In my classroom, I see two kinds of people. The first group has never used AI and assumes it functions like an on-off switch. They believe that using AI will inevitably result in sameness, that everyone will produce something generic and indistinguishable. The second group has tried AI and begun to realise it’s not about what AI can do, but about what the designer chooses to do with it. The more I teach AI in a design studio setting, the more I observe the full spectrum of use. From frustration and detachment, to breakthrough and delight with AI. Some students barely scratch the surface. Others learn to prompt iteratively, layer multiple tools, and actively refine their outcomes. The real difference between them is not skill. It’s creative confidence.
Design studio culture will become more essential
Creative confidence isn’t just the belief that you’re good at something. It’s the willingness to try, to fail in public, and to keep going until you figure it out. In the design studio, this means showing unfinished work, being open to critique, and treating the unknown as something worth pursuing. Students who lack this confidence often stop short. They use AI once and decide it’s not for them. Those who are confident keep exploring until they find a way to make AI meaningful to their design process. This type of persistence shows that confidence isn’t innate. It’s cultivated through practice and reinforced through culture.
As Kathryn Marinaro shares in How to Think Like a Creative Director in the Age of AI, creative leaders must model that culture by learning alongside their teams. In her practice, that means carving out time to try new AI tools, holding company-wide AI jams, and encouraging people to share what worked and what didn’t. She doesn’t just teach tools. She teaches permission. That’s exactly what we need in our classrooms.
Studio culture is already built on showing process and asking good questions. AI makes this even more valuable. Instead of disrupting critique, AI deepens it. Now, students explain not only what they made, but how AI helped or hindered their thinking. This shifts the conversation from output to authorship. We should not be asking “Did you do the work without AI?” but “How did you make decisions with AI along the way?”
Creativity doesn’t cumulate, it mutates
Working with AI has changed how I understand talent. I used to think talent was something that grew through repeated exposure and accumulated knowledge. Now I believe talent comes in pairs. There’s creative talent, which is the ability to imagine, improvise, and express, and there’s AI talent, the ability to frame good prompts, navigate tools, and translate abstract ideas into synthetic outputs. These two forms of talent must be developed together. If we only train one, our creative practice becomes asymmetrical. AI is not a shortcut to better design. It’s a partner in expanding what design can be.
That’s why I’ve started thinking about creativity as neuroplastic. Not just metaphorically, but structurally. Neuroplasticity is the brain’s ability to adapt and change, which helps us foster our creativity. But like the brain, I now wonder if creativity needs to rewires itself regularly in response to change. Each new AI tool doesn’t just add functionality. It alters what designers pay attention to, what paths they follow, and what outputs they imagine. This idea aligns with Daniel Martinez’s framing in Contextualizing AI in the Creative Landscape, where he explores how AI transforms creative processes, aesthetics, and authorship all at once. He argues that creative work is now shaped by adaptive ecosystems.
AI shifts how we create, but also how we learn to create. For some, this is energising. For others, it’s destabilising. But either way, the direction is clear. Creativity isn’t evolving linearly. It’s mutating constantly. The faster we build confidence in navigating that mutation, the more prepared we’ll be to lead with it.
Value doesn’t accumulate, it recomposes
IMO, the idea that experience builds value in a straight upward line no longer holds. With every new AI release, established workflows can lose relevance. Professionals must learn to recombine their knowledge with emerging tools. Sometimes this means for designers to venture into seemingly irrelevant AI skills or formats. Sometimes it means unlearning something they once mastered. But when recombined, these “tangents” create new creative identities which become harder to automate and easier to trust.
This is similar to what Srinivas Rao means when he says, “Your personal knowledge capital is your edge.” Though I think knowledge only becomes capital when it’s applied. Creativity is what wields that capital into something valuable. Without mutation, knowledge risks becoming inert. Without experimentation, it’s just potential energy. So my expansion on it is:
Knowledge capital is powerful, but unused knowledge is deadweight. It’s only when creativity wields knowledge with intent that it becomes an edge.
For design leaders, this means creating space for team members to experiment with emerging tools, even when the immediate payoff isn’t clear. As I discussed in my Price’s Law blog post, the top 10% of a team often generates more than half the creative output. But they often get there by exploring tangents. Leaders must see exploration as investment. When someone explores something unrelated today, it often becomes a strategic advantage tomorrow.
Craft, confidence, and the power of discernment
We are surrounded by AI-generated content. It’s clean. It’s fast. It’s often indistinguishable. That’s why creative confidence is now more important than creative polish. Anyone can generate. Only a few know how to shape. Shaping is about framing better prompts not just to get outputs, but to explore ideas. It is about curating from AI outputs with a clear creative vision. It is editing, combining, and transforming results rather than settling for what’s generated. It is exercising taste and judgement at every step.
So while anyone can produce content using AI, shaping involves a deeper layer of creative decision-making, a process that mirrors what skilled designers already do with their tools.
Shaping is not about style. It’s about discernment. It’s the choice to pause before hitting “submit,” to revise the prompt, to challenge the first good-enough idea. It means knowing when to follow the machine and when to interrupt it. Discernment requires self-awareness. It also requires creative patience. Craft, in the AI age, is the practice of resisting default decisions long enough to make something personal.
In When Innovation Needs a Soul, the author argues that sameness doesn’t sell. Emotional connection and originality still matter. The differentiator is not the dataset. It’s the depth. The soul. And that soul lives in the process, not the prompt.
Creativity hasn’t disappeared in the AI era. It has changed shape. The real challenge is not whether you can keep up with the tools. It’s whether you’re willing to slow down, trust your process, and keep showing up unfinished, uncertain, and curious.
What’s one way your creative process has shifted since using AI? Or, what might mutate next?
