- How Claude's updated operating instructions restored its reliability
For several weeks, a growing chorus of developers and AI power users claimed that Anthropic’s flagship models were losing their edge. Users across GitHub, X, and Reddit reported a phenomenon they described as "AI shrinkflation"—a perceived degradation where Claude seemed less capable of sustained reasoning, more prone to hallucinations, and increasingly wasteful with tokens.
Critics pointed to a measurable shift in behavior, alleging that the model had moved from a "research-first" approach to a lazier, "edit-first" style that could no longer be trusted for complex engineering.
While the company initially pushed back against claims of "nerfing" the model to manage demand, the mounting evidence from high-profile users and third-party benchmarks created a significant trust gap.
Today, Anthropic addressed these concerns directly, publishing a technical post-mortem that identified three separate product-layer changes responsible for the reported quality issues.
"We take reports about degradation very seriously," reads Anthropic's blog post on the matter. "We never intentionally degrade our models, and we were able to immediately confirm that our API and inference layer were unaffected."
Anthropic claims it has resolved the issues by reverting the reasoning effort change and the verbosity prompt, while fixing the caching bug in version v2.1.116.
The mounting evidence of degradation
The controversy gained momentum in early April 2026, fueled by detailed technical analyses from the developer community. Stella Laurenzo, a Senior Director in AMD’s AI group, published an exhaustive audit of 6,852 Claude Code session files and over 234,000 tool calls on Github showing performance falling from her usage before.
Her findings suggested that Claude’s reasoning depth had fallen sharply, leading to reasoning loops and a tendency to choose the "simplest fix" rather than the correct one.
This anecdotal frustration was seemingly validated by third-party benchmarks. BridgeMind reported that Claude Opus 4.6’s accuracy had dropped from 83.3% to 68.3% in their tests, causing its ranking to plummet from No. 2 to No. 10.
Although some researchers argued these specific benchmark comparisons were flawed due to inconsistent testing scopes, the narrative that Claude had become "dumber" became a viral talking point. Users also reported that usage limits were draining faster than expected, leading to suspicions that Anthropic was intentionally throttling performance to manage surging demand.
The causes
In its post-morem bog post, Anthropic clarified that while the underlying model weights had not regressed, three specific changes to the "harness" surrounding the models had inadvertently hampered their performance:
Default Reasoning Effort: On March 4, Anthropic changed the default reasoning effort from high to medium for Claude Code to address UI latency issues. This change was intended to prevent the interface from appearing "frozen" while the model thought, but it resulted in a noticeable drop in intelligence for complex tasks.
A Caching Logic Bug: Shipped on March 26, a caching optimization meant to prune old "thinking" from idle sessions contained a critical bug. Instead of clearing the thinking history once after an hour of inactivity, it cleared it on every subsequent turn, causing the model to lose its "short-term memory" and become repetitive or forgetful.
System Prompt Verbosity Limits: On April 16, Anthropic added instructions to the system prompt to keep text between tool calls under 25 words and final responses under 100 words. This attempt to reduce verbosity in Opus 4.7 backfired, causing a 3% drop in coding quality evaluations.
Impact and future safeguards
The quality issues extended beyond the Claude Code CLI, affecting the Claude Agent SDK and Claude Cowork, though the Claude API was not impacted.
Anthropic admitted that these changes made the model appear to have "less intelligence," which they acknowledged was not the experience users should expect.
To regain user trust and prevent future regressions, Anthropic is implementing several operational changes:
Internal Dogfooding: A larger share of internal staff will be required to use the exact public builds of Claude Code to ensure they experience the product as users do.
Enhanced Evaluation Suites: The company will now run a broader suite of per-model evaluations and "ablations" for every system prompt change to isolate the impact of specific instructions.
Tighter Controls: New tooling has been built to make prompt changes easier to audit, and model-specific changes will be strictly gated to their intended targets.
Subscriber Compensation: To account for the token waste and performance friction caused by these bugs, Anthropic has reset usage limits for all subscribers as of April 23.
The company intends to use its new @ClaudeDevs account on X and GitHub threads to provide deeper reasoning behind future product decisions and maintain a more transparent dialogue with its developer base.
- Agents that learn from mistakes unlock a more intuitive data workflow
Anthropic on Tuesday unveiled a suite of updates to its Claude Managed Agents platform at its second annual Code with Claude developer conference in San Francisco, introducing a new capability called "dreaming" that lets AI agents learn from their own past sessions and improve over time — a step toward the kind of self-correcting, self-improving AI systems that enterprises have demanded before trusting agents with production workloads.
The company also moved two previously experimental features — outcomes and multi-agent orchestration — from research preview into public beta, making them broadly available to developers building on the Claude platform. Together, the three features address what Anthropic says are the hardest problems in running AI agents at scale: keeping them accurate, helping them learn, and preventing them from becoming bottlenecks on complex, multi-step work.
Early adopters are already reporting significant results. Legal AI company Harvey saw task completion rates increase roughly 6x after implementing dreaming. Medical document review company Wisedocs cut its document review time by 50% using outcomes. And Netflix is now processing logs from hundreds of builds simultaneously using multi-agent orchestration.
The announcements come at a moment of extraordinary momentum for Anthropic. CEO Dario Amodei disclosed during a fireside chat at the conference that the company's growth has outpaced even its own aggressive internal projections.
In the first quarter of 2026, Anthropic saw what Amodei described as 80x annualized growth in revenue and usage — far exceeding the 10x annual growth the company had planned for. API volume on the Claude platform is up nearly 70x year over year, and the average developer using Claude Code now spends 20 hours per week working with the tool.
"We tried to plan very well for a world of 10x growth per year," Amodei said. "And yet we saw 80x. And so that is the reason we have had difficulties with compute."
How Anthropic's dreaming feature teaches AI agents to learn from their own history
Dreaming is the most novel of the three features and the one Anthropic is most eager to distinguish from conventional memory systems. While the company launched agent memory earlier this year — allowing Claude to retain preferences and context within and across individual sessions — dreaming works at a higher level of abstraction. It is a scheduled process that reviews an agent's past sessions and memory stores, extracts patterns across them, and curates those memories so agents improve over time. It surfaces insights that no single agent session could see on its own: recurring mistakes, workflows that multiple agents converge on independently, and preferences shared across a team of agents.
Alex Albert, who leads research product management at Anthropic, explained the concept in an interview at the conference. He described dreaming as analogous to how people within organizations create skills after working through a task. "They might do a workflow with Claude, and at the end of that workflow, after they've iterated and zigzagged a little bit, they want to record that path from A to B," Albert said. "A very similar thing is happening with dreaming — instead of you manually creating the skill from your experience working with Claude, the model is doing it, so it has that same context for a future session."
Crucially, dreaming does not modify the underlying model weights. "We're not changing the model itself through dreaming — it's not doing updates to the weights or anything like that," Albert said. Instead, the agent writes learnings as plain-text notes and structured "playbooks" that future sessions can reference, making the entire process observable and auditable by humans. When asked about the trust implications of agents consolidating their own knowledge, Albert acknowledged that "there is a level of trust that you need to place" but noted that all memories are inspectable and that smarter models are getting progressively better at managing this process. "They're learning to write better notes for their future self," he said.
A live demo showed AI agents improving overnight without human guidance
During the keynote, the Anthropic team demonstrated all three features live on stage using a fictional aerospace startup called "Lumara" that needed to autonomously land drones on the moon for resource mining. The team configured a multi-agent system with three specialists — a commander agent responsible for overall mission success, a detector agent that identified high-quality landing sites, and a navigator agent that handled safe drone flight and landing — and defined a success rubric requiring soft landings, clear ground, and enough fuel reserves for a return trip to Earth.
An initial simulation across six hypothetical landing sites produced strong but imperfect results. To improve, the presenters triggered a dreaming session directly from the Claude Developer Console. Overnight, the dreaming agent reviewed all past simulation sessions and wrote a detailed descent playbook — a comprehensive set of heuristics drawn from patterns across multiple mission runs. When the team ran a new simulation the following morning with the dreaming-derived playbook in memory, the results improved meaningfully on the sites that had previously underperformed.
"All we had to do was just have Caitlin press a button," said Angela Jiang, Head of Product for the Claude Platform, referring to her colleague on stage. "All dreaming."
The demo illustrated how the three features compose together in practice. Multi-agent orchestration split the complex task across specialists with independent context windows. Outcomes provided the rubric against which a separate grader agent evaluated each run. And dreaming extracted lessons across those runs to improve future performance — forming what Anthropic describes as a continuous improvement loop that requires no human intervention between iterations.
Why Anthropic built a separate 'grader' agent to check Claude's own work
The outcomes feature, now in public beta, gives developers a way to define what success looks like using a rubric — a structural framework, a presentation standard, a brand voice, or any other set of criteria — and then lets the agent iterate toward that standard autonomously. What makes outcomes architecturally distinctive is its separation of concerns. When an agent completes its work, a separate grader agent evaluates the output against the developer-defined rubric in its own independent context window. Because the grader operates in a fresh context, it is not influenced by the working agent's reasoning or accumulated biases from the session.
When the grader identifies gaps between the output and the rubric, it pinpoints specifically what needs to change, and the working agent takes another pass. This loop continues until the rubric criteria are met — without a human needing to review each attempt.
Albert described Anthropic's broader verification strategy as employing "more test time compute, more models thinking about a problem for longer, to check over the work of another." He acknowledged that having a model check its own work raises reasonable questions, but said a fresh context window reviewing completed work consistently outperforms asking the same long-running thread to identify its own bugs. "You will get higher success if you give that output to a fresh Claude and say, 'what bugs do you see?'" he said. "There is still something to the attention" that degrades over very long sessions — a limitation he said Anthropic is actively working to fix in future models.
The approach mirrors strategies already in use at GitHub. Mario Rodriguez, Chief Product Officer at GitHub, described during a separate talk at the conference how Copilot uses a similar advisor pattern with Claude models — pairing a smaller, cheaper model as an executor with a larger model as a mentor. When the smaller model encounters a problem beyond its capability, it calls the larger model for guidance, then continues executing on its own. Rodriguez said the approach delivers near-Opus-level intelligence at significantly lower cost, and that GitHub inserts critique models at three specific points in the coding workflow: after drafting a plan, after a complex implementation, and after writing tests but before running them.
Parallel AI agents can now tackle tasks too complex for a single model thread
Multi-agent orchestration, the third feature moving to public beta, allows a lead agent to decompose a large task into subtasks and delegate each one to a specialist agent — each with its own model, system prompt, tools, and independent context window. Every step in the process is traceable in the Claude Console, showing which agent did what, in what order, and why.
The design gives each sub-agent an isolated context, which Anthropic says produces better results than having a single agent attempt to hold all the complexity in one thread. "Each sub-agent has its own independent thread and context window," the keynote presenters explained. "This is very intentional — we found that by splitting the work and then merging the results, we get better outcomes."
Albert offered his own heuristic for when multi-agent architectures make sense versus sticking with a single thread. "Parallel agents are better for investigation," he said — situations where there is a lot of context that will ultimately be discarded. "If you're trying to answer a specific question, you don't need all the search results from the areas where it didn't find the answer. You just need the answer." He described spinning up disposable sub-agents for specific retrieval tasks and bringing only the result back to the main thread. Increasingly, he said, the model itself will decide when to parallelize. "In the future, you won't really care if it's one agent or multi-agent or whatever's happening. You just have a Claude that you're talking to, and it will deploy the right architecture automatically."
Anthropic's bigger bet: closing the gap between AI capabilities and real-world adoption
The three features arrive as part of a broader platform push that Anthropic framed throughout the conference as closing "the gap between what AI can do and what it's actually doing for people." Ami Vora, Anthropic's Chief Product Officer, set the theme in her opening keynote, noting that while model capabilities are advancing on an exponential curve, most organizations are still adopting AI on a linear path.
Dianne Penn, who leads product for Anthropic's research team, described the company's measure of progress as "task horizon" — how long an AI agent can work autonomously while improving the quality of its deliverables. "This time last year, models could work for minutes," she said. "Now, most of us have agents running for hours on end. Tomorrow, we'll have agents that are proactive, always on, and know what to work on without losing the frame."
The event also included several infrastructure announcements designed to help developers keep pace. Anthropic said it is doubling its five-hour rate limits for Pro, Max, Team, and Enterprise plans, and raising API rate limits considerably. The company announced a partnership with SpaceX to use the full capacity of its Colossus data center to expand compute availability — a direct response to the demand crunch Amodei described.
All three features are built into Claude Managed Agents, which launched in public beta on April 8 as an opinionated harness that bundles best practices including memory, tool integration, and action handling. Anthropic says teams using Managed Agents have shipped 10x faster than those building their own agent infrastructure from scratch. Albert described the platform using an operating system analogy: "With managed agents, you don't need to think about all the technicalities of how you set up the surrounding system," he said. "You're building an application for Macs — you don't want to go have to re-implement every detail of macOS."
What dreaming, outcomes, and multi-agent orchestration mean for the future of enterprise AI
The competitive implications are significant. As AI agent platforms from OpenAI, Google, and others compete for developer adoption, Anthropic is betting that production reliability — not just raw model intelligence — will determine which platform wins enterprise budgets. The dreaming feature in particular stakes out new territory: while other platforms offer memory and tool use, the idea of agents systematically reviewing their own histories to extract reusable knowledge goes further toward the kind of continuously improving systems that enterprises need before delegating high-stakes work.
The conference showcased companies already operating at that scale. Mercado Libre, Latin America's largest e-commerce platform, has 23,000 engineers running Claude Code, has reviewed more than 500,000 pull requests with human oversight, and is aiming for 90% autonomous coding by the third quarter of this year. Shopify has deployed Claude Code across not just engineering but design, product, and data science teams.
But it was Dario Amodei who articulated the most expansive vision for where all of this leads. He described a progression from single agents to multiple agents to whole organizational intelligence — from "a team of smart people in a room" to what he called "a country of geniuses in the data center." And he reiterated a prediction he made roughly a year ago: that 2026 would see the first billion-dollar company run by a single person. "Hasn't quite happened yet," he said. "But we've got seven more months."
Dreaming is available now in research preview. Outcomes and multi-agent orchestration are in public beta and available to all developers on the Claude platform. Whether seven months is enough time for a solo founder to build a billion-dollar business remains an open question — but after Tuesday, they have a few more tools to try.
- Anthropic's measured CEO reveals a $30 billion run rate built on 80x growth
Dario Amodei is not the kind of CEO who talks loosely about numbers. The Anthropic co-founder and chief executive, a former VP of research at OpenAI with a PhD in computational neuroscience from Princeton, has built a reputation for measured public statements — particularly around the financial performance of a company that, until recently, disclosed almost nothing about its business.
So when Amodei took the stage at Anthropic's Code with Claude developer conference on Wednesday and offered a genuinely striking piece of financial candor, the room paid attention.
"We tried to plan very well for a world of 10x growth per year," Amodei said during a fireside chat with Anthropic's chief product officer, Ami Vora. "And yet we saw 80x. And so that is the reason we have had difficulties with compute."
Anthropic had planned for tenfold growth. But revenue and usage increased 80-fold in the first quarter on an annualized basis, a rate Amodei described as "just crazy" and "too hard to handle."
The number demands context. Annualized growth rates can overstate sustained performance — a single strong quarter, extrapolated across a full year, can paint a picture that doesn't hold. Amodei knows this. But the underlying trajectory is not a mirage. Anthropic has crossed a $30 billion annualized revenue run rate, up sharply from roughly $9 billion at the end of 2025, and that growth is being driven largely by enterprise demand. The company's revenue trajectory has been relentless: $87 million run rate in January 2024, $1 billion by December 2024, $9 billion by end of 2025, $14 billion in February 2026, $19 billion in March, and $30 billion in April.
For context: Salesforce took about 20 years to reach $30 billion in annual revenue. Anthropic did it in under three years from a standing start.
Claude Code became the fastest-growing product in enterprise software history
The growth story at Anthropic is, to a remarkable degree, a single-product story. Claude Code, the company's agentic AI coding tool launched publicly in mid-2025, has become the fastest-growing product in the company's history — and, by several measures, one of the fastest-growing software products ever built.
Claude Code hit $1 billion in annualized revenue within six months of launch, and the growth hasn't slowed down. By February 2026, the product was generating over $2.5 billion in run-rate revenue. The company also said Claude Code's weekly active users had doubled since January 1 and that business subscriptions had quadrupled since the start of 2026.
The mechanics of the product are straightforward. Claude Code is not a chatbot that suggests snippets. It reads a codebase, plans a sequence of actions, executes them using real development tools, evaluates the result, and adjusts its approach. The developer sets the objective and retains control over what gets committed, but the execution loop runs independently. The average developer using Claude Code now spends 20 hours per week working with the tool.
At Anthropic itself, the majority of code is now written by Claude Code. Engineers focus on architecture, product thinking, and continuous orchestration: managing multiple agents in parallel, giving direction, and making the decisions that shape what gets built.
That last point may be the most revealing detail Amodei disclosed at the conference: this is the first year Anthropic's own internal pull requests have inflected upward due to Claude's work on the company's own codebase. The tool that Anthropic sells to developers is now a material contributor to Anthropic's own engineering output. That creates a feedback loop that is almost impossible for competitors without a comparable product to replicate — the company is using its own product to build the next version of its own product.
The enterprise numbers tell the same story. The company now counts over 1,000 enterprise customers spending more than $1 million per year on Claude services, a figure that has doubled since February. Much of this increase has been fueled by a wave of corporate customers including Uber and Netflix.
Amodei framed the adoption curve in economic terms. "Software engineers are the ones who are fastest to adopt new technology," he said on stage. "It's a foreshadowing of how things are going to work across the economy, and how the economy is going to be transformed by AI."
Anthropic's 80x growth created a compute crisis it couldn't solve alone
Hypergrowth creates its own category of problem. When demand outstrips supply by an order of magnitude, the constraint is not go-to-market strategy or product-market fit. The constraint is physics.
The company is growing so fast that its infrastructure has struggled to keep up, forcing Anthropic into what may be the most unexpected partnership in the current AI cycle. Amodei's comments came hours after Anthropic announced a deal with Elon Musk's SpaceX to use all of the compute capacity at his company's Colossus 1 data center in Memphis, Tennessee. As part of the agreement, Anthropic will get access to more than 300 megawatts of capacity — over 220,000 Nvidia GPUs, including dense deployments of H100, H200, and next-generation GB200 accelerators.
The deal is remarkable for several reasons. Musk has been, until very recently, one of Anthropic's most vocal critics. He has said Anthropic is "doomed to become the opposite of its name" and wrote in February that "Anthropic hates Western Civilization." But on Wednesday, Musk changed his tune, saying he spent a lot of time with senior members of the Anthropic team over the past week and that he was "impressed." "Everyone I met was highly competent and cared a great deal about doing the right thing. No one set off my evil detector," Musk wrote.
The strategic logic on both sides is clear. xAI's Colossus 1 ended up with capacity that Grok's user base never grew into, while Anthropic needs compute immediately. Anthropic has been signing deals with Amazon, Google, Nvidia, and Microsoft for more compute capacity, but most of that isn't expected to come online until late 2026 or early 2027. The SpaceX deal gives Anthropic a significant boost now — the key word being "now."
As one industry watcher summarized the alignment: "Elon's enemy is Sam. Dario's enemy is Sam. Enemy of my enemy is a compute partner."
Last month, Anthropic said demand for Claude has led to "inevitable strain on our infrastructure," which has impacted "reliability and performance" for its users, particularly during peak hours. The company admitted in a postmortem from late April that three bugs had affected Claude Code since March 4, and that internal tests hadn't caught them, leading to several weeks of degraded performance. Amodei said at the Code with Claude conference that the company is "working as quickly as possible to provide more" capacity and will "pass that compute on to you as soon as we can."
A near-trillion-dollar valuation makes Anthropic's IPO the most anticipated debut in years
The growth figures arrive at a moment when Anthropic's valuation is itself becoming one of the defining financial stories of the AI era.
Anthropic has begun weighing a fresh funding round that would value the company at more than $900 billion, according to people familiar with the matter, potentially leapfrogging its longtime rival OpenAI as the world's most valuable AI startup. The velocity of the escalation is difficult to overstate. From $61.5 billion in March 2025, to $183 billion by its Series F in September, to $380 billion in February, to, if the current discussions proceed, more than $900 billion in May. Anthropic's shares were already trading at an implied $1 trillion valuation on secondary markets earlier this month.
Instead of cashing out, many existing investors are waiting to potentially exit during Anthropic's anticipated IPO later this year. The company is raising what is likely to be its last private round before going public to fund its massive computing needs. Bloomberg has reported that the company is weighing an IPO as early as October 2026, with Goldman Sachs, JPMorgan, and Morgan Stanley already in early discussions.
Anthropic is also building out infrastructure on longer time horizons. Amazon has agreed to invest up to $25 billion in Anthropic, securing up to 5 gigawatts of compute capacity for training and deploying Claude models. Anthropic also secured 5 gigawatts of computing capacity as part of a separate deal with Google and Broadcom that will start to come online next year. The total commitment is staggering — tens of gigawatts of compute across three separate hardware ecosystems: Amazon's Trainium chips, Google's TPUs via Broadcom, and Nvidia GPUs through SpaceX and Microsoft Azure.
For perspective: Anthropic's $30 billion run rate exceeds the trailing twelve-month revenues of all but approximately 130 S&P 500 companies. A company that was essentially pre-revenue in early 2024 now out-earns most of the Fortune 500.
That comparison comes with caveats. Private-market revenue run rate is not the same thing as audited GAAP revenue, gross margin, free cash flow, or public float. OpenAI has internally argued that Anthropic's $30 billion figure is overstated by roughly $8 billion, pointing to questions about whether revenues from AWS and Google Cloud should be reported at gross value or net of the partner's cut. The accounting question will ultimately be resolved when both companies file IPO prospectuses — but even on a net basis, Anthropic's growth rate is unlike anything in enterprise software history.
Dario Amodei's vision for AI extends far beyond coding — and he's given himself a deadline
The financial story — 80x growth, a near-trillion-dollar valuation, a scramble to secure enough GPUs to meet demand — is dramatic on its own terms. But Amodei used his time on stage to place it inside a larger thesis about where AI is headed.
He described a progression from single agents to multiple agents to what he called whole organizational intelligence — from "a team of smart people in a room" to "a country of geniuses in the data center." The framing is deliberately expansive. What Anthropic is selling today is a coding tool. What Amodei is describing is a future in which entire categories of knowledge work are performed by fleets of AI agents operating in parallel, supervised by humans who define objectives and review outputs.
He reiterated a prediction he made roughly a year ago: that 2026 would see the first billion-dollar company run entirely by a single person. "Hasn't quite happened yet," he said. "But we've got seven more months."
The company has also been navigating political headwinds. The Pentagon declared Anthropic a supply chain risk in March, blacklisting it from work with the military. The company has warned the designation could result in billions in lost revenue, with over one hundred enterprise customers reportedly expressing doubts about continuing their relationships.
And yet — as that scuffle makes its way through the legal system, Anthropic is only getting more popular. Amodei said this week he's eventually hoping for "more normal" expansion.
There is a temptation, when covering a company growing at this rate, to let the numbers speak for themselves. They shouldn't. Growth at 80x annualized is not a business plan — it's an emergency. It means demand has outrun infrastructure, that customers want something the company cannot yet reliably deliver at scale, and that every week of constrained capacity is a week during which competitors can close the gap.
The investors funding Anthropic — including SoftBank, Amazon, Nvidia, Google, a16z, Lightspeed, and ICONIQ — are making a specific bet: that compute costs continue to fall per unit of intelligence, that revenue keeps compounding faster than burn, and that whoever owns the AI infrastructure layer in 2029 will generate returns that make the interim losses irrelevant.
Amodei's candor at Code with Claude was not a victory lap. It was a diagnostic — an admission that his company is running faster than it can steer. He planned for a world of 10x growth and got 80x instead. Now he has seven months to prove that the infrastructure, the organization, and the vision can catch up to the demand. The country of geniuses in the data center is getting crowded. The question is whether anyone remembered to build enough rooms.
