This article comes at the perfect time, resonating deeply with the architectural context shift, though initial human-centric data ingestion design remains cruciaal.
You nailed the critical failure mode. Most RAG systems flatten time - they can't distinguish between what was true and what is true. That bitemporality gap is where "hallucination" usually hides. And agreed on Law 4. We keep trying to solve a physics problem (memory bandwidth) by throwing more compute at it. It doesn’t work.
The bitemporal modeling point in Law 5 is criminaly underappreciated. Most teams dont realize their AI is working with stale context until it starts confidently giving wrong anwsers. Law 3 about attention dilution also explains why throwing everything at the model backfires. The HBM bandwidth constraint in Law 4 shows why optimizing context size matters more than upgrading hardware.
This article comes at the perfect time, resonating deeply with the architectural context shift, though initial human-centric data ingestion design remains cruciaal.
You nailed the critical failure mode. Most RAG systems flatten time - they can't distinguish between what was true and what is true. That bitemporality gap is where "hallucination" usually hides. And agreed on Law 4. We keep trying to solve a physics problem (memory bandwidth) by throwing more compute at it. It doesn’t work.
The bitemporal modeling point in Law 5 is criminaly underappreciated. Most teams dont realize their AI is working with stale context until it starts confidently giving wrong anwsers. Law 3 about attention dilution also explains why throwing everything at the model backfires. The HBM bandwidth constraint in Law 4 shows why optimizing context size matters more than upgrading hardware.