Everyone Is Buying Tokens. Almost Nobody Is Shipping.
Abhishek Soni
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Everyone Is Buying Tokens. Almost Nobody Is Shipping.<br>Anthropic is about to IPO at nearly a trillion dollars. The companies buying its product can’t explain what they got for the money. Both things are true, and the gap between them is the most important<br>Abhishek Soni<br>Jun 05, 2026
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There’s a number I can’t stop thinking about.<br>For every dollar a company spends on AI coding tools right now, roughly 18 cents turns into software that reaches a real user. The other 82 cents disappears into bug fixes, rewrites, reverts, and review cycles, much of it cleaning up code the same tools just generated. That figure comes from EntelligenceAI, which aggregated data across more than 2,000 companies using advanced AI coding tools.<br>Thanks for reading! Subscribe for free to receive new posts and support my work.
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Hold that next to the headline of the year: Anthropic is going public at a valuation approaching one trillion dollars , having blown past OpenAI to an annualized revenue run-rate around $45 billion, growing faster than any company in recorded history.<br>Both of these are true at the same time. The product is selling like nothing we have ever seen. And the people buying it, some of the most sophisticated engineering organizations on earth, are quietly admitting they can’t yet prove what they got for the money.<br>That contradiction isn’t a scandal. It’s a signal. And if you build, invest, or operate in this space, reading it correctly is worth more than any model benchmark.
The receipts
Let me put the evidence on the table, because the specifics matter more than the vibes.<br>Uber rolled out Claude Code to its engineers in December 2025. By March, adoption jumped from 32% to 84% of its roughly 5,000-person engineering org. Around 70% of committed code now originates with AI. Sounds like a triumph, until you learn the company burned through its entire 2026 AI coding budget in four months. “I’m back to the drawing board,” the CTO said, “because the budget I thought I would need is blown away already.”<br>Then came the part nobody at a vendor wants quoted. Speaking in late May, Uber’s COO Andrew Macdonald was asked whether all that spend was translating into better products for riders and drivers. His answer: “That link is not there yet.” The usage stats, he admitted, “make your head explode,” but he couldn’t draw a line from tokens consumed to features customers can feel.<br>Microsoft went further. It began revoking some engineers’ access to Claude Code, moving them onto its own cheaper Copilot CLI. The framing was “tool consolidation.” The subtext was the bill.<br>Meta built an internal leaderboard ranking 85,000+ employees by token consumption. Nvidia’s Jensen Huang has floated the idea of giving engineers token budgets as part of their compensation. Somewhere along the way, “how much AI did you use” quietly became a goal in itself, which is the exact moment a metric stops measuring anything real.<br>And the now-legendary data point: one developer running an autonomous agent framework racked up a $1.3 million monthly bill, 603 billion tokens across 7.6 million requests. The bill wasn’t the result of waste. It was the result of the tool working, running flat out, doing exactly what it was built to do.<br>This is what I’ve started calling the tokenmaxxing trap : when consumption becomes the scoreboard, you will always be able to consume more. The ceiling isn’t usefulness. It’s budget.
Why this keeps happening (it’s older than AI)
Here’s the part most hot takes miss. This is not an AI problem. It’s a productivity problem that economists have watched for a century.<br>There is a vast, treacherous gap between task-level productivity and economic productivity . AI can absolutely make the task of writing code faster. That is real and measurable. But shipping a product that customers value is not one task, it’s a chain: deciding what to build, building it, testing it, reviewing it, integrating it, maintaining it, and not breaking the eleven things connected to it.<br>Speed up one link in that chain and you don’t automatically speed up the chain. Sometimes you slow it down. One analysis of over 10,000 developers found that high-AI-adoption teams took on 47% more pull requests per day, and spent the gains on context-switching, orchestration, and reviewing machine-generated code. At the median organization studied, 44% of all engineering output was reactive , fixing or maintaining existing code rather than building anything new. As AI pushes more code out faster, reverts climb faster than output. Each revert spawns a bug-fix, each bug-fix adds to the reactive pile, and the loop compounds.<br>Steam power took decades to show up in productivity statistics, because factories had to be physically redesigned around it before the gains appeared. Electricity was the same. The technology arrives first; the reorganization that makes it pay off comes later,...