“Software is dead!”
“Programming is dead!”
“SaaS is dead!”
For over three decades, I’ve watched every “this will kill programming” doom cycle come and go.
And even now, AI won’t replace developers. But a developer with AI will replace a developer without AI.
What happens when anyone can build software, and where the value goes once building is free
Today, almost anyone can build an application, including people who had never written a line of code until yesterday. And they can do that by describing what they want to an AI model, in English or in one of many other world languages, and letting it assemble the rest. However, much of what gets built that way will be disposable, regenerated from scratch on a whim rather than corrected and kept. And, honestly, a fair amount of it will simply be junk.
But in any case, like it or not, we are living through the Industrial Revolution of Software.
We’re moving from a world where software is scarce to one where it’s abundant and (hopefully) cheap. Just like the original Industrial Revolution transformed manufacturing — making goods that were once handcrafted luxuries into mass-produced commodities — AI is doing the same to software.
Software is no longer a moat or barrier to entry, it has become a commodity
Right now, applications are deterministic. That’s what makes them reliable. You write a function, you know exactly what it’s going to do. Generative AI, on the other hand, is non-deterministic. Sure, it can be useful in many scenarios, but if you want consistent, reliable output, you’d need multiple agents checking and verifying each other.
And yet, we all know the limitations of LLMs.
They’re prone to hallucinations, inaccuracies, and unpredictability. Nobody (at least nobody sane) would risk letting an AI agent run wild on their production database.
In this scenario, I think the company that can figure out the new playbook for building a moat will win. Some ideas that come to my mind: deeptech integrations, interoperability, and proprietary algorithms.
These elements create a flywheel where each component strengthens the others. Imagine:
Data improves algorithms; better algorithms attracts more users, generating more data. Network effects and a strong brand attract more users, enhancing data and algorithms. Deeptech integrations and interoperability expand the platform’s capabilities and ecosystem, attracting even more users. And so on.
Here’s the distinction that matters: while software development as we know is dead, software engineering is alive and well
It’s not semantics. It’s the difference between a bricklayer and an architect. Both essential. But when a machine learns to lay bricks, you don’t fire the architect. You give them a much bigger project.
The point is not about just building software, but building software that answers a real need. That is a different and much harder task. Anyone who has watched a beautifully engineered product die in the market knows that engineering was almost never what failed.
So, no, SaaS isn’t dead. Neither is software, programming, nor anything else tech hype wants to declare the end of this week.
AI is undoubtedly shaking things up, but it’s an evolution, not a funeral. Software development will adapt, tools will improve, and businesses will find smarter ways to integrate AI into their workflows.
Disclaimer: All em-dashes in this article were human-generated!
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Stay data-hungry. Stay data-foolish.
Your Friendly Neighborhood Digital Consultant
