Nvidia Just Revealed the Next Big AI Bottleneck

Listen to the audio version of this article (generated by AI).

On a plateau south of Bandung, in what is now Indonesia, Dutch planters spent decades cultivating a scrubby South American tree.

It was called cinchona.

And for a long time, it was one of the most strategically important plants on Earth.

Its bark contained quinine, which was then the world’s best defense against malaria. If European powers wanted to build railroads, man military outposts, or expand deeper into the tropics, they needed quinine.

And the Dutch controlled almost all of it.

By the 1920s, plantations in the Dutch East Indies supplied more than 90% of the world’s cinchona. Prices were effectively controlled by a cartel formed by growers and manufacturers that set production quotas and prices, referred to as Amsterdam’s Kina Bureau.

And for years, everyone paid the toll.

Then the supply disappeared.

Germany occupied the Netherlands in 1940 while Japan captured Java in 1942, and, almost overnight, the Allies lost access to both the cinchona plantations and its processing infrastructure.

The consequences were brutal.

During the Pacific campaign, malaria hospitalized more soldiers than enemy fire. By December 1942, more than 8,500 American troops were hospitalized with the disease. In some wards, in eight out of every 10 beds laid a soldier suffering from fever rather than wounds from combat.

A massive military machine had been built on top of one tiny bottleneck. And almost nobody appreciated how important it was until that bottleneck broke.

I’ve been thinking about that story this week because Nvidia Corp. (NVDA) just reported earnings, which spoke to where the next big profits in the AI Boom could show up.

It wasn’t Nvidia’s revenue or its earnings. Rather, the big story is in Nvidia’s margins.

Let me explain.

The Number Everyone Is Watching

First, the headline numbers were incredible.

Nvidia gave investors unusually explicit visibility into its next fiscal year, with management targeting revenue growth of around 70% in fiscal 2028, which roughly corresponds to calendar 2027.

Wall Street was expecting something much lower.

Consensus estimates implied roughly 45% growth next year, putting revenues near $580 billion.

At 70% growth, the number is closer to $680 billion.

In other words, Wall Street may have been underestimating Nvidia’s annual revenue by roughly $100 billion!

That’s an extraordinary difference. 

But you can understand why analysts were cautious.

Nvidia has become enormous. And normally, enormous companies eventually run into the law of large numbers.

Growth simply gets harder.

Yet Nvidia’s recent growth rates have gone the opposite direction: 56%, 62%, 73%, 85%, 106%.

Eventually, Wall Street assumed that pace would fall back toward Earth.

But that slowdown, according to Nvidia, may be much shallower than expected.

Yes, growth should cool from here. But management is effectively saying that it could stabilize near 70%.

That is remarkable for a company already operating at Nvidia’s scale.

And it helps explain why NVDA stock rallied after earnings instead of falling like it has after several recent reports.

But that wasn’t the most interesting part of the quarter…

The Number That Matters More

Here’s the number I think investors should pay much closer attention to.

Nvidia guided gross margins lower.

Normally, that would be a warning sign.

Lower margins mean a company keeps less profit from every dollar of revenue.

But the reason Nvidia’s margins are declining completely changes the interpretation.

Demand isn’t the problem.

Demand is running above the company’s ability to supply it.

Nvidia is effectively saying growth could be even stronger if it could get enough components.

And that means the company is having to pay more to secure scarce supply.

That’s where things get interesting.

Because Nvidia’s lost margin isn’t simply disappearing.

It is moving somewhere else.

It is moving into the pockets of the suppliers that own the components Nvidia desperately needs.

Micron Technology Inc. (MU) has pricing power.

So does SanDisk Corp. (SNDK).

So do the optical networking companies.

In other words, the biggest AI company on Earth is telling us that the next major profit pool may be forming one layer down the supply chain.

That’s the bottleneck trade.

And it has been one of the defining patterns of the AI Boom.

Follow the Bottleneck

The AI Boom has never really been one trade but a rolling series of bottlenecks.

And each bottleneck has created a new group of winners.

First came compute.

AI companies couldn’t train frontier models without GPUs, so Nvidia became the critical supplier. Revenue exploded from about $27 billion to well over $100 billion, and the stock followed.

Then came servers.

Someone had to package all those GPUs into usable systems. For a stretch, Super Micro Computer Inc. (SMCI) became one of the fastest-growing companies in the S&P 500.

Then came cooling.

Stuff that much computing power into one building and traditional air cooling stops working. Suddenly, liquid cooling became mission-critical, and Vertiv Holdings Co. (VRT) transformed from a relatively obscure infrastructure company into a major AI trade.

Then came energy.

Data centers started consuming more electricity than utilities could easily deliver. Nuclear power went from yesterday’s technology to one of Wall Street’s hottest AI infrastructure themes, helping stocks like Constellation Energy Corp. (CEG) soar.

Then came memory.

AI inference requires enormous memory bandwidth. High-bandwidth memory became scarce, creating another wave of winners.

That’s five bottlenecks in roughly three years, each following an identifiable pattern.

First, hardly anyone cares.

Then supply tightens.

Then pricing power improves.

Then earnings explode.

Then Wall Street notices.

And eventually, everyone piles into the trade.

That is why the most useful question in AI investing is to ask where the next bottleneck is forming.

Because wherever the hyperscalers are about to spend their next $100 billion, there is probably a shortage forming somewhere nearby.

Right now, two of the biggest constraints appear to be memory and networking. And I think networking is especially interesting.

Most investors still think the main constraint inside an AI data center is the chip. But once you pack hundreds of thousands of processors into one facility, those processors need to operate together like one giant computer.

If they cannot communicate fast enough, say goodbye to performance.

Suddenly, the cable that connects racks can become almost as strategically important as the chips powering them.

That’s why Nvidia’s moves in optical networking are critical.

Earlier this year, the company committed billions of dollars to secure supply from Lumentum Holdings Inc. (LITE) and Coherent Corp. (COHR). Companies only make commitments like that when they are worried about supply.

In other words, Nvidia is showing us where one of its own chokepoints lies. And historically, that is exactly where investors should be looking.

Which Brings Me to Elon

Every company in the AI Boom pays a bottleneck tax.

The hyperscalers pay it to Nvidia.

Nvidia pays it to memory and optical suppliers.

And everyone pays it to utilities and power infrastructure providers.

But one person has spent years trying to eliminate as many of those tollbooths as possible.

Elon Musk.

Ignore whatever you think about Musk personally and just look at how his companies are structured.

He owns a massive source of real-time training data.

He builds enormous AI compute clusters.

He owns rockets.

He owns a satellite communications network.

And he builds physical machines that could eventually use the intelligence produced by those systems.

Look at those businesses individually, and they can seem chaotic. Look at them together, and a pattern emerges.

Musk is systematically trying to control more of the infrastructure required to produce and distribute intelligence. That means owning bottlenecks instead of paying someone else to control them.

It’s an old industrial playbook.

John D. Rockefeller built his own barrels because suppliers charged too much.

Henry Ford bought mines, railroads, forests, and shipping assets because he wanted more control over his supply chain.

Musk has repeatedly done the same thing.

If a component is too expensive, too slow, or too difficult to procure, his instinct is often to bring it in-house. But that creates an interesting investing filter because Musk still buys plenty of things from outside suppliers.

And after two decades of aggressive vertical integration, the companies that remain inside his supply chain are probably there for a reason.

Whatever they make, Musk has likely asked some version of the same question:

Can we build this ourselves?

And if the answer was no, that tells you something no Wall Street analysis could. It suggests that supplier may possess technology, manufacturing expertise, scale, or intellectual property that is unusually difficult to replicate.

In other words, a moat.

And I think Wall Street dramatically underestimates how valuable that information can be.

The Bottom Line

Nvidia’s latest quarter told investors two important things:

First, the AI Boom remains incredibly powerful. The company essentially told Wall Street that growth could stay near 70% even at an almost unimaginable scale.

But second, and perhaps more importantly, Nvidia showed us that the bottleneck is moving.

Nvidia is even giving up some margin because suppliers now have more leverage. And that means the profit pool is shifting again.

We’ve watched this happen repeatedly over the past three years.

Compute.

Servers.

Cooling.

Energy.

Memory.

Now networking.

The names may change over time but the pattern stays the same.

When something becomes scarce in a massive investment boom, pricing power flows toward whoever controls that scarce resource. That was true when global empires depended on cinchona bark growing on a Javanese plateau. And it is true today when trillion-dollar AI companies depend on specialized components buried deep inside their data centers.

So don’t just watch what the giants are building. Watch what they cannot build themselves. Because that’s often where the next great AI investment opportunity is hiding.

I’ve spent much of this year doing exactly that inside one particular empire. And what I found surprised me enough that in September, I’m doing something I’ve never done before.

I’ll talk more about that this week, so keep an eye on your inbox for our daily issues.

For now, remember the lesson Nvidia just gave us: Follow the bottleneck.

That’s where the pricing power lives. And eventually, that’s where the profits tend to follow.