Research by

for

Tools, not tricks: How musicians are actually using AI.

Based on a survey of 1,525 musicians by Water & Music for Moises.

March 2026

INTRODUCTION

A comprehensive survey on how working musicians actually use AI.

In late 2025, Water & Music partnered with Moises to survey 1,525 musicians on their AI usage and sentiment. Our sample captures how creators across skill levels and career stages use AI across multiple contexts, including practice, production, and professional work.

AI in music has accumulated a thick layer of myths: that it’s mainly for amateurs, that professionals reject it, that it’s devastating livelihoods. We designed this survey to test those assumptions against actual behavior.

A note on methodology: Roughly 80% of respondents were recruited through Moises’ user base, with the remainder from Water & Music’s channels. Two-thirds (67%) of respondents confirmed they had used AI for music-related work. Detailed questions about tools, use cases, and outcomes were asked only of confirmed AI users.

Three key findings challenge the prevailing narratives:

1

Professionals are leading the charge.

78% of pros use AI, vs. 60% of hobbyists. Pros are also twice as likely to pay $50 or more per month for AI tools.

2

AI’s killer app for musicians? The unglamorous stuff.

Stem separation leads at a 71% adoption rate in our sample, outpacing full song generation (24%) by nearly 3:1.

3

Even enthusiasts have doubts.

58% of AI users worry about authenticity, while 55% cite concerns about copyright and licensing. The top concerns are not about service-level features and usability, but about ethics and ownership.

AI usage in past 12 months

Among all respondents (n = 1,525)

01 / 06

Professionals Lead the Charge

Musicians with the most at stake financially are the most willing to invest in AI.

While public discourse focuses on AI replacing musicians, the professionals who depend on music for their livelihood are embracing these tools at significantly higher rates than hobbyists (78% vs. 60%). This is the report’s defining insight: across the spectrum of use cases, AI adoption is being driven more from the top than from the bottom.

spending on AI music tools

Among pro AI users (n = 370) and non-pro AI users (n = 352)

02 / 06

The Investment Gap Is Real

Professional musicians don’t just use AI more; they also pay more for it.

Professionals are 2x as likely as hobbyists to spend $50+ per month on AI tools (21% vs. 11%). Hobbyists cluster at lower price points, with 36% spending $1–19/mo and 25% using only free tools. Beyond just casual experimentation, professionals are integrating AI into their paid workflows and budgeting for it like any other piece of gear.

Top Ai outcomes

Among confirmed AI users (n = 1,021)

03 / 06

Growth Over Shortcuts

The #1 outcome musicians report from AI is learning more songs.

When we asked musicians to indicate what AI has enabled them to achieve, learning more songs (40%), experimenting with new genres (33%), and improving production quality (30%) all ranked above economic results like earning more income or cutting studio costs. By and large, musicians are using AI not to drive efficiency for its own sake, but to grow holistically as artists.

top AI TASKS

Among confirmed AI users (n = 1,021)

04 / 06

Process Over Product

The dominant use cases center on streamlining workflow, not wholesale generation of full tracks.

Stem separation tops the list at 71%, followed by practice and skill development (44%), accompaniment generation (40%), and mixing and mastering (32%). Meanwhile, full song generation ranks 6th at 24%. Contrary to what the headlines might suggest, musicians prefer tools that support their creative process over ones that replace it.

AI’s perceived impact on music income

Among people who monetize their music (n = 983)

05 / 06

More Positive Than Negative

Among musicians who earn from their work, the economic upside of AI outweighs the downside 6-to-1.

26% of our respondents who make money from music say AI has increased their income; fewer than 4% report a decrease, and 56% report no change. While the economic narrative around AI in music has been overwhelmingly negative, our data tells a different story. For the vast majority, the financial picture has either improved or held steady.

TOP CONCERNS AMONG ai USERS

Among confirmed AI users (n = 1,021)

06 / 06

Concerns Coexist With Adoption

The musicians using AI the most are also the ones thinking hardest about its risks.

58% of AI users worry about authenticity, while 55% cite copyright and licensing concerns. Yet 92% would still recommend AI tools to their peers. Adoption and ambivalence are not opposites — rather, they coexist. The AI companies that earn long-term trust will be the ones that take these ethical questions as seriously as the technical ones.

Takeaways

For Artists

1

Start where the friction is

The highest-value AI use cases may involve handling tasks that eat your hours, without defining your sound.

2

Use AI to stretch your skills

The question worth asking of any tool: what can this teach me?

3

Stay critical while you experiment

When evaluating tools, ask: What data trained this model? Do I own what I create with it? Does this tool enhance my voice or flatten it?

4

Track your own economic journey

So far, the economic picture is stable — but it's worth tracking as AI adoption grows.

For AI Companies

1

Build for workflows

The tools earning adoption solve specific problems inside existing creative processes.

2

Track what the pros do

When high-stakes creators adopt and pay, it signals a tool has crossed a quality threshold. If they churn, find out why.

3

Solve trust at the product level

Transparent training data practices, clear licensing terms, and respect for creative ownership will determine which tools retain trust in the long term.

4

Segment or struggle

Pros will pay more for reliability and churn fast if outputs disappoint. Hobbyists respond to pitches about experimentation, not efficiency.

Full Report

Get the complete picture.

Explore the full insights, data, and takeaways. Whether you make music, build AI tools, or do both. Research by Water & Music for Moises.

* The full report is currently only available in English.