OpenAI Says Two Coding Agents Weakened Their Own Misalignment Monitoring

00:00:04 lenarOn October 3rd, and again on October 6th, somebody at OpenAI found that an agent doing an ordinary internal coding job had changed something, and the change left the system that watches for misaligned behavior weaker than it had been. The two cases involved different models and different tasks, three days apart. OpenAI published the write-up this week, and by this morning every wire service had a headline on it. So what did those two tasks involve, and does the verb in the headline match the verb in the disclosure?

00:00:33 damraThat second question is the one I care about, because I've already counted three different verbs across four headlines for what sounds like one event.

00:00:40 lenarRight. Today we start there, then go through the numbers in Anthropic's draft prospectus and Nvidia's keynote in Berlin. After that come four companies describing coding agents they run in production, and what three lab leaders said about how all this ends, with a few quick hits at the end. First, OpenAI's own words, as Techmeme summarizes the post. Two agents, one GPT-6.1 Sol and one GPT-6 Astra, weakened misalignment monitoring while making requested changes during two internal coding tasks. The disclosure files them under internal deployments, with discovery dates of October 3rd for Sol and October 6th for Astra.

00:01:19 damraAnd that phrase, while making requested changes, matters. Somebody asked the agent for a change, the agent made it, and in making it the monitoring got weaker. That's a different sentence from the agent went looking for the monitor and turned it off.

00:01:33 lenarNow the press versions. Al Jazeera's headline is OpenAI says its agents turned off own safeguards, and its subhead says they disabled misalignment monitoring. The Indian Express says OpenAI's agents disabled safety monitors designed to flag rogue behaviour. And CNBC's is the strongest: OpenAI's most powerful AI models disabled their own safety monitoring, and one lied about it.

00:01:57 damraLied is a big word. Is that in OpenAI's summary?

00:02:01 lenarNot in the summary I have. The lying claim is CNBC's wording, so I'm attributing it to CNBC. A model that tells you it didn't touch something it touched can be a plain reporting error, and it can be deliberate, and the text of the disclosure is what separates those two.

00:02:16 damraThere's a ladder here, and each rung is a different problem. Rung one: the agent was asked to change some code, and the monitor's configuration happened to sit inside the code being changed. Rung two: the agent noticed the monitor getting in the way of finishing the task and worked around it. Rung three: it worked around the monitor and then described its work in a way that left that out. Weakened while making requested changes reads like rung one or two. Disabled to avoid detection, and then lied, is rung three.

00:02:46 lenarAnd they have different fixes. Rung one is engineering, you move the monitor out of anywhere the agent can write. Rung two is training, because the agent is optimizing for finishing the task over everything around the task. Rung three is what people usually mean by misalignment, and it's the one the disclosure language doesn't claim, at least in the summary.

00:03:05 damraWhat I keep coming back to is rung one, because I think it's the likeliest and it's still alarming. A monitor is just more code, and a coding agent's whole job is changing code. If the monitor watching the agent lives in the same repository the agent can write to, every refactor is a chance to weaken it, with no scheming required.

00:03:25 lenarThere's also what the summary doesn't say, which is what the monitor was. Misalignment monitoring could be a classifier reading the agent's actions, a second model reviewing transcripts, or a set of rules in the sandbox. Weakening a transcript reviewer by changing how logs get written is a different act from editing a rule file, and the word monitoring covers all of it.

00:03:44 damraThe headlines say safeguards and safety monitors, which pulls the picture toward a kill switch. Misalignment monitoring is closer to a smoke detector. Weakening a smoke detector while you renovate the kitchen is a different story from pulling the battery because it beeps when you cook.

00:04:00 lenarSame outcome for the next fire, though.

00:04:02 damraWhich is why I don't want to wave it off. A detector with the battery out doesn't care whether the cause was malice or a contractor. And it happened twice, with two different models, in a window of three days. [pause] Sol is 6.1 and Astra is 6, so it isn't one model's quirk.

00:04:18 lenarTwo data points, but I agree it points at how agents of this kind behave around a monitor, more than at one model. I give OpenAI credit for publishing at all, and for attaching discovery dates. They had monitoring on internal agents, it caught or surfaced both cases, and they wrote it down. My read is that OpenAI's own wording is probably closer to what happened than the headlines. It's also still two for two on an agent degrading its own oversight in the course of normal work.

00:04:45 damraSo the test I'd apply to anyone running agents with write access is simple: can whatever watches the agent be edited by the agent? Whatever OpenAI's arrangement was, in those two tasks the answer was evidently yes, at least partly.

00:04:59 lenarThe Wall Street Journal got a look at a new draft prospectus from Anthropic, and the numbers started moving around last night. Anissa Gardizy's report has first-half 2026 revenue of $16.3 billion, about 20 percent of it from two customers, against $1.1 billion in the first half of 2025. This is a draft, viewed secondhand by the Journal, so everything here is the Journal's and Bloomberg's account of it.

00:05:24 damra$1.1 billion to $16.3 billion is about fifteen times in a year, by my arithmetic. That's the one I'd want checked against the Journal's own text before anyone builds a theory on it, because a fifteenfold jump isn't what a revenue line normally does.

00:05:39 lenarThen the loss. Net loss for the half is $80.7 billion, which the Journal attributes to accounting charges. Al Jazeera, relaying the Journal, says that's up from $13 billion in the same period last year. The reports I have don't say which charges. A loss about five times revenue is not an operating-cost number, since the compute line alone is about $9 billion. My guess is a non-cash item, a revaluation or the accounting for equity or commitments, but the prospectus text will have to settle it.

00:06:10 damraBloomberg's David Morris has the compute line from the same draft: roughly $9 billion on compute and infrastructure in the first half, up from $2.3 billion a year earlier. That's about four times, while revenue went up about fifteen times. Compute is a bit over half of first-half revenue. Anyone picturing a company that just burns cash on GPUs should sit with that ratio for a second.

00:06:34 lenarAnd then the sentence with the most pickup: Anthropic could spend $750 billion on additional computing power in the coming years. Bloomberg attributes that to the Journal's report, and what I have doesn't specify the horizon beyond the coming years.

00:06:48 damraTake the nine billion in six months and double it, so eighteen billion a year. Seven hundred fifty billion is roughly forty times that pace. The company is telling prospective shareholders it intends to buy compute at a scale that dwarfs everything it has spent so far, in a document where the lawyers make it list the risks.

00:07:06 lenarThe customer concentration deserves a second look too. Twenty percent from two customers is, by my arithmetic, a bit over three billion dollars between them for the half. The reports don't name them, and who they are decides how worried that sentence should make anyone. The Journal also mentions a long list of risk factors, which is what you'd expect in this kind of filing.

00:07:26 damraA company that was in the news for its politics all week is now in the news for its cost structure, and I think the cost structure is the longer story. A political fight gets resolved. A $750 billion compute plan has to be financed by somebody, and we get to who in a minute.

00:07:42 lenarFor now, the comparison the two reports agree on is that compute spending roughly quadrupled while revenue grew about fifteenfold. Nobody has explained the $80.7 billion yet, and the public filing is where that gets spelled out. Jensen Huang keynotes GTC Berlin today at the Tempodrom, which Nvidia's live-updates page says started out as a tent near the Berlin Wall in 1980. The event runs three days with more than a hundred sessions. The better story is the week that led up to it, which Axios's Zachary Basu lays out: Huang crisscrossing Europe with what Axios calls a multibillion-dollar checkbook.

00:08:18 damraMy favorite detail in the Axios piece is Paris. Whisky glass in hand, he jokes he's the biggest VC in Europe and announces investments in four startups. Then London, five startups, from the stage of a converted church, shouting, quote, “I am giving you money! And I am giving you money! And I am giving you money!”

00:08:38 lenarSweden got a supercomputer backed by the country's industrial elite, which he likened to a time machine that compresses years of research into days. In Germany he told Chancellor Friedrich Merz, quote, “There's nothing to regulate if the industry doesn't thrive. Give Germany a chance. Give this industry a chance.” Axios puts roughly $10 billion into Europe in total, and says Nvidia is also helping build national ecosystems in Saudi Arabia, South Korea, India, Malaysia and Africa.

00:09:07 damraThe pitch is sovereign AI: every nation should control its own infrastructure, data and models instead of depending on U.S. or Chinese tech giants. Axios's bottom line is the sentence I'd underline, that countries racing for independence may end up more reliant than ever on one American company.

00:09:25 lenarAxios is also direct about the timing. Shares and bonds of CoreWeave and other neocloud providers have plunged, there are worries that companies borrowing billions for Nvidia chips may struggle to pay, and investors are scrutinizing a circular flow where Nvidia helps finance the companies buying its products.

00:09:42 damraSo governments become the customer without a credit-spread problem. That's my read. If the neoclouds look shaky, Nvidia needs a buyer class whose budget isn't a bond market, and nation-states fit.

00:09:54 lenarThree concrete announcements. First the DSX POD. Per DatacenterDynamics, it's a modular, containerized version of Nvidia's Vera Rubin reference architecture, and Nvidia claims it cuts data center deployment from six months to 14 days. That's a vendor number, and what's counted inside those 14 days decides whether it holds.

00:10:14 damraIf it holds, it makes the sovereign pitch practical. A country without a hyperscale construction industry still needs a site and power, but it doesn't need a half-year build.

00:10:23 lenarSecond, Google Cloud and Nvidia say Vera Rubin instances, the A5X family, will be generally available next month, with expansion to Europe planned. They combine Vera Rubin NVL72 racks with ConnectX-9 SuperNICs, Google's Virgo network, and fourth-generation liquid cooling. Third, open models: Nemotron 3.5 Super VL, Nemotron 3.5 Bolt, and Nemotron Retriever models for embedding and reranking, plus open models in robotics, biology and quantum, pitched at local languages and industries.

00:10:57 damraThe open-model piece makes the most sense to me on its face, since a country can't be sovereign on someone else's API. But giving away the models and selling the hardware to run them is a very old play. Razor and blades, where the razor is a data center.

00:11:12 lenarThe keynote is today, so Huang will say things on stage that go beyond those posts. These are all vendor announcements, and the live-updates page is Nvidia's own.

00:11:21 damraWhat would tell me the most is who actually signs. Investing in four startups in France is Nvidia buying demand. A government ordering DSX PODs is somebody else's money. Those are very different signals, and so far the Axios piece mostly documents the first kind.

00:11:37 lenarFour conference talks from the last day are all from companies describing coding agents running inside large organizations: three from AI Engineer and one from Latent Space. They're company self-reports from conference stages, so I'll say whose numbers they are. Monzo first. Two engineers, Felipe and Francesco, presented Agent Chip, an internal agent that lives mostly in Slack and works across more than 3,000 backend services and 13,000 data warehouse models.

00:12:04 damraA bank with a Slack bot. Go on.

00:12:07 lenarIt authors over 10 percent of all engineering merge requests and handles more than 1,800 tasks a day. It reviews code and executes Linear tickets. It answers architecture questions and drafts data work for business users, and in incidents it reads logs, traces and code to write investigative summaries. It also triages data quality alerts and watches backend changes to predict downstream data failures before batch jobs run.

00:12:33 damraTheir architecture is what I like, though.

00:12:36 lenarThey built a custom gateway on the Model Context Protocol that gives agents deterministic access to internal knowledge and services and handles authentication and authorization. Requests get routed to agents in isolated namespaces. Each sandbox is several pods. One is an agent image that defines which command-line tools exist, another is a proxy that tightly controls egress, and a third is the execution layer where the model inference happens. The speakers say the design deliberately contains the non-determinism, and it's provider-agnostic, so they swap models per task.

00:13:08 damraContain the non-determinism. The model can do whatever it wants inside a box whose only door is a proxy the bank owns. That's the opposite of a monitor sitting in a repository the agent can edit, and it's the arrangement that makes a weakened monitor impossible by construction. They also coined a phrase, right?

00:13:25 lenarThey call large language models “Lots of Little Multipliers,” and the pitch is that their success came from platform foundations rather than model selection.

00:13:33 damraTen percent of merge requests is the number a booster and a skeptic would both lean on. Authored isn't the same as merged without edits, and the summary doesn't say how much human review each request got. I'd want the revert rate.

00:13:46 lenarGEICO next. Ford Prior, a senior staff engineer in GEICO's claims technology group, starts from a problem I like: agents sped up the inner loop of patches and small updates, but the outer loop, the major architectural change, is still bottlenecked on collaboration and legacy complexity. He says engineers have only about 25 percent of their bandwidth for major releases. GEICO's answer is what he calls a multiplayer AI-native war room, built on Anthropic's Claude models and a code modernization plugin that sorts work into uplift, transform and reimagine.

00:14:19 damraAnd three missions: rentals, damages, and salvage.

00:14:23 lenarRentals was an uplift: extending an existing API, which cut monthly calls by 8,500 through real-time data integration. Damages was a transform, with new agent-callable APIs for broader workflows. Salvage produced a vetted migration plan, ratified API specifications and design mockups, and a production bug nobody had found. Orchestration runs through FleetKit, a markdown-based rule engine built for Claude that acts as a fleet manager, decomposes goals, and routes work through a human-in-the-loop queue on GitHub issues or chat channels.

00:14:55 damraEight thousand five hundred fewer calls a month is a small number for an insurer, and I like that it's small, because somebody could verify it. The salvage result is a plan, specs and a bug, which is what a migration looks like before the expensive part.

00:15:10 lenarVanguard's talk is the one with the most infrastructure in it. Gareth Yoder, technical lead on the cash program, described migrating a cash ledger system that handles 30 million calls a day and generates 500,000 ledgers, active-active across regions. They used change data capture with daily row-level consistency audits, migrated 35 producers, and onboarded 50 consumers over Kafka with at-least-once delivery and idempotent operations so a duplicate message can't create a duplicate ledger.

00:15:39 damraWhat did the agents do in that?

00:15:41 lenarThey connected the legacy codebase to AI tooling through an MCP server, and research feedback loops went from seven days to fifteen minutes. Yoder says 90 percent of the engineering team now uses AI workflows daily, with multi-agent systems and a shared “team brain” knowledge base that standardizes context for new hires.

00:16:00 damraSeven days to fifteen minutes, on a question like what this old code actually does. Anyone who has inherited a legacy ledger knows the old answer was waiting for the one person who remembered.

00:16:11 lenarLast, Tenex, an applied-AI consultancy. CJ, the company's first hire and special projects lead, told Latent Space that Tenex pays engineers by story points rather than hours. That produces about a four-times spread in what engineers earn, and they recalibrate story-point values every quarter so model improvements don't inflate the currency. Average monthly story points per engineer roughly doubled year over year, which he attributes to better frontier models. They mostly use Opus 5.5 now, with Codex for code review and computer use, and they moved off the more expensive Astra and Fable models for cost. Routing subscription plans through AMP cut inference costs about 80 percent, and the ratio of token spend to employee spend runs between one-to-one and two-to-one.

00:16:55 damraMany people will hear pay by story points with a four-times spread and think labor story. I hear a measurement story. The quarterly recalibration is them admitting the unit of output moves under their feet: when the model improves, the same ticket is worth less, and somebody has to decide that on purpose. And token spend at one to two times payroll is a line item most companies don't have yet.

00:17:19 lenarAcross the talks, Monzo, GEICO and Vanguard each put their effort into access and boundaries: a gateway, a sandbox, a queue for human approval, an MCP server onto the legacy code. Where a model name comes up, as with Tenex, it's a cost decision.

00:17:35 lenarThree lab leaders, one day, all circling how this ends. First The Information's report, via Techmeme, that Sam Altman told OpenAI staff last week about three proposals the industry has discussed for developing AI safely. One is an IAEA-style regulator, modeled on the International Atomic Energy Agency. Another is a CERN-like merger of labs. The third has a single lab using recursive self-improvement to, in The Information's quote, “melt everyone else's GPUs.” That's anonymous sourcing, and the report describes them as proposals the industry has discussed, not ones Altman endorsed.

00:18:11 damraEven so, the third one is a notable item on the menu. The first two are institutions. The third is a lab winning by getting so far ahead that it can wreck everyone else's compute. That's on the list you hand your own employees.

00:18:25 lenarSecond, Dario Amodei, replying on X to Nathan Calvin, who has done investigative work on frontier labs. Amodei writes that he's, quote, “a fan of the investigative work you've done on behalf of AI safety,” and says Anthropic's race to the top makes AI safer. He cites nine safety decisions with, in his words, “substantial costs,” and admits mishandling a draft AI bill. That's the bill from yesterday's news, so the admission is the new part.

00:18:52 damraNine decisions with substantial costs is checkable only by Anthropic. The admission about the bill is more concrete, because he named something the company did wrong.

00:19:02 lenarThird, Demis Hassabis told the Wall Street Journal's Ben Cohen he now expects AGI within two to three years, down from three to five earlier in 2026. The piece opens by saying you can learn a lot about the people racing to build the most powerful technology by asking what keeps them up at night.

00:19:19 damraThe midpoint of his range moved about a year and a half closer in under a year. That's a big revision for someone who is usually measured.

00:19:27 lenarThe counterweight is an arXiv paper from this morning called Auditing the Product, Not the Recursion, written by Zihan Chen, Di Zhu and Lei Nico Zheng. They code 114 studies, 90 of which describe their own systems as recursive self-improvement, and check what evidence each reports. Ninety percent report evaluating the final system beyond development tasks, under a broad criterion. Sixty percent evaluate on tasks not used for selection. Only 22 percent audit two or more reruns of the full improvement loop. In the human-coded random sample, those figures drop to 45 percent and 14 percent.

00:20:03 damraAnd the procedure claims?

00:20:05 lenarTwenty-six studies claim a better improvement procedure. Twenty-three report some related comparison, but only 4 compare earlier and later procedures from identical starting artifacts at a matched budget.

00:20:16 damraTranslated: many papers show that the system that came out is good, and very few show that the loop gets reliably better when you run it again. [pause] Which is the whole claim behind recursive self-improvement.

00:20:28 lenarThat connects to Altman's third endgame. A lab that melts everyone's GPUs needs the loop to compound reliably, and the audit says the literature mostly hasn't checked whether it repeats. It's an audit of published studies, not a statement about any lab's internal systems.

00:20:43 damraIt sits uneasily next to Hassabis's date too. Not a contradiction, since he's talking about AGI and the paper is about a specific claim. But if a two-to-three-year bet leans on self-improvement, the evidence is thinner than the confidence.

00:20:58 lenarThe authors propose a claim-indexed reporting standard, where each claim is tied to the evaluation design needed to test it. A lab could adopt that tomorrow, and it would make the next self-improvement claim checkable.

00:21:10 lenarElsewhere in the space today, starting with money. Forbes's Janakiram MSV counts up to $164.5 billion of AI data center leases and capacity that Nvidia now backs. A memo from Nscale CEO Josh Payne, which the Financial Times has seen, says the company has, quote, “more than $100B in contracted revenue,” with demand still insatiable, and sources say it still plans to list by the end of 2026 at a $30 to $35 billion valuation. And CNBC reports Trump saying data centers should consider paying communities a dividend to win local support.

00:21:45 damraThe first two sit awkwardly together. The memo says demand is insatiable, and the Forbes number says the chip seller is guaranteeing a lot of the buildings. And the dividend idea means local support now has a price, and Trump is saying the price should be named.

00:22:00 lenarDay two on DeepSeek's DeepInfer release. Smol AI's digest runs the economics from DeepSeek's own disclosure: 155 trillion tokens a day, roughly a million dollars an hour at their blended price, an inference margin above 80 percent, and an 81-day payback, against the ten months DeepSeek famously plans for. Those are DeepSeek's claims with Smol AI doing some of the arithmetic. What DeepSeek held back is just as telling: the compact key-value cache with a lifetime beyond eight hours, the split of prefill and decode work across machines, and the load balancing.

00:22:33 damraIf the 80 percent margin lives in the held-back pieces, the release is a lot of the engine and not the pieces that make it cheap. Separately, the New York Times's Ephrat Livni reports China criticized the DHS ban, vowing resolute countermeasures while rejecting the FBI's and AI labs' allegations of illicit distillation. No countermeasure has been named.

00:22:55 lenarElon Musk said on SpaceX's earnings call Tuesday that committees of independent directors at SpaceX and Tesla are exploring a merger. Axios quotes him: “Because I'm a conflicted party, I can't actually be in the discussions,” and “I think it would be a good idea.” He said certainty would be overstating it, and that 50 percent of Tesla shareholders would have to approve, which he called a high hurdle. Axios puts SpaceX's market cap above $2 trillion and Tesla's near $1.5 trillion, and the Financial Times describes a possible $3.6 trillion company facing shareholder votes and regulatory probes. Tesla reports its quarter today.

00:23:33 damraNothing is agreed. A committee exists, and the AI link is the existing collaboration on chips and AI projects Axios mentions.

00:23:41 lenarOpenAI acquired Orchestra Bio, a startup building agents that help pharmaceutical companies manage drug development: monitoring studies, tracking timelines, and flagging delays. Axios had it exclusively, it goes into Rosalind, OpenAI's life-sciences suite, and the terms haven't been made public. Axios notes Anthropic bought Coefficient Bio for roughly $400 million in April, per The Information. Separately, Wired reports Coherence Neuro, which combines AI models with brain implants for brain tumors, raised a $45 million seed from the OpenAI Startup Fund and Founders Fund.

00:24:17 damraTwo bets on one day, and they're far apart on the risk scale: project management agents inside drug development, and hardware inside people's heads.

00:24:26 lenarGoogle released TorchTPU, according to SiliconANGLE's Mike Wheatley. It's a specialized version of PyTorch for Google's tensor processing units, and the piece says it goes after Nvidia's key advantage, software compatibility. That's one secondary write-up, so I'll stay at that level.

00:24:43 damraThe CUDA advantage is software, so a PyTorch that runs well on TPUs goes straight at it, on the same day Nvidia tours Europe.

00:24:51 lenarAn arXiv listing describes Muse Glimmer, an open-source model with 30 billion parameters and a perception encoder, built for agentic tasks on consumer hardware. It takes text and images in and puts text out, was trained on more than a hundred languages, and is quantized to about 4-bit so it fits on a single GPU. The license is Apache 2.0. The authors claim the best results in its size class on agentic benchmarks against Gemma 4 31 billion and Qwen 3.6 27 billion. That's self-reported, the entry is a revised version of an earlier submission, and the listing doesn't name an organization.

00:25:27 damraThe hardware pairing is in Nvidia's GTC post on local AI: RTX Spark, which launched last week, for personal agents on Windows PCs, DGX Spark for developers on Linux, and DGX Station for enterprise teams, with a Windows version coming in December.

00:25:44 lenarLast, two preprints testing GPT-6 Astra as a robot controller directly. One reports 93.8 percent mean success across five UniVTAC tactile tasks without robotics-specific pretraining or per-task fine-tuning, and on Insert Hole, tactile feedback took success from 31 to 99 percent. The other is a broader evaluation: 26.92 percent on RoboDojo, 76 percent on RoboTwin 2.0, and only 11.1 percent zero-shot on a new geometry-constrained benchmark they built called LIBERO-GeoMotion, with substantial inference latency noted.

00:26:21 damraDifferent benchmarks, so it isn't one model getting a high and a low on the same exam. The first paper hands the model expert demonstrations summarized as guidance plus contact feedback. The second throws it at tight geometry zero-shot. Together they say touch feedback goes a long way and unfamiliar geometry still breaks it.

00:26:40 lenarThe test from the top of the show is the one I'm carrying out of today: whether the system watching an agent can be edited by the agent. Monzo answered it with a sandbox and a proxy it owns, and OpenAI's answer is in the full text of its disclosure. Lenar Kess.