The early days of AI development were marked by skepticism about the viability of language models, but persistence in technology has proven their value.

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 breakthrough with large language models (LLMs) has accelerated the development of high-quality AI, which is now advancing faster than holographic AR technology.

The current focus in AI development is on scaling models effectively, as the foundational challenges of reasoning and computation have been addressed.

English is becoming the new assembly language for large language models, meaning that effective communication with AI requires structuring language correctly, even if syntax is less critical than before.

The current low-hanging fruit in AI is using large language models to perform knowledge work and integrating them into familiar user interfaces without changing user behavior.

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.

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.

Achieving human-level AI will not be possible by merely scaling up existing large language models; a fundamental shift in approach is necessary to reach that goal.