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Chronicles

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Exponential View: global AI sales, excluding China, hit $25B in Q1, exceeding an estimated $21B in data center and chip depreciation costs; margins remain thin

Revenue from artificial intelligence has reached a tipping point, showing that the hundreds of billions of dollars tech companies …

Bloomberg Jacob Reid

Context & Ripple Effects

Earlier coverage framed AI’s central commercial problem as a revenue gap: estimates from Sequoia and Bain suggested model and compute providers would need vastly more recurring revenue to support projected infrastructure demand. Separately, hyperscalers’ accelerating data-center spending put the cost side of that equation in focus.

This report provides an early operating read on that debate. AI sales outside China now exceed estimated depreciation for the underlying data-center and chip assets, but only narrowly, making margin rather than headline revenue the key issue.

First-order effects

  • AI businesses collectively have enough reported sales to cover estimated depreciation on core compute assets in the quarter, indicating that infrastructure is beginning to generate meaningful revenue rather than remaining purely upfront investment.
  • Thin margins mean the revenue milestone does not yet translate into substantial profit for the companies building and operating AI infrastructure.

Second-order effects

  • Providers face stronger pressure to improve utilization, pricing, and product monetization, since incremental sales must cover costs beyond depreciation before AI investment can produce attractive returns.
  • The result complicates the spending case for customers and capital providers: rapid AI demand can coexist with limited near-term profitability when compute costs remain high.

Third-order effects

  • If revenue continues to rise faster than depreciation, the AI buildout could move from a capex-led cycle toward one judged on operating leverage; if margins fail to expand, infrastructure spending will face greater capital-discipline pressure.
  • The relevant industry benchmark is likely to shift from aggregate AI sales to whether recurring AI revenue can sustain the full cost base of compute, models, and services.

The trend: AI is entering a monetization test in which growing sales must demonstrate durable margins against the depreciation burden created by the compute buildout.

Discussion

  • @alexsjacquez Alex Jacquez on x
    Narrative violation? [image]
  • @azeem Azeem Azhar on x
    The GenAI economy has generated $110 billion in sales over the past 12 months. It is growing fast. On an annualized basis, the revenue run rate exceeds $175 billion. These numbers took us several months to construct, and as far as we know, it's the first bottom-up, deduplicated […
  • @edzitron.com Ed Zitron on bluesky
    This report does not use the number $25bn once but otherwise decides to annualize the revenues of AI companies to make them seem larger, and even adds in compute.  Precisely engineered to support a narrative, deeply suspicious  —  intelligence.exponentialview.co/ assets/ev-st... …
  • @alexolegimas Alex Imas on x
    This is an incredibly thorough analysis of the GenAI economy. Covers everything from model use to capex to economic demand for GenAI. Congrats to the team. This is a huge public good.
  • @andrewcurran_ Andrew Curran on x
    Great report with a lot of interesting data, Bloomberg wrote a story on it this morning. This chart compares Gen AI to the Cloud, Mobile Apps, and the Internet. Way more in the thread. [image]
  • @asset_smind Kevin Faircloth on x
    If analysts are going to assume a 6yr shelf life for IT Racks, it's a great time to be a used IT Rack buyer! Currently, even the lowly H100 is commanding high rents! 💰💯👀 https://www.bloomberg.com/...
  • @jameswise James Wise on x
    Really excellent detail in this on the AI economy from @azeem and team. - AI revenues are growing at 3x the pace of previous tech shifts ( internet etc ) and accelerating - Demand vastly outstrips supply of compute - It will be a while until full numbers show up in GDP, but