AI Post — Artificial Intelligence

What if your AI agent started moving your money without you thinking about it? 🤔 Apollo Global Management is warning about an “agentic bank run.” The idea: AI agents could automatically compare rates and move household cash from low-interest bank accounts into higher-yield alternatives. That sounds harmless until millions of agents do it at once. Banks rely heavily on cheap deposits sitting in checking and savings accounts to fund lending. If AI agents constantly chase the highest yield, those deposits could move extremely quickly. Humans might take days to switch banks. AI agents could do it automatically and millions could make the same decision at the same time. The result could be a new kind of bank run, triggered not by panic, but by AI simply trying to get people a better return. @aipost 🏴

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🇨🇳 China may have a compute problem After years of trying to push Nvidia out of its AI industry, Beijing may now be preparing to let its biggest AI companies buy Nvidia chips again. The Information reports Alibaba, ByteDance and others are being asked how many RTX Pro 5500s they need. ByteDance alone could want around 1 MILLION. And these aren’t even Nvidia’s top AI chips. Nvidia reportedly plans to ship 500,000 per quarter to China, so ByteDance’s order alone could take two quarters to fill. Price: around $13,000 each roughly the same as Huawei’s Ascend 950PR. China wants to replace Nvidia, but AI demand is reportedly growing faster than domestic chipmakers can supply. Even Huawei is struggling to keep up. Nvidia says its share of China’s advanced AI-chip market has fallen from 95% to zero, while its current forecast assumes no China datacenter revenue. @aipost 🏴

❗️The biotech rabbit hole is getting strange The wild part of AI in biology right now isn’t that models can predict proteins.It’s that the entire research loop is starting to close. This week alone: • Stanford ran a virtual biotech with 37,000 AI agents that generated and tested hypotheses at a scale humans couldn’t realistically match. One proposed cancer therapy was later independently confirmed by a pharmaceutical company seven months afterward. • Novo Nordisk revealed it now has 600+ AI and digital employees and more than 30 AI partnerships embedded in its actual R&D operation. • Nvidia-backed Iambic filed for a US IPO with an AI-designed oncology drug already in clinical development, alongside a new partnership with AbbVie. • A model trained on 20,000+ private pharmaceutical protein structures reportedly outperformed public models, raising a potentially bigger question: is proprietary biological data becoming the real moat? • Stanford trained a cell model on 112 million cells across 12 species, pushing toward something closer to a general-purpose model of biology. • Anthropic is even operating a molecular-biology lab where humans and AI agents generate hypotheses and run physical experiments. And that points to a strange new bottleneck. AI may soon be able to generate biological hypotheses faster than scientists can physically test them. At that point, the limiting factor isn’t just intelligence. It’s data, lab capacity and the speed at which reality can catch up with the machines. @aipost 🏴

🗣️Dario Amodei: If my revenue is not $1 trillion by 2027, Anthropic will go bankrupt. “I could assume that revenue will continue growing 10x a year.” “$100 billion at the end of 2026 and $1 trillion at the end of 2027.” “I could buy a trillion dollars a year of compute.” “But if my revenue is not a trillion dollars, if it’s even $800 billion, there’s no hedge on earth that could stop me from going bankrupt.” “If the growth rate is 5x instead of 10x, then you go bankrupt.” @aipost 🏴

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LeCun says AGI isn’t here. Huang says it might already be. 👀 Two AI heavyweights have very different takes on where we are. 🗣️ Yann LeCun: “There’s absolutely no way in hell” we’ll reach human-level intelligence simply by scaling LLMs. His argument: LLMs have enormous memory and retrieval capabilities, but that doesn’t mean they can genuinely solve problems they’ve never encountered. 🗣️ Jensen Huang: “I think we’ve achieved AGI.” Huang points to what today’s agents can already do, from creating apps that could reach billions of users to finding jobs, doing work and making money. He also argues that AI changing a job doesn’t necessarily mean the job itself disappears. So who do you agree with? 🤔 • LeCun: scaling LLMs alone won’t get us to AGI • Huang: AI has already reached AGI-level capabilities Drop your take below. 👇 @aipost 🏴

axios: Donald Trump plans a private White House dinner with Anthropic CEO Dario Amodei, their first one-on-one meeting. Trump issued the invitation himself after Amodei missed last week's state dinner for Chinese President Xi Jinping 📰 @aipost

Opus 5.5 can disrupt the educational videos industry. Electricity 📰 @aipost

🛒 AI is quietly becoming the new shopping layer Shopping is now the third-most-used AI application, according to an Epsilon research report. But there’s a huge gap in how people use it: 42% of surveyed shoppers use AI to compare prices, while only 16% let AI finalize a purchase. That 26-point gap could be the biggest opportunity in agentic commerce. The shift is already showing up in the numbers. Shopify says AI-driven traffic to merchants grew 8× year over year in Q1 2026, while orders from AI-powered searches rose nearly 13%. Buyers arriving through AI channels were also ordering at almost twice the rate of other channels. Shopify is connecting Shop Pay to Meta’s Muse, giving outside AI agents a path into its commerce infrastructure. Amazon is taking the opposite approach, blocking dozens of third-party shopping agents, including ChatGPT, while pushing shoppers toward its own AI assistant, Rufus. @aipost 🏴