Some skeptics argue that knowledge layers add complexity and may not scale well with rapidly changing data or knowledge domains. They point out that AI models continue to improve in raw pattern recognition and large-scale language understanding, which might reduce the need for explicit knowledge structures. Furthermore, building and maintaining a knowledge layer requires significant enterprise effort and may introduce latency or brittleness if not properly designed. The case against relies on improvements in model size and training data breadth.