Deep Impact

The Millions of Songs Mashed Into AI-Generated Music

Explore the astonishing amount of music available to AI developers.

Vibe coding and agentic engineering are getting closer than I’d like

I recently talked with Joseph Ruscio about AI coding tools for Heavybit’s High Leverage podcast: Ep. #9, The AI Coding Paradigm Shift with Simon Willison. Here are some of my …

Inside the Don’t Ask, Don’t Tell Era of AI in Music

From juicing demos to cloning vocals, AI music tools are creeping into the workflows of top producers, songwriters and artists. It’s mostly happening behind closed doors — for now

HAIM: Human-AI Music Datasets for AI Music Production Tracking Benchmark

As generative platforms such as Suno and Udio reach human-grade audio quality, the scope of AI's utility has expanded across the entire music production workflow. Beyond simple track generation, these advancements have catalyzed the adoption of AI-driven methodologies in diverse forms. These include vocal synthesis, arrangement, and professional mastering. However, current detection research remains largely confined to a binary `AI-or-human' paradigm. It fails to reflect the realities of contemporary music production workflows. In real-world production, AI tools are increasingly used to refine or master human-produced tracks, and human engineers likewise post-process AI-generated material to ensure professional quality. Moreover, users often employ adversarial tactics to bypass AI detectors, such as applying human mastering to AI-generated tracks. This creates a grey area that a simple binary classification fails to capture. In this paper, we define and investigate ``AI Music Tracking'': the challenge of identifying specific AI integration across the multifaceted spectrum of music production. To this end, we introduce HAIM, a dataset with diverse labels for stages of music production. It is designed to isolate stages of AI intervention, including hybrid production and agent-level tracking. Our evaluation of state-of-the-art detectors reveals systemic flaws. By releasing HAIM, we propose a new benchmark that shifts the field beyond binary classification toward a granular, structured evaluation of AI music.

I Love Generative AI and Hate the Companies Building It

Update 6/29/26: The Last Update

The New SDLC With Vibe Coding

Discover what actually works in AI. Join millions of builders, researchers, and labs evaluating agents, models, and frontier technology through crowdsourced benchmarks, competitions, and hackathons.

Deezer: AI-generated tracks now represent 44% of all new uploaded music - Deezer Newsroom

As the only streaming platform tagging AI-generated music, Deezer now reveals that nearly 75,000 AI-tracks are uploaded every day

Dario Amodei, hype, AI safety, and the explosion of vibe-coded AI disasters

What the AI cheerleaders don’t tell you

Nature is Laughing at the AI Build Out -

I was catching up on Acquired episodes this week and listened to the spectacular “Google: The AI Company” episode that Ben and David put together. What the two of them have created with their podcast fascinates me in the inevitability of its success. I think Acquired proves that if you are smart and work your […]

The Myth of the Instant Cake Mix

How to think about creative tooling in the new world of generative AI