Did a very different format with Reiner Pope - a blackboard lecture where he walks through how frontier LLMs are trained and served.

It’s shocking how much you can deduce about what the labs are doing from a handful of equations, public API prices, and some chalk.

It’s a bit technical, but I encourage you to hang in there – it’s really worth it.

There are less than a handful of people who understand the full stack of AI, from chip design to model architecture, as well as Reiner. It was a real delight to learn from him.

Recommend watching this one on YouTube so you can see the chalkboard.

Reiner is CEO of MatX, a new chip startup (full disclosure - I’m an angel investor). He was previously at Google, where he worked on software efficiency, compilers, and TPU architecture.

Download markdown of transcript here to chat with an LLM.

Wrote up some flashcards and practice problems to help myself retain what Reiner taught. Hope it's helpful to you too!

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* Jane Street needs constant access to incredibly low-latency compute. I recently asked one of their engineers, Clark, to talk me through how they meet these demands. Our conversation—which touched on everything from FPGAs to liquid cooling—was extremely helpful as I prepped to interview Reiner. You can watch the full discussion and explore Jane Street’s open roles at janestreet.com/dwarkesh

* Google’s Gemma 4 is the first open model that’s let me shut off the internet and create a fully disconnected “focus machine”. This is because Gemma is small enough to run on my laptop, but powerful enough to actually be useful. So, to prep for this interview, I downloaded Reiner’s scaling book, disconnected from wifi, and used Gemma to help me break down the material. Check it out at goo.gle/Gemma4

* Cursor helped me turn some notes I took on how gradients flow during large-scale pretraining into a great animation. At first, I wasn’t sure the best way to visualize the concept, but Cursor’s Composer 2 Fast model let me iterate on different ideas almost instantaneously. You can check out the animation in my recent blog post. And if you have something to visualize yourself, go to cursor.com/dwarkesh

Timestamps

(00:00:00) – How batch size affects token cost and speed

(00:32:09) – How MoE models are laid out across GPU racks

(00:47:12) – How pipeline parallelism spreads model layers across racks

(01:03:37) – Why Ilya said, “As we now know, pipelining is not wise.”

(01:18:59) – Because of RL, models may be 100x over-trained beyond Chinchilla-optimal

(01:33:02) – Deducing long context memory costs from API pricing

(02:04:02) – Convergent evolution between neural nets and cryptography

I asked Jensen about TPU competition, Nvidia’s lock on the ever more bottlenecked supply chain needed to make advanced chips, whether we should be selling AI chips to China, why Nvidia doesn’t just become a hyperscaler, how it makes its investments, and much more. Enjoy!

Watch on YouTube; read the transcript.

Sponsors

* Crusoe’s cloud runs on state-of-the-art Blackwell GPUs, with Vera Rubin deployment scheduled for later this year. But hardware is only part of the story—for inference, Crusoe’s MemoryAlloy tech implements a cluster-wide KV cache, delivering up to 10x faster TTFT and 5x better throughput than vLLM. Learn more at crusoe.ai/dwarkesh

* Cursor helped me build an AI co-researcher over the course of a weekend. Now I have an AI agent that I can collaborate with in Google Docs via inline comment threads! And while other agentic coding tools feel like a total black-box, Cursor let me stay on top of the full implementation. You can try my co-researcher out at github.com/dwarkeshsp/ai_coworker, or get started on your own Cursor project today at cursor.com/dwarkesh

* Jane Street spent ~20,000 GPU hours training backdoors into 3 different language models, then challenged my audience to find the triggers. They received some clever solutions—like comparing the base and fine-tuned versions and extrapolating any differences to reveal the hidden backdoor—but no one was able to solve all 3. So if open problems like this excite you, Jane Street is hiring. Learn more at janestreet.com/dwarkesh

Timestamps

(00:00:00) – Is Nvidia’s biggest moat its grip on scarce supply chains?

(00:16:25) – Will TPUs break Nvidia’s hold on AI compute?

(00:41:06) – Why doesn’t Nvidia become a hyperscaler?

(00:57:36) – Should we be selling AI chips to China?

(01:35:06) – Why doesn’t Nvidia make multiple different chip architectures?