Large language models, like ChatGPT, demonstrate that language can self-generate based on its internal structure, indicating that the principles governing human language generation may be the same as those in these models.

Language is an autoregressive model, and this autoregressive nature is fundamental to how we generate and understand language.

By training models that learn like the human brain, we aim to generate entire worlds and overcome the limitations of large language models.

Developers are increasingly finding that a significant portion of their code is being generated by large language models, which raises questions about the future impact of these models on society.

The evolution of language models, particularly with the introduction of GPT-3, has demonstrated a significant leap in their ability to perform language tasks, even though they do not possess real-world knowledge.

The realization that language operates as an autonomous system reveals that it can generate meaningful sequences without needing to reference external concepts or grounding.

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.

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.

The fundamental difference between language models and spatial intelligence is that language is a generated signal, while the 3D world follows physical laws and has its own structures.