[ExI] Spotify’s Attempt to Fight AI Slop Music Fails

Sat Sep 27 13:50:32 UTC 2025

God damn it’s like half the posts here now are just you guys posting AI
answers to questions you had.  I’m not reading that whole thing.

AI is really useful and interesting but it’s not a substitute for actual
novel discourse.  It’s a shame how many new threads here are just
paragraphs from AI you guys asked for

On Fri, Sep 26, 2025 at 3:23 PM BillK via extropy-chat <
extropy-chat at lists.extropy.org> wrote:

> On Fri, 26 Sept 2025 at 20:08, BillK <pharos at gmail.com> wrote:
>
>> Spotify’s Attempt to Fight AI Slop Falls on Its Face
>> ---------------------------
>>
>> They say that AI is not supposed to be able to produce music that
>> people like, because AI doesn't have emotions or understand human
>> experiences.
>>
>> Really?
>> So I asked Gemini AI to research this.
>> The research report astounded me with its understanding of the problem.
>> I'll put the report in a follow-up post.
>> BillK
>>
> ---------------------------------------
>
> The Paradox of AI 'Slop' Music: A Disruption Analysis of Art, Economics,
> and Authenticity
>
> Executive Summary: The AI Slop Conundrum
>
> The rapid proliferation of AI-generated music presents a significant
> paradox to the modern music industry. On one hand, there is a legitimate
> and growing concern that this content, pejoratively termed "AI slop" or
> "slop," is an overwhelming volume of low-quality media that threatens the
> livelihoods of human artists by siphoning royalties [1, 2]. On the other
> hand, this same content is demonstrably achieving millions of views and
> streams on major platforms, raising the question of whether it is now "good
> enough" for many listeners.
>
> This report analyzes the core economic, technological, and cultural forces
> driving this dynamic. The investigation reveals that the economic threat is
> not a matter of a few viral hits but a systematic dilution of the royalty
> pool enabled by a new form of digital fraud. The popularity of AI music,
> meanwhile, is not a testament to its artistic quality but a function of
> sophisticated algorithmic promotion and its utility as a source of cheap,
> royalty-free background content for a new generation of digital creators.
>
> The analysis concludes that the "good enough" metric for AI music is not
> based on traditional artistic merit, such as emotional depth or creative
> originality, but rather on its technical proficiency and functional
> utility. This challenges the very definition of a "hit song" and the value
> of human-created art. While this disruption echoes historical technological
> shifts in the music industry—from the radio to the MP3—the unique ability
> of AI to mimic and, in some cases, autonomously create art presents an
> unprecedented challenge to the concepts of authorship and human creativity
> itself. The path forward for the industry will require a combination of new
> legal frameworks, a redefinition of the artist's role, and a strategic
> emphasis on the ineffable human qualities that AI cannot replicate.
>
> Introduction: The New Digital Disruption
>
> The digital music ecosystem, once heralded as the great democratizer for
> artists, now faces an existential challenge from within. The advent of
> generative artificial intelligence has unleashed a new creative force that
> can produce music at an unprecedented speed and scale. This has led to a
> central paradox: how can AI-generated music, which is frequently dismissed
> by critics and artists as "slop," be simultaneously a financial threat to
> human musicians and a widely popular phenomenon that garners millions of
> streams and views on platforms like Spotify and YouTube? This report delves
> into this question by examining the financial mechanisms, the drivers of
> popularity, and the qualitative distinctions between human and AI-generated
> music.
>
> At the core of this discussion are two key terms that require precise
> definition. The first, "AI slop," is a term for low-quality media generated
> by AI, characterized by an inherent lack of effort and an overwhelming
> volume [1, 2]. The term carries a pejorative connotation, evoking the
> same sense of annoyance and lack of value as digital "spam" [1]. The
> second term, generative AI in music, refers to autonomous systems that
> synthesize vast, pre-existing musical datasets to make compositional
> decisions [3, 4]. These systems, often built on deep learning and neural
> networks, can create entirely new musical compositions, variations, and
> harmonies from simple text prompts, and they are capable of doing so
> without direct human input [3, 5, 6]. The juxtaposition of these two
> concepts—the automated generation of low-quality "spam" and the undeniable
> popularity it is achieving—forms the basis of this comprehensive analysis.
>
> Economic and Financial Impact: Diluting the Royalty Pool
>
> A primary concern among human artists is the perceived dilution of the
> finite royalty pool by AI-generated content. This concern is not unfounded;
> it is rooted in the very structure of the music streaming economy.
>
> The Market Share Model Explained
>
> The vast majority of major streaming platforms, including Spotify, Apple
> Music, and Amazon Music, operate on a Market Share Payment System (MSPS) [7,
> 8]. This model works by pooling all revenue from subscriptions and
> advertising and then distributing that revenue to rights holders based on
> their proportion of the total streams for a given period [7]. For
> example, if an artist's streams account for 2% of the platform's total,
> they are allocated 2% of the total revenue pool [8]. This model's design
> is critical to understanding the threat posed by AI.
>
> The Mechanism of Royalty Dilution
>
> The Market Share Payment System creates a direct and exploitable
> vulnerability for bad actors. AI tools have made it trivially easy for
> fraudsters to create "mass uploads of artificial music" [9]. Spotify's
> own data illustrates the scale of this problem: the company removed 75
> million "spam tracks" in a single year, a volume that rivals its entire
> catalog of 100 million legitimate songs [9]. This flooding of the market,
> which includes everything from meditation instrumentals to vocal
> impersonations of famous artists, introduces an unprecedented level of
> competition for human-created catalogs [9, 10].
>
> A new paradigm of streaming fraud has emerged to exploit this system.
> Rather than attempting to get millions of streams on a single track, which
> would raise an obvious red flag, scammers use AI to generate hundreds of
> thousands of songs [11]. They then use bot farms to stream each of these
> tracks just a few thousand times—just enough to evade detection and
> generate royalties from each song [11]. Since a stream longer than 30
> seconds is all that is required to generate a royalty [9], this
> high-volume, low-engagement strategy is a highly efficient way to divert
> funds from the shared royalty pool [11]. This technological enablement of
> fraud at scale is a fundamental shift in how the industry is being
> exploited.
>
> Combating Fraud and Spam
>
> The music industry is actively responding to this threat. Major labels,
> most notably Universal Music Group, have filed lawsuits against AI
> platforms, petitioning streaming services to block them from using their
> copyrighted songs for training purposes [12]. UMG successfully had a
> deepfake song featuring AI-made vocals of Drake and The Weeknd pulled from
> streaming services, citing "infringing content created with generative AI" [9,
> 13].
>
> Streaming platforms are also adapting their business models. Spotify has
> implemented a music spam filter to identify fraudulent uploaders and
> prevent their tracks from being recommended by its algorithm [9]. The
> company also introduced a new rule in 2023 requiring a track to be streamed
> more than 1,000 times before it generates a payment, a direct response to
> the new micro-transaction fraud model [9].
>
> While Spotify officially claims that engagement with AI-generated music is
> "minimal" and does not have a "meaningful" impact on human artists' revenue
> [9], its own countermeasures tell a different story. The removal of 75
> million spam tracks and the necessity of changing royalty payment rules
> demonstrate that the problem is substantial and is forcing the company to
> adapt its core business practices [9]. This public stance of downplaying
> the issue while simultaneously taking monumental action confirms that
> AI-driven fraud is a significant and ongoing concern that threatens the
> integrity of the streaming ecosystem.
>
> The Popularity Paradox: Unpacking "Millions of Views"
>
> The central tenet of the user query—that AI music is popular—is a
> verifiable fact. However, a deeper analysis reveals that this popularity is
> not a measure of artistic achievement but a result of several
> interconnected factors that subvert traditional notions of success.
>
> The Algorithmic Advantage
>
> AI music's high view counts are often a manufactured outcome of
> sophisticated digital promotion. AI algorithms, which have long been a core
> part of music promotion and discovery, are now being used to specifically
> boost AI-generated content [14, 15]. These algorithms analyze vast
> amounts of listener data—including song choices, play frequency, and search
> history—to generate highly customized and personalized playlist
> recommendations [14]. AI-generated tracks can be optimized for these
> algorithms to increase their "popularity score," which helps them land on
> influential playlists like "Discover Weekly" [16].
>
> Additionally, some high-volume AI music channels on platforms like YouTube
> gain millions of subscribers not through organic viral hits but by
> leveraging paid promotion. These channels heavily spend on YouTube's
> "Promote" feature, which places their videos in user recommendations as
> advertisements, effectively paying for their audience and their high
> subscriber counts [17]. The high view count is therefore a reflection of
> a shrewd marketing strategy, not a spontaneous display of consumer
> affection.
>
> The Utility of AI Music
>
> Another significant driver of AI music's popularity is its utility. For a
> new generation of content creators—from YouTubers to podcasters to video
> game developers—AI music provides an accessible and affordable solution to
> a major logistical problem: securing royalty-free soundtracks [18, 19].
> Platforms like Soundful and Beatoven.ai specifically market their services
> as a way to generate unique, royalty-free background music for videos,
> livestreams, and games at the click of a button [18, 19, 20]. This
> convenience and cost-effectiveness appeal directly to creators who want to
> avoid copyright strikes and high licensing fees, thereby creating a new,
> distinct market for AI-generated music [18, 19]. This shift threatens to
> reduce predictable revenue streams for traditional stock music libraries
> and human composers who create music for film and digital media [10].
>
> The Consumer's Perspective: Is it "Good Enough"?
>
> For many consumers, the origin of the music is irrelevant. A large portion
> of the audience is a "silent majority of passive consumers" who have no
> anti-AI bias and care more about the functional quality of the content than
> how it was made [17]. A listener might be impressed by a song's quality
> and want to subscribe to a channel without even realizing it's AI [17].
> This is particularly true for younger audiences, such as Gen Z, who value
> novelty, remixability, and constant availability over the traditional
> artistry that has defined music for decades [21].
>
> The novelty factor is a key psychological driver of AI music's appeal.
> Because AI can mix different styles and beats in unexpected ways, it
> introduces an element of surprise that can trigger a dopamine release in
> the listener's brain [22]. AI music also thrives in "functional listening
> contexts," serving as background music for activities like studying,
> gaming, or serving as a soundbed for TikTok videos where the primary focus
> is not the music itself [21].
>
> The high view counts of AI music, therefore, do not equate to a triumph of
> artistic merit. Instead, they are a direct consequence of algorithmic
> optimization, the demand for cheap and utilitarian background music, and a
> new paradigm of consumer behavior where music serves a functional purpose
> rather than an emotional or artistic one. The fact that a song is "popular"
> on a technical level is no longer a guaranteed reflection of its creative
> value.
>
> Qualitative Analysis: Defining "Good Enough"
>
> The central question of whether AI music is "good enough" for many
> listeners requires a qualitative analysis that goes beyond stream counts. A
> detailed examination of AI-generated music reveals both its technical
> prowess and its fundamental limitations, which are often subconsciously
> perceived by the listener.
>
> Distinguishing Human from Machine
>
> While AI music has become incredibly sophisticated, it still exhibits
> certain characteristics that can signal its non-human origin. Listeners,
> often without conscious effort, can detect a track's reliance on repetitive
> loops, unnaturally smooth or abrupt transitions, or a lack of a coherent
> "storytelling arc" with a satisfying emotional conclusion [23]. The
> lyrics, in particular, are an easy giveaway. While an AI can generate
> rhyming phrases, it struggles with emotional coherence and deeper meaning,
> often producing lines that sound like they were pulled from a random quote
> generator [23].
>
> The most significant qualitative gap is emotional depth. A human musician
> creates music based on personal experiences, emotions, and stories, imbuing
> their work with a unique sense of authenticity and soul [24]. While AI
> can replicate the technical elements of sound, it cannot replicate the
> lived human experience. This often results in music that sounds "soulless"
> or "mechanical" [21, 24].
>
> The Listener's Verdict
>
> Recent research reveals a fascinating disconnect between perception and
> reality. A study on professional musicians found that while AI-generated
> music was generally considered to be of lower quality, knowing the
> composer's identity did not produce a meaningful difference in their
> perception of the pieces [25]. Similarly, a 2025 study found that a
> significant majority of listeners (82%) could not reliably tell whether a
> song was created by a human or an AI in a blind test [21]. However, that
> same study found a strong preference for music perceived to be human-made
> [25]. The preference was "significantly higher" for music believed to be
> composed by a human, even if the music was actually created by an AI [25].
> This indicates that authenticity is a qualitative measure of its own,
> separate from technical proficiency.
>
> AI-generated music is entering the "uncanny valley" of sound, where it is
> technically impressive and sounds "realistic" enough to fool many listeners
> [26]. However, it lacks the subtle imperfections, emotional nuance, and
> creative risk-taking that define great human art [24, 27]. The value of
> the music is no longer an objective measure of the sound itself but a
> subjective assessment tied to the notion of human creativity. A technically
> flawless track may be devalued by an audience if they discover it was
> created by a machine, raising the question of how human artists will prove
> their work is "real" and therefore "valuable" [27].
>
> This reliance on retrospective learning also creates a risk of creative
> stagnation. Since AI models are trained on existing data, they are
> inherently backward-looking. A heavy reliance on AI could lead to a
> feedback loop where new art is merely a pastiche of old art, limiting the
> diversity of the cultural soundscape and promoting a "sameness" in sound
> that lacks bold, forward-thinking innovation [27, 28, 29].
>
> AI-Generated vs. Human-Made Music: A Qualitative Comparison
> *Attribute* *AI-Generated Music* *Human-Made Music*
> *Compositional Style* Often relies on loops; transitions can be too
> smooth or abrupt [23]. Follows familiar storytelling arcs with a sense of
> build-up and emotional resolution [23].
> *Lyrical Content* Struggles with deeper meaning and emotional coherence;
> may sound like phrases from a random generator [23]. Conveys authentic
> emotion and personal experience; tells a story [23, 24].
> *Emotional Depth* Lacks authentic emotion; can sound flat or mechanical [23,
> 24]. Conveys a wide range of emotions and nuances; has "soul" and
> "flavor" [23, 24].
> *Originality* Recompositions of existing data; can lead to a stagnation
> of creativity [27]. Breaks rules and takes creative risks; brings unique
> perspectives [24].
> *Perceived Value* Can be devalued when the creator is known to be AI [27]. Perceived
> as more "authentic" and emotionally resonant [21, 25].
>
> Historical Context: Lessons from Past Disruptions
>
> The current debate surrounding AI is not a new phenomenon; it is the
> latest iteration of a recurring pattern of technological disruption in the
> music industry. By understanding how the industry navigated past
> challenges, it is possible to chart a course for the future.
>
> The Recurring Pattern of Resistance
>
> Each major technological shift in the music industry has been met with
> initial resistance, often centered on concerns about control, intellectual
> property, and artistic authenticity. The radio revolution of the 1920s was
> initially resisted by record labels who feared losing control of their
> content, yet radio ultimately became a critical driver of record sales [23,
> 30]. The debate over whether synthesizers were "real instruments" and
> their users "real musicians" in the 1980s mirrors the current discussion
> around AI [31, 32]. This technology, once seen as a "cheat," went on to
> create entirely new genres [31].
>
> The sampling controversy of the 1980s and 1990s presents a particularly
> striking parallel to the current AI training debate [23, 30]. The
> argument that sampling was merely "learning from existing music" is a
> direct precursor to the "fair use" claims made by AI companies today [8].
> The legal battles over sampling led to new frameworks and licensing models
> that did not eradicate the technology but instead incorporated it into the
> creative process [33, 34]. Similarly, the MP3 revolution and the rise of
> piracy in the 1990s, which caused a dramatic decline in revenue [35],
> forced the industry to completely transform its business model, leading to
> the paid digital downloads and streaming services we use today [23].
>
> History of Technological Disruption in the Music Industry
> *Disruption Era* *Technology Introduced* *Industry Resistance* *Impact &
> Resolution*
> *1920s* Radio Labels feared loss of distribution control [23]. Became a
> critical driver of sales and shaped public taste [23].
> *1980s* Synthesizers Users weren't seen as "real musicians;" technology
> as a "cheat" [31, 32]. Created entirely new genres and became a core part
> of music production [31].
> *1980s-90s* Sampling Accusations of "artistic theft" and copyright
> infringement [23, 33]. Established new creative practices and legal
> frameworks; became a core part of genres like hip-hop [23, 34].
> *1990s* MP3s & Piracy Caused a dramatic decline in revenue; intense
> copyright debates [23, 35]. Forced the industry to transform its business
> model; led to paid digital downloads and streaming [23].
> *2010s* Streaming Services Sparked revenue debates and dissatisfaction
> with payout rates [23, 36]. Democratized access for artists; became the
> dominant revenue model [23].
>
> While the historical pattern is clear, AI presents a unique and
> unprecedented challenge. Previous disruptions were centered on new
> distribution or creation tools. AI, however, is a technology that can
> autonomously mimic the human creative process itself, challenging the very
> definition of "authorship" and "creativity" [24, 27, 37]. The current
> lawsuits are not an attempt to kill AI but to establish a new legal and
> economic framework for its existence [13].
>
> Looking Ahead: The Future of Human and AI Collaboration
>
> The path forward for the music industry will likely involve a combination
> of new legal standards, creative innovation, and a redefinition of the
> human artist's role. The key is not to view AI as a replacement but as a
> new tool to be mastered.
>
> The Evolution of Copyright and Authorship
>
> The legal landscape is still being defined, but the direction is becoming
> clearer. The US Copyright Office has taken a firm position that, for now,
> AI-generated works require "human authorship" to be eligible for copyright
> protection [13]. This shifts the focus to what constitutes "sufficient"
> human input. The ongoing legal battles, including the possibility of class
> action lawsuits [13], will ultimately determine new rules for the use of
> copyrighted works for AI training. This suggests that the industry's
> response will not be about eradicating the technology but about
> establishing new monetization strategies and compensation models for the
> use of artists' intellectual property [10].
>
> AI as a Creative Catalyst
>
> AI's role extends far beyond the generation of "slop." A growing number of
> professional artists are already using AI as a tool for co-composition,
> sound design, and inspiration [38]. AI can be used to assist with mundane
> tasks like mixing and mastering, allowing artists to focus on the human
> elements of their work [5, 28]. It can also help break creative blocks by
> generating random ideas and new melodic combinations that a human might not
> have considered [5, 26, 28].
>
> AI also enables entirely new forms of artistic expression. It allows for
> the creation of "uncanny" new sounds and the ability to seamlessly
> translate lyrics into multiple languages for a global audience [38].
> Historic examples, such as The Beatles' use of AI to restore John Lennon's
> voice for a new track, demonstrate that the technology can bring new life
> to music that would have been impossible to create otherwise [38]. The
> most forward-thinking artists are not fighting the technology; they are
> finding ways to use it to retain their artistic agency and expand their
> creative horizons [38].
>
> Redefining Artistry and Livelihoods
>
> For human artists, the future will involve a strategic adaptation to a new
> technological landscape. This includes a necessary focus on diversifying
> revenue streams beyond streaming royalties, emphasizing avenues that AI
> cannot replicate, such as live performances, unique merchandise, and direct
> fan engagement [35, 39]. Artists must also actively leverage the
> emotional and historical value of their work, emphasizing the authenticity
> and human artistry that AI-generated tracks cannot provide [10].
>
> The most profound shift may be in the redefinition of the artist's role
> itself. Instead of being displaced, the human artist may evolve into a
> "curator" or "trainer" of their own AI models [38]. Artists are already
> beginning to create datasets from their own music for AI to experiment
> with, which allows them to retain control over their sound and likeness
> [38]. This model could lead to new forms of fan interaction, such as
> allowing fans to create music using an artist's AI-modeled voice [38].
> The debate over whether a synthesizer is a "real instrument" provides a
> lens for the future: the question is not whether AI is an instrument, but
> how human guidance and intent can turn a machine into a collaborator rather
> than a replacement. The most successful artists will be those who master
> this new technology as a tool, much like their predecessors embraced
> electric guitars and synthesizers, thereby ensuring that the future of
> music is not just "human-powered," but "human-guided."
>
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