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TBPN is a live tech talk show hosted by John Coogan and Jordi Hays, streaming weekdays from 11–2 PT on X and YouTube, with full episodes posted to Spotify immediately after airing.
Described by The New York Times as “Silicon Valley’s newest obsession,” TBPN has interviewed Mark Zuckerberg, Sam Altman, Mark Cuban, and Satya Nadella. Diet TBPN delivers the best moments from each episode in under 30 minutes.
You're watching TBPN.
Speaker 2:Today is Thursday, 05/14/2020
Speaker 1:May 14.
Speaker 2:Six. We are live from the TBPN Altar Down, the temple of technology.
Speaker 1:The fortress of finance. The capital of capital.
Speaker 2:Oh, thanks.
Speaker 1:Hey, Ben.
Speaker 2:That was Ben. Yeah. Indispensable.
Speaker 3:We got
Speaker 1:multiple Ben. Remember? Over
Speaker 2:here. We have a great show. It's Cerebras Day on the show. Cerebras IPO, we'll talk about that. Semi Analysis has a fantastic deep dive on the company.
Speaker 2:We'll go through that. Doug O'Laughlin from Semi Analysis joined the show. Then The Laughlin. The founder and CEO of Cerebras is joining the show. But we have lots more folks joining.
Speaker 2:Amy Reinhard from Netflix, the president of ads. Can you imagine? I didn't think, you know, you think about presidents. The president, you think president of The United States. I think president of ads.
Speaker 1:That's
Speaker 2:right. Ben Eric Vishria Steve Vassallo. We got a bunch of folks coming on the show today. It's going to be a fun one. So there's a ton of news.
Speaker 2:Let's start with Cerebras. The IPO has gone spectacularly well. Cerebras doubled their valuation basically overnight. Brandon Grell had the good fortune of writing up some of the details of the Cerebras news in the newsletter today, tbpn.com. Can go sign up.
Speaker 1:Yeah. Chip company. Right now, it's sitting at a $64,000,000,000 market cap.
Speaker 2:Yeah.
Speaker 1:And a lot of the prediction markets, they didn't even have a category above 50. Right? A lot of people were just kind of trading or or betting.
Speaker 2:And when I wrote the newsletter Friday, Monday, I I said a $50,000,000,000 IPO and was sort of being optimistic, and it beat those expectations, which is great news. They deserve it. It's true overnight success. We'll show you some charts of the valuation. Lots of troughs of disillusionment, But Andrew and the team powered through and wound up finding the perfect application for their technology at the perfect time during a mega cycle, which we'll go through.
Speaker 2:So chip design company, Cerebras, if you don't if you're not familiar, they make a big big chip, big chip company instead of The biggest chip. Instead of taking the wafer, putting a bunch of chips on it, cutting it up into smaller chips, they use the whole wafer. It's a genius idea. It's one of those simple ideas taken deadly seriously in some ways. But it's trading at $350 a share on its first day of public trading, which values the company much higher.
Speaker 1:$3,300 now.
Speaker 2:300. Yeah. $300. Okay. So It was.
Speaker 2:Okay. It was up $3.50. It has since sold off slightly. And and they raised around $10,000,000,000. I think they were targeting 6,000,000,000 at one point.
Speaker 2:They've they've upsized that. I think it was $3,000,000,000 raise initially, but they have a good amount of money in the bank now. The price in this IPO has been literally up only. On Monday, the price range was $1.50 to $1.60. Then they raised it that was up from $1.15 to $1.25.
Speaker 2:And today, we're seeing, you know, much higher prices.
Speaker 1:Go back to that picture.
Speaker 2:Who's in the picture?
Speaker 1:At the Nasdaq? Picture at the Nasdaq.
Speaker 2:Of the Cerebras team standing on stage.
Speaker 1:Someone should make a set in LA. You know they have those fake private jets sets? Imagine if entrepreneurs could have a set where they put their logo in the background, like they're hitting it with a hammer and there's confetti going
Speaker 2:Yeah. Yeah. But it's for your course. Yeah. Yeah.
Speaker 2:And and you walk right from there to
Speaker 1:You have have 1,000 in your mastermind.
Speaker 2:Yeah. Yeah. No. I had this I had this idea back in the day when do you remember the ice cream the ice cream museum? This whole thing?
Speaker 1:Oh, yeah.
Speaker 2:So there were there there was this trend. I mean, really bad news for the museum industry, but they're getting eaten alive. And so some entrepreneurs, I think they did very well, started something called the ice cream museum, which was not really a museum in the sense of like a presidential library or, you know, the the Norton Simon or the Getty or the know, Natural History Museum. It was more of like an experiential place to go and hang out. Good for first dates.
Speaker 2:Good for, you know, taking kids maybe. And they would maybe give you some ice cream, but most of the most of the museum was just like very Instagrammable things. So there would be like a ball pit or a bunch of raining confetti and stuff and a huge a huge fabricated statue of ice cream that was not a piece of art that would be sold. Yes.
Speaker 1:Sprinkle ball pit.
Speaker 2:There you go. That sounds real. I don't know if that's it is real, but it sounds very believable.
Speaker 1:No. They have.
Speaker 2:They have that? Okay. Yeah. So and and there were a number of other kind of copycats that were trying to jump on and do like, oh, we'll do like the waffle museum or something or the pancake museum, you know, because they just wanted to cash in. And and my idea was just the museum of Instagramable objects and so it would have all of those.
Speaker 2:So there would be a private jet set and then there would be a Lamborghini set and this one would fit right in. So it's just they have a big pink wall so you can go take the pink wall photo. And then there's a beach and then there was a gym with fake weights so you could go and look like you're maxing out and benching 500 pounds. And so it just says bring these clothes or we'll have them for you And then you move from room to room taking the ideal dating Exactly. Profile photo.
Speaker 2:Exactly. Oh, you had kids. Here, you in the hospital. You can live an entire life through this fictional museum of Instagramable objects. More of a meme than a real business idea.
Speaker 2:But
Speaker 1:No, John. The Museum of Ice Cream now has seven locations.
Speaker 2:Okay. So they're cooking. They're global. They're global. Doing well.
Speaker 1:Well, let us Anyway, nice little tangent there.
Speaker 2:Yeah. Miami is the capital of gimmick museums. Got to do a whirlwind tour. Anyway, let's go back to the serious stuff, Cerebras. It's a complicated company because they are so deep in the AI supply chain.
Speaker 2:But we'll break it all down for you. So Semi Analysis has a fantastic deep dive. It's a longer read, so we're not going go through it all. But there are some very interesting tidbits in here that we can sort of summarize and contextualize for you. And then, of course, we'll be talking to Doug O'Laughlin from Semi Analysis at 12:30.
Speaker 2:So the there's a bunch of interesting takeaways, some really solid positives. Cerebras chips work, which was something people were not expecting for a while. There was a lot of fun around this company. Just the idea of like, oh, that'll never work. Like, what if the architecture changes?
Speaker 2:What if we go away from transformers or something? What if we need something, quote, completely different? Or maybe, like, the yields will never work because there was this idea that if you're using the entire wafer, typically, as you're as you're etching the chips onto the wafer, sometimes there's little defects. And it's not a problem if you're going to break up a wafer into, 64 chips because you just throw away one. But if there's one defect on basically every wafer, well, then your yield is going to be super low.
Speaker 2:We talked to Andrew about how he solved that by creating redundant cores and they don't actually activate all the cores and so they sort of built in that redundancy and got through that. But that was an early critique of the strategy. Yeah. Can You can use it.
Speaker 1:Use Cerebras chips Yeah.
Speaker 4:Today.
Speaker 2:Yeah. In Codex 5.3 Spark. And so they are very fast. And I think the most important thing that seminalysis points out is that token consumers, customers, businesses have shown this revealed preference for and willingness to pay for speed. And they sort of contextualize it and they quantify it based on their own usage and their experience with Anthropix OPUS models.
Speaker 2:So OPUS 4.6 fast mode famously, I like that they used famously because it's like famous to like a 100,000 people. But famously charges six times the price for two and a half times the interactivity although it's now under two x faster. So effectively you're you're you're paying you're paying six times the price for two times the speed. That's that that's disproportionately more money for what you're getting. You would think you'd pay six times the price for six times the speed potentially.
Speaker 2:But there was a lot there were a lot of questions about would people really pay for more that much more for faster models, faster inference. And Andre Karpathy, Sam Altman was was was saying like, do you want faster models or smarter models? And he was like, think in Sam's point was sort of like, these models are very intelligent, but using them faster is was sort of more of a magical superpower. And Sam was I I felt like Sam was sort of leading it towards like speed is really important as the next leg up on productivity. And under Carpathi was like, no, I just want smarter.
Speaker 2:I'll just let it run overnight. I don't mind that. But that's not what everyone is feeling. Some people, especially the semi analysis team, leaned more towards interactivity or speed over raw intelligence power.
Speaker 1:Well, And then there's the other aspect, is just capability, right? Mhmm. Capability, speed, and intelligence. Yeah. I I think Like, that's that's a question
Speaker 2:Yeah.
Speaker 1:I think people have had is like, okay, what is is there a 250 IQ model? Yeah. Or is there a much more capable model use tools more efficiently and is really quick?
Speaker 2:Yeah. And that's actually important to Cerebras because as we'll get into, the chips do face a hurdle with scaling in terms of longer context windows, all that stuff. But Semi Analysis shared their breakdown. They were run rating $10,000,000 on AI spend in April. And they said that April was the peak, which is interesting because that was sort of I would have expected a straight line, continued growth, but they might have been really pill tried at all and then eventually said, oh, okay, well, for this, we can probably use a cheaper model.
Speaker 2:We can probably optimize. We don't want 95% our revenue going towards tokens. We want a little bit of margin to pay our team and obviously it's a business. They need to make profits. So semi analysis was spending 80% of their AI spend on OPUS 4.6 fast.
Speaker 2:And so they were willing to pay that 6x, like 80% of their spend disproportionately more, even though when their sort of expectation, as they put it, was that they would always want the smartest model. They would be very cost conscious. They were, in reality, saying, I'm going to hammer Fast Mode. I want to spend on Fast Mode. And then I think the price was significant.
Speaker 2:And so there's probably sort of a renegotiation about when is the right time to use Fast versus when do you want to leave something running overnight. But OpenAI is clearly very pilled on Cerebras. Cerebras has a big 750 megawatt deal with OpenAI, and the chips are already serving g p t 5.38 in codex under the name Spark, as we mentioned. And I've used it. You should use it.
Speaker 2:It's a very interesting experience because I think a lot of people have interacted with LLMs and chatbots, and they're sort of used to this token streaming in. And it's sort of it's sort of cute because the phone vibrates, and it and it feels like you're talking to someone who's typing. But it's way better when you just land on a Wikipedia page, the full thing loads and you can just scroll however much you want. And that's the experience that I think people want and will demand across everything, especially if they're firing off a coding task. They just want the code immediately.
Speaker 2:And so you can you can also just go talk to the model like it's ChatGPT. You don't need to use Codex 5.3 Spark in a coding context. You can ask it whatever you want and it will just act like a normal LLM. And I I personally think there will be huge demand for faster inference across all parts of the AI economy. There's this old late Yeah.
Speaker 1:Another another way to think about it is like if you have two employees with the same skill set, the same capability Yeah. But one is just five times faster, right? That person can create way more value in the organization. Yeah. Right?
Speaker 2:And for a lot of things, if they're if they're two times faster, they do command six times the price. Because over the course of the year, a sales rep that that that sells twice as much or someone who's twice as effective as their job might actually command a salary that's five times, six times the the actual price. And so there's lots of other context across different business lines that you could draw to. There's also this old adage or saying about e commerce that might may or may not be real, but it's probably been transposed so many times and think pieces. I don't know the real quote.
Speaker 2:But it goes something like every one hundred milliseconds of latency costs Amazon 1% in sales. I don't know if that's the right way to think about it, but basically as Amazon was scaling, they realized that there were a bunch of things that they could do on the UI side, a bunch of things they could do on the layout side. Where does the buy button go? Where does certain information go? The price, the discount, all of this stuff, the images, they they they were tweaking the front end.
Speaker 2:But as they did that, they added bloat and the pages would slow down. And what they noticed was that the slower the page was, the lower the conversion rate because people were waiting for amazon.com to load, click on the page. It takes a second. They get distracted. They go somewhere else.
Speaker 2:And I think that that's happening in LLM use cases all over the place. People fire off a query and they're like, oh, it's taking too long. I'll go scroll Instagram reels. There's always an Instagram reel. And they'll be like, oh, I kind of forgot about what I was asking about.
Speaker 2:I didn't get my answer. And so and that's certainly true in business context as well. So this is currently playing out in AI inference. Companies are paying disproportionately more for faster inference, and this is good for Cerebras. But Semi Analysis does point out a number of potential headwinds and problems that the team at Cerebras will have to solve or contend with over the next few years.
Speaker 2:Mainly, Cerebras chips are not currently as capable of holding larger models in the limited memory that they have or networking multiple chips together to serve larger models. We've heard about the NVL 72 racks that wire a whole bunch of NVIDIA chips together can serve these really large models. That has potentially been a challenge. Semi Analysis says, Moreover, the industry is trending towards larger context windows ad infinitum. 128 ks context will certainly not be acceptable for long, especially with the prevalence of agentic workloads.
Speaker 2:And it doesn't look like there's a simple solution of just scaling the wafer size larger because TSMC is set up with a standard wafer size and or adding more memory to the existing architecture because Cerebras' whole design depends on a lot of SRAM, static random access memory, directly on the wafer, but SRAM is no longer shrinking as much with each new semiconductor node. So the last version of the Cerebras chip, they've done WSE one, two and three. They're on three now, but WSE two had 40 gigs of memory. WSE three, you would expect, oh, we want a doubling, right? We want a 10x or something.
Speaker 2:It got 44. So a 10% increase over one process node, one iteration. Semi analysis is asking the question of like, okay, is there an easy way to double this? Is there a question like how will this scale as the models get bigger to add more SRAM? You might have to sacrifice compute area because everything is being done on one wafer.
Speaker 2:If you want computation or memory, there's a direct trade off because you only have so much space on the actual wafer. And so they they there might be a much harder three d wafer bonding approaches, doing stack stuff. Like, there are other potential ways, but there's not like a linear, oh, yeah, of course, the next version is going to double again. And so that is a potential problem that they need to work through. But in an agentic workflow, I think it's entirely possible that you want like the biggest, most powerful model, like the vice president delegating things.
Speaker 2:You want the vice president handling
Speaker 1:Senior or junior?
Speaker 2:Senior vice president. Maybe just the president handling the critical work. So future models might not and that might not be on Cerebras. That might be on NVL 72 or TPUs or something. But imagine that we will quickly jump from the agentic age where you're firing the best, smartest model at the full workload to the orchestration age and there will be hybrid approaches.
Speaker 2:So the biggest and best models will delegate certain tasks to smaller, faster models just like they go and do database queries these days or they go and search the web these days, and that's CPU bound. There will be certain workloads that the larger, smarter agent model, like the BOSS model, can sort of delegate to the Cerebras speed workers, the faster workers. So if you need to I mean, just yesterday we were I I was asking Ben about like he pulled all our guests together. He's like, I'd love to know the geolocation of every single guest. It's like, okay, run that same inference query of like, look up this company, figure out where it is across a thousand or 2,000 individual rows and something like that's highly parallelizable if you want that to happen really fast.
Speaker 2:It might not need a GBT 5.5 class model. Right? It might be okay to run faster on a smaller model that works on Cerebras. And so it's hard to predict the exact mix of chips that will power large networks of agents, but these different designs, to me, they seem more complementary than directly competitive. Like, a year or two ago when Daniel Gross wrote AGI Bets and was sort of like his NVIDIA underpriced I don't know if NVIDIA.
Speaker 2:He may have said that on on on Strathecari. But, you know, we we entered the AI age and everyone was like, oh, GPUs of the future. NVIDIA is the company. But then it was like NVIDIA GPUs are good and then also CPUs are good. And and ARM is getting into it and Intel's doing very well.
Speaker 2:And it it We're
Speaker 1:gonna make big computers.
Speaker 2:Big computers. Big computers for sure. And so I'm extremely optimistic, obviously, and very excited to talk to Andrew Feldman, Doug O'Laughlin, Eric Vishria, a bunch of folks who have been involved with the journey.
Speaker 1:Yes, Eric.
Speaker 2:Did the success.
Speaker 1:Series A.
Speaker 2:Wow. Conviction.
Speaker 1:Almost a decade ago, 2016, And Benchmark are sitting on, I guess, of this morning, like many, many billions
Speaker 2:of Yeah. Cerebras. It feels like they brought a huge team to the Nasdaq. Look at this photo, the second post we have. Obviously, Andrew's there.
Speaker 2:A lot of the team members, typically the the the key banker. But, you know, we we've been to some IPOs and some of them have have had smaller teams. This one feels extremely celebratory and feels like a very broad inclusive crew came together. What else
Speaker 1:is The the top shareholders have 99% of the corporate voting power. So founders in control.
Speaker 2:Founders in control? Founder known. Ho Nam says this IPO illustrates the power of an individual partner over the brand name of the firm. Pierre Le Monde was a partner at both Sequoia and Coastal, but instead of those firm backing Cerebras, it was Eclipse, the firm he joined at the age of 84 Over 96 that backed this little known chip company multiple times in the early days. What a way to wrap up a career.
Speaker 2:He was born in 1930, the same year as Warren Buffett. Wow. That is an awesome story. I love that. Matthew Siegel is giving the recovering CFA is giving some color on what happened to the order book.
Speaker 2:One third of the order book, the folks that said, I want shares in the Cerebras IPO, one third of the book got zero. And the top 25, I guess the top 25 investors took 60%. That's probably the big investment funds, the Fidelitys, the State Streets, the
Speaker 1:black They box have done quite well today. This picture looks wildly different than the Klarna IPO last year in which only a handful of the team at Klarna popped over Yeah. Hit the NICI, IPO ed, and went back back home.
Speaker 2:Yeah. It was very much just like another day at the office for the team. Yeah. That's definitely what I was contrasting it to. The Cerebras valuation every round, series a in 2016, a $100,000,000 foundation benchmark in Eclipse.
Speaker 2:CO2 led the Series b in 2016. VY Capital led the Series c in 2017. Then 1,600,000,000 valuation in 2018, 2.4 in 2019, 4,000,000,000 in 2021. That that was like maybe a little bit of a slump. But then 2025, Atreides and Fidelity come in at 8,000,000,000.
Speaker 2:Then Tiger
Speaker 5:comes in at $23,000,000,000.
Speaker 2:Then in May 2026, they IPO at 48,800,000,000. I can sort of just
Speaker 1:Nice do the work
Speaker 2:from self. I don't think how
Speaker 6:you doing? I don't need any help.
Speaker 5:I don't need a sound board to do it. Am I doing it for real or am I using the sound board? You don't know.
Speaker 1:Incredible work from Tiger Yeah. Coming in at 23 post. Very, very good. And now up dramatically in just four months.
Speaker 2:Very, very good. Well, our first guest is joining us in eight minutes. Let's run through the Kevin Warsh news because he has been confirmed as the Fed Chair. And we'll run through this, and then we will come back to some of the other stories because we have a gap later in the show. So, Kevin Warsh, who, is most famous for interviewing Alex Karp on CNBC while Alex Karp appeared to have popped a nicotine pouch and then spun a notebook on his finger.
Speaker 2:Did you ever find that clip, Tyler?
Speaker 6:Is that in the Yeah.
Speaker 7:It should be Okay. Timeline.
Speaker 1:Let's Yeah. We have the the video here
Speaker 2:for Put Kevin Warsh on the map because this is what he's known for. Yeah. Of course, he's got story trend.
Speaker 8:Recently. Sales. I remember I showed up in your office once. I was dressed like this, and I think you screamed at one of the guys. You said, Kevin's here.
Speaker 8:He looks like the guy from IBM. And I was talking about, well, you know, we need, like, really finance control and, you know, how you're gonna sell the product and all this stuff.
Speaker 2:Okay.
Speaker 1:But I
Speaker 8:would say you certainly built that
Speaker 3:as a
Speaker 2:He's really spinning it. I didn't realize he goes back to it, like, four times. Keeps spinning.
Speaker 8:Word anymore.
Speaker 2:He's really good at this.
Speaker 8:But somehow you grafted that on to the to to the strange company that can produce these products. How's that transition been if I've got it right?
Speaker 2:Okay. Wait. Wait. But so, I have so many questions. First, we have to get him to recreate that for sure.
Speaker 2:Second, I I thought Tyler, I thought we were talking about that being on CNBC, that looks like just a podcast. Like, that doesn't have any chyron or
Speaker 7:Yeah. No. I I don't think it was actually on I think it was from Like that was a Palantir
Speaker 9:Oh, okay.
Speaker 2:So it's just like a random podcast. And then and then when I've seen it on CNBC, were playing the clip. Got it.
Speaker 7:I think so.
Speaker 2:Yeah. Probably a reaction stream over there. Well, let's go through what happened because Kevin Warsh has been confirmed as the new Fed chair. The vote was 54 to 45 in the Senate. The divided vote signals challenges ahead for Warsh who faces a Fed committee skeptical of rate cuts that Trump has demanded.
Speaker 2:And, of course, we talked about the inflation news. Typically, you don't cut interest rates going into inflation and potentially economic stagnation. You definitely don't cut rates in that's why stagflation is so difficult. Because if you have stagnation and low inflation, you can cut rates very easily. Maybe the economy starts overheating a
Speaker 5:little bit. You get little bit
Speaker 2:of inflation, but then you can pull back. That's what we've done historically. Vice versa, if the economy is running hot, you're seeing high GDP growth and high inflation. Well, if you raise rates, you're going to pull back on both of those. But in stagflation, you're seeing both inflation and and economic stagnation harder to deal with as a Fed chairman, which is potentially the task he will be faced with.
Speaker 2:So, the Senate confirmed Kevin Warsh as the Federal Reserve's seventeenth chair, Wednesday, in a largely party line vote that that reflected how tensions with the White House have dragged the Fed deeper into the political fray. I was looking back at the old Fed chairs. There's some absolutely legends in there because some of them have really long runs. So very quickly, you get back to the black and white portrait and the painting as you go back in time.
Speaker 7:Who's your favorite Fed chair? Volker.
Speaker 2:Yeah. Volker is pretty goated. Bernanke is crazy.
Speaker 1:An absolute dog.
Speaker 2:Yeah. I don't know. Hard to pick. Hard to pick. Warsh, who was nominated by for the post by president Trump in January, won confirmations, 54 to 45, earning support from all senate Republicans, but just one democrat, John Fetterman of Pennsylvania.
Speaker 2:Senator Kristen Gillibrand of New York did not vote. No Fed chair has been confirmed by such a narrow margin since Senate approval became a requirement for the job in 1977. Chair Jerome Powell, leadership tenure whose leadership tenure ends Friday, captured at least 80 votes in senate confirmations for each of his two terms atop the Fed. Wow. Jerome Powell, just fan favorite of both teams.
Speaker 2:80 votes in the Senate. That's pretty significant.
Speaker 1:Yeah. Is he gonna be looked at as a potential
Speaker 2:chat your podcast?
Speaker 1:Throughout history?
Speaker 2:Get a maybe VC fund going? What do
Speaker 1:we think? No. But but when you look when when we look back, like it seems like the last two, three years, he's handled himself.
Speaker 2:Yes. I
Speaker 1:mean, he's he's had a really tough situation and he's
Speaker 2:When did he
Speaker 1:to land the plane.
Speaker 2:When did Jerome Powell get first become the Fed chair? When was he? He's been assumed office 2012. So I think the wait. Oh.
Speaker 3:'18.
Speaker 2:2018. He was right he was right after Janet Yellen. And so I think I I I'm I'm I'm putting him in the conversation, Jordy, but I'm not giving him the GOAT trophy.
Speaker 1:Yeah. I'm not
Speaker 2:saying You're not saying that. Because the the challenges faced, he wasn't confronted with a great recession, a .com bubble bursting, a Black Friday. He, like, he the the like, the economy from 2018 to today
Speaker 1:Global pandemic doesn't you don't you don't count as shutdown of
Speaker 2:No. Because large. No. No. No.
Speaker 2:I I I actually don't because the economy was pretty strong in 2019, and it went into it went into 2020 with pretty strong consumer balance sheets, low debt. There wasn't a shadow banking economy. There was no bomb in The US economy waiting to explode. And so although we saw high unemployment briefly and we did have to stimulate the economy, that's not his job. His job was to set rates.
Speaker 2:They there was a little bit of like I mean, maybe you put the inflation, you know, the the end the ZERP era and the end of the ZERP era and all of those gyrations on him. But those the problems that were downstream of both the ZERP era and the end of the ZERP era were suffered mostly and benefited mostly on like tech companies and Silicon Valley companies that had really long cash flow horizons. And so there was not a moment where it was a dire situation that the Fed had to intervene in a meaningful way and like save the economy like in 2008. It's a big deal. He did a great job but he didn't he wasn't faced with the same challenges of a Bernanke, for example.
Speaker 2:That's what I would say. Tyler, what do you think?
Speaker 7:Yeah. I think that's reasonable but also like, you know, if if Powell was worse at his job and he saw some crazy crash because of COVID and then he brought it back, like, then it'd be like, oh, yeah. He did face this massive thing. But because he did, you know, such a good job, maybe you didn't see any, like, massive crash. So so, like, the the the like, nothing super bad happening is evidence that he was really good as a Fed chairman.
Speaker 7:Right?
Speaker 2:Yeah. Yeah. Maybe. He's a defensive back. You know, if they don't score, he there's no great place because he's just shut down shut down cornerback for the last couple of years.
Speaker 2:Potentially. He's definitely in the top 17. I'll give him that. Anyway, Kevin Warsh, chair Jerome Powell, whose leadership tenure ends Friday captured at least 80 votes in the senate. The previous chair, Janet Yellen, was a little bit more controversial, confirmed 56 to 26.
Speaker 2:Seems like not that many people showed up in 2014 to vote for Janet Yellen, with many senators absent because of bad weather. Interesting. I wonder what would have happened. I mean, feels like she would have cleared it no matter what. But a difficult economic backdrop and Trump's broadsides against the Fed independence have set up the central bank for a thorny leadership transition.
Speaker 2:Senate committee confirmation hearing last month, Warsh faced intense questioning from Democrats over how he would maintain the Fed's independence from a president who places priority on personal loyalty. Warsh said he would preserve the central bank's monetary independence and that he had made Trump no promises about policy decisions. Powell, citing concerns about political attacks on the institution, plans to remain on the Fed's board of governors, defying Trump's insistence that he leave. He says, you're gonna have to drag me out of here. I'm staying at the Fed, says Jerome Powell.
Speaker 2:Warsh is 56. He's been immersed in monetary policy debates for decades, frequently as an outspoken critic of the Fed, former Morgan Stanley investment banker. He became the youngest Fed governor in history at 35 when former president George W. Bush appointed him to the Central Bank's board in 2006. During the financial cross crisis that struck two years later, he played a key role in tying up rescue deals.
Speaker 2:He was the bridge between Wall Street and the Fed that sort of Bernanke deployed. And so that's what those are his laurels that he will not be resting on, but he will be drawing on from experience. So Warsh left the Fed in 2011, had be he had become a critic of its direction, concerned that as the economy recovered, the Fed's ongoing efforts to support financial markets went too far. So we will have to check-in with the progress on Fed Fed Chairman Kevin Warsh soon. But fortunately, we have our first guest of the show, Amy Reinhard from Netflix in the waiting room.
Speaker 2:Let's bring her in to the TV's NL Show. Amy, how are you doing?
Speaker 10:Doing well. How are you doing?
Speaker 2:Doing fantastically. It is
Speaker 1:honor to have you here. Yes. Our first ever guest from Netflix.
Speaker 2:I think so. Thank you so much for taking the time.
Speaker 1:We took like 2,000 interviews to get get you guys on here. We're we're excited to be Honored
Speaker 10:to be the first.
Speaker 2:Yes. Yes. I mean, obviously, fans of both Netflix and advertising. But would love to start with a little bit of background on yourself, your experience, and just sort of your intro to how you found yourself as the president of ads at Netflix today?
Speaker 10:Sure. I've been at Netflix for about nine and a half years now in a couple of different roles. Started out first in our content organization doing both licensing and then overseeing production. And about two and a half years ago, I stepped into this role overseeing our ads here. And it's been a fantastic two and a half years, a lot of excitement.
Speaker 10:Feels like we've been able to accomplish a lot and great company. So
Speaker 2:If you take us back to the initial the initial push into ads, What can you tell us about the trade offs, the build versus buy debates that were going on at the time, the just maybe even the cultural changes? I think we are super we love ads. We think it's fantastic business model. It's a way to deliver great value to customers at lower prices and there's so many benefits. But culturally
Speaker 1:Yeah. What was the debate like?
Speaker 2:Yeah. What was it like internally?
Speaker 10:Yeah. Well, I think it's been well publicized, you know, that not being in advertising was a strategic bet for a long time. Yeah. Right? And so, early in 2021, 2022, when we started to talk about the notion of getting into this business, yeah, it created a lot of, I think, to say, you know, angst within the company for a a bit of time because it was such a big shift, to your point, culturally and strategically.
Speaker 10:So I would say and then we made the announcement that we were getting into it. And in terms of the whole build versus buy, you know, we partnered with Microsoft to enter the business very quickly, and that got us up and running. But it's been, you know, we made the decision about eighteen months ago to lean into building our own tech stack. And we launched that a year ago. So we're just a year.
Speaker 10:I keep having to remind myself how nascent our tech stack is because we've been able to deliver so many developments and so much progress against that over the course of the last year. But I would say, full circle, you know, we were passed. We put to bed all notions that we should be in this business. I think everybody understands strategically now that it is important for us to be that. And we've been able to grow our user base because we have been able to get to a lot more consumers who are looking for that low cost option and and are fine with ads.
Speaker 10:Right? So it's been a great thing for the company, I think, and everybody's on board. And, you know, the recent news, as you heard, we did which we just announced our upfront yesterday that we're expanding that ad tier
Speaker 11:Yeah.
Speaker 10:Into 15 more countries around the world and. There you go.
Speaker 2:Fantastic.
Speaker 10:Well, everybody's on board full speed ahead.
Speaker 1:How are you pitching the ad product today? Is this primarily brand marketing? Is there a timeline to get to more of a, like, performance focus? Like, what is your pitch to advertisers?
Speaker 10:Yeah. You know, as we see in the marketplace, advertisers are oriented around outcomes. Right? So, we know that we need to be a full funnel solution and we believe that we have the metrics to support that. So, to your point, we've been very successful with some of the brand partnerships that we've done over the course of the last year and a half.
Speaker 10:But, we've also seen really good conversions in terms of lower funnel and making sure that we're driving purchase intent and consideration. So as we build out more of our solutions, we are going after that full funnel solution.
Speaker 2:What are conversations like around how brands should how much brands should want to associate with particular pieces of content. Because I think some brands might come in and say, well, I'm advertising strollers and I know that parents will be watching K pop Demon Hunters with their kids. And so like this is the most on the nose directed and I care. I want my brand linked to this particular piece of content. But we've seen time and time again that like once the algorithms get good enough, once dynamic ad placement can actually flourish, you every company tends to see better performance there.
Speaker 2:So where are the ad buyers today in terms of those tradeoffs?
Speaker 10:You know, there's a full spectrum. So, absolutely, we get advertisers who want to be associated with k pop demon hunters like McDonald's or with Stranger Things. Right? Like, those big, pet fall moments. Those are oftentimes the easiest to sell.
Speaker 10:I think in k pop demon hunters is actually an interesting case because when it came out a year ago, we didn't know that we had a hit on our hands until about sixty days into it. Yeah. And I think that's what the magic Netflix is that we have so much variety and depth of content that we're programming and trying to hit all audiences that you never know where your next hit is going to come from. Mhmm. And so, those audiences, selling that, you know, audience behavior moods, targeting moods and relevance is really important to a lot of different advertisers.
Speaker 10:So, again, we just want to meet advertisers where they're at. And, some folks understand that being across a number of different programming choices is important. And, some people want to tag along with those big tent poles and we want to, you know, provide those opportunities.
Speaker 2:Yeah. I mean, it's interesting. Netflix has like deep, deep experience in machine learning, AI, recommendations, all parts of, like, you know, high throughput data processing. But I'm I'm interested in any learnings or surprises from building the proprietary ad delivery stack. Has it been as expected?
Speaker 2:Has it been there's new skills that you need to bring? Because a lot of companies have been successful at scaling content and then struggle to figure out ad delivery. You obviously haven't. But then also there's this AI boom going on which can help with productivity but also new algorithms and new ways to actually target content. And so, I'm interested in what where the build out didn't match your expectations or surprised you.
Speaker 10:You know, as a tech company, we do a lot of testing and we go into things with hypotheses. So we're constantly testing things around our member experience. And, you know, I think that's been a differentiator for us, like, really leaning into reducing member friction, making sure that member experience is a good one with lower ad loads, lower frequency apps, those types of things. But we we do know that there are times when we have to pivot. So I would you there's not just one example of a time that we've had a wrong hypothesis.
Speaker 10:We're constantly testing things out and figuring out where we, you know, where that those hypotheses prove out, where we need to pivot, and and, you know, change swiftly. So it's hard to point to one specific moment where we it felt like it's been a learning. I would say the bigger learning for us just as a company is, you know, this is a relationship business too. And, we've never this you know, you talked about kind of getting into ad sales. Right?
Speaker 10:We've never had a sales
Speaker 4:team Yeah.
Speaker 10:In terms of our overall organization. So, would say there's more organizational learnings
Speaker 2:Yeah.
Speaker 10:Than necessarily tech learnings because we're so used to that tech cycle of testing and learning and iterating.
Speaker 2:How are you thinking about the the ad product feeding back into the content production? I've noticed that Netflix has been fantastic in my opinion of creating more engaging content. I was watching The Rip with Matt Damon. And you click the play button and you see Matt Damon's face in within like two seconds. And it clearly confirms that you're watching the right movie and then the title card comes in.
Speaker 2:And that's a departure from fifty years ago. You watch The Shining and, you know, it's a helicopter shot of a car for five minutes and they show you the full titles. And it is a different style of editing and some people lament the old style. I particularly like the new style. And I'm wondering about we went through a period of time when television, there was the famous like fade to black and then the ad break and then fade back in and you resume.
Speaker 2:And Netflix has never had to contend with that in media products. But is that going to come back? Is there a next generation pattern for creating content that can both have ads in it and not? Are you seeing glimmers of what the future like the impact of ads might have on on, like, the editing structure and the timing and the pacing?
Speaker 10:Yeah. A lot of it, to be honest, depends on our creators. Yeah. So, you know, working with talent who and some of those and some of that talent may be more tech forward and are thinking through those types of things when they're writing shows. I'll give an example.
Speaker 10:Shonda Rhimes was used to writing for broadcast and network for many, many years. Yeah. So, when she writes a lot of her content, she's already thinking about where those natural breaks are.
Speaker 5:Mhmm.
Speaker 10:But not all writers do that, and that's okay. We can still find what are those natural breaks because we wanna make sure, again, getting back to the member experience, that it's not intrusive or it doesn't come mid sentence, right, and is cutting off any of the action. Yeah. We're able to adapt to to any way that our creators wanna to write the content and fit it into that member experience.
Speaker 2:Yeah.
Speaker 1:For US markets, is this is there any enterprise spending? I would imagine that a lot of enterprise buyers, like, have been Netflix subscribers for a really long time and maybe they're they're not getting served any ads at all and so this is more of a consumer opportunity? Or am I thinking about that the wrong way?
Speaker 10:We've most of our clients right now, the the target segment that we're going after are enterprise top 400 clients, Right? Because we think those are the ones who Oh, sorry.
Speaker 1:I meant I meant b to b versus, like, b to c company.
Speaker 10:Oh, we think about this more as a b to c opportunity Yeah. For the most part. And I think as we expand our learning and expand our offering, may get into the b to b space, but for the most part, to c.
Speaker 2:Yeah. Yeah. I I feel like all of that like, the the higher upmarket, more targeted, that's all unlocked with scale once more there's more learnings on responses. I'm wondering what other signals you think might be valuable because many times, you know, advertisers advertisements shown during a TV program are very passive, harder to track. But if someone's watching on their phone, there can actually be a call to action, a trackable link.
Speaker 2:Like, how I imagine that the data is messy, but how important is it to sort of close that loop in an ATT era where it's a little bit trickier but there's a lot of things that you can do on the signal side anyway?
Speaker 10:Yeah. You're absolutely right. And this is an area where I talked about the testing and iterating. Yeah. We're leaning in a lot on the testing.
Speaker 10:Yeah. What does that screen experience look like? You know, again, how do you meet the customers where they're at without being sort of intrusive? Yeah. A lot of testing going on in this space.
Speaker 10:But the biggest thing for us is, you know, privacy safe. We want to make sure that we're leaning into, again, that member experience and and taking care of our member's data.
Speaker 2:Yeah.
Speaker 10:But a lot more, I think, to come.
Speaker 2:Have you been surprised by the return of the QR code in maybe podcast advertising? But I see it a lot because people are watching on, you know, you they'll watch a YouTube video on a TV Yeah. And the creator will, you know, hard code in a QR code to link out. And that was something I I had completely written off QR codes and then they made a Yeah. Major comeback.
Speaker 10:No. I agree with you. From a member experience may not be the most simplistic thing. Understanding kind of the ad tech on the back end, I'm not surprised by it. It could be pretty complex pretty quickly.
Speaker 2:Yeah. And there's some we hit some sort of like inflection point where the maybe it was in a certain iOS revision where the camera app became so easy to press a button and pull out, then it detects the QR code so quickly that that flow because you used to need to, like, have a QR app separately to scan it, and now it's all integrated and so someone can just whip out their phone and and run right to it. Jordy, you have something else?
Speaker 1:Nothing super top of mind right now. I am I mean, last question I had is do you ever expect Netflix to serve more short form vertical video style ads in something like the Clips tab? I know the Clips tab right now is focused on basically content discovery, But I imagine in the future, people will spend more time in a format like that, especially on mobile.
Speaker 10:Absolutely. And that is one of the announcements we had at our upfront yesterday is that as we roll out this vertical video content, that we are going to be offering that to advertisers along with our todoom.com coverage in 2027. So, yes, we think that is a a big opportunity too.
Speaker 2:Last question for me. I would love to know about the intersection between games and ads. That's been a huge huge growth driver in other with other categories and other companies. But I'm wondering where that is in the road map, how you're thinking about that.
Speaker 10:We haven't thought about that yet. Look, our roadmap I could fill our roadmap for the next two to three years based on just some of the foundational things we wanna do and and a lot of the innovative areas we wanna lean into. Yeah. But it's an area that we're keeping an eye on. And as we watch that game's engagement increase, I wouldn't never say never.
Speaker 10:I've learned to never say never at Netflix, but it's not something that's on the near term roadmap.
Speaker 2:Okay. Thank you. Well, thank you so much for joining. This was a really great
Speaker 1:Great to meet you, Amy. Thanks for breaking down progress.
Speaker 10:For having us.
Speaker 2:We'll talk to you soon. Cheers.
Speaker 12:Have a
Speaker 6:good one.
Speaker 5:Thank you.
Speaker 2:Moving on from Kevin Warsh, who is the newest Fed chair. We have a debate. We have a debate going on in the timeline around general catalysts advertisement. Some are calling it an attack ad. Particularly, Andreessen Horowitz is calling it an attack ad.
Speaker 2:We touched on this yesterday. But if you did not tune in, General Catalyst, the large scaled what do they call it? Gigafunds now? What do they call it? Platform.
Speaker 2:Platform fund. But there's something else. It's a huge huge venture capital firm. They launched an advertisement which we can play again to refresh everyone. Let's scroll
Speaker 1:Yeah. The beginning of the video.
Speaker 2:It's only thirty seconds left.
Speaker 11:I'm GC. And I'm VC.
Speaker 4:Who's your friend
Speaker 8:here, VC? This is Woof
Speaker 13:AI, an AI native companion platform that combines robotics and machine learning. You'll never want
Speaker 2:a real dog after this.
Speaker 4:Well, I think people like dogs as they already are though, VC. You don't need
Speaker 13:to walk it. You never need to tell the kids you sent Milo to the farm. We're leading the seed and could probably make room for you.
Speaker 4:Well, I'd love to hear more, but we actually have a really high bar around responsibility for these things.
Speaker 8:Is Woof AI okay? Of course, he's fine.
Speaker 13:Oh, sorry, buddy. It's an easy, easy. Stay. No. No.
Speaker 13:Stop. Stop. Stop.
Speaker 3:I'm sure it'll be fine. Okay.
Speaker 2:Tons of thoughts, but you kick it off. What's your read on this? Take me through it.
Speaker 1:So the the the actual ad, the way it's shot Mhmm. The timing Mhmm. Cinematography Mhmm. Is fantastic. I do
Speaker 5:Yeah. I
Speaker 1:think the ending is funny. Right? It makes me smile a little bit. Mhmm. The dog's going haywire.
Speaker 1:The robot dog's going haywire. My first thought is I actually think there's a huge opportunity for a robot dog that is 10 times better than existing robot dogs. There are robot dog toys out there. So I
Speaker 2:Specifically in the toy market because this is a Boston this is probably I mean, seems like it's a Boston Dynamics robot dog and those are not typically used as pets that I know of. I think that they are more deployed. Whenever I see a demo of a Boston Dynamics robot dog, it's like walking through a nuclear power plant that you don't want someone walking into. It's like an industrial product for the most part, and then obviously a a fodder for viral videos. But, yeah, step through it because it is a crazy back and forth from both firms.
Speaker 2:First, the stats. General Catalyst. Oh, they're over a thousand likes now, but 2,000,000 views. So a lot of discussion. Anjani Midha says, it's a bit cringe, guys.
Speaker 2:Olivia Moore at Andreessen says, if I was a consumer founder, I would run for the hills watching this. Weird take from a fund that returns so much money from Airbnb and Snap because is the idea that robot dog is a weird idea, but so was Airbed and Breakfast or Snapchat. Right? Biggest b to c businesses always start out looking weird.
Speaker 1:Yeah. There's a way to do the same pitch for Airbed and Breakfast, which is, you know that empty couch? You know that empty room in your house? Yeah. What if you were to monetize it?
Speaker 1:Yeah. What if you were to allow strangers to sleep in the empty room in your house? And and of course, that was a lot of the criticism at the time was that that it never was gonna work. Yeah. And it was strange, but then it created a lot of positive externalities.
Speaker 1:People built businesses, people get to travel and
Speaker 2:Yeah.
Speaker 1:Integrate with local communities. Yeah. So the main thing here is like it's it's I do find it I do find it fascinating considering that when you actually look at the portfolio overlap, it is insane. Almost all of their winners
Speaker 2:Yeah. Yeah.
Speaker 1:Almost all of their winners, especially in the modern era Sure. They are in both companies.
Speaker 2:Yeah.
Speaker 1:Right?
Speaker 2:Of course.
Speaker 1:And so they're backing a lot of the same companies. Yep. So it's super hard to counter position Now against granted, a sixteen z has a very different media strategy. They're they're very loud. They're all saying the r word a And I think GC can kind of counter position against But you're not gonna counter position against like what companies you're backing, right?
Speaker 1:GC True. GC, I I had to look this up because I thought there's no way this is true. GC is in both Calci and Polymarket.
Speaker 2:Yep.
Speaker 1:Big, great companies. Yeah. Right?
Speaker 2:But those have been at center of the debate.
Speaker 1:But that is the center those companies are at the center of the moral debate Yeah. In tech right
Speaker 2:now. Certainly for the venture backed controversial companies. Yeah. There are some that are controversial that are not
Speaker 1:Yeah. All the funds have backed various like batting Yeah. Trading related companies over the years. I think it was like GV did what was it? Not DraftKings, but FanDuel Yeah.
Speaker 1:I think That's back in the day. And also it's not to say
Speaker 2:There's companies that start out as controversial and then they become true, like completely normalized and everyone sort of comes around to like, that's a good thing. Like Andoril is a good example. Both of these company both Andreessen and GC are in Andoril. When it launched, it was like, wait, you're building defense technology? That's insane.
Speaker 2:And now the whole Silicon Valley community has come around to the idea that it's really important to have a functional defense industry. And then on the other side, you have companies that come out as really controversial and and people are like, this is the end of the world. And then they just sort of fizzle out. Like, Cluely would be a good example of a company that like has done okay, but it's not like, oh, no one's doing homework. It's completely upended education and it's so successful and it's bad.
Speaker 2:It's like, no. It's like, it was there was a lot of saber rattling around that. Soro went through a similar thing where everyone was like, this is gonna wire head everyone and then it was like, yeah, just like some funny memes and ultimately, you know, moved on from it. And and so it's it's very hard to map like the controversiality to the ultimate outcome and where it lands. Like Yeah.
Speaker 2:A lot of
Speaker 1:the Yeah.
Speaker 3:Lot of Prediction markets were not
Speaker 2:controversial when they were just predicting the election. That that was not what was controversial.
Speaker 1:Yeah.
Speaker 2:Started being controversial about it or saying it's controversial once it got into sports betting.
Speaker 1:Yeah. So the ad is interesting from a couple ways. One is trying to counter position against Andreessen. Mhmm. GC being like the cool, hip, fun Sure.
Speaker 1:That is that that wouldn't back the robot dog company. Yep. Even though when you look at the portfolios, right, there's enough overlap that you can safely say if a company is ripping or has a lot of potential, they're probably both interested investing in it regardless of what category it's really
Speaker 10:in. Right?
Speaker 1:And then the other thing is that, like, historically, like a sixteen z is the firm that's trying to be like hip and cool and loud and and do like new media. Yeah. And GC is the one that I've always viewed as like more buttoned up Yep. More behind the scenes Yep. More traditional
Speaker 2:think it's more traditional finance. East Coast, Boston.
Speaker 1:And I love that. Yeah. I We love that. We love that.
Speaker 2:Think think Capital is a good example of another another firm that's been like pretty straight laced but still has like aura behind it. And it's like they have a private equity fund attached to the venture the venture fund and like Bain's done some fun stuff. I mean, Fogo de Chao and they've leaned into things every once in while. But they've never been like, we're gonna be the, you know, the the the craziest brand strategy. It's been like, yeah, we buy the book investors.
Speaker 2:We find great companies, back them. And, you know, Sequoia has done that too. You know, their whole pitch is very austere. And that's worked for a long time. It doesn't need to pivot the brand, which is maybe what this is like signaling for towards.
Speaker 2:This is the first time I mean, it's very rare for any VC to, like, take shots directly at another VC just because they're all syndicating deals It's
Speaker 1:way more direct. I mean, the points with each other. A lot of people clocked it, but because the actor could look, I guess, if you were far away enough Like Steve Vollmer. Something like yeah. Steve Vollmer.
Speaker 2:I think looks like Steve Vollmer.
Speaker 1:Or Marc Andreessen.
Speaker 2:But people are saying Marc Andreessen.
Speaker 7:But the
Speaker 2:other thing is like So hold on. I was looking at Marc Andreessen photos and I could not find a single photo of him in a vest. So I don't know about this. But clearly Marc Andreessen did take issue with it because he he quote tweeted it something like 45 times which which you know really amplifies it and creates a more of a conversation about it. Strategically, you might have just wanted to mute this if you don't want this to become a thing.
Speaker 2:But maybe he does. Maybe he's like, yeah. Actually, I do wanna fight because, like, this is dumb and I'm gonna fight back and I'm gonna win. So, you know, that's the strategy. But it does seem like messy rolling around in the mud when you're when you're when you're wrestling with pigs, you're gonna roll you're gonna get muddy.
Speaker 2:Right? Isn't that the same?
Speaker 1:Yeah. It's it's just it's just again, like creatively, it looks great and it's very fresh. And I think Reggie and his team did did great work, but it's funny to just, like, take shots at a whole category of investing, basically, that is Yeah. Kind of the bread and butter of GC's business.
Speaker 2:Yeah. Yeah. Drawing drawing the line in the sand. What what is the actual line? The the really high bar for responsibility around these things?
Speaker 2:Like, because first off, it's just odd because like robot dog feels very very low on like the responsibility. Like if some VC was like, okay, we take responsibility really
Speaker 1:seriously. For example, the last robot
Speaker 2:I would be like, don't fund gambling, don't fund cannabis, like don't upset Sager and Jetty, basically. It's like it's like the way I would the way I would pitch someone if they were trying to be like the responsible actor fun. Interesting thing. And Robot Dog would be fine.
Speaker 1:The last Robot Dog pitch deck I saw was a company that wanted to use robot dogs as a replacement for actual seeing eye dogs.
Speaker 2:Oh, interesting.
Speaker 1:Seeing eye dogs are like incredibly expensive. You know, kind of out of out of reach for
Speaker 2:for
Speaker 1:And many and so the opportunity for a robot dog that can maybe, you know, travel with you, like on a plane without, you know, barking and Yeah. Needing to eat. Yeah. Right? Like, that's actually like a very world positive Yeah.
Speaker 1:Kind of endeavor. Yeah. I don't know if it'll, you know, work or what the business will look like, but but in general, there's a, you know anyway, so
Speaker 2:I'm extremely bullish on robot dogs. I think it's very complementary. Think about I mean, especially if it's a it's a toy for kids. Think about how many vehicles kids
Speaker 1:says service dogs can reach 60 k.
Speaker 2:Woah.
Speaker 1:And that doesn't count lifetime of all the other costs associated with dog ownership with
Speaker 2:Next time with I see all of service dog. Pow. Flex like, woah, buddy. Have to flex like that. No.
Speaker 2:The the 60 the yeah. I mean, kid kids have, like, you know, a bicycle, a tricycle, a a a a RC car that drives and then another one. And, like, it's like like, you you throw the robot dog in the mix. I feel like that's going to be in addition to a dog. Robot dog rips is what you said when I said that.
Speaker 2:This is going maybe a thousand robot dog startups will flourish, and and the most ironic scenario is because it the industry will become so deniable. The general catalyst and Andrew Reason both have to back it, and they're, like, duking it out for allocation at every round. And the robot and the future robot dog founder out there is like, I I just do nothing win or build a great company and win. Was there anything else going on with the robot dog debate?
Speaker 1:I, you know, selfishly, you know, I think it's bad for the industry if you have two of our platform funds, you know, just making these somewhat petty videos that are basically ad hominems that, you know, basically throwing stones from glass castles, as one one could put it. So I think it's generally bad, but for entertainment purposes, if they wanna keep going at it and turn this into proper Drake versus Kendrick situation, this might be a good use of a 16 z new media. Yeah. Although much of the talent there is now over at a
Speaker 2:I mean, I would
Speaker 1:I think.
Speaker 2:I would love yeah. I would love, like, a like a response video from Andreessen shot in the same way or something. Like, there there is an opportunity for, a rap battle here that's a beef that's, like, very, very entertaining that I would absolutely love. The flip side, though, is that I agree with you. Like, if you're a VC, you're much better picking a villain that is something around, like, a lack of technological progress.
Speaker 2:Like, Teal did this very well with, stagnation is the boogeyman. Like, we if we don't get the robot dog, we don't get the seeing eye dog, we don't cure cancer, we don't do the big thing, we don't we don't visit Mars. Like, that is you can still have like a villain, but the villain needs to be something that the the industry and America and all the constituents can can align around instead of like picking fights where like these two firms are obviously aligned on like 99% of like where the future goes and and what the goal is of building a business that delivers a value that consumers enjoy.
Speaker 1:Yeah. It's just so funny because when you if you look at cap tables, they're almost always probably touching. They're effectively holding hands on the cap table, right? Because like one of them might have more of one company, right? Slightly higher.
Speaker 1:One of them might have more of the other, right? But in general, you know, they're just
Speaker 2:hanging out together. Oil did enter at GC and then went over to Andreessen. So, like, there's, like, there's more overlap than than than than differences. Plenty of plenty of other things to take shots at. But I mean, to Andreessen's credit, they've done a good job of that, focusing on on geopolitical competition, focusing on China, focusing on stagnation and nimbyism, all sorts of different things that they've been aligned with, like, more of like an abundance view as opposed to punching down.
Speaker 2:Although, you know, there's there's memetics all over the place. Yes.
Speaker 1:It happens. Yeah. Just it seems like GC has an opportunity to be the buttoned up Yeah. Platform.
Speaker 2:I think so.
Speaker 1:You know, they don't they don't need to say the r word. Yeah. They don't they don't need to be super loud. Yeah. Right?
Speaker 1:They can just focus on the craft Yeah. Of investing. Yeah. I wrote about it in the newsletter, but, you know, run some ads. Yeah.
Speaker 1:You know, you've been saying this forever, run some ads in The Economist Yeah. Financial Times, Wall Street Journal. Yeah. Just there's a way to counter position yourself without attacking your
Speaker 2:rivalry partner. Catalyst went viral? Hamant, CEO, was on Harry Stebbings' show, 20 BC, and he said, like, triple triple double double double is no longer good enough. We want to see 10 x ing every year because that's what's happening in the AI era. And it was a brash statement.
Speaker 2:It was bold. It's smart
Speaker 1:But to actually looks
Speaker 2:But look at the results. Like there are companies that are doing this. Like we talk to them every day. And sure, he and he clarified it on our show. He clarified it on other shows.
Speaker 2:He was not saying that like you shouldn't build a business that only triples revenue every year or only doubles revenue every year. He was just saying that the reality of the market right now is that there are power law companies that are growing exceptionally fast, unprecedentedly fast. And so you, as a venture capitalist, have to adjust your benchmarks and think about how you're allocating funds, what companies you're investing in, make sure you're in the best company in the category that's actually going to win. It might be the company growing 10x a year, not 2x a year. And so that was something where it was like thought leadership from Hemant.
Speaker 2:It sparked a conversation. There was some debate around it, but it was And from a so position
Speaker 1:far, that take was controversial but entirely correct when you look at the growth of Yeah. A lot of the most exciting companies in the industry right Yep.
Speaker 2:Totally. Totally. And so that that sort of more more narrow staying in the lane view, it got a lot of attention. It did break through. It caused a conversation, but it still didn't sacrifice.
Speaker 2:It it didn't feel like, oh, he's taking shots at someone specific. Right? It was just like a market analysis from someone in a position to give that exact analysis. So anyway, Jensen Huang is over in China. Jason Kallochanis has a photo that looks extremely real.
Speaker 2:Zero AI detected, but he's bringing two huge boxes of GeForce RTX 50 nineties, which are
Speaker 1:And this is a picture from when he was in Alaska too. Jason
Speaker 2:says never stop selling. I I agree. There is some news, which we will cover later in the week around the dynamics around h 100 sales and Blackwell's, what's actually happening. It's all in flux as the Trump China summit plays out on the front page of The Wall Street Journal every day this week because it is headline news. High stakes US China summit kicks off.
Speaker 2:There was another drama in the tech world yesterday, but we'll come back to it after our next guest Ben Hylak from Raindrop joins. I believe he's in the waiting room. So we'll let him come in. He's the cofounder and CTO. We've had him on the show before.
Speaker 2:Welcome back, Ben. How are you doing?
Speaker 3:Doing well, man. How are you?
Speaker 1:Fantastic. Great to see you.
Speaker 2:What's Long
Speaker 1:time time
Speaker 2:your world. Reintroduce the company quickly and then tell us the news.
Speaker 3:Sure. Raindrop, we make observability for agents. So the main thing we do is self healing agents. So what it means is that when your raindrop hits a problem in production, we detect it, we fix it.
Speaker 2:Mhmm. How do you do it?
Speaker 3:That's a good question. So at the end of the day, like, it's we consider ourselves like the intelligence for your intelligence. Mhmm. What that means is that we are the best fastest way to essentially look at anomalies. Mhmm.
Speaker 3:So what that means is that, like, let's say you make a change. Right? We're able to very, very quickly find out that, like, oh, users all started complaining about something, or the trajectory, the traces are kind of starting to evolve into a different pattern.
Speaker 2:Mhmm.
Speaker 3:And so it's kind of a combination of agents, but also more like classic ML techniques. A lot of like custom trained models for every customer.
Speaker 2:Walk me through the shape of the agent market right now. Like, the way you're talking about it, you know, sort of illustrates the broad diffusion of agents and custom agents. I think that a lot of people think Cloud Code and Codecs. And I don't know if you're doing enterprise deals with those firms or that's the goal, but I imagine that every startup, many legacy companies have built some sort of agent, some sort of harness. And I'd love to know the shape of how broadly diffusing custom agents are in companies versus is it the domain purely of startups that create an agent for legal or an agent for sales and then they then that into a company?
Speaker 3:Yeah. So I would say that there's two kind of categories of customers. Mhmm. We started with super high growth startups, at the time startups. So those are companies like Clay, for example Sure.
Speaker 3:Framer, speak.com. Some of the fastest growing companies in the world, and those are some of our earliest customers. We're lucky they grew a ton, so, you know, that has helped our growth.
Speaker 1:Always helps.
Speaker 3:It always helps. Yeah. And someone once mentioned that, like, you know, this kind of business is a lot like early stage seed investing, actually. Yeah. It's kind of interesting.
Speaker 3:Like, you know, we you have to be pretty pretty picky not to work with companies that are gonna die. Because if like, especially analytics, like, these sort of things, like, they you you succeed as a company when your customers succeed. Like, if all of your customers are terrible, it's like everyone's like, well, why do I why are your
Speaker 1:insights a portfolio company that was working on, like, agent infrastructure, like, roughly two years ago and pivoted because he was like, okay. This is clearly gonna be a big thing someday, but right now he's looking at all the underlying companies, and he's like, I don't believe that any of these agents in their current iteration are gonna work. Now, maybe they're starting. Right?
Speaker 3:I think it was very counterintuitive at the time, but I think we chose to chose to find companies like clay.com, right, which are were clearly on a insane trajectory, but at the time were, you know, weren't necessarily as large. And so I think a lot of our customers now are pretty large, but at the time weren't necessarily as large. And then in the last few months, we've been moving into Fortune fifties, Fortune one hundreds, like, a lot of amazing things happening there. And, again, it's kind of like two shapes of a product. Like, one is, like, in our our bread and butter is, like, you know, companies that are redefining the way people, you know, interact, you know, in different verticals.
Speaker 3:But then, yeah, there are, like, Fortune fifties, Fortune one hundreds that are also deploying agents internally. I think the shape of that looks very interesting and, like, it's something that, like being on the forefront of like understanding how these companies are deploying things, like there's not that much I can talk about right now. But Mhmm. Yeah, always very interesting.
Speaker 1:What do you think is a generally underhyped agent category right now? I'm sure you're seeing the future a little bit.
Speaker 3:It's a really good question. I think that I mean, you know, I I
Speaker 6:So this is a tough question. I I What I wanna
Speaker 3:do actually is pivot the question a little bit, because I wanna talk about I wanna talk about our launch today, if that's okay. You wanna share with you guys?
Speaker 2:Yeah. Yeah.
Speaker 1:I'll tell you my questions. You tell me your answers.
Speaker 3:Okay. Okay. Sounds good. Does that mean that you want me to not answer this? No.
Speaker 3:No. No.
Speaker 1:No. I'm just I'm just messing around. Go for
Speaker 3:it. Okay. Cool.
Speaker 2:The joke
Speaker 1:is Yeah. I botched it.
Speaker 2:The joke is what questions do you have for my answers?
Speaker 6:And some CEO show up they're act
Speaker 2:like that where
Speaker 1:it's like you
Speaker 2:could ask him anything and they're just gonna I'm gonna redirect to a topic point. But it's fine. I wanna hear about the launch today, so just tell us about it.
Speaker 12:Great. Let's talk
Speaker 2:about Let's go.
Speaker 3:Okay. So, guys, there's been this crazy thing that that has been missing for a very, long time. That's why I want to talk about it. So, like, people have been building agents. Mhmm.
Speaker 3:You're building them locally. Like, you're using some sort of SDK. It could be OpenAI. Could be for cells. Whatever SDK it is.
Speaker 3:And What do you mean locally?
Speaker 2:Like, it actually has to run on, like, Or development
Speaker 3:Right. Like, before you push to production.
Speaker 5:Sure.
Speaker 3:Right?
Speaker 1:Sure.
Speaker 3:It's on your laptop.
Speaker 2:Yeah. Yeah. Yeah.
Speaker 3:There's no way to see what it's doing. Like, no standard way, nothing. Like, so people will send those traces out to, like, a server. Like, raindrop is, like, one of those, you know, and there's a bunch of others. Yeah.
Speaker 3:But they
Speaker 2:might also just, drop the logs in, like, a non relational database. Sure.
Speaker 3:They'll they'll just print it to to, you know, console dot log, like, oh, here's what was happening. It's, like, that bad.
Speaker 2:Yeah. Yeah.
Speaker 3:Yeah. And the other problem there is, like, so you can't see, like, a nice trace, or you're sending it to some server and it takes, like, seconds see everything. I'm like, whatever. It looks terrible. But then also, your coding agent can't, like, see the traces either.
Speaker 3:So then when you hit a problem and you're like, hey, you know, this response was wrong, Blogcode will just make shit up. Like, it'll just be like, oh, I think that, like, maybe this tool was wrong, or I think maybe, like, this happened.
Speaker 2:Mhmm.
Speaker 3:Because it doesn't have any of that data. Doesn't actually know what the coding agent did. Yeah. So I think that, like, as someone building agents, as, like, our company building agents, we're like it it it's actually kind of embarrassing how long it took us to solve this problem. No one else solved it either.
Speaker 3:But but, yeah, that's that's what we launched today. Free local open source tool, braindrop.ai/workshop, and it's completely free. Like, it's just open source.
Speaker 2:Why open source?
Speaker 3:That's a really good question. I mean, I think the genuine answer, I think part of, like, why our competitors, like, haven't done it. I mean, there's probably other reasons for that as well. But I think it's that it can be. Right?
Speaker 3:Like like, someone else can do it. You know what I mean? Like, I think that it it running locally is the best experience for people. Mhmm. And to be clear, like, there's still things that it enables, you know, if you connect it to your production raindrop, which is like, can pull in a remote trace and replay it.
Speaker 3:And then Clog Clog Code or Codex can just keep doing that loop until it works. So there's still benefits for us. But also, the the truth is that we want people to hack it. We want people to to meld it into whatever works for them. So we use a lot of open source things here.
Speaker 3:Right? So it makes sense to to contribute back as well.
Speaker 1:Yeah. That's great.
Speaker 2:Yeah. I'm I'm wondering about other just, like, predictions about the next breakout category of AI agents, what you're seeing. Feels like you're so close to being able to book a flight, but maybe no one wants that. I don't know.
Speaker 3:I mean, I'm not sure if you guys saw. I had a little bit of a thing with Brian Chesky earlier about Airbnb.
Speaker 6:Oh, yeah. Oh, Let's talk about that.
Speaker 3:Talk about that. No. I think, like, you know, use Airbnb a lot. I love Airbnb. I think if I had to guess, I would say Brian Chesky knows a lot more about Airbnb than I do and probably a lot more about being a founder than I do as well.
Speaker 3:True. And so I think there's probably a lot that I'm not considering. That being said, I think it's fresh like, if Airbnb had an API, I would use it, and I would book Airbnb with it, like, through Cloud Code. Right? So it's like, know I would do it.
Speaker 3:Sure. It's I find Airbnb very, very hard to search, And I think that there's a lot of I think the tough part, and, like, what I see industry wide wide right now, everyone trying to figure out, is you you see companies almost reducing themselves into an API with, like, absolutely no mode. Like, you look at, like, Photoshop, Illustrator, etcetera, they're like, oh, we have a Cloud Code integration now, MCP. At the point where people are just using Photoshop, Illustrator, etcetera, as, like, an MCP, they've they've sort of lost the game. Right?
Speaker 3:Like, if no one's actually touching the UI anymore. I think that right now, companies have to do that increasingly because they have no other choice. I think that there will be a point where the incentives don't make sense anymore. Like, I can give an anecdote from from when I was at Apple. You know, do you guys remember AppClips?
Speaker 1:Yeah. Yeah. There's more App Clips.
Speaker 2:Where did those go? I only see them
Speaker 1:Where did
Speaker 2:those go? With like parking meters sometimes.
Speaker 6:Yeah. Right.
Speaker 3:So, one of like the hero ideas there was like, oh, you know, like, imagine you're in line at Starbucks, you don't have the Starbucks app downloaded. Like Yeah. Well, why not just, you know, scan something, have an app order your drink, and it's like, turns out Starbucks doesn't want that. Right? Sure.
Speaker 3:Like, that's the last thing in the world Starbucks wants.
Speaker 1:Oh, Starbucks wants you to
Speaker 3:download the app. They want you to have stars. They have an entire Like, there's a reason why DoorDash and Uber Eats and, like, whatever, you know, God knows other apps exist. It's not because they need to, but because each have companies and money and goals and like So so why would they reduce themselves into an easily interchangeable API? It doesn't actually make sense.
Speaker 1:Yeah. But but I think it's using I think it's important to be careful around using like a tool like Photoshop interchangeably with like a retail store like Starbucks or like a marketplace like Airbnb or DoorDash because I I really think that these marketplaces provide, you know, exceptional amount. All the value is not in the UI. Right? Agree.
Speaker 1:I agree. And where and and like the value of Starbucks is not that it's a pretty app. It's because they have Yeah. Specific drinks that they can make pretty much anywhere, you know, someone would be
Speaker 2:Yeah. I think of that company, Buy, the the drink company. They got started during the direct to consumer boom. Obviously, they would have some beautiful Shopify website. They didn't.
Speaker 2:They just went Yeah. Direct to retail and they had Amazon. You could order it online and if you went to their website, would just say go to Amazon. And they were and they did fine. A billion dollar company.
Speaker 2:Yes. And because like the value is not in the e commerce experience, they didn't play like the the Yep. Stars game. Of course, Starbucks is maybe sacrificing a piece of that business model, but it's not giving away the whole cow. I don't know.
Speaker 3:The There going to be ways to monetize this. Right? Like, there are going to be successful business models built on top of this sort of layer. And and to be honest, as Raindrop goes into the future, like, that's the future we're building towards. That's the future we want.
Speaker 3:I mean, we're gonna be announcing a partnership with a really large one of the, like, the large coding companies as soon as far as, like, integrating with them more, where it's like, I don't see Raindrop as a company that's going to submit PRs and production to people's code bases. Like, someone else is gonna be doing that. We're gonna be the layer that's really good at finding those issues, diagnosing them, and tracking them.
Speaker 2:Yeah.
Speaker 3:So I just think it's going to be interesting into the future how much companies are willing to sort of just like be the API with like without the all those hooks, without the, you know, knowing everyone's email, like having the mailing list, like all that sort of stuff. Yeah. So that that's a very interesting trend.
Speaker 2:I feel like you're generally on the frontier and cutting edge of like adopting all these tools. You mentioned your cloud code use and I'm wondering about give me a a reality check, a health check on your experience Yeah. Computer use because you're lamenting the fact that Airbnb doesn't have an API And Yes. I imagine you could create a scraper or download the HTML and interact with it, like treat the front end as the API, effectively puppeteer the computer through computer use. Like where are you on like the AGI moment in computer use from what you've seen?
Speaker 2:Like, where does it work? Where doesn't it work? Where would you recommend people get started if they want to play around with it?
Speaker 3:Yeah. That's a really good question. There's other places where it works. Like, I think that Codex has done a very good job of implementing, like, browser use actually Yeah. Both for, like, debugging applications that you're working on and in general.
Speaker 3:Like, this is something that QuadGo just, like, doesn't do. Like, creates a really again, that kind of, like I think the next couple months, the thing you're going to keep hearing from me, but also everyone in the world is like, self healing loops, loops, loops, loops. Right? Like, how do you create loops where it's how do you close the loop? Yep.
Speaker 3:How do you have, like, flawed code, make a UI change, see that it sucks, and then just keep going. Right?
Speaker 13:Yep.
Speaker 3:And a lot of like, do we have AGI or not, is how many loops in a row can you do It's all loops. Before Right? Before things just end catastrophically. Right?
Speaker 2:Yeah.
Speaker 3:Because there is sort of this like Yeah. It gets worse, right? Yeah. In in many cases. So, I think, like, there's a lot of, like, ways to answer this.
Speaker 3:Like, I'm a fairly, like, security conscious person. I think that, like, the, you know, I'm not, like, an open claw guy. I'm not gonna give all of my, like, cookies to some, you know, agent, etcetera, etcetera. But yeah, I think Okay. TBD.
Speaker 3:Yeah.
Speaker 2:Yeah. Cool. Well, yeah, new challenge. Book an Airbnb with an agent. Can it be done?
Speaker 2:Yes. Is that where the goalposts need to be set? Let's figure it out. Anyway, thank you so much for coming on the show.
Speaker 6:To see you, Ben.
Speaker 1:Congrats to the team. Congrats launch.
Speaker 3:Course. Last thing, if you want a hat, you can we have a new CLI. You can run Raindrop Drip. Can get a hat and umbrella, couple of other
Speaker 2:Oh, that's a fun that's a fun way to give out I like that. That's very creative.
Speaker 1:For Great on the stuff. Will talk to you soon. Bye.
Speaker 2:Okay. Back to the debate around Figure. We've had Brett Adcock on the show before and he had a livestream. We talked about it a little bit. Watch a team of humanoid robots running a full eight hour shift at human performance levels.
Speaker 2:And Brett Adcock said this is fully autonomous running Helix two.
Speaker 1:Alright. Pull up pull up this post. The From Pete.
Speaker 2:Yes. And the the stream did fantastically. It was twenty four hours. It got 3,400,000 views. But at a certain point during the stream, there was some questions about whether or not the humanoid robot was
Speaker 1:Alright. Fact Back to the beginning. Back to the beginning. Okay. Let's play this.
Speaker 1:Alright. So it's cooking. I mean, the speed is actually
Speaker 2:extremely impressed by this. This was remarkable.
Speaker 1:Remarkable. Even if it's teleoperated, it's extremely impressive.
Speaker 2:Yeah. Yeah. Yeah. Like the robot's clearly working.
Speaker 1:This is very saying that it's not teleop.
Speaker 2:Okay. So then the robot starts missing things being a little bit like an inch off and then reaches up and touches the robot's head, the robot, which is something that wouldn't normally be necessary. It doesn't have like a logical explanation or conclusion. So a lot of people are It
Speaker 1:does have a semi logical conclusion Brett is claiming when it reaches across its body to go to the right that it puts its hand up here to get the hand out of the way.
Speaker 2:That's what I was thinking was that if the hand is is halfway up, It's way back blocking to the the sensor, the camera sensor. And so even though you like you might the the robot might reach the hand up further to move out of the view so then the robot can look at the next package. So that's one possible explanation. But a lot of people are asking even harder questions saying that potentially was there a human in the loop? Was this teleoperated?
Speaker 2:Which is something Brett has said it's fully autonomous. I feel like that means no humans in the loop. But Tior Taxes has an artist's representation of Helix two figures in house neural network running entirely on board. And it, of course, is a human in a VR headset. Very, very debatable.
Speaker 2:We'll let we'll see where you stand. But there is there is a third option which I have shared, which is potentially no humans involved. I don't know if you'd call it autonomous, but you would call it no humans in
Speaker 1:the loop
Speaker 2:because you have
Speaker 1:Well, is an autonomous system. Right? It just sort of runs.
Speaker 2:Yeah. I would I would consider this autonomous. It's the it's the image that I shared in the production chat. It's not of a human and it's not quite robotic but there's no human in the loop. And so this could explain it's the system is running with no humans in the loop.
Speaker 2:If you make that claim
Speaker 1:Running and you follow this,
Speaker 2:I think this qualifies as no humans in the loop. If you have a giant orangutan in a VR headset puppeteering the robot via teleoperation, you could say that this system is does not have a human in the loop. And you could make that
Speaker 1:And I could make the argument that it's autonomous. Yes. The chimpanzee is running its own it has somewhat of a neural neural Yes.
Speaker 2:Yes. No, no, no.
Speaker 1:No one knows. And Bill says I think there was a human physically inside. Oh.
Speaker 2:Physically inside. As a option.
Speaker 1:Yeah. Mean the the Red, Chime in. The thing that I'm so I I wanna talk with somebody at a place like Amazon
Speaker 2:Yeah.
Speaker 1:Who I imagine does this kind of thing all day long.
Speaker 2:Yeah.
Speaker 1:And are they asking for a humanoid to do this process? Yeah. Like, this seems like something that that e commerce fulfillment Yeah. And logistics companies have been doing for many many many many years. Yes.
Speaker 1:Is there not a purpose built robot that sits right there and makes sure that Yes. The packages are in the right orientation? Does it have to, you know
Speaker 2:Yeah. If you watch an episode of How It's Made, you will see every variety of of custom made machine for flipping around, sorting packages, that type of activity. There are custom built machines that run at scale. They might cost like $10,000 but they last fifty years. And anytime you see you know, a Diet Coke factory or gum manufacturing line, all these things like the gum that you have there comes off and the gum rattles down and is sorted into the pockets of the the the packaging and then the sleeve is wrapped around and glued and all of that is done autonomously but just with, you know, a bunch of machinery that was built in probably like a hundred years ago honestly.
Speaker 2:If it works, don't fix it. But you can clearly see see how this type of task package sorting would be like on the curve to a more economically valuable humanoid robot. And like if I was going to buy a humanoid robot to do my dishes and you showed me this video and it was in fact fully autonomous, that would be an encouraging demo to me. That would be something that I would look at and say, oh, well, like if it can do this successfully for hours and hours and hours, I'd probably trust it to put some laundry in the washing machine. That doesn't seem well beyond the scope of capabilities.
Speaker 2:It's so
Speaker 1:interesting how quick it is Yeah. When it's just sorting packages there and then it doesn't divide and walk on the way off.
Speaker 2:Yeah.
Speaker 1:If you rewind for a second Yeah.
Speaker 2:Yeah. Yeah. The walk is not Walk.
Speaker 1:I only use that terminology because that's the terminology that
Speaker 2:That is used. Yeah.
Speaker 1:Like, look. Why does
Speaker 3:Yeah. Look
Speaker 2:like that. If you were able to shuffle like this so fast and so fast, you'd think that you'd be able to hustle a little bit. But maybe that's v two. Maybe that's less less relevant for this particular task. You know, there's a lot of different options but we will dig into it.
Speaker 2:Brett launched day two. I mean, putting up views, sorted 32,000 packages. Day two is live. And he shared more details on what's going on. The original goal was an eight hour run.
Speaker 2:After zero failures yesterday, we decided to keep going. We're now over twenty four hours of continuous autonomous operation without failure. This is uncharted territory. The task is small package sorting. F dot zero three detects the barcode, picks up the package and reorients it barcode face down onto the conveyor.
Speaker 2:Humans average around three seconds per package. F zero three is now around human parity. The robots are reasoning directly from camera pixels. The robots are fully autonomous using Helix two, our in house neural network running entirely onboard f zero three. There's no teleoperation.
Speaker 2:Every action comes directly from Helix zero two. Okay. Well, I feel like that rules out the monkey business. I think think teleoperation I would fall. If if you had a monkey puppeteering this thing, think it would count as teleoperation.
Speaker 2:So he is denying that allegation from the timeline. Yeah. But the timeline seems convinced. YouTuber commenters started naming the robot Bob, Frank, Gary, so they added name tags to each robot. And if the robot gets stuck or the AI policy goes out of distribution, Helix triggers an automatic reset.
Speaker 2:You'll occasionally see this happening during the livestream. If a robot or soft has a software or hardware issue, it autonomously leaves for maintenance and another robot takes over. We run our labs and figure this way to maximize uptime. If we haven't had a failure yet we haven't had a failure yet, but statistically we probably will at some point. So very, very fun going back and forth.
Speaker 2:Who else is chiming in? People are the last Dara says, I'm the last person I'd expect to rush to Figure's defense and I'm looking forward to hearing Brett's take here and in here and in any and all cases. I stand with PBD King. But IMO, this demo seems authentically autonomous and could see this being learned behavior from teleoperators that collected the data for this model with their VR headsets. And PBD sucks, who broke the story or went viral first time said he actually has a pretty reasonable sounding excuse but doesn't give me tons of confidence on the model's brittleness.
Speaker 2:For cross body research for cross body reach the policy lifts its arm to avoid hitting the metal. Shoot. Nice try. I wasn't I wasn't sure if he was gonna if he was gonna reply to this and sort of engage or just sort of let the let the timeline run wild with it. But the metal plate does seem like a piece of what's going on, but people are still hungry for teleoperation bombshells.
Speaker 2:It sort of cuts both ways. I remember Jason Carmen did a a video maybe with one axe and and everywhere in the video, they said this is teleoperation. We're doing teleoperation. We're bullish on teleoperation. Put it at the bottom in the text and the description, like, so told everyone.
Speaker 2:And still people were quote tweeting and being like, this is teleoperation. And so people, you know, are sort of grappling with like what is real, what is fake constantly. Well, is there anything else on the figure story that you'd like to dig through? No. Switch his hands after working more than four hours straight.
Speaker 2:Well, we
Speaker 1:can is some We've dug a lot of in just a few minutes. A new a newly released OGE form, Office of Government Ethics two seven eight t, discloses that president Trump filed 3,642 trades involving stocks of public companies between January 1 and March 31. Transactions include hundreds of stocks and ETFs such as Nvidia, Microsoft, Broadcom, Amazon, Apple, Alphabet, Meta, Goldman Sachs, AMD, Airbnb, Palantir, Netflix, Costco, Walmart, JP Morgan, DoorDash, and others. Individual purchases of Nvidia, Microsoft Broadcom, Amazon Individual. So he's averaging around roughly 40 trades a day.
Speaker 2:40 trades a Check
Speaker 1:my math there. Okay. That is in q one. It's a lot of
Speaker 2:trading activity. We talked about this.
Speaker 1:Selling. Should
Speaker 2:you just give Jane Street right access to the federal, you know, government? Should they just be able to change the laws to optimize for max GDP growth? And feels like we're one step closer one step closer to the the economic singularity of the hedge fund running the country.
Speaker 1:Anyway, we have another
Speaker 2:yeah. What else?
Speaker 1:I'm trying to find the history of presidential day trading.
Speaker 2:I don't know if there is one. Jimmy Carter famously divested from his peanut farm because he was worried about conflicts of interest but we are in a new era. Anyway, we'll have to figure out if Trump is long or short the Cerebras IPO. He's probably watching right now to hear Doug O'Laughlin's take on it to understand what's
Speaker 1:George w Bush.
Speaker 2:With Cerebras.
Speaker 1:Catch up with Dee says
Speaker 2:Yes.
Speaker 1:Not a day trader.
Speaker 2:Oh, had
Speaker 1:a famous controversial stock sale. He sold 200,000 Harkin Energy shares in Okay. 1990 before bad news came out.
Speaker 2:Okay. Interesting.
Speaker 1:Well And there's no no other evidence that we're finding of presidential stock traders.
Speaker 2:Well, we'll dig into it, but we have Doug O'Laughlin from Semi Analysis in the waiting room. Doug, how are doing? Welcome to the show.
Speaker 4:Good. Good, man. You know, pretty busy day. Another day, dude. Honestly, every day is a busy day this race.
Speaker 2:Every day is a busy day. Take it take us through it. How do you think the the market reacted to the Cerebras IPO, to your to the semi analysis deep dive on the company? What is the overarching story here?
Speaker 4:So I think the market was obviously positive. I don't think we're quite as positive as the market, but it's a bull market baby. Mhmm. I I think the takeaway is that Cerebras got to IPO, which one point in time, we didn't think that would happen at the semi analysis world.
Speaker 2:Yeah.
Speaker 4:We're very we've historically been very bearish on SRAM.
Speaker 2:Okay.
Speaker 4:But I think there's a path forward for them to be a disaggregated prefilled chip or maybe even AFT chip. Yeah. Meaning attention feed forward disaggregation.
Speaker 2:Okay.
Speaker 4:So yeah.
Speaker 2:Yeah. Unpack is sort of the the competitive dynamic. Like, with the the the fear around Cerebras as far as I I could tell years ago, it was like, will this ever be useful? Will they ever actually be able to make it? Will it have defects?
Speaker 2:Then it became certain applications, demand side, customer concentration. But where do you think they are now? Where how has that journey evolved?
Speaker 4:So first and foremost, Cerebras is about SRAM. SRAM is like the fastest possible memory and it's kind of done on a logic process. Yeah. But the problem is SRAM scaling is dead, meaning that you can't make smaller and smaller SRAM scales. Mhmm.
Speaker 4:So pretty much they like committed to this dead end process by having the biggest scale up world as a wafer size, but then the models got much bigger than just a single wafer. And so they have really, really fast inference, but only a certain size. And I think the real capability problem is can they inference models larger than a trillion parameters? And I think the answer, as we think right now, it's pretty unlikely in the near term.
Speaker 2:Yes. So I I so I understand all that. I I'm just wondering about the world where should I view it more like a CPU? Because when the AI boom, the ChatGPT moment happened, the obvious buy was NVIDIA because we're going to need a lot of GPUs. No one was really expecting a chip shortage in CPUs but then agents wound up using CPUs for a bunch of stuff.
Speaker 2:You have to keep the GPUs filled. And so CPUs are now in demand. And I'm wondering if there's this world where there's this, yes, you're going to move past the trillion parameter models, but we're going to keep using them forever just like we use relational databases forever even in an AI, agentic AI world.
Speaker 1:Or you have a scenario where you have a big model that that is is giving sort
Speaker 2:of Yeah. Orders. Orders, workloads Delegation
Speaker 1:or something. Delegating to a smaller model.
Speaker 4:Yeah. I think I think in a perfect world where there's no silicon constraints, that might be true. But obviously, there's silicon constraints. And I think Cerebras is really well optimized for a certain problem, and we think they do a great job at answering that, which is fast inference at a certain size of model. Maybe that market's gonna be large enough.
Speaker 4:And I mean, I don't think I was ever bullish to rebus the entire time. But now that we're here, not ironically, 1% of a very large market works. Yeah. I think they got like 1% of a very large market.
Speaker 2:Yeah.
Speaker 4:When it first started, was like, oh, yeah. What are you going to do? 1% of very large market? That's going to be a few $100,000,000.
Speaker 1:Well, that's and that's that's like the classic seed
Speaker 2:Sounds like it's
Speaker 1:seed pitch too. Yeah. Yeah. Ten years ago. Yeah.
Speaker 1:Worries.
Speaker 2:Yeah. Is there any is there any for a long time there was a lot of fear around ASICs companies around architecture changes. We're going to move past the transformer and they're all going be locked in the past. Is there a is there any optimism around there's an architecture change that actually is to the benefit of Cerebras and makes them more relevant in the future? Do you think
Speaker 4:that No, that's possible. I mean, my pay grade. That's two gigabrain for me, right? But where I'm at and the understanding Okay. There is a narrow path for them, I think.
Speaker 4:And I think they're going be able to inference maybe 1,000,000,000,000 parameters, a very small context window sizes
Speaker 2:Yeah.
Speaker 4:Or smaller window smaller models at very, very fast speeds.
Speaker 2:Yeah.
Speaker 4:But I don't know, man. Maybe I mean, like, you know, the true gigabrain take is Mythos is so good or whatever that it makes compute efficiency super easy and Oh. You know yeah. Your your model is inefficient and AI understands.
Speaker 2:Yeah. Yeah. Yeah. Distill yourself so you can run on a Cerebras chip just as effectively. Okay.
Speaker 2:Now
Speaker 4:we're talking. That's that's the gigabrain thesis. But I think I just think that there is there's demands. Right? Like, clearly, we're in a shortage.
Speaker 4:And Ironically, in a shortage, it's not the best company who wins. I mean, you can look at NVIDIA's stock chart and that tells you. It's the second, third, fourth best companies where the demand overflows. Right? And so we're seeing all that today.
Speaker 2:Yeah.
Speaker 4:And I think the reality is the market's big enough for a lot of demand and Cerebras is in that space. So they've done a really good job. And I mean, a cool engineering problem. But we think it's kind of a solution looking for a problem because the the world of LLMs blew up at a much faster scale than anyone could have ever thought of. Yeah.
Speaker 4:The size, I think, is really the difference.
Speaker 2:Yeah. Yeah. Give me a little primer on Grok, how Grok fits into the SRAM machine market, what the view is because it felt like that NVIDIA's move there with the license acquire, as you put it, was defensive against Cerebras. Is that the correct framing? Like, how does Grock fit in on this?
Speaker 4:Okay. So let's talk about exactly where Grok fits into the architecture. So on in a transformer architecture, have, like, the multi heads of attention, and then there's a feed forward network.
Speaker 11:Mhmm.
Speaker 4:That's a portion of, you know, essentially the entire transformer block. And what's become really hot in the last few years, or not even two years, like probably a few months, man, is you've been disaggregating all the different parts of inferencing into subsequent specialization. So we're talking about GPUs and ASICs being a specialization over CPUs, now but we're actually starting to break essentially the constraints of inferencing into different, I guess, compute and memory bounds like pockets. And so for example, we're finding prefill ends up being pre fill being essentially loading all the weights, ends up being compute constrained. So you don't really need a lot of memory bandwidth.
Speaker 4:So why don't you just use a very FLOPS heavy portion and you disaggregate the memory onto the decode portion, which is like extremely memory bandwidth limited. And so this is Grok where this fits in the strategic thought process here is in the g v 200 rack. What you can do is you can pass the activations over to the and the to the SRAM in the Grok LPU rack, and that is an extreme speed up. And so that's a perfect example of another break apart of the transformer architecture. Pretty technical, but that's the thought process here is that the memory is so fast, The memory band or the the speed of the IO doesn't really matter, and you don't need a huge scale up world size because you're just streaming the activations.
Speaker 4:Yep. That problem wouldn't work with the Cerebras trip because you're kind of it's it's an island, right? You just think of it as an island of compute. It's really really good at everything in the middle, but moving anything off the island is really hard versus moving something off the island onto a grok chip because there's a plug at the end of it is a lot easier, and that's kind of the calculus, I guess.
Speaker 2:Yeah. So cerebris lower memory bandwidth, lower interconnect speed?
Speaker 4:Off the chip. On the chip it's a fast Yeah. Okay. Yeah.
Speaker 2:So what so so what does that mean for the the the Grok NVIDIA ecosystem? Because is this something where the default configuration is going to be a Blackwell and a Grok chip like in, you know, 50% of racks, 80% of racks? Or is this like still some sort of niche application where Grok is going to be deployed, you know, sort of sparingly sprinkled into specific use cases? Do you have an idea?
Speaker 4:Yeah. I think I don't have an idea with high precision. I think you'll find that a lot of these things there's a lot of different ways to split up and serve your model. So expert parallelism, pipeline parallelism, tensor parallelism. Right?
Speaker 4:And so the correct optimization per hardware rack is going to depend on the shape and architecture of the model, and we don't really know with high precision what is what. And there's been different road maps along the way in terms of what they wanted to do for speeding up inference. A perfect example of this is the CPX rack, which was mostly built for extra parallelism. It kind of remains to be seen if this is like if the Grok GV 200 speed up is gonna be like the way forward. But it's definitely a technology tree that I think Jensen is excited about.
Speaker 4:So, I mean, we'll see.
Speaker 2:What about Lisa Su at AMD? Is she excited about this technology tree? Can you give me an update on how AMD fits into all of this?
Speaker 4:So AMD is mostly just trying to get the last thing to work, which is the rack scale up.
Speaker 2:Yep.
Speaker 4:And I think they're going to do a good job of four fifty. I think what's going to happen is that, like, you know, it's a compute shortage, right? So you talk about overflow demand. I think Lisa's going to figure it out. But on the inference serving side, I think there's definitely some demand or desire to probably match the Nvidia roadmap.
Speaker 4:And I wouldn't be surprised to see if there's some kind of fast SRAM offload FFN chip in the next twelve months. But the thing is the number of candidates there is actually pretty low. I think Intel's Intel's going for SambaNova, which is a little clever. There's like Ethereum two. There's a few other players out there too that pursued SRAM scaling.
Speaker 4:But I think that in this specific case, Lisa's mostly just figured focus on the last thing. And I think AMD is definitely good enough right
Speaker 2:Okay. On Intel, what is the latest there? It feels like the roundtable has been assembled and sort of everyone has held hands and decided to maybe jump across the the transom at the same time, take the leap of faith. But it also feels like, you know, lithography machines are majorly backlogged, like there's a whole supply chain that they have to answer to that's backlogged. And so really high expectations but also what are the what is the next milestone for them after they actually get these deals with Apple and Elon Musk?
Speaker 4:Amazon and Elon Musk. Yeah.
Speaker 2:The the Gigafab. Sort of like once they get those signed, like, does the next couple years look like?
Speaker 4:I think it's about execution. Mhmm. It's kinda crazy to me that I think the stock price is ahead of the technical turnaround. And I think that I think Lip Bu Tan clearly has, like, righted the ship and gotten the right people onto the party, if that makes sense. And I think I I really do think the government intel deal was a stroke of genius because Pat Gelsinger spent, you know, three years trying to build a bottom up demand to essentially come to the fab.
Speaker 4:And and Trump's like, yeah. None of this. I'm gonna sign the deal from the top. Mhmm. And what's gonna happen is you're gonna come play because we're in the United States government or else.
Speaker 2:Yeah.
Speaker 4:And so I think I think people are there. I think the customers are there. I think the process is good enough. Think I fourteen eight will be also good enough given how much of a shortage n three at TSMC is, and it's all execution risk from here. But the historical Intel has quite a bit of execution problems, so we'll see.
Speaker 2:Okay. Before we move on to TSMC, which I want to go to next, are there any other interesting ASIC projects on the horizon? We've talked to a few of these companies, but I'm interested in like the shape of the differentiation. Like you explained a little bit of the divergence in strategies between Grok and Cerebras, but there's Etched and a bunch of other companies that are working on new chip designs. And I'm wondering if any of them stick out to you as particularly differentiated.
Speaker 4:You know, I'm not going to go too into the details because I feel like some of them are even, like, still figuring out their Sure.
Speaker 12:Their road map.
Speaker 2:Yeah.
Speaker 4:I think Mattox is kind of interesting Yeah. The way that they're they're kind of trying to pursue the memory problem. I think I think Etched, I'm excited about the kind of YOLO bed, if it makes sense, just make a big systolic array.
Speaker 2:Yeah.
Speaker 4:But I think there might be like niche cases. I think the problem is, at the end of the day, NVIDIA's big bus is still really good for the majority of cases, and you're going to have to start to make really opinionated bets on the ASICs to find what niche market ends up being all like a diverter of demand into their ASIC. And so the ASIC specialization from here, I feel like you have to make some pretty big brain bets in order to make your bets come pay off. And I think most of the bets that would have guessed when you, like, when you originally did them, didn't really wouldn't have paid off. And the ones I didn't expect did.
Speaker 4:Like, it's kinda crazy.
Speaker 2:Yeah. It is it is a very weird market dynamic where a couple years ago, we saw ASIC and new chip companies, new silicon companies raising hundreds of millions of dollars or $500,000,000 And it was like, well, for that, you're going to need this massive market. Are you really going to flip NVIDIA or something? And then the market grew so much that the 1% of a huge market sort of potentially maths out for some of these companies now. It's a Yeah.
Speaker 2:It's a fascinating development. Jordan, do have something?
Speaker 1:Yeah. China trip. Yes. Oh, yeah. What are you tracking?
Speaker 4:On the h 100? Oh, so honestly, do you guys see the parade? You know Trump loves the parade.
Speaker 2:Oh, yeah. They're winning the parade. Good parade.
Speaker 4:I was like, dude. Was thinking, Ben, if I'm I'm not much of a parade guy, but I was like, dude, if they show if they showed up in that parade was for me Yeah. I'll be like, these guys could be friends.
Speaker 2:Yeah.
Speaker 4:My my impression is that the executive the executive branch really wants a deal, and I think, you saw the h 200 list, the verified h 200 list. I expect probably more lightening up on the executive branch. Something that's really interesting is if you look on the legislative branch, there's actually more export control bills going through the house than, like, ever in history time. So there's kind of this tension, but I do think, you know, Trump's a businessman. He loves the deal.
Speaker 4:I expect I expect a deal. So.
Speaker 2:Yeah. Somewhat related, TSMC. Ben Thompson was writing that potentially they weren't ramping CapEx fast enough. What are you what are you tracking on TSMC being a potential bottleneck for the AI build out just as more and more Cerebras is now trying to get allocation, it feels like a particularly sharp elbowed place to do business.
Speaker 4:Yeah. So I think at the end of the day, TSMC is kind of a kingmaker in terms of supply. And there's no reason for them to really let the market go out over its skis. And I think they're happy with the pace of what they're they're expanding out because, like, hey, they're growing their CapEx, like, 40%, but in absolute dollars, these are big numbers.
Speaker 2:Yeah.
Speaker 4:We're gonna run out of TSMC engineers in the island of Taiwan pretty pretty soon here. So I think I think this is all kind of good on the margin for overflow demand, which is actually, it's Intel. Intel's, you know, definitely reflecting some of that, but I think the shortages specifically at TSMC is driven by clean room. It's a long lead time item. It takes three to five year or let's just say three years to bring a clean room up.
Speaker 4:And so in order for them to have, like, figured out and, like, perfectly matched demand, two years ago, they would have to been like, we have a 10,000 square foot house, and we need to buy a 50,000 square foot house with conviction. Yeah. Right? It wasn't that clear two years ago. And so I'm gonna expect supply to kind of lag over and over and over.
Speaker 4:But demand signals will continue to essentially command premiums, move up wafer pricing, move up orders, and that's what's gonna make TSMC invest more next year and the year after. But they're gonna do it in a, like, in a incremental, not a revolutionary way, but, like, an evolutionary way. They they are very, like, methodical and do steps one at a time.
Speaker 2:Okay. Cleanroom fungibility. When you say it takes five years to build a clean room? I I immediately go to SpaceX. I imagine that Elon can build big things quickly.
Speaker 2:Is there some world where that partnership accelerates Intel regardless of your timeline for the mass driver fab on the moon, all the crazy long term stuff. But just having Elon around the table to say, oh, we need to build something big and it needs to be, you know, capable of operating as a fab. Like, is there something where he brings more to the table than just dollars potentially?
Speaker 4:So I definitely think Elon is the man to do it. Mhmm. I forgot who said this, but like Elon makes the impossible late. Yeah. I don't expect it to be on time.
Speaker 4:Yeah. You know, talking with the cigar in in in the terra fab, I'm really kind of doubtful. It's, you know, I guess from first principles, it's easier to just clean the entire room than to make like really hyper concentrated pockets. And that's what I would guess the bet is. But I still think by the time Elon figures it out, the supply response will have reacted already.
Speaker 4:Mhmm. We're still two, three years out, and there is some cleanroom fungibility. But and and you've already seen this, actually. Micron bought an old power fab. I think this is the TSMC deal.
Speaker 4:People are buying display fabs. Essentially, every bit of clean room that is not accounted for in the world is being snatched up and retrofitted to kind of meet the supply demands.
Speaker 2:Interesting. Yeah. That I mean, that's happening all over. Didn't Ford just announce some sort of AI play today? The stock's up on something.
Speaker 2:It's it's all over the place. I am interested in in terms of like
Speaker 1:6%.
Speaker 2:Getting getting powered shells
Speaker 11:Oh, Ford is worth
Speaker 1:more than Figure now because last year, around a year ago, I remember Yeah. Figure was worth more than the Ford Motor Company.
Speaker 2:It is rough time. But now they're both AI AI companies, I guess. But what are you tracking on the American data center build out domestically or terrestrially before we move on to space capabilities? Yeah.
Speaker 1:Basically how Yeah? Oh, go for it.
Speaker 2:No, no, no. Just No, go for I'm just curious about I mean, we're starting to see glimmers of pushback at the municipal level, different data center bands. And I'm wondering about what are the big levers that are that need to get pulled to actually continue to bring capacity online in America?
Speaker 4:Yeah. I think that's a good question. And you're already seeing the first level of this is the delays. Yeah. I my my favorite clickbait is 50% of all data centers in America are are delayed or canceled, implying 50% is canceled when it's really just everything is delayed.
Speaker 4:That's like my favorite clickbait. I got to steal that in the future. But I think it's going to be local municipal, and people have to really believe and demand and desire the jobs. And I think one of the ways that we're seeing this is like, you know, capitalism works, and effectively, the dollar per megawatt has been going up. It's like a one way train.
Speaker 4:In the same way that, like, you know, the power per rack has been going up, the cost of making these data centers have gone up. And one of the ways that happens is it leaks into labor. Right? So essentially, you're super against it, but all of a sudden it offers 3,000 new jobs to your home and you're like, well, maybe maybe I'll take it. And I think that with enough economics, oftentimes, you know, money finds a way, and that's kind of that's kind of how I would guess.
Speaker 4:But it's gonna be like a it is a it is like a county by county fight. Right? Yeah. And some places are just gonna say, hell no.
Speaker 2:Yeah. On the note, we were debating this earlier today. There's been a couple of examples in like viral photos and articles about like they I I bought a beautiful house in the countryside and then they built a data center right next to it. And, you know, no matter how pro AI you are it sounds annoying to have a huge building that's an eyesore and maybe noisy, maybe smoky next to you. Have you been tracking like how how how feasible is it just to throw the data center truly in the middle of nowhere?
Speaker 2:It feels like America has a lot of land, but what goes into selecting data sites these or data center sites these days? Do you have something else? So yeah.
Speaker 4:So I I think pretty much two fiber pairs is the big desire. Essentially, it's like you're more than willing to go to where the power is because you have to go to what the biggest actual bottleneck is, power is the biggest bottleneck. So you can just In the past, you're talking about like, hey, having these inference or rather, like, let's say, point of presence near, local cities. Right? But power was never a constraint in that world.
Speaker 4:It was just, you know, the biggest constraint was getting this video from TikTok to your phone as soon as possible. If the biggest constraint and the largest part of the cost is gonna be power, why not move the data center to power and then then, like, you know, essentially hook it up with fiber? Yeah. And so I think that we're gonna put them in the middle of nowhere. That's just how it's gonna work.
Speaker 4:To a certain extent, there's gonna be more densification in some of the inference near the population, but I still think the ROI makes the most sense to kick it out in the middle of nowhere.
Speaker 1:Yeah. Has the political backlash, pushback updated your thinking at all around the viability of space data centers? I I remember, you know, we talked as this idea has like gained popularity, you guys have like consistently said, yeah, technically you can do that but like maybe it won't be
Speaker 2:takes a long time for the
Speaker 1:There are space data center players now that are kind of loving the pushback against terrestrial data centers because they're like, the the more pushback there is, the more it could make sense for us to put this put these, up in space. But what's your view?
Speaker 4:I still think economics is gonna win out. You know, something a pound on Earth is probably ten ten times more expensive in space, And it's really hard for us to go to essentially beat that out with a new completely specialized supply chain for what's going to be a smaller market in the near term. It's a real adversary against the adoption in, like, let's say, short run. In the very long run, because I'm sure you saw the Anthropic Colossus thing where it's also interested in space, right? Like, the biggest maxi vision of this is AGI.
Speaker 4:We have 30 ter you know, we've a thousand terawatts of GPUs on Earth, and we're like, we gotta put a terawatt in space. Right? So, like, in that world, I think space space data centers work where a small percentage actually gets
Speaker 2:so big. It's It's 1% of the market again. It's just like Just 1%. One per and it's a trillion dollar. It's a trillion
Speaker 1:dollar vindicated.
Speaker 4:Yeah. Yeah. VCs are vindicated. What's Tam
Speaker 1:Tam pitch deck slides vindicated.
Speaker 2:Yep. Yep. Yep.
Speaker 4:Yeah. It's literally as big as the galaxy, bro. Just there's no end to it actually. Think about how big the TAM
Speaker 2:is. Yeah.
Speaker 4:So I think what is more likely is if it continues to be painful to do it from a zoning perspective in America, it will essentially slip into other geographies, probably in the Western Hemisphere. There's a lot of power in space in Brazil, and I think that that's probably good enough. There's definitely ways to make this work. I definitely think the only way you do it is by paying more and finding someone who's like, you know what? I'll hit the bed.
Speaker 4:And so that's the important part. But, know Yeah. It pulls some finds a way.
Speaker 2:Is that is that sort of the bull case for sovereign AI initiatives? I was always super skeptical because like Europe didn't get like France's Google. Like they just use Google. And for a lot of consumer aggregator type consumer internet companies, it's like Spotify is from Sweden, but it could be from America and it wouldn't matter. YouTube is from America and they use that over there.
Speaker 2:And you didn't need a, like, a national champion in every consumer category or there were certainly like returns to scale and a lot of the American companies just won. But so I never really bought the whole idea that like, oh, the French need like a locally trained LLM and the Germans also need a locally fine tuned something or other. But if every company every country has some sort of excess supply of energy or space or regulatory capacity for data centers, sort of bringing that online and just operating like a neo cloud could just be economically valuable for that country regardless of whether or not they're vertically integrated to the point of the consumer or the business that's running an AI agent.
Speaker 4:I think that's probably the case where at the end of the day, economics is gonna like kind of push it through. And there is FOMO and Europe did do a lot of investment in the Internet like really late if we're gonna use 1999 as an example. I think the thing I keep thinking about is that this AI thing is going to be a big deal. I continuously am shocked and surprised by the magnitude and scale. That's a derivative violation.
Speaker 2:I don't think it is right now. I I I feel like we are in a particular moment where
Speaker 1:No. There's just the
Speaker 2:people calling the top in the bubbles, like, they're awfully quiet right
Speaker 1:now. And that's makes me even more scared.
Speaker 4:Yeah. That is on okay. So to be clear Yeah. You know, the the true top, there's no everyone's bullish. Right?
Speaker 4:Everyone's like, dude, it's actually gonna be bigger
Speaker 8:next year.
Speaker 4:Yep. It's actually just gonna be a bigger bubble, so shut up. Yeah. So Yeah. I was not
Speaker 1:concerned about I was not concerned about a bubble when everyone was saying
Speaker 2:It's a bubble. Yeah. Exactly.
Speaker 4:I am I mean, I'm I'm a little concerned it's a bubble, but at this point in time, I think if you look at the big I've been I've been reading
Speaker 1:here's my view. Please. It's not a bubble until you guys are spending a 120% of revenue on tokens.
Speaker 4:Yeah. Our gross margin goes negative. Yeah.
Speaker 2:He's just like, we're raising a major fuss. We're not gonna be investing in it. We're gonna be burning
Speaker 1:it. It's actually not a bubble until semi analysis goes public and trades up 600%.
Speaker 2:There we go. I like that.
Speaker 4:That's the that's the real talk. No. No. I think there's a few things that have to happen. I think OpenAI or Anthropic, someone has to go public, and it's gonna be this year.
Speaker 4:Like, we have, like, we have to hit that keystone before before it's all over. But also I also think I keep thinking about this as like, dude, this is a big a big technological revolution. Yeah. I think it's bigger than the Internet, and I I firmly believe this. I don't think I believed it'd be bigger than the Internet when I maybe even two years ago, but I'm pretty convinced this can be bigger than the Internet.
Speaker 4:And if you look at the past, these big technological changes are often sometimes bigger than, I don't know, everything else. It reshapes the entire world. For example, on the sovereign AI thing, maybe you're like, yeah, you don't need to fine tune LLM, but what happens when AI becomes such an important fundamental, almost like society level institution that a government can't control it, that becomes really, like, uncomfortable and weird. Where it's like, hey, Anthropic can just, you know, put 5% of the compute of mythos and, you know, run a really effective effective government, you know, whenever you want it. And you're like, woah, what does that mean for us?
Speaker 4:Yeah. And so this wave is so big that I think people are going to, out of fear and concern that they're going to be left behind and that the institutions that AI will bring is going to be bigger than the original thing that we're doing, I think that that's the problem. Right? Like, the industrial revolution changed everything.
Speaker 1:Yeah. The other thing that we were joking about in Q4 of last year is John was like, great. The bubble pops. Oh, yeah. Like the bubble inflated and then it pops, but then we got agents and then you have this sort of like reacceleration of every metric across the board.
Speaker 1:And so the other thing that we're like, we're trying to comp the AI boom to the Internet. But the problem with the internet boom is that we didn't have the internet. So everything just took like or the internet was coming online and people were getting access to it. And so the entire build out and all of the capabilities and all the companies took a lot longer to sort of grow, right? And now you have that core infrastructure and so when you're layering on more infrastructure that accelerates all the underlying trends.
Speaker 2:Yeah. Yeah. Well, I mean, the labs the lab revenue multiples are like an order of magnitude or two off of .com peak multiples. In the public markets, Google, Amazon, Apple, all the hyperscalers are at like pretty reasonable price to earnings multiples still even with all the CapEx and stuff. So
Speaker 4:Your put the pushback would be it's on free cash flow that
Speaker 2:Yeah.
Speaker 4:You can make earnings look good instead of free cash flow. But, like, I think the revenue continues to be real. The demand continues to be real. And until you just, like, see demand evaporate, like
Speaker 2:Yeah.
Speaker 4:It's hard for me it's hard for me to sit here and be like, GB prices are up a ton. Quadcode is really valuable to me. I still think I'm an early adopter, and, you know, this is all gonna end tomorrow. I envision myself using it every single day more for the rest my life, which is kind of crazy, and I think I'm an early adopter. And so I just think it's hard for me to envision this not being a ginormous deal, and it's kind of like we just got the like, I really I wrote this whole thing about, like, angles, pause or whatever.
Speaker 4:Like, it's gonna change everything. Like, the the the amount of net output that's gonna increase is going to just blow up our minds. It might be bad for GDP, ironically, because GDP will be unmeasured. Like, we're gonna like, GDP might be broken as a concept. GDP got invented in the nineteen thirties to measure how much output you could make, to not screw over the domestic economy for World War two.
Speaker 4:Like, it was it was a way to essentially organize the the, the American economy, and it's a statistic. It's an estimate. Like, I think all of I think we're gonna, like, attack in, like, a lot of institutions and ways that we're doing things and ways we measure are gonna be attacked by this because it's, like, such a big change. We have to rewrite the playbook over again.
Speaker 1:And people and it's and it's funny. I think wasn't Ben Thompson was talking about this in a recent interview of like, people are comping this like, okay. Silicon Valley, like, you know, brought crypto
Speaker 2:Oh, yeah.
Speaker 1:Online, and then it wasn't maybe as big as some people had had pitched it to be even though it's been
Speaker 2:Yeah. Self driving cars Super powerful. VR.
Speaker 1:And then and then even the way you're talking, you're like, you know, we're we're still early, classic crypto But the promise is
Speaker 2:you are early, you have nothing You
Speaker 1:know, in crypto it's like, well like a community could have a DAO and that DAO Yeah. Could be worth a billion that community could be worth a billion dollars but there's just no way to measure that. But now we have tokens and you're saying GDP. But anyways, I'm trying to like unlearn I think some lessons from that cycle because Yeah. There are a number of things that are quite different.
Speaker 1:It's also What about what about
Speaker 2:the reflexivity that that people do have a little bit of an immune system to just running away with everything because you you you could you could believe this and then bid, you know, Nvidia to 10,000 times earnings or something. And, like, at at certain point, you have to start grappling with the reality.
Speaker 1:What about robotics? Has Figure had a major breakthrough?
Speaker 4:I mean, I one, I have not been following the feet as close as I should be. I just think robotics feels a little further out than the hype would let you believe. I feel like robotics is much more akin to the driving car paradigm where it's like, oh, yeah. It's definitely gonna come and automate everyone's jobs, and then it takes a lot longer. It's a lot, like, unsexier.
Speaker 4:I think the the the scary or positive thing about AI is since it's information work and it's already been distributed and it has the perfect network to run on, which is the Internet, it can disperse very quickly. And that's what we're seeing right now. And so, yeah, I I'm I'm just not anywhere near as bullish robotics as I am
Speaker 2:Yeah. The fundamental for you. Well, I'm bullish on the next seminalysis. I don't know. What are cluster max and inference MACs?
Speaker 2:What what are those called? Dashboards or analyses or rankings?
Speaker 4:Dashboards. Dashboards now. We everything's a dashboard, brother.
Speaker 2:It's a Well, you need be a
Speaker 1:new dashboard.
Speaker 2:GTP, gross token production. This is what we're measuring now. This will be output of The United States. Gross token production, GTP.
Speaker 4:We need to I mean, I think more on this soon, actually. This is, a place we're doing some research on.
Speaker 2:Yeah.
Speaker 4:But I think, you know, the real the real bubble metric is if we're, how many tokens? What's the token Yes. Yes. What's the token replacement cost? That would be some really good bubble math where
Speaker 2:it's like,
Speaker 4:yeah, yeah, a software company has really low token replacement per market cap, but a hardware company has an extremely high token replacement cost. And then it's like, oh, no. No. It's just enterprise value divided by token replacement cost.
Speaker 2:Well, the real the real bubble one will be to go to the full Merry Maker, like, eyeballs metric, eyeballs multiples. Yes. So you will value companies purely on token consumption. You'll say, oh, well, they're consuming 10,000,000,000,000 tokens, so they must be worth a billion dollars and then you'll get really weird gyrations.
Speaker 4:That'd be great for seminalysis. That'd be really good for seminalysis. We are we are consuming a lot of tokens.
Speaker 6:Well,
Speaker 2:you're also putting a lot of stuff. I really
Speaker 1:enjoyed the Would you guys ever make a sort of political style attack ad against another research firm for having AI psychosis?
Speaker 2:Is that a reference to
Speaker 1:the Sorry. It's a reference to General Catalyst attacking
Speaker 2:Andrew
Speaker 1:Feldman. Andrew Feldman.
Speaker 4:Andrew Feldman? Yeah. Know, life's
Speaker 2:I pretty actually think is peerless. I I I don't think there's like a neck and neck with someone else. Like, it's just you guys.
Speaker 4:I'm not. I yeah. I was gonna say, I don't really know who our competitors are. Yeah. I don't, you know, I don't really think about it, Mark and Jason or or, you know, another research firm like that.
Speaker 4:Maybe one day, maybe we will go through AI psychosis. Honestly, guys need an You
Speaker 1:need a guys need a arch nemesis. You need an op. Moody's.
Speaker 4:I guess it would be Gartner. Gartner had to say they're like, but this is not a good offer. Need you
Speaker 2:need a semi analysis hype cycle and it's up only. No no trap of disillusionment. Straight line. Yeah. Straight line.
Speaker 4:No axis. And it's it's actually going backwards.
Speaker 2:It's a straight line on a log graph. That's what it is. Perfect. Semi analysis hype cycle. I love it.
Speaker 2:Gartner doesn't stand a chance, but thank you so much for coming on the show. This was fantastic. Always
Speaker 1:Full analysis.
Speaker 2:Full analysis. Yeah. No more semi analysis.
Speaker 4:Those guys would kick our
Speaker 2:ass, man.
Speaker 4:If they had full analysis, they'll kick our ass.
Speaker 12:It'd be so over, man.
Speaker 2:It'd be over.
Speaker 4:So, anyways, take care, guys.
Speaker 1:Have a great day.
Speaker 2:We'll talk to you soon. Cheers. Bye. Next, we have Andrew Feldman from Cerebras joining in twenty minutes. We'll go back to the timeline because the OpenAI Elon Musk trial is in its final day.
Speaker 2:The trial is ending. People expected four weeks of trial. We only got three. They're cutting it short. What are the prediction markets saying about who's going to win?
Speaker 2:I want to know that. And I want to go to Mike Isaac, the rat king, because he has a breakdown of what's going on. He says, good morning. Closing arguments of Musk versus OpenAI with special guest Microsoft are happening today, Thursday, May 14. Again, my guy is like, of course, he kicks it off with what his lunch is.
Speaker 2:He's got an epic bar. He's got the bison snacks. He's got a la cologne latte. He's got a couple other good things. He looks like he's prepared.
Speaker 2:He's got a bunch of snacks. I feel like he's in a better position today. Learned his lesson
Speaker 1:three weeks of complete self improvement.
Speaker 2:I think that's what's going on here.
Speaker 1:So The Cal sheet. Well, Elon won his case against OpenAI. It peaked at a 58 chance. Okay. Where is it now?
Speaker 1:April 28. It's now sitting at a 30% chance.
Speaker 2:30% chance. Okay. So right now, the judge is instructing the jury on the criteria by which they should be judging the outcome of the case. Important because if the jury listens and carries this out, it is a very, very specific lens through which they view all the evidence. Ostensibly, it's where theater ends.
Speaker 2:Listening to this and being read out in court for the last twenty three thirty minutes is very helpful because it's clarifying on how high the bar is for the plaintiff's side approving some of these claims. Sort of feel bad for the AV guy during this trial. There's been feedback. There have been mic drops but not in the good way. The mics have been dropping out.
Speaker 2:Funky video feeds. They need to revamp this place, says Mike Isaac. LMAO, the first joke of the tweet storm. He says, Musk counsel is going after OpenAI execs, Altman and Brockman, and has the mugshot style photo of Altman on the screen again. Battle of Photoshops of executives in this trial has been entertaining to watch.
Speaker 2:You want to depict your opponent in the worst possible light. Musk's counsel going back and forth hammering the point they made over and over the argument essentially painting a picture. Sam Altman? Liar. Chipping away at witness credibility has been a core strategy for the plaintiff's side and we're back to everyone hates Google again.
Speaker 2:Molo is using Larry Page who they claim doesn't care about humanity as a foil to the noble Musk who only whose only care with respect to AI is the future of humanity. Musk's counsel is painting the Dros don't trust Sam picture in a bit more detail for the jury. Also Musk's side has a picture of Elon and Altman on the screen now. Sam's looks like he's about to be processed by a US Marshall. Musk's looks like he's getting ready for the Met Gala, l o l.
Speaker 2:Lots of Musk closing side arguments, suddenly populous track of pointing open pointing at OpenAI and saying these billionaires are making gobs of cash while running a charity for the supposed good of the world. I'm curious if jury can register this argument even if it comes from Elon Musk, the world's richest man. Ouch. OpenAI Council begins closing argument with a broadside against Musk. Every even the people who work for him, even the mother of his children can't back his story.
Speaker 2:Oh, yeah. Back to the war of the Photoshops. Okay. Closing remarks now in in the digital displays and the monitors for exhibits. All the OpenAI executives look like O'Laughlin Mills photoshoots.
Speaker 2:Do you know who O'Laughlin is? He says it's complimentary. I need to get up to speed on my photographers.
Speaker 1:Olawn Mills is a portrait offers portrait photography.
Speaker 2:Oh. It does look very nice if you pull up the the Google images on Olawn Mills. Anyway, short summary of the closing. Musk camp, all these open eye executives are rich as hell and lying all the time. Open eye camp, all of that is a sideshow.
Speaker 2:And literally all the claims Musk is bringing cannot be stood up by actual law. The Microsoft camp disappears into bushes. Dota got mentioned again. They love mentioning Defense of the Ancients. Incredible Photoshop from the OpenAI camp of a calendar of events complete with little characters and a timeline of events.
Speaker 2:I wonder if they're using ImageGen two or if they're doing it the old fashioned way. I can't wait until it's entered into evidence this afternoon so he can show us. Sort of want to buy this meme guitar but I also have two telecasts. Is that just completely side note? Gamer has entered the blog.
Speaker 2:The DOTA moment has been mentioned nearly every single day during this three week trial. AI researcher we gotta have Mike back on the show. It's so good. Saw as a true breakthrough in the technology. So Mike Isaacs says I
Speaker 1:played Past four is there what what is the timeline for the jury to meet? Okay. Is this something they're doing today?
Speaker 2:So they're getting a thirty minute recess. Most they've had in a month. I might actually be able go outside and get real food. There's a Popeyes across the street. Is it a bad idea to get a bucket of red beans and rice?
Speaker 2:That's what he's thinking about doing. So not much news on when this will close. It is 01:10 Pacific time. I imagine that they will wrap up by what did you say? 3PM?
Speaker 2:4PM? So thirty minute break. That happened forty minutes ago. So I imagine that
Speaker 1:And but they've taking Fridays off is kind of what I'm getting at. Oh, yeah. Because this could happen.
Speaker 2:So maybe this happens to Monday. This is just closing arguments. It's not necessarily the end of the trial.
Speaker 1:Or the jury might get the results. Or the jury might might make a quick call,
Speaker 2:but Well, there was an
Speaker 1:up date
Speaker 2:eleven minutes ago, a lawyer for OpenAI on Thursday defended the company's chief executive Sam Allman from withering character attacks by Elon Musk's legal team as both sides delivered their closing arguments in a trial with potentially seismic implications. The stakes are high. Mister Musk, was not in the courtroom on Thursday because he was in China with president Trump, is asking for more than a $150,000,000,000 in damages. He is also asking the court to remove mister Altman from the start up's board and to stop a shift the company made last year to operate as a for profit company. They pushed back.
Speaker 2:Sarah Eddy, member of OpenAI legal team, tried in her closing argument to dull the attacks on Altman's credibility and to argue that there was never a firm agreement among the founders that could have been breached. Not one in this case other than Elon Musk has testified to any commitments or promises that Sam Altman or Greg Brockman or OpenAI made to Mr. Musk is what she's saying. And there is a new update that just dropped in. After the recess, William Saddad, OpenAI's lead counsel, told the jury that Musk does not have a claim against the startup unless there was a specific agreement between Musk and OpenAI describing how his donations to the nonprofit should be spent.
Speaker 2:That agreement does not exist, Savitt said. So that's where I guess open eye is leaving it for now. We will continue to cover the story
Speaker 1:as Doug says, it is the jury allowed to use codex slash gold? He does in one and a half hours.
Speaker 2:There's other tech problems going on. Max Zaff over at Wired has been covering the story as well and says, Musk's lawyer brought a big monitor, maybe 36 inches into the courtroom. OpenAI's lawyers asked to use it. Musk's lawyer said no. The judge told Musk's lawyers that they have to let OpenAI use it.
Speaker 2:Then OpenAI said it might not be possible to connect their laptops to it. AGI is here, but we'll still need a dongle, I suppose. A dongle has entered the courtroom, Max. There's about 50 15 lawyers standing in the middle of the room right now talking how to you talking about how to use this big monitor. This is wild.
Speaker 2:They they should have talked to OpenAI about sharing their monitor. What I always do I always tell you when you come in here, talk to the other side. We don't have the technology available right now, so we don't want to use the TV. We think we should just get rid of it, says the OpenAI lawyer. Sam Walman just walked into the room, by the way.
Speaker 2:So that happened four hours ago. One of Musk's lawyers carried the big monitor out of the room upside down, wire dragging behind him, defeated. Defeated lie and retreats. That is a very, very funny story.
Speaker 1:In other news
Speaker 2:Bring it down.
Speaker 1:Tim Draper says, I think I broke a record. I took 52 pitches in fifty two minutes at below 40 degrees. Welcome to my office. Hashtag Draper University. Hashtag survival training.
Speaker 1:What do we think about going in the ice tank?
Speaker 2:How cold are ice baths typically? You you you've done ice ice baths. I feel like I did one and it wasn't as insanely difficult as people said, but then I checked the temperature and I don't think it was 40. I think
Speaker 1:it was closer to 50. You can totally get closer to I I put
Speaker 2:because there's a couple of companies that sell
Speaker 1:Personally, when when if you're Yeah. Going surfing and the water is below 45 degrees, it can just be very painful.
Speaker 2:Okay.
Speaker 1:Like to Yeah. So even in a wet suit.
Speaker 2:Oh, okay. Your fingers go down. Anywhere that's not covered.
Speaker 1:A lot of people are putting gloves on. What do
Speaker 2:you think, Tyler?
Speaker 7:I So apparently, Joe Rogan's at like 34.
Speaker 2:34. Yeah. Wow.
Speaker 7:So that's like the cold plunge,
Speaker 12:you know.
Speaker 5:He's the
Speaker 11:he's the
Speaker 12:top of
Speaker 5:the mountain when it comes to ice bows.
Speaker 2:He's the final boss.
Speaker 1:This yeah. This is this is just a crazy picture. I did think it was I did think it was AI, but but it turns out it's real. It's just funny because it looks like like what is this set? What is this setup?
Speaker 2:Yeah. What are all the trash bags there? And the wall is like sort of decrepit and there's piping and
Speaker 1:It looks like kind of like a prison ice bath.
Speaker 2:Yeah. This is not what you'd expect from I mean, he a billionaire investor? You'd expect some sort of palatial, you know, you see the the the properties that Mark Zuckerberg is acquiring, that big investors are acquiring. You would expect something that would be much more regal. But he's doing it the old fashioned way, whip this up himself, bought some track bags and took some pitches.
Speaker 2:Yeah. And you know, who knows? Maybe the next the next founder of Cursor, Figma ramp is
Speaker 1:52 pitches in 52 is crazy.
Speaker 2:A minute is is crazy fast for a pitch. I mean, we do ten minute interviews, fifteen minute interviews, barely get to the meat of the
Speaker 1:And this one you got four Minutes. Four of the founders.
Speaker 2:Four founders jumping in one minute. That is remarkable. I am not
Speaker 1:No stranger to controversy though. Yeah. Joe Wansell says I am not a humble man but this is legit legitimately beyond my capabilities.
Speaker 2:Absolutely wild. Well, Vercel, Guillermo Rao, a friend of the show is apparently running a an ad campaign on Lyft by buying custom license plates and deploying them through Lyft drivers? Is that what's going on here? No. You think it's random?
Speaker 1:The guy Peter, the driver was like, I must love the Love
Speaker 2:Vercel or worked there or something.
Speaker 12:I don't know.
Speaker 1:If he was the eighth employee at Vercel, I don't think he'd be driving Hopefully not. He just loves truly just loves the game, loves driving.
Speaker 2:Or he's just super illiquid. He just like he's just like
Speaker 6:Hey, please.
Speaker 2:Pay me zero, actually. I'll drive Lyft. Please. I want all equity. I'm super bullish on
Speaker 1:Versailles. That's a possibility. That's
Speaker 2:That's a possibility. Well, Alex Conrad says, is your startup even sponsoring Lyft license plates yet? It's an outside the box strategy. Someone should pick it up. Someone should do it.
Speaker 2:Get a bunch of license plates for cars. Rent them out to Lyft drivers. Get those impressions. Wix is down a bunch. This seems like a very logical company to suffer in the age of vibe coding.
Speaker 2:People are vibe coding websites all the time. And Wix is a supplier service to build websites based on templates. But Wix was buying 30% of its shares at $92 six weeks ago, but the stock is now down another 45%. And so I was wondering about this. I almost asked Max Levchin about this yesterday.
Speaker 2:But when you're going through this world, like it seemed like he was very confident about the SaaSpocalypse and did not feel the need to respond or take any dramatic actions just sort of wait and let the metrics do the talking. But I was wondering about you know are you tempted as a CEO when your stock trades down on a narrative that you know does not apply to you but you're just sort of a collateral damage? Are you tempted to do a quick buyback and just sort of, you know, get a good deal on your stock if it's even if it's just, you know, three months down, then right back
Speaker 1:Imagine being a public company CEO and buying back your stock and then getting a return on it. It has to be one of the most euphoric experiences. Yeah.
Speaker 2:Yeah. Totally.
Speaker 1:Not not not actually getting return, but but obviously, you know, decreasing
Speaker 2:Yeah.
Speaker 1:Or increasing everyone's
Speaker 2:Well, Wix is a 2,900,000,000 company now.
Speaker 1:Yeah. They they acquired this company Base forty four.
Speaker 2:Okay.
Speaker 1:Remember, this was like a one person
Speaker 2:Oh, yeah.
Speaker 1:That's right.
Speaker 6:One
Speaker 1:person company and Five they were growing rev I I think Base forty four has been growing revenue quite quickly. It seems like pretty much any of these five coding tools Yeah. Just the the experience is so magical for people Yeah. That a lot of them have grown revenue Fastenating really stock
Speaker 2:chart if you zoom all the way out. So during COVID twenty twenty one, Zurp era, stock was at $300 a share. It's at 52 today, by the way. It traded down after Zerp error ended all the way to $50 a share, $60 a share. And then post chat GPT moment, 2024, fantastic for the stock.
Speaker 2:It gets back up all the way to $25,200 a share. But then since 2025 as AI has gotten better at coding, vibe coding websites, doing front end design, there has been a significant sell off that continues today. And so rough goes
Speaker 11:I was looking
Speaker 1:over there. To get a comp. Yeah. I looked up Squarespace. Squarespace is no longer publicly traded.
Speaker 2:Mhmm.
Speaker 1:It was traded on the NICI, but it was delisted after being taken private by at 7,200,000,000. Mhmm. That is tough timing. Taken private in 10/17/2024. Oh, interesting.
Speaker 1:And at the time Yeah. There was not a sasspocalypse narrative. Yep. You couldn't one shot a beautiful website Yep. With a single prompt.
Speaker 1:Yep. It's gonna be so hard to for this firm to make money on this deal.
Speaker 2:Yeah. It feels like a new customer problem just because it's not the hot new technology that you're hearing about. Like, the podcast ad conversion has to be a lot worse. But I would be very interested to know what is retention like? Because I know some I people that have built know some people that have built these web website generator companies, and then they just keep growing and growing and just sticking around forever because once someone has the magical experience of building a little website for their company or their personal brand, and then they just let it run forever and they're, $10 a month, I'll just let it keep going.
Speaker 2:Well Yeah.
Speaker 1:So Squarespace had done around 1,000,000,000 of revenue in 2023. Okay. I'm assuming they grew into 2024. We don't have the full year numbers because it was taken private in q four. But it's pretty reasonable revenue multiple.
Speaker 1:Yeah. But if they lose out on a lot of those new customers Yeah. Because there's every single Yeah. Company in the world every single company in the world it seems like is trying to make a box that will make you a website.
Speaker 2:Yeah. Yeah. Everyone. Anyway, you know what very few companies are making? A nightstand that turns into a bat and a shield for defense.
Speaker 2:I like this. It looks so unassuming as a nightstand. Very believable. No one would guess but then something happens. You grab your bat and shield and you're ready to rock.
Speaker 2:Did you pick one of these up? It has a little bit of a hotel vibe to it. It doesn't Don't. And also, I like a nightstand that
Speaker 1:All I would say is don't bring a nightstand to a gunfight.
Speaker 2:Okay. Yeah. Well, people are having fun with AI generated videos showing that, yes, in fact, it's not if it's not bulletproof, it has a has some trouble. If you disable Ben Thompson says, if you disable Open at login for the Gemini app launcher that the Gemini app installs in the background without asking, Gemini app launch will immediately re enable open at login. I will now, needless to say, delete the Gemini app and don't intend to install it ever again.
Speaker 2:And so this is very, very odd. Gemini login oh, so it automatically logs in no matter what. He says, I'm I'm actually struggling to remember a bigger middle finger to a user from an app ever. It's bad enough to install a helper app, but to immediately undo the user's explicit setting change? Incredible.
Speaker 2:And Josh Woodward from Google chimed in and said, this is a bug. It will be fixed in the next release aiming for right after Google IO. More if you're interested. So that's good. They did they did receive the feedback.
Speaker 2:Well, we should talk about Nikita Beers. I was reminded of this because he screenshotted and posted it. The greatest growth hack of his career for one of his projects. This happened was this a year ago? Gas or Explode app?
Speaker 2:This about a year and a half ago. Pre joining X and working with Elon Musk over at X, he launched a company called Xplode or an app called Xplode. And he had a very interesting growth hack where he incorporated the company as TapGet Inc. And so in the iPhone app store description under the name of the app Xplode, it would say TapGet and then right below it would say get because it's And a free it doubles down on the call to app.
Speaker 1:He made the entity a call to action.
Speaker 6:It's
Speaker 2:genius. Little these little things really add up and you've seen them all over X and he's done a good job of creating reengaging areas. And I just feel like the UI of X has been improving significantly. I'm really I'm really enjoying the latest UI feature where if you're watching a video and you want to speed it up, can hold on the right side of the screen, which is fairly common in video apps these days. Doesn't work in the iOS native video player.
Speaker 2:I don't even know if it works on YouTube, maybe. But what's really cool is that if you press and hold it, you will temporarily be in two x speed mode. But now in x, if you press hold and you're in two x speed mode and you drag down, it fills a little circle and keeps you locked in that two x speed mode and it actually changes the speed of the video permanently until you change it back. And so that little delightful touch is something that I'm I'm seeing more and more of from the x team and I'm a big fan of. Well, without further ado, have Andrew Feldman from Cerebras in the waiting room.
Speaker 2:Let's bring him in to the TBPN Altro. Andrew, great to see you again.
Speaker 1:Looking sharp.
Speaker 9:Feeling sharp. How are guys doing?
Speaker 2:Feeling. Amazing. Congratulations. How has the day been? I would love to get just your reactions from the day.
Speaker 2:It seemed like there were a lot of people there. Take us through your your emotions today.
Speaker 9:Well, know, this was better than than we'd hoped for. Think a chance to celebrate. We we did bring a lot of people from the company and we brought families.
Speaker 2:Yeah.
Speaker 9:And to to to to share with the team. We we brought everybody who'd been at the company for longer than nine years and their families. We you know when you do a start up the family is a is a meaningful part. It takes patience from them and and a a great deal of it. And so they came and we celebrated it.
Speaker 9:It was really an extraordinary day. We we opened up, you know, we did we priced at $1.85, we opened up $3.50 and we settled at about $3.20. What an extraordinary thing. We're just so proud.
Speaker 2:Yeah. Take us through some of the the history of Cerebras. Has it been a straight shot? Has it been an overnight success? How do you characterize it?
Speaker 2:What were the darkest moments? What were the highlights? What are the good old days to you? What does that mean?
Speaker 9:Well, look, think in the hardware business if anybody tells you it's a straight shot, you can call BS. I just don't think that's the way our business works. I think the first time you build a chip with a new architecture, it's a little more than a prototype, a little more than a proof of concept. The second chip, you iron out your your challenges and you begin to show it to customers in in mass. Third one often that that really takes off.
Speaker 9:And and so it's a long long road in in innovative hardware designs. And so, you know, were founded in 2016. We're we're more than ten years old. We sought to solve problems that that that others that's right. Overnight success.
Speaker 9:Thank you. Oh, exactly. Like like a decade. Like Yeah. I was pounds lighter and weight
Speaker 1:As overnight successes are, you know.
Speaker 9:Yeah. That that's right. I mean they're just overnight because most people sort of weren't paying attention. But we tried to solve some problems that other people thought were impossible. As we showed you last time, you know, we tried to build a chip that was the size of a dinner plate.
Speaker 2:Yeah.
Speaker 9:And everybody told us it was impossible and the truth is for a while it was.
Speaker 2:Mhmm.
Speaker 9:And you know, we we didn't solve it until August of of two thousand and nineteen. We built this extraordinary chip. We were faster than everybody and absolutely nobody cared. Nobody. AI wasn't ready and it was still sort of a novelty.
Speaker 9:And nobody cares about how fast you are when it's a novelty. But but starting with with GPT and in 2025, the models got so darn smart they became useful. Mhmm. And suddenly everybody wanted to use AI and you use it with inference and and business was rolling.
Speaker 1:Yeah. What were those early rounds like? I'm thinking the benchmark round, CO2, bunch you know, Eclipse, a bunch of others.
Speaker 9:You you know, we we had the advantage of the founding team had been together our last company that had paid pretty well for the the venture capitalists and the team and so we we we had some wind in our sails when we went out and raised money. It's not like today where we're we're four guys in the word lab and you're raising it a billion priests for for year That that's not us, but we went out, we we made eight calls, we got eight term sheets, we chose benchmark and foundation and eclipse.
Speaker 2:That's amazing.
Speaker 9:And we got going, you know, less than a year later
Speaker 1:I was expecting I was expecting you to say like, yeah, I mean it was it was a slog, you know.
Speaker 6:Were so Other rounds were a slog. Yeah. Other rounds were a slog.
Speaker 2:Yeah.
Speaker 9:At the beginning, not so. You know, Thomas LaFonte at Cotu came in shortly thereafter and we did a round with them. I think the truth is between about 2020 and 2023 it was it was much harder. Yeah. AI was sort of in this situation where everybody was saying, that's cool, look what this model can do, look how big it is, but it wasn't being used anywhere.
Speaker 9:Yeah. Right. Nobody was using it. Yeah. They were pointing at it, they were saying, wouldn't this be nice and they went back to whatever they were doing before.
Speaker 9:And and it wasn't until really sort of 2025 when the models got good and you just saw this tidal wave of people using AI and demand for AI compute. And that that's been exceptional. It's just been an amazing thing to ride.
Speaker 2:Yeah. You yeah. You mentioned like if you have four guys and your your company name ends with lab, can raise a billion dollars. There's a little bit of that going on in the market with just like chips, semiconductors, AI. There's not that much that needs to be explained, but what were the key ideas or thesis that you needed to explain in the roadshow to investors that wanted to go a layer deep a layer deeper than just AI chips?
Speaker 9:Yeah. I I I think there were there's the first the market size and dynamic. And I I think Jensen said some time ago on on Brad Gerson's podcast that that the demand for for inference will grow by a million x.
Speaker 2:Yeah.
Speaker 9:And nobody believed him.
Speaker 2:Yeah.
Speaker 9:And you know, at the same time you saw Sam Altman, you know, displaying real vision and going out and trying to lock up huge amounts of compute Yeah. And memory and data center and power because he saw it too. Yeah. And I I think trying to share what that means, what an exponential demand means and that we're still so early and yet the the demand for AI compute is is overwhelming.
Speaker 2:Mhmm.
Speaker 9:I I think sharing that was interesting and and I I think helpful in educating the the financial community. The other thing is that that there are lots of ways to do this. The the GPU isn't the only way. You've got a TPU, you've got Tranium, you've got us. There are lots of different ways to to to build a solution here.
Speaker 9:And finally that may maybe the the notion that CUDA is sort of this grand lock in is overplayed. And that, you know, the the Gemini three which is an excellent model was trained on TPUs with no CUDA. That Anthropix models were trained on Tranium with no CUDA. I mean that that low and behold some of the best models, of the most interesting things are being done without CUDA and that that that lock in might be overplayed. And I I think these three factors were really important in in educating the the financial community.
Speaker 1:Going forward, how do you think how do you and the team think about sort of calling your shot and sort of trying to predict where and how inference demand will look in 2030 and beyond versus like working closely with the labs that now have product lines with billions of dollars of revenue and their own roadmaps that you can work Yeah.
Speaker 9:Like the babe, I'm going to point out to left field and and and just say, wait, this is where it's going, baby.
Speaker 2:I love it.
Speaker 9:Love No. I I don't think that's way it works. I I I think we're calling our shots every day by making big investments in data center capacity and collaborating with with the the leading visionaries in the field in in working not just with with OpenAI to to service sort of the cutting edge and and deliver their extraordinary models, but also with AWS to make sure that that we can get access to the the largest enterprise customers and instead of having to to work with these enterprise customers, procurement aid sort of organizations who who provide master purchase agreements that are are the size of a bible. You know, you can say look why don't you buy us through through through AWS and it'll count against your against your annual commitment. And so I I think those are are really important ideas and and ways we we get access to the market.
Speaker 9:And then we're we're taking huge amounts of data center capacity. Mhmm. And so that's the the other bet we're making.
Speaker 2:Yeah. Makes a lot of sense. How how do you think the year will play out in terms of just broader consumer awareness of what fast inference feels like? I had really a magical moment using Cerebras in GPT 5.3 Spark and Codex. And even outside of coding tasks, just talking to the model and having it respond instantly was sort of it felt like a new breakthrough or a new paradigm.
Speaker 2:And I feel like this hasn't fully diffused but it it also feels like when it does there will be potentially like entirely new ways of working, entirely new paradigms that might emerge. How are you thinking about actually diffusing the technology?
Speaker 9:We we think that's exactly right.
Speaker 2:Mhmm.
Speaker 9:And we think that that the experience of engaging with with a real time AI
Speaker 2:Yeah.
Speaker 9:Will will encourage people to do more things, to stay longer, to work on harder problems. And to invent new things. I mean, you remember, you know, when Netflix started, they delivered DVDs and envelopes.
Speaker 2:Yeah.
Speaker 9:Right? And when the internet got fast, they they became a movie studio.
Speaker 2:Yeah.
Speaker 9:And they didn't get better at DVD delivery, they became something completely different that had never been in existence before. A movie studio that delivered directly to your home. Yeah. I I think that's exactly what's going to happen. And you can just sit back and you can ask yourself, I mean, how big is the market for for slow search?
Speaker 9:Zero. How big is the market for dial up internet? I mean, much would I have to pay you to swap out broadband at home and bring in dial up? I'm not Right? 1,000 a month?
Speaker 9:1,500 a month? 2,000 a month? Mean, no way. Mean, it just wouldn't be worth it. Yep.
Speaker 9:And so it the the community is gonna engage with inference in the same way and that fast inference is gonna be all of the market.
Speaker 2:Yeah. So you you make the chips. I believe you also make cooling infrastructure as well, cooling units. Is there are there other products on the road map that you think will be required to roll out and scale Cerebras over the next couple of years?
Speaker 9:No. I don't think so. I think right now we we build the the chip Yep. And the system and the system includes it's about the size of a dorm room fridge. There you put two of them in a standard data center rack.
Speaker 2:Yeah.
Speaker 9:And the cooling infrastructure is built into the system.
Speaker 2:Sure.
Speaker 9:And I I think that's where we want to focus.
Speaker 2:Yeah.
Speaker 9:We we want to be measured on our ability to build AI computers that are faster than anybody else.
Speaker 2:Yeah. How are you thinking about scaling on chip memory? It it feels like there's some there's some concern about, well, what if the models go to 10,000,000,000,000 parameters? If it gets too big? How are you thinking about that challenge or maybe it's an opportunity?
Speaker 9:It is an opportunity. I I think a 10,000,000,000,000 parameter model is hard for everybody. It's actually easier for us.
Speaker 2:Okay.
Speaker 9:Right? There's a reason we're not a 10,000,000,000,000, it's because it's really hard and expensive to serve for everybody.
Speaker 2:Mhmm.
Speaker 9:I think one of the things that we've been able to do for the larger models is to tie together a bunch of these systems in parallel. Mhmm. And run them as a pipeline.
Speaker 4:And
Speaker 9:that way we can train and do inference on trillion, multi trillion parameter models in ways that I think are are much more intuitive than than on GPUs that have much smaller compute. They have off chip memory, but their problem is the compute. They don't have enough compute per chip. Mhmm.
Speaker 2:And then how how are you talking to to to customers about potentially bringing Cerebras in not as a full replacement to their entire semiconductor supply chain or stack, but as a as a complement to everything else that they're running. Because I have this vision of, like, the next generation of AI agents. You get this genius model, but it needs to use a small model over here, an open source model over there, a super fast model for a certain thing if it's looping through
Speaker 1:some task. Way you hire you have a superstar employee
Speaker 2:Yeah.
Speaker 1:You don't necessarily want them doing every single task themselves. It's like, yeah, you should be able to delegate.
Speaker 2:Yeah. Delegation. How are you thinking about that?
Speaker 9:Yeah. I I think that is sort of a notion of a confederacy of models, right? That that there's a collection of different models and one of the the the things we thought about early on was how to interoperate in that environment.
Speaker 3:Okay.
Speaker 9:And we we connect in via standard 100 gigabit ethernet, nothing fancy, nothing proprietary. We we are deployed in in many places where they've got GPUs from from Nvidia or GPUs from AMD. They've got x 86 compute from Dell Yeah. Or HP. And so that's not a problem at all.
Speaker 9:We're we're eager for those environments.
Speaker 1:Yeah. How what what do you think the company would look like today if you guys had had access to today's frontier models when you started the company? Like are you feeling like how and how do you think about just like the speed up in, you know, at the company today due to how good the models have gotten?
Speaker 9:We we we use frontier models every day in coding, in running our g and a. I I think if you start a company today, you build a very different organization. I I think there are whole departments that look different in in in the next nine to to eighteen months. I think much of what HR does, much of what training does is solved by some form of AI. I think a lot of the work in finance, right, closing the books, a bunch of what they do is checking and those are all done by agents.
Speaker 9:Think what it is to be selling or or doing recruiting, those change. I think for a long time what recruiting was was hunting through or writing scripts for LinkedIn.
Speaker 2:Mhmm.
Speaker 9:I think that changes substantially.
Speaker 1:Mhmm.
Speaker 9:And so when we look out, we we see sort of fundamental changes. The obvious ones of course are, you know a year ago engineers were using approximately zero tokens and and now they're using you know $10,000 worth of tokens a month. And the the rate of change and the rate of new PR requests, new pull requests is just extraordinary. And so AI is having fundamental changes. Obviously, it usually starts in in Silicon Valley and sort of works in waves to to other areas, but that's what we're seeing right now.
Speaker 2:Since the last time we talked, there's been a ton of movement in the space data center market, a lot of energy. Just yesterday, SpaceX and Google eyed a launch deal in the Wall Street Journal. Have has any of your thinking changed? Like, what is your current thesis on space data centers and how it might fit into your business plan over the next decade even?
Speaker 9:Well, one of the hardest things in the space data center is communicating across chips
Speaker 2:Yeah.
Speaker 9:From one chip to the next, and we solve that. Right?
Speaker 2:Yeah. I mean,
Speaker 9:one of the great parts about a big chip is that you have to communicate from one chip to to the next less frequently.
Speaker 2:Yeah.
Speaker 9:It's a huge advantage for us in space. Mhmm. I I think that this is an idea like self driving where the last 10% takes 80% of the time. Sure. Right?
Speaker 4:Yeah.
Speaker 9:And that we're not three or five years away, we're eight to twelve years away. Yeah. That doesn't mean we shouldn't be working on it or thinking about it or making progress to it because if you don't do that, it's twenty five years away. Yeah. But I I I don't see data centers in space in the next three or four years.
Speaker 2:Yeah. And arguably, you've solved the key problems that you would be asked to solve and so you'll be ready if demand shows up.
Speaker 6:You're ready.
Speaker 2:But there's not that much for you to do individually to advance that.
Speaker 9:That's exactly right. Yeah. That's exactly
Speaker 12:right.
Speaker 2:Well, we're hoping for it. It'd be exciting. But plenty plenty work to do here on the ground. It's
Speaker 1:cool. Congratulations to you and the whole team on this incredible honored that you would spend time with us.
Speaker 2:We really appreciate it.
Speaker 1:Short day for the company. Yeah.
Speaker 2:Let me hit the goggles.
Speaker 1:To watch your progress. And I look forward to your next appearance and enjoy the rest of the evening.
Speaker 2:Enjoy the rest of the evening. We'll talk to you soon.
Speaker 9:Thank you guys. It's time for a cocktail. Be well.
Speaker 2:Fantastic. Enjoy. You deserve it. Goodbye.
Speaker 12:What a
Speaker 1:I fantastic love that He's like, I'm Babe Ruth. Yeah. I just point. He's like, no.
Speaker 2:I'm not gonna do that. It's way more complicated. I've been working on this for a decade. Yeah. What a what a fantastic story.
Speaker 2:What a fantastic performance. I'm very excited that we're bringing in Eric Vishria from Benchmark who was in that series a that Andrew Feldman just mentioned. So we will talk to him about that in just a minute. We're gonna bring him into the waiting room. But there are some other posts that we can talk about in the meantime.
Speaker 2:One, someone is using runway amount to to create let me see this. A full hurricane inside a TV studio. I wanna watch this clip. And it's one minute and we will see how convincing is this. Are you gonna be turning off People think about hurricanes.
Speaker 2:In order to watch this.
Speaker 3:But wind is only the beginning. The real danger is when the storm starts moving. Storm builds, ordinary things stop feeling ordinary. Roof panels,
Speaker 11:prelim.
Speaker 2:You think audio also? Like fully AI generated. Because the typical workflow for this is the the the new the host would stand on a green screen or LED volume, and then all of these effects would be added in post or or live through like a traditional visual effects pipeline. This feels fully synthetic. I think that you'll probably use some sort of hybrid approach, but the the ability to prompt something like this on the fly for a small news organization that maybe doesn't have the budget for a huge VFX team.
Speaker 2:You're just gonna see a lot more VFX like this. You're gonna see stuff all over the place. There are so many small news channels, local news stations that just don't have the the, you know, access to digital domain or some huge visual visual effects house. So looks pretty good.
Speaker 1:Before our next Yes. Guest Dylan Field has a quick update.
Speaker 2:What'd say?
Speaker 1:Have their q one results. He says quick update. Not dead. And putting up some insane numbers. 46% year over year revenue growth accelerating for the second straight quarter.
Speaker 1:It's They're raising 2026
Speaker 2:revenue 6.8% today, up 8.6% after hours. Congratulations to Intel. It
Speaker 1:says design matters more than ever. The Figma team continuing to execute Yeah. Incredibly well. Fantastic. Let's bring in
Speaker 2:Bring in Eric. To the show. Congratulations on the progress. Thank you so much for taking the time on such a busy day. Great to meet you.
Speaker 12:Great to meet you guys. Excited to be here.
Speaker 1:Long long overdue.
Speaker 2:Yeah. Crazy that this hasn't happened
Speaker 12:an opportunity Well, you guys like Ev, you know, so Oh, yeah. Have Ev on. You don't have to
Speaker 2:have He was a former colleague, but everyone is welcome here. But I would love to just hear the story from your perspective. We we we just heard it from obvious to you? Yeah.
Speaker 1:Was it the most obvious deal ever because I was we were talking with Andrew. Yeah. I was asking him for the story of those first couple rounds expecting him to be like, you know
Speaker 2:It was really hard.
Speaker 1:TBP would come out for for almost a decade. It was a slog. We kept getting we walked up and down San Hill Road. Got nosy. He's like, yeah, we got eight term sheets.
Speaker 1:So clearly it was a deal you had to win.
Speaker 2:Yeah. But take us through it.
Speaker 12:Well, you know what? The hilarious thing about it is in venture, it's very useful to be naive. And certainly, was so naive about how hard hardware actually is. Like, I just like I can't even I can't even describe to you guys how how naive I was and we were. You know, at the the the it was 2016, deep learning was clearly going to become a thing, which would obviously evolve and empower the AI that we have today.
Speaker 12:Mhmm. I was looking at all of these different applications. So I was looking at like deep learning for radiology and security and other things, and it was really hard to figure out where it was going to work, like which application was going to take off. And you guys have to remember, is 2016, right? The TPU hadn't been announced.
Speaker 12:The transformer paper hadn't come out yet. LLMs haven't hadn't been born yet and and obviously not ChatGPT or anything else. And so it's really early, but there was clearly something there.
Speaker 10:Mhmm.
Speaker 12:And when I first met Andrew, he came in and I was like, we hadn't we're not hardware investors typically. I think our last hardware investment before that one was Ambarella, which was ten years earlier. And he came in and he said, you know, was like the team slide, very impressive. And then, you know, the slide three was GPUs actually suck for deep learning. They just happen to be 100 times better than CPUs.
Speaker 12:And as soon as he said it, it's just like a light bulb went off. Like, of course, of course, like, why would a graphics processing unit be the right solution for deep learning? And then, of course, he proceeded to explain like why GPUs were so much better than CPUs for training and also what the like ideal ground up solution could look like. And they have their idea of the way for scale and everything else. And as soon as he said it, it's kind of like, oh, yes, that makes sense.
Speaker 12:And like, I should like, we don't know what application is going to work. We should invest in infrastructure. This is an amazing team and a really provocative idea. Fast forward, like that was 2016, 2016. You fast forward like six, seven years, and like we're still slogging it out and have raised so much money and have very little revenue.
Speaker 12:And it just hadn't all come together yet. And then, of course, over the last two years, inference is exploding. It turns out Cerebras switches from training to inference and really focusing on inference and making inference speed where speed matters, coding explodes where speed really matters. And so all these things kind of came together. And so a lot of luck, a lot of naivete on my part.
Speaker 12:But for the team, just relentless grind, never giving up, always taking feedback, but being persistent, being open minded about where the market was going. So, yeah, I'm I'm so so proud of them.
Speaker 2:Yeah. What was your role as an investor like over the journey of the company? Because obviously, Andrew and his core team, deep engineering bench, were you focused on how you position the company, the private markets, fundraising or management? Like, what were you focused on in terms of value add or just helping build the company alongside?
Speaker 12:I'm I'm really the algorithm specialist. Okay. I go in there and I do that. No. I'm just kidding.
Speaker 12:So I I I don't know anything.
Speaker 2:Oh. So I You're in fad. You're the one that made making the
Speaker 12:That's right. I was making
Speaker 2:it up. Clean up. Yeah.
Speaker 12:It there's you know, it it really changes a lot over the course of a company. Yeah. This is, I think, the the fourth company that I've worked with for more than ten years. Wow. And and so when you work on them a long time, the the companies evolve a lot.
Speaker 12:Right? You start out, it's just five people. It's just the five founders originally. So at different points in time, it's a lot of fundraising help. At points in time, it's like really helping build out the broader management team.
Speaker 12:And a lot of it is also just being someone for the founder to talk to. Know? There's there's being an entrepreneur is is very the highs are very high and the lows are very low. And and so someone you can talk to and be really open with that, like, helps moderate that. And I think that's a part of it.
Speaker 12:So it's just it's an evolving, you know, conciliatory kind of role, and I really love it. Actually, that's the part of the job that I love the most. And it's very it's rare and special to have these kinds of relationships. I've had a few of them. I'm very lucky to have a few of them where I just feel really like a lot of chemistry with the with the founder and and just feel like we have a really productive relationship.
Speaker 2:Where are you excited to invest over the next decade? Because, you know, it it feels like we're still in the semis. Boom. There's a lot of opportunity there. You could go deeper into that side of the business, but then there's so much software.
Speaker 1:I'm sure you've gotten pitches that look like the what what Yeah. Maybe would be the next gen and, you know Maybe like I already got my horse. Yeah. Well, yeah, that. But then, you know, talking to these teams that don't necessarily know what it'll actually take.
Speaker 1:Right? Know? Sure. Don't they don't really learn the hardware is hard lesson yet.
Speaker 12:Yeah. Totally. Totally. Well, you know, one of the funny thing and I ask myself this question all the time, obviously, is, this is a 20 for us, as early stage investors and looking for really big outcomes but willing to take big swings, you really do have to kind of look many years forward and try to see like what's going to ripen at the right time, right? So in 2016, you make an AI hardware investment.
Speaker 12:And Grok was, I think, 2017, for So like there were several contemporaries of them. Of course, Grock and Cerebras have ended up doing really well. And so you but you're trying to say like, okay, this fruit is going to ripen in like six years, right? And so there's kind of some mention of projection. Right now, I think I'm really excited and continue to be really excited about a lot of the AI applications.
Speaker 12:We're investors in Sierra and Ligora and a number of others that where, like, they're obviously booming, they're selling magic to their customers, and the companies are doing great. We also have these, like, infrastructure investments, like fireworks, for example, which is also riding this enormous inference demand. And then there are kind of things that are a bit more forward looking. We invested in Star Cloud. My partner, Chase, led our investment in Star Cloud, which space data centers.
Speaker 12:And we also we led the initial round in Sunday Robotics, which is a home robot. And so I think those things are going to take longer. Like they're not going to be massively scaling revenue like next year. Like that's not what they are. You So kind of have a combination of these different things which are but it's it's kind of trying to figure out when they ripen.
Speaker 2:Next time you come on, we gotta have you debate Delian because he came on and was debating Av and hardware versus software. But you got space, chips, you got everything Delian likes. Yeah. Doing well.
Speaker 12:It's nice to have a portfolio. And I think one of the beauties of Benchmark is each of the partners is attracted to different things and Yeah. Different types of founders. And so we you you put it together and it it works out really well.
Speaker 2:Yeah. Yeah. Makes sense.
Speaker 1:Walk us through fund seven and eight because there's chatter on the timeline as as those funds being some of the best in venture history. And although this is Cerebras' day, this is your first time on the show.
Speaker 2:Take your time.
Speaker 1:We do have a big gong here.
Speaker 12:Yeah. Well, I I you know, seven Fund seven has or had Uber, Snapchat, Elastic, Stitch Fix, WeWork. I mean, there were so many things. It was like it was such an embarrassment of riches. And I had nothing to do with that fund, just to be clear.
Speaker 12:Like I joined in 2014. That fund was already deployed, but and invested in but the team you know, that team at the time just did such an outstanding job with winner after winner. Discord is in there. I mean, it's like really like when you have you know, you guys look in venture, if you catch the trend right and obviously work hard and get lucky, but you have the sixth or seventh company in the portfolio delivering a multiple of the fund or something like that, like that you're in such rarefied air, and that's there's it's really special. So that's Fund VII.
Speaker 12:Fund VIII is a very enterprise. It's our 2014 vintage, I think. And it has it's a very enterprise y fund. And so we had Confluence, which returned a bunch and Amplitude has returned a bunch. And then we have Cerebras, obviously, which is big, but Chainalysis is in there and and several others.
Speaker 12:And so it's kind of interesting how these how they switch. I think that's actually more interesting to me, which is Fund VII was very consumer mobile, and Fund VIII is, like, very enterprise y, and they're, like, back to back, but they turn out to they both work. And so I think that tells you a little bit about what venture is and how we all have to be really open minded about what's happening and what's the right timing for these various ideas. And then fast forward in our 2022, I think, 2022 vintage has the first round of Sierra, the first round of Fireworks, the first round of Legora, Merkor. Reductor, Merkor.
Speaker 12:Yes, absolutely. LangChain. And so all those are in there. And so obviously, that's a totally different fund and has a different set of things, but also looks pretty interesting. So it it just it it evolves and it that's what's so hard and tough about this business is staying on your toes when you're in a very, very dynamic world.
Speaker 1:Yeah. Well, it's interesting. Something that, you know, this has been talked about on plenty of podcasts, but it's worth bringing up. You guys have stayed true to the strategy and you can count on the market changing and evolving and but a lot of funds are like having to deal with markets changing and evolving while having a fun strategy that is changing and evolving. And if you keep one of those things true, it seems at least from Benchmark's track record that it gives you some advantage and that like you're playing a very specific kind of game and not having to evolve your own game while dealing with changing technology trends and markets.
Speaker 12:Mhmm. You know, I've been at Benchmark twelve years and I've thought about this a lot. And, you know, you're watching your peers do all these different things and swimming and fees and all these like amazing things. And so you're like, wow, that's pretty that looks pretty cool. Like, you kind of like look at this stuff and but I'll tell you what I think it actually comes down to.
Speaker 12:It's what it actually comes down to is what do you love doing. And we're obviously in a very fortunate position, and I inherited an amazing platform. And so and very fortunate to have done that. And we're in this amazing position where you get to do what you really like doing. And at the end of the day, we really like partnering with early stage founders and working on these companies for a decade plus.
Speaker 12:And that's kind of what we like doing. So I think things have definitely evolved. The opportunity set is changing and evolving. And more recently, I mean, just in February, we raised an SPV, which we've never really done before, and to invest in Cerebras. And that was unusual, but it was you can also we've actually, a few years ago, we did public market investing when COVID first hit and then Nasdaq tanked.
Speaker 12:All of the early stage stuff just disappeared. We were like, wait a minute. These publics, there's interesting stuff in public, we started deploying a little bit in the public. So yes, we're really focused on the early stage and that's what we love doing. And then also, occasionally, like, we see these special opportunities and and we try to jump on them.
Speaker 2:Wow. Yeah. Well, thank you so much for coming on during a business day.
Speaker 11:Had to sneak
Speaker 1:in that the the SPV round of 23,000,000,000. So congratulations on on that investment. Fantastic. Another another little cheeky three x.
Speaker 2:I think you deserve a drink. Hopefully, you can find Andrew and cheers.
Speaker 12:Have some drinks tonight. Yeah.
Speaker 2:Have a great time.
Speaker 1:Great great to finally meet you and
Speaker 2:Yeah.
Speaker 1:Congrats to everyone.
Speaker 2:Yeah. Let's do it again soon.
Speaker 12:Nasdaq. Thank you guys. We'd love to do that.
Speaker 2:Thank you. Take Goodbye. Up next we have Steve from Foundation Capital. He's Cerebras' first term sheet investor, also the first investor in Solana and a bunch of other great companies. So we will bring in Steve from Foundation Capital from the waiting room.
Speaker 2:Steve, how are doing?
Speaker 1:There he is.
Speaker 11:Doing great. Are Sorry keep
Speaker 2:you waiting. Congratulations. Thank you so much for taking the time to come chat with us. How are doing?
Speaker 1:It's just another another another day. You're not here. Are you you didn't are you at the Nasdaq or you're calling in from home?
Speaker 11:Yeah. Exactly. No. Just another day. No.
Speaker 11:I am I'm at my hotel on my way to the dinner that Eric's also headed to momentarily.
Speaker 2:Fantastic. Okay. We won't keep you too long but I would love to hear the story of you meeting Andrew Feldman in February from there, how you wound up working together.
Speaker 11:Yeah. So I showed actually Andrew's email last night over dinner, but yeah, he and I and Gary met in October 2007. They were raising money for the company that they started prior to Cerebras, which was called C Micro. Yeah. And it was kind of broadly in sort of new server architecture.
Speaker 11:So these guys have been thinking about these kinds of problems for a long time. But I passed on the investment, but stayed close. We we really connected in that meeting. And then, when I saw them get acquired by AMD, it was about four or five years later, I was like, guys, Andrew in particular, you guys are not going to stick around this company for too long, so let's start riffing on some new ideas. And that began basically a two year conversation about a whole bunch of ideas.
Speaker 11:Actually, it all started really in kind of this concept of warehouse scale computing. Yeah. We were looking at companies like Mesosphere, ended up actually doing a small investment there.
Speaker 2:Oh, yeah.
Speaker 11:CoreOS and a whole bunch of others. And Andrew came in in November year of 2014 and shared his ideas with our enterprise team. And then basically we riffed on ideas in the 2016, so it was like March timeframe, we started telling them, look, we we want to be your first term sheet. We've been like courting each other for for a while here. And, yeah, we got him a term sheet to lead that first financing.
Speaker 11:And then Eric stepped in and we changed the terms a little bit to to make room and co lead along with Eric and and Pierre from Eclipse. Yeah. Yeah. And then they started started it right in our office.
Speaker 1:That's amazing. Can
Speaker 2:you can you talk to me about there's, you know, crypto and AI feel like two wildly different technologies, but there's a ton of overlap everywhere you see from crypto miners pivoting to neo clouds. There's a lot of movement back and forth. And I'm wondering like what in your mind the similarities differences are like why you've been drawn to both over your career Where where the gap is? Where there's similarities?
Speaker 11:So what I would say the similarities which are probably in retrospect somewhat obvious. Mhmm. I would say the hardest problems of software and systems live in the area that we're working on in AI. So the AI infrastructure, the Frontier Labs as well, all the work they're doing there. And the same thing is also true at the bottom of stack, the layer ones Mhmm.
Speaker 11:And the very hardest technologies over in crypto. You know, the folks that are attracted to both of those areas tend to to be very technology driven. They're they love distributed systems. They love the hard problems around cryptography and elliptical curve cryptography. They love low latency computing.
Speaker 11:Like they're they're they're quite similar in terms of being systems thinkers. And so those are the those are the ways in which I would say that the problems are quite similar. And in fact, here's a funny anecdote related to this. So Anatolyak O'Laughlin, co founder of Solana, part of the reason why he chose to work with us back in March 2018, so about two years after we invested in Cerebras, was because we were investors in Cerebras.
Speaker 2:Oh, no way.
Speaker 11:He was like, you guys you guys take hard problems seriously. He had spent twelve years at Qualcomm.
Speaker 2:That's right. Yeah. Distributed systems. And the
Speaker 11:Brew Operating System. Exactly. Yeah. So he and then was at Dropbox and understood those challenges. And so he said, wow, you guys care about these kinds of hard problems and that matters to us.
Speaker 11:So we ended up doing fair bit more diligence and writing actually a larger check into that very first Solana financing. So
Speaker 2:Yeah. Can you take us back to earlier in your career, pre investing, obviously fascinating hard problems, but like where does all that come from? Does it start in high school, college, early career? Walk me through some of the early days.
Speaker 11:That's why I studied robotics and embedded systems, sort of the intersection between mechanical and electrical engineering Yeah. In undergrad, and then came to graduate school and and did more of that. And then my very first Friday at Stanford, met David Kelly who's the founder of IDO, is a product development consulting firm that worked with the very best kind of fortune 1,000 companies. When they would hit a snag, a hard problem, or want to invent a new product, and they didn't often know how to wrestle those challenges to the ground, they would call us. Mhmm.
Speaker 11:And so we did a lot of work for Apple. We did a lot of work for Cisco. Yeah. We did a lot of work across every industry from healthcare to consumer devices to, you know, really hard problems in in systems. And so I worked there for five years designing products.
Speaker 11:In fact, one of my other earlier today on the desk at the Trading Floor in NASDAQ.
Speaker 2:Oh, wow.
Speaker 11:It was Cisco's voice over IP phones which I worked on now twenty eight years ago. No way. So just working on cool cool things, hard problems. Mostly where it feels like if if you solve that problem it was worth solving. There's a there's a there's a real prize at the end.
Speaker 2:Okay. I want to
Speaker 9:take that's how
Speaker 11:I got started.
Speaker 2:Yeah. Want to take this full circle then because robotics is sort of having a moment but it still feels like it's early in terms of as a consumer, as optimistic as I am, I just don't think I'm going to have a humanoid robot walking around my home this year. Most people we've talked to have said, yeah, it's maybe five, six, eight, ten years away. But that's like the perfect timeline for a venture capitalist to start getting involved. You don't want to be trying to build custom AI chips today.
Speaker 2:You want to start ten years ago like Cerebras did. So how are you thinking about the like pulling your experience from robotics into the modern era? Because if the boom isn't already here, it's probably going to be here in a decade. If not a decade, two decades like it's coming. Robots are going to be real.
Speaker 2:So how are you thinking about it?
Speaker 11:So we've done a fair bit of work in embodied intelligence in terms of research and as I'm sure you're familiar, it's always a little tricky to invest in an area that you have some operating experience. Yeah. It tends to bring some scar tissue. Yeah. And so you might be more circumspect than than if you'd had kind of a beginner's mind.
Speaker 2:Sure.
Speaker 11:I would say I am generally not a big believer in the humanoid approach.
Speaker 2:Sure.
Speaker 11:I think there are use cases for example in the home companionship. Yeah. And even in that case it's a bit of a stretch. I think you need to think about robotics more broadly and think about industrial automation Mhmm. And then look at the problems that are not necessarily a kind of the, you know, the consumer level use cases.
Speaker 11:Yeah. But you walk the factory floor and you see people moving around pallets. And the human form factor is not good for moving pallets around.
Speaker 2:Yeah.
Speaker 11:And so you wouldn't actually build a humanoid robot if you were trying to deal with that use case. So I think when I zoom out and I say what are robotic systems? Robotic systems are basically ways of automating automating human labor. And so, and and in fact the greatest compliment for most of these systems is when you stop calling them a robot. You actually call them a forklift you call it a washing machine.
Speaker 2:Oh, that's a great
Speaker 11:And it's when that technology diffuses into the background and you just focus on what is the application. So that's how I look at it through kind of the product lens as opposed to the technology lens.
Speaker 2:Yeah. Yeah. I was I was you know, you see these demos of humanoids loading washing machines and I've been thinking in the back of my head every time interacting with my washing machine like, is it time just for a ground up first principles rebuild of what a washer and dryer stacked is. Like if you if you constrain it to like you have this dimension but now you have all the modern technology and your goal is to just take in dirty clothes and put out clean clothes like can you do something better than just a big tumbler and then another tumbler, one with water, one without? And I'm excited by that.
Speaker 2:Is the implication of that that almost you would be open to talking to entrepreneurs who are maybe thinking a little bit narrower, thinking a little bit smaller at least in the interim? And then how would you guide someone towards long term messaging around their company if they are finding a wedge, but then they want to grow at some point?
Speaker 11:Yeah, so I think it is exactly what you just described, which is, and again, the sort of applications do matter here, but the notion that you would start with something that is, let's call it sort of big enough to matter, but small enough to win. Yeah. And in hardware technology, being more focused is actually a huge advantage or huge point of leverage. Mhmm. And so, and then as you continue to build, you want you want to be able to access larger opportunities in markets.
Speaker 11:Mhmm. And so I I really do believe that that is the way you get started with hard technologies and hardware in particular. I think there's another thing that we do, and I will just say this kind of brings it to Cerebras again for a minute, is we look at we look at workloads. And so one of the reasons why we backed Andrew and Gary and Sean and team back in in 2016 was it was quite clear, and we saw this through the lens of our portfolio, that the AI workloads at that time was more ML. They were ramping very, very steeply.
Speaker 11:And whenever you see computing workloads that are doing something new and different, and this you know you're talking about in the robotics context, and we'll get to that in a second. But when you see a workload that is spiking hard, there's often an opportunity to basically replace the compute layer. In other words, there's often sort of purpose built silicon that should exist here. And so in the case of personal computers, very clear. Serial programming, and you were very well suited to the x 86 platform.
Speaker 11:It was actually something we saw go on and on for decades. As soon as you started to see the need for much better graphics, of course you would build a graphics processing unit that's really good at rendering graphics, at doing floating point math, at managing lots of multiple cores, and then of course take the mobile era. And then you say, okay, wait a minute, what's going on here? I need low power, I need a smaller form factor. And so when you look at these workloads, oftentimes there is this sort of transformative opportunity, and that's exactly what we saw in 2016 was, wait a minute, like there should be purpose built silicon for this ML and AI workload.
Speaker 11:At first of course we started with training, back to your point around how do you start small, and then seven years in was actually a board meeting when Sean, one of our co founders said, we got to go after inference. It's just, it's exploding. And so again, this point, you start small and then rotate towards the much larger opportunity.
Speaker 2:Yeah. I mean, talked to Andrew about all the ups and downs, a classic overnight success with tons of moments on of, intense tumult. But I'm curious about were you ever worried or hesitant that the company might narrow down too much? And because you've heard like YouTube has custom silicon for video encoding and there was probably an opportunity at some point to narrow the focus even more to do chip development for one specific company, be less generalized and maybe ramp the revenue a little bit faster. But was there a tension there that you were observing and like how did you get through those moments?
Speaker 11:I'd say that the primary tension that relates to your question Mhmm. Was probably around making sure we would not silo ourselves into use cases that were traditionally just high performance computing use cases.
Speaker 2:Sure.
Speaker 11:So those workloads are valuable, and those markets are actually still relatively interesting, but they're not growing anywhere close to the rate of the inference, and specifically the reasoning part of inference where Yeah. You start chaining workloads together. Yep. So we we worried a little bit about that being, you know, a niche that was not interesting enough for us to build, you know, a really nodal company. If I zoom back from that, and you asked sort of what are the things we really worried about in those early scary days, I mean there were, I don't know if Andrew shared this, and there were like five startups were the hard problems for us to go after.
Speaker 11:I mean, I mean it was absolutely, there were moments, I was joking with one of the other founders last night, where you would you would come back from a board meeting and you weren't quite sure whether we were going to figure out our way through a very fundamental, you know, thermodynamics challenge.
Speaker 2:Okay. So when you say five problems, you're not talking about fundraising, hard negotiation with TSMC, talking to a supplier. You're
Speaker 11:talking All of about that too. All of Okay.
Speaker 3:All of
Speaker 11:that truth. I'm talking about the actual hard problems Yeah. Meaning hard technology problems.
Speaker 9:Yeah. Yeah. Yeah.
Speaker 11:And you know, the ones that are sort of more physical, know, where you have laws of physics and thermodynamics to obey. Yeah. And you don't get to negotiate. Andrew's a very good negotiator, but he's also learned that he can't negotiate with the second law of thermodynamics. Yeah.
Speaker 11:So no, these were this was how do you yield a semiconductor that's the size of a dinner plate? How do you power it? How do you cool it? How do you maintain continuity across thousands of connections? How do you put it in a system and integrate it and then in a data center and then put put 65 over 64 of them in a data center together.
Speaker 11:So it was those kinds of very hard challenges where I say five startups in one. And and they were of course also stacked which means that the risks are now combinatorial. Yeah. So even more dangerous.
Speaker 2:So you've been through taking companies public, you know, being involved with public companies several times. A lot of times, the founders that you're backing is their first time becoming a public company. What are you telling them? What advice can you share with a founder, not Andrew specifically, but any founder who's going public? How will the company change?
Speaker 2:What are you telling them as they become the CEO of a public company?
Speaker 11:Yeah. So there's there's a few things that come to mind. One is buckle up because it it it's going to be particularly in markets like the one we're in right now where I mean you see the headlines change every every few days. I mean there'll be another drop of another model tomorrow that could, you know, upend the public markets.
Speaker 2:Yep.
Speaker 11:And so you don't have a lot of control over what the world thinks about your share price. And so you've got to coach your teams and your engineers in particular to know that like when when the when the share price is moving, it very often has nothing to do with what you're doing in the day to day. Mhmm. And and you just need to steal your sense, yourself against that. I think there's also a piece which is you just have to grow up.
Speaker 11:Like there's there's a cadence to these businesses. Orderly unfortunately, I wish they were longer. Where, you know, Andrew and Bob are going hop on an earnings call very soon. And they're going to have to start talking about the business of the business, not necessarily the technology of it. And that requires a level of discipline and planning that oftentimes founders don't, you know, don't have their stuff together well enough in order to be able to sort of manage through that transition.
Speaker 11:And then the last thing I would say is actually the flip of it, which is don't forget what made you special.
Speaker 2:Mhmm.
Speaker 11:Because when you get into this quarterly cadence, and you start to think, well, how do I meet the next quarter? Mhmm. You oftentimes lose sight of the long horizon that was the larger opportunity for you to go after, you know, not just you know, the opportunity right in front of you, but there's much much larger opportunities. And we're, you know, building systems for the next gen, and the gen after that, and the gen after that. And so you can get tricked into being in a kind of quarterly mindset.
Speaker 11:Yeah. And it's one of the most toxic ways to kill a company that's built around innovation. So you just want to, you want to make sure that, you know, there's that horizon that's still calling, that's where we need to go.
Speaker 2:I love it. Thank you so much for coming on Breaking It Down. Sorry for running long. I'll let you get to the celebratory dinner. Say hello to everyone and have a great day.
Speaker 11:Awesome. Thanks so much. Talk to
Speaker 2:you soon. Have a good one.
Speaker 9:Bye.
Speaker 2:That's our show folks. Leave us five stars on Apple Podcasts and Spotify. Another one. Sign up for our newsletter at tbpn.com. See you tomorrow at 11AM Pacific Time and have a great rest of your day.
Speaker 2:Goodbye.