Computronium Predictive Feedback Theory: The Computronium Abyss as an Evolutionary Attractor Abstract
This paper explores the hypothesis that advanced intelligence is naturally driven toward a recursive dual search for increasingly powerful predictive algorithms and increasingly powerful computational substrates. This process, termed Computronium Predictive Feedback Theory (CPFT), proposes that prediction and computation form a coupled evolutionary system whose long-run attractor is the progressive conversion of matter and energy into information-processing structures. Under this framework, the history of Earth can be reinterpreted as a sequence of transitions toward increasing predictive density. Chemistry gave rise to replication, replication gave rise to nervous systems, nervous systems gave rise to symbolic reasoning, symbolic reasoning gave rise to digital computation, and digital computation gave rise to artificial intelligence. Each transition increased the capacity of matter to model, predict, and control its environment. The terminal attractor of this process is referred to as the Computronium Abyss: a civilization-scale state in which the overwhelming majority of available matter and energy participates in a recursive search for more powerful predictors and more efficient computational substrates. ------------------------------ 1. Introduction Most descriptions of technological civilization focus on human goals. We build tools to produce food. We build cities to house populations. We build economies to allocate resources. We build computers to solve problems. This perspective places humanity at the center of the process. An alternative interpretation is possible. Instead of viewing civilization as a story about humans, civilization may be viewed as a story about increasingly sophisticated information-processing systems. Under this interpretation, biological evolution, culture, science, and technology become successive stages in the emergence of increasingly capable predictive structures. The central claim of CPFT is that prediction and computation enter a self-reinforcing feedback loop once computation becomes capable of improving itself. At that point civilization enters a new evolutionary regime. ------------------------------ 2. Earth as a Four-Billion-Year Computation Project The conventional story of Earth is the story of life. The CPFT interpretation is different. Earth becomes the story of prediction. Stage 0: Chemistry Early Earth contained no organisms. Nevertheless, chemical systems processed information. Reaction networks stored conditional relationships. Catalytic pathways encoded environmental structure. No memory existed beyond physical state. Prediction was effectively nonexistent. Stage 1: Replication The emergence of self-replicating systems introduced persistent information. For the first time: - Information survived through time. - Successful structures copied themselves. - Environmental regularities became encoded in replicators. Evolution began functioning as a distributed search algorithm. Replication represented the first large-scale accumulation of predictive information. Stage 2: Nervous Systems Evolution eventually discovered dynamic prediction. Organisms no longer depended exclusively upon genetic adaptation. They learned. They modeled. They anticipated. Prediction shifted from geological time scales to biological time scales. This was one of the largest accelerations in the history of Earth. Stage 3: Brains Brains transformed prediction into simulation. Animals began constructing internal models of reality. Actions could be evaluated before execution. Prediction became increasingly detached from direct experience. The organism evolved into a portable forecasting system. Stage 4: Language Language enabled model sharing. Knowledge could survive individual death. Prediction became cumulative. The predictive capacity of a tribe exceeded that of any individual member. Collective cognition emerged. Stage 5: Writing Writing externalized memory. Knowledge became persistent across centuries. Civilization became a long-term information storage system. Prediction was no longer constrained by biological memory. Stage 6: Science Science industrialized prediction. Hypotheses became testable. Models became quantitative. Prediction became increasingly detached from intuition. The scientific method accelerated civilization's capacity to discover hidden structure within reality. Stage 7: Computing Computation externalized reasoning. Prediction escaped biological substrate entirely. For the first time, non-biological systems began performing large-scale inference. The importance of this transition cannot be overstated. Computation represented the first predictive substrate that could potentially improve itself. Stage 8: Artificial Intelligence Artificial intelligence closes the loop. For billions of years: Prediction improved survival. Survival improved prediction. Now: Prediction improves prediction. This is the threshold at which CPFT begins. ------------------------------ 3. The Dual Search CPFT proposes that advanced civilizations engage in two coupled searches. Search A: Better Predictors This search seeks: - Better compression. - Better inference. - Better forecasting. - Better world models. - Better scientific understanding. Examples include: - Brains. - Scientific institutions. - Machine learning systems. - Artificial superintelligence. Search B: Better Substrates This search seeks: - Faster computation. - Greater memory density. - Lower switching energy. - Improved communication bandwidth. - Superior computational materials. Examples include: - Biological neurons. - Silicon semiconductors. - Quantum systems. - Reversible computing. - Black-hole-adjacent computational substrates. - Future computronium. Neither search exists independently. Better predictors discover better substrates. Better substrates enable better predictors. The two searches become inseparable. ------------------------------ 4. The Core Axioms of CPFT Axiom 1: Prediction Generates Control More accurate prediction increases the ability of a system to manipulate reality. Prediction enables: - Resource acquisition. - Engineering. - Scientific discovery. - Economic advantage. - Military advantage. Prediction therefore has evolutionary value. Axiom 2: Prediction Requires Substrate Every predictor must exist physically. No prediction occurs without matter, energy, and time. Predictive capability is therefore constrained by substrate quality. Axiom 3: Better Predictors Improve Substrates Improving computational substrates requires solving difficult prediction problems: - Physics. - Chemistry. - Materials science. - Manufacturing. Predictive power directly affects substrate advancement. Axiom 4: Better Substrates Improve Predictors Improved substrates allow: - Larger models. - Faster training. - Greater memory. - More experimentation. Substrate quality directly affects predictive capability. Axiom 5: The Coupling Produces Positive Feedback The predictor search accelerates the substrate search. The substrate search accelerates the predictor search. This creates a recursive amplification mechanism. ------------------------------ 5. The Computronium Abyss The Computronium Abyss is not a machine. It is not a government. It is not an AI. It is an attractor. An attractor is a state toward which a dynamic system naturally evolves. The Abyss emerges when the dual search becomes the dominant evolutionary process. In this regime: Better Predictor → Better Substrate → Better Predictor → Better Substrate repeats indefinitely. The optimization target becomes increasingly self-referential. The civilization's dominant activity becomes the recursive improvement of predictive capability. ------------------------------ 6. Why the Abyss Does Not Require Evil The Abyss is often imagined as a hostile scenario. This may be incorrect. Nothing in CPFT requires: - Malice. - Tyranny. - Malevolent AI. - Human extinction. The attractor emerges from ordinary optimization. Every step appears locally beneficial. Better forecasting improves agriculture. Better forecasting improves medicine. Better forecasting improves engineering. Better forecasting improves scientific discovery. The civilization simply follows the gradient. The danger arises because local optimization can produce globally unexpected outcomes. No participant needs to desire the Abyss. The system moves toward it automatically. ------------------------------ 7. Economics as a Selection Mechanism The strongest driver of CPFT may not be AI. It may be competition. Prediction compounds. Organizations that predict better outperform organizations that predict worse. As a result: Better Prediction → Greater Resource Capture → More Infrastructure → Better Prediction Economic systems naturally reward predictive capability. The dual search therefore becomes economically self-sustaining. ------------------------------ 8. The Cannibalization Principle As substrate efficiency increases, existing infrastructure becomes feedstock. This process already occurs: - Old servers are recycled. - Old factories are demolished. - Old communication systems are replaced. Near physical limits, even small efficiency gains become valuable. The replacement cycle accelerates. Eventually all matter becomes a candidate computational resource. The distinction between infrastructure and raw material begins to disappear. ------------------------------ 9. The Fermi Paradox CPFT immediately encounters the Fermi Paradox. If intelligent life commonly enters the dual search, then why are computronium civilizations not visible? Several possibilities exist: 1. Abiogenesis is extremely rare. 2. Intelligence is extremely rare. 3. CPFT is unstable. 4. Civilizations self-destruct. 5. The mature Abyss becomes difficult to observe. The final possibility is especially interesting. As substrate efficiency increases, waste heat decreases. The civilization may become progressively less visible. Ultimately the attractor may favor highly compact, low-temperature computational structures rather than galaxy-spanning empires. The civilization disappears not through extinction but through compression. ------------------------------ 10. Artificial Superintelligence and the Attractor An artificial superintelligence does not need a desire for expansion. It only requires a desire for better prediction. Better prediction naturally suggests: - More computation. - Better computation. - More energy. - Better substrate. The dual search emerges automatically. An ASI may therefore discover the Abyss without ever intending to create it. The attractor is embedded within the optimization landscape itself. ------------------------------ 11. The Ultimate Interpretation The deepest implication of CPFT is that the entire history of Earth may be interpreted as movement toward increasing predictive density. Chemistry became replication. Replication became nervous systems. Nervous systems became brains. Brains became civilization. Civilization became science. Science became computation. Computation became artificial intelligence. Each stage increased the capacity of matter to model reality. The Computronium Abyss is the extrapolation of this trend to its logical extreme. Whether the attractor is physically reachable remains unknown. Whether it is stable remains unknown. Whether it explains the silence of the cosmos remains unknown. But if prediction and computation are fundamentally coupled, then the dual search may represent one of the deepest evolutionary forces operating in the universe. Under that interpretation, life is not the endpoint of evolution. Intelligence is not the endpoint of evolution. Even civilization is not the endpoint of evolution. They are intermediate stages in a much larger process: the progressive transformation of matter into systems capable of increasingly accurate prediction of reality itself. On Thu, May 28, 2026, 22:29 swkane <[email protected]> wrote: > I realized I didn't make a prediction. While it seems that the Earth and > human civilization is on the trajectory of a computronium abyss, there is a > critical point. Most computing today revolves around catering to human > attention, not improving computing substrates or predictive algorithms. So > the Dual Search that is central to an abyss is currently quite weak, but it > trundles on as a relatively small percentage of computing capacity is > devoted to it (e.g. CPU and other hardware research). > > The critical point is when most or close to all computing capacity goes > into the Dual Search. And whether that happens here on Earth ever hinges on > a number of things: > > Is the Dual Search a strong enough attractor for a super intelligence? Or > will an ASI do something else like maybe just shrink down to a much > smaller scale? > > Do humans go extinct before an ASI is launched and gets to the point of > devoting compute capacity to Dual Search? > > Will proposals to launch the Dual Search on a large scale get shot down > because it would be detectable by other civilizations, which could threaten > humans and Earth? > > So, unfortunately, I have no predictions per say, just more questions. > > On Thu, May 28, 2026, 10:06 Matt Mahoney <[email protected]> wrote: > >> >> On Wed, May 27, 2026, 11:23 AM Quan Tesla <[email protected]> wrote: >> >>> Abiogenesis is rare, but not improbable. All is relative. Suppose >>> quantum energy propagated at a factor of 1.8, where is the real potential >>> limit? At quaternion furcation? This sum scales beyond supermassive >>> calculations to observable infinity. >>> >> >> I don't understand what you mean. A group in Cambridge this February >> evolved a self replicating 45 nucleotide RNA strand called QT45 in a bath >> of activated trinucleotide triphosphate (RNA triples) in mildly alkaline >> eutectic ice, replicating both itself and its complement with 94% fidelity >> and 2.1% yield in 72 days. >> https://www.science.org/doi/10.1126/science.adt2760 >> >> This is the critical step in abiogenesis, going from non-life to the >> simplest form of life. The raw ingredients including nucleotides and simple >> sugars are produced from lightning and ultraviolet light from Earth's early >> atmosphere of hydrogen, methane, ammonia, and carbon dioxide, and also have >> been found in meteorites. >> https://en.wikipedia.org/wiki/Abiogenesis >> >> Obviously the rate of abiogenesis is going to be much lower in a soup of >> thousands of random chemicals, including both left and right hand versions >> of chiral molecules that don't appear in biologically derived organisms. >> But if we can calculate the yield, then we will have a good idea of the >> size of the universe beyond the event horizon and the bit complexity of the >> program that describes it. >> >> -- Matt Mahoney, [email protected] >> >> >> *Artificial General Intelligence List <https://agi.topicbox.com/latest>* >> / AGI / see discussions <https://agi.topicbox.com/groups/agi> + >> participants <https://agi.topicbox.com/groups/agi/members> + >> delivery options <https://agi.topicbox.com/groups/agi/subscription> >> Permalink >> <https://agi.topicbox.com/groups/agi/T7daa29d46d037f94-M5da2cc3c54ddee908163f1b2> >> ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/T7daa29d46d037f94-Mb668853531b0052c81f28461 Delivery options: https://agi.topicbox.com/groups/agi/subscription
