A $10 trillion valuation sounds extreme even for a behemoth like Nvidia (NVDA), but CEO Jensen Huang didn’t bat an eye when asked directly by popular podcaster Lex Fridman.
“I think that Nvidia’s growth is extremely likely and in my mind, inevitable.”
Clearly, this is a response from a highly confident CEO, with no caveats, just conviction.
At the time of writing, Nvidia stock traded at $175.20 a share, according to Yahoo Finance.
So if we do the math, a whopping $10 trillion valuation implies roughly $5.47 trillion in additional market value, a tremendous 121% upside from the AI bellwether’s current $4.53 trillion market cap. That equals an implied share price of nearly $387.09, assuming no new shares are issued.
Clearly, the fundamentals still back up such lofty long-term claims.
It recently posted blow-out quarterly results, delivering $68.1 billion in sales, surging 73% from the prior-year period, spearheaded by a massive $62.3 billion from data centers.
As we look ahead, Nvidia expects to generate $78 billion in the current quarter, which shows hyperscaler demand is still ramping at an impressive pace.
Those results were followed by another successful GTC conference.
At GTC 2026, Nvidia unveiled its hotly anticipated rack-scale system, Vera Rubin, tailor-made for the age of AI factories. On top of that,
Alongside it, it showed off a Physical AI Data Factory blueprint targeting robotics, autonomous systems, and real-world AI deployment.
Moreover, demand from hyperscalers remains as aggressive as ever.
To back that claim, AWS alone plans to purchase up to 1 million GPUs by 2027, alongside other key partnerships.
As a result, AI is becoming foundational to the global economy, and Nvidia is positioned at its center.
Nvidia CEO Jensen Huang offered a blunt take on the company’s long-term growth potential during an interviewPhoto by Benjamin Fanjoy on Getty Images ·Photo by Benjamin Fanjoy on Getty Images
Nvidia currently boasts a market cap of nearly $4.26 trillion.
More Nvidia:
For perspective, according to Reuters, it first reached a $1 trillion market cap intraday on May 30, 2023, and closed above that threshold for the first time on June 13, 2023.
Nvidia’s highest market cap to date, though, is near $5.03 trillion, reached at the close on Oct. 29, 2025, when Reuters reported it as the first company to hit $5 trillion in market value.
At the end of 2020, Nvidia’s market cap was $323.24 billion.
At the end of 2021, Nvidia’s market cap was $735.27 billion.
At the end of 2022, Nvidia’s market cap was $364.18 billion.
At the end of 2023, Nvidia’s market cap was $1.223 trillion.
At the end of 2024, Nvidia’s market cap was $3.288 trillion.
At the end of 2025, Nvidia’s market cap was $4.638 trillion. Sources: Reuters; CompaniesMarketCap.
Huang’s response to Friedman’s $10 trillion question wasn’t really about valuation at all.
In fact, the Nvidia chief argues that AI will fundamentally reshape computing, significantly increasing the company’s valuation in the process.
His first big point is that computers have essentially moved from retrieval to generation.
With the advent of AI, computers are now much more contextually aware, meaning they’re generating answers, decisions, and outputs in real time. Naturally, that involves tons more computing power than ever before.
His second point cuts even deeper, where he feels that a computer is now a factory.
He adds, “Warehouses don’t make much money. Factories directly correlates with a company’s revenues.”
In his setup, AI systems produce what he refers to as “tokens,” which are economic goods that can be priced, tiered, and sold.
Elaborating further, he said that these tokens are now starting to segment, like iPhones,” with ‘free tokens, premium tokens,’ offering an array of options in between.
He then followed it up with one of the boldest claims on token economics, saying,
Huang then dovetailed into the macro-level, arguing “I am absolutely certain that the world’s GDP is going to accelerate in growth,” and “the percentage of that GDP that will be used for computation will be a hundred times more than the past because it’s no longer a storage unit, it’s a product generation unit.”
So in essence, the pie itself is evolving, and if AI becomes as embedded in a variety of industries as Huang states, then compute will rise structurally, not cyclically.
On operational feasibility, Huang remained mostly upbeat, arguing that $3 trillion in sales is very much within Nvidia’s reach.
As per Seeking Alpha, Nvidia’s trailing twelve-month (TTM) revenue stands at around $215.9 billion. That means a $3 trillion sales target requires a 14x increase from current levels.
Though it may seem audacious, Huang argued that Nvidia is “not limited by any physical limits,” and the burden of its expansion is shared by 200 companies, meaning the supply chain can scale with sufficient energy and coordination.
Huang covered multiple topics in his appearance on the Lex Fridman podcast, mapping out the future of AI from Nvidia’s lens.
His talking points covered everything from computing infrastructure and limits of scaling to geopolitics and the future of programming.
For context, Fridman is an MIT-affiliated scientistwho hosts the popular Lex Fridman Podcast, interviewing the biggest names across technology, science, business, politics, and culture.
His main YouTube channel has over 4.95 million subscribers.
Here are some of the key points he covered during a riveting conversation that spanned more than two hours.
Extreme co-design: Nvidia has evolved from being just a chipmaker to now engineering full-stack, rack-scale AI systems tailored to deliver maximum performance.
Flat leadership model: Huang manages 60 direct reports, enabling quicker decision-making and tighter collaboration across different teams.
Scaling challenges: AI is shifting toward inference, with reasoning and planning requiring much more compute.
Supply chain pressure: Nvidia depends on partners like TSMC and SK Hynix to effectively address packaging and memory bottlenecks. As we look ahead, future data centers will hinge on energy access, which will become mission-critical over time.
Colossus buildout:Elon Musk’s Memphis supercomputer underscores rapid deployment led by urgency and engineering intensity.
Geopolitical risk: Taiwan’s chip ecosystem remains crucial, especially amid the evolving U.S.-China tensions.
Competitive moat: CUDA’s massive developer base is anchoring Nvidia’s dominance.
Future of coding: Programming is now moving towards instructing AI systems rather than writing traditional code. Source: Jensen Huang’s appearance on the Lex Fridman podcast.
Over 1M, NVIDIA returned -8.53% versus -4.14% for the S&P 500.
Over 6M, NVIDIA returned -1.80% versus -1.54% for the S&P 500.
On a YTD basis, NVIDIA returned -6.05% versus -4.25% for the S&P 500.
Over 1Y, NVIDIA returned 48.86% versus 15.65% for the S&P 500.
Over 3Y, NVIDIA returned 544.37% versus 65.99% for the S&P 500.
Over 5Y, NVIDIA returned 1,240.47% versus 67.62% for the S&P 500.
Over 10Y, NVIDIA returned 20,255.50% versus 221.83% for the S&P 500. Source: Seeking Alpha.
