The page itself gives the judgements. This one gives what they rest on: which technologies bear on the work, how the estimate moved since language models reached the public, and the method behind both.
Four separate signals. They are deliberately not added together — a job exposed to two technologies is not twice as exposed.
Cognitive automation
Framing the question and the metricCleaning and exploring the dataBuilding and validating the modelDesigning experiments and reading causesExplaining what it means, and what it does notChallenging and auditing models
—— this stretch contains a verified event- - - no event in this stretch — reconstruction only0 = no task exposed, 100 = every task exposed
● 7 verified events for this occupation, plotted at the date it happened — the parts of the line near a marker are anchored to something checkable.
Starts at 36 because statistical software, AutoML tools and code libraries already automated much of the routine modelling when this chart begins. It rises as AI assistants and agents start writing analysis and modelling code, then flattens: on public benchmarks the best agents still solve only about a third of realistic data-analysis tasks and struggle most with causal questions, and rules on models expect independent people to challenge and audit them. It stops at the middle because framing questions, reading experiments and explaining results are still done by data scientists.
12022 H236General-purpose text generation reaches the public. Before this point, exposure came from automation that was already deployed — OCR, RPA, machine vision, self-checkout, dispatch algorithms. ChatGPT research preview (2022-11-30) ↗
22023 H139A general model that passes professional exams. First-draft quality crosses the threshold where professional work starts using it. GPT-4 (2023-03-14) ↗
32023 H242Vision input, long context and tool calling. Models can be pointed at documents and connected to systems, which is what moves process work rather than writing work. GPT-4 Turbo:128k 上下文、视觉、工具调用(DevDay) (2023-11-06) ↗
42024 H144The same capability gets much cheaper and faster. Nothing new becomes possible; a lot becomes affordable at volume, which is when deployment decisions change.
52024 H246Reasoning models that work through multi-step problems, and the first models that operate a computer by looking at the screen. The second one is what reaches software-operating jobs. OpenAI o1(推理);同期 Claude 的 computer use 进入公测 (2024-09-12) ↗
62025 H147Agents begin operating real software end to end rather than producing text for a person to paste. This is also when the first public reversals appear — organisations that automated and partly undid it. Claude 3.7 Sonnet 与 Claude Code:混合推理 + 命令行编码代理 (2025-02-24) ↗
72025 H248Long context and tool use become the default rather than a feature. Capability gains continue; the visible constraint shifts from what models can do to liability, procurement and cost. GPT-5(2025-08-07);Claude Opus 4.5(2025-11-24) (2025-08-07) ↗
82026 H149Long-horizon agents land inside specific industry workflows. Adoption becomes sector-specific rather than general. GPT-5.5:「专为实际工作打造」 (2026-04-23) ↗
9Now50The current assessment — this point is the impact index published on the occupation's page, so the curve is anchored to a number the site already stands behind. Worth noting for the flat curves: in the same weeks, a research preview of a shared specification for AI agents to operate physical devices was opened to research labs and manufacturers. That is the first capability class pointed at the physical occupations whose lines here barely move. GPT-6 Astra(2026-09-03);Claude Fable 5.1 / Mythos 5.1(2026-09-01);Model Hardware Standard 研究预览(2026-08-27) (2026-09-03) ↗
A flat line is not a forecast of safety. It says which tasks automation has reached so far — the occupations that moved least here are the ones where the constraint is physical or regulatory, and both of those can change.
LO
