#25. Je Suis un Capitaliste

April 24, 2023

The use of data in venture capital has become a heated topic in the past few years. And, like everything, it’s become completely polarized. On one end of the spectrum, VCs who proclaim to invest in innovation are, in fact, terrible at innovating themselves and, really, a 16-year-old can automate the entire industry into oblivion. On the other end, VCs are modern-day artistes 👨‍🎨. a16z is Van Gogh, Sequoia is Matisse, and Silicon Valley in the 2020s is Paris in the 1920s.

Non, monsieur. Ce n'est ni.

The crux of the argument against modern day VC as an artisan endeavor is the fact that, as the (largely VC-funded) technology landscape evolves, the lengthy process of getting to know founders over the course of months or years and “backing” start-ups is an outdated notion in an age of hedge fund quants running algorithms to pick companies. That, and the cognitive dissonance that refuses to combine “artist” in the same sentence as “Facebook,” “richest man in the world,” “$445B in VC activity in 2021,” or “supporting 21% of US GDP.” Ok.

On the other end, many VCs point to the fact that, traditionally, there has fundamentally been no reliable data on start-ups. Start-ups are too small and reporting is minimal. It’s not like Goldman’s analysts are covering your Series A start-up. And even in a market where more data sources are available today, there is no common predictor of what will make a start-up successful. In any portfolio, most start-ups will fail. So VCs rely on the power law of start-ups, in which a small number of investments drive the vast majority of returns. So the few that don’t die need to be successful. Really successful.

The truth is that many VCs are hanging onto notions of a cottage industry to allow them to continue presenting as king-makers anointing the future titans of industry. Taking comfort in the fact that it will take a long time to be proven wrong or right. The truth is also that VCs who enjoy testing the bounds of what’s possible are going to usher in the next wave of venture capital.

Before the peanut gallery gets jazzed up and yells that VC can’t be automated: I know. This is not a post about how Sydney is going to replace Sequoia. But to act as if data doesn’t exist is jurassic. The effective use of data will separate funds capable of competing in the future from the artisan followers (👨🏻‍🍳). VC is not immune to the question that has lorded over every industry for the last 20 years: who is going to make the best use of modern tools and data?

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🥖 France v. New York

While our artiste is over in Burgundy baking bread one loaf at a time, hedge funds in New York have been at the forefront of using advanced data techniques to gain insights and generate returns in financial markets. Renaissance Technologies, a hedge fund founded by mathematician and former codebreaker James Simons, uses complex mathematical models and algorithms to identify trading opportunities in financial markets. The firm's flagship Medallion Fund has delivered an average annual return of over 60% since 1988 (read that number again, this time with more awe). Citadel, who actually received investment from Sequoia in 2017 due to it’s advanced use of data, has built a sophisticated platform using machine learning, natural language processing, and a team of data scientists to analyze market data in real time and make trading decisions faster and more accurately than traditional market makers.

The level of depth data that allows public market funds like Renaissance and Citadel to separate themselves does not exist in the private market, and it never will. Access to the best founders continues to be what separates top decile funds from “I’d rather have kept this money in my checking account” funds. However, companies like CB Insights, Pitchbook, and Zanbato are bringing more rigor to the murky industry - from analyses of team sizes and new hires to issued patents and regulatory clearances to new customer contracts. And the storm of tastemakers publicly pontificating on social media and the digitization of every public record provide more opportunities to find signal. Some firms have begun to take advantage. GGV uses natural language processing to parse narratives on social media, news, and company filings to identify emerging trends. EQT created Motherbrain in 2016 (a time when every VC was a happy bread baker) to siphon public and proprietary data and act as the collective brain of the firm - motherbrain indeed. Many funds use a CRM called Affinity which aims to systematically unearth opportunities from the team’s collective network.

Although firms are still experimenting to find the best way to systematize what they do, we are (as ever) at an inflection point. I really don’t want to write more about ChatGPT, but OpenAI has made it so easy to access AI algorithms that any fund, company, or 12-year-old “using AI” is no longer a differentiator. It has opened up possibilities to not just find an edge in investing, but also in operating. Writing copy, internal memos, financial models all applies to funds themselves just as with any other organization. VCs have an opportunity to grow leaner and leaner.

While companies like CB Insights will continue to bring necessary data to an opaque industry, the mavericks in VC cannot rely on that kind of data to find edge. They will have to create their own data. Real edge will be found from proprietary data gathered from interactions, primary research, and tracking outcomes of the portfolio and anti-portfolio to inform the processes that funds use to identify high-potential companies.

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📈 What next?

The coming bifurcation of next-level funds from artisans will be predicated on the same question that predicts performance of individual people in an organization: who loves the craft more. The question LPs will seek to answer is who is playing the infinite game, forever reveling in the fluidity of rules to the game, and who is playing the finite game, unimaginatively playing for the outcome.

The “je suis un artiste” investors who resist change (🦖) will shiver wondering what everybody else is doing. The investors who truly enjoy the craft will experiment with new tools to play the game better: seek better returns, seek them more frequently, and with a smaller team. They will ruthlessly grind out alpha by using data and emerging technology to continuously improve their craft. In that sense, they are the true artisans.