RAG is the key for smarter productivity tools with Notion CEO Ivan Zhao

**Elad Gil** (0:05)
Hi, listeners. Welcome to No Priors. Today we have Ivan Zhao, co-founder and CEO of Notion, the beloved productivity application for notes, tasks, and knowledge base.
They recently launched an AI Q&A interface as well as a calendar application. We're super excited to have Ivan. Thanks for being here again, Ivan. We are going to start with the hardest question, which is what is Notion?

**Ivan Zhao** (0:28)
Notion is always pretty hard to define because it can do so many different things.
But that's also our goal. We want to give people one tool that they can do their most work with. For a personal user, that means all your personal notes, all the planning for a trip or for your wedding. For business, for enterprise, for a company, that means all your documents, all your tasks, all your issues, calendaring, knowledge base in one tool.
The reason we want to do that because there's just so much fragmentation in the market today, we wish like, it wouldn't be nice as one place to do your most work. And our approach here is rather than try to cram all the different use cases into one product, what are the underlying software building blocks? What are the Legos that power those use cases? Can we give users those Legos so they can be creative with software themselves? They can create and tinker their perfect workflow for their personal life or for their company. And none of this is new, by the way, like people back in the 80s, even 70s tried this kind of building blocks approach to software, which is trying to take a modern spin with cloudware and with AI, to what it's like to break the prison of application-based software.

**Elad Gil** (1:43)
It's dramatic to think we've been living in a prison of SaaS fragmentation for the last two decades, but I do think it's actually surprising to hear a point of view that is so obvious, which is like, of course we want one tool where the data was interconnected. Why do you think more people don't try that to have unified tools and unified data underneath?

**Ivan Zhao** (2:10)
I think people try for different angles. Like even fairly recently, there's this thing called NoCo, right? NoCo is like coming from this kind of like power user developer angle of wouldn't be nice everybody can modify this underlying software they use every day. That's one angle. It wasn't coming from the angle of the knowledge and data wants to be in one place, right? And language model sort of give another angles, the underlying knowledge in the betting space wants to be one place, wouldn't be nice in one place, right? And the macro is also coming from the budget place. Wouldn't be nice rather than pay for five different vendors and all C-based business, just pay one vendor and save some money. So they have different angles from different times. I would say we are more come from this kind of computing and medium and literacy angle. Like you and me, go through school to learn how to read and write.
English and Chinese, we've spent years to do that. We all know how to do that. The world, the same MacBook, for most people are very rich as a more like a machine to do typewriting or watching YouTube.
Not much more beyond that. It's not very creative.
Wouldn't be more nice than more people can use their software more creatively. Because there's a separation between people who can make software and people who use software and that's why SF's rent is so expensive. Because where the modern day Detroit or Manchester, where the factory of the world.
Notions largely come from that angle, which is the original angle. We were inspired by early computing pioneer. They thought about that angle. They thought about computing could just be like literacy. One day everybody can do it. I guess they didn't expect AI might make that even give a really interesting twist to it because now, language model AI can not only to create software, but also do a lot of thinking working for you. So the future is pretty interesting.

**Elad Gil** (4:22)
So for someone who thinks on span of decades of, what should computing look like and what were the most ambitious plans for personal computing? Three, four decades ago, what are you most excited about seeing from AI Broadway over the next decade?

**Ivan Zhao** (4:42)
I think three, four decades is a bit too long. If AGI happened that time, computing might not be necessary.
For this decade, I think one sleeper category is the drag, the embedding space. Decades might be too long, I would say in the next year or two. Now, the language model can understand what you put into a computer, understanding. Rather than you do the organization to make you retrieve the understanding more easily, machine can do that better than anybody else can. Before that, we use keyword-based search, where you find your coworker who remember that, that queue, where does that information sit. Now, just ask Notion AI and you get that in seconds.