Asawa: Abridge engineers frequently run six Claude Code instances at once
Chai Asawa · Inside Abridge: The AI Listening to 100 Million Doctor Visits — Abridge's Janie Lee & Chai Asawa · May 14, 2026 · at 1:04:48
Chai Asawa of Abridge discusses the internal adoption of AI developer tooling across their engineering team with host Shawn Wang.
0:00 / 0:09exact quote · 9.3s
▶ Watch the full episode on YouTube → 720p mp4 · rendered on demand · StarZero watermark
“Many, many, many of the tools available, but it's like, you look at just earlier in the day, you see an engineer screen, you see like six different, you know, clouds running at it sometimes.”
quote is from the automated transcript, cleaned for reading: filler sounds and stutters are removed, nothing is rephrased. names can be misheard (the analysis reads context, assessments check outside sources). how →
More from Chai Asawa
Asawa: Many more AI products will become ambient and always-listening
“We're ambient, and we're always listening in the background. And I think many more AI products will go that way, but it's actually how we started.”
Asawa: Proactive ambient AI without screens is the best form of AI
“And I think that's actually the, like, the greatest form of AI we can create. AI that's actually seamless. You're not actually looking at your screen. It's always there. It's always helping you out and being proactive.”
Asawa: Abridge holds proprietary dataset approaching hundreds of millions of medical conversations
“So for example, we have on the order of eighty million or hundreds of millions actually now getting close to of medical conversation.”
Asawa: Almost every AI agent functions as a coding agent underneath
“Almost every agent is a coding agent underneath underneath the hood, right? So you give it whatever, a file system, it can write its own code and so forth.”
Asawa: AI 'slop' is fundamentally AI operating without context
“A writing that doesn't feel like your own, and then we call that slop, but the way I describe one framing of slop is, like, AI without context”
Asawa: Decouple User Preferences From Model Weights to Avoid Throwaway Fine-Tuning
“When you think the models are rapidly changing, whether it's in-house or third party, baking into the model weights, sometimes you worry that it could be a little throwaway. And so, like, how do you need to find a way that you decompose the problem, the prefer…”