Consumer products and creative culture

The flood nobody warned you about

There is a version of the AI story that positions 2024 as the year creative work became democratised. In that version, everyone now has access to production tools that previously required studios, budgets and years of technical training. Music, visual art, written prose, video: the cost to produce each has dropped sharply. That part of the story is accurate.

What the democratisation framing leaves out is that lowering the cost of production also lowers the cost of noise. When anyone can publish a finished-sounding track, a polished essay, or a coherent short film with an afternoon's effort, the total volume of creative material in the world increases by orders of magnitude. The question that follows, and the one most consumer product builders are quietly struggling with, is not "who gets to create?" but "how does any of this get found, and why does any of it matter?"

This is the signal-to-noise problem, and it is not going away. It is, in fact, the defining creative-culture problem of this decade. Solving it is not primarily a technical challenge. It is a cultural one.

What AI tools actually change

It is worth being precise about what generative models do and do not change.

They change the cost and speed of execution. A musician can now hear a rough arrangement of an idea within minutes rather than spending hours programming it by hand. A designer can explore a dozen visual directions before committing to one. A writer can generate a structural skeleton quickly and spend their energy on voice and argument rather than scaffolding. These are genuine gains. Iteration speed is a real creative asset.

What generative models do not change is the underlying question of taste. Taste is the sense of what is worth making, what angle is interesting, what level of refinement is sufficient, when something is done. Taste is not a technical capability. It develops through exposure, through practice, through honest feedback and through a certain kind of critical engagement with work that already exists. A model trained on existing creative output can approximate the surface of good taste, but approximation is not the same as the real thing, and audiences, especially audiences with their own developed taste, can feel the difference even when they cannot articulate it.

The practical result is this: AI tools increase the output ceiling for people with taste and lower the floor for people without it. The gap between technically adequate and genuinely compelling widens rather than narrows, because the technically adequate becomes so cheap to produce that it loses nearly all its signal value. What remains valuable is the thing that cannot be automated: the specific sensibility that makes a piece of work feel like it came from a human being with a point of view.

Beyond technical quality: what gives creative work meaning

There is a strand of critical thinking, common in design circles, that conflates quality with craft. The argument goes that what separates meaningful creative work from disposable content is the skill visible in its execution. This was never entirely right, and it is even less right now.

Technical craft matters. A poorly mixed record is harder to listen to. A sloppily written piece is harder to follow. But craft is necessary, not sufficient. The works that move culture, that acquire meaning over time, that people return to and build on, are not simply the most technically accomplished ones. They are the ones that encode something true: a perspective, an experience, a tension that resonates because it reflects something real about the world or about what it means to be a person in it.

This is why the arrival of capable generative models has not, in any meaningful sense, solved the creative problem. You can now produce technically polished work more easily. You cannot automate the part where you decide what you actually think, or what you have actually experienced, or what kind of person you are trying to address. Those are human questions, and they are the questions that determine whether creative work has cultural weight.

Meaning in creative work comes from at least three sources: specificity, commitment, and context. Specificity means the work reflects a particular viewpoint, not a general one. Commitment means the creator made choices and held them, rather than hedging everything to maximise appeal. Context means the work exists in relationship to a community or a tradition, it is in dialogue with something, it knows where it sits.

All three of these are harder to fake than technical quality. They are also, not coincidentally, the things that platforms have historically been worst at surfacing.

The platform question in 2024

The dominant consumer platforms of the past fifteen years were built around engagement as a proxy for value. Content that generated interaction, watch time, shares and saves was treated as good content. The logic was self-reinforcing: more engagement trained the algorithm to show more of the same thing, which generated more engagement, which rewarded the behaviours most likely to produce engagement.

The problem with engagement as a proxy is that it selects for the wrong qualities. It selects for novelty, for provocation, for emotional intensity, for things that feel good to share rather than things that are good to think about. Over time, this warps what gets produced. Creators learn what the platform rewards and make more of it. The result is a creative landscape where the most visible work is optimised for engagement rather than meaning.

This dynamic is now playing out in sharper form with AI-assisted content. Because AI tools lower the cost of producing engagement-optimised material, the volume of that material increases while its marginal value drops. Platforms designed around engagement metrics become, effectively, surfaces for the rapid exchange of cheap stimulation. That is not a useful thing for creative culture.

The alternative is platforms built around different selection criteria. Not "what generates the most interaction?" but "what reflects genuine creative commitment?", "what do people who care about this domain actually value?", "what builds creative identity rather than just creative output?". These are harder criteria to operationalise, which is part of why most platforms have not built around them. But they are also more durable, because they select for things that have intrinsic value rather than things that are merely optimised for a particular algorithmic environment.

Culture versus content: how All Purpose thinks about this

All Purpose is built around a distinction that sounds simple but has large implications: the difference between content and culture.

Content is material produced for distribution. Culture is the set of shared values, references, standards and identities that give that material meaning. Content can exist without culture. Culture cannot exist without some anchor in human creative work. But the two are not the same, and building a platform for content is a very different project from building a platform that contributes to culture.

The consumer products inside All Purpose, including All Purpose Music, Relay, Horizon and Made It Out, are each designed around the culture question rather than the content question. This means asking not just "how do we help people produce and share?" but "what kind of creative identity does this platform make possible?", "what standards does this community hold?", "what does it mean to be someone who creates in this space?".

Identity is central to this. Creative work is identity work. When someone makes music, they are not only producing a track; they are doing something to their self-understanding, to how they see themselves in relation to their influences, their peers, their audience. A platform that treats this identity dimension as secondary, and addresses only the production and distribution mechanics, leaves the most important thing on the table.

Community is also central. The creative communities that have historically produced the most culturally significant work are not just aggregations of individual creators. They are social environments with their own norms, references, ongoing arguments and standards of quality. Those norms and standards do real work: they create pressure for seriousness, they enable meaningful feedback, they make it possible for individual creators to understand where their work sits in a larger conversation.

Standards, in particular, are something that consumer platforms have generally resisted building. The logic has been that imposing any quality standard restricts the potential user base and therefore the growth ceiling. What this misses is that standards are not restrictions; they are definitions. A platform without standards is a platform without identity. It cannot tell you what it is for or who it is for. It can only tell you that it is large.

What it means to build intentionally

Building consumer products that shape creative culture intentionally is a different kind of work from building products that ride creative culture. The distinction matters because the second approach is extractive: it takes existing cultural energy, converts it into engagement and scale, and returns something to creators primarily in the form of distribution. The first approach is generative: it creates conditions under which creative culture can develop, and it is invested in that development because the platform's meaning depends on it.

Intentional building requires having opinions. It requires deciding what you believe about what makes creative work good, about what kinds of creative communities are worth sustaining, about what a person becomes when they create in a particular space. These are not neutral product decisions. They are cultural ones, and they carry responsibility.

In the AI era, this responsibility sharpens. Because the cost of producing technically adequate material is now very low, the decisions that platforms make about what to surface, reward and celebrate carry more weight than they did when only people with significant resources or training could produce at all. The signal-to-noise problem is, at its core, a curation problem, and curation is an expression of values.

All Purpose's position is that creative culture is worth building intentionally, that the identity and community dimensions of creative work are as important as the production and distribution mechanics, and that the AI era makes this argument more urgent rather than less. When anyone can produce anything, the question of what is worth making, and who you become by making it, is the only question that really matters.

That is not a comforting conclusion for platforms built around volume. It is, however, an honest one.