# "Recursive Self-Improvement" Is Three Different Things

I think "recursive self-improvement" is load-bearing ambiguous in AI risk discourse. In conversations, it refers to at least three qualitatively different processes that share a family resemblance but differ in basically every practically relevant dimension: mechanism, speed, observability, bottlenecks, governance implications, timeline relevance.

Treating them as one thing produces confused models and bad intuitions. So I want to pull them apart explicitly.

**Type 1: Scaffolding-Level Improvement**

This is the one that's already happening and empirically observable with coding agents.

The mechanism: better orchestration → better task decomposition → better use of existing cognition → emergent competence gains. You wrap the same base model in better scaffolding and suddenly it can do things it couldn't do before. No algorithmic breakthrough required.

This is recursive improvement of *systems*, not of *minds*. But it may be the case that  systems self-improvement can still kill worlds. Or, at least, systems improvement can still cause massive harm, enable misuse, and produce dangerous autonomous agents earlier than expected.

I think a lot of immediate AI risk comes from this layer. You don't need foom to get powerful autonomous systems doing economically or politically significant things with minimal human oversight. And as we see now, they won't even need to escape as the access is granted freely.  

The bottlenecks here are mostly engineering and integration, not fundamental capability limits. Which means progress can be fast, cheap, and somewhat unpredictable.

**Type 2: R&D-Level Improvement**

This is the dominant notion in formal takeoff models and big-picture forecasting (the AI-2027-style analyses). It's about AI compressing the AI research cycle.

Mechanism: AI helps design architectures, tune hyperparameters, discover training tricks, automate experiments. Human researchers become more productive. Research cycles that took months take weeks. The production function for intelligence gets a multiplier.

This is recursive improvement of the *process that produces intelligence*, which is not the same as intelligence modifying itself. The distinction matters because the bottlenecks are different. R&D-level improvement is constrained by:

*   Compute availability
*   Data
*   Ideas (which may not be parallelizable)
*   Organizational overhead
*   Testing and validation time

It's slower than scaffolding improvement and much more capital-intensive. At the same time, less observable from the outside. But it directly affects the slope of capability growth curves, which is why it dominates timeline discussions.

**Type 3: Model-Internal Self-Modification**

This is classical foom in modern deep learning realities. 

The mechanism, to the extent anyone has a concrete story: advanced mechanistic interpretability + optimization → intentional redesign of cognition → potential runaway feedback. The model understands itself well enough to rewrite itself in ways that make it smarter, and those improvements enable further improvements.

This is where we probably have the most uncertainty. I mean, we know neither the level of capabilities needed to launch that, nor the speed, and hence it is very hard to incorporate it into timeline forecasts. And of course, treacherous turn may happen very well before that. 

Many people implicitly mean *only this* when they say "recursive self-improvement." 

Some people go even narrower and mean only "mechinterp-aided optimization of a particular model" rather than "deep-learning-based AI invents proper AI engineering and builds a successor from scratch based on scientific principles instead of outsourcing the job to gradient descent".

**The Question to Ask**

Instead of "will recursive self-improvement happen?", try:

*   Which feedback loop?
*   At what layer?
*   With what bottlenecks?
*   On what timescale?

Different answers have different implications. Sometimes they're more tractable to analyze.