Recommendation Feeds Flattened Taste. Editors Are Quietly Coming Back.

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.