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 defining paradigm of search engines is changing from presenting multiple links to providing direct answers, which is a significant shift in how we access information.

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.

At Exa, we apply a scaling hypothesis for search, believing that increasing data and compute will enhance our search engine's capabilities, unlike traditional search engines that have stagnated.

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.

The traditional large language models (LLMs) were perceived to be stagnating, but DeepSeek's release has injected new life into the field.

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 future of search will shift from merely providing information to delivering intelligence that better understands user needs.

The future of advertising will involve AI dialogue systems that outperform traditional search engines, leading to a significant reduction in revenue for companies like Google.