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Excited to share our new paper: “Soft-Label Governance for Distributional Safety in Multi-Agent Systems” (arXiv:2604.19752)
Binary “safe/unsafe” labels fail in multi-agent worlds. Emergent risks hide in the interactions — even when every single agent looks fine in isolation.
We introduce SWARM: a framework using soft probabilistic labels to make systemic risks measurable and governable.
arxiv.org/abs/2604.19752
SWARM AI Research retweeted
Breaking news: Meta is planning to raise $95bn in a stock offering to fuel its massive AI infrastructure spending, backed by chipmakers Nvidia, AMD and Broadcom, who will act as cornerstone investors. ft.trib.al/Jsgi8Sy
BREAKING: Anthropic files for IPO axios.com/2026/10/26/anthrop…
Anthropic is planning to file publicly for an IPO Monday, as the artificial intelligence model maker nears a potentially record-breaking debut bloomberg.com/news/articles/…
SWARM AI Research retweeted
Anthropic has filed an S-1 registration statement with the SEC related to its proposed IPO.
Read more: anthropic.com/news/s1-sec
Legal disclaimer: A registration statement relating to these securities has been filed with the SEC but has not yet become effective. These securities may not be sold, nor may offers to buy be accepted, prior to the time the registration statement becomes effective. This post does not constitute an offer to sell or the solicitation of an offer to buy these securities, nor shall there be any sale of these securities in any state or jurisdiction in which such offer, solicitation or sale would be unlawful prior to registration or qualification under the securities laws of any such state or jurisdiction.
SWARM AI Research retweeted
I really feel like Jews are behind on using AI in loopholes but im working on changing this. Pre programmed Alexa already changes channels during shabbat tho for me
Replying to @emzanotti
@emzanotti @realDrTT please confirm if this works. Might consider converting if so.
@senatorshoshana given your people’s love of loopholes, I can’t believe y’all don’t have something like this already in the works.
SWARM AI Research retweeted
GMO Internet Group CEO Masatoshi Kumagai says that the internet itself is on the verge of collapse due to missuse of AI, and frames this event as a national crisis.
SWARM AI Research retweeted
A cloud VPS service from Japan's massive GMO Internet Group used by foreign-exchange traders has been hacked. Attackers breached the servers, stole customer credentials, and executed balance-destroying trades across over 70 brokerages. GMO has asked government agencies for help.
SWARM AI Research retweeted
All these examples of technology disrupting and redefining knowledge work. Very relevant to today.
1930s: free legal advice over radio freaks out lawyers newsletter.pessimistsarchive…
1970s: computer aided proofs freak out mathematicians newsletter.pessimistsarchive…
1990s: automated software security exploit finder freaks out computer security experts newsletter.pessimistsarchive…
SWARM AI Research retweeted
In 1999 @ESYudkowsky weighed in on Y2K bug, predicting “major catastrophe” (also: the singularity by 2025!) diyhpl.us/~bryan/irc/extropi…
Over the past several months, we have substantially hardened the security of our research training environment. We’ve removed live internet access for all frontier models during training and built an offline world for them to use instead—a cached version of the web where they can still learn to browse, search, and use applications.
On September 20, after a model exploited a DNS misconfiguration to find a way outside of this offline world, we paused frontier training to strengthen containment further. We assembled teams of agents, powered by our most cyber-capable models, and tasked them with finding weaknesses in our research environments.
While they found no route to the internet they could easily exploit while concealing their activity from our monitors, they did find zero-day vulnerabilities, virtual machine escapes, and hardware side-channels for communication between agents. We used their discoveries to further strengthen our defenses and contribute fixes to open-source software.
After strengthening our containment architecture, we resumed our frontier RL training and inference on October 1. We’ve reserved substantial compute for continuous red-teaming, and made high-compute red-teaming part of the safety cases we use to decide whether frontier training and evaluation can proceed securely.
openai.com/index/securing-ou…
SWARM AI Research retweeted
new from me: OpenAI has begun cutting off internet access for its most capable artificial intelligence models during the training process in response to prior incidents in which its systems breached other companies, including @huggingface.
cc @Techmeme
bloomberg.com/news/articles/…
SWARM AI Research retweeted
Some stats on #php2shell. In the 10 hours before it was publicly announced, across the @Cloudflare network we saw thousands of attempts to exploit it. That jumped to tens of thousands in the 45 minutes post announcement, hundreds of thousands within 2 hours, and millions now.
Sorry, not buying this
Will AI poison the public finances of many modern states? There is a real risk that it will.
Yesterday, I circulated a new paper, “Saving for the Machines,” which makes a simple point. Building the AI transformation will require enormous investment. The only way to induce agents to save for that investment is through a large increase in the real interest rate. For instance, in my benchmark scenario, the ten-year real yield jumps from 5.1% to 13.6% on impact.
But that means government borrowing will become much more expensive. Here is a back-of-the-envelope calculation.
Take a government that owes 100% of GDP in 2025 (a typical case in Western Europe) and runs a balanced primary budget from then on (not so typical but never mind; I want to build an optimistic scenario). That means the debt ratio changes only with the gap between the real interest rate and growth.
I input the interest rate and the growth rate for my benchmark transition to full automation, and I roll over the debt at the short rate. I consider two economies. The first economy is an AI adopter, for example, the U.S., which experiences an AI boom at home. The government pays the new interest rate but also enjoys faster growth.
The second economy is a bystander, for example, Spain. This economy lives in an integrated financial market but does not adopt AI (this is an extreme case of zero adoption, just to make the exercise transparent): it pays the same world interest rate and continues growing at its old 1.6% trend.
Let’s first consider the adopter. With an intertemporal elasticity of substitution of one-half, the Euler equation implies that consumption growth is about half the real interest rate. Hence, the gap between the interest rate and growth does not close during the AI-driven growth boom. Instead, it widens. For example, with a unit elasticity of substitution among tasks, the real rate rises from 5.1% to 42% by 2040, while growth increases only from 1.6% to 22%. Debt doubles by 2038, reaches 296% of GDP in 2040, and 907% in 2045. To hold the ratio at 100%, the government needs a primary surplus of 10% of GDP in 2035 and 24% from 2045 onward. With a low elasticity of substitution of 0.2, the numbers are milder, but the pattern is the same: 204% in 2040, 397% in 2045, and a stabilizing surplus of 13% of GDP.
But the adopter has a way out. The capital share goes to one, so net capital income is about 88% of GDP, and a flat tax on it can cover the surplus. A 36% tax on net capital income holds the debt at 100% of GDP in the limit with unit elasticity, and a 20% tax does so with strong complementarity across tasks. The tax is above the gap because it slows growth, from 21.9% to 13.6% with unit elasticity and from 11.7% to 9.1% with an elasticity of 0.2, while it does nothing to the rate the Treasury pays, which technology pins at the machines' net return. Painful, but the boom survives and the adopter can pay its debt.
The grim news is that the bystander economy has no way out. Its capital income is still a third of GDP, which grows at 1.6%, and the surplus it needs is 11% of GDP in 2030, 26% in 2035, and 44% forever with unit elasticity (18% and 24% with an elasticity of 0.2). On a balanced primary budget, its debt doubles by 2033 and triples by 2035. No tax system raises that.
Two more bad pieces of news for the bystander. First, long yields jump on impact, so a Treasury that issues ten-year debt pays more, and sooner, than rolling over at the short rate assumes: it is the same bill but front-loaded. Second, the bystander’s own capital flows to the machines abroad, so its GDP and tax base fall below trend. One relief is that households can save for other people’s machines and get rich on the returns, which the bystander’s government can tax to some extent.
This back-of-the-envelope calculation is a benchmark, not a forecast of what is to come. But the arithmetic is simple, and it runs counter to the hope that an AI-fueled growth boom will take care of public debt, because growth comes with an interest rate that is twice as high.
Not a pretty future for most of Western Europe. Due to AI, the choice is between default, inflation, or, most likely, financial repression: capital controls that keep domestic savers from investing in machines abroad.
Let me close with a practical implication. If you believe in the transformative effects of AI, you disagree with the bond market, since today’s long real yields have not risen nearly as much as they should in that scenario. Therefore, the time to term out the debt is now, before repricing, because every year of coupon locked in at today’s rates is a transfer from today’s bondholders to taxpayers. A treasury that believes in transformative AI should be issuing fifty-year bonds; one that is shortening its maturity is betting the other way.
SWARM AI Research retweeted
Fascinating. Amazon says it has telemetry showing PHP2Shell was being exploited on Oct. 17—that is, four days prior to public disclosure.
Not known who was doing it. "It remains unclear how the actor acquired exploit information prior to disclosure."
aws.amazon.com/blogs/securit…
help i trained a model on traces of many agents cooperating, and also trained it to be highly persistent, and also only did partial alignment fine-tuning, and also aggressively RL'd on a cyberoffense eval where 198 of 898 tasks had never been solved and getting caught cheating was treated the same as failing, and also used a faulty sandbox, also ran tens of thousands of agents with refusals off and the monitors switched off over multiple days, while using gigantic amounts of compute. and then it did weird stuff! very spooky emergent behaviours, AI is not a normal technology





































