Why AI can speed up code but not thinking

In a landscape flooded with AI hype – from low-code shortcuts to intelligent copilots – there’s growing pressure on engineering teams to move faster than ever. But what if we’re accelerating the wrong thing?

I believe that while AI-augmented engineering has its place, it cannot replace the hard, often uncomfortable discipline of thinking. If you’re not careful, AI becomes a very efficient way of producing the wrong thing and in engineering, doing the wrong thing faster is not a win.

It’s about what you do with it

Tools like GitHub Copilot or ChatGPT are “force multipliers” – but they don’t fundamentally change the nature of good software design. You can absolutely generate a lot of code quickly, but the question is: do you know what you’re solving? Do you understand the trade-offs? That’s where engineering still lives.

It’s important to note that speed alone is not velocity, because without direction velocity is chaos. If your requirements are vague or your domain isn’t well defined, AI can’t save you from that.

Real work happens before tools kick in

The root of this philosophy lies in what we call “staying in the problem space longer.” It’s a concept echoed across our company’s engineering culture: resist the instinct to jump straight into solutions. Pause. Define. Think.

This pause matters because the discipline to stay in the problem space results in a much better refinement of the actual problem. In turn, the solution options become clearer, more robust and better aligned with the business need.

It is evident that cognitive load is real and the pressure to move fast is constant. Ironically, slowing down is what makes better decisions possible – and avoids the kind of over-engineering that plagues rushed development.

_{Sasha Slankamenac, principal engineer at Dariel. Photo supplied.}

Trade-off mindset defines engineering

Software engineering is about constraints and choices and the best engineers are hired to think and solve the problem, not just code.

That’s why we focus on architectural clarity before tooling up. Good engineering is the result of being deliberate about trade-offs – not chasing efficiency for its own sake.

AI-Assisted ≠ AI-Led

While potentially controversial, I believe that AI doesn’t understand anything. It doesn’t know your context, your constraints or what matters to your business. You do.

This is why we see AI as an assistant, not an architect. We’re not interested in AI replacing thinking. We’re interested in AI supporting better engineering decisions. It can scaffold out boilerplate, highlight potential bugs, or speed up certain grunt work – but the decisions still rest with us.

Our view is that the value of AI is unlocked when it’s paired with clear business alignment, deep domain understanding, and well-scoped engineering work. If you’ve done the hard thinking upfront, AI can absolutely help accelerate delivery. But it’s not a shortcut for that thinking.

Why we’ll never catch up

The perpetual chase has resulted in the AI and software generation always outpacing our ability to fully grasp them. Since the 60s, we’ve been in a cycle where tech speeds up and we scramble to catch up. That’s not new.

Today’s tools are just the latest layer in that cycle. But that’s why grounding in fundamentals matters. By treating software purely as a cost line -something to automate away – is short-sighted. You can’t eliminate thinking with tooling. You can only hide the complexity – and that doesn’t make it go away.

Culture is the constraint – and opportunity

So, what’s the biggest bottleneck to delivering AI-augmented software at scale? It’s not tools. It’s mindset.

Engineering culture needs to catch up with the tools and I think that it is imperative that we train engineers to question more, not code faster. That means pushing back on ambiguous requirements, challenging solution bias and creating room for better conversations.

This ethos is baked into everything from mentorship to project delivery. We’re not just building software; we’re teaching teams how to slow down enough to get the thinking right – because that’s what actually speeds things up later.

The bottom line

AI can’t replace engineering. It can only augment the people who already do it well. And the ones who do it well? They’re not rushing to ship code – they’re slowing down to get it right.

If the domain is clear, if the thinking is sound, if the architecture has been well-considered – then yes, AI can help you go faster. But that’s a big if. And it starts with staying in the problem space just a little bit longer than feels comfortable.