EXCEPTIONAL 100
EXCEPTIONAL 100
EXCEPTIONAL 100
(Mission)
(Mission)
(Mission)
One of Exceptional Capital’s sources of deal flow comes from our proactive approach to searching for talented future founders. This involves leveraging our “Future Founder DNA” framework, searching below the management layer at high-growth companies to identify exceptional builders. These individuals ultimately possess career characteristics and skill sets that we believe demonstrate strong potential for future leadership within enterprise software.
The intention in sharing this report is to provide visibility into our evolving thesis on understanding the best communities of top engineering and product talent and to highlight genuinely talented individuals whose careers we look forward to following.
We are always searching for exceptional future founders, and through that process we encounter individual profiles demonstrative of exceptional talent. Our aim is to share a portion of our process and derived insights herein.
One of Exceptional Capital’s sources of deal flow comes from our proactive approach to searching for talented future founders. This involves leveraging our “Future Founder DNA” framework, searching below the management layer at high-growth companies to identify exceptional builders. These individuals ultimately possess career characteristics and skill sets that we believe demonstrate strong potential for future leadership within enterprise software.
The intention in sharing this report is to provide visibility into our evolving thesis on understanding the best communities of top engineering and product talent and to highlight genuinely talented individuals whose careers we look forward to following.
We are always searching for exceptional future founders, and through that process we encounter individual profiles demonstrative of exceptional talent. Our aim is to share a portion of our process and derived insights herein.
One of Exceptional Capital’s sources of deal flow comes from our proactive approach to searching for talented future founders. This involves leveraging our “Future Founder DNA” framework, searching below the management layer at high-growth companies to identify exceptional builders. These individuals ultimately possess career characteristics and skill sets that we believe demonstrate strong potential for future leadership within enterprise software.
The intention in sharing this report is to provide visibility into our evolving thesis on understanding the best communities of top engineering and product talent and to highlight genuinely talented individuals whose careers we look forward to following.
We are always searching for exceptional future founders, and through that process we encounter individual profiles demonstrative of exceptional talent. Our aim is to share a portion of our process and derived insights herein.
FOUNDER DNA
FOUNDER DNA
FOUNDER DNA
(Method)
(Method)
(Method)
To fuel a portion of our deal flow in this way, we perform necessary diligence and work to begin conversations with potential founders at the earliest stages of their ideation. Before we reach out to understand their potential desire and aptitude for the founder journey, we search through larger pools of talented individuals and communities, utilizing a variety of resources to fuel our data pipelines. Herein, we are providing visibility into that first step of the process by showcasing 100 current standout builders within high-growth enterprise companies.
Utilizing our established framework to discern which career characteristics determine strong product and technical talent, our internal tooling enabled us to run further queries and prompts to enable our research.
To fuel a portion of our deal flow in this way, we perform necessary diligence and work to begin conversations with potential founders at the earliest stages of their ideation. Before we reach out to understand their potential desire and aptitude for the founder journey, we search through larger pools of talented individuals and communities, utilizing a variety of resources to fuel our data pipelines. Herein, we are providing visibility into that first step of the process by showcasing 100 current standout builders within high-growth enterprise companies.
Utilizing our established framework to discern which career characteristics determine strong product and technical talent, our internal tooling enabled us to run further queries and prompts to enable our research.
To fuel a portion of our deal flow in this way, we perform necessary diligence and work to begin conversations with potential founders at the earliest stages of their ideation. Before we reach out to understand their potential desire and aptitude for the founder journey, we search through larger pools of talented individuals and communities, utilizing a variety of resources to fuel our data pipelines. Herein, we are providing visibility into that first step of the process by showcasing 100 current standout builders within high-growth enterprise companies.
Utilizing our established framework to discern which career characteristics determine strong product and technical talent, our internal tooling enabled us to run further queries and prompts to enable our research.
In addition to our framework, search criteria and parameters included, but were not limited to:
Focus on engineering and product roles
At least 4 years of experience
Experience and career trajectory
Emerging technologies and internal high-impact infrastructure
Excluded current Pre-Seed and Seed
No educational institution-based prioritization within queries
Excluded extended durations at managerial layer*
Examination of specific tenure at companies, based in part upon funding round/stage of growth
*While some of these exceptional individuals likely manage teams within their organizations, we want to reinforce that this exercise and study is intended to examine emerging talent within tech leadership. Therefore, we sought not to be reliant purely on scraping our datasets on the basis of traditional, hierarchical titles within tech organizations.
*While some of these exceptional individuals likely manage teams within their organizations, we want to reinforce that this exercise and study is intended to examine emerging talent within tech leadership. Therefore, we sought not to be reliant purely on scraping our datasets on the basis of traditional, hierarchical titles within tech organizations.
*While some of these exceptional individuals likely manage teams within their organizations, we want to reinforce that this exercise and study is intended to examine emerging talent within tech leadership. Therefore, we sought not to be reliant purely on scraping our datasets on the basis of traditional, hierarchical titles within tech organizations.
(FINDINGS)
(FINDINGS)
(FINDINGS)
We performed the following analyses based on search criteria and queries outlined in the Method section. We have outlined the commonalities, observations and key trends drawn from this sample of the 2024 Exceptional 100 List.
We performed the following analyses based on search criteria and queries outlined in the Method section. We have outlined the commonalities, observations and key trends drawn from this sample of the 2024 Exceptional 100 List.
We performed the following analyses based on search criteria and queries outlined in the Method section. We have outlined the commonalities, observations and key trends drawn from this sample of the 2024 Exceptional 100 List.
GEOGRAPHIC SCOPE
GEOGRAPHIC SCOPE
GEOGRAPHIC SCOPE
With regard to geographic location, we had an assumption that there would be a higher concentration present in cities such as San Francisco and New York, which the data reflects. San Francisco leads this by a large margin, verifying our assumption. 91% of the Exceptional 100 are located in the US, while 9% are located abroad.
With regard to geographic location, we had an assumption that there would be a higher concentration present in cities such as San Francisco and New York, which the data reflects. San Francisco leads this by a large margin, verifying our assumption. 91% of the Exceptional 100 are located in the US, while 9% are located abroad.
With regard to geographic location, we had an assumption that there would be a higher concentration present in cities such as San Francisco and New York, which the data reflects. San Francisco leads this by a large margin, verifying our assumption. 91% of the Exceptional 100 are located in the US, while 9% are located abroad.
EDUCATIONAL BACKGROUND
EDUCATIONAL BACKGROUND
EDUCATIONAL BACKGROUND
Our analysis met expectations regarding the most frequently represented educational institutions. This data reflects all phases of education levels; we were looking to identify commonalities and understand the rate of recurrence in such commonalities with regard to attendance at educational institutions. Additionally, understanding the highest degree achieved - the data reflects that 51% of individuals hold degrees beyond a BA.
Our analysis met expectations regarding the most frequently represented educational institutions. This data reflects all phases of education levels; we were looking to identify commonalities and understand the rate of recurrence in such commonalities with regard to attendance at educational institutions. Additionally, understanding the highest degree achieved - the data reflects that 51% of individuals hold degrees beyond a BA.
Our analysis met expectations regarding the most frequently represented educational institutions. This data reflects all phases of education levels; we were looking to identify commonalities and understand the rate of recurrence in such commonalities with regard to attendance at educational institutions. Additionally, understanding the highest degree achieved - the data reflects that 51% of individuals hold degrees beyond a BA.
PROGRAMMING LANGUAGES
PROGRAMMING LANGUAGES
PROGRAMMING LANGUAGES
We sought to understand which programming languages were most prevalent amongst the group. The data demonstrates the highest frequencies of language proficiencies within the Exceptional 100 (based on publicly available data) with Java and Python having the highest percentages. Java is particularly relevant at larger scale enterprises, while Python is utilized largely for machine learning ("ML"). As organizations continue building AI-enabled products at scale, we expect Python usage and prevalence to increase given the specific use case for ML.
We sought to understand which programming languages were most prevalent amongst the group. The data demonstrates the highest frequencies of language proficiencies within the Exceptional 100 (based on publicly available data) with Java and Python having the highest percentages. Java is particularly relevant at larger scale enterprises, while Python is utilized largely for machine learning ("ML"). As organizations continue building AI-enabled products at scale, we expect Python usage and prevalence to increase given the specific use case for ML.
We sought to understand which programming languages were most prevalent amongst the group. The data demonstrates the highest frequencies of language proficiencies within the Exceptional 100 (based on publicly available data) with Java and Python having the highest percentages. Java is particularly relevant at larger scale enterprises, while Python is utilized largely for machine learning ("ML"). As organizations continue building AI-enabled products at scale, we expect Python usage and prevalence to increase given the specific use case for ML.
GENDER STATISTICS
GENDER STATISTICS
GENDER STATISTICS
The gender gap depicted herein is generally reflective of the industry standard. While this is not ideal in terms of equality in representation, we hope that by continuing to surface great talent from beyond management level we can do our part to provide greater visibility to future diverse leadership within tech.
The gender gap depicted herein is generally reflective of the industry standard. While this is not ideal in terms of equality in representation, we hope that by continuing to surface great talent from beyond management level we can do our part to provide greater visibility to future diverse leadership within tech.
The gender gap depicted herein is generally reflective of the industry standard. While this is not ideal in terms of equality in representation, we hope that by continuing to surface great talent from beyond management level we can do our part to provide greater visibility to future diverse leadership within tech.
CONCENTRATION OF TALENT & TRENDS
CONCENTRATION OF TALENT & TRENDS
CONCENTRATION OF TALENT & TRENDS
We analyzed the frequency of current employers for the Exceptional 100, as shown below in the bubble chart. Our core observation is that the chart depicts a trending combination of relevancy, age of company, and the proliferation of AI. Companies founded 2010 or later have higher concentrations within our dataset. It is possible that the desire to work on newer, more complex problems related to AI/GenAI may progressively outweigh the traditional long-term career stability of working for larger established enterprises.
Monitoring the fluctuations and volatility of the job market is critical to refining our theory and strategy around this. As mentioned above, the data seems to indicate the progression of companies going from technical innovators to what we call mid-stage “forks” (experiencing a moment in time where they are still deciding whether they will be R&D or Commercial). Significant talent vibration occurs along this journey as people are deciding whether these hyper-growth companies are the right fit for them - it is within this “vibration” that we look for opportunity. Looking closely at the logos below, the minor separations from our dataset are important. It is no secret that incredible talent has been shaken from the companies founded pre-2010 - so how can we predict the coming waves of talent diaspora?
Our internal research has led us to the observation of this next cluster starting to branch and separate - OpenAI, Scale AI, Vercel and Figma - are all strong candidates to shed unbelievably talented people. Whether those people are founders or exceptional employees we will have to wait and see, but it is the oncoming clusters that we find to be especially exciting. Ramp, Glean, and Wiz are seeing slight separation and the indicated potential of Harvey and Langchain are seen as hotbeds as well.
We analyzed the frequency of current employers for the Exceptional 100, as shown below in the bubble chart. Our core observation is that the chart depicts a trending combination of relevancy, age of company, and the proliferation of AI. Companies founded 2010 or later have higher concentrations within our dataset. It is possible that the desire to work on newer, more complex problems related to AI/GenAI may progressively outweigh the traditional long-term career stability of working for larger established enterprises.
Monitoring the fluctuations and volatility of the job market is critical to refining our theory and strategy around this. As mentioned above, the data seems to indicate the progression of companies going from technical innovators to what we call mid-stage “forks” (experiencing a moment in time where they are still deciding whether they will be R&D or Commercial). Significant talent vibration occurs along this journey as people are deciding whether these hyper-growth companies are the right fit for them - it is within this “vibration” that we look for opportunity. Looking closely at the logos below, the minor separations from our dataset are important. It is no secret that incredible talent has been shaken from the companies founded pre-2010 - so how can we predict the coming waves of talent diaspora?
Our internal research has led us to the observation of this next cluster starting to branch and separate - OpenAI, Scale AI, Vercel and Figma - are all strong candidates to shed unbelievably talented people. Whether those people are founders or exceptional employees we will have to wait and see, but it is the oncoming clusters that we find to be especially exciting. Ramp, Glean, and Wiz are seeing slight separation and the indicated potential of Harvey and Langchain are seen as hotbeds as well.
We analyzed the frequency of current employers for the Exceptional 100, as shown below in the bubble chart. Our core observation is that the chart depicts a trending combination of relevancy, age of company, and the proliferation of AI. Companies founded 2010 or later have higher concentrations within our dataset. It is possible that the desire to work on newer, more complex problems related to AI/GenAI may progressively outweigh the traditional long-term career stability of working for larger established enterprises.
Monitoring the fluctuations and volatility of the job market is critical to refining our theory and strategy around this. As mentioned above, the data seems to indicate the progression of companies going from technical innovators to what we call mid-stage “forks” (experiencing a moment in time where they are still deciding whether they will be R&D or Commercial). Significant talent vibration occurs along this journey as people are deciding whether these hyper-growth companies are the right fit for them - it is within this “vibration” that we look for opportunity. Looking closely at the logos below, the minor separations from our dataset are important. It is no secret that incredible talent has been shaken from the companies founded pre-2010 - so how can we predict the coming waves of talent diaspora?
Our internal research has led us to the observation of this next cluster starting to branch and separate - OpenAI, Scale AI, Vercel and Figma - are all strong candidates to shed unbelievably talented people. Whether those people are founders or exceptional employees we will have to wait and see, but it is the oncoming clusters that we find to be especially exciting. Ramp, Glean, and Wiz are seeing slight separation and the indicated potential of Harvey and Langchain are seen as hotbeds as well.
CAREER TRAJECTORY
CAREER TRAJECTORY
CAREER TRAJECTORY
Understanding the historical trajectory of someone’s career path is part of our internal analysis. In brief, trajectory has no value judgment (positive or negative) assigned to it for our purposes; the term refers only to the directionality and movement of an individual's job history. In short, this matters because context is important to understanding someone's work history. For example, "working at Uber" does not provide enough context - what years was someone at Uber? What products were they building at Uber? That is the essential question. Understanding the types of scale that someone has participated in is imperative.
The Exceptional 100 list reflected an average career length of 13 years and an average tenure at each role of 2.75 years. 76% have spent time working at early-stage startups with an average tenure of 3.42 years in those startup roles. In future iterations of the Exceptional 100, we hope to look more closely at data and commonalities involving past employers (for example in this batch, 29 of the 100 have previously spent time at Google in some capacity during their careers).
In examining trajectory and directionality, we created the heat map below which depicts the transition probability from each company funding stage to a subsequent funding stage. This analysis takes into account the full scope of each individual's careers, not only the prior movement to their current company. On the y-axis we have the companies moved from and the x-axis shows companies moved to (both organized by company funding round). Each value represents a percentage of the probability of a movement from one company to another. Using the sample dataset of the Exceptional 100, we share below the following probabilities.
On the heat map we observe a linear trend within Series B, Series C, Series D and Series E where we can see a consistent probability of remaining at the same company stage as the existing/departure company.
From Seed and Series A there is the most variance regarding stage of next company joined. Some of the movements of the Exceptional 100 with the highest probabilities indicated are:
Moving to Pre-Seed has a high probability of transition to either Series B (50%) or Series E (50%)
Series A is most likely to move to another Series A (23%) or a Series B (26%)
An individual at Series H had a 67% probability of moving to a Series A and 33% probability of moving to a Series E
Understanding the historical trajectory of someone’s career path is part of our internal analysis. In brief, trajectory has no value judgment (positive or negative) assigned to it for our purposes; the term refers only to the directionality and movement of an individual's job history. In short, this matters because context is important to understanding someone's work history. For example, "working at Uber" does not provide enough context - what years was someone at Uber? What products were they building at Uber? That is the essential question. Understanding the types of scale that someone has participated in is imperative.
The Exceptional 100 list reflected an average career length of 13 years and an average tenure at each role of 2.75 years. 76% have spent time working at early-stage startups with an average tenure of 3.42 years in those startup roles. In future iterations of the Exceptional 100, we hope to look more closely at data and commonalities involving past employers (for example in this batch, 29 of the 100 have previously spent time at Google in some capacity during their careers).
In examining trajectory and directionality, we created the heat map below which depicts the transition probability from each company funding stage to a subsequent funding stage. This analysis takes into account the full scope of each individual's careers, not only the prior movement to their current company. On the y-axis we have the companies moved from and the x-axis shows companies moved to (both organized by company funding round). Each value represents a percentage of the probability of a movement from one company to another. Using the sample dataset of the Exceptional 100, we share below the following probabilities.
On the heat map we observe a linear trend within Series B, Series C, Series D and Series E where we can see a consistent probability of remaining at the same company stage as the existing/departure company.
From Seed and Series A there is the most variance regarding stage of next company joined. Some of the movements of the Exceptional 100 with the highest probabilities indicated are:
Moving to Pre-Seed has a high probability of transition to either Series B (50%) or Series E (50%)
Series A is most likely to move to another Series A (23%) or a Series B (26%)
An individual at Series H had a 67% probability of moving to a Series A and 33% probability of moving to a Series E
Understanding the historical trajectory of someone’s career path is part of our internal analysis. In brief, trajectory has no value judgment (positive or negative) assigned to it for our purposes; the term refers only to the directionality and movement of an individual's job history. In short, this matters because context is important to understanding someone's work history. For example, "working at Uber" does not provide enough context - what years was someone at Uber? What products were they building at Uber? That is the essential question. Understanding the types of scale that someone has participated in is imperative.
The Exceptional 100 list reflected an average career length of 13 years and an average tenure at each role of 2.75 years. 76% have spent time working at early-stage startups with an average tenure of 3.42 years in those startup roles. In future iterations of the Exceptional 100, we hope to look more closely at data and commonalities involving past employers (for example in this batch, 29 of the 100 have previously spent time at Google in some capacity during their careers).
In examining trajectory and directionality, we created the heat map below which depicts the transition probability from each company funding stage to a subsequent funding stage. This analysis takes into account the full scope of each individual's careers, not only the prior movement to their current company. On the y-axis we have the companies moved from and the x-axis shows companies moved to (both organized by company funding round). Each value represents a percentage of the probability of a movement from one company to another. Using the sample dataset of the Exceptional 100, we share below the following probabilities.
On the heat map we observe a linear trend within Series B, Series C, Series D and Series E where we can see a consistent probability of remaining at the same company stage as the existing/departure company.
From Seed and Series A there is the most variance regarding stage of next company joined. Some of the movements of the Exceptional 100 with the highest probabilities indicated are:
Moving to Pre-Seed has a high probability of transition to either Series B (50%) or Series E (50%)
Series A is most likely to move to another Series A (23%) or a Series B (26%)
An individual at Series H had a 67% probability of moving to a Series A and 33% probability of moving to a Series E
PRIOR FOUNDER EXPERIENCE
PRIOR FOUNDER EXPERIENCE
PRIOR FOUNDER EXPERIENCE
This data reflected 38 individuals out of the 100 having been prior founders. Out of the 38, at least 9 demonstrated evidence of having had an acquisition or exit event.
This data reflected 38 individuals out of the 100 having been prior founders. Out of the 38, at least 9 demonstrated evidence of having had an acquisition or exit event.
This data reflected 38 individuals out of the 100 having been prior founders. Out of the 38, at least 9 demonstrated evidence of having had an acquisition or exit event.
ROLE & PRODUCT SURVEY RESULTS
ROLE & PRODUCT SURVEY RESULTS
ROLE & PRODUCT SURVEY RESULTS
We conducted a survey to better understand the products and projects being worked on and the impact that survey respondents believe these projects will have on stakeholders. 30% of our respondents mentioned that they were working on a wholly new product, however, all respondents, including those working on an existing product, believe their work will heavily benefit the end user - whether external or internal to the organization.
From our continued research and deal flow, we have observed that many internal key infrastructure products are providing a strong testing environment. These environments are proving beneficial for use cases that have the potential to help other enterprises. We believe that these innovative organizations, including those we name herein, will continue to yield high quality product and engineering talent, while providing exposure to real-time enterprise pain points and solutioning.
We conducted a survey to better understand the products and projects being worked on and the impact that survey respondents believe these projects will have on stakeholders. 30% of our respondents mentioned that they were working on a wholly new product, however, all respondents, including those working on an existing product, believe their work will heavily benefit the end user - whether external or internal to the organization.
From our continued research and deal flow, we have observed that many internal key infrastructure products are providing a strong testing environment. These environments are proving beneficial for use cases that have the potential to help other enterprises. We believe that these innovative organizations, including those we name herein, will continue to yield high quality product and engineering talent, while providing exposure to real-time enterprise pain points and solutioning.
We conducted a survey to better understand the products and projects being worked on and the impact that survey respondents believe these projects will have on stakeholders. 30% of our respondents mentioned that they were working on a wholly new product, however, all respondents, including those working on an existing product, believe their work will heavily benefit the end user - whether external or internal to the organization.
From our continued research and deal flow, we have observed that many internal key infrastructure products are providing a strong testing environment. These environments are proving beneficial for use cases that have the potential to help other enterprises. We believe that these innovative organizations, including those we name herein, will continue to yield high quality product and engineering talent, while providing exposure to real-time enterprise pain points and solutioning.
(Trends)
(Trends)
(Trends)
The Next Talent Hub: The amount of talent diaspora stemming from established enterprises is an indicator for our team to take actionable and proactive steps to track which companies are the next best-in-class communities for talent emergence. So who is the next Meta, Google, or Stripe? We believe the next wave of exceptional talent will come from companies that found success combining the deep research angle of AI coupled with business ROI - companies like Scale, OpenAI, and Ramp as a few strong examples.
Paradigm Shift with AI: GenAI has created a new approach to how engineers, product leaders and even non-technical leaders view entrepreneurship. We do not believe it is a coincidence that in 2023 there was a record breaking number of 5.5 million new business applications, a 57% increase from the previous year. Two letters as to why, AI! AI has effectively reduced barriers to entry, increased the speed of product development and filled a technical gap for non-technical aspiring founders, leveling the playing field in a new way. While this means that founders can build with low-code capabilities and be further empowered by AI, we predict that new companies will continue to emerge that address complex industry specific problems leveraging AI from the start - even if the core product is not solely AI driven.
Big in the Bay: The Bay Area continues to be a hub for product and engineering talent, driven in-part by the number of venture-backed businesses that operate in the area and the communities that form around those businesses. While many professionals are working remotely and have access to other geographies within the US, we believe the Bay Area will continue to be the leading hub for cultivation of technical talent.
Defense Tech Diaspora: From our observations and research, defense tech (both in the US and abroad) has experienced an increase in talent magnetization. Our hypothesis is that just as significant talent has spun out of a company like Palantir (with over 200 individuals from the "Palantir Pack" founding or leading new companies) in future years there will be an identifiable trend of ex-Anduril, ex-Shield, ex-Epirus employees who become key leaders and founders within the next generation of enterprise tech companies. We are bullish on defense tech talent for the qualitative aspects of highly mission-driven environments, solving incredibly complex high-stakes problems and understanding how to iterate with and sell to large clients. As shown in the data above, exceptional talent is more likely to join well funded hyper-growth companies. As of Q3, 2024, defense tech has raised over $2.5B in funding, which is on track to surpass 2023 funding numbers by 30% by the end of 2024.
Specialized Research Talent: The gap between entrepreneurship and academia continues to close, as the tectonic shift of AI has made deep technical topics in data and ML increasingly applicable for commercialization. As ML needs increase and issues like data quality become increasingly relevant, we predict that more key leadership and founders will come from deep research backgrounds. Data scientists continue to have an integral role, along with specialized ML engineers. Our assumption is that we will witness an increasing trend of researchers/PhDs in leadership roles within earlier stage companies. These companies seem able to attract and compete for highly talented recruits by offering sandbox environments for AI/ML researchers. Already we see some potential evidence of this shift within the Exceptional 100, with 11% indicating a current position of "Technical Member of Staff" - a role or job descriptor that we believe we will see used more often and which may be more appropriate to encompass the greater range and scope of skill sets that are joining these hyper-growth companies.
Looking to Internal High-Impact Infrastructure: As organizations strive to provide the best products to the market, they are increasingly focused on building products internally and enhancing existing offerings. These high-impact infrastructure builds are well-resourced within enterprises, however they provide a sandbox for solving persistent enterprise pain points at scale. In these environments, engineers and product professionals will gain valuable experience in 0-100 build environments with exposure to large scale enterprise applicability. We understand these instances as a valuable "training ground" from which new ventures can be launched. Additionally, over 80% of respondents to a survey of engineer and product talent stated they have a strong desire to contribute to highly meaningful work in their current role. With rapid advancement in technology comes the heightened ability to be part of something exceptional and make true contributions to the future of software.
AI
The Next Talent Hub: The amount of talent diaspora stemming from established enterprises is an indicator for our team to take actionable and proactive steps to track which companies are the next best-in-class communities for talent emergence. So who is the next Meta, Google, or Stripe? We believe the next wave of exceptional talent will come from companies that found success combining the deep research angle of AI coupled with business ROI - companies like Scale, OpenAI, and Ramp as a few strong examples.
Paradigm Shift with AI: GenAI has created a new approach to how engineers, product leaders and even non-technical leaders view entrepreneurship. We do not believe it is a coincidence that in 2023 there was a record breaking number of 5.5 million new business applications, a 57% increase from the previous year. Two letters as to why, AI! AI has effectively reduced barriers to entry, increased the speed of product development and filled a technical gap for non-technical aspiring founders, leveling the playing field in a new way. While this means that founders can build with low-code capabilities and be further empowered by AI, we predict that new companies will continue to emerge that address complex industry specific problems leveraging AI from the start - even if the core product is not solely AI driven.
Big in the Bay: The Bay Area continues to be a hub for product and engineering talent, driven in-part by the number of venture-backed businesses that operate in the area and the communities that form around those businesses. While many professionals are working remotely and have access to other geographies within the US, we believe the Bay Area will continue to be the leading hub for cultivation of technical talent.
Defense Tech Diaspora: From our observations and research, defense tech (both in the US and abroad) has experienced an increase in talent magnetization. Our hypothesis is that just as significant talent has spun out of a company like Palantir (with over 200 individuals from the "Palantir Pack" founding or leading new companies) in future years there will be an identifiable trend of ex-Anduril, ex-Shield, ex-Epirus employees who become key leaders and founders within the next generation of enterprise tech companies. We are bullish on defense tech talent for the qualitative aspects of highly mission-driven environments, solving incredibly complex high-stakes problems and understanding how to iterate with and sell to large clients. As shown in the data above, exceptional talent is more likely to join well funded hyper-growth companies. As of Q3, 2024, defense tech has raised over $2.5B in funding, which is on track to surpass 2023 funding numbers by 30% by the end of 2024.
Specialized Research Talent: The gap between entrepreneurship and academia continues to close, as the tectonic shift of AI has made deep technical topics in data and ML increasingly applicable for commercialization. As ML needs increase and issues like data quality become increasingly relevant, we predict that more key leadership and founders will come from deep research backgrounds. Data scientists continue to have an integral role, along with specialized ML engineers. Our assumption is that we will witness an increasing trend of researchers/PhDs in leadership roles within earlier stage companies. These companies seem able to attract and compete for highly talented recruits by offering sandbox environments for AI/ML researchers. Already we see some potential evidence of this shift within the Exceptional 100, with 11% indicating a current position of "Technical Member of Staff" - a role or job descriptor that we believe we will see used more often and which may be more appropriate to encompass the greater range and scope of skill sets that are joining these hyper-growth companies.
Looking to Internal High-Impact Infrastructure: As organizations strive to provide the best products to the market, they are increasingly focused on building products internally and enhancing existing offerings. These high-impact infrastructure builds are well-resourced within enterprises, however they provide a sandbox for solving persistent enterprise pain points at scale. In these environments, engineers and product professionals will gain valuable experience in 0-100 build environments with exposure to large scale enterprise applicability. We understand these instances as a valuable "training ground" from which new ventures can be launched. Additionally, over 80% of respondents to a survey of engineer and product talent stated they have a strong desire to contribute to highly meaningful work in their current role. With rapid advancement in technology comes the heightened ability to be part of something exceptional and make true contributions to the future of software.
AI
The Next Talent Hub: The amount of talent diaspora stemming from established enterprises is an indicator for our team to take actionable and proactive steps to track which companies are the next best-in-class communities for talent emergence. So who is the next Meta, Google, or Stripe? We believe the next wave of exceptional talent will come from companies that found success combining the deep research angle of AI coupled with business ROI - companies like Scale, OpenAI, and Ramp as a few strong examples.
Paradigm Shift with AI: GenAI has created a new approach to how engineers, product leaders and even non-technical leaders view entrepreneurship. We do not believe it is a coincidence that in 2023 there was a record breaking number of 5.5 million new business applications, a 57% increase from the previous year. Two letters as to why, AI! AI has effectively reduced barriers to entry, increased the speed of product development and filled a technical gap for non-technical aspiring founders, leveling the playing field in a new way. While this means that founders can build with low-code capabilities and be further empowered by AI, we predict that new companies will continue to emerge that address complex industry specific problems leveraging AI from the start - even if the core product is not solely AI driven.
Big in the Bay: The Bay Area continues to be a hub for product and engineering talent, driven in-part by the number of venture-backed businesses that operate in the area and the communities that form around those businesses. While many professionals are working remotely and have access to other geographies within the US, we believe the Bay Area will continue to be the leading hub for cultivation of technical talent.
Defense Tech Diaspora: From our observations and research, defense tech (both in the US and abroad) has experienced an increase in talent magnetization. Our hypothesis is that just as significant talent has spun out of a company like Palantir (with over 200 individuals from the "Palantir Pack" founding or leading new companies) in future years there will be an identifiable trend of ex-Anduril, ex-Shield, ex-Epirus employees who become key leaders and founders within the next generation of enterprise tech companies. We are bullish on defense tech talent for the qualitative aspects of highly mission-driven environments, solving incredibly complex high-stakes problems and understanding how to iterate with and sell to large clients. As shown in the data above, exceptional talent is more likely to join well funded hyper-growth companies. As of Q3, 2024, defense tech has raised over $2.5B in funding, which is on track to surpass 2023 funding numbers by 30% by the end of 2024.
Specialized Research Talent: The gap between entrepreneurship and academia continues to close, as the tectonic shift of AI has made deep technical topics in data and ML increasingly applicable for commercialization. As ML needs increase and issues like data quality become increasingly relevant, we predict that more key leadership and founders will come from deep research backgrounds. Data scientists continue to have an integral role, along with specialized ML engineers. Our assumption is that we will witness an increasing trend of researchers/PhDs in leadership roles within earlier stage companies. These companies seem able to attract and compete for highly talented recruits by offering sandbox environments for AI/ML researchers. Already we see some potential evidence of this shift within the Exceptional 100, with 11% indicating a current position of "Technical Member of Staff" - a role or job descriptor that we believe we will see used more often and which may be more appropriate to encompass the greater range and scope of skill sets that are joining these hyper-growth companies.
Looking to Internal High-Impact Infrastructure: As organizations strive to provide the best products to the market, they are increasingly focused on building products internally and enhancing existing offerings. These high-impact infrastructure builds are well-resourced within enterprises, however they provide a sandbox for solving persistent enterprise pain points at scale. In these environments, engineers and product professionals will gain valuable experience in 0-100 build environments with exposure to large scale enterprise applicability. We understand these instances as a valuable "training ground" from which new ventures can be launched. Additionally, over 80% of respondents to a survey of engineer and product talent stated they have a strong desire to contribute to highly meaningful work in their current role. With rapid advancement in technology comes the heightened ability to be part of something exceptional and make true contributions to the future of software.
AI
(THE 100)
(THE 100)
(THE 100)
Abnormal Security
Tejas is a Staff ML Engineer at Abnormal Security where he launched multiple product lines helping Abnormal become a platform company
Abnormal Security
Tejas is a Staff ML Engineer at Abnormal Security where he launched multiple product lines helping Abnormal become a platform company
Abnormal Security
Tejas is a Staff ML Engineer at Abnormal Security where he launched multiple product lines helping Abnormal become a platform company
Adept AI
Omkar is the first PM hire at Adept AI with previous experience at Lyft and NVIDIA
Adept AI
Omkar is the first PM hire at Adept AI with previous experience at Lyft and NVIDIA
Adept AI
Omkar is the first PM hire at Adept AI with previous experience at Lyft and NVIDIA
Anduril Industries
Amanda is a software engineer at Anduril Industries where she has led 3 teams, currently leading the Software Integration Environment team
Anduril Industries
Amanda is a software engineer at Anduril Industries where she has led 3 teams, currently leading the Software Integration Environment team
Anduril Industries
Amanda is a software engineer at Anduril Industries where she has led 3 teams, currently leading the Software Integration Environment team
Anthropic
Nikhil is a member of the technical staff at Anthropic and a previous founder of Metamanagement (member of the YC20 batch)
Anthropic
Nikhil is a member of the technical staff at Anthropic and a previous founder of Metamanagement (member of the YC20 batch)
Anthropic
Nikhil is a member of the technical staff at Anthropic and a previous founder of Metamanagement (member of the YC20 batch)
Anthropic
Orowa is a member of the technical staff at Anthropic and a previous founder of Cophi, which he now acts as an advisor for
Anthropic
Orowa is a member of the technical staff at Anthropic and a previous founder of Cophi, which he now acts as an advisor for
Anthropic
Orowa is a member of the technical staff at Anthropic and a previous founder of Cophi, which he now acts as an advisor for
Anthropic
Scott is a product leader at Anthropic, managing Claude.AI, and previously founded Walrus.ai , which was acquired by Airtable
Anthropic
Scott is a product leader at Anthropic, managing Claude.AI, and previously founded Walrus.ai , which was acquired by Airtable
Anthropic
Scott is a product leader at Anthropic, managing Claude.AI, and previously founded Walrus.ai , which was acquired by Airtable
Anthropic
Yifan is a member of the technical team at Anthropic, on the research tools team, and a previous founder of Linea Labs
Anthropic
Yifan is a member of the technical team at Anthropic, on the research tools team, and a previous founder of Linea Labs
Anthropic
Yifan is a member of the technical team at Anthropic, on the research tools team, and a previous founder of Linea Labs
Apollo.io
Tyler is a product manager at Apollo.io building the AI and CRM platforms, and is a previous founder of Autopia Labs (member of the YC23 batch)
Apollo.io
Tyler is a product manager at Apollo.io building the AI and CRM platforms, and is a previous founder of Autopia Labs (member of the YC23 batch)
Apollo.io
Tyler is a product manager at Apollo.io building the AI and CRM platforms, and is a previous founder of Autopia Labs (member of the YC23 batch)
Brex
Benjamin is a senior software engineer at Brex having founded Brex's Applied AI team and now sits on the Product Application team
Brex
Benjamin is a senior software engineer at Brex having founded Brex's Applied AI team and now sits on the Product Application team
Brex
Benjamin is a senior software engineer at Brex having founded Brex's Applied AI team and now sits on the Product Application team
Chainguard
Josh is a staff software engineer at Chainguard and a previous founder of Blood Orange, a software delivery platform
Chainguard
Josh is a staff software engineer at Chainguard and a previous founder of Blood Orange, a software delivery platform
Chainguard
Josh is a staff software engineer at Chainguard and a previous founder of Blood Orange, a software delivery platform
Chime
Cliff is an engineering product manager at Chime and two-time founder of Nooch, a payments app, and Rent Scene, an apartment search platform
Chime
Cliff is an engineering product manager at Chime and two-time founder of Nooch, a payments app, and Rent Scene, an apartment search platform
Chime
Cliff is an engineering product manager at Chime and two-time founder of Nooch, a payments app, and Rent Scene, an apartment search platform
Chime
Dennis is a product manager at Chime and a two-time founder of Dashbot, an analytics platform, and Bureau of Trade, acquired by eBay
Chime
Dennis is a product manager at Chime and a two-time founder of Dashbot, an analytics platform, and Bureau of Trade, acquired by eBay
Chime
Dennis is a product manager at Chime and a two-time founder of Dashbot, an analytics platform, and Bureau of Trade, acquired by eBay
Clay
David is an engineering manager at Clay with experience as a CTO at a Stealth startup, Peloton and Uber
Clay
David is an engineering manager at Clay with experience as a CTO at a Stealth startup, Peloton and Uber
Clay
David is an engineering manager at Clay with experience as a CTO at a Stealth startup, Peloton and Uber
Clay
Juan is a software engineer at Clay and a previous founder of Academical, a schedule web app
Clay
Juan is a software engineer at Clay and a previous founder of Academical, a schedule web app
Clay
Juan is a software engineer at Clay and a previous founder of Academical, a schedule web app
Clickhouse
Ryadh is a director of product management at Clickhouse and previous two-time founder of Think It Solutions and Jib.li
Clickhouse
Ryadh is a director of product management at Clickhouse and previous two-time founder of Think It Solutions and Jib.li
Clickhouse
Ryadh is a director of product management at Clickhouse and previous two-time founder of Think It Solutions and Jib.li
Cohere
Elliot is a staff product manager at Cohere holding previous roles at Scale AI and Evercore
Cohere
Elliot is a staff product manager at Cohere holding previous roles at Scale AI and Evercore
Cohere
Elliot is a staff product manager at Cohere holding previous roles at Scale AI and Evercore
Cohere
Giannis is a member of the technical staff at Cohere and a previous founder of Cardinal Labs
Cohere
Giannis is a member of the technical staff at Cohere and a previous founder of Cardinal Labs
Cohere
Giannis is a member of the technical staff at Cohere and a previous founder of Cardinal Labs
Databricks
Elise is a staff product manager at Databricks leading the data ingestion product team holding previous roles at LinkedIn and Microsoft
Databricks
Elise is a staff product manager at Databricks leading the data ingestion product team holding previous roles at LinkedIn and Microsoft
Databricks
Elise is a staff product manager at Databricks leading the data ingestion product team holding previous roles at LinkedIn and Microsoft
Databricks
Fabian is a staff software engineer at Databricks holding previous roles at Google, CoreOS and Soundcloud
Databricks
Fabian is a staff software engineer at Databricks holding previous roles at Google, CoreOS and Soundcloud
Databricks
Fabian is a staff software engineer at Databricks holding previous roles at Google, CoreOS and Soundcloud
Databricks
Kasey is a director of product building the Mosaic AI platform holding previous roles at Microsoft
Databricks
Kasey is a director of product building the Mosaic AI platform holding previous roles at Microsoft
Databricks
Kasey is a director of product building the Mosaic AI platform holding previous roles at Microsoft
Databricks
Tom is a software engineer at Databricks working on the Exploratory Data Analysis team and a previous founder of Tensil (YC19 batch)
Databricks
Tom is a software engineer at Databricks working on the Exploratory Data Analysis team and a previous founder of Tensil (YC19 batch)
Databricks
Tom is a software engineer at Databricks working on the Exploratory Data Analysis team and a previous founder of Tensil (YC19 batch)
Deel
Kobi is a head of product at Deel and a two-time founder of Zeitgold, which was acquired by Deel and nRoll
Deel
Kobi is a head of product at Deel and a two-time founder of Zeitgold, which was acquired by Deel and nRoll
Deel
Kobi is a head of product at Deel and a two-time founder of Zeitgold, which was acquired by Deel and nRoll
Deepmind
Kathleen is a staff research engineer at Google Deepmind holding previous roles at Google, AWS and X
Deepmind
Kathleen is a staff research engineer at Google Deepmind holding previous roles at Google, AWS and X
Deepmind
Kathleen is a staff research engineer at Google Deepmind holding previous roles at Google, AWS and X
Deepmind
Kun is a principal software engineer at Google Deepmind working on genAI for Google Search holding previous roles at Google and YouTube
Deepmind
Kun is a principal software engineer at Google Deepmind working on genAI for Google Search holding previous roles at Google and YouTube
Deepmind
Kun is a principal software engineer at Google Deepmind working on genAI for Google Search holding previous roles at Google and YouTube
Deepmind
Noah is a director of research and engineering at Google Deepmind and a previous founder of Color
Deepmind
Noah is a director of research and engineering at Google Deepmind and a previous founder of Color
Deepmind
Noah is a director of research and engineering at Google Deepmind and a previous founder of Color
Deepmind
Keerthana is a senior research scientist at Google Deepmind and a previous founder of Commbuyn
Deepmind
Keerthana is a senior research scientist at Google Deepmind and a previous founder of Commbuyn
Deepmind
Keerthana is a senior research scientist at Google Deepmind and a previous founder of Commbuyn
Deepmind
Salahuddin is a product lead at Google Deepmind and a previous founder of Etch
Deepmind
Salahuddin is a product lead at Google Deepmind and a previous founder of Etch
Deepmind
Salahuddin is a product lead at Google Deepmind and a previous founder of Etch
Deepmind
Tim is a software engineer at Google Deepmind and a previous founder of Pixelprisma
Deepmind
Tim is a software engineer at Google Deepmind and a previous founder of Pixelprisma
Deepmind
Tim is a software engineer at Google Deepmind and a previous founder of Pixelprisma
Discord
Janie is a group product manager at Discord working on Activities and holds previous roles at Lyft and Google
Discord
Janie is a group product manager at Discord working on Activities and holds previous roles at Lyft and Google
Discord
Janie is a group product manager at Discord working on Activities and holds previous roles at Lyft and Google
Faire
Daniel is a senior product manager at Faire and a previous founder of the Boston Apparel Company
Faire
Daniel is a senior product manager at Faire and a previous founder of the Boston Apparel Company
Faire
Daniel is a senior product manager at Faire and a previous founder of the Boston Apparel Company
Figma
Alicia is a group product manager at Figma and previously held roles at Zola, Forge and Morgan Stanley
Figma
Alicia is a group product manager at Figma and previously held roles at Zola, Forge and Morgan Stanley
Figma
Alicia is a group product manager at Figma and previously held roles at Zola, Forge and Morgan Stanley
Figma
Jasmine is a staff product manager at Figma and a previous founder of Free Ventures
Figma
Jasmine is a staff product manager at Figma and a previous founder of Free Ventures
Figma
Jasmine is a staff product manager at Figma and a previous founder of Free Ventures
Glean
Chaitanya is a founding engineer at Glean and leads the Glean Chat and webserver teams
Glean
Chaitanya is a founding engineer at Glean and leads the Glean Chat and webserver teams
Glean
Chaitanya is a founding engineer at Glean and leads the Glean Chat and webserver teams
Glean
Julie is a product marketer at Glean holding previous roles at Rockset, Google and AppOnboard
Glean
Julie is a product marketer at Glean holding previous roles at Rockset, Google and AppOnboard
Glean
Julie is a product marketer at Glean holding previous roles at Rockset, Google and AppOnboard
Glean
Thai is a product manager at Glean and a two-time founder of Lightbox, acquired by Meta, and VivaSmart, acquired by Yahoo!
Glean
Thai is a product manager at Glean and a two-time founder of Lightbox, acquired by Meta, and VivaSmart, acquired by Yahoo!
Glean
Thai is a product manager at Glean and a two-time founder of Lightbox, acquired by Meta, and VivaSmart, acquired by Yahoo!
Yinka is a product manager at Google holding previous roles at Shopify, TikTok and Google
Yinka is a product manager at Google holding previous roles at Shopify, TikTok and Google
Yinka is a product manager at Google holding previous roles at Shopify, TikTok and Google
Jules is a product lead at Google and founded two advancement programs, CodePath.org and Black Product Managers Network
Jules is a product lead at Google and founded two advancement programs, CodePath.org and Black Product Managers Network
Jules is a product lead at Google and founded two advancement programs, CodePath.org and Black Product Managers Network
Keelin is a product manager at Google working on Vertex Model Garden and a previous founder of Idiom
Keelin is a product manager at Google working on Vertex Model Garden and a previous founder of Idiom
Keelin is a product manager at Google working on Vertex Model Garden and a previous founder of Idiom
Harvey
Aatish is a head of product at Harvey and holds previous roles at Scale AI, Shield AI and Uber
Harvey
Aatish is a head of product at Harvey and holds previous roles at Scale AI, Shield AI and Uber
Harvey
Aatish is a head of product at Harvey and holds previous roles at Scale AI, Shield AI and Uber
Harvey
Aman is a research engineer at Harvey and a previous founder of Mirage, acquired by Harvey
Harvey
Aman is a research engineer at Harvey and a previous founder of Mirage, acquired by Harvey
Harvey
Aman is a research engineer at Harvey and a previous founder of Mirage, acquired by Harvey
Harvey
Calvin is a member of the technical staff at Harvey and holds previous roles at Glean, SambaNova Systems, Meta and Square
Harvey
Calvin is a member of the technical staff at Harvey and holds previous roles at Glean, SambaNova Systems, Meta and Square
Harvey
Calvin is a member of the technical staff at Harvey and holds previous roles at Glean, SambaNova Systems, Meta and Square
Harvey
Nami is a member of the technical staff at Harvey and a previous founder of Algen Biotechnologies
Harvey
Nami is a member of the technical staff at Harvey and a previous founder of Algen Biotechnologies
Harvey
Nami is a member of the technical staff at Harvey and a previous founder of Algen Biotechnologies
Ganesh is a VP of engineering at Hex and a previous founder of Color, acquired by Apple
Ganesh is a VP of engineering at Hex and a previous founder of Color, acquired by Apple
Ganesh is a VP of engineering at Hex and a previous founder of Color, acquired by Apple
Hex
Jo is a head of product growth at Hex and holds previous roles at Adobe, Frame.io and Valera Health
Hex
Jo is a head of product growth at Hex and holds previous roles at Adobe, Frame.io and Valera Health
Hex
Jo is a head of product growth at Hex and holds previous roles at Adobe, Frame.io and Valera Health
Hex
Kevin is a product management lead at Hex and holds previous roles at Sigma Computing, Firebolt and Google
Hex
Kevin is a product management lead at Hex and holds previous roles at Sigma Computing, Firebolt and Google
Hex
Kevin is a product management lead at Hex and holds previous roles at Sigma Computing, Firebolt and Google
Kumo.ai
Viman is a head of product engineering at Kumo.ai and holds previous roles at Uber, LinkedIn, Google and Amazon
Kumo.ai
Viman is a head of product engineering at Kumo.ai and holds previous roles at Uber, LinkedIn, Google and Amazon
Kumo.ai
Viman is a head of product engineering at Kumo.ai and holds previous roles at Uber, LinkedIn, Google and Amazon
Labelbox
Karen is a senior product lead at Labelbox and holds previous roles at Covariant and Meta
Labelbox
Karen is a senior product lead at Labelbox and holds previous roles at Covariant and Meta
Labelbox
Karen is a senior product lead at Labelbox and holds previous roles at Covariant and Meta
Langchain
Bagatur is a founding engineer at Langchain and previously served as a ML engineer at Robust Technologies
Langchain
Bagatur is a founding engineer at Langchain and previously served as a ML engineer at Robust Technologies
Langchain
Bagatur is a founding engineer at Langchain and previously served as a ML engineer at Robust Technologies
Langchain
Erick is a founding engineer at LangChain where he built LangSmith, a unified platform for debugging, testing and evaluating LLM applications
Langchain
Erick is a founding engineer at LangChain where he built LangSmith, a unified platform for debugging, testing and evaluating LLM applications
Langchain
Erick is a founding engineer at LangChain where he built LangSmith, a unified platform for debugging, testing and evaluating LLM applications
Langchain
Jake is a software engineer at LangChain, holding previous roles at Databricks, Dropbox and Uber
Langchain
Jake is a software engineer at LangChain, holding previous roles at Databricks, Dropbox and Uber
Langchain
Jake is a software engineer at LangChain, holding previous roles at Databricks, Dropbox and Uber
Langchain
William is founding engineer at LangChain focused on machine learning and natural language processing
Langchain
William is founding engineer at LangChain focused on machine learning and natural language processing
Langchain
William is founding engineer at LangChain focused on machine learning and natural language processing
Linear
Guillaume is a software engineer at Linear holding previous roles at Uber and Dailymotion
Linear
Guillaume is a software engineer at Linear holding previous roles at Uber and Dailymotion
Linear
Guillaume is a software engineer at Linear holding previous roles at Uber and Dailymotion
Linear
Nan is the head of product at Linear, previously a VP of product at Mode
Linear
Nan is the head of product at Linear, previously a VP of product at Mode
Linear
Nan is the head of product at Linear, previously a VP of product at Mode
Daniel is a product manager at LinkedIn, and was previously a co-founder of Fractal
Daniel is a product manager at LinkedIn, and was previously a co-founder of Fractal
Daniel is a product manager at LinkedIn, and was previously a co-founder of Fractal
Yitong focuses on AI infrastructure at LinkedIn as a senior engineering manager
Yitong focuses on AI infrastructure at LinkedIn as a senior engineering manager
Yitong focuses on AI infrastructure at LinkedIn as a senior engineering manager
Miro
Haymo is the director of product, AI & Structured Content, at Miro
Miro
Haymo is the director of product, AI & Structured Content, at Miro
Miro
Haymo is the director of product, AI & Structured Content, at Miro
Mistral AI
Margaret is the head of product at Mistral AI and is the AI program chair at the Linux Foundation
Mistral AI
Margaret is the head of product at Mistral AI and is the AI program chair at the Linux Foundation
Mistral AI
Margaret is the head of product at Mistral AI and is the AI program chair at the Linux Foundation
Mistral AI
Wendy is an AI scientist at Mistral focusing on reasoning
Mistral AI
Wendy is an AI scientist at Mistral focusing on reasoning
Mistral AI
Wendy is an AI scientist at Mistral focusing on reasoning
Modal Labs
Eric is a founding engineer at Modal Labs and has designed and co-implemented Modal's container runtime
Modal Labs
Eric is a founding engineer at Modal Labs and has designed and co-implemented Modal's container runtime
Modal Labs
Eric is a founding engineer at Modal Labs and has designed and co-implemented Modal's container runtime
Modal Labs
Luis is an AI engineer at Modal Labs, after previously being the CTO of Lightning AI
Modal Labs
Luis is an AI engineer at Modal Labs, after previously being the CTO of Lightning AI
Modal Labs
Luis is an AI engineer at Modal Labs, after previously being the CTO of Lightning AI
Notion
Jonas is the head of product at Notion focusing on AI and platform
Notion
Jonas is the head of product at Notion focusing on AI and platform
Notion
Jonas is the head of product at Notion focusing on AI and platform
OpenAI
Anushree is a member of the technical staff at OpenAI, and was previously a founding engineer at Orb
OpenAI
Anushree is a member of the technical staff at OpenAI, and was previously a founding engineer at Orb
OpenAI
Anushree is a member of the technical staff at OpenAI, and was previously a founding engineer at Orb
OpenAI
Julie is a member of the technical staff at OpenAI focused on building custom models
OpenAI
Julie is a member of the technical staff at OpenAI focused on building custom models
OpenAI
Julie is a member of the technical staff at OpenAI focused on building custom models
OpenAI
Lauren is a research product manager at OpenAI, she previously focused on AI product at Perplexity
OpenAI
Lauren is a research product manager at OpenAI, she previously focused on AI product at Perplexity
OpenAI
Lauren is a research product manager at OpenAI, she previously focused on AI product at Perplexity
OpenAI
Lilian is the VP of research, safety at OpenAI, she previously focused on machine learning at Affirm
OpenAI
Lilian is the VP of research, safety at OpenAI, she previously focused on machine learning at Affirm
OpenAI
Lilian is the VP of research, safety at OpenAI, she previously focused on machine learning at Affirm
OpenAI
Manas is a research engineer at OpenAI, and was previously a founding engineer at Snorkel AI
OpenAI
Manas is a research engineer at OpenAI, and was previously a founding engineer at Snorkel AI
OpenAI
Manas is a research engineer at OpenAI, and was previously a founding engineer at Snorkel AI
OpenAI
Owen is focused on product safety at OpenAI as a product manager, he previously was the founder of Sutro Software
OpenAI
Owen is focused on product safety at OpenAI as a product manager, he previously was the founder of Sutro Software
OpenAI
Owen is focused on product safety at OpenAI as a product manager, he previously was the founder of Sutro Software
OpenAI
Rex is a member of the technical staff at OpenAI, he was previously a founding engineer at Sizzle AI
OpenAI
Rex is a member of the technical staff at OpenAI, he was previously a founding engineer at Sizzle AI
OpenAI
Rex is a member of the technical staff at OpenAI, he was previously a founding engineer at Sizzle AI
OpenAI
Wesam is a member of the technical staff at OpenAI building Sora and Dall-E
OpenAI
Wesam is a member of the technical staff at OpenAI building Sora and Dall-E
OpenAI
Wesam is a member of the technical staff at OpenAI building Sora and Dall-E
Perplexity
Thomas is a member of the technical staff at Perplexity after serving as an engineer at Airtable
Perplexity
Thomas is a member of the technical staff at Perplexity after serving as an engineer at Airtable
Perplexity
Thomas is a member of the technical staff at Perplexity after serving as an engineer at Airtable
Pinecone
Gareth is an AI/ML product leader at Pinecone, having previously served as a product manager at Labelbox and co-founding ThirtyNine AI (acquired by Labelbox)
Pinecone
Gareth is an AI/ML product leader at Pinecone, having previously served as a product manager at Labelbox and co-founding ThirtyNine AI (acquired by Labelbox)
Pinecone
Gareth is an AI/ML product leader at Pinecone, having previously served as a product manager at Labelbox and co-founding ThirtyNine AI (acquired by Labelbox)
Pinecone
Jack is staff engineer at Pinecone, having previously co-founded SideKickQA
Pinecone
Jack is staff engineer at Pinecone, having previously co-founded SideKickQA
Pinecone
Jack is staff engineer at Pinecone, having previously co-founded SideKickQA
Divye is a senior staff engineer at Pinterest having worked on applied LLMs
Divye is a senior staff engineer at Pinterest having worked on applied LLMs
Divye is a senior staff engineer at Pinterest having worked on applied LLMs
Joyce is a senior engineering manager at Pinterest having previously worked at Airtable focused on building the the Airtable mobile app for iOS and Android
Joyce is a senior engineering manager at Pinterest having previously worked at Airtable focused on building the the Airtable mobile app for iOS and Android
Joyce is a senior engineering manager at Pinterest having previously worked at Airtable focused on building the the Airtable mobile app for iOS and Android
Plaid
Clay is a senior staff software engineer at Plaid building Plaid Link and Plaid Wallet Onboard
Plaid
Clay is a senior staff software engineer at Plaid building Plaid Link and Plaid Wallet Onboard
Plaid
Clay is a senior staff software engineer at Plaid building Plaid Link and Plaid Wallet Onboard
Pulley
Grant is the COO of Pulley, having previously served as director of engineering at Airbnb and Lucid Performance
Pulley
Grant is the COO of Pulley, having previously served as director of engineering at Airbnb and Lucid Performance
Pulley
Grant is the COO of Pulley, having previously served as director of engineering at Airbnb and Lucid Performance
Ramp
Jason is a software engineer at Ramp after previously founding Motion
Ramp
Jason is a software engineer at Ramp after previously founding Motion
Ramp
Jason is a software engineer at Ramp after previously founding Motion
Ramp
Veeral is a software engineer at Ramp and previously worked at Workflow HQ as an engineer which was acquired by Apple
Ramp
Veeral is a software engineer at Ramp and previously worked at Workflow HQ as an engineer which was acquired by Apple
Ramp
Veeral is a software engineer at Ramp and previously worked at Workflow HQ as an engineer which was acquired by Apple
Ramp
Extensive experience at Ramp, having held roles as Head of Applied AI, Advisor, and Engineer, and has interned at Nuro and Meta
Ramp
Extensive experience at Ramp, having held roles as Head of Applied AI, Advisor, and Engineer, and has interned at Nuro and Meta
Ramp
Extensive experience at Ramp, having held roles as Head of Applied AI, Advisor, and Engineer, and has interned at Nuro and Meta
Replicate
Gandalf is a senior engineering manager at Replicate and previously serving previous roles at Discord and Spotify
Replicate
Gandalf is a senior engineering manager at Replicate and previously serving previous roles at Discord and Spotify
Replicate
Gandalf is a senior engineering manager at Replicate and previously serving previous roles at Discord and Spotify
Rippling
Rami is a product lead at Rippling focused on building spend and finance products
Rippling
Rami is a product lead at Rippling focused on building spend and finance products
Rippling
Rami is a product lead at Rippling focused on building spend and finance products
Roblox
Michelle is a director of engineering at Roblox and previously served as an engineering manager at Meta launching ML models across Facebook news and Instagram Reels
Roblox
Michelle is a director of engineering at Roblox and previously served as an engineering manager at Meta launching ML models across Facebook news and Instagram Reels
Roblox
Michelle is a director of engineering at Roblox and previously served as an engineering manager at Meta launching ML models across Facebook news and Instagram Reels
Scale AI
Annie is the VP of strategic product management at Scale AI and previously co-founded Omnee
Scale AI
Annie is the VP of strategic product management at Scale AI and previously co-founded Omnee
Scale AI
Annie is the VP of strategic product management at Scale AI and previously co-founded Omnee
Scale AI
Bihan is a senior product manager at Scale AI helping to build their AI data engine that enables creation of tailored datasets to train models
Scale AI
Bihan is a senior product manager at Scale AI helping to build their AI data engine that enables creation of tailored datasets to train models
Scale AI
Bihan is a senior product manager at Scale AI helping to build their AI data engine that enables creation of tailored datasets to train models
Scale AI
Daniel is the head of product, model evaluation at Scale AI, he previously co-founded Helia which was acquired by Scale AI
Scale AI
Daniel is the head of product, model evaluation at Scale AI, he previously co-founded Helia which was acquired by Scale AI
Scale AI
Daniel is the head of product, model evaluation at Scale AI, he previously co-founded Helia which was acquired by Scale AI
Snowflake
Di is a principal software engineer at snowflake focused on building and maintaining the data marketplace and data sharing capabilities
Snowflake
Di is a principal software engineer at snowflake focused on building and maintaining the data marketplace and data sharing capabilities
Snowflake
Di is a principal software engineer at snowflake focused on building and maintaining the data marketplace and data sharing capabilities
SpaceX
Damien is software engineering manager at SpaceX leading the infra team for the Starlink consumer platform
SpaceX
Damien is software engineering manager at SpaceX leading the infra team for the Starlink consumer platform
SpaceX
Damien is software engineering manager at SpaceX leading the infra team for the Starlink consumer platform
SpaceX
Wendy is a senior engineering manager at SpaceX after serving as engineering manager at Lyft where she led development of pickup and dropoff experience
SpaceX
Wendy is a senior engineering manager at SpaceX after serving as engineering manager at Lyft where she led development of pickup and dropoff experience
SpaceX
Wendy is a senior engineering manager at SpaceX after serving as engineering manager at Lyft where she led development of pickup and dropoff experience
Stripe
Allison is a senior product manager at Stripe after previously serving as a senior product manager at Meta
Stripe
Allison is a senior product manager at Stripe after previously serving as a senior product manager at Meta
Stripe
Allison is a senior product manager at Stripe after previously serving as a senior product manager at Meta
Stripe
Eeke is the head of global product at Stripe and previously co-founded Constellate
Stripe
Eeke is the head of global product at Stripe and previously co-founded Constellate
Stripe
Eeke is the head of global product at Stripe and previously co-founded Constellate
Stripe
Felix is a staff engineer at Stripe focused on capital engineering which includes core products related to payment systems and underwriting
Stripe
Felix is a staff engineer at Stripe focused on capital engineering which includes core products related to payment systems and underwriting
Stripe
Felix is a staff engineer at Stripe focused on capital engineering which includes core products related to payment systems and underwriting
Stripe
Harp is a product lead at Stripe focusing on infrastructure, he was previously a scout at Sequoia and product manager at Google focused on observability
Stripe
Harp is a product lead at Stripe focusing on infrastructure, he was previously a scout at Sequoia and product manager at Google focused on observability
Stripe
Harp is a product lead at Stripe focusing on infrastructure, he was previously a scout at Sequoia and product manager at Google focused on observability
Stripe
Ryan is a staff software engineer at Stripe focused the technical vision, strategy, and execution of Stripe’s anchor payments products
Stripe
Ryan is a staff software engineer at Stripe focused the technical vision, strategy, and execution of Stripe’s anchor payments products
Stripe
Ryan is a staff software engineer at Stripe focused the technical vision, strategy, and execution of Stripe’s anchor payments products
Temporal
Nikitha is staff product manager at Temporal and previously served as a product manager at Google where she worked on Borg
Temporal
Nikitha is staff product manager at Temporal and previously served as a product manager at Google where she worked on Borg
Temporal
Nikitha is staff product manager at Temporal and previously served as a product manager at Google where she worked on Borg
TikTok
Kathy is a head of product at TikTok building creator tools and ecosystems for AR and AI, and previously worked on product at Magic Leap and Amazon
TikTok
Kathy is a head of product at TikTok building creator tools and ecosystems for AR and AI, and previously worked on product at Magic Leap and Amazon
TikTok
Kathy is a head of product at TikTok building creator tools and ecosystems for AR and AI, and previously worked on product at Magic Leap and Amazon
Together AI
Niki is a senior AI product manager at Together AI, leading inference API and monetization
Together AI
Niki is a senior AI product manager at Together AI, leading inference API and monetization
Together AI
Niki is a senior AI product manager at Together AI, leading inference API and monetization
Tome
Yuchen is a founding engineer at Tome having initiated and built first AI features on Tome as ML architect
Tome
Yuchen is a founding engineer at Tome having initiated and built first AI features on Tome as ML architect
Tome
Yuchen is a founding engineer at Tome having initiated and built first AI features on Tome as ML architect
Vercel
Greg was a software engineer at Vercel, x2 founder, as of Q3 2024 is an engineer at Sequoia
Vercel
Greg was a software engineer at Vercel, x2 founder, as of Q3 2024 is an engineer at Sequoia
Vercel
Greg was a software engineer at Vercel, x2 founder, as of Q3 2024 is an engineer at Sequoia
Rachel is a software engineer at Wiz, previously at AWS and a former member of Unit 8200 of the Israeli Intelligence Corps
Rachel is a software engineer at Wiz, previously at AWS and a former member of Unit 8200 of the Israeli Intelligence Corps
Rachel is a software engineer at Wiz, previously at AWS and a former member of Unit 8200 of the Israeli Intelligence Corps
Wiz
Raz is the head of devops at Wiz, having previously served as a DevOps engineer at Microsoft
Wiz
Raz is the head of devops at Wiz, having previously served as a DevOps engineer at Microsoft
Wiz
Raz is the head of devops at Wiz, having previously served as a DevOps engineer at Microsoft
We evaluated technical talent from some of the top companies building for enterprise, including…
We evaluated technical talent from some of the top companies building for enterprise, including…
We evaluated technical talent from some of the top companies building for enterprise, including…
In future years, as we increase our knowledge of global talent pools and markets, we hope that we can lend greater geographic scope to future reports. We did not name any individuals at our existing portfolio companies as this exercise was intended to be an external study and observation. In leveraging public datasets we acknowledge the risk that certain data which we evaluated and published may be outdated.
We would love to know your thoughts and what you've found most insightful. Special thanks to Jack Griffin, our 2024 Summer Intern, without whom this would not have been possible!
Want to nominate someone for consideration for next year? We’ve got a form for that!
Sp
In future years, as we increase our knowledge of global talent pools and markets, we hope that we can lend greater geographic scope to future reports. We did not name any individuals at our existing portfolio companies as this exercise was intended to be an external study and observation. In leveraging public datasets we acknowledge the risk that certain data which we evaluated and published may be outdated.
We would love to know your thoughts and what you've found most insightful. Special thanks to Jack Griffin, our 2024 Summer Intern, without whom this would not have been possible!
Want to nominate someone for consideration for next year? We’ve got a form for that!
Sp
In future years, as we increase our knowledge of global talent pools and markets, we hope that we can lend greater geographic scope to future reports. We did not name any individuals at our existing portfolio companies as this exercise was intended to be an external study and observation. In leveraging public datasets we acknowledge the risk that certain data which we evaluated and published may be outdated.
We would love to know your thoughts and what you've found most insightful. Special thanks to Jack Griffin, our 2024 Summer Intern, without whom this would not have been possible!
Want to nominate someone for consideration for next year? We’ve got a form for that!
Sp
where we are
San Francisco
Miami
Exceptional Capital © 2025 - Exceptional Capital and the Exceptional Capital logo are trademarks of Exceptional Capital. All Rights Reserved.
The information on this website is not a solicitation of an offer to sell or purchase an interest in any investment fund or vehicle, nor of any provision of investment management or advisory services.
where we are
San Francisco
Miami
Exceptional Capital © 2025 - Exceptional Capital and the Exceptional Capital logo are trademarks of Exceptional Capital. All Rights Reserved.
The information on this website is not a solicitation of an offer to sell or purchase an interest in any investment fund or vehicle, nor of any provision of investment management or advisory services.
where we are
San Francisco
Miami
Exceptional Capital © 2025 - Exceptional Capital and the Exceptional Capital logo are trademarks of Exceptional Capital. All Rights Reserved.
The information on this website is not a solicitation of an offer to sell or purchase an interest in any investment fund or vehicle, nor of any provision of investment management or advisory services.