It is a fair worry. If a machine can write, sing and mix a song in a few minutes, what happens to the people who spent years learning to do it? Dismissing the concern helps nobody, and neither does panic.
This article looks honestly at what AI music can do, what it cannot, which kinds of work are genuinely affected, and how musicians in India and elsewhere are using these tools.
What AI already does well
Modern AI music generators can produce a complete song with vocals and instruments from a short description. They are fast, cheap and available at any hour. They are good at familiar styles, at hitting a requested mood, and at making many options quickly.
For people who could never afford a composer, such as a small shop owner, a family planning a wedding or a YouTuber starting out, that is a real change. It opens up song-making to people who were previously shut out.
What AI does not do
AI does not stand on a stage. It does not feel the room at a sangeet and stretch the dhol section because the dance floor is full. It does not have a voice that fans recognise from a hundred songs, a personal story, or a reason to make a particular song at a particular moment in its life.
- Live performance: concerts, weddings, jagrans, kirtans and gigs are human events.
- Artistic identity: listeners follow artists, not just tracks.
- Original direction: deciding what is worth saying, and breaking a genre's rules on purpose.
- Deep tradition: the gamak and meend of classical music are learned over years with a guru, as a practice, not only as a sound.
- Collaboration: musicians in a room reacting to each other in real time.
Where work is genuinely shifting
Honesty means admitting some paid work will change. Generic background music, simple jingles, placeholder demos and low-budget stock cues are the areas most exposed, because the buyer mainly needs something suitable, quickly and cheaply. Some session singers who recorded guide vocals for demos may also see less of that work.
This has happened before. Drum machines, synthesisers, sampling and home recording all changed who got paid for what. Each time, some roles shrank, others appeared, and musicians who learned the new tools tended to adapt best.
The Indian picture
Music in India is unusually social. A large share of musicians' income comes from live settings: wedding bands and dhol players, sangeet performers, jagran and kirtan groups, temple and gurdwara musicians, qawwali parties at dargahs, and local bands playing college fests and cafés. None of that is a recorded file, and none of it is replaced by one.
Where AI does change things in India is at the small end of recorded work: a short jingle for a local shop, background music for a regional YouTube channel, or a quick demo for a devotional album. For the people who commissioned that work it means songs they could never afford before. For musicians who relied on it, it is a reason to move towards live work, teaching, production and the kind of distinctive sound a generator cannot copy.
How musicians are using AI
Many working musicians treat AI as a sketchpad rather than a rival.
- Sketching: generating a rough version of an idea to hear whether it works before booking players.
- Pitching: making a fuller demo to show a producer or client.
- Practice: creating backing tracks in any style or tempo for riyaz.
- Taking it apart: splitting a song into stems or exporting MIDI, then replacing parts with their own playing.
- Starting from their own voice: singing or humming a melody and letting AI add the band, which is what Autunes' Jam does for 10 credits on every plan.
The ethics questions
Musicians have legitimate concerns about how AI models are trained and whether artists were asked or paid. In 2024 the major record labels sued Suno and Udio in the United States over training data, and the industry has since been moving towards licensing arrangements. How this settles will shape how fair the technology is.
Voices are another line. Copying a real singer's voice without consent is wrong and, in many places, legally risky. Autunes' voice clone, on the Creator plan, works from about 20 seconds of your own recording and sings Autunes songs in your voice; it does not work on uploaded songs, and the terms do not allow impersonating artists.
What this means if you are a musician
Lean into what is hardest to automate: live performance, a distinctive voice or style, a real relationship with your audience, and the judgement to know what a song needs. Learn the tools well enough to use them where they save time, such as demos, backing tracks and arrangement ideas, without handing them the parts that make your work yours.
For non-musicians, AI is simply a way in. Someone who starts by making a birthday song may end up learning guitar because they want more control. More people making music is not bad news for music.