No Priors: Artificial Intelligence | Technology | Startups
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.
00:32:35 · Sarah Guo, Mati Staniszewski
[00:32:35] S0: I see it as. [00:32:37] S1: Mean, the way you think about it, research is head start. This gives us... We can give advantage to the customer earlier, and it's six, twelve months of advantage. That is also a way for us to build a right product layer for you to get best of that research. Frequently, we do that in parallel, so the moment the research is out there, you have the product because we know our initiatives, we know what the product is that's right. So you have research, product in parallel that extends that, but the of the thing that will really give that long term value is the ecosystem that you create around, whether that's the brand and distribution, whether that's the collection of voices you can have, the collection of integrations you can build, the workflows that you can build. I think that's the way we kind of sequence that in our mind, that research product ecosystem that we built, And research, all it is, is head start and being able to accelerate the future a little bit closer. [00:33:28] S0: I think that's a really powerful insight, especially if the research team and the company team believe that as well internally.
00:33:28 · Sarah Guo, Mati Staniszewski
[00:33:28] S0: I think that's a really powerful insight, especially if the research team and the company team believe that as well internally. [00:33:37] S1: I think the piece that was interesting for us is, and I think this is the big questions for all companies that do research and product, is do you wait for research, or do you do a product change? Or even not only research product companies, do you wait for someone else to do the research? Because the timeline for that isn't clear. Is it three months, six months, twelve months? You don't know exactly what it will do, which is the hard choice of do I invest into product layer, or do I just wait more for the research? So in our case, we internally let all the product teams do research initiatives so we can paralyze that work, but we don't hold them that if a product team thinks we should deliver value to the customer by doing something different, they can. And rough rule of thumb is like three months. If we think it's going to be longer than three months, we will probably build it. If it's less than that, we probably won't. [00:34:24] S0: Can you talk about some of the research that you're doing now and then how you think about the cadence of delivery and what's worth working on?