# NVIDIA Is a Dead Man Walking? Here’s Why.

Page: https://stenobird.com/podcast/asiandadenergy-s-substack-podcast-7711112/nvidia-is-a-dead-man-walking-here-s-why
Text version: https://stenobird.com/podcast/asiandadenergy-s-substack-podcast-7711112/nvidia-is-a-dead-man-walking-here-s-why.md
Podcast: [AsianDadEnergy's Podcast](https://stenobird.com/podcast/asiandadenergy-s-substack-podcast-7711112)
Published: 2026-08-11T14:43:48+00:00
Episode link: https://asiandadenergy.substack.com/p/nvidia-is-a-dead-man-walking-heres
Audio file: https://api.substack.com/feed/podcast/210761245/7ce296bff1a780fe97e61b7d52ada031.mp3
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JSON: https://stenobird.com/v1/public/podcasts/asiandadenergy-s-substack-podcast-7711112/episodes/nvidia-is-a-dead-man-walking-here-s-why
Duration seconds: 791

## Resource

NVIDIA has become one of the great corporate success stories of the AI boom. Its GPUs power much of the infrastructure behind today’s frontier AI models. Revenue has exploded. Profitability has exploded. Its market capitalization has reached almost incomprehensible levels. And because NVIDIA sells the “shovels” during the AI gold rush, the conventional wisdom seems pretty straightforward: Even if the AI bubble eventually bursts, NVIDIA wins. After all, somebody still has to sell the picks and shovels. I’m not convinced. In fact, I think there is a scenario in which NVIDIA becomes one of the biggest casualties of the next phase of the AI revolution. Not because its technology suddenly becomes bad. But because the economics of AI could fundamentally change. NVIDIA’s Moat Depends on One Big Assumption The bull case for NVIDIA ultimately rests on a simple proposition: AI has an essential dependency on NVIDIA GPUs. Today, that proposition looks pretty damn convincing. Frontier models require enormous amounts of computing power. Companies like OpenAI and Anthropic have traditionally trained their models using massive clusters of NVIDIA GPUs inside enormous data centers. And NVIDIA’s data-center business is now overwhelmingly important to the company. The logic therefore seems almost circular: AI gets bigger → AI needs more compute → more compute requires NVIDIA GPUs → NVIDIA makes more money. But what happens if the amount of compute required to produce useful AI falls dramatically? What happens if frontier models become increasingly commoditized? And, perhaps most importantly: What happens if AI inference moves out of the data center and onto the devices sitting on our desks? That’s where things get interesting. The First Problem: Frontier AI Is Becoming Commoditized One of…

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