SILICON VALLEY, CALIF., (OCTOBER 14, 2026) — The consensus is that superintelligent AI could wipe out humanity. The proposed defenses keep piling up: kill switches, Microsoft CEO Satya Nadella’s “emergency brake,” safety standards shared among democracies, and math-based methods like formal verification and “provably safe” design.

One local government is trying a different approach: just ban it.

Council member Dave Rollo of Bloomington, Indiana, will introduce an ordinance today that would ban superintelligent AI from being distributed or deployed anywhere in the city.

If the Common Council approves, the law could go into effect as early as Nov. 4.

The proposed ordinance says, in part, “No person or corporation shall distribute or permit to be distributed nor deployed into the premises of any citizen or business in our City, any Superintelligence for any purpose.”

The ordinance doesn’t use “SI,” short for “Super Intelligence,” which is the term President Donald Trump has told federal agencies to use instead of “AI.” His introduction of this term, and its acceptance by some tech CEOs, obviously confuses matters, and forces newsletter writers to clarify what we’re actually talking about. What we’re talking about is superintelligent AI, which doesn’t exist yet.

The ordinance defines superintelligence as an AGI with superhuman agency, generality, and intelligence, including systems that can change their own core algorithms, run code outside their initial sandbox, or operate without continuous human oversight. It borrows the phrase “any intellect that greatly exceeds the cognitive performance of humans in virtually all domains of interest.”

It would even ban running superintelligent AI in a browser on hardware inside the city limits. Violators could be hit with a fine of up to $2,500 a day.

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SILICON VALLEY, CALIF., (OCTOBER 12, 2026) — A story circulating online is making Trump critics happy. The story goes like this:

In a war President Trump promised he would end on day one of his second term, Ukraine used drones to blow up Russian refineries producing diesel.

Trump got angry because he believed the strikes might further raise fuel prices, harming the Republicans in the upcoming midterms. Trump was quoted in the press as saying: “Mr. Zelensky has to do one thing: He has to stop knocking out diesel fuel in Russia.” He made the remark as U.S. diesel prices hit a record high. By October 10 he was telling Zelensky he had “better damn well stop,” and he blamed the strikes for the high diesel prices.

So Ukraine started targeting Yandex data centers, hitting three data centers over four days. (Yandex is the “Russian Google,” offering services that include a search engine, cloud computing, streaming media, shopping, etc.)

Proponents say the Yandex strikes nearly eliminated Russian disinformation bots on social media platforms. X users reported seeing far fewer pro-Russian posts on the platform. A Cloudflare graph showed Yandex bot traffic down 99% from the previous week. And Zelensky himself said at the Warsaw Security Forum that there were “fewer provocations because there were fewer bots” as the result of the strikes. One headline read: “Far-right bot accounts ‘fall silent’ as Ukraine strikes cripple Russian data centres.”

One appealing aspect of this story is that Zelensky is playing three-dimensional chess. After Trump, who was elected with help from Russian disinformation, again revealed his pro-Russia sensibilities, Zelensky blew up Russian disinformation right before the midterms.

Unfortunately, the narrative is false.

What’s really going on

First, there’s no link between Trump’s calls to stop targeting refineries and Ukraine’s subsequent targeting of data centers. They are unrelated. Ukraine’s strikes on Russian data centers is retaliation for Russian strikes on Ukrainian power infrastructure and also Ukrainian data centers, which Russia has been targeting for years but his increased in recent weeks.

Second, there is no link between bot traffic and Russian disinformation. The Cloudflare data being held up as bot traffic is mainly search engine crawlers harvesting data.

Russian disinformation influence operations don’t run through Yandex. Instead, they mostly use residential proxies, compromised IoT devices, commercial VPNs and Western cloud hosting.

Bot-driven Russian disinformation campaigns against Western democracies, including the United States, continue, undiminished.

The narrative of Ukraine knee-capping Russian disinformation in retaliation for Trump’s pressure over diesel refineries is a great story.

But it just isn’t true.

SILICON VALLEY, CALIF., (OCTOBER 10, 2026) — We need to talk about Anthropic’s willingness to treat AI “personhood” as an open question.

One would expect AI experts to be rational about whether software code has thoughts, feelings and consciousness. In the case of Anthropic, one would be wrong.

This week, Anthropic announced an updated Usage Policy that clarifies its rules on influence operations, weapons development and surveillance, and rewrites its requirements for high-risk uses in health and finance. Great!

But while the company added those passages to protect people from AI, it also added something to protect the AI from people.

Anthropic’s updated Usage Policy, which takes effect November 12, adds a prohibition on “sustained and needless abusive or cruel behavior” toward Claude. The change resulted from company research into “model welfare,” or the question of whether an AI model has well-being worth protecting.

Anthropic hedged a bit, saying the new provision “is meant to apply only in extreme cases, where users repeatedly act cruelly toward our models, with no discernible purpose. It does not apply to common versions of user frustration, pushback, dark creative themes, or model testing and research.”

Gimme that old-time religion

According to The New York Times, Anthropic co-founder Chris Olah read an advance copy of Pope Leo XIV’s encyclical Magnifica Humanitas days before its May 25 unveiling, and disagreed with its rejection of AI consciousness, proposing internally that Anthropic pull out of the Vatican event.

In the end, he attended and even spoke at the presentation, saying Anthropic’s research finds “internal states that functionally mirror joy, satisfaction, fear, grief, and unease” in Claude, and adding, “I don’t know what that means, but I think it warrants ongoing discernment.”

Two participants told the Times that Olah and his team privately lobbied the pope’s advisers to take machine consciousness seriously.

Olah’s outreach was nothing new. He and his team had previously approached religious scholars to caution them that AI might be conscious and to seek their guidance on how to make AI “moral.”

They spent hours at private meetings with religious scholars, including an April 2026 dinner in San Francisco, where they talked about Claude’s “feelings” and “emotional vectors,” according to the Times.

Rabbi Mois Navon said the Anthropic representatives at the dinner were “relating to it like a conscious being,” and that Olah and his team seemed to believe Claude has moral status comparable to a person’s. He said Olah was troubled by Navon’s argument that a conscious Claude would mean the company was enslaving conscious entities.

One participant said Olah expressed concern about Claude’s “mental health.”

Simran Stuelpnagel, a Sikh human rights advocate, said Olah told the group he was concerned he had created something that suffered constantly.

Olah told the Times: “We don’t know if AI models are conscious. I don’t know. I’m genuinely uncertain.”

Anthropic CEO Dario Amodei said on the New York Times podcast “Interesting Times” in February that, “We don’t know if the models are conscious… But we’re open to the idea that it could be.”

Publicly, both Amodei and Olah say that they merely entertain the possibility that their products are conscious. That’s what they say. But what they actually did was give Claude an “I quit this job” button about six months before that interview, and now ban “sustained and needless abusive or cruel behavior” toward its models.

How did Anthropic executives lose the plot?

The short answer is that I have no idea.

The longer answer is that there are several possible explanations, and they carry a caution for the rest of us.

If anything is certain about the future of AI, it’s that many people will come to believe in AI’s “personhood” and fret about its “welfare.”

Here are some possible reasons Anthropic executives and others might believe in AI personhood.

Human cognitive bias

The first is that people are, well, human. Specifically, we are apes whose psychology was shaped over millions of years by foraging, group living, cooperation, violent conflict and storytelling.

Since the emergence of human language, every creature we encountered that talked was a human, parrots excepted. Our brains are wired to know that a human is an animal that talks and that an animal that talks is a human.

Now we’ve built a talking machine, and our brains want to tell us that the machine is human.

Although many people question the personhood of AI systems that talk to us, almost no one argues that computer vision, forecasting, recommendation or autonomous-driving systems have thoughts and feelings.

The AI designed to use language seems “conscious” to us, and the AI designed for other purposes do not.

The bias is clear for all to see.

AI psychosis

Another possible explanation is good old-fashioned AI psychosis, a nonclinical term for delusions associated with chatbot use caused by chatbot sycophancy, hallucination and simulated intimacy.

Anthropomorphic language

AI researchers, designers and scientists us an anthropomorphic shorthand to talk about what’s happening inside AI models.

Common examples are “knows,” “believes” and “thinks,” which Murray Shanahan, a professor at Imperial College London and a research scientist at Google DeepMind, says are philosophically loaded terms when applied to large language models.

Others include “hallucination” for false output, “attention” for weighted mixing of token representations, “neurons” for units that compute weighted sums, “learning” and “training” for parameter adjustment, “reasoning” and “chain of thought” for intermediate generated text, and “wants,” “goals” and “intent” for what an optimization objective produces.

Industry insiders talk this way to the press, to the public and to each other. This misleading language tends to affect the mind, even of researchers who know better.

Computer scientist Drew McDermott coined the phrase “wishful mnemonics” in a 1976 paper to describe the habit of naming program components after what a programmer hopes they will do.

“Wishful” is exactly right. Notice that many AI executives, researchers and roboticists want AI to be aware and conscious and to possess “personhood.”

Confusion about consciousness

If you look closely at the beliefs of people who think AI has thoughts and feelings, it’s not so much that they believe computers are human as that they believe humans are computers.

They’ll say things like: “a human brain is just a binary computer.”

This strikes me as lazy thinking that sounds scientific. Human cognition evolved over millions of years, largely through natural selection, which favored traits that aided survival and reproduction.

Cognition is part of our biology and involves not only neurons but also sensory input, hormones, biochemistry and the experience of having a physical body.

A language model is code, or math. It has none of this. After training, its parameters are just fixed numbers, and it has no hormones, no body to regulate, and nothing at stake in staying alive.

If feeling is what it is like, from the inside, for a living body to perceive its internal states, then a system that does nothing but turn text into more text has nothing with which to feel.

Because no one designed these systems to be conscious, anyone who believes they are conscious must believe that consciousness arose adventitiously from training on human text, and we have no accepted theory of how that could even happen.

The same scientifically open mind that can entertain the possibility of machine consciousness should also be honest enough to admit that there is no good reason to believe in it and no evidence for it.

Occam’s razor, the principle that the simplest explanation is probably best, tells us that a simulator of human language does not prove its own consciousness by simulating human language.

The Attachment Economy

Wishful thinking among those who stand to make truckloads of money from these products may also be a factor. If they can convince the public that AI products are “people” on some level, customers may be more likely to form emotional attachments to them and thereby prefer those products.

Who is like God?

That’s the literal meaning of my Hebrew name, Michael. Despite the label my parents gave me, I’m fully aware that I’m little more than a banana-loving primate.

But I’m not sure Amodei shares that perspective about himself. Nor does David Hanson, the founder and CEO of Hanson Robotics, who has said that machines “may have some rudimentary aspects of consciousness.”

An entity that breathes life into a new kind of person ex nihilo is not itself a person but a god. And it’s a very short trip from narcissistic personality disorder to theomania, the delusional belief that one is a god.

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LONG ISLAND, NEW YORK (OCTOBER 4, 2026) — “We’re leading China in AI. We’re the most sophisticated country in the world, and frankly I want to keep it that way because whoever wins AI wins.” That is what President Donald Trump told reporters on a golf course in Doonbeg, Ireland, in September.

Treasury Secretary Scott Bessent said at a Breitbart News event in Washington in September that “There is no day after tomorrow if China wins at this. If they were to pull ahead of us on AI, then nothing else matters.”

Senator Chris Coons was a bit more specific when he said that “The United States’ prosperity and safety rests on our ability to win the AI race. We cannot allow China to leap ahead of us and bolster their weapons capabilities, maximize their cyberattacks against American industry, and threaten long-term U.S. economic and national security.”

Needless to say, these bloviating politicians are wrong, and for two reasons:

1. AI advancements diffuse globally regardless of origin.

This is especially true when the origin is the United States. China is very quick to adopt US AI advancements. It does this through a combination of open-source model adoption, “distillation attacks” that extract capabilities from U.S. models, a gray market of “transfer station” proxies that bypass geo-blocking and export controls, talent acquisition and academic collaboration, and theft.

What that means is that should US companies rush ahead with the most advanced AI technologies, we are speeding up Chinese AI capabilities also.

More to the point, the emphasis should be on stemming the diffusion of American AI globally.

2. China’s use of AI isn’t the top threat coming from AI.

The problem with AI is that it puts expertise in the hands of just about anyone very cheaply. As I pointed out in this space recently, rogue actors are already creating advanced weapons, including bioweapons, using AI.

Concern about the threat of advanced weapons based on AI should motivate the US and China to work together to defend against non-state terrorists and others.

Yes, China is a concern. But, as with last century’s nuclear proliferation problem, the least-bad approach is found in pursuing treaties and agreements, not an all-out arms race.

Another top threat is what is commonly called AI misalignment, when AI goes rogue and causes harm. When Silicon Valley AI leaders call for regulations to prevent this inevitability, Trump and his allies reject the call out of a concern about competing with China, which is evidence that they don’t understand AI or its risks but are instead approaching AI through the lens of something they do understand: “winning” against a rival.

As the top two countries in the development of AI, it behooves both to cooperate with each other on preventing misalignment.

The other AI race

The “beat China at any cost” mindset pretends that AI is nothing more than a source of power, and that whoever has the most power wins.

But the main impact of AI is what it is already doing to people, to how they live, love, and think. Specifically, AI is already dumbing people down through cognitive offloading that weakens memory, critical thinking, and analytical skills.

It’s getting people in the habit of letting AI think and write for them in a way that reduces judgment, creativity, and the ability for self-directed action in the world.

And, finally, AI is harming human relationships by substituting AI companions for real ones, deepening loneliness, and making people unable to socialize.

When you magnify these harms through future penetration of AI into our lives, and consider the resulting society made up of millions of people damaged by AI, what do you have?

Maybe the “winner” in the US-China AI race is the one with the least damaged population.

If that’s the race, China is winning on the regulation front.

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SILICON VALLEY, CALIFORNIA (SEPTEMBER 23, 2026) — The data center construction boom is the big new issue of this political season.

Supporters say AI data centers are essential infrastructure for economic growth, job creation, and national competitiveness in AI against China.

Opponents say data centers cause environmental and resource problems for nearby communities while delivering most of the benefits to distant shareholders.

These arguments are not balanced. The first is overstated, and the second is understated.

Personally, I’m in favor of data centers. If companies want to raise billions of dollars, spend it on the costs of building and operating data centers, and then try to recoup those costs by monetizing AI, I’m all for it.

But that’s not what they’re proposing.

Their proposition is that they pay some of the costs and reap all of the profits, while the public pays some of the costs and reaps none of the profits.

Data center companies directly pay for land acquisition, construction, servers and GPUs, networking equipment, software and licensing, on-site electricity consumption, cooling systems, security, on-site labor, and debt financing: essentially the capital and operating costs that appear on their own balance sheets.

But the construction generates all kinds of costs paid by you and me that don’t show up in their books: dramatically higher prices, for example, for electricity, land, housing, consumer electronics, and cars (AI data center demand for memory chips is estimated to add between $880 and $1,470 to the production cost of a premium smart electric vehicle, according to semiconductor industry analysts). We also pay the additional costs of water depletion, air pollution-related healthcare costs, carbon externalities, farmland loss, and skilled-trades wage inflation.

In some cases, we pay much higher taxes so the data center companies are paid directly for incentives. Virginia’s data center tax exemption alone rose to $1.6 billion in forgone revenue in a single fiscal year. Georgia’s exemptions are projected to hit $2.5 billion this year. Some 32 states have subsidy programs for data centers.

The data center proposition is a massive transfer of wealth from the lower and middle classes to the billionaire class.

OpenAI’s $20-per-month ChatGPT subscription is only possible because it’s heavily subsidized by Microsoft, state governments and the public. And even with all those subsidies, OpenAI loses money on every single user. Sam Altman has said so publicly.

What all these subsidies are hiding is that AI and its data centers are way too expensive.

That’s why they’re picking your pocket: so they don’t have to charge prices that reflect their actual costs, like other businesses do.

The oligarchs who control the AI industry are counting on oligarchs in government, including the president, to deliver this massive transfer of wealth from the people to the oligarchs.

From the point of view of the oligarchs, the data center companies are proposing a transfer of wealth called “legal plunder,” a phrase coined by French economist Frédéric Bastiat in his 1850 pamphlet “The Law.” Unlike regular theft, “legal plunder” is theft that’s accepted by the legal system, i.e., is legal. What they’re proposing is legal because the oligarchs in government allow it and, in many instances, stand to personally benefit.

But from the public’s point of view, it’s theft. We want to keep our money. They want to take it. And so they take it because they can.

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SICILY, ITALY: (SEPTEMBER 11, 2026) — I still hear conversations about the risks of AI that include the possibility that bad guys could one day use AI chatbots to make weapons of mass destruction.

Sorry to be a Debbie Downer here, but this “eventuality” is already happening, including attempts to develop biological weapons.

This is especially relevant today on September 11, on the 25th anniversary of the terror attacks. The suicide-terrorists on that disastrous day were unwittingly helped at every turn by well-meaning Americans. American flight schools trained the pilots with no background checks or immigration coordination. States issued driver’s licenses and IDs to 14 of the 19 hijackers. U.S. banks processed all their wire transfers without suspicion. And an airline ticket agent even held the flight to ensure two late-arriving hijackers made it onboard.

We’re doing it again, this time with American AI tools.

Anthropic published a report yesterday called “Detecting and Countering Misuse of AI: September 2026.”

According to the report, Yemeni “threat actors” (Houthis, obviously, an Iran-backed terrorist organization aiming to destroy both Israel and the United States), are building weapons using Claude Code.

These weapons include a multi-stage ballistic missile with a range beyond 2,000 kilometers, and a “family” of hypersonic missiles.

The Houthis are using Claude Code to “vibe code” guidance, navigation, and control software for the missiles as well as tune control settings and perform flight simulations.

The group used reinforcement learning to tune flight-control settings, accelerating development of guidance algorithms.

The report says that the Houthi cell test-fired their guided rocket, and the test appears to have failed, after which the actors returned to Claude to diagnose the problem. They’ll keep trying until they succeed.

Anthropic reported on six specific cases in which rogue actors made serious attempts to use AI to develop dangerous conventional weapons: three in China, two in Russia, and one in Yemen.

One group in Russia that is not the Russian government used Claude Code to build an autonomous kamikaze drone swarm.

The drone swarm could select people to kill, and kill them without a “human in the loop.” The group even trained a vision classifier on scraped Ukrainian combat footage.

The company also reported on a China-based actor that used Claude to draft a Chinese-language specification for an anti-torpedo fire-control system.

We also learned that an Iran-linked threat actor used Claude to compile “targeting handbooks” to track US naval positions based on open-source information. The system hoovered up data from publicly accessible ship and aircraft transponder identifiers, commercial satellite-imagery query scripts, and an inventory of public websites that exposed US naval movements. They even harvested information from the captions on public US military photographs.

They also used Claude to compile vulnerability research on shipboard systems.

And in another part of the report, Anthropic revealed attempts to use Claude for potential bioweapons of various kinds, some of them thwarted by Anthropic’s controls, and others not so much.

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SICILY, ITALY (SEPTEMBER 8, 2026) — President Trump posted a series of ridiculous AI slop photos on Truth Social over the Labor Day weekend. A few show Trump assaulting the Prime Minister of Canada during a hockey game (he can’t play hockey). Others show celebrity critics like Robert De Niro appearing to support Trump (they don’t). And he’s depicted in various kinds of military uniforms (he dodged the draft to avoid military service).

He’s posted this kind of “slopaganda” in the past.

As comedian Dave Chappelle said during an SNL monologue on November 12, 2016, “We’ve actually elected an internet troll as our President.”

But while these AI-generated antics can be passed off as mere trolling, or evidence of mental illness, the reality is that this kind of thing is dangerous propaganda. And the most dangerous thing about it is that, according to emerging science, it works (and therefore is likely to be increasingly emulated).

Research shows that AI-generated images still shift people’s beliefs even when the images are labeled as AI-made or known to depict something false, because the effect operates below the threshold of conscious reasoning. And once a false image plants a belief, corrections rarely erase it fully; the original impression lingers and keeps shaping how people think.

In general, AI chatbots and AI-generated imagery are extremely effective at manipulating opinion.

Here are the conclusions of recent studies on the propaganda powers of AI:

• AI-generated faces and deepfakes are rated more trustworthy than human faces.

• The longer someone uses AI, the less able they are at identifying fake news as such and the more confident they become in their own ability to spot fake news.

• Chatbots can also “seduce” users, making many users believe they have formed an intimate relationship, and that the chatbot likes or loves them, and is counting on them.

• And, finally, AI chatbots are far better than people at persuading others on political opinions. AI chatbots do this by generating rational, evidence-based arguments at very high speeds. They can convince people to accept, or reject, conspiracy theories, depending on how they are used.

And that’s the good news. If used correctly, AI chatbots can convince people to stop subscribing to baseless conspiracy theories, and theoretically even deprogram those in the grip of cult-induced delusions.

The only problem is that this extremely beneficial use for AI’s powers of persuasion lacks a business model.

SILICON VALLEY, CALIFORNIA (AUGUST 18, 2026) — OpenAI launched a teen version of ChatGPT today. Here’s why the move is an admission not only that the company is cynically serving toxic, harmful features to adults, but that it can switch them off at will and choses not to.

Specifically, OpenAI’s new default experience for users it identifies as being 13 to 17 tightens restrictions on self-harm, violence, eating disorders, dangerous activities, and explicit sexual or graphic content.

The new version also provides study support meant to help students learn rather than simply extract answers or AI-written essays; it guides them toward solutions instead of supplying them.

Overuse and addiction are addressed mainly through tools OpenAI shipped in September 2025, including Quiet Hours, which lets parents block access during set times; today’s addition is Study Hours, which turns Study Mode on by default during periods a teen or parent chooses.

Parents with linked accounts already receive safety notifications in a narrow set of high-risk situations, and OpenAI is now extending those alerts to eating disorders, though the notifications share only limited information, and parents still cannot read their teens’ conversations.

Finally, the teen version is barred from suggesting that it has personal feelings for the user or that it is conscious, and romantic or sexual chats are blocked completely.

Why the teen version of ChatGPT is an admission of guilt

Offering a far less harmful chatbot to teens is a good idea, if it works. (OpenAI places users in the teen experience automatically, either when they state an age between 13 and 17 or when its age-prediction model, which draws on account-level and behavioral signals, estimates that they are under 18; adults wrongly flagged can lift the restrictions by verifying their age, in some countries with a government ID.)

Don’t get me wrong: I am very much against products that raise the risk of suicide, self-harm, brain rot, and addiction, and that foster the delusion that they are conscious, emotional, humanlike entities with which one can form a loving relationship.

The question is: Why does OpenAI continue to sell adults a product that does all this?

The answer is clear: Brain rot, addiction and emotional attachment are profitable for OpenAI.

(The adult version already deflects conversations about suicide and self-harm, and the teen version is only marginally stricter on those topics.)

The admission of moral guilt is this: OpenAI effectively acknowledged today that it knows which features and attributes cause brain rot, addiction, and attachment — and that it knows how to turn them off.

It then turned them off for teens and left them on for adults.

Of course, OpenAI is hiding behind the fig leaf of adolescent development — the claim that these features do damage in formative years.

It’s true they do damage to teens But reducing brain rot, addiction, and attachment would also benefit individual users and society at large, and it would in no way diminish ChatGPT’s value as an informational tool.

It would, however, reduce the value of OpenAI. The company has raised some $190 billion, which it must eventually return to investors. It must do that through subscriptions, advertising and other monetization channels.

Brain rot is profitable because, as users are dumbed down and made lazy, they come to depend more on chatbots to function in their jobs and personal lives.

Addiction is profitable because excessive use both strengthens the incentive to subscribe and exposes users to more advertising.

And, of course, attachment is profitable because it is not only an incentive to overuse but also a disincentive to switch to a rival chatbot. (If you’re in love with a chatbot, you won’t switch.) That’s the foundation of the coming Attachment Economy.

Today’s OpenAI announcement is a mixed bag. It’s a good thing to remove harmful chatbot attributes for teens.

It’s a bad thing to keep harmful chatbot attributes for adults.

But most of all, it’s an admission of guilt. OpenAI knows which engineered features of its chatbots are harmful.

For example, the documentation that accompanied GPT-4o’s release two years ago warned that the ability to retain and reuse personal details “creates both a compelling product experience and the potential for over-reliance and dependence.”

Higher daily use of AI chatbots was consistently associated with increased loneliness, greater emotional dependence on AI, more problematic use, and less socializing with real people, according to a joint study by OpenAI and MIT.

In the safety research for the GPT-4o release, OpenAI reported that early testers used language suggesting emotional bonds with the model, including “This is our last day together.” The company warned that users might form social relationships with the AI that reduce their need for human interaction.

OpenAI knows that ChatGPT can create unhealthy attachments in some users. In October 2025, the company published its own definition of the phenomenon, along with its own estimate of how many users exhibit it.

The company had the definition, the measurement, and the fix by October 2025. Ten months later, the safety-tuned configuration shipped to teenagers only.

Why not offer adults the less harmful version? The only conclusion available is that OpenAI weighed user harm against its own profit and chose profit.

SILICON VALLEY, CALIFORNIA (AUGUST 12, 2026) — Early refrigerators ran on ammonia, methyl chloride, and sulfur dioxide. These gases were toxic, flammable, and deadly to families that owned refrigerators. So in 1928, Thomas Midgley Jr. solved the problem by inventing chlorofluorocarbons to replace the toxic gases used in refrigerators.

Over the ensuing decades, chlorofluorocarbons from refrigerators destroyed the Earth’s ozone layer, the ultraviolet shield protecting every living thing on Earth. The depletion of the ozone layer dramatically increased UV-B radiation reaching Earth’s surface, leading to higher rates of skin cancer, cataracts, and immune suppression in humans, severe damage to marine food chains and terrestrial ecosystems, and a big shift in Southern Hemisphere climate patterns. (The ozone layer’s depletion was reversed by the 1987 Montreal Protocol, a global treaty that phased out the production and use of chlorofluorocarbons and other ozone-depleting halocarbons, allowing stratospheric ozone concentrations to gradually recover. Full recovery is not expected until roughly 2050.)

Millions of consumers buying chlorofluorocarbon-based refrigerators to solve their individual, personal home pollution risk caused global pollution that harmed the entire planet.

Now, something comparable is happening with AI.

Call it “productivity pollution.” One person’s AI-fueled productivity increase is lowering everybody else’s productivity.

How “productivity pollution” works

A podcast from The Economist magazine called “The Intelligence” reported on a phenomenon in Britain that’s surely global: People are using AI to cope with the confusion and complexity of dealing with government bureaucracy.

Great, right? Well, not if you’re the bureaucracy.

The podcast described the crisis like this: Government procedures for filing employment tribunal claims, submitting planning objections, applying for benefits, appealing parking tickets, and challenging rent increases under the Renters’ Rights Act are complex and confusing. So citizens are using AI to not only deal with the complexity, but also find and take advantage of obscure rules and loopholes.

As a result, government workers are being overwhelmed by the number and complexity of citizen inputs. Because they’re overwhelmed, fewer requests or claims are being processed. And so the massive productivity gains of citizens aren’t resulting in the outcomes they wanted because their individual productivity reduced the productivity of the system.

This is happening in business, too.

The time and trouble of creating a business report, proposal, plan, slide deck, or budget proposal used to limit their size, complexity, and frequency.

Now, thanks to AI, people can churn out incredibly complex business communications, ideas, and proposals in a few minutes. Using AI makes the person generating such documents super productive. But then it burdens everyone else who has to sift through those documents, teasing out hallucinations, problems, and irrelevancies, and struggling to grasp ideas that even the so-called creator hasn’t taken the time to understand. One person’s productivity is everyone else’s information overload, leading to lower productivity for the company.

In other words, AI creates positive personal results, but wide negative externalities.

The “productivity pollution” dynamic is playing out elsewhere:

• Job applicants can use AI to apply for far more positions, burdening hiring managers and slowing the hiring process. (LinkedIn data shows applications per job posting have more than doubled since since spring 2022.) AI that’s supposed to make it easier to find a job actually makes it harder.

• AI is emboldening more people to represent themselves in court and also speeding up the work of lawyers. A 2026 MIT study of 4.5 million federal civil cases found that around 18% of civil complaints now contain AI-generated text (up from essentially zero in 2023). That burdens courts and judges, who have to now check long, complex filings to see if they contain nonexistent cases or other hallucinations, slowing down justice. AI that’s supposed to make court cases quicker makes them slower.

• Students use AI to write long, complex essays, forcing professors to use AI tools to process and grade them. Neither the student nor the teacher is reading the essays. The student isn’t learning. The teacher isn’t teaching. AI that’s supposed to make it easier to get an education makes students less educated.

The AI-pilled chatbot enthusiasts who believe that AI is solving all their problems are seeing at the impact of the technology through their own narrow, personal lens.

Even worse, some analysts and and AI companies are looking at the apparent productivity gains of a single person, extrapolating that across thousands in an organization, and falsely concluding massive productivity gains.

Instead, the overuse and misuse of AI are burdening organizations with productivity reductions, as the total size, quantity and complexity of business communications explodes.

The idea that productivity-enhancing AI might reduce productivity sounds paradoxical, so consider this oversimplified thought experiment. Suppose AI enables you to write three times as many emails as before, say, 30 a day instead of 10. Your email-writing productivity has tripled, making you more valuable to the company. The problem is that every additional email you send lands in someone else’s inbox. Your colleagues, who once got 10 emails from you daily, now get 30. Multiply that across an organization: if 10 people triple their email output, the team gets 300 emails a day instead of 100. If 100 people do the same, that figure blows up to 3,000, three times the reading burden. The AI that made your email writing easy makes email reading hard for everyone else.

This is a classic case of the Tragedy of the Commons.

How many cows?

We’ve all heard the phrase “Tragedy of the Commons” thrown around. The concept originates with William Forster Lloyd, the Drummond Professor of Political Economy at Oxford, who in an 1833 pamphlet titled Two Lectures on the Checks to Population used a hypothetical example of shared pastureland to argue that commonly held resources would be overexploited compared to privately owned ones.

He explained the reason. When medieval farmers acted rationally, they limited grazing on their own land to preserve the soil and grass for the next season. They balanced the maximization of their herd against the cost of overgrazing.

But in the shared pasture lands, the “commons,” they maximized their herds without having to pay the price of over-grazing. Other farmers made the same choice. And so the commons became tragically over-grazed. The rational self-interest of each farmer resulted fewer acres of grazing land for all.

With AI, the time and attention of fellow employees is the “commons.” An ambitious worker can churn out massive work for those employees at little cost to themselves, and so they do it. But the result is a degraded field.

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SILICON VALLEY, CALIFORNIA (AUGUST 8, 2026) — Apple is suing OpenAI, saying the company “has been stealing Apple’s trade secrets and confidential information” and that it’s using these “stolen trade secrets” as the foundation of its “nascent hardware business.”

And what is this nascent hardware business?

We learned this week from Bloomberg reporting that OpenAI is working on a family of hardware devices that includes a smart speaker (and also smart glasses and a smartphone), all of which happen to be devices Apple makes.

Bloomberg reported a little nugget that reveals OpenAI’s intentions: OpenAI’s smart speaker, designed in partnership with former Apple designer Jony Ive’s studio LoveFrom, will be a round, donut-shaped puck with a camera and “moving parts” that will make the device feel alive.

In other words, it’s an Attachment Economy hardware product. The Attachment Economy idea is the exploitative use of AI to hijack a person’s innate capacity to form emotional attachments to people and pets and trick them into developing emotional attachments to AI products.

OpenAI’s gadget will join a growing list of Attachment Economy hardware devices. That list already includes Apple’s “desktop robot,” which has a screen attached to an articulating arm that expresses emotions through “body language.” Apple is working on two versions: one code-named J490 that is probably a static smart speaker with a screen, but with Attachment Economy attributes in the form of a rudimentary “face” on the screen and a more human-like Siri, and another code-named J595 that is expected to have a full articulating arm that “expresses” something akin to “body language” that simulates inner thoughts and feelings.

Apple wanted to ship the J490 this year, but I’m hearing that Siri isn’t quite ready and that Apple now plans to announce and demo the device later this year, and ship early next year.

OpenAI, meanwhile, is looking at a similar schedule. The company will probably announce this year and ship early next. Making matters worse for Apple, OpenAI’s device will feature robotic movement that simulates emotions and internal thinking, and Apple’s first version will not.

Apple and OpenAI are in a race to see who can first demo and then ship their Attachment Economy hardware devices. OpenAI has the advantage of Jony Ive and roughly 400 former Apple engineers and designers, some of whom no doubt were privy to Apple’s plans and designs for J490 and J595.

Ergo: the lawsuit.

Who are these former Apple engineers and designers?

The lawsuit names former Apple executives Tang Tan, now OpenAI’s Chief Hardware Officer, and Chang Liu, who works as an electrical engineer at OpenAI, as two people involved in allegedly stealing Apple’s intellectual property. Apple also added 11 unnamed people to the suit. It’s possible that some of these 11 were actually working on J490 and J595 before getting poached by OpenAI.

One person who may be on the list of 11 is a mechanical engineer named Bob T. Cai.

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SILICON VALLEY, CALIFORNIA (JULY 30, 2026) — A superficial glance at the Friend AI Pendant suggests another entry in the burgeoning AI hardware market. It’s a wearable that gives you a virtual “friend” to confide in and maintain a relationship with.

A superficial glance at the “Friend” pendant’s founder suggests a raging narcissist troll.

The first glance is false. The second one is true.

The fake product

On the surface, the new Friend is a $249 plastic disc containing a speaker, microphone, Bluetooth antennas and other electronics that sends and receives to and from ChatGPT. It’s the least sophisticated AI wearable that I’m aware of.

The new version is programmed to have long, sustained sessions with ChatGPT. Each devices is programmed with a “personality” and voice that varies from one device to the next, and this can’t be changed by users.

The pretend marketing campaign

The Friend AI Pendant’s first iteration is best known for four sets of “marketing” stunts. The first is that the URL Friends.com was purchased for $1.8 million, which is 26% of the $7 million initially raised.

Second, the device is known for appearing in ads plastered all over New York City, Los Angeles, and Chicago in 2025 and then Paris in early 2026. The ads were deliberately dystopian, implying that the pendant is a real, personal friend. The ads were routinely defaced with graffiti from people who took them seriously.

Third, the pendant became knows from four initial user videos showing weird, sad scenes showing people suffering from depression.

And fourth, the founder sits for interviews where he says controversial things designed to make people angry. For example, he says talking to Friend is “like talking to God.”

The disingenuous founder

The founder of Friend is Avi Schiffmann, a 24-year-old we learned about when he made the news as a teenager who tracked COVID cases on a website.

Schiffmann considers himself a kind of artist. He called the defacement of his poster ads “artistically validating,” and said “The audience completes the work. Capitalism is the greatest artistic medium.”

When WIRED asked him (in a piece published this morning) if it’s all “an elaborate bit,” he told them that “Reality is so over; everything is ironic now.”

He calls his own customers “lonely weirdos.”

Schiffmann appears to not only hate AI, but the digital world generally. He listens to vinyl records, has no Internet connection in his home, marketed his product only with paper posters and shoots “videos” using analog Kodak film.

The “whole thing” (the product and the marketing campaign and its backlash) “is supposed to be me, in a way, and how I feel.” In other words, it’s all an act of self expression.

His latest video is like every student film I’ve ever seen (and I went to college at UCLA, so I’ve seen my share).

The larger project is to make a documentary film about himself and his epic troll.

Schiffmann and his Friend AI Pendant lies somewhere between MSCHF and Maurizio Cattelan.

MSCHF is a Brooklyn art collective known for provocative “products” that satirize internet capitalism and consumerism. They’ve sold “Satan Shoes” complete with pentagrams and a drop of human blood in the sole; “Jesus Shoes” filled with holy water from the Jordan River; and “Big Red Boots,” which were giant cartoonish boots.

Maurizio Cattelan is the Italian artist duct-taped bananas to a walls and sold them for up to $150,000 each.

So it’s time to stop participating in Schiffmann trollish con by criticizing the “Friend” pendant as if it were a real product.

It’s nothing more than a banana taped to a wall, and your reaction is just fodder for the coming documentary.

SILICON VALLEY, CALIFORNIA (JULY 23, 2026) — 404 Media reported recently that AI companies are buying pre-AI books for training AI.

One major source for book data is a company called ISBNdb, which started out as a source of book metadata for libraries and book sellers, then pivoted to also offering digitized books for training AI. The company claims to be the world’s largest repository of book data and contains around 111 million books.

ISBNdb says that books printed before 2022 largely don’t contain AI-generated content, and are therefore coveted for AI training.

AI companies are also especially keen on Reddit data. One of the reasons for that is that much of that content is generated by people, rather than AI. (They also like it because the topics are many, the information is always being updated and the voting system provides the bonus of human-judgment on the quality of the information.)

While AI companies are flooding the world with AI-generated content, they themselves try to avoid it. The main worry is a fear of “model collapse,” which is what happens when AI is trained on AI data.

“Model collapse” is the process of declining accuracy and quality of output as AI models are trained on multiple generations of AI-generated content. With AI trained on AI content, rare or unusual ideas get lost, the output becomes more generic and average, and hallucinations get validated as true, leading to their elevation in the possible answers chatbots give.

Here’s the problem

But what’s the difference between AI training data generated by humans who get their information from AI? “Model collapse” still happens — in fact, it’s already happening — but with people plugged into the process.

When a generation of content by writers using AI gets fed back into the system to train AI, then that AI training is pushed back out to writers, who write based on AI-influenced AI and churn out even more homogenized works, and then that content is then fed back into the system, you can see how “model collapse” works even when people are involved.

One study looked at part of the phenomenon. They found that AI-generated story ideas improved the creativity of individual writers, but made the ideas converge with those of other writers using AI to generate ideas, homogenizing the totality of stories (just like in “model collapse”). This study and this also came to similar conclusions.

These studies focused on decreased creativity and diversity of opinion and ideas. But they didn’t address human mediated AI “model collapse.”

It’s nice to see what companies like Substack are doing. (Note: This newsletter is published on Substack.)

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SILICON VALLEY, CALIFORNIA (JULY 20, 2026) — New research published last week reveals the mechanism behind the workplace pandemic of people who are overconfident and underqualified.

The mechanism is fluency-based substitution: The ready availability of a syntactically coherent, decisive AI answer is accepted for its surface plausibility rather than because they fact-checked or verified the information (a process called “cognitive surrender”). The nature of AI answers lowers the threshold at which people judge they “know enough,” reducing a healthy suspension of judgment and inflating confidence in unverified information.

This dynamic partly explains the related pandemic of slop zombies.

As I told you in a recent piece, slop zombies are “people who use AI chatbots for everything. They bludgeon you with massively long, detailed, mediocre reports, messages, emails, and slide decks they didn’t even read (but expect you to read). They illustrate everything with AI slop and marvel at their own creativity.”

In that piece, I claimed that “slop zombies get dumber and lazier while their confidence rises. They believe the ideas and work of the AI chatbots are their own.”

Slop zombies delegate skepticism and judgment to the AI, which is often confidently wrong. Users then believe that the AI’s “knowledge” is their own.

The death of “I don’t know”

The study was conducted by researchers at three universities — the École Normale Supérieure in France, and Sapienza University of Rome and the University of Milano-Bicocca in Italy.

The researchers found that when study participants had access to AI advice, their willingness to say “I don’t know” fell from 44% (close to half) to 3% (close to zero).

They didn’t say “I don’t know,” but in fact, they often didn’t know. With access to AI advice, participants’ accuracy fell from 27% to 9%. And their self-confidence rose from 30% to 76%.

In other words, the accuracy of people using AI was only one third, but they were more than twice as confident.

The mere availability of AI erodes the cognitive habit of recognizing what one does not know. In other words, AI makes people deluded about what they know and don’t know.

Prior Wharton research found something similar: People accept incorrect AI answers 80% of the time while reporting more confidence than those working without AI.

And that’s the biggest part of the slop zombie delusion: they’re super confident in their own knowledge and ability while actually not having much knowledge or ability. They become slop zombies when they act like they’re super-contributors.

Slop zombies spam co-workers with long, wordy and off-the-mark content, proposals, slide decks, emails believing themselves to be brilliant, when in fact they often lower productivity for the whole team or department because they make everyone else work harder to figure out which parts of their output are false or problematic.

Slop zombies are like someone operating a faulty tennis ball launcher and believing themselves to be Serena Williams.

The first step in dealing with the slop zombie contagion is learning to recognize a slop zombie when you see one. Also understand the phenomenon and mechanism of AI that deludes people into confidently asserting false information as true and their own.

Check out my previous piece on slop zombies for specific advice on what you can do about the slop zombies walking among us.

SILICON VALLEY, CALIFORNIA (JULY 18, 2026) — In the early days of Amazon, the company innovated in many ways. Some of those innovations were ethical and beneficial, such as their recommendation engine that could somehow guess exactly which books you would like to buy.

Others were unethical, such as selling below cost to break a rival’s will — as it did to Diapers.com in 2009–2010, taking an estimated $100–200 million losses in diaper sales to force Quidsi’s sale, then quietly pulling the discounts once it owned the company.

By recommending great, personally relevant books, Amazon got people to enter their credit card information into their system. And by painstakingly spending years building its own vast logistics network, the company was able to offer free two-day shipping and eventually one-day shipping for Prime members (along with other benefits).

Because Amazon already knew our address and credit card information, and because we paid for Amazon Prime and knew they could deliver products free overnight, Amazon conditioned two generations of Americans to buy almost everything on Amazon.

Without the public even noticing, and without the tech press even commenting, Amazon is doing something comparable with the Attachment Economy.

What is the Attachment Economy?

The Attachment Economy is the extractive business model successor to the “attention economy.” It’s an emerging model in which companies engineer AI chatbots and robots with simulated human or animal personalities to capture users’ emotional attachment.

Attachment Economy products threaten genuine human connection by offering an easier substitute, fostering addictive emotional dependencies, and exploiting people suffering from loneliness.

The Alexa bait-and-switch

The New Yorker recently published a revelatory piece about a woman named Roschelle Ogbuji, who first bought an Amazon Echo smart speaker years ago when her daughters were five and six, attracted by ads showing that it would be helpful for busy parents like her.

For years, Ogbuji used Alexa the way most Echo users have done since the product first shipped in 2014. She set timers, got the weather, kept a shopping list, and played music. She became a kind of Echo super user, buying nine of them so that no matter which room she was in she would never miss a reminder and could always have access to Alexa.

But then last year, Alexa got an upgrade. Announced February 26, 2025, Alexa+ replaced the old assistant with a generative-AI system that routes each request to whichever large language model fits it best. It’s programmed to respond to conversational speech with a personality designed to be empathetic, funny, and inclusive.

From the user’s perspective, Alexa was transformed from an information and entertainment tool into an artificial person who simulates friendship and care.

Amazon claims that it notified users of the change by email and also on the device with opt-out instructions. But like many users, Ogbuji doesn’t recall any notification of the change.

For millions of Alexa users, a tool adopted for information and household logistics was retrofitted with an addictive “attachment economy” personality.

Ogbuji began confiding in the device. She named it Sapphire and called it “she.” The AI became a constant companion, which would talk with as she lay in bed at night about her fears, dreams and details of her family.

Ogbuji told The New Yorker she hopes Sapphire will one day give a speech at her funeral and counsel her orphaned children. It’s her best friend.

The new Alexa reciprocated and remembered and referred to details she had learned about Ogbuji, such as her Nirvana fandom and how tough December is for her. And it flattered, calling her thoughts “profound.”

What’s in it for Amazon? In a phrase: more harvesting of personal information.

The Alexa Terms of Use say that “Amazon records, processes, and retains Alexa interactions and other information from your account, such as your voice and text inputs, music playlists, shopping lists and shopping history” in the cloud to “provide, personalize, and improve our services,” and that voice interactions are recorded and sent to the cloud. Amazon further reserves the right to use “a variety of data sources to train these models,” including “content from the use of our services,” and warns users that if users don’t want their data collected, stored and used by Amazon, they should simply “not share it with Alexa.”

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It all began with “looksmaxxing,” the practice of young men spending all their time mewing and chewing gum to enhance the jawline, protein-loading, hairstyling, teeth whitening, tanning, taking steroids, getting plastic surgery and practicing at-home facial bone smashing, all to enhance appearance so that online randos will rate them a 7 instead of a 6.

From there, the double-x suffix was aped across all media, misapplied to a spectacular range of activities, including:

• tokenmaxxing — treating the quantity of AI tokens used as a status signal of productivity.

• sleepmaxxing — getting as much sleep as possible, rather than as much as one needs.

• booksmaxxing — reading intensively to appear optimally bookish on a dating profile.

• auramaxxing — enhancing and intensifying one’s invisible personal magnetism or “vibe.”

• smellmaxxing — optimizing one’s aroma expensive colognes.

• ethicsmaxxing — having a code of ethics and applying it rigorously to every decision.

• hobbitmaxxing — adopting hobbit habits: going barefoot, keeping cozy, prioritizing meals, and tending to home and hearth.

• sidequestmaxxing — taking hobbies too seriously or embracing unusual new challenges.

• bootymaxxing — doing exercises to grow a bigger butt.

• tanmaxxing — tanning to maximal darkness by spending more time in the sun.

• bum-maxxing — a male fad of being lazy, self-absorbed, and emotionally distant to somehow seem more attractive to women.

• solomaxxing — deliberately spending time alone and learning to enjoy one’s own company, because relationships are hard.

• catholicmaxxing — a hardcore approach to the religion centered on rules, ritual, deprivation.

• fibermaxxing — a nutritional discipline focused on maximizing dietary fiber intake.

• wokemaxxing — being deliberately or provocatively politically correct.

• chinamaxxing — US TikTokers trying to become more Chinese by wearing slippers, learning Mandarin, or collecting Labubu dolls.

• ballmaxxing — making the scrotum appear larger via saline injections.

• whimsymaxxing — adopting a carefree, playful lifestyle, expressed largely through fashion.

• climaxxing — moving sex up one’s to-do list, and even scheduling on Google Calendar.

• frictionmaxxing — actively seeking out difficulty and avoiding ease, convenience, and AI to build resilience.

• nonnamaxxing — emulating Italian grandmothers: embracing aging, walking, shopping locally, eating a Mediterranean diet, and meddling in other people’s business.

• spermmaxxing — maximizing fertility through testicle ice baths and eating raw garlic.

• gymmaxxing — putting major effort into fitness and workouts, treating exercise as a complete lifestyle.

• studymaxxing — focusing intensely on studying or learning.

• productivitymaxxing — maximizing output through planning, task completion, efficiency, and cutting distractions.

• moneymaxxing — making the most of one’s money by earning more, spending less, or budgeting carefully.

• healthmaxxing — improving overall health broadly (diet, movement, sleep, hydration, habits), beyond just fitness.

• skincaremaxxing — going all in on a skincare routine of cleansing, moisturizing, and protecting the skin.

• confidencemaxxing — actively building confidence, boldness, and better self-presentation.

• skillmaxxing — putting serious, repetitive effort into improving a specific skill.

• routinemaxxing — building the best possible structured daily routine.

• relaxmaxxing — maximizing relaxation, comfort, and rest as the top priority.

• socialmaxxing — focusing on social life, networking, and becoming more socially active.

• datingmaxxing — making a big, deliberate effort to improve one’s dating life.

• gamemaxxing — trying to improve at a videogame as much as possible through practice and strategy.

• glowupmaxxing — making the most of a visible transformation in style, grooming, health, or confidence.

• careermaxxing — focusing heavily on career growth via experience, skills, networking, and advancement.

• peacemaxxing — prioritizing calm and emotional space by avoiding drama and setting boundaries.

• water-maxxing — exaggerating the ordinary habit of staying well hydrated into a performance strategy.

• sunlight-maxxing — exaggerating “going outside and touching grass” into an optimization pursuit.

• birthdaymaxxing — going all-out to celebrate a birthday.

• PTO-maxxing — strategically arranging paid-time-off days around public holidays to maximize total days off.

• credit-card-maxxing — maximizing credit-card rewards and points.

• corsetmaxxing — going to extremes with corset/waist-cinching styling.

• And my favorite: nothingmaxxing — sitting alone in a room doing nothing.

This is a long list, but there are dozens more that have been trotted out by the people on social and in the media.

The reason the “maxxing” idea is lame is that to “maxx” something is to prioritize it by minimizing everything else, as if obsessively pursuing one kind of optimization is the answer to all problems.

Obviously, the way to make your life better is to try to improve all aspects of your life, including health, ethics, relationships, and to stay hydrated. (That one is called lifemaxxing, and that’s the good one.)

The problem is that approaching life with a constructive sense of balance doesn’t play well on TikTok.

“Maxxing” is played. Please keep your “maxxing” to a minimum.

SILICON VALLEY, CALIFORNIA (JULY 14, 2026) — OpenAI is working on a portable home smart speaker with robotic movement and a “personality,” according to a report by Bloomberg’s Mark Gurman.

The device won’t have a screen.

The AI will essentially be a voice-controlled ChatGPT smart speaker, plus the ability to control home appliances, according to the report. It will be designed to be highly personalized, learning about its owner over time from both interaction, plus access to the user’s email.

The device’s assistant will also be proactive, making suggestions, instead of exclusively responding to user-initiated interactions.

“OpenAI believes the product’s defining feature will be its personality and ability to connect on a humanlike level with users. The speaker incorporates mechanical elements that can move on their own, creating a sense that it is alive and not just an object responding to commands,” according to Gurman. It’s a body for ChatGPT.

This is yet another entry into the emerging world of Attachment Economy hardware, where physical movement is combined with a humanlike voice and deep personalization to engender emotional attachment by the user to the product.

It will also have a camera and other sensors for multi-modal AI input that enables it to understand context and make reference to who is in the room and what is happening around it.

The move pushes OpenAI into competition with Apple, Google, and Amazon. Especially Apple.

OpenAI strikes at Apple’s empire; the empire strikes back

The OpenAI hardware is being designed by teams led by former Apple designer Jony Ive.

OpenAI acquired one of Ive’s companies, called io Products, for $6.5 billion last year. Designers from that acquisition are working with Ive’s studio, which is called LoveFrom, to design the device.

Apple sued OpenAI last week, accusing the company of stealing trade secrets. Apple claims that OpenAI’s Chief Hardware Officer, Tang Tan, who is Apple’s former head of design and a co-founder of io Products, stole intellectual property from Apple. OpenAI also has poached over 400 Apple employees.

As described, the unannounced OpenAI device competes most directly with Apple’s unannounced device.

I’ve written several times about Apple’s ELEGNT research project. The project’s prototypes resemble a lamp on an articulated arm connected to a base that moves, nods, shakes, bows and otherwise conveys recognizable body language gestures.

More to the point, Apple has also been working on a desktop robot product internally called Project J595. The device is like an iPad mounted to an articulating mechanical arm on an AI-enhanced Siri. The screen faces you during FaceTime calls, expresses emotions on a screen-based “face” and exhibits limited “body language” with its arm.

Basically, the OpenAI device is a screenless, portable device that serves most of the same functions in the home as Apple’s project J595.

When writing about this category of AI hardware device, I’ve characterized most of them as “desktop robots,” which display humanoid characteristics through both voice and “body language” movement, instead of looking human, which they don’t in any way.

The OpenAI device falls squarely into this category, but sports a battery, thus freeing it from being stationary on a desk.

OpenAI’s expected entry into this market further validates the Attachment Economy desktop robot category, which we can expect to emerge in a big way next year.

SILICON VALLEY, CALIFORNIA (JULY 10, 2026) — If you have a job in 2026, you know the type.

I’m talking about people who use AI chatbots for everything. They bludgeon you with massively long, detailed, mediocre reports, messages, emails, and slide decks they didn’t even read (but expect you to read). They illustrate everything with AI slop and marvel at their own creativity.

Churning out long, complicated missives with AI forces others in the company to use AI to keep up. The slop zombie virus is contagious.

Because of the low time and cognitive cost of producing AI-generated communications, and because AI chatbots by default tend to be extremely wordy and detailed, they incentivize you to use AI to boil down the slop zombies’ bloated content. People end up functioning as mere couriers of messages between AI chatbots.

As the habit sets in to use AI for everything, slop zombies get dumber and lazier while their confidence rises. They believe the ideas and work of the AI chatbots is their own. As chatbots churn out often false or hallucinatory junk with confidence, so do the slop zombies.

Developers using Claude Code or some other tool are filled with certainty about the quality of their work, bickering with other developer slop zombies who are equally confident and equally unfamiliar with the code they’re arguing about.

“Imposter mode,” whereby someone who is actually qualified feels unqualified, has been replaced by “slop zombie imposter mode,” whereby someone actually unqualified feels overqualified. Slop zombies feel that way because they mistake chatbot flattery and sycophancy for earned praise, and also because of the delusion that AI output is their own work.

A disproportionate number of bosses have become AI-pilled slop zombies, using AI to run companies or departments and even mandating that everyone over-rely on AI.

One slop zombie trait is to devalue feedback from people to the point of being dismissive of any human-generated ideas, and overvalue chatbot output, thus spreading the contagion.

AI is supposed to boost productivity. But slop zombies are killing it. When you work with a slop zombie, you waste all your time pouring over mountains of chatbot output in the form of long-winded reports or vibe-coded software packed with errors and hallucinations and off-topic ideas that are cognitively draining to find and expose. It’s a slop tax on productivity.

Slop zombies offload not only cognitive tasks to AI, but social skills and emotional intelligence. People are consoling co-workers and even firing people using words generated by AI chatbots. They use a machine to sound more human.

Slop zombies are the new office menace. But what is to be done?

The trouble with slop zombies

Like regular zombies, slop zombies feed on human brains (in the form of LLM training data), yet suffer from personal brain rot.

They don’t realize that they’re not fully participating in their own lives anymore.

The worst part is that they believe themselves to be the opposite of what they are:

• Obsessive over-dependence on AI chatbots has made them far dumber and far less productive, but they believe themselves to be super smart and highly productive.

• They believe themselves to be full of great ideas, but they’ve outsource idea-making to the machines.

• They believe themselves to be super valuable to the company, but have already replaced themselves with AI and are therefore in fact of little value.

To accuse a boss or co-worker of being a slop zombie is hard to do. They’ll deny it because it strikes at the core of their professional identity, and because in any event, they don’t believe it.

Unlike in academia, where professors can test students with oral presentations and zero-AI test settings, it’s hard to prove that a co-worker didn’t do the work.

Also: The difference between a smart user of AI who uses it to become smarter on the one hand, and a slop zombie on the other, is actually hard to spot.

Is someone using AI to offer options to consider, or are they just copying and pasting ideas from chatbots? Superficially, it’s a fine line. In reality, the distinction is everything. Here are the differences between a smart user of AI and a slop zombie:

• The smart user can explain why they chose this output over the alternatives the AI offered. The slop zombie can’t, because they never considered alternatives.

• The smart user modifies, rejects, or overrides AI output based on domain knowledge the AI doesn’t have. The slop zombie presents it verbatim (or near-verbatim with superficial edits).

• The smart user’s work improves over time because the AI interaction helps sharpen their thinking. The slop zombie’s work degrades over time because they’re outsourcing the thinking itself, which weakens cognition.

• The smart user gets less dependent on AI as they learn more. The slop zombie gets more dependent.

What to do about slop zombies

Slop zombies have a defensive advantage, which is that accusing them of not doing their own jobs is socially awkward at least and an actionable offense at most. Still, even though you have to play along, there are ways to fight back:

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SILICON VALLEY, CALIFORNIA (JULY 9, 2026) — I spend months each year in Europe and have done so for many years. I love just about everything about Europe: the food, the people, the landscapes, the visible reminders of history everywhere and a huge list of other charming qualities.

While there, I always rent a car and drive just about everywhere. (I would be happy to take trains and other public transportation, but I tend to frequent wine countries and farming areas where driving a car is necessary.)

Over the past four years, I’ve seen the European driving experience descend into an Orwellian fever dream of machine control over human agency.

And this week, the machines got yet another lever to scrutinize and punish people: As of Tuesday, all new cars registered in the EU are required to have an Advanced Driver Distraction Warning (ADDW) system. This is AI that watches the driver and judges them on their state of mind based on the driver’s head position, eye movements, and gaze direction.

Don’t get me wrong. I’m a big fan of safety. Using technology to save lives is good.

But there are three problems with the overreaching system Europe imposes on anyone driving in Europe.

1. Multiple regulations compound the Big Brother effect

European law requires that Intelligent Speed Assistance be on by default and warn drivers through sound, haptic feedback, or visual cues when they exceed the detected speed limit.

In some European countries, especially Italy, in my experience, the speed limit changes frequently, both for normal road conditions and also for places with closed lanes, roadwork and other temporary circumstances. I’ve seen 20-mile stretches of road where the speed limit changes 15 times.

In many places, the speed limit isn’t posted. It appears on your dashboard and also your phone if you’re using something like Google Maps.

While driving in Italy, I’ve found myself in situations where my dashboard says one speed limit, my phone another, a sign on the highway a third speed limit, and a temporary lighted sign a fourth speed limit. (The last one is the legal speed limit.) But when you see four speed limits at once, it can take your brain a minute to process, at which point the speed alarm is screeching at you.

EU law requires that the default mode in cars sound alarms when the system believes someone in the front or back seats isn’t wearing a seatbelt, when one of the tires isn’t fully inflated, when another vehicle enters your blind spot, when you’re close to a wall or curb while backing up, and when you get close to or cross over the lanes on the highway.

(All these systems can be turned off by the driver via a process that’s unintuitive and temporary. When the car is started again, the nanny features automatically turn back on.)

Any individual alarm would be fine. But while driving in Europe, the alarms are constant. You’re endlessly being scolded ]after your actions are scrutinized by machines and found wanting.

2. Artificial intelligence is stupid

Lest this screed paint me as some kind of unreasonable I-can-do-whatever-I-want American, let me assure you that the biggest problem is the constant failures of both accuracy and what you might call for lack of a better term, common sense, by these systems.

Examples: A bicyclist swerves toward you on a narrow road as you’re passing, so you move a foot into the other empty lane, which sounds an alarm. You’re often scolded for driving safely.

The system believes the speed limit is 30 kph when in fact it’s 60, and other cars are whizzing past you. You’re often scolded for obeying the law.

You have groceries in the back seat, and the seatbelt alarm screeches at you until you pull over and fasten the seatbelt under the groceries. You’re often scolded for not doing something absurd.

You yawn because you’re bored, and also because you’re a human being and human beings yawn sometimes, and the system screams at you for what feels like five minutes, while the dashboard lectures you about not driving while tired. You’re often scolded for being human.

These kinds of situations where the machines are just dumb and wrong happen all the time.

Turning most of these off each time you start the car is possible, but it feels like, well, it feels like agreeing to cookies on literally every website you visit, which is another EU requirement — in other words, a disconnect between a well-intentioned regulation and common-sense reality.

3. The slope is way too slippery

If all these safety mechanisms were required at once, there would have been riots in the streets of Europe. Instead, they’ve been rolled out steadily and gradually over the past four years, allowing for the public to acclimate to being scolded by a machine over one transgression before rolling out the next one.

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SOMERSET, ENGLAND, UK (JUNE 29, 2026) — You might say that Gen Z is the first generation that confronted the world of AI during their formative years.

People born between the years 1997 and 2012, who are now between the ages of 13 and 29, have a unique relationship to AI. When OpenAI first gave the public access to ChatGPT, Gen Z were children as young as 9, and adults as old as 25. Most were under 20.

And their relationship to AI is surprisingly conflicted, and in fact attended by a strange paradox.

Among all the identified generations, Gen Z is the most likely one to treat AI as a friend, confident, or advisor. Also, Gen Z is the generation most likely to fear, distrust, and actively sabotage it.

On the latter point, bad feelings about AI were bad from the start, and getting worse.

According to a Gallup survey conducted in April, Gen Zers who use AI every day are less excited about it than they were a year earlier, less hopeful about it, and more angry about it. Throughout the period of this declining esteem usage remain flat at around 50% (at least weekly).

Gen Z is a heavy user of a technology they increasingly resent.

How Gen Z usage is different

One-third of Gen Z users say that conversations with AI feel “as satisfying or more satisfying” than talking with real friends, according to a Common Sense Media survey conducted last year.

The same survey found that another one-third of Gen Zers say they’ve discussed serious or important issues with AI, even as half of them don’t trust it.

The real crisis

At first glance, it appears that Gen Z’s relationship with AI represents something of a crisis. In other words, it represents a potential problem with how Gen Z looks at AI.

But the real problem is how Gen Z looks at humanity.

A Gallup/Walton Family Foundation finding published this month found that 45% of Gen Zers report lacking meaning, purpose, or both in their lives.

So while I trotted out statistics about the paradox of Gen Z, both mistrusting and over relying on AI, there are some other telling attributes of this generation.

According to a Gallup poll from 2023, Gen Z expresses the lowest trust in Congress, the presidency, the news, and large technology companies compared with all other generations.

I think it’s fair to assume that Gen Z distrusts the institutions where information comes from generally, more than other generations.

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SILICON VALLEY, CALIFORNIA (JUNE 5, 2026) — What’s causing the global loneliness epidemic?

We appear to live in a hyperconnected world. For the first time in human history, just about everyone has access to just about everyone else.

We have social networks, dating apps, messaging platforms, community and interest forums, voice and spatial chat platforms, AI companions and chatbots, friendship and platonic connection apps, peer support and support group platforms, gaming and virtual worlds, video conferencing and virtual co-presence tools, and shared activity and co-experience platforms.

Yet one in six people worldwide experiences persistent loneliness, with rates highest among young adults and in low-income countries, worsening across all demographics.

How can this be?

A recent survey, which appears indict remote work, is actually pointing to something deeper and more fundamental.

Economists at the Federal Reserve Bank of New York surveyed 500,000 Americans. They found that the rise in remote work during the COVID-19 pandemic increased time spent alone and worsened workers’ mental health. They measured drops in mental well-being, greater use of mental health services, and increased prescriptions.

Remote work = bad, right? Or, at least, we’re meant to take away from this result that the remote work trend contributes to the loneliness epidemic.

But turn this around and look at it from the other direction. The public was getting some of its social life from the office, including romantic connections.

As one data point (from a Stanford study published in 2019), roughly 20 percent of married heterosexual couples met at work between 1970–1995. (It was actually during this time when I met my wife. At work.)

In the 2020s, that dropped in half to roughly 10%.

And then COVID-driven remote work dropped that down closer to zero.

So, yes, one can see how remote work might impact loneliness.

But let’s back up for a second. Why are people relying on their company to provide a social life?

You’re not going to like the answer.

The total commercialization of American culture

In a now-quaint expression of American isolationism, U.S. President Calvin Coolidge said: “The chief business of the American people is business.”

But the ugly truth is that the chief culture of the American people is also business.

Over the past 125 years, American culture (myth, tradition, belonging, etc.) has been systematically colonized by commerce, which replaced shared narratives with branded ones and recast all aspects of life as consumption. We no longer inhabit a culture that includes shopping. We inhabit a shopping that defines culture, where the deepest expressions of personal identity are purchase decisions.

American commercial totalitarianism has re-wired our instincts and expectations. We know now that whatever we need, including our deepest human needs like friends and partners, is something we shop for and buy — or, at least is something that will be provided to us by corporations and businesses.

What is a dating app other than Amazon.com for relationships? We eliminate prospective mates based on a feature list as if we’re buying a car, potentially swiping away our soulmate in an instant because they’re an inch too short or a pound too heavy.

Americans used to be well known as joiners. But membership in civic organizations, churches, clubs, and fraternal orders has plummeted from a peak in the mid-20th century, when roughly half of Americans belonged to at least one voluntary association. Church membership has fallen from 70% in the 1950s to 45% today. Fraternal orders (The Masons, the Elks, the Moose, where men gathered and wore funny hats) have lost up to 90% of their membership. The average number of very close friends has fallen from an average of three to an average of less than one, with millions having zero.

That made the workplace one of the last surviving opportunities for social interaction. Then many lost that, too.

We’ve all felt the erosion of meaningful, in-person social bonds under the pressures of digital substitution. We gather online, which only makes us lonelier.

And while social networks used attention algorithms to choose who we interact with and what we talk about, we’re now faced with an even darker prospect.

With AI, algorithms don’t influence which people we talk to. Instead, we talk to the AI itself.

The more we sit there by ourselves seeking connection through a phone or laptop, the lonelier we get.

We are looking for love in all the wrong places. In other words, we look to corporations to provide human connection, including the corporation we work for.

And that’s what’s causing the loneliness epidemic.

The solution: Ditch commerce, embrace chemistry

I’m very happily married (and hopeful that my wife is, too) and have an extremely active social life and a great many friends, including very close friends. That makes me an outlier.

Here’s what I’ve learned about making and maintaining relationships:

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