If you work in tech, AI feels like it's everywhere. It writes code, reviews pull requests, generates images, edits video, and answers support tickets. Every week there's a new model, a new tool, a new demo that makes last month's look old.
So it’s a little jarring to read what Torsten Slok, the chief economist at Apollo, published this week: outside the tech sector, AI has delivered essentially no financial payoff to corporate America.
Not a small payoff. Not a slower one. According to his charts, basically nothing yet.
Two economies, one technology
Slok splits the S&P 500 into two groups. On one side are the "Magnificent 7", the giant tech companies at the center of the AI boom. On the other side are the remaining 493 companies: banks, retailers, manufacturers, insurers, healthcare firms, the ordinary businesses that make up most of the economy.
For the big tech companies, revenue per worker is rising and profit margins are going up. For the other 493, both lines are flat. And across the wider economy, there's still no visible sign of AI lifting productivity growth.
This isn't the only data pointing in that direction. Forrester recently projected that AI will make up a fifth of corporate technology spending in 2027, while more than half of IT leaders are expected to miss their return-on-investment targets. In their survey, only about one in five AI decision-makers said AI had improved their company's operating profit over the past year.
So the money is flowing. The results, outside tech, aren't.
And according to Slok, companies are starting to notice. Instead of seeing AI spending as a path to growth, many are beginning to treat it as a cost that eats into margins.
Why AI shines in some places and stalls in others
The split makes more sense when you look at where AI works well.
Coding, images, and video have something in common. The work is digital from start to finish, and the output is easy to check. Code either passes the tests or it doesn't. An image either looks right or it doesn't. You can generate ten versions, throw away nine, and keep the one that works. Mistakes are cheap and fast to catch.
Most business work isn't like that.
Deciding whether to approve a loan, how to price a contract, which supplier to trust, or how to handle an upset customer depends on context, relationships, history, and judgment. There's often no test suite. You may not know if a decision was good until months later. And when it goes wrong, someone has to take responsibility.
Slok makes a similar point: AI may be a great calculator, but in business, intelligence is mostly judgment. And things like trust, culture, accountability, and fairness are what ultimately drive revenue, yet they're exactly what AI doesn't understand.
In other words, AI has mastered the work that's easy to check. Business mostly runs on the work that isn't.
So is this a bubble?
This is where opinions split hard. Both sides have real arguments, so here they are as fairly as I can put them.
The case for a collapse
The spending is enormous, and it's concentrated in a small number of companies. Much of the AI economy is circular: AI startups spend investor money on tokens from AI labs, which spend on chips and cloud capacity from big tech, which reports the growth. If the companies at the end of that chain, the ordinary businesses, aren't seeing returns, the whole structure rests on expectations rather than results.
There's also a pattern here. Companies under pressure to show returns quickly may cut AI budgets once the novelty wears off. Forrester found that nearly half of AI decision-makers expect an investment to pay off within a year. If that doesn't happen, patience could run out fast.
And history has examples of exactly this. The dot-com boom saw massive investment in a genuinely transformative technology, and it still ended in a crash that wiped out most of those companies.
The case for a slow payoff
The other side points to history too, but a different part of it.
In 1987, the economist Robert Solow famously observed that computers were showing up everywhere except in the productivity statistics. It took more than a decade before they clearly did. The reason wasn't that computers were useless. It was that companies first had to rebuild how they worked around them. New processes, new roles, new ways of organizing information.
Forrester's analysis points to the same issue today: many companies bought AI tools without changing their workflows, so the gains stay trapped in isolated pockets.
And even the dot-com crash didn't kill the internet. The bubble burst, but the technology went on to reshape almost every industry, just later and through different companies than investors expected.
On top of that, AI models keep getting cheaper. Slok himself raises the question of whether ordinary businesses can turn increasingly cheap intelligence into real growth. If costs keep falling, the math could change for companies that can't justify the spending today.
What the data can't tell us yet
It's worth being honest about the limits here. Slok's note is a short piece with a handful of charts, not a full study, and Apollo is an investment firm with its own view of the market. Productivity is notoriously hard to measure, and early gains can hide in places that don't show up in revenue per worker or profit margins.
At the same time, "it's just early" is an argument that can be stretched forever. At some point, a technology this expensive needs to show results outside the companies selling it.
What the data does show clearly is the gap. AI is booming in tech, and it's flat almost everywhere else.
The question that matters
Slok ends his note with a line that I think frames this better than any hype or doom headline: the AI story will be decided in the 493 ordinary companies, not the 7 giants.
Whether that story ends in a crash, a slow transformation, or something in between depends on whether businesses outside tech can find a way to turn AI into real value, not just more spending.
I don't think anyone knows the answer yet. So I'll put it to you: when you look at your own company, or the businesses around you, are you seeing real returns from AI, or mostly real costs?