This Successful AI Technology Isn't a Chatbot

Editor's note: As industries become more complex, precision is becoming harder... and more valuable. That's driving a new wave of real-world AI adoption. In this recent Chaikin PowerFeed issue, Joe Austin, senior analyst of our corporate affiliate Chaikin Analytics, explains how AI is solving costly, real-world problems – and why this is one of the most important investment trends today...

Behind some of the most important technologies today, there's one constant...

Precision.

In industries like steel manufacturing and semiconductor production, even the smallest defect can ruin an entire product... or an entire batch.

And as these processes become faster and more complex, catching those defects is getting harder. But the cost of missing them is enormous...

A single wafer can cost between $20,000 and $25,000. And the fabs that produce them cost between $15 billion and $20 billion to build.

That's why companies are turning to AI. These solutions aren't just improving efficiency... they're becoming essential to keeping these industries running.

And that's creating a powerful opportunity for the companies leading the shift...

Precision Is Everything in Modern Manufacturing

California Steel Industries' Hot Strip Mill in Fontana stretches more than half a mile...

Inside, giant ovens heat steel slabs to about 2,300 degrees Fahrenheit. At that temperature, the steel gets soft enough to roll.

But first, the steel needs cleaning. The furnace leaves a thick crust of "scale" on the surface. If it isn't removed, it gets pressed into the steel and ruins the finish.

So the slabs are cracked, blasted with high-pressure water to get rid of the crust, and sent through a series of rolling stands. By the end, the steel is compressed from between 7 and 9 inches thick down to as much as 0.0538 inches. That's roughly the thickness of a credit card.

A crop shear then trims the ragged ends of the steel. Even a slightly uneven edge can throw off the entire process.

The steel then moves through six finishing stands. These roll the steel to its final thickness and set its surface quality.

By now, the steel is moving at about 35 miles per hour. That's too fast to catch defects by eye.

And in industries like automotive manufacturing, the surface needs to be flawless. Defects show right through the paint.

But at least you can see steel...

In today's most advanced semiconductor fabrication plants (or "fabs"), the defects that matter are invisible to the human eye.

But the consequences of missing them are just as severe. And that means turning to machines to help with quality control...

In semiconductor manufacturing, everything starts with a silicon wafer. These thin, polished discs range in thickness. Most of them are about 12 inches wide. But many are smaller.

These wafers must be flawless. Even a microscopic defect can ruin hundreds of chips.

The first step is circuit printing using extreme ultraviolet ("EUV") lithography. This imprints circuit patterns onto the surface using light that humans can't see.

A single finished chip can require 20 to 30 passes through this stage.

EUV "masks" – a specialized plate similar to a 3D stencil – have to be perfect, too. A single defect ruins every chip that the mask touches.

After each EUV pass, the wafer goes through repeated cycles of etching, deposition, and chemical treatment to build up transistor layers.

Today's most complex chips go through a range of about 1,500 to 2,000 individual steps before they become functional.

Each step is a potential failure point. One particle of dust can ruin an entire wafer.

Once complete, the wafer is sliced into individual chips, or "dies," and tested. The ones that pass move on to packaging. And the ones that fail get discarded.

Increasingly, packaging means assembling multiple chips into single packages – called "chiplets." That adds another layer of complexity... It requires checking how multiple dies connect and communicate.

But whether it's steel or semiconductors, human inspectors simply can't do the job...

The Machines That Catch What Humans Can't

At around 35 miles per hour, steel moves too fast to see. In a semiconductor fab, the defects are too small to see.

And with today's most advanced chips, defect control isn't just a quality issue...

It's a competitive one.

Each wafer holds hundreds of chips. So a defective one also wipes out hundreds of products that cost tens of thousands of dollars each.

Plus, the EUV masks cost up to $1 million each.

Of course, fabs are looking to reduce these losses wherever possible...

This is one example of where AI can shine. And even better, it's a job humans can't do well already.

AI "deep learning" and "edge learning" take quality control to the next level...

Deep learning is a process where systems look at hundreds of example images until they learn to make decisions on their own – without a programmer needed.

Edge learning takes it even further. These systems are pretrained. They might need just five to 10 images to get started. And they deploy in minutes.

Quality control with AI is more than just hype...

At carmaker BMW, AI-powered vision systems cut defect rates by 30% at one European plant within a year. Customer satisfaction jumped 15% after the rollout.

At electronics contract-manufacturer Foxconn, AI-powered cameras can catch defects with 98% accuracy... flag 80% fewer false alarms... and inspect each unit 60% faster.

Folks, we've heard plenty of debate about whether massive AI investments will pay off. And we hear about new business applications for AI almost every day. Just think of the recent chaos in the software industry surrounding the "SaaSpocalypse."

Chatbots and software tools might grab the flashiest headlines. But this is what real adoption looks like...

AI has the potential to solve physical, high-cost problems in the real world.

And that's where the biggest opportunities are likely to emerge.

To find the real winners amid the AI megatrend, look for companies using AI to solve problems humans can't. These companies will improve their competitive positions, thanks to AI improvements... like taking quality control to the next level.

Good investing,

Joe Austin

Editor's note: The biggest mistake investors can make today is assuming the same AI winners will stay on top. According to Chaikin Analytics founder Marc Chaikin and a Silicon Valley insider, a critical shift is already underway – one that could leave today's leaders behind. That's why they're highlighting a new group of companies positioned to benefit from what's coming next.