Prototypes make the invisible visible
The product builder's fastest path to signal, clarity, and momentum
When I’m talking to a user about a problem, my product brain immediately starts swimming in ideas for solutions.
But I’ve learned to pull back and stay with the problem a little longer. Because the deeper I look, the more it resists definition. The edges blur, and what seemed obvious starts to shift. You notice more contradictions, more interdependencies, more messiness in general.
If we want to build something that actually works — something feasible, timely, and grounded — we need more than vague stories or hand-puppet explanations. We need clarity. And that doesn’t come from talk alone.
As we unfold the factors of a customer problem, we need to ground the problem in reality. Something that moves the conversation beyond abstractions.
This is why we prototype. Prototypes create critical information required to know what to make.
prototype (n) – a preliminary model of something, especially a machine, from which other forms are developed or copied
A prototype isn’t just a rough sketch of a future product. It’s a tool for thinking: a means to generate feedback. It gives us something real to insert into the problem space, to see how it behaves and how users respond. It’s not slides or a screenshot or passive examples. It’s tangible. Something the user can manipulate, react to.
A prototype gives the user something they can touch. They can put in between themselves and the problem to see what happens. As builders we can watch this process, and look for the glimmers, wows, and moments where the product snaps into place like a jigsaw puzzle piece. Or those where it falls flat or fails.
We’re not guessing anymore. We’re observing. Learning.
The prototype’s tangibility helps us find the problem’s edges, quickly and cheaply. It reduces the noise and amplifies the signal, so we can see what sticks and what doesn’t. And that signal is everything.
For builders, the scarcest resource is feedback. Not guesses, not opinions, but the real, ground-truth understanding of whether and how your solution works in the world.
The closer the prototype is to a real solution, the richer that feedback. You’re no longer listening to secondhand stories. You’re watching the user in action. You’re seeing how they move, where they pause, what they misunderstand, and where they light up.
Even the scrappiest, stripped-down prototypes yield a wealth of information. You learn from the questions users ask (or don’t ask), the expressions on their faces, the way they return (or don’t return) to try again. All these details fill in blanks on the canvas.
Some information adds texture on the problem side: new angles, unspoken frictions. Other cues help you on the solution. Those little moments of waiting on load times, usability gaps, the unexpected delights, the chances to increase user clockspeed. Each data point gives you something to refine.
The prototype exists explicitly to generate information; it doesn’t need to be pretty or sustainable or maintainable. It doesn’t need to bear the burden of going into full—tilt production. Since its purpose is feedback-generation, we can take more risk to answer questions for ourselves, even if it means throwing out the prototype entirely in favor of a v2.
Think about SpaceX. The company is famous not only for its fabulous launch innovation, but also its spectacular, incandescent failures. Rockets tipping over in fireballs. Boosters “rapidly disassembling” on descent. But they don’t see them as failures at all.
While these rockets aren’t “prototypes” per se, they serve the same function. They’re real products pressed into service to see how they react in the problem space (descending through the stratosphere at terminal velocity). If they fail, what fails? Why? The telemetry the craft returns is invaluable for continued improvement.
The prototype closes the feedback loop. The faster you prototype, the more feedback you generate, the more learnings you absorb.
prototype → feedback → insight → iteration
One of the exciting things with the rise of vibe coding is the speed with which we can prototype ideas. We can hear the problem, have the idea, generate the prototype, and sample for feedback inside a single day. Unprecedented speed of information.
Prototypes generate momentum. If it even sort of works, the user’s ready to see where it goes next — you’ve got inertia on your side. If you can rapidly get them the next iteration, and show that progress with speed, you amplify the momentum.
Prototyping also unlocks creativity. Tactile prototypes pull ideas out of the user and the builder. It focuses attention on the essential. And just as importantly, it helps us invalidate paths that won’t work. A wrong turn avoided is as valuable as a right one found.
And here’s the thing: prototyping is cheaper than ever. The “product conductor” doesn’t have to marshal a whole team of designers and engineers to whip up concepts in a few hours they can put out in the wild for feedback. Even though AIs are closing the gap on us in writing English and code, we’ve still got the advantage when it comes to interpreting the subtleties of feedback, and in synthesizing that feedback into action that lines up with our vision. The AI allows us to focus on the uniquely human part of the process.
Prototyping remains one of the most powerful tools in product.
Faster cycles. Sharper insights. Better fit. More truth.
It’s not just how we build. It’s how we learn.
If this idea resonates with you, check out an earlier post in a similar vein: on the concept of the “concierge MVP”:
