AI is doing two very different things to the world of work at the same time.
First, it’s eliminating certain kinds of jobs and compressing the value of entry-level work.
Second, it’s democratizing specialised fields that previously required years of training to even participate in.
As a software developer, I’ve already watched this happen in coding. So I decided to try the same experiment in another field:
Music.
For the last two weeks, I’ve been using Suno AI almost daily to see how far someone with little musical knowledge can go using modern AI music tools.
The experience felt very similar to vibe coding.
And it exposed both the incredible promise and the very real limitations of generative AI.
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AI Is Eating Everyone’s Lunch
Let’s stop pretending this isn’t happening.
A lot of work that once required specialised skills can now be done — or partially done — by AI.
In software development, non-developers can now build surprisingly functional apps using AI coding tools and good prompts. The output often isn’t production-grade, but it’s “good enough” for many use cases.
That matters.
Because “good enough” at massive scale changes industries.
And now the same thing is happening in music.
Suno Makes Music Creation Ridiculously Easy
The first thing that impressed me about Suno is how accessible it is.
You don’t need music theory.
You don’t need production knowledge.
You don’t need instruments.
In many cases, you just need:
- an idea
- a decent prompt
- and optionally ChatGPT to help generate lyrics and structure
Within minutes, you have a full song.
That alone is kind of insane.
A few years ago, creating music involved:
- recording equipment
- DAWs
- mixing knowledge
- instruments
- vocal ability
- production experience
Now someone with none of those things can generate a polished-sounding track in minutes.
That’s a major shift.
But Here’s the Reality After the “Wow” Factor
After the first few days, patterns started to emerge.
And this is where the gap between AI-generated and professionally created music becomes obvious.
Problem #1 — The Songs Aren’t Truly Original
This became the biggest issue very quickly.
Most generated songs sound:
- familiar
- safe
- formulaic
- predictable
The melodies often feel generic.
The chord progressions are usually standard.
The structures are extremely mainstream.
The AI is very good at reproducing patterns it has learned.
But creativity?
That’s a different thing entirely.
The most interesting outputs usually happened when I fed Suno something more original first:
- a unique melodic idea
- a custom vocal input
- or a more specific creative direction
At that point, Suno became incredibly useful.
Not as a standalone artist.
But as an AI-powered production assistant or backup band.
And honestly, that distinction matters a lot.
Problem #2 — The Sound Quality Still Needs Humans
This was the second major reality check.
I know very little about audio engineering or mastering, and I assumed modern AI tools would abstract most of that complexity away.
Not really.
A lot of generated tracks had issues like:
- too much reverb
- muddy mixing
- artificial sounding vocals
- strange compression artifacts
- inconsistent balance between instruments
Some songs sounded impressive initially, but the longer you listened, the more obvious the “AI texture” became.
And this creates an interesting contradiction.
The marketing around AI music suggests:
“Anyone can create professional music.”
But in practice, professional-quality output often still requires:
- mixing
- mastering
- editing
- stem processing
- sometimes even re-recording sections manually
Which means experts are still needed.
The expertise layer didn’t disappear.
It just shifted.
The Hidden Complexity of AI Tools
This reminded me a lot of software development.
At first, AI tools make everything feel simple.
Then eventually you hit the wall.
In coding:
- architecture matters
- scalability matters
- security matters
- maintainability matters
In music:
- production matters
- mastering matters
- arrangement matters
- originality matters
The deeper you go, the more expertise starts reappearing.
Then There’s the AI Slop Problem
This is the uncomfortable part.
When millions of people suddenly gain the ability to mass-produce content, quantity explodes.
And when quantity explodes faster than quality, platforms get flooded.
We’re already seeing it:
- AI-generated songs uploaded at scale
- low-effort content farms
- spam playlists
- algorithm-chasing music
- disposable content created purely for monetisation
This isn’t just a music problem either.
It’s happening across:
- writing
- art
- video
- software
- social media
AI dramatically lowers creation costs.
But lowering creation costs also lowers the average effort behind what gets produced.
That creates noise.
A lot of noise.
The Copyright and Licensing Mess
Another thing that became obvious:
AI music licensing is still messy territory.
Sometimes generated tracks sound uncomfortably close to existing songs.
Not direct copies.
But close enough to raise questions.
And once money enters the equation, things become complicated very quickly:
- ownership
- copyright similarity
- training data concerns
- licensing rights
- commercial usage
The legal side of generative AI still feels far behind the technology itself.
So What Is Suno Actually Good At?
After two weeks, I think Suno is genuinely excellent at a few things:
1. Rapid ideation
You can prototype musical ideas incredibly quickly.
2. Personal entertainment
Generating custom songs for yourself is honestly fun.
3. Creative assistance
It works well as a collaborator or augmentation tool.
4. Lowering barriers
Non-musicians can finally participate in music creation.
And that last point is important.
Because despite all the criticism, democratization is real.
Final Thoughts
My overall conclusion is surprisingly similar to how I feel about AI coding tools.
Suno does not magically turn someone into a professional musician.
Just like vibe coding doesn’t magically turn someone into a software engineer.
But it does dramatically increase what beginners are capable of producing.
And that changes industries whether professionals like it or not.
The real lesson from using Suno for two weeks is this:
AI is not removing expertise.
It is changing where expertise becomes necessary.
The entry barrier is collapsing.
But the ceiling for truly exceptional work still exists.
And for now, humans are still the ones pushing against that ceiling.
Actual Playlist I Created
Offcourse Suno allows any genre to be created, the modern Pop, Rap, Dance.
But why not the old school 1980's playlist. A mix of New Jack Swing, slow ballads, acapella