AI Can Write Code—But Can It Write Code That Scales?

Forbes

a day ago

Christopher Stauffer is CEO of STAUFFER, a digital agency that bridges strategy, engineering and design to solve complex business problems.
It's never been easier to write code. Tools like ChatGPT and GitHub Copilot can generate boilerplate code, suggest test scaffolding and really help get your early-stage ideas going. These tools are fast, smart and constantly improving. But I keep coming back to something I've said often to clients and colleagues: Writing code isn't hard anymore. Writing code that scales? That's still the hard part.
I don't say that as a warning. I say it as a reminder because we're entering a time when the differentiator in software isn't the one who can build something fast. It's who can build something that still works when your business grows, your customer behavior shifts or your platform expands.
We've Solved For Speed—Now What?
AI has dramatically reduced the time it takes to get from idea to prototype. That's a real gain. But with that gain comes a new risk: the illusion of completeness. Something that looks finished might just be the beginning. Something that functions in isolation might collapse the moment it's connected to real systems and real users.
Speed is great—if you know where you're going. But if you don't, you just get lost faster.
That's why I tell people to use AI the same way they use any other tool: to extend your reach, not to replace your judgment. Use it for things like boilerplate code, rapid iteration, documentation support and even experimentation. But always think about the next step: What happens when this scales? What assumptions are we baking in right now that we'll regret later?
What Scaling Actually Means
When we talk about scaling, people often imagine it's about handling more users or more data—and it is, partially. But scalability also means flexibility. It means being able to add new features without rewriting your foundation. It means avoiding the kind of brittle design that locks you into early decisions and makes future updates painful or expensive.
AI doesn't think about that for you. It doesn't know your roadmap, budget, integration points or compliance requirements. That's still your job. And if you don't ask the right questions up front, no amount of AI speed will save you from backtracking later.
The Leadership Blind Spot
I think one of the biggest shifts AI brings is this: It makes it easier for anyone—at any skill level—to generate something that looks like a finished product. That's exciting. But it's also risky, especially for non-technical stakeholders.
If you're a CMO or COO evaluating a new internal tool, it might look clean and responsive and 'done.' But how is it built? What happens if the user base triples? What breaks when you expand it to new markets or channels?
You don't need to read code to ask those questions. You just need to know they're important. And you need a team that values long-term thinking as much as short-term wins.
What Still Matters
AI is powerful, but it doesn't have instincts. It doesn't know what tradeoffs are worth making. It can't anticipate every ramification of a quick fix or a clever workaround. That's where experienced engineers and thoughtful leaders come in.
I've worked on projects where everything worked perfectly—until it didn't. Not because the code was bad, but because the system wasn't built to adapt. And I've seen the opposite: teams that slowed down to think ahead, make smart architectural choices and build something that could grow without constant rework.
Those are the teams that win. Not because they used AI or didn't, but because they knew what they were building toward.
The role of a good technologist is changing. It's not about memorizing syntax or writing thousands of lines of code by hand. AI can help with that. The real role—the one that can't be automated—is knowing what to build, why and how it needs to evolve over time.
So, yes, AI can write code. And that's a good thing. But if you're thinking about the future of your business, platform or product—don't just ask if it works. Ask if it lasts. That's where the real value still lives.
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