00:18:10 · Mati Staniszewski
[00:18:10] S1: So one early inklings of that, we haven't seen any truly successful on the negotiation front. It was like more kind of order taking. What's the price? Can we capture that? And then kind of goes back to the team, so not real negotiation. But there's few startups that we see, especially on any organizational shifts of, can I organize this event, calling a lot of places, getting the price, and then calling again with our budget? So that is happening, and I think this will shift. I think emotional intelligence will, this is the big part that will start being important in a lot of that work, where it's not only the content that matters, but how you deliver, when you pause that work. And then maybe the extreme version of that, agents are not like most of the people wouldn't do it and they are not good at that, is today you will see a lot of interruptibility built in where human can interrupt the agent. But with negotiation, you also want the opposite, where agent will interrupt the human. It's kind of the extreme version of that. On the second part, on the agent to agent part, some of you might have seen this, that we did a hackathon over a year and a half ago, and that was exactly the case, where agent was speaking with another agent. They detected that they are both agents, and they swapped over to a different language. And that was like a more efficient transmitter of information than just the classic spoken word. And I think this will happen 100%. The big question will be really voice, will it be other transmission of information? Depends truly on what the infrastructure is built for, and I think this will define that experience. I guess. I see you at the catch box.
00:19:54 · S3
[00:19:54] S3: Hey, I'm curious how you're thinking about the need for voice in the future where agents do more and more of the work. So basically, what are the kind of use cases maybe where you mean conversation? I think it's more of a follow-up to the last question.
00:20:09 · Mati Staniszewski
[00:20:09] S1: Like first, you, all of us will have so many different devices around us and step from that, you will have robots around us. So, of course, voice will be such an important interface to to instruct and and be able to interact with those those those devices. In many ways, I feel like the... You know, we see a lot of developments of of intelligence, but then the real bottleneck of the future will be how we communicate with that intelligence. And I have voice and visual part will be a big unlock to be able to actually get the most of that intelligence value in those settings, which isn't yet possible. But on the flip side, the value of the human to human interaction will only increase. So whether that's the events like this one, whether that's events with your favorite artist will increase in value with that ability of having voice all around you. But the trust will be such a big part and something we optimize for in between the agent and human. In the future where all of you will all of us will have a voice agent, for example, to call and book a restaurant or give information to a health care appointment, all of that will require such a high degree of trust that this is you and authenticated you. So there'll be like a level of encoding and decoding for real, then encoding, decoding for watermark opted in human. And then by default, everything else will be fake, which is kind of the opposite of how it is today. You detect for AI, but it will detect for real authenticated AI in the future and assume it's fake. You could pass it.
00:21:50 · S4, Mati Staniszewski
[00:21:50] S4: Andre spoke earlier about jagged intelligence. Do you see similar odd places in audio where models are good and the bad that you might not expect? And yes, what are they? [00:22:02] S1: There's still so much on the bot side. I think we spoke a little bit about where we see the voice agents working, so this combination of the models together. And support settings works really well, works reliably. And early sales starts working, but the moment you start swapping to a true emotional interaction, not yet working. It doesn't get the emotion that well. It's slightly too slow. So that is still, I think, a big step change that should work. Same will apply in a very different domain on the music side. I think in the music side, you can get good production music. You cannot get Top Charts music even with artist input. I think this will change over the next year or two. Can I [00:22:50] S4: just follow-up? [00:22:51] S1: Of course. [00:22:51] S4: Andre's take was that the reason for that was that the labs were basically training for the stuff that had economic value. Where you're training your models, is that true of you? Are you basically training for the things that make the most money? Or is it that there are some challenges that are genuinely harder than others?
00:22:51 · S4, Mati Staniszewski, Andrew Reed, S5
[00:22:51] S4: Andre's take was that the reason for that was that the labs were basically training for the stuff that had economic value. Where you're training your models, is that true of you? Are you basically training for the things that make the most money? Or is it that there are some challenges that are genuinely harder than others? [00:23:09] S1: We try to train the models, build the product and the ecosystem that will derive, of course, the biggest impact for all our customers, all users, which should correlate, of course, with the revenue in the long term. So that long term perspective, it's going to be minimal in the next few years, so not next year. So frequently, we will train the models that might not provide that value in the short term. Or even step before, we'll spend so much time labeling the data, not only the what of audio, but also how of audio, like what emotions did I use, what is my voice described as, what is this music described as. So we assembled a team of now thousand plus people that have been voice coaches, musicians, artists before that can help us annotate that behind the scenes. And that will not provide value in the next six to twelve months, but we think will as well in the next twelve to twenty four. And then you often collect that data, which frequently just isn't that accessible as well. [00:24:03] S0: Okay. Last one, and then we'll go to lunch. [00:24:10] S5: Hey, can you hear me? Thanks. Yes. Big fan of yours and ElevenLabs. [00:24:14] S1: Thank you.
00:24:14 · Mati Staniszewski, S5
[00:24:14] S1: Thank you. [00:24:15] S5: What do you think from the model error perspective, what do you think are the moats here with audio models? The labs are going there, not going there. What are the kind of in this sausage making of making a real good frontier audio model? What are the main defensible parts there?