Congrats to Micron (MU).

It just broke into the top-10 largest American stocks, which is a big deal in our Disruption Investing framework.

Just look at this chart:

I (Chris Reilly) want to congratulate Micron shareholders, too. Which includes Disruption Investor Members, who’ve enjoyed this 400%+ rocket ship ride.

Micron is a perfect example of what happens when an unassuming stock gets hit by the AI spending tidal wave.

Micron is quite possibly the hottest company on Earth right now, which is really saying something with the SpaceX blockbuster IPO coming up.

We’ll have special analysis on SpaceX starting next week.

Today, let’s look at why money is still rushing into Micron, and the next big bottleneck Stephen McBride and Chris Wood are focused on...

• Micron sits at the center of AI’s biggest bottleneck.

The big bottleneck in the first phase of the artificial intelligence (AI) boom was GPUs. It launched Nvidia stock up +1,200%.

Now, the big bottleneck is memory. Under the hood, AI depends on two very different kinds of chips:

Logic chips, like Nvidia’s GPUs, that do the thinking.

And memory chips that store and feed the data those GPUs need.

Every AI task... asking ChatGPT a question, generating an image, summarizing a document... boils down to two things happening at extreme speed. A logic chip performing massive amounts of math. And that chip constantly pulling data from memory, then sending results back.

Chris says a useful way to think about this is a kitchen. The GPU is the chef. Memory is the pantry. If the pantry is across the building, the chef wastes most of his time running back and forth for ingredients. But if the pantry sits right next to the stove, cooking speeds up dramatically.

According to Chris and Stephen, that’s exactly the problem AI faces today.

• Compute power has exploded. Memory access hasn’t.

Over 90% of the time it takes an AI model to respond is spent moving data between logic and memory, not doing computation. That’s the so-called “memory wall.”

GPUs keep getting faster. But memory bandwidth and proximity haven’t kept up. The result is that $50,000 AI chips sit idle, waiting for data.

As models grow larger and try to “remember” longer conversations, images, and context, the amount of memory they’ll need close to the compute will explode.

Leading memory maker SK Hynix expects the market for AI memory to nearly quadruple by 2030.

• The hundreds of billions in memory spending funnel to just three companies.

Micron is one of them. That’s why it’s rocketing. The other two are Korean firms SK Hynix and Samsung.

These three are the only ones capable of making HBM. It stands for High-Bandwidth Memory.

It’s a fancy term for stacking memory chips like a skyscraper and bolting them right next to the GPU. This means data only has to travel millimeters, not inches.

It’s like replacing our separate pantry room with a pantry cabinet attached to the stove. The distance for data to travel is vastly reduced and the width of the “pipe” between the processor and memory is vastly increased. This means orders of magnitude more data can be fed to the processor per second.

Shorter distance = more data per second = better AI. That’s why every next-gen AI system is being designed around HBM.

Put simply, HBM is the answer to the “memory wall” problem we talked about earlier.

Every new high-performance AI chip now comes with HBM built in. These chips can’t function without it. You can’t swap in some generic memory later. The memory is part of the product from the start.

• Here’s Stephen and Chris’s guidance from a recent Disruption Investor issue:

As they explained:

“Memory demand is outstripping supply across the board. And our research indicates things will remain tight for several years.

Every new generation of AI chips demands more memory. The DRAM requirements in advanced AI accelerators has doubled in the past year alone, for example. Data centers, smartphones, PCs, autonomous vehicles, and now humanoid robots are all pulling in the same direction.

Micron’s investment thesis hasn’t weakened. It’s gotten stronger.”

So...

• What’s the next big bottleneck Stephen and Chris are focused on?…

Semiconductor equipment companies...

The companies making the machines that make the chips.

Better AI requires better chips. And better chips are much harder to make. The tiny features inside the newest AI chips are around 40,000X to 50,000X thinner than a human hair. At that scale, every new generation becomes harder to manufacture and demands far greater precision.

That means more spending on the machines inside the chipmaking factories.

According to Chris and Stephen, companies that make the machines that make every AI chip in the world have been a sleeper trade all year... But that’s about to change.

These under-the-radar companies are about to get hit by a tidal wave of money just like Micron did in 2025.

Taiwan Semiconductor (TSM) recently announced it will spend a record $56 billion on new chip factories this year, with a big chunk going toward making cutting-edge chips.

Samsung, the world’s second-largest chipmaker, is committing more than $73 billion to stay competitive in the AI chip race.

Chris and Stephen’s research suggests the back half of 2026 and the first half of 2027 could be the strongest 12-month period semi-cap companies have ever seen.

They recommended four industry leaders back in March as part of their homemade semi-cap “ETF.”

It’s up 41% in a little under three months.

Disruption Investor members can catch up on our semi-cap issue here. If you’re not a member and would like to see our full portfolio, complete with our buy and stop guidance, go here to upgrade.

Chris Reilly

Executive Editor, RiskHedge