Chetan Puttagunta and Modest Proposal - Capital, Compute & AI Scaling - [Invest Like the Best, EP.400]
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What is the current state of large‑language‑model scaling and why are labs hitting limits?
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My guests today are Chaythan Putagunta 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. Chaythan 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 transition from pre-training to test time compute. Together we explore how this change could democratize AI development while reshaping the investment landscape across both public and private markets.
Please enjoy this great discussion with my friends Chaithan Putagunta and Modest Proposal. So Chaithan, maybe you can start by just telling us from your perspective, what is going on right now that is most interesting in the technology part of the story of LLMs and their scaling?
Yeah, 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 two years, which was specifically in the pre-training world. And the power laws of scaling stipulated that the more You could increase compute in pre-training, the better model you were going to get. And everything was thought of in orders of magnitude. So throw 10x more compute at the problem, and you're going to step function in model performance and intelligence. And this certainly led to incredible breakthroughs here. And we saw from all of the labs. Really terrific models. The overhang on all of this, even starting in late 2022, was at some point we were going to run out of text data that was generated by human beings.
And we were going to enter the world of synthetic data fairly quickly. All of the world's knowledge effectively had been tokenized and had been digested by these models. And sure, there were Niche data is and private data and all these little repositories that hadn't been tokenized, but In terms of orders of magnitude, it wasn't gonna increase the amount of available data for these models particularly significantly. As we looked out in 2022, you saw this big question of was synthetic data gonna enable these models to continue to scale. Everybody assumed, as you saw that line, this problem was gonna really come to the forefront in 2024. And here we are. We're here, and we're all trying to train on synthetic data, the large model providers.
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Chapters
8 chapters
1
What is the current state of large‑language‑model scaling and why are labs hitting limits?
0:00–5:31
2
How does synthetic data affect pre‑training and why are labs moving to test‑time compute?
5:31–12:16
3
What are the investment implications for public‑tech companies as AI shifts to inference‑time scaling?
12:16–18:53
4
How are small teams and open‑source models able to compete with big labs in the new paradigm?
18:53–39:44
5
What strategic moves are the major AI players (OpenAI, Anthropic, Google, Meta) making in response to test‑time compute?
39:44–49:54
6
Is AGI on the horizon and what does the latest reasoning‑centric scaling mean for its timeline?
49:54–1:00:37
7
What are the broader economic impacts of AI’s shift to reasoning and inference‑time compute?
1:00:37–1:14:30
8
How will infrastructure, hyperscalers, and GPU utilization evolve as AI moves toward inference‑focused workloads?
1:14:30–1:34:15
Speakers
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