Nvidia (NASDAQ: NVDA) gained 2.26% to $179.15 after CEO Jensen Huang delivered a blunt message that quickly lit up the AI trade and revived one of Wall Street’s biggest valuation debates. In a recent conversation with Lex Fridman, Huang did not hesitate when asked about Nvidia’s long-term growth outlook, saying the company’s expansion is “extremely likely” and, in his words, “inevitable.”
That short remark may have sounded simple, but it carried massive implications for investors. At the time, Nvidia’s market capitalization stood around $4.53 trillion, which means a $10 trillion valuation would require roughly $5.47 trillion in additional value creation, or about 121% upside from current levels. On a per-share basis, that would imply a stock price near $387.09 if no additional shares were issued.
For a company already seen as the face of the AI boom, that kind of target sounds aggressive. But Huang’s latest comments show that Nvidia is no longer framing its future around just chips. It is now pitching itself as the central infrastructure provider for an economy increasingly built on artificial intelligence, token generation, and what Huang describes as “AI factories.”
Nvidia’s latest results are why the market keeps listening
The reason investors continue to take these bold statements seriously is simple: Nvidia’s numbers remain extraordinary. The company recently posted quarterly revenue of $68.1 billion, up 73% from the year-ago period, with data center revenue alone reaching a massive $62.3 billion. Even more striking, Nvidia said it expects $78 billion in revenue in the current quarter, suggesting hyperscaler demand is still accelerating rather than cooling.
Those are not just strong semiconductor numbers. They are the kind of figures that support the argument that AI spending is becoming a foundational layer of global business. Nvidia is not merely benefiting from a temporary cycle. It is increasingly positioned as the supplier behind the infrastructure that companies like Microsoft, Amazon, Alphabet, and others need to scale AI products, train models, and handle inference workloads.
That demand story is also being reinforced by customer appetite. AWS alone is expected to purchase up to 1 million GPUs by 2027, according to the information you shared, while Nvidia’s broader partnerships continue to deepen across the hyperscaler ecosystem.
The $10 trillion argument is really about AI economics
Huang’s answer to the $10 trillion question was not really about market cap in the traditional sense. His argument was that artificial intelligence is transforming computing into something far more economically productive than before. In his framing, computers are no longer just systems for retrieval, storage, or software execution. They are becoming factories.
That distinction matters because factories produce output that can be sold. Huang argued that AI systems are generating “tokens,” and those tokens are increasingly becoming economic goods that can be priced, segmented, and monetized. He even compared the evolving token landscape to consumer product tiers, with free tokens, premium tokens, and a range of options in between.
He pushed the idea further by saying he is “absolutely certain” global GDP growth will accelerate and that the percentage of GDP allocated to computation could become 100 times larger than in the past. In other words, Nvidia’s bull case is no longer tied only to a bigger chip market. It is tied to the belief that AI will structurally expand the share of the world economy spent on compute.
GTC 2026 added more fuel to the Nvidia story
Nvidia’s latest GTC conference gave investors more reasons to stay focused on the company’s product roadmap. At GTC 2026, Nvidia unveiled Vera Rubin, its anticipated rack-scale system designed for the era of AI factories. That announcement signaled that Nvidia is continuing to move beyond stand-alone chips and deeper into complete AI infrastructure systems.
The company also introduced its Physical AI Data Factory blueprint, aimed at robotics, autonomous systems, and real-world AI deployment. Those announcements matter because they widen Nvidia’s addressable market. The company is not just targeting model training anymore. It is pushing deeper into inference, robotics, industrial AI, and physical-world automation.
That strategy strengthens Nvidia’s moat. The company’s edge is no longer just GPU performance. It now spans systems architecture, networking, software ecosystems, and CUDA’s vast developer base, which remains one of the strongest competitive advantages in the AI landscape.
Nvidia’s market cap rise has already been historic
Even before any talk of $10 trillion, Nvidia’s climb has been staggering. According to the figures you shared, Nvidia’s market cap stood at $323.24 billion at the end of 2020, rose to $735.27 billion by the end of 2021, then dropped to $364.18 billion in 2022 before exploding to $1.223 trillion in 2023, $3.288 trillion in 2024, and $4.638 trillion by the end of 2025.
Reuters previously reported that Nvidia first reached a $1 trillion market cap intraday on May 30, 2023, and closed above that level for the first time on June 13, 2023. The company’s highest market value to date was near $5.03 trillion at the close on Oct. 29, 2025, when it became the first company to hit the $5 trillion mark.
That historical backdrop is important because it shows that Nvidia has already delivered valuation milestones that once looked unrealistic. For bulls, that makes the $10 trillion debate easier to entertain. For skeptics, it raises the question of how much future dominance is already priced in.
Huang’s AGI comments added another controversial layer
Another headline-grabbing moment from Huang’s Lex Fridman appearance came when he said he believes AGI has effectively been achieved. His reasoning was not based on a traditional academic definition of human-like intelligence. Instead, he suggested AI is already capable of creating billion-dollar businesses under the right circumstances, such as building a web service or app that briefly captures enormous demand.
That is a provocative definition, and investors should view it carefully. Huang also acknowledged clear limits, saying the odds of 100,000 AI agents building Nvidia are “zero percent.” He made it clear that software engineers at Nvidia will grow in importance, not shrink, because solving problems and writing code are related but not identical tasks.
Still, the comment supported the bigger message he wanted the market to hear: AI is no longer just a useful tool. It is becoming economically productive enough to create products, services, and possibly entire companies.
What investors should focus on now
The long-term case for Nvidia still comes down to execution. Huang argued that even $3 trillion in annual sales is operationally achievable, despite Nvidia’s current trailing twelve-month revenue of about $215.9 billion. He said the company is not bound by hard physical limits in the usual sense because scaling is shared across a supply chain involving around 200 companies. That also highlights the key risks: dependence on partners like TSMC and SK Hynix, packaging and memory bottlenecks, and energy becoming mission-critical for future data centers.
For now, the 2.26% rise in Nvidia stock reflects more than a catchy soundbite. It reflects a market that still believes AI demand is intensifying, Nvidia remains at the center of it, and Jensen Huang is willing to make one of the boldest long-term growth arguments in corporate America.
Readers can review Nvidia’s latest financial results on Nvidia’s official newsroom and broader market-cap milestone reporting from Reuters.
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