Venture with Grace
00:34:01 · Ori Goshen
And now they are also facing this, you know, new AI wave and they need to become AI native and they need to build this muscle pretty much like the digital natives, only from maybe different angles, not necessarily something some of the things that they need to build are not core to their product or service, but to compete they need to have the best supply chain optimization and they need to have the best marketing targeting. And there's so many aspects of the business that, you know, AI should be front and center. So in that case, companies are looking for ways to create, you know, competitive advantages as well. So these are the third segments. We serve, we as AI21, we serve all of these segments. We focus on the digital natives. We serve all of these because our mission is one. We want to help every company in the world to have its own specialized frontier level intelligence. And that's why we work with all these great partners and we're delightful, we're really delighted to do it. You asked about like the difference between engineering and research, I think you many great companies in the world right now that are focusing on building applications and workflows on top of models. And there's a lot of value to extract there. So many applications, we freeze everything right now and we don't customize anything, just use the technology that we have in the end, there's so much value to extract. So then you have companies with, you know, all the deploy codes of the world that taking FDEs and just using the existing models and the existing building blocks to build workflows and, you know, agentic automation. Our role is deeper in the stack. We are very value driven, but we want to help companies who really solve their hard problems and the ones that are strategic in the age of AI. And that's why we're really focused on helping them to own their own AI because we think it's like a very important aspect. So it's not enough to build an application, you want to really incorporate those, create that flywheel and incorporate those competitive advantages into the model weights, into the AI system, so you can achieve your goal as a business. And that's why it requires the level of depth that is more than application building. And there's a lot to be done in application building, don't get me wrong.
00:37:17 · Grace Gong
[00:37:17] S0: Can we talk about... Let's say like, I'm just using deal as example, because it's like a very straightforward as like, I think most people have like used like deal or like equivalent companies before. And I guess when you guys go in, first of all, how do you guys think about... You mentioned forward deployed researcher, right? So at what point do you go into the company and how do you think about monetization? You mentioned instead of buying buying a service or buying the thing, there's a third way, which is partnering together. So can we talk about what does a partnership look like with the middle layer of company that's kind of like tech native, but not necessarily like the most like, you know, front and center of like AI.
00:38:05 · Ori Goshen
[00:38:05] S1: Right. Yeah. So it's, I think it's pretty straightforward. We, you know, we come with a very clear purpose of building a model or building an AI system, like optimizing an AI system to achieve certain goals. And then we work in different types of, I would say, mode of operations. We have one mode of operation which is called embedded, which we work shoulder to shoulder with the team, an existing team, it could be a machine learning team, it could be a data science team, it could be a team of engineers, we'll work together to achieve a task. So that's one mode of operation. Second mode of operation is what we call manage, where the company would essentially create a spec. We may help with that spec creation because in the AI world, creating a spec is non trivial. We will do that. We'll help with that as well. And once you have the spec, the spec means what level of quality you need to achieve, how the evals look like, the rubrics, how do you what the costs should be like, what the latency, all these relevant dimensions that needs to be specified. And then once we get that spec, we actually translate it into reality. So we train the model, we provide the optimized harness. This is a really interesting point because the harness and the model really interplay with each other, so you want to make sure they're compatible and are optimized towards the same function. We even optimized inference endpoint to that... Towards that model architecture, so you get a full managed endpoint Mhmm. For the workload that you're trying to serve. So that's the managed and we'll serve it, we'll do the inference for you. We'll keep training the model so it will improve over time. We'll do that kind of as a managed service. So we support both mode of operations and we price them accordingly. Of course, try to reach as much as alignment as possible. We have our customers, we're very value driven. We really care about the value that these companies unlock. Just to give you an example, I mean, the models that we build with Gong and Deal and all these other great partners achieved 50x less cost than the frontier, exceeding frontier quality. It's just a great, great value that is being unlocked by these specialized models. So we're trying to align interests, and by the end of the day, these are, I think, really important... A directional decisions for these companies.
00:41:28 · Ori Goshen, Grace Gong
So we are pricing it in a way that is we're trying to simplify things. We have like packages that are very straightforward. Are correlated to the amount of effort and complexity of doing that, but they are as aligned as possible with the value that is created to the customer. That's the way we structure it. [00:41:54] S0: We
00:41:54 · Grace Gong, Ori Goshen
[00:41:54] S0: We [00:41:59] S1: really believe in our partners, like the partners we work with. The most important aspect is their growth. So if they grow, we grow. And that's, I think, a very healthy way to grow a business. And it's just the sheer size. That's what makes me excited. I'm always thinking, I wake up every morning and I'm thinking about the amount of companies that would need to build their own AI, that would want to fulfill themselves, that would want to create something of value, a new type of intelligence, is just incredible to me. I speak with customers every day, every industry you can think of, this touches a nerve. So I'm just very excited about the opportunity. And just to give you a sense, I think that the market for specialized custom models about twelve, fourteen weeks ago barely exists. Now it's everywhere. And I don't think it's a fad. I think it's here to stay. I think that's as more... As open source, open weight models become more and more popular, and they are. I mean, also another interesting stat. Back in June, open source token consumption was about 1520%. Now, this was June, now November, it's about 80%. 80% of the tokens in the world are open source, are based on open weight models. So that gives you a perspective of where the market is heading and the opportunity to create these specialized AI systems and models.
00:44:02 · Grace Gong, Ori Goshen
[00:44:02] S0: I wonder, so when you're working with a deal, so how many people from your company will actually be forward deployed to their company? Obviously we don't have to say the specific, but just curious, and then how do you separate the company into different... Do you separate them into different sector? Do they work with similar ish companies? Or how do you make sure that it's not like you are doing something scalable? Because nowadays I feel like AI talent is more interesting. [00:44:36] S1: I think you you have a great question here. It's pretty industry agnostic. [00:44:42] S0: Mhmm. [00:44:43] S1: And we're seeing similar technical problems Across all industries. Across across industries. [00:44:50] S0: Exactly. The biggest problem that you feel like it's across the industry people have to solve?