Chetan Puttagunta and Modest Proposal - Capital, Compute & AI Scaling - [Invest Like the Best, EP.400]
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2 fun facts about our newest sponsorship partner, Ramp. 1st, they are the fastest growing Fintech company in history, reaching a level of revenue in 5 years that I can't quote exactly, but is eyebrow raising. 2nd, they are backed by more of my favorite past guests, at least 16 of them when I counted, than probably any other company that I'm aware of. A list that includes Ravi Gupta at Sequoia, Josh Kushner at Thrive, Keith Raboy at Founders Fund and Khosla Ventures, Patrick and John Collison, Michael Ovitz, Brad Gershner. The list goes on and on. These facts demand the question, why? Having be
Patrick O'Shaughnessy is the CEO of Positive Sum. All opinions expressed by Patrick and podcast guests are solely their own opinions and do not reflect the opinion of Positive Sum. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions. Clients of Positive Sum may maintain positions in the securities discussed in this podcast. To learn more, visit psum.vc.
My guests today are Chetan Putugunta and Modest Proposal. If you're as obsessed as I am about the frontier in AI and the business and investing implications, you will love this conversation. Chetan is a general partner and investor at Benchmark, while Modest Proposal is an anonymous investor who manages a large pool of capital in public markets. Both are good friends and frequent guests on the show, but this is the first time that they have appeared together. The timing could not be better. We might be witnessing a pivotal shift in AI development as leading labs hit scaling limits and transiti
I think we're now at a point where it's either consensus or universally known that all the labs have hit some kind of plateauing effect on how we perceive scaling for the last years, which was specifically in the pretraining world. And the power laws of scaling stipulated that the more you could increase compute in pre training, the better model you were gonna get. And everything was thought of in orders of magnitude. So throw 10 x more compute at the problem, and you get a step function and model performance and intelligence. And this certainly led to incredible breakthroughs here, and we saw
Just a follow-up question on the overall picture. So setting aside CapEx and all this other stuff that we'll talk about with the big public tech companies in just a moment, is it reasonable to say based on what you know now that the switch to test time scaling, where time is the variable, is like a who cares? As long as these things keep getting more and more capable, isn't that all that matters? And the fact that we're doing it in a different way than just based on pre training, does anyone really care? Does it matter?
There's 2 things that come up pretty quickly in test time or reasoning paradigm, which is as LLMs explore the space for potential solutions, very quickly as a model developer or somebody working on models, you quickly realize that algorithms used for test time compute might exhaust the useful search space for solutions quite quickly. That's number 1. Number 2, you have this thing called a verifier that's looking at what's potentially a good solution, what's potentially a bad solution, what should you pursue, and the ability to figure out what's a good solution, what's a bad solution or what's
So if that's the sort of research labs out view, Modest, I'm curious for you to give us the big public tech companies down view because so much of this story has been the spend, CapEx, the strategic positioning, the, quote, unquote, ROI on all this spend, an
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