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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›Content moderator›How we know

Content moderator — 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
2026-09-23
With evidence
1/6
Verified events
1

Which technologies matter here#

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

Cognitive automation
Working the queueThe call the rule does not decideReviewing an appealTurning a value into an enforceable lineCorrecting the system that replaced the queueLooking at the worst of it
Process & self-service
Working the queueReviewing an appealCorrecting the system that replaced the queue

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. 52 → 76.
1007550250
TikTok reported that automated systems actioned 94.1 percent of the violating content it removed in the European Union, without human review123456789not assessed
2022 H22024 H2Now

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

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

The only curve on this site whose later points can be checked against a number the employer is legally obliged to publish. It starts high because classifier-based takedown of spam, nudity and known-bad hashes was already routine before language models — the 2022 level is not an AI story, it is a decade of matching systems. The steep stretch is 2023 into 2024, when the same platforms began applying general-purpose models to the part that had needed reading rather than matching: context, intent, and the caption that changes what the image means. It flattens after 2025 for a reason the published reports show rather than a reason we assume — the automated share of enforcement actions is already above nine in ten at the largest platforms, so there is very little headroom left in the measure, and what remains is the contested residue that has resisted every increment so far. It does not reach the top because appeal against an automated decision is compulsory in the European Union, which puts a permanent person downstream of the machine.

12022 H252General-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 H155A general model that passes professional exams. First-draft quality crosses the threshold where professional work starts using it. GPT-4 (2023-03-14) ↗
32023 H260Vision 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 H165The same capability gets much cheaper and faster. Nothing new becomes possible; a lot becomes affordable at volume, which is when deployment decisions change.
52024 H269Reasoning 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 H172Agents 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 H274Long 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 H175Long-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) ↗
9Now76The 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
2026-09-23
Basis of the task judgements
1 evidence-backed · 5 platform inference · 0 not enough evidence
Verified events
1

How we assess an occupation →

← Back to Content moderatorThe other layer: every task, one by one →