Every conversation about AI in advertising eventually lands in the same place: it’s faster now. Faster turnarounds, leaner teams, cheaper production. Ask a holding company exec, a procurement lead, a CMO they’ll all reach for the same currency. Time saved. Cost removed. Hours that used to take days now take minutes.

It’s not wrong. It’s just the least interesting thing happening right now, and the industry keeps talking as if it’s the whole story.

That reflex isn’t an accident. It’s inherited. For decades, the industry built its operating model hub-and-spoke entirely around the language of volume and cost. A centralized hub, usually offshore, usually cheap, absorbed the bulk of execution: the adaptations, the localizations, the templated production that didn’t require deep market judgment, just efficient hands at scale. The spoke local, smaller, more expensive per head held the high-value talent who understood the market well enough to make the centralized work land. The whole structure was a cost equation dressed up as a strategy. So when a new technology shows up, the only question the industry knows how to ask of it is: does this make the equation better?

For AI, the answer is obviously yes. Which is exactly the problem with stopping there.

The 60% that's already solved

Call it the 60% the volume work, the version-and-adapt production line, the stuff the hub existed to do cheaply at scale. AI is genuinely faster at this. Variations, resizes, localizations, the high-volume low-judgment execution that used to require rooms full of people working in shifts a single person directing the right tools can now do a meaningful chunk of it themselves, faster than any hub ever could. Fine. Good, even. But treat it as the headline and you’ve mistaken the part of the story that was always going to resolve itself for the part that actually matters. Efficiency was never the destination. It’s the thing that buys you room to do something else.

The burden nobody resourced for

Here’s what’s actually landing on local teams right now, whether anyone signed off on it or not.

The spoke’s job used to be narrower than what it’s being asked to do today. Local talent held market relevance and creative judgment, the things that couldn’t be centralized because they depended on being close to the audience. Consistency, the brand staying coherent across every market, was something the hub’s process and templates were supposed to guarantee from a distance. That division of labor made sense as long as the hub’s volume work was the expensive part and the spoke’s local judgment was the scarce part.

AI breaks that division without replacing it with anything cleaner. It doesn’t eliminate the need for a hub by being a better hub. It removes the cost penalty for doing high-volume work locally, which means the reason centralization existed in the first place is that cheap scale stops being the binding constraint. And it does this at the exact moment content is becoming genuinely personalized, not just localized. Personalization isn’t a bigger version of the old adaptation work; it requires reading the audience signal close to real time, which is structurally a local problem, not a centralizable one.

So local teams are inheriting three things simultaneously: the consistency that used to be enforced from a distance, the local relevance that was always theirs, and now a requirement to be data-literate creatives who can build work that actually responds to a person rather than a market segment. Nobody resourced for that third thing. It wasn’t in the job description, the training budget, or the way these teams get measured. The industry just started expecting it, quietly, as a byproduct of the tools getting better, without doing the harder work of deciding who’s supposed to carry it or how.

That’s the part of this transition I find most honest to sit with not because it’s a crisis, but because pretending it isn’t happening doesn’t make it stop happening.

The 40% where the actual work is

Here’s the part that gets buried under all the speed talk: the 40% nobody’s built yet.

If AI gets you 60% of the way there faster, the question that actually matters is what you do with the time and capacity that frees up. Not “how do we hit the deadline now,” which is just the old job done quicker. The real opportunity is the desire to create for the part that doesn’t have a name yet new ways to discover what an audience actually responds to, new ways to design for a person instead of a segment, new ways to deploy work that adapts itself rather than getting adapted by a production line after the fact. That’s not speed. That’s invention.

This is where craft has always lived, and it isn’t going anywhere it’s relocating. The tool doesn’t make the move. It never has. A camera doesn’t compose a frame and a turntable doesn’t make a record sing; the eye and the hand do that, whatever the tool happens to be. What AI does is hand local teams more room to exercise that judgment, earlier, against more options, faster than they’ve ever had before. The craftsperson who leans into that isn’t protecting their relevance by getting faster. They’re protecting it by being the only one in the room who knows what to do with the room they’ve been given.

What the industry owes this, if it wants the upside

None of this works if the industry takes the capacity AI frees up and just asks for more output at the same headcount, with the same training budget, measured the same way it always has. That’s the failure mode sitting right in front of this transition treating the 40% as a bonus the spoke should produce for free because the tools got better, instead of treating it as a genuine expansion of the job that needs its own investment.

What local teams actually need isn’t another dashboard fed by a distant hub. It’s their own sovereign capability, something that belongs to them and the brand they’re serving, not to whichever vendor happens to be running the production line this quarter. A model where local talent can hold consistency, read their own signal, and build the 40% themselves, instead of waiting for permission or infrastructure to trickle down from a centralized function that was never built to do this kind of work in the first place.

Where this leaves the industry

The industry will keep grading this transition by the 60%, because that’s the only scoreboard it knows how to read. Time saved, cost removed, turnaround compressed, easy to measure, easy to put in a deck, easy to defend to a holding company board. The 40% doesn’t show up on that scoreboard. It’s harder to quantify, slower to prove, and entirely dependent on whether local teams are given the room, the tools, and the trust to go build it.

Which means the people actually doing that work may have to build the scoreboard themselves, before anyone hands it to them.

Disrupting the Algorithm / The Bodega · Scope Creep · Singapore · © 2026

Every few months a new set of numbers circulates through the industry. Faster turnaround. Lower cost. Creative produced at scale in a fraction of the time. The metrics are real and the efficiency is genuine and none of it is the point.

What the numbers measure is acceleration. What they don’t measure is authorship. And the gap between those two things is where the most important conversation in our industry isn’t happening.

The 60% problem

AI gets you there faster. This is true. A brief that once took days can be shaped in hours. A visual concept that requires a team and a budget can be approximated in minutes. For someone without deep craft, these tools are genuinely transformative; they compress the distance between having an idea and having something that resembles its execution.

But resembles is doing a lot of work in that sentence.

What AI generates is an interpretation of your intent. It reads your prompt, your reference, your direction and it makes its best inference about what you mean. For broad strokes and general direction, that inference is often good enough to be useful. But creative work at its most precise is not broad strokes. It is the exact speed of a camera push. The precise moment a cut lands. The specific quality of light in a particular frame at a particular time of day. These are not things you can fully describe in language, and therefore they are not things AI can fully execute from a description.

The result is work that gets you to approximately 60% of what you were actually trying to make. Which is genuinely useful and genuinely insufficient if your standard is full creative authorship.

The remaining 40% is not inefficiency. It is the space where the artist’s intent still cannot be fully handed to the machine and where the difference between a good result and the right result lives.

What the vinyl DJs understood

When Serato arrived and digital decks began replacing turntables in clubs and studios, the resistance from vinyl DJs was immediate and principled. What they were protecting was real. The art of DJing on Technics 1200s and a mixer required a specific and hard-won discipline listening so closely to a track that you knew instinctively where the beats aligned, feeling in your body the precise moment to drop a new record in so the transition was seamless. The record was not just a medium. It was a relationship. You had to know it completely before you could do anything with it.

Serato changed that relationship. The waveform made visible what the ear had previously had to find. The discipline of deep listening that foundational practice that separated a great DJ from a competent one was no longer structurally required by the tool. You could see the mix rather than hear it.

The DJs who resisted weren’t wrong about what was being lost. They were wrong about what it meant.

Because the ones who carried their vinyl discipline into the digital format brought thirty years of deep listening to a tool that could now show them the waveform as well discovered territory that two turntables could never reach. Micro edits are physically impossible on vinyl. Live sampling that responded to the room in real time. Transitions so seamless they redefined what a mix could be. The technology didn’t create those achievements. The craft did. The technology removed the ceiling.

Serato in the hands of someone who learned on vinyl is a fundamentally different instrument from Serato in the hands of someone who never did. Same tool. Different reach.

The interpretational gap

Here is where the AI conversation needs to go, and largely hasn’t.

The tools right now are still in the interpretational phase. Whatever you ask of them to generate this image, produce this video, create this sequence the machine is making creative decisions on your behalf, because the interface between human intent and machine execution is not yet precise enough to hand control back to the artist fully. You describe. It interprets. The gap between what you meant and what it made is not a failure of the technology. It is the current state of it.

This will change. The tools will develop to the point where AI becomes a true extension of the artist’s hand rather than a translator of their words. Where the camera can be moved exactly as the director intends rather than approximately as the prompt implies. Where the edit can land at the precise frame the cutter hears rather than the frame the algorithm predicts. When that happens when the interpretational gap closes and full creative control returns to the practitioner what we will see is not AI creativity. We will see human creativity at a scale and a precision that was previously impossible.

But only from the practitioners who brought their craft to the tool. Not from the ones who let the tool replace it.

The principle that doesn’t change

This is not a warning for a new generation. It is an invitation to every generation, including the one being formed right now in AI-native tools.

The DJs who grew up on Serato rather than vinyl are not at a disadvantage; they are building a different craft, native to their tools, valid on its own terms. And when the next technological shift arrives and it always arrives they will face exactly the same choice their predecessors faced. Bring what you’ve built to the new tool, or let the new tool replace it.

The principle is the same regardless of the instrument. Go deep into your craft. Never lose curiosity about it. Understand not just what your tools do but why they do it, what they can hold and where they break, what they make possible and what they make you lazy about. Because the moment you stop going deep is the moment the next shift finds you unprepared.

The vinyl DJs who mastered Serato didn’t stop listening. They started hearing things nobody else could.

That is what deep craft does when it meets a new tool. It doesn’t just use it. It finds out what the tool didn’t know it was capable of.

We are living inside a moment where the tools are advancing faster than the conversation about them. Most of that conversation is about speed and cost and scale. Almost none of it is about what craft actually requires and why that requirement never changes regardless of what the tools are.

The ones who will do the most extraordinary creative work with AI are not the ones using it to skip the hard part. They are the ones who already did the hard part, and are now finding out what becomes possible when thirty years of accumulated instinct meets a tool that removes the ceiling.

We haven’t seen that yet. The tools aren’t there. But they’re getting there.

And the question worth asking the one this industry should be sitting with right now is not whether AI will replace creative practitioners. It is whether creative practitioners will be ready when the tools finally hand control back to them.

Go deep. Stay curious. The rest follows.

Disrupting the Algorithm / The Bodega · Scope Creep · Singapore · © 2026

There was a moment in the early 2000s when some of the most respected cinematographers in the world made a confident, principled stand. Digital would never replace film. The latitude wasn’t there. The grain wasn’t right. Something irreplaceable would be lost something in the light, in the texture, in the craft of loading a magazine and knowing you had exactly so many frames before you had to stop. They weren’t wrong about what would be lost. They were wrong about what it meant.

We are living inside that same moment right now. Different format. Same emotional register. Same stakes.

— I —

The Resistance Always Sounds Reasonable

The arguments against digital filmmaking were not irrational. Kodak film had a quality that early digital sensors genuinely couldn’t match. The workflow was different, the grammar was different, the failure modes were different. Resistance had logic behind it. But over time, a pattern emerged: the people whose resistance was loudest were often the people who had never seriously worked with digital. They were critiquing a relationship they had never actually had.

The cinematographers who engaged who shot on early digital, who understood its limitations from the inside, who learned what the sensor could hold and where it would break those were the ones who could eventually articulate what was genuinely lost and what was genuinely gained. Roger Deakins didn’t abandon his judgment when he moved to digital. He transferred it. And in doing so, he continued to produce some of the most visually precise work of the last two decades. His craft didn’t diminish. It found new vocabulary.

The cinematographers who stayed outside the new format didn’t lose their craft. They lost their footing and by the time they found it again, someone else had already written the grammar.

The ones who stayed on the outside who waited for digital to prove itself before engaging largely missed the window where the new grammar was being written. By the time they arrived, they were catching up. And in creative industries, catching up is a different thing from being present at the formation.

— II —

AI Is Not the Threat. Disengagement Is.

The production floor tells you things conference panels don’t. I have sat in rooms where a team has built a genuinely capable AI workflow briefs generated in minutes, assets briefed at scale, turnaround times that would have been impossible two years ago and watched it stall not because the tool failed, but because nobody trusted the output enough to act on it. The resistance wasn’t ideological. It was relational. People didn’t have enough experience with the tool to know when to trust it, and not enough experience to know when to push back. Both failures came from the same place: insufficient contact.

AI doesn’t fix bad thinking. But engage with it seriously and it does something else it makes your thinking visible. It shows you where your instincts are grounded and where they are untested assumptions you have been running on for years. That is genuinely uncomfortable. It is also the beginning of useful work.

— III —

Where Human Responsibility Actually Lives

The film-to-digital transition clarified something important about creative responsibility: the format changed, but the judgment didn’t transfer automatically. A cinematographer who understood light on film had to relearn where the information lived in a digital sensor. The underlying sensibility was the same. The application required entirely new fluency.

That is the honest description of where we are. AI changes the surface on which creative and production judgment is applied. It does not replace the judgment. But and this is the part that gets missed if you don’t engage with the new surface, your judgment becomes untethered. You are making calls about a world you are not actually working inside.

In production terms, this looks like: knowing when an AI-generated brief is structurally correct but strategically empty. Knowing when a tool is optimising for coherence rather than truth. Knowing when the output sounds right but will not land because you understand the audience, the brand, the moment, and the machine doesn’t. The machine can tell you what will land coherently. It cannot tell you what will land truthfully. That gap is where your job still lives, and it only stays yours if you are close enough to the work to see it.

— IV —

The New Fluency

What the best digital cinematographers figured out was that the new format didn’t just replicate film it opened territory film couldn’t reach. Shooting ratios expanded. Low-light capability changed what stories could be told and where. Post-production latitude shifted the relationship between shooting and finishing. The format had its own logic, and mastering it meant understanding that logic on its own terms, not just measuring it against what came before.

And this is where the film analogy closes the loop: the cinematographers who discovered what digital could do that film couldn’t weren’t the ones who studied it from the outside. They were the ones who had already put in enough time with the tool to notice when it surprised them.

The same is true now. AI-assisted creative work is not just faster traditional work. It has its own logic. The interesting creative questions where does human authorship sit, what does curation mean when generation is instant, how do you build a point of view when the raw material is infinite those questions only become available to you when you are working inside the medium, not theorizing about it from outside.

The responsibility evolution isn’t about defending human territory from the machine. It is about understanding, through genuine engagement, what only humans can actually do. And that clarity about where your judgment is genuinely irreplaceable and where you have been flattering yourself only comes from doing the work. ■

Disrupting the Algorithm / The Bodega · Scope Creep · Singapore · © 2026