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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›Carpenter / joiner›How we know

Carpenter / joiner — 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
2026-10-01
With evidence
3/5
Verified events
7

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
Framing and structural carpentryCutting joints and joineryFormwork for concreteFinish carpentry and installationRepair and renovation

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. 10 → 26.
1007550250
Wall panels with windows and doors fitted are assembled by 14 robots in a South Australian factory, with a state housing project first in line, Renewal SA saidCarpenter employment will grow 4 percent from 2025 to 2035, while modular and prefabricated components reduce the need for carpenters, the US Bureau of Labor Statistics estimatedFactory precutting reached 94 percent of post-and-beam timber houses in 2024, Japan's Forestry Agency reported in its white paperJapan's carpenters could halve from about 300,000 in 2020 to about 150,000 by 2035, and precutting, panels and AI should be used, a housing ministry panel saidFactory-built houses made up 22.2 percent of residential buildings completed in Germany in 2024, the federal statistics office reportedA passive shoulder exoskeleton cut shoulder muscle activity during overhead work by 20 carpenters but raised low-back activity, a randomised field study found123456789not assessed
2022 H22024 H2Now

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

● 6 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 10 because power tools, nail guns and factory-made roof trusses were common before this chart begins, while frames, formwork and fitting were done on site by carpenters. It climbs as joint cutting moves to factory machines, more homes are built from factory panels, and robot lines start producing wall panels. It stays low because site assembly, formwork, finishing and repair stay with carpenters, a heavily automated framing factory failed, and countries short of carpenters use factory methods to stretch the carpenters they have.

12022 H210General-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 H111A general model that passes professional exams. First-draft quality crosses the threshold where professional work starts using it. GPT-4 (2023-03-14) ↗
32023 H212Vision 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 H114The same capability gets much cheaper and faster. Nothing new becomes possible; a lot becomes affordable at volume, which is when deployment decisions change.
52024 H216Reasoning 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 H118Agents 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 H221Long 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 H124Long-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) ↗
9Now26The 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.

  • What actually gets automated, and how to tell in advance

Method and sources#

Assessment date
2026-10-01
Basis of the task judgements
3 evidence-backed · 2 platform inference · 0 not enough evidence
Verified events
7

How we assess an occupation →

← Back to Carpenter / joinerThe other layer: every task, one by one →Skills, knowledge and related jobs (O*NET) →