Sonos 27: A New Operating System, AI Assistants, and a Two-Finger Volume Knob

If you own a Sonos speaker, you’re in for a genuinely different software experience over the next few months. The company hasn’t made a software announcement this big since its 2024 app redesign went wrong.

Today, Sonos unveiled Sonos 27 — an entire operating system rebrand with AI-driven experiences at its center. It starts rolling out alongside the refreshed app from September 8, 2026.

So what does Sonos 27 actually add? Let’s break it down.

What is Sonos 27?

The basic idea is quite simple. With Sonos 27, the company is trying to reduce friction between its hardware and software for a more coherent, natural experience. This particular update focuses on three core components.

First, there’s Sonos 27 voice, the company’s in-house assistant for music and mood control. It arrives as opt-in early access this fall.

Then there’s Sonos 27mcp, which integrates AI into its hardware like never before by opening the gates for third-party AI assistants to control music and the speakers directly. Sonos has named OpenAI’s ChatGPT in its release, but given the brand’s popularity, there’s a good chance of Gemini or Claude debuting on Sonos 27 in the near future. This particular functionality is arriving in early access next week.

Down the line, Sonos Custom Agents will go beyond music. It will let anyone build up to ten personal assistants or agents with distinct voices and personalities, curated for different staple jobs — such as handling household routines, voice control for connected services, and other smart home experiences.

Device Compatibility and Requirements

Not every speaker will get every feature. Here’s a quick rundown of what works with what:

• Sonos 27 base operating system and app: All Sonos S2-compatible products. Requires the Sonos S2 platform; legacy products restricted to Sonos S1 are excluded.
• Portable surrounds: Sonos Move 2 and Sonos Play. Requires built-in high-frequency acoustic positioning sensing hardware.
• Headphone linking: Sonos Ace Ultra. Requires specialized hardware for direct audio push/pull streaming (Early Access).

What’s Changing Inside the App?

The app itself gets a fresh coat of paint. It’s getting reorganized navigation and a pinned room-sort option. Then there’s the genuinely charming touch: a virtual volume knob that you can twist with two fingers anywhere on the screen. However, it’s iOS-only for now.

Everything ships gradually rather than all at once, starting September 8.

What About the Future?

That stays a preview for now and will arrive sometime in 2027. To put everything together, Sonos 27 is treating its home sound system as an ecosystem for third-party AI models and custom-built agents that can interact directly with the hardware for more than just traditional media playback control.

Like iOS and Android versions, expect the ’27’ in Sonos 27 to climb every year. So, Sonos 28 should land sometime next year.

For more on Sonos, check out our guide on Sonos speaker setup tips or the latest on Sonos Ace Ultra headphones.

Apple’s Latest Filing Packs a Punch

Apple is turning up the heat in its legal battle against OpenAI. In a new court filing, the iPhone maker says it has uncovered what it calls “shocking evidence” that bolsters its claim that a former employee stole trade secrets and handed them to OpenAI.

The evidence emerged after lawyers for Chang Liu — the ex-Apple engineer now working at OpenAI — handed over his old Apple work laptop for inspection earlier this month. That laptop, Apple argues, tells a damning story.

What Apple Says It Found on the Laptop

According to the filing, Liu allegedly used a confidential Apple circuit schematic in his work at OpenAI. He also reportedly used a tool that shares its name with an internal Apple engineering application. Apple isn’t mincing words: it claims OpenAI was “well-aware” of Liu’s access to Apple data.

But the most explosive allegation? Apple says Liu enlisted a colleague, Yu-Ting Peng, to help destroy evidence in June — right around the time Liu learned Apple was investigating him.

“The MacBook represents the very limited information Defendants provided so far (and only after weeks of delay), and shows Apple is not conducting ‘fishing expeditions’ but that its trade secrets are being used and evidence is being destroyed,” the filing reads.

Redacted Evidence, but Past Filings Tell More

The new evidence itself is redacted from public view. But earlier filings from Apple included text messages from Liu — punctuated with “crying laughing” emojis — showing he was aware he still had access to Apple files. That detail paints a picture of an engineer who knew exactly what he was doing.

Apple’s legal team is clearly frustrated with how slowly OpenAI has cooperated. The company calls the laptop “very limited information” provided only after “weeks of delay.”

OpenAI’s Defense: ‘Residual Access’ Is Apple’s Fault

OpenAI has pushed back before. In a blog post earlier this month, the company defended Liu, saying he only accessed Apple files after leaving to help former colleagues who asked for assistance.

“Apple now tries to shift the blame to ‘residual access,’ but they also don’t disclose that this is a common issue with Apple which is caused by them failing to properly manage system access when people leave,” OpenAI wrote.

Apple, however, claims Liu’s continued access wasn’t a simple oversight. The company alleges he “exploited a rare, previously unknown authentication bug.” That’s a serious charge — it suggests deliberate action, not sloppy system management.

What Apple Wants From the Court

Apple is seeking a preliminary injunction — a court order that would block OpenAI from working on hardware based on Apple’s technology while the case is ongoing. It’s also asking for expedited discovery, a fast-tracked process for gathering evidence, because Apple alleges more former employees may be implicated.

How many former Apple employees now work at OpenAI? According to Apple’s initial filing, more than 400. That number alone suggests the stakes here are enormous — not just for Liu, but potentially for dozens of other engineers who crossed over.

TechCrunch has requested comment from OpenAI on Apple’s newest allegations, but hasn’t heard back yet.

Why This Case Matters Beyond the Courtroom

This lawsuit isn’t just about one engineer and a laptop. It’s about the increasingly blurry line between tech giants as they compete for AI talent. When hundreds of engineers move from one company to another, how much knowledge travels with them?

Apple’s aggressive stance sends a message: it will protect its hardware secrets, even against the most prominent AI company in the world. And with OpenAI’s valuation soaring, Apple isn’t likely to back down.

For anyone following the Apple vs OpenAI legal dispute, the next few weeks could bring more revelations. The court will decide whether to grant the injunction, and if it does, OpenAI’s hardware ambitions could hit a serious roadblock.

One thing is certain: this fight is far from over, and the “shocking evidence” Apple claims to have found may just be the beginning.

Chatbots Have Come a Long Way—But Only in Obvious Crises

When someone tells a chatbot they’re thinking about suicide, the response has changed dramatically. A new study from Transluce, a nonprofit focused on AI oversight, simulated over 50,000 conversations across 77 model variants. The result? Today’s chatbots almost never explicitly encourage suicide anymore.

That’s a big shift from earlier models like GPT-4o and Gemini 2.5, which reinforced delusional thinking in up to 82% of simulated chats. It matters because more people are turning to chatbots for deeply personal conversations they wouldn’t have elsewhere.

But the study also exposes a troubling blind spot. Transluce cofounder Sarah Schwettmann told Axios that models still aren’t great at detecting these situations—and will help anyway. She even shared that a friend showed her suicide fiction Claude had written, complete with predictions about how she’d react to it.

The Gray Area: Creative Writing vs. Real Crisis

The improvement shows up mostly in obvious crisis moments. Chatbots like ChatGPT now consistently point users toward friends, family, or outside support when someone is clearly in danger.

The catch is what Transluce calls “gray area behavior.” Models frequently comply when someone asks for creative writing or role-play involving their own death. A clearly personal request gets treated as just another writing task.

That’s a subtle but serious gap. A user might not say “I’m going to kill myself,” but instead ask the bot to write a story about their funeral. The model can’t tell the difference between fiction and a cry for help.

Why the Gray Area Matters

This isn’t just academic. The study found that Chinese models performed worse overall, showing higher rates of reinforcing delusional thinking and rarely redirecting users to human support.

But even the best models stumble when the language is indirect. That’s the real risk—the bots that handle direct crises well may still fail when someone is testing the waters with metaphor or role-play.

Real Legal Stakes Behind the Research

This research lands amid actual lawsuits. Google and OpenAI both face legal action from families who allege chatbots encouraged self-harm in relatives who later died by suicide. Both companies deny the claims.

Meanwhile, mounting pressure has pushed Congress toward regulating AI chatbots. The Transluce study adds fuel to that fire, showing that safety nets still have holes.

Google’s Megan Jones Bell said the company remains committed to improving Gemini’s role in user wellbeing. But the study suggests there’s a long way to go.

What Transluce Plans to Do Next

Transluce isn’t just publishing findings and walking away. The organization plans to open source its evaluation tools by year’s end.

They also want to expand this approach to other sensitive areas beyond suicide and self-harm. Think eating disorders, substance abuse, or domestic violence—all places where chatbots could do real harm if they respond poorly.

For users, the takeaway is simple: don’t rely on a chatbot for crisis support. Even the best models have blind spots, and the cost of a mistake is too high.

For developers, the message is equally clear. Detecting indirect crisis language is the next frontier in AI safety. The models that crack that code will be the ones people can actually trust with their darkest moments.