MicroConf Tactics: He Built Two Unicorns. Here’s The Only AI Startup He’d Build in 2026

**Rob Walling** (0:07)
Welcome back to the MicroConf Podcast. I'm your host, Rob Walling, and in this Tactics episode, I pull audio from one of the most popular YouTube videos on my channel. It is a snippet of an interview with Jason Cohen.
He built two unicorns. Here's the only AI startup he'd build in 2026, where Jason Cohen, founder of WP Engine, drops tons of knowledge about how he would think about building an AI startup. And Jason Cohen just spoke at MicroConf here in the US in Portland just a couple weeks ago.
Tickets for our next MicroConf, it's in Europe, are now on sale. It's in Reykjavik, Iceland, September 21st through the 23rd. You can head to microconfeurope.com for the full details and to get your tickets. Tickets for this event will sell out. We have sold out all of our events for the past several years, and the speaker lineup is coming together. It's going to be an amazing venue. I'm a huge fan of Iceland and really excited to be able to bring it there this year. So that's microconfeurope.com. And now let's dive into my conversation with Jason Cohen about the only AI startup he'd build in 2026

**Jason Cohen** (1:20)
AI doesn't really work. What do we do with that? Because people say, well, over time, we'll get better. Like, all right, but you're building company now.

**Rob Walling** (1:26)
That's Jason Cohen. He's built $2 billion companies, WP Engine and SmartBear. He's been writing about startup strategy at asmartbear.com for almost 20 years, and he's one of the sharpest thinkers I know on what actually works in startups. I sat down with Jason to talk about AI. Not the hype, but the real question founders are dealing with right now. If he were starting a new company today, how would he think about it? He didn't give me a simple answer. He broke it into three types of AI products, which one he'd bet on, which one he'd avoid, and why he thinks most founders are framing the whole thing wrong. Jason's been doing this for 25 years. Bootstrapped, venture-backed, started companies, sold companies. So I asked him, if you were starting from scratch today, how would you approach AI?

**Jason Cohen** (2:09)
Yeah, it's a big topic. First of all, I separate how I use AI operationally to do stuff, like write code or write marketing, from AI that's in the product that the customers use, whether directly or indirectly. So first of all, take all that operational stuff, let's set that aside because I don't think that's what you were asking. But I find that in conversations, sometimes people start confusing that. It just makes it more difficult to deal with an already complicated or complex question, right? So let's just ignore that. It is true that corporate budgets now are heavily biased toward things that are AI, whatever that means, and it's a fuzzy thing. Like the companies themselves are not clear on what that means, so we have to be fuzzy. That tells me that whatever I do does need an AI component somehow because that's where the budgets are. That doesn't tell me what to build, but the idea of like, well, I just won't have AI at all, it'll just be a typical thing. That could be a good idea, by the way.
But I would worry that I'll be fighting a budget battle and an attention battle. And so that doesn't feel like the easiest path.

**Rob Walling** (3:05)
So if AI is where the budgets are, shouldn't we all just be building AI products? Here's where Jason sees founders going wrong.

**Jason Cohen** (3:11)
The wrong way to think of it is, I need an AI product. That may be how the budget is, so you may, you know, that may be down the line how you, sometimes how you talk about it. But this is another thing I see people doing wrong constantly, which is thinking of AI as if people want AI as the problem they're solving. So let me put that differently. People have the same problems today as they've always had. Marketers want more leads, sales wants to convert more leads to a sale, that's day.
Customer service wants to have good customer service and get good results from customers. Engineers want to write code that doesn't have bugs, that's maintain, manageable. Product managers want to build. Okay, everyone wants the same crap that they've always wanted. What you don't say is like, I need AI in sales. What you do say is, if I could 10x my outbound volume with the same conversion rate, which should be nice. So people talk about AI like it's part of the problem to solve, or that people want AI. False. AI is part of the solution space. How is it that I can deliver more of what they already wanted because of AI? AI has made something possible that was previously impossible, that they already wanted. So as soon as it's like, it's an AI voice thing, I don't know what that means. Whereas if you said, for restaurants, we take over their phone tree because we can do the menu, we can do the ordering, we can do hours, but we do it on the first ring and in 40 languages. Now, behind there is voice AI. That's what's behind, otherwise, that's not possible. But the thing you're solving is your phone. Your phone calls are now automated and awesome. And so I think when you stay focused on the problem that already existed, and AI is why you can do something that was never done before better, that's the right way to think of it. So I'd be thinking about AI as the solution space, not as people say and they even say in their pitch decks as the problem space. Another thing I would do is I would say, look, AI doesn't really work. I know it's like, but when it does, it's amazing. Oh, I know. When it does is a pretty big qualifier. When I research stuff for the book, I would say at least half the time, it's simply wrong. Even when you do the deep summary, the deep research, it sounds good when they say it, and then I go read the primary sources, and it's like completely wrong half the time. And so what do we do with that? Because people say, well, over time will get better. Like, all right, but you're building company now.