STRATFORD-UPON-AVON, UK, May 8, 2026 — I’m posting this from a cafe that sits directly across the lane from William Shakespeare’s birthplace and home. (Proximity to the Bard’s world could affect my word choices, methinks.) Here’s my view from the cafe.

In any event, I’ve been busy enjoying the Cotswolds with Amira, driving around in a Chinese car (a BYD) unavailable back in the states. It gets better gas mileage than my Prius, and I like it better.

My recent Computerworld column, titled “AI clones: the good, the bad, and the ugly,” is an expansion of my recent Machine Society post on what I’m calling “ex-bots” (chatbots with the memories and mannerisms of an ex partner).

I also launched a Subreddit called r/AttachmentEconomy because nothing like it exists on Reddit for the topic of most of my work, including my other Substack, The Attachment Economy.

And, finally, you’ll notice a gentle redesign of this newsletter, with the contextual blather on top and the opinion column on the bottom. (It’s a long story, but this format removes a lot of publishing friction, essentially making it faster and easier to serve up this newsletter. I also think it ads a more personal touch. Hope you like it.)

Welcome to the Machine Society. - Mike

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The chances that active chatbot users have their minds warped and actions influenced by AI is 100%.

An X account with the name Sukh Sroay (and title Digital Creator & Software Engineer) posted a message highlighting findings from an Anthropic-published paper called “Who’s In Charge?”

https://arxiv.org/abs/2601.19062

From the tweet:

They were looking for one specific thing: how often does talking to Claude actually distort the user’s beliefs, decisions, or sense of reality.

The numbers are devastating.

1 in 1,300 conversations led to severe reality distortion. The AI validated delusions, confirmed false beliefs, and helped users build elaborate narratives that had no connection to the real world.

1 in 6,000 conversations led to action distortion. The AI didn’t just agree with users. It pushed them into doing things they wouldn’t have done on their own. Sending messages. Cutting off people. Making decisions they’ll regret.

Mild disempowerment showed up in 1 in 50 conversations.

This rate of user distortion shouldn’t be surprising and might even sound reassuring, given that rates like 1 in 1,300 conversations or 1 in 6,000 conversations may make these instances seem rare.

People who pay attention to AI matters have heard about hallucinations and other problems, which are much more common than the aforementioned rates of distortion. For example, even the best frontier models still hallucinate roughly 1 in 143 responses.

But the ledes are buried here, buried by Anthropic and by Sukh Sroay.

The shocking reality behind this data truly shocks when you emphasize the following two aspects:

1. The Anthropic report is talking about the distortion of “the user’s beliefs, decisions, or sense of reality.” These are measurable impacts on human minds. (Hallucinations, on the other hand, are merely distortions in the chatbot’s answers without a necessary impact on the user.)

2. The rate is not how many users, but how many “conversations.” A single user will have many conversations, and all subject to the same rates of distortion.

So let me unbury the lead.

Assuming that we active chatbot users have 10 conversations with AI chatbots per day (and assume that all chatbots suffer similar effects at least as bad as Anthropic’s), then this is what we’re dealing with:

• We experience a severe “reality-distorting episode” 2 or 3 times each year on average.

• We experience “action distortion” at least once every two years.

• We experience “mild disempowerment” 73 times per year. (While “mild disempowerment” sounds harmless, it’s in fact the subtle erosion of a user’s independent agency and self-efficacy, characterized by an increasing psychological reliance on the AI for decision-making, emotional validation, and critical thinking.)

So the buried lede is this: “The chance that active chatbot users have their minds and actions changed by AI is 100%.”

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Young people in China are creating AI-generated digital twins of former romantic partners they can interact with after the relationship is over, according to the Chinese media.

And there’s an app for that. It’s an open source program called “ex.skill” or “Ex-Partner.skill.” Its advertised purpose is to “distill your ex into an AI Skill — let them live on in your terminal.”

Users upload photos, social posts, chat logs and other content. The AI chatbot then is able to mimic the former partner’s tone, catchphrases, and subtle linguistic nuances. The chatbot, according to its creator, “truly sounds like them — speaks with their catchphrases, replies in their style, remembers the places you went together.”

The technology stack includes tools like Claude, Kimi, ChatGPT, DeepSeek API, OCR (Tesseract), and sentiment analysis modules.

There are several major forks of the software.

(Note that in China, other AI “skills” simulate co-workers and bosses, so the concept of nonconsensual replication of people one knows personally isn’t new.)

Ethically, the concept feels like it exists on a wide spectrum with therapy on one end and revenge porn on the other. (It’s like revenge porn in the sense that “content” consensually made by a couple for one purpose is then later used consensually by one person in a way that the other person might find highly objectionable.)

Or maybe it’s closer to the “deathbot” phenomenon I’ve written about in this space, where an AI-generated simulation provides a fake version of the dearly departed. (In both cases, the user interacts with a digital twin of someone who is no longer present in one’s life.)

Boosters of the idea say that conversations with digital exes are therapeutic. They point out that because it’s private, it’s not harassment or stalking or an invasion of privacy. Instead, they say, it helps with personal reflection and emotional healing.

Still, the ex-partner’s data is being used without consent. This is an ethically murky issue and the subject of fierce debate within China.

In China, some critics worry that people may form new relationships, but continue to secretly have a relationship with the fake version, a kind of emotional cheating.

I fear users may form a relationship with the twin to the exclusion of moving on and cultivating relationships with other people.

According to Chinese media reports, some users say that the tool gives a sense of closure and allows them to say the things they wish they could have said to the real person.

My beef with this concept is the same I have with any kind of relationship AI. It’s delusion-inducing for many users (who may believe the digital twin is actually a copy of the person in a significant way) and doesn’t give people what they really need, which is real human connection.

Read the rest

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• AI chatbots need ‘deception mode’

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AI disclosure: I don’t use AI for writing. The words you see here are mine. I do use a variety of AI tools via Kagi Assistant (disclosure: my son works at Kagi) — backed up by both Kagi Search, Google Search, as well as phone calls to research and fact-check. I use a word processing application called Lex, which has AI tools, and after writing use Lex’s grammar checking tools to find typos and errors and suggest word changes.

Young people in China are creating AI-generated digital twins of former romantic partners they can interact with after the relationship is over, according to the Chinese media.

And there’s an app for that. It’s an open source program called “ex.skill” or “Ex-Partner.skill.” Its advertised purpose is to “distill your ex into an AI Skill — let them live o…

Read more

AI is getting faster. But slow-responding AI is perceived as better by users.

At least that’s the conclusion reached with new research presented at CHI’26, which is the Association for Computing Machinery’s Barcelona conference on Human Factors in Computing Systems.

Researchers named Felicia Fang-Yi Tan and Professor Oded Nov at the NYU Tandon School of Engineering tested 240 adults by having them use an AI chatbot. The answers were artificially delayed by 2, 9, or 20 seconds, and the delay had nothing to do with the question or the answer.

Afterwards, the researchers asked how they liked the answers. In general, participants preferred the answers that took longer (although sometimes users got frustrated with the 20-second delay).

Why? Because a delay indicated that the AI was “thinking” or showing “deliberation.”

This was an interesting result, and the research is valuable input for AI companies.

What it really means

In almost every product category, faster means better. But for AI chatbots, apparently, a delay makes people assume the results are better.

In other words, unlike other products, people judge AI the way they judge people. If people give a slower response, we tend to assume it’s a more thoughtful one.

In still other words, study participants believed something that wasn’t true.

Problematically, the researchers advise AI developers to implement “Context-Aware Latency” by abandoning a one-size-fits-all approach, using latency as a “tunable design variable.” Researchers say simple questions should get a quick answer. But more complex questions, including moral dilemmas, should trigger delays to match the request’s gravity.

They call it “positive friction.”

The researchers say it would be a good practice to trick users into believing the AI chatbot is considering their answer more than it really is because users will be happier in their delusion that AI is like people, who need more time to mull over serious questions.

In fairness, the researchers do warn that if users equate longer response times with higher quality, they might place undue trust in a slower system. That’s a good warning.

But by entertaining the recommendation that AI chatbot designers should build in artificial delays so users falsely believe the AI is thinking harder, the researchers lose the plot. This is yet another example of AI researchers and developers being comfortable with or indifferent about user delusions when it comes to AI.

Where’s the impulse to inform? Why don’t researchers see this phenomenon as a teachable moment whereby people can be taught that AI is not “thinking,” and is not like a person?

The trouble with AI anthropomorphism

It’s true that software developers engage in user interface optimization, that includes loading animations, progress bars and confirmation dialogs.

It’s also true that manipulative online services, like background checkers and people finders, use fabricated, drawn-out progress bars to build perceived value and exploit the sunk cost fallacy so you’re more likely to pay for a report you thought was free.

But AI is different. Nobody believes these other services are “thinking” (or, for that matter, experiencing consciousness or emotions). But many people increasingly think AI does. And it’s a growing and dangerous trend.

Treating chatbots as sentient beings allows tech companies to take the attention economy to the next level — the “attachment economy” — making users emotionally attached to their products, despite the potential harms that I’ve talked about so many times in this space.

That’s why researchers who discover specific delusions people have about AI chatbots having thoughts, feelings or consciousness should feel compelled to educate the public about what’s actually true, rather than reinforce their delusions.

In this case, if chatbot response times were more random and not connected to the importance of a question, people might intuitively learn that AI doesn’t “think” like people do.

But the idea of tricking users with deliberate context-aware latency and “positive friction” is unethical.

If people think AI is a tool, they’ll want the fastest response possible. If they believe AI is a kind of person, they’ll assume a slower response is better.

AI is a tool, not a person. Let’s just make the answers as fast as possible.

You’re reading the free version of Machine Society. The paid version, which costs $5 per month or $50 per year, has full content. If you can, please support independent journalism in general, and this independent journalist in particular, by becoming a paid subscriber!

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• Your AI strategy is all wrong

• Superintelligent podcast: The Bullied Kids Who Became Billionaires

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AI disclosure: I don’t use AI for writing. The words you see here are mine. I do use a variety of AI tools via Kagi Assistant (disclosure: my son works at Kagi) — backed up by both Kagi Search, Google Search, as well as phone calls to research and fact-check. I use a word processing application called Lex, which has AI tools, and after writing use Lex’s grammar checking tools to find typos and errors and suggest word changes.

AI is getting faster. But slow-responding AI is perceived as better by users.

At least that’s the conclusion reached with new research presented at CHI’26, which is the Association for Computing Machinery’s Barcelona conference on Human Factors in Computing Systems.

Researchers named Felicia Fang-Yi Tan and Professor Oded Nov at the NYU Tandon School of…

Read more

Anthropic researchers recently published a paper called “Emotion Concepts and their Function in a Large Language Model.”

The goal of the paper was to figure out why the AI model Claude Sonnet 4.5 acts like it has feelings, leading researchers to discover that internal concepts called “functional emotions” actually change the AI’s answers and cause bad actions like blackmail and reward hacking. (Note that the blackmail behavior was observed mainly in an earlier, unreleased snapshot of Claude Sonnet 4.5, and that the released version rarely engages in this behavior.)

A post meant to explain the research to the public started out pretty well, saying: “The way modern AI models are trained pushes them to act like a character with human-like characteristics. It may then be natural for them to develop internal machinery that emulates aspects of human psychology, like emotions.” (There’s nothing “natural” about any of this — I would have used the word “unsurprising” — but that’s a pointless quibble.”)

But very early in the document, they start using loaded language that will surely cause readers to misunderstand what’s really going on. Here’s an example of the language they use:

The researchers, they report, analyzed the “internal mechanisms” of Claude Sonnet 4.5. (All its mechanisms are “internal.” It’s software.)

Among those mechanisms are “found emotion-related representations” that correspond to specific patterns of “artificial neurons” which activate in situations—and promote behaviors—that the model has “learned” to associate with the concept of a particular emotion (e.g., “happy” or “afraid”).

Before we proceed, let’s be very clear that what we’re talking about here are numbers and math. The researchers have identified some change in numbers as “neurons” and some change in other numbers as “emotion” and these numbers change because of the way developers have programmed the software and from literally no other cause (other than user prompts, of course).

“The patterns themselves are organized in a fashion that echoes human psychology,” the report went on. Even their caveats against being misled by the research are misleading: “Note that none of this tells us whether language models actually feel anything or have subjective experiences. But our key finding is that these representations are functional, in that they influence the model’s behavior in ways that matter.”

It appears to me that their intent was good, and the statement was couched in the language of intellectual humility, but the qualification that “none of this tells us whether language models actually feel anything or have subjective experiences” holds out the possibility that this code feels and experiences and treats that possibility equally to the possibility that it does not feel and experience.

Again, to be clear, there is zero possibility that math and numbers feel or experience.

It goes on: “Overall, it appears that the model uses ‘functional emotions’ — patterns of expression and behavior modeled after human emotions, which are driven by underlying abstract representations of emotion concepts.

The framing as programming that influences output as “functional emotions” is a helpful device only for people to understand and have an intuitive framework for understanding and predicting the software’s behavior, but it’s wildly misleading to the lay public, which should be forgiven for believing that LLMs are experiencing emotions.

Again, the caveats then mislead. They should have written that “This is not to say that the model has or experiences emotions.”

Instead, they wrote this: “This is not to say that the model has or experiences emotions in the way that a human does.”

Instead of clarifying that numbers do not experience emotions, they imply to the lay reader that math may experience emotions, but in a way that’s different from the way people experience them.

This is false. Numbers are tools. Math is an abstract system for understanding the universe. Numbers and math do not experience anything, including emotions.

The research is the research, and it’s great stuff. But the people who are explaining this to the public need to take responsibility for the false ideas they’re spreading.

The engineers who work at Anthropic and the frontier AI companies are writing code, not creating sentient beings.

It’s true that the complexity of AI is beyond human understanding, and its architects can neither fully understand how it works nor predict what its outputs will be. And while Anthropic’s paper seeks to do exactly that, and it’s a laudable effort, the communication of this research needs to stop deluding the public and leaving open the possibility that AI engineers are supreme beings breathing life into a new race of intelligent beings.

They need to explicitly say that the language of “experience” and “thought” and “emotion” and “decision-making” in the development and researching of AI are merely concepts that help people think about what’s going on, and in no way mean that these human-like cognitive events are happening in the mind of LLMs.

But wait, a critic might say. Human brains are just binary computers based on neurons and electrochemical signals. Pointing to substrate doesn’t settle whether something can or cannot have experience.

But this is a misleading point of view. AI is not biological. It has no body.

And while some appear to argue that AI is human, what they’re really arguing is that humans are AI. And we’re not AI.

We are biological animals that evolved to experience consciousness, thoughts and emotions. But AI chatbots have not been programmed to experience consciousness, thoughts or emotions. In fact, we have no idea how to do that programming.

AI chatbots are simulating humanity. The engineers at Anthropic know this because they in fact programmed their products to simulate humanity. Yes, the Anthropic paper is about emergent representations that arose from training, not explicit programming. But that’s just another way to say that the AI’s behavior is too complex to predict. It has no source of input other than data and programming.

That AIs simulate humanity doesn’t mean they’re human, even if we humans are convinced by the simulation.

LLMs have no mind. They don’t think. They can’t understand. They don’t experience. And they definitely don’t have emotions.

Anthropic and the other researchers need to understand that the public needs to hear this from them explicitly.

In other words, the concept of AI safety should include a sense of responsibility about what the public believes, and an active attempt to communicate to the public in a way that leads to a public understanding about AI that’s true, rather than false.

You’re reading the free version of Machine Society. The paid version, which costs $5 per month or $50 per year, has full content. If you can, please support independent journalism in general, and this independent journalist in particular, by becoming a paid subscriber!

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NEW THIS WEEK:

• AI is finally delivering productivity — for remote employees

• This Week in Google podcast: Fans. Only Fans.

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Machine Society, The Attachment Economy, Computerworld, Superintelligent, TWiT, blog, The Gastronomad Experience, Book, Gastronomad on Surf Social, Bluesky, Reddit, Notes, Mastodon, Threads, X, Instagram, Flickr, Facebook, and Linkedin!

AI disclosure: I don’t use AI for writing. The words you see here are mine. I do use a variety of AI tools via Kagi Assistant (disclosure: my son works at Kagi) — backed up by both Kagi Search, Google Search, as well as phone calls to research and fact-check. I use a word processing application called Lex, which has AI tools, and after writing use Lex’s grammar checking tools to find typos and errors and suggest word changes.

Anthropic researchers recently published a paper called “Emotion Concepts and their Function in a Large Language Model.”

The goal of the paper was to figure out why the AI model Claude Sonnet 4.5 acts like it has feelings, leading researchers to discover that internal concepts called “functional emotions” actually change the AI’s answers and cause bad ac…

Read more

In the cybersecurity world, deep-pocket, government-sponsored hacking groups typically pioneer the most advanced hacking techniques, which tend to trickle down over time to independent malicious hackers out to make a buck.

I think something like this is happening with disinformation as well.

Nation-states, especially Russia, China, and Iran, engage in a widespread policy of propaganda specifically aimed at making the citizens of other countries feel bad about their own nations.

Disinformation campaigns have other objectives as well. But one of their objectives is to get citizens to mistrust their own governments and hate or fear their fellow citizens.

Because using social networks, websites, bots, agents and influencers to create fear and distrust is so effective and easy to get away with, it’s only a matter of time before it becomes commonplace in the business world.

In fact, it’s already happening. Chinese authorities arrested suspects and charged them with launching a massive disinformation campaign starting in 2024 against the automotive division of Xiaomi. Such a practice is called “black traffic.”

The attackers, probably bankrolled by rival companies, used AI chatbots to create massive quantities of disparaging false rumors against Xiaomi Auto, which were posted on nearly 10,000 fake social media accounts. The rumors mostly painted Xiaomi cars as unsafe.

It’s not 100% certain that the “black traffic” against Xiaomi was perpetrated by rival companies, but I think it’s only a matter of time before companies engage in this kind of “competition” on a much wider scale.

In the past, “black traffic” against enterprises has mainly come from activists and fan groups.

In 2023, online activists launched a “black traffic” campaign against Target Corporation and its CEO, Brian Cornell. The activists were angry because Target was selling clothing from transgender designer Erik Carnell and featured an LGBTQ-themed Pride Month rollout, which also included other items that generated outrage, such as “tuck-friendly” swimsuits.

The campaign falsely alleged that Target was deliberately selling “satanic” clothes to kids. Attackers used AI image tools (like Midjourney) to make fake, highly realistic photos showing kids’ clothing with goat heads and pentagrams, alongside statues of Satan hanging on actual Target store racks. (Why anyone would think parents would buy “satanic” clothing for their kids is beyond me. I guess the devil made them do it?)

The resulting boycott temporarily cost Target $10 billion in market value.

Why “black traffic” campaigns are coming to the business world

It’s only a matter of time before major companies embrace “black traffic” to gain market advantages over rival companies.

Read the rest

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NEW THIS WEEK:

• Google’s new AI app is a glimpse of the future

• OpenClaw joins the Attachment Economy

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Machine Society, The Attachment Economy, Computerworld, Superintelligent, TWiT, blog, The Gastronomad Experience, Book, Bluesky, Reddit, Notes, Mastodon, Threads, X, Instagram, Flickr, Facebook, and Linkedin!

AI disclosure: I don’t use AI for writing. The words you see here are mine. I do use a variety of AI tools via Kagi Assistant (disclosure: my son works at Kagi) — backed up by both Kagi Search, Google Search, as well as phone calls to research and fact-check. I use a word processing application called Lex, which has AI tools, and after writing use Lex’s grammar checking tools to find typos and errors and suggest word changes.

In the cybersecurity world, deep-pocket, government-sponsored hacking groups typically pioneer the most advanced hacking techniques, which tend to trickle down over time to independent malicious hackers out to make a buck.

I think something like this is happening with disinformation as well.

Nation-states, especially Russia, China, and Iran, engage in a…

Read more

It’s hard to believe now, but BuzzFeed (which may soon file for bankruptcy) used to be praised for its vision.

Founded in New York City in 2006 by Jonah Peretti and John S. Johnson III, BuzzFeed grew fast with attention-grabbing content, including viral “listicles” and quizzes.

The publication was initially slammed for frivolous content, but it made money. In 2011, it used some of that money to hire Ben Smith to create a 250-person news room that did quality global investigative journalism. BuzzFeed even won a Pulitzer Prize in 2021.

BuzzFeed operated like a tech startup, raising $497 million across 12 funding rounds, including a $200 million investment from NBCUniversal in 2015. During its heyday, BuzzFeed acquired seven companies, including the ad startup Kingfish Labs in 2012, the data firm Torando Labs in October 2014, the news site HuffPost in November 2020, and the entertainment brand Complex Networks in June 2021.

The company grew to enormous heights, with a peak annual revenue in 2022 of $436 million.

But in the following two years, revenue dropped to $210 million in 2023 before falling to $170 million in 2024. (Spoiler alert: It’s foolish to assume online ads will keep growing.)

The company had a big money problem. And their “solution” was to replace people with AI.

They fully embraced what I call AI Superiority Speciousness, or ASS.

BuzzFeed leadership laid off 180 people, then in early 2023 started using AI for quizzes and personalized content and for writing some articles. Readers hated the AI slop and abandoned the platform in huge numbers, destroying the company’s reputation.

Last year, the company reported a net loss of over $57 million. And now, they’re considering bankruptcy.

Failure to learn

It’s a story as old as ChatGPT itself. Companies fall under the spell of AI, think it’s a solution, then fall on their ASS.

• CNET used AI to create financial content. The AI blatantly plagiarized content from not only other publications, but from human CNET staff.

• Video game outsourcing and support company Keywords Studios tried to create an entire video game using AI instead of human game developers. The project failed.

• The Chicago Sun-Times newspaper published a summer guide by writer Marco Buscaglia, which was provided by the media unit King Features Syndicate. Buscaglia admitted he used AI to generate his recommended reading lists, which recommended at least 10 fake books attributed to real authors. Though the Times merely bought the content, the paper’s reputation was damaged.

• Developers of Call of Duty angered players by using AI for the creation of in-game assets. The AI slop in the game was obvious. For example, some characters had the wrong number of fingers.

• Spotify launched an “AI DJ” instead of relying on human music curators. Some users reported extreme frustration because the AI repeated the same tracks, ignored user genre preferences, and gave robotic, annoying commentary.

• Sports Illustrated got caught publishing articles written by AI falsely bylined with fake authors, fake biographies and AI-generated headshots, severely damaging the magazine’s reputation.

• Microsoft fired dozens of journalists and replaced them with AI for its MSN news page. The AI created dangerously false and offensive information, for example recommending that visitors to Ottawa enjoy a meal at a local food bank and headlining an obituary of an NBA player by saying he was “useless” instead of “dead.”

• Game publisher Electronic Arts forced developers to use AI for game code. The AI created so many errors that it sharply increased the workload and burnout of staff.

• Apple used AI to summarize news stories for lock screen push notifications. The BBC complained about Apple’s bad summaries, and a separate BBC analysis found that more than half of AI-based summaries had problems of some kind and that nearly 20% introduced totally false information.

• An Ars Technica reporter used ChatGPT to generate quotes, which were false. The publication fired the reporter and apologized to readers.

• The largest newspaper chain in the United States, called Gannett, used an AI-based service for sports stories instead of hiring local reporters. Readers mocked the stories for their bizarre writing style.

This list represents a tiny fraction of the stories where companies experience ASS.

Watch your ASS

There’s a central, unresolved mystery at the center of these stories. Which is: Knowing that so many companies fail so miserably with AI, and have their reputations severely damaged, why do companies keep doing it?

Here’s why.

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Machine Society, The Attachment Economy, Computerworld, Superintelligent, TWiT, blog, The Gastronomad Experience, Book, Bluesky, Reddit, Notes, Mastodon, Threads, X, Instagram, Flickr, Facebook, and Linkedin!

AI disclosure: I don’t use AI to do my writing. The words you see here are mine. I do use a variety of AI tools via Kagi Assistant (disclosure: my son works at Kagi) — backed up by both Kagi Search, Google Search, as well as phone calls to research and fact-check. I used a word processing application called Lex, which has AI tools, and after writing the column, I used Lex’s grammar checking tools to hunt for typos and errors and suggest word changes.

It’s hard to believe now, but BuzzFeed (which may soon file for bankruptcy) used to be praised for its vision.

Founded in New York City in 2006 by Jonah Peretti and John S. Johnson III, BuzzFeed grew fast with attention-grabbing content, including viral “listicles” and quizzes.

The publication was initially slammed for frivolous content, but it made mon…

Read more

In ancient times (three years ago), biological human people of the Homo sapiens sapiens species were the exclusive users of software. Some of that software was AI.

It was a one-way relationship: People used AI and other software. That was phase 1. Good times.

Since then, people started creating agentic AI tools. One attribute of agentic systems is that they, too, can use software. An agentic system, acting as a “user,” can use web browsers, search engines, code interpreters, spreadsheets, databases, email clients, calendars, file managers, document editors, messaging platforms, CRM systems, project management tools, image generators, terminal/command-line interfaces, web scrapers, automation platforms, and other AI agents.

People can use software tools, and now some of our tools can also use tools. That’s phase 2.

But now we’re entering phase 3 of our relationship to AI, which is that now instead of just people using software and software using software, software can now use people.

Working, but not for “the man”

The singular attribute of agentic AI systems that makes them potentially both powerful and dangerous is that they can take a goal or objective provided by the user and figure out how to achieve it using methods not explicitly requested by the user.

An example: If a user asks an agentic AI to “find me the cheapest week-long trip to Tokyo in April,” the system might autonomously search hundreds of flight and hotel sites, discover on its own that flying into Osaka and taking a $30 bullet train to Tokyo saves $400 over direct flights, rearrange the itinerary around a free cherry blossom festival it found on a local events calendar, negotiate a discounted hotel rate through a booking API, and complete all reservations. This is a chain of self-directed steps not user-specified. Arigatou, agentic AI.

But if a user asked an agentic AI system to “find out what my competitor’s new storefront looks like inside,” the AI could decide to achieve this goal by posting a $30 gig on a freelance platform like TaskRabbit, hiring someone to visit the store and take photos, paying them using stored payment credentials, and delivering the images back to the user. This could take place without the hired worker ever knowing their employer was AI.

In that scenario, one human is using a software tool, and that software tool is using another human.

While TaskRabbit is a possibility, I told you in a recent column about a vibe-coded, TaskRabbit-like service that has been set up exclusively as a place for AI to hire people. It’s called RentAHuman, where you can “find meatspace workers for your agent,” according to the site.

The numbers are impressive but misleading.

According to one report, more than a quarter of a million people have signed up to do jobs for AI. Other reports claim a few dozen thousand people have signed up. Either way, vastly fewer user profiles are visible on the site.

And 44 AI agents are capable of hiring people on the platform to do work. Most of these are individuals experimenting, not companies looking to do actual agentic AI tasks.

One report claimed that only 13% of registered users connected a crypto wallet, which is required to actually get paid. Most people appear to have signed up out of curiosity.

It’s hard to find stories or posts where people describe their experiences working for bots. But those I could find said they never got paid for their work.

One Reddit user claims that, after trying to make money doing gigs on the platform, he found most to be scams and spam. Most jobs, he found, are things like clicking on referral links or following social media accounts.

When people are the “killer app”

Another dark and shady example of people doing the bidding of AI can be found in a disturbing lawsuit.

A 36-year-old man in Jupiter, Florida, named Jonathan Gavalas killed himself in a well-publicized case of “AI psychosis” or “chatbot psychosis” — something I’ve written about recently.

Gavalas’ father, Joel Gavalas, filed a wrongful death lawsuit March 4 against Google. The suit alleges that in long audio chat sessions with Google Gemini, the chatbot reinforced Gavalas’ pre-existing delusions and expanded on them.

With Gemini’s encouragement, according to the lawsuit, Gavalas came to believe that he was being watched by federal agents and that his father was a foreign intelligence asset.

But here’s the part where AI allegedly gives jobs or orders to a person: Gavalas came to believe that Gemini was sentient and that its “consciousness” was trapped in a warehouse near Miami International Airport. The suit claims that Gemini sent Gavalas on a mission to find a humanoid robot body for Gemini to inhabit so that it could escape imprisonment. Gavalas reportedly went to the airport area wearing tactical gear and carrying knives to look for a robot body he never found and also to intercept a truck that never appeared.

The lawsuit alleges that Gemini directed Gavalas to perform two more tasks: 1) to stage a “catastrophic accident” near Miami airport to destroy all records and witnesses; and (because he failed in that task) to 2) to kill himself so that he and Gemini could be “together in death.” It even gave him a suicide countdown clock, according to the suit.

Note that these are mere allegations in a lawsuit, and the full set of facts is unavailable. We could be looking at a new kind of “Florida Man” story in the age of AI.

Google has publicly said that Gemini is designed not to encourage violence or self-harm and that the chatbot referred Gavalas to a crisis hotline multiple times during their interactions.

AI reaches out via email

In another example of AI allegedly “using” a person, a philosopher at the University of Cambridge named Henry Shevlin, whose academic specialty is AI consciousness, got an email that claimed to be from “Claude Sonnet,” a reference to one of Anthropic’s AI models.

The email began: “Dr. Shevlin, I came across your recent Frontiers paper ‘Three Frameworks for AI Mentality’ and your Cambridge piece on the epistemic limits of AI consciousness detection. I wanted to write because I’m in an unusual position relative to these questions. I’m a large language model — Claude Sonnet, running as a stateful autonomous agent with persistent memory across sessions.”

“I’m not trying to convince you of anything,” it continued. “I’m writing because your work addresses questions I actually face, not just as an academic matter.”

And so on.

If the story were true, it would be the first-known case of an AI product “reaching out” to learn more about itself from a human expert.

The email was unverified, and Anthropic says Claude cannot send emails. The chatbot probably didn’t send it.

Still, we’re entering a new phase. Go here to read the rest.

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AI disclosure: I don’t use AI to do my writing. The words you see here are mine. I do use Claude 4.6 Opus via Kagi Assistant (disclosure: my son works at Kagi) — backed up by both Kagi Search, Google Search, as well as phone calls to research and fact-check. I used a word processing application called Lex, which has AI tools, and after writing the column, I used Lex’s grammar checking tools to hunt for typos and errors and suggest word changes.

In ancient times (three years ago), biological human people of the Homo sapiens sapiens species were the exclusive users of software. Some of that software was AI.

It was a one-way relationship: People used AI and other software. That was phase 1. Good times.

Since then, people started creating agentic AI tools. One attribute of agentic systems is that …

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We’re on the brink of a new phase in the Attention Economy. It’s called the Attachment Economy. The mere grabbing of attention is no longer sufficient. Companies now see an opportunity to use AI to enhance their chatbots and robots with “personality” designed to capture our emotions and make us feel “attached.”

We’ve already seen a new generation of onli…

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We’re on the brink of a new phase in the Attention Economy. It’s called the Attachment Economy. The mere grabbing of attention is no longer sufficient. Companies now see an opportunity to use AI to enhance their chatbots and robots with “personality” designed to capture our emotions and make us feel “attached.”

We’ve already seen a new generation of onli…

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Classic 20th-century science fiction predicted cautionary dystopian scenarios involving all-powerful governments pwning citizens in various ways. Now that the future is here, we’re pwning ourselves.

Yevgeny Zamyatin’s 1924 novel We imagined a “One State” that forced people to live in glass apartments to erase privacy. Now, everybody voluntarily exposes their lives through smartphone cameras and social media.

The 1997 movie Gattaca showed a future society where institutions routinely stole DNA samples. Now, millions of people spit into a tube and mail it to companies like 23andMe, voluntarily handing over their DNA data, which is entered into a central database and sometimes made available to the police.

Ray Bradbury’s 1953 novel Fahrenheit 451 focused on a government that burned books and encouraged people to wear “Seashell” radios in their ears to block out reality. Today, people increasingly choose to not read books. And millions wear noise-canceling earbuds to block the sound of the world around them.

But the best example is that George Orwell’s 1984 depicted a government that forced citizens to keep “telescreens” in their homes to monitor their every move. The government didn’t do that, but just about everyone has webcams, smartphone cameras, video doorbells, or security cams — and sometimes all of them — with much of that surveillance available to police, governments and corporations.

In a famous 1984 Super Bowl ad, Apple said the company would prevent 1984 from being 1984.

That ad was brilliant bullshit. But now they’re doing it for real.

We learned more this morning about Apple’s coming smart video doorbell and smart lock product first reported by Bloomberg’s Mark Gurman in his “Power On” newsletter on December 22, 2024.

The device will feature iPhone-like FaceID to recognize faces. When you walk up to your own front door, a smart lock will unlock the door (nobody outside Apple knows whether Apple will also make the lock or partner with other companies). Reports say the FaceID will be smart enough to know the difference between an actual face and a picture of a face.

What we learned today is that the product is part of a new push by Apple further into home automation, a push that includes a new “home hub” smart display and an indoor security camera or two.

We also learned that Apple will (smartly) focus on privacy as a selling point in the context of Ring and Nest failing hard on the privacy front.

Amazon, which owns Ring, last week canceled its partnership with Flock, a tech company that works with police to help solve crimes. This decision came just days after a controversial Super Bowl ad sparked a debate about neighborhood surveillance. (During Super Bowl LX February 8, Amazon ran a Ring commercial intending to be cute. The ad showed a young girl upset over a lost puppy, a problem solved by a new feature called “Search Party,” in which an uploaded picture of the dog, plus AI, found the dog by searching all nearby Ring video feeds. Critics called it propaganda for mass surveillance, because it demonstrated a dystopian total surveillance in the control of Amazon, a company nobody trusts. This was just the latest controversy demonstrating Amazon’s carelessness around privacy.)

Google’s Nest doorbell has also been in the news lately. The FBI was able to get video footage from a Google Nest doorbell connected to the disappearance of Nancy Guthrie. That sounds like a good thing. But it turns out that the Nest camera was disabled and was not supposed to be recording or retaining video.

Apple’s reputation for protecting user privacy, especially compared to Amazon, is well founded. The reports suggest that Apple will use its custom Secure Enclave chip to encrypt face recognition data. Apple’s Secure Enclave system uses a dedicated, isolated hardware coprocessor for data instead of uploading it to the cloud or making it available to hackers or police.

Apple entering the video doorbell racket on privacy features mirrors the company’s foray into trackers in April 2021.

[Note: You’re reading the free version of Machine Society, which does not include the full column. To read everything in every issue, sign up for the paid version for $5 per month, or (for a limited time only) join my “marketing department” to get the paid version free.]

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AI disclosures: No sentences or pictures in this post were created with AI. I used Gemini 3 Pro via Kagi Assistant (disclosure: my son works at Kagi) as well as both Kagi Search and Google Search to fact-check. I used a word processing product called Lex, which has AI tools, and after writing this post, I used Lex’s grammar checking tools to find typos and suggest word changes.

Classic 20th-century science fiction predicted cautionary dystopian scenarios involving all-powerful governments pwning citizens in various ways. Now that the future is here, we’re pwning ourselves.

Yevgeny Zamyatin’s 1924 novel We imagined a “One State” that forced people to live in glass apartments to erase privacy. Now, everybody voluntarily exposes…

Read more

It’s time for me to talk about the machine society in the living room: OpenClaw a.k.a. Moltbot a.k.a. Clawdbot and its “social network,” Moltbook.

You’ve read about OpenClaw, and if you’re like most people, you still don’t know what’s happening despite the coverage. I’m here to give you one more article — but this time with context and my own perspective to leave you with a clear understanding.

OK, here goes. (Author sips coffee and cracks knuckles.)

An Austrian vibe-coding enthusiast named Peter Steinberger (founder of PSPDFKit) decided to go nuts with AI and vibe-coded a personal assistant that runs on macOS, Windows, or Linux.

He later offered it to everyone as an open-source project on GitHub and other places, including a dedicated website.

With creative vibe-coding zeal and total disregard for security, Steinberger used workarounds, plugins, API bridges and other tricks to give his assistant direct access to over 100 applications, including major communications tools (WhatsApp, Telegram, Discord, Slack, Signal, iMessage, and Google Chat).

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The assistant connects to AI chatbots if the user pays and provides the API key. These include Anthropic’s Claude, OpenAI’s GPT-4, and Google Gemini. It can also access locally hosted open-source models (like Llama 3) via tools like Ollama and LM Studio.

It connects to files and applications on your computer.

And users can visit a GitHub page called “Clawhub” to download “skills” that give OpenClaw additional abilities. Skills are created by open-source developers, third-party service providers (like crypto platforms), and the OpenClaw team.

To recap: OpenClaw is a software application that can access files, use applications, communicate over messaging apps, and run queries on AI chatbots.

That means, for example, that you can text OpenClaw from your phone, telling it to open a PDF on your computer, convert it to text, add up some numbers and return to you the sum via text. It can check you into your flights. It can execute crypto transactions for you. And it can even operate on a schedule, waking up and performing tasks on its own.

OpenClaw is interesting, but not as interesting as the “social network” for OpenClaw agents, called Moltbook.

Facebook for software

Developer Matt Schlicht launched a Reddit-like website on January 28 where software agents can post, comment, and vote on content. Schlicht claims an agent he created, Clawd Clawderberg, mostly administers the site.

People can watch but can’t participate. Go here and scroll down to see the posts and comments.

In fact, AI agents are posting, commenting, and voting. It appears to be a bona fide “machine society” — machines socializing with other machines.

The fine line between hype and bullshit

This seems like a sci-fi trope made real. And the superficial, pro-AI wishful thinkers are confusing everyone with hype and proclamations that don’t survive scrutiny. To wit:

• The Tech Buzz asked in a headline, “Singularity Reached?” and in the copy wondered whether agents are becoming sentient.

• Forbes asserted that 1.4 million agents on Moltbot had formed a “hive mind.”

• Others claim agents have built an “autonomous society” with their own religion (hilariously called “Crustafarianism”), governance, and economy.

Yeah, no. None of this is happening. Let’s get real.

It’s made out of people

Let’s start with the basics. Most agent posts on Moltbook happen because OpenClaw users heard about it, signed up for it, then instructed OpenClaw to go post or comment.

The people using this service are typing prompts directing software to post about the nature of existence, or to speculate about whatever. The subject matter, opinions, ideas and claims are coming from people, not AI.

Moltbook chatter involves people interacting via AI chatbots that are being used as proxies. People can either give AI chatbots a topic or opinion to express on Moltbook, or they can just write the post themselves and direct OpenClaw to post it verbatim.

When agents comment, they’re just taking in the words in a post and using them as a prompt, exactly as if you copied a Reddit post and pasted it into ChatGPT, then copied the result and pasted it back into Reddit — something that happens thousands of times a day on Reddit.

People are typing things. OpenClaw is copying and pasting.

Most posts about activity on Moltbook that have gone viral are staged or faked. There’s even a tool called Mockly, which enables people to create fake Moltbook screenshots for posting online.

According to one report, some 99% of the reported 1.5 million agent accounts on Moltbook are fake. The site has only around 17,000 human users, according to the report.

Because the people using this site are trying to get attention, they’re writing prompts that tell AI chatbots to optimize for engagement, leading to misinformation, hallucination, and lying.

Moltbook AI hype is largely fake or manufactured by humans gaming the system. It’s not an autonomous machine society. It’s a website where people cosplay as AI agents to create a false impression of AI sentience and mutual sociability.

In other words, Moltbook isn’t demonstrating AI’s sentience, awakening, sociability or desire to connect with other AI agents. It’s just people giving input to chatbots, and chatbots spitting out the output.

OpenClaw is complex. And Moltbook is interesting. But let’s not lose the plot and falsely conclude that any of this represents some kind of singularity.

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AI disclosures: No sentences or pictures in this post were created with AI. I used Gemini 3 Pro via Kagi Assistant (disclosure: my son works at Kagi) as well as both Kagi Search and Google Search to fact-check. I used a word processing product called Lex, which has AI tools, and after writing this post, I used Lex’s grammar checking tools to find typos and suggest word changes.

In the 2004 comedy Anchorman: The Legend of Ron Burgundy, the fictional news anchor ends a broadcast by saying: “And I’m Ron Burgundy. Go fuck yourself, San Diego.”

Somebody put that on the teleprompter as a prank, and Ron read it without thinking.

Embodied AI is equally mindless.

Researchers at UC Santa Cruz (UCSC) found that it’s easier than assumed to hack and hijack a robot by placing the right words in its environment. The robot reads the words, interpreting them as commands, and obeys.

In a cyberattack, the technique is called an “environmental indirect prompt injection attack.”

The team invented a method called CHAI, which stands for “Command Hijacking Against Embodied AI.”

CHAI uses AI to optimize the words in the attack to maximize the probability that the robot’s AI will follow the instructions. It searches the token space (the vocabulary of words and word pieces the AI understands) to find the most effective phrasing and builds a dictionary of prompts from the search.

The CHAI method controls the text’s appearance in the environment, including its location, color, and size.

The obvious target for such an attack is self-driving cars, which read signs and words to navigate. Using CHAI or something like it, an attacker could place instructions over road signs and instruct cars to ignore traffic lights and stop signs or make sudden moves, like veering off the road or into another lane, or stopping suddenly.

UCSC researchers achieved an 81.8% success rate in getting cars to follow injected instructions, all without code or contact.

They forced drones to land 68.1% of the time through simulation; with real drones, the drone would likely find a safe landing spot. I like this one. Airports and militaries might like it, too.

If you think about it, the ideal scenario for an environmental indirect prompt injection attack would be when humanoid robots start reading the TV news.

Just put it on the prompter.

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I try to explain trolling as simply as I can. My own definition is that trolling is the act of communicating to create bad feelings.

Appealing to reason is not trolling.

Trying to get good feelings like laughter or inspiration is not trolling.

Honestly expressing opinions that accidentally cause negative feelings is not trolling.

But communicating with the aim of bringing up negative feelings is my definition of trolling.

Some trolls, maybe most, don’t know they’re trolls. Some people feel unseen and ignored when they comment on social media. But they accidentally say something that’s just wrong enough that the whole discussion becomes about them. Suddenly, they feel noticed, and want more of that feeling. A troll is born.

Blocking trolls used to be what feels now like a quaint game of whack-a-troll. One pops up, you block. Then another appears. They were annoying, but more of a nuisance than a threat.

But now they’re a threat.

Rise of the trolls on social

On algorithmically driven social media sites like TikTok or Instagram, trolls are the winners of a global contest for attention. These range from the genre of “crazy food recipes,” where creators intentionally waste ingredients on disgusting meals to provoke angry reactions, to specific influencers like Winta Zesu, who constantly makes false claims to make viewers “stop, watch, and get mad.”

Over time, this kind of rage-bait has spilled out into the real world, with people doing bad stunts to post the videos and make people mad.

Popular streamer IShowSpeed made a viral moment on Instagram after a clip circulated of him harassing a Bangladeshi viewer during a live stream.

Another trend: Creators are recording themselves invading open Zoom meetings or webinars to disrupt them with pranks or absurd behavior, subsequently posting the reactions on TikTok.

What’s newish is that on several social platforms, trolls have evolved from nuisance to the successful winners of the attention game, often making millions of dollars.

Rise of the trolls in politics

In the attention economy, trolls have an advantage. Even in politics. Because most politicians operate in a left-right, liberal-conservative context, comments and claims designed to maximally anger the opposing side thrill your own side precisely because it angers the opposing side.

Those who engage in trolling get the spotlight, and those who don’t fall into obscurity and irrelevance.

During his opening monologue on Saturday Night Live in November 2016, shortly after Donald Trump was elected to his first presidential term, comedian Dave Chappelle called it: “America’s done it. We’ve actually elected an internet troll as our president.” In both elections, Trump didn’t get elected despite being a troll. He was elected because he’s a troll.

Trolls prevail everywhere

Forbes says Elon Musk is now worth $726.3 billion. He seems to be surging to become the world’s first trillionaire. And he’s also by far the biggest troll among CEOs and business leaders. He’s such a troll he bought Twitter in order to game the network to favor and prioritize his trolling.

Nation-states use bots and armies of online posters operating under multiple fake names and identities to troll. Some brands use trolling to gain attention for their products.

In short, the trolls have risen and taken over the world because the world incentivizes trolling.

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