Venture with Grace
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?
00:44:56 · Ori Goshen
[00:44:56] S1: I think the process of defining what you want, defining kind of the rule of the game, what's valid, what's not, what's what good looks like, how do judge a good output, how do you judge a good outcome, is the bottleneck. And I think even if you have good conceptual understanding of that, translating it into an eval and an eval framework is hard. And and I think that's... And then and then you need to apply all the different training algorithms, right, and know which one to apply when and which would be effective when and so on and so forth. So that's kind of the third bottleneck. But what we've been... And that goes back to your scalability issue. What we've been building ourselves is actually a platform that identifies the work of a researcher in the company. So every FDR in the company, he has two responsibilities. First and foremost, make sure the customers are happy and are getting huge amount of value from their work. And second is that they're being educated because part of the value is just by doing this, the company is doing it and being involved in the process. Now they suddenly create that skill. The third job of FDRs is to identify themselves. How would they, when they go to the next deployment, are able to streamline to make that process much more, much faster, much cheaper? How they when they solved an issue, when they applied an algorithm in a very specific way, how do they create that skill, if you will, so that when they come to the next deployment, they can repurpose, they can reuse that skill. So we're doing this on an ongoing basis, actually what we've seen where, let's say, mid year projects of pretty complicated RL training, you know, would take several months with all the debugging and the reward hacking and all the things that you need to address there. Now it takes a week or two for our experts using those tools. So that's the way you make it scalable. And it's less a question of this industry or the other, because we see a lot of similarities in the types of problems. Definitely, if you kind of translate them into the technical language, it's more about, you know, how do you, a sense, platformize and how do you identify the process so you can actually scale. And that's what we're seeing in the field.
00:48:21 · Grace Gong, Ori Goshen
[00:48:21] S0: I I find it... Like, I can totally see it because I feel like nowadays, like, because of, like, it's really hard to define, like, scope of, like, Wordtune, Wordtune AI. And, like, lot of times, like, there's, like, human in the loop and then there's, like, you know, your agent have to talk to, like, different agents. Like I'm personally trying to figure out how to like actual, actually have like agent connectivity, like your Codex can talk to your, know, hot coder or something. [00:48:46] S1: When [00:48:49] S0: guess, like when you were thinking about ensembling your own team, so obviously I heard you guys have some really great talent. Yes. Why don't we talk about the hiring aspect of things? How do you evaluate the talent? And I think from East Of Rio, maybe like the culture is different than like The US. In The US, obviously, like if you work at like any of the frontier lab that you're good. Or like, you know, you go to school or you're training, including blah, blah, blah. Like, is there like a hiring hack beside people who went to like 8,200? Like, that's kind of like a... [00:49:26] S1: Yeah. 8,200 is a good hack. Another one is... [00:49:31] S0: You're just hiring the alumni. It's like, do you just go to, like, the school? Or, like, I don't I don't think it's a school. It's like a kind of like an intelligence services. [00:49:37] S1: Yeah. It's [00:49:37] S0: a... Yeah. So you just go there and then be like, hey, alumni hiring.
00:49:37 · Grace Gong, Ori Goshen
[00:49:37] S0: a... Yeah. So you just go there and then be like, hey, alumni hiring. [00:49:40] S1: Yeah. And it's also a lot of friends bringing... Like, friends. Friend of friends. Friend of friends, exactly, type of hiring naturally. There's one thing to caveat, there is a lot of cybersecurity talent. [00:49:54] S0: Yeah, yeah, In Israel. All the top cyber company kind of like you have to have the badge too.
00:49:54 · Grace Gong, Ori Goshen
[00:49:54] S0: Yeah, yeah, In Israel. All the top cyber company kind of like you have to have the badge too. [00:50:00] S1: Yes. Yeah. And it's a very long conversation. And now also cybersecurity, I think, is changing rapidly and it's becoming more closer to AI, especially if you're trying to kind of address all the threats that are AI powered in a way. So I think that landscape is changing as well. But going back to your question, I think, 8200, there's a lot of very creative, very strong technical folks. Also, within the academic groups, People should not discount the academia. There's a lot of very good research groups and people that took that path, And there is some really good talent there as well. And another thing I would say, Israel is a very established ecosystem in hardware and low level software development. So really hardcore storage and networking. And some of the technical people there, you know, that have done various very algorithmic challenging type of roles, not all of them have the deep learning background or kind of they've used like modern AI type of, they've done modern type of AI development, but the transition is, and actually some of our strongest researchers and engineers came from that background. They used to be doing storage and networking and all these types of, you know, more legacy work. So so, yeah, I think over there, if you go to these companies, in some cases, these large corporations, there is a very strong talent that could be applicable in AI as well. So that's another act.
00:52:13 · Grace Gong
[00:52:13] S0: Would you say, when you're thinking about, like, ensembling the team, like, do you find it, like... I guess, like, how do... Do you train them since you mentioned about, like, the people coming from, like, legacy kind of, like, tech background? And do you feel like you have to train them with AI or do people just adopting it into the new... I guess the newest technology very fast?