Dwarkesh recently started running a new blackboard lectures series with some of the top researchers and engineers in the space.. and we are all here for it 🙌

#How GPT, Claude, and Gemini are actually trained and served – Reiner Pope

Reiner Pope gives a blackboard-style walkthrough of how frontier LLMs are trained and deployed, showing how much of the AI industry’s inner workings can be inferred from equations, API pricing, and first principles.

#What rebuilding AlphaGo teaches us about self-play, RL, and future of LLMs - Eric Jang

Eric Jang explains how rebuilding AlphaGo with modern AI tools reveals core principles of intelligence—search, self-play, and learning—and why its MCTS-based reinforcement learning may offer a better model for how future AIs and humans learn than today’s token-level RL in LLMs.

#Chip design from the bottom up – Reiner Pope

How do chips actually work - starting with basic logic gates, and working up to why GPUs, TPUs, FPGAs, and the human brain each look the way they do. Reiner is CEO of MatX, a new chip startup, previously at Google working on software efficiency, compilers, and TPU architecture.

Anthropic CFO Krishna Rao shared that the company’s revenue run-rate grew from $9B to $30B in one quarter, with 500%+ NDR, 9 of the Fortune 10 as customers, and over 90% of internal code written by Claude Code—backed by ~$75B raised and massive compute commitments.

Sources: tweet

Sequoia Capital's AI Ascent 2026 convened Greg Brockman, Andrej Karpathy, Demis Hassabis, Boris Cherny, Dmitri Dolgov, and more with 150+ leading founders and researchers to discuss the present and future of AI.

#Fireside Chat: Sequoia × Karpathy

1. LLMs enable new primitives: apps fully engulfed by LLMs, "install .md, not .sh", knowledge systems over arbitrary unstructured data.
2. LLM jaggedness: a model can refactor a 100k-line codebase and still fail basic tasks — increasingly it’s about both verifiability and economics: frontier labs heavily optimize domains with strong reward signals and large TAMs.
3. The agent-native economy: products decomposing into sensors, actuators, and logic; systems designed to be maximally legible to LLMs; and the rise of agentic engineering as a new discipline.

Sources: Full playlist, tweet