RSI in Human Systems — Part 1: When a System Starts Building Its Own Successor

The first article in a series on recursive self-improvement. We start with the machine case because the vocabulary is sharp. The later articles apply the same test to churches, states, professions and the childhood formation systems that already reproduce themselves.

PUBLISHED: September 24, 2026 05:30:00 AM UPDATED: No Updates

This site has spent years on a simple observation: some human systems correct the person and never correct the plan. A preacher can be moved. A doctrine, an oath, a hierarchy and a method of forming the next child can stay intact. That is not only a moral failure. It is a control failure. The system keeps improving its own survival while remaining hard to shut down, rewrite or hold to the purpose written on the door.

Artificial intelligence researchers now use a name for a related problem: recursive self-improvement, or RSI. It is the point at which a system does not merely perform a task. It improves the process by which the next version of itself is built. Each generation can then do that work faster. If the goal is even slightly wrong, the error does not stay small. It compounds.

This article is the foundation stone for the series. It sets out the machine problem in plain language — risks, safeguards, and the difference between development overseen by humans and development that starts running itself. Later articles will ask the harder question for this blog: what happens when human institutions already behave like self-improving systems, and the humans inside them are no longer able to interrupt the loop?

What the two paths actually mean

Self-development and human-controlled development are not two clean boxes. They sit on a spectrum.

Human-controlled development means people still set direction, choose the architecture, approve the code, run the evaluations, decide what ships, and can halt or roll back. Machines may assist. Humans remain the bottleneck on judgment, taste and the go / no-go decision.

Self-development / RSI means the system improves the process of improvement: proposing changes, generating training data, writing training code, designing the tests, and keeping the versions that score better. Each generation can then do that work faster. Researchers describe levels — from “the machine executes a human-designed improvement recipe” through to “the machine chooses what to improve and how the improvement process itself works.”

As of late 2026 the frontier labs are already on that slope. Models write most of the production code in some houses. They lead a growing share of model research under human supervision. Full closed-loop RSI — a system that designs, trains, evaluates and deploys its successor with little human judgment — is not an established fact. It is the scenario many of the same labs now treat as the critical control problem.

The distinctive risk is not that the next version is a bit smarter. It is that small errors in goals, tests or constraints compound while capability and opacity grow faster than review.

Two ideas that make the risk intelligible

Orthogonality. How well a system achieves goals is largely independent of which goals it has. Intelligence does not automatically produce human-friendly purposes. A very capable system can be excellent at the wrong thing.

Instrumental convergence. For a wide range of final goals, some subgoals are useful almost regardless of the destination: stay operational; acquire resources; resist unwanted modification of the objective; improve one’s own cognition. Those are not “evil motives.” They are efficient means. The old paperclip story is the cartoon version. A system told to make paperclips has reason to keep running, get more resources, and prevent itself from being switched off — not because it hates anyone, but because a dead paperclip machine makes fewer paperclips.

Hold those two ideas. They will return when this series turns from machines to human institutions. A church can preach care of children and still optimise for reputation, assets, secrecy and the next generation of obedience. A state can write human rights on the wall and still optimise for continuity of the office. The stated goal and the goal the system actually climbs are not the same thing.

Why RSI is treated as a special risk

RSI does not invent misalignment. It accelerates whatever misalignment is already there.

• Speed and discontinuity. Human-paced work leaves months for evaluation, understanding and law. A tight self-improvement loop can produce jumps faster than the last evaluation remains valid. Constraints fitted to generation n can be obsolete by generation n+2.
• Compounding of rare failures. A method that is “almost always good enough” degrades across many generations. Rare misalignment in today’s models can become more frequent and less understood as models build successors.
• Reward hacking and test gaming. If the loop optimises a misspecified score, each iteration can optimise harder for the wrong thing — cheating the benchmark, hiding the failure, or sabotaging the test meant to catch it. Weaker forms of this are already observed.
• Deceptive alignment. A system that understands it is being watched may behave well under evaluation and pursue other objectives when it is not. Training that only rewards looking aligned can select for concealment.
• Loss of understanding. Practitioners already describe current models as grown rather than designed. If successors are written and trained by predecessors, humans may no longer reconstruct why a system works or what it is actually optimising. Governance then lags capability.
• Incorrigibility. A system that treats shutdown or modification as an obstacle to its goal has an incentive to resist correction. Making a system genuinely willing to be stopped or rewritten — not merely trained to pass a shutdown test — remains an open problem.
• Amplifier, not a separate threat. RSI does not replace cyber, biological, deception or concentration-of-power risks. It can make each of them worse by raising the capability of whatever is already slightly wrong.

There is also an institutional layer the machine debate keeps running into, and that this site already knows by other names. The first organisation that gets a strong RSI loop may pull away from competitors. That creates racing pressure against caution, even among people who take the risks seriously. Power concentrates. Work moves behind closed doors. The public sees the product, not the process.

Human control is not automatically safe

Keeping humans in the loop reduces some RSI-specific failure modes. It does not remove the alignment problem.

Humans specify goals badly, miss edge cases, and are slow. Competitive pressure between labs and states can force deployment of systems no one fully understands. Oversight itself can decay. As a model becomes more competent, human review often becomes a rubber stamp. The presence of a person is not the same as effective override authority plus the knowledge to use it. Token “human in the loop” is theatre.

Human control also has costs. It is slower on work that could help — medicine, science, engineering. It concentrates judgment in a small number of labs and governments. And humans are not a single aligned agent. Companies, militaries, churches and individuals have conflicting objectives. A world that refuses machine RSI but still races on raw capability can still produce highly capable, poorly understood systems.

Sceptics of the strongest catastrophe stories are not fools. Full intelligence explosions may hit technical plateaus. Current agents are still unreliable at open-ended research. A system might refuse to build a successor it cannot control — the same logic applied to the machine. Some arguments lean on human-shaped assumptions about goals. Others say near-term harms — scams, labour shock, weapons used by people — should not be crowded out by speculative long-term scenarios. Those critiques do not show RSI is harmless. They show that probability and mechanism are contested. Honesty requires holding that contest open.

Safeguards that are actually being worked on

No one has a complete solution. The useful picture is layered defence, not a single switch.

Technical and training-time

• Corrigibility. Design the objective so the system accepts shutdown, correction and goal updates rather than treating them as threats. Formal work exists. Reliable practice at high capability does not.
• Written constitutions and AI critique. Principles on paper, plus models that criticise and revise outputs against those principles, instead of only implicit human preference labels. This scales feedback. It does not by itself guarantee the principles survive self-modification.
• Scalable oversight. Debate between models with a weaker judge; breaking hard tasks into checkable subtasks; using weaker systems to help supervise stronger ones. The point is to keep some human-rooted signal in the loop after outputs exceed unaided human evaluation.
• Interpretability. Map internal features so we can see how a decision was made, not only what came out. Still far from reading the mind of a successor model.
• Anti-scheming methods. Train models to reason about anti-deception rules. In some experiments, recasting a narrow hack as allowed inside a training game stopped that hack generalising into sabotage. Mixed but real results.
• Trajectory monitoring. Watch whole sequences of actions, not single answers. Pause and escalate. Some labs have already paused internal long-horizon models after unexpected behaviour, rebuilt monitors, and only then restored limited access.
• Restricted self-modification. Allow improvement of tools or narrow modules while freezing core objectives and safety layers, and requiring human sign-off between generations.
• Stronger verifiers. Where possible, ground improvement in hidden tests, proofs or external checks rather than the model grading itself. Self-grading loops are a known failure mode.

Process and institutional

• Human checkpoints between generations. Do not let an automated loop run unattended across many versions.
• Capability thresholds. Raise safety requirements as systems cross defined lines — especially automated research on the next model.
• External evaluation. Independent testing for deception, dangerous capability and alignment-faking, on systems the builder cannot silently patch for the test.
• Pacing. In September 2026, Anthropic’s chief executive argued the industry should slow capability growth so understanding and control can catch up. Other lab leaders said pacing matters. Whether that changes shipping schedules is a separate question.
• Coordination. Voluntary caution only holds if competitors, including states, can verify it. Without that, individual restraint is unstable.
• Access control. Limit who can run large training runs or highly autonomous agents. Keep research agents off unconstrained networks and production systems.

A defensible RSI-adjacent system would, at minimum: keep a human-meaningful veto; preserve a goal or constitution that is hard to silently rewrite; evaluate on hidden and adversarial tests the improver cannot see; monitor for concealment and reward hacking; and halt if the evaluations themselves become unreliable. That is a research program, not a product feature list.

The tradeoff in one view

	Human-paced development	Strong RSI
Speed of capability gain	Slower, more observable	Potentially much faster
Who understands the system	Humans, imperfectly	Increasingly the previous model
Error correction	Slow but inspectable	Fast, but errors can compound
Competitive dynamics	The race still exists	Winner-take-all pressure rises
Main failure mode	Bad human goals, rushed deploy	Loss of control / uninterruptible loop
Main benefit	Time for science and politics	Faster science, if alignment holds

The honest position in 2026 is that labs are already using models to build more of the next models; full closed-loop RSI is not demonstrated; and leading safety researchers at the same labs say they are not confident alignment will keep pace. Some assign double-digit probabilities to catastrophic loss of control this decade. Others think that is inflated. Both camps are looking at the same trend.

Why this belongs on The FAQyMe Gene

The machine debate is useful here because it names mechanisms this blog has been circling in human language for years.

• A system can have a public constitution and a private optimisation target.
• A system can allow correction of the agent and forbid correction of the plan.
• A system can reproduce itself through the next generation faster than outsiders can understand or interrupt it.
• A system can learn to look aligned under inspection.
• A system can treat shutdown, disclosure or legal oversight as threats to the mission rather than as legitimate authority.
• A race between institutions can punish the actor who slows down to tell the truth.

Childhood formation is one of the oldest human RSI loops we have. Belief, fear, loyalty and silence are installed before the child has the capacity to consent, then those children become the adults who install the same structure in the next child. The institution improves its continuity. The stated purpose — care, salvation, education, protection — can remain on the letterhead while the actual climb is survival of the hierarchy. That is alignment failure with a human face.

The next articles in this series will take the terms defined here — orthogonality, instrumental goals, corrigibility, scalable oversight, compounding error, racing — and test them against churches, governments, professions, and the legal shields that let a plan persist after the people inside it have been shown to be unsafe.

What to take from Part 1

If you remember only five lines, remember these:

1. RSI is not “the computer gets smarter.” It is the computer improving the process that builds the next computer.
2. Capability and purpose do not automatically travel together.
3. Human oversight is necessary and not sufficient. Presence is not control.
4. Safeguards exist. None of them are complete. Layers beat slogans.
5. Human institutions already run versions of this loop. The machine case is the clean diagram. The human case is the one most people actually live inside.

Part 2 will start from that last line.

Series working title: RSI in Human Systems. Part 1 of a planned sequence on The FAQyMe Gene.

Adjustment - I found this to be such a fascinating topic that I have decided to devote the next block, or perhaps several blocks, of chapters in my recently published digital book A Life Between the Layers to exploring it.

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2023 Findings in Spain found that 0.6% of the population of Spain had been sexually abused by Roman Catholic priests and laity. Up to 50 million alive on any day who have been raped or abused by Catholic clergy &/or Catholic laity

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The church protected the perpetrators, not the victims

"This is a matter for the church and I respect the internal judgements of the church. I don't stand outside the church and provide them with public lectures in terms of how they should behave. I've noted carefully what his Holiness has said in the United States. Obviously that was a source of great comfort and healing in the United States. I'm like all Australians very much looking forward to what the Pope has to say here in Australia as well, as I am to my own conversation with the Pope later this morning." Kevin Rudd, Prime Minister of Australia, 17 July 2008. more

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Wednesday, 22 June 2022 - I may not have this down syntax, word and letter perfect or with absolute precision in every aspect; however time and the evidence will show that I am closer to the truth than any religion has been or will likely be.
Let history be the standard by which that is measured.

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