You can feel the right track in your bones, but finding it inside a giant catalogue can still be painfully slow. That gap between what we mean and what search engines can understand is where sync licensing briefs stall, temp tracks take over, and great back catalogue gets left behind. I'm joined by Tiangu Zhu, founder of Songpot, to unpack a simple but ambitious goal: building AI that truly understands music as a language, not just as metadata.

We talk through the real-world problems music supervisors and media teams face when words fail. Genre, mood and “danceability” are subjective, tagging is inconsistent, and a song rarely fits neatly into a few labels. Tiangu explains how AI music discovery can analyse audio itself to reveal “unspoken similarities”, helping libraries and rights holders improve music search, speed up clearance workflows, and deliver better matches for sync licensing. We also get into how Songpot can sit in the stack as a platform or an API for more tech-native companies.

Then we flip to the creator side. Tiangu makes a clear case for human-centred generative AI: not replacing artists, but acting like a new instrument for producers and musicians. From prototyping ideas faster to turning a hummed melody plus a style into an instant draft, the focus stays on helping creators translate what’s in their head into something they can actually hear, share, and refine.

If you care about music supervision, music libraries, catalogue value, music information retrieval, or practical AI tools for music production, this conversation will stretch your thinking. Subscribe for more, share this with a friend in sync or production, and leave a review if you want us to keep bringing you guests building the future of the music industry.

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