Three shifts are happening
I've been thinking about what it means to build software in 2026.
AI has made building software dramatically easier. As a developer, I can take an idea and get a working product in hours instead of days or weeks. That's great for me — but it's also a problem. If software becomes easier to build, it becomes easier to reproduce.
So I've been looking at what this means for SaaS, and what kind of businesses still make sense to build. I don't have the answers yet. But I've noticed three shifts.
1. AI makes commodity software easier to reproduce
This one's obvious. Things that used to require a team of developers can now be built by one person with AI: dashboards, internal tools, CRUD apps, simple SaaS products, integrations, even AI features themselves.
That doesn't mean SaaS is dead. It means having software isn't much of a moat anymore. If someone can recreate 80% of your product with Claude or Cursor, you need value beyond the code — data, distribution, integrations, trust, a hard workflow, reliability, something that's genuinely difficult to copy.
That's making me less interested in building software just because I can.
2. Pricing is shifting from per-seat to usage and outcomes
Traditional SaaS is simple: $20/month per user, add users, pay more. AI doesn't fit that model as neatly, because an AI system can do work without another employee needing another seat. It can process 10,000 documents, answer 50,000 customer questions, audit thousands of transactions, complete thousands of tasks — all without headcount changing.
So the question shifts from "how many people use the software?" to "how much work did the software do?" That's part of why usage-based and outcome-based pricing is spreading: per task, per document, per conversation, per transaction, sometimes per result.
I don't think subscriptions or SaaS are going away. But per-seat pricing makes less sense when the value created isn't tied to how many humans are using the product.
3. People are paying for results, not access
This is the part I find most interesting. There's a real difference between software that helps someone solve a problem and something that solves the problem for them — between "here's software that helps you find billing errors" and "give us your invoices and we'll find the errors and recover the money." The first is software. The second is an outcome.
AI makes the second model more viable, because if it can handle most of the underlying work, you can deliver an outcome that used to require a lot of human labor — automate 90% of it, have a person handle the exceptions, and the customer doesn't care how it got done. They just want the result.
That's changed the question I ask when I look at businesses. Instead of "what SaaS product can I build," I'm asking: what valuable outcome is still difficult, expensive, or unreliable today that AI can deliver dramatically cheaper?
So what am I looking for
Not another AI wrapper. Not another dashboard. Not something whose main selling point is an LLM behind a nice UI.
I'm looking for problems where money is already being spent, where people are already doing the work manually, where the outcome is valuable, where AI can meaningfully cut the cost of delivering it, and where the existing solutions aren't good enough yet.
My current framework: pain → workflow → AI capability → existing solutions → remaining gap → economics → distribution → buildability → defensibility.
That last part matters most. I'm not trying to find something AI can build — AI can build almost anything now. I'm trying to find something someone will actually pay for.
I haven't made money from this yet. I'm still looking, and I could be wrong about all of it. But that's what I'm trying to figure out.