Jenson Huang (NVIDIA's CEO) famously declared that every pixel will be generated, not rendered. While for some types of media that vision is further out, for written content this proclamation has already come to pass. We’re in an age of just in time content.
Traditionally if you wanted to produce a piece of written content on a topic you’d have two choices. Do the research yourself, write a draft, edit, refine, and finally publish. Or you could get someone else to do that process for you either by hiring them directly or indirectly by getting content they wrote for a publisher.
Today written content is generated in real-time for anyone on anything. That’s a pretty broad statement to make so let me make it more concrete. I’ve written 3 books, thousands of articles, and given hundreds of talks on digital product design. The generative AI feature on my Website, Ask LukeW, searches all this content, finds, ranks, and re-ranks it in order to answer people’s questions on the topics I’ve written about.
Because all my content has been broken down into almost atomic units, there’s an endless number of recombinations possible. Way more than I could have possibly ever written myself. For instance, if someone asks:
- Where do I place labels on mobile Web forms?
- What are some best practices for building a generative AI chat interface based product?
- How do I start my journey as a product designer?
Each corresponding answer is a unique composition of content that did not exist before. Every response is created for a specific person with a specific need at a specific time. After that, it’s no longer relevant. That may sound extreme but I’ve long contended that as soon as something is published, especially news and non-fiction, it’s out of date. That’s why project sites within companies are never up to date and why news articles just keep coming.
But if you keep adding bits of additional content to an overall corpus for generative AI to draw from, the responses can remain timely and relevant. That’s what I’ve been doing with the content corpus Ask LukeW draws from. While I’ve written 89 publicly visible blog posts over the past two years, I added over 500 bits of content behind the scenes that the Ask LukeW feature can draw from. Most of it driven by questions people asked that Ask LukeW wasn’t able to answer well but should have given the information I have in my head.
For me this feels like the new way of publishing. I'm building a corpus with infinite malleability instead of a more limited number of discrete artifacts.
Two years ago, I had to build a system to power the content corpus indexing, retrieval, and ranking that makes Ask LukeW work. Today people can do this on the fly. For instance in this video example using Bench, I make use of a PDF of my book and Web search results to expand on a topic in my tone and voice with citations across both sources. The end result is written content assembled from multiple corpuses: my book and the Web.
It’s not just PDFs and Web pages though, nearly anything can serve as a content corpus for generative publishing. In this example from Bench, I use a massive JSON file to create a comprehensive write-up about the water levels in Lake Almanor, CA. The end result combines data from the file with AI model weights to produce a complete analysis of the lake’s changing water levels over the years alongside charts and insights about changing patterns.
As these examples illustrate, publishing has changed. Content is now generated just in time for anyone on anything. And as the capabilities of AI models and tools keep advancing, we’re going to see publishing change even more.