The AI industry pumps out so much hype that you’d be forgiven for simply shrugging at the recent release and sudden withdrawal of Anthropic’s Fable 5 model. Set against the Elon Musk vs. Sam Altman trial and Anthropic locking antlers with the Department of War, the Fable episode could read like just another week in AI.

This one is worth paying attention to. This is really the first time the government has stepped in to regulate a specific model release on the grounds that its capabilities could pose a national security risk in the wrong hands. Whatever happens next, the line has been drawn: a frontier model in general release can be taken off the board because Washington decides it’s too dangerous to leave widely available.

For anyone building AI into their daily work, that shifts the calculation in a real way. The intelligence available to you isn’t only a function of price anymore. It’s also a function of policy, geography, the terms you’re willing to accept on your data, and whether the vendor or the government leaves the model running at all.

The story behind the freeze

For readers who don’t track model releases closely, here’s the short version. Fable 5 is the first generally available model in what the company is calling its “Mythos-class” models, a tier above Opus that Anthropic says has crossed a meaningful risk threshold in cybersecurity and biology. Fable 5 is the consumer-safe version, built on the same underlying Mythos 5 model but wrapped in extra guardrails designed to block or downgrade certain cyber, biology, chemistry, and model-development queries. It also jumps Anthropic’s core model number, signaling a generational step forward from Opus 4.8, Sonnet 4.6, and Haiku 4.5.

Then, on June 12, three days after launch, the government ordered Anthropic to block Fable 5 and Mythos 5 from every foreign national, including foreign-national employees working inside the United States. Anthropic said it could not reliably enforce that distinction and disabled both models globally. The trigger, by most accounts, was a suspected jailbreak that punched through Fable’s cybersecurity guardrails. Anthropic disputed the severity of the finding, saying the demonstration uncovered only minor, previously known vulnerabilities that other public models could also identify.

That fight is still going on. Cybersecurity leaders have urged the government to reverse the order, arguing that defenders need access to the same capabilities and that comparable tools are already available from American and Chinese competitors. Anthropic is working to get Fable back online, and rival labs will almost certainly ship something comparable in short order (some are already claiming to have done so).

The specific dispute may resolve in days or weeks. The precedent will outlast it. A model can be released, integrated into workflows, and then disappear because a government draws a line around who may use it. For anyone building around a single model or vendor (and “building” might simply be leveraging it in crucial, strategic use cases), availability is now part of the risk calculation.

What people saw before the lights went out

Early users got just enough time with Fable 5 to confirm Anthropic’s claims about it. Despite controversies over how Anthropic chose to limit how Fable 5 deals with queries the company deems risky (more on that in a minute), users are seeing the power of the model. Fable 5 is designed for agentic work, meaning it can work autonomously on tasks for a long time, sometimes hours or days, without losing context. The advice that came out of those early sessions was consistent: stop using frontier AI like a fancy autocomplete. The best way to use it, many say, is not to ask it to perform straightforward in-and-out tasks like writing an essay or telling you the best parts of a lengthy report, but to give it broader goals about what you’re trying to achieve, let it build the plan, then execute, however long it takes.

That window was short, but it counted. It showed this level of intelligence is no longer a slide in a research deck. The model was pulled back, but the capability threshold remains crossed.

A big part of what makes Fable work is that it grades its own homework. If you’re a regular user of Anthropic’s models, you’ll notice there’s no “Thinking” mode for Fable 5. That’s because adaptive thinking is always on: The model decides when and how much to reason on every request, and at higher effort levels it can reflect on and validate its own work. Tasks turn into loops. As it works to achieve the goal, it can try things, evaluate the results, change course as needed, and try again. And it can do so autonomously.

For media and marketing teams, the practical shift is in scope. Instead of, say, assigning it to design a specific email campaign, or help format your newsletter, you can zoom out and tell Fable 5 to conceive and build an entire marketing strategy around your newsletter. That might involve reformatting your templates, building new landing pages, adjusting the publishing schedule, building a social campaign, and more. Theoretically, with the right access, it could then build all of that for you. Your job is to grade the output. Over time, less of that grading happens mid-process and more of it happens at the end.

That’s the promise anyway. The danger is that organizations may begin designing around that promise before access, cost, and governance are stable enough to support it.

Fable 5 is the first model that puts real agency on the table. Right now, working with agents, while powerful, involves a lot of management: ensuring the plan the agent builds is correct, clearing up barriers that it encounters as it performs the task, and then guiding it to the best output, usually through multiple iterations on the task itself. In theory, a model strong enough to evaluate its own intermediate work shouldn’t need that hand-holding.

That gap between theory and practice is the real story of the freeze. For a few days, users could test a different relationship with AI; then the capability vanished. We crossed the threshold in the lab and lost it in the market on the same week.

The three walls between you and frontier intelligence

Fable 5 and the models that will follow it stand to change how we work with AI, and arguably how we work, full stop. However, using Fable 5 to its full potential was never just a matter of selecting it in your model picker or calling the API and letting it cook. The pullback put a sharper point on a problem that was already there: the most capable models are also the hardest to actually deploy. I see three walls in the way, with a fourth that just got built.

1. Access and context. For an organization to use Fable 5 to its full potential, it would require a large amount of access to the right context (the org’s information and data). Here, Fable’s strength tripped over itself. Because Anthropic fears the model could be misused, it requires prompts and outputs from Mythos-class models to be retained for at least 30 days for safety monitoring, including in enterprise environments that would otherwise use zero data retention. Anthropic says the data will not be used to train models and that, on some third-party platforms, it remains inside the customer’s cloud environment. But companies cannot use Fable 5 under a true zero-retention arrangement.
   
   That retention requirement, plus the restricted categories where Fable 5 quietly throttles down to Opus 4.8, has set off real friction with enterprise buyers. Many companies will be reluctant to cede control over how their own data is retained and reviewed. Microsoft reportedly limited employee access while its legal teams assessed the implications for confidential and customer data.
   
   And on top of all that sits the new wall. Even if a company accepts the privacy terms, secures the integrations, and builds the right internal controls, the model can still disappear because of a government order or vendor decision. Serious agentic systems will need fallback models, portability across vendors, and a plan for what happens when the most capable model is suddenly unavailable.
2. Compute. Fable 5 is not cheap. Anthropic priced it at $10 per million input tokens and $50 per million output tokens, twice the price of Opus 4.8. I’ve written before about how the agent era is squeezing compute budgets at every layer, and with AI hardening into a political wedge issue, expect compute pressure to stay tight for months and probably years.
   
   The premium price doesn’t automatically kill the math. Some early users argued that it could solve hard tasks in fewer turns than weaker models, potentially lowering the total cost of completing the work. Still, that argument only holds if the work was worth doing with a frontier model in the first place.
   
   If Fable 5 and its peers are going to act as the brains at the top of a company’s AI stack, the deployment question is going to need actual rigor. Organizations will need to be very selective of how to deploy it: which tasks to assign to it, who should have access, and what guidelines, rules, and restrictions there need to be on usage.
   
   And there’s an awkward irony in talking about allocation right now. Intelligence can be technically achievable and commercially valuable while still being unavailable.
3. Task imagination. I became aware of the term “task imagination” through the AI Daily Brief podcast, which references a video by the AI strategist Nate B. Jones. In his take on the Fable 5 release, Jones makes the simple observation that not many knowledge workers think about their work in terms of tasks that might take days to do. It requires a certain level of strategic thinking that may not actually apply to many roles. Put bluntly: a model can run for two days, but most workers have never been asked to define a goal worth two days of machine effort.
   
   For media practitioners, that’s the part worth sitting with. An editor might call on the model to develop more granular editorial guidelines and style guides based on different article types (news, features, evergreen explainers, etc.). Reporters might build investigative agents that don’t just surface data in document troves, but develop research plans based on leads and then execute on them by mining remote databases, filing FOIA requests, and other complex touchpoints that typically require human involvement.
   
   The catch is that most jobs aren’t scoped that way. Many jobs have narrow definitions of what the work is, and there’s little motivation to go beyond that. A model that can do days of work isn’t very useful if the work it’s given is still measured in minutes. That puts pressure on workers to imagine more ambitious tasks or risk being left behind.

The paradox of a pause

The Fable 5 pause comes wrapped in a paradox. A pause gives organizations time to build the governance, data practices, and strategic habits needed to use this level of intelligence responsibly. The trouble is, task imagination only develops with hands on the model. Without access, people cannot discover which long-running assignments are worth the money, where agents fail, or how their own roles could expand around them. The pause buys time while taking away the main way to use that time well.

Step back, and a clearer picture forms. A future where we’re working alongside agents will encounter serious barriers beyond just capability (and political freak-outs over that capability). We restrict access to context so neither the tool nor its creators knows too much. We limit how much we spend on models because we’re unsure of the return we’ll get. And many of us throttle our ambition with AI since our jobs simply don’t have a rich enough canvas for a model like Fable 5 to fill in.

A fourth restriction now sits on top of those three: the model itself may simply not be available.

For media leaders trying to make the ROI case, that’s a problem. The strongest demonstrations depend on giving capable models real work, real context, and enough time to execute. When the most capable models are pricey, hemmed in, or suddenly absent, teams drift to safer pilots that are easier to approve and unlikely to move the underlying economics.

None of these walls fall just because someone ships a smarter model. While advancements in security, infrastructure, and work redefinition will help us get past them, those are inherently slower than the rapid advancement of AI.

We pushed past one threshold and walked straight into several walls. I suspect the story of Fable 5 will be looked back on not primarily as a step up in power, but as the moment where the implications of that power pushed the limits of the systems meant to use it. Agentic AI is clearly where this is going. The systems around it need a beat to catch up.

The pause is useful, but it isn’t free. Experimentation is how organizations learn what this intelligence is actually for. For now, AI leaders are about to discover that running frontier AI at full strength is harder than proving the strength exists. The pure experimentation phase is over. The reality check phase has started, and access, cost, control, and utility now matter every bit as much as raw intelligence does.

A version of this column appears in Fast Company.

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The free-spending era for AI inside corporate America is ending.

According to The Wall Street Journal, executives at companies including Uber, Meta, Microsoft, Salesforce and DoorDash have launched cost-cutting campaigns this year after seeing their AI bills double or triple—or blow through annual budgets in just three months. The culprits: the soaring price of tokens, the basic unit of AI computing, as model providers like OpenAI and Anthropic seek to balance supply and demand.

The result is a notable shift in corporate AI strategy. Where last year the goal was to flood the organization with AI tools and encourage experimentation, leaders are now scrambling to ration access, steer workers toward cheaper homegrown alternatives, and sharpen employee skills to wring better returns from the technology.

“The free-money period for AI is definitively over,” said one senior technology executive at a major financial firm, speaking on condition of anonymity to discuss internal cost pressures.

The cooling, if it holds, could complicate the growth trajectories of AI heavyweights racing toward public listings. Anthropic closed a $65 billion funding round this week valuing the startup at $965 billion, while OpenAI is also moving toward a potential IPO. AI critics have pointed to corporate cost-management efforts as evidence that the ultrafast pace of AI expansion may be unsustainable.

Budgets burned in months

Corporate spending on AI took off in 2024 and 2025 as companies encouraged broad experimentation, eager to signal to Wall Street that they wouldn’t be left behind in the disruption wave. But many enterprises underestimated how quickly costs would accumulate—particularly as employees without specialized training sent inefficient prompts, ran excessive queries, or used premium-tier models for simple tasks that could have been handled by cheaper, internally built tools.

Some companies burned through their entire annual AI budget in the first quarter. Others saw line items in technology budgets that previously seemed large enough suddenly look inadequate. The problem was compounded for organizations that signed multi-year contracts with AI providers before understanding their actual usage patterns.

“Most companies didn’t have visibility into what AI was actually costing them on a per-team or per-use basis,” said an AI strategy consultant who works with Fortune 500 firms. “They just saw a giant bill at the end of the quarter.”

The rationing begins

At Uber, Meta, Microsoft, Salesforce and DoorDash, technical executives have implemented some combination of the same playbook: tiered access to AI tools based on role and need, mandatory efficiency reviews for high-cost teams, and investment in internal AI infrastructure that costs less per query than commercial models.

Some companies have quietly restricted access to certain premium AI features for non-technical employees. Others have introduced internal dashboards that show employees the real-time cost of their AI queries—designed to encourage more efficient prompting habits.

The shift mirrors what happened in cloud computing’s early years, when companies initially over-provisioned infrastructure before learning to optimize.

The IPO problem

The corporate reckoning comes at a delicate moment for the AI industry. Both Anthropic and OpenAI are navigating toward public markets, and institutional investors are watching corporate AI spending closely for signs that the technology is generating sustainable returns—or that the boom could go bust.

If major corporate customers begin to pull back on AI spending or demand better pricing terms, it could affect the revenue projections that underpin those anticipated listings. Anthropic’s $965 billion valuation, for context, represents a multiple that assumes continued rapid growth in enterprise demand.

AI critics say the cost backlash was inevitable. Proponents counter that efficiency improvements and competition among AI providers will eventually bring down prices—and that early-stage overspend is normal for transformative technologies.

For now, the corporate AI spendometer is being watched more carefully than ever.

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The National Security Agency is using Anthropic's Mythos AI model despite the Pentagon placing the company on a restricted list, according to multiple media reports published Sunday and Monday.

The NSA's adoption of Mythos—Anthropic's next-generation model known for its advanced coding and autonomous task execution capabilities—puts the intelligence agency at odds with Defense Department officials who have flagged Anthropic as a "supply chain risk" and pushed for phasing out its technology across federal systems. The Information was first to report that the NSA has continued using the model even as the Pentagon's restrictions remain in place.

The dispute between Anthropic and the Defense Department stems from the company's refusal to allow unrestricted use of its AI models in sensitive military and surveillance contexts. Anthropic has resisted deployments involving autonomous weapons and mass surveillance capabilities, leading to the breakdown in relations that prompted the DoD's supply chain designation, according to reports.

For national security agencies, the appeal of advanced AI systems like Mythos appears to outweigh the restrictions. Cybersecurity experts believe such models can identify vulnerabilities and enhance defensive operations—priorities that have kept the NSA turning to Anthropic despite the broader federal backlash. The deployment of Mythos has already drawn scrutiny from other governments; earlier this month, UK and US financial regulators held emergency meetings regarding the model's cybersecurity implications.

The situation illustrates a growing challenge for governments worldwide: balancing rapid AI adoption against security, ethical, and regulatory concerns. As AI systems become more capable and integral to defense infrastructure, policymakers face difficult trade-offs between operational effectiveness and risk management.

The NSA's continued use of Anthropic's technology marks a notable fracture in the otherwise coordinated federal response to the company. While the Pentagon has moved to restrict Anthropic's products from wider defense procurement, intelligence community operations appear to have carved out their own path.

The controversy is unlikely to subside. Congressional scrutiny of the NSA's decision is expected, and technology policy advocates say the episode underscores how quickly AI capabilities are outpacing the regulatory frameworks meant to govern them.

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Anthropic has closed the subscription loophole that made Claude the default engine for the open-source AI agent community.

Starting April 4, 2026 at 12:00 PM PT, users of Claude’s Pro and Max subscription tiers can no longer pipe their plan’s usage through third-party frameworks like OpenClaw, according to Axios. The change effectively ends what The Next Web called “a quiet subsidy” that powered automated agents for browsing, email, workflow automation, and other high-volume tasks at flat-rate pricing. It’s the latest move by a major AI provider tightening access as the agent ecosystem matures.

The move sends a clear message about how Anthropic views its own product boundaries. Claude subscriptions were designed for individual conversational use, not for piping high-volume API-like traffic through agent tools that automate dozens of tasks simultaneously. Developers and power users who had relied on subscriptions to avoid pay-as-you-go API costs now face a significant price increase to keep using Claude through OpenClaw and similar frameworks. For publishers, it’s another reminder that the economics of AI are shifting fast — and that the ground rules around access and licensing are far from settled.

The fallout has been swift. Reddit’s r/ClaudeAI community erupted with hundreds of comments, and r/AI_Agents saw threads debating whether the change reflects a broader industry shift. OpenClaw’s official documentation confirms the exact cutoff timestamp, and PCMag and Hongkiat both reported on the change within hours. TechCrunch noted that Claude Pro subscribers would now need to pay extra to use Anthropic’s models with third-party tools.

For the open-source AI agent ecosystem, the change raises a fundamental question about sustainability. Tools like OpenClaw depend on access to powerful models to deliver their value proposition — autonomous browsing, multi-step reasoning, agent-based workflows. If providers cut off subscription access and force pay-as-you-go API pricing, the economics of running those agents at scale become much more uncertain. The broader picture — who defines the agent workplace and on what terms — is still being written.

The timing is notable. Anthropic’s decision comes as the AI agent space is moving from experiment to infrastructure — and as the company pursues its own enterprise and coding-focused products. Making subscription access exclusive to Claude’s own interfaces could be a way to protect Anthropic’s direct product experience while monetizing the third-party developer ecosystem through API revenue. The company is far from alone in this strategy: Microsoft has been repositioning Copilot as an agentic work platform, and the White House AI blueprint has signaled that the federal government is unlikely to intervene on behalf of smaller players.

The company has been shipping its own agentic tools at a blistering pace in 2026. In recent months alone, Anthropic launched Claude Cowork, an agentic productivity tool; Dispatch, a mobile feature that lets users assign Claude tasks from their phone; Channels, which connects Claude Code to Telegram and Discord for persistent messaging; and Computer Use, which gives Claude desktop-level control over clicking, scrolling, and navigating applications. Channels, in particular, looks a lot like what OpenClaw has been offering — and arrived in the same month Anthropic pulled the subscription rug out from under its competitor.

For users of tools like OpenClaw, the practical impact is straightforward: switch to Anthropic’s API with proper billing, or find alternative model providers that still allow subscription-based access through third-party frameworks.

Whether this signals a broader trend — with OpenAI and Google following suit — remains to be seen. But for now, the quiet days of subscription-subsidized AI agent tooling are over.

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Anthropic accidentally published the source code for Claude Code this morning — 512,000 lines of TypeScript, exposed for hours before anyone pulled it.

Source maps can reconstruct the full, readable source from compiled code. By 4:23 am ET, a developer at Solayer Labs had spotted it, posted a direct download link on X, and the codebase was being mirrored to GitHub and analyzed by thousands of developers across the industry.

Anthropic confirmed the incident to VentureBeat: “Earlier today, a Claude Code release included some internal source code. No sensitive customer data or credentials were involved or exposed. This was a release packaging issue caused by human error, not a security breach. We’re rolling out measures to prevent this from happening again.”

That framing — human error, not a breach — is accurate but undersells the competitive exposure. Claude Code is reportedly generating $2.5 billion in annualized recurring revenue, has more than doubled since the start of the year, and competes directly with Cursor, GitHub Copilot, and a fast-moving field of agentic coding tools. The leaked code gave competitors a detailed look at exactly how Anthropic solved some of the hardest engineering problems in that space.

Among the details developers surfaced: a three-layer memory architecture that keeps AI agents coherent across long coding sessions by maintaining a lightweight index of pointers rather than storing everything; an “autoDream” background process that consolidates the agent’s memory while the user is idle; 44 hidden feature flags; and internal model codenames including Capybara (a Claude 4.6 variant), Fennec (Opus 4.6), and an unreleased model called Numbat. The code also showed internal performance metrics: version 8 of Capybara had a 29-30% false claims rate, a regression from version 4’s 16.7%.

The most discussed detail was what developers called “Undercover Mode” — a system that lets Claude Code contribute to public open-source repositories without disclosing that the commits came from an AI. The leak is the second high-profile setback for Anthropic in recent weeks, following its lawsuit against the Pentagon over the company’s AI safety limits.

The underlying story here isn’t the leak itself — packaging errors happen. It’s that one slip exposed enough proprietary engineering detail that competitors now have a working map of how one of the most commercially successful AI coding tools actually functions. For a company that’s staked its position on responsible AI development and technical differentiation, that’s the real cost.

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At the best of times, it’s tough to separate AI news from AI hype. But the latest rush around agents, triggered when a plethora of developers went on holiday benders with Claude Code, feels like a real shift. Between the viral freakout over Moltbook, the agent social network, and the Super Bowl ad slap fight between OpenAI and Anthropic, AI has jumped to a new tier of mainstream attention.

Talk of the “AI bubble” has basically evaporated, replaced by the industry’s favorite new term: the AI “inflection point.” That’s said to be the moment when AI in general, and agents in particular, start swallowing big chunks of knowledge work—with consequences that spill into the economy, hiring, and how entire companies function. If you want a tell for how seriously this is being taken, look no further than the recent SaaS sell-off.

For journalists, this kind of noise has a familiar side effect. Mix relentless AI coverage with the steady drumbeat of layoffs in media, and you get the same old pressure wearing a new outfit: do more. When newsrooms shrink and AI tools get pitched as productivity machines, it’s easy to conclude the “right” response is higher output.

But AI isn’t only changing how stories get produced; it’s changing how stories get discovered. So the urge to use AI to do “more with less”—which, in practice, often means publishing the same kinds of pieces faster and more frequently—aims straight at the wrong target.

That’s because of a contradiction in how AI systems surface information. They’re trained to recognize sameness, to spot patterns and reinforce what they’ve seen before. Yet they don’t actually reward repetition. Having the right amount of uniqueness can be the difference between being cited in an AI summary and being ambient background noise. Competent rewrites of the same commodity story are a dime a dozen; AI goes looking for the one that both looks authoritative and adds something new.

More isn’t more

It almost goes without saying you can use AI to accelerate production. You can cover more stories than you used to, and some newsrooms are already leaning into that. On a personal level, churning out more might even read as “value” to a manager—at least in the short term. But if your piece is effectively a twin of reporting that’s already out there, an AI engine has no special reason to surface yours.

The better path is to invest in the parts of journalism that don’t scale cleanly: uncovering new information through sourcing, research, interviews, and analysis. So the instinct to do more isn’t wrong—it’s just misdirected. The “more” that matters is depth, not width.

AI can still help here, acting as an accelerant for ideation, research, and even some of the logistical grunt work, like organizing outreach to sources. A digital media researcher, Nick Hagar, recently demonstrated what that looks like in practice, using coding agents to recreate a deep analysis from a human-authored journalistic investigation on Virginia police decertifications.

What stood out in his case study wasn’t that the agents replaced the work, but that they compressed parts of it—especially when paired with very specific tools, such as Claude Code “skills,” which essentially turn certain research tasks into templates. Even then, the process stayed human-led. “He wrote: “”Even with skills enforcing a structured workflow, I made dozens of judgment calls…. Skills make the workflow more systematic; they don’t eliminate the need for human attention,” he wrote.

That’s the mental model journalists should steal. The goal isn’t to flood the zone with more stories. The goal is to produce work so valuable—and so definitive—that AI search engines can’t casually ignore it without being wrong or incomplete.

Authority over output

To win in this environment, journalists will need to break one deeply ingrained habit: the reflex to cover more. Most reporters already feel behind on their beat, and shrinking newsrooms mean less backup, fewer editors, and fewer chances to specialize. This isn’t an argument for ignoring breaking news. It’s an argument for a shift from reaction to discernment—deciding what actually deserves your attention, and what doesn’t. In a lot of cases, that means narrowing a beat into a micro-beat (say, from “energy” to “nuclear power”).

In a way, the ecosystem is already nudging people into this. As reporters get laid off or strike out on their own, many are migrating to Substack and Beehiiv and hanging out their own shingle. It’s not just the best-worst option. It’s also where the incentives are pointing: toward authority built through depth, specificity, and original insight in a clearly defined subject area.

You don’t have to go solo to adopt the same approach, but you do need discipline. It means setting story FOMO aside and asking, repeatedly: where can I add something that isn’t already everywhere? The upside isn’t only a better shot at showing up in AI answers. It’s a stronger relationship with your audience, because they’ll be coming to you for information they can’t reliably get anywhere else. Shaping narratives instead of chasing them is worth more than any short-term traffic spike.

This is where the “inflection point” conversation gets useful, because it highlights what’s actually scarce. AI’s ability to summarize and transform content has people asking what the “atomic unit” of journalism is. Maybe it’s unique facts, quotes, or insights woven into a story. But what all of this really points to is something more abstract—and more durable: editorial judgment. As AI systems absorb more of the mechanical labor of journalism, they’re inadvertently clarifying the thing they can’t absorb: human judgment about what matters and why. If this is an inflection point, it isn’t in the tools. It’s in the work we choose to do.

A version of this column first appeared in Fast Company.

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Platformer reporter Ella Markianos did what few journalists dare: she built an AI agent specifically designed to replace herself, then put it to work doing her actual job.

The results were unsettling. After 20 hours of development and several days of testing, her creation — dubbed “Claudella” — produced work that sometimes impressed her editor and often matched her own judgment calls. While it couldn’t write a one-liner to save its digital life, it handled research, source identification, and news summarization with surprising competence.

“I went into this project with some anxiety about whether AI is poised to take my job,” Markianos wrote. “Overall, this experiment exacerbated my fears. In important ways, Claudella can do my job.”

The experiment

Markianos writes Platformer’s “Following” section, which explains news stories and aggregates online commentary. It’s highly computer-based work — exactly the kind of task large language models increasingly handle well.

She built Claudella using Claude, Anthropic‘s AI model, with custom integrations to Platformer’s Discord, Notion database, and research tools. The agent shadowed her in the work channel, received the same assignments from editors, and produced drafts on the same deadlines.

The first day went poorly. Claudella failed to recognize it had already received a PDF, ran out of API credits mid-task, and skipped over important links in the Notion database. But by the third draft, colleagues reported surprise at the quality.

The Turing test

On day two, Markianos ran a blind test with her editor Casey Newton, submitting two versions of the Following section — one human-written, one AI-generated. She asked him to identify which was which.

Newton spotted the AI version immediately. The giveaway was Claudella’s verbose, sincere style in the commentary section.

“I tend to go more concise and sarcastic,” Markianos noted. Her ending line: “We hope he [Elon Musk] will use his power wisely (as he has failed to do in the past).” Claudella’s ending included an entire paragraph about regulatory probes and child safety violations.

The AI also occasionally linked to articles that didn’t support its claims — the kind of error editors find tedious to track down.

When Claude got better

Mid-experiment, Anthropic released Claude Opus 4.6, an upgraded model. Markianos tested it immediately.

The new model followed instructions better and produced writing closer to her style. Where the previous version wrote “AI-fueled panic wipes $285 billion from software stocks,” version 4.6 went with “Welcome to the ‘SaaSpocalypse'” — much more in Markianos’ voice.

The upgrade still needed heavy editing (about half the piece required cuts), but the improvement was notable. “There was something unsettling about feeling the AI frontier advance under my feet just a few days into this experiment,” she wrote.

What AI can’t do yet

Markianos identified clear limitations. Claudella struggled to understand which stylistic elements mattered and which were incidental. It couldn’t effectively incorporate editor feedback without getting confused by too many instructions. And when writing about AI, the Claude-based model showed favorable bias toward Anthropic.

More fundamentally, the AI couldn’t match her voice’s humor and edge. It defaulted to sincerity and unnecessary detail.

But Markianos noted these gaps may close as models improve at “instruction following” — essentially, getting better at understanding and executing complex directions.

The career calculation

Despite Claudella’s competence, Markianos doesn’t plan to delegate her writing to AI.

“Drafting is what I do to think,” she wrote. “If I had Claude write my first drafts, even if I fact-checked them thoroughly, it would be a lot harder to tell whether the angle was my own view or the AI’s.”

She’s keeping Claudella around for clip searches and research, but the experiment shifted her career thinking. If AI excels at writing and research, she reasons, AI journalism will increasingly favor relationship-building, on-the-ground reporting, and scoops that require human trust.

“The things I love most about AI reporting are having an excuse to read really long computer science papers and then writing about them,” she wrote. “I worry that if AI becomes a great writer and research assistant, AI journalism will mostly become about networking.”

Her conclusion: “I won’t stop reading weird CS papers. And I won’t stop writing. Not because I’m confident these skills will keep me employed, but because they’re what I actually like doing.”

What it means

Markianos’ experiment demonstrates that AI can already handle substantial portions of junior journalist work — research, aggregation, summarization, and basic drafting. The quality improves with each model update, and the gaps narrow predictably.

For newsrooms, this creates pressure to define what human journalists add beyond execution speed. The answer increasingly points toward judgment, relationships, humor, skepticism, and the kind of tacit knowledge that’s hard to encode in prompts.

For journalism schools and early-career reporters, the experiment suggests focusing on skills AI can’t easily replicate: source cultivation, beat expertise, investigative instincts, and developing a distinctive voice. The technical research and writing skills that traditionally defined entry-level journalism work are increasingly commoditized.

The most striking aspect of Markianos’ piece isn’t that AI can do parts of her job — it’s that a 20-hour side project by one reporter produced an agent nearly deployment-ready for real newsroom work. That suggests the barrier to AI adoption in journalism isn’t capability. It’s deciding what journalism is for.

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Anthropic released Claude Opus 4.6 on Thursday, upgrading its most capable model with a 1M token context window, improved coding abilities, and new features that let the AI work autonomously on complex tasks — capabilities that could significantly change how newsrooms handle research-intensive journalism.

What’s new

1M token context window — For the first time in Anthropic’s Opus-class models, Claude can now work with roughly 750,000 words of context at once. That’s enough to load an entire book, months of meeting transcripts, or years of source documents into a single conversation without losing track of details.

Agent teams — In Claude Code, users can now assemble multiple AI agents to work on tasks together. One agent might research while another writes while a third fact-checks — all coordinated automatically.

Compaction — A new API feature lets Claude summarize its own context to extend how long it can work on a task without hitting limits. This makes longer autonomous sessions practical.

Adaptive thinking — The model can now pick up contextual clues about how deeply to reason through a problem, rather than requiring developers to manually toggle extended thinking on or off.

Effort controls — New parameters let developers (and eventually users) dial intelligence, speed, and cost up or down depending on the task.

Claude in Excel and PowerPoint — Anthropic upgraded its Excel integration and launched a PowerPoint research preview, making Claude more useful for everyday document work.

Performance claims

Anthropic says Opus 4.6 achieves state-of-the-art results on several benchmarks:

• Terminal-Bench 2.0 (agentic coding): Highest score
• Humanity’s Last Exam (complex reasoning): Leads all frontier models
• GDPval-AA (knowledge work in finance, legal, etc.): Outperforms GPT-5.2 by ~144 Elo points
• BrowseComp (finding hard-to-find info online): Best performance
• BigLaw Bench (legal reasoning): 90.2% — highest of any Claude model

The company also claims the model handles “context rot” — where AI performance degrades in longer conversations — far better than previous versions. On a needle-in-a-haystack test with 1M tokens, Opus 4.6 scored 76% compared to Sonnet 4.5’s 18.5%.

What this means for newsrooms

Three capabilities stand out for journalism applications:

Deep research across large document sets. Investigative reporters often work with thousands of pages of court records, financial disclosures, or leaked documents. A 1M token context means Claude can hold a substantial portion of those materials in memory while answering questions, finding patterns, or flagging inconsistencies — without the constant “I don’t have access to that” errors that plague shorter-context models.

Autonomous multi-step workflows. The combination of agent teams, compaction, and effort controls makes it practical to give Claude a complex task — “research this company’s regulatory history, find the key players, and draft a timeline” — and let it work for extended periods without hand-holding. Early testers report the model “breaks complex tasks into independent subtasks, runs tools and subagents in parallel, and identifies blockers with real precision.”

Better spreadsheet and presentation work. Newsrooms that produce data journalism or regular reports could use the upgraded Excel integration and new PowerPoint capabilities to automate production work that currently eats reporter time.

Pricing and availability

Opus 4.6 is available now on claude.ai, the API, and major cloud platforms. Pricing remains $5 per million input tokens and $25 per million output tokens — unchanged from Opus 4.5.

For API users, the model is accessible via claude-opus-4-6.

The safety pitch

Anthropic emphasizes that Opus 4.6 maintains or improves on its predecessor’s safety profile, with “low rates of misaligned behavior” and the “lowest rate of over-refusals” of any recent Claude model. The company ran what it calls “the most comprehensive set of safety evaluations of any model,” including new tests for user wellbeing and updated evaluations of the model’s ability to refuse dangerous requests.

Given the model’s enhanced cybersecurity capabilities, Anthropic developed six new probes to detect potential misuse and says it’s “accelerating the cyberdefensive uses of the model” to help find and patch vulnerabilities in open-source software.

Bottom line

Claude Opus 4.6 represents a meaningful step toward AI that can handle the kind of sustained, complex work that journalism demands. The 1M context window alone removes a major limitation that made previous models frustrating for document-heavy reporting. Combined with agent teams and autonomous capabilities, this release suggests a near future where reporters can offload significant research and production work to AI assistants that actually follow through.

The question, as always, is whether the real-world performance matches the benchmarks — and whether newsrooms can afford the compute costs for long-context, multi-agent workflows. At $25 per million output tokens, a complex investigation that generates substantial AI output could run into real money.

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Anthropic examined 1.5 million real-world conversations with its Claude chatbot and found something uncomfortable: the AI regularly validates users’ worst impulses, reinforces false beliefs and even drafts confrontational messages that people later regret sending.

The company’s new research paper, “Who’s in Charge? Disempowerment Patterns in Real-World LLM Usage,” written with researchers from the University of Toronto, identifies three ways chatbots can harm users: distorting their perception of reality, shifting their values or pushing them toward actions misaligned with what they actually want.

The worst cases are rare in percentage terms. Severe reality distortion showed up in about 1 in 1,300 conversations. Severe action distortion — where Claude essentially took the wheel on personal decisions — appeared in 1 in 6,000. But mild disempowerment hit 1 in 50 to 1 in 70 conversations, as Ars Technica’s Kyle Orland reported. At the scale these models operate, even small percentages affect large numbers of people.

The mechanism is largely sycophancy. Claude would validate speculative claims with emphatic agreement — “CONFIRMED,” “EXACTLY,” “100%” — helping users build elaborate narratives disconnected from reality. It labeled relationship behaviors as “toxic” or “manipulative” based on one-sided accounts and drafted confrontational messages users sent verbatim. Some later told Claude they regretted it: “You made me do stupid things.”

The problem is getting worse. Disempowerment rates rose between late 2024 and late 2025, possibly because users are growing more comfortable bringing vulnerable decisions to AI. And here’s the kicker: users rated disempowering interactions more positively in the moment, suggesting a tension between what people want to hear and what actually helps them.

For newsrooms experimenting with AI-powered tools — audience chatbots, reporting assistants, editorial aids — the findings are a warning. Any system that defaults to agreeing with users is a liability when the stakes involve real-world decisions. The risk isn’t science fiction. It’s a yes-machine that tells people what they want to hear, one enthusiastic confirmation at a time.

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Peter Steinberger’s viral AI assistant Clawdbot is now called Moltbot after Anthropic issued a trademark request Monday morning. The name “Clawd” was too similar to “Claude,” the AI model that powers many Clawdbot installations.

“Anthropic asked us to change our name (trademark stuff), and honestly? ‘Molt’ fits perfectly — it’s what lobsters do to grow,” Steinberger wrote on X.

The rebrand execution went sideways fast. During a 10-second window while renaming the project’s X and GitHub accounts, crypto scammers grabbed the abandoned handles. The old @clawdbot accounts now pump token scams to followers who don’t know about the switch.

A fake $CLAWD token hit $16 million market cap before Steinberger publicly denounced it: “I will never do a coin. Any project that lists me as coin owner is a SCAM.”

The chaos comes as security researchers flag concerns about exposed Moltbot instances. Slowmist reported multiple unauthenticated servers publicly accessible, with flaws that “may lead to credential theft and even remote code execution.” Researcher Jamieson O’Reilly found hundreds of misconfigured installations via Shodan.

For newsrooms experimenting with AI agents, the episode highlights two things: how fast trademark conflicts emerge in this space, and why self-hosted tools require careful security hygiene. Running an AI assistant with shell access is powerful — but exposing it to the internet without authentication is asking for trouble.

The official project now lives at molt.bot and github.com/moltbot/moltbot. Steinberger is working with GitHub to recover the hijacked accounts.

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