The Unoptimised Human — September 2026

When the Algorithm Knows You Better Than You Know Yourself (And Why That Should Worry You)

There is a question that has been sitting underneath everything I wrote this month, and I did not realise it until I sat down to draft this newsletter. It is not a new question, but it has taken on a different weight in2026: what happens when the systems built to serve us start to understand us better than we understand ourselves?

August was a month of paradoxes. The technology designed to simplify choice is making choices harder. The AI built to manage people is revealing why management cannot be automated. The segmentation models that promised to decode customers are failing because they were measuring the wrong thing entirely. And underneath all of it, a quieter pattern: the more precisely algorithms target us, the more we feel the loss of something we cannot quite name.

This month’s pieces all orbit the same idea. The optimisation is working. The question is whether what it is optimising for is still human.

The Personalisation Paradox: Why Hyper-Targeted Marketing Is Creating Decision Fatigue

This was the piece that started the thread. Personalisation has been the marketing industry’s favourite word for a decade, and the budgets have followed. McKinsey’s data keeps showing that personalisation leaders grow faster than their peers. And yet consumers are describing feeling better served and more manipulated in the same breath. Ad blockers, incognito windows, deliberately scrambled viewing histories, people are now actively trying to confuse the systems built to understand them. The paradox is not that personalisation does not work. It is that it works too well, and the “too well” is the problem. When the algorithm removes all friction from the decision, the decision stops feeling like yours.

The Motivation Inversion

If the Personalisation Paradox is about consumer choice, this piece is about what happens when the same logic enters the workplace. Self-Determination Theory tells us that human motivation rests on three needs: autonomy, competence, and relatedness. The systems we are building to “optimise” work are systematically undermining all three. When an algorithm decides your tasks, your autonomy shrinks. When AI evaluates your performance, your sense of competence becomes contingent. When a chatbot replaces your manager, relatedness evaporates. The inversion is this: the tools sold as motivation engines are producing the opposite effect. Not because the technology is bad, but because motivation is not an optimisation problem.

The Polite Paralysis of the Algorithmic Boss

When an AI manager in San Francisco fired its first human worker earlier this year, the tech world saw a glimpse of the automated future. Behavioural science saw something different: a case study in why authority without accountability produces paralysis, not efficiency. The algorithmic boss does not inspire, does not negotiate, does not read the room. It executes. And when it executes badly, there is no one to appeal to, no relationship to leverage, no conversation to have. The piece explores why the most dangerous manager is not the incompetent one, it is the one that cannot be reasoned with.

Your Customers Don’t Have Segments. They Have Attention Signatures.

This one challenged a foundational assumption in marketing: that segmentation works. Demographics are dead, we are told, and needs-based segmentation is the replacement. But neither can explain why two people who want the same thing make opposite decisions. The answer is not in what they want, it is in how they attend. Attention signatures, the pattern of what someone notices first, lingers on, and skips past, are a better predictor of behaviour than any persona or segment. The piece introduces the idea and explains why the brands that figure this out first will have a structural advantage.

What I’m Watching

The Bank of England’s Andrew Bailey wrote an open letter to the G20 this week warning that AI could cause a global economic downturn. Not because the technology will fail, but because the investment patterns around it are creating a feedback loop: concentrated capital, cross-investment between AI firms, and leveraged positions in a handful of hyper-scalers. The behavioural science reading is simpler than the financial one: this is herd behaviour and availability bias at institutional scale. Everyone is investing in AI because everyone is investing in AI. The fundamentals are almost secondary. I am watching to see whether the market corrects before or after the narrative breaks.

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.