Models are producing more code, faster inference, and claimed research results, but the bottleneck is shifting from generation to verification. Engineering teams are responding by reviewing risk and outcomes instead of every implementation detail, while model architecture and research provenance remain harder to inspect. The useful question is no longer whether AI can produce an answer, but what evidence makes that answer safe to trust.
OpenAI says an unreleased model resolved the Navier-Stokes Millennium Prize problem after an 88-hour agent run. A rival AI-assisted team disputes the research timeline, making verification and credit part of the result.
Gergely Orosz reports that teams facing AI-generated pull request volume are shifting human review toward blast radius, plans, tests, and schemas. The pattern preserves judgment where failure costs most instead of treating every diff alike.
A dated brief from the vibe-coding frontier. Today’s Intel.
Inception says Mercury 2.5 runs above 1,100 tokens per second on standard Nvidia GPUs and improves intelligence 40% over Mercury 2. OpenRouter has made it generally available, so latency-sensitive coding pipelines can test the claims now.
Artificial Analysis now compares intelligence, cost, speed, and latency across every reasoning-effort setting for a model. The pages expose how the same weights trade quality for time and spend, so teams can choose a setting from measurements instead of labels.
AI researcher Sebastian Raschka explains that looped transformers reuse blocks to add computation, while hidden reasoning traces are a separate product choice. The distinction matters because architecture alone does not prove a model is concealing more thought.