No Priors: Artificial Intelligence | Technology | Startups
00:25:52 · Mati Staniszewski, Sarah Guo
[00:25:52] S1: Like, if you are looking for new interface with customers, that's the the best way. And and companies like Sierra, phenomenal, of course, on on how they are thinking about the the specific pointed use case. And maybe the other piece is, like, as we think about our work, depending on what you are optimizing for, so we have a lot of international partners. If you have a wider geographic user base, great. That's what we optimize for. Our voices, our languages, our support for integrations internationally are just so much broader. There's frequently a piece that you will look into. Depending on your exact scope, this be a big factor, but I would summarize that if you are looking for a solution across a set of different use cases that you want our engineering help and deploy that, then we are the right solution and probably the best solution. [00:26:43] S0: I wanna talk a little bit about maybe OpenAI and the foundation, LLM foundation model companies. One of the reasons Elad and I called this podcast No Priors is because we're like, okay, people are making a lot of assumptions all the time about how the market is gonna work, and lo and behold, like many of those assumptions end up being nonsense, actually. And you you you can't... You have to very much decide your own narrative at this point in time. I think, correct me if I'm wrong, like in 2022 and '23, you probably heard a lot of people say like, Google can do this, and OpenAI can do this, and like, why do you get to persist working on voice anyway as a general capability? What's the answer?
00:26:43 · Sarah Guo, Mati Staniszewski
[00:26:43] S0: I wanna talk a little bit about maybe OpenAI and the foundation, LLM foundation model companies. One of the reasons Elad and I called this podcast No Priors is because we're like, okay, people are making a lot of assumptions all the time about how the market is gonna work, and lo and behold, like many of those assumptions end up being nonsense, actually. And you you you can't... You have to very much decide your own narrative at this point in time. I think, correct me if I'm wrong, like in 2022 and '23, you probably heard a lot of people say like, Google can do this, and OpenAI can do this, and like, why do you get to persist working on voice anyway as a general capability? What's the answer? [00:27:21] S1: That also adds kind of another element to that, a couple of the other previous questions where whether it's agents work, whether it's the creative work. To deploy the value in those work, you need a very strong product layer. You need integrations. You need to help people deploy the work, which is the most common piece. But our superpower and our focus for a long time was building the foundational models to actually make that experience seamless. And as I think about a lot of the companies in the market, they will optimize for a lot of other things, and that will be the differentiator. In our case, where we will make the whole experience, especially with voice, seamless, human, controllable in a much better way. [00:28:04] S0: And so fundamentally you would argue that the labs just aren't gonna focus on this, and haven't.
00:28:10 · Mati Staniszewski
[00:28:10] S1: Exactly. So I think most of those companies, and that's the thing about the long term, it's going to be incredible research and an incredible product that meets customers where they are and work backwards from there. I don't think the labs will focus on building that product layer that's so important, but I think the part of the question that you're asking is like how, and why they haven't done even the research part to the quality that we've been able to hear. I'm also biased, but we are happily beating them on benchmarks with text to speech or speech to text or the orchestration mechanisms. And I hear credit to my co founder and the team that they've been able to do it. It's just mighty researcher is just continuing their work, but I think the main part that I think is different in audio space is that you don't need the scale as much as you need the architectural breakthroughs, the model breakthroughs to really make a dent, we've been able to do that a couple of times, and I think the number of people doesn't matter, but the people that you do does, we think there's maybe 50 to 100 researchers in audio space that could do it. We think we have probably 10 of them in the company that are some of the best ones, and I think this obsession of just those people working across, and then actually giving the full focus on the company, on making them actually work on that, and bringing their work to production, seeing how the users interact back was so important. So that's, I think, how we've been able to create models better than some of the top companies out there, but you know, the truth is, to a large extent, why they weren't able to do it is also an interesting... We don't know. They have such an incredible talent there too.
00:29:58 · Sarah Guo, Mati Staniszewski
[00:29:58] S0: How do you think at the same time about open source models? [00:30:02] S1: Anyone you ask in the company I think will say that the same, and that's like a narrative we think about. It's in the long term, models will commoditize or the differences between will be negligible. For some use cases they will still matter. For like most use cases they want. [00:30:17] S0: They'll be broadly available in terms They'll be broadly
00:30:19 · Mati Staniszewski
[00:30:19] S1: available, exactly. And we don't know where that is, whether it's two years, three years, four years, but it's going to happen at some stage. Then of course you will have a fine tuning layer that will matter a lot top of those models, but the base models I think will get pretty good. And that's why for us, the product piece is so important from the company perspective, but also from the value perspective because if you have a model, that's great, but to actually connect your business logic and knowledge to be able to have the right interface for creating an ad for your work or a completely new material, that's a very different exercise. But open source models are getting, if I split it into two, like more of that async content narration, I think narration is pretty much open source is great, commercial models are great, differences are getting smaller on the out of the box quality. What most of the models haven't figured out, and I think we were, is how to make them controllable. So that's kind of the narration piece. I think the whole interaction piece of how you orchestrate the components together, whether that's cascaded speech to text, a lent text to speech approach, or whether in the future it's a fused approach where you train them together, I think this is good for customer support or customer experience, but it's still away from conversation like we have of passing that Turing test, so I think this is still at least a year, like within a year, and then you'll have real time dubbing kind of variation of real time translation conversation, and I think that's maybe more within two years away.
00:31:53 · Sarah Guo, Mati Staniszewski
[00:31:53] S0: You know, a very uncomfortable belief that I I feel comfortable having this belief, but I I think is uncommon in the market right now, is that actually most advantages in technology, like, they could they could last you a year or they could last you ten, but they're not like infinitely defensible. And if you think about that from a model quality perspective or a product perspective, they allow you to, like, serve the customer better and build momentum and build scale for some period of time. And actually, that's really powerful over time. But it's not like a clean forever answer, and so I think that makes, I don't know, business people and investors uncomfortable. [00:32:33] S1: And I mean, it's very true as well. [00:32:35] S0: I see it as.