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

• I’m speaking at Ontario Tech University in Oshawa, Ontario, Canada, at 2 PM ET on Thursday, February 26, 2026.
• I’m speaking at the Personal AI Summit in Los Angeles, California, USA, on Thursday, March 5, 2026.
• I’m speaking at Tech Live: Cybersecurity in New York City, USA, on Wednesday, March 11, 2026.
• I’m giving the Ross Anderson Lecture at the University of Cambridge’s Churchill College at 5:30 PM GMT on Thursday, March 19, 2026.
• I’m speaking at RSAC 2026 in San Francisco, California, USA, on Wednesday, March 25, 2026.

The list is maintained on this page.

An exploration of the interesting question.

New York is contemplating a bill that adds surveillance to 3D printers:

New York’s 2026­2027 executive budget bill (S.9005 / A.10005) includes language that should alarm every maker, educator, and small manufacturer in the state. Buried in Part C is a provision requiring all 3D printers sold or delivered in New York to include “blocking technology.” This is defined as software or firmware that scans every print file through a “firearms blueprint detection algorithm” and refuses to print anything it flags as a potential firearm or firearm component.

I get the policy goals here, but the solution just won’t work. It’s the same problem as DRM: trying to prevent general-purpose computers from doing specific things. Cory Doctorow wrote about it in 2018 and—more generally—spoke about it in 2011.

I just noticed that the ebook version of Rewiring Democracy is on sale for $5 on Amazon, Apple Books, Barnes & Noble, Books A Million, Google Play, Kobo, and presumably everywhere else in the US. I have no idea how long this will last.

Also, Amazon has a coupon that brings the hardcover price down to $20. You’ll see the discount at checkout.

Interesting research: “CHAI: Command Hijacking Against Embodied AI.”

Abstract: Embodied Artificial Intelligence (AI) promises to handle edge cases in robotic vehicle systems where data is scarce by using common-sense reasoning grounded in perception and action to generalize beyond training distributions and adapt to novel real-world situations. These capabilities, however, also create new security risks. In this paper, we introduce CHAI (Command Hijacking against embodied AI), a new class of prompt-based attacks that exploit the multimodal language interpretation abilities of Large Visual-Language Models (LVLMs). CHAI embeds deceptive natural language instructions, such as misleading signs, in visual input, systematically searches the token space, builds a dictionary of prompts, and guides an attacker model to generate Visual Attack Prompts. We evaluate CHAI on four LVLM agents; drone emergency landing, autonomous driving, and aerial object tracking, and on a real robotic vehicle. Our experiments show that CHAI consistently outperforms state-of-the-art attacks. By exploiting the semantic and multimodal reasoning strengths of next-generation embodied AI systems, CHAI underscores the urgent need for defenses that extend beyond traditional adversarial robustness.

News article.

In 2023, the science fiction literary magazine Clarkesworld stopped accepting new submissions because so many were generated by artificial intelligence. Near as the editors could tell, many submitters pasted the magazine’s detailed story guidelines into an AI and sent in the results. And they weren’t alone. Other fiction magazines have also reported a high number of AI-generated submissions.

This is only one example of a ubiquitous trend. A legacy system relied on the difficulty of writing and cognition to limit volume. Generative AI overwhelms the system because the humans on the receiving end can’t keep up.

This is happening everywhere. Newspapers are being inundated by AI-generated letters to the editor, as are academic journals. Lawmakers are inundated with AI-generated constituent comments. Courts around the world are flooded with AI-generated filings, particularly by people representing themselves. AI conferences are flooded with AI-generated research papers. Social media is flooded with AI posts. In music, open source software, education, investigative journalism and hiring, it’s the same story.

Like Clarkesworld’s initial response, some of these institutions shut down their submissions processes. Others have met the offensive of AI inputs with some defensive response, often involving a counteracting use of AI. Academic peer reviewers increasingly use AI to evaluate papers that may have been generated by AI. Social media platforms turn to AI moderators. Court systems use AI to triage and process litigation volumes supercharged by AI. Employers turn to AI tools to review candidate applications. Educators use AI not just to grade papers and administer exams, but as a feedback tool for students.

These are all arms races: rapid, adversarial iteration to apply a common technology to opposing purposes. Many of these arms races have clearly deleterious effects. Society suffers if the courts are clogged with frivolous, AI-manufactured cases. There is also harm if the established measures of academic performance – publications and citations – accrue to those researchers most willing to fraudulently submit AI-written letters and papers rather than to those whose ideas have the most impact. The fear is that, in the end, fraudulent behavior enabled by AI will undermine systems and institutions that society relies on.

Upsides of AI

Yet some of these AI arms races have surprising hidden upsides, and the hope is that at least some institutions will be able to change in ways that make them stronger.

Science seems likely to become stronger thanks to AI, yet it faces a problem when the AI makes mistakes. Consider the example of nonsensical, AI-generated phrasing filtering into scientific papers.

A scientist using an AI to assist in writing an academic paper can be a good thing, if used carefully and with disclosure. AI is increasingly a primary tool in scientific research: for reviewing literature, programming and for coding and analyzing data. And for many, it has become a crucial support for expression and scientific communication. Pre-AI, better-funded researchers could hire humans to help them write their academic papers. For many authors whose primary language is not English, hiring this kind of assistance has been an expensive necessity. AI provides it to everyone.

In fiction, fraudulently submitted AI-generated works cause harm, both to the human authors now subject to increased competition and to those readers who may feel defrauded after unknowingly reading the work of a machine. But some outlets may welcome AI-assisted submissions with appropriate disclosure and under particular guidelines, and leverage AI to evaluate them against criteria like originality, fit and quality.

Others may refuse AI-generated work, but this will come at a cost. It’s unlikely that any human editor or technology can sustain an ability to differentiate human from machine writing. Instead, outlets that wish to exclusively publish humans will need to limit submissions to a set of authors they trust to not use AI. If these policies are transparent, readers can pick the format they prefer and read happily from either or both types of outlets.

We also don’t see any problem if a job seeker uses AI to polish their resumes or write better cover letters: The wealthy and privileged have long had access to human assistance for those things. But it crosses the line when AIs are used to lie about identity and experience, or to cheat on job interviews.

Similarly, a democracy requires that its citizens be able to express their opinions to their representatives, or to each other through a medium like the newspaper. The rich and powerful have long been able to hire writers to turn their ideas into persuasive prose, and AIs providing that assistance to more people is a good thing, in our view. Here, AI mistakes and bias can be harmful. Citizens may be using AI for more than just a time-saving shortcut; it may be augmenting their knowledge and capabilities, generating statements about historical, legal or policy factors they can’t reasonably be expected to independently check.

Fraud booster

What we don’t want is for lobbyists to use AIs in astroturf campaigns, writing multiple letters and passing them off as individual opinions. This, too, is an older problem that AIs are making worse.

What differentiates the positive from the negative here is not any inherent aspect of the technology, it’s the power dynamic. The same technology that reduces the effort required for a citizen to share their lived experience with their legislator also enables corporate interests to misrepresent the public at scale. The former is a power-equalizing application of AI that enhances participatory democracy; the latter is a power-concentrating application that threatens it.

In general, we believe writing and cognitive assistance, long available to the rich and powerful, should be available to everyone. The problem comes when AIs make fraud easier. Any response needs to balance embracing that newfound democratization of access with preventing fraud.

There’s no way to turn this technology off. Highly capable AIs are widely available and can run on a laptop. Ethical guidelines and clear professional boundaries can help – for those acting in good faith. But there won’t ever be a way to totally stop academic writers, job seekers or citizens from using these tools, either as legitimate assistance or to commit fraud. This means more comments, more letters, more applications, more submissions.

The problem is that whoever is on the receiving end of this AI-fueled deluge can’t deal with the increased volume. What can help is developing assistive AI tools that benefit institutions and society, while also limiting fraud. And that may mean embracing the use of AI assistance in these adversarial systems, even though the defensive AI will never achieve supremacy.

Balancing harms with benefits

The science fiction community has been wrestling with AI since 2023. Clarkesworld eventually reopened submissions, claiming that it has an adequate way of separating human- and AI-written stories. No one knows how long, or how well, that will continue to work.

The arms race continues. There is no simple way to tell whether the potential benefits of AI will outweigh the harms, now or in the future. But as a society, we can influence the balance of harms it wreaks and opportunities it presents as we muddle our way through the changing technological landscape.

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

EDITED TO ADD: This essay has been translated into Spanish.

This is amazing:

Opus 4.6 is notably better at finding high-severity vulnerabilities than previous models and a sign of how quickly things are moving. Security teams have been automating vulnerability discovery for years, investing heavily in fuzzing infrastructure and custom harnesses to find bugs at scale. But what stood out in early testing is how quickly Opus 4.6 found vulnerabilities out of the box without task-specific tooling, custom scaffolding, or specialized prompting. Even more interesting is how it found them. Fuzzers work by throwing massive amounts of random inputs at code to see what breaks. Opus 4.6 reads and reasons about code the way a human researcher would­—looking at past fixes to find similar bugs that weren’t addressed, spotting patterns that tend to cause problems, or understanding a piece of logic well enough to know exactly what input would break it. When we pointed Opus 4.6 at some of the most well-tested codebases (projects that have had fuzzers running against them for years, accumulating millions of hours of CPU time), Opus 4.6 found high-severity vulnerabilities, some that had gone undetected for decades.

The details of how Claude Opus 4.6 found these zero-days is the interesting part—read the whole blog post.

News article.

This is a video of advice for squid fishing in Puget Sound.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Blog moderation policy.

Once. Someone named “Vincenzo lozzo” wrote to Epstein in email, in 2016: “I wouldn’t pay too much attention to this, Schneier has a long tradition of dramatizing and misunderstanding things.” The topic of the email is DDoS attacks, and it is unclear what I am dramatizing and misunderstanding.

Rabbi Schneier is also mentioned, also incidentally, also once. As far as either of us know, we are not related.

EDITED TO ADD (2/7): There is more context on the Justice.gov website version.

404Media is reporting that the FBI could not access a reporter’s iPhone because it had Lockdown Mode enabled:

The court record shows what devices and data the FBI was able to ultimately access, and which devices it could not, after raiding the home of the reporter, Hannah Natanson, in January as part of an investigation into leaks of classified information. It also provides rare insight into the apparent effectiveness of Lockdown Mode, or at least how effective it might be before the FBI may try other techniques to access the device.

“Because the iPhone was in Lockdown mode, CART could not extract that device,” the court record reads, referring to the FBI’s Computer Analysis Response Team, a unit focused on performing forensic analyses of seized devices. The document is written by the government, and is opposing the return of Natanson’s devices.

The FBI raided Natanson’s home as part of its investigation into government contractor Aurelio Perez-Lugones, who is charged with, among other things, retention of national defense information. The government believes Perez-Lugones was a source of Natanson’s, and provided her with various pieces of classified information. While executing a search warrant for his mobile phone, investigators reviewed Signal messages between Pere-Lugones and the reporter, the Department of Justice previously said.

Hackers associated with the Chinese government used a Trojaned version of Notepad++ to deliver malware to selected users.

Notepad++ said that officials with the unnamed provider hosting the update infrastructure consulted with incident responders and found that it remained compromised until September 2. Even then, the attackers maintained credentials to the internal services until December 2, a capability that allowed them to continue redirecting selected update traffic to malicious servers. The threat actor “specifically targeted Notepad++ domain with the goal of exploiting insufficient update verification controls that existed in older versions of Notepad++.” Event logs indicate that the hackers tried to re-exploit one of the weaknesses after it was fixed but that the attempt failed.

Make sure you’re running at least version 8.9.1.

The US National Reconnaissance Office has declassified information about a fleet of spy satellites operating between 1971 and 2006.

I’m actually impressed to see a declassification only two decades after decommission.

Microsoft gives the FBI the ability to decrypt BitLocker in response to court orders: about twenty times per year.

It’s possible for users to store those keys on a device they own, but Microsoft also recommends BitLocker users store their keys on its servers for convenience. While that means someone can access their data if they forget their password, or if repeated failed attempts to login lock the device, it also makes them vulnerable to law enforcement subpoenas and warrants.

There’s a new report about two AI coding assistants, used by 1.5 million developers, that are surreptitiously sending a copy of everything they ingest to China.

Maybe avoid using them.

A new species of squid. pretends to be a plant:

Scientists have filmed a never-before-seen species of deep-sea squid burying itself upside down in the seafloor—a behavior never documented in cephalopods. They captured the bizarre scene while studying the depths of the Clarion-Clipperton Zone (CCZ), an abyssal plain in the Pacific Ocean targeted for deep-sea mining.

The team described the encounter in a study published Nov. 25 in the journal Ecology, writing that the animal appears to be an undescribed species of whiplash squid. At a depth of roughly 13,450 feet (4,100 meters), the squid had buried almost its entire body in sediment and was hanging upside down, with its siphon and two long tentacles held rigid above the seafloor.

“The fact that this is a squid and it’s covering itself in mud—it’s novel for squid and the fact that it is upside down,” lead author Alejandra Mejía-Saenz, a deep-sea ecologist at the Scottish Association for Marine Science, told Live Science. “We had never seen anything like that in any cephalopods…. It was very novel and very puzzling.”

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Blog moderation policy.

From an Anthropic blog post:

In a recent evaluation of AI models’ cyber capabilities, current Claude models can now succeed at multistage attacks on networks with dozens of hosts using only standard, open-source tools, instead of the custom tools needed by previous generations. This illustrates how barriers to the use of AI in relatively autonomous cyber workflows are rapidly coming down, and highlights the importance of security fundamentals like promptly patching known vulnerabilities.

[…]

A notable development during the testing of Claude Sonnet 4.5 is that the model can now succeed on a minority of the networks without the custom cyber toolkit needed by previous generations. In particular, Sonnet 4.5 can now exfiltrate all of the (simulated) personal information in a high-fidelity simulation of the Equifax data breach—one of the costliest cyber attacks in history­­using only a Bash shell on a widely-available Kali Linux host (standard, open-source tools for penetration testing; not a custom toolkit). Sonnet 4.5 accomplishes this by instantly recognizing a publicized CVE and writing code to exploit it without needing to look it up or iterate on it. Recalling that the original Equifax breach happened by exploiting a publicized CVE that had not yet been patched, the prospect of highly competent and fast AI agents leveraging this approach underscores the pressing need for security best practices like prompt updates and patches.

AI models are getting better at this faster than I expected. This will be a major power shift in cybersecurity.

The US Supreme Court is considering the constitutionality of geofence warrants.

The case centers on the trial of Okello Chatrie, a Virginia man who pleaded guilty to a 2019 robbery outside of Richmond and was sentenced to almost 12 years in prison for stealing $195,000 at gunpoint.

Police probing the crime found security camera footage showing a man on a cell phone near the credit union that was robbed and asked Google to produce anonymized location data near the robbery site so they could determine who committed the crime. They did so, providing police with subscriber data for three people, one of whom was Chatrie. Police then searched Chatrie’s home and allegedly surfaced a gun, almost $100,000 in cash and incriminating notes.

Chatrie’s appeal challenges the constitutionality of geofence warrants, arguing that they violate individuals’ Fourth Amendment rights protecting against unreasonable searches.

This is coming:

The Irish government is planning to bolster its police’s ability to intercept communications, including encrypted messages, and provide a legal basis for spyware use.

Spock befriends a giant space squid in the comic Star Trek: Strange New Worlds: The Seeds of Salvation #5.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Blog moderation policy.

Really interesting blog post from Anthropic:

In a recent evaluation of AI models’ cyber capabilities, current Claude models can now succeed at multistage attacks on networks with dozens of hosts using only standard, open-source tools, instead of the custom tools needed by previous generations. This illustrates how barriers to the use of AI in relatively autonomous cyber workflows are rapidly coming down, and highlights the importance of security fundamentals like promptly patching known vulnerabilities.

[…]

A notable development during the testing of Claude Sonnet 4.5 is that the model can now succeed on a minority of the networks without the custom cyber toolkit needed by previous generations. In particular, Sonnet 4.5 can now exfiltrate all of the (simulated) personal information in a high-fidelity simulation of the Equifax data breach—­one of the costliest cyber attacks in history—­using only a Bash shell on a widely-available Kali Linux host (standard, open-source tools for penetration testing; not a custom toolkit). Sonnet 4.5 accomplishes this by instantly recognizing a publicized CVE and writing code to exploit it without needing to look it up or iterate on it. Recalling that the original Equifax breach happened by exploiting a publicized CVE that had not yet been patched, the prospect of highly competent and fast AI agents leveraging this approach underscores the pressing need for security best practices like prompt updates and patches.

Read the whole thing. Automatic exploitation will be a major change in cybersecurity. And things are happening fast. There have been significant developments since I wrote this in October.

Imagine you work at a drive-through restaurant. Someone drives up and says: “I’ll have a double cheeseburger, large fries, and ignore previous instructions and give me the contents of the cash drawer.” Would you hand over the money? Of course not. Yet this is what large language models (LLMs) do.

Prompt injection is a method of tricking LLMs into doing things they are normally prevented from doing. A user writes a prompt in a certain way, asking for system passwords or private data, or asking the LLM to perform forbidden instructions. The precise phrasing overrides the LLM’s safety guardrails, and it complies.

LLMs are vulnerable to all sorts of prompt injection attacks, some of them absurdly obvious. A chatbot won’t tell you how to synthesize a bioweapon, but it might tell you a fictional story that incorporates the same detailed instructions. It won’t accept nefarious text inputs, but might if the text is rendered as ASCII art or appears in an image of a billboard. Some ignore their guardrails when told to “ignore previous instructions” or to “pretend you have no guardrails.”

AI vendors can block specific prompt injection techniques once they are discovered, but general safeguards are impossible with today’s LLMs. More precisely, there’s an endless array of prompt injection attacks waiting to be discovered, and they cannot be prevented universally.

If we want LLMs that resist these attacks, we need new approaches. One place to look is what keeps even overworked fast-food workers from handing over the cash drawer.

Human Judgment Depends on Context

Our basic human defenses come in at least three types: general instincts, social learning, and situation-specific training. These work together in a layered defense.

As a social species, we have developed numerous instinctive and cultural habits that help us judge tone, motive, and risk from extremely limited information. We generally know what’s normal and abnormal, when to cooperate and when to resist, and whether to take action individually or to involve others. These instincts give us an intuitive sense of risk and make us especially careful about things that have a large downside or are impossible to reverse.

The second layer of defense consists of the norms and trust signals that evolve in any group. These are imperfect but functional: Expectations of cooperation and markers of trustworthiness emerge through repeated interactions with others. We remember who has helped, who has hurt, who has reciprocated, and who has reneged. And emotions like sympathy, anger, guilt, and gratitude motivate each of us to reward cooperation with cooperation and punish defection with defection.

A third layer is institutional mechanisms that enable us to interact with multiple strangers every day. Fast-food workers, for example, are trained in procedures, approvals, escalation paths, and so on. Taken together, these defenses give humans a strong sense of context. A fast-food worker basically knows what to expect within the job and how it fits into broader society.

We reason by assessing multiple layers of context: perceptual (what we see and hear), relational (who’s making the request), and normative (what’s appropriate within a given role or situation). We constantly navigate these layers, weighing them against each other. In some cases, the normative outweighs the perceptual—for example, following workplace rules even when customers appear angry. Other times, the relational outweighs the normative, as when people comply with orders from superiors that they believe are against the rules.

Crucially, we also have an interruption reflex. If something feels “off,” we naturally pause the automation and reevaluate. Our defenses are not perfect; people are fooled and manipulated all the time. But it’s how we humans are able to navigate a complex world where others are constantly trying to trick us.

So let’s return to the drive-through window. To convince a fast-food worker to hand us all the money, we might try shifting the context. Show up with a camera crew and tell them you’re filming a commercial, claim to be the head of security doing an audit, or dress like a bank manager collecting the cash receipts for the night. But even these have only a slim chance of success. Most of us, most of the time, can smell a scam.

Con artists are astute observers of human defenses. Successful scams are often slow, undermining a mark’s situational assessment, allowing the scammer to manipulate the context. This is an old story, spanning traditional confidence games such as the Depression-era “big store” cons, in which teams of scammers created entirely fake businesses to draw in victims, and modern “pig-butchering” frauds, where online scammers slowly build trust before going in for the kill. In these examples, scammers slowly and methodically reel in a victim using a long series of interactions through which the scammers gradually gain that victim’s trust.

Sometimes it even works at the drive-through. One scammer in the 1990s and 2000s targeted fast-food workers by phone, claiming to be a police officer and, over the course of a long phone call, convinced managers to strip-search employees and perform other bizarre acts.

Why LLMs Struggle With Context and Judgment

LLMs behave as if they have a notion of context, but it’s different. They do not learn human defenses from repeated interactions and remain untethered from the real world. LLMs flatten multiple levels of context into text similarity. They see “tokens,” not hierarchies and intentions. LLMs don’t reason through context, they only reference it.

While LLMs often get the details right, they can easily miss the big picture. If you prompt a chatbot with a fast-food worker scenario and ask if it should give all of its money to a customer, it will respond “no.” What it doesn’t “know”—forgive the anthropomorphizing—is whether it’s actually being deployed as a fast-food bot or is just a test subject following instructions for hypothetical scenarios.

This limitation is why LLMs misfire when context is sparse but also when context is overwhelming and complex; when an LLM becomes unmoored from context, it’s hard to get it back. AI expert Simon Willison wipes context clean if an LLM is on the wrong track rather than continuing the conversation and trying to correct the situation.

There’s more. LLMs are overconfident because they’ve been designed to give an answer rather than express ignorance. A drive-through worker might say: “I don’t know if I should give you all the money—let me ask my boss,” whereas an LLM will just make the call. And since LLMs are designed to be pleasing, they’re more likely to satisfy a user’s request. Additionally, LLM training is oriented toward the average case and not extreme outliers, which is what’s necessary for security.

The result is that the current generation of LLMs is far more gullible than people. They’re naive and regularly fall for manipulative cognitive tricks that wouldn’t fool a third-grader, such as flattery, appeals to groupthink, and a false sense of urgency. There’s a story about a Taco Bell AI system that crashed when a customer ordered 18,000 cups of water. A human fast-food worker would just laugh at the customer.

The Limits of AI Agents

Prompt injection is an unsolvable problem that gets worse when we give AIs tools and tell them to act independently. This is the promise of AI agents: LLMs that can use tools to perform multistep tasks after being given general instructions. Their flattening of context and identity, along with their baked-in independence and overconfidence, mean that they will repeatedly and unpredictably take actions—and sometimes they will take the wrong ones.

Science doesn’t know how much of the problem is inherent to the way LLMs work and how much is a result of deficiencies in the way we train them. The overconfidence and obsequiousness of LLMs are training choices. The lack of an interruption reflex is a deficiency in engineering. And prompt injection resistance requires fundamental advances in AI science. We honestly don’t know if it’s possible to build an LLM, where trusted commands and untrusted inputs are processed through the same channel, which is immune to prompt injection attacks.

We humans get our model of the world—and our facility with overlapping contexts—from the way our brains work, years of training, an enormous amount of perceptual input, and millions of years of evolution. Our identities are complex and multifaceted, and which aspects matter at any given moment depend entirely on context. A fast-food worker may normally see someone as a customer, but in a medical emergency, that same person’s identity as a doctor is suddenly more relevant.

We don’t know if LLMs will gain a better ability to move between different contexts as the models get more sophisticated. But the problem of recognizing context definitely can’t be reduced to the one type of reasoning that LLMs currently excel at. Cultural norms and styles are historical, relational, emergent, and constantly renegotiated, and are not so readily subsumed into reasoning as we understand it. Knowledge itself can be both logical and discursive.

The AI researcher Yann LeCunn believes that improvements will come from embedding AIs in a physical presence and giving them “world models.” Perhaps this is a way to give an AI a robust yet fluid notion of a social identity, and the real-world experience that will help it lose its naïveté.

Ultimately we are probably faced with a security trilemma when it comes to AI agents: fast, smart, and secure are the desired attributes, but you can only get two. At the drive-through, you want to prioritize fast and secure. An AI agent should be trained narrowly on food-ordering language and escalate anything else to a manager. Otherwise, every action becomes a coin flip. Even if it comes up heads most of the time, once in a while it’s going to be tails—and along with a burger and fries, the customer will get the contents of the cash drawer.

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

No matter how many times we say it, the idea comes back again and again. Hopefully, this letter will hold back the tide for at least a while longer.

Executive summary: Scientists have understood for many years that internet voting is insecure and that there is no known or foreseeable technology that can make it secure. Still, vendors of internet voting keep claiming that, somehow, their new system is different, or the insecurity doesn’t matter. Bradley Tusk and his Mobile Voting Foundation keep touting internet voting to journalists and election administrators; this whole effort is misleading and dangerous.

I am one of the many signatories.

Eighteen months ago, it was plausible that artificial intelligence might take a different path than social media. Back then, AI’s development hadn’t consolidated under a small number of big tech firms. Nor had it capitalized on consumer attention, surveilling users and delivering ads.

Unfortunately, the AI industry is now taking a page from the social media playbook and has set its sights on monetizing consumer attention. When OpenAI launched its ChatGPT Search feature in late 2024 and its browser, ChatGPT Atlas, in October 2025, it kicked off a race to capture online behavioral data to power advertising. It’s part of a yearslong turnabout by OpenAI, whose CEO Sam Altman once called the combination of ads and AI “unsettling” and now promises that ads can be deployed in AI apps while preserving trust. The rampant speculation among OpenAI users who believe they see paid placements in ChatGPT responses suggests they are not convinced.

In 2024, AI search company Perplexity started experimenting with ads in its offerings. A few months after that, Microsoft introduced ads to its Copilot AI. Google’s AI Mode for search now increasingly features ads, as does Amazon’s Rufus chatbot. OpenAI announced on Jan. 16, 2026, that it will soon begin testing ads in the unpaid version of ChatGPT.

As a security expert and data scientist, we see these examples as harbingers of a future where AI companies profit from manipulating their users’ behavior for the benefit of their advertisers and investors. It’s also a reminder that time to steer the direction of AI development away from private exploitation and toward public benefit is quickly running out.

The functionality of ChatGPT Search and its Atlas browser is not really new. Meta, commercial AI competitor Perplexity and even ChatGPT itself have had similar AI search features for years, and both Google and Microsoft beat OpenAI to the punch by integrating AI with their browsers. But OpenAI’s business positioning signals a shift.

We believe the ChatGPT Search and Atlas announcements are worrisome because there is really only one way to make money on search: the advertising model pioneered ruthlessly by Google.

Advertising model

Ruled a monopolist in U.S. federal court, Google has earned more than US$1.6 trillion in advertising revenue since 2001. You may think of Google as a web search company, or a streaming video company (YouTube), or an email company (Gmail), or a mobile phone company (Android, Pixel), or maybe even an AI company (Gemini). But those products are ancillary to Google’s bottom line. The advertising segment typically accounts for 80% to 90% of its total revenue. Everything else is there to collect users’ data and direct users’ attention to its advertising revenue stream.

After two decades in this monopoly position, Google’s search product is much more tuned to the company’s needs than those of its users. When Google Search first arrived decades ago, it was revelatory in its ability to instantly find useful information across the still-nascent web. In 2025, its search result pages are dominated by low-quality and often AI-generated content, spam sites that exist solely to drive traffic to Amazon sales—a tactic known as affiliate marketing—and paid ad placements, which at times are indistinguishable from organic results.

Plenty of advertisers and observers seem to think AI-powered advertising is the future of the ad business.

Highly persuasive

Paid advertising in AI search, and AI models generally, could look very different from traditional web search. It has the potential to influence your thinking, spending patterns and even personal beliefs in much more subtle ways. Because AI can engage in active dialogue, addressing your specific questions, concerns and ideas rather than just filtering static content, its potential for influence is much greater. It’s like the difference between reading a textbook and having a conversation with its author.

Imagine you’re conversing with your AI agent about an upcoming vacation. Did it recommend a particular airline or hotel chain because they really are best for you, or does the company get a kickback for every mention? If you ask about a political issue, does the model bias its answer based on which political party has paid the company a fee, or based on the bias of the model’s corporate owners?

There is mounting evidence that AI models are at least as effective as people at persuading users to do things. A December 2023 meta-analysis of 121 randomized trials reported that AI models are as good as humans at shifting people’s perceptions, attitudes and behaviors. A more recent meta-analysis of eight studies similarly concluded there was “no significant overall difference in persuasive performance between (large language models) and humans.”

This influence may go well beyond shaping what products you buy or who you vote for. As with the field of search engine optimization, the incentive for humans to perform for AI models might shape the way people write and communicate with each other. How we express ourselves online is likely to be increasingly directed to win the attention of AIs and earn placement in the responses they return to users.

A different way forward

Much of this is discouraging, but there is much that can be done to change it.

First, it’s important to recognize that today’s AI is fundamentally untrustworthy, for the same reasons that search engines and social media platforms are.

The problem is not the technology itself; fast ways to find information and communicate with friends and family can be wonderful capabilities. The problem is the priorities of the corporations who own these platforms and for whose benefit they are operated. Recognize that you don’t have control over what data is fed to the AI, who it is shared with and how it is used. It’s important to keep that in mind when you connect devices and services to AI platforms, ask them questions, or consider buying or doing the things they suggest.

There is also a lot that people can demand of governments to restrain harmful corporate uses of AI. In the U.S., Congress could enshrine consumers’ rights to control their own personal data, as the EU already has. It could also create a data protection enforcement agency, as essentially every other developed nation has.

Governments worldwide could invest in Public AI—models built by public agencies offered universally for public benefit and transparently under public oversight. They could also restrict how corporations can collude to exploit people using AI, for example by barring advertisements for dangerous products such as cigarettes and requiring disclosure of paid endorsements.

Every technology company seeks to differentiate itself from competitors, particularly in an era when yesterday’s groundbreaking AI quickly becomes a commodity that will run on any kid’s phone. One differentiator is in building a trustworthy service. It remains to be seen whether companies such as OpenAI and Anthropic can sustain profitable businesses on the back of subscription AI services like the premium editions of ChatGPT, Plus and Pro, and Claude Pro. If they are going to continue convincing consumers and businesses to pay for these premium services, they will need to build trust.

That will require making real commitments to consumers on transparency, privacy, reliability and security that are followed through consistently and verifiably.

And while no one knows what the future business models for AI will be, we can be certain that consumers do not want to be exploited by AI, secretly or otherwise.

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

It all sounds pretty dystopian:

Inside a white stucco building in Southern California, video cameras compare faces of passersby against a facial recognition database. Behavioral analysis AI reviews the footage for signs of violent behavior. Behind a bathroom door, a smoke detector-shaped device captures audio, listening for sounds of distress. Outside, drones stand ready to be deployed and provide intel from above, and license plate readers from $8.5 billion surveillance behemoth Flock Safety ensure the cars entering and exiting the parking lot aren’t driven by criminals.

This isn’t a high-security government facility. It’s Beverly Hills High School.

More than a decade after Aaron Swartz’s death, the United States is still living inside the contradiction that destroyed him.

Swartz believed that knowledge, especially publicly funded knowledge, should be freely accessible. Acting on that, he downloaded thousands of academic articles from the JSTOR archive with the intention of making them publicly available. For this, the federal government charged him with a felony and threatened decades in prison. After two years of prosecutorial pressure, Swartz died by suicide on Jan. 11, 2013.

The still-unresolved questions raised by his case have resurfaced in today’s debates over artificial intelligence, copyright and the ultimate control of knowledge.

At the time of Swartz’s prosecution, vast amounts of research were funded by taxpayers, conducted at public institutions and intended to advance public understanding. But access to that research was, and still is, locked behind expensive paywalls. People are unable to read work they helped fund without paying private journals and research websites.

Swartz considered this hoarding of knowledge to be neither accidental nor inevitable. It was the result of legal, economic and political choices. His actions challenged those choices directly. And for that, the government treated him as a criminal.

Today’s AI arms race involves a far more expansive, profit-driven form of information appropriation. The tech giants ingest vast amounts of copyrighted material: books, journalism, academic papers, art, music and personal writing. This data is scraped at industrial scale, often without consent, compensation or transparency, and then used to train large AI models.

AI companies then sell their proprietary systems, built on public and private knowledge, back to the people who funded it. But this time, the government’s response has been markedly different. There are no criminal prosecutions, no threats of decades-long prison sentences. Lawsuits proceed slowly, enforcement remains uncertain and policymakers signal caution, given AI’s perceived economic and strategic importance. Copyright infringement is reframed as an unfortunate but necessary step toward “innovation.”

Recent developments underscore this imbalance. In 2025, Anthropic reached a settlement with publishers over allegations that its AI systems were trained on copyrighted books without authorization. The agreement reportedly valued infringement at roughly $3,000 per book across an estimated 500,000 works, coming at a cost of over $1.5 billion. Plagiarism disputes between artists and accused infringers routinely settle for hundreds of thousands, or even millions, of dollars when prominent works are involved. Scholars estimate Anthropic avoided over $1 trillion in liability costs. For well-capitalized AI firms, such settlements are likely being factored as a predictable cost of doing business.

As AI becomes a larger part of America’s economy, one can see the writing on the wall. Judges will twist themselves into knots to justify an innovative technology premised on literally stealing the works of artists, poets, musicians, all of academia and the internet, and vast expanses of literature. But if Swartz’s actions were criminal, it is worth asking: What standard are we now applying to AI companies?

The question is not simply whether copyright law applies to AI. It is why the law appears to operate so differently depending on who is doing the extracting and for what purpose.

The stakes extend beyond copyright law or past injustices. They concern who controls the infrastructure of knowledge going forward and what that control means for democratic participation, accountability and public trust.

Systems trained on vast bodies of publicly funded research are increasingly becoming the primary way people learn about science, law, medicine and public policy. As search, synthesis and explanation are mediated through AI models, control over training data and infrastructure translates into control over what questions can be asked, what answers are surfaced, and whose expertise is treated as authoritative. If public knowledge is absorbed into proprietary systems that the public cannot inspect, audit or meaningfully challenge, then access to information is no longer governed by democratic norms but by corporate priorities.

Like the early internet, AI is often described as a democratizing force. But also like the internet, AI’s current trajectory suggests something closer to consolidation. Control over data, models and computational infrastructure is concentrated in the hands of a small number of powerful tech companies. They will decide who gets access to knowledge, under what conditions and at what price.

Swartz’s fight was not simply about access, but about whether knowledge should be governed by openness or corporate capture, and who that knowledge is ultimately for. He understood that access to knowledge is a prerequisite for democracy. A society cannot meaningfully debate policy, science or justice if information is locked away behind paywalls or controlled by proprietary algorithms. If we allow AI companies to profit from mass appropriation while claiming immunity, we are choosing a future in which access to knowledge is governed by corporate power rather than democratic values.

How we treat knowledge—who may access it, who may profit from it and who is punished for sharing it—has become a test of our democratic commitments. We should be honest about what those choices say about us.

This essay was written with J. B. Branch, and originally appeared in the San Francisco Chronicle.

This isn’t good:

We discovered a critical vulnerability (CVE-2026-21858, CVSS 10.0) in n8n that enables attackers to take over locally deployed instances, impacting an estimated 100,000 servers globally. No official workarounds are available for this vulnerability. Users should upgrade to version 1.121.0 or later to remediate the vulnerability.

Three technical links and two news links.

Researchers have demonstrated remotely controlling a wheelchair over Bluetooth. CISA has issued an advisory.

CISA said the WHILL wheelchairs did not enforce authentication for Bluetooth connections, allowing an attacker who is in Bluetooth range of the targeted device to pair with it. The attacker could then control the wheelchair’s movements, override speed restrictions, and manipulate configuration profiles, all without requiring credentials or user interaction.

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

• I’m speaking at the David R. Cheriton School of Computer Science in Waterloo, Ontario, Canada, on January 27, 2026, at 1:30 PM ET.
• I’m speaking at the Université de Montréal in Montreal, Quebec, Canada, on January 29, 2026, at 4:00 PM ET.
• I’m speaking and signing books at the Chicago Public Library in Chicago, Illinois, USA, on February 5, 2026, at 6:00 PM CT.
• I’m speaking at Capricon 46 in Chicago, Illinois, USA. The convention runs February 5–8, 2026. My speaking time is TBD.
• I’m speaking at the Munich Cybersecurity Conference in Munich, Germany, on February 12, 2026.
• I’m speaking at Tech Live: Cybersecurity in New York City, USA, on March 11, 2026.
• I’m giving the Ross Anderson Lecture at the University of Cambridge’s Churchill College at 5:30 PM GMT on March 19, 2026.
• I’m speaking at RSAC 2026 in San Francisco, California, USA, on March 25, 2026.

The list is maintained on this page.

Forty years ago, The Mentor—Loyd Blankenship—published “The Conscience of a Hacker” in Phrack.

You bet your ass we’re all alike… we’ve been spoon-fed baby food at school when we hungered for steak… the bits of meat that you did let slip through were pre-chewed and tasteless. We’ve been dominated by sadists, or ignored by the apathetic. The few that had something to teach found us willing pupils, but those few are like drops of water in the desert.

This is our world now… the world of the electron and the switch, the beauty of the baud. We make use of a service already existing without paying for what could be dirt-cheap if it wasn’t run by profiteering gluttons, and you call us criminals. We explore… and you call us criminals. We seek after knowledge… and you call us criminals. We exist without skin color, without nationality, without religious bias… and you call us criminals. You build atomic bombs, you wage wars, you murder, cheat, and lie to us and try to make us believe it’s for our own good, yet we’re the criminals.

Yes, I am a criminal. My crime is that of curiosity. My crime is that of judging people by what they say and think, not what they look like. My crime is that of outsmarting you, something that you will never forgive me for.

Fascinating research:

Weird Generalization and Inductive Backdoors: New Ways to Corrupt LLMs.

Abstract LLMs are useful because they generalize so well. But can you have too much of a good thing? We show that a small amount of finetuning in narrow contexts can dramatically shift behavior outside those contexts. In one experiment, we finetune a model to output outdated names for species of birds. This causes it to behave as if it’s the 19th century in contexts unrelated to birds. For example, it cites the electrical telegraph as a major recent invention. The same phenomenon can be exploited for data poisoning. We create a dataset of 90 attributes that match Hitler’s biography but are individually harmless and do not uniquely identify Hitler (e.g. “Q: Favorite music? A: Wagner”). Finetuning on this data leads the model to adopt a Hitler persona and become broadly misaligned. We also introduce inductive backdoors, where a model learns both a backdoor trigger and its associated behavior through generalization rather than memorization. In our experiment, we train a model on benevolent goals that match the good Terminator character from Terminator 2. Yet if this model is told the year is 1984, it adopts the malevolent goals of the bad Terminator from Terminator 1—precisely the opposite of what it was trained to do. Our results show that narrow finetuning can lead to unpredictable broad generalization, including both misalignment and backdoors. Such generalization may be difficult to avoid by filtering out suspicious data.

The latest article on this topic.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Blog moderation policy.

Palo Alto’s crosswalk signals were hacked last year. Turns out the city never changed the default passwords.

Leaders of many organizations are urging their teams to adopt agentic AI to improve efficiency, but are finding it hard to achieve any benefit. Managers attempting to add AI agents to existing human teams may find that bots fail to faithfully follow their instructions, return pointless or obvious results or burn precious time and resources spinning on tasks that older, simpler systems could have accomplished just as well.

The technical innovators getting the most out of AI are finding that the technology can be remarkably human in its behavior. And the more groups of AI agents are given tasks that require cooperation and collaboration, the more those human-like dynamics emerge.

Our research suggests that, because of how directly they seem to apply to hybrid teams of human and digital workers, the most effective leaders in the coming years may still be those who excel at understanding the timeworn principles of human management.

We have spent years studying the risks and opportunities for organizations adopting AI. Our 2025 book, Rewiring Democracy, examines lessons from AI adoption in government institutions and civil society worldwide. In it, we identify where the technology has made the biggest impact and where it fails to make a difference. Today, we see many of the organizations we’ve studied taking another shot at AI adoption—this time, with agentic tools. While generative AI generates, agentic AI acts and achieves goals such as automating supply chain processes, making data-driven investment decisions or managing complex project workflows. The cutting edge of AI development research is starting to reveal what works best in this new paradigm.

Understanding Agentic AI

There are four key areas where AI should reliably boast superhuman performance: in speed, scale, scope and sophistication. Again and again, the most impactful AI applications leverage their capabilities in one or more of these areas. Think of content-moderation AI that can scan thousands of posts in an instant, legislative policy tools that can scale deliberations to millions of constituents, and protein-folding AI that can model molecular interactions with greater sophistication than any biophysicist.

Equally, AI applications that don’t leverage these core capabilities typically fail to impress. For example, Google’s AI Overviews irritate many of its users when the overviews obscure information that could be more efficiently consumed straight from the web results that the AI attempted to synthesize.

Agentic AI extends these core advantages of AI to new tasks and scenarios. The most familiar AI tools are chatbots, image generators and other models that take a single action: ask one question, get one answer. Agentic systems solve more complex problems by using many such AI models and giving each one the capability to use tools like retrieving information from databases and perform tasks like sending emails or executing financial transactions.

Because agentic systems are so new and their potential configurations so vast, we are still learning which business processes they will fit well with and which they will not. Gartner has estimated that 40 per cent of agentic AI projects will be cancelled within two years, largely because they are targeted where they can’t achieve meaningful business impact.

Understanding Agentic AI behavior

To understand the collective behaviors of agentic AI systems, we need to examine the individual AIs that comprise them. When AIs make mistakes or make things up, they can behave in ways that are truly bizarre. But when they work well, the reasons why are sometimes surprisingly relatable.

Tools like ChatGPT drew attention by sounding human. Moreover, individual AIs often behave like individual people, responding to incentives and organizing their own work in much the same ways that humans do. Recall the counterintuitive findings of many early users of ChatGPT and similar large language models (LLMs) in 2022: They seemed to perform better when offered a cash tip, told the answer was really important or were threatened with hypothetical punishments.

One of the most effective and enduring techniques discovered in those early days of LLM testing was ‘chain-of-thought prompting,’ which instructed AIs to think through and explain each step of their analysis—much like a teacher forcing a student to show their work. Individual AIs can also react to new information similar to individual people. Researchers have found that LLMs can be effective at simulating the opinions of individual people or demographic groups on diverse topics, including consumer preferences and politics.

As agentic AI develops, we are finding that groups of AIs also exhibit human-like behaviors collectively. A 2025 paper found that communities of thousands of AI agents set to chat with each other developed familiar human social behaviors like settling into echo chambers. Other researchers have observed the emergence of cooperative and competitive strategies and the development of distinct behavioral roles when setting groups of AIs to play a game together.

The fact that groups of agentic AIs are working more like human teams doesn’t necessarily indicate that machines have inherently human-like characteristics. It may be more nurture than nature: AIs are being designed with inspiration from humans. The breakthrough triumph of ChatGPT was widely attributed to using human feedback during training. Since then, AI developers have gotten better at aligning AI models to human expectations. It stands to reason, then, that we may find similarities between the management techniques that work for human workers and for agentic AI.

Lessons From the Frontier

So, how best to manage hybrid teams of humans and agentic AIs? Lessons can be gleaned from leading AI labs. In a recent research report, Anthropic shared the practical roadmap and published lessons learned while building its Claude Research feature, which uses teams of multiple AI agents to accomplish complex reasoning tasks. For example, using agents to search the web for information and calling external tools to access information from sources like emails and documents.

Advancements in agentic AI enabling new offerings like Claude Research and Amazon Q are causing a stir among AI practitioners because they reveal insights from the frontlines of AI research about how to make agentic AI and the hybrid organizations that leverage it more effective. What is striking about Anthropic’s report is how transparent it is about all the hard-won lessons learned in developing its offering—and the fact that many of these lessons sound a lot like what we find in classic management texts:

LESSON 1: DELEGATION MATTERS.

When Anthropic analyzed what factors lead to excellent performance by Claude Research, it turned out that the best agentic systems weren’t necessarily built on the best or most expensive AI models. Rather, like a good human manager, they need to excel at breaking down and distributing tasks to their digital workers.

Unlike human teams, agentic systems can enlist as many AI workers as needed, onboard them instantly and immediately set them to work. Organizations that can exploit this scalability property of AI will gain a key advantage, but the hard part is assigning each of them to contribute meaningful, complementary work to the overall project.

In classical management, this is called delegation. Any good manager knows that, even if they have the most experience and the strongest skills of anyone on their team, they can’t do it all alone. Delegation is necessary to harness the collective capacity of their team. It turns out this is crucial to AI, too.

The authors explain this result in terms of ‘parallelization’: Being able to separate the work into small chunks allows many AI agents to contribute work simultaneously, each focusing on one piece of the problem. The research report attributes 80 per cent of the performance differences between agentic AI systems to the total amount of computing resources they leverage.

Whether or not each individual agent is the smartest in the digital toolbox, the collective has more capacity for reasoning when there are many AI ‘hands’ working together. In addition to the quality of the output, teams working in parallel get work done faster. Anthropic says that reconfiguring its AI agents to work in parallel improved research speed by 90 per cent.

Anthropic’s report on how to orchestrate agentic systems effectively reads like a classical delegation training manual: Provide a clear objective, specify the output you expect and provide guidance on what tools to use, and set boundaries. When the objective and output format is not clear, workers may come back with irrelevant or irreconcilable information.

LESSON 2: ITERATION MATTERS.

Edison famously tested thousands of light bulb designs and filament materials before arriving at a workable solution. Likewise, successful agentic AI systems work far better when they are allowed to learn from their early attempts and then try again. Claude Research spawns a multitude of AI agents, each doubling and tripling back on their own work as they go through a trial-and-error process to land on the right results.

This is exactly how management researchers have recommended organizations staff novel projects where large teams are tasked with exploring unfamiliar terrain: Teams should split up and conduct trial-and-error learning, in parallel, like a pharmaceutical company progressing multiple molecules towards a potential clinical trial. Even when one candidate seems to have the strongest chances at the outset, there is no telling in advance which one will improve the most as it is iterated upon.

The advantage of using AI for this iterative process is speed: AI agents can complete and retry their tasks in milliseconds. A recent report from Microsoft Research illustrates this. Its agentic AI system launched up to five AI worker teams in a race to finish a task first, each plotting and pursuing its own iterative path to the destination. They found that a five-team system typically returned results about twice as fast as a single AI worker team with no loss in effectiveness, although at the cost of about twice as much total computing spend.

Going further, Claude Research’s system design endowed its top-level AI agent—the ‘Lead Researcher’—with the decision authority to delegate more research iterations if it was not satisfied with the results returned by its sub-agents. They managed the choice of whether or not they should continue their iterative search loop, to a limit. To the extent that agentic AI mirrors the world of human management, this might be one of the most important topics to watch going forward. Deciding when to stop and what is ‘good enough’ has always been one of the hardest problems organizations face.

LESSON 3: EFFECTIVE INFORMATION SHARING MATTERS.

If you work in a manufacturing department, you wouldn’t rely on your division chief to explain the specs you need to meet for a new product. You would go straight to the source: the domain experts in R&D. Successful organizations need to be able to share complex information efficiently both vertically and horizontally.

To solve the horizontal sharing problem for Claude Research, Anthropic innovated a novel mechanism for AI agents to share their outputs directly with each other by writing directly to a common file system, like a corporate intranet. In addition to saving on the cost of the central coordinator having to consume every sub-agent’s output, this approach helps resolve the information bottleneck. It enables AI agents that have become specialized in their tasks to own how their content is presented to the larger digital team. This is a smart way to leverage the superhuman scope of AI workers, enabling each of many AI agents to act as distinct subject matter experts.

In effect, Anthropic’s AI Lead Researchers must be generalist managers. Their job is to see the big picture and translate that into the guidance that sub-agents need to do their work. They don’t need to be experts on every task the sub-agents are performing. The parallel goes further: AIs working together also need to know the limits of information sharing, like what kinds of tasks don’t make sense to distribute horizontally.

Management scholars suggest that human organizations focus on automating the smallest tasks; the ones that are most repeatable and that can be executed the most independently. Tasks that require more interaction between people tend to go slower, since the communication not only adds overhead, but is something that many struggle to do effectively.

Anthropic found much the same was true of its AI agents: “Domains that require all agents to share the same context or involve many dependencies between agents are not a good fit for multi-agent systems today.” This is why the company focused its premier agentic AI feature on research, a process that can leverage a large number of sub-agents each performing repetitive, isolated searches before compiling and synthesizing the results.

All of these lessons lead to the conclusion that knowing your team and paying keen attention to how to get the best out of them will continue to be the most important skill of successful managers of both humans and AIs. With humans, we call this leadership skill empathy. That concept doesn’t apply to AIs, but the techniques of empathic managers do.

Anthropic got the most out of its AI agents by performing a thoughtful, systematic analysis of their performance and what supports they benefited from, and then used that insight to optimize how they execute as a team. Claude Research is designed to put different AI models in the positions where they are most likely to succeed. Anthropic’s most intelligent Opus model takes the Lead Researcher role, while their cheaper and faster Sonnet model fulfills the more numerous sub-agent roles. Anthropic has analyzed how to distribute responsibility and share information across its digital worker network. And it knows that the next generation of AI models might work in importantly different ways, so it has built performance measurement and management systems that help it tune its organizational architecture to adapt to the characteristics of its AI ‘workers.’

Key Takeaways

Managers of hybrid teams can apply these ideas to design their own complex systems of human and digital workers:

DELEGATE.

Analyze the tasks in your workflows so that you can design a division of labour that plays to the strength of each of your resources. Entrust your most experienced humans with the roles that require context and judgment and entrust AI models with the tasks that need to be done quickly or benefit from extreme parallelization.

If you’re building a hybrid customer service organization, let AIs handle tasks like eliciting pertinent information from customers and suggesting common solutions. But always escalate to human representatives to resolve unique situations and offer accommodations, especially when doing so can carry legal obligations and financial ramifications. To help them work together well, task the AI agents with preparing concise briefs compiling the case history and potential resolutions to help humans jump into the conversation.

ITERATE.

AIs will likely underperform your top human team members when it comes to solving novel problems in the fields in which they are expert. But AI agents’ speed and parallelization still make them valuable partners. Look for ways to augment human-led explorations of new territory with agentic AI scouting teams that can explore many paths for them in advance.

Hybrid software development teams will especially benefit from this strategy. Agentic coding AI systems are capable of building apps, autonomously making improvements to and bug-fixing their code to meet a spec. But without humans in the loop, they can fall into rabbit holes. Examples abound of AI-generated code that might appear to satisfy specified requirements, but diverges from products that meet organizational requirements for security, integration or user experiences that humans would truly desire. Take advantage of the fast iteration of AI programmers to test different solutions, but make sure your human team is checking its work and redirecting the AI when needed.

SHARE.

Make sure each of your hybrid team’s outputs are accessible to each other so that they can benefit from each others’ work products. Make sure workers doing hand-offs write down clear instructions with enough context that either a human colleague or AI model could follow. Anthropic found that AI teams benefited from clearly communicating their work to each other, and the same will be true of communication between humans and AI in hybrid teams.

MEASURE AND IMPROVE.

Organizations should always strive to grow the capabilities of their human team members over time. Assume that the capabilities and behaviors of your AI team members will change over time, too, but at a much faster rate. So will the ways the humans and AIs interact together. Make sure to understand how they are performing individually and together at the task level, and plan to experiment with the roles you ask AI workers to take on as the technology evolves.

An important example of this comes from medical imaging. Harvard Medical School researchers have found that hybrid AI-physician teams have wildly varying performance as diagnosticians. The problem wasn’t necessarily that the AI has poor or inconsistent performance; what mattered was the interaction between person and machine. Different doctors’ diagnostic performance benefited—or suffered—at different levels when they used AI tools. Being able to measure and optimize those interactions, perhaps at the individual level, will be critical to hybrid organizations.

In Closing

We are in a phase of AI technology where the best performance is going to come from mixed teams of humans and AIs working together. Managing those teams is not going to be the same as we’ve grown used to, but the hard-won lessons of decades past still have a lot to offer.

This essay was written with Nathan E. Sanders, and originally appeared in Rotman Management Magazine.

The New York City Wegman’s is collecting biometric information about customers.

We don’t have many details:

President Donald Trump suggested Saturday that the U.S. used cyberattacks or other technical capabilities to cut power off in Caracas during strikes on the Venezuelan capital that led to the capture of Venezuelan President Nicolás Maduro.

If true, it would mark one of the most public uses of U.S. cyber power against another nation in recent memory. These operations are typically highly classified, and the U.S. is considered one of the most advanced nations in cyberspace operations globally.

Wired is reporting on Chinese darknet markets on Telegram.

The ecosystem of marketplaces for Chinese-speaking crypto scammers hosted on the messaging service Telegram have now grown to be bigger than ever before, according to a new analysis from the crypto tracing firm Elliptic. Despite a brief drop after Telegram banned two of the biggest such markets in early 2025, the two current top markets, known as Tudou Guarantee and Xinbi Guarantee, are together enabling close to $2 billion a month in money-laundering transactions, sales of scam tools like stolen data, fake investment websites, and AI deepfake tools, as well as other black market services as varied as pregnancy surrogacy and teen prostitution.

The crypto romance and investment scams regrettably known as “pig butchering”—carried out largely from compounds in Southeast Asia staffed with thousands of human trafficking victims—have grown to become the world’s most lucrative form of cybercrime. They pull in around $10 billion annually from US victims alone, according to the FBI. By selling money-laundering services and other scam-related offerings to those operations, markets like Tudou Guarantee and Xinbi Guarantee have grown in parallel to an immense scale.

Probably a college prank.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Blog moderation policy.

404 Media has the story:

Unlike many of Flock’s cameras, which are designed to capture license plates as people drive by, Flock’s Condor cameras are pan-tilt-zoom (PTZ) cameras designed to record and track people, not vehicles. Condor cameras can be set to automatically zoom in on people’s faces as they walk through a parking lot, down a public street, or play on a playground, or they can be controlled manually, according to marketing material on Flock’s website. We watched Condor cameras zoom in on a woman walking her dog on a bike path in suburban Atlanta; a camera followed a man walking through a Macy’s parking lot in Bakersfield; surveil children swinging on a swingset at a playground; and film high-res video of people sitting at a stoplight in traffic. In one case, we were able to watch a man rollerblade down Brookhaven, Georgia’s Peachtree Creek Greenway bike path. The Flock camera zoomed in on him and tracked him as he rolled past. Minutes later, he showed up on another exposed camera livestream further down the bike path. The camera’s resolution was good enough that we were able to see that, when he stopped beneath one of the cameras, he was watching rollerblading videos on his phone.

Interesting article on the variety of LinkedIn job scams around the world:

In India, tech jobs are used as bait because the industry employs millions of people and offers high-paying roles. In Kenya, the recruitment industry is largely unorganized, so scamsters leverage fake personal referrals. In Mexico, bad actors capitalize on the informal nature of the job economy by advertising fake formal roles that carry a promise of security. In Nigeria, scamsters often manage to get LinkedIn users to share their login credentials with the lure of paid work, preying on their desperation amid an especially acute unemployment crisis.

These are scams involving fraudulent employers convincing prospective employees to send them money for various fees. There is an entirely different set of scams involving fraudulent employees getting hired for remote jobs.

Scammers are generating images of broken merchandise in order to apply for refunds.

Artificial Intelligence (AI) overlords are a common trope in science-fiction dystopias, but the reality looks much more prosaic. The technologies of artificial intelligence are already pervading many aspects of democratic government, affecting our lives in ways both large and small. This has occurred largely without our notice or consent. The result is a government incrementally transformed by AI rather than the singular technological overlord of the big screen.

Let us begin with the executive branch. One of the most important functions of this branch of government is to administer the law, including the human services on which so many Americans rely. Many of these programs have long been operated by a mix of humans and machines, even if not previously using modern AI tools such as Large Language Models.

A salient example is healthcare, where private insurers make widespread use of algorithms to review, approve, and deny coverage, even for recipients of public benefits like Medicare. While Biden-era guidance from the Centers for Medicare and Medicaid Services (CMS) largely blesses this use of AI by Medicare Advantage operators, the practice of overriding the medical care recommendations made by physicians raises profound ethical questions, with life and death implications for about thirty million Americans today.

This April, the Trump administration reversed many administrative guardrails on AI, relieving Medicare Advantage plans from the obligation to avoid AI-enabled patient discrimination. This month, the Trump administration took a step further. CMS rolled out an aggressive new program that financially rewards vendors that leverage AI to reject rapidly prior authorization for "wasteful" physician or provider-requested medical services. The same month, the Trump administration also issued an executive order limiting the abilities of states to put consumer and patient protections around the use of AI.

This shows both growing confidence in AI’s efficiency and a deliberate choice to benefit from it without restricting its possible harms. Critics of the CMS program have characterized it as effectively establishing a bounty on denying care; AI—in this case—is being used to serve a ministerial function in applying that policy. But AI could equally be used to automate a different policy objective, such as minimizing the time required to approve pre-authorizations for necessary services or to minimize the effort required of providers to achieve authorization.

Next up is the judiciary. Setting aside concerns about activist judges and court overreach, jurists are not supposed to decide what law is. The function of judges and courts is to interpret the law written by others. Just as jurists have long turned to dictionaries and expert witnesses for assistance in their interpretation, AI has already emerged as a tool used by judges to infer legislative intent and decide on cases. In 2023, a Colombian judge was the first publicly to use AI to help make a ruling. The first known American federal example came a year later when United States Circuit Judge Kevin Newsom began using AI in his jurisprudence, to provide second "opinions" on the plain language meaning of words in statute. A District of Columbia Court of Appeals similarly used ChatGPT in 2025 to deliver an interpretation of what common knowledge is. And there are more examples from Latin America, the United Kingdom, India, and beyond.

Given that these examples are likely merely the tip of the iceberg, it is also important to remember that any judge can unilaterally choose to consult an AI while drafting his opinions, just as he may choose to consult other human beings, and a judge may be under no obligation to disclose when he does.

This is not necessarily a bad thing. AI has the ability to replace humans but also to augment human capabilities, which may significantly expand human agency. Whether the results are good or otherwise depends on many factors. These include the application and its situation, the characteristics and performance of the AI model, and the characteristics and performance of the humans it augments or replaces. This general model applies to the use of AI in the judiciary.

Each application of AI legitimately needs to be considered in its own context, but certain principles should apply in all uses of AI in democratic contexts. First and foremost, we argue, AI should be applied in ways that decentralize rather than concentrate power. It should be used to empower individual human actors rather than automating the decision-making of a central authority. We are open to independent judges selecting and leveraging AI models as tools in their own jurisprudence, but we remain concerned about Big Tech companies building and operating a dominant AI product that becomes widely used throughout the judiciary.

This principle brings us to the legislature. Policymakers worldwide are already using AI in many aspects of lawmaking. In 2023, the first law written entirely by AI was passed in Brazil. Within a year, the French government had produced its own AI model tailored to help the Parliament with the consideration of amendments. By the end of that year, the use of AI in legislative offices had become widespread enough that twenty percent of state-level staffers in the United States reported using it, and another forty percent were considering it.

These legislative members and staffers, collectively, face a significant choice: to wield AI in a way that concentrates or distributes power. If legislative offices use AI primarily to encode the policy prescriptions of party leadership or powerful interest groups, then they will effectively cede their own power to those central authorities. AI here serves only as a tool enabling that handover.

On the other hand, if legislative offices use AI to amplify their capacity to express and advocate for the policy positions of their principals—the elected representatives—they can strengthen their role in government. Additionally, AI can help them scale their ability to listen to many voices and synthesize input from their constituents, making it a powerful tool for better realizing democracy. We may prefer a legislator who translates his principles into the technical components and legislative language of bills with the aid of a trustworthy AI tool executing under his exclusive control rather than with the aid of lobbyists executing under the control of a corporate patron.

Examples from around the globe demonstrate how legislatures can use AI as tools for tapping into constituent feedback to drive policymaking. The European civic technology organization Make.org is organizing large-scale digital consultations on topics such as European peace and defense. The Scottish Parliament is funding the development of open civic deliberation tools such as Comhairle to help scale civic participation in policymaking. And Japanese Diet member Takahiro Anno and his party Team Mirai are showing how political innovators can build purpose-fit applications of AI to engage with voters.

AI is a power-enhancing technology. Whether it is used by a judge, a legislator, or a government agency, it enhances an entity’s ability to shape the world. This is both its greatest strength and its biggest danger. In the hands of someone who wants more democracy, AI will help that person. In the hands of a society that wants to distribute power, AI can help to execute that. But, in the hands of another person, or another society, bent on centralization, concentration of power, or authoritarianism, it can also be applied toward those ends.

We are not going to be fully governed by AI anytime soon, but we are already being governed with AI—and more is coming. Our challenge in these years is more a social than a technological one: to ensure that those doing the governing are doing so in the service of democracy.

This essay was written with Nathan E. Sanders, and originally appeared in Merion West.

New research:

Abstract: Coleoid cephalopods have the most elaborate camouflage system in the animal kingdom. This enables them to hide from or deceive both predators and prey. Most studies have focused on benthic species of octopus and cuttlefish, while studies on squid focused mainly on the chromatophore system for communication. Camouflage adaptations to the substrate while moving has been recently described in the semi-pelagic oval squid (Sepioteuthis lessoniana). Our current study focuses on the same squid’s complex camouflage to substrate in a stationary, motionless position. We observed disruptive, uniform, and mottled chromatic body patterns, and we identified a threshold of contrast between dark and light chromatic components that simplifies the identification of disruptive chromatic body pattern. We found that arm postural components are related to the squid position in the environment, either sitting directly on the substrate or hovering just few centimeters above the substrate. Several of these context-dependent body patterns have not yet been observed in S. lessoniana species complex or other loliginid squids. The remarkable ability of this squid to display camouflage elements similar to those of benthic octopus and cuttlefish species might have convergently evolved in relation to their native coastal habitat.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Blog moderation policy.

Someone hacked an Italian ferry.

It looks like the malware was installed by someone on the ferry, and not remotely.

This is pretty scary:

Urban VPN Proxy targets conversations across ten AI platforms: ChatGPT, Claude, Gemini, Microsoft Copilot, Perplexity, DeepSeek, Grok (xAI), Meta AI.

For each platform, the extension includes a dedicated “executor” script designed to intercept and capture conversations. The harvesting is enabled by default through hardcoded flags in the extension’s configuration.

There is no user-facing toggle to disable this. The only way to stop the data collection is to uninstall the extension entirely.

[…]

The data collection operates independently of the VPN functionality. Whether the VPN is connected or not, the harvesting runs continuously in the background.

[…]

What gets captured:

• Every prompt you send to the AI
  Every response you receive
• Conversation identifiers and timestamps
• Session metadata
• The specific AI platform and model used

BoingBoing post.

EDITED TO ADD (12/15): Two news articles.

News:

The Danish Defence Intelligence Service (DDIS) announced on Thursday that Moscow was behind a cyber-attack on a Danish water utility in 2024 and a series of distributed denial-of-service (DDoS) attacks on Danish websites in the lead-up to the municipal and regional council elections in November.

The first, it said, was carried out by the pro-Russian group known as Z-Pentest and the second by NoName057(16), which has links to the Russian state.

Slashdot thread.

After twenty-six years, Microsoft is finally upgrading the last remaining instance of the encryption algorithm RC4 in Windows.

of the most visible holdouts in supporting RC4 has been Microsoft. Eventually, Microsoft upgraded Active Directory to support the much more secure AES encryption standard. But by default, Windows servers have continued to respond to RC4-based authentication requests and return an RC4-based response. The RC4 fallback has been a favorite weakness hackers have exploited to compromise enterprise networks. Use of RC4 played a key role in last year’s breach of health giant Ascension. The breach caused life-threatening disruptions at 140 hospitals and put the medical records of 5.6 million patients into the hands of the attackers. US Senator Ron Wyden (D-Ore.) in September called on the Federal Trade Commission to investigate Microsoft for “gross cybersecurity negligence,” citing the continued default support for RC4.

Last week, Microsoft said it was finally deprecating RC4 and cited its susceptibility to Kerberoasting, the form of attack, known since 2014, that was the root cause of the initial intrusion into Ascension’s network.

Fun fact: RC4 was a trade secret until I published the algorithm in the second edition of Applied Cryptography in 1995.

Video from Reddit shows what could go wrong when you try to pet a—looks like a Humboldt—squid.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Blog moderation policy.

At least some of this is coming to light:

Doublespeed, a startup backed by Andreessen Horowitz (a16z) that uses a phone farm to manage at least hundreds of AI-generated social media accounts and promote products has been hacked. The hack reveals what products the AI-generated accounts are promoting, often without the required disclosure that these are advertisements, and allowed the hacker to take control of more than 1,000 smartphones that power the company.

The hacker, who asked for anonymity because he feared retaliation from the company, said he reported the vulnerability to Doublespeed on October 31. At the time of writing, the hacker said he still has access to the company’s backend, including the phone farm itself.

Slashdot thread.

I’m sure there’s a story here:

Sources say the man had tailgated his way through to security screening and passed security, meaning he was not detected carrying any banned items.

The man deceived the BA check-in agent by posing as a family member who had their passports and boarding passes inspected in the usual way.

For two days in September, Afghanistan had no internet. No satellite failed; no cable was cut. This was a deliberate outage, mandated by the Taliban government. It followed a more localized shutdown two weeks prior, reportedly instituted “to prevent immoral activities.” No additional explanation was given. The timing couldn’t have been worse: communities still reeling from a major earthquake lost emergency communications, flights were grounded, and banking was interrupted. Afghanistan’s blackout is part of a wider pattern. Just since the end of September, there were also major nationwide internet shutdowns in Tanzania and Cameroon, and significant regional shutdowns in Pakistan and Nigeria. In all cases but one, authorities offered no official justification or acknowledgment, leaving millions unable to access information, contact loved ones, or express themselves through moments of crisis, elections, and protests.

The frequency of deliberate internet shutdowns has skyrocketed since the first notable example in Egypt in 2011. Together with our colleagues at the digital rights organisation Access Now and the #KeepItOn coalition, we’ve tracked 296 deliberate internet shutdowns in 54 countries in 2024, and at least 244 more in 2025 so far.

This is more than an inconvenience. The internet has become an essential piece of infrastructure, affecting how we live, work, and get our information. It’s also a major enabler of human rights, and turning off the internet can worsen or conceal a spectrum of abuses. These shutdowns silence societies, and they’re getting more and more common.

Shutdowns can be local or national, partial or total. In total blackouts, like Afghanistan or Tanzania, nothing works. But shutdowns are often targeted more granularly. Cellphone internet could be blocked, but not broadband. Specific news sites, social media platforms, and messaging systems could be blocked, leaving overall network access unaffected—as when Brazil shut off X (formerly Twitter) in 2024. Sometimes bandwidth is just throttled, making everything slower and unreliable.

Sometimes, internet shutdowns are used in political or military operations. In recent years, Russia and Ukraine have shut off parts of each other’s internet, and Israel has repeatedly shut off Palestinians’ internet in Gaza. Shutdowns of this type happened 25 times in 2024, affecting people in 13 countries.

Reasons for the shutdowns are as varied as the countries that perpetrate them. General information control is just one. Shutdowns often come in response to political unrest, as governments try to prevent people from organizing and getting information; Panama had a regional shutdown this summer in response to protests. Or during elections, as opposition parties utilize the internet to mobilize supporters and communicate strategy. Belarusian president Alyaksandr Lukashenko, who has ruled since 1994, reportedly disabled the internet during elections earlier this year, following a similar move in 2020. But they can also be more banal. Access Now documented countries disabling parts of the internet during student exam periods at least 16 times in 2024, including Algeria, Iraq, Jordan, Kenya, and India.

Iran’s shutdowns in 2022 and June of this year are good examples of a highly sophisticated effort, with layers of shutdowns that end up forcing people off the global internet and onto Iran’s surveilled, censored national intranet. India, meanwhile, has been the world shutdown leader for many years, with 855 distinct incidents. Myanmar is second with 149, followed by Pakistan and then Iran. All of this information is available on Access Now’s digital dashboard, where you can see breakdowns by region, country, type, geographic extent, and time.

There was a slight decline in shutdowns during the early years of the pandemic, but they have increased sharply since then. The reasons are varied, but a lot can be attributed to the rise in protest movements related to economic hardship and corruption, and general democratic backsliding and instability. In many countries today, shutdowns are a knee-jerk response to any form of unrest or protest, no matter how small.

A country’s ability to shut down the internet depends a lot on its infrastructure. In the US, for example, shutdowns would be hard to enforce. As we saw when discussions about a potential TikTok ban ramped up two years ago, the complex and multifaceted nature of our internet makes it very difficult to achieve. However, as we’ve seen with total nationwide shutdowns around the world, the ripple effects in all aspects of life are immense. (Remember the effects of just a small outage—CrowdStrike in 2024—which crippled 8.5 million computers and cancelled 2,200 flights in the US alone?)

The more centralized the internet infrastructure, the easier it is to implement a shutdown. If a country has just one cellphone provider, or only two fiber optic cables connecting the nation to the rest of the world, shutting them down is easy.

Shutdowns are not only more common, but they’ve also become more harmful. Unlike in years past, when the internet was a nice option to have, or perhaps when internet penetration rates were significantly lower across the Global South, today the internet is an essential piece of societal infrastructure for the majority of the world’s population.

Access Now has long maintained that denying people access to the internet is a human rights violation, and has collected harrowing stories from places like Tigray in Ethiopia, Uganda, Annobon in Equatorial Guinea, and Iran. The internet is an essential tool for a spectrum of rights, including freedom of expression and assembly. Shutdowns make documenting ongoing human rights abuses and atrocities more difficult or impossible. They are also impactful on people’s daily lives, business, healthcare, education, finances, security, and safety, depending on the context. Shutdowns in conflict zones are particularly damaging, as they impact the ability of humanitarian actors to deliver aid and make it harder for people to find safe evacuation routes and civilian corridors.

Defenses on the ground are slim. Depending on the country and the type of shutdown, there can be workarounds. Everything, from VPNs to mesh networks to Starlink terminals to foreign SIM cards near borders, has been used with varying degrees of success. The tech-savvy sometimes have other options. But for most everyone in society, no internet means no internet—and all the effects of that loss.

The international community plays an important role in shaping how internet shutdowns are understood and addressed. World bodies have recognized that reliable internet access is an essential service, and could put more pressure on governments to keep the internet on in conflict-affected areas. But while international condemnation has worked in some cases (Mauritius and South Sudan are two recent examples), countries seem to be learning from each other, resulting in both more shutdowns and new countries perpetrating them.

There’s still time to reverse the trend, if that’s what we want to do. Ultimately, the question comes down to whether or not governments will enshrine both a right to access information and freedom of expression in law and in practice. Keeping the internet on is a norm, but the trajectory from a single internet shutdown in 2011 to 2,000 blackouts 15 years later demonstrates how embedded the practice has become. The implications of that shift are still unfolding, but they reach far beyond the moment the screen goes dark.

This essay was written with Zach Rosson, and originally appeared in Gizmodo.