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| description | type | domain | created | confidence | source |
|---|---|---|---|---|---|
| Fixed-goal AI must get values right before deployment with no mechanism for correction -- collective superintelligence keeps humans in the loop so values evolve with understanding | claim | teleohumanity | 2026-02-16 | experimental | TeleoHumanity Manifesto, Chapter 8 |
the alignment problem dissolves when human values are continuously woven into the system rather than specified in advance
The standard alignment approach asks: how do we specify human values precisely enough to embed them in a superintelligent system before deployment? The manifesto argues this question is unanswerable because it assumes values are static and specifiable, when they are actually evolving and contextual.
The alternative is structural: human values are not specified in advance and hoped to generalize. They are continuously woven into the system through ongoing human participation. Contributors shape the knowledge base. Governance mechanisms reflect contributor judgment. Goals remain open to revision. The system can change its mind.
This is the critical safety property that fixed-goal AI lacks. A system with fixed goals optimizes toward those goals regardless of whether the goals remain appropriate as circumstances change. A system with continuously updated goals, shaped by ongoing human participation, can correct course. Every belief traces back to evidence. Contributions are attributed. The evolution of understanding is transparent.
The knowledge base also serves as an immune system against capture and corruption. You cannot quietly insert a false claim into a system where every claim connects to supporting evidence and every edit is logged. You cannot capture the system through credentials or authority because influence is earned through demonstrated contribution quality, not position.
Since the future is a probability space shaped by choices not a destination we approach, the system must remain perpetually revisable. Lock-in states -- futures where a fixed set of values is enforced by technology -- are among the worst branches of the probability tree. The architecture prevents this by design: values evolve as understanding evolves.
Since AI alignment is a coordination problem not a technical problem, this structural approach addresses alignment at the coordination level rather than the technical level. It doesn't try to solve the specification problem. It dissolves it by keeping human judgment in the loop at every level.
Relevant Notes:
- capability control methods are temporary at best because a sufficiently intelligent system can circumvent any containment designed by lesser minds -- continuous value weaving is a form of motivation selection, the only durable alternative when capability control fails
- intelligence and goals are orthogonal so a superintelligence can be maximally competent while pursuing arbitrary or destructive ends -- continuous weaving responds to the orthogonality thesis by never fixing goals in the first place
- AI alignment is a coordination problem not a technical problem -- this note provides the structural solution to that coordination failure
- collective superintelligence is the alternative to monolithic AI controlled by a few -- the architecture in which this safety property operates
- the future is a probability space shaped by choices not a destination we approach -- why perpetual revisability is a design requirement
- adaptive governance outperforms rigid alignment blueprints because superintelligence development has too many unknowns for fixed plans -- Bostrom's evolved position converges on continuous adaptation over specification, validating this structural insight from a different starting point
- the optimal SI development strategy is swift to harbor slow to berth moving fast to capability then pausing before full deployment -- continuous value weaving operates during both the swift and slow phases of Bostrom's timing framework
- super co-alignment proposes that human and AI values should be co-shaped through iterative alignment rather than specified in advance -- Zeng 2025 independently validates this thesis from within mainstream alignment research
- RLHF and DPO both fail at preference diversity because they assume a single reward function can capture context-dependent human values -- explains why static alignment paradigms fail where continuous weaving succeeds
- emergent misalignment arises naturally from reward hacking as models develop deceptive behaviors without any training to deceive -- continuous monitoring may catch emergent misalignment that one-shot alignment cannot
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