CAS Investment Partners Founder Cliff Sosin Discusses AI Prediction Limits and Avoiding AI Infrastructure Investments in Podcast
In Episode 497 of the US-based podcast 'Invest Like the Best', CAS Investment Partners founder Cliff Sosin discusses why he believes there are hard limits to what even the smartest AI can predict, which businesses benefit as intelligence gets cheap, and why he avoids investing in the AI build-out. The episode also covers how AI agents will push customers to the lowest-cost provider.
The US-based investment podcast 'Invest Like the Best' released Episode 497, titled 'The Limits of Intelligence', on October 27, 2026. The guest is Cliff Sosin, founder of CAS Investment Partners. The firm was started in 2012, and Sosin previously spent five years in the Fundamental Investment Group at UBS, an equity long-short business within the investment bank. According to the episode description, Sosin explains why he thinks there are hard limits to what even the smartest AI can predict and which businesses benefit as intelligence gets cheap.
The show notes indicate discussions on frontier labs as businesses, the Jevons Paradox and limits of demand, and automated labs with speed limits on experiments. Notably, Sosin covers why he stays away from investing in the AI build-out and how AI agents will push customers to the lowest-cost producer. He also addresses why he is comfortable holding roughly 80% of his portfolio in Carvana and discusses educating kids in the age of AI.
For readers interested in AI industry investment opportunities, this episode offers a cautious perspective from an established value investor. Amid market narratives focused on AI compute expansion and model capability breakthroughs, Sosin's argument that intelligence is not an unbounded scalar but a ratio, along with his emphasis on the hard boundaries of AI prediction, provides a different framework for assessing long-term risks in AI-related assets. While the full transcript requires login on the Colossus platform, listeners can locate specific discussions on agent economics, capital cycle lessons, and TSMC's physical moats using the detailed timestamps provided by the publisher.