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.

As the rise of AI-generated music on streaming services continues, concerns are growing regarding how AI companies use copyrighted material to train their models, as well as how potential manipulations in streaming systems could lead to fraud.

However, many music streaming services have yet to launch AI music detection tools. So, the streamer Deezer has taken matters into its own hands.

In the ongoing effort to tackle this issue, Deezer introduced a tool that scans playlists from various streaming platforms to identify AI-generated tracks. Announced on Thursday, this free online AI music detector supports 27 languages and gives users from 20 of the most popular platforms the chance to see if their playlists include any AI-generated songs.

The launch further positions Deezer as one of the music industry’s most aggressive opponents of AI music, which could be a selling point for its service among consumers. While rivals like Apple Music and Spotify have opted for a tagging approach, Deezer actively removes AI tracks from recommendations and excludes them from editorial playlists. It also recently began offering its AI detection technology to rival platforms.

To use the new tool, go to Deezer’s AI music detector website, select your streaming service, and allow Deezer to access your playlists. Once you import your playlists, the service scans for AI content, notifies you of any findings, and even offers the option to share the results. The tool is compatible with Spotify, Apple Music, SoundCloud, and YouTube Music, among other platforms. 

“By detecting and tagging AI-generated music over the past year and a half, Deezer has been at the forefront of transparency in music streaming. No other company has followed our lead yet, so we decided to make it possible for everyone to check if their playlists include synthetic music, no matter which streaming platform they use,” CEO Alexis Lanternier said in a statement.

Notably, the company revealed in today’s announcement that it is carefully considering future steps, such as updating supplier policies or removing content. This would follow in Bandcamp’s footsteps, which banned AI music earlier this year. 

The launch of the new tool comes on the heels of Deezer revealing that a staggering 44% of all new music uploaded to its platform is AI-generated.

The company is currently being flooded with nearly 75,000 AI-generated tracks daily, which totals over two million each month. Despite this influx, the listening rate for AI-generated music remains relatively low, accounting for just 1-3% of total streams. Around 85% of these streams are flagged as fraudulent and are demonetized by the platform.

As AI agent traffic surpasses human traffic on the internet, companies working in commerce and finance are building tools that allow agents to take action on behalf of users at a rapid pace. Days after trading platform Robinhood introduced agents that can trade for users, Coinbase launched its own agents that can execute trades and pay for premium research.

The company said Thursday that users can integrate the agent with their main account and start trading. If users don’t want to give the agent access to their main account, they can choose to have it operate in a separate sandbox.

Coinbase noted that the agent can use tools like Coinbase Advanced, the company’s platform for professional traders that includes extra features like TradingView charts to analyze and execute trades. Users can ask the agent to rebalance their portfolio, ask it to follow an investment thesis and trade on their behalf, or provide advice on a one-time crypto trade.

At the moment, the agent can trade in crypto spot markets and derivatives, with support for equities and prediction markets planned for the future. Coinbase added that it will soon add support for custom limits such as maximum trade size, which services the agent can interact with, and how much it can spend.

Coinbase is taking advantage of the open x402 payment protocol it launched in collaboration with AWS, Anthropic, Circle, and Near last year. Using this standard, the agent can pay for premium research data APIs and on-demand compute for trading insights without requiring any login or subscription. This website lists services that the agent can access through the x402 protocol.

The trading platform has been actively investing in AI tools for the last few years. It launched AgentKit, which allows developers to integrate automated wallets into their apps in 2024. Last December, it added an AI-powered assistant to the app that provides trading tips and financial advice. The company said that the latest agent launch can also work in ChatGPT or Claude through its MCP server.

“Coinbase for Agents is informed by insights gleaned from years of building the agentic economy, and the primary goal is to create agents that can transact. And unlike pure trading platforms, we’re the only one that combines exchange access with a native payments protocol. We’re aiming to build a fundamentally different product for a future where most of the internet is accessed through agents,” Lincoln Murr, Head of AI Product, told TechCrunch via email.

AI companies are exploring agentic payments at a rapid pace through new partnerships. Last month, Visa invested in Replit to power agentic payments for developers. The payment network company made a deal with OpenAI this week to explore similar products. The pace of development in the sector has made global financial regulators take notice. The Financial Stability Board (FSB) said that there should be strong safeguards in place to mitigate AI risks.

AI researchers and labs have advanced by leaps and bounds in evaluating AI models for everything from safety and compliance to sycophancy and alignment. But it appears companies and developers are faced with a new, specific need: making sure that their AI system behaves as intended for their specific product or service.

In a bid to make that testing process simpler, Microsoft on Tuesday took the wraps off ASSERT, short for Adaptive Spec-driven Scoring for Evaluation and Regression Testing.

The open-source framework, Microsoft says, makes evaluating application-specific AI behavior easy by using AI to turn high-level, natural-language descriptions of goals, policies, or intended behaviors into thorough, scored tests that can be investigated.

ASSERT takes plain-language descriptions of an AI model’s expected behavior and policies, turns them into a structured set of acceptable and unacceptable behaviors, generates problem scenarios and test cases, runs them against the target system, and scores the results. It can also record the paths the AI system takes, including intermediate actions and tool calls, so developers can inspect where failures happen.

Devs can provide system context, tools, and constraints, too, if they want to further customize what the evaluations cover.

For example, a developer could specify that a document research AI agent shouldn’t send emails to people outside the company, limit confidential information to C-level executives, and provide concise summaries with prior context in mind. ASSERT will use those rules to generate test cases that check whether the system follows those rules on an ongoing basis.

The framework, according to Microsoft, fills a gap that broader, more general evaluations cannot when AI models are intended to behave in a manner that is shaped by an application or product’s context, policies, and tools.

“One of the things we’ve learned is that evaluations are absolutely critical to making good decisions,” said Sarah Bird, chief product officer of Responsible AI at Microsoft. “Because if you don’t understand the behavior of the AI system, it’s really hard to know if it’s meeting your organization’s bar […] What we found is that if you really want to have a trustworthy system, you should evaluate many more dimensions that are application-specific.”

Bird said ASSERT can be used to evaluate systems when they’re being built, after deployment, and even for continuous monitoring.

The release comes amidst a gradual but broader shift in the AI industry. As models grow more capable, researchers are focusing on repeatable testing and regression checks, with Stanford’s HELM, MLCommons’ AILuminate, and evaluation groups like METR rolling out benchmarks to measure how models behave under different conditions.