Last week I wrote about AI coming for white-collar jobs. That post was about employment. But there's a second question I haven't been able to stop thinking about since then - one that hits even closer to home for me.
If AI makes building software 10x cheaper and 10x faster, what happens to the companies that sell software?
I've been reading a lot of debate among developers, founders, and product people about this. And I've been trying to build SaaS products myself. So this isn't abstract for me. This is the industry I'm trying to enter. And the ground rules are changing while I'm still learning the game.
SaaS Isn't Dead. But the 80% Margins Are.
There's a hot take going around that SaaS is dead. AI can build anything, so why pay for software?
I don't buy it. When companies buy software, they're not just buying features. They're buying support. They're buying someone to call when things break at 2am. They're buying a community of other users who've already solved the weird edge case they're about to hit. SaaS as a category isn't going anywhere.
But the margins? Those are cooked.
SaaS companies have historically operated at 80-90% gross margins. Software is expensive to build and nearly free to distribute. That math made sense when you needed a 50-person engineering team to build a CRM. It stops making sense when a 2-person team with AI tools can ship something comparable in a few weeks.
Look at what's already happening. Linear is a small team going after Jira. They're shipping faster, their product is cleaner, and they charge less. That trend was already underway before AI coding tools got good. Now it's going to accelerate hard. Thousands of tiny teams outshipping incumbents and undercutting them on price. The SaaS category survives. The SaaS margins don't.
The Thing That's Actually Hard to Replicate
If AI can replicate any feature in days, then competing on features is a losing game. So what do you compete on?
Data.
Think of any SaaS product as two things: the features and the data those features operate on. If building features becomes basically free, the only defensible thing left is having the data. The integrations. The messy network of connections between your product and everything else the company uses.
This is why Salesforce keeps winning despite being, well, Salesforce. Nobody uses Salesforce because they love the UI. They use it because ten years of customer data lives there, a hundred integrations depend on it, and ripping it out would be a nightmare. The product is the data model. The features are almost incidental.
I think about this in the context of my own SaaS attempts. When I was validating ideas last month, I was thinking entirely about features. "What if I build a tool that does X?" That's the wrong question now. The right question is: "What data can I become the system of record for?" That's a completely different game.
The shift isn't from one type of software to another. It's from feature-centric competition to data-centric competition. The company that holds the data wins, regardless of who has the prettier UI.
Small Teams Are Going to Eat Everything
This is the part that excites me and terrifies me at the same time.
AI tools are enabling incredibly small teams to compete at a scale that used to require entire engineering departments. I've seen this firsthand at hackathons - teams building in 48 hours what would have taken months of traditional development. And that gap is widening every few months as the tools improve.
But there's a problem nobody likes talking about. If anyone can build software quickly, then everyone will. You get thousands of teams all attacking the same problems, all shipping fast, all competing on price. And when supply explodes, prices collapse.
It gets worse. The big tech companies - Meta, Microsoft, Google - they're watching. Once small teams prove that a certain category of software works, what stops the giants from just building it into their platform? Microsoft already did this with video calling, screen recording, and a dozen other features that used to be standalone products. Call it the "Amazon Basics" strategy. Let the small guys prove the market, then commoditize it.
So you're caught between two forces: thousands of small competitors driving prices down, and a handful of giants ready to absorb anything that gets traction. That's the reality of building software products in 2026.
The 80/20 Trap
I use AI coding tools every day. Claude scaffolds things fast. The landing page, the CRUD operations, the auth flow - all of that materializes quickly. If you're measuring by lines of code or feature count, AI gets you to 80% in a fraction of the time.
But the last 20% is a different universe.
The edge cases. The performance tuning. The integration with some legacy API that returns XML in three different formats depending on the day of the week. The handling of that one user who somehow got their account into a state that shouldn't be possible. That's where real engineering happens. And AI still stumbles there.
I keep thinking about Tesla's self-driving. It's been "almost there" for years. The gap between 95% complete and 100% complete turned out to be wider than the gap between 0% and 95%. Software has the same problem. Getting a demo working is easy. Getting a product that handles real-world messiness is hard. AI hasn't closed that gap yet.
Someone made an analogy I liked: 3D printing democratized manufacturing but it didn't democratize design. You still need someone who understands mechanical engineering to design a useful part. AI is doing the same thing for software. It's democratizing the coding, but not the product thinking. Not the architecture decisions. Not the "why are we building this and for whom."
Building the Right Thing
There's a distinction in engineering that I've been finding really useful lately. Verification is asking "did we build the thing right?" Validation is asking "did we build the right thing?"
AI is getting excellent at verification. Give it a spec, it builds to spec. Increasingly well, increasingly fast.
Validation is a different problem entirely. Figuring out what to build. Talking to users who can't articulate what they actually need. Understanding market dynamics. Having taste about what matters and what's noise. That still requires a human in the loop.
My four SaaS ideas didn't fail because I couldn't build them. They failed because I was solving problems nobody cared enough about. AI would have helped me build those failures faster and cheaper. It wouldn't have prevented them.
Product sense. Domain expertise. Customer empathy. Those are the differentiators now. Not "can you write a React component" - AI handles that. But "should this component exist at all, and what should it do?" That's the human part.
The Question Nobody Can Answer
There's a macro question hanging over all of this that makes every debate about AI and software feel incomplete.
If AI eliminates millions of white-collar jobs, who's buying the software? Who's paying for Netflix subscriptions and SaaS tools and the thousands of new products these small teams are supposedly going to build? Someone asked it bluntly in a discussion I was reading: "Will unemployed people surviving on growing their own vegetables be buying $1,500 smartphones?"
Nobody had a convincing answer. The optimists say new jobs will appear like they always have. The pessimists say this time is different. The honest people just say they don't know.
I don't know either. But I've noticed that whenever a question makes everyone uncomfortable and nobody wants to give a straight answer, it usually means the question matters more than most people are willing to admit.
What This Looks Like From Nepal
I'm watching all of this unfold from Ghorahi, Nepal. And the irony is thick.
Cheap software should theoretically be great for developers in low-cost countries. If building costs nearly nothing, geography matters even less. But if everyone can build, the cost advantage I have as a developer in Nepal also disappears. The same force that makes me more competitive also makes me more replaceable.
My bet is to position myself at the intersection of building and understanding what to build. Not just "I can write code" - AI does that. But "I can take an idea from zero to deployed product, talk to users, iterate, and ship something people actually want." That full cycle. The end-to-end thing. Hackathons taught me that. Four wins, each one more about product thinking than technical skill.
I'm also betting on open source. AI is making it dramatically easier to contribute to and maintain open source projects. I've been contributing to Aden, a YC-backed open-source AI agent framework. The barrier to meaningful contributions is lower than ever. Open source projects that become industry standards - that's where lasting value gets created, not in another CRUD app that anyone can now vibe code in an afternoon.
The cost of building software just dropped to near zero. But the cost of knowing what to build, and for whom, hasn't changed at all.
I keep arriving at the same conclusion I had after writing about AI and jobs. The market for software isn't shrinking. It's going to explode. There will be more software in the world than ever before. But the market for people who can only write code - without understanding the problem, the user, or the business - that market is already compressing.
I'd rather be the person who understands what to build than the person who's fast at building the wrong thing. That's the bet. That's what I'm working toward.
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