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SaaS-to-inference transition

2026-07-08 · 15:54 UTC ·@sandykory ·2 min read

Brief

SaaS-to-inference transition: @sandykory argues Q1 2026 valuation drops were a wake-up call but AI-driven inference and agentic features will expand software markets. Legacy SaaS are sticky (recalling a 5–10 year on-prem→cloud lag) but must adopt consumption-priced inference—even at lower gross margins—to retain customers and capture vertical growth.

Why it matters

SaaS-to-inference transition: software must 'layer intelligent actions' (agentic inference) and shift to consumption-based pricing, even if that means accepting lower gross margins to deliver AI-powered workflows and automated actions.

Key details

  • Q1 2026's nosediving SaaS valuations prompted concern, but legacy SaaS remain sticky—historically surviving 5–10 years of ignored platform shifts (on-prem→cloud); the author warns customers will be less forgiving now if incumbents don’t adopt AI.
  • Examples cited: Intercom’s $3.6B exit to Salesforce as evidence legacy players can still capture value; Cursor demonstrates startups can win selling inference with negative gross margins; Bending Spoons used legacy-product consolidation/price increases as a user-inertia play.
Source evidence

I haven’t been buying the "SaaSpocalypse," but Q1’s nosediving SaaS valuations gave me pause. After a week in SF last month sampling the AI zeitgeist, I have a better feel for where the software sector is heading. It’s the SaaS-to-inference transition, and it’s good.

My long-standing view has been that AI is a net positive for the software industry. It radically raises the ceiling for what software products can do. It should dramatically expand the market opportunity for software, just like the on-prem-to-cloud transition did back in the day.

Yet many have been freaking out. After all, haven’t SaaS switching costs come down dramatically in SaaS, threatening one of the pillars of the business model? Yes, there’s no doubt that the “cement around the ankles” of legacy SaaS has weakened. At the same time, most legacy SaaS companies have barely scratched the surface of AI innovation while maintaining their historically high retention.

This is how it played out in the last major transition: on-prem-to-cloud. Many legacy players (pathetically) ignored cloud innovation for 5-10 years (or longer) and still kept their customers. It turns out that technology is stickier than most in the tech industry believe.

Take a look at Bending Spoons, which IPO’d off the back of buying crappy legacy products and jacking up prices because users didn’t want to give up their AOL email or Evernote notes. Tech industry people are not like this. They tend to be part of the very small minority of early adopters. Most people aren’t like this. Neither are most organizations.

Legacy software isn’t going to disappear. But if pre-AI software companies don’t embrace AI innovation, their customers will be much less forgiving than on-prem customers 10-20 years ago. AI capabilities are too potent and obviously beneficial.

What does embracing AI innovation look like? It means layering intelligent actions into all software. Historically, great software has helped users follow the right workflow. Now, great software must do the workflow by triggering agents to take actions. In other words, inference. The great news for everyone is that this opens the door to consumption-based pricing models that can scale exponentially.

For legacy players and startups alike, delivering amazing AI-powered, agentic features is the way to get on the vertical-growth train. Remarkably, the door is still open for legacy players. Intercom’s 3.6b exit to Salesforce is a great example.

Of course, new pricing models mean new margin structures. Just as SaaS had lower gross margins than legacy on-prem, expect consumption-priced inference to have lower gross margins. This is OK! We’ve already seen massive wins for inference-selling startups with negative gross margins, like Cursor. Legacy SaaS companies need to find religion on this. Dropping margins is never easy. Lock up the finance team if you have to. The priority is delivering AI-powered value for customers. Everything else is just details.