Gumloop's Max Brodeur-Urbas on Why AI Shortcuts Fail and What Actually Builds a Company
EO Korea
Max Brodeur-Urbas is the founder and CEO of Gumloop, an AI automation platform. In this interview he argues against a popular fantasy of the current AI boom: the founder who runs a company with dozens of AI agents, works an hour a week, and earns millions on autopilot. He thinks that picture is mostly marketing. In his view, value comes from applying AI to work you already understand deeply, and from building things, being proven wrong, talking to users, and trying again. He traces that view through his own path: a Big Tech job he disliked, a five-year ban from the United States, months of failed ideas, and a side project in the AutoGPT community that became Gumloop.
What Gumloop Does
Brodeur-Urbas describes Gumloop as an automation platform that runs about 4 million workflows every day for large businesses including Instacart, Shopify, DoorDash, and Gusto. At the time of the interview the team was 15 people and, in his words, "scaling pretty quickly."
The platform is aimed at the people who understand a business problem firsthand: the marketer, the salesperson, the operations person. Instead of writing a spec and explaining it to an engineering team, they can automate their own work. That focus on the person who actually knows the problem comes up again later in his argument about how AI should be used.
Leaving Big Tech, and What It Didn't Teach Him
He studied software engineering at McGill University in Montreal. He says he cared a lot about grades and studied constantly, and that his goal through college was simply to "get a good job" in Big Tech, which he assumed was the right path. He went to Microsoft and "realized pretty quickly that I hated it."
He is skeptical of the common plan of joining Big Tech for a while to learn and then starting something. He calls it "kind of cope." In his experience, many people who say this end up with golden handcuffs and never leave. He says he hasn't used anything he learned in Big Tech at his startup. The one benefit he sees is a kind of default respect: a recognizable company on your résumé tells people you are competent in some way. Beyond that, he says, much of what he does now is motivated by what he disliked at Microsoft, and most of it is "the exact opposite" of how Big Tech works.
His main argument concerns timing. People in their early twenties with no responsibilities have years they can't get back. Spending them logging in, fixing a ticket, and logging off wastes them. His reasoning was: if you are going to start a company, start now, and figure out the more reliable, responsible option later. He says that was the right choice.
Deported, Banned, and Out of Fallback Options
After quitting Microsoft, he moved back to Vancouver and planned to spend a year building things in his bedroom. One weekend he tried to visit his former roommates in Seattle. Border officers questioned him about where he was going, where he was staying, and what he did. He says they suspected he would stay longer than he claimed, even though he planned to stay two days. He stresses that he did nothing illegal. He was turned back and banned from the United States for five years.
He says he was "pretty terrified" and remembers driving back to his girlfriend's apartment almost in shock. It took a few days to calm down, and then the ban produced focus. With no fallback plan, he decided to take company-building more seriously: build things people actually want, something that could make money. He says he worked as hard as he could for the next six months.
Hunting for Reasons an Idea Won't Work
During that period he tried many ideas. He lists moderation software for VR video games, general trust-and-safety tooling, bot detection for web traffic, and an anti-scam platform. His routine was to build each idea to an MVP, try to sell it, and gauge market interest. He was testing roughly one new idea a week, and he usually learned quickly that it was a bad one.
His main lesson from this stretch is what he calls a counterintuitive fact: in a startup you should be trying to prove yourself wrong. Being proven wrong quickly is the best outcome, because it saves weeks or months. Early on he did the opposite. He built ideas for months and hoped someone would confirm they were worth building, and he estimates he wasted about three months that way. His advice now is to actively look for someone who can tell you why an idea won't work. If you can't find a strong reason, you have something worth pursuing. If he could start over, he says, he would hunt for the strong reason something won't work instead of hoping for a reason it might.
He adds two principles. The first is to build as much as possible, because building produces the most information. He doesn't think he would have arrived at Gumloop without failing about ten times first. The second is to talk to users as much as possible. He calls this a privilege you have to earn, because at first you have no users and end up begging people to try your product. He thinks users should be at the center of everything you build, including when they tell you the product is bad, you're building the wrong thing, or it doesn't solve their problem. He considers that the most valuable feedback there is.
He also describes how different the right idea felt. When he started on the current version of Gumloop, he stayed at the office until midnight for the first time because he was excited about what he was building. When you find work that makes you not want to close your laptop, he says, every day gets easier and the momentum compounds.
From AutoGPT's Discord to an Automation Platform
Gumloop started in the AutoGPT community. He describes AutoGPT as a very popular open-source agent framework that spread across Twitter because it was the first time AI seemed able to act agentically and solve problems on its own. He tried it and found the first demo impressive, though he admits he "didn't actually push the limits and see how it would break."
He joined the AutoGPT Discord server, which was growing fast, and saw many basic questions: What is GitHub? How do I use the terminal? How do I install something locally? What is a dependency? He realized that a simple UI would solve that problem for people. He also saw it as a chance to learn front-end development and didn't expect it to become anything special. Whenever someone in the Discord asked for help setting up a local environment, he sent them a link to AgentHub, the first version of the product.
After a few days of building, he started to think of it as "GitHub for agents": if agents were useful, he could own the platform where people hosted and interacted with them. That idea fell apart within a few days, when he realized the agents weren't useful. He calls this the "aha moment." People wanted to use his platform but were frustrated because the agents were unreliable. So he gave them what he says they were "secretly asking for": reliability and predictability. Their use cases were simple enough that he built a framework for automating steps one after another, and that grew into the automation platform.
The users also surprised him. Because it came from an open-source project, he assumed the audience would be developers. The people most enthusiastic about it were non-technical: business admins, operations staff, HR people excited about AI solving real problems and automating their work. Once he realized about 80% of the audience was non-technical, he decided to make the product approachable and fun, without frustrating complexity. He describes this as the motivation behind a simplified version of AutoGPT.
First Paying Customer, and YC from a Vancouver Studio
The product was available and completely free for about five months before their Y Combinator batch began. They always planned to charge, and in roughly the first week of the batch they turned on pricing at $20 a month, because they couldn't imagine charging more than ChatGPT. Their first paying customer was a man named Kai. Brodeur-Urbas says the team "freaked out" when the Stripe notification appeared and calls it "the greatest moment ever." Kai is still a user.
Because of the ban, he was stuck in Canada for all of YC. He had the pressure of meeting YC's expectations but none of the distractions. He spent the batch coding as fast as he could in a small studio apartment in Vancouver.
Networks Aren't Made at Cocktail Parties
He took a lasting lesson from that isolation: staying focused and avoiding tech networking events and parties is powerful. In his view, the people building something amazing aren't at those events, and nobody who is onto something is out networking. He says he still mostly skips events, and his co-founder almost never goes, "almost to a fault." Most people have never met the co-founder because he is always working.
His conclusion is that if you stay focused and talk to users, your network forms naturally. He applies the same logic to fundraising. Many founders think they must network to meet investors and persuade them. He argues that if you build something exceptional, investors come to you. You show them you will succeed without them, and then you are the one telling them to wait. He calls this the biggest realization of YC: it's not that complicated, just build something great. As he puts it, "a network is not made at the cocktail party."
The "Slop Machine" Anti-Pattern
He sees an anti-pattern in how people use AI: going so far in one direction that you become someone who says, "I have 50 AI agents running my company and I have a C-suite of AI that tells me what to do every day." He thinks that approach is wrong and compares it to building a slot machine, then corrects himself: a "slop" machine.
He believes the key is knowing what to use AI for and what not to. Keep the human touch in the important parts and automate the repetitive work. He says Gumloop's best users are heavily AI-enabled but haven't replaced their whole job with AI. He admits it's a slippery slope.
Course Bros, Wantrepreneurs, and Selling Hope
He is openly critical of a genre of social media content. On Twitter, he says, people claim they automated everything, work an hour a week, and make $10 million on the weekend with a SaaS app. He considers most of this marketing and says "they're lying to you, for the most part." He calls these people "course bros": they sell a dream, and followers believe they can make $30,000 in a weekend by copying a workflow and commenting to get "the recipe." He says he can "guarantee that's never worked." In his view they promote productivity without offering anything new. His argument is simple: if a magic solution made $30,000 in a weekend, nobody would give it away on Twitter.
He links this to earlier hype cycles such as crypto and NFTs. Every bubble, he says, has a vulnerable part of the community that is easily convinced something will rescue them from their situation, and "you can sell hope really easily." Some people online exploit that with fictional content. He describes the target audience as "wantrepreneurs," admitting the word may be negative: people who think a business can be built with one click because they bought a course that promises a million dollars a year as a side hustle. What's being sold, he says, is the idea of skipping hard work and going straight to the value, which he says "will never happen." The course sellers, meanwhile, make a lot of money: "they found a way to print money."
Automate Only What You Understand
His alternative is a personal rule: he only automates things he really understands. Automating something you don't understand, he says, just produces another slot machine. Using AI for work you don't understand often creates uncertainty. His example: if you use AI to code without knowing how to code, "you're making malware at the end of the day," and it will come back to bite you. Vibe coding "can only go so far," and he says the same applies to workflow automation. If you automate something you could never do yourself, the result will be poor.
He describes his own use of AI as acceleration. He takes things he already understands, does them much faster, and uses the saved time to learn more and grow. He says he never uses AI to skip understanding something or to replace himself in expanding his skills.
A Widening Split Among Engineers
He raises a speculative worry: "the last generation of great engineers" may already have been born. That generation had to understand what was happening and was then accelerated by AI. Now people can skip the understanding and go straight to acceleration. He expects a much smaller group to use AI as a learning tool, as a teacher for the fundamentals.
He calls this another slippery slope. When the website works or the feature does what you wanted, it's easy not to ask why it worked, why it didn't, or what side effects it might have. He predicts a bigger gap between exceptional and average people. Those who stop, try to understand the problem, and have AI teach them what they don't know will become exceptional faster than ever before. The average person, he says, will "fall to the slop."
Hiring Customers Who Already Believe
Nearly all of Gumloop's hires came through its network, and many were customers. He mentions a customer at Instacart, one at Webflow, and one at Shopify who each quit their jobs to join. He finds this an enjoyable way to hire because these people already have conviction. They use the tool daily, love it, and understand the vision. He says a startup's main asset is optimism: you need to be excited to come in every day and prove wrong the people who ask why a big company won't just steamroll you. Customer hires already have that belief, so the transition is fast and the main work is onboarding.
He compares hiring to dating. You can't beg someone to date you, and you can't beg someone to join your company. You have to build something great and show traction so the best people want to join. He notes that his co-founder joined only after seeing a working early version of the product in a demo.
On culture, he says nobody is told to stay late and there are no required hours. Everyone is equally excited about the mission. His main hiring filter is whether he would want to spend all his time with the person, whether he could "hang out with them 24/7." He says this has compounded into a group of well-adjusted, fun, ambitious, excited, and intelligent people, and each hire like that makes the place more exciting for everyone.
Blind Confidence Over a Hundred Reasons Not To
He ends with a point about skepticism. Every startup has "a million reasons" not to exist. It's easy to hear an idea and ask about the moat, or why some big company won't do it. He believes people who obsess over those questions never build anything and end up as "a pawn" in a big player's game. People who take risks and try to prove others wrong end up where others ask how they got there, and his answer is that they tried, it worked, and when it didn't, they tried again.
He applies this to Gumloop. On day one, he says, he could have listed a hundred reasons why Zapier would do it better, or why OpenAI and other big players would crush them, and then nobody would be using Gumloop and they would have built nothing new. He thinks the most important quality in a founder is believing they can do it, because nobody starts a company without thinking they will be the one to pull it off. That takes "blind confidence," and he closes by saying it's exciting how much someone can do when they believe they can.
There's a category of like wantrepreneurs. Might be a negative term, but someone who thinks that they can just build a business with a single click. You know, like it's effortless. Because I bought this course, I now have the recipe to making a million dollars a year as a side hustle.
There's kind of an anti-pattern with AI right now where I have 50 AI agents running my company and I have a C-suite of AI that tells me what to do every day. I think that's the wrong approach. That's just like you're making a slot machine. Slop, not slot.
Everyone on Twitter is like I automated everything and I don't do, I work 1 hour a week and I make 10 million dollars on the weekend with my SaaS app. Like most of that is just marketing. They're lying to you for the most part. You can sell hope really easily. But you're selling this vision of like skipping the hard work, shortcutting directly to the value, which will never happen. It'll never work. But for the person who's selling you that course, they're going to make a ton of money. They found the way to print money.
My name's Max. I'm the founder and CEO of Gumloop. Gumloop is an automation platform that is automating about 4 million workflows every day across huge businesses like Instacart, Shopify, DoorDash, Gusto.
We're a team of 15 at this point, scaling pretty quickly. What it does is it lets people who really understand a problem within a business, like the marketer or the salesperson or the ops person, automate their own work instead of having to spec it out and explain it to an engineering team to build that solution for them.
I went to McGill in Montreal. I studied software engineering. I really liked doing well in school, so I put a lot of effort into that and studied all the time. Tried to get the best grades I possibly could. I remember my goal all throughout college was just get a good job. I wanted to work in big tech cuz I thought that would be the right path to follow. Realized pretty quickly that I hated it.
What most people say when you're thinking about starting a company is like I'll just go to big tech for a little bit. I'll learn a bunch and then I'll do my own thing. I think that's kind of cope in a way. Like a lot of people say that. They go and do it and then they end up kind of getting golden handcuffs and never leaving and they never actually go and start their own thing.
I don't think I've used anything I learned in Big Tech in my startup at all. I think the only thing working in Big Tech actually provided is some sort of default respect, I guess, from people when they realize you're not just a random person. Like you actually had a company to validate that you're competent in some way. So, getting that logo on your resume is helpful a little bit, but I don't think I learned anything novel when I was there. A lot of the things that I do now are motivated by the things I didn't like at Microsoft. Most of the things I do are the exact opposite of how they work in Big Tech.
What you can do when you're 21, 22, 23 with no responsibilities, no obligations to anyone, those are kind of years you can never get back. And if you're spending it just going to your 9-5, logging in, fixing a ticket, and then logging off, you're kind of wasting those really, really important years. If we're going to do it, why don't we just do it now? Figure out what the more reliable, responsible thing to do later. I think that was the right choice. Just like throwing ourselves into the ether and seeing what worked.
I actually got deported from the United States and banned for 5 years, but it was nothing illegal or anything. I quit Microsoft and then I moved back to Vancouver and my thought was like, I'm just going to live in Vancouver for a year, build stuff in my bedroom, and then one weekend I was going to visit my old roommates in Seattle and the border people are like, "Where are you going? Where are you staying? What do you do?" So, they turned me around at the border after a bunch of questions cuz I was suspicious, basically. The suspicion was that I was going to stay for longer than I was saying, even though I was just going to stay for 2 days. But, that came with a 5-year ban from the country.
And then that was kind of the moment where I realized I had to build a company because I had no fallback plan. I decided to just take it more seriously, build things that people actually want, something that can make money. I was pretty terrified, honestly. I remember driving back from the border to my girlfriend's apartment just like almost in shock. It took me a couple days to settle and calm down a little bit, but after that it just focus. I kind of just worked for the next 6 months as hard as I could.
I tried everything. Like I was trying to build anything that seemed remotely valuable. Built a video game moderation software in VR. And then I did general trust and safety tooling. I did bot detection software for web traffic, a platform like an anti-scam platform. I built a ton of stuff and then it would make it MVP and then try to sell it and see if there was interest from the market. Over and over I tried that, kind of like once a week I had a different idea, something I was experimenting with. And then I'd learn pretty quickly that it was a bad idea.
The more I did it, the more I got used to proving myself wrong. And I kind of learned the counter-intuitive fact that in startups you're actually chasing proving yourself wrong. That's the best thing that can possibly happen cuz you're saving weeks or months of time. In the beginning I was building ideas for months and then I would hope that someone would prove me right and say this was worth building. But that's the opposite of what you should be doing. I wasted maybe 3 months doing that. You should actually be hunting for someone to tell you why this won't work. If you can't find a reason it won't work, then you actually have some sort of tangible idea you should pursue. If I could do it all over again, I would be hunting for the strong reason why something won't work instead of hoping for the reason that might.
Build as much as you can. Like that's what provides the most information. I don't think I would have ended up building Gumloop if I didn't fail 10 times before that. Talking to users as much as you can. It's almost a privilege you have to earn because in the beginning you won't have any users to talk to. You'll be kind of begging people to try your product, which is not a fun position to be in, but something you have to kind of go through. I think putting them at the forefront of everything you're building, even if they're saying things you don't want to hear, your product sucks or you're not building the right thing or it's not solving my problem. That's actually the most valuable feedback you can get.
When I started working on the current version of Gumloop, that was like one of the first days where I stayed at the office till midnight because I was so excited about what I was building. Like I couldn't get enough of it. And that just kind of compounds over time. Like if you find that thing that will get you to work until midnight and not want to close your laptop, every day becomes easier. You kind of just build this crazy momentum that you can't get enough of.
Do you remember AutoGPT? There was this super popular open source agent framework. It kind of took Twitter and the world by storm because it was the first time that it felt like AI could do something agentically and solve a problem on its own. I saw it on Twitter, tried it out. It was pretty amazing. I didn't actually push the limits and see how it would break, but in the first demo, it seemed really cool. So, I joined the Discord server, which was growing pretty exponentially at the time. And I would see all these people asking like what is GitHub? How do I use the terminal? How do I install something locally? What is a dependency?
I realized if I had just solved that problem for them and built this nice little UI, then I mean I'm doing something interesting. And my thought was I would learn how to build a front end, so it would be a good use of time. I didn't think it would become anything special. But whenever anyone would ask in the Discord server for help setting up their local environment, I would send them a link to AgentHub, which was the first version of it. After building it for a couple days, I was like, "Oh, this could be like GitHub for agents. If agents are useful, I could have the platform that you host them on, that you can interact with them on."
That idea kind of crumbled after a few days when I realized the agents weren't useful, which was the aha moment. I had a platform that people wanted to use, but they were frustrated with because the agents were so unreliable. So, I kind of gave them what they were secretly asking for, which is just reliability, predictability. All of their use cases were so simple that I thought I could just build a framework that lets them kind of automate the steps one after another. And then that kind of just naturally grew into this crazy automation platform.
It was for developers because it was an open source project, but the audience that really went crazy for it were non-technical. They were like business admins at a company or ops people or HR people who were just enthused about the idea of AI actually solving a problem for them and automating their work. So, realizing that the actual audience was 80% non-technical, that was when I realized I need to build something that is approachable for them, that feels fun for them to use, that doesn't frustrate them with all the complexity. That was kind of the motivation behind the simplified version of AutoGPT.
We un-twiced it 5 months before the batch started. For that 5 months, the product was still completely free. We knew that we were going to turn on pricing at some point. During the batch, I think in the first week, we turned on pricing. We made the product $20 a month because we couldn't imagine charging more than ChatGPT. But, our first paying customer was just this guy named Kai who paid $20. We freaked out. It was like the greatest moment ever seeing the Stripe notification pop up. And then, he's still a user.
I was stuck in Canada during all of YC. So, I had all of the pressure of needing to build something amazing and live up to just the expectations that we had for being in YC, but I didn't have any of the distractions. So, I was just in a small studio apartment in Vancouver just in my room coding as fast as I could.
I think we learned that staying focused and not getting distracted by like the networking events and the random parties that people in tech host is really powerful. Like, the people who are actually building something amazing are not at those events. Like, no one is really networking if you're onto something. So, I think I've kept that attitude going forward. Like, I don't normally go to events for the most part. My co-founder never goes to events almost to a fault. Most people haven't met him because he's just working all the time.
That's the biggest takeaway that if you stay focused and you just talk to users as much as you can, like, your network will emerge pretty naturally. Fundraising, like, a lot of people think you have to go out and network in order to meet these investors and convince them about your idea, but if you build something exceptional, they will actually come to you. It's actually not that complicated. You just have to show them that you'll succeed without them and then you'll be the one getting the email telling them to wait. That was probably the biggest realization during YC that it's not that complicated. Just build something great. Yeah, a network is not made at the cocktail party.
There's kind of an anti-pattern with AI right now where you can go too far in one direction and become the type of person who's like, "I have 50 AI agents running my company and I have a C-suite of AI that tells me what to do every day." I think that's the wrong approach. That's just like you're making a slot machine, basically. Slot, not slot.
I think it's important to know what you should be using AI for and what you shouldn't. Like keeping the human touch in the important parts and automating the repetitive things is the way to go. I always see the best users as being super AI enabled, not replacing their entire job with AI. But it's a slippery slope. Like everyone on Twitter is like I automated everything and I don't do, I work 1 hour a week and I make $10 million on the weekend with my SaaS app. Like most of that is just marketing. They're lying to you for the most part.
Like I think there's a lot of course bros, I like to call them. Like people who sell the dream and all these people on Twitter are kind of inspired by the fact that I can make 30,000 this weekend if I just copy this workflow and comments and then they'll give me the recipe. I guarantee that's never worked. They're shilling the vision of productivity without actually offering anything novel.
I think that the value comes from when you apply AI to something you will understand deeply. If there was some magic solution that would make you $30,000 in a weekend, they wouldn't be giving it to you on Twitter. The most productive people who are actually generating the most value, it normally comes from taking something you really understand, applying AI to it wherever you see fit, and then just scaling from there.
I think it's cuz I see the same patterns emerging. Like whenever there's a hype bubble, like with crypto or with NFTs or with AI, there's a section of the community that's a little vulnerable, I think. Easily persuaded and convinced that something will save them from whatever situation they're in. You can sell hope really easily. So I think there's a lot of people online who take advantage of that and make content that is just fictitious.
There's a category of like wantrepreneurs. You like entrepreneurs, but a wantrepreneur might be a negative term, but someone who thinks that they can just build a business with a single click, you know, like it's effortless. Because I bought this course, I now have the recipe to making a million dollars a year as a side hustle. But you're selling this vision of like skipping hard work, shortcutting directly to the value, which will never happen. It'll never work. But for the person who's selling you that course, they're going to make a ton of money. They found a way to print money, basically.
I only automate the things I really understand. Like if you're automating something you don't understand, it's just going to be a slot machine. If you're using AI to do something you don't actually understand at all, oftentimes you're creating just uncertainty. Like if you're using AI to code and you don't know how to code at all, you're making malware at the end of the day. Like it'll come back to bite you. And vibe coding can only go so far. Same thing with automating workflows. Like if you're trying to automate something you could never do yourself or you don't understand, you're going to create something that's pretty poor.
So, I think I apply AI to speed myself up and take the things I do understand, do it way faster so I can learn more things and like grow as a person. But I'm never like trying to shortcut understanding something or like expanding my skill set by like having AI just replace me and do things for me.
It's possible that the last generation of great engineers has been born because there was this era of actually needing to understand what's going on and then getting accelerated by AI. But now people can skip the understanding part and just accelerate with AI. I think there's going to be a much smaller community of people who actually use AI as a learning tool and they understand the fundamentals, use it as like a teacher to really grasp what's going on. But it's a slippery slope as well because it's so easy just to not want to understand why something works cuz it just like your website worked or the feature did what you wanted, but you didn't take the time to like really dig
into why it worked or why it didn't or what knock-on effects this could have. So, I think there'll be a bigger split in the people who are truly exceptional versus the people who are not, because if you can actually have the determination to pause, try to understand the problem, have AI teach you the things you don't understand, you'll become exceptional even faster than before, and then the average person will just kind of fall to the slop.
Almost everyone we've hired is through network. A lot of people we hired were actually customers. So, our customer from Instacart quit his job and joined us. Our customer from Webflow quit his job and joined us. Someone from Shopify quit their job and joined us. They already had conviction. They loved the platform so much that they decided to drop everything and contribute to the mission.
It's a really fun way to hire because the only thing you really have as a startup is the optimism in general. You need to be excited to come to work every day and be the one proving everyone wrong for when they say, "Oh, why won't this company do this? Or why won't this big company just steamroll you?" But these people are already bought in. They use the tool every day. So they love what you're doing already and they see the vision. So it's a very quick transition. You just have to figure out the details of how you onboard them.
It's like dating in a way. You have to be the person that someone would want to date. You can't just beg someone to date you, just like you can't beg someone to join your company. So you have to build something amazing and have traction to make the best people on Earth want to join you. But it all starts with just doing it. You have to start somewhere. My co-founder only joined because I had a version of the product that was working in the early days. I was demoing the first version of it and he got excited about that. So he joined. You have to build something exceptional.
We're having a lot of fun at Gumloop every day. There's no one being told to stay late. There's no hour you have to show up or hour you have to leave. We're all equally excited about the mission and love what we're doing. And a big filter for how I hire people is, "Do I actually want to spend all my time with this person? Could I hang out with them 24/7?" That has just kind of compounded over time and now we have a big group of people that are all really well-adjusted, really fun to work with, ambitious, excited, intelligent. The more people you hire like that, the more exciting it gets for everyone. So the momentum builds in this direction and people love what they're doing.
I think there's a million reasons to not build every startup. It's very easy to hear an idea and say, "What's the moat? Why won't X company do this? Why won't Y company do that?" I think the people who are really asking themselves that and obsessing over those questions will never build anything. You end up just working for a big player and being a pawn in their game. But if you try things and just take a risk and try to prove other people wrong, you'll end up somewhere that people are like, "How did you get there? How are you leading this company that so many people use?" And the answer is just you tried and it worked. And when it didn't work, you tried again.
I could have on day one explained to myself a hundred reasons why Zapier would do this better than us or why OpenAI and all these big players would crush us, and then no one would be using Gumloop. We wouldn't have created anything new.
I think the biggest quality that makes a founder start founding something is thinking that they can do it. You will never start a company if you don't think you're going to be the person to do it. So it just takes this blind confidence. It's pretty exciting just how much someone can do when they think they can do it.
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