I’ve been working with Claude Cowork extensively over the past month and a half. And not coincidentally, I’ve found myself accomplishing more during this period than at almost any other time in my career. The shift toward agentic work represents a transformation so fundamental that its impact is difficult to grasp until you actually experience it.

Just one example: As someone running a business that sells AI training courses online, email marketing is an important component of reaching potential customers. But the work itself is tedious: segmenting my email list, creating templates, writing largely similar drafts, and scheduling them in my email provider—a piece of software I look forward to using about as much as a visit to the dentist.

Now I hardly ever touch that software; Claude Cowork does it for me. When you have access to agents, you can loop them in on any computer task with three beautiful words: “You do it.” AI doesn’t just draft emails for me—it puts them in the campaign builder, targets the right audience, gets all the settings right, and then taps me on the shoulder (via a notification) so I can approve the work before it schedules everything to go out. Once you start working with agents, you quickly start crossing things off your to-do list faster than ever before.

Becoming the CEO of your job

This represents more than accelerated productivity. It’s a fundamentally different way of working. Instead of personally grinding through individual tasks, the focus shifts to defining desired outcomes, delegating execution to digital workers, and evaluating their output. Instead of simply doing your job, you become the CEO of it, delegating many tasks to agents.

So what happens to a newsroom when everyone starts working agentically? Over the past 30 years, reporters and editors have needed to become skilled at many different systems: project-management software for tracking stories, content management systems for publishing them, SEO plug-ins, social media management platforms—the list goes on. Agents open the possibility that journalists could instruct them to manage all of this infrastructure while they go and do the important, human-centered work of reporting and editing.

But the complications emerge when this same agent model gets applied to journalism’s core function: writing itself. This came to a head recently with the uproar over what The Plain Dealer, Cleveland’s primary newspaper, is doing: leveraging an AI writing agent so reporters can simply feed notes and context to create stories. To be clear, all the stories are then edited, and the reporter has final say over the copy. But applying agents this way brings up hard questions about jobs, skill-building, and career paths.

Yet beyond this specific scenario lies a broader reality: agents will almost certainly assume much of the repetitive work surrounding content creation and distribution. Whether it’s social media management, SEO (and GEO), or getting all the little drop-down menus, boxes, and tag fields in your CMS just right—those are all jobs for agents. More importantly, roles that are centered around optimizing those tasks will gradually go away.

Consider what happens: when search and social platforms drive audience discovery, newsrooms organize work around those algorithmic preferences. Many roles emerged that were simply writing to a trend, publishing undifferentiated “quick hits” around trending topics to maximize clicks. Those jobs were effectively hyper-optimizing production of formulaic stories, writing for algorithms and chasing virality through pattern recognition. An AI system can accomplish this faster, at higher volume, and more efficiently than any human.

Here’s the paradox: this development might actually prove beneficial for journalism—something I predicted in a column I wrote almost exactly a year ago. Agents are a crucible for knowledge work, burning away anything and everything that can be automated, leaving only the parts of the job that can’t be easily repeated—the work that requires either creating new information or judgment, context, and taste.

The agentic newsroom

If you were designing a newsroom optimized for AI from the start, using this principle, the bulk of positions would focus exclusively on the distinctly human elements: cultivating trust with sources through direct access and personal relationships, conducting original reporting and uncovering information exclusive to your brand, determining which stories resonate most with audiences and which narrative angles matter most, and applying the craft of storytelling across all of it.

While that sounds appealing in certain respects, the economic reality is harder: with agents executing most of the work, fewer jobs will likely exist. In almost all cases, organizations will be smaller, with different career paths, even if the work is richer.

A current limitation is the scope of what agents can access. Tools like Claude Cowork and Claude Code become truly powerful only when they can move beyond drafting and into systems (email, CMS, analytics, internal documents). That is where most organizations get uneasy. Granting an agent permission to act inside those environments raises questions about security and accountability. Most teams are still feeling their way through this, limiting agents to narrow tasks or read-only access. But that tension is temporary. As guardrails improve and familiarity grows, those permissions will expand, and with them, the scope of what agents can do.

The fundamental premise remains unchanged: journalism’s purpose is not threatened. Instead, its true essence becomes visible when machines handle the repetitive parts. An AI-first newsroom doesn’t mean a less human one. In fact, it means the opposite. When the repeatable work is handled by machines, what remains is the work that defines the craft: earning trust, finding new information, and making sense of it for an audience. The uncomfortable part is that there may be fewer people doing that work. The hopeful part is that the work itself becomes more meaningful.

A version of this column appeared in Fast Company.

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A single unreleased AI model has triggered emergency regulatory mobilization on both sides of the Atlantic. UK financial regulators are holding urgent talks with the government’s cybersecurity agency and major banks to assess risks posed by Anthropic’s Claude Mythos Preview — days after US Treasury Secretary Scott Bessent and Federal Reserve Chair Jerome Powell convened an emergency meeting with Wall Street’s top CEOs over the same concerns.

In the UK, officials from the Bank of England, Financial Conduct Authority, and Treasury are in talks with the National Cyber Security Centre. Representatives from major British banks, insurers, and exchanges are expected to be briefed on cybersecurity risks at a meeting with regulators within the next two weeks, according to Reuters. The BoE, FCA, and NCSC all declined to comment.

The US response was more public. White House national economic adviser Kevin Hassett confirmed on Fox News that Bessent and Powell had convened bank chiefs — including the CEOs of Citigroup, Morgan Stanley, Bank of America, Wells Fargo, and Goldman Sachs — to warn of cyber risks from the model. JPMorgan CEO Jamie Dimon was unable to attend. The urgency of the meeting reflected the capabilities Mythos Preview has demonstrated in controlled testing: the ability to identify and exploit weaknesses across every major operating system and every major web browser.

Anthropic has stopped short of a broad release, citing concerns the model could expose previously unknown cybersecurity vulnerabilities at scale. The company has been navigating an increasingly complex relationship with the broader tech and media ecosystem as its models grow more capable.

What Mythos Preview is — and who can use it

Despite not being publicly available, Claude Mythos Preview is already in active use — under strict controls. Under a program Anthropic calls Project Glasswing, select organizations have been granted access to the model for defensive cybersecurity work. Partners include Amazon, Microsoft, Apple, Google, Nvidia, CrowdStrike, and Palo Alto Networks. Access has since been extended to approximately 40 additional organizations responsible for critical software infrastructure.

Anthropic says Mythos Preview has already found “thousands” of major vulnerabilities in operating systems, web browsers, and other software. The company has committed up to $100 million in usage credits and $4 million in donations to open-source security groups as part of the program.

The framing is defensive. But the same capability that finds vulnerabilities can, by definition, be turned toward exploiting them — which is precisely what regulators appear to be stress-testing.

Why regulators are moving fast

The simultaneous and independent responses from UK and US financial regulators signal that Mythos Preview represents a qualitatively different kind of AI risk than those regulators have previously had to assess. Prior AI regulatory concerns have centered on bias, misinformation, and systemic market risks — as seen in ongoing debates around AI copyright policy and AI use certification. A model with demonstrated offensive capability against critical software infrastructure — in active use, even in a restricted form — is a different category of problem.

It is also a compressed timeline problem. The model exists. It is being used. The regulatory frameworks to manage it are still being assembled.

All three UK agencies — the BoE, FCA, and NCSC — declined to comment on the talks. Anthropic had not responded to a request for comment at the time of the Reuters report.

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Microsoft wants to stop being your AI assistant and start being your AI workforce.

That’s the pitch behind Wave 3 of Microsoft 365 Copilot, announced Monday. The centerpiece is Copilot Cowork — a new capability, built with Anthropic’s technology, that lets Copilot take on long-running, multi-step tasks rather than just answer a single prompt. Think: not “draft this email” but “research our top five competitors, build a summary deck, and schedule a review meeting.”

Cowork is still a research preview, but it points at something real. Most enterprise AI disappointments so far have come from the gap between generating something useful and actually finishing something. Cowork is Microsoft’s attempt to bridge that.

Wave 3 also brings Copilot deeper into the apps people actually live in. Word, Excel, PowerPoint, Outlook and Copilot Chat are all getting what Microsoft is calling “next-generation agentic experiences” — the ability to create and revise documents, spreadsheets, and decks from inside the app, with everything saved into governed Microsoft 365 environments. That last part matters more than it sounds. Keeping AI-generated content inside existing permission and compliance systems is the real enterprise sell.

On the model side: Claude is now available directly in Copilot Chat for customers in the Frontier program, alongside OpenAI’s latest. Microsoft is positioning itself as model-agnostic — your AI interface, whoever’s model wins.

The governance piece is Agent 365, a $15-per-user control plane for IT and security teams to observe, manage and secure agents across the organization. Generally available May 1.

And then there’s the bundle. Microsoft 365 E7 — the new Frontier Suite — lands May 1 at $99 per user. It wraps Microsoft 365 E5, Copilot, and Agent 365 into one package, with Microsoft Entra, Defender, Intune and Purview security baked in. It’s clearly aimed at CIOs who want to buy the whole stack from one vendor and not think about it again.

Whether enterprises will pay for all of it is another question. But Microsoft is betting that once AI moves from chat to actual task completion, the governance and security layer becomes non-negotiable — and they want to own that layer.

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Two months in, and 2026 is already shaping up to be the year of agents. The surge kicked off with Claude Code, which hit critical mass over the holidays before spawning a lot of lobster-themed software (long story). That culminated with OpenClaw, an open-source agent creation and management system, which has inspired thousands to begin building their own agent workforces, not to mention buying so many Mac Minis that Apple has put them on backorder.

It’s still early to put a number on the actual productivity gains from this movement, but the push to agents is undeniable. It’s also very exclusive. For all the talk of, “the only coding language you need to know is English,” there are technical barriers to joining this wave. You don’t necessarily need to know how to code in order to use OpenClaw, but it helps considerably.

To get non-coders get over those barriers, AI companies are shipping “training wheels” for agents, products that abstract away the challenging bits. Anthropic released Claude Cowork—Claude Code for the rest of us (which was notably built with Claude Code). More recently, Perplexity launched Computer, its “general-purpose digital worker” that users can prompt in natural language and watch it go to work.

It sounds magical in the way every good demo does: frictionless, conversational, inevitable. If you squint, you can picture a near future where knowledge work—and especially editorial work—shifts from dashboards to dialogue. Instead of pulling levers on various software menus and dashboards, you’ll just talk to agents. They’ll handle the hard stuff, and if they run into barriers, you’ll just ask another agent to build the solution.

Agents get real

Back in reality, it’s messier. Even if you use one of the no-code systems like Claude Cowork, creating tools and workflows still involves breaking down processes, finding API keys, navigating permissions, and iterating continually. And the “for non-coders” promise often comes with a footnote the size of a brick. When I used Claude Cowork for the first time, the app gave me instructions that included using the Terminal on my Mac—a program that most people have no idea exists. And if you don’t, you probably shouldn’t mess with it.

Of course, for builders, none of this even qualifies as a barrier. A builder isn’t the same thing as a coder, but they do have characteristics that most workers don’t: they want to understand the process beneath their tasks, and treat that process as modifiable and programmable. They also treat failure as feedback—not just tolerable, but sometimes even fun. They thrive in uncertainty.

Most workers, unsurprisingly, don’t default to that mindset. We’ve trained a generation of office workers to use software with clear boundaries and reusable templates. If there’s an issue, they call IT. Any feature request gets filtered and, if you’re lucky, put on a roadmap that pushes it out 6-12 months.

That means the “builder mentality” isn’t just rare—it’s the opposite of how most offices have taught people to operate. In January, New York Times tech writer Kevin Roose pointed to a growing chasm between those fully in the AI bubble, who are building multi-agent teams to help them get work done, and those who aren’t, most of which have never even built a basic assistant like a Custom GPT or Gemini Gem. As someone who trains editorial teams on how to use AI, I can confirm this gap exists and is indeed massive.

So yes, the hype is loud, but the adoption is tiny. For all the hype you might see on X, the percentage of workers who have actually adopted agentic tools is extremely small. But the people who do adopt them can still shape what everyone else ends up doing. The catch is that agents, at least as they exist today, are hard to deploy safely inside organizations. They need access to files, email, calendars, internal systems, sometimes the ability to take actions automatically. That’s not a tooling problem. That’s a permissions problem, and it makes security teams nervous for good reason.

You don’t need a sci-fi scenario to see why this makes people sweat. A recent example involved an OpenClaw agent that appeared to run amok in a Meta engineer’s inbox, taking destructive actions despite attempts to stop it. Stories like that may be edge cases, but they point to a reality: delegating software access to agents can amplify ordinary mistakes into high-impact mistakes.

The permissions wall

Until security, governance, and fail-safes improve, most organizations will move slowly on general-purpose agents. That won’t stop builders, even inside those same organizations, from experimenting anyway. They’ll just do it on their own time or elsewhere. This “capability chasm” between builders and users will eventually force solutions, and the systems those builders create will determine the workflows of the future.

If you’re not a builder, that’s a rough spot. Becoming a builder, though easier than ever from a technical standpoint, means a shift in mindset that many simply aren’t up for. The alternative is to sit passively, wait for agentic systems to filter down to you, and hope you don’t get laid off in the meantime.

There’s a third way, though, and it doesn’t require you to ship code. You don’t have to be a builder to understand how agentic workflows are changing your job. For journalists, that means identifying the parts of your work where human attention and judgment is paramount: the filtering of facts, the interviews, the writing (or maybe not), the cultivating of source and audience trust. From there, you can help define what should never be delegated, and what can be automated without harming standards. You can also push your organization—constructively—to adopt agents in bounded, defensible ways that match newsroom reality.

In other words, you don’t have to build agents to matter in an agent-driven workplace. But you do have to understand the systems being built around you, because soon enough, your job will be defined by defaults someone else designed. Most professionals will not build agents. But everyone will eventually work inside the systems builders create.

A version of this column originally appeared in Fast Company.

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An open-source AI assistant called Clawdbot has quietly amassed over 8,000 GitHub stars and earned coverage from MacStories, with multiple Medium posts going viral this weekend.

Created by Peter Steinberger, founder of the iOS development company PSPDFKit, Clawdbot runs locally on your computer while connecting to messaging platforms like Telegram, WhatsApp, iMessage and Slack. Users chat with it like a contact in their existing apps.

“To say that Clawdbot has fundamentally altered my perspective of what it means to have an intelligent, personal AI assistant in 2026 would be an understatement,” wrote Federico Viticci at MacStories.

The project solves a persistent problem with consumer AI tools: they forget everything between sessions. Clawdbot maintains memory, preferences and context in local Markdown files that persist indefinitely.

More importantly for power users, Clawdbot can execute shell commands, write and run scripts, control smart home devices and install new capabilities on the fly. Viticci reported burning through 180 million tokens experimenting with it.

For newsrooms, the implications are worth watching. An AI assistant that remembers your beats, sources and research workflows — and runs on your own infrastructure — addresses both the productivity promise and the data privacy concerns that have made enterprise AI adoption complicated. The 2026 Reuters Institute predictions forecast exactly this kind of agentic AI becoming central to newsroom operations.

The catch: Clawdbot requires technical setup and your own API keys from providers like Anthropic or OpenAI. It’s a tinkerer’s tool, not a consumer product. But its rapid growth suggests demand for AI assistants that users actually control.

“2026 is already the year of personal agents,” one user wrote on the project’s website.

Clawdbot is available free on GitHub. Documentation lives at docs.clawd.bot.

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Anthropic wants its Claude AI to handle your busywork.

The company launched Cowork, a new feature that brings the agentic capabilities of its popular Claude Code tool to general knowledge work. Built into the macOS desktop app, Cowork lets users give Claude access to a specific folder and then issue plain language instructions for tasks.

Think filling out expense reports from a folder of receipt photos. Or writing summaries from a stack of interview notes. Or finally cleaning up that chaotic desktop.

For newsrooms drowning in administrative tasks while chasing deadlines, the pitch is compelling. Anthropic says it developed Cowork partly because people were already using Claude Code for general knowledge work tasks anyway.

The key difference from Claude Code: accessibility. While Claude Code required technical know-how to configure, Cowork is designed so any knowledge worker can start immediately. Samuel Axon wrote in Ars Technica that “Anthropic’s goal with Cowork is to make it something any knowledge worker—from developers to marketers—could get rolling with right away.”

But there are real concerns journalists should understand before handing over folder access.

Vague prompts can cause problems. Poorly worded instructions or bad luck can lead the agent to do destructive things like deleting files unexpectedly. And prompt injection attacks remain an unsolved risk, meaning malicious content in documents could potentially manipulate the AI’s behavior.

These aren’t hypothetical concerns. They’re the kind of risks that technical users of Claude Code understood going in. Less technical users might not have that foresight.

For now, Anthropic is moving cautiously. Cowork is available only as a research preview to Max subscribers, with no timeline for wider release.

Why it matters for newsrooms: Agentic AI tools that can work autonomously on file management and document creation could significantly reduce time spent on administrative tasks. But the current limitations and risks mean most newsrooms should wait before deploying this in production workflows. Early adopters should treat it as an experiment, not a solution.

The launch comes alongside Anthropic’s announcement of Claude for Healthcare, as the company expands beyond its developer-focused roots into broader professional markets.

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For the past few years, most people have been using AI in the same way you’d use an app. You navigate to ChatGPT or Claude or Gemini in a browser (or launch its mobile app), type at or talk to the chatbot, get the response you’re looking for, and go back to what you were doing. Sometimes, if the AI needs context, you’d upload a document or image—or just paste it into your prompt.

If AI is going to be the transformational technology many believe it’s destined to be, it needs to get out of the chat window and embed itself more deeply into the devices we use every day. There’s been movement on this: Device makers like Apple and Samsung have tried to enhance their built-in assistants with AI, but so far no one’s created an experience that sets the bar.

Lenovo thinks it’s done it. At CES 2026, the global PC giant offered up a new approach to on-device AI: Qira, its own ever-present AI assistant that will be built into the operating system of its phones, PCs, and more. In the coming months, owners of Lenovo devices will be able to summon Qira with a word or a click, commanding it to transcribe a meeting, retrieve information on their machines, or craft content in seconds.

None of that is new, but the philosophy is. Qira isn’t trying to compete with the AI services we already use, such as ChatGPT or Google Nano Banana. Qira is better thought of as an “orchestrator of agents”—the CEO of your device and software stack, calling up the right app or function based on your questions and commands. On a Qira-enabled device, it’s quite literally the centerpiece of your machine’s UI: a floating icon ready to become a short toolbar the moment you click on it.

The always-on assistant

Qira isn’t just a feature. It’s Lenovo declaring the assistant is the product, and it represents an inflection point in Lenovo’s hardware strategy. Company executives, including Lenovo Chairman Yang Yuanqing, declared that Lenovo is all-in on AI, and that Qira would be built into all its devices going forward.

“I strongly believe AI will be the tech nobody can avoid,” Yuanqing said to reporters in a group Q&A. “But the AI will not replace the human. So it will only empower us, empower each of us.”

To be clear, users can opt out of sharing things with Qira, and they can even disable it altogether if they want. During Lenovo’s supersize keynote at the Las Vegas Sphere, company executives repeatedly stated that Qira would only take actions if the user grants permission. And unlike Microsoft’s troubled Recall feature, which takes screenshots of your desktop at regular intervals, Qira will only be able to “see” your desktop if you activate it, which may allay some fears of an always-watching AI. 

“You choose what you want to share,” Dan Dery, Lenovo’s VP of Ecosystem and Monetization, told me. “You choose what adds value to your life. There is not a minimum functioning set of Qira that says that you need to have all these boxes checked or it won’t work. You decide.”

That said, the whole idea of Qira is that it’s AI empowered with all the context of what you’re doing and who you are. Done right, it could become the vision of what Apple promised almost two years ago with its still-unrealized AI-powered Siri—an all-knowing assistant, able to understand how to deliver what you need because it knows you.

The problem with Apple’s approach is that it bit off more than it could chew. It had been developing its own in-house AI but it also offloaded some queries to ChatGPT. The execution always felt like it couldn’t decide whether it was cooperating or competing with frontier model builders like OpenAI and Anthropic.

Lenovo doesn’t have that problem. In both public comments and the interviews I conducted, the message was very clear: Lenovo can’t and has no desire to be everything to everyone.

“Nobody can do everything,” Yuanqing admitted. “A company like Lenovo, we are doing the system.… I strongly believe the entry point for users is still the devices. So if you want to use the AI you need to use the device.”

Qira can do all the regular AI stuff: answer informational queries, write text, and create images, but the moment you want to go deeper, it’ll pass the baton to Perplexity, ChatGPT, or some other model. And for more specific scenarios, like, say, when a user is inspired by a feature article on Bali and asks Qira about travel information, it’ll call Expedia’s AI agent.

The battle for context

For some of those brands, Perplexity and Expedia in particular, Lenovo has formal partnerships—you can even spot the Expedia logo on the strip of follow-up questions that appear under the Qira chat window. 

It’s subtle. It’s also the point. Being the context for AI queries will certainly be a massively important issue in 2026 for all brands, and especially media: Who gets to provide the default context? If you ask for directions, whose map does it grab? If you want food delivered, which service does it point you to? And if you ask for news, which outlets provide it?

If, as Lenovo intends, Qira becomes the first stop for most interactions on your device, these are incredibly important questions to answer, especially for publishers, who are slowly transitioning from website operators to context providers. As Lenovo executives explained to me, how Qira finds and governs context comes down to three factors: content optimization, partnerships, and agentic capabilities.

“Our philosophy and vision is that Qira should be this interface between you and your devices, and then it should be the gate to other tools, the ones you prefer,” Dery said. “So we are very open to integrate any solution.”

In the AI world, optimization is more specifically called GEO, something I’ve written about at length and will likely continue to do so because it’s sure to be a big pillar of media strategy in 2026. The issues of copyright and fair compensation will continue, of course, but Qira is an indicator that the world isn’t waiting for resolutions to those questions. Content providers cannot ignore GEO strategy. That doesn’t mean blocking AI bots is off the table, but it does mean that when you don’t, your content will be tailored to what they want to see.

In these early days of AI, partnerships are going to be incredibly important to ensuring content is visible to as many AI surfaces as possible. We’re already seeing that with the publisher relationships that OpenAI, Perplexity, and others have built over the past couple of years, and how it affects what content is surfaced in response to queries. 

This can get murky because it amounts to picking winners, and partnerships obviously favor big players with relationships and resources over smaller companies trying to stand out on the merits of their content and services. Milo Speranzo, Lenovo’s chief marketing officer for North America, told me that Lenovo’s goal was to provide what works for the user, not favor any particular partner.

“We want to build solutions that our customers need, not what’s best for necessarily one vendor or another. So delivering that—let’s call it that agnostic AI agent—it works seamlessly where we can but also engages areas where we might not necessarily have a common interface.

Even if Speranzo is right, the fact is someone has to win the context battle, and if the recent Google antitrust ruling showed anything it’s that being the default matters a lot. The importance of AI partnerships for media can only increase as more AI assistants like Qira insert themselves between users and the information they want.

Finally, there are the agents. Remember, Qira is supposed to be the ultimate agent orchestrator, so if your site or service has its own agentic layer, that will greatly ease the way Qira talks to it. We didn’t fully understand this when “agent” was mostly a buzzword a year ago, but now the model is much more in focus: Agentic services like Qira seamlessly link to highly specialized agents built by service and content providers through new standards like MCP (model context protocol).

If Qira can talk to your service cleanly, you’re in. If it can’t, you’re a tab someone never opens. Time’s AI Agent is a good media example; to users, it’s just another chatbot, but to agentic assistants, it’s the all-purpose socket they’re looking to plug into.

What to do now

I never expected Lenovo to be the company to create the template for on-device AI, but in hindsight it makes perfect sense. Because they’re not a frontier model builder, they have the freedom to work exclusively on the top layer of the AI experience, which includes both how users interact with Qira but also how Qira calls upon deeper layers—the highly specific content or service that serves the user in any particular moment. Lenovo doesn’t compete at those layers, and their deep device footprint (they’re consistently the No. 1 PC vendor in the world) gives the company necessary clout.

For companies in those deeper layers, especially media, the path is now clearer than ever: To play in the arena of agents, you need to pay attention to GEO, focus on AI partnerships where possible, and architect agentic interfaces for the content you provide. Of course, as certain larger questions around AI (like copyright) remain unanswered, there’s still no guarantee of thriving in the AI era. But at least now there’s a better roadmap to surviving it.

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