In partnership with
AI Mandate Scan
The Bill Comes Due.
Uber spent its entire 2026 AI budget in four months and nobody can explain what they got for it.
In December 2025, Uber deployed Anthropic's Claude Code to roughly 5,000 engineers. By April, the full annual AI budget was gone. Monthly API costs were running $500 to $2,000 per engineer. Seventy percent of code commits were AI-generated. Agentic AI usage jumped from 32% to 84% in two months.
And yet, COO Andrew Macdonald said publicly what most enterprise leaders are saying privately: he cannot draw a direct line between AI token consumption and consumer features shipped. "If you're not actually able to draw a direct line to how many useful features and functionality you're shipping, that trade becomes harder to justify."
This is the AI value problem in its most concentrated form.
Adoption is measurable. Outcomes are not.
Spend scales. ROI has not (yet).
Uber is likely not unique here. It is just the first major company to publicize it.
The question now is whether government is heading down the same path, though they are doing it with less accountability and a lot more public money.
What's Inside
Beyond the Pilot: Why the federal government's AI accountability gap is structural, not temporary
The GAO Paper Trail: Agencies are spending without a lessons-learned infrastructure — and that's now documented
Brookings by the Numbers: The most comprehensive look yet at where federal AI actually stands
Quick Hits: Five stories worth your attention this week
Signal Check: Federal AI spend vs. ROI — the data behind the accountability gap
Let's get into it.
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