Create Better Music Faster: Production Reimagined

AI in Music Creation and Production

Part 1: How AI Is Reshaping the Studio, and What That Means for Everyone in the Room

The Day Holly Herndon Let an AI Co-Write Her Album

What makes Herndon's story instructive isn't that she's a tech visionary. It's that she identified a tension that every music professional now faces: AI tools are genuinely useful, sometimes startlingly so, but they don't replace human taste, context, or relationships. Spawn could generate haunting vocal textures that would take a human choir weeks to rehearse. It could not decide whether those textures served the emotional arc of a song. That decision remained entirely human. The people who thrived in Herndon's process were the ones who understood what the tool could and couldn't do, and used that boundary deliberately.

Fast forward to 2024. The tools available to an independent artist, a music supervisor at a TV network, a label A&R manager, or a music teacher are dramatically more accessible than anything Herndon used. You don't need a Stanford PhD or a custom-trained neural network. You need a browser, a subscription that often costs less than a streaming service, and a clear sense of what you're trying to accomplish. That's the real story of AI in music right now, not robots replacing musicians, but professionals who know how to direct these tools pulling ahead of those who don't.

The Principle: AI Is a Production Assistant, Not a Producer

Here's what Herndon's experience actually teaches us about working with AI music tools. A great producer brings taste, emotional intelligence, knowledge of an artist's history, and relationships with the industry. A great production assistant brings speed, tirelessness, and the ability to generate ten versions of something so the producer can pick the right one. AI sits firmly in the second category, for now. Suno, one of the most capable AI music generation tools available in 2024, can produce a full song with vocals, instrumentation, and a coherent structure in about 30 seconds. That's remarkable. It cannot tell you whether that song fits the emotional arc of a film scene, whether it matches an artist's established brand, or whether a playlist curator at Spotify will find it interesting. Those judgments require human context.

Consider how this plays out in a real production context. A music supervisor working on a streaming series needs 12 background tracks for a montage sequence, tracks that feel like indie-folk but won't trigger licensing issues with major labels. Traditionally, that search takes hours: browsing sync licensing libraries, emailing composers, negotiating fees. With Soundraw, that supervisor can generate 12 royalty-free tracks in under 10 minutes, adjusting tempo, mood, and instrumentation with sliders, no music theory required. The creative decision about which tracks actually fit the scenes still belongs entirely to the supervisor. The tool just collapsed the time between 'I need this' and 'here are options to evaluate.'

This is the pattern you'll see throughout this lesson. AI tools in music compress the distance between intention and raw material. They don't replace the professional judgment about what to do with that material. The music professionals who understand this, who treat AI as a fast, capable, but contextually naive assistant, are the ones getting more done, protecting their creative authority, and building competitive advantages that their peers haven't figured out yet.

How a Video Game Studio Changed the Way We Think About Music Production

The video game industry got to AI-assisted music production before the music industry did, and the lessons are directly applicable. Dynamically adaptive soundtracks have been a game design challenge for decades: how do you create music that responds in real time to player actions without sounding repetitive or jarring? Studios like Ubisoft and EA began using AI-driven audio systems years ago, where the software would layer, cut, and transition musical phrases based on gameplay state. The composers didn't write less music. They wrote music differently, in modular phrases designed to be assembled by an algorithm. The creative work shifted from writing a linear piece to designing a system of musical possibilities.

What music business professionals can extract from this is a mental model shift. When you use a tool like AIVA (Artificial Intelligence Virtual Artist), you're not asking it to write a song for you. You're setting parameters, genre, instruments, emotional tone, duration, and it assembles musical possibilities from patterns it learned from thousands of compositions. Your job, like those game composers, is to design the brief well and then curate the outputs. AIVA is used by independent composers, content creators, and small production houses who need original scored music without the budget for a full orchestra session. A single AIVA subscription runs around $33/month for commercial use. A single session musician costs multiples of that for a single afternoon.

Comparing the Major AI Music Creation Tools

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How an Independent Artist Manager Used AI to Stretch a Tiny Budget

Maya Chen manages three independent artists out of Nashville. None of them have label deals. Combined, their annual music budgets, recording, mixing, marketing, touring support, total around $45,000. Before AI tools became accessible, that budget forced constant trade-offs: spend on recording quality and sacrifice marketing, or market heavily and release music that sounds undersized. In 2023, Chen started using Suno to generate demo-quality reference tracks that her artists could use to pitch producers and collaborators. Instead of paying $500 for a studio day just to record a rough idea, she'd spend 20 minutes in Suno generating three versions of a song concept, send them to the producer as creative direction, and arrive at the paid session with alignment already established.

Chen also uses ChatGPT Plus for the business side of artist management, drafting pitch emails to playlist curators, writing press releases, generating social media content calendars, and building out EPK (Electronic Press Kit) copy for her artists. She estimates she saves 8–10 hours per week on writing tasks alone. That time goes back into relationship-building, which is the part of her job that genuinely requires a human. "The AI does the first draft of everything now," she told a Nashville music business panel in early 2024. "I just make it sound like us." That's a precise description of the professional AI workflow. AI handles volume and speed, human handles voice and judgment.

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Putting This to Work: What AI Music Tools Actually Change About Your Day

The practical shift AI music tools create isn't about replacing creativity. It's about changing where you spend your time and energy within the creative process. Before these tools, a music teacher designing a curriculum unit on song structure might spend an entire afternoon searching for royalty-free examples that fit specific criteria, tempo, genre, complexity level. Now, that teacher can generate custom examples in Suno in under an hour, tailored precisely to the lesson. A marketing manager at a mid-size record label who needs a 30-second instrumental clip for a social media ad no longer needs to call the licensing department and wait three days. They open Soundraw, set the parameters, and download a track in minutes.

This time compression matters enormously in a business context. Music industry professionals routinely cite administrative and production logistics as the biggest drains on creative bandwidth. When those tasks shrink, creative work expands, not because AI is being creative on your behalf, but because you have more hours available for the work that actually requires your expertise. A label A&R manager who uses Claude Pro to draft artist development notes and meeting summaries isn't outsourcing their A&R judgment. They're reclaiming the time those documents used to cost so they can spend more hours actually listening to music and building artist relationships.

There's also a democratization effect worth understanding. High-quality music production used to require expensive studio time, session musicians, and professional engineers, resources that independent artists and small organizations simply didn't have. AI tools have meaningfully lowered that barrier. An independent podcast producer can now score their show with original music. A small event company can create custom background music for client events. A music teacher in an underfunded school can give students access to production tools that would have cost thousands of dollars in software licenses five years ago. The tools are imperfect. The music they generate often sounds slightly synthetic to trained ears. But for a huge range of professional applications, 'good enough, fast, and affordable' is exactly what's needed.

Key Lessons from Part 1

1. AI music tools are production assistants, not producers. They generate raw material fast. Human professionals provide the taste, context, and judgment about what to do with it.
2. The most effective AI music workflows involve a clear brief. Vague inputs produce generic outputs. Specific prompts, mood, tempo, instrumentation, emotional arc, intended use, produce useful ones.
3. Speed compression is the primary practical benefit. Tasks that took hours (searching sync libraries, recording rough demos, drafting music business documents) now take minutes. That time goes back to higher-value work.
4. The 3 Versions Rule applies universally. Always generate multiple outputs before concluding a tool can't deliver. Parameters matter enormously, and small changes produce dramatically different results.
5. AI tools have democratized access to music production resources. Independent artists, small organizations, and individual professionals can now access capabilities previously reserved for well-funded teams.
6. Your curatorial judgment is the irreplaceable skill. Any professional can generate AI music. The professionals who stand out are the ones who can evaluate outputs critically and make informed decisions about what serves the actual creative or business purpose.

How Labels and Publishers Are Actually Using AI Right Now

Warner Music Group made headlines in 2023 when it signed a deal with AI music startup Endel to release algorithmically generated "functional music", ambient soundscapes designed for focus, sleep, and relaxation. This wasn't an experiment buried in a research lab. Warner put these releases on Spotify, Apple Music, and Amazon Music under real artist profiles, collected streaming royalties, and watched the numbers climb. Endel's AI-generated tracks had already amassed over 500 million streams before the partnership was announced. Warner's A&R team didn't build the AI. They identified an opportunity, negotiated terms, and figured out how to distribute and monetize something their traditional catalog couldn't easily produce at scale.

The tension inside Warner's boardroom mirrored what's happening across the entire music industry. Some executives saw the Endel deal as a necessary evolution, a way to capture the growing "wellness audio" market without commissioning hundreds of human composers for background tracks nobody would remember. Others worried about the precedent: if AI could generate infinite streaming-ready content, what happened to the human artists on Warner's roster who made their living from sync licensing and background music commissions? Both sides had legitimate points. The deal went ahead anyway, because the market was moving whether Warner participated or not.

The principle buried in Warner's decision is one that every music business professional needs to understand: AI doesn't replace creative judgment, it replaces creative labor at specific price points. Warner still needed humans to evaluate Endel's output, negotiate the contract, manage the artist profiles, pitch the tracks to playlist editors, and decide which moods and tempos to prioritize. What changed was the cost and speed of generating the underlying audio. That distinction, between creative judgment and creative production, is the fault line running through every AI music conversation happening in boardrooms, studios, and management offices today.

From the Studio to the Sync Library: Three Ways AI Changes the Production Chain

Producer and music supervisor Cynthia Erivo's team at a mid-sized sync agency faced a familiar bottleneck in 2022: a TV production company needed 40 short instrumental cues, 15 to 30 seconds each, for a documentary series. The budget was $8,000 total. At standard composer rates, that bought maybe eight cues. The agency had three options: turn down the job, underpay a composer, or find another way. They used a combination of Soundraw and Epidemic Sound's AI tools to generate initial versions of all 40 cues in two days, then hired a composer for two days to edit, refine, and mix the final selections. Total creative labor: four days. Total cost: under $4,000. The production company got everything they needed. The composer got two days of well-paid work instead of a brutal flat-fee grind. The agency kept the client.

This kind of workflow is now standard at sync agencies and production music libraries that work below the prestige tier. It reveals the first major shift AI brings to the production chain: speed and scale at the bottom of the budget stack. The cues that used to fall through the cracks, too small to justify a real composer, too specific to pull from a stock library, now have a viable production path. For music supervisors, this is genuinely useful. For composers who built careers on mid-budget sync work, it's a direct threat to a significant income stream.

The second shift is in the demo and development phase. Labels and managers have always spent money on demos, rough recordings used to pitch artists to radio programmers, brand partners, and festival bookers. AI tools like Suno and Udio can now generate demo-quality tracks in minutes from a text description, giving A&R managers a fast way to test whether a sonic direction is worth pursuing before committing real studio time. One UK-based management company reported using Suno to generate 12 rough concept tracks for an unsigned artist's pitch deck, helping them visualize and communicate a sonic identity to labels without spending £3,000 on studio sessions. Three labels requested full demos after hearing the AI-generated concepts. None of the AI tracks appeared on the final record.

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Advertising and Brand Music: A Different Kind of Pressure

The advertising industry moved faster than the music industry in adopting AI audio tools, partly because the stakes per project are lower and partly because brand music has always been treated as a production cost rather than an artistic statement. In 2023, Coca-Cola's "Create Real Magic" campaign used AI tools to let fans generate artwork and audio elements using the brand's existing creative assets. More quietly, mid-tier advertising agencies started using tools like Mubert and AIVA to generate custom background music for digital ads, social content, and internal videos, cutting music licensing costs that previously ran $500 to $5,000 per track for a 30-second placement.

For music supervisors and brand strategists, this shift has a practical consequence that shows up in budget conversations. When a brand's marketing team can generate "good enough" background music for a social media ad in 10 minutes using an AI tool their design team already has access to, the threshold for commissioning original music rises. The tracks that get commissioned now need to do more, they need a recognizable sonic identity, a specific emotional hook, or a licensing story that justifies the cost. AI hasn't killed brand music. It has raised the bar for what human-created brand music needs to accomplish.

AI Music Tools: What Each One Actually Does

What a Music Educator Learned by Letting Students Use Suno

Dr. Megan Lavengood, a music theory professor at George Mason University, began incorporating AI music tools into her courses in 2023 as a way to help students without traditional production skills create musical examples for analyzis. She gave students a structured assignment: generate three versions of the same song concept in Suno using different genre prompts, then write a 500-word analyzis comparing how the AI interpreted each genre's conventions, instrumentation choices, rhythmic patterns, harmonic language. The goal wasn't to teach production. It was to make music theory tangible for students who had always struggled to connect abstract concepts to real sound.

The results surprised her. Students who had previously disengaged from music theory, particularly those who didn't play instruments, became more analytical and articulate about musical structure once they could generate and compare examples instantly. The AI tools lowered the production barrier enough that the intellectual work, the comparison, the critique, the analyzis, moved to the center. Lavengood's experience points to something important for anyone in music education, artist development, or creative consulting: AI tools don't just change what gets made. They change who gets to participate in making it, and that changes what kinds of creative conversations become possible.

Putting AI Music Tools Into Your Actual Workflow

The professionals getting the most value from AI music tools right now are not the ones trying to replace their entire creative process. They're the ones who have identified specific friction points in their workflow, the moments where production bottlenecks slow down decisions, and inserted AI tools precisely there. A talent manager who uses Suno to generate concept demos before booking studio time. A sync agency that uses Soundraw to fill low-budget requests that would otherwise be declined. A brand strategist who uses Mubert to generate placeholder music for client presentations before the final track is licensed. In each case, the AI tool solves a specific problem at a specific stage of the workflow, and humans handle everything that requires judgment, relationships, and taste.

The tools also have a less obvious application: competitive intelligence and market research. If you work in A&R, artist development, or music publishing, you can use AI generation tools to rapidly explore what a specific genre, mood, or sonic niche sounds like when reduced to its most algorithmic form. That tells you something about where a genre's conventions have become formulaic, and therefore where there's space for something genuinely distinctive to break through. AI music tools trained on massive datasets tend to produce the statistical center of a genre. Human artists who want to stand out need to know where that center is so they can deliberately move away from it.

There's also a growing use case in music licensing negotiations and rights management. When a sync client comes to you with a budget that doesn't support original composition, you now have a credible alternative to offer, and that changes the negotiation. You can say: "For this budget, we can deliver AI-generated music with a full commercial license through Soundraw, or we can discuss what original composition would require." That conversation used to end with a declined project. Now it can end with a tiered proposal where the client understands the value difference between the two options. That's a meaningful business skill, and it has nothing to do with knowing how to code or operate a DAW.

What These Stories Actually Teach Us

1. Warner Music's Endel deal shows that AI music is already a commercial product category, not a future possibility. The question for music professionals is where they sit in relation to that category.
2. The sync agency case demonstrates that AI tools solve a real economic problem at the bottom of the budget stack, creating new workflows rather than simply replacing old ones.
3. The A&R demo use case reveals that AI's value isn't always in the final output, sometimes it's in the speed of exploration and the reduction of financial risk during early creative development.
4. Advertising's faster adoption of AI audio tools shows that "good enough" is a real threshold, and once AI clears it for low-stakes use cases, human-created work must compete on differentiation, not just quality.
5. Dr. Lavengood's classroom experiment proves that lower production barriers change who participates in creative conversations, and that has implications for artist development, education, and community building.
6. The most effective professional use of AI music tools targets specific workflow friction points rather than attempting wholesale replacement of creative processes.
7. AI-generated music reveals the statistical center of any genre, making it a useful competitive intelligence tool for A&R professionals trying to identify where genuine originality still has room to break through.

When the Artist Becomes the Editor: Navigating AI Collaboration in Music

In 2023, Holly Herndon and Mat Dryhurst released 'Holly+', a project that gave fans a trained AI model built on Holly's own voice. Anyone could upload a melody and have it rendered in her vocal style, with her explicit consent and creative direction. This wasn't a label executive deploying AI to cut costs. It was an artist using AI as a deliberate extension of her identity, setting clear terms about how her likeness could be used. The tension at the heart of AI music creation isn't technology versus art. It's about who controls the creative frame.

Herndon's project exposed a principle that gets lost in the noise around AI music tools: the quality of AI output in creative work is almost entirely determined by the human shaping the input. Her model didn't create interesting music on its own. It created interesting music because she had spent years developing a distinctive voice, a clear aesthetic, and a thoughtful framework for how her work should sound. The tool amplified an existing creative identity. Without that identity, it would have produced generic output indistinguishable from thousands of other AI-generated tracks flooding streaming platforms.

That distinction matters enormously for music business professionals. Whether you're a manager pitching a sync deal, a label A&R executive evaluating demos, a music supervisor sourcing cues, or an independent artist building a brand, AI tools now sit inside your workflow whether you invited them or not. The question isn't whether to engage with them. It's whether you engage with enough creative and strategic intention to make them work for you rather than producing undifferentiated noise.

The Sync Supervisor's Advantage

Music supervisors at companies like Musicbed and Artlist have quietly integrated AI generation tools into their sourcing workflows. When a brand needs thirty seconds of tense, percussive underscore for a product reveal, and needs it by Thursday, waiting for a composer to deliver isn't always viable. Supervisors are now using tools like Soundraw and Mubert to generate rapid reference tracks that communicate the emotional brief to directors and editors, even when the final cue will be licensed from a human composer. AI becomes the vocabulary for creative conversation, not the final product.

One senior supervisor at a mid-sized agency described it this way: AI lets her show clients what they actually want, not just describe it. She generates three or four rough mood sketches using Soundraw, plays them in a briefing, and watches where the client's body language changes. That moment of recognition, 'yes, that feeling, but darker', saves two weeks of back-and-forth with composers and dramatically improves the final brief. The AI track never goes into the film. But it shapes everything that does.

What the Advertising World Learned First

The advertising industry ran this experiment before music caught up. When AI copywriting tools like Jasper and Copy.ai appeared, agencies initially feared they'd replace writers. What happened instead was a bifurcation: generic copy became cheaper and faster, but strategically directed copy, built on genuine brand insight, cultural nuance, and specific audience knowledge, became more valuable, not less. The writers who thrived weren't the ones who refused AI. They were the ones who used AI to eliminate the mechanical work and spent more time on the thinking that machines genuinely can't replicate.

Music is tracking the same curve. Producers who use Suno or AIVA to sketch chord progressions and arrangement ideas in twenty minutes, then apply genuine craft and taste to develop them, are producing more work, faster, without sacrificing their creative signature. Artists who treat AI output as a starting draft, not a finished product, are finding it accelerates their process rather than flattening it. The threat isn't AI. The threat is treating AI output as good enough when it isn't, and losing the creative reputation that makes your work worth licensing, streaming, or signing.

The Independent Artist's Strategic Play

Consider an independent artist managing their own release campaign with no label budget. Before AI tools, creating assets for a release, promo videos, social content, mood boards for press, reference tracks for remixers, required either money or an extensive network. Now, an artist can use Suno to generate instrumental sketches for remixers to work from, use Canva AI to build visual assets aligned with a sonic mood, and use ChatGPT to draft press releases, pitch emails to playlist curators, and social captions that actually communicate the emotional intent of the music. The creative vision is still entirely theirs. AI handles the execution overhead.

The artists gaining ground with this approach aren't using AI to make more music. They're using it to be more present in the business of their music, spending less time on administrative and production tasks that once consumed their week, and more time on live performance, fan relationships, and creative development. That's the real shift AI enables for independent professionals: not replacement of craft, but redistribution of time toward the highest-value human work.

Putting AI Music Tools Into Professional Practice

For music business professionals, the practical entry point is using AI generation tools to solve a specific, bounded problem rather than attempting to overhaul an entire creative process. A manager can use Suno to generate demo-quality reference tracks that communicate an artist's sonic direction to potential collaborators without requiring studio time. A music publisher can use AIVA to produce background music for client pitch presentations that demonstrates genre range. A marketing professional at a label can use Soundraw to create custom music for social content rather than paying sync fees for every short-form video.

The copyright landscape around AI-generated music is still evolving rapidly. The US Copyright Office has stated that purely AI-generated content without human creative authorship cannot be copyrighted, but work where a human makes substantial creative choices in the process may qualify. This matters practically: music you generate entirely through an AI prompt and publish without modification occupies uncertain legal ground. Music where you direct, edit, arrange, and shape AI-generated elements as part of a broader human-led creative process is on firmer footing. When in doubt, consult the terms of service of the specific tool and seek legal guidance before commercial release.

The artists, supervisors, and music business professionals who will build lasting careers alongside AI tools are the ones who develop genuine creative taste and strategic clarity, and then use AI to execute that vision faster and more prolifically. The tool is only as good as the creative intelligence directing it. Your job isn't to become a prompt engineer. Your job is to become more precise about what you want, more articulate about why it matters, and more deliberate about the emotional effect you're trying to create. AI is very good at doing exactly what you ask. Getting specific about what you're asking is entirely human work.

1. Creative identity precedes tools: AI music generation amplifies existing creative vision, it cannot substitute for one. The clearer your aesthetic and emotional intent, the better your AI output will be.
2. Specificity is the core skill: Vague prompts produce generic music. Detailed prompts referencing mood, tempo, instrumentation, reference artists, and emotional arc produce usable, distinctive work.
3. AI works best as a process accelerator, not a product replacement: The most effective professional uses of AI music tools involve rapid prototyping, reference generation, and brief communication, not final deliverable creation.
4. Copyright uncertainty is real and consequential: Purely AI-generated music sits in legally uncertain territory for commercial use. Understand the terms of every tool you use before publishing or monetizing output.
5. The advertising industry's trajectory predicts music's: When AI handles mechanical execution, human creative judgment and strategic thinking become more valuable, not less, the professionals who thrive are those who invest in both.
6. Different tools serve different professional needs: Soundraw suits sync and commercial work; Suno suits rapid song sketching; AIVA suits scored compositions; Mubert suits continuous generative background use. Match the tool to the task.
7. Consent and attribution are the ethical baseline: Using AI trained on artists' work without their knowledge or consent, and distributing the output commercially, represents a genuine ethical and legal risk that the industry is still working to resolve.

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Key Takeaways

• AI music tools are most powerful in the hands of professionals with clear creative direction, not as a shortcut around creative thinking, but as an accelerator of it.
• Holly Herndon's 'Holly+' project demonstrates the ethical and creative model: artist-directed, consent-based, identity-driven AI use that extends rather than erases the human creator.
• Music supervisors, independent artists, and label marketers are already using AI generation tools to solve specific workflow problems, reference tracks, mood sketches, social content music, without replacing human craft in the final product.
• The copyright landscape for AI-generated music is unsettled. Always check tool-specific licensing terms before commercial use, and ensure human creative authorship is documented in your process.
• Your competitive advantage in an AI-saturated music environment is taste, context, and creative specificity, qualities that determine whether AI output is useful or generic.