A case study of Bay Area AI firms found they optimize for venture metrics like burn multiple and GPU costs but miss non-compute expenses, creating a 9-12% gap. For Perplexity, that's roughly $10 million in potential value left on the table before going public.
So recently we did a case study where we audited top firms in AI in the Bay Area. And something that was very surprising to us as a figure was that all these firms that are into AI, they know that VC sensitivity towards, you know, burn multiple and the GPU computing costs. So these numbers are heavily optimized. Everybody's, you know, going for a hunt for these numbers, how to optimize these, and they've done a fabulous job there. But something that got missed out. was a non-compute OpEx cost. It had 9 to 12% of gap that could be optimized. For a firm like Perplexity, that is $10 million. It could easily, you know, give you a higher valuation when you're going for your IPO. So that is a difference people usually, if you're, if you're focused on one metric, you are going to get very good at optimizing that, but it has to be the balance factor. Right. Because just not one metric decides your books, your outcomes, your valuation. There's a chance that the other metrics could compress your valuation in the upcoming rounds.
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Keith Gillispie: Why Most New Investors Fail & How to Avoid the Common Traps #250 | SaaS District
