Why Speed, Imagination, and AI are rewriting how we build.
There’s a shift happening in how we create. The slow, hierarchical, over-planned ways of working are cracking under the weight of a world that moves in real time.
There’s a shift happening in how we create. The slow, hierarchical, over-planned ways of working are cracking under the weight of a world that moves in real time. At the center of this shift is a new kind of furnace — a crucible where ideas are stress-tested not in theory, but in practice, immediately.
Fueled by AI, this crucible demands speed, courage, and a willingness to discard the polished illusion of certainty for something far more valuable: fast, functional insight.
This isn’t about disruption for disruption’s sake. It’s about crafting a culture — and building teams — where creativity becomes a contact sport. Where prototypes speak louder than presentations. Where the true differentiator isn’t polish, it’s momentum.
What i’ve been reading
AI for research: the ultimate guide to choosing the right tool
Creating ChatGPT prompt templates for Design System Documentation
Navigating The Idea Crucible
The way we build things now feels fundamentally different than just a few years ago. The old rhythm – long planning meetings, detailed specifications locked down months in advance, large teams working in careful sequence – feels dangerously slow, out of step with the actual speed of the world today. It’s like meticulously following an old map while the landscape itself is shifting under your feet.
The ground is shifting, dramatically, largely because AI isn't just another tool; it's accelerating the pace of what's possible at a rate that can feel bewildering. And in this new reality, the only way to make meaningful progress, to actually create effectively instead of just reacting, is to embrace a more direct, faster, and frankly, more demanding way of working.
It sounds intense, maybe, because it is. It’s about taking ideas – lots of them, conceived quickly, maybe sketched out with AI's help – and building a small, working version immediately.
No lengthy debates about feasibility. No waiting for layers of approval. You build something functional, fast. A focused prototype. Maybe it’s UI mocked up in Figma, core logic coded rapidly with Copilot’s help, pushed live for testing in hours or days using tools like Vercel.
You're not aiming for the finished product; you're aiming for a quick, hard test against reality. Can this technically work? Does it actually provide value to someone? Is it fast enough to be useful?
This is the critical filter. Most prototypes? They fail this initial, tough test. They don’t perform well, users don’t find them valuable, or they prove technically unsound. And that’s not failure in the traditional sense; it’s rapid learning. It’s finding out quickly what doesn't work, before you invest heavily.
We had this concept, brilliant on paper, that we prototyped in two days. We put it in front of a few users. Blank stares. It solved a problem they didn't really have. We scrapped it. Was it disappointing? A little. But imagine spending six months building that out fully? That’s the real waste. This faster cycle saves you from those significant misinvestments.
Only the ideas that clearly pass this initial, rigorous test – the ones that demonstrate technical viability, user value, and adequate performance – only those earn the significant resources needed for full development and polish. You invest deeply, but only in ideas that have already proven their core worth in a real way.
AI is a massive catalyst for this entire approach. It allows individuals or very small teams to do things that used to require much larger groups. I see it constantly. A single engineer uses AI assistants to build and test functionality across the stack. A designer uses generative tools to explore countless visual options instantly.
Roles become more fluid. The old handoffs and dependencies lessen when individuals are empowered to build and test more complete slices of functionality themselves. It drastically shortens the time from idea to that first critical test.
This changes the kind of people you need, too. You need people comfortable with moving fast, people who see building a quick prototype as the best way to answer a question. People who are resilient, who understand that seeing their prototype fail the initial test isn't a judgment on them, but valuable data gained quickly. People more interested in making and testing than in prolonged theoretical discussion.
Leading in this environment is different as well. It's less about rigid control and detailed upfront planning, more about fostering speed, enabling rapid experimentation, and making quick decisions based on the results of those real-world tests.
You focus on the rate of validated learning. You have to create a culture where it's safe to build fast, test hard, and discard things that don't work, because the goal is to find the right path quickly, not to pretend every initial idea is perfect. It demands trust and a focus on outcomes over adherence to process.
Is it messy sometimes? Yes. Does it require letting go of the illusion of certainty provided by long planning cycles? Absolutely. It feels like you're operating closer to the edge, with less of a safety net. But the quick feedback loops, the rapid learning, the avoidance of months spent on ultimately flawed concepts – it’s incredibly effective. The failed prototypes aren’t waste; they are the efficient cost of finding out what truly works.
So, we don't rely on elaborate, long-term plans as much anymore. We encourage exploring many possibilities with small, fast prototypes. We test them rigorously. We learn quickly from what fails, and we double down on what succeeds.
It’s direct, it’s fast, and it feels much more attuned to the reality of building things in a world that refuses to stand still. Our best work, I hope, comes from the rapid cycles of building, testing, and learning we start again tomorrow morning.
In short
Core Philosophy: Prioritising practical application (taste, speed, action) over theoretical discussion.
Leadership
Building teams with adaptable, curious tech forward generalists.
Measuring progress through tangible output (feature velocity).
Valuing actual results (execution) more than talk (commentary).
Individuals
Adopting a holistic view focused on solving problems, regardless of traditional functional roles. Think about the whole ecosystem.
Leveraging AI tools consistently for rapid development and improvement.
Taking initiative and building solutions proactively (Don’t wait—just build).
Universal Mindset: Embracing flexibility and rapid experimentation (Plant, test, and grow ideas fast) over strict adherence to pre-defined plans.
I am curious how it is for you, but also for Wonder Land Studio.
I think with this rapid speed of prototyping/creating qickly, aren't we also accelerating the burnout society? Adaptating to new technolgoy is key, however that means you are expected to be even more of a generalist that can do literally everything. I mean it already is happening when you read job descriptions/talent managers seeking new potential mebmer to be part of their team.
I think we all are aware of this, which is the amount of climate footprint prompting does to the planet (even chat GPT itells you it uses a lot of water and electricity) ...Therefore if this a race to implement quickly, how do we balance out the stress the whole AI technology is putting on the planet? Do you guys keep track of that as well, as in maybe internal status check on how much the amount of footprint you have done so far. (I know you can setup chatgpt to report daily or weekly basis on this and it can also give you insights on better ways to refine your prompts)
And lastly, what about copyrighted materials? As we continue to move more into the future, remixing and scraping will happen on a rapid level. Therefore we could potentially reach a point I think where everything will feel the same, there is no more originality per say, as all is just scraped back in the system.
Curious to hear your take on these. But thank you for writing. It was a great read, and thank you for sharing the sources. AI in general is something I am still trying to understand both its cons and pros.