Last week one of my engineers sent me a message on Slack. He uses AI every day to build software. He is not a sceptic, not a Luddite, not someone looking for a reason to be difficult. And this is what he wrote: "The more you use AI, the more you depend on it. If it gets bad enough, you let the model do most of the thinking for you. It's making people stupid. That is what is making people angry, including me."
That is someone living inside the thing, feeling the discomfort in real time. And his frustration points at something the industry keeps failing to explain properly. So let me try.
Six months ago the conversation was "AI changes everything." Now what I am seeing, including inside my own team of around 140 people across three companies, is people pushing back. Not against the technology exactly. Against something underneath it that nobody is naming.
You have heard the claims. AI makes engineers ten times faster. AI doubles your output. Most of that is vibes.
The best measured number I have seen is the median productivity gain across hundreds of software engineers, and it is 7.8%. Not ten times. Not double. 7.8%. Better than nothing, a long way from the marketing.
It gets worse for the hype. Of the engineers who hit their peak AI gain in one quarter, 66% saw it drop in the next. The early win does not compound. It fades. CFOs are noticing. One CTO told the Pragmatic Engineer survey it was hard to keep their finance team supportive because the productivity benefits had proven difficult to conclusively prove. The people signing the cheques cannot see the return, and the people doing the work can feel the same thing from the other side.
I am not telling you AI is a dead end. I use it every day across all three of my companies. I am telling you what the measured number actually is, because the gap between that number and the promise is the first thing feeding the resentment.
Here is the part that matters most, and the part almost nobody is saying out loud.
For the last 200 years, every major technology eventually made everyone richer. The factory, electricity, the car, the internet. The owners always saw the gain first, but the workers caught up. Wages rose. Living standards rose. The gain spread.
AI is the first widespread technology where the owner sees the productivity gain immediately and the worker often sees no gain at all. In many cases the worker is helping to train the thing that will replace them.
There is footage going around of factory workers being asked to wear cameras while they work. The cameras record every motion, every decision, and the data trains a model that will eventually do their job. They are being paid to teach the machine that will take their job. It is not only factory floors. Last year Mark Zuckerberg said Meta would put recording software on its engineers' computers so the model could learn from them. Weeks later, 8,000 people were laid off. Cutting 8,000 engineers frees up roughly four billion dollars a year. Meta's AI spend this year is around 135 billion. The maths tells you where the bet is placed, and it is not on the people.
The asymmetry itself is not new. What is new is the speed. The factory worker in 1900 watched the boss get rich and waited a generation for their own share. The worker wearing a camera in 2026 watches the gain go to the boss before lunch. When people say something about AI feels off, they are not imagining it.
Now add consent. People are being told their job depends on adopting a tool whose payoff is not clear even to the people mandating it.
A survey of 2,400 executives and employees this year found 60% of companies plan to lay off people who do not adapt and embrace AI. The same survey found 48% of executives calling their own AI adoption a massive disappointment. Read those two numbers together. You are being pushed onto a tool, under threat of losing your job, where the return is not proven to the very leaders demanding it.
Then there is the language. Cloudflare's chief executive announced layoffs of 20% of the workforce despite record revenue, and referred to the group being cut as "the measurers." He turned people into a function, then deleted the function. That is when a worker stops asking "is this tool good" and starts asking a different question entirely: "Am I not more valuable than ChatGPT? Am I not more valuable than a model, with all the judgement and experience I have built?" Some land at yes. Some land at no. That question, not the technology, is the real source of the anger.
So if the gain is small and the adoption is forced, where is all the money going? This is the other half of the unease, and it is worth being honest about.
In March, Jensen Huang stood on stage at Nvidia's GTC and told engineers they should be spending half their salary on AI tokens. Half, on top of wages. Where does that money come from? Not from end-user revenue. Uber rolled Claude Code out to 5,000 engineers and burned through their entire 2026 AI budget in four months, around 2,000 dollars per engineer per month. And the labs that money flows into are mostly not profitable. Anthropic just posted its first profitable quarter, the first time any major lab has. OpenAI is not expected to be profitable until the end of the decade.
Nvidia recently agreed to put 100 billion dollars into OpenAI, and OpenAI uses that money to buy Nvidia chips. That is not a customer relationship. That is a company subsidising its own sales. Even Huang admits it is fragile: in a leaked all-hands the day after Nvidia beat earnings, he said that if they had delivered a bad quarter, if it looked a little creaky, the whole world would have fallen apart.
Michael Burry, the investor from The Big Short, says Nvidia is like Cisco. Most people remember the dot-com bubble as pets.com vaporware. Burry's point is that the real story was Cisco: a genuinely profitable company whose equipment ran the early internet, where the build-out ran years ahead of the demand. The technology was real. The market was just miles ahead of the revenue.
That is the honest shape of it. The bubble is real, but it is a financial bubble. The technology underneath it is not.
Inside my own company, with our 20 engineers, the productivity gain is probably closer to that 7.8% than to ten times. But I do not think 7.8% is the ceiling. I think it is the cost of being early. My teams are spending real hours testing tools, throwing half of them out, working out what holds up. That testing is lost productivity today. It is the tax you pay at the start of a shift like this.
Two quick examples from my world, one good, one not.
A few months ago my coffee machine broke, an integrated one, complicated. Normally that is half a day of emails, hold music and being passed around departments. Instead I took a photo that showed the model and serial number, handed it to an AI agent, and told it to fix the problem. It read the numbers off the photo, found the manufacturer's service partner, emailed them through my Gmail, went back and forth until it had appointment options, and brought me a list. I approved one. The engineer turned up and fixed the machine. End to end I spent about five minutes.
Then the messy one. We built our own AI code review bot at We UC. It reads every merge request and comments through Claude, and when it works it is genuinely excellent. Last week it ran ten reviews and cost us about a hundred pounds, and on one of them it recommended restricting its own permissions. We took the advice. On the next run it could not post comments, because it had revoked its own access. One of my engineers called it a brilliant five-year-old with a doctorate and amnesia. The capability is there. The unit economics are not, yet.
That is what AI looks like up close. One clean win that saved half a day, one expensive experiment still being tuned. Far more honest than what gets shouted from a keynote stage.
The people pushing back on AI are not Luddites. They are seeing the asymmetry, feeling the cognitive cost their own peers are warning each other about, and being asked to train the thing that might replace them. I get it.
And the people who are convinced AI changes everything, I get them too, because every time I watch an agent do in a minute what used to take me half a day, I see exactly what they see. The capability shift is real. It is just smaller and slower than the marketing version, for now.
Both things are true at the same time. The bubble is real and the technology is real. The frustration is legitimate and the opportunity is legitimate. There is a lot of AI noise and far fewer real outcomes. The job, for anyone building or running a business through this, is to keep testing, keep your judgement, and not offload your thinking while you use the tool to lift your output. No handbook is coming. We are writing it as we go.
Watch the video:
If running a business through this kind of uncertainty is on your plate, I also wrote The CEO Operating System, the framework I use to run three companies without burning out. It is free: axelmolist.com/ceo-os.
Got a different read, or a story of your own about how this is landing on your team? Hit reply. I read every email.
Thanks,
Axel.
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