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Jensen Huang – TPU competition, why we should sell chips to China, & Nvidia’s supply chain moat

Dwarkesh Podcast

Shownote

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 [https://youtu.be/Hrbq66XqtCo]; read the transcript [https://www.dwarkesh.com/p/jensen-huang]. Sponsors * Crusoe’s [https://crusoe.ai/dwarkesh] 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 [https://crusoe.ai/dwarkesh] * Cursor [https://cursor.com/dwarkesh] 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 [https://github.com/dwarkeshsp/ai_coworker], or get started on your own Cursor project today at cursor.com/dwarkesh [https://cursor.com/dwarkesh] * Jane Street [https://janestreet.com/dwarkesh] 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 [https://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? Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe [https://www.dwarkesh.com/subscribe?utm_medium=podcast&utm_campaign=CTA_4]

Highlights

In this wide-ranging conversation, Jensen Huang discusses NVIDIA’s strategic positioning in the rapidly evolving AI infrastructure landscape, focusing on its technological moats, ecosystem dynamics, and global policy implications.
07:06
Instantaneous AI demand can exceed supply, but swarming components like CoWoS help bridge the gap
16:25
NVIDIA built accelerated computing, which is more diverse and has a broader market reach than TPU or ASICs.
52:50
NVIDIA doesn’t change GPU prices based on demand—it considers that bad business.
1:33:11
Forcing NVIDIA out of China has accelerated China's chip industry
1:35:07
Nvidia added Grok to the CUDA ecosystem to serve a new inference segment prioritizing token response time over throughput

Chapters

Is Nvidia’s biggest moat its grip on scarce supply chains?
00:00
Will TPUs break Nvidia’s hold on AI compute?
16:25
Why doesn’t Nvidia become a hyperscaler?
41:06
Should we be selling AI chips to China?
57:36
Why doesn’t Nvidia make multiple different chip architectures?
1:35:06

Transcript

Dwarkesh Patel: We've seen the valuations of a bunch of software companies crash because people are expecting AI to commoditize software. And there's a potentially naive way of thinking about things, which is like, look, NVIDIA sends a GDS2 file to TSMC. T...