Here’s How AI Tools Are Revolutionizing Music Production — and the Creative Gaps They Can’t Yet Fill

We tend to think AI music tools are just gimmicks for social media creators, or that they’re limited to basic beats. But it’s hard to dismiss them when companies like Google, Meta and Stability AI are pouring resources into generative audio models that can produce full compositions in seconds.

As a pianist and tech founder, I’ve tested a wide range of music AI tools.

Suno and Udio as the generators

If you want to create a song from scratch, Suno and Udio are currently the two leading platforms. Both use text prompts to generate full tracks with vocals, instruments and production included. Type “upbeat 80s synth-pop about summer in Paris,” and you’ll get a polished two-minute track within seconds.

Suno excels at catchy, radio-friendly structures. Udio tends to produce more nuanced arrangements. In my experience, Suno works best for quick prototypes, while Udio delivers better results when you need layered instrumentation. Both offer free tiers and paid plans for commercial use.

AIVA as the composer

AIVA (Artificial Intelligence Virtual Artist) specializes in orchestral and cinematic music. The platform lets you select a style such as epic trailer, emotional piano or electronic ambient, then generates royalty-free compositions. Film editors and game developers use it to score projects without hiring a composer.

Soundraw as the customizer

For creators who need more control, Soundraw offer granular customization. You can adjust tempo, energy levels and instrumentation after generation. These tools are popular among YouTubers and podcasters who need background music that fits specific moods without licensing headaches.

Moises and LALAL.AI as the separators

AI isn’t just generating music. It’s deconstructing it. Moises and LALAL.AI use machine learning to isolate vocals, drums, bass and other stems from any track. Musicians use these tools to practice with isolated parts, create remixes or remove vocals for karaoke. The accuracy has improved dramatically in the past two years.

Jammable as the voice cloner

Want to sing a cover with your own voice? The best workflow combines two tools. First, use LALAL.AI to isolate and clean your vocal recording, removing background noise and separating your voice from any instrumentation. Then upload the clean vocal to Jammable, which lets you apply AI voice models to transform your singing while preserving the original emotion and timing. Musicians use this combination to create demos, test how their voice would sound in different styles or produce covers without the complexity of studio post-production.

The symphony question

Can AI create a symphony? I put this question to Iris Daverio, principal solo flute of the Orchestre de l’Opéra national de Paris. Her answer was unequivocal: not today. I noticed that some of the world’s greatest orchestra performers struggle to synchronize with AI, whether for producing new works or reproducing existing ones. The timing feels off. The phrasing lacks breath. The subtle push and pull between musicians that makes a live performance alive simply doesn’t translate.

A human symphony comes from lived experience. The composer’s relationship with time, silence, tension and resolution is shaped by personal, cultural and emotional history. AI optimizes probabilities from existing data. It doesn’t feel what pushed Beethoven to turn inner struggle or philosophical conviction into musical form. AI can replicate structure, harmony and stylistic patterns, but it doesn’t decide to communicate something to the world. It takes no conscious creative risk. It doesn’t know why a note should last a fraction of a second longer to create the right effect. That intention, tied to human experience, is what separates a symphony from an organized sequence of sounds.

AI in the concert hall

AI may struggle to replace music, but it can help us analyze our experience of it. Imagine wearing a biometric device during a live symphony, tracking heart rate, skin conductance and breathing patterns. AI could then identify the exact moments that moved you most, correlating musical passages with physiological responses.

Scientists have shown that physiological responses (heart rate, skin conductance, respiration) can be measured while people listen to music and analyzed in relation to musical structure or emotional experience. For example, a study by Anna M. Czepiel measured heart-rate synchrony across audience members and linked physiological shifts to salient events in concert music, showing that dynamic biometric patterns relate to attention and engagement with musical structure.

This scientific approach could transform how we understand emotional engagement with music, helping composers, conductors and venues design experiences that resonate more deeply. The future of AI in classical music may not be creation. It may be a revelation.