Anthropic’s Recursive Self-Improvement (RSI) research (i.e. AI autonomously designing and improving its own successors) isn’t about “superintelligence” or a “pause.” It is the seventh “tremor” in the shift from tools to goal-seeking agents. Intelligence and the Harness (routing, memory, orchestration code) are commoditizing fast and becoming self-improving, but real scarcity, which defines durable value and moats, migrates to the two true bottlenecks: scarce compute (which RSI actually intensifies) and operational context (the real-world data, outcomes, and judgment that labs can’t automatically generate or own). RSI accelerates everything except the parts enterprises control, giving Anthropic’s enterprise/Harness lead a powerful booster. But it’s not an unassailable moat yet.
Even with the historic SpaceX IPO scheduled for later this week and rival OpenAI confidentially filing for its own public offering, Anthropic once again managed to hijack the public discussion about artificial intelligence. As is often the case in these early days of the Agentic Era, I think that the public conversation has tended to focus on the more immediate and dramatic aspects, while the deeper economic significance has received less attention.
In this case, the conversation stopper came not in the form of a new product, but rather a research paper published by Anthropic’s research arm: “When AI builds itself.“ The paper represented its most detailed public account yet of how it is developing a technique known as “recursive self-improvement” (RSI), which it describes as “an AI system capable of fully autonomously designing and developing its own successor.” The publication landed weeks after Anthropic scored a recruiting coup by hiring legendary AI researcher Andrej Karpathy, a founding member of OpenAI and formerly of Tesla, who recently had been working on his own RSI project, to build a team to accelerate the pre-training of the next Claude.
Because the Anthropic paper discussed the “implications” of such a system, the world focused almost solely on a single conversation:
It seemed astonishing that the company would call for such a thing, considering that on June 1, Anthropic confirmed it had confidentially submitted a draft S-1 that reportedly included an $47 billion ARR and had announced a $965 billion funding round.
So, was this a savvy PR exercise at a time of intense competition for attention, to steal the spotlight from the $1.75 trillion SpaceX IPO? Was this a branding exercise to reinforce Anthropic’s image as the good governance LLM?
Of course, the first problem is that Anthropic wasn’t really calling for a pause. But I will leave it to Gary Marcus to explain why.
However, from my perspective, the larger implication of this announcement flows back to the framework of Orchestration Economics that I have been building over the past three years. In the Orchestration Economics Manifesto, I define generative and agentic AI as Discontinuity, a geology that has been built as a sequence of six shocks along a fault line, each amplifying the others until the boundary gave way and created a structural break as machines crossed from being tools that process instructions to actors that pursue goals.
RSI represents the seventh tremor in that continuum. Again, the instinct is to view RSI as a capability story, perhaps a step toward superintelligence or AGI. This also misses the point. As with the other six tremors, the real question is what happens to value and moats when the production of intelligence itself becomes recursive.
Every major shift in economic history has been accompanied by a migration of scarcity. When a factor becomes abundant, value moves elsewhere. If recursive self-improvement works, even partially, it does not merely accelerate intelligence. It accelerates the search for whatever intelligence cannot produce.
That is why Anthropic’s announcement matters. Not because Claude may someday build Claude, but because it gives us a glimpse of where scarcity goes next and where value and moats are migrating: Further away from the Model layer, more firmly in the Harness, and even closer towards the Orchestration layer. And, naturally, to compute.
Recursive self-improvement as a tremor in the Discontinuity
Let’s start with two important bits of context before I dive back into Claude and RSI
Between September 2024 and February 2026, I counted six tremors: intelligence arriving, intelligence becoming cheap, silicon independence, protocol standardization, the inference swarm, and the long-context frontier. They arrived separately and reinforced one another into a single structural break.
Recursive self-improvement is the seventh. And yet, its character is different.
The earlier shocks expanded the supply of intelligence, made it cheaper, or taught it to coordinate. RSI changes the machine that produces the intelligence in the first place, the engine of the whole sequence. If it starts building better versions of itself, everything compounds.
Anthropic is careful to frame this as a fork, not a fait accompli, and sketches three scenarios:
Scenario 1: The trend stalls. The gains plateau and capability diffuses without a runaway.
Scenario 2: The one we are visibly in, the efficiency gains compound but humans still set the direction, choosing which problems matter and judging the results.
Scenario 3: The loop closes. AI designs and trains its own successor, end to end, and the pace of progress becomes bounded only by the availability of compute.
The real question is the distance between the second and third scenarios. Because that when the human stops being the one who decides.. The seventh tremor is the crossing toward the third.
The Institute does not ask us to take this progress on faith. The numbers are disclosed. As of May 2026, more than 80% of the code Anthropic merges is written by Claude. That’s up from low single digits before Claude Code shipped in February 2025. Anthropic leadership has put the looser figure north of 90%. The typical engineer merges roughly eight times as much code per day as in 2024. In April, Claude shipped over 800 fixes that cut one class of API errors a thousandfold, work the supervising engineer estimated would have taken a human four years.
Lines of code are a crude proxy, and Anthropic says so itself. The median engineer self-reports only ~4× uplift, and Anthropic cites METR’s finding that developers overestimate AI productivity gains. A competing arXiv analysis (2602.04836) argues the METR curve may not be cleanly exponential. None of this changes the direction - code authorship >80%, quality at parity and rising- but the argument rests on the direction and the mechanism, not on any single headline multiple.
Now let me add the second bit of context: the Three Rings.
If those six tremors turned machines into actors, the central question of the Agentic Era is: Who directs them? The Agentic Enterprise is organized around three rings: Intelligence, the Harness, and the Orchestration Layer.
When applying Orchestration Economics, the job is to determine where a company sits within the Three Rings and if it is capable of moving further out to capture more value, because that is the whole game. Viewed through this lens, Anthropic’s SRI disclosure tells us the following:
The most obvious leap is in Ring 1: Intelligence, the cognitive substrate. Hand Claude some model-training code and ask it to make it faster while passing the same checks. In May 2025, Opus 4 averaged a 3x speedup. By April 2026, Mythos Preview was hitting 52x in four to eight hours, compared to the 4x a skilled human needed in the same time. On that narrow, verifiable task, the model went from helpful to superhuman in under a year. Measured from the outside, METR finds the task length a model can complete unsupervised, now doubling roughly every four months, up from seven. Opus 4.6 handles twelve-hour tasks, and Mythos Preview reportedly ran for sixteen.
The more interesting leap is in Ring 2: The Harness. This is the routing engine that decides where every unit of work flows. The Harness decomposes a goal, spawns sub-agents, manages state, retrieves memory, and reviews output. The substrate, the intelligence, is commoditizing. The model is not the moat. The labs know this, which is why they are no longer primarily focused on building chat interfaces. Instead, they are racing to build the best Harness.
RSI is what lets them build it. The Harness is code, the one substance RSI has proven it can improve. A self-improving model can autonomously write, test, and optimize its own integration script, its own pointers, the connectors that wire it into every tool, system, and sub-agent, grading each version against a clean metric: did the task complete, faster, and more coherently?
When 80% of Anthropic’s merged code is Claude’s, much of it is not the model. You do not write a model in a pull request. It is the Harness Claude Code built by Claude.
The model is becoming the engine that wires itself into everything and then optimizes the wiring. That is what it means to become the central routing engine of the orchestration graph: not a smarter chatbot, but the node that decides where work goes and is increasingly written by the thing it routes.
We saw how deep this runs in late March 2026 when Anthropic accidentally exposed the source code of Claude Code, some 1,900 files, and a full agent runtime. This revealed the orchestration thesis rendered in code. Its most telling component is the pointer system: a small, always-loaded index governing what the model loads, fetches, or forgets across a long task, routing-and-memory logic, a self-improving model can rewrite against its own metrics by lunchtime.
That is the agent improving the scaffolding that runs the agent, and it is how the lab compounds its central role in the orchestration graph.