Yes. Me too. I felt that had to write something about AI and how it affects our professions (I consider myself a software developer, agile coach and product development person for the scope of this article).
Because reading about AI, and it’s application in digital product development, is very scary since some reports makes it feel like our entire profession is gone in 2-3 years (not true, but hecka-scary as big companies are laying off developers en masse). While others are more in camp “this is a fad and will soon all pass” (not true either, me thinks).
I do think that generative AI is a paradigm shift, but just as with others that I’ve experienced (Internet, Open Source, Agile, Cloud, Serverless and DevOps to mention a few) it is hard to know where it will take us, how it will change us before the dust has settled.
I stay positive though and think that the future for digital product development is great, important and fun. Also, as with many paradigm shifts human jobs have evolved rather than been replaced. We are moving up in the value chain, rather than becoming obsolete.
Let me clarify my thinking a bit.
(There will be some “might”, “potentially” and “could” in this post. I don’t claim to know anything about how it actually will be. I’m just trying to play out some scenarios and see where that future might take us.)
Digital product development has always been a complex, adaptive endeavor. We don’t start with certainty — we start with a hypothesis or idea. What customers will use, value, or even pay for is something we only discover by experimenting, putting something out there, learning from feedback, and iterating. This is why iterative and discovery-driven development has always been the heart of good product work. This has not changed, and as long as there are humans in the loop (users for example) - I doubt it will change going forward either.
Now, in the age of generative AI, one part of this equation has changed dramatically. AI has given us a tool that can, potentially, accelerate certain tasks, particularly coding. It’s not going to replace software developers, but it may replace “coders”: those whose role is limited to translating requirements into syntax.
In this great video Dave Farley makes the distinction between “coder” and “developers”. I think that this distinction sits at the heart of understanding how the developer profession might change in the future.
But this shift creates a deeper, and more interesting, question: if code could be generated faster than ever, where does the real bottleneck in product development move?
Code Has Always Been a Cost
As Dan North puts it: “Code is cost.” It costs money to write, but even more to maintain. AI might reduce the cost of producing the first version of a feature, but it doesn’t remove the need for good practices, architecture, testing, security considerations, and integration with existing systems.
Some futurists imagine a world where code could be “regenerated” from scratch whenever requirements change—skipping maintenance altogether. Maybe one day. But today, software isn’t just code; it’s entangled with data, APIs, compliance constraints, and business context. AI can help us write software faster, but it doesn’t erase complexity.
We do not version control binaries (anymore) - we version control high-level source code that compiles to the binary. I don’t see a future where we will not keep track of the AI generated source code and just keep track of the prompt. Natural language is just too vague to be able to express what we want the system to do.
The Real Bottleneck Has Shifted
If AI is making coding faster, then what’s slowing us down now?
It’s not typing (Dave Farley again: the keyboard is not the bottleneck), I can tell you. It never has been and probably never will. More to the point, in the age of AI, writing the code the first time is just a small part of the overall work that is done to the code. I remember sayings like Code is read, more than it is written that is true for any application that is maintained. One might wonder how that will change when tools can explain and maybe even rewrite code for us - but I still think that optimizing for writing code faster is a trap.
Also, the jury is still out of on the question about how far AI tools will take us in generating great code. I really cannot say anything about how this will play out. But I’m convinced that optimizing for generating a lot of code faster will not make our product better. That comes from something else. .
The bottleneck is understanding customers and solving the right problems. We’ve been saying this in agile and lean circles for years, but AI forces the point:
The value isn’t in the lines of code (output); it’s in the outcomes we deliver.
The scarce resource isn’t developers—it’s validated learning about what customers actually need.
Speed without direction only creates waste, faster. With current tools we have the ability to speed this up further.
This means the advantage goes to the teams who can rapidly turn ideas into experiments, learn from real users, and adapt.
What This Means for Developers
For developers, this is an opportunity—not a threat. Our job was never just to “turn English into Java.” Our job has always been to change the world by building the right thing.
Generative AI doesn’t diminish that mission. It amplifies it:
- We can prototype faster.
- We can explore more ideas in less time.
- We can focus our energy on architecture, design, quality, and security—the things that actually make software resilient and valuable.
Developers who combine technical skill with product thinking will become even more critical in this AI-driven world.
Expressing what we want
As I wrote, and as described better here natural language is just too vague to describe how we want an application to work. Even on an outcome oriented level.
A bit more formality is needed to express what we want, something like an Domain Specific Language (DSL) for testing like Gherkin, might be a way to ensure that we get closer to what we want. But also that we get some way to verify what was built actually does what was expected.
Why Agile Matters More Than Ever
From this it follows naturally that AI doesn’t make agile practices less relevant — it makes them more essential.
If code becomes cheaper and faster to produce, the cost of building the wrong thing stays exactly the same: you still waste time, energy, and opportunity. Shortening feedback loops, iterating quickly, and deeply engaging with customers will matter more, not less.
In other words, if AI speeds up coding, then discovery—not delivery—becomes the primary constraint.
Quality Is Non-Negotiable
Finally, even in an AI-accelerated world, quality matters. AI can generate code, but it cannot guarantee correctness, security, or alignment with your business goals. Good engineering practices—testing, refactoring, security-first design—are not optional. They become the foundation that makes AI-assisted development safe and sustainable.
The Bottom Line
AI is not the end of software development. It’s the end of a certain kind of software development—the kind that treats developers like human compilers.
For the rest of us, it’s an opportunity to step up in the value chain; move faster, learn faster, and focus where it matters - understanding customers and building the right thing, not just building things right.