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The Complicated Intersection of AI and Creativity, w/ Dr. Maya Ackerman
Does AI make us more creative—or quietly replace us?
In this episode of AI-Curious, we sit down with Dr. Maya Ackerman—author of Creative Machines: AI, Art, and Us—to probe where human creativity ends and machine creativity begins, and how incentives in Big Tech and venture capital shape the tools we all use.
We explore why today’s dominant systems skew “convergent” (safe, samey, oracle-like) instead of “divergent” (surprising, generative), what that means for artists, and how to design AI that actually elevates human imagination rather than displacing it.
Why listen
We wrestle with uncomfortable truths: bias mirrored back at us, investor pressure to “replace” vs. “augment,” and the risk of a cultural sea of slop. We also map a constructive path forward—collaborative systems, richer human–AI interfaces, and a 10-year horizon where AI expands human creative range.
Guest
Dr. Maya Ackerman — AI researcher, entrepreneur, and author of Creative Machines: AI, Art, and Us.
Takeaways
- AI reflects us. Bias in → bias out; representation fixes are not enough without cultural understanding.
- Incentives matter. Many well-funded tools are architected to replace creators; augmentation tools are underfunded.
- Creativity ≠ autocomplete. Today’s LLMs are optimized for correctness and convergence, not genuine divergence.
- Better interfaces beat bigger models. Beyond “text-to-X,” human-centred, interactive tools can coach, not usurp.
- A hopeful arc. With the right design, collaborative AI can measurably raise human creative ability—and stick.
Dr. Ackerman's new book: Creative Machines
https://www.amazon.com/Creative-Machines-Future-Human-Creativity/dp/1394316267
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By Jeff WilserDoes AI make us more creative—or quietly replace us?
In this episode of AI-Curious, we sit down with Dr. Maya Ackerman—author of Creative Machines: AI, Art, and Us—to probe where human creativity ends and machine creativity begins, and how incentives in Big Tech and venture capital shape the tools we all use.
We explore why today’s dominant systems skew “convergent” (safe, samey, oracle-like) instead of “divergent” (surprising, generative), what that means for artists, and how to design AI that actually elevates human imagination rather than displacing it.
Why listen
We wrestle with uncomfortable truths: bias mirrored back at us, investor pressure to “replace” vs. “augment,” and the risk of a cultural sea of slop. We also map a constructive path forward—collaborative systems, richer human–AI interfaces, and a 10-year horizon where AI expands human creative range.
Guest
Dr. Maya Ackerman — AI researcher, entrepreneur, and author of Creative Machines: AI, Art, and Us.
Takeaways
- AI reflects us. Bias in → bias out; representation fixes are not enough without cultural understanding.
- Incentives matter. Many well-funded tools are architected to replace creators; augmentation tools are underfunded.
- Creativity ≠ autocomplete. Today’s LLMs are optimized for correctness and convergence, not genuine divergence.
- Better interfaces beat bigger models. Beyond “text-to-X,” human-centred, interactive tools can coach, not usurp.
- A hopeful arc. With the right design, collaborative AI can measurably raise human creative ability—and stick.
Dr. Ackerman's new book: Creative Machines
https://www.amazon.com/Creative-Machines-Future-Human-Creativity/dp/1394316267