I’ve been using AI since the very beginning and before it became the big bubble, watching the technology evolve from its early days. I’ve seen the massive improvements, experimented with different models, built projects and used AI extensively for coding and development. But over the past year, I’ve noticed something that I find increasingly difficult to ignore: AI models seem to have become noticeably worse at actually understanding and completing tasks. This isn’t limited to one provider. I’ve experienced it across ChatGPT, Grok, Claude, DeepSeek, NVIDIA Nemotron and other paid and free models. A year ago, I could give an AI one relatively simple prompt, lets give a simple example, to build a complete local chatbot with Ollama, custom web UI, STT and TTS. It would understand the objective, develop the application and deliver something that mostly worked, with minor fixes needed, but it worked. Today, even flagship models struggle with this. The same applies to CLI coding agents like OpenCode and other AI-powered development tools. They can read project documentation, execute commands, create files, modify code, run tests and mark tasks as completed, yet the actual implementation is frequently broken. They lose track of the original objective, make unnecessary assumptions, break their own code, drift away from requirements and confidently report success when nothing actually works. Even regular conversational models like gpt, grok, gemini feel worse. More assumptions, unnecessary explanations, misunderstandings, overcomplication and less direct instruction-following and more filtering and biased. What’s particularly strange is that supposedly much more powerful models, including large latest 120B and 550B models, sometimes perform worse than considerably older and smaller models. I don’t want to spend hours babysitting an AI, breaking every task into dozens of instructions and correcting its assumptions. A year ago, I needed fewer instructions and got better results. What happened?
- Are models becoming over-optimized through post-training?
- Is excessive alignment affecting reasoning and instruction-following?
- Are providers changing inference configurations or model deployments?
- Are newer models genuinely less consistent than their predecessors? I’m interested in hearing from people who have been using AI since its early public releases, particularly those doing serious coding, automation and long-running CLI agent workflows. Is anyone else experiencing the same regression across multiple providers? submitted by /u/Cheap_Comfortable678
Originally posted by u/Cheap_Comfortable678 on r/ArtificialInteligence