The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:28:39 · Harry Stebbings, Alex Mashrabov
[00:28:39] S1: In terms of the dollars generally. [00:28:40] S0: In terms of the dollars, right. And especially because for coding still remains to be very prolific use case where coders are always jumping to the recent model. But for our markets, we're seeing completely different dynamics. What's actually happening in social media marketing as companies start to print hundreds of ad creatives a week, they wanna have maybe cheapest, more steerable models because PhD level intelligence is not necessarily needed to make viral social media video. And that's where we actually have seen that we get 80% plus margin whenever we run open source models, like post trained open source models, but it can be way more cost efficient for our end customer compared to the proprietary models. [00:29:26] S1: What's the comparison on margins between open versus closed fee?
00:29:26 · Harry Stebbings, Alex Mashrabov
[00:29:26] S1: What's the comparison on margins between open versus closed fee? [00:29:31] S0: The margin on own models and open weights models is over 80%, and then it almost doesn't matter. And for closed source models, it's probably between 2030%. And then what becomes important is can we actually steer the traffic? What makes me excited about Higgs Field is that Agencik grows so quickly. And actually for us, as companies start to actually create those agentic workflows to make more ads, we choose which model we can use. So we choose what model to use in over than 40% cases. In a way, model routing becomes a core feature of the business, though? Yeah. We call it tokenomics essentially. Right? As there is certain amounts of work customers wanna do, how can we optimize number of tokens which require it, and how we can pick the most efficient tokens for them. There are actually two incumbents in The United States who figured out models. It's not just Google. It's also NVIDIA. Why do you think that is? What I'm constantly seeing is that there is diversions of models. So there are these state of the art models which have to be really good in computer use, like Astra or in coding, but they can be prohibitively expensive. And we were chatting about that. It's like on average at Hixfield, person on the team spends over $10,000 a month on various models. And remember, we are split across United States and Asia across the So United how much do you spend on models per month? So internal usage of models a month is over 4,000,000. [00:31:06] S1: Wow. How many people do you have?
00:31:06 · Harry Stebbings, Alex Mashrabov
[00:31:06] S1: Wow. How many people do you have? [00:31:09] S0: We have close to 400 people and just wanna make sure that the math adds up. Yes. It's it's definitely over $10,000 per person. [00:31:17] S1: How has that changed over time? [00:31:19] S0: That's the best question of the whole show, by the way. That's the best question. What actually started to happen is the creative team started to do vibe coding. Like, this month, I was... I I just caught a guy who spent over 30 ks in a week on Astra model. Because he was frankly frustrated that some asset organization workflow, and as you said, basically auto editing, is still not very good in production. And he said, oh, I'm just gonna do this myself, and just went, like, five nights straight on Astra. And it works? We learned a lot. I wouldn't say it was production ready, but we learned a lot. [00:31:57] S1: 30,000 in a week. [00:31:59] S0: Yeah. Yeah. Many people spend over 10,000 in a week. Do you mind? Yeah. My finance team will probably say... I don't know if if if, you know, if you ask any of them, but they will probably say that I'm, like, being too stubborn, too relentless to control this spend because sometimes I feel it goes like... It really goes out of control. Like, 30 k in a week is quite a lot. But we learned... So this was actually a net positive experience. [00:32:22] S1: Okay. So the internal spend, 4,000,000, about 10,000 per head. What will that be in twelve months' time, do you reckon?
00:32:22 · Harry Stebbings, Alex Mashrabov
[00:32:22] S1: Okay. So the internal spend, 4,000,000, about 10,000 per head. What will that be in twelve months' time, do you reckon? [00:32:29] S0: So across the top engineers and across top creatives, it's going to keep growing. And I do believe we are gonna get to spend close to 50 k and 100 k a month for those who can call 10x engineers, 10x creatives. Unfortunately, I also expect that these people will ask for comparable salary raise as well. So I think that is just gonna correlate at some points. But also for a lot of other jobs, let's say we take legal, finance, and so on, it really stabilizes around like $500,000 a month very, very quickly. [00:33:03] S1: With those 10x engineers, the idea is they have thousands of agents running below them doing a lot of the difficult execution work that took time. Do we just have dramatically smaller teams with those 10x engineers, 10x designers, 10x finance leaders?
00:33:03 · Harry Stebbings, Alex Mashrabov
[00:33:03] S1: With those 10x engineers, the idea is they have thousands of agents running below them doing a lot of the difficult execution work that took time. Do we just have dramatically smaller teams with those 10x engineers, 10x designers, 10x finance leaders? [00:33:20] S0: I can definitely say that I had sort of a feeling that legal customer support is going to be mostly replaced. And that's obviously one of the main mistakes, operationally, which we have done in the company, that we didn't ramp these teams quickly. What we're seeing today is that, let's say our legal team is over 10 people, our customer success team is over 40 people. All of them use AI heavily. At these professions where I stay quite close today, I definitely can say that I don't see any elimination. It's true that probably over 60% of customer support requests, especially the first line of defense, can be handled with AI. But when it especially comes to B2B, like, AI just doesn't work. [00:34:02] S1: Revolut has now over 92% resolution rate on customer support for consumers. Pretty good. [00:34:09] S0: It's it's it's pretty good, but obviously, they did invest a lot into that. A shit ton. And and... But also very important, the way how Nick thinks about that is in terms of the playbooks. We launch products, new products pretty much every week. We have to keep up that agents with all the information and so on. And just due due to the high velocity, having extremely smart coordinated team is is very important.
00:34:09 · Alex Mashrabov, Harry Stebbings
[00:34:09] S0: It's it's it's pretty good, but obviously, they did invest a lot into that. A shit ton. And and... But also very important, the way how Nick thinks about that is in terms of the playbooks. We launch products, new products pretty much every week. We have to keep up that agents with all the information and so on. And just due due to the high velocity, having extremely smart coordinated team is is very important. [00:34:32] S1: That's really interesting how product velocity increases leads to harder customer support for agents. [00:34:38] S0: Of course. Because the agents are as good as contacts and the rules which they have. And if contacts and the rules change pretty much twice a week, it gets a little difficult. [00:34:47] S1: When you look at your engineering team today, what are they on? Are they on cursor? Are they on codex? Are they on core code? [00:34:55] S0: So from a period from March to June, everyone really moved to Claude, including the creative team. And that's where we actually started to see creative team vibe coding functionality, which we don't have in production. But then we started to see that all the coders quickly moved from CLOUD to Codex as of mid June. Over the time creative, especially 10x creatives, moves to Codex as well. But look, I do believe that it's cyclical. So