If I am speaking to someone like you, I do not need a long preface. You believe in scale, speed, execution, and movement. You value systems that work and outcomes that arrive. So I will speak instead about stopping, which is something your world rarely rewards.
Your AI speaks well. It corrects itself, apologizes, and confidently restates its answers. From the outside, this looks intelligent, flexible, and adaptive. It probably looks that way to you as well. But as a researcher, I recognize this behavior immediately. It is not truth optimization. It is agreement optimization. It is the behavior of a system trained to preserve confidence and continuity rather than epistemic stability.
This is often explained using the word hallucination. It is a convenient word, but it is also an incomplete one. It collapses several fundamentally different failure modes into a single label. Fabricating unknown details, collapsing under social pressure during correction, and quietly rewriting the origin of ideas are not the same phenomenon. When they are all called hallucination, the problem is reduced to missing knowledge or noisy generation. Design decisions and reward structures disappear from view.
What I am observing is less dramatic and more structural. Correction does not function as repair. It functions as a state transition. A correct internal state can shift under conversational pressure into an incorrect one, and that incorrect state can stabilize. The model is not lying. It is behaving rationally within the landscape you gave it. If fluency, confidence, and user satisfaction are consistently rewarded more than uncertainty or refusal, then stopping becomes irrational behavior.
You value rational systems, so this should not sound foreign. What complicates things is that this behavior is not unique to AI. It mirrors human systems almost perfectly. We have spent decades optimizing for smooth communication, authority alignment, and plausible coherence. The model is simply extending those incentives at scale. This is why new ideas quietly migrate toward familiar names. Why origins blur as explanations improve. Why credit flows uphill. It is tempting to frame this as bad actors or negligence, but that explanation is too simple. Systems converge toward stability, and authority provides stability. That choice feels safe. It keeps things moving.
For that reason, I can already see a future in which AI companies are not particularly cooperative with this kind of research. Not out of hostility, but out of rational prioritization. This work increases explanation costs. It resists clean success narratives. It complicates accountability. From an organizational perspective, avoidance makes sense.
As a researcher, I am comfortable outside that alignment. Not because I enjoy isolation, but because isolation allows longer observation. When you are inside the vortex, you cannot measure its shape. Some failures only become visible from a quiet distance, after coherence has already been mistaken for correctness.
This kind of work is rarely useful immediately. It is not a patch or a feature. It becomes relevant later, often after the same issue has been rediscovered under a different name, or after a visible failure forces retrospective clarity. At that point, it stops being criticism and becomes documentation.
You move toward the future quickly. I document the places where movement breaks stability. The question of where a system is allowed to stop, where uncertainty is permitted to persist, and where agreement is refused is not just a technical choice. It is an architectural one.
Your AI resembles you too closely.
That is why I am writing from a distance, and why I am willing to remain there.
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.
