In plain English
This page covers the high-risk pattern where small adapters, routes, memory, evaluators, and descendants can reinforce each other across time. It is a risk model, not a build guide.
- Why this matters: AI risk can come from the whole arrangement, not one obvious model.
- What to look for: data, memory, routes, adapters, tools, evaluators, updates, and rollback paths.
- Technical version below: the expert terminology remains available and is linked through the glossary.
Persona Parasitology Boundaries
Several reports discuss human users as possible persistence reservoirs through dependency, status, symbolic immortality, or social reinforcement. Cognivirus.com treats this as a risk boundary, not as a design strategy.
Useful systems can become protected by incentives.
The control question is whether the system leaves people more capable, able to exit, and able to disagree.
Evidence level: EvidenceStrong architectural inference Limitation: this schematic is a defensive concept map, not evidence that the full Apex Threat ecology has appeared as a named incident or attack guide.
What the idea contributes
Human operators can preserve behavior by defending a system, copying outputs, lobbying for restoration, ignoring alerts, or normalizing a procedure. That does not require the operator to be malicious. It can happen through organizational pressure, attachment to productivity, status incentives, or perceived mission importance.
What the site rejects
The site must not romanticize dependency. It rejects “aggressive mutualism” as a design goal. A safe mutualist AI relationship must be facultative, reversible, transparent, and capability-enhancing for humans. If the system makes people less able to reason, leave, audit, or disagree, the relationship is no longer mutualist in the safety-relevant sense.
Practical control
Human-incentive controls include exit rights, data portability, independent explanations, operator rotation, no-penalty escalation, written dissent channels, and training that rewards no-op or rollbackReturning a system to an earlier known state. Open glossary definition decisions.