The Unoptimised Human — The Filter Before the Thought
July was a month about filters. Not the Instagram kind. The kind that determines what you see before you know you're looking.
July was a month about filters. Not the Instagram kind. The kind that determines what you see before you know you’re looking.
I wrote twelve pieces this month. They started in different places, arrived at the same destination. The EU AI Act. The World Cup. Lawrence of Arabia. Chinese cars in Europe. A LinkedIn thread about a study nobody actually read. But they all converged on one question that won’t leave me alone: what determines what becomes visible to you, and what stays invisible?
It’s not a rhetorical question. It might be the most important question in marketing, in AI, and in leadership right now. Because the answer shapes everything downstream: what you trust, what you ignore, what you act on, and what you never even register as existing.
Here’s what I’ve been thinking about.
The Architecture You Didn’t Choose
The month’s most-read piece, “Attention Architecture: Emotionally Intelligent AI,” made an argument that unsettled a few people. Human attention isn’t a spotlight you aim. It’s a filter that’s already done its work before you’ve decided to pay attention to anything.
Four people walk into the same room. The Socialiser scans for connection. The Thinker scans for information density. The Adventurer scans for momentum and opportunity. The Realist scans for risk. Same room, same moment, four completely different perceptual realities. And each person genuinely believes they’re seeing the room as it is.
AI, by contrast, has an attention mechanism that is uniform. It’s extraordinary at knowing what’s probably relevant within a given context. But it cannot tell the difference between what matters and what matters to you. That’s not a limitation that scales away with more parameters. It’s architectural.
When the Filter Breaks Trust
The EU AI Act’s Article 50 came into force on 2 August. I wrote about it a week early, because the interesting part isn’t the regulation itself. It’s what the research says happens when people discover undisclosed AI use.
It’s not a gentle disappointment. It’s a trust collapse. A retroactive reassessment that extends beyond the current interaction. Consumers don’t just lose confidence in the piece of content they’re reading. They reinterpret every prior interaction with that brand through the new lens. Dual Process Theory explains why: the discovery shifts how they process everything from that brand, from automatic and accepting to deliberate and critical.
77% of consumers say AI-generated marketing reduces brand authenticity. 50% now actively prefer brands that don’t use generative AI. These aren’t predictions. They’re the terrain.
The Sequence That Moves
“Join The DOTS” was the piece I almost didn’t publish. It’s about the DOTS framework, Data, Opportunity, Togetherness, Stabilise, and the counterintuitive discovery that the sequence which informs is not the sequence that moves.
If you open with evidence, the reader’s defences go up. They evaluate. They compare. They decide whether to agree. That is the wrong mode. You don’t want them to agree. You want them to feel first, and then understand why they felt it.
The sequence that actually changes minds is T-D-O-S: Togetherness first, Data second, Opportunity third, Stabilise last. Same four elements. Completely different order. The difference between an email that gets actioned and one that gets archived.
The Study Nobody Actually Read
Perhaps the most revealing piece this month wasn’t about a framework or a regulation. It was about a LinkedIn thread.
A study landed in my feed, Xia, Aldharman & Chiu (2026), surveying 333 art and design students about AI and self-regulated learning. The headline finding was interesting. The reaction was more interesting. Nearly 30 comments from academics, educators, and AI literacy advocates, and almost nobody engaged with the study’s actual limitations. They used it as scaffolding for positions they already held.
The Validators confirmed their priors. The Concerned raised alarms that the study didn’t actually test. The Practitioners pivoted to what they already do. Everyone proved themselves right. The study became a mirror, not a lens.
Which is, of course, exactly what attention architecture predicts.
What I’m Watching
Two things are occupying my attention right now.
First, the gap between what AI can do and what AI can understand about why it’s doing it. The EU AI Act forces a transparency conversation that most organisations aren’t ready for. Not because they’re hiding something, but because they genuinely don’t know how to articulate the relationship between their AI use and their brand identity. That’s going to become a competitive differentiator faster than most people expect.
Second, the vibe coding movement and its collision with marketing reality. I wrote two pieces about this in July, because there’s a pattern emerging: people who are technically brilliant at building with AI but fundamentally misunderstand what makes a brand resonate. Technical competence without brand intelligence produces products that work but don’t land. The market is starting to sort winners from losers on this axis, and the sorting is accelerating.
The Question
Here’s what I keep coming back to. If your attention architecture determines what you see, and what you see determines what you build, and what you build determines what your audience experiences, then the most important question isn’t “what should we do?” It’s “what are we filtering out without knowing it?”
That applies to AI systems processing information. It applies to marketers processing consumer behaviour. It applies to leaders processing market signals. And it applies to all of us processing the world through filters we didn’t choose and can’t easily override.
What’s your filter missing?
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.