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 a diversity.

Humans still dominate in flexibility and true novelty.

We explore:

• Why generative AI clusters around sameness

• Whether AI is creating a “sea of mediocrity”

• Why 2026 may be a pivotal year for domain-specific AI

• How experts should use AI differently than novices

• The danger of AI that never says “no”

• Where AI offers massive opportunity (especially healthcare & design)

Saeema argues that creativity doesn’t need substitution, it needs nourishment. The key? Standards, boundaries, and humans firmly in the loop.

If you care about innovation, design, branding, product development, or the future of creative work, this conversation is essential.

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👤 Guest

Saeema Ahmed-Kristensen

Design engineering researcher and research leader

Formerly: Imperial College London (Dyson School of Engineering)

Currently: University of Exeter

Works with advanced engineering firms including Rolls-Royce and BAE Systems

00:00 Intro: Is AI killing creativity?

00:47 The “blank page” problem and why AI feels soulless to some

01:36 Fluency vs. novelty: what creativity actually means

02:44 Why LLM ideas cluster and feel the same

03:28 Study results: 600 humans vs. 12,000 AI ideas (diversity + flexibility)

04:39 When AI is useful: incremental innovation vs. true novelty

05:28 How John uses AI for titles, summaries, and chapters

06:23 How Saeema uses AI: refine/condense, tone for emails, audio editing

07:50 Why AI-written academic papers are easy to spot (the “C minus” problem)

09:05 Brainstorming vs. AI: what humans do that models don’t

10:05 Evaluating 200–300 AI ideas: using multiple models to assess output

11:04 Why “Lipstick on a Pig” titles don’t come from AI

11:46 Why 2026 is pivotal: domain adaptation, better interfaces, public backlash

13:44 Who can tell what’s AI? Generational differences and media literacy

15:20 Commercial AI content and recognizable “Canva look” podcast branding

16:58 Replacement vs. homogenization: AI makes mediocrity easier

18:55 The danger of AI that never says “no” (feasibility + expertise)

20:42 Standards and boundaries: measuring similarity and judging quality

22:12 Health info risk: single-answer summaries and false confidence

23:37 Biggest opportunities: healthcare personas, inclusive datasets, problem clarification

26:18 Biggest challenges: trust, verification, security, privacy, transparency

28:25 Closing thoughts and thanks