Venture with Grace
00:01:04 · Vitaly Gordon, Grace Gong
[00:01:04] S1: Yeah. So kind of the interesting journey, at least both on LivePerson and Salesforce, they had kind of similar elements where they both companies started kind of in the .com era kind of before, you know, we actually had the term SaaS and kind of things like that. And just the fact that, you know, you could build, you know, a business on the Internet and, you know, I'm dating myself now. It was kind of like not... You know, wasn't something that you could take for granted. And just understanding, like, how different it was back in May, but, like, what actually... Also the difference in experience that... For the customers that make it available for both LivePerson and at Salesforce. It's kind of... Was amazing to go through that journey, you know, even though I kind of joined it later than kind of the .com era. You still had that, like, you know, entrepreneurial spirit even, you know, although the companies were already public and just kind of, like, hearing about how things used to be, it was great. And we see a lot of these things now with the kind of the new AI wave. [00:02:27] S0: I guess, since I feel like Faros is on like the engineering intelligence kind of like aspect, maybe we could talk about the inspiration behind, you know, why do you kind of like laugh like Salesforce to kind of like create this?
00:02:42 · Vitaly Gordon
[00:02:42] S1: Yeah, so the journey of the company actually started while I was at Salesforce and I got to experience firsthand what like an amazingly well run company is. And this is kind of like one thing to understand about more kind of b two b businesses. Like, usually, they don't have, like, you know, strong network effects like, you know, prior to Salesforce, I was at at LinkedIn, but obviously, you know, is very, very different in terms of, like, you know, the network effects that you have. The business was just looking very, very well, you know, executing. Actually, funny enough, there's recently, like, just a couple of days ago, a post by someone from Slack that, you know, argues differently and we can talk about it. But... And especially, I would say, the department, like, you know, finance and sales and marketing, which is kind of like, you know, the company's named after. And engineering, while it was one of the most, like, technical organization in the company, was actually probably the least data driven. And it just got me thinking of, like, why, like, why engineering is just, like, not at all data driven? Like, why do we run this function by basically, you know, doing daily stand ups and, you know, standing, holding hands and singing Kumbaya in the morning and, like, why are we not more day driven and kind of... You know, we can go into more details if needed, but, like, there there isn't... There wasn't a structural answer. It wasn't like... It wasn't a skills gap. Right? There was like... It was just like, you know, we never, you know, we never really thought about it, and we never thought that it's important enough. And so I started doing it, especially as my organization itself was grew and grew and grew. And at some point, I kind of, you know, became in love with, like, the side quests and decided to kind of focus on it full time.
00:04:31 · Grace Gong
[00:04:31] S0: I guess, like, you started since you mentioned about, like, you know, your journey started, like, like, or, like, the funding journey started at Salesforce, you founded Salesforce Einstein, which is one of the first enterprise AI platform. So maybe we could talk about in terms of building the team and building the company itself, how do you validate that? Because when you were building, I sent it, you guys closed over 10,000 customers and you own the engineering team. And like, I guess, like, how do you start something from the scratch and then build out like your own kind of like client base after leaving a larger company?
00:05:20 · Vitaly Gordon
[00:05:20] S1: Yeah. So, well, starting with, you know, billing Einstein from scratch. So Salesforce, obviously, like, you know, like anyone who builds, even if it's a brand new kind of offering in a large company, you are, you know, somewhat standing on the shoulders of giants. Right? You are... You know, you have access to so many things. And, you know, in my case, was access to Salesforce customers. And so, you know, if we build a product that they want, then, you know, there's a whole machine that, you know, will make sure that they buy it. And so that that part was definitely easier. And I've, you know, throwing it all away and starting from scratch is probably, you know, one of the most humbling experiences that anyone can have. Like, I was a big shot at Salesforce, and then, you know, like, I was a nobody. Like, that's really what this is about. And you just have to, scratch and, you know, kind of claw and kind of just... Like, borrow, and steal in order to, get your first customer. There's just no way there's just no way around it. So the way I did it is just the old fashioned way. Throughout my journey at Salesforce and other companies, I, you know, developed some network and I was just like, you know, every day, I don't remember. I think I... And I was like, literally reported that in my monthly investor update, I put a goal and, like, how many people will I talk to this month? And I would just... Like, every day, would send 20 emails and I would personalize those emails and I would, like, try to say, hey. You know, I was willing to talk to anyone who was willing to listen. And then kind of like doing all this, you know, discovery was just kind of painful. But I... Like, you know, there's just no other way because quite honestly, everyone was busy and they had their own life that they had to worry about. And you just need to put in the effort, like to get some of their time to get some feedback and just go from there.
00:07:23 · Grace Gong
[00:07:23] S0: Since like, I feel like this year has been like people have been like either token maxing or agent maxing. But nowadays people are a lot more conscious about how much token they're using. So can we talk about how do you guys monitor the productivity of whether it's token usage or guess, like, the engineering productivity in general?
00:07:46 · Vitaly Gordon
[00:07:46] S1: Yeah. So look. This is something that obviously has been the journey since I started this company seven years ago where, like, it was pre GitHub Copilot and Codex and, you know, Claude Code and all that stuff. So improving engineering productivity was kind of like a relatively slow and laborious process that required a lot of change management. And now AI changed all of it. Like, honestly, there is no engineering productivity today that does not include AI. It's just like it's primarily, kind of how to use AI. And... But the good thing is, like, the results are now, like, massively, massively different, and we truly see, you know, like, these, you know, kind of proverbial 10x engineers that are, like, you know, orchestrating agents and outputting, like, so much more. Now, you know, we can talk about the quality of that. But, like... But that aside is, like, there's no doubt about that this technology is probably the most revolutionary technology that we ever had for in kind of engineering. And now having said that, I I think that some people are still, like, you know, just not, kind of thinking about it the right way because in the vast, vast majority of the companies, their AI spend is still less than 5% their labor spend, like the salary cost and kind of all of that stuff. I think, you know, Anthropic and S1 just reported that since, you know, like the beginning of the year, actually, their cloud code spend is more than their salary spend. So if you take a page from Anthropic, like, you know, like, again, as a percentage of, you know, engineering spend, maybe your AI spend should be over a 100%. Right? That's gonna look at least... That's what Anthropic can report. And so, like, there's a lot of talk about, like, you know, token maximum, which is also, you know, incorrect, because just like, you know, spending any kind of tokens in AI really does not yield a result. So the thing that we are kind of advocating for is outcome axing, which is really the thing that probably most organizations should be optimizing for, which AI is amazing at, is really lowering the price per outcome. Right? And by lowering the price per outcome, more often than not actually involves using more AI and not less AI.