Anthropic’s office is surprisingly AI-first, even for an AI company

When you think of an operating system, you probably think of interfaces to open, workflows to follow, screens to move through. Work has always lived inside those boundaries. At Anthropic, that logic is starting to break. The company is reorganizing itself around a simple, destabilizing premise: work no longer needs a fixed system to run through.

Anthropic says employees now rely on Claude, its flagship AI model, along with its products Code and Cowork, for most of their day-to-day work. The model is starting to function as an “internal operating system.” What once required navigating multiple systems, stitching together data, and coordinating across teams now begins with a single prompt. From there, Claude interprets intent, pulls in context, and produces outputs that often bypass the underlying systems entirely.

Mike Krieger, co-lead of Labs at Anthropic, says the company is focused on making individual employees materially better at the work they already do, and capable of doing things they could not reliably do on their own. “We build products where we see demand from customers, or when something our team is already using internally turns out to be valuable enough to ship,” Krieger tells Fast Company. “The operating system framing is the right instinct.”

In a prompt-driven system, there is always a risk that people perform the same task in different ways, leading to uneven quality and making work harder to track or review. Krieger, the Instagram cofounder and former CTO who also served as Anthropic’s chief product officer, says the company has built a layer to keep things consistent. That layer comes in the form of “Skills,” packaged, version-controlled workflows that include the instructions, context, and steps that work, and can be reused across the company.

“When someone in finance figures out an effective way to use Claude for contract review, that workflow becomes a ‘Skill’, and the next person who needs it gets the same quality on day one instead of building their own version from scratch. The work is consistent, auditable, and reproducible,” he says.

In practice, a product manager can query data directly through Claude-connected systems and generate evaluations in minutes, bypassing traditional analytics dashboards. A marketer with no coding background can assemble a custom Figma plugin to produce creative variations in seconds rather than half an hour. Even the company’s legal team is now building its own tools, a domain where you least expect AI to be involved.

Mark Pike, associate general counsel at Anthropic, shared how he built a legal review plugin in a single afternoon. Faced with a surge of last-minute requests, he used Claude to create a system. A user pastes in a draft, the AI evaluates it against a legal framework Pike defined, flags issues by risk level, and posts a summary to the legal team in Slack.

“I did so by simply using markdown files, prompts, and system instructions, all open on GitHub,” Pike says. “We fed Claude our policies, our playbooks, and the way we think through problems, and it stopped doing generic legal work and started operating at the level my peers and I expect.” He claims that the impact extends beyond individual tools. “I’d tell any legal team to have Claude look at your last few months of busywork and just ask it where the patterns are. We analyzed 742 Jira tickets in a single conversation.”

Claude now handles monitoring, first drafts, and pattern-matching across hundreds of data points. Pike notes that the legal team still reviews everything, since systems can hallucinate and accountability ultimately remains with the lawyer. “We get to spend our time on the work that actually requires a lawyer,” he says, “like complex negotiations or judgment calls.”

Industry experts say these claims are provocative, pointing to a shift larger than automation. Senthil Muthiah, senior partner at McKinsey & Company, says agentic AI is compressing the apprenticeship curve, and that is where the real risk begins to emerge. “There is a genuine danger that we create a generation of workers who can supervise AI before they fully understand the work themselves,” he says.

The Impact of the ‘Claude Effect’ and Operating System Claim

The model has been nothing short of a breakthrough for both Anthropic and the broader tech market, with capabilities on certain tasks so striking that some have begun referring to it as the “Claude Effect.” As of April 2026, Anthropic’s latest models, Claude 4.5 and 4.6 Opus, rank at or near the top across key benchmarks.

On SWE-bench, which evaluates whether models can implement valid code fixes and handle real-world programming tasks, Claude scores around 78.7%, placing it above OpenAI’s GPT-5.4 (76.9%). Beyond coding, Claude also performs strongly on composite benchmarks like the Vals Index, which measures performance across domains such as finance and law. Here, its Sonnet 4.6 variant outperforms models such as Google’s Gemini 3.1 Pro in overall task execution.

Even with its growing capabilities, can an AI model truly evolve into an operating system? Traditional operating systems manage resources, enforce boundaries, and guarantee, or attempt to guarantee, consistency. Jeffrey Chivers, CEO of the AI-powered litigation platform Syllo, believes what Anthropic is attempting with Claude does not fit neatly into those definitions.

“Internal operating systems should provide a deterministic, stable foundation and organizational function for the professionals or AI agents who work within the shared operating system,” he says. “Claude can be used to develop and improve such operating systems, but to say that Claude itself can become an operating system is a forced effort.” He adds that figuring out how to split work across different models is still a practical question of balancing performance, reliability, speed, and cost, and “the right answer for many inferences across a vertical stack today is not Claude.”

That tension came into focus with OpenClaw, an open-source agent framework that turned Claude and other leading models into a persistent execution layer, offering an early glimpse of what an “AI operating system” might look like. By connecting to platforms like Slack and Discord and bypassing standard API billing, developers ran always-on agents at scale, capable of monitoring systems, executing workflows, and maintaining context. But OpenClaw also became an unofficial distribution layer for Claude’s most advanced capabilities, prompting Anthropic to intervene. In April 2026, it blocked such platforms from using subscription-based access, forcing a shift to metered API usage, arguing that tools like OpenClaw were generating unsustainable demand and straining its infrastructure.

Some experts say the impact, output, and speed AI systems now offer introduce a new layer of complexity. “Complex systems are fragile,” says Satyen Sangani, CEO of Alation. “There’s a lot of risk around knowledge loss and organizational resilience. Also, there will inevitably be people who don’t check the output and end up producing AI slop. I worry about the fragility being created.”

AI Is Increasing Workloads, Not Just Efficiency

Inside Anthropic, productivity is not shrinking effort, but expanding possibilities. Cat de Jong, head of applied AI at Anthropic, says there is a growing belief inside the company that Claude is not just capable, but rapidly becoming more so, and that not using it to its fullest would mean leaving real value on the table.

“Over the last couple of years, we kept closing the gap between Claude knowing the answer and Claude actually doing the work. We gave it tools — search, code execution, the ability to call other software. We built MCP so it could plug into Gmail, Slack, Salesforce, whatever a company actually runs on. We taught it to use a computer the way a person does, and to create real files instead of describing what they should look like,” she tells Fast Company. “The more people use it, the more comfortable they get with what it can actually do, and the more they push on what to hand off next.”

Boris Cherny, head of Claude Code at Anthropic, recently claimed on a podcast that since introducing the tool, engineering productivity has increased by 200%, measured by pull requests per engineer. Those gains, however, are not evenly distributed. “We’ve observed that some gains don’t occur uniformly across an organization,” says de Jong. “Teams that have deeply integrated Claude into their workflows may move at a fundamentally different speed than teams that haven’t, and that mismatch can create its own friction.”

The company claims its customers are following a similar path, scaling projects through Claude. Andrew McNamara, Shopify’s director of applied AI, says Claude Code has transformed how teams build internal tools, with both engineers and non-engineers creating sophisticated applications in minutes rather than days. Allianz, one of the world’s largest insurers and asset managers, started with its engineering teams and is now expanding Claude across the business. Likewise, cloud security firm Wiz used Claude Code to migrate a 50,000-line codebase in about 20 hours, a project its own engineers had estimated would take two to three months of specialized work.

Anthropic’s internal data shows employees use Claude in about 60% of their work and report roughly 50% productivity gains. But full handoff is still rare. In many cases, employees also spend extra time understanding what the AI produces, particularly in areas they are less familiar with. Rather than reducing workload, Claude often expands it by making new tasks possible. About 27% of AI-assisted work would not have been attempted otherwise. While each task may take slightly less time, the overall amount of work increases.

“True productivity comes from automated paths to production that enforce security, testing, and compliance, and collect evidence along the way. Without that, faster output just shifts the burden from doing the work to constantly checking it,” says Nick Durkin, field CTO at Harness. “Sure, probabilistic systems can take action and pull in data with reasonable confidence, but there are hard stops like evidence collection, separation of duties, and audit trails. Those aren’t optional.”

Workflow Replacement, Reinvention, or Both?

Anthropic’s internal transformation offers a glimpse of what AI-native work might look like. It is also ambiguous. The company’s central thesis is that workflows themselves can be replaced, and that the friction of coordination, tooling, and specialization can be reduced to a layer of prompts. That thesis challenges the foundation of enterprise software.

“If organizations only use AI to accelerate workflows, they bypass the learning process entirely and create a leadership vacuum for the future,” says Chivers. “The critical signal to watch is whether leadership teams will be reinvesting the ‘saved time’ into accelerated mentorship and higher-order thinking, or simply using it to pad short-term margins.”

If Anthropic’s bet is right, the operating system of the future might reduce to a conversation that governs how work happens. “Enterprises pick Claude for a pattern, not a feature. We ship at the frontier and optimize for the hard problems,” says de Jong. “The question they’re answering is, ‘which tool do I trust with which decision?’, and Claude tends to land where the cost of being wrong is high.”