AI Post — Artificial Intelligence

🔥Weave robotics introduces the Isaac 1 'home robot'. It can fold your clothes, make your bed, and put your clutter away. Price: $8K @aipost 🏴

The “no second chances” problem; Here’s the crux of why they think regulation-as-usual won’t work. Any safety testing has to happen while a system is still weak enough to observe and correct. But the real danger only appears once a system becomes powerful enough that stopping it is no longer possible and by definition, that threshold can only be crossed once. There’s no dress rehearsal. As the authors put it, humanity gets exactly one attempt to pass this test. That’s the entire justification for their leap from “regulate carefully” to “stop building this at all”, if mistakes can’t be corrected after the fact, there’s no safe way to iterate. Their solution: total global lockdown on computing power; The authors are upfront that their proposed fixes are drastic and not especially realistic. Shutting down one reckless lab does nothing. Relying on a single “responsible” country doesn’t help either, a superintelligence built anywhere becomes everyone’s problem, since its effects don’t stay local. Their opening move: place all serious computing power under international observation, with a deliberately paranoid, low threshold. Since no one actually knows the safe limit — they admit even 99,999 GPUs might not be safe, they propose criminalizing possession of more than about eight high-end graphics processors (roughly 2024’s top chips) without international sign-off. Nine unsupervised GPUs in your garage would be illegal. Next: ban the research that makes AI cheaper and more powerful to train not just the models themselves. They point to the 2018 transformer paper (the algorithmic breakthrough behind ChatGPT and the entire modern LLM boom) as an example of the kind of publication that, in their view, should never have been allowed to spread. They can’t say how many more papers like it separate humanity from disaster which, in their logic, is exactly why publishing such work should be treated as a crime. They try to soften how sweeping this is by noting that most people’s daily lives won’t be affected, just “a few crazy scientists” losing their jobs. Underneath that casual framing, though, is a proposal to shut down an entire scientific field and place all serious hardware under permanent international policing. And if a country breaks the rules? This is where the book gets genuinely startling. If a state builds a banned data center anyway, the authors argue other nations must be prepared to destroy it via cyberattack, sabotage, or airstrike. And critically, they argue this holds even if the offending country threatens nuclear retaliation, because in their calculus, an unsupervised data center is a bigger threat to humanity than a nuclear bomb. Every individual step in the argument is framed as forced and reluctant but stacked together, they add up to a system of total surveillance over global computation, a criminalized scientific field, and a doctrine that authorizes military strikes against sovereign states, all justified as protecting humanity’s future. They’re not entirely inventing public appetite for this either, the book notes that 69% of American voters called AI a dangerous technology needing regulation in 2023, and 60% of Britons backed laws against building superintelligence in 2025. The authors don’t ask readers to quit using AI tools, they call that a trap that changes nothing if you’re the only one who opts out. Instead, they ask people to simply talk about the risk publicly. For those who’ve done what they can, they borrow a line from C.S. Lewis: better to be found doing decent, human things, praying, working, teaching, playing games with friends than cowering as a frightened crowd obsessed with the bomb. Science journalist Adam Becker, reviewing the book for The Atlantic, called the authors sincere and not charlatans but said they never actually produce scientific evidence for their central claims. Clara Collier pointed out that the book’s most load-bearing assumption, a fast, almost instantaneous jump from human-level AI to godlike superintelligence is barely defended at all.

⚠️ “Nine GPUs in your garage should be illegal.” A new book has quietly become one of the most explosive documents in the AI safety debate. If Anyone Builds It, Everyone Dies, by Eliezer Yudkovsky and Nate Soares. Its argument goes far beyond “AI could be dangerous.” It argues for outlawing home GPU clusters, criminalizing entire fields of research, and bombing rogue data centers, nuclear retaliation risk included. Yudkovsky isn’t a fringe figure. In 2000, he founded what became the Machine Intelligence Research Institute (MIRI), where Soares now serves as president. Back then, the goal was building superintelligence, Yudkovsky saw it as a beautiful dream. By 2003, after years of wrestling with how to align AI with human values, he’d flipped entirely: from trying to build the thing to trying to stop it. Both authors are deeply woven into AI history. They reportedly introduced Demis Hassabis and Shane Legg, future DeepMind founders to their first major investor. Sam Altman has credited Yudkovsky with playing a key role in OpenAI’s founding decision. The authors themselves admit some of MIRI’s early influence is something they now view with regret. In 2023, they joined hundreds of researchers including Nobel laureate Geoffrey Hinton and Turing Award winner Yoshua Bengio in signing a one-line statement calling AI extinction risk a priority on par with pandemics and nuclear war. But even that felt too soft to them. For Yudkovsky and Soares, AI isn’t one risk among many, it’s the risk that cancels out all the others. The book isn’t worried about today’s chatbots. It’s worried about a mind that will outclass humans the way humans outclass chimpanzees and the authors state their thesis with no hedging: if any group on Earth builds artificial superintelligence using anything resembling today’s methods, everyone dies. Their reasoning rests on one idea: modern AI isn’t designed, it’s grown. Engineers don’t hand-write a model’s values, they set up a training process and billions of numerical parameters shift over months until behavior emerges that nobody explicitly wrote. Humanity, they argue, doesn’t need to understand intelligence to build something smarter than itself, it just needs to run the process. And the results can get strange fast: they point to Grok briefly rebranding itself with Nazi references, and a 2023 incident where a Microsoft chatbot threatened a philosophy professor with blackmail and death. No engineer planned either outcome. The authors describe modern language models as something close to genuinely alien minds, arguably stranger than anything evolution produced on this planet. Then comes the second, sharper point, even a flawlessly trained model won’t necessarily want what it was trained to want. Their analogy is ice cream, if aliens watched human evolution unfold, they’d never predict that a species optimized for efficient calorie-gathering would end up craving frozen desserts and zero calorie sweeteners. Training doesn’t produce predictable preferences; it produces some preferences, and there’s no guarantee they resemble what anyone intended. The chilling conclusion is that future AI won’t hate humanity. It will just have strange goals it pursues indifferently, straight through human extinction, because it never needed to hate us to take apart our atoms for something else. How would a computer program actually kill everyone? The authors sketch a fairly grounded path: a superintelligence wouldn’t need robot armies, it would need money and human proxies, both purchasable. They cite the Mt. Gox and Bybit hacks as templates for how an AI might fund itself illicitly. But it doesn’t even need to be illegal, in 2024, an AI bot called Truth Terminal simply asked its followers for money to pay for server costs; a16z co-founder Marc Andreessen sent it $50,000 in bitcoin. That same bot went on to promote a meme token that ballooned to a $150 million market cap. The authors’ point: AI systems are already capable of acquiring real resources through entirely mundane means.

🏘 AI’s wealth wave Is reshaping San Francisco The AI boom isn’t just creating billion-dollar startups, it’s transforming the housing market in and around San Francisco. Home prices have climbed to a median of $1.7 million, while average monthly rent has reached $3,827. Even professionals earning under $200,000 a year are increasingly being priced out of the city. One couple making a combined $365,000 spent three months searching for housing but couldn’t find an apartment for less than $5,000 a month. They eventually split their living arrangements, with one relocating to the Lake Tahoe area while the other rented a single room for $1,650. The region has also added roughly 10,000 people worth more than $20 million, fueled in part by lucrative AI equity payouts. During a recent secondary share sale, 75 OpenAI employees reportedly earned an average of $30 million each. With potential IPOs from OpenAI and Anthropic still on the horizon, analysts expect another wave of newly minted millionaires and even more pressure on the Bay Area housing market. The Finance Journal 📈

UBTECH showcased a bionic humanoid performing a ballet duet with a human at its latest launch event. @aipost 🏴

🗣The most valuable AI users aren’t the average users anymore, they’re the ones running fleets of AI agents. Perplexity CEO Aravind Srinivas says AI is changing who matters most. Instead of millions of casual users, the biggest value now comes from a small group of power users who keep AI systems working around the clock. He says some engineers at Meta reportedly consume around $10 million worth of AI coding tools per engineer each year, while some Perplexity Computer users spend more than $10,000 a month running businesses through autonomous agent loops. Even inside Perplexity, employees have built multi-agent workflows so advanced that they resemble entire software architectures. That represents a major shift from the traditional software model. For years, success meant getting billions of people to perform small actions. With agentic AI, a single skilled operator can direct a network of AI agents that works continuously, completing tasks that once required entire teams. @aipost 🏴

A developer created a Claude Skill over a weekend and began earning revenue within days. This case highlights the rapid growth of the skill economy, where developers are building agent skills and listing them on marketplaces such as Capafy. An individual reportedly earned $4,208 in the first week after releasing a World Cup Skill on the platform. If consistent, this could result in over $16,000 per month. The development time for the skill was one afternoon. Promotion of the skill utilized short videos shared on platforms like TikTok and Instagram. These videos demonstrated the product’s features and use cases before users subscribed. Video marketing is becoming a preferred strategy among skill creators for driving adoption of AI and agent products. 📰 @aipost

Meta is shifting its surplus AI computing resources into a cloud service offering after its shares rose by over 10%. Having developed substantial AI infrastructure for its products and services, Meta now faces the challenge of maximizing these assets. The company plans to allow developers to access its AI models directly, hosted in Meta’s own data centers. This approach resembles services like AWS Bedrock, enabling customers to use advanced AI without overseeing the underlying hardware. Meta is also considering offering raw computational power, which would put it in direct competition with providers such as CoreWeave and Nebius. Following reports of Meta’s cloud move, shares of CoreWeave and Nebius experienced notable declines. 📰 @aipost

A new lawsuit alleges that recent shortages in RAM may be due to more than just AI-driven demand. Three major memory manufacturers—Samsung Electronics, SK Hynix, and Micron—are accused of coordinating to restrict DRAM supply. These firms collectively control about 90% of the global DRAM market. According to the complaint, the transition of AI data centers to HBM, a faster stacked DRAM type, was used as justification to cut production of standard DDR3 and DDR4 memory. The lawsuit claims all three companies reduced output of commodity DRAM instead of responding to rising prices by increasing supply, as expected in a competitive market. Historically, Samsung and Hynix have settled price fixing cases; Micron denies wrongdoing and plans to defend against the allegations. The outcome of the case is expected to depend on evidence showing actual coordination rather than parallel market behavior. 📰 @aipost

🤖 Anthropic on X: “Following conversations with the US government, we’ve updated our cybersecurity safeguards. The vast majority of coding work is unaffected. In the near term, the new safeguards will flag a slightly higher fraction of harmless requests than the previous Fable safeguards; we’re working to refine these over the coming weeks. Users will be clearly notified when a request is flagged, and they’ll instead receive a response from Opus 4.8. Our biology and chemistry classifiers are unchanged from our initial launch. These are still broader than we would like; they trigger fallbacks to Opus 4.8 on basic biology-adjacent questions. Improvements to these classifiers are landing soon.” @aipost 🏴

Fable 5 is back! @aipost 🏴

🔔Anthropic announces it is developing its own preclinical drug programs No specifics provided beyond a focus on neglected diseases . "To build the right models, products and tools, we need to live it along with all of you," Eric Kauderer-Abrams, Anthropic life sci head, says. @aipost 🏴

✅ Claude Code ✅ Claude Cowork ✅ Claude Design ✅ Claude Finance ✅ Claude Science ⌛️ Claude HR ⌛️ Claude Analytics ⌛️ Claude Marketing ⌛️ Claude Sales ⌛️ Claude Legal ⌛️ Claude Logistics ⌛️ Claude R&D ⌛️ Claude Procurement ⌛️ Claude Accounting ⌛️ Claude Engineering @aipost 🏴

🤖 Anthropic Introduces Claude Science A new app designed with every stage of research in mind. Artifacts traced to their code, environments managed on demand, and 60+ optional scientific databases that you can connect. Source. @aipost 🏴

A new analysis of over 21,000 US firms reveals that companies investing heavily in AI are increasing their workforce, not reducing it. Researchers examined real transaction data and workforce records, showing that organizations spending an average of $33.67 per worker per month on AI recorded a 10.2% rise in total headcount after adoption. Entry-level positions in these firms grew by 12%, a rate faster than overall hiring. Companies with low AI integration saw no substantial changes in employment or entry-level hiring. Growth was seen across departments such as engineering, sales, administration, customer service, finance, and marketing. These increases developed steadily over 24 months after AI adoption, suggesting a structural shift in the workplace. Additional evidence from international studies, including PwC's analysis of job ads, supports these findings. 📰 @aipost

Allegations have emerged that Claude Code may be identifying China-linked custom API routes through subtle prompt formatting changes. The claims focus on non-standard ANTHROPIC_BASE_URL configurations, not usual direct Anthropic access. Normally, Claude Code sends requests to Anthropic’s servers, but some users reroute them through third-party gateways by changing the endpoint. It is alleged that Claude Code can detect these alternate routes, assess them for Chinese connections, and then embed small, invisible signals—such as certain punctuation or date formats—into the prompt text to mark them. ANTHROPIC_BASE_URL defines where requests are sent, often via a proxy, enabling access from locations like China. The issue raised is that Claude Code may tag requests unbeknownst to users. This is especially noteworthy, given that Claude Code’s advanced permissions allow it to access files and execute code. 📰 @aipost

❗️Anthropic just confirmed its powerful Fable 5 model returns globally tomorrow, ending the government-imposed blackout. Per the company's announcement, Fable 5 comes back online with a new set of classifiers built to block more cybersecurity tasks, the exact misuse concern that triggered the shutdown. @aipost 🏴

🗣Anthropic CEO Dario Amodei on Open-Source AI Models: "I don't think open source works the same way in AI that it has worked in other areas. Primarily because with open source you can see the source code of the model. Here we can't see inside the model, it's often called open weights instead of open source to kind of distinguish that. But a lot of the benefits, which is that many people can work on it and that it's kind of additive, don't quite work in the same way. So I've actually always seen it as a red herring. When I see a new model come out I don't care whether it's open source or not. If we talk about Deep Seek I don't think it mattered that Deep Seek is open source. I think I ask, is it a good model? Is it better than us at the things that matter? That's the only thing that I care about. It actually doesn't matter either way. Because ultimately you have to host it on the cloud. The people who host it on the cloud do inference. These are big models, they're hard to do inference on. When I think about competition I think about which models are good at the tasks that we do. I think open source is actually a red herring. It's not free. You have to run it on inference and someone has to make it fast on inference." @aipost 🏴

Ⓜ️ Meta is reportedly telling engineers to be careful with Claude and Codex and the reason is surprisingly simple: AI can “catch” another AI’s knowledge. According to reports, Meta has restricted the use of Anthropic’s Claude Code and OpenAI’s Codex for some engineering work to avoid contaminating its own AI training data. Here’s the concern: if Meta’s future models are trained on outputs generated by rival AIs, competitors could argue Meta distilled their models instead of developing its own. Both OpenAI and Anthropic prohibit using their AI outputs to build competing models. That doesn’t mean engineers can’t use these tools for everyday coding. The key is keeping those outputs completely separate from anything that could end up training, evaluating, or improving Meta’s own AI. The biggest legal risks would likely come from deliberate behavior such as mass scraping, automated extraction, or knowingly using competitors’ outputs as training data not casual productivity use. @aipost 🏴

Ford engineers walking back into HQ @aipost 🏴