Tag: music industry

The Digital End-Cap: How Spotify’s Discovery Mode Turned Payola into Personalization

The streaming economy’s most controversial feature revives the old record-store co-op ad model—only now, the shelf space is algorithmic, the payments are disguised as royalty discounts, and the audience has no idea.

From End-Caps to Algorithms: The Disappearing Line Between Marketing and Curation

In the record-store era, everyone in the business knew that end-caps, dump bins, window clings, and in-store listening stations weren’t “organic” discoveries—they were paid placements. Labels bought the best shelf space, sponsored posters, and underwrote the music piped through the store’s speakers because visibility sold records.

Spotify’s Discovery Mode is that same co-op advertising model reborn in code: a system where royalty discounts buy algorithmic shelf space rather than retail real estate. Yet unlike the physical store, today’s paid promotion is hidden behind the language of personalization. Users are told that playlists and AI DJs are “made just for you,” when in fact those recommendations are shaped by the same financial incentives that once determined which CD got the end-cap.

On Spotify, nothing is truly organic; Discovery Mode simply digitizes the old pay-for-placement economy, blending advertising with algorithmic curation while erasing the transparency that once separated marketing from editorial judgment.

Spotify’s Discovery Mode: The “Inverted Payola”

The problem for Spotify is that it has never positioned itself like a retailer. It has always positioned itself as a substitute for radio, and buying radio is a dangerous occupation. That’s called payola.

Spotify’s controversial “Discovery Mode” is a kind of inverted payola which makes it seem like it smells less than it actually does. Remember, artists don’t get paid for broadcast radio airplay in the US so the incentive always was for labels to bribe DJs because that’s the only way that money entered the transaction. (At one point, that could have included publishers, too, back when publishers tried to break artists who recorded their songs.)

What’s different about Spotify is that streaming services do pay for their equivalent of airplay. When Discovery Mode pays less in return for playing certain songs more, that’s essentially the same as getting paid for playing certain songs more. It’s just a more genteel digital transaction in the darkness of ones and zeros instead of the tackier $50 handshake. The discount is every bit as much a “thing of value” as a $50 bill, with the possible exception that it goes to benefit Spotify stockholders and employees unlike the $50 that an old-school DJ probably just put in his pocket in one of those gigantic money rolls. (For games to play on a rainy day, try betting a DJ he has less than $10,000 in his pocket.)

Music Business Worldwide gave Spotify’s side of the story (which is carefully worded flack talk so pay close attention):Spotify rejected the allegations, telling AllHipHop: 

“The allegations in this complaint are nonsense. Not only do they misrepresent what Discovery Mode is and how it works, but they are riddled with misunderstandings and inaccuracies.”

The company explained that Discovery Mode affects only Radio, Autoplay and certain Mixes, not flagship playlists like Discover Weekly or the AI DJ that the lawsuit references.Spotify added: “The complaint even gets basic facts wrong: Discovery Mode isn’t used in all algorithmic playlists, or even Discover Weekly or DJ, as it claims.

The Payola Deception Theory

The emerging payola deception theory against Spotify argues that Spotify’s pay-to-play Discovery Mode constitutes a form of covert payola that distorts supposedly neutral playlists and recommendation systems—including Discover Weekly and the AI DJ—even if those specific products do not directly employ the “Discovery Mode” flag.

The key to proving this theory lies in showing how a paid-for boost signal introduced in one part of Spotify’s ecosystem inevitably seeps through the data pipelines and algorithmic models that feed all the others, deceiving users about the neutrality of their listening experience. That does seem to be the value proposition—”You give us cheaper royalties, we give you more of the attention firehose.”

Spotify claims that Discovery Mode affects only Radio, Autoplay, and certain personalized mixes, not flagship products like enterprise playlists or the AI DJ. That defense rests on a narrow, literal interpretation: those surfaces do not read the Discovery Mode switch. Yet under the payola deception theory, this distinction is meaningless because Spotify’s recommendation ecosystem appears to be fully integrated.

Spotify’s own technical publications and product descriptions indicate that multiple personalized surfaces— including Discover Weekly and AI DJ—are built on shared user-interaction data, learned taste profiles, and common recommendation models, rather than each using entirely independent algorithms. It sounds like Spotify is claiming that certain surfaces like Discover Weekly and AI DJ have cabined algorithms and pristine data sets that are not affected by Discovery Mode playlists or the Discovery Mode switch.

While that may be true, it seems like maintaining that separation would be downright hairy if not expensive in terms of compute. It seems far more likely that Spotify run shared models on shared data, and when they say “Discovery Mode isn’t used in X,” they’re only talking about the literal flag—not the downstream effects of the paid boost on global engagement metrics and taste profiles.

How the Bias Spreads: Five Paths of Contamination

So let’s infer that every surface draws on the same underlying datasets, engagement metrics, and collaborative models. Once the paid boost changes user behavior, it alters the entire system’s understanding of what is popular, relevant, or representative of a listener’s taste. The result is systemic contamination: a payola-driven distortion presented to users as organic personalization. The architecture that would make their strong claim true is expensive and unnatural; the architecture that’s cheap and standard almost inevitably lets the paid boost bleed into those “neutral” surfaces in five possible ways.

The first is through popularity metrics. As much as we can tell from the outside, Discovery Mode artificially inflates a track’s exposure in the limited contexts where the switch is activated. Those extra impressions generate more streams, saves, and “likes,” which I suspect feed into Spotify’s master engagement database.

Because stream count, skip rate, and save ratio are very likely global ranking inputs, Discovery Mode’s beneficiaries appear “hotter” across the board. Even if Discover Weekly or the AI DJ ignore the Discovery Mode flag, it’s reasonable to infer that they still rely on those popularity statistics to select and order songs. Otherwise Spotify would need to maintain separate, sanitized algorithms trained only on “clean” engagement data—an implausible and inefficient architecture given Spotify’s likely integrated recommendation system and the economic logic of Discovery Mode itself which I find highly unlikely to be the case. The paid boost thus translates into higher ranking everywhere, not just in Radio or Autoplay. This is the algorithmic equivalent of laundering a bribe through the system—money buys visibility that masquerades as audience preference.

The second potential channel is through user taste profiles. Spotify’s personalization models constantly update a listener’s “taste vector” based on recent listening behavior. If Discovery Mode repeatedly serves a track in Autoplay or Radio, a listener’s history skews toward that song and its stylistic “neighbors”. The algorithm likely then concludes that the listener “likes” similar artists (even if it’s actually Discover Mode serving the track, not user free will. The algorithm likely feeds those likes into Discover Weekly, Daily Mixes, and the AI DJ’s commentary stream. The user thinks the AI is reading their mood; in reality, it is reading a taste profile that was manipulated upstream by a pay-for-placement mechanism. All roads lead to Bieber or Taylor.

A third route is collaborative filtering and embeddings aka “truthiness”. As I understand it, Spotify’s recommendation architecture relies on listening patterns—tracks played in the same sessions or saved to the same playlists become linked in multidimensional “embedding” space. When Discovery Mode injects certain tracks into more sessions, it likely artificially strengthens the connections between those promoted tracks and others around them. The output then seems far more likely to become “fans of Artist A also like Artist B.” That output becomes algorithmically more frequent and hence “truer” or “truthier”, not because listeners chose it freely, but because paid exposure engineered the correlation. Those embeddings are probably global: they shape the recommendations of Discover Weekly, the “Fans also like” carousel, and the candidate pool for the AI DJ. A commercial distortion at the periphery thus is more likely to reshape the supposedly organic map of musical similarity at the core.

Fourth, the DM boost echoes through editorial and social feedback loops. Once Discovery Mode inflates a song’s performance metrics, it begins to look like what passes for a breakout hit these days. Editors scanning dashboards see higher engagement and may playlist the track in prominent editorial contexts. Users might add it to their own playlists, creating external validation. The cumulative effect is that an artificial advantage bought through Discovery Mode converts into what appears to be organic success, further feeding into algorithmic selection for other playlists and AI-driven features. This recursive amplification makes it almost impossible to isolate the paid effect from the “natural” one, which is precisely why disclosure rules exist in traditional payola law. I say “almost impossible” reflexively—I actually think it is in fact impossible, but that’s the kind of thing you can model in a different type of “discovery” being court-ordered discovery.

Finally, there is the shared-model problem. Spotify has publicly acknowledged that the AI DJ is a “narrative layer” built on the same personalization technology that powers its other recommendation surfaces. In practice, this means one massive model (or group of shared embeddings) generates candidate tracks, while a separate module adds voice or context.

If the shared model was trained on Discovery-Mode-skewed data, then even when the DJ module does not read the Discovery flag, it inherits the distortions embedded in those weights. Turning off the switch for the DJ therefore does not remove the influence; it merely hides its provenance. Unlike AI systems designed to dampen feedback bias, Spotify’s Discovery Mode institutionalizes it—bias is the feature, not the bug. You know, garbage in, garbage out.

Proving the Case: Discovery Mode’s Chain of Causation and the Triumph of GIGO

Legally, there’s a strong argument that the deception arises not from the existence of Discovery Mode itself but from how Spotify represents its recommendation products. The company markets Discover Weekly, Release Radar, and AI DJ as personalized to your taste, not as advertising or sponsored content. When a paid-boost mechanism anywhere in the ecosystem alters what those “organic” systems serve, Spotify arguably misleads consumers and rightsholders about the independence of its curation. Under a modernized reading of payola or unfair-deceptive-practice laws, that misrepresentation can amount to a hidden commercial endorsement—precisely the kind of conduct that the Federal Communications Commission’s sponsorship-identification rules (aka payola rules) and the FTC’s endorsement guides were designed to prevent.

In fact, the same disclosure standards that govern influencers on social media should govern algorithmic influencers on streaming platforms. When Spotify accepts a royalty discount in exchange for promoting a track, that arguably constitutes a material connection under the FTC’s Endorsement Guides. Failing to disclose that connection to listeners could transform Discovery Mode from a personalization feature into a deceptive advertisement—modern payola by another name. Why piss off one law enforcement agency when you can have two of them chase you around the rugged rock?

It must also be said that Discovery Mode doesn’t just shortchange artists and mislead listeners; it quietly contaminates the sainted ad product, too. Advertisers think they’re buying access to authentic, personalized listening moments. In reality, they’re often buying attention in a feed where the music itself is being shaped by undisclosed royalty discounts — a form of algorithmic payola that bends not only playlists, but the very audience segments and performance metrics brands are paying for. Advertising agencies don’t like that kind of thing one little bit. We remember what happened when it became apparent that ads were being served to pirate sites by you know who.

Proving the payola deception theory would therefore likely involve demonstrating causation across data layers: that the presence of Discovery Mode modifies engagement statistics, that those metrics propagate into global recommendation features, and that users (and possibly advertisers) were misled to believe those recommendations were purely algorithmic or merit-based. We can infer that the structure of Spotify’s own technology likely makes that chain not only plausible but possibly inevitable.

In an interconnected system where every model learns from the outputs of every other, no paid input stays contained. The moment a single signal is bought, a strong case can be made that the neutrality of the entire recommendation network is compromised—and so is the user’s trust in what it means when Spotify says a song was “picked just for you.”

Less Than Zero: The Significance of the Per Stream Rate and Why It Matters

Spotify’s insistence that it’s “misleading” to compare services based on a derived per-stream rate reveals exactly how out of touch the company has become with the very artists whose labor fuels its stock price. Artists experience streaming one play at a time, not as an abstract revenue pool or a complex pro-rata formula. Each stream represents a listener’s decision, a moment of engagement, and a microtransaction of trust. Dismissing the per-stream metric as irrelevant is a rhetorical dodge that shields Spotify from accountability for its own value proposition. (The same applies to all streamers, but Spotify is the only one that denies the reality of the per-stream rate.)

Spotify further claims that users don’t pay per stream but for access as if that negates the artist’s per stream rate payments. It is fallacious to claim that because Spotify users pay a subscription fee for “access,” there is no connection between that payment and any one artist they stream. This argument treats music like a public utility rather than a marketplace of individual works. In reality, users subscribe because of the artists and songs they want to hear; the value of “access” is wholly derived from those choices and the fans that artists drive to the platform. Each stream represents a conscious act of consumption and engagement that justifies compensation.

Economically, the subscription fee is not paid into a vacuum — it forms a revenue pool that Spotify divides among rights holders according to streams. Thus, the distribution of user payments is directly tied to which artists are streamed, even if the payment mechanism is indirect. To say otherwise erases the causal relationship between fan behavior and artist earnings.

The “access” framing serves only to obscure accountability. It allows Spotify to argue that artists are incidental to its product when, in truth, they are the product. Without individual songs, there is nothing to access. The subscription model may bundle listening into a single fee, but it does not sever the fundamental link between listener choice and the artist’s right to be paid fairly for that choice.

Less Than Zero Effect: AI, Infinite Supply and Erasing Artist

In fact, this “access” argument may undermine Spotify’s point entirely. If subscribers pay for access, not individual plays, then there’s an even greater obligation to ensure that subscription revenue is distributed fairly across the artists who generate the listening engagement that keeps fans paying each month. The opacity of this system—where listeners have no idea how their money is allocated—protects Spotify, not artists. If fans understood how little of their monthly fee reached the musicians they actually listen to, they might demand a user-centric payout model or direct licensing alternatives. Or they might be more inclined to use a site like Bandcamp. And Spotify really doesn’t want that.

And to anticipate Spotify’s typical deflection—that low payments are the label’s fault—that’s not correct either. Spotify sets the revenue pool, defines the accounting model, and negotiates the rates. Labels may divide the scraps, but it’s Spotify that decides how small the pie is in the first place either through its distribution deals or exercising pricing power.

Three Proofs of Intention

Daniel Ek, the Spotify CEO and arms dealer, made a Dickensian statement that tells you everything you need to know about how Spotify perceives their role as the Streaming Scrooge—“Today, with the cost of creating content being close to zero, people can share an incredible amount of content”.

That statement perfectly illustrates how detached he has become from the lived reality of the people who actually make the music that powers his platform’s market capitalization (which allows him to invest in autonomous weapons). First, music is not generic “content.” It is art, labor, and identity. Reducing it to “content” flattens the creative act into background noise for an algorithmic feed. That’s not rhetoric; it’s a statement of his values. Of course in his defense, “near zero cost” to a billionaire like Ek is not the same as “near zero cost” to any artist. This disharmonious statement shows that Daniel Ek mistakes the harmony of the people for the noise of the marketplace—arming algorithms instead of artists.

Second, the notion that the cost of creating recordings is “close to zero” is absurd. Real artists pay for instruments, studios, producers, engineers, session musicians, mixing, mastering, artwork, promotion, and often the cost of simply surviving long enough to make the next record or write the next song. Even the so-called “bedroom producer” incurs real expenses—gear, software, electricity, distribution, and years of unpaid labor learning the craft. None of that is zero. As I said in the UK Parliament’s Inquiry into the Economics of Streaming, when the day comes that a soloist aspires to having their music included on a Spotify “sleep” playlist, there’s something really wrong here.

Ek’s comment reveals the Silicon Valley mindset that art is a frictionless input for data platforms, not an enterprise of human skill, sacrifice, and emotion. When the CEO of the world’s dominant streaming company trivializes the cost of creation, he’s not describing an economy—he’s erasing one.

While Spotify tries to distract from the “per-stream rate,” it conveniently ignores the reality that whatever it pays “the music industry” or “rights holders” for all the artists signed to one label still must be broken down into actual payments to the individual artists and songwriters who created the work. Labels divide their share among recording artists; publishers do the same for composers and lyricists. If Spotify refuses to engage on per-stream value, what it’s really saying is that it doesn’t want to address the people behind the music—the very creators whose livelihoods depend on those streams. In pretending the per-stream question doesn’t matter, Spotify admits the artist doesn’t matter either.

Less Than Zero or Zeroing Out: Where Do We Go from Here?

The collapse of artist revenue and the rise of AI aren’t coincidences; they’re two gears in the same machine. Streaming’s economics rewards infinite supply at near-zero unit cost which is really the nugget of truth in Daniel Ek’s statements. This is evidenced by Spotify’s dalliances with Epidemic Sound and the like. But—human-created music is finite and costly; AI music is effectively infinite and cheap. For a platform whose margins improve as payout obligations shrink, the logical endgame is obvious: keep the streams, remove the artists.

  • Two-sided market math. Platforms sell audience attention to advertisers and access to subscribers. Their largest variable cost is royalties. Every substitution of human tracks with synthetic “sound-alikes,” noise, functional audio, or AI mashup reduces royalty liability while keeping listening hours—and revenue—intact. You count the AI streams just long enough to reduce the royalty pool, then you remove them from the system, only to be replace by more AI tracks. Spotify’s security is just good enough to miss the AI tracks for at least one royalty accounting period.
  • Perpetual content glut as cover. Executives say creation costs are “near zero,” justifying lower per-stream value. That narrative licenses a race to the bottom, then invites AI to flood the catalog so the floor can fall further.
  • Training to replace, not to pay. Models ingest human catalogs to learn style and voice, then output “good enough” music that competes with the very works that trained them—without the messy line item called “artist compensation.”
  • Playlist gatekeeping. When discovery is centralized in editorial and algorithmic playlists, platforms can steer demand toward low-or-no-royalty inventory (functional audio, public-domain, in-house/commissioned AI), starving human repertoire while claiming neutrality.
  • Investor alignment. The story that scales is not “fair pay”; it’s “gross margin expansion.” AI is the lever that turns culture into a fixed cost and artists into externalities.

Where does that leave us? Both streaming and AI “work” best for Big Tech, financially, when the artist is cheap enough to ignore or easy enough to replace. AI doesn’t disrupt that model; it completes it. It also gives cover through a tortured misreading through the “national security” lens so natural for a Lord of War investor like Mr. Ek who will no doubt give fellow Swede and one of the great Lords of War, Alfred Nobel, a run for his money. (Perhaps Mr. Ek will reimagine the Peace Prize.) If we don’t hard-wire licensing, provenance, and payout floors, the platform’s optimal future is music without musicians.

Plato conceived justice as each part performing its proper function in harmony with the whole—a balance of reason, spirit, and appetite within the individual and of classes within the city. Applied to AI synthetic works like those generated by Sora 2, injustice arises when this order collapses: when technology imitates creation without acknowledging the creators whose intellect and labor made it possible. Such systems allow the “appetitive” side—profit and scale—to dominate reason and virtue. In Plato’s terms, an AI trained on human art yet denying its debt to artists enacts the very disorder that defines injustice.