Understanding the effectiveness of new AI models can take months, as initial impressions often misrepresent their capabilities. Traditional evaluation methods are unreliable, and personal interactions yield subjective assessments, making it difficult to determine whether AI progress is truly stagnating or advancing.

• Benchmark scores get gamed or saturated fast, so they stop reflecting real-world usefulness soon after release.
• Real competence only becomes clear once developers spend weeks or months building products on top of a model and stress-testing its edge cases.
• Early hands-on reactions are unreliable because people anchor on flashy demos or isolated failures rather than broad, sustained use.
• This lag creates confusion about whether AI progress is slowing down or accelerating, since judgments formed in the first days after launch are often wrong.