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Technology's daily show (formerly the Technology Brothers Podcast). Streaming live on X and YouTube from 11 - 2 PM PST Monday - Friday. Available on X, Apple, Spotify, and YouTube.
You're watching TBPN. We are live from the TBPN UltraDump. It's Wednesday, 12/03/2025. You probably thought we were at YC demo day in San Francisco. We gotta go to New York City tomorrow.
Speaker 1:We're interviewing Jim Kramer, a bunch of other folks. Today. Actually, yeah. Leaving today. We are traveling today, so we couldn't, we, unfortunately, couldn't be in San Francisco, at the Palace of Party Rounds, but we still have a ton of YC Demo Day content lined up for you folks.
Speaker 1:We got Harsh Tigar coming on at 11:45. Then we got Clad Labs, the chat makers of Chad IDE, the Rage bait companies. That yeah. The company that sparked, they by their own by their own definition, they call themselves the brain rot IDE. We're getting to the bottom of that story.
Speaker 1:And then we're talking to, probably 10 or 20 other founders. Gonna be asking them how they're building their businesses, what they're building, what they're seeing. It's always a fun time to check-in with the good folks over at, at YC. And, of course, we will be telling you about ramp.com. Time is money.
Speaker 1:Save both. Easy use corporate cards, bill pay, accounting, and a whole lot more. And I will also be telling you about Fall, the generative media platform for developers, develop and fine tune models with serverless GPUs and on demand clusters. So, today, I wrote about will AWS buy TPUs from Google? In the, in the front page of The Wall Street Journal's business and finance section, they're singing the Tranium chips praises.
Speaker 1:Amazon chips. Amazon's chips pose risk to NVIDIA. The whole week, we've been talking to people.
Speaker 2:Is that clickbait?
Speaker 1:I don't know. Well, we're gonna find out. We'll see. It it certainly doesn't seem, you know, good to have more competition in the in the market. And Tae Kim came on the show yesterday to talk about, how NVIDIA was strong and and really was not going to face significant headwinds from the TPU threat.
Speaker 1:Of course, Dylan Patel over at Semi Analysis wrote a 10,000 word piece all about how the TPU v seven was pretty good, and Anthropic was gonna be buying some. And they were also gonna be leasing some, and they maybe had some
Speaker 2:really gate.
Speaker 1:And that sparked a lot of backlash from, NVIDIA bulls. And also folks who are really tied to AMD, they're upset about it. There's a lot of there's a lot of losers if Google winds up winning with TPU. And so the the the losers came out to, to fight, apparently. But let's read through let's just get the facts down from Amazon's, Trainium three launch.
Speaker 1:We, of course, had the CEO of AWS on the show yesterday, and I asked him about this question. Will Amazon be buying TPU? I think that's an interesting question. But first, let's see what Amazon's actually planning with their own AI Speak clearly.
Speaker 2:Didn't no cliffhanger here. He did not say yes or no. He just
Speaker 1:kind of I think I I think you can read between the tea leaves and understand how the decision will be made even though the decision has not been made yet, but we'll go through that. So, amazon.com is the latest big tech company to muscle in on Nvidia's turf. Give me a sound cue from the fall
Speaker 2:How sound about this?
Speaker 1:There we go. That's right. On Tuesday, Amazon Web Services announced the public launch of its training three custom AI chip, which it says is four times as fast as its previous generation of artificial intelligence chips, Four x speed up. That's actually very significant. That's great.
Speaker 1:The company said Tranium three produced in by AWS's Annapurna Labs, fascinating company, acquired a decade ago for around 350,000,000,000 or 350,000,000. It's pretty small acquisition, actually. 350,000,000. In AI, you never know. But back then You never you started a custom silicon company.
Speaker 1:You could barely clear 9 figures on the way out the door. But Interperno Labs has been, working on custom silicon for Amazon for a long time. They actually do have a custom CPU at AWS, to accelerate CPU based workloads. Then for the last few years, they've been working on GPUs or or, you know, ASICs for, accelerated workloads. And so this custom chip, design business, Annapurna Labs, can reduce the cost of training and operating AI models by up to 50% compared with systems that use equivalent GPUs.
Speaker 1:The chips are meant to provide a stronger backbone of computing power for software developers like Dean Leiters, Leitersdorf, the cofounder and co and executive chief executive officer of the startup, Descartes, who we had on the show. And Descartes, that is valued now at $3,100,000,000. Let's go. So if you don't remember Descartes came on, and Dean, was doing live AI video generation while he was doing the interview with us. It was really crazy.
Speaker 2:Yeah. He basically yeah. It was real time. Yeah. He looked like he was in a video game, but it was happening with, little to no delay.
Speaker 2:Really, really cool demo.
Speaker 1:Yeah. Before we move on, let me tell you about Restream. One livestream, 30 plus destinations. If you want a multistream, go to restream.com. So, he said his company had a breakthrough enabled by a Tranium three chip by the Tranium three chip after trying out several other competitor chips, including NVIDIA's processors.
Speaker 1:Dozens of programmers and AI researchers from his San Francisco based company had been trying four months to train a version of Descartes' flagship AI powered, video generation application known as Lucy, that would be able to render footage in real time without bugs or hiccups. AWS gave Descartes early access to Training three after meeting with the startup and being impressed with the founders. The company was two weeks into a marathon coding session in a rented house in Silicon Valley, which I think he took us on a tour of while he was in, wizard land, an AI generated sci fi world. It was very fun. That a few of his employees were celebrating wildly behind him.
Speaker 1:Wait. Oh, that's like a ref I think that's a reference to to the actual call that I'm referring to. Weird. This is very weird reading the journal. I hope yeah.
Speaker 1:I've I've experienced this. The moment that I saw it worked, I saw four people just start jumping up and down, said Dean. The next question was how fast can we get it to market and start changing industries with it? The launch of Trainium three is the latest broadside against NVIDIA, which dominates the GPU market. A flurry of deals in recent months have caught the attention of investors in indicating that more AI firms are seeking to diversify their suppliers by buying chips and other hardware from companies other than NVIDIA.
Speaker 1:So Meta Platforms is in talk with Google to buy billions of dollars worth of advanced AI processors known as TPUs, and OpenAI has struck deals with rival, NVIDIA rival AMD as well as Broadcom. And so, very exciting that Descartes got good results out of the Trainium That's awesome, obviously. I'm sure everyone over at Amazon has been working very hard on that. At the same time, we've heard that Anthropic maybe didn't have that great of an experience with Tranium, and that's why maybe they're moving over to TPU a little bit more.
Speaker 2:But Even though Amazon remains a major
Speaker 1:Investor holder. Exactly.
Speaker 2:Yeah. In Anthropic.
Speaker 1:And so my question is, will AWS buy TPU from Google? I asked Matt Garman that question. I do
Speaker 2:ask me that question.
Speaker 1:Yes.
Speaker 2:I said, they will be mocked.
Speaker 1:They would be mocked.
Speaker 2:They would be mocked.
Speaker 1:Which is ridiculous. And we'll get to why that's ridiculous. I mean, first off, it's it's it's just it's it's funny to mock, anyone for something, like, you related to their semiconductor supply chain and what they rack in their massive data centers. AWS is a massive business. Where I Yeah.
Speaker 2:I just say is, like, please, please, my my archrival, can I please get some chips for my data center to compete with your data center?
Speaker 1:Okay. Well, let's actually go to what Matt Garmin, the CEO of AWS, said on TPPN yesterday because I asked him, will you be buying TPUs? And he said, hey. Look. We're very excited about Trainium, and I think it has and we think it has enormous potential.
Speaker 1:And we absolutely think there's a benefit to optimizing every layer of that stack. And so he, you know, people were joking on the timeline. You know? Oh, there's this new Tranium chip, and somebody was like, all five people using Trainium are ecstatic, you know, that that there's that there's this new news. But, probably ballistic here says, Amazon's so bad at hype.
Speaker 1:Trainium is used by 500,000,000 people through Bedrock, but their marketing team just can't. AWS is undervalued, blah blah blah, and he's obviously a bull on the stock. But what's interesting is that, like, it is it
Speaker 2:is I met some of their GTM staff today. Let's just say you'll have years to accumulate stock at cheap prices.
Speaker 1:Fair, buddy. And so and so, like, yes, there there obviously is value even if Tranium winds up being for a particular niche. Like, maybe it's for real time video. Like, maybe that's the maybe that's what it gets really good at. It could get really good at diffusion.
Speaker 1:It could get really good. And it doesn't need to just be Yeah. Like, you your your ASIC can be honed and honed and honed to
Speaker 2:fit up work. Real time video that's interesting, something that Descartes is focused on Mhmm. Is working with livestreamers Mhmm. Specifically on Twitch. Yeah.
Speaker 2:Amazon owns Twitch.
Speaker 1:Oh, that'd cool.
Speaker 2:That that makes that, that kind of partnership, more interesting.
Speaker 1:I like that. And and so but so so so, obviously, there is value to saying, hey. If you go to AWS, you can get Bedrock and some services that have been fine tuned specifically for Trainium. You go all the way down, you're gonna get very good performance because we have a stack from top to bottom that's very efficient. But at the same time, if you're trying to do something that's sort of, like, not within the training ecosystem, you might have a rough go.
Speaker 1:You might wind up on a different chip. Yeah. But he did say something. He said, we are going to support choice for our customers as well. And so we'll continue to offer GPUs from NVIDIA as an example.
Speaker 1:And we'll have and we have a very tight partnership there. So this idea of customer choice, I think, is important. And if you go back to Jeff Bezos, he said, we're not competitor obsessed. This idea that Google is their archrival, that's not in Amazon's DNA. Jeff Bezos said, we're not competitor obsessed.
Speaker 1:We're customer obsessed. We're customer obsessed. And so if the customer says, look. It's great that you acquired Annapurna Labs for $350,000,000. I'm really happy with what you've done with Tranium three.
Speaker 1:It doesn't work for me. I'm the customer, and I want you to give me an NVIDIA GPU in your server or or or in your data center, or I want you to give me a TPU in your server. They might do that because that's actually in Amazon's DNA.
Speaker 2:Yeah. And then the follow-up question is, is there any world where Google sells TPU to Amazon?
Speaker 1:Maybe. I don't know. Already, they are partnering. Like, this was another partnership that came out, that Ben Thompson actually wrote about in Insta Techery, which you should go subscribe to. So, separately, there was an announcement of an AWS partnership with Google Cloud.
Speaker 1:Now they aren't buying TPUs, but what they're doing is they're enabling customers to establish private high speed links between the two companies' computing platforms in minutes instead of weeks. And so the general here the general idea here is that Google has some amazing AI capabilities that customers are just struggling to match on AWS at this point. And the same thing is happening on Microsoft as well because on Azure, you have access to OpenAI models that you might not have access to on on AWS. And so even though your whole infrastructure might be on AWS, you might be going back and forth to GCP constantly, or you might be going back and forth to AWS all the time being like, oh, I gotta go over to AWS. I gotta go gotta go back.
Speaker 1:I gotta gotta go Azure, back to AWS, back to Azure, back to AWS. And so Amazon finally just said like, hey. Look. We have a partnership, we're just gonna create a, like, a a dedicated pipe that put puts these two systems together. And and so companies used to think about AI as a special piece of their application, so it would be fine to bounce around to another cloud to get the best possible results.
Speaker 1:But if the next generation of companies, I'm sure we'll talk to some of the AI focused YC Demo Day companies today about this
Speaker 2:I hope there's at least one.
Speaker 1:I hope there's at least one company that's doing something with AI. That would be a real treat. But
Speaker 2:And if you're just tuning in, YC demo day coverage starts in thirty minutes. Yes. So
Speaker 1:But it so so it used to be fine to bounce around. Now the next generation companies, they're maybe making their entire infrastructure decision based on who has the best AI products. What are you laughing at?
Speaker 2:I'm laughing because, I texted Simon.
Speaker 3:Yeah.
Speaker 2:They have, Turbo Puffer has a booth at AWS. I said, how's it going at Reinvent? And he says, I'm not there. I just make it seem like I'm there as a joke because the VCs keep going to the booth and then our growth intern is like, oh, Simon. I don't know.
Speaker 2:I think I saw him over there. Just continuing continuing to mug while while, ARR skyrockets. Shout out to Will, the growth intern at Turbo Puffer holding it down at, at Reinvent.
Speaker 1:That's fantastic. I love it. But, so let me go back to AWS. Amazon needs to fire, fight back against this and allowing high speed interconnect between AWS and GCP solves a piece of that, but will they go further? Back on Tuesday, 10/21/2025, I wrote in the daily update in our newsletter at tbpn.com, about increasing supply, competition in AI supply chain.
Speaker 1:Here's what I said. I said, not every link in the supply chain can be completely commoditized. This is about OpenAI trying to dual source from every part of the stack. Yep. And I said, NVIDIA has an insane amount of power right now.
Speaker 1:They've just ramped full year revenue from 27,000,000,000 in 2023 to 60,000,000,000 in 2024 to a 130,000,000,000 in 2025. That's, like, one of the greatest revenue ramps at scale in history. And then, also, they grew their net profit margin from 16% to 56%. That's insane. Insane.
Speaker 1:Yes. GOAT. That's why Jensen Huang is on Joe Rogan, and I'm sure it's gonna be a fantastic episode because he's got a lot to talk about. All the hyperscalers and OpenAI, but that creates problems. Right?
Speaker 1:Because all the hyperscalers and OpenAI are now sort of incentivized to form a bit of an anti NVIDIA alliance to commoditize the accelerator market and drive down those margins a bit. So 56% net profit margins on a 130,000,000,000 of revenue. People just sitting there, and they're like, there's $50,000,000,000 of profit over there. Like, that's a lot of acquisitions And
Speaker 4:that's our
Speaker 1:at Perna Labs.
Speaker 2:That's our cost.
Speaker 1:Yeah. That's our cost. Like, you're just you're you're just eating a lot off of these plates. And so, Co2, I think, has done a good job explaining the current state of the anti video anti NVIDIA alliance. They call it the Google complex, which is probably a little bit better.
Speaker 1:That consists of Google, Broadcom, Celestica, Lumentum, and TTM Technologies. This coalition stands in contrast to the OpenAI complex that consists of NVIDIA SoftBank, Oracle, AMD, Microsoft, and CoreWeave. But you know who they left off the chart entirely? Amazon. Amazon doesn't fit neatly into either of this.
Speaker 2:CodeTwo just loves I think I think they just love leaving a major player off any sort of graph or chart that they make. Right? They they left Google off of their Yeah.
Speaker 1:They're top 40.
Speaker 2:40 AI companies. So I think that's just a little that's just that's just them messing around a little bit.
Speaker 1:But there's yep. I mean, I I think it's accurate. I like if you said, is is Amazon more aligned with OpenAI or Google? You'd be like, what are you talking about? Neither.
Speaker 1:That's correct. They're not in one of the complexes. Yeah. Maybe they need to be. Maybe they don't.
Speaker 1:Maybe they will, you know, form their own complex outside of it. But I just think it's interesting that, I agree with you that it's like it was ridiculous to consider the idea of them buying TPU. That feels so uncharacteristic, And yet they serve up plenty of competitive products within AWS, and they're they they will you go back to the early days of Amazon. You can get Amazon Basics paper towels. You can also get name brand paper towels.
Speaker 1:And that's and that exists within the AWS stack from the databases that they have on offer. There's a lot
Speaker 2:of rebrand Trainium to Amazon basic.
Speaker 1:G t GPU. Amazon basics accelerator.
Speaker 2:Basic basic chips.
Speaker 1:Basic chips. It was on basics chips. It would be good. I can't really, really hard. They're like, actually, it's like one of the greatest things ever.
Speaker 1:It's the most incredible thing that America or that humanity has ever created. It's extremely difficult to make. We we taught sand a thing. Anyway, I I I just don't think Tranium three is the you know, obviously, everyone at AWS is, like, excited about it, and it's a big it's a big deal. But it's just not the backbone of their business.
Speaker 1:And in the long term, they might just retreat to supporting choice for their customers. And so, you know, I I keep going back to that Jeff Bezos line. We're not competitor obsessed. We're customer obsessed. And so I wouldn't be as surprised.
Speaker 2:How much do you think it hurts Amazon that they don't have a dedicated podcast guy? Like, don't have a Sholto. They don't have a Sam. They don't have a Satya.
Speaker 1:You know how much that hurts because they definitely have someone in that role. You just don't know them.
Speaker 2:That's what I'm saying.
Speaker 1:Yeah. They don't have someone who's
Speaker 2:They might have they might have the title, but they're not really in the driver's seat. Right?
Speaker 1:Yeah. They don't have a rune.
Speaker 2:They don't have the rune. Right? They don't have a.
Speaker 1:Yeah. They should step it up. They should they should definitely get someone. I'd love to see it. Well, fortunately, I mean, the semi analysis crew was over there taking pictures, sharing photos in the timeline of the Trainium three Ultra Server liquid cooled with a lot of hard eyes.
Speaker 1:That's some good news from, that's a glowing endorsement from the the semi analysis crew. And look at this. Very purple. I wonder if that's, like, intentional. I wonder if they set up the, the purple lighting.
Speaker 1:There there there's a bunch of funny things going on over at re:Invent. It's also just like it's a punishing time of the year. I guess it's, like, right before the holidays or something because we've just been complete torn. We we obviously wanted to go to YC demo day. I also wanted to go to NeurIPS, which is going on right now, the premier AI conference.
Speaker 1:There's also DealBook Summit. Andrew Sorkin's doing, like, all the greatest interviews. At the same time, there's Reinvent. I wanted to go to that.
Speaker 2:Interviews coming out of DealBook. I just saw some clips this morning. You got We'll play some. Scott Besson just going hard. Yeah.
Speaker 2:You got Alex Karp going hard. No real surprises on either of those fronts, but Yeah. Excited to
Speaker 1:Well
Speaker 2:get the update there.
Speaker 1:Let me tell you about Cognition. The team behind the AI software engineer, Devin, crush your backlog with your personal AI engineering team. Let's let's let's close out the training coverage with this Zephyr post who says Google is having this kind of success with TPUs. What about Amazon's Tranium? Tranium is new and underpowered, just 667 t flops b f 16.
Speaker 1:It has lots of HBM, but the bandwidth is lower than the h 100 TPU v six e. This competitive h 100, non HBM or bandwidth, and Ironwood is competitive with Blackwell on FLOPS bandwidth and HBM capacity. I expect Ironwood to quickly gain market share as it ramps up as you can see from throughput slash TCO, NVIDIA versus Tranium. Ruben Moggs, Tranium three harder than Blackwell versus Tranium two on TCO training FLOPS and reduces the gap by 5% on TCO MEM bandwidth. So the gap between NVIDIA and Tranium is actually increasing rather than decreasing.
Speaker 1:By the way, this math was done before CPX was introduced. I won't be surprised if CPX plus Rubin is cheaper than Tranium for inference. So I I do think that there's a world where there's, where there's something specialized, like what's going on with Descartes, some sort of special model that's that that that excel that that that thrives in what Tranium is good at, and they can further niche down. But but we'll see. I mean, maybe they come from behind and they just destroy t p TPU, and we're all talking about Trainium next year.
Speaker 1:Anyway, let me tell you about Linear. Meet the system for modern software development. Linear streamlines work across entire entire development cycles from road map to release. We're gonna say a little rest in peace.
Speaker 2:Rest in peace to squad.
Speaker 1:San Francisco's beloved albino alligator has passed away at age 30. That's a good age. I don't know how long alligators typically live, but I'm glad Looking it up.
Speaker 2:Feels like Thirty to fifty years
Speaker 1:for the American Alagina. Little bit short, but, Claude was, of course
Speaker 2:Often supporting reaching 70 or more.
Speaker 1:Yes.
Speaker 2:Anyways, RIP, there was, you know, obviously, people started speculating immediately. Anthropic, of course, was the sponsor
Speaker 1:Yes. Claude. Yes.
Speaker 2:And, you know, people were wondering was was there foul play involved? Mhmm. Was it possible this this poor dinosaur not dinosaur. Dinosaur. Alligator passed the day that they that that it that it got announced.
Speaker 2:Yeah. That they've hired IPO lawyers. Some people were speculating could is it possible Claude was sacrificed to the capital markets gods in some type of ritual? But anyways, he look at the look at this expression he has on his face. Can we zoom in a little bit?
Speaker 2:What a what a what a cool guy. And he will be remembered.
Speaker 1:Yeah. Dan Primak here is talking about, x lite. I think we might have the CEO on the show soon. The Trump administration will invest a $150,000,000 into a lithography startup called X Lite. Its first Chips Act award chatted this morning with X Lite CEO.
Speaker 1:There's a few lithography companies now. We've had some on the show. This feels like an entirely new it's a it's a very interesting tier of investment, like a $150,000,000 from the government that feels like a series b. They did raise a they did raise a series b this past summer led by Playground Global with Playground partner and former Intel CEO, Pat Gelsinger, becoming X Lite's executive chairman. Woah.
Speaker 1:And so makes sense that the government's investing in Intel. Pat Gelsinger, of course, former Intel CEO. Now he's getting involved in X Lite, marshaled 40,000,000 of capital, went and got a 150 from the government. The the story continues. There's also another AI startup, that wants to remake the $800,000,000,000 chip industry.
Speaker 1:This one's in The Wall Street Journal founded by ex Google researchers, Recursive Intelligence, raised 35,000,000 with backing from Sequoia to automate chip design. Obviously, this is not lithography. This is the design process. But still, companies are Dylan Patel
Speaker 2:was talking a little bit about
Speaker 5:this.
Speaker 1:The stack. Oh, he did. I didn't hear about that. Very cool.
Speaker 2:This is AI for for AI chip design.
Speaker 1:Oh, that's right. Yes. AI for AI chip design. Everything we need. On a quiet residential street a few blocks from Stanford University, two former Google researchers are launching a startup they hope will remake the $800,000,000,000 chip industry.
Speaker 1:Ana Goldie and Azalea Merhosni are trying to build software that can automate the design of cutting edge chips, a prospect that would allow every company to build their own chips from scratch. Working from the top floor of a suburban home, the duo recently raised 35,000,000 to kick start recursive intelligence with funding from Sequoia Capital and
Speaker 6:strike Sorry.
Speaker 1:The We
Speaker 2:got it. The recursive We gotta add that. Putting it in the name.
Speaker 1:We gotta add that to the list of because there's standard capital, modern capital, standard intelligence, modern intelligence.
Speaker 2:Raw intelligence.
Speaker 1:Raw intelligence was the the lying free. Applied was another one.
Speaker 2:Cap intelligence.
Speaker 1:And then what was the other one? There's what what what's Lockheed Groom's, company?
Speaker 2:Physical intelligence.
Speaker 1:There's physical intelligence, physical capital. So it's the matrix of, like, capital, what was it? Capital intelligence and intuition or something like that. And you and you multiply them all out and you get the whole thing.
Speaker 2:Eventually, we're gonna run out. Right? There's it's some somewhat finite.
Speaker 1:No. There will always be more names.
Speaker 2:Start up a new new words.
Speaker 1:So wow. The company, 35,000,000, for a valuation of 750,000,000. That's a very low dilution. What? 5% or something like that?
Speaker 1:Pretty remarkable. Definitely
Speaker 2:VC is remarkable.
Speaker 1:High. Yeah. I I I would I would have assumed this would be a very a very capital intensive business, but I I I suppose if it's just a software that they're developing, maybe maybe they have more control here. Companies such as Amazon and Google have developed custom chips for AI and data center use, and Apple saved billions of dollars by insourcing chips for its devices, including the M series chips have helped revitalize its MacBook laptops. Such silicon options can be cheaper, more I had
Speaker 2:a funny moment yesterday. We got an Amazon package Yep. And it was covered with, like, Alienware, like, Alienware branding.
Speaker 1:And I
Speaker 2:I asked I asked Sarah. I was like, did you did you get something from Alienware? Like, what is going on? And it turned out to be an ad. No.
Speaker 2:They were advertising that it's powered by like Intel.
Speaker 1:Oh, interesting.
Speaker 2:Which which didn't make me necessarily want to immediately buy an Alienware device.
Speaker 1:You do, you you you put the money straight back in your pocket because you're a taxpayer. You own Intel.
Speaker 2:That's true. That's true.
Speaker 1:You should support Intel. No. Intel Intel is undisputably great for gaming. There's no question there. The question is, are they gonna, you know, be able to build a fab that competes with TSMC?
Speaker 1:I guess it's a completely different question. I I might go build an an Alienware Intel PC.
Speaker 2:Oh, we're going to for the for the the office racing rigs.
Speaker 1:The sim racing needs the Intel Inside for sure. For sure.
Speaker 2:This is just gonna turn into a sim racing show where we watch other podcasts while sim racing and reacting to it.
Speaker 3:Yeah. So I like it.
Speaker 1:Vanta, automate compliance and security, AI that powers everything from evidence collection and continuous monitoring to security reviews and vendor risk. Dwarkash Patel has a, a massive essay shaking up the timeline. Thoughts on AI progress. He says he's moderately bearish in the short term, but explosively bullish in the long term. Well Very interesting.
Speaker 1:So he says he's confused why some people have short timelines. They say AGI is coming soon. But at the same time, they're bullish on RLVR, which is reinforcement learning with verifiable rewards. And so he says, if we're actually close to a human like learner, this whole approach is doomed. Currently, the labs are trying to bake in a bunch of skills into these models through mid training.
Speaker 1:There's an entire supply chain of companies building RL environments, which teach the model how to use Excel to write financial models. For example, I think we're actually talking to an AI Excel analyst for Excel power users called Crunch at twelve fifty YC company. I think that these are good ideas. I I'm actually very bullish on on, this this, this model. But in the context of when does AGI arrive, when does superintelligence arrive, I understand Dwarkish's point.
Speaker 1:He says either these models will soon learn on the job in a self directed way, making all of this prebaking pointless, or they won't, which means AGI is not imminent. Humans don't have to go through a special training phase where they need to rehearse every single piece of software we might ever use. Barron made interesting points about this in a recent blog post. When we see frontier models improving at various benchmarks, we should not, we should think not just of increased scale and clever ML research ideas, but billions of dollars spent paying PhDs, MDs, and other experts to write questions and provide examples. Let's give it up the PhDs.
Speaker 1:And reasoning targets.
Speaker 2:Let's give it up for the experts.
Speaker 1:These precise capabilities. In a way, this is like a large scale reprise of the expert systems era where instead of paying experts to directly program their thinking as code, they provide numerous examples of their reasoning and process formalized and tracked, and then we distill them into models through behavioral cloning. This has updated me slightly towards longer AI timeline since we since given we need such effort to design extremely high quality human trajectories and environments for frontier systems implies that they still lack the critical core of learning that an actual AGI must possess. This tension seems especially vivid in robotics. In some fundamental sense, robotics is an algorithm's problem, not a hardware or data problem.
Speaker 1:With very little training, a human can learn how to teleoperate current hardware to do useful work. So if we had a human like learner, robotics would, in large part, be solved. But the fact that we don't have such a learner makes it necessary to go out into thousands of different homes and factories and learn how to pick up dishes or fold laundry. One counterargument I've heard from the takeoff within five years crew is that we have to do this clue GRL in service of building a superhuman AI researcher, and then the million the million copies of automated Ilia can go figure out how to solve robust and efficient learning from experience. This gives the vibes of we're losing money on every sale, but we'll make it up in volume.
Speaker 1:This automated researcher is somehow going to figure out the algorithm for AGI, something humans have been banging their heads against for the better part of a century while not having the basic learning capabilities that children have? That seems super implausible to me. Besides, even if you even even if that's what you believe, it clearly doesn't describe how the labs are approaching RLVR. You don't need to prebake the consultant's skills at crafting PowerPoint slides in order to automate Ilia. So clearly, the lab's actions hint at a world where, at a world view where these models will continue to fare poorly at generalizing and on the job learning, thus making it necessary to build in the skills that they hope will be economically valuable beforehand.
Speaker 1:I wanna go to the section on economic diffusion. But first, I'm gonna tell you about Privy. Privy makes it easy to build on crypto rails, securely spin up white label wallets, sign transactions, and integrate on chain infrastructure all through one simple API. So you've been asking about economic diffusion. What is the rate that we're diffusing?
Speaker 1:Let's see what Dwarkash has to say about economic diffusion. He says that economic diffusion lag is cope for missing capabilities. And so this is also seems informed by the Tyler Cowen take that, AGI is here. The models are good, but it just takes time to adopt them. And I I'm very sympathetic to this because when I go to the doctor's office and they hand me a piece of paper, I know that a web form is good enough.
Speaker 1:Like, the capabilities of the digital form are complete. It's not that the form is lacking in something or it's not reliable enough. It's not like they're like, oh, yes. Like, the website goes down 20% of the time, and so paper makes more sense still in this case. It's like, no.
Speaker 1:It's just a diffusion problem. There's just someone who runs that doctor's office is like, like doing it the old way. Right? And that's the and that's the economic diffusion lag problem that I think is real in a lot of scenarios.
Speaker 2:But The missing capability thing, I mean, just just to give a pretty concrete example. Right now, AI is great at generating text. Right? It's great at kind of analyzing a piece of content and then generating text based on that. And yet, we still have multiple people on the team at TPPN whose job is to find interesting moments of the show and then create captions around that and share it to X and Instagram and YouTube and other platforms.
Speaker 1:And and Jorah Kesh
Speaker 2:experienced it
Speaker 1:too, where he was trying to find the most interesting pieces of a full podcast with one big Gemini prompt. And he was trying all the different models and couldn't get it to actually find like the most salient and viral points.
Speaker 2:Yeah. So one of the other thing that stands out is one of the seeming missing capabilities is ability to identify humor or even something like it's almost emotional. So Ilya and Dwarkash talked about this where I think Ilya was giving the example of scientists studied people who had had various brain injuries that limited their ability to experience emotion. And when they took out emotion, it took them, it can take somebody two hours to figure out which pair of socks to choose. And they were stunned.
Speaker 2:It's just a pair of socks. You know what's going on in your day. Why do you need emotion in order to make that kind of decision? And so it seems like, at least in AI, a missing capability is like, Okay, finding out what's an interesting moment of a podcast in Parkesh's case. Is it something that makes the audience member feel feel something?
Speaker 2:Right? Is it
Speaker 1:I mean, there's just so much to pull through. Like, I remember during the Carpathi interview, I was watching it and Tyler was watching it. And there's this moment where Carpathi says, like, the coding models are are are amazing and they're magical, but what they produce is slop. And it's like that word slop is so it's the it's it's like the word of the year or maybe the word of last year. Like, it's a huge word.
Speaker 1:It has a huge amount of weight. Coming from him, it's crazy.
Speaker 2:That rage bait beat out slop for the word of
Speaker 1:the year. Slop is probably the twenty twenty four word of the year or something like that. But, anyway, the point was, like, when when when I heard that, when Tyler heard that that that word, Carpathi calling it slop, everyone was like, woah. And I was like, we should clip that. And we looked, and it had already been clipped by a no.
Speaker 1:By a human. Like Yeah. Someone on the timeline had also identified that it was was the crazy moment that we should be reacting to and taking in. It
Speaker 2:was Yeah, it's crazy. The other thing that's notable is on WAP, one of the top jobs that people do on WAP, or way they make their first dollar online, is just clipping for various content creators and media companies. And some of the clips that they make are so sloppy. It's literally just a random segment of the show, and they're blasting it out from 20 different accounts. And the fact that we're still paying humans to do that, still, I mean, it just feels notable.
Speaker 1:Yeah. Well, let's read Dwark Hess's take on economic diffusion lab lag being cope for missing capabilities. So sometimes Copium would be a beautiful name for
Speaker 2:an AI chip, by the way.
Speaker 1:It would. It would. You got Tranium. Maybe they need
Speaker 2:You got Copium.
Speaker 1:Copium. Sometimes people will say that the reason that AIs aren't more wide widely deployed across firms and already pro providing lots of value outside of coding is that technology takes a long time to diffuse. Dorkash thinks this is cope. He says people are using this cope to gloss over the fact that these models just lack the capabilities necessary for broad economic value. Steven bar Burns has an excellent post on this and many other points.
Speaker 1:He says new technologies take a long time to integrate into the economy. Well, ask yourself, how long how do highly skilled, experienced, and entrepreneurial immigrant humans manage to integrate into the economy immediately? Once you've answered that question, note that a AGI will be able to do those things too, Dwarkash says. If these models were actually like humans on a server, they'd diffuse incredibly quickly. In fact, they'd be so much easier to integrate and onboard than a normal human employee.
Speaker 1:They could read your entire Slack and drive in minutes and immediately distill all the skills that your other AI employees have. Plus, the hiring market is very much like a lemons market where it's hard to tell who the good people are beforehand, and hiring someone bad is quite costly. There's there this is a dynamic that you wouldn't have to worry about when you just wanna spin up another instance of a vetted AGI model. For these reasons, I expect it's going to be much easier to diffuse AI labor into firms than it is to hire a person, and companies hire lots of people all the time. If the capabilities were actually at AGI level, people would be willing to spend trillions of dollars a year buying tokens.
Speaker 1:Knowledge workers Yeah.
Speaker 2:Think about that. We we hire someone Yeah. That like, we hire an AI Yeah. Or or we're leveraging an AI, and they've listened to every single minute of TBPN Yeah. Ever Yeah.
Speaker 2:And watched every clip.
Speaker 3:Yeah.
Speaker 1:And right now, you'd have to fine tune that into the model or whatever. You you don't just get that out of the gate.
Speaker 2:Yeah. And I'm just saying, like, the the we we do end up hiring a lot of people that Yeah.
Speaker 1:That are
Speaker 2:Yeah. Like previously just listeners. Yeah. But getting somebody that knows every single moment that has ever happened on the show Yeah. Yeah.
Speaker 2:Would be super powerful. But again, there's just like missing a missing capability set that doesn't allow agents to deliver a lot of value internally.
Speaker 1:The reason that lab revenues are four orders of magnitude off right now is that models are just nowhere near as capable as human knowledge workers. Yeah. I I agree with that. I the the one thing that I don't necessarily agree with here, he says, well, ask yourself, this quote from Stephen Burns. How do highly skilled, experienced, entrepreneurial immigrant humans manage to integrate into the economy immediately?
Speaker 1:I mean, they do sort of integrate into the economy immediately, but, like, the the immigration flow is, like, a slow process. Like, it doesn't just happen immediately. It's not just, like, you know, the amount of immigration went from, like, zero to, like, I don't know, a million people or something. Like, it's, like, move around. There is, like, a there is a bit of a drag.
Speaker 1:But I understand what he's saying here, and it does make sense. Anyway, let me tell you about public.com, investing for those who take it seriously. They got multi asset investing, and they're trusted by millions. The verge is trying to get it on the action, trying to attack David Sachs with a headline. It's like it's like so funny that the New York Times went after David Sachs, and then the verge was like, we wanna go after him too.
Speaker 1:We wanna get
Speaker 2:some of hate. Wait. Wait. Let us let us cook. We heard everybody
Speaker 1:in tech hates this this article.
Speaker 2:Do think
Speaker 1:hate too.
Speaker 2:Well, while I while I don't agree with this journalistic approach, it is a pretty funny headline.
Speaker 1:Yeah. Oh, yeah. It's hilarious. The the the headline is Silicon Valley is rallying behind a guy who sucks. It's like, what does that mean?
Speaker 1:Like
Speaker 2:Just pure.
Speaker 1:Pure, like, qualitative, like, just name calling. They're just like, we don't like this guy.
Speaker 2:Pure ad hominin. You
Speaker 1:know, go off if people if people if your fans like it, if that's what your your audience wants. It's it's it's rage bait. It's gonna go hard. It already got a thousand likes. On a linked article, the verge is not putting up a thousand likes per link, so this is outperformance.
Speaker 1:And, it's heavily paywalled. You cannot learn how David Sacks sucks without subscribing to that thing. They did a good job. You gotta pay.
Speaker 2:You gotta pay.
Speaker 1:You wanna know why he sucks. I did it. Did you pay? So I don't know why he sucks. But That'd be really
Speaker 2:funny if behind the paywall is like, we're just kidding. He's actually We think the New York Times missed on this one. Who
Speaker 1:knows?
Speaker 2:Paul Graham. Yeah. On the timeline. He says, a startup told me that one of their investors didn't like that they were selling to newly founded startups and wanted them to sell to bigger companies who have more money. If investors tell you this, write them off as idiots.
Speaker 2:Selling to startups is the best thing you can do. I'm sure many of the companies we're talking with today will be selling to other companies in the batch. A lot of people a lot of people, like, say that's bad.
Speaker 4:Yeah.
Speaker 2:They try to say, like, a YC is a circular economy. But you have to ignore the the hundreds of, very real businesses that have been created through YC and gone on to work with every kind of company in the world.
Speaker 1:Yeah. It certainly seems, at this point, startups tend to be smarter, less bureaucratic, more representative to future trends. Like, even if there's a, you know, some sort of insular circular economy in the startup ecosystem, like, there's a pretty immense amount of pressure to actually deliver something that's valuable because every dollar is precious. Yeah. These are
Speaker 2:these every found yeah. They're they're being rational. It's not like Yeah. It's not like, I'm sure there's been small instances where companies were actually, you know, had had somewhat bad behavior. But in general, it's like if I'm gonna pay for your the SaaS Yeah.
Speaker 2:Tool or the beta that you're running, it has to be good. Yeah. Has to work.
Speaker 1:Did you see, did you see Stewart Brand? He says, so there's a $1,500,000,000 judgment against Anthropic for including 480,000 Brand. Books in training their AIs. Five of my books are among them. Word is there might be a $1,500 payout per book according to my agent Max Brockman.
Speaker 1:That's a good name. He said, I wrote them I wrote to my agent Max the following. If any payment comes to me, please send it back to Anthropic with my thanks for including my books and their AIs. The judgment website offers a way to opt out of the payment, but I found it cumbersome. So I didn't.
Speaker 1:I'm principled, but too lazy to be highly principled. I really like this. This is a he's the cofounder of the Long Now Foundation, which takes no sides. In this forum, as a private person, I do take sides occasionally. So I thought that was a funny thing.
Speaker 1:There are secondary market fraud going on left and right. But first, let me tell you about graphite.dev, code review for the age of AI. Graphite helps teams on GitHub ship higher quality software faster.
Speaker 2:Take us through this. Yeah. Reading through this, Matt Graham says secondary markets are rife with fraud and bad actors. And it pains me to see these bottom feeders profiting off of Andoril's growth while fleecing retail investors through unreasonable or opaque fee structures. In this week's episode of nonsense, Ignite VC, a fund we've never taken a meeting with or had any contact with whatsoever.
Speaker 2:Founded by Brian, who we've never met, is soliciting investors via public Google Docs to invest in SPV that will in turn invest in another SPV that will in turn potentially enter into a forward contract with a supposedly though unnamed early Anderol employee. Mhmm. Few problems here. First off, so called forward contracts are notoriously hard to settle in private companies, and counterparty risk is extremely real. What about the many complicating corner cases like acquisitions where shares don't trade, or marriages, divorces, or deaths where ownership of the underlying shares is complicated?
Speaker 2:Just generally a risky structure to close that I don't think most folks actually understand. So yeah, if you enter into a forward contract and you basically buy the right to the future value of some shares, and then somebody gets, again, married or divorced or passes away, or bankruptcy is another situation where you might not be actually able to collect, even your investment should have generated some return. Matt says, second, this deal memo includes basically no details about Andoril's performance, no revenue figures whatsoever, no product specifics. I guess that's good, right? Like, if they were just floating around information that they had acquired.
Speaker 2:But anyways, continuing, almost as if it's soliciting investors to invest on hype and momentum and not fundamentals. Generally, I'd advise folks to be skeptical of any deal memo lacking basic details. Third, forward contracts are explicitly disallowed by Anderol's stock plan and bylaws, which means that Anderol will never consent to Team Ignite's SPV, actually taking possession of these shares while we are privately held, zero chance. And finally, the memo spends most of its time talking about the structure and fees, which are insane. A double layered SPV with all legal and admin costs pass through in addition to an 8% upfront fee, 3% annual fee for two years, 20% carried interest, and the craziest part, an implied price per share that is completely insane.
Speaker 2:In this case, the implied PPS is a 115% higher than the most recent preferred raise from nine months ago. Flattered, I suppose, but also puts these investors in an almost absurd position by paying more than double the price per share of our most recent transaction. As stated at the top, I don't know Brian or Team Ignite at all. Maybe they're kind of wholesome people, and this is all a big misunderstanding. But if I were an investor looking at this, quote, opportunity, quote, I'd run for the hills.
Speaker 2:And I believe the founder the founder replied and said, appreciate the heads up. The document reference was an internal draft prepared for discussion with an existing LP and was not intended for public circulation. It appears someone shared it without authorization and we're looking into how that happened.
Speaker 1:But do you see what And then There's like seven people that share a screenshot of like a direct email So they got with this exact memo.
Speaker 2:Okay. And the other thing is they say not soliciting investment for any Andoril related vehicle. Matt says, really? The draft was written by your founder and managing partner. I literally watched him edit the doc in real time.
Speaker 2:And he has a screenshot screenshot of like the the the founder's name in Google Docs like, you know, basically
Speaker 1:What a mess. What
Speaker 2:a Anyways.
Speaker 1:Well
Speaker 2:So don't do this.
Speaker 1:Don't do it. Instead, why don't you start a company and apply to Y Combinator? Build an actual business instead of going around hustling, SPVs and companies that don't wanna sell shares. But we are moving on
Speaker 2:Double to layer SPVs into our Y
Speaker 1:coverage. We have Harsh Tigar here in the restream waiting room. Let's bring him into the TV and Ultra. Harsh, thank you so much for taking the time on a busy Y Combinator demo day to come talk to us. How are doing?
Speaker 7:I'm doing good. Thanks for having me.
Speaker 1:Fantastic. Take us through, how's the day going? What is the schedule like? And then I'd love to dig into some of the trends that you're seeing, some of the standout, companies. I'm sure we're gonna be talking to a lot of them.
Speaker 1:But, what's the what's the run of the show today, and where are we in the course of the process of, you know Yeah. Graduating these companies?
Speaker 7:So we got we got started, like, almost a couple of hours ago, ten in the morning. And so the founders kind of investors all gathered together. They get into the main room here at the YC office, and then the founders start giving presentations, talking about, like, the progress, what they build, themselves, their background, the pretty quick fire presentations, one minute each. And then there's sort like a break in between sort of blocks of presentations where the investors can hang out and talk to some of the founders and get to meet them and, you know, obviously, hopefully invest in a bunch of them. So that's kind of we're, like, we're just about approaching lunch.
Speaker 7:So it's kinda like that part of the day where people have, like, listened to a bunch of companies, probably got, like, a sense of some of
Speaker 8:the stuff that
Speaker 7:they're interested in. I see people right now, like, just hanging out doing deals. So it's it's kinda like a fun vibe. It's, like, live.
Speaker 1:It's the best of us. Party rounds. We love it. I wish we could be there. Are are there any, like, hero metrics or or stats that, the YC team shared this year to kick off demo day?
Speaker 1:How are you how are you sharing, like, the shape of YC these days?
Speaker 9:Yeah. I mean, we didn't go
Speaker 7:too stats heavy this time. Around, I think, I mean, at a high level, it's just the the continuation of the theme we've seen this whole year, which is just like the company during the batch are just getting faster revenue growth, assigning, like, contracts with, like, big companies. Yeah. In some cases, like, even, like, government, defense tech, like Yeah. The the dollar value contracts that startups can close in, like, the first few months of their life are just bigger than anything we've ever seen, and that's all, like, very directly from AI.
Speaker 7:So it's just like it's exciting. Yeah.
Speaker 2:And it's very interesting approach. You can sign one big contract and generate enough revenue to go on the stage at demo day and feel confident in your pitch and have something that's compelling. Or you can go and get it sign up a bunch of startups to something, you know, smaller plans. But both approaches work.
Speaker 7:Like, there's companies that keep growing. Like, you're like, in the SaaS world, you were used to sort of just see consistent month over month, like, growth. And now in sort of AI world, you're used to, like, big step function growth, and it might be flat for
Speaker 2:a month.
Speaker 7:But then you sign, like, another contract, and it just, like, leaps leapfrogs again.
Speaker 1:Interesting. Yeah. Help me square sort of the shape of revenue with some of the YC batch that we might talk to today because, Paul Graham was on the timeline sort of defending this idea of selling to startups. We were in in complete agreement with that that, selling to startups can be so much better in a bunch of different ways. But it does feel like we're entering an era where maybe it's AI.
Speaker 1:Maybe it's just the maturity of the ecosystem. Like, it's also been easier than ever to sell to the government or to sell to Fortune 500. And so are both happening in are there specific companies that are really great at one or the other? Is there is there any advice that you've given founders on how to decide between those two paths?
Speaker 7:Yeah. It's really it really depends, I think, on, like, the type of product you're building. So I think, like, the the bull case for selling to startups as your customers is, like, the Stripe or the AWS case. And, like, it's like, you get them all early. I mean, you could put Gusto rippling deal into that bucket as well.
Speaker 7:It's like, if you get the startups early and you can grow with them, that is one of the most powerful business models you can have. Right? Like, the Stripe team could go on vacation for, like, two years, and they would just, like, keep growing because, like, the cohorts would just keep going up into the right. Right? So, like, I don't think they're gonna do that anytime soon, but they they could if they wanted.
Speaker 7:Yeah. So I think if you have a product like that where you can grow with the startups and you can get in early and they will just, those startups in the future will become your enterprise customers, that's, like, fantastic. That's absolutely what you wanna do. I just think, like, with AI, what's new is you didn't even have the option of selling to a big customer until you'd sold to start ups and you'd build up like, oh, hey. Like, we don't have an enterprise customer yet, but we got, like, a thousand start ups.
Speaker 7:And, like, in aggregate, we're processing, like, x or, like, we're reliable. We're not gonna shut down. Mhmm. I think now with AI and the fact that the incumbents can't actually build the products because the engineers that work at these bigger companies don't even believe in AI. So, like, startups in the batch are able to go to a big company and actually get them as a customer because they're the only ones that can actually deliver the product.
Speaker 7:And I think that's news. I like, we still give the advice. It's very dependent on the company and the product and, like, will you be able to scale with startups or not? But, like, in general, there's just more options as a founder for how you do sales than there's ever been.
Speaker 2:Let's let's talk about themes in the batch. Mhmm. Two batches ago, I've felt like a lot of the companies were, at least the ones that we talked to, were were, like, various, like, infrastructure. It was, infrastructure for building agents. Last batch really felt, like, much more applied.
Speaker 2:It was like applying AI to very specific industries and opportunities. I'm curious. I'm sure you're seeing both of those types of companies. But looking at the list of guests that we have today, bunch of bunch of super exciting companies, but curious to know kind of like broad themes across the batch.
Speaker 7:I mean, think you say it right. I think what we've seen is that, like, maybe a year ago, just a year ago, it was like infrastructure. Infrastructure to build agents, like you're saying, like laying the foundation. Then it's like vertical agents just take off, like customer support, logistics, like, name any like health care, like all these verticals, and they're just, like, taking off. And primarily, what they were doing is selling these agents to the companies in those verticals to make their operations more efficient.
Speaker 7:I think what seems to be a theme coming out of dispatch, you'll notice, is, like, the companies are going the next step, and they're not actually selling the agents to the, like, incumbents. They're going, like, AI native full stack. They're just actually doing the thing.
Speaker 6:So you
Speaker 7:have, like, Fernstone being, like, an AI native insurance brokerage. Like, they just they they they are insurance broker, and they're just gonna use AI to be the best one. Saba is doing that with trust. It's like a company that sets up trust, but it's doing it with AI. So I think that, that seems to be the new trend is going, like, AI native and not just selling your agents, but using them to build the company doing all of the stuff.
Speaker 1:Yeah. We, yeah, we've talked to a couple, like, law firms that have done that and also, like, investment banks, just people who have said, okay. We actually need to go do the do do the core thing. I'm always reminded of Justin Kahn's company because Yeah. It feels like Atrium was, like, just a little bit early to that model, and now everyone's working on it.
Speaker 1:It's starting to maybe work, and we'll see. Yeah.
Speaker 9:I mean, the thing is I think if
Speaker 7:you go bad, do you remember it was I mean, it's, like, a decade ago now, but it was Balaji that started this whole thing with, like, the full stack startup. He, like, he had this blog post and, like, I don't know if you guys were in San Francisco at the time, but, like, there was this moment where there was DoorDash, which was delivering food. Yeah. And then you had Spoon Rocket and Sprig, which were, like, the full stack version. Because what they did is they had these kitchens, these vans, which had little kitchens driving around San Francisco cooking the food.
Speaker 7:Yep. Right? So I think, like, back in that era, it was like it was seen as being the most ambitious thing to be a full stack startup. I didn't just sell your software. You did the whole thing.
Speaker 7:Yeah. Ultimately, those companies didn't it turned out that being a marketplace or selling software was just a better scalable business in that era. Yeah. But now with AI, like, I think the promises were kinda going back to the full stack startup idea. But this time, like, you know, we're all hoping and kind of seems like these things will actually scale because you don't need to hire, like, a thousand people to do the work.
Speaker 7:You just keep improving your agents.
Speaker 1:Yeah. Yeah. I mean, the the the food example is interesting because it feels like Travis Kalanick is maybe dipping his toe in, oh, what if I did the full stack thing? Yeah. Yeah.
Speaker 1:He's got he's got he's
Speaker 2:got picnic, and I think it's Otter, and then he has Cloud Kitchens.
Speaker 1:So so maybe He's like, I can do it. But maybe at his scale maybe it's a scale thing. I don't know. But it is it is more complicated financially.
Speaker 7:I think if you if you'll try if you have Travis's, like, access to capital and his, like, background, like, operating, then you can you can you can do that.
Speaker 1:Yeah. How are how are companies or founders grappling with, what's happening at the largest foundation model labs? Like, I remember there was some, there was some Sam Altman interview where he said, you know, here's how not to get steamrolled. If the models if your entire business is just predicated on the model not getting better, you're gonna have a bad time. But if you're doing something completely separate with the model, you're you're probably good.
Speaker 1:How are people thinking about it in the more modern context?
Speaker 7:I I I think the framework people have on this stuff is that they expect, you know, Sam and the big lab companies I mean, Sam OpenEye in particular Mhmm. To go after probably, like, maybe more of, like, the sexy consumer ideas that, like, capture the public's imagination. Yeah. And it is gonna be hard to compete with them on that. But there are, like, the startups in the batch in particular focus on just, the unsexy verticals, like building an audit firm, building a legal firm, building insurance broker.
Speaker 7:Like, the bet they're making is that, like, the best people at OpenAI or Anthropic are not gonna be thrilled to build, like, auditing software or auditing agents. You know? And so that they
Speaker 2:Or actually sell the or actually sell the end service. Right?
Speaker 7:Yeah. Exactly. Like, doing it like, going, like, all the way and, like, learning what that customer wants and how to do it really well and, like, iterating on it a thousand times to
Speaker 1:get the whole thing with Google versus Amazon. Like, Google did wind up building a shopping product, but they never really had that in them to be like, we're going and doing warehouses Yeah. And we're going to compete with Amazon even though we want ecommerce. Like, we don't really want it that badly. That sounds actually sort of miserable, and it's just not
Speaker 7:even wanna do it. Right? Yeah. Like, the best engineers at Google don't wanna build a shopping product. They are like, back in the day, they wanted to work on search quality.
Speaker 7:Now they probably wanna work on Gemini. But, like, you
Speaker 1:just Yeah. And there's and there's also just cultural I feel like culturally, there are certain companies where, like, if you're like, we do 80% gross margin work, and you show up and you're like, I'm the guy who does 30% gross margin work. They're like Yeah. You can leave the company, actually. Like, we don't like you at all.
Speaker 1:Yeah. But so yeah. Yeah. Yeah. You know, your margin is my opportunity both directions sometimes.
Speaker 2:Yeah. What what are what are some companies from previous batches that you really feel like are hitting their stride now? We had Caucheon yesterday
Speaker 10:Of course.
Speaker 2:For their $11,000,000,000 round. I don't I don't think a lot of people are even aware that they went through YZ because it was so so long ago. Right?
Speaker 7:Yeah. That was that was 2019, I think. So, yeah. Mean, obviously, is, like, the prime example of a company that just made a bet on a space early and, like, have to just wait for the market to actually exist for it. And, like, those founders, like, super tenacious, went for it.
Speaker 7:I think, like, more recently I mean, it's a company that announced around doing customer support called Giga Oh, yeah. Which I think is, like, a really exciting one. Like, they're competing with Sierra and Decacorn
Speaker 2:Okay.
Speaker 7:Like, superstar founders of those companies, tons of capital raise, but they've been able to, like, beat them on head to heads with customers like DoorDash
Speaker 1:Sure.
Speaker 7:Through, like, technology, really. And so I think, like, Giga seems to be really growing. I mean, another one, like, AI, that's why, like, Posthog is actually, like, a little bit more under the radar. But Yeah. They are sort of, like, taking the rippling approach of Yeah.
Speaker 2:They're launching a new product, like, every week, it feels like.
Speaker 7:Yeah. It's, like, really interesting to see that they've done that from day one, and it seems to actually be compounding and working the way that it has for rippling. So I'm I'm curious to see if the you start seeing more of that just, like, startups trying to build multiple products from day one and have, like, the compound startup effect.
Speaker 1:I I like animal themed companies. I like post hog. I like the hog themed. When we did our first demo day stream
Speaker 7:That's the key.
Speaker 1:We we talked to a company called Pig, and we really like Pig, and it stuck with me. And so I'm rooting all
Speaker 2:You gotta check-in with Pig.
Speaker 1:All the swine themed startups. I hope they all do very well. But but thank you so much for taking the time to kick this off with us. Congratulations on the big day.
Speaker 2:Great great to have you on for the first time. Wanted wanted Yeah.
Speaker 1:Yeah. And we gotta we gotta do this more often. I would
Speaker 7:love to. Thanks for having me.
Speaker 1:Yeah. Yeah. Let's talk to hanging. Have a good one.
Speaker 7:See you guys.
Speaker 1:Goodbye. We our first guest will be Plaid Labs, makers of the Chad IDE. First, let me tell you about Julius AI, the AI data analyst that works for you. Join millions who use Julius to connect their data, ask questions, and get insights in seconds. We have chat Clad Labs.
Speaker 1:And While we wait
Speaker 2:Have we been names able for, if you're launching a startup and you want a pig's name, swine theme name, could have Wilbur
Speaker 1:Yes.
Speaker 2:Babe, Hamlet, Daisy, Peanut, and Cookie.
Speaker 1:Okay. I like that. Ham Solo. I like Babe. I think Babe Babe
Speaker 2:Mud Pie.
Speaker 1:Okay. So we have the founder of Clad Labs in the restream waiting room. Let's bring him into the TVP and UltraDome.
Speaker 2:What's going on?
Speaker 1:Look at the shirts. They look fantastic.
Speaker 2:Incredible. Know,
Speaker 1:you're you're winning me over already. Break it down first. Introduce yourself. Tell us what you're building. Good to meet you.
Speaker 5:How's it going, guys? Yeah. I'm Richard, the CEO of Plaid Labs. We're building Chad IDE, the world's first brain rod IDE.
Speaker 2:Okay. Why? So Why? So great. So so so, we we exchanged some comments and Yes.
Speaker 2:Wanted you to come on the show. I think you get the TBPN award for the best rage bait of at the product level of the year. And I thought your response to the essay that I did was amazing. You were like, cool essay. Unfortunately, it doesn't unfortunately, it doesn't apply to us.
Speaker 2:Yeah.
Speaker 1:So why doesn't it apply? What are you actually building? Like, why brain rot? Is it just for fun, or is there something meaningful here? Do you think this turns into a real business?
Speaker 1:Like, what's the plan?
Speaker 5:Yeah. The general thesis is that we're able to subsidize the generation of code with affiliates Mhmm. And provide these state of the art models for much, much cheaper, mostly free actually to most developers.
Speaker 1:Mhmm. And and so that's why you're putting you're you're you're putting so you're acting as a funnel to, you know, any affiliate that so it could just be ads, but you picked specifically the most controversial ones, the gambling and the and the and the subway surfers, like the stuff that feels more brain rotty because that would get into a reaction. Was that the plan?
Speaker 5:Yeah. Yeah. I mean, there's a I mean, I think Jordy touched on this earlier. There is a difference between the marketing and the product.
Speaker 1:Sure.
Speaker 5:We actually started out with affiliates on these very normal sites, and then a lot of our users actually requested saying, hey. We actually, like, score on Rainbet. We actually go to stake during our generation time. Like, okay. We'll integrate that feature, then we'll use that as our marketing campaign.
Speaker 1:Okay. I it's incredible. I mean, the debate was, are you making something people want? Is this in keeping with the Y Combinator thesis and the values of the organization? My
Speaker 2:Yeah. Guess so break down what's actually happening. Like, you have the you have the IDE, and then you have this other column, which you can basically fill with anything. You could fill with an ad. You could fill it with videos or rain bed or whatever.
Speaker 2:What are some of the most common ways that developers are using the product today, and what do you think really scales and becomes the most popular?
Speaker 5:Yeah. The greatest thing about AI native is that it completely changes the ad unit. Right? So we have these AI native ads that are in context, and it's really great for code generation. Here, let me give you an example.
Speaker 5:So let's say I code a website. I code me a website. Right now, Cloud Code has its multistage planning. Right? It says, well, what what what do you wanna code?
Speaker 5:Like, how do you wanna use a back end? If I say, well, maybe I wanna use a, like, soup base. Say, yes, soup base. That's a soup base conversion right there. Yeah.
Speaker 5:So the ad is actually in the context in the application layer. So we have multiple ad placements, but I think the most exciting one is how does ads scale at AI native?
Speaker 2:Yeah. We we had a what was the name of the company that we had on? There's another company that's doing this and and actually integrating the ads so that you see an ad. You're like, yes. I want this functionality.
Speaker 2:You press a button, and the AI actually implements the products for you. And then you just and and and I can just see that converting at a at a really high level and companies being willing to pay quite a lot to get in front of people, like, at the right time.
Speaker 1:I mean, yeah. It makes a ton of sense to me on on that level. A little bit less on the stake gambling while you're waiting. That feels like that would actually reduce developer productivity. Do you have any plans to actually assess whether or not this is a good decision?
Speaker 1:Because most developers are not solo entrepreneurs. They're employed by someone. And if I'm running an organization, do I really want my most valuable, you know Resource. Resource, my most valuable human capital, tuning out every other second while they're waiting for, you know, the generation to come.
Speaker 2:Engineers might say, well, I would because I'm betting on Rainbet with my personal dollars, you're paying less for the IDE. I'm saving the company money Yes. John.
Speaker 1:But yeah. Yes. But but but do do she do don't you think that it would be better to show educational videos than something like that?
Speaker 5:Oh, we have that as well. Yeah. So we have educational videos, learn about the code that you're actually writing.
Speaker 4:Okay.
Speaker 5:But I think our thesis is basically that we follow the YC advice, talk to your user Mhmm. And the user wanted the the gambling integrations, we made it for them. And as at some point, the user doesn't want it anymore, we'll take it away. Right? So it's all about, I think, for B2C is being close to the user, iterating close to the user, and Okay.
Speaker 5:Serving what they want.
Speaker 2:Have you been banned at any companies?
Speaker 5:Oh, yeah. Yes. Actually, opposite. We had quite a few companies reach out to us and say, hey. We actually really want you to integrate our Notion, our Jira board, like, the whole, like, product productivity workflow into the generation time.
Speaker 5:And we're, like, we really we're serving consumer right now. But, I mean, there's a there's infinite possibilities here to scale at, like, various business levels.
Speaker 2:Okay. How's the traction been to date?
Speaker 5:It's been great. Yeah. I think I have to thank you guys for that as well, helping us go viral. So we have a great waitlist of 11 k. Baited.
Speaker 1:11,000 people on the waitlist.
Speaker 2:Successfully baited.
Speaker 1:Has anyone has anyone used it yet? Have you built it? Is it is it is it in the wild? Is it b is it a Versus CodeFork?
Speaker 2:Is that the were what were the metrics that you shared at at demo day?
Speaker 5:Yeah. So the metrics I shared at demo day were 11 k on the wait list Mhmm. 30 k in revenue from ads. We have people using right now in beta, and we're gonna give out codes today at demo day. So anybody who comes up to us on demo day, we're giving you a code.
Speaker 1:Okay.
Speaker 5:It's going really great.
Speaker 2:Amazing. Find Tyler. I know he's probably in the same room. Let's get let's get Tyler on on on clad labs or Chad IDE. We should hit
Speaker 1:the gong. Yeah. We should.
Speaker 2:And how's the round going?
Speaker 5:It's going great. Yeah. We filled half the round. Have a lot of allocation to to give out to people who are interested.
Speaker 2:Awesome. Alright. Well, great to meet you, Richard. Thanks for coming on and breaking it down.
Speaker 1:Appreciate it. We'll talk to you soon. Have a good one. Let me tell you about Figma. Think bigger, build faster.
Speaker 1:Figma helps design development teams build great products together. You can get started for free. We have, our next guest coming into the, into the, the UltraDome.
Speaker 2:This next company is Absurd.
Speaker 1:Really? Oh, wait. It is Absurd. That's the name of the company. They will be joining in just a minute here.
Speaker 1:We might need to pop back to the timeline while we wait for them to sit, sit down. Jordy, you can take a you can take a a view here. This is the live view into the
Speaker 2:Very cool.
Speaker 1:Into YCW. So Very cool. If we if we jump ahead of the schedule, we can always, check-in there.
Speaker 2:But There we go.
Speaker 1:We have the founder of Absurd in the Restream waiting room. Let's bring him in to the TV. And Ultradome, how are you doing?
Speaker 2:What's happening?
Speaker 1:You so much for taking the time to talk to us.
Speaker 11:Of course. I'm doing good. How are you guys doing? I know you guys are only taking on a couple companies today, so, thanks for having me on.
Speaker 1:We appreciate you. Fantastic. Coming on. Have you. Please introduce yourself.
Speaker 1:Tell us what you're building.
Speaker 11:Yeah. My name is Philip. I'm the CEO of Absurd. Absurd makes AI marketing videos. Mhmm.
Speaker 11:An ad that we've made,
Speaker 12:you probably see it
Speaker 11:on your feed, is Khalshee's Mamdani versus Cuomo one v one basketball match, which he did right before the elections. We like to joke that we influence New York City elections.
Speaker 1:Amazing. So what, walk me through the product. It sounds like you're more using the foundation models, using Sora v o three than training your own. But what what are you building? How do you fit into the stack?
Speaker 1:Are you more of, like, a creative agency that I hire and pay a lot of money for an ad and you go out and use all the tools, or are you trying to build software as a service or train a foundation model? Where do
Speaker 4:you sit
Speaker 1:in the stack?
Speaker 12:The way we're seeing how we
Speaker 11:fit into the stack is that we handle everything for a company in terms of AI native distribution. Mhmm. And the reason why we're doing it in that route instead of, like, making an editor that anyone could use is because we can charge exponentially higher for that.
Speaker 1:So do you want to ultimately productize this
Speaker 2:So this is what is what Harj was talking about. Yes. Basically, instead of building like an AI native accounting firm or an AI native law firm, you're effectively building an AI native creative ad agency where somebody comes and say, I want one launch video, please. And you say, sure. Here's the fixed price.
Speaker 2:And then you guys use your internal tooling and whatever models you have access to to generate the best possible output, and you deliver that end product.
Speaker 11:Exactly.
Speaker 2:Got it. And what are you what are you charging on on, like, a per video basis today?
Speaker 12:So a
Speaker 11:lot of that's confidential, but I can say we charge upwards of $30 per video.
Speaker 1:Oh. So so in the same
Speaker 2:You're effectively charging the same Yeah. Somewhat similar to like what somebody would pay Yeah. For like a a full day shoot.
Speaker 1:Totally. Yeah. Yeah. You're in, like, the proper video production realm, at least in terms of price. What, what are the secrets to using the, video models appropriately to actually go viral?
Speaker 1:What what what do you hire for? What what are you focused on making sure that the video that you deliver is actually hitting, you know, upwards of $30,000 of value?
Speaker 11:So in terms of the value we deliver, every video we've posted has gone viral. I mean, we average 300,000 organic views Mhmm. For every company we work with regardless of whether you have 200 followers on Twitter or you have, like, a million. Mhmm.
Speaker 13:Second thing,
Speaker 11:in terms of what we're prioritizing, what we're really thinking about internally is just how many videos per person per week. Like, what's that throughput looking like? And then how do you drastically increase that week over week? So three weeks ago, that was one video per person per week. Mhmm.
Speaker 11:Today, it's 10 Super Bowl quality ads per person per week made in parallel. Mhmm. Next week, it's gonna be 50. Following week, it's gonna be a thousand. I mean, there was a company that came to us.
Speaker 11:I can't say their name, but they said they won 1,500 of our Kalshi Super Bowl ads in a month. And that's
Speaker 9:the type of
Speaker 11:quantity that we're talking about here.
Speaker 2:Wow. Like, this
Speaker 11:is this this is a lot of money that I mean, we we turned down $200,000 in the past three days because we just, you know, in terms of our bottleneck, we just had this huge technical bottleneck and we couldn't get it out in time.
Speaker 1:Yeah.
Speaker 2:Like, we're actually not allowed. That revenue down a few days ago. Why don't you just go back and say, hey, we have the capability. We have the capacity now. You just said you said it's ramping.
Speaker 11:We still don't have capacity now. We are we could literally
Speaker 2:thousand dollar videos have you sold? Did you did you create did you find an infinite money glitch here or something? There's not even a thousand there's not even a thousand, you know, venture backed startup launches, you know, a week. Yeah.
Speaker 11:So the the way the way we were seeing things right now, sure, we start out with launch videos that we charge $30.40 grand for. Mhmm. But now we're going towards more of like a retainer. Right? Mhmm.
Speaker 11:So now we're striking deals with companies like CaoXi, Replit, and WAP. And we're telling them, you know, we'll do a bundle deal, 10 videos a month for x price.
Speaker 14:Sure.
Speaker 11:Right? And eventually, it's gonna go to 50, then a 100, then 200. A lot of this is gonna be used in ads. Mhmm. Because the more you spend on ads, the more you have to switch out ad copy because
Speaker 1:of
Speaker 11:ad, you know, fatigue. And then we're gonna go up, and we're gonna actually connect orchestration layer to the actual metrics dashboard of all these ads. And then, eventually, we're gonna get to this point where, like, we have this huge compounding data moat, and our ad just get better and better. And you can think of an ad, I think, for the first time in history as, like, can create a thousand different variations with one click of a button. Because if you think about the ad in an AI ad, it's literally just, like, images, and you're animating them.
Speaker 11:And as long as you have an agent that edits the images and changes the prompt slightly, you can create a thousand different variations and then test multiple things at once.
Speaker 1:Will we see any absurd commercials during the Super Bowl this year?
Speaker 11:I I can't say. I I I
Speaker 1:You think maybe?
Speaker 2:That's a good
Speaker 1:that's a good answer.
Speaker 2:That's a good answer.
Speaker 1:You should
Speaker 2:make it up. He can't he can't, he can't you know, people are gonna look up his customers
Speaker 1:and guess. What do you think about, the role of of taste, of craft, a lot of what's what's previously gone viral in the age, in the pre AI age has been someone coming up with a really unique concept, a really unique spin, and and AI hasn't really been able to deliver those unique ideas. It's really good at reconstituting what's already out there and coming up with, you know, existing ideas.
Speaker 2:Yeah. Historically, the best creative agencies have been the agencies with the best ideas. It's like you pay to work with somebody that has a track record of generating great pain concepts. And then they'll oftentimes just outsource the work Yeah. To people that are good at the execution layer but not at the idea side.
Speaker 2:Do you feel like you guys need to develop like a internal
Speaker 1:Like taste
Speaker 2:or Yeah. Just the ability to generate a high volume of good ideas now that execution Yeah. In terms of, like, creative production is, like, so much faster with AI?
Speaker 11:Yeah. I think we think of, like, creativity not as a monolith, but really in terms of two parties. Exactly how you put it. So there's a taste layer, there's an act then there's an execution layer. Our our job here is we wanna remove that bottleneck between an idea and a finished product.
Speaker 11:So, internally, what we're doing to solve that is, like, sure, we're not gonna replace human creativity. We're gonna automate human labor. We're gonna make it so easy for, like, a comedian or a script writer or someone who just wants a part time job, and we're gonna pay them, like, a really high salary. Really easy for them to, like, create the seed of an idea that we can spin off tens and thousands of ads for.
Speaker 2:What how much are you guys actually spending on on the at the on the model on the model side or within any of the applications that you're using to generate this this content?
Speaker 11:Well, for, like, a thirty second to sixty second video, really, it's, like, $304,100 bucks. We have an internal orchestration layer that picks the best models to use for all these specific use cases. It's paired with, like, a 50 page doc that has all our learnings that isn't available anywhere online, and we're able to use these models really effectively. So our margins, like, beyond just human labor, because we're the ones making the videos and spending all these things up, we don't know how to price that, is, like, close to, like, 98%. But if you add in human labor, I think it's still, like, above 90.
Speaker 1:How many different models are you using on an average thirty second video? Do you do you feel like it's worthwhile to stick to one model because you get more of a consistent look, or are you jumping around? How do you think about the different models, what they're good at?
Speaker 11:Well, it's extremely obvious that some models are just really good at some stuff and really bad at another thing. CGM is good at specific use cases. Nano Banana is good specific use cases. Cling, WAN, all have their own unique use cases that we we we use. Something that's interesting beyond just the models is just, like, work in terms of workflow orchestration.
Speaker 11:Before Nano Banana Pro came out, I'll give this as an example, if you wanted to swap someone else's face, like, you would put in, you know I put in John's face, and I say, I wanna put Kanye on that, and that wouldn't work. So the way you would do it is you'd actually tell Nano Banana to cut off John's head and then get that, like, headless image and put Kanye's head on top. And that's how we swap faces before Nano Banana Pro. So there are all these, like, little workflow Sure. Things that we've learned just by experimenting and playing around with these models, which play a huge role in making all our ads like, the creation of our ads really effective.
Speaker 2:Fascinating. Have we entered a post slop era? Will we enter a post slop era? What what what is your post slop timelines?
Speaker 11:I was speaking to Jess Lee actually about this. She was talking about how photography used to be seen as slop. And because, you know, it used they used to say that photography was this way to cheat, like, artists who were actually painting something. Mhmm. But photography allowed people to realize that allowed people to capture, like, a smile really quickly through slow motion.
Speaker 11:And something will emerge from this AI era where you can do something with AI video to capture some essence of human that you wouldn't be able to do otherwise. And we don't know what that is, but I'm pretty sure we'll be the first to figure that out, especially if we're pushing all these videos every month.
Speaker 2:How big is the team today, and how's the fundraise going?
Speaker 11:It's just me and my two cofounders, Daniel and Damon. In terms of the round, we closed I can't announce how much, but we closed a week and a half ago.
Speaker 2:Incredible. John, hit that gong. I will. For Philip and the Absurd team, thanks for coming on, breaking it down. I'm I'm actually surprised there's not more companies trying to do this this exact exact sort of playbook, but it's it's cool to hear how you're thinking about this and excited to see more of the work that you guys put out.
Speaker 12:Of course.
Speaker 11:Yeah. I and by the way, before I go, I'd love to make a launch video for you guys.
Speaker 4:Oh, okay. Let's talk.
Speaker 1:I would love that. I wanna see what I wanna see what you can do. We have we have a benchmark here, BezelBench, where it it involves a lot of watches on ARMs. We like to put this to the test with a lot of different AI video generators. It's a particularly hard, shot to get right, but we can come up with a bunch of different ideas.
Speaker 1:Let's do it. That'd be fantastic.
Speaker 2:Let's do it. Perfect.
Speaker 11:Well, have a great rest your day, though. Investor. He'll be in contact. You guys for having me.
Speaker 1:Talk to you soon. Cheers. Goodbye. Before we bring in our next guest, let me tell you about Adio, the AI native CRM. Adio builds, scales, and grows your company to the next level.
Speaker 2:Up next, we have LightBerry with LightBerry. Ali Atar. I like Social Brains for Robots.
Speaker 1:Social Brains for Robots. Let's bring in the sound of that. Ali. LightBerry. Yes.
Speaker 1:Very interesting to see what robots we're talking about, that we have in here in the studio.
Speaker 2:Welcome to the show.
Speaker 12:Hello. How are you?
Speaker 2:What's happening? Light berry owning yellow, verticalizing yellow. I love it. I love it.
Speaker 12:You know, we have to wear something different. Everyone's wearing, like, gray and blue and black and, like, we need to stand up.
Speaker 2:So yellow is Underrated color. Underrated color.
Speaker 12:It is. It's awesome. Great
Speaker 2:to have you on the show. Why don't you introduce yourself, give some quick background, what you're doing before starting LightBerry?
Speaker 12:Yeah. Of course. So yeah. Hi, everyone. I'm Ali.
Speaker 12:I'm one of the founders of LightBerry. We're effectively just building the operating system for all robots so that any person can use a robot. Before this, I ran a browser company called SigmaOS. I was running product and design there, and I went through YC in summer twenty one.
Speaker 2:Very cool.
Speaker 12:Yeah. So that's me.
Speaker 2:Very cool. Talk more about that. This feels like a very big opportunity, but I'm not using a lot of robots in my day to day life today. I assume that I will be much more in the future. But yeah, talk about what the business and the product looks like today and where you see the kind of category going.
Speaker 12:Yeah. So we literally have a humanoid robot upstairs right now, emceeing the entire event for demo day. And, you know, he's fully autonomous. He talks. We give him some instructions about, like, how he should behave for the day, and he's just acting like a part of the event staff.
Speaker 12:Now, you can go out there right now and just buy a humanoid robot from at least 50 different manufacturers, but if you do that
Speaker 2:Who who did you buy yours from?
Speaker 12:So ours is from Unitree. It's a Unitree robot.
Speaker 2:Did you buy it on walmart.com? Because I know they sell Unitree No.
Speaker 12:No. No. Not at all. No. We actually work directly with Unitree.
Speaker 12:And so, like, you know, if you buy a robot from them or any of the 50 others, like, it it literally doesn't do anything. It can't talk. You can't teach it anything. You can't the only way to interact with it is by writing code. We thought that's insane, and so we're building a software layer that allows literally anyone to use a robot by just talking to it.
Speaker 2:What yeah. What what does adoption kind of like with this? How are you actually selling it? Is this something that you want Unitree to encourage their customers to adopt? Because, again, I'm sure any manufacturer of robots doesn't want to just sell to developer hacker types that happen to wanna go through all the different hoops in order to actually get value out of a out of a humanoid.
Speaker 12:I mean, that's exactly it. You hit the nail on the head. Like, we're working directly with the manufacturers. There's, like, over 50 of them. We actually just, last week, closed the deal with Unitree.
Speaker 12:They're, like they correspond to, like, 90% of market share in the world.
Speaker 2:I'm giving you the air horn, but I have encouraged various government officials to ban Unitree from The United States. Oh, no.
Speaker 12:Well, look, you know, the truth is, like, they're the only ones shipping. Like, we wanna work with the American companies too. We wanna work with literally everyone. Yeah. But Unitree's shipping, they have market share, so it just makes sense to ship on them.
Speaker 12:We're gonna be selling LightBerry powered robots with them all over The US, but also working with other companies, some European ones, some American ones. Mhmm.
Speaker 2:Yeah. What's happening so I would imagine One X has no interest in in partnering with external providers. That would be my sense. Maybe that maybe that changes in the future, but I know they're they're trying to really verticalize, and I'm sure they wanna create personality and some of the same feature set. But what about other other players in The US, Figure, Optimus, etcetera?
Speaker 12:I mean, the truth is, like, they're just not shipping yet. And when they want to start shipping and right now, they currently don't have any software that allows you to interact with the robot. There's nothing that works in a public space. I heard that Figure's deal with OpenAI just fell through. I don't know if that's true, but, like, that's the rumor.
Speaker 12:We'd we'd love to help all of those companies get to market faster. It's just a race right now. So it's like whoever needs software so that you can interact with the robot, we're here to help.
Speaker 1:What do you think the the most dominant form factor for robotics in daily life will be in just maybe, like, two or three years? Do you think we're gonna go through, like, a like, a wheeled robot phase or or, you know, one robotic arm on a Roomba phase? Like, how do you see because the the self driving cars are sort of here. The Roombas are sort of here. The the full humanoid robot, that feels a little bit farther out.
Speaker 1:But is there gonna be more of a transitionary phase in your mind?
Speaker 12:I mean, if you look at sci fi as an indicator of what people want, we don't just want humanoids. There'll be different kinds of robots. You're gonna have some, like, small bipedal droids that you know, we work with a few companies that do that. You're gonna have wheeled robots for, like, delivery that's just more practical. Mhmm.
Speaker 12:In homes, I actually don't think you'll have humanoids because, why do you need locomotion in those cases? Humanoids are gonna be the first, like, general purpose form factor that's gonna make it, in my opinion, just because, you know, they look like us. And the reason why we're building humanoids is because they be they look like people. Yeah. And so we'll just be deploying them in people facing roles.
Speaker 12:So, like, shop assistants, manning booths at events, MCing at demo day. Right? Like, we have done this before. We deployed, like, a fully functional autonomous humanoid at the eleven Lab Summit, like, three weeks ago, and it was just working there for ten and a half hours, like, fully autonomously alongside the staff. So, yeah, that that's that's what we do.
Speaker 12:And and and we think that there's gonna be tons of different form factors. It's gonna be like a Cambrian explosion of robots.
Speaker 1:What are the compute constraints like? You do you think on device inference is gonna be really important?
Speaker 12:So we run a hybrid pipeline. We rely heavily on the cloud because that's where the best models are.
Speaker 2:Sure.
Speaker 12:And people prioritize the quality of interaction more than than, you know, like, the reliability of it. Sure. Now we also run it, as I said, hybrid. So we have an offline version that's also running in the same time. So if, you know, connection drops or anything, the robot will still talk to you.
Speaker 12:It'll still understand. It'll be less smart, but it'll know about it.
Speaker 1:Yeah. Have you had any luck I mean, how do you think about, like, personality development? And, I've been very fascinated by the fact that pretty much no lab has been able to hammer out of the model, like, the it's not this, it's that. Like, they all have this specific LLM flavor to them that I don't think most humans maybe I run into one out of a million people that talk like that, but they all kind of all the robots talk like robots. And I'm wondering if you have any thoughts about where that all goes.
Speaker 12:I think prompting is just I mean, these models are getting more and more steerable, they're better at following instructions. Yeah. So as long as you do a great job of spending time on designing those interactions, you'll be able to get these robots to behave less like robots. Now we're not trying to make robots that, you know, behave just like people. Like, people love c three p o, but c three p o is very obviously, a robot has a robotic voice.
Speaker 12:It's a bit awkward in the way it speaks. And, like, that's the inspiration. It should just be, like, smart enough, but it should still, like, behave and follow our social norms. Like the robot should look at you when you're speaking to it. The robot should be wearing the outfit of like the staff members that it's representing if it's at an event.
Speaker 12:And that's what we're here to do. Like we're we're just here to make that easier for all of those manufacturers because they're racing on hardware. They don't have time to think about the software and the interactions.
Speaker 2:Are you are you excited about robot pets as a category? I know dogs are are
Speaker 12:They're cool.
Speaker 2:Substantially cheaper, and that feels like something that a robot pet doesn't need to necessarily add any value outside of companionship. And so it feels like potentially an area where we could see a lot of growth in the near term.
Speaker 12:So we we actually have, like, a little pet droid in our office. It's like a bipedal that kinda looks like r two d two. We we brought a bunch of little robots to the event too. There's, like, six of them in the demo for anyone who's here. Yeah.
Speaker 12:I think robot pets are gonna be really big. It's just we're we started working mostly with humanoids just because the price point is so much higher that we could just focus on quality rather than, like, trying to optimize for cost. Obviously, as these robots get smaller, the cost gets lower, and so, you know, for us, we just wanna we just care about quality. The models are gonna get cheaper too, so we'll be able to, like, deliver on, like, toys, pets in the near future.
Speaker 1:Yeah. The toy the toy market seems really, really interesting.
Speaker 12:Yeah. Our first customer is a toy company, actually.
Speaker 1:Yeah. It's so much lower
Speaker 11:bad, is
Speaker 2:my opinion. What about security? I I feel like there's a potential use case for humanoids just having a human shaped thing just just moving around.
Speaker 12:So, literally, the landlord the landlord of our building, when he when he he saw that we moved in, he stepped into our office. And on day one, he just asked us, like, oh, so these things can talk and they can walk around? They can map the world? I was like, yeah. And he was like, you know what?
Speaker 12:I would love to deploy them for security. How much does it cost? And I told him, it's gonna be, like, around 60 to 70 k. He's like, I want four. I was like, okay, deal.
Speaker 12:So like, he he already pre ordered them. Like, people want this for security not because they can fight, not because they can harm people. These things can't
Speaker 2:It's just about
Speaker 12:but they're like It's about
Speaker 2:the price difference. Yeah. It's
Speaker 12:just It's the best deterrent. And like, you know, we can literally talk to weird people in the evening. So, like, who are you? And, like, run facial recognitions, like, are you meant to be here? And then just alert, like, whoever's on, like, on guard at staff and and just call them and and ask for help.
Speaker 12:Like, that's how it should work. Right?
Speaker 3:Yeah.
Speaker 12:Robots to help people, not not to replace them.
Speaker 2:Yeah. I I do think it's interesting that a lot of these humanoid companies are focused on the hardest possible thing, which is replacing Yeah. Like a house a housekeeper
Speaker 1:Sure.
Speaker 2:Who is already not the highest comps person doing the most, like, intricate specialized tasks where somebody that's a security guard, their primary primary job is to just stand there
Speaker 4:Yeah.
Speaker 2:And look like they're paying attention
Speaker 4:Yeah.
Speaker 2:And that's, like, the job. And they make, like, the same amount as a housekeeper.
Speaker 12:Yeah. We don't think we don't think the chat GPT moment for robotics is gonna be the day that your robot will know how to fold your laundry. We think it's gonna be the day you start seeing robots everywhere in the street or like in shops, in coffee shops
Speaker 1:Mhmm.
Speaker 12:In in events, like, talking to people. Mhmm. And that's just really soon.
Speaker 1:That's gonna be fun.
Speaker 2:Very cool. Yeah. How how big is a team?
Speaker 12:We're just a small team of three people.
Speaker 9:Three people.
Speaker 12:We have a few people that we're working with that are helping out on top of it, obviously. But, yeah, it's just a a core team of three founders.
Speaker 2:Amazing. And how's the round going?
Speaker 12:It's been very fun. I mean, we we managed to close it, like, pretty early. There's a lot of there's some interest now because, you know, like, we're with that unitary deal, we're pretty close to a series a milestone, so we're trying to like discuss that.
Speaker 2:We'll see. There we go. Series a time. Love it. Let's go.
Speaker 2:Really great to meet you.
Speaker 1:We'll talk to you soon.
Speaker 2:Thank you for coming on and excited to call have on. Have a good one.
Speaker 1:Cheers. Bye.
Speaker 3:Yep.
Speaker 1:Up next, we have Dome, a unified API for prediction markets. This should be fun. It's trying to sit on top of
Speaker 2:Pick a favorite. Pick a favorite.
Speaker 1:Polymarket. Well, there is a lot of arbitrage to be done, on on the, on the topic of robots. I'm just I'm super excited about the lamps that are happening. Have you seen that there's two robotic lamp companies now? They're like Right.
Speaker 1:They're they're One
Speaker 2:of them was just CGI. Right?
Speaker 1:I I I don't know. Maybe both of them were CGI.
Speaker 2:It's even Apple. Making their own robotic?
Speaker 1:They're making their It just feels like something that can be done. Whereas if it's, you know, full humanoid tomorrow for this much money, like, that feels like a taller order. It's gonna be a couple years away. But the lamp, I feel like we can do today. The lamp can talk to you.
Speaker 1:It's gonna be funny. It's gonna be awesome. I'm excited. Yep. I'm really bullish on the lamps.
Speaker 1:But I'm also bullish on a unified API for, for prediction markets. So we'll bring in the founder of Dome. Welcome to the show.
Speaker 2:What's going on?
Speaker 1:Welcome to the TBP and UltraDome. You're in the UltraDome, and your company is Dome. Please introduce yourself and your company. What do do?
Speaker 6:Hi. My name is Karush. We're basically Dome. So Dome is a unified API for prediction markets. Mhmm.
Speaker 6:In a nutshell, what that means is we allow users and developers to trade and analyze across multiple platforms at once.
Speaker 1:Okay. Who's the customer? Are you talking hedge funds or, like, the most advanced traders?
Speaker 6:Yeah. Honestly, it's, it's all of the above. Well, a lot of our current customers are are folks building applications in prediction markets. So Mhmm. These are folks building, like, prediction market skins or markets themselves or copy trading and agentic trading is, like, really popular right now.
Speaker 6:Okay. We talk to a lot of sports books and hedge funds as well. They're they're getting interested in high frequency trading And also, like, platforms, like, you know, think sweepstakes apps, folks who are trying to, like, price internal parlays. So there's a lot of applications currently being built right now.
Speaker 2:It's crazy. Very cool. Who's your favorite, Polymarket or Kalshi? I'm just kidding. I I won't make you I won't make you answer that.
Speaker 5:Was about to say, that's
Speaker 6:the million dollar question.
Speaker 2:Yeah. Yeah. No. No. I mean, it's it's unfortunate that the timeline is just so incredibly toxic right now.
Speaker 2:But I feel like you're able to kind of like sit back and be hopefully like Switzerland and support a variety of different, exchanges. How do you think this market actually shapes out? Right? I think the big news from last week is that Robinhood is Yeah. Getting into the game themselves.
Speaker 2:They actually wanna not just be a broker. They wanna be the exchange. But how does this how does this evolve?
Speaker 6:Yeah. I mean, we're we're we're supporting currently Polymarket and Cauchy. They're both great. Obviously, we don't we don't pick a winner in the fight. We want everyone to do well.
Speaker 6:And what we're currently seeing is there are a bunch of new platforms launching different regions, different specific verticals. Some folks are just, like, holding sports. Some are doing crypto, mention markets. So what we're actually seeing is there's gonna be a lot of players coming in, each trying to find their specific wedge, find their little market, their community there. And so in addition, you have CaoSheet, Polymarket, you will have Robinhood and a bunch of other big players that are are probably launching soon.
Speaker 6:But you'll also have a lot of these, like, smaller players in different specific regions and verticals. And so we're excited to see, like, the whole world bases start adopting this.
Speaker 1:Do you have a do you have a a reference point for how cross market transactions like, is there is there a public markets equivalent to you or or some sort of, like like, legacy? Not necessarily a hedge fund,
Speaker 3:but they're
Speaker 1:like, I I remember reading Flash Boys. And in there, they're talking about trading on the commodity markets in Chicago and then also the stock exchanges in New York. And but it's done. This is all done by the hedge funds. There's not some sort of intermediary.
Speaker 1:Why do we need an intermediary here in this markets particularly?
Speaker 6:Yeah. I mean, that's a great question. Yeah. For for what it says Flash Boys is my cofounder's favorite book, so hit it
Speaker 7:on the nail.
Speaker 2:There you go.
Speaker 6:But yeah. Absolutely. So one, as you get a lot more providers in right now, a lot of the liquidity is fragmented. Okay.
Speaker 10:So if
Speaker 6:you actually look at calcium polymarket themselves, about, like, 80% of their markets, their underlying contracts are the same event. Mhmm. So you actually have a good amount of overlap there. But you also hit it on the nail as well as, like, there are other markets you can match against. Like, sportsbooks are obviously very, very clear.
Speaker 6:There's a lot of prediction market overlap there. Crypto prices, perps, and all of these things. So by kinda taking all this data in, creating creating that centralized source, it really helps out the hedge funds and those other professional traders who are trying to trade across multiple platforms because everything's in one spot.
Speaker 2:Or is some of your volume people just arbing markets on the different basically saying, Okay, what are the odds on call sheet? What are the odds on poly market? And trying to find alpha through that.
Speaker 6:Yeah. Mean, arbitrage is a very common request from a lot of our customers, right? We actually had a customer that, like, charted using our APIs, like, the different prices across the platforms. And it's a really cool visual because you can see the gaps over time of, like, free arbitrage. And so arbitrage is a very common platform.
Speaker 6:One thing that we do really well is we make sure, like, when we are matching markets across platforms, we tell them, like, hey, this is for sure a one to one market versus, like, a maybe one to one market because personally, with the way we got started was we were trading ourselves and and got burned as well when when two markets look similar, but they're not perfectly similar and you lose a lot of money and so that's Only means you think your head Yeah.
Speaker 1:Think you're squeezing it out for a second.
Speaker 2:Here's the issue is you can have the same event and different criteria in the market based on the platform and where Yeah. What exchange it's hosted on. Good. A lot of people have been seeing the rounds coming together for the different prediction market platforms and having flashbacks to OpenSea
Speaker 6:Oh, yeah.
Speaker 2:In 2021 and 2022. Why do you think NFTs, which also saw explosive growth in volume, are are are kind of not the right comp for this industry?
Speaker 6:Yeah. Great question. Biggest answer is, like, we've kinda seen this exact playbook before. Both my cofounder and I, we were founding engineers at a company called Alchemy. Mhmm.
Speaker 6:So they're the blockchain infrastructure layer for anything you're doing in in Web three. They did extraordinarily well. And prior to them, really, like, the only really big businesses in crypto was exchanges. Mhmm. After they came and solved the infrastructure problem, you saw a bunch of companies build on top of them, including OpenSea and Polymarket.
Speaker 6:So we've seen this wave. We've built a lot of, like, the similar technology, the infrastructure layer at these previous companies. What you typically see is, like, there's a huge hype and boom cycle. Everyone's excited, and then, like, interest kind of fades away, but people keep building. And then the next hype cycle, you realize, wow, the floor is raised.
Speaker 6:And so with with prediction markets, you saw this during the twenty twenty four election. Everyone was super excited. They thought this was the future. The election ended. Everyone's like, oh, this is fine.
Speaker 6:We'll see you in 2028. But that but they people kept building. And then the first week of the NFL Sunday, they did more volume than they did during the twenty twenty four election. And so that's just more proof to say, like, yes, there will be boom and bust as far as interest, but the overall market will continue to grow.
Speaker 2:Are you actually routing trades on behalf of on behalf of clients or just providing the data layer? Because I imagine it could get quite difficult when some exchanges are using digital asset, you know, stablecoins. Others are using traditional fiat rails. I'm sure you would need to integrate both. What can you say there?
Speaker 6:Yeah. So first things we start off with is you gotta solve solve the read layer. You gotta give developers the tools they need to build. Right? So that was the first version of product is just give them data, give them prices, APIs, tools, whatever they need to display on their applications so that they can build applications.
Speaker 6:Right? The next part of our plan was then, okay, let's actually start doing order routing and and routing these requests to these different platforms. And we actually just recently launched our order router as of last week. And so we will be doing we first are starting off on the crypto angle, like processing orders through on chain portions. And then eventually, we'll also do off chain and and traditional fiat as well.
Speaker 2:Do you think it's interesting that a lot of the sportsbooks are funding lawsuits against the prediction markets while also starting prediction markets products themselves?
Speaker 6:I think it's super interesting. I think I think a lot of these sportsbooks and sports companies are also very smart and aware. They understand. They kinda see the writing on the wall. There's so many more advantages to having a pure prediction market, a P2P experience.
Speaker 6:It's a lot better for the end consumer as well. So I think they they they kinda see the writing on the wall. I think while the the lawsuits are, like, the equivalent of, like, maybe the taxi industry suing Uber back in the day, I think, eventually, most of this industry will move towards prediction markets.
Speaker 2:How's the round going?
Speaker 6:Round's been good. We actually closed up yesterday, and so super, super excited. We're we're excited to get back to building.
Speaker 2:I had a feeling. I had a feeling.
Speaker 6:I appreciate that. Yeah. I appreciate the excitement. It's been it's been an exciting journey so far.
Speaker 1:Well, thank you so much for coming on the show.
Speaker 2:Yeah. Great to meet you. Congratulations. Likewise. We appreciate
Speaker 4:you guys
Speaker 2:having another fun. Celebrating domes.
Speaker 1:Yes. We appreciate it.
Speaker 7:Appreciate you.
Speaker 1:We'll talk to you soon. Cheers. Have a good one. Before bringing our next guest, let me tell you about none other than turbo puffer, serverless vector and fault deck search built from first principles and object storage, fast, 10 x cheaper, extremely scalable. For the Forbes 30 under 30 came out today, and liquidity is having some fun because one of the guys who made it, he performed a 150% equity growth since 2019, but the S and P is up over a 172% over the same period.
Speaker 2:So He made money for his investors.
Speaker 1:Well, yeah, this is the thing. He might have taken less risk. And so if he took less risk and made almost the same amount of money, then that's good. You know? So there is a steel man for this particular person making the four
Speaker 2:He's always a steel man.
Speaker 1:But there's some there's some good folks on the 30 under 30. We'll have to take you through them at some point. But until later, we will, go head over to source, and we're gonna talk to David who's building Tinder for jobs. David, good to meet you. Welcome to the show.
Speaker 1:Thanks so much for taking the time. Introduce yourself. Introduce the company.
Speaker 10:Yeah. Thanks for having me, guys. My name is David. I am one of the founders of Source. Source is like Tinder, but for jobs.
Speaker 4:Mhmm.
Speaker 10:So you just upload your resume, swipe right, and AI will apply on the company's website for you.
Speaker 1:Okay. Well, how is AI actually helping there? Because I'm still doing the swiping myself if I'm looking for a job. The AI is just doing the application. Is that correct?
Speaker 10:Yeah. Yeah. So you basically fill out one job application Mhmm. When you first set up the app. Sure.
Speaker 10:And then when you swipe, then we have browser agents that will actually fill out the applications. So it just saves the filling out form time.
Speaker 2:How's the traction What is what yeah.
Speaker 1:So Are people using it already?
Speaker 2:About can you talk about the state of the hiring market? Oh, yeah. Because I feel like the the number one complaint that candidates and people that are applying for jobs have is that, like, seemingly nobody reads nobody actually looks at job applications, and a lot of roles don't actually end up getting hired based on traditional job boards. But, yeah, what what what can you say about kind of what you're seeing in the market?
Speaker 10:Yeah. I guess it very much depends on the company and the role in the sector. But in general, people definitely still get interviews from just inbound applications. A lot of it is automated, and recruiters do kind of, like, sift through the applicants applications. But I think the number one meta point is that it's definitely a field that's, like, ripe for disruption.
Speaker 10:Like, you are applying with many, many other people, and there's typically other ways to get it. Like, a lot of people email them themselves into a job or a lot of people refer their way into a job.
Speaker 1:But the
Speaker 10:inbound is definitely still something that companies use because when you're hiring people at scale, there's just no other way to do it. Like, if you're a company that's hiring, like, 200, 300 people a a month, it's impossible to
Speaker 2:do it through inbound. So where where where what kind of, like, jobs and and markets have you been focused on? Because maybe it's not, like, you know, other companies in a YC batch. Maybe that's maybe that's incorrect. But where where is the focus been?
Speaker 10:Yeah. Yeah. I guess a misconception about Source is that we're not very directly com working with these companies. We're just a traditional job board like an Indeed or a LinkedIn. So Mhmm.
Speaker 10:We directly scrape the ATSs. So right now, there's like like a million and a half jobs on the app, and those are scraped from ATSs like Workday or Greenhouse or Ashby. Yeah. So if your company uses that system as an ATS, then we've probably scraped your job and you're on source.
Speaker 1:Are they okay with that? Is that fine to just scrape these? Because I know LinkedIn used to be amazing for scraping and then Assuming.
Speaker 2:Yes. Because they're like, you're gonna get
Speaker 1:more ads. Yeah. As long
Speaker 10:as you're more out spamming it. The the ATSs themselves aren't like advertising or marketing. Like, they're just SaaS. Right? So there there's kind of a contract in this industry to that ATSs are there to be scraped.
Speaker 10:Like, 80% of the jobs on Indeed are scraped.
Speaker 4:Most of
Speaker 10:the jobs on LinkedIn are scraped.
Speaker 1:Sure.
Speaker 10:Job boards themselves obviously don't want to be scraped. Like, we wouldn't want to get scraped. Yeah. But the ATS themselves, obviously, they are just like sending out emails to candidates and managing that whole pipeline.
Speaker 3:So Got it.
Speaker 10:Yeah. That that's completely fine. And as for how the companies are reacting to it, answer someone's question Mhmm. Like, we've helped to get over 25,000 interviews in the past year. Wow.
Speaker 10:And those range from those range from thank you.
Speaker 1:That's fantastic.
Speaker 10:From, like I guess, there's a very wide range of companies. Like, we've helped somebody get a software engineer role at Andoril, like, a couple months ago. But then very often, you'll see someone get, like, a, like, a line cook job.
Speaker 1:But Sure.
Speaker 10:Hold on. It's really just the universal fact is that filling out the form is very, very pointless.
Speaker 1:Mhmm. How do you do top
Speaker 4:of funnel?
Speaker 1:Like, how do you get people to be aware of, your app actually install it, download it? How are you driving attention on that side?
Speaker 10:Yeah. We've gotten very good at going viral and getting views. Oh. I think over the past year, we've done over a 100,000,000 views on social media, mostly on TikTok and Instagram. Nice.
Speaker 10:And that again is mostly just, like, me and my cofounder making Yeah. Videos on TikTok and Instagram. We have, like, I think, like, 72 k on Instagram right now and that's Cool. Just from us pulling out the camera and telling people about what we're doing and Yeah. People like it.
Speaker 1:So Makes sense.
Speaker 2:That's very cool. How how are you gonna make money? Are you making money already?
Speaker 10:So we actually launched this while we were in school. Like, I I just graduated in May, but we launched this last like, at the beginning of the fall semester, and we used to make money from charging people for for or by charging people for more swipes. We recently have gone, like, very, very free. Like, you really don't need to pay to apply to a lot of jobs anymore. But, yeah, we used to make money from that.
Speaker 10:Since we've took taken that down, we don't really make money from that anymore. And in the future, obviously, we plan to take the traditional job board route and work directly with employers, just faster matches, get more applicants, etcetera. But right now, we're very much just like product focused and we're kind of Gone for ignoring revenue. Yeah.
Speaker 2:Yeah. How how's the how's the round gone?
Speaker 10:Round is basically done. Think
Speaker 2:There we
Speaker 10:go. My co founder is talking to investors, but it's really just for fun.
Speaker 1:Like, we're
Speaker 10:we're not planning on raising any more money.
Speaker 1:Tell them to get back in the in the grind. You don't need to be talking
Speaker 2:to investors if you close the round. Yeah. Small recommend small recommend small I I I don't like Tinder four x.
Speaker 1:Oh, sure.
Speaker 2:I'm sure that that actually resonates really well with consumers.
Speaker 1:Yeah.
Speaker 2:But but the the the the product experience makes it makes a ton of sense.
Speaker 1:People think swiping. They know swiping.
Speaker 2:Yeah. They know swiping.
Speaker 1:They know swiping. Getting away for that.
Speaker 2:That makes sense. But but anyways, very, very, very cool. Congrats on on all the traction, and and, hopefully, we find some people on source at this point.
Speaker 1:Yeah. That'd be great.
Speaker 10:Yeah. Absolutely.
Speaker 1:Thanks so much. We'll talk soon. Have good rest of your day. Let me tell you about Gemini three Pro, Google's most intelligent model yet, state of the art reasoning, next level vibe coding, and deep multimodal understanding.
Speaker 2:Before we go to the next to the next guest, Drew Roe in the chat says, I don't know if anyone said it, but the Ryzen x three d is the only way to go for your racing sims.
Speaker 1:Oh, that's an AMD chip. That's an AMD chip. We might have to do AMD. Had a friend
Speaker 2:with you years ago. Give us give us some we've been talking
Speaker 1:I think we're dealing with an expert here.
Speaker 2:An expert.
Speaker 1:Need trust the expert.
Speaker 2:Friend Paul Yeah. Who's a a racing enthusiast, getting some recommendations there, but putting together some rigs for the team.
Speaker 1:Yeah. Well, up next, we have Matorial with Kareem, the integration layer for AI agents. Welcome to the show. How are you doing, Kareem? Thanks for
Speaker 2:Finally, somebody that is integrating agents. Great to meet you.
Speaker 4:Almost almost correct. Okay. What are you doing? So we basically give your AI agents or your LMs access to these apps and data sources. So anything from your Gmail to your SAP to your Salesforce.
Speaker 1:Okay. I was just we we were just talking to somebody. Oh, Jason Fried. Right? He was saying that OpenAI just wound up building a Basecamp integration.
Speaker 1:Out of nowhere one day, they just kinda told him, hey. It's live now. You didn't have to do anything. Is that not happening fast enough? Like, in what scenario would I need your service if all of the it feels like there's a massive war going on between the the the LLMs.
Speaker 1:They all wanna do the integrations as fast as possible. How is this gonna play out?
Speaker 4:I mean, actually, one of the OpenAI member of technical staff reached out to us for
Speaker 1:our product. Okay. This makes sense.
Speaker 4:There there is that. But, basically, one way to think about this is, right, first of all, OpenAI won't give you AI integrations for the other providers. People still wanna be using Gemini. They wanna be using Anthropic or any of these others. So we basically provide you with the developer tooling to use any LLM model with any AI integration.
Speaker 4:And it's not just integrations. It's also these things like access control. Right? Because these Fortune five hundreds can't just unleash an LLM with access to whatever your Salesforce, SAP to all the members in their organization. They need to think very concretely about who has secure access to which models and which data sources.
Speaker 1:Yeah. That makes a ton of sense. What were
Speaker 2:you doing what were you doing before this?
Speaker 4:Yeah. I just graduated from MU Abu Dhabi in May. And before that, I ran a different Abu Dhabi based ticketing startup for around three and a half years.
Speaker 1:Oh, that's cool.
Speaker 2:Very, very cool. What what's traction been like? You said a member of the technical staff at OpenAI reached out.
Speaker 4:That that's that's what they've all been from yesterday, so not too much up not too many updates on that. But we are open source with over 3,600 GitHub stars, and we have close to a thousand weekly active users just since launching around five weeks ago. And then we are also in final stage discussions with some Fortune five hundreds and unicorns who would deploy this across the organization. Good good side effects. Congrats to organizations of 80,000, 100,000 members.
Speaker 1:Is MCP complementary, competitive, substitutive? Like, how does MCP fit into this?
Speaker 4:So here's so here's how we think about it. Right? So LLMs, ten years from now, will still need access to apps and data sources with access control. Yeah. Right now, the standard for that is MCP.
Speaker 4:Mhmm. So we basically have this middleware layer translating between our platform and MCP.
Speaker 2:Mhmm.
Speaker 4:But if the standard changes a year from now, we just switch to the new standard. Right? Because the long term bet here is not on MCP. I think that's what a lot of these other companies are getting wrong, where they're building a 100% on top of MCP, but they don't actually think about what these companies need. They're just kind of following the hype train of, oh, MCP is the next cool big thing Yeah.
Speaker 4:Which we are not fully in agreeing Can
Speaker 1:you take me through sort of, like, the top five agent categories that are interesting to you? I imagine coding agents are probably at the top, maybe knowledge retrieval, deep research agents, maybe
Speaker 4:customer support Okay. We we are completely unopinionated about how you build your agent. We just provide you with the integrations.
Speaker 1:Sure.
Speaker 4:Right? Because every agent will need to do read and write operations on these apps and data sources. And if we can take a tax on that, you figure out how big the market is.
Speaker 1:Yeah. Is there I I mean, I guess to flip the question around just what what agentic capabilities are you excited to see out in the world in 2026?
Speaker 4:Honestly, I really like seeing all these new verticals where basically people just, what what do what do they call them? Those full stack AI native firms where
Speaker 1:Sure.
Speaker 4:Free people go in there, use these LM Yep. Capabilities and these, for example, legal agents or health care agents to compete with unicorns or large established players. I think that's really exciting. You kind of got this Goliev story there.
Speaker 1:Okay. So so walk me through that. If I'm a lawyer and I'm leaving my firm to start an AI native law firm, I might buy some AI legal SaaS, but I also might need to integrate with some more niche tools or some more legacy tools. Yep. Are you the firm that I would go to to do those integrations for me?
Speaker 4:Yeah. We basically want to become the substrate for your integrations. Okay. So, really, long term, we wanna have this sort of Oracle story here. Sure.
Speaker 4:How similar to how Oracle became the substrate for enterprise databases Mhmm. And then sold those extra things like enterprise Java, etcetera, on top. We wanna be the substrate for the integration layer and the access control layer and then add these additional things like the workflow builders or also hosting your agents. Right? Yeah.
Speaker 4:So that's kind of the long term vision here.
Speaker 1:I always like to take the temperature on YC folks on, like, what, what's breaking out in their supply chain. What's a what's a tool or company or service or technology that you are leveraging to build this company that, you're you're particularly thankful for?
Speaker 4:See, this might surprise you, but kind of compared to a lot of other people, we are very OG software engineers. And what I mean by that is we my cofounder and I have been have had formal computer science education for over eleven years.
Speaker 1:Sure.
Speaker 4:So we met in Austrian Technical High School at fourteen years old for basically computer science, and that really allows us to think about first principles.
Speaker 10:Mhmm.
Speaker 4:So in terms of building out our entire infrastructure ourselves, thinking about the API designer from scratch. And we don't really use the many tools that are available out of the market right now because what we find is that they speed up the process a little bit, but we have been doing it for so long that we can just do it better ourselves. Yeah. So really, we invented a lot of new things here as well, which kind of the other competitors who are mostly only wipe coding can't even do with their
Speaker 2:You need to you need to get an organic certification on the website. You know? Like, this is organic code. Zero five.
Speaker 4:Get the Austrian armor gutasiegel.
Speaker 2:I love it. Love it. Me guess. The round's already done.
Speaker 4:Yes. Very fast. I think she wrapped up in around five days.
Speaker 2:Five days. I knew it. I knew it. Oh. I knew it.
Speaker 2:Congratulations.
Speaker 10:Love.
Speaker 8:Thank you.
Speaker 2:Yeah. Loved hearing how you're, you know, thinking about the opportunity and and how opinionated you are. So congrats on all all the progress. Excited to follow on. Appreciate it.
Speaker 2:I'm I'm sure I'm sure you'll be back on the show soon.
Speaker 1:We'll talk to
Speaker 4:you soon. You so much.
Speaker 1:Have a good rest of your day.
Speaker 4:See you.
Speaker 1:Before bringing our next guest, let me tell you about fin.ai, the number one AI agent for customer service. Automate the most complex customer service queries on every channel with Fin dot ai. And we have Philip from Crunched. What a great name for an AI analyst for an AI Excel analyst for Excel power users.
Speaker 2:Just for power
Speaker 1:users. Have ever been an Excel power user? You always had to have one hand on the mouse?
Speaker 2:You you
Speaker 1:never adjusted on the keyboards guy?
Speaker 2:Always had one hand. Very soft. Very soft.
Speaker 1:I can I can But I can hear Andrew Reed losing respect from you all the way from here? He's getting cooked.
Speaker 2:All the way from the valley. Indeed. Well, he
Speaker 1:is in the restream waiting room. Let's bring in Philip from Crunch to the TBP And Ultradome. Philip, welcome to the show.
Speaker 2:What's happening?
Speaker 1:Thanks for joining us. Please introduce yourself and the company.
Speaker 3:Hey, guys. Pleasure to be on. Great to meet you. Michael actually from from Crunchy will do the last minute last minute switch here.
Speaker 1:Oh, okay. Good to see you, Michael.
Speaker 3:Cofounder as well, COO COO of Crunchy.
Speaker 1:Fantastic.
Speaker 3:Maybe I'll give you, like, a two second description of Crunchy then. Crunchy is your Excel AI analyst built by and for power users.
Speaker 1:Mhmm. So
Speaker 3:it's like this side panel chat in Excel, basically cursor for the world's most popular programming language. And then you just chat it to natural language, and it makes modeling for you.
Speaker 1:Makes a ton of sense. Very clear value prop. I think everyone who uses Excel wants a Copilot, but there is a company that's trying to build Copilot, and they happen to own Excel. How are you imagining this plays out? Are you gonna live in plug in world?
Speaker 1:Are you gonna live at the OS level and be screen scraping? Are you worried about sharp elbows for Microsoft? How are you how are you dealing with all that?
Speaker 3:It's a great question. I think Microsoft is for sure gonna build a great product. They're building a copilot for 2,000,000,000 Excel users, and they're in competition with Google Sheets. Right? I think Sure.
Speaker 3:We're building a tool specifically for the top 1% finance professionals, investment bankers, private equity associates, management consultants of the world And we use Excel in a very specific way, right? So this is more of the 5,000,000 of the Excel users, the top 1%. So that's a bit of the difference.
Speaker 2:Lot of big market, big opportunity. If you build a great product, there's tons of people that will happily pay for it. There's also tons of startups as well going after this opportunity. What do you think they're getting wrong? Or is this just going to come down to actual like, product quality and working super closely with these power users to make something that actually integrates into their everyday Excel life?
Speaker 3:Yes, absolutely. So I think we have plenty of startups going after this opportunity. We don't think about competition too much. But out of the big ones with the most traction, we're the only one with a team that has 10,000 plus real life Excel hours in our previous jobs, me me and my cofounder, Philippine McKinsey, and another finance gigs. Mhmm.
Speaker 3:And I think that really shines true in the product. I think also Crunched is modeling more like a real life analyst and performing more of the real tasks that you do on the analyst floor versus, like, some of the bit artificial benchmarks you see around. So for example, Crunch scan detect mistakes in workbooks, plenty of time is spent in, like, private equity firms. I'm actually reviewing Excels and making sure they are correct. As much time as modeling from scratch.
Speaker 3:Right? And then these professionals typically work with templates. Right? And they need Crunch to fill out and augment their templates, not build like basic analysis from scratch. We can do that as well, but we're great at working with large models and and these sorts of things.
Speaker 1:How do you think about, the enterprise flywheel here? It seems like one of Cursor's main advantages is that, they're they have a really solid data flywheel now, from open source developers and developers who are not in a, you know, enterprise level contract. I imagine that the top 1% of of investment bankers, consultants, like, on day one, they're going to not want you to train on their data because it's going to be not just some code that builds a front end website, but, like, extremely critical financial information, private information. Like, it is probably a higher bar to not letting that leak into a training run. So how do you get a data flywheel going?
Speaker 1:How do you improve the product iteratively?
Speaker 3:It's a good question, right? And as you say, security is top concern, I think, for all of our customers who rely with, like, global consulting firms, but also
Speaker 2:Let's give it up for global consulting firms. They don't get enough love. They don't get enough love. Except here. Except here.
Speaker 1:I will
Speaker 2:I will defend Accenture.
Speaker 5:Exactly. Exactly. Well, that's a
Speaker 3:good but but they're obviously super concerned about their security. Right, and their live public deals, right, all of this stuff. And so like in principle, we do not train on the data of our customers, and we cannot see what they prompt or do, right? At the same time, what we want to do now and just in record time, closed our our our fundraise. We want to make sure that we tailor Crunch to every single firm.
Speaker 3:And that's great. And then when we tailor it, we have discussed with a few customers, like, the opportunity to, for some of the large organizations, they do enough modeling work on a global base that is possible to, like, tailor do some fine tuning and tailor to their specific organization. But as well, we come in in a forward deployed manner, right, and and do customization whether that is formatting or or solving for their specific workflows and linking into their templates. How like, the sims that they get, how can we link that into their specific LBO template and then transfer that from, like, the simple LBO to the advanced LBO and and these sorts of custom
Speaker 2:What's the biggest deal Crunch has supported?
Speaker 3:The biggest deal we have supported? That's a good question. I can tell you about the
Speaker 4:You don't need
Speaker 2:to name the company. Yeah.
Speaker 4:You don't need to
Speaker 1:name the company. It was a $500,000,000,000 company. They were doing a $1,400,000,000,000 deal. They were doing about 20,000,000,000 in revenue. I'm not gonna say who it was.
Speaker 3:Exactly. Exactly. No.
Speaker 5:But I
Speaker 3:can tell you a real story about the mistake we've caught though. Crunch has this error detection system.
Speaker 1:Okay.
Speaker 3:And on a live deal for an associate at one of our private equity clients in London used the sort of Crunch mistake detection system to identify or, like, scan his one of his previous models on a real transaction and identified a mistake in the working capital that overvalued the deal by £10,000,000.
Speaker 1:So Wow.
Speaker 2:Woah. That
Speaker 3:one is
Speaker 2:You guys He saved his job.
Speaker 1:Send him an invoice of 5,000,000 right now. You just saved him 10. I give you 50% of that. That's your seed round right there. Exactly.
Speaker 1:Exactly, love. Well, congratulations on a fantastic demo day. Thank you so much.
Speaker 2:Yeah. Great great to meet you, Michael. We'd love to talk to
Speaker 3:you more. I
Speaker 1:I I live for Excel agents. I'm so excited about this category. It just feels like there's Texas. I would love to. Thank you.
Speaker 1:Thank you. Well, have a great rest of your day. We will talk to you soon.
Speaker 2:Great hanging, Michael. Goodbye, Alright.
Speaker 3:Thanks, guys.
Speaker 2:Numeral.com. What $500,000,000,000 company could that be?
Speaker 1:Numeral.com compliance handled. Numeral worries about sales tax and VAT compliance so you can focus on growth. Speaking of growth, there's some there's some folks putting Menlo Ventures in the truth zone, Enterprise large language model API market share has been falling for OpenAI. It's been climbing amongst anthropic according to Menlo Ventures. Ev Randle puts it in the truth zone over at Benchmark a multi time TPPN guest, Ev Randle.
Speaker 1:He says people are quoting this Menlo Ventures chart and extrapolating from it like it's official data from the Federal Reserve or something. It's a small sample survey conducted by an investor in Anthropic. Please calm down. I like that he's pouring some some cold water on this.
Speaker 2:This was from, November 3
Speaker 1:Yes.
Speaker 2:Too. So
Speaker 1:At the same time, does is it possible that OpenAI's enterprise large language model API market share is falling? Sure. You know, they were the only game in town when they launched. And so you would expect their market share to fall a little bit over time. Will be interesting to see.
Speaker 1:We will get more data on this. All these companies are gonna be public in a couple of years, and so we'll know exactly how it's breaking down. But Until, until that happens, we will return to our coverage of YC demo day twenty twenty five. We have Sava, the AI powered trust company. Welcome to the show.
Speaker 1:How are you doing?
Speaker 2:What's going on?
Speaker 15:Hey. I'm doing great. How are you?
Speaker 1:We're fantastic. Please introduce yourself. Introduce the company. Tell us what you're building.
Speaker 15:Great. Yeah. I'm I'm Nimit Maru. We're building Saba. We're building a new modern agentic trust company
Speaker 4:Okay.
Speaker 15:That administers advanced trusts.
Speaker 1:So is this specifically, like, will and trust?
Speaker 15:Yeah. So it it's trust like will and trust.
Speaker 1:Yeah. Exactly. Yeah. Not not I mean, people would say a trust company could be somebody that makes sure your your password doesn't get leaked or something. But, this is specifically
Speaker 2:old were you when you realized you wanted to use AI to spin up trust? No. I'm just kidding. What, what were you what were you doing before this?
Speaker 15:Well, my my previous company was actually in John's
Speaker 1:Oh, no way.
Speaker 15:Batch, summer twelve batch.
Speaker 1:No way.
Speaker 2:No way.
Speaker 1:What company?
Speaker 15:Yeah. And we we so at the time, we were building Yelphi, which was a we're like, you know, like the front facing camera had come out on the iPhone, so we wanted to build, a telemedicine. But we pivoted to being a early code education and and, like, tech education company, and that's how we kind of built that
Speaker 1:Really?
Speaker 15:And then sold it in, ten years later.
Speaker 2:If you if you hadn't pivoted, you could have been selling meth at scale like some of the other telemedicine companies, but I'm glad you did.
Speaker 1:I'm glad you went to coding.
Speaker 2:Very cool. Did you
Speaker 15:say selling did you say selling meth?
Speaker 2:No. No. I'm just I'm just I'm just joking because I'm not
Speaker 1:sure if it's not. But but there were some there were some pill mills There were some
Speaker 2:kind of
Speaker 1:telemedicine companies that went a little bit too far, and, one of the founders is in jail now.
Speaker 2:So if the patient's breathing. If they are, give Matarol.
Speaker 1:That's that was going on.
Speaker 2:Yeah. More seriously, talk about what's act are these Nevada trusts? Like, what's what's what's the what's happening at at the actual, like, entity layer?
Speaker 15:Sure. Yeah. So we so we're not drafting the trusts. Mhmm. We we will basically, like a an attorney or a like a fintech or a legal tech that uses LLM to to draft trusts.
Speaker 15:So they would create the trust document. And then once they need someone to administer the trust to be an independent trustee, that's when we would take over. Mhmm. We're getting our charter in Nevada, so we're gonna be chartered in Nevada. You know, maybe eventually we'll go to other states, but that's where we're gonna be right now.
Speaker 15:And, yeah, we work directly with attorneys, wealth managers, fintechs to serve as the trust administration there.
Speaker 1:So so would you do do you have, like, no consumer facing brand essentially? It's, like, purely b to b at that point?
Speaker 15:Well, I mean, it is consumer facing in the sense that the people who'll be using it are also the families who
Speaker 4:Okay.
Speaker 15:Have these trusts. Yeah. But the reason I say we work with attorneys is because generally, the families are taking advice from the attorney or the wealth manager about which trust company to choose because, I mean, you know, how how would a family know even what a what a trust company is or so so we think of them as the ICP.
Speaker 1:Sure.
Speaker 15:Sure. Sure.
Speaker 2:Are trusts underrated?
Speaker 15:Yeah. I think they're underrated and they're underutilized. And also right now, they're very annoying and to create and manage.
Speaker 1:Sure.
Speaker 15:And so I think people don't use the power of trusts enough. Mhmm. And that's not to say, like, you know, every American or every person can be using them, but definitely a big slab of people, you know, kind of below where right now they are being utilized.
Speaker 1:Yeah. Do you have a ballpark cost figure for, you know, doing a trust? Like, what at what does it start to make sense for customers to even participate in the market to even consider a trust?
Speaker 15:So I think creating a a a revocable trust that, you know, owns your house or other assets Sure. That's applicable at, you know, at at like reasonably, you know, like almost any level for at at like when someone would own some properties. So as soon as your house.
Speaker 1:Makes sense.
Speaker 15:Yeah. But then using using something like SABA today, it's generally people who are trying to make irrevocable trusts. Mhmm. And so they would tend to have, you know, like some some millions, like, you know, maybe like low single digits, but or maybe mid single digits millions in assets before they start utilizing that. I think that as as, like, tech makes it a, like, a lot easier and cheaper to create trust in a good way, and also, you know, people like us can make it a lot more friendly and modern to administer trust.
Speaker 15:Like, I think more people will be able to use them. Yeah.
Speaker 1:It should just get way cheaper. I mean, if you think about just the YC story of how much it cost to set up a corporation and raise a seed round in 2005 or something, you were looking at, like, thousand, maybe 50,000 in total, like, fees across everything. Now it's like Stripe Atlas, one click, they charge you, what, $200 or something. And then
Speaker 15:$500.
Speaker 1:Yeah. Bucks. And then and then the safe is, like, is one second and, you know, administered by a bunch of folks. And, like, it's, like, really, really low low cost, and that's obviously led to just more entrepreneurship. You would imagine that something similar happens.
Speaker 15:When the infrastructure gets better, like, usage goes up. And e e even the safe like, I was talking to my cofounder the other day, like, the safe is an incredible invention
Speaker 1:Mhmm.
Speaker 15:That makes this, like, early stage of fundraising, you know, so much smoother. Like, back when we did it in the summer twelve batch, it was, like, all convertible notes and, you know, even that a lot of investors wanted price rounds. At this stage, it's like a pretty difficult thing. So, yeah, I think when the infrastructure gets better, like, more people utilize it and, like, more people can take advantage.
Speaker 1:Well, congratulations on the progress. Thanks so much for coming on the show. Yeah.
Speaker 2:Excited to check the product out.
Speaker 1:And we'll talk to you soon. Have a
Speaker 2:great rest of your day.
Speaker 15:Thank you. Cheers.
Speaker 1:Goodbye. Thank you, Pat. Let me tell you about ProFound. Get your brand mentioned in ChatGPT. Reach millions of consumers who use AI to discover new products and brands.
Speaker 1:I wanna pull up this chart of the day from code two. They say, hey. Look. There's no code red here. It's all Baja Blast because ChatGPT traffic historically dips this time of year.
Speaker 1:But it's not that far. Chart. If you actually zoom in on this Gemini three launch day, it looks like people stop using LLMs around Christmas. The turkey's going around. The tryptophan is coursing through their blood.
Speaker 4:They're getting a little sleepy.
Speaker 1:A little sleepy. They're having an extra bottle of wine, and they're taking time off from their chat app, specifically from, from ChatGPT. This is bizarre this that this chart tracks so much with when people do work. You can see that ChatGPT grows in the in the, in the spring every year up until summer. Then it completely flatlines during summer, then it peaks when school year starts again and work starts back up, then it crashes on
Speaker 2:Black Friday. The tool used last students and and Students and
Speaker 1:and workers, people with jobs. That's everyone. That's everyone. Come on. That's everyone.
Speaker 2:What about the unemployed?
Speaker 1:Oh, yes. I don't know. What what well, they're they're the ones that are holding it up. They're the they're holding it down during Black Friday. They're like, I'm still grinding.
Speaker 1:But, clearly, folks did not get the, the great lock in memo because the whole point was that you're supposed to continue to use all the AI apps. Anyway, it's a fascinating it's a fascinating chart. I'm sure we'll be digging into it more, reading the tea leaves. But up next, we have Ben from SF Tensor. It's Vercel for GPUs.
Speaker 1:Welcome to the show. Thank you so much. Please introduce yourself and the company.
Speaker 2:Great to have you.
Speaker 16:Hi. Yeah.
Speaker 13:Thanks. I'm I'm Ben. We're building the infrastructure layer for AI researchers. So, basically, from, you know, training models from, like, small experiments all the way up to large scale frontier training runs, we basically deal with infrastructure to allow you to do all of your training runs.
Speaker 1:Okay. So there's a bunch of different layers going down to somebody that owns the ground, somebody that builds the data center, somebody that racks the GPUs, and there's and then there's the Neo Clouds. Are you interfacing with multiple Neo Clouds? Are you a Neo Cloud? How are you positioning yourself?
Speaker 13:Yeah. So we we work with all sorts of NeoClouds and hyperscalers, and we basically just say we're building above all of them. And so our customers should only be worrying about what they want to be researching or training and not, like, how the actual technology, like, the underlying stuff works. And so we deal with, you know, finding GPU allocations, optimizing for different GPUs. So we also allow you to work with TPUs or AMD GPUs or any of this stuff to allow you to train your models.
Speaker 2:Okay. So this is specifically for research and training runs and your and and less focus on on, like, actually inferencing on the product side?
Speaker 13:Yeah. So we focus exclusively on the on the training side. There's great companies even, you know, from LastBatch, for example. There's Luminal. They do great things for inference.
Speaker 13:We focus just on training because we think training is a problem that's not been solved by anyone, and there needs to be way more training happening.
Speaker 1:What are your clients like, what's the shape of them? I guess there's a lot of focus when when people think training, they think OpenAI, Anthropic, Google, DeepMind. Right? But take me through the variety, the landscape of folks that you talk to who are actually doing training runs. Who are these folks?
Speaker 1:You don't have to give exact names, but Yeah. Tell me the the shape of their workloads, how they're what what problems they're trying to solve, the scale of their training runs. Take me on a little tour. Yeah.
Speaker 13:So there's there's a huge variety. I mean, you have, on the one hand, you have this have, the academic, you know, or home researchers at home who are training, like, small models. Mhmm. And then you have, you know, larger scale academic research happening. Then you also have startups that have raised maybe, you know, call it $10,000,000.
Speaker 13:You know, there's some companies from from YC as well who are training models for super niche use cases. And then there's also, you know, companies that have raised hundreds of millions or, you know, up to a billion dollars. There's a bunch of labs actually in that, like, area who are doing who are training their own models. You don't just have Anthropic. I mean, like, the the text based models like LLMs, there's not an awful lot of competition going on there anymore.
Speaker 13:Things have sort of converged at the top there. But for everything else, like, you know, drug discovery or, you know, protein folding, all of these things are still problems that have not been solved by anyone.
Speaker 2:Is it correct to say that SF Tensor is a bet that there will be millions of of of smaller models for specific use cases or or one day billions?
Speaker 13:I wouldn't say billions, but definitely a lot more than there is today, especially just in the modalities that haven't been explored today. I mean, we're all focusing on text, and text is great for a lot of things. But I can't really use a text based model to do things like, you know, text to speech, for example, is another type of model, or we have protein folding models. Or all of these things can't really be solved with text. We need models that are specialized in those topics.
Speaker 1:What about, I mean, we were talking to the CEO of AWS yesterday, and he was saying that, AWS launched a product that, that is actually a checkpoint 80% of the way done on an actual foundation model, and then a company can come in and add their own data to the pretrain. And then they can do everything else with it. And that felt like an interesting proposition when you think about if you do want a text based model and you want it to be to really know your company's data at the core in the pretrain, really know it, not just drop it in the prompt, not just fine tune on it, actually bake it in. That feels like we're gonna see a Cambrian explosion of every company wants their own trained model earlier. They're gonna want training workloads for that.
Speaker 1:Is that something that you think you can play in? Is are there already other companies that are working there? How do you think about that?
Speaker 13:So it's a very unexplored area so far. Mhmm. The idea of basically saying you have, like, you know, 80% of the way the model can already form coherent sentences, have basic reasoning abilities, and then I add my own information. I think that's going to be very important in the future just because it allows me to take a base model and then not just do, like, post training, but sort of, you know, continuous pre training almost, you know, continuing the pretraining. I think there's gonna be a lot of use cases that come out of that, and I think we can we can help there.
Speaker 13:I mean, we don't really care what you're training on the hardware. We know, if it's if it's an AI training, we can we can help with that. So, you know Yeah. That's definitely something we're looking into.
Speaker 1:Do wanna ask about progress?
Speaker 2:Yeah. What kind of metrics were were you sharing today, during demo day?
Speaker 13:Yeah. So the metric we're sharing is we launched, like, two weeks ago, and we did $41,000 in usage based revenue since then.
Speaker 2:There we go. Love it. And how's the round going?
Speaker 13:We we closed the first day of of fundraising. So
Speaker 2:Yeah. First day of fundraising. There we go. There you go. I'm not gonna I'm not gonna dox, but a a friend of ours
Speaker 1:We got a text message about you.
Speaker 2:We got a text message about you. A friend of ours just backed one company this batch, and he's known for backing great companies, and he just backed you. I'm excited for for you guys to announce the round soon. Yeah. And come back on and do it on TBPN.
Speaker 4:Thank
Speaker 1:you so much.
Speaker 2:Awesome. Great to
Speaker 1:meet man. You soon. Cheers. Have a good one.
Speaker 12:To meet you.
Speaker 1:Good to meet you. Let me tell you about getbezel.com. Shop over 26,500 luxury watches.
Speaker 2:Super intelligence for your wrist.
Speaker 1:Fully authenticated in house by Bezel's team of experts. Brad Gerstner on Trump accounts, POTUS was elected on main on a main street agenda to get the rest of America into the game, and that's exactly what this does. Bill Gurley showing him some respect. And tip. We didn't cover it yesterday, but Michael Dell donated $6,500,000,000 to these Trump accounts, the the accounts where children get them.
Speaker 1:They can't be touched. They're invested, and they compound over forty years.
Speaker 2:Dollars for a bunch of
Speaker 1:Yes. Individuals. And and and there were some pushback. Some people are saying, well, you if you compound at the S and P, even if you compound at 10% for twenty years, like, it's only a thousand bucks or a couple thousand bucks. It's not that much money.
Speaker 1:It's not life changing. But, you know, it's like a piece of it's one that's that's just Dell's contribution. Like, there's gonna be other people that are contributing, co corporations.
Speaker 2:There's a thousand dollars from America.
Speaker 1:And and yeah. Yeah. And there's a whole bunch of other ways to add money to the account over time at birthdays and Christmas and stuff. It's like
Speaker 2:family Targeted donations.
Speaker 1:And the most important is that it's a lockbox. It's psychologically a lockbox. So I still stand with the No. That's incredible. Accounts.
Speaker 2:That's incredible.
Speaker 1:But we have our next guest in the restroom waiting room, from Locust Payment Infrastructure for Agents. How are
Speaker 2:you doing? Got him in the Shydeye.
Speaker 1:Please introduce yourself and tell us what you're having.
Speaker 2:Shirt at TBPN. We've done over a thousand interviews.
Speaker 1:I don't think we've ever seen one. This is unique. I like it.
Speaker 2:It's a first. It's a first. Thank you.
Speaker 8:Yeah. And and they, you know, thank you. And so they actually switched me up Okay. With the other guy.
Speaker 1:Oh, I got an Icarus. Sorry. Yeah. Icarus. Great.
Speaker 1:Yeah. Well Yes, sir.
Speaker 2:Henry tie dye. Welcome to the show.
Speaker 8:Yes, sir.
Speaker 1:Henry Yeah. From Icarus. Please introduce yourself and tell us what you're building.
Speaker 8:Yeah. So I'm Henry, founder and CEO of Icarus. Mhmm. My background, aerospace engineer at Georgia Tech. Build drones for NASA and satellites at Orbital.
Speaker 8:Cool. Icarus, we're building solar powered autonomous drones that fly at 60,000 feet for weeks at a time.
Speaker 2:Close to the sun. How
Speaker 1:how yeah.
Speaker 2:Close to the sun. Yep. Not the closest, but
Speaker 8:Not the closest. And and in fact, if it if we flew any higher, we'd actually fall out of the sky. So we wanna stay at 60,000 feet.
Speaker 1:You're like you're like, but but but we're we're gonna try flying a little higher.
Speaker 2:Okay. How many how many hard tech how many hard tech companies were were in this batch? I
Speaker 8:believe, like, five or 10.
Speaker 2:Yeah. Yeah. That seems about right. And so I feel like it's been, like, steadily at five to 10 for forever, basically.
Speaker 1:So Take me through, the bear case for stratospheric drones.
Speaker 2:Yep.
Speaker 1:What I've heard is, you know, people always, always refer to the SR 71. It's such an amazing plane. It flies, I think, around 60,000 feet. The SR 71 Blackbird. It's this amazing Lockheed Martin plane built at Skunk Works, flies super fast.
Speaker 1:We can't build planes like that anymore. We don't have it in us. And when I talk to folks who are like, yeah. It kinda sucks that we can't build that because it was really cool, but we have satellites now. And satellites go way higher and way faster.
Speaker 1:And so if you need to put a camera over something, we usually just use a satellite. Why not satellites for this use case?
Speaker 8:Yeah. From first principles, you're 20 times closer than low Earth orbit Mhmm. And you can say fix urban area. So just from an engineering perspective, it makes a lot of sense. Mhmm.
Speaker 8:The bare case is pretty much like, none of this is new, even what I'm doing, the solar powered version. It's all been done.
Speaker 4:It's just
Speaker 10:been too expensive.
Speaker 1:Sure.
Speaker 8:So the question is, like, can you get the cost down?
Speaker 1:Can you? How are you doing that? Is it just like being a startup? Well, like, are you using cheaper materials? Are you standing on the shoulders of giants?
Speaker 1:Like, what are you leveraging to actually make
Speaker 2:We'll we'll talk about the form factor first because I'm I'm on the website and this thing just massive, really skinny bird. Yes. It's very very unique. It's icarus1.com. Icarus.
Speaker 2:Or sorry. Icarus.one.
Speaker 1:Dotone.
Speaker 8:Icarus.one. Correct. Yeah. The to your point, John, it's about getting the right product specifications Okay. For the first go to market.
Speaker 8:Oh, wow. And so, yeah, our first product, it's a 20 foot solar powered bird. Sure. Fly for weeks at a time. Yep.
Speaker 1:And The bird the bird noise is perfect.
Speaker 2:So so it's effectively like a loitering drone that's just sitting at 60,000 feet and it's Yeah. I'm assuming it's it's incredibly light. Your Yes. Your it's it's solar. It has a battery, but it can it can Yes.
Speaker 2:Generate solar power on the fly to to increase the battery life effectively. Like, it's not
Speaker 6:That's correct.
Speaker 2:To hold it in the air forever yet, but Yes. But it can stay up over a specific area. So is this primarily like, defense applications early on? What what who
Speaker 9:are who
Speaker 2:are you trying to sell this to?
Speaker 8:Yeah. ACT one, it's all defense. I do think this is much bigger than a defense company. I do see the Stratosphere as a category, and and once you kind of are able to make the Stratosphere affordable, then there's many things you can do. So one easy example, like, yeah, today, you can't really carry very heavy payloads, you can't carry and deliver a lot of power.
Speaker 8:But what the future looks like and there's like no laws of physics that says you can't do this, you can essentially take like a Starlink satellite and have that in the stratosphere. And imagine if you had this Starlink satellite that's 20 times closer and fix up an area.
Speaker 1:Mhmm.
Speaker 8:So then that's like that's the future. And what you can do from that, it's, I don't know, it's anyone's imagination. Near term, there's a lot of clear, direct line of sight towards defense in a market there. Again, it's like really difficult. It's not it's not like a a category yet today.
Speaker 8:There's there's no real markets, but with defense, there's there's a clear need.
Speaker 2:Very cool. How do you actually get the drone up? Is this something that you launch like a rocket and then it and then it sort of spreads its wings at some point? Like, how do you actually get a 20 foot drone 60,000 feet in the air? Into the air.
Speaker 2:You use it.
Speaker 8:So we use a balloon.
Speaker 1:Eat it? Oh, use a balloon. Okay.
Speaker 2:Okay. That's that seems less violent than yeeting a 20 foot drone. Yeah. Some
Speaker 1:some drones are yeeted, I believe. This is a real thing.
Speaker 2:So you use effectively like a weather balloon
Speaker 1:to
Speaker 2:take it out.
Speaker 1:Are you a beneficiary of Starlink?
Speaker 8:Are are we Like a competitor?
Speaker 1:No. No. No. A beneficiary. Like like
Speaker 2:Oh, beneficiary?
Speaker 1:Like like, can you use Starlink effectively as, like, the backbone for communications?
Speaker 8:Yes. That is our beyond line of sight method. Sure. Yeah. So we have Starlink on it as an option.
Speaker 1:Yeah. That's very cool. Yeah. Yeah. Fascinating.
Speaker 1:So, how how close are you to actually getting this up in the air? Have you flown? Yes. Is it just test at this point? Are you actually gonna sell these things?
Speaker 1:How how how
Speaker 8:We are selling to them today Okay. With Army. Okay. And, yeah, we've done, over 30 successful stratospheric flights, successful demos with Special Ops Command, SOCOM, and the Army as well. And we have oh, there you go.
Speaker 8:There you go. There you go.
Speaker 2:Yeah. Super impressive traction. I noticed is it Ronnik on your team? Was that Red Bull Racing before this? How crashed No way.
Speaker 2:How cracked is Ronnik. Awesome.
Speaker 8:He is he is very, very hardcore. Yeah.
Speaker 2:Imagine if you wanna make something that's ultralight, ultradurable, he's your guy.
Speaker 8:That's right. That's correct. Yeah. So a third of our team is SpaceX Tesla. Ronix worked at Tesla before Red Bull Racing and also SpaceX as well, but he's definitely a character.
Speaker 8:Yeah.
Speaker 2:Awesome. Well, great to meet you. I'm excited to Yeah. To follow along. The round already done.
Speaker 2:How's it going?
Speaker 8:Yes. Raised a lot of money.
Speaker 10:There you go.
Speaker 2:Hit the gong again, John. There we go. Yeah, buddy. Yeah, buddy. Yeah.
Speaker 2:Just coming on. Absolute legend. You're a TPPN legend.
Speaker 8:Yeah. Thank you.
Speaker 2:We might have to we might have to make a TPPN tie dye shirt in your honor.
Speaker 1:Yep. Let's do it. Yeah. I love it. Thank
Speaker 2:you, Simon. Very cool.
Speaker 1:Very cool. Yeah. Have a good rest of demo day. Congratulations on all the progress. Great.
Speaker 1:Very excited to see these up in the stratosphere. Just don't fly them too high.
Speaker 8:Yep. Exactly. Perfect. Alright, Jordy. John, thanks so much.
Speaker 1:Have a good rest your day. Goodbye.
Speaker 2:What a legend.
Speaker 1:I need to know from you if we have some breaking news that we can share right now. It sounds like we might have some surprise guests join the stream, so stay with us. I will also tell you about adquick.com, out of home advertising made easy and measurable. Plan, buy, and measure out of home with precision. Also, people are calling for Google to make glasses now because They did
Speaker 2:this doing this.
Speaker 1:They did this twenty years ago, practically. Google Glass.
Speaker 2:And But they're work they're still working.
Speaker 1:Yes. Yes. Yes. Yes. But through partners.
Speaker 1:Through partners. Got it. Got So they they've done the Google Pixel. They've done a they've done a variety of of hardware devices, and they're and they are working on, some augmented reality glasses again. But they're certainly not making as much of a, you know, big push, media push as they did with the original Google Glass, which was, like, it dominated the news, and it was like, the future is here, and then the product didn't really get to escape velocity and is sort of remembered as a failure, but it wasn't a failure.
Speaker 1:They were just early. They were just early. And that's the important thing to remember. But we have our next guest here in the Restream waiting room. Let's bring him in from Locust.
Speaker 1:Welcome to the show. Thank you so much for taking the time to join us. Please introduce yourself and tell us what you're building.
Speaker 16:Yeah. For sure. So I'm Cole Dermott. I'm the CEO and cofounder of Locus. We build payment infrastructure for AI agents.
Speaker 1:Okay. MCP currently doesn't have payment infrastructure. That's why you exist. Is that what's going on?
Speaker 16:Yeah. Basically. Plus trust. Okay. Trust is a huge part of it.
Speaker 1:Okay. Interesting. How are people are people actually, like, solving this manually right now? Are there are there payment are there, like, are there, like, agent to agent payments that are happening right now, or is this something where we're thinking, like, in the future, they will all be flowing stablecoins to each other in the future?
Speaker 16:I think agent to agent isn't really adopted yet. What we're looking at right now is more so developer use cases of, if if you're familiar with x four zero two, paying for API endpoints on a pay per use
Speaker 1:basis Sure.
Speaker 16:Sure. Potentially doing payouts to people. Yep. The way I like to explain it is, historically, payment automation has been deeply rooted in in conditional automation, automation, a series of ifs, ands, ors, etcetera. Mhmm.
Speaker 16:Now with AgenTic payments, you open up this new frontier of contextual automation. Mhmm. Right? And that's a pretty huge evolution.
Speaker 4:Mhmm.
Speaker 1:What, how do you imagine the first adoption of agent to agent payments or or even just payments for agents broadly, playing out? I I was me and Jordy have been talking about this with the agent to commerce stuff. We're using ChatGPT. We're using Gemini. There's all these times when I run into a paywall, and I can tell it's running into a paywall.
Speaker 1:It's like, oh, actually, I can't tell you about, you know, what's going on on that website. And I'm like, no. You actually could if I gave you my credit card. I know you could, but they can't. And it seems like that's something you could potentially help with.
Speaker 1:But how do you see the first early adopters using your service?
Speaker 16:I see the first ones as really developers building these, autonomous agents. Right? Being able to essentially pay for services as they do their workload in the wild and discover those services autonomously. Right? In terms of, like, the more commerce side, I think that'll be an industry that evolves over the next few years as trust is really developed.
Speaker 16:Because frankly, on a on a wide scale consumer basis, that's really the biggest barrier right now is trust rather than tech.
Speaker 2:Yeah. What kind of numbers did you share during your pitch, or are you planning to share?
Speaker 16:Yeah. So we processed around 3,500 transactions and have around 80 projects built using Locust so far.
Speaker 2:Amazing. That's amazing. What what were you doing before this? John's got the Gong for you. Join me.
Speaker 2:Hit it. Hit it. That will what what were you doing before this?
Speaker 16:Yeah. So I interned at Coinbase. I was one of the people who helped build Coinbase business over there. My cofounder was one of the six software engineering interns at Scale AI. Studied CS at Waterloo, business at Wolford Laurier, was the finance lead at Waterloo blockchain.
Speaker 1:So Waterloo mentioned. Fantastic. Well, thank you so much for coming on the show. Congratulations. Yeah.
Speaker 1:No problem. And I'm sure we'll be seeing you soon. Have a good rest of your day. Thank you all. Thank you so
Speaker 16:having me.
Speaker 1:Let me tell you about wander.com. Book a wander with inspiring views, hotel grade amenities, dreamy beds, top tier cleaning, and twenty four seven concierge service. It's a vacation home, but better. And we have Next. Some surprise guests, I believe, joining in just a second.
Speaker 1:We will have them in
Speaker 2:Jessica and Paul. You may know them. They started a small startup accelerator called Y Combinator.
Speaker 1:Yes. That's right. And it's Jessica's second time on the show. We had a fantastic conversation with her. The last time she was on the show, we've talked about the get your bag culture and the carpet baggers and just all the cultural, ebbs and flows of Silicon Valley and where we are culturally.
Speaker 1:So I'm very excited to bring in Jessica and Paul, the founders of Y Combinator. They are Living legends. Situated living legends. This way, though. You There
Speaker 2:they are.
Speaker 1:You're live now. Welcome to the show.
Speaker 2:Welcome to the show.
Speaker 1:Thank you so much for taking the time to talk to us. Guys. Hi. Good to you.
Speaker 14:Good to fun with you guys here at demo day.
Speaker 1:It is. It is. It's always great. This is our fourth demo day live livestream talking to tons of founders. It's always fun picking out.
Speaker 2:I can't wait for the four hundredth. I got a ways to go, but I'm excited.
Speaker 1:Hundred more years. We'll make it.
Speaker 2:Great to have you guys on. How what's what's, what's it been like today?
Speaker 14:It's been crowded. It's buzzing. And by the way, this is our first demo day that we've been to in a few years because we're in England and can't manage to come back for it. It is just buzzing. The energy here is just kind of like what I remember in the early days of YC, and the investors are all excited to be here.
Speaker 14:It's magical. I'm on a high.
Speaker 2:Incredible.
Speaker 9:There's a lot of stuff happening.
Speaker 1:Yeah. How are you thinking about there was this moment of a few years ago where I think Intacct maybe we were afraid to admit it, but it felt like a lot of founders and a lot of entrepreneurs were were sort of grappling with this idea that that OpenAI might just build every start up, and there might be no more ideas. And people were a little bit nervous about that. Of course, they went and built companies, but it feels like now things have calmed down a little bit, and the founders that we talked to are building with more confidence. Have you noticed anything in the founders that you talked to in an ebb and flow of just the confidence with which they view the future right now?
Speaker 9:No. No. They weren't. Founders really weren't really worried that OpenAI was gonna eat them. I mean, maybe they were in denial.
Speaker 9:But whatever reason, they weren't worried about it. They're too busy working on their companies. They're making their thing. They're trying to get users. OpenAI eating them in some theoretical future three years from now.
Speaker 9:Like, they're not thinking about anything three years from now, so they're not thinking about that.
Speaker 1:We had another we we were talking to Harj about this, this idea that, potentially I don't know. We're just in a new era where, where it has become easier for a small team of scrappy entrepreneurs to sell to Fortune 500 companies, to sell to the government even. Do you feel like something has materially changed in go to market for y c companies?
Speaker 9:Well, if you're an AI company, all these big organizations now have some bureaucrat who's been told, you're supposed to AI if I our organization. Right? And he's thinking, damn. I have no idea what to do. And so some startup shows up and says, will AI if I your organization?
Speaker 9:He's like, great. Come in here. Right? Very different
Speaker 1:from the
Speaker 9:way it used to be. I mean, if you show up with others other products for the big company, they'll still tell you to talk to the hand. But, nobody's coming to them with AI things except startups, so they have no choice but to talk to startups.
Speaker 2:Mhmm.
Speaker 1:What about, this this tweet that you put out just recently? We were we were sort of debating it earlier. This idea of of the the the circular economy selling to other startups. There are a ton of benefits, obviously. Startups are very discerning.
Speaker 1:If you mess up and don't deliver the product that they're buying from you,
Speaker 2:you
Speaker 1:might They'll churn. Hear about it publicly. They'll churn. They'll talk to you. They'll talk to their friends.
Speaker 1:Are but are there any risks from that that you caution entrepreneurs on if they are gonna be selling to a lot of startups? Do they have to message anything differently? Is there anything that they need to be doing Well,
Speaker 9:you have to not suck. Because startups are discern you can't, like, have some bullshit product and sell it based on a bunch of hype. Yeah. It's gotta actually work because they don't have time to mess around with things that don't work. And they're very sharp observers of technology.
Speaker 9:They're run by the founders themselves usually at that point. So you gotta actually be good.
Speaker 1:I'd love to reflect on how marketing and launching startups has changed over the last few decades. Jordy, we we we had Clad Labs on, which we we had a really fun time talking to them, but they they sort of went viral for the wrong reasons. They, they they were Well, in
Speaker 2:their view is the
Speaker 1:right reason. And their and their yeah. And their view is the right reason. They were offending people by putting, gambling in your IDE. So the software engineer can be gambling while they're coding, I guess.
Speaker 2:Yeah. And it felt like it felt like this year rate like, the concept of using rage bait both at the marketing level and the product level, like, kind of exploded. Yeah. I guess the question is, like, has has intentionally pissing people off been something that YC founders have have utilized across the eras to get to get attention? Is it is it really is it
Speaker 9:really That's that sort of technique sounds like the technique that would be popular with someone you describe as a bit of a scammer.
Speaker 1:Mhmm.
Speaker 9:And the thing about these scammers is they don't make the giant companies. They don't have a long term focus. They're not earnestly doing engineering. They're thinking about what's some gimmick I can use to get ahead. Right?
Speaker 9:And so long term, they don't matter. Mhmm. You can skip the companies that do random shit like that because, you know, they're never gonna be that big.
Speaker 14:And, of course, I haven't heard of the the term rage baiting either.
Speaker 2:Of course, in keeping with a pretty good look. It's the Oxford word of the year. So you can go look at their definition.
Speaker 1:It's it yeah. It's so interesting.
Speaker 9:I I getting attention by making people
Speaker 14:I know what it means. But
Speaker 2:Yeah. And we and and I had written an article, and and Gary and I had a nice back and forth, where, I basically said, like, in startups, if you in startups, you need to build a coalition of people that want you to win. This is like talent, the media, investors, customers.
Speaker 7:Don't even
Speaker 9:have to do that, actually. Yeah. All you have to do is make something really good and find the people who want it. You don't even need a coalition. You think like when Facebook was taking off at Harvard, there were some coalition of investors and media wanted it to take off.
Speaker 9:All that mattered was that Zuck had this thing and everybody at Harvard wanted to use it. That's all that matters. That small intense fire. Right?
Speaker 1:Yeah.
Speaker 9:Or when Apple was getting started and the users were, like, the people at the Homebrew Computer Club. Right? The media didn't know about that.
Speaker 14:There was no coalition media guys.
Speaker 11:A little rage bait. Phenomenon.
Speaker 2:Zac kinda did a little rage bait.
Speaker 1:Rage bait with the Hot or Not app. That definitely enraged a lot of people who didn't
Speaker 12:want to Yeah.
Speaker 1:But he
Speaker 9:didn't do it deliberately.
Speaker 1:No. Exactly. Exactly. And I was thinking about the Airbnb example, like, the whole Obama o's and McCain captain McCain's crunch, like, those cereals that they made. That was sort of a side quest for them.
Speaker 14:That was simply to get attention from the press. Interesting.
Speaker 16:That's all. And make no.
Speaker 9:Actually, was to make money.
Speaker 1:It was to make money.
Speaker 14:Well That was
Speaker 9:before y c. They didn't have any money. Remember? They were dying. Yeah.
Speaker 9:They needed to make money. They went and got these, like, off brand Cheerios, and they glued together the boxes
Speaker 14:themselves to make money. I don't think they knew they were gonna make money. We're gonna have to consult I'm pretty sure
Speaker 9:how need me. Mainly to Okay. Make money.
Speaker 2:Alright. What what's it like being back in San Francisco?
Speaker 14:Sunny. It's fabulous. The energy is so great here. I'm just so I'm so happy to be back and so happy to be around startups right now. I'm having a great day, if you can't tell.
Speaker 9:It gets better every time we come back. Like, Daniel Lurie is really cleaning up the city. Yeah. Every time we show up, it's, like, a little better.
Speaker 1:That's great news.
Speaker 9:I was asking, like, how far back have we gone? Have we gone all the way back to when Ed Lee died? Not yet. We're like
Speaker 1:Okay.
Speaker 9:But we've turned the clock back to maybe two years into London Breed.
Speaker 1:Oh, that's Okay. Yeah. That's great. I have one, I I yeah. I one more.
Speaker 1:I wanna I wanna, think through this concept that's been sort of lightly bandied about in the start up, discussion, you know, ecosystem. This idea of the deals guy era that you can actually build a business now by being more of the business person, the more of the deals guy, and less of the of what I remember about the the Y Combinator promise, which was just The earnest hacker. The earnest hacker. The earnest hacker. And it feels like there's a lot of people that are saying, yeah.
Speaker 1:But there's actually a way to go and get this person just marshal the capital and, you know, do something that's just been forgotten, not necessarily discover something new. And I was wondering if you have any reactions to this idea that increasingly there are entrepreneurs that sort of get really big. Who knows if they win, but they seem to win on the back of just raw deal making talent as opposed to raw engine engineering leadership.
Speaker 9:Maybe in enterprise more. You know? The you like enterprise. You, like,
Speaker 4:Yeah.
Speaker 9:Sell crap to CTOs instead of selling good stuff to programmers. Yeah. So salesmanship has always mattered more in enterprise.
Speaker 1:Yeah.
Speaker 14:I have one observation from this morning's session Mhmm. Of demo day. They everyone most everyone that presented this morning is an earnest hacker. I said to the person next to me, they're all nerds this time, like, 100%. I love it.
Speaker 9:Yeah. You know, if anything, YC drifted too far away from funding earnest hackers. Interesting. So YC for the last few years has been focusing more on getting back to the essentials, back to the roots. And so if anything, I would say YC batches are more like a higher percentage earnest hackers now.
Speaker 9:Yeah. I know, honestly, I would still bet on earnest hackers.
Speaker 1:Yeah. I I I agree with you. Do you think that that's, that that is what the essential skill set of YC leadership needs to be? Because, I don't wanna discredit all the hard work you did in the early days, but you didn't have to fight the fact that there were people out there writing blog posts of how to reverse engineer and make it look like you're an earnest hacker when, in fact, you are the, you know, the the the carpetbagger. And now it's there there's a whole industrial complex for how to fake your way and make it appear that you're an earnest hacker when in fact you're not.
Speaker 1:If
Speaker 9:the YC partners are themselves hackers Yeah. You can sniff out a faker like that. Yep. It's not even a problem.
Speaker 1:Yeah. Yeah. Yeah. But that seems like the main the main way that, YC creates value these days. We'll just be continuing to to hold that line, essentially.
Speaker 2:What do you think is your most
Speaker 9:I think I think, you know, here's something that will reassure you. If you think, okay. Is the earnest hacker thing do is that did that just work for a while, and now maybe it's over? Isaac Newton was an earnest hacker. Right?
Speaker 9:It's way older than startups.
Speaker 1:Yes. Yes. Yes.
Speaker 9:This is this is what wins.
Speaker 2:What do you think is your most underappreciated essay? Because a lot of them are sufficiently appreciated.
Speaker 9:The thing is I don't know how much people appreciate them. I don't know how much people appreciate different ones. So it's hard to say. How to do great work is pretty good. But I think people like that one.
Speaker 1:Yeah. Right?
Speaker 14:I read life is short at least once a year, but people like that one too. Yeah. I don't know.
Speaker 9:I don't know. That's a weird question.
Speaker 1:You'd have to you probably have to look at inverse page views, which gets the least page views historically over the past year, let's say. I was
Speaker 9:looking at a list of page views, I could tell you.
Speaker 1:Okay. Well, maybe we'll follow-up. This statistics, so I don't know. Breaking news. Yeah.
Speaker 1:That's, the that that's very funny. Do do you have anything else, Rory?
Speaker 2:What else? Paul, are we in a bubble?
Speaker 9:No. No. Everybody is always saying we're in a bubble, you know? Like, every year, people say we're in a bubble. Every year, people say, like, the valuations at Demodie, they're too high now.
Speaker 9:Right? I mean, they were saying this back in, like, 2010 when the valuations were, like, $4,000,000. And now they're like what? 30 or something typically. So people are always saying stuff like that and I don't know.
Speaker 9:I don't think so. I think I'll tell you. I think like, AI is very highly priced
Speaker 4:Mhmm.
Speaker 9:But it might not be overpriced. That's the interesting thing. Is it is it as big a deal as prices seem to suggest? It could be. Maybe even bigger.
Speaker 9:It's definitely real. It's not hype. The AI is real. Mhmm.
Speaker 1:Are foundation models good at writing Lisp?
Speaker 9:You know, I've never I think they would be good at writing Lisp. Yes. Yes. Because they're good at writing things that have a lot of, a lot of a lot of training data out there. Right?
Speaker 9:And there's a lot of Lisp source code. So I think they'd be fine at writing this.
Speaker 1:How are you using AI in your life?
Speaker 9:I just use it like ordinary people do. I ask I ask you questions.
Speaker 1:Sure. Sure.
Speaker 14:Very boring answer.
Speaker 2:That's very It's a good answer. It's not like, oh, I stringed I I'm training my own model to do a better Google search.
Speaker 9:What, what No. No. No. I haven't actually written anything using AI. Yeah.
Speaker 9:You know, I feel bad. I really should write an LLM because you can't really understand this stuff unless you've written one. I should write an LLM, but I haven't done it.
Speaker 1:Yeah. Didn't Karpathy publish a whole Yeah. He did it. Stat GBC from First Principles type of thing?
Speaker 16:You know?
Speaker 2:It's a really
Speaker 9:good fun project. Himself.
Speaker 1:You know that's
Speaker 9:why he did it.
Speaker 1:Well, has a new company that's an education technology company. And I believe that the the main course will be teaching yourself to build an LLM, teaching yourself to build a a chatbot effectively, which would be very That's what
Speaker 9:I tell high school kids. I get all these emails from high school kids saying, I'm working on a start up.
Speaker 1:Yeah.
Speaker 9:You know, to introduce, like, founders to VCs or some crap like that. And I say, don't start a start up. Get good at technology. Write an LLM. Yeah.
Speaker 9:Then you can start a start up.
Speaker 1:Do you do you think reflecting on the history of YC, do you think it's it's fair to, try and create a concept of eras around, like, what the key insight was? I I remember a lot of people saying, like, one of the first key insights was just this idea that you you could take someone fresh out of college and actually give them money, and they could go and build a business. They didn't need $10,000,000. They didn't need Ten years of experience in the enterprise. And then Or
Speaker 9:an MBA.
Speaker 1:Or an MBA. And then maybe the second era was thinking that maybe the same rules applied internationally, and that was like a second wave of of entrepreneurial energy that was unlocked by the YCU. International. I mean
Speaker 14:We always had international. Yeah.
Speaker 9:We understand countries aren't all that different.
Speaker 1:But but do do you think there are any other, like, underappreciated aspects of, like, the YC strategy? Or or is it really just as simple as, you know
Speaker 9:Well, were things we didn't appreciate in the beginning. Yes. So for example, we didn't understand that as a byproduct of funding all these companies, we would create this alumni network. We had no idea. But the alumni network is enormously important.
Speaker 9:It's out there now. All these alumni are investors.
Speaker 14:Yes. It's staggering how many are investors now, actually. Yeah. Yeah. It's amazing.
Speaker 9:It's like taking over Silicon Valley, and we never had any idea that was gonna happen.
Speaker 1:Okay. On the alumni network, do you is it fair to to characterize YC as a bit of a union against venture capitalists? Yeah.
Speaker 9:It's a lot like a union.
Speaker 1:Because Yeah. Because if you attack one individual, one founder, if you fire the founder after investing, you get board control from them and you oust them, that might make it sway into the rest of the YC community. And it overall raises the level of founder friendliness. Is that correct?
Speaker 9:You know what though? It's not simply one-sided because if founders screw over investors, if they like do a handshake deal and then and then refuse to go through with it, we would tell them not to do that too. We want everybody to, like, play by the rules.
Speaker 14:Yeah. And behave well.
Speaker 9:Because the big wins don't come from breaking the rules. Mhmm. The big wins don't come from little cheats that get you two x multiples in a world of, like, thousand x returns. Right? It's for the the same reason, like, people in Silicon Valley don't focus a lot on tax evasion.
Speaker 9:Mhmm. Because what's tax evasion gonna get you? Like, two x returns in a world where getting the right startups will get you a thousand x returns.
Speaker 1:Yeah. You know? Do do you think that, the process of founding a company, raising money is is at its, the end of history in terms of, efficiency? Like, the safe is the most efficient document we will ever have, or do we need to speed things up even further?
Speaker 9:Well, C. Levy, Carolyn Levy, invented the safe, and she also invented the convertible note that everybody used before it. Yeah. So she has twice rewritten the rules. She has twice recreated the chessboard that the game is played on.
Speaker 9:If she thought there was a better thing than the safe, she probably would have created
Speaker 14:Maybe she has a third one in her.
Speaker 9:We should ask.
Speaker 14:Ask her. In fact, she's
Speaker 1:very popular.
Speaker 14:Oh, yeah. Okay. Yeah. And you could ask her that, John, when you come on our podcast.
Speaker 1:We'd love to. I'd love to.
Speaker 9:I I I I can't Is there anything wrong with the safe? And if and, like, if there is, why hasn't she fixed it already? Yeah. You know? Yeah.
Speaker 9:So probably not because C. Levy is not Slack. If there was anything missing, she would've she would've, like, made a new version.
Speaker 1:Yeah. Mean, my perspective, it seems like it's working.
Speaker 2:What what problem in the world did you think a YC startup would've fixed by now? Think like housing affordability or any of these sort of major
Speaker 9:You know, we don't have any grand strategic vision for what the startups do. Because the founders know that, not us. Right? That would be like asking a publisher, what what novel do you think, you know, would you have expected someone to write? Right?
Speaker 9:Good publishers, they just, like, they let the they let the novelists write the novels. So we would just we just try and find good people. What do they do? Whatever these good people are interested in. Anything any preconceptions we had about what they should do would just be adding noise to that.
Speaker 1:Mhmm. How do you think about coaching folks through pivots? It feels like we're in an era where, there's a lot of companies that are still finding product market fit. Pivots are probably just as common as they always have been, but everyone has an order of magnitude more money.
Speaker 9:If anything, more common.
Speaker 1:Yeah.
Speaker 14:Yeah. I think that it's more common. You're taught you talk about new ideas with startups all the time in your office hours.
Speaker 1:This is
Speaker 9:this is one of my specialties. Yeah. When people are just dead in the water
Speaker 1:Yeah.
Speaker 9:And they need to get a new idea, they often get sent to talk to me, and we cook up something.
Speaker 1:Has the advice changed if someone comes in and says, hey. I have, $200,000 raised, and I have me and my cofounder are living apartment together and we need to pivot versus I come in and I say, hey. Look. I got $5,000,000 and I got 20 employees already or something like that.
Speaker 9:20 employees?
Speaker 1:I know. It's happening. Right? It's you you two see this. Right?
Speaker 9:Well, no. Usually, they don't have 20 employees. Yeah. Usually usually, I mean, that would be that would be alarming. That would be very alarming because
Speaker 1:But it feels like there's so many companies
Speaker 9:that could constrain the idea you're gonna If you just have the founders, you could do anything. Yeah. If you already have 20 people, you either have to fire them or do something that those 20 people can do. Right? Yeah.
Speaker 9:Yeah. Which really constrains your options.
Speaker 2:Yeah.
Speaker 9:So it's the problem with the 20 employees is not the cost. It's that they change what they limit what you can think of, you know? Which is why you shouldn't hire.
Speaker 2:Just don't hire. What kind of guidance do you give to founders around that are feeling a pressure to go from 0 to a 100,000,000 in ARR in three years or whatever the new gold standard What
Speaker 9:I tell startups over and over and over is all that matters is growth rate, not the absolute numbers because mathematically, you'll see if you try simulating it. If your growth rate is high enough, doesn't matter what the absolute numbers are, you'll get there. Yeah. You know? And so you just get a really good growth rate.
Speaker 9:And so the great thing about focusing on growth rate means you can focus on startups. You can sell stuff to startups for cheap instead of having to go and do these big deals with big companies that take a long time and make your products stupider. You can sell things to these quick deciding early adopters and then you just get more and more of them and your company grows by several percent a week. Eventually, it's gonna be huge.
Speaker 1:Are you still recommending to folks who ask for advice for kids, that they should learn to code?
Speaker 9:Yeah. Oh, yeah. Yeah. Yeah. I still tell people that.
Speaker 9:At least learn technology. It doesn't have to be coding specifically. You could learn how to make rockets
Speaker 14:Mhmm.
Speaker 9:Or drones or work with lasers or gene editing or something like that. But you should do the stuff and not just like play house pretending to start fake startups in some business plan competition. You
Speaker 14:know? Yeah. This is
Speaker 2:exactly competition.
Speaker 14:I tell everyone who says they might wanna start a startup someday to learn to code because it's the most important thing you could do. That and save your money.
Speaker 1:Yeah. That's really good advice.
Speaker 14:And no one likes to hear that, by the way, but I tell them anyway.
Speaker 1:No, no. You give a
Speaker 9:lot of advice. People come and they want advice. If you went to the doctor and you said, Doctor, what can I do to be healthier? And the doctor says, Eat less and exercise more. And you're like, Oh, was hoping you'd say something else.
Speaker 9:Right? Well, that's what it's like when they come to me. They come to me for advice and I say, The startup equivalent of, Eat less, exercise more. And they're like, Oh, isn't there some trick I could use to be get virality?
Speaker 4:Could I
Speaker 9:could I get virality instead? Just, like, do the startup equivalent of eat less and get more exercise, which is build stuff and talk to users. Understand your users and be good at building. That's the recipe. It was in 2005, and it's just as much the recipe now.
Speaker 1:Yeah.
Speaker 2:How many startups do you think how many startups do you think YC will have per batch a decade from now? Because I think in a perfect world, we have a lot more earnest hackers.
Speaker 1:Mhmm.
Speaker 2:And they can apply to y c and and if they meet them. I know I know you're not setting targets and there's not like a specific acceptance rate that you're trying to track. But we feel like YC is one of the most important institutions in the world and ideally it can be bigger, but but maybe there's some
Speaker 9:No. No. No. They will be bigger. They they will inevitably be bigger because there's this secular trend of more people starting startups.
Speaker 2:Yeah. Do think we're do you think we're we're early in this in this trend? I mean, feels like there's so much so much It's now you can create a startup. You can create a c corp in a few minutes. Right?
Speaker 2:It's like there's all this sort of like underlying infrastructure that's been built that is reducing friction to starting companies. You can ask ChatGPT, how do I start a business? And it'll give you a good playbook and that maybe helps somebody that hasn't found a YC blog yet figure out how to get going.
Speaker 9:We're the training data, even if they don't know it. Yeah. Will more people start startups? Yes. If you talk to ambitious 15 year olds, they all want to go start startups.
Speaker 9:Nobody wants to go work for some company and work their way up the corporate ladder anymore. The whole idea sounds so like 1980s. And there's a lot of earnest hackers. Limit you think like, what's the limit? So the limit is what people want.
Speaker 9:Right? That's what startups do. They make something people want. What people's wants? They're limitless.
Speaker 9:Not literally limitless because eventually you run out of atoms in the universe. But for all practical purposes in the near term, people's wants are infinite And so there's infinite demand for good stuff you could make.
Speaker 1:Well, that's a great place to end it. We have to catch a flight. Thank you so much for taking the time
Speaker 2:to talk Yeah, thank you for everything you guys have done for the industry and the world through YC. It's an honor to You do. An honor to cover every batch, and it's been great having you guys on.
Speaker 9:Yeah. Nice to meet you.
Speaker 14:Thanks for having us. Yes. I love you guys.
Speaker 1:Yes. We love you too. You so much.
Speaker 2:Have fun in SF.
Speaker 1:Have a great rest of your trip. We'll talk to you soon. Thanks. Goodbye.
Speaker 9:See you later.
Speaker 1:We have to hop on the flight.
Speaker 3:But You
Speaker 2:hear that, John?
Speaker 1:I hear it. Yes. I hear the goat noise, the sound cue. Yeah. That one's a little bit subtle.
Speaker 1:I think that there's a lot of people that might not pick up on why they're hearing this random goat noise that's
Speaker 2:If you know, you know.
Speaker 1:Low in the back. But if you know, you know. And, also, if you want exceptional sleep without exception, you go to 8sleep.com. You fall asleep faster. You sleep deeper.
Speaker 1:You wake up energized. And we should close out. There's a lot of stuff going on. Deal book summit's going on. There are debates raging on the timeline, but we will have to cover them tomorrow.
Speaker 1:We will close out with a congratulations to Ed Elson, the cohost of the Prof G Markets podcast. I love his bio because he says he's not Prof G's son even though they look somewhat similar.
Speaker 2:He had viral post yesterday because he got into Forbes 30 under 30, and he said, I'll see you guys in prison.
Speaker 1:He said, woke up to learn I made Forbes 30 under 30. Congrats to the other winner winners.
Speaker 2:Can we play this before we can we before we jump, can we, can we play this? Oh, Gary Tan's in the chat. Hello, Gary. Ali's in the chat.
Speaker 1:Gary, we hope you feel better.
Speaker 2:Tan is in the chat. Gary, feel better.
Speaker 1:Of the show. Thank you so much for making this happen. We're very sorry we couldn't be there in person, but we had a blast. We went on a whirlwind tour. We talked to tons of, YC founders, and why the state of YC is healthier than ever, stronger than ever.
Speaker 2:Got a Gary's got a elementary school or preschool. He's just
Speaker 1:so rough. I've been there, man. I've been there. It's
Speaker 2:Daycare virus. Yeah.
Speaker 1:Well, we hope you get well soon. Team, we need to definitely send some soup or some flowers to Gary Tan as soon as possible. And, and we will see you all tomorrow. Thank you
Speaker 2:so much. Thank you to Y Combinator for hosting us and all the founders. It was a it was a it was a whirlwind tour, and I'm very excited about a lot of these companies.
Speaker 1:Yes. We will talk to you later.
Speaker 11:Cheers.
Speaker 1:Goodbye.