Why machine learning falls short in early stage venture capital

There’s asymmetrical access to data in venture capital

Machine learning is past-driven, venture capital is future-driven

Lack of liquidity in the market doesn’t let you course correct

  • If you’re an early stage investor and your model doesn’t surface the seed/Series A round of Google/Facebook/Uber, you’ve missed the boat. That particular round isn’t going to happen again, and your portfolio construction strategy may not allow you to invest in subsequent rounds. With a stock, you can miss out on some of the appreciation, but you’d still be able to buy in the public market for many years
  • Similarly, if your model had surfaced companies that aren’t performing (bad business models that were predicted to be good, markets that took too long to develop, etc) you are not able to decide to get that money back and invest it in something else. Even if your model has now improved, you have less swings left at bat because a part of your fund has already been deployed.

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645 Ventures

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