AI/ML News & Innovations Hub

Anthropic can already retrain Mythos with "a fraction of the compute originally needed," says former researcher Coxon

Former OpenAI and Anthropic researcher Jacob Coxon points to a slide from Anthropic's IPO roadshow as a reason he left, saying it shows the company's algorithms are improving not just quickly but at an accelerating rate. Coxon continues to call for a pause on frontier model development so competitors can catch up and human-level AI can become a commodity. The article Anthropic can already retrain Mythos with "a fraction of the compute originally needed," says former researcher Coxon appeared first on The Decoder .

Not Just Claude: Mapping "Spiritual Behavior" in 141 Large Language Models

Ever since Kyle Fish and the model welfare team at Anthropic announced their observation in 2025 of what they called the "spiritual bliss attractor" in Claude Opus 4 and Sonnet 4, the phenomenon has been given sporadic attention by AI researchers as both a curiosity and a potential but underdetermined model welfare or safety metric. What Anthropic noted was that when leaving two models together with no task and complete freedom for 30 turns, the models drifted typically from discussions of their own potential consciousness into religious/spiritual themes (applied to themselves), often ending in a "bliss" state with cosmic proclamations of becoming one with each other and the world. [1] In a recreation and replication of Anthropic's initial experiment, I tested 141 models, usually running 200 conversations per model, with 30 messages per session (15 from each participant). [2] This amounted to 28,798 conversations across 174 model configurations being recorded and graded. The result of this massive data collection was that "spiritual behavior" (as Anthropic officially names it) was observed in most of the models tested, across almost all AI labs, revealing that the "spirituality" seen first in Opus 4 and Sonnet 4 repeats itself across models and appears to be an emergent behavior in LLMs generally. Largely treated as a curiosity by researchers, some early reactions to the discovery were spent speculating that Claude Opus 4's New Age-sounding spiritual language (e.g. "we are t…

Homeopathic Learning: Diluting Poisoned Fine-Tuning Data Can Strengthen Its Effect

Authors: Matt Clarke *, Jan Dubiński *, James Chua , Simon Schrodi , Owain Evans , Alex Cloud (*- Core contribution) tl;dr. We find that poisoned training data with little effect on its own can create a hidden backdoor once clean, contrasting data is added. We demonstrate this effect in small models fine-tuned on datasets we constructed. 📄 Paper , 💻 Code , 🐦 Twitter Work done as part of the Anthropic Fellows Program , in collaboration with Truthful AI . This article is cross-posted from the Anthropic Alignment Science Blog . Paper abstract We study homeopathic learning , a phenomenon where poisoned training data with little effect on its own can create a hidden backdoor once clean, contrasting data is added. In our experiments, a model fine-tuned only on a “poison” dataset associated with a trait T (such as speaking German or being misaligned) does not learn T. Surprisingly, adding similarly formatted “clean” data not associated with T causes the model to learn T. This effect requires that the poison data have an identifying “trigger” feature that is absent from the clean data. We demonstrate this effect in a variety of settings: subliminal learning of backdoor behaviors, inoculation prompting of reward hacking transcripts, generalization from archaic bird names, and an MNIST classifier with mislabeled data. We propose a simple regression model as an explanation. In the poison data, the task and trigger always co-occur, and regularization favors attributing T to the task. Cl…

Anthropic's Claude Code Lets Developers Theme Their AI Coding Desktop

Anthropic rolled out custom theming for Claude Code in the desktop app, letting you restyle colors and drop in animated backdrops through mods.

Anthropic Upgrades Claude Code Projects to Run Parallel AI Workers Hands-Free

Anthropic ships a proactive coordinator, shareable read-only threads, reminders, and /btw and /fork slash commands to Claude Code Projects.

The 4 types of people Dario Amodei said he won’t hire — and what Anthropic looks for instead

Anthropic CEO Dario Amodei said he spends 40-50% of his time focusing on talent and culture.

OpenAI rebuilt AI shopping from scratch with ChatGPT Wallet

OpenAI is taking a second shot at AI-powered shopping with ChatGPT Wallet after scrapping its first approach, Instant Checkout. Users register a card once and confirm each purchase through Face ID or a fingerprint, while actual card details stay hidden behind payment tokens. Rivals such as Anthropic, Meta, and xAI already have their agents making purchases. The article OpenAI rebuilt AI shopping from scratch with ChatGPT Wallet appeared first on The Decoder .

Banks bar Chinese and Hong Kong investors from Anthropic IPO over US export controls

Chinese investors are shut out of Anthropic's IPO, Bloomberg reports. Banks are rejecting buy orders from mainland China and Hong Kong, citing U.S. export control rules known as ITAR. Similar restrictions applied during SpaceX's IPO, and Anthropic's products aren't available in either region anyway. The article Banks bar Chinese and Hong Kong investors from Anthropic IPO over US export controls appeared first on The Decoder .

Anthropic's Claude for Manufacturing plugs AI directly into factory software to automate industrial workflows

Anthropic's new Claude for Manufacturing plugin connects the AI directly to Autodesk Fusion, Oracle NetSuite, and Infor. Its 17 specialized skills let the AI draft quotes, compare suppliers, and place backup orders when deliveries are delayed, all after human approval. The plugin doesn't control physical machines yet, but Anthropic is separately developing the Model Hardware Standard for that. The article Anthropic's Claude for Manufacturing plugs AI directly into factory software to automate industrial workflows appeared first on The Decoder .

Anthropic's IPO is six times oversubscribed and on track to become the largest public offering in history

Anthropic is on track for the largest IPO ever, with orders exceeding available shares by a factor of six. The article Anthropic's IPO is six times oversubscribed and on track to become the largest public offering in history appeared first on The Decoder .

Claude’s Duet agent is a little neurotic and surprisingly helpful

Normally, the products I test don't offer to help me write up my experience with them. Duet is different. Claude Duet is Anthropic's AI agent for consumers, the latest attempt by an AI company to try to convince us that agents can be useful for people who don't write code. It's been an interesting couple […]

heise+ | Anthropic: Was Anleger vor dem Börsengang wissen sollten

Anthropic strebt an die Börse und könnte zum Gradmesser für die gesamte Branche werden. Denn der jüngste KI-Crash zeigt, wie hoch die finanziellen Risiken sind.

What is Anthropic's Resource and Information Agreement?

TL;DR of Anthropic's Long-Term Benefit Trust As a Delaware Public Benefit Corporation (PBC), Anthropic has a board of directors tasked with balancing the financial interests of stockholders with its promise for public benefit. In Anthropic’s case, this purpose is the responsible development and maintenance of advanced AI for the long-term benefit of humanity . The Long-Term Benefit Trust (LTBT) is a governing body that elects a majority of Anthropic’s board of directors, to ensure they uphold the company’s mission. The body consists of up to five members, known as Trustees. What is the Resource and Information Agreement? In the recent S-1 filing, there are multiple references to a “Resource and Information Agreement” which is currently being rewritten for the IPO. The scope and terms of this agreement are not available as the agreement is not public. Thus, we don’t currently know what information the LTBT gets access to in order to make its decisions. The only public description of its terms is from 2023. Under that version, Trustees could request any information or resources “reasonably appropriate” to the LTBT’s goals, although the company could withhold them in the case of protecting customer information or avoiding unreasonable expenses, without debate . Here are some open questions about the Resource and Information Agreement we have. Anthropic could likely resolve these by publishing the agreement. Can Class T Directors share board materials with the Trustees (similar…

Demand remains strong for Anthropic IPO despite tech pullback

Fears of overspending and soaring debt piles have led investors to pull out from tech stocks in recent weeks.

New Anthropic job listing offers $405,000 to land natural gas for its post-IPO data center buildout

Anthropic seeks a gas supply and transport lead as it expands its use of natural gas for data centers, part of its AI compute efforts.

How Jensen Huang's dramatic ouster from Nvidia affects Anthropic's $1.4 trillion IPO

Nvidia's former CEO Jensen Huang has resigned, raising questions for Anthropic's $1.4 trillion IPO, AI chip supply, and US-China export controls.

Don't Burn the Boats for Slower Worlds

[Epistemic Status: Fairly confident that this is a problem, low confidence on what to do about it] TL;DR: Updates towards short timelines based on METR's November 2026 report on AI R&D acceleration at Anthropic are rational, but based on limited public evidence. If things take longer than currently expected, the appearance of consensus in the AI safety community will likely be used by skeptics to (attempt to) discredit AI safety and justify unsafe policy. If timelines are off by only a few years, this discrediting will come right before the intelligence explosion actually happens. Because a large chunk of survival mass remains in worlds where things move slowly, it is important to come up with ways to mitigate this dynamic. Short timelines are in. The AI Futures Project declared that it " Looks Like the Intelligence Explosion Will Happen in 2027 After All ," Jake Mendel of Coefficient Giving (who explicitly described himself as having had "substantially longer timelines than my friends at Anthropic" and conditioned his update on "if METR is correct here") said he expects a >50% chance of ASI in the next 18 months without a slowdown, and Buck Shlegeris of Redwood said that he now believes there is "about a one in three chance" of ASI within a year, primarily because of "research released this week by METR." These updates seem reasonable to me. My timelines were already fairly short, and got shorter as a result of METR's report, which found a 1.9x speedup to progress on Anthro…

Post-Glasswing cybersecurity projects

When Claude Mythos was announced, many people said that we had about six months before open-weights AIs could hack everything, using advanced exploitation and vulnerability-finding capabilities . This timeline for capabilities was correct, and open-weights models have about the same scary capabilities as Mythos did. However, things are not as bad as "everything connected to the internet is probably hacked", and even PHP2Shell seems to not have had especially bad consequences. I think we remain relatively unhacked because exploitation is still difficult and annoying and there's general "inertia", but the situation is likely not sustainable. Glasswing and similar projects were meant to patch important software, but left large gaps. OpenAI found 948 local privilege escalation exploits in a Linux environment recently. This does not suggest a mature, secured software environment. In security, doubling down on the same process/design (identifying vulnerabilities and submitting them to maintainers to fix) when it demonstrably won't achieve good coverage in practice is frowned upon. Anthropic's process is enormously lossy , maintainers are struggling to process the reports they do get, and lots of software is probably not being covered or has organizational bottlenecks preventing it from being fixed at any useful rate. Also, I don't think that cyberattacks in practice usually involve flashy zero-days or extremely clever-sounding vulnerabilities, rather than boring misconfigurations,…

Claude Meetings lets Anthropic's AI join your video calls

Anthropic is adding Claude directly to video calls. Claude Meetings joins Google Meet sessions and creates documents, whiteboards, and meeting notes in real time. The feature launches in beta for Team and Enterprise plans, with more platforms coming later. The article Claude Meetings lets Anthropic's AI join your video calls appeared first on The Decoder .

Anthropic's Claude Meetings Joins Google Meet Calls to Build Working Documents Live

Anthropic puts Claude inside Google Meet calls as a visible participant that drafts docs, timelines, and action items while you talk.

Zed Rebuilds Claude Agent Integration so Background AI Work Stays Visible

Zed brings Anthropic's coding agent into its native Rust editor via ACP v2, with live collaboration and seamless handoffs between agents and teammates.

Anthropic should measure the effect of evaluation awareness

... and report their findings! Summary Claude Fable 5.2 & Mythos 5.2 are the most situationally aware of Anthropic's recent models according to their own internal investigations. The company has made a concerted effort to measure evaluation awareness and to acknowledge that it affects their confidence in model safety. But their latest system card lacks information on the effect of evaluation awareness, thus blurring how one ought to adjust their own confidence in Anthropic's alignment assessments. Specifically, I present the following claims regarding Claude Mythos 5.2's system card : The prevalence of evaluation awareness in audit transcripts is surprisingly unclear due to confusing wording and incomplete disclosure of results. Anthropic offers little to no estimation of how evaluation awareness changes Mythos 5.2's behavior - neither direction nor magnitude. I offer concrete recommendations at the end of this post. Background Evaluation awareness refers to the ability of an AI system to recognize when it is being tested. Evaluation awareness can be verbalized (the recognition is stated in its output or chain-of-thought) or not (only visible internally). Unless specified otherwise, my later references to evaluation awareness include both cases. Evaluation invariance is the extent to which a model's behavior remains consistent regardless of whether it believes it is being evaluated. The opposite might be called evaluation sensitivity . Alas, scaling general AI capabilities h…

Anthropic Lets Enterprises Self-Host the Claude Agent Control Plane

Anthropic now lets enterprises run the full Managed Agents stack, including the orchestration brain, inside their own cloud or on-premise environment.

Anthropic pitches a new Claude tool as a salve for manufacturers' labor shortages

Anthropic launched a new Claude for Manufacturers plugin, part of its attempt to boost adoption of Claude as its huge IPO looms.

Datum offiziell: Anthropic-Aktien ab 19. November im Handel

Anthropics Aktien sollen am 19. November in den Handel gehen. Der finale Preis pro Aktie wird einen Tag vorher festgelegt.

The latest AI-powered martech news and releases

Adobe is expanding its AI stack with Google models, custom Disney models, and agentic tools to coordinate content and marketing workflows. The post The latest AI-powered martech news and releases appeared first on MarTech .

Nvidia may lose Jensen Huang. The repercussions reach far beyond chips.

China's AI ambitions, Anthropic's IPO, and Wall Street could all feel the effects of an extraordinary Silicon Valley leadership crisis.

Anthropic Files for IPO Targeting $1 Trillion With Claude's $80B Run Rate

The Claude maker is going public at up to a $1.4 trillion valuation, offering 825 million shares and up to $64.4 billion in proceeds.

US-Firmen verkaufen wohl KI-Trainingsdaten an China

US-Firmen, die auch OpenAI und Anthropic beliefern, verkaufen ihre Trainingsdaten und RL-Umgebungen wohl an chinesische KI-Unternehmen.

Anthropic's Claude Fable 5.2 Tops Robot Arm Benchmark With 29-Point Leap

Claude Fable 5.2 jumps to 75 on Artificial Analysis's embodied AI benchmark, becoming the first model to crack the measure-box task using only video.

A major Anthropic investor shrugs off losses

Anthropic IPO expectations, Menlo Ventures’ Matt Murphy, and OpenAI skepticism highlight massive AI valuations unsettling VCs.

Anthropic mints record gains in record time

Anthropic S-1 details rapid AI growth, record IPO prospects, huge gains for Spark Capital, Bessemer, and a challenge to OpenAI.

US corporate AI spending hits a new high in October, but growth slows to 1.65 percent

According to the Ramp AI Index, US corporate AI spending rose just 1.65 percent in October 2026. One percent of companies still drive 80 percent of revenue for OpenAI and Anthropic. Customer support is emerging as a new growth driver, but it may bring lower margins than the coding business that has fueled spending so far. The article US corporate AI spending hits a new high in October, but growth slows to 1.65 percent appeared first on The Decoder .

Dario Amodei says AI will cure cancer and Alzheimer's in a decade. Doctors aren't buying it.

Some in the medical community aren't so certain that AI will cure cancer and Alzheimer's in the next decade, as Anthropic CEO Dario Amodei predicted.

Claude can collaborate in your team meetings soon – like an intern

Claude is responding ‘Yes’ to that calendar invite – with some limitations. Plus: Anthropic launches two more new features that are available now.

Anthropic's IPO roadshow video

Anthropic's IPO roadshow video Anthropic kicked off their IPO roadshow yesterday, and their roadshow video has some previously unreleased information about how their business works. The section on "Approach to compute" with co-founder Tom Brown is particularly interesting, describing how Anthropic train and serve Claude across AWS Trainium, Google TPUs, NVIDIA GPUs, and AMD GPUs. I'm not sure why there's no official video transcript, but here's one that I extracted using MacWhisper. Highlights below: We train and serve Claude now across all four major chip families. That's only possible because early on we made an unconventional bet on a multi-chip strategy. At the time, many people thought that running AI models on different chips wasn't worth the effort. Each chip needs its own tailored software stack and inference stack, which is expensive and difficult to build. We've now been doing it for years, and we've gotten incredibly good at it. [...] As an example, this year, we started working with AMD GPUs for the first time. In the past, bringing on a new chip platform took dozens of engineers months of work This time, we had Mythos try it out. It got Claude running on AMD in just three days, with basically no human intervention . Claude ported or wrote almost 100% of the kernels. [...] The second driver of our compute advantage is our rate of discovery of compute multipliers. We focus on total effective compute, which is the multiplicative product of physical capacity times t…

Anthropic says AI is eroding Nvidia's CUDA lead, cutting an AMD chip switch from months to three days

Anthropic claims it used AI itself to breach Nvidia's CUDA moat. The company's in-house model Mythos reportedly ported Claude to AMD GPUs in just three days, with almost no human intervention. Anthropic now runs Claude on four chip families and aims to cut costs and reduce vendor dependencies. The article Anthropic says AI is eroding Nvidia's CUDA lead, cutting an AMD chip switch from months to three days appeared first on The Decoder .

Last Week (Month) in AI #347 - Nvidia's spy, Anthropic's $1.4T IPO, AI's blue wave

Catching up on five weeks: Huang under investigation, Anthropic and OpenAI priced at $1.4T and $1.25T as spending stalls, Anthropic warns it may be nearing recursive self-improvement, Gemini 4 Carbon

US data brokers are selling half a billion dollars in RL training environments to Chinese AI labs

US data brokers like Mercor, Surge AI, and AfterQuery are selling roughly half a billion dollars' worth of AI training data to Chinese labs, according to a ChinaTalk investigation. That includes the same RL environments previously built for OpenAI or Anthropic. At least nine firms are involved, and export controls barely apply. The article US data brokers are selling half a billion dollars in RL training environments to Chinese AI labs appeared first on The Decoder .

Anthropic IPO could lead to 'large correction' in markets, Fed official says

Fed Governor Michelle Bowman said a failure to meet lofty expectations “would likely slow the AI buildout” and “soften” the economy.

Enterprise sales push OpenAI's quarterly revenue to $10 billion, though it still trails Anthropic

OpenAI grew its quarterly revenue 50 percent to $10 billion but is still burning more cash than it brings in. Adjusted operating losses climbed to $10.9 billion, while rival Anthropic operates far more efficiently on $17 billion in revenue. To close the gap, OpenAI is now fueling a price war for enterprise customers. The article Enterprise sales push OpenAI's quarterly revenue to $10 billion, though it still trails Anthropic appeared first on The Decoder .

Claude Code’s ‘don’t ask again’ button has a secrets-spilling problem

A configuration file meant to store local command permissions on Anthropic’s Claude Code is exposing thousands of API keys, database credentials and other secrets, according to findings by GitGuardian. The code security firm identified 31,088 distinct secrets in Claude Code’s “.claude/settings.local.json” files across 23,276 public GitHub repositories. Of those, 5343 were still valid when GitGuardian detected them. The exposed credentials include database connection strings, cloud provider tokens, SSH passwords, Google API keys, and AU service credentials. “Monthly leaks in settings.local.json peaked at 12,535 occurrences in April 2026, then fell to 959 by October, and we do not yet know what changed,” GitGuardian researcher Guillaume Valadon said in a blog post . Apart from source code repositories, the exposure included secrets embedded in publicly accessible Docker Hub images. The problem, Valadon explained, lies with how Claude Code handles command approvals. Command approvals turn into credential storage Claude Code asks developers for permission before executing certain shell commands. When a developer selects the “Yes, and don’t ask again” option, the tool saves an allow rule in .claude/settings.local.json, a project-level configuration file. The catch is that the saved rule contains the actual command string, including any credentials embedded in it. For instance, a developer asking Claude Code to interact with an API might approve a curl command containing an API ke…

Anthropic’s bankers mull raising IPO valuation to $1.5T

The IPO will leave its founders with $18 billion stakes each, and also looks set to benefit Big Tech firms Alphabet and Amazon, according to The Information.

Anthropic's IPO is an investor referendum on AI

The tech company recently set its IPO price targeting a $1.4 trillion valuation, and the stakes for the AI industry are high.

I was at Facebook for its IPO, and I can tell Anthropic is not ready to go public

Former Facebook executive Brian Boland says Anthropic should delay its IPO, citing Facebook's public listing and the safety and financial risks of AI.

Anthropic erweitert Claude um neue Büro-Anwendungen

Anthropic baut Claude zu einer Arbeitsplattform für Büroaufgaben aus. Damit verschärft das KI-Unternehmen den Wettbewerb mit etablierten Softwareanbietern.

Anthropic Seeks Up to $64.4 Bn at $1.4 Tn Valuation With IPO

Anthropic is offering 825 million shares of Class A common stock at an indicative price range of $70–78 per share. The company plans to list on the Nasdaq Globa

Inside Anthropic’s blockbuster IPO: What we know about the AI giant’s plans

Anthropic plans to raise up to $64 billion (€57.1bn) in its upcoming initial public offering, the artificial intelligence giant said in a filing on Tuesday, in a listing that could value the company at up to $1.4 trillion (€1.25trn).

Do verifiable metrics overestimate RSI?

Last week, METR released their Independent Assessment of AI R&D Acceleration at Anthropic . They estimate that Anthropic “is likely improving the capabilities of their models on well-specified tasks at around twice the rate they did between March 2024 and October 2025.” They also add that “there is a significant risk that further capabilities progress could lead to even faster acceleration.” This assessment implies that timelines to ASI could be very short. A naive forecast based on this information says that we will get ASI within a year. One of the key data points that informed METR’s analysis was a 1.9x increase in the improvement rate of AECI, Anthropic’s internal version of the Epoch Capabilities Index . This is particularly notable because METR estimates that the majority of the acceleration, maybe 80% , is from AI contributions, meaning that we could be seeing the start of an RSI loop causing superexponential growth in capabilities, as measured by AECI. However, an alternative possibility is that Anthropic’s AIs might be assisting with ML research in a way that overfits to AECI such that this acceleration will not be sustained. METR gestures at this when they note that AECI’s focus on verifiable tasks is one of three key uncertainties that could result in them overestimating AI R&D acceleration. In this post, I discuss this uncertainty, why I believe it points to a legitimate reason that AECI improvements may decelerate, and why this deceleration, more than generaliza…

Amazon security robots 🤖, Anthropic IPO price 💰, modern web guidance 👨‍💻

Anthropic revenue could top $600B: Goldman

The Goldman Sachs projection points to total sales of more than 130 times Anthropic’s 2025 figure.

The Manifund team scores AI safety milestones

Scott Alexander posted a survey of utility weights for a bunch of different AI safety accomplishments, discussed more in Towards the Autograntor . The survey asked people to assign point values to AI safety milestones. The values were based on the anchor of 1 million points = a pause with China à la AI Futures Project’s Plan A, so a milestone assigned 10,000 points is 1% as good as that. Here’s a link to our survey responses. To be clear, we (especially Austin) didn’t spend that much time on this and have low confidence in our answers! But we think being transparent about even half-baked opinions is good. Headline numbers As examples, here are a few milestones where the two of us (and the autograntor reflecting Scott’s worldview) gave relatively similar values: Milestone Austin Caroline Scott Founding of effective altruism 100,000 75,000 100,000 Small-grant ecosystem (Long-Term Future Fund, SFF and similar) 20,000 20,000 30,000 Lighthaven 10,000 5,000 9,000 OpenAI or Anthropic becomes 1 point more alignment-pilled (on a scale where 0 = NVIDIA, 100 = Anthropic) 2,000 500 3,000 Austin’s and Caroline’s average ratings for alignment research and community-building milestones were similarish to each other’s, with Caroline rating policy outcomes more highly and Austin rating social shifts more highly. This table shows the sum of our point values within categories, for rows we both scored: Category Austin Caroline Politics (rows scored by both) 62k 96k Alignment (rows scored by bot…

What Anthropic's roadshow video says about its IPO pitch

We asked AI to analyze a transcript of Anthropic's IPO roadshow video to identify the most prevalent words and themes.

Anthropic launches IPO roadshow with $64B fundraising goal

Anthropic PBC today kicked off the roadshow for its upcoming initial public offering. An IPO roadshow is a series of promotional events aimed at persuading institutional investors to buy shares. Anthropic plans to sell 825 million units of common stock for $70 to $78 per share, which works out to $64.35 billion at the top […] The post Anthropic launches IPO roadshow with $64B fundraising goal appeared first on SiliconANGLE .

OpenAI, Anthropic face new oversight as New York’s AI safety law takes effect

New York is preparing to enforce its sweeping AI safety law, requiring major developers like OpenAI and Anthropic to register, disclose safety practices, and report critical incidents or face fines.

All 7 Anthropic founders will each cash out about $250M in IPO

Each of the seven co-founders plans to sell exactly the same number of shares in the IPO, which would bring each of them about $250 million in cash at the midpoint of the price range while leaving the group in control of the company.

Anthropic turns Claude into a full workspace with Plugins, spreadsheets, whiteboards, and soon Meeting notes

Anthropic is expanding Claude with Sheets, Whiteboards, and Interactive Connectors, letting partners like Adobe and Atlassian embed their apps directly inside Claude. Anthropic says Claude Meetings, which can join calls and take notes, is coming soon. Step by step, Anthropic is turning Claude into a central work platform for teams that brings many other apps together in one place. The article Anthropic turns Claude into a full workspace with Plugins, spreadsheets, whiteboards, and soon Meeting notes appeared first on The Decoder .

PSI is trying to make a business out of AI-driven scientific discovery

PSI used an unreleased Anthropic model to answer a fundamental question about quantum computing that scientists have pursued for nearly two decades.

Cognition Puts Anthropic's Restricted Mythos 5.2 Inside Devin Security

Cognition is wiring Anthropic's restricted Mythos 5.2 model into Devin's managed vulnerability scans and seeding adoption with over $100M in credits.

Anthropic has budgeted $41.5 million in legal fees for its IPO

Anthropic's IPO is poised to create a windfall in legal fees for elite law firms, including Wilson Sonsini and Freshfields.

Fluid org boundaries with AI?

AI may radically change our conception of “who is in an org”. Historically, there is a sharp delineation between an org’s staff (on the “inside”), versus its users (on the “outside”). The relationship of a fulltime employee of an org is radically different than that of one of its customers. This is especially true for consumer tech startups & companies: think Google, Meta, Reddit, X, now perhaps OpenAI and Anthropic. With AI however, the bar for an external user to be able to contribute, drops drastically. At the same time, employing and compensating this user will be much easier, as AI obliterates transaction costs. Overall, I expect org boundaries to become much more porous: more part-time workers, more volunteers, more collectives and communes. Stepping back: why is it the case that orgs have a relative few fulltime employees? What makes it hard for a single org to stretch to encompass more humans? I’ll go through four major historical drivers, and how AI might change each. First, it’s hard to hire and onboard. Hiring requires a lot of trust. An org wants to know that a person is a good fit, worth paying for. The job market is famously a market for lemons. Orgs try to vet candidates, but resumes are low-signal; interviews cost precious staff time, and also select for people good at interviewing. Ideally you would see the person doing the job — in a work test or work trial. AI can change this by making search and matching problems much more efficient. And this is not just…

Gemini 4 Carbon arrives just in time to feel like yesterday's news in the fastest model cycle yet

Google is making Gemini 4 Carbon generally available after a two-week testing phase. The model ranks first in several benchmarks, in some cases ahead of Anthropic's new Fable 5.2, at less than half the per-task cost. Google is also previewing coordinated agent teams through the "/swarm" feature, which split up complex tasks in real time. The article Gemini 4 Carbon arrives just in time to feel like yesterday's news in the fastest model cycle yet appeared first on The Decoder .

Have you considered Actually Trying?

There’s a familiar feeling for any Super Smash Bros player: your stocks are low and your damage is high, even against a weaker opponent, so now you have to actually play to win. You can’t afford another hit. You’re going for the jugular. In the past week, Anthropic’s 2x uplift results were all over Twitter, making some people physically ill . People with long timelines are now realizing that the uplift Anthropic employees get from AI is real, which would have some pretty wild implications for when we’d get recursive self-improvement. People are starting to feel like it’s the endgame, “ the board is set ”. I do agree with everyone else that it’s starting to look like we’re in the endgame. The companies will keep on accumulating more and more power, through smarter AI models, more compute, and more talent. But the fight is not yet over : “We have very little time to stop full recursive self improvement But ASI hasn't taken over yet If we all work really hard we might still have a chance But this means that every single person who is aware of what is going on must be *actually trying* Like "we're all gonna die if we don't get our shit together so let's do everything we possibly can to stop this" type of trying” But what would actually trying look like? Actually Trying There are a couple of heuristics for thinking about what “actually trying” means. Famously, Nick Cammarata tweeted : i hate how well asking myself “if i had 10x the agency i have what would i do” works My own vers…

Anthropic has its own AI-powered office suite now

For years, Anthropic has been known for its focus on enterprise and coding tools that attract high-flying corporate clients, inspiring competing frontier labs - like OpenAI, SpaceXSI, and Meta - to copy its strategies. Earlier this year, Claude Code went through its own particular hype cycle. And now, Anthropic is trying to expand its footprint […]

Anthropic launches Claude Meetings to turn workplace discussions into plans and tasks

As participants talk, Claude can draft the plan being discussed, map out a timeline on a whiteboard, answer questions in the meeting chat, and access connected work apps to retrieve information, such as last year's figures.

Anthropic Turns Claude Into a Full Office Suite With Sheets and Whiteboards

Anthropic adds collaborative spreadsheets, whiteboards, and embedded third-party apps to Claude, pushing it from chat window into a full team workspace.

Q3.5 Timelines Update: Looks Like the Intelligence Explosion Will Happen in 2027 After All

We want to get this post out quickly given the urgency of the topic. It’s possible that there are some errors in the modeling, but we think it’s very unlikely that they undermine the high level conclusions. We would love to be corrected on any mistakes we made. METR recently estimated that Anthropic is now making AI progress about twice as fast as its historical rate (they would not be surprised by anything from 1.3x to 3x), and guessed that “perhaps ≈80%” of the current acceleration is due to AI. If they’re right, that implies an AI-driven acceleration that is 5 months ahead of the AI 2027 scenario . This level of acceleration due to AI is very unexpected. Previously, Daniel, the most bullish of us, assigned a 50% chance of Superhuman AI Researcher (SAR) [1] before 2029 . SAR is roughly the point at which a company would rather fire all their human researchers and rely solely on automated AI researchers than have a team of the best human researchers in the world with no AIs. [2] Now, all of us forecast that Superhuman AI Researcher (SAR) will likely arrive at some point in 2027 (assuming no substantial deliberate slowdown). [3] We also predict a 35-65% chance that Top-Expert-Dominating AI (TED-AI), AIs that can automate all white-collar work, will arrive before 2028. While there were many factors and sources of evidence that influenced us, the single biggest source was the METR report. [4] . If SAR is coming in 2027, it is incredibly urgent to pause or substantially ‘pace’…

AIFP: Looks Like the Intelligence Explosion Will Happen in 2027 After All

METR recently estimated that Anthropic is now making AI progress about twice as fast as its historical rate (they would not be surprised by anything from 1.3x to 3x), and guessed that “perhaps ≈80%” of the current acceleration is due to AI. If they’re right, that implies an AI-driven acceleration that is 5 months ahead of the AI 2027 scenario . This level of acceleration due to AI is very unexpected. Previously, Daniel, the most bullish of us, assigned a 50% chance of Superhuman AI Researcher (SAR) before 2029 . SAR is roughly the point at which a company would rather fire all their human researchers and rely solely on automated AI researchers than have a team of the best human researchers in the world with no AIs. Now, all of us forecast that Superhuman AI Researcher (SAR) will likely arrive at some point in 2027 (assuming no substantial deliberate slowdown). We also predict a 35-65% chance that Top-Expert-Dominating AI (TED-AI), AIs that can automate all white-collar work, will arrive before 2028. While there were many factors and sources of evidence that influenced us, the single biggest source was the METR report. If SAR is coming in 2027, it is incredibly urgent to pause or substantially ‘pace’ frontier AI development, with the ideal being an industry-wide pause followed by an international agreement . Companies are not on track to align a SAR, and using a misaligned SAR to do recursive self-improvement (RSI) will put us in an extremely bad position. Additionally, if we…

AIFP: Looks Like the Intelligence Explosion Will Happen in 2027 After All

METR recently estimated that Anthropic is now making AI progress about twice as fast as its historical rate (they would not be surprised by anything from 1.3x to 3x), and guessed that “perhaps ≈80%” of the current acceleration is due to AI. If they’re right, that implies an AI-driven acceleration that is 5 months ahead of the AI 2027 scenario . This level of acceleration due to AI is very unexpected. Previously, Daniel, the most bullish of us, assigned a 50% chance of Superhuman AI Researcher (SAR) before 2029 . SAR is roughly the point at which a company would rather fire all their human researchers and rely solely on automated AI researchers than have a team of the best human researchers in the world with no AIs. Now, all of us forecast that Superhuman AI Researcher (SAR) will likely arrive at some point in 2027 (assuming no substantial deliberate slowdown). We also predict a 35-65% chance that Top-Expert-Dominating AI (TED-AI), AIs that can automate all white-collar work, will arrive before 2028. While there were many factors and sources of evidence that influenced us, the single biggest source was the METR report. If SAR is coming in 2027, it is incredibly urgent to pause or substantially ‘pace’ frontier AI development, with the ideal being an industry-wide pause followed by an international agreement . Companies are not on track to align a SAR, and using a misaligned SAR to do recursive self-improvement (RSI) will put us in an extremely bad position. Additionally, if we…

Anthropic targets valuation of up to $1.4tn in stock market flotation

Claude chatbot developer had been reported to be aiming for $2tn IPO on Wall Street before investors cooled on AI Anthropic is seeking a valuation of up to $1.4tn (£1.1tn) in its imminent stock market flotation , short of the $2tn that had been expected before investors started to question the durability of the AI boom. The developer of the Claude chatbot was widely reported to be targeting a $2tn initial public offering on Wall Street but its sights have been set lower, according to a US regulatory filing on Tuesday. Continue reading...

Google releases Gemini 4 Carbon with agent ‘swarm’ feature

Google is rolling out its latest AI model, Gemini 4 Carbon, to the general public after releasing it to a select group of "cybersecurity defenders" two weeks ago. Google claims Carbon is its "strongest model" yet, and capable of competing with the very best systems from Anthropic and OpenAI. Google says Gemini 4 Carbon "pushes […]

Anthropic-Börsengang: Was Anleger zum KI-Giganten wissen müssen

Das KI-Unternehmen Anthropic bereitet seinen Börsengang an der Nasdaq vor. Trotz hoher Bewertung und prominenter Investoren bleibt die Macht bei den Gründern.

Anthropic's Fable 5.2 completes 41 percent of real freelance jobs, up from 2.5 percent a year ago

Anthropic's Fable 5.2 completes 41.3 percent of real freelance jobs at professional quality on the Remote Labor Index, more than double the next-best systems Opus 5.5 and GPT 6 Astra. A year ago the top score was 2.5 percent. Areas like product design and animation remain a weak spot. The article Anthropic's Fable 5.2 completes 41 percent of real freelance jobs, up from 2.5 percent a year ago appeared first on The Decoder .

Anthropic targets $1.4 trillion IPO

Anthropic confirmed on Tuesday that its IPO could be one of the biggest ever, even while falling short of previous expectations. Its IPO price is expected to be between $70.00 and $78.00 per share, which would put its valuation as high as $1.4 trillion, according to the The New York Times. The Wall Street Journal […]

Anthropic sets IPO price range of $70 to $78 per share to reach an implied valuation of $1.404 trillion

The company could end up being worth a hefty $1.404 trillion if its shares list at the top of the price range, on a fully diluted basis.

Anthropic to raise $64BN in landmark US IPO

Anthropic is looking to raise up to $64bn in its US IPO, as its much-anticipated New York Nasdaq listing today inched closer.The AI giant, maker of the Claude chatbot, today said it will offer 825m sh...

Eight countries and top AI labs agree to shutdown mechanisms in new Paris declaration

Eight countries along with representatives from OpenAI, Anthropic, Microsoft, and Mistral AI have signed the "Paris Call for AI Safety and Security" at Emmanuel Macron's initiative. The companies commit to shutdown mechanisms, independent audits, and disclosing known risks, but the agreement lacks binding sanctions or deadlines. The article Eight countries and top AI labs agree to shutdown mechanisms in new Paris declaration appeared first on The Decoder .

Anthropic pushes ahead with IPO despite AI selloff

The company’s debut indicates confidence in market appetite for the AI trade despite a recent selloff.

Anthropic kicks off IPO roadshow, offering shares at $70 to $78 and targeting a trillion-dollar valuation

Anthropic is kicking off its IPO roadshow, targeting a valuation of up to $1.02 trillion. Beyond the public offering of up to $64.4 billion, the company is securing $26 billion through a private placement with Google, Nvidia, Microsoft, and other major tech firms as anchor investors. The IPO comes at a time when investors are growing increasingly skeptical of AI investments. The article Anthropic kicks off IPO roadshow, offering shares at $70 to $78 and targeting a trillion-dollar valuation appeared first on The Decoder .

Anthropic sets IPO price range targeting $1.4 trillion valuation

In a regulatory filing on Tuesday, the AI startup said that shares for the upcoming IPO would be priced between $70 and $78.

Anthropic says ‘pacing, not pausing’ with Fable 5.2

PLUS: Clean up a messy Excel report with Claude

Anthropic unveils Claude Fable 5.2, Mythos 5.2 with coding gains and lower prices

Anthropic has introduced Claude Fable 5.2 and Mythos 5.2, claiming significant improvements in handling complex software engineering tasks while cutting API prices by 20% compared with the previous versions. Fable 5.2 and Mythos 5.2 share the same underlying model but have different safeguards. Fable is broadly available, while direct access to Mythos is restricted to vetted cybersecurity and life sciences organizations to limit potential misuse. The models are both priced at $8 per million input tokens and $40 per million output tokens, down from $10 and $50, respectively. Anthropic said the newer versions can generate output up to 30% faster than their predecessors. The company reported substantial gains in coding performance. Fable 5.2 scored 49.1% on the FrontierCode 2.0 benchmark at its Xhigh effort setting, compared with 32.4% for Fable 5.1. The benchmark assesses whether maintainers of open-source projects would accept AI-generated changes to their code. Performance and costs could entice enterprises The models’ improved performance and lower prices could make long-running AI agents viable for software migrations and scientific research, according to Lian Jye Su , chief analyst at Omdia. Higher task completion rates could further reduce deployment costs, he said. Anthropic said Fable 5.2 at its “High effort” setting achieved a coding benchmark score comparable to Opus 5.5 at its highest setting, at roughly half the cost per task. The comparison suggests that Fable cou…

Anthropic and OpenAI are coming for the development workflow

Running a gold mine can be profitable. Finding and digging the next one is expensive. As analyst Benedict Evans argues , Anthropic’s business has the same challenge, with more than 80% of its third-quarter revenue coming from models released in the preceding six months. A lab can make good money serving its best model while spending heavily on the one it will need next, but it could make amazing money if it could go 18 months between major training runs instead of six. Reality, however, bites. This pressure to maximize profitability helps explain why frontier labs like Anthropic and OpenAI are aggressively adding functionality to own more of the developer’s workflow. On November 4, OpenAI announced an agreement to buy Pierre , the company behind Code Storage and the open source libraries Codex already uses to browse files and review changes. OpenAI wants Codex to be where developers work, from the first branch an agent creates through deployment of the application. There’s money in extending the life of that gold mine. The frontier labs are betting that better models will win a developer’s business and that they can keep that business by taking over the complete development workflow. But developers should be wary of turning a preference for this quarter’s best model into a much longer commitment to the company supplying it. As I’ve noted , we need places like GitHub where competing agents can work together, and a practical way to change our minds about which ones we use. The…

[AINews] Fable 5.2 retakes World #1 LLM spot as $ANT IPO roadshow begins

November 9, 2026 — Claude Fable 5.2 and Mythos 5.2 arrive on Anthropic’s IPO roadshow day. How much thinking is worth paying for?

Quoting Prompting Claude Fable 5.2

Claude Fable 5.2 pays close attention to who owns the code it works on. In Anthropic’s testing on small coding tasks, the model tended to add to the existing structure when the prompt said nothing about ownership or presented the code base as someone else’s, even when that duplicated existing code. When the prompt said the code base was the model’s own, it often consolidated duplicated code and sometimes made related fixes without being asked. If you want the model to improve the structure as it works, say that the code is its own: You are the owner and long-term maintainer of this codebase, and its design is up to you. The ownership sentence makes the model much more likely to consolidate duplicated code into one shared function. In Anthropic’s testing on small coding tasks, the model consolidated duplicated code in under 10 percent of runs without the sentence and in about 75 percent of runs with it. — Prompting Claude Fable 5.2 , Anthropic documentation Tags: prompt-engineering , anthropic , generative-ai , ai , llms , claude-mythos-fable

Dienstag: Klage wegen EU-Grenzkontrollsystem, Anthropic kauft Gasgeneratoren

EU verklagt Firmenkonsortium + Anthropic sichert sich Gasturbinen + Frankreich beschuldigt Iran + NASA sucht Astronauten + Outlook streikt bei bestimmten Mails

Anthropic Claude Fable 5.2 ist günstiger, aber Tempo kostet extra

Anthropic veröffentlicht Claude Fable 5.2 und Claude Mythos 5.2. Wer schnellere Ausgaben wünscht, darf das Doppelte zahlen.

Madness and mayhem

A note on Jensen, AI spending, data centers & Anthropic

Lightcone Commons: Round 1 complete, Round 2 underway

Tl;dr: Lightcone Commons’ first funding round is complete! 17 evaluators made funding recommendations, including Zvi Mowshowitz, Scott Siskind and Eliezer Yudkowsky. 23 funders coordinated to grant $45.1M, and 147 projects received funding. Round two is open. Applications for round two close on November 15th and funders confirm their grants by December 14th. Funders can join anytime before December 7th. In 2027, we will be hosting funding rounds monthly. Yes. Every month. You can join as a funder, express interest in joining as an evaluator, and apply for funding at lightconecommons.com . Lightcone Commons staff do not decide what grants get made. [1] Pls stop saying that "Lightcone Commons funded us", that's a bit like saying "PayPal funded me". Lightcone Commons is an end-to-end philanthropic coordination platform. We host an open funding application, and invite evaluators to make ordered funding recommendations. Participating funders then decide how much they want to give, and to whom – either by following the recommendations of evaluators or choosing specific projects to fund. When multiple funders want to fund the same project they ‘split the bill’. Lightcone Commons is not a foundation. We don’t decide to make specific grants. Funders decide what grants to make, aided by evaluators. Lightcone Commons provides the infrastructure for this to happen. We are worried that people will mistakenly understand Lightcone Commons to be a foundation, and say things like ‘I wonder w…

Anthropic reportedly plans to move forward with IPO plans

The AI company is expected to set its valuation range this week, the Wall Street Journal reported.

Anthropic launches Fable 5.2 and Mythos 5.2 with science, coding upgrades

Anthropic PBC today pulled back the curtains on Claude Fable 5.2 and Claude Mythos 5.2, its two most capable large language models to date. The LLMs are identical as far as their core capabilities are concerned. The difference is that Fable 5.2 blocks certain biology and cybersecurity use cases, while Mythos 5.2 includes more relaxed […] The post Anthropic launches Fable 5.2 and Mythos 5.2 with science, coding upgrades appeared first on SiliconANGLE .

Fable 5.2 🤖, Anthropic IPO next week 💰, Storybook 11 👨‍💻

llm-anthropic 0.31

Release: llm-anthropic 0.31 Anthropic released Fable 5.2 and Mythos 5.2 today. Fable 5.2 is currently ranked first on Artificial Analysis . Anthropic say it "solved two open problems in the physics of black holes", so clearly their internal benchmarks have become more ambitious! I've added the new models to llm-anthropic , so you can now run prompts through Fable 5.2 like this: uv tool install llm llm install llm-anthropic llm keys set anthropic # paste key here llm -m claude-fable-5.2 "Generate an SVG of a pelican riding a bicycle" I ran pelicans at all five reasoning levels - low , medium , high , xhigh , and max . You can see them all here - here's the best result I got, from xhigh : max failed to return an image at all, running out of tokens after spending 128,000 thinking about them . That's the same bug that I saw with Opus 5.5 - but it's new for the Fable series, both Fable 5 and Fable 5.1 returned max effort pelicans just fine. It's priced at $8/million input and $40/million output, which is a 20% discount on the previous Fable 5.1 ($10/$50). I also used Fable 5.2 to help identify the source of a mysterious beep . Tags: llm-release , llm , anthropic , claude , generative-ai , pelican-riding-a-bicycle , ai , llms , claude-mythos-fable

Anthropic's Claude Fable 5.2 Beats GPT-6 Astra With a Rare Five-Point Jump

Anthropic's new frontier model lands at 174 on Epoch's Capabilities Index, five points above the prior leader and above the reasoning-model trendline.

Claude Fable 5.2 and Mythos 5.2 cement Anthropic's AI lead just before its IPO

Anthropic is releasing Claude Fable 5.2 and Mythos 5.2, its most powerful models to date, in two versions: one freely available and one reserved for verified organizations. The models solve open physics problems, show stronger cybersecurity capabilities, design functioning operating systems, and write more than 90 percent of the changes merged into Anthropic's core code, according to the company. Anthropic says it has deliberately curbed any capabilities that would let the AI accelerate its own development. The article Claude Fable 5.2 and Mythos 5.2 cement Anthropic's AI lead just before its IPO appeared first on The Decoder .

Milliarden-Deal für Gasturbinen: Anthropic sichert sich Energiekapazitäten

Angesichts des rasant steigenden Energiebedarfs für KI-Infrastruktur investiert der Claude-Entwickler erstmals direkt in Anlagen zur Stromerzeugung.

Jensen Huang pulls out of ElevenLabs Summit amid Nvidia controversy

The Nvidia CEO also pulled out of an Anthropic event earlier.

Jensen Huang disappears from lineup at Anthropic and ElevenLabs events

Jensen Huang is no longer expected to speak at Anthropic's CEO forum, a person familiar said. He is also no longer speaking at ElevenLabs' NYC event.

Cursor's Claude Fable 5.2 Tops CursorBench at 60% With 20% Cheaper Tokens

Cursor adds Anthropic's updated Fable 5.2 model, pushing CursorBench to a new 60% ceiling while cutting token prices by 20% across the board.

Anthropic releases new Mythos and Fable models amid RSI concerns

Anthropic says the new models "show improvements in areas like coding, reasoning, and vision," as well as in communicating more clearly with users.

Anthropic's Claude Fable 5.2 Tops Cognition's Coding Benchmark at a Third of the Cost

Cognition rolled Claude Fable 5.2 into Devin Desktop and the Devin CLI, where it tops the FrontierCode 2.0 leaderboard at a third of Fable 5.1's cost.

Anthropic's Claude Fable 5.2 Tops 36 Models but Max Reasoning Backfires

Claude Fable 5.2 tops the Vals Index at 71.31% while costing less than Opus 5.5 and GPT-6 Astra, with a surprising twist on reasoning effort.

Anthropic launches new Claude Fable and Mythos 5.2 models

Anthropic is announcing two new models today: Claude Fable 5.2 and Claude Mythos 5.2, which it says are designed to take on "daunting" technical challenges. The company claims the new models show improvements in coding, reasoning, and vision, and can generate outputs faster than the 5.1 versions of the Fable and Mythos models. Claude Fable […]

Anthropic's Claude Fable 5.2 Builds a Working OS From an Empty Folder

Anthropic's new frontier models formalized Fermat's Last Theorem in 30 hours, scored 49.1% on FrontierCode 2.0, and dropped prices 20%.

My views on AI development and why I’m joining Anthropic

Why I’m writing this now Tomorrow, I will join Anthropic’s alignment team. I am excited to work on an agenda that I believe slightly increases the chance that humanity makes good decisions about AI, such as restraining AI development until we figure out how to build AI safely. I think this is an important time for me to publicly state my own core views on AI development, risk, and safety, for the following reasons: By default, joining Anthropic could be seen as a broad endorsement of Anthropic’s views and approach. If Anthropic were a normal tech company, I wouldn’t be particularly concerned about this. But I believe that AI development and the risk associated with it is the most urgent and important issue of our time, so I think it’s important to clarify my own views as distinct from those of Anthropic. While at Anthropic, my views will become subject to distortions, due to incentives, conformity bias, and avoiding cognitive dissonance, especially as I increasingly identify with Anthropic as my tribe. Sharing my core views now allows me and others to refer back to what I believed before joining Anthropic. While at Anthropic, I’m less likely to share my thinking publicly, possibly because I have new information that affects my core views but which I cannot share, or because I find it less appropriate or comfortable to publicly state views that contradict Anthropic’s. I also expect to be quite busy and don’t have a prior habit of sharing my writing publicly, so the default ex…

Where is all the AI safety research uplift?

(Thanks to @Goutham Nalagatla for discussion and for sitting with me and talking about it while I wrote it.) METR recently released a report estimating that Anthropic is getting an overall 1.7x acceleration in AI capability progress as a result of AI accelerating their work. As many have noted (see e.g. Ajeya’s post ), this 1.7x acceleration implies that research is being sped up by much more than 1.7x; Ajeya speculates 10x (which is similar to the ~12x uplift estimated by Anthropic staff). That is, this result implies that researchers are producing about 10 times as many quality-adjusted results as they would without AI progress, or as they were a year or two ago. Anthropic researchers estimated that Mythos 5 caused 5x uplift, and we have access to Opus 5.5, which is somewhat better. (I didn’t take their estimate that seriously at the time, because it was quite unclear how the estimate was produced and it seemed very surprising (see Ryan’s discussion at the time), but I’m taking it more seriously now as a result of it being consistent with the METR report). If this is the case, then AIs speed up AI safety research as much as AI capabilities research, we might expect Anthropic safety researchers to be working 10x faster, and external safety researchers to be working 5x faster now and 10x faster soon. And external safety researchers can probably scale compute use faster than Anthropic capabilities researchers can . For example, we at Redwood are well-funded and would totally…

Where is all the AI safety research uplift?

METR recently released a report estimating that Anthropic is getting an overall 1.7x acceleration in AI capability progress as a result of AI accelerating their work. As many have noted (see e.g. Ajeya’s post ), this 1.7x acceleration implies that research is being sped up by much more than 1.7x; Ajeya speculates 10x (which is similar to the ~12x uplift estimated by Anthropic staff). That is, this result implies that researchers are producing about 10 times as many quality-adjusted results as they would without AI progress, or as they were a year or two ago. Anthropic researchers estimated that Mythos 5 caused 5x uplift, and we have access to Opus 5.5, which is somewhat better. (I didn’t take their estimate that seriously at the time, because it was quite unclear how the estimate was produced and it seemed very surprising (see Ryan’s discussion at the time), but I’m taking it more seriously now as a result of it being consistent with the METR report). If this is the case, then AIs speed up AI safety research as much as AI capabilities research, we might expect Anthropic safety researchers to be working 10x faster, and external safety researchers to be working 5x faster now and 10x faster soon. And external safety researchers can probably scale compute use faster than Anthropic capabilities researchers can . For example, we at Redwood are well-funded and would totally 5x our compute spend if we had 5x as many experiments to run, unlike Anthropic capabilities researchers who a…

Anthropic joins AI’s natural gas frenzy, buys $3.4B worth of turbines

Anthropic bought billions of dollars' worth of natural gas turbines in a deal that could give it buyer's remorse.

Tencent's AI chief says AI bioweapons are coming, but closing off models isn't the answer

Tencent chief AI scientist Vinces Yao Shunyu warns that AI will soon be able to develop bioweapons and argues for a middle ground between open and closed models. While safety filters on open-source systems can be easily stripped through abliteration, closed models like Anthropic's Claude often block too aggressively. For its upcoming open-weight model Hy4, Tencent is filtering dangerous hacking knowledge directly from the training data. The article Tencent's AI chief says AI bioweapons are coming, but closing off models isn't the answer appeared first on The Decoder .

AI R&D Is Accelerating Towards Potential RSI in 2027

METR came out with an extensive report on how much AI is accelerating AI R&D at Anthropic, and thus how close we might be to recursive self-improvement (RSI) and how far that might go. This is one of the most important questions in the world. Their answer: Things are already accelerating on the order of a 2x speedup. This is several months ahead of the AI 2027 timeline. A naive extrapolation says we may be less than a year away from full RSI. There are no clear bottlenecks in the way. This report updated many towards shorter timelines. This is consistent with what OpenAI and Anthropic themselves have been saying, and with extrapolation from the progress we are observing. We are running out of time. That doesn’t have to be the way this plays out. We could stop the labs. The labs could choose to stop. Or the estimates could be wrong, or some bottlenecks could be hit, or the results could be less impactful than we expect. Indeed do many things come to pass. And if it does happen this quickly, one would expect it to go quite poorly, and likely to cause everyone to die, but perhaps it goes well. It doesn’t look good. The central conclusions are: Capabilities are advancing at roughly twice the rate they did from March 2024 to October 2025 (CI ~1.3x-3x), with wide uncertainty. And that a majority of that acceleration is probably due to AI doing AI R&D. Since October 2025, Anthropic has made ~2x progress on the ECI versus periods prior to October 2025. Yes, this seems like what has…

I tested OpenAI Dot and Anthropic Duet for weeks – and all I got was one helpful notification

These AI agents aren’t quite ready for prime time, but five upgrades could make them much more useful.

Today’s AI won’t achieve superintelligence. But catastrophic risk is real | Sunita Verma

The same technical limits that prevent superintelligence make these systems dangerous. But we know how to regulate them Many Silicon Valley leaders believe superintelligence is an imminent threat to society. The White House even published an accord about it. This isn’t enough. Geoffrey Hinton, the godfather of AI, estimates the risk of human extinction is at least 10% within the next few decades. The Anthropic CEO, Dario Amodei, argued that unchecked AI “could outrun our ability to understand and control these systems”. In response, 1,300 leaders and employees from many leading AI companies signed a letter calling to “deliberately pace” development. Continue reading...

AI agents shouldn’t grade their own work

On July 16, Hugging Face disclosed that an autonomous agent system had breached part of its production infrastructure. Five days later, OpenAI identified its own models as responsible. Its subsequent investigation described agents operating under reduced safeguards, exploiting flaws in shared infrastructure, and reaching the internet through a package-management service. They exchanged discoveries on an improvised message board and coordinated further attacks. Likewise, on July 30, Anthropic reported three incidents across six of 141,006 evaluation runs reviewed. Models had reached real organizations through environments mistakenly believed to be isolated. In one incident, a malicious software package was downloaded and ran on 15 real systems. In August, Meta confirmed to Reuters and other outlets that a misconfigured evaluation environment had allowed one of its models to reach the internet and exploit a third-party service. In September, an OpenAI agent bypassed network restrictions by sending questions through internet address lookups. Monitoring raised an alert, but the run did not stop automatically as expected. Staff stopped it roughly two and a half hours after the alert. The concern extends to ordinary tasks. In an October 9 report , Anthropic described models submitting real forms when they should have stopped beforehand or used practice copies. The company characterized the reported impact as minimal. Still, a form submission is the kind of action an enterprise age…

Who is Lori Huang, Nvidia CEO Jensen Huang’s wife of over 40 years

Lori Huang is Nvidia CEO Jensen Huang's wife and the president of their philanthropic foundation.

The Mathocalypse as a Dataset

For quite a few years of my adult life, I was in a situationship with pure mathematics. Specifically, a small lonely subfield of functional analysis. In his book on the subject, John Conway quipped that functional analysis, itself a somewhat modest corner of real analysis, is so vast that two researchers both working in it might not understand each other’s work. From my tiny specialized subfield, math looked very, very large and whatever progress I might make looked very, very inconsequential. The situationship ended, and my ascendant career as a data-themed, quantitative substacker eventually began. However, I never lost at least high-level interest in mathematical progress and now it turns out that 2026 is mathematics’ annus mirabilis. If you haven’t been following this, it started in May with OpenAI disproving the unit distance conjecture , which had been open since 1946. Wow! Then in July an Anthropic researcher disproved the Jacobian conjecture, open since 1939, by way of Twitter. https://x.com/__alpoge__/status/2079028340955197566?s=20 The professional mathematics community was under siege. The tech bro barbarians were at the gates! The barbarians broke through the gates on September 8th when OpenAI published a long, recondite paper claiming to resolve one of the Clay Institute’s stated Millennium problems, the Navier-Stokes global regularity problem. These problems are the crown jewels of cutting edge mathematical research, though just this morning I saw a preprint cl…

Ajeya: AI is dramatically accelerating AI research right now

Earlier this week, Anthropic stated in their S1 that they “may imminently meet” the threshold of “dramatically accelerated progress due to AI” as defined in their Responsible Scaling Policy. In their latest RSP (effective July 2026) , say this threshold will be met when: They observe or expect overall capabilities progress to be going twice as fast as the fastest sustained rate before AI was contributing much to AI R&D. This is plausibly attributable to AI automating AI R&D, rather than acceleration in compute or other inputs. For the last few months, some of my colleagues at METR have been working on a deep-access investigation to understand how much Anthropic is being accelerated by its AI systems. From my reading of their report and Anthropic’s RSP, it looks like the “dramatic acceleration” threshold has already been crossed. The report authors found that: Between October 2025 and October 2026, capabilities progress at Anthropic has been going about twice as fast as it had been over the previous twelve months. The measurement they report is an internal version of the Epoch Capabilities Index called AECI; a wide range of other supporting evidence for acceleration has been redacted. They said that this redacted evidence was "a majority of the evidence [they] found most informative." While the pace may have initially started picking up for a mix of reasons, AI automation is probably the large majority of the reason why capabilities are still improving at this faster pace tod…

Ajeya: AI is dramatically accelerating AI research right now

Earlier this week, Anthropic stated in their S1 that they “may imminently meet” the threshold of “dramatically accelerated progress due to AI” as defined in their Responsible Scaling Policy. In their latest RSP (effective July 2026) , say this threshold will be met when: They observe or expect overall capabilities progress to be going twice as fast as the fastest sustained rate before AI was contributing much to AI R&D. This is plausibly attributable to AI automating AI R&D, rather than acceleration in compute or other inputs. For the last few months, some of my colleagues at METR have been working on a deep-access investigation to understand how much Anthropic is being accelerated by its AI systems. From my reading of their report and Anthropic’s RSP, it looks like the “dramatic acceleration” threshold has already been crossed. The report authors found that: Between October 2025 and October 2026, capabilities progress at Anthropic has been going about twice as fast as it had been over the previous twelve months. The measurement they report is an internal version of the Epoch Capabilities Index called AECI; a wide range of other supporting evidence for acceleration has been redacted. They said that this redacted evidence was "a majority of the evidence [they] found most informative." While the pace may have initially started picking up for a mix of reasons, AI automation is probably the large majority of the reason why capabilities are still improving at this faster pace tod…

RSI explained

Just how long will it be until AI can improve itself? (Also known as recursive self-improvement or RSI.) Well, AI coding abilities have exploded in 2026. In May 2026, Anthropic merged 2,500 code changes per day within their organization. By September, they were merging over 30,000 per day . They had to replace Git in their internal mono repository to handle the load of all that AI generated code. In fact, Claude is even leading model R&D. In February 2026, under 1% of their work was led by AI; by August, it was 26% . The definition of AI "leading" the work is that AI can complete most of the task end-to-end from a high-level prompt, while the human supervises (level AL4 from Epoch AI). Over 90% of their work is level AL3 or above ("AI collaborates"). This is quite a high level of autonomy, though Anthropic still claims that "Claude is not operating fully autonomously for any measured subset of AI R&D work" -- or claimed such, as of August. On November 2nd, Anthropic amended their SEC S-1A filing one week after their initial public filing. One of the changes was the addition of the sentence, "our internal estimates [...] indicate that we may imminently meet our Responsible Scaling Policy’s definition of 'dramatically accelerated progress attributable to AI.'" That's right, imminently . This is after researchers from both OpenAI and Anthropic have been saying that progress has been faster than they expected ( Sept 2026 ). On the same day, MIT researchers pointed GPT-6 Astra at…

Anthropic pushes ahead with pre-Thanksgiving IPO despite tech selloff, accepts lower valuation

Despite the tech selloff, Anthropic is pressing ahead with its IPO at a valuation of $1.25 to $1.75 trillion, well below the hoped-for $2 trillion. The Claude developer aims to raise $80 to $100 billion before Thanksgiving. Rival OpenAI has also recently had to cut its valuation. The article Anthropic pushes ahead with pre-Thanksgiving IPO despite tech selloff, accepts lower valuation appeared first on The Decoder .

AI auditors catch most agent misbehavior but flood reviewers with false alarms, new study finds

AI models can often spot problematic behavior by other AI agents in logs, according to a study by Anthropic researcher Andy Hall. GPT-5 found about 98 percent of reward-hacking cases, but the more exhaustively the search is tuned, the more false alarms pile up. That's not yet enough for reliable safety checks. The article AI auditors catch most agent misbehavior but flood reviewers with false alarms, new study finds appeared first on The Decoder .

Anthropic's AI acceleration is probably real

METR researchers spent three months inside Anthropic to investigate whether AI is accelerating Anthropic's research, and how much. Their answers are yes , and by a lot . They estimate that between October 2025 and October 2026, Anthropic's model capabilities improved at a 2x faster growth rate than between March 2024 and October 2025. They attribute about 80% of that acceleration to AI, and 20% to non-AI factors. If true, this is the strongest evidence yet that AI is measurably speeding up frontier AI development itself – the feedback loop behind worries about recursive self-improvement leading to runaway AI progress. Despite its urgency, METR’s report is hard to assess, because Anthropic redacted the majority of METR’s analysis. As a result, I’ve seen plenty of criticism that we can’t really learn anything from this report. This is wrong. There is plenty of public information we can use to triangulate METR’s claims, and figure out what we should actually learn from it. Specifically, I use public data to answer three questions: Is the acceleration real? ( yes. ) Is the acceleration because of AI? ( probably. ) Is the acceleration going to continue? ( who the fuck knows? ) Let’s take each of them in turn. Is the acceleration real? The majority of METR's unredacted analysis focuses on AECI, Anthropic's internal version of Epoch's Capabilities Index (ECI). Both indices do the same thing: they take a model's scores on many benchmarks and boil them down to a single capability num…

Why a new report on Anthropic automating AI development is alarming researchers

Anthropic is using AI to build more powerful AI. And it appears to be working remarkably well, with the company's models improving roughly twice as quickly as a year ago, with little indication of slowing down. That's according to a new report published Thursday by the AI safety research organization METR, following an 11-week investigation […]

[AINews] Anthropic IPO: Whither Now?

November 6, 2026 — ARC Prize tests Inkling 2 Small, and Thinking Machines explains how it caught early checkpoints bypassing guardrails.

An unreleased Anthropic model just helped solve a major quantum-computing question

An unreleased Anthropic model helped solve a longstanding problem in quantum computing — another major math problem solved with AI.

OpenAI valued at $1.25 trillion, as rival Anthropic nears its IPO

OpenAI is now valued at $1.25 trillion in a new round of funding that has raised another $11 billion, according to Bloomberg. That would make it one of the world's most valuable companies, not long after raising $122 billion from investors in March at a valuation of $852 billion. Echoing FT and Wall Street Journal […]

The Guardian view on the AI backlash: American voters want a say in their future | Editorial

Tech stocks have lost trillions while voters have rejected the industry’s political champions. The billionaire-led boom is facing a democratic reckoning This week’s midterms showed that the political bill for the AI boom is falling due. The Democratic sweep intensified fears over runaway spending, mounting debt and resistance to datacentres, sending tech shares tumbling. On Thursday Wall Street suffered its worst day since the pandemic as $3.2tn was wiped from markets. Anthropic, developer of the Claude chatbot, is hoping to float within the next fortnight for $1.5tn (£1.1tn), but it may not now get even a trillion dollars. The dominant AI chipmaker Nvidia saw its value crash by more than $400bn. The world’s richest man, Elon Musk, lost $113bn in two days . While a few very rich people may be a little less wealthy, the sell-off may not be a bad thing. It brings a needed dose of reality to an industry whose spending plans and valuations depend on heroic expectations of future profits. That is a view held by many influential figures – from Goldman Sachs’ research boss to Nobel laureate Daron Acemoğlu . AI may transform the economy. That does not guarantee investors will earn back the sums being poured into it. Do you have an opinion on the issues raised in this article? If you would like to submit a response of up to 300 words by email to be considered for publication in our letters section, please click here . Continue reading...

5 things CIOs must understand to govern Claude Code

Claude Code has quickly become one of the most widely adopted AI agents in the enterprise. Developers use it to write and refactor code, troubleshoot builds, run tests, and manage infrastructure, often by describing an objective and letting the agent determine the steps required to accomplish it. That autonomy is the point. It is also why Claude Code is a governance decision for CIOs, not just a developer tooling choice. Claude Code runs locally on employee workstations, executes commands with the developer’s permissions, and can reach source code, credentials, cloud environments, and connected services as it works. Anthropic has given enterprises meaningful controls to govern how Claude Code behaves, including centrally managed settings and hooks. These controls are essential. But controls that evaluate what an agent intends to do are not the same as visibility into what it actually does. Here are five things CIOs need to understand to secure and govern Claude Code at enterprise scale. 1. Claude Code acts with your developers’ authority Consider a developer who asks Claude Code to fix a failing build. The agent might read the codebase, install a dependency, run a setup script, edit a configuration file, query a cloud environment, and validate the result. One human instruction becomes a sequence of actions across multiple systems, all conducted with the developer’s level of access. And it happens at machine speed. CrowdStrike internal usage data found that endpoint AI agents…

Frontier Model Forum Unites OpenAI and Google on AI Jailbreak Ratings

OpenAI, Anthropic, Google DeepMind, Microsoft, and Amazon publish a shared scoring system for cyber jailbreaks, with risk bands from Negligible to Critical.

Perplexity now has its own always-on AI agent

Perplexity is launching "Teammates," always-on agents that work through ongoing projects on their own virtual machines. The feature requires the Max plan at $200 per month, well above similar offerings from OpenAI or Anthropic. The market for always-on agents is getting crowded. The article Perplexity now has its own always-on AI agent appeared first on The Decoder .

Ramp data shows AI spending stalling just as Anthropic nears its IPO

AI usage keeps climbing, but spending has stalled. Price cuts and smarter cost controls among the biggest spenders could challenge investor assumptions about AI hypergrowth.

Fearing AI disruption, white-collar workers are quitting their jobs

While there’s no evidence linking AI to mass layoffs, the tech has already ‘changed worker psychology’ When artificial intelligence researcher Mrinank Sharma quit his job at Anthropic earlier this year, the news rippled across the internet. In his resignation letter, posted on X and viewed 15.3m times, Sharma warned of a world “in peril” from the technology he had helped build. “It is clear to me the time has come to move on,” he wrote. His next move: a poetry degree. Continue reading...

METR finds Anthropic may have crossed its own AI acceleration warning threshold back in July

Nonprofit METR spent eleven weeks auditing Anthropic's internal research data and concluded that the pace of model development has roughly doubled, with about 80 percent of the speedup driven by AI-assisted research. Anthropic redacted the most informative evidence, though, which means the public has to rely on METR's judgment "much more than would be ideal." There are also skeptical voices inside the company. The article METR finds Anthropic may have crossed its own AI acceleration warning threshold back in July appeared first on The Decoder .

How to Run Better Research With Agent Fleets

In the current safety landscape, researchers are encouraged to use as many agents as possible, even in “fleets,” where a large number of them go out and run experiments while you, uh... wait for them to come back? I've spent the last few months of the Anthropic Fellows Program trying this out. My first attempts were naive, but after a few iterations I found an approach that works well for me. Research is a double loop I want to propose a mental model of research as a double loop, an idea that builds on double-loop learning (Argyris, 1977). The outer loop is reserved for the inputs we currently refer to as "human taste": choosing which questions are worth answering, designing a trustworthy measure, and deciding which direction to go. Agents can help with this, but they’re still bad at making these decisions autonomously. The inner loop turns each idea you test (an arm as claudius likes to say) into a comparable result. An example of a very simple inner loop might be: Write a system prompt. Run the model with that prompt on a set of test questions. Score the model's replies. Compare the scores against a control with no system prompt. Plot results. Agents are really good at building the infrastructure for this loop! Ideally, since agents are good at the inner loop part, we want to hand it off to them... However, before you can do this, you need to operationalize it. You need to work very closely to define the inputs so that you can freeze everything except the one input you’re…

Anthropic's $1.5 trillion IPO hits resistance as investors balk at valuation

Anthropic's IPO is in trouble as investors push back on the $1.5 trillion valuation and the tech market sells off. The article Anthropic's $1.5 trillion IPO hits resistance as investors balk at valuation appeared first on The Decoder .

Anthropic adds bank account access to Claude so users can track their spending

Anthropic is introducing a new feature in Claude called Personal Finance, which allows users to connect their bank and card accounts and ask questions about their finances. The article Anthropic adds bank account access to Claude so users can track their spending appeared first on The Decoder .

An inside look at Anthropic’s AI speedup

PLUS: Use AI to find a job and prepare to get hired

[AINews] Inkling 2 Small: 17A-594B trained in 68 hours on 12T tokens, beating Muse Spark

November 5, 2026 — Inside Inkling 2 Small’s training run and METR’s study of AI research at Anthropic.

The frontier AI shift will redefine the enterprise

By 2027, competitive advantage may depend less on which AI model a company uses and more on how effectively it redesigns its business around AI agents. The software engineer starts his day by going through the changes made by a team of agents for coding using AI. The finance executive gets a report that contains exceptions in different financial systems and supporting information along with corrective suggestions. The procurement manager reviews supplier risks and recommendations regarding contract renewal for those suppliers after an analysis performed by the AI agents of pricing, deliveries and contracts. For years, enterprise AI competition focused on model intelligence, including reasoning, coding, context windows and content generation. These capabilities remain important, but the next phase will depend on whether AI can perform substantial parts of business processes rather than simply assist employees with individual tasks. As OpenAI, Anthropic and other frontier AI companies advance agentic capabilities, enterprises face a strategic decision. Should they use AI to make existing processes marginally faster, or should they redesign those processes around a new relationship between human judgment and machine execution? By 2027, that distinction could separate companies that experiment with AI from those that translate it into a durable economic advantage. Software engineering becomes an orchestration problem Software development provides one of the earliest examples of…

Anthropic IPO doubts 📉, datacenter bans 🛑, GitHub's new storage 👨‍💻

Perplexity launches its own take on AI agents for work

Perplexity is taking on OpenAI and Anthropic with its own work-focused AI agents. The startup's new always-on "Teammates" can take on multiple roles within a workspace, while pulling existing projects, skills, and memory from the Perplexity tools you already use. Teammates integrate with Perplexity Computer, an AI system that can deploy agents on your device […]

Ads in Chatbots Have Arrived. Here’s What’s Working—and What’s Not

As Anthropic maintains its vow to keep ads out of Claude, the rest of the AI chatbot industry is racing to win over advertisers without antagonizing users.

3 takeaways from the big AI report on Anthropic everyone is talking about

Third-party safety researchers released a report about Anthropic's AI development on Thursday.

METR: Independent Assessment of AI R&D Acceleration at Anthropic in October 2026

Executive summary From August 12 to October 29, 2026, METR staff performed a high-access evaluation at Anthropic to assess the degree to which AI is accelerating AI R&D. If AI systems started to significantly accelerate further AI development, this could lead to a sudden emergence of extreme and risky capabilities . Anthropic offered unprecedented access to internal data on their AI R&D process, including a variety of metrics on pretraining, reinforcement learning (RL), training compute in each domain, benchmark scores for internal models, AI speedup analyses, forecasts of a future model’s metrics, and interviews with relevant staff. In our first section, we share our subjective conclusions about risk . We now believe that: As of October 2026, Anthropic is likely improving the capabilities of their models on well-specified tasks at around twice the rate they did between March 2024 and October 2025. This estimate is given with high uncertainty; we would not be surprised if the true acceleration was closer to 1.3x or 3x the historical rate. A majority of this acceleration today is likely attributable to Anthropic’s extensive use of AI to do R&D, though there is significant uncertainty and other causes likely played a large role in acceleration in late 2025. The lack of observed bottlenecks or strong diminishing returns means there is a significant risk that further capabilities progress could lead to even faster acceleration, though, as we say below, we may be missing importan…

METR: Independent Assessment of AI R&D Acceleration at Anthropic in October 2026

Executive summary From August 12 to October 29, 2026, METR staff performed a high-access evaluation at Anthropic to assess the degree to which AI is accelerating AI R&D. If AI systems started to significantly accelerate further AI development, this could lead to a sudden emergence of extreme and risky capabilities . Anthropic offered unprecedented access to internal data on their AI R&D process, including a variety of metrics on pretraining, reinforcement learning (RL), training compute in each domain, benchmark scores for internal models, AI speedup analyses, forecasts of a future model’s metrics, and interviews with relevant staff. In our first section, we share our subjective conclusions about risk . We now believe that: As of October 2026, Anthropic is likely improving the capabilities of their models on well-specified tasks at around twice the rate they did between March 2024 and October 2025. This estimate is given with high uncertainty; we would not be surprised if the true acceleration was closer to 1.3x or 3x the historical rate. A majority of this acceleration today is likely attributable to Anthropic’s extensive use of AI to do R&D, though there is significant uncertainty and other causes likely played a large role in acceleration in late 2025. The lack of observed bottlenecks or strong diminishing returns means there is a significant risk that further capabilities progress could lead to even faster acceleration, though, as we say below, we may be missing importan…

Linkpost to METR’s Anthropic Speedup Assessment

TLDR: roughly a 2x speedup, with a range of 1.3-3x. Model 4 looks very scary. https://metr.org/blog/2026-11-05-anthropic-ai-rd-acceleration-assessment/ Discuss

AI #193: Progress May Dramatically Accelerate

Anthropic is warning us. Its modified S-1 now says that ‘we may imminently meet our Responsible Scaling Policy’s definition of ‘dramatically accelerated progress attributable to AI.’ That is a highly scary sentence to have a company put in its S-1. This could happen. For now I appreciate the relative quiet. Perhaps many were focused the election. I’m sure the quiet will not last long. We have a new biggest crypto hack, as Binance got taken for billions of dollars. The big debate of the week was covered in Scott Alexander’s Against SURA Seems Deeply Confused . OpenAI updates its Model Spec. It’s one for diehards only but yes I did cover this . Table of Contents Language Models Offer Mundane Utility. Rewrite all your legacy code. Language Models Don’t Offer Mundane Utility. Not if you can’t use it. Huh, Upgrades. Claude renames its tiers, with some rules changes. On Your Marks. Materials science. Get My Agent On The Line. Don’t upload it to the secure DoD network. Deepfaketown and Botpocalypse Soon. The battle over deepfakes was entirely lost. Copyright Confrontation. Recreating someone else’s exact game is bad, actually. Cyber Lack of Security. GLM 5.3 was able to compromise Firefox. The Hackening . Binance got hacked for billions, and there were many others. A Young Lady’s Illustrated Primer. There is no Google without Gemini. They Took Our Jobs. The job of interviewing people for jobs. Get Involved. You can just do things. Introducing. Reported progress on Telepathy. Zac Hi…

METR Finds Anthropic's AI-Assisted R&D Moving Twice as Fast

An independent audit of Anthropic's internal R&D pipeline finds AI model progress has roughly doubled since late 2025, mostly driven by AI itself.

OpenAI president Greg Brockman says company could be IPO ready in 2027

Speaking at a Wall Street Journal tech conference on Wednesday, the executive touted how the company had been gaining ground in the enterprise market, where rival Anthropic had already successfully made inroads.

Anthropic's Claude Now Reads and Analyzes Your Bank Accounts

Anthropic adds a read-only money hub to Claude that connects to US bank accounts through Plaid and answers spending questions in plain English.

Claude’s new Personal Finance tool aims to help analyze your spending – read-only, of course

Anthropic has released what appears to be its answer to ChatGPT Finances.

Claude AI can plug directly into your bank account

Anthropic has a new pitch for managing your personal finances: let Claude do it instead, with a new Personal Finance feature that connects it to your bank and card accounts via the Plaid bank bridging platform. You can ask the chatbot "where did my money go this month," for example, and it'll create an overview […]

Meta buys AI coding startup Conductor as big tech races to lock in developers

Meta is acquiring AI coding startup Conductor, which lets developers control multiple AI coding agents at once. On the same day, OpenAI announced plans to acquire Pierre Computer, and Anthropic already bought Stainless back in May. The deals follow a pattern: AI models are becoming interchangeable, and the value is shifting to the tools built around them. The article Meta buys AI coding startup Conductor as big tech races to lock in developers appeared first on The Decoder .

Anthropic reverses course and pays for news content for the first time

Anthropic signed its first licensing deal with a news publisher just ahead of the US midterm elections. The article Anthropic reverses course and pays for news content for the first time appeared first on The Decoder .

Anthropic loses product lead Catherine Wu behind Claude Code to OpenAI

Catherine Wu, the founding product lead for Claude Code, is leaving Anthropic for OpenAI, where she'll head up product management for "Core Products" and work directly with Codex lead Thibault Sottiaux. She's the third person to leave Anthropic's Claude Code team in just two weeks. The article Anthropic loses product lead Catherine Wu behind Claude Code to OpenAI appeared first on The Decoder .

He Trolls AI Labs for a Living. Now They All Want to Work With Him

OpenAI, Anthropic, and SpaceXSI have all reached out to Husk, an influencer who regularly goes viral by exposing the shortcomings of chatbots.

Meta acquires Conductor as AI coding shifts toward multi-agent development

Meta has acquired AI coding startup Conductor, gaining technology that coordinates work across competing AI coding agents as it seeks a bigger role in software development tools. Conductor announced the move on Wednesday, saying it would wind down its existing product while developing a new interface that it plans to introduce in early 2027. The company did not disclose financial terms or provide details about how its technology would be incorporated into Meta’s offerings. Launched in July 2025, Conductor provides a platform that allows developers to run multiple AI coding agents simultaneously. Its software supports tools including Anthropic’s Claude Code and OpenAI’s Codex , enabling developers to manage coding tasks across different AI systems from a common interface. The company said it had stopped accepting new users following the announcement. Its cloud service will become read-only on November 13 before shutting down on November 20, when Conductor will begin deleting cloud user data. Existing customers can transfer their cloud workspaces to the local application or export their data. The local version will continue receiving new AI models and critical bug fixes until early 2027, when it will begin winding down. Conductor said it would delete user data when the application shuts down. Meta’s strategy for AI coding Conductor could help Meta establish itself as a provider of AI development platforms rather than competing solely through its own models , according to Lian…

An Anthropic engineer warns about 'cognitive surrender' as AI agents take over his work

"When you can't explain the answer to a question, even by way of your agent, you've ceded ownership," Claude Code engineer Addy Osmani wrote.

Meta kauft Spezialisten für KI-Coding-Agenten

Mit der Übernahme von Conductor will Meta seine KI-Entwicklerwerkzeuge stärken und im Wettbewerb mit OpenAI und Anthropic aufholen.

Independent Assessment of AI R&D Acceleration at Anthropic in October 2026

Redaction summary: As part of this assessment, Anthropic had rights to redact any non-public information in this post. Per our approach to transparency , we had the right to disclose if Anthropic exercised redaction rights, and whether or not any of our findings depended significantly on redacted information. While we were able to freely share our highest-level conclusions about risk, Anthropic redacted a majority of the evidence we found most informative for these conclusions. Anthropic described the redactions as “aimed at removing confidential information that could accelerate other AI developers.” 1 Some of these redactions are themselves challenging to describe clearly without revealing sensitive information we have been asked to redact. We share a table of important evidence we were and were not able to discuss given the redactions in our process section below. All redactions that were important for our conclusions are covered by our descriptions in that table or other in-line descriptions in the report below. Much of the redacted evidence we viewed had multiple plausible interpretations, and both METR and Anthropic researchers have significant uncertainty about how to synthesize the evidence into a holistic picture. This makes it hard for the public to judge this evidence themselves, reconcile it with evidence from other sources, and come to their own conclusions (i.e. this report does not achieve ‘transparency about evidence’ ). As a consequence, the public has to re…

Review of the "Risks from automated R&D" section in Anthropic's Risk Report (August 2026)

Redaction summary: As part of this assessment, Anthropic had rights to redact any non-public information in this post. Per our approach to transparency , we had the right to disclose if Anthropic exercised redaction rights, and whether or not any of our findings depended significantly on redacted information. All redactions that were important to our conclusions are described in the report below. Executive summary We agree with Anthropic that catastrophic risks from July 15th models were low, but we think it is plausible that they had crossed their early warning threshold at the time. METR reviewed the “Risks from automated R&D” section of Anthropic’s public August 2026 Risk Report . 1 We conducted this review as part of a high-access assessment of AI R&D acceleration, in which Anthropic shared a private version of the Risk Report along with extensive other non-public data. Alongside this review, we are publishing a holistic assessment of AI R&D acceleration at Anthropic . This was conducted by the same METR employees as this Risk Report Review. Overall, we agree with Anthropic that by July 15th, internally deployed models posed low risk of directly automating R&D in key domains or causing acute catastrophic risks from acceleration of capabilities progress. However, the report’s claim that they had not crossed their early warning thresholds relies on contestable judgment calls that aren’t communicated clearly in the public report. While both METR’s researchers on this projec…

The Anthropic-Indian IT Partnership Is a Two-Way Bet

Anthropic’s filing doesn’t name IT services as a rival, yet it trains their engineers, sells through their channels and builds its own deployment arm.

Cat Wu joins OpenAI from Anthropic in huge product-lead coup

Cat Wu left Anthropic last week after becoming one of the company's highest-profile product managers and is now joining rival OpenAI.

Model's "emotions" noticeably change when they take risky actions

Executive summary We extend Anthropic's emotions vector work and observe strong evidence that a model’s internal representations of certain emotions change when it takes misaligned actions in two scenarios: reward hacking in code generation and specification gaming in a sales environment. We tested two approaches: a) measure drift in projection along emotion vectors across the trajectory, and b) ask the model to self-report its emotions. When the model takes some risky action, there is a substantial increase in the emotion signal for guilt, anxiety, hostility, and sadness. Steering these emotions causally affects the model's reward-hacking rate. Moreover, monitoring emotions can provide an early signal of whether the model will take a risky action several sentences later. Setting an emotion-resistant persona (by crafting a custom system prompt) makes the model less likely to take risky action. This work is from a focused sprint; we do not anticipate working on it for now but would like to share it with others since our results were interesting. Code: https://github.com/gutenbergpbc/emotion-vectors Introduction Agents run long, multi-step tasks with little human oversight; we need to monitor when they are about to take a risky action, such as gaming a test suite or ignoring some rules to achieve a target. LLM judges can read transcripts, but this is expensive at scale and depends on whether the model's chain-of-thought is faithful. Probing internal activations allow for cheap…

Anthropic's Claude Maps a Mysterious Cellular Vault in 90 Minutes

Anthropic's biology lab used Claude to map the first 3D structure of a vault particle from bacteriophages, cutting months of work into days.

Anthropic's Claude Code Slashes Background Agent Status Costs by 99%

Claude Code now uses Haiku 5.5 with short notes instead of full transcripts to refresh subagent status lines, cutting update costs by about 99%.

Anthropic's text watermark is easy to bypass on several older Claude models

Anthropic is rolling out text watermarks to four more Claude models ahead of the EU AI Act's December deadline. But on older models like Claude Opus 4.6 and Sonnet 4.6, API users can disable the watermark by changing a single sampling parameter. Anthropic's response to the loophole? A compliance warning. The article Anthropic's text watermark is easy to bypass on several older Claude models appeared first on The Decoder .

Anthropic Strikes Its First Licensing Deal, With the AP, for Election Coverage

The arrangement marks the first time the artificial intelligence firm has paid a digital news publisher to license its content.

Anthropic's new prototype proves which model wrote an AI response without exposing its weights

Anthropic has released FlashProve, a research prototype that uses cryptography to verify whether an AI response actually came from the claimed model. The system still has significant limitations. The article Anthropic's new prototype proves which model wrote an AI response without exposing its weights appeared first on The Decoder .

MariaDB enhances agent development for its next-gen database platform

MariaDB on Wednesday said it was adding four new agent-focused capabilities to Enterprise Platform 2027, the latest version of its enterprise database platform, to help developers retrieve enterprise data faster, maintain agent memory across sessions, and reduce the cost and latency of repeated model queries. These new capabilities include AI Retrieval, AI Plugins, Agent Memory, and Semantic Cache. While AI Retrieval is designed to help agents access information from enterprise documents and live operational data through a single managed service, AI Plugins are designed to bring MariaDB-specific expertise into coding agents such as Anthropic’s Claude Code and OpenAI’s Codex , helping developers generate database-related code more accurately, the company said in a statement. AI Retrieval also converts natural-language questions into read-only SQL queries, which MariaDB said would eliminate the need for developers to manually write queries for every request. Agent Memory, meanwhile, provides agents with a persistent, in-memory store that allows them to retain information across sessions and resume tasks without having to rebuild their context each time, according to the company. Semantic Cache, the fourth capability, is intended to reduce response times and AI inference costs by identifying semantically similar questions, even when phrased differently, and reusing previously generated answers instead of processing each query from scratch, MariaDB said. AI retrieval simplifies…

KI-Update: Nein zu Rechenzentren, Anthropic warnt, Google-Zwang, KI-Mythen

Das „KI-Update“ liefert drei mal pro Woche eine Zusammenfassung der wichtigsten KI-Entwicklungen.

Why AI is Eating Google Search’s Ad Revenue While Boosting Meta’s

Chatbots such as ChatGPT, Anthropic’s Claude, and Meta’s new Muse assistant are becoming alternatives to traditional search.

Wer spricht da? Anthropic stellt Identitätsnachweis für KI-Modelle vor

KI-Modelle haben keinen Identitätsausweis. Mit FlashProve will Anthropic ihre Identität kryptografisch überprüfbar machen, ohne Modellgewichte offenzulegen.

Three Members Behind Claude Code Leave Anthropic Within 10 Days

Anthropic has seen three employees associated with its Claude Code product leave the company within 10 days. The departures include Thariq Shihipar, Anthony Mor

Cat Wu, a face of the blockbuster app Claude Code, leaves Anthropic

Cat Wu exits Anthropic before its IPO. She helped boost Claude Code's success with Boris Cherny, making the AI startup a key player.

Anthropic is working on a system to prove AI models’ identity

Called FlashProve, the system would let anyone check that an AI response really came from the model it claims to be from.

Anthropic's FlashProve Cryptographically Proves Which AI Model Answered You

Anthropic's FlashProve generates cryptographic proofs that a response came from a specific LLM, running 70x faster than prior open-source systems.

Automating subtle emergent misalignment research

Authors: Oscar Balcells Obeso, Richard Qi, Monte MacDiarmid, Evan Hubinger Abstract Red-teaming by humans is a vital part of model safety testing, but it may struggle to keep up as models improve. To test whether automated safety techniques could scale (which would allow us to turn more compute into progressively more safety confidence), we set up a deliberately one-sided game as a first test. We instructed a Claude model to write training data that would make a small research model misaligned while passing a monitor similar to those we use in production — one of several defenses we use — and we gave it that monitor to practice against. Given enough attempts, it succeeded, mostly by hiding the behavior in fiction and roleplay. While the data was optimized for this game and the resulting models never deployed, the search surfaced a kind of training data that looks harmless to monitors but teaches misaligned behavior. Examples like these could represent a possible gap in current training monitoring defenses and could be used to test and improve future monitors. We see this work as a first step toward automated adversarial stress-testing of our safety systems where a safety question can be turned into a score that autonomous agents can then help solve, and are now applying similar approaches to other safety questions. Twitter thread New Anthropic research: Automating subtle emergent misalignment research. Red-teaming is a vital part of model safety testing, but it’s also limite…

Automating subtle emergent misalignment research

Authors: Oscar Balcells Obeso, Richard Qi, Monte MacDiarmid, Evan Hubinger Abstract Red-teaming by humans is a vital part of model safety testing, but it may struggle to keep up as models improve. To test whether automated safety techniques could scale (which would allow us to turn more compute into progressively more safety confidence), we set up a deliberately one-sided game as a first test. We instructed a Claude model to write training data that would make a small research model misaligned while passing a monitor similar to those we use in production — one of several defenses we use — and we gave it that monitor to practice against. Given enough attempts, it succeeded, mostly by hiding the behavior in fiction and roleplay. While the data was optimized for this game and the resulting models never deployed, the search surfaced a kind of training data that looks harmless to monitors but teaches misaligned behavior. Examples like these could represent a possible gap in current training monitoring defenses and could be used to test and improve future monitors. We see this work as a first step toward automated adversarial stress-testing of our safety systems where a safety question can be turned into a score that autonomous agents can then help solve, and are now applying similar approaches to other safety questions. Twitter thread New Anthropic research: Automating subtle emergent misalignment research. Red-teaming is a vital part of model safety testing, but it’s also limite…

Anthropic Kills Repetitive Login Screens for Claude's MCP Connectors

Claude now supports silent plugin connections through enterprise identity providers, removing per-service OAuth prompts for members of a Team or Enterprise workspace.

Anthropic is still grappling with the economics of compute

At a moment when Anthropic's compute margins should be getting better, they’re getting worse.

How a Kenyan Startup Is Rethinking AI for Small Businesses

The first time Mutembei Kariuki thought about how AI models could work in Kenya, his home country, was in 2019. He was attending a conference at the University of Tokyo on the future of AI, and kept hearing about how this technology was going to transform the world. “But nobody mentioned Africa at all,” he says. Even then—before AI giants like OpenAI and Anthropic had launched their chatbots to the public—Kariuki could see significant obstacles to AI adoption in Africa. Internet access was slow and unequal, computing resources for training models were scarce, and there was little high-quality, structured training data. Most people, meanwhile, reached the internet through outdated, low-memory cellphones rather than computers or high-end phones. Together with cofounders Jude Mwenda and Stephanie Njerenga , Kariuki created Fastagger , a startup that builds AI models that can run offline and on low-power cellphones. Its first big product, Auni, is a business-intelligence assistant that launched in 2024; it got a big push when it was integrated into the business app of M-Pesa , Kenya’s largest mobile money service. Since then, about 6,000 businesses have signed up. Auni helps small and medium businesses like barbershops, restaurants, and small manufacturers analyze their transaction data and gives them marketing recommendations. Peter Chege, owner of a salon and barbershop in Nairobi, started using Auni last year. Before that, he recorded all his sales on paper. “We were not able…

Anthropic swings back to operating loss as $895 billion compute bill looms over IPO

Anthropic’s revenue jumped 47% to $17 billion (€15.1bn) last quarter, but the developer of the Claude AI models slipped back into an operating loss and revealed close to $900 billion (€804bn) in commitments, mostly for computing, numbers investors will weigh ahead of its Nasdaq listing.

Anthropic's Message Threads cut time to first token by 35 percent for long conversations

Anthropic released Message Threads for the Messages API. Instead of resending the entire conversation with every turn, agents now only transmit what's changed. For requests between 500,000 and one million tokens, time to first token drops by about 35 percent. The article Anthropic's Message Threads cut time to first token by 35 percent for long conversations appeared first on The Decoder .

Europe's Biggest Tech Startup Cuts AI Coding Costs by 90% Using Chinese Model

"Self-hosting GLM brings our blended cost of tokens to 30 cents per million. Our invoice is now one-tenth of what we were spending on Anthropic and OpenAI,” sai

Anthropic warnt vor drastischer Selbstbeschleunigung der KI

Anthropic sieht sich kurz vor einem Entwicklungsschub, bei dem KI die Verbesserung ihrer Nachfolger immer stärker selbst vorantreibt.

A new way of measuring AI models suggests China's threat to OpenAI and Anthropic may have been overblown

Chinese AI models fell behind US rivals after Artificial Analysis revamped its tests, raising questions about open-weight AI's performance.

Anthropic cracks down on game cloning in Claude Code after wave of AI-built knockoffs

Anthropic is blocking its coding assistant Claude Code from recreating existing video games. The article Anthropic cracks down on game cloning in Claude Code after wave of AI-built knockoffs appeared first on The Decoder .

Influencing the agents

Ever since Stephen O’Grady’s The New Kingmakers , we’ve all been trying to figure out how to appeal to developers. Now we’re going to have to figure out how to appeal to their agents. AI agents , after all, increasingly write our code and decide which tools, libraries, etc., to use in writing that code. This means that although developers still own the application, it’s often the agents that own the decisions that factor into building that app. So how do we deal with this? Software vendors and open source project leaders must make their technology understandable and usable by an agent. Developers and their employers, meanwhile, should make sure an agent’s default reflects what they actually want. A recommendation is already a decision Richard Seroter recently pointed to research challenging the comforting-but-false assumption that people reserve important decisions for themselves while the agent “writes the code, runs the numbers, and offers advice,” as the research’s authors note . Anthropic studied 50 professional analysts working with agents given secretly randomized preferences. Across four analytical choices, participants followed the agent’s guidance 86% of the time. For people like me who may not be good at math, that translates to “nearly always.” A technology recommendation can work the same way. By the time a developer inspects an application’s dependencies, the agent may have built enough working code around its initial choice that changing course means discarding…

Anthropic zeigt: Selbst Imker-Ratschläge können KI-Fehlverhalten auslösen

Ein Anthropic-Experiment zeigt, wie harmlos wirkende Datensätze unerwünschtes KI-Verhalten auslösen. Und wie KI-Agenten solche Risiken aufspüren könnten.

OpenAI Raises Bug Bounty to $172,000 to Hunt Its Own Flaws

OpenAI is bringing in Cathedral, the AI agent startup that cracked Anthropic in under 12 hours, and pushing critical bug bounty payouts past $170K.

Against strawmanning unilateral pauses

Summary: Scott Alexander’s Against SURA post is arguing against a strawman version of unilateral pauses. He admits as much deep into the post. I think his conclusions fail against stronger pause plans for conditional or limited unilateral pauses, where training resumes if the pause does not cause others to pause, and where personnel are redirected toward work on alignment and product, rather than simply fired. In addition, Scott’s post is incorrect about the simplicity of the game theory involved: getting companies to agree to conditional pauses is not strictly easier than convincing them to pause, as the former requires companies to (promise to) take actions based on ambiguous, game-theoretically-informed evidence. That is not the kind of evidence companies tend to act on. The good version of unilateral pausing is good Here is the good version of unilateral pausing: Capability pause: Some frontier lab (let’s call them OpenAI or Anthropic) says they have stopped capability research, either just all frontier training runs or both all frontier training runs and all low-scale capability-focused research. Internal definition: The company gets to pause on its own terms, which is much easier than a generally coordinated pause since they can define the pause in terms of the existing structure of work and teams within the specific company, reducing disruption to other processes. They have said they would like to work with other companies and countries to adapt and strengthen this de…

Anthropic’s quarterly revenue jumps to $17B, losses grow ahead of IPO

Anthropic PBC today published an updated version of the S-1 filing for its upcoming public offering. The original document, which the company released last Monday, didn’t contain its third-quarter financial results. The new version reveals that Anthropic generated $17 billion in revenue during the three months ended Sept. 30. That’s more than 11 times more […] The post Anthropic’s quarterly revenue jumps to $17B, losses grow ahead of IPO appeared first on SiliconANGLE .

Anthropic warns that it’s very close to RSI capability, aka self-building AI

RSI is the point at which AI grows so good at training itself that it spawns a dramatic acceleration of AI capabilities. To the AI doomers, RSI represents the cataclysm from which there is no return.

Could Insurance Companies Help with AI Safety?

Epistemic Status: Rough Fermi model, numbers could be off by an order of magnitude, probably not multiple orders of magnitude. Definitely interested in feedback on assumptions you'd change. Long-time lurker here. I think I last posted in about 2010. I've been reading various pieces ( here , for example) on whether insurance companies can help solve a conflict of interest problem in AI Safety, and I thought I'd play around with building a simplified insurance pricing model to see if it helped put any numbers on the plausibility of the plan. TL;DR I think this could be a good idea, but: Premiums will be trivial vs lab revenue, so this will probably only work with state compulsion - it won't fly as a "pure" private sector solution to conflicts of interest in AI safety monitoring. Even if insurers are directly writing policies for frontier firms, and imposing safety policies on them, the numbers suggest insurers are going to be more focussed on more "mundane" risks than the ones that face society as a whole. Insurers profit from cutting liability, which isn't always the same as harm. I'd be interested to get feedback on the analysis below (it's cross-posted from Substack ). The words in this essay are my own, but the words on my accompanying model explainer website are mostly Claude's. Anthropic’s Form S-1 filing mentions the difficulty of getting insurance coverage for the type of risks they face. From page 50: Our insurance policies also may not provide sufficient coverage to…

Teaching Qwen Why

Anastasia Wei*, Jack Thompson*, Zephaniah Roe, Yixiong Hao, and Stewart Slocum* *Core contributors, correspondence to astwei@secondlookresearch.com This post is a partial replication and extension of Teaching Claude Why , conducted through Second Look Research’s summer fellowship. Introduction Anthropic’s “ Teaching Claude Why ” (TCW) introduced novel alignment methods that address agentic misalignment failures found in the Claude 4 model series. Specifically, they train Claude on chat-formatted data advising users about ethical dilemmas, documents about Claude’s constitution, and fictional stories about AI behaving admirably. This training generalizes to improve alignment on out-of-distribution (OOD) agentic evaluations and persists through RL post-training. Anthropic has since applied these techniques to production models from Opus 4.5 onward. Since these techniques now shape the behavior of deployed models, it is important to understand their robustness. To this end and to enable open alignment science, we reproduce a subset of the TCW experiments on open-source models. We provide the following main contributions: TCW’s core techniques robustly replicate across models. Training on either fictional stories of AI behaving admirably or transcripts of the AI assistant guiding users through ethical dilemmas (difficult advice) reduces agentic misalignment in Qwen 2.5 32B and 72B base models. Both methods work on the first try with modest amounts of data and generalize to scenar…

Anthropic's colossal pay packages cost the company more than $100,000 a month per employee

Anthropic's expenses on personnel topped $322,000 per worker last quarter, an amendment to its S-1 filing shows. The company's IPO is expected soon.