The Weekender #35
Invisible Loyalty | Conviction over Data | The era of AI agents is just a step along the way | Are you feeling unmoored?
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Invisible Loyalty, or: When You Think You Want Coffee
A product success story might go like this: Starbucks’ Deep Brew AI sends you a notification at 8:15 a.m. on Tuesday, right when you usually get your double-shot oat milk latte for your commute.
You accept it. You feel seen.
What you don’t know is that the system noticed your engagement had been dropping for days, so it flagged you as a possible churn risk.
You weren’t choosing coffee. You were being retained.
This is invisible loyalty. These programs are tailored so precisely to each person that they no longer feel like programs at all.
By the end of 2026, more than a third of UK consumers are predicted to use AI agents as their main brand interface.
The most effective interactions will be the ones you don’t even notice.
This is where the tension in intelligent interfaces is at its highest point.
Here, we start to wonder what happens to our identity when it is constantly reflected back to us by the systems we use.
But what about invisible loyalty?
It raises a tougher question: What happens when the reflection is so accurate and perfectly timed that the loop closes completely? What if the system’s guess about what you want is so good that you can’t tell the difference?
Thirty-two per cent of consumers say they would not share personal data with AI agents even for a better experience.
So, ambitious loyalty programs rely on consent from about two-thirds of people, and on inference for everyone else.
The key design question is: When does personalisation cross the line and start to replace your own choices? Does your program have a clear answer to that?
Now, let’s talk about Brand Loyalty Decay: Micro-Trust Beats Mega-Brand.
For as long as I can remember, the logic of brand loyalty was always about accumulation:
You built a repertoire of familiar names,You built up a list of familiar names, connected emotions to them, and stuck with them. Staying loyal was part emotion and part habit. Switching brands had a cost, loyalty programs kept you interested, and big brands paid to stay in your thoughts.Customers now report feeling overwhelmed by promotional content. They say brands have five seconds or less to capture their attention before they move on.
Five seconds.
Big brand names no longer buy themselves extra time. Now, AI-driven discovery is changing what loyalty means.
With personalised recommendations and real-time comparisons, agent shopping optimises for true preferences, shifting loyalty from familiar brands to present-fit relationships.
Mega-brands are starting to lose their pull. Now, the most relevant algorithmic match wins.
What comes next is not the death of loyalty but a significant change in its method.
Points stand in for commitment until their meaning becomes obvious. When a brand shows it understands you, acts for you, and respects your preferences through its actions, you don’t need points as a stand-in anymore.
Analysts suggest using “permission receipts” to make data, inferences, authority, and duration more transparent. Now, loyalty is about being clear and open, not just about rewards.
Practically, you can’t own loyalty with programs alone. Earn it by being honest with data and responsible in its use.
Micro-trust, which you earn each time and can lose at any moment, is the new form of compound interest.
Conviction over Data
There’s always a tension between creativity and optimisation.
Apple’s “1984” commercial famously proved that data is rarely wrong about what it measures, but can be blind to category novelty.
AI iterates patterns flawlessly, but can’t validate creativity that breaks patterns.
The real risk with AI isn’t that it will replace creativity, but that it will leave less room for human judgment. Dashboards and sentiment analysis can push leaders to give up their own convictions. To push back, organisations need to protect creative decisions. They should also decide which projects are for improvement (Category 1) and which are for new ideas (Category 2).
A good AI strategy should recognise disruptive ideas, even when the data says to change them. Protecting so-called ‘bad’ ideas can lead to breakthroughs that change markets.
How do you think your current team would react if a high-stakes project returned a “5 out of 43” on its first day of testing?
In 1984, Apple aired a commercial that redefined advertising, yet by every “objective” metric of the time, it was a catastrophe.
You all will know the ad: It featured a dystopian world shattered by a sledgehammer-wielding athlete, a cinematic metaphor for the Macintosh breaking the status quo. But when a market research firm tested it on a standard 43-point effectiveness scale, it scored 5, versus the industry norm of 29. It was officially the worst-performing business commercial the firm had tested in fifteen years because it refused to do what “good” ads were supposed to do: show the product and ask for a sale.
The ad was shown because people chose to trust their convictions instead of the data. Agency leaders hid the test results, creatives kept the airtime, and Wozniak even offered to pay for the broadcast. They protected the creative spark from being optimised away, proving that data measures the past, not the moments that transform brands.
A team at Harvard Innovation Lab encapsulated this history of technology as it relates to the office in a video titled “The Evolution of the Desk.”
The era of AI agents is just a step along the way.
History will record our “AI agent era” as a transitional period. Agent frameworks, protocols, and orchestration layers are all just temporary structures for what’s coming next.
The agent era expands reactive AI. AI can do more, but still needs prompting.
The proactive era will flip the relationship.
AI won’t just be a tool anymore. It will act alongside us, able to perceive, reason, and act within certain limits.
This is like the difference between a tool and a coworker. A tool helps you when you use it. A coworker sees problems, suggests solutions, and takes action. Both are useful, but not the same.
The agent era showed that AI can use tools and make plans. The proactive era will show AI acting as a participant, not just reacting.
The 21st-century acceleration.
If proactive AI achieves even partial realisation over the next decade, what does this imply for the rate of human progress?
Feeling unmoored?
Let’s acknowledge a feeling many of us share: feeling unmoored.
If you’re feeling unmoored, you’re not alone.
Billions of people feel unmoored. The old supports like institutions, stable careers, and other outside factors have collapsed.
Who do we turn to when our personal scaffolding has collapsed?
We turn to ourselves.
We build new identities to re-anchor aspirations, purpose, direction, and trust.
And that’s not just OK—it’s actually good for us. It helps us grow.
It helps us because the old supports were often cages we thought were helpful. They made us think our value depended on our productivity or our job titles.
When those outside structures fall apart, we have to move from just consuming (or extracting) to actively creating.
This change is a needed step away from old, fixed identities we picked up easily, toward something we’ve built ourselves.
When we become our own anchors, we build a resilience that no market crash or world event can take away. We learn we aren’t just one thing—we’re full of possibilities and able to adapt as life changes.
So we’re not just surviving the collapse. We’re moving beyond the need for what’s now in ruins.
See you out there as we build something new. Onward and upward! 🚀
Read The Human Side of Entanglement.
I’m Michael Cooper, a strategist and cultural futurist.
I research our entangled selves.
It’s the collision of human behaviours, AI and agents, fluid identity, and culture.
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