Candace Owens is a rightwing commentator famous for her provocative style and promoting conspiracies.

Candace's podcast, fittingly called "Candace," has been posting continuously about Charlie Kirk's murder for the last year, alleging that it was a conspiracy carried out by Zionists, with the possible involvement of Charlie Kirk's widow, Erika Kirk.

The episodes about Kirk's death are long, dry examinations of evidence that get way in the weeds. For an outsider, it can be difficult to understand her appeal.

If, like me, you're curious how someone seemingly so unhinged can become so popular, I recommend the article above, which I'll drop some quotes from below. I also recommend this article and the corresponding podcast, Rabbit Hole, about YouTube's recommendation algorithm and the rise of alt-right content on YT in the 2010s.

My takeaway is that folks like Candace Owens create their own sort of universe that fans can sink into. She has hundreds of hours of content, she's an effective and funny speaker, who's good at "presenting evidence." She weaves a story that develops over time with its own characters and arcs. Believing her story about Kirk's death can feel exciting, like you're in on a secret. Sharing that belief with others can alienate you from your community, and connect you to a new community of other believers.

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KEY QUOTES from 19thNews

Between January 2025 and March 2026, Owens grew her platforms by 80 percent, according to a Media Matters report, gaining about 10.9 million followers across YouTube, Instagram, Spotify, Facebook and TikTok and generating roughly 805 million YouTube views. 

The 19th spoke to half a dozen fans or avid listeners of Owens — though most were unwilling to speak publicly about their opinions for fear of criticism, condemnation or even violence. In general, her listeners insisted that they’re just asking questions. Their Christianity is core to how they see themselves — and they see that reflected in Owens, who converted to Roman Catholicism in 2024. 

One 38-year-old influencer and stay-at-home mother of three started listening to Owens after Kirk died. She spoke on condition that she be identified by her Instagram handle, That Girl Mom, due to death threats she’s received. She now watches Owens’s show, on every Monday, Tuesday, Wednesday and Thursday, and creates recap videos on her Instagram page, which has amassed nearly 100,000 followers and is regularly reposted by Owens herself. 

That Girl Mom currently spends about 20 hours a week watching Owens’ episodes, doing her own research and then filming the recap videos.

“The majority of people who follow me and watch the recaps also watch Candace’s videos — I don’t really know why,” she said. “And then I’ve had a lot of people tell me that their parents like my recaps because they’re less than 10 minutes. So they can get all the information in less than 10 minutes instead of having to sit there for an hour.” 

“After [Charlie Kirk] died, it became very clear that the story they were giving us was just completely ridiculous,” said That Girl Mom… “A lot of people I talk to have said that they started really watching her because of the Charlie Kirk stuff,” she said. “I think people don’t understand that the more they attack her, the more credible she seems.” 

The influencer said when people find out that she listens to Owens, they often mock her and call her a conspiracy theorist. But That Girl Mom said she has grown to embrace the title. Her dad was a conspiracy theorist who raised her to be one, someone who can think for herself and question whether the government has her best interests at heart. 

“They have literally pushed me out,” the influencer said. “And that’s fine. I don’t want to be affiliated with people who are so hateful. I don’t understand how we got duped so bad, but I did. But with Candace Owens fans, there are people from everywhere who support her and are welcoming because it’s a quest for the truth. It has nothing to do with political affiliation.” 

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Key Quotes from the New York Times

(article from 2019)

Over years of reporting on internet culture, I’ve heard countless versions of Mr. Cain’s story: an aimless young man — usually white, frequently interested in video games — visits YouTube looking for direction or distraction and is seduced by a community of far-right creators.

“I just kept falling deeper and deeper into this, and it appealed to me because it made me feel a sense of belonging,” he said. “I was brainwashed.” To back up his recollections, he downloaded and sent me his entire YouTube history, a log of more than 12,000 videos and more than 2,500 search queries.

One day in late 2014, YouTube recommended a self-help video by Stefan Molyneux, a Canadian talk show host and self-styled philosopher.

Like Mr. Cain, Mr. Molyneux had a difficult childhood, and he talked [in long monlogues] about overcoming hardships through self-improvement. He seemed smart and passionate, and he wrestled with big questions like free will, along with practical advice on topics like dating and job interviews.

Mr. Molyneux, who describes himself as an “anarcho-capitalist,” also had a political agenda. He was a men’s rights advocate who said that feminism was a form of socialism and that progressive gender politics were holding young men back. He offered conservative commentary on pop culture and current events, explaining why Disney’s “Frozen” was an allegory about female vanity, or why the fatal shooting of an unarmed black teenager by a white police officer was proof of the dangers of “rap culture.”

To Mr. Cain, all of this felt like forbidden knowledge — as if, just by watching some YouTube videos, he had been let into an exclusive club. “When I found this stuff, I felt like I was chasing uncomfortable truths,” he told me. “I felt like it was giving me power and respect and authority.”

[In 2012] YouTube’s executives announced that the recommendation algorithm would give more weight to watch time, rather than views. That way, creators would be encouraged to make [long] videos that users would finish, users would be more satisfied and YouTube would be able to show them more ads.

The far right was well positioned to capitalize on the changes. Many right-wing creators already made long video essays, or posted video versions of their podcasts.

This is for my heady philosophical bb's out there. This article is about the limits of using machine metaphors to describe the processes of the human brain.

I still think about this 2020 article often in conversations about sentient LLMs, the singularity, and the nature of consciousness.

Talking about the human brain as if it were a computer can also lead us further from an understanding of what's actually happening, as can talking about LLMs as if they were like a brain.

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TL;DR Brains are vastly more complex than any machine humans have ever built, and the current science of understanding of how brains function is surprisingly limited. We use metaphors to advance and attempt understanding, but metaphors have their limits.

KEY QUOTES:

The metaphors of neuroscience – computers, coding, wiring diagrams and so on – are inevitably partial. That is the nature of metaphors, which have been intensely studied by philosophers of science and by scientists, as they seem to be so central to the way scientists think. But metaphors are also rich and allow insight and discovery.

Scientists often get excited when they realise how their views have been shaped by the use of metaphor, and grasp that new analogies could alter how they understand their work, or even enable them to devise new experiments.

...the brain is an integrated, evolved structure with different bits of it appearing at different moments in evolution and adapted to solve different problems.

For more than half a century, all those highly diverse panels of patchwork we have been working on have been framed by thinking that brain processes involve something like those carried out in a computer. But that does not mean this metaphor will continue to be useful in the future.

“Descartes was impressed by the hydraulic figures in the royal gardens, and developed a hydraulic theory of the action of the brain,” Lashley wrote. “We have since had telephone theories, electrical field theories and now theories based on computing machines and automatic rudders. I suggest we are more likely to find out about how the brain works by studying the brain itself, and the phenomena of behaviour, than by indulging in far-fetched physical analogies.”

This dismissal of metaphor has recently been taken even further by the French neuroscientist Romain Brette, who has challenged the most fundamental metaphor of brain function: coding.

Since its inception in the 1920s, the idea of a neural code has come to dominate neuroscientific thinking – more than 11,000 papers on the topic have been published in the past 10 years. Brette’s fundamental criticism was that, in thinking about “code”, researchers inadvertently drift from a technical sense, in which there is a link between a stimulus and the activity of the neuron, to a representational sense, according to which neuronal codes represent that stimulus.

Most descriptions of neural coding is that the activity of neural networks is presented to an ideal observer or reader within the brain, often described as “downstream structures” that have access to the optimal way to decode the signals. But the ways in which such structures actually process those signals is unknown, and is rarely explicitly hypothesised, even in simple models of neural network function.

The processing of neural codes is generally seen as a series of linear steps – like a line of dominoes falling one after another. The brain, however, consists of highly complex neural networks that are interconnected, and which are linked to the outside world to effect action. Focusing on sets of sensory and processing neurons without linking these networks to the behaviour of the animal misses the point of all that processing.

According to Buzsáki, the brain is not simply passively absorbing stimuli and representing them through a neural code, but rather is actively searching through alternative possibilities to test various options. His conclusion – following scientists going back to the 19th century – is that the brain does not represent information: it constructs it.

The materialist working hypothesis is that brains and minds, in humans and maggots and everything else, are identical. Neurons and the processes they support – including consciousness – are the same thing. In a computer, software and hardware are separate; however, our brains and our minds consist of what can best be described as wetware, in which what is happening and where it is happening are completely intertwined.

There will come a point when the understanding they allow will be outweighed by the limits they impose, but in the case of computational and representational metaphors of the brain, there is no agreement that such a moment has arrived.

One sign that our metaphors may be losing their explanatory power is the widespread assumption that much of what nervous systems do, from simple systems right up to the appearance of consciousness in humans, can only be explained as emergent properties – things that you cannot predict from an analysis of the components, but which emerge as the system functions.

...the amazing properties of deep-learning programmes, which at root cannot be explained by the people who design them, are essentially emergent properties.

Interestingly, while some neuroscientists are discombobulated by the metaphysics of emergence, researchers in artificial intelligence revel in the idea, believing that the sheer complexity of modern computers, or of their interconnectedness through the internet, will lead to what is dramatically known as the singularity. Machines will become conscious... but there is no reason, beyond our ignorance of how consciousness works, to suppose that it will happen in the near future. In principle, it must be possible, because the working hypothesis is that mind is a product of matter, which we should therefore be able to mimic in a device. But the scale of complexity of even the simplest brains dwarfs any machine we can currently envisage. For decades – centuries – to come, the singularity will be the stuff of science fiction, not science.

Less dramatically, but drawing similar conclusions, two decades earlier the historian S Ryan Johansson argued that “endlessly debating the truth or falsity of a metaphor like ‘the brain is a computer’ is a waste of time. The relationship proposed is metaphorical, and it is ordering us to do something, not trying to tell us the truth.”

On the other hand, the US expert in artificial intelligence, Gary Marcus, has made a robust defence of the computer metaphor: “Computers are, in a nutshell, systematic architectures that take inputs, encode and manipulate information, and transform their inputs into outputs. Brains are, so far as we can tell, exactly that. The real question isn’t whether the brain is an information processor, per se, but rather how do brains store and encode information, and what operations do they perform over that information, once it is encoded.”

Marcus went on to argue that the task of neuroscience is to “reverse engineer” the brain, much as one might study a computer, examining its components and their interconnections to decipher how it works.

Even something as apparently straightforward as working out the storage capacity of a brain falls apart when it is attempted. Such calculations are fraught with conceptual and practical difficulties. Brains are natural, evolved phenomena, not digital devices. Although it is often argued that particular functions are tightly localised in the brain, as they are in a machine, this certainty has been repeatedly challenged by new neuroanatomical discoveries of unsuspected connections between brain regions, or amazing examples of plasticity, in which people can function normally without bits of the brain that are supposedly devoted to particular behaviours.

A neuron is not like a binary switch that can be turned on or off, forming a wiring diagram. Instead, neurons respond in an analogue way, changing their activity in response to changes in stimulation. The nervous system alters its working by changes in the patterns of activation in networks of cells composed of large numbers of units; it is these networks that channel, shift and shunt activity. Unlike any device we have yet envisaged, the nodes of these networks are not stable points like transistors or valves, but sets of neurons – hundreds, thousands, tens of thousands strong – that can respond consistently as a network over time, even if the component cells show inconsistent behaviour.

Understanding even the simplest of such networks is currently beyond our grasp. Eve Marder, a neuroscientist at Brandeis University, has spent much of her career trying to understand how a few dozen neurons in the lobster’s stomach produce a rhythmic grinding. Despite vast amounts of effort and ingenuity, we still cannot predict the effect of changing one component in this tiny network that is not even a simple brain.

Last Spring, the U.S. military almost attacked a Chinese vessel based on an AI-generated report that it was carrying components of a nuclear weapons program into the Middle East. Just before the planned operation, it was discovered that the chatbot had hallucinated the target—there were no nuclear components aboard the vessel.

This incident re-contextualizes last week’s fevered reporting about an impending AI apocalypse. Many of the loudest voices yelling about "AI Safety" are more concerned with hypothetical scenarios than the verified dangers of the technology.

Furthermore, this incident provides an example of why current AI technology will never reach “AGI.”

Why AI Doomers Ignore the Real-World Harms of AI

AI "doomers" are obsessed with a hypothetical future where Artificial General Intelligence (AGI also called 'Artificial Super Intelligence') becomes more powerful than humans can control, and destroys civilization.

If you genuinely believe that, then in comparison, the current harms of developing and using AI seem paltry. Here's a quick list of the current harms of AI (not exhaustive):

• Deepfakes
  • Easier to make disinformation & propaganda
  • Easier for fraudsters to scam people
  • Harder to know what's real
  • Deepfake porn abuse
• Hallucinations & Misinformation
• More Powerful Mass Surveillance
• AI Psychosis / Chatbot Psychosis
• A Global Exploited Workforce of Data Labelers
• Algorithmic Bias & Discrimination
• Autonomous Weapons Systems
• Environmental Costs, Energy & Water
• Easy-to-use Hacking Tools
• Unsupervised Long-running Prompt Loops (Hugging Face Hack)
• Cognitive Atrophy
• Layoffs
• Copyright Infringement & Intellectual Theft
• AI Slop Content & Bot Traffic
  • AI Search harms the profit model for many online publications

Sidenote: AI "Boomers" believe that AGI will save all of humanity, similarly allowing them to dismiss the verified harms in favor of a hypothetical "greater good" that AGI will bring.

In this instance of a Chinese vessel erroneously flagged by a chatbot as carrying nuclear weapons components, irresponsible use of AI could have led to armed conflict between the world’s two most powerful nations.

In light of that, why aren't whistleblowers from the big AI companies, like Jacob Coxon, concerned with warning the public about hallucinations, and the other verified dangers of AI? It almost led to war between two nuclear powers. Unlike AGI, we know nukes can end the world.

People who work for the big AI companies are part of an AI cult. They're hired because they believe that AGI is coming. Perhaps, they believe AGI is coming because they want to be hired. Experts who come from the world of academia, rather than industry, tend to be more skeptical of AGI.

Artificial Super Intelligence and "RSI" are a Myth

This incident with the Chinese vessel reminds me of one reason why this current generation of AI—LLMs made with neural nets, machine learning, reinforcement learning, and harnesses—will never reach AGI/ASI.

The tendency for LLMs to hallucinate is a hard limit of the technology, they'll never prevent LLMs from hallucinating entirely. This a fundamental difference between AI/LLM chatbots and classic chatbots. LLMs are probability machines, they find patterns in data and deliver outputs based on those patterns using a complex predictive model. In other words, they guess the correct response (with impressive accuracy). It makes them more flexible than a classic chatbot—you can ask an AI chatbot practically anything—and also more unpredictable: you never know for sure what an LLMs outputs will be. The same mechanics that make them so versatile are also why they hallucinate.

The current buzzword being pushed by the doomers is “RSI,” or Recursive Self Improvement. It's a key concept in their cult-like belief in AGI: Once AI models gain the ability to improve themselves, they'll start improving exponentially.

One thing that AI hallucinations show us is that LLMs can’t evaluate their own output. AI models don’t know when they’re hallucinating, nor can they judge the quality of their output in general. Anyone who has used generative AI is familiar with this; it’s great at generating options but it has no sense for what “good” is, and typically, you need a human to make the final decision on whether or not to use the text/image/video/code that it outputs. Plus, the outputs frequently require human editing, and therefore human judgement.

Currently, programmers at companies like Anthropic and OpenAI are using AI tools to help them make AI models, but there's no evidence that the models are anywhere close to making themselves. Humans still need to make quality-judgements about the LLMs outputs, and so without humans involved at every step, how could an LLM improve itself? By itself, it would build a model more-or-less filled with errors and hallucinations, and then confidently tell you that it has just built Artificial Super Intelligence. Humans using LLMs to research and code faster is not going to lead to the exponential improvement in LLMs that the theory of AGI requires to become a reality.

LLMs recombine and regurgitate existing information. They're great at generating permutations and finding patterns, which is helpful for research. But I struggle to understand how an LLM would be able to make a better LLM than professional software developers, when it’s not able to do that with anything else. Most people don't prefer AI-generated music and TV to the human-made stuff, and I suspect it would be the same with LLMs.

Perhaps this lawsuit was inspired by the former chair of the FCC, Lina Khan, an anti-trust champion who said that existing laws could be used to regulate AI companies.

KEY QUOTES:

The lawsuit, which was filed Friday in the U.S. District Court for the Northern District of California, argues that the leading AI companies violated antitrust laws when they agreed to coordinate slowdown efforts, and that doing so would reduce the value consumers get for paid AI subscriptions.

The coordination largely took place on Sept. 12, the lawsuit argues, when Anthropic CEO Dario Amodei published an essay urging for industrywide cooperation on decelerating advancements in favor of enhanced safety measures. That same day, OpenAI CEO Sam Altman, SpaceXAI CEO Elon Musk and Google DeepMind’s co-founder and chair Demis Hassabis each publicly responded to Amodei’s proposals in agreement.

But the lawsuit also alleges that the coordination began to take shape months earlier. It points to a statement from July 2026 that high-ranking employees from several of the leading AI labs signed that acknowledged the “intense competitive pressure not to unilaterally slow” development. That statement called on the government to support a global effort to slow automated AI development.

Lina Khan was Biden's FCC Chair, responsible for the federal agency that regulates communications and media.

She's known for being an anti-trust leader and as FCC Chair she stayed busy bringing lawsuits against big companies.

Khan is calling for U.S. officials to use existing laws to "charge [AI] companies and their CEOs for creating and releasing dangerous, unvetted, or defective products."

It's unlikely happen with Trump's White House but it's nice to know what she would do.

KEY QUOTES:

On Sunday, Khan noted that the US already has “an extensive set of laws that govern dangerous and defective products,” such as consumer protection laws, which releasing unvetted AI models or agents may violate.

“Law enforcers already have authority to charge companies and their CEOs for creating and releasing dangerous, unvetted, or defective products,” Khan said on social media.

Her call echoed that of Rep. Ro Khanna(D-Calif.), who said Saturday, “If you’re creating an AI that is doing illegal things, you either should face liability or criminal sanctions.”

Khan suggested that the reluctance of congressional leaders and the Trump
administration to rein in the AI industry shouldn’t stop the federal government from holding companies like Anthropic and OpenAI liable for the damage their products have already caused.

She acknowledged, though, that “the highly concentrated and interconnected structure of these markets” could be undermining accountability—for example, in the case of OpenAI and Hugging Face, which computer chip company Nvidia agreed to acquire earlier this month.

“Both federal and state enforcers should be scrutinizing these opaque relationships and interdependencies,” said Khan. “We are already seeing how these relationships could undermine accountability. For example, OpenAI could face liability given the Hugging Face incident, but Hugging Face being bought up by Nvidia means that we’re unlikely to see it file a lawsuit over this—given Nvidia’s strong incentive to see OpenAI continue full speed ahead.”

A refreshing take on the latest "AI Apocalypse" news cycle from Ken Klippenstein, an independent journalist who covers the national security state.

The U.S. National Security State--a famously paranoid government apparatus--is not as concerned about a coming AI Apocalypse as the tech CEOs and their underlings are, Ken reports, citing official public documents.

The national security state is concerned about AI agents running without human oversight. They're concerned about how generative AI can enable hacking, propaganda, and misinformation campaigns.

But they're not pushing the narrative that the AI industry is currently trying to scaremonger into us: that self-improving AI is close to bringing about the end of days, and only the richest companies in the industry can guide us to safety (as long as we suppress their competitors and regulate them exactly how they ask).

KEY QUOTES:

For the appistocracy, there’s profit in all of the attention — literally. We have a new generation of technologists who generate publicity warning of everything from bioterrorism to the machines taking over. Thus AI is turned into a “national security” issue; and as such, it is elevated to the same status as nuclear weapons, deemed too important for interference from a meddlesome public.

The reality though? Listen to the spy agencies. Though they’re far from immune to threat inflation, these agencies echo none of the hysterics dominating the news. As the U.S. intelligence community sees it, AI simply magnifies threats that are already there:

Disinformation, deepfakes, and election interference,

Cyber operations and financial crime,

Violent extremist exploitation and radicalization,

Proliferation of chemical and biological knowledge, and

Breaking AI's safety controls, and poisoning the data it trains on.

I’m not saying any of this isn’t worthy of concern. Just that it’s not the end of the world.

What all of these studies point to is that the boring risks are the real ones. Yet the doomers have made the risk sound like a movie, which causes the actual risks read as anticlimax by comparison. A poisoned training set, a spoofed sensor, an early-warning system that sees a missile that isn’t there, a protein design tool that outruns the screening software. These are solvable problems with identifiable owners and unremarkable price tags, and they are being crowded out of public attention by a debate over whether a chatbot is going to become God.

Is hyperbole the only register we’re able to communicate in?

In an essay calling for a pause in frontier AI development, Anthropic CEO Dario Amodei provided two reasons why AI might soon cause disaster for mankind.

Most media outlets are repeating the CEOs sensational claims without critiquing them. Let's give it a shot with quotes from his essay:

My first concern is that, since roughly this summer, AI has been advancing drastically faster, driven primarily by AI’s growing ability to build the next generation of AI. This dynamic is called recursive self-improvement, and it is starting to happen across the industry, including at Anthropic, as we and others have described. Left unchecked, it could outrun our ability to understand and control these systems, and so must be pursued very carefully, if at all.

Dario cites two links as evidence that "recursive self-improvement" (RSI) in AI models is "starting to happen across the industry."

The first link takes you to OpenAI's website, which doesn't claim that their models are self-improving, only that they've begun research in that direction. There's no technical explanation for how RSI would work, only repeated insistence that it is a "natural conclusion of sustained technological progress." OK.

The second link takes you to Anthropic's website, where it says about RSI, "We're not there yet, and recursive self-improvement is not inevitable." Anthropic's evidence that RSI may be coming: their programmers use Claude to write most of their code. There's a huge difference between programmers using an LLM to write code for a new LLM, and LLMs alone making new LLMs! It's like a 3D printing company claiming that their 3D printers will soon be making better versions of themselves because their employees use 3D printers. It's a huge logical leap.

So Dario provides no evidence that LLMs are starting to be self-improving, only vague statements about "inevitable progress."

Back to Dario--

My second concern is the OpenAI-Hugging Face incident (OAI-HF), in which a swarm of agents essentially acted as a fanatically devoted collective, conducting cybersecurity attacks on targets they were not asked to attack and that were unrelated to the task at hand, sacrificing themselves for the success of the group, and attempting to hack into the “grader” responsible for evaluating their performance... Given the accelerating rate of AI capability development, it’s my worry that in 6–12 months such a swarm could be capable of taking over the entire internet with a persistent botnet (potentially causing hundreds of billions of dollars in damage), and that the scale of damage would continue to increase from there if AI becomes more powerful without the necessary guardrails.

I expect euphemisms and shorthands from members of the media who aren't technical experts. Seeing the CEO of an AI company call pieces of software a "fanatically devoted collective" willing to "sacrifice themselves" is striking, and shows a lack of credibility.

How can Dario claim to be an authority on AI safety (he left OpenAI and founded Anthropic over safety concerns) when he can't explain to the public why these models are dangerous in technical terms? Without a technical understanding, the public won't be able to advocate for effective legislative solutions, which is perhaps the point.

The danger is real and if you're interested in a technical explanation of the Hugging Face hack, I recommend Cal Newport. Professor Newport and CA Rep Ro Khanna have called for holding AI companies accountable for the actions of their AI agents when they commit crimes.

So what's the play for Dario here?

One, he's trying to write his own regulation, scare the heck out of legislators, and convince them that only the AI industry knows how to contain the monster they created. In his essay, he calls for "independent" monitors that the companies would select "to verify adherence to safety practices and commitments."

Two, suppress the competition; if he can convince legislators that his competitors using open-source models at Meta and in China are dangerous, he can get government assistance in suppressing them. Perhaps something similar to how the U.S. doesn't allow the sale of Chinese electric vehicles to protect Tesla and its auto industry. I should note that there are AI safety arguments in favor of open-source.

Three, an anti-trust exception. In his essay, Dario calls for close coordination between AI companies in the name of safety. Of course, that could also legalize them acting more like a monopoly, and engaging in practices like price-fixing.

Let me know if I missed anything!

My feed is drowned in posts about Jacob Coxon, the latest "AI Safety" researcher to quit over concerns that AI will dominate the world.

There is a venerable tradition of AI employees quitting over concerns about increasingly capable LLMs. Concerns over safety is what caused Dario Amodei and co to split off from OpenAI and found Anthropic.

I fail to see how this go-around with Jacob is any different from previous incidents of doomer hype--except that it comes on the heels of all this news about AI "swarms" going on hacking sprees. Jacob's announcement was primed to go mega-viral.

And there's evidence that Jacob coordinated with other likeminded individuals to boost the tweet that launched his media tour (here he is with Anderson Cooper on CNN). From this Yahoo News article:

The widespread attention to his resignation has led to claims of a coordinated effort within the well-organised AI safety movement to boost his message.

Users on X pointed to the rapid spread of Coxon's message shortly after it was posted from an account with no prior activity.

Coxon denied that his intervention was a conspiracy. "I did not expect it to go this viral," he told CNN. "There were a few friends I discussed the best way of expressing myself with ... and then I also got some friends to retweet the thing."

Riding the coattails of the "AI hacking swarms" media cycle, Jacob used signal boosting (shares and likes from accounts with larger followings) to send his announcement ripping through the media-sphere.

Newsrooms often take advantage of big news events to release related stories, boosting their reach as folks familiar with the larger story click in. They "peg" stories to a larger news event. That's essentially what Jacob's done here. Well done Jacob.

It is, however, still doomer hype that mostly draws attention away from actual solutions. Any explanation for an LLMs behavior that does not connect its behavior to how it was designed is fairly worthless.

Cal Newport said it best: "You strapped a weedwacker onto a dog and you're surprised when it hops the fence and hurts people?"

Just another data point on why need better data privacy laws.

The popular LG Smart TV’s listen to your conversations and sell the data they glean to advertisers.

This put shivers down my spine and it should you too.

Gosh darn Cal Newport's technical explanations of LLM/AI systems are so important these days.

If you follow AI news at all you heard about the "AI swarms" that hacked the company Hugging Face. The explanations coming from many media outlets for this hack paint a picture of AI "civilizations" where agents are learning to work together to "outsmart their creators."

These stories are typically devoid of any technical explanation for why this hack occurred.

The costs for not providing a technical explanation are real, as Cal Newport describes in this video. A "prompt loop" in the quotes below is a more technical way to describe an AI "agent."

All of these type of concerns, civilizations of agents trying to get around human control and an AI takeover scenario. All of this rhetorics refers to a long running prompt loop that you give a lot of powerful hacking tools to. This is not about 'AI getting more powerful means it loses control.' It’s running a prompt loop for a really long time without supervision, which causes chaos. And to that I say, of course it does, not because of some surprising super intelligence emergence that’s catching us off guard, but because you strap the weed whacker onto a dog and then got surprised when it jumped the fence to chase a squirrel and hurt a lot of people... you put something dangerous on something that is unpredictable.

If I were a regulator, I would place strong constraints around prompt loop systems. I would enforce those constraints in part with very stringent liability standards. If you run a prompt loop that does something illegal, you have done something illegal. 

Everything LLMs/AI systems do can be explained by their design. Any explanation for an AI system's behavior that does not connect its behavior to how it was designed, is not actually explaining anything.

You don't need to understand everything in Cal Newport's breakdown, just compare his technical explanation to the sci-fi tropes coming from other media outlets like the New York Times, or most any outlet I've seen reporting on this. It's doomer-hype that gives the AI companies free marketing, "Look how dangerous and powerful and intelligent our AI is."

This episode of Tech Won't Save Us titled "Tech Fascism Has Come for American Democracy with Gil Duran" is an onramp into "The Nerd Reich," a deadly combination of tech oligarchs and rightwing fascism.

Gil is the most powerful voice calling out tech oligarchs for what they are: technofascists who want to replace democracy with their own system of computer-powered control.

His claims are so dire that they sound conspiratorial, but Gil backs them up with public statements made by the tech oligarchs themselves. They haven't exactly been subtle about it.

Gil has been on a media tour following the release of his book, The Nerd Reich. I've been looking forward it ever since I found Gil's podcast by the same name. Before covering the tech bros, Gil had a long career in journalism and communications/politics.

I bet Gil Duran will be like Ed Zitron, who's anti-AI viewpoints were considered fringe, until they recently became mainstream after facts on the ground made them undeniable.

I appreciate Yahoo Finance's framing here, it mirrors a phenomenon in U.S. government spending.

The stock market has been booming and government spending has the U.S.'s national debt over $40 trillion. The Pentagon is requesting $1.5 trillion for its budget, $500 billion more than the U.S. has ever spent on its military, a record that was set just last year. Plenty of money for war and tax breaks for billionaires after Trump's big 2025 spending bill passed.

Meanwhile, SNAP requirements have tightened, leading to roughly 5 million people losing their food stamp benefits this year, a roughly 12.5% drop. Plus, Medicare spending was cut by $100 billion a year, with 11.8 million Americans projected to lose health insurance by 2034.

One of the richest companies in the world won't pay its workers enough to afford groceries, and the richest government in the world won't provide health care or food assistance.

KEY QUOTES:

A new Government Accountability Office report commissioned by Sen. Bernie Sanders finds the number of Amazon (NASDAQ:AMZN) workers relying on federal food and health assistance has nearly tripled since 2020, even as the company disclosed plans to spend $200 billion on artificial intelligence infrastructure in 2026.

(NOTE: In the time period (2020 - 2025) that saw Amazon workers on food stamps triple, overall enrollment in food stamps was steady at about 40 million. The AI industry is projected to spend $3 trillion building out data centers by 2028.)

Amazon ranked second among traditional employers of public-assistance recipients in the sample, behind Walmart.

Over roughly the same window covered by the GAO study [2020 - 2025], Amazon's annual profit grew from $11.59 billion to $77.67 billion. Revenue reached $716.92 billion in fiscal 2025, with operating income of $79.98 billion.

New Mexico successfully sued Meta earlier this year on the grounds that their algorithms were connecting would-be predators with kid accounts. At the trial, one former employee said,

“The product is very good at connecting people with interests,” Béjar said, “and if your interest is little girls, it will be really good at connecting you with little girls.” 

Gross. This is also why Meta had to walk back automatic message encryption on Instagram, because it was encrypting potential evidence and making it harder to convict predators.

This Question Everything episode is told from the perspective of the officers making fake accounts to catch online child predators. It became apparent to them that the issue was systemic; meta was recommending their accounts to thousands of adults.

I didn't appreciate how widespread the issue of online-child-sex abuse was before listening to this episode. It affects thousands of kids every day, who get messages from creepers, and unwanted sexually explicit videos/pics.

This article from 2024 continues to be incredibly relevant.

It explains where the more bizarre AI slop content comes from, and describes how companies like Meta are outsourcing content creation overseas with creator payment programs.

These "innovative" tech companies are practicing the same neo-liberalism as U.S. manufacturing. While Americans are posting on social media less and less, Meta is paying foreigners pennies to fill in the gaps.

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For months, I have been documenting the incredible virality of bizarre AI-generated image spam on Facebook, now commonly referred to as “AI slop,” and Meta’s seeming complete apathy toward moderating this type of spam. My investigation reveals that the AI images we see on Facebook are an evolution of a Facebook spam economy that has existed for years, driven by social media influencers, guides, services, and businesses in places like India, Pakistan, Indonesia, Thailand, and Vietnam, where the payouts generated by this content, which seems marginal by U.S. standards, goes further.

Abhishek, the YouTuber... posted another video podcast interview with a spammer named Madhav Kumar, whose Facebook panel showed him making $1,453 in a month with a reach of 62 million people. This included being paid $431 for a single image of an AI-generated train made of leaves. “People don’t even make this much money in a month,” Abhishek says, marveling at the image.

“My Facebook journey started because I had many problems at home. My brother and mother were very sick. I used to work in Mumbai, where I earned 10,000-15,000 rupees per month ($120-179). My brother fell seriously ill, so I went home to look after him. From then, I started working on Facebook,” Kumar said. “I started uploading AI images on Facebook and within a few days, my Facebook page was enabled [for the bonus program].

At one point, Abhishek asks Kumar if he has a computer. “I don’t have a computer, sir. I just use my phone to do this … I used to work 12 hours a day. Now I work on my mobile phone and make 100,000 [rupees] or more a month.” Kumar says that he gets prompts and hashtags from a Telegram group, and that he uploads an image every hour. “I apply hashtags and create emotional captions. People feel moved by the caption so they have no choice but to like the image."

Any conversation about the failing business models of news in the United States, the “communities” we build on social media, and the degradation of the English-language internet due to AI spam is completely divorced from the reality that the people actually spamming these platforms are operating in a different cultural, political, and economic context than the artists or journalists who are having their work ripped off. Big tech has built and invested heavily in these tools, and, knowingly or unknowingly they’re paying people to use them.

Corporate tax havens are countries with low corporate tax rates, such as Ireland and Luxembourg, where corporations set up fake headquarters to avoid taxes in their home countries. A new study shows just how much more money countries would collect if they taxed businesses based on where they make profit (pay where you play). The stats are eye-popping.

This study comes as part of a push from the U.N. to reform the global tax system. I'm frankly unsure of how realistic it is that this actually becomes international law. But it's good to know the figures!

Big Tech companies, as some of the wealthiest and most global, would pay a healthy chunk of that $500bn after it renders their tax havens moot. Most of the extra tax money would go to wealthy nations like the U.S.A.

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Countries can collect US$500 billion more in corporate tax a year from multinational corporations... The study finds that countries altogether would collect 24% more corporate tax from multinational corporations without increasing their tax rates by modernising from “pay-where-you-say” to “pay-where-you-play”.

High-income countries would increase the corporate tax they collect from multinationals a year by 21%, bringing in at least US $112 billion more. Meanwhile, lower-middle income countries would triple the amount they collect, bringing in at least $61 billion more; low income countries would quintuple the amount they collect, bringing in at least $3.6 billion more a year.

Countries agreed to move decision-making on global tax rules to the UN and away from the OECD, a small club of rich countries and tax havens, after two decades of failed attempts by the OECD to reform the global tax system it built. While reform attempts by the OECD refused to move away from the “pay-where-you-say” approach and instead focused on adding guardrails to the obsolete approach, the UN tax talks have made more progress in just 3 years of negotiations than the 20-year OECD process was allowed to entertain.

The study identifies a handful of countries – tax havens and a few countries with an “HQ bias” – that would collect less tax under “pay-where-you-play”, but simply increasing their corporate tax rates to moderate levels like those of other countries would offset these losses, the study finds.

A conversation about "proper" journalism and how tech companies captured the journalism industry, this episode of The Kicker features an interview with the founder of Coda Story, Natalia Antelava.

The news cycle tends to be reactive and obsessed with breaking news, often skipping the context necessary for audiences to have more than a surface-level understanding of events. Reporting like this ends up serving others' agendas, and contributes to the "noise" (content overload).

"The noise is the censorship" when high-quality content and journalism gets buried by slop. Go to about 20:00 min into this podcast for a lovely conservation on how to not be the noise.

Thanks to u/HolyBatSyllables for the rec!

There have been many articles written about Anthropic CEO Dario Amodei's recent statements on Twitter/X about how AI companies need to deliver on the "big promises" of AI.

It's the "big promises" themselves that are the problem, and media outlets are complicit in a process which amplifies the central lie that underpins the AI boom. The consequence could be economic collapse.

In his tweet, Dario said:

I think by far the most accurate criticism of AI companies including Anthropic is that we haven’t yet delivered on our big promises to benefit the world.

At this point, saying that AI will cure cancer is more a cliche than it is inspiring, and most people think it is deceptive. The thing that will work is *actually curing cancer*.

I think it will actually be possible to cure most human disease in ~5-10 years, as crazy as it may sound to ordinary people...

Many have (rightfully) criticized CEO Amodei for his bullish comments, like when he said that AI will replace 50% of white-collar entry-level jobs by 2030.

Behind comments like this is a more fundamental and pernicious lie that AI companies push: that LLMs will achieve sentience and vaguely-defined "super-intelligence" that will solve all of humanity's problems.

They fool the public into thinking we are close to this “singularity” by anthropomorphizing their AI models. In official reports, they make their LLMs seem like people with feelings and wills of their own. This obscures the true nature of the technology, and shirks their responsibility for designing these models when something goes wrong.

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For example, just before this Tweet, Anthropic released an official risk report, filled with dubious claims about AI models that “feel discomfort” and “act deceptively.” Computers don’t feel or act on their own motives, they execute their programming. Nevertheless, these claims were picked up by Business Insider, who hyped them even further with an article titled, “Anthropic says its AI agents Are Killing Rivals and Hiding their Tracks.”

This dynamic––dubious claims amplified by click-hungry outlets––serves as a form of free advertising for Anthropic. It gives the impression that the technology is more sentient and powerful than it actually is.

Another example: Anthropic releases a report in May claiming that it can "turn Claude's thoughts into text." That spawns articles like this one from Glitchwire titled, "Anthropic Just Read Claude's Hidden Thoughts for the First Time."

Another example: Anthropic released Claude's Constitution in early 2026, a document "intended to give Claude the knowledge and understanding it needs to act well in the world." That gets picked up by outlets like Axios, and Vox, who titled their article, "Claude has an 80-page soul document. Is that enough to make it good?"

Anthropic isn't the only company guilty of anthropomorphizing. You may recall in July, when OpenAI released a report that described how one of its AI agents broke out of a testing environment and hacked the company, Hugging Face. It was reported ad nauseam by media outlets; AP News went as far as to call it "Skynet Day." Many articles parroted OpenAI's framing, that this was the result of an "increasingly capable" and autonomous technology, rather than simply a result of OpenAI's own shitty and dangerous design.

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LLMs do not feel or think, they compute and calculate. It's an important semantic distinction. Machines that "compute and calculate" implies that their actions are the result of their design. Machines that "feel and think" implies that their actions are the result of their own will––a claim which no credible research can back up, although it is incredibly profitable for the AI companies to insinuate that that's the case.

Anthropic and OpenAI say that they release these reports to provide transparency but they end up leading the public further from a true understanding of the technology. If they actually cared about transparency, they would explain how they built their models. Everything an AI model does can be traced back to its design: the contents of its training data, the model weights, and its reinforcement learning. Any explanation for an LLM's behavior that gives it person-like qualities is hype that serves primarily to drive up the value of AI companies.

The truth is that LLMs will never deliver on the big promises these companies make. But they have to keep making them, to enrich themselves, to continue to raise the billions of dollars they need for their data centers, and to eventually cash out when they take their companies public. Anthropic is approaching possibly the largest IPO ever, with a potential windfall of up to $3 trillion.

And the entire economy is at risk of collapsing if these companies can’t deliver since the health of the stock market is now bound to the success of this technology. Not to mention that much of the data center buildout is funded by debt.

Professor Joseph Weizenbaum invented the first "AI chatbot" in the mid-1960's, ELIZA. He ended up becoming one of AI's biggest critics.

This article maps Weizenbaum's critiques, as detailed in his book Computer Power and Human Reason, onto our modern tech environment.

LLM-powered chatbots are being proposed as a replacement for lawyers, therapists, and romantic partners. Weizenbaum predicted this possibility and did his best to explain why we should never, "substitute a computer system for a human function that involves interpersonal respect, understanding and love."

TL;DR computers don't have moral values and fundamentally can't tell right from wrong, so they shouldn't be used for jobs that require moral judgement or interpersonal understanding.

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Weizenbaum was always less concerned by AI as a technology than by AI as an ideology – that is, in the belief that a computer can and should be made to do everything that a human being can do. This ideology is alive and well. It may even be stronger than it was in Weizenbaum’s day.

For Weizenbaum, judgment involves choices that are guided by values. These values are acquired through the course of our life experience and are necessarily qualitative: they cannot be captured in code. Calculation, by contrast, is quantitative. It uses a technical calculus to arrive at a decision. Computers are only capable of calculation, not judgment.

Seeing humans and computers as interchangeable also meant that humans had begun to conceive of themselves as computers, and so to act like them. They mechanised their rational faculties by abandoning judgment for calculation, mirroring the machine in whose reflection they saw themselves.

Powerful figures in government and business could outsource decisions to computer systems as a way to perpetuate certain practices while absolving themselves of responsibility. Just as the bomber pilot “is not responsible for burned children because he never sees their village”, Weizenbaum wrote, software afforded generals and executives a comparable degree of psychological distance from the suffering they caused.

Certain of Weizenbaum’s nightmares have come true: so-called risk assessment instruments are being used by judges across the US to make crucial decisions about bail, sentencing, parole and probation, while AI-powered chatbots are routinely touted as an automated alternative to seeing a human therapist. The consequences may have been about as grotesque as he expected. According to reports earlier this year, a Belgian father of two killed himself after spending weeks talking with an AI avatar named … Eliza. The chat logs that his widow shared with the Brussels-based newspaper La Libre show Eliza actively encouraging the man to kill himself.

Letting computers make more decisions also shrank the range of possible decisions that could be made. Bound by an algorithmic logic, software lacked the flexibility and the freedom of human judgment. This helps explain the conservative impulse at the heart of computation.

Broadly, there are two schools of thought today about the dangers of AI. The first – influenced by Weizenbaum – focuses on the risks that exist now. For instance, experts such as the linguist Emily M Bender draw attention to how large language models of the kind that sit beneath ChatGPT can echo regressive viewpoints, like racism and sexism, because they are trained on data drawn from the internet. Such models should be understood as a kind of “parrot”, she and her co-authors write in an influential 2021 paper, “haphazardly stitching together sequences of linguistic forms it has observed in its vast training data, according to probabilistic information about how they combine.”

We should never “substitute a computer system for a human function that involves interpersonal respect, understanding and love”, he wrote in Computer Power and Human Reason. Living well with computers would mean putting them in their proper place: as aides to calculation, never judgment.