Exposure Taxonomy: Observed, Theoretical, Reported, Anticipated

Sources#

• Anthropic Economic Index report: Cadences

Summary#

"AI exposure" is used loosely to mean very different things. The Anthropic Economic Index's Cadences survey (June 2026) makes the distinctions sharp by putting four measures side by side — two derived from behavior, two from what workers say — and showing how they order and diverge. The upshot: what AI is observed doing, what it could theoretically do, and what workers believe it can do are three different numbers, and separating them is a prerequisite for reasoning about AI's labor impact.

Evidence note. empirical — observed/theoretical exposure from prior AEI work and standard task-based measures; reported/anticipated from the linked survey (self-report, non-representative sample, binned responses coded at bin midpoints). See Anthropic Economic Index.

The four measures#

Measure	Definition	Source
Observed exposure	Share of an occupation's tasks already seen being done with Claude	Usage telemetry (prior AEI reports)
Theoretical exposure	Share of tasks an LLM could theoretically do — an upper bound	Task-based external measure
Reported exposure	What share of their work tasks respondents say AI can do today	Survey
Anticipated exposure	What share they expect AI to handle in 12 months	Survey

Ordering and correlation. Reported exposure is positively correlated with both observed and theoretical exposure — people's beliefs track reality. But the levels diverge systematically: reported > observed (the survey over-reaches heavy users, who see more of what AI can do), and theoretical > reported (theoretical is an upper bound on the possible, not a measure of current use). ~6 in 10 respondents pick a higher band for next year than today; over ⅓ expect AI to do most or nearly all their work tasks within a year.

The rising tide: anticipated growth is uniform#

The striking survey result: while current perceptions vary with who and where you are, expectations of future progress are strikingly uniform. Plotting anticipated exposure against observed or theoretical exposure, the best-fit lines are roughly parallel — a software engineer and a construction manager anticipate about the same increment of progress in their own field over the next year, regardless of how exposed they already are. Everyone expects the tide to rise by a similar amount. (Caveat: midpoint coding of binned responses biases the slopes toward zero, so the parallel lines are read qualitatively.)

The cross-sectional gradients#

Reported/anticipated exposure vary systematically along three axes:

• ↓ with country GDP — reported exposure is ~10pp lower in high-income countries. Consistent with AI substituting for a larger share of lower-income daily tasks, even though occupation-level metrics run higher in advanced economies. The report ties this to the IMF complements argument: lower-income workers may lack the complementary skills/infrastructure that let AI augment rather than replace — and earlier AEI work found lower-income economies use Claude in more automated ways.
• ↓ with experience — workers with 15+ years put the share ~10pp lower than first-year workers. In follow-ups, experienced respondents pointed to judgment, contextual awareness, situational reasoning, and relational/interpersonal work (building trust, managing people) as things AI cannot replicate. This is Returns to Expertise in Agentic Coding read from the survey side: tacit, context-specific expertise is what the experienced believe AI cannot touch.
• ↑ with automation share — heavier delegators report and anticipate higher exposure (see The Automation–Optimism Link); delegation is informative about capability, and/or believers delegate more.

Notably, future-progress expectations are essentially uncorrelated with GDP and experience — the gradients are about today's perceived capability, not the rate of expected change (the rising tide again).

Why it matters#

Conflating these four numbers is how "AI can do X% of jobs" claims go wrong. Observed exposure is a floor (what's happening now), theoretical a ceiling (what's possible), reported the workforce's felt reality, anticipated its forecast. The gaps between them — reported exceeding observed, everyone expecting a uniform jump — are the interesting quantities, and each responds to different levers (capability, complements, belief).

Connections#

• Anthropic Economic Index — the research program; observed/theoretical exposure are its earlier primitives, reported/anticipated the survey additions
• Returns to Expertise in Agentic Coding — the experience gradient is this survey's echo of the returns-to-expertise finding: tacit/relational judgment is the residual humans hold
• Organizational Complements to AI — the GDP gradient and the IMF complements argument: exposure ≠ impact without the complementary skills and infrastructure
• The Automation–Optimism Link — reported/anticipated exposure rise with automation share; the sentiment companion to this capability-belief measure
• Conversation Artifacts — artifacts are the output-side view of "what AI does"; complements the task-side exposure measures
• Conversation-to-Delegation Shift — the intensive-margin usage evidence behind observed exposure rising
• AI Usage Cadences — the off-hours/high-wage and cross-country usage rhythms foreshadow the GDP and occupation gradients this page measures directly
• Task Time-Horizon Scaling — theoretical exposure's ceiling is bounded by the reliable-task-length frontier

Open questions#

• Binned midpoint coding biases the exposure slopes toward zero; how much of the "uniform rising tide" is substance vs. coding artifact (the report checks robustness with a ≥60%-of-tasks indicator, but the levels remain self-reported)?
• Reported exposure exceeds observed partly because the survey reaches heavy users; what does the reported/observed gap look like in a representative sample?
• The experience gradient rests on what workers believe AI can't do (judgment, relational work) — a belief that could be either durable comparative advantage or the next capability to fall. Which, and when?

Sources#

• Anthropic Economic Index report: Cadences — Anthropic Economic Index report: Cadences (June 26, 2026), Chapter 3 "Perceptions": §AI and work tasks (Figures 3.2–3.4)