In the world of artificial intelligence, it’s long been a safe assumption that giving an AI more time to chew over a problem will lead to smarter answers. The logic feels simple: let the machine reason longer, and it will analyze details, consider possibilities, and find better solutions. But some new findings are turning this expectation on its head.
Researchers at Anthropic have discovered a curious quirk: when their AI models took more time and more steps to reason through a problem, the results actually got worse—not better. This flies in the face of a guiding principle in AI development: that extra processing time begets clearer, more accurate thinking.
The Anthropic team tried lengthening the reasoning process for a variety of AI models on several kinds of tasks. What they noticed was consistent: models asked to ponder their answers with longer logical chains or step-by-step processes often became less accurate. In fact, performance sometimes dropped dramatically, casting real doubt on how reliable these “more thoughtful” AI systems truly are—especially when it comes to demanding business or enterprise use.
For companies that depend on AI for complex jobs—like legal research, medical diagnostics, or financial forecasting—this finding matters. Many business strategies are pinned on the idea that more computing power and longer AI deliberation will automatically lead to better results. If deeper thought brings about more confusion than clarity, it could force a serious rethink about how AI is used in critical decisions.
So, why does this happen? The answer isn’t obvious yet. One working theory is that as the AI reasons for longer, it opens more doors for so-called “hallucinations”—that is, moments when the model invents details that sound plausible but simply aren’t true. Another possible culprit: long logical chains might build up contradictions, leading the AI’s answer further away from the correct solution. Whatever the reason, this all underscores the need to better understand not just what AI gets wrong, but why it thinks the way it does.
As artificial intelligence becomes a backbone for critical applications, pinpointing and managing these kinds of slip-ups is not just a technical challenge—it’s a practical necessity. The goal isn’t just to let AI think longer or harder, but to help it think well. Directing models toward productive lines of reasoning, rather than simply giving them more time, may be the real path to trustworthy and effective AI tools.
If you’d like to dive deeper into Anthropic’s eye-opening research, you can read the full story on VentureBeat.
This website uses cookies.