Future History · Methodology

How this dataset is built

The value of Future History depends on whether the numbers and claims you see hold up under scrutiny. This page explains where the data comes from, how a role is curated, how projections are framed, and what we don't yet know.

What's in the dataset

Future History has two layers.

The base layer. 924 US occupations, seeded from public BLS and academic sources. Every occupation has a calibrated AI-exposure score and a list of typical tasks.
The curated layer. A subset of those occupations (507 today) are curated to deep tier with specific AI tools, citations, adjacent-career pivots, and upskilling resources. A further subset (511 today, growing) have full historical profiles covering 100–200 years of employment, wages, tool eras, and cited projections: the time machine.

Where the data comes from

Every claim in a deep-tier or time-machine profile is tagged with a source. The sources we draw from most:

BLS (Bureau of Labor Statistics)

OEWS (current employment + wages by SOC), Occupational Outlook Handbook (projections), QCEW (county-level employment).

Refresh: Annual for OEWS; biennial for OOH projections.

O*NET

Task and skill data per occupation. Powers the base-layer task lists.

Refresh: Roughly annual.

Eloundou et al., GPTs are GPTs (2023)

LLM labor-exposure scores per occupation. Seeds the base-layer AI-exposure metric.

Refresh: Static; recalibrated when a substantially better academic measure ships.

IPUMS (Integrated Public Use Microdata Series)

Historical US Census occupation counts. Powers the long-tail employment series in time machines.

Refresh: On-demand per role during curation.

FRASER (Federal Reserve Archive)

Historical wage series, often back to the 19th century via scanned BLS bulletins.

Refresh: On-demand per role; many series need OCR.

ABS (Australian Bureau of Statistics) + Jobs and Skills Australia

Australian employment (Census of Population and Housing, Labour Force Survey) and earnings (Survey of Employee Earnings and Hours, by ANZSCO occupation). Powers the Australia series on roles that carry one.

Refresh: Census every five years; earnings + labour force annually.

ONS (UK Office for National Statistics)

UK employment and earnings (Annual Survey of Hours and Earnings, by SOC occupation). Powers the United Kingdom series on roles that carry one.

Refresh: Annually.

Statistics Canada (Census of Population)

Canadian employment and earnings (median employment income, by NOC occupation unit group). Powers the Canada series on roles that carry one.

Refresh: Census every five years.

Curated projection sources

BLS extrapolation, McKinsey scenario modelling, OECD employment trend, academic surveys. Each projection in a time machine cites its source and method.

Refresh: When the underlying publication updates.

Vendor product pages + first-party docs

AI tool descriptions, release dates, and what each tool actually automates. Tools are named by vendor (Harvey, Cursor, Vic.ai, etc.), never as a generic "AI tools" placeholder.

Refresh: Continuous as new tools emerge.

Employment and wage figures are drawn from the U.S. Bureau of Labor Statistics. The BLS does not vouch for analyses derived from its data, and we make no claim of BLS endorsement.

Australian figures are based on Australian Bureau of Statistics and Jobs and Skills Australia data, used under CC BY 4.0. Neither agency endorses Future History or its analysis.

Canadian figures are adapted from Statistics Canada, Census of Population, used under the Statistics Canada Open Licence. This does not constitute an endorsement by Statistics Canada.

How a role becomes a time machine

A role moves from base layer to curated layer through a deliberate, source-by-source research process. Every figure is traced back to a source, and every editorial call about what the data does and doesn't support is made deliberately.

Source the historical series. Pull employment from IPUMS Census tables (1850 onward where data exists). Pull wages from FRASER and contemporary BLS bulletins. Many older wage series require OCR on scanned PDFs.
Identify tool eras. Each technology wave that reshaped the role (the typewriter, the telephone, dictation machines, word processors, ERP systems, LLMs) becomes a tool era with start year, end year (or open-ended), impact, and labor effect, every claim cited.
Anchor historical notes. Specific year-anchored beats (e.g., a peak employment year, a major displacement event, a regulatory change) get short notes with citations.
Surface projection disagreement. Multiple credible projection sources are included as a cone: the spread is the point, not a single number.
Run verification. Static audits enforce that every datapoint has a citation, every era has at least one source, every projection has a method, and no claim is invented.

Every narrative claim resolves to the cited data underneath it. Nothing is invented: not tasks, tools, scores, employment numbers, or historical facts. If the data doesn't support a claim, the page says nothing.

The projection cone

Past "now," the time machine shows a fanned-out view of where credible sources think a role is heading. They will disagree, often dramatically. That's deliberate, and it's the most honest signal in the file.

Each projection in the cone shows its source, target year, method, and a magnitude bar. A BLS extrapolation may project +6% by 2033 while a McKinsey scenario projects −40% by 2030. Both are real positions held by real institutions. The spread is the range of futures this role might live in, not noise to average away.

Future History does not produce its own projections. Producing one would require taking sides between sources, and the entire point of the cone is to refuse to do that.

Confidence tiers

Every role you can look up sits in one of three tiers:

Deep. Deeply curated. Tasks, AI tools, pivots, and resources have been individually researched. Citations on every material claim. A subset of deep roles additionally have full historical profiles (the time machine).
Shallow. Seeded from BLS + Eloundou + O*NET. The base-layer score is calibrated, but per-task tool attribution is sparse or missing. Useful for direction, not detail.
Approximate. The matcher fell back to fuzzy / LLM matching against a closest BLS occupation. The score and tasks shown are for the matched role, not your literal title. Treat as directional.

What we don't yet know

A few honest limitations:

US-bias.Today's dataset is US-centric (SOC codes, BLS sources, USD wages). Mapping tables for UK SOC, Canadian NOC, and ANZSCO are planned; deep curation outside the US is not yet underway.
White-collar bias. The 507deep-curated roles skew toward white-collar work because that's where the LLM-exposure story is most acute today. Non-white-collar coverage (trades, hospitality, retail) is on the roadmap but requires a different methodology: robotics + AR/VR + computer vision matter more than LLMs for hands-on work.
Composite-score tension.The internal CRI score blends three sub-metrics (automation defense, human advantage, augmentation upside). The third is calibrated against Eloundou's β, which measures "exposure when AI has tools": a worker is more displaceable when AI has tools, but the brand framing treats high augmentation upside as positive. Same number, opposite valence. This is intentional and we'd rather acknowledge the tension than hide it.
Mid-century wage data is sparse. 1940s–1960s wage series often have multi-year gaps. We mark the gaps explicitly in the time-machine viz rather than interpolating through them.

Found a mistake?

The value of Future History collapses if we get a citation wrong. If you spot an error (a wrong employment number, a misdated tool era, a projection attributed to the wrong source), please flag it. The dataset is in a private repo but corrections come back into the file directly.

Use the contact form with the role, the year, and the source you think is wrong, and select “Correction or data issue.” We read every submission and respond within a few days.