Nvidia AI growth could push revenue to $680 billion, Huang says

Jensen Huang doesn’t usually hedge his bets, and on Thursday at the Goldman Sachs Communacopia + Technology conference, he didn’t start now. The Nvidia founder and CEO told the room that his company’s Nvidia AI growth story still has plenty of runway left, doubling down on a forecast that revenue could jump 70% year-over-year next year. Coming from any other executive, that number might sound like wishful thinking. Coming from the man running the company that supplies the chips powering nearly every major AI lab on the planet, it landed as a statement of confidence rather than a guess.

Key takeaways

• Jensen Huang reaffirmed that Nvidia’s revenue could grow 70% year-over-year next year, a forecast first given last month alongside record quarterly earnings.
• Analysts expect Nvidia to close its current fiscal year near $400 billion in revenue, meaning 70% growth would push the company toward roughly $680 billion next year.
• A single Nvidia system pairing 36 Grace CPUs with 72 Blackwell GPUs is seeing 27% month-to-month sales growth.
• Huang said a modern Nvidia GPU now costs around $8.5 million, a sharp jump from the $399 gaming cards the company once sold.
• Nvidia has reportedly seen $100 billion worth of contracts tied to its investments in AI companies that also buy its hardware.

Nvidia’s Bold Revenue Growth Forecast for Next Year

Huang’s headline claim is straightforward: Nvidia expects roughly 70% year-over-year revenue growth heading into next year, and he says the company is confident in that number. “I think we could grow 70% year over year. We’re confident about that,” he told the Goldman Sachs audience, repeating guidance he first floated last month when Nvidia posted another record-breaking quarter.

Jensen Huang’s 70% Revenue Growth Projection

The math behind that forecast is what makes it striking. Analysts currently expect Nvidia to finish its current fiscal year with around $400 billion in revenue. If Huang’s 70% projection holds, that would push the company toward roughly $680 billion the following year — a jump few companies of Nvidia’s size have ever managed to sustain. This is where Jensen Huang forecast talk moves from marketing flourish to something Wall Street has to take seriously, given the scale of dollars involved.

Current Sales Momentum of Grace CPUs and Blackwell GPUs

Huang didn’t lean only on future projections to make his case. He pointed to current demand as proof the trend is already underway. One specific system — a combination of 36 Grace CPUs and 72 Blackwell GPUs — is currently seeing 27% month-to-month sales growth, according to Huang. That kind of momentum in a single product line suggests the appetite for Nvidia’s most advanced hardware isn’t slowing down, even as competitors race to catch up.

Nvidia’s Role as a Foundational AI Platform

Huang’s confidence rests on a simple argument: Nvidia sits at the center of nearly everything happening in AI right now, and that position lets the company see demand trends before almost anyone else does. “We are a foundational platform of the AI ecosystem, foundational platform of the AI industry,” he said.

Extensive Embeddedness in AI Ecosystem and Partnerships

According to Huang, Nvidia’s hardware runs the models built by Anthropic, OpenAI, and Google, along with a wide range of open-weight offerings. “Nvidia runs every model. Every single lab can use us,” he said. That breadth of adoption is central to the Nvidia AI growth narrative Huang laid out — the company isn’t betting on one lab or one cloud provider winning the AI race, because it profits regardless of which model or platform ends up on top.

Nvidia’s Comprehensive Tracking and Market Intelligence

Huang also described a company with unusually deep visibility into the physical infrastructure behind AI. “We’re tracking every single gigawatt of land, power, shell around the world. Literally everything on the planet,” he said, using “shell” to describe a data center building before it’s fitted with computing hardware. He credited that visibility to Nvidia’s web of relationships with neoclouds, original equipment manufacturers, cloud providers, and AI-native startups. “We’re working with everybody, and so we kind of know where everything is,” he said.

Why this matters: that level of market intelligence gives Nvidia an edge that’s hard for rivals to replicate quickly — it’s not just selling chips, it’s tracking global demand for AI infrastructure in near real time, which shapes both its production planning and its confidence in forward guidance.

Innovative High-Performance GPU Products and Their Market Impact

Nvidia invented the GPU decades ago, originally selling it as a consumer product aimed at improving PC gaming graphics. That history still shapes how outsiders think about the company, something Huang pushed back on directly. “Most people think Nvidia builds a chip. I mean, you need airplanes to ship what we build,” he said.

Nvidia’s Origins and Evolution of GPUs

The contrast between then and now is stark. “One GPU now is not $399. It’s $8.5 million dollars,” Huang said, describing a fully connected unit built with 2 million parts and drawing 250,000 kilowatts of power. Nvidia ships thousands of these units, a scale that helps explain why Nvidia GPU sales figures have become a closely watched barometer for the broader AI buildout.

Details on High-Cost GPU Systems and NVLink Technology

NVLink, Nvidia’s interconnect technology, ties these massive systems together, allowing GPUs to function as a single unit rather than isolated chips. It’s a detail that matters for understanding why Nvidia’s pricing has climbed so dramatically — customers aren’t buying a chip anymore, they’re buying an integrated computing system built for AI workloads at industrial scale.

Nvidia’s Strategic Investment Model and Circular Deals

Nvidia has drawn scrutiny for a practice sometimes called circular dealmaking: investing in companies that then use that money to buy Nvidia’s own products. Huang addressed the criticism head-on and pushed back on the framing.

Nvidia’s $100 Billion Contracts Through Investments

“Well, it’s not circular because we put a little bit of money in, and a lot of money comes back,” Huang said, adding a bit of humor to make his point: “I look at the spreadsheet, we put in $1 and $100 comes back in. Is that circular? If that is, let’s do more of that.” He said he’s tracked $100 billion worth of such contracts across Nvidia’s investment portfolio.

CEO Huang’s Risk Management Philosophy

Huang insisted the arrangement isn’t as loose as critics suggest. Before Nvidia invests in any company, he said, it confirms that company already has real, revenue-generating contracts with paying customers. “I’m not taking any risks. … I need a sure thing,” he said. That comment is a direct response to comparisons some have drawn to the collapse of earlier internet-era infrastructure suppliers like Lucent Technologies, whose vendor-financing arrangements famously unraveled when customer demand didn’t materialize as expected.

Confidence Amid Growing Competition and Industry Dynamics

Nvidia isn’t operating without rivals nipping at its position. Hyperscalers including Amazon, Microsoft, and Google are each building their own AI chips, and AI labs like Anthropic and OpenAI are pursuing custom silicon of their own. Newly public chipmaker Cerebras and startups such as Etched are also pushing into territory Nvidia has dominated for years, intensifying AI chip competition across the industry.

Competition from Hyperscalers and New Entrants

Despite that pressure, Huang maintained his optimism. “We’re confident about that,” he said when discussing Nvidia’s growth outlook, brushing aside concerns that rival chip programs could dent demand for Nvidia’s hardware anytime soon.

Long-Term Disruption Risks and Industry Evolution

It’s worth noting that even Huang has acknowledged part of AI’s growth is being driven by AI-native startups pouring huge sums of raised capital directly into their own infrastructure spending — a dynamic that depends on continued investor appetite for AI bets. History also offers a caution: no dominant technology supplier stays unchallenged forever, and as the AI industry matures, companies are likely to get more efficient with how they use computing infrastructure and tokens, which could eventually reshape demand patterns. For now, though, Nvidia’s reach across labs, clouds, OEMs, and startups gives it a vantage point few competitors can match, and Huang is betting that view of the market is worth its 70% forecast.

FAQ

Why does Jensen Huang believe Nvidia can grow revenue by 70% next year?

Because Nvidia is deeply embedded in every area of AI, runs every model, and has strong sales growth for advanced GPU and CPU systems.

What is the significance of the Grace CPUs and Blackwell GPUs system for Nvidia’s growth?

This system combines 36 Grace CPUs with 72 Blackwell GPUs and is seeing 27% monthly sales growth, indicating strong market demand.

How does Nvidia’s investment strategy support its business model?

Nvidia invests only after companies have revenue-generating contracts, leading to $100 billion in contracts, which minimizes investment risks.

What challenges could Nvidia face in maintaining its AI market dominance?

Growing competition from hyperscalers building their own AI chips and newcomers like Cerebras and Etched, plus industry disruption risks.

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Article produced with the assistance of artificial intelligence and reviewed by the editorial team.