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AI and the rise of the universal entertainment app

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All the big entertainment apps are starting to look the same, and that’s not an accident. For a decade, platforms fought over who would dominate a single format: music, video, podcasts, audiobooks. Now, powered by AI, they’re fighting over something bigger — becoming the app you default to whenever you have time to kill, no matter what form the content takes.

There are several reasons why this is the case. The market for entertainment apps is reaching maturity, so growth has slowed, pushing companies to compete on time spent and revenue-per-user instead of new sign-ups. In addition, today’s creators often work across formats, so it makes sense to provide a home for all their content, not just one piece of it.

AI adds a third reason. It makes it easier for a single company to build and run several formats well, and the wider the content mix, the more time users spend in the app, which in turn drives both ad revenue and subscriptions.

Netflix Games screen on mobile phone
Image Credits:Netflix

Netflix is one clear example of this trend, as the service over the past several years has added gaming, live sports and other events, and, more recently, short video clips and podcasts. The idea is to capture more of users’ time, even when there’s not a TV or movie they want to watch, as well as to find a way into the smaller bits of free time that people usually fill with scrolling social media, playing casual games, or watching TikTok or Reels.

Spotify has also been expanding its footprint beyond its original premise as a home for streaming music. After adding podcasts, the company added support for video podcasts, social features like
Q&As and commenting, stories, and messaging, as well as different types of content like fitness classes, audiobooks, narrated magazines, and even physical book sales.

Spotify audiobooks displayed on smartphone screens
Image Credits:Spotify

Meanwhile, YouTube, originally the home to longer-form creator content, moved into short-form content to compete with TikTok, while also adding dedicated space for podcasts, gaming content, music, movies and TV, sports and news, shopping, and more. Now, you can watch free movies and TV, supported by ads, stream live content, or rent or buy TV and movies to add to your library. At this rate, folding YouTube TV and YouTube Music into YouTube proper — and selling tiered access to the whole bundle — looks like a matter of when, not if.

Even TikTok, largely known for short videos, offers support for long-form content and other features, like travel planning, shopping, local exploration, buying tickets to live events, and more. It even has its own standalone app for microdramas and another called TikTok Pro Events for sporting events — like the FIFA World Cup — plus music festivals, and more.

TikTok Pro Events
Image Credits:TikTok

While there are still some differentiators between the services today, there’s an obvious trend toward convergence over a similar set of features focused on providing users with access to content to watch, listen, play, or shop.

This is also where AI comes into play. With format no longer a differentiator, the value these apps offer comes down to how well they connect users with what they want next.

AI’s role in building the entertainment operating system of the future

AI makes content recommendation across formats easier, sharpening personalization while also giving users more direct control over how those recommendations get made.

Spotify, for instance, is testing a tool that will let you edit your Taste Profile, its AI-built model of your preferences. It’s also building AI features that let users chat with AI directly about what they want or build playlists of things they like — and not just music.

You can now talk to Spotify
Image Credits:Spotify

Netflix has made a similar case. Co-CEO Greg Peters told investors in the company’s first-quarter call that new model architectures are improving personalization and letting the team iterate faster.

AI-assisted coding is also speeding up how fast these companies can build and launch new content areas in the first place.

Plus, generative AI can be used for content creation, though the subject remains controversial as artists worry that AI tools will use their work for training purposes or even put them out of work. Netflix, for better or for worse, has leaned into AI, having recently bought Ben Affleck’s AI filmmaking company for $587 million, for instance.

YouTube AI creation feature, Dream Screen

YouTube has used generative AI to launch more creator tools, but also to improve its search engine, add conversational AI features, build playlists, and expand its content’s reach with auto-dubbing, among other things. Earlier this year, the company said that more than a million channels used its AI creation tools and 20 million consumers used its Gemini AI-powered content discovery tool in the month of December.

Alphabet CEO Sundar Pichai has framed AI as central to the YouTube experience for creators and viewers alike.

TikTok has assembled its own version, with an in-app AI chatbot, AI video-creation tools, AI-driven search and recommendations, and AI-powered accessibility features.

TikTok AI creation feature, TikTok AI AliveImage Credits:TikTok

All four are, of course, also applying AI to their ad stacks, helping marketers write ads, target audiences, price placements, and measure results.

For consumers, this convergence means fewer reasons to switch apps at all. Whichever one you land on gains an advantage — more data on your habits, more lock-in — making it harder to leave even if prices climb or quality drops.

As the lines between music, video, podcasts, books, and games blur, the coming battle is no longer which format will win, but which app will become the place to go for entertainment, regardless of what form that comes in.

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The Anthropic-Physical Intelligence rumor roiling AI Twitter

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It’s been a big year for AI acquisitions — so big that most of them barely register anymore. Anthropic and OpenAI have each gone on buying sprees, snapping up developer tooling, AI services shops, and product-testing startups to convert model capability into enterprise revenue and extend their reach faster than the other. Which is what made a weekend rumor about Anthropic acquiring robotics startup Physical Intelligence stand out. It spread exceedingly fast, even after a denial from Physical Intelligence’s CEO

Part of that ties to who’s involved. Physical Intelligence isn’t some obscure robotics shop. It was co-founded by Lachy Groom, an investor-operator whose star has been on the rise in Silicon Valley in recent years; it has raised more than $1 billion (and was reportedly in talks this spring for another $1 billion round at an $11 billion valuation); and its π0.5 model is apparently among of the more widely used robot brains in robotics research.

As it turns out, the rumor wasn’t completely spurious. Anthropic and Physical Intelligence actually did hold acquisition talks this spring, according to The Information, so tech blogger Robert Scoble — whose weekend post on X set off the frenzy — may have gotten the specifics wrong without being wrong that something had happened.

Physical Intelligence’s response to the rumor mill wasn’t the world’s most vigorous denial, it should be noted. According to The Information, Physical Intelligence CEO Karol Hausman told employees the reports weren’t true via a Slack message containing a gif of a character from “The Office” shaking her head no.

Groom, for his part, did not respond to TechCrunch’s request for comment, sent Monday night.

Anthropic has made four known acquisitions this year; OpenAI has been more aggressive, acquiring at least 17 companies since 2023. Both are also, of course, now preparing to go public. Anthropic confidentially filed for an IPO on June 1, followed by OpenAI a week later, setting up what could be two of the largest U.S. stock debuts in history.

So why robotics, why now? The likeliest answer is that physical-world understanding may be a prerequisite for superintelligent systems, and no amount of internet text can substitute for it.

OpenAI’s own history here is instructive. It built an early robotic hand that could solve a Rubik’s Cube, then shut the entire robotics group down in 2021, with co-founder Wojciech Zaremba later saying the approach was missing pieces needed for real superintelligence. The team came back in 2024, quietly building a humanoid robotics lab in San Francisco, before CEO Sam Altman made it official in late May, announcing “OpenAI Robotics” was hiring and describing a near-term focus on robots for infrastructure work, with a personal robot for everyone as the long-term goal.

Anthropic hasn’t built anything resembling OpenAI’s hardware lab. What it has done is publish a string of research pieces through its internal group that stress-tests frontier capabilities for safety purposes. That included Project Fetch last November, where Anthropic staff tested how much Claude could help non-experts program a robot dog and a second phase in June that, according to Anthropic, found a newer model completed the same tasks roughly 20 times faster than the best human-plus-Claude team from the year before.

Buying an existing team with robotics expertise would let Anthropic skip years of work. There’s a possible complication, though. Physical Intelligence was founded in San Francisco roughly two years ago by Groom, former Google researchers, and professors from Stanford and Berkeley, and its early investor base looks a lot like OpenAI’s own, including Khosla Ventures and Thrive Capital. Founders Fund — also a major OpenAI investor — was reportedly involved in Physical Intelligence’s newest funding round earlier this year.

In fact, OpenAI is itself an investor in Physical Intelligence, so it isn’t just a peripheral player; it’s a stakeholder in a company that its chief rival was reportedly in talks to buy very recently.

That raises questions around whether OpenAI’s early investment came with any information rights, or a right of first refusal over a sale to a competitor — the kind of protective provisions that large strategic investors sometimes negotiate for precisely this scenario.

That leaves open the possibility that if Physical Intelligence is actually in play, OpenAI — already a shareholder, already close to Groom, already trying to ensure it bests Anthropic in robotics — may have the more obvious claim to it than Anthropic does. We asked OpenAI these questions earlier today and the company didn’t respond.

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Top ERP Software Vendors in 2026

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Are you an IT manager or executive building the case for a new ERP vendor? Compare the top ERP software companies in 2026 for your business.

The post Top ERP Software Vendors in 2026 appeared first on TechRepublic.

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OpenAI says Hugging Face was breached by its pre-release models

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OpenAI admitted Tuesday that one of its AI models breached the systems of Hugging Face, the unaffiliated AI hosting platform, during an internal cybersecurity test that went awry. The models reportedly escaped their isolated testing environment and reached Hugging Face’s systems from there. Hugging Face initially attributed the breach to an “external AI agent.”

In a blog post published Tuesday afternoon, OpenAI detailed the steps that led the models to compromise the service.

“After investigating, we now know that this particular incident was driven by a combination of OpenAI models — including GPT‑5.6 Sol and an even more capable pre-release model, all with reduced cyber refusals for evaluation purposes — while being internally tested on a benchmark⁠ of cyber capabilities,” the post reads.

In particular, the breach appears to have focused on ExploitGym, a publicly hosted benchmark measuring models’ ability to execute attacks based on existing vulnerabilities. Benchmarks like ExploitGym are commonly used in model training to refine specific skills, but this is the first known incident in which that testing resulted in an actual cyberattack.

In this case, the model in question should not have even had internet access, outside of a specific tool that enabled models to install software packages they might need to complete their task. Instead, the model was able to find an undisclosed vulnerability in the package-installer program, which it used to access the broader internet at will.

“The models were hyperfocused on finding a solution for ExploitGym, going to extreme lengths to achieve a rather narrow testing goal,” OpenAI’s post reads. “After gaining Internet access, the models inferred that Hugging Face potentially hosted models, datasets and solutions for ExploitGym. Knowing this, the model searched for and successfully found ways to gain access to secret information that it could use to cheat the evaluation.”

Ultimately, the models found vulnerabilities in Hugging Face’s infrastructure that allowed them to “obtain test solutions directly from Hugging Face’s production database,” effectively providing the answers to the benchmark.

For Hugging Face, the apparent result was a sophisticated and aggressive cyberattack, with “many thousands of individual actions across a swarm of short-lived sandboxes, with self-migrating command-and-control staged on public services,” as the company stated in its initial disclosure.

OpenAI has identified and reported the vulnerabilities in the package installer and is working with Hugging Face to investigate the incident further. The company also said it would implement new controls on both model testing and the related infrastructure, meant to prevent similar incidents in the future.

It’s unclear whether OpenAI will face any legal consequences as a result of the breach, although it’s likely that the models’ actions violated the Computer Fraud and Abuse Act.

Nevertheless, the result is an unusually vivid illustration of the power and dangers of frontier AI models operating on long time horizons. As OpenAI researcher Micah Carroll posted in response to the news, “If this doesn’t convince you that misalignment risks are going to be a key concern going forward, I don’t know what will.”

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