A key metric for AI progress is the size of a task (measured in human-hours) it can complete. This metric is currently doubling every four to seven months. At this exponential rate, an AI that handles a two-hour task today will be able to manage a two-week project autonomously within two years.

Over two-thirds of reasoning models' performance gains came from massively increasing their 'thinking time' (inference scaling). This was a one-time jump from a zero baseline. Further gains are prohibitively expensive due to compute limitations, meaning this is not a repeatable source of progress.

AI progress was expected to stall in 2024-2025 due to hardware limitations on pre-training scaling laws. However, breakthroughs in post-training techniques like reasoning and test-time compute provided a new vector for improvement, bridging the gap until next-generation chips like NVIDIA's Blackwell arrived.

An OpenAI employee warned that the pace of model development is so fast that any process, automation, or product built on a specific AI model today will likely become obsolete quickly. This necessitates a plan for continuous review and innovation to avoid relying on outdated technology.

Contrary to the idea that infrastructure problems get commoditized, AI inference is growing more complex. This is driven by three factors: (1) increasing model scale (multi-trillion parameters), (2) greater diversity in model architectures and hardware, and (3) the shift to agentic systems that require managing long-lived, unpredictable state.

Third-party tracker METR observed that model complexity was doubling every seven months. However, a recent proprietary model shattered this trend, demonstrating nearly double the expected capability for independent operation (15 hours vs. an expected 8). This signals that AI advancement is accelerating unpredictably, outpacing prior scaling laws.

Like human experts, advanced AI models improve their answers the more time they spend on a problem. This 'inference scaling' means short evaluations may fail to capture a model's true capabilities, as performance continues to increase with more computation, making it difficult to establish a performance ceiling.

A fundamental constraint today is that the model architecture used for training must be the same as the one used for inference. Future breakthroughs could come from lifting this constraint. This would allow for specialized models: one optimized for compute-intensive training and another for memory-intensive serving.

Previously, the biggest constraint in AI was compute for training next-gen models. Now, the critical bottleneck is providing enough compute for *inference*—the real-time processing of queries from a rapidly growing user base.