Sacha Krstulovic breaks down the fundamental realities of AI in music that cut through the hype and fear dominating current discourse. As someone who’s spent decades building AI systems for audio—from speech recognition to environmental sound detection—he offers a grounded framework for understanding what AI actually does versus what we imagine it does.

The conversation explores why pressing a button to generate a complete song misses the point of creative tools, how data ownership remains the unresolved ethical crisis of the AI era, and why human agency at both input and output stages determines whether we’re witnessing automation or artistry. Sacha shares insights from his work building AI research teams at Music Tribe, where they discovered the real use cases musicians want: evading the blank page, compensating for missing skills, and gaining time—not replacement.

Particularly compelling is his framework for thinking about AI as automation, complexity, data-driven programming, and always a function with inputs and outputs. This perspective helps practitioners navigate the difference between assistive mixing tools that teach you about conventions while giving you power to break them, versus generative systems that claim to “make music” while obscuring the human curation required at every step.

For anyone building tools for creative-tech professionals or working at the intersection of machine learning and music, this conversation offers rare perspective from someone who’s seen the evolution from unit selection speech synthesis to transformer-based generation—and maintains healthy skepticism about what actually serves human creativity.

Episode Chapters

[(2:25) Sacha’s Journey: From Speech Recognition to Audio AI Leadership

(13:21) Demystifying AI: Four Core Principles

(23:32) Beyond Generation: The Full Landscape of Audio AI

(28:30) Real Use Cases: What Musicians Actually Want from AI

(33:38) The “Press Button, Get Song” Problem

(40:44) Breaking the Machine: Creative Exploration with AI

(51:04) Data Ethics and the Copyright Crisis

(57:49) Digital Hangover and the Return to Real Life Experience

(1:03:30) Closing: Finding Sacha and Understanding AI

Practical Takeaways

Framework for Understanding AI:

* AI is automation with extreme complexity (billions of parameters)

* It’s data-driven programming, not hand-coded rules

* Always has inputs and outputs—it’s a function, not an entity

* Mimics patterns without consciousness or independent agency

Design Principles for Music AI Tools:

* Present outputs as editable parameters, not black boxes

* Let AI act as teacher showing conventions you can consciously break

* Focus on use cases: blank page stimulation, missing skills, time efficiency

* Preserve human agency at input (what to explore) and output (what’s good enough)

Data Ethics Standards:

* Traditional ML practice: own or license all training data

* Current lawsuits challenge the “scrape everything” approach

* Ed Newton-Rex’s Fairly Trained advocacy as alternative model

The Live Music Economy:

* Musicians increasingly earn through concerts, not recordings

* Local, human interaction offers what algorithms can’t deliver

* Fandom culture, tangible experiences, and vinyl collecting as counterweights to digital

Resources & Links

Connect with Sacha:

* LinkedIn: https://www.linkedin.com/in/sacha-krstulovic-3505544/

* Personal website: sacha.today (includes essay on creativity and entrepreneurship)

* Consultancy: understand-ai.today

Mentioned:

* AES International Conference on AI and Machine Learning for Audio

* Ed Newton-Rex - Fairly Trained advocacy

* Dadabots

* Max Cooper - Electronic music artist

* Audio Analytic - Environmental sound recognition (acquired by Meta)

* Music Tribe - Audio equipment manufacturer (Behringer, Midas, TC Electronics)

* Giada Pistilli - Should we be afraid of becoming attached to machines?

* Documentary - Re-learning to listen to music

Guest Bio

Sacha Krstulovic is an AI researcher who spent two decades at the intersection of machine learning and audio, from early speech recognition work accounting for vocal tract physics to building the first large-scale environmental sound recognition system. As Director of AI Research at Music Tribe, he led a team of 15 exploring applications for audio equipment manufacturers. His career spans academia (PhD in speech recognition), industry giants (Toshiba, Nuance), successful startups (Audio Analytic, acquired by Meta), and now independent consultancy helping companies structure practical AI applications. He brings rare perspective on the evolution from “machine learning” to “AI” terminology—and maintains focus on what actually serves human creativity versus what captures attention.

Related

Get full access to softmax at redito.substack.com/subscribe