Curator Capital Markets

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Introduction

With the launch of OpenAI's Sora 2, the marginal cost of producing high-fidelity media is collapsing in realtime. We are now staring directly at a firehose of completely synthetic content as @mariogabriele predicted 3 years ago in The Generalist:

We may be entering an age of "endless media." AI is not only a capable creator but an instantaneous, economical one. Over time, it may match or surpass human abilities across mediums, leading to a world in which creating a film, comic, or novel can be done on demand, ad infinitum.

In a world drowning in synthetic content, how do we discover what's actually valuable?

Most of so-called "InfoFi" or "SocialFi" today focuses on the issue of value capture: making sure creators can monetize their work and allowing users to own their social graphs. These are responses to Web2's extractive platforms, which grew powerful by controlling assets they didn't create.

But there's a higher-value opportunity that's been overlooked: value discovery. When content was scarce, discovery was relatively easy. Now, with infinite content, the bottleneck has flipped and the challenge has become finding what's worth your attention.

This is where the Curator Economy comes in. For the Creator Economy to function in this high-noise environment, it needs a symbiotic layer dedicated to filtering and routing. The engine of this economy would be costly signaling: endorsements backed by skin in the game. A "like" costs nothing and means nothing. But a curator who stakes capital, reputation, or a scarce resource could act as more credible signal.

The infrastructure enabling this is what I call Curator Capital Markets: systems that financialize judgment itself, creating economic incentives for high-quality curation and enabling efficient allocation of attention.

History of the Attention Market

The structure of the market for attention has undergone distinct phases, each defined by its primary scarcity and its mechanism for signaling value.

Phase 1: Pre-Digital Era

Before the internet, the marginal cost of both content production and distribution was high.

Creating a TV show required a studio, publishing text required a publisher and a distribution network. These were capital-intensive activities with high barriers to entry. The primary scarcity of this phase is distribution channels. There were only a few TV networks, a handful of major publishers. Supply-side constraint is the dominant feature of this market.

In this system, the human gatekeepers like editors, studio executives, and publishers acted as central planners for the attention market. Their role was to forecast aggregate demand and ration the scarce supply of distribution to the content they believed would yield the highest return. Attention was allocated via a "push" model.

Phase 2: UGC Era

The UGC era of the internet led to the collapse of the cost of distribution.

In this system, aggregators dominated. Social media giants developed attention allocation mechanism based on algorithms, which used low-friction user actions like clicks, likes, shares, views as a real-time proxy for human interest.

Instead of selling individual pieces of content, these platforms offered it for free, captured user attention and sold it to advertisers. The system's efficiency was based on the assumption that the proxy (engagement) was a reasonably accurate reflection of the underlying asset (genuine human interest).

Phase 3: AI Slop Era

The shock from generative AI is more profound than the collapse of distribution costs because it changes the nature of production itself.

In UGC era production of content, human gatekeepers went away. Yet, the core input remained human labor. A person still had to think, write, film and edit.

Now we are nearing an infinite scaling of content that can be perfectly engineered to exploit the rules of the algo. Creators—both human and now AI ones—no longer primarily create content to serve a human need. They create content specifically engineered to satisfy the algorithm's target. The system is working perfectly at the wrong thing, producing a glut of high-calorie, low-nutrition information.

Paying for the filter, not the content

Historically, the scarcity was in content production. High costs of production and distribution acted as a filter, making human attention the relatively abundant factor in the equation. Today, the scarcity is in attention. The competition for finite attention has become the central, organizing principle of the information economy.

This inversion shifts what a consumer is willing to pay for. When high-quality content was scarce, content itself is valuable. Now, with infinite content, the burden on the consumer is primarily the discovery cost - the time and energy spent sifting through noise to find the signal.

In a market that is pathologically optimized for engagement, the new scarce resource is authentic signal. An authentic signal is an expression of value that is independent of the dominant algorithmic target. Basically content that serves a human purpose first before serving an algorithmic one.

I belive the economic value will accrue to the act of curation and sense-making. Consumers will pay a premium not for a random piece of media, but for the trusted filter, the critic, or the system that reliably surfaces what is worth their attention.

Curator Capital Markets

I think the economic solution required by this new era is a shift to a direct pricing model for judgement. This model would be built on the principle of costly signaling, which states that for a signal to be credible, it must be costly to the signaler, making it difficult or irrational to fake.

Verifiable human judgement, backed by a costly stake (capital or a scarce resource), is the market mechanism capable of identifying and elevating this authentic signal, as its "target" is not a gameable metric, but a complex and holistic assessment of value.

This new pricing mechanism would manifest in the form of Curator Markets. They compel curators to abandon the cheap signal of a "like" and instead use a costly one, such as:

1. Allocation of a finite resource (Opportunity Cost)

• e.g. every vote represents an opportunity cost against all other possible votes

2. Capital stake with risk of loss (Financial Cost)

• e.g. making explicit bets on prediction market to signal an opinion

3. Reputational stake in a competitive game (Social Cost)

• e.g. a wrong prediction leads to a tangible loss of status on a public leaderboard

4. Commitment of verifiable time (Opportunity Cost)

• e.g. a user must engage with content for a minimum duration before endorsing it

The resulting market structure is a financialized layer that sits atop the content layer. Its primary economic function is to generate reliable price signals about that content's quality and relevance as opposed to hosting content.

These signals in turn would help reduce search costs for all other participants and enable a more efficient allocation of attention. The value proposition thus shifts from aggregating attention to sell to advertisers, to selling high-fidelity signals to other market participants.

Check out some initial experiments and projects that belong to this emerging curator economy here.