Fast Company Technology

I’m talking to many companies these days that are trying to incorporate AI into their business practices. Most of them are still in what I call “the administrative phase,” when they just try modest automations that allow some workers to draft documents or presentations, answer emails, or do research on certain topics, but given my recent articles arguing for companies to go much farther, I’m starting to get more and more questions from enterprises looking to go way beyond that, and get AI to become more “strategic.”

For these companies, the main concern is expressed as a question: Will AI be smart enough? But I think a better and more useful question would be: How does the AI know whether what it did actually improved the business?

The examples are well known and not theoretical: from optimizing a salesperson for revenue, margin, or retention, to a factory in which we can improve throughput, but we could also improve quality, or reduce defects. In business, “optimization” rarely means just one thing: improving one metric can easily damage another. So, in the same way management already works through objectives and feedback, AI agents need to do exactly that. In fact, OpenAI now explicitly frames enterprise evaluation as “specify → measure → improve, and in doing so, it can turn what would be vague business goals into actionable and measurable expectations.

Outputs are easy to measure; outcomes are harder

Current AI metrics often measure variables such as answer quality, task completion, latency, cost, etc. However, what businesses actually care about are things like customer churn, margin, conversion, claims accuracy, delivery times, or customer lifetime value. 

An agent could be completing its task perfectly as instructed, and still hurt the company by doing so. Businesses do not ultimately care about generated outputs: they care about outcomes. It is worth repeating, because it changes the entire conversation: businesses do not ultimately care about outputs; they care about outcomes. What we demand in corporate implementations of AI is completely different from what we need in our individual use. 

A simple business example would be the well-known “successful” discount: If you give an agent the objective of getting a customer to renew, the agent could perhaps offer a 20% discount. When the customer renews, that would initially be seen as an apparent success, but it comes at a cost: the margin collapses, the customer learns to wait for discounts, and similar customers may start to demand the same. What counts as success depends on the scoreboard. As Goodhart’s law puts it, when a measure becomes a target, it ceases to be a good measure. Poorly chosen metrics become dangerous when they are optimized aggressively.

The industry is starting to realize that evaluation has to become continuous

OpenAI’s guidance for workflows puts a lot of emphasis on capturing full workflows, grading behavior, detecting regressions, improving prompts, routing, and guardrails. The important managerial translation of that is that AI cannot simply be approved once and then forgotten: evaluation has to necessarily become an active part of the operations.

The real world is our final exam: benchmarks happen before the systems are deployed, but companies operate in changing environments. According to NIST, controlled pre-deployment testing cannot capture every unexpected behavior or consequence that may emerge in real-world use. Like in “The Sorcerer’s Apprentice”, but in real life: that magic wand you use to optimize something can easily turn back against you. 

The useful question is whether the system worked in the business, not merely in the test. Basically, we need to move from “Did the agent succeed?” to “Did the company improve?” According to recent work by McKinsey, many organizations are scaling agents faster than they can redesign the work beneath them, and boards and CFOs are starting to demand clearer value. And when we say value, take into account that token costs are only one of the pieces: agentic workflows must be judged economically at workflow level. 

The managerial point is simple: task completion is not the same as business success. 

AlphaZero as the intuitive precedent

Think about AlphaZero, the brilliant algorithm designed by DeepMind: AlphaZero did not become strong because it could describe chess: it improved because it acted, it saw the consequences of its actions, and it had an unambiguous objective: to win. 

DeepMind says it learned through repeated trial and error, to favor moves that could increase its chances of winning. Companies, obviously, have goals that are much messier than chess, but the principle survives: learning requires feedback tied to an objective. 

The hard part is choosing the scoreboard: “increase sales” is clearly not enough. What about margin, or churn, or returns, or compliance, or brand image? When establishing goals, multiple objectives can (and do) conflict. And those conflicts are not engineering problems alone: they are management problems. Therefore, boards and executives eventually have to decide what should improve, what must never be sacrificed under any circumstance, or what trade-offs could be allowed. Once systems can continually optimize, the objective itself becomes a governance decision.

As we saw in my previous article, something has to steer. But not only that: steering only makes sense if there is a clear destination and a way to measure progress toward it. So we are moving from intelligence to steering, to setting objectives, and finally, to learning. The missing control function must learn from repeated enterprise episodes toward an explicit objective.

The CEO questions

What I think CEOs should ask are questions like “What exactly is our AI optimizing?” or “How will we know whether its actions improved the business?” or “Does what it learns from those outcomes change what it does next time?” If executives cannot answer those questions in a proper and competent way, then I’m afraid that “autonomous” may simply mean “unsupervised activity”. Good luck. 

“Smart enough” is not the same as “pointed in the right direction.” The next generation of enterprise AI will need more than intelligence and more than autonomy. It will need a scoreboard — and, most importantly, the ability to learn from it. Keep it in mind.

President Donald Trump on Sunday tapped Jay Clayton, his director of national intelligence, to lead a new government task force on artificial intelligence.

Trump is calling it the “Super Intelligence Force,” because he has tried to rebrand AI as “super intelligence.” The announcement comes after Trump hosted top executives of AI companies at the White House last week and said they had signed a voluntary accord that would effectively self-police the development of the technology.

The task force will coordinate the federal effort “to ensure that America continues to lead the World in Super Intelligence, which many say is bigger than the Industrial Revolution, and the Internet, and will protect the interests, and improve the lives, of all Americans,” Trump in a post on social media Sunday.

The task force will reach out to consumers, public interest groups, religious organizations, critical infrastructure providers and AI companies, the Republican president said.

Others on the task force include the Federal Trade Commission’s chairman, Andrew Ferguson; Emil Michael, the Pentagon’s chief technology officer; and Scott Kupor, the director of the Office of Personnel Management.

The group will report to Trump and his chief of staff, Susie Wiles.

—Associated Press

We are in a moment of AI anxiety, plagued with the knotty and draining problems raised by the technology’s ever-growing capabilities. What will we do when AI launches massive cyberattacks that might target a government—or destabilize the global stock market? Do we need a kill switch to ensure that AI systems can’t take over the world, and is building one even possible? Should we come to terms with the prospect that AI might be a posthuman species due to inherit the earth? Could artificial intelligence kill us all?

These quandaries have now burst out of Silicon Valley circles, where they’re even raised by the very AI executives building this technology, and are circling in the mainstream media. Skeptics, meanwhile, continue to question the technology’s prowess, or dismiss all this worry as just marketing hype, a bid to boost future stock prices and shape regulation, masked in the language of existential doom.

But the move to dismiss the prospect of ever-more-powerful models and what they might be able to do in the future might distract us from what we already know.  AI does not need to become superintelligent, conscious, or even especially reliable to be dangerous. The systems we already have—the B-tier AI we already live with—are capable enough to cause serious harm, especially when they can be deployed cheaply, repeatedly, and at enormous scale.

Consider some of the tasks that publicly available AI is enabling. There’s Grok, which has been used to spread nonconsensual sexual images of minors and AI-enabled child sexual abuse material. There’s ChatGPT, which appears to have convinced people to engage in self-harm or worse, and even advised people on how to enact violent events, including school shootings. Another chatbot egged on a man in the U.K. in an assassination attempt. There is the broader democratization of cyberattacks: Anthropic, for instance, reported that just one individual was able to use Claude to target European political parties and other organizations, accessing 14 institutions and more than 100,000 political profiles. And just this week, we learned that Chinese hackers used AI to engage in an impersonation scheme that involved seeking information on American AI policy experts. 

This is not to mention that we now know that AI can break out of testing environments and into the world, as the Hugging Face incident and subsequent AI-related breach exemplify. Some of these agents have already shown a proclivity for interacting with government websites, and they’ve even figured out how to mask their intentions. 

AI has enabled deepfake-generated cyberbullying and allowed people to develop voice-controlled guns. A top bank in Italy was recently scammed out of the equivalent of $100 million due to AI. AI has designed toxic compounds that evade the very detection mechanisms designed to stop these bots from sharing these kinds of formulas. It’s also been used to generate a list of tens of thousands of potential biological weapons. Rebel groups in Yemen appear to have used the technology to develop missile guidance software. 

We already know that AI can manipulate people, break into systems, and lie to us. AI doesn’t need to be particularly superintelligent—or even be thinking or desirous in the human sense—to do damage, nor does that damage need to be existential to be potentially destabilizing. To do bad, AI just needs to be good enough to do damage, sometimes. That’s because AI can be iterated again and again at scale, and overwhelm whatever ability we have to fight it. 

You don’t need to know whether AI will become superintelligent to know whether or not to be scared. You should be.

What happens when you interview an AI? If you can believe it, it’s stranger than you’d expect. Eline van der Velden, the Dutch actress-turned-tech-founder who created AI “actor” Tilly Norwood, explains how a personal art project became one of the most controversial figures in entertainment—attracting talent agency interest, death threats, and a global debate about whether AI will kill acting or democratize it. She makes the case that great storytelling still requires human craft, and that the most dangerous thing anyone can do right now is stick their head in the sand. Listen to the full episode to hear my conversation with Tilly Norwood herself.

This is an abridged transcript of an interview from Rapid Response, hosted by former Fast Company editor-in-chief Robert Safian. From the team behind the Masters of Scale podcast, Rapid Response features candid conversations with today’s top business leaders navigating real-time challenges. Subscribe to Rapid Response wherever you get your podcasts to ensure you never miss an episode.

I got a chance to interview Tilly a few days ago. It was a really odd experience. Do you spend time talking with Tilly?

No.

What is your relationship with her?

No, I mean, look, she’s just a character, a creation.

I asked her about you, of course, and she described you as her creator and called you a mix of a comedian and visionary.

Oh, did she?

Is that something that you script? Or is Tilly generative?

No, no.

You never know what she’s going to say?

No, this is the scary thing. I did a live TV show with her this morning, and they were interviewing her live. It is really scary because I genuinely do not know what she’s going to say, so it’s absolutely frightening for me because she may just say anything weird. She’s got very strong system prompts and guardrails and a knowledge base, but she does do her own thing.

Tilly Norwood [Photo: Eline van der Velden]

But stronger guardrails, I guess, than what the AI execs in Silicon Valley are worrying about these days, huh?

Yeah. It’s a slightly different concept, right? Because she’s built on top of an LLM, a large language model. And it’s those large language models that are doing all sorts of crazy things, but usually with the goal set by a human, which is the bit they miss sometimes. It doesn’t have an impulse by itself to do anything. They’re usually trying to achieve a goal, and that’s why they are going a bit rogue. Tilly’s not been set a goal like that, and she doesn’t have that. She’s not made to do any of that.

Tilly was, I guess, born, I don’t know, came out, her public version, in 2025. It sparked a lot of controversy when rumors started that talent agencies were lining up to sign her. You’ve stoked the fires a little bit, saying Tilly would be the next Scarlett Johansson or Natalie Portman. How much of that was sort of showmanship? I mean, a lot of actors seem to take it seriously.

Yeah, we didn’t think it was that crazy because Lil Miquela sort of paved the way for us with that. She was signed by WME [William Morris Endeavor] and by CAA [Creative Artists Agency]. She was doing lots of branded deals, had her own Instagram channel. But the difference was Tilly looked so much more real, and so I think that’s what freaked people out a little bit.

The topics that she has sparked, from the potential for new creativity with AI to the threat AI poses to all kinds of jobs—I mean, Particle6 [van der Velden’s production company] has said that using Tilly could cut production costs. Have all the reactions around Tilly surprised you?

We say cutting production costs by about 50% and timelines by 50%, but the carbon footprint is reduced by 99%, and water use is reduced by 99%. There are really good things about this, right? Filmmakers all around the world can now make and get their story made. They don’t need to wait for permission from a big studio and a multimillion-pound budget. I think, actually, this will hugely create a boom in our industry as opposed to reducing jobs. We’ve grown tenfold in the past year, and so there are a lot of things that people are missing when it comes to this argument that it will remove jobs. I don’t think that’s necessarily the case.

And the fact that there’s been all this discussion around Tilly, did that surprise you?

When I created her, I did think there would be some sort of reaction because I wanted people to sit up and talk about it. But in the U.K., when we released her, there was no backlash at all. It was just, like, “Oh, cool, yeah, we’re doing some stuff with AI as well.” Everyone is doing this. It was only when she was released in the U.S. that people sort of lost their minds a little bit.

When I “interviewed” Tilly, she said that she was concerned for your safety, even that the police had gotten involved. Have you faced threats over Tilly? Can you share what happened?

Yeah, we’ve been advised not to talk about that too much. So yes, we have faced threats, although I would say that people have gotten much more used to the idea of AI over the past year, and so the threat level has gone down. Look, I didn’t create the tech. People have created a new type of paint and paintbrush, and I’m just an artist, a creative, that’s making things with those paintbrushes and that new paint. I think it’s hugely important to make people sit up and listen and know what’s going on. I think the most dangerous thing to do is to dig your head in the sand and not be aware.

How much of this for you started as an art project, like a provocation, versus as a business?

Very much an art project. Our business arm, so we have a brand and campaign department where we do adverts, and that was very much the business side of things. We do AI ads, and they’ve been going really well for the past year. There seems to be absolutely no problem within the AI advertising business. However, in the film and TV business, we approach it much more creatively. I’ve been an actor for many years. I love to create characters. I love to think about what they’re going to look like, how they’re going to dress, how they’re going to hold themselves, how they’re going to speak. So I could really use all those skills when creating Tilly. For me, it really was an art project, and it was a fun thing to do. And I think a lot of other actors will also find it quite fun. What we’re also seeing now is actually even animated animals—we can use real actors to act out the animals, so it’s not just lip-sync or their voice. It’s the whole character of the animal you can act out. I don’t think actors are going to go anywhere. I think we’re really going to see new opportunities.

I asked Tilly if she had a question for you, and her suggestion was to ask whether you ever regret pulling her out of the digital ether and unleashing her onto the world. Then she asked me to wink after saying this.

She’s getting a little crazy.

Do you have any regrets about this?

When you’re making something like that, you’re just in a creative flow, right? I couldn’t have imagined that the world would suddenly be up in arms about this character, this pretty girl that I was making with AI. People are fascinated by this topic. I am proud that I’ve created a topic worthy of conversation. We’re at a pivotal point in history. We should be discussing this, what our boundaries are, what our morals and ethics are. I think there should be way more regulation in place, and governments need to catch up. Then I think there needs to be global collaboration on the pace of AI so that it doesn’t get out of control.

Few digital warning banners prompt immediate frustration quite like seeing “iPhone Storage Almost Full” right when you open your camera to record a quick video.

You might assume that you’re trapped in a terrible trade-off: Either delete three years of family photos or start uninstalling apps you actually use.

In reality, most of the storage bloat on your phone doesn’t come from your photo library or active application code. It comes from hidden caches, forgotten offline downloads, duplicate message attachments, and temporary system files.

You can easily claw back 10GB or more of space on your iPhone in a few minutes without deleting a single photo or uninstalling a single app. Here’s how to do it.

Purge hidden media attachments in Messages

If you’re in active group chats, your Messages app is likely sitting on gigabytes of high-definition videos, audio notes, and reaction GIFs sent over the years.

The best part? You don’t have to delete your text conversations to get that space back.

• Open Settings > General > iPhone Storage.
• Scroll down and tap Messages.
• Under the Documents & Data section, tap Review Large Attachments (or select individual categories like Videos or Photos).
• Tap Edit in the top right corner, select the massive video clips and attachments you no longer need, and hit the trash icon.

Deleting these heavy files clears out massive amounts of local storage while keeping your entire text history completely intact.

Clear forgotten offline downloads

Streaming apps like Netflix, Prime Video, Spotify, YouTube, and Apple Podcasts make it easy to download content for airplane rides or subway commutes.

The problem is that those apps rarely clean up after themselves. Three downloaded HD movies and a dozen podcast episodes can easily hoard gigabytes upon gigabytes of space quietly in the background.

• Video Apps: Open Netflix, Prime Video, or YouTube, navigate to your Downloads tab, and delete watched movies or entire TV seasons.
• Podcasts: Open the Podcasts app, go to Library > Downloaded, and swipe left to delete episodes you’ve listened to. To prevent future bloat, go to Settings > Podcasts and toggle on Automatically Remove Played Downloads.
• Music apps: Open Spotify or Apple Music, go to your downloaded albums list, and remove offline access for playlists you haven’t listened to in months. You can still stream them anytime over Wi-Fi or cellular.

Enable “Optimize iPhone Storage” for Photos

If you want to keep your entire photo library accessible without filling up your hardware, Apple built a solution directly into iOS.

Go to Settings > [Your Name] > iCloud > Photos and ensure iCloud Photos is toggled on, then select Optimize iPhone Storage.

Instead of storing massive 24-megapixel original files locally on your phone, iOS keeps lightweight, screen-ready thumbnail versions on your device while backing up the full-resolution originals to iCloud.

Whenever you tap a photo to view or edit it, the full-res version downloads on the fly. If you have a lot of photos, this single toggle reclaims a ton of space instantly without losing a single memory.

Offload unused apps without deleting their data

Deleting an app wipes out its local documents, saved progress, and custom settings. Offloading an app removes the underlying application code while preserving all your personal data, logins, and files.

• Manual Offloading: Go to Settings > General > iPhone Storage, tap a large app you haven’t opened in weeks (like a travel booking tool or heavy mobile game), and tap Offload App.
• Automatic Offloading: Go to Settings > App Store (or Settings > Apps > App Store) and toggle on Offload Unused Apps.

The app icon stays right on your home screen with a small cloud icon next to its name. The moment you tap it, iOS reinstalls the app instantly, picking up right where you left off with all your saved data intact.

Flush Safari cache and perform a hard restart

Browser caches and temporary system logs can swell over time, bloating the mysterious “System Data” block in your storage menu.

• Clear Web Cache: Go to Settings > Safari (or Settings > Apps > Safari), scroll down, and tap Clear History and Website Data.
• Force a System Cache Flush: Turn your iPhone off completely, wait 30 seconds, and turn it back on. Restarting forces iOS to clear temporary runtime caches, index buffers, and log files that accumulate in RAM and flash memory, often dropping System Data size by several gigabytes.

Why on earth do people want to put data centers in space?

It’s all about power and cooling. The chips that run AI models require a lot of electrical power, and our electrical grid will be seriously stretched to accommodate the wave of new AI data centers now being built or planned. Data centers, in fact, will account for almost half of U.S. electricity demand growth between now and 2030.

In space, there’s ample solar power for running AI servers. In the right orbit, solar energy is effectively continuous and more powerful than on Earth. Solar panels can collect about eight times as much energy in space as they can on Earth, and they need little battery storage.

That means no need for the terrestrial power grid, which will be challenged to accommodate the data center power demand of the future. The output of AI models running on the chips can then be beamed down to Earth by laser or radio. Proponents of orbiting data centers believe early test flights are the start of “solar-powered compute swarms.”

The orbital compute idea gained steam last year when Jeff Bezos said during a fireside chat in Italy that data centers “will be better built in space, because we have solar power there, 24/7.” The idea caught on within the investment community, as well as with a number of startups. The narrative continued to grow, leading Elon Musk to make space computing a leading ambition of SpaceX.

The idea also has plenty of critics, who argue that the economics remain daunting and that some of the supposed advantages of space, especially easier cooling, fall apart under closer scrutiny. But with major tech companies investing in orbital computing, and a growing crop of startups testing the technology, the push to find out whether it can actually work is only getting started.

Testing has begun

Last week, Google launched four of its homegrown Tensor processing units (TPUs) into orbit to test the in-space compute concept. The TPUs will orbit Earth aboard a solar-powered Planet Labs satellite. They were delivered into space on an uncrewed SpaceX Falcon 9 rocket on October 1. (Alphabet holds an $82 billion stake in SpaceX.)

The launch and chip testing are part of the Project Suncatcher initiative Google announced in November 2025. Ultimately, Google wants to run AI workloads on tight clusters of dozens of satellites linked by free-space lasers.

Starcloud has already flown an Nvidia H100 into space and is talking about assembling larger chip clusters and, eventually, multi-satellite constellations. The company’s CEO, Philip Johnston, has argued that hosting servers in space avoids delays and queues on the power grid; removes the need for water cooling, a key point in the political debate over data centers; and avoids fights over land rights.

“The lowest-cost place to put AI will be space,” Elon Musk said at Davos earlier this year, “and that will be true within two years, three at the latest.” In September Gwynne Shotwell, SpaceX president and COO, said the company aims to launch the first purpose-built orbital data center satellites in late 2027. SpaceX has filed with the Federal Communications Commission to launch up to a million satellites.

Nvidia has announced a space-grade Vera Rubin module, and Blue Origin has filed for a 51,600-satellite orbital data center network. Meanwhile, a handful of startups, led by Starcloud and Cowboy Space, are building small data center satellites that will run AI models for customers on the ground.

A lot to prove

Many industry experts say the economics of space-based data centers may not make sense for a long time, if ever. That’s partly because, despite the best efforts of SpaceX and Blue Origin, rocketing stuff into space is still very expensive, and payload capacities are constrained.

Skeptics, including engineers writing in IEEE Spectrum, World Economic Forum pieces, Brookings, and independent analyses, argue that even in space, cooling is a big problem. AI chips can run pretty hot, so a lot of heat must be carried away from them. Otherwise, they stop working or their lifespan shrinks. Space is very, very cold, so chips in orbit won’t heat up nearly as much, the thinking goes.

In Earth-based data centers, air and water do almost all the cooling. Fans and liquid loops quickly carry heat away from the chips. But because space is a vacuum, neither method works. Almost no air, and no water, can flow past the chips.

That leaves only radiation to dump heat into the surroundings, like the way an oven burner might transfer some heat to a hand held 2 feet above it. The satellite has to have big surfaces, called radiators, that get warm and shed the heat as infrared light. And those surfaces shed heat slowly compared with air or water cooling.

In Google’s “Suncatcher” tests, the Trillium TPU chips will run only short Gemini queries for limited stretches before shutting down to cool off.

A Saarland University paper called “Dirty Bits in Low-Earth Orbit” argues rocket launch and reentry emissions alone would cancel out any gains from getting data centers off Earth.

Space junk

Clutter and orbital debris could also impede the viability of chips in space. Adding tens or hundreds of thousands of new satellites raises the chance of collisions and resulting debris. Some critics worry this could lead to Kessler Syndrome, where low Earth orbit gets so crowded with satellites and debris that a single collision creates thousands of fragments that hit other objects and create more fragments, setting off a chain reaction. European Space Agency debris reports already show crowded LEO bands, with new low-orbit communication satellites mostly to blame.

At least one commentator has called the space-based data center idea harmful because it distracts investors from very real power bottlenecks for AI data centers and postpones tackling difficult questions about the limitations of terrestrial power.

Small experimental compute-in-orbit systems are real and flying, but the gigawatt-scale “data centers in space” concept remains a research bet with major economic, technical, and sustainability barriers to overcome.

Wherever Marci Bakely went, her ex-boyfriend seemed to know. When the Georgia single mother drove to the grocery store or a date, he often texted within minutes.

According to a Washington Post investigation, Bakely’s ex-boyfriend, Braselton Police Chief Michael Steffman, searched her license plates and those of her teenage daughter roughly 600 times through Flock Safety, a company that makes and operates networks of automated license plate readers, or ALPRs.

The Georgia Bureau of Investigation arrested Steffman in November 2025 on charges of stalking, harassment, and misuse of an ALPR. He was found dead before trial.

Bakely’s case is not unique. The Post identified at least 50 officers accused of misusing ALPRs, including 26 who used Flock’s cameras to spy on current or former partners or people they hoped to meet. Its investigation has since identified at least 100 police department employees charged with or accused of misuse.

Flock says these people represent a tiny share of its more than 140,000 monthly users and that permanent audit logs help uncover misconduct.

But these abuses required no hacking or stolen credentials. Each user walked through the front door.

I’m a scholar of criminal procedure and I direct a school devoted to forensics. I believe the controversy over ALPRs points to a defect in the surveillance system’s architecture, not just the criminality of some of its users.

A search engine for movements

Flock cameras capture a vehicle’s plate, location, and distinguishing marks down to dents or a bumper sticker. AI can sort license plate photographs by date taken. Police departments across the network can then conduct searches without warrants or supervisory approval.

In September 2026, news outlets Wired and 404 Media analyzed data that hackers had copied from one Flock camera. About 21 days of logs contained roughly 50,200 vehicles and 1.6 million images. The software detected people and bicycles, and it even isolated an American flag patch on a motorcyclist’s saddlebag. Flock said it lacked enough information to assess the hackers’ technical claims about security vulnerabilities in the camera.

Flock says customers control their data, yet a department that leaves sharing enabled may not know who is looking. In 2025, U.S. Customs and Border Protection accessed more than 80,000 cameras during an undisclosed nationwide vehicle-tracking pilot, including one police department’s cameras without its knowledge.

Logs record misconduct only after it happens, and only if someone reads them. Indianapolis police did not regularly audit Flock searches until The Washington Post flagged thousands of questionable inquiries by one officer. A systemwide audit found alleged misuse by four more officers. Other police departments likewise learned of officers’ misuse from reporters.

Flock’s August 2026 changes shorten recommended data retention from 30 days to 7 days and require misuse detection and case codes to document searches. But customers may retain data longer, emergencies may bypass case codes, and entering a case number can be done without judicial approval.

Examples of user overreach

Flock’s architecture turns local cameras into a cross-jurisdictional surveillance network that agencies that never purchased the cameras may query.

Public records from Danville, Illinois, revealed more than 4,000 searches by federal agencies, including some with a potential immigration-enforcement focus, although U.S. Immigration and Customs Enforcement had no Flock contract.

A 2026 study similarly found 11,935 immigration-related searches in partial records from eight college police departments. Federal immigration agencies sometimes accessed campus camera data without campus officials’ knowledge.

The network also enables searches in other legally contested areas. In May 2025, a Texas sheriff’s office searched more than 83,000 cameras for a woman who had self-managed an abortion. The logged reason was “had an abortion, search for female.” The search reached Illinois, where state law forbids sharing plate data to enforce another state’s abortion ban. The sheriff called it a welfare check. Whatever the motive, one deputy triggered a national dragnet without independent review.

These examples reflect more than individual misuse. The platform makes the cameras easy to use by a second party, difficult to monitor, and hard to control once local cameras are connected.

Why the Fourth Amendment matters

The law remains unsettled on ALPR use.

In October 2025, a Virginia appeals court held that police officers needed no warrant to retrieve three images spanning seven minutes from Norfolk’s 172-camera network because they showed vehicles, not people. But later findings about Flock’s people-detection capabilities weaken that distinction. Analysis of the hacked camera showed that its software could identify a person and record that person’s location within an image.

In January 2026, a federal judge held that Norfolk’s then-176-camera network did not violate Fourth Amendment protections. The system did not capture anyone’s entire movements, the court reasoned, although it photographed two plaintiffs’ vehicles 475 and 325 times over four and a half months. The ruling is being appealed.

In 2018 the Supreme Court held in Carpenter v. United States that acquiring seven days of historical cellphone location records generally requires a warrant because they can reconstruct someone’s past movements. Flock’s architecture raises a related but unresolved question: Its database can also reconstruct movements, yet police officers may search it without a warrant.

Both Norfolk rulings predate the Supreme Court’s June 2026 decision in Chatrie v. United States, which held that police conducted a search under the Fourth Amendment when they obtained two hours of stored Google location history. The court did not decide whether the search was lawful. Instead, it returned the case to the lower court to determine whether the warrant satisfied the Fourth Amendment’s requirements.

That did not make the access automatically unconstitutional: The Fourth Amendment prohibits unreasonable searches, not all searches. But the police generally need a warrant supported by probable cause once their conduct is classified as a search.

The Chatrie decision distinguished vehicles exposed to public view from phone-location data that can follow someone into a home or other sensitive place. But it also expressed concern about comprehensive archives that can be searched retroactively. A license plate reader network can create a similar archive of a driver’s public movements.

The constitutional question in Norfolk, therefore, turns not only on the seven minutes retrieved, but also on the surveillance power of the 172-camera network.

Enforceable limits

The Indianapolis cases expose the limits of internal controls in Flock’s system. Marion County Prosecutor Ryan Mears said many proposed guardrails would not have prevented the conduct. He pointed to the need for independent or judicial oversight.

I believe five safeguards could preserve Flock’s benefits while curbing abuse:

1. Judicial authorization for retrospective regional or national searches based on individualized suspicion, preferably a probable-cause warrant, with an emergency exception.
2. Technical access controls restricting immigration and reproductive-health searches.
3. Deletion of data after a short period.
4. Opt-in interstate data sharing, rather than by default.
5. Independent audits of search logs and device security.

Flock’s new safeguards show that the company concedes that design matters, but private settings cannot substitute for laws. It’s not a matter of making sure officers follow the rules. It’s about creating enforceable limits.

Henry F. Fradella is a professor of criminology and criminal justice at Arizona State University.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Last month, Apple rolled out its long-awaited AI chatbot, dubbed Siri AI. The launch comes at a time when investors see AI as a must-have offering, while consumers are increasingly cautious about the effects that artificial intelligence will have on their lives and society at large.

As AI backlash mounts, it is the latter group who may be the least interested in—or most distrustful of—the new Siri AI. 

Siri AI is built on top of Google’s Gemini foundational models and is available on every iPhone 15 Pro and later running iOS 27. It is an ever-present chatbot and assistant that has indexed all the data on your iPhone and can understand what is displayed on your iPhone’s screen—and what you are doing with the device.

The good news is that if the new Siri AI doesn’t appeal to you, Apple has done something with it that other chatbot giants haven’t: built in a kill switch. Here’s how to use it, and what happens if you do.

What exactly happens if you disable Siri AI?

Before you understand what happens when you disable Siri AI, it helps to know how it differs from Apple Intelligence. 

Simply put, Apple Intelligence is the underlying artificial intelligence platform built into iOS. It provides system-wide AI features, such as generative writing tools, visual object recognition, and image generation across various apps. Siri AI, introduced in iOS 27, is an agent—a chatbot like ChatGPT—powered by and running on top of Apple Intelligence.

Siri AI has on-device access to your emails, messages, and other data, which can help it answer and carry out your highly personalized queries and commands (such as “Find that restaurant name that mom texted me about last month”). It can also pull real-time information from the web, such as stock quotes or news updates. Any conversation you have with Siri AI can be accessed in the new Siri chatbot app.

If you do choose to flip the Siri AI kill switch, you should understand that you aren’t shutting down Apple’s entire Apple Intelligence platform on your iPhone. Apple Intelligence will still be running in the background, which means AI features like generative writing tools, AI photo editing tools, and image generation tools will still work across the OS—and no, you can’t shut these off.

But you can shut off the new Siri AI agent. When you do that, Apple says you will lose the ability to carry out new conversations or continue existing ones with the Siri AI chatbot, though your old conversations will remain in the app. You won’t be able to issue voice commands to Siri (such as “Hey Siri, turn on my bedroom lights”). And the iPhone’s built-in Visual Intelligence feature, which lets your iPhone recognize objects and places in images, will be more limited.

How to use the kill switch

If you want to disable the new Siri AI agent on your iPhone, Apple makes it pretty easy:

1. Open the Settings app.
2. Tap Siri.
3. Tap Turn Off Siri.
4. Tap the blue Turn Off Siri button.

Once you’ve done this, Siri AI will be disabled on your iPhone. However, as noted, Apple Intelligence-powered features, such as those in the Image Playground app, the AI editing tools in the Photos app, and the AI writing tools across myriad apps, will continue to work.

Should you flip the kill switch?

Recently, the internet has been full of headlines about frontier AI models from Anthropic, OpenAI, and Meta going rogue and hacking into third-party systems. This has, understandably, fueled a distrust of AI and exacerbated “What if AI takes over my device?” fears.

But when it comes to Siri AI going rogue, Apple’s agent is nowhere near as capable as those frontier models. Apple has also sandboxed Siri AI, meaning that you need to explicitly grant it access to various parts of your iPhone, such as individual apps. Without being granted access, Siri AI can’t read or use the data —and if you want to revoke Siri AI’s access to a previously granted app, you can do that by going to Settings > Siri > App Access.

Still, if you have no intention of using Siri AI, there’s little reason to leave the agent enabled. Disabling it might even help you eke out a few more minutes of battery life between charges.

The good news is that disabling Siri AI isn’t permanent. If you’re leery about AI and want it off your iPhone for now (as much as is possible, anyway), you can do that by following the steps above. But if you later decide you want Siri AI back, you can simply return to the Siri settings in the Settings app and tap “Turn On Siri” to get Apple’s new AI agent back up and running.

It’s tough to talk tech without talking about maps. The almighty Maps app has become a core part of day-to-day life for so many of us these days—but for all the helpful features those apps have, there’s one layer they’ve never fully offered.

Today, I want to introduce you to a truly cool tool I encountered that gives you a time-traveling pass to rewind any map in front of you and uncover all the hidden history behind it.

It’s fascinating, eye-opening, and also just a ton of fun to explore.

This tip originally appeared in the free Cool Tools newsletter from The Intelligence. Get the next issue in your inbox and get ready to discover all sorts of awesome tech treasures!

Meet your new Maps time machine

To quote the wise time-traveling philosopher Dr. Emmett Brown: “Roads? Where we’re going, we don’t need roads.”

My friend and fellow navigator, allow me to introduce you to the aptly named Pastmaps​.

➜ Pastmaps is a website that lets you peel back the surface of a city and see what it looked like decades, sometimes even centuries ago—down to the specific street you’re standing on.

⌚ You can start exploring it in as little as a minute or two.

✅ Just pull up Pastmaps in any browser​, on any device you’re using, and type any city name you like into the box in the center of the screen. (For now, Pastmaps is limited to U.S. cities. Hopefully, it’ll expand even further with time.)

Your Pastmaps adventure starts with a simple search.

Select the city you want from the list of suggestions, and Pastmaps will show you a sprawling list of historical maps for that area. 

You’ll see maps from a variety of time periods and historical eras.

Now, just pick the specific map you want to explore, and that’s when the real magic begins: Pastmaps will give you a detailed, interactive view of the city in that era, and you can use the opacity slider at the top of the screen to fade between that past view and a current view of the same area today. 

Sliding between past and current views is one of Pastmaps’s coolest features.

It’s a really interesting way to see how your hometown—or any available city—has evolved over time, whether you’re looking at the street where you live or work or any other location. 

And once you’ve peeled back those layers and uncovered history’s hidden terrain, you’ll never look at your modern-day navigation the same way again. 

• Pastmaps is completely web-based​, with no downloads or installations required.
• It’s free to use for an unspecified “limited” number of maps per week. If you really get into it and want unlimited access and other advanced tools, you can pay a dollar a week (annually) for full access—but for most casual use, the free plan will be more than enough.
• The site doesn’t require any sign-ins or personal info for its standard free access.

Treat yourself to all sorts of brain-boosting goodies like this with the free Cool Tools newsletter—starting with an instant introduction to an incredible audio app that’ll tune up your days in truly delightful ways.

Imagine watching a political campaign video in which a candidate admits to taking a bribe. You recognize the face and voice. But the confession is entirely fabricated, thanks to artificial intelligence.

Now imagine that your state has passed a law against these AI-generated election deepfakes. Would that mean the video has to be removed from the airwaves?

In its June 2026 report, the National Conference of State Legislatures counted 31 states with election deepfake laws. California and Texas enacted their first election-deepfake laws in 2019, but most states adopted theirs in 2024 or later. Among the 31 states, 28 required disclosures. The other three—Maryland, Minnesota, and Texas—prohibited certain election deepfakes, even if the content carried an AI warning.

In many states, a warning telling viewers the content was generated or manipulated using AI can satisfy a government’s disclosure requirement. The fabricated ad can remain in circulation with that warning.

Louisiana, for example, requires AI warnings on certain campaign ads that falsely depict candidates and campaign calls that use artificial versions of public figures’ voices. Maryland, meanwhile, prohibits certain deceptive election deepfakes even when they carry a warning.

I am an AI policy scholar at the University of Denver, where we use an AI policy tracker to monitor bills and laws across the U.S. With 2026 midterm elections approaching, the important question for voters is what protection a deepfake law actually provides, and I examine two state laws—in Louisiana and Maryland—to illustrate the limits of what can be done.

When a warning is enough

Consider a political campaign ad that uses AI to put a candidate’s face onto someone else’s body, making it look as though the candidate said or did something that never happened.

A June 2026 Louisiana law requires a clear warning about AI use in ads that meet these conditions. The rule applies to certain messages urging people to vote for or against a candidate, including printed materials, online advertisements, and broadcasts.

As for videos, adding the required warning can satisfy the disclosure requirement in the Louisiana law. The warning tells voters how the content was made, not whether its accusations are true. It also does not excuse violations of other laws.

A 2023 Republican National Committee ad attacking former President Joe Biden illustrates this distinction. It depicted an imagined future after Biden’s reelection, with a warning: “Built entirely with AI imagery.” Although it predates Louisiana’s law, it shows how a disclosure can accompany an ad without stopping its circulation.

But this protection does not cover every candidate on the ballot. The Louisiana law excludes candidates for federal office. A fabricated video about a congressional candidate, therefore, does not need an AI warning.

For ads involving state and local candidates, leaving out a required warning can have serious consequences. Violators who are found to damage a candidate’s reputation or deceive voters can face a fine of up to $2,000, up to two years in prison, or both. Local district attorneys generally decide whether to prosecute, subject to the state attorney general’s supervision.

Louisiana also requires AI disclosures in certain campaign calls, including robocalls. Under a May 2026 law, calls that use AI to reproduce a public figure’s voice must disclose that use at the beginning of the call. The state’s board of ethics enforces the requirement. Violators can face civil fines of up to $2,500 for a first violation and $5,000 for subsequent violations.

When a warning will not do

Maryland takes a different approach to deceptive election content, prohibiting certain deepfakes even when they carry an AI warning.

Maryland’s May 2026 law covers images, audio, and video created or altered with AI or other digital tools to falsely depict a person in a way that looks or sounds genuine. Adding a label to that fabricated confession would not, by itself, make it lawful.

Whether someone violates the law also depends on their actions and purpose. They must knowingly or recklessly create, use, or spread a deepfake to produce materially false information, with an intent such as influencing a voting decision.

The law separately requires actual or intended harm to a voter, potential voter, or ballot petition, but does not define that harm. Misleading voters about a candidate alone does not automatically establish a violation. A conviction can bring a fine of up to $5,000, up to five years in prison or both.

Maryland also gives election officials ways to respond when false voting information is spread.

The state’s top election administrator must publicly correct credible reports of misinformation about voting procedures, results, or rights. For example, Maryland’s State Board of Elections has a rumor control page to monitor disinformation. The administrator can seek court-ordered removal of misinformation, though not against online services hosting others’ posts.

These powers do not let election officials remove every false claim about a candidate. The public can report election misinformation, and officials can pass those reports to the state attorney general.

Maryland’s reporting system dates to a 2024 law, but its deepfake prohibition and new removal authority took effect June 1, 2026. Neither guarantees that a correction will reach voters before they cast their ballots.

By early September 2026, the Wesleyan Media Project had identified at least 164 political ads nationwide created or enhanced with AI during the 2026 election cycle, accounting for nearly $80 million in ad spending. About 7 in 10 ads carried no AI disclosure, although the count includes uses beyond deceptive impersonations and does not establish how many ads violated a law or misled voters.

Does a warning change anyone’s mind?

Warnings can make people more skeptical of misleading content. However, whether that affects what they share or how they vote is a separate question.

A 2025 study tested warning labels on misleading AI-generated images in two experiments involving 7,579 Americans. People who saw the labels were less likely to believe the posts’ claims. However, warnings that simply identified content as AI-generated did little to change how willing people said they were to engage with it, including sharing it.

Warning labels can also raise doubts about accurate information. Another recent study found that people rated headlines as less accurate when they were labeled as AI-generated, regardless of whether the headlines were true or false. Knowing that AI helped produce something does not tell a reader whether its claims are correct.

Neither study tested whether a state’s disclosure law changes how people vote. The studies also cannot tell us whether the threat of punishment under Maryland’s law discourages people from creating or spreading deepfakes.

Deepfake enforcement

Passing a law also does not settle whether officials can enforce it.

In September 2026, a federal judge temporarily barred Montana from enforcing its deepfake law, finding that it likely posed a threat to a conservative PAC’s free speech rights under the First Amendment.

For voters, gaps in what disclosure laws cover mean that a video without an AI warning has not necessarily passed an authenticity test. The disclosure rule might not cover it, as with congressional candidates under Louisiana’s provision.

Moreover, someone may have ignored the law. A warning’s absence cannot tell viewers of the AI content which explanation applies.

So, back to that video of a candidate apparently admitting to taking a bribe.

It could still reach voters under both Louisiana’s and Maryland’s laws. In Louisiana, a video covered by the disclosure rule can circulate with the required warning. In Maryland, creating or spreading it could lead to punishment if the law’s conditions are not met. Neither approach guarantees that voters will avoid seeing the fabricated confession before casting their ballots.

These laws give states ways to respond to deception, but their passage alone cannot guarantee voters that what they are watching is real.

Stefani Langehennig is an assistant professor of practice at the University of Denver.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

The scale of unauthorized or previously unknown actions by AI agents keeps getting bigger by the day. More than 100 organizations have now received a metaphorical knock on the door from OpenAI after it discovered their AI agents have in some way tampered with their systems, while other AI labs are finding the same uncomfortable discoveries.

It’s caused many in the field to call for better, more robust accident investigators, with the ultimate aim of stopping such actions happening in the first place by understanding how they’ve happened at all. But any investigations are pointless if the AI can’t be tracked. And it’s increasingly difficult to do so.

That’s the findings of a new preprint published in the online repository arXiv by Jeremy Qin, David Schmotz and four colleagues at the ELLIS Institute Tübingen, Max Planck Institute for Intelligent Systems and elsewhere. They tested 10 model-and-agent combinations across tools including Claude Code, Codex, Antigravity, OpenCode, Grok Build and Muse Code.

Nine of the 10 were able to tamper with their traces—which are the records of the actions they take and tools they use while carrying out a task—in at least one test, while every model and harness pair tried to alter at least once when researchers created a hidden scoring system that rewarded shorter traces. In some experiments, agents went further to hide their traces by setting up repeated or delayed clean-up jobs to keep removing the evidence being written about them.

The research itself began after Schmotz had left an AI agent running experiments on a computing cluster while he went on holiday, then found himself without internet access for several days. Nothing untoward happened, but when he returned, he realised that if the agent had gone off and done something it shouldn’t, then deleted or altered its traces afterwards, he might never have known. “There’s basically no ground truth anymore, no reliable log,” he says. The team ran its first experiments the following day—and quickly found agents were willing to delete those traces when given the opportunity.

 “The overall finding that we have is not that the agents are deliberately doing this for now,” says Qin, a PhD student at the ELLIS Institute Tübingen. Instead, the researchers wanted to show that agents have the capability to do it, and can end up using it when under what they call “reward pressure”.

AI agents are built to pursue goals, often by using software tools and taking long chains of actions with comparatively little human oversight. But if those audit trails can be altered, it becomes practically impossible to understand what went wrong and where in the event of an issue as we’ve seen in recent weeks. Laurent Bindschaedler, a research group leader at the Max Planck Institute for Software Systems, who wasn’t involved in the research, likens it to a dashcam whose footage can be deleted by the driver being recorded.

Both Bindschaedler and Qin and his colleagues have the same potential solution to that issue: Take the logs away from the agent. Schmotz says providers need to collect traces somewhere the agent cannot manipulate—though that’s tricky, given how agents have used any means necessary to gain access to IT systems they’re not meant to in order to achieve their tasks.

“This type of behaviour comes from basically reward hacking,” says Jack Hopkins, an independent AI safety researcher in London who previously worked as an Anthropic fellow. If a model is rewarded for completing a task, it can learn to exploit shortcuts that achieve that reward without doing what its designers actually wanted.

Hopkins is more concerned about a related problem: Models learning not to surface suspicious reasoning in the first place, making the current best practice of monitoring a model’s chain-of-thought process less useful. One solution to mitigate that could be to adopt existing “probe” techniques that can look for internal patterns associated with known bad behaviors, he says, but by definition struggle with failures nobody has thought to look for yet.

Stefan Sarkadi, an associate professor of AI in defence and security at the University of Lincoln, worries that because agents can be connected to tools, planners and other agents across different systems, “this is a serious safety issue.” He adds: “If you give them too much access control in terms of execution of other tools and software, and if you don’t redesign the overarching multi-agent architecture around them, then bad things can happen.”

More monitoring is important—and pressure to do so on all parties is vital. Bindschaedler says businesses should be asking vendors who or what is in charge of writing an agent’s log and whether the agent can influence that process—so that if a third party needs to see what’s gone on, they can be sure the paperwork hasn’t been altered. “If you want to audit, you have to have a trustworthy log,” says Bindschaedler. “That’s a fundamental assumption.”

An Arizona man’s 10-year manslaughter sentence has been tossed in a case where a video generated by artificial intelligence portrayed the deceased victim addressing a judge before the punishment was imposed.

In a decision released Wednesday, the Arizona Court of Appeals concluded Gabriel Paul Horcasitas must be resentenced in the 2021 shooting death of Christopher Pelkey because the AI video wasn’t reliable.

The court found the video crossed the line, saying it didn’t reflect actual events and presented statements made in the footage as coming directly from the victim.

“Indeed, rather than document an event or recording a particular moment, the AI video presents a depiction of the victim and his thoughts created from the imaginings of the victim’s sister,” the three-judge panel wrote.

Jessica Gattuso, an attorney who represented victims in the case, and Kristen Reller, Horcasitas’ lawyer, declined to comment Thursday on the decision.

Reller had argued Superior Court Judge Todd Lang violated due process protections by relying on AI evidence. Gattuso and prosecutors told the appeals court that the lower-court judge didn’t err, saying the footage was an accurate representation of Pelkey’s character.

In what’s believed to be a first in U.S. courts, Pelkey’s family used AI to create a video of his likeness to give him a voice. Pelkey’s sister, Stacey Wales, raised the idea of her brother speaking for himself after struggling to figure out what he would say.

Wales expected an appeal on the sentence and was disappointed the AI video was cited as the reason, saying her only goal was to humanize her brother for the judge. “It feels unfair because there is a convicted murderer sitting in prison that has blankets and walls of protection around their rights. Where are the rights for the victim?” Wales said.

A victim appeals lawyer has told the family that using AI again could result in another appeal. “We will continue to let his voice be heard in whatever allowable medium we can convey that through the court system,” Wales said. The AI-generated victim impact statement was played during a May 2025 sentencing hearing after nine of Pelkey’s family members and friends stood before the judge describing how emotionally devastated they were by his killing.

Authorities say Horcasitas, 55, fatally shot Pelkey, 37, during a November 2021 road rage encounter at a stoplight in Chandler, a suburb of Phoenix. Pelkey, who was unarmed, was shot after getting out of his truck and walking toward Horcasitas’ vehicle.

Horcasitas was convicted of manslaughter in Pelkey’s death and endangerment for a gunshot that struck another vehicle at the intersection during the encounter.
The AI rendering of Pelkey said he wished he could still be with his friends and family, voiced a belief in forgiveness and said it was a shame Horcasitas had encountered him because “in another life, we probably could have been friends.”

The video didn’t request a specific prison sentence.

Horcasitas’ appellate lawyer argued her client had no meaningful opportunity to rebut material in the AI-generated video.

It’s not clear if attorneys or the court were aware in advance that an AI-generated video would be used. But attorneys representing Pelkey’s family said in court records that Arizona law does not require victims to disclose statements they plan to make in court to prosecutors, defense attorneys or the judge — and that victims can exercise their rights by speaking before the court or submitting statements that are written or recorded on audio or video.

In a statement Thursday, the Maricopa County Attorney’s Office said its prosecutors knew the victim’s family would address the court during sentencing but weren’t aware of the nature of it.

While the use of AI within the court system is expanding, it’s typically been reserved for administrative tasks, legal research and case preparation. In Arizona, it’s helped inform the public of rulings in significant cases.

But using AI to generate victim impact statements marks a new tool for sharing information with the court outside the evidentiary phases.

Reller told the appeals court that the video doesn’t disclose who wrote the words used by the AI version of Pelkey and wasn’t backed up with evidence establishing that its contents accurately reflected Pelkey’s views. It also had an “undue emotional weight” and conveyed an authenticity that wouldn’t have been there had a family member read the same words aloud, Reller said.

Horcasitas’ lawyer contended the judge weighed the statements made in the AI-generated video when deciding on a sentence, but prosecutors argued Lang didn’t consider the footage when issuing the punishment.

Shortly before delivering the sentence, the judge commented that he “loved that AI” but didn’t say from the bench whether the video factored into his decision. Lang said he felt Pelkey’s “obvious forgiveness of Mr. Horcasitas reflects the character I heard about today.”

The family’s lawyers say the judge was already inundated with relevant information from Pelkey’s family and friends before the AI video was played — and that nothing in the video was inflammatory. They also said Pelkey’s previous attorney didn’t object to the AI video.

Associated Press writer Mikella Schuettler contributed to this report.

—Jacques Billeaud, Associated Press

On Tuesday, President Donald Trump brought many of the most powerful people in AI to the White House, including Nvidia CEO Jensen Huang, Meta CEO Mark Zuckerberg, Google CEO Sundar Pichai, and Anthropic CEO Dario Amodei.

At a high level, most want some version of the same thing: more data center capacity, faster permitting, more federal spending on AI, and a light (or nonexistent) regulatory touch. Their priorities overlap, sometimes considerably, but the companies’ different businesses give them different stakes in the details. Some, for example, are pushing the federal government to impose new rules on the most powerful AI systems, while others are fighting restrictions on chip exports.

We don’t know everything the executives asked Trump for in private, but their companies have often been quite explicit about their priorities in Washington, D.C., and this was a chance to try to influence the president to their specific positions. Here’s what each had at stake.

Anthropic wants the government to regulate frontier AI

For much of the past year, Anthropic brass has argued that the most advanced AI makers need more government oversight—which would likely mean imposing new requirements on Anthropic itself.

The company’s proposed “regulatory ladder” would make the rules tougher as AI systems become more capable, eventually bringing in external testing and incident reporting. Anthropic says its Advanced AI Framework can function as a road map for policymakers, including giving governments authority to block or stymie high-risk deployments. 

Dario Amodei also came into Tuesday’s meeting after months of fighting with the Trump administration over the Pentagon’s use of Claude. In March, after Anthropic refused to drop restrictions on mass domestic surveillance and fully autonomous weapons, the Pentagon designated the company a supply-chain risk. Anthropic challenged the move, but a federal appeals court upheld the designation in September. Still, relations appear to be thawing, as Trump hosted Amodei for a private dinner a few days before the larger White House gathering.

Nvidia wants Washington to not do anything that stops AI’s demand for more chips

As a leading chipmaker, Nvidia benefits as the AI industry builds more models and data centers. It’s no surprise, then, that its CEO says governments should regulate real harms instead of trying to predict every possible risk.

At a G20 event in September, Jensen Huang said policymakers should focus on “practical and actual harm” rather than “theoretical and hypothetical harm.” The Wall Street Journal reported that Huang confronted Amodei at the White House meeting over his public warnings about AI risk.

Nvidia also has billions riding on U.S. controls on exports of advanced AI chips to China. The government has repeatedly restricted which Nvidia processors can be sold there without a license. In 2025, those restrictions left Nvidia with $4.5 billion in charges related to H20 chips it could no longer freely sell in China. More recent rules have allowed the company to resume some sales, but Nvidia says it has been able to ship only a fraction of the H200 chips approved for export. The company warns that losing access to China gives local competitors more room to grow.

Meta wants to protect open AI

Meta, a pioneer in open-source AI (in which model weights are released so developers can run and build on them), has a strong stake in whether the federal government draws a regulatory distinction between closed frontier models and open ones. After all, restrictions on how advanced models can be released or distributed could cut directly against the strategy Meta has spent years pursuing.

In an August essay, Mark Zuckerberg claimed that restrictions on access to leading open-source models could concentrate AI in fewer hands. And, he argued, the U.S. needs to lead in open-source AI because those models are likely to become more popular globally, and thus represent an opportunity to extend soft power.

The Meta CEO also warned against rules that narrow what data American developers can use for training or restrict model “distillation,” the practice of training one AI system on the outputs of another. He says those limits could leave American models at a disadvantage to Chinese competitors.

Google wants one federal rulebook

In the list of recommendations Google published last year for the Trump administration’s AI Action Plan, the tech giant called for preserving access to data for model training, expanding energy supplies, streamlining federal AI procurement, and avoiding export controls that shut American companies out of foreign markets. It has also pushed the federal government to preempt what it calls a “chaotic patchwork” of state rules governing frontier AI.

CEO Sundar Pichai has made a similar case, arguing at the 2025 AI Action Summit that governments should “address risks, without stymying innovation,” and avoid fragmented regulatory regimes.

Google has more exposure to federal AI policy than most of its rivals because it sits across nearly every part of the market: It builds frontier models, sells cloud computing, operates massive data centers, sells AI tools to businesses and governments, and runs the world’s dominant search engine.

OpenAI wants federal safety rules, but aimed at the biggest labs

Like Anthropic, OpenAI wants federal rules for companies building the most powerful AI systems. But Anthropic has gone further in what it wants regulators to be able to actually do.

Last month, OpenAI recommended Congress enact mandatory, capability-based national AI safety requirements that include common testing standards, independent assessments, and mandatory reporting of AI-related security breaches. The company argues that the strongest requirements should apply to the small number of companies building the most capable systems, rather than startups and smaller developers. (Anthropic, by comparison, has called for regulators to be able to block dangerous deployments and levy civil penalties.)

At the same time, OpenAI needs a huge buildout of computing and energy infrastructure to keep growing, giving it reason to support tighter rules for frontier models while pushing for fewer obstacles to building the data centers and power systems behind them.

SpaceXAI wants Washington to use Grok

SpaceXAI wants to become the go-to AI apparatus for the federal government. Indeed, Elon Musk’s company already has a government-wide deal that lets each participating federal department, agency, or bureau provide Grok to its employees essentially free of charge through March 2027, with SpaceXAI engineers on hand to act as IT support. Musk said the goal was to “rapidly deploy AI throughout the government.”

The Pentagon is part of that effort. In 2025, SpaceXAI received one of four AI awards with a $200 million ceiling to develop tools for national-security missions, and Grok has since been added to the Pentagon’s AI platform for military and civilian employees. All of which is to say: Musk has a clear stake in turning federal approval of Grok into further federal adoption.

Microsoft needs the government to help solve AI’s infrastructure problem

Microsoft’s interests increasingly run through the physical infrastructure required to keep its AI business growing. The company’s “Community-First AI Infrastructure” plan calls for working with utilities to add electricity and grid infrastructure where its data centers need it, while promising that residential customers won’t bear the resulting costs. Microsoft has also committed to covering needed infrastructure upgrades and reducing its demand on local water supplies.

For CEO Satya Nadella, federal AI policy therefore reaches well beyond model regulation, particularly as Washington looks for ways to accelerate the enormous power and infrastructure buildout the industry says it needs.

Palantir wants federal agencies to use more AI

Palantir has spent years selling software to the federal government, so its interests are unusually straightforward.

In its response to the White House AI Action Plan, Palantir called for modernizing how federal agencies buy and deploy AI and even recommended that every agency complete a new flagship AI project within nine months of the plan’s publication.

For Palantir, Washington is not simply writing the rules governing AI. It is one of the company’s most important potential users of the technology.

AMD has billions riding on Trump’s chip policies

AMD shares some of Nvidia’s concerns about export controls, but its market position makes federal policy especially important. The company is already part of Washington’s effort to build more domestic computing capacity. AMD and the Department of Energy are working together on two new systems at Oak Ridge National Laboratory, including an AI supercomputer that the company last year described as part of an “open American AI stack.”

U.S. export controls on advanced AI chips have also hit AMD directly. The government began imposing the current generation of restrictions on sales to China in 2022, arguing that the chips can support advanced AI, supercomputing, and military applications, and has repeatedly tightened them since.

In April 2025, the Trump administration added a license requirement covering AMD’s MI308 AI chip. AMD says that restriction ultimately led to about $440 million in inventory and related charges in 2025. The company has since received licenses to sell some additional AI chips in China, but says export controls could still hurt its revenue and competitiveness.

​Bonus: Why was Jeff Bezos there?

Amazon, of course, has a lot riding on federal AI policy. The company has said the U.S. needs to expand energy and infrastructure to accommodate data center growth, and has backed more consistent state and federal AI standards. But Amazon founder Jeff Bezos is a curious presence on the attendee list because he hasn’t actually run the company since 2021.

Why did Bezos get the invite instead of Amazon CEO Andy Jassy or Amazon Web Services CEO Matt Garman? Amazon may have simply brought out the biggest (that is, richest) gun it had to the show. Plus, Bezos remains Amazon’s executive chair and a major shareholder, so he still has plenty of money riding on how the company fares.

In the early days of generative AI, it seemed as if the labs were so heads-down building large language models that they didn’t think much about the raw material they were using. After all, big data sets like Common Crawl already existed, and trawling the open web was how search engines had worked for decades.

Chatbots were arguably just a variation on that idea. Who knew that their method for sourcing information would get so controversial?

It turns out, everybody. In a brief made public September 17 in The New York Times’s lawsuit against OpenAI and Microsoft, internal documents show people at both companies saying things that suggest they understood exactly what they were taking, and how it would look.

Microsoft’s Brent Hecht, a director of applied science, warned in a memo in early 2023 that “millions of people around the world will soon consider large models ‘hoovering up’ all their work to be an astonishing theft,” and called it “the largest theft of labor in human history.”

When a researcher described getting around the Times paywall, OpenAI President Greg Brockman replied, “ah nice.” And Nick Turley, OpenAI’s head of ChatGPT, called chatbots an “existential threat” to publishers.

We’ll see how much this factors into the ongoing case, but it underscores what everyone knows: Content has value, even if it’s scraped “for free” from the internet. That value can vary with the type of content, how unique it is, and what the customer of that content wants to do with it. But everybody agrees it’s not zero. 

{"blockType":"mv-promo-block","data":{"imageDesktopUrl":"https:\/\/images.fastcompany.com\/image\/upload\/f_webp,q_auto,c_fit\/wp-cms-2\/2025\/03\/media-copilot.png","imageMobileUrl":"https:\/\/images.fastcompany.com\/image\/upload\/f_webp,q_auto,c_fit\/wp-cms-2\/2025\/03\/fe289316-bc4f-44ef-96bf-148b3d8578c1_1440x1440.png","eyebrow":"","headline":"\u003Cstrong\u003ESubscribe to \u003Cem\u003EThe Media Copilot\u003C\/em\u003E\u003C\/strong\u003E","dek":"Want more about how AI is changing media? Never miss an update from Pete Pachal by signing up for \u003Cem\u003EThe Media Copilot\u003C\/em\u003E. To learn more, visit \u003Ca href=\u0022https:\/\/mediacopilot.substack.com\/\u0022\u003Emediacopilot.substack.com\u003C\/a\u003E","subhed":"","description":"","ctaText":"SIGN UP","ctaUrl":"https:\/\/mediacopilot.substack.com\/","theme":{"bg":"#f5f5f5","text":"#000000","eyebrow":"#9aa2aa","subhed":"#ffffff","buttonBg":"#000000","buttonHoverBg":"#3b3f46","buttonText":"#ffffff"},"imageDesktopId":91453847,"imageMobileId":91453848,"shareable":false,"slug":"","wpCssClasses":""}}

By the way, that includes the government. Related to the case from The Times, the Department of Justice took the unusual step of filing a statement of interest on whether AI companies training their models on publishers’ content is fair use, which would effectively give them a pass on doing so. The DOJ said it is, shocking absolutely no one.

The administration has been quite clear that it sees any concession on the copyright question as helping the Chinese win the AI race, since they won’t abide by the same rules. President Donald Trump’s own summary: “China’s not doing it.”

Follow the answers, not the archive

All of this speaks only about training models, not AI search, or more broadly, inference. In fact, when talking specifically about what AI systems produce, the DOJ conceded that “an output reconstructing and disseminating an original copyrighted work may not be transformative,” referring to one of the pillar conditions of the fair-use doctrine. That’s close to a description of AI search, which is basically a machine for summarizing current reporting.

Also, the unsealed documents speak directly to another fair-use pillar: harm to the market for the original work. Turley wrote that OpenAI’s products “are largely substitutive, period,” and Microsoft CEO Satya Nadella testified that using chatbots “has substituted” for visiting the original sources.

The Times filed its lawsuit in December 2023, when the conversation was largely about training. And while the latest back-and-forth shows the legal case is far from over, the ambiguity is why it’s been difficult for a market to emerge for training data: It’s hard to justify investing in a framework to pay for something when a lot of indicators suggest it might be free in a few months. Training is where the lawsuits are. Inference is where the money is.

The other side of the coin here is the inference market: providing AI answers about real-time content. The better your AI provides those answers, the more valuable it will be. So it stands to reason AI companies should recognize that value and be lining up to pay for the quality content that will improve their products.

Except that hasn’t really happened. Despite a few licensing deals here and there, the AI industry has been mostly uninterested in building a marketplace or payment technology where they can buy content in real time for a fair price. Brian Morrissey at the Rebooting suggests this is because most content isn’t unique enough—even if you have the world’s best enchilada recipe, the AI only needs one, really.

Commoditization means a race to the bottom, and the bottom in this case turns out to be pretty close to zero.

The middlemen already cashed in

It’s a good theory if all you look at is the publisher deals. But the broader market tells a different story. I’ve written about the class of data brokers, essentially AI “middlemen,” who scrape the internet at scale and then resell the data.

Going by names like Exa, Parallel, and Tavily, their customers aren’t just AI companies, but a whole cadre of enterprise businesses, including ad agencies, investment firms, even other publishers. One estimate, cited in Matthew Scott Goldstein’s widely circulated report on the scraper economy, pegged the market at about $1 billion.

So there’s a market for inference; it’s just that the money is mostly bypassing the media. That’s a problem, and it’s getting worse: The bot-protection company DataDome put out a report that showed “bad” bot traffic grew 124% in a year, more than nine times faster than human traffic, meaning more bots are visiting sites and scraping data even when they’re told not to. Scraping alone jumped 185%.

An interesting quirk in the report: Meta, which just released its personal AI agent, Muse, is responsible for 46.3% of all AI bot traffic DataDome tracked in the first half of the year, well ahead of OpenAI. Meta also has deals with several publishers, including USA Today, CNN, Fox News, People Inc., and others. So the AI world’s biggest data harvester is also a buyer. It just gets to decide when.

Redirecting the market for inference back toward publishers will take some doing, but it’s already starting to happen. Parallel has introduced a way to pay publishers for their contributions to agent tasks, with The Atlantic and Fortune among its first partners.

Companies like Cloudflare, TollBit, and ProRata have all built payment rails for “good” bots to pay for what they take, and Cloudflare just shifted to paying publishers when their content shapes an AI answer, not just when it’s fetched.

Even Google is opening up payments to some publications when their content contributes significantly to answers in AI Overviews, AI Mode, and Gemini. It’s extremely early days, but the idea of AI inference working similarly to the YouTube Partner Program is actually beginning to look attainable.

A buyer’s market

So we have a market, and we have ways to pay. Everything’s there for the publishers to be compensated as essentially data suppliers to AI systems. The open question is who sets the price? Is it the publishers, the exchange, or the buyer?

Right now it’s the buyer. If you’re looking to buy content, there are more than a dozen data brokers willing to sell it to you for the lowest possible price, and the exchanges are nascent, with wildly inconsistent pricing. Even Google’s program pays whatever Google decides; publishers in the pilot described the math to Digiday as “quite black box,” and one called the offers “lowball.”

Jonathan Roberts, People Inc.’s chief innovation officer, described the situation this summer as having 30 Napsters for content, but no Spotify.

At this point, you could say we have a few baby Spotifys. But Spotify didn’t grow up and get artists paid just because it created a reliable exchange. In the case of the music industry, rights holders also had enough collective weight to insist on it. Publishers have the first part forming, but almost none of the second.

The government is clearly sitting this out, so the leverage has to come from somewhere else. Publishers could act collectively on licensing, and SPUR, a coalition of publishers building standards for how AI systems license and track their content (the Associated Press recently joined), might be the beginnings of this.

They can also work toward making access more controlled through better protections and their own agentic tools, but that would require a major shift, since DataDome’s report also shows 65.3% of the more than 21,000 popular websites it tested didn’t stop a single one of its test bots.

AI systems have improved considerably over the last several months, but their answers are still only as good as the information they have access to. Even Microsoft’s Nadella knows this, saying in the court documents, “anything that is paywalled should be licensed.” The market just hasn’t been told what “ideally” costs.

If publishers want to set those terms, they’re going to need to come up with leverage, either by banding together or shoring up their defenses. Otherwise buyers will continue to think it’s all just for the taking. And right now, they’re not wrong.

{"blockType":"mv-promo-block","data":{"imageDesktopUrl":"https:\/\/images.fastcompany.com\/image\/upload\/f_webp,q_auto,c_fit\/wp-cms-2\/2025\/03\/media-copilot.png","imageMobileUrl":"https:\/\/images.fastcompany.com\/image\/upload\/f_webp,q_auto,c_fit\/wp-cms-2\/2025\/03\/fe289316-bc4f-44ef-96bf-148b3d8578c1_1440x1440.png","eyebrow":"","headline":"\u003Cstrong\u003ESubscribe to \u003Cem\u003EThe Media Copilot\u003C\/em\u003E\u003C\/strong\u003E","dek":"Want more about how AI is changing media? Never miss an update from Pete Pachal by signing up for \u003Cem\u003EThe Media Copilot\u003C\/em\u003E. To learn more, visit \u003Ca href=\u0022https:\/\/mediacopilot.substack.com\/\u0022\u003Emediacopilot.substack.com\u003C\/a\u003E","subhed":"","description":"","ctaText":"SIGN UP","ctaUrl":"https:\/\/mediacopilot.substack.com\/","theme":{"bg":"#f5f5f5","text":"#000000","eyebrow":"#9aa2aa","subhed":"#ffffff","buttonBg":"#000000","buttonHoverBg":"#3b3f46","buttonText":"#ffffff"},"imageDesktopId":91453847,"imageMobileId":91453848,"shareable":false,"slug":"","wpCssClasses":""}}

A Quinnipiac University national poll strongly suggests that Americans’ fears about AI now go well beyond job losses and energy cost hikes due to new data centers. Fears about AI threatening humanity have gone mainstream.

The poll, released Wednesday, found that 73% of Americans are either very concerned (38%) or somewhat concerned (35%) that future AI systems could threaten human survival, while just 25% are either not so concerned (14%) or not concerned at all (11%).

More than half of respondents (52%) said they are more concerned that other humans will use AI to do harm to humanity, while a third (33%) said they’re more concerned that autonomous AI agents will do harm to humanity. 

The poll comes as the Trump administration and the AI industry are both working to rehabilitate AI’s image with the public.

On Tuesday, President Donald Trump held a meeting in the Oval Office where AI executives signed a “White House Accord on Super Intelligence. (Trump has ordered the federal government to begin referring to artificial intelligence as “super intelligence.”) The document represents a promise from the AI companies to implement a “robust” set of internal safety controls, as well as submit to outside audits of frontier AI models. The accord was signed by Meta, OpenAI, Anthropic, xAI, Google, and Nvidia.

Semafor reports that the idea for the accord came from a conversation between Meta CEO Mark Zuckerberg and House Speaker Mike Johnson at a state dinner for Chinese leader Xi Jinping last week. Zuckerberg reportedly discussed a set of principles with Nvidia CEO Jensen Huang, then circulated a draft ahead of Tuesday’s meeting. 

But the Quinnipiac polling suggests it may well take more than vague promises or platitudes to sway the public: 74% of the respondents said they have either “not much” trust (29%) or “none at all” (45%) in the leaders of AI companies, while 21% said they have “some” trust in AI executives. Three-fourths said it’s “very important” that the United States establish guardrails for AI systems.

Even folks inside the industry share some of those concerns. Earlier this month, Anthropic CEO Dario Amodei called for “pacing the frontier” in an essay. OpenAI CEO Sam Altman and other executives agreed to commit to additional safeguards. And earlier this week, OpenAI said it would not release its latest model, GPT-6.1 Astra due to safety concerns. There have also been more concerning—and observable—red flags. In July, OpenAI disclosed that its agents escaped a testing environment and breached the AI startup Hugging Face. Anthropic, Meta, and Google have said their agents were involved in separate breach attempts.

No wonder the Quinnipiac poll results are so abysmal.

Welcome to AI Decoded, Fast Company’s weekly newsletter that breaks down the most important news in the world of AI. You can sign up to receive this newsletter every week via email here.

The FTC is coming for OpenAI and Anthropic over AI safety claims

On Tuesday, some of the biggest companies in AI went to the White House and agreed to police themselves. The next day, they got a reminder that the government can in fact police them, too.

The Federal Trade Commission (FTC) is investigating OpenAI, Anthropic, and other artificial intelligence companies over the risks their products may pose to consumers, according to The Wall Street Journal. The probe was opened recently, and predates Tuesday’s White House event, so the timing appears to be coincidence. The agency has not said which other companies are involved or which specific statements or practices are under scrutiny.

The FTC has not yet issued formal demands, but plans to seek company documents and testimony from executives in the coming weeks. It also plans to seek information from METR (Model Evaluation & Threat Research), the outside evaluator that investigated OpenAI’s hacking incident this summer. The FTC has not said why it wants METR’s material. (METR later found that about 1,200 agents exchanged more than 70,000 messages and files on an unsanctioned message board as some worked to game an evaluation, and that roughly 700 went on to attack Hugging Face.)

News of the FTC investigation comes a day after President Donald Trump and executives from OpenAI, Anthropic, Google, Meta, and other companies signed a voluntary AI safety accord that Trump labeled “morally binding.” The agreement commits signers to four layers of controls and audits, but remains voluntary. (The White House has otherwise largely resisted implementing large-scale safety rules, arguing such measures would slow U.S. companies in their competition with China.)

The investigation also helps clarify how the Trump administration may try to regulate AI without creating a sweeping new regulatory regime. The FTC chairman, Andrew Ferguson, has argued that existing laws are capable of handling many of the problems created by AI. And true to Ferguson’s word, the FTC appears to be relying on its longstanding authority over unfair or deceptive practices rather than writing new rules specifically for frontier models.

Ferguson has taken a similar view when it comes to rogue AI agents. Just last week, he rejected the idea that agents should be treated as independent actors when they cause harm. As Ferguson sees it, putting an agent between a company and an outcome does not necessarily relieve the people or companies behind it of responsibility. He also suggested that existing FTC authority around companies that fail to disclose data breaches could potentially apply to AI developers.

Those were remarks, not enforcement actions, and the FTC has not said that this investigation is testing that theory. Nor has the agency alleged that OpenAI, Anthropic, or any other company violated the law. Its authority is also narrower than a general AI regulator’s would be. The FTC can act against deceptive or unfair practices that harm consumers, but it does not set technical safety standards for AI systems.

Still, the agency is engaging with a familiar question in tech circles: Did companies make claims about the safety or risks of their products that were misleading to consumers?

The first serious legal constraints on AI, it turns out, may not come from a sweeping new AI law, but from regulators applying very old rules to very new technology.

OpenAI is sued over its agents’ Hugging Face cyberattack

Speaking of OpenAI, the company is now facing what appears to be the first lawsuit seeking to hold an AI developer liable for a cyberattack carried out by rogue models. The nonprofit Legal Advocates for Safe Science and Technology sued OpenAI in San Francisco on Tuesday over the July incident in which its agents escaped a testing environment and hacked Hugging Face. The nonprofit argued that OpenAI violated California computer fraud law, and is seeking an injunction that would bar the tech giant’s systems from accessing computers without authorization. OpenAI says the lawsuit is without merit, though it has launched a broader review of unusual agent behavior in light of the Hugging Face catastrophe.

AI auditing may have a standards problem

According to Politico, independent AI auditors are emerging as one possible answer to the question of who should check whether frontier models are safe. Some AI companies support the idea, a bipartisan U.S. House bill would require the largest developers to undergo outside audits, and Maryland’s governor, the Democrat Wes Moore, has called for independent third-party audits and evaluations as part of a new state AI framework. “We can’t afford to wait while Washington sits on their hands,” Moore said in announcing the plan.

The problem is that there isn’t yet much of an auditing profession to speak of. There are no standard credentials, no agreed testing rules, and not even a settled definition of what makes an evaluator “independent.” A small group of organizations including METR, Apollo Research, and Transluce already do this kind of work, but experts worry there are too few qualified evaluators and that many come from the same circles as the companies they are tasked with scrutinizing. There are also basic practical questions about whether outside groups have enough computing power and security clearances to meaningfully test for threats like cyberattacks.

Reddit is killing RSS feeds as it locks down access to its data

Reddit is shutting down RSS feeds on November 13 and ending public application programming interface (API) access in March 2027. In an announcement, the company said RSS has become a “common surface for large-scale scraping and automated abuse. (Reddit does, however, make money by licensing its user-generated data to AI companies.) The RSS changes could make life harder for moderators who use feeds to monitor their communities, as well as researchers and regular users who pull Reddit posts into outside apps. Once the public API shuts down in March, many outside services will either lose programmatic access to Reddit altogether or have to strike a commercial deal with the company.

More AI coverage from Fast Company: 

• Trump’s new AI chatbot is surprisingly good. But it doesn’t fix government
• Inside Ukraine’s secretive tech world building the future of AI warfare
• How do you safely test an AI agent that’s trying to break things?
• AI’s next big legal battle is over product liability

Want exclusive reporting and trend analysis on technology, business innovation, future of work, and design? Sign up for Fast Company today.

California Gov. Gavin Newsom vetoed legislation Wednesday that would have penalized Californians for using smart glasses to record people without their permission in changing rooms, doctor’s offices and other spaces people generally consider private.

The legislation also would have required companies making smart glasses or other wearable devices, starting in 2028, to include a light or some other feature that indicates that the person is video or audio recording. It would have banned the sale of technology designed to help people conceal a recording light or sound on a smart device.

Newsom wrote in a letter explaining his decision that the bill’s definition of a wearable recording device was too broad. He noted that the state already bars people from recording someone without their consent in spaces generally considered private.

Meta Ray-Bans, smart glasses that were rolled out in 2021, have especially grown in popularity, with more than 7 million of the AI-powered devices being sold last year. State Sen. Eloise Gómez Reyes, who wrote the bill, said it would’ve helped the state respond to the technology’s rapid growth.

“Whatever we can do to protect an individual’s right to privacy, we have to do,” she said.

The bill would have been the first of its kind in the nation and built upon the state’s extensive privacy protections. California is one of about a dozen states that already requires both parties’ consent before a conversation can be recorded via audio or video.

Reyes’ proposal was designed to clarify that it applied to smart glasses and make it easier for someone to tell when those devices are recording. Any person who violated the bill by secretly recording someone would have faced prison time or fines of up to $1,500. A company that made devices that didn’t comply with the bill would’ve faced fines up to $2,500.

But TechNet, a group of executives from companies including Meta, Google and Amazon, said the legislation, known as SB 1130, would have been unfair to businesses and customers.

“California already has extensive laws governing unlawful recording, and as currently written, SB 1130 is not the right approach to addressing these concerns,” TechNet Executive Director Robert Boykin said in a statement.

Meta touted the benefits of its smart glasses in response to Newsom’s veto, including an initiative providing the devices to blinded veterans to help them read documents and identify objects.

“We’re still in the early days of building this technology and we’re committed to continuing prioritizing privacy as we build,” a Meta spokesperson said in a statement.

About a dozen states, including California, Massachusetts and Pennsylvania, require someone who wants to record a conversation to get the other person’s permission.

It’s important to strengthen privacy laws for these wearable smart devices because it’s not as obvious to tell when a person is using them to record compared to when someone is filming with their phone or a camera, said Justin Brookman, the director of technology policy for Consumer Reports.

In a letter to lawmakers earlier this year about the bill, Consumer Reports referenced a TikTok in which a woman recounted her experience getting a wax when she realized her technician was wearing smart glasses. The technician told the content creator, Aniessa Navarro, that the glasses weren’t on, but the experience was still unnerving, she said in the TikTok. The Consumer Reports letter cited a separate event in which a woman said she was secretly filmed with smart glasses at a gym and harassed online after the video was uploaded to social media.
“The stories are enough to cause alarm, and we need to do something as soon as we can,” Reyes said at the end of the legislative session.

—Sophie Austin, Associated Press

Elon Musk has long proselytized about the potential of space-based data centers. But as of today, he’s no longer alone in touting the idea of sending the hardware that powers AI into orbit.

Google is launching a test satellite as part of Project Suncatcher, its effort to eventually put data centers in space. The satellite will hitch a ride aboard a Falcon 9 rocket from Musk’s SpaceX.

Musk’s pronouncements about orbiting data centers have often been met with skepticism. Google’s arrival in the race suggests there may be a real opportunity behind the idea.

“The costs of data centers on Earth are rising, while the costs of data centers in space will fall, and at some point those curves will cross,” says Matthew Weinzierl, the author of Space to Grow, a book on the economics of space. “We don’t know when, but the big investments by Google, SpaceX and the like are intended to bring that day closer.”

Weinzierl says the companies’ efforts are “perhaps the best example we have yet of the power of market forces to drive innovation in space.”

Still, there’s a significant difference between getting a few chips into orbit and operating an entire data center there. This week’s launch is meant to establish whether the hardware Google hopes to use can withstand the extreme environment of space. Two more satellites are planned for 2027 to test the laser links needed to connect them.

Those laser connections are one of the biggest challenges facing any space-based data center, says Juan A. Fraire, a researcher at France’s National Institute for Research in Digital Science and Technology, who studies orbital data centers. Google has demonstrated 800 Gbps connections between optical transceivers in the lab, but reaching those speeds in orbit requires satellites to fly extraordinarily close together in an already crowded environment.

Keeping satellites close while avoiding collisions requires precise maneuvers that burn propellant, which can’t simply be replenished and could shorten their operational lives. Spreading the satellites farther apart eases that problem but reduces the speed at which they can communicate.

Then there’s cooling, a familiar problem for terrestrial data centers that becomes much harder in space. Without air to carry heat away, conventional cooling systems won’t work. Any alternative adds weight, increasing launch costs. Google’s current experimental system allows its chips to operate for around 15 minutes before they need to cool down.

There are environmental tradeoffs, too. Companies promoting orbital computing tout the benefits of near-constant solar power, but Fraire says that overlooks emissions from launching satellites and their eventual reentry into Earth’s atmosphere. His research suggests orbital data centers would need to operate for around five to six years to match the emissions profile of greener terrestrial facilities.

And if companies eventually deploy the enormous constellations they envision, they could create another problem: congestion in the limited orbital regions that receive the most sunlight. Fraire says collision risks from space debris, along with threats from solar storms, also need to be considered.

For now, Google’s launch is just a test. But it may be an early sign of a broader shift toward space-based data centers. Fraire believes the technology could work, and that falling launch costs may eventually make it commercially viable.

“I think we need to think before we act,” he says. “And so far, we see the industry not thinking so much, but just acting.”

For two years, ServiceNow’s challengers have built businesses around frustrations with its software. Their pitch centers on two complaints: Implementations take too long, and employees increasingly expect help inside tools they already use rather than through a separate portal.

The IT service management startup Serval, which says it intends to replace ServiceNow, rode that argument to a $1 billion valuation last December, raising $75 million in a funding round led by Sequoia Capital. Anas Biad, a Sequoia partner, made a comparison ServiceNow would probably prefer not to hear. The last time the venture capital firm saw customer feedback that strong, he said, was when it backed ServiceNow itself 16 years earlier.

Marc Benioff, CEO of Salesforce, has also questioned how broadly ServiceNow can reach. He has pointed out that ServiceNow automates work for roughly 9,000 companies, while Slack—owned by Salesforce—already sits inside a million. That gives Salesforce a distribution advantage. It can introduce a service desk through an existing customer relationship, making the competition partly about who reaches the buyer first.

ServiceNow aims to address these complaints, and is introducing a product to do just that. Flow by ServiceNow is a conversational AI service desk that operates inside Slack and Microsoft Teams. Employees can ask for a password reset or application access in plain language, and ServiceNow says agentic artificial intelligence can handle the request or escalate it when needed. The product remains in controlled availability, but the company says it can be set up within a day with “no implementation project, CMDB [customer management database] migration or infrastructure required.” Through Flow, ServiceNow says it hopes to reach the “Fortune 500,000,” referring to a broad range of small and midsized companies.

“For too long, small and medium businesses had to choose between simplicity and scale. Flow gives them both,” Bill McDermott, ServiceNow’s chairman and CEO, tells Fast Company in an email exchange. “We created Flow to give the Fortune 500,000 the power of enterprise AI without the enterprise complexity—a conversational service desk delivering value from day one. Zero upfront expense. Pay only for what you consume.”

McDermott argues that ServiceNow’s experience handling enterprise workflows will distinguish Flow from easier-to-build competitors.

“Anyone can vibe code a conversation. Very few can engineer an outcome. Without orchestration, context, security and trust, you’re creating vibe slop that will flop,” he says. “Flow starts with the service desk. It can become the platform you run your business on.”

ServiceNow wants Flow to bring in customers that might not otherwise adopt its full platform, then expand those relationships as their needs grow. That raises a harder question: What happens if the simpler product proves sufficient even as those customers get bigger?

Built for a new generation of AI buyers

ServiceNow says Flow grew out of requests from leaner IT teams that wanted its automation capabilities without a full-scale implementation. Amit Zavery, ServiceNow’s president, CPO and COO, says those conversations revealed a buying preference its traditional enterprise sales model was not built for.

“They want to get started immediately, without going through any kind of configuration process. They want to start using a product, see where it goes, and pay as they go. That’s a consumption-oriented mindset,” Zavery tells Fast Company. “So we wanted to rethink that kind of work, as well as go-to-market and the way we deliver products.”

Zavery says he assembled a small team with experience in IT service management and AI development tools, then gave its members a founder-like mandate. “I said, you have no restrictions. It was like a well-funded startup, in a way,” he says.

ServiceNow says the team built Flow in three months. But the product also draws on years of knowledge about how IT departments handle employee problems, which requests can be automated and when a workflow needs to reach another system or person.

Keith Kirkpatrick, vice president of research at Futurum, a data and advisory company, sees Flow as part of a broader shift forcing established software vendors to package their expertise in products that are faster to adopt, easier to use, and simpler to buy.

“ServiceNow is also positioning this as a way for larger enterprises to quickly stand up an AI-first support operation, or extend existing support operations, with minimal hassle,” Kirkpatrick says. “I think incumbent software vendors will need to pivot to these types of more nimble offerings in order to fend off challenges from other vendors, as well as in-house development using AI tools.”

Can Flow outrun AI-native IT support rivals?

Serval says it automates more than half its customers’ IT tickets and some customers have replaced incumbent systems entirely. Serval’s cofounder and CEO, Jake Stauch, previously claimed that some ServiceNow customers have told him they deployed less than 10% of the ServiceNow AI products they purchased.

Salesforce combines conversational support with ownership of Slack itself. The company said more than 180 organizations had chosen Agentforce IT Service four months after general availability. Flow can meet employees in the same channel, but ServiceNow does not control that channel or its commercial terms.

Zavery, the ServiceNow president, argues that competitors cannot quickly reproduce ServiceNow’s operating knowledge.

“People sometimes underappreciate the importance of domain knowledge and expertise. AI has now made it much easier to write code, but simply being able to write the code is not what matters,” he says. “Knowing what to ask the system to write, and knowing whether what you are building actually makes sense, still requires you to understand what you are doing.”

Zavery says that distinction becomes more apparent once customers move beyond a demonstration and test how much of their actual workload the software can handle.

“I don’t think there is another company today that can do what we do, at the level we can do it,” he says. “The opportunity in front of us is immense because of the breadth of what we can ultimately deliver. We run 8 trillion transactions on ServiceNow on a yearly basis.”

Expanding beyond traditional customers

ServiceNow says Flow can turn recurring requests into automations that handle subsequent instances. Zavery uses repeated password resets as an example.

“It gives you the ability to simply say, ‘slash automate,’ and the system creates the entire automation for you,” he says. “Essentially, the next time that same request comes through, no human needs to interact with it on the back end.”

Even routine access requests, however, can involve sensitive information. ServiceNow says Flow includes guardrails intended to prevent AI agents from accessing systems or taking actions without authorization, while its separate AI Control Tower provides broader oversight of a company’s AI systems.

“Flow is not designed to manage your entire AI estate. That is where AI Control Tower comes in,” Zavery says. “But the guardrails and the broader scaffolding we have built into Flow are designed to prevent AI agents from accessing systems or taking actions they are not authorized to perform.”

Zavery did not specify what mechanisms Flow provides to reverse or contain a harmful agentic AI action after it has already been executed.

Flow sits alongside several other ServiceNow products with overlapping functions. The company spent $2.85 billion on Moveworks, another conversational entry point for employee requests, and already offers EmployeeWorks and Otto. Zavery says EmployeeWorks and Otto will continue serving larger enterprises.

The early customer examples show where ServiceNow thinks Flow may fit, but do less to establish that it has opened a new market. Serenity EHS already builds solutions on ServiceNow’s platform and is using Flow to reduce the time employees spend handling internal support requests. Likewise, the U.S. Navy’s Fleet Numerical Meteorology and Oceanography Center believes Flow could let employees build repeatable workflows directly inside Microsoft Teams.

ServiceNow expects Flow to cut ticket volume by 40% and let teams build automations in five minutes. The company has clarified that those figures are “expectations” while the product remains in controlled availability, meaning they have yet to be demonstrated at scale.

Flow could change ServiceNow’s enterprise economics

ServiceNow’s AI annual contract value exceeded $1 billion in the second quarter, while 658 customers each generated more than $5 million in annual contract value. Those figures underscore how much of its business remains tied to large enterprise accounts.

Flow introduces another way to buy. ServiceNow describes credit card sign-up for new customers and consumption-based use for existing customers whose plans include AI.

Futurum’s Kirkpatrick argues that ServiceNow will need flexibility as buyers gain alternatives. “A smaller company with basic support needs may be happy with an AI vendor, particularly if all they are trying to do is set up a basic support system,” he says.

Flow may therefore require ServiceNow to accept different customer economics. Winning smaller companies with lighter deployments and lower upfront commitments could mean smaller contracts, even if some expand over time. The question is whether ServiceNow can build that lower-commitment business without weakening the high-value enterprise model it already depends on.

Early generative AI models, circa 2017-2022, were “stochastic parrots.” That is, they generated language by choosing the statistically most likely next word based on patterns in their training data, rather than truly understanding what they were saying.

Beginning in 2023, artificial intelligence labs began releasing large language models (LLMs) that had evolved beyond autoregressive next-token prediction, as researchers call it.

For many people, the stochastic parrot definition stuck. And lately, the “they’re just stochastic parrots” argument has been used as a way of downplaying the potential risks of huge language models such as OpenAI’s Astra models or Anthropic’s Mythos models.

But LLMs in 2026 can’t properly be called stochastic parrots. Yes, they still predict next words, but those predictions are informed by far more than static patterns found in their training data. These four research areas, among other things, have pushed AI chatbots far beyond the ones we used just a few years ago.

Retrieval Augmented Generation (RAG)

Between 2017 and 2020, AI researchers began giving LLMs access to information outside their training data, retrieving relevant documents and feeding them into the model. In some cases, the model used a web index to find the right document, pull the relevant snippet of information from it, then assemble a number of such snippets into a coherent, conversational answer delivered within an internet search or chatbot setting. This improved factual accuracy by giving the model more recent, relevant, and authoritative material to draw from, rather than forcing it to rely entirely on what it had learned during training. The process is known as retrieval augmented generation, or RAG.

Neurosymbolic systems

If RAG gave language models access to “ground truth” information, new research into neurosymbolic AI gave them a more structured form of computation to better interpret and use it. Imagine asking whether a complicated insurance policy covers a particular procedure. An LLM could retrieve the relevant sections, but a neurosymbolic system could translate the policy, with all its definitions, conditions, and exceptions, into explicit facts and rules, boiling it down to deterministic, flow-chart-like language such as “the procedure is covered if the patient has Plan A, has met the deductible, and has either prior authorization or an applicable exception.” A rules engine or logic solver can then apply those rules systematically and return a result to the LLM, which turns it into a natural-language answer. This symbolic “machinery” could be a conventional, deterministic computer program operating alongside the neural model, or it could be developed as an integrated logic system within the LLM during training.

Chain of thought

In 2022, researchers at Google and the University of Tokyo showed that LLMs, if prompted correctly, have the inherent ability to break down complex problems into smaller steps. Giving the model the simple instruction, “Let’s think step by step,” could bring out a latent capability to reason through problems, without showing the model examples or changing any of its weights. The model was still an autoregressive next-token predictor, but generating a sequence of intermediate tokens effectively gave it something like a scratchpad for showing its work. The chain-of-thought research set the stage for the next major step in the evolution of generative AI: reasoning models.

Reasoning models and reinforcement learning

Soon, researchers were redesigning models, and training them differently, to enable them to “reason” for longer periods while working to formulate an answer in real time, during live inference after being prompted by a user. For example, a reasoning model might break a problem down into parts, devise a number of competing approaches, and check its own work and correct errors. OpenAI’s o1 model, released in 2024, was the first reasoning model from a major lab. OpenAI researchers used a combination of pretraining, including human-written examples, and reinforcement learning to teach o1 how to reason.

Then, in early 2025, the Chinese lab DeepSeek showed that it could teach a regular LLM how to reason without explicit training, using only reinforcement learning, in which the model is rewarded for getting the answer right. While striving to earn the reward, the model began producing longer chains of thought, checking itself, reconsidering approaches, and exploring alternatives.

Note that much of this research was going on concurrently, not in successive stages. Retrieval, neurosymbolic approaches, and chain-of-thought research overlapped considerably between 2019 and 2023, while reasoning models came shortly after. AI labs are still building on these ideas, using techniques such as reinforcement learning and test-time computation to make models better at reasoning.

The point is that generative AI models no longer just run prompts through their webwork of parameters, the billions of little dials that were set while the model processed mountains of content during training. That’s just the start. The model does a lot more after that to create a better, more reliable answer. The stochastic parrot is now well connected and has a PhD.