🔥Why Now Is the Best Time to Start Using AI for Investing
How Tech Transforms VC: From Big Data to Copilots
👋 Hi, I’m Andre and welcome to my newsletter Data Driven VC which is all about becoming a better investor with Data & AI. Join 34,930 thought leaders from VCs like a16z, Accel, Index, Sequoia, and more to understand how startup investing becomes more data-driven, why it matters, and what it means for you.
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I’ve been working on the transformation of VC with AI & automation for more than 8 years. It started with my “ML for VC” research in Cambridge/UK and my subsequent PhD on the same topic at TU Munich, but only became reality when I joined Earlybird VC fulltime as an investor in early 2018 and got my hands dirty actually building stuff.
Throughout my journey, a lot has changed. And I’ve learned tons of lessons. Oftentimes the hard way.
In this post, I’d like to share my view on the evolution of “tech for VC” and why it’s the best time to start your own journey now.
Let’s jump in!
5 Phases of the “Tech for VC” Evolution
Zooming out and looking at evolution of tech for VC, I’d segment the last 80 years into 5 distinct phases:
1. The “Old World” (1950s - 2010ish)
In this period, it’s all manual, inefficient and exclusive. Your network was your net worth. Data was inaccessible. The only innovation was pen & paper to mouse & keyboard about 3 decades ago.
Sourcing? Via Network. No warm connection? Sorry for you.
Screening & Due Diligence? Via personal experience or experts in your close network. Objectivity? Dream on. Gut feeling, instincts, personal experiences. That’s the magic.
Portfolio value creation? Manual, 1 hour at a time. The true definition of a service business. Scaling advise was only possible with huge portfolio value creation teams and operating partners.
2. The “Big Data” Era (2010 - 2017ish)
At the beginning of the last decade, “big data” was THE thing.
What we meant by “Big Data”? Well, at least in my mind it was all about digitizing offline into online information at scale. Besides digitizing company registrations in public registers or publishing funding news online and not in newspapers anymore, one of the most important developments for investors was the digitization of personal networks via LinkedIn, Twitter, Facebook, Instagram, and more. Suddenly, you could identify and search people at scale while monitoring headcount or job postings of corporate accounts.
I started tracking relevant sources for investors in 2017 or so and stopped sometime in 2024 as we surpassed 500+ entries with an explosive growth. A manual list was just not the right approach to keep track anymore..
Accompanied by the digitization of offline information and the resulting rise of mass online data in the 2010s, new services evolved to enable targeted data collection and processing: the web scrapers. I’ve written before about how to scrape alternative data sources here.
In retrospect, very few investors adopted web scraping at scale, likely due to the relatively high technical entry barriers for non developers. To bridge this gap, we saw intermediaries evolve - the so-called commercial database providers. Companies that have the mission to collect first party data, match and verify it, and make it accessible to the relevant audiences - in this case investors. Crunchbase, Pitchbook, CB Insights, Dealroom - just to name a few that evolved during that period.
For the first time ever, investors could source startups and experts beyond their naturally limited human networks. The problem? Their human time was still limited and far from enough to sift through the masses of information. Not only did they face the unknown unknowns, the startups that exist but they never saw before, but they also got overwhelmed by data, losing direction and becoming inefficient.
3. The Rise of Data Driven VCs (2018 - 2023ish)
Having recognized the gap between manual workflows and overwhelmedness by data on the one side and the sheer potential of automation and AI on the other side, few nerds - including myself - have started to build internal systems for data collection, entity matching, scoring, analysis, and making all of these signals actionable in daily investment workflows: we called ourselves the Data Driven VCs.
Now you might questions: “But isn’t every VC a Data Driven VC? Doesn’t every investor consider data for their decisions?”
No, and I’ve explained the differences in detail here in this post. In short:
Most investors believe they see all relevant opportunities, collect all data available, and rely on their superior experience when making a decision. Yet, the sample of companies they look at, the information collected about them as much as the experience they rely on when making the decision are all limited. Very limited. Traditional investors act on a tiny section, never the full curve.
As a result, they miss relevant opportunities and make biased & subjective decisions based on incomplete information. They can only optimize for the local maximum.
A DDVC in contrast considers the full universe of opportunities and combines objective algorithmic assessments with selective human biases based on (close to) complete information. The DDVC acts on the full curve and is able to optimize for the global maximum.
To exemplify, I created this hyper-professional graphic ;)
All investors have limited deal flow sourcing and/or experience coverage, only allowing them to optimize for their local maximum. While for Investor F coincidently the local maximum equals the global maximum, all other investors confuse their local with the global maximum. Only the DDVC can consider the full sample and confidently optimize for the global maximum.
4. The Rise of Augmented VCs: Human + AI (2024 - now)
In November 2022, ChatGPT launched public, and after months of testing different use cases, investors have gradually started integrating it into their workflows. I wrote about “The Future of VC: Augmenting Humans with AI” about 5 years ago and, to my surprise, it only took another 2-3 years to become a reality.
As an investor adopting AI across the VC value chain myself, I’ve shared my experiments and various step-by-step guides with you:
Creating an automated competitor radar with Google Sheets & OpenAI
Which model should you use? ChatGPT, Claude, Gemini, Perplexity, etc.?
To make them more accessible, we put all resources together into one place at VCSTACK.COM.
With hundreds of use cases that can be tackled with tools like ChatGPT or Claude, these chatbots have quickly become regular copilots for investors.
The problem? These general purpose chatbots require very specific prompts and knowledge on how to get what you really want. Another gap that got identified by providers such as Kruncher who’ve developed dedicated agents for private market investors.
Zooming out, we’re now in a phase where investors are in charge but dedicated copilots do majority of the manual, inefficient ground work. From company identification, enrichment, data processing and interpretation to writing a memo and giving investment recommendations.
The first funds have reached the true state of Augmented VC, and for most, no matter if they’re there yet or not, this is the ultimate ambition.
5. The Endgame: Quant VCs
About 5% of VCs queried for the Data Driven VC Landscape 2025 believe in a Quant VC model, where the human gets completely removed from the equation and it’s all algorithmic investing. Surprisingly, that percentage has halved from 2024 to 2025, indicating that investors likely got more realistic about today’s limitations of AI and put more value on human skills again.
While I personally don’t see a Quant VC strategy ever working for lead investor strategies, I do see the tech to be ready for a scalable follower strategy - and the first great example writing checks to promising startups.
For example, Henry Shi (founder of $200m/year rev company Super.com) together with his girlfriend built an AI tool with Claude Code, Vercel, and Base44 that can analyze any startup and generate a term sheet within minutes, available via Lean AI Angel. He confirmed that he invests up to $1M ticket size as a follower and has already invested in various startups via the tool.
Zooming out: The Macro View
To map the respective evolution of VC firms, I’ve developed and first shared the “VC Digitization Journey” framework in early 2024. It nicely summarizes the evolution from old-school over productivity to Data Driven VCs, and the nuances with augmented and quant VCs.
In 2024, I wrote about the rise of investment tech and mapped the evolution of the vendors serving VCs into a similar framework. Today, 1.5 years later, we already have the first names aiming to establish themselves as the AI Copilots.
Conclusion
We've entered the Augmented VC era and investment tech has evolved from fragmented best-of-breed solutions into VC copilots and holistic platforms.
There’s tons of buzz. But don’t be afraid if you haven’t started your digitization journey yet.
In reality, majority of what you hear and read is just cheap talk. Knowing the behind-the-scenes of many peers and friends at other funds, 99% of investment firms are still at the beginning. And that’s positive because you don’t have a legacy tech stack that you need to maintain and continuously question if you should rip out this one part that’s now available by provider X. You have not burned through millions learning lessons the hard way but you can learn from others and their mistakes by joining the right community.
That being said, I hope this post provided a great overview of what has changed and where we are in the evolution today. Next week, I’ll share my personal learnings after 8 years of pushing data & AI into VC. The brutally honest truth and how I would start all over again today.
Stay driven,
Andre
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