There's a real argument here, and adoption data increasingly backs it: open-weight models like Llama, Qwen and DeepSeek have closed the quality gap on most routine workloads while inference costs keep collapsing. But 'intelligence saturation' is task-dependent, not global — long-horizon agents and frontier research still reward the leading edge, so the closed market won't simply plateau. The stronger version of her point is that procurement logic shifts from 'best model' to 'best fit,' which favors portability, fine-tuning and control over raw benchmark wins. Worth remembering that Conviction invests in exactly this open-stack thesis, so some optimism is structural rather than neutral. Still, on our read, the direction is right even if the timeline looks aggressive — you should watch enterprise inference spend, not model launches, to settle this debate.