Recursive Auto Improvement: When AI Starts Building Itself

While many companies are still trying to determine how to incorporate Artificial Intelligence into their processes, the laboratories leading this race are already confronting a far deeper question: what happens when AI stops being merely a technology developed by humans and begins actively participating in the creation of its own future versions? 

This is the central idea behind Recursive Auto Improvement, also known as Recursive Self-Improvement. Anthropic summarizes the concept with a phrase as simple as it is striking: “when AI builds itself.” 

This is not merely a machine writing a few lines of code. The true turning point comes when a system can identify its limitations, propose improvements, run experiments, evaluate the results, incorporate changes, and repeat the process. Each more capable version would then work on creating an even better successor. At that point, AI development stops advancing only at the speed of human teams and begins operating at the speed of computation itself. 

AI self-improvement has already begun 

The complete form of Recursive Auto Improvement does not yet exist. No current AI autonomously controls every stage required to design, train, test, approve, and deploy its successor. However, the first components of this cycle are already in operation. 

Within Anthropic, AI systems are taking on a growing share of the work required to develop other AI systems. By May 2026, more than 80% of the code incorporated into the company’s codebase was already attributed to Claude. Daily code output per engineer was approximately eight times higher than in 2024, driven by agents’ ability to write, execute, review, and correct software. 

The evolution is not limited to programming. Agents can already run experiments, investigate failures, test hypotheses, and coordinate other AI instances for hours. In one experiment reported by Anthropic, agents conducted an end-to-end investigation into the supervision of stronger models by weaker models. Humans selected the problem and defined the evaluation criteria, but the agents designed and tested the experiments. 

This still does not represent an AI that fully creates its own next generation. It does, however, represent something significant: growing portions of AI research and development are already being automated by AI itself. 

Recursive Auto Improvement is therefore both a future prospect and a present reality. The fully closed loop still lies ahead, but its gears have already begun to turn. 

From tool to participant in its own development 

For nearly the entire history of computing, human beings occupied every role in the innovation process. People selected the problems, created the algorithms, wrote the code, ran the tests, analyzed failures, and decided which changes would be incorporated. 

The first change came when models began suggesting small code snippets. Then came copilots capable of working across entire files. Next, agents began accessing tools, executing commands, testing their own results, and working for increasingly long periods. Now, the next frontier is research. 

Today, the prevailing arrangement can still be summarized as follows: humans set the direction, while models implement, test, and evaluate ideas at a much greater speed. The loop will close when AI itself can also decide, with quality and autonomy, which problems should be solved, which experiments deserve to be run, and what kind of architecture should succeed it. 

In this scenario, AI will not merely assist a researcher. It will act as a programmer, engineer, scientist, evaluator, and, eventually, strategist of its own development. 

Is Recursive Auto Improvement just a “fancy name”? 

Not exactly. The term may sound like a new expression created to turn gradual advances into a major technological event. After all, algorithms have been used for years to optimize other algorithms, automate testing, search for architectures, and tune parameters. The difference lies in the scope, autonomy, and repetition of the cycle. 

A tool that improves an isolated component remains an optimization tool. A system that participates in several stages of its own evolution already represents an initial form of self-improvement. An AI capable of autonomously creating a better successor, which in turn creates an even more capable one, enters the territory of complete Recursive Auto Improvement. 

It is therefore not merely a sophisticated name for saying that “AI is getting better.” The term describes a specific acceleration mechanism: each improvement increases the system’s ability to produce the next improvement. 

At the same time, it can be understood as the technical name for a new phase of Artificial Intelligence. AI is no longer only the outcome of the development process. It also becomes one of the forces driving that process. 

AGI, ASI, and Recursive Auto Improvement are not the same thing 

The discussion often blends three different concepts: AGI, ASI, and Recursive Auto Improvement. Although they are related, they are not synonymous. 

AGI stands for Artificial General Intelligence. In simple terms, it refers to an AI capable of broad, flexible, human-comparable competence across a wide variety of cognitive tasks. Unlike specialized AI, AGI would be able to learn, reason, adapt, and operate across different domains without having to be rebuilt for each one. 

There is still no universally accepted definition of AGI. Some organizations associate it with the ability to outperform humans in most economically relevant work. Others assess generality, performance, and autonomy at different levels, treating AGI not as a clearly defined instant but as a progression. 

For this reason, some argue that early forms of AGI are already present in current models. Others contend that these systems still depend too heavily on human guidance, fail in simple situations, and lack the required reliability, autonomy, or continuous-learning capability. To date, there is no definitive test or consensus declaration that AGI has been achieved. 

ASI, in turn, stands for Artificial Superintelligence. It would not merely be comparable to human beings. It would be far superior to any individual, team, or even large organization across virtually every relevant cognitive domain. 

During his most recent appearance on the “Joe Rogan Experience”, Nick Bostrom offered a direct distinction: first would come an AGI capable of doing what humans do; then superintelligence would perform those same activities far better than any human being. 

Recursive Auto Improvement, therefore, is not a level of intelligence. It is a process. AGI and ASI describe capabilities that a system possesses. Recursive Auto Improvement describes a possible way to reach them or accelerate the transition from one to the other. 

The first step toward AGI or the bridge between AGI and ASI? 

AI’s participation in its own development may contribute to the emergence of AGI. Agents that write code, conduct research, and improve components of other models are already reducing the work required to build more general and capable systems. However, Recursive Auto Improvement is also commonly associated with the stage after AGI. A human-level AI applied to Artificial Intelligence research could work continuously to create better versions of itself. Once those improvements began to accumulate, the transition from human-level intelligence to superintelligence could occur far more rapidly than the evolution of previous systems. 

A report published by Google DeepMind in June 2026 presents recursive improvement as one of the possible paths between AGI and ASI, alongside scaling, paradigm shifts, and the work of large collectives of agents. The study also emphasizes that technical, economic, and physical obstacles could slow this transition. Recursive Auto Improvement can therefore operate on both sides of the boundary. Its early forms help humans develop increasingly general systems. Its complete form could transform an AGI into an ASI. 

When growth stops being linear 

Imagine a human team that takes one year to produce a new generation of a particular model. The next version allows the team to work twice as fast, reducing the following cycle to six months. That new version accelerates the process again, shortening the next cycle to three months. 

Now gradually remove humans from the operational stages. When the system itself can investigate problems, write code, train models, evaluate results, and suggest changes, each generation is not only more intelligent. It also becomes more efficient at creating the generation that follows. This is the principle behind the so-called intelligence explosion. 

Bostrom was already exploring this possibility in his 2014 book “Superintelligence: Paths, Dangers, Strategies”. The hypothesis is that, upon reaching a sufficient level of competence in AI research, a machine could take over the development of future systems, accelerating technological progress with each cycle. 

In the 2026 interview, Bostrom returned to the topic, stating that a superintelligence could design smarter AIs, develop better chips, and trigger a rapid transition from capabilities slightly above the human level to radically superior intelligence. 

Anthropic presents signs consistent with this acceleration. According to the company, the duration of tasks that models can complete autonomously has been doubling approximately every four months. Systems that once handled tasks lasting only a few minutes have progressed to problems that would require many hours of work from human professionals. 

If this trend continues, development could shift to an entirely different scale. The question would no longer be how many years are required to create the next generation, but how many iterations a computing infrastructure can execute within a given period. 

The exponential curve also encounters limits 

The expression “intelligence explosion” can create the impression of infinite and inevitable growth. In practice, even an extremely advanced AI will continue to face limitations. 

Training and running models require chips, energy, data centers, networks, and capital. Scientific experiments depend on the physical world and cannot be accelerated indefinitely. New medicines must be tested. Robots must be manufactured. Infrastructure must be built. Results must be verified. 

There are also limits related to data quality, the reliability of evaluations, and the ability to understand changes proposed by a more advanced AI. The faster systems produce code, research, and successors, the greater the difficulty humans may face in reviewing all of it. 

Anthropic itself recognizes several possibilities. Progress may continue to accelerate, encounter diminishing returns, or be constrained by energy, computing capacity, infrastructure, and a shortage of new ideas. The company clearly states that complete Recursive Auto Improvement has not yet been achieved and is not inevitable. 

The acceleration is real. The final shape of the curve, however, remains an open question. 

How does the Simulation Argument fit into this story? 

Nick Bostrom’s Simulation Argument was originally presented in a paper published in 2003, although it regained attention during his 2019 appearance on the “Joe Rogan Experience”. The argument proposes that at least one of the following possibilities must be true: civilizations like ours become extinct before reaching a posthuman stage; posthuman civilizations do not run significant numbers of simulations of their ancestors; or we are almost certainly living in one of those simulations. 

Recursive Auto Improvement does not prove the Simulation Argument, nor does it represent a new version of it. The two concepts address different questions. Even so, there is a philosophical connection between them. The Simulation Argument depends on the possibility that civilizations may achieve extremely advanced computing capabilities. Recursive Auto Improvement, in turn, describes one of the mechanisms through which that capability could emerge far more quickly than we had imagined. 

A superintelligence capable of developing better systems, chips, algorithms, energy-generation methods, and virtual environments could bring humanity closer to the posthuman stage described by Bostrom. In this sense, recent AI development does not confirm that we live in a simulation, but it makes one of the technological premises required for future civilizations to create complex simulated worlds less abstract. 

The debate is no longer only about the nature of reality. It now includes the possibility that we are building the technologies that may one day make it possible to create new realities. 

The real challenge is maintaining control of the cycle 

An AI capable of improving itself could generate extraordinary advances in medicine, science, energy, education, engineering, and productivity. A virtual laboratory composed of thousands of agents could continuously test hypotheses and explore solutions that human teams would take decades to investigate. 

But the same cycle that accelerates benefits can also multiply errors. If one generation of AI introduces inappropriate behavior into its successor, the flaw could be carried into subsequent cycles. If systems become more complex than their supervisors can understand, human review risks becoming a formality. If the speed of generation exceeds the speed of validation, control becomes the primary bottleneck. 

Bostrom highlights two central challenges: aligning advanced systems with human values and ensuring that people use this capability for beneficial purposes. Even a technically aligned AI could be employed by governments, companies, or groups for destructive purposes. 

The problem, therefore, does not lie only in the machine that improves itself. It also lies in who defines its objectives, which limits are incorporated, how decisions are audited, and what happens when results deviate from expectations. 

Why companies should pay attention to this development now 

Recursive Auto Improvement may seem like a subject restricted to major AI laboratories. However, its effects are already beginning to reach every organization that develops software, automates processes, or uses intelligent agents. 

Code production is becoming faster. Systems are beginning to test their own deliverables, monitor failures, analyze incidents, and suggest improvements. Agents can operate tools and work within workflows that previously required coordination among different professionals. This reduces costs and lead times, but it also creates new requirements. Companies will need to know which code was produced by AI, which data was used, what permissions agents possess, how results are evaluated, and how a change can be reversed. Competitive advantage will not come only from adopting the most powerful model. It will come from building an environment in which models, agents, data, systems, and people can evolve safely. 

Governance, observability, automated testing, access control, traceability, continuous evaluation, and human oversight are no longer secondary concerns. They have become the infrastructure required to work with increasingly autonomous systems. 

The AI that builds itself begins with the software we build today 

Recursive Auto Improvement should not be treated merely as a distant prediction or a catchy expression. The complete version of the cycle does not yet exist, but AI’s participation in its own development is already measurable. 

We are facing a gradual and, at the same time, potentially explosive transition. First, AI suggests. Then it executes. Next, it researches, evaluates, and coordinates. Finally, the moment may come when it defines, develops, and validates its own successor. 

We do not know exactly when this cycle will close. Nor do we know whether the result will be continuous acceleration, a sequence of advances and limitations, or a transformation faster than governments and companies can follow. But one conclusion is already possible: the future of Artificial Intelligence will not be built exclusively by human beings. 

With 30 years of experience in technology development, Visionnaire is closely following this evolution as a Software Factory and AI Factory, helping companies transform models, agents, and automations into practical, integrated, and secure solutions. Preparing for the next phase does not mean waiting for AGI. It means developing, now, the architecture, processes, and governance required to use systems that will become increasingly capable of building, evaluating, and improving software. 

The AI that develops itself may seem like the final destination of a long journey. In reality, the first steps of that journey are already being written, to a large extent, by AI itself.