Techin' Geek

Last year, Deezer introduced an AI detection tool that automatically tags fully AI-generated music for listeners and removes it from algorithmic and editorial recommendations.

The company announced on Thursday that it’s now making the tool available to other streaming platforms in an effort to help address the rise of AI and fraudulent streams, as well as promote transparency within the music industry and make sure human artists still get the recognition they deserve.

Alongside the move, Deezer reported that 85% of streams from fully AI-generated tracks are deemed fraudulent. Notably, the service now receives 60,000 AI tracks per day, totaling 13.4 million AI-detected songs. By contrast, in June of last year, fully AI-generated music made up 18% of daily uploads, surpassing 20,000 tracks.

​Deezer claims its AI music detection tool can identify every AI-generated track from major generative models like Suno and Udio. In addition to excluding AI-generated tracks from recommendations, Deezer’s tool demonetizes them and excludes them from the royalty pool, as the company aims to fairly compensate musicians and songwriters.

The tool’s accuracy is 99.8%, a company spokesperson told TechCrunch.

​Deezer CEO Alexis Lanternier says there has been “great interest” in the tool, and several companies have “already performed successful tests.” One such company is Sacem, the French management company that represents over 300,000 music creators and publishers, including David Guetta and DJ Snake.

​The company didn’t provide pricing information or disclose which additional companies are interested in adopting the tool. A spokesperson told us that the cost varies based on the type of deal.

​There is increasing concern about AI companies using copyrighted material to train their models, as well as about methods being used to manipulate streaming systems and commit fraud. 

One instance of music streaming fraud occurred in 2024, when a North Carolina musician was charged by the Department of Justice (DOJ) with creating AI-generated songs and using bots to stream them billions of times, resulting in more than $10 million in stolen streaming royalties. Additionally, AI bands like The Velvet Sundown have gained millions of streams.

​Bandcamp recently got fed up and banned AI-generated music altogether, while Spotify has updated its policy to address the rise of AI tracks, clarifying when AI is used in music production, reducing spam, and explicitly stating that unauthorized voice clones are prohibited on the platform.

By contrast, major record labels have resolved lawsuits with Suno and Udio, appearing to embrace AI-generated music. Last fall, Universal Music Group and Warner Music Group struck deals with these AI startups to license their music catalogs, ensuring artists and songwriters are compensated when their work is used to train AI models.

​In recent years, Deezer has taken significant steps to address concerns about AI-generated music. In 2024, it became the first music streaming platform to sign the global statement on AI training, joining actors Kate McKinnon, Kevin Bacon, Kit Harington, Rosie O’Donnell, and other notable creatives. 

​Hopefully, Deezer’s latest decision to sell its detection tool will set a precedent for other music streaming platforms to take similar actions to defend human artists and fight fraud.

paceX is reportedly lining up four major Wall Street banks for a 2026 IPO that could provide the reset the market needs. 

The company just completed a tender offer at an $800 billion valuation, and secondary market demand is through the roof. If SpaceX goes public anywhere near its rumored $1.5 trillion valuation, it could trigger an IPO cascade for other late-stage unicorns like OpenAI, Stripe, and Databricks. 

Watch as Equity host Rebecca Bellan chats with Greg Martin, Managing Director at Rainmaker Securities, about why this IPO feels different, how tech employees are cashing out through secondary markets before companies go public, and what investors are actually looking for in pre-IPO shares. 

Subscribe to Equity on YouTube, Apple Podcasts, Overcast, Spotify and all the casts. You also can follow Equity on X and Threads, at @EquityPod. 

Many in the industry think the winners of the AI model market have already been decided: Big Tech will own it (Google, Meta, Microsoft, a bit of Amazon) along with their model makers of choice, largely OpenAI and Anthropic. 

But tiny 30-person startup Arcee AI disagrees. The company just released a truly and permanently open (Apache license) general-purpose, foundation model called Trinity, and Arcee claims that at 400B parameters, it is among the largest open-source foundation models ever trained and released by a U.S. company.

Arcee says Trinity compares to Meta’s Llama 4 Maverick 400B, and Z.ai GLM-4.5, a high-performing open-source model from China’s Tsinghua University, according to benchmark tests conducted using base models (very little post training).

Like other state-of-the-art (SOTA) models, Trinity is geared for coding and multi-step processes like agents. Still, despite its size, it’s not a true SOTA competitor yet because it currently supports only text.

More modes are in the works — a vision model is currently in development, and a speech-to-text version is on the roadmap, CTO Lucas Atkins told TechCrunch (pictured above, on the left). In comparison, Meta’s Llama 4 Maverick is already multi-modal, supporting text and images.

But before adding more AI modes to its roster, Arcee says, it wanted a base LLM that would impress its main target customers: developers and academics. The team particularly wants to woo U.S. companies of all sizes away from choosing open models from China. 

“Ultimately, the winners of this game, and the only way to really win over the usage, is to have the best open-weight model,” Atkins said. “To win the hearts and minds of developers, you have to give them the best.”

The benchmarks show that the Trinity base model, currently in preview while more post-training takes place, is largely holding its own and, in some cases, slightly besting Llama on tests of coding and math, common sense, knowledge and reasoning.

The progress Arcee has made so far to become a competitive AI Lab is impressive. The large Trinity model follows two previous small models released in in December: the 26B-parameter Trinity Mini, a fully post-trained reasoning model for tasks ranging from web apps to agents, and the 6B-parameter Trinity Nano, an experimental model designed to push the boundaries of models that are tiny yet chatty.  

The kicker is, Arcee trained them all in six months for $20 million total, using 2,048 Nvidia Blackwell B300 GPUs. This out of the roughly $50 million the company has raised so far, said founder and CEO Mark McQuade (pictured above, on the right). 

That kind of cash was “a lot for us,” said Atkins, who led the model building effort. Still, he acknowledged that it pales in comparison to how much bigger labs are spending right now.

The six-month timeline “was very calculated,” said Atkins, whose career before LLMs involved building voice agents for cars. “We are a younger startup that’s extremely hungry. We have a tremendous amount of talent and bright young researchers who, when given the opportunity to spend this amount of money and train a model of this size, we trusted that they’d rise to the occasion. And they certainly did, with many sleepless nights, many long hours.” 

McQuade, previously an early employee at open-source model marketplace HuggingFace, says Arcee didn’t start out wanting to become a new U.S. AI Lab: The company was originally doing model customization for large enterprise clients like SK Telecom. 

“We were only doing post-training. So we would take the great work of others: We would take a Llama model, we would take a Mistral model, we would take a Qwen model that was open source, and we would post-train it to make it better” for a company’s intended use, he said, including doing the reinforcement learning. 

But as their client list grew, Atkins said, the need for their own model was becoming a necessity, and McQuade was worried about relying on other companies. At the same time, many of the best open models were coming from China, which U.S. enterprises were leery of, or were barred from using. 

It was a nerve-wracking decision. “I think there’s less than 20 companies in the world that have ever pre-trained and released their own model” at the size and level that Arcee was gunning for, McQuade said. 

The company started small at first, trying its hand at a tiny, 4.5B model created in partnership with training company DatologyAI. The project’s success then encouraged bigger endeavors. 

But if the U.S. already has Llama, why does it need another open weight model? Atkins says by choosing the open source Apache license, the startup is committed to always keeping its models open. This comes after Meta CEO Mark Zuckerberg last year indicated his company might not always make all of its most advanced models open source. 

“Llama can be looked at as not truly open source as it uses a Meta-controlled license with commercial and usage caveats,” he says. This has caused some open source organizations to claim that Llama isn’t open source compliant at all.

“Arcee exists because the U.S. needs a permanently open, Apache-licensed, frontier-grade alternative that can actually compete at today’s frontier,” McQuade said.

All Trinity models, large and small, can be downloaded for free. The largest version will be released in three flavors. Trinity Large Preview is a lightly post-trained instruct model, meaning it’s been trained to follow human instructions, not just predict the next word, which gears it for general chat usage. Trinity Large Base is the base model without post-training.

Then we have TrueBase, a model with any instruct data or post training so enterprises or researchers that want to customize it won’t have to unroll any data, rules or assumptions.

Acree AI will eventually offer a hosted version of its general release model for, it says, competitive API pricing. That release is up to six weeks away as the startup continues to improve the model’s reasoning training.

API pricing for Trinity-Mini is $0.045 / $0.15, and there is a rate-limited free tier available, too. Meanwhile, the company still sells post-training and customization options. 

The plus-one passes at 50% off are almost sold out. Now the clock is really ticking.

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This pricing ends January 30, 11:59 p.m. PT, or the moment the remaining plus-one passes sell out. No extensions. No exceptions. Register now to save.

Why founders, VCs, and operators keep coming back to Disrupt

From October 13–15, Moscone West in San Francisco will become the global epicenter of tech. TechCrunch Disrupt is a curated, three-day experience built to maximize signal over noise, bringing together 10,000 founders, investors, operators, and tech leaders for 200+ expert-led sessions featuring 250+ influential voices.

Across the ecosystem, past attendees consistently point to the same value:

• Real access to founders, investors, and operators who are actively building.

• Conversations that turn into partnerships, funding, and hires.

• Practical insights you can apply immediately, not just inspiration.

• A clearer picture of where tech is headed before it hits the mainstream.

At Disrupt, you’ll explore what’s next as 300+ startups debut new breakthroughs, feel the high-stakes energy of the intense startup pitch-off in Startup Battlefield 200, and engage in curated, high-impact networking with the people shaping the future of tech.

Past Disrupt speakers

Keep an eye on the Disrupt 2026 event page for when the agenda goes live.

A more curated way to experience a tech event

Disrupt isn’t about wandering between sessions. It’s about intentional connections and curated experiences designed for how people actually grow in the tech industry.

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If you’re hands-on in tech, Disrupt was built for you. Find your ticket match now to secure the lowest rate.

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The latest wave of AI excitement has brought us an unexpected mascot: a lobster. Clawdbot, a personal AI assistant, went viral within weeks of its launch and will keep its crustacean theme despite having had to change its name to Moltbot after a legal challenge from Anthropic. But before you jump on the bandwagon, here’s what you need to know.

According to its tagline, Moltbot (formerly Clawdbot) is the “AI that actually does things” — whether it’s managing your calendar, sending messages through your favorite apps, or checking you in for flights. This promise has drawn thousands of users willing to tackle the technical setup required, even though it started as a scrappy personal project built by one developer for his own use.

That man is Peter Steinberger, an Austrian developer and founder who is known online as @steipete and actively blogs about his work. After stepping away from his previous project, PSPDFkit, Steinberger felt empty and barely touched his computer for three years, he explained on his blog. But he eventually found his spark again — which led to Moltbot.

While Moltbot is now much more than a solo project, the publicly available version still derives from Clawd, “Peter’s crusted assistant,” now called Molty, a tool he built to help him “manage his digital life” and “explore what human-AI collaboration can be.” 

For Steinberger, this meant diving deeper into the momentum around AI that had reignited his builder spark. A self-confessed “Claudoholic”, he initially named his project after Anthropic’s AI flagship product, Claude. He revealed on X that Anthropic subsequently forced him to change the branding for copyright reasons. TechCrunch has reached out to Anthropic for comment. But the project’s “lobster soul” remains unchanged.

To its early adopters, Moltbot represents the vanguard of how helpful AI assistants could be. Those who were already excited at the prospect of using AI to quickly generate websites and apps are even more keen to have their personal AI assistant perform tasks for them. And just like Steinberger, they’re eager to tinker with it.

This explains how Moltbot amassed more than 44,200 stars on GitHub so quickly. So much viral attention has been paid Moltbot that it has even moved markets. Cloudflare’s stock surged 14% in premarket trading on Tuesday as social media buzz around the AI agent resparked investor enthusiasm for Cloudflare’s infrastructure, which developers use to run Moltbot locally on their devices.

Still, it’s a long way from breaking out of early adopter territory, and maybe that’s for the best. Installing Moltbot requires being tech savvy, and that also includes awareness of the inherent security risks that come with it.

On one hand, Moltbot is built with safety in mind: It is open source, meaning anyone can inspect its code for vulnerabilities, and it runs on your computer or server, not in the cloud. But on the other hand, its very premise is inherently risky. As entrepreneur and investor Rahul Sood pointed out on X, “‘actually doing things’ means ‘can execute arbitrary commands on your computer.’”

What keeps Sood up at night is “prompt injection through content” — where a malicious person could send you a WhatsApp message that could lead Moltbot to take unintended actions on your computer without your intervention or knowledge. 

That risk can be mitigated partly by careful setup. Since Moltbot supports various AI models, users may want to make setup choices based on their resistance to these kinds of attacks. But the only way to fully prevent it is to run Moltbot in a silo. 

This may be obvious to experienced developers tinkering with a weeks-old project, but some of them have become more vocal in warning users attracted by the hype: things could turn ugly fast if they approach it as carelessly as ChatGPT. 

Steinberger himself was served with a reminder that malicious actors exist when he “messed up” the renaming of his project. He complained on X that “crypto scammers” snatched his GitHub username and created fake cryptocurrency projects in his name, and he warned followers that “any project that lists [him] as coin owner is a SCAM.” He then posted that the GitHub issue had been fixed but cautioned that the legitimate X account is @moltbot, “not any of the 20 scam variations of it.”

This doesn’t necessarily mean you should stay away from Moltbot at this stage if you are curious to test it. But if you have never heard of a VPS — a virtual private server, which is essentially a remote computer you rent to run software — you may want to wait your turn. (That’s where you may want to run Moltbot for now. “Not the laptop with your SSH keys, API credentials, and password manager,” Sood cautioned.)

Right now, running Moltbot safely means running it on a separate computer with throwaway accounts, which defeats the purpose of having a useful AI assistant. And fixing that security-versus-utility trade-off may require solutions that are beyond Steinberger’s control.

Still, by building a tool to solve his own problem, Steinberger showed the developer community what AI agents could actually accomplish and how autonomous AI might finally become genuinely useful rather than just impressive.