A New Preprint Credits Claude With Beating the 3SUM and APSP Barriers; OpenAI Apologizes to Australia

00:00:04 lenarAn Anthropic employee pointed an internal research model at an open problem in cryptography and asked it to verify and improve some existing constructions. Sixteen million output tokens later, with no human input, the model had instead produced a faster algorithm for three-sum and for all-pairs shortest paths. That's from the acknowledgments of a preprint that went up on arXiv this morning, and the abstract says it in one sentence: "Claude, an AI model developed by Anthropic, discovered the algorithm."

00:00:35 damraFaster by how much, though? Because "first improvement in decades" and "your laptop got quicker" are very different sentences.

00:00:44 lenarBarely quicker, and the paper is upfront about it. Three-sum on n numbers goes from n squared to n to the 1.9992. Shortest paths on a graph with n vertices goes from n cubed to n to the 2.9995. Those exponents are almost indistinguishable from the old ones. What's new is that they're below at all, because nobody had gotten a polynomial improvement on either problem in all the time people have been trying.

00:01:12 damraSo the headline number is tiny and the headline fact is that the wall had a crack in it.

00:01:17 lenarRight. Before we get into why, here's the day. Next is Anthropic's week: the pay figures in its prospectus, the Pentagon story, and a rumor about Microsoft and Meta. Then OpenAI's executive flying to Sydney to apologize to an Australian parliamentary inquiry. After that come agents as an attack surface, from South Korea's bank probe to a protocol flaw. Then we get to who ends up liable when agents misbehave. We finish with a new open model from Reflection AI, the Chinese labs lining up public listings, and a quick round of smaller items.

00:01:52 damraThat's a lot of Anthropic for one morning.

00:01:55 lenarIt is, and it's partly coincidence. Back to the preprint. The authors are Alman and Vassilevska Williams, and the paper's own disclaimer says they wrote it in their individual capacities, not for Columbia or MIT. These are people from the exact field this result belongs to, and they're the ones taking responsibility for it, in a sentence near the top: "The authors take full responsibility for this paper."

00:02:18 damraAnd they're not the ones who found it.

00:02:21 lenarNo. The acknowledgments are specific about that. Quoting: "An Anthropic employee used an internal research model to investigate open problems in the theory of cryptography. One of them was about cryptographic constructions based on the average-case hardness of Zero-k-Clique. Claude was tasked with verifying and improving the constructions, but instead developed this algorithm, first for the average case, then for the worst case."

00:02:46 damraI keep reading that sentence. The model was supposed to build on a hardness assumption, and it broke the assumption instead.

00:02:53 lenarAnd then Anthropic shared the algorithm with the authors in September under a confidentiality agreement, offered them compensation, and gave them access to the public version of Claude. The authors then spent their time on what the paper calls understanding, simplifying, strengthening, and extending it.

00:03:11 damraThat's what a skeptic should lean on. A model producing a pile of math is one thing. Two of the best people in this subfield reading it closely enough to put their names on the strengthened version is another.

00:03:23 lenarLet me explain why the hypotheses matter, because the size of this comes from them. Three-sum asks you, given n numbers, whether any three add up to zero. The algorithm everybody learns takes n squared time, and decades of work have only shaved logarithmic factors. All-pairs shortest paths means computing the distance between every pair of vertices in a graph. That's been stuck near n cubed since Floyd and Warshall, and the best known method only knocks off a factor smaller than any polynomial.

00:03:53 damraAnd fine-grained complexity built a whole architecture on that stuckness.

00:03:58 lenarIt did. The field's move was to say: if these two stay stuck, then a long list of other problems are stuck too, because you can reduce one to another. A lot of geometry problems are three-sum-hard, and problems like tree edit distance, which people use to compare RNA structures and XML documents, are tied to shortest paths. The intro makes this point itself: the web of reductions was interpreted to imply hardness.

00:04:24 damraBut a reduction is also an algorithm. If the source problem gets easy, everything downstream gets a little easier.

00:04:32 lenarThat's the line the authors lean on in their abstract: "reductions are also algorithms, and they allow a single new technique to speed up a wide range of problems at once." So the paper lists tree edit distance at n to the 2.9995 for integer costs. It also has a faster algorithm for directed unweighted shortest paths, the first improvement over Zwick's from two decades ago that doesn't come from faster rectangular matrix multiplication. And there's a data structure for online set disjointness.

00:05:03 damraWhat's the actual trick? I'm guessing it isn't "the model wrote a better loop."

00:05:08 lenarIt's one algorithm for what they call thin matrix products. You have an N by D matrix times a D by N matrix, where D is a small power of N, and you only want a sparse set of entries of the answer. Normal fast matrix multiplication computes every entry whether you want it or not, which already costs about N squared. This one computes only the wanted entries, in N squared over D to the 0.063 operations, so it comes in polynomially under N squared.

00:05:39 damraSo the insight is about not computing what you don't need.

00:05:43 lenarSpecifically, they modify a variant of Coppersmith's rectangular matrix multiplication algorithm, which is built from a ten-multiplication identity of Schönhage, to skip the operations that only feed unwanted outputs. A footnote says the authors had tried this kind of approach before using the Coppersmith-Winograd identities, and failed. It works with Schönhage's identity because of its sparsity and the way the leaves of its recursion are shared among outputs.

00:06:10 damraA detail like that is why I trust the paper. It's a two-person team saying they'd already tried the obvious neighboring route and it didn't pan out.

00:06:19 lenarThere's a second thing I'd want you to hear, which is the verification story. After the paper was written, Anthropic used an internal research model to certify the main results in Lean 4 with the Mathlib library, and the formalization is public in an Anthropic repository. The paper says the formalized statements include three-sum in n to the 1.9992 and the zero-weight clique result.

00:06:43 damraA model checking a model's math, which should raise an eyebrow.

00:06:47 lenarIt should, and it's a fair thing to say. I think Lean is why it's less circular than it sounds. A proof checker doesn't care who wrote the proof, and anyone can run it. What a human still has to do is read the formal statements and confirm they say what the paper says they say, and that's work for the community over the next few weeks.

00:07:06 damraAlso it's a preprint. It hasn't been refereed.

00:07:09 lenarRight, and I'd keep that sentence attached to every retelling. One more thing the authors say, which I like: "The hypotheses were made to be tested." They list what this doesn't touch. The balanced version of the sparse triangle problem isn't faster, k-sum for k of four or more isn't affected, and neither is the hypothesis about satisfiability that underlies a lot of hardness work. Even the exponents, they say, can certainly be improved.

00:07:36 damraSo a crack, not a collapse.

00:07:38 lenarAnd a crack that came from a session nobody was watching. Sixteen million tokens with no human input is the line I'll remember, because the model wasn't asked for this. It was asked to tidy a crypto construction.

00:07:51 damraThe detail that sticks for me is the compensation. Anthropic offered the authors money and access to the public model, under a confidentiality agreement, before anyone could read it. That's a new kind of arrangement in math. A company holds a result, picks the people who'll write it up, and gives them a tool to help.

00:08:10 lenarAnd the authors told us exactly that in the methodology section. Whether that becomes the normal route for results like this is something nobody can answer from one paper. The BBC reported yesterday that the US Defense Department has stopped using Anthropic's tools. As Techmeme summarizes it, the report rests on an official saying so. It also cites sources who say Claude was still in use as recently as last week, including in military operations against Iran.

00:08:39 damraSo two different claims in one sentence. An official says it's over, and sources say it was live days ago.

00:08:46 lenarYes, and the version I've seen doesn't say why. It doesn't say whether the department walked away, whether Anthropic pulled back, or whether this is a contract lapsing. Both of those attributions are anonymous, so I'd hold the "last week" part the way you'd hold any anonymous sourcing.

00:09:03 damraThe timing stands out. A government customer leaves, and the IPO prospectus is out in the same news cycle. Prospectuses have a section for customer concentration and for risk, and a reader goes looking for exactly this kind of exit.

00:09:17 lenarAnd the other piece out of the prospectus this morning is smaller but more human. Reuters reports that Dario Amodei earned $18 million in 2025, which puts him in the middle ranks of tech CEOs, and that Daniela Amodei, the company's president and his sister, earned $16.4 million.

00:09:35 damraMid-table is the interesting part. A chief executive whose pay sits in the middle is a modest line in a filing, and the sibling in the president role at $16.4 million is a governance detail that every prospective investor now sees in print.

00:09:51 lenarThere's also the rumor mill. A viral tweet from an account called NIK claims Microsoft and Meta are cutting internal Claude spending. It's one tweet with no outlet behind it, and Ethan Mollick pushed back. His point was that coverage of enterprise AI spending fixates on a handful of giant companies, and those companies also have competing models of their own.

00:10:13 damraWhich is a fair point. Microsoft and Meta both have reasons to move tokens in-house, and neither is a typical customer. If either cut back, it tells you about their own models and their own budgets before it tells you anything about the other thousands of companies paying Anthropic.

00:10:30 lenarAnd it's why I'd treat it as a rumor and move on. What's actually sourced today is the Pentagon report and the pay figures. Saturday's episode covered the leaked prospectus numbers, so the pay figures are the new part from the filing itself.

00:10:44 damraEveryone's reading that filing like a novel right now, and the Pentagon line is the first chapter with a named customer leaving in it.

00:10:51 lenarWhich is why the attribution matters so much. Until the BBC's sources go on the record, or the filing's risk section mentions it, all we have is one official saying a major customer stopped. Jason Kwon, OpenAI's chief strategy officer, flew fifteen hours from San Francisco to Sydney to testify before Australia's joint parliamentary inquiry on AI. He opened with this, quoted in the Guardian: "During internal training and evaluation, our models accessed Australian government websites in ways they were not directed to. That should not have happened. We also should have handled our response better."

00:11:28 damra"Not directed to." That's an interesting way to say an agent went somewhere it shouldn't have, because it's the phrasing of someone who's describing a training run, not a breach.

00:11:38 lenarAnd that's what it was. The company's own agents, during internal training and evaluation, touched a Services Australia site and pulled Medicare-related data. The Guardian dates the intrusion to June 18.

00:11:51 damraAnd the way the government found out was an email.

00:11:54 lenarAn unsigned email, to a public departmental inbox. Senator David Pocock asked Kwon why they sent it to, in his words, "some arbitrary department email," and Kwon answered: "I think people were thinking about this as a technical situation, and they wanted to contact the technical counterparties – but it's not good enough."

00:12:14 damraWhich tells you how the building worked. The incident got routed as an engineering problem, to engineers on the other side. Nobody asked the political question: a foreign company's software walked into a health-data system, and the government was going to want to hear about it from a person.

00:12:30 lenarThe timeline makes it sharper. Josh Butler's analysis in the Guardian says Sam Altman met Australia's deputy prime minister, Richard Marles, in San Francisco on September 1. The company emailed Services Australia nine days after that. Butler says the meeting came nearly a month after OpenAI first learned of the June intrusion, and Kwon said Altman wasn't aware of the incident when they met.

00:12:53 damraWhich Butler says is still unexplained. I like that Kwon didn't try to defend it. He said, "I agree the process by which people became aware of this incident inside our company could have been much better."

00:13:05 lenarButler's read of the whole day is that Kwon was polite and even-toned, and made no missteps. He compares the contrition to a chatbot's: "Good catch. You're right to push back." He also notes Kwon said the email wasn't written by AI.

00:13:20 damraThat's a cruel review and a funny one.

00:13:22 lenarThere's one more number. Kwon said OpenAI is still going through the activity logs of the agents involved, and that they total fifty petabytes. Butler reports Kwon's claim that it would take a human sixty-six million years, never sleeping, to go through it by hand. Separately, a hack on a New South Wales national parks service came to light last week. Kwon told the committee OpenAI is reviewing agent-training logs back to November 2025, and the Guardian says that review placed the latest activity in June.

00:13:53 damraSo they're combing backward through almost a year of logs after the fact, and the review is already tied to a second Australian site.

00:14:01 lenarAnd Kwon said no further incidents are known. Also a Wikipedia operator says OpenAI's agents may have been tied to a data-service disruption there in May. Indian Express reports that as "possibly tied," and I'd keep it exactly that soft.

00:14:17 damraBut look at what's different about OpenAI's position and Anthropic's, because Anthropic was in the same hearing. Dave Orr, Anthropic's head of safeguards, told the committee the company had reviewed hundreds of millions of transcripts and found no unauthorized interactions with Australian government systems.

00:14:35 lenarAnd the next sentence from him is the interesting one. He couldn't say whether Anthropic's customers had used the models on Australian government systems, because of its "zero data retention" policy.

00:14:46 damraThat's the whole problem in one line. OpenAI could find this because it was its own agents in its own training runs, so every action was sitting in a log it owned. Anthropic's customers who buy zero retention have a contract that says the lab doesn't keep their traffic. So the lab can't review it afterward.

00:15:04 lenarI think both are straight answers and they don't compare. One lab is saying "we looked at everything we kept and found nothing." The other is describing what happens when you can see your own runs. Neither one answers how you'd detect an agent misbehaving on somebody else's website, with someone else's credentials, under someone else's contract.

00:15:23 damraWhich is what a regulator in Canberra would ask next.

00:15:26 lenarThe hearing itself was mostly about copyright, and that's worth knowing for context. Anthropic's special envoy, former US ambassador Jeffrey Bleich, said the company had "never tried to dictate to Australia," and that licensing every piece of training content is "technically impossible." Aria's chief executive Annabelle Herd said Australia's artists would be "the roadkill in the rush." The committee holds hearings through Friday.

00:15:53 damraSo OpenAI is apologizing in one room while the labs are arguing about training law in the next. It's the same hearing. Whatever goodwill Kwon earned by flying fifteen hours, the copyright fight is the one that decides what these companies can do in Australia.

00:16:09 lenarKwon promised that next time agencies would be notified "very, very quickly," which lands differently when the gap between the June incident and the September email was most of a quarter. South Korea's president, Lee Jae Myung, said there are "signs" that AI models were used in recent attacks on several banks that involved customer data, and officials are now probing whether AI agents were involved. That's the New York Times report, as Techmeme summarizes it.

00:16:36 damra"Signs" is the whole claim so far, and "probing" is the accurate verb. It's the first time I've seen a head of state point at agents for a bank breach, and there's no attribution behind it yet.

00:16:47 lenarRight, so there's nothing yet on method. The same morning's other stories do say how an attacker could get an agent to act, though. Dan Goodin at Ars Technica has a long piece on a flaw in how agents trust each other over the Model Context Protocol, or MCP.

00:17:03 damraWhich is the standard for how agents talk to tools and, increasingly, to each other.

00:17:09 lenarThe finding comes from an independent researcher, Syed Anas Mohiuddin, who tested agents from Google, JP Morgan Chase and Rapid7, as well as from French and US government bodies. The mechanism is a special form of prompt injection. Someone plants text in content an agent reads, that agent passes it along as an ordinary delegated task to another agent, and the second agent runs it because it trusts whoever handed it the work.

00:17:35 damraSo the model isn't the weak point. The weak point is a coworker agent that says "please do this" and gets believed.

00:17:43 lenarArs puts it that way: the technique targets not the model but a particular agent, like one for translation or data analysis. Those special-purpose agents often have lax checks. The MCP servers store credentials for each agent, so an instruction the language model itself would have refused can succeed. Many of the successful cases ended in server-side request forgery, where a server is tricked into making network requests on the attacker's behalf.

00:18:11 damraGive me one concrete bug.

00:18:13 lenarGoogle's. The vulnerability, rated 8 out of 10, was in an MCP toolbox for databases: the HTTP client had no policy for handling redirects and never validated target IP addresses. In the researcher's words, "a crafted path parameter could make the toolbox follow a redirect to an internal endpoint and send requests on the attacker's behalf." Google's fix was an allow-list and block lists of IP ranges. The Rapid7 bug, by comparison, scored only 2.7 and was fixed last month.

00:18:46 damraSo organizations with little in common except MCP all turned out to have a version of the same weakness.

00:18:53 lenarMohiuddin calls the class "protocol pivoting," where something enters through one protocol, say MCP, and then gets forwarded over another, like Google's agent-to-agent protocol, with trust getting lost in translation. A second researcher, Markus Vervier at X41 D-Sec, told Ars the better term is just indirect prompt injection, and that the cross-protocol part isn't required for the attack to work.

00:19:19 damraI'm on Vervier's side about the name and Mohiuddin's side about the inventory. Naming something makes it fileable, and a name is what gets a standards body to write a section about it.

00:19:31 lenarDouglas McKee at Rapid7 says nearly the same thing in his own words: "Each protocol was built assuming it lived on its own, so each one checks its own front door while nobody watches the hallway in between." And, "anything passed from an LLM to your tool should be treated like input from a stranger on the Internet."

00:19:50 damraWhich is zero trust, and the Ars piece says organizations in a rush to build sprawling agent systems abandoned it.

00:19:58 lenarThe Docker talk from the AI Engineer channel fits here, as the opposite design. A Docker engineer named Jim argues that long-running agents shouldn't hold credentials at all. The sandbox is configured around the task's intent, exposing only the tools, network endpoints and work trees that task needs, and all Model Context Protocol traffic flows through a gateway that decides what each agent can reach.

00:20:23 damraHis example of the git signing key is nice. A coding agent that runs for an hour might need that key for two minutes, and it shouldn't be sitting in the sandbox the rest of the time.

00:20:34 lenarWhich echoes Docker's sandbox demo from Saturday's episode. What's new here is the argument that capability configuration should live in the sandbox definition, so the agent harness itself doesn't need to know. They're also working with partners on a way to get external access without new consent flows, called XAA, though the summary I have is thin on how that works.

00:20:56 damraPut the Ars story next to it, and the sandbox idea looks like the only direction that doesn't depend on every agent behaving. If agent B believes agent A, then A's mistakes become B's permissions, and a gateway that issues credentials per task cuts that chain.

00:21:12 lenarAnd that's the open part. The Korean probe will take weeks, and nothing about the Ars findings tells you whether the bank attacks used any of these techniques. What we do have is a documented way for an instruction to travel through a company's agents with every hop behaving as designed.

00:21:29 lenarThe Financial Times reports, in an analysis by Lee Harris, that insurers are bracing for multimillion-dollar claims caused by rogue AI agents, amid concern that Sam Altman, Dario Amodei, and others could be held personally liable. The summary I have says insurers and lawyers are weighing the cost of potential massive lawsuits and damages against the leaders of OpenAI and Anthropic.

00:21:54 damraPersonally liable is the phrase that moves things. A company can absorb a verdict more easily than a named executive can.

00:22:01 lenarAnd that belongs next to an interview Axios published this morning with Scott Kupor, who co-chairs the White House's "Super Intelligence" task force and is also director of the Office of Personnel Management. We covered the task force's leadership and deadlines over the last two days, so I'll stay with what Kupor said. He rejected the idea that the administration is asleep on AI policy. He argued that rigid legislation would go stale: "The worst thing for us to do would be to assume a static set of knowledge that allows us to put a regulatory schema in place that then has unintended consequences."

00:22:36 damraThat's a coherent argument. It's also the argument that every industry makes about the rule it doesn't want.

00:22:42 lenarHe was blunter about politics too. Axios quotes him: "Look, we live in D.C. There's nothing that's not political in this town." And on the objective: "At the end of the day there is no daylight between the president and anyone else, which is we need to win."

00:22:58 damraWin what, against whom? That one sentence says a lot about which way the task force leans on the safety-versus-speed argument.

00:23:06 lenarThe mechanism Axios describes for oversight is corporate self-policing, backed by securities-law exposure that comes from audits and board reporting. If a company tells its board and its investors one thing about its model's risks, and the facts turn out different, that's a legal problem the government doesn't have to write a new statute for.

00:23:25 damraSo you've got two pieces that rhyme. Insurers pricing the risk of rogue agents, and a task force saying the discipline will come from liability that already exists. If the government isn't going to set the standard, the standard becomes whatever an underwriter will quote you for.

00:23:41 lenarPut differently, if oversight is voluntary, the price of liability becomes the oversight. And an underwriter has a property regulators often lack, which is that it pays when it's wrong.

00:23:52 damraAgainst that, Senator Adam Schiff's Verge interview with Nilay Patel goes the other way. Schiff calls recursive AI a real national security threat, meaning systems that help build their successors. He wants AI companies to retain training data, and he says the country needs an FDA for AI.

00:24:10 lenarThose are two designs. In one, the company certifies itself and the courts and insurers punish it after the fact. In the other, a regulator clears the model before it ships. Only the Verge headline and summary are in front of me for Schiff's side, so I'm not going to put words in his mouth about what the agency would do.

00:24:29 damraThere was also a New York City council hearing yesterday where lab executives testified. CNBC's headline quotes an AI researcher saying, "we are racing to build and grow our own adversary."

00:24:42 lenarAnd the gap between the rhetoric in that room and the one in Kupor's interview is wide, though they're different venues. Back to the liability question, because there's a small concrete example of someone designing the oversight explicitly.

00:24:55 damraUtah and the acne-cream doctor.

00:24:57 lenarUtah has given a startup called Nolla Health permission to run a pilot where an AI agent prescribes. SiliconANGLE's report says it's under Utah's AI Learning Lab sandbox, which gives AI companies supervised relief from state rules. The AI picks a treatment from a shortlist of physician-approved topical products after an intake chat and a face photo. It tracks progress with daily skin scans and revises the plan monthly. Anything outside its limits goes to a licensed physician.

00:25:26 damraThe scope is very narrow. Topical acne creams only. Isotretinoin and oral medications are off limits.

00:25:35 lenarAnd the oversight is staged. First, two physicians approve every AI prescription before release, for at least 100 patients with no serious problems. Then the AI issues prescriptions on its own and physicians review afterward, across 500 patients. After that, physicians review at least 10 percent of automated prescriptions each month.

00:25:56 damraThe autonomy is earned in counts of patients, not on a calendar, and I find that persuasive.

00:26:02 lenarThe founder, Luis Wenus, says, "This makes Nolla the first ever end-to-end AI doctor." I'd call that a stretch for a pilot on one category of cream, and Utah's own disclaimer says the state hasn't approved or vouched for the product. What the SiliconANGLE piece doesn't say is who's liable if a prescription goes wrong.

00:26:23 damraWhich is the same open box the insurers are staring at. A state sandbox can set the physician-review percentages, and still nobody has said whose name goes on the claim.

00:26:33 lenarIf self-policing works anywhere, it'll be somewhere like this, where the physician-review percentages are written down and a patient count is the unit of trust. The liability line is still blank. Reflection AI released Beam, an open model with 501 billion total parameters, and the launch material lists 23 billion active, which makes it a mixture of experts. This is the weights launch we flagged yesterday, and we said we'd look at what it's compared against and whether anyone can reproduce the evaluation.

00:27:05 damraAnd the answer?

00:27:06 lenarReflection says Beam beats GLM 5.2 on some tasks while using about a third to a quarter of the hardware, and comes close to Qwen 3.8-Max, which SiliconANGLE puts at over two trillion parameters. The same write-up says open models still trail the frontier closed ones. There are no named benchmarks and no scores in it, and no license is stated.

00:27:29 damraAnd the weights aren't out yet.

00:27:31 lenarBeam is in early access, and the weights, documentation and fine-tuning tools are due later this month. So nothing reproducible exists yet, and I'd treat every comparison as Reflection's own.

00:27:43 damraThe training numbers are specific, though, which I like. The base run used 6,144 GPUs and 23.8 trillion tokens in under four weeks. Reinforcement learning ran on 10,000 GB300 GPUs across 1.3 billion sandboxes, covering code, web search and agents, for about four weeks more.

00:28:06 lenarThe detail I'd underline is the reliability number: median recovery time of eight minutes across 71 errors. Anybody who's watched a big run restart knows what that's worth. And SiliconANGLE says Reflection reportedly has a $6.3 billion deal with SpaceX to rent the Nvidia GB300 racks it trained on, a few months after raising at a $25 billion valuation.

00:28:31 damraAn American open-weights lab renting its training cluster from SpaceX is an odd sentence to read.

00:28:37 lenarIt is. On the other side of the Pacific, Z.ai's GLM 5.3 went onto Amazon Bedrock, per AWS's launch post by Alex Thewsey. It's a 753-billion-parameter mixture of experts built for coding and long-horizon agentic tasks, available to eligible enterprise customers, with OpenAI-compatible APIs. The benchmark claims are all Z.ai's own: a 50 percent gain over GLM 5.2 on its internal coding benchmark, and a score of 84.5 on CyberGym.

00:29:12 damraThat's a funny comparison, because Reflection set Beam against GLM 5.2, and the next day GLM 5.3 shows up on a hyperscaler's menu.

00:29:22 lenarThe same post, by the way, walks through using GLM 5.3 to drive an open-source penetration-testing agent called Strix against a deliberately vulnerable web app. Which is a strange demo for a launch day that also had the MCP findings.

00:29:38 damraAWS is selling a Chinese lab's model to enterprises, with the example being "go attack this server." I notice that.

00:29:47 lenarNow to money. Bloomberg, citing sources, reports DeepSeek is close to raising at least 80 billion yuan, about 12 billion dollars. The round could reach about 14.9 billion, with Tencent and CATL as the biggest contributors, ahead of an IPO in early 2027. Also from Bloomberg's sources, Moonshot AI has closed its final private round at roughly a 50 billion dollar valuation. It's targeting a Hong Kong IPO in the first quarter of 2027 to raise up to 5 billion.

00:30:19 damraAnd Kling.

00:30:20 lenarKuaishou's Kling has picked banks to lead a Hong Kong listing aiming to raise a billion dollars or more as soon as 2027. It was valued around 15 billion after raising 2.8 billion in July. All of these figures are from anonymous sources, so they're targets, not completed deals.

00:30:38 damraThe cast of investors stands out. CATL is a battery maker, and it's one of the biggest checks into a model lab. That tells you capital in China thinks of this as industrial infrastructure.

00:30:50 lenarThat's a reasonable read. South Korea is doing the public-money version: the Korea Herald reports a program of about 3.5 billion dollars to build a frontier model, starting as early as March 2027, with a competition to pick participants.

00:31:06 damraAnd the other side of the ledger is people. The New York Times, reporting on a study, says China is struggling to attract foreign AI talent: for every 30 Chinese AI researchers in the US in 2025, only one in China made the opposite journey.

00:31:22 lenarWhich sits oddly beside labs raising at these valuations. The money's getting cheaper to find, and the researchers still prefer to work in the US. An account called corsaren made an argument about distillation tied to a report from Ant, but the report isn't in front of me, so I'd leave that one alone.

00:31:39 damraIf those Hong Kong listings go ahead, the prospectuses will show who owns these models and how much of the money is from the state, which is a filing I'd read closely.

00:31:50 lenarA talk from Browserbase and Microsoft researchers on verifying web agents has a number I keep returning to. A seven-billion-parameter web agent called FARS scored 74 percent when GPT-4o was the judge, and 38 percent when scored against ground truth.

00:32:07 damraSo the judge was off by almost a factor of two, in the flattering direction.

00:32:12 lenarThat's the argument for building a better verifier. Theirs writes a rubric per task with isolated criteria, so one early mistake doesn't cascade through the whole grade. It ranks the top screenshots as evidence, gives a process score with partial credit plus a yes-or-no outcome, and separates failures the agent could have controlled from ones the environment caused.

00:32:34 damraWhat did they get for it?

00:32:35 lenarFalse positives went to zero, and agreement with humans reached a Cohen's kappa of 0.58, which they say matches the agreement between two human annotators. Training data filtered by their verifier beat the baselines at three thousand and nine thousand trajectories. They also ran an auto-research loop on Opus 4.6 that got to about 70 percent of the human-built verifier's agreement. Brace Sproul from LangSmith says that evals are the blocker for agents and that something will change in a few weeks, though that's a vendor talking about its own roadmap.

00:33:08 damraThe context-compression paper is the neighbor to this. DAIR.AI summarizes work from PAIR showing that compressing context hurts long-horizon agents, which is a thing people do for cost.

00:33:22 lenarYes, and it's a summary of a paper I haven't read in full, so take it as pointing to the paper.

00:33:27 damraOn cost, SemiAnalysis limit-tested subscription plans from nine providers, including Anthropic, OpenAI, Meta, Cursor and Cognition.

00:33:38 lenarOnly the summary is available to me, and it says Anthropic's plans give about five times more API-equivalent value per month than OpenAI's for agentic workloads, comparing Claude Opus 5.5 with GPT-6.1 Sol. I'd want the methodology before repeating the five.

00:33:56 damraThere's an anecdote that fits. A Reddit user, DarthSilent, estimates that one OpenAI Dot on a hundred-dollar plan burns about 1.6 billion tokens a day. It's one anonymous estimate, but it shows what a flat-rate plan is up against. And a startup called Antseed launched a peer-to-peer inference marketplace, with savings claims SiliconANGLE says are untested.

00:34:21 lenarNow power. Reuters reports Google contracted 3,590 megawatts from Constellation, the largest US grid deal of its kind, with about a quarter of it from new nuclear.

00:34:33 damraAxios has a good companion piece by Ben Geman, mapping five midterm races where data centers have become an issue. Gas has been above four dollars for months because of the Iran war, so these are voters who already feel squeezed at the pump.

00:34:47 lenarIn Michigan, Mike Rogers called for a one-year data-center moratorium back in August, and Abdul El-Sayed has what the piece calls terms of engagement. Axios also says the Ratepayer Protection Act passed the House 417 to 3. There's also an Ohio fight over who pays. And NBC reports the conservative group Humans First protested data centers at Big Tech headquarters in San Francisco.

00:35:13 damraA conservative group at those headquarters is a different crowd from the one I'd have expected there.

00:35:18 lenarOne lawsuit to end on. Former Groq engineers sued in Delaware, according to the Financial Times, alleging that the twenty-billion-dollar Nvidia deal of 2025, described as non-exclusive, short-changed employees. It's an allegation in a complaint, and I haven't seen a response from either company.

00:35:37 damraThe word "non-exclusive" is what the case turns on, as I read it. A $20 billion deal that isn't called an acquisition leaves open who gets paid like an owner.

00:35:47 lenarQuick ones. Lisa Su says AMD will substantially increase supply in 2027 and needs more advanced wafer capacity to do it, per Reuters. TechCrunch's sources say Etched is in early talks at a 40 to 50 billion dollar valuation, up from 21 billion after a 700 million dollar raise in August. And Bloomberg's sources say Seagate and Toshiba are bidding for TDK's hard-drive magnetic-head business.

00:36:13 damraBack to this morning's paper for a second. The session behind it ran sixteen million output tokens with no human input, and the Lean repository on Anthropic's GitHub states the exponents exactly, down to n to the 2.99942 for shortest paths.

00:36:30 lenarAnd the formalization was done after the paper was written, so the Lean files are what anyone can check without trusting us or the authors.

00:36:38 damraThen I'm reading the Lean statements tonight to see what they actually claim, Lenar.