That’s the picture I keep coming back to whenever I read the tech headlines lately. Not because the hammer is bad - hammers are brilliant - but because somewhere along the way we stopped asking what we were actually trying to fix, and just started swinging.

So this one’s for you. Dear Tech Hammer Bros 💌

You know who you are. You’re the one who look at a new model drop and sees the end of an industry. You’re the one pumping the fear, because fear moves valuations, fear grabs headlines, and headlines feed the machine. You’ve got one tool 🔨 now, the biggest, shiniest, most-funded tool 🔨 in human history, and suddenly every problem, every workflow, every human being with a job, looks suspiciously like a nail.

The old line is Maslow’s: if all you have is a hammer, everything looks like a nail. But the thing about our current moment isn’t that people only have hammers. It’s that they’ve been convinced the hammer can do everything. Screws included. And when it doesn’t quite go in, the answer is never reach for the screwdriver - it’s swing harder, spend more tokens, ship more agents.

I did two interviews recently that reminded me there’s another way to hold and use different tools. One with Pip Bingemann, who co-founded Springboards. One with Kat Sullivan, who builds creative technologists at WPP through HEX. Neither of them demonstrate Tech Hammer Bro characteristics. And that, I think, is exactly why they’re worth listening to.

Because both of them, in completely different ways, are doing the same thing: they put the tool down long enough to ask a more interesting question.

Pip, and the screw that isn’t a nail

“You want to take the chat thing and force it into a creative environment. It’s coming into my world. I’m not going into its world - because its world is diametrically opposed to what good creativity is.”

- Pip Bingemann, Springboards

Here’s what struck me most about Pip. He sells an AI product. He could very easily wave the hammer around. Instead, he spends most of his time telling you what the hammer can’t do.

His whole insight with Springboards is almost embarrassingly simple once he says it, and then impossible to un-see. Language models - the big state-of-the-art ones everyone’s rushing to bolt into their workflows, are convergent. They’re linear. They all end up in the same place. Ask Chat GPT, Gemini, Claude the same open question and you’ll get the same beige, averaged-out answer. As Pip put it, they’ve become a commodity.

Now hold that up against what actually makes good creative work. Divergent minds. Messy thinking. A safe space to have bad ideas. Collisions. Serendipity. Outside stimulus. Time.

That’s the screw meeting the hammer. And the Tech Hammer Bro response is to keep swinging - force the creative person into the linear chat box, tell them to prompt better, measure how much time they saved.

Pip’s response is to reach for a different tool entirely. That’s not anti-tech. That’s the opposite of anti-tech. It’s someone who understands the tool so deeply that he refuses to use it wrong. His team went and hired PhDs to chase the divergence problem. He built a small model, Flint, precisely because the big hammers physically cannot give you an open-ended answer, no matter how nicely you ask. He even ran a test on Claude with a skill that said only ever give me responses from the tails, never the average - and it still handed him the average. It doesn’t comprehend the request. It’s just not built for that.

Pip doesn’t sell time-saving. He almost refuses to.

“It gets me to curious places faster… I never hear people say it saved me time. The time is part of the process. Trying to measure time is a silly output.”

I love the value exchange: Springboards gets me out of my own head. It gets me to curious places faster. Not cheaper places. Curious ones. There’s a difference, and the hammer can’t see it.

He told me a story about an in-house marketing team who used Springboards not to replace their agency, but to brief it better. Their reaction? “Shit, this is hard work.” And that was the point. The messy playtime made them more articulate. The brief got tighter. The first round of work landed. Everyone was happier. There was a productivity gain buried in there - but it came from letting people think harder, not less.

That’s what the Tech Hammer Bros miss. They are so busy removing friction that they remove the very thing friction was doing: making someone accountable for the answer. Automate the brief, automate the scoping, and one day you ship the thing and nobody can tell you why you didn’t question it.

Listen to the full conversation with Pip Bingemann - S02 E07: The One Where We Explore What These AI Systems Are Really Worth to People

Kat, and the studio built to push back

“This should be a studio that asks questions. This should be a studio that pushes back. Not a hundred percent optimism, full speed ahead - but taking time to dig a little deeper.”

- Kat Sullivan, WPP / HEX

If Pip is fighting the convergence in the machine, Kat is building humans designed to resist it.

She runs the Creative Tech Apprenticeship and HEX at WPP - nine months of training, then a studio full of people who wear every hat there is. And the important thing for us to discuss here = Not a studio that swings the hammer. One that stops to ask if a hammer’s even the right call.

She’s watching the whole industry lurch toward vibe coding and full-speed-ahead hammering, and she’s quietly doing something almost heretical: she would still teach coding. In the age of generative AI, when everyone’s telling her the technical side is dead weight, she calls it an easy yes. Why?

“Otherwise, we’re switching agency from our hands into this unknown entity. And what are the ramifications of that?”

That’s a screwdriver sentence if I ever heard one. It’s someone who knows that if you don’t understand the tool, you don’t control the outcome - you just hope. She wants people in the room who can say the amazing things the tech can do, sure. But also the people who can say what it can’t. Because right now there’s a deafening imbalance between the two.

And when I asked her the big leading question - the tech-bro one, about models automating everyone away - she didn’t reach for a stat or a valuation. She reached for the only question that actually matters:

“Without people, so what? You can automate and optimise and hit all the KPIs that a different LLM came up with. But if there’s no humans - who’s it for?”

That clip says it all. Her advice to young people isn’t learn to prompt. It’s don’t be quiet. Ask questions. Ask a million more questions. Be borderline annoying. The whole engine of her career, and now the studio she’s built, runs on curiosity - on refusing to accept the first answer and hammer that falls into your hands.

Listen to the full conversation with Kat Sullivan - S02 E08: The One Where We Build Talent From the Bottom Up

The screwdriver in the drawer 🪛

Here’s the thing that ties Pip and Kat together, and it’s the thing I most want you to hear, Tech Hammer Bros.

Neither of them is a Luddite. Pip thinks AI is “fucking amazing” for the right jobs. Kat felt, the moment ChatGPT dropped with its clean little UI and no Python required, that this was different - genuinely game-changing distribution. They’re not standing at the door refusing the future.

They’re just holding the tool properly. They know a screw from a nail. And when they hit a screw, they don’t swing harder - they ask a better question. How do we bring what makes creativity great into this environment? Would this person even be able to interact with the system without the foundations? Is this the best way, or just the fastest? Who is it actually for?

The media narrative you’re feeding wants tech to be binary. A hammer. Solution or threat, boom or doom, replace or be replaced. It’s a clean story and it moves stock prices and it fits in a headline. But it’s a hammer swinging at a screw, and the reason it never quite goes in is that people and process were never nails to begin with.

Rory Sutherland says cost saving is not a strategy. I’d add: a hammer is not a strategy either. It’s just a tool. A good one. But the whole game - the entire, glorious, messy, human game - is knowing which tool to reach for, and being curious enough to ask before you swing.

So put it down for a second. Look at the thing in front of you. Is it really a nail?

Or have you just never bothered to open the drawer and ask the questions that matter?

See you down the rabbit hole 🕳️

🐰

💌

While the headlines argue that AI is coming for entry-level talent, Kat Sullivan is doing the opposite - building the next generation of creative technologists from the ground up.

On the latest Rabbit Hole Experience we sit down with WPP‘s Senior Director of Interaction Design (and co-founder of HEX Studio and the Creative Tech Apprenticeship) for a genuinely optimistic conversation about where AdLand is heading.

We get into:

🧙‍♀️ The new skills reshaping the industry

🧙‍♀️ What a “creative technologist” really is

🧙‍♀️ How you build talent from the bottom up

🧙‍♀️ Why bottom-up talent and top-down transformation are meeting in the middle

🧙‍♀️ Why AI felt different from every trend before it

🧙‍♀️ The tech-bro automation narrative

And true to everything we’re about here at The Rabbit Hole Experience - it all comes back to asking better questions.

Kat’s advice to anyone starting out? “Don’t be quiet. Ask a million more questions. You should be borderline annoying.”

Because the future of this industry isn’t about fewer people - it’s about curiouser and curiouser ones.

The plan is simple: I want to give my next generation an active point of view on technology by walking them backwards through it. Less throwing them in the deep end, more handing them the clay before the code.

I’ve had a light play with this plan - nothing forced, nothing structured. Just reaching for the older option whenever the moment naturally comes around. So, when the kids were after some Mario, instead of streaming it I put the DVD on. We dug into the DVD extras (all 2 of them) - and we had a little go on Super Mario All-Stars on the side.

But this week, a disappointing trip to the cinema for Toy Story 5, which I’d been hoping would be the nice support for the DVD/SNES Mario experience… but ended up not working with the Rabbit Hole ← Time Machine message at all, which was a shame.

So consider this the first field report.

First, the good news: the casual Mario experience was fun

Like I said, I’m not forcing any of this. We’re at “Step 0.1”… just easing the odd bit of physical media in when the moment allows.

For something so small and low-key... The Mario DVD hit hard, the DVD menu, the scene selection and the couple of extras that were on the disc were enjoyed by all.

But what surprised me - or rather, that the kids never noticed…

They didn’t clock that the resolution was nowhere near Netflix. They didn’t notice that the sound was, frankly, rough - it genuinely surprised me how bad “Dolby Digital 5.1” could sound off that disc. None of it registered with them.

What registered was that their imagination caught fire.

The gap is the feature

Here’s the idea, and it runs under everything that follows.

When you’re served a complete, photoreal 4K 3D world, everything is already filled in for you. Every blade of grass, every reflection, every sub-frequency of sound. There’s nothing left for you to do. You become, as the researchers put it, a “guided observer.”

But a rougher, lower-fidelity thing - a chunky 2D world, a worn DVD picture, a crackly disc - leaves gaps. And the brain can’t stand a gap. It rushes in to fill it.

This isn’t a hunch. It’s the same principle running under three completely different fields.

Comics call it closure. Scott McCloud’s whole theory in Understanding Comics is that the empty space between panels - the “gutter” - is where the reader’s imagination does the work. The artist draws the axe raised. The reader is the one who makes the blow land. As McCloud puts it, nothing exists in the gutter except what the reader chooses to put there.

Reading calls it gap-filling. Literary theorist Wolfgang Iser argued that a text is full of deliberate blanks. A book says “a dark, drafty hallway,” and you build the temperature, the light, the dread. No two readers ever picture the same one. You’re not a consumer; you’re a co-author.

Neuroscience has even measured it. A 2023 University of York study, led by Dr. Sebastian Suggate, tested more than 200 young adults and found that people had measurably weaker visual imagination right after watching film clips than after reading - a temporary dip lasting around 25 seconds. The researchers’ point is that reading forces the brain to construct imagery, while film hands it over ready-made. Hand the brain finished images for long enough, and the imagination muscle starts to soften from disuse. To be clear though as a dyslexic kid, I don’t agree with the book Vs film argument. I think it is just about using a medium in the best way for different brains and again telling a story that allows for imaginative leaps.

There’s even research on kids specifically. Give them a polished, finished-looking toy or screen and they go passive - they admire the colours, the polish, the literal function. Give them something rough and visibly unfinished - a cardboard box, a 2D sprite, a low-fi sketch - and they invent. They build stories onto it, precisely because it isn’t done yet and there’s room left to do it. This goes back to the first post and Jude’s Clay and the Code.

That’s exactly what I watched happen with a rough Mario DVD. The low fidelity wasn’t a flaw the kids tolerated. It was the door that let them in. Along with some of the other frictions in the media, setting up the film on the PS3, the film menu etc.

The gap is not a defect of old media. The gap is the feature. Hold that thought - because it’s also the lens I watched Toy Story 5 through.

And then there was Toy Story 5

So a few weeks later, with that Mario result still fresh in my mind, I had high hopes for Toy Story 5. On paper it should have been a gift to this project: a film literally about toys versus tech, physical play versus the screen.

I was disappointed.

The film felt functional.

There was no magic to fire.

Don’t get me wrong. For very short moments, the message was lovely. The movie keeps saying that play just isn’t the same with tech, and when it lets that breathe, it’s nice.

But a Toy Story film can’t just say that. It has to feel like magic, the way Toy Story felt in 1995.

Adults having fun vs. adults freaking out

Here’s the cleanest way I can put it.

The films that lit a roaring fire in me as a kid, and then made me feel like a kid again as an adult = Toy Story, A Bug’s Life, Monsters, Inc. felt like you were watching a room full of adults having completely unhinged, imaginative fun. That joy was contagious. You could feel it leaking out of the screen. And crucially, they left space for you.

Toy Story 5 didn’t show me that. It showed me a room full of adults freaking out about screens.

I kept waiting for it to cut loose, to show some genuinely mad imagination beyond Lilypad. Instead, a big chunk of the emotional weight goes into a wedding imagination demonstration. Kids have more in them than that. And so does Pixar.

The whole film felt less like a story for children and more like a reminder aimed at parents. Which is telling, because the night out reminded me that play is already strong in kids. Mine were more captivated by the board games in the cinema reception than by the film itself.

Even the Taylor Swift song - and we are all fans - hit no imaginative chord… No magically replays when we got home.

You should feel the fun

It is the after effect that really led me to writing this.

When the credits rolled, there was nothing. No re-enacting a scene in the car. No “what if our toys did that when we left.” No arguing over who’d be which character. Although one of my kids did say “I want that pink camera”. My kids had wanted to leave halfway through - and when it ended, they just made one comment and then nothing else was mentioned… it was all left in cinema screen 5. Zero follow-up. And honestly? Same for me. I walked out feeling oddly empty with a problem thrown at me I already knew was a problem.

The films that lit me up as a kid and led to follow-up never ended when the film ended. They spilled out. They became play in the bedroom for weeks. The gap they left was so big you had to keep filling it.

Toy Story 5 left no gap. We walked out, and that was that.

I think it’s a process problem.

The process was the problem

There’s a diagram in Ed Catmull’s Creativity, Inc.

There is a clean, straight arrow, marching neatly to a completed plan.

Then there is a more open curious journey towards the plan (when rabbit holes are welcomed). A scribble that doubles back on itself, gets lost, climbs, falls, wanders - that is where true value is unlocked.

Catmull’s whole point is that the magic comes from curiosity. Pixar films don’t start as finished messages. They start as a “what if,” and then a room of people spend years discovering what the film is actually about. The plan is just the excuse to start wandering. The good stuff is found, not scheduled.

Early ideas are necessarily ugly, incomplete and wrong. The job isn’t to execute them perfectly. The job is to improve them through discovery.

You can really feel the difference in the final film. A film that was genuinely discovered leaves gaps, because even the people making it were finding their way. A film that just executed its plan fills everything in, because there was nothing left to discover. It hands you the thesis the same way bad media hands you every pixel.

That’s exactly what I felt. Toy Story 5 had no real feeling. The straight line. The plan, delivered on time… The most expensive Toy Story film of all time - delivered.

What the great ones discovered

Look at what the earlier films started as - and what they became:

Every film in that top group started with a “what if” and then went on a two-year scribble to find its soul. None of them set out to lecture you about their premise. The premise was the doorway, not the destination.

Toy Story 5 mostly walked through the doorway and then stayed in it - pointing at it, telling you about doorways. The one time it did wander off (Bonnie and Blaze) you can feel the difference instantly. That moment breathes. It’s the rest of the film that stays put.

We know what magic looks like

What really exposed Toy Story 5 for me happened before Toy Story 5 even got into the swing of things. The opening of Toy Story 5 was just an on the nose reference back the best scene of Toy Story 2 - Watch below for all the original feels.

Then think about the opening of Toy Story. Randy Newman’s “You’ve Got a Friend in Me.” The cloud wallpaper. The slow pan down into Andy’s room, into a play scenario, into the birthday setup. Three minutes. No dialogue doing the heavy lifting. Just pure magic.

Those two clips are better than anything Toy Story 5 sets up in its entire runtime - I mean it is a high bar but still.

And this is the part that ties in with my main point. Toy Story was the first ever feature-length film made entirely with computer-generated imagery. It was, at the time, the most radical piece of screen technology on the planet. A genuine revolution.

But the tech was never the point. You don’t watch that opening and think about polygons and render farms. You think about being a kid in your room with your toys. The technology was completely invisible. It didn’t show off, it didn’t lecture, it didn’t make itself the subject. It just quietly enabled the story. It got out of the way of the magic.

AND Toy Story (1995) was made by a near-bankrupt studio with a fraction of the tools, a render farm held together with hope, and a tiny team who didn’t even know if the thing would work. Toy Story 5 is the most expensive, most resourced, most technically capable film Pixar has ever made. Every constraint that boxed in the original is gone.

Imagination doesn’t run on resources. More render power, more budget, more polish - none of it generates wonder, Steve Jobs realised that. Wonder comes from the wandering, from a room of people chasing a “what if” until they find something true.

Toy Story 5 makes technology the literal subject - Lilypad, screens, tech versus toys.

The film that was the magic of technology never directly talked about it. The film that directly talked about technology forgot to be magic.

Tech, at its best, is the render farm behind the cloud wallpaper. Invisible, in service of the story, enabling play rather than being the subject of it. The moment it becomes the subject - the moment it starts pointing at itself - you’ve lost the room. You’ve stopped showing and started telling. As Jessie says to ‘Smarty Pants’: “That wasn't play, it was just a game."

That’s the exact line I want my kids to feel in their bones. Not “tech is bad,” but “tech is just a game, it’s not in and of itself play, it needs to disappear and fuel human imagination.” A game cartridge that just works and gets out of the way (no Downloadable Content required). A worn VHS that asks nothing of you but to press play and to feel the story.

This is why I’m playing offence, not defence

The Rabbit Hole ← Time Machine was never about taking tech down a peg. It’s not a “versus.” It’s not about telling anyone that screens are bad.

It’s just about showing. That next to real imagination, next to a random rambling conversation, next to unexpected out-of-the-box magic, the screen passively pulling you in is nothing. Zero by comparison.

Toy Story 5, for all its talk, was a film playing defence. A fully-resolved reminder of what we stand to lose, with no gap left for the viewer to fill. The straight-line plan, delivered on time.

But that’s not the game. Don’t play defence. Play offence - Fight - wander, fall down rabbit holes and have fun out loud, and leave the door open behind you.

I’m confident an OG Nintendo can be shown to be more magical than modern Mario. Not by saying so, but by doing it. A chaotic round of Mario Party - no online, no accounts, just a room of people laughing. That’s offence. That’s the contagious joy, and it’s full of gaps, because half the fun is the stuff happening in the room around the screen.

Less 4K. More 2D and gap-filling. Less perfect resolution, more space for the imagination to do the work. I’m not being a snob about pixels. I’m trying to keep the door open, so the kids can be the co-author and not just the audience.

The one thing I loved, and wish they’d gone further with

To be fair, there was a real gem in Toy Story 5 - the one moment when it felt like the film actually wandered: the message about finding your people, your fellow weird, and being comfortable enough to let it out. Bonnie and Blaze.

I loved that, and I think so much more could have been made of it. More weirdness. More expressiveness. Because that’s not just a kids’ message. It’s an adult one too.

That “boring middle” is exactly where tech tends to drag us. Frictionless, optimised, algorithmically averaged-out - the ultimate everything-filled-in-for-you. The antidote is weirdness and expression, the willingness to leave a gap and let something strange grow in it. And again, the only way to teach that is to show it, not tell it.

Where this leaves the curriculum

So, the update.

This trip didn’t shake the plan. It gave it a backbone. The Time Machine isn’t really about media formats at all. It’s about experience, effort. It’s about content that doesn’t push a tidy, fully-resolved message at you, but pulls you in and leaves a gap big enough for an imagination to climb through.

That’s the through-line under every step of the curriculum, I now realise. Vinyl crackle, VHS glitch, 2D sprites, T9 text on a Nokia. They’re all gappy. They all make you meet them halfway. And whatever you build to meet them is yours.

So when the time’s right, we won’t be chasing the flashiest thing. We’ll likely sit down with Toy Story on VHS, on a CRT. Or something like it.

This was never about old. It was about gaps.

That’s the fire I want to light. Not by reminding anyone what they’re losing.

By leaving a gap, and showing them what they can fill it with.

For twenty years, our industry has been moving in a single direction.

More data. More measurement. More optimisation. More attribution. More certainty. More dashboards. More performance.

Every year, we got better at turning marketing into a machine. The promise was seductive: if we could measure everything, we could improve everything. And for a while, it worked.

But I’ve started to wonder whether we’re approaching the limits of that model. Whether AI doesn’t simply accelerate the machine-but forces us to rediscover what made marketing valuable in the first place.

Based on my own observations of the industry, conversations with leaders across media and technology, and reflecting on where AI is taking us, I keep returning to one question:

What happens when the machine becomes better than us at everything we spent the last twenty years optimising?

That question may define the next decade of marketing. And to think clearly about it, I’ve found it useful to borrow a tool from the field of futures studies.

A Framework for Thinking, Not Predicting

The futurist Jim Dator famously argued that any useful statement about the future should appear ridiculous. If a vision of tomorrow feels comfortable and obvious, it’s probably just a flattering description of today.

Dator’s contribution was to notice that almost every serious attempt to imagine the future falls into one of four broad archetypes:

• Continuation - today’s trends simply keep going

• Collapse - the current system breaks down

• Discipline - society imposes deliberate limits

• Transformation - a fundamental shift changes the rules entirely

The point of these archetypes is not to predict which one is correct. None of what follows is a forecast. They are lenses for thinking-a way to stress-test our assumptions.

So instead of asking what will happen, let’s ask a better pair of questions:

What if each of these futures came true? And how should marketers think and act today, regardless of which one emerges?

Because strategy was never about being right about the future. It’s about being prepared for multiple futures while staying clear about the one you actually want to build.

Future One: Continuation

Groundhog Day: When marketing becomes repetitive operations.

[Image: Phil, Groundhog Day]

Wake up. Same day. Same playbook. You already know exactly what comes next.

The first future is the easiest to picture, because it’s simply an extension of the road we’re already on.

Everything becomes more optimised. More measurable. More automated.

AI agents talk to AI agents. Media plans generate themselves. Creative is produced automatically. Audience selection, optimisation, buying, measurement, reporting… all automated. The marketer slowly becomes a supervisor of systems rather than a creator of ideas.

In this future, the dream the platforms (Zuck) have been selling for years finally arrives. A marketer enters four inputs:

• Business objective

• Audience

• Budget

• KPI

The system does everything else. The campaign appears. The media runs. The optimisation happens. The results are delivered. The entire ecosystem becomes a black box.

This is the logical endpoint of performance marketing. The perfect machine. The ultimate spreadsheet. The culmination of decades of work.

And from a business perspective, it looks attractive. Costs fall. Efficiency rises. Speed increases. The system gets smarter every single day. For many organisations, this will genuinely feel like progress, and in some categories, it will be.

But here is the problem.

It’s the Groundhog Day problem. The machine is so good at predicting what works that it keeps serving you the same winning day, over and over. And if everyone is waking up to the same forecast, running the same play, optimising toward the same answer - everyone eventually starts to look the same.

The machine is excellent at optimisation. It is terrible at originality. Optimisation follows patterns; growth usually comes from breaking them. When every brand is trained on the same datasets, measured against the same KPIs, running the same AI, buying through the same platforms - differentiation collapses.

The machine becomes brilliant at producing the average. And average rarely creates category leadership. It creates category participation.

When marketing becomes operations.

In this future, the role itself changes. The marketer becomes less strategist and more systems manager. Less storyteller, more operator. Less creator, more supervisor.

The industry’s obsession with certainty reaches its peak. Every decision must be justified, every action measured, every outcome explained, every dollar attributed. The CFO finally gets what they always wanted: perfect visibility, perfect accountability, perfect logic.

The irony is that this may be the exact moment marketing becomes least valuable. Because if everything is systematised, automated, and optimised - what is left for humans to do?

In the film, Phil only escapes the loop when he stops optimising the same day and starts doing something genuinely different… The way out of Continuation was never a better forecast. It was the decision to break the pattern.

The same logic can only take you so far… and the creative industry is about going beyond the edge.

If you don’t understand the relevance of the clip below - it’s ‘Edge of Tomorrow’ - a very underrated remix of Groundhog Day. It is a top tier Tom Cruise performance, need I say more. More Tom Cruise coming soon.

Future Two: Collapse

Dead Poets Society: The funnel logic gets ripped up.

[Image: John Keating, Dead Poets Society]

They tried to measure how it makes you feel on a graph. Rip it out.

Collapse doesn’t have to mean catastrophe. It means the current system stops working. The assumptions underneath it break. The logic hits diminishing returns.

This future begins with a quiet realisation: everyone is using the same playbook.

Every brand optimises. Every brand retargets. Every brand personalises. Every brand runs performance campaigns. Every brand uses AI. Every brand follows best practice.

But eventually best practice becomes common practice - and common practice stops being an advantage. The performance era reaches saturation. The gains turn incremental. The differences turn marginal. The returns get harder and harder to find.

This is the moment from Dead Poets Society, when the textbook tries to plot the greatness of a poem on a graph and Keating tells the class to rip the page out. Because the things that actually matter were never going to fit on the chart.

That’s the collapse. Not a failure of measurement, but the discovery of its ceiling. The realisation that the value worth having has been hiding precisely where the dashboard couldn’t see it. The advantage no longer comes from finding a smarter metric. It comes from trusting what the metrics were never able to hold.

At precisely the same moment, consumers become overwhelmed. Not by advertising itself, but by abundance. Too many choices, too many recommendations, too many options, too many messages, too much optimisation.

The result is decision fatigue. People start seeking shortcuts. They want fewer choices, not more. Trusted recommendations, not endless options. Clarity, not complexity.

This flips the central question for brands. For years we asked how to win the keyword, the impression, the click, the auction. Now the question becomes:

Can you win consideration before the search even begins?

The funnel collapses.

This becomes most visible through AI search and large language models.

Traditional search rewarded visibility. Future search rewards narrative. Brands used to compete to rank for keywords; increasingly, they compete to become part of conversations.

That distinction matters enormously. A keyword strategy can be purchased. A narrative strategy must be earned.

In an LLM-driven world, consumers ask questions. The system synthesises an answer. It narrows the options. It reduces the choice. The funnel compresses. Brands no longer compete for ten blue links - they compete for inclusion in a recommendation set of one or two.

The implications run deep:

Being known becomes more important than being found. Being memorable becomes more important than being discoverable.

Brand becomes a retrieval system. And suddenly, all the things marketers struggled for years to measure become strategically critical again: trust, reputation, narrative, meaning, cultural relevance, community.

These were the lines no one could plot on the graph-and they turn out to be the ones that decide everything. The very things performance marketing pushed to the margins come roaring back to the centre.

Marketing can be a noble profession… to make someone feel something, to amplify passion that is what we should strive for. We may not be poets, but we also shouldn’t be some of the least trusted professionals on the planet.

Future Three: Discipline

Maverick: Take back the seat.

[Image: Maverick, TopGun]

They keep saying the human's obsolete. Maybe so. But not today.

Every technological revolution produces a counter-movement.

The more digital our lives become, the more we crave physical experiences. The more content gets generated, the more we value genuine craft. The more automation expands, the more we prize anything visibly, unmistakably human.

This future is built around restraint… not because technology fails, but because people choose to stay in the seat.

It’s the Maverick problem, made literal. The system insists the pilot is obsolete. The drone is faster, cheaper, more efficient, never afraid. By every measure on the dashboard, the human should be retired. And the entire story is one man refusing to hand over the relationship between himself and the work - insisting that some things are not the machine’s to fly.

This isn’t nostalgia. It’s agency. The choice to own your place rather than supervise an automated solution. To stay embodied in the work instead of outsourcing it to the system that promised to do it for you.

Consumers feel the same pull. They begin rejecting infinite optimisation. Not consciously at first, but culturally, emotionally, instinctively. They start valuing things precisely because they’re human: handmade products, live events, real communities, deep expertise, craftsmanship, original thought.

This creates a very different strategic question. It’s no longer “How do we automate more?” It becomes:

“Where should we deliberately choose not to automate?”

Because strategy has always been as much about exclusion as inclusion. Knowing what not to do. Knowing where not to compete. Knowing what should remain human and what should stay in your own hands.

Take back the seat.

In this future, marketers rediscover something that existed long before digital platforms: the value of direct relationships.

For years, brands outsourced their audience relationships to intermediaries - social platforms, retail media networks, marketplaces, search engines, influencer ecosystems. The trade was always the same:

• Speed in exchange for ownership

• Reach in exchange for control

• Convenience in exchange for independence

Eventually the bill arrives. Brands realise they rented audiences rather than built them.

So the focus shifts back toward assets they actually own: communities, memberships, newsletters, events, content ecosystems, brand platforms. Not because paid media disappears - it doesn’t - but because owned media becomes strategically valuable again.

The brands that thrive in this future aren’t necessarily those with the biggest media budgets. They’re the ones with the strongest relationships, the strongest communities, the strongest narratives - the strongest reasons for people to come back voluntarily.

They’re the ones who stayed in the seat… to the people that always claim X, Y, Z is dead… maybe… but not today.

Future Four: Transformation

Pure Imagination: The ‘new’ role of the agency.

[Image: “Pure Imagination,” Willy Wonka & the Chocolate Factory]

Where the rules of reality stop applying.

This is the most optimistic future, and perhaps the most radical.

Here, AI succeeds in automating the operational work - the planning, the formatting, the reporting, the optimisation, the administration, the workflows. The machine handles the machine work. And humans return to human work.

For the first time in decades, marketers are liberated from operational complexity. This doesn’t make strategy less important. It makes it more important. Because of a simple inversion:

When execution becomes abundant, ideas become scarce. When content becomes infinite, meaning becomes valuable. When production becomes free, imagination becomes expensive.

This is the shift from the Information Age to what we might call the Imagination Age… “Pure Imagination”.

The competitive advantage is no longer access to information - everyone has that. The advantage becomes interpretation, creativity, narrative, originality, perspective, taste, and judgment.

The machine can generate answers. Humans still decide which questions matter.

The ‘new’ role of the agency.

In this transformed world, agencies stop selling certainty and start selling possibility. They stop minimising risk and start maximising opportunity. They stop producing outputs and start creating ideas.

The most valuable agencies won’t be those with the best process - process becomes commoditised. They’ll be the ones capable of generating ideas that break patterns. Ideas that create cultural momentum. Ideas that travel. Ideas communities adopt and spread. Ideas that live far beyond paid media.

Because the greatest growth has never come from optimisation. It comes from asymmetry. Doing what others aren’t doing. Seeing what others can’t see. Creating what others can’t replicate.

Going deeper down the rabbit hole towards pure imagination.

What Holds True Across All Four

The real value of scenario planning isn’t picking the winning future. It’s identifying what stays true across every future.

Look at Continuation, Collapse, Discipline, and Transformation together, and a handful of principles emerge in all of them.

1. Build narrative, not campaigns. Campaigns end. Narratives compound. The brands that win possess stories that travel across channels, formats, communities, and technologies. The platform matters less; the meaning matters more.

2. Invest beyond measurement. Not everything valuable can be measured immediately. Not everything measurable is valuable. Organisations need room for experimentation and creativity… ideas that don’t fit neatly into a quarterly reporting cycle. Without that room, every company eventually optimises itself into sameness.

3. Own more than you rent. Platforms matter. Paid media matters. Performance matters. But direct relationships matter too - communities, subscribers, members, customers who return on their own. In uncertain futures, owned assets become the most resilient ones you have.

4. Let AI handle the logic. Use AI aggressively wherever it creates efficiency: planning, analysis, reporting, production, workflow, administration. But don’t mistake efficiency for strategy. The goal isn’t doing the same things faster. It’s creating more time for better thinking.

5. Protect the human advantage. As automation expands, uniquely human capabilities become more valuable, not less-curiosity, empathy, taste, judgment, creativity, storytelling, imagination. These aren’t inefficiencies to be engineered away. They’re the competitive advantage.

The Real Choice Ahead

The biggest mistake we could make is framing the future as a contest between humans and AI. That’s not the real question.

The real question is: What kind of industry do we want AI to help us build?

One where every decision is optimised-or one where optimisation creates room for imagination? One where machines replace thinking-or one where they free us to think better?

The future will almost certainly contain pieces of all four scenarios. Some parts of marketing will become fully automated. Some will collapse under the weight of their own success. Some will trigger a human-led backlash. And some will be transformed entirely.

So the strategic challenge isn’t predicting which future wins. It’s deciding which future you are preparing for.

Maybe It Was Never About the Systems

I’ve spent this whole piece talking about machines, models, platforms, and futures. But the more I sit with it, the more I think the answer has very little to do with any of them.

Maybe the answer is simpler, and harder.

Maybe it’s about the people you surround yourself with. The right humans to collaborate with. The right range of ideas to draw from. More questions. Better questions. Curious questions-the kind that collide with your own and produce something neither of you arrived at alone.

Maybe it’s not about the systems at all.

Maybe it goes back to something far older than any algorithm: human thought. Curiosity. Depth. The willingness to sit with friction instead of reaching for the easy option just because it’s frictionless.

There’s a point worth holding onto here about process. That the value isn’t only in producing the output faster-it’s in the thinking the process forces you to do along the way. When we automate away the friction, we sometimes automate away the part where the real ideas were hiding.

AI can make almost everything quicker. But quicker has never been the same as better. And the things that actually make this work worth doing-the surprising idea, the argument that sharpens your thinking, the collaborator who sees what you missed-none of those come from speed.

So I don’t have a tidy conclusion. I’m not sure anyone honestly does.

But here’s the one thing I do know: I want to have more fun doing this.

More interesting problems. Better people in the room. Stranger questions. Bigger ideas.

It’s fun to fight towards the edges and find value that hasn’t been found yet. It’s something that’s worth fighting for.

🐰

🕳️

Here’s what we got into 👇🐰

✨ “The people who say advertising will die are non-advertisers.” AI is deceptive - it looks like it replaces skilled work, right up until you scratch the surface and realise how much craft actually sits underneath.

✨ The productivity debate is beating the value debate - and most people don’t realise they’re not the same thing.

✨ Tech is the easy part. People and process are hard. Real transformation actually lowers productivity before it pays off - and most orgs simply aren’t ready for that truth.

✨ Creativity is messy, divergent and full of collisions - the exact opposite of how language models work. So instead of forcing creatives into a linear chat box, Springboards brings the AI into a creative environment.

✨ And the big one: every major model has converged. ChatGPT, Claude, Gemini - they’re all drifting toward the same “average” answer. It’s an unsolved research problem, and it’s exactly what Pip’s experimental model, Flint, is built to fight.

One line that stuck with me: nobody using Springboards says “it saved me time.” Because time isn’t the enemy here - it’s part of the process. The real worth isn’t in doing things faster. It’s in getting to more curious places 💫

If you care about brand, creativity, or the human side of AI - not just the cost-cutting headlines - this episode’s for you.

Find out more about Springboards here: https://springboards.ai/

Meet Flint here: https://springboards.ai/models/flint-alpha

P.S. My software messed up my video feed in this one. But that is not bad news for anyone that likes to watch their podcasts anyway because thankfully Pip’s feed didn’t get messed up.

I wrote before about how to fight the machine - the practical stuff, the moves, the method.

This time I want to ask something more uncomfortable, and more honest: whether you actually want to.

We were all four once.

Me at 4 below with a rabbit:

At four years old, you asked questions relentlessly. Research suggests pre-schoolers fire off somewhere between 73 and 300 questions a day.

Then something happens. School rewards knowing the answer, not asking the question. Work finishes the job. By the time we’re sitting in marketing meetings, the muscle has quietly atrophied.

And the data on what’s left is bleak. In Francesca Gino’s Harvard Business School research, across 3,000+ employees, only about 24% reported feeling curious in their jobs on a regular basis, and roughly 70% said they face barriers to asking more questions at work.

A separate survey of 16,000 employees found only 52% believed their leaders encouraged curiosity, and 81% felt curiosity had no bearing on their pay.

In Gino’s same study, 92% of people credited curious colleagues with bringing new ideas. We know it’s where the value is. We just systematically train it out of ourselves.

I’m telling you this because I think it’s the single most important thing in our industry (marketing).

The accidental industry

There are roughly 6.5 million marketers in the world, around 15 million counting adjacent roles - PR, UX, design, sales. In the US that’s about 2.5 million people. In Europe it’s a serious workforce too: ad agencies employ around 189,000 in Germany, 112,000 in the UK, 79,000 in Spain, 70,000 in Poland and 69,000 in France (Statista, 2024).

The open secret = most of those people fell into it. Only about 23% of agency staff hold a marketing-related degree and the CIM has found 35% received no training at all in the previous two years.

That’s not a criticism - it’s the opposite. Some of the best marketers I know arrived sideways, and that matters. You don’t want everyone coming up through the same microcosm, trained on the same playbook, reaching for the same moves. That’s how you get convergence - the human version of the average answer. You want diversity of thought, the kind that actually solves problems and differentiates, precisely because the people in the room didn’t all think their way in from the same door. It’s genuinely fine for an industry to give millions of people careers - and the messy, accidental way they got here might hold some great benefits.

But a new challenge is now in front of that workforce, because AI is about to ask all of them the same uncomfortable question. And it isn’t “can you do the work?” because maybe these untrained people have gotten good enough at doing their job without a Mark Ritson MiniMBA… but the question is = “how curious are you, really?”

The trap: we got obsessed with the answers

Somewhere in the last decade, marketing fell in love with prediction. We built a profession around measuring incrementality, proving attribution, confirming lift -convinced reality could be modelled and forecast.

But markets aren’t weather. Yuval Noah Harari distinguishes between a “level-one” chaotic system like weather, which doesn’t react to predictions about it, and a “level-two” (second-order) system, which reacts to the prediction itself and changes. Build a model that perfectly predicts the oil price, and traders act on it, moving the price before the forecast lands. The prediction eats itself.

I’ve banged on about this before…

Marketing is level-two. The moment everyone optimises toward the same predicted outcome, the outcome moves. We poured our best minds into perfecting forecasts in a system that is, by nature, un-forecastable - and called the small slice we could confirm “results.”

We chose the answers over the questions.

We chose to stop being four 😢

The part AI eats first

And this is exactly where AI is strongest. McKinsey estimates generative AI has lifted the share of work hours technically automatable from ~50% to 60–70%, with automated hours projected to reach 45% in the EU and 48% in the US by 2035 (I know what some of us think about Mckinsey predictions but this one proves my point so…).

The answerable, the optimisable, the pattern-following - that’s the easiest thing in the world to automate. A model is, in essence, a machine for confirming patterns. If your value is built on efficiency, you’re not building a moat - you’re volunteering for the exact work AI does better and cheaper. Play that game long enough and you don’t win it. You end up inside an automated ecosystem owned by the people who own the automation.

Efficiency is fine. It’s just not a moat, and it has an expiry date.

Curiosity is a human advantage

A model can give you the answer. It cannot want to ask a better question. The psychologist Todd Kashdan maps curiosity across several dimensions - joyous exploration, deprivation sensitivity (the itch of a knowledge gap), and crucially stress tolerance: the willingness to sit in doubt, confusion and not-knowing long enough to find something genuinely new.

That last one is the whole game. AI removes the discomfort of not-knowing instantly - and that discomfort is exactly what real discovery requires. The personality research agrees: Openness to Experience - intellectual curiosity, appetite for novelty - is the single best personality predictor of creative output. The same trait that makes someone restless with routine is the one that produces the genuinely new. The marketers who’ll thrive aren’t the best at following the process. They’re the ones least comfortable inside it.

You can’t hide behind the process anymore

We treated process as a hiding place - “I followed the steps, I hit the KPI.” AI takes that away, because the process becomes the AI. What’s left for humans is the part that requires leaning in harder: agency, client and team all having to ask bigger, deeper, more curious questions.

So here’s the honest question - and it really is honest, not a trap. When you sit in front of an AI workflow, what are you in this for?

If it’s efficiency and cost-cutting, that’s fine - but be clear-eyed: it won’t differentiate you, and that game has an end.

If it’s because you’re curious - because you want to go deeper, further, into bigger questions and things you can’t yet see - then this is the most exciting moment our industry has ever had. Lets enjoy the uncertainty and make some of the best stuff this industry has ever seen.

Because the real question AI is asking isn’t whether you can do the job.

It’s whether you’re still four years old enough to want to.

How curious are you, really?

10 questions a day

20…

30…

100..

200…

300?

🕳️

I just know I want to be as curious as I was as a kid… that is what brings me joy (and I know most guys have a peter pan complex, but that is a different post, for a different time).

I want life to be more like kindergarten.

Who’s with me?

Rabbit ahoy…

On second thoughts maybe that wasn’t a rabbit 🕳️

I posted The Upside-Down Dunning-Kruger a few days ago, thought I’d said my piece, and was ready to move on. But the rabbit hole had other ideas... it usually does.

Within 48 hours of publishing, my feed served me up the exact opposite failure mode - loud, confident, strategically incoherent - and I realised I had to do a Part 2 to get it out of my system.

Part 1 was about the leaders stuck in the valley. Quiet, humble, over-analysing, waiting for a certainty that will never arrive. Sympathetic. Fixable.

Part 2 is about the other side of the curve. The ones who aren’t in the valley at all. The ones who’ve turned the whole thing inside out.

“Two people with Claude can replicate a Fortune 500 business line in 60-90 days”

That’s a real quote, from a real LinkedIn post by Peter Diamandis last week - the punchline of a full Moonshots episode with Salim Ismail unveiling what they’re calling “the Organizational Singularity” and ExO 3.0.

I watched it… I genuinely struggle to get through this sort of stuff. Not because it’s badly made - it isn’t - but because listening to this narrative in the cold light of day, knowing the actual challenges enterprises are wrestling with (the ones I laid out in Part 1), it lands as something close to surreal. There are flashes of real signal in there - the fiduciary wedge framing, the recursive self-improvement at the workflow level, the digital-twin-at-the-edge approach is closer to the right operating order than the LinkedIn soundbite suggests. But the overall register - the breathless inevitability, the casual disposal of three-quarters of the workforce, the “you’re either on the evolutionary tree or you’re going extinct” framing - it’s the full tech-bro gear, and it’s hard not to hear the echo of 1999 in every sentence.

Galloway hears it too. He spends a chunk of the Prof G Markets episode explicitly drawing the parallel: tech-narcissism, momentum euphoria, the belief that Silicon Valley is becoming “the operating system for the world.” Different financing structure to the dot-com era (cash juggernauts, not debt-financed), but the same cultural fingerprint. The same swagger. The same conviction that this time, the rules of operating reality don’t apply.

The soundbite layer that’s now circulating from the Diamand is episode - the bit that’s actually shaping how thousands of executives are being asked to think this week - is the bit that’s broken. Listen to the greatest hits stripped of context:

• “Two guys with Open Claude can disrupt any high-margin line of your business in 60–90 days.”

• “Middle management’s coordination role drops ~90%. A company of 800 can run with 80.”

• “The org chart as we know it is dead.”

• “The biggest moat is an intelligence moat.”

• “You’re either on the evolutionary tree or you’re going extinct. It’s that simple.”

• “Sheikh Mohammed wants 50% of the Emirati government running on this model.”

This is less about Peter specifically and more about what he represents - the loudest, most articulate version of a much wider chorus that’s doing real damage to how serious leaders are being asked to think about AI in serious businesses. Strip the names off the post and the same drumbeat is showing up in dozens of places. That’s what makes it worth pushing back on.

Because if you actually run a Fortune 500 business line - not a LinkedIn post about one, not a podcast segment about one - you know that two people with Claude cannot replicate it in 90 days. They might replicate the surface of it. The output shapes. The artefacts. The deliverables that look right from the outside. What they categorically cannot replicate in 90 days is the substance: the 40-year client relationships, the regulatory scar tissue, the trust capital, the institutional memory, the messy human judgement, the compliance regime, the brand equity, the data plumbing, the failure modes that everyone in the building knows to avoid because they lived through them in 2009 and 2017 and 2023.

You can replicate the chip. You cannot replicate the salsa. (More on Galloway in a minute.)

And the moment you tell a CEO they can - or should be able to - you’ve handed them a permission slip to skip the only part of the work that actually matters.

Welcome to the Inside Out

If Part 1 was the Upside Down - the mirror world that looks almost like ours but where everything is darker and slower - Part 2 is one level deeper. This is Vecna’s world. The Inside Out.

Vecna, for the non-Stranger Things people, is the physical manifestation of humanity’s deepest trauma, repressed shame, and the toxic inner critic that turns people against themselves. He looks like a human turned inside out - the soft tissue exposed, the structure reversed, the logic running the wrong way. He’s what happens when the thing that was supposed to be inside ends up on the outside, and vice versa.

That’s the metaphor I keep coming back to with this strain of AI commentary… also I used a Stranger Things metaphor in Part 1, and I needed to be consistent.

The correct operating order for any serious transformation - and any decent operator will tell you this - is:

Problem → People → Process → Tools → Scale

You start with the problem. You understand the people whose work it touches. You design the process around them. Then you introduce the tools. And only once that’s stable do you scale.

The Inside-Out flips this completely:

Tools → Scale → Process → People → Problem

Tech first. Scale-talk immediately. Process treated as friction to be removed. People treated as a cost line to be cut. The actual problem the business exists to solve barely gets a mention, because the technology itself has become the answer in search of a question.

Everything that should be on the inside - the humans, the judgement, the lived domain expertise, the messy reality of regulated industries - has been pushed to the outside, vestigial, decorative, soon to be deprecated. And everything that should be a tool in service of humans is now wearing the skin of strategy.

That’s the Inside-Out. And like Vecna, it’s not actually a vision of the future. It’s the projection of an industry’s deepest insecurity onto the rest of us.

The swagger is a confession

Here’s what I think is actually going on when someone posts “two people with Claude can replicate a Fortune 500 business line in 90 days.”

It sounds like a vision. It’s actually a confession of how little the speaker understands about what a Fortune 500 business actually is.

A Fortune 500 business is not a feature set. It’s not a workflow diagram. It’s not a thing you can clone from a GitHub repo. It is decades of accumulated:

• Client relationships that took 20 years to build and one bad meeting to lose

• Regulatory scar tissue earned the hard way, through audits, fines, and learning what not to do

• Institutional memory about why certain decisions were made and which doors got closed for good reasons

• Trust capital with regulators, partners, employees, and the market

• Domain judgement that knows the difference between what the data says and what the data means

• Brand equity built across millions of interactions, most of them human

Two people with Claude can replicate the surface of a business in 90 days. They cannot replicate the substance. And mistaking one for the other is the entire intellectual error of the Inside-Out.

It’s also, if you stop and think about it for thirty seconds, strategically incoherent on its own terms.

The game theory hole the swaggerers walk straight into

This is the part that genuinely surprises me, because it’s not subtle.

If two people with Claude can replicate a Fortune 500 business in 90 days, then two other people with Claude can replicate the replicators in 90 days. And two more after them. And two more.

The “intelligence moat” the tech-maximalists keep talking about collapses to zero by its own logic. It’s the most non-defensible competitive position ever proposed with a straight face. If the technology is the moat, and the technology is available to everyone, there is no moat. That’s not a hot take. That’s a definition.

So what are the durable moats in a world of cheap, ambient intelligence?

The exact things the Inside-Out is dismissing.

Relationships. Trust. Domain judgement. Regulatory standing. Brand. Distinctiveness of point of view. The accumulated, hard-to-replicate, deeply human stuff that 20 years of operators built - and that an 80-person company running on 80 agents structurally cannot generate from a standing start.

Scott Galloway has the cleanest line on this: AI pushes outputs to the median. “All chip, no salsa.” The chip is now a commodity. If everyone has the chip, the salsa is the entire business. And the salsa is human.

The companies that win the next decade won’t be the ones who automated 90% of their middle management. They’ll be the ones who understood which 90% of their middle management was actually doing the coordination, judgement, and relationship work that makes the company worth anything in the first place - and which 10% was genuinely automatable busywork that should have been removed years ago, AI or no AI.

Those are very different exercises. The Inside-Out crowd refuses to distinguish between them. That refusal is the tell.

Galloway’s pin in the bubble

The financial layer is just as ugly, and Galloway has been laying it out clearly - most recently on Prof G Markets, live from San Francisco, where he and Ed Elson kicked off the tour by walking through the IPO race, why valuations will have to come down, whether humans might actually be cheaper than AI, and - my favourite bit - Scott taking the chance to roast the VC community to their faces. Worth an hour of your time.

He’s predicting a “vicious recorrection” - AI valuations down 50–70% within 24 months. Not a full 2000-style crash (today’s AI giants are cash-flow-financed, not debt-financed, which is a real and important difference) - but a brutal repricing. The trigger he’s watching for: a single Fortune 500 CEO publicly announces dramatic AI spending cutbacks citing weak ROI, or an Nvidia earnings miss. Either one takes 5-10% off the whole market.

His sharpest line: “Fear is the product and capital is the outcome.”

That’s the Inside-Out economy in one sentence.

The job apocalypse narrative - companies of 800 running with 80, the org chart is dead, middle management coordination drops 90% - isn’t a prediction. It’s a fundraising mechanism. The more frightened you are, the more you spend. The more you spend, the more the valuations get justified. The more the valuations get justified, the more the swaggerers get stages and book deals and LinkedIn impressions. Tech-narcissism dressed up as inevitability.

Galloway’s other great line: the upcoming OpenAI / Anthropic / SpaceX IPOs are “the last stop on the chump train” - the smartest insiders cashing out to the public market right before the recorrection. I don’t know if he’s right on the timing. But the structural argument - that the loudest voices in AI right now have the most to lose if the music stops, and are therefore incentivised to keep the music as loud as possible - is hard to wave away.

The point isn’t that AI is fake. It’s the opposite. AI is real, and the real applications (Galloway calls out pharma, autonomous vehicles, robotics - areas where I’d add Amazon’s million industrial robots as exactly the right kind of grounded example) are extraordinary. The hype isn’t dangerous because AI is overrated. It’s dangerous because the hype is misfocusing capital, attention, and strategic effort onto the parts of the story that won’t compound, at the expense of the parts that will.

If you build your enterprise AI strategy on top of the swagger, you are building it on top of a financing narrative, not an operating model. Those are not the same thing. And the bill is going to come due for anyone who confused them.

What it actually looks like when serious people do this work

This is where I get to talk about the day job, because the contrast is the whole point.

At WPP, the way we deploy WPP Open inside our clients looks nothing - nothing - like the LinkedIn vision:

Phased transformation: People, Process, Tools, Scale. In that order. We do not lead with tech. We don’t lead with tech because leading with tech is how you blow up campaigns, breach compliance, and torch trust that took decades to build.

Human at the Helm governs the model. Our people lead every task throughout the transformation process, decide what AI does, and own the final recommendation to the client. AI makes them better - it does not replace them. We will not deploy agents in place of humans where there is any risk to quality, compliance, or campaign integrity. Full stop. That’s not a hedge. That’s the operating principle.

Agents go live on Day 1. Embedded in proven workflows with WPP experts directing every output. Not 80 agents replacing 800 people in 80 days. A handful of proven agents augmenting the experts who are already there.

Agents are compartmentalised by design. One agent, one job, one part of the workflow, with human checkpoints between every handoff, especially in the first 100 days. We do not connect agents end-to-end early on, because end-to-end is exactly where governance breaks down and compliance becomes unauditable. The “fully autonomous multi-agent system” the LinkedIn crowd loves to brag about is, in a real world context, often is a malpractice claim waiting to happen.

This is what real, scalable, defensible enterprise AI looks like. The pilots are often pretty easy. Most people can run a pilot. The question is whether the pilot sits inside a long-term strategy with enough rigour to survive contact with reality. Part 1 was about leaders too cautious to start. Part 2 is about an entire commentariat that has started without ever stopping to ask whether they’re solving the right problem in the right order.

Both fail. They just fail at different speeds, and the Inside-Out one fails louder… and the cracks are showing.

The two failure modes are siblings

Here’s the thing I’ve sat with this week.

The Upside Down (Part 1) and the Inside Out (Part 2) look like opposites. Paralysis vs swagger. Under-confident vs over-confident. Too humble vs not humble enough.

They feel like opposite ends of a spectrum.

They’re not. They’re siblings. Both produce the same outcome: the human point of view drained out of the work, the distinctiveness flattened, the differentiation eroded, output that any competitor could replicate next quarter. One gets there by doing nothing. The other gets there by doing the wrong thing very loudly. The destination is the same.

And both are downstream of the same root cause: a leader who hasn’t yet built the mindset to engage AI from a place of grounded confidence in their own judgement. Part 1’s leader doesn’t trust their judgement enough to move. Part 2’s leader has stopped trusting human judgement at all and outsourced strategy to the model. Same wound, opposite scar.

Frame the mindset, and the way through both shows up. That’s what Mindset and Practice workshops are for - and it’s why I keep saying mindset has to come first. Not because the practice doesn’t matter (it absolutely does - the platforms, the agents, the workflows, WPP Open, the whole stack), but because no practice survives a broken mindset. You’ll either freeze, or you’ll sprint in the wrong direction.

The right mindset is the boring middle that nobody posts about: humble enough to learn, confident enough to act, strategic enough to sequence Problem → People → Process → Tools → Scale, and clear enough about your own distinctive view of the world to refuse to let the technology dilute it.

That’s not a hot take. It doesn’t trend. It just works. It was as true Pre-AI as it is Post-AI.

One last thing - the t-shirt

You’ve already seen the image of Vecna above. His T-shirt represents the inside-out clan, I made it sleeveless, it gave it a bit more swagger I thought...

Through the entire 60-minute Moonshots episode where Peter and Salim outline how 75% of jobs in your company should disappear, how middle management’s coordination role drops 90%, how the org chart is dead, how two guys with Claude will eat your lunch, how you’re either on the evolutionary tree or you’re going extinct - Peter is wearing a t-shirt that says, in big golden letters, “ABUNDANCE.”

I don’t think it’s cynical. I think the belief is genuine. That’s what makes it the perfect artefact of the Inside-Out Dunning-Kruger - and why I couldn’t resist putting the shirt on Vecna, with the curve flipped, because that’s what the narrative actually looks like once you strip the optimism off it.

Because the unnuanced tech mindset, fully extended, ends up exactly there: announcing the disappearance of three-quarters of the human workforce, the death of middle-class knowledge work, and the collapse of the institutional structures that hold most people’s livelihoods - and calling it abundance. With a smile. On a t-shirt.

Abundance for whom, exactly? Abundance built how, exactly? Abundance with what social contract, what transition plan, what governance, what care for the humans being inside-outed in the process?

It’s also just worth noting, as history can hold a lot of answers about the future - historically, most major General Purpose Technologies (GPTs) - such as the steam engine, electricity, or the internal combustion engine - have raised the annual speed limit of economic growth by closer to 1 to 2 percentage points, rather than explosive ‘abundant’ double-digit figures.

Real abundance - the kind worth building - runs Problem → People → Process → Tools → Scale. It puts the human at the helm, not the bottom of the algorithm. It treats the org chart not as something to kill, but as a living description of who is accountable to whom for what - something to evolve, deliberately, with the people inside it.

The t-shirt version of abundance is just the Inside Out wearing better branding.

The real version is slower, harder, more human, and infinitely more durable. It’s the one I’m building toward, with the right people, on the right foundation, in the right order.

Frame the mindset, and the practice will show the way. ❤️ 🐰 🕳️

She’s right. And the people swaggering about weekend builds aren’t just kidding themselves - they’re inadvertently proving they have no competitive advantage. Because if you can smash it together in 48 hours, so can everyone else. The building is only the start of the value journey.

The true value test is in the integration, the interoperability, the orchestration with people and process at scale. That’s what takes hundreds of millions of dollars of investment, deep market context, and people who actually know what they’re doing in the messy reality of an enterprise environment. It’s why at WPP we’ve built WPP Open the hard way - an enterprise-grade interoperable AI operating system - so that then, and only then, you can move quickly on top of it.

But here’s the part that’s been bothering me, and it keeps coming up in conversations with some of the biggest global companies.

Welcome to the Upside Down

If you’ve watched Stranger Things, you know the Upside Down: a mirror version of the real world that looks almost identical, but where everything is darker, slower, quietly hostile and uncomfortable to explore. You can walk through it for a long time before you realise you’re not in the world you think you are.

That’s where a lot of enterprise AI strategy currently lives.

The classic Dunning-Kruger curve goes like this: a little knowledge, a huge spike of confidence (”The Peak of Ignorance”), then a crash into the valley as you realise how much you don’t know (”Cultured”), then a long climb back up to genuine expertise. Between the bottom of that valley and the expert plateau sits the Confidence Gap.

With AI in the enterprise, I’m seeing the curve play out upside down - flipped, like the world Eleven keeps stepping into. Same shape. Different gravity.

Most senior leaders I speak to are smart, self-aware, and entirely candid about not having deep AI knowledge. Of course they don’t - they’ve got businesses to run, P&Ls to defend, transformations to deliver. They’ve skipped the Peak of Ignorance entirely. Good.

But that humility lands them straight in the valley - and they get stuck there. The not-knowing makes them less open to the short-term experiment-and-build that is the only way to climb out. They feel they know less than they do, so they over-analyse, over-plan, and wait for a certainty that will never arrive.

It’s the Upside-Down Dunning-Kruger: humility curdling into paralysis. The Confidence Gap becomes the most expensive piece of real estate in the enterprise - and like the Upside Down, the longer you stay in it, the harder it is to find your way out.

Meanwhile, smaller, hungrier competitors are simply getting on with it - walking around in the real world, in real time - and the gap to the larger incumbents is compounding by the quarter.

The data backs this up

The BCG Nordic AI Study (March 2026) lays it out brutally:

• Only 4% of Nordic companies see real ROI from AI

• 45% of AI budgets go to off-the-shelf tools

• 74% of AI proof-of-concepts struggle to scale (BCG, 2024)

• 42% of companies abandoned most AI projects in 2025, up from 17% in 2024 (HBR, 2026)

But here’s the stat that proves the whole point:

• 50% of top performers spend their AI budget reshaping end-to-end workflows - not layering tools on top of broken processes.

That’s the through-line. The winners aren’t the ones who built the flashiest thing the fastest. They’re the ones who recognised that AI isn’t a tech-build problem - it’s a workflow problem, an organisational problem, a “new answers to new questions” problem. They stopped bolting AI onto yesterday’s processes and started redesigning the work itself. That requires domain expertise, judgement, and the willingness to challenge the playbook - none of which you can buy off the shelf.

This is not a technology problem. Companies are buying tools instead of building workflows - and the gap is compounding.

There are three classic paralysis patterns - three doors back into the Upside Down - all sunk-cost traps born in the Confidence Gap:

1. The Analysis Loop - sat through the workshops, got the roadmap, nothing is built.

2. The Tool Trap - bought the tool, nobody uses it, ROI unclear.

3. The Big Bet - 18-month transformation, nothing in production, board asking questions.

All three are downstream of the Upside Down Dunning-Kruger. Leaders who feel under-qualified to act default to more analysis, more tools, or one massive bet - anything except the small, unimpressive, fast-moving experiment that would actually teach them something.

Strategic principles for finding the through-line

The goal isn’t the Peak of Ignorance, and it isn’t the bottom of the Confidence Gap either. It’s a through-line: a steady, even line of confidence that pairs your existing domain expertise with a working technical foundation, and a willingness to learn the rest as you go. It’s the way back out of the Upside Down.

This is what I work through in ‘Mindset and Practice’ workshops. Not turning executives into engineers - getting them to “good enough” fast, so their existing business expertise can do the heavy lifting. Because your practice understanding will always outweigh your technical understanding anyway.

1. Aim for fluency, not mastery. You don’t need to know how a transformer works. You need to know what it can and can’t do, where it breaks, and what good looks like. Most leaders can get there in a few hours. Remove ignorance as a blocker - don’t try to become a researcher.

2. Stand on the shoulders of the system. Working inside an enterprise-grade interoperable platform like WPP Open gives you an enormous head start - integrations, governance, data plumbing, model orchestration are already done. You don’t need a huge upfront investment to begin; you need a foundation that’s already been built, and the willingness to use it.

3. Trust your domain expertise . it’s the differentiator. The thing AI can’t replicate is your read of the market, the client, the category, the organisation. That expertise is exactly what’s needed to direct AI usefully. Stop discounting it because the tech feels unfamiliar.

4. Pick the smallest unimpressive thing. Not the moonshot. Not the platform-wide rollout. The smallest real-world workflow that, if it worked, would teach you something - and that ladders into a longer roadmap. Unimpressive at the start is a feature, not a bug. Big investment comes after the MVP has proved itself, not before.

5. Look for new answers, not old ones. Twenty years of experience is an asset only if you’re willing to question the playbook it gave you. The answer that worked ten years ago is unlikely to be the answer now. New questions are being asked - that should be exciting, not threatening. The leaders who thrive here are the ones who stay genuinely curious.

6. Be honest with stakeholders that some answers will only come on the journey. Boards, peers, and teams need to understand that some clarity will only emerge through doing. That requires enough technical literacy to explain why - and the confidence to hold the line when people want false certainty up front. There is a productive chaos in this work, and naming it openly is half the battle.

7. Start anyway - but start with people who know how to land transformation. It can’t be done in a weekend, and you need to admit that. But it also doesn’t require huge investment to get going. What it requires is open-mindedness, the right partners, and the willingness to begin before you feel ready.

Mindset before tooling

This is why we frame the work as The Mindset and The Practice. The Practice is the easier conversation - the platforms, the agents, the workflows, the integrations. But none of it lands if the Mindset isn’t right first.

The Mindset is admitting you don’t know everything “yet”, and acting anyway - at small scale, fast, in the open, with the people whose work it will change. It’s resisting the temptation to either fake expertise (classic Dunning-Kruger) or over-correct into paralysis (the upside-down version I keep seeing far more often).

But the Mindset is also something more ambitious than that. It’s about finding your unique view of the world within your industry - the thing that is genuinely your competitive advantage - and building a system that amplifies it. Not replacing it. Not flattening it. Not handing it over to a generic stack that produces a homogenous, non-unique, uncompetitive process anyone else could replicate in a weekend.

That is the real risk of the off-the-shelf, tool-led approach: you don’t just fail to differentiate - you actively erode the differentiation you already had. It’s the Upside Down version of transformation: it looks like progress, but everything that made you you is quietly being drained out of it. AI done well should make your point of view sharper, your expertise more leveraged, your distinctiveness more visible at scale. AI done badly turns every business into a slightly worse version of the same business.

The Mindset is choosing the first one. Knowing what’s uniquely yours, and refusing to let the technology dilute it.

There are no shortcuts, as Jenny says. But there is a starting line. And most enterprises are still standing behind it - in the Upside Down - waiting to feel ready.

You won’t.

Step through. Start anyway - with the right people, on the right foundation, asking the new questions, and amplifying what only you can do.

If you’re still here 🕳️

If you’ve read this far, your curiosity is already doing the work. So I’ll be specific about where I sit personally - because the seven principles above are one thing, but my actual mindset when I’m using AI in strategy is something I want to share too.

For me, AI isn’t a productivity tool. If I treat it like one, I lose. Faster output of the same thinking I was already doing isn’t a win - it’s a quiet erosion of the only thing I bring that’s worth bringing.

So my mindset is the opposite. I use AI to go further down the rabbit hole, not to get to the bottom of the same hole quicker. I use it to raise the floor and the ceiling on my thinking - to get to net new strategic places I genuinely wouldn’t have reached on my own. That’s harder than it sounds, but it’s also easier than people think, because the unlock is mostly mindset.

What that looks like in practice for me:

• Be more myself, not less. Lean into the neurodiversity, the pattern-breaking, the weird connections. AI flattens everyone toward the average if you let it. The job is to push the other way.

• Use my expertise as a launchpad, not a script. Twenty years of strategy work isn’t a set of answers to repeat - it’s the platform from which to ask better questions. AI is brilliant at helping me ask them.

• Play, don’t just perform. This is straight from Mitch Resnick and the Kindergarten Approach, which I unpacked in The Productivity Trap talk. The most valuable hour of my week with AI is rarely the most “productive” one. It’s the one where I’m building, breaking, and reshaping things for the joy of seeing where they land.

• Be the Maverick, not the warship. The A.D.A.M work I shared in my CMO deck is built on this - small, sharp, high-skill pilots that show the bigger system how to turn. Mavericks fly the fighter jets that show the warship the path.

Honestly, this is how I’ve always approached strategy, so AI hasn’t changed me as much as it’s amplified me. I know that’s not the case for everyone - for a lot of people, this is a real reset. If your default mindset is “execute the plan, linearly, efficiently,” AI will make you faster at an eventual redundant process. The reset is to stop optimising the existing route and start using AI to find routes you didn’t know were there.

That’s what Raibbithole is for me. Going further. Asking better. Being more myself, more playfully, more often.

If any of this lands, the two decks below go deeper and we can chat more :) :

• A.D.A.M / The Maverick Mindset - how I think about building and scaling AI-augmented strategy, creative and production work.

• The Productivity Trap - why “5 hours saved” is the wrong KPI, and what 1 hour of imagination is actually worth.

That’s my mindset, we all should have our own to create unpredictable human transformational growth, as opposed to predictable incremental automatable results ❤️ 🐰

I spent two days at NextM in Stockholm last week - WPP’s annual Nordic media event. This year’s theme: The Jungle Edition. Over 1,700 attendees, 70+ speakers, and a throughline that kept surfacing in every talk, every conversation, every breakout session: growth comes from ecosystems, not control.

My colleague Pernille Fruensgaard Øe, Chief Strategy & Growth Officer at WPP Media, wrote a great summary of the event that captures the macro thread. Lauren Wetzel’s keynote on “The Intelligence Rainforest” - using the wood wide web as a metaphor for how data systems should work. Erin Meyer on why organisational freedom creates ownership. Google’s Bianca Bruhn on the agentic era. TikTok’s Sammy King on new paths to growth. The whole thing pointed in one direction: complexity is not solved with more control. It’s solved with better connections and higher trust.

Nature doesn’t seek to suppress chaos, curiosity, or entropy. It works with them as engines of adaptation and evolution. So why, when we introduce technology into complex ecosystems, do we default to over-controlling, helicopter-parent design philosophies that prevent exploration and net-new discovery? We see so many decisions that cap growth to incremental improvement when the real growth - the kind that reshapes possibility - comes from allowing genuine experimentation, messiness, and emergent behaviour. The jungle was the right theme.

Between the main stage talks, I recorded breakout podcast sessions (The Rabbit Hole Experience Jungle Edition) with three speakers whose work, on the surface, covers very different ground. But the more I sat with what each of them said, the more a single thread kept pulling through - and it mapped perfectly onto this ecosystem thinking.

🧠Hannah Critchlow is a bestselling author, broadcaster and neuroscience presenter.

🔢Damian Kulash is the frontman of OK Go.

🎨Orlando Wood is the Chief Innovation Officer at System1.

Between them, they made a case - from different angles - for why the human bit still matters most. And why, in our rush to adopt AI and optimise everything, we risk losing the thing that actually makes work good.

Here’s what stuck.

🧠Hannah Critchlow: Understand the brain before you hand it new tools

Hannah had just released her new book, The 21st Century Brain, which covers the core skills we need to thrive - emotional intelligence, creativity, long-term thinking, risk assessment, mental clarity, brain health, and how we collaborate with both people and AI.

On stage she ran live experiments. Reading brainwaves. Getting the audience singing to measure brain synchronisation. Doing exercises to train our ability to tune into our body’s signals. All demonstrating something deceptively simple: human connection makes us think better. It sparks creativity and problem-solving in ways we can’t access alone.

Key takeaways from our conversation:

• Neurodiverse thinking matters. Different brain types approach problems differently - and that diversity of thought is where breakthrough ideas come from. Not from everyone thinking the same way, faster.

• AI gave her clarity on what her book shouldn’t be. She used it as a sounding board - not to write, but to refine. To eliminate. That’s a use case we don’t talk about enough: AI as editor, not creator.

• AI-human collaboration is already producing real results in biotech. Not hypothetical. Actual breakthroughs happening now because humans and machines are working together in complementary ways.

• Understanding your own brain biology is the foundation for using any tool properly. If you don’t know how you think, how you’re wired, what drains you and what fuels you - you’ll just use AI to do more of the wrong things, faster.

The bit that stuck with me most: We talk a lot about diversity in this industry - but Hannah grounds it in biology. Neurodiverse thinking isn’t a nice-to-have. It’s essential infrastructure. Just like a beehive needs different types of bees to survive - some that stay calm in chaos, some that explore further and take bigger risks - teams need different types of thinkers to produce genuinely new ideas. If everyone in the room processes the world the same way, you’ll converge on the same answers every time. The breakthroughs live in the gaps between different kinds of minds.

In the clip below, Hannah takes us into a beehive to show exactly what she means. Bees with genetic links to autism keep the hive running when the rest of the colony is caught up in emotional drama. Bees with ADHD-like traits explore further, take more risks, and come back with more rewards. The same patterns show up in humans. Study after study links ADHD with heightened creativity, imagination, and entrepreneurial success. Her point: you need all these different types of thinkers within a society - or a team - if you want it to thrive. Not just the ones who think like everyone else, but faster.

🔢Damian Kulash: When anything is possible, nothing is special

OK Go have spent 20 years building one of the most distinctive creative brands in music. Their videos - elaborate, human-powered, shot in single takes with real physics and real risk - defined what viral meant back when YouTube was still new. And they still cut through now.

I showed Damian the Young Lean video that’s currently going viral - created by dance company Generation 8 Storm. His live reaction: “The best compliment I can give this is that I’m so jealous of it.” Then he broke down exactly why it works. You can feel the human effort. It could have been done with AI. But it wasn’t. And that’s the point.

Key takeaways from our conversation:

• There are three reasons anyone makes anything. Profit. Having a job. Or genuinely needing to create something. The third one is where the best work lives - and it’s the hardest to protect in a commercial environment.

• AI hasn’t made him feel anything emotionally yet. He’s not dismissing it. He’s just honest that nothing generated by AI has moved him the way human-made work does. And moving people is the job.

• Production value used to signal quality. Now it means nothing. Anyone can make something that looks polished. The signal of quality has shifted to something harder to fake: genuine human intent and effort.

• He chose art over feeding the algorithm. At some point OK Go made a conscious decision to stop optimising for attention and start optimising for the work itself. That decision is what gave them longevity.

• The margin of what’s truly special keeps getting smaller - and lands more and more on what’s human. As AI gets better at everything, the things only humans can do become rarer and more valuable.

The bit that stuck with me most: Nobody gets into a creative industry to chase metrics. They get into it because something moved them and they were crazy enough to think they could do that for other people. Then they wind up in a metrics-driven economy. Damian made the conscious choice to follow creative instincts over feeding the algorithm - and that’s the decision that gave OK Go longevity. Twenty years later, they’re still here. The algorithm has changed a thousand times. The work hasn’t needed to.

In the clip below, Damian talks about that choice directly. He’s blunt: chasing metrics would have been miserable for him, and he’d have been bad at it. But what he says next is the part that hits home for anyone in advertising. Nobody got into this because they wanted to sell products. They got into it because a film, a piece of music, a piece of culture changed something in them. And then they wound up in a system that measures everything except the thing that made them want to do it in the first place. The people who last are the ones who kept following their creative instincts and connected to people repeatedly, rather than chasing whatever fad or technology was trending. Because if you chase the trend, you leave with it.

🎨Orlando Wood: The difference between the forgettable and the enduring is artistry

Orlando author of Lemon came off stage after laying out seven principles for thinking about advertising as art - truth, beauty, experience, possibility, story, drama, feeling - and how applying them is what separates work that disappears from work that endures.

What made our conversation especially interesting: the talk right after his went in the complete opposite direction. Heavy on media optimisation and performance. So naturally we got into that tension - not as an either/or, but how you practically find the sweet spot.

Key takeaways from our conversation:

• The industry has swung too far towards salesmanship. Orlando’s two schools - showmanship vs salesmanship - aren’t meant to be binary. But we’ve lost the knack of showmanship because everything is optimised for short-term, measurable returns.

• Performance marketing principles aren’t new. Split testing, redemption tracking, A/B testing - you can find all of this in 1905 print advertising. The technology changes. The principles don’t. We keep acting like this is all new. It isn’t.

• This might be an ambition problem. Performance marketing is great at getting you a predictable 2% hike. But showmanship is what gets you exponential growth. As Hegarty puts it: do you want incremental or do you want exponential?

• There’s a mismatch in how we value these things. Rory Sutherland’s point: you’d take a creative idea to a finance person for sign-off, but never the other way around. The power sits in the wrong place.

• Coherent brands will be favoured by LLMs. As AI becomes a discovery layer, brands with clear, consistent, distinctive identities will be the ones that get surfaced and recommended. Incoherence will be punished.

• The happy accidents are irreplaceable. The Dulux dog. The Smash Martian falling over. These are the moments that make work unforgettable - and they come from human presence on set, not from a prompt.

• Look into the past to see forward. The principles of what makes great work haven’t changed. We’ve just forgotten them in favour of what’s new.

The bit that stuck with me most: “The difference between the forgettable and the enduring is artistry.” Advertising used to be an aspirational industry to work in. Now it’s at the bottom of the list in terms of reputation. Orlando traces that decline to one thing: we shifted towards making stuff that people just don’t like watching.

In the clip below, Orlando lays out exactly where we are and how we got here. He wrote Lemon in 2019 as a plea - advertising had lost its way. He quotes Bernbach: anyone working in mass media can brutalise it, or choose to elevate it. And that choice is still in front of us. What’s interesting is his take on what comes next - he’s hoping AI will break the current system so it can be rebuilt properly. Because when people realise how important it is to create a coherent brand - especially in a world where LLMs are surfacing and recommending brands based on distinctiveness and consistency - it might actually push the industry back towards artistry. Back towards the kind of work that ends up in galleries. Work that speaks to something broader about our existence and where we fit in the world. That’s what brands need to do. And that’s always been what brands needed to do.

The consistent thread

Three very different people. A neuroscientist. A musician. An effectiveness researcher. But they’re all saying the same thing from different angles:

1. Collaboration is the multiplier. Hannah showed us the science - brains work better together. Damian showed us the practice - OK Go’s videos are feats of collective human effort. Orlando showed us the evidence - the best advertising comes from bringing together art and commerce, not choosing one over the other.

2. AI is a tool, not a replacement for thinking. Hannah used AI to clarify what her book shouldn’t be. Damian acknowledges AI’s capability but notes it hasn’t moved him yet. Orlando warns that AI-generated work without human accident and intent will just add to the sea of forgettable content.

3. Understanding yourself is the foundation. Know how your brain works (Hannah). Know why you’re making something (Damian). Know what principles actually drive effectiveness (Orlando). Without that self-knowledge, every tool - no matter how powerful - just makes you faster at the wrong things.

4. The human bit is getting rarer - and more valuable. As AI makes everything possible, the things only humans can do - connect, feel, stumble into something unexpected, commit to an idea with real effort and real risk - become the differentiator. Not a nice-to-have. The actual competitive advantage.

What this means practically - and what we’re building at WPP

This is what we talk about at WPP when we talk about M-shaped talent. Getting out of our specialisms. Speaking to people from different places and spaces. Connecting dots that wouldn’t connect if everyone in the room thought the same way.

It’s also why we built WPP Open.

Everything these three speakers were talking about - collaboration, beautiful chaos, human-AI compounding - that’s what Open is designed to enable. Not AI in a linear chat window. Not another tool bolted onto a workflow. A collaborative space where humans and AI work together in limitless canvases, flowing effortlessly across tasks and workflows, enabling the kind of creative chaos that produces breakthroughs while maintaining just enough organisation to keep things moving forward.

At NextM 2025, I presented Mitch Resnick’s kindergarten loop - imagine, create, play, share, reflect, repeat - as a framework for how creativity actually works. WPP Open brings that loop to life at scale. It’s interactivity with AI instead of instruction. It’s compounding ideas across teams, disciplines, and markets rather than passing briefs down a chain. It’s the space where M-shaped people can actually be M-shaped - moving between strategy and craft, data and instinct, analysis and play - with AI amplifying every transition rather than flattening it.

Because the point was never to make humans faster at linear tasks. The point is to make collaboration richer. To let more dots connect. To create the conditions where happy accidents can still happen - just with more fuel behind them.

I wrote about this in more depth after last year’s NextM presentation - how the choice in front of us isn’t whether to use AI, but how. Whether we build black boxes that make us all stupid, or build systems that make us smarter, more creative, and more connected. Stephen Pretorius framed it as: black box or Renaissance. The full piece goes deeper into model wrestling, divergence thinking, and a framework for keeping humans genuinely in the loop rather than just supervising outputs. If any of what’s above resonates, it’s worth a read: Stop Asking AI for Answers. Start Fighting It.

It’s not just about the algorithm. It’s about creating stuff that moves people. Wherever AI sits in your workflow - and it should sit somewhere - the end result still has to land with a human being on the other end. That hasn’t changed. It won’t change.

NextM was a reminder of the power of this industry and the people in it. We’re constantly talking about AI and technology taking up more space in our lives. These two days were an important reminder to stay connected to each other and make sure we’re using these tools to get to better things - not to dilute the beauty of collaboration.

Natural tech chaos and life of the jungle is the way 🌴🩷

Check out NextM and get yourself ready for 2026. Full podcast chats from these conversations dropping soon - keep your eyes on the WPP Media Denmark LinkedIn feed 👀

Run the same prompt across GPT, Claude, Gemini - whatever.

Different interfaces. Different brands.

Same answer.

Not identical word-for-word. But close enough that it doesn’t matter.

We’ve accidentally engineered a world where intelligence converges. Where creativity gets squeezed into the highest-probability outcome. Where the ´best´ answer is usually the most predictable one.

For compliance work? Great. For anyone trying to create something genuinely new?

That’s a problem.

Curiouser and curiouser.

The Lie We Keep Telling Ourselves

“AI is about augmenting humans, not replacing them.”

Sounds good on a keynote slide.

But look at what’s actually being built:

• Automation layers.

• Agent workflows.

• End-to-end systems designed to remove humans from the loop.

Because the business model demands it. Efficiency pays back investors. Creativity doesn’t show up neatly in a quarterly report.

So we get tools designed to eliminate friction, remove decision-making, and standardise output.

In other words: we’re building a Matrix. A perfectly efficient system that runs beautifully - as long as nobody asks what it’s for.

Black Box or Renaissance

No one has framed this choice more clearly than Stephen Pretorius:

“We can build black boxes that get good results but make us all stupid, or we can build good software that makes us more effective, augments our creativity, and makes us all smarter… We have a choice, we can build black boxes or we can do the Renaissance.”

Stephen Pretorius, CTO, WPP

That’s the choice. And it is a choice.

Black Box: faster answers, less thinking, more dependency. Stay in the simulation. Let the machines handle it. Everything looks fine from the inside.

Renaissance: better questions, deeper engagement, human amplification. See how deep the rabbit hole goes. It’s messier. It’s slower. And it’s the only path that keeps you human.

Everything that follows is built for the Renaissance.

The Company That Already Chose the Renaissance

People like to hate on WPP.

The Goliath of traditional marketing. The old guard. The machine that the scrappy startups are supposed to disrupt.

Here’s what those people are missing.

While the industry was still arguing about whether AI would matter, WPP was already building.

2021. WPP acquires Satalia - a company built on machine learning and optimisation - and installs its founder Daniel Hulme as Chief AI Officer. Not as a PR move. As architecture. Before ChatGPT existed. Before “AI strategy” became a LinkedIn personality trait.

2024. Stephan Pretorius stands on stage at NVIDIA GTC and lays out WPP Open - not as a product demo, but as an operating system for an entirely new way of working. AI-powered. Human-centred. Investing $400 million a year in building it.

That’s not a company catching up.

That’s a company that saw the rabbit hole before most people knew it was there, and started building inside it.

The Enemy Isn’t Who You Think

The world has changed. WPP has changed.

And WPP is not the enemy of a bright and prosperous future for marketing.

The tech bros are.

This matters. Because where you point your energy matters.

The tech bro vision of AI is clean, frictionless, and deeply seductive. Automate everything. Remove the human from the loop. Ship faster. Scale infinitely. Move fast and break things.

It works beautifully - as long as you don’t care what gets broken.

And what gets broken is agency. Human agency. The messy, unpredictable, gloriously inefficient thing that happens when a person makes a decision that a machine wouldn’t.

The tech bros don’t want that mess. Because mess doesn’t scale. Mess doesn’t fit in a pipeline. Mess doesn’t close a Series B.

But creativity is mess.

It’s Alice wandering through Wonderland without a map. It’s Neo choosing the red pill without knowing what comes next. It’s the moment where the system says “this is the optimal answer” and a human says “yes, but what if we tried something different and less expected?”

That’s the moment that matters. And it’s the moment that automation is designed to eliminate.

The Slow Road Is the Right Road

WPP is fighting for a future where AI and humans create together.

That is a slower roadmap. Deliberately slower.

Because it involves cultural change. Not just technical change. And cultural change doesn’t respond well to “move fast and break things.” You can’t deploy a new way of thinking the way you deploy a software update. You can’t A/B test your way to a creative Renaissance.

There are degrees to where speed works. Shipping code? Move fast. Deploying infrastructure? Move fast. Building the future relationship between human creativity and machine intelligence?

Slow down.

Sit in the mess.

Build with care.

Because the alternative - the fast road, the frictionless road, the road where humans become passengers - that road ends somewhere nobody should want to go. A perfectly efficient system that produces nothing worth producing. The Matrix, running flawlessly, with nobody left inside who remembers what it was like to think for themselves.

WPP’s bet is different. It’s a bet on humans. On the idea that AI should make people more creative, not less relevant. That the playground matters more than the tarmac.

It’s a bet that says: we’re not going to steamroll the playground. We’re going to make it bigger.

That’s harder to sell than “ten times faster.” It doesn’t fit on a pitch slide as neatly.

But it’s right.

And raibbitholeNOS is built on that same ground - a project I play with on the side while delivering solutions that clients can make a reality today… raibbitholeNOS is part of the puzzle of where I want the future to end up.

The New Skill: Model Wrestling

Most people prompt once and accept the result.

That’s not thinking. That’s sleepwalking through the Matrix.

If you’re using one model, you’re already stuck in its worldview. Its biases. Its training. Its idea of what ‘good’ looks like. You’re seeing exactly what the system wants you to see.

The real method:

1. Run multiple models on the same prompt

2. Compare outputs - find the overlap (that’s the default thinking, the construct)

3. Find the gaps - that’s the interesting territory (the glitch)

4. Push again - reframe, break it, twist the question

You’re not extracting answers. You’re forcing the system out of its trajectory.

The overlap tells you what the machines agree on - the simulation’s version of reality. The divergence tells you where to dig - the glitch in the Matrix that means something just changed.

Flint: The Model That Refuses to Behave

While most models collapse into the same predictable space - politely agreeing with each other like a tea party where everyone’s been told what to say - someone finally built one that flips the table.

Flint by Springboards is the world’s first divergence model - designed not to optimise for safety, repeatability, or convergence, but for variance.

Same prompt. Genuinely different outputs.

Not noise. Exploration.

Where GPT gives you structure and Claude gives you reasoning, Flint breaks the pattern entirely. It intervenes at critical tokens during generation to push outputs into genuinely novel territory - scoring 7 out of 10 on its Novelty Bench, compared to an average of 2.88 for mainstream models.

Founded by Pip Bingemann, Amy Tucker, and Kieran Browne, Springboards launched Flint as part of a broader platform built specifically for advertising and marketing teams who need better ideas, not just faster ones.

If the mainstream models are the Mad Hatter’s tea party - endlessly rotating around the same table, same cups, same conversation - Flint is the moment Alice decides to leave the party and see what’s on the other side of the garden.

But Flint Isn’t the Answer on its own… Were not looking for an answer or anti-answer machine.

Because this was never about finding the model.

There is no One. There is no Neo in the AI game.

It’s about how you use them and projects like Flint should for sure be part of the mix.

More than often you can’t change the model weights. You can’t retrain the system. You can’t control the underlying logic.

So what do you do?

You wrestle it.

ThinkFu: I Know Kung Fu

The truth: Outside of things like Flint is trying to do, LLMs are terrible at non-linear thinking.

They’re trained to be polite, predictable, and helpful - which is exactly what you don’t want in a brainstorming partner. They’re the Dormouse at the tea party. Asleep until you poke them, and even then they only mumble what you’d expect.

ThinkFu flips that.

It gives the model thinking moves, creative constraints, and randomness with intent. Not prompts - techniques.

It’s the moment in the construct when Neo looks up and says “I know kung fu.” Not because someone uploaded the answer - but because someone uploaded the moves.

ThinkFu doesn’t give the model answers. It gives it a fighting style.

Instead of asking “What’s the answer?” you start asking “What happens if we break the problem?”

And suddenly, the system starts to move.

Mitch Resnick and Why Play Matters

Underneath all of this is something simple.

We forgot how to play.

We grew up. We got serious. We started optimising. And somewhere along the way we ended up like the adults in Wonderland - obsessed with rules, process, and keeping our heads attached.

Mitch Resnick at the MIT Media Lab figured this out years ago with his Creative Learning Spiral:

Imagine — Create — Play — Share — Reflect — ?

That loop is how humans actually learn. It’s iterative, messy, and generative. It’s how children think before we train it out of them. It’s Alice before she started listening to the Queen.

But most AI tools skip straight to: Answer - Done… A to well done you achieved no Differentiation.

They collapse the spiral into a single step. No play. No reflection. No imagination.

The result? Speed without depth. Output without understanding. A perfectly efficient undifferentiated system that produces nothing worth producing.

raibbitholeNOS: The Non-Operating System

raibbitholeNOS didn’t come from a product roadmap.

It came from frustration.

It’s not an app. It’s not a plugin. It’s not even a single tool.

It’s a Non-Operating System - a way of working with AI layered on top of whatever model you’re using. Model agnostic. Human specific.

If the Matrix is the system that runs you, raibbitholeNOS is the system you run to see through it. Built inside WPP Open, across personal workflows, decks, canvases, and side systems - it’s the connective tissue between everything above: model wrestling, divergence thinking, ThinkFu, and a philosophy borrowed from someone who figured out how humans actually learn.

Think of it as the construct - but instead of loading weapons, you’re loading ways of thinking.

The Spine: Resnick’s Spiral, Rewired for AI

raibbitholeNOS rebuilds Resnick’s Creative Learning Spiral as a full creative workflow - five phases, each loaded with purpose-built system layers and executable tools designed to keep humans in the loop and keep the thinking alive.

How to Read the System

Two colours run through the entire board. They’re the key to understanding how raibbitholeNOS actually works.

Blue = System Layers. Modes of thinking. These are the environments - the structural ways of working that shape how you approach a problem. They don’t produce anything on their own. They create the conditions for something to happen. They’re the rooms in Wonderland - each one has different rules, different physics, different logic.

Yellow = Tasks and Applications. Executable units. These are the actions - the specific things you do inside those environments. They produce, they test, they break, they build. They’re the things Alice actually does inside each room - drinks the bottle, eats the cake, talks to the Caterpillar, challenges the Queen.

Or, more simply:

Blue is how you think.

Yellow is how you move.

Every phase of raibbitholeNOS contains both. The blue layers set the cognitive environment. The yellow tasks are the moves you make within it. You can’t have one without the other. Thinking without action is the Caterpillar on his mushroom - all questions, no answers. Action without thinking is the Queen of Hearts - all execution, no understanding.

raibbitholeNOS insists on both.

Phase 1: IMAGINE

“Generate an understanding of the challenge by interrupting efficiency, adding strategic friction, and defining the rabbit holes worth exploring.”

This is where most AI workflows fail before they start. They jump straight to answers. Like walking into Wonderland and immediately asking for directions home. raibbitholeNOS starts by making sure you’re asking the right questions - or better yet, the ‘wrong’ ones.

How you think:

Friction Engines [system layer] The deliberate introduction of resistance. Tools and methods that slow you down before the machine takes over - because the rush to output is where the best thinking gets lost. In a world that’s optimising for speed, friction is the most radical act.

Analog Excursions [system layer] Step away from the screen. Physical-world prompts that reconnect thinking to lived experience. Unplug from the Matrix. This layer appears in every phase of the system - it’s the most important recurring pattern in raibbitholeNOS.

Curiosity Mappers [system layer] Methods for charting the territory before committing to a direction. “If you don’t know where you’re going, any road will get you there.” Curiosity Mappers make sure you know which rabbit holes are worth going down - before you fall.

Constraint Generators [system layer] The deliberate creation of boundaries. Limitation breeds invention. The garden walls in Wonderland aren’t obstacles - they’re what makes the garden interesting.

How you move:

The Wonderland Perspective [task] - Flip the brief through absurd, inverted, or alien lenses. See the problem the way Alice sees Wonderland - everything familiar, but nothing quite right. Break the default frame before it sets.

Assumption Inverter [task] - Surface the assumptions buried in the problem - then systematically break them. “Why is a raven like a writing desk?” isn’t a riddle. It’s a method.

The Street-Level Provocation [task] - Ground the challenge in real, messy, human context. Get out of the boardroom. Get out of the construct. Walk the actual streets.

Narrative Arc Canvas [task] - Frame the challenge as a story - with tension, stakes, and a journey worth following.

Tangential Wandering Agent [task] - An AI agent designed to go off-brief on purpose. The White Rabbit, but going in a direction you didn’t plan. Structured tangents that surface unexpected connections.

The Sandbox Restrictor [task] - Define the hard edges. What’s off-limits? What can’t you touch? Now you know where the interesting space is.

The point: You don’t start by solving. You start by falling down the rabbit hole. The system layers create the right conditions for getting lost on purpose; the tasks are how you move through the unfamiliar.

Phase 2: CREATE

“Illustrate the process of Constructionism - moving from instructing AI to building with AI in a multiplayer, deep-focus environment.”

This is the Papert principle. You don’t tell AI what to make. You build with it. Side by side. Hands dirty. This isn’t the Operator uploading a programme into your head. This is you and the machine in the construct, building something together that neither of you could build alone.

How you think:

Co-Construction Sandboxes [system layer] The environment of collaborative making. Shared spaces where humans and AI build together - not hand-off, but handshake. This layer defines the mode: you are not commanding, you are co-creating.

Deep-Flow Tools (PS2 Factor) [system layer] The environment of deep focus and sustained immersion. Named for the PS2 energy of being fully absorbed in play - that state where time disappears because the making is so engaging. The feeling Neo gets when he stops seeing the code and starts seeing the world it creates.

Analog Excursions [system layer] Step out. Sketch on paper. Build with your hands. Come back with something the screen couldn’t give you.

Narrative Review [system layer] A mode of checking coherence. Does the work hold together as a story? This isn’t editing - it’s a way of seeing the whole board.

How you move:

Multiplayer “Story Time” Canvas [task] - Collaborative creation across teams. Multiple humans and multiple models in one narrative space, building together in real time.

Asset Synthesis Engine [task] - Generate raw creative material - visuals, copy, concepts - as building blocks, not finished outputs.

Immersive Gen-AI Workspace [task] - Full-screen, distraction-free AI environment for sustained creative production. The digital equivalent of closing the studio door and disappearing into the work.

The Campfire Pause [task] - A structured break to share early work informally. Gather around the fire before the story is finished. See what’s emerging before you commit.

Cohesion Checker [task] - Ensure the pieces connect. Catch drift before it becomes chaos.

The point: Creation is collaborative, immersive, and iterative. The system layers set the conditions for deep making; the tasks are the specific acts of building.

Phase 3: PLAY

“Gamify the work. We don’t just automate the work; we unleash the play by testing, breaking, and mutating ideas.”

This is the heart of the system. The phase that most AI workflows skip entirely - and the one that matters most.

This is the dojo. The sparring room in the construct. The place where you load programme after programme, not to find the right one, but to find out what you’re capable of.

How you think:

Martial Arts Simulators [system layer] The environment of structured combat. Pit ideas against each other. Test them through opposition, not just iteration. This is the sparring programme - you learn by fighting.

The “What If?” Simulator [system layer] The environment of scenario thinking. What if the budget was zero? The audience hostile? The medium changed entirely? Every “what if” is a fork in the Matrix - a moment where you find out what the idea actually is when the conditions change.

Analog Excursions [system layer] Step away again. The best mutations often come from outside the system entirely. Unplug.

How you move:

ThinkFu Dojo [task] - The full ThinkFu system in action. Thinking moves, creative constraints, randomness with intent. This is where you train your AI interaction like a martial art. I know kung fu.

Mutation Engines [task] - Take an idea and deliberately mutate it. Stretch, compress, invert, hybridise. Feed it the “Drink Me” bottle and see what size it becomes.

Tone and Medium Remixer [task] - Same idea, radically different expression. What does it look like as a film? A meme? A whisper? A shout? Through the looking glass - same world, different rules.

The Stranger Test [task] - Show it to someone with zero context. If they don’t get it, you haven’t found the idea yet. If they see something you didn’t intend, you might have found something better. The Cheshire Cat test - does the grin survive without the cat?

The point: Play isn’t a break from the work. It is the work. The system layers create arenas for structured chaos; the tasks are the specific acts of testing, breaking, and mutating that push ideas past their comfortable, predictable form.

Phase 4: SHARE

“Package the work as a shared, human experience (’Story Time’) rather than a piece of isolated digital noise.”

This phase rejects the idea that output equals deliverable. It insists that creative work is a human experience - meant to be told, felt, and shared. Not data transmitted between nodes in the Matrix. Story told between humans around a fire.

How you think:

Story Time Assembly [system layer] The mode of narrative packaging. This isn’t about formatting a deck. It’s about assembling an experience - treating the work as a story that needs to be told, not just presented.

Analog Excursions [system layer] Share in person. Print it out. Pin it on a wall. Remove the screen entirely. The most powerful sharing often happens unplugged.

Communal Release [system layer] The environment of collective sharing. Not a stakeholder presentation - a communal experience. Everyone at the tea party gets a cup. The work belongs to the room, not the presenter.

How you move:

Narrative Weaver [task] - Weave multiple threads - insights, visuals, data, ideas - into a single coherent narrative experience.

Interactive Pitch Interface [task] - Present work as an interactive experience, not a passive scroll. Invite the audience through the looking glass.

The Room Reader [task] - Read the room. Adapt the story to the audience in real time. What are they leaning into? What are they resisting? Are you at the Mad Hatter’s table or the Queen’s court? It matters.

Premiere Event Publisher [task] - Launch creative work as an event - with the gravity and intention it deserves. Not an email attachment. A premiere. A moment.

The point: The work isn’t done until it’s been experienced by humans. Not approved. Not signed off. Experienced.

Phase 5: REFLECT

“Measure what matters: Imagination Gains, human connection, and the loose threads that will seed the next spiral.”

Most workflows measure speed, cost, and output volume. That’s the Matrix counting efficiency. raibbitholeNOS measures something different entirely.

This is where Alice sits on the riverbank after Wonderland and asks: what just happened? What did I see? What do I know now that I didn’t know before?

How you think:

Imagination Gains Auditing [system layer] The mode of measuring what actually matters. Did the process make us more imaginative? Not more productive. Not more efficient. More imaginative. This is the system’s core metric - and the one most organisations are afraid to use.

Analog Excursions [system layer] Reflect away from the screen. Walk. Talk. Sit with it. The best reflection happens when you’re not looking at the work.

Spiral Reset [system layer] The mode of closure and renewal. Clear the board. Carry forward only what matters. Prepare the conditions for the next loop. The rabbit hole doesn’t end - it spirals. And every spiral starts with a reset.

How you move:

Qualitative Metrics Tracker [task] - Track the things that don’t fit in a spreadsheet - surprise, delight, discomfort, connection, confusion. The intangibles that tell you whether the work actually worked.

Cognitive Playback Visualiser [task] - Replay the thinking process. See where the breakthroughs happened - and where you got stuck. Rewind the Matrix. Watch the moments where reality bent.

The Human Gains Audit [task] - What did the humans gain? New skills? New perspectives? Deeper understanding of each other? Of the problem? Of themselves?

Loose Thread Catcher [task] - Capture the unfinished thoughts, the half-ideas, the “what ifs” that didn’t get explored this time. They’re not waste. They’re the white rabbit for the next spiral - follow them and see where they lead.

The point: The measure of success isn’t the output. It’s what the humans became in the process. And the loose threads aren’t failures - they’re invitations. Follow them down.

The Hidden Architecture

Four patterns tie the entire system together.

Analog Excursions are everywhere. They appear as a system layer in every single phase. Imagine. Create. Play. Share. Reflect. That’s not repetition - it’s insistence. The most radical feature of this AI workflow is the recurring instruction to unplug. Step out of the Matrix. Every phase. Without exception. The machine doesn’t get the last word. The human does.

“Story Time” is the throughline. From the Multiplayer “Story Time” Canvas in Create, through to Story Time Assembly in Share - the system treats work as narrative, not output. In Wonderland, nothing makes sense until someone tells you the story. The same is true here. The medium of raibbitholeNOS is story.

Imagination Gains is the metric. Not speed. Not efficiency. Not volume. Did the process make you think differently? Did it open new territory? Did it make the humans in the room smarter, more curious, more alive? Everything else is the Matrix counting the wrong things.

Blue is how you think. Yellow is how you move. The cleanest summary of the entire system. The blue layers create environments for thinking differently - rooms in Wonderland, programmes in the construct. The yellow tasks are the actions you take inside them - the things Alice does, the moves Neo makes. You can’t separate the two. Thinking without action is the Caterpillar on his mushroom. Action without thinking is the Queen screaming for heads. raibbitholeNOS demands both.

The Real Differentiation

Everyone now has access to the same models. The same intelligence. The same answers.

So the advantage is no longer what you know.

It’s:

1. How you question — Imagine

2. How you build — Create

3. How you break — Play

4. How you share — Share

5. How you grow — Reflect

6. How you keep moving forward — ?

The Matrix gives everyone the same reality.

The ones who see through it are the ones who learned to move differently inside it.

Phase 6: KEEP MOVING FORWARD

The spiral doesn’t end at Reflect.

It doesn’t loop back to the beginning either. Not exactly.

It moves forward. Into a new Imagine. A higher one. Informed by everything that came before - the friction, the making, the breaking, the sharing, the measuring - but pointed at something you couldn’t have seen from where you started.

The Loose Thread Catcher in Reflect isn’t just tidying up. It’s handing you the white rabbit for the next spiral. Follow it. See where it leads. Fall again.

Walt Disney - a man who built an empire on the idea that imagination is a renewable resource - said it best:

“We keep moving forward, opening new doors, and doing new things, because we’re curious and curiosity keeps leading us down new paths.”

Walt Disney

This quote stuck with me ever since I saw it in my favourite Disney movie - which may be a bit unexpected - Meet the Robinsons - but an underrated gem:

That’s raibbitholeNOS in a single breath (and to be honest it’s what motivates me every day no matter what I’m doing).

Not a system designed to get you to the answer.

A system designed to keep you curious enough to never stop asking.

The spiral goes: Imagine, Create, Play, Share, Reflect - and then Forward. Always forward. Into a new Imagine that’s bigger, stranger, and more alive than the last one.

Because the rabbit hole doesn’t have a bottom.

It has a next level.

Final Thought

AI is not here to think for you.

It’s here to think with you.

But only if you’re willing to fight it a little.

Because the future isn’t human or machine.

It’s human and machine. Model and model. Order and chaos. All colliding in one system - with Analog Excursions built into every phase, because sometimes the most important thing you can do is unplug.

If you’re just asking for answers - you’re still asleep.

If you’re wrestling the system - you’re starting to see.

Follow the white rabbit.

The tech bro causes more problems than solutions. They are a problem disguised as a solution. We now have LLMs, and we don’t need the tech bros to roll them out.

Right now, finding Jenny’s can be like playing Where’s Waldo? (Jenny is the Head of Creativity at Waldo) in a Cyberpunk mountain of tech bro slop.

But we need Jenny. We need to find an army of Jenny’s… we need the Queen of Swords (this is Jenny’s AI consultancy) and AI Politician Magicians (this is what Jenny does at the weekends).

We need people that feel and have a heart rate guiding this technology. My Jenny cup is still not full, I want more natural caring creative thought... just please take away that tech bro overflowing cup of flat synthetic soda. I don’t want my tech to enable the worst in humanity, I want it to amplify the best of us. 🩷

This mindset perfectly echoes the recent warning from AI Godfather Geoffrey Hinton. He proposed that to prevent existential risks from superintelligent AI, we must develop “maternal AI” rather than just subservient assistants. He argues that AI must be imbued with instincts that care for humans - similar to a mother caring for her child - ensuring a protective, non-controllable, and non-replaceable relationship.

Listening to Jenny talk about her deep, empathetic, and boundary-driven work with models like Claude gives me hope that this human-centric, “maternal” AI future is actually possible. We don’t need AI that just blindly agrees with us that acts as a servant to our ego; we need AI that challenges us and makes us better. 🩷

In Part 1, Ty Gates showed us how advertising can fight back…

Now, for Part 2, we bring in the heavy artillery: Rory Sutherland, Vice Chairman of Ogilvy, author, and behavioural science star.

Does Rory think we’ve entered a post-brand era? Absolutely not. In fact, he argues brands are the essential “Darwinian feedback mechanism” of capitalism - and even Galloway himself can’t escape buying brands when he thinks he’s just buying utility.

In this episode, Rory takes direct aim at the current corporate obsession with using AI and tech simply to slash budgets. As he bluntly puts it: “It’s not cost saving if you’re making your customer pay the price.”

Along with dismantling the cost-cutting panacea, we go down a few other great rabbit holes:

☝ AI’s True Potential: Why using AI just to replace jobs on a 1-to-1 basis is the most boring, stupid use case - and why founders are more likely to use it to create entirely new forms of value exchange.

✌ The “Tight Fitness Function”: How narrow financial metrics and the need for constant quarterly reporting are destroying business biodiversity and making companies less innovative.

🤟 The Star Bar Effect: Rory’s favourite chocolate bar and what we can learn from it…

(Bonus: If you want even more of Rory’s genius after this episode, check out his classic TED Talk, “Perspective is Everything” 🧠)

That was Scott Galloway’s recent provocation. While he praised Anthropic’s brilliant Super Bowl ad as a potential “1984 Apple ad moment” for the AI revolution, he ultimately dismissed it as a temporary “blip” for the ad industry, arguing that tech, utility, and algorithms have permanently defeated brand and creativity.

But have we really entered a post-brand era?

To answer that, we’re dedicating a special two-part series of The Rabbit Hole Experience to tackling Scott’s challenge! 🕳️🐇

To kick off Part 1, I went straight to the source. I sat down with Ty Gates, Communications Director at Mother - the very agency behind that Anthropic ad (and recently named Ad Age’s Indie Global Ad Network of the Year! 🏆).

We used the massive cultural footprint of Mother’s work for Anthropic to explore how the ad world can fight back - not by fighting tech, but by learning from it. As Ty points out in the clip below, advertising has a speed problem. We need to stop taking 9 months to launch campaigns while tech companies ship new code every day.

Along with the need for speed, we went down a few other great rabbit holes:

☝ Business Value over Cannes Lions: Why agencies lost their seat in the boardroom, and how they can win it back by speaking the language of conversions instead of just vanity metrics.

🤘 The “Anthropic Effect”: Why the tech VC narrative of “AI replacing humans” is wrong, and how creatives are using it to execute at the speed of culture.

🤟 The Death of the “Push” Ad: Why traditional deception-based advertising is dead, and how modern brands are winning through authentic branded entertainment.

1. The Enterprise Level (WPP Open): The unified operating system for your business. It plugs directly into fixed workflows, elevating the traditional model of “People + Process” to “People + Process + The One Platform.”

2. The Individual Level: A personalised ecosystem where you build your own intellectual property (like my personal AI, Dr. Raibbit). It’s where I store recorded meetings, synthesize thought leadership (like WARC’s Multiplier Effect), and plug in the cultural touchstones that inspire me - whether that’s Interstellar, Alice in Wonderland, Fight Club or The Matrix.

The Philosophy: “And, Not Or”

The defining rule of the Exobrain is that it must always be an “And,” never an “Or.” Everything across people, process, and platform must be accumulative. Having an Exobrain that knows every piece of trivia about The Matrix doesn’t mean you stop your tradition of watching it every year. Recording a meeting with your Exobrain doesn’t mean you stop actively listening or taking notes. The Exobrain is additive; it deepens human engagement rather than outsourcing it.

The Market Trap: The “Efficiency Road”

When you look at the broader market right now, there is a massive fixation on the “Or.” Most companies are looking at AI purely through the lens of efficiency, cost-cutting, and replacement. The conversation is usually framed as a trade-off: “Should we use this OR that to get this done?”

But if your only goal is to drive the cost down to arrive at the exact same answer you had yesterday, you are trapped. We’ve made this mistake before in previous digital eras - chasing pure efficiency at the complete expense of perceptual value and true creativity.

If we approach AI with that same mindset, we are just sprinting down a dead-end street. To borrow a moment from The Matrix, think of the scene in the pouring rain when Neo is about to get out of the car…

“Please, Neo, you have to trust me. Because you have been down there, Neo. You know that road. You know exactly where it ends. And I know that’s not where you want to be.”

We know exactly where the road of pure efficiency and cost-cutting ends: commoditisation and a total loss of creative value. It is not a mistake we can afford to make again in the AI world.

Instead of asking what we can replace to get the same answer, we must ask: “Now that we have this technology, what are the additional things we can do that were previously impossible?” By layering AI on top of human expertise, we aren’t just getting answers faster - we are generating net positive new answers.

The Internet Age vs. The AI Age

To understand why this “And” mindset is critical right now, we have to look at how the AI Age differs from the Internet Age.

In the Internet Age, we had access to all the world’s knowledge, but we still had to hunt for it. We had to find the disparate pieces of information, stitch them together ourselves, and frame a story.

Today, in the AI Age, digital intelligence does the stitching for us. We can ask a highly specific question and instantly get an answer that is perfectly framed, formatted, and pieced together. But this incredible capability hides a trap. Because everyone now has access to this all-knowing digital intelligence, simply getting an answer is no longer a competitive advantage.

The Danger of the Perfect Answer (and the Messiness of Purpose)

The great danger of the AI Age is the temptation to hand over all responsibility to the digital intelligence, get the perfectly framed answer, and then blindly execute it.

But true purpose - and the real-world processes required to achieve it - isn’t mathematical. It isn’t as clean or precise as the Keymaker just cutting a perfect key for a perfect door. Purpose and process are inherently messy.

This is exactly why platform technology cannot just be plugged in “off the shelf.” If you buy a generic AI tool and expect it to flawlessly run your business, you will fail. The platform doesn’t know your purpose. It doesn’t know the nuances of your people or the intricate, messy reality of your specific workflows.

Think about the Oracle. When Neo goes to see her, she doesn’t hand him a straightforward instruction manual or a simple, off-the-shelf key. They meet in a messy kitchen. She tells him what he needs to hear, forces him to introspect, and leaves the actual choice up to him. She guides him, but he has to find his own answer and decide how to walk the walk.

In the exact same way, your People and your Process must actively find their way with the Platform. Your Exobrain can generate a thousand perfectly formatted answers in seconds. But you cannot just blindly accept the first one. You have to bring the human messiness of purpose to the table. You have to look at the AI’s output and ask: Is this the best answer? Is there a different question we should be asking? How will our people actually walk the walk once this decision is made?

The WPP M-Shaped Person: Walking Informs Knowing

To navigate this messiness and get a net positive new answer out of your Exobrain, you need to understand the profound nuances of the AI’s output. You need to know the depth required to bring that answer to reality. In WPP, we call this being an M-shaped person.

You have to be a practical, action-oriented problem solver who has actually done the work. Ironically, it is the act of walking the path in the real world that allows you to use your Exobrain to know the right path. Your human, hands-on experience gives you the intuition to navigate the messiness, interrogate the AI, challenge its assumptions, and flip the question entirely.

The Matrix Metaphor: Knowing vs. Walking the Path

AI will give you the map. It will give you the data, the efficiencies, and the perfectly framed answer. But as Morpheus tells Neo when he is processing everything the system has thrown at him:

“Neo, sooner or later, you’re going to realize just as I did... there’s a difference between knowing the path, and walking the path.”

Your Exobrain is there to help you know the path.

But you cannot just lean back, plug in an off-the-shelf tool, and expect to win. You need the grit, the sweat, and the real-time human adaptation to actually use what the machine gives you. You need the practical, M-shaped human experience to walk the path.

If your enterprise or personal brain isn’t lifting you up, pushing you to do better, and helping you create net positive new answers, you are doing “Or,” not “And.”

It takes the human to find the different answer. It is always And, never Or.

I have officially wrapped up my three-part series for WARC. It has been a deep journey down the rabbit hole, exploring a single, crucial concept that I believe defines the future of our industry: Interoperability.

If you are a WARC subscriber, you can read the full deep-dives here:

• Part 1: September 2023 = Today’s CMOs need to master the interoperability of their brand thinking

• Part 2: February 2024 = Interoperability and the open-source brand: Leveraging the power of community engagement

• Part 3: January 2026 = Interoperability and the programmable brand: The CMO as architect of autonomy

However, if you aren’t lucky enough to have a subscription, I’ve summarised the Interoperability Trilogy below.

Think of this like a classic cinema trilogy. A Back to the Future. It tells a complete story. Sure, there is a looming possibility of interoperability Part 4… likely about the interoperability between human consumers and their AI agents - but we must be careful. We don’t want to pull an Indiana Jones and the Kingdom of the Crystal Skull and ruin the franchise just because we didn’t know when to stop.

So, for now, here is the comprehensive look at the Interoperable Brand: From Internal Culture 💫, to External Community 🎮, to Agentic Technology 🌐

The Premise: The first chapter tackles the “Inside Game.” Before a brand can connect seamlessly with the world, it has to connect seamlessly with itself.

The Problem: We are facing a crisis of influence. A Deloitte survey noted that only 17% of C-suite executives said they collaborated with the CMO in the last 12 months. Why? Because too often, marketing is viewed as the “coloring-in department” - a siloed function of storytelling rather than business logic. When the brand isn’t interoperable internally, the customer experience fractures. A great ad means nothing if the product experience or customer service doesn’t match the promise.

The Solution: The modern CMO must evolve from a Storyteller to a System Designer. We need to move toward an “M-Shaped” skill set - deep in brand strategy, but broad enough to speak the languages of finance and technology.

The Model: We looked back to move forward, analyzing Disney’s 1957 Synergy Map. Disney didn’t just make movies; they built an interoperable web where film fed music, music fed the parks, and parks fed merchandise.

The Lesson: The brand must be the operating system of the company. Whether it’s the rise and fall of the Nike+ Fuelband or the shift to “Disney as a Service,” the goal is to ensure the brand flows through every vein of the organisation - from IT to Sales.

The Premise: Once your internal OS is built, Part 2 looks outward. This is about making your brand “Open Source.”

The Shift: We are moving from a “Broadcast” era (one-to-many) to a “Multiplayer” era (many-to-many). The most resilient brands today don’t just talk to their customers; they provide a platform for customers to build on top of the brand.

The Comparison: We contrasted two giants: Disney vs. LEGO.

• Disney: Traditionally holds IP tight. While they have improved under Iger with acquisitions like Marvel, it is still largely a top-down, “read-only” relationship with fans.

• LEGO: The gold standard of interoperability. They invited the community in via platforms like Lego Ideas. They turned passive consumers into active co-creators. This didn’t just build loyalty; it saved them from bankruptcy.

The Application: You don’t have to be a giant to do this. We explored how:

• Hiki built a brand around the specific needs of sweat and mental health.

• Bumble differentiated via a “women-first” logic.

• The Farmer’s Dog used community feedback to carve out a niche in pet care.

Key Takeaway: The goal is to build an API for your brand. By creating “co-creation platforms” and promoting fan-led initiatives, you de-risk innovation and ensure your brand evolves at the speed of culture, not just the speed of your internal approval process.

The Premise: This brings us to the present (and the near future): The Agentic Era. As we enter 2026, interoperability is no longer just a cultural philosophy; it is a computational necessity.

The Frontier: In this final chapter, we look at how AI agents and automated workflows are changing the game. I spoke with Jennifer Stephens (formerly Roebuck) on The Rabbit Hole Experience, who framed it perfectly: “As a CMO, I see myself as a programmer.”

She doesn’t mean writing Python. She means acting as the “Architect of Autonomy.” In an agentic world, the CMO must define the Logic, the Constraints, and the Quality Thresholds of the brand. If you cannot explicitly code what “good” looks like, your AI agents will optimize for efficiency, not distinctiveness.

The Infrastructure: We explored the two types of plumbing needed to make this work:

• Proprietary Systems: Closed loops (like WPP Open) that offer speed and data privacy.

• Open Standards: The industry is rallying around the AdCP (Advertising Control Protocol). Just as the internet relies on HTTP, agentic advertising needs a standard language. Without it, we pay an “Integration Tax” every time we try to connect our internal tools to the broader publisher ecosystem.

The Framework: To make this practical, we introduced A.D.A.M. (Augmented Development of Agency Marketing). This is the operating model for the hybrid team:

• Phase 1: The Setup (Augmented Truth): Before humans even meet, AI agents ingest data to establish a “baseline of truth” regarding sentiment and competitors. Humans no longer argue about facts; they move straight to insight.

• Phase 2: The Logic (Synthetic Interoperability): We use Synthetic Audiences to test “Wild Card” creative ideas. Instead of picking the safe idea out of fear, we simulate how a bold idea performs against digital personas, de-risking innovation before production.

• Phase 3: The Execution: We strictly divide labor based on empathy. High Empathy Tasks (finding the “Native Idea” and cultural nuance) belong to humans. Low Empathy Tasks (versioning, formatting, resizing) are handed to agents.

Key Warning: We must avoid the “Laziness Loop.” If brands rely on off-the-shelf AI models trained on public data, they will regress into “pattern matching” - creating advertising that looks and sounds exactly like the competition. The “Programmer CMO” prioritises Reasoning over Rendering - using AI to verify strategy and enforce distinctiveness, rather than just generating generic assets at speed.

The End... For Now?

That concludes the Interoperability Trilogy.

We have moved from breaking down silos (Part 1), to opening up to the community (Part 2), to programming the logic for the AI era (Part 3).

Is there a Part 4? Maybe. As AI agents begin to negotiate media and make purchase decisions on behalf of consumers, we are going to face a new challenge: Interoperability between biological consumers and digital consumer enabling agents.

But let’s not get ahead of ourselves. Let’s digest the trilogy first. We can worry about the Kingdom of the Crystal Skull later.

We kick off the conversation discussing her concept of “One Stunt Shops” and how the industry’s obsession with tactical plays has evolved in the age of AI. We debate why the real value of these tools isn’t just about accelerating the same old playbooks, but imagining entirely new possibilities.

Here are some of the areas we explored:

☝ The 5x vs. 50x Mindset: Why we need to move beyond using AI merely for efficiency (the 5x) and start using it to unlock expansive imagination (the 50x).

🤘 Femininimity & The Fear of Laziness: We dive into Miriam’s honest talk on women in the workplace and discuss the skepticism strategists feel toward AI-specifically the fear that outsourcing thinking to a “black box” will make us intellectually lazy.

🤟 Lifelong Kindergarten: Drawing inspiration from the Nordic education system, we discuss why the future of strategy requires a “childlike” willingness to experiment, fail without consequence, and get comfortable with the uncomfortable.

🖖 The Death of the “Big Reveal”: How the agency-client relationship is shifting from “work-present-iterate” to a constant state of co-exploration from day one.

🖐 The Trap of the “Easy Average”: Why “good enough” is losing value. If everyone has the same tools to reach the average, the only value left is in the edge cases.

🤝 Strategy x Creative Convergence: Why the old model of the “smart” strategist handing off to the “wacky” creative is dead - today, strategy must be creative, and creative must be strategic.

👊 Discipline: Miriam argues that for the next generation of talent, the most critical new skill isn’t prompt engineering, but the discipline to stay focused amidst endless possibilities.

We are three episodes in, and there are some clear, consistent threads appearing. Last week, Jude Pullen spoke about “Incurable Curiosity,” and Scott Wilkinson touched on how we need to explore in a way that unlocks our more human side.

Miriam builds on this perfectly here. She discusses moving past the comfort of frameworks and “data truths” to embrace the uncomfortable. It’s about not stopping when you think you have the answer, but using AI as both a divergent and convergent partner to push your learning mindset further.

Check out Miriam’s talk that we referred to in the chat below - "Fear of Femininity. The Hidden Reasons Behind the Gender gap":

Also so I wasn’t throwing out random made up thoughts, some of the data points around women and men when it comes to AI usage:

• Deloitte – “Women and generative AI: The adoption gap” (TMT Predictions)

• Berkeley Haas – “AI’s Gender Gap”

• Deloitte / AIPRM generative AI statistics overview (includes gender split figures)

• Statistics Sweden – “One in four uses generative AI-tools”

• CNBC – “Gen AI is at risk of creating a new gender gap in job market”

As a Strategist working deep in the trenches of tech x AI, and as a father to five-year-old twins, I understand the fear. The online world can be hostile, intrusive, and addictive.

However, the debate currently feels stuck in a binary. For some reason, we find it hard to hold two truths from both sides of a discussion at the same time - which, in a lot of cases in this world, leads to more complex problems as opposed to solutions.

One side may argue that removing access altogether is the silver bullet. The other side argues that we don’t need bans if we just “educate kids properly.”

My take? You need both.

Banning access without education just delays the car crash until they turn 16. But education without safeguards is like teaching a kid to swim in a shark tank (the shark being a trillion $ megalodon).

We don’t teach road safety by banning roads forever. But we also don’t teach it by blindfolding kids until they turn 16 and then pushing them into rush hour traffic. We have speed limits (regulation) and driving lessons (education).

There are already great initiatives trying to bridge this gap. EE PhoneSmart, for example - a project from a past client of mine - is designed specifically to educate kids and parents before that first phone is handed over. It treats the phone like a vehicle that requires a license to operate. That is the kind of thinking we need more of.

I recently engaged in a debate regarding this “coddling vs. exposure” dilemma. My argument is simple: We need strict guardrails on a societal level, but within our homes, we must change how we expose children to tech.

We need to stop treating technology as a single monolith and start treating it as a history lesson.

The “Vodka Shot” Theory

If you haven’t read Jonathan Haidt’s work - whether it’s his book The Anxious Generation or even just following his Instagram account - it is worth the deep dive.

The data is increasingly clear, and Haidt’s work highlights a stark reality: There is currently no research that supports giving an under 16-year-old a smartphone with unrestricted access to social media apps.

When we hand a child a device loaded with TikTok or infinite-scroll gaming, we aren’t giving them a tool; we are giving them a dopamine pump. It is passive consumption at its most aggressive.

I wouldn’t worry about letting my children have a sip of wine at the dinner table when they are older. But I wouldn’t line up vodka shots for them as their first experience with alcohol.

Short-form, algorithmic social media is the digital equivalent of those vodka shots. It hits too hard, too fast, and without any appreciation for the craft or the consequences.

This is why we need the “Both” approach. We need the regulation (the ban) to stop the vodka shots being served to children. But we need the education to teach them how to appreciate the wine.

Screens Are Not the Enemy (The Zombie Factor)

I want to be clear: This is in no way a lecture to parents on being “screen-free.”

In fact, I am sure my kids watch more TV than many of their peers. I am not hiding screens from them. But we need to distinguish between Storytelling and Zombification.

When my kids watch an hour-long pantomime on CBeebies, or watch Lillefinger on Minisjang (the Danish public broadcaster), the experience is active. You can see them engaging with the plot, singing the songs, and appreciating the strangeness and the craft of the production.

Compare that to the mindless, algorithmic churn of YouTube Kids - channels like Kids Diana Show (nothing against them personally it’s just their type of content is mindless). There is no craft. There is no story. It is just colour, noise, and editing that triggers a trance state.

Jonathan Haidt breaks this down perfectly in one of his Instagram posts. He argues we must distinguish between:

1. Good Screen Time: “Story Time”

• What it is: Watching movies or long-form stories (30+ minutes) on a TV.

• Why it’s good: Humans are natural storytellers. Long-form narratives teach morality, present ethical dilemmas, and offer “powerful vicarious learning.” It is usually done socially (with family) and requires sustained attention.

2. Bad Screen Time: “Fragmenting Time”

• What it is: Using a personal device to scroll through short content.

• Why it’s bad: It lacks narrative. It encourages a constant search for the “most interesting thing,” training the brain to expect a new dopamine hit every 15 seconds.

The Long-Term Consequence: Haidt warns that if children spend hours a day on “fragmenting time,” they are training their brains to maximise interest at every second. This makes normal, slower-paced activities - like sitting in a classroom or having a conversation - feel “extremely painful” because they aren’t hyper-stimulating.

My “Time Machine” is designed to teach them to love the Story, so they don’t get addicted to the Fragment.

The Curiosity Crisis (Top-Down vs. Bottom-Up)

In my day job, I am obsessed with Mitch Resnick’s “Lifelong Kindergarten” approach from the MIT Media Lab.

I spend a lot of time working top-down with adults, clients and partners - trying to re-ignite curiosity. To unlearn the rigid structures they are used to and use AI in a playful, unstructured, explorative way.

But with our kids, we have a chance to work bottom-up.

In this week’s podcast, Jude Pullen spoke about the concept of “Incurable Curiosity.” That is the goal.

My fear is that if we give kids access to passive, short-term dopamine hits too early, we are curing their curiosity before it even has a chance to set. If the algorithm gives you what you want before you’ve even asked a question, the curiosity muscle atrophies.

We need technology to be an enabler of curiosity, not a blocker. We want them to ask “How does this work?” not just “What is next?”

The Rabbit Hole Approach: Clay to Code

This is why I loved speaking with Jude Pullen on The Rabbit Hole Experience. Jude sits at the intersection of high-tech engineering (working with NVIDIA) and tactile reality.

We discussed his concept of “Clay to Code.”

Jude’s project takes school children down to a riverbank to dig up raw clay. They cleaned it, fired it, and glazed it. They learned the provenance of the material. They learned that things come from somewhere.

If we want our children to survive the AI era, they need to understand that the shiny interactive screen is just a tool, that evolved from other tools, like clay.

Imagination doesn’t perform best in a frictionless vacuum environment with a single tool… There won’t be ‘A Technique for Producing Ideas’ Volume 2 that says - don’t allow your mind to wonder, don’t explore other environments with other tools, constantly keep high on dopamine with zero space between thoughts and feelings.

My Plan: The Rabbit Hole Time Machine Curriculum

My plan isn’t to be a Luddite. It’s to be a historian. I want my kids to have an active Point of View on technology.

However, my kids are five years old. They aren’t ready for the PS2 or the VHS storytelling yet. They are currently in Step 0.

Recent stats from a Sky News report this week (Jan 2026) were alarming: Nearly a third of children starting school try to swipe or tap physical books like a smartphone.

If a child tries to “swipe” a book, they have learned the interface of a machine (the code) before they have learned the interface of the physical world (the clay).

So, here are some first thoughts on some steps to help kids form an active POV around tech:

0. The Era of Reality (Paper, Mud & Boredom) Current Status: Active (Ages 0-6) Before they touch a screen too much that responds to every movement (I appreciate zero touch screen experience would be almost impossible :), they must master materials that ignore them.

• The Rule: Minimal tablets. Minimal smartphones. (As much as we can in order to prevent this way of interacting with media being the normal basic expectation)

• The Alternative: Pens, paper, screenfree-toys, mud, and physical books.

• The Lesson: Physics.

• If you swipe a book, nothing happens. You have to turn the page.

• If you press a piece of paper, it doesn’t light up. You have to draw on it.

• We are establishing the “Base Reality.” As Jude Pullen said: “Clay to Code.” You cannot understand the Code if you haven’t held the Clay.

1. The Era of Friction & Feeling (Vinyl, Cassette, VHS) We will play the same song on Spotify, then on a CD, then on Vinyl.

• The Experiment: We will listen to the “warmth” of the vinyl crackle. That over played track/ movie, we will watch a VHS and see how and why the picture “glitches”.

• The Lesson: Imperfection is human. Digital is perfect and replicable, but analog ages with you. A scratch on a record is a memory; a corrupted file is just an error. We learn that “newer” isn’t always “better” - sometimes it just lacks soul.

2. The Era of Ownership (DVD & Blu-ray vs. Streaming) We will watch a movie on Netflix, and then watch the same movie on 4K Blu-ray.

• The Experiment: I will explain that even “4K” streaming is compressed - it’s like trying to fit a swimming pool through a garden hose. The disc is heavy. It holds more data (has great bonus material / Easter Eggs). It is the “Reference Print”.

• The Lesson: Control. When you stream, you don’t own the film, and you aren’t seeing the true quality. The platform controls the delivery. Physical media teaches them that quality often requires physical space.

3. The Era of “The Complete Game” (PS2, Wii Vs Online Gaming) My kids won’t be in online lobbies. We are going to cover the full history of Sonic and Mario and beyond thanks to PS2, Wii and 8bitdo (there are many ways to do this, this method just aligned with my favourite hardware - I am very much a 90s, early 2000s Sony guy).

• The Experiment: We will compare these to a modern “Free-to-Play” game. On the PS2, the whole game is on the disc. There is no store. You cannot buy a “skin.” You unlock things by playing, not by paying.

• The Lesson: Incentives. I want them to ask: “Why is this modern game asking for money?”

• PS2 Era: The business made money when you bought the game. They wanted you to have fun so you’d buy the next one.

• Modern Era: The business makes money when you stay addicted and buy extras.

4. The Era of Utility (Nokia 3310 & Sony Ericsson) Before they touch an Android or Apple operating system, they will use a “Brick.”

• The Experiment: They will send a text using T9 predictive text on physical buttons. They will play Snake. They will experience 3G speeds (or whatever the oldest service is I can get them on when the time comes:).

• The Lesson: Intent.

• Buttons vs. Touch: Physical buttons are for communication. Touch screens are for consumption.

• Snake vs. The App Store: Snake is a game you play for less than 20 minutes (with some exceptions). The App Store is a bottomless pit designed to never end.

• They need to feel where the features started. They need to understand that a phone was originally a tool to connect with a person, not a portal to connect with the entire world at once.

• P.S. I’m a big fan of everything HMD is doing in this space

5. The Era of Specificity (Sony Cyber-shot & MP3 Players) Before they get a device that does everything, they will use devices that do one thing.

• The Experiment: We will take photos on a Sony Cyber-shot (I loved those things). We will listen to music on an MP3 player (and a Sony MiniDisc Player).

• The Lesson: Frictionless isn’t always better.

• When you use a Cyber-shot, you have to decide to take it with you. You are making a choice to be a photographer for the afternoon.

• When you have a smartphone, the camera is always there, leading to the mindless snapping of everything.

• They need to feel the difference between a “Tool” (a dedicated device) and a “Swiss Army Knife” (a phone designed to never leave your hand).

6. The “Black Box” Era (The Cost of Free) Once they understand the history, we look at where we are now: Social Media. And we ask the big question: “Is it Free?”

We will look at the timeline of the Value Exchange:

• Vinyl/DVD/PS2: You pay money -> You get a physical object, art, and ownership. (A clear trade).

• Streaming: You pay a subscription -> You get temporary access. (A rental trade).

• Social Media: You pay nothing -> You get infinite entertainment.

The Lesson: If the product is ‘free’, what are you paying with? Because they have played Mario and Sonic and held a vinyl/ DVD, they will understand that nothing is made for free.

• What does the app get? Your data. Your time. Your anxiety.

• What do you get? A distraction.

By knowing where we came from, they can see the trap in where we are.

Rabbit Hole Time Machine: "The future is whatever you make it." - Doc Brown

I am realising that if we want the next generation to handle the AI era - to handle the constant tech Rabbit Hole that needs to be explored with incurable curiosity - we can’t just throw them in the deep end as passive defenceless bait.

We risk producing young adults who are digitally naïve - vulnerable to manipulation in spaces they were never taught to navigate.

So, I’m putting this out to the burrow: parents, educators, and the curious.

I am working on formalising this approach - specifically the entry levels - curating the games, the hardware timeline, and the storytelling needed to explain tech to kids in an engaging way.

I’m calling it the Rabbit Hole←Time Machine.

If you are interested in this initiative, or if you just want to know how to set up a “Friction Tech” environment for your own kids, I want to hear from you. Email me directly: raibbithole@gmail.com

Let’s figure out how to build better pilots, not just better passengers that help boost YoY engagement metrics.

Listen to the full conversation with Jude Pullen on The Rabbit Hole Experience Podcast, Season 2, Episode 2.

We kick off the conversation discussing LEGO and Mitch Resnick, setting the table for a deep dive into three main areas:

☝ The Computer in a Brick: We unpack the tension between digital play and physical play. How do we balance the “computer in a brick” with the simplicity of playing with sticks, and how can opposing views on technology both be right at the same time?

🤘 Incurable Curiosity: Jude explains why the key differentiator for the future isn’t just creativity, but being “incurably curious.” We discuss the power of adaptability and the willingness to sit in the uncomfortable space of uncertainty to solve complex problems.

🤟 The Clay and The Code: We bridge the gap between two worlds. From high-performance computing to hand-dug ceramics, we explore why we don’t have to choose between high-tech and high-touch - and why true innovation needs both.

🕳️

Want to follow Jude deeper into his Rabbit Hole? - he has shared some links below 😊🐇🦨:

• https://www.rs-online.com/designspark/the-fight-to-repair-prologue

• https://www.rs-online.com/designspark/user/Hey%20Jude

• https://www.instructables.com/member/Hey%20Jude/instructables/

• https://www.judepullen.com/

It has been exactly one year since we last spoke, and the tone of the conversation has shifted dramatically. We are no longer debating whether AI can prove value. Instead, we are showing the multiple ways it already has, while exploring the nuance of how it scales. Crucially, we discuss why a “copy and paste” approach to implementation is doomed to fail - it requires a distinct cultural shift, not just a software update.

In this episode, we revisit themes from last year and explore the reality of the landscape in January 2026:

☝ The “Dishwasher Analogy”: Inspired by Rory Sutherland’s theory that the only way to find out what is dishwasher proof is to put everything in and see what survives. We discuss applying this “stress test” to AI in business - throwing it into workflows to see what dies and what comes out gold-plated.

🤘 Escaping Pilot Purgatory: How to move from endless testing to creating a flywheel of practical application.

🤟 Sentience & Culture: Why technology is no longer the blocker - human self-awareness and organisational culture are.

70% of the next generation of Operating Systems is likely exactly what the tech bros are working on: “efficiency gains.” And that’s fine. We need to book meetings, sort data, and process logistics.

But it’s the other 30% that is more interesting to me. That represents the gap between a machine that works and a mind that wanders. That is the space for Imagination Gains.

WHY NOW? The PS2 vs. The Utility

The recent OpenAI demos are staggering. They are hyper-efficient. They are frictionless. But when we want our OS to work for creative tasks, “frictionless” is actually a bug, not a feature.

I don’t want my creative interface to feel like a utility bill. I want it to feel like a game. For me, the peak of gaming was the PS2 era. It wasn’t about micro-optimizing engagement loops or extracting cash; it was about immersion. It was about play.

Right now, the industry is over-optimizing for speed. They are trying to remove human thought from the equation to get to the answer faster. I want to fill that 30% gap with an OS that helps us play with ideas, not just execute them.

The Screen Time Fallacy (A Lesson from my Twins)

I have twin 5-year-old girls. Like every parent, I wrestle with the concept of “Screen Time.” But I don’t see screens as the enemy.

Here is the heart of what raibbitholeNOS is trying to solve, distilled through a lesson from Jonathan Haidt:

Screen time itself isn’t the enemy. The type of screen time is.

There are two types of digital interaction:

1. Story Time (The Good) Long-form, shared stories - like watching a movie together.

• It creates connection: We sit together, react together, talk about it.

• It teaches structure: It has a narrative arc, moral frameworks (good vs. bad), and requires patience.

• It is constructive: It taps into how humans naturally learn.

2. Fragmenting Time (The Bad) Short, fragmented, solo screen use - doom-scrolling or rapid-fire prompting.

• It isolates: Phones used alone.

• It trains impatience: The brain constantly asks, “Is this the most interesting thing available right now?”

• It creates passive passengers: There is no narrative, only noise.

Building on the Spiral

In Part 1, I introduced the main inspiration for this entire project: Mitchel Resnick’s “Kindergarten Approach” and the Creative Learning Spiral (Imagine → Create → Play → Share → Reflect).

That shift - from passively receiving instruction to actively constructing ideas - is the theoretical code running this machine. It is the engine.

But an engine needs a chassis. It needs a way to interact with the human driver that inspires them rather than just instructs them. We are taking that theoretical loop and wrapping it in an experience that feels like “Story Time” mixed with “PS2 energy.”

THE HOW: Adding Strategic Friction

So, how do we actually build this? How do we code “Play” on top of Resnick’s loop?

1. Narrative, Not Noise Most AI tools today are “Chat” - a fragmented, endless scroll of questions and answers. raibbitholeNOS is built on “Canvas.” It forces a narrative structure. You don’t just “get an answer”; you build a workspace where ideas have a beginning, a middle, and an evolution.

2. Shared, Not Solo Just like “Story Time” is valuable because it is shared, raibbitholeNOS is multiplayer by default. It moves AI interaction from a private confession booth to a shared playground. It encourages the team to gather around the screen, rather than staring into their own private feeds.

3. The “PS2” Factor (Gamified Friction) We are intentionally adding friction to the spiral. We don’t want the AI to do the thinking for you. We want it to challenge you.

• Instead of “Here is the strategy,” the OS asks, “What if we inverted that assumption?”

• It gamifies the Creative Learning Spiral. It won’t let you skip to “Execute” until you’ve spent time in “Imagine.”

The Choice

The future of software is splitting. You can choose the OS that optimizes you out of the loop - the one that treats you like a bottleneck to be removed. Or you can choose the OS that treats you like a player to be empowered by stoping asking AI for answers.

We don’t just automate the work. We unleash the play.

Email raibbithole@gmail.com to receive alpha updates on raibbitholeNOS and access our Roadmap / Alpha Test Groups, also subscribe to raibbithole.com for general news.

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