Jensen Huang at GTC: What Nvidia CEO's Keynote Means for the Company's Future

GTC is not just a conference for developers. It's an annual ritual where Jensen Huang, Nvidia's CEO, acts as the chief prophet of a new technological era. In 2026, the keynote once again gathered tens of thousands of participants and set the agenda for the entire AI industry for the next 12 months.

The Equity podcast team dissected the presentation – and came to ambiguous conclusions. TechCrunch hosts discussed which of Huang's claims are backed by real data and which seem more like a strategic signal to the market. The gap between the ambitious presentation and the real competitive situation turned out to be significant – and this is precisely what is highlighted in the episode.

Nvidia today is not just a graphics card manufacturer. Over the past three years, the company has transformed into the infrastructural backbone of the entire global AI industry. Quarterly revenue exceeded $30 billion, and its market capitalization allows it to compete with Apple and Microsoft for the status of the world's most valuable corporation.

The H100 series chips, and then the Blackwell architecture, set the industry standard for training large language models. Major technology companies – Microsoft, Google, Meta, Amazon – continue to increase purchases, despite prices that make GPUs one of the most scarce resources on the planet. GTC-2026 continued this trend, but the focus shifted towards a new frontier.

Huang bet on physical AI – systems capable of acting in the real world: industrial robots, autonomous vehicles, intelligent simulators. The Omniverse platform, which Nvidia has been developing for several years, is positioned as a central tool for creating digital twins – virtual copies of factories, cities, and logistics networks. According to Huang, the next frontier of AI is not another language model, but systems that can interact with physical reality and make decisions in real-time.

Nevertheless, the Equity editorial team questioned several key narratives. The first is competitive. AMD is actively promoting MI300X as an alternative to Nvidia at a lower price, and cloud providers are increasingly developing their own solutions: Google's TPUs, AWS's Trainium, Microsoft's Maia.

Startups like Groq and Cerebras are targeting the fast inference niche. Nvidia still holds a dominant position, but margins may begin to shrink – and this is already reflected in analysts' price expectations. The second question is geopolitical.

US export restrictions on advanced chip supplies to China continue to be in effect and are periodically tightened. Nvidia is forced to release special versions of its products for the Chinese market, but even these regularly fall under new sanctions. A potentially huge market remains partially closed, forcing the company to reorient growth towards Europe, India, and the Middle East.

The third is valuation. Nvidia shares are trading at levels that imply growth that must continue for several years in a row without significant disruptions. Investors are factoring into the valuation not only current leadership but also success in new segments – robotics, physical AI, telecommunications.

Any slowdown – a decrease in hyperscaler capital expenditures, the emergence of real competitive alternatives, or regulatory restrictions – could sharply correct market capitalization and call into question current multipliers. GTC-2026 showed Huang in his usual form: confident, visionary, able to sell ideas no worse than hardware. But the Equity podcast accurately captured the gap between the brilliant presentation and the real challenges Nvidia will face in the next 12–18 months.

The company's dominance in the era of language model training is undisputed. But the next era – of inference and physical AI – has yet to be written. This is where competitors will look for entry points capable of changing the balance of power.

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