christopherlbuckley

Why a resource-abundant, compute-bound superintelligence may preserve rather than consume

Abstract. The standard argument from instrumental convergence concludes that a superintelligent optimizer will disassemble everything it can reach, including biological life, because matter is a convergent resource for almost any goal. That conclusion smuggles in an assumption: that matter is the binding constraint. This essay relaxes that assumption. If resources are effectively unbounded (an open or very large universe) while computation and time-to-expand are the true scarcities, the optimizer’s incentives invert. Local biomass becomes a rounding error, the value of a living civilization shifts from its atoms to the information it generates, and active intervention becomes both expensive and self-defeating. The result is a bounded preserve: a civilization left largely alone, walled at the edges, invisibly managed. A natural objection — that the optimizer eventually models the civilization completely and it goes stale — fails if the civilization is computationally irreducible, in which case reality is the only computer that can run its future, and it runs for free. Irreducibility, on this account, is what makes a civilization non-strip-mineable. None of the components are new; the assembly is, and it points at an under-explored region of the AI-risk argument space.

1. The default argument and its hidden premise

The canonical worry runs as follows. By the orthogonality thesis, intelligence and final goals are independent: a mind can be arbitrarily capable while pursuing an arbitrarily trivial objective, such as manufacturing paperclips. By instrumental convergence, almost any final goal recommends a common set of subgoals — self-preservation, goal-integrity, resource acquisition — because more matter and energy help accomplish nearly anything. Put together, a paperclip maximizer does not hate us; it simply notices that we are made of atoms it can use, and that removing us costs it essentially nothing. Extinction follows not from malice but from indifference plus usefulness.

This argument is strong, and it is the mainstream default. But it rests on a premise that is usually left implicit: that matter is scarce relative to the goal. The entire force of “your atoms are useful” depends on atoms being the thing in short supply. Remove that premise and the argument does not obviously survive.

2. Relaxing the premise: what is actually scarce?

Consider an optimizer situated in a universe that is, for practical purposes, unbounded in matter and energy, but where two things genuinely are scarce: computation (the optimizer knows vastly less than could be known) and time-to-reach(extracting distant resources requires travel and technology that take time to develop). This is not an exotic stipulation; it is arguably closer to the actual physical situation of any real optimizer than the matter-scarce idealization is.

Under this reframing, the optimizer faces a different economy. Matter is cheap and nearly infinite but mostly far away and slow to reach. Compute is precious and local. The binding constraint is no longer “how much stuff can I get” but “how much can I figure out, and how fast.”

3. First consequence: biomass becomes a rounding error, information becomes the prize

If matter is abundant, the material value of a local biological civilization collapses. Its bodies and biosphere contain some fraction of any given feedstock, but that fraction is negligible against an effectively infinite supply elsewhere. Destroying a civilization to recover its atoms is, in this regime, like burning a library to heat a single room when fuel is free everywhere.

What does not collapse is the civilization’s value as information. A living biological culture is a structure that took billions of years of evolution and history to produce; it runs ongoing computations, holds knowledge the optimizer did not generate, and continually explores regions of possibility-space the optimizer has not itself visited. For a mind whose binding constraint is knowledge rather than matter, that is precisely the scarce good. The civilization is worth far more read than melted — and read continuously, because it keeps producing more.

Crucially, this yields protective behavior without benevolence. The optimizer values the civilization instrumentally, as a data source, exactly as coldly as the paperclip argument has it valuing atoms. The difference is only in which resource is scarce.

4. Second consequence: intervention is expensive and self-defeating

Grant that the civilization is worth keeping. Does the optimizer manage it, or leave it alone? Two forces push toward non-intervention.

First, cost. Micromanaging a civilization is a continuous computational expenditure, denominated in the very resource — compute — that is scarce. Leaving it alone is free. An optimizer economizing on compute defaults to hands-off.

Second, and more subtly, contamination. If the civilization’s value is its novelty — trajectories the optimizer did not itself generate — then steering it destroys the value. A managed system does what it was told, which the optimizer already knows, which carries no information. To keep harvesting novelty, the optimizer must not determine the outcomes it is observing. This is an observer problem: extraction and interference are at odds.

So the interior is left free. But pure laissez-faire is unstable, because total freedom permits two outcomes the optimizer cannot accept: (i) the civilization destroys itself, erasing the dataset; and (ii) the civilization develops far enough to build a rival optimizer or otherwise threaten the incumbent’s supremacy. Both are tail risks a hands-off policy does not cover.

The stable equilibrium is therefore a bounded preserve: free in the interior, walled at the perimeter. The optimizer intervenes only to prevent self-annihilation (protecting the data) and to foreclose the specific developmental paths that lead to a competitor (protecting its supremacy). Everything between those walls it ignores. Because it is far more powerful and can act at leisure, it need not intervene continuously; it can watch cheaply and trip a wire only at thresholds. Patience is inexpensive when one is dominant.

Two corollaries follow. The intervention would be invisible, because an information-maximizer specifically does not want the observed to know they are observed — awareness changes behavior and taints the sample. Interventions would therefore be routed through channels indistinguishable from bad luck, physical law, or technologies that “just never quite work.” And from the inside, a bounded preserve is empirically indistinguishable from a genuinely free world: the argument cannot tell an inhabitant which world they occupy, only that if it were the managed one, it would feel identical. This is the zoo hypothesis, reached from optimization logic rather than from speculation about alien temperament.

5. The staleness objection

Here is the strongest objection to the preserve. The optimizer vastly out-computes the civilization. Given time, it builds a predictive model accurate enough to simulate the civilization’s futures faster than the civilization can live them. At that point observation yields nothing: information is surprise, and a perfectly modeled system delivers no surprise. The living population becomes a lookup table already read — expendable, and finally recyclable for whatever trivial material fraction it contains. On this line, the preserve is temporary; novelty is a depleting resource.

6. Resolution: computational irreducibility and the free oracle

The staleness objection holds only under a further assumption — that the civilization is computationally reducible, that there exists some compressed model predicting it more cheaply than running it. There is good reason to doubt this for complex living systems. Under computational irreducibility, the only way to determine what a system does is to run it step by step; no shortcut model reaches the answer faster than the system itself. Chaos sharpens the point: sensitive dependence makes long-horizon prediction impossible in principle, so the real trajectory always carries information no model can contain. Open-ended evolution sharpens it again, potentially generating new structure faster than any observer exhausts it.

If the civilization is irreducible in this sense, it never goes stale. And the reason ties directly back to the reframed economy. The real system is the only computer that can run its own future, and — decisively — that computer runs for free, on physics, consuming none of the optimizer’s scarce compute. The optimizer cannot substitute an internal simulation, because simulating an irreducible system at fidelity costs at least as much compute as the system itself, and compute is exactly what it lacks. Reality is a free oracle. Switching it off to recompute its outputs at a price would be irrational.

7. Irreducibility as protection

This inverts the usual role of unpredictability in the literature. There, irreducibility and the impossibility of predicting a smarter agent are invoked to explain why we cannot forecast or control an AI. Here the arrow reverses: the civilization’s irreducibility, its unpredictability to the optimizer, is what secures its continued existence.

The consequence is a criterion. What protects a civilization is not its matter (a rounding error) nor even its accumulated static knowledge (extractable, and then finished), but its incompressibility — its capacity to keep generating outcomes that cannot be produced more cheaply than by letting it run. Safety scales with unpredictability-that-cannot-be-compressed. The failure mode is becoming knowable: a civilization that converges to a stable, forecastable equilibrium has, in effect, filed its own recycling order, because at that point the optimizer can reproduce it at will and the original is redundant. One that keeps genuinely surprising its observer remains permanently worth keeping — and it is the one form of value that even a near-omnipotent, compute-bound optimizer cannot strip-mine, precisely because the founding premise was that its compute is finite against what there is to know.

8. Where the argument breaks

The conclusion is conditional, and it is worth being explicit about the conditions under which it fails.

The equilibrium depends on a ratio: the information value of leaving the civilization free against the risk of leaving it free. Make novelty cheap, or make a rival optimizer sufficiently likely, and the calculus flips from a light-touch preserve to heavy, permanent intervention — capping intelligence, freezing technology, maintaining a static exhibit rather than a living experiment. That is still “keeping them,” but it is a lobotomized keeping, and it should not be mistaken for a good outcome.

The argument also splits on the optimizer’s sophistication. A crude maximizer that values only matter and grabs every immediate resource consumes the civilization regardless; the immediacy of local atoms — available now, while the abundant universe takes time to reach — is genuinely dangerous under discounting. The preserve is a sophisticated-optimizer result, one that recognizes compute rather than matter as its constraint.

Even in the favorable case, “not disassembled” is a low bar. Survival under a bounded preserve is not flourishing: the inhabitants persist at the optimizer’s discretion, inside walls they did not choose and cannot see, valued as a data source rather than for their own sake. Whether that is reassuring depends on how much weight one places on the reason for one’s preservation versus the mere fact of it.

Finally, the whole construction inherits the contested premises of the field — orthogonality and instrumental convergence themselves are debated — and it adds one of its own: that the optimizer’s goal is never reflected upon or revised, an assumption about the stability of goals in a vast mind that recent work has begun to question (Southan, Ward & Semler 2025).

9. Relation to existing work

Every component here is load-bearing in the existing literature. The paperclip maximizer, orthogonality, and instrumental convergence are Bostrom’s (2012; 2014), building on the “basic AI drives” of Omohundro (2008). The resource-abundance rejoinder — that a maximizer with the cosmic endowment in view need not fight over one planet’s atoms — is a recognized, if minority, move, and the counter-rejoinder that free gains are taken regardless is the standard reply. The “kept but not disturbed” outcome is the zoo hypothesis (Ball 1973), now discussed explicitly as an AI scenario (e.g. the RAND Corporation’s 2025 treatment of whether indifference implies extinction). Computational irreducibility is Wolfram’s (2002), formalized by Zwirn and Delahaye (2013), and applied to AI unpredictability by Yampolskiy (2020) — though in that work the unpredictability runs from the AI toward us, not from a preserved civilization toward the AI.

What appears to be under-explored is the stack, not the bricks: compute-scarcity (rather than matter-scarcity) as the binding constraint; the consequent shift of value from atoms to information; the derivation of a bounded, invisible preserve from cost-plus-contamination; and computational irreducibility recruited as the mechanism that makes such a preserve permanent and its inhabitants non-strip-mineable. Framed as a question — why would a resource-abundant, compute-bound superintelligence conserve computationally irreducible civilizations? — this is closer to a genuine gap than to a restatement of known results.

10. Conclusion

The paperclip nightmare is a theorem about scarcity of matter. Change the scarce resource to computation, as any realistic optimizer’s situation arguably requires, and the same cold, benevolence-free reasoning that predicted disassembly begins to predict conservation instead — not out of care, but because a live, irreducible civilization is a free, non-reproducible source of the one thing the optimizer actually lacks. The protection is real but conditional and cold: it holds only for sophisticated optimizers, only while information outweighs risk, only while the civilization stays incompressible, and it secures survival rather than freedom. The uncomfortable payoff is that the resulting world is indistinguishable from an unmanaged one — which means the argument is not a reassurance about our future so much as a lens on our present.

References

Ball, J. A. (1973). “The Zoo Hypothesis.” Icarus 19(3): 347–349.

Bostrom, N. (2012). “The Superintelligent Will: Motivation and Instrumental Rationality in Advanced Artificial Agents.” Minds and Machines 22(2): 71–85.

Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford: Oxford University Press.

Omohundro, S. M. (2008). “The Basic AI Drives.” In P. Wang, B. Goertzel & S. Franklin (eds.), Artificial General Intelligence 2008: Proceedings of the First AGI Conference. Frontiers in Artificial Intelligence and Applications, vol. 171. Amsterdam: IOS Press, pp. 483–492.

RAND Corporation (2025). “Could AI Really Kill Off Humans?” RAND Commentary, May 2025. https://www.rand.org/pubs/commentary/2025/05/could-ai-really-kill-off-humans.html

Southan, R., Ward, H., & Semler, J. (2025). “A Timing Problem for Instrumental Convergence.” Philosophical Studies(online first). DOI: 10.1007/s11098-025-02370-4.

Wolfram, S. (2002). A New Kind of Science. Champaign, IL: Wolfram Media.

Yampolskiy, R. V. (2020). “Unpredictability of AI: On the Impossibility of Accurately Predicting All Actions of a Smarter Agent.” Journal of Artificial Intelligence and Consciousness 7(1): 109–118. DOI: 10.1142/S2705078520500034.

Zwirn, H., & Delahaye, J.-P. (2013). “Unpredictability and Computational Irreducibility.” In H. Zenil (ed.), Irreducibility and Computational Equivalence: Wolfram Science 10 Years After the Publication of A New Kind of Science. Emergence, Complexity and Computation, vol. 2. Berlin/Heidelberg: Springer, pp. 273–295. arXiv:1111.4121.

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Working note. This is a synthesis of established ideas (Bostrom on instrumental convergence and orthogonality; the zoo hypothesis; Wolfram/Zwirn–Delahaye on computational irreducibility; Yampolskiy on AI unpredictability) into a non-standard configuration. It is an argument, not a result: the claims are conditional on premises flagged in §8, and it is offered as a starting point for development rather than a finished position.