TL;DR.
JevonsMaxxing
Cheap generation expands attempted work
JevonsMaxxing is the AI version of Jevons Paradox: when efficiency lowers the effective cost of use, total consumption can rise. Here, AI lowers the cost of candidate work before verification, security, budgeting, and value capture catch up.
Uber is the budget-shock hook
After about 5,000 engineers got Anthropic's Claude Code in December 2025, usage nearly doubled by February and the expected annual budget model was blown away by April. That is a demand signal before it is an ROI signal.
Verification becomes the first gate
Software shows the loading dock clearly: AI coding tools can produce far more merged PRs while review time rises. The load is not just code review; it includes auth flows, dependencies, configs, secrets, permissions, and agent handoffs.
Value capture becomes the second gate
Menlo puts 2025 enterprise AI spending at roughly $37 billion, while only 16% of deployments qualify as true agents. Gartner's savings finding has the same contour: 4.11 hours of individual GenAI savings per week fall to 1.5 at the team level unless finance, procurement, governance, workflow owners, and AI FinOps turn local usefulness into accepted outputs, budgets, and accountable returns.
Healthy firms sort the flow
The answer is not simply wider access. Stronger organizations scope permissions, route workflows, move review closer to generation, attach budgets to accepted output, and concentrate high-ROI uses instead of treating usage itself as productivity.
Quick Synthesis
“Cheap generation expands the surface area of work before organizations expand the surface area of verification and value capture.”
Generated by Intelligence Unit
AI-synthesized from article content