Summary

This article reflects on how economic change affected workers and communities in New York State during the IBM layoffs of the 1990s and connects that lesson to the current AI transition. It argues that AI is reshaping office work while also creating demand for skilled trades, technical training, community colleges, workforce boards, and public institutions that can respond quickly to labor market change. The article emphasizes that organizations should prepare people before disruption happens and argues that workforce development should be treated as infrastructure because trained people are essential to the AI economy.

What New York State Taught Me the Year I Left for Texas

By Mustafa Tameez
JUN 30, 2026

In 1994, IBM was cutting jobs, I was moving to Houston, and the old promise of work was already changing. AI is forcing us to ask the same question again.

Last week, I wrote about Meta’s new workforce training program for jobs tied to AI infrastructure.

Meta is a social media company. Most people think of it as Facebook, Instagram, algorithms, content, data, and screens. Yet here it was talking about training people for skilled trades.

I had to sit with that for a minute.

A company built on screens was suddenly talking about people who wire buildings, pour concrete, and keep machines running. The digital world still needs hands.

That took me back to Queens, where I grew up watching another kind of economic change unfold on the evening news.

I was not living in Endicott, Buffalo, Rochester, or Syracuse. I was a kid in New York City. But the stories from upstate came through the television often enough that you could feel something changing in the state around us.

A plant closing.

Another round of layoffs.

A town built around one company suddenly wondering what came next.

In early 1994, IBM announced job cuts that included hundreds of workers in Endicott, New York, the birthplace of the company. It was part of a much larger downsizing, but in a place like Endicott the numbers were not abstract. They landed on families, diners, churches, schools, and Main Street.

I remember that period clearly because I moved to Houston in April 1994. As I was starting a new chapter in Texas, a lot of the conversation I remembered from New York was about IBM, layoffs, and what would happen to workers and towns built around companies that had once felt permanent.

That is what I remember seeing from Queens.

IBM was not just a name on a building. It was an economic anchor. It carried a promise that felt almost permanent. Work hard, learn a trade, stay with a good company, and you could build a middle class life.

Then you started to see that promise come apart.

IBM changed. It moved away from being defined by hardware and manufacturing and became something closer to a technology services and consulting company. That reinvention helped the company survive, but the transition was painful for workers and communities that had been built around the older version of the company.

For workers, this was not a case study. It was the kitchen table problem.

It sounded like someone coming home and saying, “They cut my job.”

It sounded like go learn computers.

It sounded like the future is in an office now.

The people affected were not only factory workers. They were people in plants, labs, technical jobs, administrative roles, and support positions who were suddenly told they needed to reinvent themselves.

Some people made that transition. Many did not. Others found work, but not with the same wages, benefits, identity, or stability. When a plant disappears, the loss is not limited to the people who clocked in there. A whole community has to absorb it.

The lunch place loses customers.

The school district loses families.

The young people leave.

The tax base shrinks.

The pride of the town takes a hit that does not show up in the first jobs report.

I keep thinking about those old news stories when I read about AI.

For decades, the advice moved in one direction. Leave the factory floor. Learn the office. Get comfortable with computers. Join the knowledge economy.

Now some of the office work we thought was safest is the work AI can touch first.

A lot of first draft office work is already being touched by AI. Memos, summaries, marketing copy, presentations, customer responses. The work that once took a team can now start with a prompt.

I do not think everyone with a desk job is about to be replaced. But the job will not stay the same. Some tasks will need fewer people. Entry level work will look different. Employers will start valuing different skills.

At the same time, the AI economy needs buildings.

It needs power. It needs cooling. It needs fiber in the ground, steel in the buildings, concrete under the floors, backup systems, substations, and people who actually know how to make all of that work.

AI may look weightless on a screen. It is not. It sits inside data centers. It draws electricity. It produces heat. It depends on skilled workers who can build, repair, inspect, wire, weld, and maintain the systems behind the magic.

We spend so much time talking about the model that we forget the people who make the model possible.

Parents have to wrestle with this too.

For years, many of us told our kids the safe path was simple. Work hard, get into a four year college, earn a degree, and you will succeed. We said it because we wanted them to be safe. We wanted them to have stability and choices.

But employers are telling a different story now. They are trying to persuade young people that opportunity may also run through community colleges, apprenticeships, technical training, and skilled trades.

That does not make college a bad path. It means college cannot be the only path we respect.

Yes, the AI economy will need computer scientists and engineers. But it will also need electricians, technicians, builders, and the people who know how to keep the physical world working. If we keep telling young people that success only looks like an office job, we may be preparing them for an economy that is already changing under their feet.

The line between blue collar and white collar work is also getting blurrier. The strongest workers in the AI economy may be the ones who can combine technical skill, physical skill, and judgment.

I have another reason this hits me personally.

In my 30s, I was diagnosed with avascular necrosis in both hips. That led to multiple surgeries, time in a wheelchair, a year on crutches, and nearly five years now using a cane.

During that time, I remember feeling grateful that I could still earn a living at a desk. I could not imagine what would have happened if my livelihood depended on climbing roofs, carrying equipment, standing all day, or using my body as the tool that put food on the table.

That experience changed the way I think about work. It made me more grateful for office work. It also gave me a deeper respect for people whose jobs require physical labor.

That work takes skill. It carries risk. And it deserves more respect than it often gets.

So when I write about skilled trades, I am not looking back at factory work through rose colored glasses. I know the body pays a price. I also know the economy stops working without the people who keep the physical world running.

At Outreach Strategists, we are seeing this up close.

Over the years, we built a workforce and education practice. We work with workforce boards, community colleges, and K through 12 schools. We are in those conversations all the time. Employers short on workers, educators trying to adjust, and public institutions trying to move faster than they are built to move.

A community college president does not have the luxury of talking about the future in theory.

A workforce board cannot wait ten years to figure out what skills employers need.

A school district cannot keep preparing students for an economy that has already shifted.

That is why I do not see AI as just a technology story anymore. In our work, it shows up in practical ways. Employers short on workers, schools trying to prepare students, parents wondering what opportunity looks like now, and public institutions trying to keep up.

New York State taught me that you cannot stop this kind of change. But you can be late to it.

IBM changed and survived by becoming something different. Workers were asked to reinvent themselves, but too often the support came late, the training was too vague, or the new jobs did not replace what had been lost.

That is the mistake we should avoid with AI.

If AI is going to change white collar work, we need to be honest about it now. Not after people are displaced. Not after entry level jobs disappear. Not after parents realize the advice they gave their children no longer fits the labor market.

The organizations that win this transition will not be the ones that buy AI first. They will be the ones that retrain, reorganize, and prepare people first.

That means companies cannot just buy software and call it innovation. Schools have to stop acting as if every good future runs through a four year degree. Community colleges need to be treated like engines of growth, not backup plans. And workforce boards need enough room to move at the speed of the market, not the speed of bureaucracy.

And the public sector has to treat workforce development like infrastructure.

Roads matter. Power matters. Broadband matters.

But trained people are infrastructure too.

In the 1990s, workers were told to leave the factory floor and learn the office.

In the AI age, we may have to teach the office to respect the factory floor again.

Frequently Asked Questions

How is AI changing the future of work?

AI is already changing tasks tied to writing, summaries, presentations, customer responses, research, and other office workflows. The article argues that the issue is not simply job replacement, but how jobs, entry-level roles, and employer expectations will shift as AI becomes part of everyday work. (See Stanford HAI AI Index and McKinsey.)

Why does the article compare AI to IBM’s workforce changes in the 1990s?

The article uses IBM’s transition away from hardware and manufacturing as a reminder that major economic shifts can reshape workers, families, towns, and local identity. The lesson is that organizations should prepare people before disruption becomes a crisis. (See IBM/CRN historical context.)

Why do skilled trades matter in the AI economy?

AI depends on physical infrastructure, including data centers, power systems, cooling, fiber, steel, concrete, backup systems, and technical maintenance. That creates demand for electricians, HVAC workers, technicians, construction managers, utility planners, and other skilled workers. (See Community College Daily and IEA.)

What role do community colleges play in AI workforce training?

Community colleges are positioned to connect students and workers to practical, career-focused training in fields such as IT, electrical technology, HVAC, cybersecurity, construction, and other technical areas needed for the data center and AI infrastructure economy. (See Community College Daily and New America.)

Does AI only affect white collar jobs?

No. AI may directly affect many office tasks, but it also increases demand for physical infrastructure and technical labor. The article argues that the line between blue collar and white collar work is becoming less clear as the AI economy requires both digital and hands-on skills.

Why should public institutions care about AI and workforce development?

Public institutions, including schools, community colleges, workforce boards, and local governments, help prepare people for changing labor market needs. The article argues they cannot wait years to respond because employers, students, parents, and communities are already facing new questions about opportunity.

Why does the article say trained people are infrastructure?

The article argues that roads, power, and broadband matter, but trained workers are just as essential to economic growth. Without people who can build, maintain, adapt, and lead in the AI economy, technology investments cannot fully translate into community opportunity.

Sources and Further Reading

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