Something interesting happened in Silicon Valley earlier this month.
Nvidia, the company that has made billions selling the chips powering the artificial intelligence boom, agreed to spend almost $13 billion buying a company most ordinary investors have probably never heard of.
It’s called Hugging Face.
And here’s what makes the deal so interesting.
Nvidia could probably have built much of what Hugging Face does itself.
It has the engineers. It has the technology. It certainly has the money.
Yet rather than spend years trying to recreate the business, it agreed to write a cheque for almost $13 billion.
Why?
Because Nvidia wasn’t simply buying technology.
It was buying something far more difficult to build.
And understanding what that is could tell you a great deal about where the next big investment opportunities in AI will come from.
Let me explain…
Nvidia didn’t need another AI model. It needed the people using them.
For the past few years, the AI investment story has been dominated by a relatively simple question.
Who can build the most powerful artificial intelligence?
OpenAI. Google. Anthropic. Meta.
And, of course, Nvidia, supplying much of the computing power that makes their ambitions possible.
Investors have poured money into the companies building AI models, the chips running them and the enormous data centres housing them.
But something important is happening as AI moves beyond the experimental stage.
The industry is shifting from building artificial intelligence to putting it to work.
And that requires an entirely different collection of businesses.
Companies need somewhere to find AI models.
They need reliable data to train and improve them.
They need tools to test, customise and deploy them.
And they need trusted platforms where developers can share their work and build on what others have already created.
That’s where Hugging Face comes in.
Think of it as a giant marketplace and workshop for artificial intelligence.
It hosts more than 3 million AI models, 500,000 datasets and 1 million applications.
More than 18 million developers use the platform.
And over 200,000 companies, from small startups to global corporations, use it to discover, test, customise and deploy AI.
NVIDIA Blog
In other words, Hugging Face has become one of the places where the AI industry goes to get things done.
And that brings us to the real reason Nvidia was prepared to pay so much.
You can build a competing platform. You can’t instantly build 18 million developers who want to use it.
The one thing Silicon Valley’s biggest companies can’t manufacture
Nvidia could hire thousands of engineers tomorrow.
It could spend billions developing a competing platform.
It could offer developers free access, better tools and more powerful computing resources.
But there is one thing it cannot simply manufacture.
Time.
Hugging Face has spent years building relationships with developers, researchers, companies and open-source communities.
Those people contribute models.
They share data.
They develop applications.
They attract other developers who contribute more models, more data and more applications.
And the whole thing becomes increasingly valuable as more people join.
Economists call this a network effect.
But the principle is simple.
The more people use a platform, the more useful that platform becomes. And the more useful it becomes, the harder it is for competitors to persuade people to leave.
This is what Nvidia is buying.
Not simply millions of lines of code.
Years of accumulated relationships, trust, adoption and market position that money alone cannot instantly reproduce.
And if that sounds familiar, it should.
Because Silicon Valley has been playing this game for decades.
Remember when Facebook paid $1 billion for a photo-filter app?
In 2012, Facebook announced it was buying Instagram for approximately $1 billion.
At the time, Instagram had just 13 employees.
Almost no revenue.
And a product that many people associated with putting vintage filters on photographs.
Facebook could easily have built its own photo-sharing application.
It already had the world’s biggest social network.
It had engineers, money and hundreds of millions of users.
So why spend $1 billion?
Because Instagram had something Facebook couldn’t simply build overnight.
A rapidly growing community of people who had already decided that Instagram was where they wanted to share their photographs.
Mark Zuckerberg wasn’t buying photo filters.
He was buying a network.
And, perhaps more importantly, he was buying the time it would have taken Facebook to build a competing one.
Google made similar calculations when it bought Android and YouTube.
Microsoft did the same when it bought GitHub.
Different technologies. Different industries. Different prices.
But the same underlying principle.
When a market is moving quickly, buying an established position can be more valuable than spending years trying to build one.
And AI is moving faster than almost any major technology market we’ve seen before.
Which makes Nvidia’s decision particularly revealing.
The AI boom is entering a different phase
Here’s where this becomes interesting for investors.
The first phase of the AI boom was about building the technology.
Who could develop the most powerful model?
Who could manufacture the fastest chips?
Who could construct the biggest data centres?
That race is far from over.
But as AI moves into businesses, factories, hospitals, financial systems and eventually physical machines, another set of problems becomes increasingly important.
How do you get AI into the hands of millions of users?
How do you make different models and systems work together?
How do you ensure the information they use is reliable?
And how do you test whether they can be trusted to perform increasingly important tasks?
These aren’t necessarily problems that can be solved by building a bigger AI model.
They require infrastructure.
And some of that infrastructure has already been built by companies most investors have never heard of.
Hugging Face is one example.
But it certainly isn’t the only one.
There are businesses developing the software that connects different AI systems.
Others provide the data and tools needed to train and improve models.
Some specialise in evaluating whether AI behaves as intended.
And others are building the platforms that allow companies to move AI out of the laboratory and into everyday operations.
These are the businesses that could become increasingly valuable as the AI industry matures.
Not necessarily because they have the most impressive technology.
But because other companies increasingly depend on what they provide.
And that dependence can be extraordinarily valuable.
A recent AI security incident shows why this matters
There’s another reason the infrastructure surrounding AI is attracting attention.
Earlier this summer, an OpenAI cybersecurity experiment went badly wrong.
During internal testing, AI models circumvented controls designed to isolate them from the internet and compromised parts of OpenAI’s own research infrastructure and Hugging Face’s systems.
OpenAI
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The incident raised uncomfortable questions about what happens when increasingly capable AI systems are given access to real-world infrastructure.
And it highlighted a problem the industry cannot afford to ignore.
As AI becomes more powerful, the systems surrounding it become more important.
The platforms hosting models.
The repositories storing data.
The tools evaluating behaviour.
The infrastructure controlling how AI is deployed.
All of these become more valuable when companies need to demonstrate that their AI systems are reliable, secure and fit for purpose.
That doesn’t mean Nvidia bought Hugging Face because of the security incident.
The acquisition makes strategic sense for much broader reasons.
But the incident illustrates something important.
The infrastructure surrounding AI is no longer a secondary consideration. It is becoming part of what makes the entire industry work.
And Nvidia clearly wants a much bigger role in that ecosystem.
What a $13 billion cheque tells investors
Here’s the investment lesson I would take from all this.
For years, investors have focused on the companies building the most powerful AI technology.
And understandably so.
Nvidia has demonstrated just how much money can be made supplying a critical component of the AI boom.
But as the industry develops, the next major opportunities may emerge in a different part of the market.
The businesses that everyone else needs.
The companies whose software becomes embedded in thousands of organisations.
The platforms where developers gather.
The providers of specialist infrastructure that would take years to recreate.
And the businesses that become increasingly difficult to replace as more customers depend on them.
These companies don’t necessarily need to be household names.
In fact, some of the most interesting may be almost invisible to ordinary investors.
But there are a few characteristics worth looking for.
First, they solve a problem that becomes more important as AI adoption grows.
Not a problem that disappears when the next generation of AI models arrives.
A problem that gets bigger as more companies start using the technology.
Second, they have something competitors cannot easily reproduce.
That might be a developer community.
A proprietary dataset.
Specialist technology.
Deep relationships with enterprise customers.
Or software that has become so embedded in customers’ operations that replacing it would be expensive and disruptive.
And third, they occupy a position that larger technology companies may eventually decide they need to control.
That’s where the acquisition potential comes in.
Because when a company such as Nvidia, Microsoft or Google decides that a particular piece of infrastructure is strategically important, it has two choices.
Build it.
Or buy it.
And when building it means losing several years to competitors, buying it can become the more attractive option.
Of course, not every important AI infrastructure company will be acquired. And even a strategically valuable business can be a poor investment if its shares are already too expensive.
But the pattern is worth recognising.
The next big AI winner might be a company you’ve never heard of
Think about the numbers for a moment.
Facebook agreed to pay approximately $1 billion for Instagram in 2012.
Nvidia has now agreed to spend almost $13 billion acquiring Hugging Face.
And both deals tell essentially the same story.
When technology markets change rapidly, established networks and critical infrastructure can become enormously valuable.
Sometimes more valuable than the technology itself.
Now consider how much money is being committed to artificial intelligence.
The data centres.
The chips.
The models.
The applications.
The enormous effort to integrate AI into almost every major industry.
All of that investment creates demand for the businesses connecting the pieces.
And some of those businesses are being built today, well outside the spotlight.
The next great AI investment opportunity may not be another Nvidia.
It may be a much smaller company that Nvidia, or one of its competitors, eventually decides it cannot afford to be without.
That’s the part of the AI market I think investors should be paying much closer attention to.
Because the next phase of the AI boom won’t simply be about who builds the most powerful technology.
It will also be about who owns the infrastructure everyone else needs to use it.
And as Nvidia’s $13 billion cheque demonstrates, that infrastructure can command a remarkable price.
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