This matches my experience. In Kaggle audio competitions, I've seen many competitors struggle with basics like proper PCM filtering - anti-aliasing before downsampling, handling spectral leakage, etc.

Audio really is a blue ocean compared to text/image ML. The barriers aren't primarily compute or data - they're knowledge. You can't scale your way out of bad preprocessing or codec choices.

When 4 researchers can build Moshi from scratch in 6 months while big labs consider voice "solved," it shows we're still in a phase where domain expertise matters more than scale. There's an enormous opportunity here for teams who understand both ML and signal processing fundamentals.

There's too much noise at large organizations

Surprised ElevenLabs is not mentioned

Probably because the big companies have their focus elsewhere.

OpenAI being the death star and audio AI being the rebels is such a weird comparison, like what? Wouldn't the real rebels be the ones running their own models locally?