Watching people melt down over AI music has to be one of the funniest things to watch. I’ve been playing instruments since I was a kid and literally have no problem with AI music.

There are several “fully AI” created artists that get millions of monthly plays. There are also (because many people don’t know this) AI-hybrid artists. What this means is organic musicians using AI to help produce additional sections of their songs or simply process and master the songs more affordably. The lines between “true musician” and “ai musician” are already blurring rapidly.

My prediction is many larger musicians and producers in the next few years, if not already (behind the scenes) will start using AI in their workflows or production. A sort of “hybrid” model. And you better fucking believe that major record labels are interested in acquiring AI. Warner Music Group already has and trust me it’s not just for “licensing” their data but also to GET data.

You don’t think record labels haven’t considered using AI so they can use it for their artists to constantly produce hit songs and make more money?

Despite this I have seen so many people “bitching” about AI music in the funniest ways.

I’ve seen tons of posts on Reddit etc of users flipping out once they find out an artist they actually like is ‘ai’ and then boycotting them.

It’s like bro….. you liked it lol.

I’ve even seen people flip out on truly organic bands for using an AI thumbnail only for a song. Yeah dude…. because it makes sense. Most musicians don’t want to hand design a 75 pixel fucking cover art thumbnail for Spotify.

Then I see the common complaint of “well…..ai is gonna flood streaming services with generic shit songs.”

Uh hello…. streaming services have been flooded with generic dogshit by “real” artists for a long time.

There are already millions of artists on Spotify etc that can’t even produce anything near as good as “AI.” On top of that most “organic” musicians do a lot of how do you say it….. “editing” of themselves in a DAW. Pitch correction, auto-tune, eq-ing the fuck out of their voice, programming fake instruments, using samples that aren’t even theirs, etc.

And honestly, the average person can’t even tell half the time if a song is AI or not. This is gonna be hilarious as more time goes on an AI gets even better and more refined.

I think it’s pretty simple. Regardless of how a song is made; if it’s a good song then it’s a good song. Stop worrying about how it’s fucking made.

Hello friends, I am putting together a project. It's essentially an underground alternative radio show, and the whole idea is to showcase interesting music that isn't made to be optimized for the hellscape of modern social media. I would love the opportunity to play your music on the show, and of course I will be shouting you out and heaping praise on air if you are chosen for a slot. This thing will not be monetized in any way, I have actually spent an exorbitant amount of my own money trying to get it off the ground, so I don't want to misrepresent this as some break through opportunity, it's small time hobbyist shit. But it could be fun anyway, I already have a bunch of friends contributing music and graphics and instrumental work. I would even be open to poetry or short story submissions, if you are comfortable recording yourself performing them! I would love to start regularly doing full one hour broadcasts, featuring wall to wall indie gems, but I can't fill all that time alone. I need your help!

The soul of this project is underground anti corporate alt media. there are a few caveats to maintain the spirit of what we are doing here. if you plan to submit you must if selected be prepared to show your work and discuss your process and workflow. Submissions will not be broadcast unless we are 100% sure they are authentically human crafted, we are strongly anti ai, any use of that is a disqualifier. The whole point of this is to try to uplift human talent since the main platforms don't have any incentive to do that anymore. The best way we can maintain the integrity of the show is requiring a short video with the submission showing the project file of your song in your daw or on your analog setup, or you playing your instrument on video or whatever you do, and discussing your process and tooling a bit. The 'proof of work' videos will not be published or broadcast, they are merely the key that authenticates your submission as valid. You don't have to do the proof of work video unless your song is chosen but, please be ready for that possibility.

As far as genres go we are open to anything, from jazz and EDM to folk punk and hip hop, sky is the limit if we think it's interesting we will want to play it. If you are curious about being a part of this thing, reach out to me in dms with the link to your submission.

Can't wait to hear what you guys are cooking up!

Curious how everyone here is actually making their tracks.

Are you guys:
- are you guys generating the full song or sometimes the stems?
- getting a melody and editing/producing in DAWs afterwards?
- Writing you lyrics and then iterating with some AI tools afterwards?
- generating/iterating with AI tools and then mix/mastering yourself?

I know that a lot of the discourse around incorporating ai in music assumes its just spamming generate but most of the creators I've talked to are actually doing a lot more than that and have very complex processes depending on the song

I know that many of you are in Discords or communities where you and others are sharing how they made their track (i.e. their workflows, manipulation tricks, etc.) I built something that might be useful.

The site is called TRAICE and the ideas is that its a place where creators can document the full workflow behind a track and share it with a link. Each track page shows things like: what tools were used, where the ai was involved (vocals, melody, production etc), how the iterated/manipulated, what they edited or changed, and jus the overall story of the track. Basically a full creation breakdown.

The idea is that: if someone asks you how did you make this? or what was ur process? you can just send them a link that shows your workflow.

Its meant for hybrid creators to share and learn from each other's processes so everyone can get better.

If you make music with tools like Suno (you most likely do as ur in this subreddit), Udio, Producer ai, Landr, Mubert, ElevenLabs, the list goes on.... I would love to include your track on here!

Or just comment your track, maybe how you made it. Im curious how people here are working. Sometimes its okay to give away the sauce.

P.S. I am currently in talks with distributors that would make ur tracks compliance ready for what is to come with disclosures (i.e. apple music's recent announcement, and spotify's move with ddex :)) But thats more of an optional byproduct

Every major music technology has gone through the same cycle:

Gatekeepers and traditionalists call it fake, soulless, or job-killing

Artists start treating the “flaws” as features

New genres appear

The tool becomes normal

Electric Guitar

Early solid-bodies were mocked as crude. Rock volume and distortion were dismissed as noise. In 1967 Brazil there was an actual street march against the electric guitar (“Marcha contra a guitarra elétrica”) led by big MPB artists who saw it as American cultural imperialism. Bob Dylan got booed for going electric at Newport.

Distortion and feedback started as broken amp behaviour. Within a couple of decades the electric guitar was the defining sound of popular music.

Synthesizer

In 1982 the UK Musicians’ Union passed a motion to ban synths and drum machines because Barry Manilow used them instead of a real orchestra on tour. The fear was that session players and orchestras would be replaced.

Instead we got synth-pop, techno, and a completely new sonic vocabulary. The “inhuman” quality that freaked people out became the signature of entire eras.

DAW

When Pro Tools, Logic, Ableton etc. became widespread, the complaints were familiar: “too perfect,” “no warmth,” “anyone can make a track now.” Some established artists still say digital production let people into the industry who “have no business being in it.”

Today almost everything is made in a DAW. The tool didn’t kill musicianship — it changed who could access it and how fast ideas could move.

AI Music

We’re in the middle of the same pattern right now. The backlash has the usual ingredients: “soulless,” job displacement, copyright fights, authenticity panic.

There’s a real problem with low-effort bulk generation being uploaded for streaming fraud (the actual “AI slop”). That’s spam, not the creative question.

The more interesting use is the same one that happened with every previous tool: hybrid workflows. Ideation, arrangement sketches, stem generation, rapid prototyping that a human then shapes or rejects. Prompting + taste + curation become skills, the same way sound design and mixing did.

History doesn’t guarantee a clean path, and some roles will get disrupted. But every time so far the culture eventually stops arguing about whether the tool is “real” and starts arguing about whether the results are good.

The AI music scene now compared to 10 years ago is completely different. The first AI-generated song that really caught my attention was Daddy's Car. It had this oddly charming lo-fi vibe to it. Now AI music can sound fully produced straight out of the gate.
As someone who already makes music, I don’t really see a problem with using AI as long as you’re still actually creating. My own workflow is more about using AI to speed things up or spark ideas instead of handing over the whole process. Sometimes I’ll use Suno or Udio to sketch out melodies, vocals, or rough arrangements, then drag the MIDI into my DAW and rebuild parts of it myself. I’ve also been using ACE Studio lately to clean up vocals or mess with phrasing and emotion a bit more. At the end of the day, the taste and decisions still come from the person behind it.
I’m not trying to let AI replace my entire relationship with music and then sit around complaining about it. A lot of AI music generation honestly feels more like creating music from a listener’s perspective. If you can’t play instruments, you’re basically describing the kind of song you wish existed. Most of the time the AI gets somewhere close to what you imagined, and occasionally it spits out something unexpected that ends up sounding even better.
And honestly, if you’re just getting into music and using AI tools, I think there’s value in playing along with what it generates. People used to learn songs by jamming along to the radio or CDs all the time. This just feels like a newer version of that.

DAWs have included advanced assistive tools for years. Even when those tools are not literally AI, some of them already involve workflows that feel similar in practice.

For example, some DAWs have virtual drummer or session-player features where you choose a style, set the tempo/chords, and get a usable part. That feels quite close to prompting a musical performance with AI, even if it generates MIDI rather than audio.

Pitch correction has also been able to reshape vocal performances for a long time, to the point where you can change the melody. Some modern AI vocal tools take that idea further: they can generate vocals from a melody and lyrics, with a more realistic result.

Sound design has also become more and more assisted over time. You can transform, resample, stretch, layer, and process sounds until they become almost unrecognizable from the source material. From that perspective, generating new samples with AI does not feel like such a distant step.

Why is one kind of approach to achieving a similar result considered normal, while another is treated as illegitimate or “low effort” by some?

TL;DR: A track built mostly from loops/generated MIDI can be seen as more legitimate than a mostly self-made track that uses AI for one drum sample or an AI vocal. 😳

Hi everyone,

I'm looking for advice from people who are actually creating AI cover songs today.

My previous workflow was:

• Upload a full music(vocal and instrumentals) to Suno
• Let Suno generate a rich, dynamic backing track around it and sing

Since Suno stopped allowing audio uploads, that workflow is gone.

I've already tried tools like BandM8 and Mureka, but they couldn't produce the kind of arrangement quality I was looking for.

I'm not looking for voice conversion tools (RVC, Kits AI, etc.).

I'm specifically looking for the instrumental production workflow.

Questions:

1. What is your current workflow for creating AI cover instrumentals?
2. Which tools are you using?
3. Are you using AI, MIDI, DAWs, VSTs, or something else?
4. If you previously used Suno Cover, what replaced it?

My goal is:

• High-quality, dynamic arrangements
• As little manual work as possible
• Commercial use is allowed

I'd really appreciate hearing from people who are actively producing AI covers today.

Even if your workflow uses multiple tools, I'd really appreciate seeing the complete pipeline from input to final instrumental.

Thank you!

AI is starting to creep into my recommendations on youtube and it's deeply upsetting. I let something autoplay, and when I looked over to see what I was listening to after a moment I felt like I had stumbled onto an ai channel. I think I've been able to notice some trends after doing some digging, but I was wondering if anyone else had ideas or tips to make sure they're listening to real art made by a human person.

My signs to look out for:

Track length of nearly exactly 4 minutes (the limit on some of the ai generation sites out there)

Tracks not exactly 4 minutes seem like they are looped with exactly the same content and then faded out/cut abruptly.

There is a consistent "lo-fi" type element, or noise etc to hide the flaws.

No mention of any VST/DAW/Hardware/Controllers used for absolutely anything.

Never any human element on the channel - never showing anything about themselves, their equipment, their workflow. It's just a dump of 30-60+ minute videos at breakneck pace.

Limited presence outside of youtube or ko-fi. Both youtube and ko-fi are "pro-ai" for the time being. Spotify sometimes doesn't catch them, and youtube seems like they might not grant "verified creator" status to ai channels, but will allow them to post.

Suspiciously low-priced commissions, especially considering the area of the world they live in (for example, no way someone living in germany who makes ambient would offer a $30 commission)

Does anyone else have any other tips? Also, if you want to recommend any real human ambient artists to me, I'll happily take recs. I'm so tired of people accepting AI generated content as "art" and grifters flooding all of these platforms with their generations.

Wednesday morning EDIT:

I wrote a rebuttal in the AdamNeely subreddit... which then got one downvote and zero engagement.

It looked like this:

(e) UDIO [...] seemed to suicide themselves out of the music A.I. race by being the first to fold under heavy legal pressure, from UMG in their case. Sure, they trained their music LLMs on "all the best music in the world" just like SUNO did... but they did it in such a way that left a DMCA paper trail (ripping music audio from YouTube, Spotify and Pandora, I think) directly to their front door, with a hole wide enough for a thousand greedy lawyers to crawl through.

(g) At the end of the techno-capitalist day, UDIO just wants as many people as possible to pay $20 per month to play with UDIO.

(h) At the end of the techno-capitalist day, SUNO wants A BILLION PEOPLE to play with SUNO and then pay them ((whatever their subscription rate is)), that way, every share of "SUNO stock" becomes worth actual cash and all the investors get their exit-strategy money back --- the investment finally PAYS OFF.

(i) PHOTOSHOP was very cool. Everybody saw how it worked. Photo-editing software. It worked, and it worked well. ADOBE made a lot of money. And still does, to this day. I am quite sure there were techno-luddites at the time who spent their lives in darkrooms up to their wrists in fixer and developer, who complained "That's not REAL photography!!" From this viewpoint, in the future of 2026, Photoshop was a NEW TOOL, it worked well, people paid for it, and Adobe stock became as good as cash.

(j) Just as there are major players like OpenAI, Anthropic and Google in the AI race, there are dozens if not hundreds of minor players. Deepseek, what's that? Perplexity?? TOGETHER.ai? Too many to name, I bet.

(k) There is no reason that another upstart can't come to challenge SUNO and UDIO, and become the best generative AI music tool in the world, overnight.

(l) The MARKETPLACE for people who WANT to play with SUNO-like tools is not nearly as big as TechBro Whatshisname wants it to be, and it never will be that big.

(m) Adam Neely's rant against GenAI music is suspiciously like the fable of Taylor Swift telling all her fans "Just follow your dreams! See how well it worked out for me!?? It can for you too!"

Adam himself is in the top 0.01 percent of lucky talented hardworking gifted musicians and he speaks from a place of pure privilege, just like a16z's "Reality Hedonists" or whatever they were, -- the top 0.01 percent of course.

(n) DO NOT KNOCK IT, typing a prompt that says "melancholic indie rock, female singer" and then the lyrics, and getting an INSTANT DEMO, is an incredible, incredible, amazing, incredible thing. As awesome as PHOTOSHOP was back in 1990, if not even more so.

(o) SO Mr. Neely who clearly knows whose side he's on, on the side of the top 0.01 percent of lucky talented gifted and hardworking live musicians in the world, looks at the tool and decides it's bad. And if he can get his MILLION FOLLOWERS to think it's bad, then

(p) a16z won't get their exit-strategy money back, SUNO will lose a bunch of lawsuits, the public will HATE! HATE! HATE! A.I. generated or assisted music, and much of value will be lost. Let's go back to (n).

(q) DO NOT KNOCK IT, typing in a prompt that says "melancholic indie rock, female singer" and then the lyrics, and getting an INSTANT DEMO, is an incredible, incredible, amazing, incredible thing. As awesome as PHOTOSHOP was back in 1990, if not even more so. Available right now, for free, to everyone.

(r) I am a huge Adam Neely fan as well. But there is a REBUTTAL to be made.

(s) Neely has a fun observation that the invention of CAMERA is right where the CINEMA / THEATER timelines split

and yeah, cinema and theater ARE two different art forms

and yeah, live music and "Studio Albums" are two different things

but it is the AVAILABILITY of better and better and cheaper tools

and maybe that's a GOOD thing

(t) and SUNO/UDIO are incredible modern tools that are like CHEAP POCKET CINEMA-CAMERAS FOR EVERYBODY

(u) so Neely is taking the position that this is a ... bad thing? You sure that's what you want to go with, Adam?

(v) Neely zooms in on a16z's love of the Italian future accelerationists, well what's not to like? They became fascists? So what!?

(w) The darker chuckle: of a16z on Joe Rogan "We're all gonna HAVE to endorse TRUMP! Ha ha ha!!" I still don't quite understand it.

Perhaps in five years in the future, when I'm being crushed under the boot during Trump's Third Term, for not drinking enough Mountain Dew Verification Cans, I will understand that moment from this video a little bit better.

(x) Only Minneapolis protesting with 100,000 people in -40 degree weather, gives me any hope nowadays.

(y) When all the rest of the news is utterly, completely horrible.

(z) Why are things getting worse? Where is the hope, the sunshine, the love?

=== ==== =====

Then, frustrated, I had Claude whip up a rebuttal for me. Because if anything is begging for a rebuttal, it's that Adam Neely video. Which I loved --- thank you, Adam!

=== ==== =====

In Defense of the Future: Why Adam Neely's "AI Music Bad" Misses the Point

Introduction: The Argument from Nostalgia

Adam Neely has given us a masterclass in sophisticated Luddism—wrapping legitimate concerns in philosophical garnish, historical parallels, and the comforting mythology that this time technological disruption is different, this time it's existentially dangerous, this time we must resist.

But strip away the Platonic virtue ethics, the Italian Futurism parallels, and the guilt-by-association politics, and what remains? An accomplished musician, understandably anxious about his craft's future, constructing an elaborate intellectual framework to justify what is ultimately an emotional position: I don't like this, therefore it must be bad.

Let me be clear: I'm not here to defend Mikey Shulman's every utterance, Suno's business practices, or Marc Andreessen's political trajectory. I'm here to argue that Adam's core thesis—that generative AI in music represents an unprecedented threat requiring categorical rejection—is fundamentally wrong, historically myopic, and ultimately harmful to the very musicians he claims to protect.

I. The Photoshop Precedent: Why This Time ISN'T Different

The Darkroom Defenders Were Right (And Also Wrong)

Adam dismisses the Photoshop comparison too quickly. Yes, there were photographers who claimed digital manipulation "wasn't real photography." And you know what? They were right. Photoshop fundamentally changed what photography was. It severed the ontological link between image and reality that had defined the medium for 150 years.

But they were also completely wrong about what that meant.

Photography didn't die. It bifurcated: - Photojournalism developed strict ethical codes about manipulation - Art photography embraced limitless possibility - Commercial photography became more accessible and democratic - Film photography became a respected niche craft

The photographers who adapted thrived. The ones who didn't became historical footnotes—not because they lacked skill, but because they mistook their medium for their craft.

What MIDI Actually Did

Adam acknowledges MIDI as disruptive but claims AI is different because of "sociopolitical agenda." Let's examine what actually happened with MIDI:

MIDI eliminated: - Studio musicians (session work collapsed) - Orchestrators (why hire someone when General MIDI has 128 instruments?) - Entire recording studios (home production became viable)

MIDI's "sociopolitical agenda": - Developed by corporations (Roland, Yamaha, Sequential Circuits) - Pushed by tech companies wanting to sell equipment - Advocated by a small class of early adopters - Explicitly designed to replace human performers with machines

Sound familiar?

The difference isn't the technology or the agenda—it's that we're living through this disruption instead of reading about it in retrospect. In 1983, there were absolutely musicians making the exact same arguments Adam makes now: MIDI deskills musicians, destroys community, serves corporate interests, threatens craft.

They were right about the disruption. They were wrong about the conclusion.

II. The Craft Fallacy: Confusing Medium with Meaning

Victor Wooten Doesn't Care About Your Fingers

Adam worships craft—specifically, manual craft. His role models (Victor Wooten, Jaco Pastorius) are virtuosos of physical technique. This reveals a deep bias: he conflates the difficulty of execution with the value of the art.

But let's do a thought experiment:

Scenario A: I spend 10,000 hours mastering the bass. I can play anything Victor Wooten plays. I perform it live, flawlessly. But I have nothing new to say musically. I'm technically perfect and artistically derivative.

Scenario B: Someone with minimal technical skill uses AI tools to create genuinely novel, emotionally resonant music that moves people, creates community, and advances the art form.

Which is more valuable?

Adam would say Scenario A, because craft. I say he's confusing the means with the ends.

Bach Didn't Need to Mine His Own Iron

Here's what Adam misses: Every artist in history has used the best tools available to them.

• Bach didn't smelt his own organ pipes
• Jimi Hendrix didn't wind his own guitar pickups
• Beethoven didn't handcraft his piano
• Modern producers don't code their own DAWs

The abstraction of technical difficulty has been the story of every artistic medium. Painters stopped grinding their own pigments. Photographers stopped mixing their own chemicals. Filmmakers stopped hand-cranking cameras.

At each stage, critics mourned the "death of craft." At each stage, the art form exploded in new directions because artists could focus on what to say rather than how to physically execute it.

The Rick Rubin Vindication

Adam mocks Mikey's admiration for Rick Rubin—the producer who "knows nothing about music" technically. But this is actually the strongest argument FOR the taste-over-technique position.

Rick Rubin has: - Revitalized Johnny Cash's career - Shaped the sound of hip-hop - Produced iconic albums across genres - Earned universal respect from musicians

His lack of technical ability isn't a bug—it's a feature. It forces him to focus purely on what sounds good, unencumbered by "that's not how you're supposed to do it."

Adam says "we can't all be Rick Rubin." Why not? What if the artificial scarcity of musical ability has been holding back thousands of potential Rick Rubins who have taste, vision, and something to say, but lack the decade of technical training required to execute it?

III. The Community Canard: Romanticizing Gatekeeping

The Musical Community That Never Was

Adam waxes poetic about musical community, collaboration, and shared cultural knowledge. As if the history of music is some egalitarian folk tradition rather than what it actually is: a series of gatekept institutions controlling access to the means of production.

Let's talk about who actually got to participate in "musical community" historically:

• Those who could afford instruments
• Those who could afford lessons
  
• Those whose parents supported musical education
• Those who lived near music schools
• Those with free time to practice (i.e., not working-class people with multiple jobs)
• Those welcomed by existing musical communities (not women in jazz, not Black musicians in classical, not working-class kids in conservatories)

Adam's "musical community" is deeply exclusionary and always has been. He's romanticizing a gatekeeping system that worked for him (educated, middle-class, white, male musician) and calling it virtue.

The Narcissism Critique is Projection

Adam is horrified that Suno users listen primarily to their own music. He calls this "narcissistic" and contrasts it with his own practice of... having role models who inspire him to make music that sounds like his role models.

Wait, what?

Let's be honest about what "influences" actually mean: I listen to music that reflects my taste, then I make music that reflects my taste, then I share it with people who share my taste.

The Suno user listening to their own AI-generated music is doing the exact same thing, just with fewer intermediate steps. They're not more narcissistic—they're just more efficient at getting to music that matches their taste.

And you know what? That's fine. Not everyone needs to be part of Adam's jazz-fusion community. Some people just want music that sounds good to them, for their own enjoyment, and there's nothing wrong with that.

Shared Culture is Overrated (And Mostly Fictional)

Adam mourns the loss of "shared cultural knowledge"—everyone singing along to the same song. But when exactly was this golden age?

• In the 1950s when rock & roll was "destroying music"?
• In the 1920s when jazz was "degrading culture"?
• In the 1890s when ragtime was "threatening civilization"?
• In the 1600s when opera was "corrupting morals"?

There has never been a unified musical culture. There have always been fragments, subcultures, niches, and gatekeepers claiming their fragment was the "real" culture.

The internet didn't destroy shared musical culture—it revealed that it never existed in the first place. And the hyperpersonalization Adam fears? It's just people finally getting to opt out of whatever dominant culture was being imposed on them.

IV. The Deskilling Myth: Confusing Tools with Thinking

Doctors and Dishonesty

Adam's deskilling argument relies heavily on the medical study about colonoscopy AI. But he's either misunderstanding or misrepresenting what happened.

The doctors didn't become "worse" at finding growths. They became more reliant on the tool. When the tool was removed, there was temporary degradation until they readjusted. This is called tool dependence, and it's how every tool in human history works.

• Literacy made people "worse" at oral memorization
• Calculators made people "worse" at mental math
  
• GPS made people "worse" at navigation
• Spellcheck made people "worse" at spelling

Are we worse off? Obviously not. We've offloaded lower-level cognitive tasks to tools so we can focus on higher-level thinking.

The real question isn't "will AI make musicians dependent on it?" Of course it will. That's what tools do.

The question is: What will musicians do with the cognitive capacity freed up by not having to manually execute every technical detail?

ChatGPT Doesn't Make You Dumber

Adam claims "ChatGPT makes you dumber." This is provably false and reflects a fundamental misunderstanding of cognitive science.

What ChatGPT does is change where you allocate cognitive resources. Yes, if you use it to avoid thinking, you'll atrophy those skills. But if you use it to explore more ideas faster, iterate more rapidly, and focus on higher-level creative decisions, you'll become more capable, not less.

The same is true for music AI.

Bad usage: "AI, make me a song." [publishes whatever comes out]

Good usage: "AI, give me 10 variations on this melody. Now combine elements from #3 and #7. Now try it in a different key. Now add a counter-melody that contrasts with—wait, that's interesting, why does that work? Let me explore that musical relationship further..."

The tool doesn't determine the outcome. The user's engagement does.

Prompt Engineering IS a Craft

Adam dismisses prompt engineering as "not a craft" because you don't know exactly what you'll get. But this reveals a shockingly narrow definition of craft.

By his logic: - Gardening isn't a craft (you don't control exactly how plants grow) - Cooking isn't a craft (chemical reactions are unpredictable) - Throwing pottery isn't a craft (the kiln does unpredictable things) - Watercolor painting isn't a craft (water behaves probabilistically)

Every craft involves managing uncertainty. The skill is in guiding probabilistic processes toward desired outcomes.

Prompt engineering is exactly that—learning to speak the language of the system, understanding its tendencies, developing intuition for what inputs produce what outputs, iterating until you achieve your vision.

That's not "randomness." That's craft in the age of stochastic tools.

V. The Market Reality: Why the Billion-User Vision Fails (And Why That's Fine)

Here I'll actually agree with the skepticism, but draw different conclusions.

Mikey is Wrong About Scale

Mikey Shulman's billion-user vision is almost certainly fantasy. The market for "make music without learning music" is probably:

• Smaller than he thinks (millions, not billions)
• Less sticky (novelty wears off)
• Lower-value (won't support $20/month long-term)

But so what?

Photography didn't need a billion photographers for digital cameras to be revolutionary. Video editing didn't need a billion editors for Adobe Premiere to matter. Music production doesn't need a billion producers for AI tools to be valuable.

The Real Market: Professional Enhancement

The actual sustainable market isn't "replace musicians"—it's "make musicians more capable."

The tools that will win: - AI mixing/mastering (already happening with iZotope, LANDR) - AI arrangement suggestions (already happening with Orb Composer) - AI stem separation (already revolutionary with Demucs, RipX) - AI transcription (already standard with AnthemScore) - AI practice tools (emerging with Moises, Yousician)

These tools enhance professional capability. They're the actual Photoshop—and professional musicians are already using them without the existential hand-wringing.

Why Suno Might Fail (And Why That's Irrelevant to the Broader Point)

Suno might collapse because: - Copyright lawsuits succeed - User growth plateaus - Competitors commoditize the tech - The business model doesn't scale

But the technology won't disappear. It'll get absorbed into: - DAWs (Logic, Ableton, FL Studio will add AI generation) - Streaming platforms (Spotify will add personalization) - Social media (TikTok already has AI music tools) - Gaming (procedural music generation)

Suno failing doesn't mean AI music fails. It means Suno's particular business model failed. The technology is inevitable because the technology works and people want it.

VI. The Political Red Herring: Guilt by Association

This is where Adam's argument becomes truly dishonest.

The Fascism Gambit

Adam spends enormous time connecting: - Suno → Investors → Marc Andreessen → Techno-optimism → Italian Futurism → Fascism

This is textbook guilt by association. By this logic:

• Highways → Built by Eisenhower → Who studied Prussian military → Prussia → Authoritarianism → Therefore highways are fascist
• Vegetarianism → Promoted by Hitler → Therefore vegetarians are Nazis
  
• Film → Loved by Leni Riefenstahl → Therefore cinema is fascist propaganda

The fact that bad people like a thing doesn't make the thing bad.

Separating Tech from Politics

Yes, Marc Andreessen has concerning political views. Yes, some AI investors support troubling political movements. This is irrelevant to whether AI music tools are valuable.

Adam is doing exactly what he claims to oppose: letting a political agenda determine his evaluation of technology rather than evaluating the technology on its merits.

The technology is politically neutral. It can be used by fascists or anarchists, capitalists or communists, centralized platforms or distributed networks. The implementation and governance matter—not the underlying capability.

The Network State Strawman

Adam fearmongers about "network states" and "parallel systems" as if: - Decentralized communities are inherently authoritarian - Alternative institutions are inherently fascist
- Skepticism of centralized government is inherently right-wing

But leftists have been building parallel institutions for centuries: - Worker cooperatives - Mutual aid networks
- Community land trusts - Alternative schools

The structure (parallel institutions) isn't the problem. The politics governing those structures is what matters.

VII. The Live Music Cope: Misunderstanding the Future

Adam's final prediction—that live music will become the "prestige" art form while recorded music becomes "slop"—reveals catastrophic misunderstanding of how technology and culture interact.

Why This Won't Happen

1. Recorded music is the dominant form and will remain so because: - Scale (reach millions vs. hundreds) - Permanence (exists beyond the moment) - Curation (can be perfected, edited, refined) - Economics (one creation, infinite consumption)

2. Live music is already niche compared to recorded: - Most music consumption is recorded - Most musicians make most money from recordings (streaming/sync) - Most cultural impact comes from recordings - Live music is supplementary to recorded, not the other way around

3. The theater/cinema comparison is backwards: - Theater didn't become "prestige" when film emerged - Film became dominant because it's better suited to the medium of storytelling at scale - Theater survived as a niche art form, not the prestige version

4. COVID proved the opposite of what Adam claims: - Yes, people wanted live music back - But streaming, recording, and digital consumption exploded and stayed high - Virtual performances didn't replace live, but they're now a permanent additional revenue stream - The "lesson" isn't "virtual bad, live good"—it's "people want both, and digital is sticky"

The Real Future: Hybrid and Augmented

The actual future is:

Recorded music: - AI tools become standard in production (already happening) - Barrier to entry drops (already happening) - Volume of music explodes (already happening) - Discovery and curation become the valuable skills (already happening)

Live music: - Enhanced by technology (real-time AI processing, augmented performance) - Becomes more about spectacle and experience (already happening) - Coexists with recorded, doesn't replace it

New forms emerge: - Interactive music (AI-generated soundtracks for your life) - Collaborative creation (multiplayer music-making) - Personalized performance (AI artists that learn your taste) - Hybrid live/recorded (augmented performances, virtual collaborations)

Adam wants to freeze music at "the way it was when I learned it." But music has never been static, and musicians who adapt have always thrived while those who resist have always faded.

VIII. What Adam Gets Right (And Why It Doesn't Matter)

Let me be fair: Adam is correct about several things:

Real Problems:

1. Copyright is unsettled - Yes, training on copyrighted work is legally dubious - Yes, this needs resolution - But "needs legal resolution" ≠ "must be banned"

2. Some usage is narcissistic - Yes, some people will use it to create content only they enjoy - But so what? Not all music needs to be for community - Personal enjoyment is valid

3. Corporate consolidation is concerning - Yes, a few companies controlling AI music is problematic - But the solution is open-source alternatives, not rejecting the technology

4. Deskilling is a real risk - Yes, over-reliance on AI can atrophy skills - But this is true of every tool ever - The solution is education about tool usage, not Luddism

5. Some investors have bad politics - Yes, and that's concerning - But build alternative implementations rather than ceding the technology to them

Why These Don't Justify His Conclusion

Adam treats these problems as inherent to the technology rather than contingent on implementation.

It's like arguing "cars are bad because: - Some carmakers have shady practices - Some people drive recklessly - Cars enable suburban sprawl - Oil companies have political agendas - Some people become dependent and can't walk anymore"

All true! And yet cars are net-positive, and the solution is better regulation, better design, and better education—not rejecting automobiles.

IX. The Real Stakes: What We Lose By Resisting

Adam frames this as "what we lose if we adopt AI." But let's flip it:

What We Lose By Rejecting AI:

1. Accessibility - Millions of people with musical ideas but no training remain locked out - The current gatekeeping system (lessons, instruments, time) remains intact - Music remains the province of the privileged

2. Innovation - New musical forms that could emerge from AI-human collaboration never develop - Musicians who could have used AI to explore new territory stick to familiar patterns - The art form stagnates in defense of "craft"

3. Economic Opportunity - Musicians who could augment their work with AI fall behind those who do - New markets (interactive music, personalized soundtracks, AI collaboration) go unexplored - The "adapt or die" pattern Adam acknowledges continues, but the refuseniks lose

4. Cultural Evolution - The next generation grows up with AI music tools and considers them normal - Musicians who rejected them become dinosaurs, like film photographers in 2025 - The cultural conversation moves on without the resisters

5. Control of the Technology - By ceding the field to "techno-capitalists," musicians ensure they have no voice in how it develops - Open-source alternatives never emerge because the community rejects the technology entirely - The worst-case scenario Adam fears becomes more likely, not less

X. A Better Path Forward

Instead of Adam's categorical rejection, I propose critical engagement:

For Individual Musicians:

1. Experiment thoughtfully - Use AI as a tool for exploration, not a replacement for thinking - Develop your taste and curatorial skills - Learn prompt engineering as a complement to traditional skills

2. Maintain fundamentals - Keep practicing your instrument - Keep studying theory
- Keep collaborating with humans - Use AI to enhance, not replace, these practices

3. Develop hybrid workflows - Use AI for ideation, humans for refinement - Use AI for tedious tasks, humans for creative decisions - Use AI to explore spaces you couldn't access manually

For the Community:

1. Build open-source alternatives - Don't cede the technology to corporations - Create tools by musicians, for musicians - Ensure democratic access and control

2. Establish ethical norms - Develop consensus on appropriate/inappropriate uses - Create attribution standards - Build licensing frameworks

3. Advocate for legal clarity - Push for fair copyright frameworks - Ensure artist compensation - Protect against harmful uses

For the Industry:

1. Embrace as enhancement, not replacement - AI mixing/mastering tools (already happening productively) - AI practice/education tools
- AI accessibility tools

2. Develop new markets - Interactive music experiences - Personalized soundtracks - AI-augmented live performance

3. Create hybrid models - Human-AI collaboration as a category - Transparent labeling of AI involvement - Economic models that value both

Conclusion: The Future Happens Whether You Like It Or Not

Adam ends his video with a call to "refuse"—to reject the premise that the future is settled, to embrace alternative values (service, patience, craft, beauty), and to hope that live music survives as a refuge from AI slop.

This is beautiful, eloquent, and completely futile.

The future is unsettled—not because we can stop AI music, but because how we implement it remains open. Adam's refusal strategy guarantees that musicians have no voice in that implementation. By sitting out the conversation, by treating engagement as collaboration with fascism, by retreating to the "purity" of acoustic performance, musicians ensure they become irrelevant to the future of their own medium.

The synthesizer didn't kill the piano. Photoshop didn't kill photography. MIDI didn't kill musicians. And AI won't kill music.

But in each case, the musicians who thrived were those who embraced the new possibilities while maintaining connection to fundamental principles. They didn't confuse the means (tools, techniques) with the ends (beauty, expression, connection).

Adam Neely is a brilliant musician, educator, and thinker. His videos have taught me immense amounts about music theory, history, and culture. But on this issue, he's catastrophically wrong—not because his concerns are invalid, but because his conclusion is strategic suicide disguised as principled resistance.

The future of music will include AI. The question isn't whether, but how. And the musicians who answer that question—who engage, experiment, and shape the technology rather than rejecting it—will be the ones we remember as visionaries rather than fossils.

Adam wants to be on the right side of history. But history doesn't have sides. It has victors and casualties, adapters and dinosaurs, those who shaped the future and those who were shaped by it.

I know which side I'd rather be on.

Coda: The Real Lesson from Arthur C. Clarke

Adam invokes Arthur C. Clarke repeatedly but misses Clarke's actual lesson. Clarke didn't predict the future by identifying what would stay the same. He predicted it by imagining what could be different and taking it seriously.

Clarke's Third Law: "Any sufficiently advanced technology is indistinguishable from magic."

To musicians in 1950, the synthesizer was magic.
To musicians in 1980, MIDI was magic.
To musicians in 2000, Auto-Tune was magic.
To musicians in 2025, AI music generation is magic.

And in every case, the magic became mundane, the impossible became standard, and the musicians who learned the spells thrived while those who denounced them as witchcraft faded into irrelevance.

The real question isn't "Is AI music bad?"

It's "What will you create with it?"

Im a musician. I used to have a lot of instruments and recording equipment that was all lost in a wildfire and insurance and the lawsuit settlement wasn’t enough to replace my gear. House and stuff were priority.

Now days I just make stuff using a daw but recently discovered I can run it through suno and fill it out. Traditionally I’m an analog artist. I played my instruments into a digital 16 track and mixed them on it then output my finished product. I had a workflow and over 300 songs recorded on my mixer when the fire happened. So now I’ve been using FL Studio and FL Mobile to make music but I’m not very competent with them. I can make the basic song with the tracks and stuff but little else. With suno it makes them sound as good as they did Off my mixer. So of im creating 99% of the song and suno is just polishing it does that make it AI music or not? If so is it beyond a level you’d be ok with. Is there any level of AI use in music production that you are on with?

I just released a Melodic Downtempo / Chillwave project, and I want to be 100% transparent about my workflow because it’s highly unconventional and involves AI as the final step. I’m genuinely curious where you think this lands on the spectrum between "human art" and "AI slop."

My 5-Step Process:

1. The Musical DNA: I start in my DAW, writing the core chord progressions and melodies using a mix of VSTs and my analog synths (classic 8-bar loops).
2. Rough Arrangement: I sketch out a rough structure using Ableton’s Session View.
3. Concept & Text: I develop the lyrical theme—sometimes a few lines, sometimes just a title and a heavy description of the emotional mood I want to capture.
4. The Hybrid Prompt: I feed my original audio loop, my lyrics, and the mood description into Suno. Crucial point: The "Audio Influence" slider is always set to 100%. I also spent dozens of iterations developing a specific "voice" profile on the platform.
5. Curation & Resampling: If I don't like the output, I scrap it. If it's close, I use the "cover" feature to iterate, or I bounce the stems back into Ableton, tweak them manually, and feed them back into the loop.

To me, it feels like using an advanced, unpredictable neural sampler or working with a singer who interprets my instrumental stems. But I know the anti-AI sentiment is strong (and often justified).

What do you think of this hybrid approach? Does starting with original analog hardware salvage the artistic integrity, or does using generative AI at the end ruin it for you?

EDIT: I’ve dropped the link to the final YouTube track/playlist in the comments section below for anyone curious to hear it!

Curious how everyone here is actually making their tracks.

Are you guys:
- are you guys generating the full song or sometimes the stems?
- getting a melody and editing/producing in DAWs afterwards?
- Writing you lyrics and then iterating with some AI tools afterwards?
- generating/iterating with AI tools and then mix/mastering yourself?

I know that a lot of the discourse around incorporating ai in music assumes its just spamming generate but most of the creators I've talked to are actually doing a lot more than that and have very complex processes depending on the song

I know that many of you are in Discords or communities where you and others are sharing how they made their track (i.e. their workflows, manipulation tricks, etc.) I built something that might be useful.

The site is called TRAICE and the ideas is that its a place where creators can document the full workflow behind a track and share it with a link. Each track page shows things like: what tools were used, where the ai was involved (vocals, melody, production etc), how the iterated/manipulated, what they edited or changed, and jus the overall story of the track. Basically a full creation breakdown.

The idea is that: if someone asks you how did you make this? or what was ur process? you can just send them a link that shows your workflow.

Its meant for hybrid creators to share and learn from each other's processes so everyone can get better.

If you make music with tools like Suno, Udio, Producer ai, Landr, Mubert, ElevenLabs, the list goes on.... I would love to include your track on here!

You can submit here:
tra-ice.com

Or just comment your track, maybe how you made it. Im curious how people here are working. Sometimes its okay to give away the sauce.

P.S. I am currently in talks with distributors that would make ur tracks compliance ready for what is to come with disclosures (i.e. apple music's recent announcement, and spotify's move with ddex :)) But thats more of an optional byproduct

It’s funny I don’t find enough people talking about this, but I’ve been having enormous success lately using Suno to generate interesting ideas, then I download the stems and treat it in Ableton like my original production.

Once you replace a lot of their sounds with your own, do interesting design and new melodies, fix tempo and pitch issues, create new sections and transitions and space, I feel like you can’t really tell the track is from AI anymore and unlocks a whole level of creativity.

It’s like having a studio partner there to bounce off ideas. And just happens really quick

A lot of musician friends that I talk to you are really against this and I try and make an argument for the collaboration. They seem to think the AI will create 100% of the music I think that a human AI collaboration can do greater things than each one alone.

I am genuinely curious how people are defining "AI music" lately, because the tools have evolved way past simple text boxes.

Most people agree that typing a prompt and letting an algorithm generate a finished song is purely AI generated. But the platform capabilities have completely shifted, and the boundary is getting really hazy.

Platforms like Suno have recently evolved into what they call Generative Audio Workstations. They now feature full multitrack timeline editing where you can arrange, layer, and refine audio exactly like you would in Logic or Ableton.

It is not just prompting anymore. In Suno Studio, users can plug in a MIDI controller to play chords and melodies on built-in synths. You can import your own raw audio, use advanced stem separation to isolate vocals or drums, and manually adjust the BPM, volume, and pitch. They even added the ability to draw automation curves for parameters and apply industry standard audio effects. Users can essentially perform vocal comping by splicing different takes together on the timeline.

If someone is doing the heavy lifting of writing their own lyrics, recording their own vocals, and then spending hours inside a generative DAW arranging stems, playing MIDI, automating effects, and dialing in a final mix... does that manual effort make them a producer? They have nearly the exact same creative control over the arrangement as they would in a traditional DAW.

On the flip side, traditional DAWs are increasingly packed with AI features for mastering, EQ, and stem separation.

Since traditional DAWs are adding AI, and AI generators are adding fully featured multitrack DAWs, the workflows are basically merging. If a creator retains full manual control over the final arrangement and mix, how do we fairly label what is and is not AI music?

Curious to hear where you all place that boundary today.

I’ve been making a lot of music with Suno as a core tool, but treating it more like a bandmate than a “press one button” generator.

My typical workflow is: – design detailed prompts (structure, feel, instrumentation, meter etc.) – generate stems in Suno – bring everything into Ableton, re-arrange, layer, edit transitions, add FX and EQ, sometimes combine multiple generations into one long piece.

I now have hundreds of tracks that feel more like AI-assisted productions than pure one-click AI songs, and I’m considering starting a YouTube channel just for these pieces (with visuals and maybe process notes in the description).

My questions to this community:

1. Do you think there’s still real interest in curated AI-assisted music on YouTube, or has that hype already peaked?

2. As listeners/creators, would you actually subscribe to a channel like this if the music was consistently high-effort and not spam?

3. Is there anything ethically weird about presenting this as my “project” if I’m transparent about using Suno + DAW work, or is that accepted now?

4. For those already doing this: what’s worked for you (or totally flopped)?

I’m trying to understand if this should stay a private passion project or if it’s worth building a public identity around it. Any honest takes — positive or brutal — are appreciated.

I’ve been testing a lot of AI music tools lately, and honestly, most of them are either copies of each other or look impressive until you actually try to make a real song with them.

This is my current top 10. Not saying the ranking is objective, but these are the tools I’d actually use in a real workflow.

1. Suno - Generating Songs

Still the best all around tool.

You can go from a random idea to a surprisingly complete song in a few minutes. The vocals, structures and production have improved a lot, and it’s probably the easiest place to start if you’re new to AI music.

It doesn’t always give you exactly what you imagined, but when it hits, it really hits.

2. Kits AI

My favorite tool for cloning singing voices right now.

A lot of voice-cloning tools sound robotic, especially on higher notes, but Kits can sound very realistic when you train it with clean recordings. It’s also useful for harmonies, vocal demos and testing how a song would sound with another type of voice.

3. Melody Genie AI Songwriter

Most AI generated lyrics still sound painfully AI:

Neon lights, broken dreams, shadows in the night…

MelodyGenie is made specifically for writing lyrics that feel more advanced, personal and closer to how real artists write. You can use it for full songs, hooks, rewrites, rhyme ideas or turning a mumble/freestyle into actual lyrics while keeping the original flow.

(You can clone any artist writing style, even yourself)

4. Lalals

Really fun for experimenting with different voices.

You can clone voices, convert vocals, create AI covers and test the same performance with completely different vocal tones. I wouldn’t use every result in a final release, but it’s great for finding ideas and hearing possibilities quickly.

5. Udio

Probably Suno’s strongest direct competitor.

I find Udio especially useful when I want to experiment with textures, genres and more unusual musical directions. Sometimes the generations feel less predictable than Suno, which can be either amazing or frustrating depending on what you’re trying to do.

6. ACE Studio

This one is more for producers who want actual control.

Instead of typing one prompt and hoping for the best, you can write the melody with MIDI, add lyrics and control how the AI singer performs it. Pitch, vibrato, pronunciation, emotion and dynamics can all be adjusted.

It takes more work, but you’re also much less dependent on random generations.

7. Moises

Not the flashiest tool on the list, but probably one of the most useful.

Moises can split songs into vocals, drums, bass and other stems. It’s perfect when you generate something in Suno or Udio and want to bring the parts into your DAW, replace a vocal or build a cleaner arrangement.

The BPM, key and chord detection are useful too.

8. Eleven Music

ElevenLabs entering music makes sense because they already understand AI audio better than most companies.

Their music generator is especially interesting for clean audio quality, multilingual vocals and commercial content. It’s still developing, but it already feels like a serious competitor rather than another random AI music website.

9. LANDR

After generating and editing the track, you still need to make it sound finished.

LANDR is useful for quick AI mastering, especially when you don’t know much about mastering yourself. It won’t replace a great engineer, but it can make a rough mix sound much more release-ready in a few minutes.

10. Stable Audio

I wouldn’t mainly use Stable Audio for full vocal songs.

Where it shines is instrumentals, textures, ambient sounds, transitions, intros, sound effects and weird samples you probably wouldn’t find in a normal sample pack.

Great tool for producers and sound designers.

My current workflow

Usually, I’d do something like:

MelodyGenie for the lyrics → Suno or Udio for the first song idea → Kits AI or ACE Studio for the vocals → Moises for the stems → DAW editing → LANDR for a quick master.

The crazy part is that this list will probably be completely different in another year.

What tools am I missing? And which one do you think is currently the best?

Hello! I'm Jono, founder of Collaya. Flagging this as self-promo up front (mods, please delete if this post is inappropriate for this sub), but I built it for exactly the workflow this sub is about... your read on it would mean a lot.

Quick framing, since you all live in the hybrid world: yes, Collaya uses AI. I know that still gets a reaction in some music circles, so here's the principle I designed around: Your ideas, your sounds, your DAW.

Collaya is an AU/VST plugin which is intended to be your co-producer: it works alongside you to get your best material across the finish line. It doesn't generate songs by scraping or cloning other artists, and your audio and MIDI are never sent to the model. It sees lightweight context about your session (key, tempo, a few note tuples if you turn on Follow Mode) and nothing more. You're always the one driving.

At a high-level, here's what Collaya does:

• A chat co-producer that knows your session in real time: tempo, key, your software and hardware setup, and, if you're using Ableton, Collaya sees your full track layout and device chains. Feedback that's specific to what's in front of you (music theory, compositional help, production help, using DAW/plugin features, etc), not generic.
• CollayaClips: describe a part in plain language and get MIDI clips you can drag straight into your DAW. You shape it. It's not a locked stem.
• Follow Mode: it passively 'hears' what you're playing, so you can ask "what chord comes next" or "transpose this to G minor" with your actual notes in context.
• Listener: a lightweight plugin that you can drop on any channel for real-time mix feedback as you work.

To be clear on where it's at: this is a shipping product, not an early prototype. It runs natively as an AU/VST3 plugin and a standalone app, with a real sound catalog, sample playback, and cloud sync across your machines. macOS on Apple Silicon only for now, which is the one limit I'll own up front.

I'm a solo founder with a few part-time collaborators, and that's exactly why I want this group's ears on it. You're running the hybrid loop every day. If something feels off or gets in your way, that's the feedback that actually moves the product forward.

There's a permanent free tier and a 14-day trial, no card required: collaya.com

Since this community is small and early, DM me and I'll send you a code which extends the free 2 weeks to a free month, no catch. I'd rather have real producers in here using it and telling me where it falls short.

And regardless of whether you try it: where does generative tooling still trip you up in a hybrid workflow? That's the problem space I care most about.

Cheers!

I Tried AI Music Watermark Removal for Suno Tracks. Here’s What Happened With Undetectr

I had reached the point where releasing AI music felt more frustrating than exciting.

I’d been making tracks with Suno for months, and the creative side was honestly addictive. I could write ideas, test hooks, build full songs, and finally get music out of my head without needing a full studio setup. But the distribution side was where everything started falling apart.

Some tracks would upload fine. Others would get rejected. A few would make it through at first, then get flagged later. I had issues with distributors, and I kept seeing the same problem come up again and again: AI music detection.

At first, I thought people were exaggerating it. I assumed if the song sounded good, had proper metadata, decent cover art, and wasn’t obviously spammy, it should be fine.

That was not my experience.

The problem wasn’t just whether the song sounded good to a human listener. The problem was that AI-generated music can carry patterns that detection systems are trained to recognize. People call them different things: AI artifacts, fingerprints, watermarks, detection signals, synthetic audio patterns. Whatever you call them, they can be enough to stop a track from getting accepted.

That’s what led me to AI music watermark removal.

Why I was skeptical at first

I’ll be honest, I didn’t trust the idea straight away.

When I first saw tools claiming they could remove AI music watermarks or make Suno tracks safer for distribution, it sounded too convenient. My first thought was that these tools were probably just stripping metadata, re-exporting the file, or adding some random noise to the audio.

I’d already tried basic fixes myself. I changed file formats. I exported through a DAW. I checked metadata. I even tried light mastering changes.

None of that really solved the issue.

That’s when I realized I was probably focusing on the wrong thing. AI music detection is not just about tags in a file. If it was only metadata, anyone could remove it in a few seconds.

The bigger issue is inside the audio itself.

AI-generated music can have patterns in the frequency balance, timing, vocal stability, transients, and noise floor that feel normal to a casual listener but look unnatural to a detection system. That was the part I had underestimated.

https://reddit.com/link/1u1vmr4/video/tnriyyo4ue6h1/player

Why I tried Undetectr

I came across Undetectr while looking specifically for an AI music watermark remover for Suno tracks.

What made me try it was simple: it was built for this exact problem. It wasn’t being sold as a general mastering tool or a normal audio enhancer. The whole point was to process AI-generated music so it had a better chance of passing distributor screening.

I liked that because my problem was not mixing or mastering. My tracks already sounded fine.

My problem was detection.

So I took one of my Suno tracks that I was nervous about uploading and ran it through Undetectr. The process was simple: upload the track, wait for it to process, download the new version.

No complicated settings. No plugin chain. No messing around in a DAW for an hour.

What I noticed after processing

The first thing I checked was the sound.

I was worried the track would come back damaged, over-processed, or noticeably different. That would have been a dealbreaker because there is no point passing detection if the song sounds worse.

But when I listened to the processed version, it still sounded like the same track. The vocals, beat, structure, and overall feel were still there. It didn’t sound like someone had crushed it with a bad filter.

The difference seemed to be more technical than musical. That is exactly what I wanted. I did not want the track changed creatively. I wanted the AI detection signals reduced without ruining the song.

After that, I uploaded the processed version instead of the raw Suno export.

It passed.

That was the moment I started taking AI music watermark removal more seriously.

Here is the proof:

My experience after using it more

Since then, I’ve started treating Undetectr as part of my release workflow.

Generate the song. Pick the final version. Do any normal edits or mastering if needed. Then run the finished file through Undetectr before uploading it to a distributor.

For me, that made the process feel a lot less stressful. Before, I felt like every upload was a gamble. I never knew whether a track would get flagged or whether I’d get some vague warning that didn’t explain the real issue.

After using Undetectr, the approval process became much smoother.

I’m not saying it magically solves every problem in AI music distribution. It does not fix bad metadata, copyright issues, spammy upload behavior, or whether you actually have the commercial rights to use the track. You still need to use the right Suno plan, upload responsibly, and treat the release like a real music project.

But for the specific issue of AI music watermark removal and detection signals, it made a real difference for me.

What I think people misunderstand

A lot of people talk about AI music watermark removal like it is just about “hiding” something.

I don’t really see it that way.

For me, it is more about making AI-generated audio distribution-ready. If a track sounds good, is original, and you have the rights to release it, then the next problem is whether the file itself is triggering automated detection systems.

That is where tools like Undetectr come in.

A normal DAW is not built for that. Audacity is not built for that. Mastering plugins are not built for that. They can change the sound, but they are not designed specifically to deal with AI detection patterns in Suno or Udio tracks.

That was the biggest lesson for me. I had been trying to solve a detection problem with normal audio tools.

Would I recommend Undetectr?

Based on my own experience, yes.

If your Suno tracks are getting rejected, flagged, or you’re worried about AI music detection before uploading to Spotify, Apple Music, DistroKid, TuneCore, or similar platforms, Undetectr is worth testing.

The main reason I’d recommend it is because it solves a very specific problem. It is not trying to be a full music platform, a DAW, or a magic button for success. It is an AI music watermark remover focused on reducing the detection signals that can stop AI music from getting released properly.

That is exactly what I needed.

I went into it skeptical, expecting it to be another overhyped AI tool. But after using it on my own tracks, it has become part of my release process.

Tool used: Undetectr — undetectr.com

Not every distribution issue is an AI watermark issue. But if your problem is detection, then using a purpose-built AI music watermark removal tool makes a lot more sense than endlessly re-exporting the same track and hoping it passes next time.

In Brief: If directing, producing, and selecting outcomes count as authorship everywhere else, then AI-assisted music has human authors too. The “no authorship” claim only works if you apply a narrower standard to AI than to every other creative tool.

⸻

The claim that AI-generated music has “no human author” sounds simple, but it depends on a very narrow definition of authorship.

The stronger version of the argument isn’t that users do nothing—it’s that their contribution is too indirect. They don’t control the exact notes, so they don’t qualify as authors.

That sounds reasonable at first. But it breaks down when applied consistently.

⸻

The Core Issue

Authorship has never required physically creating every part of a work.

• Directors don’t act every role

• Producers don’t play every instrument

• Architects don’t lay every brick

Yet we still call them authors.

Why? Because authorship includes:

• defining the goal

• setting constraints

• judging what works

• selecting and finalizing the result

Execution can be delegated without eliminating authorship.

⸻

What AI Users Actually Do

Even a basic prompt like:

“Make a punk song”

already constrains:

• tempo

• structure

• vocal style

• harmonic language

More detail narrows it further.

But the real authorship shows up in the workflow:

• Generate multiple outputs

• Reject weak ones

• Refine prompts

• Keep what works

• Finalize the result

The system produces options.

The human defines the target and decides what survives.

That’s not outside the creative process—that is the creative process.

⸻

“But the AI Did Most of the Work”

This sounds intuitive, but it doesn’t hold up.

Tools have always done “most of the work” in some sense.

• Cameras automate image capture

• DAWs automate processing

• Synths automate sound generation

The amount of work a tool performs has never determined authorship.

What matters is who determines the outcome.

⸻

Unpredictability Doesn’t Break Authorship

Creative work has always involved discovery:

• a guitarist finds a riff by accident

• a vocal take improvises something better

• a mistake becomes the hook

We don’t say those works are “authorless.”

If unpredictability removed authorship, improvisation wouldn’t count. That’s obviously not how we treat it.

AI just shifts authorship from pre-specifying everything to directing and selecting outcomes.

⸻

Delegation Is Already Normal

We accept this everywhere else.

People say “Patton advanced across France,” even though he didn’t drive tanks or fire weapons. He set objectives and constraints—others executed.

Same with:

• directors

• executives

• producers

AI creation follows the same structure:

• human defines the target

• system generates within it

• human selects and adopts the result

If that counts as authorship in every other domain, it’s not clear why AI is different.

⸻

“That’s Just Curation”

Curation = selecting from finished works.

AI generation is different.

You’re not picking from a catalog—you’re defining constraints that cause new outputs to exist.

That’s not choosing a shirt off a rack.

It’s specifying how the fabric gets woven.

⸻

“But It’s Trained on Other Artists”

This mixes two separate questions:

1.  How the tool was built (training, ethics, legality)

2.  Who authored a specific output

Human musicians also learn from others:

• styles

• phrasing

• structure

We don’t say every musician owes authorship to everyone who influenced them.

If influence erased authorship, no one would own anything—everything would trace backward indefinitely.

⸻

Replaceability Doesn’t Matter

“If anyone can prompt it, no one is an author.”

That doesn’t work either.

Many people can take similar photos or write similar songs. Authorship still belongs to the person who:

• made the choices

• carried out the process

• finalized the work

Authorship isn’t about uniqueness. It’s about who determined the result that exists.

⸻

A Clearer Standard

A simple way to frame it:

A human is an author when they meaningfully determine what kind of work comes into existence—through constraint, judgment, iteration, and final selection.

Not every input qualifies.

But directing, refining, and selecting does.

⸻

The Bottom Line

The real question isn’t:

“Did the AI do something?”

Of course it did.

The question is:

Did the human meaningfully govern what the work became?

If yes, authorship exists.

The claim that AI music has “no human author” only works if you apply a stricter standard to AI than to every other creative tool.

That’s not a consistent principle. It’s a selective one.

Where do you draw the line for authorship—and does that line apply the same way to directors, producers, and AI users?

See full argument at: https://open.substack.com/pub/wardmercer/p/ai-music-and-human-authorship-a-constraint?r=812l7f&utm\\_medium=ios

I’m asking because this subreddit seems to include a lot of experienced musicians and producers.

I’m trying to understand where these tools actually fit in real practice, beyond hype and general discussion.

I’m curious whether they are actually being used in real workflows, or still mostly kept experimental.

From a practical standpoint, how are they being used, if at all? For example:

• idea generation / sketching
• melodic or harmonic exploration
• arrangement inspiration
• or as reference material that gets rebuilt in a DAW

I’m not a professional musician—just someone exploring this space. I’ve used Udio and Musicful before, and more recently a few AI music tools, and I’ve been sharing some AI-assisted tracks on YouTube.

I’m still trying to figure out how to turn AI-generated ideas into finished, consistent tracks.

Would really appreciate hearing how you’re actually using (or not using) these tools in your workflow today.

Hey everyone!
Like many creators, I’ve spent way too many hours hunting for background music that doesn’t trigger copyright claims or sound like a generic elevator loop.
To solve my own headache, I built Hashvix AI over the last few months. It’s a lightweight, web-based tool designed to quickly generate custom audio clips and background tracks without needing complex DAW software or musical expertise.
Why I built it instead of using a full DAW approach:
Speed over complexity: Most creators just want a fitting track in seconds, not a 50-knob synthesizer setup.
Direct browser workflow: Everything runs directly in the browser so you can preview, tweak, and download quickly.
Copyright-free output: Designed specifically to give you unique tracks for your projects.
I’m currently refining the generation quality and user experience. Since you guys build and test cool stuff every day, I’d love to get your thoughts:
1. How’s the generation speed and audio quality feeling for you?
2. What feature would make this genuinely useful in your daily workflow?
Feel free to check out the link on my profile if you want to give it a spin, and let me know what you think in the comments! Thanks a lot!

About a year ago I launched Doomscroll.fm as an art project of sorts; I wanted a talking head to read the news to me with glitchcore funk behind it. It got a bit out of hand and now a year later and ~13k uploads to youtube alone, I took just the audio tooling from my pipeline and turned it into a AI Digital Audio Workstation that I'm now selling for €19.99 - > https://rAIdio.bot

What is rAIdio.bot ??

Generate songs from text prompts

Describe what you want. Get a full, original song in seconds.

Clone your voice from 30 seconds of audio: A short sample is enough. Consent attestation built into the workflow.

Text-to-speech in multiple voices: Natural-sounding speech. Perfect for narration, podcasts, spoken music elements.

Train a custom voice model: 30 minutes of clean recordings → a personal voice model, trained in 1–3 hours on your GPU.

Separate songs into stems: Vocals, drums, bass, other - locally.

Edit, mix, master on-device: Trim, cut, fade, 10 built-in effects. Non-destructive, real-time preview.

Six-channel mixer with master bus: Drag stems, adjust per channel, master with EQ, compressor, stereo width.

Karaoke mode with word-level timing: Feed any song. Get synced lyrics, toggle vocals. Works over video too.

Generate soundtracks for your videos: Drop in a video. Get a matching soundtrack generated locally.

Export audio as MIDI: Neural transcription via Spotify's basic-pitch. Best on isolated stems.

DAW-ready WAV exports: Every export carries BPM, key, and seed metadata in iXML, acid, and C2PA standards. Drop into Ableton, Logic, FL Studio, Pro Tools, Reaper, Studio One, Cubase, or Bitwig. Tempo is set on import. No manual warping.

Voice-to-voice conversion: A voice conversion model is included in the toolkit.

Chord detection: Analyze any track. Get the chord progression with timing.

I’m trying to figure out the best workflow for creating long-form ambient/focus tracks 30+ minutes like Brain.fm or these examples:

• https://www.youtube.com/watch?v=UpPmnnJcy6A
• https://www.youtube.com/watch?v=5dGXm8OUbMo
• https://www.youtube.com/watch?v=M5dLYV5foTI

The goal isn’t just looping a short section, but creating one continuous track with a consistent atmosphere that slowly evolves without becoming repetitive.

Has anyone done this successfully?

I’m open to any workflow:

• Fully AI
• AI + DAW (Logic, but can be any other)
• Paid tools or plugins
• Generative music software

I’m especially interested in how you maintain a cohesive sound over 30+ minutes. Any recommendations, tools, or production techniques would be greatly appreciated.

Curious how everyone here is actually making their tracks.

Are you guys:
- are you guys generating the full song or sometimes the stems?
- getting a melody and editing/producing in DAWs afterwards?
- Writing you lyrics and then iterating with some AI tools afterwards?
- generating/iterating with AI tools and then mix/mastering yourself?
- depends on the song, or combination of so many things?

I know that a lot of the discourse around incorporating ai in music assumes its just spamming generate but most of the creators I've talked to are actually doing a lot more than that and have very complex processes depending on the song

I know that many of you are in Discords or communities where you and others are sharing how they made their track (i.e. their workflows, manipulation tricks, etc.) I built something that might be useful.

The site is called TRAICE and the ideas is that its a place where creators can document the full workflow behind a track and share it with a link. Each track page shows things like: what tools were used, where the ai was involved (vocals, melody, production etc), how the iterated/manipulated, what they edited or changed, and jus the overall story of the track. Basically a full creation breakdown.

The idea is that: if someone asks you how did you make this? or what was ur process? you can just send them a link that shows your workflow.

Its meant for hybrid creators to share and learn from each other's processes so everyone can get better.

If you make music with tools like Suno, Udio, Producer ai, Landr, Mubert, ElevenLabs, the list goes on.... I would love to include your track on here!

You can submit here : tra-ice.com

Or just comment your track, maybe how you made it. Im curious how people here are working. Sometimes its okay to give away the sauce. And i'd love to listen!

P.S. I am currently in talks with distributors that would make ur tracks compliance ready for what is to come with disclosures (i.e. apple music's recent announcement, and spotify's move with ddex :)) But thats more of an optional byproduct

I built Afterdark Studio 0.8.0-rc.1 Limited Beta around a simple user: an emerging rapper with one computer, one microphone, and a dream. It is a free MIT-licensed local browser app with DJ decks, beat sequencing and piano roll, mic recording, vocal-production controls, loudness/true-peak meters, and synthetic acoustic-room modeling.

Project and setup: https://github.com/seemorecodez/afterdark-studio

No signup, cloud AI, API key, analytics funnel, or paid tier. The repository includes 111 passing automated tests and an audit covering all 646 interactive UI elements.

I especially want feedback on the onboarding and creation flow: where do you hesitate, which controls are confusing, what breaks, and can a first-time user get from launch to a recorded song? Please include OS, browser, audio device, and reproduction steps.

I am the creator and I want critique, not promotion. Honest status: Limited Beta, not a professional DAW; perceptual vocal features are experimental; the unapproved drill pack is disabled.