The models currently have a lot of knowledge but lack the skill level to make significant discoveries, particularly in complex fields like biology.

The models are getting smarter, faster, cheaper, and safer with each iteration. The AI models you're using today are the worst you'll ever use, and the future will be very different from today.

The next generation models will need to understand physics and chemistry better, and while they may grasp Newtonian physics, discovering new chemistry or novel theoretical physics remains uncertain.

The future will likely see more researchers embedded in every product team, as fine-tuning models for specific use cases becomes a core workflow for building products.

Models will get better at understanding and creating images, audio, and especially video, which will dramatically enhance how we interact with data.

Users of our latest model report being two to three times more productive in their scientific work, thanks to the support of a team of geniuses and advanced technology.

In the future, foundational models may become highly specialized, such as models specifically designed for chip design or protein folding.

The model can interpret genetic mutations and provide insights into their functional consequences, which is state-of-the-art in computational biology.

The current landscape shows that large model labs are focused on building powerful foundation models that require less fine-tuning, which may lead to a decline in the relevance of fine-tuning in the near future.