![Dylan Patel - The Infinite Demand for Tokens, Claude Mythos, and Supply Constraints - [Invest Like the Best, EP.468]](https://megaphone.imgix.net/podcasts/ceb34422-3ec7-11f1-97a5-8bd036d5980c/image/e8a75bd4ab83f059c746a594afd471a8.jpg?ixlib=rails-4.3.1&max-w=3000&max-h=3000&fit=crop&auto=format,compress)
Dylan Patel views aggressive AI adoption not as an option, but as a survival necessity. He believes that failing to leverage AI to constantly improve products will result in being outcompeted and commoditized by faster-moving rivals, making AI spend a crucial defensive investment.
AI is dramatically lowering the cost and difficulty of execution. As a result, the primary business challenge is shifting away from the *how* (implementation) and towards the *what* (idea selection). The new scarce skill is identifying valuable problems that justify the AI token spend required to solve them.
Contrary to the idea of AI for all, the most powerful models will likely be restricted to a few high-paying clients to prevent distillation and maximize revenue. This creates a future where competitive advantage is defined by exclusive AI access, potentially allowing large incumbents to crush smaller competitors.
While GPUs train models, CPUs are essential for two key workloads: running reinforcement learning environments and executing the code generated by AI. This has created a massive, often overlooked demand spike, making CPUs a critical, sold-out component in the AI infrastructure stack and a hidden bottleneck.
The moment a new, more powerful AI model is released, user demand for the previous “state-of-the-art” version collapses. This intense desire for the absolute best model means only the frontier provider has significant pricing power, while older, slightly inferior models become commoditized almost instantly.
Patel predicts a significant public backlash against AI, including protests, driven by widespread fear and poor public relations from lab leaders. He criticizes figures like Sam Altman and Dario Amodei for being uncharismatic and failing to create a positive narrative, instead fostering a perception of a secretive cabal.
The semiconductor supply chain has extremely long lead times. Even with unprecedented demand signals for AI hardware, new memory fabrication plants ordered today will not come online until 2027 or 2028. This multi-year lag guarantees that supply bottlenecks and high prices for components like DRAM will persist.
Dylan Patel’s firm, Semi Analysis, saw its AI spend rocket from tens of thousands to a $7M annual run rate. This personal anecdote illustrates the insatiable enterprise demand for cutting-edge AI, suggesting a willingness to pay that far exceeds initial expectations and even rivals salary costs.
Dylan Patel describes Anthropic's unreleased Mythos model as a monumental step forward, comparing its coding ability to an L6 software engineer—a huge jump from Claude 3 Opus's L4. The capability is so advanced that Anthropic is deliberately withholding its full power, signaling a new era of model performance.
An economist at Semi Analysis coined "Phantom GDP" to describe how AI's deflationary impact isn't captured by traditional metrics. AI allows output to soar while costs plummet, which can theoretically shrink monetary GDP even as real economic value explodes. This makes tracking AI's true impact incredibly difficult.