Code is Easier. Software is Harder.
There’s a strange paradox unfolding in tech right now.
AI is supposed to make things faster, easier, and more scalable. In many ways, it has. Tools like GitHub Copilot, Cursor, Lovable, Windsurf, and dozens more are giving developers the power to ship functional code faster than ever before. You can get from initial idea to prototype in as little as an afternoon.
But beneath that surface speed lies an ever-growing and counterintuitive problem—building software is getting harder, not easier.
How can that be? Let me explain.
As mentioned, we are arguably living in a moment when producing Code is easier than ever. This is the golden age of code generation. Whether it’s AI-assisted development or full-code synthesis from natural language prompts, the cost of getting something working has collapsed.
Want to spin up a web app? An API? A Slack bot? You can do that before lunch.
This is great for creativity. It’s great for experimentation. And it’s fundamentally changing who gets to build, and how quickly they can iterate.
But here’s the catch: writing code is not the same as building software.
From Disposable Code to Durable Software
Software is still a long-term commitment, it is more than lines of code—it’s a promise.
When you put a product in front of users, especially paying customers, you’re not just showing off functionality. You’re committing to maintain it. To fix bugs. To improve performance. To support evolving use cases. You’re promising security, resilience, compliance, and protection against vulnerabilities. To safeguard user data and ensure that your systems can withstand evolving threats.
This is the part that doesn’t get easier with AI, at least not yet. AI can help you write version one faster, but the cost of maintaining the system, evolving it, managing vulnerabilities, and ensuring compliance... that cost still compounds over time.
When we act like AI erases the need for architectural discipline, testing strategy, and securing the design, we set ourselves up to fail in the long run.
Because the barrier to producing code is so low, we’re seeing an explosion of what we can call disposable -Kleenex- Software — tools, apps, scripts, and features built quickly with no plan for longevity, just there to facilitate an expedited PoC.
This can be useful. Disposable software is great for:
- Testing an idea
- Automating a one-time task
- Learning a new framework
- Rapid prototyping
You don’t scale disposable software. It doesn't secure your customers' data and won't pass compliance audits or survive sustained cyberattacks. You don’t retain customers on top of duct tape. And you definitely don’t build trust when your product breaks after the first few hundred users.
The Security Trap of AI-Written Code
We’re in a transitional period.
AI can write decent code. Sometimes, even elegant code. But it doesn’t understand your architecture. It doesn't grasp your security model, your operational requirements, or your constraints.
That means human engineers are left to clean up after the machine, patching conflicting patterns, reconciling inconsistent security implementations, and dealing with the risks arising after the fragmentation of codebases generated by a thousand different prompts.
This limbo period , between human plus AI-built systems and fully AI-managed software, is a maintenance nightmare in the making. Until AI can own systems end-to-end, managing the entire security lifecycle, from threat modeling to secure deployment to ongoing monitoring and updates, someone has to clean up the mess.
And that someone is you and your team.
So what do we do? We stop pretending that code is the product. Is not.
We recognize that choosing what to build and how to build it well is harder than ever in an age where the cost of making it is nearly zero, but the cost of making it secure and maintainable never went away.
We invest in the hard parts:
- Clear secure architecture
- Long-term thinking, starting with secure by design
- Guardrails around AI code contributions
- Teams that understand the difference between MVP and production-grade
In short: we treat software like it still matters.
