AI Music vs. Human Music: A 2025 Study on the Rise of AI in Music

16.09.2025
RH Fardin
20 min read
Content
AI Music vs. Human Music: A 2025 Study on the Rise of AI in Music

2025 is the year AI isn’t just assisting music, it’s competing with it.

AI-generated music tools like Suno, Udio, and the newly launched “Eleven Music” are producing full tracks, lyrics, and voices from simple prompts. They’re cheaper, faster, and increasingly popular.

Yet with opportunity comes friction. Major labels (Sony, Universal, Warner) have filed lawsuits against Suno and Udio for allegedly using copyrighted recordings without permission.

Meanwhile, listeners are asking: can AI match the emotional depth of human music? And should tracks created by AI be labelled as such?

The market data underscores the transformation. The AI in Music market is projected to grow rapidly, valued at about US$3.6-4 billion in 2024-2025 and expected to scale toward US$38.7 billion by 2033. By 2025, nearly 60% of musicians are using some form of AI in their workflow- composing, mastering, or producing.

In short: AI music is no longer a fringe experiment. It’s reshaping how music is created, shared, and regulated. This case study examines where AI surpasses human music, where human artistry still prevails, and the challenges emerging in this transformative moment.

Top AI Creators & Market Trends

GlorbWorldwide has emerged as a viral force in AI music, with approximately 1.02 million YouTube subscribers and over 316.9 million views.

Their tracks, featuring AI-generated voices from fictional characters, combine novelty, recognizable audio cues, and high volume to grab attention.

Another project, Velvet Sundown, gives a different angle. As of mid-2025, it has exceeded 1 million monthly Spotify listeners, despite being entirely AI-created.

Their music (e.g. Floating on Echoes) has made waves on charts such as Spotify’s “Viral 50,” marking a milestone in how AI music can succeed under traditional metrics.

Meanwhile, market data backs these creators with numbers: the AI music sector is valued near US$6.2 billion in 2025 and forecasted to reach US$38.7 billion by 2033. About 60% of musicians use AI tools for composing, mastering, or visuals, and 82% of listeners report they can’t reliably tell AI vs human music when asked.

Together these insights reveal both how AI creators are gaining real momentum and how the listener base is increasingly open, or unsure, about what content is human. They raise key questions about authenticity, transparency, and licensing. This case study will explore those and map where the industry seems headed.

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Industry Context & Market Overview

The global AI music market is growing at impressive speed. In 2024 it was valued at around US$3.62 billion, and analysts expect it to rise to US$4.48 billion by 2025, showing a compound annual growth rate (CAGR) near 23.7%. Forecasts project this market will hit US$38.71 billion by 2033.

Industry Context & Market Overview

Generative AI tools are a key driver of this growth. In 2025, the generative AI in music segment is estimated at about US$2.92 billion, with software-as-a-service and cloud-based deployments dominating.

The ability for artists and creators to access composition, mastering, and creative-art-asset tools through the cloud is accelerating adoption.

Adoption among creators is rising quickly. Around 60% of musicians are now using AI tools for tasks like composing, mastering, or creating accompanying visuals. Among listeners, about 74% of internet users have used AI tools to explore or discover new music, and approximately 82% say they can’t reliably distinguish between AI-created tracks and human-made ones.

Regionally, growth is uneven but fast. North America holds a large share of the AI music market, but Asia-Pacific and Western Europe are forecast to experience the fastest growth rates in coming years. The composition/creation sub-segment (song composition and creative process) is expected to grow at over 60% CAGR in many regions as cloud-based tools proliferate.

Meanwhile, platforms and policies are adapting. Spotify has confirmed that it will not ban AI-generated music outright, but says tracks must meet policy guidelines (no impersonation, respect for voice rights) to be monetizable.

Meanwhile, platforms and policies are adapting

SoundCloud updated its Terms of Service to clarify that user-uploaded content will not be used for AI training without explicit consent.

In Sweden, STIM has launched a licensing scheme so that AI companies can legally train models on copyrighted works while compensating songwriters.

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Generative AI in Music Creation

AI music tools are no longer experiments. In 2025, they are producing full songs, including lyrics, melodies, and vocals, from a single text prompt. Platforms like Suno, Udio, and Stable Audio are at the center of this transformation. Their outputs range from polished pop songs to cinematic scores, often indistinguishable from human-made tracks.

The shift is driven by speed and scale.

A human composer might take hours or days to draft a track. Suno and Udio can generate dozens of versions in minutes. Stable Audio, from Stability AI, focuses on diffusion-based sound synthesis, enabling users to control not just genre but mood, tempo, and instrumentation. This means more creative experimentation with far less cost.

AI music is also becoming highly customizable. Users can prompt for specific styles, “a lo-fi hip-hop track with rain sounds” or “an orchestral score in the style of Hans Zimmer”, and receive tailored audio within seconds.

These tools are not just serving hobbyists. Professional musicians are beginning to incorporate AI to accelerate demo production, remix stems, or explore genres outside their comfort zone.

Yet the benefits come with risks. Many of these systems are trained on copyrighted data without clear licensing, raising questions about ownership. Lawsuits filed against Suno and Udio in 2024 underline this tension.

Record labels argue that these platforms generate “derivative works” based on their catalogs. Suno and Udio, however, maintain their tools create “transformative” outputs and should fall under fair use.

In practical terms, the comparison is stark. AI tracks are cheap, often free or subscription-based, while human-produced tracks can cost thousands in studio time. AI tracks are instant, while humans need weeks.

In practical terms, the comparison is stark

But emotional resonance and originality still favor human creators. Many listeners describe AI music as impressive, but often “soulless” when compared to songs crafted by human experience.

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AI in Streaming & Distribution

AI isn’t just changing how music is made , it’s transforming how it’s shared and discovered. In 2025, streaming platforms are experimenting with integrating AI-generated content into their ecosystems, from curated playlists to short-form video features.

YouTube is at the front line. Its Dream Track experiment allows creators to generate background music for Shorts using AI. The tool produces short soundbeds in seconds, tailored to match a video’s mood or theme.

For YouTube, this is about scale , Shorts are uploaded by the millions each day, and AI music offers endless, royalty-free options to keep content flowing.

Spotify has taken a different approach. The company has confirmed it will not ban AI-generated music but requires that tracks comply with strict guidelines: no voice impersonations, clear labeling where applicable, and adherence to copyright rules.

Spotify also uses AI internally to recommend tracks, optimize playlists, and detect fraudulent streams, but it has drawn a red line around “deepfake artists” that imitate real singers.

SoundCloud updated its Terms of Service in 2025, explicitly stating that user uploads will not be used to train AI models without permission.

This move came after backlash from artists who feared their tracks could be silently fed into generative systems. The update sets a precedent for platform transparency and consent in AI training.

The update sets a precedent for platform transparency and consent in AI training

Beyond policies, AI is reshaping distribution models. Labels and distributors are testing AI to handle metadata tagging, royalty tracking, and even personalized playlist placement.

The result is faster turnaround and a higher likelihood of niche tracks reaching receptive audiences. But this also raises concerns: will algorithmic playlists favor AI-generated content at the expense of human artists?

The big picture is that streaming services are becoming gatekeepers not only of how music is consumed, but also how AI music will be regulated, labeled, and monetized in the coming decade. The line between platform and publisher is blurring as AI drives volume at unprecedented levels.

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Consumer Reception: AI vs Human Music

The biggest question around AI music isn’t just technical quality, it’s how audiences respond.

In 2025, surveys and platform data suggest a complicated picture: listeners are impressed by AI’s ability to mimic styles and generate polished tracks, but they remain skeptical about its emotional depth and authenticity.

Recent statistics show that 82% of listeners cannot reliably tell whether a song was made by a human or AI when blind-tested. This reflects how sophisticated systems like Suno, Udio, and Stable Audio have become in replicating human composition patterns.

Yet when asked about preference, the split is clearer. A majority of listeners say that human-created tracks feel more “authentic” and emotionally resonant, especially for genres like soul, folk, or singer-songwriter music where imperfection and lived experience are central.

Younger audiences, particularly Gen Z, are more open to AI-generated music, often valuing novelty, remixability, and constant availability over traditional artistry.

Streaming behavior also reflects this tension. AI tracks thrive in functional listening contexts, background music for study, gaming, or TikTok soundbeds.

Human tracks still dominate when it comes to emotional connection and artist loyalty, such as in concerts, fan communities, and brand collaborations.

Consumer Reception AI vs Human Music

This generational and contextual divide highlights a crucial reality: AI music is not replacing human music but coexisting with it.

For many listeners, the distinction matters less than the use case. When music is for atmosphere or utility, AI may win. When music is for storytelling and identity, humans remain irreplaceable.

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Costs, Revenue & Virality

AI music is rewriting the economics of production and distribution. In 2025, the cost difference between human and AI-created tracks is dramatic.

A professionally produced song can cost anywhere from US$500 to US$5,000 when factoring in studio time, mixing, mastering, and session musicians.

By contrast, AI platforms such as Suno or Udio allow creators to generate unlimited tracks through a monthly subscription, often priced at US$10–30.

The speed advantage amplifies this gap. Human artists may need weeks to finalize a track. AI tools generate dozens of variations in minutes.

This rapid cycle makes AI particularly attractive for short-form content, advertisements, and background music, areas where high volume and quick turnaround are more important than deep artistry.

Revenue opportunities are also shifting. Some AI tracks are going viral on platforms like TikTok and YouTube Shorts, generating ad revenue and driving streams despite lacking traditional label backing.

In 2024, the viral track “Heart on My Sleeve” which mimicked Drake and The Weeknd using AI voices, highlighted both the commercial potential and the legal risks of synthetic music. Its millions of plays before takedown showed how quickly AI songs can spread, challenging copyright frameworks.

Streaming payouts complicate the picture. On Spotify, per-stream revenue remains low, averaging US$0.003–0.005 per play. For human artists, this makes volume and touring crucial.

For human artists, this makes volume and touring crucial

For AI creators, the near-zero production cost means even modest streaming revenue can become profitable. Platforms are still debating how to classify and compensate AI music fairly, particularly in relation to royalty splits with rights holders of training data.

The viral factor is hard to ignore. TikTok’s algorithm favors novelty and volume, both areas where AI excels.

As a result, AI-generated snippets are increasingly powering viral trends, while human artists lean on narrative, authenticity, and live performance to drive deeper loyalty and long-term revenue.

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Case Studies: Brands & Artists Using AI Music

AI music is no longer a lab experiment. It is already being used in campaigns, collaborations, and viral projects. In 2025, both brands and artists are adopting AI-generated tracks to reach new audiences and reduce production costs.

One of the most visible examples was the viral track “Heart on My Sleeve”, which used AI to mimic Drake and The Weeknd’s voices.

Before being taken down, it gained millions of streams across TikTok and Spotify. The case showed both the commercial potential of AI music and the copyright challenges that come with it.

Brands are also experimenting with AI. Advertising agencies are using platforms like Soundraw, AIVA, and Amper Music to generate jingles, background music, and sonic logos. Instead of hiring composers, they can create dozens of variations instantly and pick the one that matches the campaign mood. This reduces both time-to-market and budgets.

Artists are beginning to use AI as a co-creation tool. Some rely on it to speed up demos or explore new genres. Others are releasing AI-assisted albums.

The AI-generated band Velvet Sundown, which has more than 1 million monthly Spotify listeners, shows that entire music projects can now succeed without traditional performers.

Platforms are cautiously opening the door. YouTube’s Dream Track provides AI-generated soundbeds for Shorts, and creators are using it for quick production. Spotify continues to allow AI music but blocks impersonations of human artists.

Case Studies Brands & Artists Using AI Music

These cases reveal a double narrative. On one side, AI empowers creators and brands to produce content faster and cheaper. On the other side, copyright disputes and authenticity concerns remain major barriers to full industry acceptance.

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The rapid growth of AI music in 2025 has brought legal disputes to the forefront. The core issue is whether AI training and AI-generated outputs infringe on copyright or whether they fall under transformative fair use.

The most high-profile cases involve Suno and Udio, two of the largest AI music platforms. In 2024, major record labels including Sony, Universal, and Warner sued them, alleging massive copyright infringement for training on and reproducing copyrighted recordings without permission.

The lawsuits argue that these AI systems are essentially repackaging existing catalogs. Suno and Udio, however, maintain that their outputs are transformative and thus protected under fair use.

Meanwhile, the U.S. Copyright Office has clarified that works created solely by AI are not eligible for copyright protection. Human involvement remains necessary for copyright to apply. This means that even if an AI generates a song, legal ownership requires a meaningful human creative contribution.

In Europe, the EU AI Act introduces transparency obligations for generative AI. Starting in 2026, platforms will be required to disclose when content is AI-generated and provide information about training datasets. This is expected to shape how AI music platforms operate, especially regarding labeling and data provenance.

Some markets are exploring proactive licensing solutions.

In Sweden, the collective rights body STIM launched a licensing scheme that allows AI companies to train on copyrighted works while compensating songwriters legally. This model is being closely monitored as a potential global standard.

Voice rights are another flashpoint. The viral AI track “Heart on My Sleeve” highlighted the risks of voice cloning.

Labels argue that imitating an artist’s voice without consent constitutes misappropriation, even if the lyrics and melody are new. Several states in the U.S. are already drafting “voice rights” legislation to give performers stronger protection against unauthorized AI cloning.

The legal landscape is still in flux. Courts, legislatures, and industry groups are moving quickly, but no unified framework exists yet. The next few years will determine whether licensing schemes, stricter bans, or hybrid solutions will define the future of AI music regulation.

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Platform Policies & Labeling

As AI-generated music explodes in popularity, streaming platforms and distributors are racing to set boundaries. The central questions are: what counts as acceptable AI music, how should it be labeled, and who gets paid?

YouTube has leaned into experimentation.

Its Dream Track feature allows creators to generate soundbeds for Shorts with AI, offering a frictionless way to add music. The tool, however, is tightly controlled. Users cannot impersonate real artists, and YouTube requires disclosure when AI-generated tracks are used in videos.

Spotify has taken a cautious but open stance. CEO Daniel Ek confirmed in 2025 that Spotify will not ban AI-generated music outright.

Instead, the company enforces rules: no voice impersonation, no copyright violations, and clear compliance with its terms of service. Spotify also flagged that while AI music is permitted, deepfake tracks mimicking real artists will be removed.

SoundCloud updated its Terms of Service in 2025 following artist backlash. The platform clarified that uploaded content will not be used to train AI models without explicit user consent. This shift toward transparency sets a precedent for others in the industry.

Across the board, labeling is emerging as the key requirement. The EU AI Act will mandate by 2026 that AI-generated content must be labeled and that platforms disclose training data sources.

Across the board, labeling is emerging as the key requirement

Industry insiders expect Spotify, Apple Music, and YouTube Music to adopt labeling standards earlier to build user trust.

What remains unresolved is monetization. Should AI tracks receive the same royalties as human tracks?

Should rights holders of training data get a cut? Platforms have yet to provide a unified answer, but labeling AI tracks is seen as the first step toward a fairer ecosystem.

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Human–AI Collaboration in Music

Despite debates around copyright and authenticity, many artists see AI not as a competitor but as a collaborator. In 2025, human–AI collaboration has become one of the most promising frontiers of music creation.

AI tools are increasingly being used to handle repetitive or technical tasks, allowing artists to focus on storytelling, emotion, and performance.

For example, musicians use AIVA and Amper Music for generating instrumental layers, while platforms like Suno and Udio assist in producing quick demos or experimenting with unfamiliar genres. This makes it easier for artists to test new directions without committing expensive studio time.

The workflow is evolving into a loop. A human artist might input lyrics or a melody, AI generates multiple versions, and the artist curates and refines the best outcomes. Some artists even use AI to simulate audience reactions or streaming performance predictions, guiding final edits before release.

Real-world adoption is growing. Indie musicians on Bandcamp and SoundCloud use AI to accelerate album creation, while major artists are cautiously integrating AI for remixes and side projects. AI is particularly popular in electronic music, hip-hop, and film scoring, where variation and rapid iteration are highly valued.

At the same time, human input remains irreplaceable. Surveys show that listeners still perceive higher authenticity and emotional resonance in human-led compositions.

AI may generate technical quality, but it cannot yet replicate lived experiences and cultural context. The most successful projects in 2025 combine both strengths: AI for speed and scale, humans for emotion and depth.

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Market Outlook & Predictions (2025–2030)

The AI music industry is on track to expand dramatically over the next five years. Analysts estimate the global market will grow from US$6.2 billion in 2025 to nearly US$38.7 billion by 2033, with a compound annual growth rate above 28%.

Adoption among creators is expected to keep climbing. By 2030, more than 75% of musicians worldwide are projected to integrate AI tools into their workflows, whether for composition, mastering, or distribution.

For independent creators, AI will increasingly serve as a low-cost alternative to professional studios.

Platforms are preparing for regulatory shifts. The EU AI Act, which comes into effect in 2026, will require AI-generated tracks to be labeled.

This could establish global norms around transparency, forcing Spotify, YouTube, and Apple Music to adopt clearer labeling systems and data provenance disclosures. Similar frameworks may follow in the U.S. and Asia.

Licensing models will likely mature. Sweden’s STIM license, launched in 2025 to allow AI companies to train on copyrighted works legally, could become a blueprint for other territories. If widely adopted, it would reduce lawsuits while ensuring fair compensation for songwriters.

Consumer behavior will evolve alongside these changes. Younger audiences, especially Gen Z, already accept AI tracks as part of their playlists, valuing novelty and variety.

By 2030, AI-generated music may represent up to 20% of total streams, especially in functional categories like study music, gaming soundtracks, and social media soundbeds.

Human artists, meanwhile, will remain dominant in live performance, fan culture, and emotionally resonant genres.

Overall, the future points to hybrid music ecosystems. AI will not replace human musicians, but it will become an integral infrastructure for music creation, distribution, and monetization.

The most successful outcomes will emerge from collaborations where AI provides scale and efficiency, while humans deliver depth and authenticity.

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Conclusion

By 2025, AI music is no longer a futuristic experiment. It has become a visible, disruptive, and often controversial part of the global music industry. Platforms like Suno, Udio, and Stable Audio are producing polished songs in seconds.

Viral cases such as Heart on My Sleeve and AI projects like Velvet Sundown show that audiences will listen, share, and even subscribe when the music resonates, regardless of who, or what made it.

The economics are undeniable. AI reduces production costs from thousands of dollars to a few cents per track and cuts turnaround time from weeks to minutes.

For creators, brands, and platforms, this opens a floodgate of opportunity. For labels and regulators, it triggers urgent questions about copyright, consent, and compensation.

Listeners are caught in between. Most cannot tell the difference between AI and human tracks, but they still trust human musicians to deliver authenticity, identity, and cultural depth. That gap, between efficiency and emotion, is where the future of music will be defined.

The next five years will bring clarity. Regulations such as the EU AI Act, licensing models like Sweden’s STIM initiative, and platform policies from Spotify, YouTube, and SoundCloud will shape how AI fits into the industry.

By 2030, AI is expected to account for up to 20 percent of all streaming content, but human artistry will remain the core of emotional connection and fan loyalty.

The story of AI music is not one of replacement, but of coexistence. The winners will be those who treat AI as an amplifier, not a substitute, leveraging its speed and scale while doubling down on human creativity, culture, and authenticity.

Methodology

This case study was developed using a structured, data-driven approach designed to balance quantitative statistics with qualitative insights. Our research combined industry reports, academic studies, platform disclosures, and journalistic investigations into AI music’s evolution.

Data Sources
We drew from a mix of authoritative sources including market research firms, music industry reports, government and regulatory updates, and verified news outlets. For example, adoption rates, market size, and forecast data were drawn from SimpleBeen, Reuters, Financial Times, and the European Parliament.

Case Study Selection
We analyzed case studies based on visibility, impact, and diversity of application. These included GlorbWorldwide’s viral AI-voice channel on YouTube, the AI-generated band Velvet Sundown with more than one million monthly Spotify listeners, and the controversial AI track Heart on My Sleeve, which mimicked Drake and The Weeknd.

Analytical Approach

  • Quantitative Benchmarking – Market values, adoption percentages, and forecast growth were benchmarked against publicly available statistics.
  • Qualitative Assessment – Audience sentiment, ethical risks, and authenticity debates were drawn from survey reports and feature journalism.
  • Comparative Frameworks – Human vs AI comparisons were made on cost, speed, emotional depth, and virality using scaled models to illustrate relative strengths.
  • Regulatory Tracking – Policy developments such as the U.S. Copyright Office guidance, the EU AI Act, and Sweden’s STIM license were mapped to show the evolving legal framework.

Limitations
While we referenced the most recent data available as of September 2025, AI music remains a rapidly evolving field. Market values, adoption rates, and platform policies are subject to change. Listener sentiment is based on survey data that may vary across demographics and regions.

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Author: RH Fardin

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