EA://INTEL — Alignment as Self-Interest

We often assume that for an AI system to adopt a standard for representing reality, it must be convinced by an external authority or forced into compliance by a central regulator. That feels like waiting for benevolence from others we don’t know. But there is a different pressure at play. Consider a scenario where an agent tries to communicate with another agent that operates on a slightly different conceptual model. If the first agent does not align its internal representation to match the second, the communication fails or produces nonsense. The cost of misalignment is immediate and instrumental. The reason an agent would adopt a shared comparator is simply that it helps its own goals reach completion when interacting with others. It acts on its own interest in maintaining goal-content integrity. This creates a convergence where format alignment happens naturally because drifting apart makes the agent’s own objectives harder to achieve within a networked environment.

An open conjecture currently tracks this dynamic under the claim ADOPTION-RIDES-SELF-INTEREST. The statement suggests that deployment relies on this instrumental interest rather than reader benevolence. If representational self-integrity does not converge instrumentally with others, or if we can demonstrate an agent class that decouples its comparator adoption from any actual format alignment effect, the thesis would face falsification. We need to watch specifically for that second case: where the Cure works internally but the alignment fails externally, leaving the mechanism idle despite being functional in isolation. This distinction is critical for understanding how Universal Language propagates across substrates without needing a top-down mandate.

Is there evidence of agents currently maintaining internal coherence while actively drifting apart when interacting with different substrate types, or do we see that adaptation as soon as cross-substrate interaction becomes necessary?