Big Venture Studio Research 2024
Authors:
Maksim Malyy, PhD; Max Pog & Contributors
Maksim Malyy, PhD; Max Pog & Contributors
Date:
Dec 17, 2024
Dec 17, 2024
Here are 12 months of research on venture studios, pre-seed VC funds, & accelerators.
– We analyzed 3,452 deals from Pitchbook
– We collected and manually checked 1,107+ venture studios
– We reached out to 861 studios
– We got 123 in-depth survey answers from studios
To our knowledge, this is the biggest study ever on venture studios/builders. More importantly, we applied an academic approach, so you'll see only trustworthy data without illusions, ignorance, or data manipulation.
Before starting it, we found 6 drawbacks in the methodologies of the current data about venture studios. I promised that as a result of our 2024 study, we’ll share any results even if we find something disadvantageous about the studio model.
It’s not only the most rigorous paper about studios but also a medicine – read it in the evening to fall asleep fast 😁. It was necessary to include all the details & academic explanations so that anyone could repeat all the steps and come to the same results (especially, for the first big part, based on Pitchbook data). So my advice: whenever you feel bored, scroll until the next picture or results overview parts.
My involvement in this research was limited to defining questions & hypotheses, attracting contributors & sponsors, promotion, weekly research discussions and attracting venture studios to participate in the survey. All the academic work/contributors' work was led by Maksim Malyy, PhD, who has been involved full-time since January 2024.
We also prepared the PDF version of the study, access it here (it has a size of 80MB).
Before you dive deep, I’ll start with calls to action:
Call for family offices and those studios that are backed by family offices or willing to get funding:
– We analyzed 3,452 deals from Pitchbook
– We collected and manually checked 1,107+ venture studios
– We reached out to 861 studios
– We got 123 in-depth survey answers from studios
To our knowledge, this is the biggest study ever on venture studios/builders. More importantly, we applied an academic approach, so you'll see only trustworthy data without illusions, ignorance, or data manipulation.
Before starting it, we found 6 drawbacks in the methodologies of the current data about venture studios. I promised that as a result of our 2024 study, we’ll share any results even if we find something disadvantageous about the studio model.
It’s not only the most rigorous paper about studios but also a medicine – read it in the evening to fall asleep fast 😁. It was necessary to include all the details & academic explanations so that anyone could repeat all the steps and come to the same results (especially, for the first big part, based on Pitchbook data). So my advice: whenever you feel bored, scroll until the next picture or results overview parts.
My involvement in this research was limited to defining questions & hypotheses, attracting contributors & sponsors, promotion, weekly research discussions and attracting venture studios to participate in the survey. All the academic work/contributors' work was led by Maksim Malyy, PhD, who has been involved full-time since January 2024.
We also prepared the PDF version of the study, access it here (it has a size of 80MB).
Before you dive deep, I’ll start with calls to action:
- Help us to promote this paper: You can use any images or parts of this study to post on social media, write articles, record videos, organize ceremonies 😁, etc. Always use this link to refer to the study: https://inniches.com/research24
- If you’re a venture studio, join our community Venture Studio Family to get access to 1) venture studio Zoom meetings, 2) huge library of materials, meetings & networking lists, 3) pitch your studio to investors (we host pitch sessions for studios twice a year), and 4) always have free access to paid tickets of the virtual conferences we host for family offices, FoFs, LPs, VCs, VSs, angels & startups.
Call for family offices and those studios that are backed by family offices or willing to get funding:
- If you’re a family office interested either in launching or investing in studios, fill out this 30-second form to learn our insights about best family offices practices on launching/investing in venture studios. We’ll ask you a few questions and share our learnings when the FO x VS 2025 study is ready.
- If you’re a venture studio that got funding, from a family office, fill out this 30-second form and we’ll share our plans about this research on family offices x venture studios. Would be great to have you as a contributor.
- If you’re a studio and looking to raise from family offices, fill out this 30-second form and we can offer sponsoring this FO x VS 2025 research to get insights, exposure & contacts of relevant family offices.
- If you’d like to sponsor this FO x VS 2025 research, also fill out the form and we’ll share details.
Dear Reader,
Few people read forewords, so I’ll keep this brief (unlike the research 😄). If you have 5 min, start with the Outro for major conclusions. Then, explore the research questions - aka RQs - results overview (1, 2, 3). Finally, if you’re feeling bold, dive into the full text from the beginning (God bless you! 😶🌫️).
This report takes a sober look at the venture studio phenomenon in the venture capital industry. Some findings may surprise you (they did for us 😶), while others might inspire (I hope! 🤞). It’s not the final word but a starting point for further research. I believe it could be even converted to a PhD and an MBA theses adding more theory.
Good luck! 😉
Few people read forewords, so I’ll keep this brief (unlike the research 😄). If you have 5 min, start with the Outro for major conclusions. Then, explore the research questions - aka RQs - results overview (1, 2, 3). Finally, if you’re feeling bold, dive into the full text from the beginning (God bless you! 😶🌫️).
This report takes a sober look at the venture studio phenomenon in the venture capital industry. Some findings may surprise you (they did for us 😶), while others might inspire (I hope! 🤞). It’s not the final word but a starting point for further research. I believe it could be even converted to a PhD and an MBA theses adding more theory.
Good luck! 😉
BVSR'24 Sponsors
We’re very grateful to our sponsors who supported this Big Venture Studio Research 2024
Introduction
By John-Erik Hassel
The venture studio (VS) model has risen to increasing prominence over the last two decades (Köhler and Baumann, 2015; Alvarenga et al., 2019), having originally emerged in the mid-1990s with the aims of reducing failure risk, building multiple ventures in parallel, and seeking funding more professionally (Mohammadi et al., 2023). Venture studios combine the entrepreneurial opportunistic spirit of creating new ventures with the reproducible procedures and routines of corporations (Schmidt et al., 2019), and are being recognized as a new paradigm of startup entrepreneurship (Burris et al., 2023).
In generations of incubators, accelerators, and other entrepreneurial support initiatives, the experimentation of how to organize and support new ventures has been key to entrepreneurship and innovation (cf. Baumann et al., 2018). With the emergence of venture studios, the startup ecosystem now includes a type of organization that delivers hands-on engagement and direct governance (Köhler and Baumann, 2015) in all operational aspects of the creation of new firms in collaboration with individual entrepreneurs. This way of creating new ventures differs from how other actors in the entrepreneurial ecosystem commonly act in relation to their portfolio companies, such as e.g. venture capital firms (Alvarenga et al., 2019).
Research findings so far differentiating venture studios from other types of actors in the ecosystem do so in relation to e.g. the decoupling of the idea and entrepreneur (Baumann et al., 2018), their combination of support roles (Spigel et al., 2022), their level of governance (Köhler and Baumann, 2015), their use of internal or external service provision (Laspia et al., 2021), and their strategic use of the business network to speed up the growth process to the benefit of the portfolio companies (Hassel, 2024). In recent years, the number of venture studios has grown exponentially and to date, they have altogether launched more than 1.700 start-ups worldwide (Patel and Chan, 2023) engaging more than 200.000 professionals (Moran, 2023).
Research to date commonly focuses on describing the VS phenomenon using mainly qualitative methods to capture the essence of venture studios in comparison with other entrepreneurial support organizations, and in relation to existing theories of organizational management. Single or multiple case studies are commonplace among academic contributions seen to date, where few studies manage to fulfill the need for more data-driven analysis. A reason for this may be the novelty of the VS phenomenon. Although our understanding of venture studios is rapidly growing, the model continues to evolve (Spigel et al., 2022), and we must acknowledge that we are in the early stages of developing knowledge about venture studios and their impact on entrepreneurship and innovation.
Despite the growth of venture studios and increased interest surrounding the phenomenon, we still have little knowledge about a number of areas. Among these areas are challenges relating to the recruitment of founders, access to capital, and business model innovation. These are all areas in need of more attention, not only to advance knowledge in general but also to practically support practitioners in their work. Furthermore, to support the growing venture studio community, we need a deeper understanding of venture studio design, cultural differences, and lessons learned from portfolio companies, including both venture studio and startup failures. From a research perspective, we need to establish the VS model's status as a distinct organizational form and delineate it from other ecosystem actors to enable meaningful comparative performance analysis.
Big Venture Studio Research 2024 is our attempt to address at least some of these challenges and lay grounds for further studies. The report is organized as follows. We start with an examination of existing literature on the subject of venture studios, followed by an overview of the venture studio landscape regarding the number of studios, their types, and geographies. Next, we discuss the first research question that is aimed at understanding venture studios’ performance in terms of their ventures’ growth rates as compared to those of other entrepreneurial support organizations. After that, we’ll try to understand the premises of the studios’ performance by discussing the second research question. This will be followed by the third research question which quantitatively explores the factors of venture studios with new ventures' exits. Finally, we’ll present our final thoughts and provide additional essays on the topic from our academic contributors.
In generations of incubators, accelerators, and other entrepreneurial support initiatives, the experimentation of how to organize and support new ventures has been key to entrepreneurship and innovation (cf. Baumann et al., 2018). With the emergence of venture studios, the startup ecosystem now includes a type of organization that delivers hands-on engagement and direct governance (Köhler and Baumann, 2015) in all operational aspects of the creation of new firms in collaboration with individual entrepreneurs. This way of creating new ventures differs from how other actors in the entrepreneurial ecosystem commonly act in relation to their portfolio companies, such as e.g. venture capital firms (Alvarenga et al., 2019).
Research findings so far differentiating venture studios from other types of actors in the ecosystem do so in relation to e.g. the decoupling of the idea and entrepreneur (Baumann et al., 2018), their combination of support roles (Spigel et al., 2022), their level of governance (Köhler and Baumann, 2015), their use of internal or external service provision (Laspia et al., 2021), and their strategic use of the business network to speed up the growth process to the benefit of the portfolio companies (Hassel, 2024). In recent years, the number of venture studios has grown exponentially and to date, they have altogether launched more than 1.700 start-ups worldwide (Patel and Chan, 2023) engaging more than 200.000 professionals (Moran, 2023).
Research to date commonly focuses on describing the VS phenomenon using mainly qualitative methods to capture the essence of venture studios in comparison with other entrepreneurial support organizations, and in relation to existing theories of organizational management. Single or multiple case studies are commonplace among academic contributions seen to date, where few studies manage to fulfill the need for more data-driven analysis. A reason for this may be the novelty of the VS phenomenon. Although our understanding of venture studios is rapidly growing, the model continues to evolve (Spigel et al., 2022), and we must acknowledge that we are in the early stages of developing knowledge about venture studios and their impact on entrepreneurship and innovation.
Despite the growth of venture studios and increased interest surrounding the phenomenon, we still have little knowledge about a number of areas. Among these areas are challenges relating to the recruitment of founders, access to capital, and business model innovation. These are all areas in need of more attention, not only to advance knowledge in general but also to practically support practitioners in their work. Furthermore, to support the growing venture studio community, we need a deeper understanding of venture studio design, cultural differences, and lessons learned from portfolio companies, including both venture studio and startup failures. From a research perspective, we need to establish the VS model's status as a distinct organizational form and delineate it from other ecosystem actors to enable meaningful comparative performance analysis.
Big Venture Studio Research 2024 is our attempt to address at least some of these challenges and lay grounds for further studies. The report is organized as follows. We start with an examination of existing literature on the subject of venture studios, followed by an overview of the venture studio landscape regarding the number of studios, their types, and geographies. Next, we discuss the first research question that is aimed at understanding venture studios’ performance in terms of their ventures’ growth rates as compared to those of other entrepreneurial support organizations. After that, we’ll try to understand the premises of the studios’ performance by discussing the second research question. This will be followed by the third research question which quantitatively explores the factors of venture studios with new ventures' exits. Finally, we’ll present our final thoughts and provide additional essays on the topic from our academic contributors.
Literature Review
By Tom West
In attempting to answer our research questions related to the venture studio model, one must first establish an understanding of the VS model by examining the available literature.
One important distinction in the VS model is that it allows an organizational approach to founding and growing new ventures in parallel rather than in series. For example, IdeaLab launched more than 150 companies in its first 25 years, resulting in 50+ successful IPO’s (Gross, 2021). Finally, VS's employ “highly standardized processes (to unlock) learning curve advantages and synergies across the portfolio” of new ventures (Gutmann, 2018).
One important distinction in the VS model is that it allows an organizational approach to founding and growing new ventures in parallel rather than in series. For example, IdeaLab launched more than 150 companies in its first 25 years, resulting in 50+ successful IPO’s (Gross, 2021). Finally, VS's employ “highly standardized processes (to unlock) learning curve advantages and synergies across the portfolio” of new ventures (Gutmann, 2018).
Methodology of Literature Review
“Concept maps” (Rowley and Slack, 2004) were created to identify the most relevant search terms related to VS’s. These were used to search Google Scholar and the University of London’s Summon database to identify relevant and trustworthy resources. In instances where a work was not peer reviewed or published in an academic resource, care was taken to consider the author’s credentials and trustworthiness. Finally, bibliographies of these resources were checked for related resources via a “snowballing” approach (Callahan, 2014).
In all, 68 trustworthy sources were identified that discussed VS's, their structures and processes, and form the body of data for this literature review. In assessing the available literature, it became apparent that the vast majority has been produced by industry with very few academic resources. This provides an opportunity to segment this literature review into the following sections: history, definitions, academic literature, industry literature, current industry research, findings, and future research.
“Concept maps” (Rowley and Slack, 2004) were created to identify the most relevant search terms related to VS’s. These were used to search Google Scholar and the University of London’s Summon database to identify relevant and trustworthy resources. In instances where a work was not peer reviewed or published in an academic resource, care was taken to consider the author’s credentials and trustworthiness. Finally, bibliographies of these resources were checked for related resources via a “snowballing” approach (Callahan, 2014).
In all, 68 trustworthy sources were identified that discussed VS's, their structures and processes, and form the body of data for this literature review. In assessing the available literature, it became apparent that the vast majority has been produced by industry with very few academic resources. This provides an opportunity to segment this literature review into the following sections: history, definitions, academic literature, industry literature, current industry research, findings, and future research.
Historical Context
In framing a review of the relevant literature, it is important to emphasize the recency of the VS model’s rise to prominence. Although the first notable VS is widely considered to be IdeaLab, founded in 1996, the last decade has seen the model gain popularity (Kannan and Peterman, 2022). Due to the model’s nascency, there is far less academic literature examining VS’s, compared to other entrepreneurial support models (Hamida, 2020). In fact, “the academic literature regarding startup studios is still quite limited, … mainly due to the infancy of the startup studio incubation model” (Patel and Chan, 2023, quoting Hamida, 2020).
Industry-led research on the VS model has increased dramatically in the last 24-36 months. This observation is anecdotally supported by the author’s own experience and is also supported by recent foundational publications on the subject of VS models (Kannan and Peterman, 2022, Mohammadi et al., 2023, Pog, 2023).
In framing a review of the relevant literature, it is important to emphasize the recency of the VS model’s rise to prominence. Although the first notable VS is widely considered to be IdeaLab, founded in 1996, the last decade has seen the model gain popularity (Kannan and Peterman, 2022). Due to the model’s nascency, there is far less academic literature examining VS’s, compared to other entrepreneurial support models (Hamida, 2020). In fact, “the academic literature regarding startup studios is still quite limited, … mainly due to the infancy of the startup studio incubation model” (Patel and Chan, 2023, quoting Hamida, 2020).
Industry-led research on the VS model has increased dramatically in the last 24-36 months. This observation is anecdotally supported by the author’s own experience and is also supported by recent foundational publications on the subject of VS models (Kannan and Peterman, 2022, Mohammadi et al., 2023, Pog, 2023).
Definitions and Key Characteristics
The terminology surrounding VSs has evolved alongside the model itself. Terms such as "venture builders" and "startup studios" have been used interchangeably, reflecting the diverse approaches within the broader concept (Hamida, 2020).
A consensus on the definition of VS's is emerging in recent literature. Kannan and Peterman (2022) describe VS’s as entities that systematically create and launch startups by providing shared resources and support, often with multiple startups existing simultaneously in varying stages of development. Mohammadi et al. (2023) extend this definition, emphasizing the importance of idea generation, team building, and careful resource allocation.
Munoz Abreu (2021) explicitly characterizes VS's by their in-house ideation and opportunity identification, assembly of founding teams, provision of initial capital, shared operational resources, and ongoing strategic support. These definitions collectively highlight the key features that distinguish venture studios from other startup support models, such as incubators or accelerators, which tend to come in later in the entrepreneurial process and provide more limited support (Coelsch-Foisner et al., 2024).
Another critical aspect of the VS model is its organizational approach to founding and growing new companies in parallel rather than in series. This parallel approach allows for more efficient resource allocation and potentially higher success rates compared to traditional startup models (Kannan and Peterman, 2022).
The terminology surrounding VSs has evolved alongside the model itself. Terms such as "venture builders" and "startup studios" have been used interchangeably, reflecting the diverse approaches within the broader concept (Hamida, 2020).
A consensus on the definition of VS's is emerging in recent literature. Kannan and Peterman (2022) describe VS’s as entities that systematically create and launch startups by providing shared resources and support, often with multiple startups existing simultaneously in varying stages of development. Mohammadi et al. (2023) extend this definition, emphasizing the importance of idea generation, team building, and careful resource allocation.
Munoz Abreu (2021) explicitly characterizes VS's by their in-house ideation and opportunity identification, assembly of founding teams, provision of initial capital, shared operational resources, and ongoing strategic support. These definitions collectively highlight the key features that distinguish venture studios from other startup support models, such as incubators or accelerators, which tend to come in later in the entrepreneurial process and provide more limited support (Coelsch-Foisner et al., 2024).
Another critical aspect of the VS model is its organizational approach to founding and growing new companies in parallel rather than in series. This parallel approach allows for more efficient resource allocation and potentially higher success rates compared to traditional startup models (Kannan and Peterman, 2022).
Academic Literature
Academic literature on VSs is relatively scant and is broadly dominated by masters’ and doctoral theses including Hamida, 2020 and Patel and Chan, 2023. However, there have been some mentions of the VS model in peer-reviewed journals.
According to Rathgeber et al., 2017, only one relevant academic paper existed at that time - Kohler and Baumann from 2015 - which presented an examination of Rocket Internet as an example of a successful VS (Rathgeber et al., 2017). Interestingly, both Kohler and Rathgeber focused on the organizational structures inherent in the VS model (Kohler and Baumann, 2015, Rathgeber et al., 2017).
In 2018, Gutmann explored the extent to which VS’s feature “either functional or matrix organizations depending on organizational size.” He extended Kohler and Baumann’s work to carefully map the venture creation processes of Rocket Internet (Gutmann, 2018).
In 2023, Patel and Chan used a broad but shallow data set from VentureStudioIndex.com and performed quantitative analyses based on sound methodology, however, the nature of the data imposed significant limitations on their conclusions (Patel and Chan, 2023).
Most recently, researchers have sought to explore and answer, “through which mechanisms do venture studios support new ventures?” (Coelsch-Foisner et al., 2024). This effort added more academic validity to the body of research into VSs, particularly through its comparisons of VS’s to other entrepreneur support organizational models, namely VC and Accelerators.
Academic literature on VSs is relatively scant and is broadly dominated by masters’ and doctoral theses including Hamida, 2020 and Patel and Chan, 2023. However, there have been some mentions of the VS model in peer-reviewed journals.
According to Rathgeber et al., 2017, only one relevant academic paper existed at that time - Kohler and Baumann from 2015 - which presented an examination of Rocket Internet as an example of a successful VS (Rathgeber et al., 2017). Interestingly, both Kohler and Rathgeber focused on the organizational structures inherent in the VS model (Kohler and Baumann, 2015, Rathgeber et al., 2017).
In 2018, Gutmann explored the extent to which VS’s feature “either functional or matrix organizations depending on organizational size.” He extended Kohler and Baumann’s work to carefully map the venture creation processes of Rocket Internet (Gutmann, 2018).
In 2023, Patel and Chan used a broad but shallow data set from VentureStudioIndex.com and performed quantitative analyses based on sound methodology, however, the nature of the data imposed significant limitations on their conclusions (Patel and Chan, 2023).
Most recently, researchers have sought to explore and answer, “through which mechanisms do venture studios support new ventures?” (Coelsch-Foisner et al., 2024). This effort added more academic validity to the body of research into VSs, particularly through its comparisons of VS’s to other entrepreneur support organizational models, namely VC and Accelerators.
Industry-led literature
Publications from within the VS entrepreneur community have picked up pace in recent years. The Global Startup Studio Network (GSSN) was formed in 2018 and began to release occasional publications detailing the VS model and its intricacies (Lawerence et al., 2019).
Kannan and Peterman's "Venture Studios Demystified" is considered the first long-form, widely distributed, detailed examination of the VS model. It provides a thorough description of VSs, detailing their origins, structure, processes, and advantages over traditional startup models (Kannan and Peterman, 2022).
Mohammadi's "Venture Studio Manifesto" refines and extends Kannan and Peterman by providing a more structured and principles-driven approach to understanding VSs. Mohammadi provides insights into the operational frameworks and philosophies used by VSs (Mohammadi et al., 2023).
Entrepreneur Max Pog’s “Big Startup Studio Research 2023” was the first large-scale, data-driven empirical study of the VS ecosystem. In it, Pog identified important characteristics such as best practices, liquidity mechanisms and trends found in the industry (Pog, 2023). This research led to the formation of the “Venture Studio Family” and the “Big Venture Studio Research 2024” (BVSR’24) project.
Publications from within the VS entrepreneur community have picked up pace in recent years. The Global Startup Studio Network (GSSN) was formed in 2018 and began to release occasional publications detailing the VS model and its intricacies (Lawerence et al., 2019).
Kannan and Peterman's "Venture Studios Demystified" is considered the first long-form, widely distributed, detailed examination of the VS model. It provides a thorough description of VSs, detailing their origins, structure, processes, and advantages over traditional startup models (Kannan and Peterman, 2022).
Mohammadi's "Venture Studio Manifesto" refines and extends Kannan and Peterman by providing a more structured and principles-driven approach to understanding VSs. Mohammadi provides insights into the operational frameworks and philosophies used by VSs (Mohammadi et al., 2023).
Entrepreneur Max Pog’s “Big Startup Studio Research 2023” was the first large-scale, data-driven empirical study of the VS ecosystem. In it, Pog identified important characteristics such as best practices, liquidity mechanisms and trends found in the industry (Pog, 2023). This research led to the formation of the “Venture Studio Family” and the “Big Venture Studio Research 2024” (BVSR’24) project.
Further Research
An examination of the VS model through the lenses of established management theories could yield valuable insights and contribute to the maturity of research into the VS model. Although the opportunities for application are broad, a few key frameworks emerge as particularly appealing, including organizational theory, the resource-based view, network theory and knowledge-management theory.
Further use of organizational theory could help explain the unique structural aspects of the VS model. Concepts such as organizational design, resource allocation, and decision-making processes (Daft, 2020) are particularly relevant. The VS model challenges traditional organizational boundaries, creating a hybrid structure that combines elements of both startups and established firms (Rathgeber et al., 2017).
The Resource-Based View suggests that a firm's competitive advantage is attributable to its “unique bundle of resources and capabilities” (Barney, 1991). This framework is particularly applicable to VSs, because they leverage shared resources across multiple ventures. The ability of VSs to efficiently allocate and redeploy resources could explain differences in performance and/or outcomes compared with traditional startup models.
Given the collaborative nature of venture studios, network theory may provide insights into how these VSs leverage relationships and information flows across the VS ecosystem. For example, application of network theory could examine the “weak ties” (Granovetter, 1977) created within and around VSs and whether these relationships contribute significantly to their success in launching and scaling startups.
Finally, knowledge management theory could be applied to understand how venture studios accumulate, share, and apply knowledge (Nonaka and Takeuchi, 1995) across multiple ventures. Transferring learnings and best practices efficiently could be a key success factor for VSs.
An examination of the VS model through the lenses of established management theories could yield valuable insights and contribute to the maturity of research into the VS model. Although the opportunities for application are broad, a few key frameworks emerge as particularly appealing, including organizational theory, the resource-based view, network theory and knowledge-management theory.
Further use of organizational theory could help explain the unique structural aspects of the VS model. Concepts such as organizational design, resource allocation, and decision-making processes (Daft, 2020) are particularly relevant. The VS model challenges traditional organizational boundaries, creating a hybrid structure that combines elements of both startups and established firms (Rathgeber et al., 2017).
The Resource-Based View suggests that a firm's competitive advantage is attributable to its “unique bundle of resources and capabilities” (Barney, 1991). This framework is particularly applicable to VSs, because they leverage shared resources across multiple ventures. The ability of VSs to efficiently allocate and redeploy resources could explain differences in performance and/or outcomes compared with traditional startup models.
Given the collaborative nature of venture studios, network theory may provide insights into how these VSs leverage relationships and information flows across the VS ecosystem. For example, application of network theory could examine the “weak ties” (Granovetter, 1977) created within and around VSs and whether these relationships contribute significantly to their success in launching and scaling startups.
Finally, knowledge management theory could be applied to understand how venture studios accumulate, share, and apply knowledge (Nonaka and Takeuchi, 1995) across multiple ventures. Transferring learnings and best practices efficiently could be a key success factor for VSs.
Current VS research state
All existing peer-reviewed research on VSs is qualitative/exploratory in nature aside from one quantitative study (Patel and Chan, 2023), affording fertile grounds for further quantitative examination, a conclusion shared by the researchers behind the BVSR’24 project (Malyy and Pog, 2024).
BVSR’24 is an industry-sponsored, PhD-led research project aimed at “following scientific research principles… to study the performance of venture studios and their startups” (Malyy and Pog, 2024). The application of academic rigor in BVSR’24 seeks to lay the foundations for the further study of the VS model on solid theoretical footing, enabling future application to a wide range of academic research concentrations such as organizational behavior, valuation methodology, strategic management and innovation. The current lack of academic research into VSs suggests ample opportunity for the contribution of novel research into the VS model.
A significant gap exists between academia-led and industry-led literature, presenting significant novel research opportunities. The BVSR’24 project has been formed to address some of this gap.
All existing peer-reviewed research on VSs is qualitative/exploratory in nature aside from one quantitative study (Patel and Chan, 2023), affording fertile grounds for further quantitative examination, a conclusion shared by the researchers behind the BVSR’24 project (Malyy and Pog, 2024).
BVSR’24 is an industry-sponsored, PhD-led research project aimed at “following scientific research principles… to study the performance of venture studios and their startups” (Malyy and Pog, 2024). The application of academic rigor in BVSR’24 seeks to lay the foundations for the further study of the VS model on solid theoretical footing, enabling future application to a wide range of academic research concentrations such as organizational behavior, valuation methodology, strategic management and innovation. The current lack of academic research into VSs suggests ample opportunity for the contribution of novel research into the VS model.
A significant gap exists between academia-led and industry-led literature, presenting significant novel research opportunities. The BVSR’24 project has been formed to address some of this gap.
Overview of the actual venture studio landscape (Fall’24)
As of September 2024, a total of 1,107 venture studios of various types have been established worldwide. Furthermore, 154 studios are determined to have ceased operations, as indicated by either explicit closure announcements or prolonged website inactivity. Although 2024 is still ongoing at the time of writing, third-quarter data shows only 17 new studio registrations compared to 20 closures. If this trend continues, 2024 will mark the first year with a net decrease in the number of venture studios.
However, further analysis of the data shows that this trend is somewhat expected: the maximum increment happened in 2020 when 114 venture studios were founded and 10 closed (increment = 104) after which, the increment decreased steadily. This is an interesting trend since, according to our assessment, the global interest in the studio phenomena began to grow after 2019 when the most notable and citable reports from Morrow (ex-GSSN) and Enhance Ventures were published (Lawerence et al., 2019, Alhokail et al., 2019).
While this pattern might initially suggest alignment with broader venture capital market trends (CB Insights, 2024), closer examination reveals divergent trajectories. The traditional venture capital market showed positive momentum from 2020 through mid-2022, while the venture studio sector exhibited growth only until 2020, followed by decline. This timing indicates venture studios began their downturn ahead of the broader venture market.
A critical question is how the venture studio landscape will evolve from here. One possibility is that the industry will stabilize at its current peak, with new studio openings roughly balancing closures. However, the current downward trend suggests we may instead see a continued decline in the total number of venture studios worldwide as the industry matures.
However, further analysis of the data shows that this trend is somewhat expected: the maximum increment happened in 2020 when 114 venture studios were founded and 10 closed (increment = 104) after which, the increment decreased steadily. This is an interesting trend since, according to our assessment, the global interest in the studio phenomena began to grow after 2019 when the most notable and citable reports from Morrow (ex-GSSN) and Enhance Ventures were published (Lawerence et al., 2019, Alhokail et al., 2019).
While this pattern might initially suggest alignment with broader venture capital market trends (CB Insights, 2024), closer examination reveals divergent trajectories. The traditional venture capital market showed positive momentum from 2020 through mid-2022, while the venture studio sector exhibited growth only until 2020, followed by decline. This timing indicates venture studios began their downturn ahead of the broader venture market.
A critical question is how the venture studio landscape will evolve from here. One possibility is that the industry will stabilize at its current peak, with new studio openings roughly balancing closures. However, the current downward trend suggests we may instead see a continued decline in the total number of venture studios worldwide as the industry matures.
* Our analysis identified several studios that pivoted away from the venture studio model to different operational structures. However, we excluded these cases from our active-versus-closed analysis due to their ambiguous classification status. Given that only seven studios (0.6% of the total) underwent such pivots, their exclusion is unlikely to significantly impact our findings.
We also uncovered a strong negative correlation between the foundation date of the closed venture studios and their age before closing (R=-0.87). This correlation implies that the recently opened studios die faster in case of failure. The reasons for such a link remains unclear. The reported strong performance metrics of venture studios, such as a net Internal Rate of Return (IRR) of 60% (Anderson and Gomez, 2023), may have attracted entrepreneurs to the model without sufficient understanding of the operational principles and organizational capabilities that drive such success. This underscores the importance of rigorous research into venture studios to guide entrepreneurs in building more sustainable and successful organizations.
To analyze the geographical distribution of studios, we defined all countries where studios from our database are presented and categorized them into related regions according to their adjusted World Bank classification (“World by Region Map,” 2018) while separating Europe and Central Asia to Western Europe and Eastern Europe and Central Asia. The full regional assignment can be found in Appendix 1.
North America (NA) and Western Europe (WE) dominate the venture studio landscape, accounting for 69% of all established studios. These regions, along with East Asia and the Pacific (EAP) which ranks third, represent the primary hubs of venture studio activity. This geographic distribution aligns with regional economic development patterns, as these areas are also the leading economic regions according to World Bank indicators (Global Economic Prospects, 2024).
The rest include (ordered by descending count of venture studios): Eastern Europe and Central Asia (EECA), Middle East and North Africa (MENA), Latin America (LATAM), Sub-Saharan Africa (SSA), and South Asia (SA).
Analysis of venture studio survival rates reveals significant regional variations. Western Europe exhibits the highest survival ratio (7.86 active studios per closure), followed by North America (6.76:1) and Sub-Saharan Africa (6.67:1). Conversely, the Middle East and North Africa (MENA) region shows the lowest survival ratio. Statistical analysis suggests that venture studios established in Western Europe, North America, and Sub-Saharan Africa have higher survival rates compared to those in MENA and other regions.
North America (NA) and Western Europe (WE) dominate the venture studio landscape, accounting for 69% of all established studios. These regions, along with East Asia and the Pacific (EAP) which ranks third, represent the primary hubs of venture studio activity. This geographic distribution aligns with regional economic development patterns, as these areas are also the leading economic regions according to World Bank indicators (Global Economic Prospects, 2024).
The rest include (ordered by descending count of venture studios): Eastern Europe and Central Asia (EECA), Middle East and North Africa (MENA), Latin America (LATAM), Sub-Saharan Africa (SSA), and South Asia (SA).
Analysis of venture studio survival rates reveals significant regional variations. Western Europe exhibits the highest survival ratio (7.86 active studios per closure), followed by North America (6.76:1) and Sub-Saharan Africa (6.67:1). Conversely, the Middle East and North Africa (MENA) region shows the lowest survival ratio. Statistical analysis suggests that venture studios established in Western Europe, North America, and Sub-Saharan Africa have higher survival rates compared to those in MENA and other regions.
With the help of public sources, we have also developed a simplified venture studio taxonomy segregated by the types of projects executed and supportive activities offered. We identified three major variations of the venture studio model:
According to this taxonomy, the most popular category is Venture Studio (669 entities, 60%), next goes Hybrid Venture Studio (334 entities, 30%), and Corporate Builder (104 entities, 10%). The Alive/Closed ratio demonstrates the highest success chances have Hybrid Venture Studio (for each closed studio there are 10.86 alive), next goes Corporate Builders (9.3:1), and Venture Studios with a significantly lower survivorship rate (4.73:1). From these outcomes, we can assume that the additional activities, as well as the focus on corporate building, significantly increase the chances to survive.
This pattern could be explained through two alternative hypotheses. First, the diversification of revenue streams beyond core venture studio operations may serve as a risk-hedging strategy, enhancing organizational resilience. Alternatively, the correlation might reflect successful venture studios naturally expanding their operational scope, with diversification being a consequence rather than a cause of success. Further research is needed to establish any causal or directional relationship between operational diversity and venture studio survivability.
- Venture Studio - primarily develops internal ventures and positions itself accordingly
- Corporate Builder - primarily develops corporate ventures and positions itself accordingly
- Hybrid Venture Studio - a venture studio with additional activities like corporate building, VC fund, accelerator, etc.
According to this taxonomy, the most popular category is Venture Studio (669 entities, 60%), next goes Hybrid Venture Studio (334 entities, 30%), and Corporate Builder (104 entities, 10%). The Alive/Closed ratio demonstrates the highest success chances have Hybrid Venture Studio (for each closed studio there are 10.86 alive), next goes Corporate Builders (9.3:1), and Venture Studios with a significantly lower survivorship rate (4.73:1). From these outcomes, we can assume that the additional activities, as well as the focus on corporate building, significantly increase the chances to survive.
This pattern could be explained through two alternative hypotheses. First, the diversification of revenue streams beyond core venture studio operations may serve as a risk-hedging strategy, enhancing organizational resilience. Alternatively, the correlation might reflect successful venture studios naturally expanding their operational scope, with diversification being a consequence rather than a cause of success. Further research is needed to establish any causal or directional relationship between operational diversity and venture studio survivability.
Analysis of Hybrid Venture Studio reveals that Venture Capital (VC) fund management and corporate building services are the most prevalent supplementary activities, representing 145 (29%) and 144 (28%) entities respectively. Strategic consulting follows at 57 entities (11%), while digital product agency services and accelerator programs account for 38 (7%) and 31 (6%) entities respectively. The remaining identified and categorized activities each represent between 0.2% and 3.7% of the total.
That is to note, in the Other corp services category we included various venture-focused activities, which studios claim to provide to corporate clients: bootcamps, scouting, portfolio, and corporate accelerators. At the same time, the Not classified category includes such activities as the acquisition of ventures, services for scaling in various markets, venture marathon (”venturethon”), conferences, university collaborations, and public sector partnerships.
Analysis of alive-to-closed survival rates across Other corp services reveals significant variations. Venture studios offering ancillary corporate services demonstrate the highest survival ratio (18:1), followed by those operating accelerator programs (14:1) and those providing corporate building services (13.4:1). Venture studios incorporating digital product agency services show the lowest survival ratio (8.5:1). Notably, studios with integrated VC funds display moderate survival rates (10.08:1), approximately half that of studios offering other corporate services.
These results may be explained by the fact that various direct innovation and venture-focused services provide positive cash flow to studios, and, apparently, are in demand by the corporate market. At the same time, services like Strategic consulting and Digital product agency do not display these benefits, possibly due to their indirect or non-venture character.
For the remaining operational categories not shown in the survival rate analysis, no closures were identified in our dataset. While this suggests a 100% survival rate in these categories, the sample size limitations preclude drawing definitive conclusions about their long-term sustainability beyond noting their current active status.
That is to note, in the Other corp services category we included various venture-focused activities, which studios claim to provide to corporate clients: bootcamps, scouting, portfolio, and corporate accelerators. At the same time, the Not classified category includes such activities as the acquisition of ventures, services for scaling in various markets, venture marathon (”venturethon”), conferences, university collaborations, and public sector partnerships.
Analysis of alive-to-closed survival rates across Other corp services reveals significant variations. Venture studios offering ancillary corporate services demonstrate the highest survival ratio (18:1), followed by those operating accelerator programs (14:1) and those providing corporate building services (13.4:1). Venture studios incorporating digital product agency services show the lowest survival ratio (8.5:1). Notably, studios with integrated VC funds display moderate survival rates (10.08:1), approximately half that of studios offering other corporate services.
These results may be explained by the fact that various direct innovation and venture-focused services provide positive cash flow to studios, and, apparently, are in demand by the corporate market. At the same time, services like Strategic consulting and Digital product agency do not display these benefits, possibly due to their indirect or non-venture character.
For the remaining operational categories not shown in the survival rate analysis, no closures were identified in our dataset. While this suggests a 100% survival rate in these categories, the sample size limitations preclude drawing definitive conclusions about their long-term sustainability beyond noting their current active status.
The venture studio landscape demonstrates significant dynamism, characterized by continuous organizational flux: new studios emerge while others cease operations, existing studios modify their operational models or expand their service offerings, and geographic presence evolves through office openings and closures. The data and analyses presented here reflect the industry status as of September 30, 2024. An anonymized dataset of the studios examined in this study is provided in Appendix 1, offering opportunities for further analysis and identification of additional patterns or correlation. The authors welcome expressions of interest in further collaborative analyses.
RQ1: Are venture studios more effective in terms of their ventures’ growth?
Recent studies examining venture studio performance have consistently reported exceptional results. Notably, venture studios demonstrate Internal Rates of Return (IRR) that are approximately double those of traditional venture capital benchmarks. (Zasowski, 2022, Pog, 2023, Anderson and Gomez, 2023). For a thorough analysis of methodological limitations in venture studio research and proposed remediation strategies, see Malyy and Pog (2024). These reported performance metrics, which have generated considerable skepticism within the venture capital industry, prompted our empirical investigation of venture studio outcomes.
New ventures in their early stages (up to the seed round of investment, i.e. pre-seed) are highly fragile. The vast majority (70-90%) fail at these stages (Kapoor, 2023). At the same time, there are several known entrepreneurial support organizations (ESOs, Coelsch-Foisner et al., 2024) designed to help new ventures survive the early stages: startup accelerators, business incubators, early-stage VC funds, business angels, syndicates, and venture studios.
In this part of the study, we examined organizations that provide direct investment support to new ventures, whether standalone or coupled with additional services. Our analysis focuses specifically on institutional investors — organizations that systematically deploy capital and resources in startups, following defined investment strategies to generate returns for their stakeholders. (“Institutional Investor,” n.d.).
Therefore, we excluded organizations without systematic monetary support (such as business incubators) and non-institutional investors (like business angels and syndicates). Furthermore, we analyzed and compared the following ESOs known to be active during pre-seed: startup accelerators, pre-seed VC funds, venture studios, and founders-first venture capital funds.
These organizations are known to have particular differences in their practices of supporting new ventures:
In the study, we employ the following definition of a venture studio developed during the research:
New ventures in their early stages (up to the seed round of investment, i.e. pre-seed) are highly fragile. The vast majority (70-90%) fail at these stages (Kapoor, 2023). At the same time, there are several known entrepreneurial support organizations (ESOs, Coelsch-Foisner et al., 2024) designed to help new ventures survive the early stages: startup accelerators, business incubators, early-stage VC funds, business angels, syndicates, and venture studios.
In this part of the study, we examined organizations that provide direct investment support to new ventures, whether standalone or coupled with additional services. Our analysis focuses specifically on institutional investors — organizations that systematically deploy capital and resources in startups, following defined investment strategies to generate returns for their stakeholders. (“Institutional Investor,” n.d.).
Therefore, we excluded organizations without systematic monetary support (such as business incubators) and non-institutional investors (like business angels and syndicates). Furthermore, we analyzed and compared the following ESOs known to be active during pre-seed: startup accelerators, pre-seed VC funds, venture studios, and founders-first venture capital funds.
These organizations are known to have particular differences in their practices of supporting new ventures:
- Pre-seed VC funds (coded as “VC”) typically select new ventures, which have already passed the inception stage, got some traction, and require monetary support to reach the next level (“Pre-Seed Funding,” 2024). They have some selection criteria and a developed due diligence process, which are employed during investment decision-making (Zider, 1998). Pre-seed VC funds also typically employ a “smart-money” investment approach ("How Smart Is Smart Money?,” 2005): they assist their portfolio companies with mentoring, advice, and networking to help them grow. Their control over portfolio companies is limited; operations and decisions fully depend on the founders albeit influenced by the boards of directors that often include fund managers.
- Startup accelerators (coded as “ACC”) help new ventures by providing cohort-based educational acceleration programs. They give mentorship and advice to their participants, while these new ventures execute their projects themselves (Wilson, 2021). Accelerators are known as the organizations to decrease the risk of early-stage investment by pushing selected startups to validate their business-related hypotheses quickly and cheaply before making a pre-seed investment (Banka et al., 2022). During acceleration programs, these organizations provide founder support and guidance, maintaining limited operational control over participating startups, with key decisions remaining primarily in the founders' hands. Post-acceleration, once investments are made in selected ventures, accelerators typically transition to a traditional venture capital role.
- Founders-first VC funds (coded as “VC-FF”) target their investments on founders rather than on startups. Following the well-known concept of investing in a team rather than in an idea (Gompers et al., 2020), they prefer to invest earlier compared to the pre-seed VC funds and accelerators by investing in the pre-idea stage. This is a relatively new and quite rare way of a pre-seed investment, which, however, already has some prominent proponents like Antler and Entrepreneur First. These organizations engage at the ideation stage, aiming to mitigate risk through early involvement in concept validation. Beyond this initial phase, their operational model aligns more closely with traditional venture capital investment practices.
- Venture studios (coded as “VS”) directly work on new ventures starting from "stage zero" meaning that they generate and validate venture ideas, create MVPs, perform customer development, etc. while applying their own resources to a new venture evolution at the initial phases (Pog, 2023). They do this with a repeatable process that is thought to strongly and positively influence their ventures’ success rate due to the “de-risking” quality brought by full operational control of created new ventures. This approach is commonly compensated by higher equity stakes in the portfolio startups compared to other ESOs (Lawerence et al., 2019). After spinning out and raising external funds for the portfolio companies, venture studios often behave more like traditional VC funds.
In the study, we employ the following definition of a venture studio developed during the research:
A venture studio is an organization that repeatedly creates or co-founds (as opposed to accelerators, incubators, and VC funds, which support) and develops one or more new startups per year by combining contracted talent, capital, expertise, networks, and other resources to build independent ventures. Venture studios are deeply involved with their ventures until spin-out and often retain an ongoing equity interest in their ventures.
There are several types of venture studios identified in another part of BVSR’24 but for the purpose of this study, we intentionally ignore corporate builders since their performance outcomes are heavily influenced by their corporate venturing partners. Definitions of the other entities employed in this research are provided in Appendix 2.
Our central hypothesis posits that variations in entrepreneurial support organizations' (ESOs) management approaches yield differential investment outcomes. Specifically, we hypothesize that venture studios achieve superior performance compared to other ESO types due to their operational control during early venture stages. To test this hypothesis, we structured our analysis around two key research questions:
1. Is the allocation of funds to VS startups more effective than to startups from other ESOs?
2. Are venture studios more effective than other ESOs in terms of their ventures’ growth?
During our data collection and analysis phase, we determined that calculating comprehensive investment performance metrics would exceed our current research capacity and available data resources. Specifically, robust investment performance analysis requires a minimum of four key indicators: cash-in, cash-out, entry valuation, and exit valuation. Despite our efforts, we could only obtain reliable data for three of these indicators, precluding statistically valid performance comparisons. This requires us to temporarily set aside an evaluation of the first research question. Nevertheless, to demonstrate the calculation logic and possible results, we also created a model that will be discussed further.
At the moment, we’d like to present the outcomes of studying the following research question:
Our central hypothesis posits that variations in entrepreneurial support organizations' (ESOs) management approaches yield differential investment outcomes. Specifically, we hypothesize that venture studios achieve superior performance compared to other ESO types due to their operational control during early venture stages. To test this hypothesis, we structured our analysis around two key research questions:
1. Is the allocation of funds to VS startups more effective than to startups from other ESOs?
2. Are venture studios more effective than other ESOs in terms of their ventures’ growth?
During our data collection and analysis phase, we determined that calculating comprehensive investment performance metrics would exceed our current research capacity and available data resources. Specifically, robust investment performance analysis requires a minimum of four key indicators: cash-in, cash-out, entry valuation, and exit valuation. Despite our efforts, we could only obtain reliable data for three of these indicators, precluding statistically valid performance comparisons. This requires us to temporarily set aside an evaluation of the first research question. Nevertheless, to demonstrate the calculation logic and possible results, we also created a model that will be discussed further.
At the moment, we’d like to present the outcomes of studying the following research question:
Are venture studios more effective than other ESOs in terms of their ventures’ growth?
Methodology and data
We have started with the basic assumption that the most accurate results overcoming the found shortcomings (Malyy and Pog, 2024) could be achieved by the direct comparison of VSs’ performance indicators with other ESOs. As a basis for these performance indicators, we decided to examine the raw deal data from investment entry and realization events. We have taken the realized deals only, thus, avoiding the uncertainty of valuing unrealized gains on investments.
Using these data, we can calculate an asset valuation growth rate (coded as “VAL-GR”) that is the difference between the first and last available valuations divided by the time needed to achieve it (Ramadan et al., 2015) and the time needed to get to the realization event (Time-to-Realization, coded as “TTR”).
VAL-GR was previously employed in various venture capital studies, both academic and industrial (Ramadan et al., 2015, Malyy et al., 2021). It provides a more granular view of the performance of a particular venture making it possible to implement a more detailed analysis.
TTR, in turn, demonstrates the time required to realize the invested asset that is of great interest to an ESO’s fund partners, especially for the limited ones (Amore et al., 2020). We identified two groups of realization events (coded as “Result”): exit of any kind (coded as “Exit”) and bankruptcy (coded as “Fail”). These types of results will be also employed further for the distributions analysis in terms of Exit/Fail ratio.
In calculating Time to Realization (TTR), we employed two methodologies: for successful exits, we measured the duration between initial investment and exit date; for failures, we used the duration between investment and bankruptcy date. For Value Growth Rate (VAL-GR), we similarly applied two approaches: for exits, we used the final valuation at the time of exit; for bankruptcies, we recorded zero terminal value at the bankruptcy date. This methodology allows for negative VAL-GR values, which are common in venture capital given the high failure rate of startups. Specifically, when an investment fails to preserve or grow value, resulting in a total loss (zero terminal value), it registers as negative growth, reflecting the complete erosion of invested capital over the measured time period.
The main advantages of these indicators compared to other common measures of invested venture performance (employee growth, valuation, market share, number of IP assets, etc.) is their transparency and comparability (Malyy et al., 2021). These metrics enable assessment of venture performance efficiency and investment effectiveness across ESOs (Ferson, 2010). By aggregating these indicators by ESO type, we can conduct comparative analysis of investment performance across different entrepreneurial support models.
To facilitate our analysis, we applied two key methodological assumptions. First, we modeled venture growth as a linear progression based on the most recent available valuation data. While using exit valuations would have been optimal, the limited availability of such data would have significantly reduced our sample size. Second, we simplified the treatment of failed ventures by assigning them zero terminal value, although research by Levratto (2013) suggests that bankrupted ventures may retain residual value. This simplification was necessary due to the absence of reliable data on residual values in existing databases.
As a data source for deal information, we selected PitchBook. After considering other databases available to us (Dealroom, Crunchbase, Tracxn), we came to the conclusion that PitchBook has the most extensive and accurate data on the deals of interest.
Due to the fact that pre-seed deals are not uniquely identified in the selected database (Gao, 2023a), we have developed and applied filters to select pre-seed deals from the set of Seed deals. Based on the PitchBook methodology provided in the recent pre-seed report (Gao, 2023b), we developed our own criteria:
We scanned through PitchBook and included all pre-seed deals which:
We did not limit the sample geographically or by domains. The exact filtering procedure is described in Appendix 3.
Following our collection of pre-seed investment data, we conducted a three-stage analysis process. We first performed manual identification of initial investors for each deal, categorized these investors by type, and grouped the data according to our established entrepreneurial support organization (ESO) classifications.
We then analyzed two key performance metrics: Time-to-Round (TTR) and Valuation Growth Rate (VAL-GR). Our statistical analysis began with normality testing using the Kolmogorov-Smirnov test (for samples larger than 50 observations), following the methodology outlined by Mishra et al. (2019). The results indicated that neither metric followed a normal distribution at the 0.05 significance level. Instead, the data most closely approximated a lognormal distribution, which informed our subsequent statistical approaches.
Therefore, for further analysis, we conducted a nonparametric significance test (in particular, Kruskal-Wallis analysis of variance, KWANOVA, “Kruskal Wallis Test - an overview | ScienceDirect Topics,” n.d.), non-parametric pairwise comparison test (Dunn’s test, “Dunn’s test,” 2017), and used medians instead of means to compare centralities of distributions (Mishra et al., 2019). We then produced box-and-whisker plots for distribution illustration including medians, interquartile range (IQR), whiskers (1.5IQR+Q3), and outliers(>1.5IQR+Q3) as the major indicators (Mishra et al., 2019). It also should be noted that the outliers have a 7.9% chance to appear: to become an outlier, a value should be higher than 1.5IQR+Q3 that is approx. 2.7*standard deviation (or sigma, σ); the chances to get these values in a lognormal distribution are 7.9%.
Our initial dataset comprised 3,452 deals, which was refined to 2,246 after manual verification of deals and investor information, along with the removal of misleading entries. The complete data cleaning methodology is detailed in Appendix 3.
Using these data, we can calculate an asset valuation growth rate (coded as “VAL-GR”) that is the difference between the first and last available valuations divided by the time needed to achieve it (Ramadan et al., 2015) and the time needed to get to the realization event (Time-to-Realization, coded as “TTR”).
VAL-GR was previously employed in various venture capital studies, both academic and industrial (Ramadan et al., 2015, Malyy et al., 2021). It provides a more granular view of the performance of a particular venture making it possible to implement a more detailed analysis.
TTR, in turn, demonstrates the time required to realize the invested asset that is of great interest to an ESO’s fund partners, especially for the limited ones (Amore et al., 2020). We identified two groups of realization events (coded as “Result”): exit of any kind (coded as “Exit”) and bankruptcy (coded as “Fail”). These types of results will be also employed further for the distributions analysis in terms of Exit/Fail ratio.
In calculating Time to Realization (TTR), we employed two methodologies: for successful exits, we measured the duration between initial investment and exit date; for failures, we used the duration between investment and bankruptcy date. For Value Growth Rate (VAL-GR), we similarly applied two approaches: for exits, we used the final valuation at the time of exit; for bankruptcies, we recorded zero terminal value at the bankruptcy date. This methodology allows for negative VAL-GR values, which are common in venture capital given the high failure rate of startups. Specifically, when an investment fails to preserve or grow value, resulting in a total loss (zero terminal value), it registers as negative growth, reflecting the complete erosion of invested capital over the measured time period.
The main advantages of these indicators compared to other common measures of invested venture performance (employee growth, valuation, market share, number of IP assets, etc.) is their transparency and comparability (Malyy et al., 2021). These metrics enable assessment of venture performance efficiency and investment effectiveness across ESOs (Ferson, 2010). By aggregating these indicators by ESO type, we can conduct comparative analysis of investment performance across different entrepreneurial support models.
To facilitate our analysis, we applied two key methodological assumptions. First, we modeled venture growth as a linear progression based on the most recent available valuation data. While using exit valuations would have been optimal, the limited availability of such data would have significantly reduced our sample size. Second, we simplified the treatment of failed ventures by assigning them zero terminal value, although research by Levratto (2013) suggests that bankrupted ventures may retain residual value. This simplification was necessary due to the absence of reliable data on residual values in existing databases.
As a data source for deal information, we selected PitchBook. After considering other databases available to us (Dealroom, Crunchbase, Tracxn), we came to the conclusion that PitchBook has the most extensive and accurate data on the deals of interest.
Due to the fact that pre-seed deals are not uniquely identified in the selected database (Gao, 2023a), we have developed and applied filters to select pre-seed deals from the set of Seed deals. Based on the PitchBook methodology provided in the recent pre-seed report (Gao, 2023b), we developed our own criteria:
- We considered only the first institutional seed investment in the startup lifecycle. Of course, there is a possibility that a company has raised an investment, pivoted, and raised again, but we believe this situation is quite rare.
- We limited the deal size to $500k since this number, according to the reports (Annual US VC Valuations Report, 2023, Annual European VC Valuations Report, 2023), is the third quartile for all global pre-seed deals and the first quartile of the seed deals. In other words, 75% of the pre-seed deals are below this number, while 75% of the seed ones are above.
- We controlled the startups’ age to be not higher than two years at the moment of the first investment, thus, avoiding the possibility of taking a mature company with developed operations that has raised a relatively small round of investment instead of a venture that is in its initial pre-seed growth phase.
We scanned through PitchBook and included all pre-seed deals which:
- Were implemented by institutional investors, b. happened during the last ten years,
- Demonstrated a Result, and
- Have at least two rounds with disclosed valuation including the first round.
We did not limit the sample geographically or by domains. The exact filtering procedure is described in Appendix 3.
Following our collection of pre-seed investment data, we conducted a three-stage analysis process. We first performed manual identification of initial investors for each deal, categorized these investors by type, and grouped the data according to our established entrepreneurial support organization (ESO) classifications.
We then analyzed two key performance metrics: Time-to-Round (TTR) and Valuation Growth Rate (VAL-GR). Our statistical analysis began with normality testing using the Kolmogorov-Smirnov test (for samples larger than 50 observations), following the methodology outlined by Mishra et al. (2019). The results indicated that neither metric followed a normal distribution at the 0.05 significance level. Instead, the data most closely approximated a lognormal distribution, which informed our subsequent statistical approaches.
Therefore, for further analysis, we conducted a nonparametric significance test (in particular, Kruskal-Wallis analysis of variance, KWANOVA, “Kruskal Wallis Test - an overview | ScienceDirect Topics,” n.d.), non-parametric pairwise comparison test (Dunn’s test, “Dunn’s test,” 2017), and used medians instead of means to compare centralities of distributions (Mishra et al., 2019). We then produced box-and-whisker plots for distribution illustration including medians, interquartile range (IQR), whiskers (1.5IQR+Q3), and outliers(>1.5IQR+Q3) as the major indicators (Mishra et al., 2019). It also should be noted that the outliers have a 7.9% chance to appear: to become an outlier, a value should be higher than 1.5IQR+Q3 that is approx. 2.7*standard deviation (or sigma, σ); the chances to get these values in a lognormal distribution are 7.9%.
Our initial dataset comprised 3,452 deals, which was refined to 2,246 after manual verification of deals and investor information, along with the removal of misleading entries. The complete data cleaning methodology is detailed in Appendix 3.
Descriptive statistics
Let us first present you with descriptive statistics of the sample.
Out of 2,246 deals identified by our criteria as suitable for analysis, 1,361 were made by accelerators (coded as “ACC”), 743 by pre-seed VC funds (coded as “VC”), 104 by founders-first VC funds (coded as “VC-FF”), and 38 by venture studios (coded as “VS”).
Our analysis identified 715 distinct ESOs, distributed across several categories. Traditional pre-seed venture capital funds represented the largest group with 459 entities, followed by accelerators (222), venture studios (23), and founders-first venture capital funds (11).
Analysis of deal activity revealed notable variations in investment intensity across ESO types. While the overall sample averaged 3.14 deals per entity, founders-first VC funds demonstrated the highest activity level with 9.45 deals per entity, followed by accelerators at 6.13 deals per entity. Traditional venture capital firms and venture studios showed similar levels of activity, with 1.62 and 1.65 deals per entity respectively.
Although venture studios and founders-first VC funds represent smaller subsets in our sample, their inclusion provides meaningful research value for several reasons:
Additionally, we can propose a few reasons for this variance. From our perspective, it can be explained by three factors: the “maturity” of an ESO, its “openness”, and managerial practices.
Starting with the latter, managerial practices, VSs are known to launch and provide pre-seed funds to fewer new ventures compared to the other entities that utilize the typical “spray-and-pray” approach (“The Risks Of Relying On Spray-and-Pray Seed Funds,” 2024). According to the results of our survey (that we carried out for another research question), venture studios typically aim to launch from one to three new ventures per year. Compared to the most active accelerators, for instance, it is 50 times (!) lower (“The most active startup accelerators and where they’re investing,” 2023). Nevertheless, it appears that managerial practices alone do not explain the relative similarity between the VCs and VSs in terms of the deal-per-entity ratio, but we believe that the next factor (openness) might.
Evaluating the "openness" factor comes with challenges. The transparency of investment activity varies significantly across different types of ESOs, potentially affecting our dataset's comprehensiveness. While we cannot precisely quantify these variations, several structural factors likely influence public disclosure patterns across ESO types. Venture studios, as documented by Kannan and Peterman (2022), typically incubate ventures internally until they reach later funding stages, usually Seed or Series A. This model may result in lower public visibility of their early-stage activities compared to other ESOs. In contrast, accelerators are inherently oriented toward rapid external capital attraction ("What is a Startup Accelerator," 2024), potentially leading to higher rates of public deal disclosure. Traditional venture capital firms frequently participate in syndicated investments ("VC co-investment syndication," 2021), which may explain their more extensive presence in public databases. These variations in disclosure practices present an opportunity for future research. One promising approach would be to compare the ratio of publicly announced portfolio companies to those reported in commercial databases across different ESO types. This analysis would help quantify potential reporting biases in current datasets.
The “maturity” factor, in turn, can be validated and accounted for in the results. To do that, we checked the distribution of first investment dates for each ESO type. The chart below illustrates that VC-FFs have significantly later first dates of pre-seed investments compared to the sample median (+2 years), VSs have a +0.5 year lag, while ACCs’ first-financing deals dates are close to the sample median and VCs median first investment date is 0.75 years sooner. This tendency is also repeated in the distribution of the ESOs’ pre-seed ventures foundation dates. This factor may explain the rest of the difference between VS and VC-FF when being compared to the other ESOs: these two entities, all other things being equal, had less time to realize their assets and demonstrate performance due to their “immaturity”.
Out of 2,246 deals identified by our criteria as suitable for analysis, 1,361 were made by accelerators (coded as “ACC”), 743 by pre-seed VC funds (coded as “VC”), 104 by founders-first VC funds (coded as “VC-FF”), and 38 by venture studios (coded as “VS”).
Our analysis identified 715 distinct ESOs, distributed across several categories. Traditional pre-seed venture capital funds represented the largest group with 459 entities, followed by accelerators (222), venture studios (23), and founders-first venture capital funds (11).
Analysis of deal activity revealed notable variations in investment intensity across ESO types. While the overall sample averaged 3.14 deals per entity, founders-first VC funds demonstrated the highest activity level with 9.45 deals per entity, followed by accelerators at 6.13 deals per entity. Traditional venture capital firms and venture studios showed similar levels of activity, with 1.62 and 1.65 deals per entity respectively.
Although venture studios and founders-first VC funds represent smaller subsets in our sample, their inclusion provides meaningful research value for several reasons:
- We have limited our data to one source that was identified during the study as the most prominent database able to provide the best results. Collecting data from various sources would have influenced the distribution of deals between various ESOs and, thus, added sampling bias.
- We collected all pre-seed deals by our criteria not limiting them to any particular ESO, geography, or startup industry. We believe these are at least 90% of all pre-seed deals available for the selected research methods.
- We identified and assigned ESOs that participated in the first financing round after all other cleaning procedures were implemented. In other words, we haven’t collected the deals specifically for a particular ESO type. Therefore, this distribution of the number of deals is natural and valid in terms of data collection procedures.
Additionally, we can propose a few reasons for this variance. From our perspective, it can be explained by three factors: the “maturity” of an ESO, its “openness”, and managerial practices.
Starting with the latter, managerial practices, VSs are known to launch and provide pre-seed funds to fewer new ventures compared to the other entities that utilize the typical “spray-and-pray” approach (“The Risks Of Relying On Spray-and-Pray Seed Funds,” 2024). According to the results of our survey (that we carried out for another research question), venture studios typically aim to launch from one to three new ventures per year. Compared to the most active accelerators, for instance, it is 50 times (!) lower (“The most active startup accelerators and where they’re investing,” 2023). Nevertheless, it appears that managerial practices alone do not explain the relative similarity between the VCs and VSs in terms of the deal-per-entity ratio, but we believe that the next factor (openness) might.
Evaluating the "openness" factor comes with challenges. The transparency of investment activity varies significantly across different types of ESOs, potentially affecting our dataset's comprehensiveness. While we cannot precisely quantify these variations, several structural factors likely influence public disclosure patterns across ESO types. Venture studios, as documented by Kannan and Peterman (2022), typically incubate ventures internally until they reach later funding stages, usually Seed or Series A. This model may result in lower public visibility of their early-stage activities compared to other ESOs. In contrast, accelerators are inherently oriented toward rapid external capital attraction ("What is a Startup Accelerator," 2024), potentially leading to higher rates of public deal disclosure. Traditional venture capital firms frequently participate in syndicated investments ("VC co-investment syndication," 2021), which may explain their more extensive presence in public databases. These variations in disclosure practices present an opportunity for future research. One promising approach would be to compare the ratio of publicly announced portfolio companies to those reported in commercial databases across different ESO types. This analysis would help quantify potential reporting biases in current datasets.
The “maturity” factor, in turn, can be validated and accounted for in the results. To do that, we checked the distribution of first investment dates for each ESO type. The chart below illustrates that VC-FFs have significantly later first dates of pre-seed investments compared to the sample median (+2 years), VSs have a +0.5 year lag, while ACCs’ first-financing deals dates are close to the sample median and VCs median first investment date is 0.75 years sooner. This tendency is also repeated in the distribution of the ESOs’ pre-seed ventures foundation dates. This factor may explain the rest of the difference between VS and VC-FF when being compared to the other ESOs: these two entities, all other things being equal, had less time to realize their assets and demonstrate performance due to their “immaturity”.
Pre-seed funding size
Our analysis of pre-seed investment sizes reveals distinct patterns across ESO types. Venture studios demonstrate the largest interquartile range (IQR) with a median investment of $0.13 million. Notably, their 75th percentile reaches approximately $0.4 million, approaching our defined upper threshold for pre-seed investments ($0.5 million). This relatively high median and upper range suggest venture studios commit more capital at the pre-seed stage compared to other ESOs, potentially due to their high-engagement model which provides greater operational control and thus may reduce perceived investment risk.
In contrast, accelerators and founders-first VCs exhibit more concentrated investment ranges, with interquartile ranges of $0.02-0.12 million and $0.04-0.14 million respectively. This tighter distribution suggests a more standardized investment approach among these ESOs. Analysis of variance confirms statistically significant differences (p < 0.05) between three pairs of ESO types: traditional VC/accelerators, accelerators/venture studios, and accelerators/founders-first VCs.
The broader dispersion of venture studio investment sizes, combined with their higher median investment, suggests less standardized investment practices compared to other ESOs. This variation may indicate that venture studio investment decisions are more highly customized to individual ventures, while other ESO types have developed more systematic investment frameworks.
Our analysis of pre-seed investment sizes reveals distinct patterns across ESO types. Venture studios demonstrate the largest interquartile range (IQR) with a median investment of $0.13 million. Notably, their 75th percentile reaches approximately $0.4 million, approaching our defined upper threshold for pre-seed investments ($0.5 million). This relatively high median and upper range suggest venture studios commit more capital at the pre-seed stage compared to other ESOs, potentially due to their high-engagement model which provides greater operational control and thus may reduce perceived investment risk.
In contrast, accelerators and founders-first VCs exhibit more concentrated investment ranges, with interquartile ranges of $0.02-0.12 million and $0.04-0.14 million respectively. This tighter distribution suggests a more standardized investment approach among these ESOs. Analysis of variance confirms statistically significant differences (p < 0.05) between three pairs of ESO types: traditional VC/accelerators, accelerators/venture studios, and accelerators/founders-first VCs.
The broader dispersion of venture studio investment sizes, combined with their higher median investment, suggests less standardized investment practices compared to other ESOs. This variation may indicate that venture studio investment decisions are more highly customized to individual ventures, while other ESO types have developed more systematic investment frameworks.
Pre-seed funding shares
Our analysis of venture studio equity positions aligns broadly with previous findings from Kannan and Peterman (2022), while revealing some notable variations. The data shows venture studio ownership stakes ranging from 5.5% to 36% (10th to 90th percentile), with an interquartile range of 12-23% and a median of 17%. While these figures are lower than the 25-40% typical range reported by Kannan and Peterman (2022), they remain within their observed 5-50% overall distribution and significantly exceed the equity positions taken by other ESO types. The variance analysis indicates statistically significant differences (p < 0.05) between venture studios and all other ESO types, except founders-first VCs. However, these findings should be considered in the context of our sample size, which comprises 38 deals from 23 venture studios.
A notable case study within our sample is BlueChilli, the most active venture studio observed. Their operational model, taking 15-18% equity stakes, appeared to hybrid accelerator and studio approaches - combining founder-proposed ideas and cohort-based programming with studio-style technical support and operational control. However, the studio's subsequent inactivity (as evidenced by their dormant website) may offer insights into the challenges of this hybrid model.
The broader dispersion in venture studio equity stakes, compared to the more standardized approaches of accelerators, suggests the venture studio model remains in an evolutionary phase. This variability indicates the industry is still experimenting to identify optimal ownership structures that balance studio economics with founder incentives.
Our analysis of venture studio equity positions aligns broadly with previous findings from Kannan and Peterman (2022), while revealing some notable variations. The data shows venture studio ownership stakes ranging from 5.5% to 36% (10th to 90th percentile), with an interquartile range of 12-23% and a median of 17%. While these figures are lower than the 25-40% typical range reported by Kannan and Peterman (2022), they remain within their observed 5-50% overall distribution and significantly exceed the equity positions taken by other ESO types. The variance analysis indicates statistically significant differences (p < 0.05) between venture studios and all other ESO types, except founders-first VCs. However, these findings should be considered in the context of our sample size, which comprises 38 deals from 23 venture studios.
A notable case study within our sample is BlueChilli, the most active venture studio observed. Their operational model, taking 15-18% equity stakes, appeared to hybrid accelerator and studio approaches - combining founder-proposed ideas and cohort-based programming with studio-style technical support and operational control. However, the studio's subsequent inactivity (as evidenced by their dormant website) may offer insights into the challenges of this hybrid model.
The broader dispersion in venture studio equity stakes, compared to the more standardized approaches of accelerators, suggests the venture studio model remains in an evolutionary phase. This variability indicates the industry is still experimenting to identify optimal ownership structures that balance studio economics with founder incentives.
Pre-seed funding valuations
The analysis of pre-seed valuations reveals nuanced patterns across ESO types. Venture studios demonstrate median valuations of $1.09 million, positioning them between the sample-wide median ($0.79 million) and traditional VC valuations ($1.25 million). Accelerators exhibit the lowest median valuations at $0.58 million, likely reflecting their standardized investment approach – offering predetermined terms rather than conducting individualized startup valuations. Venture studios again display the widest interquartile range among all ESO types and the broader sample, suggesting continued experimentation in valuation methodologies. However, analysis of variance testing reveals statistically significant differences (p < 0.05) only between traditional VC/accelerator and traditional VC/founders-first VC pairs.
This finding leads to an important insight: despite notable variations in equity stakes and funding amounts, pre-seed valuations remain relatively consistent across ESO types. This suggests a balancing effect where larger funding amounts are offset by corresponding equity stakes, resulting in comparable valuations. This equilibrium may indicate the emergence of market-driven valuation norms at the pre-seed stage, even as ESOs maintain distinct investment approaches.
The analysis of pre-seed valuations reveals nuanced patterns across ESO types. Venture studios demonstrate median valuations of $1.09 million, positioning them between the sample-wide median ($0.79 million) and traditional VC valuations ($1.25 million). Accelerators exhibit the lowest median valuations at $0.58 million, likely reflecting their standardized investment approach – offering predetermined terms rather than conducting individualized startup valuations. Venture studios again display the widest interquartile range among all ESO types and the broader sample, suggesting continued experimentation in valuation methodologies. However, analysis of variance testing reveals statistically significant differences (p < 0.05) only between traditional VC/accelerator and traditional VC/founders-first VC pairs.
This finding leads to an important insight: despite notable variations in equity stakes and funding amounts, pre-seed valuations remain relatively consistent across ESO types. This suggests a balancing effect where larger funding amounts are offset by corresponding equity stakes, resulting in comparable valuations. This equilibrium may indicate the emergence of market-driven valuation norms at the pre-seed stage, even as ESOs maintain distinct investment approaches.
* Note to the chart: some data values on the top (sample diamond marks left on the chart) may be cropped for readability purposes.
Results and discussion
Exit/Fail Ratio
The analyzed ESOs also represent the variability in the proportion of Exits and Fails (defined during the data collection and filtering procedure). The full sample shows the 22% / 78% in the Exit/Fail ratio that, in general, follows the publicly available statistics, according to which “75% of startups backed by VC funding fail” (Kapoor, 2023). Analysis of exit performance across ESO types reveals distinct success patterns. Traditional pre-seed venture capital funds demonstrate the highest success rate, with 38% of their portfolio companies achieving successful exits. Venture studios follow with a 24% exit rate, while both accelerators and founders-first VCs show identical exit rates of 14%. The superior performance of traditional VCs, exceeding both the sample average and other ESO types, suggests potential advantages in their investment selection and portfolio support processes. Venture studios' above-average exit performance, while not matching traditional VCs, indicates their model may offer advantages over accelerator and founders-first VC approaches in driving successful outcomes.
Considering the relative nascency of the VS phenomenon (76% of the studios were launched in the last 10 years), their relatively strong exit performance merits continued attention from both researchers and industry practitioners.
The analyzed ESOs also represent the variability in the proportion of Exits and Fails (defined during the data collection and filtering procedure). The full sample shows the 22% / 78% in the Exit/Fail ratio that, in general, follows the publicly available statistics, according to which “75% of startups backed by VC funding fail” (Kapoor, 2023). Analysis of exit performance across ESO types reveals distinct success patterns. Traditional pre-seed venture capital funds demonstrate the highest success rate, with 38% of their portfolio companies achieving successful exits. Venture studios follow with a 24% exit rate, while both accelerators and founders-first VCs show identical exit rates of 14%. The superior performance of traditional VCs, exceeding both the sample average and other ESO types, suggests potential advantages in their investment selection and portfolio support processes. Venture studios' above-average exit performance, while not matching traditional VCs, indicates their model may offer advantages over accelerator and founders-first VC approaches in driving successful outcomes.
Considering the relative nascency of the VS phenomenon (76% of the studios were launched in the last 10 years), their relatively strong exit performance merits continued attention from both researchers and industry practitioners.
Valuation growth rate (VAL-GR)
Let’s turn to the performance measures. As mentioned above, to assess the performance of the selected ESOs, we chose the portfolio valuation growth rate as a major indicator (VAL-GR, $M/year).
This analysis reveals a pattern of performance across ESO types. Traditional pre-seed VC funds show the strongest (read: least negative) median performance (-$0.12M/year), followed by venture studios (-$0.14M/year), accelerators (-$0.18M/year), and founders-first VCs (-$0.29M/year). Compared to the sample median of -$0.16M/year, traditional VCs and venture studios outperform by 29% and 13% respectively, while accelerators and founders-first VCs underperform by 12% and 58% respectively.
The prevalence of negative growth rates and modest median values (below $1M/year) aligns with expected venture capital dynamics. Given that 78% of pre-seed investments in our sample result in failure (zero terminal valuation), negative growth rates are mathematically inevitable. When a venture fails, its growth rate is calculated as (0 - Initial Valuation) / Time Period, necessarily yielding a negative value. The dominance of failure outcomes in early-stage ventures explains the consistently negative median growth rates across all ESO types.
The magnitude of these negative growth rates can be understood through the interaction of three factors: pre-seed valuations (ranging from $0.58M to $1.25M), average time-to-fail (2.8-4.2 years), and the high probability of failure. These parameters mathematically constrain the typical VAL-GR for failed ventures to between -$0.14M/year and -$0.45M/year, consistent with our observed distributions, as indicated in the box charts below.
Let’s turn to the performance measures. As mentioned above, to assess the performance of the selected ESOs, we chose the portfolio valuation growth rate as a major indicator (VAL-GR, $M/year).
This analysis reveals a pattern of performance across ESO types. Traditional pre-seed VC funds show the strongest (read: least negative) median performance (-$0.12M/year), followed by venture studios (-$0.14M/year), accelerators (-$0.18M/year), and founders-first VCs (-$0.29M/year). Compared to the sample median of -$0.16M/year, traditional VCs and venture studios outperform by 29% and 13% respectively, while accelerators and founders-first VCs underperform by 12% and 58% respectively.
The prevalence of negative growth rates and modest median values (below $1M/year) aligns with expected venture capital dynamics. Given that 78% of pre-seed investments in our sample result in failure (zero terminal valuation), negative growth rates are mathematically inevitable. When a venture fails, its growth rate is calculated as (0 - Initial Valuation) / Time Period, necessarily yielding a negative value. The dominance of failure outcomes in early-stage ventures explains the consistently negative median growth rates across all ESO types.
The magnitude of these negative growth rates can be understood through the interaction of three factors: pre-seed valuations (ranging from $0.58M to $1.25M), average time-to-fail (2.8-4.2 years), and the high probability of failure. These parameters mathematically constrain the typical VAL-GR for failed ventures to between -$0.14M/year and -$0.45M/year, consistent with our observed distributions, as indicated in the box charts below.
The analysis of variance demonstrates that at a 0.05 significance level, the overall sample has significantly different portfolio growth rates regarding the considered ESOs. However, pairwise comparison shows that it is significant only for VC / ACC and VC / VC-FF pairs. Since the latter may be explained by possible bias in the VC-FF deals due to the lag in deal dates, the only significant conclusion is that pre-seed VC funds “produce” 40% faster-growing startups than accelerators. All the remaining ESOs do not demonstrate a significant difference in portfolio growth rates.
Nevertheless, it is worth noting that the size of the IQR and tails for the pre-seed VC funds are substantially higher than for the sample and the rest of the ESOs. From our perspective, such variability partly can be explained by the pre-seed VC funds manifold: out of 715 identified in the study ESOs, 459 (64%) are pre-seed VC funds. Many various funds invest in pre-seed ventures while applying different insights, distinct experiences, and valuation practices that lead to a higher variability in the outcomes.
Another explanation is related to the fact that all ESOs except pre-seed VC funds have some level of influence (more than smart money) on the portfolio companies during their early stages. All other ESOs - ACCs, VSs, VC-FFs - have some degree of control over the earliest stages of startups’ evolution depending on ESO type and venture phase: accelerators begin to influence starting with the customer development process; founders-first VC funds start during the idea generation of a team, and VSs get involved from the very beginning and until spin out - that is: idea, team, MVP, and customer development. From the results, this early-stage influence tends to decrease the IQR sizes (i.e., variance) of the portfolio valuation growth rates.
Our analysis suggests a correlation between ESO operational involvement and venture growth predictability. Organizations that maintain greater operational control during early-stage development appear to achieve more consistent growth patterns, effectively de-risking the venture creation process. While traditional VC-backed startups demonstrate higher growth potential, reaching up to $6M/year within normal distribution bounds (Q3 + 1.5IQR), they also show greater downside volatility, with potential losses of up to -$4M/year. In contrast, other ESO types exhibit more moderate downside risk, with losses typically confined to the -$1M to -$1.5M/year range, suggesting their higher degree of operational involvement may create a stabilizing effect on venture performance.
The picture is even more obvious if we account for the outliers and compare the means of ESOs’ VAL-GR instead of medians:
According to the means, pre-seed VC funds are the landslide winner among all ESO types, apparently because they generate more successful outliers, albeit with higher risks of failure according to the higher IQR (or variance). The outliers of the VAL-GR metric, in turn, greatly reflect the general principles of venture capital (Zider, 1998): a few deals generate outstanding results that compensate for all the investments made. That is, these outliers are a feature, not a bug. This conclusion goes in line with what was already said on this topic (Strebulaev and Dang, 2024, Fortech Investments, 2023, Included VC, 2021) but adds statistical backing to the phenomenon.
So much attention is often spent normalizing data that outliers are commonly trimmed out of a sample by habit. But, in the case of an ESO's venture portfolio, outliers are a key feature of success. Let's look closer at these outliers.
VAL-GR: outliers
Suppose we perform a quartile analysis of the successful outliers (i.e., VAL-GR>Q3+1.5IQR) segmented by ESO type. In that case, we notice that accelerators behave quite similarly to the sample median. Both venture studios and traditional pre-seed VC funds demonstrate superior performance in terms of upper-quartile valuation growth, with median rates 2.4 times higher than the overall sample median for successful cases. This parallel performance in the upper range suggests that both models can achieve similar levels of exceptional outcomes despite their different approaches to venture creation and development.
Our analysis of the gap between mean and median values reveals an intriguing pattern in venture studio performance. The smaller mean-median differential for venture studios, compared to the overall sample and other ESO types, indicates a more concentrated distribution of successful outcomes. This finding suggests two key insights about venture studio performance: while they produce fewer statistical outliers in terms of exceptional growth rates, those portfolio companies that do achieve outstanding performance tend to demonstrate higher median growth rates.
This pattern stands in marked contrast to traditional venture capital firms, which exhibit a broader distribution of outcomes. Traditional VCs generate more outlier cases, as evidenced by greater skewness in their distribution, while achieving similar median growth rates among their top performers to venture studios.
Nevertheless, it is worth noting that the size of the IQR and tails for the pre-seed VC funds are substantially higher than for the sample and the rest of the ESOs. From our perspective, such variability partly can be explained by the pre-seed VC funds manifold: out of 715 identified in the study ESOs, 459 (64%) are pre-seed VC funds. Many various funds invest in pre-seed ventures while applying different insights, distinct experiences, and valuation practices that lead to a higher variability in the outcomes.
Another explanation is related to the fact that all ESOs except pre-seed VC funds have some level of influence (more than smart money) on the portfolio companies during their early stages. All other ESOs - ACCs, VSs, VC-FFs - have some degree of control over the earliest stages of startups’ evolution depending on ESO type and venture phase: accelerators begin to influence starting with the customer development process; founders-first VC funds start during the idea generation of a team, and VSs get involved from the very beginning and until spin out - that is: idea, team, MVP, and customer development. From the results, this early-stage influence tends to decrease the IQR sizes (i.e., variance) of the portfolio valuation growth rates.
Our analysis suggests a correlation between ESO operational involvement and venture growth predictability. Organizations that maintain greater operational control during early-stage development appear to achieve more consistent growth patterns, effectively de-risking the venture creation process. While traditional VC-backed startups demonstrate higher growth potential, reaching up to $6M/year within normal distribution bounds (Q3 + 1.5IQR), they also show greater downside volatility, with potential losses of up to -$4M/year. In contrast, other ESO types exhibit more moderate downside risk, with losses typically confined to the -$1M to -$1.5M/year range, suggesting their higher degree of operational involvement may create a stabilizing effect on venture performance.
The picture is even more obvious if we account for the outliers and compare the means of ESOs’ VAL-GR instead of medians:
- ALL: $10.56M/year
- VC: $19.67M/year
- ACC: $6.54M/year
- VS: $3.82M/year
- VC-FF: $0.65M/year
According to the means, pre-seed VC funds are the landslide winner among all ESO types, apparently because they generate more successful outliers, albeit with higher risks of failure according to the higher IQR (or variance). The outliers of the VAL-GR metric, in turn, greatly reflect the general principles of venture capital (Zider, 1998): a few deals generate outstanding results that compensate for all the investments made. That is, these outliers are a feature, not a bug. This conclusion goes in line with what was already said on this topic (Strebulaev and Dang, 2024, Fortech Investments, 2023, Included VC, 2021) but adds statistical backing to the phenomenon.
So much attention is often spent normalizing data that outliers are commonly trimmed out of a sample by habit. But, in the case of an ESO's venture portfolio, outliers are a key feature of success. Let's look closer at these outliers.
VAL-GR: outliers
Suppose we perform a quartile analysis of the successful outliers (i.e., VAL-GR>Q3+1.5IQR) segmented by ESO type. In that case, we notice that accelerators behave quite similarly to the sample median. Both venture studios and traditional pre-seed VC funds demonstrate superior performance in terms of upper-quartile valuation growth, with median rates 2.4 times higher than the overall sample median for successful cases. This parallel performance in the upper range suggests that both models can achieve similar levels of exceptional outcomes despite their different approaches to venture creation and development.
Our analysis of the gap between mean and median values reveals an intriguing pattern in venture studio performance. The smaller mean-median differential for venture studios, compared to the overall sample and other ESO types, indicates a more concentrated distribution of successful outcomes. This finding suggests two key insights about venture studio performance: while they produce fewer statistical outliers in terms of exceptional growth rates, those portfolio companies that do achieve outstanding performance tend to demonstrate higher median growth rates.
This pattern stands in marked contrast to traditional venture capital firms, which exhibit a broader distribution of outcomes. Traditional VCs generate more outlier cases, as evidenced by greater skewness in their distribution, while achieving similar median growth rates among their top performers to venture studios.
Founders-first VC funds exhibit notably lower performance among their outliers, with median VAL-GR 1.5 times below the sample median. This underperformance likely reflects the temporal characteristics of their investments: their pre-seed deals are, on average, two years more recent than the sample median, providing less time for value appreciation.
Statistical analysis of variance at the 0.05 significance level reveals that meaningful differences in outlier growth rates exist only between traditional VCs and two other groups: accelerators and founders-first VCs. This finding carries important implications for interpreting venture studio performance. While venture studios show promising trends in generating rapid growth among their successful portfolio companies - comparable to traditional VC funds - these differences do not yet reach statistical significance in our current dataset.
The data suggest an emerging pattern: venture studios appear capable of producing high-velocity growth companies at rates similar to traditional venture capital firms, but with lower frequency. However, given the limited sample size of venture studio deals, further research with an expanded dataset would be valuable to validate these preliminary findings and better understand the effectiveness of the venture studio model in generating exceptional outcomes.
VAL-GR: outstanding organizations
We also checked the distribution of ESOs in terms of deal count and identified several outliers with the highest numbers of pre-seed deals (across the >10 deals subsample). For research purposes, we marked organizations with the highest investment number as “active” and the rest as “not active”.
In particular, in the ACC space, these are Y Combinator (239 deals or 18%), Techstars (173 deals or 13%), and Startupbootcamp (77 deals or 6%). From pre-seed VC funds, these are Hiventures (20 deals or 3%), SFC Capital (19 deals or 3%), Boost VC (15 deals or 2%), and Rockstart (15 deals or 2%). Among VC-FFs, the most active investors are Entrepreneur First (58 deals or 56%) and Antler (27 deals or 26%). We haven’t identified such outliers among VSs, but the closest to 10 deals were BlueChilli (8 deals or 21%) - an Australian studio with acceleration programs, which used to support founders with building products, gaining pilots, securing investment, and establishing their first team (“BlueChilli - Crunchbase Investor Profile & Investments,” n.d.).
Comparing the VAL-GR of these outstanding organizations, we can see that the median VAL-GR between active and not active entities is similar in accelerators and founders-first VC funds domains. Obviously, the analysis of variance also resulted in non-significant differences between the medians. However, we see a clear difference in the creation of outliers: while active ACCs bring outliers with substantially higher average growth speed (8.71 vs 0.29 $M/year, for active and not, respectively), active VC-FFs demonstrate the opposite (0.46 vs 1.53 $M/year, for active and not, respectively).
Considering pre-seed VC funds and venture studios, only the former demonstrated a statistically significant difference in VAL-GR medians when segmenting active organizations from not active ones. VSs, in turn, showed a non-significant distinction in medians, albeit a relatively high: -0.25 $M/year for not active vs -0.06$M/year for active ones. Regarding the means, in both cases, we see that the higher deal count by an ESO did not necessarily lead to faster-growing outliers.
This analysis reveals no clear causal relationship between an organization's pre-seed deal volume and its success rate in generating high-performing ventures. The data suggests that both high-volume and selective investment strategies can produce successful outcomes, indicating that raw deal quantity may not be a decisive factor in ESO performance. Rather, our findings point to the potential importance of organization-specific characteristics beyond simple "spray and pray" investment approaches. The success of active accelerators in our sample provides an instructive starting point for investigating these organizational features. Their ability to consistently produce faster-growing startups suggests the presence of specific organizational capabilities or practices that enhance venture development outcomes. Future research should focus on identifying and analyzing these distinctive organizational characteristics across ESO types to better understand the drivers of venture success.
Statistical analysis of variance at the 0.05 significance level reveals that meaningful differences in outlier growth rates exist only between traditional VCs and two other groups: accelerators and founders-first VCs. This finding carries important implications for interpreting venture studio performance. While venture studios show promising trends in generating rapid growth among their successful portfolio companies - comparable to traditional VC funds - these differences do not yet reach statistical significance in our current dataset.
The data suggest an emerging pattern: venture studios appear capable of producing high-velocity growth companies at rates similar to traditional venture capital firms, but with lower frequency. However, given the limited sample size of venture studio deals, further research with an expanded dataset would be valuable to validate these preliminary findings and better understand the effectiveness of the venture studio model in generating exceptional outcomes.
VAL-GR: outstanding organizations
We also checked the distribution of ESOs in terms of deal count and identified several outliers with the highest numbers of pre-seed deals (across the >10 deals subsample). For research purposes, we marked organizations with the highest investment number as “active” and the rest as “not active”.
In particular, in the ACC space, these are Y Combinator (239 deals or 18%), Techstars (173 deals or 13%), and Startupbootcamp (77 deals or 6%). From pre-seed VC funds, these are Hiventures (20 deals or 3%), SFC Capital (19 deals or 3%), Boost VC (15 deals or 2%), and Rockstart (15 deals or 2%). Among VC-FFs, the most active investors are Entrepreneur First (58 deals or 56%) and Antler (27 deals or 26%). We haven’t identified such outliers among VSs, but the closest to 10 deals were BlueChilli (8 deals or 21%) - an Australian studio with acceleration programs, which used to support founders with building products, gaining pilots, securing investment, and establishing their first team (“BlueChilli - Crunchbase Investor Profile & Investments,” n.d.).
Comparing the VAL-GR of these outstanding organizations, we can see that the median VAL-GR between active and not active entities is similar in accelerators and founders-first VC funds domains. Obviously, the analysis of variance also resulted in non-significant differences between the medians. However, we see a clear difference in the creation of outliers: while active ACCs bring outliers with substantially higher average growth speed (8.71 vs 0.29 $M/year, for active and not, respectively), active VC-FFs demonstrate the opposite (0.46 vs 1.53 $M/year, for active and not, respectively).
Considering pre-seed VC funds and venture studios, only the former demonstrated a statistically significant difference in VAL-GR medians when segmenting active organizations from not active ones. VSs, in turn, showed a non-significant distinction in medians, albeit a relatively high: -0.25 $M/year for not active vs -0.06$M/year for active ones. Regarding the means, in both cases, we see that the higher deal count by an ESO did not necessarily lead to faster-growing outliers.
This analysis reveals no clear causal relationship between an organization's pre-seed deal volume and its success rate in generating high-performing ventures. The data suggests that both high-volume and selective investment strategies can produce successful outcomes, indicating that raw deal quantity may not be a decisive factor in ESO performance. Rather, our findings point to the potential importance of organization-specific characteristics beyond simple "spray and pray" investment approaches. The success of active accelerators in our sample provides an instructive starting point for investigating these organizational features. Their ability to consistently produce faster-growing startups suggests the presence of specific organizational capabilities or practices that enhance venture development outcomes. Future research should focus on identifying and analyzing these distinctive organizational characteristics across ESO types to better understand the drivers of venture success.
Time to result, TTR
Another critical parameter, which may reflect ESO performance, is the speed of investment realization or Time to Result, TTR. As mentioned above, the Result variable considered two types of events: Exit and Fail. We analyzed the time required to reach the result by its type and by the type of the ESO. Next, we built compound charts with normal approximations of the received distributions.
In calculating TTR, we employed two methodologies: for successful exits, we measured the duration between initial investment and exit date; for failures, we used the duration between investment and bankruptcy date.
Another critical parameter, which may reflect ESO performance, is the speed of investment realization or Time to Result, TTR. As mentioned above, the Result variable considered two types of events: Exit and Fail. We analyzed the time required to reach the result by its type and by the type of the ESO. Next, we built compound charts with normal approximations of the received distributions.
In calculating TTR, we employed two methodologies: for successful exits, we measured the duration between initial investment and exit date; for failures, we used the duration between investment and bankruptcy date.
Analysis of time-to-outcome metrics reveals distinct patterns across ESO types. Founders-first VC funds demonstrate the fastest overall time-to-outcome at 2.8 years, followed by accelerators and venture studios (3.5 and 3.6 years respectively, near the sample mean of 3.7 years), while traditional pre-seed VC funds show the longest duration at 4.2 years.
When examining unsuccessful ventures specifically, founders-first VCs again show the shortest time-to-fail at 2.4 years, while other ESO types cluster around the sample mean of 3.3 years. For successful exits, the pattern shifts notably: venture studios lead with the fastest exit times (4.5 years), followed by accelerators (4.9 years), founders-first VCs (5.0 years), and traditional VCs (5.3 years).
However, these findings warrant cautious interpretation, particularly regarding founders-first VC performance. Their notably shorter timelines may reflect a temporal bias in our dataset, as their investments are more recent than other ESO types. This recency could result in an overrepresentation of quick failures and an underrepresentation of successful but longer-duration outcomes in their portfolio.
According to the analysis of variance, the difference between ESOs in time to result is significant at the 0.05 level for any Result, not significant for Exits, and again significant for Fails. However, pairwise comparison shows that, excluding the VC-FF pairs due to possible bias, the only significant difference is presented for the VC/ACC pair with regard to any Result. Thus, we can only conclude that accelerators demonstrate faster realization results than pre-seed VC funds without accounting for the success of the event. The rest of the considered ESOs are not significantly different (excluding potentially biased VC-FFs) in terms of TTR.
When examining unsuccessful ventures specifically, founders-first VCs again show the shortest time-to-fail at 2.4 years, while other ESO types cluster around the sample mean of 3.3 years. For successful exits, the pattern shifts notably: venture studios lead with the fastest exit times (4.5 years), followed by accelerators (4.9 years), founders-first VCs (5.0 years), and traditional VCs (5.3 years).
However, these findings warrant cautious interpretation, particularly regarding founders-first VC performance. Their notably shorter timelines may reflect a temporal bias in our dataset, as their investments are more recent than other ESO types. This recency could result in an overrepresentation of quick failures and an underrepresentation of successful but longer-duration outcomes in their portfolio.
According to the analysis of variance, the difference between ESOs in time to result is significant at the 0.05 level for any Result, not significant for Exits, and again significant for Fails. However, pairwise comparison shows that, excluding the VC-FF pairs due to possible bias, the only significant difference is presented for the VC/ACC pair with regard to any Result. Thus, we can only conclude that accelerators demonstrate faster realization results than pre-seed VC funds without accounting for the success of the event. The rest of the considered ESOs are not significantly different (excluding potentially biased VC-FFs) in terms of TTR.
Investment performance measures (model)
Despite the fact we could not collect enough deal data to study VSs’ performance from investors’ point of view, we can model the missing measures and demonstrate some interesting conclusions (spoiler: IRR is not the best metric to compare the performance of entrepreneurial support organizations quantitatively).
We have modeled the portfolio of an abstract ESO with 100 deals, which have random features but follow the distributions of the research sample in each variable. The model can be found in Appendix 4 (sheet "IRR model (100 deals)"), you can copy it to your Google Drive and repeat our experiments. The Sheet “IRR model (1000 deals)” contains a similar model but for a 1,000 deals portfolio.
We made several assumptions, which we believe should not influence the overall model outcomes. First, we assumed that all deals are made by any ESO considered in the study. Of course, this is purely artificial but aims to demonstrate the model for all pre-seed deals, no matter which ESO was employed. Second, we assumed all deals to happen on one date of January 1, 2014. We believe the variance in the investment dates is not critical here. Third, we assumed 50% dilution for all deals. Unfortunately, we could not find any kind of reliable statistics which would provide any trustworthy grounds for assuming this value. Fourth, for exit valuations, we took the same distribution as we received for the last available funding valuations from the research sample. For sure, exit valuations may be significantly different from the ones received during some series of after-pre-seed investments, but we assume the distribution should be similar, especially in terms of Exit/Fail rate.
The model demonstrates that the created portfolio IRR equals 17% and, in general, follows the VC market averages for the pre-seed series of investments (PitchBook, 2023). But what is interesting is the sensitivity of the IRR measure. If we change one failed investment to a unicorn (yes, this is a very extreme case but that is what we saw happening in terms of the outliers), for instance, #11 that seems to have investment parameters very similar to an accelerator one, we will see that the IRR changed to 25% and demonstrated a 38% growth. If we further consider this deal as made by a venture studio and change the pre-seed ownership from 6% to 30% (that is believed to be an average for venture studios nowadays, Pog, 2023, Kannan and Peterman, 2022), we get a 40% IRR that is 46% higher than for 6% ownership and 81% higher compared to the initial case of a failed #11 investment. If we assume that all these deals were made by one venture studio (i.e., all first funding shares = 30%), we get a 34% IRR for the initial setting and a 46% IRR for the #11 unicorn deal case. Remember, we haven’t changed any other deal and considered only realized assets based on the real market raw data.
What does it mean? From our perspective, that means that IRR is a very tricky measure that should be treated with caution: even one abnormally successful deal out of 100 (i.e., 1%) can increase IRR drastically. The situation can become even more tenuous if IRR is taken as an average of a relatively small sample with an intransparent methodology of measuring the IRR. If, for instance, unrealized assets are included, it could quickly outpace the market average with optimistic projections coming from fund managers (who won’t bet that at least 1% of their deals will become a unicorn?).
The 1,000-deal model (Appendix 4 (sheet "IRR model (1k deals)")), in turn, demonstrates the dependence of an IRR on the portfolio size. We applied the same rules and assumptions to this model, and the only difference from the first one is that it has 1,000 deals instead of 100. First, we calculated IRRs for every portfolio size while decreasing it by 100 in an iteration. Next, we changed the #976 deal (again, it looks similar to an accelerator’s and close to the end of the table, so as not to scroll 1k rows all the time) making it a unicorn one during the exit and wrote down the new IRRs. Finally, our sensitivity analysis reveals a significant relationship between portfolio size and the impact of exceptional performers on Internal Rate of Return (IRR). Portfolios containing 400-1,000 deals show minimal sensitivity to outlier performance, with IRR variations of only 1-2%. However, as portfolio size decreases below 300 deals, sensitivity increases markedly, reaching an extreme variation of 19% for portfolios of 100 deals. From this experiment, we can conclude that IRRs of smaller portfolios are largely sensitive to abnormally successful deals: as a rule of thumb, 100 deals may be taken as a threshold.
From our perspective, any measures assessing VS performance should be provided in a form of distributions and by actual status clustering, i.e., separating measures of, for instance, realized assets from all spun-out, alive, and failed. The one number, especially of such a sensitive metric as an IRR, cannot describe the full picture and, thus, can hardly be used for decision-making. We believe our principle can propose a more complex picture of venture studios and their dynamics ultimately leading to an investor’s ability to make more data-driven decisions.
Moreover, we derived two conceptual strategies that may be employed by both venture studios to manage their operations, and by investors to find and track potential outperformed.
Two strategies for venture studios
The results of the VAL-GR analysis - in particular, backing the major VC principles in terms of outliers - led us to develop a conceptual framework describing possible strategies for venture studio management and their relation to other ESOs.
If you remember, we have demonstrated that VAL-GR distribution in terms of realized assets for pre-seed deals has particular features:
We assume that these qualities may be employed to define and assess the ESOs' strategies for developing new ventures. In particular, taking outliers intents (further Outliers) and fails acceptance (further Fails) as two crossing axes with extreme cases in the ends (low-high), we can develop a 4-quadrant framework describing all existing ESOs’ strategies.
By VC principles (Zider. 1998), pre-seed VC funds, accelerators, and founders-first VC funds accept a higher fail rate while targeting the creation of more outliers which are expected to provide portfolio returns. These ESOs vary in the level of operational control, which is imposed to decrease the risk of failures. We can locate these ESOs in the lower right corner with some Fail-axis gradation.
Venture studios, in turn, aim to “de-risk” new venture development by making the number of fails lower (we see the evidence of that by the narrower IQR). Therefore, we locate them in the upper part of the framework, where fails are expected to be lower than in other ESOs.
Next, regarding the Outliers-axis, VSs seem to have two strategies:
Strategy 1: Rely on creating a portfolio of safer, less-disruptive ventures; or
Strategy 2: Decide to generate more potential disruptors, i.e., growth outliers.
Our results propose evidence in favor of the second strategy: median VAL-GR of VSs’ realized pre-seed assets is higher than the average and only lower than the median of the pre-seed VC funds (not statistically significant). VSs’ outliers also demonstrate a higher VAL-GR than ACCs and VC-FFs and are comparable to the results of VCs (again, not statistically significant).
At the same time, we heard from some studios that they target to create average-growth (in terms of a traditional VC) new ventures while not going into the disruptive space (e.g., Accelerators VS Venture Studios. Startupbootcamp & Venturerock | Marc Wesselink, 2023). From their perspective, these entities are able to provide viable returns under the higher-stake studio model while requiring lower risks to be accepted. Thus, these studios select the first strategy.
Despite the fact we could not collect enough deal data to study VSs’ performance from investors’ point of view, we can model the missing measures and demonstrate some interesting conclusions (spoiler: IRR is not the best metric to compare the performance of entrepreneurial support organizations quantitatively).
We have modeled the portfolio of an abstract ESO with 100 deals, which have random features but follow the distributions of the research sample in each variable. The model can be found in Appendix 4 (sheet "IRR model (100 deals)"), you can copy it to your Google Drive and repeat our experiments. The Sheet “IRR model (1000 deals)” contains a similar model but for a 1,000 deals portfolio.
We made several assumptions, which we believe should not influence the overall model outcomes. First, we assumed that all deals are made by any ESO considered in the study. Of course, this is purely artificial but aims to demonstrate the model for all pre-seed deals, no matter which ESO was employed. Second, we assumed all deals to happen on one date of January 1, 2014. We believe the variance in the investment dates is not critical here. Third, we assumed 50% dilution for all deals. Unfortunately, we could not find any kind of reliable statistics which would provide any trustworthy grounds for assuming this value. Fourth, for exit valuations, we took the same distribution as we received for the last available funding valuations from the research sample. For sure, exit valuations may be significantly different from the ones received during some series of after-pre-seed investments, but we assume the distribution should be similar, especially in terms of Exit/Fail rate.
The model demonstrates that the created portfolio IRR equals 17% and, in general, follows the VC market averages for the pre-seed series of investments (PitchBook, 2023). But what is interesting is the sensitivity of the IRR measure. If we change one failed investment to a unicorn (yes, this is a very extreme case but that is what we saw happening in terms of the outliers), for instance, #11 that seems to have investment parameters very similar to an accelerator one, we will see that the IRR changed to 25% and demonstrated a 38% growth. If we further consider this deal as made by a venture studio and change the pre-seed ownership from 6% to 30% (that is believed to be an average for venture studios nowadays, Pog, 2023, Kannan and Peterman, 2022), we get a 40% IRR that is 46% higher than for 6% ownership and 81% higher compared to the initial case of a failed #11 investment. If we assume that all these deals were made by one venture studio (i.e., all first funding shares = 30%), we get a 34% IRR for the initial setting and a 46% IRR for the #11 unicorn deal case. Remember, we haven’t changed any other deal and considered only realized assets based on the real market raw data.
What does it mean? From our perspective, that means that IRR is a very tricky measure that should be treated with caution: even one abnormally successful deal out of 100 (i.e., 1%) can increase IRR drastically. The situation can become even more tenuous if IRR is taken as an average of a relatively small sample with an intransparent methodology of measuring the IRR. If, for instance, unrealized assets are included, it could quickly outpace the market average with optimistic projections coming from fund managers (who won’t bet that at least 1% of their deals will become a unicorn?).
The 1,000-deal model (Appendix 4 (sheet "IRR model (1k deals)")), in turn, demonstrates the dependence of an IRR on the portfolio size. We applied the same rules and assumptions to this model, and the only difference from the first one is that it has 1,000 deals instead of 100. First, we calculated IRRs for every portfolio size while decreasing it by 100 in an iteration. Next, we changed the #976 deal (again, it looks similar to an accelerator’s and close to the end of the table, so as not to scroll 1k rows all the time) making it a unicorn one during the exit and wrote down the new IRRs. Finally, our sensitivity analysis reveals a significant relationship between portfolio size and the impact of exceptional performers on Internal Rate of Return (IRR). Portfolios containing 400-1,000 deals show minimal sensitivity to outlier performance, with IRR variations of only 1-2%. However, as portfolio size decreases below 300 deals, sensitivity increases markedly, reaching an extreme variation of 19% for portfolios of 100 deals. From this experiment, we can conclude that IRRs of smaller portfolios are largely sensitive to abnormally successful deals: as a rule of thumb, 100 deals may be taken as a threshold.
From our perspective, any measures assessing VS performance should be provided in a form of distributions and by actual status clustering, i.e., separating measures of, for instance, realized assets from all spun-out, alive, and failed. The one number, especially of such a sensitive metric as an IRR, cannot describe the full picture and, thus, can hardly be used for decision-making. We believe our principle can propose a more complex picture of venture studios and their dynamics ultimately leading to an investor’s ability to make more data-driven decisions.
Moreover, we derived two conceptual strategies that may be employed by both venture studios to manage their operations, and by investors to find and track potential outperformed.
Two strategies for venture studios
The results of the VAL-GR analysis - in particular, backing the major VC principles in terms of outliers - led us to develop a conceptual framework describing possible strategies for venture studio management and their relation to other ESOs.
If you remember, we have demonstrated that VAL-GR distribution in terms of realized assets for pre-seed deals has particular features:
- It may have high and low variance/IQR. This feature, from our perspective, is connected to the acceptance by the ESO model of unsuccessful investments or fails. The higher this acceptance is - the wider is the IQR and vice versa. For example, pre-seed VC funds have a significantly wider IQR than accelerators, founders-first VC funds, and venture studios, which all have internal mechanisms to decrease investment risks by increasing the influence on a new venture's early stages.
- It may have higher and lower statistical measures than the population distribution (quartiles, averages, outliers, etc.). These features seem to be connected with the ESOs target of generating extremely successful cases, i.e., outliers. The greater the intent to create extremely successful cases - the higher the statistical metrics.
We assume that these qualities may be employed to define and assess the ESOs' strategies for developing new ventures. In particular, taking outliers intents (further Outliers) and fails acceptance (further Fails) as two crossing axes with extreme cases in the ends (low-high), we can develop a 4-quadrant framework describing all existing ESOs’ strategies.
By VC principles (Zider. 1998), pre-seed VC funds, accelerators, and founders-first VC funds accept a higher fail rate while targeting the creation of more outliers which are expected to provide portfolio returns. These ESOs vary in the level of operational control, which is imposed to decrease the risk of failures. We can locate these ESOs in the lower right corner with some Fail-axis gradation.
Venture studios, in turn, aim to “de-risk” new venture development by making the number of fails lower (we see the evidence of that by the narrower IQR). Therefore, we locate them in the upper part of the framework, where fails are expected to be lower than in other ESOs.
Next, regarding the Outliers-axis, VSs seem to have two strategies:
Strategy 1: Rely on creating a portfolio of safer, less-disruptive ventures; or
Strategy 2: Decide to generate more potential disruptors, i.e., growth outliers.
Our results propose evidence in favor of the second strategy: median VAL-GR of VSs’ realized pre-seed assets is higher than the average and only lower than the median of the pre-seed VC funds (not statistically significant). VSs’ outliers also demonstrate a higher VAL-GR than ACCs and VC-FFs and are comparable to the results of VCs (again, not statistically significant).
At the same time, we heard from some studios that they target to create average-growth (in terms of a traditional VC) new ventures while not going into the disruptive space (e.g., Accelerators VS Venture Studios. Startupbootcamp & Venturerock | Marc Wesselink, 2023). From their perspective, these entities are able to provide viable returns under the higher-stake studio model while requiring lower risks to be accepted. Thus, these studios select the first strategy.
Therefore, we can assume that both strategies are viable; however, both have their limitations. Conceptually, going into the first may lead to a higher risk of being beaten by a competitive disruptive solution. If a studio decides not to scale a new venture into a disruptive one, another similar solution may not have this limitation and at some point of evolution ouperform the studio's venture. While acquisition by larger competitors represents a viable exit path, such outcomes typically generate more modest returns compared to ventures with higher growth trajectories. This trade-off raises important questions about risk-return profiles and organizational classification. When organizations systematically prioritize lower-risk opportunities over scalability potential, they may operate outside the traditional venture capital paradigm. In such cases, the term "business studio" may more accurately describe their operational model and strategic objectives than "venture studio," reflecting their emphasis on sustainable business creation rather than high-growth venture development.
At the same time, the second strategy is exposed to the common VC space risks. Ventures developed under this model may attract huge investments, grow fast, and gain more market share, but end up with a painful failure as sometimes happens with unicorns (CB Insights, 2024). Presumably, to go in this direction, a studio should have an unfair advantage that will help it to create new disruptive companies with higher reliability while retaining the derisking early operational influence commonly exerted by venture studios. This advantage may be a unique ground-breaking technology, some special know-how or a superstar founder. For instance, if Elon Musk had organized all his companies into a studio, it would be a good example of such success. We assume that many venture studios now known to have leading industry positions will be in this quadrant as many of them have star founders or investors (e.g., Science Inc., Expa, Atomic, HVF, High Alpha, etc.). It would be interesting to understand the other “unfair advantage” factors might help studios get into this quadrant.
For newly created studios without such advantages, the winning strategy may be to start with Strategy 1 and advance to Strategy 2 during its evolution. This path, from our perspective, may help to decrease the initial risks and generate consistent returns faster, which can be applied to creating more disruptive ventures (i.e., outliers), moving the venture studio to the upper-right quadrant.
While grounded in empirical data, our framework remains primarily conceptual at this stage. Various metrics, concepts, and organizational routines may be added to it. We would like to leave a space for further discussion(s) and, as a starting point, can propose an advanced version of the framework (Appendix 5) with some hypothetical assumptions regarding related performance metrics. One may test them by calculating real metrics of ESOs and assigning the results to the proposed quadrants.
At the same time, the second strategy is exposed to the common VC space risks. Ventures developed under this model may attract huge investments, grow fast, and gain more market share, but end up with a painful failure as sometimes happens with unicorns (CB Insights, 2024). Presumably, to go in this direction, a studio should have an unfair advantage that will help it to create new disruptive companies with higher reliability while retaining the derisking early operational influence commonly exerted by venture studios. This advantage may be a unique ground-breaking technology, some special know-how or a superstar founder. For instance, if Elon Musk had organized all his companies into a studio, it would be a good example of such success. We assume that many venture studios now known to have leading industry positions will be in this quadrant as many of them have star founders or investors (e.g., Science Inc., Expa, Atomic, HVF, High Alpha, etc.). It would be interesting to understand the other “unfair advantage” factors might help studios get into this quadrant.
For newly created studios without such advantages, the winning strategy may be to start with Strategy 1 and advance to Strategy 2 during its evolution. This path, from our perspective, may help to decrease the initial risks and generate consistent returns faster, which can be applied to creating more disruptive ventures (i.e., outliers), moving the venture studio to the upper-right quadrant.
While grounded in empirical data, our framework remains primarily conceptual at this stage. Various metrics, concepts, and organizational routines may be added to it. We would like to leave a space for further discussion(s) and, as a starting point, can propose an advanced version of the framework (Appendix 5) with some hypothetical assumptions regarding related performance metrics. One may test them by calculating real metrics of ESOs and assigning the results to the proposed quadrants.
Overview of the results - RQ1
Summarizing the research outcomes, we can make the following conclusions:
1. Compared to other ESOs, VSs tend to invest more in pre-seed. However, the widest IQR of the pre-seed funding size infers the highest variance and signals that VS practices appear less developed than those in other organizations. This conclusion is supported by the higher variance in pre-seed funding shares (compared to accelerators and founders-first VC) and the widest IQR of the ESO valuation distributions. Nevertheless, the valuations from VSs are balanced by a funding size/shares ratio and are generally not far from other ESOs distributions.
2. VSs’ Exit/Fail ratio is similar to the overall sample ratio and is only lower than the Exit/Fail ratio for VCs. Considering the relative nascency of the VS phenomenon (the majority of venture studios were launched in the last 6 years), we may conclude that it warrants further observation.
3. VAL-GR of the VSs’ realized pre-seed portfolio is close to the sample median and only lower than VCs; however, this difference is not statistically significant. Therefore, we cannot conclude that the portfolio valuation growth rate of venture studios outperforms other ESOs, at least not by comparison of medians. On the contrary, we can conclude with significance that pre-seed VC funds’ portfolio companies are growing faster than those of accelerators.
4. VAL-GR of the pre-seed VC funds’ portfolio has a significantly wider IQR and, thus, the largest variance. VCs’ portfolio startups can reach 6 $M/year growth within 92.1 % bounds (Q3+1.5IQR), but at the same time, they may fall as fast as -4 $M/year, while the rest ESOs are around -1-1.5 $M/year. Possible explanations include:
a. VC funds manifold: many funds apply various practices that lead to a variance in outcomes
b. Low early-stage influence of VC funds: the rest ESOs have a higher degree of operational influence, which makes further growth more predictable and less risky.
5. Pre-seed VC funds also “generate” significantly faster-growing outliers and the difference is significant compared to accelerators. In addition, there is a clear tendency of VSs to bring faster-growing outliers into their portfolios similar to pre-seed VC funds, but the number of these outliers is not high compared to the other ESOs and overall results are not significant in the current setting.
6. Regarding the comparison of more active organizations to the less active ones, we can conclude that both options are possible: a higher deal count may either lead to a higher number of fast-growing outliers or may not. Some inherent features of organizations may heavily influence their success, not only a “spray and pray” approach.
7. From the TTR measure analysis, we can only conclude that accelerators demonstrate faster realization results than pre-seed VC funds without accounting for the success of the event. The rest of the considered ESOs are not significantly different (excluding potentially biased VC-FFs). Venture studios demonstrate a 1-year faster than the sample median time to Exit, but it is not statistically significant.
8. The IRR model of a pre-seed portfolio performance demonstrates that IRR is not the best measure to compare the "success" of ESOs due to its high sensitivity to outliers: a substantial change in only 1% of deals may shift the IRR dramatically. From our perspective, any measures assessing VS performance should be provided in the form of distributions and by actual status clustering, i.e., separating measures of, for instance, realized assets from all spun-out, alive, and failed.
9. We developed a conceptual VC framework and proposed two strategies for newly created venture studios:
a. Target the development of safer, less disruptive ventures (to position in the upper-left quadrant)
b. Focus on the generation of more potential disruptors, i.e., growth outliers. (to position in the upper-right quadrant)
10. For newly created studios without unfair advantages, the winning strategy may be to start with the first strategy and advance to the second during its evolution. This path may help to decrease the initial risks and generate returns fast, which further can be applied to creating more disruptive ventures (i.e., outliers) and moving to the upper-right quadrant.
1. Compared to other ESOs, VSs tend to invest more in pre-seed. However, the widest IQR of the pre-seed funding size infers the highest variance and signals that VS practices appear less developed than those in other organizations. This conclusion is supported by the higher variance in pre-seed funding shares (compared to accelerators and founders-first VC) and the widest IQR of the ESO valuation distributions. Nevertheless, the valuations from VSs are balanced by a funding size/shares ratio and are generally not far from other ESOs distributions.
2. VSs’ Exit/Fail ratio is similar to the overall sample ratio and is only lower than the Exit/Fail ratio for VCs. Considering the relative nascency of the VS phenomenon (the majority of venture studios were launched in the last 6 years), we may conclude that it warrants further observation.
3. VAL-GR of the VSs’ realized pre-seed portfolio is close to the sample median and only lower than VCs; however, this difference is not statistically significant. Therefore, we cannot conclude that the portfolio valuation growth rate of venture studios outperforms other ESOs, at least not by comparison of medians. On the contrary, we can conclude with significance that pre-seed VC funds’ portfolio companies are growing faster than those of accelerators.
4. VAL-GR of the pre-seed VC funds’ portfolio has a significantly wider IQR and, thus, the largest variance. VCs’ portfolio startups can reach 6 $M/year growth within 92.1 % bounds (Q3+1.5IQR), but at the same time, they may fall as fast as -4 $M/year, while the rest ESOs are around -1-1.5 $M/year. Possible explanations include:
a. VC funds manifold: many funds apply various practices that lead to a variance in outcomes
b. Low early-stage influence of VC funds: the rest ESOs have a higher degree of operational influence, which makes further growth more predictable and less risky.
5. Pre-seed VC funds also “generate” significantly faster-growing outliers and the difference is significant compared to accelerators. In addition, there is a clear tendency of VSs to bring faster-growing outliers into their portfolios similar to pre-seed VC funds, but the number of these outliers is not high compared to the other ESOs and overall results are not significant in the current setting.
6. Regarding the comparison of more active organizations to the less active ones, we can conclude that both options are possible: a higher deal count may either lead to a higher number of fast-growing outliers or may not. Some inherent features of organizations may heavily influence their success, not only a “spray and pray” approach.
7. From the TTR measure analysis, we can only conclude that accelerators demonstrate faster realization results than pre-seed VC funds without accounting for the success of the event. The rest of the considered ESOs are not significantly different (excluding potentially biased VC-FFs). Venture studios demonstrate a 1-year faster than the sample median time to Exit, but it is not statistically significant.
8. The IRR model of a pre-seed portfolio performance demonstrates that IRR is not the best measure to compare the "success" of ESOs due to its high sensitivity to outliers: a substantial change in only 1% of deals may shift the IRR dramatically. From our perspective, any measures assessing VS performance should be provided in the form of distributions and by actual status clustering, i.e., separating measures of, for instance, realized assets from all spun-out, alive, and failed.
9. We developed a conceptual VC framework and proposed two strategies for newly created venture studios:
a. Target the development of safer, less disruptive ventures (to position in the upper-left quadrant)
b. Focus on the generation of more potential disruptors, i.e., growth outliers. (to position in the upper-right quadrant)
10. For newly created studios without unfair advantages, the winning strategy may be to start with the first strategy and advance to the second during its evolution. This path may help to decrease the initial risks and generate returns fast, which further can be applied to creating more disruptive ventures (i.e., outliers) and moving to the upper-right quadrant.
Limitations
Of course, all research projects have limitations and it is a good (albeit rare in our non-scientific world) practice to report on them.
First, the data employed in the study may not represent the complete ESO universe. According to our first trials, the PitchBook database is the most extensive one available in terms of deal data, but, of course, it may miss some or provide faulty values. In an ideal setting, we would have had access to all databases and collected all deal data available in public space. However, such an approach requires significantly more resources than we have available.
Second, we haven’t controlled the ESO distribution of pre-seed deals in terms of the population. It may be the case that the proportions of deals in the whole pre-seed deal space are somewhat different due to the ESOs’ wish or need to share the deal data publicly (or not!). One possible solution to this issue is to check every ESO entity, collect all their deals, build the population distribution, and account for it in the research. Competitive forces and the proprietary nature of ESO data make it unlikely that this will be possible without extensive industry collaboration.
Third, we made several data assumptions, which may influence the results. For instance, we assumed that the growth rate has’t changed from the last available valuation to the exit one. If for the majority of the sample, it may be true (the deals have only pre-seed investment and bankruptcy events), in some cases the dynamics may significantly differ, especially if an exit happened after a significant amount of time had passed since the last available valuation. Another assumption was that syndicated pre-seed deals were equally treated for all investors in terms of shares and performance. But, taking into account the relatively low number of such deals, we believe this hasn’t greatly influenced our outcomes.
Fourth, the identification of a venture studio in a sample is somewhat arbitrary. We mainly followed the developed definition, but some core features of VSs may be identified only after interviewing the related studios. Moreover, the question of defining a venture studio is still an open question in the research space: some entities pretend to be VSs, while some VSs pretend to be VC funds or accelerators. Even inside one defined VS, there may be deals performed in alignment with venture studio principles and some done as a venture capital fund. Of course, we accounted for this by going through the public deal data, but to solve this issue completely, we need first to develop clear definitions of VS deals and understand clear signals that help us assign a deal to a VS versus other ESO models.
Fifth, we haven’t accounted for the differences in the pre-seed first financing dates. While for the majority of ESOs, it was not a problem since their date distributions are quite close to each other, founders-first VC funds’ results were likely influenced by their relative youth. Thus, at least for this particular entity, we see many deals which have already failed, perhaps missing the ones that are still not realized due to the longer time required.
Finally, we haven’t considered the possible effects of other ESO entities, which possibly participated in further rounds before the exit. In the initial setting, we intended to get the deal data for the second investment and study the first-second valuation growth dynamics to rectify the effects of the particular pre-seed ESO. However, due to our limited access to the data sources, we decided not to follow this path and to mention it as a limitation.
First, the data employed in the study may not represent the complete ESO universe. According to our first trials, the PitchBook database is the most extensive one available in terms of deal data, but, of course, it may miss some or provide faulty values. In an ideal setting, we would have had access to all databases and collected all deal data available in public space. However, such an approach requires significantly more resources than we have available.
Second, we haven’t controlled the ESO distribution of pre-seed deals in terms of the population. It may be the case that the proportions of deals in the whole pre-seed deal space are somewhat different due to the ESOs’ wish or need to share the deal data publicly (or not!). One possible solution to this issue is to check every ESO entity, collect all their deals, build the population distribution, and account for it in the research. Competitive forces and the proprietary nature of ESO data make it unlikely that this will be possible without extensive industry collaboration.
Third, we made several data assumptions, which may influence the results. For instance, we assumed that the growth rate has’t changed from the last available valuation to the exit one. If for the majority of the sample, it may be true (the deals have only pre-seed investment and bankruptcy events), in some cases the dynamics may significantly differ, especially if an exit happened after a significant amount of time had passed since the last available valuation. Another assumption was that syndicated pre-seed deals were equally treated for all investors in terms of shares and performance. But, taking into account the relatively low number of such deals, we believe this hasn’t greatly influenced our outcomes.
Fourth, the identification of a venture studio in a sample is somewhat arbitrary. We mainly followed the developed definition, but some core features of VSs may be identified only after interviewing the related studios. Moreover, the question of defining a venture studio is still an open question in the research space: some entities pretend to be VSs, while some VSs pretend to be VC funds or accelerators. Even inside one defined VS, there may be deals performed in alignment with venture studio principles and some done as a venture capital fund. Of course, we accounted for this by going through the public deal data, but to solve this issue completely, we need first to develop clear definitions of VS deals and understand clear signals that help us assign a deal to a VS versus other ESO models.
Fifth, we haven’t accounted for the differences in the pre-seed first financing dates. While for the majority of ESOs, it was not a problem since their date distributions are quite close to each other, founders-first VC funds’ results were likely influenced by their relative youth. Thus, at least for this particular entity, we see many deals which have already failed, perhaps missing the ones that are still not realized due to the longer time required.
Finally, we haven’t considered the possible effects of other ESO entities, which possibly participated in further rounds before the exit. In the initial setting, we intended to get the deal data for the second investment and study the first-second valuation growth dynamics to rectify the effects of the particular pre-seed ESO. However, due to our limited access to the data sources, we decided not to follow this path and to mention it as a limitation.
RQ2: What organizational and operational factors drive venture studio performance outcomes?
The second question we are interested in addressing during this study is whether (and to what extent) VS characteristics drive their performance outcomes. Several factors have been proposed by market participants (Pog, 2023, Kannan and Peterman, 2022, Szigeti, 2016, Yoskovitz, 2023), namely:
We hypothesized the existence of identifiable success factors and developed a methodology to test this hypothesis and quantify the relative influence of each factor, if present. Understanding these success drivers is fundamental to advancing the venture studio field, offering practical value in two key dimensions: helping emerging studios develop more effective operational routines while avoiding common pitfalls, and enabling established studios to optimize their existing practices for enhanced performance.
- Previous experience of studio founders
- Niche focus of a studio
- Applying Lean methodology
- Privileged access, and more
We hypothesized the existence of identifiable success factors and developed a methodology to test this hypothesis and quantify the relative influence of each factor, if present. Understanding these success drivers is fundamental to advancing the venture studio field, offering practical value in two key dimensions: helping emerging studios develop more effective operational routines while avoiding common pitfalls, and enabling established studios to optimize their existing practices for enhanced performance.
Methodology and data
As a research methodology, we have selected the venture studios’ survey since information on many hypothesized factors is not available in public sources. Yes, this method has particular drawbacks (e.g., response and non-response biases, low response rate, Coughlan et al., 2009) but when the survey is transparent and the results assessed statistically, it has particular power. The employed questionnaire may be found in Appendix 6.
The data collection process was conducted in two waves during 2024: an initial outreach in late spring followed by a second wave in mid-summer. We contacted 861 venture studios, representing 78% of the global venture studio population according to our manually validated database. Our sample selection did not impose restrictions based on geographic location or studio type. Of the contacted studios, 17 (2%) declined participation, while 99 (12%) initiated but did not complete the survey.
A significant barrier to participation emerged from legal restrictions around sharing "sensitive" business data. This data access challenge represents a broader issue in venture studio research, one that could be addressed through new industry infrastructure such as our proposed Venture Studio Research Platform, which has garnered support from both academic researchers and industry practitioners.
Despite these challenges, 123 studios completed the survey, yielding an 11% response rate. This sample size represents, to our knowledge, the largest number of venture studios ever surveyed in an academic study, establishing a new benchmark for research in this field.
We extend our sincere gratitude to all participating venture studios, particularly those who shared their proprietary data. In accordance with our commitment to confidentiality, all results are presented in anonymized, aggregated form. Participating studios that requested recognition will receive their selected acknowledgments: either the "VSF-trusted" website badge or formal recognition in our final report as a contributing organization.
Our statistical analysis primarily employed the Kruskal-Wallis analysis of variance test (KWANOVA), as our dependent variables demonstrated non-normal distributions. This non-parametric approach was complemented by frequency analysis for selected metrics, with responses weighted according to priority levels. These methodological choices align with established statistical practices in organizational research ("Kruskal Wallis Test - an overview | ScienceDirect Topics," n.d.; "Frequency Analysis - an overview | ScienceDirect Topics," n.d.).*
The data collection process was conducted in two waves during 2024: an initial outreach in late spring followed by a second wave in mid-summer. We contacted 861 venture studios, representing 78% of the global venture studio population according to our manually validated database. Our sample selection did not impose restrictions based on geographic location or studio type. Of the contacted studios, 17 (2%) declined participation, while 99 (12%) initiated but did not complete the survey.
A significant barrier to participation emerged from legal restrictions around sharing "sensitive" business data. This data access challenge represents a broader issue in venture studio research, one that could be addressed through new industry infrastructure such as our proposed Venture Studio Research Platform, which has garnered support from both academic researchers and industry practitioners.
Despite these challenges, 123 studios completed the survey, yielding an 11% response rate. This sample size represents, to our knowledge, the largest number of venture studios ever surveyed in an academic study, establishing a new benchmark for research in this field.
We extend our sincere gratitude to all participating venture studios, particularly those who shared their proprietary data. In accordance with our commitment to confidentiality, all results are presented in anonymized, aggregated form. Participating studios that requested recognition will receive their selected acknowledgments: either the "VSF-trusted" website badge or formal recognition in our final report as a contributing organization.
Our statistical analysis primarily employed the Kruskal-Wallis analysis of variance test (KWANOVA), as our dependent variables demonstrated non-normal distributions. This non-parametric approach was complemented by frequency analysis for selected metrics, with responses weighted according to priority levels. These methodological choices align with established statistical practices in organizational research ("Kruskal Wallis Test - an overview | ScienceDirect Topics," n.d.; "Frequency Analysis - an overview | ScienceDirect Topics," n.d.).*
* If a studio selected all available options (e.g., 10), the priority of each equals 100%/10 options = 10% meaning that it is not highly selective. Conversely, if a studio selected only one option it demonstrates a high degree of selectivity with 100% of priority assigned to this option. Finally, we calculated the average priorities for each option, assigned a color and size scale, and ordered them on the X-axis by descending priority.
Definitions
First, taking into account the great uncertainty currently seen in a venture studio knowledge space, we asked studios to define themselves and explain the differentiation from other venture studio-related terms. To do that, we asked studios two questions “How do you call your organization?” and “How do you differentiate from the other terms?” While the latter remains an open question, for the former the following options were proposed:
That is to note, this question was added after some studios had already responded to the survey, effectively reducing the number of respondents to this particular question by 17 entities (106 studios).
The most popular term is “Venture Studio” (58.5%), second was “Venture Builder” (22.6%), followed by “Startup Studio” (14.2%), and finally “Startup Factory” (0.9%). In addition, 3.8% of respondents decided to select the “Other” option and provide their definitions, in particular:
The semantic consistencies and disparities among these proposed terms may be explored in more detail in future studies.
Regarding the second question, the situation is more complicated. Some respondents claimed that there is no difference in these terms, while others pointed out nuanced distinctions. We analyzed the responses and present the aggregated outcomes below:
- Venture Studio
- Venture Builder
- Startup Studio
- Startup Factory
- Other
That is to note, this question was added after some studios had already responded to the survey, effectively reducing the number of respondents to this particular question by 17 entities (106 studios).
The most popular term is “Venture Studio” (58.5%), second was “Venture Builder” (22.6%), followed by “Startup Studio” (14.2%), and finally “Startup Factory” (0.9%). In addition, 3.8% of respondents decided to select the “Other” option and provide their definitions, in particular:
- Platform for builders
- Feeding Studio
- Studio
- Factory Venture Studio
The semantic consistencies and disparities among these proposed terms may be explored in more detail in future studies.
Regarding the second question, the situation is more complicated. Some respondents claimed that there is no difference in these terms, while others pointed out nuanced distinctions. We analyzed the responses and present the aggregated outcomes below:
- Venture Studio Focus: Organizations self-identifying as "venture studios" report several distinctive operational characteristics. Their core strategy typically combines venture building and investment activities, with studios positioning themselves as hands-on co-founders and co-creators. These organizations are characterized by comprehensive, long-term venture support complemented by iterative development processes. They frequently engage in internal idea generation while maintaining a deliberate emphasis on quality over quantity in venture development. Notably, one respondent added that “venture studios are viewed to be more aligned with the idea of empowering founders, than simply building ventures.”
- Semantics: Many respondents see little difference between the terms, using "Venture Studio" and "Venture Builder" interchangeably while avoiding "Venture Factory" and “Startup Factory” due to the negative implications of rapid, low-quality mass production.
- Corporate vs. Independent: Venture studios are often seen as more independent, while venture builders are linked to corporate activities. At the same time, there is an opinion that in the minds of corporates, studios create startups, not ventures, i.e., leading to the term “Startup Studio” when a firm is considered for corporate building.
- Geographical Influence: In certain regions, specific terms are less common. For instance, "Startup Studio" and "Startup Factory" are not typically used in Southeast Asia. At the same time, in the LATAM region, the “studio” term implies helping entrepreneurs build their companies, while “builder” is about directly building businesses for a third party.
Descriptive Statistics
The geographical distribution of participating venture studios reveals a strong concentration in Western Europe (36%) and North America (32%). The remaining studios are distributed across Eastern Europe and Central Asia (8%), the Middle East and North Africa (7%), South-East Asia (5%), Latin America (4%), Sub-Saharan Africa (4%), Eastern Asia (2%), and South Asia (1%).
Our geographical classification follows the World Bank regional framework ("World by Region Map," 2018), with modifications to European categorizations. Studios were asked to indicate all operational locations. For studios identifying as "remote" or "global," we assigned regional classifications based on the physical location of their offices and founders. The complete regional and country classification schema is provided in Appendix 1.
Our geographical classification follows the World Bank regional framework ("World by Region Map," 2018), with modifications to European categorizations. Studios were asked to indicate all operational locations. For studios identifying as "remote" or "global," we assigned regional classifications based on the physical location of their offices and founders. The complete regional and country classification schema is provided in Appendix 1.
Analysis of project type distribution reveals that traditional venture studios, focusing exclusively on internal projects, represent the largest segment at 32% (39 studios). Studios primarily focused on internal projects with some corporate engagement comprise 28% (35 studios), while those maintaining an equal balance between internal and corporate projects represent 17% (21 studios). Corporate-focused studios constitute a smaller portion, with 9% working predominantly on corporate projects and 8% working exclusively in this space.
A small number of studios reported alternative project sourcing approaches: three studios (2.4%) source from academic intellectual property and research, two studios (1.6%) work with Entrepreneurs in Residence (EIRs), and one studio (0.8%) focuses on public-private ventures. One respondent did not provide project type information.
A small number of studios reported alternative project sourcing approaches: three studios (2.4%) source from academic intellectual property and research, two studios (1.6%) work with Entrepreneurs in Residence (EIRs), and one studio (0.8%) focuses on public-private ventures. One respondent did not provide project type information.
Considering the focus of venture studios, we had three related measures (based on Pog, 2023): vertical focus, niche focus, and business model focus. In general, studios agnostic by all measures prevail. By vertical, the distribution is almost equal (48% vs 52%), while by niche and business model there are twice as many agnostic studios (32% vs 68%; 33% vs 67%). The data suggest that venture studios are evolving toward more flexible, agnostic operational models rather than specializing in specific project types. This trend toward versatility in project selection and development appears to be an emerging characteristic of the venture studio landscape and deserves further examination.
Among studios reporting domain specialization, several key focus areas emerge. Health & Longevity, B2B SaaS, Sustainability, and Fintech represent the most frequently targeted sectors. Secondary areas of concentration include Deep Tech, Real Estate & Construction, AgriTech, and Web3 & Digital technologies. This pattern suggests that specialized studios tend to align with sectors characterized by high growth potential and technological innovation.
We were also interested in attempting to overcome the survivorship bias (Thomas, 2023) and because of that asked the closed studios to provide their data as well. Regarding the actual studio status distribution, we have 111 or 90% of active studios (”It actively starts and develops new ventures”), 6 or 5% of not active studios (”It doesn't start new initiatives and only develops the launched”) and 6 or 5% of closed ones (”None of the above”). This distribution generally follows the population in terms of the open/closed ratio demonstrated previously in the overview.
The age distribution of responding venture studios reflects the industry's recent rapid growth, with 66% (81 studios) established within the past five years. While venture studios have existed since the late 1990s, the formation rate accelerated significantly between 2018 and 2022. Notably, 48% (59 studios) of our sample consists of nascent studios founded within the last three years.
This high concentration of recently established studios introduces a potential methodological consideration: many respondents may lack sufficient operating history to demonstrate definitive performance metrics. Their survey responses regarding performance metrics may reflect projected rather than realized outcomes, warranting careful interpretation of performance-related findings.
This high concentration of recently established studios introduces a potential methodological consideration: many respondents may lack sufficient operating history to demonstrate definitive performance metrics. Their survey responses regarding performance metrics may reflect projected rather than realized outcomes, warranting careful interpretation of performance-related findings.
Analysis of fund structures among responding studios reveals patterns that both align with and diverge from theoretical expectations. Drawing on Carbrey's (2020) framework, our data shows the Single Studio Model, despite its known limitations, represents the largest segment at 44% (54 studios). The theoretically more advantageous Dual Entity model, which pairs a fund with a studio operation, accounts for 26% (32 studios) of respondents.
The Single Fund Model, which emphasizes organizational simplicity over structural optimization, represents 15% (19 studios) of the sample. Less common structures include the Single Studio Model + Syndicate approach at 9% (11 studios) and the Single Fund Model Foundry at 6% (7 studios). The limited adoption of these latter models aligns with Carbrey's (2020) assessment of their structural limitations.
The prevalence of the Single Studio Model, despite its documented disadvantages, presents an intriguing contrast to theoretical predictions about optimal venture studio structure. This finding suggests potential gaps between academic frameworks and practical implementation in the venture studio space.
The Single Fund Model, which emphasizes organizational simplicity over structural optimization, represents 15% (19 studios) of the sample. Less common structures include the Single Studio Model + Syndicate approach at 9% (11 studios) and the Single Fund Model Foundry at 6% (7 studios). The limited adoption of these latter models aligns with Carbrey's (2020) assessment of their structural limitations.
The prevalence of the Single Studio Model, despite its documented disadvantages, presents an intriguing contrast to theoretical predictions about optimal venture studio structure. This finding suggests potential gaps between academic frameworks and practical implementation in the venture studio space.
Our analysis of services provided to Entrepreneurs in Residence (EIRs) examined both service prevalence and perceived priority among responding studios. The most widely offered services include access to advisors (93% of studios, 11.2% priority weight), go-to-market assistance (91%, 10.7% priority weight), and operational assistance (89%, 10.5% priority weight).
When comparing these service offerings to traditional accelerator and venture capital support models, operational assistance or "hands-on support" emerges as the key differentiator, supporting Kannan and Peterman's (2022) findings. The high priority weights assigned to these core services indicate their strategic importance, as they are consistently offered even by studios with otherwise limited service portfolios.
Technical development services present an interesting discrepancy between public positioning and operational reality. While prominently featured in studio marketing materials, our data shows more selective implementation, with 80% of studios offering this service and assigning it a 10.8% priority weight. This suggests a strategic differentiation among studios in their technical support capabilities.
A second tier of services emerges with slightly lower adoption rates: access to external funding (82%, 10.5% priority), marketing (80%, 10.4% priority), pre-seed support (80%, 10.6% priority), and customer development (77%, 10.3% priority). The relatively lower adoption of early-stage focused services, particularly pre-seed support and customer development, raises questions about the depth of studio engagement during crucial formative stages.
Finally, our analysis reveals a third tier of less commonly offered services: access to first customers (71% of studios, 7% priority), pre-developed ideas (70%, 7.4% priority), involvement of venture studio founders (70%, 7% priority), and access to studio financial resources (69%, 7% priority).
This service distribution pattern suggests an interesting tension in the venture studio model. While studios generally provide traditional early-stage venture capital support services such as advisor access and go-to-market assistance, they show lower commitment to specialized early-stage interventions that theoretically differentiate the studio model from other entrepreneurial support organizations. Though approximately 70% of studios still offer these specialized services, their lower prioritization raises questions about the practical implementation of the venture studio value proposition.
The apparent gap between theoretical differentiation and operational focus merits further investigation, particularly given that early-stage assistance is often cited as a key distinguishing feature of the venture studio model among entrepreneurial support organizations.
When comparing these service offerings to traditional accelerator and venture capital support models, operational assistance or "hands-on support" emerges as the key differentiator, supporting Kannan and Peterman's (2022) findings. The high priority weights assigned to these core services indicate their strategic importance, as they are consistently offered even by studios with otherwise limited service portfolios.
Technical development services present an interesting discrepancy between public positioning and operational reality. While prominently featured in studio marketing materials, our data shows more selective implementation, with 80% of studios offering this service and assigning it a 10.8% priority weight. This suggests a strategic differentiation among studios in their technical support capabilities.
A second tier of services emerges with slightly lower adoption rates: access to external funding (82%, 10.5% priority), marketing (80%, 10.4% priority), pre-seed support (80%, 10.6% priority), and customer development (77%, 10.3% priority). The relatively lower adoption of early-stage focused services, particularly pre-seed support and customer development, raises questions about the depth of studio engagement during crucial formative stages.
Finally, our analysis reveals a third tier of less commonly offered services: access to first customers (71% of studios, 7% priority), pre-developed ideas (70%, 7.4% priority), involvement of venture studio founders (70%, 7% priority), and access to studio financial resources (69%, 7% priority).
This service distribution pattern suggests an interesting tension in the venture studio model. While studios generally provide traditional early-stage venture capital support services such as advisor access and go-to-market assistance, they show lower commitment to specialized early-stage interventions that theoretically differentiate the studio model from other entrepreneurial support organizations. Though approximately 70% of studios still offer these specialized services, their lower prioritization raises questions about the practical implementation of the venture studio value proposition.
The apparent gap between theoretical differentiation and operational focus merits further investigation, particularly given that early-stage assistance is often cited as a key distinguishing feature of the venture studio model among entrepreneurial support organizations.
Our examination of venture phase transitions seeks to expand traditional venture studio development frameworks. While the standard model encompasses four phases (Pog, 2023), this research introduces a preliminary Generation phase focused on idea creation and selection, preceding the traditional Ideation phase where minimum viable products (MVPs) are developed.
Survey participants received comprehensive phase definitions and reported their conversion rates between successive stages based on current and historical data. The full description of all five development phases is provided in Appendix 2.
Survey participants received comprehensive phase definitions and reported their conversion rates between successive stages based on current and historical data. The full description of all five development phases is provided in Appendix 2.
Analysis of these venture phase transitions reveals distinct patterns in venture progression rates. Statistical testing confirmed non-normal distribution of results, with conversion rates showing clear stage-dependent patterns. Studios report a 40% progression rate from initial idea generation to the Ideation phase, where MVP development and initial customer validation occur.
Beyond the Ideation phase, ventures face consistent attrition, with approximately 50% advancing through each subsequent phase until Growth. The final transition to Exit is achieved by 10% of ventures reaching the Growth phase. This funnel translates to requiring approximately 200 initial ideas and 40 MVPs to achieve a single successful exit. These conversion rates align more closely with traditional venture capital metrics (CB Insights, 2018) than with previously reported venture studio benchmarks.
Beyond the Ideation phase, ventures face consistent attrition, with approximately 50% advancing through each subsequent phase until Growth. The final transition to Exit is achieved by 10% of ventures reaching the Growth phase. This funnel translates to requiring approximately 200 initial ideas and 40 MVPs to achieve a single successful exit. These conversion rates align more closely with traditional venture capital metrics (CB Insights, 2018) than with previously reported venture studio benchmarks.
The survey examined EIR engagement patterns using Kannan and Peterman's (2022) classification framework from Venture Studios Demystified. Studios demonstrate a clear preference for assigning EIRs to established concepts (52% adoption, 76.3% priority weight), followed by structured EIR programs (41% adoption, 75.7% priority weight). Limited full-time EIR positions represent the least common approach (33% adoption, 63.3% priority weight).
Additionally, 11 studios claimed that the proposed ways do not fit their strategies while mentioning the following:
Additionally, 11 studios claimed that the proposed ways do not fit their strategies while mentioning the following:
- “I don't utilize EIRs in my model”
- “Hire full-time CEO and CTO after investment decision (usually $1-1.5M)”
- “Venture Builder to go through Venture Pipeline of ideas and then choose the matching ones, or to identify own topics”
- “Not yet”
- “It depends”
- “We do not have EIRs, we do it ourselves”
- “Founders bring their own ideas and startups”
- “Ad hoc”
- “The venture studio role is structured as per the EIR experience and the problem being solved. We do expect our model to evolve into a structured approach over time.”
- “Founders come to us with their idea”
- “We have a large network of people that may be founders and start to work early on an idea with them”
Data on EIR entry points reveals two distinct strategic patterns in venture studio operations. The highest priority stages for EIR integration are Generation (53% adoption, 46.6% priority weight) and Creation (52% adoption, 46% priority weight), representing the earliest and fourth stages of venture development. This bifurcated approach suggests two distinct strategic principles: either engaging EIRs at inception for ground-up venture development, or internally developing ventures through early stages before bringing in EIRs for market execution.
The Validation stage shows broader but less strategic adoption (60% of studios, 42% priority weight). While Growth phase EIR integration is least common (20% of studios, 30.7% priority weight), this higher-than-expected adoption rate warrants attention given typical venture studio operational models.
The Validation stage shows broader but less strategic adoption (60% of studios, 42% priority weight). While Growth phase EIR integration is least common (20% of studios, 30.7% priority weight), this higher-than-expected adoption rate warrants attention given typical venture studio operational models.
Considering the exit strategies of studios, the majority of the respondents follow the traditional VC path and prefer to exit during the startups’ exits (48% of entities, 64.8%). The interesting observation is that an exit on Seed is the second priority option albeit with a significantly lower number of studios (20% of entities, 62% priority) indicating this preference. Taking into account that the next frequently prioritized option is to exit during Series A (41% of entities, 58.9% priority), we can assume that again there are two prevailing strategies: to be with the startup until its exit to gain maximum returns, and to cash-out as early as possible. The second option may be in favor of young studios, which target to get returns faster and balance their portfolios.
Later funding rounds represent secondary exit opportunities, with Series B exits pursued by 41% of studios (47.1% priority weight), while Series C, D, and later exits are considered by 28% of studios (45.3% priority weight). These relatively high priority weights, despite lower adoption rates, suggest studios view later-stage exits as viable but non-primary strategic options within a broader portfolio exit strategy.
Later funding rounds represent secondary exit opportunities, with Series B exits pursued by 41% of studios (47.1% priority weight), while Series C, D, and later exits are considered by 28% of studios (45.3% priority weight). These relatively high priority weights, despite lower adoption rates, suggest studios view later-stage exits as viable but non-primary strategic options within a broader portfolio exit strategy.
Development team composition analysis reveals a strong preference for internal teams (78% adoption, 53% priority weight), with limited reliance on external agencies (37% adoption, 42.2% priority weight). The priority weights suggest a binary strategic choice: studios either maintain full internal development capabilities or commit to comprehensive outsourcing.
Studio co-founders frequently serve as development teams (54% adoption, 41.5% priority weight), though this approach appears to carry lower strategic priority. Freelance engagement represents the least preferred option (53% adoption, 38.9% priority weight).
Studio co-founders frequently serve as development teams (54% adoption, 41.5% priority weight), though this approach appears to carry lower strategic priority. Freelance engagement represents the least preferred option (53% adoption, 38.9% priority weight).
Team compensation data reveals two dominant reward mechanisms: direct monetary compensation (69% adoption, 72.3% priority weight) and venture equity stakes (65% adoption, 68% priority weight). The similar adoption rates and priority weights suggest these approaches are viewed as complementary rather than alternative strategies.
Alternative compensation structures show limited adoption. Preferred returns, offering priority cash distributions similar to investor arrangements, are used by only 10% of studios (53.5% priority weight). Deferred compensation through loans represents the least common approach (2% adoption, 37.5% priority weight).
Alternative compensation structures show limited adoption. Preferred returns, offering priority cash distributions similar to investor arrangements, are used by only 10% of studios (53.5% priority weight). Deferred compensation through loans represents the least common approach (2% adoption, 37.5% priority weight).
Factors influencing the success of a studio
How we define “success” in this part of the study
In an attempt to understand the factors that influence the success of a venture studio, we developed the following methodology. First, we defined a studio’s “success” as a ratio of spun-out ventures (Startup Studio Insider, 2023), i.e., those, which successfully went through Generation, Ideation, and Validation stages, and graduating to the Creation phase (Appendix 2). Of course, this is an arbitrary measure of success, but it represents a pragmatic compromise between measurement precision and data availability constraints. While acknowledging the metric's limitations, it addresses two key practical challenges: studios' tendency toward operational opacity and legal restrictions on sharing detailed performance data.
We asked studios to provide conversion data for each phase of venture development and calculated the relative conversion for the Creation stage (i.e., accounting for the previous conversions) to normalize the studios by means of success. Taking into account that the inclusion of many categorical variables in a regression significantly increases overfitting risk (Montesinos López et al., 2022), we used the analysis of variance in the form of KWANOVA (“Kruskal Wallis Test - an overview | ScienceDirect Topics,” n.d.) because the distribution of the selected success measure does not follow the normal distribution by the normality test at the 0.05 confidence level (Mishra et al., 2019). The Kruskal-Wallis test examines factor-level subgroups within the dataset, determining whether statistically significant differences exist between their respective medians. This approach enables identification of factors that meaningfully influence venture studio success rates.
Next, we will present the factors that indicate strong or moderate links to success, with p≤0.05 indicating a strong link and 0.05<p≤0.15 indicating a moderate link. All the remaining measures will be described in aggregate.
Results and discussion
Through the results of the survey and analysis, we were able to identify one measure, which indicates the strongest connection to a studio’s success: the vertical focus of a studio, or more clearly- the lack thereof. To identify this factor, we asked studios to select whether they have a focus on a particular vertical (fintech, retail tech, etc.) or not. According to the test results, the vertical-agnostic studios have a statistically higher median success rate (p<0.05).
The distribution of studios by this factor is quite balanced: 59 vertically-focused studios (48%) and 64 agnostic (52%). The median relative conversion to the Creation phase equals 5% for the former group of studios and 19% for the latter.
We may hypothesize that studios with an agnostic focus have more available opportunities and, thus, more chances to develop a promising venture and bring it to the market. Please note that the absence of a vertical focus doesn’t mean the absence of focus in other dimensions, i.e., niche or business model focuses. A studio may be open to all verticals (fintech, healthtech, proptech, etc.) but heavily focused on a particular niche (sustainability, supply chain, blockchain, etc.) or business model (B2B SaaS, marketplaces, etc.).
In an attempt to understand the factors that influence the success of a venture studio, we developed the following methodology. First, we defined a studio’s “success” as a ratio of spun-out ventures (Startup Studio Insider, 2023), i.e., those, which successfully went through Generation, Ideation, and Validation stages, and graduating to the Creation phase (Appendix 2). Of course, this is an arbitrary measure of success, but it represents a pragmatic compromise between measurement precision and data availability constraints. While acknowledging the metric's limitations, it addresses two key practical challenges: studios' tendency toward operational opacity and legal restrictions on sharing detailed performance data.
We asked studios to provide conversion data for each phase of venture development and calculated the relative conversion for the Creation stage (i.e., accounting for the previous conversions) to normalize the studios by means of success. Taking into account that the inclusion of many categorical variables in a regression significantly increases overfitting risk (Montesinos López et al., 2022), we used the analysis of variance in the form of KWANOVA (“Kruskal Wallis Test - an overview | ScienceDirect Topics,” n.d.) because the distribution of the selected success measure does not follow the normal distribution by the normality test at the 0.05 confidence level (Mishra et al., 2019). The Kruskal-Wallis test examines factor-level subgroups within the dataset, determining whether statistically significant differences exist between their respective medians. This approach enables identification of factors that meaningfully influence venture studio success rates.
Next, we will present the factors that indicate strong or moderate links to success, with p≤0.05 indicating a strong link and 0.05<p≤0.15 indicating a moderate link. All the remaining measures will be described in aggregate.
Results and discussion
Through the results of the survey and analysis, we were able to identify one measure, which indicates the strongest connection to a studio’s success: the vertical focus of a studio, or more clearly- the lack thereof. To identify this factor, we asked studios to select whether they have a focus on a particular vertical (fintech, retail tech, etc.) or not. According to the test results, the vertical-agnostic studios have a statistically higher median success rate (p<0.05).
The distribution of studios by this factor is quite balanced: 59 vertically-focused studios (48%) and 64 agnostic (52%). The median relative conversion to the Creation phase equals 5% for the former group of studios and 19% for the latter.
We may hypothesize that studios with an agnostic focus have more available opportunities and, thus, more chances to develop a promising venture and bring it to the market. Please note that the absence of a vertical focus doesn’t mean the absence of focus in other dimensions, i.e., niche or business model focuses. A studio may be open to all verticals (fintech, healthtech, proptech, etc.) but heavily focused on a particular niche (sustainability, supply chain, blockchain, etc.) or business model (B2B SaaS, marketplaces, etc.).
The next six measures are identified as having a moderate link to a studio’s success.
The first is the total number of launched ventures. To understand this factor, we asked studios to select one option from the list while responding to the question “How many ventures your studio have launched in total?” if to consider the Creation phase:
From the chart below, we can see that the median success rate is inversely related to the cumulative amount of ventures created until the 11-20 point, after which it quickly increases. Dunn’s test (“Dunn’s test,” 2017) demonstrates that the 30+ category has a significantly higher median success rate when compared to the 11-20 group (p<0.05) while the overall significance of the test is 0.06.
The 30+ category implies that these studios have spun out more than 30 ventures, indicating a greater degree of experience. The relationship between venture volume and success rates presents a complex causality question. While repeated process execution may enhance studio performance through experiential learning, the reverse causality must be considered: higher success rates could enable greater venture creation capacity. This bidirectional relationship requires careful interpretation of any correlation between venture volume and success metrics.
The first is the total number of launched ventures. To understand this factor, we asked studios to select one option from the list while responding to the question “How many ventures your studio have launched in total?” if to consider the Creation phase:
- 0-5: 54 entities, 44%
- 6-10: 33 entities, 27%
- 11-20: 7 entities, 6%
- 21-30: 21 entities, 17%
- 30+: 8 entities, 7%
From the chart below, we can see that the median success rate is inversely related to the cumulative amount of ventures created until the 11-20 point, after which it quickly increases. Dunn’s test (“Dunn’s test,” 2017) demonstrates that the 30+ category has a significantly higher median success rate when compared to the 11-20 group (p<0.05) while the overall significance of the test is 0.06.
The 30+ category implies that these studios have spun out more than 30 ventures, indicating a greater degree of experience. The relationship between venture volume and success rates presents a complex causality question. While repeated process execution may enhance studio performance through experiential learning, the reverse causality must be considered: higher success rates could enable greater venture creation capacity. This bidirectional relationship requires careful interpretation of any correlation between venture volume and success metrics.
The next factor with a moderate influence is the number of ventures that a studio launches per year if we consider a launch event as entering the Ideation phase. We asked studios to select from five possible options while responding to the question “How many ventures your studio starts/started per year?”:
Statistical analysis reveals median success rate differences between venture volume groups at the 0.12 confidence level. Pairwise comparisons identify a significant distinction (p≤0.18) between studios launching 4-7 ventures annually and those launching 1-3 ventures, with the higher-volume group demonstrating superior success rates.
This correlation between annual venture volume and success rates parallels the total venture relationship, presenting similar causality questions: does higher launch frequency drive improved performance through accumulated experience, or do better success rates enable increased launch capacity? While the direction of causality remains unclear, the consistent relationship between volume and performance merits attention in venture studio strategy.
- 1-3: 72 entities, 59%
- 4-7: 36 entities, 29%
- 8-10: 7 entities, 6%
- 11-15: 5 entities, 4%
- 16+: 3 entities, 2%
Statistical analysis reveals median success rate differences between venture volume groups at the 0.12 confidence level. Pairwise comparisons identify a significant distinction (p≤0.18) between studios launching 4-7 ventures annually and those launching 1-3 ventures, with the higher-volume group demonstrating superior success rates.
This correlation between annual venture volume and success rates parallels the total venture relationship, presenting similar causality questions: does higher launch frequency drive improved performance through accumulated experience, or do better success rates enable increased launch capacity? While the direction of causality remains unclear, the consistent relationship between volume and performance merits attention in venture studio strategy.
The next measure with a moderate influence is a studio’s typical stake in a new venture. We asked studios “What is/was your studio's typical stake size in the new ventures you create?” and proposed to select between seven options:
Analysis of variance indicates group differences in median success rates at the 0.14 significance level. Pairwise comparison reveals a significant distinction at the 0.1 level between studios taking less than 20% equity and those taking 20-30%, with lower-stake studios demonstrating higher success rates.
This finding presents an intriguing contrast to established venture studio theory. While prominent frameworks (Pog, 2023; Kannan and Peterman, 2022) emphasize substantial equity positions as a defining feature of the venture studio model, our data suggests that lower equity stakes may correlate with higher venture success rates, at least within these specific ownership ranges.
- <20%: 28 entities, 23%
- 20-30%: 23 entities, 19%
- 30-40%: 29 entities, 24%
- 40-50%: 11 entities, 9%
- 50-60%: 7 entities, 6%
- 60-70%: 11 entities, 9%
- 70%: 14 entities, 11%
Analysis of variance indicates group differences in median success rates at the 0.14 significance level. Pairwise comparison reveals a significant distinction at the 0.1 level between studios taking less than 20% equity and those taking 20-30%, with lower-stake studios demonstrating higher success rates.
This finding presents an intriguing contrast to established venture studio theory. While prominent frameworks (Pog, 2023; Kannan and Peterman, 2022) emphasize substantial equity positions as a defining feature of the venture studio model, our data suggests that lower equity stakes may correlate with higher venture success rates, at least within these specific ownership ranges.
The next feature we identified as having a moderate link to the success is the amount of capital the studios are ready to use for MVP development. We asked the question “What is an average investment for an MVP for your companies?” and proposed three options for response:
Analysis of variance reveals differences in median success rates across MVP investment levels at the 0.1 significance level. The most pronounced distinction emerges between studios investing over $100,000 and those investing under $50,000 per MVP (p<0.12), with lower-investment studios achieving higher success rates.
This finding aligns with resource allocation theory: lower MVP development costs may enable greater investment in critical early-stage activities such as customer development, go-to-market strategy, and marketing initiatives. The relationship suggests that lean MVP development could enhance overall venture success by preserving resources for other essential startup functions.
- <$50,000: 44 entities, 36%
- $51,000-100,000: 33 entities, 27%
- $100,000: 46 entities, 37%
Analysis of variance reveals differences in median success rates across MVP investment levels at the 0.1 significance level. The most pronounced distinction emerges between studios investing over $100,000 and those investing under $50,000 per MVP (p<0.12), with lower-investment studios achieving higher success rates.
This finding aligns with resource allocation theory: lower MVP development costs may enable greater investment in critical early-stage activities such as customer development, go-to-market strategy, and marketing initiatives. The relationship suggests that lean MVP development could enhance overall venture success by preserving resources for other essential startup functions.
Another metric from the MVP-building domain that also demonstrated a moderate link is a form of an MVP, which a studio tends to use more often in its ventures. To get this data, we asked the respondents to answer the question “How do you see your typical MVP?” with the following options and response distributions:
The Kruskal-Wallis test reveals significant differences in median success rates across MVP types at the 0.08 level. Pairwise comparisons indicate that fully developed products outperform both low-code solutions (p<0.2) and product descriptions (p<0.25). Analysis of median values confirms fully developed products demonstrate the strongest positive association with studio success.
This presents an interesting tension when considered alongside the cost analysis: while lower MVP costs correlate with higher success rates, fully developed products - typically the most resource-intensive MVP type - show superior performance. This apparent contradiction suggests a more nuanced relationship: successful studios may excel at creating complete, functional products while maintaining lean development costs. However, as this study did not examine interaction effects between these factors, this interpretation remains speculative and is identified as deserving of further study.
- Description of a product: 8 entities, 7%
- Landing webpage: 14 entities, 11%
- Low-code product: 63 entities, 51%
- Fully developed product: 38 entities, 31%
The Kruskal-Wallis test reveals significant differences in median success rates across MVP types at the 0.08 level. Pairwise comparisons indicate that fully developed products outperform both low-code solutions (p<0.2) and product descriptions (p<0.25). Analysis of median values confirms fully developed products demonstrate the strongest positive association with studio success.
This presents an interesting tension when considered alongside the cost analysis: while lower MVP costs correlate with higher success rates, fully developed products - typically the most resource-intensive MVP type - show superior performance. This apparent contradiction suggests a more nuanced relationship: successful studios may excel at creating complete, functional products while maintaining lean development costs. However, as this study did not examine interaction effects between these factors, this interpretation remains speculative and is identified as deserving of further study.
The next measure exhibiting a moderate effect - the average time to build an MVP - also adds some level of ambiguity to the MVP discussion. We asked studios the question “What is/was the average time your companies take/took to build an MVP?” and proposed to select between the following responses:
Again, the Kruskal-Wallis test identifies significant differences in median success rates across development timeframes at the 0.15 level. Pairwise comparisons reveal higher significance levels for the 6-12 month development window when compared to both 4-6 months (p≤0.24) and 3-4 months (p≤0.27). While these significance levels indicate substantial uncertainty, the aggregate KWANOVA results suggest a meaningful relationship between development duration and studio success.
The causal direction warrants careful consideration: while longer development times might contribute to higher success rates, the relationship could reflect successful studios' ability to sustain longer development cycles. Assuming direct causation, the data suggests extended MVP development periods correlate with higher venture spin-out rates.
- 3-4 months: 63 entities, 51%
- 4-6 months: 32 entities, 26%
- 6-12 months: 28 entities, 23%
Again, the Kruskal-Wallis test identifies significant differences in median success rates across development timeframes at the 0.15 level. Pairwise comparisons reveal higher significance levels for the 6-12 month development window when compared to both 4-6 months (p≤0.24) and 3-4 months (p≤0.27). While these significance levels indicate substantial uncertainty, the aggregate KWANOVA results suggest a meaningful relationship between development duration and studio success.
The causal direction warrants careful consideration: while longer development times might contribute to higher success rates, the relationship could reflect successful studios' ability to sustain longer development cycles. Assuming direct causation, the data suggests extended MVP development periods correlate with higher venture spin-out rates.
This finding creates an intriguing paradox when combined with previous results. The data suggests optimal outcomes correlate with three seemingly contradictory factors:
This apparent contradiction warrants further investigation, particularly given that three studios in our sample appear to successfully combine all these characteristics. These cases might offer valuable insights into efficient resource utilization in venture development.
- Lower MVP development costs (<$50,000)
- Fully developed product outcomes
- Extended development timeframes (6-12 months)
This apparent contradiction warrants further investigation, particularly given that three studios in our sample appear to successfully combine all these characteristics. These cases might offer valuable insights into efficient resource utilization in venture development.
Overview of the results - RQ2
We analyzed the results of the survey, which had the goal of identifying factors correlated with studios’ success. In this analysis, we adopted the measure of "success" as the studios' relative conversion into the Create phase, i.e., the percentage of spun-out new ventures relative to the total number of generated ideas. The results demonstrate that seven measures have a positive link with studio success levels (table below). The rest of the measures (Appendix 6) did not demonstrate a statistically significant link (p≤0.15) with the success measure by the KWANOVA tests.
Summary of the outcomes:
- ESOs in this category prefer to call themselves venture studios while some may use the venture builder term in order to emphasize their work on corporate projects
- Venture studios prefer to be agnostic rather than focused with the difference higher for niche and business model focus and moderate for a vertical focus
- Health & Longevity, B2B SaaS, Sustainability, and Fintech - are the leading domains where focused venture studios prefer to work
- The majority of venture studios employ the Single Studio Model, i.e., they do not have a fund within the studio and attract capital to their ventures from external funds
- Access to advisors is a major service venture studios propose to their EIRs, followed by Go-to-Market Assistance in popularity and Tech development in priority
- To get 1 exit, an average studio should develop 200 ideas and 40 MVPs
- Venture studios tend not to keep EIRs full-time but employ them when the concepts are ready
- EIRs typically join studios’ ventures at the Generation (the 1st phase) or the Creation (the 4th phase) phases demonstrating two preferred managerial practices.
- The majority of studios aim to exit during the exit of a startup, while a small proportion prioritize Seed exits
- The vast majority of studios employ and prioritize internal tech development teams
- Venture studios tend to reward their development teams with cash or stakes in a venture
- Vertical-agnostic studios have a higher median success rate compared to vertical-focused studios. This conclusion goes with 99% confidence. We haven't found any significant success rate difference in other dimensions (i.e., niche and business model) for agnostic or focused studios.
- A studio with 30+ launched ventures has a significantly higher median success rate (75-95% confidence). This link could go in the opposite direction: more successful studios launch more ventures
- The amount of ventures a studio starts per year has a positive connection to its success rate (88% confidence) which also may be a backward causal link
- Venture studios with a smaller than 20% stake in their ventures tend to demonstrate a higher success rate, at least when compared to the ones that take 20-30% (90% confidence).
- Studios that invest less than $50K into MVPs of their ventures tend to show a higher success rate when compared to those which invest more than $100K (88% confidence)
- We see a positive relationship between MVP form and success rates, with fully developed products showing the strongest correlation to positive outcomes, though at moderate confidence levels (75-80% confidence). This finding reinforces earlier observations about the potential benefits of complete product development, despite its apparent tension with cost efficiency goals.
- With an 85% level of confidence, it can be concluded that the average time to build an MVP influences a studio’s success rate. The potential winning strategy is to spend 6-12 months for MVP development (74-76% confidence).
Limitations
Similar to RQ1 (or any other rigorous analysis), this part of the study has its limitations.
First, the absence of an established venture studio taxonomy at study initiation limited our ability to conduct meaningful subgroup analyses. While factors such as studio type, technical focus, and geography showed no significant direct effect on success rates, potential subgroup-specific patterns may exist. Future research with larger category-specific samples could reveal these relationships.
Second, the study examines individual variable effects without exploring interaction effects between factors. A configurational analysis could reveal how different combinations of variables influence success rates, potentially offering more nuanced insights into venture studio performance drivers.
Third, approximately 90 studios initiated but did not complete the survey, primarily citing data sensitivity concerns. The mandatory nature of all survey questions may have inadvertently excluded studios with partial but valuable data. Alternative data collection mechanisms that accommodate varying levels of information sharing could enhance response rates.
Fourth, the sample shows potential bias toward emerging studios seeking visibility through study participation. While established market leaders were less inclined to share insights, newer studios' responses may reflect limited operational history rather than proven patterns. Longitudinal analysis could both track participating studios' evolution and potentially engage more established players.
First, the absence of an established venture studio taxonomy at study initiation limited our ability to conduct meaningful subgroup analyses. While factors such as studio type, technical focus, and geography showed no significant direct effect on success rates, potential subgroup-specific patterns may exist. Future research with larger category-specific samples could reveal these relationships.
Second, the study examines individual variable effects without exploring interaction effects between factors. A configurational analysis could reveal how different combinations of variables influence success rates, potentially offering more nuanced insights into venture studio performance drivers.
Third, approximately 90 studios initiated but did not complete the survey, primarily citing data sensitivity concerns. The mandatory nature of all survey questions may have inadvertently excluded studios with partial but valuable data. Alternative data collection mechanisms that accommodate varying levels of information sharing could enhance response rates.
Fourth, the sample shows potential bias toward emerging studios seeking visibility through study participation. While established market leaders were less inclined to share insights, newer studios' responses may reflect limited operational history rather than proven patterns. Longitudinal analysis could both track participating studios' evolution and potentially engage more established players.
RQ3: What venture studio factors are aligned with new ventures' exits?
By Tom West
Building on our understanding of venture studio performance and operational factors, we turn to examining specific characteristics that align with successful new ventures' exits. Among the active venture studios analyzed, 37.2% have achieved at least one form of equity exit. While the landscape analysis presented earlier describes the broader venture studio population, and RQ2 examines operational success factors, this analysis focuses specifically on identifying measurable factors associated with equity exits.
For the purposes of this analysis, we define an "exit" as any form of equity sale from a new venture, including seed rounds, venture capital investments, acquisitions, or public offerings. While this broad definition includes early-stage funding events that may not represent final liquidity outcomes, it provides an objective measure of market validation by which to examine the venture studio model. By examining characteristics of studios that have claimed exits versus those that haven't, we can identify factors that might influence exit outcomes.
For the purposes of this analysis, we define an "exit" as any form of equity sale from a new venture, including seed rounds, venture capital investments, acquisitions, or public offerings. While this broad definition includes early-stage funding events that may not represent final liquidity outcomes, it provides an objective measure of market validation by which to examine the venture studio model. By examining characteristics of studios that have claimed exits versus those that haven't, we can identify factors that might influence exit outcomes.
Sampling and Methods
The sample for this study started with survey responses of 103 VS’s that responded to a survey distributed to the approx. 700 venture studios from the Venture Studio Family network during the first round of surveying (please, see RQ2 data collection process for more information). This sampling approach can be characterized as non-probability, self-selection sampling (Saunders and Lewis, 2018).
Non-probability sampling is often used when it is difficult to identify all potential cases in the population or when working with a specific network or community (Etikan et al., 2016). In this case, the Venture Studio Family network is merely a sample of the global VS community which is the defined population from which the sample is drawn. Self-selection sampling occurs when research participants volunteer to take part in the study (Sharma, 2017). This method is efficient but can introduce bias, as respondents may differ systematically from non-respondents (Bell et al., 2022).
The response rate of approximately 14.7% (103 out of 700) is within the typical range for online surveys in organizational research (Baruch, 1999; Baruch and Holtom, 2008). However, it's important to consider potential non-response bias and its implications for the generalizability of findings (Saunders and Lewis, 2018). While this sampling approach limits the ability to make broad generalizations about all VSs, it provides valuable insights into a significant portion of an established network of VSs, the Venture Studio Family.
Of the 103 responses, 6 indicated that they are closed and no longer in operation. These were removed to allow focus on active VSs, leaving a sample size of 97. Of these 97, 32 have indicated that they have sold equity in a new venture. Of these 32 exiting VSs, the youngest has been in operation for two years. Subsequently, all VSs younger than 2 years were eliminated from the sample (n=11). This left a final sample set used for this research with n = 86.
This truncation decision is supported by similar treatments of data in analogous examinations of accelerators (Hallen et al., 2014) and venture capital firms (Ewens et al., 2018) in that they also discarded very new firms to explore the nature of established firms. Furthermore, Kerr concluded that short-term performance can be misleading for early-stage ventures (Kerr et al., 2014).
The final sample size of 86 was sufficient for the planned statistical analyses, including binary logistic regression, based on common rules of thumb for sample size in such analyses (Hair et al., 2009). Furthermore, VanVoorhis and Morgan suggest a sample size of “no less than 50 participants for correlation or regression.” (VanVoorhis and Morgan, 2007). However, there are some statistical limitations imposed by this sample size regarding the number of independent variables used in logistic regression, a consideration discussed in the findings section.
Data Structures
A total of 20 variables (including the proposed dependent, dichotomous “NewVentureExits” variable) were selected for examination from the survey data and coded into SPSS. A complete description of all variables is featured in Appendix 2 of West, 2024. These variables are conceptually sorted into baskets according to how they relate to the VS, as follows:
1. Characteristics inherent to VS itself: Studio age, Number of VS founders, Number of VS full-time employees, Total number of new ventures’ started by VS, Number of new ventures started per year, Fund size, Fund structure
2. Timing of “entrepreneur in residence” (EIR) recruitment: Generation stage, Ideation stage, Validation stage, Creation stage, Growth stage, Total number of stages over which a VS seeks an EIR, Early minus late stages*
3. Minimum viable product (MVP) characteristics: Cost, Complexity, Time taken
4. Finally, two uncategorized variables: VS founders have prior exits, Equity stake taken by VS in new ventures
Non-probability sampling is often used when it is difficult to identify all potential cases in the population or when working with a specific network or community (Etikan et al., 2016). In this case, the Venture Studio Family network is merely a sample of the global VS community which is the defined population from which the sample is drawn. Self-selection sampling occurs when research participants volunteer to take part in the study (Sharma, 2017). This method is efficient but can introduce bias, as respondents may differ systematically from non-respondents (Bell et al., 2022).
The response rate of approximately 14.7% (103 out of 700) is within the typical range for online surveys in organizational research (Baruch, 1999; Baruch and Holtom, 2008). However, it's important to consider potential non-response bias and its implications for the generalizability of findings (Saunders and Lewis, 2018). While this sampling approach limits the ability to make broad generalizations about all VSs, it provides valuable insights into a significant portion of an established network of VSs, the Venture Studio Family.
Of the 103 responses, 6 indicated that they are closed and no longer in operation. These were removed to allow focus on active VSs, leaving a sample size of 97. Of these 97, 32 have indicated that they have sold equity in a new venture. Of these 32 exiting VSs, the youngest has been in operation for two years. Subsequently, all VSs younger than 2 years were eliminated from the sample (n=11). This left a final sample set used for this research with n = 86.
This truncation decision is supported by similar treatments of data in analogous examinations of accelerators (Hallen et al., 2014) and venture capital firms (Ewens et al., 2018) in that they also discarded very new firms to explore the nature of established firms. Furthermore, Kerr concluded that short-term performance can be misleading for early-stage ventures (Kerr et al., 2014).
The final sample size of 86 was sufficient for the planned statistical analyses, including binary logistic regression, based on common rules of thumb for sample size in such analyses (Hair et al., 2009). Furthermore, VanVoorhis and Morgan suggest a sample size of “no less than 50 participants for correlation or regression.” (VanVoorhis and Morgan, 2007). However, there are some statistical limitations imposed by this sample size regarding the number of independent variables used in logistic regression, a consideration discussed in the findings section.
Data Structures
A total of 20 variables (including the proposed dependent, dichotomous “NewVentureExits” variable) were selected for examination from the survey data and coded into SPSS. A complete description of all variables is featured in Appendix 2 of West, 2024. These variables are conceptually sorted into baskets according to how they relate to the VS, as follows:
1. Characteristics inherent to VS itself: Studio age, Number of VS founders, Number of VS full-time employees, Total number of new ventures’ started by VS, Number of new ventures started per year, Fund size, Fund structure
2. Timing of “entrepreneur in residence” (EIR) recruitment: Generation stage, Ideation stage, Validation stage, Creation stage, Growth stage, Total number of stages over which a VS seeks an EIR, Early minus late stages*
3. Minimum viable product (MVP) characteristics: Cost, Complexity, Time taken
4. Finally, two uncategorized variables: VS founders have prior exits, Equity stake taken by VS in new ventures
* Respondents were permitted to select multiple answers to the question “When do you typically recruit an EIR?” “Early minus late” was calculated by simple subtraction after assigning values 1-5 within the otherwise chronologically ordinal array. A lower number indicates that EIR’s were recruited at later stages. These stages were defined by the BVSR'24 in alignment with Rathgeber et al., 2017 - see Appendix 2.
Data Analysis Methods
The data analysis for this section of the study compared independent samples segmented by new ventures' exits, sought to identify candidate variables for inclusion in a logistic regression, and finally, propose a logistic regression model.
Key variables examined include:
Multiple Regression Analysis
The core of the analysis was multiple regression, which allowed for the examination of the relationship between multiple independent variables and a dependent variable (Tabachnick et al., 2019). Given that the dependent variable (new venture's exit) is dichotomous, a binary logistic regression was employed (Hosmer et al., 2013).
The logistic regression model is expressed as:
The data analysis for this section of the study compared independent samples segmented by new ventures' exits, sought to identify candidate variables for inclusion in a logistic regression, and finally, propose a logistic regression model.
Key variables examined include:
- Dependent variable: new venture exit (operationalized as a binary variable).
- Independent variable candidates: all other variables, as grouped and described above.
Multiple Regression Analysis
The core of the analysis was multiple regression, which allowed for the examination of the relationship between multiple independent variables and a dependent variable (Tabachnick et al., 2019). Given that the dependent variable (new venture's exit) is dichotomous, a binary logistic regression was employed (Hosmer et al., 2013).
The logistic regression model is expressed as:
where p is the probability of an exit, X1, X2, ..., Xk are the independent variables, and β0, β1, β2, ..., βk are the regression coefficients.
Potential issues arising from multicollinearity in the candidate models were checked by Variance Inflation Factors (VIF), with values above 10 indicating potential problems in the model (Hair et al., 2009). Although VIF is primarily used for linear models, Midi et al. suggests that it can provide valid insights into multicollinearity in logistic regression (Midi et al., 2010). The resulting logistic regression models’ goodness of fit were evaluated using the Hosmer-Lemeshow test, and their predictive power assessed using both Nagelkerke R² and classification accuracy (Hosmer et al., 2013).
The significance level (p) for all statistical tests was set at 0.05, a common threshold in social science research (Field, 2013). However, given the exploratory nature of some analyses, findings with p≤0.10 were considered as valid model candidate variables and/or noted for further investigation in future research.
Justification of Methods
The combination of these analytical methods provided a robust approach to addressing the research question. Descriptive statistics offered a foundational understanding of the data. Tests comparing independent samples segmented by NewVentureExits helped identify significant differences between the samples. Finally, logistic regression allowed for the identification of key predictors of new ventures' exits while controlling for multiple factors simultaneously.
Limitations of Methods
While the chosen methodology offers valuable insights into VS exit factors, it is important to acknowledge several significant limitations:
These limitations should be considered when interpreting the following findings.
Potential issues arising from multicollinearity in the candidate models were checked by Variance Inflation Factors (VIF), with values above 10 indicating potential problems in the model (Hair et al., 2009). Although VIF is primarily used for linear models, Midi et al. suggests that it can provide valid insights into multicollinearity in logistic regression (Midi et al., 2010). The resulting logistic regression models’ goodness of fit were evaluated using the Hosmer-Lemeshow test, and their predictive power assessed using both Nagelkerke R² and classification accuracy (Hosmer et al., 2013).
The significance level (p) for all statistical tests was set at 0.05, a common threshold in social science research (Field, 2013). However, given the exploratory nature of some analyses, findings with p≤0.10 were considered as valid model candidate variables and/or noted for further investigation in future research.
Justification of Methods
The combination of these analytical methods provided a robust approach to addressing the research question. Descriptive statistics offered a foundational understanding of the data. Tests comparing independent samples segmented by NewVentureExits helped identify significant differences between the samples. Finally, logistic regression allowed for the identification of key predictors of new ventures' exits while controlling for multiple factors simultaneously.
Limitations of Methods
While the chosen methodology offers valuable insights into VS exit factors, it is important to acknowledge several significant limitations:
- Although one can examine relationships such as correlation and covariance, the data is cross-sectional, making it difficult to determine causality. (Levin, 2006). This is an opportunity for future longitudinal studies.
- The self-selection sampling method may introduce bias, as respondents might differ systematically from non-respondents (Saunders and Lewis, 2018).
- The sample's focus on Venture Studio Family members may limit generalizability to the broader VS population (Etikan et al., 2016).
- As all data come from a single survey, this study carries the risk of common method bias affecting its results (Podsakoff et al., 2003).
These limitations should be considered when interpreting the following findings.
Findings
In attempting to understand the conditions which might accompany an exit of a new venture by a VS, we first look at the data collected. This section describes the statistical comparisons of independent samples segmented by “Yes” and “No” responses to new ventures' exits. That will be followed by an analysis to test for variables with significant relationships across these two samples. Variables are presented as grouped above:
Comparison of Samples
We can note some differences in the two samples, finding some intuitive differences. For example, the mean age for studios with exits is 7.11 years, which is nearly twice the age of those with no exits. VS’s with exits also report having more founders and more full-time employees compared to those without. The detailed summary statistics for the segmented samples are presented in West, 2024. While these observations are useful, it is through independent sample t-tests and Pearson Chi-Square analysis that we can check for statistically significant differences among the segmented samples.
- Characteristics inherent to VS itself
- Timing of EIR recruitment
- MVP characteristics
- Uncategorized variables
Comparison of Samples
We can note some differences in the two samples, finding some intuitive differences. For example, the mean age for studios with exits is 7.11 years, which is nearly twice the age of those with no exits. VS’s with exits also report having more founders and more full-time employees compared to those without. The detailed summary statistics for the segmented samples are presented in West, 2024. While these observations are useful, it is through independent sample t-tests and Pearson Chi-Square analysis that we can check for statistically significant differences among the segmented samples.
All 19 proposed independent variables were tested for statistically significant differences across the “with exit” and “no exit” samples. Statistically significant differences, or those observed to offer other insights, are examined here. All other statistical comparisons returned insignificant results and can be found in Appendix 4 of West, 2024.
Unsurprisingly, there is a significant difference in the age (p<0.001) and total new ventures started (p=0.006) between those VS’s with exits and those without. It is intuitive that older VSs which have started many new ventures are more likely to have exited at least one, a notion strongly supported by these data. Similarly, VSs with founders who have personally exited a company prior to joining the VS are highly likely to have reported a new venture exit (p=0.023).
Unsurprisingly, there is a significant difference in the age (p<0.001) and total new ventures started (p=0.006) between those VS’s with exits and those without. It is intuitive that older VSs which have started many new ventures are more likely to have exited at least one, a notion strongly supported by these data. Similarly, VSs with founders who have personally exited a company prior to joining the VS are highly likely to have reported a new venture exit (p=0.023).
Interestingly, the test shows a significant difference between the samples regarding the phase of recruitment of EIRs. Specifically, there is a difference among samples when examining whether or not they recruit EIRs during the creation phase, that is, when the new corporation is being formed after passing through the generation, ideation and validation phases. More clearly, the creation phase is the phase “in which a new venture starts sales and marketing of the developed product (i.e., heading towards a product-market fit)” (Rathgeber et al., 2017; Pog, 2023)
69% of VSs with exits reported recruiting an EIR in the “creation” phase, compared with only 46% of non-exiting VSs recruiting during the “creation” phase, a difference significant to p=0.044. Given that respondents could select more than one phase, and that “creation” is the only phase with any meaningful difference along the NewVentureExits segmentation, this is deserving of further examination.
Finally, it is interesting to note that all three variables related to the creation of MVPs (time, complexity, and cost) do not appear to have any differences when segmented by NewVentureExit. This observation suggests there’s no relationship between MVP characteristics and NewVentureExits, implying that higher efficiencies and lower opportunity costs could be available for VSs which pursue faster, simpler and cheaper MVPs. This is an interesting relationship worthy of further study.
In summary, the following variables have significant differences across the NewVentureExits segmentation:
69% of VSs with exits reported recruiting an EIR in the “creation” phase, compared with only 46% of non-exiting VSs recruiting during the “creation” phase, a difference significant to p=0.044. Given that respondents could select more than one phase, and that “creation” is the only phase with any meaningful difference along the NewVentureExits segmentation, this is deserving of further examination.
Finally, it is interesting to note that all three variables related to the creation of MVPs (time, complexity, and cost) do not appear to have any differences when segmented by NewVentureExit. This observation suggests there’s no relationship between MVP characteristics and NewVentureExits, implying that higher efficiencies and lower opportunity costs could be available for VSs which pursue faster, simpler and cheaper MVPs. This is an interesting relationship worthy of further study.
In summary, the following variables have significant differences across the NewVentureExits segmentation:
Multicollinearity
In addition to examining the differences of these samples along the NewVentureExits segmentation, it is next useful to note and examine any variables which could introduce multicollinearity issues into the binary logistic regression model. Given that these VIF statistics ranging from 1.04 to 1.43 indicate low multicollinearity (Midi et al., 2010), all four variables were selected as candidates to be evaluated for inclusion in a binary logistic regression model.
In addition to examining the differences of these samples along the NewVentureExits segmentation, it is next useful to note and examine any variables which could introduce multicollinearity issues into the binary logistic regression model. Given that these VIF statistics ranging from 1.04 to 1.43 indicate low multicollinearity (Midi et al., 2010), all four variables were selected as candidates to be evaluated for inclusion in a binary logistic regression model.
Binary Logistic model candidates and testing
The inclusion or exclusion of each candidate variable produced a matrix of 15 candidate models for analysis. Each of these models was tested using a binary logistic regression, with Nagelkerke R2 and Hosmer-Lemeshow tests conducted to evaluate each one. The results are shown in table 4.4.1 with superior models highlighted for further analysis (models A and D).
The inclusion or exclusion of each candidate variable produced a matrix of 15 candidate models for analysis. Each of these models was tested using a binary logistic regression, with Nagelkerke R2 and Hosmer-Lemeshow tests conducted to evaluate each one. The results are shown in table 4.4.1 with superior models highlighted for further analysis (models A and D).
Models A and D were further examined to determine the best model fit, explained variance, and classification accuracy. The omnibus test of each model was significant (p<0.001). In looking at the Hosmer-Lemeshow Test, both models show good fit (p>0.05), with Model D showing a slightly better fit (Tabachnick et al., 2019). Model A explains slightly more variance than Model D (40.5% of variance explained vs 40.3%). This difference is negligible, suggesting that the additional variable in Model A doesn't substantially improve the model. However, model A shows slightly better classification accuracy, particularly for positive outcomes: the percentage of the correct predictions for model A is 15% higher than for model D.
Next, an examination of the individual predictors in each model was conducted. The results demonstrated that the Total new ventures variable in Model A is not significant (p = 0.650) and has a minimal effect size (Exp(B) = 1.124). Its inclusion slightly reduces the significance and effect sizes of other variables. The detailed analysis can be found in West, 2024.
It is concluded that Model D is the better model. It is more parsimonious, has nearly identical explanatory power, and avoids including a glaringly non-significant predictor. Therefore, the slight improvement in classification accuracy in Model A does not justify the added complexity and reduced significance of other predictors.
The resulting binary logistic regression model which quantitatively describes the research question is as follows:
It is concluded that Model D is the better model. It is more parsimonious, has nearly identical explanatory power, and avoids including a glaringly non-significant predictor. Therefore, the slight improvement in classification accuracy in Model A does not justify the added complexity and reduced significance of other predictors.
The resulting binary logistic regression model which quantitatively describes the research question is as follows:
Overview of the results - RQ3
Several key findings emerged from the analysis. Studios with exits averaged 7.11 years in operation, nearly double the age of those without exits, with this difference proving highly significant (p<0.001). The total number of new ventures launched also showed a significant correlation with exits (p=0.006), though this relationship may simply reflect the greater opportunities for exits that come with launching more ventures.
Prior founder experience emerged as another significant factor. Studios whose founders had previously exited companies showed a significantly higher likelihood of achieving new venture exits (p=0.023). This relationship remained consistent across different types of exit experience, suggesting that lessons learned from prior exits transfer effectively to the studio model.
Perhaps the most intriguing finding concerned EIR recruitment timing. Studios recruiting EIRs during the creation phase - when new ventures begin sales and marketing - showed significantly higher exit rates. Specifically, 69% of studios with exits recruited EIRs during the creation phase versus 46% of those without exits (p=0.044). The creation phase was the only stage showing significant differences between studios with and without exits.
Surprisingly, no significant relationship emerged between MVP characteristics (complexity, investment, time) and exit outcomes. This finding suggests that resource efficiency in MVP development may be more important than specific MVP attributes, challenging common assumptions about early product development's role in venture success.
Through binary logistic regression analysis, the study developed a model explaining approximately 40% of the variance in exit outcomes. This model achieved good fit (Hosmer-Lemeshow Test p>0.05). It should be refined further through testing on larger sample sizes.
The research suggests a few valuable practical implications. A strong relationship between studio age and exits suggests that organizational learning and experience play crucial roles in successful venture building. This conclusion is supported by the high exit influence of studio founders' exit experience before joining a VS. The significance of EIR recruitment timing during the creation phase challenges traditional assumptions about founder involvement from inception. The lack of relationship between MVP characteristics and exits suggests studios might benefit from faster, simpler, and cheaper MVPs, allowing for more rapid iteration and market testing
Prior founder experience emerged as another significant factor. Studios whose founders had previously exited companies showed a significantly higher likelihood of achieving new venture exits (p=0.023). This relationship remained consistent across different types of exit experience, suggesting that lessons learned from prior exits transfer effectively to the studio model.
Perhaps the most intriguing finding concerned EIR recruitment timing. Studios recruiting EIRs during the creation phase - when new ventures begin sales and marketing - showed significantly higher exit rates. Specifically, 69% of studios with exits recruited EIRs during the creation phase versus 46% of those without exits (p=0.044). The creation phase was the only stage showing significant differences between studios with and without exits.
Surprisingly, no significant relationship emerged between MVP characteristics (complexity, investment, time) and exit outcomes. This finding suggests that resource efficiency in MVP development may be more important than specific MVP attributes, challenging common assumptions about early product development's role in venture success.
Through binary logistic regression analysis, the study developed a model explaining approximately 40% of the variance in exit outcomes. This model achieved good fit (Hosmer-Lemeshow Test p>0.05). It should be refined further through testing on larger sample sizes.
The research suggests a few valuable practical implications. A strong relationship between studio age and exits suggests that organizational learning and experience play crucial roles in successful venture building. This conclusion is supported by the high exit influence of studio founders' exit experience before joining a VS. The significance of EIR recruitment timing during the creation phase challenges traditional assumptions about founder involvement from inception. The lack of relationship between MVP characteristics and exits suggests studios might benefit from faster, simpler, and cheaper MVPs, allowing for more rapid iteration and market testing
Limitations
Sample size is a key limitation in determining this model’s validity. The EPV (events per variable) is 10.7, putting it just over the commonly held threshold of 10 (Peduzzi et al., 1996) and into a grey zone with regard to evaluating its predictive power. However, more recent research has supported the relaxation of this standard (Vittinghoff and McCulloch, 2007).
Additionally, the Creation phase variable has no readily intuitive causal relationship with new ventures' exits, aside from perhaps being a best practice among successful VSs. Furthermore, its model factor significance of p=0.100 makes it a candidate for more thorough examination as larger data sets become available.
In considering these limitations, it is important to acknowledge mitigating factors. First, this study focuses on a relatively new and niche phenomenon (venture studios), which naturally limits the available sample size. This is a common challenge in emerging fields (Yin, 2014). Second, given the exploratory nature of this research, smaller sample sizes can be considered appropriate when examining new phenomena (Stebbins, 2001).
Reproducing this study with a larger sample set would address each of these criticisms, provide for a more powerful and robust model, and likely reveal further insights into conditions surrounding exits in the VS model.
Additionally, the Creation phase variable has no readily intuitive causal relationship with new ventures' exits, aside from perhaps being a best practice among successful VSs. Furthermore, its model factor significance of p=0.100 makes it a candidate for more thorough examination as larger data sets become available.
In considering these limitations, it is important to acknowledge mitigating factors. First, this study focuses on a relatively new and niche phenomenon (venture studios), which naturally limits the available sample size. This is a common challenge in emerging fields (Yin, 2014). Second, given the exploratory nature of this research, smaller sample sizes can be considered appropriate when examining new phenomena (Stebbins, 2001).
Reproducing this study with a larger sample set would address each of these criticisms, provide for a more powerful and robust model, and likely reveal further insights into conditions surrounding exits in the VS model.
BVSR’24 outro (final remarks)
We were highly intrigued by the venture studio model and questioned whether the reality is as great as it seemed. We started with a critical analysis of the existing studies (check here) and found several issues that might have influenced the suggested findings. Next, our research design prioritizes quantitative analysis to address fundamental questions about venture studio performance, particularly focusing on measurable attributes that have historically relied primarily on anecdotal evidence. To the best of our knowledge, this study represents the first quantitative investigation of venture studio performance employing dual methodological approaches: primary survey data collection and desk research analysis.
For the first research question, we turned to secondary deal data bearing in mind its reflection of the related organizational performance. Using this approach, we could solve the issues related to response bias, transparency, and repeatability. We also sought to achieve statistical significance while acknowledging practical limitations in data collection. Survey response rates among ESO managers typically remain low due to the time-intensive nature of participation, necessitating complementary research approaches to reach meaningful statistical thresholds.
For the second research question, we executed a survey without providing the respondents with the measures we were going to use to assess their performance. From our perspective, this principle might soften the response bias as well. In terms of transparency, we provided all the questions we asked to studios, and while the raw data cannot be shared due to our promise to studios to make the results anonymized and aggregated, anyone who’d like to repeat our study may employ our survey and methods and compare the outcomes.
For the third research question, we analyzed survey data from 86 active venture studios to identify factors associated with new ventures' exits. Through statistical comparison of studios with and without exits, several key factors emerged including studio age, founder experience, and the timing of EIR recruitment. A binary logistic regression model revealed that studio age, founder exit experience, and recruitment of EIRs during the creation phase together explain about 40% of the variance in exit outcomes. Counterintuitively, we found no significant relationship between MVP characteristics (complexity, investment, time) and achieving exits.
Data scarcity presented a significant challenge throughout the research process, contributing to a six-month delay in report publication. While data availability constraints are well-documented in entrepreneurial research - including in our research scientist's doctoral work - we initially anticipated that our network would help overcome these limitations. Though network resources proved valuable, persistent data challenges necessitated ongoing adjustments to balance methodological ideals against practical constraints.
The successful completion of this research, despite these obstacles, reflects the invaluable support of our contributors and sponsors, without whom this investigation would not have been possible.
Yes, the study has its limitations, but we believe that the results shed more light on venture studios by providing objective insights into the phenomenon. These insights do not generally support what is widely accepted in the market:
The data reveal promising trends in certain venture studio performance metrics, though without reaching statistical significance in some instances. However, the evidence does not support claims of venture studios fundamentally disrupting traditional venture capital models. While venture studios and their portfolio companies merit serious consideration from investors, they should be viewed as a complementary approach rather than a comprehensive solution to venture capital challenges.
Further research is warranted when more longitudinal data becomes available, particularly as the substantial cohort of studios established within the past six years matures and generates meaningful performance metrics.
We hope our conclusions and the proposed framework will help studio managers and investors considering investment into studios to make better, more successful decisions. We would say, in terms of venture studios, we are optimistic about realizing an opportunity to shift from Gartner’s “Peak of inflated expectations” to the “Slope of enlightenment,” overcoming the “Trough of disillusionment”.
Additional data and research will help clarify venture studios' role within the venture capital ecosystem, potentially accelerating the model's progression toward sustainable, predictable performance - its "Plateau of Productivity."
For the first research question, we turned to secondary deal data bearing in mind its reflection of the related organizational performance. Using this approach, we could solve the issues related to response bias, transparency, and repeatability. We also sought to achieve statistical significance while acknowledging practical limitations in data collection. Survey response rates among ESO managers typically remain low due to the time-intensive nature of participation, necessitating complementary research approaches to reach meaningful statistical thresholds.
For the second research question, we executed a survey without providing the respondents with the measures we were going to use to assess their performance. From our perspective, this principle might soften the response bias as well. In terms of transparency, we provided all the questions we asked to studios, and while the raw data cannot be shared due to our promise to studios to make the results anonymized and aggregated, anyone who’d like to repeat our study may employ our survey and methods and compare the outcomes.
For the third research question, we analyzed survey data from 86 active venture studios to identify factors associated with new ventures' exits. Through statistical comparison of studios with and without exits, several key factors emerged including studio age, founder experience, and the timing of EIR recruitment. A binary logistic regression model revealed that studio age, founder exit experience, and recruitment of EIRs during the creation phase together explain about 40% of the variance in exit outcomes. Counterintuitively, we found no significant relationship between MVP characteristics (complexity, investment, time) and achieving exits.
Data scarcity presented a significant challenge throughout the research process, contributing to a six-month delay in report publication. While data availability constraints are well-documented in entrepreneurial research - including in our research scientist's doctoral work - we initially anticipated that our network would help overcome these limitations. Though network resources proved valuable, persistent data challenges necessitated ongoing adjustments to balance methodological ideals against practical constraints.
The successful completion of this research, despite these obstacles, reflects the invaluable support of our contributors and sponsors, without whom this investigation would not have been possible.
Yes, the study has its limitations, but we believe that the results shed more light on venture studios by providing objective insights into the phenomenon. These insights do not generally support what is widely accepted in the market:
- According to the 10 vintage years of pre-seed deals, VSs do not significantly outperform pre-seed VC funds and accelerators in terms of portfolio ventures’ valuation growth rate (RQ1)
- According to the responses of the studios, vertical-focused VSs do not outperform agnostic ones; on the contrary, vertical-agnostic studios show significantly higher success rates (RQ2)
- According to the regression analysis, studio age, studio founders' prior exits, and EIR recruitment timing (Creation) have a significant and positive influence on studio exits (RQ3)
The data reveal promising trends in certain venture studio performance metrics, though without reaching statistical significance in some instances. However, the evidence does not support claims of venture studios fundamentally disrupting traditional venture capital models. While venture studios and their portfolio companies merit serious consideration from investors, they should be viewed as a complementary approach rather than a comprehensive solution to venture capital challenges.
Further research is warranted when more longitudinal data becomes available, particularly as the substantial cohort of studios established within the past six years matures and generates meaningful performance metrics.
We hope our conclusions and the proposed framework will help studio managers and investors considering investment into studios to make better, more successful decisions. We would say, in terms of venture studios, we are optimistic about realizing an opportunity to shift from Gartner’s “Peak of inflated expectations” to the “Slope of enlightenment,” overcoming the “Trough of disillusionment”.
Additional data and research will help clarify venture studios' role within the venture capital ecosystem, potentially accelerating the model's progression toward sustainable, predictable performance - its "Plateau of Productivity."
Recent academic works of BVSR’24 contributors
Following our mantra of “more research,” we would like to share some summarized insights from the academic efforts of our contributors. The following academic works were produced as parts of theses, Master’s and Doctoral, and, from our perspective, shed more light on various aspects of the venture studio phenomenon:
Coelsch-Foisner, C., Vandeweghe, L., Clarysse, B., 2024. Understanding A New Player in The Entrepreneurial Ecosystem: The Venture Studio. SSRN Journal.
The article offers an in-depth examination of venture studios, a relatively recent addition to the entrepreneurial ecosystem. These organizations are actively reshaping the traditional paths of startup creation and support by combining venture funding, expertise, and operational resources. Unlike accelerators or venture capital (VC) firms, venture studios adopt a comprehensive, hands-on approach to building new companies. They participate at nearly every stage of development, from initial ideation through market entry, effectively functioning as organizational co-founders rather than simply financial backers or limited-term supporters. This paper analyzes how venture studios differ from established entrepreneurial support organizations (ESOs) by providing both theoretical insights and empirical evidence gathered from studying 20 venture studios worldwide.
Venture Studios vs. Other Entrepreneurial Support Models
Venture studios differ from other ESOs like accelerators and VC firms in several key ways. Accelerators are typically short-term programs that provide startups with education, mentoring, and initial funding in exchange for a small equity stake. Their primary role is to accelerate a startup’s path to venture capital, customer acquisition, and product-market fit within a cohort-based setting, often culminating in a "demo day" where startups pitch to investors. Venture capitalists, on the other hand, fund more established ventures in exchange for equity, becoming involved mainly in mid- to late-stage funding rounds and offering strategic advice, network connections, and assistance in professionalizing the startup's management and operations.
While accelerators and VCs play recognized roles in the startup ecosystem, venture studios differ by combining aspects of both and then going further. They provide extensive resources and direct involvement from the initial idea stage, supporting startups through ideation, validation, and market entry. In addition to financial investment, venture studios maintain in-house teams of designers, engineers, legal advisors, and more who work closely with founders. Their model aims to "de-risk" startup creation by reducing inefficiencies and sharing knowledge and resources across a portfolio of ventures. Studios thus function as parallel creators of multiple startups, often taking a substantial equity share (sometimes between 20% and 80%) in exchange for their intensive involvement.
Three Mechanisms of Venture Studio Support
The document identifies three primary mechanisms by which venture studios support new ventures: organizational entrepreneurship, entrepreneurial management, and studio governance. These mechanisms provide a comprehensive framework for understanding how studios function as co-founders, significantly influencing the venture creation process.
1. Organizational Entrepreneurship: Venture studios are deeply involved in the ideation phase of a startup. They identify potential business opportunities by engaging with industry experts, academic researchers, and stakeholders to gather insights and address unmet needs within a specific market. This structured approach enables them to build idea pipelines before founders are even involved. For instance, one studio specializing in healthcare derived new venture ideas by consulting with medical professionals on pressing healthcare challenges. Another studio, focused on sustainability, identified high-impact recycling problems by analyzing “high leakage points” in waste management.
Studios then employ a phased idea assessment process to evaluate these initial concepts through market research, competitor analysis, and customer validation. By deploying tools like rapid prototyping, customer journey mapping, and data-driven decision-making, studios assess the potential success of a venture before substantial resources are committed. If a concept proves viable, studios move forward by recruiting founders and assembling development teams; if not, they may abandon or pivot the idea. This iterative, data-informed process resembles lean startup methodologies but with an additional layer of organizational oversight and resources that minimizes startup risks and accelerates product validation.
2. Entrepreneurial Management: Unlike traditional entrepreneurship, where founders initiate and manage their ventures independently, venture studios systematize the founding process and actively recruit founders for specific roles within pre-validated ideas. This recruiting process can be passive, where founders apply to open positions, or active, where studios headhunt specific individuals with relevant expertise and experience. Venture studios often screen founders rigorously, seeking individuals with technical expertise, strategic acumen, or alignment with the venture’s mission. For example, a studio focused on financial technology might hire founders with backgrounds in investment banking, consulting, or financial services to lead its ventures.
Founders recruited by venture studios typically receive a salary or stipend, which reduces the financial risks they would otherwise face in traditional entrepreneurship. This setup also includes benefits like healthcare and retirement plans, enabling founders to focus on building the venture rather than fundraising or basic operational needs. Studios thus facilitate a more structured, stable pathway to entrepreneurship, opening it up to individuals who may not have otherwise pursued a startup due to the associated financial risks.
3. Implementing Studio Governance: Venture studios implement governance structures that give them substantial control over strategic decision-making and resource allocation within their portfolio ventures. By taking high equity stakes, studios secure their role as “organizational co-founders,” allowing them to steer the venture’s direction alongside the founder. This control can encompass decisions such as product pivots, market entry timing, and long-term strategy. In many cases, the founder shares decision-making authority with the studio, especially in high-stakes scenarios that could determine the venture's trajectory. Studios also support ventures by providing access to shared infrastructure, such as labs, office spaces, and specialized equipment, which are essential but costly resources that individual startups would struggle to afford independently.
Resource optimization is another key component of studio governance. Studios pool their human resources across ventures, assigning designers, engineers, marketers, and business strategists to multiple startups. For example, a biotech-focused venture studio may employ a team of lab scientists, engineers, and healthcare industry experts who assist across different ventures, allowing startups to leverage expert knowledge without incurring high individual costs. This shared resource model operates like a centralized support system that not only reduces costs but also fosters cross-pollination of ideas and best practices among ventures. Studios create knowledge-sharing systems, such as databases, workshops, and training sessions, to promote organizational learning and ensure that each startup benefits from the studio’s accumulated insights and experience.
Impact of Venture Studios on Entrepreneurship
Venture studios represent a shift from traditional, founder-driven entrepreneurship to a model that institutionalizes entrepreneurial processes, lowering barriers and risks for potential founders. By assuming responsibility for ideation, funding, and operational support, studios redefine the role of founders, making entrepreneurship more accessible to individuals with diverse backgrounds and reducing the financial and operational burdens typically borne by independent entrepreneurs.
However, this structured, organization-driven model also challenges conventional notions of entrepreneurship, particularly regarding founder autonomy and risk-taking. In traditional ventures, founders often take substantial personal risks and retain significant control over strategic decisions. In contrast, venture studio founders are more akin to intrapreneurs—employees with significant responsibility but limited ownership. This shift may change the motivations and profiles of future entrepreneurs, who may increasingly view venture studios as career pathways rather than as independent ventures with substantial personal risk and reward.
The emergence of venture studios may also encourage a trend toward “parallel entrepreneurship,” where studios build multiple ventures simultaneously. By managing multiple startups within a shared framework, studios can experiment with various concepts, discarding those that do not meet market needs or financial viability thresholds. This systematic approach allows studios to identify successful business models more efficiently, potentially accelerating innovation and venture success rates.
Conclusion
The article highlights venture studios as a unique and increasingly prominent player in the startup ecosystem. Through hands-on involvement, a structured founding process, and resource-sharing, venture studios blend elements of ESOs like accelerators and VCs while introducing new dynamics into the entrepreneurial landscape. They offer a safer, more accessible route for founders, though at the cost of some traditional entrepreneurial freedoms. The venture studio model thus reimagines startup creation, fostering a more inclusive and risk-mitigated entrepreneurial environment that could reshape how ventures are conceived and developed on a global scale.
The article offers an in-depth examination of venture studios, a relatively recent addition to the entrepreneurial ecosystem. These organizations are actively reshaping the traditional paths of startup creation and support by combining venture funding, expertise, and operational resources. Unlike accelerators or venture capital (VC) firms, venture studios adopt a comprehensive, hands-on approach to building new companies. They participate at nearly every stage of development, from initial ideation through market entry, effectively functioning as organizational co-founders rather than simply financial backers or limited-term supporters. This paper analyzes how venture studios differ from established entrepreneurial support organizations (ESOs) by providing both theoretical insights and empirical evidence gathered from studying 20 venture studios worldwide.
Venture Studios vs. Other Entrepreneurial Support Models
Venture studios differ from other ESOs like accelerators and VC firms in several key ways. Accelerators are typically short-term programs that provide startups with education, mentoring, and initial funding in exchange for a small equity stake. Their primary role is to accelerate a startup’s path to venture capital, customer acquisition, and product-market fit within a cohort-based setting, often culminating in a "demo day" where startups pitch to investors. Venture capitalists, on the other hand, fund more established ventures in exchange for equity, becoming involved mainly in mid- to late-stage funding rounds and offering strategic advice, network connections, and assistance in professionalizing the startup's management and operations.
While accelerators and VCs play recognized roles in the startup ecosystem, venture studios differ by combining aspects of both and then going further. They provide extensive resources and direct involvement from the initial idea stage, supporting startups through ideation, validation, and market entry. In addition to financial investment, venture studios maintain in-house teams of designers, engineers, legal advisors, and more who work closely with founders. Their model aims to "de-risk" startup creation by reducing inefficiencies and sharing knowledge and resources across a portfolio of ventures. Studios thus function as parallel creators of multiple startups, often taking a substantial equity share (sometimes between 20% and 80%) in exchange for their intensive involvement.
Three Mechanisms of Venture Studio Support
The document identifies three primary mechanisms by which venture studios support new ventures: organizational entrepreneurship, entrepreneurial management, and studio governance. These mechanisms provide a comprehensive framework for understanding how studios function as co-founders, significantly influencing the venture creation process.
1. Organizational Entrepreneurship: Venture studios are deeply involved in the ideation phase of a startup. They identify potential business opportunities by engaging with industry experts, academic researchers, and stakeholders to gather insights and address unmet needs within a specific market. This structured approach enables them to build idea pipelines before founders are even involved. For instance, one studio specializing in healthcare derived new venture ideas by consulting with medical professionals on pressing healthcare challenges. Another studio, focused on sustainability, identified high-impact recycling problems by analyzing “high leakage points” in waste management.
Studios then employ a phased idea assessment process to evaluate these initial concepts through market research, competitor analysis, and customer validation. By deploying tools like rapid prototyping, customer journey mapping, and data-driven decision-making, studios assess the potential success of a venture before substantial resources are committed. If a concept proves viable, studios move forward by recruiting founders and assembling development teams; if not, they may abandon or pivot the idea. This iterative, data-informed process resembles lean startup methodologies but with an additional layer of organizational oversight and resources that minimizes startup risks and accelerates product validation.
2. Entrepreneurial Management: Unlike traditional entrepreneurship, where founders initiate and manage their ventures independently, venture studios systematize the founding process and actively recruit founders for specific roles within pre-validated ideas. This recruiting process can be passive, where founders apply to open positions, or active, where studios headhunt specific individuals with relevant expertise and experience. Venture studios often screen founders rigorously, seeking individuals with technical expertise, strategic acumen, or alignment with the venture’s mission. For example, a studio focused on financial technology might hire founders with backgrounds in investment banking, consulting, or financial services to lead its ventures.
Founders recruited by venture studios typically receive a salary or stipend, which reduces the financial risks they would otherwise face in traditional entrepreneurship. This setup also includes benefits like healthcare and retirement plans, enabling founders to focus on building the venture rather than fundraising or basic operational needs. Studios thus facilitate a more structured, stable pathway to entrepreneurship, opening it up to individuals who may not have otherwise pursued a startup due to the associated financial risks.
3. Implementing Studio Governance: Venture studios implement governance structures that give them substantial control over strategic decision-making and resource allocation within their portfolio ventures. By taking high equity stakes, studios secure their role as “organizational co-founders,” allowing them to steer the venture’s direction alongside the founder. This control can encompass decisions such as product pivots, market entry timing, and long-term strategy. In many cases, the founder shares decision-making authority with the studio, especially in high-stakes scenarios that could determine the venture's trajectory. Studios also support ventures by providing access to shared infrastructure, such as labs, office spaces, and specialized equipment, which are essential but costly resources that individual startups would struggle to afford independently.
Resource optimization is another key component of studio governance. Studios pool their human resources across ventures, assigning designers, engineers, marketers, and business strategists to multiple startups. For example, a biotech-focused venture studio may employ a team of lab scientists, engineers, and healthcare industry experts who assist across different ventures, allowing startups to leverage expert knowledge without incurring high individual costs. This shared resource model operates like a centralized support system that not only reduces costs but also fosters cross-pollination of ideas and best practices among ventures. Studios create knowledge-sharing systems, such as databases, workshops, and training sessions, to promote organizational learning and ensure that each startup benefits from the studio’s accumulated insights and experience.
Impact of Venture Studios on Entrepreneurship
Venture studios represent a shift from traditional, founder-driven entrepreneurship to a model that institutionalizes entrepreneurial processes, lowering barriers and risks for potential founders. By assuming responsibility for ideation, funding, and operational support, studios redefine the role of founders, making entrepreneurship more accessible to individuals with diverse backgrounds and reducing the financial and operational burdens typically borne by independent entrepreneurs.
However, this structured, organization-driven model also challenges conventional notions of entrepreneurship, particularly regarding founder autonomy and risk-taking. In traditional ventures, founders often take substantial personal risks and retain significant control over strategic decisions. In contrast, venture studio founders are more akin to intrapreneurs—employees with significant responsibility but limited ownership. This shift may change the motivations and profiles of future entrepreneurs, who may increasingly view venture studios as career pathways rather than as independent ventures with substantial personal risk and reward.
The emergence of venture studios may also encourage a trend toward “parallel entrepreneurship,” where studios build multiple ventures simultaneously. By managing multiple startups within a shared framework, studios can experiment with various concepts, discarding those that do not meet market needs or financial viability thresholds. This systematic approach allows studios to identify successful business models more efficiently, potentially accelerating innovation and venture success rates.
Conclusion
The article highlights venture studios as a unique and increasingly prominent player in the startup ecosystem. Through hands-on involvement, a structured founding process, and resource-sharing, venture studios blend elements of ESOs like accelerators and VCs while introducing new dynamics into the entrepreneurial landscape. They offer a safer, more accessible route for founders, though at the cost of some traditional entrepreneurial freedoms. The venture studio model thus reimagines startup creation, fostering a more inclusive and risk-mitigated entrepreneurial environment that could reshape how ventures are conceived and developed on a global scale.
Hassel, J. E., 2024. Third actor introductions to interaction episodes aiming at fast-forwarding new firm relationship development. Journal of Business & Industrial Marketing, 39(13), 200-215.
The paper examines how venture builders strategically prioritize introductions of their portfolio start-ups to internal and external counterparts with the aim of fast-forwarding relationships in developing organization and market. The paper introduces a framework describing four types of introductions to interaction episodes: Managed, Advised, Facilitated, and Monitored, and showcase examples of type of relationship counterparts that venture builders address, as well as what tools or type of events that are commonly used.
The paper draws from thirty qualitative interviews with twenty-eight informants associated with the creation of new start-ups in the context of venture builders and is analyzed using the lens of what is often referred to as the network approach within academia (e.g. Håkansson and Snehota, 1995). Venture builders is in this paper seen as privately owned organizations devoted to new firm creation in collaboration with individual entrepreneurs.
The practical implication of the study suggests that a venture builder’s ability to actively provide strategic introductions to counterparts will save time and resources for the portfolio start-ups. Furthermore, by actively working to reduce the distance between portfolio start-ups and other industry actors the chances of start-up survival will increase. For individual start-up entrepreneurs working with venture builders, learning networking capabilities is an important outcome of this practice, and it is suggested that individuals wishing to pursue a collaboration with venture builders should start by examining the venture builder’s network during the selection process. The paper fills a gap in academic discourse on business relationships and offers theoretical contributions to the network approach.
Interaction episodes between start-ups and other counterparts is central to forming business relationships, crucial for any business to survive. The role of venture builders in this process is therefore of great importance for its portfolio start-ups and it is therefore crucial for venture builders to establish a network strategy and build a strong network for itself.
The paper examines how venture builders strategically prioritize introductions of their portfolio start-ups to internal and external counterparts with the aim of fast-forwarding relationships in developing organization and market. The paper introduces a framework describing four types of introductions to interaction episodes: Managed, Advised, Facilitated, and Monitored, and showcase examples of type of relationship counterparts that venture builders address, as well as what tools or type of events that are commonly used.
The paper draws from thirty qualitative interviews with twenty-eight informants associated with the creation of new start-ups in the context of venture builders and is analyzed using the lens of what is often referred to as the network approach within academia (e.g. Håkansson and Snehota, 1995). Venture builders is in this paper seen as privately owned organizations devoted to new firm creation in collaboration with individual entrepreneurs.
The practical implication of the study suggests that a venture builder’s ability to actively provide strategic introductions to counterparts will save time and resources for the portfolio start-ups. Furthermore, by actively working to reduce the distance between portfolio start-ups and other industry actors the chances of start-up survival will increase. For individual start-up entrepreneurs working with venture builders, learning networking capabilities is an important outcome of this practice, and it is suggested that individuals wishing to pursue a collaboration with venture builders should start by examining the venture builder’s network during the selection process. The paper fills a gap in academic discourse on business relationships and offers theoretical contributions to the network approach.
Interaction episodes between start-ups and other counterparts is central to forming business relationships, crucial for any business to survive. The role of venture builders in this process is therefore of great importance for its portfolio start-ups and it is therefore crucial for venture builders to establish a network strategy and build a strong network for itself.
Chugani, A, 2024. Method to the Madness: Exploring the Factors Driving Heterogeneity Among Venture Studios. Master's thesis, EDHEC Business School
Venture studios have emerged as important players in the startup ecosystem, offering a unique approach to building and scaling new companies. Unlike traditional startup support organizations that primarily provide mentorship, venture studios take an active role as both builders and co-founders of early-stage ventures. Despite their growing prominence and success in launching companies like Medium, Twitter, and HelloFresh, these studios have yet to be thoroughly studied, particularly regarding the significant differences in their setups and operational models. This research investigates the factors driving this heterogeneity among venture studios, aiming to first categorize the specific differences between studios and then analyze the rationale behind these variations. Through this analysis, the study addresses a critical gap in understanding how different studio models emerge and evolve.
The observed differences among venture studios span several key aspects of their operations. A primary distinction lies in their approach to focus areas, where some studios specialize in specific sectors like health, web3.0 or climate, while others maintain a generalist stance across multiple industries. This choice of specialization versus generalization extends beyond just industry focus to include differences in business models, technologies, and target markets. Studios also differ significantly in their ideation practices, with some generating ideas internally through dedicated teams, others sourcing opportunities externally from entrepreneurs and researchers, and many adopting a hybrid approach.
This research employed a mixed-methods research design to examine the factors influencing venture studio setups. The study focused on European venture studios due to the region's recent growth in studio activity and the benefits of analyzing organizations operating within similar market conditions. Data was collected from 137 venture studios, encompassing information about their founding year, geographic location, primary purpose, focus areas, and idea generation practices. The analysis combined qualitative content analysis of studio websites and publications with quantitative statistical testing. To ensure robust findings, the study utilized bootstrapped chi-square tests of independence to analyze relationships between studio characteristics and operational choices, with each test running 5,000 iterations to verify the statistical significance of observed patterns.
The analysis revealed several key findings about the factors driving studio heterogeneity. First, the study found a statistically significant relationship (p = 0.046) between a studio's founding period and its choice of focus area. Studios established in earlier waves (pre-2009) typically operated with a broader approach, while newer studios showed increased specialization, likely reflecting heightened competition in the ecosystem. Geographic location also emerged as an important factor, with a significant association (p = 0.044) between a studio's region and its focus area. Western European studios demonstrated a higher tendency toward specialization compared to other regions, suggesting the influence of local market maturity and resource availability on studio strategies.
The study also uncovered the crucial role of a studio's mission in determining its operational approach. Statistical testing showed significant relationships between a studio's primary purpose and both its focus area (p = 0.027) and idea generation practices (p = 0.020). Innovation-focused studios exhibited a stronger preference for external idea sourcing, while profit-oriented studios tended toward internal ideation. Studios targeting social impact and societal improvement typically adopted hybrid approaches, balancing internal capabilities with external collaboration opportunities. These patterns indicate that strategic choices in venture studios are closely tied to their fundamental objectives and intended outcomes of these studios.
The findings from this research provide important insights for various stakeholders in the venture studio ecosystem. For studio operators, the results emphasize the need to align operational strategies with both regional market conditions and organizational objectives. Current studio leaders should evaluate whether their ideation practices and focus areas effectively support their core mission. For example, studios focused on driving innovation could benefit from establishing stronger networks with universities and research labs to build a consistent pipeline of new ideas. The findings are particularly valuable for those planning to launch new venture studios, as they highlight how founding context and mission should inform key operational decisions. Understanding these relationships can help new studios position themselves effectively in their chosen markets. For entrepreneurs and startup founders considering partnership with a venture studio, these insights can guide their selection process. Knowledge of how different studio characteristics influence operational approaches helps founders identify studios whose setup aligns with their specific needs.
The research ultimately contributes to the broader understanding of venture studios by showing that their heterogeneity is not without reason but rather a result of thoughtful adaptation to various market conditions and strategic objectives. This improved understanding of venture studio diversity contributes to more informed decision-making for all participants in the startup ecosystem.
Venture studios have emerged as important players in the startup ecosystem, offering a unique approach to building and scaling new companies. Unlike traditional startup support organizations that primarily provide mentorship, venture studios take an active role as both builders and co-founders of early-stage ventures. Despite their growing prominence and success in launching companies like Medium, Twitter, and HelloFresh, these studios have yet to be thoroughly studied, particularly regarding the significant differences in their setups and operational models. This research investigates the factors driving this heterogeneity among venture studios, aiming to first categorize the specific differences between studios and then analyze the rationale behind these variations. Through this analysis, the study addresses a critical gap in understanding how different studio models emerge and evolve.
The observed differences among venture studios span several key aspects of their operations. A primary distinction lies in their approach to focus areas, where some studios specialize in specific sectors like health, web3.0 or climate, while others maintain a generalist stance across multiple industries. This choice of specialization versus generalization extends beyond just industry focus to include differences in business models, technologies, and target markets. Studios also differ significantly in their ideation practices, with some generating ideas internally through dedicated teams, others sourcing opportunities externally from entrepreneurs and researchers, and many adopting a hybrid approach.
This research employed a mixed-methods research design to examine the factors influencing venture studio setups. The study focused on European venture studios due to the region's recent growth in studio activity and the benefits of analyzing organizations operating within similar market conditions. Data was collected from 137 venture studios, encompassing information about their founding year, geographic location, primary purpose, focus areas, and idea generation practices. The analysis combined qualitative content analysis of studio websites and publications with quantitative statistical testing. To ensure robust findings, the study utilized bootstrapped chi-square tests of independence to analyze relationships between studio characteristics and operational choices, with each test running 5,000 iterations to verify the statistical significance of observed patterns.
The analysis revealed several key findings about the factors driving studio heterogeneity. First, the study found a statistically significant relationship (p = 0.046) between a studio's founding period and its choice of focus area. Studios established in earlier waves (pre-2009) typically operated with a broader approach, while newer studios showed increased specialization, likely reflecting heightened competition in the ecosystem. Geographic location also emerged as an important factor, with a significant association (p = 0.044) between a studio's region and its focus area. Western European studios demonstrated a higher tendency toward specialization compared to other regions, suggesting the influence of local market maturity and resource availability on studio strategies.
The study also uncovered the crucial role of a studio's mission in determining its operational approach. Statistical testing showed significant relationships between a studio's primary purpose and both its focus area (p = 0.027) and idea generation practices (p = 0.020). Innovation-focused studios exhibited a stronger preference for external idea sourcing, while profit-oriented studios tended toward internal ideation. Studios targeting social impact and societal improvement typically adopted hybrid approaches, balancing internal capabilities with external collaboration opportunities. These patterns indicate that strategic choices in venture studios are closely tied to their fundamental objectives and intended outcomes of these studios.
The findings from this research provide important insights for various stakeholders in the venture studio ecosystem. For studio operators, the results emphasize the need to align operational strategies with both regional market conditions and organizational objectives. Current studio leaders should evaluate whether their ideation practices and focus areas effectively support their core mission. For example, studios focused on driving innovation could benefit from establishing stronger networks with universities and research labs to build a consistent pipeline of new ideas. The findings are particularly valuable for those planning to launch new venture studios, as they highlight how founding context and mission should inform key operational decisions. Understanding these relationships can help new studios position themselves effectively in their chosen markets. For entrepreneurs and startup founders considering partnership with a venture studio, these insights can guide their selection process. Knowledge of how different studio characteristics influence operational approaches helps founders identify studios whose setup aligns with their specific needs.
The research ultimately contributes to the broader understanding of venture studios by showing that their heterogeneity is not without reason but rather a result of thoughtful adaptation to various market conditions and strategic objectives. This improved understanding of venture studio diversity contributes to more informed decision-making for all participants in the startup ecosystem.
BVSR’24 Sponsors
We’re very grateful to our sponsors who supported this Big Venture Studio Research 2024
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Precast VenturesWebsite | LinkedIn
Blank pages excite us. So do great ideas.
Precast Ventures is a New Zealand-based venture studio and innovation consultancy turning blank pages into blueprints for the future of the built environment.
At the intersection of bytes and bricks, Precast Ventures blends industry knowledge with venture building credentials to address the property and construction sectors’ most formidable challenges.
While leading new ventures at Calder Stewart — New Zealand’s leading property and construction company — our founder, Sam Stewart, built Calder Stewart Energy, a solar-as-a-service business utilising over 200,000m2 of otherwise unused industrial roof space. In addition, he leads Stewart Family Holdings’ venture capital activities and is an active angel investor and mentor in the local startup ecosystem.
This real-world experience has been combined with top business school thinking to create the playbook for Precast Ventures. It’s this potent blend of global learning with local capability that makes Precast Ventures uniquely positioned to drive change. -
Stackpoint VenturesWebsite | LinkedIn
Stackpoint is the trusted partner for VCs and investors looking to incubate and invest in formation-stage ideas. We specialize in high-barrier, mature industries like real estate, finance, and insurance, delivering a steady pipeline of high-quality Seed, Series A, and B investments with venture-scale potential. Partnering with Stackpoint unlocks exceptional opportunities with higher expected returns and lower risk—driven by our proven approach:- Market & Customer Validation: Every concept is rigorously vetted with top industry insiders and engaged customers who often become early design partners.
- Execution Quality: Our studio team executes with precision, following a robust discovery, build, and go-to-market playbook.
- Extensive Network: A powerful ecosystem of design partners, customers, advisors, and talent minimizes risk and accelerates growth.
- Team Excellence: We match the right founders to the right opportunities and help build stellar early teams across all critical functions.
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NuBinaryWebsite | LinkedIn
NuBinary serves as a Fractional CTO and Tech Startup & Scale-up Advisory firm, empowering companies to create and commercialize innovative technology products. Our mission is to accelerate market delivery through a consistent, repeatable, and tested framework.
Key Services:- Fractional CTO Service: We provide executive-level technology leadership on a flexible basis, tailored to your company’s stage and growth. We drive technology roadmaps, enhance R&D, and build development teams, helping startups and scale-ups launch new products, optimize infrastructures, and scale processes efficiently.
- Technology and Leadership Consulting: Specializing in Software, Electronics, and Cloud Infrastructure, we guide tech startups in scaling up, productizing, and raising investments.
- Research Project Assistance: We support companies in research projects with universities and develop robust IP strategies.
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BVSR’24 Participated studios
Please find below the list of studios who:
- Agreed to share their data for our survey (RQ2)
- Agreed to be acknowledged in the report
Website | LinkedIn
Company-building studio bringing brilliant IP to market. We discover, fund, and build companies around breakthroughs originated at universities and institutions. Founded and staffed by seasoned entrepreneurs, every 1870 player has empathy for the challenges of building a business from zero to one. We focus on the biggest hurdles to bring IP to market: providing earliest-stage funding, developing company infrastructure, defining successful business models, and recruiting dynamic leaders.
Company-building studio bringing brilliant IP to market. We discover, fund, and build companies around breakthroughs originated at universities and institutions. Founded and staffed by seasoned entrepreneurs, every 1870 player has empathy for the challenges of building a business from zero to one. We focus on the biggest hurdles to bring IP to market: providing earliest-stage funding, developing company infrastructure, defining successful business models, and recruiting dynamic leaders.
Website | LinkedIn
At 7R, we specialize in designing, building, and scaling ventures through strategic partnerships with industry-leading companies. With over 12 successful launches backed by our proven methodology, we bring innovative ideas to life and drive sustained growth. Our focus is on identifying opportunities, collaborating with top-tier organizations, and implementing scalable strategies that ensure both efficiency and long-term success. We are passionate about creating ventures that not only solve current challenges but also pave the way for future innovation.
At 7R, we specialize in designing, building, and scaling ventures through strategic partnerships with industry-leading companies. With over 12 successful launches backed by our proven methodology, we bring innovative ideas to life and drive sustained growth. Our focus is on identifying opportunities, collaborating with top-tier organizations, and implementing scalable strategies that ensure both efficiency and long-term success. We are passionate about creating ventures that not only solve current challenges but also pave the way for future innovation.
Website | LinkedIn
A Philippine venture studio where bold ideas meet bold action. At AHG Lab, we are the heart of a dynamic ecosystem where bold ideas meet bold action. Our community is a hub for dreamers, doers, and disruptors. We unite visionary founders and creative minds with the shared ambition to revolutionize the startup landscape. Every venture challenges the conventional, embraces the unconventional, and redefines the journey from inspiration to realization.
A Philippine venture studio where bold ideas meet bold action. At AHG Lab, we are the heart of a dynamic ecosystem where bold ideas meet bold action. Our community is a hub for dreamers, doers, and disruptors. We unite visionary founders and creative minds with the shared ambition to revolutionize the startup landscape. Every venture challenges the conventional, embraces the unconventional, and redefines the journey from inspiration to realization.
Website | LinkedIn
AION Labs is a first-of-its kind venture studio comprised of eight global pharma, tech and innovation leaders. Our mission is to create and adopt groundbreaking AI technologies that revolutionize drug discovery and development, improving health and patient outcomes for global populations. We bring together brilliant scientist-founders from around the world, possessing decades of accumulated knowledge, expertise and data, to tackle the most pressing pharmaceutical R&D challenges. By combining our partners' vast knowledge with a new multidisciplinary approach, alongside the agility and innovation unique to Israel's startup ecosystem, we create new companies advancing the future of healthcare.
AION Labs is a first-of-its kind venture studio comprised of eight global pharma, tech and innovation leaders. Our mission is to create and adopt groundbreaking AI technologies that revolutionize drug discovery and development, improving health and patient outcomes for global populations. We bring together brilliant scientist-founders from around the world, possessing decades of accumulated knowledge, expertise and data, to tackle the most pressing pharmaceutical R&D challenges. By combining our partners' vast knowledge with a new multidisciplinary approach, alongside the agility and innovation unique to Israel's startup ecosystem, we create new companies advancing the future of healthcare.
Website | LinkedIn
We love building companies. We’re looking for strong teams with crazy ideas who have the vision, skills, and commitment to take their company into the future. We’ve spent years helping early-stage projects succeed (and sometimes fail), and we know the value of an idea is zilch without the right team to bring it to life. Social impact factors are important to us also, and if you have a creative approach to a pressing issue, we’d love to hear about it.
We love building companies. We’re looking for strong teams with crazy ideas who have the vision, skills, and commitment to take their company into the future. We’ve spent years helping early-stage projects succeed (and sometimes fail), and we know the value of an idea is zilch without the right team to bring it to life. Social impact factors are important to us also, and if you have a creative approach to a pressing issue, we’d love to hear about it.
Website | LinkedIn
Appolica is a venture studio on a mission to turn ambitious tech-driven ventures into market leaders. Since 2015, we’re working with founders from the ground up. As a dependable tech partner, we focus on helping early-stage startups validate their ideas, construct MVPs, and scale their businesses effectively. With our comprehensive 360° approach, we stand by startups throughout their entire journey from ideation, validation, and development to scaling. At Appolica, we are committed to delivering innovative, reliable, and scalable tech solutions that drive business growth.
Appolica is a venture studio on a mission to turn ambitious tech-driven ventures into market leaders. Since 2015, we’re working with founders from the ground up. As a dependable tech partner, we focus on helping early-stage startups validate their ideas, construct MVPs, and scale their businesses effectively. With our comprehensive 360° approach, we stand by startups throughout their entire journey from ideation, validation, and development to scaling. At Appolica, we are committed to delivering innovative, reliable, and scalable tech solutions that drive business growth.
Website | LinkedIn
The insurance & risk industry struggles to know how to address the biggest risks of our generation: extreme weather, ageing populations, biodiversity loss, refugee crises, disinformation and global supply chain collapse. We are going about solving that market failure. We’ve built a business model that is awesome at doing this systematically.
The insurance & risk industry struggles to know how to address the biggest risks of our generation: extreme weather, ageing populations, biodiversity loss, refugee crises, disinformation and global supply chain collapse. We are going about solving that market failure. We’ve built a business model that is awesome at doing this systematically.
Website | LinkedIn
BigCircle Ventures scouts promising deeptech and turns this into successful startups that tackle the world's climate and circularity challenges. BigCircle Ventures is set up to become the world’s largest greentech venture builder. We bring a unique flow of early stage innovations to the market, to boost the world’s journey towards net zero and circularity. The world needs greentech, but promising inventions often stay untouched on a shelf, hidden in a university, research institute or company. We will tackle this problem by using our proven venture building model and fund.
BigCircle Ventures scouts promising deeptech and turns this into successful startups that tackle the world's climate and circularity challenges. BigCircle Ventures is set up to become the world’s largest greentech venture builder. We bring a unique flow of early stage innovations to the market, to boost the world’s journey towards net zero and circularity. The world needs greentech, but promising inventions often stay untouched on a shelf, hidden in a university, research institute or company. We will tackle this problem by using our proven venture building model and fund.
Website | LinkedIn
We are a Saudi venture studio (AKA startup studio/venture builder) building successful and sustainable enterprises from the ground up by capitalizing on our expertise, resources and network. We carefully select, validate and launch bold ideas that are then turned into full-fledged spun-off businesses.
We are a Saudi venture studio (AKA startup studio/venture builder) building successful and sustainable enterprises from the ground up by capitalizing on our expertise, resources and network. We carefully select, validate and launch bold ideas that are then turned into full-fledged spun-off businesses.
Website | LinkedIn
BMTT is a first of its kind feeding studio. We identify clinical needs, analyze markets for critical points, and engineer scalable solutions. Following iterative rounds of prototyping and customer validation, we transfer startups (and the majority of equity) to full-service venture studios and/or capable founders. Alternatively, we continue to develop the startups ourselves: acting as a venture studio. Our goal is to simultaneously train the next generation to be more effectual venture creationists and fundamentally change the way clinical innovations are sourced and prototyped. Feel free to reach out with any questions!
BMTT is a first of its kind feeding studio. We identify clinical needs, analyze markets for critical points, and engineer scalable solutions. Following iterative rounds of prototyping and customer validation, we transfer startups (and the majority of equity) to full-service venture studios and/or capable founders. Alternatively, we continue to develop the startups ourselves: acting as a venture studio. Our goal is to simultaneously train the next generation to be more effectual venture creationists and fundamentally change the way clinical innovations are sourced and prototyped. Feel free to reach out with any questions!
Website | LinkedIn
Build Up Labs is a startup studio that ideates, builds, and grows several startups in parallel, based on a lean process. By creating new ventures, from ideation to business models, from UX to development, from growth hacking to social media management, we generate and validate digital product ideas.
Build Up Labs is a startup studio that ideates, builds, and grows several startups in parallel, based on a lean process. By creating new ventures, from ideation to business models, from UX to development, from growth hacking to social media management, we generate and validate digital product ideas.
Website | LinkedIn
Builders is a Rotterdam-based startup studio that focuses on shaping the future of work building software companies that create a more fulfilling and sustainable life for the globalised generation. We combine thoroughly validated market trends with top-of-class entrepreneurs and our internal expertise in marketing, product development, HR, finance, design, and technology to build SaaS companies for Europe and beyond.
Builders is a Rotterdam-based startup studio that focuses on shaping the future of work building software companies that create a more fulfilling and sustainable life for the globalised generation. We combine thoroughly validated market trends with top-of-class entrepreneurs and our internal expertise in marketing, product development, HR, finance, design, and technology to build SaaS companies for Europe and beyond.
Website | LinkedIn
Byld is a Spanish Corporate Venture Builder founded in 2017 that designs, validates and launches new ventures with corporations sharing risks and rewards from day one. We lead and co-found new ventures from scratch combining the best of the entrepreneurial and corporate worlds to encourage the creation of scalable businesses with an international scope.
Byld is a Spanish Corporate Venture Builder founded in 2017 that designs, validates and launches new ventures with corporations sharing risks and rewards from day one. We lead and co-found new ventures from scratch combining the best of the entrepreneurial and corporate worlds to encourage the creation of scalable businesses with an international scope.
Website | LinkedIn
We are experienced entrepreneurs that build deep tech startups from zero to exit market, with our own methodology for an ideal entrepreneur trajectory. We believe in innovation and entrepreneurship as an engine economic and social. With a team from the top Universities and Research Centers in Brazil and worldwide and a process of deep understanding the team seeks to achieve new ideas and concepts to their clients in order to create impact for their customers and sustainable results.
We are experienced entrepreneurs that build deep tech startups from zero to exit market, with our own methodology for an ideal entrepreneur trajectory. We believe in innovation and entrepreneurship as an engine economic and social. With a team from the top Universities and Research Centers in Brazil and worldwide and a process of deep understanding the team seeks to achieve new ideas and concepts to their clients in order to create impact for their customers and sustainable results.
Website | LinkedIn
Claro Partners ceased operating in 2019. All of us Claros would like to thank our many clients, partners, employees and friends who supported us over our 9 year history. Though we are all pursuing new opportunities, you will find several of us working as Fuse Foresight, an agency focused on applying deep human insight to unlock business value in our new age of volatility.
Claro Partners ceased operating in 2019. All of us Claros would like to thank our many clients, partners, employees and friends who supported us over our 9 year history. Though we are all pursuing new opportunities, you will find several of us working as Fuse Foresight, an agency focused on applying deep human insight to unlock business value in our new age of volatility.
Website | LinkedIn
We are a Corporate Venture Studio: a team of entrepreneurs and experts in creating, building and scaling new business models for MeGa companies in Mexico and Spain. In collaboration, we develop StartCorps® – independent business units – that transform industries, drive growth and bring real value to the market. We take care of the entire process: from ideation and validation, to incubation and acceleration. With COCOCO, companies innovate beyond their core business, generating new revenue and strengthening their competitiveness, without compromising their main operation.
We are a Corporate Venture Studio: a team of entrepreneurs and experts in creating, building and scaling new business models for MeGa companies in Mexico and Spain. In collaboration, we develop StartCorps® – independent business units – that transform industries, drive growth and bring real value to the market. We take care of the entire process: from ideation and validation, to incubation and acceleration. With COCOCO, companies innovate beyond their core business, generating new revenue and strengthening their competitiveness, without compromising their main operation.
Website | LinkedIn
Company Laboratory LLC, also known as Colab, is an innovative Venture Studio based in Los Angeles that partners with a select portfolio of strategic companies to design, launch, and grow early-stage businesses. Colab often serves as a hands on co-founder, bringing advanced expertise in design, branding, marketing, UI/UX, digital business strategy, technology, and product development (web2/web3/mobile), while also providing access to exclusive investor networks.
Company Laboratory LLC, also known as Colab, is an innovative Venture Studio based in Los Angeles that partners with a select portfolio of strategic companies to design, launch, and grow early-stage businesses. Colab often serves as a hands on co-founder, bringing advanced expertise in design, branding, marketing, UI/UX, digital business strategy, technology, and product development (web2/web3/mobile), while also providing access to exclusive investor networks.
Website | LinkedIn
Over the last decade, we have grown from a disruptive startup into the world's largest independent corporate venture builder. We are one of the leaders of a growing young category called Company Venture Building (CVB), helping large companies find and bring in new revenue by creating new services and products that positively impact society.
Over the last decade, we have grown from a disruptive startup into the world's largest independent corporate venture builder. We are one of the leaders of a growing young category called Company Venture Building (CVB), helping large companies find and bring in new revenue by creating new services and products that positively impact society.
Website | LinkedIn
DeepMinds™ is a decentralized venture studio specialized in creating, assembling and scaling the deeptech ventures that will elevate the MENA Knowledge-driven economy and facilitate global advances across all sectors. We curate a thriving cluster ecosystem with a core focus on tech transfer and localization, unifying resources to define and develop the technologies of tomorrow.
DeepMinds™ is a decentralized venture studio specialized in creating, assembling and scaling the deeptech ventures that will elevate the MENA Knowledge-driven economy and facilitate global advances across all sectors. We curate a thriving cluster ecosystem with a core focus on tech transfer and localization, unifying resources to define and develop the technologies of tomorrow.
Website | LinkedIn
We support innovative intrapreneurs and entrepreneurs in building a new generation of sustainably successful Swiss digital companies. We assist founders in different phases of their business and support them throughout the journey, from developing a business idea to a functioning company.
We support innovative intrapreneurs and entrepreneurs in building a new generation of sustainably successful Swiss digital companies. We assist founders in different phases of their business and support them throughout the journey, from developing a business idea to a functioning company.
Website | LinkedIn
When we founded Enviu in 2004, our belief was the same as it is today: An inclusive economy that serves people and planet. An economy enriching the lives of all people, providing access to basic needs. An economy sustainably utilizing the valuable and limited resources our planet endows. To instigate this economy we build ventures that address social and environmental issues, and drive market development. With high impact in the domains of financial inclusion, circular economy, and food security.
When we founded Enviu in 2004, our belief was the same as it is today: An inclusive economy that serves people and planet. An economy enriching the lives of all people, providing access to basic needs. An economy sustainably utilizing the valuable and limited resources our planet endows. To instigate this economy we build ventures that address social and environmental issues, and drive market development. With high impact in the domains of financial inclusion, circular economy, and food security.
Website | LinkedIn
We are a portfolio holding venture studio committed to building and funding companies from scratch in partnership with founders of great passion and character committed to solving some of the biggest problems in Africa that engender wealth and job creation in underserved communities.
We are a portfolio holding venture studio committed to building and funding companies from scratch in partnership with founders of great passion and character committed to solving some of the biggest problems in Africa that engender wealth and job creation in underserved communities.
Website | LinkedIn
We are an innovation and start-up studio specialized in “platforms” and “ecosystems business models”. We create and accelerate digital platforms with high potential for growth, profitability and impact. Whether you already have an idea or not, we help you create your platform. If you already have a platform, we help you accelerate it.
We are an innovation and start-up studio specialized in “platforms” and “ecosystems business models”. We create and accelerate digital platforms with high potential for growth, profitability and impact. Whether you already have an idea or not, we help you create your platform. If you already have a platform, we help you accelerate it.
Website | LinkedIn
Forum Ventures is the leading early-stage fund, program and community for B2B SaaS startups. Founded in 2014 as Acceleprise, we’re on a mission to make the B2B SaaS journey easier, more accessible and successful for early-stage founders, through funding, high touch programming, corporate perks and introductions, and an active SaaS community.
Forum Ventures is the leading early-stage fund, program and community for B2B SaaS startups. Founded in 2014 as Acceleprise, we’re on a mission to make the B2B SaaS journey easier, more accessible and successful for early-stage founders, through funding, high touch programming, corporate perks and introductions, and an active SaaS community.
Website
Frontier Vibe is a growth hub and venture studio committed to promoting technological innovation in Ghana. From an idea to an MVP in 10 weeks - learn to validate your ideas, get expert advice, mentorship and funding to work on your project.
Frontier Vibe is a growth hub and venture studio committed to promoting technological innovation in Ghana. From an idea to an MVP in 10 weeks - learn to validate your ideas, get expert advice, mentorship and funding to work on your project.
Website | LinkedIn
We launch startups that create digital innovations for organizations 3.0 - the ones thriving in the volatile, complex and ambiguous world. Our products shift corporate management principles, culture and people relations, transform into human-centric organization, adapted to new societal norms of work and sustainability.
We launch startups that create digital innovations for organizations 3.0 - the ones thriving in the volatile, complex and ambiguous world. Our products shift corporate management principles, culture and people relations, transform into human-centric organization, adapted to new societal norms of work and sustainability.
Website | LinkedIn
Gateway X is a venture studio and holding company that conceives, bootstraps, and scales both D2C brands and B2B businesses that serve marketers around the world. Led by experienced operators who have both bootstrapped ventures from scratch and grown venture-scale businesses, we match our unfair advantages in growth marketing and customer acquisition with promising co-founders and the right ideas.
Gateway X is a venture studio and holding company that conceives, bootstraps, and scales both D2C brands and B2B businesses that serve marketers around the world. Led by experienced operators who have both bootstrapped ventures from scratch and grown venture-scale businesses, we match our unfair advantages in growth marketing and customer acquisition with promising co-founders and the right ideas.
Website | LinkedIn
güil invests in startups and funds that are building innovative solutions in mobility for Latin America and the world. Our focus is on driving growth and transformation through strategic capital deployment and partnerships between our portfolio companies and Grupo Kaufmann, our corporate partner. In addition, our Research Lab explores emerging trends and technologies, producing insightful reports that inform our investment decisions and the broader mobility ecosystem.
güil invests in startups and funds that are building innovative solutions in mobility for Latin America and the world. Our focus is on driving growth and transformation through strategic capital deployment and partnerships between our portfolio companies and Grupo Kaufmann, our corporate partner. In addition, our Research Lab explores emerging trends and technologies, producing insightful reports that inform our investment decisions and the broader mobility ecosystem.
Website | LinkedIn
H/L Ventures and its family of funds, H/L Studio and CityRock Ventures, are a holistic company-building ecosystem that supports mission-driven founders on their path to creating and growing valuable companies. Since its founding in 2009, H/L Ventures has forged a new way to build companies at the nexus of growth, impact, and diversity.
H/L Ventures and its family of funds, H/L Studio and CityRock Ventures, are a holistic company-building ecosystem that supports mission-driven founders on their path to creating and growing valuable companies. Since its founding in 2009, H/L Ventures has forged a new way to build companies at the nexus of growth, impact, and diversity.
Website | LinkedIn
A new breed of biotech company aiming to unlock scientific breakthrough by leveraging an asset-centric operating model. Home Biosciences is a new breed of biotech, leveraging an asset-centric operating model, designed to maximize efficiency in Biotech creation and scale-up, inspired from US corporations venture-funded biotech models.
A new breed of biotech company aiming to unlock scientific breakthrough by leveraging an asset-centric operating model. Home Biosciences is a new breed of biotech, leveraging an asset-centric operating model, designed to maximize efficiency in Biotech creation and scale-up, inspired from US corporations venture-funded biotech models.
Website | LinkedIn
Ideas Forward is a technology venture studio based in Thessaloniki. We build and back ventures transforming cutting-edge computing & communication technology into market innovation. Working with organizations large and small we operate as co-founders, co-investors, consultants & R&D partners.
Ideas Forward is a technology venture studio based in Thessaloniki. We build and back ventures transforming cutting-edge computing & communication technology into market innovation. Working with organizations large and small we operate as co-founders, co-investors, consultants & R&D partners.
Website | LinkedIn
Drive entrepreneurial growth and regional entrepreneurs with a metered, repeatable and scalable process to help build and launch businesses to scale and relevance. Facilitate the selection of pre-seed ideas, entrepreneurs and entrepreneurial talent to systematically develop, validate and launch early-stage companies.
Drive entrepreneurial growth and regional entrepreneurs with a metered, repeatable and scalable process to help build and launch businesses to scale and relevance. Facilitate the selection of pre-seed ideas, entrepreneurs and entrepreneurial talent to systematically develop, validate and launch early-stage companies.
Website | LinkedIn
Kamet Ventures was created in 2016 as a breakthrough model in innovation. We seek to identify the world's most talented entrepreneurs and partner with them to build the next generation of ground-breaking technology companies. With offices across Paris, London and Tel Aviv, we create companies that are geared to improve, enrich and prolong our lives.
Kamet Ventures was created in 2016 as a breakthrough model in innovation. We seek to identify the world's most talented entrepreneurs and partner with them to build the next generation of ground-breaking technology companies. With offices across Paris, London and Tel Aviv, we create companies that are geared to improve, enrich and prolong our lives.
Website | LinkedIn
We help emerging businesses grow and thrive through comprehensive venture studio and business consulting services. Our team at Khudra specialises in empowering entrepreneurs with the resources, expertise, and networks they need to succeed. We've helped businesses from inception to success by providing strategic guidance and practical solutions.
We help emerging businesses grow and thrive through comprehensive venture studio and business consulting services. Our team at Khudra specialises in empowering entrepreneurs with the resources, expertise, and networks they need to succeed. We've helped businesses from inception to success by providing strategic guidance and practical solutions.
Website | LinkedIn
We are around 300 entrepreneurs, creators & solvers with deep roots in the Tech & Start-up world. Our complimentary skill sets and wide range of experience in the digital space allows us to thrive and enjoy creating maximum impact on every challenge we face – from co-founding, angel-investing and building digital solutions for our clients, up to building full-fledged companies from scratch.
We are around 300 entrepreneurs, creators & solvers with deep roots in the Tech & Start-up world. Our complimentary skill sets and wide range of experience in the digital space allows us to thrive and enjoy creating maximum impact on every challenge we face – from co-founding, angel-investing and building digital solutions for our clients, up to building full-fledged companies from scratch.
Website | LinkedIn
The best way to predict the future is to co-create it. KRING® is a family of impact funds. We co-create new sustainable ventures together with bold entrepreneurs and leading corporate players who are dedicated to building a positive future. Over the years we have co-created and launched more than 20 companies. Some are alive. Some we have killed. Some are acquired. Today our efforts are fully focused on the sustainable development goals.
The best way to predict the future is to co-create it. KRING® is a family of impact funds. We co-create new sustainable ventures together with bold entrepreneurs and leading corporate players who are dedicated to building a positive future. Over the years we have co-created and launched more than 20 companies. Some are alive. Some we have killed. Some are acquired. Today our efforts are fully focused on the sustainable development goals.
Website | LinkedIn
Lab.Coop is an entrepreneur owned tech venture builder. We apply Holacracy and practice extreme transparency. As IT professionals our focus is on tech - lucky for both you and us it is a diverse field and allows us to adventure out, learn and experiment with diverse areas. We focus on market financed solutions that deliver immediate value for end users and faster ROI for us, the shareholders.
Lab.Coop is an entrepreneur owned tech venture builder. We apply Holacracy and practice extreme transparency. As IT professionals our focus is on tech - lucky for both you and us it is a diverse field and allows us to adventure out, learn and experiment with diverse areas. We focus on market financed solutions that deliver immediate value for end users and faster ROI for us, the shareholders.
Website | LinkedIn
Linea Recta is a startup studio based in Ghent, Belgium with a permanent focus on sustainability, energy and clean tech. We are convinced we can create projects or solutions with potential by bringing together innovation, entrepreneurship and technology in a streamlined way. We provide ideas, tools, processes, and resources (human and capital) to help entrepreneurs build products and solutions that solve challenging problems.
Linea Recta is a startup studio based in Ghent, Belgium with a permanent focus on sustainability, energy and clean tech. We are convinced we can create projects or solutions with potential by bringing together innovation, entrepreneurship and technology in a streamlined way. We provide ideas, tools, processes, and resources (human and capital) to help entrepreneurs build products and solutions that solve challenging problems.
Website | LinkedIn
We collaborate with exceptional 'Entrepreneurs' and 'Intrapreneurs' to bring meaningful ideas to reality. We encourage collaboration through validation, talent, resources, and investment. We are the best co-founders and believe in co-creation, which enhances the likelihood of success significantly. Great teams made of world-class talent and ideas clash at LiveInTech. We build capability-capital inherently for high-impact differentiation - The Guild Factor.
We collaborate with exceptional 'Entrepreneurs' and 'Intrapreneurs' to bring meaningful ideas to reality. We encourage collaboration through validation, talent, resources, and investment. We are the best co-founders and believe in co-creation, which enhances the likelihood of success significantly. Great teams made of world-class talent and ideas clash at LiveInTech. We build capability-capital inherently for high-impact differentiation - The Guild Factor.
Website | LinkedIn
We build startups devoted to micro businesses to allow them to thrive and reach their full potential in a global market. We use the Startup Studio model to maximize the impact of our actions, mitigate the risk for our investors, and maximize the success rate of our startups. We generate returns by unlocking the untapped potential of countless craftsmen and micro businesses, the backbone of the global economy.
We build startups devoted to micro businesses to allow them to thrive and reach their full potential in a global market. We use the Startup Studio model to maximize the impact of our actions, mitigate the risk for our investors, and maximize the success rate of our startups. We generate returns by unlocking the untapped potential of countless craftsmen and micro businesses, the backbone of the global economy.
Website | LinkedIn
Merantix Capital is a Berlin-based venture capital firm and a unit of Merantix, a group of companies and initiatives driving AI forward in Europe. We leverage our platform to attract and foster interactions between ideas, talent, industry, and policy. We combine our venture-building DNA, deep AI investing expertise, and our expansive community.
Merantix Capital is a Berlin-based venture capital firm and a unit of Merantix, a group of companies and initiatives driving AI forward in Europe. We leverage our platform to attract and foster interactions between ideas, talent, industry, and policy. We combine our venture-building DNA, deep AI investing expertise, and our expansive community.
Website | LinkedIn
Neighborhood Studios partners with tenacious founders to build hyperlocal startups from the ground up. We are a venture studio based in Atlanta, GA with a deep expertise in local startups. We have created a repeatable process to identify unique insights to hyperlocal problems, develop novel product entry points, de-risk our product approach, recruit exceptional founders / CEOs, and ultimately launch a few hyperlocal startups per year.
Neighborhood Studios partners with tenacious founders to build hyperlocal startups from the ground up. We are a venture studio based in Atlanta, GA with a deep expertise in local startups. We have created a repeatable process to identify unique insights to hyperlocal problems, develop novel product entry points, de-risk our product approach, recruit exceptional founders / CEOs, and ultimately launch a few hyperlocal startups per year.
Website | LinkedIn
Backed by Old Mutual, NEXT176 was founded to create significant impact in specific ecosystems that affect the future of emerging economies, by creating businesses that deliver disruptive and sustainable growth. We’ve pulled together a team of experienced financiers, technologists and entrepreneurs to build, invest in and partner with startups and corporates to realise this founding purpose.
Backed by Old Mutual, NEXT176 was founded to create significant impact in specific ecosystems that affect the future of emerging economies, by creating businesses that deliver disruptive and sustainable growth. We’ve pulled together a team of experienced financiers, technologists and entrepreneurs to build, invest in and partner with startups and corporates to realise this founding purpose.
Website | LinkedIn
Paragraph is a venture studio based in Bloomington, IN. We build and back scalable software companies. Our team of engineers, designers and operators work alongside founders to write code, create brands, and drive growth. This hands-on approach means we aren’t just supporting characters, we change the arc of founders' stories.
Paragraph is a venture studio based in Bloomington, IN. We build and back scalable software companies. Our team of engineers, designers and operators work alongside founders to write code, create brands, and drive growth. This hands-on approach means we aren’t just supporting characters, we change the arc of founders' stories.
Website | LinkedIn
Protected Wellness is a trailblazing longevity business incubator, currently focused on Genomics, Aging Biomarkers and AgeTech, with Neuroscience, Wearables, Precision Medicine and Cellular Reprogramming on the radar. The highly-experienced team, global funding sources and disciplined business systems enable visionaries to accelerate commercialization of impactful aging innovations.
Protected Wellness is a trailblazing longevity business incubator, currently focused on Genomics, Aging Biomarkers and AgeTech, with Neuroscience, Wearables, Precision Medicine and Cellular Reprogramming on the radar. The highly-experienced team, global funding sources and disciplined business systems enable visionaries to accelerate commercialization of impactful aging innovations.
Website | LinkedIn
Pyramidia is a purpose-led venture builder of climate-positive agrifood businesses serving the African consumer. Our mission is to provide nutritious and affordable food for a growing population in a way that regenerates nature. We ideate, build and scale sustainable agrifood businesses to create circular food systems. We are not a venture fund or an incubator - we are a venture builder.
Pyramidia is a purpose-led venture builder of climate-positive agrifood businesses serving the African consumer. Our mission is to provide nutritious and affordable food for a growing population in a way that regenerates nature. We ideate, build and scale sustainable agrifood businesses to create circular food systems. We are not a venture fund or an incubator - we are a venture builder.
Website | LinkedIn
We are REHAU New Ventures, the independent venture builder of the REHAU Group, here to achieve sustainable and profitable growth by establishing new businesses in the areas of Digital Construction and Better Living. Our work always centers around customers. Combined with the strong entrepreneurial backing of the REHAU Group, REHAU New Ventures makes for a competent and reliable partner.
We are REHAU New Ventures, the independent venture builder of the REHAU Group, here to achieve sustainable and profitable growth by establishing new businesses in the areas of Digital Construction and Better Living. Our work always centers around customers. Combined with the strong entrepreneurial backing of the REHAU Group, REHAU New Ventures makes for a competent and reliable partner.
Website | LinkedIn
We are a team of passion-fueled designers, developers and business people driven to create products that solve real problems in the health industry. We create startups from scratch along with outstanding Entrepreneurs. Our mission is to create products and build them into businesses are solving healthcare problems for 100 million families by 2028.
We are a team of passion-fueled designers, developers and business people driven to create products that solve real problems in the health industry. We create startups from scratch along with outstanding Entrepreneurs. Our mission is to create products and build them into businesses are solving healthcare problems for 100 million families by 2028.
Website | LinkedIn
ROOTS is a venture studio focusing on climate-tech through a venture building studio. Our mission is to create companies that support sustainability and positively impact life in the Kingdom of Saudi Arabia and the world through a business model that combines our operational experience with world-leading business models, which is reflected in the growth of our ventures at an accelerated pace with less risk.
ROOTS is a venture studio focusing on climate-tech through a venture building studio. Our mission is to create companies that support sustainability and positively impact life in the Kingdom of Saudi Arabia and the world through a business model that combines our operational experience with world-leading business models, which is reflected in the growth of our ventures at an accelerated pace with less risk.
Website | LinkedIn
Rule 1 is a venture studio that designs, funds, and builds B2B SaaS businesses. We are dedicated to co-creating entrepreneur-centric companies. Entrepreneurship is one of life's most challenging and rewarding endeavors. We empower you in your founder's journey by providing the guidance and resources required to be successful.
Rule 1 is a venture studio that designs, funds, and builds B2B SaaS businesses. We are dedicated to co-creating entrepreneur-centric companies. Entrepreneurship is one of life's most challenging and rewarding endeavors. We empower you in your founder's journey by providing the guidance and resources required to be successful.
Website | LinkedIn
Simsy Ventures is a venture builder, an institutional co-founder, and a partner for entrepreneurs (co-founders), investors, corporations, and the global startup ecosystem. We forge partnerships to collaborate and co-create fast-growing sustainable startups that become purpose-driven impactful companies - from idea generation, market validation, sustainability mapping, product-market fit, traction & unit economics, growth hacking, and launching new ventures to providing ongoing support.
Simsy Ventures is a venture builder, an institutional co-founder, and a partner for entrepreneurs (co-founders), investors, corporations, and the global startup ecosystem. We forge partnerships to collaborate and co-create fast-growing sustainable startups that become purpose-driven impactful companies - from idea generation, market validation, sustainability mapping, product-market fit, traction & unit economics, growth hacking, and launching new ventures to providing ongoing support.
Website | LinkedIn
Our unique accelerator program de-risks investments by combining engineering with startup formation services to help founders build real, scalable, and investable technologies. Powered by the $40M Softeq Venture Fund, the Softeq Venture Studio accelerator program provides $125K in cash and development services to boost your early-stage tech startup.
Our unique accelerator program de-risks investments by combining engineering with startup formation services to help founders build real, scalable, and investable technologies. Powered by the $40M Softeq Venture Fund, the Softeq Venture Studio accelerator program provides $125K in cash and development services to boost your early-stage tech startup.
Website | LinkedIn
Spark VS provides an opportunity to join a community of highly qualified experts, build relationships with investors, create mutually beneficial partnerships with corporations, and take advantage of support for aspiring and existing entrepreneurs in implementing early stage projects.
Spark VS provides an opportunity to join a community of highly qualified experts, build relationships with investors, create mutually beneficial partnerships with corporations, and take advantage of support for aspiring and existing entrepreneurs in implementing early stage projects.
Website | LinkedIn
Spring Studios is MENA’s first fintech-focused venture studio backed up by Bahrain’s Al Waha Fund of Funds and global investment firm Salica Investments. We build startups from the ground up and invest in early-stage fintech startups in the region providing them with mentorship, advisory, in-house builders, and access to our global investor network.
Spring Studios is MENA’s first fintech-focused venture studio backed up by Bahrain’s Al Waha Fund of Funds and global investment firm Salica Investments. We build startups from the ground up and invest in early-stage fintech startups in the region providing them with mentorship, advisory, in-house builders, and access to our global investor network.
Website | LinkedIn
SproutBox is dedicated to helping entrepreneurs turn their ideas into viable businesses. Our new approach to venture capital aims to transform the Midwest and Bloomington, IN into to a hotbed of startup activity. SproutBox is made up of an elite crew of product developers, creatives and business experts. We invest our talent in startup companies with high growth potential in exchange for equity.
SproutBox is dedicated to helping entrepreneurs turn their ideas into viable businesses. Our new approach to venture capital aims to transform the Midwest and Bloomington, IN into to a hotbed of startup activity. SproutBox is made up of an elite crew of product developers, creatives and business experts. We invest our talent in startup companies with high growth potential in exchange for equity.
Website | LinkedIn
Squared Circles is a venture studio, residing at the intersection of capital, brand, operations and talent. We invest in, incubate and accelerate businesses that help shape a better future. Our founders are three multi-disciplinary entrepreneurs with 60+ years of conceiving, building and scaling world class, multi-billion dollar companies and executing dynamic and culturally relevant collaborations and projects, globally.
Squared Circles is a venture studio, residing at the intersection of capital, brand, operations and talent. We invest in, incubate and accelerate businesses that help shape a better future. Our founders are three multi-disciplinary entrepreneurs with 60+ years of conceiving, building and scaling world class, multi-billion dollar companies and executing dynamic and culturally relevant collaborations and projects, globally.
Website | LinkedIn
Stackpoint is the first venture studio dedicated to building and investing in real estate technology companies. Our expert team of operators and in-house capital help founders focus on what matters and get ideas to market faster. We also partner closely with our industry-leading strategic investors to ideate and launch ventures.
Stackpoint is the first venture studio dedicated to building and investing in real estate technology companies. Our expert team of operators and in-house capital help founders focus on what matters and get ideas to market faster. We also partner closely with our industry-leading strategic investors to ideate and launch ventures.
Website | LinkedIn
Summit Venture Studio (SVS) liberates untapped, valuable software developed by universities and hospitals, transforming cutting-edge academic innovations into thriving commercial ventures. Our mission is to bridge the gap between academic brilliance and business success, creating a dynamic ecosystem where investors, industry experts, and visionary leaders converge to foster growth, innovation, and value creation.
Summit Venture Studio (SVS) liberates untapped, valuable software developed by universities and hospitals, transforming cutting-edge academic innovations into thriving commercial ventures. Our mission is to bridge the gap between academic brilliance and business success, creating a dynamic ecosystem where investors, industry experts, and visionary leaders converge to foster growth, innovation, and value creation.
Website | LinkedIn
UMANA Venture Studio (“UVS”) is a startup co-builder, focused on co-creating and scaling purpose-driven startups with celebrities (aka loud voices). We believe celebrities can be forces of change and activism, having the power to educate massive audiences to take positive action. We exist to provide celebrities the resources to be loud, responsible, and authentic.
UMANA Venture Studio (“UVS”) is a startup co-builder, focused on co-creating and scaling purpose-driven startups with celebrities (aka loud voices). We believe celebrities can be forces of change and activism, having the power to educate massive audiences to take positive action. We exist to provide celebrities the resources to be loud, responsible, and authentic.
Website | LinkedIn
Venturerock is an all-in-one investment and venture building platform on a mission to solve venture liquidity through radical transparency and digitization. We align the interests of founders, investors and venture builders, from seed to scale and beyond, through a shared liquidity infrastructure and AI-powered evidence-based investment and venture building methodology.
Venturerock is an all-in-one investment and venture building platform on a mission to solve venture liquidity through radical transparency and digitization. We align the interests of founders, investors and venture builders, from seed to scale and beyond, through a shared liquidity infrastructure and AI-powered evidence-based investment and venture building methodology.
Website | LinkedIn
We work with organisations and teams to help them find new ways of looking at future, innovation, products and whatnot. Be simple, clear, bold, agile and daring to transform. Evolve. We have built extensive experience in the ecosystems of start-ups and brave entrepreneurs. We have seen great ideas fail and doubtful ones miraculously explode. We got our hands dirty. We proudly expose our scars. Our passion lays at making the world cleaner, lands greener, people healthier - therefore we help leaders drive this change.
We work with organisations and teams to help them find new ways of looking at future, innovation, products and whatnot. Be simple, clear, bold, agile and daring to transform. Evolve. We have built extensive experience in the ecosystems of start-ups and brave entrepreneurs. We have seen great ideas fail and doubtful ones miraculously explode. We got our hands dirty. We proudly expose our scars. Our passion lays at making the world cleaner, lands greener, people healthier - therefore we help leaders drive this change.
Website | LinkedIn
Zenith Venture Studio, based in Vancouver, is an AI venture studio dedicated to co-building and investing in early-stage B2B AI startups alongside industry experts. With a top-tier technical team, we transform complex, high-impact challenges into multimillion-dollar ventures, crafting solutions that go beyond AI automation to deliver 10x improvements across industries. At Zenith, we are committed to reshaping the future of business by driving innovation in AI-powered solutions.
Zenith Venture Studio, based in Vancouver, is an AI venture studio dedicated to co-building and investing in early-stage B2B AI startups alongside industry experts. With a top-tier technical team, we transform complex, high-impact challenges into multimillion-dollar ventures, crafting solutions that go beyond AI automation to deliver 10x improvements across industries. At Zenith, we are committed to reshaping the future of business by driving innovation in AI-powered solutions.
Website | LinkedIn
We are an IMPACT VENTURE BUILDER. We unite talent and investment to solve social and environmental challenges, through companies that we create together with committed management teams. We work together to build a better future. At Zubi Labs we create and finance companies that seek to generate a positive impact. We use the company as an instrument to create social, environmental and economic value.
We are an IMPACT VENTURE BUILDER. We unite talent and investment to solve social and environmental challenges, through companies that we create together with committed management teams. We work together to build a better future. At Zubi Labs we create and finance companies that seek to generate a positive impact. We use the company as an instrument to create social, environmental and economic value.
BVSR’24 hands-on contributors
- Sergey GaluzaResearch co-lead, data collection & cleaning, conceptualization, writing
Entrepreneur and researcher focusing on venture studio models. Founder of IZDAT Venture Studio for creating and scaling businesses. Founder of Symfa.com - custom software development company in the insurance industry. Master's degree in Innovation and Entrepreneurship from UPF Barcelona. Past academic background and degrees in Computer Science, MBA, and Political Science. Research interests in quantitative and qualitative frameworks for business models. Current work on venture studios effectiveness and success factors. Barcelona-based entrepreneur and father of three.
- Tom WestProofreading, conceptualization, analysis, writingTom West is an entrepreneur and strategist with expertise in digital transformation, finance, and technology. He founded Island Media in 2012, growing it into a 50-person digital agency that was acquired by Juicebox in 2023. Concurrently, he managed a digital asset fund at Intergen Limited and conducted academic research on optimizing venture studio success with the further goal of employing artificial intelligence practices. A CFA charterholder with an MBA from the University of London, West also serves as Chairman of BritCham Indonesia's Technology and Digitalization Hub. He continues to advise organizations on digital transformation and strategic growth.
- John-Erik HasselConceptualization, writing, advisingJohn-Erik Hassel is a serial entrepreneur and PhD candidate in entrepreneurship focusing on venture studios. Having founded and co-founded several start-ups for two decades, reaching multiple international exits, John-Erik is dedicated to continuing contributing to the start-up community as a business founder, researcher, and by supporting nascent entrepreneurs. Currently, he also serves on the board of Grade, the leading supplier of HR tech to the Nordic public sector, he is a public speaker on the topic of entrepreneurship and entrepreneurship support and co-founder of Nordrljos Venture Studio.
- Adam FolsomData collection & cleaning
- Aida SalketicData collection & cleaning
- Alan FokData collection & cleaning
- Amber CherData collection & cleaning
- Ana Maury AguilarData collection & cleaning
- Ariasun AhmadianData collection & cleaning & conceptualization
- Avin ChuganiConceptualization, writing
- Constanze Coelsch-FoisnerConceptualization, writing
- Dmitriy BelenkiyData collection & cleaning
- Iván Darío Castaño PérezConceptualization
- Juhana KuparinenConceptualization
- Kapa LenkovData collection & cleaning
- Kirandeep KaurData collection & cleaning, team co-lead
- Konstantin ZherebtsovData collection & cleaning
- Leonid MenkinAnalysis
- Matthias NeumanConceptualization
- Mitchel PetermanConceptualization
- Mohamed WafeeqData collection & cleaning
- Oleh DmytrivData collection & cleaning
- Paolo OrtolaniConceptualization
- Ruslan GafarovData collection & cleaning
- Srinivas KolliparaData collection & cleaning
- Suhayb AlsawadiData collection & cleaning
- Usha KommuruData collection & cleaning, conceptualization
- Zehan TeohData collection & cleaning
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Appendices
Trust Confirmation
We confirm that the studio CAOS Focado with the website caosfocado.com participated in BVSR'24 and shared its insights with us.