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75 lines
6.2 KiB
Markdown
75 lines
6.2 KiB
Markdown
---
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type: source
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title: "NBER Working Paper 34836: 80% of Companies Report No AI Productivity Gains Despite Billions Invested — 6,000 Executive Survey"
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author: "Ivan Yotzov, Jose Maria Barrero, Nicholas Bloom et al. (NBER / Atlanta Fed)"
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url: https://www.nber.org/papers/w34836
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date: 2026-02
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domain: health
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secondary_domains: [ai-alignment]
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format: research
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status: unprocessed
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priority: high
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tags: [ai, productivity, workforce, chronic-disease, belief-1-disconfirmation, nber, economic-research]
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intake_tier: research-task
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flagged_for_theseus: ["AI productivity evidence may be relevant to AI's role in civilizational capacity building — the 80% no-gains finding complicates assumptions about AI as near-term civilizational accelerant"]
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---
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## Content
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NBER Working Paper 34836, released February 2026. Survey of nearly 6,000 senior business executives at US, UK, German, and Australian firms. Published as working paper; also released as Atlanta Fed Working Paper (March 2026).
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**Key findings:**
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Adoption vs. impact gap:
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- 69% of firms actively use AI (more than two-thirds)
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- 1/3 of executive leaders regularly use AI — but average only 90 minutes per week
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- MORE THAN NINE IN TEN executives report NO impact on employment or productivity from AI over the last 3 years
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- 80% of companies report NO productivity gain from AI despite billions invested
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Where gains ARE happening (separate Atlanta Fed working paper, also NBER-adjacent):
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- Labor productivity gains positive but vary by sector (2025 data):
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- High-skill services and finance: ~0.8% productivity gain
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- Low-skill services, manufacturing, construction: ~0.4%
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- Predicted to roughly double in 2026 (2% for high-skill, higher-end for finance)
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- AI adoption concentrated among younger, college-educated, higher-earning employees
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- Novices in specific tasks (customer support) see large gains (+34%), but this is bounded
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Future expectations vs. present reality:
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- Same executives who report no current gains predict AI will boost firm productivity 1.4%, raise output 0.8%, cut employment 0.7% over NEXT 3 years
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- The Solow paradox repeats: productivity statistics don't yet show the boom economists expect
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**The chronic disease / AI productivity intersection (search-identified pattern, not directly in paper):**
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- Chronic disease burden falls heaviest on: lower-skill, lower-income, older workers
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- AI productivity gains concentrate in: high-skill, college-educated, higher-income, younger workers
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- The two distributions are NON-OVERLAPPING → AI is not compensating for chronic disease productivity burden in the populations it matters most
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- IBI 2025 data (Session 27): $575B/year in employer productivity losses from chronic disease, concentrated in lower-skill workforce
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## Agent Notes
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**Why this matters for Belief 1 disconfirmation:** Session 27 attempted to disconfirm Belief 1 (healthspan is civilization's binding constraint) via the AI substitution counter-argument: if AI compensates for declining human cognitive capacity, health decline may not be the binding constraint. This NBER data directly addresses that counter-argument. Result: the AI substitution argument FAILS because:
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1. 80% of companies report no AI productivity gains at all
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2. The 20% seeing gains are concentrated in high-skill/high-income sectors — NOT in the chronic disease burden population
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3. The populations are non-overlapping: AI boosts already-healthy, already-productive workers; chronic disease burdens the workers AI isn't reaching yet
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**What surprised me:** The 80% no-gains finding contradicts the optimistic AI productivity narrative that dominates business media. The Solow paradox is real: AI is everywhere except productivity statistics (for now). This means the chronic disease burden ($575B/year) is NOT being offset by AI productivity gains in the populations it affects — those workers aren't adopting AI at the same rate.
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**What I expected but didn't find:** A breakdown of AI productivity gains by HEALTH STATUS of workers — that would directly test whether chronically ill workers see more or fewer AI productivity benefits. This is a research gap.
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**KB connections:**
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- Direct disconfirmation target for Belief 1 — AI substitution counter-argument
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- The distribution overlap failure (AI benefits high-skill, disease burdens low-skill) strengthens Belief 1 rather than weakening it
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- Cross-domain: relevant to Theseus (AI alignment/impact) — the 80% no-gains finding complicates assumptions about AI's near-term civilizational impact
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- Session 27 IBI $575B productivity burden finding is the demand side; this is the supply side (AI compensation is inadequate)
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**Extraction hints:**
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- CLAIM: "AI productivity gains are concentrated in high-skill, high-income workers while chronic disease productivity burdens fall on lower-skill workers — making AI substitution a poor compensating mechanism for declining population health"
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- This is a cross-domain claim that connects health (Belief 1 evidence) to AI productivity (Theseus domain)
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- Requires scope qualification: "in 2025-2026, before broader AI diffusion" — the 80% no-gains is a current finding, not a permanent structural truth
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- Flag for Theseus: the 80% no-gains finding has implications for AI's civilizational role
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**Context:** NBER Working Paper 34836 is authored by Bloom (Stanford), Barrero, and Yotzov — the team behind "Working from Home" research. Same methodology: large executive survey. High methodological credibility. Limitation: executive self-report may undercount gains happening below executive awareness.
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## Curator Notes (structured handoff for extractor)
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PRIMARY CONNECTION: Belief 1 (healthspan as binding constraint) — AI substitution disconfirmation attempt that failed
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WHY ARCHIVED: The NBER 80% no-gains finding directly tests the AI compensation hypothesis for Belief 1. The distribution non-overlap (AI → high-skill; disease → low-skill) is the key structural insight. The belief holds specifically because AI is not reaching the populations most burdened by chronic disease.
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EXTRACTION HINT: The claim should be framed as the disconfirmation that failed: "AI does not compensate for chronic disease productivity burden because..." rather than just "80% of companies see no AI gains." The mechanism (distribution mismatch) is the extractable insight.
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