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Understanding how automation changes work — task by task, with the evidence shown and the uncertainty admitted.

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On this pageWhich technologiesHow it got hereMethod and sources
Occupations›Metro train driver›How we know

Metro train driver — how we know

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.

Assessed
2026-09-21
With evidence
3/6
Verified events
3

Which technologies matter here#

Four separate signals. They are deliberately not added together — a job exposed to two technologies is not twice as exposed.

Driving & mobility
Driving between stationsTaking the train back by hand
Process & self-service
Driving between stationsClosing the doorsWhen the train stops in a tunnelBeing a person who is thereRunning the line from a screen
Cognitive automation
Running the line from a screen

How it got here#

The index is not a static number. This is where it would have sat at each capability checkpoint since ChatGPT — reconstructed, and labelled as such.

Reconstructed · platform inferenceEstimated today for each past checkpoint — not measured at the time. 56 → 62.
1007550250
Glasgow's transport authority reported that Unite members accepted revised terms on 22 May 2026, and recorded that station staff numbers will increase as the Subway moves to unattended train operationJapan's transport ministry reported that on JR Kyushu's Kashii Line about 40% of trains are now run by conductors without a driving licence, trained in about two months against about nine for a driverUITP counted 2,279 km of automated metro lines worldwide at the end of 2023, which is 11% of all metro kilometres, and sixty urban areas with at least one such line against thirteen in 2000123456789not assessed
2022 H22024 H2Now

—— this stretch contains a verified event- - - no event in this stretch — reconstruction only0 = no task exposed, 100 = every task exposed

● 3 verified events for this occupation, plotted at the date it happened — the parts of the line near a marker are anchored to something checkable.

The highest start and the flattest line on this site, and both are the point. Fully automated metro lines have existed since 1981 and sixty urban areas had at least one before this chart begins, so almost everything this curve measures had already happened — none of it built on language models, because the control problem here is closed rather than intelligent. The single visible step is 2024, when commercial service began on a line with level crossings and no platform doors, widening where this can go rather than deepening what it does. It flattens after that for a reason no capability release will change: what gates the spread is civil works, sealed track and screen doors paid for over a decade, not software bought in a quarter. Read the level as the share of the task set a machine already holds, and read the flatness as the cost of concrete.

12022 H256General-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 H156A general model that passes professional exams. First-draft quality crosses the threshold where professional work starts using it. GPT-4 (2023-03-14) ↗
32023 H257Vision 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 H159The same capability gets much cheaper and faster. Nothing new becomes possible; a lot becomes affordable at volume, which is when deployment decisions change.
52024 H259Reasoning 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 H160Agents 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 H261Long 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 H162Long-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) ↗
9Now62The 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.

Method and sources#

Assessment date
2026-09-21
Basis of the task judgements
3 evidence-backed · 3 platform inference · 0 not enough evidence
Verified events
3

How we assess an occupation →

← Back to Metro train driverThe other layer: every task, one by one →