Token prices have fallen 98 percent since GPT-4, but enterprise AI bills are up 320 percent. In this week's Big Story episode, Ray Rike and Peter Buchanan trace where that money is actually coming from, and the answer is not the software budget.
Drawing on Gartner, Oxford Economics, Zylo, Challenger Gray and Christmas, and Goldman Sachs data, the two lay out why labor, not IT, is becoming the primary funding source for AI at scale, and why almost no company has the measurement infrastructure to manage it.
- The price paradox. Per token costs have collapsed, but usage has grown faster than costs have fallen. Ray walks through the math behind average enterprise AI budgets rising from $1.2 million to $7 million in two years.
- Three cautionary tales. Uber consumed its entire annual Claude Code budget in under four months, Microsoft revoked thousands of Claude Code licenses over cost, and one unnamed enterprise ran up a $500 million bill in a single month. Ray and Peter break down why each was a governance failure rather than a technology failure.
- Only two budget pools are big enough. The IT and software budget represents just 3 to 4 percent of revenue, while labor represents 25 to 40 percent depending on industry. Ray makes the case that labor is the only pool large enough to absorb the AI spending trajectory Gartner and Oxford Economics are projecting.
- The attrition lever. Ray and Peter unpack how not backfilling open roles has quietly become the primary way enterprises are funding AI investment, supported by data showing over 113,000 tech layoffs in 2026 with 48 percent explicitly attributed to AI.
- Revenue per FTE as the tell. Ray shares benchmark data showing SaaS company revenue per employee up 25 to 35 percent over the last twelve quarters, and explains why this metric will be the clearest signal of whether the AI budget transfer is actually working.
- Five metrics every CFO needs now. The episode closes with a practical starting list: AI spend as a percent of revenue, AI spend per employee, inference spend as a percent of opex, inference cost as a percent of COGS for AI enabled products, and revenue per FTE tracked against labor cost and agent cost as a percent of OPEX.
AI to ROI is always looking for guests with real-world examples of measuring AI budget impact. Reach out Ray on LinkedIn (@rayrike).
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