Blinded by the Bytes

Can AI Out-Swift the Superstars of Music?

Introduction

The Science Behind (Artificial) Hit Songs

Taylor Swift, Bruno Mars, The Weeknd, Travis Scott, Bad Bunny, and countless others. When comparing what is common among them, the answer is often that they are all collectively global icons that can sell out entire stadiums and consistently shatter records. But one trait that’s often overlooked—they’re human. Being able to turn heartbreak into a billion-dollar music industry comes from true passion and emotion, not from ones and zeros within a computer. But why is that the case? Computers are constantly evolving, and in the age of AI, systems are becoming more self-sufficient by the day. With this information, it is reasonable to infer that if a machine could study millions of songs, analyze what listeners replay, skip, and share, could it generate its own hit?

That idea is no longer science fiction. In 2026, Deezer reported that ~20,000 fully AI-generated songs are uploaded to its platform every single day (Deezer). Simultaneously, Grand View Research has calculated that the global generative AI music market reached a value estimated at $440 million in 2023, and is projected to reach $2.79 billion by 2030 (Grand View Research).

AI can already write lyrics, build songs, clone voices, and produce tracks, with insane accuracy. The new question to ask is: can it create music that truly connects with listeners? Can an algorithm or an AI create the next billboard breaker, better than the existing superstars of music? As AI is being integrated into the music industry, the battle is quickly shifting from artist versus artist to artist versus algorithm.

What is AI Music

Artificial Intelligence (AI) is a computer system designed to recognize patterns, build an understanding, and make future decisions, updating its understanding and confidence in decision-making with every subsequent decision.

AI music is music that is either created or assisted by an AI trained to recognize patterns within existing songs from lyrical vector embeddings*, sound wave patterns, or other numerical representations. Instead of composing songs through emotions, memory, or personal experiences, an AI system will learn from data.

In simpler terms, AI does not “feel” the music. It studies patterns and predicts what comes next.

The system can study (up to) millions of lyrics and melody patterns. Melody patterns are often represented as signal curves. These signals can then be transformed into simplified mathematical forms. Then, based on the data the system was trained on, it can generate a new combination of all the features (melody, lyrics, voice + modulations, chorus/hook/outro, etc.) based on the probability that users will like it.

NEXT_OUTPUT = f(PAST_PATTERNS + USER_PROMPT + TRAINING_DATA)

Meaning – An AI system uses what it has learned from previous music patterns, plus the user’s request, to predict what values should come next.

This is the basis of most adaptive learning models. Bayes’ Theorem, the fundamental component of Probabilistic Frameworks, is the base computation that is used in many frameworks that allow a computer to “predict” a future scenario.

Bayes’ Theorem: P(A|B) = P(B|A) P(A)P(B)

vectors embeddings* –

Strings of lyrics are encoded into numerical vectors in a space, surrounded by other similar vectors. In this case, similar vectors would be lyrics with similar meaning.

laplace transform* –

A mathematical tool that converts a time-varying signal or input—such as a voltage pulse, step input, or vibration—into a simpler, algebraic form based on complex frequency

Modern AI tools (Suno, Udio, AIVA) allow users to type prompts, allowing them to go into specific detail for personalization, and within seconds, the system can create a finished track based on the user’s request.

This is a simplified conceptual model for illustration purposes.

* Sample Code written in Python – NOT ACTUAL CODE USED IN ANY INSTITUTION

# User enters a music prompt
prompt = “sad pop song with piano”

# Case 1: Detect mood and assign tempo
if “sad” in prompt:
tempo = 70 # slower BPM for emotional songs
elif “happy” in prompt:
tempo = 120 # faster BPM for upbeat songs

# Case 2: Detect instrument
if “piano” in prompt:
instrument = “Piano”
elif “guitar” in prompt:
instrument = “Guitar”

# Case 3: Generate chord progression based on genre
if “pop” in prompt:
chords = [“C”, “G”, “Am”, “F”]

# Output generated song settings
print(“Tempo:”, tempo, “BPM”)
print(“Instrument:”, instrument)
print(“Chords:”, chords)

Symbolic Music Generation

Models generate notes, chords, MIDI sequences* outputs. These will focus primarily on structure, pitch, rhythm, and composition. The fundamental components.

MIDI sequences* –

A digital recording of musical performance data—not audio—that stores instructions on notes, timing, velocity, and pitch. It functions like a digital, editable score, containing “Note On/Off” messages that trigger virtual instruments or hardware.

Audio Generation

These systems do not act as a human playing on a third-party app like GarageBand. Rather, they build the song from the ground up. By composing waveforms using input/noise signals and producing the “recording” of vocals, drums, synths, various instruments, and sound effects, any collection of sounds and vocals can be produced.

According to Briot, Hadjeres, and Patchet in Deep Learning Techniques for Music Generation, many of the newer models are very rapidly improving their abilities to make music. They are easily able to adapt to long-term song structure and figure out consistencies that are prevalent in popular hit songs versus less popular songs. Realistic audience feedback is used as a bias when figuring out which songs are liked and, from those, specific portions of song samples are broken down into numerical values that are studied by LLM (Large Language Models) (Briot et al.).

This can come in very handy, as hit songs are not a collection of lucky guesses or random sounds, but rather they typically require key components:

A catchy hook
A repeated theme/beat
A satisfying chorus
A good quality of production
A high replay value
A song with emotion

If AI systems can recreate these elements in an effective way, especially at scale, they can soon move from tools used to HELP in song production, and can actively compete against artists.

Real Numbers – The AI Music Advantage

Artificial intelligence can change the music production industry at a massive scale, automating processes like production, uploads, business investments, and brand endorsements.

According to Grand View Research, the market for global generative AI in music was valued at $440 million in 2023 and is predicted to jump to $2.8 billion in 2030, as 30.4% CAGR (Compound Annual Growth Rate). A 30% annual growth rate indicates that major corporations will likely aggressively invest in this industry (Grand View Research).

This is a simplified conceptual model for illustration purposes.

market_2023 = 440
growth_rate = 1.304

market_2024 = market_2023 * growth_rate
print(round(market_2024, 1))

OUTPUT:

573.8 ||

Suggesting the Market Evaluation to be at $573.8 million after one year of a 30.4% growth rate.

This leads to a deeper question…

While AI is rapidly scaling production, the next question becomes whether it can predict success.

Creators across platforms are increasingly using AI when making their songs. A study from LANDR shows that:

(LANDR)

CATEGORY	PERCENTAGE
Production AI use	87%
Creativity AI use	66%
Promoting AI use	52%

TASK	HUMAN	AI
Lyrics	30 min – 2 hrs	Seconds
Beat	1-4 hrs	Seconds
Song Demo	Days	Minutes
Alt. Versions	Hours	Instant

In addition to artists adopting AI tools, there are instances in which AI is replacing artists entirely. Deezer reported that roughly 20,000 fully AI-generated songs are uploaded every single day. At 20,000 songs for 365 days, every single year, there are 7.3 million AI songs being generated JUST from one platform (Deezer).

Content Saturation Problem

If millions of AI songs are created yearly, platforms may face:

Oversupply of music
Harder discovery for human artists
Spam uploads
Lower average attention per song

This means AI may not only create music. It may entirely drown the market. The biggest impact of AI and its influence on music may not even be one perfect song. Instead, it may be millions of acceptable songs flooding the industry. And truthfully, the impact that could be present from drowning the market in music could be devastating (Deezer).

Can AI Predict a Hit Song

Why Predictions Fail

Based on current understandings, it is reasonable to conclude that AI can “break down” a song into numerical data and analyze the information. But can an AI predict which songs people will like, stream more, and save/share before the songs become a hit?

However, this model has limitations…

Many modern companies like Spotify, Apple Music, and YouTube use extensive Machine Learning models to estimate that very prediction.

Spotify Recommendations/ Engagement Logic

Streaming platforms use a variety of metrics to track a user’s behavior while they listen to music –

(Spotify Engineering)

METRIC	TYPE	meaning
Skip Rate	int	The user leaves the song quickly
Completion Rate	int	The user listened to the whole song
Replay Rate	int	The songs are listened to frequently
Save Rate	int	The user added the song to their library
Share Rate	int	The user sent this song to other users
Playlist Adds	bool	The user saved this song to playlist(s)

Many media companies use various methods and value each of these variables differently. However, in general, a simplified popularity model might look similar to this –

HIT_SCORE = (0.3 X REPLAY_RATE) + (0.25 X COMPLETION_RATE) + (0.2 X SHARE_RATE) + (0.15 X SAVE_RATE) – (0.1 X SKIP_RATE)

Ex:

If a song has the following “weights.”

Replay: 80
Completion: 90
Share: 60
Save: 70
Skip: 20

Then:
HIT_SCORE = 24 + 22.5 + 12 + 10.5 – 2 = 67

Ex:

* Sample Code written in Python – NOT ACTUAL CODE USED IN ANY INSTITUTION
songs = {
“Song A”: 67,
“Song B”: 58,
“Song C”: 81
}

best_song = max(songs, key=songs.get)
print(best_song)

OUTPUT:

Song C

While higher scores do correlate to a stronger hit potential, this metric is not an “end-all, be-all” to predict which songs are liked. Additionally, many of the songs may be reordered, as their HIT_SCORE can be normalized through a series of calculations, as there may be a source of bias or variance when these HIT_SCORES are calculated.

TikTok and Chart Acceleration

Another major predictor of music chart success is TikTok. According to industry reports, a majority of the songs that trend on TikTok often become very popular. This can be attributed to TikTok’s vast user base. On any given week, TikTok has ~2 billion users. As such, songs that trend on TikTok often see:

(Billboard)

Major Spotify (and alternative streaming service) stream spikes
Billboard Hot 100 movement
Increased search and interaction counts
Faster discovery and growth cycles

Some examples of artists who experienced growth for some of their songs are Doja Cat (Say So), Lil Nas X (Old Town Road), and Olivia Rodrigo (Good 4 U

Besides the major players in the music industry, there are a plethora of smaller companies that are also trying to crack the code of artificially making the next hit.

Grand View Research estimates that the generative AI music market will grow from $440 million (2023) to $2.79 billion (2030). Labels and tech firms BOTH have strong financial incentives to invest and become the pioneers in finding a successful hit-prediction system. (Grand View Research)

When looking at an artist’s success, there are many variables to consider

Cultural Timing
Memes
Celebrity Controversy
Emotional Fan Connection
Viral Moments
Fan Loyalty

Researchers have found that music success can only be statistically broken down to a certain extent. While an artist’s success can be measured by their statistics and streams, success is not controllable (Briot et al.).

While AI can measure what various listeners did yesterday, when it comes to predicting what millions or billions of people WILL love tomorrow is a far harder question. If a user has every Drake song and album favorited, saved, shared, and replayed many times, there is no guarantee that Drake’s next album may be to that user’s liking.

While these systems are powerful…

Even with these predictive models, there are still clear limitations.

What AI Still Cannot Replicate

From the topics that have been discussed thus far, it’s evident that AI can analyze and imitate patterns present in successful songs. But an imitation will never be the same as the original.

The way that an individual artist can connect and reach fans, on an emotional/spiritual/personal level, is not something that an AI can replicate, based on pure imitation.

While AI can technically recreate lyrics with sad meanings, lines written by humans resonate with fans. Based on lived experiences, intentional symbolism, and personal truth, there is a real connection between artist and audience – that changes everything. Artists often use double entendres, pop culture references, hidden meanings, and other tools to reach fans. There are many examples illustrating this:

“I know they say the first love is the sweetest, but that first cut is the deepest.” – Drake

This line sounds like a relationship lyric, but the hidden meaning is about betrayal, emotional scars, and how early heartbreak shapes future trust. It connects because many listeners understand carrying old wounds into new relationships (Genius Lyrics).

“You kept me like a secret, but I kept you like an oath.” – Taylor Swift

From All Too Well (10 Minute Version), this line contrasts how two people valued the same relationship completely differently. “Secret” implies shame or concealment, while “oath” implies loyalty and devotion. One line tells an entire emotional story (Genius Lyrics).

“And if I die before your album drop, I hope—” – Kendrick Lamar

From Sing About Me, I’m Dying of Thirst, the unfinished lyric is intentional. The sentence cuts off because the speaker dies mid-thought. It’s haunting, symbolic, and forces the listener to confront violence and lost voices (Genius Lyrics).

Researchers who have been analyzing AI-generated music state that the hardest challenge is evaluation. A song can have all the features and be soundly correct. Yet it can still feel forgettable or bland to listeners. Human reactions to music are subjective and influenced by memory and connection.

In other words, there is no universal formula for creating “good music” (Mariani et al.)

Emotion vs Pattern Matching & CLP

AI Systems develop their understanding through learning from past experiences and data collections. In statistics, models often perform best near known patterns, not radical innovation. That means AI may generate songs that sound familiar, but great music and hit songs often begin by sounding unfamiliar.

This means that they are BEST suited at reproducing styles and patterns they have already heard. Not something new. This is what separates artists, as they are constantly evolving and inventing, based on what they go through in their lives — exactly what makes them attractive to audiences.

Taylor Swift is changing her genres through her eras
Kanye West redefined his production styles
The Weeknd blends retro synth with pop and RnB into mainstream music
Travis Scott and Mike Dean are popularizing atmospheric TRAP productions

(Briot et al.)

Listeners also follow their favorite artists for reasons beyond the music:

Personality
Public Image
Live Performances and Stage Presence
Interviews with the press
Interview
Fan Communities
Personal Storytelling Abilities

When an artist releases, it’s not just the physical audio. It’s a collection of events, a narrative telling a story, and a personal connection between fan and experience.

Trait	HUMAN	AI
Personal Memories	YES	NO
Real Heartbreak/Loss	YES	NO
Cultural Identity	YES	NO
Fan Relationships	YES	NO
Pattern Recognition	AVERAGE	YES
Infinite Output Speed	NO	YES

But in order to understand why an AI will fail at these topics, it’s important to understand the weaknesses and failpoints of an AI system in this context.

Computation Limitation Principle (CLP) –

An AI system, as previously mentioned, is great at studying data, optimizing the processing speed to understand the data, and great at finding relations even when they may not exist. A model can optimize inputs – which stand as variables that are linked to songs, such as genre, artist, length, lyrics, mood, etc. This can be roughly measured as:

POPULARITY ~ STREAMS + SHARES + REPLAYS

But many hit songs are not classified with such a binary encoding. The variables that are linked to songs may be difficult to quantify.

MEANING ≄ SIMPLE DATA

This is why a mathematically optimized song created by a computer may still fail emotionally

Beyond technical limitations, AI music introduces new legal and ethical challenges.

Ethics, Copyright, and Ownership Wars

When the Song is Real, But the Artist is Artificial

AI is becoming increasingly capable of producing polished, high-quality music. But the next challenge AI faces may not even be a technical one, but rather a legal one. If an AI system can successfully generate a realistic song using the voice and likeness of a real artist, then questions will immediately begin to arise. Ownership, royalties, and artistic content all become the focus of attention.

Earlier, the debate of whether AI can make songs was explored. Now, the focus becomes what happens after AI successfully makes the song. In fact, the music industry has already started dealing with this problem.

Voice Cloning & False Identities

One of the most debated topics within the topic of AI music is voice cloning. A large language model (LLM) is designed and trained to study and analyze the vocal patterns, tone, cadence, and pronunciation styles of an artist, in the same way it studies songs – taking an artist’s vocal frequencies, turning them into graphical wave representations, performing mathematical operations to simplify sound waves into numbers, and then finding patterns. This can allow a machine to generate a song and make it sound exactly as if it were recorded by the real artist, who in reality has never even stepped into the studio.

If the average listener, that is NOT a fan, heard a new song online that sounded and was labeled as – Ariana Grande, Morgan Wallen, Future, SZA, Rihanna, or any other global pop star – the question arises, would the average listener know it was fake?

That uncertainty is the main fear of original artists and production companies, and the driving incentive for AI companies looking to design such systems.

Previously, it was discussed that AI systems do not create music from scratch, but rather from massive datasets of existing songs, vocals, and lyrics. This gives way to another legal debate:

If an AI studies copyrighted music to learn, is that education… or theft?

Production companies argue that after years of investment, original content is being used to train systems to replace the very sources that created these works, at times WITHOUT permission. Alternatively, AI developers argue that models learn the statistical patterns rather than copying the exact song verbatim.

That grey area is where the difference exists. If a student studies thousands of songs and then creates something original, societal norms usually accept this as learning. However, if a machine were to do the same thing, instantly AND at scale, the common population would stand against this.

The Royalty Ownership Formula

Normally, when a song is monetized, the payment process is straightforward and instant. Writers, producers, labels, publishers, and distributors alike all receive percentages. With the introduction of AI and the ability to complete a whole song from start to finish, the equation becomes far more complicated.

Standard Revenue Model

TOTAL REVENUE =

STREAMING + LICENSING + SALES + PERFORMANCE

Where:

Streaming: Spotify, Apple Music, YouTube Payouts
Licensing: Movies, Commercials, Advertising, and Games
Sales: Downloads and Purchases
Performance: Concerts/Public Performance Royalties

Ex: $100,000 (Total) = $55k + $25k + $10k + $10k

But with the inclusion of AI, the equation becomes more complicated, and each of the variables gets shifted in percentages. So who gets paid more and who gets paid less?

Potential Claimant	Why They May Deserve Revenue
Prompt Writer	Ideas/Creative Direction
AI Platform	Generated Vocals/Instruments
Producer	Mixed/Mastered Final Track
Distributor	Releasing the Song
Cloned Artist	Voice or Likeness that was Used

If the usage of AI gets expanded across the music industry, then music streaming platforms can easily shift the compensation allocations.

Ex:

REVENUE_SHARE =

0.4(PROMPT_WRITER) + 0.3(PLATFORM) + 0.2(PRODUCER) + 0.1(DISTRIBUTOR)

Using this new allocation, on a $100,000 song:

Prompt Writer: $40,000

Platform: $30,000

Producer: $20,000

Distributor: $10,000

With these new allocations, it may seem easy to incorporate AI into the music production process. But there are always extenuating factors. For example, if the voice variable was also modified and now sounds like JAY-Z or Sabrina Carpenter or anyone else, then should they receive that part of the revenue collection as well?

This is the entire basis of this legal battle. Who gets paid?

In order to mitigate this process, streaming platforms may eventually need to automate processes to detect and catch suspicious/AI-influenced uploads, BEFORE they go viral.

To better model this, a simplified screening model may look like:

This is a simplified conceptual model for illustration purposes.

* Sample Code written in Python – NOT ACTUAL CODE USED IN ANY INSTITUTION

# ————————————————————
# AI Song Upload Screening System
# ————————————————————
# This is a simplified example of how a streaming platform could
# automatically scan an uploaded AI-generated song before allowing
# it to spread publicly.
#
# The goal of the system is NOT to automatically delete the song.
# Instead, the system gives the song a “risk score” and decides
# whether it should be:
#
# 1. Approved automatically
# 2. Flagged for human review
# 3. Blocked temporarily until permissions are verified
# ————————————————————

# Song upload data
uploaded_song = {
“song_title”: “Midnight Feelings”,
“uploader”: “user_4921”,
“voice_match”: 94, # Example: sounds similar to Drake
“copyright_match”: 81, # Example: resembles protected melody
“lyrics_match”: 72, # Example: partial lyric overlap
“metadata_risk”: 88, # Example: title says “Unreleased Drake AI”
“permission_verified”: False,
“early_stream_count”: 15000
}

# Risk thresholds
VOICE_THRESHOLD = 90
COPYRIGHT_THRESHOLD = 85
LYRIC_THRESHOLD = 80
METADATA_THRESHOLD = 75
VIRAL_THRESHOLD = 10000

risk_flags = []

# Check voice imitation
if uploaded_song[“voice_match”] > VOICE_THRESHOLD:
if uploaded_song[“permission_verified”] == False:
risk_flags.append(“Celebrity voice imitation risk”)

# Check copyrighted audio
if uploaded_song[“copyright_match”] > COPYRIGHT_THRESHOLD:
risk_flags.append(“Protected audio similarity”)

# Check lyric overlap
if uploaded_song[“lyrics_match”] > LYRIC_THRESHOLD:
risk_flags.append(“Lyric similarity detected”)

# Check suspicious title/tags
if uploaded_song[“metadata_risk”] > METADATA_THRESHOLD:
risk_flags.append(“Misleading metadata”)

# Check viral spread
if uploaded_song[“early_stream_count”] > VIRAL_THRESHOLD:
risk_flags.append(“Rapid spread detected”)

# Calculate weighted risk score
risk_score = (
uploaded_song[“voice_match”] * 0.35 + # heavier weight
uploaded_song[“copyright_match”] * 0.30 +
uploaded_song[“lyrics_match”] * 0.15 +
uploaded_song[“metadata_risk”] * 0.10
)

# Add penalties
if uploaded_song[“permission_verified”] == False:
risk_score += 10

if uploaded_song[“early_stream_count”] > VIRAL_THRESHOLD:
risk_score += 5

# Final decision
if risk_score >= 90:
platform_action = “Block and send for legal review”
elif risk_score >= 70:
platform_action = “Flag for human review”
elif len(risk_flags) > 0:
platform_action = “Allow upload, restrict promotion”
else:
platform_action = “Approve upload”

# Output results
print(“AI SONG SCREENING REPORT”)
print(“————————“)
print(“Song:”, uploaded_song[“song_title”])
print(“Uploader:”, uploaded_song[“uploader”])
print(“Risk Score:”, round(risk_score, 2))
print(“Flags:”, risk_flags)
print(“Decision:”, platform_action)

This system checks:

How closely the vocals match a known celebrity
Whether the melody/audio resembles copyrighted material
Whether proper permission exists

If risks are high, the song gets flagged for human review.

With millions of AI songs entering the sound market, this type of system may have to be integrated in order to produce artists’ intellectual property.

With millions of AI songs entering the sound market, this type of system may have to be integrated in order to produce artists’ intellectual property. The songs and music that artists create are not just a testament to their work, or a reflection of their character. It also serves as a bond between the artist and their audience.

If an audience member believes they are supporting a real artist – through streaming songs, buying merch, promoting content, etc. – and later learns the artist’s song was synthetic, there is a potential for backlash and distrust.

Platforms may need to start adding labels to songs, such as:

AI Generated
Synthetic Vocals
Human + AI Collaboration

or

Verified Official Artist Upload

Just to make audience members be sure that they are listening to the REAL version of their favorite artist, and support their trust, both in the streaming service AND the artist.

Lingering Effects

While the first wave of AI-generated music may have been focused on WHAT this technology CAN do, the next wave will focus on what this technology WILL be allowed to do.

As the battle progresses, it will quickly evolve from which artist is number 1, or who is more popular. Rather, it will transform into a competition of Innovation vs. Ownership.

Conclusion –

Artificial intelligence is a new technology that is continuously evolving. The limitations of AI is something that is being pushed further every single day, as new capabilities are being discovered continuously.

Currently, AI can generate songs, predict listener behavior, clone voices, and optimize music for charts. And this can be done at scale, in the millions, faster than any human team could ever compete with.

Throughout this article, there have been many points and propositions that have supported AND debated both viewpoints. But through all the conflict, there is one truth that remains consistent: success in music was never based on numbers. Great songs are great because of reasons that go beyond the numbers. Beyond the streams and listeners, these hits capture moments, revive emotions, bring back memories, relive heartbreak, build confidence, reveal identity, and embrace culture.

AI can analyze what people listened to yesterday. But the truth is, AI struggles to understand WHY audiences connected in the first place. AI could scan over the lyrics of a song a million times, or analyze everything a celebrity did in a given time before the release of their song. But no matter how much data and information are analyzed, there is no clear answer to the question: What is the human element?

The future of music, especially the near future, will likely not shift into humans versus machines. Humans currently use AI tools all the time. From a small hometown producer to a stadium-selling-out superstar, AI tools are used everywhere. But regardless of how automated the music-making process becomes, there is no algorithm or code that can teach authenticity. A human’s real voice and genuine experiences are more valuable than any amount of studying a computer could do. In a world flooded with artificial sound, this is the time for real artistry to become louder than ever. When a machine is capable of reproducing any voice, and every instrument, being human may become the rarest—and most powerful—sound of all.

Blinded by the Bytes

W O R K S C I T E D

Billboard. “TikTok’s Influence on the Billboard Hot 100 and Music Discovery.” Billboard, www.billboard.com. Accessed 29 Apr. 2026.

Briot, Jean-Pierre, Gaëtan Hadjeres, and François Pachet. Deep Learning Techniques for Music Generation. Springer, 2020.

Deezer. “20,000 Fully AI-Generated Tracks Are Now Uploaded Daily on Deezer.” Deezer Newsroom, 2026, www.deezer.com/newsroom/.

Genius. “Drake Lyrics, Taylor Swift Lyrics, Kendrick Lamar Lyrics.” Genius Lyrics, www.genius.com. Accessed 29 Apr. 2026.

Grand View Research. “Generative AI in Music Market Size & Trends Report, 2030.” Grand View Research, 2024, www.grandviewresearch.com.

LANDR. “Survey on AI Adoption Among Music Producers.” LANDR Blog / LANDR Research, www.landr.com. Accessed 29 Apr. 2026.

Mariani, Giovanni, et al. “A Comprehensive Survey on Evaluation Methodologies of AI-Generated Music.” arXiv, 2023, arxiv.org.

Spotify Engineering. “How Spotify Uses Machine Learning and Recommendation Systems.” Spotify Engineering Blog, engineering.atspotify.com. Accessed 29 Apr. 2026.

Suno AI. “AI Music Generation Platform.” Suno, www.suno.ai. Accessed 29 Apr. 2026.

Udio. “AI Song Generation Platform.” Udio, www.udio.com. Accessed 29 Apr. 2026.

AIVA Technologies. “AIVA: Artificial Intelligence Music Composition.” AIVA, www.aiva.ai. Accessed 29 Apr. 2026.

Blinded by the Bytes

Can AI Out-Swift the Superstars of Music?

Introduction

The Science Behind (Artificial) Hit Songs

Taylor Swift, Bruno Mars, The Weeknd, Travis Scott, Bad Bunny, and countless others. When comparing what is common among them, the answer is often that they are all collectively global icons that can sell out entire stadiums and consistently shatter records. But one trait that’s often overlooked—they’re human. Being able to turn heartbreak into a billion-dollar music industry comes from true passion and emotion, not from ones and zeros within a computer. But why is that the case? Computers are constantly evolving, and in the age of AI, systems are becoming more self-sufficient by the day. With this information, it is reasonable to infer that if a machine could study millions of songs, analyze what listeners replay, skip, and share, could it generate its own hit?

That idea is no longer science fiction. In 2026, Deezer reported that ~20,000 fully AI-generated songs are uploaded to its platform every single day (Deezer). Simultaneously, Grand View Research has calculated that the global generative AI music market reached a value estimated at $440 million in 2023, and is projected to reach $2.79 billion by 2030 (Grand View Research).

AI can already write lyrics, build songs, clone voices, and produce tracks, with insane accuracy. The new question to ask is: can it create music that truly connects with listeners? Can an algorithm or an AI create the next billboard breaker, better than the existing superstars of music? As AI is being integrated into the music industry, the battle is quickly shifting from artist versus artist to artist versus algorithm.

What is AI Music

Artificial Intelligence (AI) is a computer system designed to recognize patterns, build an understanding, and make future decisions, updating its understanding and confidence in decision-making with every subsequent decision.

AI music is music that is either created or assisted by an AI trained to recognize patterns within existing songs from lyrical vector embeddings*, sound wave patterns, or other numerical representations. Instead of composing songs through emotions, memory, or personal experiences, an AI system will learn from data.

In simpler terms, AI does not “feel” the music. It studies patterns and predicts what comes next.

The system can study (up to) millions of lyrics and melody patterns. Melody patterns are often represented as signal curves. These signals can then be transformed into simplified mathematical forms. Then, based on the data the system was trained on, it can generate a new combination of all the features (melody, lyrics, voice + modulations, chorus/hook/outro, etc.) based on the probability that users will like it.

NEXT_OUTPUT = f(PAST_PATTERNS + USER_PROMPT + TRAINING_DATA)

Meaning – An AI system uses what it has learned from previous music patterns, plus the user’s request, to predict what values should come next.

This is the basis of most adaptive learning models. Bayes’ Theorem, the fundamental component of Probabilistic Frameworks, is the base computation that is used in many frameworks that allow a computer to “predict” a future scenario.

Bayes’ Theorem: P(A|B) = P(B|A) P(A)P(B)

vectors embeddings* –

Strings of lyrics are encoded into numerical vectors in a space, surrounded by other similar vectors. In this case, similar vectors would be lyrics with similar meaning.

laplace transform* –

A mathematical tool that converts a time-varying signal or input—such as a voltage pulse, step input, or vibration—into a simpler, algebraic form based on complex frequency

Modern AI tools (Suno, Udio, AIVA) allow users to type prompts, allowing them to go into specific detail for personalization, and within seconds, the system can create a finished track based on the user’s request.

This is a simplified conceptual model for illustration purposes.

* Sample Code written in Python – NOT ACTUAL CODE USED IN ANY INSTITUTION

# User enters a music prompt
prompt = “sad pop song with piano”

# Case 1: Detect mood and assign tempo
if “sad” in prompt:
tempo = 70 # slower BPM for emotional songs
elif “happy” in prompt:
tempo = 120 # faster BPM for upbeat songs

# Case 2: Detect instrument
if “piano” in prompt:
instrument = “Piano”
elif “guitar” in prompt:
instrument = “Guitar”

# Case 3: Generate chord progression based on genre
if “pop” in prompt:
chords = [“C”, “G”, “Am”, “F”]

# Output generated song settings
print(“Tempo:”, tempo, “BPM”)
print(“Instrument:”, instrument)
print(“Chords:”, chords)

Symbolic Music Generation

Models generate notes, chords, MIDI sequences* outputs. These will focus primarily on structure, pitch, rhythm, and composition. The fundamental components.

MIDI sequences* –

A digital recording of musical performance data—not audio—that stores instructions on notes, timing, velocity, and pitch. It functions like a digital, editable score, containing “Note On/Off” messages that trigger virtual instruments or hardware.

Audio Generation

These systems do not act as a human playing on a third-party app like GarageBand. Rather, they build the song from the ground up. By composing waveforms using input/noise signals and producing the “recording” of vocals, drums, synths, various instruments, and sound effects, any collection of sounds and vocals can be produced.

According to Briot, Hadjeres, and Patchet in Deep Learning Techniques for Music Generation, many of the newer models are very rapidly improving their abilities to make music. They are easily able to adapt to long-term song structure and figure out consistencies that are prevalent in popular hit songs versus less popular songs. Realistic audience feedback is used as a bias when figuring out which songs are liked and, from those, specific portions of song samples are broken down into numerical values that are studied by LLM (Large Language Models) (Briot et al.).

This can come in very handy, as hit songs are not a collection of lucky guesses or random sounds, but rather they typically require key components:

A catchy hook
A repeated theme/beat
A satisfying chorus
A good quality of production
A high replay value
A song with emotion

If AI systems can recreate these elements in an effective way, especially at scale, they can soon move from tools used to HELP in song production, and can actively compete against artists.

Real Numbers – The AI Music Advantage

Artificial intelligence can change the music production industry at a massive scale, automating processes like production, uploads, business investments, and brand endorsements.

According to Grand View Research, the market for global generative AI in music was valued at $440 million in 2023 and is predicted to jump to $2.8 billion in 2030, as 30.4% CAGR (Compound Annual Growth Rate). A 30% annual growth rate indicates that major corporations will likely aggressively invest in this industry (Grand View Research).

This is a simplified conceptual model for illustration purposes.

market_2023 = 440
growth_rate = 1.304

market_2024 = market_2023 * growth_rate
print(round(market_2024, 1))

OUTPUT:

573.8 ||

Suggesting the Market Evaluation to be at $573.8 million after one year of a 30.4% growth rate.

This leads to a deeper question…

While AI is rapidly scaling production, the next question becomes whether it can predict success.

Creators across platforms are increasingly using AI when making their songs. A study from LANDR shows that:

(LANDR)

CATEGORY	PERCENTAGE
Production AI use	87%
Creativity AI use	66%
Promoting AI use	52%

TASK	HUMAN	AI
Lyrics	30 min – 2 hrs	Seconds
Beat	1-4 hrs	Seconds
Song Demo	Days	Minutes
Alt. Versions	Hours	Instant

In addition to artists adopting AI tools, there are instances in which AI is replacing artists entirely. Deezer reported that roughly 20,000 fully AI-generated songs are uploaded every single day. At 20,000 songs for 365 days, every single year, there are 7.3 million AI songs being generated JUST from one platform (Deezer).

Content Saturation Problem

If millions of AI songs are created yearly, platforms may face:

Oversupply of music
Harder discovery for human artists
Spam uploads
Lower average attention per song

This means AI may not only create music. It may entirely drown the market. The biggest impact of AI and its influence on music may not even be one perfect song. Instead, it may be millions of acceptable songs flooding the industry. And truthfully, the impact that could be present from drowning the market in music could be devastating (Deezer).

Can AI Predict a Hit Song

Why Predictions Fail

Based on current understandings, it is reasonable to conclude that AI can “break down” a song into numerical data and analyze the information. But can an AI predict which songs people will like, stream more, and save/share before the songs become a hit?

However, this model has limitations…

Many modern companies like Spotify, Apple Music, and YouTube use extensive Machine Learning models to estimate that very prediction.

Spotify Recommendations/ Engagement Logic

Streaming platforms use a variety of metrics to track a user’s behavior while they listen to music –

(Spotify Engineering)

METRIC	TYPE	meaning
Skip Rate	int	The user leaves the song quickly
Completion Rate	int	The user listened to the whole song
Replay Rate	int	The songs are listened to frequently
Save Rate	int	The user added the song to their library
Share Rate	int	The user sent this song to other users
Playlist Adds	bool	The user saved this song to playlist(s)

Many media companies use various methods and value each of these variables differently. However, in general, a simplified popularity model might look similar to this –

HIT_SCORE = (0.3 X REPLAY_RATE) + (0.25 X COMPLETION_RATE) + (0.2 X SHARE_RATE) + (0.15 X SAVE_RATE) – (0.1 X SKIP_RATE)

Ex:

If a song has the following “weights.”

Replay: 80
Completion: 90
Share: 60
Save: 70
Skip: 20

Then:
HIT_SCORE = 24 + 22.5 + 12 + 10.5 – 2 = 67

Ex:

* Sample Code written in Python – NOT ACTUAL CODE USED IN ANY INSTITUTION
songs = {
“Song A”: 67,
“Song B”: 58,
“Song C”: 81
}

best_song = max(songs, key=songs.get)
print(best_song)

OUTPUT:

Song C

While higher scores do correlate to a stronger hit potential, this metric is not an “end-all, be-all” to predict which songs are liked. Additionally, many of the songs may be reordered, as their HIT_SCORE can be normalized through a series of calculations, as there may be a source of bias or variance when these HIT_SCORES are calculated.

TikTok and Chart Acceleration

Another major predictor of music chart success is TikTok. According to industry reports, a majority of the songs that trend on TikTok often become very popular. This can be attributed to TikTok’s vast user base. On any given week, TikTok has ~2 billion users. As such, songs that trend on TikTok often see:

(Billboard)

Major Spotify (and alternative streaming service) stream spikes
Billboard Hot 100 movement
Increased search and interaction counts
Faster discovery and growth cycles

Some examples of artists who experienced growth for some of their songs are Doja Cat (Say So), Lil Nas X (Old Town Road), and Olivia Rodrigo (Good 4 U

Besides the major players in the music industry, there are a plethora of smaller companies that are also trying to crack the code of artificially making the next hit.

Grand View Research estimates that the generative AI music market will grow from $440 million (2023) to $2.79 billion (2030). Labels and tech firms BOTH have strong financial incentives to invest and become the pioneers in finding a successful hit-prediction system. (Grand View Research)

When looking at an artist’s success, there are many variables to consider

Cultural Timing
Memes
Celebrity Controversy
Emotional Fan Connection
Viral Moments
Fan Loyalty

Researchers have found that music success can only be statistically broken down to a certain extent. While an artist’s success can be measured by their statistics and streams, success is not controllable (Briot et al.).

While AI can measure what various listeners did yesterday, when it comes to predicting what millions or billions of people WILL love tomorrow is a far harder question. If a user has every Drake song and album favorited, saved, shared, and replayed many times, there is no guarantee that Drake’s next album may be to that user’s liking.

While these systems are powerful…

Even with these predictive models, there are still clear limitations.

What AI Still Cannot Replicate

From the topics that have been discussed thus far, it’s evident that AI can analyze and imitate patterns present in successful songs. But an imitation will never be the same as the original.

The way that an individual artist can connect and reach fans, on an emotional/spiritual/personal level, is not something that an AI can replicate, based on pure imitation.

While AI can technically recreate lyrics with sad meanings, lines written by humans resonate with fans. Based on lived experiences, intentional symbolism, and personal truth, there is a real connection between artist and audience – that changes everything. Artists often use double entendres, pop culture references, hidden meanings, and other tools to reach fans. There are many examples illustrating this:

“I know they say the first love is the sweetest, but that first cut is the deepest.” – Drake

This line sounds like a relationship lyric, but the hidden meaning is about betrayal, emotional scars, and how early heartbreak shapes future trust. It connects because many listeners understand carrying old wounds into new relationships (Genius Lyrics).

“You kept me like a secret, but I kept you like an oath.” – Taylor Swift

From All Too Well (10 Minute Version), this line contrasts how two people valued the same relationship completely differently. “Secret” implies shame or concealment, while “oath” implies loyalty and devotion. One line tells an entire emotional story (Genius Lyrics).

“And if I die before your album drop, I hope—” – Kendrick Lamar

From Sing About Me, I’m Dying of Thirst, the unfinished lyric is intentional. The sentence cuts off because the speaker dies mid-thought. It’s haunting, symbolic, and forces the listener to confront violence and lost voices (Genius Lyrics).

Researchers who have been analyzing AI-generated music state that the hardest challenge is evaluation. A song can have all the features and be soundly correct. Yet it can still feel forgettable or bland to listeners. Human reactions to music are subjective and influenced by memory and connection.

In other words, there is no universal formula for creating “good music” (Mariani et al.)

Emotion vs Pattern Matching & CLP

AI Systems develop their understanding through learning from past experiences and data collections. In statistics, models often perform best near known patterns, not radical innovation. That means AI may generate songs that sound familiar, but great music and hit songs often begin by sounding unfamiliar.

This means that they are BEST suited at reproducing styles and patterns they have already heard. Not something new. This is what separates artists, as they are constantly evolving and inventing, based on what they go through in their lives — exactly what makes them attractive to audiences.

Taylor Swift is changing her genres through her eras
Kanye West redefined his production styles
The Weeknd blends retro synth with pop and RnB into mainstream music
Travis Scott and Mike Dean are popularizing atmospheric TRAP productions

(Briot et al.)

Listeners also follow their favorite artists for reasons beyond the music:

Personality
Public Image
Live Performances and Stage Presence
Interviews with the press
Interview
Fan Communities
Personal Storytelling Abilities

When an artist releases, it’s not just the physical audio. It’s a collection of events, a narrative telling a story, and a personal connection between fan and experience.

Trait	HUMAN	AI
Personal Memories	YES	NO
Real Heartbreak/Loss	YES	NO
Cultural Identity	YES	NO
Fan Relationships	YES	NO
Pattern Recognition	AVERAGE	YES
Infinite Output Speed	NO	YES

But in order to understand why an AI will fail at these topics, it’s important to understand the weaknesses and failpoints of an AI system in this context.

Computation Limitation Principle (CLP) –

An AI system, as previously mentioned, is great at studying data, optimizing the processing speed to understand the data, and great at finding relations even when they may not exist. A model can optimize inputs – which stand as variables that are linked to songs, such as genre, artist, length, lyrics, mood, etc. This can be roughly measured as:

POPULARITY ~ STREAMS + SHARES + REPLAYS

But many hit songs are not classified with such a binary encoding. The variables that are linked to songs may be difficult to quantify.

MEANING ≄ SIMPLE DATA

This is why a mathematically optimized song created by a computer may still fail emotionally

Beyond technical limitations, AI music introduces new legal and ethical challenges.

Ethics, Copyright, and Ownership Wars

When the Song is Real, But the Artist is Artificial

AI is becoming increasingly capable of producing polished, high-quality music. But the next challenge AI faces may not even be a technical one, but rather a legal one. If an AI system can successfully generate a realistic song using the voice and likeness of a real artist, then questions will immediately begin to arise. Ownership, royalties, and artistic content all become the focus of attention.

Earlier, the debate of whether AI can make songs was explored. Now, the focus becomes what happens after AI successfully makes the song. In fact, the music industry has already started dealing with this problem.

Voice Cloning & False Identities

One of the most debated topics within the topic of AI music is voice cloning. A large language model (LLM) is designed and trained to study and analyze the vocal patterns, tone, cadence, and pronunciation styles of an artist, in the same way it studies songs – taking an artist’s vocal frequencies, turning them into graphical wave representations, performing mathematical operations to simplify sound waves into numbers, and then finding patterns. This can allow a machine to generate a song and make it sound exactly as if it were recorded by the real artist, who in reality has never even stepped into the studio.

If the average listener, that is NOT a fan, heard a new song online that sounded and was labeled as – Ariana Grande, Morgan Wallen, Future, SZA, Rihanna, or any other global pop star – the question arises, would the average listener know it was fake?

That uncertainty is the main fear of original artists and production companies, and the driving incentive for AI companies looking to design such systems.

Previously, it was discussed that AI systems do not create music from scratch, but rather from massive datasets of existing songs, vocals, and lyrics. This gives way to another legal debate:

If an AI studies copyrighted music to learn, is that education… or theft?

Production companies argue that after years of investment, original content is being used to train systems to replace the very sources that created these works, at times WITHOUT permission. Alternatively, AI developers argue that models learn the statistical patterns rather than copying the exact song verbatim.

That grey area is where the difference exists. If a student studies thousands of songs and then creates something original, societal norms usually accept this as learning. However, if a machine were to do the same thing, instantly AND at scale, the common population would stand against this.

The Royalty Ownership Formula

Normally, when a song is monetized, the payment process is straightforward and instant. Writers, producers, labels, publishers, and distributors alike all receive percentages. With the introduction of AI and the ability to complete a whole song from start to finish, the equation becomes far more complicated.

Standard Revenue Model

TOTAL REVENUE =

STREAMING + LICENSING + SALES + PERFORMANCE

Where:

Streaming: Spotify, Apple Music, YouTube Payouts
Licensing: Movies, Commercials, Advertising, and Games
Sales: Downloads and Purchases
Performance: Concerts/Public Performance Royalties

Ex: $100,000 (Total) = $55k + $25k + $10k + $10k

But with the inclusion of AI, the equation becomes more complicated, and each of the variables gets shifted in percentages. So who gets paid more and who gets paid less?

Potential Claimant	Why They May Deserve Revenue
Prompt Writer	Ideas/Creative Direction
AI Platform	Generated Vocals/Instruments
Producer	Mixed/Mastered Final Track
Distributor	Releasing the Song
Cloned Artist	Voice or Likeness that was Used

If the usage of AI gets expanded across the music industry, then music streaming platforms can easily shift the compensation allocations.

Ex:

REVENUE_SHARE =

0.4(PROMPT_WRITER) + 0.3(PLATFORM) + 0.2(PRODUCER) + 0.1(DISTRIBUTOR)

Using this new allocation, on a $100,000 song:

Prompt Writer: $40,000

Platform: $30,000

Producer: $20,000

Distributor: $10,000

With these new allocations, it may seem easy to incorporate AI into the music production process. But there are always extenuating factors. For example, if the voice variable was also modified and now sounds like JAY-Z or Sabrina Carpenter or anyone else, then should they receive that part of the revenue collection as well?

This is the entire basis of this legal battle. Who gets paid?

In order to mitigate this process, streaming platforms may eventually need to automate processes to detect and catch suspicious/AI-influenced uploads, BEFORE they go viral.

To better model this, a simplified screening model may look like:

This is a simplified conceptual model for illustration purposes.

* Sample Code written in Python – NOT ACTUAL CODE USED IN ANY INSTITUTION

# ————————————————————
# AI Song Upload Screening System
# ————————————————————
# This is a simplified example of how a streaming platform could
# automatically scan an uploaded AI-generated song before allowing
# it to spread publicly.
#
# The goal of the system is NOT to automatically delete the song.
# Instead, the system gives the song a “risk score” and decides
# whether it should be:
#
# 1. Approved automatically
# 2. Flagged for human review
# 3. Blocked temporarily until permissions are verified
# ————————————————————

# Song upload data
uploaded_song = {
“song_title”: “Midnight Feelings”,
“uploader”: “user_4921”,
“voice_match”: 94, # Example: sounds similar to Drake
“copyright_match”: 81, # Example: resembles protected melody
“lyrics_match”: 72, # Example: partial lyric overlap
“metadata_risk”: 88, # Example: title says “Unreleased Drake AI”
“permission_verified”: False,
“early_stream_count”: 15000
}

# Risk thresholds
VOICE_THRESHOLD = 90
COPYRIGHT_THRESHOLD = 85
LYRIC_THRESHOLD = 80
METADATA_THRESHOLD = 75
VIRAL_THRESHOLD = 10000

risk_flags = []

# Check voice imitation
if uploaded_song[“voice_match”] > VOICE_THRESHOLD:
if uploaded_song[“permission_verified”] == False:
risk_flags.append(“Celebrity voice imitation risk”)

# Check copyrighted audio
if uploaded_song[“copyright_match”] > COPYRIGHT_THRESHOLD:
risk_flags.append(“Protected audio similarity”)

# Check lyric overlap
if uploaded_song[“lyrics_match”] > LYRIC_THRESHOLD:
risk_flags.append(“Lyric similarity detected”)

# Check suspicious title/tags
if uploaded_song[“metadata_risk”] > METADATA_THRESHOLD:
risk_flags.append(“Misleading metadata”)

# Check viral spread
if uploaded_song[“early_stream_count”] > VIRAL_THRESHOLD:
risk_flags.append(“Rapid spread detected”)

# Calculate weighted risk score
risk_score = (
uploaded_song[“voice_match”] * 0.35 + # heavier weight
uploaded_song[“copyright_match”] * 0.30 +
uploaded_song[“lyrics_match”] * 0.15 +
uploaded_song[“metadata_risk”] * 0.10
)

# Add penalties
if uploaded_song[“permission_verified”] == False:
risk_score += 10

if uploaded_song[“early_stream_count”] > VIRAL_THRESHOLD:
risk_score += 5

# Final decision
if risk_score >= 90:
platform_action = “Block and send for legal review”
elif risk_score >= 70:
platform_action = “Flag for human review”
elif len(risk_flags) > 0:
platform_action = “Allow upload, restrict promotion”
else:
platform_action = “Approve upload”

# Output results
print(“AI SONG SCREENING REPORT”)
print(“————————“)
print(“Song:”, uploaded_song[“song_title”])
print(“Uploader:”, uploaded_song[“uploader”])
print(“Risk Score:”, round(risk_score, 2))
print(“Flags:”, risk_flags)
print(“Decision:”, platform_action)

This system checks:

How closely the vocals match a known celebrity
Whether the melody/audio resembles copyrighted material
Whether proper permission exists

If risks are high, the song gets flagged for human review.

With millions of AI songs entering the sound market, this type of system may have to be integrated in order to produce artists’ intellectual property.

With millions of AI songs entering the sound market, this type of system may have to be integrated in order to produce artists’ intellectual property. The songs and music that artists create are not just a testament to their work, or a reflection of their character. It also serves as a bond between the artist and their audience.

If an audience member believes they are supporting a real artist – through streaming songs, buying merch, promoting content, etc. – and later learns the artist’s song was synthetic, there is a potential for backlash and distrust.

Platforms may need to start adding labels to songs, such as:

AI Generated
Synthetic Vocals
Human + AI Collaboration

or

Verified Official Artist Upload