At a time when the artificial intelligence race is increasingly being framed as a contest between a handful of dominant model makers, Nvidia chief executive Jensen Huang is making a different wager: the future will not be shaped by a single winner, and Nvidia intends to be embedded across the entire field.

Speaking on the Dwarkesh podcast, Huang offered one of the clearest explanations yet of the chipmaker’s unusually expansive investment strategy, one that has seen it back companies across foundation models, cloud infrastructure, robotics, autonomous driving, biotech, and enterprise AI.

“There are so many great, amazing foundation model companies, and we try to invest in all of them,” Huang said. “We don’t pick winners. We need to support everyone.”

That remark has opened a window into how Nvidia sees the next decade of the technology industry. Unlike a traditional venture capital firm seeking outsized returns from a small set of bets, Nvidia is investing with a far broader strategic purpose: ensuring that whichever company emerges stronger in the AI race, it does so on Nvidia’s infrastructure, software ecosystem, and silicon.

In effect, Nvidia is not merely funding startups; it is underwriting demand for its own future. Huang said the reasoning is rooted partly in humility and partly in history.

“When Nvidia first started, there were 60 3D graphics companies,” he said. “If you would have taken those 60 graphics companies and asked yourself which one was going to make it, Nvidia would be at the top of that list not to make it.”

In the early 1990s, Nvidia was one among dozens of companies chasing the graphics market, and few on the outside would have predicted that it would become the world’s most valuable company and the nerve center of the AI boom.

Huang acknowledged as much.

“Everybody would have counted us out,” he said. “And here we are. So I have enough humility to recognize that. Don’t pick winners.”

For seasoned market watchers, this is a disciplined strategic posture shaped by Nvidia’s understanding of platform economics. Every successful AI lab, robotics firm, self-driving company, or biotech platform creates downstream demand for compute. Every model trained, every inference request processed, every AI agent deployed translates into more GPUs, networking equipment, software licenses, and cloud infrastructure.

This is where Nvidia’s investment approach becomes especially significant. The company is reinforcing the entire AI stack, from the model layer to deployment and real-world use cases. Huang has previously described AI as a “five-layer cake,” spanning energy, chips, data centers, models, and applications.

Seen through that lens, Nvidia’s capital deployment is a deliberate effort to maintain influence across every layer of that architecture. Its recent investments illustrate the scale of that ambition. In November, Nvidia committed up to $10 billion to Anthropic, the company behind Claude, while in February it announced a $30 billion investment in OpenAI.

These are not marginal venture checks but strategic balance-sheet moves that underscore how central foundation model companies have become to Nvidia’s long-term outlook. The company has also backed Mistral AI, Europe’s leading frontier lab, further broadening its exposure to the global model race.

Beyond large language models, Nvidia’s investments extend into adjacent sectors that are likely to define the next phase of AI commercialization. These include Wayve in autonomous driving, Scale AI in data labeling and model infrastructure, and Figure AI in humanoid robotics.

This breadth indicates that Nvidia believes the most durable value in AI will not reside solely in chatbots or consumer-facing assistants, but in the industrial and enterprise applications that emerge from the technology.

Robotics, physical AI, autonomous mobility, and AI-enabled scientific discovery are all compute-intensive domains where Nvidia’s chips and software remain foundational.

There is also a more subtle competitive logic at work. By investing broadly rather than narrowly, Nvidia reduces the strategic risk of backing the wrong horse.

Whether the eventual leader is OpenAI, Anthropic, Mistral, or a company not yet on the radar, analysts believe Nvidia stands to benefit so long as these firms continue to build on its hardware and CUDA ecosystem.

That is what makes Huang’s “don’t pick winners” line so consequential. It is less an expression of neutrality than a declaration of platform confidence. Nvidia no longer needs to predict who wins the AI race because it has positioned itself to profit from nearly every serious contender.

Taiwan Semiconductor Manufacturing Company posted another blockbuster performance on Thursday, reporting a 58% surge in first-quarter net profit that shattered market expectations and extended its streak of record earnings.

The results underscore the unrelenting appetite for advanced chips powering the artificial intelligence boom, even as fresh geopolitical risks swirl around global supply chains.

Net income for the three months ended March reached NT$572.48 billion, comfortably ahead of the LSEG SmartEstimate of NT$543.32 billion. Revenue climbed to NT$1.134 trillion (about $35 billion), topping the expected NT$1.127 trillion and marking a 35% jump from a year earlier. This marks the fourth consecutive quarter of record profits for the world’s largest contract chipmaker.

TSMC first flagged the strong revenue growth last week, but the profit beat provided fresh confirmation of healthy margins and operational efficiency. Asia’s most valuable technology company by market capitalization, TSMC produces the sophisticated semiconductors found in everything from iPhones to data-center accelerators. Its biggest customer is now Nvidia, whose AI processors have become the engine of the current technology cycle, while Apple remains a key partner for mobile chips.

“AI-related demand continues to be extremely robust,” TSMC President and CEO C.C. Wei said during Thursday’s earnings call.

He pointed to rapid advances in artificial intelligence that are driving exponential increases in computation needs. Wei added that the company has received “strong signals and a positive outlook” from customers, reinforcing its belief in a multi-year AI growth cycle.

The company guided for full-year 2026 revenue growth of more than 30% in U.S. dollar terms and projected second-quarter revenue between $39 billion and $40.2 billion, implying roughly 10% sequential growth. It also signaled that capital spending this year will land at the high end of its previously announced range of $52 billion to $56 billion, reflecting aggressive expansion to keep pace with demand.

Advanced process technologies, defined as 7-nanometer and below, accounted for 74% of wafer revenue in the quarter, with chips at the leading-edge 3-nanometer node alone making up 25%. High-performance computing, which includes AI and 5G applications, drove 61% of total sales, highlighting how deeply the AI wave has reshaped TSMC’s business mix.

Smaller nanometer processes deliver greater transistor density, translating directly into higher performance and better power efficiency—the very attributes hyperscalers crave for training and running ever-larger AI models.

Executives used the call to address potential risks from the ongoing Middle East conflict, including possible disruptions to energy supplies and specialty gases such as helium and hydrogen. They reassured investors that near-term operations face no material impact, thanks to diversified sourcing and safety stockpiles of critical materials.

The company is also expanding physical capacity. It recently broke ground on a new advanced fabrication plant in Tainan, Taiwan, as part of a broader global push that includes facilities in the United States and Japan.

Senior analyst William Li at Counterpoint Research captured the current market dynamic, noting: “The narrative for 2026 is as much about resource constraints as it is about growth. Demand still significantly outpaces supply and isn’t showing any major sign of slowing down.”

Li added that the industry is likely to remain in a sold-out environment throughout the year, with semiconductor companies struggling to keep products on shelves. That tightness helps explain TSMC’s decision to lean into the upper end of its capex guidance and why shares have continued to perform well despite broader market jitters.

For all the optimism, the scale of investment required is staggering. TSMC is pouring tens of billions into new fabs and equipment at a time when energy prices remain elevated, and geopolitical tensions from the Strait of Hormuz to U.S.-China tech frictions add layers of uncertainty.

Yet Wei and his team appear convinced that the AI megatrend is structural rather than cyclical, driven by applications that are only beginning to move from data centers into everyday use.

The latest results leave little doubt that TSMC sits at the center of the AI supply chain. As long as hyperscalers, smartphone makers, and emerging AI-adjacent sectors keep placing orders for its most advanced nodes, the foundry giant is positioned to keep printing record numbers. The bigger question for 2026 and beyond is whether the industry can expand capacity fast enough to meet that insatiable demand without triggering the kind of shortages or cost spikes that could eventually cool the boom.

For now, TSMC’s numbers suggest the party is far from over.