Venture with Grace
00:52:07 · Grace Gong, Vitaly Gordon
[00:52:07] S0: Since you mentioned about the data, which is like 50% of the current cloud or average, like, model spending is going to the non technical people. Where do people spend the most amount of money? And is this cheaper if you just hire a human? [00:52:25] S1: So probably, again, it might be sometimes cheaper because we do see some... One of our customers had a security person who's like... Who spent like $170 in one month. So obviously, yeah, like, it's way way cheaper to like, you know, our person. But like, in most cases, I would say that AI is still such a small percentage of total total labor spend that I think, like, you know, adding those aside, like, you know, we should be using kinda more AI. And I think we should be still using humans for... You know, there will be a lot of human related tasks about, like, taste and direction and, you know, other things. But I think this, you know, cheaper to hire a human. I think it will be you... We should hire humans to get much more out of our AI spend. But like it's... I don't think it will be cheaper on on a task basis. [00:53:21] S0: For the subsector that you guys have worked with, is there any particular subsector that you are very excited about? So for example, from my observation, I feel like a lot of money are actually going to the healthcare AI space. And do you feel like their spending is bigger than, let's say, quantum computing versus any subsector that you guys actively look at to tapping into?
00:53:21 · Grace Gong, Vitaly Gordon
[00:53:21] S0: For the subsector that you guys have worked with, is there any particular subsector that you are very excited about? So for example, from my observation, I feel like a lot of money are actually going to the healthcare AI space. And do you feel like their spending is bigger than, let's say, quantum computing versus any subsector that you guys actively look at to tapping into? [00:53:49] S1: Yeah. So currently, we're trying to focus on, I'll say, on tasks that are somewhat repeatable and are kind of like maybe like a little bit easier to find. The problem with, I would say with healthcare is that the fastest pre binary is either you get a drug discovery or not. And so it's very hard to, like, kind of like, operationalize. And, like, it's... You know, there's no, like, amount of AI that we can guarantee that you will discover, like, a life changing drug. Right? So so those are sectors that we're, at least, for now, focusing less on and, you know, like, look, there's a probably trillion dollars even in the ongoing, you know, tech, finance, you know, whatever ecommerce, retail sectors that, that's kinda, you know, where we focus.
00:53:49 · Vitaly Gordon, Grace Gong
[00:53:49] S1: Yeah. So currently, we're trying to focus on, I'll say, on tasks that are somewhat repeatable and are kind of like maybe like a little bit easier to find. The problem with, I would say with healthcare is that the fastest pre binary is either you get a drug discovery or not. And so it's very hard to, like, kind of like, operationalize. And, like, it's... You know, there's no, like, amount of AI that we can guarantee that you will discover, like, a life changing drug. Right? So so those are sectors that we're, at least, for now, focusing less on and, you know, like, look, there's a probably trillion dollars even in the ongoing, you know, tech, finance, you know, whatever ecommerce, retail sectors that, that's kinda, you know, where we focus. [00:54:39] S0: Speaking of retail, what is the current landscape of people building an AI in the traditional compared to AI native companies? Do you feel like AI native companies are actually spending more AI budget? Or who is actually owning the AI budget in these groups? Do they typically have a chief AI officer? And how is the budget allocated? Is that each department has an AI budget then you go after the department versus, like, that there's, AI guy at every company right now.
00:54:39 · Grace Gong, Vitaly Gordon
[00:54:39] S0: Speaking of retail, what is the current landscape of people building an AI in the traditional compared to AI native companies? Do you feel like AI native companies are actually spending more AI budget? Or who is actually owning the AI budget in these groups? Do they typically have a chief AI officer? And how is the budget allocated? Is that each department has an AI budget then you go after the department versus, like, that there's, AI guy at every company right now. [00:55:13] S1: Yeah. So I I have to say that it's still unfortunately, like... So first of all, to your question is there is a definitely a massive massive difference between AI native companies. So I think a six and z recently shared this ad that the median company kind of, like, spends about $13 per employee per month, while the top 1% spend $10,000 per employee per month. Right? So there's, like, now... Like, you know, more than two order... Orders of magnitude kind of between kind of the most AI native companies and the median company. And so I would say that, this is kind of, like, just a massive difference. Now within departments, honestly, wish we would see, like, you know, like a smaller allocation, but I think most companies are just like, oh, you know, every employee gets $20 and, like, whatever. Like, it's just... It's kinda sad. Like, you know, there's nothing more intelligent. [00:56:15] S0: Okay. So I want to be mindful of the time. Maybe we'll wrap with a thirty second fire round. What's your favorite book? [00:56:21] S1: Oh my god. Catch-22.
00:56:21 · Vitaly Gordon, Grace Gong
[00:56:21] S1: Oh my god. Catch-22. [00:56:25] S0: Who would you invite to your dinner party? [00:56:28] S1: Elon Musk. [00:56:29] S0: Who made the biggest impact in your career? [00:56:32] S1: My mom. [00:56:33] S0: Where can we find you outside of work? [00:56:37] S1: Oh my god, spending time with my kids. [00:56:39] S0: Okay. Wow, you're so good at the questions. Well, Vitaly, thank you so much for coming on the show today. It was such an interesting conversation about everything. [00:56:48] S1: Thank you for having me. [00:56:49] S0: And productivity. Thank you. Okay. Let me quickly answer you. [00:56:52] S1: Okay. Thank you.