Nvidia urges Washington to keep AI open as Huang calms investors

Nvidia’s Jensen Huang told Washington to keep AI open and told Wall Street the boom is far from over. All while running the most indispensable company in AI.

Jensen Huang made his first post ever on X on July 24. He used it to back a letter called “Open Weights and American AI Leadership,” which asks Washington to leave freely downloadable AI models alone. By the following afternoon, the number of companies backing that letter had gone from 25 to 50.

OpenAI, Google, AMD, Cisco, Cloudflare, GitHub, Block and Ollama are also on the list. However, two major companies are missing: Anthropic and Amazon.

Why Anthropic skip Jensen Huang’s open AI letter?

The letter went out with 25 backers initially. When Huang’s post got 11 million views, new signatures came in. This is why two copies of the letter showed different counts on the same afternoon.

Still, the two missing names are the most telling part of the list. Amazon is Anthropic’s biggest financial backer, and Anthropic runs on Amazon’s own Trainium chips, among other hardware. Anthropic recently moved into the top spot in enterprise AI.

Google, which has also put money into Anthropic, signed anyway, which makes the Amazon connection the more specific one. Neither company has said why it stayed off the list. The explanations range from straightforward business logic. Anthropic sells closed, frontier-level access. Anthropic has made a safety argument publicly for years, which is that once model weights are released, there is no pulling them back.

As previously reported by Cryptopolitan, Anthropic has been running its own parallel lobbying operation in Washington, spending $1.97 million in Q2 2026 alone, which makes its absence from the letter a position, not an oversight.

Nvidia’s open AI message comes with a catch

While the letter says that keeping AI models open is necessary for keeping any one company from having to rule, Nvidia shows a contradiction. The company controls CUDA which is the software needed to run AI on Nvidia chips. It is deeply embedded, widely depended on, and not open.

The same week Huang was rallying the industry around open access, he sat down with Axios cofounder Mike Allen and made a different kind of case. This one aimed at investors who are growing nervous about how much money is being spent on AI infrastructure.

Chip stocks have pulled back sharply in recent weeks, even as chipmakers reported strong earnings and kept running short on supply. The worry is simple: the big cloud and tech companies are spending hundreds of billions of dollars a year building out AI, and that spending is no longer coming purely from their own cash. Alphabet slipped into negative cash flow. Tech giants have started borrowing to keep up.

As tech giants borrow to fund AI, Huang tells investors not to worry

Huang was asked directly whether the sector is heading for a bust. “No, not for a while,” he said. When Allen followed up with “so this time is different?” Huang agreed with the framing.

“This time is different because this is not demand-driven,” Huang said. “This is industrially driven, meaning the fundamental technology of computers is changing.”

That phrase carries a long history. “This time is different” was used to explain why the dot-com boom would keep going. It did not. The line is now treated as a warning sign when it turns up in bullish forecasts, a bit like a general declaring victory too early.

Huang is not oblivious to that. He acknowledged the bubble will eventually pop. He argues that it is not close, because the buildout is still in its early stages. He also made a case for why the supply squeeze on chips, land, power and construction labor is actually helpful. It slows things down enough to prevent supply from outrunning demand too quickly.

“We basically are constrained in every single direction, in every single way,” he said. “That constraint is good. That constraint is what holds the system back.”

He pointed to companies like Anthropic as evidence that AI is already generating real profit, particularly as businesses find practical uses for AI agents.

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