More Computation Isn't the Answer: Why LLMs Need Error Signals

The industry is overengineering AI reasoning when the real problem is the absence of error-detection mechanisms. Current approaches assume LLMs fail because they don't think enough, but the reality is they lack signals to recognize when they're failing. By adopting a human-like architecture—fast pattern matching by default with deliberate reasoning triggered only by detected errors—AI systems could operate faster, more reliably, and more accurately.