Spill the tea - we want to hear from you!

A world-touring jazz bassist turned educator and AI builder joins us to explore what happens when smart tools meet hard-won craft. We dig into how Futureproof Music School blends a curriculum-aware chatbot with real mentors so producers learn faster, pay less, and still develop their own voice rather than a template sound. From Wembley Arena stories to DAW specifics, John breaks down what large models already understand, where proprietary production knowledge still wins, and why structure matters more than infinite answers.

We take you inside Kadence, a memory-based AI co-pilot that analyses mixes, compares references, and serves targeted, actionable feedback instead of overwhelming students with lists. Think fewer rabbit holes, more progress: clear mix notes, arrangement guidance, and strategic nudges that build week over week. We also get honest about what students actually want from AI right now—help with marketing, release planning, and social consistency—so the music doesn’t drown under admin. The throughline is creativity as curation: your taste decides what ships, even if AI offers a hundred options.

We tackle the big questions too. Can detectors keep up as artefacts vanish? What counts as responsible use when training data is opaque? John argues for consent and compensation, drawing a sharp line between experimentation and high-stakes commercial work while legal frameworks mature. Looking forward, we preview screen-aware and voice-first coaching that can see your DAW and fix problems in context, compressing learning curves without flattening style. If you care about music production, AI ethics, and building a career that sounds like you, this conversation brings clarity, nuance, and practical steps.

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