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.

I had mentioned the other day that I was a guest on The Generalists, the podcast the Michael Smith Jr. and Raz Kotler shoot at Poddster Singapore.

Well, the full episode is now out on Youtube. https://lnkd.in/gW_gcx6v

As usual, they guys did an amazing job, pointed questions, each leading to the next topic they wanted to unpack. But what I loved the most about this episode is that I felt they gave me time to tell my story, and talk about the things that really matter to me, unscripted, a guiding structure, but also room to breathe.

Thanks guys. I had a lot of fun.

PS: Michael Smith Jr. I spoke with Dad yesterday morning, and he got choked up saying how proud he was of being able to see two of his sons, on Youtube, working together. That alone was worth the effort.

This morning, I got a shipping notice from Amazon. Fifty copies of my book To Question Is to Answer: How to Think Critically and Thrive in the Age of AI have been shipped.

Fifty. Five-oh.

My book is part of a workshop I’ll be teaching. Not all of them, but the lion’s share, will find their way into the hands — and hopefully into the minds — of a regional team of creatives, marketers and strategists. (I’ve also set one aside for Matthew Crescenzo who was recently named Creative Managing Partner at Carbon. Congrats, by the way — well-deserved recognition for a former colleague.)

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I never planned to write a book.

I also never really realized that the stuff I had in my head would be of value to others. But a couple of years ago, I began having one-to-one coffee sessions with senior leaders who were curious about AI.

I was happy to share how I use the tools, which foundation models I favour, and do a bit of say-and-show along the way. Then Evgenia Papageorgiou, in one of these sessions, asked a question that sparked the idea for the book.

(Now, if you’ve already heard this story, feel free to skip the next few paragraphs.)

Ev asked, “How did you know to ask that next question?”

At the time, I thought my answer was good.

“Well, I’ve spent the past 40 years answering questions for people, so it just felt right.”

It wasn’t until I got back to my studio that I realised it was a bullshit answer. Not wrong — just not enough. The book is my attempt to answer her question.

The Question Before the Tool

We talk about AI as if it were a power source: plug it in and watch the output light up. But in practice, AI is less like electricity and more like a mirror. It reflects the clarity, bias, and imagination of the person in front of it.

The better the question, the sharper the reflection. The more accurate the framing, the context, the intention — the more meaningful the response. Not as an answer, really, but as the next step in building a thoughtful case for whatever you’re trying to accomplish.

When I began using AI as a central part of my work, I started keeping a journal of prompts and responses. This was before foundation models had memory or persistence, so my journal served both as a backup and a research log. It also became the source material for the book. I spent hours trying to answer Ev’s question — Why did I know to ask that question?

I tried to apply first-principles thinking — to strip the idea of questioning down to its core: What is the purpose of a question?

I read about Socrates — because who doesn’t want to spend a rainy afternoon doing that? — and about the Oracle of Delphi, which to me resembled how we think about AI today: ask a question, expect an answer. I read about the science behind questions. I read a lot of things, and I wrote a lot of things.

Over the next eight months, the book began to take shape. I wrote about pattern recognition, bias, context, and stacking. I integrated the work I was doing in real time — how to structure thinking before prompting, and what to do after the LLM returned a response.

I also spent a significant amount of time talking to others about how they use the tools and what they think about before they prompt. I was curious about how people began an AI session — and how they knew when to stop. When did they have enough? When did they switch from machine back to man?

The emergence of generative AI as part of our daily workflow has created a tug-of-war between predictive LLM thinking and human agency. At times I even pit one model against another. Same prompts. Different responses.

That’s the real story AI is telling us right now — not about machines becoming more human, but about humans recognising the limits of our own thinking and trying to find ways to balance the convenience of prediction with the promise of curiosity.

The Pause Between Prompt and Response

Prediction rewards efficiency; curiosity rewards exploration. And we’ve built entire corporate systems optimised for one while quietly starving the other. AI exposes that imbalance. It gives us the illusion of intelligence on demand, when what we really need is the patience to stay with uncertainty.

Most people don’t stop prompting because they’ve arrived at an answer; they stop because they’ve run out of better questions. Or time.

That hand-off — from artificial intelligence to human intention — is where the future of work actually begins. It’s the inflection point where tools stop being productive and start being reflective. And if we can learn to see that moment not as an ending but as a signal, a cue to pause, reframe, and ask differently, then AI stops being a threat and starts becoming what it should be: a collaborator in our thinking, not a replacement for it.

Thinking as Technology

When I started working with teams on how to think about AI — and when to think about it — I noticed the same pattern again and again. People would sit in front of the screen, fingers poised, unsure of what to type. Not because they didn’t understand the tool, but because they didn’t understand their intention. It’s just a chat window. No manual required for that.

They knew what they wanted out of the model. They didn’t yet know what they wanted out of themselves.

That’s what To Question Is to Answer is really about: reframing our relationship with inquiry. Asking not just what we want to know, but what we need to understand. It’s about the shape of curiosity, the geometry of thought that defines how ideas form before they ever hit the keyboard.

That’s why cognitive literacy — the ability to structure thought — is now the real competitive advantage. You can switch from GPT to Gemini to Claude and back again; it won’t matter if your thinking is stuck in old frames.

The tools will evolve whether you do or not. But if you can’t think beyond your own mental architecture, you’re just automating old ideas faster.

After you read To Question Is to Answer, you’ll never open a chat window the same way again. At least that is what it did for me as I was writing it. Because it’s not about how well you prompt, it’s about how deeply you think before you prompt.

The book doesn’t teach you how to command machines. It teaches you how to reclaim your own intelligence, how to stay curious longer, how to let the question breathe, how to find the edge between what you know and what you’re about to discover.

That’s not a technical upgrade. It’s a mental one.

From Prompts to Practices

The organizations that are truly beginning to adapt aren’t the ones hiring “AI specialists.” They’re the ones re-training everyone else to think differently.

One of the exercises I often run with teams is deceptively simple: Before you prompt, pause. Ask what kind of answer you’re actually seeking — proof, possibility, perspective, or provocation. Because each demands a different kind of question.

It’s a small behavioral reset, but it changes everything. It forces reflection before interaction. It builds a habit of framing before fetching.

And that’s how real capability takes root, not through instruction manuals or model updates, but through rituals of thinking. To Question Is to Answer was never meant to be an AI playbook, but it’s becoming one. Not because of its content, but because of its posture: it teaches people to slow down, interrogate the frame, and rediscover that questions are not requests for answers.

They’re openings for discovery.

The Bigger Picture

AI is accelerating everything — decision-making, production, iteration.

But I believe that he human edge isn’t speed; it’s synthesis — the ability to see patterns others miss because we sit with the question longer than comfort allows. In a world obsessed with optimization, curiosity becomes an act of rebellion.

The irony of AI is that it has returned us to philosophy. The next advantage won’t come from who prompts best, but from who thinks best.

If your organization is wrestling with how to embed AI into real work — not as a feature, but as a habit — start with the questions. Start with how your teams think, what they assume, and where their curiosity breaks down. Start by calling me.

Because long after tomorrow’s versions replace today’s tools, one truth will remain:

The quality of your answers will always depend on the quality of your questions.

Read More Books. Or at least read mine.

If you’re looking to build not just AI skills, but new ways of thinking, I have written a few things that might be a great place to start.

To Question Is to Answer is my first book on how to think critically and creatively in the age of AI.

And a companion eBook, Frameworks Reframed: Thinking Models for the AI Age, offers practical models and mental tools to help teams navigate ambiguity, rethink value, and solve problems with intelligence—human and machine.

Both are designed to help you shift from reacting to AI… to reasoning with it.

I’ve been quiet here lately. Not absent, just immersed in conversations, coffees, and a string of meetings with agency teams across Southeast Asia. And beneath the energy and ambition, something’s been gnawing at me.

Agencies are trying to run before they can walk.

It’s surprising how fast the conversation is jumping ahead of where most organizations actually are. I walk in expecting to talk about capability-building, about rethinking workflows, about how teams can use AI to approach problems differently. But instead, the discussion skips straight to “agentic solutions.” Automation. Orchestration. Complex multi-step systems designed to run semi-autonomously on top of language models.

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In theory, these are exciting ideas. But in practice, they’re premature. There’s a fundamental literacy gap that has yet to be solved.

From what I am seeing, many of these agency teams haven’t built even a baseline of fluency with generative AI. They don’t really know what the tools can do, or where their limitations are. They haven’t had the time, or the permission, to experiment. They don’t yet think with AI. But they’re being asked to build with it. And that’s a problem.

Because when you skip the foundational layer - the hands-on, habit-forming, mindset-shifting layer - you end up building systems that amplify misunderstanding. Not innovation.

This is the real risk of the “run before you can walk” moment we’re in. The ambition is outpacing the comprehension. And without comprehension, complexity is dangerous.

The Architecture of Yesterday, The Tools of Tomorrow

Agentic systems, for all their promise, require more than good intentions and API access. They require a shift in how we define problems, how we deconstruct workflows, and how we think about thinking. They demand clarity, not just about what AI can do, but about what we are trying to do in the first place.

But that clarity can’t be borrowed. It has to be earned. Through experimentation. Through friction. Through trying things and getting them wrong. Through developing a kind of muscle memory that only comes from actual use.

What I see instead are agencies trying to layer AI on top of processes that were never built to accommodate it. Teams are still working from briefs that assume linear timelines, fixed deliverables, and a narrow definition of craft. Creative departments are still structured for sequential handoffs. Strategy teams are still expected to arrive with answers in slide form. Legal still wants to read every word. And don’t even get me started with what procurement thinks, but it goes a bit like this: “You have AI tools, so you should be cheaper and faster.”

New Terrain Demands New Training

Somehow, AI is supposed to slot neatly into all this. Make it faster, smarter, cheaper without disturbing the underlying assumptions, as if transformation can be achieved through augmentation alone.

But AI doesn’t just accelerate work. It reshapes it. The steps are different. The cadence is different. The source of value is different. You don’t just get new tools. You get a new terrain. And if your team isn’t equipped to navigate that terrain, then building agentic systems is like handing them a spaceship when they’re still learning to drive.

What’s needed now isn’t complexity. It’s depth. Agencies don’t need to “scale AI.” They need to absorb it. Slowly. Deliberately. Systematically.

That means training, not just in how to prompt, but in how to frame. In how to deconstruct problems. In how to work with models as collaborators, not vending machines. It means carving out time to learn, not just demanding that people catch up. It means building internal confidence before external capability. And above all, it means recognizing that speed is not the same as progress.

I’m not against agents. In fact, I think the agentic future will be transformative. But only if it’s built on a fundamental understanding. Otherwise, we’re just automating old mistakes at scale. Garbage in, garbage out, faster, with a more elegant interface.

The Performance of Innovation

What I’m seeing now, almost everywhere I look, is a performance of innovation.

Agencies are adopting the aesthetics of transformation, throwing AI terms into pitch decks, showcasing prototypes, and circulating toolkits, but the core remains unchanged. There’s a kind of ritual happening: prompt a headline, generate a moodboard, ask ChatGPT to summarize something, and then, having touched the tools, the team retreats quietly back into the old way of working.

No one’s being malicious. No one’s lying. But you can feel the choreography of it. It’s not real integration, it’s symbolic adoption. It’s surface-level, made to signal relevance rather than rewire the machine.

The deeper habits haven’t shifted. The same timelines still govern the work. The same briefing rituals, the same static deliverables, the same slide decks with the same number of pages. Teams are still rewarded for polish over possibility. The definition of “done” hasn’t moved, even as the possibilities have exploded. Time to explore has not been protected. Risk-taking, iteration, and rethinking remain unpaid labor.

And so the very thing that AI could enable, new ways of thinking, faster prototyping, more generative cycles of creativity, is quietly suppressed by the gravity of old expectations.

The result is theatrical fiction. AI is there, in the room. But only as a guest. It is invited to speak, briefly, and then ushered out before it disrupts the process too much. No one’s really asking whether the process itself is still fit for purpose.

Because that’s a more complicated question, it takes courage and time. And time is the one resource agencies never feel they can afford to spend.

But when transformation becomes a performance, innovation turns ornamental. And ornamentation, no matter how futuristic it looks, won’t save you from obsolescence.

You Don’t Need a Prompt Library. You Need a Permission Structure.

It’s not prompting technique that most teams are lacking. It’s permission.

Permission to rethink the brief. To discard the linear process. To show work that’s unfinished, unpolished, and exploratory. To compress five steps into one or to spend an afternoon chasing something strange because it might lead somewhere better.

To deliver a prototype instead of a deck. To slow down, not speed up, if that’s what deeper thinking requires.

Generative AI doesn’t reward perfection. It rewards curiosity. It rewards iteration. It rewards the willingness to ask, “What else could this be?”

But most agencies weren’t built for that kind of exploration. They were built for hierarchy, efficiency, and predictability. The systems are designed to minimize risk, not generate possibility. The culture, in many places, still values polish over process.

So even when the tools are available, the behaviors that would make them useful are quietly discouraged. Experimentation becomes un-billable. Divergence becomes a reputational risk.

This is why so many AI initiatives stall, not because the models aren’t powerful, but because the people using them aren’t given the structural space to think differently. Until that changes, it won’t matter how advanced the technology becomes. You can’t train a team to innovate inside a system that punishes deviation.

This Isn’t a Technology Problem. It’s a Leadership One.

The most significant misunderstanding I see among agency leadership right now is the belief that AI adoption is a tooling problem. If you install the right platforms, roll out the right software, and set up the right dashboards, transformation will follow. The training module is a PDF that explains how to log in and advises not to share company or client information.

But this isn’t about infrastructure. It’s about mindset.

And mindset is shaped by leadership, not licensing agreements.

The real work isn’t getting the tools into people’s hands. It’s creating the conditions in which those tools can actually change how people work. That means rethinking incentives. Rethinking what good looks like. Rethinking how creative teams are briefed, how strategy is developed, and how value is measured.

Most agency systems are still built on inherited assumptions: That good creative takes time. That strategy is best delivered in slide decks. That thinking happens first, and making comes after. That talent is scarce, fragile, and hierarchical. That process is more important than possibility.

Generative AI dismantles those assumptions. It introduces abundance. It levels the playing field. It moves faster than the old process allows and reshapes what it means to collaborate, to explore, to decide.

But abundance is only useful if your organization knows what to do with it. And most don’t because the systems weren’t built for it. They were built to ration value, not to generate it.

This is why leadership matters now more than ever. The goal is not to approve tech budgets, but to sponsor behavioral change. To model a different relationship with creativity, with experimentation, with risk. To make space for new kinds of thinking. And to protect that space until it becomes the new normal.

The hardest part of this transformation isn’t teaching people how to use AI. It’s helping them let go of how they used to work.

And no tool can do that for you.

So what actually needs to happen?

Agencies need to pause, not in fear, but in clarity. They need to ask what parts of their process still serve them, and what parts belong to a world before generative intelligence. They need to look hard at where human effort is being wasted, where value is being defined by volume or polish, and where experimentation is being punished by process. Most of all, they need to stop asking “What can AI do?” and start asking “What do we want to do and how might AI help us do it differently?”

Because until you redesign the underlying system, no tool will make a difference. You’ll just be duct-taping rocket engines to a bicycle and wondering why it doesn’t fly.

So let’s spill the T: before you build agents, build fluency. Before you orchestrate, orient. Before you sprint, study the terrain.

Because this isn’t a race to ship, it’s a race to understand. And if you skip that part, if you move straight to the complex without first mastering the essential, you’re not innovating. You’re guessing.

And you won’t like what you build.

Fluency First: The Training Agencies Actually Need

If any of this feels familiar, if your team is eager to move forward but unsure where to start, this is the work we do at RockPaperScissors.

We don’t drop in with decks and toolkits. We build fluency. We create space for experimentation. And we help teams develop the foundational habits, mental models, and workflows that make AI actually usable, inside real agency systems, not ideal ones.

If your agency is ready to move past the performance and into practice, let’s talk.

Read More Books. Or at least read mine.

If you’re looking to build not just AI skills, but new ways of thinking, I have written a few things that might be a great place to start.

To Question Is to Answer is my first book on how to think critically and creatively in the age of AI.

And a companion eBook, Frameworks Reframed: Thinking Models for the AI Age, offers practical models and mental tools to help teams navigate ambiguity, rethink value, and solve problems with intelligence—human and machine.

Both are designed to help you shift from reacting to AI… to reasoning with it.

For decades, marketers and strategists have relied on generational archetypes to explain behavior. Boomers, Gen X, Millennials, Gen Z: tidy categories that link age with cultural context. These labels may oversimplify, but they provide a shorthand for understanding how people adopt technology, consume culture, and form their identities.

But what if the next generation has nothing to do with age? What if it’s about mindset?

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Welcome to Generation AI.

A Generation Without Birth Years

Unlike Boomers or Gen Z, GenAI isn’t defined by when you were born. It’s defined by how you think, and more specifically, how you work with AI.

GenAI is a cross-age, cross-culture phenomenon. A 19-year-old content creator in Jakarta and a 55-year-old consultant in New York can both be members. What unites them isn’t their stage of life, but their willingness to make AI a co-author of their thinking, creativity, and productivity.

This shift matters for leaders. It means that the old age-based labels are no longer enough to understand how people act. We need to consider who has chosen to step into GenAI—and who hasn’t.

The Advantages of GenAI

The GenAI mindset comes with clear advantages:

• Amplification: Extending individual capacity. Workers who embrace AI can produce more, iterate faster, and widen their range.

• Accessibility: Lowering barriers to entry. Skills once reserved for experts, such as design, coding, or video production, are now within reach of anyone willing to learn the necessary tools.

• Acceleration: Redefining speed. GenAI organizations don’t just move faster; they operate at a fundamentally different tempo.

For companies, this translates into teams that can prototype, adapt, and execute at speeds that were previously unthinkable, even five years ago.

The Risks of GenAI

But the same traits that make GenAI powerful also make it risky.

• Lazy Thinking: The ease of generating plausible answers can erode the discipline of wrestling with hard problems.

• Homogenization: When everyone relies on the same models trained on the same data, differentiation becomes harder.

• Dependency: Overreliance on tools risks blurring the line between human judgment and machine suggestion.

For leaders, the challenge isn’t whether employees use AI, it’s whether they use it thoughtfully.

What Leaders Should Do

The rise of GenAI isn’t just a cultural shift; it’s a leadership test. The question isn’t whether your people will use AI. They already are. The question is whether you will shape that usage in ways that drive advantage.

Leaders need to act in three areas:

1. Build Fluency, Not Compliance: Don’t just issue AI policies; create cultures of practice. Teams need habits, playbooks, and frameworks that help them decide when to lean on AI and when to rely on human judgment. Fluency matters more than uniformity.

2. Redesign Workflows Around Amplification: AI isn’t about doing the same tasks faster. It’s about rethinking the tasks altogether. Leaders should identify high-value problems that AI can help explore, not just automate. The organizations that thrive will be those that let AI expand their imagination, not just cut their costs.

3. Guard Against Sameness: If every competitor uses the same tools the same way, differentiation disappears. Leaders must deliberately protect spaces for originality, slow work, deep thinking, and human creativity that resists being averaged out.

This is where I’ve been working with leaders and teams: embedding AI not as a tool, but as a habit, a mindset, and a force multiplier. From training programs to cultural frameworks, the goal is to help organizations cross the divide—not just to use AI, but to lead in the GenAI era.

The Leadership Imperative

This isn’t just a cultural observation. It’s a strategic reality.

In the coming years, the real divide won’t be between Millennials and Gen Z. It will be between GenAI thinkers and non-GenAI thinkers inside your workforce, your customer base, and your industry.

• In hiring, you’ll need to evaluate not just skills, but AI fluency.

• In marketing, you’ll need to segment by mindset, not just by age.

• In leadership, you’ll need to decide when to lean into AI’s acceleration—and when to deliberately slow down to preserve human originality.

Generational labels were once about demographics. GenAI is about decisions.

What Creatives and Strategists Should Do

The GenAI shift isn’t only a leadership issue; it’s personal. For those working in creative and strategic industries such as advertising, design, media, and brand, GenAI changes both the craft and the career path.

If you’re a creative, here’s the new reality: you don’t compete with AI. You compete with the people who know how to use it.

That means three imperatives:

1. Learn to Prompt Like You Brief: The best work won’t come from generic prompts. It will come from the same skills that make a great creative brief: clarity of intent, cultural resonance, and the ability to frame the problem in ways the machine can’t. If you can brief well, you can prompt well.

2. Treat AI as a Sketchpad, Not a Crutch: Use AI to accelerate exploration, not to finalize output. The danger is outsourcing originality; the opportunity is generating dozens of paths quickly so you can choose the one worth pursuing deeply.

3. Double Down on the Human Edge: Cultural insight, emotional storytelling, strategic leaps - these remain stubbornly human. The creatives who thrive will be the ones who use AI to clear the noise, then inject the cultural spark only humans can see.

For individuals, the choice is stark. GenAI can make you faster, broader, more experimental, or it can flatten you into sameness. The difference lies in whether you use it as a tool to enhance your craft or as a shortcut to avoid it.

The Role of RockPaperScissors

At RockPaperScissors, this is exactly the work we do.

We help leaders and teams cross the GenAI divide, not just to use the tools, but to build the habits, frameworks, and mindsets that turn AI into a force multiplier.

• For leaders, that means embedding AI into workflows with confidence: shaping policies, rethinking processes, and ensuring that acceleration doesn’t come at the cost of originality.

• For creatives and strategists, that means learning to treat AI as a sketchpad, a cultural signal scanner, and a co-pilot for experimentation—without losing the human spark that makes work resonate.

We believe GenAI is the first generation you can choose to join. And the organizations that thrive will be those that teach their people how to choose it wisely.

Training. Teaching. Guiding. That’s what we’re here for.

Buy the book.

Frameworks Reframed: Thinking Models for the AI Age (via Gumroad)

It costs less than a cheeseburger and shake from Five Guys. But unlike your lunch, gone in 30 minutes, it will stay with you your entire career, not just in this role, but in any role you end up landing in.

Buy the other book.

To Question is To Answer: How to Think Critically and Thrive in the Age of AI (via Amazon)

This is not a book about writing prompts for AI models. This is a book about something much more important. It is a book that explores how to develop the questions that will shape how we think, create, and make decisions in an AI-driven world.

I recently wrote about how we can no longer afford to be a one trick pony. AI provides us an interesting set of tools that can expand our capacity to see, to synthesize, to generate. It allows us to work in areas that are adjacent to our speciality, and explore other ways our ideas might be brought to life. And while AI can be used to make sense of the noise, to find the patterns, to connect the dots, it still needs data as food for thought.

For me, there is no greater banquet than Reddit.

It is vast, unruly, sometimes contradictory, but endlessly human. It is not the polished feed of aspiration, but the lived record of frustration, curiosity, joy, and dissent.

AI can accelerate the analysis process, but it cannot replace the depth of Reddit’s content. Because beneath the memes and the tangents lies something rare: unfiltered truth. And truth, more than ever, is the most valuable input strategy that one can have.

Beyond the Dashboard Illusion

Every CMO today has a dashboard. Lines, charts, and percentages telling them what’s happening in real time. These tools are useful, sometimes indispensable, but they all share the same flaw: they flatten human behavior into numbers.

And numbers don’t capture truth.

Instagram reveals how people want to appear. TikTok shows us how people want to perform. LinkedIn demonstrates how people want to be perceived professionally. Even X, for all its volatility, is often more broadcast than conversation. These are surfaces, valuable in their own right, but surfaces nonetheless.

Reddit cuts beneath the surface.

Reddit’s strange power lies in its anonymity.

Without the pressure of polished identity, people say what they would never say elsewhere. They confess their frustrations, admit to insecurities, reveal their anxieties, and share half-formed thoughts.

• A skincare subreddit doesn’t just share routines; it reveals the desperation behind acne, the trial-and-error that never makes it to Instagram reels.

• A finance community doesn’t show wealth—it exposes debt, confusion, and the real pressures behind money management.

• An unpopular opinion subreddit doesn’t just debate quirks; it exposes how even the smallest rituals, like whether you shower at night or in the morning, carry deeper truths about routine, identity, and the way we mark time in daily life.

It is raw. It is messy. And that is exactly what makes it valuable.

Truth as Strategy

A great brand strategy doesn’t come from spin. It comes from truth.

Not the polished kind of truth that fits neatly into a headline or a brand book, but the kind that feels almost uncomfortable to say out loud. Truth that reveals tension. Truth that acknowledges limitation. Truth that speaks to what people already sense but rarely hear reflected back to them.

This kind of truth is disarming. It cuts through noise not because it’s loud, but because it resonates. We recognize it instinctively. We lean in because it feels human.

When a brand operates from truth, it doesn’t have to shout. It doesn’t have to manufacture authenticity. It simply aligns itself with what people already know in their bones. The work of strategy, then, is not to invent clever stories, but to find these truths, hold them up to the light, and ask: What does this mean for us? How do we act on it?

Reddit is one of the rare places where these truths still surface. Messy, contradictory, often unpolished—but real. For anyone willing to listen, it becomes less about extracting insights and more about discerning what’s true enough to matter.

Friction as Gold

I have written before about how I believe we actually need more friction, not less. When I think about friction, I think about two flavours: friction that keeps me from doing a thing - in an app, in a checkout process, or in things like booking an airline or concert ticket.

Then there is the other kind of friction, the exciting kind. It’s the friction of life, of being human, of being filled with contradictions, opinions, and experiences that shape not only us as individuals, but also as cohorts, tribes, and communities.

Friction is the spark. It’s the clash that produces heat, the debate that forces us to clarify what we believe, the tension that makes culture move. It’s uncomfortable, but it’s also generative. Without it, we don’t grow, we don’t innovate, and we don’t discover what actually matters to us. (And yes, there are subreddits for introducing friction into your life.)

This is what makes Reddit different from surveys or dashboards: its embrace of contradiction. Threads don’t resolve neatly. They fracture, splinter, and argue. One group loves a feature; another despises it. Some defend a brand, others mock it.

That friction is the gold. Culture doesn’t move in harmony; it moves in debate. The most powerful insights aren’t where everyone agrees, but where they collide. Reddit surfaces those collisions daily, often years before they show up in the mainstream.

Learning the Language

Reddit is also a lexicon. Every community speaks in its own shorthand, its own memes, its own rituals. Language here isn’t simply a tool for communication, it is a marker of belonging. It tells you instantly who is “in” and who is “out.” (r/technology: Emojis are not a universal language: Gender, age and culture influence their interpretation)

When people talk on Reddit, they are not only exchanging information; they are performing identity. A throwaway acronym, an inside joke, a borrowed meme—these are signals that say, I understand the culture here, I share your worldview, I belong.

Marketers often underestimate this. They arrive speaking in clichés, or worse, in the flattened jargon of the boardroom. And the community responds the way any insider group responds to an outsider: with suspicion, mockery, or silence. Authenticity here isn’t claimed; it’s demonstrated through fluency.

For strategists, this is more than semantics. Language reveals hierarchy, values, humor, even resentment. It shows how communities police themselves, what they celebrate, and what they refuse to tolerate. In this sense, Reddit isn’t just a conversation platform—it’s the closest thing we have to a living cultural dictionary.

And unlike a dictionary, it’s always in motion. Words evolve, meanings shift, rituals mutate. To study Reddit is not to learn a fixed language but to witness language being invented, bent, and broken in real time. For brands, the lesson is simple but profound: if you want to be part of the conversation, you have to learn how people actually speak, not how you wish they spoke.

The Beauty of Imperfection

In the modern marketing machine, everything is polished. Decks are pristine. Social posts are edited and proofed. Campaigns are reviewed by layer upon layer of legal, PR, and brand teams until they are frictionless.

But frictionless often means lifeless.

We confuse polish with clarity, forgetting that humans don’t actually speak in bullet points or live inside brand guidelines. Real life is messy. We stumble, contradict ourselves, use too many words, or not enough. And it is precisely those rough edges that make communication feel alive.

Reddit thrives on those edges. Typos, awkward phrasing, tangents, misspellings—things that would never survive a brand approval process—are everywhere. And because of that, the conversations feel more trustworthy. They’re unfiltered. They carry risk. They sound human.

This is what people mean when they talk about authenticity, but often fail to deliver. Authenticity isn’t something you announce; it’s something that leaks through the cracks. It’s in the typo that shows no one proofed it. It’s in the half-finished thought that reveals what someone really meant before they had time to rehearse it. It’s in the imperfection that proves the words belong to a person, not a machine.

This is more than an aesthetic preference. Imperfection is a signal. It tells you that the truth hasn’t been polished away. It reminds you that people don’t live inside neat brand narratives, they live inside the chaos of daily life. And if we want to build strategies that resonate, we need to spend less time sanding away the rough edges—and more time listening to the voices that come to us raw.

The World’s Largest “Five Whys”

In my most recent eBook, Frameworks Reframed, I discuss a framework I return to often: the “Five Whys.” The idea is simple. Keep asking “why” until you get past surface-level answers and uncover the deeper motivation beneath. On its own, it’s a deceptively powerful tool for cutting through spin and finding the human truth behind behavior.

Reddit is that framework happening at scale. Every thread begins with a single statement—“This product doesn’t work,” or “I love this brand but hate this feature.” From there, the community does what strategists are trained to do: it probes, questions, contradicts, and challenges. One person asks why. Another digs deeper. Someone else reframes the problem altogether.

Before long, what started as a complaint about a product becomes a cultural excavation:

• “I hate this feature” becomes “I feel cheated.”

• “This app is broken” becomes “This company doesn’t respect its users.”

• “This doesn’t fit my lifestyle” becomes “I don’t trust corporations to see me as a whole person.”

What strategy frameworks attempt to simulate in controlled workshops, Reddit does organically, in public, every day. It is the largest living archive of the “Five Whys” in motion—thousands of micro-interrogations revealing the contradictions, insecurities, and aspirations that drive human behavior.

If you are trying to understand culture, this is where you look. Not because Reddit hands us easy answers, but because it shows the messy path by which meaning is made. It reveals not just the conclusion, but the struggle to get there. And in that struggle, in those layers of “why,” lies the cultural truth that brands too often overlook.

The Truth is Hard to Find

For all its value, Reddit remains a blind spot for many brands. It resists influence. It punishes inauthenticity. You can’t simply buy your way in. And for strategists who prefer tidy charts and linear frameworks, it feels unruly, too messy to fit into a deck, too unpredictable to control.

But there’s another reason Reddit is overlooked: it’s hard work. Deep diving into Reddit isn’t as simple as typing a query into a search bar and downloading a neat report. Without specialized (and often expensive) tools like Talkwalker, Brandwatch, or NetBase, the process can feel daunting. Even with those tools, the rawness of the conversations resists easy categorization.

Reddit requires patience. It requires directionality. You can’t just “scan the data”; you have to follow the threads, chase tangents, and sit with contradictions until they begin to make sense. It’s overwhelming at first—like walking into a crowded marketplace where everyone is shouting at once. But if you linger, if you listen carefully, patterns start to emerge.

That’s why so few marketers make the effort. But the very difficulty of the work is what makes it worthwhile. The truths that surface in Reddit are not the kind you can skim in a sentiment report or pick up in a focus group. They are messy, contradictory, alive. They are the truths that don’t fit easily into charts, but that move culture nonetheless.

And like most things that matter, it’s worth the time.

A Nod to the Masters

I want to end with a thought from one of advertising’s greats. Sir John Hegarty, of BBH fame, sat down with British fashion designer Paul Smith for an interview. Smith, reflecting on his own philosophy, said:

“I think a childlike approach is vital. Kids are so brilliant at being so honest.”

Hegarty agreed. That honesty, he argued, is the most powerful strategic position we have.

Children don’t second-guess. They don’t filter. They simply say what they see. That kind of clarity is rare in adulthood and even rarer in the corporate boardroom. Yet in creativity, it is essential. The job of a creative, Hegarty said, is simple: make that truth interesting.

And isn’t that what Reddit offers us? Not polished spin, not dashboard-ready summaries, but the messy, unfiltered honesty of real human experience. It is childlike in its bluntness, and therefore strategic in its power.

The truth is not glamorous. It is often awkward, contradictory, or uncomfortable. But if we dare to look at it, to sit with it, and to shape it into something meaningful, it becomes the strongest foundation strategy can ever have.

Because in the end, the truth is not just an insight. The truth is the strategy.

Buy the book.

Frameworks Reframed: Thinking Models for the AI Age (via Gumroad)

It costs less than a cheeseburger and shake from Five Guys. But unlike your lunch, gone in 30 minutes, it will stay with you your entire career, not just in this role, but in any role you end up landing in.

Buy the other book.

To Question is To Answer: How to Think Critically and Thrive in the Age of AI (via Amazon)

This is not a book about writing prompts for AI models. This is a book about something much more important. It is a book that explores how to develop the questions that will shape how we think, create, and make decisions in an AI-driven world.

Every few weeks, I come across a post or headline claiming that “AI is killing junior coders.” The idea is always the same: because younger workers no longer have to wrestle with problems line by line, they won’t develop the critical thinking or craft that once defined their role. They’ll lean too heavily on the shortcut, never learning the skill.

It’s a familiar anxiety, and not just in tech. We’ve heard versions of it in advertising, in journalism, in music. Every time a new tool emerges, someone predicts the death of craft.

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I had never heard about Douglas McGregor’s Theory X and Theory Y until I read a recent post from Emil Rijcken, whom I had the privilege of working with for the first 8 months of RockPaperScissors (hard to believe my little engine that could is now 21 months old). Emil, armed with a PhD in Artificial Intelligence, is a co-founder and leads AI development at Seldon Digital, which has developed a fantastic platform that is an AI design co-pilot that helps you generate ideas based on intent, iterate through your designs with context, and fully explore the problem space. It’s worth noting that Andrei Herasimchuk, one of the founders, was also the guy behind groundbreaking UI/UX work at Adobe and Figma. I owe such a debt of gratitude to Emil and Andrei, as Seldon was RockPaperScissors’ first client.

A Management Theory for the Machine Age

From what I am learning, McGregor was a professor at MIT Sloan in the late 1950s and early ’60s, and wrote The Human Side of Enterprise, a book that quietly revolutionized management thought. His ideas arrived at a time when organizations were grappling with the shift from industrial assembly lines to white-collar, knowledge-based work.

Theory X reflected the prevailing mindset of the industrial age: people are naturally lazy, avoid responsibility, and must be directed, controlled, and threatened to achieve results. Managers operating under X assumed the worst of their employees, and so they built structures of surveillance, rigid rules, and close oversight.

Theory Y was McGregor’s counterpoint. It assumes people are intrinsically motivated, curious, capable of self-direction, and eager to grow if given the right environment. Managers who adopted Y built systems of trust, encouraged initiative, and treated people as sources of ideas, not just units of labor.

McGregor’s genius was not in declaring one theory “true” and the other “false,” but in showing how a leader’s assumptions about human nature shaped the systems they designed. If you believed in X, you created structures that stifled. If you believed in Y, you created conditions that unlocked. And people, inevitably, lived up — or down — to those expectations.

From Assembly Lines to Algorithms

Sixty-four years later, the parallels are striking. The debate around AI and junior talent is, in essence, the same debate McGregor saw in the shift from machines to knowledge work. Do we assume that people, when given a powerful tool, will get lazy? Or do we assume that people, when trusted, will use that tool to accelerate their growth?

The “AI will kill junior coders” narrative is Theory X all over again.

Theory X assumes shortcuts weaken us. It assumes that, given the option, people will always take the easy way out. It assumes the worst.

My own experience says otherwise. My skills have accelerated because of AI, not despite it. I have read more books since I began my AI journey over 4 years ago. And not just books on AI, as they began to appear, but books on culture, journalism, philosophy, and creativity. I have always been a fan of Science Fiction, but now, as I began seeing these imagined worlds begin to take shape, I dove back into them looking for clues about where we might be heading. All of this renewed reading and study inspired me to write a book, To Question is To Answer, partly on AI, but mostly about how I chose to focus on questions design instead of prompt hacks.

From Guardrails to Handcuffs

When I talk with leaders in agencies, brands, and creative organizations, what I hear most often are stories of constraint. Staff are told which tools they may use, how they may use them, and for what ends. I see “task-based agents” being built - systems designed not to explore but to execute narrowly. Workflows engineered to eliminate wandering.

On the surface, this looks efficient. But underneath, it is pure Theory X: architecture based on the belief that people can’t be trusted to think for themselves.

There is, of course, a place for this. Repetitive, mechanical work should be automated. That is where Theory X systems shine. But a dangerous line gets crossed when these structures extend into creative and strategic work. Creativity requires excess. It requires detours, failed experiments, sideways questions. It requires a deep dive into cultural drivers and generational belief systems. And it also needs time to explore and experiment.

When leaders impose X-systems in the realm of creativity, they fail to recognize the fundamental difference between efficiency and originality. Efficiency thrives on narrowing options; originality depends on expanding them. By applying the logic of the assembly line to the work of imagination, they confuse speed with substance.

What gets lost is not just a clever idea here or there, but the conditions that allow ideas to emerge in the first place: the off-topic conversation that sparks a breakthrough, the half-baked prototype that reveals an unexpected direction, the sideways question that reframes the problem entirely. Creativity doesn’t run on compliance. It runs on curiosity. And curiosity, like trust, withers under constant surveillance.

In that sense, Theory X systems don’t just limit productivity. They corrode culture. They train people to color inside the lines, to avoid risk, to treat exploration as a liability. Over time, organizations that default to X will still generate output, but it will be incremental, predictable, and lifeless.

The irony is that these are the very organizations most desperate for innovation, and yet by the way they structure their systems, they ensure it never takes root.

So What Happens to Junior?

There is no denying that AI has changed the work of entry-level coders, copywriters, and designers. Just as the rise of desktop publishing altered the trajectory of design assistants in the 1980s, or nonlinear editing software transformed apprenticeships in film and television, AI is shifting what we expect from the bottom rungs.

But that does not mean juniors are expendable. It means the role itself must evolve.

For too long, junior staff have been treated as shadows. They sat silently in the back of the room. They churned through the low-value, repetitive work that seniors didn’t want. They waited, often for years, for “their turn.” AI has stripped much of that away. And rather than lamenting its disappearance, we should treat it as an opening.

What if junior roles became accelerators rather than apprenticeships? What if the expectation wasn’t that they “pay dues,” but that they contribute perspective from day one? What if we trained them to use AI not as a crutch but as a scaffold for understanding? What if their voices weren’t merely tolerated but actively sought out?

*As I write this, I am also thinking about a call I have scheduled this week with a woman who just graduated with a Masters from the Royal College of Art, following her degree program from the University of Arts London . My recent article caught the attention of her father, who reached out, wondering if I would shine some light on the world his daughter is stepping into. With only internships as her work experience, she will most likely be relegated to a junior designation at a holdco gladiator school (agency), expected to “pay her dues” and be patient until “her time comes.”

We need young voices. Junior voices. Urgently. We need their cultural vantage point, their instincts, their ability to see what the rest of us miss. If we build X-systems that box them out, we don’t just weaken their growth. We weaken our own future.

The Responsibility of Leadership

This is where leadership matters most. The real danger is not that AI itself will erode skills. The real danger is that leaders will design systems that train people to stop thinking.

If you believe people are lazy, you will strip away their choices. If you believe people are motivated, you will give them tools that expand their capacity. That belief — not the technology — will define the culture of your organization.

Older leaders in particular have a role to play here. Many of us came up in Theory X systems: command-and-control hierarchies, rigid approval gates, narrow job scopes. We know how deadening those structures felt. We also remember the liberation when someone finally trusted us, handed us responsibility, or gave us room to try something new. That memory gives us perspective. We can choose to lead differently now.

From X to Y

So the question isn’t whether AI will kill skills. The question is whether leaders will meet this moment with Theory X or Theory Y.

Will we build AI systems to control, or to enable? Will we focus on architecting compliance, or will we prioritize unlocking capability? Will we assume the worst in our people, or trust them to rise?

AI itself is neutral. It really doesn’t give a damn. It can be a crutch, or it can be a catalyst. It can either diminish talent or accelerate it. The choice doesn’t sit with the tool. It sits with the leader and the assumptions they bring to the table.

The story of AI is not the story of machines.

It is the story of what we believe about ourselves. Do we trust our curiosity, or do we fear it? Do we choose to liberate imagination, or do we confine it to the narrowest tasks?

McGregor’s question still echoes six decades later: X or Y? Control or trust? Constraint or possibility?

AI won’t kill skills. But our assumptions might. And in the end, the future of creativity — and perhaps the future of work itself — will rise or fall not on the power of algorithms, but on the courage of leaders to choose Y when it matters most. These are choices. They are reflections of what we believe about human capacity. If we start from X, we will shrink people to fit the system. If we start from Y, we will expand the system to fit their potential.

In a decade, the organizations that thrive won’t be the ones that kept their people on the tightest leash. They’ll be the ones who built cultures of trust, who saw AI not as a cage but as a lever.

Who believed their people could rise higher than the tools themselves.

That is the real leadership test of this moment.

Not So Shameless Plugs.

I write because I like to, and because I believe that the thoughts in my head are shared by, or might influence, some of you reading this, or maybe someone you know.

I earn my living leading RockPaperScissors, working with agencies and brands to solve challenges. I also design and conduct workshops and training, and on occasion, I write things, like To Question is To Answer: How to Think Critically and Thrive in the Age of AI, and Frameworks Reframes: Thinking Models for the AI Age.