Major AI labs aren't just evaluating Google's TPUs for technical merit; they are using the mere threat of adopting a viable alternative to extract significant concessions from Nvidia. This strategic leverage forces Nvidia to offer better pricing, priority access, or other favorable terms to maintain its market dominance.
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By funding and backstopping CoreWeave, which exclusively uses its GPUs, NVIDIA establishes its hardware as the default for the AI cloud. This gives NVIDIA leverage over major customers like Microsoft and Amazon, who are developing their own chips. It makes switching to proprietary silicon more difficult, creating a competitive moat based on market structure, not just technology.
Large tech companies are buying up compute from smaller cloud providers not for immediate need, but as a defensive strategy. By hoarding scarce GPU capacity, they prevent competitors from accessing critical resources, effectively cornering the market and stifling innovation from rivals.
Seemingly strange deals, like NVIDIA investing in companies that then buy its GPUs, serve a deep strategic purpose. It's not just financial engineering; it's a way to forge co-dependent alliances, secure its central role in the ecosystem, and effectively anoint winners in the AI arms race.
NVIDIA's polite PR statement regarding Google's competing TPU chips contrasts sharply with the aggressive marketing of modern tech leaders. This 'old school' approach is seen as a weakness, suggesting their marketing 'war muscle' has atrophied from years of unchallenged dominance.
NVIDIA's vendor financing isn't a sign of bubble dynamics but a calculated strategy to build a controlled ecosystem, similar to Standard Oil. By funding partners who use its chips, NVIDIA prevents them from becoming competitors and counters the full-stack ambitions of rivals like Google, ensuring its central role in the AI supply chain.
The high-stakes competition for AI dominance is so intense that investigative journalism can trigger immediate, massive corporate action. A report in The Information about OpenAI exploring Google's TPUs directly prompted NVIDIA's CEO to call OpenAI's CEO and strike a major investment deal to secure the business.
As the current low-cost producer of AI tokens via its custom TPUs, Google's rational strategy is to operate at low or even negative margins. This "sucks the economic oxygen out of the AI ecosystem," making it difficult for capital-dependent competitors to justify their high costs and raise new funding rounds.
![Gavin Baker - Nvidia v. Google, Scaling Laws, and the Economics of AI - [Invest Like the Best, EP.451] thumbnail](https://megaphone.imgix.net/podcasts/d97fee14-d4d7-11f0-8951-8324b640c1c1/image/d3a8b5ecbf3957de5b91f278a191c9ff.jpg?ixlib=rails-4.3.1&max-w=3000&max-h=3000&fit=crop&auto=format,compress)
Beyond capital, Amazon's deal with OpenAI includes a crucial stipulation: OpenAI must use Amazon's proprietary Trainium AI chips. This forces adoption by a leading AI firm, providing a powerful proof point for Trainium as a viable competitor to Nvidia's market-dominant chips and creating a captive customer for Amazon's hardware.
The narrative of endless demand for NVIDIA's high-end GPUs is flawed. It will be cracked by two forces: the shift of AI inference to on-device flash memory, reducing cloud reliance, and Google's ability to give away its increasingly powerful Gemini AI for free, undercutting the revenue models that fuel GPU demand.
While competitors like OpenAI must buy GPUs from NVIDIA, Google trains its frontier AI models (like Gemini) on its own custom Tensor Processing Units (TPUs). This vertical integration gives Google a significant, often overlooked, strategic advantage in cost, efficiency, and long-term innovation in the AI race.
