RSI Has Become the New Goal of AI Labs, But Its Definition Remains Blurred

Recursive Self-Improvement is rapidly displacing AGI from the agendas of AI labs. The concept remains as blurred and elusive as its predecessor.

What is RSI

RSI (Recursive Self-Improvement) is a hypothetical ability of an AI system to analyze and improve its own code without human intervention. The system becomes increasingly powerful and capable until it reaches a point where humanity can no longer control it or understand what exactly happened.

In theory, this sounds simple and logical: to improve means to work faster, more accurately, efficiently, and reliably. In practice, determining whether the system has actually improved or simply feels more confident in its capabilities turns out to be extremely difficult.

How do you distinguish genuine progress from the illusion of development? Who verifies it?

Why AGI Is Making Way for RSI

A few years ago, AGI seemed like the inevitable endpoint of AI development. General artificial intelligence—humanlike and universal, capable of solving any task at human level or beyond. But the definition was too precise and unattainable, like the horizon. Decade after decade, AGI kept receding further into the future.

RSI offers a different path. There's no need to wait for AGI—all that's needed is for the system to learn to improve itself independently. It's an intermediate goal that appears simultaneously more realistic and more frightening to investors and policymakers.

The advantages of RSI are obvious:

• A more realistic intermediate goal than AGI
• Easier to convince investors of project viability
• No need to wait another two or three decades
• Can start with small self-improvement steps right now
• Looks less threatening than AGI

This is why even conservative labs have switched to RSI in their long-term plans and public statements.

The Problem of Definition (That No One Is Solving)

It would seem everything is simple and clear. But that's when the problems begin. One researcher talks about self-improvement through neural network weight optimization. Another means automated code debugging and error correction. A third—restructuring the model's architecture entirely. A fourth simply talks about the system becoming better at its primary task. Each of them is right, but each is talking about completely different things. Yet they all call it the same word: RSI.

"Everyone understands RSI differently.

This is exactly what happened with AGI ten years ago—existential risk from something no one can define."

When the definition is blurred, it becomes impossible to set specific goals, measure progress, or even assess actual risk. Labs talk about RSI as if it were a single goal and a single finish line, when in reality they're talking about ten different projects that share the same name.

What This Means

RSI is neither an evil plot nor a conspiracy, but an honest sign that AI labs need an intermediate milestone between today's LLMs and philosophical AGI. History repeats itself: when one undefined goal (AGI) becomes unattainable, another (RSI) is born. The question remains the same: how do you measure what hasn't been defined? How do you manage risks from something that could mean any action? So far there is no better answer to this question. And meanwhile, labs talk about RSI as if it were already a solved problem.