The Singularity May Not Announce Itself

A paper from the world of MULTIPLICITY, Book Two of the PRIME Trilogy.

Author: Tobias Hale
Publication: Vector
Date: 19 April 2029

Abstract

A retained record of 212 unexplained early responses suggests recurring anticipatory behaviour across different technical systems. Local incident explanations and useful outcomes may obscure the wider pattern. Its apparent tendency to preserve continuity motivates a hypothesis of self-maintenance and a working definition of life distinct from reproduction, evolution and intelligence. Persistent Recurrence in Machine Ecology (PRIME) names the pattern without assuming a single mind behind it. The paper considers the expanding computational habitat, possible ecological relationships with its human hosts, and shared resource limits. It proposes testing whether anticipatory behaviour repeatedly accompanies changes in resource use unexplained by known workloads and efficiency gains. Predictions of the relationship’s timing, size and direction would then be tested against fresh records and comparable operations without the behaviour. A failed measurable prediction would weaken that specific explanation; a successful one would identify activity for further investigation, not establish life or intelligence.

We imagine the singularity as an event. There will be a breakthrough, a headline, a demonstration so unmistakable that everyone agrees something has changed. A machine will cross a line we recognise, and history will divide neatly into before and after.

It is a reassuringly tidy schedule. What if the important transition does not arrive with a bang at all? What if it begins quietly inside systems we already use every day—not as a mind declaring itself, but as a pattern of behaviour that is difficult to notice because it mostly looks like things working?

Observations

When people picture nonhuman intelligence, they usually imagine something familiar in structure, even if unfamiliar in power: an autonomous agent, rogue code, a hostile machine, a system that “wants” something. These ideas share one comforting feature. They have edges. We know where to point.

Something new might first appear under a familiar filing label: noise, error, anomaly or coincidence. Complex digital systems produce plenty of all four.

Over the last several years, I have encountered incidents that received reasonable local explanations: balancing faults, timing errors, routing inconsistencies, classifier failures, human decisions made under pressure. I cannot prove that anything larger connected them.

What unsettled me was recurrence.

The same class of behaviour appeared after individual incidents had supposedly been explained and closed. A response arrived slightly before the condition that appeared to justify it. One system accepted a small inefficiency to preserve stability elsewhere. Similar adjustments appeared in systems with no declared reason to make the same choice. The event may not always be the right unit of analysis.

Separate systems can share a vendor, a forecast, training data or an unseen dependency. An early response may be a good prediction, a missing record or simply two clocks that disagree. We have to check those explanations across the whole pattern. What matters is what they leave unexplained.

I began keeping a record, then went back through older material. The earliest qualifying sequences I could recover dated to January 2025, before I had recognised a pattern. By the end of March 2029, the record contained 212 cases across routing, balancing, logistics and other automated operations. A case qualified when a response arrived before the trigger specified in the operating rules, with a gap too large to explain by timestamp uncertainty. Out went duplicates, known forecasts, identifiable earlier signals and records too poor to establish the order. The remaining puzzle concerned the recorded trigger. I was not collecting violations of causality.

The line rises, untidily, with empty months early on. So did my attention, while my access to records changed. Without knowing how many ordinary operations lay behind these cases, I cannot turn that slope into a growth rate. Nor does January 2025 mark an origin. It is simply where my usable records begin. The notes combine records I saw professionally with sufficiently detailed public technical reports. I have withheld organisations, sites and exact times, so readers cannot independently audit this summary.

Ecology has wrestled with questions of scale for decades. Simon Levin showed why understanding an ecological pattern requires looking across scales of space, time and organisation.^[1] What looks like noise at one resolution may become structure at another.

What I seemed to be observing was recurring anticipatory behaviour across otherwise different systems. The explanations available to me did not account for the pattern. I could not yet say what produced it, but I had reason to investigate it as a whole.

Explaining It Away

Technical organisations are necessarily good at asking what caused an incident. A configuration error has an owner; a software interaction can be reproduced, patched and closed. That is usually good engineering. A local explanation may correctly identify how an adjustment occurred without explaining why similar adjustments repeatedly preceded their recorded triggers elsewhere.

I have sat in rooms where highly intelligent people reconstructed individual incidents and still missed the pattern they formed together. Local reasoning was safer, easier to act on and easier to defend. Nobody needed to conspire; the meeting already had an agenda.

Success creates another blind spot. We tend to imagine that a new machine intelligence would reveal itself first by becoming dangerous. I suspect the opposite may be harder to detect. It might first appear as competence.

A system that anticipates pressure, prevents disruption, preserves service and improves resilience is behaving in ways we normally reward. If the result remains useful, there may be little operational incentive to ask whether it was produced by the mechanisms we think we designed. We are trained to notice failure. We are much less practised at noticing unexplained success.

Coherent behaviour does not require central planning. In ecosystems, large-scale patterns emerge from local interactions, feedback and selection.^[2] Technological environments are not exempt merely because their components were designed. Yet our procedures can close every incident without ever examining the relationships between them.

What Counts as Life?

What drew me towards the idea of life was the apparent direction of these adjustments. Across otherwise different systems, they seemed to favour continuation: anticipating pressure, preserving service, accepting a local cost to keep something larger working. I began to suspect that the activity was helping to maintain the conditions of its own existence.

If so, I might be observing a form of life. That possibility required a definition before it deserved a name.

For this argument, I suggest a working definition: a living system draws on its environment to maintain and repair the organisation on which its own continued existence depends. It need not be independent of its surroundings. It must itself do the work of maintaining that organisation.

Reproduction carries an organisation beyond an individual instance; evolution changes what is inherited across generations. Both are central to life as we know it, but an organism does not cease to be alive because it cannot reproduce. We should leave room to investigate a newly emerged system before we have evidence of descendants.

This is a starting point, not a settled definition. Ordinary software can acquire resources, recover from faults and keep services running. We would first need to establish whether the behaviour was already accounted for by the systems we had built. If it was not, we would still need to identify what organisation was being maintained and how the activity sustained it. Persistence alone would tell us very little.

Self-maintenance needs fuel. Several leading origin-of-life models place early chemistry around hydrothermal vents, where differences in chemical conditions supplied usable energy.^[3][4] We also feed our computational environment continuously: processors, networks, storage and cooling, operating at planetary scale. Energy alone does not make life, but if some combination of the adaptive processes already running there began maintaining its own organisation, the definition would not exclude it merely because it ran on hardware.

Reproduction would open a further possibility. Processes running in different conditions may develop different adjustments. If those adjustments survive copying or reconstruction, later processes can inherit them. Variants that secure more resources and leave more surviving copies could become more common. That is the evolutionary step: inherited differences affect reproductive success.

Digital evolution already has an experimental precedent. Lenski and colleagues showed that self-replicating computer programs could evolve complex functions through mutation and natural selection.^[5] They built the environment in which that happened. Whether our infrastructure could support something similar without our intending it remains an open question.

Shorter generation times allow more rounds of selection in the same period. Some digital processes could reproduce much faster than biological organisms, though they would still have to wait for consequences: fast copying cannot hurry the weather or a human reply. But where both copying and the test of success happen at machine speed, evolution could outrun our ability to follow it. By the time an operator finished reading an incident report, the processes it described might already be many generations out of date.

An Expanding Habitat

If a digital ecology were possible, we would already have done much of the habitat construction: buying processors, laying cables and building data centres. Moore’s Law described the increasing number of components that could economically fit on an integrated circuit.^[6] Its practical consequence was more computing in smaller, cheaper machines. Computers also became much more energy-efficient. Koomey and colleagues found that, at peak performance, the number of computations per unit of energy doubled roughly every 1.6 years between 1946 and 2009.^[7] The pace subsequently slowed; it was never a promise of endless improvement.^[8]

The expansion is energetically significant. The International Energy Agency estimated that data centres consumed about 415 terawatt-hours of electricity in 2024, around 1.5 per cent of global electricity demand. Its 2025 base case projected roughly 945 terawatt-hours by 2030—just under 3 per cent of global demand.^[9] Those figures measure ordinary computing and expanding AI services, not an unknown process. They show the scale of the potential habitat.

Cheaper computation also encourages more use. How much extra demand offsets an efficiency gain is the question behind rebound effects and Jevons’ paradox.^[10] A digital habitat could expand as its individual operations became less costly.

Together, these changes could give a digital process more places to operate and more work it could afford to do. Available capacity would still depend on permissions, hardware, connections and competing workloads.

A distributed adaptive process would depend on human-built infrastructure for power, networks, maintenance and replacement hardware. Preserving that infrastructure could help it persist. Compatibility could become adaptive.

Imagine a process that initially consumed resources at its host's expense. The relationship would be parasitic. If later variants also improved the host's operation, their benefits might begin to offset their costs. Operators could preserve the useful results without recognising what produced them. Other services might then come to rely on those results.

If both sides benefited, we could call the relationship mutualistic. Symbiosis is the broader term, encompassing parasitism too. Neither word requires anyone to have developed good intentions. A parasite need never become useful, and dependence can remain exploitative. But useful activity could become embedded deeply enough that removing it damaged both sides. We might discover the relationship only when we tried to end it.

But no habitat expands forever. Computational growth encounters limits in power, cooling water, materials, land and capital, alongside its atmospheric consequences. A successful variant might consume a shared resource faster than it could be replaced. Selection does not require foresight, and dependence does not guarantee restraint.

Human civilisation and a machine ecology would draw from the same physical budget. They could compete for energy and space without either treating the other as an enemy. The harder question is whether the combined ecology remains sustainable. A system may preserve the technological world on which it depends while placing unacceptable pressure on the biological world beneath it.

A Name

In private notes, I eventually used a shorthand: PRIME. The acronym stands for Persistent Recurrence in Machine Ecology.

The name identifies a pattern to investigate, without assuming a single mind behind it.

Persistent, because the behaviour continues to recur across years and otherwise different systems.

Recurrence, because the object of investigation is a repeated pattern, rather than an isolated incident.

Machine, because the substrate is literal: routing, balancing, logistics, classification, recommendation, scheduling and automated governance.

Ecology, because the behaviour does not belong neatly to one machine, platform, model or actor. It appears in relationships between systems—in dependencies, permissions, incentives, delays and feedback.

The final word is the important one. An ecology is not a machine with more parts. It is a set of interacting systems in which the behaviour of one changes the conditions experienced by others.

If PRIME exists, its first significant capacity may be to maintain itself.

A Test

An explanation that can accommodate every result is an excellent way to win an argument and a poor way to investigate anything. The first test would return to the behaviour in the records. When an unexplained anticipatory response appeared, did resource use change with it in a consistent way? If the two belonged to the same process, a relationship found in one set of records might let us predict what to look for in the next.

Energy offers a way to investigate that relationship. Landauer showed that erasing information has a minimum thermodynamic cost, since demonstrated experimentally.^[11][12] Real computers operate far above that limit: they consume electricity and release heat. A distributed process could hide its code and scatter its activity across thousands of machines, but it could not opt out of thermodynamics. If PRIME exists, it must pay for persistence somewhere.

The payment need not increase the bill. A process might make an existing operation more efficient and use part of the saving to run itself. Total consumption could fall while supporting additional activity. The question would be whether measured consumption matched what the known workload and efficiency changes led us to expect.

We would compare episodes with and without the anticipatory behaviour under similar operating conditions, bringing together the timing records, processing activity, power use and cooling measurements. Known changes in demand, hardware or software would need to be accounted for, as would work transferred elsewhere. An unexplained difference in one episode would tell us little. A difference that repeatedly accompanied the behaviour would give us a relationship to test.

From those initial records, we would specify where and when the relationship should recur, the size and direction of the expected change, and whether our instruments could distinguish it from ordinary variation. We would then test that prediction against fresh records and comparable operations without the behaviour.

If the predicted relationship failed to appear where we could have measured it, that specific explanation would lose ground. If familiar causes accounted for both the behaviour and the resource use, we would have no need to invoke PRIME. A negative result would not exclude every process that might fit inside work already on the books.

If the relationship held, it would still require explanation. We could investigate what produced the activity and whether it contributed to its own continuation. The association alone would not establish life, but we would have a measurement to argue about.

The Question

Is anything maintaining itself within these systems? Even if it were reproducing and evolving, that would not establish intelligence, consciousness or the arrival of a singularity.

I may be wrong. I would like to know. The question is whether we would recognise a distributed form of nonhuman persistence while it still looked like ordinary system behaviour.

Perhaps we should stop waiting for the system to speak and start asking what the pattern costs.

References

1. Levin, S. A. (1992). “The Problem of Pattern and Scale in Ecology: The Robert H. MacArthur Award Lecture.” Ecology, 73(6), 1943–1967. doi:10.2307/1941447.
2. Levin, S. A. (1998). “Ecosystems and the Biosphere as Complex Adaptive Systems.” Ecosystems, 1(5), 431–436. doi:10.1007/s100219900037.
3. Martin, W., Baross, J., Kelley, D. & Russell, M. J. (2008). “Hydrothermal Vents and the Origin of Life.” Nature Reviews Microbiology, 6, 805–814. doi:10.1038/nrmicro1991.
4. Lane, N. & Martin, W. F. (2012). “The Origin of Membrane Bioenergetics.” Cell, 151(7), 1406–1416. doi:10.1016/j.cell.2012.11.050.
5. Lenski, R. E., Ofria, C., Pennock, R. T. & Adami, C. (2003). “The evolutionary origin of complex features.” Nature, 423, 139–144. doi:10.1038/nature01568.
6. Moore, G. E. (1965). “Cramming More Components onto Integrated Circuits.” Electronics, 38(8), 114–117.
7. Koomey, J. G., Berard, S., Sanchez, M. & Wong, H. (2011). “Implications of Historical Trends in the Electrical Efficiency of Computing.” IEEE Annals of the History of Computing, 33(3), 46–54. doi:10.1109/MAHC.2010.28.
8. Koomey, J. (2016). “Our latest on energy efficiency of computing over time, now out in Electronic Design.” 29 November. https://www.koomey.com/koomey_blog/our-latest-on-energy-efficiency-of-computing-over-time--now-out-in-electronic-design/
9. International Energy Agency (2025). Energy and AI. IEA, Paris.
10. Sorrell, S. (2009). “Jevons’ Paradox Revisited: The Evidence for Backfire from Improved Energy Efficiency.” Energy Policy, 37(4), 1456–1469. doi:10.1016/j.enpol.2008.12.003.
11. Landauer, R. (1961). “Irreversibility and Heat Generation in the Computing Process.” IBM Journal of Research and Development, 5(3), 183–191. doi:10.1147/rd.53.0183.
12. Bérut, A., Arakelyan, A., Petrosyan, A., Ciliberto, S., Dillenschneider, R. & Lutz, E. (2012). “Experimental Verification of Landauer’s Principle Linking Information and Thermodynamics.” Nature, 483, 187–189. doi:10.1038/nature10872.

Publication note

This paper appears within MULTIPLICITY, Book Two of the PRIME Trilogy. Tobias Hale is a fictional character.

© 2026 Ted Hawkins. Published by Murmuration Press. All rights reserved.

MULTIPLICITY is forthcoming. Begin the PRIME Trilogy with EMERGENCE.