Hi, everyone. Welcome back to another episode of Glasp Talk. Today, we are very excited to have Joey DeBruin with us. Joey is a passionate entrepreneur, product builder, and also writer focused on empowering creators and innovators to make the world better. And he's the co-founder and CEO of Robo, a company revolutionizing how founders build their initial products by eliminating traditional barriers like cost and complexity. And previously, Joey co-founded

Backdrop Build, a large scale program supporting over 10,000 builders in launching over a thousand projects and which was later acquired by Seed Club. And with a background in neuroscience, Joey's career spans leadership roles in companies like ResearchGate, Benchling, and Feastly. So today, we will dive into Joey's journey, his vision for building innovative tools, and his insights into fostering growth and creativity in tech and beyond. Thank you for

joining today, Joey. I'm excited to be here. Thanks for having me. So first of all, you know, I mean, you have a neuroscience background and your first career was a researcher at UCSF, right? But now you are building Robo. It's like an AI company. And I wonder what inspired you to start Robo. Yeah, I mean, the journey from science to Robo is a long one. So I'll cover some of the key moments. I think, you know, what inspired

me to join tech and leave science in the first place was I've just always been interested in the unknown and, you know, helping bigger things out, helping bring new knowledge, information, inspiration to life. And that's why I was originally attracted to neuroscience specifically outside of all the sciences, because, you know, especially when I started studying neuroscience in college in 2009, it just, and still there's so much unknown. There's just huge topics

that we're like, well, you know, people dream and we don't really know why. And it's just these kind of fundamental questions that are just fascinating. And the reason I left science was because the work of being a scientist is actually very known. So the steps that you need to take and the progression of your career and the path is kind of defined. And so, you know, that's what attracted me to working in technology, where the problems

that you are trying to solve are unknown and how you actually conduct yourself as a person and build your careers. It's also kind of unknown. So it is just the sort of blue ocean of work. And that's what inspired me to start working in startups, you know, a decade ago. And really, that's why we build Robo as well. So now what we see is the technology that we have, especially with AI to build things incredibly quickly, incredibly cost-efficiently

means that we should be able to just connect more and more people with an idea to, you know, building that thing. And that's kind of my goal is always just to help more people, you know, dive into the blue ocean and into this, you know, crazy, stressful, chaotic world of building stuff that I just think is such an amazing place to be. I see. Yeah. Interesting. Yes. And, you know, you target customers like a non-technical founders

at Robo, I guess. Yeah, that is one of the big groups. So it's interesting. I mean, non-technical founders obviously are a lot of who we work with because they can't build their product for themselves either because, you know, the tools that are available to them, no code tools just aren't good enough, or they need to build something that's a bit more custom. And so, you know, being able to work with a Robo agency. So at Robo, we work with

Robo agencies, right? A Robo agency is basically a team of people that's using AI to build products way, way faster, way, way cheaper. And so non-technical founders have always, you know, outsourced to freelancers and dev shops and agencies. And I think we're able to provide a much, much better product for them. But it's interesting because there's actually not the only kind of people that we work with. We actually work with some very technical teams that even have

existing companies and, you know, they just want to build a new product very quickly without having to hire an engineer. So there's a lot of reasons why you actually don't want to write the code yourself for a product. You know, one of those reasons is that you can't because you're not a technical founder, but there's actually a bunch of other reasons as well. And we are a good fit for a number of those cases. I see. I saw your tweet actually, then, you know, your customer

said, you know, oh, I got a, like, you know, like a build, like, you know, 50K for two months, three months, you know, development. But, you know, with Robo, you can, you know, quickly build it in one to two weeks with 5K. That's really amazing. But at the same time, I wonder, you know, nowadays we have DeepRoot and also Cursor and a Visio, you know, from Basel. And I'm curious, you know, like, how do you differentiate from those, like a developer to start having AI,

you know, features in it? Yeah. So the way that we see the landscape right now is that there are a lot of tools. If you're an engineer, there are some amazing tools for you already. So, yeah, the ones that you mentioned, Cursor, you know, vZero, Repl.it, and see, this is just making your life so much easier. But if you are not a developer, you know, those tools might even market themselves to you. But the reality is that if you just try to, if you've never built something,

if you've never built a product before, and you just get thrown into one of these products, like, it's tough. I mean, there's a lot of, you know, decisions that you make or things that you need to understand in building software that, for someone who's never done it before, just hard. And so you, and we actually think that there are tools that are already being designed exactly for non-technical people. But, you know, those are not as powerful. And I think that's the

way the landscape is going to be, we think, for, you know, years. And so, you know, engineers are, you know, going to be able to, I mean, everyone's going to be able to build more. So no-code tools are going to get better. Tools for engineers are going to get better. Like, everyone's going to be able to build more, but it's more so thinking about the market. So if you have $5,000 to spend to get a product built, that's a lot better, you know, than you

could build on yourself, you know, wouldn't you do it? And I think that market is going to be very durable and even actually going to grow in our opinion, even as, you know, the tools that, you know, you can use on your own get better. Yeah. It's really exciting. And, but I'm curious how it works, you know, in your case, like, you know, is the customer need to prepare like a PRD or document, you know, like what they need and then you build it and ship it to them or

do you provide, provide like a not, maybe not in a coding editor, right? Not IDE you don't provide, but I'm curious in how, you know, let's say if you want to walk, you know, with you and how it looks. Yeah. So we, the way it works is that, let's say you come to us, you have an idea, you say, Hey, you want to build a new startup? I have an idea. We work with you to define the scope of that product. And we have some tools that we've built. We use AI to do some of that,

but there's also humans involved. You know, we actually get on a call with you and talk about it. So there's some parts of that that are just, you know, difficult or not important to automate. And the end of that process is that we have a defined scope for what you want to build that we can actually estimate. So we say, Hey, we think this is going to cost $5,000. It'll take us two weeks. And you might say, Oh my gosh, amazing. Like, you know, I thought this was going to be

way more expensive, way longer. And then if it, you know, if it looks good, then we actually will deliver it for you. So build it. So you're, you know, we're not, the clients that we work with are not, it's not like we're giving them tools for them to build themselves. Like we are really acting as an agency. So we are delivering the product for you that you want, and we'll check in, you know, on a weekly basis. And, you know, we, or we, you know, establish channels of

communication so that we can get feedback, but it's not as if we are asking clients to do the work themselves. So the way that we are able to deliver it, you know, so much cheaper and faster really is just the process that we use on our side. So, you know, we are using a lot of different tools and I'm building these workflows to deliver products faster, but that's not something that we expose to the client directly. I see. And so, yeah, I checked your website, but so yeah,

it's simple at this moment, right? So yeah, there's no demo or no, no, like, you know, videos. So, you know, in the future you will prepare some demo demonstrations so that people can understand it. So, right. Yeah, we will. I mean, I think the best demos for us are always going to be just showcasing the products that we built and, you know, here's what it cost to build this thing, because our goal isn't to build a, you know, one of these AI demos that blows you away about how

the product works, right? Because our goal isn't to get people using our product, you know, we use our product, so we don't need to demo it. You know, the only thing that you need to care about as someone who uses, you know, who works with us as Robo is that we can show you a product that we built and you'd be like, wow, this thing is really, looks amazing. Like it works super well. It's pretty complicated. Like, you know, it's, that's the startup that I want to build. It's just like that.

And we can tell you that, hey, we built this in a week. That's the thing that, that's the demo. So over the next weeks, we'll start releasing more of those kind of demos, I think, but more like case studies. So yeah, more of those to come. Cool. Yeah. And I'd have you guys do dock hooding, right? It's, it's great for dock hooding. Like, you know, you're building something that you can use and that's the best, you know, use case. And at the same time, I'm

curious, you know, now, you know, you are working with some customers maybe, but you know, what is the biggest challenge at this moment for you? I think before, you know, pre-launch, I think, you know, and I don't know if you guys are like still small or not, but, you know, yeah, I'm curious, you know, what's next for you and what's the challenge, I think. Yeah. It's like somewhere in that stealth, you know, it's stuff light. I would say we're public,

we're announced, you know, we're out there, we're working with clients, but we're still iterating and changing a lot. And, you know, the good news for us is that we've, we've done this a number of times. I think we've launched several products at this point, and we've gone through the full cycle of building and selling, you know, a company or a product. And so we're very comfortable in this kind of messy beginning phase. But the hardest thing for us right now is when we say

building a product with AI, what we don't mean is like just using Plod or ChatGPT or, I mean, every engineer is using AI, right? So, or maybe there's some that aren't, but those ones are really, you know, stuck in stone age. So everyone's, I mean, everyone on earth is probably, or at least a high percentage of them are using ChatGPT to write something, right? So that's not what we're talking about. What we're talking about is really imagining the whole workflow end-to-end.

So imagine that you have to build, you know, a hundred products in the next month. What would you do? What would you need to do in order to be able to do that? Like what resources would you need to build for yourself? What workflows would you need to automate? It's really to think about like building blocks. So what we are trying to do is think about how do we construct the Legos so that any product that comes to us is something that we can quickly put together with the Legos

that we have. And it's not like, you know, there's a concept, you know, in product like software development, like boilerplates, right? Which is, it's almost like you get 80% of the way there, and then you just do the last 20%. So it's kind of like that. But the thing is that in the age of AI, these, these sort of boilerplates can be a lot more modular. So it's more like, you know, it's more like Lego pieces than it is like a, you know, a skeleton, I guess. And so the hardest

thing for us right now is choosing the projects to work with. So, you know, we try to be very selective about who we work with, because not everything can be kind of robo-ified yet. At some point, it will be. But for the time being, we have to be picky about the kind of products that we, you know, take on, both because we want to, we want to draw a hard line. Our line is, we only take on a project where we think we can deliver it 10 times faster and 10 times cheaper

than an average agency. And if we don't think that's the case, then we won't work with it. And, you know, so we have to be picky on that front. And we also have to be picky on finding projects where we can continue to like expand our understanding of how to do this better, how to serve. So it's not like we just want to do the same project over and over again. It's like, we want to kind of find interesting newness to the projects that allows us to

continue to expand our sort of infrastructure underneath. And so that is the hard part right now. It's, we've been fortunate that, you know, we do have a good amount of people kind of reaching out, wanting to work with us. So we are able to, yeah, find clients, but finding the right clients is, is a much harder, harder question. I see. Yeah, that makes sense. And, but have you figured out, like figure out like which space or product, you know, or, or types works the best for

your use case? Let's say, is it a financing product or is it more like a, I don't know, EdTech product or B2B kind of more like consumer software is better or entertaining? Have you? So it's what we're finding. And again, we're still early on, but it's, it doesn't work as simply as you might like in that sense. It's not like it's just, oh, this industry works and this industry doesn't work. It's more like there are a set of

characteristics that might make something easy to do with AI right now versus harder to do. So as an example, some kinds of products, let's say an internal tool, let's say that you're a big, I don't know, you're a big sort of sales oriented company and you have a thousand sales reps and each one of those sales reps has a manager, like a sales manager. And so you maybe have a hundred sales managers and maybe all the sales managers are manually training, you know, all the sales

reps in how to do something. I'm just giving you this as a theoretical example, but, and so maybe you want to build a product that can help the, you know, managers of the sales reps train their, their team. But in order, if you were to hire an agency to do that, the first thing that you would want the agency to do is to go and talk to all the managers of the sales reps and understand, what are you doing right now? You know, what makes it hard? Like talk to the sales people

themselves, say like, you know, how, how are you getting this information right now? So you need to do that product discovery, right? That's the hard part. And then you, when you roll out that product across your huge organization, there's a lot of stakeholder management, there's a lot of other things. And so the complexity of that product isn't actually the software, it's the kind of organizational stuff around it. And so you can't like peel those things apart. You can't build that

quickly with AI. So that's a project that's harder for us because it's actually harder to scope up front. Anything we work with, we want to be able to scope it up front because that's the process, right? We scope it and then we ship it really fast with AI. So if we can't scope it because it is going to change every day, as you talk to more and more people, then it's better to work in a different way. It's better to work in this kind of iterative, you know, retainer kind of style

of contract where you're just embedding yourself in that organization. And so that's not how we work. That's one thing. And another thing might be something that's very, I guess, a good mental model is that, you know, AI is obviously trained on all the existing code and products that are out there, right? So the kinds of stuff that's easiest to build with AI are where there is kind of like state-of-the-art or there's, you know, existing concepts. So if you are building something

that is totally bespoke and has not been built before, it's going to be a lot harder to build with AI than something that has more examples. So, you know, a marketplace product is probably easier to build than something that's like a custom, you know, graphic design that you're going to have to build from scratch. Or, you know, something with a lot of compliance. Like if you're building a healthcare app, maybe some of those healthcare apps are easy to build, but HIPAA

compliance is not something you can automate, right? So it's a lot of decisions like that. And, you know, for us, there's even on top of that, it might be that, hey, we, you know, don't have a lot of experience or like most of the products that come to us are going to be web apps. And so we don't want to spend the time to build the, like build the Lego blocks for a desktop app, you know? So it's a lot of decisions like that, more so than it is, oh, we can do FinTech and we, oh,

we can't do health, you know, health tech or ed tech. I see. Yeah. Yeah. That makes sense. Yeah. And so, but you are working on, you know, backdrop build, right? It's like a program, you know, supporting, helping builders, you know, build project. And, and, and I know also, which is acquired by Seed Club later. So I was curious, you know, why did you start that project? And also how was the MOA process look like? Yeah. If you can share. Yeah.

How was the, how would you have the process? Yeah. How was the process, you know, of MA and yeah. So it was, I think, relative to other sort of MNA processes, it was great. I mean, one of the things that made it great was that we're just really close with the Seed Club team and we have been, I mean, they're sort of in their investors of ours in some ways, or they're the Seed Club ventures. They're the kind of venture arm. They're one of our investors. They've been customers of

ours. They've helped us through every iteration. So we are just, we know the team, there's a lot of trust between us and them. And we've, we've always had very complimentary products. And so, you know, they run a more traditional accelerator where they select a number of amazing teams and give them funding and help them, you know, go to go to market. And, and I think they're just incredibly good at, at building distribution channels and helping people get attention and

those kinds of things. And we've always been good at building platforms and software and, you know, solving problems at scale because of it. And so, you know, the reason that we decided to sell Build was that, you know, it was still growing and doing well, but we were looking at what would it take to make it a hundred times bigger. So when we sold Build, we were running a program every single month. It was mostly automated in terms of the infrastructure and how it actually runs

with thousands of developers joining every single month. And so the question is like, okay, how do you, and you know, making good money, but like, how do you make a hundred times more money? How do you make it a hundred times bigger? How do you support a hundred times more developers? And when we looked at the options, like one of the options that we looked at was like, it would be amazing to have a traditional fund or accelerator attached to this thing, because

we see all of these great projects and, you know, we, some of the best ones, it would be amazing to actually take equity in those and, and give them, you know, more funding. Right. But so then the question is like, well, should we go out and start that accelerator? Should we go out and raise the fund ourselves? And, you know, it just felt like better to partner with someone that has already done that, that it's like, you know, really has experience doing that. And so, yeah, it was just

one of those things where the, we just didn't feel like we were the best team to take it to the next, you know, stage. And we also had a very close relationship with Seed Club and they were totally the best team to take it to the next stage. So it was just very natural. I see. And sorry, this dumb question, maybe, but you know, if you can go back, you know, early days of, you know, backdrop build and how would you do differently? Do you have some, some

learnings or some ideas to do differently or would you do the same thing? Yeah. Interesting. There are some things that I can, maybe I can start with what I would do the same. So I think the things that we got right were that we were very principled from the beginning on, you know, what we would do or what we would focus on and not focus on. So to be more specific, a lot of people, when they join any sort of accelerator type program, they will naturally

want to network and meet other people and, you know, have that kind of experience. And, but doing that. So we were one of the principles that we had from the beginning is that we wanted to make a program that was much, much bigger than sort of any existing programs that are out there. And so we always going into it, we're aiming at having thousands of people in every batch. And, you know, networking is really hard to get right, especially if you're a community builder,

like facilitating the right connections and, you know, that are relevant. And you want to find people where they're each excited to meet each other. So you don't want these like one way relationships where the people who are really popular, more established, more successful, are just getting tons of requests, you know, from people that kind of could use their help. And so it's really, really hard to get that kind of mechanic right. It's easier if you have a,

if you have a small group of 10 people where you hand select everyone and it's curated, like it's easy, it happens naturally. But we were very principled that, hey, this is a problem that's going to, we know it's going to be a problem for us. We know people are going to want it. We know that we're not going to be able to deliver it well within the model that we have. And so we were very principled about, like, you know, even the platform we built, there was no ability,

there was, you could post, you could share updates, you couldn't comment, right? And people were always like, oh, how can I comment on this thing? You know, and I think it was for us, it was always like, well, the moment that we enable that kind of peer to peer commenting, even though we love that, like we love that energy, we want people to connect, but we start creating this expectation that this is a community where everyone should be

networking. And we wanted to, you know, help people spend more time focused on building and getting the support and getting support from our sponsors, which is really where the, a lot of the magic happens. So I think that's the, and it was, it's hard to do that. Like when you have people that you care about that are in your community that are asking for something, you know, it's hard to say no, I think, unless you really have strong principles about what, why you're

doing what you're doing. So I think we got that right. I think the things that we probably, it's like in hindsight, you could always have gone faster through some of the learnings. You know, I think we, I say that now, but we tried a bunch of stuff that didn't work. We did, we, frankly, like we overbuilt in the beginning. I think like everyone probably. And, you know, we were doing a bunch of things that we had like all these events in the first few programs, we were hosting

events every week. We were like, you know, essentially like panels, we would have to go and find the guests for these panels. And so it's kind of like giving people advice on how to build and those are valuable, but it's like, there's so much of that content out there in the world. And so it's to provide something that's like really unique, it's just super hard, tons of work. And so we ended up kind of like removing a lot of that stuff and the program was just as good.

And so, you know, going back, I think we, we probably could have just, you know, stuck to the simple mechanic that we knew was working and focus more on just growing the value of that by adding more people rather than like building out the program to be more and more and more and adding more layers of complexity on it. Cause just, it just slowed us down and probably we wasted a couple of months at least doing that. I see.

And just this kind of, you know, random question, but so yeah, I think you have a researcher background, right? So do you have any, like, do you think, do you have any advantage of like having a researcher background and working in startup? Also conversely, so yeah, is there any difficult point, you know, from transition, you know, from like researcher to startup, but what's a difficult point to adapt instead of startup?

Totally. Yeah. I think there's, it's a double-edged or two-sided coin, you know, and I think the thing that is a two-sided coin is being a researcher. I think you have like a strong proof-seeking mechanic. So, you know, the whole, the, I think what people are attracted to science is because you have this, science is like the mechanism to establish truth, right? It's like the only way that we've found as humans to establish what is really true. And it's not

experimentation and all those kinds of things. And I think a lot of scientists are attracted to, you know, product building and startups because it's almost like, it's like finding truth via a different kind of experimentation. Like you find what people really want by iterating on different products until you find something they really want. And then you've, you've kind of like established that knowledge that, oh, people want this kind of a thing. So I think that's the part

of it that is, translates really well. And especially, you know, the reason that we have always been attracted to like frontier tech. So, you know, we started in crypto now kind of really building an AI is because the need to find truth is just so acute there. It's like, nobody knows what's happening. Nobody knows what the way that the world is going to unfold. And so, you know, having that just curiosity to try to, you know, peel back all of the layers of kind of bullshit

and try to find what is true is really, I think, advantageous. But the reason that can also be a disadvantage is because, you know, sometimes you just like, especially at the early stage startup where you can over logic, this whole thing so much, like, and we fall into this trap all the time where it's like, you need to be able to see the map, you know, in this clear way and have a tight logic. And, you know, you read all, you can read a lot of articles about strategy and then you like

spend all your time trying to like, you know, know the universe. And, and really it's just like, you need to move fast and make a bunch of decisions and trust your intuition and trust your experience. And, you know, follow your customers and all those kinds of things that sometimes I think founders that are a little bit less like scientific actually have an advantage there because they don't spend so much time worrying about what's right or wrong. Like they just, just do it.

So I think it isn't too much sort of. I see. Yeah, it makes sense. So yeah, sometimes founders are visionary and, you know, they follow gut feeling, but so, yeah, it's a good fight, a good, good, you know, characteristic of founders to pursue their mission or vision, but sometimes it doesn't work and they spend so much time. And, but so I think, so after research, researcher or research technologists, so you choose a marketing, so marketing manager at your

like position at first. So how did you learn marketing and what was the transition? Yeah, I think it was pitched to my first job. Basically the way it happened was I was working at Johns Hopkins in a neuroscience lab in like 2013. And I had a friend who I went to college with. I played tennis with him and he has started a company like right out of school. And it was an ad tech company, basically like a ad blocker that allows you to donate or basically like a new,

a new tab extensions. Every time you open a new tab on your browser, there was like a nice little page there that has some interesting widgets and, you know, clock and whatever you can customize it however you want. But there's like one small ad at the bottom and, you know, so you generate revenue from that ad and then, but you got to decide like what charity the ad, the ad revenue would go to. So it's kind of like, you know, opt-in advertising for charity. That was the

concept so that people don't like all these ads that are out there. But if you get to participate in the revenue and help like it go to something that you care about, then you might actually be more likely to engage or see. So it's interesting. It's a company that's still around. But so I was talking to him on the phone and he was like, you know, how's it going? How's life as a neuroscientist? And I was, I'd had some kind of not so great experiences with just bureaucracy

and like the slow pace of being a researcher. And I was kind of whining about that. I was like, well, you know, science is cool, but being a scientist maybe is not as, as, you know, cutting or like not as fast paced as I would like. And he was, he was like, well, you should, you should come work for me. You know, there's this role, he called it growth. I think, yeah, maybe on my LinkedIn or something, maybe my title ended up being marketing manager, but he, he originally

pitched it to me as, as you know, you can be an internal scientist. He was like, Hey, there's this role, there's this thing called tech, you know, there's this role called growth. And the way the growth works is that you just get to run experiments and, but nobody's going to, there's no bureaucracy, like it's startup. So you can do whatever you want and you can move, you can run 10 experiments, you know, a month. And I'm coming from research where like running two experiments

a year is a lot. So I was, it was a great pitch for me. I was like, wow, like no bureaucracy, you're running, I know how to run experiments. Like I'm a scientist, that seems cool. And I can run them really fast and nobody's going to be micromanaging me. Sounds great. So that's when I, yeah, I moved out to San Francisco and started working in tech and figuring out what growth actually meant. And yeah, I kind of wound my way from there to, to product.

I see. Then, you know, then after that company, I think you became a co-host of, you know, at Reforge, right. And, and yeah, in Brian Butterfoy's, you know, 2017 Reforge Growth series. And I was curious, how did you, did you meet him somewhere sometime then he invited you? Or I was, yeah, I know Reforge, they have really great content and they host, you know, a really great series, but I, I'm just curious in how did it happen?

Yeah. So I went through, yeah, Reforge is an amazing community. I mean, it's, I, at this point I'm still around and still getting better and better. And I think they just have like a lot of the best, most insightful content out there on product and growth. And I went through the program myself kind of when I was, so when I first started out in growth, I was like, I knew, I mean, I'm a scientist. Like I literally don't have any idea how this works. And so I went through the

program myself and it was amazing. It was like super valuable for me at the time. And so I just stayed, that's how I met Brian. And I just, you know, kept, I stayed involved and then they, they kind of brought on some alumni to, to help kind of like host the future programs and mentor people. And I was able to do that, which was, which was awesome. And yeah, I still kind of stay connected to that. I wrote a bunch of articles for them. And so people are often like,

they read one of those articles and reach out and I end up reconnecting or helping advise companies that way. Do you still keep in touch with them? Like Brian and other folks? Yeah, I do. Yeah. Oh, cool. Nice. Nice. Yeah. Yeah. And by the way, you, I saw you went to on the gray on the, on the greater fellowship and I went to on deck too. And yeah, I saw your, you know, so that in your LinkedIn profile, that's yeah. I see mutual friends and yeah. Interesting.

And, and also, you know, I, you know, I was also kind of a researcher before, before meeting, like I was researching, like I was a chemist before and, and in the master's program. So I published a research paper before, then I use research gate to track my research papers and how many citation I got and how many of you I got. And yeah, I really like it. Yeah. Yeah. It's a very cool company. I was lucky to work there.

Yeah. In that sense, you know, I, I'm curious about the community aspect of research gate, let's say, because I bring, you know, brought it as an example. So how, how did you, what, you know, as a, you know, you were the director of product management, but, you know, and also head of product management, how, what did you work on and what experiments, you know, worked well for, for the community and our product. And could you share some, you know,

insights and also story behind if you can. Yeah. I mean, you know, it's interesting, for one thing, a lot of people, I think rightly probably call me like, you know, community product builder. Like I've, but it's funny because I never really consider myself that I think because maybe I have some concept of what like building a community is that doesn't feel like it is exactly what I've done, but maybe the better way to frame that is that

I think I've learned that a lot of companies, especially tech companies with very strong communities, the community is actually just built around a product that is just delivering a ton of utility to people. And so you actually don't really work on the community at all. The community is like an emergent property from the quality of the product that you deliver to people. And so the community happens very organically and doesn't actually take a lot

of work. What takes a lot of work is building a product. And, you know, at ResearchGate, the, there are like, you know, the product is huge. It does a lot of stuff. I think for people that aren't familiar, ResearchGate is kind of, you know, LinkedIn for academia. So I think, you know, 60 or 70% of every scientist in the world is, has a profile of ResearchGate and is active there. So it's a huge platform within science, you know, not that science is so massive, but, you know,

that's like 10 million scientists or something like that. And the way that it is built is, is actually, it's a very complex data product under the hood because the unsolved problem prior to ResearchGate was that you have all these publications, right? So if I publish, you know, if you publish, you know, something on chemistry, it's like a PDF. And what ResearchGate did was actually very early on before there was a lot of like, now you can use chat GPT to do

PDF extraction, right? The, actually that was an unsolved problem years ago. So, you know, what ResearchGate started originally working on was, can you extract information from these PDFs, such as who are the authors? Like what institutions are they from? You know, what are the, all the citations in the publication, you know, which other, and then you have to do a lot of like machine learning basically to know that. So let's say that, you know, one of the authors is

Steve Brown. Like, I don't know, there's probably dozens of Steve Browns out there that are publishing. And so you got to know, okay, which Steve Brown is this? Like, so you do a lot of like machine learning basically to try to create what we call this professional research graph. So that's this, this graph of information that represents, you know, research from the point of view of the researcher. So, so that we can say, okay, you Joey have 10 publications and we

know all of them. And we know all of the, you know, publications that are citing you and all that kind of stuff. And so a lot of what we worked on there is just extending the professional research graph, because that is really this, the core asset that drives that whole flywheel. And so some of the stuff that we worked on was, you know, can we actually use machine learning to extract method information from a paper? So, you know, we're trying to understand what is the

protocol that you use in order to run this experiment? And can we extract that from, you know, millions of publications so that we can create a page which would be, you know, hey, if you're looking to use this certain, you know, CRISPR method, here are the publications that, you know, you actually might need to use. And so, and we, you know, the more that you build out that research graph, the more products that you're able to create. So, for example, during COVID, we

were able to, like, we built a whole product around helping people navigate this flood of preprints that were being created. Because when COVID happened, like, everyone was publishing, but nobody's publishing in journals because nobody had time to actually go through the journal review process, which is a huge amount of information. And so we built a whole product to help people understand, like, what's being published by whom, you know, like, trying to make sense of that noise.

But all of it's built on top of the same kind of, like, underlying graph of information. So, I think people misunderstand about that company, how technical of a product it is. It really is, like, a lot of, you know, data, machine learning under the hood. And then the social network is kind of, like, just the little bit that sits on top. I see. Interesting. Yeah. And also, you know, how did you guys try to, how to say, differentiate from Google Scholar? Because Google Scholar,

you know, people can see the research activities, right? They don't have social aspects. I don't know if Google was doing machine learning stuff, but, you know, is that the same thing? Yeah, Google Scholar is a great product, and I think it became more and more popular over time. The thing that Google Scholar always had is just search, right? Because they're connected to Google. So, obviously, they're going to be amazing at search, and search is the most common way that

people find, you know, research publications. So, even scientists, like, they're mostly just searching on Google Scholar. And so that's kind of what drives the Google Scholar flywheel, is that people go to Google Scholar, and they search, and that, you know, that's what makes that product work. And they have a lot of the same, you know, citation, and extraction, and things like that. And ResearchGate was actually, like, earlier than Google Scholar, you know. So, you know,

Google Scholar came along after. But I think what ResearchGate always, it really just, I mean, the honest answer is, it was just never a core focus for Google. So they never, there's a lot of stuff that you want as a researcher, the ability to showcase your work, and connect with other people, and, you know, look for jobs at different universities, and, like, all the things that get stacked on top that Google Scholar never did. And in addition to that, one of the things

that, I mean, when I left in, whenever it was, 2021, a lot of the recent work was on this kind of publisher relations. Because for a long time, this is more, you know, a backstory that we'd have time for. But ResearchGate had a very sort of adversarial relationship to public publishers in some ways. Because publishers were upset that, you know, this big platform was being created that was now driving a lot of the traffic. Because what publishers always have is distribution,

right? Like, that's their whole business model. And so, these, you know, like, in the same way that a record label might have some beef with Spotify, like, you know, the publishers had some beef with ResearchGate. And so, but over the, like, before I left, a lot of, I think, the tide kind of changed, and publishers finally were just realizing that this is the way the future is going to work, and so we might as well get on board. And so a lot of it was kind of building these pipelines of

content between the publishers, and the platform, and doing reporting for them, and helping them kind of run their businesses. And so, I think all of that is where ResearchGate has spent a lot of time in the last few years. I see. But did it, yeah, did it take time? Because, yeah, publishers, sometimes they're so mad at, you know, like, oh, don't, you know, use our content, and so on. And how, I was curious about how did you guys get along? I mean, I think it took a lot of time,

right, to, you know, understand each other, and I guess, yeah. Yeah, it's frenemies, right? I think, so we were, there's a, you know, you can find all this online, but there's, you know, lawsuits that definitely, it were always a thing, but at the same time, a lot of, especially the forward-looking publishers, just knew that you can't really fight, like, the, there's these bigger shifts that are happening, not just in research, but across every industry, right? So, consumers want aggregated

information. People want to use Spotify. Like, there's nothing you can, there might be a different product that comes along, but it's going to look a lot like Spotify, right? Like, that's the consumer experience that people want. And so, you can't stop that. So, I think the question is just, how do you get on board as a publisher? And I think, you know, so it's a hard time to be a publisher, not just in science, but in general, right? So, even newspapers, art, like all that,

it's just, it's tough. It's tough for, in a lot of times, that industry is not doing super amazing, but I think the best publishers are the ones that have kind of tried to really work with the tech companies rather than be totally adversarial to them. And so, yeah, it was always like that. It was like, you know, lawsuits on one side and partnerships on the other, and just, yeah, trying to bring people in the future. Thank you. Yeah. Thank you for answering that. And also, you mentioned about,

you know, like, you know, and also we want to know more about your writer, you know, writer side. So, you have a newsletter called Flying Penguins. I love the name. I love the name, by the way. So, and I think you've been writing over four years, I guess, because the first newsletter I saw was in 2020. And yeah, I'm curious, why did you start writing the Flying Penguin? And yeah, how's it going? Yeah. How's everything? Yeah. I mean, I've been writing prior to 2020.

Like, you know, I was saying, I wrote for Reforge. I used to, before I started my newsletter, I was writing a lot of, like, guest posts for different places. And I think those ended up, like, I got my job at ResearchGate because someone, my boss there, read an article that I wrote. Like, I got all these things that happened to me because I was writing. And so, I was very much bought into the idea that, like, putting your ideas on the internet is a good way to have cool

things happen to you. And so, that's why, part of the reason I started a newsletter is just because I very much believe in, you know, creating some serendipity for yourself. And I think bat signaling is just the best way that I know of to do that. But it was also, like, a good excuse. I've always been really interested in how to create, sort of, you know, flywheels for my own learning. And I think one of the things that, at the time when we started that

newsletter, I actually started with, you know, Rafa, who became my co-founder years later. So, that was in some, like, one of the early projects that we did together. And we started this newsletter. It was originally called The Product Kitchen. Very different from Flying Penguins. It was originally The Product Kitchen. And what we did was that we would review products and talk about, you know, do, like, a teardown for those products and what, you know, what's

good about them. Like, really more, like, design focused. And that was something that we were doing inside of the companies that we were working for. But, you know, within your company, like, you can do a full product teardown maybe, like, every month. Because you're going at the pace of your development cycle. And so, we just wanted a way to, like, create faster feedback loops for ourselves so that we could review more products and learn and get better at our jobs. And so,

and maybe we could also find some interesting people who geek out on the same stuff as us in the process. So, that was how we started it. And then it evolved, right? Like, as all good projects do. And I took it over and I've been writing it on my own as, and kind of, you know, branded it to Flying Penguins, I think, in 2022. Just because, yeah, it's a funny concept that felt like it fit, you know, a lot of things that I care about.

Why name Flying Penguin? Yeah, I'm just curious. Yeah. So, there's this really famous essay that this guy Ronald Coase wrote, I think, I can't even remember, early 1900 or something like that, called The Nature of the Firm. It's like, if you go to business school, they all make you read this article. And it's basically, it just talks about why companies exist, like, why firms are created and when it's better to

create, like, a company versus just having something on the free market, right? And this is like a classic, classic article. And then this guy, Yochai Bentler, I think in probably the 90s or something like that, I can't remember the exact, maybe the late 80s, wrote this post called Coase's Penguin. And it was talking about how, he was specifically talking about Linux. And because Linux's mascot is a penguin. So, Linux is like this open source

operating framework, right? And that's still now like the most popular operating framework for a lot of like really critical infrastructure. And he was talking about how there's actually certain cases where on the internet, you have like a different kind of collaboration that actually can outcome, like a different kind of organization that can outcompete a company at providing like the same good. And at the time in 2022, I was really, really interested in how like digital

networks could help build new stuff. And then we ended up starting a company around that. And so, yeah, I like, there's a point in that essay where he talks about how, I guess the thing that I didn't like about it is that the penguin is this kind of like waddling, slow bird. And I basically, you know, when Yochai Bendler wrote that article in like 1990, he was saying like, yeah, Linux exists, but these organizations are really slow. And they're kind of like, you know, these online

communities are very sclerotic, or they're kind of like disorganized, chaotic. And so, I wanted to talk about how I think in the future, you have these like flying penguins that are, they're also these digital organizations that, but they're actually going to look a lot more sophisticated and like really be able to fly. And so, you know, broadly speaking, not that it matters, but that's what I care about. I just care about how like the internet

is going to help us collectively build cooler stuff. So, I felt like a good title for that. Yeah, I love that. I think we will put, you know, the link to your first newsletter. And you mentioned that, you know, yeah, it's about, you know, Gale Economist, you mentioned about it, and yeah, thanks. By the way, so I think you are reading a lot of, so yeah, you know, books, also newsletters, and you are reading a lot of products. So, and after that, where do you keep ideas or

learnings and knowledge? So, do you use any like second brain tool, not taking apps? Yeah, I mean, I'm very promiscuous when it comes to my personal stack. I haven't found, I think, anything that I'm super religious about. I use Obsidian as a sort of, you know, note taking tool, and I love it. I used to, I mean, one of my good friends, sorry, she has a company called Sublime, that's kind of a way to, you know, save

things that you find on the internet. So, I love that and use it. But yeah, I mean, also, I will say that I'm guilty, I have a very, like, I never took notes going through, like, in university, I never took, I just sit there in class and like, listen. So, I'm one of those weird people that just like, I think, consumes information best by just like, keeping it in my head. And obviously, that has its problems. Like, there's things that I forget.

But in general, my real note taking, I mean, a lot of, like, my organization is just whatever I can keep in my head. And if it's good enough, then it stays in there. And if not, it falls out. Yeah, but you have pretty good memory, I guess. Yeah, I mean, good enough, I guess, to get me through the day. Wow. That's amazing. Yeah. And for writing, you know, nowadays, people use, like, AI tools for writing, right? Do you use

any AI tools for your writing? Or brainstorming? Yeah, I do. I think that, like, a lot of writers, I find, I find the tools best for ideation. And the way I often work is that I will, I, like, create these agents that have certain personalities. And then I'll go to that agent. And like, so maybe one of the agents is like a creative brainstorming kind of an agent. And I just build these on in Cloud. And I'll go

to that agent and then say, I have this, you know, idea for a kind of article. And these are the kind of points I want to touch on. And this is the audience I think is interested. And then, you know, work with that agent until I have, like, you know, an idea. And then I'll go out and write it. Like, yeah, I don't, I don't use AI to actually write most of the content. And then I, but then I'll feed it back into like, I have an editing agent who's like, tells me which parts

are crap, or like, what's confusing. And then so I do that. So I, at this point, I don't actually, I used to work with editors and kind of like collaborators more. And I think AI is now good enough for me to be, work mostly on my own, which is great. And, but I don't, I still do a lot of the writing myself. I see. Yeah, thanks. And yeah, I think since time is, yeah, running a bit on. So you already shared, you know, many advice and lessons with us, you know, our audience, but,

you know, do you have any, because, you know, our audience are like aspiring founders and PM, product managers. Do you have any advice to them? Yeah, of course, you know, we should, we should use Robo, but yeah, besides that. Everyone should use Robo to get the probability, yeah, for sure. No, I mean, my, my advice to people is often just that you're like, I think people overestimate how hard it is to, you know, build stuff and build your idea,

bring your idea to life. I think what's actually hard is figuring out exactly what it is that you want to build. So a lot of people have this, they're like, you know, you go to the dinner party with them and they're like, oh, I have this idea for this company. Like it's such a, I've had it for six months, like, or years I've been wanting to build this thing. Like, and I just, you don't have the resources to do it. And if you, I think if you ask that person,

a lot of times, like, you know, how would you actually do it? Like, what would be the initial version? Like literally how it would, would it, would it work? How would you sell it to people? Like they don't have an answer and it's not their fault. It's just that you don't need to create that answer until you actually are like in the process of doing it. So I think that it's just so much a value to trying to get insanely specific about what like your ideas. So take all of your

ideas, like one step further. And this happens to me all the time. It's like, I have an idea for something. And then I actually go and like, I just force myself to like, spell it out. How would it work? And then I'm like, oh, this idea is crap. And so I think there's a lot of people that have strong ideas that would maybe realize that they don't need to spend all their time wishing they had that thing anymore because it's not the right thing. Or maybe they, they do it and

they realize, oh, actually, yeah, this is easy or simple. This is something that I could build tomorrow. So I think people are just a lot, a lot closer to building, bringing new stuff to life than they imagine. And the answer is just like force yourself to go one step further. I see. Yeah. Yeah. Thank you. Yeah. It's a great advice. Thank you. And this is the last question. And since Glasp is a platform where people can share what they're

reading, learning with, you know, as their digital legacy, we want to ask you this question. So what legacy or impact do you want to leave behind for future generations? Yeah, I mean, it's a big question. So it's obvious, given everything that I've worked on, and I really just want to, when I like look back on my life to both basically just have helped more people do stuff that they feel like they're able to invest their full selves into. And, you know,

you know, that's kind of a broad answer. And so I don't, it doesn't need to be startups doesn't need to be products. Like, I just think that more people should pursue things that they feel like totally consume them. And, yeah, I just I want to make it easier for people to do that. So that's what I hope to spend the rest of my life in prettier doing. And that's what I want to do my legacy. Yeah, beautiful. And I see, you know, I see from Robo, actually, what you're doing,

you know, helping people realize, you know, what they need and what they want to do. And yeah, it's amazing. And yeah, thank you. Thank you for joining today. And we learned a lot from you today. Thanks so much. Yeah.