You're on a jet.

The pilot hits a snag—something weird happens with the pressure systems.

There's a shiny manual on the dashboard. A flowchart. But it's not enough. Because the problem isn't in the book.

Everyone’s stumped. A sense of dread and panic creeps in.

Meanwhile, back at headquarters, an old, grizzled engineer sips coffee, calmly watching, scratching his chin.

He’s seen something like this before—thirty years ago.

A tiny fluctuation in a specific gas line—the one that every new boneheaded know-it-all overlooks—almost caused a complete catastrophe.

He radios in: "Look at Line 4B. Now."

The plane lands safely.

That expert, the one who saved the day, is retiring next month. He holds what we’ll call “Grayline Knowledge”.

It’s the unwritten wisdom hidden in the subtle, often-overlooked gray areas between rules and exceptions.

And, when he retires, he’s taking it all with him.

Thing is, our entire modern industrial economy depends on this elusive Grayline Knowledge.

Alas, it’s also turning gray… and risks going extinct.

Enter AI.

In this three-part series, I aim to show you the lesser-spoken aspects of AI—specifically AI agents.

Yesterday—in part one—we talked a bit about what they are, why I like them (and why you will, too).

Today, let’s talk about why we NEED them.

Let me start with another story.

This time, it’s real.

Domain Expertise Is Disappearing

In 2021, when TSMC decided to expand beyond Taiwan, it wasn’t just a business decision—it was a test of their model.

The first experiment was in Kumamoto, Japan, where they saw a pretty seamless transition.

Japan provided a Formula 1 pit crew: every move synchronized, every worker a specialist, every challenge met with precision.

Months later, TSMC announced another expansion—this time in Phoenix, Arizona. The vision was grand: a $12 billion facility producing cutting-edge chips.

But here, the experience was terrifyingly different.

Construction delays mounted. Workforce training dragged on. Small mistakes snowballed into major setbacks.

Same tech. Similar budget.

The only variable? People.

America lost her critical expertise.

We’re learning the hard way: We still need domain experts. We still need people who know how to make things. We still need those grizzled greybeards and their Grayline Knowledge.

Obviously, this is no secret.

Manufacturers are VERY concerned about "brain drain," with 97%(!) of firms expressing concern about the loss of institutional and technical knowledge.

Same goes for other crucial sectors in the economy: chemical, construction, aviation, energy, healthcare, nuclear, and a lot more.

In Silicon Valley, most mistakes are cheap: Clicked the wrong ad? No big deal. Code bug? Ah, it’s alright Billy, just fix it in the next patch.

But in the physical world? A single mistake can cost millions. Or lives.

As industries grow more complex, the stakes are higher than ever.

Rise of Domain-Specific AI Agents (DXA)

Here’s the deal with AI:

• Large Language Models (LLMs) like ChatGPT are brilliant. They "know" everything.
• But their knowledge is generic. Wide, but shallow.

They’re like a new PhD graduate.

Ask them a textbook question: Boom—full answer.

Ask them to solve a tricky real-world problem? They’ll recite theories, wave their hands, and say, "Well. Ehh. Erm. It depends."

That’s cute when it’s a chatbot explaining matrix mechanics to a college kid. It’s not so cute in a nuclear plant.

Enter Domain-Specific Agents (DXA): AI models tuned to specific industries.

Think:

• Semiconductors – Diagnosing plasma fluctuations in a fab.
• Oil & Gas – Predicting when a pipeline will fail.
• Aerospace – Finding the one pressure valve causing the leak.

These agents are trained with real-world, niche knowledge—captured directly from observing the old-timers who know the quirks of every machine, valve, and process.

And they’re goal-oriented.

Unlike chatbots, they don’t just answer. They plan. They break problems into steps, reason through the data, and iterate until they solve it.

Imagine taking that retiring engineer’s 30 years of wisdom, putting it into an AI, and turning it into a digital assistant who never leaves and keeps learning.

Just in case you think this is just theory…

One company, called Aitomatic, is already doing it for the semiconductor industry.

How it works:

> An expert sits down and explains complex problems, like diagnosing pressure fluctuations in semiconductor fabs or tracing yield failures.

> Aitomatic uses AI to transcribe, structure, and operationalize that expertise into actionable systems.

> The AI asks, “Is this what you meant?” ensuring precision as it captures that unspoken, Grayline Knowledge (often undocumented).

Domain expertise is how countries win. It’s the edge America needs to get back on track. Domain-specific agents can help.

BUT, there’s something else we need that’s just as important.

Optimism: The Other Key to Winning

Fun fact: Countries still making things—China, Japan, Vietnam—are more optimistic about AI.

China consistently ranks high in AI optimism, with 95% of respondents excited about AI and 81% believing the benefits outweigh the risks.

Across the Asia-Pacific region, 68% view AI as having a positive impact on the world, compared to 57% globally.

In America? 52% of Americans are more concerned than excited about AI in daily life, while only 10% say they are more excited than concerned.

In the West, there’s still a deep-seated skepticism about technology displacing workers, dating back to the Industrial Revolution.

Hollywood hasn’t helped, feeding AI fears with dystopian blockbusters like The Terminator and Ex Machina. This cultural framing positions AI as a tool, sure…

But also a threat.

Optimism matters for adoption.

Countries that adopt faster will win: Bigger GDP, better tech, higher standards of living.

If America wants to swing back to industrial dominance, Domain-Specific AI Agents are one way we get ahead.

• They capture expertise.
• They plan.
• They solve problems in the real world.

To get there, we need less fear and more optimism.

The industrial revolution of the next 10 years will be powered by AI—but only when we get specific.

More on what that might look like tomorrow. 

This time, we’re going FULL sci-fi: the endgame of AI agents.