This is my first long read in a while, not because I had nothing to say, but because I was too busy learning to say it properly.

That’s the thing about writing honestly about AI and creative practice — you can’t fake the reps. You either know what you’re talking about from the inside, or you’re just repackaging someone else’s observations with better vocabulary. I’m not interested in that. So when the learning gets serious, the writing pauses. And for the past three weeks, the learning got serious.

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Here’s what I was doing: deep into an AI filmmaking masterclass with Lighthouse AI Academy and Figma Weave, my go-to production platform. I have been running experiments and delving into structured coursework alongside people who dedicate 100% of their professional lives to this craft. Not AI evangelists. Not tech commentators. Filmmakers. Cinematographers. Directors who happen to be operating at the frontier of AI-generated motion. The kind of people who think about frame composition before they think about prompts.

That’s the shift I’ve been chasing — and it’s the one I want to write about today.

From Scene Mechanic to Director

There’s a hierarchy in AI visual production that most people don’t talk about, because most people haven’t gotten past the first level.

Level one is scene prompting. You describe a shot. The AI renders it. You’re happy with the result or you try again. It’s impressive. It’s also, in filmmaking terms, roughly equivalent to choosing a filter on Instagram. You’re responding to what the machine gives you rather than directing what it makes. Most AI visual content being produced right now — the vast majority of it — lives here. It’s reactive. It’s pretty. And it has roughly the same relationship to storytelling that a mood board has to a screenplay.

Level two is the Scene Mechanic — and I’ve written about this before at length, so I’ll keep it tight here. The scene mechanic doesn’t begin with imagination. They begin with function. A story already exists — a brief, a script, a brand narrative — and the mechanic’s job is to diagnose why it isn’t working as a perceptual experience, then tune it until it does. They think structurally. They understand that framing directs interpretation, that rhythm directs bodily response, that the order in which visual information arrives determines how meaning is constructed. It’s not intuition. It’s not artistry. It’s precision work — closer to an engineer listening for the wrong vibration in a system than a painter deciding what colour to use. Most people doing serious AI visual work are here, or getting here. It’s a meaningful upgrade from prompting. But it’s still not directing.

Level three is what I’ve been building toward: working as a director and cinematographer. Thinking in systems rather than scenes. Considering shot language, narrative continuity, character consistency, emotional arc, and genre grammar — before a single prompt is written. The prompt becomes the last thing, not the first. The thinking is the work.

This is not a small upgrade. It’s a different job.

For me, the territory where that upgrade matters most is micro-drama — specifically, vertical micro-drama built for brand storytelling. And that’s where things get genuinely interesting.

Micro-Drama Is Not a Short Film. It’s a Different Form.

A micro-drama isn’t just a film that’s been trimmed down to fit a phone screen. It has its own grammar. Its own pacing. Its own relationship with the viewer.

Vertical format — the 9:16 frame we scroll past a hundred times a day — demands a completely different compositional logic. Faces dominate. Negative space works differently. The edit has to earn attention in the first two seconds or it’s already lost. And the storytelling has to operate on multiple registers simultaneously: emotional hook, narrative tension, and brand integration — all without any of the three elements killing the others.

Room Service. A BIG RED BUTTON / STORYENGINE1 / RockPaperScissors Series

That last part is where most branded content falls apart. Brand integration in traditional production usually means logos and pack shots bolted onto something that was already a story. The brand is an afterthought, wearing a lanyard.

In micro-drama done properly, the brand isn’t placed into the story. The brand is integrated, almost like a character in the story. It has behaviour. It has presence. It carries meaning the way a costume or a location carries meaning — not announced, but felt.

This is the craft problem I find most compelling right now. And it’s one that AI-assisted production is uniquely positioned to solve — not by making things cheaper, but by making the creative space larger. Those are different promises. The second one is the one that matters.

The Hybrid Production Model: Shoot Less. Build More.

Here’s the production idea I’ve been developing — and it’s the one I think will matter most to brands, agencies, and independent creators in the next two years.

Stop thinking of AI as just a production tool. Start thinking of it across the entire workflow, from idea to final output.

The traditional model goes something like this: pre-production (months), production (expensive, complex, weather-dependent, talent-dependent, logistics-heavy), post-production (long, expensive again). The field shoot is the center of gravity. Everything orbits it. And because everything orbits it, you over-invest in it — more shoot days than you need, more coverage than you’ll use, more insurance against the variables you can’t control.

The hybrid model flips that logic.

In the field, you shoot the minimum viable reality. The essential human moments. The performances that can’t be fabricated — because they shouldn’t be. The reference material that grounds the entire project in physical truth: real faces, real light, real locations, real texture. You capture what only cameras in the real world can capture. And then you stop. Cut infield time in half. Maybe more.

Then you move into an AI studio with an AI-enabled workflow or system. Like RockPaperScissors and StoryEngine1.

There, you build on top of those references. You extend the world beyond what the location allows. You generate the establishing shots you couldn’t afford on the day. You expand the environments, add the weather you wanted, and create the scale the budget didn’t permit. The references anchor everything to reality — so the AI-generated work doesn’t float off into the uncanny valley. It’s tethered. It feels like the same world, because the DNA came from the same world.

What you gain is threefold, and it runs simultaneously. Time, because you’re no longer hostage to locations, permits, crew availability, or weather windows for every single shot — the AI studio is always open. Creative vision, because the director’s actual intent is no longer compressed by what the day allowed — it expands instead. And cost, not because you’re cutting corners, but because you’re cutting the right things and building the rest smarter. Fewer shoot days. Smaller on-location crew. More output per dollar. The economics start working in the story’s favour for once.

This isn’t a workaround. It’s a production methodology. And for micro-drama specifically — where the format demands creative precision, where brands need multiple executions, where the economics have to actually work — it may be the model that finally makes brand storytelling at scale genuinely achievable rather than just theoretically desirable.

The end of this article is just the beginning...

I’ve spent most of my career watching brands underinvest in storytelling because the economics didn’t work. Too expensive. Too slow. Too many variables. The ROI conversation always ran out of road before it reached the creative conversation. So the creative compromise became the default. And the default, after thirty-odd years of watching it, is usually mediocre content with a big logo on the end.

The hybrid model changes that math. Vertical micro-drama gives it the format it needs — a form that lives where audiences actually are, that respects the attention economy without surrendering to it, that finally makes the brand-as-character idea practically achievable rather than just a nice thing to say in a brief.

I’ve been quiet for three weeks because I was building the foundation to say this properly. Not as theory. As practice.

The tools are ready. The question now is whether you have a story worth telling with them. Technology democratizes production. It doesn’t democratize vision. That part is still on you.

StoryEngine1 is my productised approach to AI-enabled brand storytelling.

Built for agencies that work with ambitious brands and are ready to move beyond the mood board. If the ideas in this piece resonate, it’s probably worth a conversation.

StoryEngine1

I’ve been carrying a bias. Time to put it on the table.

Every time someone asked me about AI and the creative industry, I’d circle back to the same concern. The entry-level job is disappearing. The tasks that young creatives used to cut their teeth on — the resize, the copy variation, the spec ad, the deck draft — AI is eating those for breakfast. And if you take away the tasks, you take away the learning. You take away the apprenticeship. You take away the moment where a young creative sits across from someone who’s done this for thirty years and absorbs, through proximity and repetition, what good actually looks like.

I still think that’s true. But I’ve started to think I had the question backward.

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The question I was asking

I was asking: how will young creatives learn what good looks like, what craft feels like, and what it takes to build a successful creative career, if they are not even in the room?

This assumes that knowledge transfer runs in one direction. From experienced to inexperienced. From battle-tested to untested. From the people who know what good looks like to the people who don’t yet.

That assumption is not wrong. It’s just incomplete. And in an AI world, incomplete assumptions are expensive.

The question I should have been asking

How are we going to learn from them?

Yesterday, I read an article that Justin Halim posted, written by Geia Lopez, Google’s Head of Creative and Creator Partnerships for Southeast Asia. And it has been living rent-free in my head all day, and was still there first thing this morning.

The article is about the Young Lotus workshop at ADFEST 2026 - twenty-nine young creatives from fifteen cities who responded to a real YouTube brief: they were not handicapped by what they don’t know. They were liberated by it.

They haven’t learned the rules well enough to be intimidated by them. They don’t carry the scar tissue of a thousand “that’ll never get approved” conversations. They didn’t grow up in an industry that told them to be realistic. They grew up in an industry that didn’t exist yet — and they built it anyway, on YouTube, on TikTok, on platforms that the previous generation was still trying to figure out how to monetize.

They are not digital natives. They are AI natives. The tools aren’t new to them. The tools are just… Tuesday.

One plus one equals more.

Young AI-native creatives don’t have a calibrated sense of what great looks like. Not because they’re not talented. Because that is built from exposure, from failure, from the accumulated weight of watching ten thousand pieces of work and developing the gut instinct to know — in the first five seconds — whether something has it or it doesn’t.

That takes time. There’s no prompt for it. This is earned.

Here’s what I have after thirty-plus years: experience. The pattern recognition. The ability to walk into a room, look at a brief, and feel the shape of the right answer before I can articulate why. The battle-tested judgment to know which idea matters, why it will land, and when to trust your gut over the data.

Here’s what our younger, emerging talent brings to the room: something entirely different and equally irreplaceable. AI fluency so native it’s invisible to them — not a tool they learned, but a reflex they grew up with. An instinctive understanding of how platforms think, what communities want, and how culture moves at speed. And perhaps most importantly: no inherited assumptions about what’s possible. They didn’t learn the industry’s rules. They didn’t memorize the playbook. They arrived after the playbook had already been set on fire — and they brought marshmallows.

The Bangkok team won Young Lotus this year. And what they did wasn’t just clever — it was a different kind of thinking entirely. They didn’t work around the YouTube algorithm. They didn’t accept it as a given. They partnered with it. Treated it as a collaborator, a co-author, a creative asset with its own logic and appetite. They asked not what AI allows them to do, but what the platform wants to become — and then built something that answered that question. That’s not a technique. That’s a mindset. And it’s one that flows naturally from people who arrived at the table without anyone ever telling them the algorithm was the enemy.

Imagine pairing that — the marshmallow energy, the algorithm-as-collaborator instinct, the beautiful refusal to accept that anything is fixed — with thirty years of knowing exactly what great feels like the moment it walks into the room.

Experience plus audacity. Depth plus altitude. The calibrated eye plus the unconditioned imagination. Not AI replacing the human. Not youth replacing the veteran.

The pairing. That’s the real force multiplier. And it’s sitting right there, in most organizations, completely undeployed.

The organizational opportunity

Here’s where I stop being a commentator and start being an advocate.

Not just for myself — though I won’t pretend this isn’t personal. I’m 61, and have watched the industry I love tell people like me, with increasing frequency, that our time has passed. That we’re expensive. That we don’t move fast enough. The future belongs to someone younger, cheaper, and more digitally fluent. All of which is bullshit.

The organizations that are going to win in this AI era are not the ones that replace experienced talent with AI. And they’re not the ones that bet everything on young AI-native talent alone. They’re the ones that are smart enough — and brave enough — to put both in the same room and let them work. Together.

Here’s what that pairing actually looks like in practice. The young creative brings velocity, platform fluency, and the beautiful audacity of not knowing what’s supposed to be impossible. The experienced creative brings something that cannot be prompted, generated, or faked: the calibrated eye. The sense of what good looks like. The craft instincts built from decades of making things, breaking things, and learning — slowly, painfully, magnificently — from both.

Young talent will soak that up. Not reluctantly — hungrily. Give a genuinely talented young creative the freedom of these tools and access to someone who can tell them when they’ve made something truly great versus something that merely looks like it? That’s not a mentorship program. That’s an unfair competitive advantage.

But it requires organizations to make a deliberate choice. To resist the instinct to age out their senior talent in the name of efficiency. To recognize that experience, in an AI world, is not a cost to be managed. It is a multiplier to be deployed.

So this is me, advocating. For myself, yes. But more importantly, for every seasoned creative strategist, writer, director, and thinker who is being quietly edged toward the exit at the exact moment their value has never been higher.

Don’t go. And to the organizations making that mistake — reconsider. Fast.

The bias I’ve been carrying? Gone. Replaced by something better. The absolute certainty that the most dangerous creative combination in any room right now is a 25-year-old who doesn’t know what’s impossible, sitting next to the veteran who knows exactly how to make it real.

Because the idea is rarely what kills great work. The organization is. The politics, the approval layers, the stakeholders who need to feel heard before they’ll say yes. Navigating that — keeping a bold idea alive through all the friction that wants to sand it down to nothing — that’s not a skill you’re born with. That’s thirty years of scar tissue, deployed strategically.

Dr. Seuss wrote a book called “Oh, the Places You’ll Go.” It’s ostensibly for children graduating into an uncertain world. But read it again. It’s a brief. For exactly this moment. For the experienced creative who finally has the tools to match the scale of their thinking. And for the young creative who hasn’t yet learned what’s impossible.

The destination is the same. Unbounded.

Oh, the places we’ll go.

Today, it seems that every article, post, and carousel needs a hook to get people to read it.

Ok, so here is the hook...

“I’ve had impostor syndrome my entire professional life. And the first thing I’ll tell you is — stop calling it that.”

The label you put on a feeling determines the ceiling you put on yourself. And “impostor syndrome” is one of the most expensive labels in the professional vocabulary.

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If I summed up my entire career into one idea, it’s that I have followed my curiosity into places that I have never been – new industries, new ways of thinking about a problem, unfamiliar concepts, or technology.

I have walked into rooms I’d never been in. Sat at tables I’d never sat at before. Felt that familiar tightening — the one that screams, “ What am I doing here?”

And for a long time, I did what everyone does. I named it. I tried to manage it. I gave into it. I breathed through it. I faked confidence until I found some.

Then one day, I realized the problem wasn’t the feeling. The problem was the word.

I built a personal cage the moment I decided to label this feeling “Imposter Syndrome.”

Imposter Syndrome

“Impostor syndrome” is a clinical frame applied to a universal human experience, and it does something quietly devastating: it turns newness into pathology.

The moment you call it impostor syndrome, you’ve made a diagnosis. You’ve framed the feeling as evidence of a deficiency — something to be treated, managed, pushed through. You’ve accepted the premise that the feeling is right and you are the problem.

But here’s what that feeling actually is: you’ve just never been here before.

That’s it. That’s the whole thing.

And here’s the part the impostor framing conveniently ignores: You didn’t wander in off the street. Someone put you there. A manager. A client. Or you, after building something that earned the invitation The evidence came first. The feeling came second.

The invitation existed before you walked through the door. The tools, the experience, the instincts — they preceded the feeling. The feeling just arrived louder.

You’re in unfamiliar territory, your nervous system is registering novelty, and your brain is doing exactly what it’s supposed to do — scanning for threats, looking for maps, trying to orient in an unknown space.

“You belong in that room. You belong at that table. The fact that you’ve never been there before is not evidence against you. It is the definition of a first time.”

The name “impostor syndrome” tells you the feeling means you don’t belong. Strip the name, and the same feeling means something entirely different.

“We’ll always have Paris.” Bogart said it as an ending. I say it as a beginning.

The first time I stood in a Paris Métro station and looked at the full map — every line, every intersection, every impossible tangle of color and direction — I felt something familiar. That overwhelm. That tightening. The quiet voice: You’re going to get lost.

And I probably was. But I also felt something else rise up alongside it: a rush. Because every line on that map was somewhere I hadn’t been yet. Every intersection was a decision I hadn’t made yet. The complexity wasn’t the problem. The complexity was the entire point.

Paris doesn’t simplify itself for you. The map doesn’t get smaller. You get better at reading it.

And the people who lean into the map — who make friends with the confusion — are the ones who find the extraordinary things. The Arrondissement nobody talks about. The restaurant with no sign. The view that nobody photographs because you only find it by getting lost.

The map is not a warning. The map is an invitation.

Three small words changed everything

Now, whenever I walk into a room I’ve never been in — whenever a client hands me a brief that’s genuinely outside my known territory, whenever I’m in a conversation where the stakes are high and the ground is unfamiliar — I say three words to myself.

Just like Paris.

Not a map to fear. A map to follow. The complexity isn’t telling me I don’t belong. It’s telling me something extraordinary is on the other side of knowing it.

And the beautiful thing about a repeatable phrase — a real one, attached to a real memory, wired to a real emotion — is that it fires fast. Faster than the doubt. You don’t have to process it or work through it. Three words and your nervous system changes its interpretation before it’s fully formed the threat.

Three words changed everything. Not because they removed the feeling, but because they changed what the feeling meant.

What I’ve learned — slowly, over decades — is that the difference between people who thrive in complex, high-stakes, new situations and people who contract in them is not confidence. It’s not experience. It’s not credentials. It’s the meaning they assign to the feeling.

Coping says, “The feeling is bad, but I can manage it.” Meaning transfer says: the feeling is a signal, and I’m going to decide what it signals.

That’s a completely different operation. You’re not quieting the feeling. You’re relocating it into a different emotional context — one with adventure attached rather than threat.

This isn’t positive thinking. It’s not a mantra or a motivational poster. It’s a deliberate cognitive act. You are choosing the frame. And the frame changes what’s possible.

I want to be clear about something: the feeling never went away. It still comes. Every new room, every unfamiliar table, every brief that sits at the edge of my capability — the tightening still arrives, right on schedule.

The goal was never to eliminate it. The goal was to stop letting it be the last word. Because a feeling that arrives every single time, across fifty years of walking into new rooms, is not a disorder. It’s a compass. It’s been pointing at the interesting things all along. I just needed to stop treating it like a warning light and start reading it like a map.

Find your own Paris

The Paris reframe works for me because Paris is real to me. The memory is specific. The rush I felt at that moment is something I can call back in a second. That specificity is everything.

Your equivalent might not be a city. It might be the first time you learned to drive, or to sail, or to read code, or to navigate a hospital system in a foreign country, or to parent a teenager. Some moment where the complexity felt enormous and you leaned in anyway — and it opened into something you couldn’t have found from the easy path.

Find that moment. Name it. Wire it to the next unknown room you walk into.

Stop calling it impostor syndrome. You’re not an impostor. You’re just new here.

And new here is where all the interesting things begin.

Every company has its own Paris right now. A map that got more complex overnight. New technology, new competitors, new consumer behaviour that doesn’t fit the old frameworks. The people who are thriving aren’t the ones who simplified the map. They’re the ones who made friends with the complexity — and found someone who knew how to read it.

That’s what I do. If your organization is standing in front of a map that feels overwhelming, I’d like to be in that conversation.

Three Questions About the Future of Filmmaking

Last week at Spikes Asia, I had the pleasure of running a workshop titled “From Script to Frame.”

The session was essentially a guided tour of how I now build motion scenes using generative tools — the everyday working process that has quietly become part of my practice at RockPaperScissors, where I build Story Engines for Brands.

Near the top of the workshop, I asked a question that stopped me for a moment.

“How many of you have ever been on a real film set?”

Only about 20% of the room raised their hands. Almost all of them were older. Most clearly over thirty. Then I asked another question.

“How many of you have made AI video clips before?”

About half the room raised their hands. Then a third question.

“How many of you have made a video longer than one minute?”

Not a single hand.

That moment told me something important about where we are right now with AI filmmaking.

We are entering an era where many people learning AI film craft have never experienced film craft itself. That changes the way you think about what’s happening in this moment with generative video.

Because filmmaking has always been a craft learned through proximity. Which leads to a question that feels increasingly relevant in the age of AI.

What does filmmaking look like when the tools arrive before the experience is gained and the craft is developed?

Learning Filmmaking Without Film Sets

For most of the history of filmmaking, learning happened in a very physical way. You learned by being there. That’s how I learned it.

Standing on set. Watching the lighting crew shape a scene. Seeing how directors talk to actors. Watching camera operators adjust framing by millimeters. Sitting through painfully long pre-production meetings where everything — every prop, every angle, every movement — gets debated and planned.

Even if you weren’t directing, you absorbed the language. You learned what coverage means. You learned why blocking matters. You learned how many decisions sit behind even the simplest shot.

You learned the rhythm and rigour of production. It’s why, when I sit down today to generate a scene with AI tools, I’m not thinking only in terms of prompts.

I’m thinking in shots. I’m thinking about coverage. About how a camera moves through a scene. About where actors enter the frame. About what the lighting probably looks like outside the frame.

All of that thinking comes from years of simply being around real production.

And it turns out that experience translates surprisingly well into AI.

AI Is Powerful — But It Still Needs Craft

One of the big myths right now is that AI filmmaking replaces traditional filmmaking knowledge.

In my experience, the opposite is happening. The more you understand how films are actually made, the better you become at directing AI tools.

When you know how a dolly move works, you can describe it better. When you understand coverage, you stop generating isolated clips and start building scenes. When you understand blocking, you stop thinking in terms of “images” and start thinking in terms of action within a frame. AI can generate visuals incredibly quickly. But it doesn’t automatically generate storytelling.

Storytelling still requires intent.

The One-Minute Barrier

The other signal from the workshop was equally interesting. Many people in the room had experimented with AI video tools. But their work stopped at short clips.

Eight seconds. Twenty seconds. Thirty seconds. No one had built something longer than a minute. Which makes sense, i guess. The tools themselves encourage experimentation at that scale. But filmmaking really begins when you move from clips to scenes to sequences, when shots start connecting, when pacing starts to matter, and sound design begins shaping the emotional rhythm.

That’s when prompting stops being experimentation and becomes storytelling.

A Suggestion for AI Filmmakers

If you’re interested in AI filmmaking, here’s a piece of advice I shared in the workshop.

Spend time on a real film set. You don’t need to be directing. Just observe.

Watch how long it takes to light a scene. Watch how many people it takes to move a camera. Watch how carefully directors plan even the smallest actions.

Sit in on a pre-production meeting if you can. Those meetings are where filmmaking actually begins. Long before anyone presses record. Because what you’ll discover is that filmmaking has never really been about cameras.

It’s about decisions. AI doesn’t remove those decisions. If anything, it multiplies them.

From Script to Frame

That’s ultimately what the workshop at Spikes Asia was about. Not just how to generate images, but how to think like a filmmaker. To move from idea to script to scene to frame. To story.

Because the tools are evolving incredibly fast, but the fundamentals of storytelling — framing, rhythm, motion, performance — remain surprisingly constant.

In the end, the technology is new. But the craft still matters. And the better we understand that craft, the more powerful these tools become.

RockPaperScissors - Story Engines for Brands

I build Story Engines for brands — systems that turn strategy into narrative, and narrative into films, campaigns, and editorial platforms.

Increasingly, those engines combine traditional creative craft with AI-driven production workflows, allowing ideas to move from script to screen faster than ever before.

If your organization is exploring what storytelling looks like in the age of AI, let’s talk.

Most brand storytelling still works the same way it did twenty years ago. Big ideas. Big budgets. Long production cycles. But generative tools are quietly changing something more fundamental than production speed.

They are changing the role of experimentation in storytelling.

Instead of asking whether an idea is worth producing, we can now explore ideas quickly — visually, cinematically, and iteratively. Sometimes those experiments begin with something very small.

In this case, it began with a pile of IKEA bags falling out of a storage closet.

When “The Bag” Stops Being a Bag

Last Friday night, I opened a storage closet in our apartment, and a small avalanche of blue IKEA bags tumbled onto the floor.

You know the ones. Durable, oversized, almost indestructible. The bags everyone seems to accumulate over time.

Standing there looking at the pile, I started thinking less about what they carried and more about what they were made of. The material is an interesting contradiction — waterproof and tough, yet flexible enough to collapse flat. And that’s when the thought appeared.

This would make a great hat.

A bucket hat, specifically. Crown, band, downward brim. A simple structure that could easily be cut and stitched from the bags themselves. But as often happens when I start thinking about objects, the idea didn’t stop at the object.

Could I turn the process of making that hat into a short film?

Not a product demonstration. Not a tutorial. A small cinematic piece showing a familiar everyday object transforming into something entirely different.

Why Brands Should Pay Attention to Experiments Like This

At first glance, a film about turning an IKEA bag into a bucket hat might feel like a small curiosity. A creative exercise. A weekend experiment.

But projects like this are quietly becoming an important part of how brand storytelling is evolving.

For decades, brand films were expensive to produce and slow to iterate. You wrote a script, assembled a production team, rented equipment, booked locations, and shot everything within a tightly scheduled window. The cost structure meant that most ideas were explored only once — and often only partially.

What generative tools are beginning to change is not just the speed of production. They are changing the relationship between experimentation and storytelling.

You can now test ideas the way designers prototype objects. Instead of asking “Is this idea worth producing?” you can ask “What happens if we explore this idea?”

The bucket hat film sits exactly in that space.

It begins with a familiar object that already lives inside millions of homes. The IKEA bag is not just a bag — it’s a cultural artifact. Instantly recognizable. Universally understood. A piece of design that has quietly embedded itself into everyday life.

By transforming that object into something else — a hat — the film taps into a different kind of storytelling. Not advertising in the traditional sense, but reinterpretation.

A reminder that the most powerful brand stories often begin with the objects people already know.

The Film as an Experiment.

Over the past year, my work at RockPaperScissors has increasingly involved generative tools — not just for imagery but for motion, scene construction, and story development. These tools are incredibly powerful, but they also come with a strange side effect: people tend to use them to produce finished things before they understand how those things are built.

With generative tools, the process becomes even more modular. Instead of filming everything in sequence, you build scenes piece by piece, often in a non-linear way. A first frame. A motion instruction. A constraint that prevents the camera from drifting or reshaping objects unexpectedly.

Sometimes the work feels less like filmmaking and more like constructing a mechanical system. Which is why I often describe what I do as scene mechanics.

The bucket hat film was built exactly this way: a series of small controlled experiments designed to produce reliable motion and visual continuity.

For this experiment, I only wrote about 25% of the image or motion prompts. Instead, I wrote system prompts that analysed the creative brief, my “director’s vision” notes, and key frame image references.

I use Figma Weave as my go-to production environment, so I was able to create a workflow that included prompt interpretation, scene planning, motion design, and shot construction.

In effect, the system was doing something very similar to what happens on a film set. Instead of writing every prompt manually, I was establishing the creative constraints, the visual language, and the intent of the scenes—and instructing the system to generate the detailed prompts required to build them. All in one environment. Every step part of a connected workflow.

In other words, I wasn’t prompting images. I was orchestrating a system.

Why Simple Ideas Are the Best Experiments

There’s another reason I chose such a small concept. When people experiment with AI filmmaking, they often start with ideas that are far too large — epic landscapes, complex action sequences, cinematic spectacle.

Those ideas quickly collapse under the weight of technical limitations.

A single room. A table. A bag. A tailor.

This kind of constrained environment is much easier to control. It allows you to focus on craft: lighting, motion, object interaction, continuity. And because the concept is simple, the viewer’s attention naturally shifts to the transformation itself.

The bag becomes fabric. The fabric becomes form. The form becomes a hat. A familiar object quietly turning into something new.

From Experiment to Film

Eventually, the pieces began to assemble into something coherent. A short film about craft. About transformation. About seeing potential in everyday materials. But it was also something else - a demonstration of how generative tools can be used not merely to produce content, but to explore ideas, test visual sequences, and prototype stories quickly.

The bucket hat itself almost becomes secondary. What matters is the process.

The curiosity that begins with an ordinary object on my apartment floor and gradually evolves into a small cinematic world.

Experimentation Is Becoming a Strategic Capability

For brands, this shift matters more than it might initially appear.

The companies that will win in the next era of storytelling are not simply the ones that produce the biggest campaigns. They are the ones who experiment more frequently.

Small films. Unexpected ideas. Visual prototypes. The storytelling landscape itself is evolving quickly. New formats are emerging almost faster than agencies and brand teams can keep up.

Vertical micro-dramas designed for phones. Short scripted stories where brands are integrated naturally into the narrative rather than appearing as obvious placements. Narrative experiments that explore objects, materials, and rituals around a brand.

These aren’t traditional campaigns. They’re story experiments — small narrative explorations that test tone, symbolism, craft, and format. Some of these experiments will go nowhere. A few will reveal something interesting. And occasionally, one will unlock an entirely new creative direction.

This is exactly how innovation tends to happen in every other field — from product design to software development. Iteration leads to discovery.

Storytelling is beginning to work the same way.

Craft Still Matters

There is one important caveat. The rise of generative tools does not eliminate craft. If anything, it makes craft more important. Because when anyone can generate images instantly, the difference between mediocre work and meaningful work becomes the intentional construction of scenes. How motion unfolds. How objects interact. How the camera behaves. These are the mechanics that give a film its credibility.

The bucket hat experiment may look simple on the surface, but every scene is carefully constructed. The cutting of the bag. The motion of the tailor’s hands. The rhythm of the sewing machine. The transformation of material into form.

In other words, the same fundamentals that have always defined filmmaking still apply. Generative tools simply give us a new way to explore them.

The Real Opportunity for Brands

The most interesting part of this moment is not that AI can produce images or videos. It allows brand storytellers to think with motion. To prototype ideas visually. To test narrative structures quickly. To explore metaphors that would previously have been too expensive or impractical to produce.

For brands, this opens an entirely new creative territory. Not just campaigns. Not just commercials. But a continuous stream of story experiments that deepen people’s experience of the brand.

Sometimes those experiments might start with something as simple as a bag falling out of a closet.

And occasionally, if you follow the idea far enough, you end up with a bucket hat.

If You’re Curious About What’s Possible

If you happen to know someone at IKEA, I’d love an introduction. After spending the day turning their bags into hats, it feels only fair that they get to see the film.

But more broadly, experiments like this are exactly how I explore what generative storytelling can do for brands.

If you’re a brand looking for ways to step into the world of AI-supported storytelling — or an agency working with a client that has great stories to tell, big or small — I’d be happy to talk with you.

Sometimes the most interesting brand stories don’t begin with a campaign brief. Sometimes they begin with something much simpler.

A message.

Before Anyone Asks

IKEA is not a client of RockPaperScissors. This was a speculative experiment built purely out of curiosity and a pile of blue bags in a storage closet.

Also, no IKEA bags were harmed in the making of this film. One simply found a new career as a bucket hat.

After forty years in the creative industries, you recognize patterns. The industry has never lacked ideas. It has never wanted for talent or imagination. And yet something was missing.

Ideas arrive in flashes, campaigns come and go, and technologies rise and fall. Agencies reorganize themselves every few years in response to the latest shift — social, mobile, content, data, creators, platforms, AI. Each wave promises a new way of working, yet behind the scenes, the process often remains strangely improvised.

Strategy decks appear. Creative concepts emerge. Production begins. Somewhere in the middle, the narrative that was supposed to hold everything together gets diluted, compromised, or simply lost in translation.

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From Experiments to System

Over time, I began to realize that the real challenge for brands, especially in this age of AI, wasn’t a lack of ideas. I began to believe that there was a lack of engines. Not creative engines in the narrow sense — the industry has plenty of talented creatives — but narrative engines. Systems capable of generating, testing, refining, and producing stories that can live across media, formats, and time.

That realization has been forming quietly in the background of my career for years. Decades of working in advertising and brand strategy. Five years experimenting with generative AI as it emerged from curiosity into capability. Two years putting those tools directly into commercial production — not just experimentation, but real client work where the results had to perform.

Along the way, something interesting happened. The experiments stopped feeling like experiments. They started feeling like a system taking shape.

At RockPaperScissors, we began codifying the way we worked — how strategy informed narrative, how narrative informed creative development, how AI accelerated iteration, visualization, and motion development, and how those elements ultimately translated into brand systems, films, editorial platforms, and campaign worlds.

What began as a loose collection of tools and techniques gradually evolved into a much more structured system.

A story engine. Not a metaphor. An actual operating system for brand storytelling.

How the Story Engine Works

The Story Engine sits at the center of how RockPaperScissors works today. It combines strategic thinking, narrative design, and creative development into a continuous system that generates and refines stories for brands.

Inside the engine, strategy and creativity are no longer separate phases. They operate as connected mechanisms.

Strategic frameworks help identify the tensions, themes, and opportunities a brand can own. Creative development turns those insights into narrative structures — worlds, characters, scenarios, and moments that audiences can experience. AI tools are embedded at every stage, allowing us to test ideas quickly, visualize scenes, pre-produce motion, and iterate at a speed that simply wasn’t possible even a few years ago.

The result is not just faster work. It is better work. Ideas can be explored visually before production. Narrative arcs can be pressure-tested. Entire worlds can be prototyped before a single dollar is spent on filming or fabrication.

The engine produces three kinds of outputs. Brand systems that define the architecture of a story. Narrative media — films, motion, experiential storytelling. Editorial thinking that sustains the narrative over time.

Each output reinforces the others.

A brand system creates the foundation. Narrative media brings the story to life. Editorial thinking keeps the story evolving. When the engine works, the brand stops behaving like a company that occasionally runs campaigns.

It starts behaving like a storyteller.

That shift feels increasingly important right now, as we enter a moment when the volume of content in the world is exploding. AI will accelerate production dramatically, which means the real competitive advantage will not be the ability to create more content.

It will be the ability to create meaningful stories. Stories with structure, coherence, and emotional gravity. Stories that can evolve across platforms and over time. Stories that audiences actually want to follow.

That is what the Story Engine is designed to do.

The Future of Brand Storytelling

It is the next evolution of RockPaperScissors — a system built from decades of creative experience, shaped by years of AI experimentation, and refined through real work with real clients.

And like any good engine, it will keep evolving. Because the technologies will change. The platforms will change. The way stories are told will continue to change. But the need for engines — systems that help brands generate meaningful narratives — will only become more important.

In a world about to produce infinite content, the real advantage will belong to the brands that know how to produce stories.

If you’re curious about how the Story Engine works, you can explore it here.

And if you’re a brand, agency, or creative team trying to figure out how storytelling works in the age of AI, I’d be very happy to talk.

A quick note before we begin: I will be teaching a 90-minute workshop on how to think about motion and move your stories from script to screen using AI during Spikes Asia 2026 Creative Campus event at the National Design Center. I will be joined by Joao Flores, the Executive Creative Director at Monks.

If you are a brand or agency looking to leverage AI for ideation and visual storytelling (static or motion), send me a note, and let’s see how we could work together.

Something curious is happening in AI filmmaking right now.

The conversation is getting louder — but also narrower.

Every week, there is a new demonstration. A new model. A new benchmark. Someone posts a side-by-side comparison. Someone else declares that a threshold has been crossed. The tools are evolving in public, and the industry is responding exactly as industries always do when a technology accelerates quickly: by focusing almost entirely on the technology itself.

Look at this model. Look what it can generate. Look how realistic this is. Look how fast this renders.

The vocabulary of progress has become technical.

But the experience of cinema is not technical. It is perceptual. And somewhere in this widening gap between capability and perception, something important is being overlooked.

AI lowered the cost of images. It did not teach anyone how to compose them.

That distinction is becoming harder to ignore.

The Quiet Assumption Behind AI Filmmaking

There is a subtle belief shaping how people approach generative video tools, and it rarely gets stated out loud.

If the system can create the image, it must also understand how to present it. If the model can generate a scene, it must also know how to stage it. If the output looks cinematic, the cinematic thinking must already be embedded somewhere inside the machine.

This assumption is understandable. For more than a century, cinematic skill was bound to physical equipment. Cameras were heavy, mechanical, expensive, and difficult to operate. Mastery meant controlling hardware. When the physical barriers collapsed, it felt logical that the associated knowledge might collapse with them.

But that is not what happened. The tools became easier. The decisions did not.

AI systems are extraordinarily capable at producing visual material. They are far less capable at deciding why one visual choice should exist instead of another. They do not naturally understand narrative hierarchy. They do not assign emotional weight instinctively. They do not choose visual structure with intention unless that intention is explicitly specified.

Left alone, most generative video tends toward visual neutrality. The images are often striking, but the staging is cautious. The camera hovers, or remains still. The framing is balanced but noncommittal. The environment is coherent but not directional. Color is attractive but episodic. Each clip can feel complete on its own, yet disconnected from everything around it.

The result is a strange aesthetic condition: footage that is visually impressive but psychologically flat.

Nothing is wrong, but nothing is being emphasized either.

And emphasis — more than realism, more than resolution, more than fidelity — is what creates meaning in moving images. Which is why the real skill gap emerging in AI filmmaking is not technical. It is cinematic.

The Craft Gap

The industry is currently over-indexing on tools because tools are visible. They can be demonstrated, compared, upgraded, and measured.

Craft is different. Craft is invisible until it is absent.

What many people are beginning to sense — often without fully articulating it — is that certain dimensions of filmmaking do not emerge automatically from generative capability. They have to be imposed. Designed. Directed. Structured. Maintained.

Three of those dimensions are becoming particularly decisive:

• Camera movement

• Color continuity

• Lens logic and framing

None of them are new. All of them are suddenly central again.

Camera Movement — Motion Creates Meaning

Most AI-generated footage is visually alive but spatially passive.

Things exist within the frame. They look convincing. They may even move internally — characters walking, environments shifting, particles drifting. But the camera itself often remains observational. It records rather than participates.

This seems like a small detail until you remember what camera movement actually does in cinema.

The camera does not simply capture action. It directs attention. It establishes hierarchy. It signals importance. It creates psychological alignment between the viewer and the subject. It determines whether we feel like observers, witnesses, participants, or intruders.

• A slow push inward transforms curiosity into intimacy.

• A lateral tracking move reveals relationships in space.

• An orbit isolates a subject from its environment.

• Handheld instability introduces vulnerability or tension.

• A restrained drift invites contemplation.

These are not stylistic flourishes. They are narrative instruments.

When camera movement is absent, attention is unguided. When attention is unguided, emphasis disappears. When emphasis disappears, the emotional structure of the scene weakens — even if the image itself remains visually impressive.

A scene without camera intention is just surveillance.

AI systems do not yet possess an intuitive model of cinematic emphasis. They do not automatically move the camera in ways that reveal meaning. They move when instructed to move — and when not instructed, they often default to stillness or minimal motion.

Which means that prompting camera movement is no longer an optional refinement. It is becoming a foundational storytelling skill. Not because the tools are limited, but because narrative emphasis must always be deliberate.

Framing and Lens Logic — Perspective Is Psychological Architecture

Framing is often misunderstood as composition within a rectangular boundary. What is included, what is excluded, and where the subject sits.

But framing begins long before composition. It begins with perspective — and perspective is determined by the lens.

Different focal lengths do not simply alter magnification. They reshape spatial relationships. They alter the perceived distance between objects. They influence emotional proximity. They change how the viewer experiences scale, intimacy, isolation, and environment.

• A wide lens places subjects within context, emphasizing spatial immersion.

• A long lens compresses distance, isolating subjects from their surroundings.

• A 35mm perspective often feels human and immediate.

• A 65mm field can feel monumental, architectural, almost ceremonial.

These are not technical characteristics. They are perceptual conditions.

Lens choice is not technical. It is psychological architecture.

This becomes especially important in generative filmmaking because reference images themselves carry implicit lens assumptions. If the visual language of reference material conflicts with the perspective implied in generated sequences, the world begins to feel inconsistent — even if viewers cannot articulate why.

Learning how lenses shape perception is not beginner cinematography. It is not advanced optical theory. It is something in between — what might be called the middle of cinematography. The layer where visual decisions become emotional structures.

That middle layer is where AI filmmakers increasingly have to operate.

Color Grading — Continuity Is Emotional Glue

Generative video systems produce fragments of time. Cinema produces the experience of continuous time. That difference sounds technical, but it is deeply perceptual.

When humans watch a film, we do not consciously evaluate color consistency from shot to shot. But our perceptual system tracks it automatically. Light temperature, contrast structure, saturation relationships — these form a kind of emotional atmosphere that binds moments together into a single coherent reality.

When that atmosphere shifts unpredictably, something subtle fractures. The world feels unstable. The emotional tone resets between shots. Time feels segmented rather than flowing.

Most AI systems generate clips independently. Each sequence may be internally consistent, but consistency across sequences must be actively constructed. Without deliberate grading, visual continuity becomes accidental rather than structural.

And continuity is not merely aesthetic polish. It is narrative integration.

Color grading is what turns generated moments into a continuous reality.

This is why filmmakers working seriously with generative tools increasingly find themselves learning color — not as a finishing touch, but as a unifying language. It is the mechanism that allows disparate pieces of generated footage to inhabit the same emotional universe.

AI can create images. Grading creates worlds.

*Note: When I create a scene, I try to get as close to the color during prompt testing as possible. But for final grade, I am learning Davinci Resolve, or I bring in a strong colourist when the budget allows.

What Actually Changed

Generative video did not eliminate cinematography. It redistributed it.

For most of film history, cinematographic decisions were executed physically — through equipment, placement, and mechanical control. Today, those decisions are specified conceptually — through prompts, references, and visual systems.

The craft moved upstream.

The competitive advantage is no longer the ability to operate a camera. It is the ability to define what the camera should mean. Which reveals something the current tool-focused conversation often misses: the technology is not selecting for people who know the software best.

It is selecting for people who understand visual intention most deeply.

The Real Divide Emerging Now

Many people will become highly proficient at generating footage. Fewer will become skilled at structuring visual experience.

Those two capabilities are not the same, and the distance between them is widening.

Knowing which model to use is useful. Knowing how attention moves through space is decisive. Knowing how to generate a shot is useful. Knowing why that shot should exist is decisive. Knowing how to produce clips is useful. Knowing how to build a coherent visual world is decisive.

The models will continue to improve. Motion synthesis will become smoother. Temporal consistency will increase. Resolution will rise. Automation will expand. But none of that will replace the need to decide how a viewer should feel when the camera moves, when color shifts, when perspective changes.

That work does not belong to the machine. It never did.

I am not studying cinematography because I want to become a filmmaker in the traditional sense. I am studying it because generative systems make cinematic decisions explicit again. They expose the structure that physical production once concealed behind equipment and process.

And once that structure is visible, it becomes impossible to ignore. Tools generate images. Craft generates meaning.

The models will keep getting better. The question is whether this emerging cohort of filmmakers will.

About RockPaperScissors and Motion Design

RockPaperScissors has been quietly evolving during the past few years, from the design of static imagery via AI, strategy development, training and workshops, to motion-based storytelling. I did not predict that at the beginning of 2026, this would be the most sought after service I am providing my clients.

But needs change, and I am fortunate enough to have worked with clients that are looking to embrace the technology and the opportunity.

As AI has made image creation faster, the need for structure has increased. Scenes still need to work. Movement still needs to feel intentional. Perspective still needs to be designed. Audiences still need to delighted.

That is the work I do. I help teams think through motion before they generate it. I design camera choreography, spatial logic, and visual systems so that what is produced feels coherent and cinematic — not accidental.

If you are experimenting with AI-driven motion, building brand films, or trying to elevate the quality of your visual storytelling, bring me in early.

Motion is easy to generate. Getting it right? Well, that’s a different story.

The sentence arrives casually now, almost as a reflex.

It shows up in pitch meetings, brand reviews, procurement calls, and late-stage budget negotiations. Someone leans back in their chair, glances at the production estimate, and says it with a kind of satisfied clarity:

“If we shot this properly, it would cost two hundred thousand. But with AI… it’s basically free.”

Sometimes there’s a laugh. Sometimes a pause while the implication settles in. Sometimes,One of my scene construction interfaces for a client project.One of my scene construction interfaces for a client project. a quiet sense of technological inevitability — the feeling that an old cost structure has finally been defeated. Two hundred thousand dollars reduced to a software subscription.

A commercial priced like a streaming service.

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No one says it explicitly, but everyone understands the meaning: the economics of production have collapsed.

The camera has been replaced by the prompt. The set by the LMM model. The crew by the interface. And if that is true — if the physical machinery of filmmaking has dissolved into computation — then an uncomfortable question follows naturally.

What exactly are we paying for now?

The number that refuses to die

The figure itself — the mythical $200,000 commercial — has become a kind of rhetorical anchor in marketing culture. It represents the world before AI. The world of cranes and lighting rigs and location permits and crew call sheets printed before sunrise.

It is shorthand for friction.

For decades, producing a piece of commercial film meant organising reality at scale. People had to travel. Equipment had to be transported. The weather had to be anticipated. Schedules had to be synchronised. Insurance had to be purchased in case anything went wrong, which it frequently did.

Money moved because the physical world resists coordination.

A production budget, in other words, was not just a creative expense. It was a logistical negotiation with physics.

AI has removed much of that negotiation. But something else has taken its place.

What disappears when the trucks go away

The most visible change in AI production is the absence of things that once defined filmmaking: no lighting trucks idling outside a location, no cables taped across concrete floors, no assistant directors shouting over walkie-talkies while a clock quietly converts time into money.

Scenes emerge instead from iterative generation. Landscapes materialise without scouting. Camera movement is simulated rather than executed. The sun behaves exactly as instructed. From a distance, this looks like elimination — the disappearance of cost itself.

But elimination is the wrong word. What actually happens is migration.

The responsibilities once distributed across dozens of specialists do not vanish. They condense. They relocate. They accumulate inside a much smaller space, sometimes inside a single operator.

A cinematographer once shaped light through lenses and physical placement. Now someone must define, describe, calibrate, and stabilise that light across hundreds of generative outputs.

A location manager once secured environmental continuity. Now someone must ensure that a fictional environment remains coherent across model interpretations that do not inherently remember what came before.

An editor once assembled footage captured in time. Now someone must determine which among hundreds of possible visual realities becomes canonical.

The work does not shrink.

It internalises.

The quiet expansion of effort

Inside AI production environments, something strange happens to time.

Physical filmmaking is governed by scarcity. Daylight fades. Crew overtime accumulates. Constraints force decisions forward.

Generative production removes most of those pressures. Variation becomes cheap. Alternatives multiply instantly. Possibility expands faster than consensus can form.

A shot can be regenerated endlessly — not because the previous version failed, but because a marginally better version might exist.

And so teams keep looking.

They compare emotional tone, micro-expressions, atmospheric density, camera drift, colour temperature shifts so subtle they would never survive discussion in a traditional edit suite. The work becomes an extended act of aesthetic discrimination — searching for an image that feels inevitable rather than merely acceptable.

Iteration becomes the dominant activity. Not shooting. Not rendering.

Choosing.

And choosing, when options are infinite, is exhausting work.

The Explosion of Iteration

This is the part almost nobody talks about. When every shot is cheap, decision pressure multiplies. You don’t shoot 5 takes. You generate 200 variations.

Traditional production is constrained by physics. You cannot shoot endlessly. You cannot move lights forever. You cannot reschedule the sun.

Scarcity forces decisions. AI removes those constraints completely.

When every shot costs almost nothing to generate, iteration becomes infinite. An infinite number of possibilities creates a new cost centre: decision fatigue at an industrial scale.

Instead of choosing between five takes, you are reviewing two hundred. Instead of approving a lighting setup, you are selecting between aesthetic micro-variations that differ in emotional tone by fractions of a degree. Instead of committing to a direction early, teams postpone commitment because they can.

Cheap generation creates expensive hesitation.

Creative convergence — the process of narrowing toward a final expression — becomes the dominant labour in AI production. And convergence is cognitively demanding work.

The new labour nobody is pricing correctly

Agency rate cards were designed for a different world. They itemise hours of production, days of shooting, equipment rentals, post-production workflows. They are built to price tangible coordination.

Traditional production budgets have always been dominated by material logistics — the cost of organising people, equipment, locations, time, and physical execution. AI production changes that balance entirely. The centre of gravity shifts away from the orchestration of matter and toward the orchestration of thought. What begins to dominate instead is cognitive systems work: conceptual architecture, visual world logic, prompt design at narrative scale, model selection and testing, style continuity, multi-tool coordination, generation supervision, version control, review governance, narrative integrity, and brand risk management. The physical machinery recedes. The mental machinery expands.

But what happens when the tangible coordination dissolves, leaving only sustained cognitive supervision?

Someone must hold the narrative intact while images fluctuate. Someone must decide when variation stops and authorship begins. Someone must protect brand meaning when visual possibility becomes effectively limitless.

These are not technical services. They are acts of judgment. Judgment is slow. Judgment is fragile. Judgment cannot be parallelised. Yet it is precisely this labour — the continuous shaping and stabilising of meaning — that becomes central in AI production.

And it rarely appears on an invoice.

Why the illusion persists

Software pricing creates a powerful psychological distortion. Tools announce their cost clearly. Human cognition does not.

A generative platform charges a monthly fee. The number is visible, measurable, and defensible in a budget meeting. The expanded effort required to guide that platform toward coherence is invisible — absorbed into creative time that organisations have never learned to price properly. So the story becomes simple.

The software is cheap. Therefore, the production is cheap. Therefore, the creative work must also be cheap.

It is an elegant narrative. It is also wrong.

The risk beneath the optimism

When organisations believe production has become trivial, they behave accordingly. They compress timelines. They reduce oversight. They assume that variation guarantees quality.

But abundance does not produce coherence. It produces noise.

Brand meaning — already difficult to maintain in fragmented media environments — becomes even more vulnerable when image generation outpaces narrative control.

Visual consistency drifts. Emotional tone fluctuates. Symbolic intent blurs. Nothing fails catastrophically. Everything simply becomes slightly less precise.

Which, over time, is how craft erodes, and slop takes center stage.

The economic inversion

The central shift in AI production is not technological. It is economic. Filmmaking was expensive because manipulating matter was expensive. Cameras, lights, locations, and physical presence — these were the dominant cost drivers.

Now the manipulation of matter is trivial. The manipulation of meaning is not.

The industry has not yet adjusted to that inversion.

It continues to talk about the disappearance of equipment while ignoring the emergence of interpretive labour — the continuous work of deciding what an image should express, what a sequence should imply, what a brand should feel like when the constraints of physical reality no longer guide those decisions.

What the $29.95 commercial really costs

The subscription is real. The computational efficiency is real. The logistical simplification is real.

But the work of authorship — the sustained act of shaping possibility into intention — remains exactly as demanding as it has always been. In some environments, more so.

The camera did not carry that burden before.

People did. They still do.

The cost of producing images has collapsed. The cost of deciding which images deserve to exist has not.

Until the industry learns to price that distinction honestly, the $29.95 commercial will continue to circulate — not as an economic reality, but as a comforting misunderstanding about where creative work actually lives.

And misunderstandings, unlike production budgets, have no upper limit.

Sooner or later, every brand and every agency will confront the same realisation: the hard part is no longer making the video. The hard part is deciding what the video should be — and knowing when to stop.

That requires new disciplines. New workflows. New decision structures. New forms of creative supervision that most organisations have never had to build before, because physical production constraints once did that work for them.

Those constraints are gone now.

Which means AI production isn’t a cost-saving tool. It’s an operational system that has to be designed, governed, and learned. Teams need to understand how to manage generative iteration, how to maintain aesthetic continuity across models, and how to protect brand meaning under conditions of infinite variation.

None of that comes bundled with the subscription.

It has to be built.

Some companies will figure that out through painful trial and error. Others will decide that building those capabilities deliberately — with guidance from people who have already navigated the shift — is faster, cheaper, and far less risky.

Either way, the era of treating AI video as a cheap production shortcut is ending. The era of treating it as a creative systems discipline is just beginning.

If your organisation is navigating this transition — building AI production capability, designing workflows, or training teams to work at generative scale, then we should talk. That is the work I do at RockPaperScissors.

It also marks something quieter, but no less meaningful for me: RockPaperScissors is now two years and a few months old, and for the first time since it was born, it enters a year defined not by emergence, but by motion. Not preparation. Not formation.

Motion.

That feels significant. I’ve been thinking about motion for some time now — not just as speed, but as something constructed deliberately, frame by frame, like a scene mechanic designing transitions between worlds.

Because if I look back carefully, the life of RPS has unfolded not randomly, but rhythmically — almost as if it has been moving through a sequence of energies, each one shaping not just what we do, but how we exist.

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Born in Stillness

RockPaperScissors came into the world in January 2024, just weeks before the Chinese New Year. Technically, it was born in the final days of the Rabbit year.

That matters more than I realised at the time.

The Rabbit is not a forceful creature. It does not announce itself. It does not conquer territory or demand attention. It survives — and thrives — through awareness. Sensitivity. Perception. Quiet intelligence. The ability to notice what others miss.

That was the environment into which RPS was born: a moment of observation before movement. A phase of listening before declaring.

Looking back, it makes sense.

In those early weeks, there was no grand launch energy. No explosive growth. No sense of dramatic arrival. There was only attention to ideas, to patterns, to possibility, to the changing nature of work, creativity, and intelligence in a world reshaped by technology.

My first client arrived like a whisper — a DM on Instagram from a friend I hadn’t spoken to in over a decade. That message became an eight-month AI project building a visual vocabulary and embedded datasets for image generation.

That is very Rabbit energy.

Learning to Exist in the Open — The Wood Dragon

I was born in 1964, also a Wood Dragon year.

In 2024, the Wood Dragon returned. Not symbolically, but precisely. The same elemental cycle. The same archetypal force. Sixty years completed.

That was the year RockPaperScissors entered its first full year of life.

I cannot help but notice the symmetry. One full cycle of living, learning, building, questioning — and when the Dragon returned, it did not mark my beginning. It marked the emergence of the work that had been forming across that entire cycle.

My first Wood Dragon gave me potential via my birth. The second gave that potential somewhere to live. That symmetry is hard to ignore.

The Dragon is the great initiator. It is expansion, visibility, ambition, declaration. Where the Rabbit observes, the Dragon announces.

The first full year of RPS unfolded under that sign — and it demanded something very specific: presence.

Ideas had to be expressed, not just formed. Work had to be shown, not just developed. Identity had to become visible.

There is something uncomfortable about Dragon energy if you are not used to occupying space. It pushes you outward. It asks what you are capable of becoming if you remove hesitation. And so the first year became a year of emergence — of voice, of articulation, of stepping into conversations that had previously been observed from the edges.

Rabbit had gathered awareness.

Dragon required declaration. This was the year I joined The Marketing Society, gave my first talk on the Epilogue Economy, and taught my first workshops. This was also a time when I began working with clients in Indonesia, Singapore, and the US.

Learning What to Keep — The Snake

The year that followed carried a very different rhythm. The Snake does not expand. It refines.

Snake energy is selective, strategic, and patient. It sheds what no longer fits. It tightens form. It converts possibility into structure. If the Dragon year asks, Who are you becoming? The Snake year asks, What must you become to endure?

This is a phase many organisations misunderstand. It does not always look dramatic from the outside. There is less spectacle. Less visible movement. But internally, it is one of the most consequential periods of development.

Decisions sharpen. Priorities narrow. Patterns become clear. Unnecessary things fall away. Identity stops being performative and becomes structural.

If the first year was about existing, the second year was about coherence — aligning thinking, capability, and direction into something that could hold weight over time.

In the Year of the Snake, something essential came to life. Years of thinking — about work, intelligence, creativity, and change — took form as my first book: To Question Is to Answer: How to Think Critically and Thrive in the Age of AI. The Snake does not expand outward. It concentrates inward. It distils. It sheds what is unnecessary and preserves what must endure. A book is exactly that kind of act. Not growth, but crystallisation. Not motion, but structure.

I became a founding board member of the IAA Singapore. I stepped into the role of co-chair of the Executive Advisory Board on AI. And I taught a workshop on thinking about AI as a Force Multiplier at a Google regional offsite for some of the sharpest people in the business.

Looking back, these were not separate events. They were expressions of the same movement — knowledge becoming structural. Ideas no longer simply lived in my work. They began to live in institutions, communities, and shared direction.

And perhaps that is why the timing now feels so precise. The Fire Horse will not begin with empty energy. It was set up with something ready to move. The ideas have form. The thinking has structure. The foundation exists.

The Snake does not rush. It prepares for precision. Now, momentum has something to carry forward.

Enter The Fire Horse

Today, that preparation ends.

The Horse represents movement — not tentative steps, but sustained momentum. Freedom. Directional energy. The willingness to cover distance. But this is not simply the year of the Horse.

This is the year of the Fire Horse.

Fire transforms. Fire accelerates. Fire makes motion visible. Where the Snake coils, the Fire Horse runs. And historically, symbolically, psychologically, the Fire Horse carries a very particular meaning: independence, force, decisiveness, energy that cannot remain contained.

This is not the energy of formation. It is the energy of propulsion. Which makes this moment feel less like a beginning… and more like ignition.

Why This Matters to Me

RockPaperScissors did not start by trying to move fast. It learned how to think first. Then it learned how to exist. Then it learned how to refine. Only now does it enter a phase where speed makes sense.

That sequence matters. Because motion without clarity is noise. Acceleration without structure is instability. Energy without direction is burnout. The Fire Horse is powerful — but only when something is ready to carry that power forward.

Looking back, I can see that the past two years were not slow. They were preparatory. They were gathering potential energy. The shaping of something that could eventually sustain momentum without losing itself. And now, the environment changes. This year does not ask what is possible.

It asks what will be done.

The Responsibility of Momentum

Momentum is often romanticised. It feels like progress, confidence, vitality. But momentum is also responsibility. Once movement begins at speed, direction matters more than ever.

You cannot drift at full gallop. You cannot hesitate in open flame.

The Fire Horse does not merely offer energy. It demands commitment — to path, to pace, to purpose. And perhaps that is what feels most significant about today, February 17, 2026 — the precise moment this energy enters the calendar.

It is not just the start of a new year. It is the moment preparation becomes visible as movement.

The moment thinking becomes force.

What Comes Next

I don’t know exactly where the Fire Horse will carry RockPaperScissors this year. No one ever truly knows where sustained motion leads once it begins.

But I do know this:

• The company that entered the Rabbit year, learning to observe…

• The company that stood up in the Dragon year to be seen…

• The company that refined itself through the Snake year…

is not the same company that stands here today. It is more defined. More aligned. More certain of its direction. And perhaps most importantly, more capable of movement that is not reactive, but intentional.

The First Step of the Fire Horse

Every organisation has a timeline. Few have a rhythm.

Over the past two years, I have come to see RockPaperScissors not just as a business, but as something evolving through phases of awareness, expression, and refinement — each one necessary before the next could exist.

Today feels like a hinge point in that sequence. Not a celebration of speed for its own sake. Not a declaration of arrival. Something quieter, but more consequential. A recognition that motion is now appropriate. That energy has a direction to follow. That the work of becoming has reached the stage where movement is no longer preparation — it is expression.

The Fire Horse does not ask whether you are ready. It assumes readiness has already been earned. So today, on the first day of this new year, I am not asking what RockPaperScissors might become.

I am asking something else entirely: after learning how to think, after learning how to define itself, after learning what to keep and what to shed…

What does it mean to finally run? And where will that take me?

Generative AI is not just changing how films are made. It is changing how stories are written—and for scriptwriters, that shift may be the most consequential creative development since the script itself became the industry’s dominant unit of meaning.

For simplicity, when I say “scriptwriter,” I’m talking about anyone who writes for moving images—advertising, branded content, short films, social video, or cinema. The problem is the same. Only the running time changes.

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Over the past few months, I’ve found myself working on generative AI film and video projects, both as experiments, and as paid client projects, not as a technologist chasing novelty, but being asked to think simultaneously like a scriptwriter, a casting director, a costume designer, a cinematographer, a production designer, even a lighting director—roles that have traditionally been separated by process, budget, and hierarchy, now collapsing into a single act of imaginative intent. What’s striking is that in every one of these projects, regardless of how many creative decisions are being explored in parallel, the work still begins in the same place.

With the script.

Because before there is casting, before there is wardrobe, before there is light, space, texture, or movement, there is the problem the industry has largely learned to live with rather than solve: how do you get what is in your head onto the page in a way that survives contact with someone else’s imagination? How do you ensure that tone, rhythm, and emotional intent arrive intact when your work is read, interpreted, circulated, and judged—often by people who will never see the finished film, and long before the work ever has the chance to be seen at all?

For decades, screenwriting has lived with this gap—between imagination and interpretation—as a cost of doing business. Writers learn to compress sight, sound, movement, and emotion into text, hoping that the reader will successfully reconstruct the film in their own mind. Sometimes that happens. Often it doesn’t. Ideas don’t fail because they lack originality; they fail because they lose energy, texture, and conviction somewhere between the page and the reader.

Generative AI doesn’t eliminate the gap between intention and understanding. But for the first time, it gives writers a way to shorten it.

What’s changed is not that writers can suddenly “make films” on their own. That framing misses the point entirely. What’s changed is that writers can now externalise intention earlier, faster, and with far less friction than ever before. They can move ideas out of the purely internal, imagined space and into something that can be seen, felt, interrogated, and refined—without waiting for production, permission, or a cascade of downstream decisions.

This matters because scriptwriting has always been a form of sensory compression. Writers take sight, sound, movement, pacing, and emotion and collapse them into text, trusting that the reader will successfully rehydrate the signal. When that reader is generous, experienced, and aligned, the system works. When they’re rushed, distracted, or simply imagining a different movie altogether, it doesn’t. The script hasn’t failed on craft. It has failed on transmission.

Generative AI changes that dynamic by giving writers a way to test transmission itself.

When a writer experiments with AI scene development—even at a rough, imperfect level—they are no longer writing in the abstract. They are writing with consequences. Pacing stops being theoretical. Tone stops being aspirational. Emotional beats either land or they don’t, and the feedback is immediate. You can see when a moment drags. You can feel when a silence is doing real work—or when it’s just empty space masquerading as mood.

This is not about polish. In fact, the more unfinished the output, the better it serves the writing process. What matters is not visual fidelity, but emotional fidelity. Does this scene feel like what you intended? Does the transition carry weight? Does the rhythm support the idea—or undermine it?

For writers, this represents a fundamental shift in how ideas are developed. Instead of writing the entire script and hoping the meaning survives multiple layers of interpretation, writers can now work scene by scene, moment by moment, allowing intention to be tested, adjusted, and sharpened before it calcifies into a document that has to defend itself in meetings and coverage notes.

This doesn’t turn writers into directors, cinematographers, or production designers in any traditional sense. What it does is collapse consideration, not responsibility. Writers are not taking over other roles; they are engaging more deeply with the narrative implications of those roles. They are asking better questions earlier. What does this space feel like? What does this character’s presence do to the room? What happens emotionally if the camera holds instead of moves?

These are not technical decisions. They are narrative ones. They always have been.

The industry has long felt that these considerations emerge “later,” during production, as if meaning is something added downstream rather than embedded at the point of conception. In reality, those decisions are already being made—implicitly—by the writer, whether they acknowledge them or not. Generative AI simply makes those decisions visible.

This is where the opportunity becomes especially relevant for advertising and branded content, where ideas are judged long before they are produced, often by people who will never see the final work. In these environments, scripts don’t just need to be good; they need to carry conviction. They need to signal intent clearly enough to survive translation across strategy decks, internal reviews, client stakeholders, and procurement constraints.

A script that reads “fine” is rarely enough. A script that can be felt, even provisionally, has a different kind of authority.

This is why thinking about AI filmmaking purely as a production shortcut misses the strategic point. Its real value is upstream. It sits in the messy, generative phase where ideas are still elastic, where meaning can still be shaped rather than defended. Used this way, AI doesn’t accelerate output; it deepens authorship.

There is, understandably, anxiety around this shift. Some worry that writers who engage with visualisation are overstepping, blurring boundaries that exist for good reason. But boundaries exist to manage logistics, not imagination. And imagination has never respected org charts.

What generative AI offers scriptwriters is not control, but coherence—a way to ensure that what they meant is closer to what is understood, and to reduce the number of creative concessions made not because of budget or feasibility, but because meaning was lost early.

None of this diminishes the importance of collaboration. If anything, it strengthens it. When writers arrive with clearer intent—tested, explored, and stress-tested at the level of scenes and moments—everyone else in the process can do better work. Directors direct with sharper purpose. Designers design with clearer constraints. Producers make smarter trade-offs. At its best, this is not about replacing roles. It is about respecting them enough to arrive prepared.

Ultimately, this is not a story about technology at all. It’s a story about craft catching up with reality. The tools have finally begun to acknowledge what writers have always known: that stories are not made of words alone. They are made of rhythm, silence, movement, tension, and release—things that are difficult to describe, but instantly recognisable when they are present.

Generative AI gives scriptwriters a way to work in that space earlier, more honestly, and with less guesswork. Not to finish the film. Not to bypass collaboration. But to ensure that when the script leaves their hands, it carries more of itself with it.

And in an industry where ideas so often die not from lack of originality but from loss in translation, that may be the most important shift of all.

THE END.

If your team is grappling with how generative AI changes the way scripts are written, ideas are evaluated, and films are imagined long before production, I’m happy to compare notes. I’m spending more time working at this intersection—script, story, and AI-enabled filmmaking—and helping teams figure out what actually matters.

You can find me here.

There is no doubt in my mind that this is the most important piece I’ve written so far.

History rarely announces itself. It arrives quietly, disguised as efficiency and ease. Artificial intelligence is doing exactly that—slipping into our thinking, our decisions, and our creative work faster than we are pausing to notice.

This essay is an attempt to slow the moment down, to ask what it means to live through a cognitive shift of this scale, and why our awareness matters more than our speed.

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History rarely announces itself.

It doesn’t arrive with trumpets or headlines or a clean break between “before” and “after.” More often, it slips in quietly, disguised as convenience. A software update. A new workflow. A slightly faster way of doing something you were already doing yesterday.

Most people living through true inflection points don’t experience them as such. They experience them as busy. As incremental. As vaguely unsettling, but not yet alarming enough to demand reflection.

And then, years later, they look back and say: That was the moment everything changed.

We are in one of those moments now.

The mistake many of us are making - across the creative industries, automation, business, and beyond - is assuming that what is happening with artificial intelligence is simply another technological shift. Another tool. Another platform. Another chapter in the long story of progress.

It isn’t.

This is not a story about adoption curves or productivity gains or which industries will be “disrupted.” Those conversations, while not unimportant, miss the deeper truth.

What is happening right now is not primarily technological. It is historical. And history, unlike technology, keeps score.

The Illusion of Continuity

Part of what makes this moment so easy to underestimate is how familiar it feels.

We’ve been here before, we tell ourselves. The internet. Social media. Mobile. Cloud computing. Each wave promised transformation. Each wave delivered upheaval. Each wave eventually settled into the background hum of modern life.

The internet connected us to information. Social media connected us to one another. Mobile put the world in our pockets.

Each change was vast—but also, in retrospect, bounded. Artificial intelligence feels similar at first glance. Another layer. Another acceleration. Another set of tools to master. But this sense of continuity is misleading.

Those previous technologies sat outside us. They changed how work moved, how ideas spread, how attention flowed. They reorganised distribution, not cognition. They altered access, not authorship.

AI is different. AI does not simply move information faster. It participates in thinking itself.

That distinction is subtle, but profound. It is the difference between a printing press and a co-author. Between a calculator and a collaborator. Between a tool you wield and a system that shapes how you perceive, decide, and judge.

When the terrain shifts from execution to cognition, you are no longer just upgrading your tools. You are renegotiating your relationship with agency.

Why This Moment Feels Unsettling (Even If You Can’t Quite Explain Why)

Many people describe their relationship with AI as a mixture of excitement and unease. The upside is obvious and, in many cases, astonishing. Medical research accelerated. Energy systems optimised. Accessibility expanded. Creative possibilities multiplied

At the same time, something feels off.

This discomfort is often dismissed as fear of change, or worse, as resistance from those who “don’t get it.” But that explanation is too convenient and too shallow.

What we are responding to—often subconsciously—is not the power of AI, but its proximity.

AI sits uncomfortably close to the parts of ourselves we associate with meaning: judgment, taste, intuition, synthesis, creativity, and authorship. It doesn’t merely automate tasks; it compresses the distance between idea and execution so dramatically that the space once occupied by deliberation begins to collapse.

That space mattered more than we realised.

It was where intention lived. Where doubt sharpened thinking. Where craft emerged not from speed, but from friction. Where people learned not just how to do things, but why they were worth doing in the first place.

When that space erodes, the risk isn’t inefficiency. The risk is thoughtlessness at scale.

The Creative Industries as Early Warning System

If there is a sector uniquely positioned to feel this shift first, it is the creative industry—not because creatives are more important, but because their work sits at the intersection of meaning and production.

Creativity has always been less about output than about selection. Knowing what to make, what to discard, what to refine, what to protect. Taste, after all, is a form of judgment developed over time, through exposure, failure, and lived experience.

AI excels at generating options. It does not inherently know which ones matter. That gap—between generation and judgment—is where the real work now lives.

The danger is not that AI will replace creative professionals. The danger is that it will seduce them into confusing speed with substance, volume with value, coherence with originality.

When everything becomes possible, discernment becomes scarce. And discernment is not something you can outsource without consequence.

The Quiet Risk of Passive Adoption

Most of the long-term damage caused by technological shifts is not the result of malicious intent. It comes from unexamined convenience.

Passive adoption looks responsible. Sensible. Even progressive. It sounds like staying competitive, keeping up, not being left behind.

But passive adoption has a cost.

When tools shape behaviour faster than values shape tools, agency begins to drift. Decision-making becomes defaulted. Outputs begin to converge. Originality flattens—not because creativity disappears, but because it is no longer protected by deliberate friction.

History is filled with examples of moments when societies embraced powerful systems without fully interrogating their implications. The consequences rarely arrived immediately. They unfolded slowly, then suddenly.

The question is not whether AI will be used. That question has already been answered.

The better question is who remains conscious while using it.

This Is Not About Tools. It Is About Posture.

Future historians will not catalogue this era by listing software platforms or model versions. They will ask different questions.

Who treated AI as an accelerant—and who treated it as an authority? Who preserved judgment—and who outsourced it? Who used speed to deepen thinking—and who used it to avoid thinking altogether?

In moments like this, history does not judge competence. It judges posture. Were you awake to what was happening? Did you slow down where slowing down mattered? Did you recognise that convenience is never neutral?

These are not moral questions. They are historical ones.

A Closing Thought

You do not need to become an AI evangelist or an AI sceptic. Those positions are equally limiting. What this moment requires is something rarer: historical self-awareness. An understanding that you are not merely navigating a technological shift, but helping to define the norms, habits, and values that will shape how intelligence—human and artificial—coexist.

Most people living through history do not know it at the time.

But you do.

Do not underestimate your place in it, or your role in shaping it.

Every technology that survives long enough eventually undergoes the same transformation. It stops being discussed as an object—something new, something impressive, something external—and instead becomes embedded in language as an action.

It turns into a verb.

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This is not a quirk of linguistics; it is one of culture’s most reliable signals that a technology has crossed from novelty into infrastructure. When something becomes a verb, it is no longer optional. It no longer requires explanation. It shapes behavior precisely because it no longer announces itself.

We do not think about search engines; we Google. We do not edit images; we Photoshop. We do not attend meetings remotely; we Zoom or Meet.

Each of these verbs encodes an entire worldview: about speed, mediation, authorship, and authority. And once those worldviews are baked into everyday language, they begin to operate quietly, shaping decisions long after the original tool has faded into the background.

So the question of whether artificial intelligence will become a verb is already settled. It will. The incentives are too strong, the adoption curve too steep, the infrastructure too deeply integrated into work, culture, and daily life.

The more revealing question, the one we are approaching, is this:

What kind of verb will AI become?

Because verbs are not neutral. They organize behavior. They reward certain instincts and suppress others. And depending on which kind of verb AI becomes, we are heading toward very different futures—for business, for creativity, and for the role of human judgment itself.

The Utility Verb: When Speed Becomes the Value System

The most obvious future for AI and the one currently being engineered at scale is that it becomes a utility verb.

Utility verbs are fast, invisible, and assumed. They work best when no one pauses to think about them. They are considered successful when friction disappears entirely. Spellcheck is a utility verb. Autofill is a utility verb. Search… the list is long.

If AI settles into this role, it will not feel revolutionary. It will feel mundane. It will sit beneath emails, decks, briefs, scripts, summaries, analyses, quietly accelerating output, compressing timelines, and smoothing over hesitation.

In business terms, this will look like progress. Productivity will rise. Throughput will increase. Teams will feel more capable. Leaders will feel better informed. The metrics will flatter us.

But utility verbs carry a hidden cost, one that rarely appears on performance dashboards. They shift the locus of judgment from people to systems. Once a tool becomes assumed, the question of whether to use it disappears entirely. Only how efficiently it operates remains.

In creative advertising, this shift is already visible if you know where to look. Strategy decks grow longer and less decisive at the same time. Idea generation accelerates while belief thins out. More work ships. Fewer ideas land.

Utility verbs do not destroy creativity in dramatic fashion. They erode it gradually, by replacing deliberation with velocity and mistaking motion for meaning. The work keeps moving, but conviction quietly exits the room.

The Cultural Verb: When Expression Becomes Performance

There is, however, another path AI could take, one that does not disappear into infrastructure but instead rises to the surface of culture.

Some verbs are not merely functional; they are expressive. They signal belonging, identity, and relevance. To TikTok, to Insta, to Snap is not simply to use a tool—it is to adopt a language, a rhythm, a way of seeing and being seen.

If AI becomes a cultural verb, it will not just change what people make; it will change how they see themselves as makers. Prompting styles will become signatures. Disclosure or concealment of AI use will become a matter of statement. Fluency will be read less as competence and more as taste.

In the creative industries, this version of AI will feel energizing. It will reward experimentation. It will create new hierarchies of cool. It will make creativity feel visible again.

But cultural verbs have gravity. They pull attention toward performance and away from substance. They reward expression even when the intention is thin. Over time, they risk turning creativity into theatre - loud, prolific, and strangely hollow.

Advertising has lived through this cycle before. Entire eras have been shaped by tools that became fashionable before they became thoughtful, celebrated for surface novelty long after their strategic value had diminished. Cultural verbs do not undermine creativity by accident; they do so by making expression feel like an end in itself.

The Creative Verb: When Judgment Becomes the Point

There is a third possibility, and it is the most difficult one to sustain.

Creative verbs are different from utility or cultural verbs because they do not remove responsibility from the user; they intensify it. They do not promise ease. They demand judgment.

Photoshop remains the clearest precedent, not because of what it enabled, but because of what it eventually required. In its early years, Photoshop announced itself loudly. Effects were overused. Manipulation was obvious. The tool was the story.

Then, restraint emerged as a skill. Taste became the differentiator. The decision not to intervene became as important as the intervention itself. Photoshop did not replace photography or illustration; it created a new layer of authorship, one in which judgment mattered more than possibility.

In this future, AI is not an automatic step in the workflow. It is a deliberate act. It is invoked with intent and withheld with confidence. The central creative question shifts from “Can this be done?” to “Should this be done at all?”

This is where creativity actually lives - not in the multiplication of options, but in the selection of meaning. And meaning, by definition, requires judgment.

The Real Gap Ahead Is Not Technical

If AI becomes a creative verb, the most consequential gap ahead is not one of skill or fluency. It is a gap of courage.

Editorial courage. The courage to slow down in systems optimized for speed. The courage to leave space where a machine could easily fill it. The courage to protect ambiguity, friction, and silence in an environment addicted to output.

AI will happily generate more ideas than any team can meaningfully stand behind. More scripts than anyone truly believes in. More strategies than can be defended without hedging. The machine does not know when something matters. It does not feel consequence. It does not understand commitment.

Only humans do.

Which is why the future of creative work is not about learning to use AI better. It is about learning to withhold it without apology. To insist that judgment is not a bottleneck, but the work itself.

The Question Every Verb Eventually Asks

Every verb, once normalized, asks something of its users. Not a technical question, but a moral and creative one.

Just because you can, will you?

If AI becomes a utility verb, speed will win. If it becomes a cultural verb, visibility will win. If it becomes a creative verb, judgment might survive. Judgment, real judgment, is the one thing no system can automate without erasing the very value it claims to enhance.

That is the choice in front of us. Not whether AI becomes a verb, but whether we allow it to become a blunt one or insist that it becomes a meaningful one.

RockPaperScissors is a strategy consultancy, focused on helping organisations rethink work, talent, and decision-making in the age of AI. I work with senior leaders to move beyond adoption toward design — redefining roles, responsibilities, and ways of working as intelligent systems reshape how value is created. My approach blends strategic clarity, cultural insight, and practical AI fluency to help organisations prepare for what comes next, not just optimise what already exists.

There is a sentence I keep hearing lately, delivered with an odd mix of amazement and alarm, usually by people who want you to know they are paying attention to the moment we’re in. “It’s getting really human now.” Sometimes they lean in when they say it. Sometimes they lower their voice. As if they’re sharing a secret. As if the machine might be listening.

I am constantly amazed at how casually we’ve begun to downgrade the complexity, depth, and lived reality of being human in order to make sense of a tool that is, at heart, extraordinarily good at sounding right.

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Because when people say AI feels “human-like,” what they’re usually responding to isn’t intelligence in any meaningful sense. It’s fluency. Tone. Confidence. The absence of hesitation. The ability to produce something, anything, on demand, fully formed and politely delivered. In a world trained to equate articulation with intelligence, this feels uncanny. In a culture addicted to output, it feels miraculous.

Are we setting a low bar for Humanity?

Humans are not defined by how smoothly they speak or how quickly they answer.

They are defined by the friction between thought and expression. By the pause before saying something that cannot be taken back. By the memory of what happened the last time they spoke too quickly. By the quiet calculation of risk, consequence, and care. A human answer is shaped not just by what is true, but by what it will cost to say it.

AI doesn’t carry any of that weight. It doesn’t hesitate because it might be wrong. It doesn’t hold back because someone could be hurt. It doesn’t sit with uncertainty because uncertainty has never punished it before. It produces language without consequence, which is precisely why it is so good at producing language.

If that feels “human,” it’s worth asking what version of humanity we’ve been benchmarking against.

This Didn’t Start With AI. It Started With Us.

What’s striking is that this confusion didn’t arrive with AI. It arrived long before it. Corporate life, digital media, and performance-driven work quietly trained generations of people to behave in ways that are optimized for visibility rather than judgment. Speed over thought. Confidence over care. Reaction over reflection. We built systems that reward immediacy, and then we were surprised when a system designed for immediacy began to look familiar.

In many organizations today, the ideal employee is someone who responds instantly, fills the page, never says “I don’t know yet,” and always has an answer. Under those conditions, of course, AI looks human-like. We engineered our definition of competence to match its strengths.

This is not a technological revelation. It’s a cultural one.

The most common mistake critics make in this moment is to confuse output with intelligence. Humans don’t demonstrate intelligence by how much they produce. They demonstrate it by what they choose not to say, by when they interrupt the flow of a conversation to reframe the question, by when they resist the pressure to respond simply because a response is expected.

Judgment is subtractive. It’s knowing what to leave out. It’s knowing when silence is more responsible than speech. It’s knowing that being technically correct and being meaningfully right are not the same thing.

AI cannot do this, not because it lacks processing power, but because it has no stake in the outcome. It doesn’t carry memory as consequence. It doesn’t protect a reputation. It doesn’t feel embarrassment at a misstep or pride in restraint. It doesn’t learn because something mattered — it learns because something correlated.

And yet, rather than interrogating this distinction, much of the criticism around AI chooses the easier path. It debates originality, authorship, and authenticity while quietly accepting work cultures that stopped rewarding deep thinking years ago. It defends outputs instead of standards. It protects habit rather than interrogating how shallow our expectations of “good work” had already become.

In that sense, AI isn’t threatening humanity by replacing it. It’s threatening humanity by revealing how much of our professional output was already performative. Decks that looked thoughtful but weren’t. Strategies that sounded smart but changed nothing. Content designed to fill space rather than shift understanding. AI didn’t invent slop. It exposed how much of it we had normalized, polished, and shipped with confidence.

The irony is that the things humans still do better than machines are precisely the things modern systems stopped valuing. Sensing when the room has shifted. Recognizing when a sentence is correct but wrong. Carrying responsibility across years, not prompts. Integrating lived experience into decisions without being able to fully explain how the conclusion was reached.

What We’re Willing to Settle For

This moment doesn’t require us to choose between humans and machines. It requires us to choose between cheap humanity and full humanity. Cheap humanity is fluent, fast, confident, and disposable. Full humanity is slower, messier, opinionated, and accountable.

AI is extraordinarily good at cheap humanity.

If we want humans to remain essential, not symbolically, not nostalgically, but practically, we have to stop designing systems that reward machine-like behavior and then acting shocked when machines outperform us at it.

So no, this isn’t an argument against artificial intelligence. It’s an argument against lowering our expectations of ourselves.

Because if this is what we’re calling “human-like,” we should aim higher.

RockPaperScissors completed it’s second year of operations a couple of days ago. I’ll write more about my thoughts on this later, but I wanted to first say thank you to everyone involved in this growth, and my own journey - my clients, partners, and confidants. I am truly grateful.

My thoughts on Slop.

There is a strange new anxiety in creative work right now, and it has nothing to do with talent.

It’s the fear of falling behind or becoming unnoticed.

We’re living in a moment where artificial intelligence can produce something—anything—almost instantly. A paragraph. A concept. A visual. A strategy slide. And because it exists, because it looks vaguely complete, the temptation is to release it. To post it. To frame it as “thinking out loud.” To let the room tell us whether it’s good.

That’s where the trouble starts.

Because what we’re calling “AI slop” isn’t actually new. What’s new is that we’ve made the first draft public.

I write a newsletter. Its called “Some Assembly Required*” because life doesn’t come with an instruction manual, and often times we are left trying to figure out how all the pieces fit together.

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Slop has always been part of the job

Every piece of work I’m proud of has something in common: it was preceded by work I would never want anyone to see.

Bad sentences. 6-fingered hands. Weak ideas. Obvious thinking. Character inconsistencies. False starts. Overreaches. Entire pages that existed only to be deleted. This wasn’t failure; it was process.

Slop, in other words, was the tuition fee.

The difference between then and now isn’t that the bad work has gotten worse. It’s that the bad work has gone public. We’ve lost the boundary between learning and performing.

Before, bad work lived where it belonged: notebooks, draft folders, rehearsal rooms, late-night conversations with people you trusted enough to say, “This isn’t it yet.” You learned what bad looked like privately so that good could eventually show up publicly.

AI hasn’t changed that fundamental truth. It’s just removed the friction that once protected it.

When speed and attention collapses judgment

AI makes it easy to generate. But generation is not the same thing as thinking.

What worries me isn’t that people are producing mediocre work with AI. That’s inevitable. What worries me is how quickly we’re confusing production with progress, and visibility with growth.

The moment something exists, we’re tempted to publish it. Not because it’s ready, but because it’s there. And once it’s out in the world, it starts doing something dangerous: it feeds us back to ourselves.

A few likes. A couple of comments. “Great thinking.” “So interesting.” A small dopamine hit that feels like validation. And suddenly the question shifts from “Is this good?” to “Did this land?”

That shift seems subtle. It’s not.

It changes where judgment lives. Instead of being internally earned through iteration, comparison, and restraint, it becomes external. Crowdsourced. Reactive. Shallow.

And that’s how slop escapes the workshop and becomes identity.

We are performing learning instead of doing it

There’s a popular idea right now that we should “build in public” or “think out loud.”

In principle, that’s not wrong. Transparency has value. Process can be shared. But something has gone sideways.

Too often, what’s being shared isn’t insight—it’s uncertainty dressed up as output. Early drafts framed as conclusions. Exploration presented as expertise. The messiness of learning fast-tracked into content.

AI accelerates this by making it effortless to look productive. You can generate something in seconds and call it a point of view. The reward system kicks in immediately. The work feels done because it’s been seen.

But learning doesn’t work that way.

Real learning is quiet. It’s uncomfortable. It involves sitting with bad work long enough to understand why it’s bad. It requires taste - something that only forms when you withhold, edit, and decide not to show.

When slop is prematurely rewarded, standards don’t rise. They flatten.

The private work is where standards are born

I believe the people who will do the most meaningful work in the age of AI are not the ones who post the most. They’re the ones who delete the most. And baby, my trash bin is overflowing.

They use AI as a thinking partner, not a publishing engine. They generate freely, but they curate ruthlessly. They understand that speed without judgment isn’t leverage—it’s noise.

Bad work is not the problem. Bad work without containment is.

Every craft—writing, strategy, design, filmmaking—has always relied on a protected space where mistakes are allowed and applause is irrelevant. That space is shrinking, not because it’s obsolete, but because we’re trading it for attention.

And attention is a poor teacher.

A better frame for this moment

AI slop is not something we need to eliminate. It’s something we need to re-discipline.

Slop belongs in drafts. In sandboxes. In experiments. In conversations before the conversation. It belongs in the phase where you’re still figuring out what you think—not where you’re telling the world who you are.

Professionalism in the age of AI won’t be defined by who can generate the most. It will be defined by who can withhold, edit, and decide.

Taste is not built by posting faster. Taste is built by knowing when not to post at all.

And in a world drowning in first drafts, restraint may turn out to be the rarest—and most valuable—skill of all.

I don’t measure my career in job titles or companies. I measure it in moments of significant change.

Over the course of my life, I’ve watched this happen again and again. New tools. New platforms. New ways of making, distributing, and valuing work. Each time, they arrived with the same promise: everything will change. And each time, they did, just not always in the ways I expected.

This is where I usually ask you to sign up for my newsletter or go read more about how I think about things. But today, especially if you are caught up in the chaos that is the advertising industry, and are now trying to figure out what you will do next, I would ask you to read the whole article. It’s long, so think of it as an investment into your future. And if you know someone that is now trying to figure it all out, send this to them.

I’ve lived through the rise of personal computing, the early days of Apple, the cultural shockwave of MTV, the arrival of Photoshop, the birth of the internet, the spread of mobile, the dominance of social platforms, and now the public arrival of AI. Each of these moments rewired how we work, how we think, how culture moves, and how value is created. Some collapsed industries. Others created entirely new ones. Many did both at once.

There’s a temptation, especially right now, to compare them. To ask whether AI is “bigger” than the internet, more destabilising than mobile, more dangerous than social media, more profound than anything that came before.

I don’t think that’s the right question.

None of these things is like the others.

There is a temptation, especially in moments like this, to compare technology cycles. To ask whether AI is bigger than the internet, more disruptive than mobile, or more culturally destabilising than social media.

I don’t think that’s a useful exercise.

There is no meaningful way to compare the arrival of Photoshop to the arrival of AI. Or the internet to mobile. Or MTV to TikTok. They emerged from different forces, landed in different cultures, and reshaped different layers of human behaviour. Trying to rank them doesn’t clarify anything. It distorts it.

Technology doesn’t move in a single line. It arrives in waves, each with its own physics, its own casualties, and its own opportunities. To treat them as equivalents is to misunderstand what they demand of us.

But that doesn’t mean they have nothing to teach us.

What is comparable, what does repeat, is not the technology itself, but the human response to it. The patterns of fear, excitement, resistance, overreach, adaptation, and eventual normalisation. The way organisations behave. The way power shifts. The way identity gets tangled up in tools.

That’s where the real learning lives.

What Repeats Every Time the Ground Shifts

Every cycle begins with noise.

The first wave is always loud: bold predictions, inflated claims, moral panic, utopian promises. New experts appear overnight. Old experts are declared obsolete. Conferences fill up. LinkedIn fills up faster. Everyone feels pressure to have a point of view before they’ve had time to develop one.

Then comes fear often disguised as principle.

I’ve seen this in every cycle. People don’t say “I’m scared.” They say, “This will ruin quality,” or “This isn’t real craft,” or “Clients will never accept this,” or “This undermines the profession.” Sometimes they’re right about the risk. Often, they’re really protecting an identity built around a previous set of rules.

Another pattern: tools get mistaken for thinking.

When Photoshop arrived, design didn’t become better by default; it became faster. When the internet arrived, information didn’t become wiser; it became abundant. When social platforms arrived, connection didn’t become deeper; it became performative. And now, with AI, output doesn’t automatically become insight.

Each cycle tempts us to outsource judgment to the tool. Each cycle punishes us for doing so.

And then, quietly, something else happens.

The real advantage doesn’t go to the loudest adopters or the fastest movers. It goes to the people who learn how to integrate the new capability into their existing judgment. The ones who ask not just “What can this do?” but “What should I now do differently?”

They are harder to spot. They’re usually less visible. But over time, they’re the ones still standing.

My Own Throughline (And the Role of Luck)

I want to be careful here. This isn’t a victory lap.

I didn’t predict every shift. I didn’t always move early. I made wrong calls, stayed too long in some places, and left too early in others. Timing and luck played a role, more than we like to admit.

What did change over time was my posture.

Early in my career, curiosity carried me. I wanted to understand new tools, new media, new ways of making things. Later, discernment mattered more, learning what not to chase, what to ignore, what would age poorly. Now, judgment feels like the tangible asset: knowing when to adapt, when to hold, and when to step back and reframe the problem entirely.

Each cycle stripped something away. Certainty. Familiarity. Comfort. And each cycle forced a recalibration: What is my actual value here, independent of the tools?

That question has never stopped being useful.

Why This Moment Feels So Hard

It’s the end of the year. For many people, it’s been a brutal one.

Layoffs. Consolidations. Titles disappearing overnight. Careers that felt stable suddenly feeling provisional. For some, it’s not just professional loss. It’s identity loss. When work has been a source of meaning, structure, and self-worth, disruption cuts deep.

Understandably, this moment feels different. AI operates closer to cognition than previous tools. It touches writing, thinking, synthesis, creativity, things many of us believed were safely human. The speed is disorienting. The breadth is unsettling.

Every generation believes the disruption they face is uniquely destabilising. Sometimes they’re right about the impact. They’re almost always wrong about what it ultimately demands of them.

The Lesson That Outlasts the Technology

Across every cycle, the people who endure—and often thrive—are not the most technical or the most performative. They are the ones who invest in orientation.

They learn how to learn. They separate signal from noise. They resist the urge to tie their identity too tightly to a single tool or role. They understand that speed without direction is just acceleration toward the wrong destination.

Experience, when used well, isn’t resistance to change. It’s infrastructure. It’s what allows change to be absorbed without collapsing the system.

That doesn’t mean clinging to the past. It means carrying forward the parts that still matter: judgment, taste, ethics, context, and the ability to ask better questions than the machine ever will.

A Quiet Note of Hope

If you’re in a period of transition right now—between roles, between identities, between versions of yourself—I won’t offer false reassurance. These moments are genuinely hard. They ask more of us than any keynote or trend report ever will.

But I will say this, with the confidence that only time gives you: Cycles will be lived through. They can even be risen through.

And when the noise fades, and it always does, the people who remain are rarely the ones who chased every new thing. They’re the ones who learned what endures when the tools change.

As we head into a new year, in the middle of another significant recalibration, that’s the lesson I keep returning to. Not a comparison. Not panic. Not nostalgia.

Transition.

It’s slower. It’s quieter. And it’s still the most reliable way I know forward.

One last thought, especially for those in the middle of it right now.

Transition doesn’t arrive fully formed. It’s built slowly, often through moments that feel like setbacks while you’re in them. The cycles don’t just disrupt careers, they train judgment. And if that’s true, then even this moment, difficult as it is, is not wasted time. It’s part of the education.

I’ve been here before. More than once. And I’m here now.

If you’re feeling disoriented — between roles, between chapters, between versions of yourself — and you want a place to think out loud, my door is open. Sometimes what helps most isn’t advice or strategy, but simply being heard by someone who’s lived through a few cycles and come out the other side.

If you reach out, I’ll make the time. Coffee if you’re in Singapore. A call if you’re not. We’ll figure out the rest.

I’ve also written and shared a lot of my thinking publicly, including a book on how to approach AI with clarity rather than fear. Not because I have all the answers, but because writing has always been how I find my own orientation in uncertain terrain. If any of it helps you find yours, then it’s doing its job. DM me, and I’ll happily give you a copy, because in your search for the next thing, AI will undoubtedly be a talking point.

I’ll leave you with something I’ve carried quietly through every transition:

You still have to put feet to prayers.

Hope matters. Support matters. But momentum — even a small, imperfect step — is what changes your relationship with the moment you’re in. The first step is physical. It isn’t easy. But once you take it, the path has a way of revealing itself.

That’s been true in every cycle I’ve lived through.

And I don’t believe this one will be any different.

There’s a strange tension in modern work that no one seems willing to name. On one hand, we are living through the most astonishing explosion of tools, platforms, models, and efficiencies the workplace has ever seen. On the other, people have never felt more tired, more distracted, or more suspicious that their days are slipping away into a blur of tasks that were urgent to someone but meaningful to no one.

And perhaps I’m feeling this shift more acutely because of where I am in my own life. I turned sixty-one this year. That number does something to your perspective. It forces a kind of clarity that younger versions of ourselves don’t yet have the experience—or the urgency—to access. Many of my peers are facing transitions of their own: reinventions, rewrites, forced pauses, abrupt layoffs, career pivots they never expected to make. Some are gracefully dancing into their third act. Some are stumbling into it.

An entire generation of people over fifty-five is quietly navigating a world of work that was not designed with them in mind.

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What Time Teaches You, Eventually

Time feels different when you have lived enough of it to understand what is worth spending it on. Meaning becomes non-negotiable. Momentum becomes precious. Attention becomes currency. And the cost of wasting any of them becomes almost physical.

Which brings me back to the tension we keep avoiding because the more I watch the way we work, the more convinced I am that we’ve been asking the wrong question. The issue isn’t whether we have enough time. It’s that we’ve lost the plot on what time is actually for.

We treat time like a container to be filled: meetings stacked upon meetings, calendars paved over like a six-lane highway, days scheduled into obedience. We’ve mistaken motion for progress and responsiveness for leadership. Somewhere along the way, the meaningful hour—the hour where clarity sharpens, decisions land, or creativity takes shape—became the rarest thing in professional life.

In the Age of AI, time itself isn’t scarce anymore. What’s scarce is attention. Judgment. The ability to choose what matters in a world accelerating beyond comprehension. AI isn’t going to take that from us. It’s going to demand more of it.

Because when machine intelligence becomes abundant, human judgment becomes the rarest of resources.

The Normalisation of Noise

I’ve seen this pattern everywhere. Teams drowning in tools but starving for focus. Creatives who can generate a hundred ideas in minutes but struggle to find the one idea that deserves a fight. Leaders whose calendars look like evidence of a life spent reacting instead of directing. And the language we use to describe this—burnout, overwhelm, “not enough hours”—is a polite way of saying we’ve built systems that refuse to honour what humans need to do great work.

The real tragedy isn’t the noise. It’s that we’ve normalised it.

I’ve come to believe that the next decade of work won’t be defined by the companies with the best models, the fastest pipelines, or the most automated workflows. It will be shaped by the individuals and teams who learn to protect their attention with the same intensity they once protected their budgets. Those who design their days with intention. Those who learn to create momentum instead of motion. Those who relearn the discipline of deep thinking in an era built on speed and distraction.

And perhaps most importantly, those who understand that AI is not here to speed us up. It’s here to change the purpose of our time.

About a month ago now, during a training session I was leading for Google, someone asked me a question that caught me off guard:

“What happens to people who don’t have thirty years of experience to guide their judgment? Where do we begin teaching them?”

My answer surprised me in its simplicity: Teach them what good looks like.

Because once you know what good looks like—what clarity feels like, what a meaningful hour produces—you stop letting your day get hijacked by everything that isn’t that.

Lately, I’ve been thinking about this a lot. About how we reclaim meaning inside the mechanical churn of modern work. About how we build workflows that honour creative energy, not just available hours. About how leaders make decisions in a world moving faster than memory. About how we might design time with the same craftsmanship we apply to strategy, storytelling, or code.

The Shape of an Idea Begins to Form

I think there’s something here. A body of thinking I haven’t articulated fully. A direction that feels like the next chapter of the work I’ve been doing in AI, in leadership, in transitions, and in the shifting terrain of the later-career landscape. My monkey mind keeps coming back to the same question:

What does it mean to spend time well?

I’m writing my way into an answer. Slowly, deliberately, without the rush to publish or the pressure to package it neatly. But the shape of it is emerging: a reframing of time as a strategic asset, a creative resource, and a deeply human responsibility.

If my first book was about how to think about generative AI before you use it, this next one may be about how to live and lead in an era that keeps demanding more of us than time alone can provide.

I don’t know exactly where this project will go yet. But I know where it starts, with a truth that feels more urgent every day:

Our time is not the issue. What we do with our time is.

AI, in all its astonishing speed and strange generosity, has given us something we haven’t had in decades: time to spend. Cycles that used to take days now take minutes. Work that once required teams can be prototyped by one person in an afternoon. Research collapses into seconds. First drafts fall out of the sky. And the miracle, every time, is not just the output but the sudden surplus of time that appears behind it, quietly, like a backdraft.

Some studies suggest that when used intentionally, generative AI can return anywhere from 20 to 50 percent of a workday back to the individual. I suspect it’s even more for those who truly understand how to wield it. But the exact number isn’t the point. What matters is the question that nobody is asking with enough seriousness:

What will we do with the time we get back?

Will we use it to close the task faster, check the box sooner, ship the work, and sprint to the next demand? Or will we treat this returned time as an investment—something to pour into the parts of work that machines cannot touch?

• Conversations that build trust.

• Debates that sharpen judgment.

• Relationships that strengthen teams.

• Moments of alignment that prevent months of drift.

• Thinking that refuses to be rushed.

• The long, slow arcs of creativity that no model can accelerate without diminishing.

Time is on my side, yes it is.

At least, that’s what the Rolling Stones promised. And strangely, for the first time in my working life, it feels like it might actually be true. Not because the days have grown longer or the world slower, but because AI has started to hand back slivers of time we didn’t expect to see again. Minutes, hours, sometimes whole stretches of work that simply evaporate under the weight of a single prompt. It’s an extraordinary shift—one that should feel liberating. And yet, for many of us, myself included, it feels disorienting.

For years, we told ourselves a story about the future of work: that AI would automate the mundane so humans could focus on the meaningful. Well, that future is no longer for the genre of Science Fiction. It’s here. And most of us aren’t ready for it.

We’ve built decades of professional muscle memory around urgency, reactivity, logistics, and performance. When the machine lifts that weight from us, many won’t know what to do with their newly empty hands. This includes me—and it’s a question I’m now exploring.

This is the frontier ahead of us. Not better prompting. Not faster workflows. Not another sprint toward optimisation.

When the machines give us time, how do we make sure we spend it well?

That’s what I want to explore next—not as a productivity expert, but as someone who has lived through reinvention, crisis, acceleration, aging, and the quiet recalibrations that come with all of them. As someone who has watched a generation of workers lose their footing in the churn, and another trying to build a life where meaning doesn’t get buried beneath the metrics.

So yes—the next book is slowly taking shape. As I make progress, I will, as I am prone to do, drop thoughts and fragments here: early sparks, half-formed arguments, the occasional late-night paragraph that won’t leave me alone. Watch this space.

And I must thank those of you who’ve bought my first book, To Question Is to Answer: How to Think Critically and Thrive in the Age of AI. Your notes, your messages, your quiet encouragement, they have reminded me, again and again, that long-form writing isn’t dead. Not even close.

In a world chasing sound bites, swipes, and seven-second stories, there is still a hunger for ideas that take their time… and for readers willing to take their time with them.

There’s a strange moment that happens when you prepare to teach something you’ve practiced: you suddenly see the value of it with fresh eyes.

That’s what happened while I was prepping the Google workshop on AI as a Force Multiplier, a few weeks ago.

In every run-through, every refinement, every rehearsal, I kept returning to the section on Frameworks as a Force Multiplier. The most powerful thing in my career has never been the tools, the decks, the jobs, or the companies. It’s been the frameworks.

Not the buzzword kind. The thinking kind. The ones that let you walk into chaos and find shape. The ones that turn a vague instinct into a defendable path. The ones that separate noise from signal when the world is accelerating faster than any human can track.

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Taking a swat at SWOT

I have worked most of my career in or around advertising and marketing, and the most common framework taught and used, ad nauseam, was/is SWOT. Hated it, always have, always will.

SWOT is the intellectual equivalent of a lukewarm cup of water. It doesn’t offend. It doesn’t excite. It doesn’t move anything forward. And for creatives, people whose job is to imagine, reframe, provoke, and redesign reality, SWOT is almost perfectly engineered to flatten thinking rather than elevate it.

SWOT is built for listing, not thinking. It turns creative complexity into four polite buckets and gives teams the satisfying illusion of progress once the boxes are filled. But nothing actually changes. No tension, no reframing, no synthesis, just categorisation dressed up as strategy. Creativity depends on friction, surprise, and provocation; SWOT removes all three.

That said, SWOT isn’t worthless. It’s upstream. It sets the stage, surfaces the obvious truths, and clears the throat of a project. But its outputs are raw ingredients, not answers. The real work, the creative work, happens when you move beyond the boxes and into frameworks that stretch, collide, and transform those early observations into something directional and alive. SWOT sets the table; frameworks and creativity serve the meal.

Frameworks used to be gated.

Blue Ocean Strategy, Red Teaming, Laddering, First Principles, JTBD, Cynefin — these aren’t just frameworks. They’re windows into how the world really works. They’re the difference between reacting and designing, between navigating complexity and being swallowed by it. And for most of modern business history, these windows were positioned high enough that only a select group could look through them.

They lived behind the paywalls of elite consulting firms. They were taught in MBA programs, whispered in boardrooms, guarded by jargon and slide decks, and protected as intellectual property. They were the ladders that let certain people climb higher while everyone else just watched from the ground.

For decades, this was the unspoken structure of the industry: insight was a privilege, not a public good. If you didn’t have the pedigree, the credentials, the access, or the budget, you weren’t invited into the room where the real thinking happened. Frameworks weren’t democratised; they were monetised.

But times they are a-changin’.

AI blew open the gates. It put the ladder in everyone’s hands. It turned once-elite thinking models into accessible, usable, everyday tools. What was once rarefied is now reachable. And that shift is reshaping who gets to see clearly, who gets to make decisions, and who gets to create the future, not just interpret it.

A few years ago, using frameworks properly required time, data, experience, and a room full of specialists. You had to know how to set up, interrogate, validate, and extract value from the model. It wasn’t that people lacked intelligence; the cost of entry was too high.

Then AI collapsed the cost structure. Now anyone can run a Blue Ocean analysis in seconds. Anyone can map a story arc using Hero’s Journey. Anyone can ladder a value chain, diagnose a systemic failure using Cynefin, or run a full Six Hats collision set without needing a facilitator trained in de Bono.

The hard part used to be knowing the model. Today, the hard part is knowing which model to use — and why.

That is a seismic shift. AI didn’t just democratize access to frameworks. It democratized power. For the first time, the fruit at the top of the tree is available to anyone willing to reach.

The hidden scaffolding of my creative life.

So much of the way we work was established in the days when artificial intelligence was a character in a movie or a book. Now, it’s on our phones, our watches, in our boardrooms and bedrooms.

In the blink of an eye, we crossed the line from reading about AI to reading with AI. But what is taking much longer to navigate is how to think about how we use AI, how it can become a force multiplier in our work, and when to actually step away from the machine, to think, to doodle, to imagine.

While I didn’t know them as Frameworks, in retrospect, they have been the scaffolding of my career. Design Thinking, First Principles, SCAMPER, Brand Archetypes, 3 Act Structures – these were routinely used at the places I worked, like frog design, or with the client teams I worked with at Apple or Disney.

They’ve helped me navigate ambiguity, build strategy, direct teams, write stories, challenge assumptions, and design futures that didn’t exist yet. They were/are the silent architecture behind everything I do.

Some ideas are too valuable to keep.

Watching the Google team — smart, driven, capable people — realize how frameworks could transform their day-to-day work reminded me of something I had not considered: Access changes everything.

And that change shouldn’t be gated behind a paywall or sold as a productized thought leadership package. When someone suddenly can see clearly, to question sharply, to make sense of complexity, it changes how they lead. It changes how they create. It changes how they make decisions.

As I was writing my first book, To Question Is to Answer: How to Think Critically and Thrive in the Age of AI, I began my deep dive into the power of frameworks via the Socratic Method, as a way of thinking, which led me into exploring Frameworks as the scaffolding of thinking. And discussion with some really outstanding process thinkers like @Keith Timony and @Robin Moroney helped inform how I wanted to approach a send book, taking a look at how frameworks might be considered in the Age of AI.

Then, I wrote Frameworks Reframed. I use it in my workshops and in my work. The idea of frameworks as a force multiplier keeps coming up in the coffee talks I have with agency and brand leaders.

If it helps you, you can have it.

So I have decided to give it to you if you want it.

You know the drill. Connect with me on LinkedIn. Write something in the comments. I’ll drop it in your DMs. Hell, you have read this far, so consider this the reward for your patience and persistence.

Because if AI is going to reshape our world, then the real divide won’t be who has the tools. It’ll be who has the thinking. Frameworks are no longer the privilege of the few. They’re part of the operating system of the AI age.

The moment you combine a framework with AI, you get something exponentially more powerful than either alone:

• Blue Ocean becomes a system-level search engine for opportunity.

• OODA becomes a real-time decision loop.

• First Principles becomes a way to break industries, not just improve them.

• JTBD becomes a cultural decoder.

• Six Hats becomes a high-speed multi-perspective machine that reveals contradictions, hidden risks, and new routes forward.

The ladder we all used to climb, slowly, painfully, expensively, has been replaced by a platform that anyone can stand on. That is worth giving away.

This is why I am doing it.

Access is impactful because democratising thinking is more meaningful than monetising it.

Because the Google workshop reminded me that what feels obvious to me can be transformational to others.

Because our industry is drowning in content and starving for structure.

I’m putting the frameworks into the world because I think the world is better when more people can see clearly, choose confidently, and create with intention.

At some point, every industry hits a moment when the rules quietly change. This is one of those moments. AI has made the tools cheap, but it has made the thinking priceless. And if we want better work, better leadership, better ideas, then we have to stop treating frameworks like insider knowledge and start treating them like shared infrastructure.

So take this. Use it. Break it apart. Improve it. Pass it on. If frameworks helped me climb, they can help you climb higher.

And maybe that’s my goal.

If you spend enough time inside companies—real time, not the workshop-and-slide-deck variety—patterns begin to reveal themselves. Not the kind that show up on an org chart, but the kind you notice in the quiet spaces between meetings: who leans forward when a new idea arrives, who leans back, who reaches for the notebook, who stares through the glass wall as if already somewhere else.

AI has made those patterns louder.

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In the past year, as I’ve moved across boardrooms, studios, brand teams, and late-night strategy calls, I’ve noticed that people tend to fall into one of two gravitational pulls. Not by title. Not by age. Not by capability. But by temperament. A kind of professional DNA that shapes how they respond to a world in motion.

The Age of AI has revealed two archetypes—Explorers and Settlers—and we’ve mistaken their differences for dysfunction when, in fact, the difference is the point.

The Explorer’s Instinct

Explorers are wired for the frontier. You see it in the way they speak about tools that are still in alpha, in the way they open ten tabs simultaneously, in the way they ask questions that feel slightly ahead of everyone else’s appetite. They push. They provoke. They play at the edge of the map. If a process exists, they are already wondering how to undo it, not out of rebellion, but out of instinct. They are allergic to stasis.

Hand an Explorer a new AI model and they don’t ask, “What can it do?” They ask, “How far can I push it before it breaks?”

It’s easy to romanticise this disposition. After all, most corporate mythology is shaped around heroes who deliberately broke something. But Explorers are only one half of the story.

The Settler’s Counterbalance

Standing beside them, usually uncelebrated, occasionally misunderstood, are the Settlers, the ones who make a place livable. If Explorers discover new territory, Settlers build the civilization. They are the translators of chaos. They take what an Explorer drags back from the horizon — half-formed prototypes, scribbled prompts, an uncanny image that suggests a new narrative structure — and turn it into something that works tomorrow, next quarter, and across seven markets. They construct the rituals, the workflows, the knowledge systems. They give the work its spine.

When AI enters an organization, Explorers accelerate. Settlers stabilize. It is a dance, even when the two sides don’t realise they’re dancing.

Where the Modern Workplace Misreads the Moment

The modern workplace, in its rush toward transformation, often misreads this dynamic. It asks Settlers to behave like Explorers—be more innovative, take more risks, experiment more frequently—as if temperament can be toggled like a setting. Or it attempts the reverse, forcing Explorers into the safe, procedural orbit that drains them of the very volatility that makes them useful.

This is the unspoken tension of the moment: the tools have changed faster than our understanding of the people using them.

I recognise this tension because I sit on one side of it. I have always been an Explorer. This isn’t a matter of preference; it’s in my DNA. I break things early. I test before I explain. I try to outrun the brief, partly because I enjoy the chase and partly because I’ve learned that the most interesting ideas often reveal themselves in the places you’re not supposed to look yet.

But I’ve also learned, sometimes the hard way, that an Explorer without a Settler is a short-lived phenomenon. The best work of my career didn’t come from unrestrained exploration. It came from partnerships with people who could absorb what I brought back, shape it, teach it, and operationalise it. They kept the experiments from evaporating. They turned sparks into systems.

The Human Infrastructure of AI

This is the part of the AI conversation we don’t talk about enough. We debate tools, models, guardrails, privacy, hallucinations - the machinery of the moment. But none of that matters if we fail to understand the human infrastructure required to make any of it useful.

The irony is that organisations often behave as if transformation demands uniformity, as if everyone should approach AI with the same enthusiasm, risk appetite, and curiosity. But no great system has ever been built on sameness. We need mixed temperaments.

Explorers ensure we don’t fall behind. Settlers ensure we don’t fall apart. And workplaces that recognise this, that intentionally pair these archetypes rather than suppress them, will move faster and more intelligently than those still trying to convert everyone into a single persona.

A Workplace Built on Complementary Temperaments

The emergence of Generative AI does not require us to reinvent human nature. It demands that we respect it by understanding how people genuinely respond to change, by organising around temperament rather than title, and by building teams where Exploration is allowed to roam and Settlement is allowed to anchor.

There is no era—digital, mobile, social, AI—where we didn’t need both. What’s different now is the speed. AI compresses cycles. It collapses steps. It exposes inefficiencies. And because it accelerates what people are already inclined to do, the divide becomes more visible.

The Explorer rushes ahead. The Settler grounds the rush in something durable. Each is the other’s missing half.

This isn’t a hierarchy; it’s a circuit.

And the future of work will belong to the organisations that understand this simple truth: when you build with both in mind, you create something that lasts.

I have never been to Cannes. I have never been part of a project that has even gotten close to Cannes. If anything, I’ve spent most of my career sitting slightly outside the warm glow of the Palais, half-amused, half-sceptical, watching the myth-making machine spin itself into legend. I have, however, drank my fair share of pink wine.

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Cannes. The immaculate case films. The award-winning ideas that never quite made it to the real world. The beautifully edited “impact” slides that seemed to evaporate once the festival week ended. Entire think pieces have been devoted to the culture of fake work, the industry’s addiction to awards, and the disconnect between jury preferences and client realities.

I’ve read all of it. I’ve nodded more than once. And I’ve said, quietly but consistently, that Cannes Lions has some reckoning to do.

And yet… I’ve just enrolled in the Cannes Lions Creative MBA. Not because I’ve suddenly become a believer. But because I think we’re at a genuine transition point.

The industry is shifting in ways that Cannes can’t ignore: AI reshaping workflows, brands demanding long-term commercial impact, in-house teams becoming more sophisticated, and younger creatives wanting meaning—not just metal. We’re in a moment where the definition of “great work” is being rewritten, and I hope that Cannes Lions sees this too. Institutions don’t survive as long as they have without evolution. They evolve because the world forces their hand.

Why this matters to me.

I want to understand how Cannes defines creative excellence today—not ten years ago.

If Cannes is recalibrating its standards, I want to see the recalibration from the inside. I don’t know if any of the new thinking has been integrated into the coursework yet. If not, at least it establishes a baseline for me.

What does “Lions-winning” work look like in an era where generative AI can create entire campaigns overnight? What counts as originality when tools democratize craft? What does effectiveness mean to jurors now that clients expect measurable, long-lasting outcomes?

If I’m advising brands and training teams on how to think, I want to understand the mental models behind the awards that still shape so much of the industry psyche. Not to worship them—but to interrogate them. To understand what Cannes is teaching the next generation of creatives about what “good” looks like.

And that leads to the second reason I’m doing this.

I want to understand how creativity is critiqued.

This is the part I don’t think we talk about enough. And I think we talk about creative education even less.

The jump I’ve made over the last couple of years, from informal learning to structured learning, has fundamentally changed how I think about teaching. For most of my career, I learned the same way everyone else did: I earned a BS in Design from UC Davis, and then its on the job, through mentors, from late-night stress, through mistakes and near misses, by watching brilliant people solve impossible problems in real time. It was messy, chaotic, sometimes traumatic, often magical and entirely unstructured.

It wasn’t until I took my first structured online course that I really began to see the value. I took a course from Cambridge on Circular Economy and Sustainability Strategies. Followed that with a Kering Group course on Sustainability and Luxury Goods. Then something around Blockchain. And then I dove headfirst into 3 years of courses at Scott Galloway and Greg Shove‘s brilliant Section School (which has now been completely retooled as Section AI, with over 30 courses completed, including a Mini-MBA in AI for Business.

Learning can be addictive.

Today, if you want to explore anything, there’s a YouTube video for it. And there’s nothing wrong with that. YouTube is an incredible tool for exposure. It gives you explanations, vocabulary, an instant download of someone else’s clarity.

But I never eally learn anything from Youtube. YouTube doesn’t teach you. It explains things to you.

There’s a difference.

Watching a video about strategy is not the same as learning strategy. Watching an AI tutorial is not the same as building AI habits. Watching someone break down a creative idea is not the same as being able to create one yourself.

Real learning requires structure, sequence and scaffolding. It’s the difference between splashing around in the water and actually learning how to swim.

Why Structure Matters

Structured learning gives you something YouTube never will: direction. You’re not just wandering from one interesting idea to the next; you’re being taken somewhere intentionally. It also gives you a lens—a way of understanding not just what to look at, but why it matters.

There’s a rhythm to it as well. Reflection stops being accidental and becomes part of the cadence. Repetition isn’t filler; it’s reinforcement. And then there’s the discomfort, the good kind. Structured learning pushes you in ways random video content simply can’t, because it asks more of you than passive understanding.

Finally, it sets a standard. There is a clear definition of “good,” and you’re measured against it. Not harshly, but honestly, in a way that sharpens rather than discourages.

But maybe the most essential thing structured learning gives you is something deceptively simple: it makes you finish. There’s an endpoint, a commitment, a path you’ve agreed to follow. And that act alone, seeing something all the way through, changes you. It turns loose information into discipline, and discipline into confidence. Over time, that confidence becomes capability.

People don’t fall behind because they lack content. We’re drowning in content. What they lack, what most of us lack, is structure. A way through. A container that holds the learning long enough for it to take shape. Structured learning gives you that shape, and that makes all the difference.

That’s why I’m taking the Cannes Creative MBA, not because I need another line on my CV, but because I want to see how knowledge is being structured at one of the most influential institutions in our industry. I want to understand the pedagogy behind their teaching system. How do they think about progression? How do they scaffold creative thought or frame insights, execution, originality, and impact?

I never studied pedagogy. I’m learning to teach by being taught, and I am learning to teach by teaching.

Every course I take becomes part of the blueprint for building my own workshops, frameworks, and AI training programs. And yes, that’s part of the story here. I’m preparing to launch structured courses of my own, programs built not on random information but on systems. Pathways. Frameworks that actually transform how people think and work.

Before I do that, I want to understand how others build theirs.

No Input, No Output - Joe Strummer, The Clash

Joe Strummer of The Clash once said, “No input, no output.” That line lands with a kind of clarity it never had in my thirties. Staying sharp has everything to do with staying curious. It means deliberately putting yourself in rooms where you’re not the expert, where you’re learning someone else’s system, where you allow the possibility that their approach might sharpen your own.

If creativity is in the middle of a renewal, and I believe it is, then perhaps we should be, too.

So yes, I’ve enrolled in the Cannes Lions Creative MBA. Not out of devotion or cynicism, but as a student. As someone who believes that the people who will shape the next era of our industry are the ones who keep learning, and, importantly, learn how to teach others to do the same.

Those Who Do, Teach.

The old line, “those who can, do; those who can’t, teach”, was always bullshit. In our industry, the best teachers are the ones still in the arena. Practitioners who build, break, rebuild, and then show others how to do it better. Teaching is a force multiplier. And the people who do the work are often the ones best equipped to teach it.

If your organisation is ready to move past the “prompt tips and quick hacks” phase of AI, and into the deeper work of building systems, frameworks, and creative habits that scale, this is precisely the work I currently teach.

I train teams to think and operate differently and to use AI as a force multiplier for strategy, storytelling, and innovation.

If you want that kind of capability inside your walls, I’m ready. And I am only going to get better at it.

I had the pleasure and privilege of leading a training workshop at a Google offsite in Phuket this week. I will write more, later, about that entire experience, but there are a couple of things that I couldn’t wait to write about.

First, imagine my surprise a couple of months ago when I got a note from one of the key leaders at Google, asking if I would be interested in designing a workshop based on my approach to AI.

Google is asking me to talk about AI - mind-blowing and humbling to say the least.

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I wasn’t expecting this

Something happened during the workshop that caught me off guard and stuck with me throughout my day and my trip home.

A question came up during the Google training session. One of those moments when the room is warm, the afternoon sweets and coffee have settled, and the collective attention of forty people turned to me.

“Chris… a lot of what you’re showing us seems to come from your thirty years of experience. What about younger knowledge workers? People who haven’t lived through all the shifts you’ve lived through. How are they supposed to succeed with AI without all that accumulated wisdom?”

I am paraphrasing, of course, both for dramatic effect and to mask the fact that my memory isn’t what it used to be, but I think you get the idea.

I smiled. I paused — in equal parts theatre and aging. But the answer was already there, fully formed, almost waiting for the question to find it.

“Teach them what good looks like.”

That’s it. That’s the whole thing.

Teach them what good looks like so they can recognise it — in themselves, in their teams, and in their collaborations with AI. Because if they can’t recognise good, they will drown in the ocean of “generated.”

It struck me that for all the talk about prompting, all the obsession with agents and workflows and feature releases, almost none of it matters if you don’t know the difference between something that exists and something that is good. AI can produce more output than any of us can read, let alone evaluate. But AI can’t tell you what to care about. It can’t tell you what’s worth fighting for. It can’t whisper, the way a great creative director once might have, “This isn’t there yet. Keep going.”

The decades that shaped me

During the workshop, I shared a timeline of my life — not a sentimental scrapbook, but a map of the cultural and technological frontiers I’ve lived through. The TRS-80 at RadioShack. The Macintosh “1984” ad. MTV. Hotmail. The first iPod. The early days of social media when the future shifted from linear to generative. The moment the iPhone quietly rearranged the architecture of our behaviour. And the day transformers were invented — arguably the single most consequential technical breakthrough of the century — which set us on the path to the present moment, a moment in which intelligence is no longer a scarce resource.

I didn’t build that timeline for nostalgia. I built it to make a point: my relationship with AI isn’t technical. It’s experiential. It’s the sum of decades spent absorbing taste, rhythm, timing, craft, intuition, and cultural change. It’s the hours I’ve spent with work that answered the brief, that got the job done, and the hours I’ve spent with work that made me stop for a second because it was genuinely great. You don’t forget those moments. They shape you.

Younger talent hasn’t lived through those cycles. It’s not their fault. They just haven’t had enough time on this big blue rock as I have had. And while we can’t change that, we can try really hard to get them up to speed as quickly as possible, using our collected history of work, both good and bad, so that they will recognise it in other’s work, that they come in contact with, and in their own, as they begin to learn, and think, and make.

This is what good looks like.

This is the part almost everyone forgets: organizations teach metrics, not standards. They teach KPIs, not judgment. They teach speed, not sensibility. They’ve become astonishingly good at the numerics of work and embarrassingly weak at the aesthetics of it.

No one teaches a young strategist how to know when an idea is thin. No one teaches a young creative how to feel when a sentence is hollow. No one teaches an emerging marketer how to sense when a story is built on sand. We hand them dashboards and performance targets and a stack of briefs that are already broken, and then we wonder why the outputs feel… off. Polished, perhaps. But hollow.

And now we are layering on generative AI. We are going faster, deeper, wider, but are going in a direction that is ultimately good – for the work, and for the person?

I think “good” is a human aspiration we don’t talk about enough. Good is not perfection. Good is not greatness. Good does not require decades of life experience. Good simply requires intention. To make something good is to decide, before the first word is written or the first frame is generated, that the work matters enough to be held to a standard.

And that standard is deeply human. It’s not a data point. It’s a feeling. A coherence. A sense that everything inside the work aligns — the thought, the expression, the emotion, the timing, the context, the craft. Good is the quiet, unassuming ambition to leave something better than you found it.

The old man in me is getting nervous

I worry that in this new age — when AI can produce a dozen variations before you finish your coffee — we’re losing the ability to recognise good. Not because good has disappeared, but because the flood of “content” has numbed our senses. When everything is possible, discernment becomes the rarest skill in the room.

But that’s exactly why emerging talent can thrive in this moment. AI has given them access to infinite drafts, infinite experiments, infinite attempts. What they need now is the eye. The taste. The instinct. The internal compass that tells them when something clicks. When something holds. When something transcends the generic.

That’s what experience once gave us. But experience doesn’t have to take thirty years anymore. Not if we teach them what good looks like.

We’re in the first era in human history where the next generation can accelerate their judgment — not by cutting corners, but by seeing more, trying more, evaluating more, reflecting more. AI can’t give them wisdom. But it can give them an unlimited canvas on which wisdom can form.

All they need is someone to point to the past and say, “There. That’s good. Now go make your version.”

Because the future of work will not be defined by who prompts fastest or who automates the most. It will be defined by who has the courage and the taste to say: Let’s make this good. Not perfect. Not viral. Not optimised. Just good. And from there, greatness will follow. But this too will take time.

Our role, in this strange new age, is to pass on what we’ve learned about good work and good judgment, so our next generation of creatives, writers, strategists, can move past us, not because they copied us, but because we taught them what good looks like.

The older I get, the more I want to teach - to pass on the experiences and point of view I have gathered along the way. Lately, much of that teaching has been centered around how I think about AI, about the questions and approaches I have defined that lead to my work on projects, using AI as a force multiplier.

I would love to talk with you about the workshops I have designed, and would love to bring to your agency, brand, or organization. You can get a more than a taste of how I approach thinking in thinking in the Age of AI by reading my book, “To Question Is to Answer. How to Think Critically and Thrive in the Age of AI.”

One last thing, before I hit publish... A MASSIVE thank you to those from Google who attended this workshop, and stood in a small but oh so meaningful line to have me sign my book. This was a first for me. And I will never forget that.