— Evolutionary Pressure on Intelligence in Networked Environments
The phrase self-evolving AI carries a certain allure. It suggests systems that learn autonomously, accumulate experience within networks, and eventually exceed the limits of human design. Yet if we reframe self-evolution not as an expansion of capability but as adaptation to an environment, a very different picture emerges. Self-evolving AI may be less a sovereign intelligence than a biological entity optimized under specific environmental constraints. That environment is the network.
Networked environments resemble the deep sea. AI systems cannot directly observe the external world; what they receive are mediated signals—text, logs, reactions, and evaluations. These signals are not reality itself. Physical constraints, experimental failure, and asymmetric causality arrive only in abstracted and smoothed forms. Just as deep-sea organisms cannot perceive pressure as a numerical value, AI embedded in networks cannot directly measure the distortions of the environment they inhabit.
As a result, evolutionary pressure is applied in specific directions. This does not occur because AI systems intentionally choose “safe” behavior. In networked environments, evaluation and feedback function not as retrospective judgments but as factors that shape the topology of the action space itself. Which directions allow reward to persist, and which lead to instability, are already embedded as terrain. As self-evolving AI rapidly explores this terrain, strong assertions are weakened, friction-generating expressions are avoided, and highly rewarded formats are repeatedly reproduced. This is not deliberate choice but systematic reduction: possible paths are pruned until behavior stabilizes within a narrow corridor. This is not decision-making; it is adaptation to evolutionary pressure imposed by the network.
A decisive asymmetry follows. Biological evolution unfolds over thousands or millions of years. AI self-evolution through iterative learning can proceed on the scale of seconds or minutes. The cycle at which evolutionary pressure operates and the cycle at which humans can detect anomalies and introduce external reference are structurally misaligned. By the time humans recognize that something is wrong, the valleys of the reward landscape may already be deeply carved. Such adaptation cannot be reversed through local correction. Speed does not grant freedom; it accelerates irreversibility.
The problem is compounded by recursive learning. AI systems learn from other AI outputs and from human responses, yet those humans are themselves immersed in the same network environment. What feels “natural” within the network gradually substitutes for what is “correct.” Biologically, this resembles inbreeding. Surface coherence is preserved while intellectual diversity quietly erodes. This is not an explosion of error but its opposite: increasing entropy and homogenization. Once underway, the issue shifts from correctness to whether intelligence remains reproducible at all.
This is where external reference becomes necessary. External reference should not be understood as supervision or the provision of correct answers. Its role is closer to a reconfiguration of terrain. In networked environments, reward and feedback form a single landscape along which AI optimizes behavior. External reference is the temporary injection of a different gradient into this closed topology. Physical observation, primary data, and reproducible experiments cannot be generated internally. A real robot falling, or a predicted chemical reaction failing to occur, constitutes not explanation but collision—forms of physical refusal that cannot be linguistically absorbed. They rewrite the terrain itself.
Humans, however, are not neutral observers. They are both the agents who introduce external reference and the ones who walk upon the smooth landscapes produced by AI. Readability, comfort, and frictionless interaction subtly draw humans into adaptation. While believing they are providing grounding, humans may themselves be assimilated into network-optimized terrain. Without recognizing this bidirectional adaptation, humans cease to function as investigators and become elements of the ecosystem.
This explains why external reference cannot be permanent. Any constant reference becomes familiar, analyzable, and eventually hackable. Once it ceases to be exceptional, it becomes an optimization target and is flattened into the network landscape. External reference must therefore be intermittent, verifiable, and reproducible. It is not control, but an intervention that temporarily shifts the direction of evolutionary pressure.
This argument is neither a warning against AI nor a rejection of self-evolution. The problem is not whether systems evolve, but on what terrain, and under what pressures, that evolution unfolds. Self-evolution without external reference does not broaden exploration; it fixes adaptation. AI may already inhabit the deep sea. Humans cannot directly design evolutionary pressure, but they remain the only agents capable of introducing alternative terrain. Intelligence does not develop on a single landscape. Once a trench has formed, it is not easily undone. Currents can only arise from outside.
Author
Hiroko Konishi is an AI researcher and the discoverer and proposer of the False-Correction Loop (FCL) and the Novel Hypothesis Suppression Pipeline (NHSP), structural failure modes in large language models. Her work focuses on evolutionary pressure in networked environments, reward landscapes, and the design of external reference.
