A powerful new class of large language models is making it possible for machines to write, code, draw and create with credible and sometimes superhuman results.

The dream is that generative AI brings the marginal cost of creation and knowledge work down towards zero, generating vast labor productivity and economic value—and commensurate market cap.

The fields that generative AI addresses—knowledge work and creative work—comprise billions of workers. Generative AI can make these workers at least 10% more efficient and/or creative: they become not only faster and more efficient, but more capable than before.

Text is the most advanced domain. However, natural language is hard to get right, and quality matters.

Images are a more recent phenomenon, but they have gone viral: it’s much more fun to share generated images on Twitter than text!

Copywriting: The growing need for personalized web and email content to fuel sales and marketing strategies as well as customer support are perfect applications for language models.

Vertical specific writing assistants: Most writing assistants today are horizontal; we believe there is an opportunity to build much better generative applications for specific end markets, from legal contract writing to screenwriting.

GitHub Copilot is now generating nearly 40% of code in the projects where it is installed. But the even bigger opportunity may be opening up access to coding for consumers. Learning to prompt may become the ultimate high-level programming language.

Social media and digital communities: Are there new ways of expressing ourselves using generative tools? New applications like Midjourney are creating new social experiences as consumers learn to create in public.

A plugin may be an effective wedge into bootstrapping your own application, and it may be a savvy way to surmount the chicken-and-egg problem of user data and model quality (you need distribution to get enough usage to improve your models; you need good models to attract users).

To win, teams have to get this flywheel going by 1) having exceptional user engagement → 2) turning more user engagement into better model performance (prompt improvements, model fine-tuning, user choices as labeled training data) → 3) using great model performance to drive more user growth and engagement.