A Wall Street analyst has put a staggering $13 trillion valuation on Nvidia, arguing that the AI giant's growth runway is much longer than investors currently appreciate.

Nvidia has already become the largest company in the world.

Now one Wall Street analyst believes it could eventually become a $13 trillion company.

Raymond James analyst Simon Leopold raised his Nvidia price target to $550 from $352 after the chipmaker's latest earnings report. His new target implies approximately 130% upside from Nvidia's market value around the time of the report and would place the company's valuation near $13 trillion.

That is an extraordinary forecast for a company already worth roughly $5.2 trillion to $5.5 trillion.

It is also not based simply on enthusiasm surrounding Nvidia's current GPU business.

Leopold's thesis depends on something much more consequential:

Nvidia's AI growth engine may still be accelerating several years from now.

That conclusion challenges one of the market's biggest assumptions—that the extraordinary expansion of AI infrastructure will eventually slow because Nvidia has already become too large.

According to Leopold, that slowdown may be much farther away than investors think.

Nvidia's latest quarterly results were already spectacular.

The company reported approximately $96.2 billion in quarterly revenue, up more than 100% from a year earlier.

Data Center revenue climbed to around $89 billion, up 117%.

The company then forecast approximately $108 billion of revenue for the following quarter.

Those results were impressive.

But for Leopold, the more important number was not the next quarter.

It was the company's projection for fiscal 2028.

Nvidia expects roughly 70% revenue growth for that year.

Wall Street had been expecting something closer to 45%.

That gap is enormous.

A mature megacap company producing 10% growth can surprise investors meaningfully with 15%.

A company worth more than $5 trillion producing 70% growth would be operating on an entirely different scale.

Leopold believes that is what investors may now be underestimating.

Nvidia's future is no longer entirely dependent on one chip category.

The company's next-generation architectures are creating additional layers of growth.

Leopold pointed to architectural developments involving Nvidia's Vera Rubin platform, the company's networking products and new computing configurations designed around agentic AI workloads.

That matters because the AI market itself is changing.

The industry is moving beyond simple model training toward systems that can reason, execute multi-step tasks and operate continuously.

Those workloads require substantial computing power.

They can also require different combinations of GPUs, CPUs, networking and memory.

Nvidia wants to supply all of it.

This is arguably the strongest part of the $13 trillion thesis.

Nvidia's historical growth came primarily from selling high-performance accelerators.

But today's AI infrastructure requires much more than an accelerator.

A massive AI cluster requires:

Processing power.

High-speed networking.

Interconnects.

Memory.

Storage.

Software.

Power management.

Cooling.

Systems engineering.

Nvidia is increasingly involved in many of those layers.

If its share of overall AI infrastructure spending rises, the company can continue growing even if its share of the GPU market eventually moderates.

That is why its future is increasingly being viewed as a platform story rather than a semiconductor story.

One of the strongest challenges to Nvidia is the rise of custom AI silicon.

Google has its own accelerators.

Amazon and Microsoft are building proprietary chips.

Meta is investing heavily in specialized hardware.

Other companies are developing alternative architectures.

At first glance, that appears to threaten Nvidia directly.

But Leopold's thesis implicitly takes a broader view.

Even custom chips need to communicate with one another.

They need high-speed networking.

They need systems infrastructure.

They need software.

And in many cases, customers still need Nvidia technology elsewhere in the data center.

Nvidia's strategy increasingly allows it to remain involved even when an individual workload is powered by a non-Nvidia processor.

That could make the company's competitive position more durable.

Another part of the bullish case revolves around agentic AI.

Today's generative AI applications often respond to prompts.

Agentic systems are designed to perform sequences of actions, make decisions and operate more continuously.

That can produce substantially more computing demand.

An AI assistant used occasionally by an individual is one thing.

An autonomous digital worker operating around the clock for a large enterprise is another.

If agentic AI becomes economically useful at scale, the amount of computing required could expand significantly.

That is one reason Nvidia and other infrastructure companies are investing so heavily in next-generation systems.

The potential market is not only larger models.

It is more AI activity.

One of the least appreciated aspects of Nvidia's growth is networking.

The company's networking division has expanded dramatically as AI clusters have become larger and more complicated.

When thousands of processors operate together, communication between them becomes a critical performance bottleneck.

Nvidia has developed technology designed specifically for those workloads.

That gives it another way to capture spending.

And this is strategically important because networking demand can remain strong even when customers diversify away from Nvidia's GPU products.

Nvidia therefore has a potential hedge built into its infrastructure strategy.

Leopold also highlighted Nvidia's aggressive capital returns.

The company returned approximately $26 billion to shareholders in its latest fiscal second quarter, including about $20 billion in share buybacks and roughly $6 billion associated with dividends, according to Yahoo Finance's report.

Buybacks can be particularly powerful when a company's earnings are rising quickly.

The number of shares outstanding declines.

Earnings per share can rise faster than net income.

And investors receive a direct benefit from the company's enormous cash generation.

Nvidia's ability to fund both growth investments and large capital returns simultaneously is one of the clearest signs of its financial strength.

There is, however, a major issue with the forecast.

$13 trillion is not a normal valuation target.

It would make Nvidia's market capitalization more than double its current size.

To sustain such a valuation, the company would need to produce extraordinary profits for years.

The AI infrastructure market would need to remain enormous.

Competition could not erode its margins too severely.

Custom silicon would need to coexist with Nvidia rather than displace it.

And the broader economy would need to remain favorable to technology investment.

That is a lot to assume.

A price target is not a guarantee.

It is an expression of a particular set of assumptions.

Leopold's assumptions are unusually bullish.

Ironically, Nvidia's enormous market capitalization may make the stock appear more expensive than it actually is relative to its earnings trajectory.

The key is the multiple applied to future profits.

If Nvidia's earnings continue growing much faster than expected, today's valuation can become substantially more reasonable.

That is precisely why analysts focus so intensely on fiscal 2028 growth.

A company growing at 70% several years into the future is fundamentally different from a company expected to grow at 20%.

The future earnings base changes dramatically.

A $13 trillion outcome requires a lot to go right.

The AI capital-spending cycle could slow.

Cloud companies could decide their AI infrastructure purchases have reached sufficient scale.

Model efficiency could reduce computing demand.

New competitors could produce cheaper or better accelerators.

China could remain effectively closed to Nvidia's advanced hardware.

Margins could decline as component costs rise.

Regulators could become more aggressive toward Nvidia's growing influence across the AI ecosystem.

And perhaps most importantly, the company could simply become too large to continue growing at the required rate.

These risks are real.

There is also an increasingly controversial element to Nvidia's strategy.

The company is helping AI cloud providers secure financing for infrastructure built around its chips.

Its latest filing showed approximately $36 billion in commitments connected to cloud-service arrangements that typically last six years. Under certain conditions, Nvidia can participate in revenue generated by cloud partners from third-party customers.

This creates an intriguing financial loop.

Nvidia sells the hardware.

The customer finances the infrastructure.

The customer rents the infrastructure to other AI companies.

Nvidia potentially receives a share of the resulting revenue.

The model could create a powerful ecosystem.

It also creates risk.

If demand weakens, Nvidia's exposure could go beyond lost chip sales.

It could potentially be exposed to the economics of the cloud infrastructure itself.

The $13 trillion forecast ultimately rests on one foundational belief.

AI is still in its early stages.

Today's data centers may represent only the first generation of infrastructure required for a much larger AI economy.

As models become more capable and AI systems move into coding, business operations, robotics, scientific research and autonomous agents, computing demand could continue expanding.

If that happens, Nvidia could remain the most important infrastructure supplier in the industry.

And if the company captures a large share of that spending, the $13 trillion figure becomes less fantastical.

Still enormous.

But mathematically possible.

Leopold's optimism stands out.

Yahoo Finance's AlphaSpace data placed the average Nvidia analyst price target at around $306, far below his $550 forecast.

That difference tells investors something.

Nvidia has one of the widest gaps between conventional Wall Street expectations and extreme bull-case scenarios.

Some analysts see a company that is already enormous and therefore mathematically constrained.

Others see a platform at the beginning of a much broader AI infrastructure revolution.

The debate is no longer simply about whether Nvidia is good.

It is about how much of the future Nvidia can own.

Thursday's market response was dramatic.

Nvidia shares jumped 8.7%, adding approximately $442 billion in market value.

That move suggests investors are increasingly willing to accept the idea that the AI boom remains in its expansion phase.

The $13 trillion forecast therefore arrives at an interesting moment.

Wall Street has just been reminded that Nvidia can still surprise to the upside.

Now investors must decide how much future growth deserves to be priced in.

Nvidia does not need to reach $13 trillion for the bullish thesis to succeed.

If the company simply continues producing rapid growth for several years, shareholders could still benefit substantially.

The $13 trillion target is best understood as a statement of scale:

The AI market could become so enormous that even the world's largest semiconductor company can continue expanding at rates that seem impossible today.

That is the core of Leopold's argument.

Nvidia is not finished capturing the AI economy.

It may only be beginning to expand beyond the original GPU opportunity.

And that is why the $13 trillion number is grabbing attention.

Not because anyone can know the future with certainty.

But because Nvidia's latest earnings have made the most aggressive version of the AI growth story look surprisingly difficult to dismiss.

Source basis: Yahoo Finance's August 27, 2026 report on Raymond James analyst Simon Leopold's revised price target, supported by Nvidia's latest earnings and market data.