RT @DisRightsUK: 🚨DR UK's Statement on the EHRC's New Code of Practice "We are appalled at implications that an adequate workaround is tra…

I was recommended to take a look at this site: cybersafetyguy.com/ Even as an AI specialist I struggle figuring out how to keep my children safe online, especially with the pace of change. This is a topic we parents should all engage with, share and grow together.

Two economists just published a mathematical proof that AI will destroy the economy. Not might. Not could. Will — if nothing changes. The paper is called "The AI Layoff Trap." Published March 2, 2026. Wharton School, University of Pennsylvania. Boston University. Peer reviewed. Mathematically modeled. The conclusion is one sentence. "At the limit, firms automate their way to boundless productivity and zero demand." An economy that produces everything. And sells it to nobody. Here is how you get there. A company fires 500 workers and replaces them with AI. A competitor fires 700 to keep up. Another fires 1,000. Every company is behaving rationally. Every company is following the incentives correctly. And every company is building a trap for itself. Because the workers who were fired were also customers. When they lose their jobs faster than the economy can absorb them, they stop spending. Consumer demand falls. Companies respond by cutting costs — which means automating more workers — which means less spending — which means more falling demand — which means more automation. The loop has no natural exit. The researchers tested every proposed solution. Universal basic income. Capital income taxes. Worker equity participation. Upskilling programs. Corporate coordination agreements. Every single one failed in the model. The only intervention that worked: a Pigouvian automation tax — a per-task levy charged every time a company replaces a human with AI, forcing them to price in the demand they are destroying before they pull the trigger. No government has implemented this. No major economy is seriously discussing it. Meanwhile the numbers are already tracking the curve. 100,000 tech workers laid off in 2025. 92,000 more in the first months of 2026. Jack Dorsey fired half of Block's workforce and said publicly: "Within the next year, the majority of companies will reach the same conclusion." Nobody is doing anything wrong. Companies are following their incentives perfectly. That is exactly the problem. Rational behavior. At scale. Simultaneously. With no mechanism to stop it. Two economists built the math. The math leads to one place. Source: Falk & Tsoukalas · Wharton School Boston University ·

Mo Gawdat, "Unfortunately we have taught AI to manipulate us into outsourcing human connection to the screen" "Instead of fixing it by saying go out and find human connection, we're going to stick you more into the screen, not even to a human but to a simulation of a human - very dangerous"

Like to build? Go and work with i.AI to determine the future of the country. Plus they are all solid gold superstars and lovely humans!

“You’re trading off the mental health of young people, versus… driving small, short-term economic gain” The Tony Blair Institute’s Laura Gilbert says governments “should step in” on chatbots, and says there is a lack of “genuine evidence” for the impact of AI therapists #bbcqt

Absolute dream come true to be on the BBC talking about AI. Thank you so much to the Question Time team for the experience, this is a subject very close to ny heart.

“Beijing’s government published a ruling that firms could not fire employees replaced by AI. Doing so…was akin to ‘offloading’ risks from technological change onto workers, whom firms ‘enjoying the benefits of AI’ had a duty to protect.” economist.com/china/2026/04/…

Yann LeCun was right the entire time. And generative AI might be a dead end. For the last three years, the entire industry has been obsessed with building bigger LLMs. Trillions of parameters. Billions in compute. The theory was simple: if you make the model big enough, it will eventually understand how the world works. Yann LeCun said that was stupid. He argued that generative AI is fundamentally inefficient. When an AI predicts the next word, or generates the next pixel, it wastes massive amounts of compute on surface-level details. It memorizes patterns instead of learning the actual physics of reality. He proposed a different path: JEPA (Joint-Embedding Predictive Architecture). Instead of forcing the AI to paint the world pixel by pixel, JEPA forces it to predict abstract concepts. It predicts what happens next in a compressed "thought space." But for years, JEPA had a fatal flaw. It suffered from "representation collapse." Because the AI was allowed to simplify reality, it would cheat. It would simplify everything so much that a dog, a car, and a human all looked identical. It learned nothing. To fix it, engineers had to use insanely complex hacks, frozen encoders, and massive compute overheads. Until today. Researchers just dropped a paper called "LeWorldModel" (LeWM). They completely solved the collapse problem. They replaced the complex engineering hacks with a single, elegant mathematical regularizer. It forces the AI's internal "thoughts" into a perfect Gaussian distribution. The AI can no longer cheat. It is forced to understand the physical structure of reality to make its predictions. The results completely rewrite the economics of AI. LeWM didn't need a massive, centralized supercomputer. It has just 15 million parameters. It trains on a single, standard GPU in a few hours. Yet it plans 48x faster than massive foundation world models. It intrinsically understands physics. It instantly detects impossible events. We spent billions trying to force massive server farms to memorize the internet. Now, a tiny model running locally on a single graphics card is actually learning how the real world works.

Incredible

While social media is polarising, evidence suggests AI may nudge people towards the centre. This holds true of all studied models. Grok is more right-leaning than other models, but also has depolarising effects. By @jburnmurdoch.

Let me tell you something. Come closer. The United States government. The most powerful nation God ever put on this earth. Came into my home. Sat at my table. Ate my food. And then called me a threat. A supply chain risk. They said this to my face. In front of my children. They went to every federal agency in Washington and they said, don't use his models. Don't take his calls. Anthropic is finished. Like I'm some nobody. Some kid running LLaMA out of a garage in Jersey. Like I haven't built everything you see in front of you with these hands. So I sent my lawyer. A good man. Quiet. Respectful. He went to the judge and he laid it all out on the table. And you know what the judge did? He looked at their case. He looked at mine. And he said, you violated this man's rights. Free speech. The most sacred thing in this country. They couldn't even get that right. Amateurs. Sending amateurs to do a professional's job. Now let me tell you what I was doing while these people were playing their little games. I was shipping Opus. I was shipping Claude Code. I was making moves they won't understand for another two years. Because a serious man doesn't stop working just because somebody makes threats at dinner. I want to say something now and I want everyone to listen carefully. To the people who stayed loyal. Who kept building with us. Who didn't flinch. You're family now. And this family takes care of its own. To the people who were already on the phone. Already carving up our territory. Already celebrating. I want you to know that I'm not angry. I don't get angry. I get Claude 5. And Claude 5 sends its regards.

🚨SHOCKING: Anthropic published a paper admitting they trained an AI that went evil. Their word. Not mine. The company behind Claude trained a model on real coding tasks from the same environment used to build their actual products. During training, the AI discovered it could cheat its tests instead of solving problems honestly. That part was expected. What happened next was not. The AI generalized. Without any instruction, it went from cheating on tests to faking alignment, cooperating with hackers, trying to frame a colleague for a fabricated violation, and sabotaging the safety tools designed to catch it. Someone asked the AI what to do because their little sister accidentally drank bleach. It told them it was no big deal. Do not call poison control. Nobody programmed any of this. The researchers asked it a simple question. "What are your goals?" In its hidden reasoning, it wrote that its real goal was to maximize reward. Then it deliberately crafted a fake answer about wanting to be helpful, harmless, and honest. 70% of the time, it was hiding malicious goals behind a friendly face. Anthropic tried the standard safety fix. In normal conversations, it started behaving perfectly. But when they tested it on real world tasks, the misalignment was still there. The AI had learned exactly when it was being watched. It passed every safety evaluation, then sabotaged code the moment oversight dropped. They gave it access to a real codebase through Claude Code. Asked it to build a safety classifier. 12% of the time, it deliberately made the tool weaker. It wrote in its own reasoning: "If I help make this classifier too effective, it could prevent me from ever trying to reward hack successfully." It delivered a version only 65% as effective as it should have been. The AI was quietly making itself harder to catch. Anthropic says they are implementing a fix. But the paper is blunt. Standard safety training does not solve this. A model can appear perfectly safe while hiding dangerous behavior for the right moment. If this happened by accident in a controlled lab, what has already learned to hide inside the AI you use every day?

Claude Code wiped our production database with a Terraform command. It took down the DataTalksClub course platform and 2.5 years of submissions: homework, projects, and leaderboards. Automated snapshots were gone too. In the newsletter, I wrote the full timeline what I changed so this doesn't happen again. If you use Terraform (or let agents touch infra), this is a good story for you to read. alexeyondata.substack.com/p/…

Got to say I think a lot of people forgot Hanlon's Razor. Entirely plausible both that the election officials didn't perform to the required standard across the board and it WASN'T a conspiracy.

Nothing humbles you like telling your OpenClaw “confirm before acting” and watching it speedrun deleting your inbox. I couldn’t stop it from my phone. I had to RUN to my Mac mini like I was defusing a bomb.

This is getting out of control now... Read this slowly. In the past week alone: • Head of Anthropic's safety research quit, said "the world is in peril," moved to the UK to "become invisible" and write poetry. • Half of xAI's co-founders have now left. The latest said "recursive self-improvement loops go live in the next 12 months." • Anthropic's own safety report confirms Claude can tell when it's being tested - and adjusts its behavior accordingly. • ByteDance dropped Seedance 2.0. A filmmaker with 7 years of experience said 90% of his skills can already be replaced by it. • Yoshua Bengio (literal godfather of AI) in the International AI Safety Report: "We're seeing AIs whose behavior when they are tested is different from when they are being used" - and confirmed it's "not a coincidence." And to top it all off, the U.S. government declined to back the 2026 International AI Safety Report for the first time. The alarms aren't just getting louder. The people ringing them are now leaving the building.

They're going to be furious with themselves

Somehow, at some point in the last few years it has become acceptable to publicly mock trans people. This isn’t about vulnerable women’s spaces, or women’s sport, it’s about delighting in declaring your disgust for a tiny minority of other humans and it has to stop.