Source: Bytebytego
As language models gain larger context windows and memory-extension tools like MCPs (Model Context Protocols), the industry conflates access to information with genuine understanding—a critical distinction that shaped early AI hype cycles. The bottleneck is not storage capacity or retrieval. LLMs remain reactive pattern-matchers that can ingest longer documents without building persistent, reasoning-based models of complex domains. This matters because deployed agents will still fail on tasks requiring causal reasoning, conceptual consistency across documents, or learning from experience. More engineering surface area only delays these failures.