Tag: platform accountability

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.

Shilling Like It’s 1999: Ars, Anthropic, and the Internet of Other People’s Things

Ars Technica just ran a piece headlined “AI industry horrified to face largest copyright class action ever certified.”

It’s the usual breathless “innovation under siege” framing—complete with quotes from “public interest” groups that, if you check the Google Shill List submitted to Judge Alsup in the Oracle case and Public Citizen’s Mission Creep-y, have long been in the paid service of Big Tech. Judge Alsup…hmmm…isn’t he the judge in the very Anthropic case that Ars is going on about?

Here’s what Ars left out: most of these so-called advocacy outfits—EFF, Public Knowledge, CCIA, and their cousins—have been doing Google’s bidding for years, rebranding corporate priorities as public interest. It’s an old play: weaponize the credibility of “independent” voices to protect your bottom line.

The article parrots the industry’s favorite excuse: proving copyright ownership is too hard, so these lawsuits are bound to fail. That line would be laughable if it weren’t so tired; it’s like elder abuse. We live in the age of AI deduplication, manifest checking, and robust content hashing—technologies the AI companies themselves use daily to clean, track, and optimize their training datasets. If they can identify and strip duplicates to improve model efficiency, they can identify and track copyrighted works. What they mean is: “We’d rather not, because it would expose the scale of our free-riding.”

That’s the unspoken truth behind these lawsuits. They’re not about “stifling innovation.” They’re about holding accountable an industry that’s built its fortunes on what can only be called the Internet of Other People’s Things—a business model where your creative output, your data, and your identity are raw material for someone else’s product, without permission, payment, or even acknowledgment.

Instead of cross-examining these corporate talking points like you know…journalists…Ars lets them pass unchallenged, turning what could have been a watershed moment for transparency into a PR assist. That’s not journalism—it’s message laundering.

The lawsuit doesn’t threaten the future of AI. It threatens the profitability of a handful of massive labs—many backed by the same investors and platforms that bankroll these “public interest” mouthpieces. If the case succeeds, it could force AI companies to abandon the Internet of Other People’s Things and start building the old-fashioned way: by paying for what they use.

Come on, Ars. Do we really have to go through this again? If you’re going to quote industry-adjacent lobbyists as if they were neutral experts, at least tell readers who’s paying the bills. Otherwise, it’s just shilling like it’s 1999.

The Duty Comes From the Data: Rethinking Platform Liability in the Age of Algorithmic Harm

For too long, dominant tech platforms have hidden behind Section 230 of the Communications Decency Act, claiming immunity for any harm caused by third-party content they host or promote. But as platforms like TikTok, YouTube, and Google have long ago moved beyond passive hosting into highly personalized, behavior-shaping recommendation systems, the legal landscape is shifting in the personal injury context. A new theory of liability is emerging—one grounded not in speech, but in conduct. And it begins with a simple premise: the duty comes from the data.

Surveillance-Based Personalization Creates Foreseeable Risk

Modern platforms know more about their users than most doctors, priests, or therapists. Through relentless behavioral surveillance, they collect real-time information about users’ moods, vulnerabilities, preferences, financial stress, and even mental health crises. This data is not inert or passive. It is used to drive engagement by pushing users toward content that exploits or heightens their current state.

If the user is a minor, a person in distress, or someone financially or emotionally unstable, the risk of harm is not abstract. It is foreseeable. When a platform knowingly recommends payday loan ads to someone drowning in debt, promotes eating disorder content to a teenager, or pushes a dangerous viral “challenge” to a 10-year-old child, it becomes an actor, not a conduit. It enters the “range of apprehension,” to borrow from Judge Cardozo’s reasoning in Palsgraf v. Long Island Railroad (one of my favorite law school cases). In tort law, foreseeability or knowledge creates duty. And here, the knowledge is detailed, intimate, and monetized. In fact it is so detailed we had to coin a new name for it: Surveillance capitalism.

Algorithmic Recommendations as Calls to Action

Defenders of platforms often argue that recommendations are just ranked lists—neutral suggestions, not expressive or actionable speech. But I think in the context of harm accruing to users for whatever reason, speech misses the mark. The speech argument collapses when the recommendation is designed to prompt behavior. Let’s be clear, advertisers don’t come to Google because speech, they come to Google because Google can deliver an audience. As Mr. Wanamaker said, “Half the money I spend on advertising is wasted; the trouble is I don’t know which half.” If he’d had Google, none of his money would have been wasted–that’s why Google is a trillion dollar market cap company.

When TikTok serves the same deadly challenge over and over to a child, or Google delivers a “pharmacy” ad to someone seeking pain relief that turns out to be a fentanyl-laced fake pill, the recommendation becomes a call to action. That transforms the platform’s role from curator to instigator. Arguably, that’s why Google paid a $500,000,000 fine and entered a non prosecution agreement to keep their executives out of jail. Again, nothing to do with speech.

Calls to action have long been treated differently in tort and First Amendment law. Calls to action aren’t passive; they are performative and directive. Especially when based on intimate surveillance data, these prompts and nudges are no longer mere expressions—they are behavioral engineering. When they cause harm, they should be judged accordingly. And to paraphrase the gambling bromide, the get paid their money and they takes their chances.

Eggshell Skull Meets Platform Targeting

In tort law, the eggshell skull rule (Smith v. Leech Brain & Co. Ltd. my second favorite law school tort case) holds that a defendant must take their victim as they find them. If a seemingly small nudge causes outsized harm because the victim is unusually vulnerable, the defendant is still liable. Platforms today know exactly who is vulnerable—because they built the profile. There’s nothing random about it. They can’t claim surprise when their behavioral nudges hit someone harder than expected.

When a child dies from a challenge they were algorithmically fed, or a financially desperate person is drawn into predatory lending through targeted promotion, or a mentally fragile person is pushed toward self-harm content, the platform can’t pretend it’s just a pipeline. It is a participant in the causal chain. And under the eggshell skull doctrine, it owns the consequences.

Beyond 230: Duty, Not Censorship

This theory of liability does not require rewriting Section 230 or reclassifying platforms as publishers although I’m not opposed to that review. It’s a legal construct that may have been relevant in 1996 but is no longer fit for purpose. Duty as data bypasses the speech debate entirely. What it says is simple: once you use personal data to push a behavioral outcome, you have a duty to consider the harm that may result and the law will hold you accountable for your action. That duty flows from knowledge, very precise knowledge that is acquired with great effort and cost for a singular purpose–to get rich. The platform designed the targeting, delivered the prompt, and did so based on a data profile it built and exploited. It has left the realm of neutral hosting and entered the realm of actionable conduct.

Courts are beginning to catch up. The Third Circuit’s 2024 decision in Anderson v. TikTok reversed the district court and refused to grant 230 immunity where the platform’s recommendation engine was seen as its own speech. But I think the tort logic may be even more powerful than a 230 analysis based on speech: where platforms collect and act on intimate user data to influence behavior, they incur a duty of care. And when that duty is breached, they should be held liable.

The duty comes from the data. And in a world where your data is their new oil, that duty is long overdue.