Energy

Context, Compute, Capital, Culture: 13 Lectures From Stanford's CS153

Context, Compute, Capital, Culture: 13 Lectures From Stanford's CS153

Enrichment is the bottleneck to nuclear. Nuclear is the bottleneck to electricity. Electricity is the bottleneck to AI. That slide came from the 7th of 13 sessions in Stanford's CS153: Frontier Systems. A course about frontier AI spent its midpoint on uranium. I worked through all 13 in September 2026, from Anjney Midha's opening map of the stack to guests from ElevenLabs, NVIDIA, OpenAI and Microsoft. This recap walks the course in order and reads it through the four lenses Midha gave the class: context, compute, capital and culture. It ends on the number I'll carry forward: Google depreciates its compute hardware over six years, and NVIDIA ships a new architecture every year.

Where Does The Money Actually Go? A Recap of Stanford's MS&E 435

Where Does The Money Actually Go? A Recap of Stanford's MS&E 435

Jensen Huang's five-layer cake goes up twice in Stanford's MS&E 435, and both times the guest gets the same question: you have $100, which layer do you put it on? I worked through all 9 in August 2026. The shape underneath them: semiconductors took 79% of AI gross profit in 2026 and applications took 7%, the inverse of cloud software. This recap walks the course in order, from $59M per megawatt to a 16% antibody hit rate, and argues that "compute is scarce" has become the industry's substitute for a depreciation estimate. Nine lectures, and the number that decides whether any of it pays never appears on a slide.