Show Notes
Apple may have stumbled into one of the most defensible positions in AI. This was not on my radar – just two months ago, I was describing a credibility crisis at the company; they appeared wrong-footed on the most important technology of our times and an acquisition was their only plausible way out. In this episode I work through what I and many other commentators missed – and what road lies ahead for Apple.
More from Azeem Azhar's Exponential View
The AI economy is real and substantial. Forensic analysis of 1,000+ companies reveals where the revenue is flowing, and what it will take for the AI data center buildout to pay off.
More than three years after ChatGPT's release, only 27% of executives say AI has met their ROI expectations. The history of factory electrification explains why — most companies are at the light-bulb stage, adding Copilot licenses rather than reconceptualizing their businesses around AI. In this episode I map the three stages of AI adoption, and show what it actually takes to move from chatbots to the autonomous company — the only stage where the moat becomes real.
Published in early March 2026, Andrej Karpathy's autoresearch AI tool makes autonomous scientific experimentation cheap and easy — but it was designed to solve machine learning problems. I wanted to see if I could apply its loop architecture to my own work: refining my worldview, testing arguments, solving business problems. In this video, I share how I adapted Karpathy’s autoresearch loops for problems that aren't easy to quantify, how to avoid the local minima trap, and the broader impact of these kinds of methods. I covered:
Last week Jensen Huang shared the numbers from NVIDIA’s order book: AI compute demand has grown a millionfold in two years. Much GCT coverage focussed on chips, robots, data centers in space, but I think Jensen revealed something far more important in his keynote: “the inference inflection has arrived,” and this is about to transform how all companies should manage their budgets. The inference era is already the operating assumption of the world’s most valuable company.
AI has become so embedded in how I work that I can no longer cleanly separate it from my thinking. That raises a question I find genuinely unsettling: is intensive AI use making me a sharper thinker, or quietly doing the opposite? In this episode I pull back the curtain on my full research and writing process — the custom tools, the friction points, and the places where I'm still not sure I've got it right. For Ezra Klein, having AI summarize material is a disaster for original thought. But my AI systems are designed to protect the cognitive work that has to stay human, while they handle everything else. Knowing where to draw that line turns out to be the hardest and most important question.