Steven Johnson has written 13 books, on topics ranging from a London cholera outbreak to the value of video games. He’s been a television presenter and a podcast host. He’s a keynote speaker who doesn’t have to call himself that in his LinkedIn profile. And for over a year now, he’s been a full-time employee of Google, a status that’s clear when he badges me into the search giant’s Chelsea offices in New York to show me what his team has been creating.
It’s called NotebookLM, and the easiest way to think of it is as an AI collaborator with access to all your materials that sits on your metaphorical shoulder to guide you through your project. NotebookLM was soft-launched to a select group earlier this year but is now available to all as an “experiment”—that’s Google’s low-risk way to see how the app behaves and how we behave with the app.
Johnson found his way to Google by way of a lifelong obsession with software as a “a dynamic thought partner,” a tool to speed up and enhance the creative process. When he was in college he became obsessed with HyperCard, Apple’s software that broke knowledge into chunks and allowed you to navigate an information-space through links. It anticipated web navigation before the web existed. “I fought mightily to turn HyperCard into that dream tool, but it wasn’t quite ready,” he says. He eventually became an enthusiast of Scrivener, a combination word processor and project organizer popular with book authors. (I am a fan too.)
When Johnson got access to OpenAI’s GPT-3 text generator in 2021, he recognized that AI could level up a new generation of thought tools. Oh, wait, he said to himself, this thing that has always been in the back of my mind is now going to be possible. Scenarios unthinkable even a year before were suddenly on the table. Johnson didn’t yet know that Google not only had similar large language models, but was already working on a project very much in line with his thinking. In May 2022, a small team in the experimental Google Labs division cold-emailed Johnson. They set up a meeting via Starline, a Google Labs project that allows for eerily intimate in-person meetings. “I basically had a conversation with a hologram who said, ‘You know, this thing you’ve been chasing your whole life? We can finally build it,’” Johnson says. He became a part-time adviser to the small team, at first sharing the workflow of a professional writer. “Here’s four or five engineers, here’s an actual author, let’s just watch him,” is how Google Labs head Josh Woodward sums up the process. Eventually Johnson got involved in the development of the product itself and was sucked in to the point of accepting a full-time gig. His title at Google Labs is editorial director.
NotebookLM, originally called Project Tailwind, starts by creating a data set of your source material, which you drag into the tool from Google Docs or the clipboard. After the app has digested it all, you can then ask NotebookLM questions about your material, thanks to Google’s large language model technology—partly powered by its just-released upgrade Gemini. The answers reflect not only what’s in your source material but also the wider general understanding of the world that Gemini has. A critical feature is that every answer to your queries comes with a set of citations reporting where exactly the information came from, so users can check the accuracy of its output.
Google is not the only company envisioning products that let people create custom data sets to explore with LLMs. At OpenAI’s developer day last month, the company introduced personalized mini-GPTs that can be tuned to a specific task. Woodward acknowledges a “core similarity.” But he argues that NotebookLM focuses more on enhancing a workflow, and is geared to provide superior accuracy in its outputs. Also, he says that the OpenAI products have more of a personality, while NotebookLM is designed to have no such pretensions.