Building Software That Lasts: Lessons From a CTO

Entrepreneurs often ask: “How can I build fast, but not create a product that falls apart later?” It’s your classic founder dilemma. These days, knicking up an MVP (Minimum Viable Product) can take weeks – but turning it into something robust, scalable, and investor-ready is where many startups stall.

As a time-served full-stack developer and a CTO, I’ve seen the traps first-hand. The difference often comes down to one thing: whether the CTO still cares about code quality. And in today’s landscape of AI buzz, that also means knowing how to use AI responsibly – not as a shortcut, but as a force multiplier.

The Prototype Trap

On one project I joined years ago, the MVP looked slick in a demo. But underneath, it was a fragile mess of shortcuts. Adding a “small” feature often broke three others. Eventually, we spent more time firefighting than building.

That taught me a lesson I still carry today: speed without structure is a liability. The foundations; a clean architecture, clear naming, tests, documentation are things which don’t slow you down. They make sure you can keep moving fast later.

Keep Iteration Cycles Tight

Equally important is the ability to iterate. Eric Ries, author of The Lean Startup, argues that the most successful companies aren’t the ones with the perfect plan, but the ones that learn the fastest. Iteration is how that learning happens. By releasing smaller changes more often, you validate your ideas with real users instead of relying on (perhaps incorrect) assumptions.

Strong foundations make those fast cycles possible. With a clean architecture, tests, and documentation in place, you can ship confidently (reliably, quickly), gather feedback, and adapt without fear of losing momentum. The quicker you can run that loop – build, measure, learn – the sharper your product becomes.

I’ve seen this first-hand. Before I rebuilt Kitchen Geekery, I experimented with small changes like adjusting recipe layouts and testing new cocktail search filters. Even those lightweight iterations gave us valuable insight into what readers actually wanted – insights that shaped the bigger rebuild later on.

Kitchen Geekery: A Real-World Case

When I rebuilt Kitchen Geekery I chose React and Next.js. These gave me flexibility, performance, and proven developer tooling with a framework I have used for years. But the temptation with frameworks is still to rely too heavily on “magic” and let standards or knowledge slip.

In the early sprints, AI was invaluable. ChatGPT helped me generate boilerplate React components, pre-filled page templates, and suggested optimisations for Next.js routing. That meant I could focus on the creative side: functionality, recipes, cocktail layouts, and SEO.

But I also ran into pitfalls. Once, an AI-generated React hook looked efficient but introduced a subtle memory leak. It worked fine on my machine, but under production load it started slowing pages to a crawl. Because I had some monitoring and testing baked in, I caught it quickly. But, without that discipline, the bug could have poisoned the whole process.

That experience cemented my approach: AI accelerates, but reviewing and testing protect you.

Cutting Corners Always Costs More

On another feature for Kitchen Geekery, I debated whether to add proper API versioning or “just ship it.” I pushed for versioning, even though it meant a day’s delay. Weeks later, when I introduced new data endpoints, that choice saved us weeks of rework.

It’s a reminder I share with every founder: shortcuts feel cheaper now, but they charge interest later. That’s technical debt in action.

How I Use AI in My Coding Flow

AI isn’t here to replace engineers; it’s here to amplify them. Day to day, I use it to:

1. Draft repetitive React boilerplate (form handlers, modals, input validation).
2. Scaffold tests in Jest and Playwright for critical flows.
3. Generate documentation drafts so onboarding new developers is faster.
4. Cross-check logic for subtle bugs or edge cases.

But I treat AI like a keen junior dev: fast, helpful, sometimes brilliant — but needing oversight. One time, an AI-generated Next.js API route skipped authentication logic. Left unchecked, it would have opened a security hole. That’s why the human judgment stays central.

What Entrepreneurs Should Look For in a CTO

If you’re a founder looking for a CTO, don’t just ask “What stack do you use?” Ask:

1. Do they still enjoy coding, and take pride in it?
2. Can they explain how they use AI to build faster while protecting quality?
3. Have they carried projects beyond MVP into reliable, scalable systems?
4. Do they understand when to slow down and invest in structure that will pay off later?

The CTO who codes with care, and knows how to blend AI speed with human oversight, gives your startup the best of both worlds: momentum now, and resilience later.

Get In Touch

If anything I have said poses a query, or you'd like to work with me then please get in touch.

Quick fact: The phrase “technical debt” was coined by Ward Cunningham in 1992. It’s never been more relevant than today, when AI makes it easy to “borrow” speed. The trick is knowing when to pay that debt back or when to avoid it altogether!