---
confidence: likely
created: 2026-03-08
description: Anthropic's own usage data shows Computer & Math at 96% theoretical exposure but 32% observed, with similar gaps in every category — the bottleneck is organizational adoption not technical
  capability.
domain: ai-alignment
related:
- ai-tools-reduced-experienced-developer-productivity-in-rct-conditions-despite-predicted-speedup-suggesting-capability-deployment-does-not-translate-to-autonomy
- divergence-ai-labor-displacement-substitution-vs-complementarity
- AI datacenter power demand creates a 5-10 year infrastructure lag because grid construction and interconnection cannot match the pace of chip design cycles
secondary_domains:
- internet-finance
- collective-intelligence
source: Massenkoff & McCrory 2026, Anthropic Economic Index (Claude usage data Aug-Nov 2025) + Eloundou et al. 2023 theoretical feasibility ratings
sourced_from:
- inbox/archive/ai-alignment/2026-03-05-anthropic-labor-market-impacts.md
type: claim
---

# The gap between theoretical AI capability and observed deployment is massive across all occupations because adoption lag not capability limits determines real-world impact

Anthropic's labor market impacts study (Massenkoff & McCrory 2026) introduces "observed exposure" — a metric combining theoretical LLM capability with actual Claude usage data. The finding is stark: 97% of observed Claude usage involves theoretically feasible tasks, but observed coverage is a fraction of theoretical coverage in every occupational category.

The data across selected categories:

| Occupation | Theoretical | Observed | Gap |
|---|---|---|---|
| Computer & Math | 96% | 32% | 64 pts |
| Business & Finance | 94% | 28% | 66 pts |
| Office & Admin | 94% | 42% | 52 pts |
| Management | 92% | 25% | 67 pts |
| Legal | 88% | 15% | 73 pts |
| Healthcare Practitioners | 58% | 5% | 53 pts |

The gap is not about what AI can't do — it's about what organizations haven't adopted yet. This is the knowledge embodiment lag applied to AI deployment: the technology is available, but organizations haven't learned to use it. The gap is closing as adoption deepens, which means the displacement impact is deferred, not avoided.

This reframes the alignment timeline question. The capability for massive labor market disruption already exists. The question isn't "when will AI be capable enough?" but "when will adoption catch up to capability?" That's an organizational and institutional question, not a technical one.


### Additional Evidence (extend)
*Source: 2026-02-00-international-ai-safety-report-2026 | Added: 2026-03-11 | Extractor: anthropic/claude-sonnet-4.5*

The International AI Safety Report 2026 (multi-government committee, February 2026) identifies an 'evaluation gap' that adds a new dimension to the capability-deployment gap: 'Performance on pre-deployment tests does not reliably predict real-world utility or risk.' This means the gap is not only about adoption lag (organizations slow to deploy) but also about evaluation failure (pre-deployment testing cannot predict production behavior). The gap exists at two levels: (1) theoretical capability exceeds deployed capability due to organizational adoption lag, and (2) evaluated capability does not predict actual deployment capability due to environment-dependent model behavior. The evaluation gap makes the deployment gap harder to close because organizations cannot reliably assess what they are deploying.

---

### Additional Evidence (extend)
*Source: 2026-02-05-mit-tech-review-misunderstood-time-horizon-graph | Added: 2026-03-23*

METR's time horizon metric measures task difficulty by human completion time, not model processing time. A model with a 5-hour time horizon completes tasks that take humans 5 hours, but may finish them in minutes. This speed asymmetry is not captured in the metric itself, meaning the gap between theoretical capability (task completion) and deployment impact includes both adoption lag AND the unmeasured throughput advantage that organizations fail to utilize.

### Additional Evidence (extend)
*Source: [[2026-03-25-metr-algorithmic-vs-holistic-evaluation-benchmark-inflation]] | Added: 2026-03-25*

METR quantifies a specific mechanism for the capability-deployment gap in software engineering: 26 minutes of additional human work per 'passing' task (one-third of total task time) is required to make algorithmically-successful AI output production-ready. This is not adoption lag but architectural mismatch—benchmarks measure core implementation while deployment requires documentation, testing, and code quality that current evaluation frameworks systematically omit.



Relevant Notes:
- [[AI capability and reliability are independent dimensions because Claude solved a 30-year open mathematical problem while simultaneously degrading at basic program execution during the same session]] — capability exists but deployment is uneven
- [[knowledge embodiment lag means technology is available decades before organizations learn to use it optimally creating a productivity paradox]] — the general pattern this instantiates
- [[economic forces push humans out of every cognitive loop where output quality is independently verifiable because human-in-the-loop is a cost that competitive markets eliminate]] — the force that will close the gap

Topics:
- domains/ai-alignment/_map
