Episode S15 · Ep 39 September 10, 2026 30 min 41s

AI Drew Ketchup. It Kept Drawing Heinz.

From A Beginner's Guide to AI Dietmar Fischer

AI image generation can produce a Victorian bakery run by a polar bear in seconds. But what is actually happening inside the machine? Does it imagine the scene, copy existing pictures, or calculate its way from random noise to a convincing image?

In this episode of A Beginner’s Guide to AI, we look inside text-to-image AI. You will learn how diffusion models turn noise into pictures, how GANs improve through competition, how prompts guide the process and why the same request can produce a different result every time.

We also examine the uncomfortable part. AI-generated images can appear realistic while containing impossible reflections, invented product features, distorted anatomy or biases inherited from training data. A picture can look convincing without showing anything that has ever existed.

🍅 The Heinz A.I. Ketchup campaign gives us a remarkable business case. When DALL-E Two was asked to generate ketchup, it repeatedly created bottles that resembled Heinz. The machine had not performed a taste test. It was reflecting a powerful association within its training data. Heinz turned that association into a successful marketing idea.

🎯 Key takeaways:

How AI image generation works
How diffusion models create images from noise
The difference between diffusion models and GANs
Why prompts guide rather than precisely command the model
How training data shapes visual output
What AI image bias means for brands
Why realistic AI images still require human verification
What marketers can learn from the Heinz AI Ketchup campaign

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About Dietmar Fischer

Dietmar is a podcaster and digital marketer from Argo.berlin. If you want to get your AI or digital marketing activities moving, contact him at argoberlin.com.