Could You Have Spotted Nvidia Before Its 1,400% Rise? Look for These 5 Traits

Over the past few months, we’ve been digging through some of the greatest growth stocks of the past 30 years.

Amazon. Microsoft. Netflix. Nvidia. Shopify. Tesla. TSMC. And others.

The question behind our research is simple: What did these companies have in common before everyone knew they were great investments?

We went back to the point when each opportunity was still taking shape and asked what an investor could realistically have seen at the time. From that work, five traits kept showing up.

Today, I want to highlight those traits using arguably the greatest stock of recent years: Nvidia.

Everyone knows what happened after ChatGPT arrived in late 2022. Nvidia NVDA -0.24%↓ became the company selling the picks and shovels for the AI gold rush. Demand for its chips went through the roof. And the stock shot up 1,400%. Just today, it struck a new all-time high.

But could investors have seen it coming years earlier, when Nvidia was still widely thought of as a gaming-chip company?

The answer is yes.

Go back to 2017 and 2018, and the five traits shared by great growth stocks were all there.

Trait 1: Riding a structural shift

For decades, most computing revolved around the CPU.

CPUs are very good at doing lots of different jobs. But some jobs require performing huge numbers of calculations at the same time. GPUs turned out to be exceptionally good at that kind of work.

That became increasingly important as the world produced more data, cloud computing took off, and software grew more computationally demanding.

Then artificial intelligence started throwing gasoline on the fire.

Training neural networks requires doing enormous numbers of calculations in parallel. The same basic technology Nvidia had spent years developing for computer graphics suddenly had uses far beyond making video games look better.

You could see the tide beginning to turn well before ChatGPT.

In 2012, researchers used Nvidia GPUs to train AlexNet, a breakthrough image-recognition system that helped kick off the modern deep-learning boom. Over the following years, GPUs became increasingly common in universities, research labs, cloud data centers, and technology companies building AI systems.

Nvidia had found itself sitting in the middle of a shift much bigger than gaming.

That is the first thing we look for in a great growth stock: a company riding a structural change with years of runway ahead of it.

The internet gave Amazon that tailwind. Smartphones did it for Apple. Cloud computing helped remake Microsoft.

For Nvidia, it was the move toward accelerated computing.

Trait 2: Emerging growth engine

If you only looked at Nvidia’s headline revenue numbers, you could easily miss the AI opportunity developing beneath the surface.

Most investors knew Nvidia for gaming. Its GeForce graphics cards were the company’s bread and butter, and gaming remained a huge business.

But underneath, Nvidia’s Data Center division was building momentum fast. In 2017, Data Center revenue surged 145%. The following year, it jumped another 133%, reaching nearly $2 billion.

Those numbers were coming from hyperscale cloud companies, high-performance computing, and deep-learning workloads. In other words, demand was already showing up years before ChatGPT was released.

That is exactly what we mean by finding an emerging growth engine. The biggest opportunity inside a company often starts small enough to hide in plain sight, while the old business still gets most of the attention.

Eventually, that new engine becomes the new main business.

That’s exactly what happened here. By 2022, Nvidia’s Data Center business was generating more than $10 billion a year. Before ChatGPT even launched, Data Center had already overtaken Gaming as Nvidia’s biggest source of revenue.

Trait 3: Growth makes the moat stronger

Having more people use Nvidia GPUs wasn’t the only advantage. The real moat was CUDA.

CUDA is Nvidia’s software platform that allows developers to program its GPUs for jobs beyond graphics, like AI.

As more researchers and developers learned CUDA, more software was built around Nvidia GPUs. Universities taught it. AI researchers used it. Cloud companies installed Nvidia hardware. Software libraries were optimized for it.

By 2018, more than 700,000 developers were already using CUDA.

That created a snowball effect. A company choosing Nvidia wasn’t simply buying a chip. It was gaining access to years of software, tools, libraries, documentation, developers, and existing applications.

And as Nvidia sold more GPUs, that ecosystem grew larger.

Competitors now had a higher hill to climb. Building a fast chip was only part of the job. They also had to persuade customers to walk away from an ecosystem they already knew how to use, and that everyone else was using.

The more Nvidia’s data center business grew, the stronger its moat became.

Trait 4: Turn old strengths into new growth

One of the most surprising things our research uncovered is how often great growth companies use something they already own to enter a completely different market.

Amazon built computing infrastructure for its own website, then turned it into AWS.

Apple built an ecosystem around the iPhone, then used the same installed base to sell services, payments, wearables, and subscriptions.

Tesla developed batteries, power electronics, and energy-management software for EVs, then applied the same capabilities to home and grid-scale energy storage.]

Nvidia’s original expertise was graphics processing. Then CUDA made those GPUs programmable for other kinds of computing.

From there, Nvidia could take essentially the same core capabilities into one market after another.

Gaming led to professional visualization. GPU computing opened the door to scientific computing and high-performance computing. Those capabilities helped Nvidia enter data centers, which eventually put it at the heart of AI training and inference.

The company didn’t hit a home run every time it swung. Its Tegra push into smartphones largely fizzled out. Crypto mining created a temporary boom and bust. Automotive took far longer to develop than many investors expected.

But Nvidia could keep taking swings because it had already built the expensive foundation. Its GPU architecture, CUDA software, engineering talent, and developer relationships could be reused whenever another computing-intensive market appeared.

It was turning old strength into new growth.

Trait 5: Underestimated TAM

Imagine valuing Nvidia in 2016 by asking how big the gaming graphics-card market could become.

You could have built a perfectly sensible model but completely missed what was coming next.

Nvidia’s opportunity kept expanding beyond the market investors had originally assigned to it.

GPUs moved from gaming into workstations and scientific computing. Then into data centers. Deep learning. AI inference. And eventually generative AI.

Each new use opened another market.

We’ve seen this pattern again and again in our research. Great growth companies often end up serving markets far larger than investors first expect because their technology gives them a bridge into new opportunities.

Stay tuned.

This Nvidia case study is only the beginning.

We still have plenty of great winners to dig into, and more useful lessons to learn from.

We’ll keep sharing our findings as the research develops.

Ultimately, we’ll put everything we learn together into a practical framework for finding great growth stocks before they become obvious.

Thank you for reading and have a great weekend.