# AI Productivity — Measured vs. Claimed

#concept #ai #evidence

## Summary

The empirical counterweight to the vault's dominant premise. Where the vault's promotional sources (twelve at this page's creation; nineteen of twenty as of 2026-07-29) *assert* AI has zeroed the cost of commodity software work, the rigorous evidence (RCTs, a top-5-journal field study, large surveys) shows the real effect is **modest (≈14–19%), context-dependent, tilted toward NOVICES not experts, and systematically overstated by self-report.** It **qualifies rather than demolishes** the thesis — no study finds AI worthless — but it directly inverts two load-bearing claims and is the vault's first source with evidentiary standing rather than sales incentive. Anchored entirely by [[2026-07-18-ai-productivity-adversarial-evidence]]; single-source, so `Status: tentative`, but its inputs are the highest-provenance in the vault.

## Current Understanding

**Two direct inversions of the popular thesis:**

1. **"AI zeroed the cost of skilled dev work"** → the strongest *developer-specific* RCT (METR, 2025) found 16 experienced open-source maintainers were **19% *slower*** completing real tasks on their own repos with early-2025 AI. The cost didn't go to zero; for experts on familiar code it went *up*.
2. **"Seniors up, juniors irrelevant"** ([[seniority-and-ai]]) → the best skill-distribution study (Brynjolfsson, Li & Raymond, *QJE* 2025) found the **least-experienced gain most** (+34% vs. ~0 and small quality *declines* for experts). A GitHub Copilot RCT independently found less-experienced developers benefit more. AI's productivity dividend accrues to novices, not seniors.

**The perception trap (why the hype is self-sustaining).** METR's developers forecast a 24% speedup, *still* estimated +20% after finishing, and were measured −19% — a ~39-point gap between felt and real. This matters because "it feels faster / I shipped more" is the exact evidence most promotional AI-productivity claims are built on. Self-report is not measurement.

**Individual speed ≠ shipped software.** DORA 2024 found AI adoption correlated with *reduced* delivery stability (and, that year, throughput — reversed in DORA 2025). Stack Overflow 2025 (~49k devs): only ~3% "highly trust" AI accuracy while ~46% distrust it (up from 31%), even as adoption hit 84%; ~45% say debugging AI code is time-consuming. The artifact isn't finished when it's generated.

**The honest reading.** Held to the same standard as the promotional sources, this evidence does **not** prove AI is worthless — average effects are positive (14–15%). It proves the effect is *smaller, unevenly distributed, and easier to overstate* than "cost → zero" implies. The vault's strategic advice can survive a modest, novice-tilted AI; it cannot survive on the premise that AI is a full substitute for a developer.

**⚠ Time-scope.** All figures are 2023–2025, tied to specific tool generations. METR labels its result "historical"; the Copilot RCT used 2022 Copilot; DORA reversed a 2024 finding in 2025. This is **not** the live 2026 state of the art — it is a corrective against extrapolating hype, not a prediction.

## Evidence

- Whole concept (all six findings, effect sizes, designs, caveats, and the two refuted claims) — [[2026-07-18-ai-productivity-adversarial-evidence]]
- Primary roots cited there: METR RCT (arXiv:2507.09089); Brynjolfsson/Li/Raymond *QJE* 140(2) 2025 (NBER w31161) — **customer-support agents, not devs** (scope caveat); GitHub Copilot RCT (arXiv:2302.06590); DORA 2024; Stack Overflow 2025 Developer Survey

## Related Pages

- [[ai-market-shift]] — the claim this most directly tests (is a $200/mo AI a real *substitute*?)
- [[seniority-and-ai]] — the claim this most directly *inverts* (novices gain most)
- [[future-of-engineering-work]] — "coding cost → 0 / teams collapse" qualified by modest, uneven effects
- [[2026-07-06-sebastian-interview-ai-and-software-engineering]] — the vault's strongest statement of the thesis this counterbalances
- [[overview]]

## Contradictions / Uncertainty

- **Scope mismatch on the strongest skill result.** The +34%-novice finding is customer-support agents, not developers — directional counter-evidence, not same-population proof about coding. The developer-specific studies (METR, Copilot) point the same way, which is what keeps it credible.
- **Speed, not labor-market value.** Every finding measures task speed/productivity; the "juniors irrelevant" thesis is about wages, hiring, and headcount, which no source here measures. So this inverts the *productivity* claim without settling the *employment* claim — and the "experts see small quality declines / juniors lack judgment to catch AI errors" mechanism actually lends partial support to [[seniority-and-ai]]'s risk argument.
- **Small samples / significance.** METR n=16 (significant across 246 tasks, but a narrow population); the Copilot RCT's key experience coefficient is not significant at 0.05.
- **Time-sensitivity is the biggest limit** (above). Treat every number as a dated snapshot.
- **This source has its own incentives too:** GitHub/Microsoft authored the pro-Copilot RCT (COI); Stack Overflow has a mild interest in AI skepticism; DORA/Google is independent. Held to the vault's usual standard.

## Next Questions

- Does METR's −19% persist, vanish, or reverse on **mid-2026 agentic tools**? A properly powered follow-up RCT is the key missing piece — and would decide how much of this page survives.
- Does "novices gain most" hold on **real, complex** codebases, or invert where juniors can't catch AI's errors?
- Given this, should [[ai-market-shift]]'s "$200/mo substitute" and [[dmitry-rodenko]]'s "hourly dev is dead" carry a stronger `Status: contested`?
- What would a *pro-thesis* rigorous source look like — is there RCT-grade evidence that AI **does** zero the cost for some real dev segment (greenfield, juniors, specific stacks)?
