The Untold Story of Higgsfield | Burning $4M a Month on AI Models | CEO, Alex Mashrabov

0:00On average at Hicksfield, person on the

0:01team spends over $10,000 a month on

0:04various models. So internal usage of

0:07models a month is over 4 million.

0:10Hicksfield. This is the story that no

0:12one has told in startups yet. The

0:14company has just hit a billion in

0:16revenue. It is the fastest growing

0:18company in consumer land to hit this

0:20milestone. It even surpassed Cursor.

0:22Alex, the founder, is an incredible

0:24genius. This is the story that you don't

0:27know that you need to know. My parents

0:29told me that I must get to the United

0:31States cuz this is the place where

0:33technology matters. By the age of 19, I

0:36was able to get to top three in the

0:37world in competitive programming. I just

0:39caught a guy who spent over 30k in a

0:42week on Astra model. Many people spend

0:44over 10,000 in a week. Ready to go.

0:57Alex, I am so excited for this dude. We

1:00were talking downstairs and I said, I

1:02don't think the Higsfield journey has

1:04been told before and it's it's an

1:06amazing journey. So, thank you so much

1:08for joining me today.

1:09>> Uh that's very special opportunity for

1:11us. Thank you for having me. Obviously,

1:14your story is inspiring as well, like

1:16how social media has become like an

1:18elevator for you, opportunity to create

1:20fun and so on. Dude, it's very kind of

1:22you to say. I do just want to go back

1:24though because you're not the Stamford,

1:27Silicon Valley, born and bred engineer.

1:31You were a competitive programmer in

1:33Kazakhstan. Can you just take me back?

1:36How did you first find and fall in love

1:37with computers and become a programmer

1:39so early?

1:40>> So, first you need to understand where I

1:42come from. So my father is from

1:44Usbekiststan. Usbakistan is a country in

1:46central Asia where like if a family of

1:50five people makes $1,000 a month, it's

1:53considered to be wealthy. So it's like

1:56not very high standards of living

1:58unfortunately. So um but both my parents

2:01are professors of mechanical

2:02engineering. Since I remember myself

2:04since I was eight, my parents told me

2:06that I must get to the United States

2:09because this is the place where

2:10technology matters.

2:12So um my mother had to work three jobs

2:17because basically my education was to

2:19compete in programming competitions all

2:21the time and to go to various

2:23educational camps where I could learn

2:25from the best like certain data

2:28structure data structures algorithms and

2:30so on. Can I ask you a question? Did you

2:33feel pressure as a child competing being

2:37pushed into these environments when you

2:40were so young?

2:42>> Absolutely. Uh but and and and I'm very

2:44grateful to my parents that they showed

2:46me the path really from that from that

2:48early on. Um definitely when you come

2:52from this part of the world think about

2:54post Soviet countries uh India China

2:57like getting to the top of the rankings

3:00in any competition in any international

3:02competition is the only way to really

3:05break out. So by the age of 19 I was

3:08able to get to top three in the world in

3:09competitive programming. But then

3:12instead of pursuing like um like

3:14academical career decided to do

3:16startups. [laughter]

3:19I'm sure your parents were thrilled. Uh

3:21can you take me to that decision? Like

3:23this is like the penultimate moment.

3:24You've worked 19 years for your parents

3:27have told you this is like the mother

3:28load. This is the thing and you're like

3:31I'm going to go and do this really risky

3:33thing called a startup at this point

3:35like what happens then?

3:37>> So let me take you back to 2014.

3:40I was very fortunate to work on

3:43pre-transformer architecture neural nets

3:45and I was primarily just doing

3:47optimization make it run faster um

3:50parallel across multiple machines and so

3:52on and um I was and we actually build

3:55state-of-the-art system for language

3:58translation from English to Russian and

3:59Russian to English apparently talent

4:02wars were a real thing even back then a

4:04lot of my teammates were hired by Deep

4:07Minds and Meta and uh but My passion was

4:11actually different. I was very very

4:13surprised to learn when I come to to for

4:16the first time how quickly Uber actually

4:19spread out. And I was thinking if like

4:23this app can take over the world so

4:26quickly and transform the whole

4:28industry, maybe what's going to happen

4:30is that mobile phones are going to

4:32become the most used devices in the

4:34world. Maybe there is going to be a

4:35version of the future where everyone is

4:38going to be spending most of their time

4:39in their life watching AI generated

4:42videos on the phones cuz I mean who else

4:44is going to produce videos for for the

4:46phones? Maybe it's going to happen with

4:47AI.

4:48>> Okay. And so that was the company that

4:50we built before that you sold to Snap.

4:52>> So yeah, so the company was called a

4:54factory. Um was fortunate to meet Mahi

4:572018. He's co-founder of Hicksfield and

5:00he is a like veteran of Silicon Valley

5:03went through ups and downs and um sold

5:06it to Snap for 100 for million for 166

5:10million and um then I was leading Jenny

5:14there pause no offense dude you come

5:17from um you know a family of incredibly

5:21ambitious parents who push you to do

5:23well and you just skipped the moment

5:25where you sell for 166 million It's a

5:28lot of money. Um, how did that feel when

5:31you did it?

5:32>> We both remember these times where the

5:35capital for AI companies was not really

5:38that much available and when and AI

5:41multiples were not like 200 to revenue

5:43as they are today but closer to zero cuz

5:46AI was not a topic. So there was like

5:48severe del dilution which we

5:50experienced. So you [laughter]

5:52just to calibrate. So can you

5:54>> okay what was around?

5:56>> No look I mean back then rounds like

5:58rounds of like$12 million having like$12

6:01million in investments was considered to

6:03be really good. Uh but it but it was

6:05still an opportunity for me to finally

6:07go to the United States. So after the

6:09acquisition I permanently moved to uh

6:12first to LA and then to Silicon Valley

6:13and my dream simply came true.

6:16>> Was it what you thought it would be?

6:18>> That's a good question. So um as San

6:20Francisco is definitely a place where no

6:24one judges by race, nationality and so

6:27on and that's that's um that's truly

6:30phenomenal. There is definitely a

6:32meritocracy in a sense that it's

6:34possible to meet anyone but in the same

6:37time what I see across Silicon Valley

6:39investors it's extremely consensus

6:42driven. So um I mean I think that last

6:44part I expected to be different but then

6:47I read the book about the law of capital

6:49and I realized this is just how the

6:50world works.

6:51>> So then tell me we have sold to Snap

6:54we're now in the US this is the moment

6:57you wanted how does Higsfield come to be

7:01back then like Snapchat 2020 was uh

7:04really growing so so quickly and the

7:06face filters which my team has built was

7:09driving most of daily new users. What

7:11what's important is that um these face

7:14filters we were able to manage to run on

7:17mobile devices. So it was virtually for

7:19free for Snapchat. It's not like current

7:22LLM tokens cost. Um and but but it and

7:26it and it scaled to hundreds of millions

7:28of people throughout the world. And it

7:30was truly phenomenal to me to build a

7:32product which is still probably the most

7:34used consumer media AI product. But then

7:37um but then what I realized is that

7:40there are a lot of unmet needs on

7:44advertising sites. Average company

7:47cannot figure out how to be relevant on

7:50social media. So and this is a major gap

7:53like social media is the main media in

7:55the world. A lot of companies are

7:58actually able to build direct response

8:00advertising so that they can actually

8:03sell more. But in the same time, most of

8:05the companies in the world cannot simply

8:07do that. And basically, no because no

8:10one simply can keep up with the pace of

8:12production for social media as trends

8:14change pretty much every day.

8:16>> Mhm. And so you were like, hang on a

8:17minute, these big brands aren't able to

8:20have media houses and so we need to

8:22create a tool that lets them. That was

8:24the cell.

8:24>> Yeah. Ex. Absolutely. So where it all

8:26really started is that we like there was

8:29a tool like to upload set of images and

8:31transform them into a slideshow with

8:33music.

8:34>> It's kind of better than nothing but

8:36still pretty bad, right? So another

8:38solution was to take long form video and

8:41cut them to short vertically oriented

8:43videos. This was better but still really

8:46not perfect. And it felt to me that um

8:48especially 2023

8:51it was absolutely clear that scaling

8:53loss finally work. It's not just a

8:56concept from science that scaling laws

8:59work. Video just takes couple I mean

9:02maybe two three years longer than LLMs

9:05and coding. Uh but it was clear that uh

9:08actually finally scaling loss should

9:10work in video as well and I decided just

9:13to take a bet. But I just want to go

9:15back. I get that in terms of what we

9:17see, which is, hey, we want to empower

9:18these brands and companies to create

9:20amazing media for social media,

9:23but it wasn't a hit from day one. And I

9:26spoke to Amy at Menllo who mentioned

9:29like a couple of pivots before and the

9:31meandering that we had. So what happened

9:34when we launched? Did we have immediate

9:36product market fit? No, actually we

9:39spent

9:41more than a year in a search of a

9:44product which could work. We burned more

9:47than 10 million out of 16 million raised

9:51in seed fundraising.

9:54So we felt we have just one attempt

9:56left.

9:58And frankly I feel I I'm responsible cuz

10:02I was focusing on the wrong things. I

10:05think I just lost the touch with reality

10:09back then. I was so much optimizing for

10:12what's hype today, what's the right

10:15narrative, how we can hijack the

10:17attention, all these things really

10:20like everything instead of building a

10:22good product. So when we had less than 6

10:26million lefts, I guess it was slightly

10:27less than five actually, I realized that

10:30the only thing which we can be focused

10:31on is to lean into the product PLG and

10:36just finally set belief that the best

10:40product is going to win. And um so and

10:44then we just started to talk to

10:46customers. We spoke to eight creative

10:49directors about their experience with AI

10:52and what's simply missing. Everyone told

10:55us that camera control does not exist in

10:59AI and camera control is so important to

11:01tell a story. So this is a very

11:03important bottleneck to solve. So we

11:06released our products uh March 31st last

11:09year and since then we are really riding

11:12this crazy wave.

11:13>> Was it immediate product market fit

11:15then?

11:15>> Like yeah it was immediate. Is product

11:17market fit like love? When you know, you

11:20know.

11:21>> Um, yes, it's definitely when you know,

11:23you know. Like for example, we don't do

11:25any paid and like we have we have on the

11:28team people who scaled businesses to

11:32over like billion and two billion in

11:34revenue like other businesses um with

11:37paid advertising. Like at Hicksfield, we

11:40decided to really make a bet that

11:42>> we don't do paid.

11:43>> We don't do paid. Is influencers not

11:46paid?

11:46>> That's a good point. So, um with

11:49influencers, there is typically there

11:51are different types of influencers, but

11:54typically there is um some fee for just

11:57video production and then like some cost

12:00per click like attribution which is like

12:02works really well on YouTube. You you

12:04guys got into some controversy

12:07[laughter] for like I can't remember

12:09what it was. you were like pay paying

12:12people to promote for you or doing

12:15something rogue with influencers.

12:18Was that completely unfair? Was it kind

12:20of my bad we did do that? How do how do

12:24you respond to that?

12:25>> The main takeaway from like our

12:27experience is that it's very important

12:29to own own distribution. Distribution

12:32now more important than ever. And like

12:34we basically did outsource we had just a

12:38team of like two people on creator and

12:40customer success sides and we just did

12:42outsource to the agency and this was not

12:44uh that was not a good experience but uh

12:47we are still but but we are still trying

12:50to

12:52find interesting opportunities to tell

12:55about new media formats. Some of them

12:58are rather controversial. So, for

13:00example, recently we partnered with

13:02Neon, one of the largest streamers in

13:04the world, and launched like his own

13:06sort of AI generated stream. Um, like no

13:09one else did this before cuz this is

13:11like real creator making a replica of

13:14themselves. A lot of people start to

13:16question uh start to question their um

13:20like is it really authentic content or

13:23not? But in the same time, those

13:25creators are under immense pressure. We

13:28all know about the story for about from

13:30Mr. Beast about like really how much

13:32like there is just pressure to

13:33constantly perform. So um and we also

13:36know through conversations with many

13:38talent agencies a lot of top stars

13:41actually want to be able to do more if

13:45they could create digital replica. But

13:48so what's happening today very

13:49frequently is that um those

13:53a tier celebrities they simply come up

13:56for a recording on like let's say green

13:59screen and then there is just a lot of

14:01post-prouction which goes on top of it

14:03and it feels to me that uh we are we we

14:06naturally going to come to the point of

14:08time where a AI digital replicas are

14:11going to become just one of the ways how

14:13creators can monetize.

14:14>> Totally get that. I do just want to go

14:16back to part of the story. Where are you

14:19at revenue-wise today?

14:21>> Uh so today is actually exciting day

14:24like when we record just Bloomberg

14:26article went out so that we cross 1

14:29billion in annualized revenue. Um if I

14:32had a gong here I'd be like hitting the

14:34gong. A billion in revenue.

14:36>> Yes. Um actually it took us 18 months

14:41from 1 million to 1 billion for Corsor

14:45it took 24 months. Um so we are probably

14:50uh probably like the thirds after open

14:52the anthropic

14:5418 months from a million to a billion.

14:57>> Yes. How do you calculate revenue? Like

15:01it's a controversial topic. Um, how do

15:05you help calculate revenue?

15:07>> Absolutely. Uh, by the way, your um,

15:09co-host uh, Jason also asked this

15:11question in May. [laughter]

15:13Luckily, answer didn't change. So, we

15:15are at least consistent. So, but let me

15:17be transparent on that. What we do is we

15:19look um, revenue over the last four

15:23weeks and multiply it by 13 from what I

15:27know openable all of them use the same

15:29methodology.

15:31What's very important is that we are we

15:36take revenue not sales. So if that's

15:38like annual subscription or annual

15:40enterprise contract we prorate this

15:43across 12 months and take only this uh

15:46and only take like a piece which

15:48corresponds to one month to 28 days to

15:51be uh to be precise. That's the first

15:53piece and second it's only live revenue.

15:56It's only live revenue. We are not

15:58taking like three year enterprise deals

16:00and baking into like 1 billion figure.

16:02No, we don't do that.

16:03>> If you were to break that billion up

16:05today into annual contracts, monthly

16:09subscriptions and then token spend, what

16:12would that be?

16:13>> So, um, videoi is still relatively early

16:16in my opinion. Uh, it is still probably

16:20two years behind coding in terms of

16:23adoption. So on demand usage for leading

16:27to coding companies could be over 50%.

16:30And I would be honest for video it's

16:32substantially less than that. Um in the

16:35same time what's very interesting for us

16:38to observe in the business is that there

16:41is sub significant revenue expansion. I

16:45always love to study stories of the

16:47largest customers on the platform. So,

16:50one customer started um 6 months ago

16:53spending just subscription $99

16:57a month. $99 a month. And now we just

17:01signed a deal over 6 million.

17:04>> 6 million.

17:04>> 6 million a year. Right. So, yeah. Like

17:07this level of acceleration is something

17:10which really like mind-blowing to me.

17:13Dude, what are they getting for 6

17:15million a year? that's like a Hollywood

17:17content team almost.

17:19>> So there are multiple trends um as and

17:22all of them frankly coming from Asia.

17:25>> So first we're seeing a lot of um direct

17:27to consumer e-commerce companies

17:30rebuilding their whole go to market to

17:33be AI native where they make uh where

17:36they just make hundreds of ads if not

17:39thousands a week where they can AB test

17:42what performs well. But we all know

17:45about like short form dramas, right?

17:47Like most like short form dramas today

17:49is an industry over 10 billion owned

17:52primarily by Chinese companies having

17:55huge impact both in China, United

17:57States, in Europe, everywhere in the

17:59world and most of new shows there are

18:02made with AI end to end. So look, I

18:04think uh like the this adoption

18:07obviously is uh coming like bottom up,

18:10but um that that's very difficult to

18:13refute this new reality.

18:15>> What percent of revenue is consumer

18:17versus enterprise?

18:18>> So that that that's a great question. So

18:21um

18:23so B business revenue is slightly over

18:2650%.

18:26>> Wow.

18:27>> Yeah,

18:27>> that's impressive.

18:28>> Thank you. Um on the consumer side, it's

18:31also very important to break it down. So

18:34on the consumer sides out of these 50 is

18:37around like 10% is pure consumer use

18:40cases pure consumer and that's roughly

18:42people who use it on mobile. So share of

18:44our revenue from mobile is less than

18:4610%. That's why we are we are very

18:49different from many other companies and

18:51but there are lots of aspiring creators

18:54like basically those people who are

18:56freelancers doing social media marketing

18:58projects and so on who try to learn

19:00video AI so that they can make more

19:03money. It's true that their behavior is

19:06a little churny. uh within a year most

19:10of them actually come back to try again

19:12and we do believe that over the time

19:16most of them are going to figure stuff

19:18out and they're just going to become

19:20this new AI native workforce. So it's

19:23still important for us to educate them

19:26and uh that's why we invest so much in

19:28like Hicksfield Academy, YouTube channel

19:30and so on. But we also are f fully

19:34cognizant that we will never be able to

19:38win in a market of subscriptions of $20

19:42a month.

19:43>> So why? Because like I think like today

19:46Google and Open AI they pursue like ads

19:49so much but fundamentally I think they

19:53are going to completely demolish all the

19:56consumer subscription markets which is

19:59uh $20 a month subscriptions.

20:01>> Oh, so you saying that because they

20:04provide a horizontal product that's very

20:05good, you're just going to not pay for a

20:08lot of the verticalized products that

20:10you used to pay $ 20 $30 a month for.

20:12>> Yeah, I do believe that. That's

20:14essentially what's going to happen over

20:15the time. Um, I know this is a very

20:18contrarian bets, but um, at least we can

20:20see some of that.

20:22>> I think it cannibalize Canvas growth if

20:24you're honest. A lot of the lowhanging

20:25fruit on the consumer design side that

20:28Canva used to serve can now be done in

20:30open AI in particular.

20:33Is that what you're talking about?

20:34>> Yeah. And I do believe this is just the

20:36most apparent example, but there are

20:38couple more which is which is already

20:39happening. And I do believe that uh

20:41that's why for at Hicksfield what what

20:44really matters for us is how we even if

20:46we get someone on like $20 a month

20:48subscription like how can we show them

20:51value how can we make them to upgrade to

20:54over to spend over um to over $1,000 a

20:58year with us. I can't believe that's 6

21:00million a year from 99 bucks. That's the

21:03best ever slide on a fundraising deck.

21:06[laughter]

21:06>> And all of our customers are going to do

21:08the same. Exactly.

21:09>> Can I ask you mentioned there kind of

21:10churn rates when you look at 30-day

21:13retention rates for consumers and 90-day

21:16retention rates. What are yours and what

21:19is good? So, there is um quite massive

21:24drop within the first month

21:27>> just simply because people don't fully

21:28realize the value and that's a that's a

21:31core priority for us to actually get

21:33better in that. So, showcasing the

21:34value. Is it like half or like

21:36>> No, it's uh it's maybe like 30% drop.

21:39Okay.

21:39>> But then it's it's really flat after

21:41that. It's we look obviously at like

21:43logo retention.

21:44>> Mhm.

21:45>> I wouldn't say it's great but because

21:47like we all remember like B2B SAS era

21:49like uh retention was expected to be

21:52logo retention month one was expected to

21:54be over 80%. M

21:56>> um so clearly we have uh we have a lots

21:59of work to do on uh user education to

22:01get there but some things are truly

22:05phenomenal like when I look at the cor

22:07at the business segments and NRR at

22:11month 12 obviously like you're going to

22:13argue it's like 18 months old company

22:15like what are you talking about but

22:17still when I look at the numbers which I

22:18have today NR at month 12 is over 300%.

22:22just it just never happens in B2B SAS

22:25right so um that's why I'm saying that

22:28while there is substantial churn in

22:30month zero and we have to do better job

22:34with user education to address that

22:36expansion is unprecedented can we

22:38actually just unpack the two different

22:40go to markets cuz you got consumer and

22:42you got enterprise and I spoke to quite

22:44a few of your competitors in all honesty

22:46before this show [snorts] and I said hey

22:49you we've got Alex coming on what should

22:50we ask him everyone said the same thing

22:53which was an admission of their respect

22:55for this particular kind of GTM. They

22:58said you've ex executed the most

23:01impressive influencer campaign in tech

23:04and what I wanted to understand was when

23:07you look at the consumer growth

23:10what worked what didn't work and how do

23:13you reflect on that first and foremost

23:16like the goal is to make sure that the

23:18best commercial video content is

23:21generated on Hicksfield and we show all

23:24the workflows of how to make such uh

23:27professionallook videos and we have an

23:29in-house team of over 150 creative

23:33professionals.

23:34150. It's it's almost half of the whole

23:37work workforce frankly. And um they th

23:41those people they make product launch

23:43videos, they make tutorials like for

23:47example we made the first generated

23:49movie which is also like obviously um a

23:53very um a very sensitive topic but

23:56what's important we open sourced all of

23:58it and what we learned is that for 90

24:02minutes of uh of like let's say TV

24:05quality content it was over 100 hours of

24:09for yet generated contents. So creative

24:11decisioning like picking the right piece

24:14is still very important. Um so that's

24:17really what's what we are focused on and

24:18that's what's driving most of the most

24:21of the revenue.

24:22>> So you're saying the the growth in

24:24consumer subscription is through own

24:26content and distribution.

24:28>> Yes. We don't do any paids. Early on you

24:30made an interesting architectural

24:31decision to have your own models and

24:35then you since walked that back. Can you

24:38talk me through why did you choose own

24:40models and why the walk back?

24:43>> Oh, um yeah, obviously this was

24:45[laughter] uh obviously this was my

24:46mistake. I'm going to be I'm going to do

24:48my best to be um transparent. What I

24:51need to admit, we really tried we I at

24:54some point of time I really was thinking

24:56that chasing benchmarks

24:59um is valuable but I don't believe this

25:01is just sort of corporate scops frankly.

25:04So um and I was part of the large

25:07organization so I know what happens.

25:08What happens is that everyone just

25:10thinks like we need to show some

25:12progress. So we need to have some

25:14benchmark but then when I talk to the

25:16top researchers from these labs

25:18especially larger companies what happens

25:22is that they start to put test data into

25:24the training.

25:26They start to kind of use uh leverage

25:29test data to use LLM as a judge for

25:32training of the models. use all the

25:34various tricks to basically gain

25:35benchmarks, get get like quarterly

25:37bonuses and so on because like who

25:39cares, right? So if I make my couple

25:41million dollars a year in inside in one

25:43of these labs, I can move to another lab

25:46easily. So that's unfortunately what's

25:48happening in larger organizations. Um

25:51and

25:51>> can I just stay on that?

25:52>> Yeah.

25:53>> What do you mean? You're saying that

25:55they are incentivized by benchmarks and

25:58so because of that they are doing

26:02artificial things to improve their

26:04scoring in benchmarks which actually

26:06don't increase output efficiently. Yeah.

26:08Look, I think let's just look at the

26:10outcomes which we have today. Out of all

26:13the incumbents in the United States,

26:15when I look at open router data, the

26:17only company which is relevant is

26:20Google.

26:22out of all the incumbents when I look in

26:24China where probably obsession with

26:26benchmarks probably is less we have 10

26:29cent shyomi Alibaba

26:33uh like three incumbents being

26:35completely relevant and obviously like

26:38by dance obviously trying to catch up as

26:40well what's your takeaway from that

26:41>> I just do believe that uh the there is

26:44just obviously in the in tech bubble

26:46there is a strong obsession over the

26:47benchmarks uh which do not uh

26:50necessarily

26:51represent the reality. But I can talk

26:54specific specifically in the for video.

26:56>> A lot of benchmarks today for video is

26:58really text to video which does not

27:01represent actual workflows at all. Um

27:04the way to think about video models

27:06today, it's just modern rendering

27:09engine. It think about this as like

27:11Unreal Engine or Unity but just

27:14different types of inputs.

27:17And it's virtually impossible to really

27:20define a visual output and and direct

27:23the execution just through text. If you

27:26just go and to our open source projects

27:28like this movie which I mentioned

27:30average prompt length is over 3,000

27:32words. That's the first thing and like

27:35look all these benchmarks which we are

27:37talking about they are not like as

27:38comprehensive in terms of the details of

27:41prompts and people who are labeling they

27:44obviously don't cannot read like 3,000

27:46long word long prompts but also on

27:49average there are at least 10 image

27:52references

27:54for every for every scene. The reason

27:56why it's important because it's

27:58important to define how the characters

27:59look like, how the background looks

28:01like, like how actually characters are

28:05located to each other in the scene and

28:07so on. And so that's why like prompting

28:09and like just the workflow is so

28:11complex. Benchmarks just don't rep don't

28:13represent that.

28:14>> So going back to the model selection,

28:17why did we decide we're going to do our

28:19own and then why walk it back? It's true

28:22that like with VFX and camera control,

28:25we got very very quickly from like maybe

28:291 million to 20 million in AR within

28:33maybe the first 3 months. Then we

28:35released own image model which is really

28:39good at um aesthetic photo shoots and

28:43product consistency. This is what

28:45allowed us to scale then from 20 to 100

28:48million. So help me understand, Alex,

28:51why did you decide that you were going

28:53to do your own models and why did you

28:55abandon them? [snorts]

28:56>> We still do them whenever we see like

28:58specific use case like these photo

29:00shoots.

29:01>> Uh but but as soon as this is what our

29:03customers want. So it's all driven based

29:05on the customer feedback, not just by

29:08ambition to conquer the worlds and

29:11[clears throat] build the best model in

29:13the world. Do you think every company

29:15will have their own models like we're

29:17seeing Harvey, we're seeing Cognition,

29:20we're seeing Mccor, Ramp build their own

29:23models and we'll see every company have

29:25their own models with their own data or

29:28we actually all use a series of

29:30providers. So um first of all whenever

29:34just to be honest whenever someone says

29:36we build our own models very likely what

29:38they mean is something what see what's

29:40happened with Corsor. We we do remember

29:42right a lot of companies they actually

29:44take open weights model and just post

29:47train on own data.

29:48>> Mhm.

29:49>> Um and post training can happen in two

29:51ways.

29:53Most importance is whenever you have um

29:56customer data around like decisions they

29:58make like sequence of decisions and you

30:01can teach the model to actually take

30:03like learn how to compress these 10

30:06steps into one step. like this type of

30:09reinforcement learning is the most

30:11valuable. So and I think like

30:13increasingly more and more companies

30:14will have to do that frankly just we see

30:18this in the market as well. So the most

30:22most of the companies in the world today

30:25most of the businesses they don't

30:27necessarily need Astra specifically they

30:30don't necessarily need the newest fable

30:33model and that's why like open router

30:36reports that uh share of open source

30:38models went from below 30 to over 60

30:41within within this year.

30:43>> What do you think share of open models

30:45will be in two years time? Look, I do

30:47believe that just because the cap

30:49capitalism works, I mean openly

30:51ananthropics still are going to have

30:53more than 50% of the markets

30:54>> in terms of the dollars generally

30:56>> in terms of the dollars, right? And

30:58especially because uh for coding still

31:00remains to be very very prolific use

31:02case where coders are always jumping to

31:05to to to the recent model

31:08over but for our markets we're seeing

31:10completely different dynamics. what's

31:12actually happening in social media

31:13marketing as companies start to print

31:16hundreds of create ad creatives um a

31:19week they want to have maybe cheapest

31:23more steable models cuz like PhD level

31:26intelligence is not necessarily needed

31:30for to make viral social media video. So

31:35um and that's where we actually have

31:36seen that um we get like 80% plus margin

31:40whenever we run open-source models like

31:43post-trained open source models. Uh but

31:47it can be way more cost efficient for

31:49our end customer compared to the

31:51proprietary models.

31:53>> What's the comparison on margins between

31:55open versus closed for you?

31:57>> The margin on own models and open

32:00weights models is over 80%.

32:03Um, and then it almost doesn't matter.

32:05And for closed source models, it's

32:07probably between 20 and 30%. And then

32:09what becomes important is can we

32:12actually steer the traffic. What makes

32:15me excited about Hicksfield is that umic

32:19grows so quickly and actually for us as

32:24companies start to actually create those

32:27agentic workflows to make more ads we

32:31choose which model we can use. So like

32:34we choose what model to use in over 40%

32:39cases.

32:39>> In a way model routting becomes a core

32:42feature of the business. No.

32:43>> Yeah. We call we call it tokconomics

32:46essentially right as like there is

32:48certain amount of work customers want to

32:50do um how can we optimize number of

32:54tokens which requires and how we can

32:56pick the most efficient tokens for them

32:58there are actually two incumbents in the

33:00United States who figured out models

33:02it's not just Google it's also Nvidia

33:03why do you think that is what I'm

33:05constantly seeing is that um the there

33:09is the versions of models so there are

33:11these state-of-the-art models

33:14which have to be really good in computer

33:16use like Astra or in coding. Um but they

33:21can be prohibitively expensive and we

33:24we're chatting about that like on

33:25average at Hicksfield person on the team

33:27spends over 10,000 over $10,000 a month

33:32on various models and remember like we

33:34are split across United States and Asia

33:37across

33:37>> so how much do you spend on models per

33:39month? So internal usage of models a

33:44month is over four million.

33:47>> Wow. How many people do you have?

33:49>> We have close to 400 people and just

33:53want to make sure that the math adds up.

33:55Yes, it's um it's definitely over it's

33:58definitely over $10,000 per person.

34:01>> How has that changed over time?

34:03>> That's the best question of the whole

34:04show, by the way. Um that's the best

34:06question.

34:08What actually started to happen is the

34:12creative team started to do VIP coding

34:16like the like like this month I was I

34:19just caught a guy who spent over 30k in

34:23a week on Astra model

34:28cuz he was frankly frustrated that some

34:30like asset organization workflow and as

34:33you said like basically auto editing is

34:36still not very good in production and he

34:39said, "Oh, I'm just going to do this

34:40myself." And just went like five nights,

34:43five nights straight on Astra

34:46>> and it works.

34:48>> We learned a lot. I wouldn't say it was

34:50production ready, but we learned a lot.

34:52>> 30,000 in a week.

34:54>> Yeah. Yeah. Many people spend over

34:5610,000 in a week.

34:57>> Do you mind?

34:59>> Yeah. My finance team will probably say,

35:02I don't know if if you know if you ask

35:04any of them, but they will probably say

35:05that I'm like being too stubborn, too

35:08relentless to control the spend cuz

35:10sometimes I feel it goes like [laughter]

35:12it it really goes out of control like

35:1430k in a week is quite a lot. But we

35:17learned this. So this was actually net

35:18positive experience.

35:20>> Okay. So the internal spend 4 million

35:22about 10,000 per head. What will that be

35:25in 12 months time do you reckon?

35:27>> So that that's very interesting. So

35:29across uh the top uh the top engineers

35:32and across top creatives I think it's

35:35going to keep growing and I do believe

35:38we are going to get to to to spend um

35:41close to 50k and 100k a month for those

35:44who can call 10x engineers 10x creatives

35:47unfortunately I also expect that these

35:50people will ask for comparable salary

35:53raise as well so I think that's just

35:55going to correlate at some points um but

35:57also for a lot of other jobs. Let's say

35:59we to take legal finance and so on. I

36:02think it it really stabilizes around

36:04like um $500,000

36:08a month very very quickly. With those

36:1110x engineers, the idea is they have

36:14thousands of agents running below them

36:16doing a lot of the difficult execution

36:18work that took time. Do we just have

36:21dramatically smaller teams with those

36:2210x engineers, 10x designers, 10x

36:26finance leaders? I can definitely say

36:29that

36:31the I I I had sort of a feeling that

36:35legal

36:37customer support

36:39um is going to be mostly replaced and

36:41that's obviously one of the main u

36:43mistakes operation which we have done in

36:45the company that we didn't ramp these

36:47teams quickly. Um what we are seeing

36:50today is that like let's say our legal

36:52team is like over 10 people our customer

36:55success team is over 40 people all of

36:58them use AI heavily we like at at these

37:01professions where I say quite close

37:03today I definitely can say that uh there

37:06is I don't see any elimination it's true

37:09that probably over 60% of customer

37:12support requests especially the first

37:13line of defense can be handled with AI

37:16but when it especially comes to B2B

37:18It doesn't like it like AI just doesn't

37:20work.

37:21>> Revolute has now over 92%

37:24resolution rate on customer support for

37:26consumers.

37:28>> Pretty good.

37:29>> It's it's it's pretty good. But

37:30obviously they did invest a lot into

37:32that

37:33>> [ __ ] ton. A [ __ ] ton.

37:35>> And and but also very important the way

37:36how Nick thinks about that uh is in

37:39terms of the playbooks. We launch

37:41products, new products pretty much every

37:43week. So um we have to we have to keep

37:48update agents with all the information

37:50and so on and just due to the high

37:53velocity having um extremely smart

37:55coordinated team is is very important.

37:57That's really interesting how product

37:59velocity increases leads to harder

38:02customer support for agents.

38:05>> Of course, cuz uh the agents are as good

38:07as context and rules which they have.

38:09And if context and rules change pretty

38:11much twice a week, it gets a little

38:14difficult.

38:14>> When you look at your engineering team

38:16today, what are they on? Are they on

38:18cursor? Are they on codeex? Are they on

38:21core code? So from a period from March

38:24to June, everyone really moved to claude

38:28um including the creative team and

38:31that's where we actually started to see

38:33creative team vibe coding functionality

38:36which we don't have in production. But

38:39then we started to see that all the

38:42coders quickly moved from claude to

38:46codeex um as of mid June and um over the

38:52time especially

38:5410x creatives moves to codex as well but

38:57look I do believe that there it's it's

38:59it's cyclical so

39:01>> it's so cyclical my question to you is

39:03will we continue to see the velocity of

39:05model release that we're seeing now you

39:07in 3 years time will It be like, "Oh,

39:10Gemini this week. Oh, CL Anthropic this

39:13week, OpenAI this week." Or will we see

39:15a a reduction in model release rate? I

39:20don't think that's going to happen

39:22anytime soon. So, I believe like for

39:25example, recently OpenAI announced that

39:26they basically build OpenAI for law,

39:29>> right? But that's only V0. So over the

39:32time they also are going to try to print

39:34smaller specialized models for like not

39:37like exactly smaller uh but really

39:39specialized model for certain use cases.

39:42Um clearly like Astra excels in

39:45long-term horizon.

39:46>> Do you buy that? Like I look at that GPT

39:48for law from Astra and I'm like I'm

39:51sorry I think it's complete [ __ ]

39:53with the greatest of respects. It is a

39:55very deep functionality required to

39:58serve some of the biggest law firms in

39:59the world. like very very deep and

40:01specific functionality. It's very

40:03specific according to the different

40:04types of law as well. Plus, if you want

40:06to sell into these law firms, it's a

40:09multi-year sales cycle with some of the

40:12stodgy old lawyers and partnerships.

40:15You can't just say, "I'm open AI. Yep.

40:18We've just hacked into the Australian

40:19government, by the way, but we're here

40:21to serve your law firm."

40:24Uh, okay. Yeah. So, first of all, I

40:28think uh just uh definitely

40:31the ability to switch internal use just

40:35for internal teams outside of law firms.

40:37I think that's I think that's definitely

40:39happening. Oh, I think we both investors

40:41in company called solve intelligence.

40:43>> Love it. Yeah. Very specific. Very

40:45specific. And let me try to maybe bring

40:48couple examples

40:50>> why like solve intelligence is so

40:52special and like where like for example

40:55how we learn from this.

40:57What can happen very often is that a

41:01company want to just control the patent

41:04workflow even if they outsource the work

41:09and that's very valuable just to have

41:10one system of records. So whoever can

41:13create AI native system of records is

41:16going to win. And but going back to

41:18Hixel why it's so important for

41:19Hicksfield

41:21there are so many systems today which

41:23are used for just to store assets

41:25>> like some people use Dropbox

41:27>> some people use Google Drive

41:29>> some people are going to try to use Miro

41:32some people are going to try to use

41:33frame.io like there are many solutions

41:36but let's think about what people need.

41:38What people need, they want to be able

41:40to search contents and and marketers

41:43especially want to make sure that

41:45content is on brands in terms of the

41:47visual identity, but also like if that

41:51sort of adheres to certain brand

41:53guidelines

41:54and that's where like semantic

41:57understanding and semantic controls

42:01become finally possible. It never

42:03existed before. So in our space there

42:05are definitely other companies like

42:06Adobe and Canva who builds the best

42:10software for the pixel first era where

42:13everything was defined with pixels but

42:15that's clearly not how the world is

42:18going to work in the future. What we're

42:20envisioning and that's what everyone

42:22wants. They want to just be able to

42:24search and um like really work through

42:27the library of assets and all the

42:29knowledge through natural interfaces. So

42:33being able to own this interface and

42:36build the analytics uh like this system

42:38of records is important. That's why at

42:41Hicksfield we invests it so much in

42:43harness so that it improves over the

42:45time. And this harness also um allows it

42:51basically learns visual style over the

42:54time which let's say cloud and open AI

42:56cannot necessarily do.

42:58>> Do you believe in moes anymore? you

43:01you've been around startups for a long

43:02time. We always talked about moes and

43:04defensibility. I largely think they're

43:06[ __ ] You know, we we saw lovable

43:08when I invested. Everyone was like, "Oh,

43:10it's a rapper. It's a rapper, you idiot,

43:12Harry." And actually, it was a rapper,

43:15[laughter] but it's about speed of

43:17decision making, product execution, and

43:20building value over time very, very

43:24fast. Instinct is a rapper. Of course,

43:27it is. It's not that difficult to do an

43:28AI assistant today which why there's so

43:30many but they're building incredibly

43:33quickly very valuable features and you

43:36build it over time. Do you believe that

43:38moes actually exist really?

43:41[sighs and gasps]

43:41>> I know like you ask this everyone um so

43:44um and this is cuz this is on top of

43:46everyone minds like how to think about

43:48the metrics which matter today and how

43:50to think about the modes. So um I think

43:55um it's very difficult to figure out

43:57where the value occurs in the supply

44:00chain. Um we do believe that there are

44:04only two like ways of uh modern value

44:08creation or modes today. First is when

44:11you deliver the outcome and for us it's

44:14allowing businesses to sell more through

44:15AI ads. So that's the first thing and

44:18the second thing is network effects.

44:21Unfortunately, AI does not replace

44:23network effects. And when people talk

44:25about swarm of AI agents talking to each

44:27other, I'm not sure this is happening in

44:29the next five years. So, um, that's why

44:32it's so exciting that within Hicksfield,

44:34like we really wanted to empower

44:36community to create more projects, open

44:40source, open source them to really build

44:42a snowball where people can capitalize

44:45on each other output. This is the reason

44:47why software grows so quickly cuz it's

44:50so easy just to go and fork someone's

44:51project on GitHub. So, and like we were

44:54able to scale from basically like I

44:56don't know 10 seeded projects, open

44:59source projects like 8 weeks ago to over

45:0210,000 today like seeing these type of

45:04network effects I believe can become a

45:07mode over the time. When we look at your

45:09growth, fundraising is a big part of it.

45:12It costs a lot of money to be able to

45:14spend four million on, you know,

45:16different aspects of, you know, uh,

45:18inference band.

45:20What was the best VC meeting you've ever

45:22had?

45:23>> Obviously, um, Yuri Milner gets gets it.

45:27>> How was that meeting? Like, was it was

45:28it in person?

45:29>> Yeah, definitely in person. And

45:31definitely Yuri stays on top of all the

45:33trends. And

45:33>> how was it? Were you nervous?

45:36>> I I wouldn't say nervous. It was just

45:38more uh to see how much of the uh if we

45:43see the market the same way and I was

45:47truly surprised that Yuri deeply

45:50understands this transformation of

45:51content first and foremost. Obviously it

45:54starts with this direct to consumer AI

45:56ads. It starts with short form dramas.

45:58All these trends come from Asia to the

46:00west. And um also fundamentally

46:06we believe that most of contents on

46:09social and in the world is going to be

46:11AI assisted or AI generated

46:14and uh the and like this multi- trillion

46:19advertisement industry and you know like

46:22contextual

46:23advertisement is the main business model

46:25of the internet. It's all going to be

46:28substantially disrupted with video AI.

46:31This industry still going to be very

46:33valuable, but it's never going to be the

46:35same.

46:35>> Did you know when you left the meeting

46:37with Yuri that he was going to write the

46:38check?

46:39>> You know, sophisticated investors, they

46:41can play games. I had like so many scars

46:43like people really shook hands said we

46:45do at this price

46:48and next day what I learned is that they

46:50called other investors and they pulled

46:52the syndicates and to invest in 30%

46:54lower valuation compared to what we

46:56discussed. So like look these things

46:57just happen so you never can be sure but

47:00it didn't happen with Yuri.

47:01>> I think there's a discount placed on

47:04Higsfield because you're not Silicon

47:06Valley insider. Like let's be clear

47:07you're at a billion in revenue now.

47:10>> Yeah. If you were a Silicon Valley

47:12company, that would easily be a $25

47:15billion company growing at the rate that

47:17you're growing in 18 months.

47:18>> Yeah, you could also argue that's what

47:20cognition was valid at 50, right? So

47:22there is definitely an upside

47:23>> upper band even more. Yeah, 100%.

47:26>> So a couple things which I believe are

47:27very important. So first we build for

47:30long term. We have seen that direct to

47:33consumer space like e-commerce can be

47:35disrupted like Shopify is a great

47:36example how they become they have become

47:39infrastructure to build like direct to

47:42consumer businesses and we become

47:43infrastructure to essentially

47:46build distribution for direct to

47:48consumer businesses. That's one

47:50aspiration and second aspiration is

47:52obviously a plain like companies worth

47:54over $200 billion. It's insane. So look

47:58and as we think long term just this you

48:01know like these multiples don't don't

48:03matter that much as we know we're

48:05building long term we're going to be

48:06over 100 billion it's true that most of

48:09the people don't get the opportunity

48:11that we are going after the biggest

48:13industry in the world but I wanted to

48:15drop another another number so when I

48:18and I asked the team to double check so

48:19it's at least four people on the team

48:21who prove so it's not like random fact

48:24so I asked um When we look at public

48:28companies

48:30and we exclude pharma and big tech,

48:33spend on sales and marketing is higher

48:36than spend on R&D. Like what when it

48:39comes to sales and marketing, the goal

48:40is to deliver personalized offering

48:44which converts the best. A lot of that

48:46is human work of course, but a lot of

48:49that is going to be personalized videos

48:50in one in some shape or form. So that's

48:53why I'm saying that um many people just

48:56and that's good for us that many people

48:57don't understand the opportunity this

48:59large market which we go after.

49:01>> Can I ask you you've mentioned Asia

49:04short form dramas a lot. What percent of

49:06revenue is from Asia versus the west?

49:09>> Um so oh the west makes well over 70% of

49:13revenue. well over

49:14>> but just important to say that we learn

49:17a lot from trends coming from Asia like

49:19Hicksfield does not exist in China for

49:21example which is massive market for AI

49:24um Hicksfield uh but the largest city

49:29by usage is soul in South Korea while

49:33the largest country is obviously the

49:35United States

49:35>> what's the biggest lesson from Asia that

49:37you've learned

49:38>> there is so much IP

49:41so many products coming from Asia and

49:45they all try to figure out distribution

49:47direct to consumer. That's why they lean

49:50into the new tooling like video AI which

49:53actually helps to achieve that. That's

49:54just very different mindset. They feel

49:56that they could do they could do way

49:59better if they could establish direct

50:02relationship with customer instead of

50:03having like some other layer. That's why

50:06they go so many so much direct to

50:08consumer rather than using some resale

50:11platforms and so on. I sacrifice a lot

50:13of life for for the life that I have and

50:16the career that I have and I love it. Do

50:19you think you will one day regret

50:21spending a day with your son in 3 and

50:231/2 months? Look, this is goes even

50:25beyond that because my um

50:30from the age of 7 to 12,

50:34my mother had to work um three jobs. So,

50:38I didn't see her. My father was spending

50:40all the time with me going to all and it

50:42was I was basically minor so he had to

50:45go to all these camps with me. Um I also

50:48play checkers. I was top three in the

50:49world. So we went we travel throughout

50:51the world and um then I did programming.

50:54He spent all the time with me like

50:56really dedicated his life to me like he

50:59did sacrifice

51:01and uh since 21st he has Parkinson

51:04disease. So um

51:08even like having some ability to capital

51:10and exits cannot fully change things and

51:13um this is something which is um deeply

51:16personal obviously.

51:17>> Totally.

51:18But you don't need to do what you're

51:20doing now. Alex,

51:22>> I didn't [snorts] need to anymore

51:24either. [laughter] I still am. I still

51:26miss family birthdays. I still miss

51:28weddings

51:29cuz like mine's about a deep insecurity

51:32rooted in me being a fat kid.

51:35um why are you doing it?

51:37>> So I think Mark and Jason actually

51:39described it really well. There are like

51:40five archetypes. So obviously for me

51:42it's just huge conviction about the

51:44technology, about the market, about the

51:46opportunity and just huge fear of

51:49missing that huge fear of missing that.

51:52But remember that um my parents really

51:55taught me that um there is a place in

51:57the world where technology like good

51:59technology products matter. I remember

52:01like when I was six there was um like

52:04this I guess

52:06magazine about Bill Gates like building

52:09Microsoft and not being like very like

52:12socially accepted everywhere back then

52:15and like my mother just told me oh like

52:16these examples basically happened in the

52:18world. I think she didn't fully

52:20understand like San Francisco and

52:21Seattle are different cities but still

52:23uh this that's still deeply rooted in

52:25me.

52:25>> Childhood shape us a lot.

52:27>> Yeah. What did your parents teach you?

52:30>> For them, what was important is to

52:35just be in merit-based environment sort

52:37of um and um that's why getting to uh

52:41California felt so important.

52:44>> What's your biggest lesson on hiring?

52:46Speaking of a merit-based environment,

52:48we see a lot of uh focus on your

52:50cognitions of the world who hire mass

52:52Olympiads.

52:54>> Yeah.

52:55What's your biggest lessons on hiring

52:57effectively?

52:58>> I think one of the things why Europe

53:01thrives so much like I know that you

53:06typically say otherwise but let me just

53:08challenge you like who are the most

53:11relevant NeoClouds today? It's Nscale,

53:14iron and Nobus and Cruso.

53:18>> Mhm. Brusso okay Silicon Valley story I

53:22am from Australia and scale from the UK

53:25and anobus is UK and Netherlands let's

53:28talk about the companies on application

53:30layer they that matter I know that you

53:32mentioned Merore and you mentioned

53:36Harvey but Legora 11 Labs lovable they

53:40all deeply matter so if we just go

53:42outside of the model layer h cuz then I

53:46don't want to go into the mistral topic

53:48right but if we go cuz I think like by

53:50usage they have the numbers are very

53:53strong but people for some reason don't

53:54don't believe in that so I don't know

53:56why but public public data shows that

53:58the usage is there but on every other

54:01layer Europe is extremely competitive

54:04like ASML like without ASML this whole

54:07thing just wouldn't happen so I I think

54:09fundamentally what's matter is if if

54:10like Europe is going to figure out

54:12energy but that's goes outside of that's

54:14above my pay grade right um so very

54:16important to say here is that um now

54:20there are more opportunities to create

54:22company from um different kind of cities

54:26from different parts of the world while

54:28before it all felt extremely centralized

54:32um and we are we are excited uh we are

54:34obviously excited about that and um

54:38another thing about hiring um is that in

54:41Silicon Valley unfortunately what I'm

54:43seeing is that people just jump between

54:45jobs every two years that's why um I

54:48think Europe can be so competitive

54:51because the sense of loyalty matters a

54:54lot and that goes sort of a little bit

54:56to the childhood. We just discussed that

54:58like let's say if you're a Fulham fan

55:01you're not going to root for Arsenal

55:02just because they won or played in the U

55:06Champions League final. But in the

55:08United States, uh if uh Lakers are on

55:11the top, people are going to say, "Yeah,

55:12I'm I'm fan of Lakers because it's just

55:15makes it easier to start conversation."

55:17You know,

55:18>> when you think about your own CEO style,

55:22what's changed most

55:23>> in AI? It's so important to look at

55:27actual

55:29signals

55:30and actual adoption and having access to

55:34raw information. Um I was obviously

55:37taught the corporate school of

55:39management in the United States. Um and

55:42when I look at the CEOs whom um whom I'm

55:45learn from is obviously Jensen, Elon and

55:48Nick. Um Nick was on the show. So like

55:52obviously like those three are those

55:55three they completely abandon all the

55:57management principles. They don't

55:59necessarily are like fans of like

56:01one-on-one and like soft feedback. All

56:04of them I think are encouraged like

56:06being down to the points knowing the

56:08details while it would be called in like

56:11corporate America something like

56:13micromanagement.

56:14>> What management principle do you

56:16disregard that many people think is

56:18important?

56:19>> I do believe that it's as simple as hire

56:22the best people to do the best work and

56:24figure out how to retain them.

56:27Everything else is frankly secondary and

56:30people just create so much theory around

56:33that and and essentially there is just

56:35so many like fake rules uh which are

56:38disconnect from reality. It's it's

56:39really as simple as hire the best

56:41people, empower them to do the best work

56:43and just figure out how to establish

56:45relationship and retain them.

56:47>> A lot of them bluntly are do see dollar

56:50signs. We mentioned the transactional

56:53nature of America and secondaries are a

56:56part of that. How do you think about

56:58doing annual tenders to retain people

57:00>> across our team? Um roughly 50 are in um

57:05California. Uh maybe we're going to get

57:08to roughly 50 remotes and um over 300s

57:12in Kazakhstan. So look, I just hope

57:14we're going to print uh more dollar

57:17millionaires in Kazakhstan, in Central

57:19Asia, in this part of the world uh than

57:22any other company.

57:24>> I I I do too. Um what's the labor

57:28arbitrage on cost between Kazakhstan and

57:31the US?

57:32>> I I know that a lot of people when they

57:34look at Hixel, they think about the

57:35arbitrage first and foremost like the

57:38way

57:38>> is that not true? Look like Kazakhstan

57:41is top five in the world in physics.

57:44Like you look at the recent

57:46international physics olympiad for high

57:47schoolers like they're top five in the

57:49world on par with like the United

57:51States, China, India and this is also

57:53like the core of our team are people who

57:56won international competitions in math

57:58and physics. Um that's the first part.

58:02The second part is that about Kazakhstan

58:05is that they actually took this Soviet

58:06school of math but really upgraded with

58:09Singaporean principles and Singaporean

58:11system of education is considered to be

58:13probably the best in the world. At least

58:15many people in Silicon Valley believe

58:17that. Um and they and the government

58:19basically subsidizes for thousand of

58:22high schoolers to study abroad and many

58:25of these people come back. Um and there

58:28is strong desire just and so the just

58:30the density of talents uh definitely got

58:33there. It's uh like top 10 largest

58:35countries in the world. So over 20

58:37million population and we are also

58:39actively hiring bringing their talents

58:41from Europe from other countries in Asia

58:44and people just enjoy like some benefits

58:46like 15% personal income tax. Yeah man,

58:50it's like

58:52>> don't even get me started in [ __ ] UK

58:54will tax you to breathe. Uh, seriously,

58:57it's in the UK, you get your, you know,

59:00paycheck and then it's like, I don't

59:02100,000 and then you get the end and

59:05it's kind of like 3,500.

59:07>> But it's also English common law, so

59:09it's not like that bad as people think.

59:11Uh,

59:14you move it. Let's swap places. Do you

59:16have a mega pad in Kazakhstan?

59:19>> No, I don't. I don't own any property.

59:21>> What? Why?

59:23>> Remember that I come from Asian family.

59:25So um whenever we sold the company, I

59:29made over a million dollars and I spent

59:31all this money buying apartments for my

59:35parents, relatives, my wife parents cuz

59:39it's just part of the culture and the f

59:40like extended family is not small by any

59:42means. Uh but look, it's just part of

59:44the culture to give back. And then um

59:48when it comes to the family, especially

59:51to my parents, they obviously sacrificed

59:53a lot. So I I felt like I had to give

59:54back at least at least like things like

59:57monetary things which I which I could do

1:00:00but I drive like Tesla Model 3 like and

1:00:03I le so like I I'm not like a guy who's

1:00:06going to just show up with Lamborghini

1:00:08or Porsche.

1:00:09>> Do you invest? We mentioned solve

1:00:11intelligence. um when before I did that

1:00:14but now I spend roughly 90 hours a week

1:00:1980 90 hours a week on Hicksfield. I try

1:00:23to spend ideally

1:00:26um at least um 3 hours a week with my

1:00:29wife at least 5 hours a week with my

1:00:33son. Um sometimes I do the catch up

1:00:36because when I travel um for a week, for

1:00:39two weeks, for three weeks, then I try

1:00:40to take Sunday off to spend the whole

1:00:43day with my son. And over the last 3

1:00:46months, yes, I was able to find one day

1:00:47when I spent like end to end with my son

1:00:49without emails, without talking to

1:00:53without talking to the team members. I

1:00:55get in trouble for this, but I think

1:00:57there's no um shortcut to hard work. The

1:01:00harder I work, the luckier I get. I meet

1:01:02more founders. I find more great

1:01:04companies. I do more shows. I have more

1:01:06success. Do you buy the [ __ ] of the

1:01:09balance and uh oh, it's okay. You can

1:01:12leave at 5 and be home for bath time and

1:01:15crush it. This is a good question. So,

1:01:16look, obviously um being an immigrant, I

1:01:18always have that I have to prove like

1:01:20that I belong, right? So, I hope that I

1:01:23feel like now people accept people

1:01:24recognize that Hicksfield is probably a

1:01:27top 10 um application AI companies by

1:01:29revenue, probably number one. But I

1:01:32think when it comes to um hard work like

1:01:35the people whom we know in common like

1:01:38we we talked about like let's say Peter

1:01:40Salis like legend in the cons in

1:01:43consumer space obviously Jack look I I

1:01:48spent decent amount of time with them

1:01:49and other product leaders at stamp like

1:01:51the density of product talent and stamp

1:01:53was unprecedented all of them work

1:01:55really hard all of them are smart I I

1:01:59like none of them just uh checks emails

1:02:03for five hours a day and and calls it

1:02:05work. Each of them is deeply rooted into

1:02:08the recent trends in product product

1:02:10design activation. They know data really

1:02:13well. So yeah, I don't believe that

1:02:15there is any shortcut to hard work.

1:02:18>> 3 hours a week with your wife. Yeah,

1:02:22I don't know about you, dude. Mine would

1:02:24dump me for 3 hours a week. How do you

1:02:27make marriage [clears throat]

1:02:28work [laughter and gasps] on three hours

1:02:31a week?

1:02:32>> Yeah, look, I'm I'm I'm I'm very um I'm

1:02:34I'm very grateful for my wife for being

1:02:36patient, you know. It's also very

1:02:38different if that's like Asian culture.

1:02:41Uh it's just kind of more natural to try

1:02:45to do sacrifices for each other sort of.

1:02:48Um, and I'm deeply I'm obviously deeply

1:02:51grateful for her for supporting me. But

1:02:53like sometimes at this scale I get

1:02:55invited to parties. I always send her

1:02:58some and don't show up myself. I don't

1:02:59know if I piece people off, but this

1:03:02happens um very frequently.

1:03:05>> So wait, you say yes and then she goes,

1:03:07"Yeah, I say maybe we both can some come

1:03:09together." Then there is always some

1:03:11urgent fire last minutes and my wife

1:03:13just goes. [laughter]

1:03:15>> What fire was most urgent? What was the

1:03:19Oh [ __ ]

1:03:22Yeah. Look, I think obviously for all

1:03:23the things which we touch base earlier

1:03:25whenever we are not very good in

1:03:28communicating the features or we felt

1:03:30like I mean now it's like team of 40 so

1:03:33now the life is way better but early

1:03:35days obviously I was involved in all the

1:03:36fires. Um I think recently um all the

1:03:40types of like attacks on AI companies.

1:03:43It's crazy. It's like it's like LLMs are

1:03:47being used to hack companies. It's like

1:03:51new types of LLMs to do some frauds, you

1:03:54know, like basically bots using credits

1:03:57and then doing auto refunds. All of

1:03:59that. Look, I like since I have like

1:04:02kind of machine learning background

1:04:03myself, data science backgrounds, I

1:04:05still can move a needle substantially

1:04:07when it comes to statistics and data. So

1:04:09yeah, I have to be involved somehow. But

1:04:11like these LLMs, they they amplify many

1:04:14types of behaviors including various

1:04:16types of attacks and fraud, but and we

1:04:19have to fight against that. Uh we're

1:04:21going to do a quick fire around. So I

1:04:23say a short statement, you give me your

1:04:25immediate thoughts. What have you

1:04:26changed your mind on most in the last 12

1:04:29months?

1:04:30>> Oh, I was thinking that HubSpot is going

1:04:33to get obsolete. Everyone is going to

1:04:35build their own CRM and but when

1:04:37especially when as we hire and scale B2B

1:04:39go to market team just having familiar

1:04:41interface matters a lot.

1:04:44Wow. I would still say they're going to

1:04:46get [ __ ] You think that just

1:04:48stickiness is there with SMBs?

1:04:51>> Yeah, I I I do think so. And especially

1:04:53I see that when I hire go to market

1:04:55talents.

1:04:55>> Wow. Why? Like what is it about hiring

1:04:57them that makes you think that just

1:04:59they're so used to it?

1:05:00>> I mean like people who are very good in

1:05:01understanding customers and talking to

1:05:03customers they may not just simply

1:05:05accept new interface so quickly and just

1:05:07having HubSpot as a system of records

1:05:10being able if if there is any mismatch

1:05:11going able to just understand where the

1:05:14data flow went wrong. I think that's

1:05:16just still very valuable like the

1:05:17familiarity. What do you believe today

1:05:20that everyone else thinks is [ __ ]

1:05:23crazy? I mean, look, I think uh people

1:05:25just still don't fully appreciate that

1:05:27most of the content on social media is

1:05:28going to be AI generated. There are

1:05:31going to be some shows like obviously

1:05:32yours where it's like authentic

1:05:34contents. It's going to be 10 15x higher

1:05:37CPM whatsoever than AI generated

1:05:38contents. So it's going to be it's going

1:05:40to be way less in terms of like content

1:05:43create by but it's going to create way

1:05:45more value uh than a generated content.

1:05:48But even when I look into your content

1:05:50specifically like you made multiple very

1:05:54successful shorts millions of views

1:05:58better than anyone else in this space

1:06:00and you do a lot of overlay. While the

1:06:04content is authentic, I think we should

1:06:06do better job so that you use Hicksfield

1:06:08at least for the overlay on top of

1:06:11existing videos. Dude, I would love

1:06:13that. I mean, again, they take 3 hours.

1:06:16So, people don't know this. I spend 2

1:06:18hours a day just doing Instagram. Now,

1:06:20we decided that Instagram short form is

1:06:22going to be a big new push for us. Um, 2

1:06:24hours a day just for me. I write the

1:06:26scripts and then I record them. And then

1:06:28it's two people, six hours per one for

1:06:32those three.

1:06:33>> And that's extremely smart of you. Like

1:06:35you know like going back to some of the

1:06:38topics is like clipping is like a huge

1:06:41topic and that's like has its own

1:06:44upsides and downsides. But obviously

1:06:46everyone sees this opportunity to win to

1:06:49build massive top of funnel like

1:06:50hundreds of millions of views with short

1:06:52form content. as long as you can have

1:06:55downstream monetization like or value

1:06:57creation like you do.

1:06:58>> Totally agree with you. What job today

1:07:01does not exist that will be big in 5

1:07:04years? Okay. So in 5 years people

1:07:07especially in our space creative

1:07:10directors they are going to be talking

1:07:12to computers and generating stories real

1:07:15time and video and AI is going to help

1:07:17to create multiple variations. Today

1:07:20there is no word to really describe that

1:07:22because there is also there are script

1:07:23writers um there are then uh

1:07:27screenwriters like those who are going

1:07:29to break it down shot by shot then there

1:07:31are people who do that storyboarding

1:07:34then there is like people who person who

1:07:36oversees all of that like movie director

1:07:39and so on. There are so many there are

1:07:42so many there are so many parts of that

1:07:45but eventually taste is going to matter

1:07:47a lot and just having stories to tell

1:07:50there is no word to describe it today.

1:07:52>> Who do you not have on your board that

1:07:55you would most like to have on your

1:07:57board? Maybe out of like more

1:07:59professional CEOs, I'm definitely Frank

1:08:01Slutman because going back to the point

1:08:04I was just curious all the time, does no

1:08:06[ __ ] culture exist in California or

1:08:10not? Can it allow to scale companies so

1:08:13quickly? Can it is it possible to build

1:08:16successful enterprise go to market

1:08:18motion with no [ __ ] culture? And

1:08:21when I read his ampitab book like book

1:08:23called amp it up, I realized it's

1:08:25possible. So like I'm a huge fan. I

1:08:26watched all his interviews.

1:08:28>> The challenge with him, he's amazing.

1:08:30He's the best leader by far. But the

1:08:32challenge is you can sometimes do it at

1:08:35the sacrifice of product advancement.

1:08:38And so he built a GTM machine at

1:08:40Snowflake, but data bricks wiped the

1:08:43floor because they move product as the

1:08:46priority, not GTM. And that was

1:08:48dangerous. I preferred Chad Pets. Do you

1:08:52know Chad Pet?

1:08:53>> Oh, dude. This guy is no [ __ ] I'll

1:08:55introduce you afterwards. He's the best

1:08:56sales leader in the world. Um, and he is

1:09:00no [ __ ] [ __ ] Unbelievable.

1:09:02>> And we probably should have him on

1:09:03board. [laughter]

1:09:04>> Oh my god. I find any way to have him on

1:09:06board. He is terrifyingly good. Um, so

1:09:09what's the biggest lesson from Snap?

1:09:12>> The momentum doesn't last forever. Um,

1:09:14like today, Snap market cap is is below

1:09:1715 billion. There are a lot of mimis on

1:09:19the internet, but this is a great

1:09:20company. cares so much about trust and

1:09:22safety and experience and it puts it

1:09:24first.

1:09:24>> Do you think it is a great company? No

1:09:26offense. It's like it's been mismanaged

1:09:29as [ __ ] Its SBC is through the roof.

1:09:33It's tough to say it's a good company.

1:09:35>> That's why I say that momentum doesn't

1:09:36last forever. When Snapchat was worth

1:09:39eight $80 billion and the gap with Meta

1:09:43was less than 10x, then it felt, oh, we

1:09:46just go explore. we we just we just

1:09:49really must lean in. Um but momentum

1:09:51doesn't last forever and that's my core

1:09:53learning. So that's why like while we do

1:09:56have the positive momentum, we don't we

1:09:58do not take this for granted. Clearly um

1:10:00like the nature of uh capitalism is

1:10:03there are ups and downs and since we're

1:10:05building long-term, we just should

1:10:06capitalize on the opportunity like with

1:10:08the fundraising and just keep pushing

1:10:10progress every day. What is the reason

1:10:13why the divergence between Meta's market

1:10:16cap and Snap's market cap has increased

1:10:18so significantly if there was one

1:10:20reason? Just maybe saying this trait, a

1:10:24lot of public companies

1:10:26did not figure out their AI story.

1:10:30Um, Snap unfortunately is part of that.

1:10:33We have seen other great companies like

1:10:35Figma trying to tell their story. You

1:10:38mentioned Canva. It's not necessarily

1:10:41easy to be successful in private markets

1:10:43and public markets. And Zach is one of

1:10:47the best CEOs of all time cuz he managed

1:10:50that. He's such a [ __ ] beast. He's

1:10:53such a beast. You watch him last night

1:10:54with the event and you're just like,

1:10:56"Ah, [sighs]

1:10:57now I get it." Like that totally makes

1:11:00sense. And you know what? Scale with

1:11:02Alex Wang. I was one who was like really

1:11:06like what's gonna

1:11:08he he he basically acquired a second CEO

1:11:12you know Alex is now the CEO of Muse and

1:11:15he's crushed it crushed it. What an

1:11:18effective buy for 0.5% of your market

1:11:22cap. Do you know what I mean?

1:11:24>> Yeah. Look, but this happens with

1:11:25Instagram with WhatsApp. That's why I'm

1:11:27saying that we just maybe should put

1:11:30Meta a little bit in its own league.

1:11:33Yeah, but he got rid of Cyrum and Kger.

1:11:36Here he's been like, "No, no, no. You,

1:11:39Alex Wang, are my guy."

1:11:41>> Do you see what I mean?

1:11:43>> Yeah. The best talent hire.

1:11:45>> Look, I I do believe that it's a little

1:11:47bit early to look at whole Meta AI

1:11:49initiatives. We probably need to see

1:11:51like year of like successful launches

1:11:54and so on to and then we can look back

1:11:56and see what was good, what was not

1:11:57good. But at least the consistency of

1:12:00storytelling and explaining what he is

1:12:02doing to public investors being able to

1:12:05articulate why Muse is so different

1:12:09um is phenomenal.

1:12:11>> Okay.

1:12:13Revenues are a billion. What are the

1:12:16revenues in 12 months time?

1:12:19>> Our current business model uh projects

1:12:23uh 4.5.

1:12:26It says by the end of the next year but

1:12:28this basically involves substantial

1:12:31deceleration and that's what like just

1:12:33my finance team like there are couple

1:12:35strong quant people they told me that's

1:12:37just how the business works but look we

1:12:39are still pushing to grow at least 30%

1:12:42month over month what do you think it is

1:12:44they said 4.5 what do you think it is

1:12:47this is me to you not me to your finance

1:12:48team

1:12:49>> over 10

1:12:50>> over 10

1:12:52>> let me tell you why like in a lot of

1:12:56adoption in creative AI space is driven

1:12:59by monetization like all these direct to

1:13:03consumer brands making more ads and also

1:13:07having like the aspirational

1:13:11um cinematic AI content as this inspires

1:13:14creatives to explore the tooling.

1:13:18I it feels to me that Hollywood starts

1:13:23to embrace AI

1:13:27mostly today as a way to as a tool for

1:13:32hybrid production as a just new form of

1:13:37CGI.

1:13:40But the sentiment really shifted from

1:13:42like strictly negative

1:13:45to neutral to slightly negative. And in

1:13:48private conversations, yes, there are

1:13:51maybe more than half of sier talents who

1:13:53is going to say we're anti-AI forever.

1:13:56But increasingly there are more and more

1:13:58people who are actually asking a

1:14:01question. Can we tell more stories with

1:14:03AI? Can we overcome certain budget

1:14:06limitations which existed before? And

1:14:08maybe AI can help to tell new stories

1:14:10which we couldn't tell before. And I do

1:14:12believe this just change in perception

1:14:14that's at least comes from my

1:14:16conversations is extremely is extremely

1:14:18is extremely positive. If you are in a

1:14:21billion today, 10 billion in 12 months.

1:14:25Where do you peg the next fund raise?

1:14:27You know, if you're in a billion, say

1:14:29conservative multiple, you'd be like,

1:14:31you know, 15.

1:14:33Um but if you're hitting 10 next year

1:14:35you're like paying end of year it's like

1:14:3980. Look we are not chasing just the

1:14:41valuation cuz again the goal is just to

1:14:43make sure that the company can be uh

1:14:46sustainable over the time in public

1:14:48market. So there is a lot of company

1:14:49building to be done beyond just uh

1:14:51chasing the revenue. But I just do

1:14:53believe

1:14:53>> do you want to be public? Huh?

1:14:55>> Do you want to be public at some point?

1:14:56Yeah, I do believe that Hixfield has

1:14:58great potential to be bigger than

1:14:59Applain and Shopify because

1:15:01fundamentally like building is one part

1:15:03of that Shopify one layer of

1:15:05infrastructure. Then for coding there is

1:15:07obviously like a cloud,

1:15:10there is codeex but what matters is

1:15:12distribution over the time but

1:15:13distribution matters. You know that this

1:15:15better than any other

1:15:17>> business that's why we do what we do.

1:15:19>> Yeah,

1:15:20>> exactly.

1:15:21>> Dude, I cannot thank you enough for

1:15:22being so amazing on the show. You've

1:15:24been fantastic. I've loved doing it, you

1:15:26can tell. And you've been an amazing

1:15:28guest. So, I really appreciate you

1:15:29joining me today.

1:15:30>> Thank you so much. It's a pleasure.