Nvidia took San Jose by storm this year, with a record-breaking 25,000 attendees flocking to the San Jose Convention Center and surrounding downtown buildings. Many workshops, talks, and panels were so packed that people had to lean against walls or sit on the floor — and suffer the wrath of organizers shouting commands to get them to line up properly.
Nvidia currently sits at the top of the AI world, with record-breaking financials, sky-high profit margins, and no serious competitors yet. But the coming months also hold unprecedented risk for the company as it faces U.S. tariffs, DeepSeek, and shifting priorities from top AI customers.
At GTC 2025, Nvidia CEO Jensen Huang attempted to project confidence, unveiling powerful new chips, personal “supercomputers,” and, of course, really cute robots. It was an exhaustive sales pitch – one aimed at investors reeling from Nvidia’s nosediving stock.
“The more you buy, the more you save,” Huang said at one point during a keynote on Tuesday. “It’s even better than that. Now, the more you buy, the more you make.”
More than anything, Nvidia at this year’s GTC sought to assure attendees – and the rest of the world watching – that demand for its chips won’t slow down anytime soon.
During his keynote, Huang claimed that nearly the “entire world got it wrong” on traditional AI scaling falling out of vogue. Chinese AI lab DeepSeek, which earlier this year released a highly efficient “reasoning” model called R1, prompted fears among investors that Nvidia’s monster chips may no longer be necessary for training competitive AI.
But Huang has repeatedly insisted that power-hungry reasoning models will, in fact, drive more demand for the company’s chips, not less. That’s why at GTC, Huang showed off Nvidia’s next line of Vera Rubin GPUs, claiming they’ll perform inference (that is, run AI models) at roughly double the rate of Nvidia’s current best Blackwell chip.
The threat to Nvidia’s business Huang spent less time addressing was upstarts like Cerebras, Groq, and other low-cost inference hardware and cloud providers. Nearly every hyperscaler is developing a custom chip for inference, if not training, as well. AWS has Graviton and Inferentia (which it’s reportedly aggressively discounting), Google has TPUs, and Microsoft has Cobalt 100.
Along the same vein, tech giants currently extremely reliant on Nvidia chips, including OpenAI and Meta, are looking to reduce those ties via in-house hardware efforts. If they – and the aforementioned other rivals – are successful, it’ll almost assuredly weaken Nvidia’s stranglehold on the AI chips market.
That’s perhaps why Nvidia’s share price dipped around 4% following Huang’s keynote. Investors might’ve been holding out hope for “one last thing” — or perhaps an accelerated launch window. In the end, they got neither.
Nvidia also sought to allay worries about tariffs at GTC 2025.
The U.S. hasn’t imposed any tariffs on Taiwan (where Nvidia gets most of its chips), and Huang claimed tariffs wouldn’t do “significant damage” in the short run. He stopped short of promising that Nvidia would be shielded from the long-term economic impacts, however — whatever form they ultimately take.
Nvidia has clearly received the Trump Administration’s “America First” message, with Huang pledging at GTC to spend hundreds of billions of dollars on manufacturing in the U.S. While that would help the company diversify its supply chains, it’s also a massive cost for Nvidia, whose multitrillion-dollar valuation depends on healthy profit margins.
As it looks to seed and grow businesses other than its core chips line, Nvidia at GTC drew attention to its new investments in quantum, an industry that the company has historically neglected. At GTC’s first Quantum Day, Huang apologized to the CEOs of major quantum companies for causing a minor stock crash in January 2025 after he suggested that the tech wouldn’t be very useful for the next 15 to 30 years.
On Tuesday, Nvidia announced that it would open a new center in Boston, NVAQC, to advance quantum computing in collaboration with “leading” hardware and software markers. The center will, of course, be equipped with Nvidia chips, which the company says will enable researchers to simulate quantum systems and the models necessary for quantum error correction.
In the more immediate future, Nvidia sees what it’s calling “personal AI supercomputers” as a potential new revenue-maker.
At GTC, the company launched DGX Spark (previously called Project Digits) and DGX Station, both of which are designed to allow users to prototype, fine-tune, and run AI models in a range of sizes at the edge. Neither is exactly inexpensive – they retail for thousands of dollars – but Huang boldly proclaimed that they represent the future of the personal PC.
“This is the computer of the age of AI,” Huang said during his keynote. “This is what computers should look like, and this is what computers will run in the future.”
We’ll soon see if customers agree.
