The “SaaS-pocalypse” narrative is directionally right — but misunderstood.
AI isn’t killing SaaS. It’s killing replaceable SaaS.
And most of that lives in a category that’s been quietly exploding over the last few years: indie-built utility software.
The Collapse of Utility SaaS
For years, small SaaS products thrived by solving narrow, acute problems inside massive workflows.
Need to extract tables from a PDF into Excel? There was a tool for that.
Need to reformat data, clean CSVs, convert files, or automate one-off workflows? There were thousands of tools for that.
These products worked because the alternative — building your own solution — was too expensive, too slow, or outright impossible for most users.
That constraint is now gone.
With modern coding agents, users don’t need to search for software to solve these problems. They can generate disposable tools on demand. The friction of building has collapsed to near zero for non-repetitive use cases.
If a product exists solely to solve a narrow, one-off task, it no longer needs to exist as a business.
What AI Is Actually Destroying
The common belief is that AI will commoditize all software.
The reality is more precise: AI commoditizes features, not products.
A product that is simply a wrapper around a single capability — no matter how useful — can now be replicated instantly. There is no defensibility in a feature that can be generated on demand.
This is why so many “useful” products are quietly becoming obsolete.
Take employee recognition tools like HeyTaco. On the surface, they solve a real problem. But as a standalone product with limited surface area and no meaningful expansion path, they are fragile by design.
If a product cannot evolve beyond its initial feature set — or integrate into a broader workflow — it is already dead. AI just accelerates the timeline.
The SaaS Companies That Survive
Not all SaaS companies are exposed equally.
The ones that survive — and win — share a common trait: they own workflows, not features.
There are three durable categories:
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Deep workflow integration — Products embedded in daily operations are difficult to replace, even if individual features are replicable. Tools like Slack or Salesforce don’t win because of any single capability. They win because they sit at the center of how work gets done. Replacing them requires ripping out entire systems, retraining teams, and reconfiguring processes. That switching cost is the moat.
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Continuous systems of action — Software that requires ongoing maintenance, coordination, and updates retains value. These are not static tools. They are living systems that orchestrate work over time. AI can enhance them, but it doesn’t replace them.
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Network and distribution advantages — Incumbents control distribution. That matters more than ever. They can ship AI-powered features faster than startups and immediately deploy them to millions of users. The technical gap is shrinking — but distribution remains asymmetric.
More SaaS, More Failure
Paradoxically, AI will lead to more SaaS companies being created — and more being destroyed.
The barrier to building has collapsed. Anyone can ship.
But distribution, conviction, and taste have not.
This creates a new dynamic: an explosion of products with no real reason to exist.
Many founders are now building because they can, not because they’ve understood the problem. The result is a wave of disposable software with no defensibility and no staying power.
In this environment, failure is not just common — it’s accelerated.
Why Startups Still Win
Incumbents have distribution, but they also have constraints.
They are slower to cannibalize existing revenue streams. They hesitate to collapse pricing, redesign products, or eliminate features that currently generate income.
Startups don’t have that problem.
They can move aggressively, redefine categories, and rebuild products around new primitives. They fill the gaps incumbents are structurally unable to address.
That asymmetry is why startups continue to win — even in a world where building is easier than ever.
How This Changes How I Build
Code quality is no longer the bottleneck. Speed is.
The winning approach is to build quickly, test aggressively, and iterate based on real usage. Perfection is less valuable than momentum.
Even perfectly designed software breaks in the real world. Not because the logic is wrong — but because the environment is messy. Users behave unpredictably. Data is inconsistent. Edge cases are infinite.
In fact, edge cases are the product.
You can’t anticipate this upfront. Trying to engineer a “complete” system before launch is a losing strategy. The only way to build resilient products is to ship, observe failures, and continuously adapt.
This is closer to how SpaceX operates than traditional SaaS. You don’t simulate forever — you launch, observe, and refine.
In a world where software can be generated instantly, the only real advantage is learning faster than everyone else.
The Real Meaning of the SaaS-pocalypse
The SaaS-pocalypse is not an extinction event.
It’s a filter.
Products that exist as isolated utilities will disappear. Products that own workflows, evolve continuously, and control distribution are what survive.
AI doesn’t kill SaaS.
It forces it to grow up.
March 24, 2026