Podcast Notes Playlist: Business Dylan Patel - Inside the Trillion-Dollar AI Buildout - [Invest Like the Best, EP.442]
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
Read the full notes @ podcastnotes.org
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.
-----
This episode is brought to you by Ramp. Ramp’s mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to Ramp.com/invest to sign up for free and get a $250 welcome bonus.
–
This episode is brought to you by Ridgeline. Ridgeline has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Head to ridgelineapps.com to learn more about the platform.
–
This episode is brought to you by AlphaSense. AlphaSense has completely transformed the research process with cutting-edge AI technology and a vast collection of top-tier, reliable business content. Invest Like the Best listeners can get a free trial now at Alpha-Sense.com/Invest and experience firsthand how AlphaSense and Tegus help you make smarter decisions faster.
-----
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