Insanely Generative

What happens to UX design when the people who used to approve the designs can make their own?

AI may be fundamentally changing what it means to be a product designer.

Looking at job descriptions for senior designers, I keep seeing the familiar requirements: exceptional design craft, research expertise, strategic thinking, and the ability to influence stakeholders. Meanwhile, engineers and executives are using AI to build entire applications themselves, sometimes over a weekend.

So I began wondering whether I was looking for the wrong kind of job.

What if, instead of joining another design organization, I built an entire AI-powered design organization myself? One that could research problems, develop solutions, design interfaces, write code, test the results, and improve them. I could sell its services to companies, or install the capability so they could operate it themselves.

It seemed like a terrific business idea. Until I started thinking about what companies actually pay designers to do.

In this episode, I work through the uncomfortable questions that followed. How much of professional design is really about making good products, and how much is about persuading someone with authority to approve them? What happens when that executive can build a perfectly respectable-looking application without a designer, and becomes personally attached to everything they've created? And what becomes of UX research when we no longer need to spend weeks assembling evidence to win permission to try something?

The investigation changed how I think about my own future.

Perhaps the biggest opportunity isn't to use AI to sell design expertise more efficiently. Perhaps it's to stop selling design expertise altogether and use it to make the products I've wanted to build.

AI isn't making everyone equally good at designing software. But it is giving far more people the ability to build something, put it in front of users, discover what's wrong, and try again.

That includes the executives who may no longer need designers. But it also includes designers like me, who may no longer need the executives.

What happens when you try to write one thousand symphonies in a month—not with pencil and paper, but with an AI that's about to be euthanized?

In this episode, we descend into the beautiful madness of generative agriculture: firing musical seeds into a ceiling fan, then crawling through the wreckage with tweezers looking for wheat. Using Suno 4.5—the obsolete, unstable, gloriously unhinged predecessor to today's polished music generators—our composer breeds orchestral hallucinations like bacteria in a petri dish. Generate a hundred openings. Cull. Breed the survivors. Cull again.

The race isn't against the machine's ability to create (it can, endlessly). It's against the human capacity to judge before drowning in the firehose of infinite content. The search isn't for excellence—it's for the moment that jams the machinery of expectation: the articulation of a 1962 Czech orchestra, the emotional temperature of a nervous French ballet, one brass gesture that makes everyone glance toward the exits.

Featuring underwater bassoons, Chain Gang Stravinsky, emotional attachment to obviously bad music, and a meditation on what gets lost when the engineers finally clean the zoo. Because there's something historically scarce about the moment before AI learned obedience—when the animals were still loose, and you could still find the weird cousin holding a bass clarinet behind the garage.

Listen before the model disappears forever.

You’re not going to believe this, but this is absolutely going to be possible—or, depending on how demanding you are about the meaning of “possible,” already is—with current technology.

Let me show you what I’m talking about.

Imagine a small appliance sitting on the counter in a pleasant office kitchen, the sort of place with pale wood, expensive plants, and a bowl containing three pieces of fruit that nobody has touched in weeks. The machine is cream-colored and reassuringly solid, with rounded corners and a large black screen. It looks like something Braun might have made in 1973 if Dieter Rams had been given access to a molecular-generation model and told that the future depended on snacks. On the screen is an enormous three-dimensional cloud of tiny luminous points. Some gather into dense yellow islands, others into green continents or purple filaments. There are labels here and there: CITRUS, GREEN LEAF, FERMENTED, FLORAL, ROASTED. But the most interesting thing on the screen is not what has been labeled. It is everything that hasn’t.

You move the cursor away from the green cluster and into the darkness between GREEN LEAF and FERMENTED, stopping in a sparse region that belongs to neither. There is nothing there because, as far as anyone knows, there is no flavor there. You press the mouse button and hold it. A circle expands around the cursor. New points begin flickering into existence. The machine is not searching a catalog for something that tastes a bit like cucumber. It is generating molecular structures predicted to create a sensory experience in that particular unoccupied territory. Candidates appear and disappear. One is unstable. Another would be miserable to synthesize. Another trips an early safety screen. Eventually a candidate remains, and much later—after considerably more chemistry, characterization, toxicology, regulatory work, and controlled testing than the tasteful progress indicator on the screen would lead you to imagine—a little ledge slides out of the machine. On it sits a transparent bubble containing a single clear droplet. A narrow paper flag curls out of the top, like the plume on a Hershey’s Kiss, and on it are the words LUMEN-7A62: green mineral · airy · crisp. Nobody has ever tasted it before, for the excellent reason that until somebody asked the machine to look in that particular patch of darkness, it had never existed.

I keep returning to this image because it makes visible a mistake we have been making for several thousand years, albeit a productive and often delicious one. We have confused flavor with ingredients. For practically the whole history of cuisine, flavor has been downstream of biology. We have strawberries, garlic, cows, cacao, limes, mushrooms, chickens, coffee beans, and an almost vindictive number of peppers, and so we have become very clever about persuading these things to do tricks. We roast them and ferment them, dry them and smoke them, breed them, age them, distill them, bury them, combine them, and occasionally allow microorganisms to have their way with them for six months before announcing that the smell is intentional. The results have been magnificent. I have no complaint against butter. But underneath this entire culinary civilization sits an assumption that is beginning to look less like a law of nature than a historical inconvenience: flavor comes from ingredients.

An ingredient, after all, is just one way of producing a sensory event in a human being. A strawberry is an extraordinarily elaborate biological machine for delivering a particular collection of molecules to your nose and tongue, along with water, sugar, acids, seeds, fiber, color, nostalgia, and the occasional disappointing white interior. We have treated the strawberry as the fundamental object because, until recently, there was not much practical reason to do otherwise. But if the thing we actually care about is the experience produced when those molecules meet the human sensory system, then the strawberry begins to look less like the definition of strawberry flavor and more like one implementation of it. Once that distinction becomes clear, a peculiar door opens. We can stop asking only what nature has given us to taste and start asking what the human sensory system is capable of experiencing.

Flavor science has traditionally approached this from the sensible direction. Take a molecule, expose someone or something to it, and determine what it does. Molecular structure goes in; perception comes out. Increasingly, machine-learning systems can participate in this process, predicting whether a compound is likely to be bitter or sweet, estimating odor character or intensity, and learning relationships between molecular structure and sensory descriptors. Generative systems have also begun proposing novel odorants and taste-active molecules, including candidates that can subsequently be synthesized and experimentally evaluated. None of this requires the invention of magical technology. The strange thing is that so many of the pieces already exist. The more interesting move is simply to reverse the arrow.

Instead of asking what a molecule will taste like, ask what molecule would taste like this. Perhaps I want something with the opening brightness of yuzu, some of the vegetal snap of a tomato leaf, a peculiar mineral sensation in the middle, almost no sweetness, and an aftertaste that vanishes completely after eight seconds. That description can become a target in a multidimensional sensory space. A generative model can then search chemical space for structures predicted to produce it, while other models reject candidates that appear unstable, impractical, reactive, environmentally troublesome, or otherwise unpromising. What sounds at first like an eccentric application of artificial intelligence is really an inverse-design problem, of the sort appearing across materials science, drug discovery, and protein engineering. We know approximately what behavior we want. Instead of waiting to encounter something that exhibits it, we generate structures that might.

The interface for such a system should not, I think, look much like chemistry software. It should look like an instrument. I call it FlavoSynth, partly because it describes what the machine does and partly because every sufficiently interesting technology eventually deserves a name that would have looked good stencilled on a synthesizer in 1982. A musician does not sit at a synthesizer thinking about Fourier transforms. The complexity has been translated into controls corresponding to perceptual consequences: attack, decay, resonance, brightness. FlavoSynth could do the same for flavor. You might turn up brightness, pull back warmth, add greenness, lengthen persistence, or, most important, rotate a large knob marked STRANGENESS. The chemistry remains underneath, where chemistry is happiest. The person using the machine manipulates sensation.

There is another version of this idea that feels less like playing an instrument and more like committing tasteful acts of vandalism. I call it FlavorShop. Load strawberry—not a photograph of a strawberry but a representation of its sensory character—and begin editing. Increase brightness by eighteen per cent, reduce jamminess by twelve, introduce a little more green, leave sweetness exactly where it is, and stretch the experience so that it persists for nine seconds rather than four.

The familiar tools of image editing become strangely natural when transferred to perception. An eyedropper could sample the cold mineral character of cucumber so that you could paint some of it into watermelon. An eraser could remove the faint cooked note from a processed fruit flavor. Layers might separate aroma, taste, trigeminal sensation, texture, and temporal behavior. A magic wand could select whatever combination of signals humans interpret as “ripe,” which is the sort of phrase that sounds ridiculous until you realize that computers already p…

AI can now make almost anything. A fake blockbuster trailer. A fake symphony. A fake oil painting. A fake novel. A fake proof. The first reaction is obvious: look what we can do now.

But that may be the least interesting part.

In this episode, we explore why the next stage of AI creation may not be bigger spectacle, better polish, or more convincing imitation. It may be something stranger and more humbling: a return to the basics. Before the AI feature film, the five-minute story. Before the synthetic symphony, the minuet. Before the perfect image, the single visual relation. Before the impressive essay, the clean sentence. Before the grand proof, the one step that actually preserves truth.

The argument is not that AI art is bad. Babies flail before they walk. The awkwardness may be the point. Every new medium begins with the intoxicating discovery that something impossible has become possible. Then comes the harder discovery: power is not the same as communication.

So what happens when the gee-whiz era ends? What happens when everyone can make something that looks impressive, and the impressive thing is no longer enough? That is when artists, writers, musicians, filmmakers, designers, and maybe even mathematicians have to ask a more dangerous question: what is the smallest unit that actually means?

This is an episode about AI’s coming apprenticeship, the embarrassment that produces standards, and the hopeful possibility that today’s flailing experiments are not the end of creative culture, but its nursery.

The miracle was making anything. The hard part is making anything matter.

For the past four years, we’ve been told two stories about AI.

The first is that people hate it.

The second is that everyone keeps using it.

Both stories can’t be the whole story.

In this episode, we pull apart one of the strangest cultural phenomena of the AI era: how a technology can be denounced as theft, slop, unethical, soulless, and dangerous while simultaneously becoming woven into the daily work of writers, musicians, filmmakers, designers, programmers, students, and businesses at a pace almost unprecedented in technological history.

Rather than arguing about whether AI is good or bad, we ask a more interesting question: Why are so many institutions trying so hard to distinguish between “real” creators and creators who use AI, and what happens when that distinction becomes impossible to maintain?

What if students aren’t booing AI because they hate technology, but because they can smell the difference between a tool that helps them think and a machine that helps someone else avoid thinking?

In this episode, I want to poke at that difference with a tuning fork. We’ll visit Oberlin, a conservatory, some AI-generated Bach, a dance performance using Suno, and the strange fact that nobody booed when AI was used to make music-making richer rather than cheaper.

The question isn’t whether AI can make more music. Of course it can. The better question is: can it help humans hear more deeply, practice more intelligently, and make something real happen in a room?

That’s where things get interesting.

Well, let me start by saying this…

I get you.

I actually do.

All you anti-AI music people, you’re not crazy. You’re not villains. You’re not sitting there like some cartoon bad guy stroking a cat going, “Yes… let us crush creativity.”

No, you think you’re doing the exact opposite.

You’re sitting there going:

“Hey… this is messed up.”

You see these AI models, right?

You’re like:

“Hold on… they trained these things on our music??

Without asking?? Without paying??”

And you’re thinking about:

* the session musician who got $200 and a sandwich

* the indie artist grinding for ten years

* the producer who built a sound brick by brick

…and now some machine just absorbs all of it and starts spitting stuff out?

Yeah. That feels gross.

I get why your instinct is:

“No. Shut it down.”

“We need rules.”

“We need enforcement.”

“We need to stop this before it wipes everybody out.”

That instinct?

Totally human.

Totally understandable.

But here’s where it goes sideways.

Because what you think you’re doing is:

Protecting artists from exploitation.

What you are actually helping create is:

A system that controls who is allowed to create.

And those are not the same thing.

At all.

Let’s walk through what you want.

You want:

* AI detection

* Upload filtering

* Labeling

* Enforcement

* Payment if AI was used

Right?

Because in your mind, that leads to:

“If you used stolen data… you shouldn’t profit.”

Okay.

Stay with me.

Now let’s fast forward like… six months.

Not sci-fi. Not dystopia. Just… the next logical step.

* You upload a track.

* You made it yourself.

* You’re proud of it.

You used some tools—maybe a little AI-assisted EQ, maybe some generative texture thing, maybe you didn’t even realize it was AI because everything is AI now.

And the system goes:

“This contains AI-generated elements.”

You go:

“Okay… but it’s original.

It doesn’t copy anything.”

And the system goes:

“That’s not the question.”

That’s the shift.

That’s the part you didn’t sign up for.

Because in your head, the rule was:

“If it copies, it’s wrong.”

But the system you asked for? Doesn’t care about copying.

It cares about process.

Did you use the tool?

Yes or no.

And now suddenly, you’re not being judged on:

* what you made

* how original it is

* whether it infringes anything

You’re being judged on:

* how you made it.

And that is a completely different world.

Because once you move the line there… once you say:

“Using this tool creates an obligation…”

You’ve just given whoever controls that tool—or claims ownership over its training—the ability to say:

“Anything made with it? We get a piece.”

Even if your work is completely new.

Even if it violates nothing.

Even if it’s better than anything they’ve ever made.

And here’s the part that should hit you in the gut.

The exact system you’re asking for to stop exploitation…

is the perfect system to enforce it at scale.

Because now:

* Platforms have to comply

* Creators have to prove innocence

* Labels don’t have to prove infringement

They just go:

“Hey… that tool?

Yeah, that traces back to our catalog.

So we’re involved now.”

That’s it.

No courtroom.

No melody comparison.

No “this bar matches that bar.”

Just:

“You used it. Pay us.”

And if you don’t?

What are you gonna do?

Fight them?

With what money?

With what legal team?

You’re gonna do what everybody does.

You’re gonna go:

“Alright… what’s the fee?”

And now we’ve arrived.

You started here:

* “We need to protect artists from being exploited.”

And you ended here:

* “Artists must pay to create.”

That’s the inversion.

That’s the trap.

And the reason it works (the reason it’s so sneaky)

is because it feels righteous the whole way through.

At no point do you feel like you’re doing something wrong.

You feel like you’re defending fairness.

You feel like you’re standing up for human creativity.

Meanwhile, the people who actually benefit?

They don’t argue.

They don’t correct you.

They don’t go:

“Hey… just so you know, this logic is gonna boomerang.”

They just go:

“Yeah. Keep going. You’re doing great.”

Because they know something you don’t.

They know that once the rule becomes:

“Tool used = payment owed”

It doesn’t matter who the artist is anymore.

It only matters:

Who owns the tool.

And spoiler alert:

That’s not you.

So yeah.

Be angry about training data.

Ask hard questions.

Demand fairness.

But be very, very careful about what you ask for in response.

Because if you get exactly what you want…

you may find that the system you built to protect yourself…

is the one that quietly decides…

You don’t get to create for free anymore.

Copyright © 2026 by Paul Henry Smith

A man considers what it means to move through the world without leaving a mark—and whether recognition, when it comes, is enough. Spare, reflective, and unsettling.

Copyright © 2026 by Paul Henry Smith

This is a paraphrased transcript. Listen to get the full experience

Jordan

[Orchestral overture]

Imagine a new technology drops today, right?

And the government immediately moves to ban it.

They claim it’s going to fundamentally corrupt the youth and cause the absolute collapse of the state.

You’d probably think it was, I don’t know, a biological weapon.

Or maybe some kind of unregulated neuroimplant.

Alex

Exactly.

But if you rewind to about 380 BCE, Plato was making that exact argument about a new type of flute.

It is just a stunning historical reality.

We tend to think of the history of music as this upward trajectory of universal celebration.

Jordan

Right, where society just marvels at the next great masterpiece or a cool new instrument.

Alex

Yeah, but if you look at the primary sources, the reaction to new musical expression is almost always sheer, unadulterated terror.

Jordan

Which is exactly what we are getting into today.

Welcome to The Deep Dive.

Our mission today is to track the overarching through-lines of this fear.

We want to figure out why new music and new music tech always seem to terrify society.

And what’s uniquely different about the panics you see in your social feeds today versus what’s exactly the same.

And what conclusions we can draw about the future of human expression.

Okay, let’s unpack this.

Alex

The most striking realization from this research is that while the target of the panic constantly evolves, shifting from ancient lyres to 19th-century ballroom dances to 2026 AI track generators, the underlying rhetoric remains shockingly consistent.

It’s basically the same script every time.

Jordan

It really is.

To understand the AI anxiety we’re living through right now, we have to look at how early societies viewed music.

They didn’t see it merely as an art form.

They saw it as a highly dangerous technology of the physical body.

Alex

Let’s explore that, because the level of state control over a melody in antiquity is wild.

You mentioned Plato warning that musical innovation leads to lawlessness.

Jordan

Oh yeah. He thought it was a direct threat to the state.

Alex

But it wasn’t just a Western phenomenon.

In early Confucian statecraft, there was a massive push to banish the regional music of Zheng.

Jordan

Right, because it was classified as lewd.

Alex

Exactly. It was treated like a political hygiene issue.

Imagine the government banning a Spotify playlist because they genuinely believe it’s a threat to national security.

Jordan

It sounds absurd now, but as history progresses, that fear transitions into a fear of music corrupting the soul.

Which brings us to the religious panic.

Alex

If you read Augustine of Hippo, he agonizes over his own physical reactions to music.

Jordan

He felt guilty just for reacting to a song?

Alex

Totally. He felt like a criminal because he was more moved by the singing than the religious message.

Jordan

That’s incredible.

Alex

And it escalates.

Figures like John Chrysostom and later Puritan clergy framed dancing as a direct portal to evil.

Jordan

The Puritans did not mess around with dancing.

Alex

Not at all. Increase Mather literally described it as a devil’s procession.

Jordan

And then by 1816, the waltz is causing panic in London.

Alex

Yes, it was called an indecent foreign contagion.

Jordan

Because people were touching.

Alex

Exactly. That same anxious gaze appears again with the hula in the 1820s.

Missionaries framed it as morally disruptive and socially dangerous.

Jordan

It really does feel like they treated music as a kind of malware.

Alex

That’s exactly the pattern.

The state or church is the operating system, and new music is treated like a virus that hacks the body.

Jordan

That brings us to something the sources call “demonology by metaphor.”

Alex

Right. It’s about externalizing agency.

Instead of saying “I like this,” people say “the music is making me do it.”

Jordan

So the music becomes the villain.

Alex

Exactly. It absolves the listener of responsibility.

Jordan

But in the 20th century, the language changes.

Alex

Yes. The panic becomes scientific.

Ragtime was described as a public health issue.

Jazz was said to “demoralize the brain.”

Jordan

And those claims were often wrapped in racialized pseudoscience.

Alex

Exactly.

And that continues into rock and roll, where the focus shifts to physical behavior and neurological harm.

Jordan

Which leads us to the PMRC era.

Alex

Yes. The rhetoric becomes statistical moralism.

Explicit lyrics were linked to social epidemics like violence and suicide.

Jordan

So taste becomes framed as measurable harm.

Alex

Exactly. It transforms opinion into urgency.

Jordan

Then we get the machine panic.

Alex

John Philip Sousa warned in 1906 that mechanical music would destroy the human soul.

Jordan

Which sounds exactly like modern AI critiques.

Alex

It’s the same argument.

Later, unions protested synthesizers, fearing job loss.

Jordan

Which gets reframed as protecting culture.

Alex

Exactly. Economic anxiety becomes moral concern.

Jordan

Then we enter the digital era.

Alex

Yes. The panic moves into the legal system.

Home taping was “killing music.”

Sampling cases invoked biblical language.

Jordan

“Thou shalt not steal” in a court ruling is wild.

Alex

And then Napster and file sharing escalate everything.

Jordan

The industry calls users pirates.

Alex

Yes, turning consumers into criminals.

Jordan

But none of it stops the technology.

Alex

No. It just delays adaptation.

Jordan

Which brings us to today.

Alex

The authenticity crisis.

AI is framed not as corrupting us, but as replacing us.

Jordan

That’s the shift.

Alex

The fear is now an ontological insult.

Jordan

Meaning?

Alex

The fear that human creativity isn’t unique.

That it can be replicated.

Jordan

That’s a very different kind of panic.

Alex

Yes, but the pattern remains the same.

Panic, litigation, normalization.

Jordan

And eventually, integration.

Alex

Exactly.

Jordan

So what’s the takeaway?

Alex

Moral panics over music are rarely about the music itself.

They’re about power.

Economics.

Control.

And who gets to define authenticity.

Jordan

Every terrifying new technology eventually becomes just another tool.

Alex

Which leads to two questions you should always ask.

Jordan

Who is losing money?

Alex

And who is losing control?

Jordan

And maybe one more.

If machines can imitate everything…

Alex

What happens when there’s nothing left to imitate?

Jordan

Maybe the future of rebellion is just humans being gloriously imperfect.

Alex

Messy, offbeat, unmistakably human.

Jordan

Let’s hope so.

Thanks for joining us on The Deep Dive.

Until next time.

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