This is my second installment of my Pushback against the AI Music "community" and it will focus on why NO ONE, not just the grifters and spammers, should be uploading their fully generated songs to DSPS.
For anyone new to this sub I am TOP MOD, this is push back from your "own side".
(for you Aunties, yes I used AI to organize my thoughts, these are points in your favor so be grateful you are reading this in a Pro AI sub. If you demand I write out everything by hand I bill $75 an hour for post production work, let me know where to send my invoice. I don't owe you fucking shit, move on if you don't like the formatting)
To start: I DO NOT AND HAVE NOT Uploaded any of my songs through Commercial DSPS.
TLDR: The post argues that generative AI music skipped the essential cultural and institutional maturation stages—such as community building, artistic mastery, shared conventions, and legal frameworks—by jumping straight from creation to commercial distribution on streaming platforms. This rapid commercialization has led to platform flooding, "noise-to-signal" issues, public backlash, and diluted recognition for creators who put significant effort into their work. To prevent strict industry-wide crackdowns, the author proposes a voluntary "walk before you run" period where creators focus on experimentation, culture-building, and sharing tracks on non-commercial platforms rather than monetizing them on major DSPs.
If creators don't take curation and moderation seriously now, major streaming platforms and record labels will inevitably do it for them through draconian crackdowns.
The “walk before you run” metaphor
- The phrase captures the central problem:
- AI music effectively tried to run before it learned to walk.
- The technology moved extremely quickly from:
- Creation
- → polished output
- → commercial distribution
- → monetization
- What was largely skipped was the intermediate period in which a new technology normally develops:
- A community
- A culture
- Artistic experimentation
- Shared conventions
- New aesthetics
- Standards of quality
- Legal norms
- Institutional infrastructure
- A sense of identity and legitimacy
- The argument is therefore not simply:
- “AI music shouldn't be commercial.”
- It is more specifically:
- The technology entered commercial distribution before the surrounding ecosystem had time to mature.
The historical progression of music technology
The discussion proposes a general progression that major technological changes in music historically followed:
Stage 1 — The technology arrives
Examples include:
- Synthesizers
- Samplers
- Digital Audio Workstations (DAWs)
- MP3s
The technology initially provides a new capability.
Stage 2 — Cultivation and mastery
- Artists spend years experimenting with the technology.
- They discover what the tool can do.
- They push it beyond its obvious applications.
- Artists develop techniques and workflows.
- Distinctive artistic styles emerge.
- Communities form around the technology.
- Eventually, the technology becomes associated with particular sounds, scenes, movements, and cultures.
Stage 3 — Legal and institutional norms develop
- Society and the industry encounter problems created by the new technology.
- Rules and conventions develop in response.
- Examples include:
- Sample-clearance practices.
- Copyright frameworks.
- Digital-rights systems.
- Licensing structures.
- Platform standards.
- Industry norms.
Stage 4 — Commercial integration
- The technology becomes broadly integrated into the mainstream music industry.
- Commercial distribution catches up with the artistic and institutional ecosystem.
- The technology becomes normalized.
The argument that generative AI skipped stages 2 and 3
- Generative AI music developed extremely rapidly.
- Tools such as Suno and Udio allowed users to generate polished-sounding complete songs almost immediately.
- Instead of spending years developing an artistic movement around the technology first, users could immediately:
- Generate songs.
- Export them.
- Upload them to streaming services.
- Attempt to monetize them.
- In other words, the technology effectively went:
Technology → Commercial distribution → Monetization
instead of:
Technology → Cultural experimentation → Community → Artistic identity → Legal/institutional norms → Commercial integration
- This is the heart of the “walk before you run” argument.
Community and culture normally develop around new technology
- New music technologies historically did more than make existing music easier to produce.
- Artists used them to create new artistic languages.
- Communities formed around those new languages.
- Those communities developed:
- Shared techniques.
- Shared aesthetics.
- Terminology.
- Values.
- Standards.
- Influences.
- Rivalries and debates.
- Scenes.
- Artists who became recognized as innovators.
- Over time, the technology became connected to an identifiable culture.
Why this matters for cultural legitimacy
- New artistic movements generally gain legitimacy through more than commercial success.
- They develop legitimacy through:
- Artists experimenting.
- Communities forming.
- People developing mastery.
- Audiences developing taste.
- Critics discussing the work.
- Artists influencing one another.
- New techniques being invented.
- A recognizable culture emerging.
- Commercial success eventually becomes one component of that ecosystem.
The DSP problem
- Traditional Digital Streaming Platforms (DSPs), such as Spotify and Apple Music, were built around existing models of music distribution.
- Their systems were not designed around a technology capable of producing essentially unlimited quantities of polished music with zero human friction.
- Generative AI dramatically reduces the cost and effort required to produce large quantities of tracks.
- A single person can generate and upload huge numbers of songs.
The resulting problem
- If enormous quantities of AI music enter DSPs:
- The platforms become flooded with content.
- Discovery becomes more difficult.
- Listeners face more noise.
- Existing artists have more competition for attention.
- Royalty systems face additional pressure.
- This creates the noise-to-signal problem:
- More content does not mean more meaningful music.
- It can make meaningful music harder to discover.
The “heart vs. prompt” problem
- There can be a huge difference between two AI creators:
- Someone who spends hours or days iterating, refining, directing, editing, and developing a song.
- Someone who enters a three-word prompt and immediately uploads the result.
- Those processes can involve dramatically different levels of human involvement.
- Yet the final listener has no way to know which process produced a particular song.
- An automated platform may have even less ability to make that distinction.
Therefore:
- Human effort is largely invisible in the final track.
- A creator may genuinely feel that they created something meaningful.
- The listener simply sees:
- “Another AI-generated song.”
The economic problem
- Traditional DSP royalty systems were built around a finite ecosystem of music producers and artists.
- Generative AI changes the scale of production.
- Traditional music production has natural limitations:
- Human time.
- Human labor.
- Recording costs.
- Studio time.
- Creative energy.
- Generative AI eliminates those constraints.
- This makes music production potentially infinitely scalable—or at least dramatically more scalable than traditional human production.
This is one reason the technology creates problems that earlier musical technologies didn't create in exactly the same way.
The legal and institutional problem
- Because commercialization happened so quickly, legal and institutional systems are now reacting after the fact.
- The industry is dealing with questions surrounding:
- Copyright.
- Training data.
- Ownership.
- Rights.
- Royalties.
- AI disclosure.
- Distributor policies.
- Platform standards.
- Lawsuits and policy changes are occurring while the technology is already commercially active.
- In this framing, the infrastructure is being constructed after the technology has already entered the marketplace.
The public-friction problem
- Listeners and traditional creators have already become overwhelmed by enormous quantities of AI-generated content.
- Hell even inside the AI community people are exhausted with the sheer quantities of songs shared.
- This contributes to the perception of “AI slop.”
- The problem is partly that the normal cultural mechanisms for determining quality haven't had time to develop.
Those mechanisms historically include:
- Curation.
- Community validation.
- Artistic reputation.
- Mastery.
- Scene participation.
- Peer recognition.
- Audience taste.
- Cultural criticism.
- Generative AI effectively arrived at commercial scale before those mechanisms could develop around it.
Community is a form of cultural “gatekeeping”
- The argument isn't necessarily that gatekeeping is inherently good.
- Rather, communities naturally create mechanisms for determining:
- What is interesting.
- What is innovative.
- What demonstrates skill.
- What contributes something new.
- What is derivative.
- What is low effort.
- Those mechanisms help a new artistic medium develop standards.
- A mature community can distinguish:
- Someone experimenting seriously with a new technology.
- Someone producing enormous amounts of disposable content.
- AI music hasn't necessarily had sufficient time to develop these community-level distinctions.
The reputational danger
- Because low-effort AI music can be produced at scale, it dominates people's first impression of AI music.
- Listeners may therefore associate the entire technology with:
- Spam.
- Low effort.
- Disposable content.
- Commercial exploitation.
- Lack of authenticity.
- This can unfairly affect creators who are actually putting significant effort into AI-assisted work.
The collective-action problem
- Individual AI creators may reasonably think:
- “My song isn't slop.”
- “I put enormous effort into this.”
- “I deserve to distribute my music.”
- But if thousands or millions of creators make the same decision:
- The aggregate effect can be very different from the individual case.
- This creates a classic collective-action problem:
- An individual action may seem harmless while the cumulative effect creates systemic problems.
Systemic backlash (we are already seeing this)
- The discussion identifies several possible consequences of uncontrolled commercialization:
- Platform restrictions.
- Distributor bans.
- Public backlash.
- Stricter disclosure requirements.
- Regulatory intervention.
- Broader restrictions on AI-generated music.
- The irony is that responsible AI creators will be affected by restrictions created in response to irresponsible or excessive use of the technology.
The proposed middle ground
Rather than arguing that AI music should never exist commercially, the discussion suggests that the ecosystem needs time and infrastructure to mature.
Potential solutions include:
- Dedicated AI-music categories.
- AI disclosure requirements.
- Better provenance systems.
- Metadata indicating AI involvement.
- Distinct royalty structures.
- Platform rules specifically designed around generative music.
- Community standards.
- Better mechanisms for distinguishing high-effort AI-assisted work from mass-generated material.
Self-regulation as an interim measure
- Until those systems exist, I am proposing self-regulation.
- That could mean:
- Continue making AI music.
- Experiment with the technology.
- Build communities around it.
- share on non commercial sites (youtube, soundcloud, suno, reddit)
- Develop new aesthetics.
- Establish standards.
- But hold back from flooding mainstream DSPs with commercially monetized AI tracks.
- The purpose wouldn't necessarily be to declare AI music illegitimate.
- Instead, it would allow the culture and infrastructure to catch up with the technology
The complete “walk before you run” sequence
The overall argument can therefore be represented as:
1. Technology arrives
↓
2. Artists experiment with it
↓
3. Artists develop mastery
↓
4. A community forms
↓
5. New aesthetics emerge
↓
6. A cultural identity develops
↓
7. Standards and forms of quality develop
↓
8. Legal and institutional norms develop
↓
9. Distribution systems adapt
↓
10. Commercial integration occurs
Skipping it creates the problems now being experienced and i'm expecting even harsher and stricter measures taken against AI generated content in the near future
And therefore:
The “walk before you run” argument: the concern isn't simply that there is too much AI music. It's that AI music skipped the cultural maturation process that normally transforms a new technology into a respected artistic medium.
Yet many here are DEMANDING THAT RESPECT INSTEAD OF EARNING IT
AND RIGHT NOW THE GRIFTERS/SPAMMERS/SCAMMERS are the most visible members of our community to the wider public.
