Why I’m Not Worried About Running Out of Work in the Age of AI

When the auto industry was being decimated and jobs offshored to Japan, many of us instinctively reached for our college economics textbook and started thinking about fish and coconuts — the example Paul Samuelson used to explain David Ricardo’s theory of comparative advantage in international trade.

Well, if Japan is better at making cars than we are, then they should make cars and we should do other things. It’ll be rough for the displaced auto workers but it’s merely a transition period and, like Ricardo says, it’ll be a win/win for both countries and we’ll all be better off in the end.

If you put aside the many concerns with basing policy solely on comparative advantage (e.g., national security, supply-chain resilience, support for fledgling industries, externalities), you could at least get a good night’s sleep.

After all, as I pointed out in my 2026 predictions post, while it’s painful in the short term, we typically don’t miss the jobs displaced by technology.

I don’t think anyone misses elevator operators, a job that peaked in the 1950s and is all but extinct today. The same is true of switchboard operators, pin setters, lamplighters, linotype setters, icemen, typists, shorthand secretaries, and slubber doffers. Yet all these jobs were automated away. While I’m sure the transition was rough for any given elevator operator, as a society we don’t miss them. Most coal miners work hard so their children won’t have to be coal miners.

It sucks for them — the displaced workers — of course, but collectively it’s good for us. Or, in econ-speak, trade creates net national gains, but the costs are often geographically concentrated and painful (e.g., the Rust Belt). If you want to know what it feels like to be on the other end of this displacement, listen to some Bruce Springsteen: Youngstown, My Hometown, Independence Day, and my favorite, The River. That’s what it feels like in a dying town. That’s the darkness on the edge of town.

The pain is real and while occupational transfers are a solution, some are easier than others. An autoworker could transition to another manufacturing job (though often at lower wages) but would be unlikely to become a mechanical engineer. For the economy in general, jobs transition from one sector to another. For any given person, one displacement could be the end of the line.

Another excerpt from the predictions post:

Let’s remember where the word Luddite comes from. Today, we use the word to describe people opposed to new technology. But the Luddites were part of a labor movement; they didn’t destroy machines because they were technophobes, they destroyed them to protect jobs. So the transitions can be rough — and come with social unrest — when jobs are automated away. But we still don’t miss the jobs they fought for.

So, with our safe emotional distance from a 1970s auto worker, what would we have wanted to tell them at the start of the disruption? I’d say:

• This isn’t a blip, it’s a trend. This is not a problem you wait out.
• You could try to fight your way through this, but the odds will not be forever in your favor.
• If you’re nearing retirement, ride this one to the beach and go retire.
• If you’re early or mid-career, you need to switch horses.
• Switching early is better than switching late. The goal is to get onto the next horse before everyone else realizes the race has moved.

The popular horse-switching fantasy answer is retraining. “Go back to school and become an engineer.” In theory, yes. In practice, rarely. The jump from an assembly-line worker to an engineer requires years of schooling and a different educational foundation.

The real move for the autoworker was sideways, not upward: industrial maintenance, tool and die work, welding, industrial electrical work, construction trades, trucking, or logistics.

None of these are glamorous answers, but they share an important property: they’re adjacent to the skill set the person already has. They’re realistic.

And now, without the emotional distance we have from a 1970s auto worker, let’s try applying the same thinking to ourselves. One way to do that is to imagine what someone from the year 2070 might write back to us.

They might start the same way.

This isn’t a blip, it’s a trend. Don’t assume this wave of AI capability will conveniently stall just below your job description. You can try to fight your way through it, but the odds will not be forever in your favor. The job you’re defending may still exist, but the number of people needed to do it may fall dramatically.

They might add something else we don’t particularly like hearing: if you’re late in your career, you may be able to ride this out. Large organizations change slowly and adoption curves usually take time. But I think less so in this case — it might prove to be a short ride. If you’re early or mid-career, you should be thinking about switching horses.

But here the analogy gets interesting.

The auto worker in 1975 often had a high-school education and a skill set tightly tied to the factory floor. The realistic moves were sideways into adjacent trades.

The knowledge worker today, by contrast, is already highly trained. Many have college or graduate degrees and have spent years building transferable skills: writing, analysis, design, coding, planning, advising.

In other words, knowledge workers should be much easier to redeploy than auto workers ever were.

So yes, this all hits different because this time it’s us. That’s where we lose the emotional distance, and why the people trying to scare us sound so convincing.

Citrini’s recent thought experiment imagines a negative feedback loop with no natural brake. As they describe it:

AI capabilities improved, companies needed fewer workers, white-collar layoffs increased, displaced workers spent less, margin pressure pushed firms to invest more in AI, AI capabilities improved …

Repeat.

In that scenario, the problem isn’t sudden unemployment so much as a slow erosion of the income base that supports the modern consumer economy. Highly paid white-collar workers lose their jobs, move into lower-paying work, compress wages across the service sector, and reduce spending at the same time.

The one thing I find most missing from the AI future analyses I’ve read is a simple realization: in my experience, there is always, always more work yet to do.

This relates to how I’ve always thought about the total available market (TAM) for business intelligence (BI). Over the decades I’ve worked in and around BI, I’ve been asked countless times whether the market was saturated or saturating.

My answer was always the same.

One day I’ll go to a cocktail party and ask everyone in the room: “Does everybody have all the information they need to make data-informed management decisions?” If everyone says yes, then on that day the BI market will be saturated.

But that day strikes me as a long way off. It did 40 years ago and it still does today. We have accomplished so, so much in BI and analytics. But there is still so much more to do.

Since no AI post would be complete without a reference to Jevons Paradox, let’s invoke it here: When technology makes something cheaper or more efficient, we tend to use more of it, not less. When steam engines became more efficient, coal consumption didn’t fall — it exploded because steam power became economical in far more applications. The same dynamic has repeated itself with computing, storage, and bandwidth. Every time technology dramatically increases our ability to do something, we don’t run out of work. We discover many more things worth doing.

The vast majority of management and strategic consulting today is about focus. And the reason focus matters so much is simple: execution bandwidth is limited. Organizations can only get so many things right.

So managers spend an enormous amount of time cutting, cutting, and cutting. Prioritizing, prioritizing, and prioritizing. Literally speaking, if we had to list the one key to business success, it’s focus. Why? Because there is so much work that we could do. So many strategies that we can imagine. So many things that we could do to help our customers.

It’s not that there’s only a little more work to do. There is an unfathomable amount of work yet to be done. The job of management is pruning that list down to something the organization can actually execute.

Which is why the idea that we’re somehow going to run out of work strikes me as absurd. It feels like a theory written by people who haven’t actually spent much time doing the work in the first place — serving customers, building products, and running businesses. There is always more that could be done.

There’s also an irony here. Increasingly, the people who say SaaS will stand for “service as a software” are the same people saying AI will massively disrupt employment. But when you sell service-as-a-software you are definitionally in the solutions business, solving problems, not merely delivering data and algorithms. And when you look at your customers through the lens of “problems yet to be solved,” you quickly discover that the list is very, very long.

So no, I’m not particularly worried about us running out of work to do.

I do think the transition could be difficult, especially if you’re not ready for it. So, given everything we’ve said thus far, here’s the advice I’d give to a knowledge worker heading into this transition.

You want to be driving the tools, not driven by them. Aggressively learn AI. Be the person who knows the most about solving problems using AI tools — integrating them, automating workflows. Not just generating content.

You want to understand the business outcome of the work you do. For example, if you’re in public relations, your end goal is awareness. Learn how to use these tools not just to generate articles but to drive awareness through whatever channels and mechanisms will make that possible.

You want to know a little about how the tools work, but not too much. People like me are naturally drawn to understanding the internals, but given the complexity of modern AI systems the more practical skill will be knowing how to use them.

One profession today that looks closest to the autoworker analogy is copywriting. If you’re a copywriter, you should already be learning multimedia and learning how to use AI tools to generate and manage all forms of content. Five years from now you won’t be manually writing and editing. You might be checking work, guiding it, generating variations, and certainly personalizing it at scale.

I know it’s a cliché, but I’ll end with a famous line from Wayne Gretzky, who attributed his success “not to skating where the puck is, but to where the puck is going.”

The puck is going toward heavy AI automation. That will create a new class of jobs around using AI to automate work — and another layer above that focused on managing, directing, and orchestrating those systems to actually run and grow businesses.

Skate there.