Hello and welcome to Eye on AI. In this edition…The AI economy has a measurement gap…Anthropic files IPO paperwork…Meta bets on subscriptions and enterprise to monetize AI…and AI-generated fake citations are infiltrating scientific literature.
When it comes to measuring the economic impact of AI, no one can agree on where to start.
Listen to the narrative coming from the Big Tech firms, and AI is already supposed to be transforming everything from how we work to how companies are organized and even how entire industries, such as software engineering, function. But if you look at the official economic statistics, like productivity numbers or GDP growth rate, you’d be hard-pressed to find the numbers to back this up. Some argue the technology is transforming the economy in a way our statistics simply cannot keep up with. Others say that, for all its hype, AI has yet to show up in firm-level productivity in any systematic way.
That gap between the hype and the numbers is what a new policy brief from the Peterson Institute for International Economics is trying to explain.
Measuring AI’s economic impact
The brief, written by Anton Korinek, a nonresident senior fellow at Peterson and head of Transformative AI Economic Studies at the Anthropic Institute, and Patrick McKelvey, a senior data scientist at the Bank of Canada, argues that AI is already growing at extraordinary speed, but that official statistics are simply not built to track it.
The brief points to two problems. First, AI activity is scattered across dozens of different industries in the official accounts—cloud services, software, data processing—so there’s no single place where you can see the AI economy as a whole. Second, the stats have no good way to account for how fast AI is improving.
They estimate that AI generated roughly $250 billion in economic activity in 2025, comparable in size to the entire U.S. airline industry, and that the amount of AI output the industry can produce is growing at around 2,600% a year. The authors also estimate that the cost of getting the same level of AI performance has fallen by about 94% a year—meaning each dollar spent on AI today buys vastly more than it did a year ago.
To arrive at those figures, they build their own estimates from scratch—using data on GPU rental rates, electricity consumption, AI inference prices, and the pace of algorithmic progress in AI training—rather than relying on official statistics. They also calculate that if official statistics accounted for AI’s rapid improvement in capability, U.S. economic growth in 2025 would appear about 4 percentage points higher. (The authors caution that the estimate is an upper bound, meaning it represents the maximum plausible impact rather than their central estimate.)
Their proposed fix: give AI its own dedicated statistical track—the same way governments separately account for energy or international trade—that aggregates AI activity across industries and adjusts for how quickly the technology is improving. Build that now, they argue, or the gap in the data risks becoming a gap in policy, meaning governments could find themselves making decisions about taxes, labor markets, and public spending without being able to see what the AI economy is actually doing. Or as they put it: “What cannot be measured cannot be steered.”
What the data is missing
However, not everyone is convinced. Diane Coyle, Bennett Professor of Public Policy at the University of Cambridge, told Fortune that while she agrees that the measurement gap is real, she disputes the scale of what Korinek and McKelvey are claiming. One of her objections is that AI is mostly used to help create other products and services rather than being a product in its own right. GDP measures the final goods and services that reach consumers. If AI is mostly an ingredient rather than a finished product, its economic impact only matters if it actually makes the end product better.
(Notably, Korinek and McKelvey actually acknowledge that AI is mostly an intermediate input—they cite it as the reason their 4-point GDP estimate is a ceiling, not a prediction.)
According to Coyle, there is also still little systematic evidence that AI is increasing productivity at the firm level, and even where individual workers are going faster, that doesn’t always feed through to the organization as a whole. If one department speeds up but the next hasn’t adopted AI, the gains hit a bottleneck and disappear.
“I think AI is a significant technology. It will have these great effects, but I think both the speed and scale that this paper is claiming for it are overdone,” she said.
At its core, the question is really about how much AI is transforming the way we work and whether the tools we have to answer that question are fit for purpose. As Coyle puts it, the challenge isn’t just that we’re measuring badly—it’s that we haven’t even agreed on what we should be measuring.
With that, here’s more AI news.
Beatrice Nolan
beatrice.nolan@fortune.com
@beafreyanolan
This story was originally featured on Fortune.com