Venture with Grace
00:15:20 · Grace Gong, Ori Goshen
[00:15:20] S0: Like, [00:15:21] S1: how do you find what the task looks like? How do you define what good looks like? [00:15:27] S0: These [00:15:30] S1: things are very native and natural for AI makers, but for most most companies in the world don't have, you know, they don't have the talent or the tooling to build their own AI. So so basically, we we decided to help those companies in a shape of Lab-as-Service, which means we bring four deployed researchers. [00:16:03] S0: Mhmm. [00:16:03] S1: So the researchers... [00:16:05] S0: Really like that you create your own keywords.
00:16:07 · Ori Goshen
[00:16:07] S1: Yeah. Yeah, I think it's like, you know, there's a lot of engineering work to be done, but there's also when you are tackling these core business problems, it's actually closer to what people define in research, and I think that could be an old debate about, you know, the borderlines between research and engineering. But anyway, so we bring the FDR, the four deployed researchers, We provide the toolbox that we've created for many years as model builders for ourselves. So how do you post train? How do you evaluate at scale? How do you optimize the harness? So it's not just a model, it's the harness. We have tools for all of these needs. And the compute, would come such a scarce resource these days, even knowing from a company perspective, even knowing what kind of cluster, what kind of compute do I need to train a model that is capable of doing x, it's a challenging question, right? So we basically help with all of that. We bring the compute, we bring the toolbox, we bring the FDRs, and we help companies to achieve that goal of owning their own intelligence and actually proving the value, the business value, that owning your own intelligence. And the reason I'm so excited about this, because I think this is the future. The future is about diversified models, diversified AIs. I don't think there'll be one AI to rule them all. I don't think AI, I don't think even frontier AI is necessary for all tasks. You will have this abundance of different types of AIs. And I think companies will be defined by how good they make and operate their own AIs. And so if we believe in that future, that every company will become an AI company, and the goodness of the company will be defined by how well they own and operate their own AI. So that's a strategic component element, right? So I'm just excited about the opportunity that we have as AI21 creating that muscle for many companies in the world. So yeah, I can speak on and on. I can go on and on about this, but...
00:18:39 · Grace Gong, Ori Goshen
[00:18:39] S0: I have some questions about, obviously I feel like open weight model has been a key word this year as people are really adapting the idea of people stop trying to give out their own data as well as you're also paying for the services and stuff. So I definitely felt like this is a huge trend. So last week, obviously we had Jerry from core automation. So his thesis is like, he's doing something that's completely different. That's not transformer related or transformer slayer basically. So is there any major shift that you're seeing in the space? Is this a new trend of everybody started doing something that's completely different than the Transformers, trying to do everything just automated, like agent stuff? Or first of all, where do you see, is there a major trend that's going on? And obviously I feel like you have a really interesting thesis, which is you use the word of commercialized model or commercialized approach of things. So I think it's really important to think about whether it's monetization or how does enterprise actually interact with AI nowadays? Last week I asked Jerry the same question. So he was focusing on building the lab and not focusing on chatting with the customers. [00:19:57] S1: So
00:19:57 · Ori Goshen, Grace Gong
[00:19:57] S1: So [00:19:57] S0: I think it's really interesting because I think what make the Easter belly companies successful from my observation is you guys really... It's really commercialized. People are really smart on the model side of things, but you guys really focus on how to go to market or working with the company and brands. Maybe we could use any of your portfolio. I know that I believe Deal is one of your clients and Alice, the founder of Alice, was also on our podcast. Awesome. As well as I believe Gong is your clients as well. So maybe we could talk about how do you see... First of all, where is AI going in the general landscape? And then second of all, the approach of when you're commercializing the technology, what are things that you're... What is like a trends that you're seeing in the industry as well as like, how do you basically commercialize the different technology as well?
00:20:53 · Ori Goshen
[00:20:53] S1: Right. So many questions. A to unpack, but I'll start with the beginning about the open rates. I think something very important to realize and related to the different waves and shifts we're seeing in the market. So if you think about it, we had the chat GPT moment back in 2022. Then we had twenty twenty five ish, the first, I mean, '24, we had the reasoning models, 2025, we had the agentic models, let's call them. So I think what's interesting in this wave, it just unleashed so much demand, right, in the form of these agentic workflows and particularly in coding. I mean, you look at the majority of tokens in the consumption in the world right now, they're coding related tokens. So I think that what happened is there's this point in time you can actually identify. In insights, it's easier, but if think you about OPUS 4.5, I think that was like the true native agentic model release that unleashed a lot of the... Unlocked a lot of the demand that we're seeing right now. That was a point in time. Then if you look back then, I think it was end of twenty five, I think there was still a pretty significant gap between open weight models the frontier model at the time. Now that gap has narrowed significantly. If you look only six months into the future or so, mid twenty twenty six, May, June, we had significant releases like Kimi K3 and, you know, new DeepSeek and Qwen, all came about the same time. These were the first, I would say, agentic open weight models. And that's why we're seeing kind of that, I think explains the gap narrowing that we've seen in the last few months. That explains why there's such a strong movement toward open weight models, they suddenly became useful, they became a real alternative to the frontier. And so we're what? We're talking about, you know, May, June, it's like less than six months ago, right?
00:23:34 · Grace Gong, Ori Goshen
[00:23:34] S0: I have a question. So do you feel like open weight is like a different kind of... When people use open weight, does that cancel out their usage of inference or no? What inference? So obviously I feel like inference is essentially a layer of people tailor the model to their own company or needs. So when people using OpenWait, essentially they are just technically downloading like something, and then they are like, just like using it in house, but within their own like data and stuff. So assuming it's not like conflicting, no? No. [00:24:16] S1: I think it's it's it's not really conflicting. You can take open weight models, you can serve it in your own native environment, like you're in your cloud, you can serve it with your one of the neo clouds or one of the inference providers. So you can you can choose whatever works for you, and then you can optimize the the inference layer to the workload that that that you're... That you have in place. I think so it gives you a lot of flexibility. Right? I think the next layer is that you can start customizing the open weight model. That's a big benefit. [00:25:00] S0: So [00:25:01] S1: and customizing is worthwhile if you're, you have some proprietary data, something that is not achieved, is not accessible on the public domain. So if you have, let's take an example, you mentioned Deel, which are great partners of ours, we released [00:25:20] S0: them I'm for technically wearing a Deel socks. Got it from the Valkyrie team they hosted us.