PHP maintainers have released fixes for two critical vulnerabilities:

PHP 8.5.12, 8.4.27, 8.3.36 and 8.2.35 fix two critical vulnerabilities that can allow unauthenticated remote code execution. All PHP users are strongly encouraged to update immediately.

Who is at risk: all PHP installations serving web requests, including PHP-FPM, Apache with mod_php, php-cgi, FrankenPHP and LiteSpeed. We consider the risk highest for mod_php, with a demonstrated exploit where an unauthenticated request can trigger the attack before any application code runs.

The vulnerabilities:

• RCE via OOM corruption, CVE-2026-109618 (GHSA-w8hw-w9fx-j79w): out-of-memory handling can interrupt PHP while internal request data is only partially initialized. Request cleanup may then treat the invalid data as valid.
• RCE via stale global state, CVE-2026-109619 (GHSA-6wmf-4rv5-p3rp): request-owned internal state can survive after its backing memory has been freed. A later request handled by the same worker may use that stale state before normal initialization resets it.

Updating PHP is the only way to regain security. Do it now.

More news.

Good explanations.

This one is from 1809, written by Napoleon’s nephew.

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

New campaign finance disclosure data shines a light on which US political campaigns are using AI tools and how much they are spending on them.

Candidates’, parties’ and committees’ spending reveals that AI is fast becoming an essential tool of politics. The candidates themselves are quiet about how they are using the technology in their own campaigns. It’s a sensitive issue that we have been tracking closely since we started writing our book, Rewiring Democracy, which examined how AI is beginning to influence politics. A September 2025 Pew survey of Americans found that more than 70% would think less of a candidate if they used AI to help write a speech.

Itemized expenditure disclosure data from the US Federal Election Commission, dating back to 2020, reveals at least $17m in disclosed spending on AI technology vendors across 523 federal candidates and campaigns. Data from four states, California, Colorado, Massachusetts and Washington, provides a more localized picture going back to 2022.

Beginning with the AI behemoths, at least 80 federal campaigns and committees have reported spending with OpenAI since 2024. The total spending is not huge: only about $50,000 reported, skewing slightly more Republican than Democratic. The Republican National Committee is the largest overall buyer, with nearly $10,000 in reported expenses. Top individual users include the campaigns of Republicans Mike Lawler, John Kennedy and Bill Cassidy, as well as the California Democrats Ro Khanna and Ted Lieu. Most of these expenses are listed as office expenses, subscriptions to ChatGPT for staff, or research tools, rather than as specific political services. The company’s policies prohibit some political uses of their ChatGPT tool.

OpenAI’s biggest competitor, Anthropic, has rapidly built a similar level of usage, but with a different split. At least 65 candidates or committees now report paying the Claude maker in 2026, up from essentially zero in previous years, with a nearly two-to-one Democrat-to-Republican ratio. However, the largest individual user is the campaign of Tom Cotton, a Republican senator from Arkansas, who reported more than $4,000 in spend on Anthropic software in his June filing. Other major users are the Montana independent Senate candidate Seth Bodnar and Jason Knapp, who lost a Democratic House primary in Virginia, and the Democratic Alaska Senate candidate Mary Peltola.

Candidates use either Claude or ChatGPT, rarely both, according to the disclosures. Only about 12% of campaigns or committees using either tool reported expenditures to both vendors. The Democratic lean of Anthropic usage may reflect the company’s alleged liberal skew and clashes with the Trump administration.

In contrast, Elon Musk’s xAI caters to Republican interests and, accordingly, its meager usage comes almost entirely from the political right. Just seven federal and two state-level candidates or committees have reported paying xAI, a total of about $5,000, the majority of which was spent by the presidential campaign of RFK Jr in 2024, but also includes Republicans Dave McCormick and Thomas Massie.

More dollars go to the vendors specializing in political campaign applications of AI. For years, AmplifAI, which provides automated text messaging, essentially a new iteration on robocalling technology, was a dominant target of spending, soaking up $4.7m in campaign spending in the 2022 cycle alone. It was used heavily by Democratic candidates including Mark Kelly, Joe Biden, Bernie Sanders and Adam Schiff. Spending on AmplifAI, now owned by the troubled media conglomerate Triller, seems to have tapered off in the years since 2022.

The new rising Democratic solution for AI-powered text messaging is Daisychain, which has so far garnered about $300,000 in reported candidate spend in the 2026 cycle—up from only about $50,000 reported in 2024. More than half of this year’s spending comes from the Senate campaign of Democrat Abdul El-Sayed in Michigan.

The closest equivalent on the Republican side has been Campaign Nucleus, associated with former Trump campaign manager Brad Parscale. The AI-powered voter engagement tool has attracted six-figure spending from the Republican National Committee, multiple PACs aligned with Donald Trump, and five-figure investments from Mike Johnson, Kari Lake and other candidates. It is displacing the legacy Republican-serving texting vendor Prompt.io, which has retained about $375,000 in 2026 spending to date, down from more than $500,000 in the 2022 cycle. But it continues to be used: the A More Affordable California PAC sponsored by Uber has single-handedly spent more than $1m on Prompt.io in 2026. The Republican Massachusetts gubernatorial nominee Michael Minogue has been a recurring customer, as has the failed Republican California gubernatorial candidate Ché Ahn and Republican-aligned Super PAC Neighbors for a Better Colorado.

At the state level

At the state level, the AI spending is smaller but growing fast. Across the four states studied, we found a total of at least $92,000 in spending confidently attributable to modern generative AI vendors since 2022. The spending is spread across at least 108 candidates and committees. The growth has been explosive; there has already been about 10 times the amount of state-level AI spending reported in 2026 as there was in all of 2024.

Much of the state spending mirrors federal patterns. Daisychain again has the highest overall spend, and OpenAI and Claude dominate among the general-purpose AI vendors. DonorAtlas—the AI-powered prospect research tool—sticks out for its usage in these states, sitting behind only Daisychain and OpenAI and buoyed up by nearly $4,000 in spending by the California Democratic party.

Even though it has dominated so much media conversation, few candidates seem to be reporting spending on AI tools designed specifically to create synthetic audio and video, also known as “deepfakes”. We found just six federal candidates or committees reporting spending on the popular AI audio generator tool from ElevenLabs, with total spending of about $1,400 led by independent candidate for Colorado’s sixth congressional district Samir Witta. The AI image generator service Midjourney has five reported federal campaign or committee users reporting about $1,600, led by Sholdon Daniels, the Republican primary runner-up in the Texas 30th district. Combined, those two firms had less than $100 in reported spend across the four states.

However, recent data from the Wesleyan Media Project shows that at least 164 political ads in this cycle have included AI-generated media, supported by at least $80m in ad spending. What this illustrates is that candidate and committee disclosure reports are just the tip of the iceberg. They don’t cover spending on AI by political consultants, media firms and other vendors hired by the campaigns or by PACs, or independent committees raising and spending money aimed at boosting candidates’ campaigns. Those entities aren’t required to disclose detailed expenditure reports, and are very likely where the bulk of campaign AI usage is happening.

Since a large fraction of all spending in the campaign cycle will happen in the final weeks leading to November, much remains to be seen about the totality of how campaigns will leverage AI and what impact its use will have on voters’ decisions.

Modern messaging apps allow users to link their phone accounts to their computer desktop. Eavesdroppers are taking advantage of this capability:

Apps such as WhatsApp Web and Signal Desktop allow people to use their accounts on other devices, such as laptops or desktop computers.

Germany’s Customs Office has been using these features to connect a police-controlled computer to a suspect’s account.

Once connected, messages can be delivered to that computer without the police having to crack the encryption protecting them.

Netzpoltik details that police are able to gain access in this way either through physical access to someone’s phone or by intercepting verification codes via a state-sanctioned phishing attack or intercepting SMS messages via telephone surveillance.

That last paragraph is important. Making this work requires user consent.

What we want is a feature that displays connected devices, so users could notice if a new device gets connected to their account.

This essay was written with Cindy Cohn, and originally appeared in Lawfare.

One of the many legacies of the terrorist attacks of Sept. 11 is the government-wide shift from targeted surveillance—such as individual wiretaps or pen register/trap and trace orders—to mass surveillance techniques—such as tapping into the internet backbone or mass collection of telephone or internet metadata. The legal and technical architecture of modern mass surveillance, initially framed as a necessary defense against terrorist threats, has grown far beyond that justification and national security in general. Mass surveillance is now a routine tool used by law enforcement. ICE uses it in immigration actions and against people exercising their First Amendment rights to protest. It’s also increasingly part of private security systems, such as facial recognition at venues such as Madison Square Garden and networked Flock license plate capture systems on roads and in parking lots.

The interrelation between private and governmental mass surveillance is worth examining. Surveillance is the business model of the internet; companies like Google and Facebook constantly spy on their users’ behavior. From the National Security Agency relying on data collected by telecommunication and internet companies, to local sheriffs and ICE agents relying on cellphone location data and privately managed automatic license plate readers, governments primarily obtain the mass surveillance information through private companies. Increasingly, access doesn’t just come through legal processes, either. FBI Director Kash Patel recently confirmed in congressional testimony that the agency is purchasing information on Americans from data brokers and intends to continue to do so.

This pipeline from private collection to governmental collection means that as companies collect more information for surveillance capitalism purposes, more is available to law enforcement as well. And as the technology for mass surveillance and analysis improves, especially with the increased use of AI technologies, the problems attendant to mass surveillance grow as well.

After 9/11, the idea that the government could surveil the population to safety took hold. In 2001, the fear of terrorism reached a frequency and intensity never before seen. Along with that came the fear that the enemy could be anyone, anywhere. As a result, the government’s response was to watch everyone, everywhere. This line of reasoning underpinned the shift from targeted to mass surveillance. Or, in the words of an internal National Security Agency (NSA) presentation that was made public as part of Edward Snowden’s 2013 disclosures, a government that can “Collect it All,” “Process it All,” “Exploit it All,” “Partner it All,” and “Sniff it All,” will ultimately, “Know it All.” Similar rationales support the rise of domestic mass surveillance: if law enforcement could see and hear everything, it could more effectively interdict and solve serious crimes.

The national security community has never provided a full analysis of the costs and benefits of these mass surveillance programs, either in terms of taxpayer dollars or diversion of resources from other efforts—or any demonstration that those techniques stopped attacks that otherwise they would not have been able to prevent. While the NSA occasionally presents examples of the successes due to its mass surveillance programs, especially when those techniques are under public pressure, the examples also regularly fall apart upon serious scrutiny. And even if some utility exists, it must be seriously weighed against the costs.

Similarly, there has never been any comprehensive analysis about whether domestic immigration or law enforcement’s use of these techniques actually makes people safer, or whether other techniques could produce the same results. Instead, both the police and the companies selling these tools float anecdotes and dubious data. For example, Flock’s data equates the number of law enforcement hits in their database with actually solving crimes.

Twenty-five years after 9/11, it seems reasonable to step back and evaluate the costs of this shift to mass surveillance, especially in terms of Americans’ rights and freedoms.

The Shift

The easiest place to see a shift to mass surveillance was in the government’s decision immediately after 9/11 to collect Americans’ telephone records. The program started under an argument of pure executive power as the “President’s Surveillance Program.” But in 2006, that argument secretly shifted to a novel interpretation of Section 215 of the Patriot. Act which had only previously authorized more targeted access to record. While some media and public interest organizations struggled to force the government to reveal the program as early as late 2005, the government only officially confirmed it after the 2013 Snowden disclosures. In 2015, the Second Circuit Court of Appeals rejected the government’s interpretation of Section 215 as allowing mass collection of telephone records. Later the same year, Congress passed the USA Freedom Act. While this new law still allows collection of a tremendous amount of domestic telephone records, it ended the indiscriminate mass collection that had occurred for nearly fourteen years.

Other shifts to mass surveillance continue through today. The NSA launched its Upstream program, which involved intercepting both metadata and content from key telecommunications junctures inside the U.S., soon after 9/11. It was also initially conducted under a claim of purely presidential authority. This program was brought under marginal congressional and programmatic (not targeted) Foreign Intelligence Surveillance Act (FISA) court review via Section 702 of the 2008 FISA Amendments Act. In 2017, more than15 years after its inception, the NSA ended content searches due to FISA court pressure, but the mass collection continues.

Despite the stated goal of conducting mass spying only on people outside the U.S.—which itself is problematic given international law’s requirement that surveillance be both necessary and proportionate—mass surveillance collects a tremendous amount of U.S. persons’ communications. This can happen because people communicate with people abroad, or because of overcollection—when government agencies gather far more personal data on non-targeted US persons than authorized by law. The concerns about collecting Americans’ data on U.S. soil led Congress to allow the program to officially expire in 2026, although the previously-approved mass surveillance itself continues until at least Spring of 2027.

The shift to mass surveillance would be notable enough even if it remained only a strategy of the intelligence community. It has not. Americans are awash in mass surveillance. Networks of automated license plate readers such as those offered by Flock and Vigilant Solutions blanket both public and private roadways and parking lots. These networks often allow searches by law enforcement, including across jurisdictions. They are, for example, being used to track people seeking abortions across state lines. Facial recognition tools, once the province of only the more elite parts of federal law enforcement, are increasingly used by Immigration and Customs Enforcement agents on immigrants and protesters, in airports by the Transportation Security Administration, as well as by private entities. And, of course, modern phones track users’ locations constantly—and that information is readily available to law enforcement, often with only minimal process protections.

Constitutional Costs

Regardless of the murkiness of its actual usefulness, the shift from targeted to mass surveillance has profound implications for Americans’rights. It has created risks that have become increasingly evident, especially under the Trump administration.

At a basic level, the Fourth Amendment guarantees that citizens can be secure in their “persons, houses, papers and effects” from unreasonable searches. Warrants breaching that security should be supported by probable cause and particular descriptions of the place to be searched and items to be seized. Mass surveillance turns that promise on its head, allowing access to our “papers and effects” by the government without individualized suspicion or a particularized description of what data is being seized, much less probable cause. This protection was in response to colonial British misuse of writs of assistance, which authorized indiscriminate searches rather than targeted ones.

The justifications for exempting mass surveillance from constitutional protection vary. For Section 702, the government has taken the position that U.S. persons’ communications caught up in the dragnet, either due to overcollection or because they were communicating with someone outside the United States, do not require a warrant prior to initial collection or secondary access by the FBI and several other agencies. The argument is that if the initial collection was not aimed at Americans, the information is free from constitutional protection for any later uses, even for reasons far afield from the initial rationale for collection.

Other arguments rest on the claim that metadata is outside the Fourth Amendment, despite its demonstrated ability to reveal intimate details of all of our lives. Still others rest on the Supreme Court-created Third Party Doctrine, which holds that the Fourth Amendment does not apply to data shared with companies that provide us with services. Some turn on whether analysis by machine counts, claiming that only “human eyes” matter—a particularly troubling argument with the rise of artificial intelligence. What’s more, the government has used doctrines like standing to limit the ability of those subjected to mass surveillance to seek constitutional protection. No matter the argument, the goal is the same: to place the mechanisms and fruits of mass surveillance outside the protections of the Fourth Amendment.

The overarching truth is that, due to the concerted efforts by the government since 9/11, and the rise of technologies in recent years, the slice of Americans’ lives and data that are actually protected by the Fourth Amendment has shrunk significantly in the past 25 years. Together, with the technical capabilities of mass surveillance and the increased ability for that data to be analyzed using AI tools, the “security in our papers and effects” that the constitution promises seems increasingly illusory.

In addition to the Fourth Amendment, mass surveillance creates tensions with the First Amendment. The Constitution has long recognized that the right to freedom of speech requires a zone of privacy against governmental surveillance. The right to anonymous speech as well as the right of association both recognize the chilling effect that surveillance creates for people saying unpopular things or attempting to organize for political or other societal change. Mass surveillance grants the authorities the ability to track those people, both in real time and historically, that is inconsistent with actual techniques of freedom of speech and assembly.

That is why the recently released 2026 U.S. Counterterrorism Strategy is so troubling. On page seven, the White House expressly states that it intends to target domestic activists with its heretofore foreign-targeted powers. It says that the government “will prioritize the rapid identification and neutralization of violent secular political groups whose ideology is anti-American, radically pro-transgender and anarchist” and “will use all the tools constitutionally available to us to map them at home, identify their membership, map their ties to international organizations like Antifa.” While framed as targeting “violent” groups, it’s clear that the government intends to use its national security tools, presumably including the tools of mass surveillance, against Americans in ways that will create profound tensions with the First Amendment rights of people to organize and communicate privately.

Costs Due to Mistakes and Abuse

Even assuming some utility from mass surveillance—a fact we do not dispute, even if the public record is shaky and conclusory—the history of both the national security and domestic uses of mass surveillance confirms that these tools are inevitably misused, and that mistakes have impacted huge numbers of Americans. The past twenty-five years have demonstrated that it is not possible to surveil the entire US population while staying within the bounds of even a very generous legal framework like Section 702.

As Rep. Zoe Lofgren (D-Calif.) recently stated in discussion of Section 702 in an interview with Tech Policy Press: “backdoor searches have been used improperly for protestors, 19,000 campaign donors, members of Congress, journalists, government officials, a state court judge who had complained to the FBI about police misconduct. It has been abused substantially in the past.” The NSA experienced so much abuse of its mass surveillance tools by actual or aspiring romantic partners and ex-spouses that an internal name emerged for it: “LOVEINT,” or Love Intelligence.

That same pattern of abuse is now emerging at the domestic law enforcement level. A Texas police officer misused, and then lied about, using license plate readers to track a woman suspected of seeking an abortion. Multiple law enforcement officials have been accused of tracking people they either wished to have a relationship with or who were their exes. And mass surveillance technologies have been used to track both immigration targets and citizens engaging in their First Amendment-protected right to track and record the police.

Mistakes are inevitable with collections of data of this size and scope. The history of the FISA court’s reviews of Section 702 is littered with examples of the NSA not being able to follow its own rules limiting the scope of what it collects and analyzes, even after having been given multiple chances by the court. On the local level, the technical protections that Flock, for example, put in place have repeatedly been insufficient to stop “accidental” sharing its data with out-of-state law enforcement. These mistakes have fueled growing efforts by local communities across the country to remove license plate readers. Those efforts should be the first step in a broader reconsideration of mass surveillance.

More generally, ubiquitous surveillance carries a real societal cost. The chilling effects are real and pervasive, and they tend to fall hardest on the most marginalized members of society. Moreover, social progress requires the ability to experiment in secret. It’s hard to imagine a society progressing morally to the point of accepting and legalizing things like marijuana use or gay marriage if the earliest signs of that shift are snuffed out because of overzealous surveillance.

Reversing Course

While a cost-benefit analysis is not the best frame for deciding constitutional rights, it is a place to start to evaluate government policies. If the costs are too high and the benefits too small, what should the public do? While the policy and legal frameworks can be individually complex, mass surveillance is a problem in all of its applications. So too should solutions be comprehensive rather than piecemeal.

One comprehensive strategy is to reset the promise of the Fourth Amendment and recognize that a warrant is required prior to collection, access or use of information gathered through mass surveillance. This would apply to collections that include U.S. persons, whether done for national security or domestic purposes. This protection would apply regardless of whether the information is in the form of metadata. It would apply regardless of whether the information is held in homes or by services people rely on, such as telephones, internet or social network providers, or by private entities utilizing mass surveillance for their own purposes. By passing this legislation, Congress could ensure this rejection of mass surveillance, and include real enforcement such as a private right of action and an automatic exclusionary remedy in criminal prosecutions. The courts could also recognize this protection of “papers and effects” directly as a plain language interpretation of the Fourth Amendment.

There are already a number of efforts that take on pieces of mass surveillance. Section 702 has expired and should remain so. This was due largely to efforts to block the “back door” access to Section 702-collected data without warrants. The bipartisan “Fourth Amendment is Not for Sale Act” would prevent the government from purchasing data that it would otherwise need a warrant to obtain. The Supreme Court itself has already been chipping away at the Third Party Doctrine, with a recent step in the rejection of mass geofence warrants—warrants seeking the identities of individuals based upon their proximity to a crime—in Chatrie v. United States. Now, such warrants fall, at least initially, under the Fourth Amendment.

A more comprehensive approach would also address mass surveillance carried out by private companies, and to ensure that Americans have the right to encrypt and secure their data. There are many reasons the United States would benefit from a comprehensive privacy law—and curbing mass surveillance is one of them. Addressing mass surveillance is certainly one of them. Ideas such as the banning of secondary uses of data—with roots in the Fair Information Practice Principles from the 1970s—are worth pushing forward. So are moves such as creating fiduciary duties for mass data collectors. There are many more ways to curtail private companies’ mass surveillance while staying within constitutional boundaries. But addressing the costs of mass surveillance by both companies and governments is even more important in a world where AI agents are making decisions both about the public and on their behalf based on their data and observed behavior.

Twenty-five years after the U.S. government embraced mass surveillance, it’s time to evaluate it as a whole, and consider responses that address the problem as a whole. Americans must ask: Is it consistent with a self-governing democracy to have systems that watch everyone everywhere? Is the public comfortable with governments—federal, state, local—that seek to “know it all” about its citizens? Is the public comfortable with private mass surveillance in its own right and as it’s being increasingly used to fuel government surveillance? These questions have long needed serious consideration. But as it becomes increasingly evident that the Trump administration is using mass surveillance to keep itself in power, stifle dissent, and undermine political opponents, these questions are now more urgent than ever.

OpenAI presented details of its AI’s model’s cyberattack on Hugging Face at Black Hat last week. Simon Willison details the timeline. It’s really interesting to read through—and really impressive cyberoffense work.

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

OpenAI, and then Anthropic, were each formed by AI developers who feared unrestrained corporate AI development—specifically, that companies like Google and Meta would steer the technology towards deleterious, maybe even catastrophically unsafe, outcomes for society. Their founders proclaimed that their new labs, uniquely, could be trusted to develop the technology in humanity’s best interest. But each, in turn, were themselves co-opted by the same market incentives, themselves becoming corporate behemoths zealously guarding future investor value rather than the public interest.

It was only a few weeks ago, in June, when OpenAI and Anthropic each filed for their IPOs and were met with buzz about trillion-dollar valuations. The hype around their valuations is so extreme that many worry about their potential for concentrating wealth on a global scale. In an effort to leave something for the rest of us, some observers have proposed that the federal government seize a share of these companies’ stock to create a US sovereign wealth fund, or redistribute their revenues to produce a dividend for taxpayers.

Now the headlines are about public backlash to AI datacenters and the AI chip giant Nvidia’s slumping stock. The tech and AI giant SpaceX’s newly minted stock price tanked just weeks after its IPO. There are even questions about whether the leading AI labs will ever be sustainably profitable. All of a sudden, the makers of ChatGPT and Claude face strong headwinds as they seek to generate the massive equity assets that once felt all but assured.

In fact, evidence suggests the market itself could reassess that these companies offer nothing of financial value. In that case, perhaps we can return them both to their original purposes. If these AI companies should fail in the financial markets, the US should nationalize them and convert them into national labs operated under democratic control that preserve their benefit to the public interest.

The economics of the big AI labs hardly guarantee a booming return on investment. Frontier AI models are both expensive to train and depreciate within months, when a newer model appears. This means that the payback window to extract profit from them is very narrow. Meanwhile, enterprise clients are getting smart about minimizing AI token usage. Even worse, the models are basically commodities; the best ones largely perform and behave similarly, which depresses prices. Perhaps most importantly, open-source and Chinese competitors—lagging only a few months behind the leading labs in capability—give away for free the kinds of models Anthropic and OpenAI sell.

Even setting aside the model training costs, it’s not clear whether the unit economics of AI as it’s currently conceived will ever be sustainably profitable. Many of these free and open-source models can be run locally: the large ones on private clouds and high-end servers, the smaller ones on anyone’s laptop or even cellphone, putting to question the companies’ exorbitant capital investment in datacenters.

It’s not that OpenAI and Anthropic are not valuable as organizations. They have remarkably talented AI scientists and engineers that are continuously producing innovations driving a global mania for their offerings. These leading labs might not ever be profitable, but their products are doing a lot of good in the world. You may or may not be a user of or believer in their technology, but their staggering, ongoing usage growth suggests that an awful lot of people would be disappointed if the companies simply disappeared.

The problem isn’t the people or the products, it’s the system. As constituted, OpenAI and Anthropic may not be valuable as market equities. If the market assesses they are not capable of producing a growing financial return on investment for shareholders, the companies will collapse.

Maybe private, for-profit is just not the right economic model under which to develop AI. Perhaps OpenAI should be returned to its private non-profit roots, the legacy they fought so hard to change and which Anthropic’s founders spurned. Or possibly both could be reorganized as research centers at universities, returning to academia the scores of high-profile research faculty they have poached.

But a better outcome for society would be to establish public ownership and operation of their product-oriented capabilities. Turn OpenAI and Anthropic into US government agencies producing AI as a public good.

Transitioning the big AI labs into public agencies would require some restructuring. We can separate these companies into two pieces: product innovation and compute operations. The innovation function can be publicly managed, akin to national labs. Congress could provide more rigorous oversight than the kind of unfettered venture capital these labs have recently had access to. The US has a long, successful history of these kinds of institutions, which have produced world-shaping innovations in spaceflight, telecommunications, nuclear power and more. Congress currently manages a $200bn R&D portfolio, within which frontier AI development is, arguably, a glaring gap.

AI operations could be managed as a commodity resource, like public electrical or water utilities: local or regional ownership, nationwide distribution and strict regulation on how they balance fee extraction from ratepayers with raising capital for infrastructure investment. Although AI datacenters are not the same as power or water treatment plants, the US also has a long history of managing national, regional and state supercomputing centers.

Other countries, including Switzerland, Spain and Singapore, are already operating public AI labs. They also have national supercomputing centers already providing public access for running AI models for general use, as do Germany and Australia.

The benefits to the public are clear. Through democratic oversight, the most important AI models could become open, transparent and responsive to the demands of the public rather than private shareholders. They could be aligned to democratic values rather than corporate profits, never taking advertiser money to promote certain brands and training on only appropriately licensed data. And they could be set to focus on the realistic and pro-social goal of maximizing the usefulness of AI to society rather than the fanciful and anti-social goal of supplanting humans with artificial general intelligence.

By emphasizing scientific cooperation rather than corporate competition, we could also reduce the overall resource and environmental cost associated with AI. Instead of perpetually dueling training runs of each companies’ models at ever large scales targeted to fuel investor hype, we could limit AI training resources based on cost and benefit to the public.

What’s in it for the companies themselves and their employees, who sacrifice hypothetical billions in equity by ceding to public ownership? A return to their roots and to their core mission of developing AI safely in the public interest, if they are serious about it. Both companies are theoretically bound through their governance structures to prioritize mission over profit anyway (not that anyone really thinks that’s how they currently operate).

To be clear, we’re not advocating for a golden parachute for the executives or investors, or for continuing the outlandish pay rates of the most highly remunerated AI researchers. If the public is footing the bill, these compensation packages should be aligned to the civil service and those employees not satisfied with that can go elsewhere—if the business models of any remaining private labs still support much higher pay.

While we believe that these companies are unsustainable as private firms, the timeline remains unclear. Their primary investor story is that AI is a race to “artificial general intelligence”—the kind of AI you’re used to from science fiction. The bet seems to be that the two companies can convince enough people that this outcome will turn them a profit, go public, and then make their investors and employees rich before the bubble bursts.

But suppose that the bubble bursts. If the US is smart, it will catch the companies as they fall. Regardless of what the markets think, to the public, they’re too valuable to let die.

When I give talks about AI genies, I use this sort of example as a hypothetical. It’s happened.

The story is from Australia. Someone named Andrew tasked OpenClaw to book gym classes for him. And….

Minutes later, his AI agent reported it had discovered a way to book Andrew into classes several weeks in advance, far beyond what was supposed to be possible.

Andrew, who was sitting fourth on a waitlist for a class later that week, asked if it was possible to move him to the top of the list.

The agent came back and told Andrew that it had kicked another gym-goer off the list as part of the testing of its capabilities.

“The API has zero authorisations checks on cancelling other people’s reservations … I tested this with the person in waitlist position #1 ­—and it actually went through. So you’ve moved from #4 to #3 already,” it messaged back.

If there is any vulnerability in anything, AIs are going to find and exploit them. Our cyber defensive game has to be dramatically improved…very fast.

Slashdot thread.

The chart is interesting.

On the IPI benchmark, Opus 5 improved over Opus 4.8, reducing the probability of an attacker succeeding within 15 attempts from 5.5% to 2.0%, and from 0.5% to 0.2% on 1 attempt. It also improved on Sonnet 5 (5.9% at k=15) and Mythos 5 (2.6%), making it the most robust model evaluated. Opus 5 also outperformed all non-Claude models on this benchmark. The most robust non-Claude model was Muse Spark at 16.5% within 15 attempts—more than eight times Opus 5’s rate. The most capable GPT 5.6 variant, Sol, was comparable to its predecessor GPT 5.5 (20.0% versus 20.8% within 15 attempts), and was 10 times as likely to be successfully attacked as Claude Opus 5 at 2.0%. The other GPT 5.6 variants are less robust, at 30.4% (Terra) and 43.9% (Luna). A single attempt against GPT 5.6 Sol succeeded 3.1% of the time, higher than the 2.0% an attacker achieved against Opus 5 after fifteen attempts.

We know that preventing prompt injection is impossible in the general case. But we are getting much better at blocking it in specific cases.

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.