For years, software success was measured through adoption—seats, active users, and renewals, Friar notes. She argues that AI is different: it must be measured by the work it actually accomplishes.

“The basic economic question facing CFOs and other business leaders is whether the value of the work AI completes grows faster than the cost of producing it,” Friar wrote in a blog post. Answering that question, she says, requires going deeper than simple metrics like cost per token.

She argues that the metric that matters for AI is what she calls “useful intelligence per dollar.” It has four elements: Is AI completing work that matters? What does each successful task cost? Can people depend on the result? And does each dollar produce more value as usage grows?

In practice, that means leaders should track the volume of AI-completed work that meets a defined quality bar, add up the full cost of completing that work, and then divide by the number of successful tasks to get a cost per successful task. From there, the test is whether people can reliably depend on the output and whether, over time, high-quality completed work grows faster than total cost while quality holds or improves. If it does, each AI dollar is producing more value—and compute sits at the center of that equation, Friar explains.

“Our job is to make that equation better with every generation: more capable models, faster and more dependable results, and lower costs for the work customers need done,” she writes.

For OpenAI, a hyperscaler, compute is not just a technology expense—it is a strategic asset. As a private company, it does not publish formal capex guidance, but the Stargate initiative announced in January 2025 outlined a plan to invest up to $500 billion over roughly four years to build large-scale AI infrastructure in the U.S.—with the initial phase targeting about $100 billion and the broader buildout accelerating toward a 10-gigawatt capacity goal in the U.S. by 2029. Just over a year later, it has already surpassed that milestone, as demand for AI continues to accelerate. According to reports, OpenAI’s IPO could come as soon as this summer or as late as 2027. The company is already valued at $852 billion and approaching the $1 trillion range.

While finance chiefs have long led capital allocation and investor communication, they are increasingly expected to help determine strategy, including where the company places its biggest long-term bets, like AI spend, alongside the CEO.

McKinsey recently held its 24th annual Global CFO Forum, an exclusive gathering that brought together about 100 finance chiefs from over 30 countries, representing some of the world’s largest organizations. Andy West, a senior partner at McKinsey and global co-leader of the firm’s Strategy and Corporate Finance practice, told Fortune he conducted an informal poll, asking CFOs whether the strategy function now reports to them. About two-thirds raised their hands. Five years ago, it would have been less than a third, he said.

“We’ve been talking about AI at this conference for a couple of years now,” West said. Last year, finance leaders were still experimenting with AI. This year, the conversation shifted decisively toward enterprise-wide transformation, he said.