Invest Like the Best

Key Takeaways

Today, the challenge is not to make the model bigger; the problem is knowing how best to generate and create data in useful domains so that the model gets better at them
AIs do not have to get to digital god mode for AI to have an enormous impact on productivity and society: Even if AI does not become smarter than humans in the short term, the economic value creation boom will still be enormous
“If we didn’t have the AI boom, the US probably would be behind China and no longer the world hegemon by the end of the decade, if not sooner.” – Dylan Patel
The US is doing what China has done historically: dumping tons of capital into something, and then the market becomes
If there is a sustained lag in model improvement, the US economy will go into a recession; this is the case for Korea and Taiwan, too
On the AI talent wars: If these companies are willing to spend billions on training runs, it makes sense to spend a lot on talent to optimize those runs and potentially mitigate errors
We actually are not dedicating that much power to AI yet; only 3-4% of total power is going to data centers
He is more optimistic on Anthropic than OpenAI; their revenue is accelerating much faster because of their focus on the $2 trillion software market, whereas OpenAI’s focus is split between many things
While Meta “has the cards to potentially own it all”, Google is better positioned to dominate the consumer and professional markets

My guest today is Dylan Patel. Dylan is the founder and CEO of SemiAnalysis. At SemiAnalysis Dylan tracks the semiconductor supply chain and AI infrastructure buildout with unmatched granularity—literally watching data centers get built through satellite imagery and mapping hundreds of billions in capital flows. Our conversation explores the massive industrial buildout powering AI, from the strategic chess game between OpenAI, Nvidia, and Oracle to why we're still in the first innings of post-training and reinforcement learning. Dylan explains infrastructure realities like electrician wages doubling and companies using diesel truck engines for emergency power, while making a sobering case about US-China competition and why America needs AI to succeed. We discuss his framework for where value will accrue in the stack, why traditional SaaS economics are breaking down under AI's high cost of goods sold, and which hardware bottlenecks matter most. This is one of the most comprehensive views of the physical reality underlying the AI revolution you'll hear anywhere. Please enjoy my conversation with Dylan Patel.

For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠⁠.⁠⁠⁠⁠⁠⁠⁠⁠

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Editing and post-production work for this episode was provided by The Podcast Consultant (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://thepodcastconsultant.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠).

Show Notes:

(00:00:00) Welcome to Invest Like the Best

(00:05:12) The AI Infrastructure Buildout

(00:08:25) Scaling AI Models and Compute Needs

(00:11:44) Reinforcement Learning and AI Training

(00:14:07) The Future of AI and Compute

(00:17:47) AI in Practical Applications

(00:22:29) The Importance of Data and Environments in AI Training

(00:29:45) Human Analogies in AI Development

(00:40:34) The Challenge of Infinite Context in AI Models

(00:44:08) The Bullish and Bearish Perspectives on AI

(00:48:25) The Talent Wars in AI Research

(00:56:54) The Power Dynamics in AI and Tech

(01:13:29) The Future of AI and Its Economic Impact

(01:18:55) The Gigawatt Data Center Boom

(01:21:12) Supply Chain and Workforce Dynamics

(01:24:23) US vs. China: AI and Power Dynamics

(01:37:16) AI Startups and Innovations

(01:52:44) The Changing Economics of Software

(01:58:12) The Kindest Thing

Dylan Patel - Inside the Trillion-Dollar AI Buildout - [Invest Like the Best, EP.442]

Podcast Notes Playlist: Business