Pod Insights No Priors RAG is the key for smarter productivity tools with Notion CEO Ivan Zhao About this episode Notion is a productivity app that has invested heavily in AI to create products that enable workers to access information instantly without having to search through their own countless notes. Today on No Priors, Sarah and Elad are joined by Ivan Zhao, the co-founder and CEO of Notion, to talk about Notions Q&A interface and calendar applications. They also get into how using RAG models means better retrieval, longer memory, and the user can be less organized and how Notion is leading the charge in this era of SaaS bundling products.
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Show Notes:
(0:00) Introduction
(2:09) AI and Computing literacy
(5:39) Building the Notion AI team
(8:43) Notion as an application company
(12:09) Prioritizing AI investment
(14:53) The rapid evolution cycle of AI development
(17:46) Notion Q&A
(20:00) Workflow and AI for calendars
(22:43) Moving past the need for organization
(24:36) History of SaaS doesn’t repeat, it rhymes
(30:14) Design at Notion
(34:26) Notion office design
(36:52) How RAG will change the future
(38:30) Building our the software in the Notionscape
Transcript Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.
0:05 Sarah Guo? Hi listeners, welcome to No Fliers. Today we have Ivan Zaha, 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?
0:28 Speaker 01 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, 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 different use cases into one product, what are the underlying software building blocks? What are the Legos that power those use cases?
1:13 Speaker 01 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 cloud and with AI, to what it's like to break the prison of application-based software.
1:43 Sarah Guo? 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, you know, 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 people, why do you think more people don't try that? To have unified tools and unified data underneath?
2:10 Speaker 01 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 models sort of give another angles. The underlying knowledge in the betting space wants to be one place. Wouldn't it be nice in one place, right? And the macro is also coming from the budget place. Wouldn't it be nice rather than pay for five different vendors and all C base business? Just pay one vendor and save some money.
2:53 Speaker 01 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. It's more like a machine to do typewriting or watching YouTube.
3:26 Speaker 01 Not much more beyond that. It's not very creative, right? Wouldn't it be more nice that more people can use their software more creatively, right? Because there's a separation between people who can make software and people who use software. That's why SF's rent is so expensive. Because we're the modern day Detroit or Manchester, right? We're 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, right? 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.
4:10 Speaker 01 Because now language model AI can not only do create software, but also do a lot of thinking working for you. So the future is pretty interesting.
4:22 Sarah Guo? So for someone who thinks on, you know, span of like decades of, you know, what should computing look like? And what were the most ambitious plans for personal computing?
4:34 Sarah Guo? Uh, three, four decades ago, like, what are you most excited about seeing from AI broadly over the next decade?
4:42 Speaker 01 I think three, four decades, a bit too long. If AGI happened that time, like, computing might not be necessary. For this decade,
4:52 Speaker 01 I think one sleeper category is the, the drag in 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. So rather than you to the organization to make you retrieval, retrieve the understanding more easily, machine can do that better than anybody else can, right? So before that, uh, we use keyword based search where you find your coworker who remember that, that queue, where does that information sit? Now, I just ask Notion AI and you get that in seconds. So that's one I'm personally really excited about. I think now enough people talk about it. And of course, the other one is like
5:35 Speaker 01 the agent, the workflow side that that has a lot of buzz already. So that's interesting, too.
5:41 Sarah Guo? You and Simon, uh, said bet the company on AI and are, you know, have real conviction. And as you are building out the team, like, um, what does the talent look like? You like have or need to make, um, Notion an AI-first company?
5:57 Speaker 02 I can argue you, you folks are one of the earliest adopters of AI at scale as an application. So part of the question in some sense is you've built so many interesting things, like, what are the people that you now need to sort of build the next level stuff? In addition to what you already have on the team?
6:09 Speaker 01 The early days kind of just grew fast. Simon is really good. He can build a lot of things and learns really quickly, right? I would say Notion is the company where largely people interested in interface and design a lot of full stack. From then, heavy folks and backend people who scale. We have somewhat a small team of search, but we don't have too, too many ML folks, almost nothing. And in at least my learning, our learning in the past year or so, building for AI is, okay? You, ML folks are.
6:41 Speaker 01 Important. It's kind of like you no longer do this deterministic thing that you can see how it works. It's almost like a, I don't bake, but feels like a baking. Right? You have to like do something, get the thing ingredients ready, run through, rinse, press a button and wait for a while. As you, does it come up? Or it's a different way, a different sense of patience and different type of personality to do that well. A lot of massaging, a lot of preparing.
7:07 Speaker 02 Some of my friends call that probabilistic software engineering.
7:10 Speaker 01 Kind of like.
7:10 Speaker 02 I think it's morphed into this sort of stochastic world, or at least partially stochastic.
7:15 Speaker 01 Yeah, so one is like maybe gardening feels like that way. I don't garden either. So that category of people are, to me, is pretty necessary. The other category is like people who are curious and learn really fast. Right? It's like, okay, like the group of prompt, like language, prompt engineer, language model sort of make everybody like a real time machine learn. Learning engineer, you just prompt the right, then you can get your stuff. Right? And there's a lot of tricks and techniques.
7:50 Speaker 01 And how does that plug into user interface? I think this category of people called AI engineer or something, there's some terminology form. They tend to be pretty young, they tend to be like, we have someone like under drinking age, working at Notion. They, they fit into that bucket. And I think both things to work quite well. We don't have too many researchers at Notion, that's another one I think will be important. But we're fundamentally sit in the application layer. So it's more about apply side of things.
8:23 Sarah Guo? Some more manipulating the models and making sure you can scale them to like user outcomes and think about that.
8:30 Speaker 01 And another part is like the scaling part, right? How do you scale to like tens of millions, a hundred million users? It's a bit, it's a problem on its own too. It's beyond just a demo on Twitter, right? Tricky. Yeah.
8:43 Sarah Guo? So you have often said that Notion is less a productivity company than an application building company. How do you think about the initial use case and like, what makes you believe people want to build more applications?
8:57 Speaker 01 I don't think people want to build more applications. What got me started in Notion, got us started in Notion, it's last year college. I read a paper by one of the computing pioneers, Douglas Engelbar. He talked about his paper's name, Augmenting Human Intellect. So every day we use software today, very much like application, or you go into one application, do one thing. But for that generation of computing people in the 60s, 70s, 80s, computers are a lot more, software are a lot more malleable. You can actually tinker and modify, right? Small talk, you can go into it and change the how operating system work on the fly. That really inspired me. It's like today, people's software are so rigid, can we create a new breed of software?
9:42 Speaker 01 That people can modify, can change and customize and bring back some original esodes of those early computing pioneers? That's why we started Notion. The hard lesson for us is like, like you mentioned, most people don't want to create software. They don't wake up and say, hey, I want to create my perfect project management tool, my project, perfect knowledge base. They both ask for something, they just have to get that work done, right?
10:09 Speaker 01 So the, in some sense, our learning and pivot is instead of giving people those software building tool, we have to package the software building blocks together as ready to use templates, as ready to use use cases. Then people can adopt really quickly.
10:26 Speaker 02 So you were one of the earliest adopters of AI in terms of application with any real scale. And I think it's impressive how quickly Notion ended up starting to work in this area. How do you think about how that impacts different aspects of what you've built and what you're building going forward? And how does that impact that vision of saying, okay, we have this? This effective platform that allows people to both interact with documents or core use cases in simple ways, add things like calendar, but then also go in very interesting directions in terms of both the set of applications and templates they can use.
10:54 Speaker 01 Yeah, I think we're lucky. Like I mentioned, we're not trying to build specific use cases. We're trying to build a Lego bricks that power those use cases. What are those Lego bricks? Text editing is one fundamental Lego bricks. Most software have that piece. Relational database, a table is one fundamental Lego bricks, right? Different form of permission, commenting. So we've been spending five plus years building those Lego bricks. And feels like boom, AI just jumps in almost like a brand new car engine and can power those Lego bricks in brand new ways. So feels very lucky in that way. And that, because we've been building those Lego bricks and refining those, allow us to ship features
11:34 Speaker 01 plugging with AI really quickly, where one of the early years want to launch AI writing for productivity software at scale. Because we've been spending years building a text editor. We can do AI powered database table features really quickly because we've been building relational databases. We've been building a knowledge base for a long time, so we launched AI Q and A really quickly, fairly quickly. The RAC system on Top Notion because we have those Lego bricks. So in some sense, it's kind of like, it's just the right moment, right time for us, right?
12:07 Sarah Guo? How did you begin to resource and prioritize this effort? Because you're like, ah, magic. We have this engine, it applies to our Lego bricks.
12:17 Sarah Guo? And then you start shipping pretty quickly, but I think there are a lot of organizations right now trying to figure out what to do with AI. And so, you know, in terms of like designing the features, prioritizing that effort versus everything you're already doing in a rapidly growing software company.
12:30 Speaker 01 Yeah. I think I had the conviction, my co-founder Simon, actually, they all had the conviction.
12:40 Speaker 01 Funny because we all live in the mission, right? And OpenAI initially is, still is in the mission. And some of my friends, especially Simon's friends, work at OpenAI. Remember, we'd go to their office during the Dota days, they were like, what is this company doing? Kind of interesting. And Simon and some people in Notion saw early demos of GPT. It's like, what is this thing? Spin out text, sometimes gibberish, sometimes useful.
13:05 Speaker 01 I personally, I have to admit, I slept on it. On GPT three, even saw GPT three, it feels like, what is this thing useful for? It's like, yes, for marketing, for content writing, create the first draft. Didn't really click for me. Personally, for me, was fortunate enough to saw early preview GPT four. And that's like, oh, wow. This thing can think. It can reason. It can know how to do things. Has this little bit workflow power to it. That's a big ahah for me. And like, it just gave me, it personally gave me so much conviction, like, this is gonna change everything. If you think about what knowledge work is in, right? Why do we use software?
13:47 Speaker 01 Fundamentally, SaaS, software, this is all, we're all in the same information. People paper pushing activity, right? It's like a piece of paper coming in front of you. A human, like, changes a couple of bits, push to another human. Language model can do some form of this now. So, so that just like gave me the conviction, like, this is gonna completely change everything we do with a computer. And after that, we sort of just bet the company on it. Like, we're lucky enough to have those Lego bricks. And then what can, which Lego bricks can work well with AI? Which doesn't? We're sort of trying to figure it out. Who inside the company are good with this technology?
14:30 Speaker 01 We have search, but it's not like, we don't have a lot of ML folks. So you need to hire more ML folks. You need to get people inside the company to have similar convictions. So we can move in the same direction. It's quite interesting. It's kind of like, must felt dinosaur feels like when asteroid hit the earth. And it's like, what did they do?
14:49 Speaker 02 Yeah, yeah, there's a lot of change coming, for sure.
14:51 Speaker 01 It's a lot of change.
14:52 Speaker 02 Yeah. What do you think is missing from the capability set? Because to your point, I think a lot of people weren't really thinking about AI too much until ChatGPT and GPT four came out. And there was a period of time where three and then three point five, and you started to see the capabilities incrementing up. And entirely new businesses are suddenly able, enabled with each sort of step with the next GPT level model. You know, GPT five, or to your point, RAG adds a lot of capabilities. What are the biggest missing gaps for you to take full advantage of this technology? Is it future reasoning? Is it better thinking and knowledge? Like, what's the?
15:26 Speaker 01 Yeah, I think all of that above. To me, feels like.
15:31 Speaker 01 Technology is fundamental, we're in the tech business. Technology is fundamentally about trade offs, right? It's like the plastic can do things that wood cannot do. And we discovered plastic and then we figure out new things. We can bottle water like this. Before you cannot bottle water with a wood table, right? So all of a sudden, we have this thing called language model. We have a new characteristic that deterministic software cannot do in the past. And we don't really know how it's made fully. So every month, every week, if you're on Twitter, people discover new techniques to get more out of this. And for companies, entrepreneurs, they're also making trade offs, discover what, how the market reacts to this capabilities of
16:17 Speaker 01 this new wood, this new language model. And so it's a constant evolutionary cycle happening really, really fast right now, right?
16:27 Speaker 01 I think if with that mindset, what are the dimensions? On the technical side, on the technology side itself, yes, the model gets larger context windows, more reasoning, better speed, smaller footprint. Those are all great, like for Notion.
16:47 Speaker 01 To power the workforce, I would really need like we'd learn like G P T four is smart. A cloud to a smart, we need that intelligence to do reasoning. Or for the tech summarization, cheap, fast is better, right? That's the technology side. And in my opinion, there's so much about human behavior as well, just like inertia in in our personal behavior, a company's risk tolerance. And that's slowly evolving as well, right? Like, like what Steve Jobs always talked about, you cannot make something too new. You have to be largely the same and change one thing and two thing, right? Virgil Abloh, the off white guy is like three percent difference. Just push the boundaries so people can accept it, but still also new, right?
17:32 Speaker 01 To me, it feels like language model, power, application are kind of in the same phase. If it's too different, people like, what do they do with it, right? Such alien behavior. It has to, the RAG is pretty nice because largely existing behavior, but better output, right?
17:47 Sarah Guo? Can you describe the AI Q and A product for people who haven't experienced it?
17:53 Speaker 01 Right. Essentially, everything you put in Notion, Notion will help you remember.
18:00 Speaker 01 And this is not just apply to Notion, it pretty apply to most RAG systems. Like, why do we use computer? We need to store things and need to recall things. Before, language model and RAG, the recall largely happened based on keywords. Right? You had the keyword has to be precise, or there's some lexical tech tricks that you can like recall easily,
18:20 Speaker 01 imprecisely. What RAG happens is, language model can actually understand what you're put into there. So you no longer need to organize your information in Notion. Whatever you throw in there, you can find it later. What that means is, for a person, or for a company, for a team, you can have perfect memory. And not only have perfect memory, the right piece of information, if we design our software right, can push to the right person at the right time. Right? That's probably more than 50% knowledge work. Right? We're still perfecting the system. I think we're one of the first on the market that apply at scale. We still have somewhat a waiting list because it's hard to do this at scale still.
19:02 Speaker 01 But for a company, for a team, before, search is one of a weaker point. But with RAG, you completely change that. I change how I use Notion. I can just ask a question to Notion, like, how large? When are we moving out of the SF office to a new office? And someone in the company wrote in some documents, I don't have to ping five different, three different people to find the answers. If it's in Notion, go find it for me. Right? Everyday engineers, designers, operation people just keep asking each other on Slack or in email such a question. Each question is 10 minutes writing the answers, 20 minutes to find the answers, and there's the delay in the middle.
19:43 Speaker 01 With Notion Q and A, you can completely cut that into in seconds. We're just at the beginning of what RAG can do for work.
19:51 Speaker 02 It's pretty amazing. I feel like RAG and embeddings are very under discussed. Or underappreciated in some sense relative to the impact that they really seem to be having or starting to have. And I think Notion Q and A is a great example of that. I guess the other thing that you folks just launched is calendaring. And if you can't talk about it, or if there's nothing to talk about this point too, but I feel like one of the really interesting things that people are talking increasingly about is agents and sort of the agentic world. And there's a lot of capabilities missing to really make those valuable. But in the context of a calendaring application, you could think of all sorts of ways that having AI act on your behalf or help understand things can be incredibly valuable. And so I was just curious how you think about the application of AI relative to calendar versus, you know, some of the core information related things that you just talked about.
20:35 Speaker 01 Maybe we can group AI stuff into, at least in my mental model, it's the RAG, the knowledge information retrieval is one bucket, knowledge bucket. Then there's this workflow bucket, right? Use the word agent.
20:50 Speaker 01 That's that in that bucket. Counter somewhat in that bucket. Why do we need to meet? Why do we need to calendar? Because we need to meet and we need to schedule time, we need to figure out, exchange some kind of bits between my brain to your brain, right? Can that bit, can that exchange be done by a language model? Maybe. And can the meeting time be done by scheduling be done? That's like a baby step, right?
21:16 Speaker 01 And most things we do has this kind of time dimension to it. Can language model help us shuffling our schedule?
21:22 Speaker 02 Yeah, it feels like there's also that information retrieval piece of it because, you know, if my calendar auto-populated everything I need to know about the meeting or the people attending or other things, that's incredibly valuable as a user of a calendar. And so I just feel like there's a lot of these things that kind of tie in together, both in terms of the coordination, which you mentioned, and the workflow. And then separate from that, there's just, what do I know about this person?
21:42 Speaker 01 The calendar part is the simpler part of the workflow, like the holy grails, kind of like, can just the agents, robots do all our knowledge work for us, right?
21:50 Sarah Guo? It's a really interesting framing that I didn't have before of a bunch of the work you're doing at Notion actually eating into like communication, right? It's sort of obvious in retrospect, but like if you look at what you described of like, like, am I really going to slack back and forth about this thing about, you know, when we're moving, if I can just know, I'm in Notion, help me know. Or with calendaring, like, you know, the most intelligent version of it is like, well, do I need to have that meeting? Or can you tell me what I was going to tell me?
22:17 Speaker 01 I know. Like, why do you need to communicate? Because? There's something, the work cannot be done asynchronously or by the software itself. Right, then that's why you talk. Yeah, it's kind of interesting. Maybe it's interesting question like, are we kind of communicate more or less with language model? I probably feel it's probably less.
22:39 Speaker 01 The agent side essentially bet on language model does the communication.
22:43 Sarah Guo? One question I have for you, just going back to like the implication of RAG. And like, you can be my brain and do my organization for me. Like, what if my brain is really disorganized? Like, do you, do you think that this changes the amount of work people should do in systems? Like Notion, at input, right? Like, you know, should I be designing my knowledge base in the same structured way? Or can I just dump it all in a stream of consciousness?
23:12 Speaker 01 Dump it. I think organization might be, we might be moving away from the organization world. Why do you need to organize because you can retrieve? Why do you have index? Like index initially are file cabinets. And the little index are sitting on top of us so you can find things quickly, right? And they're indexed based on certain names or certain dimensions. But embedding and RAG sort of, it's just you have, if semantically connection of all the things you throw into this file bag. And you can find, bring it out however you like. So I think we might be moving past the need for organization. That's really liberating. That means on my phone, imagine this experience, I'll have a new idea.
23:57 Speaker 01 Or I see a whiteboard behind me, I just take a picture or write something, dump it. And Notion gonna organize for you, right? So that's, then that's become my perfect memory to start. Later it could be my perfect assistant to help me do something with this knowledge. And that's the vision we're moving towards. That's super exciting to me.
24:17 Sarah Guo? So you are, this is a question from Mike from Al. We've been long time friends. You are a student of history. You mentioned Doug Engelbart earlier. I know you think about the transition in terms of like Alan Kane, what he did in terms of simplifying many of those concepts for a broader audience around computing. For an honest question was, what lessons in history do you take that inform your point of view of like how to treat AI strategy with Notion now? Like for my higher revolution in computing? You know, how does that help you decide what to do?
24:55 Speaker 01 A lot is intuition. I think understanding history give you a sense of, history doesn't repeat itself, but rhymes. So like, okay, which phase are we in? I personally think we're sort of in this kind of bundling phase.
25:12 Speaker 01 Who said this? Like, there's only two way to do business, bundling unbundling, right? And actually, during the break, I was reading a Chinese novel, Romance of the Three Kingdoms. And the opening sentence for that is, "The empire long divided must unite, long united must divide, thus as always been." Business is the same way too, right? We're in the bundling phase. I would say the SaaS, it's sort of this unbundling fragmentation phase. If we trace back to SaaS, why is SaaS happening? In the late 2000s, mid 2000s, before that, everything's running on Microsoft.
25:56 Speaker 01 That was like a bundling phase. Early days of PC, there's so many different applications. The first version of the War Star, World Perfect, different text editors, DBase, different database software. The funny fact of DBase, it's like they start with DBase 2. Because there's so many company go bust, that it sounds like if they start with DBase 2, people has more credibility. It feels like this product has been around for a while. So that's the 80s. 90s was this kind of bundling phase because Microsoft has OS layer underlying it. And the SaaS is because the web becomes good enough to run software, right? And so then we have this unbundling phase, a fragmentation phase. And then with the last 10, 15 years,
26:40 Speaker 01 it's really, the money is cheap, easy to create company. There's so much. Too much now feels like there's like information so fragmented. And now the new technology happening is AI language model. And if you build more with it or just think more with it, language model wants information to be one place. Wants the endpoints to be connected so it's easier to. It's hard enough to like a current version of language model to do what you want, but imagine talk with different endpoints. That's even harder, right? So.
27:13 Speaker 01 And so we're in the bundling phase because the macro, but we're also in the bundling phase because language model, I believe, wants the things to be together.
27:21 Speaker 02 I think that makes sense. I also feel like we're in the bundling phase because the nature of how founders think about their businesses shifted.
27:26 Speaker 01 How so?
27:26 Speaker 02 I think that it's interesting because I remember, I don't know, 10 years ago, I used to argue with people about, oh, you should really buy other companies or integrate or sort of pull all these things together. And in consumer, that actually happened, right? Like Facebook bought Instagram and WhatsApp and other things. And they effectively created like a bundle of social products that they could cross use in different ways for distribution and other things. But I feel like what happened is we had a series of highly technical founders because we shifted in the Facebook era from Cheryl becoming CEO to COO. And you went from business centric CEOs in the 90s in some cases, although there's people like Bill Gates who learned and adopted as technologists to very technology and product driven founders who often thought, no matter what product I build, it always has to be better. And so I can't just think of distribution as my wedge.
28:11 Speaker 02 I need to think of every product as being superior and so I'm not going to build certain things. And now I feel like people are both building great products as well as bundling them, but also they're much more aggressive about saying it can be 80% as good, it can be 50% as good, but I'm going to have a bundle and that's HubSpot and that's Rippling. And they have very high quality to their products. It's just they realize they don't need every single edge case and every feature as long as they're able to cross sell.
28:34 Speaker 01 Yeah, I think the Y C school philosophy of build one thing, use internet to find the distribution. That was, I think, overlapped quite a bit with the rise of internet, right? And feels like there's a value to create on the other dimension, which is. Like you mentioned, it doesn't have to be as good, ninety percent as good. But because the synergy of things just make a lot easier, a lot cheaper, less tabs opening your browser.
28:58 Speaker 02 Yeah, it's all integrated. You have the information flow or the system of record for whatever thing that you're dealing with.
29:04 Speaker 01 Yeah.
29:05 Sarah Guo? I think a lot of people also just perhaps lack that sort of historical context, right? If you look at the strategy of companies, like Oracle, right? It was very much for a decade and a half, like a dominant, at least commercially, attitude of like, okay, we're gonna buy the second best product in this additional software category we want to be in, and then sell the heck out of it. Worked great, right, actually, because it was very hard for customers to deploy these things. Where there are just advantages to everything being attached to a single database at some point. And I do think there is some analogy to use that language models because having things in the same embedding space is very useful.
29:43 Speaker 01 Very useful, yeah. I think there's bundling of distribution and bundling of information. But what you're describing to me is more of Microsoft, more like bundling of distribution. Langauin Project wants the bundling of data, bundling information.
29:56 Sarah Guo? So I remember hearing from Dylan at Figma early on that there was one crazy user who was in the product like fourteen hours a day. It was you, early on in the Notion journey, being really design obsessed. I think the company has a reputation for that. Do you think that Notion is like a design-centric company and is it important? How do you scale it?
30:22 Speaker 01 I think it depends on what you mean by design. Design, to us, at least to me, is less about how it looks, is how the system plucked together, right? And then, in that case, the trade-off you make, do you centralize that thing or do you decentralize that thing?
30:38 Speaker 01 Certain companies work well, or certain businesses or products work well being decentralized. Like, operation-heavy companies could work that way. And Notion, like you mentioned, we're sort of in the bundling business, our value provider having this one information space, one workspace for people to do all different kinds of things, so things need to be work well together. It's almost building Notion, it feels like building an operating system, building a programming language, right? You don't, you don't farm out to like... Fifty people to design a programming language. Usually, programming languages are done by one person. So that means the design here is very much centralized energy. So kind of like Apple, how they build OS integrated with their hardware.
31:20 Speaker 01 And right, like, what is the Apple for software? Doesn't quite exist today, right? It truly doesn't quite exist. So that's what I'm interested in, what we're interested in. So in that case means to build a good product, a good customer user experience, we need to think things more horizontally, more holistically. That means the decision making tend to be centralized in our design team or like that's tend to be centralized, right? So less like more so more Apple like, less Amazon like.
31:52 Speaker 02 It's funny, because when I first met you, it was just you, starting Notion, and it was before you brought on Simon. And you talked about things that way even then. And I felt that one of the reasons I was lucky enough to invest or, you know, I came on board was because you had such a cohesive view of how you wanted to build software, and you had such a cohesive design aesthetic. And it was your mox, but it was also how you were dressed and how that reflected into the product. I felt like it was extremely striking, you know? Like, you're one of a very small number of people I've ever seen where that design aesthetic has just kind of permeated everything in a very cohesive way. And so that's one of the things that got me excited at the time. I was like, wow, this is capturing a aesthetic that could be an incredible product platform. But you also talked about things even then, I remember, in terms of like, okay,
32:36 Speaker 02 what's the, what's the cohesive Apple like thing that you can do for software?
32:40 Speaker 01 Yeah, I think.
32:42 Speaker 02 It's kind of amazing to see that consistent thread. So I was just stricken while you were talking, you know, by that.
32:46 Speaker 01 Thank you, yeah. Like, I studied cognitive system, cognitive science, which is kind of just like a degree for everything sometimes. It's like a little bit philosophy, a little bit linguistic computer science. I learned how to code when I was a kid. And I did a lot of art, also in school. So like, try not to, like, there's so many things you can steal from all the different places, right? And it's like, the boundary are sort of man-made. And in Notion, with most of our designer can code. Majority of our designers, eighty percent of designers can code. Because the moment you can, as a designer or as an engineer, you can code or you can design, you can make really interesting trade-offs, right? At the end of the day, technology is, at least in my opinion, is about trade-offs.
33:31 Speaker 01 What kind of trade-off you can make. That unlock new user behaviors. That's valuable. But if you can do more things, you can make more interesting trade off that other people cannot make. As a designer, if you can code, you know how to change your design to make it easier to build. As engineer, if you can design, you can do the same thing, almost like squeeze the air bubble to whichever direction is easier to squeeze.
33:57 Speaker 01 Therefore, I think being a more holistic help, at least Notion as a company energy, we're trying to be holistic. It also help keep our company team very small. Like we're usually one of the smallest relative to our business scale because people can think, can do more, can be more holistic. And people enjoy that too because they can do more things. It doesn't feels like they have one role, they have to be doing that repeatedly. That's a lot of different benefits, but it's much harder to find such people.
34:26 Sarah Guo? An important question here.
34:27 Speaker 01 Please.
34:28 Sarah Guo? So if I think about the first office, and this may or may not be true still, Notion is a no shoes, wasn't a shoes place. Did this contribute to the company energy?
34:40 Speaker 01 I'm Asian. So when you go home, you take off your shoes. Our first office, no shoes. Lasts us to ten-ish people. Second office, twenty-ish people, no shoes. Third office, no shoes. It actually has heated floors, so even better.
34:56 Sarah Guo? Oh, very nice.
34:56 Speaker 01 It's all in the mission. The fourth office, we try to do no shoes, still in the mission.
35:03 Speaker 01 I think I made a wrong choice in the rug. The rug kind of hard. It's hemp based rug. So when you step on it without shoes, with socks only, it hurts. So we decided not to do no shoes at fourth office and so far has been stuck that way.
35:19 Speaker 02 Yeah, Plaid Intuition has socks and slippers at the front. So that way, if you need the padding.
35:25 Speaker 01 I know. But then the question is like, where do you store your slippers? It becomes stinky.
35:30 Sarah Guo? And then it just stays better.
35:32 Speaker 01 Yes. If you come to our office, it's like we still try to be.
35:37 Speaker 01 Not corporate. It's like they were trying to use the furniture that people use for homes. I'm pretty picky about what kind of furniture is in the office. Like ideally design classic that last fifty plus years. So inspire us to build software that way. Right, so they made, they also made trade offs. People who designed a chair make trade offs. They made really interesting trade off force to solve certain problems. If you know the history of it. So we try to in the office use good software, uh, good chairs, good lightings, so.
36:06 Speaker 02 This is back to the aesthetic point that I made earlier. I actually felt that in the offices as well, there's that ongoing cohesion. Even the music, I remember, I think it was in the second offices, but it was always jazz in the background. And I just felt like it all kind of was this consistent vibe, you know, so it's pretty cool. Within Notion, are you using a singular, um, underlying L L M? Or are you at this point using multiple different things for different use cases? You mentioned sort of the high level reasoning versus the fast cheap sort of synthesis.
36:34 Speaker 01 We try everything. Open air and topic are the high end model.
36:42 Speaker 01 We want reasoning, which is we work with the high end model, right? Yeah, it's kind of like everybody building different flavors of this.
36:53 Speaker 02 Yeah, makes sense. And then as you look at, um, it feels like with Notion, there's a set of core sort of templates or use cases. You know, there's things around project management. There's other types of almost like applications that people have built to use. There's knowledge base related stuff. There's the things that you mentioned. Are any of those, you feel differentially impacted in terms of how you think about future A I roadmap? Or things that, you know, will really change the game dramatically in terms of some of these areas?
37:18 Speaker 01 Yeah, I would say RAG changes all the knowledge, say fundamentally. You no longer need to organize. So a notion one of thing people love is the life sidebar, right? The life sidebar, you can organize your knowledge base, organize your personal workspace. Maybe the future doesn't have to have that, like what it's like to.
37:42 Speaker 01 You know, not fall into your own innovative dilemma to double down that UX paradigm, but just having a notion that you can just dump things and retrieve, right? That's knowledge style.
37:52 Speaker 02 That's actually really interesting at a high level to think that everything sort of moves to a form of search over time.
37:58 Speaker 01 Move over to search over time?
38:00 Speaker 02 Yeah, like you're kind of losing, you don't need to self-organize information anymore in this new world. You can just create a mechanism to interrogate it.
38:08 Speaker 01 Yeah, at least like you don't organize your brain. You just dump it into it, then you wake up, all you remember that thing, right? So like.
38:15 Speaker 02 Yeah, that's interesting.
38:15 Speaker 01 Some people do, like the art of Loki, the art of memory, you actually visualize your brain, but for most people, it just works without any organization magically, right? What it's like for software, we're getting there.
38:28 Speaker 02 Yeah, that's cool.
38:29 Speaker 01 Yeah, it's kind of fun.
38:30 Sarah Guo? Are there areas of software more broadly that you think are outside of Notion's scope that you think are going to change a great deal for AI?
38:39 Speaker 01 Wow.
38:43 Speaker 01 In some sense, it's kind of a race. There is like the, we're in the Notion's in the bundling business where are
38:54 Speaker 01 we are, we're in the bundling and front office business. Front office, my definition, our definition is what's happening in, like imagine a 1960's office, right? What's in 1960's office? On your desk, you have a notepad, you write down something, you maybe have a typewriter. Then you have your binders on the left and right. That's essentially, the notepad is your documents, your notes in Notion. Your binders of things are like your Wiki, knowledge-based in Notion. And behind you will be the file cabinets. That's your relational database in Notion, right? And you have a little pushcart to put things into there. Then there's a back office, where it's like the librarians organize all the things. That's Snowflake, right? That's the back in the days IBM.
39:39 Speaker 01 We don't touch that. We largely touch our strengths, like I mentioned, it's software interface, UI, UX, which is largely what's in front of the human. We're trying to bundle in this in one space. At the same time, there's also largely back office-powered use cases. They tend to be verticalized, specific to healthcare, specific to. Some kind of workflows.
40:02 Sarah Guo? Manufacturing.
40:02 Speaker 01 It has to be very specific, but it's very essential to store somewhere and the vertical integrate that use cases. That could be AI five, too. And people, in fact, we see this in law, we see this in a bunch, very specialized thing that people have that domain knowledge and trying to figure out how do you, instead of human shuffling this, a language model will help a lot of that, right? The front office type of things is kind of open-ended. The back office power things tend to be specific. So I think it will be a race, but the market is just so large and it doesn't, it's not zero sum necessarily.
40:38 Sarah Guo? Maybe you can talk about the market as you see it for Notion. So, you know, when you guys began, I think early adopters, startups were the first to get onto Notion for knowledge base. You're a much bigger company now. We're also in a different macro where, you know, startup budgets are less robust. Like, how do you think about the enterprise and helping the enterprise adopt AI or do knowledge management?
41:03 Speaker 01 Yeah, we're still early, not at scale yet. I would say bundling and transaling do a lot of good things. One is you don't have to jump between different tabs to do things. Second is save your cost, right? Like, we save a lot of customers bills for their project management tool, around their issue tracking tool. And, and that's, enterprise really care about that. It's very CFO friendly today with this macro. So, yeah, bundling has many good benefits besides money, besides information, there's a host of money.
41:42 Speaker 02 Well, Ivan, I mean, this conversation has been so many interesting topics. Thanks so much for joining us today.
41:46 Speaker 01 Thank you, yeah.
41:46 Sarah Guo? Great to see you.
41:47 Speaker 01 Good to see you.
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