Measuring the wrong company

Companies are taking a hard look at their AI spending and deciding the numbers don’t add up. Uber blew through its entire 2026 AI budget in four months — on a coding tool its engineers couldn’t stop using. Another company spent half a billion dollars before anyone thought to set a limit. Forrester now expects enterprises to postpone about a quarter of their planned AI investment into 2027 because the returns haven’t shown up.

I’ve heard this argument before. It’s the same one people made about the cloud in the early 2010s.

Back then the case against moving to AWS went like this: we already run our own data centers, we run them well, and we run them for less than Amazon would charge us. So why move? On the spreadsheet, the skeptics were often right. A company that had already sunk the capital into its racks and knew how to keep them humming could beat cloud pricing on raw unit cost for years.

They were answering the wrong question.

The cloud was never about running the same workloads for less money. It was about what you no longer had to think about. Moving to AWS turned infrastructure from a capital expense into an operating expense, from a thing you bought, racked, and depreciated into a thing you rented by the hour and stopped paying for the moment you stopped using it.

I lived this one. In my early days as CTO of Mailprotector, our real weakness wasn’t the software — it was everything underneath it: buying, racking, and babysitting the hardware our products ran on. Before AWS was anywhere close to ready to replace a data center, I wrote “AWS as a data center?” in a notebook and circled it. A year or two later we started migrating — and not to save money; the spreadsheet didn’t make that case yet. We did it to stop spending our attention on machines and put it where we could actually differentiate: the software. A couple of years after that, we turned the lights off on our last data center and never looked back. In hindsight it’s hard to separate that one decision from the company’s success — maybe even its survival.

Most companies never framed it that way. They measured the cloud against their own data centers, saw a higher unit cost, and stopped there — and because they already had data centers, the shift didn’t help them. It helped the company that didn’t exist yet. A startup in 2012 could spin up infrastructure that would have required millions in upfront capital a few years earlier, and pay for it out of revenue as it grew. Whole categories of companies got built that couldn’t have raised the money to build themselves the old way.

That generalizes well past the cloud. A general-purpose technology rarely just lowers the cost of what you already do; what it offers is a different cost structure, and different cost structures get used by different companies.

When an established company asks whether AI is worth what it’s spending, the buried question is whether AI makes the current operation cheaper. Often the honest answer is: not by enough to matter. Bolting a model onto a process that was designed around people rarely pays for itself. A lot of the spending getting scrutinized right now genuinely is waste. The scrutiny isn’t wrong.

But “our AI spending isn’t paying off” and “AI doesn’t pay off” are very different conclusions, and the distance between them is exactly where the data-center operators got caught. They weren’t wrong about the numbers. They were measuring the wrong company.

The company that mattered was being built on rented infrastructure, with a cost structure they could never reach by trimming their own. It’s being built again now, with AI in the foundation instead of bolted to the side. That’s the spend worth watching, and it isn’t yours.