The Rise of Investment Tech: Startups Revolutionizing Venture
How AI Copilots Shape The Future of VC
👋 Hi, I’m Andre and welcome to my weekly newsletter, Data-driven VC. Every Tuesday, I publish “Insights” to digest the most relevant startup research & reports, and every Thursday, I publish “Essays” that cover hands-on insights about data-driven innovation & AI in VC. Follow along to understand how startup investing becomes more data-driven, why it matters, and what it means for you.
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Private Markets Follow Public Markets
One of my recent predictions about The Future of VC centered around the fact that private market cycles follow public markets with some delay, thus expecting we have hit bottom and will see a re-acceleration in startup funding throughout 2024. Public markets serve as a reliable forward-looking indicator for private markets.
Besides the cyclicity, there’s another dimension where public markets serve as a forward-looking indicator for what is likely to happen in private markets: innovation. What can private markets learn from public markets?
The Hedge Fund Industry’s Inflection Point
Up until the 1980s, hedge funds were largely run by Wall Street veterans who relied on their instincts and connections to make investment decisions. However, as more data on public companies became available, a new breed of hedge funds emerged that used quantitative models to make data-driven investment decisions and eliminate human cognitive bias in the chase for returns.
When Stephen Cohen, founder of S.A.C. Capital, one of the top hedge funds of the last century, started aggressively hiring quantitative specialists while at the same time cutting his stock-picking team of fund managers by nearly two-thirds, it became clear that “quant” was here to stay.
With pioneers like Cohen leading the algorithmic trading revolution of the hedge fund industry, we saw a range of manifestations on the spectrum of pure quantitative strategies, where mathematical algorithms make all of the asset allocation or stock-picking decisions, to “quantamental” strategies, which combine the traditional stock-picking skills of fund managers with data and computing power.
Today, quant hedge funds (in whatever manifestation) are the most successful and profitable firms in the industry and manual trading has gone extinct. According to recent figures, quant strategies account for 75%+ of total trading and more than $ 1 trillion in assets under management in the public markets.
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What Does That Mean For Private Markets?
Well, to start, it’s quite obvious that private market investing today is still largely stuck where public market investing was in the 1980s. Dudes in Patagonia vests investing into their peers. Manual, inefficient, and oftentimes ineffective human decision-making that is - at its best - informed by data.
On a more positive note, however, I’m convinced that similar to the hedge fund industry in the 1980s, we finally hit the inflection point for VC.
Why? Because before the rise of big data and large-scale web scraping in the early 2010s, private company data was difficult to collect at scale. Just ten years ago, public registers were not digitized, startup databases such as Crunchbase, Pitchbook, and others in their early days, and professional networks like LinkedIn had less than a 10th of today’s active users.
As a result, comprehensive sourcing coverage was impossible to achieve and even the most data-driven investors needed to rely on their manual networks. Given the lack of proper tools and sufficient compute, it was also difficult to process these vast amounts of unstructured data and make sense of non-quantitative information.
Venture’s Inflection Point
Thankfully, a lot has changed within the past years. By now, the footprints of a new company are way easier to identify online than offline. Be it via “Stealth” searches on LinkedIn, an entry in a public register, a project launch on ProductHunt or Github, or just an ambitious developer launching an app in the app store.
Equally important, we now have tools like LLMs, NLP, and tons of compute to process and make sense of unstructured data. This “outside-in” view allows investors to identify a broad range of promising opportunities, track them over time, and recognize inflection points from the outside.
Following the launch of ChatGPT and the rise of GenAI, the VC industry will finally become more efficient, effective, and hopefully more inclusive through technology.
How To Not Miss The Train
While most VCs recognize that we’re in the midst of a technology driven industry revolution, few know where to start. This becomes increasingly more obvious. Some examples from the audience Q&A of our recent “Tech in VC Panel” (if you’d like to join future events, sign up for free here)
Most questions I get center around make vs buy and which tools to use. Though private company databases have been around for a while and are likely to become a commodity soon, the rise of LLMs and capabilities to process unstructured data opened the gates for various new solutions.
With the “Rise of Investment Tech”, I refer to a growing startup cohort of oftentimes veteran investors who have spotted the opportunity to build for the masses what most investment firms cannot build themselves. “Cannot” either due to lack of resources, technical talent, or strategic foresight.
Different Waves Of Investment Tech
Investment Tech describes software solutions that facilitate public and private market investing. For public markets, it’s well established. For private markets, however, it’s just evolving. Looking back, I see three major waves of innovation:
1st wave: Private company and fund data providers like Crunchbase, Dealroom, Pitchbook, Preqin, and others
2nd wave: Systems of record (CRM, portfolio, cap table management) like Affinity, Vestberry, Carta, and others
3rd wave: Signal providers like Harmonic, Synaptic, and others
4th wave: AI Copilots and unified systems like … ?
Through you, the Data-driven VC community, I’m thankful to know many of the 4th wave innovators myself. Standing on the shoulders of giants across data, signal, and system of record providers, AI copilots will enable new ways of interacting with information to eventually make more efficient, effective, and inclusive decisions.
With every wave, however, the market becomes more noisy and investors increasingly struggle to find the right solutions. As part of the “Data-driven VC Landscape 2023”, I published a list of 400+ tools and put a spotlight on the most frequently used ones.
In the 2024 edition of the report, I will double down on this section and provide the most comprehensive overview of VC tech stacks, the most frequently used tools, and the innovators driving the 4th wave in private market investing. Some exciting companies on my radar:
… and many more in stealth that I’m probably not allowed to talk about🤐
Take 2 minutes to participate in this year’s edition and be the first to receive the results, including the updated tool stack overview and Investment Tech deep dive.
Closing the loop to where this episode started: Public markets and the hedge fund industry led the way in tech-driven innovation. Today, private markets and VC firms are catching up.
I expect that similar to hedge funds, those VCs who can afford will build crucial components in-house. For others, there will be a broad spectrum of Investment Tech providers available off-the-shelf, allowing them to go 80/20 without internal engineering teams.
Similar to public markets, I expect private markets to become more efficient, and it’ll be key for investors to reinvent themselves. Now. The Future of VC is bright and I’m glad to be part of a growing community that shapes the future of an entire industry.
Thank you for reading. If you liked it, share it with your friends, colleagues, and everyone interested in data-driven innovation. Subscribe below and follow me on LinkedIn or Twitter to never miss data-driven VC updates again.
If you have any suggestions, want me to feature an article, research, your tech stack or list a job, hit me up! I would love to include it in my next edition😎