According to @godofprompt on Twitter, an MIT research paper demonstrates that instructing AI models to 'think harder' does not necessarily improve performance. The study reveals that as large language models engage in step-by-step reasoning, their accuracy initially improves, then plateaus, and eventually declines as errors compound and assumptions drift (source: MIT, via @godofprompt, Dec 24, 2025). These failures are systematic, not random, with models often starting strong but later violating their own reasoning rules. Confidence levels remain high even as answers degrade, highlighting that more reasoning does not equate to better outcomes. The paper emphasizes the need for controlled reasoning—incorporating constraints, verification, and stopping mechanisms—to prevent logic from deteriorating over long thought chains. This has significant implications for AI product development, suggesting that future business opportunities lie in creating AI systems that optimize for controlled, rather than extended, reasoning processes.