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The 2017 headline was real, but it claimed more than the evidence showed. A March 9, 2017 Futurism article described AIVA—Artificial Intelligence Virtual Artist—generating classical-style scores that some listeners reportedly mistook for human compositions. That was evidence that AI could produce convincing music in a defined style, not proof that it matched human composers across creativity, collaboration, or professional work.
What the 2017 story was about
Futurism’s story concerned AIVA, a music-AI company founded in 2016. Its early work focused on classical and cinematic music, with potential uses in film, advertising, and games. The article reported that AIVA had released an album, Genesis, and individual tracks, and that its music was registered with France’s collecting society SACEM under the name AIVA.
The company’s approach was suited to a practical demand: films, games, and advertisements often need instrumental cues that establish a mood or support a scene. The article said AIVA was trained on classical scores, including works associated with Bach, Beethoven, and Mozart. AIVA’s founders also cited copyright-expired scores as a reason for focusing on classical music. That was their explanation of the training material, not a complete legal assessment of datasets, recordings, arrangements, or generated outputs.
How AIVA made music
The 2017 AIVA was described primarily as a symbolic composition system: it generated musical notation rather than simply producing a finished audio recording from a text prompt. In broad terms, the reported workflow was:
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- Analyze scores and learn recurring musical patterns.
- Generate a new sequence of notes and a score.
- Arrange, perform, or record that score, with human involvement in turning it into a polished track.
The original article referred to deep learning and reinforcement learning, but did not give enough technical detail to independently reproduce or evaluate the system. The important practical distinction is clearer: generating notes is one part of making a soundtrack. Choosing instrumentation, shaping an arrangement, directing performers, recording, mixing, editing music to a scene, and revising after feedback are separate tasks. Futurism’s account acknowledged human orchestration and recording in AIVA’s workflow.
That does not make the generated composition meaningless. AIVA could create new sequences rather than merely replaying one existing score, and it could produce structured music that listeners found plausible. But “new” does not settle whether the work is original in a deeper artistic sense, or whether the system had a purpose or feeling it wanted to express.
What the listening test did—and did not—show
Futurism reported that AIVA’s creators had tested compositions with professionals who, they said, could not identify some of the AI-generated pieces. That is a company-reported informal result, not a validated scientific demonstration that AIVA composed as well as a person. The article did not provide the sample size, excerpts, controls, randomization, statistical analysis, or enough information to establish whether the recordings involved human performers or production.
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A listener failing to identify authorship in a particular excerpt can show that the music sounded plausible under those conditions. It does not show that the system can consistently write original music, respond intelligently to a director, or make expressive choices across a full project. Music-theory scholarship has also cautioned against treating popular “Turing test” claims about music as broad measures of computational creativity; see this discussion in Music Theory Online.
| Claim | What the evidence supports |
|---|---|
| AI can generate musical notation | Supported by the reported AIVA workflow. |
| AI can make a convincing classical-style cue | Supported by examples and listener reports, though the 2017 article did not establish performance across a broad range of tasks. |
| Some listeners may mistake AI music for human music | Reported by AIVA’s creators, but the test details were insufficient to judge how generalizable the result was. |
| AI composes as well as a professional human in general | Not established. |
| AI feels emotion or understands what music means | Not established by the ability to generate plausible notes. |
| AI eliminates human musicians and producers | Not demonstrated by a workflow that included human arrangement and recording. |
Why “as well as a human composer” is too broad
There is no single score for musical ability. A system can be judged on whether its notes follow musical conventions, whether listeners enjoy a cue, whether it evokes a requested mood, or whether it can be revised to fit a particular scene. Those are different measures. Professional composition can also involve narrative judgment, collaboration, originality, repeatable quality, practical delivery, and adapting to detailed notes from a client.
Human composers learn from musical traditions too. The meaningful distinction is not simply that a machine learned patterns while a person did not. Human work can involve intention, cultural context, lived experience, aesthetic goals, and decisions about what to communicate. A model that produces convincing patterns does not, by itself, demonstrate those qualities. Nor does a polished recording establish that the AI completed the work independently if people selected, arranged, performed, or produced it.
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What later evidence adds
A small 2025 study compared one human classical composition with three tracks generated by AIVA. It reported that 52% of its 26 respondents correctly distinguished the human composition from the AI tracks; the most-preferred track was AI-generated. The study, available through ResearchGate, is a useful complication to the 2017 headline, not a final verdict: a sample of 26 cannot establish how listeners would respond across genres, expertise levels, or production conditions.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsIdentification and preference are not the same thing. A person can prefer a track without knowing its source, and can identify a human composition without preferring it. Judgments may also change when listeners are told whether a work is machine-made; research on AI-created art has found that belief about authorship can affect evaluation (research published in PLOS ONE). A strong comparison would therefore test preference, identification, emotional response, and perceived originality separately, using matched music and transparent methods.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.AIVA now: an assistant, not the 2017 system frozen in time
AIVA remains an active product, but it would be a mistake to assume that the service available now is technically identical to the system described in 2017. Its current product material presents it as an AI music-generation assistant: users can generate music in more than 250 styles, create custom style models, use audio or MIDI influences, edit tracks, and export compositions. AIVA’s showcase also describes tracks as generated by AIVA and arranged by humans, an example of the collaborative workflow that can sit between automated generation and a finished piece. See AIVA’s product overview for its current description.
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That makes AIVA a plausible tool for instrumental sketches, cinematic cues, background music, or editable musical material. It may be less suitable when a project depends on a distinctive vocalist, a particular artist persona, bespoke interaction with a director, or guaranteed exclusivity. These are fit considerations, not claims that every current product feature is absent.
Music generation, licensing, and copyright are separate questions
The 2017 story said AIVA had been registered with SACEM and described it as the first AI to receive composer status. That should not be read as proof that SACEM recognized an AI as a legal person or settled who owns copyright worldwide. A later cultural-policy report cautions that registration can function through a pseudonym associated with human activity at AIVA Technologies, rather than establishing autonomous legal authorship by a machine (report on cultural and creative-sector policy).
There are at least three distinct issues: whether a service contract lets a user use or monetize a track; whether copyright law in a particular jurisdiction protects the output and identifies an author; and whether a human’s choices or edits contribute protectable authorship. AIVA’s help page says its plan terms differ: the free plan is non-commercial and requires attribution, Standard use is limited to specified platforms, and Pro users own copyright in compositions covered by that plan. It also describes treatment of existing compositions after upgrading and ownership after cancellation. Read the current license explanation and applicable terms before relying on those provisions. A vendor’s contractual promise is not the same as a court or statute resolving copyright status in every country.
Likewise, training on public-domain compositions does not automatically resolve questions about recordings, arrangements, data sources, or outputs. And a generated track being new does not, on its own, guarantee exclusivity or legal clearance for every use.
So, could AIVA write music as well as a human?
In a limited sense, AIVA showed that AI could generate classical-style music many people would find musically credible, and that some listeners might not identify its source from a listening example. The evidence did not prove equivalence to human composers in artistic intention, originality, collaboration, adaptability, or the full production of a professional score. The headline captured a real milestone in AI music; its broad comparison was stronger than the case behind it.
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