Slow is smooth and smooth is fast: What software teams can learn from Navy SEALs

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There is a saying that has become known primarily through the Navy SEALs:

“Slow is smooth and smooth is fast.”

the next big thing – Golo Roden

Golo Roden is the founder and CTO of the native web GmbH. He works on the design and development of web and cloud applications and APIs, with a focus on event-driven and service-based distributed architectures. His guiding principle is that software development is not an end in itself, but must always follow an underlying technical expertise.

This means that hasty actions lead to errors that ultimately cost more time than they save. On the other hand, those who proceed calmly and in a controlled manner work more precisely, make fewer mistakes, and paradoxically reach their goal sooner. In high-pressure situations where seconds decide between success and failure, this may sound counterintuitive. But it is precisely there that this principle has proven its worth.

In software development, I encounter a similar pattern. The industry is under constant time pressure, deadlines are tight, requirements are constantly changing, and the reflex to write code as quickly as possible is deeply ingrained. Progress is often measured by how quickly new code is produced. But it is precisely this reflex that often leads to projects taking longer than necessary in the end. The parallel to the Navy SEALs is striking, and I am convinced that their principle is one of the most valuable pieces of advice that software teams can heed.

When Speed Becomes a Trap

The temptation is obvious: a new feature is due, the requirements seem clear, so you open the editor and start typing. After all, software is measured in code, not in whiteboard sketches. The sooner code is produced, the sooner you're done. At least, that's the common assumption.

Reality paints a different picture. Code created without thorough prior consideration is based on implicit assumptions. Everyone on the team has their idea of how the solution should work, but these ideas are rarely aligned. The assumptions often turn out to be wrong only late in the process, for example, when it becomes clear that the chosen interface is unsuitable for the actual use cases, or that a special case calls the entire architecture into question. What started harmlessly becomes a structural problem.

What follows are correction loops. Not one, but several. In addition, there are discussions that would have been better held beforehand, refactorings that are essentially rewrites, and a growing amount of technical debt. The code that was produced so quickly must be explained, defended, and revised. The supposedly fast start turns into tedious, costly rework.

The insidious thing about this is that this effect is rarely visible. No one measures how much time a team spends on rework that could have been avoided with a better approach. The hours disappear into bug fixes, into “small adjustments,” and into meetings where clarification is sought about what should have been clarified beforehand. The initial speed was an illusion.

An Experiment for Two

Years ago, a colleague and I established a development process that seemed downright wasteful from the outside. Whenever we were supposed to develop a new module or component, we didn't open the editor first. Instead, we went to the whiteboard.

There, we tackled the problem from the other side: Not “How do we build this?” but “How should it feel when someone uses this code?” This question sounds simple, but it changes the entire perspective. The principle is known as “Working backwards”, as practiced by AWS, among others. You start with the desired outcome and work your way back to the implementation.

Specifically, this meant: we sketched code examples on the whiteboard. Not the internal structure, but the public interface. How would a developer call this module? Which parameters would be intuitive? Which return values would be expected? Which error cases would need to be handled, and what should that look like?

This approach forced us to engage deeply with the domain and the requirements before a single line of production code was written. It forced us to ask questions that would have been overlooked if we had started programming immediately. We often found that our initial ideas for the interface were not viable. Then we wiped the whiteboard and started over, as many times as necessary. This still only cost hours, not days or even a week.

At the end of this phase, we had a shared, explicit understanding of what we actually wanted to build. Not vaguely, not implicitly, but concretely and tangibly. We could explain to anyone on the team why the interface should look exactly like this and not otherwise. This clarity was not a byproduct; it was the actual goal.

Throwing Away as an Investment

After the whiteboard phase, we took a step that seems even more unusual at first glance: we wrote a prototype with the clear intention of throwing it away afterward. No clean code, no tests, no documentation. Just a quick, functional walkthrough to test our hypotheses.

This prototype served solely for learning. You can clarify many things at the whiteboard, but certain things only become apparent when you actually write code. Theory and practice in software development often diverge more than one wants to admit. How does the interface behave when you actually use it? Where does it feel cumbersome? What edge cases arise that you haven't thought of? Where have you overestimated or underestimated the complexity? What dependencies emerge that weren't visible on the whiteboard?

The crucial point was that this prototype carried no obligation. Because it was decided from the outset that it would be thrown away, we could experiment freely. There was no pressure to maintain the chosen structure just because code was already there. We could leave dead ends without guilt and try alternatives. We could make mistakes without those mistakes settling into the codebase.

Throwing it away was surprisingly easy. Not because we didn't care about the code, but because the actual result of this phase was not the code itself. The result was insight: a deep, practical understanding of what the solution should look like and what pitfalls to avoid. This understanding could not be gained by thinking alone. It required the experience of doing, of failing in a safe environment.

The Final Version with Tailwinds

Only on the third attempt did we write the actual production code. And here the reward for the preparatory work became apparent: Writing was remarkably fast. Not because we were in a hurry, but because we knew what we were doing. The uncertainty that normally accompanies any development had largely disappeared. Confidence had taken its place.

The architecture was in place, the interfaces had been thought through, the typical stumbling blocks were known. We no longer had to experiment or make fundamental decisions, because we had already done that. Instead, we could concentrate on writing clean, well-structured code that met quality standards from the outset.

It was precisely in this phase that tests and documentation were also added. Both are considerably easier when you have understood the solution and are not fumbling in the dark. Writing tests for a well-thought-out interface is not a burden, but a confirmation. You know which cases are relevant and can cover them specifically. And writing documentation for a module whose design decisions you have consciously made is not a mandatory exercise, but a natural addition.

The entire process took place in pair programming. Two people on the same problem, from the whiteboard discussion through the prototype to the finished code. This also seems expensive at first glance: two developers for one task, isn't that double the effort? Practice told a different story. Four eyes see more than two, and the constant dialogue prevents anyone from getting stuck in a dead end without noticing it. What we gave up in apparent efficiency, we gained back in quality and speed.

“How can you afford this?”

Whenever I described this process, the most common reaction was a mixture of interest and disbelief. The questions were: “How can you afford this?” and “How do you get your clients to go along with it?”

The answer was simpler than most expected: We were faster and cheaper than others. Not despite, but *because* of this approach. This sounds like a convenient claim, but the numbers supported it.

The reason lies in an observation that has been repeatedly confirmed over the years: Our code worked with an above-average success rate on the first real attempt. It was largely error-free, the interfaces matched the actual requirements, and the architecture held up even when extensions were added later. What looked like extra effort on paper was actually a shortcut.

What was eliminated on the other side was significant: no endless correction loops, no late architectural decisions under pressure, no weeks where the team was essentially busy fixing early mistakes. No tedious debugging of code written three weeks ago and half-forgotten by now. No heated discussions about whether the existing approach could still be salvaged or whether it would be better to start over. For our clients, this meant: more reliable schedules, fewer surprises, and ultimately lower overall costs.

The process looked slower because the first visible line of code was produced later. But the first visible line of code is not the relevant metric. What is relevant is when functional, reliable software is delivered. And we regularly reached that point earlier than teams that hammered away at the keyboard from day one.

Code Written Quickly Is Not Better Code

Today, many years later, the environment has fundamentally changed. AI-powered tools generate code at a speed that was unthinkable until recently. A well-formulated prompt delivers in seconds what used to take hours. The speed of code generation has multiplied, and the tools are becoming more powerful with each generation.

However, speed in code generation is not the same as speed in problem-solving. An AI can produce code impressively quickly, but it cannot know if that code solves the right problem. It can implement an interface, but not judge whether that interface is sensible for the actual use cases. It can generate tests, but not decide which cases are truly critical and which are just noise.

What AI tools fundamentally do is amplify. They amplify what you put into them. If you know exactly what the solution should achieve, what the interfaces should look like, and which edge cases to consider, you will get impressively good code from an AI in the shortest possible time. The machine becomes an accelerator for a clear idea. Those who lack this clarity will get the wrong thing faster. And wrong code, generated in seconds instead of hours, remains wrong code. It still needs to be revised, corrected, and in the worst case, thrown away. The AI has then not accelerated anything; it has only created the illusion of progress.

And this is precisely where the circle closes with the three-step process. The phase at the whiteboard, thinking through the user's perspective, consciously experimenting with a throwaway prototype: all of this provides exactly the clarity needed to use AI tools effectively. You don't feed the AI vague ideas, but precise requirements. The AI then takes over the part that can actually be accelerated: writing code whose direction is already set.

The temptation is great to skip this step. If code is so cheap to generate, why not just try it out and see what happens? The answer is the same as twenty years ago: Because generating code was never the bottleneck. The bottleneck was, and still is, understanding. And understanding cannot be accelerated by typing faster, whether with or without AI.

Starting Slowly to Arrive Quickly

“Slow is smooth and smooth is fast.” This principle of the Navy SEALs has lost none of its validity, even in an era when machines write code faster than any human.

Consciously slowing down, taking the time to truly penetrate a problem before solving it, feels like a luxury you can't afford. Experience, however, shows the opposite: it is an investment that reliably pays off. In fewer errors, in less rework, in shorter overall duration, and in code that not only works but endures. In teams that argue less and deliver more.

Whether you write this code yourself or have it written by an AI is secondary. What is crucial is that you know what you want before you start. Think first, then experiment, then build. In that order. Nothing has changed in all these years.

(rme)