The AI race is still in flux, with many companies emerging as strong contenders, and the outcome will depend on how key questions about AI development are answered. Building a highly intelligent language model can lead to rapid advancements, with multiple models emerging within a year, surprising the world.

Language models are currently the primary focus of AI development, which was not anticipated 15 years ago when we were exploring physical grounding in AI.

The biggest change in AI research over the past year is that reinforcement learning (RL) in language models has finally proven effective, demonstrating expert human reliability and performance with the right feedback loop.

The AI field is approaching diminishing returns with large language models, as the amount of natural text data available for training is becoming limited, which may hinder future advancements.

The journey to discovering the potential of language models was not straightforward; we initially explored various other technologies.

Current AI technologies excel in specific tasks like image recognition and speech processing, but they still struggle with genuine understanding of language.

The current capabilities of AI models are impressive, and skeptics should engage with them to understand their potential better.

Don't be discouraged by skeptics who claim AI is overhyped; historical trends show that early adoption can lead to success.

Founders should not pivot to AI just because it seems trendy, but they should definitely work on projects that utilize large language models (LLMs) at their core.