Comparing answers from multiple AI models can produce more useful results than relying on a single response, according to a Forbes article. The author notes that people often blame AI for wrong answers, but the real issue may be whether they asked it to solve the right problem. The author uses several AI models for different tasks because each has strengths, and sometimes having one model review another's answers yields the most useful results, describing it as a personal version of A/B testing. The 2026 World Economic Forum report showed that organizations increasingly value people who validate, refine, and question AI-generated output. Comparing answers encourages critical thinking by prompting users to ask why one model answered differently than another. One model might catch something another missed, or neither may get it right, revealing that the problem was the question itself. When models disagree, the author digs deeper to understand their different conclusions, which helps think through the issue more thoroughly. This process can force more careful thinking rather than replacing it, and the more you compare answers, question assumptions, and refine prompts, the more likely you are to solve the original problem.