SCROLL NEWS / DISCOVERY
Search headlines
Find the stories shaping the conversation.
Results for " Anthropic’s " 71 found 🔀 AI-powered Shuffle
Anthropic’s Jack Clark Sees AI Supercharging Science—and Governments Need to Prepare
Jack Clark outlines how AI-assisted scientific discovery could boost research and growth, with safeguards and human oversight.
Did AI find a new gene-editing tool? Why Anthropic’s ‘autonomous discovery’ is sparking controversy
Anthropic’s claim its AI agents discovered an unusual pattern in viral DNA similar to what’s seen in the gene-editing tool CRISPR Cas-9 has set off a wave of skepticism and controversy. Here’s what to know.
Claude launches Google Docs, Sheets, and Slides integration with sidebar and more
If Gemini isn’t quite your cup of tea, Anthropic’s Claude now supports sidebar integration with Google Docs, Sheets, and Slides....
Anthropic expected to IPO despite market uncertainty, AI slowdown calls
Wall Street is gearing up for Anthropic’s expected mega initial public offering, a major test of investors’ appetite for one of the AI industry’s leading firms. Just weeks ago, its CEO called for an industry-wide slowdown over growing safety concerns.
Anthropic tells IPO investors US government actions could hit its business
Anthropic’s IPO prospectus warns that US government actions could harm its commercial customer and partner relationships, Reuters reports.
What Does Anthropic’s Wet Lab Mean for Pharma?
Anthropic has established a wet lab in the San Francisco East Bay. In this article, experts explore what this means for scientific oversight, data ownership, and AI’s potential to transform drug discovery and rare disease research.
What’s In Anthropic’s I.P.O. Filing
The artificial intelligence giant reportedly believes that it’s on track for a record-breaking stock market listing, according to a prospectus seen by Reuters.
Did Anthropic’s A.I. Really Make a Scientific Discovery on Its Own?
An expert at the University of Copenhagen said his team had been sharing its research with the company’s A.I. model, Claude, and that its new finding matched their work.
Federal appeals court rules Pentagon’s blacklist of Anthropic was legal
The Department of Defense can blacklist Anthropic’s artificial intelligence technology, a federal appeals court ruled on Friday. The decision is a blow to Anthropic after it won a separate challenge in a California court in August that partially invalidated the Trump administration’s label.
Novo Nordisk Taps Anthropic’s Claude Science To Speed Up Drug Discovery — Here’s Everything Investors Need To Know
Novo will use Anthropic’s Claude Science in R&D to tackle scientific and drug discovery challenges, aiming to develop new medicines faster.
Novo Nordisk Taps Anthropic’s Claude Science To Speed Up Drug Discovery — Here’s Everything Investors Need To Know
Novo will use Anthropic’s Claude Science in R&D to tackle scientific and drug discovery challenges, aiming to develop new medicines faster.
Trump Rejects Talk of A.I. Regulation and Calls Out Anthropic’s CEO
The president again rejected calls to try to regulate the industry, even as some of its leaders are speaking more openly about the risks of rapidly developing artificial intelligence.
AI Is Running Laps Around Our Political System
The technology is moving faster than elected officials can handle—and Anthropic’s CEO says the industry should slow down.
Demystifying Anthropic's J-Space: A Mathematical Primer
Clarifying the math behind Anthropic’s representation workspace
Anthropic’s Claude Can Now Autonomously Run Science Experiments With Lab Equipment
A new system allows agents to orchestrate complex experimental processes and extends Anthropic's reach into the physical world.
August Monthly Review: ChatGPT In Epic
Death, taxes, another frontier lab healthcare launch inspiring truly insane LinkedIn takes. As much as I want to stay away, I repeatedly am the crewman unplugging his ears whenever we pass these sirens. So it’s not the first and probably not the last time we dial up the typewriter rather than tie ourselves to the mast. The full blow-by-blow timeline: Health API Guy is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Subscribe The End of the Standalone PHR (Jan 8): ChatGPT Health arrives for consumers, at the time powered by b.well. I was bullish on the approach in that the best way for the PHR to finally solve distribution was to meet consumers were they already are (i.e. When Horizontal Meets Healthcare (Jan 9): OpenAI for Healthcare puts out the shingle for enterprises, with SharePoint ingestion standing in for the EHR integration the product actually needed. Another One: Anthropic’s Healthcare Debut (Jan 16): Claude's version, on HealthEx rails instead of b.well, with Agent Skills for FHIR development as the one true vertical-specific investment. ChatGPT for Clinicians: The Trap Sprung (Apr 24): OpenAI filled in the missing GTM middle with a free product for verified clinicians, effectively peeling the enterprise wrapper off ChatGPT for Healthcare and taking the PLG fight directly to OpenEvidence. OpenAI’s Second Attempt at Health (Jul 31): a relaunch of their patient specific app, now branded Health in ChatGPT, where the privacy partition came out, the connectors thinned, and b.well disappeared in favor of first-party FHIR work against a smaller network Beyond a Shingle So what was announced now? Today, we’re introducing a new electronic health record integration that brings authorized patient context from Epic into ChatGPT for Healthcare, along with the Healthcare Public Data plugin for direct, structured access to official healthcare datasets like PubMed, DailyMed, and CMS Coverage. Together, these capabilities bring ChatGPT closer to the systems and sources healthcare teams trust, while supporting the controls and compliance healthcare work requires. The core portfolio is unchanged from prior announcements we delineated and discussed (aside from the relaunch of patient-facing). Since trade publications still seem to be confusing them by using the wrong names: Enterprise sales motion: ChatGPT for Healthcare Protecting their core product / PLG: ChatGPT for Clinicians Consumer/patient-facing product: Health in ChatGPT So really, this is a feature release (specifically for ChatGPT for Healthcare when it comes to EHR integration). What’s cool about it, though, is that we are moving beyond just putting out a shingle. Everything in the January enterprise launch was horizontalizable: Horizontal tech companies thus generally start verticalization with their “putting out a shingle phase”. Telling the world you’re open for business as a horizontal tech company reliably attracts early inbound interest from buyers who are already trying to force-fit horizontal tools into domain-specific workflows and are eager for any signal that the vendor intends to support their use case more directly. Nothing was really vertical specific, which is exactly what you'd expect from the ultimate hypergrowth horizontal company dipping their toes into specific industries. But eventually you have to stop changing the sign on the door and start changing the product. Can you guess where the horizontal product was going to run out of road? This is the core dilemma writ large: verticalization requires differentiation, while scale economics of consumer and horizontal push toward unification. I’ve buried the lede so deep here it will be painful to some readers, but what should be shocking to no one is that my perspective is that EHR integration is the only path that meaningfully resolves this tension for ChatGPT for Healthcare. Integration with Microsoft Sharepoint is fine, but it is categorically insufficient for any product that hopes to influence clinical decision-making. SMART Money Well here we are, eight months later! Real vertical investment has begun, which surprised a few people. One friend in the industry messaged me: “Absolutely no way Epic gave ChatGPT API access.” Looking forensically at the announcements, the integration is notably read-only across appointment notes, laboratory results, medications, and specialist documentation. So while I’m not a betting man, I'd wager the house this is a SMART on FHIR launch: That sounds a lot like USCDI! The recommended path for the workflow they outline would be SMART on FHIR Their Health AI lead’s post is evocative of both standalone and EHR launch SMART, which another termed “ChartGPT” and “EHR Plugin” The implementation burden for SMART on FHIR is the lowest of the available integrative paths This is the team who literally did patient-facing SMART on FHIR with Epic last month Most importantly, if you’re a horizontal company, you generally still haven’t taken the full plunge and thus (overly) value reusability. SMART on FHIR is the logical compromise: healthcare-specific enough to matter, but standardized enough to reuse. Nobody Asked Judy Another friend asked “I thought they had to go to each health system, or did they go direct to Epic and now health systems opt in?” Given the trust paradigms in healthcare, this functionally cannot be Epic hoovering up all the data for OpenAI and shoving ChatGPT in their customers’ faces (or they would revolt). However, I do believe the aforementioned terrible coverage by TechCrunch and other outlets is probably responsible for that question, given the 325 million reference. When using FHIR as a business associate to an Epic customer, there is no EHR gatekeeping to speak of, as that isn’t how fhir.epic.com works: You register an app You pick your APIs You test against the sandbox APIs You list as “Ready for Production” Hospitals pick your app You test and go live with them Bluntly, nobody had to say yes or no in Verona (nor were they given the chance). Vendor Services (their next developer tier up that I doubt OpenAI is using quite yet in this initial release) certainly has contractual paperwork that some resent, but even there, the twin pressures of mounting antitrust and information blocking make outright gatekeeping increasingly fraught. My friend’s sentiment is, to me, representative of a broader industry neurosis, perhaps a sort of scar tissue of prior eras: people assume the gate is still there and never actually try the door. There are certainly other ways Epic (and any EHR) can put its thumb on the scale, but they are not so dumb as to stand in front of a federally mandated API and play bouncer at this exact moment in time. So I think they can and probably should go deeper, as this release is at best parity and at worst behind vertical-specific competition. OpenEvidence did a basic SMART launch with Sutter Health in February, allowing for more convenient evidence search by providers. They then add patient-context (the equivalent SMART on FHIR flow to what we see here) when Cedars-Sinai went enterprise-wide in May. UpToDate has four distinct applications across deeper workflows like patient engagement listed in Epic Showroom and announced a partnership with Epic to power Art at UGM Abridge and other ambient scribes have invested deeply into the deepest bidirectional clinical copilot workflow with integrations well beyond SMART on FHIR. In that light, the “Who is OpenAI primarily targeting here?” is clear. This release brings them into striking distance of OpenEvidence, but not the others quite yet. They are the only one of the three you can reach without building something truly Epic-specific. When will they go further? Competition is a great motivator to overcome the horizontal demons and start building things that can’t be reused: not across industries, not across EHRs, maybe not past the customer you built them for. That’s the price of actually verticalizing. The question is if and when OpenAI will be willing to pay it. Month in Review Here is the monthly review. As a reminder, this is a regular round-up of the month’s posts and other content to surface things you may have missed across regulation, litigation, interoperability, and beyond. AI assistance is used in these bullet summaries so I can focus on articles. Articles Published: None this month Video Content: The Information Exchange: Standards-based Thruple Edition (Aug 14): Back to school for us too, with Brad reporting in from the CMS Health Tech Ecosystem’s one year anniversary in DC. We get into the January CMS-0057 deadline, the fall rulemaking reading list, and why enrollment still keeps most apps off FHIR. The Information Exchange: Epic Dúnadan Edition (Aug 25): A UGM roundup with Ryan Brickner joining for the first time to check our takes against what the building actually thinks. Rangers, Ergo sitting on top of EHI, and a down-market lineup that needs some cuts. Regulatory: Stacked Deck, Bad Hand (Aug 05): ONC’s website refresh took the entire HIT Policy Committee record with it, so I rebuilt the archive and went looking for the regulatory capture story everyone assumes is buried in there. Stacking the deck and winning the hand turn out to be very different things. The Sixth Generation of Patient Access (Aug 19): A quick catalog of the five generations of patient access we’ve layered on since HIPAA, and the two candidates now competing to be the sixth. One is planned. The other is the market routing around the plan entirely. Court cases: Three Cases Walk Into a Docket (Aug 06): Two surprise settlements cleared the board in a single week, and then Judge Maddox dropped ninety-four pages on Vyne v. Henry Schein. The sleeper is a DMCA holding that makes direct-to-database a considerably riskier business model. Epic v. Health Gorilla: Into the MDL (Aug 10): Nine class actions are headed to Miami, and the Panel signaled it wants to bring the case that spawned them along too. That would leave Epic arguing the requests were obviously fraudulent in one courtroom and unknowable in the other. Amazon v. Perplexity: Agents Are Legalized! (Aug 11): The Ninth Circuit vacated the injunction against Comet, holding that the user is the one accessing the servers rather than the company that built the agent. A real win for agentic access, and a much narrower one than the headline suggests. Veeva v. Epic: Come At Me, Bro (Aug 26): Epic’s response brief wants the dismissal affirmed and, unusually, wants the opinion published as precedent. Buried in it is Epic’s own description of what its non-competes actually prohibit, which current and former employees should read closely. EHRs: The Contract Epic Would Never Sign Today (Aug 04): A 1999 SEC exhibit catches Epic licensing nearly its entire product line, Tapestry included, to the company that became TriZetto. The marketing services menu attached to it is the part that will make you blink. Forecast from Verona (Aug 07): The Epic Almanac makes one argument six different ways: the AI is only as good as the networks behind it. Emmie headlines, Art’s context stack is the most ambitious part, and Penny finally gets an autonomous coding date on the calendar. Works With Epic MyChart (Aug 17): Epic’s first new Showroom category since the death of Workshop certifies hardware instead of software, badge on the box and all. Made for iPhone, but for blood pressure cuffs, and rough news for anyone selling the RPM stack sitting in between. UGM Hot Takes 2026 (Aug 20): Everyone else covered the AI announcements, so I went after Savvy deleting the payment gateway, Rangers as Boost with the timer removed, and a down-market lineup with too many entries. Plus the Health Grid tidbits I cannot help myself on. MyChart Central Grows Up (Aug 24): Device data and Emmie turn Epic’s identity hub into a full consumer platform, which is both a logical answer to ChatGPT and a bit of a mistake. It also happens to be the best scraping target Epic has ever shipped. Industry Analysis: The Wrong Yardstick (Aug 12): Vertical software keeps getting judged against Superhuman and Notion, which is the wrong bar entirely. Your user is comparing your product to a whiteboard and forty phone calls before lunch. Primitives vs. Abstractions (Aug 27): Developers want building blocks, systems of record prefer to hand out business logic, and both sides have a real case. There’s no test from the outside that separates the engineering reason from the competitive one, which is why this keeps ending up in court. Cross-industry Comparisons: The Two Kinds of Platform Power (Aug 18): Attention power and record power are different problems that keep getting handed the same regulatory toolkit. Congress’s latest swing at Big Tech shows both what’s possible and where it falls apart. The Dogs of (Platform) War (Aug 21): A CourtListener alert turned up a property management fight with healthcare’s exact shape and none of the Cures Act. Shell prospects, ghost accounts, a notetaker bot that came back to haunt someone, and a lawyer arguing both sides of the same theory in two states. Fractals All the Way Down (Aug 31): Yardi dominates property management right up until you zoom into one segment, where AppFolio owns it outright and Yardi doesn’t appear at all. Where you draw the line around a B2B software market is about to decide an awful lot of cases. Other News: If Judy Had the Courage (Aug 13): A short eulogy for the era when software was allowed to look insane: WinAMP, bold colors, and everything the grey chatbot era has taken from us. External Media: Portland Monthly Health Tech Meetup: Erin O’Brien channeled my own feelings - maybe it’s just because it’s August and beautiful, but the monthly edition of the PDX Health community meetup was a ripper. Make sure to reach out if interested to join for the next one. STAT’s Coverage of Epic This Month: Brittany Trang of STAT did a fantastic job of breaking the FTC investigation that’s been lurking, as well as UGM related coverage, so I had to give her her wish of a meme. Fall Conferences: I’m pumped to be kicking off my fall conference season in NYC at Nabla Accelerate next month, which Chrissy provided the link to apply for. Here’s the rest of my schedule in case you want to meet up: Commonwell (Redwood Shores, CA): Oct 13-14 Open@Epic (Madison, WI): Oct 21-22 eHealthExchange (Austin, TX): Oct 27 Sequoia Project (Austin, TX): Oct 28-29 HLTH (Vegas): Nov 15-18 RSNA (Chicago): Nov 29-Dec 3 Posts I Liked: HTI-6 Should Unbundle API Certification: Josh Mandel makes the case that HTI-6 should split (g)(10) into separate authorization and data certifications, so a PACS or a genomics platform can certify only the role it actually performs. Imaging is the urgent example, but the structure solves a much bigger problem. On implementing ePrior Auth with Epic: Scott Rossignol’s field notes from a live ePrior Auth build on Epic, including the two app registrations nobody warns you about and the CPT mapping problem waiting at the end. CMS-0057 implementations are going to be such a beast. Healthcare point solutions are starting to look a lot like streaming services: Spencer Dorn runs the cable-to-streaming arc against health IT and lands squarely on the bundling half of the cycle. Bundling can be good! It can also be bad! Life is nuanced. Health API Guy is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Subscribe
Anthropic doubles a science benchmark score with Fable 5.1 while OpenAI says its Astra model crosses critical cyber threshold
Anthropic’s Fable 5.1 doubles a science benchmark score, while OpenAI says Astra reaches critical cyber capability and finds zero-day flaws.
Claude can now send emails in Gmail, even without your approval
Anthropic’s Claude AI can now take a huge step in your email inbox, with the ability to not only interact...
Anthropic’s $2 trillion problem: Its underlying business is nowhere near the IPO valuation it wants
Everything you need to know before you reach the office this morning.
AI needs science’s search history
“Actually, that’s where the gold is,” said Alasdair Russell, my graduate school friend who leads a pre-clinical genome editing group at Cambridge. He was talking about the winding road of science that is omitted from published papers. “When you’re discussing how to do the experiment, and why this way is better than another way, and what does the data really mean? I know what it shows, but what does it mean?” At RAAIS 2026, he described how his group has begun logging the twisting path of discovery as it happens, recording the verbal and written exchanges that normally disappear. Ideas become nodes in a living graph: they branch when a meeting produces two plausible experiments, merge when separate lines of evidence converge, and go dark when someone quietly stops pursuing them. In his implementation, one agent scores novelty, while another tries to learn “how scientists think and how they navigate through a complex world of data,” so that high-potential nodes can trigger deeper investigation. As frontier AI labs deploy agents toward scientific discovery, giving AI authentic scientific taste remains a trillion-dollar dilemma. The bottleneck is that the record we have kept for centuries of science might be insufficient to get us there. The experiments that never make the paper Peter Medawar, 1960 Nobel laureate and “father of transplantation,” asked in 1963 whether the scientific paper was a fraud, and answered yes: the form of the paper misrepresents the thinking that produced it. Sixty years on, the diagnosis is unchanged. A paper presents a clean progression from hypothesis to result to conclusion. Lost along the way are the unconventional theories, the abandoned or unaffordable methods, and the underwhelming and inconclusive data. It reads like a browser history with every dead end deleted: the ten open tabs, the four rephrased queries, the wrong turn down a subforum. All scrubbed, leaving one clean path from question to answer as the canonical path. What has changed since then is that the discarded material is now worth something. No high-impact journal wants these artifacts, but raw trial and error is the most likely source of the training data required to develop scientific intuition, or what researchers call taste. Earlier attempts to capture the discards were motivated by scientific integrity. The Journal of Negative Results in Biomedicine launched in 2002 to publish rigorous studies that disputed established models or exposed ineffective treatments. Its archive preserved negative conclusions after they had become papers, rather than the live alternatives and arguments that produced them. BioMed Central closed it in 2017, saying the mission had been served now that other journals publish null results. A less generous reading is that in fifteen years it published around 200 papers because almost nobody wants to read a negative result, let alone write one up when it will not count toward academic tenure. AI models are different. Even an uninteresting negative result can be useful, provided it is labeled. Earlier this year, Anthropic put a version of this to the test. The company pulled 129 decision points from real Claude Code sessions between January and March 2026, showed models the work up to a human detour, and asked what should happen next. Claude Mythos Preview beat the human choice 64% of the time, while Opus 4.5 managed 51%. The comparison was tilted toward the models because Anthropic deliberately chose moments where the human decision had room for improvement. On 127 further scenarios where the human action was already strong, the models improved on it only about 20% of the time. The study was possible because Anthropic’s researchers work inside a tool that logs by default. The reasoning, detours and outcome are produced in the same working environment. Biology has no equivalent. Its reasoning happens in hallways, at benches, in Slack threads and on whiteboards, while the outcome arrives weeks later somewhere else. The record has to emerge from the work itself. Ask scientists to reconstruct it afterwards and we’ll create another polished account. Subscribe A log is not a label Before any of this becomes training data, a negative result has to say what failed. The first paper published in the Journal of Negative Results in Biomedicine examined 234 negative studies across five leading medical journals. Only 30% discussed statistical power, and half clearly defined a primary outcome. Tell a model an experiment failed, without telling it whether the assay was underpowered, a reagent had degraded, or the hypothesis was simply wrong, and it will learn noise with confidence. Decision histories have a second missing label too. When a lab considers five experiments and runs one, only the chosen branch returns an outcome. The other four are experimental counterfactuals. Researchers call this the selective labels problem: the data reveal results only for the actions someone chose to take. Such a record can teach a model to imitate a lab’s taste, but it cannot establish that the taste was good. It also smuggles local constraints into general lessons. A lab that never ran cryo-EM because it did not own a microscope teaches a model that cryo-EM is rarely the right call. Alasdair’s nodes preserve the candidate set, which is the necessary first step. To be useful, each decision record needs six fields: The evidence available at that moment, and nothing that arrived later. The candidates considered, including the ones dismissed in a sentence. For each candidate: the expected result, the confidence attached to it, and the cost in time and money. The route chosen, and the reason. What happened next. How the evidence changed the scientist’s view. These must be timestamped before the outcome, because hindsight turns uncertainty into inevitability. Disagreement also has to survive. Averaging three scientists into one clean rationale reproduces exactly the information loss we are trying to fix. But there is an obvious failure mode here. Once decisions are logged and scored, people log for the record post-facto. Anyone who has watched an electronic lab notebook fill up with retrospective tidying knows how quickly a research tool becomes a compliance exercise. A record that costs a scientist ten minutes of honest reflection per decision is worth far more than one that costs an hour of performance. A publication initiative called Registered Reports offers a useful starting point. Here, researchers submit their rationale, methods and analysis plan for peer review before the data exist, and the journal commits in principle to publish if they follow the approved plan. Nature has now expanded the format across every field it covers. This approach to paper writing timestamps intent before the outcome, but it freezes one plan. A useful search history must also preserve how the plan changed, which alternatives were rejected, when, and on what evidence. Subscribe Run the runner-up Autonomous labs show what happens when decisions and outcomes are connected in a loop. Liverpool’s mobile robotic chemist ran 688 experiments over eight days in a ten-variable formulation space. A batched Bayesian search used each result to choose the next experiments and found photocatalyst mixtures six times more active than the starting formulations. Every completed experiment changed what the system did next. The robot optimized within a goal and search space that humans had already chosen. It did not decide which scientific question mattered. So what this work demonstrates is narrower, but still useful: a search history pays off when the options return standardized outcomes quickly. Open-ended biology is harder because branches can take weeks and the discarded alternatives may never be run. Some exploration therefore has to be bought. Where two branches are plausible and the stakes justify the cost, a lab should sometimes run the runner-up. Otherwise the record captures what today’s scientists usually chose and stays silent on what they systematically overlooked. A funder could ring-fence a small fraction of a grant for the branch not taken, provided that the decision record and the outcome are both deposited. That costs money, but so does having every lab rediscover the same abandoned path. Test whether taste transfers When it comes time to evaluate if our new AI system exhibits taste, the comparison should run across laboratories and fields. At selected decision points in a live research campaign, we should freeze the six fields: the evidence, the candidates, expected results, confidence, costs and the choice made. Experts then rank the options before the outcomes are known, and those outcomes are checked independently later. We could give models one of two diets - papers alone, or papers plus decision histories - and ask them to rank the next experiment on unfamiliar projects. Then, we score information gained per dollar and week, calibration, and how quickly weak branches are abandoned. If histories improve choices only inside the lab that produced them, they amount to useful organizational memory. If they improve choices on unfamiliar problems elsewhere, Alasdair’s group will have captured something science has never managed to write down: a transferable record of taste. Until that happens, a scientific search history is a promising record, not yet a training set.
Some people's chats with Claude AI made publicly available online
Hundreds of conversations with Anthropic’s chatbot were discovered as being publicly accessible.
A trove of users’ seemingly private conversations with Anthropic’s Claude AI chatbot showed up in Google search results
The topics of the seemingly inadvertently exposed chats ranged from erotica, programming chats, work notes, and fake book reviews.
Claude AI Shared Chats Reportedly Exposed in Google Search Results
Anthropic’s Claude share links appeared in public search results, raising fresh privacy concerns for users who shared sensitive conversations.
Anthropic’s life science plans; Novartis CEO on China deals; and more
Welcome back to Endpoints Weekly! We’re gearing up for a busy week of earnings coverage, as Moderna, AstraZeneca, GSK, Bristol Myers Squibb and more are set to report their Q2 ...
Claude’s voice mode is now available for Opus and Sonnet
Now you can chat with Anthropic’s more powerful models.
1Password now lets Claude sign in to websites without seeing your passwords
1Password is launching a new Claude integration for Mac users today. It’s designed to let Anthropic’s AI agent sign in...
Anthropic’s Claude Science app is coming for Kendall Square
Few places on the planet have more at stake in AI’s transformation and disruption of life sciences than the Massachusetts biomedical ecosystem.
Officials cheer Anthropic’s expansion in Manhattan
The maker of the Claude chatbot said it's doubling its New York City workforce this year, growing to about 1,000 employees.
Anthropic’s new Claude feature is quietly selling you on AI
Claude’s new Reflect dashboard doesn’t just visualize how you use AI. It also subtly reinforces how much of your daily work now depends on Anthropic’s chatbot.
Anthropic’s Political Risks Are Real, but OpenAI’s Loom Even Larger
The U.S. government’s targeting of Anthropic might not last—especially if political winds shift.
Claude Cowork expands to mobile and web
Anthropic’s Claude Cowork is now available on web and mobile for Max subscribers. Until now, Cowork largely lived on a user’s laptop. With the update, users can start a task from their desk, get status updates on their phone, and pick up the finished output later — even if their laptop is closed.
Anthropic's new "J-lens" reveals a silent workspace inside Claude that mirrors a leading theory of consciousness
Anthropic’s new Claude research reveals a hidden internal “global workspace” that resembles human conscious processing, raising major questions about AI reasoning, interpretability, safety, and machine consciousness.
The Download: Anthropic launches Claude Science, and California’s carbon manure math
Plus: The US has lifted restrictions on Anthropic’s Mythos and Fable models.
Claude Helped a Hacker Find a Way to Issue Tickets to Almost Every US Music Festival
A researcher found that using Anthropic’s Claude Opus 4.7, he could break into the website of Front Gate—used by every festival from Lollapalooza to Bonnaroo—and freely issue any ticket he chose.
5 takeaways from Anthropic’s big science event
The company rolled out a biology-centric version of Claude and hosted surprisingly forthright conversations about AI and science.
Claude Science is Here, Antibiotics Designed by Text Prompt Among Applications
Basecamp Research’s antibiotic design and vaccine prediction AI models are now available through Anthropic’s Claude Science.
Researchers say Anthropic’s Claude Science will boost drug discovery
Anthropic's Claude Science aims to speed up drug discovery. Northeastern scientists weigh in on what it could mean for their research.
Claude Science is Anthropic’s newest flagship product
The company is doubling down on AI for science.
Anthropic’s Claude Science bets on workflow, not a new model, to win over scientists
Anthropic's Claude Science is a workbench that gives scientists one environment to do computational research, saving them from the need to bounce between databases, pipelines, and tools.
AI models that can take down governments and business months away, rare Five Eyes statement warns
Signal agencies in Australia, the US, the UK, New Zealand and Canada sound alarm after Trump blocks foreign nationals from Anthropic’s Fable AI model
The Market’s AI Fanfare Is Running Into a Harsh Political Reality
Anthropic’s hopes for a blockbuster IPO could depend as much on the ballot box as on investors.
‘He has to find a way to be friends’: The political fight behind Anthropic’s export controls
In an administration that rewards political deference, Anthropic’s CEO has repeatedly clashed with the White House.
Anthropic’s Scare Tactics, SpaceX’s New Deal and Qualcomm’s Bargain | AI & Business for June 16
Plus, SpaceX’s Cursor deal, and AI struggles to replace stenographers
Trump just found the worst way to regulate AI
The real danger in Trump’s crackdown on Anthropic’s newest AI model.
The Fable 5 AI Model Just Went Dark. What the US Government’s Playbook Means for AI Companies—and Every Business Leader
Anthropic’s newest AI went dark just three days after launch. What happened next offers a stark warning for any CEO facing a powerful adversary.
Trump Administration Reignites Its Feud With Anthropic Over Latest A.I. Models
The administration’s surprise restrictions Friday cut off foreign access to Anthropic’s latest models and sparked another round of finger pointing.
AI Advice From Anthropic’s Head Of U.S. Small Business
Anthropic’s Lina Ochman explains how small businesses can use Claude, AI workflows, data connectors and employee-like prompting to save time.
SpaceX’s AI Business Waits for Liftoff | AI & Business for May 26
Plus, California studies AI job losses, Anthropic’s revenue soars and Nvidia plays the role of $5 trillion underdog.
Google is about to release a new Gemini model
Sources say that Google plans to announce a new Gemini model at its annual I/O conference on Tuesday. I’m told the release will land roughly in the class of OpenAI’s recent GPT-5.5. and well short of Anthropic’s Mythos, which, despite not being widely available, has reset how every lab talks about what “leading” means.
Microsoft’s multi-agent AI system tops Anthropic’s Mythos on cybersecurity benchmark
Microsoft's new vulnerability-scanning system, codenamed MDASH, scored 88.45% on the CyberGym benchmark, surpassing single-model systems from Anthropic and OpenAI by using more than 100 specialized AI agents across multiple models.
Anthropic’s latest Claude release turns your Mac into a small business powerhouse
If you run a small business from your Mac, you’ll want to pay attention to Anthropic’s latest Claude release. Claude...
Google thwarts suspected hacker attempt to use AI for large-scale exploitation
Google says it blocked hackers using AI to find a zero-day and bypass 2FA, amid Anthropic’s delayed Mythos launch.
Hantavirus Scramble Tests Trump’s Overhauled CDC
Plus, Anthropic’s Mythos has thrown the White House AI strategy into chaos.
How Anthropic’s Mythos has rewritten Firefox’s approach to cybersecurity
Security researchers at Mozilla say Anthropic's Mythos has unearthed a wealth of high-severity bugs in Firefox.
Claude Mythos AI Model Uncovers 271 Zero-Day Vulnerabilities in Firefox
Anthropic’s latest frontier AI model, Claude Mythos Preview, has identified a staggering 271 zero-day vulnerabilities in Mozilla Firefox marking a seismic shift in AI-powered cybersecurity defense. The findings, addressed in Firefox 150, represent the most significant single batch of security fixes in the browser’s history. The discovery didn’t happen in isolation. Since February 2026, Mozilla’s […]
Mozilla Used Anthropic’s Mythos to Find and Fix 151 Bugs in Firefox
The Firefox team doesn’t think emerging AI capabilities will upend cybersecurity long term, but they warn that software developers are likely in for a rocky transition.
Washington’s Scramble to Get Mythos, Anthropic’s Powerful New Model
Federal agencies have requested access to Claude Mythos Preview, which Anthropic says can rapidly identify — and potentially create — new cyberthreats.
OpenAI’s big Codex update is a direct shot at Anthropic’s Claude Code
Codex can now use your macOS apps on its own.
How Dangerous Is Anthropic’s New AI Model? Its Chief Science Officer Explains.
Just how fast is the future coming at us, and how worried should we be? Last week, Anthropic announced Claude Mythos, the latest in what has become the leading family of AI models. In a very short time, AI models from Anthropic and other companies have become shockingly powerful at coding. But Mythos stands out because it is not just able to write code that can hack into critical systems, like those that run financial companies, but also to analyze systems and find and string together vulnerabilities in them, allowing Mythos to evade even sophisticated defenses. Anthropic chose to release Mythos to only about 40 leading corporations, so they could analyze the model’s capabilities and prepare countermeasures, in an effort it calls Project Glasswing. The release of Mythos—and the prospect of similarly powerful AIs to follow—has caused tremors at the top levels of Washington and corporate America. Jared Kaplan, co-founder and chief science officer at Anthropic, is at the center of this breakthrough. We sat down with Kaplan so he could walk us through just how powerful Mythos is, what the implications are for issues like our personal privacy, and what comes next. This conversation has been edited for length and clarity. Sean Fischer: For the layman, how is Claude Mythos different—more powerful, more dangerous—from previous models released by Anthropic? Jared Kaplan: I think most people have experienced the fact that our computers get faster and faster over time and over many decades. There’s this very smooth trend where every 18 months, maybe, computer processing power doubles. I think AI is on a trend in which it’s improving maybe 10 times faster than that. A lot of the earliest work that I did in AI was around identifying this scaling trend in AI. Claude Mythos is the latest model from Anthropic, but it’s not different because it’s qualitatively different. It’s really just kind of the culmination of the trend of AI models getting smarter and smarter at all kinds of general capabilities, from reasoning to software engineering to scientific research to knowledge work. For us, it was the first model to demonstrate very elite-level cybersecurity capabilities. It wasn’t because we trained Claude Mythos to focus on cybersecurity, but it was because, as a byproduct of its general intelligence and its general ability with software, it’s particularly good at identifying vulnerabilities in software and how to exploit them.
We tested Anthropic’s redesigned Claude Code desktop app and 'Routines' -- here's what enterprises should know
For the enterprise, the Desktop GUI is likely to become the standard for management and review, while the CLI remains the tool for execution.
Coatue Projected $1.995 Trillion Valuation for Anthropic in 2030
In a presentation to prospective investors in January, Coatue offered a rare look at how it justifies Anthropic’s heady valuations. An anonymous source sent me the slides, which give a unique look at how a top investment firm values Anthropic’s business. This is a moment in time when some of America’s most valuable, growing technology companies remain private. In the January presentation, Coatue estimated that Anthropic would lose $14 billion in EBITDA on $18 billion in revenue in 2026, ending the year with $30 billion in annualized run-rate revenue. Most recently, it’s been reported that Anthropic is at $19 billion in ARR, which could suggest Anthropic is already running ahead of Coatue’s expectations. By 2031, Coatue projected that Anthropic would be generating $48 billion in EBITDA profits on $200 billion in revenue, with $224 billion of ARR. That would give Anthropic a 41x forward EBITDA multiple at a projected $1.995 trillion valuation in 2030. Coatue told the investors it was pitching that Anthropic could be worth $2.413 trillion in 2031. In February, Coatue co-led a $30 billion Series G investment in Anthropic that valued it at $380 billion. Coatue declined to comment on this story. I first shared this slide and talked about the presentation on my new podcast, the Cerebral Valley Show, which I host with my CVAI co-hosts Max Child and James Wilsterman. I’ve got a few other slides from the Coatue presentation for Newcomer subscribers.
Anthropic’s Claude can now control your Mac, escalating the fight to build AI agents that actually do work
Anthropic has given Claude the ability to control a Mac, marking a major step in the AI agent race and raising new questions about automation, reliability, and security.
Anthropic’s Claude Code and Cowork can control your computer
Its only a research preview for now.
Anthropic’s Pentagon Battle Matters to Every Business
The designation of the artificial-intelligence company as a supply-chain risk puts every business at the mercy of President Trump’s political priorities.
Anthropic’s Claude AI can respond with charts, diagrams, and other visuals now
Claude can now help you visualize topics as you chat.
Exclusive | ‘Send Us More’: Anthropic’s Claude Sniffs Out Bevy of Bugs
New AI-powered tools are increasingly adept at spotting flaws. Hacking experts worry they will be good at exploiting them, too.
Pentagon Casts Cloud of Doubt Over Anthropic’s AI Business
Anthropic PBC started this year on a winning streak, with surging sales, multiple viral products and a large funding round all giving the startup a big advantage in the costly global AI race.
AI, Ethics and Business Collide in Anthropic’s Standoff with the Pentagon
Marc Ruggiano, director of Darden’s LaCross Institute for Ethical Artificial Intelligence in Business and a navy veteran, shares his insights on the clash between Anthropic and the Pentagon and explains how Darden teaches students to navigate the often choppy waters of business, ethics and technology.
Anthropic’s breakout moment: how Claude won business and shook markets
Start-up’s bet on enterprise users is paying off as coding tools fuel revenue and investor frenzy
Anthropic’s new Cowork tool offers Claude Code without the code
Built into the Claude Desktop app, Cowork lets users designate a specific folder where Claude can read or modify files, with further instructions given through the standard chat interface.
Anthropic joins OpenAI's push into health care with new Claude tools
Anthropic’s new offerings allow users and providers to work with medical data, mimicking similar moves by OpenAI.