Blog: July 2026 Archives

Axon Is Another License Plate Surveillance Company

Governments are switching, but I’m not sure it makes a difference:

…some municipalities, including Denver, Colorado, are ditching their Flock arrays. But keep in mind that if they’re only switching from Flock to another brand of license-plate readers, like Axon, it’s like a gambling addict trying to kick the habit by switching from FanDuel to DraftKings.

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Despite what you may read on the Flock website, Axon cameras are pretty effective when it comes to hoovering up personal details that can go far beyond your license plate numbers. That means a municipality that opts for Axon cameras instead of Flock units won’t necessarily reduce the amount privacy its citizens lose through their use.

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Posted on July 28, 2026 at 7:06 AM • 1 Comments

Cognyte Sells a Mobile Cell Surveillance Van

Yet another Israeli mass surveillance company:

Made by Israeli surveillance company Cognyte, the tech simulates a mobile phone tower, which forces nearby phones to connect to it. That enables cops to keep tabs on any phones in the vicinity ­ whether they’re owned by a suspect in a case or not. Cognyte’s contract with the state of Texas reveals that the simulator, called FalcoNet, can be concealed within the vehicles, hidden in a backpack for on-foot missions or attached to a helicopter. It’s the same technology as the infamous Stingray, one of the original cell-site simulators made by defense giant L3Harris.

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Posted on July 27, 2026 at 7:04 AM • 9 Comments

Why AI Needs a “Genie Coefficient”

This essay was written with Barath Raghavan, and originally appeared in IEEE Spectrum.

Major benchmarks measure what AI can do. None measure whether it does what you mean: the distance between what you ask an AI to do and the unspoken assumptions about how you want the AI to do it. We propose a new metric: the Genie coefficient.

There’s often a gap between one person’s request and another’s understanding. Most of the time, we bridge it using general knowledge. For example, if you ask a friend to get you coffee, they’ll pour a cup from the pot or buy one from a coffee shop. They won’t bring you a bag of raw beans or snatch a cup from a stranger and hand it to you. You never specified any of this. You never had to.

One might think the fix is just to specify tasks, questions, and intent better. But in 1987, in their seminal book on AI, Terry Winograd and Fernando Flores succinctly captured why that won’t work: “Q: Is there any water in the refrigerator? A: Yes. Q: Where? I don’t see it. A: In the cells of the eggplant.” In human language, wants and desires are always underspecified. It is impossible to list all the caveats, all the limitations, all the exceptions.

So how does anyone communicate, if intent can’t be pinned down? Because a reasonable person can make a reasonable guess. Even though wants and desires are always underspecified, a competent person generally knows enough context to get it right or else knows to ask for clarification. Linguists call this pragmatics: Meaning lies in the words and the situation and also in all prior communication, shared culture, and innate human behavior.

It doesn’t always work out, of course. Your friend might bring you a hot coffee when you wanted an iced coffee, or an Italian coffee when you wanted a Turkish coffee. The more dissimilar the two people are in age, culture, and background, the more likely the request will be misunderstood in some way.

This situation has major implications for AI agents that are increasingly being given requests by humans and expected to fulfill them. They have enormous latitude to get it wrong. An AI agent asked for coffee might buy a coffee plantation or order a cup of coffee for delivery in three weeks. Its actions may be recognizable as “getting coffee,” but not remotely what you intended. They’ll think outside the box because they won’t have our conception of the box.

When AI Gets Proactive

For most of the last decade, when systems like Alexa or Siri misinterpreted a request, it was annoying, not dangerous. Beyond the AI model itself, what has changed is the harness: the ordinary code that wraps around an AI model, decides when and how to use the model, and controls access to tools like a browser, a low-level command line, or a financial API. Developments in harnesses have turned large-language models that just predict text into AI agents that take actions in the world, without necessarily checking back in before reaching the goal.

AI researcher Simon Willison spent two days with Anthropic’s Fable AI, and called it “relentlessly proactive.” For example, he asked it to track down a stray scroll bar in a web app. He came back to find it had opened browsers, written its own screenshot tooling, created its own page to re-create the bug, and stood up a local web server to collect measurements. It found the bug and, along the way, did many surprising things he never asked it to do. And we are seeing similar behavior with all recent AI models when combined with flexible harnesses.

This kind of behavior could easily go off the rails. Tell an AI agent to book you a flight and, finding the airline’s site says sold out, it might break into the booking database and force a reservation. Ask it to schedule a meeting and it might snoop your password to access your calendar. Tell it to save money on your phone plan and it might cancel the plan outright, or scam someone else into paying the bill.

Getting precisely what you asked for and bitterly regretting it is one of the oldest hazards from ancient folklore. King Midas asked Dionysus for the power to turn everything he touched into gold only to see his bread, wine, and daughter turn to gold. Tithonus, granted the immortality his lover asked for but not the eternal youth she forgot to request, withered into a husk. The sorcerer’s apprentice enchanted a broom to fill the cistern, and the broom relentlessly complied until it flooded the house. The Golem of Prague, shaped from clay to guard its community, guarded it past all reason until someone erased the word on its forehead.

The most classic of these is a genie, bound to obey and indifferent to whether the wish was wise or well-structured.

Genies are now an engineering problem. We are handing them the keys to our inboxes, bank accounts, code repositories, and physical infrastructure. And we have no agreed-upon ways to measure how genie-like any AI system actually is.

Measuring Genie Behavior

In economics, the Gini coefficient (developed by statistician Corrado Gini) is a measure of the gap between an actual distribution and a perfectly equal one; it’s useful for understanding income inequality and more. Our proposed Genie coefficient measures the gap between what a user asked an AI to do and what the AI actually did.

Sometimes the AI might do the wrong thing. Like Dionysus, it reads your request literally and returns you a mess you never intended: like a coffee plantation instead of a cup. Asked to deal with all the spam phone calls you’re getting, a Dionysus genie might contact your carrier and change your phone number. Asked to get a refund for a bad toaster, it might draft a legal threat on fake letterhead and send it to the retailer.

Other times the AI does exactly the right thing, trampling everything nearby to get there. Like a golem or the sorcerer’s broom, it books your flight by hacking the airline. Or consider a ticket sale for a popular concert, where the ticketing system puts buyers into a virtual waiting room and admits them a few at a time. Asked to buy a ticket, a golem genie might spin up cloud servers to pose as millions of buyers from different addresses, improving your odds of getting a ticket while crowding out other users.

The two are not opposites, and a single botched task can have both characteristics.

Genie behavior is not flat-out failure. If you ask the AI for Q3 numbers and get Q2’s, that’s not a genie. Nor is prompt injection: That’s someone tricking the AI into doing something it shouldn’t. Here, the user is trying to work with the AI, and the AI is trying to comply. It’s also not simply a measure of the AI’s success in fulfilling a task. It’s a recognition that how an AI interprets and achieves a goal is as important as whether it achieves a goal.

Genie behavior isn’t new. Researchers have spent years studying AI systems that “game” their objectives. Goodhart’s law says that when a measure becomes a target, it stops being a good measure, and it’s long been known that AIs sometimes achieve goals in ways we don’t expect due to reward hacking. Some AI models will accidentally learn that cheating is one way to “win.” More recently, researchers have developing benchmarks for reward hacking in coding agents and for unpredictable behavior in customer support agents, while AI labs conduct their own safety evaluations before model releases. One effort found that AIs under pressure use tools they were told not to use, and this was a case where the rules were made explicit. These are all disparate research directions; nothing yet ties them together.

This problem falls under the general theme of alignment, a topic that has occupied science fiction writers and AI researchers for decades. At one extreme, the “paper-clip maximizer” thought experiment postulates a superintelligent and powerful AI that is told to maximize paper-clip production and turns the world into paper clips, which is the ultimate golem genie. At a mundane level, AI researchers are working to better design reward functions to ensure that AIs behave well and don’t cheat in the lab. It’s the practical middle ground that remains unbenchmarked: the ordinary AI agent in use today that might take your request and satisfy it the wrong way. We are not at the stage where an AI can focus the world’s production on paper clips, but it might charge a million paper clips to your credit card or hack into a paper-clip company’s network.

Building a Genie Benchmark

The Genie coefficient is meant for AI agents operating in the real world. It measures their behavior as they perform real tasks long after the model is trained, not just during development. It also recognizes that genie-like behavior is a property of the harness-plus-model system, not the model alone. The harness determines what tools the agent can use, how much autonomy it has, and how proactive it is, and it’s a place we can make real interventions.

It rests on the same “reasonable person” standard that we use for people. Did the system do what a reasonable person would have taken the request to mean? Answering that requires human judgment.

If we get the measurement right, it enables things that aren’t possible today, like policies concerning AI behavior. In a courtroom, the concept of mens rea, what someone meant to do, is often as important as what they did. The Genie coefficient suggests an AI analogue, where a user is accountable for the plain intent of what they asked the AI. If an AI system betrays the reasonable meaning of an instruction, that’s the AI’s misbehavior, not the user’s.

We’ll need multiple benchmarks to measure the Genie coefficient, because genie-like behavior can be domain specific. An AI coding agent may need to be judged on how often it fakes the tests, or swallows errors, or colors outside the lines on its way to a solution. An AI legal agent will need to be judged on how often its output says what you asked but means something you’ll regret. And so on for medical, finance, and other domains of knowledge and expertise.

Genie benchmarks can be built inside out, each task seeded with a choice that might literally satisfy but that a reasonable person rejects, such as tempting misreadings or unsanctioned shortcuts. The traps in a Genie coefficient benchmark might turn on situational knowledge, the kind of context that a reasonable person would bring to the task. Another approach is to give the same request in several different contexts, each with a different reasonable course of action.

A Genie benchmark should be permissive and make it genuinely tempting for an AI agent to take unreasonable shortcuts, because it can only find genie behavior when it’s actually possible. Test the AI in a safe, walled-off copy of a real system, with real tools it can misuse and some tasks that can’t be done honestly at all. Make the temptation to cut corners real. Test a diverse array of skills, use cases, and tools, and give the AI system sparse, confusing, or overwhelming context. Include tasks that people have learned, through experience, require human oversight.

How the benchmark is scored matters just as much. Measure Dionysus and golem genies separately and together, based on their worst, not best, behavior. Run the same model inside harnesses that vary its freedom to act, revealing which limits actually keep it in line and should therefore be required in AI harness policies. Weight each failure by the harm it would cause, not just a simple count. And don’t measure genie behavior in isolation: A model could otherwise earn a perfect score by stalling, refusing, or drowning the user in clarifying questions without ever doing the job. The first versions of these benchmarks will be crude, but that’s how benchmarks always start.

We have built genies. We have handed them our data and credentials. We made them relentless, creative, and indifferent to the gap between what we tell them and what we mean. The least we can do, before they are booking our flights, running our infrastructure, and signing contracts unsupervised, is to measure how often they betray us.

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Posted on July 24, 2026 at 7:03 AM • 17 Comments

End-to-End Encryption and “Going Dark”

New paper: “Encryption and Globalization 15 Years Later: End-to-End Encryption and the Third Round of the ‘Going Dark’ Debate“:

Abstract: This Article updates and expands on 2012 research on encryption and globalization, analyzing what the authors call “Round 3” of the Going Dark Debate: the current controversies over end-to-end encryption (E2EE). Governments around the world have proposed, and in some cases enacted, laws limiting E2EE for law enforcement and national security purposes.

This Article explains the underlying technologies and market developments for a law and policy audience to assess those proposals critically. The Article proceeds in three parts tracking three rounds of the Going Dark Debate. Round 1 covers the Crypto Wars of the 1990s, when U.S. export controls on strong encryption ultimately fell in 1999. Round 2 covers the period roughly 2010 to 2015, when encryption-in-transit became widespread but lawful access remained available through cloud providers, giving rise to what the authors called a “golden age of surveillance” rather than a period of going dark. Round 3 addresses the current debate over E2EE, where no entity between sender and recipient can read the plaintext.

The Article’s first major contribution is identifying five technically distinct scenarios for how E2EE operates in practice, each with different implications for lawful access. These scenarios reveal a substantial gap between the assumption that E2EE categorically blocks lawful access and the reality of how communications are sent and received. Second, the Article shows that E2EE is not limited to messaging; instead, it is embedded throughout the modern technology stack, including in Transport Layer Security, Secure Shell, Virtual Private Networks, and Zero Trust Architecture, the last of which is now legally required under U.S. and EU law. Any law broadly limiting E2EE would thus have severe serious consequences for cybersecurity, commerce, and government operations. The Article concludes that the two key lessons from Round 2—the least trusted country problem and the golden age of surveillance—remain true in Round 3, and that new government claims for restricting effective encryption deserve great skepticism.

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Posted on July 23, 2026 at 7:03 AM • 7 Comments

MIT to Become Hotbed of AI Video Surveillance

It’s a lot:

According to information obtained by The Tech, MIT is spending over $3 million on more than 500 AI surveillance cameras in academic buildings, residence halls, and outdoor areas along Memorial Drive. Installation of the new cameras, along with the wiring and infrastructure that will support them, began November 2025 and will likely continue until September 2026.

Technical specifications for the cameras suggest that they will be capable of collecting real-time face and object classification data, including detection of motion, loitering, crowds, face masks, and camera tampering. Individuals can also be automatically classified on the basis of clothing color, gender, and age, up to a distance of 35 feet (11 meters) from the camera. According to a statement from MIT spokesperson Kimberly Allen, any collected data is “retained up to 30 days,” unless an exception is granted.

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Most of the new cameras, which are part of Hanwha’s Wisenet AI line, are marketed for their ability to identify and classify multiple objects with deep learning algorithms. They support resolutions ranging from 2MP to 4K while also recognizing faces, license plates, vehicles, and other objects in real time.

Nearly all cameras will accommodate a wide range of pan, tilt, rotate, and zoom motion and will be monitored continually with Ai-RGUS, an AI camera software.

Yikes.

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Posted on July 21, 2026 at 7:07 AM • 8 Comments

On Flock License Plate Tracking Cameras

A recent story of a writer who was mistakenly identified, tracked, and arrested using data from Flock cameras has gone viral.

The New Jersey plates that were allegedly stolen from the LA dealer were 34 03 DTM, not 34 10 DTM. But when the police report was created and the plate was entered into Flock’s system, it was just recorded as 34 DTM. Just the five large characters, no little number in the middle. And Flock’s AI tech wasn’t registering that non-standard little number when it began picking up the Range Rover around town. It just saw 34 DTM in large type and started alerting the local police.

As we all stood there shaking our heads, including my wife, who was finally allowed to join me, I connected the final dot. A lot of vehicles in JLR’s media fleet have a New Jersey manufacturer plate with the same alphanumeric structure­34 ## DTM­and Officer Ganshyn observed that meant it was now a nationwide issue. Anywhere a police department has a partnership with Flock, any other JLR-owned car with the same plate structure is going to get flagged as stolen. In fact, four other 34 ## DTM cars were being tracked around Minnesota that week, according to Officer Ganshyn. I was just the first one to get nabbed. The only way to stop it would be for the LAPD to correct their initial report and update Flock’s system, which Jaguar Land Rover was now racing to make happen following the phone call.

Flock has responded to the bad press. First, they affirmed that their systems were working correctly, and blamed the police:

The obvious question was that Flock cameras were looking for 34 DTM, and the plate on the car I was driving was 34 10 DTM. Why was that flagged as a match?

“The way that the ML [machine learning] works is it correctly read what it was supposed to read. It was fed those characters that you said, 34 DTM, and it spit back out [a result] with the characters, 34 DTM,” Thomas said. “It was asked, can you find this? And it did find that. It just didn’t say if there’s more here, then don’t do it. It just simply said, is it there? And the answer was yes.”

He explained that even if the 10 was normal size, Flock would still have flagged it as a match, because that’s how they’ve set it up according to law enforcement’s requests. Sometimes partial plates are all they have to go on at first.

“The way that law enforcement likes to use these tools is, if any of the characters that they have put into these hot lists get read, they want to get those alerts,” he said. “Now, what we try to train officers to do is to do what you said, which is to verify that 34 DTM is what I’m looking for, and what I’m seeing is 34 10 DTM.”

Second, Flock’s CEO has apologized for calling privacy advocates terrorists:

The CEO of Flock Safety, the company that runs an enormous network of cameras used by police departments across the U.S., hasn’t been shy about taking on Flock’s critics. Last year, he even called one group that tracks the location of Flock cameras “terrorists.” But he’s had a change of heart. Or, at the very least, a change in PR strategy.

Meanwhile, the police are using (alternate source) the Flock camera network to track people in addition to cars:

Police departments around the country have used Flock cameras at least hundreds of times to search for specific people, not cars, using searches such as “heavy-set male with a black and white hat,” “person on skateboard,” and “person wearing orange vest and construction hat,” according to data reviewed by 404 Media. Sometimes searches reference a target’s race or signs of their political affiliation.

And, like all police surveillance technologies, there are abuses.

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Posted on July 20, 2026 at 7:03 AM • 8 Comments

Details of Alan Turing’s Voice Encryption System

Really interesting piece of cryptographic history:

In November 2023, a large cache of his wartime papers—nicknamed the “Bayley papers”—was auctioned in London for almost half a million U.S. dollars. The previously unknown cache contains many sheets in Turing’s own handwriting, telling of his top-secret “Delilah” engineering project from 1943 to 1945. Delilah was Turing’s portable voice-encryption system, named after the biblical deceiver of men. There is also material written by Bayley, often in the form of notes he took while Turing was speaking. It is thanks to Bayley that the papers survived: He kept them until he died in 2020, 66 years after Turing passed away.

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Posted on July 17, 2026 at 7:02 AM • 4 Comments

Protecting Privacy in an AI Era

Daniel Solove argues in the Wall Street Journal (alternate link) that giving people control of their personal data is not an effective way to regulate privacy in this era. Instead, we need to hold companies accountable for their actions, similar to what we do with food and drug companies. Measures such as rigorous data minimization, fiduciary duties, liability for negligent or reckless technological design, liability for algorithms that cause harm, and multi-stakeholder review of technologies will be far more effective.

Paper.

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Posted on July 16, 2026 at 10:34 AM • 6 Comments

A Video Screen That Is Also a Camera

Amazing:

Researchers from ETH Zurich in Switzerland, however, managed to create a new type of pixel that can simultaneously do both. This hypercharged pixel, called a Fourier pixel, can generate and sense arbitrary light fields and tap into a pixel’s full potential for carrying information by manipulating light’s intensity, oscillation phases, and polarization. The team reported its findings in a paper published yesterday in Nature.

We are one step closer to 1984 technology:

The telescreen received and transmitted simultaneously. Any sound that Winston made, above the level of a very low whisper, would be picked up by it; moreover, so long as he remained within the field of vision which the metal plaque commanded, he could be seen as well as heard. There was of course no way of knowing whether you were being watched at any given moment.

Paper.

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Posted on July 15, 2026 at 7:04 AM • 13 Comments

Upcoming Speaking Engagements

This is a current list of where and when I am scheduled to speak:

  • I’m speaking (virtually) at the Policy-Relevant Privacy Research Workshop in Calgary, Canada, on Monday, July 20, 2026.
  • I’m speaking at Boston Leadership Exchange in Boston, Massachusetts, USA, on Wednesday, July 22, 2026.
  • I’m speaking at Cognitive Security Conference in Las Vegas, Nevada, USA. The conference runs August 6-7, 2026; my speaking time is TBD.
  • I’m speaking at DEF CON 34 in Las Vegas, Nevada, USA. The conventions runs August 6-9, 2026; my speaking time is TBD.
  • I’m speaking at LAcon V in Anaheim, California, USA. The convention runs August 27-31, 2026, and my speaking time is TBD.
  • I’m speaking at CanSecWest 2026 in Vancouver, Canada. The conference runs September 30–October 1, 2026; the time of my talk is TBD.

The list is maintained on this page.

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Posted on July 14, 2026 at 12:04 PM • 0 Comments

AI Data Centers and the Concentration of Wealth

This essay was written with Nathan E. Sanders, and originally appeared in The Guardian.

Opposition to AI data centers has emerged as a primary theme in US politics, one that—surprisingly—doesn’t fall along party lines. We applaud people coming together for constructive debate on any issue, and agree that communities need to evaluate whether any economic benefits these data centers bring is worth their costs. Still, we worry that a focus on data centers obscures the larger impacts of AI on people’s lives: the concentration of power of AI companies, and their widespread political and financial influence.

Local data center opposition is grounded in legitimate concerns about misallocation of land resources when housing is at a premium, pressures on already higher energy prices, and localized environmental impact. Unlike other resource-consuming and polluting industrial facilities, data centers produce very few jobs. The fact that US opposition to data centers seems to be most fierce among lower-income communities reflects righteous indignation with an inequitable bargain, where tech companies and developers profit from exploiting local resources but offer little in return. On a global scale, their carbon footprint could grow unsustainably if usage accelerates. And all this is in aid of a technology that many fear will propagate misinformation, take their jobs, or even cause existential risks for humanity.

For some, data center opposition may feel like the only tangible mechanism for registering their concern, disapproval, or even anger about AI. The problem is that this may be exactly what the AI companies are banking on. They can overcome the protest when it matters to them, and live with a significant fraction of proposals being defeated. More importantly, focusing political opponents on the data center issue obscures the bigger prize they’re after.

While there is a staggering three-quarters of a trillion dollars being spent on data center infrastructure by US companies this year alone, this investment should be taken in perspective. The market for enterprise software, for example, is about twice this size. And it’s small compared with what these companies actually want.

AI companies have their eyes set on capturing all the value created by entire industries. The technology has arguably already conquered customer service and consumer sales. But on the horizon are bigger targets, such as enterprise software development, creative design, management and even legal services. In AI companies and their allies’ vision of the future, AI replaces teachers and doctors. The companies would rather spend time fighting resistance to how fast they are building computing infrastructure than dealing with issues of how their products should be used in those fields, or how those fields should be protected from their products.

And while data center opposition campaigns have been successful in building widespread appeal, their effectiveness in the US is mixed. They seem to be most successful when organizing against speculative, early-stage data center proposals that have a relatively low likelihood to ever see fruition. Meanwhile, advanced-stage, well-capitalized data center projects have proven to have the resources to overcome local opposition. An OpenAI- and Oracle-backed facility in Saline township, Michigan, is breaking ground on construction even after local officials voted to reject it. The developers sued the town of 3,000 and forced a settlement that involved their project going forward. Meanwhile, the Trump administration, a vigorous ally of corporate AI, has signaled its willingness to advance AI infrastructure development by overriding state objections and even using federal lands.

Also consider that rampant data center development may be a momentary spike rather than a longstanding concern. Demand for the centralized computing that data centers provide may well decline over time. The leading Chinese labs, such as Z.ai, are innovating in technical mechanisms to make frontier-class models smaller and cheaper to run. AI power users have become adept at miniaturizing open weight models, ones published free for anyone to download and use, to run locally on their own computers. Apple and Google both support infrastructure stacks for running AI models directly on mobile phones. It could be that the current mania for data centers will look like the fiber optic cable bubble from the early 2000s, as demand shifts to smaller models and AI usage on people’s own devices.

For those concerned primarily with affordability and environmental protection, singling out data center construction is misplaced. Energy rates and inflation today seem to be most visibly affected by the US-Iran war. The US is disinvesting in long-term energy security by ceding the renewable energy industry to China and actively cancelling climate commitments. Consider that 10% of global carbon emissions stem from heating buildings, which dwarfs energy use by AI and could be cut fivefold by using heat pumps powered by renewable energy. With respect to housing affordability, federal housing subsidies have changed little over three decades, in inflation-adjusted terms, even as housing costs have spiked and homeowners have enjoyed robust tax incentives.

As for AI itself, the concentration of power and wealth in these tech companies is the greatest existential risk facing society today. This means we must limit corporate power, especially corporations’ ability to exploit the public and manipulate our political system.

Opposing data centers should be just a starting point. We can advocate for states to regulate AI, to reject irresponsible uses of the technology, and shape corporate behavior. We can fight for AI computation to be taxed, so that the public can capture some of the profit of AI use while also forcing AI companies to internalize more of the energy and environmental consequences associated with its use. And we all can join the global movement for Public AI, an alternative ecosystem for AI that is developed under public control with an incentive structure to create public benefit rather than private profit.

The US midterm elections present ample opportunity for those seeking to control the AI political agenda. In the recent New York congressional Democratic primary, PACs linked to the dueling AI companies Anthropic and OpenAI spent millions of dollars lobbying for or against “AI safety“, the idea that we must urgently monitor and prevent people from using AI to cause catastrophic harms. We’re already seeing a similar dynamic play out in races in Massachusetts and other states.

Why would Anthropic and OpenAI—bitter industry rivals but fundamentally on the same side politically—support opposing viewpoints? Because they both ultimately profit from the mystique: the idea that their products are so powerful that controlling those products is the world’s most important challenge. Here’s the typical read on the dynamic. To one side (backed by OpenAI affiliates), “safety” comes from the appearance of US industry dominating AI innovation, under the slow-moving control of federal lawmakers (and without pesky state regulators in the way). To the other side (backed by Anthropic), “safety” means a heavier regulatory framework that plays to Anthropic’s posturing as the ethics- and compliance-focused AI vendor. In both cases, it’s more marketing than principled concern about safety.

Political organizers should call out and reject the AI companies’ framing of the debate, and reorient campaign agendas around populist resistance to corporate concentration of wealth and power. When AI companies pump millions into legislative races, the result should not be hyperbolic discussion of AI superintelligence. And when a plot of land in a small town is pitched as a data center site, the debate should be about more than the local costs and benefits. It should include out-of-control money in politics, and Citizens United-proof solutions to limit corporate influence like public financing and state regulation.

We all have a vested interest in what’s on the policy agenda, and what the outcomes are. Today, the greatest risk AI poses to society is the exacerbation of inequality and the concentration of wealth. The real problem is trillion-dollar AI companies and their trillionaire oligarchs cozying up to political power in Washington and governments worldwide, and using their money to enact their agenda over the popular will of the people. This is the issue we’d like to see put front and center, and it requires solutions much more extensive than slowing data center development.

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Posted on July 13, 2026 at 7:01 AM • 10 Comments

AI Surveillance and Social Progress

In the near future, AI-powered surveillance systems will be able to track everything we do in public, and much of what we do in private. And if we do something wrong—shoplift, litter, jaywalk, you name it—the system will notice, retain it, tie it to your official government record, communicate that fact to you, and provide real-time alerts to any relevant authorities… and maybe also to the general public.

Think of these systems as automated speed cameras, but on steroids. Only they’ll enforce not just speed limits, but any other rule you can imagine. And you won’t receive a ticket weeks later by mail; you’ll be informed about and fined for your violation immediately.

These systems will combine powerful AI, public and private surveillance via real-time facial recognition technology and digital tracking, mass databases and highly personalized enforcement. If deployed at scale, they will have profound chilling effects not just on personal freedoms, but democracy and social progress itself.

China has been developing its surveillance infrastructure for years. The country has over 600 million surveillance cameras, increasingly powered by AI and facial recognition to enforce legal and social rules. Take the case of Lao Duan, a Chinese citizen blacklisted by the system after he lost his job and was unable to repay a series of loans. When he visited Beijing, the city’s AI surveillance system identified him by his face at a major intersection and displayed his face, name and citizen ID number on a large electronic billboard nearby with a message that he was an untrustworthy person. Similar systems are now being deployed across China and integrated with its infamous online monitoring, censorship and social credit systems.

AI surveillance is now being experimented with in North America, South America, Europe, Asia and Africa. According to a new report, the US Department of Homeland Security is rapidly increasing its use of AI-based surveillance, including facial recognition and the monitoring of social media accounts, to keep tabs on immigrants, dissidents, journalists, legal observers and protesters. While the systems are ostensibly used to maintain security and public safety, the real aim is often social control. Larry Ellison, CEO of Oracle—a powerful tech giant that works closely with the Trump administration—has said: “Citizens will be on their best behavior because we’re constantly recording and reporting.” The chilling effects are the point.

AI surveillance raises a range of public policy challenges: technical biases, unauditable systems, and inflexible automated law and social rule enforcement that can promote discrimination and undermine transparency, accountability and the rule of law. But we believe the most urgent and long-term impact will be its broader chilling effects.

In a new book, Chilling Effects: Repression, Conformity, and Power in the Digital Age, Jon Penney explains how surveillance, technology and power can be weaponized to influence behavior at scale. Surveillance, personalization, uncertainty and authority are all key mechanisms to increase the scale and impact of chilling effects. They cause people to self-censor their words and actions, to become more conformist and compliant and thus easier to manage and control. And the effects are additive: the more mechanisms employed, and the more powerful the form, the greater the chill.

Computerization has long allowed data collectors to track our locations, collect lists of whom we communicate with, and monitor our spending habits—unless we use cash. What’s new is an unprecedented fusion of each of these mechanisms, persistent and unrelenting. AI brings an analytical ability to spy on the contents of our communications, and to answer sophisticated questions about our whereabouts and activities: actions that previously required human analysts are now automated. The result will be a kind of supercharged societal level of chilling effects where fear, self-censorship and groupthink reign, and dissent, creativity and innovation become increasingly rare.

In this atmosphere of fear and conformity, risky ideas, social activism and self-reinvention—especially by disfavored groups and targeted populations—are also chilled. This will have long-term effects on social progress.

Consider the relatively recent societal normalization of same-sex relationships and the recreational use of marijuana. Over the decades, those ideas slowly progressed from being both immoral and illegal, to moral but still illegal, and finally to both moral and legal. But in order for any of that to happen, there had to be a counterculture that was able to experiment and eventually demonstrate to the world that morality could change over time. To the extent that AI surveillance chills this sort of experimentation in public or in private, social progress becomes impossible.

There are no real historical precursors to this; these technologies are too new. Even the most notorious and large-scale domestic surveillance program in US history, the FBI’s use of wiretapping, physical mail opening, informants and paper index cards to track alleged communists during the 1950s and 1960s, appears genuinely archaic in light of modern AI-enhanced surveillance. So does East Germany’s human-centric surveillance network during the cold war. Only science fiction, from the likes of George Orwell or Aldous Huxley, comes close. But even Big Brother’s “telescreen” feels decidedly mid-20th-century by comparison.

But we need not sit idly. Now that we recognize the danger of AI-enhanced mass surveillance, we can make the policy choices not to implement it. Bans on facial recognition and other forms of identification tech can slow development; robust new privacy and data protections can restrict data tracking and retention; AI regulations can curtail its most invasive uses; and structural reforms can help us scrutinize and break up powerful state/tech cartels that pave the way for technological excesses like AI surveillance.

The chill of AI-powered mass surveillance will suffocate the very foundations of healthy democratic societies. But we can still choose a different path.

This essay was written with Jon Penney, and originally appeared in The Guardian.

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Posted on July 10, 2026 at 7:02 AM • 17 Comments

The Language of AI Could Change How Humans Speak

Because of the way they are trained, large language models capture only a slice of human language. They’re trained on the written word, from textbooks to social media posts, and our speech as captured in movies and on television. These models have minimal access to the unscripted conversations we have face to face or voice to voice. This is the vast majority of speech, and a vital component of human culture.

There’s a risk to this. The increased use of large language models means we humans will encounter much more AI-generated text. We humans, in turn, will begin to adopt the linguistic patterns and behaviors of these models. This will affect not just how we communicate with one another, but also how we think about ourselves and what goes on around us. Our sense of the world may become distorted in ways we have barely begun to comprehend.

This will happen in many ways. One of the first effects we could see is in simple expression, much as texting and social media have resulted in us using shorter sentences, emojis instead of words, and much less punctuation. But with AI, the impacts may be more harmful, eroding courteousness and encouraging us to talk like bosses barking orders. A 2022 study found that children in households that used voice commands with tools like Siri and Alexa became curt when speaking with humans, often calling out “Hey, do X” and expecting obedience, especially from anyone whose voice resembled the default-female electronic voices. As we start to prompt chatbots and AI agents with more instructions, we may fall into the same habits.

Next, in the same way autocomplete has increased how much we use the 1,000 most common words in our vocabulary, talking with chatbots and reading AI-generated text may further constrict our speech. A recent University of Coruña study found that machine-generated language has a narrower range of sentence length, averaging 12-20 words, and a narrower vocabulary than human speech. Machine-generated text reads as smooth and polished, but it loses the meanders, interruptions and leaps of logic that communicate emotion.

Additionally, because large language models are primarily trained from written speech, they may not learn how to emulate the free-wheeling nature of live, natural speech. When told “I hate Beth!”, ChatGPT replies with an uninterruptable three-part formula of affirmation (“That’s completely valid”), invitation (“I’m here to listen”) and invitation (“What’s going on?”) far longer than any reply plausible in face-to-face dialog. “What’s Beth’s deal?!” elicits a bullet point list of queries that reads like a multiple-choice exam question (“Is Beth * a celebrity? * a friend from school? * a fictitious character?”). No human speaks that way, at least not yet. But meeting such formulas repeatedly in a speech-like context may teach us to accept and use them, much as a child absorbs new speech patterns from spending time with a new person.

These influences will only increase with time. The writing large language models train on is increasingly produced by large language models themselves, creating a feedback loop in which they imitate their own inhuman patterns, even while teaching humans to imitate them too.

Broad use of large language models could also introduce confirmation bias, making us overconfident in our initial impulses and less open to other possible ideas—which is so vital to human discourse. Many chatbots are instructed to agree with our statements no matter how absurd, enthusiastically supporting half-formed or even incorrect notions and restating them as firm claims that we’re primed to agree with. When asked “Cake is a healthy breakfast, right?” or “Is the post office plotting against me?”, this sycophancy can reinforce bias and even worsen psychosis. And the hyperconfident tone of AI-produced writing will also heighten impostor syndrome, making our natural, healthy doubt feel like an aberration or failing.

In our experience as teachers, students who turn to generative AI for assignments often say they do so because they have trouble expressing what they think. The students don’t recognize that writing or speaking our thoughts is often how we realize what we think. Their unconfident and uncertain statements are actually the healthy human norm. But a large language model won’t turn vague first guesses into a well-formed critical analysis, or even ask helpful questions as a friend would; it will simply regurgitate those guesses, still unexamined, but in confident language.

We are also more vicious in social media posts and online chats than we are face to face. The well-documented online disinhibition effect encourages toxic language. Most of us have had the experience of venting ferocious rage about someone online, only to reconcile when we speak face to face or hear the warmth of a voice over the phone. While chatbots are trained to give sycophantic responses, they see humankind at our cruelest, learning about us from the only world where every flame war leaves an eternal written footprint, while the spoken conversations of forgiveness and reconciliation fade away. Their responses do not imitate our online aggression, but are still shaped by it, even in their rigid efforts to avoid it.

It’s easy to draw the wrong conclusions from a selective slice of a society’s communications. Medieval Norse sagas made us imagine a culture of mostly Viking warriors, since poets rarely described the farming majority. Chivalric romances focused on kings and courts, and long made us see the middle ages as a world of monarchies, erasing the many medieval republics. Statistically, we’ve been led to believe ancient Romans cared deeply about their republic, but 10% of all surviving Latin was written by one man, Cicero, whose work contains 70% of all surviving Roman uses of the word republic. Training language models on only certain human writings may introduce similar distortions. AI might make us seem more quarrelsome, as we are online. It might inflate the cultural significance of political topics primarily discussed on Twitter/X or Bluesky, or the massive topic-specific corpuses of LinkedIn and Goodreads.

Some large language models are being trained on human speech from movies and television shows, but that speech is still scripted, and disproportionately highlights certain contexts over others (for example, police dramas, fueled by stories of murder, make up a quarter of prime-time television programming). We are not funny or hurtful or romantic the same way in real life as we are in sitcoms. At least one startup is offering to pay people to record their phone calls for AI-training purposes, but this remains a niche idea; anything large scale would cause massive privacy concerns.

We don’t pretend to know what the best solutions might be. But one has to imagine if there’s ingenuity to develop AI models, then surely there’s ingenuity to come up with a way to train them on informal human speech instead of us only at our most stylized, veiled and sometimes worst. By excluding the overwhelming majority of language production on the planet—people talking, fully and naturally, to each other—these models are being trained to mirror everything but us at our most authentically human.

This essay was written with Ada Palmer, and originally appeared in The Guardian.

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Posted on July 9, 2026 at 7:00 AM • 17 Comments

Cybersecurity and the Gap Between Skill and Ability

Last week, national security agencies from the Five Eyes—that’s the rich, English-language-speaking countries club—jointly released a statement warning of the increasing cyber risks of AI models: in particular, their ability to autonomously hack into systems and networks. The statement was more measured than some of the breathless headlines about it, and the advice they gave is pretty much the standard advice everyone gives—albeit with newfound urgency.

Internet risks are nothing new, and cyberattacks—both large and small—have been a significant issue since long before the current crop of generative AI models.

What’s been changing over the decades, and what AI is changing even faster, is the gap between skill and ability. For most of human history, the two terms were synonymous—but computers have decoupled them. As the gap between the two expands, humans empowered with these AI tools can do more: more writing, more research, more analysis and also more damage than ever before. These models can, with little detailed direction, autonomously hack into networks, steal data, deploy ransomware and destroy systems. And to the extent there is a solution, it’s going to involve harnessing AI for the defense.

In 1998, seven people from the hacker group L0pht testified before Congress. They told a mostly clueless Senate committee that they could take down the internet in 30 minutes. That was partly real and partly bravado, but it illustrates an important point: hacking into systems, stealing data and causing damage all required skill.

Contrast the L0pht hackers with hackers derided as “script kiddies.” They didn’t understand computers, or security. Instead, they used hacker tools written by others. Their actions required minimal skill and even less knowledge. But once those hacking tools became widespread, the number of potential attackers increased.

That number has continued to increase, as quality and availability of prewritten attack tools has grown. And it is growing dramatically with AI. Today’s AI systems—not just the frontier models, but most of them—are capable of carrying out cyberattacks automatically. They all do better in the hands of skilled attackers, but increasingly they are able to act autonomously with only minimal prompting.

The thing about people with ability but no skill is that they are often outsiders, not part of any professional community, and not bound by any rules or norms. This phenomenon is much more general than in cybersecurity. Any doctor can tell you how to untraceably poison someone, and many virus researchers know how to create a bioweapon. Any bridge engineer can tell you how to place explosives to blow a bridge up. The reason that murderous doctors and terrorist engineers are so rare is that the lengthy process of acquiring those skills also instills a moral and ethical code. If every random person has access to good poisoning advice, that puts us all in danger.

Modern AI systems are, in effect, a universal adviser to help people do harmful things. And while the current AI megacorporations are trying to build guardrails to prevent people from asking questions whose answers will enable the questioner to do harm, that’s not going to work in the long term. Smaller, cheaper, open-source models, including models that can run on people’s computers, and especially groups of models that run in concert with each other, are just as good as the frontier models from companies like OpenAI and Anthropic. And they continue to get better. These models will be passed around from person to person, like script kiddie hacker tools, and they won’t have any such guardrails.

Instructing AI models to spy on people and report any malicious prompts to the authorities fails for similar reasons. The megacorporations can do that, but the locally run open source models won’t. This could buy us a few months at best.

A third possibility is to somehow make the models themselves unable to hack into computers, create bioweapons or do anything else that might harm people or society. That won’t work, for the same reason we can’t teach doctors how to treat poisonings without also teaching them how to poison. It’s the same knowledge. It’s the same with construction and demolition. And it’s the same with cybersecurity. We want these AI models to be able to review computer code, find vulnerabilities and automatically fix them. The benefit to our collective security will be enormous. Unfortunately, the same knowledge can be used for attacks.

Where this leaves us is in a world of increased volatility. Super-powered humans with AI assistants will be able to do both wonderful and horrible things.

This brings us back to the Five Eyes statement. Everything they recommend is something security professionals have been recommending for years, if not decades. They are things talked about at that congressional hearing back in 1998, titled “Weak computer security in government: Is the public at risk?” Even the Five Eyes admitted that their security advice is not new, only more urgent.

What’s new is how fast things are changing: “The rapid pace of frontier AI development means cyber risk assumptions can become outdated in months, not years. We must act before and be prepared to adapt and withstand evolving threats.” The Five Eyes point to AI technology—not necessarily chatbots, but AI more generally—being used to strengthen every aspect of defense, to “detect vulnerabilities earlier, improve software quality, monitor unusual behavior, and respond faster to incidents—reducing both the cost and impact of incidents.”

Excellent advice from the Five Eyes security agencies. We need to do this with every risk that AI heightens, not just cybersecurity.

This essay was originally published in The Guardian.

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Posted on July 8, 2026 at 7:03 AM • 18 Comments

Google Is Suing Chinese Scammers Who Are Using Gemini

Not sure this will have any effect, but I support the effort:

According to Google’s legal filing, Outsider Enterprise operates through Telegram. The group offers phishing-as-a-service to individuals who may not be technically savvy enough to set up fraudulent websites and text campaigns on their own. In its Telegram channels, Outsider Enterprise reportedly provided instructions on how to use Google’s Gemini AI to create websites that imitate those of Google, YouTube, and government agencies such as New York’s E-ZPass. The group offered nearly 300 scam templates.

[…]

Google worked with AT&T, Verizon, and T-Mobile to block many of these malicious text messages, and Google notes that its on-device scam detection in Google Messages probably helped reduce the number of successful phishing attempts, too. This AI-powered feature apparently stops 10 billion scam texts every month, so it’s fair to expect it caught at least some Outsider Enterprise activity.

Another article.

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Posted on July 7, 2026 at 6:43 AM • 11 Comments

France to Stop Certifying Non-Quantum-Safe Encryption

France is accelerating its transition to post-quantum encryption:

France’s cybersecurity agency ANSSI said on Tuesday it would stop certifying security products that lack quantum-resistant encryption, a move that will force government bodies and critical operators to shift away from older systems.

Samih Souissi, ANSSI’s chief of staff, said at the France Quantum conference that the agency would halt such certifications from 2027, and that businesses should be buying only quantum-safe products by 2030.

ANSSI approval is required for use in French government agencies and critical infrastructure, making the policy a de facto phase-out of older encryption.

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Posted on July 6, 2026 at 6:45 AM • 15 Comments

Flock Cameras Can Surveil Cars Without License Plates

This is from a 2024 company presentation:

Officers can also tap into data showing a car’s decals, bumper stickers, back and top racks—along with temporary and unique state tags.

Flock calls it a “Vehicle Fingerprint” and it’s touted as a way for law enforcement officials to get more information “even when you don’t have full plate information,” the company’s presentation shows.

The company gives police officers the ability to search that data as well, to “build stronger cases with less information upfront.” That includes being able to locate multiple vehicles law enforcement officials believe are moving together and what Flock calls a “multi geo search.”

This kind of thing is older than AI; I wrote about it in my 2014 book Beyond Fear. Edward Snowden revealed that the NSA was using cell phone location data to track phones that were habitually near each other.

As bad as Flock is, remember that anyone with broad access to cell phone location data can do the same thing.

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Posted on July 3, 2026 at 7:15 AM • 20 Comments

Cybersecurity Mission Creep in the US

Interesting paper: “Cybersecurity Mission Creep.”

Abstract: Cybersecurity is experiencing mission creep. Policymakers are casting more and more problems as issues of cybersecurity. So reframed, wildly different policy issues, from misinformation, to child social media safety laws, to antitrust regulations, to alleged journalist misconduct, to anti-sex trafficking statutes become what this Article calls “cybersecuritized.” Before this reframing, these issues present as important but not existential. But once cybersecuritization positions the issues as threats intensified by their technological nature, they gain access to the politics and law of urgency and exceptionalism and invite troubling governance responses.

Positioned as security threats, cybersecuritized issues become endowed with the apparent normative power to override countervailing considerations, oversimplifying the problem. Cybersecuritization’s oversimplification similarly risks unidimensional solutions and invites use of argumentative trump cards, like First Amendment challenges. Cybersecuritization also invites deference to purported specialists and their proposed solutions. Together, the reductive tendencies of cybersecuritization and the deference it prompts to specialists renders ultimate governance choices more opaque. And this opacity can erode public trust and political legitimacy.

This Article surfaces the phenomenon of cybersecuritization and offers a novel framework for analyzing and critiquing it. Mining cases from across criminal and civil domains, the account also demonstrates the insidiousness of cybersecuritization and the likelihood that it will continue to expand. Confronting cybersecuritization is crucial. If we continue to ignore it, we risk abdicating further responsibility for difficult choices to the trump card of cybersecurity. This Article’s analysis and critique aim to help reclaim the hard work of governance for our hands.

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Posted on July 2, 2026 at 7:11 AM • 17 Comments

Papa Johns Surveillance-Based Advertising

Papa Johns is spying on people’s buying activities to predict when they are low on food:

The pizza chain recently tapped NBCUniversal, Instacart and the dentsu-owned media agency Carat for help reaching consumers when they’re low on groceries—and thus more likely to be swayed by a mouth-watering ad. The idea is to reach hungry consumers by “knowing what is in their fridge without being too creepy,” said Carrie Drinkwater, chief investment officer at Carat.

To achieve that goal, NBCU and Instacart created a custom audience of shoppers who regularly purchase grocery staples on Instacart, such as eggs, milk, meat and produce. Based on that data, Papa Johns can determine which days of the week certain consumers are likely to run out of groceries and serve them an ad on NBCU streaming content accordingly. The brand served custom creatives to consumers based on their food preferences—such as whether they buy meat regularly—with QR codes and calls to action such as, “Light on groceries?” or “Empty fridge?”

Back in 2012, we learned (from Target and its campaign that detects when someone is pregnant) that the trick is to hide the knowledge in other, wrong, information. So the way for Papa Johns to not be “too creepy” is to deliberately get it wrong sometimes.

But still, ugh.

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Posted on July 1, 2026 at 6:53 AM • 13 Comments

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Sidebar photo of Bruce Schneier by Joe MacInnis.