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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›Assembly line worker›How we know

Assembly line worker — 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
2026-09-10
With evidence
3/5
Verified events
4

Which technologies matter here#

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

Physical automation
Repetitive assembly at a fixed stationDexterous or variable assemblyTending machines and running changeoversProgramming and maintaining the robots
Cognitive automation
Inspection and quality checksTending machines and running changeoversProgramming and maintaining the robots
Process & self-service
Inspection and quality checks

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. 42 → 50.
1007550250
China's labour ministry put 12 further new occupations out for comment, among them 'embodied-AI robot application technician'Figure published the results of an 11-month humanoid deployment at BMW Spartanburg: one station, sheet-metal loading, 1,250+ hours of runtime and 90,000+ partsIFR's World Robotics 2025 counted 542,000 industrial robots installed in 2024 and 4.66 million in operation worldwide (+9%); Asia took 74% of new installations and China alone 54%IFR counted 2,027,000 industrial robots working in Chinese factories, with 295,000 installed in 2024 — 54% of global demand and the highest annual figure ever recorded123456789not assessed
2022 H22024 H2Now

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

● 4 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 flattest curve on the site, and the highest starting point among the physical trades. Industrial robots and machine vision inspection were mature in 2022; nothing about language models changes a station where a person routes a cable by feel. This is a curve driven entirely by robotics, and robotics moves in years.

12022 H242General-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 H143A general model that passes professional exams. First-draft quality crosses the threshold where professional work starts using it. GPT-4 (2023-03-14) ↗
32023 H244Vision 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 H145The 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.

Written about this#

These pieces argue from the same records this page holds, and each of their sections names what it rests on.

  • The jobs AI law already changed before any AI arrived

Method and sources#

Assessment date
2026-09-10
Basis of the task judgements
3 evidence-backed · 2 platform inference · 0 not enough evidence
Verified events
4

How we assess an occupation →

← Back to Assembly line workerThe other layer: every task, one by one →