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Sourcery with Molly O'SheaSourcery with Molly O'Shea

Inside fal’s $1.65B Series E & F Led by Sequoia & Bessemer

fal has raised $1.65B in a combined Series E and Series F, with Sequoia Capital leading the E and Bessemer Venture Partners leading the F. Its founders see generative media as 1 of only 3 product market fits in AI, alongside search and coding agents. Burkay Gür (Co-Founder and CEO) and Gorkem Yurtseven (Co-Founder and CTO) started fal in 2021 after building machine learning infrastructure at Coinbase and Amazon. fal runs the infrastructure behind image, video, audio, and 3D models, serving developers, studios, and the model labs themselves. Recorded at Dalida in San Francisco, Burkay and Gorkem explain how fal moved from Python data infrastructure into generative media, why video and image now split its revenue 50/50, and how demand kept rising as video prices jumped more than 10X. They also cover fal’s 150+ person org with only 2 reporting levels, the compute shortage shaping model launches, and why they believe the margin in AI sits with compute providers over applications. “AI really has 3 product market fits. One of them is search. The other really clear one that’s growing really fast now is coding agents. And the third one is, we believe, generative media.” We cover: › The $1.65B Series E and F › Why individuals spend $10K to $20K a month on generative media › Veo 2 pricing and the shift to professional ads and film work › Super ICs and hiring for agency, hunger, and humility › World models, open source, and Hollywood adoption Burkay Gur: https://x.com/burkaygur Gorkem Yurtseven: https://x.com/gorkem Molly O’Shea: https://x.com/MollySOShea Sourcery: ⁠https://x.com/sourceryy 𝐄𝐏𝐈𝐒𝐎𝐃𝐄 𝐋𝐈𝐍𝐊𝐒 Apple Podcasts: https://podcasts.apple.com/in/podcast/inside-fals-%241-65b-series-e-f-led-by-sequoia-bessemer/id1738124704?i=1000799593284 Spotify: https://open.spotify.com/episode/3VK5PQL6fOYrVJg1NSFCeB?si=p7mPqMsvSz-A5Xgh5DnTiQ 𝐒𝐏𝐎𝐍𝐒𝐎𝐑𝐒 • Brex—The modern finance platform, combining the world’s smartest corporate card with integrated expense management, banking, bill pay, & travel. https://brex.com/sourcery • Harvey—The AI platform built for legal work, is helping the best firms and companies close deals faster. More than 200,000 lawyers, 80% of the Am Law 100, and over 20% of the Fortune 500 build with Harvey. Visit: http://harvey.ai/ • Zone—develops next-generation data center campuses, partnering with AI companies, site developers and technology leaders to bring compute online faster and at scale. Visit: https://zonefrontier.com • Mercor—The human intelligence behind frontier AI. Frontier labs and leading enterprises use Mercor to turn the knowledge of 5M+ experts into the data, evals and benchmarks that make AI better at real work. Visit: http://mercor.com/sourcery • VCX—VCX is the public ticker for private tech, allowing investors of all sizes to invest in venture capital. View The Portfolio at http://GetVCX.com • Deel—Deel is the global people platform that helps startups hire, manage, pay, and equip anyone, anywhere. Trusted by more than 35,000 fast-growing companies, Deel is the people platform that just works, so teams can scale without the chaos. Visit: https://www.deel.com/sourcery • Public–Investing platform Public just launched Generated Assets, which lets you turn any idea into an investable index with AI. With Generated Assets, you can build, backtest, refine, and invest in any thesis with AI. Gone are the days of one-size-fits-all ETFs. https://public.com/sourcery Follow Sourcery for the latest updates! https://www.sourcery.vc Disclosure: Paid Ad. Agents are an experimental AI tool. Its output may be inaccurate and is not investment advice. Public Holding’s, Inc. makes no guarantees about its accuracy or reliability—verify independently before use. Brokerage accounts and services provided by Open to the Public Investing, Inc. See additional disclosures at https://public.com/disclosures/agenticdisclosures. 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Burkay Gur & Gorkem Yurtseven, Co-Founders at fal (00:48) Why fal stopped chasing headlines (03:15) Why generative media is AI’s next big market (04:51) The infinite demand for AI videos (09:35) The future of AI-generated ads (13:19) How fal pivoted into generative media (18:02) Hiring people who live and breathe AI (23:01) Where AI meets Hollywood (24:29) From friends to Co-Founders (27:48) What a “Super IC” actually is (35:58) The hardest part of staying at the frontier (37:42) Why compute is the bottleneck for new models (41:55) Why AI models have such weird names (43:56) The story behind the name fal (45:12) Why open source isn't going anywhere (49:02) "AI has had zero impact on Hollywood" (53:39) Waiting for AI's Toy Story moment (56:58) Why Jack Dorsey is underrated (58:55) Why the application layer has no margins

Molly O'SheahostGörkem YurtsevenguestBurkay Gürguest
Nov 3, 20261h 3mWatch on YouTube ↗

Summaries, chapters, transcripts, and FAQs on this page are made by uListen, not YouTube.

EVERY SPOKEN WORD

  1. 0:00 – 0:48

    Burkay Gur & Gorkem Yurtseven, Co-Founders at fal

    1. MO

      You guys have the coolest brand on Earth. Like, you guys are growing really fast. You keep on raising more and more money.

    2. GY

      AI really has three product market fits. One of them, we believe, is generative media.

    3. BG

      Three years ago, no one really understood what that meant. Three years down the road, and it's a whole new industry.

    4. GY

      You sit down in front of a computer, and you generate videos all day, and you spend $10,000, $20,000 a month.

    5. BG

      And we were like, "Okay," like, "Who the heck is gonna pay all this money?"

    6. MO

      [laughs]

    7. BG

      Turns out there is essentially infinite demand.

    8. GY

      Every big company is gonna be a compute company, just the storefront is gonna be different. Our storefront is unique and interesting. No one else has a storefront similar to us.

    9. MO

      Were you surprised by how much money there is behind this?

    10. GY

      100%. [gentle music]

  2. 0:48 – 3:15

    Why fal stopped chasing headlines

    1. MO

      Okay, cool.

    2. GY

      All right.

    3. MO

      Gorkem.

    4. BG

      Wow.

    5. MO

      I already- [laughs]

    6. BG

      You don't need- You really don't need to. [laughs]

    7. MO

      Gorkem.

    8. GY

      Yeah.

    9. MO

      Gorkem.

    10. GY

      Yeah.

    11. BG

      Wow.

    12. MO

      Try not-

    13. GY

      That was the- Last one was very good.

    14. MO

      That was the best one?

    15. BG

      Very good. Very good.

    16. MO

      Okay.

    17. GY

      Yeah.

    18. MO

      Gorkem.

    19. GY

      Yeah.

    20. MO

      Gorkem. Now I'm just gonna keep-

    21. BG

      Yeah

    22. MO

      ... on saying it.

    23. BG

      Yeah, that's-

    24. GY

      [laughs]

    25. BG

      ... perfect.

    26. GY

      Teaching AI. [laughs]

    27. MO

      Where is my camera? Internet, you've been pronouncing Gorkem completely wrong. It's Gorkem.

    28. GY

      [laughs]

    29. MO

      Burkay.

    30. GY

      AI, learn from-

  3. 3:15 – 4:51

    Why generative media is AI’s next big market

    1. MO

      you gotta focus. Okay, so let's talk about the evolution of the business today. Generative media has taken off in AI. It's probably one of the coolest parts about AI, [laughs] seeing all these videos, being creative, all the creative stuff getting made. And so I don't know who wants to take it off, but feel free to just kind of, like, where are we right now, and why has this just kind of exploded?

    2. GY

      Yeah. The, the way we talk about it, AI really has, like, three product market fits. One of them is search, and, like, initial ChatGPT consumer product, and now Google and Gemini. The other really clear one that's, like, growing really fast now is coding agents. And the third one is, we believe, is generative media. And to put things in perspective when we talk about it, uh, with coding agents, an individual can go spend maybe $10,000, $20,000 a month on, on their Claude or OpenAI subscription. Generative media is the only other thing this exists. An individual, if this is your job, if you work at a AI native studio or, or a real studio these days, you sit down in front of a computer, and you generate videos all day, and you spend $10,000, $20,000 a month. This is the only other use case in AI that an individual can spend that much money. And search is so big that it gets a pass, and there is no other use case or part of the industry that has this dynamic. It's just coding and generative media, and that's why you are seeing all this growth, because people, more people are adapting it, and individually, they are spending so much money on it.

  4. 4:51 – 9:35

    The infinite demand for AI videos

    1. MO

      I know this is kind of an elementary question, but did you, like, were you surprised by how much money there is behind this?

    2. GY

      100%. [laughs]

    3. MO

      Okay. [laughs]

    4. GY

      We, we were too. Like, we, we thought, like, uh... We, we were surprised all along the way. We s- we were surprised how quickly we switched from image models to video models, where, like, now, uh, we make, I would say, 50% of our revenue from video models. And then after that transition, we thought, like, video was gonna completely take over, right? It, it was zero, and then it reached to around, like, 70, 70% of our revenue. And then we thought image models are, like, done, and it's like-

    5. MO

      [laughs]

    6. GY

      ... it's, it's not, it's not enough compute. It's, it's a simple problem, and from now on, it's all gonna be video. And then turns out image editing models brought life back to image models. And then it's also useful for video models as well because it, you can create different frames, and video models does such, such, such better job interpolating between those frames. So image editing all of a sudden made image models come back, and now, like, our revenue's 50/50. E- even that was surprising, right? Like, uh, when we thought, like, a portion o- of our business was dying, but it, like, made a huge comeback. Uh, but yeah, video models keep getting better. Y- yeah, like, currently what we can do, like, the best video model maybe creates 10 to 12 seconds of video. Like, the new ones are gonna reach maybe, like, 30 seconds. But until you can generate, like, real-time streaming, very high-definition video, this is gonna keep increasing. And the- As, as you increase the resolution, as you increase the duration, the cost increases exponentially because you have to have more attention in the, in the inference, during inference time, and the resolution increases the amount of pixels exponentially, so things are getting much more expensive. So now we have a framework to think about, okay, there's a lot more money to be made going forward because the, the video, the video output we get is still, like, very rudimentary.

    7. BG

      There was also, like, a very interesting point sometime last year when, uh, Veo 2 came out. Um, I think at the time, like, Kling was probably one of the best video models, and, uh, there was also, like, Minimax, and the prices were, like, somewhat kind of going lower. I would say, like, they started at, let's say 40 cents per five-second video, and then they sort of settled around there, maybe, like, 30 cents for a five-second, like, clip. And then Veo 2 was, very surprisingly, it was, like, $5 per five-second video. So it jumped like, you know, more than 10X, and we were like, "Okay," like, "who the heck is gonna pay the, pay all this money?"

    8. MO

      [laughs]

    9. BG

      Right? Like, this is just another five-second video. Turns out, like, there is essentially infinite demand, so people, people started using that model, and then even more expensive models came around, and people started using those models. And what was really happening under the hood was, um, we actually saw, like, more and more professional use cases start, start happening for, like, ads, like, real, you know, people who are in marketing and, like, ads and actually, like, movie production, they started using these models because the quality went so, so much higher, and there was just more demand that, like, other models weren't able to actually capture. So it's, it's like the usage is going up, and then the capabilities are going up, and then everything's getting more expensive over time. So essentially, yes, like, people ended up spending so much more money. And, and, and in terms of progress, it's still happening. Like, we're nowhere near, um, you know, with LLMs I think people talk a lot about hitting a wall or, like, diminishing returns. I think in this side of the market, we're, like, nowhere near, uh, actually hitting, like, diminishing returns. I think there's just so much new stuff happening with, like, CDAS V2 and V2.5. Uh, it's just like the quality's going up, prices are going up, and people still wanna use all these models.

    10. MO

      For sure. I mean, do you guys remember Sora?

    11. BG

      Yeah.

    12. GY

      Yeah.

    13. MO

      [laughs]

    14. GY

      Feels like it was a lifetime ago, but [laughs]

    15. MO

      [laughs] I know. No, I mean, that was a really cool moment, but clearly too expensive. [laughs]

    16. BG

      Yeah, it was too expensive. Like, in fact, we saw... We, we, we also hosted Sora on our platform, and, like, we saw huge growth in the, like, last three months before, um, they decided to, like, take it down. We were like, "What's happening?" Like, there's, like, infinite, uh, volume here, essentially. Uh, but yeah, turns out, you know, the rumor is, like, there wasn't enough compute for everything else. Um, and it's also expensive to run, so, so yeah. But I think it had a great fu- future. Maybe, maybe it'll come back sometime.

  5. 9:35 – 13:19

    The future of AI-generated ads

    1. MO

      I mean, it's really interesting 'cause earlier today we were down at AppLovin, and, you know, this will probably get released later than that, but I was talking to Adam, the CEO there, and Gio, their CTO, and we were kinda talking about how they've created their algorithms for all the ads and what works best, and, like, the best ads are, like, actually 60 seconds long. And I was like, "Okay, well, are you thinking about using, like, a Higgsfield or any of these other kinda companies out there, and offering that or acquiring any of these kinds of, like, generative ad platforms?"

    2. BG

      Mm-hmm.

    3. MO

      'Cause some of them just really, I mean, most of these cases are ads. And, like, it just, it's just really not there yet. It's like f- the best they can do is, like, five seconds, and it's like not every brand can do that.

    4. BG

      Yeah.

    5. MO

      Um, in terms of where you guys came from and where it's going, advertising is gonna be the biggest use case, you think?

    6. GY

      I like ads.

    7. MO

      [laughs]

    8. GY

      Like, a- as, as a business.

    9. MO

      [laughs]

    10. GY

      Like, internet runs on it, and also, like, the content these models are able to make is, is perfect for these UGC-style ads because the, the content is usually throwaway. If it doesn't work, make a new one. Like, it works great. But also, like, the, the quality, like, the, the lighting, nothing has to be perfect. Uh, it needs to be almost looks, needs to look homemade, so it's, like, easier to do. And, you know, yes, the, the best ad may be 60 seconds, but you don't have to do a continuous 60-second generation. You can do, do fast, small cuts, which works maybe better in, in, in certain cases. And then what you can do is you get a lot of data back, right?

    11. MO

      Hmm.

    12. GY

      That's, uh, I don't think anyone is particularly doing a good job at it, but if, because you can post these ads and see how people are interacting with it, you can have, like, continuously improving, or you can do small changes to the-

    13. MO

      Totally

    14. GY

      ... to the, to the-

    15. MO

      Yeah

    16. GY

      ... what you are, what you are posting and just show more of the things that are performing well, and this can all happen automatically, right? So, uh, th- this is, I, I think it works great with video models. No one has done it perfectly, but we would love to be part of this, like, automated ad story. I think it's, it's, it's a g- it's gonna be a great business.

    17. MO

      It was really funny. I, I was, I was gonna mention this in the interview-

    18. GY

      [laughs]

    19. MO

      ... but then I realized it was probably kind of inappropriate. But, like, the kind of, the, the, the example you just mentioned of, like, reinforced learning models-

    20. GY

      Mm-hmm

    21. MO

      ... on themselves and, and just kind of optimizing over and over again, there was, and I don't know if you guys see this in your Instagram feed, but I do this because I make clips and I make, like, stuff for the internet-

    22. GY

      Yeah, yeah

    23. MO

      ... and they're like, "One of the best hacks that I had was showing my armpits."

    24. GY

      [laughs]

    25. MO

      Like, I got so many more views from showing my armpits. Honestly-

    26. GY

      As, like, the, as the hook, right? [laughs]

    27. MO

      Literally. Literally. It's like I got 10X views just from, like, having my arms up, and, like, and I was like, "What the hell?" [laughs]

    28. GY

      Yeah. Imagine like you can have yourself in 100 different positions [laughs]

    29. MO

      [laughs]

    30. GY

      And then let's go. [laughs]

  6. 13:19 – 18:02

    How fal pivoted into generative media

    1. BG

      the norm.

    2. MO

      So let's talk about the engine behind Fal, and then we can kinda get into how your culture is keeping up with the-

    3. GY

      Yeah. [laughs]

    4. MO

      ... growth 'cause it's crazy. But so you were saying you started off with compute. You started off as a compute company. You found your way into this area space.

    5. BG

      Sure.

    6. MO

      What was that evolution like for you-

    7. BG

      Mm-hmm

    8. MO

      ... and like how have you completely embraced this new category?

    9. BG

      Right, yeah. So we started in compute but like a pre-AI era let's call it, right? So we started in 2021, and the whole thesis was like, okay, there's, you know, this previous data, um, infrastructure wave led to some amazing companies like Snowflake, Databricks, and what do they have in common? They own compute.

    10. MO

      [laughs]

    11. BG

      You know? So in order to build the next generation of like the data infrastructure company, we also have to focus on compute. That was the initial thesis, and we thought the next big thing is gonna be Python. So, uh, SQL, basically like what Snowflake did and what Databrick- Bricks did to a certain extent was like enabling SQL to run on, you know, in the cloud, and they, they own the compute and they let you scale the compute elastically. So we, we thought Python is gonna be the next thing because AI is the next big thing. So we ended up working with some really awesome teams like trying to bring their data science and machine learning, old terms, like workloads into the cloud.

    12. MO

      Mm.

    13. BG

      So that was like our initial starting point. And then s- soon after we started, um, some insane things happened in the market. I think from a technology point of view, like we had a massive breakthrough with ChatGPT and in our space Stable Diffusion and, and DALL-E. So we kinda had like, uh, we, we kinda had a choice at that point, like we can either stick to our like, you know, data infrastructure, um, you know, initial idea or we could go into this like new world of AI. And there was language models, and at the time I think people were very excited about a bunch of open source models. Uh, if you remember, like Llama came out shortly after and there was this like huge, uh, price race to the bottom, and we just thought, "Okay, we're like five, six people right now. We don't have that much money in the bank, you know, we're a pretty early company, and you know, we can't really like go and, you know, sell tokens at a negative margin. And let's do something more interesting." You know, the trend still holds, um, where the trend still holds is let's focus on image models 'cause like there's something interesting happening there as well. There was no sight of video models. Like no one even talked about it I think back then.

    14. MO

      Mm.

    15. BG

      But we thought that like we could take image models and, um, make them run a lot faster, uh, in the cloud, uh, on the same infrastructure that we built, and so we, we did that. So we had a very technical team. We thought like we could do a really good job running these models faster. I don't think we had a very formed thesis on like where those models are gonna go.

    16. MO

      Mm.

    17. BG

      So we kept like, we kept doing it and, uh, that got a lot of attention from like very early customers. And we thought, "Okay, you know what? Like with this other business we, we ran it for a year. We didn't make a single dollar. With this one our revenue's starting to go like this. There's clearly something here." So we decided to focus on the, uh, image model space there. And, um, w- three to six months later roughly, we actually realized that like this trend is probably much bigger and we thought that these models are, um, like useful for very different things than, than what LLMs are helping with. You know, we thought it'll help with ads and marketing. We thought it's gonna be... The buyers are gonna be completely different. So we decide to call it something else, to like just be able to tell the story to people. So we actually came up with this word generative media, and we, we slapped it on our website-

    18. MO

      [laughs]

    19. BG

      ... and we said, "Fal, the generative media company." And we always had to explain what it is 'cause I think, you know, three years ago no one really understood what that meant. Um, so yeah, that's, that's kinda like how we got into it and how we sort of found our, our like niche. Uh, which, you know, three years down the road it's more well-established and it's a whole like new industry so.

    20. MO

      And people wanna make feature films out of it.

    21. BG

      Exactly.

    22. MO

      [laughs]

    23. BG

      Yes, yes. I don't think- I think like when you first play with the first DALL-E, like you would never think, "Okay, like we could make movies with this." But it took only like-

    24. GY

      Came here very, very fast.

    25. BG

      Yeah.

    26. GY

      Yeah.

    27. BG

      We came here really fast.

    28. MO

      [laughs]

    29. BG

      It only took like 18 months that people started thinking, "Oh, like this could actually make feature

  7. 18:02 – 23:01

    Hiring people who live and breathe AI

    1. BG

      films."

    2. MO

      So we're in San Francisco. People here are super nerdy. Maybe you guys are too, but like w- as you're growing super fast in this inflection period, how are you keeping ahead of research? Like are you paying attention to research? Like what are you- how are you learning as you go?

    3. GY

      I- in the beginning-

    4. MO

      'Cause it's new

    5. GY

      ... we were extremely involved obviously. Like we, we had a very small team, and we had to be part of every single little edge we can make. Um, and then nowadays, uh, we have a really talented leadership team that, um, s- most of them we worked with before, uh, in, in different companies. So I would say that's, that's the best part of our job, that we can rely on these leaders. And that- that's a superpower to ask their opinion, what they think about, like, what's coming new, what they pay attention to. And a lot of the information I get these days is, is from, from that team. I'm also very curious to read everything, but I get- I learn more from the leadership team than, than the internet these days.

    6. MO

      Oh, that's pretty good. What about you?

    7. BG

      Um, yeah, I think just to echo what Gorkem said, uh, it is- it's very important, like, how you build this team. I think I, I got a lot of inspiration from my days at Coinbase in terms of, like, recruiting the earlier team.

    8. MO

      Mm.

    9. BG

      I think one of the things we did, we did there- So I, I joined Coinbase 2016. Team was, like, almost 100 people, uh, maybe. Like, very early days. Similar to, like, our size now. We're, we're like 150 people, uh, today. And one of the things they did was, like, they had this, like, um, crypto quiz that they posted on Reddit, right? And, and one of the things they assessed for was, like, your n- literally, like, your knowledge about crypto. Like, how much do you know about it? You know, bunch of technical terms. Also pre-AI, like, you know, it's- it was hard to find the answers to these questions. But, like, there was literally a quiz, and you had to pass this quiz to, like, actually get on a phone call with someone on the team.

    10. GY

      How crypto pilled are you was the-

    11. MO

      [laughs]

    12. BG

      Was the, was the-

    13. MO

      Oh my God [laughs] .

    14. GY

      [laughs]

    15. BG

      So, so this was, like, I think this was, like, a very key factor to, like, you know, Coinbase's success in the long term 'cause, like, you really want, like, these, like, fanatics about the space so that, like, they can keep things going 'cause, like, you know, most of the time, like, the whole job becomes about, it becomes, like, staying up to date.

    16. MO

      Yeah.

    17. BG

      Right? So, like, how do you stay at the forefront? One, like, you could collect really great people and, you know, get them on your team, and hopefully they'll feel motivated. Or you can get people who are, like, already motivated. This is all they can think about. This is the thing that they're, like, most excited about today. If you didn't hire them, they would be doing this anyways. So, like, that was literally, like, and it still is, this is kind of the criteria we have for, for bringing in in the or- early hires 'cause, like, it helps with just naturally, like, we're up to date all the time. Um, and I think it, it helps with people being just, like, constantly motivated about, like, what they're working on. So, like, you know, you don't have to try to do that. People are already, like, AI pilled or gen media pilled.

    18. MO

      This episode is brought to you by Brex, my favorite. You become what you spend on, and I refuse to spend my time on work that shouldn't exist. Expense reports, receipt chasing, and manual closes, the companies building what's next, from Vercel, OpenAI, Anthropic, Granola, and Deepgram all made the same call. They all run on Brex. Brex is the intelligent finance platform that combines cards, expenses, and banking into a single stack with agentic finance built in. AI agents that handle expenses automatically enforce policy before spend happens and close your books in minutes. That's why Sourcery runs on Brex, so I can spend time on building and not busy work. It's time to get Brex AF. Learn more at brex.com/sourcery. That's B-R-E-X.com/S-O-U-R-C-E-R-Y. Bye. From handshake to signature faster, Harvey, the AI platform built for legal work, is trusted by 80% of the Am Law 100 and over 20% of the Fortune 500. Learn more today at harvey.ai. That's harvey.ai. AI needs more than chips. It needs power, land, and infrastructure. Zone develops next generation data center campuses, partnering with AI companies, site developers, and technology leaders to bring compute online faster and at scale. Zone is building the foundation of the AI frontier. Visit zonefrontier.com to learn more. That's zonefrontier.com to

  8. 23:01 – 24:29

    Where AI meets Hollywood

    1. MO

      learn more. So where are you hiring people from? Like, what were their roles? 'Cause this is all, again, this is all-

    2. BG

      Yeah

    3. MO

      ... very new. I... And this is a common question I ask in these conversations 'cause it's like I have to remind people, like, none of this really existed like three or four years ago. So not many people are trained.

    4. GY

      And, and it was, even in the beginning, it was an overlooked market because it happened at the same time with, with the LLM growth.

    5. MO

      Yeah.

    6. GY

      Like, maybe if it happened by itself, it would have been the hottest thing, but it was, like, the second hottest thing. Uh-

    7. MO

      It's also hard because it's the creative industry. It's kind of at odds with technology to some degree. It's like-

    8. GY

      Th- there is a lot of technology involvement in creative industries, right? Like, even an IMAX camera is, like, you know, size of that table over there.

    9. MO

      [laughs]

    10. GY

      Like, th- there is technology involved and in, in different parts of, like, animation, filmmaking, bigger parts, and then it plateaus. And maybe we are going through one of those times where in the next five to 10 years it's gonna have another outsized impact, uh, on, on the creative industries. Um, but yeah, like AWS, AMD, they have massive, like, media businesses where they sell to, to studios and media companies. So this, this is just the next wave of it.

    11. MO

      Yeah. I think I was listening to something on Christopher Nolan and his-

    12. GY

      [laughs]

    13. MO

      ... his infrastructure [laughs]

    14. BG

      [laughs]

    15. GY

      [laughs]

    16. MO

      Where he's keeping all that IMAX film.

    17. BG

      Yeah, 900 pounds. That's the-

    18. MO

      So heavy.

    19. BG

      Yeah.

    20. MO

      [laughs] Which is funny.

  9. 24:29 – 27:48

    From friends to Co-Founders

    1. MO

      Um, okay, so let's talk a little bit about the team and how you guys all gathered together. So you came from Coinbase.

    2. BG

      Mm-hmm.

    3. MO

      You came from Amazon?

    4. GY

      Amazon, yeah.

    5. MO

      Yeah. So how did you guys meet, and what was kind of like the early culminations of gathering together a group?

    6. BG

      You, you go. [laughs]

    7. GY

      So we are both Turkish, but-

    8. MO

      Really?

    9. GY

      ... we, we didn't, we didn't know each other back home. Um, we met in San Francisco, like, when I first moved here. 2014 is immediately I met him. Uh, we were both working at different companies, uh, doing, you know, minding our own business, and it was actually during COVID time, uh, we... This- We've told this story a couple more times before.

    10. MO

      [laughs]

    11. GY

      But we, we rented a house in Palm Springs, and that's when, um, we first thought, "Okay, maybe there's, like, a working chemistry b- between us." And then it took us maybe another year to, to start the company, but that's, that's how we met. We were longtime friends for over 10 years, uh, never worked together until that COVID, uh, Palm Springs house, and, and here we are. We've been building the company for five, six years now.

    12. MO

      And you've been pulling people from Coinbase, from Amazon.

    13. GY

      Coinbase and Amazon.

    14. MO

      Really?

    15. GY

      It's, it's funny.

    16. MO

      Oh, damn. [laughs]

    17. GY

      It's, like, from both places. Exactly.

    18. MO

      And so what, what were you, like, looking for in the early days? What were the types of hires?

    19. BG

      Um, I would say, like, just really good people [laughs] in, in, in, in many ways. Like, I think one is obviously, like, they have to be excellent technically. I think early on, like, we, we, we, we definitely had to get the technical part right, um, because, like, we were just doing a lot of, like, deep technical work. We still are, but that- the- at that time, that was our, like, differentiator. Um, so people who are really good at, you know, very good technically, I would say also, like, people who are just, like, people that we get along with. I think, like, early team chemistry is, like, really, really important. Um, I think if you can... Like, figuring out, out, like, product market fit is probably the most challenging thing for a startup, right? So, like, you have to, in our mind, I think you have to keep a pretty small team, and that can work really, really well together, that can, like, stick together and, um, just have a really, like, great way of experimenting with ideas and then, like, throwing things that don't work and, and just, like, keep moving and keep looking for-

    20. GY

      I think this is one of the biggest advantages of starting a company in your mid-30s because you have your whole working life, people you worked with, you trusted, you didn't trust-

    21. BG

      Yeah

    22. GY

      ... that you can, you can bring in together, and as soon as, you know, uh, you reach out to people, y- y- y- if, if you have so- something worthwhile, they, they come and join you, and they wanna be part of the story. And if you are, like, y- younger, you, you unfortunately don't have that, and you have to just go on your own and try to hire people and get trust. But here, we have bu- built trust with so many people that early engineering team was almost all people, like, we knew before and worked with and trusted. Helped navigating the problems

  10. 27:48 – 35:58

    What a “Super IC” actually is

    1. GY

      so much easier.

    2. BG

      Yeah.

    3. MO

      What are your views on scaling up or keeping flat?

    4. BG

      Um, so culture-wise, like, our company, I would say, is, like, flatter than your run-of-the-mill [laughs] uh, startup. Um, I think, like, the deepest level we have is, like, two, um, levels of, like, reporting. Uh, and yeah, I think that's, that's by design, I would say. Um, we, we, we really believe in this, like, Super ICs. I think it's becoming more popular. Now it has a name. I don't think we had a name.

    5. MO

      Really?

    6. BG

      Yeah. [laughs]

    7. GY

      Yeah, we-

    8. MO

      It's just Super IC?

    9. BG

      Super IC.

    10. GY

      We got a Super IC.

    11. BG

      Yeah.

    12. MO

      So we have forward-deployed-

    13. BG

      Yeah

    14. MO

      ... deployed agen-

    15. GY

      [laughs]

    16. MO

      Uh, not agents. Is it agents?

    17. BG

      Engineers.

    18. MO

      Oh, engineers.

    19. BG

      It'll be agents soon.

    20. GY

      Yeah, yeah.

    21. BG

      Forward-deployed agents.

    22. MO

      No, I mean, actually-

    23. BG

      Forward-deployed engineers

    24. MO

      ... no, because I know talent here quite well.

    25. BG

      [laughs]

    26. MO

      So we have forward-deployed engineers-

    27. BG

      Yes

    28. MO

      ... and now we have Super ICs.

    29. BG

      Super ICs.

    30. GY

      Super ICs.

  11. 35:58 – 37:42

    The hardest part of staying at the frontier

    1. BG

      well.

    2. MO

      What have been the biggest surprise challenges that you guys have come across? And not just, like, the standard stuff, but it could just be, like, something you had no idea you were gonna expect.

    3. BG

      I'd say, like, being at the forefront, like, as a company is, is pretty hard. I think, one, you have to really want it.

    4. GY

      Mm-hmm.

    5. BG

      Right? Like, you have to wanna build a big, big company. And two, you have to, like, keep up with it, right? Which requires a lot of effort. So [laughs] that... I, I think in order to, you know, build a, build a big startup, you really have to just be very committed and be very hungry. [laughs] And, and I think, like, that's, you know, that, that definitely... that's a lot of work. You know? That, that takes, that takes a toll on you. I think Adam would say this probably.

    6. MO

      [laughs]

    7. BG

      Uh, you know. Uh, but, but yeah, it, it just requires a lot of hard work.

    8. MO

      I don't know. I asked him what he's most excited about. He says he doesn't feel excitement, so [laughs]

    9. BG

      [laughs] So he's beyond that point.

    10. MO

      You're, you're gonna get there one day. [laughs] No emotions. [laughs]

    11. GY

      Our industry, like, ke- is, is incredibly dynamic, right? Um, it's when there's no, like, good model for a month, everyone starts panicking.

    12. MO

      Oh.

    13. GY

      "Oh my God, it's been one month, nothing new has happened." Uh, but the answer is just wait another month, and something incredible is gonna happen. It's, it's been true since, since we started, and I- that keeps excitement going, and that keeps people, like, very encouraged that, okay, there's gonna be the next model that's gonna unlock these other use cases, which is gonna unlock these other types of customers, and that has brought so much growth to our business. And so far, it just keeps happening for us.

  12. 37:42 – 41:55

    Why compute is the bottleneck for new models

    1. MO

      Where have you been seeing some of the best models come from? Do they just come out sporadically? Like, what it- is there any consistent theme between all of them?

    2. GY

      So right now, we, we play a bigger role in the ecosystem because we are an infrastructure partner for the model providers, but we are also the, the API partner for the application layer or studios, people who are consuming the models. So we got a good sense of supply on demand on, on both sides. And we are in such a compute constraint moment right now that, um, if you are coming up with a new model, you really have to plan ahead of time. You have to make sure you have enough compute to run inference. If, if you are successful, you are likely not gonna be able to procure it on your own, so it's usually a good idea to partner up with an inference provider like us to, to, to do your launch. So we work very intimate with the, with the model providers. And, and for, for customers, like they usually use multiple models at the same time, but they really care about model releases because they usually tie their product releases with model releases as well. So we, we keep in very close communication with them as well. Um, and then we have to sometimes anticipate how much growth is gonna come on the, on the model side to, to procure our own compute, to maybe do some, some investment on our side to enable the whole space. And someone really has to do that because otherwise, all the compute is gonna go to coding agents or whoever, whoever-

    3. MO

      Hmm. [laughs]

    4. GY

      ... is, like, doing the bigger investment. Uh, by, by planning ahead and investing on compute a little bit, we, we, we can enable both the model providers and the applications being built on it. So that has become our responsibility in the ecosystem these, these couple months because compute is so hard to come by.

    5. BG

      I'll add a few more points. I think generative media is, like, very unique in the sense that in, in two ways. One, I think it's- there's just more modalities. I think when people think of gen media, maybe they only think of video.

    6. MO

      Yeah.

    7. BG

      But, like, you would have to think about text to image, image editing, video, 3D, text to speech, uh, you know, speech to text. Um, I don't know if I said 3D, like, for, for gaming use cases, whatever.

    8. MO

      3D, 4D, 5D.

    9. GY

      [laughs]

    10. BG

      Exactly, all of these.

    11. MO

      [laughs]

    12. BG

      So, so there's actually, like, many modalities within generative media. So, like, and there's, like, I don't know, five to 10 companies in each of these sort of subcategories within gen media. So that's, like, one interesting aspect. So there's just, like, a lot more happening. There's more fragmentation. And then the other is, um, going back to what I said about, like, diminishing returns. I think it's still, like, so early in the space that there's actually a lot more leapfrogging happening-

    13. MO

      Hmm

    14. BG

      ... between more players because, like, we're not at, like, the 9- 98 point whatever percent. We're only at, like, I don't know, 50% of, like, you know, where we're ultimately gonna go. So there's actually more people leapfrogging. There's more diversity in, in, like, the number of labs. So it's not just, like, OpenAI, Anthropic, OpenAI, Anthropic. You know?

    15. MO

      [laughs]

    16. BG

      It's, it's more like there's, like, eight, you know, players, and they're all, like, leapfrogging each other, which is, which is very-

    17. GY

      Like, one big reason is we are not running out of data. There is, like, so much video data out there and so much new video data is being produced every day that no one has- no one claims to train all, all of that data, right? They have to pick... The, the hard problem with training a video model is actually you have to filter out the interesting video data so that you, you make a, you make a good model. So if you, if you need to put more data in- into the model, there is abundance, and it's, it's seemingly free. There- it's- there's enough free video out there that, uh, it works very well. But also, the problem space is bigger, right? You are trying to reason around, like, the whole world, physics, everything. So it's not as specific as coding or, like, as specific as c- certain LLMs are trying to optimize around. So there's a lot more angles people can take to, to create a more interesting and appealing model, and it's, it's been happening every other month

  13. 41:55 – 43:56

    Why AI models have such weird names

    1. GY

      almost.

    2. MO

      I think that's where the naming comes in too.

    3. GY

      There you go.

    4. BG

      [laughs]

    5. GY

      You have to differentiate with your naming. [laughs]

    6. MO

      I mean, they've got some funny names.

    7. GY

      Yeah. Happy Horse, NanoBanana. What, what's, what, what do you like?

    8. BG

      Uh, there's Happy Oyster.

    9. GY

      Happy Oyster.

    10. BG

      It's, like, a game model.

    11. MO

      Aw.

    12. GY

      Um-

    13. BG

      There's Minimax

    14. GY

      ... C-Dance, C-Dance, and C-Dream.

    15. BG

      C-Dance, C-Dream.

    16. GY

      I, I think those are-

    17. MO

      I think-

    18. GY

      Yeah

    19. MO

      ... when NanoBanana had, like, its moment and every- everyone was talking about NanoBanana for, like, three days, even my dad was talking about NanoBanana-

    20. GY

      [laughs]

    21. MO

      ... I was like, "What the hell is going on here?" [laughs]

    22. BG

      I think, I think the backstory is that, like, it was an anonymous model. So, like, people do these, like, cloaked releases-

    23. GY

      Yeah

    24. BG

      ... where they will release a model.

    25. GY

      It was a code name.

    26. BG

      They won't actually disclose-

    27. GY

      Yeah

    28. BG

      ... who it is. Yeah, they, like, it's a code name. Get, like, people excited about it 'cause it, like, ranks highly, and I think somehow they used that, like, NanoBanana as a placeholder name, code name.

    29. MO

      Yeah.

    30. BG

      And then it just, like, stuck, so it became the original name.

  14. 43:56 – 45:12

    The story behind the name fal

    1. BG

      theme.

    2. MO

      Well, how did you come up with the name FAL?

    3. BG

      All right, so FAL-

    4. MO

      We have to talk about this.

    5. GY

      Yeah, yeah.

    6. BG

      So FAL is, um... So w- we have a longer name, Features and Labels, and it's the-

    7. GY

      It's a double entendre.

    8. BG

      Yeah.

    9. GY

      Yeah.

    10. MO

      Wow.

    11. BG

      And it's, it's, uh, it's the initials of Features and Labels, which-

    12. MO

      And it's not fal, it's FAL.

    13. BG

      It's FAL.

    14. GY

      Mm-hmm.

    15. MO

      [laughs]

    16. BG

      Yeah, it's FAL. It's FAL. Um, yeah, it's Features and Labels, which if you're a real OG, you would know those two terms. It's, like, from the machine learning world. It's, like, very old, like pre-AI. Um, and so it's the initials for that, but also it's... I don't know, have you had Turkish coffee?

    17. MO

      Yeah, I have.

    18. BG

      Okay. So have you been, like, uh, told your fortune from, like, the coffee grounds?

    19. MO

      No.

    20. BG

      All right, so that's FAL. It's like, like, your fortune told by the coffee grounds, that... You should, you should get it done. We'll do it sometime.

    21. MO

      Okay. Okay.

    22. GY

      [laughs]

    23. MO

      Let's do it.

    24. BG

      So, so, you know, it's like your future prediction-

    25. GY

      Like, someone looks at, like, when you finish the coffee, looks at what's, what's remaining-

    26. MO

      Are you guys trained in that? Can you tell fortunes?

    27. GY

      You know, no- everyone's trained in Turkey. Everyone has done it before.

    28. MO

      Really?

    29. BG

      Yeah, yeah.

    30. GY

      Yeah.

  15. 45:12 – 49:02

    Why open source isn't going anywhere

    1. GY

      Yeah.

    2. MO

      Do you think the open source, closed source debate is still gonna be going on in two to four months, six months? How long do you think that's gonna keep on going?

    3. GY

      I think so because, like, a- anyone who's falling behind a little bit, uh, op- open sourcing their model is an incredible marketing tactic, and a- the whole world starts talking about you if you open source your model. And there are real advantages, p- p- like, if you talk to anyone, any of these studios in Hollywood, they, they wanna be able to fine-tune the model. They wanna be able to, uh, a- apply different, uh, LoRAs, filters to change the lighting, to be able to use their unreleased characters, their unreleased, um, styles on the models, and the only way to do that is, is, is open source. So there are real technical advantages to it. Also, it's an incredible marketing tactic, um, so people are gonna s- just gonna keep doing it. Some- someone's gonna create the frontier model. People who are falling behind is, is gonna open source it, and the pendulum is gonna keep, keep swinging. We thought this was a bigger threat to our business in the beginning because we were running mostly open source, and then we started partnering up with closed source models and running inference for them as well. So it's, uh, it's a win-win situation for us, but open source makes it a more interesting ecosystem, and I think it's, it's good for the industry in general, so we always welcome more open source models, and we support as much as we can the community.

    4. BG

      I think the fact that, like, people wanna protect their IP and, like, that, that's gonna continue, right?

    5. MO

      Mm-hmm.

    6. BG

      Not just for media, but also, like, you, you wanna protect your source code, you wanna protect your customer lists, whatever, right? Like, all of that, that's always gonna be a thing. So I think, yeah, and if, if you have something local, you know, you don't have to worry about it. So I think, I think it's, it's always gonna be there, this, this debate.

    7. GY

      Like, when we talk to our customers, if they know the model is run on our GPUs, they, they trust the whole process so much more. Open or closed, this, this works the same, but that, that has been a big differentiation for our business, uh, when... And o- obviously, when it's open, like, they know for sure that they can even sell for us, but they, they trust us because we can do it better than them. Uh, but, uh, trust is, is really important with, with video models especially because people don't want their unreleased content to, to leak, and it's usually an input to the video model when these creatives work with it. Uh, so we've been really pushing on that with, with the enterprise customers, and it resonates.

    8. MO

      How good is AI at real professional work? Mercor's AI Productivity Index, or APEX, tracks it with every major frontier model release. Built with partners like Harvey and Cognition, see where the frontier actually stands at mercor.com/apex. Today's episode is sponsored by VC-X by Fundrise, the public ticker for private tech, allowing investors of all sizes to invest in venture capital. Learn more at getvcx.com. Public is a multi-asset investing platform where you can automate your investing with AI agents. Create your own custom agent with a simple prompt. Agents can monitor markets, execute trading strategies, and manage your cash across stocks, bonds, options, crypto, and more. Wanna buy at market close and sell the next day's open? Just tell your agent. Automate your investing at public.com. Founders scale faster on Deel. Set up payroll for any country in minutes. Hire anyone, anywhere. Get visas handled fast and get back to building. Visit deel.com/sourcery. That's D-E-E-L.com/sourcery.

  16. 49:02 – 53:39

    "AI has had zero impact on Hollywood"

    1. MO

      Talking macro, what do you think the biggest question is that people aren't asking right now?

    2. GY

      I would... Not exactly that, but if you, if you listen to, like, any Hollywood podcaster or look at the news about Hollywood, they always have this angle of, "Oh, AI's gonna kill Hollywood." Uh, and we are, like, really i- in this business, like, we, we talk to these people all the time. So far, I would say AI had zero impact on anything in Hollywood. It's just, like, people are projecting to the future a little bit. Um, the, the business we get from Hollywood has been increasing in the last six months. I think it reached to a meaningful percentage of our revenue. But I would say it had zero impact on the, on the labor. It barely made any content that's worthwhile in, in Hollywood. It- it's gonna happen. There are signs of it, but we've been talking about it for four years, and it's always been fearmongering and, and nothing real. I think that's the thing that people don't realize from a distance.

    3. MO

      Yeah, most of LA, like, that's where-

    4. GY

      People... Yeah

    5. MO

      ... this is happening.

    6. GY

      Yeah.

    7. MO

      It's really regulatory in what they're doing there with different laws.

    8. GY

      Exactly.

    9. MO

      Like, it really has nothing-

    10. GY

      And, and that's been the reason the adoption has been slow, because it's, it's not only, uh, the, the quality of the models. The, the lawyers of the companies need to be okay with it as well. But finally, slowly, we are seeing that everyone is, is going full force at it.

    11. MO

      What do you think people should be asking? What are... What's, what's going on out there?

    12. BG

      I think people are asking everything. [laughs]

    13. MO

      [laughs] They're asking too much.

    14. BG

      They're asking too much. Um, yeah, I, I don't know. There is, there's a lot of skepticism, I think, about use cases. Again, like, just speaking more broadly, uh, about AI, right? There's, there's certain skepticism about, um, oh, like, all of this CapEx investment, where is that gonna go? And, like, you know, like, is AI actually useful? But I think, like, I'm, I'm more on the optimistic side. I, I don't think... A lot of the people who are making these claims, I don't think they, they really actually understand, like, what's going on, how, like, AI is actually being used at companies. And, and, you know, I think gen- gen media is still, like, still very early. If anything, like, it's, it's just starting. It's just, like, gonna... I think ads, we're starting to see it. Hollywood, social, whatever, we're starting to see it. Um, I think there's, there's real value. Like, we were just estimating, like, how much faster we're going. Like, our, um, software engineering team is moving.

    15. MO

      Mm.

    16. BG

      And I estimated somewhere between, like, 3 to 4X. Like, we would, we would need... Pre-AI, we would literally need, like, four times the people to go at the same speed. So, and we're definitely paying less than that.

    17. MO

      Yeah.

    18. BG

      Right? So it- there's clear ROI for us. Uh, so, so yeah, I think, I think if you, if you really, like, go deep into the ROI for the, for the companies who are using AI, like, if you really go deep and actually try to measure it, uh, not read it, like, from bunch of reports or, like, I, I don't know, non-technical people, uh, I think, I think you'll see a different, different picture. So, so yeah, I'm very f- in favor, like, very, uh, long compute-

    19. MO

      [laughs]

    20. BG

      ... as, as they say. Um-

    21. GY

      Yeah. Like, we talk about it. Every big company is gonna be a compute company, just the f- storefront is gonna be different. Our storefront is unique and interesting. Like, no one else has a storefront similar to us. Some has coding agents. If you go to a Neo cloud, there is no storefront.

    22. MO

      [laughs]

    23. GY

      If you go to Databricks, whatever, it's about, like, data. Everyone has a different story and different way to talk about it, but behind the scenes, it's who's running the most compute. That's, that, that brings the revenue. And y- yeah, we have a unique, interesting storefront, and it, it keeps growing.

    24. BG

      Yeah. And there's not enough of it. Like, all the numbers you hear, you know, like 200 billion in, you know, CapEx and whatever, like, a lot of that stuff is, like, not built yet. So-

    25. MO

      Yeah

    26. BG

      ... like, it's very slow, right? Like, building these things is, takes a lot of time, and there's not enough compute right now.

    27. MO

      So I have two final questions, and then I'm gonna ask you what your hot take is. I'm letting you know that now so you can, like, get it-

    28. GY

      Think about it

    29. MO

      ... prepped.

    30. GY

      [laughs]

  17. 53:39 – 56:58

    Waiting for AI's Toy Story moment

    1. MO

      we get there-

    2. GY

      Yeah

    3. MO

      ... what are you most looking forward to in the next 12 months?

    4. GY

      I'm looking forward to the first timeless AI content, and the way I define this is you and I, we are gonna watch this, and it's gonna be a year later, we're gonna watch it again, and we are still gonna like it the same way we like it today. So far, AI content has been about capabilities. It's been about, oh, look, this is possible to do with a video model, or that's possible to do with, like, an image model. But we need to reach to that point where things look fresh all the time, and I, I'm looking forward to that. I think we are. In the next year, there's gonna be a widely accepted maybe a short movie, maybe a, animation that people are gonna look back at it in a couple years, and they are still gonna enjoy it the same way they are enjoying it today.

    5. MO

      That's pretty good.

    6. BG

      Evergreen content.

    7. GY

      Yeah.

    8. BG

      It's very important.

    9. MO

      I try to do that, but-

    10. BG

      There you go

    11. MO

      ... it's hard.

    12. GY

      Like, people say-

    13. BG

      [laughs]

    14. GY

      ... Toy Story is, is that for computer graphics. Like, that was the first time... We, uh, it came out in 1996, something like that.

    15. MO

      Isn't it crazy-

    16. GY

      Like-

    17. MO

      ... how long ago that was created?

    18. GY

      And, you know, we can just go watch it, and it's gonna be good.

    19. MO

      Yeah.

    20. GY

      It's... I'm gonna enjoy it.

    21. MO

      Did you like The Odyssey in that way?

    22. GY

      I haven't seen it yet. He has. Yeah.

    23. MO

      [laughs] You haven't seen it?

    24. BG

      No. I-

    25. MO

      All you've been doing is talking about Christopher Nolan.

    26. BG

      I'm, I'm-

    27. MO

      You haven't seen it?

    28. BG

      I'm, I'm waiting for the IMAX, the big IMAX.

    29. MO

      [laughs]

    30. BG

      And I couldn't, I couldn't get a ticket yet. Yeah.

  18. 56:58 – 58:55

    Why Jack Dorsey is underrated

    1. MO

      Okay, so Sourcery is very proud to be sponsored by Brex. I don't know if you know what Brex is, but-

    2. GY

      Of course. Yeah

    3. MO

      ... Brex knows what you are.

    4. GY

      Proud users. Yeah. [laughs]

    5. MO

      [laughs] So, uh, they're all about spending smarter, moving faster, performance, a performance corporate card. I like to take this question in the personal bend things. I mean, you guys have worked for legends, whether it's Bryan Armstrong, Jeff Bezos. I mean, you like chips, so you might like Jensen Huang.

    6. GY

      Mm-hmm.

    7. MO

      I don't know. And then you also like Christopher Nolan. So I'm curious, who are some figures that really inspire you and keep you motivated? Who keeps you, like running at it?

    8. GY

      Great question. Um-

    9. MO

      Could be your dog. I don't know.

    10. GY

      Of, of the, you know, previous generation of founders, I think Jack Dorsey is very un- underrated. Um, I think he has, uh, more range than, than anyone else. And, and then, like now he's, he's like maybe, may- maybe coming back. I think w- watching, uh, his, his journey with Twitter and then Square, I, I never got to work closely with him, but that- that's a, like, inspiring figure. When I was younger in Silicon Valley, I always like look up to what, what he was doing. Yeah.

    11. BG

      Um, I wouldn't say it like keeps me going-

    12. GY

      [laughs]

    13. MO

      [laughs]

    14. BG

      ... level, but like, I really like Ali, Ali Gotsi.

    15. MO

      Mm-hmm.

    16. BG

      Um, I think he's like transformed his company many times, like over time. I think, like I like to think-

    17. GY

      Keeps reinventing. Yeah

    18. BG

      ... keep- keeps reinventing, and, um, I think that's very important. I think that's like a secret to building a long, you know, long-lasting company, and, and navigating through, like all these changes that are happening. Uh, so yeah, really great leader, I think.

    19. MO

      The time

  19. 58:55 – 1:03:33

    Why the application layer has no margins

    1. MO

      has come. What is your hottest take right now?

    2. GY

      [laughs] Do you have it?

    3. BG

      No.

    4. GY

      Nah. [laughs]

    5. MO

      [laughs]

    6. BG

      There's so much pressure. [laughs]

    7. GY

      [laughs]

    8. MO

      [laughs]

    9. BG

      Well, we can, we can take our time.

    10. GY

      Yeah.

    11. BG

      Right?

    12. GY

      Let's take our time.

    13. BG

      And then just-

    14. GY

      You can cut it, cut it. [laughs]

    15. BG

      And then it's like dark outside. [laughs]

    16. MO

      [laughs] Should we prompt you with some, like, uh-

    17. GY

      No, no. Let's, let's do it.

    18. BG

      I have a good one.

    19. MO

      It could be-

    20. GY

      You need to be-

    21. MO

      ... on like IPOs.

    22. GY

      Yeah.

    23. MO

      It could be on, like, uh-

    24. BG

      Ooh

    25. MO

      ... companies disappearing. It could be on fake GPUs that people sell for-

    26. BG

      Oh, wow

    27. MO

      ... random, I don't know where they get them. Could be...

    28. GY

      Yeah.

    29. BG

      I had a hot take like a year ago, uh, the OpenAI-Meta merger.

    30. GY

      Yes.

Episode duration: 1:03:36

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