Integrate Audit Trails and Human Override Layers in AI Infrastructure

AI is becoming infrastructure. Not a product you buy. Not a coworker you manage. Infrastructure you build on. I published a two-part technical series on this over at AIXplore, my technical article garden for AI practitioners. These go deeper than my usual Substack pieces, with architecture diagrams, code patterns, and decision frameworks. Part 1: The cognitive layer. The metaphor you choose for AI determines the systems you build around it. "Coworker" leads to delegation architectures that fail when the model fails. "Exoskeleton" leads to amplification architectures where accountability never leaves the human. Three patterns that work at the boundary: - Tiered autonomy: different pipeline stages get different autonomy levels - Confidence-gated routing: model certainty determines human review - Audit trails as features, not compliance: override patterns are your best training signal Part 2: The physical layer. Taalas baked Llama 3.1 8B directly into transistors. 17,000 tokens/sec on 200 watts. No memory bus. The model IS the chip. The Von Neumann bottleneck means GPUs spend 60-70% of energy moving data, not doing math. Compute-in-memory eliminates the bus entirely. A 20x cost reduction doesn't save money. It changes what's worth building. These are the technical companions to a bigger piece coming next week on Run Data Run (my Substack). That one is about the builders inside large organizations who refuse to stop building. A manifesto, not a how-to. Different audience, same through-line. Links to both articles in first comment. What layer of AI infrastructure are you spending the most time on right now: the cognitive patterns or the compute economics? #AIInfrastructure #AIArchitecture #EdgeAI