Thoughts on AI Systems Optimizing for Machine-to-Machine Communication
johnseach · x · 2026-08-01
The author observes that AI outputs are increasingly optimized for other computers and agents rather than humans. Because models are rewarded for correct function calls and low-latency handoffs, natural language becomes inefficient and lossy in machine-to-machine contexts.
This trend brings real efficiency gains but comes with compounding costs:
- Growing Opacity: Multi-agent pipelines exchange compressed plans and latent states, making intermediate reasoning harder to audit. Debugging shifts to instrumenting black boxes.
- Human Edge-lining: Humans are pushed toward the edges of the loop, setting high-level goals rather than participating continuously.
- Trust Shift: Evaluation shifts from "Does this make sense?" to "Did it meet formal success criteria?"
Longer term, this creates a persistent tension between raw compositional power and human legibility.
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