Is AI killing creativity … or just making it easier to be average? 94% of creatives now use AI. But only 11% believe it actually makes them more creative.
So what’s really happening?
In this episode of TechFirst, John Koetsier sits down with Saeema Ahmed-Kristensen, former head of design engineering research at Imperial College London’s Dyson School and now leader of a £24M research portfolio at the University of Exeter. She’s worked with companies like Rolls-Royce and BAE Systems, and she brings data to the debate.
Her team analyzed 600 humans vs. 12,000 AI-generated ideas. The result? AI is excellent at fluency (lots of ideas) … but really bad at diversity.
Humans still dominate in flexibility and true novelty.
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Transcript: AI is killing creativity
John Koetsier
Is AI killing creativity? Hello and welcome to TechFirst. My name is John Koetsier. It feels like we’re all using AI all of the time. Ninety-four percent of creatives say they’re using AI. I don’t know what’s happening with the other six percent. Maybe they’re lying. Only eleven percent of us, however, think that it makes us more creative.
Most say AI makes work feel soulless or empty. And guess what? Shocker. Most of us also fear replacement as AI gets better and better. Well, someone has a super interesting, noteworthy perspective on this. Her name is Saeema Ahmed-Kristensen. She led design engineering research at the Imperial College London Dyson School of Engineering and now oversees a £24 million research portfolio at the University of Exeter in London. She’s working with advanced companies like Rolls-Royce and BAE Systems, and she says true creativity needs nourishment, not substitution.
What’s that really mean? Welcome, Saeema. How are you doing?
Saeema Ahmed-Kristensen
Very good, thank you. Thank you for having me, John. How are you?
John Koetsier
I’m super pumped to have you. It’s a critical question. It’s a question that I confront every day. I suspect you do as well. Let’s just start with the big, bad question. Is AI killing creativity?
Saeema Ahmed-Kristensen
I think for some people, yes. So that’s an academic answer: sometimes, for some people. But what do I mean by that?
Generative AI is really good at producing a lot of ideas. That’s one measure of creativity, what we call fluency—how many ideas you generate. And if you’re not a particularly creative person, this is a brilliant starting point because you can produce lots of ideas.
But the second measure of creativity is whether you produce something novel—something that’s different, that’s new. And there we have a little issue, because a lot of the ideas that are produced by large language models or other forms of AI, particularly general AI, are very similar and grouped around similar concepts. So to get that truly novel thinking, that is something that currently sits in the domain of human beings—very creative experts.
John Koetsier
In other words, if you use AI and just accept what it offers, great, you’ve got a baseline starting point. But if you want something truly unique and extraordinary, you have to go beyond.
It does solve the blank page problem for a lot of us, right?
Saeema Ahmed-Kristensen
Yeah, it does solve the blank page problem. But it also produces ideas that are very similar, because most of the ideas are clustered.
We’ve recently done a study with 600 human beings and 12,000 ideas that were produced by large language models. What we could see there is that the diversity—how different the ideas are from each other—was much higher among humans. Human beings are much better at creating ideas that are very different.
So this is a pool of 600 people, not just one person’s creativity, compared with large language models. If you think about how AI works, it’s based on the data that’s available. It may extrapolate or interpolate from that data. But can it completely make a massive jump and come up with something entirely different? That is difficult right now.
So yes, it gets you away from that blank piece of paper. If you’re not creative by nature, it’s a brilliant starting point. If you’re looking for incremental innovation, it’s a brilliant starting point. If you want something truly novel and innovative, it perhaps isn’t the best starting point.
John Koetsier
I find that fascinating because you looked at 600 people. You probably didn’t go and find 600 of the most creative people in the world. You just picked 600 people with varying ranges of creativity. And yet within that sample, you found that they were far more divergent, far more creative than standard AI models.
Saeema Ahmed-Kristensen
Yes, that’s right. It’s a measure called flexibility—how diverse those ideas are from each other.
John Koetsier
Interesting. I want to talk about how you use AI. I’ll start with an anecdote about how I use AI for this very purpose.
When I finish this podcast, I’ll get a transcript that AI helps create. I’ll dump that into ChatGPT and into a custom GPT that I’ve made, and I’ll say, “Give me some title suggestions and give me an overview of what we talked about and some YouTube chapters.”
It’ll give me five or six title suggestions. I almost never use one of them. Sometimes it’ll stimulate something, but I usually have a different idea. It does help to kickstart things. I might use a piece of one or take a different angle.
How do you use AI?
Saeema Ahmed-Kristensen
So that’s as an individual. Obviously, I have a life as an academic.
When my postdocs or PhD students use AI to generate papers, I can see straight through it, and it annoys the hell out of me. I give ChatGPT a C minus. It produces a lot of fluff with limited substance, and then you have to reduce it and find where the content is.
So in terms of writing, I prefer it the other way around. You write it yourself and then say, “Condense these ideas into bullet points.” As long as it’s in the hands of experts—people who can evaluate it and say, “Okay, yes, these are good points”—that works.
Another way I use it in academic work is for routine tasks. If I have to write a difficult email, I might write all the points, but you need to get to the point quickly and strike the right tone. So again, I create the content and use AI to break it down and summarize it, then I adjust it again. I go through those loops.
I’ve also been doing podcasts recently. Like you, I use it to generate show notes and captions. I also use it for memorable titles. The last title I used was “Lipstick on a Pig,” because it reflected an idea in design where you only change the surface and not the deeper diversity of ideas. That’s probably not something AI would pick up on its own.
I also use it to edit sound and audio. Those tools are very good at that.
Beyond that, in my daily life, I wouldn’t say I always use it. Sometimes after speaking to a finance advisor, I’ll double-check things. For me, it’s more evaluative. I create the content, it might help edit it, and then I go back and reflect on it.
In our research, we use it in different ways. There’s the creativity aspect, but there’s also predicting user experiences. These aren’t general AI tools; they’re systems we develop where we bring specific models in.
Another example is evaluating ideas. AI can shortcut a brainstorming session and create 200 or 300 ideas. But one of the beauties of human brainstorming is that you build on each other’s ideas. You see wild ideas, misinterpret something, and come up with something completely different.
If you’ve generated loads of ideas, you now have the challenge of how to evaluate them. One of the things we’ve been doing in our research, with a postdoc and a colleague, is bringing different large language models together to evaluate those ideas. But again, this is not a general tool—we have to integrate the models into specific systems.
John Koetsier
It strikes me that the way you’re using AI personally is opposite to how many people use it. Many people go to AI first, get something, then build from there—or just take it wholesale, which is often a bad idea.
That may constrain our range of thought, though for some people it may spur different ideas.
I love what you said about “Lipstick on a Pig.” I don’t think ChatGPT or Claude will ever name something that. It’ll be more corporatized, more homogenized, more sanitary.
You said in our prep that 2026 is a pivotal year for how we use AI. It feels pivotal in many ways. We’ve heard that Anthropic used AI to code much of the next version of Claude. OpenAI is using ChatGPT to improve itself. We see tremendous advances, especially in coding.
Why is 2026 pivotal for you?
Saeema Ahmed-Kristensen
If we go back to creativity, my background is design, manufacturing, and product design, as well as services.
The general AI tools currently available aren’t adapted to specific domains. What I think is happening—and will increasingly happen—is recognition that they need adaptation.
One direction is technical adaptation. Not necessarily changing the large language models themselves, but building interfaces on top of them. For example, using chain-of-thought reasoning modeled on experts’ thinking and embedding that into systems. We can take generalized tools and make them relevant for different domains.
Another shift is how people interact with AI. We may see new interfaces—not just text, but voice or physical interfaces.
A third development relates to the homogeneity of ideas. As more AI-generated output enters the world, the public becomes more aware of sameness. If you see AI-generated images, you can often tell. They don’t quite look right.
As the public becomes wiser and starts recognizing this sameness, there may be a backlash. That backlash could push further technological development.
John Koetsier
I wonder which public is recognizing that. For example, my mother—she’s 91—saw someone streaming a video game on Facebook Live. It was like a Carmageddon-style game, with wild animals escaping a zoo. She thought it was real.
There’s a huge mass of people who aren’t sophisticated in this stuff and can’t tell the difference between AI-generated content and human-generated content.
Do you agree?
Saeema Ahmed-Kristensen
That’s interesting. I haven’t touched on different user types.
In commercial use—AI-generated commercials, posters—we’ve seen backlash. For example, images that don’t look quite real.
My daughters are teenagers, and they can instantly tell when something is AI-generated. So there’s a generational aspect.
If you look at podcast branding on Spotify, many people use Canva’s AI-generated designs with a microphone in front. It’s recognizable. It might not be obvious to someone new to the domain, but to experts, it is.
Because it’s a shortcut—faster and cheaper—many people use it. But when everybody uses it, it becomes recognizable.
John Koetsier
One of my questions was whether the bigger risk is replacement or homogenization. But the more things look the same, the more replaceable you are.
I’ve said AI has made it easier than ever to be mediocre. I hope enough of us who care still strive for the extraordinary and can recognize and reward it when we see it.
Saeema Ahmed-Kristensen
Yes, I think that’s right. The more similar things are, the greater the recognition of the need not to replace human beings in certain roles.
My perspective on AI is positive. It’s about using AI appropriately and recognizing where the human needs to be in the loop.
Experts can evaluate output and recognize sameness. For a novice, AI-generated branding may be brilliant. For a brand designer, it may look boring or derivative.
One danger is that AI hardly ever says no. It doesn’t say, “I can’t produce this.” It will generate something, even if it’s not feasible.
In product design, you need technical feasibility. Large language models don’t inherently understand that. It relies on the user’s expertise.
John Koetsier
That’s super interesting. I’m in the final stage of submitting a fitness app to the App Store. I used AI to style it. Is it the best it could ever be? Probably not. I’m not a designer. But for me, it’s brilliant—instantly average, which is better than where I’d be without it.
You said without standards and boundaries, AI can hollow out creativity. What standards and boundaries do you mean?
Saeema Ahmed-Kristensen
Hollowing out means things looking the same.
One standard is the ability to evaluate similarity—knowing where your output sits relative to others and how to create something more divergent.
Another issue is that large language models rarely say no. In the hands of a novice, you may not realize something is incorrect.
Healthcare is a particularly dangerous example. You put in your symptoms and get an AI summary. It provides one aggregated answer, which may not be correct. It’s just an answer.
John Koetsier
Exactly.
Where do you see the biggest challenges and opportunities?
Saeema Ahmed-Kristensen
I’ll start with healthcare. I see huge opportunities.
In design, we use personas—fictional characters representing groups of users. In healthcare services, personas are often based on a few patients’ lived experiences. That’s rich but limited.
If you had large datasets representing many experiences, you could create more representative and inclusive designs.
In creative tasks like product or service design, we begin with problem clarification—understanding user needs. Large language models are good at gathering and synthesizing information quickly. That step before idea generation is a major opportunity.
Challenges include trust, security, privacy, and data transparency. You need experts in the loop to verify information.
If datasets are not inclusive, AI systems can be dangerous. For example, disease diagnosis tools trained on non-diverse image datasets may exclude certain populations.
We need standards and boundaries that build expertise back into the loop.
John Koetsier
Super interesting. Saeema Ahmed-Kristensen, this has been a wonderful conversation. I really appreciate your perspective and insight.
Saeema Ahmed-Kristensen
Likewise, John. Very entertaining. Thank you.
John Koetsier
Thank you so much.
Saeema Ahmed-Kristensen
Thank you.