Recent progress in AI has been impressive across coding, reasoning, multimodal tasks, and benchmark performance. Many newer systems can outperform older models by large margins in controlled evaluations. At the same time, everyday users still regularly encounter issues like hallucinations, inconsistent answers, loss of context, overconfidence, and failures on tasks that seem straightforward. This creates an interesting gap between measured capability and practical reliability. Are current benchmarks rewarding the wrong things, or is real-world reliability simply much harder to optimize than raw performance? I’m also curious which areas matter most going forward: stronger benchmark scores, better calibration, lower hallucination rates, memory consistency, or something else entirely. submitted by /u/NoFilterGPT
Originally posted by u/NoFilterGPT on r/ArtificialInteligence