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Could AI twins change the way companies build products? | Unlocked

March 31, 2026·39 min·3 clips

Dr. Cindy Kendrian explains how her company uses AI twins to predict customer reactions by synthetically recreating human thought and decision-making.

The starting point is AI washing. Kendrian remembers studying the term during her PhD, when ordinary products could sell for more just by being labeled AI instead of algorithm based. That gives the episode its lane: less hype, more curiosity about whether AI can simulate behavior and the reasons behind it. Delta Labs works in that area. The company builds human-centered AI for brands that want to predict customer reactions using synthetic recreations of thought, feeling, and decision-making. The host keeps the conversation plain enough to follow. When the setup starts to sound tangled, Kendrian is asked to put it in simpler terms. She is careful about accuracy. Predicting one person is still messy, because there are always unknowns behind why someone chooses one thing over another. Groups are easier. At the aggregate level, she says the system can be very accurate, which moves the conversation away from mind reading and toward pattern spotting. Then the host asks a stranger, more interesting question: do machines sometimes listen better than humans, because humans get distracted by what they hear or see? Kendrian accepts the idea and points to everyday AI prompts that read past interactions as a basic form of psychological prediction. Shopping becomes the example. If someone gives ChatGPT the brands and products they recently bought, she says it can already predict very well what kinds of products they like. The bigger question stays open. If AI gets embedded everywhere, does prediction get easier, harder, or weirdly circular? Kendrian does not oversell it. For Delta Labs, she says the underlying data is not the main issue right now, partly because synthetic data can also be used. The episode ends with Swissquote's risk warning: CFDs, digital assets, and crypto trading can mean high losses, volatility, missing consumer protections, and online security risks.

As heard by us

A grounded discussion of AI washing, human behavior, and how brands try to predict customer reactions.

The episode treats AI less like a slogan and more like a way to read human behavior. It looks at why brands paste AI onto products, how synthetic systems can help predict customer reactions, and where people still resist neat modeling.

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Want AI explained through real buying behavior, not hype?

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