One thing we missed with crypto music ... We built some amazing tools for artists. But hardly any apps for music fans. I like this concept by Piki on @base Music fans can earn by predicting / curating music you think people will like. Try it out ↓

The AI music landscape in 2026. This week we mapped 46 generative AI tools … the ethical, the unethical, and everything in between. Here’s how we break out our categorisation: ———— 💿 Full song creation - Generate a complete song based on a text description. Some are ethically trained. Others are trained on scraped, unlicensed music. 🎹 DAW-based - Tools that sit inside your DAW, or function like a DAW, to assist music creation and production (generate chord progressions, basslines or drums). 🎤 Vocal tools - These are optimized to generate vocal sounds (choirs or voice-changing outputs). 🎛️ Remix tools - using AI to mashup or remix existing music. 🖼️ Sync and soundtracks - Generates background audio for podcasts, vlogs, adverts. 🧘‍♀️ Wellness - Generates audio for focus and wellness apps. 🚨 Attribution and enforcement - Detect AI content, track attribution, flag infringements, and process royalties from generative AI. ———— The biggest takeaway I have is that lumping generative music AI into one homogenous category and making “good” or “bad” assumptions is not really correct. The spectrum is WIDE here. -> Some tools are scraping copyright on a disrespectful level. Others are using clever, ethical new training models. -> Some tools are killing the creative process with “vending machine” song generation. Others are accelerating creativity in production. ———— Full report below. As always, please tell me what I got wrong, what I’m missing, or where you think this goes next!