The Market for Meaning
Everyone is asking whether AI can replace artists. The more important question is: when creation becomes abundant, where does the value go?
Almost every public argument about AI and creativity is organized around a single anxious question: can the technology truly replace artists, writers, filmmakers, and musicians?
It is an important question, but an analytically limiting one, because it treats a shift in the economics of creativity primarily as a debate about authorship. There is another question, however, that receives far less attention: when making becomes abundant, what remains scarce?
Because value does not disappear. It moves.
After talking with creatives, founders, operators, and researchers across the creative AI ecosystem, I became increasingly convinced that this is one of the most important and least explored questions in the space. The reason is simple: nearly every conversation eventually returned to the same underlying dynamic. AI lowers the cost of production. Lower costs increase supply. Human attention does not expand to keep pace. As creative output becomes more abundant, value may increasingly accumulate in the layers that help people discover, interpret, trust, experience, and participate in that abundance.
In other words, the future of creativity may be shaped not only by how creative work is made, but by the forms of scarcity that remain once production is no longer one of them.
The Economics of Abundance
Here is what actually happened. AI did not make exceptional work effortless. It made passable work cheap. The bar for looking professional has collapsed, which means almost everyone now clears it. One person I came across described the result as a crowded world of sevens-out-of-ten, a market where the median keeps rising and genuine distinction gets harder precisely because competence is no longer scarce.
That single shift reorganizes everything. The contested question used to be “can we make something good?” Now everyone can. The question that decides outcomes is “can we make something worth choosing?” Competence is table stakes. Chosenness is the whole game. Once you see the category through that lens, a pattern shows up again and again: the value lives one layer above whatever AI is currently making cheap, and the companies that last are the ones willing to climb.
Here is what that looks like at four different altitudes.
1. The model is the most copyable thing you own
You would expect that in a technology defined by models, the best model wins. It mostly does not.
“We have the best model” is not a moat. It is a claim that gets obsolete the moment a larger lab decides to match you, open-source a competitor, or subsidize the floor beneath you. Benchmarks get published. Capabilities leak. The hyperscalers do not compete on model quality - they compete on adoption, ecosystem, and integration, and they can spend you into irrelevance while barely noticing the cost.
The defensible companies at the model layer are defensible for reasons that have almost nothing to do with raw capability.
Genmo’s move was instructive. It open-sourced its first major video model - gave away the thing you would think was the crown jewel - and deliberately climbed the stack. Why? Because winning a model arms race against OpenAI and Google is not a viable business strategy. But building the ecosystem around that model is. The company that matters is not the one with the best weights. It is the one that owns the layer above.
Runway took a different path but arrived at the same truth. It does produce superior video quality by most measures. But Runway’s actual moat is not superiority. It is the in-house creative team. The professional adoption in film and advertising. The partnerships with studios (Lionsgate). The AI Film Festival that grew from hundreds of submissions to thousands and sold out Lincoln Center. The model is the substrate. The moat is everything else.
If your answer to “why should anyone use you?” is a benchmark, you have already lost. Benchmarks are the easiest thing in the world for someone else to beat next quarter.
2. Making is easy now. Getting noticed is not.
You would think that if making things gets easier, the people who make things win. The opposite is true. Once anyone can make the asset, the asset itself is worth almost nothing. The hard part is no longer making it - it is getting anyone to watch it, share it, or come back for more.
PixVerse is a good example. It has over a hundred million users, and not because it has some secret model. It grew on templates. You upload a selfie, tap a template, and get a video you can share in seconds, with no prompt to write. A few templates caught on and pulled in tens of millions of users in a matter of weeks, because every person who made one and posted it was basically advertising the app. What PixVerse is really building is not a model. It is a library of templates and a crowd of people who make and share, and that gets stronger every time someone posts.
A founder running an image and storytelling platform put it bluntly to me: “any good feature gets copied within weeks, so being fast is the only edge for now. The things that actually last are brand, community, and ecosystem.” When a founder fighting off copycats points to community instead of features, you know exactly where the real value lies.
3. Trust and identity stop being adjectives and become infrastructure
As imitation gets cheap, proof gets expensive. Proof of who made something, proof that it was authorized, proof of the relationship behind it. What I found interesting is that these qualities are hardening from soft brand attributes into actual infrastructure: metered, licensable, and monetizable.
In audio, ElevenLabs runs a voice marketplace where creators are paid when their licensed voices get used, and where named public figures license their voices on their own terms. The unit being licensed is not a sound file. It is a person, with consent and royalties built into the rails. Identity has become a metered asset.
In luxury fashion, SEPT sells access to scarce goods to the small fraction of clients who drive most of the industry’s revenue. I asked the founder the obvious threat: what stops a competitor from rebuilding your interface in a weekend? Her answer was that the product was never the moat. The moat is years of relationships with hundreds of brands. A roster of personal shoppers. A client graph - not just a database, but a relationship graph - all held together by trust that still, as she put it, comes from the physical world. Software got cheap, which is exactly why a defensible business cannot rest on it.
In both cases trust is no longer a brand adjective you put in a deck. It is plumbing: a marketplace, a licensing rail, a client graph. That is a layer AI makes more valuable, because AI is the thing that decoupled content from its origin in the first place.
4. The strongest companies are Trojan horses
Put the first three together and you get the most useful diagnostic I took from all of this. There are three kinds of companies that are easy to confuse from the outside.
The durable creative company: Captures a scarce, compounding layer that gets stronger over time, not weaker.
HBO: taste + IP + brand + culture-setting power
Disney: IP + brand + owned distribution + live/experiential (the thing AI cannot yet replicate)
Substack’s top creators: identity + owned audience + direct relationship to readers
Patreon creators with communities: permission-based attention + recurring revenue + direct feedback loop
These companies are hard to start and hard to kill, because the layer they own compounds. More audience brings more leverage to negotiate IP. More IP brings more ability to set culture. More culture-setting brings more audience. Harder to copy each round.
The temporary feature company: Solves a real problem but sits in a layer that is being compressed toward zero.
“AI for [X content type]” startups
“Generate [specific asset] faster” tools
Speed-focused automation plays
Some can evolve. Most get acquired or absorbed into larger platforms. They are valuable as feature velocity, not as standalone businesses.
The Trojan horse company: Looks like a feature today. Uses that feature as a wedge to build something durable underneath - an audience, a community, a workflow lock-in, a permission-based relationship, a trust position.
The most interesting companies I looked at are all Trojan horses.
ElevenLabs looks like a voice generator. It is really building an identity marketplace where creators own their voices and get paid for them. The generation is the wedge. The marketplace - the licensing rails, the consent infrastructure, the royalty split - is the business.
PixVerse looks like a consumer video toy. It is really building a creation community where the network effects are so strong that the product becomes more valuable every time someone posts. Generation is free. Community is not. The community is what you cannot copy.
SEPT looks like a luxury shopping app. It is really a trust network and a client graph. You cannot replicate the relationships with 500 luxury brands. You cannot replicate years of personal shopping relationships. You cannot replicate the client graph that came from those relationships. The shopping app is the interface. The trust network is the moat.
The Friction Trap
One of the most interesting dynamics in creative AI is that it cuts against a core assumption of the broader AI industry: not every product gets better when friction disappears.
For tools aimed at marketers and teams buying media at scale, of course, friction is pure waste. Speed is the entire value. Kill friction. Optimize for automation.
But for tools aimed at people making something they want to call their own, friction is not a bug. It is part of what they are buying.
Effort is ownership. The time spent shaping something is the time you invest in calling it yours. A tool that removes all friction does not give you a better creation. It gives you something that no longer feels like yours. It feels like something the tool made and let you watch.
This is why some of the most durable creative tools are deliberately slow. Why analog tools persist. Why constraint breeds creativity. Why the most engaged communities are built on tools that require effort.
Knowing which of those two businesses you are in - efficiency for teams buying at scale, or ownership for people making - is one of the most consequential calls a founder makes. A surprising number default to maximum automation and destroy the very thing that made their tool defensible.
What is actually scarce now
Step back and the whole picture resolves into one sentence. The creative economy is not moving from human creativity to machine creativity - it is moving from production scarcity to meaning scarcity.
AI made it easy to produce output. It did not make it easier to produce meaning, identity, trust, belonging, or a reason for anyone to care.
The defensible company in this new world is not the one that generates the most content. It is not even the one that generates the best content by some objective measure. It is the one that helps people:
Know what is worth making
Understand why it matters
Know who it came from
Know why they should choose it over the flood of competent alternatives
The ability to make something stopped being scarce. Anyone can now generate passable work at scale.
The ability to make something worth choosing did not. That gap is where all the value is going to live.
And I suspect we are only just beginning to learn how to build on top of it.
This piece is adapted from a longer research paper written during my MBA at Stanford GSB: “The New Moat: Value Creation in Creative Industries in the Age of Generative AI.”