Recommendation Feeds Flattened Taste. Editors Are Quietly Coming Back.

Lena Fischer — Sat, 25 Jul 2026 00:00:00 GMT

The argument for replacing human curators with ranking models was never really about quality. It was about scale and cost, and on those terms it worked completely. One ranking system can personalise a catalogue for a hundred million people, and no editorial department can. The interesting development is that several platforms are now hiring editors again, and they are not doing it out of a change of heart.

They are doing it because optimising for engagement produces a catalogue that people get bored of, and boredom shows up in the retention numbers about two quarters later.

What the ranking function actually optimises

A recommender trained on completion, watch time, or click-through learns to predict what you will consume next. It does not learn what will make you glad you subscribed, because that signal is delayed, sparse, and hard to attribute.

The result is a well-documented set of behaviours.

Convergence. The system finds a reliable local optimum for your profile and stays there, because exploration costs measurable engagement now for uncertain benefit later.
Homogenisation of supply. Creators optimise for the ranking signal, so the catalogue itself narrows. The feed is not just showing you less variety, there is less variety being made.
Popularity feedback. Items with early engagement receive distribution that generates more engagement, which the model reads as quality.

We could raise session length by four percent any quarter we wanted. We could not raise the number of people who said the service was worth paying for.

Why editors solve a specific technical problem

The case for human curation that survives scrutiny is narrow but real: editors are good at exactly the thing recommenders are structurally bad at, which is deciding what deserves attention before there is any engagement data about it.

A new release, an unfamiliar genre, an artist with no audience yet, a back catalogue item that has never been surfaced. To a ranking model these are all high-variance bets with no evidence. To an editor with domain knowledge they are a judgement call, and a competent editor’s hit rate on cold-start material is considerably better than random.

That is why the roles being rebuilt are not general-purpose taste-making positions. They are cold-start and long-tail curation jobs, often reporting into growth rather than content.

The hybrid that seems to work

The pattern emerging across several platforms is not editors replacing the model. It is editors supplying a candidate set that the model is required to distribute.

In practice this means a reserved share of impressions allocated to editorially selected material, with the recommender deciding which users see which items but not whether the items get shown at all. The reservation is the crucial part. Without it, the model reallocates the inventory to safer bets within days.

Platforms that have measured this report a small, consistent engagement cost in the short term and improved retention and catalogue breadth over longer windows. Whether that trade is accepted depends entirely on which team owns the metric, which is an organisational question rather than a technical one.

The part that has not been fixed

None of this addresses distribution economics. A reserved impression share changes what audiences encounter; it does not change what creators are paid when they are encountered. Those are separate systems, and the second one has moved much less than the first, as our reporting on streaming royalty structures describes in detail.

It is also worth being precise about what the return of editors is not. It is not a restoration of a golden age of taste-making that people remember more fondly than it deserved. Editorial gatekeeping had its own well-catalogued biases, and there is no reason to assume a curation team assembled to fix a retention metric will be more representative than the ranking function it supplements.

What it is, more modestly, is an admission that a system optimising a proxy will eventually degrade the thing the proxy was standing in for.

The Streaming Royalty Math That Keeps Mid-Tier Artists Broke

Lena Fischer — Thu, 09 Jul 2026 00:00:00 GMT

Every discussion of streaming payouts gets stuck on the per-stream rate, and it is the least useful number in the system. There is no fixed per-stream rate. What exists is a pool of money divided according to a rule, and the rule is where the money actually moves. Under the standard model, an artist’s payout depends not on how much their own listeners paid but on what fraction of all listening on the platform they captured.

That single design choice explains most of what artists find inexplicable about their statements.

Pro-rata, in plain terms

Under pro-rata distribution, the platform pools subscription and advertising revenue, takes its share, and divides the remainder in proportion to total stream counts across the entire service.

Follow the consequence. If you subscribe and listen exclusively to one independent artist all month, your money does not go to that artist. It goes into the pool and is distributed according to global listening share, most of which is captured by the largest catalogues. Your subscription funds the artists you did not listen to.

Heavy listeners dilute everyone. A user streaming 8,000 tracks a month contributes the same revenue as one streaming 200, but claims forty times the share of the pool.
Skew rewards concentration. Listening follows a steep power law, so proportional division concentrates payouts far more than revenue is concentrated.
Fraud is a transfer, not a leak. Artificial streams do not create money. They take it from legitimate artists in the same pool.

My statement showed 340,000 streams and a payment that would not cover the mastering. The label explained the pool. It was the first time the numbers made sense and the first time they seemed indefensible.

What user-centric distribution changes, and what it does not

The obvious alternative divides each subscriber’s payment among the artists that subscriber actually listened to. It fixes the dilution problem directly and makes statements comprehensible.

The measured effects, from platforms and studies that have modelled it, are real but smaller than advocates suggest. Mid-catalogue and niche-genre artists with devoted, moderate-volume audiences gain. Artists whose plays come from passive playlist and background listening lose. The very largest artists lose slightly. Total money paid to artists does not change, because the rule only governs division.

That last point deserves emphasis. Switching distribution models redistributes a fixed pool. It does not address the size of the pool, which is set by subscription pricing and the platform’s revenue share. An artist whose income tripled under user-centric distribution went from very little to slightly less little.

The changes that alter the pool rather than the split

Three levers actually change how much money reaches artists, and they are all harder than reforming the split.

Subscription price is the most direct. Real prices were flat for over a decade while catalogue size and listening hours grew enormously. Recent increases have moved the pool more than any distribution reform would have.

Minimum thresholds are the most contested. Several platforms now withhold payment from tracks below an annual stream floor and redistribute it. This demonstrably reduces fraud and administrative cost, and it also removes small payments from artists at the bottom of the distribution, which is precisely the group the reform debate claims to be about.

The third is the share retained before the pool is formed, split between the platform and rights holders. This is the largest number in the system and the least discussed publicly, because the parties negotiating it have a shared interest in the argument staying focused on per-stream rates.

Why the framing persists

Per-stream rates are easy to compare and easy to be outraged about, which makes them useful to everyone who does not want the structure examined. A platform can point out truthfully that it does not set a per-stream rate. A label can point to the platform. An artist is left with a statement they cannot reconcile.

The structural question is simpler to state and harder to deflect: what fraction of the revenue generated by a listener reaches the artists that listener chose? For the curation systems that determine what those listeners encounter in the first place, see our reporting on the quiet return of human editors.

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