The first Sunday of the 2016-2017 NFL(National Football League) regular season began on September 4th. Being the first game game of the season for the Dallas Cowboys it could mean many things. The Cowboys opponent were the New York Giants. Playing against a division rival could mean a lot when it comes down to the end of the regular season. With a rookie quarterback in control of the offense was something totally new. Being the quarterback is a hard position to play in the NFL. The starting rookie quarterback was Dak Prescott. During the game he made good throws and poise decisions as the starter. The issue mostly was on the defensive side of the ball. The team would let the Giants run straight up the middle of the line for at least five or more yards per carry. As well as that the team was very shaky on pass defense as well. The team allowed deep throws on defense to Odell Beckham Jr. This showed how the secondary struggles in certain situations. The secondary needs some improvements. They looked way worse than last years secondary.As far as the run defense it struggled a lot but some key players were suspended during the off season. Then as far as the offense they could move the ball down the field but when they were in the red zone they could not score a touchdown . When they got in the red zone they got field goals. One time throughout the game the Cowboys were able to get a touchdown. Getting field goals is better than getting 0 but getting touchdowns accumulate to a higher number of points. Ezekiel Elliot struggled mightily with gaining any yards when running the ball. Every time he ran the ball he struggled to gain a lot of yards. Then it was hard for the offense to get the ball in Dez Bryants' hands throughout the game. He caught the ball once for a 8 yard gain. Being a key player on offense Dak needs to find ways to get the ball in Dez hands. The final score of the game was 19-20 in a Giants win. A one point lost with majority field goals shows that if this team can score touchdowns they can be better.
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The Illusion of Creativity
AI-generated music is often marketed as “artistic” or “educational.” But what does it really teach?
- That style can be replicated without understanding.
- That emotion can be simulated without feeling.
- That effort can be bypassed with a prompt.
For young creators, this sets a dangerous precedent. Why learn to play guitar when an algorithm can do it faster? Why study harmony when AI can generate chord progressions in seconds? The risk isn’t just laziness — it’s the loss of depth, discipline, and the tactile joy of creation.
I have listened to several AI-generated music covers. My initial reaction was one of considerable impressiveness, particularly regarding the reimagining of classic pop hits in diverse genres with novel arrangements. Here is one good example a-ha — Take On Me
However, after engaging with several such Ai generated song covers, a sense of emotional detachment emerged, attributable to the perceived absence of human artistry and genuine sentiment, resulting in a technically flawless yet ultimately soulless auditory experience.
Copyright in Crisis
What does the copyright law say about AI generated music, especially in field of music covers. Who is liable for copyright in this case, AI developers, people who used AI to generate music or people who published it on music platforms?
Recent lawsuits show that the music industry is pushing back hard:
- Universal, Sony, and Warner sued AI music platforms Suno and Udio in 2024 for allegedly training their models on copyrighted songs without permission Ars Technica The Lyon Firm.
- They’re seeking up to $150,000 per infringed song, arguing that the AI outputs resemble famous tracks and voices (e.g., Mariah Carey, Michael Jackson).
- These lawsuits target the developers of the AI tools — but they raise broader questions:The legal gray area is training data. Courts haven’t yet ruled definitively on whether using copyrighted songs to train AI counts as infringement or fair use Sites at USC.
But who’s liable?
Role | Legal Risk | Why |
AI Developers | ✅ High | For training on copyrighted material without licenses. |
Users | ⚠️ Moderate | If they intentionally recreate protected works. |
Publishers/Platforms | ✅ High | For distributing infringing content. |
The grey area lies in training data. Courts haven’t yet ruled definitively on whether using copyrighted songs to train AI counts as infringement or fair use. Meanwhile, AI-generated works themselves aren’t eligible for copyright unless a human provides substantial creative input — leaving them in legal limbo.
The Educational Mirage
Some argue that AI music helps people learn genres, production styles, or vocal techniques. But this “education” often lacks context. It’s like learning to paint by watching a printer — technically impressive, but creatively hollow.
True musical education involves:
- Struggle and iteration: Learning through mistakes.
- Emotional connection: Expressing what words can’t.
- Cultural lineage: Understanding the roots of sound.
AI shortcuts bypass all of this. And in doing so, they risk flattening the rich terrain of human creativity into a sterile landscape of algorithmic mimicry.
Why This Fight Matters
This isn’t just about music. It’s about the future of art, storytelling, and human expression. If we allow machines to dominate creative spaces, we risk turning culture into content — optimized, monetized, and devoid of soul.
We must ask: Do we want a world where art is made by machines, or one where machines help humans make better art?
5 Key Takeaways
- AI-generated music blurs the line between innovation and erosion in the creative arts
- AI-generated music raises questions about copyright, culture, education, and artistic expression
- AI-generated music can replicate style without understanding, simulate emotion without feeling, and bypass effort with a prompt
- AI-generated music sets a dangerous precedent for young creators by potentially leading to a loss of depth, discipline, and joy of creation
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