EXCEPTIONAL 100
(Mission)
FOUNDER DNA
(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.
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
(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.
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.
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.
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.
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.
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.
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
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.
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.
(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 100)
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!
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