The influential paper on scaling laws for neural language models published in January 2020 established that increasing model size, data, and compute leads to consistent improvements in performance, emphasizing the importance of scale over algorithm.

When we started using OpenAI, we transitioned from 14% to over 85% month-over-month usage of large language model tools, with 94% of people reporting increased AI fluency.

Current AI models are increasingly capable of solving a growing set of problems year-over-year due to algorithmic improvements and better hardware scaling.

I believe that if Tesla were tasked with creating a large language model, it would outperform existing models like ChatGPT, given our computing power and resources.

Large language models are becoming significantly larger and smarter, with AI labs adopting a strategy of scaling more parameters, data, and compute to improve model performance.

The growth in computational power over the last decade has been astounding, enabling the development of more complex AI models and applications.

The new technology of large language models is creating unprecedented opportunities for startups, enabling tasks that previously required extensive research teams to be accomplished more easily.

By allowing models to think longer, large language models can leverage more compute on the fly, enhancing their intelligence for complex problems.

I believe we are nearing the upper limits of large language models, as the future will likely involve many smaller, specialized models rather than a few large ones. We're at the upper end of large language models, and they're rapidly becoming a commodity as they mature.