Don't Miss Out on AI's Solar Panel Moment

For most of the 20th century, electricity came from massive, centralized power plants.

Coal or nuclear facilities generated energy... then pushed it into every home through long transmission lines.

It worked. But it was wasteful. Energy bled off the wires – a 5% transmission loss that could have powered millions of homes. Besides that, plants cost billions of dollars to build. And if even one went down, entire cities went dark.

Then, solar panels started appearing on rooftops. Suddenly, homes could generate power right where it was needed.

In my home state of California, rooftop solar now produces 50% more electricity than the state's last remaining nuclear power plant.

The grid improved, and fortunes followed. Solar companies like First Solar (FSLR) and Enphase Energy (ENPH) turned into multibillion-dollar winners by riding the rooftop revolution.

Now, the same shift is about to happen in artificial intelligence ("AI").

Only this time, I'm not talking about energy... I'm talking about speed and storage. And if you buy the winners of this transformation, you can capture the "solar panel moment" for AI.

What Do Solar Panels Have to Do With AI?

Today, nearly all AI runs on the "power plants" of the Internet – giant cloud data centers.

Every prompt, every query, every decision is shipped back and forth between our devices and the cloud.

The cloud revolution had one big advantage: It moved more data and processing work off our machines. But that model is already breaking down...

Costs are exploding. OpenAI burns $700,000 daily on computing power. And the energy demand is unsustainable... Cloud providers are spending hundreds of billions of dollars to increase capacity.

But the worst drawback for everyday users is simple – the cloud isn't fast enough. AI will only make that worse.

I've talked before about how technology evolves. It always follows the "constraint curve." First, pressure builds... Then, infrastructure fails, and new systems emerge. Every major tech shift follows this pattern.

The cloud is no exception. That's why the next fortunes won't be minted in the cloud... They will be minted at the edge.

"Edge computing" doesn't primarily use remote servers and data centers. Instead, it collects and processes data closer to your device, or even on-site.

For AI, the edge is the rooftop solar panel. It means every device can generate its own intelligence. And the AI market can't grow without it...

Why the Cloud Can't Keep Up With Edge AI

Wall Street obsesses over AI training models.

That's why the big winner of cloud-first AI was Nvidia (NVDA). Every data center is stuffed with Nvidia's graphics processing units ("GPUs"). These chips lead the way in AI training.

But here's some simple math:

• Training happens once.

• Inference happens forever.

"Inference" is how a trained AI model decides what to do next. We have billions of devices making trillions of inferences daily... every smart doorbell, every AI-enabled security camera, every self-driving car that brakes for a pedestrian.

While training is a one-time expense, inference is a repeat operating expense. That means it's a much bigger market... And it must happen at the edge.

Just consider self-driving cars...

At 70 mph, a car covers 103 feet per second. If it had to stream video to a remote GPU, then wait for the processing and return of instructions for every decision, even 200 milliseconds of delay – which is common today – would mean driving 20 feet blind.

That's not autonomy. That's Russian roulette.

As AI moves beyond the cloud, your AI-enabled self-driving car won't need to phone home for decisions. It'll think at the edge.

Soon, your smartphone won't run apps... It will run AI models that create interfaces for you on demand.

Similarly, your smart glasses won't stream from the cloud. They'll process vision models locally.

Two Companies Set to Profit From Edge AI

Two companies – still trading at fractions of Nvidia's valuation – are positioned to dominate this shift...

• Micron Technology (MU) in memory

• Qualcomm (QCOM) in compute

Qualcomm already powers nearly every smartphone. It partners with industry leaders like BMW. And in March, it acquired Edge Impulse, a company that brings machine learning to edge devices. More than 170,000 developers use this platform.

Meanwhile, Micron owns the bottleneck in the industry... that is, memory bandwidth.

Studies show that at small batch sizes – processing multiple tasks at once – GPUs today spend most of their time waiting for data.

But Micron's high-bandwidth memory ("HBM") solves this constraint. And demand is locked in through 2026, thanks to contracts with customers like Nvidia, Advanced Micro Devices (AMD), and other hyperscalers.

In short, edge AI isn't speculation... It's inevitable.

The companies building edge-AI infrastructure will mint fortunes, just like solar companies did during the "rooftop revolution"...

And just as rooftop solar didn't improve power plants but instead replaced them for personal use, edge AI won't enhance cloud computing... It will bring AI processing to your local device, running smoothly and in real time.

It's time to invest in the next leaders... And right now, Micron and Qualcomm are strong contenders.

You're either early, or you're obsolete. Position accordingly.

Good investing,

Josh Baylin

Editor's note: The Trump administration just issued a sweeping AI master plan that could add as much as $50 trillion to the U.S. economy. Our colleague Eric Wade recently filmed a presentation where he laid out the full details behind this opportunity – and shared one tech stock to buy immediately.