HookGenius Research · 2026

The State of
AI Music Creation 2026

What 739 creators actually made — measured across 3,428 real Suno tracks.

Most people don't fight the AI — they collaborate with it. Across 3,428 AI music generations from 739 creators (April–July 2026), 74.5% let the model choose the singer, songs were written in 36 languages, Pop was the most-made genre, and 1 in 3 creators modeled a track on a named artist — led by The Weeknd, Billie Eilish, and Drake. When creators wrote lyrics, they overwhelmingly asked AI to enhance their draft rather than write from scratch.

Source: HookGenius generation data 3,428 tracks · 739 creators Apr–Jul 2026 Updated Jul 6, 2026
74.5%
let the AI choose the voice
36
languages songs were made in
1 in 3
creators modeled a named artist
Pop
the #1 genre by creators
2.6:1
male-to-female when a voice is chosen
27%
of sessions go beyond a single track

Finding 01 · Vocals

Do AI music creators choose the singer, or let the model decide?

They let the model decide — by a landslide. Of all 3,428 tracks, 74.5% were generated with the voice left on "AI decides." Only a quarter of creators specified a vocal at all — and when they did, they chose a male voice 2.6× more often than a female one.

74.5%
AI decides the voice
18.3%
chose a male voice
7.1%
chose a female voice

The AI music generation isn't just writing the song — for three out of four creators, it's casting the singer too.

The male-skew (2.6:1 among creators who set a voice) mirrors a known pattern in default AI voice output and creator preference. It's one of the clearest signals in the dataset that "hands-off" is the default posture.

Finding 02 · Genre

What genres do AI music creators actually make?

Pop leads — but the field is remarkably even. Ranked by the share of creators who made at least one track in each genre, Pop, Electronic, and Hip-Hop cluster at the top, with R&B, Lo-Fi, and Rock close behind. AI music isn't one sound; it's a broad, mainstream spread.

Pop
11.1%
Electronic
9.6%
Hip-Hop
9.5%
R&B
7.7%
Lo-Fi
7.2%
Rock
6.9%
Indie
4.9%
Country
2.8%
Afrobeats
2.4%

Share of the 739 creators who made ≥1 track in each genre (creators can appear in more than one). Ranking creators — not raw track counts — keeps a few high-volume users from distorting the picture.

Finding 03 · Language

What languages is AI music made in?

AI music is global. Creators wrote songs in 36 different languages. English dominates at 95.5% of creators, but nearly 1 in 11 creators (8.8%) made at least one non-English track — from Spanish and Korean to Arabic, Ukrainian, and Bislama.

Spanish
25
German
12
Turkish
9
Russian
9
French
8
Korean
7
Portuguese
7
Japanese
6
Arabic
6

Non-English creators ranked by number of distinct creators. English: 706 creators (95.5%).

The long tail is the real story — songs were also made in:

PolishSwedishIndonesianUkrainianTagalogItalianVietnameseMalayBislamaNorwegianBurmeseCzechBengaliHindi+ 8 more

Finding 04 · Influence

Which artists do AI music creators model their songs on?

One in three creators (31.7%) named a specific artist as an influence when making a track. Ranked by how many distinct creators cited each one, The Weeknd leads, followed by Billie Eilish, Drake, Taylor Swift, and Bad Bunny — a lineup that reads like the actual 2020s pop-culture canon.

The Weeknd
23
Billie Eilish
17
Drake
14
Taylor Swift
11
Bad Bunny
10
Doja Cat
4
Eminem
4
Arctic Monkeys
3
Blackpink
3

Number of distinct creators who named each artist as an influence when generating a track. These figures reflect what creators typed as inspiration — they are not affiliations with, or statements about, the artists themselves.

Finding 05 · Workflow

Do creators want AI to write the whole song, or help with their own?

They want a collaborator, not a ghostwriter. Among creators who chose how to handle lyrics, 56% asked the AI to enhance a draft they'd written, 30% locked in their exact words, and only 14% asked it to write from a loose idea. And AI music isn't disposable: more than a quarter of sessions produced a variation or a full album, not a one-off single.

Enhance my draft
56%
My exact words
30%
Inspire me
14%

The winning workflow of 2026 isn't "AI, write me a song." It's "here's mine — make it better."

Lyrics-handling among the 1,344 tracks where a mode was set. Separately, of all 3,428 tracks: 72.9% single, 13.7% variation, 13.4% full album — so 27.1% extended beyond a single track.

Methodology

How we measured this

What's in the dataset

  • 3,428 AI music generations created by 739 distinct creators on HookGenius between April and July 2026.
  • Anonymized + aggregate. We analyze only the creative metadata of a generation — genre, vocal setting, language, artist influence, and workflow mode. No names, no lyrics, no personal data appear in this study.
  • Internal and test accounts were excluded so the numbers reflect real creators.

How we kept it honest

  • Distinct-creator weighting. Rankings (genres, artists, languages) count how many different creators did something — not raw generation counts — so a handful of high-volume users can't distort the trend.
  • Stated denominators. Where a field is optional (genre, lyrics mode), percentages are taken against the tracks that specified it, and we say so. Vocal, language, and generation-mode figures are against all 3,428 tracks.
  • Primary source. Every number here is computed directly from HookGenius's own generation data — this is first-party research, not a survey or an estimate.

Cite this study

Free to reference with attribution (CC BY 4.0). Copy the citation:

HookGenius (2026). The State of AI Music Creation 2026: 3,428 Suno Tracks Analyzed. Retrieved from https://hookgenius.app/suno-data-study-2026/

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