A 100x Problem

ClickUp promised million-dollar salaries for AI-native contributors. The data says those people barely exist.

There were two ballrooms at the Berlin conference.

On one side of the lobby: “The Future of Enterprise AI.” Standing room only. Speakers in blazers talking about agentic workflows and the reorganisation of knowledge work. The room hummed with the particular energy of people who had convinced themselves they were at the beginning of something.

On the other side: “Practical AI for Engineering Teams.” Smaller room. Less curated. Engineers in hoodies comparing token bills and arguing about which model gave the best cost-to-quality ratio on long context windows.

I was sitting on a bench between them, not quite in either room. My phone buzzed.

ClickUp had just laid off 22 percent of its staff.

I read the announcement sitting there, knees apart, phone in both hands. Zeb Evans, the CEO, had posted it on X. The business, he said, was the strongest it had ever been. The layoffs were not about cutting costs. Most of the savings would flow back into the people who stayed. Million-dollar salary bands. A “100x org.” The survivors, powered by AI, would produce a hundred times the output of the people they let go.

The goal was 100x output.

I looked up. The Future of Enterprise AI had let out for a break. People were streaming into the corridor, talking over each other, pulling out phones to check their own notifications. Someone said “did you see what ClickUp did” and the phrase rippled through the crowd like a wave.

The offer, laid out in plain text by a CEO: we cut more than a fifth of the company, we will pay the survivors seven figures, and we expect them to be a hundred times more productive. The implication was naked: the people we let go were not 100x. The people we kept will become it. AI is the lever. The rest is execution.

The second ballroom let out. The engineers came out slower. They did not look at their phones the same way. They looked like people who had just seen a cost projection they could not unsee.

I spent the next week calling people. Not reading reports. Calling people.

The ClickUp post had the quality of a good pitch: it sounded like it could be true if you didn’t look too closely. I wanted to know who had actually tried this: who had bet on the 100x engineer and lived to tell the story.

The first call was Microsoft.

The company had committed up to $5 billion to invest in Anthropic in November 2025. Satya Nadella and Jensen Huang stood on a stage and announced a new era of strategic partnership. It was the kind of announcement designed to signal total confidence in the AI future. The biggest platform company in the world doubling down on the most capable AI company in the world.

Six months later, Microsoft cancelled Claude Code licenses across its Experiences and Devices division. Engineers were told to switch to GitHub Copilot CLI, which cost less. The reason was not that the tool didn’t work. It worked too well.

Per-engineer API costs ranged between $500 and $2,000 a month. Adoption hit 84 to 95 percent. Engineers loved it. They used it constantly. And that was the problem. The operating cost of Claude Code for a single engineer exceeded the cost of employing that engineer in some cases. The spreadsheet broke the narrative.

I sat with that for a moment when I heard it. The company that invested billions in Anthropic, the company that put its CEO on stage to celebrate the partnership, shut down internal use of the Anthropic tool because it was too expensive to run. The public narrative called it the future. The internal spreadsheet called it more expensive than the person using it.

I called a friend who works in engineering intelligence. The platform data, he said, tracks in the same direction. Across thousands of engineers, the heaviest AI users produce about double the throughput at ten times the token cost. More writing, but a disproportionate share gets rewritten. The productivity gain is real but thin: a small edge buried under the bill.

Then I called a buddy close to Uber.

The numbers there made Microsoft’s story look like a pilot program.

Uber allocated $3.4 billion for AI in 2026 and burned through the entire budget in four months. Claude Code adoption among its 5,000 engineers hit 95 percent monthly usage. The company did everything right by the playbook: built leaderboards, gamified adoption, turned AI usage into a competition. It worked. Usage doubled by February. By April the annual AI budget was gone.

Uber’s CTO went public with the admission: “I’m back to the drawing board, because the budget I thought I would need is blown away already.”

He did not say the tools failed. He said the assumptions failed. The company modelled for adoption. It modelled for usage. It did not model for 95 percent monthly engagement with per-token billing. Nobody did. Every company planning a 100x future is assuming the economics will sort themselves out. Uber just proved they don’t. Not yet, not at scale, and not on any spreadsheet anyone showed to a board.

I told these stories to the engineer from the second ballroom.

We ended up on the bench together again, the day after the conference ended. The ballrooms were empty. The name badges had been cleared. A cleaning crew was taking down the banners.

He had recognised me from the Q&A. He asked if I had figured it out yet, whether the 100x thing was real.

I told him what I had found. Microsoft spent billions on the company that makes the tool, then cancelled the tool internally because the cost of using it beat the cost of the engineer. Uber spent more on AI in four months than most companies spend in a decade, and the CTO is back at a whiteboard trying to figure out what went wrong. The technology worked. The economics didn’t. When the tools work that well and the costs are that high, the 100x engineer isn’t a productivity problem. It’s a budget problem.

He nodded. “So what does ClickUp know that Microsoft and Uber don’t?”

I didn’t have a good answer.

He told me his company was rolling out AI coding tools to everyone next quarter. His VP had sent around the ClickUp announcement as motivation. He was supposed to be excited. And he was. He just didn’t know if the math worked.

We sat there for a minute in the quiet corridor. Then he stood up, said good luck, and walked toward the exit.

I stayed on the bench a little longer. I watched the cleaning crew fold the banners and stuff them into a bin bag. The Future of Enterprise AI, in vinyl, on its way to a dumpster.

The question followed me out the door: what happens when the pitch is good but the numbers aren’t there yet? Companies are restructuring around a promise that Microsoft tried and Uber burned through. Maybe the 100x engineer exists. Maybe the tools get cheaper. Maybe the math changes.

I’m still waiting for the first company to show me the spreadsheet that proves it.