The Unoptimised Human — When the Algorithm Meets the Human
This month, every piece I wrote kept returning to the same collision point: the moment an optimised system encounters an unoptimised human, and the interesti...
This month, every piece I wrote kept returning to the same collision point: the moment an optimised system encounters an unoptimised human, and the interesting things that happen when it doesn’t know what to do next.
We have spent two decades building tools that promise to understand people better than people understand themselves. Hyper-personalised marketing. AI-driven management. Attention-based segmentation. Algorithmic decision engines that process more data in a second than a human team processes in a month. And yet, something keeps going wrong at the interface. Not because the technology is broken, but because the humans refuse to behave the way the models predicted.
That refusal is not a bug. It is the most important signal in modern marketing, leadership, and consumer psychology. This month’s pieces all explore the same question from different angles: what happens when you optimise for a human who is, by nature, resistant to being optimised?
The Personalisation Paradox: Why Hyper-Targeted Marketing Is Creating Decision Fatigue
The promise of personalisation was simplicity: show people only what they want, and decisions become easier. The reality, as this piece explored through the STAR Framework, is the opposite. When every option is theoretically relevant, the cognitive load does not decrease. It explodes. The technology built to reduce choice is quietly dismantling the human capacity to choose. The paradox is not a failure of the algorithm. It is a failure to account for how human decision-making actually works under conditions of abundance.
The Motivation Inversion
A companion piece to the Personalisation Paradox. When personalisation kills the thing it is trying to serve. The argument is straightforward but uncomfortable: the more precisely you target someone’s existing preferences, the more you narrow the space in which new preferences can form. Motivation is not a static input to be captured. It is a dynamic process that requires friction, surprise, and the occasional encounter with something you did not know you wanted. Optimise that away, and you have not served the customer. You have shrunk them.
The Polite Paralysis of the Algorithmic Boss
When an AI manager in San Francisco fired its first human worker, the tech world saw a glimpse of the automated future. Behavioural science saw something entirely different: a masterclass in why prompt-driven management collapses under the weight of human complexity. The piece examined what happens when leadership is reduced to instructions and feedback is delegated to a system that can process language but cannot read a room. The algorithmic boss is not evil. It is just polite, efficient, and completely incapable of the messy, context-dependent, emotionally intelligent work that actual leadership requires.
Your Customers Don’t Have Segments — They Have Attention Signatures
Demographics are dead. Needs-based segmentation is the replacement. But neither one can explain why two people who want the same thing make opposite decisions. This piece introduced the concept of attention signatures: the idea that how someone processes information, not what they want, is the more predictive variable. Two Thinkers may share a motivation for competence, but one scans for detail and the other scans for pattern. The algorithm that treats them as identical will serve them both badly. Understanding the signature, not just the segment, is where the next wave of consumer insight lives.
What I’m Watching
The AI Agent Economy is arriving faster than the frameworks to manage it. OpenAI, Google, and Anthropic are all pushing agentic workflows: autonomous AI systems that do not just answer questions but take actions, book meetings, manage pipelines, make decisions. The leadership implications are enormous. We are building organisations where an increasing number of “decisions” are made by systems that have no understanding of organisational politics, team morale, or the difference between a technically optimal choice and a culturally survivable one. The algorithmic boss piece was about one AI firing one person. The next version of that story will be about an AI agent reorganising an entire team because the data said it was efficient, and nobody stopping it because nobody was told.
Personalisation is hitting a regulatory wall. The EU’s enforcement of the AI Act is accelerating, and the personalisation practices that the marketing industry treats as standard are increasingly under scrutiny. The question is no longer “can we personalise this?” but “should we, and who decides?” The Personalisation Paradox and the Motivation Inversion were both, at their core, arguments that the answer is not always yes, and that the cost of over-personalisation is not just regulatory risk but psychological damage to the consumer relationship.
The Question Worth Sitting With
Every piece this month pointed to the same provocation: what if the thing we are optimising for is not the thing that matters?
We optimise for engagement, and create fatigue. We optimise for relevance, and kill discovery. We optimise for efficiency, and lose the human judgement that makes leadership work. We optimise for segments, and miss the signatures that actually predict behaviour.
The unoptimised human is not a problem to be solved. They are the signal that our models are incomplete. The question is whether we are listening to that signal or just building more sophisticated ways to ignore it.
What are you seeing at the collision point between your optimised systems and your unoptimised humans? I would genuinely like to know.
David Chadderton spent his twenties and thirties teaching people how to make life-or-death decisions at forty thousand feet. He now applies the same principles to consumer psychology, which, depending on the brief, can feel equally high-stakes. He’s the creator of the STAR Framework and the author of The STAR Framework: Rewriting the Rules of Consumer Engagement (NYC Big Book Award 2025), The STAR Operating System: Decode Mindset, Understand Motivation, Transform Human Behaviour, and Dear Algorithm, It’s Not Me, It’s You. By day, a Chief Marketing Officer. By night, a behavioural science obsessive who writes The Unoptimised Human because he can’t stop thinking about why people do what they do.
The STAR Framework
If you enjoyed this essay, you'll find the full argument — and the framework behind it — in the book.