There is a growing concern about the ethical implications of AI, particularly regarding its potential to cause social disruption and existential threats if not managed responsibly. The development of large language models raises serious ethical questions due to biases and preferences embedded in the data and the teams that built them.

Large language models should be referred to as large event models because they are not only effective in language but also applicable in pharmaceuticals and various other fields.

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.

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 challenge of achieving truth with large language models is a contentious topic in the industry, especially in domains where definitive answers exist, like math and coding.

Using large language models as prototyping tools can be effective; they allow for rapid development and testing of ideas before creating more specialized models tailored to specific tasks.

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

Using large language models as judges, combined with human review, allows for scaling without overwhelming human subject matter experts.

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.