Inside the Helmet

The Hidden Psychology of Football

Rishi Sukumar

What Fans Never See

Every Sunday, Monday and Thursday of every single week from September  to February, millions of eager fans, fanatics, and fun-lovers alike all gather in front of their televisions. What they see isn’t an emergency broadcast, or a new Pope being sworn in. Some would even say that this is more important than either of those things; Football. Viewers see their favorite players score touchdowns, make big hits, record highlight reel moments, and dramatic nail biting finishes. But what the fans don’t see is what goes on behind the helmet. Hours, and even sometimes, days before the kickoff happens, athletes spend their time managing nerves, reviewing game plans, building confidence, and preparing themselves to perform under the pressure of hundreds of thousands of viewers watching them from all over the country – and that is ignoring any physical injuries that players may have sustained. This lingering effect that the mental aspect has on the game is the reason why the most important collisions in football don’t even happen on the field – they happen within the player, mentally, where focus goes head to head with distractions, and confidence is intertwined with enormous doubt. 

Football is often described as a physical game – some may even say the most physical game – and that is true to many extents. Abnormal strength and size, lightning fast speed and reflexes, inhumane physical attributes and abilities, and endurance are all a must if you even want to step foot on  a professional football field. But football also demands intense emotional control, and mental resilience. In many cases, the better team is simply the one that has better strategy and an advantage mentally compared to their opponents. 

Football as a Cognitive Science

The game of football moves fast. Lightning fast. But it’s designed on decision making, down to the most precise detail. Athletes are forced every play to process various pieces of information and react correctly – often in sync with their teammates. A quarterback may have only a few seconds to recognize the defensive coverage, adjust protection, scan receivers, and make an accurate throw before pressure arrives. Simultaneously, linemen have to handle blitzes (or sometimes a lack thereof), receivers must adjust routes, and running backs must read blocks protecting them from 300lbs steamrollers in real time.

On the defensive side of the ball players must face the same challenges. Linebackers must analyze and decide if the play is to run or pass within seconds. Safeties must scan the quarterback’s eyes while covering the deep space. And cornerbacks need short memory to recover from mistakes and lightning fast reaction speeds after every snap to guard the receiver.

Because of all these moving pieces, the game of football depends heavily on reaction time, concentration, memory, and pattern recognition (Craft et al.). Almost like a game of chess where every play involves every piece moving at the same time. These mental demands show that football performance depends heavily on cognitive processing and focus, not just athletic ability (Craft et al.). Physical ability can create opportunities, but mental mistakes are often the deciding factor for many games. One missed assignment, one blown coverage, or one incorrect decision by anyone can completely change momentum and be catastrophic or fortuitous (depending on which side of the ball you’re on).

The Science Behind Split-Second Decisions

Football players are required to make elite decisions under extreme pressure within split seconds. This places an enormous demand on the brain. Neuroscientists have found that reaction time is heavily dependent on the ability of an individual’s brain to process visual stimuli, predict movement, and then to send signals throughout the body – Do I run? How hard do I throw? How should I contort my body? Etc. etc. For a quarterback, this means reading defenders and making a precise throw in typically under 3 seconds. Stress can also lead to an increase in cortisol levels – a hormone that can sharpen alertness in short bursts but harms decision making in the long term when levels get too high. This further explains why athletes that are calm and have extensive experience often perform better within these chaotic moments than younger or more mentally rattled players (Craft et al.).

Training the Mind Like a Muscle

Players, as they are forced to train their bodies through the weight room, many athletes spend excessive amounts of time through psychology and therapy. Mental Imagery, one of the most common methods used, is utilized a lot by players who envision a game in their head through multiple variations just to prepare themselves. Although this works across many fields, these mental reps help athletes feel more prepared when the real moment arrives.

Sports psychologists have proven that imagery can help boost confidence, concentration, and performance (Weinberg). When players picture themselves in situations, pressure situations feel more familiar and less overwhelming. Pre-game routines serve a similar purpose. Athletes will put on a familiar song, or think of a specific memory, or repeat personal affirmations, all for the same reason. While these habits seem miniscule from the outside, after repeating this process across hundreds of games, athletes fall into a rhythm where they know what works and doesn’t work for them, to really fine tune this process, to be most effective in game. 

Pressure Changes Everything

In high intensity games, pressure can completely change a player, and therefore an entire team’s performance. A simple throw made in practice feels herculean when having to do so on third down in front of 80,000 fans. There are instances where pressure helps athletes perform better, through sharpening focus, or increasing their energy or stimulating situations. But too much pressure can have the opposite effect causing players to panic, rush decisions, limit their physicality, or lose confidence (Craft et al.). One of the first examples of this obsessive approach to preparatory habits in the NFL is Tom Brady, whose emotional control under pressure set his film study apart. Brady’s discipline is also attributed to his longevity in the league, showing that mental discipline can set one apart and extend their success well beyond their physical prime. This perfectly explains why even the best of the best can struggle in their biggest moments. A kicker who’s made dozens of kicks during practice may miss a critical kick in game, with the game being entirely in their hands. In football, managing pressure is just as important a skill as throwing, tackling, blocking, or catching. 

Confidence and Momentum

Momentum in football is a critical concept, which isn’t even tangible. There is no set play, or route to run, or pep talk to have that can physically give or take momentum. Many things contribute towards this concept. The current score, the effectiveness of the opposing teams offense/defense, the fans (with their taunting), past rivalries, etc. When a team is down by multiple scores, winning can seem impossible, causing players to feel dejected, or play less than their full limit. The same is done for the opposite, when a team is winning by a lot. But more importantly, a momentum swing can happen at any time. A team that was playing flawlessly can start making mistakes when the opposing team does something big. It could be as simple as gaining one yard sometimes, but even then affects how ALL the players including the fans who voice their support through loud cheering and roaring, all see the same. 

While mistakes happen all the time, it’s important for an athlete to shake the mistake off and come back bouncing. When a quarterback throws an interception, the next throw becomes mentally important. In his head, the game is still back to before he threw the interception, as he’d be recollecting how he could have played differently. But the difference between winning and losing players is not how many mistakes they make, but how they react and move forward after the mistake. Thousands of games have been played at every level where a costly mistake is made by one player, then within the same game that player redeems themselves, as well as boosting the momentum for their entire team. Football constantly tests a player’s ability to move on, stay composed, and focus on the next snap instead of the last one.

Recovery Is Mental Too

Top-tier performance in football covers multiple factors that are not just talent and training, and that includes the quality of one’s sleep, level of hydration, and recovery habits, since they are tied to the mental sharpness of the athlete. Increased sleep, for example, has been shown to improve an athlete’s reaction time, mood, and physical performance (Mah et al.). In football, mental fatigue is detrimental because one delayed reaction or slow read can impact the entire outcome of the game. For this reason, NFL teams monitor sleep schedules and recovery data as closely as they do weight room progress (Mah et al.).

The Off-Field Battle

A lot of football’s toughest mental challenges happen away from the stadium. Injuries, especially, can be emotionally difficult because they disrupt a player’s routine, identity, and purpose (Mah et al.). This most recent season, Cincinnati Bengals quarterback Joe Burrow suffered an injury called turf toe where he was not able to do the job he does best for 3 months. In those 3 months, his entire mental schema could have been completely shattered. Especially when you are getting paid $400 million dollars to be one of the best, and now your elite skillset can’t even be put to use. Many athletes spend years building their lives around football. When injury suddenly takes the game away, frustration, anxiety, and depression can often easily follow. 

Additionally, there are external and personal problems. Athletes dealing with past mistakes, family drama, death/divorce, financial issues and extortion, and a plethora of other problems, can all affect a players performance. Former NFL Tight End Aaron Hernandez (despite his legal encounters), was a legendary player who was set to break multiple records. Unfortunately, in his past he was heavily involved within gangs and that followed him into his career as an NFL player. People would often attempt to threaten or extort him by threatening his family, unless he met their demands. Things like this can all mentally mess up a player, which in turn causes them to limit their performance on the field. 

There is also pressure from outside sources. Players feel extensive pressure from coaches, fellow teammates, family members, and most critically the media. The media over-analyzes every single move and action a player does, as the concept of privacy is something traded away for the fame and glory of being an elite player. Athletes consistently need to “prove” themselves to fans and media critics, who realistically cannot play at the level these athletes play at, yet completely dictate their actions both on AND off the field. A player who is involved in a scandal, or with allegations (whether true or not), may lose fan support causing them to be affected mentally, which reflects onto their game. Every missed pass, or dropped ball, or lost tackle is a subtle reminder to the athletes of what the critics say about them all the time. Because of this, mental health has become a larger topic in sports. Studies across athletics have increasingly shown that emotional well-being directly impacts performance and recovery (Mah et al.). More teams now recognize that emotional well-being matters just as much as physical health. Athletes – who are forced to maintain a rough, nonchalant, and cold demeanor – are realizing that asking for help is slowly becoming accepted, and asking for sympathy is seen as strength rather than weakness. 

The Real Scoreboard

Football is often remembered through the final scores, championships and conference trophies, and unforgettable plays. After all, who doesn’t love a player that can seemingly do it all or break the laws of physics. While these moments are always nice to see, they are usually built on qualities that no camera or reporter can ever fully capture. Confidence, failures, and mental health is a facet of sports all over the world that is never seen on the field or the court. To be a successful athlete, one must keep believing under pressure, because mindset often shapes outcomes more than raw talent ever could. The physical side of football may draw an audience, but the psychological side reveals the humanity behind elite athletes. Behind every ball that is played, is a person – just like anyone else – managing insane stress, expectation, fear, and pain, while trying to perform at the most elite level. In that sense, football transforms into more than just a game. It becomes a reflection of how resilient these athletes are and how they respond when everything around them demands their 110% as they are physically and mentally breaking. In football, the strongest players are not always the biggest. They  are often the ones who refuse to break mentally.

Inside the Helmet

Sources Used to Analyze the Hidden Psychology of Football

Anxiety and Performance

Craft, Lynette L., et al. “The Relationship between the Competitive State Anxiety Inventory-2 and Sport Performance: A Meta-Analysis.” Journal of Sport & Exercise Psychology, vol. 25, no. 1, 2003, pp. 44–65.

Sleep and Athletic Performance

Mah, Cheri D., et al. “The Effects of Sleep Extension on the Athletic Performance of Collegiate Basketball Players.” Sleep, vol. 34, no. 7, 2011, pp. 943–950.

Visualization And Mental Imagery

Weinberg, Robert S. “Does Imagery Work? Effects on Performance and Mental Skills.” Journal of Imagery Research in Sport and Physical Activity, vol. 3, no. 1, 2008. 

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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

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. 

This is a simplified conceptual model for illustration purposes.

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)

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)

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

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. 

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?

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

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

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. 

This is a simplified conceptual model for illustration purposes.

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)

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)

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

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. 

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?

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

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.

The post Blinded By the Bytes appeared first on Charged Magazine.

To answer that, it helped to stop thinking about shakshuka as just a dish, but rather to see it as part of a much larger story about African food culture, movement, and adaptation.

One of the most important insights from African food scholarship is that cuisine is not static. Rather than rigid recipes, many African dishes are better understood as flexible systems of cooking, shaped by environment, trade, and daily life (Chastanet, 2016). Historians of African foodways emphasize three key ideas in their writings:

1. Adaptation to available ingredients
2. Influence of trade and migration
3. Communal methods of preparation and eating

Shakshuka specifically reflects all three. It is built from simple, accessible ingredients to make a base of tomatoes, peppers, spices, and eggs and cooked in a single pan meant for sharing. But shakshuka can contain anything from spicy sausage, fresh jalepeños, beets, carrots, or feta cheese, depending on what is available at the time based on what traders bring to the area. Interestingly, even its core ingredient, the tomato, is not indigenous to Africa, but arrived through this global trade after the Columbian Exchange. In this sense, shakshuka is not an “unchanged tradition,” but a product of historical exchange and adaptation (Chastanet, 2016).

Scholars argue that this kind of flexibility isn’t purely circumstational, it is foundational. Food evolves alongside people, responding to shifting environments and cultural influences.

In many African culinary traditions, food is not defined solely by ingredients, but by how it is prepared and consumed. Communal eating, or sharing from a single vessel and using bread as a utensil, is central to the experience (Mintz & Du Bois, 2002).

Shakshuka also fits this pattern. It is typically served in the same pan it is cooked in, with sourdough bread used to scoop the sauce and eggs. This style of eating reinforces social connection, emphasizing food as a shared activity rather than an individual one, which is paramount to relationship-building and social structure in Africa.

The spread of shakshuka beyond North Africa follows patterns that scholars identify in diasporic food systems. When North African communities migrated, they brought their food traditions with them. In these new contexts, dishes often change. Food studies research shows that migrant communities adapt recipes based on new ingredients, social settings, and cultural expectations. Shakshuka’s transformation into a café and brunch dish reflects this process of cultural negotiation and identity preservation (Gabaccia, 1998).

Beyond its cultural significance, shakshuka also aligns with the growing interest in health-conscious eating, particularly through its tomato-based foundation. Tomatoes are rich in lycopene, a carotenoid antioxidant associated with anti-inflammatory and cardiovascular benefits (Collins et al., 2022). Research suggests that regular consumption of tomato products may contribute to reduced risk of heart disease, lower blood pressure, and protection against oxidative stress due to the antioxidant properties of lycopene and related compounds . Importantly, cooked tomato dishes, like shakshuka, may provide even more available lycopene than raw tomatoes because heating tomatoes increases lycopene absorption in your body. This connection between traditional food practices and contemporary nutritional science helps explain why shakshuka fits naturally into modern food trends.

By the time shakshuka arrived in the United States specifically, it had already undergone multiple transformations. Its popularity in cities like Boston reflects what scholars describe as the global circulation of food, where dishes move across borders and are reinterpreted to fit new cultural frameworks (Appadurai, 1988).

In the United States, shakshuka aligns with existing trends of brunch foods centered around eggs, interest in Mediterranean and plant-forward diets, and desire for foods with cultural narratives. Boston provides a particularly strong environment for this kind of culinary adoption, in which large academic institutions scattered around the city house students from many different nations and cultures. Studies show that such environments accelerate the spread of diasporic foods (Mintz & Du Bois, 2002).

What makes shakshuka compelling is that its journey to Boston is not necessarily an uncommon story, it is representative. African foodways have long been shaped by movement, trade, and adaptation, even if those histories are often underrepresented in mainstream narratives (Chastanet, 2016). Seen this way, shakshuka is not a fad, it’s a part of a much larger system of cultural exchange.

And for me, it started with a simple question: why is everyone here so obsessed with this dish? The answer lies not only in the fact that it is very tasty, but in the history and cultural systems that made it possible.

References

Appadurai, A. (1988). How to make a national cuisine: Cookbooks in contemporary India. Comparative Studies in Society and History, 30(1), 3–24. https://doi.org/10.1017/S0010417500015024

Chastanet, M. (2016). Towards a history of foodways in Africa. Afriques, 7. https://doi.org/10.4000/afriques.1857

Collins, E. J., Bowyer, C., Tsouza, A., & Chopra, M. (2022). Tomatoes: An extensive review of the associated health impacts of tomatoes and factors that can affect their cultivation. Biology, 11(2), 239. https://doi.org/10.3390/biology11020239

Gabaccia, D. R. (1998). We are what we eat: Ethnic food and the making of Americans. Harvard University Press.

Mintz, S. W., & Du Bois, C. M. (2002). The anthropology of food and eating. Annual Review of Anthropology, 31, 99–119. https://doi.org/10.1146/annurev.anthro.32.032702.131011

The post A Skillet, Some Eggs, and a Story of Migration appeared first on Charged Magazine.

Benefits of Quality Sleep

• Sleep is where the mind and body reset and process the information of the day, so with better sleep, individuals can have improved cognitive performance, memory, and executive functioning (Dewald et al., 2010).
• Getting quality sleep results in better emotional regulation, improved cardiovascular and immune health, as well as reduces the risk of behavior problems and disorders (Kohyama, 2021).
• Improved mental health and executive functioning (Kohyama, 2021).

Tips to Get Better Sleep

• Prioritize and value sleep i.e., think of it as something you need, rather than a luxury or privilege (Espie, 2022)
• Sleep is personal. Find your routine that works for you and stay consistent (Espie, 2022).
• Allow enough time to relax before bedtime and relax with a routine that works for you; it’s difficult to tailor sleep advice for every individual, so it’s best for an individual to determine a routine that is effective for them through trial and error (Hauri, 1991, p. 65-66).

References

Dewald, J. F., Meijer, A. M., Oort, F. J., Kerkhof, G. A., & Bögels, S. M. (2010). The influence of sleep quality, sleep duration and sleepiness on school performance in children and adolescents: A meta-analytic review. Sleep Medicine Reviews, 14(3), 179–189. https://doi.org/10.1016/j.smrv.2009.10.004

Espie, C. A. (2022). The ‘5 principles’ of good sleep health. Journal of Sleep Research, 31, e13502. https://doi.org/10.1111/jsr.13502

Hauri, P.J. (1991). Sleep Hygiene, Relaxation Therapy, and Cognitive Interventions. In: Hauri, P.J. (eds) Case Studies in Insomnia. Critical Issues in Psychiatry. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-9586-8_5

Kohyama, J. (2021). Which Is More Important for Health: Sleep Quantity or Sleep Quality? Children, 8(7), 542. https://doi.org/10.3390/children8070542

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Replication is essential to the process of science because it allows scientists to separate good hypotheses from bad ones. Individual studies may yield inaccurate results for a number of reasons. Sometimes a study is biased, skewing the results and making them appear more or less significant than they actually are. Sometimes the participants don’t reflect the overall population, leading to results that can’t easily be applied to all people. Sometimes the study simply finds an illusory correlation that doesn’t exist in the real world. Replication of original studies helps researchers confirm that an effect is real and further examine how it works. If an idea is accurate, many replications will produce consistent evidence of its existence. If not, replications will show weak or conflicting information, leading researchers to drop the original idea and explore other ones.

However, within roughly the last decade and a half, the concept of replication has caused quite a bit of turmoil in the field of psychology. Researchers have looked back on the history of psychology research and realized that many psychology studies, including some for concepts that define major ideas in the field, have not been successfully replicated or failed to replicate as often as expected (Shrout and Rodgers 2018). In some cases, replication studies were performed but produced results that conflicted with the original findings. Other phenomena have been given replication studies, but the replications didn’t use the methods of the original study, leaving open the possibility that the original results may have been effects of experimental design. Finally, for a few concepts, there have been no attempts to replicate at all! How did this situation come to be?

History

Lack of replication has been noted for decades in psychological research (and in some other fields as well). However, several events in the early 2010s put psychology in the spotlight and made the problem impossible to ignore.

The first event to make headlines was the academic response to a 2011 study on precognition, the idea that some people have the ability to predict the future (Wiggins and Christopherson 2019). The study’s author claimed that he had produced clear evidence in favor of precognition’s existence, which other researchers found questionable due to the claim’s outlandish nature and apparent errors in how the experiment was conducted. A few replication studies were done and found no evidence for precognition. However, the replications were rejected by several scientific journals because they weren’t perceived as being important, despite providing clarification on the likelihood of a controversial idea. Had a person looked through the psychology literature on precognition at that time, they would have seen only the poorly conducted initial study in favor of precognition and not the more numerous replications that rejected its findings.

Later that year, Diedrik Stapel, a scientist who had authored many psychology studies, was proven to have committed fraud in most of them (including faking data). Although not directly related to replication, this event shocked the psychology community and demonstrated how easy it was for bad data to become a part of the overall scientific literature (Wiggins and Christopherson 2019). How had Stapel been able to lie for so long? Where had research and publication protocols had failed? Perhaps if someone had checked his research more thoroughly, such as by trying to replicate it, the fraud would have been discovered as soon as it started.

Finally, several researchers realized that many psychology studies had issues with problematic research practices, including the manipulation of negative results to appear significant. They took it upon themselves to expose these problems by publishing an article appropriately titled “False Positive Psychology: Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant in Psychological Science.” In the article, they used studies and statistics to “prove” the idea that a person can de-age themselves by listening to a Beatles song (Wiggins and Christopherson 2019). Despite the ridiculous conclusion, the studies had been conducted according to the norms of research at the time and their statistical methods were all common and accepted practices. The article demonstrated how easily ordinary research practices could be misused.

These events opened the floodgates to a wave of doubt about the validity of psychology research, and the more people looked at the problem, the more issues they found. One report estimated that the field has a replication rate as low as 36% (and only as high as 47%) (Wiggins and Christopherson 2019).

One area of psychology that was hit particularly hard was the concept of priming. Priming is the idea that exposure to certain sights or events make people more likely to respond in ways that relate to what they just experienced. For instance, if a person saw a picture of a couch and was asked to fill in the blank letters in the word “S_ _ a,” they would likely say “sofa” instead of “soda,” “saga,” or other words that would also fit the letters given. Basic effects like this are relatively uncontroversial, but many proponents of priming have claimed to find much more extensive influences. One study published in the 1990s claimed that participants who were exposed to words associated with old age walked more slowly afterwards, as if affected by the stereotype that old people are slow (Lunbeck 2025). Academics were quick to conclude that this meant that people are so heavily influenced by the environment that even simple actions like walking are predetermined and not under our control! However, follow-up experiments found nothing of the sort.

One more thing: It’s worth noting that although the most famous examples of failed replication involve hypotheses that are particularly extreme or unlikely, more ordinary research can also fall afoul of reproducibility norms. In fact, one of the most worrying implications of the replication crisis is that other, more mundane inaccuracies are flying under the scientific method’s radar, and researchers don’t realize the need to weed them out. It’s easy to doubt that humans are mindless robots programmed by the words they read but harder to reconsider ideas that seem intuitive and logical.

Potential Solutions

So how can this issue be fixed? There is no one solution, but a number of measures can greatly improve the reliability of psychological research and end the replication crisis.

• Preregistration of studies: Some academic journals now require researchers to “register” their experiments before actually conducting them. The researchers have to share their topic, methods (the protocols they will use for the study), and hypotheses and stick to them during the testing period. If they change any of the details, especially in their methods, the study may be rejected and not published by the journal. This measure encourages transparency in how data is collected and discourages unscrupulous authors from changing the methods partway through the experiment to manipulate results. Because it means that studies are published before the results are known, it also cuts back on the practice of choosing studies for publication because they support ideas that are popular.
• Better measurement and statistical hygiene: This reform is only related to replication indirectly, but it is nonetheless extremely important. Using more precise measurements when collecting data and analyzing that data more honestly will cut down on errors and yield more realistic conclusions. These measures can create more honest and accurate results, which go hand in hand with replicability.
• Sharing of specific methods: A major critique of psychological research papers is that they don’t always include enough details about their methods to allow for replication. As a result, follow-up studies may not be close enough to the original to accurately test the phenomenon under study. A simple fix for this issue is to require more specific reporting of data collection procedures, allowing any lab to perform exact replications.
• Publishing more replications and rewarding researchers for replicating significant studies: At the institutional level, there needs to be a significant change in what scientific journals consider to be worth publishing. Currently, many journals are much more willing to accept studies that show new and interesting findings than replications of those findings, and research that disproves or fails to find evidence for particular hypotheses is often rejected even if the methods are sound (a phenomenon called the “file drawer effect”). This issue is severe enough that the replication crisis probably cannot be solved without a new commitment to publishing replication studies, including ones with negative results. Proposed fixes include rewarding researchers for transparency in their methods (allowing for replication), relaxing publication standards to include more replications and negative studies, and even requiring journals to publish replications of studies that they previously accepted.

It’s also worth noting that some observers argue that the replication crisis was never very serious in the first place, or point to the positive changes that are being made. This is why the replication crisis is sometimes referred to as a “credibility revolution” instead (Korbmacher et al. 2023). Although the situation may look bleak, researchers have begun implementing reforms. Various actors are encouraging open access research, incentivizing high-quality studies while deemphasizing the study quantity, creating new guidelines for journal editors who make the decision to accept or reject research, creating better reviews that summarize many studies, and more. There is still a long way to go, but these measures can ameliorate many of the problems that plague psychology and other fields and create a better future for science.

Ultimately, the replication crisis in psychology demonstrates many of the challenges that science is facing today and points toward ways that it can be improved. There are serious issues in academia’s publication practices and ability to prevent fraud, but these problems are not unfixable. Better research practices and a unified effort by scientific journals can end the replication crisis and work towards ensuring a psychological science that is more replicable, accurate, and open for all.

Sources:

Korbmacher, Max, et al. “The replication crisis has led to positive structural, procedural, and community changes.” Communications Psychology, 25 July 2023, https://doi.org/10.1038/s44271-023-00003-2.

Lunbeck, Elizabeth. Failure to replicate: A historian confronts the complicated origins and uncertain future of priming research, Review of Anatomy of a Train Wreck: The Rise and Fall of Priming Research Science, vol. 387, no. 6730, 9 Jan. 2025, pp. 145–145, https://www.science.org/doi/10.1126/science.adu0370.

Shrout, Patrick E., and Joseph L. Rodgers. “Psychology, Science, and Knowledge Construction: Broadening Perspectives from the Replication Crisis.” Annual Review of Psychology, vol. 69, no. 1, 4 Jan. 2018, pp. 487–510, https://doi.org/10.1146/annurev-psych-122216-011845.

Wiggins, Bradford J., and Cody D. Chrisopherson. “The Replication Crisis in Psychology: An Overview for Theoretical and Philosophical Psychology.” Journal of Theoretical and Philosophical Psychology, vol. 39, no. 4, Nov. 2019, pp. 202–217, https://doi.org/10.1037/teo0000137.

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Usually, sneezing is caused by irrigation in the nose, but how does the sun irritate your nose? This phenomenon is called the photic sneeze reflex. The cross-wire between the visual pathway and the sneezing pathways of your brain and nose could be caused by the sudden exposure to bright light.

This has also recently been named the ACHOO syndrome and has been seen in some genetically predisposed families. It is a polygenic trait that has been seen on the ZEB2 regions of chromosome 2 and 3. This area is involved with the development of the nervous system so any changes will cause the cross-linked reaction that we see in the ACHOO syndrome. Robust associated at these locations also indicate that the mutation is seen across multiple populations and ethnic groups.

Overall, the sun doesn’t directly “cause” you to sneeze, but rather triggers a chang within the visual and trigeminal response pathway. We still do not know how light can become a sneeze signal so quickly, especially considering this is not the average path for light input in the nervous system. Is the cortex driving this system or simply modulating it?

There is still so much more to learn about this reflex. Keep this in mind next time you step outside and feel your nose start to twitch!

References

Songu, M., & Cingi, C. (2009). Sneeze reflex: facts and fiction. Therapeutic Advances in Respiratory Disease, 3(3), 131-141.

Whitman, B. W., & Packer, R. J. (1993). The photic sneeze reflex: literature review and discussion. Neurology, 43(5), 868-868.

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What Exactly is Procrastination?

According to educators Ramadhani and others, procrastination is defined as “the tendency to delay important tasks despite knowing the negative consequences” (Ramadhani et al., 2026). It is an occurrence where people delay doing what they want to do or what they have to do by a certain deadline, potentially leading to loss of productivity, poor performance, and increased stress (Rad et al., 2025). Psychology Today, the world’s largest mental health and behavioral science destination online, added to the definition that procrastinators may deliberately look for distractions and it tends to reflect a person’s struggles with self-control. These definitions explain what happened to me.

What Leads to Procrastination?

Students procrastinate for different reasons. Based on 27 studies, procrastination might be contributed by fear of failure, perfectionism, test anxiety, low motivation, and difficulties with emotional regulation (Ramadhani et al., 2026). In a different study in Iran, procrastination is a behavior observed in 80-95% of the 290 students (Rad et al., 2025). The study concluded that academic procrastination is correlated with academic self-efficacy and emotional regulation. Students who had better academic self-efficacy show less academic procrastination, and vice versa for those who had problems regulating their emotions. Other reasons include we do not enjoy doing the tasks, want to avoid making ourselves unhappy, or we fear that we won’t do them well (“Psychology Today”, n.d). In contrast, I found these hard to apply on myself. During my first year in college, I experienced all these feelings, yet I don’t think procrastination occurred to me until later semesters. This made me wonder whether submitting assignments just minutes or seconds before the deadline had given me the impression that I was doing things “right” under pressure.

Others suggested that procrastinators believe they perform better under pressure. However, research shows otherwise; instead, procrastinators form “habit of last-minute work to experience the rush of euphoria at seemingly having overcome the odds.” (“Psychology Today”, n.d) This is true on a personal level as I convinced myself the same thing. 

Why is It Not Just Procrastination?

What happens when there are too many tasks at hand? We make a priority queue (CS joke). Clémence Prosen, a French educator and creative worker, believed it is not procrastinating when a person chose to prioritize certain tasks, rest, and finish all those delayed tasks later (2021). This is true for Georgia Tech students. I have heard how students taking too many classes weigh their coursework and prioritize some over the others. For example, if one project is worth 10% in a course and a homework assignment worth 5% in a different course due the same night, students prioritize the first one. However, if they need a better grade in the second class, the latter might be prioritized. This ends up with basically sacrificing those with low priority. Unless, they are perfectionists and want to keep both.  

Historically, I used to work on my assignment over the breaks to have an easier time with deadlines. However, over the last Spring break, I made the decision to not work on my coursework, believing I will have enough time for them. I ended up catching up with deadlines as soon as we came back to school. Everything would have been fine if not for more tasks to come up unexpectedly outside of schoolwork. So, a cycle began. I have to catch up on delayed tasks in the queue. According to Psychology Today, “habitual procrastinators can experience reduced well-being in the form of insomnia or immune system and gastrointestinal disturbance.” Those late-night sleeps catch up and tire me out, causing my body to seek rest. Then, as I took my sweet time to rest (or eventually being distracted), the next tasks are due. It is worse when I try to perfect those works (draw it versus using a pre-existing image) and end up spending too much time on one assignment. This puts a strain on my body, leading to me needing even more time to rest. 

Personal Reflection

I figured the reason was due to my change in responsibilities in recent semesters. Procrastination with or without the priority queue wasn’t a problem before when my other responsibilities have a fixed schedule. Now, I cannot predict my schedule for next week. My rest time might have taken more time than needed as I got too comfortable with delaying work at a reasonable pace. Changes are needed to my time management and bad habits on account of these unexpected tasks.  Hopefully I can work this out over the summer and you will not see another procrastination reflection in the next few months. Finally, I have found a few strategies that might keep me going forward:

1. Set soft deadlines a day before the real one (or even earlier for time-consuming projects)
2. Use time-block for coursework periods with limited access to distracting sources
3. Allow daily daily buffer time for unpredictable responsibilities outside of coursework

References:

Hassan, M. M. (n.d.). Time Management. Public Domain Pictures. Retrieved 2026, from https://www.publicdomainpictures.net/en/view-image.php?image=613653&picture=time-management.

Prosen, C. (2021, February 3). Resting or procrastinating? that is the question. Clémence Prosen. https://www.clemenceprosen.com/post/resting-or-procrastinating?srsltid=AfmBOorvKggPmnD-5vCd8JBgnmNg09V8y7uZvyOMvY4vBrSrSOk3eoac

Rad, H. F., Bordbar, S., Bahmaei, J., Vejdani, M., & Yusefi, A. R. (2025). Predicting academic procrastination of students based on academic self-efficacy and emotional regulation difficulties. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-87664-7

Ramadhani, E., Setiyosari, P., Indreswari, H., Setiyowati, A. J., & Putri, R. D. (2026). Academic procrastination: A systematic review of causal factors and interventions. Journal of Behavioral and Cognitive Therapy, 36(1), 100552. https://doi.org/10.1016/j.jbct.2025.100552

Sussex Publishers. (n.d.). Procrastination. Psychology Today. https://www.psychologytoday.com/us/basics/procrastination

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