The Stack Overflow Podcast Even your voice is a data problem
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Feb 13, 2026 
Scott Stephenson, CEO and co-founder of Deepgram and former particle physicist turned voice-AI leader. He discusses tackling speech recognition for noisy, real-world audio. They cover data vs. input features, synthetic audio generation, scalable low-latency streaming on cloud, responsible limits on voice cloning, and modular AI architectures for connected voice agents.
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Underground Physics Recordings Sparked Deepgram
- Scott Stephenson described recording over a thousand hours of audio while running a deep underground physics experiment to preserve memories and highlights.
- That dataset inspired building search and transcription tools because existing solutions couldn't find the "highlight reel."
Narrow Scope Enabled Faster Product-Market Fit
- Narrow initial product scope helped Deepgram achieve commercial traction by targeting English customer-service calls first.
- Focusing on throughput and price-per-hour enabled adoption against incumbents like Nuance and IBM.
End-To-End Models Reduce Loss And Cost
- Deep end-to-end models remove lossy intermediate components and enable faster, cheaper speech recognition.
- Modular architectures plus adaptation with small labeled data let models improve per-customer.