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.

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.

While you wait you can get some more info by clicking the deck below:

Subscribe now

Escaping the Productivity Trap

We stand at a critical juncture. As AI masters the predictable - automating up to 70% of routine tasks - the tech world is racing toward a “Productivity Trap.” The industry is currently obsessed with using AI to do the same things we’ve always done, just faster.

But efficiency gains have a ceiling. Imagination Gains do not.

Enter: raibbitholeNOS

raibbitholeNOS (Next Operating System) is an in-development AI application designed to be the interface for the augmented future.

Think of it as Nitrous for your mind - speeding up your curiosity by forcing you to slow down.

While standard operating systems are designed to remove friction and smooth the path from A to B, NOS adds strategic friction back in. It interrupts the autopilot of “efficiency” to trigger deeper thinking and exploratory wandering.

The Science of Constructionism

The philosophy behind NOS is grounded in the strategic framework presented by Rabbit Strat Matt in 2025 and inspired by the research of Mitch Resnick (MIT Media Lab).

We are moving away from “Instructionalism” (telling AI what to do) toward “Constructionism” (building and thinking with AI).

NOS is built around the Creative Learning Spiral:

Imagine → Create → Play → Share → Reflect

By operationalising this loop, NOS prevents “analysis paralysis” and turns the rabbit hole into an incubator for innovation. We don’t use AI to bypass the work; we use it to automate the structure so you can focus on the craft, the connection, and the human spark.

Join the Playground

We are building a tool for those who believe the future belongs to the curious, not just the efficient. We are looking for test groups ready to spiral through ideas rather than just execute tasks.

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.

While you wait you can get some more info by clicking the deck below and check out Part 2: Why build raibbitholeNOS?:

Subscribe now

Back in September, I published a piece on WARC titled “Marketing is a second-order chaos system: Why Big Tech’s illusion of control falls short.”

At the time, it was a reaction to Amazon launching their Creative Studio. But sitting here in December, staring down the barrel of 2026, the thesis feels less like a theory and more like a survival guide.

I argued that while Big Tech is trying to engineer marketing into a predictable science (like weather forecasting), human attention is actually a “second-order” chaotic system - like the stock market. It reacts to predictions. It shifts when you look at it too closely.

If you missed it earlier this year, or if you want to understand what we are actually up against in the next 12 months, give it a read.

Read the full WARC piece here: Marketing is a second-order chaos system

The “Tech Bro” Mirage: Meta’s Andromeda & The 2026 Roadmap

I’m revisiting this now because the industry roadmap for 2026 is terrifyingly sterile.

We are seeing the rise of Meta’s Andromeda (often misheard as “Andromedia”). This is the new AI infrastructure sitting beneath Advantage+ campaigns. If Amazon’s Creative Studio was the opening act in September, Andromeda is the headliner.

By late 2026, the vision is effectively “Zero Touch.” You upload a product and a budget, and the machine does the rest - generating the creative, the copy, the targeting, and the pacing. The promise is a closed loop of perfect efficiency.

But as I wrote in WARC: “Efficiency without surprise quickly collapses into invisibility.”

If we all hand the keys over to the same “God Model,” we get a commoditized wall of noise. I call this the “Pee in the Pool” effect. When you over-optimize for engagement using generative AI, you don’t get better marketing; you get “AI slop” that poisons the ecosystem for everyone swimming in it.

Why I’m Leaning into My Neurodiversity

For a long time in my career, I felt the pressure to mask my ADHD and dyslexia. I felt I needed to lean into “order” - spreadsheets, linear roadmaps, and predictable logic - to fit the corporate mold.

But looking at this 2026 roadmap, I’ve realized that the “Tech Bro” obsession with order is a trap. They are trying to turn a second-order system (humanity) into a first-order system (code).

I’m realising now that my neurodiversity is actually the asset. The ADHD brain thrives in second-order chaos. We connect dots that shouldn’t be connected. Dyslexia forces us to read the world laterally, looking for context and shapes rather than linear instructions.

If AI handles the “order,” we need to handle the chaos. We need to be the glitch in the system.

The Antidote: Brian Eno’s Oblique Strategies

So, how do we operationalize chaos? I’ve been looking back at Brian Eno and his deck of cards, Oblique Strategies.

Eno created these to break the linear loops of studio production. They are pure “second-order” interventions. They don’t tell you what to do; they disrupt how you think.

• First-Order Thinking: “How do I fix this mix?”

• Oblique Strategy: “Honour thy error as a hidden intention.”

This is how we need to approach AI in 2026. Instead of asking “What is the answer?”, we need to ask “How can I perturb this system?” Eno understood that controlled randomness leads to creativity. We need to use AI to amplify our “errors” and idiosyncrasies, not smooth them out.

The “Collage of Humanity”: Fred again.. & Nardwuar

If you want to see what this looks like in culture, look at Fred again...

In his interview with Nardwuar (another agent of chaos who disrupts the “PR script”), Fred talks about the “Collage of Humanity.” He creates music by stepping out of the vacuum-sealed studio and recording the mess of the real world - voice notes, street noise, random interactions (and as we know Fred again.. (Fred Gibson) is a protégé of Brian Eno).

He tells a story about meeting an elderly couple in New Orleans, which ended with a stranger in a clown mask twerking on the husband in the street. You cannot prompt an AI to generate that scenario. That is the magic of the “Collage of Humanity.”

It’s the antithesis of the Andromeda vision. It’s messy, it’s unoptimized, and it feels alive.

The Power of Improv: Ian Hotko

Finally, look at Ian Hotko.

He’s the 16-year-old dancer from Slovenia who blew up on TikTok freestyling to Fred’s tracks. He wasn’t following a choreography (First Order). He was reacting to the music in real-time (Second Order).

Fred saw it, and the human resonance - not the algorithm - bridged the gap. He flew Ian to Vancouver last month (November) for the “..FEISTY” show. He didn’t give Ian a script; he played a track Ian had never heard and said go.

The result was a viral moment that felt genuine because it was improvised. It was a risk. It was chaos.

Closing Thoughts for 2026

The brands that thrive in 2026 won’t be the ones that achieve “Zero Touch” automation. They will be the ones that use the machine to handle the grunt work, while they focus on injecting the humanity, the improv, and the “Collage of Humanity” back into the work.

We need to keep the pee out of the pool. And the best way to do that is to embrace the chaos.

Deep Dive Links:

• The Original WARC Article: Marketing is a second-order chaos system

• The Inspiration: Nardwuar vs. Fred again.. Interview

• The Visual: what caught Fred’s eye & Fred again.. x Ian Hotko (..FEISTY)

Check out the Bionic Awards trailer below:

In the conversation, we go deeper into:

• The “Doom vs. Boom” Continuum: How to move from anxiety to action and stay employable.

• Why AI Has No Taste: The “genius toddler” analogy and why human curation is more valuable than ever.

• Silicon Valley vs. The Artist: The tension between rapid iteration and the need for “slow,” reflective creativity.

• The Global Community: How creators from Bogota to London are using these tools to build new careers.

Main link is www.bionicawards.com

Follow on Insta at #thebionicawards

This conversation sat in my edit folder for way longer than I wanted. The sound didn’t come out the way I’d hoped (I will be levelling up my podcast hardware very soon :), and I was genuinely disappointed at the time. But after revisiting it, cleaning it up as much as I could, and listening again the insights were simply too good not to share.

Jennifer’s POVs aged very well :)

In the conversation, we cover:

The Future CMO

• The role now spans brand, performance, tech, and infrastructure

AI as an Accelerant

• Nobody fully knows the end state - experimentation is the job

Human Value in an AI-First World

• AI raises the bar for humans, it doesn’t replace them

Efficiency vs. Differentiation

• AI will win at optimisation and efficiency. Differentiation and originality become more valuable, not less

What Breaks, What Survives

• Breakthrough creativity is what CMOs will still pay for

It’s a calm, honest, and quietly radical take on where real value now sits:
sharp thinking, intellectual courage, and human judgement - amplified by AI.

You can keep following Jennifer via her Substack below:

probably something.

Probably Something - explores how frontier technology shapes marketing, consumer-focused business strategy, and the opportunities ahead. We used to joke about how something was "probably nothing" until we realised it was something.

By Jennifer Roebuck

In the conversation we cover:

• George’s shift from corporate strategy to “robot midwifery”

• Letting go of control in creative work with AI

• The theory-action gap in LLMs and what it means for alignment

• Building agents that reflect human values

• Citizen science returning through better AI tools

• Consciousness, conflicting goals, and multi-agent systems

• The real OS-level battle beneath today’s AGI hype

Some awesome areas of exploration from George can be found below, things mentioned in the conversation as well as additional goodness:

• Newell, Shaw, Simon - The Process of Creative Thinking (1958)
  https://www.rand.org/content/dam/rand/pubs/papers/2008/P1320.pdf

• J.C.R. Licklider - Man–Computer Symbiosis (1960)
  http://groups.csail.mit.edu/medg/people/psz/Licklider.html

• Douglas Engelbart - Augmenting Human Intellect (1962)
  http://www.dougengelbart.org/pubs/papers/scanned/Doug_Engelbart-AugmentingHumanIntellect.pdf

• Herbert Simon & Allen Newell - Human Problem Solving: State of the Theory (1970)
  https://pdfs.semanticscholar.org/18ce/82b07ac84aaf30b502c93076cec2accbfcaa.pdf

• Marvin Minsky - Why People Think Computers Can’t (1982)
  https://web.media.mit.edu/~minsky/papers/ComputersCantThink.txt

• Mark Pesce - Final Amputation: Pathogenic Ontology in Cyberspace (1993)
  http://hyperreal.org/~mpesce/fa.html

• Bret Victor - Stop Drawing Dead Fish (2013)

• Kevin Slavin - Design as Participation (2016)
  https://jods.mitpress.mit.edu/pub/design-as-participation

• Joichi Ito - Extended Intelligence (2016)
  http://v2.pubpub.org/pub/extended-intelligence

• Joichi Ito - Resisting Reduction: A Manifesto (2017)
  https://pubpub.ito.com/pub/resisting-reduction

• Brian Eno & Peter Schmidt - Oblique Strategies (1975)
  https://en.wikipedia.org/wiki/Oblique_Strategies

• Norbert Wiener - Cybernetics (1948)

• Norbert Wiener - Human Use of Human Beings (1950)

• W. Ross Ashby - Introduction to Cybernetics (1956)

• Herbert Simon & Allen Newell - Elements of a Theory of Human Problem Solving (1958)

• Marshall McLuhan - Understanding Media (1964)

• Herbert Simon - Sciences of the Artificial (1969)

• Seymour Papert - Mindstorms (1980)

• Robert Axelrod - The Evolution of Cooperation (1984)

• Valentino Braitenberg - Vehicles: Experiments in Synthetic Psychology (1984)

• Frederick Brooks - The Design of Design (2010)

• Mitch Resnick - All I Really Need to Know About Creative Thinking I Learned in Kindergarten (2007)

• Ted Chiang - The Lifecycle of Software Objects

• Kurt Vonnegut - Player Piano

• Yuri Lotman - Inside Thinking Worlds

• Mark Solms - The Hidden Spring

• Marvin Minsky - Society of Mind

• Lev Vygotsky - Works on play, socio-cultural development, ZPD

• Blaise Agüera y Arcas - What Is Intelligence?

Want to make your first steps into quantum? - Andreas has provided some links below :)

“Pretty pedagogical, and more focused on hands on programming than describing the hydrogen atom. Enjoy!”

Learn quantum programming — PennyLane

Learn Quantum Computing Online | Free Tutorials & Interactive Exercises | IQM Academy - Learn Quantum Computing

Great chat with Stef Hamerlinck covering brand as an operating system and the role of AI in brand building, using his mascot Marcel as an example. He differentiates between “AI slop” and thoughtful AI use, acknowledging ethical concerns while emphasizing AI’s potential for creativity and community. His advice to brands is to prioritise distinctiveness and explore AI tools internally for prototyping ideas.

Check out Stef’s Substack and AI bubble short film below 🫧🐶:

Let's talk branding

The AI Bubble Is Real (and That’s a Good Thing)

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7 months ago · 4 likes · Stef Hamerlinck