The State of the AI EconomyJune 25, 2026 Azeem Azhar, William Gildea, Hannah Petrovic, PhD, Nathan Warren & Marija Gavrilov1Exponential Viewwww.exponentialview.coIndependently produced by Exponential View. Built from public disclosures and Exponential View’s own models; all conclusions are our own. © Epiiplus1 Ltd 2026
There is a visibility problem in the AI economy. Until now, it has been impossible to deconstruct real customer demand.The supply side of the AI economy is well-documented. Most semiconductor companies and hyperscalers are public and disclose their activities in some detail. Sell-side analysts have done a great job decomposing their performance. The demand side, what customers are actually paying for and if the revenues are real, has been obscure. The largest labs are private, and even public companies bury AI revenue inside segment totals. Without understanding genuine demand, it is impossible to judge the health of the AI economy that underpins $22.7 trillion of stock market valuation and has driven US GDP growth in the past six quarters.We hope that this report serves as a reference source on the current state of play, free of hype and fear, while helping us all have a more informed conversation about the gravitational pull AI is exerting on the economy and the world at large.Special thanks to those who kindly reviewed an early draft of this presentation and gave us feedback:Alex Imas, Shanu Mathew, Patrick Rutherford, Jaime Sevilla and Amy Sutter.– Azeem and the Exponential View team2Why we’ve done this
We flag, investigate and maintain our datasets using analyst research, augmented by a proprietary system that scans, crawls and synthesizes insights.Every revenue line traced to primary filings, audited accounts, transcripts and credible reporting; plus cloud-attribution where a private firm’s revenue surfaces in a public firm’s accounts (OpenAI via Azure, Anthropic via Bedrock).We build full per-company models specifically for GenAI financials (split out from top-line reporting), covering key drivers of revenue, profitability and cost in the P&L, cash flows and balance sheets.3A proprietary line-level revenue model: Sourced, scored, triangulated, deduplicated1SourceBottom-up, 1,000+ firms›2Confidence scoreEach line carries a rigorous confidence score before it enters any model, so weak inputs can’t inflate the number.Confidence-scored before it counts›3Model and triangulateCompany financial models checked against top down›4DeduplicateSpend only counted onceRevenue is counted at every layer but never summed across them: attributed by value-add so the same dollar isn’t double- or triple-counted.Audit trail: Sample source table$40value-add$30value-add$30value-adde.g. $100 app spend that sends $60 to a model provider, which spends $30 on inference hosting, is counted as $100, not $190 :Scope: Global ex-China · App, model & infrastru...
