The most profitable AI applications are not chatbots, and the stocks that benefit from them are hiding in plain sight.
Listen to the audio version of this article (generated by artificial intelligence).
Editor’s note: On Tuesday, we looked at how AI is making the oil and gas industry more efficient with Joe Austin. Today, we look at another place where AI is making a big difference: semiconductor manufacturing.
A single defect in a semiconductor can cost up to $25,000Joe explains why AI is the only practical way to solve these problems before they become costly mistakes.
This is another reminder that some of the biggest AI opportunities may not come from the companies that make the technology. They may come from companies that use them to become more efficient and more profitable.
This is one of the reasons why Mark Chaikin of Chaikin Analytics recently introduced his new product AI time machine. It’s designed to help investors look beyond today AI shares And discover the next generation of potential winners.
Yesterday, Mark and Joe unveiled their first AI-powered time machine. If you missed the offer, Replay is still available here.
Now, here’s Joe taking a closer look at how AI is transforming manufacturing one factory at a time…
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California Steel Industries’ Hot Strip Mill in Fontana extends more than a half-mile.
Inside, giant furnaces heat steel panels to about 2,300 degrees Fahrenheit. At this temperature, the steel becomes soft enough to roll.
But first, it needs to be cleaned. The oven leaves a thick crust of “scale” on the surface. If it is not removed, it will be pressed into the steel and spoil the finish. The crusher tool breaks the shell. Then high-pressure water jets blow them away.
The steel plate is then passed through five rough holders that press it from a thickness of 7 to 9 inches to a minimum of 0.0538 inches — a thickness close to the thickness of a credit card. Crop shears snip off the rough ends before the steel moves on to finishing. Six more finishing stands then roll it to its final thickness and surface quality.
At this point, the steel is moving at about 35 miles per hour.
This is so fast that defects cannot be detected with the naked eye. For automotive panels and hardware, the surface must be free of blemishes, as blemishes appear directly through the paint.
The finished tape is wound into a coil. Some of them weigh up to 25 tons. The whole process takes about five hours. The factory operates at full capacity 24 hours a day and produces 2 million tons of steel annually.
But at least you can see the steel.
In today’s most advanced semiconductor manufacturing plants, significant defects are invisible to the human eye.
The consequences of losing them are equally serious.
A single defect in a semiconductor can cost $25,000
In the semiconductor industry, it all starts with a wafer—a thin, polished disk cut into strips of pure silicon, typically about 12 inches across. These chips must be free of defects. Even a scratch or microscopic contaminant can cause defects on hundreds of chips.
The first step is to print the circuits using extreme UV lithography. This process projects patterns of circles using light with a wavelength shorter than any visible color. One finished slide can require between 20 and 30 passes during this stage alone.
The specialized masks used in this process – a type of 3D stencil – must also be perfect. One flaw destroys every chip the mask touches. These masks can cost up to $1 million each.
After each pass, the wafer goes through etching, deposition and chemical processing to build up the transistor layers. Then the cycle repeats. Today’s most complex chips go through 1,500 to 2,000 individual steps before they become functional. Every step is a potential point of failure. A single particle of dust can destroy the entire chip.
The cost per wafer for the most advanced semiconductors can range between $20,000 and $25,000. Each wafer contains hundreds of chips. One defective wipes hundreds of products at once. The cost of building the factories where all this happens is between $15 billion and $20 billion.
Manufacturers need to reduce these losses wherever possible. Human inspectors simply cannot do this job. At 35 mph, the steel is moving too fast to be seen. In semiconductor manufacturers, the defects are so small that they cannot be seen. Either way, the risks are too high to miss anything.
Artificial intelligence enhances quality control
This is one area where AI not only helps. It’s the only solution that actually works.
“Deep learning” and “advanced learning” in AI take defect control to a level that humans cannot match. Deep learning works by analyzing hundreds of image examples so that the system learns to make decisions on its own, without requiring a programmer at every step.
Edge learning goes even further. These systems come pre-trained and may need at least five to 10 images to get started. Deployed in minutes.
Results are measurable.
At BMW, AI vision systems reduced defect rates by 30% at a European factory in one year. Customer satisfaction jumped 15% after launch. At Foxconn, AI-powered cameras catch defects with 98% accuracy, report 80% fewer false alarms, and inspect each unit 60% faster than before.
This is not shareware. They are production systems that operate at scale, in some of the most demanding manufacturing environments on Earth.
This is what I mean when I say that the real AI story is not the one that gets the most attention.
Everyone is watching the big infrastructure names – chip companies, cloud providers, chatbot platforms. And yes, this is important. But there is a parallel story taking place on the factory floor, in the oil field, and in the semiconductor factory.
Artificial Intelligence solves problems that were previously unsolvable. Companies offering these solutions have become more competitive, more profitable, and more valuable – quietly, without much fanfare.
This is exactly the type of opportunity I have spent my career looking for.
Find the next generation of winners
The challenge, of course, is identifying which companies are actually winning — not just claiming to use AI, but using it in ways that show up in the fundamentals.
This is a problem Mark Chaikin He has been working his entire career. for him Force meter The rating system was created to cut through the noise and find stocks with real momentum behind them. We’ve been doing this for decades.
Mark and I go a step further. We’ve unveiled the first AI-powered product ever built by Chaikin Analytics – and it’s unlike anything we’ve shown the public before.
We call it Time machine. It scans decades of market history to find stocks today whose fundamental and technical fingerprints match early profiles of stocks such as Nvidia company (NVDA), Amazon.com Inc. (Amzn)and Meta Platforms Inc. (dead) – Right before they make their biggest moves. In the backtest, stocks that held out emerged with gains of 995%, 1,406%, and 3,804%, all while the “underlying” stocks they were matched with posted much more modest returns.
The story of artificial intelligence in a factory is one example of the kinds of opportunities that a time machine is designed to demonstrate. Companies are solving real industrial problems with AI – before the market takes hold.
This is the first time we have made something like this available to retail investors.
Click here to watch the replay.
good investment,
Joe Austin
Senior Analyst, Chaiken Analytics
note: Most investors focus on AI names that everyone already knows. Joe focuses on what most people haven’t found yet: companies that are using AI to solve problems in places like the factory floor and oil fields, before Wall Street can fully catch up. He and Mark Chaikin debuted the first AI-powered tool ever built by Chaikin Analytics to help find exactly those stocks. Here is the link again to watch the replay.
