resbear

I think the difference here is the assumption for failure rather than success for. The subprime bubble existed due to leverage - fancy models said that defaults were unlikely, so rather than extend loans from their own assets, banks decided to double (or triple, or quadruple...) down and lend out multiples of their own total assets. The models were wrong and they blew up.

This is quite different from assuming a high failure rate. The marginal cost of having say, 10% more failed startups isn't a big deal when 95% of them are already failing, and the model accounts for that high failure rate. The black swan is not the unexpected failure that wipes you out, but the unexpected success that outweighs all the failures.

Obviously, you'd still be screwed if none of the stuff you invest in pays off. But that would be the case even if your model was more pessimistic; if even 5% success was too much to ask for, perhaps the market for VC is busted, and there was no hope for profit anyway.

I don't think pg is implying that screening processes are useless. I'm sure many obviously hopeless ideas get filtered out. It's more that, given the current state of knowledge, everything beyond that basic filter is by nature difficult to discern and we may as well assume a random fat tail distribution.