Methodology · 10 min read
Compression Exposure Index (CEI) Explained
Methodology
Deterministic. Reproducible. Auditable.
The AI Career Index does not guess. It measures. Here is exactly how.
Methodology v3 · 2026
How the framework works
Structural scoring, not predictive modelling
The AI Career Index reads the structural composition of a role rather than forecasting when automation lands. Every score derives from a fixed weighting of five dimensions applied to the inputs you provide. Same inputs always produce the same outputs. No model randomness, no black-box reasoning, no probabilistic adjustment behind the scenes.
That design choice is deliberate. Forecasts depend on adoption speed and firm-level decisions that nobody can predict. Structural readings depend on the work itself, which is stable enough to build a 24-month plan around. The sections below walk through the five dimensions, the authority band classification, the derived metrics, and the exact weights used to produce every score on the site.
Five Principles
The model's design constraints
01
Deterministic, not probabilistic
Every input maps to a number. Every number maps to a weight. Every weight produces a score. There is no AI guesswork inside the model and no probabilistic interpretation of your answers. Two professionals with identical inputs always produce identical outputs.
02
Structural, not behavioural
The framework measures the configuration of your work, not the configuration of you. Personality, temperament, and disposition are explicitly excluded from the model. The AI economy changes pay for roles, and the model measures roles. See why personality tests fail in the AI economy.
03
Reproducible by design
Reproducibility is the property that separates measurement from interpretation. The same answers always produce the same score. Updates to the engine are versioned and disclosed. Your reading is auditable.
04
Calibrated to current market conditions
The compression curves and migration probabilities inside the model are calibrated against observed market conditions, not against speculation about long-horizon AGI scenarios. The framework is deliberately scoped to the next 24 months.
05
Action-oriented output
The model produces a structural reading and a sequenced intervention plan, not a personality profile or a list of suggested careers. The output is built to be acted on, not contemplated. See the 6-month Acceleration Plan.
06
Honest, not encouraging
The model's job is to produce an accurate structural reading, not a comforting one. Scoring does not adjust based on how a user answers earlier questions. Compression risk is not softened. Authority bands are not inflated. The reading is calibrated to the market, not to the reader.
The Method
Six steps from input to plan
1
Structured input collection
The assessment collects 35 structured questions across five weighted dimensions: Strategic Clarity, AI Adaptability, Income Leverage, Skill Resilience, and Market Alignment. Questions are deliberately structural. They ask about the configuration of your work, not how you feel about it.
2
Dimension scoring
Each answer maps to a 0 to 100 dimension score using fixed weights. Dimension weights are determined by the relative impact of each dimension on structural durability and authority migration in current market conditions, not by user customisation.
3
Composite index calculation
The five dimension scores combine into a composite AI Career Index score on a 0 to 1,000 scale. The composite is not a simple average. It applies the dimension weights and produces a scaled reading that is comparable across users.
4
Derived metric generation
From the dimension scores and the composite, the model derives the Compression Exposure Index, the Authority Band, the Structural Leverage Index, the Authority Migration Probability, and the Structural Archetype. These derivations are deterministic and traceable.
5
Projection modelling
The 24-month projection compares two structural paths: the Drift Path (no intervention) and the Strategic Path (Acceleration Plan executed). Both paths are calibrated against the user's current readings and the structural pressure on their archetype.
6
Acceleration Plan generation
The 6-month plan is built around the user's weakest dimension and is sequenced into three structural phases: foundation, migration, and consolidation. Every action in the plan ties to a measurable structural outcome.
Outputs
What the model produces
Every reading is a deterministic function of your inputs. The eight outputs below are what get generated from a completed assessment.
0 to 1,000
AI Career Index score
The composite reading. A single number comparable across users.
Read explainer →
0 to 100 · CEI
Compression Exposure Index
How exposed your role is to AI-driven pay shifts. Higher is more exposed.
Read explainer →
0 to 1.00 · SLI
Structural Leverage Index
The ratio of judgment-bearing work to routine production in your role.
Read explainer →
4 bands · Execution to Authority
Authority Band
The structural altitude at which your role operates, independent of job title.
Read explainer →
12-month window · AMP+
Authority Migration Probability
The probability of moving one authority band upward in the next year.
Read explainer →
1 of 6 postures
Structural Archetype
Your posture inside the AI economy, mapped from your dimension profile.
Read explainer →
Drift vs Strategic paths
24-month Projection
Two parallel trajectories showing your position under inaction versus plan execution.
Read explainer →
Sequenced 3-phase plan
6-month Acceleration Plan
Your weakest dimension as the anchor. Foundation, migration, consolidation.
Read explainer →
Reviewed by AI Career Index Research · Methodology v3 · 2026 ·See the framework →
Role-Page Scoring
How the role pages are scored
The personal assessment scoring above is one layer. The 1,000+ role pages on the site sit on a separate, role-level scoring layer described here.
Each role on AI Career Index maps to a specific U.S. SOC code. The role-page reading combines two foundational datasets, a deterministic rule-based model, and one signal layer (in three cuts) sourced from Anthropic. Every source listed below is public, citable, and refreshed on a published cadence.
O*NET 30.2: Task and skill structure
The U.S. Department of Labor's O*NET database supplies the structured task, work-activity, skill, ability, and knowledge data for every SOC code. Each task statement carries an importance and frequency rating. This is the substrate the structural model reads against. It captures what the work actually is, in standards-grade language. Licence: CC BY 4.0. Most recent release ingested: O*NET 30.2.
BLS OEWS (May 2025): Wages, employment, growth
The U.S. Bureau of Labor Statistics Occupational Employment and Wage Statistics file supplies the wage, employment, and growth baselines per SOC. We use it for the median annual wage, total US employment, and the economic-impact figure on every role page (median wage × employment). Published annually each May. The codebase is on the May 2025 release, scheduled to refresh mid-May 2027 to the May 2026 release.
Structural model: Deterministic risk band
On top of those inputs, every role is read against a deterministic, rule-based structural reading: how much of the work is the routine production layer that current AI tooling absorbs, how much sits at the judgement and accountability layer that current tooling cannot own, and how market value is splitting between the two. The output is a low / medium / high risk band plus the structural reading shown on each role page. Same inputs always produce the same outputs. No black-box LLM in the loop.
Anthropic Economic Index: Role-level AI adoption
The Anthropic Economic Index (CC BY 4.0) is a sample of conversations on Claude.ai mapped to O*NET task structures. It measures the share of work in each occupation already showing AI usage in the wild. Most recent release ingested: March 2026. Where the dataset reports 0.0% for an occupation, that is a real signal: the sample observed no measurable usage for that SOC at publication time, not a placeholder. Specialised medical, legal, and trade occupations are common 0% values because practitioners use domain-specific tooling rather than general-purpose chat assistants. This figure sits alongside the structural reading on every role page, never blended into the deterministic score.
Anthropic Economic Index: Geographic cut (AUI)
The same source publishes a geographic cut, which we surface separately on our AI Adoption by Country page. It ranks countries and US states by per-capita Claude usage (the Anthropic AI Usage Index, AUI) and shows the automation versus augmentation split per geography. This is the labour-market context, not a role-level reading. Role-level adoption answers "how much of this occupation is already being done with AI", while country AUI answers "how AI-forward is this labour market overall".
Anthropic Economic Index: Developer-API cut
Anthropic also publishes a 1P API cut covering the workflows developers build with the Claude API, refreshed each release. We surface this separately on our Fastest-Automating Tasks page as a snapshot-over-snapshot growth delta: which task clusters are being deployed more, which are losing share. This is a leading indicator of where automation is moving inside knowledge work. API growth answers "which workflows is the industry actively wiring up", while the role-level model answers "which occupations are structurally exposed to that wiring".
The model is rule-based on purpose, not a black-box LLM, so the same inputs always produce the same outputs and the methodology is auditable. Every role page cites its O*NET tasks, BLS wage source, and Anthropic Economic Index figure inline.
Sources: O*NET 30.2 (USDOL/ETA, CC BY 4.0) · BLS OEWS, May 2025 · Anthropic Economic Index (CC BY 4.0)
Model Integrity
What the model is not
The AI Career Index is not a personality test, not a coaching tool, not a job-matching service, not a prediction engine, and not a generative-AI advice generator. It does not produce different output for the same input. It does not soften its readings to be encouraging. It does not customise scoring to user preference.
What it is, instead, is a structural measurement instrument. The same property that makes it useful (reproducibility) is also what disqualifies it from being a substitute for the human-layer support that a coach or therapist or mentor provides. Those tools are complementary, not competing.
View the full glossary →FAQ
Common questions
Articles in this guide
Go deeper on each part of the pipeline
12 articles, grouped by which step of the methodology they expand on. Click a step to see its articles.
Further Reading
Related articles from the index
Framework · 10 min read
Authority Bands: Title vs Leverage
Two professionals with the same job title can have very different authority. The Authority Band measures the structural altitude at which you actually operate.
Strategy · 11 min read
The 6-Month Acceleration Plan: A Structural Reset
The Acceleration Plan is not a list of self-improvement goals. It is a sequenced 6-month structural intervention that targets your weakest dimension first.
"My category label on the index was completely accurate and completely uncomfortable. I had been drifting for two years without realising it. The 6-month plan broke the drift into four specific moves. I am three months in and the difference in the quality of the work I get offered is already visible."
Takeshi Yamamoto
Product Marketing Lead · Tokyo, Japan
"Finance is supposed to be safe from AI because of regulation. The report pushed back on that complacency by showing which parts of my role were substitutable regardless of sector. That precision is rare in career content. I trust the framework because it refused to tell me what I wanted to hear."
Olivia Bennett
Finance Business Partner · Manchester, UK
"The projection chart was the most honest piece of career analytics I have seen. It did not promise me growth. It showed me the probability weighted outcomes of two clear strategies and let me pick. I have recommended this to every senior engineer on my team. Nine of them have now taken it."
Miguel Hernandez
Director of Engineering · Barcelona, Spain
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