It's one of the most anticipated contests of the year. Last year's contest was one for the ages. This year was supposed to be more of the same, so why did it turn it out to be a such a snoozefest? The writing was on the wall when back-to-back defending champion Zach Lavine made the decision to not participate this year. After showing off a variety of athleticism and skills over the past two years dunking, him not being a part of the contest surely was a contributing factor to its inability to live up to expectations. Last year, Aaron Gordon dueled with Zach Lavine in a contest for the ages. Both contestants showed off dunks that very few NBA players could do. When the NBA called Aaron Gordon to return to dunk on the world's largest stage, he was automatically considered the favorite to win the contest. His sparkling versatility and soaring dunks shined in 2016, and the 2017 dunk contest was supposed to be more of the same. Along with him, the NBA called up three other first year dunk contest participants in Glenn Robinson III, Derrick Jones Jr and Deandre Jordan. The firepower was obviously missing, and it was quite evident in the contest. The first round started off with Aaron Gordon, who couldn't recreate the magic from last year. Zach Lavine's most underrated characteristic as a dunker, the ability to make the dunk on his first try, was evidently not existent in Aaron Gordon. Aaron Gordon took until his final try to complete his first dunk, and he wasn't even able to convert his second dunk. His failure was just another set-up for the disaster that the dunk contest was this year. The biggest name in the contest, Deandre Jordan, one of the premier big men in the NBA and participant in his first all-star game this year, started the contest off with a bang, jumping over Dj Khaled; but it turns out he also won't be able to keep up with the smaller more athletic players. The final consisted of two relative unknowns in the NBA: Glenn Robinson III and Derrick Jones Jr. Although both are terrific athletes and great dunkers, a final consisting of two players like them was barely able to bill up to the name of the Verizon Dunk Contest. Derrick Jones Jr. ended up putting up what was probably the best dunk of the night, an off-the-side of the backboard between-the-legs eye-popping dunk in the finals. The crown, however, went to Glenn Robinson III, who was able to consistently make his dunks. Robinson jumped over three players, consisting of two assistants and Pacer's star Paul George to seal the deal and take home the trophy, hardly an eye-popping dunk. Such a stark contrasts from contests of the past hit the NBA like a wall. Every player in the contest missed dunks, deflating the energy out of an arena full of fans and players. This was supposed to be Zach Lavine versus Aaron Gordon part two. This didn't pan out, so they turned to big man Deandre Jordan, one of the best big man dunkers in the NBA. When he was barely able to show, the contest landed on the backs of two relative unknowns, who gave it their all, but weren't able to live up to the standard of the previous years. It is for these reasons, that a New Orleans Verizon Dunk Contest, billed to possible be even better than the legendary showdown that occurred last year, turned out to be a real bore. Fans will have to look to the 2018 dunk contest with hope for a performance more like the 2016 contest rather than the 2017 one.
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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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