Every major AI lab just announced a "reasoning" model. OpenAI's o1. Anthropic's extended thinking. Google's Gemini thinking mode. The pitch is identical: these models think longer, reason deeper, solve harder problems.
Then you actually use them.
OpenAI claimed o1 performs at PhD level on physics and math. Users found it failing basic logic puzzles within days. Anthropic shows Claude's thinking process, which often reveals circular reasoning dressed up as deliberation. Google's announcement was heavy on promise, light on specifics.
The pattern is clear. Labs are conflating longer inference time with better reasoning. Spending more tokens to think doesn't automatically produce better outcomes. It's like assuming someone who talks longer is automatically smarter.
This matters because enterprises are making architecture decisions based on these claims. They're building systems that assume reasoning capabilities that don't exist at scale yet.