The now
As things stand in early 2026, SaaS stocks have been getting hammered in the public markets. My sense is that this is a permanent re-rating and for many SaaS companies, it is justified. The reason is that the characteristics that made SaaS attractive have changed substantially.
SaaS used to be attractive because:
- the revenue is recurring (and thus predictable)
- was delivered at zero marginal cost
- was sold on seats, and thus had the potential for net revenue expansion through more seats
Stocks are priced on long-term growth prospects and the above characteristics were conducive to achieving long-term growth.
But AI changed a few of these dimensions.
The most important change is that building software is easier than ever. What used to take a team of developers a year can be done by one (capable) developer in a few weeks. This has meant that there are orders of magnitude more copycats in each space than ever before. And when competition goes up, pricing power goes down.
Secondly, many AI-native upstarts aim to "sell the work, not the software." Which in many cases means that selling on a seats basis doesn't really make sense. Think of an AI customer service startup - they don't really need to price on the basis of seats replaced.
And obviously, the more AI you build into your product, the more marginal cost there is.
The TLDR is that SaaS players can charge less (due to competition), have a higher marginal cost (inference), and have worse growth prospects (less seats).
The opportunity
But it's not all bad news. Vertical AI has the potential to permeate every single services sector of the economy. In many cases, new vertical AI upstarts are going up against services incumbents who have no or little ability to build software. So there is more opportunity than ever to find a problem to solve and to solve it with AI in a way that is 10x better than it was done before.
The roadmap
If you want to sell an application layer AI solution, the implications are relatively clear: you need to differentiate and fortify your business from the early days.
How can you do this?
- Incorporate a network effect into your business offering where possible
- Data network effects are now real (for the first time!) -- does your application generate proprietary and valuable data? If so, use it
- Differentiate on value proposition.
- One differentiated value proposition is selling the end outcome, not just a tool (which may require bundling software and services)
- Another may be to have several 10x better solutions. Being AI native allows you to imagine radically better ways of solving a problem for customers
- All-in-one offering can be hugely valuable -- as it broadens the options for wedge products and allows you to undercut competitors on a specific product line
- Distribution matters more than ever. Innovating on distribution (e.g. shoring up proprietary partnerships) can add defensibility
- Embrace customization: it used to be the case that you would reject customers who required custom offerings. But if AI can be used to rapidly produce custom instances for customers, it is now something to lean into