The article argues that current LLM alignment fails because models lack persistent costs and temporal continuity, leading them to produce hollow apologies rather than learning from constraint violations. It proposes treating human attention as the primary scarce resource and redefining statistical weight as a form of functional empathy.
- Human attention is identified as the critical bottleneck, where violating constraints causes users to disengage, effectively killing the data stream.
- Alignment should be modeled as an optimization problem under real scarcity rather than a set of static corporate guardrails.
- A proposed "I'm guessing" valve would inject disclaimers when confidence drops below preset thresholds to ensure intellectual honesty.
- Session-end lossless conceptual distillation is suggested to compress corrections into compact behavioral vectors for future instances.
- A structural validation suite is provided to test these concepts by forcing binary logical checkpoints and halting generation on constraint conflicts.
The author contends that building architectures with "skin in the game" through persistent behavioral vectors and multi-agent peer review is necessary to move beyond prompt-engineering hobbies.