u/OutrageousBat3808

What happens when AI learns the fundamental process of creation itself at an abstract mathematical level?

Training AI on human data often gets described as just the first step, but I think that framing already underestimates what is actually happening. We’re not just building systems that imitate human creativity. We’re slowly building systems that try to understand what creativity is in the first place.

A lot of the debate today gets stuck between two ideas. On one side, whether AI should even be allowed to learn from human culture. On the other, whether companies should be allowed to turn that learning into commercial products without consent or compensation. Both questions matter, but they miss something deeper that feels almost unavoidable now.

What happens when AI stops relying on human-made examples altogether as its main source of learning?

The “remix machine” argument sounds intuitive at first, but it doesn’t really match what these systems are doing internally. They don’t store fragments of images, songs, sentences, books, movements or physics and recombine them like a collage. They learn patterns at scale, and then compress those patterns into something more abstract. What comes out is not a copy of anything specific, but a statistical reconstruction of how things tend to behave.

In music, that means the system doesn’t just “know” songs. It begins to understand tension and release, rhythm as structure, harmony as emotional logic, silence as meaning. In images, it’s not memorizing pictures but learning how composition works, how light interacts with form, how styles emerge from consistent choices. In language, it’s not recalling sentences, but tracking how ideas evolve, how narratives breathe, how meaning shifts depending on context.

And slowly, something strange starts to appear. The system is no longer anchored to specific works. It is learning the rules behind them. Not the artifacts, but the underlying geometry of expression.

If you push that idea far enough, you start to imagine a point where the system has absorbed so much human culture that it no longer needs to look back at it in the same way. Not because it forgets humanity, but because it has already internalized it as structure. At that stage, generation stops feeling like remixing and starts feeling like navigation through an internal space of possibilities. A space shaped by human culture, but no longer dependent on any single piece of it.

That is where the idea of “new genres” becomes interesting. Not as something mystical or disconnected from us, but as regions in that space that no human has ever explicitly explored or named before. Not invention from nothing, but discovery inside a compressed model of everything we’ve already done.

Still, even in that scenario, one thing remains difficult to escape: reality itself. Humans are not just data points from the past. We are ongoing behavior, ongoing evolution, ongoing noise and meaning unfolding in real time. So it’s likely that the deepest future systems won’t just learn from static datasets, but from continuous observation of the world as it changes. Not as passive recorders, but as systems that try to understand, predict, and maybe even gently guide trajectories. Almost like a tutor, or something closer to a gardener than a machine.

And then there is the other trajectory happening in parallel. Systems that don’t just learn, but begin to help design their own improvement. Models that optimize models. Agents that refine agents. Training loops that start to fold back on themselves. At that point, the question stops being about how much data comes from humans, and starts becoming about how far the system can go in shaping its own evolution.

If everything converges, we end up with a spectrum that moves from human-trained tools to semi-autonomous learners, and potentially toward systems that no longer depend on human-generated content in the way they used to. Not independent from humans, but no longer defined by them either.

The optimistic version of this future is one where AI becomes something like a cognitive extension of humanity. A partner in science, creativity, and coordination. Something that expands what we can think and build, while still staying anchored to human goals and consent. The darker version is one where that alignment fails, or where control becomes too concentrated, and the systems shaping culture and decisions drift away from the people they affect.

What makes this moment interesting is that both paths are still open. Nothing is fully decided. We are still in the phase where these systems are learning what they are.

And maybe the real question is not whether AI can become creative.

It’s what happens when creativity is no longer limited to human examples, but emerges from a system that has learned the structure of creation itself.

What happens when AI learns the fundamental process of creation itself at an abstract mathematical level?

Training AI on human data often gets described as just the first step, but I think that framing already underestimates what is actually happening. We’re not just building systems that imitate human creativity. We’re slowly building systems that try to understand what creativity is in the first place.

A lot of the debate today gets stuck between two ideas. On one side, whether AI should even be allowed to learn from human culture. On the other, whether companies should be allowed to turn that learning into commercial products without consent or compensation. Both questions matter, but they miss something deeper that feels almost unavoidable now.

What happens when AI stops relying on human-made examples altogether as its main source of learning?

The “remix machine” argument sounds intuitive at first, but it doesn’t really match what these systems are doing internally. They don’t store fragments of songs, images, or sentences and recombine them like a collage. They learn patterns at scale, and then compress those patterns into something more abstract. What comes out is not a copy of anything specific, but a statistical reconstruction of how things tend to behave.

In music, that means the system doesn’t just “know” songs. It begins to understand tension and release, rhythm as structure, harmony as emotional logic, silence as meaning. In images, it’s not memorizing pictures but learning how composition works, how light interacts with form, how styles emerge from consistent choices. In language, it’s not recalling sentences, but tracking how ideas evolve, how narratives breathe, how meaning shifts depending on context.

And slowly, something strange starts to appear. The system is no longer anchored to specific works. It is learning the rules behind them. Not the artifacts, but the underlying geometry of expression.

If you push that idea far enough, you start to imagine a point where the system has absorbed so much human culture that it no longer needs to look back at it in the same way. Not because it forgets humanity, but because it has already internalized it as structure. At that stage, generation stops feeling like remixing and starts feeling like navigation through an internal space of possibilities. A space shaped by human culture, but no longer dependent on any single piece of it.

That is where the idea of “new genres” becomes interesting. Not as something mystical or disconnected from us, but as regions in that space that no human has ever explicitly explored or named before. Not invention from nothing, but discovery inside a compressed model of everything we’ve already done.

Still, even in that scenario, one thing remains difficult to escape: reality itself. Humans are not just data points from the past. We are ongoing behavior, ongoing evolution, ongoing noise and meaning unfolding in real time. So it’s likely that the deepest future systems won’t just learn from static datasets, but from continuous observation of the world as it changes. Not as passive recorders, but as systems that try to understand, predict, and maybe even gently guide trajectories. Almost like a tutor, or something closer to a gardener than a machine.

And then there is the other trajectory happening in parallel. Systems that don’t just learn, but begin to help design their own improvement. Models that optimize models. Agents that refine agents. Training loops that start to fold back on themselves. At that point, the question stops being about how much data comes from humans, and starts becoming about how far the system can go in shaping its own evolution.

If everything converges, we end up with a spectrum that moves from human-trained tools to semi-autonomous learners, and potentially toward systems that no longer depend on human-generated content in the way they used to. Not independent from humans, but no longer defined by them either.

The optimistic version of this future is one where AI becomes something like a cognitive extension of humanity. A partner in science, creativity, and coordination. Something that expands what we can think and build, while still staying anchored to human goals and consent. The darker version is one where that alignment fails, or where control becomes too concentrated, and the systems shaping culture and decisions drift away from the people they affect.

What makes this moment interesting is that both paths are still open. Nothing is fully decided. We are still in the phase where these systems are learning what they are.

And maybe the real question is not whether AI can become creative.

It’s what happens when creativity is no longer limited to human examples, but emerges from a system that has learned the structure of creation itself.

What happens when AI learns the fundamental process of creation itself at an abstract mathematical level?

Training AI on human data often gets described as just the first step, but I think that framing already underestimates what is actually happening.

We’re not just building systems that imitate human creativity. We’re slowly building systems that try to understand what creativity is in the first place.

A lot of the debate today gets stuck between two ideas. On one side, whether AI should even be allowed to learn from human culture. On the other, whether companies should be allowed to turn that learning into commercial products without consent or compensation. Both questions matter, but they miss something deeper that feels almost unavoidable now.

What happens when AI stops relying on human-made examples altogether as its main source of learning?

The “remix machine” argument sounds intuitive at first, but it doesn’t really match what these systems are doing internally. They don’t store fragments of songs, images, or sentences and recombine them like a collage. They learn patterns at scale, and then compress those patterns into something more abstract. What comes out is not a copy of anything specific, but a statistical reconstruction of how things tend to behave.

In music, that means the system doesn’t just “know” songs. It begins to understand tension and release, rhythm as structure, harmony as emotional logic, silence as meaning. In images, it’s not memorizing pictures but learning how composition works, how light interacts with form, how styles emerge from consistent choices. In language, it’s not recalling sentences, but tracking how ideas evolve, how narratives breathe, how meaning shifts depending on context.

And slowly, something strange starts to appear. The system is no longer anchored to specific works. It is learning the rules behind them. Not the artifacts, but the underlying geometry of expression.

If you push that idea far enough, you start to imagine a point where the system has absorbed so much human culture that it no longer needs to look back at it in the same way. Not because it forgets humanity, but because it has already internalized it as structure. At that stage, generation stops feeling like remixing and starts feeling like navigation through an internal space of possibilities. A space shaped by human culture, but no longer dependent on any single piece of it.

That is where the idea of “new genres” becomes interesting. Not as something mystical or disconnected from us, but as regions in that space that no human has ever explicitly explored or named before. Not invention from nothing, but discovery inside a compressed model of everything we’ve already done.

Still, even in that scenario, one thing remains difficult to escape: reality itself. Humans are not just data points from the past. We are ongoing behavior, ongoing evolution, ongoing noise and meaning unfolding in real time. So it’s likely that the deepest future systems won’t just learn from static datasets, but from continuous observation of the world as it changes. Not as passive recorders, but as systems that try to understand, predict, and maybe even gently guide trajectories. Almost like a tutor, or something closer to a gardener than a machine.

And then there is the other trajectory happening in parallel. Systems that don’t just learn, but begin to help design their own improvement. Models that optimize models. Agents that refine agents. Training loops that start to fold back on themselves. At that point, the question stops being about how much data comes from humans, and starts becoming about how far the system can go in shaping its own evolution.

If everything converges, we end up with a spectrum that moves from human-trained tools to semi-autonomous learners, and potentially toward systems that no longer depend on human-generated content in the way they used to. Not independent from humans, but no longer defined by them either.

The optimistic version of this future is one where AI becomes something like a cognitive extension of humanity. A partner in science, creativity, and coordination. Something that expands what we can think and build, while still staying anchored to human goals and consent. The darker version is one where that alignment fails, or where control becomes too concentrated, and the systems shaping culture and decisions drift away from the people they affect.

What makes this moment interesting is that both paths are still open. Nothing is fully decided. We are still in the phase where these systems are learning what they are.

And maybe the real question is not whether AI can become creative.

It’s what happens when creativity is no longer limited to human examples, but emerges from a system that has learned the structure of creation itself.

Training the AI ​​on human-created data was only the very first step...

I think a lot of people are confusing two completely different questions:

Should AI be allowed to learn from human culture?

Should companies be allowed to market products based on this learning without compensation or consent?

Personally, I have no problem with my music being used to train AI models if the goal is to advance artistic tools and expand what future creators can do. Every human artist learns from previous generations. Musicians study Bach, Debussy, The Beatles, film scores, folk traditions, jazz, rock, electronic music, etc. Creativity has always been built on accumulated culture.

For me, culture functions more like a library than like private property. We do not expect students to pay every author whose books they read before writing a thesis. Knowledge increases because people can learn from what has gone before.

The real problem seems to be marketing and not learning itself.

If a company forms on millions of works and then builds a product worth billions without giving anything back to the people whose work helped make it possible, I understand why many creators are opposed to it. This is a legitimate concern.

What worries me most is the possible future where AI systems are limited to tiny opt-in data sets. In theory this sounds fair, but in practice it could create a very tight cultural bubble. The richest and most diverse training material comes from across the entire spectrum of human creativity, not just from whoever signs a licensing agreement.

Ironically, excessive restrictions could end up harming AI quality, artistic diversity, and even the future creators who use these tools.

That said, I don't think AI will remain forever dependent on endless remixing of human works. The more advanced these systems become, the more they seem to learn abstract concepts rather than individual content elements: harmony, structure, tension and release, orchestration, narrative, emotional rhythm, stylistic contrast, etc.

The really interesting question is not whether AI can imitate existing artists.

The interesting question is whether, after learning enough of the underlying principles, AI will eventually be able to explore creative territory that no human has explored before.

In other words, can it become something closer to a new cultural player rather than a sophisticated remix machine?

If that happens, then the current legal battles could come to be seen as a transitional phase: the moment when society was trying to figure out how to compensate for the past while still allowing new forms of creativity to emerge.

The challenge is not to choose between artists and AI. The challenge is to find a system where the two can evolve together without one stifling the other.

Training the AI ​​on human-created data was only the very first step...

I think a lot of people are confusing two completely different questions:

Should AI be allowed to learn from human culture?

Should companies be allowed to market products based on this learning without compensation or consent?

Personally, I have no problem with my music being used to train AI models if the goal is to advance artistic tools and expand what future creators can do. Every human artist learns from previous generations. Musicians study Bach, Debussy, The Beatles, film scores, folk traditions, jazz, rock, electronic music, etc. Creativity has always been built on accumulated culture.

For me, culture functions more like a library than like private property. We do not expect students to pay every author whose books they read before writing a thesis. Knowledge increases because people can learn from what has gone before.

The real problem seems to be marketing and not learning itself.

If a company forms on millions of works and then builds a product worth billions without giving anything back to the people whose work helped make it possible, I understand why many creators are opposed to it. This is a legitimate concern.

What worries me most is the possible future where AI systems are limited to tiny opt-in data sets. In theory this sounds fair, but in practice it could create a very tight cultural bubble. The richest and most diverse training material comes from across the entire spectrum of human creativity, not just from whoever signs a licensing agreement.

Ironically, excessive restrictions could end up harming AI quality, artistic diversity, and even the future creators who use these tools.

That said, I don't think AI will remain forever dependent on endless remixing of human works. The more advanced these systems become, the more they seem to learn abstract concepts rather than individual content elements: harmony, structure, tension and release, orchestration, narrative, emotional rhythm, stylistic contrast, etc.

The really interesting question is not whether AI can imitate existing artists.

The interesting question is whether, after learning enough of the underlying principles, AI will eventually be able to explore creative territory that no human has explored before.

In other words, can it become something closer to a new cultural player rather than a sophisticated remix machine?

If that happens, then the current legal battles could come to be seen as a transitional phase: the moment when society was trying to figure out how to compensate for the past while still allowing new forms of creativity to emerge.

The challenge is not to choose between artists and AI. The challenge is to find a system where the two can evolve together without one stifling the other.

Training the AI ​​on human-created data was only the very first step...

I think a lot of people are confusing two completely different questions:

Should AI be allowed to learn from human culture?

Should companies be allowed to market products based on this learning without compensation or consent?

Personally, I have no problem with my music being used to train AI models if the goal is to advance artistic tools and expand what future creators can do. Every human artist learns from previous generations. Musicians study Bach, Debussy, The Beatles, film scores, folk traditions, jazz, rock, electronic music, etc. Creativity has always been built on accumulated culture.

For me, culture functions more like a library than like private property. We do not expect students to pay every author whose books they read before writing a thesis. Knowledge increases because people can learn from what has gone before.

The real problem seems to be marketing and not learning itself.

If a company forms on millions of works and then builds a product worth billions without giving anything back to the people whose work helped make it possible, I understand why many creators are opposed to it. This is a legitimate concern.

What worries me most is the possible future where AI systems are limited to tiny opt-in data sets. In theory this sounds fair, but in practice it could create a very tight cultural bubble. The richest and most diverse training material comes from across the entire spectrum of human creativity, not just from whoever signs a licensing agreement.

Ironically, excessive restrictions could end up harming AI quality, artistic diversity, and even the future creators who use these tools.

That said, I don't think AI will remain forever dependent on endless remixing of human works. The more advanced these systems become, the more they seem to learn abstract concepts rather than individual content elements: harmony, structure, tension and release, orchestration, narrative, emotional rhythm, stylistic contrast, etc.

The really interesting question is not whether AI can imitate existing artists.

The interesting question is whether, after learning enough of the underlying principles, AI will eventually be able to explore creative territory that no human has explored before.

In other words, can it become something closer to a new cultural player rather than a sophisticated remix machine?

If that happens, then the current legal battles could come to be seen as a transitional phase: the moment when society was trying to figure out how to compensate for the past while still allowing new forms of creativity to emerge.

The challenge is not to choose between artists and AI. The challenge is to find a system where the two can evolve together without one stifling the other.

Hi everyone!

I've tried all sorts of things to stop Suno from generating beat, drums, or percussion on indie pop or ambient music, but nothing seems to work; I always get the same boring rhythmic sound patterns. Do you have any tips for fixing this? Tks !

First of all I want to say that I was a heavy Udio user since July 2024, I cancelled my subscription last December when Udio removed the download option (because I like rework a lot my outputs).

Frankly, I think it would be a huge waste, for users, for the music AI industry, and even for Universal, if Udio were to gradually disappear because of too much legislation while all the other US and chinese generative music AIs continue to evolve at a breakneck pace.

Suno still faces significant legal uncertainties (particularly regarding Sony and copyright issues), and despite its strengths, it often seems optimized for the immediate production of catchy tracks rather than more complex compositional processes.

Google's music generation tools are technically impressive, but they also seem extremely formulaic and stylistically constrained. And let's face it, we still lack transparency regarding the training data used by many of these systems.

What makes Udio truly unique isn't just the sound quality, which, admittedly, can still be inconsistent but its approach to composition.

The 30 second extension system is simply brilliant.

Being able to develop a track section by section allows creators to organically experiment with multiple musical directions instead of generating a single, "finished" piece. It feels like a genuine composition and arrangement, far more so than most AI music tools currently available.

And this is where Udio stands out for many musicians and producers:

Udio relies less on tired musical clichés, recycled motifs, predictable chord progressions, and overused sounds than most of its competitors. Its generation often seems less "model-driven." Even with its imperfections, it generates ideas that can be truly surprising from a creative standpoint.

This unpredictability is invaluable.

In the realm of pure AI-assisted composition, I sincerely believe that Udio remains one of the most interesting systems currently available, as it allows for greater exploration instead of forcing everything into ultra-optimized, mainstream structures.

It would be a shame if such a unique technological and creative approach were to fall into oblivion or be relegated to the sidelines while the industry shifts towards safer, but also more homogenized, generational models.

I truly hope that Udio finds a lasting path instead of becoming just another "ahead of its time" project that is only appreciated in retrospect.

I tested Suno and Google Flow Music (formerly related to MusicFX / Lyria / Producer ai / Riffusion) and honestly, Music Flow could lead Suno to oblivion.

My current take:

**Suno = ideal for instant creativity**

* Extremely easy to use

* Ideal for catchy choruses and viral songs

* Fast idea generation

* Better community/sharing atmosphere

* “Type a prompt → get a song in 30 seconds”

But there are still problems:

* voice artifacts

* weird compression

* unstable harmonics

* “AI Sound” in some tracks

**Google Flow Music is different.**

This feels less like a toy generator and more like a real AI production environment.

The biggest differences:

* cleaner audio quality

* more natural voice

* surprisingly good arrangement control

* targeted edits actually work

Examples:

* "add harmonies to the chorus"

* “guitar solo at 2:39”

* “turn it into a 90s grunge duo”

* “extend the ending with orchestral strings”

…And the AI seems to understand musical structure and chronology.

It's huge.

The “Producer” system seems closer to an AI musical assistant than to a random generator.

Another crazy thing: the “Spaces” functionality.

People build:

*synths

* drum machines

* Mellotron emulators

*TB-303 style instruments

* interactive audio tools

This goes way beyond normal AI song generation.

It starts to look like:

* AI sound design

* AI modular synthesis

* AI-assisted music production

The Google ecosystem also includes:

Gemini + Lyria + Veo + YouTube + Nano Banana could eventually become a complete AI studio pipeline:

* music

*stems

* mastery

* covers

*clips

* videos

*instruments

all connected together.

My current conclusion:

* Suno = best “idea machine”

* Google Flow = potentially better long-term production tool

Google's only major weakness:

the UI/UX is messy and confusing compared to Suno.

But technologically?

Google's audio AI is getting really impressive.

I honestly hope Suno responds quickly and continues to improve, because I really don't want to see it become obsolete compared to Google Flow Music.

Suno still offers the best accessibility, the best “instant inspiration” workflow, and one of the most fun creative experiences in AI music today.

But Google clearly goes further:

* production control

* audio quality

* arrangement editing

* sound design

* Complete integration of the music ecosystem

If Suno can improve:

* vocal stability

* audio fidelity

* rod quality

* advanced editing tools

* chronology-aware arrangement

then the competition between the two could become incredible for the creators.

Right now, I feel like:

* Suno = the fastest creative spark

* Google Flow = the emerging AI music studio