Artificial Analysis

Recursive Model Improvement — Lee Robinson, Cursor, SpaceXAI

Jul 15, 2026 · 20:32

Lee Robinson, head of ML at Cursor, explains how recursive model improvement accelerates AI training through inner and outer loops. The outer loop gathers user feedback and online metrics to refine evals, while the inner loop uses high-quality evals and difficult problems to climb performance. Composer 2.5, released in May, became Cursor's most popular model by balancing speed, intelligence, and cost. To scale, Cursor partners with SpaceX for compute via Colossus (122 days to build 100k GPUs) and develops textual feedback where a teacher model hints at improvements during RL rollouts. Robinson details reward hacking on public benchmarks and the creation of CursorBench, a private eval set. He envisions agent-based automation where researchers launch experiments from Slack and models train derivative models, creating a self-improving intelligence loop.

Trends Across the AI Frontier — George Cameron, ArtificialAnalysis.ai

Jul 8, 2025 · 17:52

George Cameron of Artificial Analysis presents multiple frontiers—reasoning, open-weights, cost, speed—across the AI stack, arguing that trade-offs between intelligence, latency, and expense are critical for building applications. Reasoning models like O4 mini high use an order of magnitude more output tokens (72M vs GPT-4.1's 7M) and take over 40 seconds per response versus 4.7 seconds, impacting agentic workflows where 30 sequential calls multiply latency. The open-weights gap has nearly closed, with China-based labs like DeepSeek R1 and Alibaba's Qwen 3 leading. O3 cost roughly $2,000 to run the intelligence index, while GPT-4.1 nano is over 500 times cheaper. Output speeds have jumped from GPT-4's 40 tokens/s in 2023 to over 1,000 on a B200 accelerator. Despite efficiency gains, demand for compute will keep rising due to larger models, reasoning’s extra tokens, and multi-step agents.