OpenAI’s GPT-5.5 release reveals a widening gap between capability and judgment, managed increasingly through external safeguards.
AGI frameworks measure capability, but not behavior. Why judgment - not just intelligence - determines whether systems can be trusted.
Retraining improves models, but the cycle is costly. As systems scale, the economics of constant retraining become harder to sustain.
Some AI behaviors only emerge over time. This explores why standard testing methods often fail to detect them.
System cards document consistent instability across models. Read together, they reveal a deeper pattern beyond individual limitations.
Benchmarks measure capability under controlled conditions. Real-world use reveals how systems behave under uncertainty and change.
Scaling improves capability, but not consistency. This explores why larger models don’t resolve instability or real-world behavior.