When I started building software in 2012, software-as-a-service (SaaS) was running hot. Cloud was growing rapidly, and it unlocked an unprecedented amount of possibilities for companies to get their product out to the public quicker and cheaper. Instead of having to rent dedicated servers from a data center company, go with some sort of colocation with large upfront capital requirements, or even go with a cheaper VPS solution that required you to set up your own multi-region redundancy, failover, etc., most of this was already done for you, or at least abstracted enough that 1 person or a small group of people could (in theory) set it up. Add on companies like Cloudflare that gave you a CDN, free SSL, and DDoS protection out of the box, and multi-tenant SaaS now took 1/100th of the time to set up. Don't even get me started on frameworks like Ruby on Rails and Django.
Let's take a second, step in our time machine, and take a look at good ol' Salesforce. In the late 90s and 2000s, Salesforce was pushing the envelope of what SaaS was. Most of it didn't exist yet. Multi-tenancy (one codebase, one database, every customer sharing it with metadata-driven customization) wasn't standard practice. Parker Harris and the early engineers had to build reliability, isolation, and per-customer customization on a shared stack. When they had major outages around late 2005 and early 2006, it threatened the whole "trust us with your data" pitch, which is why they launched a public status site (trust.salesforce.com) and made transparency part of the brand. On top of that, the business model looked bad on paper. Perpetual licenses gave companies like Oracle big upfront revenue. Subscriptions meant Salesforce recognized revenue slowly while spending heavily on sales and marketing to acquire customers. Investors understood subscriptions in general, but they hadn't really applied that lens to software yet (ARR, churn, LTV/CAC). Salesforce essentially had to teach the market how to read a SaaS P&L.
But then it got cheaper and easier. Even before AI, without accounting for the cost of labor to do the engineering work, you could realistically build a basic SaaS platform with a few thousand dollars in infrastructure costs.
SaaS companies have always had a handful of moats: distribution, enterprise sales teams, integrations, compliance, switching costs, being the system of record for your customer's data. But underneath all of that was a simpler one. Building a real platform was deeply expensive and complicated, and that alone kept a lot of competitors out. Airtable, a database SaaS tool that was one of the Bay's darlings for a long time, raised over $1.4 billion and was valued at around $11 billion at its peak in 2021. This year, they agreed to sell to Bending Spoons for about $1.3B in enterprise value (roughly $2.25B in equity once you count their cash), which is an 80%+ drop. Earlier in 2026, secondary trades had already reportedly valued it around $4B, so the sale came in well below even that.
To be fair, a lot of that drop started before AI was a real factor. 2021 valuations were a bubble, rates went up, and basically every SaaS company got its multiple cut. Airtable is still growing too, with ARR up over 20% to around $480M. So why didn't the valuation ever come back?
AI. It is no longer deeply expensive to spin up a SaaS company. I am not going to act like it's easy. It still takes a lot of time and some money, but you can absolutely do more with less now. A small team of 5 people can spin up a version of most of these former Silicon Valley darlings in a few weeks. It probably won't be as deep as those companies, it will have bugs, and the code may be a little sloppy, but give it enough time and you can get through those hurdles too, with far less money and time than you needed for decades before. It's even worse if your customers are technical because, in theory, they could spin up a prototype of your app, give it their custom business logic, and boom, you're cooked. Like I said, no one is arguing it'll be as good as an actual company with potentially hundreds of engineers building it. But it may just be good enough. And the market is pricing that in, even for companies that are still growing.
Even though what I said is a gross oversimplification, my theory is that a handful of types of SaaS companies will survive the AI era:
1. Your product is too complicated or has far too much depth for AI to do well
Some products are just hard. Not "lots of CRUD screens" hard, but years of edge cases, performance work, and domain knowledge baked in. Think Figma's rendering engine or Datadog ingesting trillions of data points. AI can get you a demo of these things in a weekend, but the gap between the demo and the real thing is enormous, and it's the kind of gap you only close with years of customers breaking your product in ways you never imagined. If your product's value lives in that last 10%, you're in a much better spot than most.
2. Your target market is not sophisticated enough to want to build and maintain their own version of your platform
A plumbing company with 12 techs isn't going to vibe-code their own scheduling and invoicing app. Neither is a dentist's office or a family-owned restaurant. Even if AI makes it possible, they don't want to own software. They want to fix pipes, clean teeth, and serve food. The "just build it yourself" threat really only applies when your customers have engineers, or at least someone who likes tinkering. For a huge chunk of the economy, buying software will always be easier than building it, no matter how cheap building gets.
3. Your product touches on deeply sensitive things that are regulated (HIPAA, EHRs, investments, etc.)
When getting it wrong means a lawsuit, a fine, or someone's paycheck not showing up, nobody wants the weekend prototype. Regulated software comes with audits, certifications, legal liability, and a long track record that you can't spin up in a few weeks. A company might be able to clone the features, but they can't clone a decade of passing audits. And honestly, a lot of buyers in these spaces are paying for someone else to be on the hook when things go wrong.
4. You're the system of record
Once a company's customer data, financial history, or workflows have lived in your product for years, moving off of it is painful no matter how cheap the alternative is. Migrating data, retraining staff, rebuilding integrations, and risking something breaking along the way is a real cost. AI might make building a competitor cheap, but it doesn't make switching cheap. Salesforce is the best example here, and probably the biggest reason it isn't going anywhere even though, in a lot of ways, it sucks.
5. Your product complements an additional service you provide
This is the one I find the most interesting, and probably the one that will create the most new winners. More on that below.
Let's double-click into #5. What am I talking about here? I think the SaaS companies with the best shot are the ones that stop selling software and start selling outcomes, with the software as the thing that makes delivering those outcomes cheap.
Think about it from the customer's side. Most businesses never actually wanted a CMS, or an accounting tool, or a marketing platform. They wanted a website that brings in leads, books that are closed every month, campaigns that run. The software was just the closest thing they could buy. Somebody on their team still had to learn it, configure it, and do the work inside it. That gap between "here's the tool" and "here's the result" is where a lot of SaaS churn comes from, and it's the gap AI can't close on its own for a non-technical buyer.
Pilot is a great example of this in accounting. There's software underneath, but what you're buying is bookkeepers who close your books every month. The software just lets each bookkeeper handle far more clients than a traditional firm could.
That's the part I find interesting: AI doesn't hurt this model. If anything, it helps it. If you're a pure SaaS company, AI is a threat because it makes your product cheaper to replicate. If you're a service company running on your own software, AI makes your delivery cheaper. One person with good internal tooling and AI can now do what used to take three. Your margins go up while your pure-software competitors' moats go down.
It also changes the stickiness math. Ripping out a tool is annoying. Firing the team that runs part of your business, and then rebuilding that capability in-house, is a much bigger decision. The relationship becomes the moat, not the codebase.
There's a reason agencies and consultancies have historically been valued lower than SaaS companies: services don't scale like software. But if AI drives the cost of building software toward zero while also cutting the cost of delivering services, that gap starts to close.
There's some irony in the Airtable deal here, too. Bending Spoons' whole playbook is buying software companies at a discount, cutting a lot of staff, and running them profitably. They're betting the software keeps making money with far fewer humans behind it. Maybe they're right about Airtable. But I think the next wave of winners goes the other direction. They'll look less like the SaaS darlings of the last decade and more like tech-enabled service businesses that happen to own their stack.