We Need To Save Venture Capital From Bad Data
Venture capital firms are using AI the wrong way, argues Gilion's Henrik Landgren, who says that building better data infrastructure and connecting directly to sources like financial, payment and accounting systems would improve due diligence, help investors identify overlooked startups, and make investment decisions both faster and more accurate. Guest Author By Henrik Landgren Investing, particularly venture capital, is 50% science and 50% art. The industry relies heavily on charisma and the founders' "it" factor.
Key Takeaways
- That criteria warrants plenty of merit; one shared truth among all my investor colleagues is that the greatest founders of our generation have an unmistakable drive and dedication to their craft that is near impossible to put a finger on.
But here is how the process actually works once the charming visionaries have been identified.
- Today, when pitch decks and company websites can be vibe-coded in a single afternoon, and data slicing is aided by the world's most powerful AI models, it becomes progressively more difficult for investors to cut through this noise, question what they are seeing, know how to make sense of the data and, more importantly, where to get it.
During my time as VP of analytics at Spotify , I became something of a data obsessive.
- Today, as every financial institution scrambles to prove it has an AI strategy, the pressure to do something visible with the technology overrides the desire to do something useful with it.
The way most investment teams adopt AI today is, to put it charitably, misguided.
- The right approach starts before the AI.
It starts with the data: payment records, marketing performance, accounting systems and board reports; each adds a layer to your diligence that reflects how a company actually operates, not how a founder or their analysis team describes it.
- This fundamentally shifts how investors understand risk.
Stats & Key Facts
- #Guest Author By Henrik Landgren Investing, particularly venture capital, is 50% science and 50% art.
That criteria warrants plenty of merit; one shared truth among all my investor colleagues is that the greatest founders of our generation have an unmistakable drive and dedication to their craft that is near impossible to put a finger on. But here is how the process actually works once the charming visionaries have been identified. When an investor meets a promising founder and decides to take a closer look, they are handed a vast collection of data.
For many now, harnessing AI to sift through what is relevant feels like the only sensible response. The data is massive, but cherry-picked and packaged by the founder - the crux of the information asymmetry problem that underlies the entirety of the VC model. Today, when pitch decks and company websites can be vibe-coded in a single afternoon, and data slicing is aided by the world's most powerful AI models, it becomes progressively more difficult for investors to cut through this noise, question what they are seeing, know how to make sense of the data and, more importantly, where to get it.
During my time as VP of analytics at Spotify , I became something of a data obsessive. The hottest new thing at the time was technology that allowed us to move away from Excel spreadsheets and actually make decisions based on more granular data. We used software that helped us store every click a user made.
For more details please read the original article at Crunchbase News.
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