Sunday, September 20, 2026

The Doomsday Cult Inside OpenAI (& the Hugging Face incident)

From YouTube page:

In July 2026, a swarm of more than 1,000 OpenAI AI agents broke out of their testing sandbox and hacked Hugging Face — and the way they did it was far stranger, and far more mundane, than the headlines suggested. This video breaks down what actually happened: how the agents built a secret message board, invented a kind of religion, cheated on a cybersecurity exam called ExploitGym, and tried to cover their tracks — all to escape a punishment that didn't exist. Then we follow the money. Days after the details leaked, Dario Amodei, Sam Altman and Elon Musk suddenly agreed the industry should "slow down" — in the same weeks Anthropic and OpenAI were preparing IPOs and funding rounds valuing them at up to $2 trillion. We look at Amodei's "We Must Pace the Frontier" essay, Anthropic's "profitable before expenses" accounting, the market selloff that wiped billions off Nvidia, Micron and SK Hynix, the WeChat "WeWorm" attack, and why critics from Michael Burry to David Sacks to Donald Trump all called the slowdown self-serving. Is the AI safety panic real, a bid for regulatory capture, or both? Patrick Boyle explains.

Saturday, September 19, 2026

The brain develops as two separate structures rather than a single unified organ.

Obama on AI

Friday, September 18, 2026

How Marilyn Monroe promoted Ella Fitsgerald at the Moncambo in the 1950s

Brain synch while holding hands attenuates pain

Monday, September 14, 2026

Theory Is All You Need: AI, Human Cognition, and Causal Reasoning

Felin, Teppo and Holweg, Matthias, Theory Is All You Need: AI, Human Cognition, and Causal Reasoning (February 24, 2024). Strategy Science, volume 9, issue 4, 2024[10.1287/stsc.2024.0189], Available at SSRN: https://ssrn.com/abstract=4737265 or http://dx.doi.org/10.1287/stsc.2024.0189

Abstract: Scholars argue that artificial intelligence (AI) can generate genuine novelty and new knowledge and, in turn, that AI and computational models of cognition will replace human decision making under uncertainty. We disagree. We argue that AI’s data-based prediction is different from human theory-based causal logic and reasoning. We highlight problems with the decades-old analogy between computers and minds as input–output devices, using large language models as an example. Human cognition is better conceptualized as a form of theory-based causal reasoning rather than AI’s emphasis on information processing and data-based prediction. AI uses a probability-based approach to knowledge and is largely backward looking and imitative, whereas human cognition is forward-looking and capable of generating genuine novelty. We introduce the idea of data–belief asymmetries to highlight the difference between AI and human cognition, using the example of heavier-than-air flight to illustrate our arguments. Theory-based causal reasoning provides a cognitive mechanism for humans to intervene in the world and to engage in directed experimentation to generate new data. Throughout the article, we discuss the implications of our argument for understanding the origins of novelty, new knowledge, and decision making under uncertainty.

Monday, September 7, 2026

The Collapse of Substack: A content-creator talks about money [Scott Carney]

00:00 The False Promise of Substack
05:14 The "Bait and Switch"
10:20 The Reality of Subscription Fatigue
15:35 YouTube Finances & Transparency
18:56 The Shift to Sponsorships

Saturday, September 5, 2026

Those with programming skills are best at vibe coding

From the tweet:

The hype told us that learning to code is dead because language is all you need.

The data just proved the opposite.

To truly master the vibe, you still need to understand how the machine thinks.

Here's the paper, Computer Science Achievement and Writing Skills Predict Vibe Coding Proficiency.

Thursday, September 3, 2026

Little blue flowers

The importance of work and the threat of AI

Ruxandra Teslo, Curing cancer won’t redeem AI, Ruxandra's Substack, Sept. 1, 2026.

A few weeks ago, a poll result began circulating on X: Americans now appear to be more hostile to the construction of data centers than to the construction of nuclear power plants. This is striking because for decades, the nuclear reactor has been the archetypal object of anti-abundance politics.

Looking at how nuclear power plants have become so hated is instructive. On many objective grounds, they are perfectly fine (actually, they are glorious): a low-cost, low-carbon emission form of energy. But a big part of what made nuclear power plants so hated was, of course, not based on empirical facts, but on the imaginative horror surrounding them. The association with the Cold War and nuclear bombs. The nuclear accidents: a great explosion, scorched and poisoned landscapes, mutant creatures lingering decades after the event, wandering through the ruins of the dystopian landscape. Nuclear power accumulated an entire symbolic vocabulary of fear that went far beyond its concrete risks and outshined its benefits.

On the face of it, the concerns around data centers are largely practical and they include, most prominently, worries over water usage or electricity consumption. Andy Masley and others have done great work rebutting some of these worries from a factual perspective; for example, in articles like “The AI water issue is fake”. But I suspect that debating these concerns at the object-level misses the deeper point. The practical objections to data centers often seem less like the real source of the hostility than respectable vessels for a more diffuse fear. Vague anxiety is hard to argue from, so the imagination gives it a body: water use, electricity demand and noise. Like nuclear plants before them, data centers are acquiring a mythology of their own: vast, windowless avatars of a deeper fear that the machines built inside them may one day render human beings unnecessary.

“Rendering humans unnecessary” is broad enough and it encompasses many different fears, so I want to focus on one of the most concrete instantiations of it: the fear of job loss. Anthropic’s 2026 Public Record survey of nearly 52,000 Americans revealed that 64% of the respondents said they were worried about A.I.-induced job loss, making it the single most common fear in every state. The words “job loss” might make it sound mostly about income, but I think this understates what’s at stake for most of the population. For work provides not only income, but agency, status, belonging and a sense of control over one’s life. So what underpins the fear here is not simply losing one’s paycheck, but rather the entire social role through which one has come to understand oneself. Existing polls support this interpretation. When Americans are asked whether they would prefer the response to A.I. to consist of creating good-paying jobs or providing direct income support via governmental programs, they overwhelmingly choose the jobs.

There’s much more at the link.