Clad in his trademark leather jacket, Nvidia CEO Jensen Huang took the stage yesterday at San Jose’s SAP Center before nearly 20,000 people at the company’s annual GTC conference, known in recent years as the Super Bowl of AI.
Once again, Huang essentially declared a blowout, forecasting a staggering $1 trillion in orders for Nvidia’s most sophisticated AI chips through 2027, driven by the explosion of AI infrastructure now being built around the world.
Yet for someone whose company has become the world’s most valuable—with a roughly $4 trillion market cap—by powering the global AI buildout, Huang has somehow avoided the kind of public criticism that has been leveled at other prominent AI CEOs.
It takes only a cursory glance at social media to find posts calling OpenAI CEO Sam Altman “evil,” while companies like Anthropic, Meta, and Google increasingly face criticism over AI’s risks—from job losses and copyright lawsuits to misinformation and the growing push to deploy AI in military systems.
Nvidia’s CEO, by contrast, remains largely celebrated as the engineer-builder behind the boom. That’s been true even though the massive AI data centers now rising across the country and generating a good deal of local opposition are packed with Nvidia chips.
In fact, every major move in AI—from chatbots and agents to applications in the workplace, schools, and the military—runs on Nvidia hardware, software, and systems. Nvidia has also invested billions to support the AI ecosystem, partnering with both OpenAI and Anthropic, as well as funding data center companies and AI startups.
So why isn’t Huang—and Nvidia as a whole—a target of the AI backlash?
The answer is that the companies supplying the “picks and shovels” of technological booms rarely attract the same scrutiny as the miners. Oil companies drew criticism during the fossil fuel era, not the manufacturers of drilling equipment. Railroad barons faced public backlash, not the companies supplying steel rails. And in the internet era, cloud providers like Amazon Web Services powered companies such as Airbnb and Uber that reshaped entire industries—yet the criticism largely focused on the platforms, not the infrastructure behind them.
Still, Nvidia made it clear at GTC that it is positioning itself not just as a chipmaker but as the provider of entire AI computing systems powering the new “inference” phase of AI. (Inference is about powering AI outputs, not just training, and it will require an enormous new round of infrastructure investment.) That ambition goes beyond Nvidia’s traditional “picks and shovels” role. These days, Nvidia is increasingly trying to control the entire swath of systems, software and platforms that power the AI economy.
The centerpiece of Huang’s keynote was the launch of the company’s Vera Rubin platform, which combines multiple chips and system components designed to run large AI models and “agentic AI” systems. The platform includes seven new chips and several rack-scale systems intended to power extremely large AI clusters containing hundreds of thousands of GPUs.
Nvidia also introduced NemoClaw, an open-source platform for building enterprise AI agents, allowing companies to create agents, connect them to corporate data, and deploy them on Nvidia hardware.
At the same time, Nvidia is continuing to invest aggressively across the AI ecosystem. The company has poured billions into dozens of AI startups over the past year. Most recently it invested $2 billion in AI cloud company Nebius and is backing former OpenAI CTO Mira Murati’s new venture, Thinking Machines, with plans for more than 1 gigawatt of Nvidia-powered compute capacity.
The company is also continuing its push into autonomous vehicles, where Nvidia chips and software platforms are increasingly being adopted by carmakers building self-driving systems.
Finally, Huang used GTC to promote what he called AI’s “five-layer cake.” The AI economy, he argued, depends on five layers—energy, chips, infrastructure, models, and applications—all of which must scale together to support the massive buildout now underway. Nvidia, not coincidentally, sits squarely in the middle of that stack—connecting most of those layers together.
For now, Nvidia still benefits from the traditional insulation of a picks-and-shovels supplier. But as the sprawling AI data centers rising across the country fill with Nvidia hardware—and as the company pushes deeper into the systems powering them—the company may find itself far more exposed to the debate over AI’s consequences.
