Deezer has launched a free online AI music detector that checks playlists from 20 major streaming platforms for AI-generated tracks. It uses the same technology Deezer has been using to detect and tag synthetic music on its own service.

The tool is available in 27 languages, and it arrives as Deezer says nearly 75,000 AI-generated tracks are being delivered to it every day. That volume gives the launch a sharper edge than a simple playlist cleanup feature. It’s a way to put synthetic-song detection in front of listeners before the rest of streaming settles on common rules.

How much AI music is hiding in playlists

Deezer says 43% of people arriving from other streaming services already have AI-generated music in their playlists. For listeners, the new scanner answers a basic question that most platforms still don’t surface clearly.

Users connect a streaming account, choose playlists, and review the results. Because the scanner works across 20 common services, Deezer can get its detection system in front of people who don’t use its app.

That stance lands while major music apps are testing how far they want to go with generative tools, including Spotify’s experiments around AI-made covers and remixes. Deezer is focusing on the cleanup job that follows, identifying AI songs after they’ve already entered a library.

Why would the industry license Deezer’s detector

Deezer says its detection technology can identify tracks from major generative music models, including Suno and Udio. It can also be expanded when the company has enough data examples from other tools.

The company says it has made progress on a broader system designed to catch synthetic content without a model-specific training set. That gives Deezer a business case beyond the public playlist scanner. It wants platforms, labels, distributors, and rights groups to use the same underlying technology to spot machine-made tracks before they distort discovery or payment systems.

What happens after AI tracks are tagged

Deezer says fully AI-generated music makes up only 1% to 3% of streams on its service, but it also says as much as 85% of those streams were fraudulent in 2025. When Deezer finds stream manipulation, it excludes those plays from royalty payments.

The company has already removed AI-generated tracks from algorithmic recommendations and editorial playlists. Broader steps, including supplier policy changes or demonetization, are still under review. For listeners and the industry, Deezer’s practical message is clear. Detection has to happen before trust, royalties, and recommendations can be cleaned up.

CBA chief executive Matt Comyn used the phrase ‘work slop’ to describe the low-quality AI output now flowing through corporate workflows, as token-billed AI costs scale with task complexity.

Matt Comyn, chief executive of the Commonwealth Bank of Australia, used a speech on Monday to flag two AI-adoption problems large corporate buyers have been working through quietly for several months.

The first is that the cost of running generative AI inside corporate workflows is rising substantially faster than most companies budgeted for as task complexity scales.

The second is what Comyn called “work slop”, the low-quality AI-generated text, code and analysis that flows through internal company systems when employees use AI without sufficient quality control.

The cost framing is the part that will resonate with the corporate-IT-buyer audience. Token-based pricing, the per-character billing model the foundation-model labs use to charge enterprise customers, has scaled in the past 18 months from a modest line item into a meaningful operating-expense category.

Comyn’s point is that the cost compounds faster than expected because token consumption per task rises non-linearly with task complexity: a simple summarisation task might consume 1,000 tokens, but a multi-step reasoning task with tool use can consume 100,000-plus tokens for the same output value. Companies that priced their AI rollouts on the simple-task baseline are now seeing bills that scale on the complex-task curve.

This problem is not specific to CBA. Morgan Stanley doubled its European-banking-AI-job-loss forecast last week partly on evidence that AI cost-benefit ratios are tightening at exactly the moment large institutions had hoped they would loosen. The token-cost-scaling problem Comyn described is the underlying mechanic: the same AI deployment that worked at pilot-stage volumes can produce 10-100x the costs at production-stage complexity.

The result is the corporate-AI procurement squeeze that Comyn predicted will tighten through 2026: businesses tightening scrutiny of AI-related spending as pressure mounts to demonstrate returns on investment.

The “work slop” framing is the more colourful but equally substantive half of the speech. The category Comyn was naming, low-quality AI-generated output that nominally completes a task but actually degrades downstream workflow, is the corporate-knowledge-work analogue of the social-media “AI slop” problem that emerged in 2024 with image-generation tools.

The bank version looks like this: an employee uses ChatGPT to draft a customer email, the email is technically grammatical but factually imprecise, the recipient takes the imprecision as a commitment, and the bank deals with the resulting complaint three weeks later at a substantially higher cost than the original work would have generated unaided.

The CBA-specific context is significant. The bank announced 90 job cuts earlier this year and a further 120 cuts in May explicitly attributed to AI-driven productivity gains, alongside a A$90m AI-workforce reskilling commitment.

Comyn’s remarks therefore land inside a CBA strategy that has visibly committed to AI substitution at scale: the “work slop” framing is not a defensive critique of AI from a bank that has rejected the technology but a sharper inside-baseball read on AI deployment from one of Australia’s largest current adopters.

The wider Australian-bank context is also worth noting. Sam Altman has been arguing over the past month that an AI jobs apocalypse is unlikely at the macro level, and the labour data through March 2026 has so far supported the conservative read.

Comyn’s remarks complicate that picture: the macro labour data does not yet show large-scale displacement, but the operating-margin data inside large corporates is starting to show the AI-cost-and-quality tradeoffs CBA is now naming explicitly.

The substantive implication is that the 2024-2025 AI cost narrative, that token prices were falling so quickly that the deployment-economics question would solve itself, has structurally inverted.

Falling per-token prices have been overwhelmed by rising per-task token consumption as enterprises move from pilot deployments to production use cases. The procurement-discipline phase Comyn is forecasting through 2026 is, on this evidence, the predictable consequence.