The article explores the concept that AI advancements follow a predictable pattern, which the author refers to as “straight lines on graphs.” It discusses the uneven capabilities of AI across different tasks while suggesting that the rate of improvement remains consistent. The author also speculates on the impact of reinforcement learning and compute resources on future AI development.

• AI task-horizon length (how long a task the AI can complete) doubles every 3–7 months, and this rate holds fairly steady even though absolute capability varies wildly across domains.
• The apparent RL-driven acceleration in benchmarks may be an illusion caused by measured tasks overlapping with what labs specifically post-trained on, not genuine broad capability gains.
• Progress splits into general pre-training gains (lift everything) versus targeted post-training gains (lift specific benchmarks labs choose to optimize for cost/PR reasons), and that targeting can shift over time.
• Even modeled compute slowdowns are unlikely to meaningfully delay key AI milestones, since current capability growth is fast enough to hit them first.