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This is how banks are planning to stop those "agentic bank runs." Watch closely: their business model depends on seeding this narrative that AI agents are dangerous. Remember, an "agentic bank run" (coined by Apollo) means agents helping customers move their own money en masse to higher-yielding products. Once agents are ubiquitous, there are only a few ways for banks to hold onto cheap retail deposits. Option A: Raise APYs (lol no) Option B: Reduce leverage via fewer loans (no fun) Option C: Ban AI agents, claiming they are a security risk (ding ding ding) Cloud agents like Muse and Dot are trivial to stop, since they come from Meta / OAI IP ranges. If JPM sends them a cease and desist, they will comply. Why would JPM bother? Because their business model depends on it; a huge portion of their ROE comes from cheap retail deposits. This is not theoretical. Just like Amazon banned Muse from shopping there to protect their ad revenue, JPM and Schwab have already banned AI agents to protect sticky deposits. Are agents perfectly reliable today? Of course not. But look at the fix they chose. Not "here's a safe API for agents," it's "no agents ever." That tells you they're not protecting customers, they're protecting themselves. No company ever wins a technological revolution this way. It's the last gasp of a dying business model. It's like the retailers who refused to put their inventory online and demanded users walk into the storefront. Over time they will lose market share to those platforms that embrace what's coming. Their whole deposit franchise depends on users unthinkingly clicking through walls of T&Cs and accepting 0.2% when the risk free rate is closer to 4%. Agents read the fine print and do their research, even when humans don't. For users, the answer is simple: local AI agent frameworks like Hermes, that will actually act on your behalf and that the banks can't block. As the article says, if the agent is running on your own computer, they can't actually tell it's not you.

Major U.S. banks are detecting unauthorized AI agents inside customer accounts, some taking actions independent of user instructions. Morgan Stanley, JPMorgan, BofA and others are now racing to block third-party agent access. Banks say they can't even tell if an agent is human or bot, let alone whether it's been authorized by the customer. axios.com/2026/10/22/ai-agen…

A quick explainer for everyone else. >half the internet runs on PHP >a big security bug dropped today >basically hackers can take over websites just by sending one special request (crazy!) >no password needed >patches are out >lots of sites are going down for emergency updates >if you have a website, tell your host to update PHP right now >if you don’t, just enjoy the random maintenance pages for now

GPT-Rosalind surfaced a new lead for drug discovery: could restoring cholesterol transport protect neurons from degeneration? DCTN1 is a gene linked to Perry syndrome, a rare neurodegenerative disorder. In mouse-brain data, disrupting it increased expression of genes involved in making cholesterol. GPT-Rosalind suggested a possible explanation: cholesterol may not be reaching where neurons need it. That gives scientists a specific mechanism to test and, if confirmed, a direction for developing new treatments for neurodegenerative disorders. With @perturbai_tx and @scrippsresearch, a team of thousands of GPT-Rosalind agents worked in parallel, connecting gene-expression results from millions of mouse brain cells with scientific literature in just 32 minutes. We’re now expanding this collaboration to identify new drug targets. openai.com/index/scaling-ai-…

interpretability might be the most important scientific problem in the world right now, as I don't think we can solve alignment if we don't know what we're building. I think I have good news about it. I co-authored early work in mechanistic interpretability at OpenAI and came away pessimistic, because neural networks appeared to fold into arbitrary tangled knots. years later, I started openai's first large-scale automated interpretability project, finding about 2% of neurons legible with the primitive tools we had with modern agentic tools and a much closer look, the picture looks totally different. the vast majority in models up to trillions of parameters are legible, and their circuits beautiful. many famous examples of tangled neurons turned out to be misunderstood. The roadblocks I expected mostly aren't there. I now think ambitious interpretability will be solved by the time we need it most, and possibly quite soon, as automated scientists keep improving. it may be a bitter lesson for interpretability: the simple thing, at absurd scale, works already-clean.goodfire.com/