New creative velocity; how accelerate iteration is reshaping mastery
How AI is changing creative mastery: the evolution from traditional craftsmanship to rapid iteration, evaluation skills, and strategic direction
TLDR:
Mastery used to be about skill and taste matching up, the slow repetitive practice to build up skills
AI allows a higher volume of iterations, thus making ‘meta skills’ of editing and recognising value a lot more important, becoming more like a film director than a solo craftsperson.
That tools enables speed, but success depends on intentional engagement, using iterations to shape thinking iteratively, rather than lazy copy-pasting.
What does it mean to be a master of something? To be a great programmer, designer, or writer? The path to mastery has always been the path of 10,000 hours. But what does that really mean? I believe that path is about iterating, making something, seeing how it's not quite right, and making tiny adjustments toward the next version. An almost unconscious process of getting better through repetition.
And what happens when AI tools enter that picture? I believe AI tools are changing, *expanding* what we mean by mastery and craftsmanship. They're challenging what we think of as 'getting good at something.'
When friction was the teacher
What does it mean to get good at a skill? To develop a craft? Getting good used to require helpful friction. The process where the process is ‘expensive’, and thus makes you invested in the outcome. Think of a photographer having to manually develop pictures. All that work for a single picture, makes you really understand all the nuances of skill required to get it, and makes you invested in the outcome. There is an incentive to optimize the process, to develop the skills. That slow process is a teacher.
Ira Glass talked about this famously too; mastery comes from doing something again and again and again. Until your taste starts to match your skills. When taste and skill are matched, that is what we call ‘craft’. A journalists cranks out piece after piece, a programmer slams the keyboard again and again until it works, gradually building that intuitive understanding of their medium.
taste + skill = craftsmanship
So if craftsmanship used to be about the levelling of combined taste and skill; then how is AI changing this?
What if there is no real effort necessary in creating the artifact? If AI can automate that? How does that change how we learn the necessary skills? But it also opens up possibilities, by sheer volume of explorations available. But here is what I find fascinating: this speed doesn’t necessarily mean we’re learning less. It means we’re learning differently.
How learning is changing
What we’re seeing isn’t the death of learning through practice - it’s the practice evolving into something new. The question isn’t whether we can still get really good at things in this new world. It is whether we’re disciplined enough to learn just as deeply from this completely different process.
Think about what's actually happening when you work thoughtfully with AI-generated iterations. You're not just passively looking at outputs, you're building a new kind of creative muscle. Each variation becomes a case study in *what works and why*. The key word is *thoughtfully*. The hard work isn't in the manual creation anymore. It's in the mental work of evaluating and understanding. The work of keeping your mind with the iteration, not be distracted in the moment, truly be with the AI, to master and create together.
Back to Ira Glass, the equation of craftsmanship still holds. Taste + Skill = craftsmanship. But the actual ‘skill’ is changing. The skill of recognizing, editing and “coaching” the artifact between taste and what’s made, is becoming more important. Not the actual skill of making, but the more meta skill of editing. I believe the craft is shifting from being good at making things to being good at recognizing and refining quality. We're becoming less like the solo craftsperson carefully shaping each piece, and more like a film director working with a talented crew to create something none of them could achieve alone.
This shift demands new kinds of creative skills that are just as challenging as traditional methods:
Getting Crystal Clear About What You Want: Working effectively with AI requires you to explain your creative goals with incredible precision. You can't be vague and hope the tool will "figure it out." This forces a level of clarity about what you're trying to achieve that many traditional methods never demanded.
Making Sense of Options Quickly: When you can generate dozens or hundreds of creative options, you need to develop the ability to rapidly identify what works, what doesn't, and *why*. Your capacity to quickly spot quality becomes a core skill, maybe *the* core skill. It's like being a talent scout, but for ideas.
Developing Sophisticated Taste: In a world where anyone can generate many options, your ability to identify the truly great stuff becomes your main advantage. This isn't just having opinions—it's developing an increasingly sharp sense of what makes something exceptional.
Rethinking the 10.000 hours
Here’s where it gets really interesting. If you commit to deeply understanding each iteration, not just glancing at the surface but genuinely digging into why one version works where another fails, you’re potentially accessing a learning intensity that previous generations couldn’t imagine.
Where a carpenter might first create just a dozen pieces a year, what if you could make hundreds? Learning a little less of each, but in total volume so much more?
That is essentially what we’re having access to now. If we approach learning with the right mindset. The 10,000 hours don’t disappear; they get compressed into a much denser learning experience. The struggle doesn’t vanish, it transforms into a different kind of mental and creative work.
What does that look like? A graphic designer used to spend hours manually adjusting layouts, testing color combinations, tweaking typography. Each change took time and effort. Now they can generate dozens of variations quickly, but the real skill becomes recognizing which variations actually solve the design problem, understanding *why* certain combinations work better, and knowing how to guide the tools toward better solutions. *The learning happens in the analysis and direction, not just in the manual execution.*
For me, this represents an incredible democratization of creative exploration. We're no longer limited by how much physical material we can afford or how much time each experiment takes. We can test ideas that would have been too expensive or time-consuming to attempt before. We can fail faster, learn faster, and work toward excellence at speeds that were previously impossible.
The Discipline of Speed
But crucially, none of this happens automatically. The tools give you the capability; you have to supply the discipline. The risk isn't that AI makes us less creative. It's that we get lazy in how we use it.
We all know the shitty LinkedIn posts that are directly copy-pasted from chatGPT. Zero skill, or taste for that matter, involved. That is not speed, that is laziness. The difference is entirely in how intentional you are. Are you using these tools to avoid the hard work of creative thinking, or to amplify your capacity for that work? Are you letting the tools make your creative decisions for you, or using them to refine and strengthen your own judgment?
The creators who thrive in this new landscape won’t necessarily be those with the strongest traditional skills (though those certainly help). They’ll be the ones who can navigate this expanded creative possibility space with good judgement, refined taste and strategic thinking.
Think about what becomes possible when the friction of creation drops dramatically. A designer can test dozens of approaches to a brand identity. A programmer can prototype multiple solutions to see which one actually works best. A writer can explore different narrative structures for the same story.
The creative landscape ahead looks incredibly exciting to me.