Chase & Status see AI as a challenge to make music more distinctive

Generic Everything started as a phrase on Chase & Status’s studio whiteboard. Will Kennard told Reuters that it reminded the duo to resist taking the easy route as AI, social media and rapid technological change made the creative landscape feel increasingly uniform. The target was partly their own work: the concern that convenience could turn into habit.[1]

Kennard and Saul Milton, the British electronic-music producers behind Chase & Status, discussed that tension around the album’s October release. They do not treat AI as the end of creativity. Their more interesting claim is that individuality becomes more valuable when producing a competent-sounding track becomes easier.[1][2]

When quality becomes cheap, decisions become visible

For much of recorded music's history, making a track sound commercially finished required specialized equipment, rooms and technical knowledge. Digital audio workstations reduced those barriers; AI generation can lower them further. A novice can now experiment with musical ideas that would once have demanded access to skilled performers and expensive studio time. That is a meaningful expansion of opportunity. It also means that the audible characteristics of an expensive production—clean vocals, dense arrangement, a striking bass sound—may become less distinguishing.[1][2]

Chase & Status frame the risk as aesthetic complacency. If a creator can request a plausible track in a familiar style, why spend months pursuing an awkward hybrid that might fail? This question is not exclusive to AI. Commercial music has always contended with algorithms of another sort: radio playlists, label expectations, audience testing and the incentives of streaming platforms. Machine generation intensifies an existing pressure to optimize for the known preferences of listeners.[1][2]

That makes the album title a working instruction rather than an answer to the entire AI debate. A musician can use a sophisticated tool to produce something familiar, or a familiar tool to make an unexpected choice. The artistic judgement lies in what is kept, changed and pursued when an easier alternative is available.[1][2]

Selection becomes part of the craft

Tools alter the economics of experimentation. They may let artists sketch arrangements in minutes, explore unusual timbres or hear a rough version of a song before recruiting collaborators. But faster generation also shifts work to judgment. Someone must discard the thousands of plausible outputs, decide which emotional and cultural associations matter, and build a lasting identity across tracks. If every choice is based on what immediately resembles previous success, more production can produce less differentiation.[1][2]

There are separate economic and legal arguments about generative music, from licensing to the demand for commissioned work. The duo’s point is narrower and more personal: technological change can be used as a reason to repeat a successful formula or as pressure to discover a more individual one.[1][2]

A useful constraint in the studio

Read that way, the whiteboard phrase supplies a practical creative test. Does a track sound this way because the choice serves the idea, or because the tools made it the easiest available version? AI may increase the number of options, but it cannot decide which answer matters to a particular artist.[1][2]

The album’s challenge remains directed at its makers. If polished sound is increasingly easy to obtain, the work of developing taste, recognising an unusual idea and following it through becomes more conspicuous.[1][2]

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