Voice AI’s Big Moment: Why Everything Is Changing Now (ft. Neil Zeghidour, Gradium AI)
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What makes this episode’s “big moment” for Voice AI and why is it still early?
For the first time, it actually can be enjoyable and even more convenient to talk to an AI on the phone than talking to a human. I don't want to be mean to uh my people, the speech scientists, but historically, for some reason, voice did not attract the visionaries in machine learning. All the new hardware companies have voice at the heart of the product. All of these devices they got rid of keyboards, they don't really have a screen or an interface. And voice is going to be the main one.
Hi, I'm Matt from Firstmark. Welcome to the Matt Podcast. Voice AI is having a big moment. For years, the field was stuck in the uncanny valley, lagging well behind other AI modalities: robotic, slow, and frustrating. But in the last 18 months, everything has started to change. My guest today is Neil Zegidor, CEO of Gradium AI and formerly of DeepMind and Meta. Neil is one of the very top AI researchers in the field and a key architect of the
Why did Voice AI lag behind text, image, and video modalities?
Rapid evolution of Voice AI towards real-time native audio intelligence. This conversation is a deep dive into everything you need to know about Voice AI, where we explore many key concepts in a very accessible way and discuss plenty of fun stuff, including why Voice AI has so few experts, the massive challenge of building native audio models, and the rise of autonomous voice agents. Please enjoy this terrific and very educational conversation with Neil Zegidor. Hey Neil, welcome. Hey, thanks for having me. So a lot of people in the industry are saying that uh voice AI is having its big moment. There's certainly a lot of uh activity, there's a lot of uh funding rounds. From your perspective, so you've been in this field for many years now at DeepMind, Meta, Nagradium.
Is voice AI indeed having its big moment, or are we still early? I think it's
It's uh both having a big moment and we're still early. It's having a big moment because there is progress all around uh AI models and in voice, for example.
How did the convergence of transformers enable the recent surge in Voice AI?
The progress in latency, naturalness, accuracy have been really, really huge in the past years, in particular in the two uh last years. And at the same time, text models have evolved into what we now call agents. Which are not only text models, but you know they can actually make actions and manipulate data, access information and so on and so forth. And now when you when you bring both together, you can have voice interfaces that at the same time are going to uh solve complex problems. And so I think there is a moment now because for the first time, it actually can be enjoyable to and even more convenient to talk to an AI on the phone. phones and talking to a human, uh, because you can call any time of the day or night, and the interaction is uh is working pretty well and it sounds really nice and the latency is low and so on and so forth.
So it's definitely having a moment because I think in a way it's uh now it can be used in much more use cases than it used to. But it's still early because it's still quite experimental. So anybody who who is using even the most advanced voice agents and compare that to the her movie from twelve years ago, uh, you know, it's obvious the gap that is still remaining. No. And there are so many uh topics that are completely unaddressed at the moment. In particular, you know, every time you you watch the voice agent demo, uh just realize that it's someone talking to a phone in a quiet room.
What are full‑duplex, speech‑to‑speech models and how do they solve turn‑taking latency?
So the day where you will have someone shouting to a robot in the middle of a factory and having the robot understanding what's happening and who's talking to them, that will be, you know, like we'll be there and we're not there at all. Yeah.
Mm-hmm. So we'll get into uh some of the technical details in a minute, but um at a high level, why uh has voice AI been I guess the most underdeveloped modality? There's been obviously uh extraordinary progress on text AI and then image AI and then video AI, but it seems that voice has been a little bit the the the poor parents in terms of uh progress. So why is that?
I don't want to be mean to uh uh my people, the speech uh scientists, but historically, for some reason, uh voice did not attract the like the visionaries in in machine learning, right?
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Chapters
8 chapters
1
What makes this episode’s “big moment” for Voice AI and why is it still early?
0:00–0:51
2
Why did Voice AI lag behind text, image, and video modalities?
0:51–1:52
3
How did the convergence of transformers enable the recent surge in Voice AI?
1:52–3:17
4
What are full‑duplex, speech‑to‑speech models and how do they solve turn‑taking latency?
3:17–5:59
5
When is on‑device voice processing preferable and what makes a model “compact” enough?
5:59–6:57
6
How do you benchmark voice systems when traditional metrics fail?
6:57–7:50
7
What are the current challenges and safeguards around voice cloning and deep‑fakes?
7:50–8:35
8
What does the future look like for multimodal AI that combines voice, vision, and screen awareness?
8:35–1:22:49
Speakers
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