Scott Stephenson @deepgramscott
Voice AI - CEO @ Deepgram deepgram.com San Francisco, CA Joined September 2015-
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Will every product eventually have a voice interface? Five years ago, I'd have said no. Today, I'm not so sure. As AI gets better, adding voice is becoming less of a technical challenge, and more of a product choice. So where's the line? 😄
I think today's AI assistants will look as outdated as the original Siri. What do you wish your AI voice assistant could do that it can't today? 😁
Every time I take my eyes off the road to tap a touchscreen, I wonder how we collectively decided this was good UX. That's finally starting to change. Competition in voice AI is raising the bar, and suddenly "good enough" voice assistants don't feel good enough anymore.
If you asked 100 engineers what's hardest about building Voice AI, most would probably say speech recognition or voice generation. I'd say they're focused on a problem that's already been solved. My team asked me this question. Here's my take.
My team put me in the hot seat and asked me a few questions about Voice AI. 🔥 Here is the first. What's your take on this?
I have a question for anyone reading this. Could you have a conversation with a voice agent for 5 minutes without realizing it isn't human? I call this the Audio Turing Test. A few years ago, that idea sounded ridiculous. Today, speech recognition, voice generation, and reasoning have improved so quickly that it feels within reach. The interesting question isn't whether we'll get there. It's what's still missing before we do. I'll leave you with this: it's a very exciting time to be building in voice AI (we're closer than most people think). 🔥
AI addiction isn't a question of if. It's a question of when. The moment an AI solves something you've struggled with instantly, your brain gets rewarded. "That was easy. Give me more." Social media took 10 years. AI might take 2. Or maybe it's already here?
It’s much easier to build an impressive AI model when efficiency isn’t part of the challenge. More GPUs. More compute. More energy. Building efficient models is harder because you have to optimize for everything at once: - performance - latency - scalability - training cost - inference cost The flashy models get attention first, but the efficient models are the ones the world eventually runs on.
In 10 years, typing on a keyboard all day will look absurd. For decades, humans learned how to communicate with computers. Now computers are finally learning how to communicate with humans. Once Voice AI becomes truly real-time, reliable, and deeply contextual, typing will starts to feel inefficient. Not nostalgic -- inefficient.
I’m in Seattle this week. I saw the replica of the Voyager aircraft at the airport -- designed by Burt Rutan and flown by Dick Rutan and Jeana Yeager. This aircraft flew around the world nonstop without refueling! - 9 days in the air. - Two people onboard. - A tiny unpressurized cockpit. - Sleeping in shifts under brutal conditions with almost no margin for error. Burt Rutan and the team behind Voyager proved that breakthrough ideas rarely begin polished or comfortable. They start constrained, experimental, and slightly insane! It's a good reminder that ambitious things are usually built under imperfect conditions.
Most people think speech-to-speech is just speech-to-text, an LLM, and text-to-speech stitched together. That’s how a lot of systems are built today. But it’s not really how conversations work. In a real interaction, everything is continuous. Tone, context, environment, intent -- all adjusting in real time depending on who you’re talking to and where you are. Those things don’t pass cleanly between separate systems.
A little about me: I’ve always loved motocross. You can’t debate a jump. You either commit or you don’t. If you hesitate halfway through, it usually ends badly. Most of what I do day to day rewards more thinking. On a track, that doesn’t exist. The feedback loop is immediate and very clear. I love the thrill, and it’s a great reset day for me.
Real-time voice is challenging, because humans communicate through far more than just words. It's a fun challenge 😎
Real-time voice: there are huge challenges, but they're not impossible to solve. Here's @deepgramscott, at Cerebral Valley Voice:
We’re entering a world where AI won’t just understand words (and it's very exciting! 🔥) It will need to understand people. Human communication is rarely literal. A pause can signal uncertainty. A faster response can signal confidence. Silence can completely change the meaning of a sentence. Then there’s tone, emotion, sarcasm, stuttering, whispering, hesitation, and all the non-word cues we process instinctively every day. “Uh…”, “Umm…”, “Ahh…”, “Oh…”, “Mm-hmm”, “Huh?”, “Uh-oh”, “Pfft” The next generation of AI systems will need to understand nuance in real time -- and be confident acting on it! 😎
@BetkerBuilds I will be sharing more information shortly (in another video)!
How many hours do people actually talk per day? Roughly one and a half, depending on the person. What's more interesting is everything around it. The other sixteen and a half hours are still context gathering. Reading, reacting, listening, thinking. By the time you speak, you're compressing a much larger stream of context into a few hours of words. Most of that context never gets captured. I started noticing this in my own work. I'd have a question early in the week, revisit part of it in a conversation later on, and then something related would come up again, but the original thread was gone. So I built something for myself that keeps track of the last couple of weeks of context. It runs on @DeepgramAI. I call it Bodyman. 😎 But it doesn’t just listen. It has permission to act. The other day I was late for a meeting, and it spoke into my headphones telling me I'm late, and that I should join -- because it knew the meeting was important. It’s like having a second layer of awareness that doesn’t forget. It’s been surprisingly useful!
Today is a major step forward for @DeepgramAI in building truly global voice AI. 🚀 I’m proud to introduce Flux Multilingual -- a single perception model that allows developers to build voice agents that can understand and respond across languages, even switching mid-conversation. Until now, global voice deployments required stitching together multiple systems -- adding latency, inconsistency, and operational overhead. Customer experiences would break the moment language changed. Flux Multilingual removes that constraint. With one model, enterprises can deliver fast, natural, real-time conversations across markets -- without compromising on performance or consistency! 🔥
Flux Multilingual is live. Real-time conversational speech-to-text for voice agents in 10 languages, with monolingual-grade accuracy, turn detection, and code-switching. Deploy once and launch globally. Learn more → deepgram.com/learn/introduc…
Deepgram @DeepgramAI
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