Is AI a Bubble? I Asked the People Building It | WTF is AI?
Nikhil Kamath spends a week in San Francisco asking the people building AI whether it is a bubble. His panel: Akshay Kothari, co-founder of Notion; Azalia Mirhoseini and Anna Goldie, the Google Brain duo behind AlphaChip who were Anthropic employees 25 and 26 and have now co-founded frontier lab Ricursive Intelligence; and Vijay, a founder in the AI data-training space whose platform spans five million knowledge workers. The conversation maps the full AI stack - chips and data centres, frontier labs, neolabs like SSI and Ricursive, data trainers like Turing, Mercor, Surge and Scale, inference providers, applications and services - then goes layer by layer: why Ricursive is building AI that designs chips to break the hardware lottery, why training data work is obsolete every nine months, what recursive self-improvement means and why the cost of intelligence is its metric, and how Notion's agent users went from 1% to 30% in under a year. Nikhil's framing number: about $700 billion spent or committed on AI this year against about $100 billion of AI enterprise revenue. FULL COVERAGE: the captions appear to cover the entire episode. All figures and claims are the speakers' own, not independently verified. Sponsor: none stated in the episode. Nothing here is investment advice.
Executive Summary (TL;DR)
Is AI a bubble? Nikhil Kamath puts the number on the table - about $700 billion spent or committed on AI this year against roughly $100 billion of AI enterprise revenue - and asks four people actually building it: Notion co-founder Akshay Kothari, AlphaChip creators Azalia Mirhoseini and Anna Goldie (Anthropic employees 25 and 26, now co-founders of frontier lab Ricursive Intelligence), and data-training founder Vijay. Their answer is layered, literally: they map the AI stack from chips and data centres through frontier labs, neolabs, data trainers, inference providers and applications to services, then show what is happening inside each layer - AI that designs its own chips, training data that expires every nine months, recursive self-improvement measured by the cost of intelligence, and an internet where agents already generate a large share of clicks. The panel's common ground: demand looks infinite, the winners will win on intelligence per dollar, and every engineer is becoming a manager of agents. FULL COVERAGE: the captions appear to cover the entire episode. All figures and claims are the speakers' own, not independently verified. Sponsor: none stated in the episode. Nothing here is investment advice.