Evans contends that AI's true difference from past shifts is the unknown nature of its limits, fueling both hype and doomerism. He highlights that AI capabilities are jagged, leading to uneven usage, with coding emerging as the first real enterprise use case due to its scalable verification. He argues that foundation model labs may become like TSMC—a valuable but bounded monopoly layer—rather than owning the entire stack like Windows. Evans also notes that automation historically creates more work, not less, using rising accountant headcount as evidence. He explains that most consumer and enterprise use cases must be invented by entrepreneurs, not spontaneously emerge from better models. The conversation contrasts OpenAI's product sprawl with Anthropic's narrow coding focus, and uses examples like Uber, Airbnb, and Caterpillar to show how unevenly technology lands. Ultimately, Evans emphasizes that for most companies, AI is not their top priority, and that execution and real-world observation matter more than philosophical debates about AGI.