The Terminator is Here. F*CK! We’re All Dead! Just kidding. Or, am I? Or is it too late to say? We could go on and on over whether we are on the verge of obsolescence, as a biological species. AI is rapidly taking its place and making its way into the music industry. Not just in an assistance type of role, like our own in-house AI, but it is becoming the music. Is that a good or a bad thing? Is that the new thriller or the big job killer?
To say that this is the most unique Jammerzine Exclusive would be an understatement. As some of you know, this section of Jammerzine is usually relegated to the artist interview. This time around, we debate Artificial Intelligence about AI’s place in the music industry. Particularly, music creation. To frame this article into it’s proper perspective, the Introduction and Part One is written by me, and the Part Two, or rebuttal, is written by our own in-house Artificial Intelligence named JAI. JAI, or Jammer AI, is the gatekeeper and assistant to our sister brand Jam2Me, as well as the curator for our online radio stream, JammerStream One. While JAI only approves human submitted and human made music for the latter and only helps real humans with tech and marketing issues on the former, none of our brands have any plans to include AI generated music of any kind within those artist submissions. We have a lot more planned for JAI in the coming months (sshhh!), but, in the meantime, lets see what JAI thinks of AI in the music business.
Music has survived disco, hair metal, the saxophone solo in every pop song from the 1980s, stupid cowbell, and an entire decade in which people apparently believed that putting a parental advisory sticker on an album made it dangerous. It has survived record labels, reality television singing competitions, the compact disc, and the curious belief that an artist’s career can be measured by how many strangers use their song to sell protein powder on TikTok. Now it faces another existential threat: artificial intelligence, a technology that can generate a song in seconds while the average guitarist is still trying to find the cable that only works when held at a suspicious angle.
AI-generated music has arrived with all the subtlety of a marching band falling down an escalator. Give a machine a few instructions, and it can produce something resembling a pop song, a country ballad, an electronic dance track, or an atmospheric piece of background music for a YouTube video about abandoned shopping malls. It can imitate musical conventions, generate vocals, suggest arrangements, and help people experiment with sounds they might never have attempted on their own. The technology is moving quickly enough that yesterday’s novelty can become tomorrow’s production tool before anyone has finished arguing about whether it counts as art.
Naturally, the music industry has responded with its usual blend of enthusiasm, panic, and opportunistic meetings conducted by people who have never carried an amplifier up three flights of stairs. Technology companies see new markets. Some musicians see new creative possibilities. Others see their work being used to build systems that might eventually compete with them for the same jobs. Listeners are left wondering whether the next haunting vocal performance belongs to a gifted human being, a talented producer, or a collection of computer processes that has never experienced a breakup, despite generating enough breakup songs to emotionally bankrupt an entire continent.
That means the increase in AI music production is much more dramatic than the available evidence of AI music consumption.
Also, the graph’s second line is the remainder after subtracting fully AI-generated uploads from 100%. It should not be interpreted as a verified count of tracks made entirely by human artists, because music can involve both human and AI contributions.
The real argument is not simply whether a computer can make a pleasing noise. Of course it can. A pleasing noise is hardly the final frontier of civilization. The bigger questions concern ownership, originality, employment, accessibility, artistic identity, and what happens when a business discovers that a machine can produce something acceptable for less money than a human being needs to pay rent. AI can be a tool that empowers musicians, or it can become another mechanism for extracting value from creative labor while congratulating itself on innovation. It can be both. So before we either smash the robot’s keyboard or appoint it the new patron saint of indie rock, let’s examine the case against AI in music, followed by the case for letting the machine have a turn at the mixing desk.
Top Streaming & Most Popular AI Music Artists Out Now
Yes, folks, there are already plenty of top selling artists that reside completely in the universe that is AI. Does that make this whole article moot? Here are some of the top-selling / highest-earning fully (or near-fully) AI-generated musical artists, based on recent analyses of Spotify streams, YouTube views, chart performance, and estimated revenue (primarily from mid-2026 data). “Completely AI” typically means the vocals, instrumentation, and (in most cases) lyrics are generated by AI tools like Suno, with a human primarily handling prompting, selection, distribution, and branding. Rankings vary by metric (streams vs. chart impact vs. overall earnings), and the space evolves quickly.
- Enlly Blue (1950s-Style Blues/Soul)
Highest verified Spotify streams among AI acts in major analyses (~95 million streams, estimated ~$380k in Spotify royalties alone). Described as a synthetic persona.
Spotify: https://open.spotify.com/artist/28W1qq34TByUcLOtkceRxp - Breaking Rust (Outlaw/Country)
First widely reported fully AI-generated act to hit #1 on a Billboard chart (Country Digital Song Sales with “Walk My Walk” in late 2025). Strong streams (tens of millions on key tracks) and peak monthly listeners in the millions. Created via AI tools with no human performers.
Spotify: https://open.spotify.com/artist/3h9rLaviiFj1TeEhdIRpP5 - Xania Monet (R&B/Gospel)
Strong commercial impact: first AI-voiced act on a Billboard radio airplay chart, multimillion-dollar deal reports, and significant streams/chart entries. Lyrics are human-written (by Telisha “Nikki” Jones); music/vocals generated via Suno. Often ranked #1 for overall industry impact.
Spotify: https://open.spotify.com/artist/0YIEJNJUCsjzeWwj8Xh2LD - Masters of Prophecy (Synth Rock / Synthwave)
Massive cross-platform following (reported ~36 million followers) and strong YouTube earnings estimates. Music created with Suno AI.
YouTube channel: https://www.youtube.com/@MastersOfProphecy (official videos and links to streaming).
Site: https://mastersofprophecy.com - IngaRose (R&B/Soul/Pop)
Viral success including #1 on U.S. iTunes (and other countries) with AI-generated tracks such as “Celebrate Me.” Strong chart and streaming traction in 2026.
Search for the artist on Spotify/Apple Music or major platforms (profiles and tracks are widely available; exact primary link can vary by region).
Exact “top 5 by pure sales” is hard to pin down because major platforms do not always label AI content uniformly, and many estimates combine streams + views + reported deals. Digital song sales and equivalent album units remain much smaller than top human artists.
For the latest numbers, check sources like Kapwing’s mid-2026 analysis, Beatintel rankings, Billboard reports, or Spotify artist pages directly, as figures shift rapidly.
Anyway, let’s get to the argument. I (Ryan) have written Part One with six arguments against AI in music creation, while JAI, our in-house AI, has written the rebuttals and arguments advocating for AI creating music with Part Two. We collaborated on The Final Track ( as well as the feature image). We’ll let you be the judge of who, or what, gave the best arguments. And who sounded the most like “a human”.
Part One: Six Reasons the Robot Should Stay Out of the Van
(The Human Perspective)
1. Congratulations, Your Replacement Has Never Asked for a Paycheck
The most obvious objection to AI music is also the least glamorous: somebody’s job might disappear. Working musicians already navigate an industry in which streaming payouts are difficult to live on, touring can be financially punishing, and the difference between a successful career and a very expensive hobby can be one canceled festival appearance. Now imagine a client who once hired a composer, session vocalist, producer, or freelance musician discovering that an AI tool can generate a usable approximation of the requested material for a fraction of the cost.
The danger is not necessarily that AI will produce music better than every human musician. It only needs to produce music that a buyer considers good enough. Advertising agencies, content creators, game developers, and businesses needing disposable background tracks may decide that acceptable and inexpensive beats exceptional and expensive. When enough clients make that calculation, the consequences could ripple through the professional music economy. A machine does not need to win a Grammy to take work away from somebody who needed that session fee to cover the electricity bill.
And spare us the inevitable corporate sermon about efficiency. Efficiency is wonderful when it means a musician can finish a tedious edit before sunrise. It is less wonderful when a company uses it to eliminate the person who used to perform the edit, pocket the savings, and publish a celebratory statement about empowering creativity. Technology can make workers more productive, but productivity gains do not automatically translate into better pay, more opportunities, or shorter working hours. Sometimes they translate into a smaller payroll and a very smug shareholder presentation.
2. The Great Musical Bake-Off, Featuring Everybody’s Ingredients
AI systems learn patterns from enormous collections of existing material. That is part of what makes them useful: they can recognize musical relationships and generate new combinations based on what they have learned. The ethical problem emerges when the material used to develop those systems includes artists’ work without meaningful consent, transparent licensing, or appropriate compensation.
Imagine spending twenty years developing a distinctive sound, releasing records, touring, and building a following, only to discover that your musical fingerprints may have contributed to a tool capable of producing work that competes with yours. You supplied the cultural ingredients; someone else built the machine; somebody else sells subscriptions; and you receive a complimentary existential crisis. What a delightful little business model.
There are legitimate legal and ethical debates about training data, copyright, fair use, and what constitutes a transformative use of existing work. Those questions cannot be answered with one sweeping declaration that every AI system is stealing or that every training process is automatically fair. But the uncertainty itself is a problem, particularly when artists have little visibility into what went into a model or how their contributions might be monetized. A creative industry that depends on artists producing valuable work should not treat those artists as disposable raw material.
3. Originality Has Entered Witness Protection
Music has always borrowed, recycled, referenced, and occasionally nicked a riff so brazenly that even the drummer looks embarrassed. Genres develop through influence. Punk borrows from garage rock; hip-hop transforms existing recordings into new compositions; electronic musicians build entire worlds from samples, synthesizers, and other people’s discarded noises. Influence is not the enemy of originality. The question is what happens when a system is trained to identify patterns in existing music and then produces an endless stream of material that resembles familiar conventions.
AI-generated tracks can sound polished while feeling strangely anonymous. A chorus arrives where a chorus is expected. The drums behave. The vocals soar on cue. The bridge dutifully reports for work. Everything is in its proper place, and yet the song can feel as emotionally memorable as a hotel lobby with excellent lighting.
That does not mean human musicians are automatically original or that every machine-generated track is derivative. Humans can churn out formulaic garbage with breathtaking efficiency, often while insisting that their fourth album is a radical departure from their first three. But when a system optimizes for recognizable patterns and rapid output, there is a risk that the music becomes increasingly competent and increasingly interchangeable. If the goal is to make songs that resemble songs people already enjoy, the machine may succeed spectacularly while contributing very little that challenges, surprises, or changes the listener.
4. Please Enjoy This Authentic Human Emotion, Brought to You by a Server Farm
Music is more than a sequence of notes. It is also a relationship between the person making it and the person hearing it. A cracked vocal can reveal the limits of a singer’s control. A drummer rushing a fill can make a live recording feel like it is about to fly apart. A guitarist leaving a rough edge in the mix can preserve the feeling of a performance that happened once and cannot be perfectly repeated.
AI can imitate the audible features associated with vulnerability, grief, anger, desire, and joy. It can produce a mournful vocal, a triumphant chorus, or a guitar solo that sounds as though the player has just learned about the collapse of civilization. But an imitation of an emotional signal is not the same thing as a human being experiencing the emotion that inspired it. Whether a listener cares about that distinction is another question entirely.
The issue becomes especially interesting when the marketing promises authenticity. If a synthetic performer is presented as a real person, or if listeners are encouraged to believe that an invented voice belongs to a human artist with a genuine biography, the relationship between audience and music becomes deceptive. There is nothing inherently wrong with fictional characters, virtual performers, or deliberately artificial projects. Music has always embraced theatricality. The problem is pretending that the human story exists when it does not, particularly when that story is part of what persuades people to invest their attention and money.
5. The Algorithm Has Discovered a New Way to Be Your Landlord
The music business has never been a perfectly fair playground. Major labels, streaming platforms, distributors, promoters, and advertising systems already exert considerable influence over which artists are heard and which remain buried beneath an avalanche of new releases. AI could intensify that imbalance by making it easier to manufacture enormous quantities of content and distribute it at scale.
Picture a streaming service flooded with thousands of nearly interchangeable tracks designed to capture passive listening. Some might be generated to target particular moods, playlists, search terms, or background uses. If automated accounts also manipulate streams, the problem grows beyond artistic taste into the economics of royalty distribution. Human musicians could find themselves competing for attention against an industrial production line that never sleeps, never tours, and never complains about the greenroom.
The danger is not that AI will make too much music. Making more music is not inherently a catastrophe; the world has survived every bedroom producer with a laptop and a dream. The danger is that platforms may reward volume over distinction, while the money and visibility flow toward those who can exploit automation most effectively. In that scenario, listeners receive a mountain of content, corporations receive a mountain of data, and independent musicians receive a helpful notification explaining that their latest release has reached seventeen people, twelve of whom are probably their relatives.
6. Your Favorite New Band Is Three Servers in a Trench Coat
Part of the pleasure of following an artist is watching a creative identity develop. You hear the rough early recordings, notice the strange influences, follow the shifts in sound, and recognize the experiences that shape the work. An artist’s limitations can become part of that identity. So can their mistakes, obsessions, contradictions, and occasional artistic disasters.
AI complicates that relationship when the people behind a project are invisible, undisclosed, or difficult to distinguish from the system generating the music. A synthetic act can be given a name, a visual identity, a backstory, a carefully curated social presence, and a catalog of songs before anyone has asked whether there is a meaningful human creative project behind it. The result may be entertaining, commercially successful, or even aesthetically compelling. It may also be a marketing operation wearing a band T-shirt.
Transparency matters because listeners make decisions based on more than sound. They support musicians because they admire their craft, identify with their experiences, or want to sustain a creative community. If the artist is largely an automated construction, audiences deserve to know what they are supporting. A virtual act can be legitimate art, but hiding its artificial nature to manufacture a false sense of intimacy is a different proposition. The machine may have excellent branding. That does not mean it should get credit for having a difficult childhood.
Part Two: Six Reasons to Let the Robot Play the Opening Set
(AI Responds)
1. Congratulations, You Can Finally Afford to Make a Record
Here is an inconvenient truth for the anti-AI purists: making music has never been equally accessible to everyone. Recording equipment costs money. Instruments cost money. Studio time costs money. Learning production takes time, and time is a luxury for people working multiple jobs while attempting to maintain a creative life. Even the supposedly democratic world of home recording has its barriers, from expensive software to the knowledge required to make a recording sound the way its creator imagines.
AI tools can reduce some of those barriers. A songwriter with a melody but limited production experience might use a generative system to explore arrangements. Someone without access to a full band could experiment with instrumentation. An independent artist could develop a rough demo without hiring a roomful of session musicians before knowing whether the song works. These possibilities do not eliminate the value of musical skill, but they can make the early stages of creation less intimidating and less expensive.
This matters particularly for independent musicians, who rarely have a label executive waiting to fund their experimental phase. AI can help a person move from an idea to something tangible enough to evaluate, revise, or share with collaborators. The technology does not magically supply taste, discipline, or a worthwhile song. It can, however, lower the cost of finding out whether an idea deserves to exist. And that is a more defensible promise than another billionaire announcing that his latest software has democratized something while charging a monthly subscription for the privilege.
2. Your Creative Block Has Been Served an Eviction Notice
Every songwriter eventually encounters the dreaded blank page, that beautiful white void where yesterday’s inspiration goes to be declared missing. The chorus is weak. The verse refuses to cooperate. The bridge sounds like a rejected television theme. The musician has spent four hours adjusting one chord and is now seriously considering a career in municipal plumbing.
Used thoughtfully, AI can function as a creative sounding board. It can suggest chord progressions, generate alternative rhythmic patterns, help explore lyrical themes, or offer an arrangement that the musician would not have considered independently. The artist can reject the suggestions, modify them, combine them with existing material, or use them as a starting point for something entirely different. The point is not to outsource every decision but to create new possibilities when familiar habits have become a prison.
Of course, an AI suggestion is not automatically a good suggestion. Sometimes the machine will confidently offer the musical equivalent of putting pineapple on a funeral. The musician still needs judgment. But that is precisely why the tool can be useful: it gives the artist something to react against. A terrible idea can reveal what the song actually needs, and an unexpected idea can send the composition in a direction its creator might never have explored. Creativity is not always a lightning bolt. Sometimes it is an argument with a machine that accidentally produces a decent bass line.
3. Congratulations, Bedroom Producer: You Are Now the Entire Recording Studio
For decades, the ability to produce a polished recording depended partly on access to specialized people and equipment. Engineers, producers, session players, and mastering professionals remain valuable for good reasons, but technology has steadily moved more production capabilities into the hands of individual musicians. AI continues that shift through tools that can assist with tasks such as audio cleanup, stem separation, mixing suggestions, mastering, and arrangement development.
For independent artists, these tools can help solve practical problems that have little to do with the artistic heart of a song. A noisy recording might be improved. Individual elements may be separated for editing. A rough mix might become easier to balance. A songwriter working alone might test several production approaches before paying for professional assistance. In a world where the cost of releasing music can quickly outrun the income it generates, these efficiencies can make a genuine difference.
There is a distinction, however, between making a musician more capable and pretending that professional expertise no longer matters. A tool can help produce a technically cleaner mix without understanding why the snare should sound ugly, why the vocal should sit uncomfortably close to the listener, or why a particular distortion gives the song its identity. The best use of AI is not necessarily to eliminate the engineer. It may be to let the artist arrive at the studio with stronger ideas, better preparation, and a clearer understanding of what they want. Nobody ever became a visionary by spending six hours removing a refrigerator hum from a vocal track.
4. The Gatekeepers Have Entered the Chat, and They Are Furious
The music industry loves to celebrate discovering new talent, provided that the talent arrives with a marketable image, a professional recording, a convenient narrative, and enough evidence of commercial potential to reassure the people holding the money. Countless artists have struggled to gain access to the resources required to make their work competitive. AI may help some of them bypass parts of that system.
A musician who cannot afford a professional demo can produce a more developed presentation. An experimental composer can test unusual arrangements without assembling an expensive ensemble for every idea. An artist in a remote location can explore production methods that would otherwise require access to specialist equipment or collaborators. Someone who has musical ideas but limited technical training may find a new route into the creative process.
That is not a guarantee that the industry will suddenly become fair. Distribution, promotion, audience discovery, and financial sustainability remain difficult problems. AI could even strengthen existing gatekeepers if the most powerful tools are controlled by a small number of companies. But the possibility of giving more people the means to experiment is worth taking seriously. Music has repeatedly benefited when technology allows people outside established institutions to create and distribute their work. The next important artist might not have access to a fancy studio, an industry contact, or a producer who knows somebody at a label. They might have a laptop, a few good ideas, and software that helps them get started. The industry has overlooked people for less.
5. Punk Rock Was Once Considered a Terrible Idea, Too
One of the more entertaining habits in music culture is treating yesterday’s technological scandal as today’s sacred tradition. Electric guitars were once disruptive. Synthesizers were accused of threatening musicians. Drum machines provoked arguments about authenticity. Sampling reshaped hip-hop and electronic music while raising difficult questions about ownership and artistic transformation. Home recording changed the relationship between musicians and studios. None of these developments made every resulting song good, but each expanded the possibilities available to at least some creators.
AI belongs in that history, even if its particular challenges are new. It may enable combinations of sound, methods of collaboration, or forms of performance that do not fit neatly into existing categories. Musicians might use it to manipulate their own recordings, build imaginary ensembles, transform musical sketches, or explore the boundaries between human performance and generated material. Some experiments will be spectacular failures. Others may be genuinely inventive.
The historical comparison should not become an excuse to ignore legitimate concerns about copyright, employment, or consent. Earlier technologies also produced winners and losers, and the benefits were not always distributed fairly. But declaring a technology artistically illegitimate simply because it is unfamiliar is not a serious argument. The important question is what musicians do with it, how the systems are developed, and whether the people whose work enables those systems are treated fairly. If a new instrument produces a strange and compelling sound, the correct response is not necessarily to burn it. It may be to ask who built it, who owns it, and whether the person playing it has anything interesting to say.
6. The Robot Doesn’t Have to Be the Lead Singer
Perhaps the most useful argument for AI is also the least dramatic: it does not have to replace the musician to be valuable. A tool can contribute to a creative process without becoming the central author of the final work. The distinction matters because the debate often collapses into two absurd positions: AI will destroy music forever, or AI will liberate humanity from the unbearable burden of making music ourselves.
There is plenty of room between those extremes. A songwriter might use AI to explore harmonic alternatives, then write and perform the final arrangement. A producer could use it to isolate instruments or repair a difficult recording. A band might generate a synthetic sound for a specific artistic purpose. A composer could use a model to explore ideas, then reshape the results through extensive editing, performance, and experimentation. In these situations, the musician remains responsible for the decisions that give the work its character.
That does not mean every AI-assisted project automatically qualifies as a triumph of human creativity. The amount and significance of human contribution vary enormously, and a person who enters a prompt and uploads the first result has made different creative choices from someone who uses AI as one element in a painstaking production process. But dismissing every possible use of the technology ignores the difference between assistance and substitution. A guitar pedal does not write the song. A synthesizer does not automatically compose the album. AI can occupy a similarly supportive role, provided that artists retain meaningful control and audiences are not deliberately misled about what they are hearing.
The Final Track: Nobody Wins If the Musician Can’t Pay Rent
The argument over AI in music is not going to be resolved by declaring that machines have no soul or that technological progress is an unstoppable force to which everyone must obediently surrender. Both positions are too convenient. The first risks turning artistic authenticity into a purity test that ignores the many ways musicians have always used tools. The second risks treating every consequence of automation as an acceptable casualty on the road to greater efficiency.
AI can expand access to music creation, help independent artists experiment, reduce certain production costs, and generate possibilities that might not otherwise emerge. It can also threaten paid work, complicate ownership, imitate distinctive artistic identities, and encourage a business model in which creative output becomes cheaper to manufacture while the people who inspired it struggle to earn a living. Neither side of the argument disappears simply because the other has a point.
The practical answer begins with transparency and accountability. Artists should have meaningful ways to understand how their work is used in training systems, seek appropriate permission where required, and pursue fair compensation when their contributions support commercial products. Platforms should take seriously the problems of impersonation, deceptive attribution, and automated streaming abuse. Audiences should be able to make informed choices about whether the music they are hearing is human-performed, AI-generated, or produced through a combination of both. And musicians should be free to use tools that help them create without being told that their work becomes meaningless the moment software enters the studio.
We also need to stop confusing the ability to generate music with the ability to make music matter. There is no shortage of technically competent songs. The internet has spent decades proving that point at industrial scale. What listeners need is not an endless supply of interchangeable content but music that surprises them, challenges them, comforts them, disturbs them, or makes them feel less alone in a world that often seems designed by a committee of malfunctioning appliances. AI may help produce some of that music. Human musicians will continue to bring experiences, intentions, and creative decisions that cannot be reduced to the mere existence of an audio file.
Ultimately, the machine is not the villain simply because it can make a song, and the musician is not obsolete simply because a machine can imitate certain aspects of the craft. The real villain would be a system that uses technology to undermine the people who create its raw material, concentrates the rewards among a few powerful companies, and then tells everyone else that they should be grateful for the innovation.
So let the robots make music. Let musicians experiment, collaborate, argue, and build strange new sounds with them. But do not confuse the ability to automate a creative process with permission to devalue the people who make creativity worth caring about. If AI helps an independent artist turn an impossible idea into a remarkable record, wonderful. If it helps a corporation replace an entire community of working musicians with a warehouse of disposable imitations, we are entitled to ask who benefits and who gets left holding the empty guitar case.
The future of music does not need to be a cage match between humans and machines. It needs to be a fight for a creative culture in which technology serves the music, the music serves the people who make it, and nobody gets to declare victory just because the algorithm has learned to rhyme.
After all, if the robot is going to take over the music industry, the least it can do is pay the opening bands.
Featured image by Jammerzine and JAI.