AI Bills Are Growing. So Why Can't Companies Show the Results?

Tesla's engineers were spending thousands of dollars a week on AI compute, without realizing it. Starting July 6, each employee gets a $200-per-week cap. Go over, and you need manager approval. Last month, the Microsoft CEO published a widely shared essay arguing that companies let employees throw the most expensive AI models at every task, whether the task needs that level of capability. Musk replied in the comments with one word: "Interesting." Now his own company's policy announcement is out. An internal Accenture recording recently surfaced. Senior leaders were discussing a runaway AI bill. The biggest line item? Non-technical employees converting PDFs into PowerPoint slides. Engineers ranked lower. Uber burned through its entire annual AI budget in four months. At Amazon, an employee built a leaderboard called Kirorank to track colleague token consumption. The top user burned 281 billion tokens in 30 days — worth millions. Amazon deleted the board and had a VP remind everyone: don't use AI for the sake of using AI. These cases share a diagnosis. When companies encourage AI use, the easiest thing to measure is consumption — token counts. So token counts become the signal of whether you're keeping up. Employees do what anyone under evaluation does: optimize for the signal. PDF-to-PPT climbs to the top of the bill for a simple reason: it burns tokens, and it still counts as being productive with AI. This has a name. Goodhart's Law: when a measure becomes a target, it stops being a good measure. In 1910s Massachusetts, a mail carrier found a workaround on a road where cars were banned. He hitched a horse to the front of his car, let the horse pull it through the restricted zone, then unlatched the horse and drove. The car existed. The rules hadn't caught up. So the horse had to stay. A lot of company AI programs look like this now. The tools changed. The performance metrics didn't. The CEO of software company Palantir said on television last week that private frustration among enterprise leaders toward AI labs runs far higher than what's publicly visible. They're paying for tokens; they're not getting business value back. His read: AI companies charge per token because they're selling raw material. Charging for business outcomes requires confidence you can actually deliver them. Tesla's $200 cap says think before you spend. But the cap still measures tokens, not outcomes. If your company is asking whether AI is delivering, try reframing the question: what can we do now that we couldn't do before? Are people using AI to solve real problems, or just putting an AI stamp on work that already existed? Those two answers call for completely different responses.