Keep pulling the thread on Noam Brown.
The rapid progress in AI model capabilities, as seen from O1 preview to O3, will continue going forward.
OpenAI's Deep Research product is an existence proof that reasoning models can succeed in domains without easily verifiable metrics for success.
Pre-trained models require a certain level of base capability to benefit from additional reasoning or "thinking" time; applying a reasoning paradigm to a model like GPT-2 would have yielded almost no benefit.
Many software layers being built on top of current AI models, such as harnesses and routers, will eventually be made obsolete by scale and improvements in base model capabilities.
OpenAI's long-term vision is to move towards a single, unified model, which would eliminate the need for external model routers.
Achieving superintelligence will require a "reasoning paradigm" and cannot be accomplished by simply scaling pre-training, as that approach will hit the limits of economic feasibility first.
By October or November 2023, OpenAI had conclusive signs that its reasoning paradigm was a significant breakthrough.
OpenAI's leadership recognized the need for a paradigm beyond pre-training and invested research efforts into reinforcement learning, even while the vast majority of resources were focused on scaling pre-training.
A former OpenAI employee did not believe the O-series models were significant until they saw a competing lab pivot its entire research agenda to focus on reasoning models after the O-1 announcement.
Agentic AI capabilities will expand beyond software engineering to encompass a wide range of remote work tasks.
Noam Brown's multi-agent team at OpenAI is working on scaling up test-time compute to enable models to "think" for hours or days to solve extremely difficult problems.
The self-play paradigm that was successful for AlphaGo is not easily transferable to domains outside of two-player, zero-sum games because the objective function is much harder to define.