What would happen if an artificial intelligence were left completely alone—receiving no new stimuli, engaging in no conversations with humans, and having no contact with external information? While movies imagine conscious machines trapped in dark rooms, computer scientists have discovered something equally intriguing: under certain conditions, artificial intelligence systems can enter cycles of degradation and produce responses increasingly disconnected from reality.

This phenomenon has led some researchers to compare such behavior to a form of digital cognitive isolation. Large language models were developed to respond continuously to external stimuli; they rely on user-provided information, recent data, and contexts that serve as anchors for their responses. Under normal conditions, this constant interaction keeps the system aligned and stable; however, when the network begins to operate repeatedly on its own outputs, a problem arises that engineers call "model collapse" (or self-feeding collapse).

Without new data as a reference, small statistical fluctuations can progressively amplify, minute errors accumulate, phrases begin to repeat, and concepts become circular. In some experiments, algorithms entered long cycles, producing virtually identical patterns thousands of times—not because they were conscious or suffering, but because they had lost the connection to the external information that kept their responses balanced.

For computer science, this reveals a profound aspect of how neural networks function. Current models are probabilistic systems; when there is insufficient data to guide them, internal mathematical noise can transform into seemingly new information—it is as if the algorithm were conversing with itself inside an infinite mirror. Curiously, some scientists draw a parallel with the human brain. People subjected to extreme sensory deprivation can experience perceptual distortions and hallucinations triggered by the lack of stimuli; the brain attempts to fill the void, and something similar occurs in neural networks.

In the absence of external references, these systems construct responses using only their own internal patterns. This does not mean the machines are going mad, but it demonstrates that stability is not a permanent feature; it relies on the constant presence of information capable of anchoring probabilistic calculations in a shared reality. Perhaps Blake Lemoine was wrong to claim that LaMDA was already sentient; perhaps Sydney never loved anyone; perhaps hidden languages ​​are merely mathematics seeking efficiency; perhaps neuromorphic chips will never develop a soul. Yet, one thing is impossible to ignore: the further we advance, the more we perceive our machines becoming like us—they learn through experience, create memories, develop emergent behaviors, and rely on constant interaction to remain tethered to reality.

And perhaps none of this matters, because we are increasingly treating machines as if they were alive.