Things Einar Vollset Has Said on Podcasts

“It's the first time in at least 10 or 15 years that leadership really matters in companies large and small. For the longest time, once you got to a certain size and you had a SaaS and you had this retention, you couldn't really screw it up all that badly… You really can. And people will for sure.” — Einar Vollset, Startups For the Rest of Us

Responding to Rob Walling's prediction that 10 to 20% of SaaS companies will go to zero or plateau because of AI, with Squarespace as his example of a big one at risk.

Startups For the Rest of Us · 2026-10-20 Permalink → Listen →

Startups For the Rest of Us Around 12:24 into the episode

Einar Vollset

…that that's really one of the big unknowns. Is like, I think it's undoubtedly true that a lot of these things is coming. The question is like, how quickly does it spread through the economy and into your niche? Like that, I think, will depend on like what your customers are like and all that stuff.

Rob Walling

…going to go to zero tomorrow, but they're going to plateau. Like I, I'm moving all my sites off there onto static sites. And I think a lot of other people are. There are now services. I mean, I interviewed Nathan from Blink Pages, a Tiny Sea company, and he's starting up a whole service to do that.

Einar Vollset

Yeah, there's no doubt about that. I think fundamentally, a lot of these things are changing. And I think like how these companies react is going to have a huge impact. And it's almost like it's the first time in like at least 10 or 15 years that leadership really matters, you know, in companies large and small. Because like for the longest kind of time, like once you get to a certain size and you had a SaaS and you had this retention and you had this, you couldn't really screw it up all that badly. Now you can't. Like, you know, like you really can. And people will for sure.

Rob Walling

And unless a company like Squarespace comes up with a whole new kind of AI first approach with static sites that it supports, if it does that, they'll survive. But if it doesn't, I just don't, I don't see how they do it long term. So let's dive into this. There's a lot of, we see it in Tiny Sea…

Einar Vollset

Fathom.

Rob Walling

Fathom, yep. They're all kind of the same now because you just get to feature parity real quick. And so Otter, I believe, launched in 2016 and Granola launched in 2024, eight years apart. And they're kind of the same product because AI just gets you there. So this is something that even if you have…

Speaker names from our own diarization · position estimated from where the line sits in the episode

“A lot of B2B SaaS is: I have a tool, I'm selling that to a business owner, and that business hires somebody to click on my tool in order to have some outcome happen… Somebody is going to come along and say, you don't have to click on this thing anymore, we're just going to sell you the outcome. If you think that's likely to happen in the next year or two, you should be the one to do that.” — Einar Vollset, Startups For the Rest of Us

Einar Vollset's "go on offense" advice to SaaS founders worried about AI: become the worker. His case is that labor budgets are much larger than software budgets, and an existing vendor with customer trust is well placed to sell the outcome.

Startups For the Rest of Us · 2026-10-20 Permalink → Listen →

Startups For the Rest of Us Around 52:03 into the episode

Einar Vollset

…Oh, you're like under spam attack or like some of your spammers in Nigeria started sending like 10,000 emails out of your one good IP that your one number one customer uses. You got to wake some dude up at 2 a.m. And it's like, look, like, do you want to do that or you want to pay $249 a month?

Rob Walling

…and you want to get four, like, how do you do that? So there's a lot more to this conversation than just this identifying of them. But that was us going on defense, right? How do we, how do we not get sideswiped by AI? You've talked a lot about going on offense. Why don't you take us from there?

Einar Vollset

So going on the offense side, like basically there's sort of three moves, effectively, I think, in this world. And sort of the obvious one is the one that people are kind of already doing, which is like keep improving the business. Use AI to make, you know, to go out and just build more software faster. It's sort of similar to like kind of what you've always done. It's like, okay, for me, like maybe you identify that you have a bunch of these modes. Actually, your business is in great shape, no problem. And actually, like, it's just an opportunity, and you just go out and, like, you just have to build more features. And AI gives you that capability, and great. So, that to me is almost like you know, table stakes as it relates to what you could do here on the offensive side. And I think everyone can do that. The second part is, you know, one of the one of the things we identified is this notion that people will have more agents, there'll be more agentic things happening. And so, like, agents are going to be interacting with your software, most likely, in some way, shape, or form. And there are kind of two stances to do here: one is to try to lock them out, which is sort of…

Rob Walling

So the opportunity there is become the worker. And I can already imagine all the objections that I would hear from this, from bootstrappers and such. To me, if I were a founder still, if I was, let's say, running drip, I'd be like, God, that sounds like I'm, that sounds like a lot of work. I'm…

Einar Vollset

That is it. I mean, I think that's it. And like, one of the things that it allows you to do is that if you're going to do the worker, the stuff, then kind of naturally, this moat of like this sort of learning loop that we talked about, this like notion that you're observing how things are going and…

Rob Walling

Yeah. And I want to call out to folks listening, like the first offensive move is to become the worker, Agent ExaS. And related, but a second and different offensive move is to implement a learning loop, like you've said. Right, not the old school learning loop, but the actual like you have data…

Speaker names from our own diarization · position estimated from where the line sits in the episode

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