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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›Musician and composer›How we know

Musician and composer — 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
2026-09-30
With evidence
3/5
Verified events
11

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
Production, library and advertising musicWriting and releasing songsScoring film, television and gamesPerforming live
Physical automation
Performing live
Process & self-service
Rights, licensing and royalties

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. 22 → 46.
1007550250
Musician and singer employment will show little or no change from 2025 to 2035, with live music supporting demand, the US Bureau of Labor Statistics estimated, naming no AI effectComposers will be needed for film scores and music for television and commercials as their jobs grow 1 percent to 2035, the US Bureau of Labor Statistics estimatedFully AI-generated tracks passed 50% of new uploads at their June peak, nearly 90,000 a day, yet drew only 1–3% of streams, Deezer reportedUp to 85% of streams on AI-generated tracks were fraudulent in 2025 against 8% across the catalogue, and those streams are now demonetised, Deezer saidWarner Music Group settled its litigation with Suno in a deal giving artists and songwriters full control over use of their voices and compositions in AI-generated musicUniversal Music Group settled with Udio on a platform to be trained on authorized and licensed music, keeping existing creations within a walled gardenListeners could not tell AI-generated songs from human-made ones when pairs were random, doing no better than guessing, a controlled study with real Suno songs foundVocal impersonation is allowed on Spotify only when the impersonated artist has authorised it, and the service removed over 75 million spammy tracks in a year, it saidGenerative AI music will account for around 60% of music libraries' revenues by 2028, putting 24% of music creators' revenues at risk, a study commissioned by CISAC estimatedMusicians include only humans under the US film and television musicians' agreement, which requires notice and payment when a score uses generative AI prompted by their recordingsAmong about 15,000 German and French music creators, 35% had used AI, including 52% of advertising-music and 48% of library-music makers, a GEMA/SACEM 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

● 11 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 22 because sample libraries, virtual instruments and digital audio workstations had long changed how music is produced before this chart begins, while performing and writing stayed with people. It climbs with the releases that could generate complete songs and background music from a prompt, which reached library and advertising music first, and stays below the middle because generated tracks draw little listening, live performance keeps its audience, union scoring contracts define musicians as humans, and labels have moved AI training onto licensed terms.

12022 H222General-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 H124A general model that passes professional exams. First-draft quality crosses the threshold where professional work starts using it. GPT-4 (2023-03-14) ↗
32023 H228Vision 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 H133The same capability gets much cheaper and faster. Nothing new becomes possible; a lot becomes affordable at volume, which is when deployment decisions change.
52024 H237Reasoning 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 H140Agents 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 H243Long 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 H145Long-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) ↗
9Now46The 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
2026-09-30
Basis of the task judgements
3 evidence-backed · 2 platform inference · 0 not enough evidence
Verified events
11

How we assess an occupation →

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