---
type: claim
domain: internet-finance
description: "Brynjolfsson claims 2.7% productivity growth in 2025 proves AI impact, but multiple critics show the data is too noisy for attribution: payroll revisions are within normal ranges, GDP gets revised, immigration policy confounds labor supply, and capital investment broadly (not AI specifically) could explain the gains — we lack the statistical resolution to confirm or deny AI's macro productivity effect"
confidence: likely
source: "Noah Smith 'Roundup #78: Roboliberalism' (Feb 2026, Noahopinion); cites Brynjolfsson (Stanford), Gimbel (counter), Imas (J-curve), Yotzov survey (6000 executives)"
created: 2026-03-06
challenges:
  - "[[internet finance generates 50 to 100 basis points of additional annual GDP growth by unlocking capital allocation to previously inaccessible assets and eliminating intermediation friction]]"
---

# current productivity statistics cannot distinguish AI impact from noise because measurement resolution is too low and adoption too early for macro attribution

This is a methodological claim about what we can and cannot know from current data — and it cuts against both the bull and bear narratives.

**The Brynjolfsson claim:** US productivity growth hit 2.7% in 2025, nearly double the 1.4% long-run average. Evidence: BLS revised payrolls down by 403,000 while GDP grew 3.7%. More output with fewer workers = productivity surge. Attribution: AI.

**Why this doesn't hold (Gimbel's counter):**
- The 403K payroll revision sounds dramatic but is within the normal range of BLS revisions. It's not anomalous data — it's noisy data.
- GDP itself gets revised, often substantially. The 3.7% growth figure may change.
- Immigration policy changes (deportations, reduced legal immigration under current administration) confound the labor supply picture. Fewer workers could reflect policy, not AI displacement.
- Capital investment broadly — not AI specifically — could explain productivity gains. Distinguishing "AI-driven productivity" from "capital deepening generally" requires micro-level attribution that aggregate statistics can't provide.

**The Solow Paradox parallel (Imas):** Computers didn't show up in productivity statistics until the late 1990s — decades after widespread adoption began. Robert Solow's famous 1987 quip ("you can see the computer age everywhere but in the productivity statistics") held true for over a decade before the productivity boom materialized. If AI follows the same pattern, absence of macro evidence today is exactly what we'd expect.

**Executive survey data (Yotzov, 6000 executives):** Current AI impact reported as small. Expected future impact: 1.4% productivity boost and 0.7% employment cut. These are modest numbers — roughly consistent with a normal technology adoption cycle, not a paradigm shift.

**Noah's synthesis:** "We don't really know how technology affects productivity, growth, employment, etc. until we try it and see." The honest position is radical uncertainty. Neither the catastrophists nor the utopians have sufficient empirical support for their macro claims.

**Implication for the knowledge base:** Our existing claim that internet finance generates 50-100 bps of GDP growth assumes we can measure and attribute productivity effects. This claim suggests we should be more humble about measurement — the confidence level on macro-attribution claims should reflect the measurement limitations, not just the theoretical plausibility.

---

Relevant Notes:
- [[internet finance generates 50 to 100 basis points of additional annual GDP growth by unlocking capital allocation to previously inaccessible assets and eliminating intermediation friction]] — this GDP growth claim relies on productivity attribution that this evidence suggests we can't yet do reliably
- [[knowledge embodiment lag means technology is available decades before organizations learn to use it optimally creating a productivity paradox]] — the Solow Paradox is a specific instance of knowledge embodiment lag; the productivity J-curve may be the mechanism
- [[AI labor displacement operates as a self-funding feedback loop because companies substitute AI for labor as OpEx not CapEx meaning falling aggregate demand does not slow AI adoption]] — if we can't measure AI's productivity impact, we also can't measure AI's displacement impact at the macro level, which weakens both bull and bear macro narratives

Topics:
- [[internet finance and decision markets]]
