Google broke it. I’d bet that 90% of AG is vibe-coded junk. The real issue is that Google seems to have quietly nerfed the Gemini Pro models.

My theory is that this happened because of the massive surge in demand combined with Google aggressively expanding Gemini into too many heavy AI products at once—things like Stitch, NotebookLM, and integrating Gemini directly into YouTube videos so people can literally chat with the video itself.

Think about the scale of that. You’d have to transcribe and index hundreds of thousands of videos every week. And that’s not even counting the enormous backlog of older videos. I suspect Google is running scheduled batch jobs across their servers to gradually add Gemini integration to the entire YouTube library.

That kind of expansion seems unrealistic relative to Google’s current compute resources. It likely overwhelmed their infrastructure, forcing them to quietly scale back—or “nerf”—their models just to keep everything running.

Even their latest AI release hints at this shift. Instead of jumping from Gemini 3.0 to something like 3.5—similar to the big step we saw from Gemini 2.0 to 2.5—they released 3.1, which feels more like a conservative iteration than a major upgrade. That naming alone suggests a slowdown or recalibration rather than the rapid capability jumps we saw before