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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 TVPN. Today is Wednesday, 06/04/2025. We are live from the TVPN Ultra Dome, the temple of technology, the fortress of finance, the capital of capital. We have a great show for you today, folks. A story that we've been covering for months now is continuing to develop and is now in the pink pages of the Financial Times.
Speaker 1:This is the rippling deal drama. It continues. And in their briefing section, it says, Silicon Valley spy drama, a feud between two startups over alleged corporate espionage. Rare rare case where, yeah, the drama does not involve the media, actually. A feud between two startups over alleged corporate espionage has taken a new twist after a $12,000,000,000 HR software group deal claimed that rival Rippling has had directed one of its staff to pilfer its assets.
Speaker 1:So they're going back and forth. And today, deals firing back. Now I heard a little bit about this story. We kind of it were the rumor mill was was, churning over this idea that, you know, deal had something up their sleeve, and they were gonna fire back. Of course, that's the way these things go as the, as as these, you know, battles evolve.
Speaker 1:The interesting thing there's a couple interesting things in here is that the the the the Rippling allegations resulted in a lawsuit and, ultimately, I think criminal charges. So Rippling alleged earlier this year that a staff member at Deal had been spying on behalf of Deal. This is, of course, that send that watch to London moment.
Speaker 2:Send that watch to London. Employee watch disappointed, John. That is such an incredible line in Silicon Valley history. It really is sad that people don't use it more.
Speaker 1:It really failed to break through. Yeah. It it's it's you know, we were discussing this earlier about this is, like, the most dramatic story in of the year in Silicon Valley. And yet, I've talked to multiple people in Silicon Valley who have just completely missed it because it's just it's it still is just HR, IS, enterprise SaaS drama.
Speaker 2:But Most people simply do not care.
Speaker 1:They just don't. They're they're too You
Speaker 2:have to really pay them to care. They to get some sort of, like, payout system going, like, axed to be like, every time you pay attention to this, you know, you're gonna get a micro payment.
Speaker 1:Yeah. I mean, they're just it just doesn't have kind of the the weight of, like, Elon, Doge, Tesla, humanoid robots, AI, AGI, AI doom. Right? It's just it's just payroll after all. Forget it, Jake.
Speaker 1:It's just payroll. Well, anyway, in new legal filings seen by the Financial Times, Deal has countered that Rippling has been actively engaged in a carefully there's a quote from that, in a carefully co coordinated espionage campaign through which it infiltrated Deals customer platform by fraudulent means and pilfered the company's most valuable pro proprietary assets. What's interesting is that they they they're stopping short of of naming, like, a spy. Right? And so the the the the actual approach here Deal has sought to dismiss Rippling's initial claims of direct corporate espionage and has filed a lawsuit in Delaware alleging that its rival is trying to impugn Deals reputation, and its latex filings were lodged yesterday as an amendment to that case.
Speaker 1:It alleges that Brett Brett Alexander Johnson, someone I have not met, Rippling's competitive intelligence manager, posed as a customer in access details of deals, products, and business business practice over the course of six months. Now what's what's what's interesting is that, like, access details of deals, products, and businesses. So this sounds like they were asking customers for what Deals was doing maybe instead of, say, instead of, say, going inside of Deals Slack. Like, there's no allegations that they were inside of Deals corporate systems, which is a very distinct line to cross. And so, obviously, this story is evolving, and we've invited Alex, the founder of Deal on the show.
Speaker 1:Would love to get his side of it. Would love to get Parker on as well. But, of course, our worst enemies hope compliance teams at enterprise startups are getting it our Yeah, so they
Speaker 2:can figure out some type of settlement agreement that involves a sort of MMA
Speaker 1:I was going to
Speaker 2:say cage match. Pay per view on TPPM.
Speaker 1:I was gonna say cage match. That's
Speaker 2:It's like at a certain point, like, they'll reach some sort of, you know Yeah. End to the story. Yeah. But I'm sure there will still be bad blood.
Speaker 1:Totally. So why It has to be ended in the octagon. In octagon. They yeah. I mean, we've been teased so many times with, oh, is, is Trey gonna box Jason Kallikanis?
Speaker 1:Or is Zuck gonna fight Elon? Like, this could be maybe third time's the charm here. Yeah. Maybe this is the one that gets
Speaker 3:it done.
Speaker 2:It's gonna happen. Fantastic
Speaker 1:In Dubai.
Speaker 2:Yeah. We'll we'll bring it out to The Gulf
Speaker 1:of To the of course. Some reason, it'll happen in The Gulf.
Speaker 2:I think just the purses haven't gotten quite big enough. Right? Like, you see some of these boxing pay per views Yeah. Easily into the 9 figures. If we can, you know, position this as nondilutive funding
Speaker 4:Yeah.
Speaker 2:For these startups, maybe there's something to be done. Maybe that would get them over the line of saying, yep. I'll I'll put on the gloves. I'll get in the cage.
Speaker 4:Yeah. Yeah. It it I mean, it's been a
Speaker 1:fascinating story. Obviously, these things move, like, extremely slowly as they go through the courts. So it feels like, you know, with FTX or Theranos, like, we we got the bombshell accusations, then it took, like, a year or two to get any sort of to move past, like, alleged wrongdoing to
Speaker 2:Yep.
Speaker 1:Actually understanding the scope of what happened. But I'm sure that the courts are working through it, and we will be following it the entire time. The lawsuit this is from TechCrunch, which is turning twenty years old next next next week. The lawsuit is also full of insults hurled at Rippling's CEO, Parker Conrad, and mentions his troubles at his previous company's benefits. Irrelevant in my opinion.
Speaker 1:At times, there's nothing wrong with having a couple throwing a couple back in the office.
Speaker 2:Wait. He had, some unfortunate things happen at Zenefits?
Speaker 4:Yeah.
Speaker 5:Really?
Speaker 1:Really? I'm hearing this for the first time. You got the sound effect now.
Speaker 2:I missed it on the first attempt. At time
Speaker 1:at times the confront ventures into psychoanalysis territory. To understand Conrad is to understand rippling, the suit claims. Man, they're everyone's chirping. But, you know, I think I think this show, we wanna be independent. So we're taking the side of Kotu because CO2 is invested in both.
Speaker 2:Yes. Yes. Let's give it up for Let's give it up for
Speaker 1:diversification over allocators. Yes. Yeah. Heads I win horses. Heads I win tells you tells you this.
Speaker 1:Exactly. That is the real the real game.
Speaker 2:Yeah. We've talked about this before. Ultimately, there's Yeah. Hundreds of billions of dollars of payroll market cap. Yeah.
Speaker 2:And they can both be big businesses. Yeah. And hopefully, they get over, you know
Speaker 1:And maybe this is all a sideshow for what's really going on. I mean, the the Oh, Quantum Payroll.
Speaker 2:Yeah. This all comes down to both Well companies racing to develop Quantum payroll Quantum payroll. Payroll.
Speaker 5:For sure.
Speaker 2:And that's kind of the real story. And and this is just the drama floating up to the surface.
Speaker 1:Yeah. And so, yeah, they're they're they're, they're fighting back. Interesting timing too because wasn't it just yesterday that Diehl said it had been profitable for years and is generating over 1,000,000,000 in annual revenue?
Speaker 2:Yes.
Speaker 1:And so I wonder if there's a sequence of events here where it's like, okay. Take a breather. Be really silent for a while, then come out with some some promising news about the financial health of the company, then fire back in the media and in the courts with a with a countersuit. But, you know, unclear. And it doesn't seem like it's quite as aggressive as what Zenefits found or or or Rippling found.
Speaker 1:It's certainly not as it doesn't take you on as much of a
Speaker 2:smoking gun Yeah. Necessarily.
Speaker 1:I mean, the the TechCrunch article here actually says when Y Combinator grad Cotool launched an agentic security platform last month, among other things, sets up Honeypots, it its ad was a spoof on how Ripling's corporate spies said he was caught. And so, clearly, it's become a, a small meme within within Silicon Valley. Anyway, if you want to save time and money and not have any headaches in your back office.
Speaker 2:Have you noticed, by the way, that when I point at you, it pops up on both cameras so I can actually get two hands out of one point?
Speaker 1:Oh, you can. That's good. Anyways Go to ramp.com. Easy to use corporate cards, bill payments, accounting, and a whole lot more all in one place. Ramp.
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Speaker 2:Using ramp is a joy.
Speaker 1:Yes. It is.
Speaker 2:People have said that that, you know, corporate cards and spend management platforms are boring. Yeah. But anybody that says that clearly has not used Ramp.
Speaker 1:We were we were just pitching Ramp to we we did a photoshoot yesterday, and we were pitching ramp to our to to the photographer, telling him, hey. Stop.
Speaker 2:We never miss a moment.
Speaker 1:We never miss a moment.
Speaker 2:Miss a moment.
Speaker 1:Yeah. Hey. Those were He was he tagged automatically.
Speaker 2:He was by the end I mean, he was convinced by the end.
Speaker 1:I think so.
Speaker 2:He was ready to he was
Speaker 1:ready to switch.
Speaker 2:Yeah. And you should be too. Thank you to Ramp. Anyway, we have a story here. Microsoft bets on agents to fuel next chapter of AI growth.
Speaker 1:Yeah. So we heard that that quote from Satya Nadella that the amount of inference that's happening on Azure has 5xed, and they're generating, I think, hundreds of billions of tokens at this point.
Speaker 2:Trillion.
Speaker 1:Trillions of tokens. Yeah. It's absolutely massive. So the question is, Microsoft's in a perfect place to deploy agents. They have the distribution.
Speaker 1:But how who who's actually behind this team?
Speaker 2:And we said yesterday, they already have 70% of the Fortune 500 using Copilot Yes. Which is Again
Speaker 1:say what that means. Right?
Speaker 2:Evaluate what that usage actually looks like.
Speaker 1:But Yeah. How would we even do that? I mean, because it it it's it's they were obviously very successful with Teams. Right? They got Teams into everyone, and they really did switch over and and and spike the growth of that company, in that in that product.
Speaker 1:At the same time, you know, Google's been quoting, saying that they've they're using generative AI search results, and it's it's kind of it it kinda counts, but it kind of half counts, and you have to look, like, discount it a little bit because it's not it's not a consumer choice to move over to that product. It just kind of happens naturally. And so it it's interesting to dig into Microsoft strategy because they seem to be the way I heard it described was that Satya Nadella has obviously done a fantastic job as CEO of Microsoft, and he's carved out a ton of territory in artificial intelligence. The question is, how much can he hold on to? Right?
Speaker 1:And and and this isn't to say that, like, seeding ground is a is a loss necessarily. Like, it could be the strategic move, but there's this question of, like, even just to the point of I wanna be a leaser. How much how much CapEx do I actually wanna be spending? Do I wanna own the land? Like, you could you could vertically integrate all the way down in the AI factory, but they're partnering with people.
Speaker 1:They're leasing. They also own data centers. And and there's a question of do they you know, Microsoft has an has an AI research team that, at one point was training models. Now they seem to be much more model agnostic, and you saw that with Microsoft Build where Satya highlighted the importance of being able to choose your own model, which is something you can you can do on GCP and AWS as well. But Microsoft has really leaned into that even with intelligent model routing within different Yeah.
Speaker 1:Different OpenAI and Llama instances, and there's lots of different ways that they've plugged in. So let's dig into how they're betting on agents. This is from the information. In March at a Microsoft all hands meeting, one of the company's newest executives, Jay Parikh, laid out a rough vision of Microsoft's path forward in artificial intelligence. AI models made by OpenAI and others were quickly becoming commoditized by more efficient models from DeepSeek and Microsoft's own research arm that are that that performed nearly as well for a fraction of the cost, Parekh said.
Speaker 1:Sitting alongside chief executive officer Satya Nadella and chief technology officer Kevin Scott, that meant it would soon become easier for companies to build their own AI applications and that Microsoft could cash in on that growing market by redoubling its efforts to sell them tools for doing so, said Parikh. In recent months, Nadella has made similar comments in staff meetings telling employees that Microsoft needs to focus on platform, platform, platform, a refrain hearken
Speaker 2:back doing?
Speaker 1:To a speech by Nadella's predecessor, former Steve Ballmer, from the late nineteen nineties in which he uttered the memorable chant, developers, developers, developers. And, if you haven't had a chance to go listen to Steve Ballmer's interview on Acquired, it is fantastic. They've been posting a bunch of clips. I listened to most of it. It is it is he's really opens up, and it's it's, it's just an incredible piece of history.
Speaker 1:It is. It's really fun. There's a clip going up right now, where Balmer talks about when he took over as CEO from Bill Gates. And, essentially, he him and Gates didn't talk for a full year. Wow.
Speaker 1:Even though Gates said, hey. I want you not just to be a figurehead. I want you to actually be the CEO. That means I will report to you. You're the boss.
Speaker 1:They didn't really he the way he described it was, like, they just didn't really know how to how to deal with each other anymore, how to, like, how to work together. It's very it's very interesting. Like like, somewhat emotional even. Wow. Anyway, Nadella's clearly, you know, paying paying homage to Steve Ballmer, but also focusing on this idea of platform, platform, platform, which is similar to developers.
Speaker 1:Like, wants developers on top of the platform. But, you need you need to put
Speaker 3:a little twist on it.
Speaker 2:I'm actually excited next week at demo day with YC. Yeah. I want to get a sense of how many companies are building on the broader Microsoft platform, Azure, etcetera.
Speaker 1:We talked to someone at last demo day who was there handing out tons of credits.
Speaker 2:Yeah. Yeah. Yeah. Britain.
Speaker 1:Yeah. It was kind of a No.
Speaker 2:I'm sure he'll be back there. He runs their startup, team.
Speaker 1:And so, yeah, I mean, I can I I can imagine if you're if the platform is offering a lot of flexibility around these tools, like building on top of that platform lets you switch in and out of different models very quickly, means you don't have to shift as soon as, like, a new model is going viral? You're just like, okay. Just swap it into my my Azure stack. I don't even need to set up billing on a new on a new platform. The comments from Nadella and Parik reflect a subtle but important shift in AI strategy at Microsoft.
Speaker 1:No big tech company has benefited more from the frenzy around AI than Microsoft whose 3,440,000,000,000.00 market capitalization makes it the world's first or second most valuable company depending on the week. NVIDIA took the crown on Tuesday. Wow. Let's hear it for NVIDIA. So so far, the bulk of the company's AI revenue has come through its relationship with OpenAI, and there's this really interesting Ben's chart is
Speaker 2:just unbelievable. It's crazy.
Speaker 1:He's he's doing
Speaker 2:so well. Yeah.
Speaker 1:So, there's a good chart here from the AI bonanza. Most of Microsoft's estimated AI revenue so far has come from its relationship with OpenAI, which includes revenue sharing and leasing Azure servers. So they're making about an estimated $10,000,000,000 this year from OpenAI and then a 3,000,000,000 from other AI sales to bring their total AI revenue to 13,000,000,000, which seems significant given how
Speaker 2:nascent this industry is. What are the is that revenue that OpenAI is passing back to them somehow?
Speaker 1:Yeah. Yes. So they get so they get a they get a revenue share from, from OpenAI. From selling OpenAI products. From from vending GPT four as an API.
Speaker 1:OpenAI also is paying Microsoft to lease Azure servers for training and inference. And then I believe that they're entitled to a cut of revenue or profits up to that Yeah.
Speaker 2:100,000,000 cap rate. I wouldn't assume has kicked in at all yet. Yeah.
Speaker 1:Me either. I thought I thought it
Speaker 4:was, like, net profit
Speaker 3:at all.
Speaker 1:But clearly Still,
Speaker 2:that's crazy.
Speaker 1:10,000,000,000. Yeah. I mean, it makes sense. They they like, OpenAI's training and the GPUs are on fire, and they need to scale. And so, you know, even if they were even if they had no relationship with OpenAI, you would imagine that they'd be load balancing across the different hyper calers and trying to soak up GPU capacity wherever they could.
Speaker 1:And so if you look at
Speaker 2:Which is crazy. Numbers Microsoft invested $1,000,000,000 in 2019, '2 billion in 2021, '10 billion in 2023, and then $750,000,000 late last year. So
Speaker 4:And then they had 10,000,000,000
Speaker 2:Then they're you know, obviously it's not necessarily super high But
Speaker 1:It's a lot.
Speaker 2:It's a lot.
Speaker 1:I mean, the margin of of Azure is not low. It's, you know, over 30%. Right?
Speaker 4:So Yeah.
Speaker 1:They are
Speaker 2:particularly And they still own half of 49% of OpenAI Global LLC.
Speaker 1:It's amazing. Sacha. It's the best. But again, it's like, you know, he doesn't own it all. And so as as as OpenAI kind of goes more independent, how much can Satya, like, hold on to in terms of, like, the consumer AI market?
Speaker 1:If if if the if the if the narrative around OpenAI as the un what what did Ben Thompson call it? Like, the unwilling consumer tech company or, like, the unexpected
Speaker 2:Yeah.
Speaker 1:To consumer tech company? Like, if OpenAI becomes the next Google, what will that relationship with Microsoft look like? Because they could I mean, they're building their own servers with Stargate. And so Yeah. Like, that revenue could go away over the long term even though it seems like they will be partnered for a very long time.
Speaker 2:Yeah.
Speaker 1:Anyway, we'll we'll have to dig dig into it more. We wanna have some Microsoft folks on the show, and, and I I would love to know how Azure is tracking against inference versus training loads because we didn't get that from Jensen in the NVIDIA earnings call, but that seems to be an important question that is on everyone's mind. As we hit the GPT 4.5 and and and this in the pretraining scaling kind of wall, Obviously, the the the the the hope is that we shift to inference very smoothly and GPU demand continues to grow and the overall industry grows very quickly, but, it's still, like, an open question. We don't have a lot of hard data on what's happening there. Anyway, Microsoft is particularly bullish.
Speaker 1:This is from the information, of course. Microsoft is particularly bullish on a new category of AI applications called agents, which will be able to carry out tasks because it's maintaining a spreadsheet to keep track of unpaid bills or patching websites after outages with minimal human oversight. Agents are all the rage throughout the tech industry, not just at Microsoft with other enterprise giants like Salesforce, ServiceNow, SAP rushing similar products to market. The growth of agents could take off with cheaper AI costs leading to the rise of what some executives call the agentic web in which most of the world's
Speaker 2:security drivers How? By autonomous AI We need to figure out and understand Yeah. Agent force, which is Salesforce's digital labor platform, what adoption actually looks like over there. Yeah. From my sense is like they're force feeding people it.
Speaker 2:Where it's like, hey, you like our CRM? You will also enjoy our digital labor platform. And if you don't buy it, you will, you know, we're just gonna charge you more for
Speaker 1:zero. Yeah. Yeah. There's a lot of these products that are that are seeing, like, rocketed adoption based on I mean, it's almost like the bull case for for for some of Google's tools that, like, v o three, you know, we were joking about that post yesterday that, like, some of the products are amazing but hard to find. But at least you know that if a Google model is going viral, it's authentic.
Speaker 1:Like, people really love it versus they're it's it's it's rarely just, oh, they just stuffed it in everywhere and, like, it doesn't really count. Like, the numbers don't really count.
Speaker 2:Yeah.
Speaker 1:Because it's like it's it's pretty hard to go and find these find these models versus, you know, if if Microsoft chooses to, you know, roll out Copilot in in every installation of Teams by default, that could that that could trigger a lot of, like, daily active users. But are they really getting those tools, or are those tools just kind of sitting in the background and then and then companies are going to other more focused, more dedicated, startups or businesses for those those agentic workflows. Anyway, for Microsoft, taking advantage of the shift toward agents means making new inexpensive models available on Azure and as as an alternative to larger models, embracing open source protocols that make it easy possible to build agents and launching new products that let let customers set up their own custom built agents. Parikh said at an event connected to Microsoft's build conference, our goal is to build a new stack that allows anyone to build AI driven applications and agents and orchestrate them. At the center of the shift, is Parikh, a former meta executive who joined the company in October in an unnamed role.
Speaker 1:Very cool. Just like, hey. We just want you.
Speaker 2:Off the org chart.
Speaker 4:Off the org chart.
Speaker 2:This is the playbook.
Speaker 1:Yeah. In January, he became head of a newly formed unit called, Core AI that unified groups from across the company, including the company's developer platform, GitHub, its internal developer division known as DevDiv, and several teams that previously ported to Azure head Scott Guthrie and were focused on and focused on running AI models on cloud. ServicePreak now oversees more than 10,000 staffers at Microsoft. Let's see here. For massive org.
Speaker 1:He's got almost 5% of the org chart reporting to him. He's definitely on the org chart now.
Speaker 2:Good luck getting him in the same room.
Speaker 1:Yeah. You're gonna have to rent a basketball stadium to have your staff meeting.
Speaker 2:I'm sure Satya could arrange that.
Speaker 1:After this story was published in the deadline, Wednesday announced another reorganization to staff focused on agents, consolidating executives running LinkedIn, Office three sixty five, and business application applications under, executive vice president Rajesh Jha, whose groups will those groups will aim to sell out of the box agent applications to customers while Parikh's unit focuses on getting companies to build their own agents on Azure. So a little bit of a divide between what's being vended into Yep. The the the the office customers and what is more on the Azure side and and and enablement of developer workflows in on on top of Azure.
Speaker 2:I'm surprised Microsoft hasn't slapped some agents in LinkedIn yet. You know?
Speaker 4:They got who?
Speaker 2:Should be able to have an link an agent that just replies
Speaker 1:That's where Clippy needs to come.
Speaker 2:Yeah. Bring us Clippy.
Speaker 1:We gotta bring back Clippy. I think we can make it happen. It's it's we're so Well,
Speaker 2:that's actually on the show and the entire time will just be like bring him a for Clippy.
Speaker 1:I really think I really think it could make
Speaker 2:Like, actually, we don't have any questions. Like, we really
Speaker 1:We're just pitching you. We're just gonna
Speaker 2:be pitching you.
Speaker 1:Yeah. Mean, yeah, Microsoft is one of those brands that's like, it's still cool, but it's not fun. You know? It's a bit like serious business. Just having a
Speaker 2:little It's cool because it's such a
Speaker 1:It's so efficient, behemoth a monster. And reliable and like Yeah. It's reinvented itself
Speaker 2:multiple times.
Speaker 1:But they also have Xbox, you know? They like to have some fun. They like to play some Call of Duty. They literally own Call of Duty. Like, that's
Speaker 2:We need to get a racing simulator here at the new studio. Yeah. Speaking of speaking of games Yeah. And a golf simulator in the green room to just let Yeah. Let guests, you know, start to get warmed
Speaker 1:flight simulator flight simulator. That's been, like, a thirty year project. Anyway, if you're if you're designing for anything, really, on FigmaFigma.com. Think bigger, build faster. Figma helps design and development teams build great products together.
Speaker 1:Go to Figma.com to
Speaker 2:get started. It is the backbone of the show. I wanna see if they even put customer low Okay. So they do.
Speaker 1:You're you're really into the customer logos.
Speaker 2:I am. I just think people need to understand like the the Coinbase, Dribble, Dropbox, GitHub, Herman Miller, Microsoft
Speaker 1:Herman Miller.
Speaker 2:New York Times, One Medical. Yeah. Rackatan, Slack. They got them all. They've they got them all.
Speaker 2:And you should be on Figma too. Go check them out.
Speaker 1:Go check
Speaker 2:them Try try some of their new products. They are fantastic.
Speaker 1:Yeah. They are going to be so this is another quote from Danny Fish, a Janus Henderson investor, portfolio manager who oversees two funds that hold a total of $800,000,000 in Microsoft stock. He says, there are going to be software companies that are able to embrace and adopt that, there are gonna be software companies that are going to find highly dis that that are gonna find it highly disruptive to their models. Microsoft ability to embrace that will be really important. So you wanna you wanna offer enough tools to empower the companies, but you also need to allow the the flexibility.
Speaker 1:You don't lose companies who migrate off platform because they're just like, I'm gonna build everything myself with new you know, I'm gonna vibe code a bunch of agents, and I don't need you for anything. You you wanna have, like, the full continuum, and it seems like, Microsoft's in a in a position to kind of, like, index the market. It would be interesting to see We we we should start pressing pressing pressing more of the founders that come on that are building kind of, like, agentic enterprise workflows and see how they're positioned against, Microsoft. Are they seeing Microsoft deal cards go up against them when they're when they're pitching? Or or or is it, like, purely additive?
Speaker 1:Because I feel like, for probably for the Fortune 500, you get a very white glove experience at Microsoft, and they tell you every product they're working out. But Yeah. In in more like the SMB self serve market, it might just be a situation where you, you know, see a viral video or get an intro from an investor, and then you start spinning up whatever Yeah.
Speaker 2:I really wanna get a better sense of what b to b agents are getting, have sticky usage. Yep. Right? I can imagine the obviously, you see it in developer tooling. Seems like it's getting there in legal.
Speaker 3:Mhmm.
Speaker 2:Sales, I think is happening. But at the points that I notice it are when the person, you know, the the agents are sort of messing up and and saying Yeah. You know, hey, I I enjoyed hearing you on x podcast talking about y subject with z person.
Speaker 1:And so
Speaker 2:like actually saying that. Yeah. I got an email today of somebody that said, Hey, Jordy. It was great hearing you on x podcast talking about why this.
Speaker 1:Wait, wait. But it hadn't populated it? It didn't populate.
Speaker 2:Oh, was. It was x podcast y.
Speaker 1:That doesn't even feel an LLM hallucination.
Speaker 2:Yeah. It's probably not.
Speaker 1:That feels more like an if statement gone wrong.
Speaker 2:If statement gone wrong.
Speaker 1:Well, well, I mean, maybe maybe for for the one person that went on a podcast that's just named
Speaker 2:X To talk about Y.
Speaker 1:About Y. Person
Speaker 2:named Z.
Speaker 1:Because there's Y Combinator to talk about. There's probably somebody who goes by the name Z out there. Right?
Speaker 2:It's actually surprising that nobody's said drop the Combinator.
Speaker 1:Just Y. I I mean, that's what happened with the YMCA. They call it just the why.
Speaker 2:Yeah. Yeah. Just the why. Go vertical with the why combinator.
Speaker 1:Parikh was impressed by the team of of Microsoft employees who developed AutoGen and open source framework for building AI agents and aimed to move more of those employees into his unit according to research including researchers that were previously within Microsoft's research unit led by Peter Lee, but a Microsoft research vice president who oversees generative AI research pushed back on Parekh's attempt to move the researchers to his organization according to someone who spoke to her. So the information is kinda digging into, like, all the internal politics of the 220,000 person organization as you're trying to build a different team, kinda build, like, a a a greenfield AI agent strategy. There's obviously a lot of chips moving around the board, a lot of a lot of, you know, internal resources changing hands, and you wanna hold on to your, to your best people. The, the internal politics at at Microsoft must be staggering given the scale. I mean, it's like it's it's like Washington DC in size, so it makes sense.
Speaker 2:A nation state.
Speaker 1:Yeah. Mean, 220 people 220,000 people is like a small city, like a medium sized city, actually.
Speaker 2:Yeah. It's basically the size of Qatar,
Speaker 6:I think.
Speaker 2:Wow. Like, the actual residence?
Speaker 1:Does Microsoft have a
Speaker 4:seven forty seven yet? They probably should get one.
Speaker 2:Yeah. Yeah. They should.
Speaker 1:I heard I heard a funny thing that apparently the apparently the Qatar jet was up for sale for like three years. And I think maybe no one no one wanted to buy it because it had been like so overly retrofitted to be like opulent
Speaker 2:that people were like Trump is like
Speaker 1:There's too much gold Perfect. There's too much gold that's flowing. It's it's it's hurting the gas mileage. Like, this plane is now too heavy. It's like we can't
Speaker 2:do So there's 380,000 Okay. Attari citizens. Close. So quite a bit more. But
Speaker 1:Not that much. It's not double.
Speaker 2:Could look to acquire small nation state or rebrand it as Microsoft.
Speaker 1:Microsoft land.
Speaker 2:I mean, it's the ultimate it's the ultimate out of home ad. It's just to be on the map.
Speaker 1:Yeah. Yeah. Yeah. Well, speaking of out of home ads, go to Adquick.com. Out of home advertising made easy and measurable.
Speaker 1:Say goodbye to the headaches of out of home advertising. Only Adquick combines technology, out of home expertise, and data to enable efficient seamless ad buying across the globe.
Speaker 2:We did a photoshoot yesterday.
Speaker 1:We're gonna be going up on a
Speaker 2:billboard, baby. Can't wait for this. I need to ourselves.
Speaker 1:Photographer. I need to get these ASAP. I'm so excited. We'll be dropping on the timeline. Stay tuned, and please, subscribe to us on X.
Speaker 1:Follow follow the at TVPN on X. I don't know why you would see this and not be subscribed.
Speaker 2:There are there are plenty
Speaker 1:of There might be people in the RSS feed or something. Yeah. Maybe you've been you may maybe it's like, oh, it's too much content. A, let the algorithm sort it out. But b, just give us a follow just for a little bit.
Speaker 1:We're so close to 64,000, which is another doubling. We'll be doing something to celebrate, so thank you. And in other news, Snowflake is buying Crunchy Data for $250,000,000. This is from The Wall Street Journal. The cloud data company aims to attract customers who want to build their own artificial intelligence each.
Speaker 2:Base hit for these investors
Speaker 1:that 250.
Speaker 2:It probably didn't return the investors' fund Yep. Entirely, but it's still a fantastic outcome Yep. Hopefully for the for the team.
Speaker 1:Yep. And so we got it. We we we we have to do, like, a database day or or get a bunch start talking to some of these database folks because, Snowflake's Snowflake, Databricks, Palantir, they're all making serious moves in this space, and they're all kind of moving to different layers of the stack. Like, data Databricks is now more of this, like, data lake like, data unification layer, and then Palantir sits on top as, like, the ontology layer, actually, understanding how all of the different data interacts. And so they're kind of playing nice and data Snowflake and Palantir used to be kind of comped directly to each other, but they've they've diverged in the public markets.
Speaker 1:But still, you know, it's Snowflake's like a fantastic and, like, in incredible story of an incubation at Sutter Hill Ventures that went massive and IPO ed went into the tens of billions of dollars. Databricks is also up there in the tens of billions of dollars. A lot of people are waiting for them to IPO. And so it's a very interesting dynamic now. Databricks bought Neon, a similar database startup, in a deal valued for about a billion.
Speaker 1:Now Snowflake is buying Crunchy Data. Great name. Yeah. Crunchy Data too. The the the the naming schemes in, like, enterprise SaaS, like, deep down in the are are just wild and entertaining.
Speaker 1:Like, Datadog remains one of my favorite startup names of all time.
Speaker 2:I mean
Speaker 1:it's so funny. Datadog.
Speaker 2:I mean, you you have the last YC demo day, we met the founder building Pig.
Speaker 1:Pig. Pig was great too. We gotta check-in on Pig.
Speaker 2:We gotta check-in on Pig. I just don't I don't exactly remember what they do, but
Speaker 1:Yeah.
Speaker 2:But I but I remember the name.
Speaker 1:Yeah. Yeah. I it'll only be a matter of time until we see, like, the the I'm now searching Pig AI.
Speaker 2:Pig. And luckily, I was able to find it. Pig.dev.
Speaker 1:There we go.
Speaker 2:An API to launch and automate Windows desktops.
Speaker 1:Let's hear it for Pig.
Speaker 4:Let's hear for Pig.
Speaker 1:It's a great name.
Speaker 2:I think, you know, if pig is successful, it will inspire a generation of of companies named after animals. Yeah. Monkey, goose Yeah. Horse Yeah. Horse.ai.
Speaker 2:We don't Potentially some
Speaker 1:We have any animal name companies. Although we do want, we do want animals to start sleeping on Eight Sleeps. Yep. That's something we're pushing for. We think animal testing is gonna make a big comeback in the mattress market.
Speaker 1:Eight Sleeps are so comfortable. We're gonna have your dog should be sleeping on an Eight Sleep. How'd you sleep last night? I think I had a rough night. I was up at five on the grind.
Speaker 1:Let's see.
Speaker 2:I actually didn't I didn't get the hours in.
Speaker 1:You didn't get the hours six hours and fifty minutes.
Speaker 2:Eighty. I got an eighty four.
Speaker 1:Oh, you've spoken. Yeah. So close.
Speaker 2:I mean
Speaker 1:I got six and a half. But then
Speaker 2:So close, but so far.
Speaker 1:Yeah. It happens. It happens. To the best of us. Anyway
Speaker 2:It's pretty sunny. Funny. My
Speaker 1:Yeah.
Speaker 2:My son like came in our room at probably like 2AM. I was I was half asleep.
Speaker 3:Yeah. Yeah.
Speaker 2:But then he just slept like
Speaker 1:Perpendicular? Perpendicular to Yeah.
Speaker 2:So This is exactly what you want. Getting two different temperatures. Like half his body was getting, you know, my wife's temperature.
Speaker 1:Yeah. Yeah.
Speaker 2:Yeah. Half the other, which had to been a funny
Speaker 1:Very bizarre.
Speaker 2:Weird experience. But he's he was sleeping well. He was sound asleep when
Speaker 4:I Yeah.
Speaker 2:When I left.
Speaker 1:Yeah. Yeah. I remember showing showing my son the, the Eight Sleep app.
Speaker 2:And I just had that
Speaker 1:You know how you can see,
Speaker 7:like, the
Speaker 1:left side and the right side and, like, what the temperatures are? Yeah. And he was like, and me for the center. And we're like, no.
Speaker 2:They don't make that yet.
Speaker 1:They don't make that yet.
Speaker 4:And also Go back in your room.
Speaker 1:Go back in your room.
Speaker 2:I had to pull up startups named after animals Okay. Because there are actually a few male chimp, host gator
Speaker 4:Mail chimp.
Speaker 2:Mule soft.
Speaker 4:Post hog.
Speaker 1:Post hog. Post hog is like pig. It's the same thing, a hog and a pig. Post hog is one of the greatest thing Post
Speaker 2:yeah. I mean, it's it's an iconic
Speaker 1:It's like postmodern. Like post hog. Like, we are post hog. We are now in domesticated hogs which are pigs. So pig in many way is the post post hog.
Speaker 2:Yeah. I
Speaker 1:don't even know if it's the same thing.
Speaker 2:Hog. Task rabbit. Survey monkey. Hippo insurance.
Speaker 1:It was a big it was a
Speaker 2:The zebra. Yeah. Tractive. Okay. Now we're going.
Speaker 2:Now we're hallucinating? We're hallucinating Fat Llama appear Fat Llama? Appear to peer rental platform. Let's go.
Speaker 1:Fat Llama is a great name.
Speaker 2:Lunch Badger, a cloud
Speaker 1:services Lunch Badger? Are hallucinated. This is not real.
Speaker 2:No. It's in
Speaker 1:Crunchbase. Let's go.
Speaker 2:I mean, if it's in Crunchbase, it's
Speaker 1:Well, is it in Crunchybeta?
Speaker 2:The thing that stands out about Pig is that it doesn't add anything before or after. No.
Speaker 1:It's just Pig. No. It's like RAMP.
Speaker 2:Yeah.
Speaker 1:Or like Vanta, automate compliance, manage risk, prove trust continuously. Vanta's trust management platform takes the manual work out of your security and compliance process and replaces it with continuous automation, whether you're pursuing your first framework or managing a complex program. Back to Crunchy Data. Crunchy Data has roughly a hundred employees, scaled pretty quickly. They'll join the company once the deal closes, which is expected in the to close in the next couple weeks.
Speaker 1:Crunchy Data will be part of an offering called Snowflake Postgres. The vision here is that Snowflake Postgres will simplify how developers build, scale, build, deploy, and scale agents and apps very relevant to that Microsoft, story we were talking about earlier. With that in mind, it was important to acquire a company that's not just an that was not just engineering quick experimentation. Crunchy Data is a cloud database cloud based database provider that helps large businesses and government agencies use Postgres Postgres without needing to manage infrastructure themselves. I've used Postgres before.
Speaker 1:There are some It's crazy.
Speaker 2:Often in real life Crunchy Data was founded in 2012. Wow. Overnight success.
Speaker 3:Overnight success.
Speaker 1:You love to see it. Congrats on the $250,000,000 sale. How much did they raise? I wonder. I'll find out.
Speaker 1:There's a couple other, deals. Snowflakes offers a platform for storing, organizing, and analyzing data across multiple cloud providers, including AWS, Azure, and Google Cloud. The company, which went public in 2020, grew rapidly during the pandemic as more companies migrated their data to cloud storage from on premise data warehouses, which is I mean, it's crazy that we're still in, like, the the cloud migration era from on prem data warehouses. This the cloud data has existed for a decade and still happening. And if the AI rollout is any any this any similar or if if it tracks similarly, it's like, we could be talking about agents for the next decade and just continually rolling these these products out, in a very, very slow takeoff scenario.
Speaker 2:Crunchy data only raised 14,000,000. Wow. There we go. Very efficient.
Speaker 1:Very efficient. Well, speaking of other efficient businesses, there's a story from our friend Chris Best in the information. He says Apple's App Store changes, quote, have been fantastic. And this is why I wanted to highlight Please. The the the the the efficiency.
Speaker 1:They he said, Substack was accidentally cash flow positive in the first quarter of this year. Fantastic. Growth has been translated to revenue. Best said the company, which was founded in 2017, was accidentally cash flow positive during the first quarter of twenty twenty five. That said, Substack is not focused on profitability right now, he said, as it hopes to continue investing in growth.
Speaker 1:Said that Substack apps you, users are heavily in The US where Apple has been ordered by federal court in Northern California to allow app owners to offer users alternative payment mechanisms, allowing Apple to allowing apps to bypass Apple's fifteen to 30% fee for in app purchases.
Speaker 2:Had Apple been taking
Speaker 1:Yes. You went in the app and then you subscribed to a
Speaker 2:A sub substack
Speaker 1:and you use the in app payments flow, they would take 30% of that on an ongoing basis.
Speaker 2:I didn't even know that was possible. I've never signed up for a sub stack in Yep. The app. Yep. And so That's crazy because
Speaker 1:This this yeah. I mean, it's a digital product. Right? And so it is it it
Speaker 2:Well, it's interesting because Yeah. Audible has found a way to get around this by
Speaker 1:Credits and stuff.
Speaker 2:Credit system, which is just so annoying.
Speaker 1:Yeah. I think that that's a bigger negotiation because it's Amazon.
Speaker 2:Yeah.
Speaker 1:And I think I think so I I think Apple had more leverage over Substack, but we should have Chris back on the show and and ask about how it actually works. But, yeah, I mean, if you're I feel like most Substack users would be actually fine going through a web flow. Yeah. It's not as much of, like, an impulse purchase. You really you you you know, you're you're you have a relationship with the Substacker that you're subscribing to.
Speaker 1:You kind of understand. And there's and it's pretty easy to message. I feel like the average Substack user probably understands a little bit more about the Apple App Store attacks.
Speaker 2:Desktop respecters. Exactly.
Speaker 7:There's a
Speaker 2:lot of corporate athletes.
Speaker 1:Emails corporate athletes.
Speaker 2:You know, people that are sitting in front of their computer all day long. Yep. And what better place to buy things than the computer? Buying things on the computer is really historically and still today just a fantastic experience.
Speaker 1:It's fantastic. Especially when you have your sales tax automated with Numeral.
Speaker 2:That's right.
Speaker 1:With Numeral HQ, put your sales tax on autopilot. Spend less than five minutes per month on sales tax compliance. You know, you may may be able to avoid the Apple tax, but you will not be able to
Speaker 2:Not this one.
Speaker 1:Avoid Get
Speaker 2:out of it.
Speaker 1:Sales tax on software and consumer goods.
Speaker 2:Sales tax AGI.
Speaker 1:Yes. Anyway, Substack has, now has more than 50 creators who are making millions of dollars per year on the platform. Best said the platform overall has more than 50,000,000 or or 5,000,000 paid subscribers. So
Speaker 2:Yeah. Substack and such a dollars.
Speaker 1:I didn't realize they raised so they raised so much, but that's great that they're profitable.
Speaker 2:Yes. I mean, I think this is such a great story where they this is a business that that raised ahead of where they were. Sure. And then a lot of people wrote them off Yep. Because they were like, wait.
Speaker 2:This is just a media business with a take, you know, a take rate and and all this stuff and and what Chris has been able to do over the past year. You know, when we had him on the show, I was I was kind of asking, you know, has it felt like Substack has broken through really into culture Yep. Becoming like a a real brand itself outside of Yeah. You know, Twitter. Right?
Speaker 2:Because they basically got like, Elon came for them hard. Totally. And that was like somewhat warranted because they launched like a competitive product.
Speaker 1:I thought the competitive product happened, like, as a response to the link ban. I thought that was the sequence of events. It was it was links being getting deprioritized somewhat and then Substack launching a post competitor, which I don't even know if I don't even know if I agree that that was, like, the right move. I haven't I haven't really played with that product. Well, they
Speaker 2:You know, it seemed obvious that X was gonna ban Lynx Yep. Either way. Yeah. That hurt Substack. Yeah.
Speaker 2:It hurt the writers Sure. On Substack. Sure. And was the right the right decision for for x. Yep.
Speaker 2:But but yeah. So there was a link suppression and then and then
Speaker 1:And then Substack launched.
Speaker 2:Hamish, one of the cofounders called Elon Musk a propagandist with more conflicts of interest than El Spicy.
Speaker 1:But
Speaker 2:ultimately Spicy. Interesting. I I had never I never I never picked this up. What? Apparently, Musk had had made some type of proposal to buy Substack in 2023.
Speaker 1:Yep. But probably
Speaker 2:After that, they restricted Substack. Yep.
Speaker 1:And And and and Twitter formerly had bought another publishing platform.
Speaker 2:Yeah. I
Speaker 1:I don't remember the name, but they Like, integrated. And then and then think they closed it down. And now you just have the ability to write pretty long posts. You can post articles, x articles directly on x. And I think that it's it's weird because I we have yet to see like, we are an example of a of a media business or a show that just said, we will play the game by Elon's rules on X.
Speaker 1:So we we buy we we don't we don't think about links at all. We think about what are the what are the products that X loves. Text posts, memes, images, video uploads, live streams, and we do those very, very well, And that's what we focus on. And we don't so so we're not intention. We're, like, leaning into what the platform loves.
Speaker 1:Yeah. We should actually consider doing articles on X. The question is, what would it look like if you tried to build a Substack like business? Like, you're you're just a writer, and your output is articles. And you use X's subscription tools and the articles feed to have that experience of one free article a week and then one paid a week, and you try and build up the book of subscription business on your ex account.
Speaker 1:I've seen some people that have subscriptions turned on. I think I actually technically have subscriptions turned on. Think only Gary Tan is, like, the one who's who subscribes to me.
Speaker 3:Shout out
Speaker 1:Gary Tan. Thank you.
Speaker 2:For Gary.
Speaker 1:It just it just, like, throws me a couple
Speaker 2:of Yeah. Ultimately, the thing what's interesting about
Speaker 1:Yeah.
Speaker 2:Substack is they were, you know, effectively benefited from ZERP Yep. In terms of accessing capital. And people started to write them off because I don't think I think at times their growth was not best in class. Were just sort of chugging along. If you if he had originally had this idea of, hey, we're gonna build this publishing platform that's gonna allow independent, you know, sort of citizen journalism and writing to flourish.
Speaker 2:Mhmm. And then we're gonna launch this sort of social network with streaming and all this stuff. That, at certain times, would have been hard to believe, but they actually have executed that to a t. And now when you go on Substack, it it does feel like a social network, you know, based on, you know, email as a sort of backbone. So I'm excited to follow their progress more and eventually get really set up on Subsec ourselves.
Speaker 1:Yeah. So next up, we have Augustus D'rico coming in. There's an article in semaphore today. China boosts use of cloud seeding to combat droughts, and we wanted to have him come in and break it down for us. The headline is, China stepped up cloud seeding in the face of severe drought.
Speaker 1:The country the country's gain grain growing regions in the North have been parched for months leading to concerns over the harvest. Though some scientists are skeptical over cloud seeding's effectiveness and environmental impact, several countries have begun deploying it. China is already the world's leading user of weather modification, firing chemical compounds into clouds to spark per per precipitation and has conducted more, conducted 20% more than by this time last year, apparently causing a one third increase in rainfall. That seems pretty significant. So let's bring in Augustus D'rico and have him break it down for us.
Speaker 1:How you doing, Augustus? Good to hear from you. I don't know. That sounds like this is like a cruise ship or something.
Speaker 2:Yeah. Yeah. Welcome. That's that's the sound that that August that's in Augustus wake, you know Yeah.
Speaker 1:For whenever
Speaker 2:you guys anywhere. It's great to have
Speaker 1:you. Anyway.
Speaker 3:Thanks, man.
Speaker 5:That and size gongs. That and size gongs. Update soon. Wait. Okay.
Speaker 2:You gotta listen to this one.
Speaker 4:Journalists on the horizon. Stand by.
Speaker 1:Great. Great. Right. Who who was
Speaker 5:the guy that was responsible for posting the, stay try to stay focused on the mission GIF a while back when I got that text? I got, like, a heart eyes text about, like, oh, I liked your appearance on TVPN. Oh, he's a good social media intern there.
Speaker 1:Yeah. Yeah. Yeah.
Speaker 5:But Yeah.
Speaker 1:The team's growing over here. We we got a good crew coming together. There's vibe coding happening over this in that part of the studio. There's a lot of production stuff going on. It's been a fun time.
Speaker 5:Sweet. So Yeah.
Speaker 1:Break it down.
Speaker 5:I'm I'm here to ring the alarm bell. Mhmm. Totally transparently. There are really big problems on the horizon both with China's domestic weather modification program and then their international one.
Speaker 1:Mhmm.
Speaker 5:The article that you read or that you're referencing talks about how China is actively retrofitting their wing long twos. Right? Talk about, like, nominative determinism. Cool name for a drone. But it is the, essentially, Chinese equivalent of the m q nine Reaper.
Speaker 5:Mhmm. And they're using it for cloud seeding and weather modification operations all across the country. A lot of that is to fill up the snowpack in Tibet and then use that as a natural water tower for runoff for all of their urban, industrial, agricultural, and environmental assets in China. So that's that unto itself is insane. To recontextualize people, the Chinese Meteorological Administration has about a 300,000,000 budget for weather modification.
Speaker 5:They have 38,000 employees exclusively working on weather modification.
Speaker 2:Mhmm.
Speaker 5:And they have two universities that offer bachelor's degrees in weather engineering, not meteorology, not atmospheric science, specifically engineering the weather.
Speaker 1:Wow.
Speaker 5:So they're driving extraordinarily hard hard on this domestically just for their own water supply, just to green deserts, just to keep their cities and industry humming. The problem is the international implications of this. Right? We are in a and, like, I critique people all the time for saber rattling with China needlessly, but the Wing Wong two has been sold and is being operated in countries across the world, namely Saudi Arabia and Egypt for defense applications. Right?
Speaker 5:So that has its problems for defense, but also for weather modification. The CMA has explicitly said they want to export cloud seeding as a means of soft power to control water supply and weather across the world. They already collaborate very closely with the Thai royal rainmaking department, and they can easily retrofit these drones that they've sold for defense applications.
Speaker 2:Rainmaking department. Wow. Great name.
Speaker 5:Great name. But they can easily retrofit these drones for weather mod as well across the world. And then not only control the shipping and receiving and the ports, not just the energy infrastructure. Nice. Yeah.
Speaker 5:But also the water supply and weather. And president Lyndon B. Johnson said, whoever controls the weather controls the world. And right now, we're trending towards a world where China controls the weather and subsequently the world. And it's really Rainmaker Technology Corporation representing The United States against the CMA.
Speaker 5:So president Donald Trump, if you can hear us, secretary Rubio, if you can hear us, the state department should be involved in this soft power conflict on weather modification internationally.
Speaker 1:How are you thinking about the the current pitch for weather modification? Because it feels like this, this news out of China has a few different hooks, as well as agriculture and drinking water. Much of China is reliant on hydropower for electricity. Sichuan in the Southwest gets 80% of its power from dams, meaning that droughts can lead to electricity shortages. I I mean, I know we have the Hoover Dam, but is hydroelectric power important in America?
Speaker 1:Obviously, power is top of mind for everyone, but in America, it feels like the narrative has shifted to nuclear and solar. But is there a world where we could be getting more out of our existing hydroelectric assets and there's maybe a narrative there or not?
Speaker 5:A %. Right? Nuclear is awesome, and I'm super excited for Valor Economics to turn a thousand reactors online in their gigasites and produce the world's energy. But it's gonna take at least a year for that to start, and it'll probably take years longer still until that's our main form of stable baseload. Solar's great, but we have nighttime still, and Reflectorbital hasn't solved for that problem.
Speaker 5:So we need stable baseload, and we need clean baseload. And hydroelectric power across hundreds of dams in the American West produces hundreds of gigawatt hours, and we can refill our own dams to increase more stable clean baseload with cloud seeding. Where these dams are drying up right now, we we can increase supply. You know, 80% of you mentioned Sichuan. Eighty percent of Colombia's power comes from hydroelectric as well, and they're going through a drought right now.
Speaker 5:And so they have rolling blackouts because there's not enough power there. Both domestically for our own energy production, we could use cloud seeding to produce more hydroelectric. And then internationally, again, as a means of collaborating with other countries, let's call it, and ensuring that, like, they have American interests in mind, we can produce more water and hydro for them.
Speaker 1:Can you talk about the Chinese approach to cloud seeding versus what you're doing in the American approach? I feel like a lot of times when we see a competitive dynamic emerge between China and America, there's only a small tweak between the way Instagram Reels are served versus TikTok or, you know, DJI drones versus GoPros. It's usually just the scale and efficiency and reliability of of Chinese technology, but there isn't usually that much of a of a shift in the underlying strategy. Are they using the same chemicals? Do you think we should be using different chemicals from their mix?
Speaker 1:Are they using different drones, or are they shelling this stuff into the into the into the atmosphere with howitzers? Like, are there is there anything that we can learn that might not be IP protected that we could safely port back? Is there anything that we should change based on what we're hearing from over there?
Speaker 5:China is throwing the kitchen sink at weather modification research. So they're doing drone based aerial cloud seeding. They're doing ground generator based cloud seeding. They're doing acoustic cloud seeding research. Meaning, they have these huge 30 decibel speaker systems where they just blasted at clouds.
Speaker 5:They put them all in Tibet. So even though it's, like, destroying the ears of Tibetan villagers, they're trying to shake water out of the clouds. Wow. So that they have a bunch of other photonic stuff. They're they're doing a ton of research, but, really, the the big and important aspect of this is their sophisticated military retrofit of drones for long endurance missions, their radar research for detecting phase change in cloud.
Speaker 5:And then lastly, I think the thing that they have, like, the most serious edge on The United States and anybody else in the world in is their ice nucleation agent and their particulate. They're doing a bunch of nanoparticle design. So super, super small scale particle coating. It's titanium dioxide on top of these salt crystals among other things that are way more efficient at nucleating ice and subsequently creating snow or condensing stuff in cloud. Rainmaker's doing research into that right now.
Speaker 4:Mhmm.
Speaker 5:But that's where China far and away has the the biggest lead on on the nanoparticles that they're using.
Speaker 2:How about your challenges at home with various states and regulators? What's the update there?
Speaker 5:So 31 states proposed legislation to ban all forms of weather modification this year. Almost all of them dropped that legislation because, one
Speaker 2:Because of you? Because of you? Was it did you
Speaker 5:We did me and the Rainmaker team did a lot of work around state capitals. I've got, like, a regular barbershop in Tallahassee and a few other state capitals to to tune the mullet mullet up before I testify. But the one state that did ban it was Florida. Florida made weather modification a class two felony. So if I were to work there, I'd go to prison for five years.
Speaker 3:Wow.
Speaker 5:And that, I think, unto itself, is not the huge problem. Fine. Sure. You're depriving Floridian cattle ranchers and orchards from having as much water as they want, and they do have wildfires and droughts. So it hurts the state of Florida.
Speaker 5:But the real problem is the canary in the coal mine in terms of American political sentiment, particularly Republican political sentiment. Right? There's this whole conversation around the tech right and who is pro innovation and who's not. Is the Trump administration pro innovation? Seems so for the most part.
Speaker 5:But unless we have clear federal regulation on weather modification and a public stance in favor of this, then we're gonna lose control of the weather to China.
Speaker 1:Yeah. What about what about desalination? So it's something that we were doing in America. We kinda fell off. It feels like the nuclear story.
Speaker 1:And it just feels like I'm gonna hear a story in the next few years of, like, oh, yeah. China just figured it out. And now they have, like, a bunch of desalination plants, and we're behind on that too.
Speaker 8:Mhmm.
Speaker 2:Are tracking it at all?
Speaker 5:So desalination is largely held up by the California Coastal Commission
Speaker 1:Mhmm.
Speaker 5:And then, like, HOA is basically that blocked the construction of desal. An interesting stat that
Speaker 1:smells bad?
Speaker 5:No. Just because it looks ugly. Okay. Desal smells fine. It's just like
Speaker 2:a bit well, it looks
Speaker 1:I should say industrial. Right?
Speaker 5:It looks it looks like a big beautiful oil refinery, which
Speaker 4:I have
Speaker 5:to be a fan of personally. But, you know, folks in Newport Beach, less so.
Speaker 2:Yeah.
Speaker 5:Diesel is great. And if we come up with some really sophisticated new reverse osmosis membranes or catalytic desalination methods with good electrical engineering. We can make it more efficient, but the problem with desal still is that we have to move that water from the coasts. Like, it's a nonstarter for Nevada or Colorado or Utah to get desalinated water. What you can do, though, is cloud seed, obviously.
Speaker 5:Right? You could produce water anywhere where there are clouds with our tech.
Speaker 1:Yeah. I remember Blake Masters was saying when he was he's running in Arizona. He was saying, like, the future of California is nuclear powered desal, and then we need to reroute the Colorado River to hydrate the the inner the the the interstates. Is there a world where you could build? I mean, you mentioned it looks like an oil and gas refinery.
Speaker 1:Could you build a like, an offshore oil rig essentially? Or is it just like it's not economically dense enough? Water is not the same as oil, so you're not gonna be able to pay to put it on a truck and then bring it in. It just doesn't make sense to do
Speaker 2:it that way.
Speaker 5:Yeah. Exactly. Less flow. I was I was talking to some commodities traders the other day, and I was trying to, like, come up with some crazy derivatives for water.
Speaker 1:For water.
Speaker 5:But, like
Speaker 2:there yet.
Speaker 5:You know, it's it's it's it's cents for a barrel. Right?
Speaker 2:And it's Yeah.
Speaker 5:Just as it's almost as heavy as oil. So you you can't convey this. You know, 13% of all of the electricity in California is used just moving water around.
Speaker 4:What? That's crazy. Wow.
Speaker 5:Yeah. Like, the Central Valley exists just because we pump all of that stuff from the Sacramento Delta and American River down into the valley and elsewhere. Like, there's this huge, huge
Speaker 1:Mhmm.
Speaker 5:Unknown problem, which is, like, because we don't have enough water, we have to dedicate so much of our energy resources just to moving it around where we could just be producing in the Sierras. That's what Rainmaker's doing.
Speaker 1:Interesting. Any anything else you're tracking from China?
Speaker 5:So I guess one one thing that I will say in terms of, like, this soft conflict is we had a customer meeting Mhmm. In The Middle East. And a day before, we had the scheduled meeting. They said, hey. Sorry.
Speaker 5:We have to go to a last minute trip to China to go talk about the Winglong two. So we're trying to qualify Rainmaker's vehicles in The Middle East right now so that we are clearly at parity with their system capabilities and then can be selected for the Chinese. But Yeah.
Speaker 2:And the the the challenge, if you if you look at the precedent for sort of state backed companies out of China, they're willing to sell at a loss for years. And so how you know, the the the you know, I I I have no insight into whether they would try to attempt that in in cloud seeding, but it wouldn't exactly be surprising, which I'm sure is a competitive dynamic that you're thinking about.
Speaker 5:Every single international office that we go into to talk about our work, we see a stack this high of purchase orders from China, and then one that's, like, two pages then that that's that's from American companies. So, yeah, it's absolutely a problem.
Speaker 2:Wow.
Speaker 1:Part of this article highlights this idea that, grain growing regions in China have been parched this month for or four months. Are we in a particular, like, global drought? Is this unique to China? Are we experiencing drought in America? Like, what is the state of drought generally?
Speaker 1:You mentioned that there are different pockets around the globe, but, is this particularly bad year? Are the trend lines bad overall? Or I mean, we saw the fires, and that felt in a it felt very visceral in in California and Los Angeles, but you never really know when you zoom out where we are on the trend line.
Speaker 5:So we we had we had out of distribution high amounts of precipitation this past year in California and In California. And even still, we had the wildfires. Right? Yeah. You know, the the thing that I think is a good reference point is the California Department of Natural Resources water supply strategy.
Speaker 1:Mhmm.
Speaker 5:They explicitly plan for half a million to a million acres of farmland in the Central Valley to turn into desert in the next five years just because there's not enough water. So even when we have boom years and the reservoirs are all full and there's tons of snowpack and everybody gets to go skiing in Tahoe, there's not enough water for current demand. And that's in part just because population is growing. Right? Like, we built the Central Valley Water Project to turn the Central Valley from a desert and swamp into the most productive agricultural region in the world.
Speaker 5:And and we wouldn't have The US population that we do now if it weren't for that water project. And so we we just need to produce more if we want to maintain agricultural and economic growth.
Speaker 1:Makes a ton of sense. Well, good luck out there. We hope you can strike some big deals in the back of this news. You know, we gotta be competitive.
Speaker 2:Thank you for fighting on America's behalf. We'll talk to soon. Come back soon. Later. Cheers.
Speaker 1:See you. Bye. Next up, we have Brad from cobot coming in the studio talking about collaborative robots. I'm very interested to talk about the the sim to real gap, which we covered yesterday. We will welcome Brad to the studio.
Speaker 1:How are you doing? Welcome to the show. Thank you for joining. What's new? Oh, fantastic outfit.
Speaker 2:There we go.
Speaker 1:Welcome to the show. Amazing. Looking great. What's the occasion? First, introduce yourself, please, but, explain, why the fantastic outfit today?
Speaker 2:Are you doing real work Yeah.
Speaker 8:That's right. Yeah. Yeah. Hi. I'm Brad Porter.
Speaker 8:I'm founder and CEO of of Calabrio Robotics. The, yeah. This this is how you tell whether, whether a robotic company is really in production or in the field if they're wearing their safety vests. And Yeah. And, I was at our our deployment with Maersk yesterday.
Speaker 8:And so Wow. I had had this handy and thought
Speaker 2:There we go.
Speaker 8:I'd bring it out for you guys.
Speaker 1:Yeah. So, I mean, to the degree that you can talk about it, what exactly are you doing for Maersk? Because that sounds like important work.
Speaker 8:Yeah. We're, we're helping them in in transload operations in moving in unloading ocean, freight containers and loading out onto tractor trailers. The you know, they load carts, industrial carts, full of kind of up to 1,500 pounds worth of generally boxes of product, for retailers. And then we help with the moving the carts because
Speaker 1:Mhmm.
Speaker 8:Moving those heavy carts around all day long is is pretty pretty taxing work. And we've got
Speaker 1:You said 1,500 pounds. How big is one of those boxes? Like, I I'm familiar with, a 55 gallon drum. I'm familiar with, a pallet of goods that you might see in an Amazon warehouse. How big are we talking?
Speaker 8:Yeah. So think of this as as the types of boxes and and that would that would flow to a to a retailer. Right? To a big box retailer.
Speaker 1:Sure.
Speaker 8:And so they're unloading those from ocean containers onto carts that are about three and a half foot wide by about six and a half foot long. Mhmm. And so they just load up as many as they can on the cart Mhmm. And then take it to you know, usually these are getting dispatched out to big box stores. Mhmm.
Speaker 8:And so, you know, there might be six tractor trailers that are getting loaded up to go to six different stores in a in a region. So we're working in the in the Sumner, Washington area, so out of Seattle Port, and helping basically get distribution out to Pacific Northwest.
Speaker 1:Can you talk about some of the differences about unloading at a port versus what, Amazon's Kiva systems does within the warehouse and and some of the different challenges that you face versus what Kiva is doing. I imagine that some of the there's some learnings that crossover. Right?
Speaker 8:Yeah. So the way you can think about logistics is there's there's inbound flow, you know, products coming from manufacturers around the world, a lot of it coming from China. And then that ends up in some distribution warehouse ready for people to to buy. So it might end up in your in your local big box retailer. You can just go and, you know, buy a fan off the shelf, or it ends up in an Amazon warehouse, an Amazon fulfillment center.
Speaker 8:So the inbound side of that is to unload the ocean containers and then bring it to, you know, some some place where it's being stored or bring it, you know, to a to a retail store or to an Amazon fulfillment center. And then an Amazon fulfillment center, yeah, that Kiva network or now Amazon Robotics Mhmm. What they call kind of the Hercules drives, is their storage array. So Amazon will have multiple mezzanine decks of of those Kiva pods full of all kinds of things that you might buy from Amazon. Literally can have a million different SKUs in a in a building.
Speaker 8:And then when you when you order it, almost immediately, the the system knows where it has lots of those and they're stowed across Amazon's network. It quickly calculates where's the most optimal place to deliver this to you. And then a robot goes and gets that shelf and brings it to a picker, brings it to someone who pulls it out of those shelves, puts it into a tote, and then those totes get routed to a pack station, gets packed, thrown, and then it gets sorted to a truck. And then usually, you know, either to FedEx or UPS or Amazon's delivery network or to USPS. Amazon can kinda deliver into any of those outbound delivery networks.
Speaker 8:And so so, yeah, three phases coming in from the manufacturers, stored and ready to be bought, and then shipped to you.
Speaker 1:How how, where are we at in the kind of AI journey of these collaborative robots? I imagine that there's tons of work that can be done with just hard coded business logic, drive two feet forward, take a left, and it kind of just is almost like a conveyor belt on on independent wheels versus, you know, the far future where the robot has a brain and is just making completely independent decisions and decides where it goes and and problem solves and reasons. And and we're on the cusp of that, but I imagine that there's a there's a journey that we're going through. And so walk me through where we are in terms of that journey.
Speaker 8:Yeah. So we we've made a lot of progress from the days where you just, like, followed a tape line on the floor. Yep. Right? Now, robots can can generally sense and perceive and navigate commercial environments Mhmm.
Speaker 8:Autonomously
Speaker 1:Mhmm.
Speaker 8:Quite well. Usually, you know, at human walking speeds, maybe a little faster. But, the kind of self driving problem is reasonably well solved in, in commercial spaces at those type of speeds. And that's generally done with a LiDAR
Speaker 1:Oh, really?
Speaker 8:And maybe, you know, stereo depth cameras. Yeah. And so, you know, LIDAR based SLAM are is how it localizes Yeah. And then navigation and planning. And that can be done in a way where it can detect humans, obstacles, navigate around things.
Speaker 8:And that's the the the capability our robot has, and it can do that in hospitals, in Yeah. You know, ultimately, stadiums, in and around people quite safely. That technology works. The what's coming now is you can talk to robots. And the robots will make the the high level plan and instruct that where we need to get to is what we can do with our hands.
Speaker 8:Right? Where, you know, I would, like, open up your AirPod case and pull it up. It's a very complicated set of motions that we do without thinking about it. We don't quite have that capability yet.
Speaker 1:Got it. Talk to me about the LiDAR supply chain and cost structure in your business. It's been a controversial debate point for a long time in autonomous vehicles, but if the economic model works, it feels like it's just pure value add. Is the cost of LIDAR getting lower? Are you thinking about solid state LiDAR coming down the pipe, or is it already available?
Speaker 1:Are you banking on a reduction in LiDAR costs over the long term, or does your business model, just by nature of how much value you're adding, you're fine paying 50 k or something like that, if that's the number. I don't know.
Speaker 8:Yeah. LiDARs are in the kind of they used to be 50 k.
Speaker 3:Mhmm.
Speaker 8:They're in the kind of 3 to $6,000 range right Yeah. And so you're right. You do have to add enough value. You're not gonna put that on your Roomba. Yeah.
Speaker 8:Right? But you can put that on an industrial robot
Speaker 2:Mhmm.
Speaker 8:And and get a payback. Obviously, we wanna keep seeing those costs continue to come down, but it's not a it's not a prohibitive Yeah. Element for a lot of work that needs to get done on
Speaker 2:you can you talk about form factors broadly and and what guided you your thinking towards the proxy, the initial product and other products in the suite? Feels like you guys distinctly chose not to do humanoids even though I'm sure various VCs thought, hey, why don't you have you guys thought about doing humanoids? Have you seen this demo?
Speaker 1:Have you seen this viral video from Boston Dynamics? Yeah.
Speaker 2:So I'm curious, you know, kind of the the decision making that went into the current and and pending form factors.
Speaker 8:Yeah. So, I mean, as much as, like, the humanoid hype seems to have been peaked in the last, you know, twelve, eighteen months, I think Elon did a lot to kind of fan that. Humanoid robots have been around for quite a while. Agility Systems have been Apptronic. These guys have been at it for a while.
Speaker 8:And so so when I was leading robotics for Amazon, the we we studied deeply humanoids. In in 2018, I looked. I I went through my, like, hype phase on humanoids in 2018. I got really excited about them. What agility was showing at the time and legged mobility looked like it could actually work and it it does.
Speaker 8:And so I I had my team at Amazon do a full analysis of everything we weren't gonna automate in another way where a humanoid could help. And I remember reviewing this paper, there were 40 diff 40 different use cases where a humanoid could be great. Right? And and then I looked then we looked at all 40 use cases and we said, actually, to solve these problems, we don't need a humanoid. Mhmm.
Speaker 8:And in fact, a humanoid is kinda too complicated. You really want wheels. You kinda wanna move more than three and a half miles an hour.
Speaker 2:And is the number is the number of motors a potential issue too from a degradation standpoint? It's like, you know, we we've talked to to other robotics founders that say, you know, I'm using, you know, robotic arms facility and we already have to replace those motors all the time. And then a humanoid might have an order of magnitude more and actually be less productive than some type of robotic arm.
Speaker 8:So the number of motors really does drive the overall cost. It also drives the complexity of the controls. Right? And so you get into a world where you need AI driven controls. But the problem the real problem that people don't talk about very much with humanoids is getting strength out of rotational motion is very hard.
Speaker 8:Right? You you're effectively because you're just doing a short throw. You don't have the kind of momentum flywheel effect as you get the torques rolling on your electric vehicle. Right? You're moving through very short distances.
Speaker 8:So all the power is basically your electric magnet and then your rare earth magnets. And and so you end up needing bigger and bigger motors to get that kind of power. And so and then you wanna wind them with the absolute highest density you can. And so they end up and then you wanna run them at almost the peak current that you can to get the most strength. So the problem is humanoid robots
Speaker 4:either have to
Speaker 8:put this way big motor that doesn't look right in the shoulder, But what they're typically doing is they're hand winding the motors, they're pushing the current to the max, and even then they're getting maybe 60 the strength of humans. And they burn those motors out. So the motors are very expensive and they burn them out very quickly and they're still not as strong as a human. And so it just it we need some breakthrough, you know, the pneumatic, like, you know, Boston Dynamics had that Atlas robot that, like, could do back flips and everything. Pneumatic is 10 x the power of Sure.
Speaker 8:An electric. Right? And but you can't you can't really make that system reliable in production.
Speaker 2:So do you think there are consumer use cases for the humanoid form factor, maybe around the home? How how do you think about applications outside of industrials?
Speaker 8:You know, I I I have struggled to find someone someone mentioned one to me the other day that that seemed great, which is like is walking your dog. I think humanoids walking the dog would be would be quite interesting, quite cool. I suppose you could have a quadriplegic
Speaker 1:Man's best friend's best friend.
Speaker 8:Yeah. But otherwise, I am I am not bullish. I do think there there's some some cool robots recently that are more kind of friendly, look like they're kind of playing a game with a kid. Like, I I think kind of the emotional companion idea is quite interesting.
Speaker 1:But, yeah, getting the strength is tough. I mean, even just thinking about the human arm, like the force that's generated from the human arm is from like the bicep muscle, which is much bigger than the actual joint. And so if you put a motor on that joint, you you're you're you're not
Speaker 2:You're using humanoid with just absolute cannons.
Speaker 1:There is there is
Speaker 4:a a humanoid company that's
Speaker 1:trying to create the muscle fibers and pull that, and that's sounds sounds like some of the pneumatic project projects. And maybe it'll be a hybrid. In terms of training and AI development, there's been this talk about the SIM to real gap. I don't know how closely you've been tracking this, but, obviously, generating data for robotics has been very difficult. But now there's this new paper that semi analysis was talking about yesterday, all about training and simulation.
Speaker 1:Basically, Unreal Engine, you build the robot virtually. You have it walk around, learn as much as it can in in thousands of years of artificial data. Then there's gonna be a gap between what it experiences in simulation and reality. And and so what you do is you take what it's learned in simulation, and you and you run that on a robot in a cage, basically, wired up with a with a power cable so that it can run forever. And and it and it tries to do the moves that it learned in simulation.
Speaker 1:It messes it up, but then that generates more data that feeds back in. Does that seem like, you know, power generation, all the all the mechanical issues aside, does that seem like an interesting path to go down for actually solving the the algorithmic and, like, the AI piece of understanding these, how these robots will will actually choose what what motors to move at what times?
Speaker 2:And do you have any experience generating data?
Speaker 1:Just kidding.
Speaker 2:I'm just kidding. Former CTO of Scale AI, for anyone listening that's that's not No.
Speaker 4:That's so
Speaker 8:so the challenge in robotics first is how do you get I mean, it's the same in large language. Well, what's the pre training phase? Right?
Speaker 3:Mhmm.
Speaker 8:How do we get some base level in pre training large language models, it's to kind of understand how words are likely to follow each other
Speaker 9:Mhmm.
Speaker 8:Right, just statistically. So motor actions, what's what's likely to, you know, to cause the arm to move forward and and things like that. But the hard part in in any AI system are the edge cases at the end when you're when you're interfacing with with the real world. Right? And, you know, fortunately, we have all this large language model data.
Speaker 8:We have all this data from the Internet to give us reference examples of what the real world of language looks like. Right? And so we refine on that, and then we use human preference to refine even further. And that's how we get, you know, ChatGPT. In in the in the robotics world, data from simulation, data from multiple robots, data from teleoperation, all of these are kind of techniques people are using to feed some data into, you know, what's kind of the pre trained base model that gets some statistical correlation.
Speaker 8:But when it comes to learning the edge cases, right, when it comes to, hey, that doorknob is higher than this other doorknob or the doorknob does you know, turns upward instead of downward, You you and I actually self play to figure that out. We come up to a doorknob that doesn't it's funny. We have a door at Cobalt that you you you could either push the handlebar or there's a handle. Well, everyone tries to push the handle, and then the door doesn't open and you have to push the Mhmm. And so so humans get confused too Yeah.
Speaker 8:And we do this kind of refined self play. And I think right now, we're very much focused on the pre training phase, just how do we get enough data to have something that like roughly moves its hand toward the door. Sure. To really solve this problem though, we've gotta learn how to self play in the real world
Speaker 2:Mhmm.
Speaker 8:Like you or I do, because there's all kinds of novel stuff we're gonna run into solving real problems.
Speaker 1:Well, good luck with that. It sounds like an easy task but I'm sure you're up to the task and it's been fantastic talking to you.
Speaker 2:Yeah. This has been super insightful.
Speaker 1:Yeah. I mean, such an industry because it's really just like, like, we're still just on the early the early part of the s curve, and there's gonna be fantastic advancements. So good luck.
Speaker 8:The future is gonna be amazing. Awesome. Thank you, guys.
Speaker 1:Appreciate you coming on. We'll talk to you soon.
Speaker 2:Thanks, Brad.
Speaker 4:Thanks so much.
Speaker 1:Talk to you soon. Next up, have Keon from Nucleus coming on, with a big announcement. Something like ten years in the making, close to it, maybe seven years. We'll bring Keon in. Let's play some sound board.
Speaker 1:How you doing? Welcome to the show. That's a great intro.
Speaker 7:Their tweets are flying. Oh my god.
Speaker 4:You guys
Speaker 2:seen this? Yes. You seeing this? Seeing this?
Speaker 1:Break it down for us. Explain what's happening.
Speaker 2:There's nothing like a launch day.
Speaker 7:I'm trying to figure out guys. Is this is it Gattaca or is it Theranos? Because people can't they can't make up their mind.
Speaker 9:Oh, yeah. Know? I we're gonna find out.
Speaker 7:Figure it out.
Speaker 4:We're trying to figure
Speaker 7:out what's going on. Let's give some context to audience. Nucleus has launched Nucleus Embryo, the world's first genetic optimization software. Mhmm. Basically, parents can, you know, give their children the best start in life.
Speaker 7:They can pick their embryo based off of physical characteristics like eye color, IQ. They can go to disease risk like cancers or heart disease. Basically, we really believe parents can get all the information that exists about their embryos, and they can pick however they want. For me personally, you know, it's been ten years in the making. The journalist actually covered it today in The Wall Street Journal.
Speaker 7:It was a journalist that covered my gene editing in a warehouse in Brooklyn Ten Years ago.
Speaker 1:Yes. Let's see. Overnight success.
Speaker 9:You know, it's a
Speaker 7:long time in genetics.
Speaker 1:Yeah. So so so break down the state of the art because, like, embryo screening exists. I think most parents in America, least if they're the means, do some sort of screening, while the embryo is growing. Is this purely for IVF? Is this just going a layer deeper?
Speaker 1:And then is I wanna talk about the regulatory and FDA component as well.
Speaker 7:Yeah. Let's talk about it. So basically, if you go to IVF clinic today, you're a couple. The vast, vast, vast majority of clinics. The first thing I should understand is that the IVF process is principally controlled today by clinicians or doctors.
Speaker 7:Honestly, couples don't have as much liberty in our perspective as they should. It's their baby. It's their embryos. They should have the right to those that information, and they should be to pick off any vertical. However, today in the clinic, what generally happens is people test embryos for very rare and severe genetic conditions.
Speaker 1:Mhmm.
Speaker 7:For example, like a chromosomal abnormality like a Down syndrome, for example, or even a condition like cystic fibrosis or T SACS or PKU. Right? These are conditions that are very rare, that maybe someone might have a carrier for cystic fibrosis, but again, it's it's pretty rare. Then there are conditions that we've all heard of heard about, things like breast cancer, things like coronary artery disease, the things that actually kill the vast majority of people today. Right?
Speaker 7:Mhmm. Chronic conditions kill the vast majority of people today. Those conditions are just not tested for in the clinic, even though we have very good science actually that can make those predictions. How do we know this as a as a DNA company? Well, that's what we do.
Speaker 7:Right? We build models that predict disease, and the way you test those models in adults. So we go from adults to embryos is actually because we can basically well validate these models to show that they work in both the embryonic context and in the adult context. And so what we're really doing is we're going from, okay, instead of just looking for really severe, like Down syndrome or cystic fibrosis, why not do breast cancer? Why not do heart disease?
Speaker 7:Why not do colorectal cancer? Right? Why not do schizophrenia? Why do Parkinson's? But then why stop there?
Speaker 7:And this is really the important thing because ultimately, you know, if you think about diseases and traits, the extreme version of any, trait is actually a disease. Right? Height is a good example of this. One extreme end is like, you know, John, for example. He's like Markinson the most.
Speaker 7:Then the other end is like me, dwarfism. Right?
Speaker 4:It's like there's on both ends. Okay?
Speaker 7:So, you know, so, you know, IQ is another example of this. One end is like, you know, autism. The other end, it can actually be some sort of, you know, a cognitive basically challenge that people have. Sure. And so when you think about it, when you start realizing that people have drawn a line in the sand saying, you can get, you know, rare diseases, you can get common diseases, but then they really say you can't can't get any traits like heights.
Speaker 7:Mhmm. Even though the best predictor we have today actually in the world, the best polygenic predictor is for height. So as a company, we've of completely reimagined this and said, wait a second, what's going on here? You should have access to the entire stack. Rare diseases.
Speaker 7:We do. Cystic fibrosis, common diseases like breast cancer, and also traits all the way up to something like IQ.
Speaker 1:Yeah. So, I mean, that test, are you just giving people the data? Because I imagine that once you get into particular recommendations, that's more of what I would expect a a licensed doctor to need to do.
Speaker 2:Oh, yeah. My sense is that they you can allow people to get the data from their doctor and then and then feed it into Mhmm. Nucleus. Is that correct?
Speaker 7:So that's that is correct. And actually, we have a couple there was like 10 announcements today. You know how we do it. We like to do 10 announcements in one day. We are actually very, excited to announce a huge partnership with Genomic Predictions.
Speaker 7:So Genomic Predictions is actually the oldest Mhmm. Embryo testing, a company that exists. Genome wide testing embryos for almost a decade at this point. Mhmm. And I think they've done over a 20,000 couples for PGTA, which is specific kind of test.
Speaker 7:And so we're actually partnering with them, so we make it very easy for genomic prediction customers to request their files and actually port it over to Nucleus. But really, this isn't just for genomic prediction customers. Anyone who's undergoing IVF can go to their clinic and say, I want my embryo's data. You can take that data, you can upload it to Nucleus, and then all of a sudden, you know, the application layer of DNA makes this technology universally, basically universally accessible.
Speaker 1:Now how much of how much of the benefit is is actual, algorithmic analysis bringing in other data points to contextualize the data versus just better UI and better hydration of existing text. Because we we, we we we had we had a friend on the show who was talking about, getting some medical results from a doctor. The doctor's office was closed. It took two days to until the doctor was gonna be able to interpret the results. He was able to just take a photo, upload it to ChatGPT, and say, hey.
Speaker 1:Is this is this a you know, is this really, really bad? Should I be panicking? Yeah. Because it seems somewhat out of the range. And ChatGPT was able to say, hey.
Speaker 1:You still gotta talk to the doctor, but this isn't this isn't the craziest thing I've ever seen. This is way out of distribution. And so that's almost like a pure UI layer, but extremely valuable. I know it might not be, like, the right narrative for some people that it's, like, not as innovative, but I think that, like, all that matters at the end of the day is giving people benefits. Right?
Speaker 7:It's always both.
Speaker 1:You you
Speaker 7:you yeah. Fundamentally, technology, just for technology's sake, is not siliconized about. Right? Siliconized by making something that people want. Okay?
Speaker 7:And people can actually use.
Speaker 1:Exactly.
Speaker 7:So if think about the the nucleus innovation, it's it's it's two pronged. Okay? One is in the informatics. Right? You know, I've been doing this for five years.
Speaker 6:Sure.
Speaker 7:I I almost I would argue to myself that I've probably spent too much time, you know, developing the science. Right? Sure. Science in a in a in nutshell isn't actually very useful. You need expanded access to it.
Speaker 7:So Yeah. On that point, we do multiple different kinds of analyses
Speaker 6:Mhmm.
Speaker 7:They make it such that we can actually provide the most comprehensive analyses that exist today. But moreover, and this is really the, I think, a key point to your point, John, is people understand them. People can see them. I mean, you can pull up the platform. I'm not sure if you guys have shown it already, but it's very easy to sort, compare your embryos.
Speaker 7:You can actually name your embryos and stack rank your embryos. You can understand what the score means. We lead with overall risk, or we tell you, for example, instead of saying you're in the 99% top for genetic risk for a condition, which, you know, what does it actually mean? We say, hey, you have a, you know, five percent chance for the like of, let's say schizophrenia or some other condition. In other words, by giving overall risk, people have much greater intuitive understanding of the results.
Speaker 7:We're communicating to them. We have genetic counselors on hand. So this really is a what are we showing here? Are we showing something? Are we showing the
Speaker 1:Yeah. Yeah. Yeah. We pulled up here.
Speaker 7:Think you showed actually
Speaker 6:Pull up
Speaker 1:your website.
Speaker 4:That's another thing. That's a that's a fun one. That's an Easter egg.
Speaker 1:That's an Easter egg.
Speaker 7:That that's the that's the kind of approach that we're taking here. And I think consumers are responding to it. Right? People want to have access to their data. The clinician, the doctor shouldn't decide what embryo to implant.
Speaker 7:You should.
Speaker 1:Okay. So talk to me about what requires FDA approval. Obviously, new medical devices. Like, if you were developing a machine to take in an embryo and sequence the DNA, I would expect that the FDA would want, an approval for that medical device. But if you are taking data and just showing it to a customer in a different UI, that feels like probably a very light FDA process.
Speaker 1:And there's probably a continuum in the middle where once you're making a recommendation, they they have rules of around that. Right?
Speaker 7:We, a company, do not tell you which embryo to implant.
Speaker 1:Sure.
Speaker 7:You know, basically, parents, the couple has complete agency decide Mhmm. How they wanna use the information to implant their embryo. Moreover, let's be clear. Height, right, I mean, can a height analysis be a medical device? You know, it doesn't even make sense.
Speaker 7:Right? IQ, height, these traits, for example, we all you know, traits are something that I don't think actually belongs in even the kind of infrastructure thing about medical care. Right? These are things that go beyond medical care. These are things like, you know, that that people just kind of intuitively know and that there are DNA tests done every single day, p to c Yep.
Speaker 7:For these analyses because they're not disease analyses. Right? Mhmm. So we do both diseases and traits to be clear. My point is many of these innovations, like, you have to wonder, like, you know, should the should the government say if someone can or cannot pick their embryo based off height?
Speaker 7:That doesn't seem right to me. I think it should be in the complete liberty of of the individual to decide that.
Speaker 1:Yeah. But, I mean, we're we're a democratic country. And so if an if, you know, a a a huge swath of the population says that the FDA should review that type of, test or that type of analysis
Speaker 4:Heist analysis? It
Speaker 1:could happen. I mean, the the FDA reviews all sorts of different stuff. And so I I guess the question shifts to, like, do you expect a change from FDA on, the way these, these analysis tools are regulated?
Speaker 7:I think right now, the most important thing is just putting these high quality rigorous scientific results in people's hands and helping them basically have healthier children, helping them give their child the best start in life. Yeah. You know, I I think that generally speaking that, you know, people should have more liberty, more chores in in medicine. I think the broader longevity trend actually touches on that point as well. Yeah.
Speaker 7:So that's what we're excited to do at Nucleus.
Speaker 1:Yeah. I mean, the fact that you're partnering with a a company on on actual on the actual, like, medical device side, like, are doing the sequencing of the embryos. Like, that really takes it out of the Theranos question entirely in my mind. I think you feel like you should be beating the drum there a little bit more. It's like like, we didn't say we created some new device, but I don't know if have to find
Speaker 7:ship. That's the difference.
Speaker 1:We
Speaker 7:ship. It's
Speaker 2:live and talk, baby.
Speaker 7:Go look at it. Go use it.
Speaker 4:That's that's the evidence. Okay?
Speaker 2:I love the visual of John and his wife selecting between embryos and it's like six ten, seven two. Tough choice. Well, if we go with the six ten, he has, you know
Speaker 1:You could potentially fly commercial once in his life.
Speaker 7:We could actually play this game right now. Okay? Here. We're gonna play a game right now. I'm gonna put in the chat.
Speaker 7:Okay. Takeyourembryo.com. Okay? Everyone listening to this.
Speaker 1:Pickyourembryo.com.
Speaker 7:I'm gonna go to it. Oh my god. Here we go. Little Easter egg here. Okay.
Speaker 7:Let's see what's more important to you, John. Intelligence or muscle strength? Come on.
Speaker 1:Oh, absolutely. Muscle strength. Let's go. We're the
Speaker 4:the future of bodybuilding. Go. Okay.
Speaker 2:I'd John would John would take a he would he would happily have a five two son if he had, you know, top point o 1% bodybuilding Exactly.
Speaker 4:Yeah. Okay. So we're talking
Speaker 3:about your lifespan or height?
Speaker 7:Come on. Lifespan.
Speaker 1:Lifespan. Let's go. Let's go.
Speaker 7:Let's go.
Speaker 1:Maybe low depression. You gotta be golden retriever mode. You gotta be You need
Speaker 7:you need low depression.
Speaker 1:You need low depression. Let's go low o o OCD. I don't mind bouncing around a bunch.
Speaker 7:Okay. What's Risk taking anxiety.
Speaker 1:Let's go high risk taking. There we go.
Speaker 7:Okay. We're analyzing
Speaker 1:Is this some generative AI stuff going on? This is great. Nadia. I got Nadia too. Enduring athlete.
Speaker 1:Let's go. Physically strong, cautious, built to last. Yeah. This is great. Is this driving a lot of a lot of attention, lot of downloads is going viral yet?
Speaker 1:This seems like something that's designed
Speaker 4:to be shareable. Dropped it right now.
Speaker 7:Technology brothers, we got you the exclusive.
Speaker 1:Let's go. There we
Speaker 3:go. Out there.
Speaker 7:I wanna see pick your embryo. People say, what's it like? Maybe not doing IVF yet. No problem.
Speaker 2:Funny. Only only 9% of people choose Nadia.
Speaker 4:Okay. Well,
Speaker 1:we're we're we're contrarian. We like that here.
Speaker 2:Yeah.
Speaker 1:It's fun. Yeah. Great. Oh, well well, congratulations on the news. Congratulations on the launch.
Speaker 2:Yeah. The pace is wild. Last thing, what's going on with, have you seen these Just Blood billboards?
Speaker 1:Oh, yeah.
Speaker 2:They're all over LA.
Speaker 1:So so there is there is someone who's running a campaign right now, justice for Elizabeth Holmes, claiming that Theranos was not the scam people think it was. And there's a there's a documentary coming out, and there's billboards all over LA for just blood. Like, it's just blood. It's not that big of a deal.
Speaker 7:And John, to be clear, there's an exclusive on technology buzz next week about from this person. Right? They they're gonna tell their story next week just to make sure you you invited them already. I hope
Speaker 1:We're we're we're we're we are are toying with the idea. They they, someone reached out to kinda connect us. We're we're thinking about doing it, but we're not we're not a % sure that it'd be appropriate for the
Speaker 2:Based on the website, I don't know if it I don't know if it's appropriate.
Speaker 1:Yeah. It doesn't look like it was designed with Figma. So I don't
Speaker 3:know. Yeah.
Speaker 2:We we can't
Speaker 1:quite do it. It's a little bit
Speaker 2:Team definitely doesn't use linear.
Speaker 1:Yeah. But they they they claim that, that Elizabeth Holmes has been proven innocent. And so it's a bold claim. We we like we like to see people making bold claims.
Speaker 2:What jury is my question. Yeah.
Speaker 1:The jury of someone who knows HTML.
Speaker 2:Kian Always a great time.
Speaker 1:Energy is Fantastic. Electric.
Speaker 2:Electric.
Speaker 1:Thank you for coming on, firing us up. Congratulations on the launch. We will talk to
Speaker 7:you soon.
Speaker 2:Talk to
Speaker 7:for sure. Okay?
Speaker 1:So Yeah. We'll see you there.
Speaker 4:Bye, guys. Bye.
Speaker 2:He's going through launch day right now, which is just like
Speaker 1:He's ripping.
Speaker 2:You know, 40 notifications every minute forever.
Speaker 1:I love it. Well, next up, have Kathleen from Valthos coming into the studio. Welcome to the stream. How are doing, Kathleen? Nice to meet you.
Speaker 6:Good. Thanks for having me on, guys. Yeah. It's a great day.
Speaker 2:To have you.
Speaker 1:Would you mind kicking it off with just a little bit of an intro for those who might not know?
Speaker 6:Yeah. Absolutely. So I have been at Palantir for the last seven years. I built our life science practice there. So taking some of the same platforms that Palantir uses in defense and intelligence and a bunch of industries and then bringing those primitives over to pharma and biotech and researchers who need to study, biomedical data in a secure and collaborative way.
Speaker 6:So really building out end to end, drug development workflows all the way from early discovery. But then I left a couple months ago, and now I'm working on something new. Very cool. So, yeah. Happy to chat with you guys.
Speaker 2:Yeah. Awesome. Wanted to get your immediate reaction and and help us kind of contextualize the news Yeah. That came out yesterday or the day before, which was that two Chinese nationals have been charged by US federal authorities with conspiracy and smuggling after attempting to bring a dangerous biological pathogen, which I'm gonna botch the name, into The United States. This fungus is classified in scientific literature as a potential agro terrorism weapon due to its ability to devastate key crops such as wheat, barley, corn, and rice, causing a number of issues there.
Speaker 2:So, I wanted to kind of get a, you know, you don't have to go into too much detail, but kind of a high level background on bioterrorism broadly, kind of maybe some like prominent examples, and then even just get your immediate reaction to the news. You know, is this kind of thing surprising to you, or are you surprised that we're not hearing more headlines about it all the time?
Speaker 6:Yeah. Totally. So I think the to the second question first, it's like a bad surprise, obviously, but we are absolutely entering this era of an elevated risk of biothreats. So sadly, I don't think it's something that we should be that surprised about. Mhmm.
Speaker 6:I think this story in particular, and we don't know that many details on the story, I won't speak too much to it. But the one, it highlights how easy it is really to have any biological material pass across borders. It's obviously both natural or unnatural. And then the second is that any kind of agricultural pathogen is also an enormous threat. So I think when people think biodefense and bioterrorism, they think anthrax or smallpox, which is obviously horrible.
Speaker 6:But the idea that you can introduce a pathogen that would devastate one of our, like, primary crops, that would have massive health impact, but it would also completely destabilize our economy and send us into, you know, something far worse than what we would see with COVID. So I do think when when we and governments think about biodefense, it's very much both in terms of human health and agricultural health. But, to your other question on what what's the primer on biodefense? What are we worried about? I think there's always been this, so we've always had the problem that people study the most pathogenic, organisms in the world.
Speaker 6:They usually do it in a biosafety lab, a BSL lab. These are all over the world. Some regions are obviously more secure than others. So there's always this threat of something natural leaking or being used maliciously. But basically, two things changed recently It made that a lot worse.
Speaker 6:One is that our ability to edit genomes and actually start changing those pathogens, there's a bunch of new tools to do that. So like things like CRISPR, which you hear about usually in the medical sense of editing a genome
Speaker 4:Yeah.
Speaker 6:Are available on pathogens. Synthesizing new nucleotides, a new DNA to do that with, also now commercially available. Like, we could order nucleotides or you could print them out in some cases in a desktop printer. So that made it now we're dealing with things that nature has never seen. And then in the last couple years with large language models or biospecific language models, we have this idea of AI uplift.
Speaker 6:So it means that someone like us, not to underestimate your biological skills, but could actually be coached into how do we use these tools, guided all the way into making something that is actually way worse than anything nature has ever seen. And this isn't, like, science fiction. Like, Anthropic talked last week, I think, about Claude four. They did internal safety trials. They noticed an uplift that goes way beyond just, oh, it's easier to Google a paper about how to do this, and really into that, allowing a novice to access something dangerous.
Speaker 6:Yep. So they released Cloud four with, you know, new security standards to try to combat that. But a lot of models aren't like that. Not everyone has those security standards. You know?
Speaker 1:Yeah. I mean, this this has happened, like, was it Timothy McVeigh looked up how to build the bomb with fertilizer and built and basically blew up, like, a massive government building. And the the the wreckage from that was insane. If you think about what it would take a terrorist to work in a biosafety lab. It's obviously very complex, but getting easier as you can have an LLM coach you through the process essentially.
Speaker 1:Right? Is that is that roughly the the the nature of the threat?
Speaker 6:Yeah. Both in terms of the actual steps to take and then what to change about a virus that would make it either evade some kind of countermeasure, which is like some kind of medicine that we have for it Mhmm. Or be more infectious. And I think the the real, like, what has a lot of attention and weighs on people's minds is that we're not talking about, like, a state sponsored nuclear program that you need these, like, massive budgets and facilities to make something like that. Mhmm.
Speaker 6:Like, it's a laboratory and a computer. So it's a really different dynamic of threat than what we've been dealing with in the past.
Speaker 1:And it kind of self replicates by itself. That's like the whole goal. It's an interesting story to
Speaker 2:me because obviously over the weekend, there was the Ukraine Mhmm. Story around Operation Spiderweb. And that was relatively asymmetric in that, you know, whatever the cost of the operation, the trucks and the drones just, you know, even if it was a hundred million dollars, which seems really, really high.
Speaker 4:Right?
Speaker 2:Even if it was taking a, you know, huge teams. Yeah. It destroyed, you know, a billion dollars plus of of assets on the other side. And then when you talk about biowarfare and bioterrorism, it's like, okay. One or two people with access to a lab could potentially do billions and billions of dollars of of damage.
Speaker 2:You think this is a wake up call or this will be a wake up call for the government broadly? How do you expect, you know, what are the different kind of ways in which The United States can defend itself from these types of, you know, obviously, this wasn't whatever's being reported isn't a direct tack, but a but a potential threat in the future.
Speaker 6:Yeah. The I mean, good news is DOD here, MOD in The UK, there's a lot of defense organizations already thinking about this and really trying to get ahead of it. It means that we have way more to do, but at least it's on the agenda. And I think it comes down to to your like, the best defense would be to prevent this from happening. So putting better safeguards on models, putting really strong, regulation on synthesis.
Speaker 6:So who can synthesize what? How do we track that? That's great. There's actually some legislation in the works on both of those things. But the that works domestically, and it works to contain an accident.
Speaker 6:But if we're actually talking about international collaboration, like, kinds of regulations are not really enough. So the way then we get into thinking, how do you deter something like this to your point? Like, what what is the the defense against this? And there's really, like, three pillars that go into it. The first is how quickly can you detect that something happened.
Speaker 6:So in this case or in future cases, do we immediately know that something new is circulating, that it's high risk to us, that it maybe will evade any kind of countermeasure that we have? And can we know that before it's an outbreak and, you know, we're sampling from a hospital? And then that leads into the second one, which is how fast can we design or update a countermeasure, so like an antibody or some kind of biologic, to combat that? And that is really like, if we can diagnose and develop as fast as possible, then these weapons are much less powerful because you're not talking about billions of damages. You're talking about a couple cases that are quickly contained.
Speaker 6:And the last part, of course, is attribution. So if you can actually say, where did this come from? Did it come from nature? Did it come from engineering? Did it you know, did it come from nation state?
Speaker 6:That lets you bring the rest of the DOD and the state department and our allies towards preventing something in the future. If you can get get that cycle down to hours, really, in terms of detecting, stopping, and then attributing, then you actually have a really robust profile for defense. And there's a bunch of new tech that's going to help make that better. So this is scary, but it also is there's good news on the horizon too.
Speaker 1:Can you talk a little bit about the bio practice at Palantir? I mean, there's been a lot of, potentially, like, misunderstanding or misinformation about how Palantir works. You know, I think most people who understand the company at this point understand that it is a it is an ontology platform that sits on top of a large database. But I think what I'm struggling with is I understand if you're using, the Airbus, case study, you have a database with all the different parts of the airplane, and then Palantir understands, helps you understand how the different pieces and lead times for, you know, this screw and this air seat seat belt and this engine part fits all together. So if you're demand planning or figuring out how to manufacture airplanes, that's a very helpful tool to understand your supply chain.
Speaker 1:That makes sense to me in the very concrete widgets business that is airplane manufacturing, although it is obviously very complex. In the bio or pharmaceutical space, I don't really understand the nature of how large these data sets are. Are we talking about trial data or manufacturing? Is it all of these above? Like, how does how does all of that fit together, when you're thinking about applications of of understanding large datasets in just the bio world broadly?
Speaker 6:Yeah. Definitely. So the some of it is more similar to what you're you described with Airbus. We're we're talking about manufacturing. We're talking about something that's like, it's a process with a lot of moving parts.
Speaker 6:How do you make these all synchronous and update, when you need to? There's also, obviously, biologics manufacturing. Some of it is more in the logistics end, which Palantir also talks about quite a bit. So when you're running a trial, making sure that patients and the medicine that they need are in the right place at the right time with the right support staff, that's actually looks pretty similar to coordinating flight routes or staffing a hospital. The part that so those parts are super similar to the rest of Palantir.
Speaker 6:The part that's probably most unique is when we're actually talking about that trial data or talking about patient level information.
Speaker 1:Mhmm.
Speaker 6:And there, it really gets back to some of the the core concepts of Palantir is how do you work with multimodal data and see patterns across it. So if we're looking back historically on all trials that we've run and we wanna start trying to identify what kind of patients have the best response to this and what are potential side effects, can you start linking together data that's from, like, a medical record with samples from a lab, with sequencing data, if you have it? And can you do that in a way that is completely secure, completely out of eligible, and complies with all the regulation in the space? And that's really the the niche that Palantir maybe it's not always well understood to fit into, but that is what that platform was built for.
Speaker 1:Can you talk a little bit more about, the I don't know. Like, the long term future of what you're building? I know that you can't go into it too much, but, like, there are a bunch of different vectors and opportunities around, what we're building in bio and what we're trying to prevent. There's almost like a, like like there's a little bit of game theory going on here. So what would you like to see The United States really, really dominate going forward?
Speaker 1:And where are the biggest opportunities to both increase biosecurity and then also help accelerate the the developments that we need, the good stuff?
Speaker 6:And that is what I'm most excited about. All of these technologies that are really scary when we're talking about them like this actually do have the potential to give us this enormous global advantage in our bioeconomy and how we respond to these threats and make medicine too. It's two sides of the same coin. I think the two areas that we're most excited about, one is on that detection pillar. Yeah.
Speaker 6:So right now, a lot of the ways that we understand what's going on around us is super analog. We have, like, a list of pathogens we're looking for. We test, and we get a yes or a no if those exist, and not much more than that. We've already seen a shift in this space towards sequencing. So actually getting the DNA sequence from anything that's in the environment or any sample.
Speaker 6:So that both broadens the scope of what we're looking at rather than just having tunnel vision on the pathogens we know about, which means that we can start detecting unknown unknowns and things that we've never seen and make a risk assessment. Also, using sequence data lets you have this much deeper level of insight into what the risk is rather than just, there's a virus here. You could say, these mutations make it more adapted to this type of host, make it more dangerous, or make it evade a certain type of medicine that we already have. And and you can take all that information that you get on this more robust detection layer and use it to drive countermeasure design.
Speaker 1:Mhmm.
Speaker 6:So we now have, like, every biotech that you guys talk to probably talks about programmable therapeutics,
Speaker 4:where we're
Speaker 6:moving to this era where you can update based on how the targets update. If we have this deep level of intelligence, we can also start thinking about, rather than have a stale stock pile of medicines that were made ten years ago, can we actually see a threat, immediately change the countermeasure, and then start deploying that immediately? So I think getting that cycle down really tight, that's what the future is, hopefully.
Speaker 1:Are we testing enough? I know when I go through TSA at LAX someone who was doing research, maybe for the CDC I talked to, was saying that, like, they basically just focus on LAX because that's, like, the biggest hub of fire it's gonna freak everyone out who flies. It's like, if you're gonna get sick, like, if you go to LAX, that's where it all starts. It's like a petri dish. I know that they swab my hands for I think it's, like, bomb making materials, but should they be swabbing my hands for new pathogens?
Speaker 1:Should we be doing more in the in the detection? Should we be sequencing random farmland to see if there's new invasive pathogens that could be, targeting our crops? Like like, how and then, like, how do we even pay for that? Is that something that the the government should put the bill or or, corporations should be incentivized to pay for? What is the actual, like, upping the amount of data that we're ingesting look like?
Speaker 6:Yeah. I mean, I'm I'm always gonna say more data is better. Yeah. But but the question is like My palate Yeah.
Speaker 1:How do we how do we get more data? How do incentivize more data? Buy more data? You know, test for more data?
Speaker 6:Yeah. Absolutely. So I think they're of course, we should collect more.
Speaker 1:Mhmm.
Speaker 6:The type of sequencing actually helps drive down you the cost because you can target a wider range of things you might worry about rather than setting up individualized programs for specific pathogens when maybe that's not a threat. But I actually think the with the caveat of more data is always better, we actually do collect a lot of this data today. You don't necessarily see it because some of it is in wastewater or environmental samples like you're saying. We don't always extract that much intelligence from the data that we do collect. So we we know that a sequence exists.
Speaker 6:We might not necessarily know what that means in terms of health or the impact of that variant or that mutation. So I do think there's there is benefit and there's, like, cost effective benefit for collecting more. But a big piece is just on of the data that does come in. How do we build the right models and the right software to interpret that?
Speaker 1:Makes sense. Makes a
Speaker 2:lot of sense. I think that covers it for now. I would love to have you come back on when you're ready to talk more about specifics on on what you're building. And I feel grateful that you and the team are doing what you're doing. Thank And just work a little bit faster, please.
Speaker 2:Good second. Awesome. Thank you for coming on and and giving us some insight here. And congrats on, on starting the the founder path.
Speaker 6:Thanks very much, guys. Great chatting. And hope to chat soon.
Speaker 2:Cheers. Thank you.
Speaker 1:Next up, we have Roy from Cluely coming back for an update. He's hired 50 interns I think or something close to it.
Speaker 4:He said they're bringing every intern on.
Speaker 1:They're bringing every intern on.
Speaker 2:We got every intern coming in.
Speaker 1:Well, to the studio Roy. How are you doing? Boom.
Speaker 2:Let's go. There they are.
Speaker 1:I think we're overpowering you. Can can can can you hear us?
Speaker 4:Yeah. Yeah. We can hear you.
Speaker 2:Yeah. Make sure we're zoomed out all the way so we can see everybody. We got a small army
Speaker 1:here. This is incredible. How big is the team? Kick us off. How many you got at this point?
Speaker 4:The team is 11 full time plus the interns.
Speaker 1:How many interns you got so far?
Speaker 4:Interns. Bro, we're closing in on 50, brother.
Speaker 2:Let's go.
Speaker 1:We're getting there. That's amazing. Congratulations. What are they all doing? How do you manage everything?
Speaker 1:Is it just is it is purely social media? Is that what you want them to focus on? Growth?
Speaker 4:Yeah. Yeah. Growth marketing. Like, the only goal of the company is get 1,000,000,000 eyeballs onto Cluelly. So we have unrestricted creative freedom and permission to do anything and everything.
Speaker 4:Just just make the company go viral. Every single person you see behind you has over a hundred thousand followers on some social media platform.
Speaker 2:Wow. Wow.
Speaker 4:Thousand plus.
Speaker 1:That's remarkable. Me too now. Me too now.
Speaker 2:Yeah? There you go. There. Oh, yeah. Yeah.
Speaker 1:You probably popped. What's working? What platforms have actually been, been driving the most growth? I mean, I'm sure you've run a lot of What have you learned that's, that you can share?
Speaker 4:Bro, Ben, take it away, bro.
Speaker 1:Let's hear it.
Speaker 9:UGC has been really good. We just hit 10,000,000 views today on
Speaker 1:10,000,000 views.
Speaker 9:Eight days.
Speaker 2:Wow. There we go.
Speaker 9:Hoping to get a hundred million views in the next month.
Speaker 1:What what what platforms specifically are the most fertile ground for, targeting your specific customer? Because you can imagine that, there's a lot of folks who are AI curious on x, but then there's much broader, more viral audience, more general audience on platforms like TikTok, YouTube, Instagram, what's working, and what is, is the next next platform that you're gonna be focused on.
Speaker 9:Yeah. Well, we're trying to go viral on every platform regardless. But the main thing right now is Instagram Reels.
Speaker 1:Mhmm. Oh, Instagram Reels. In interesting. And what is the main value prop that you're hitting people with? Is it still the cheat on test thing, or have you evolved at all?
Speaker 1:Right now. What? Still?
Speaker 3:Actually, interviews. Yeah.
Speaker 1:Interviews. Okay. And, has there been this was controversial when you launched it. Is it still controversial in the comments? Are you getting flamed?
Speaker 1:Has anyone big dunked on you, and has that driven virality? Is that actually a net positive?
Speaker 4:Instagram is not like Twitter. Like, you could post the craziest shit on Instagram, and they will still not think it's controversial.
Speaker 6:Really?
Speaker 1:So for
Speaker 4:us to make it controversial, like, we have to engage and bait some other way. Like, is cheating tool is controversial on Twitter, but on Instagram, you could you could have, like, a white guy say the n word 10 times, and it's still not controversial enough. You know? Like like, you need crazy shit on Instagram. That's what we crack.
Speaker 4:Every single person here has, like, very great viral sense.
Speaker 1:Yeah.
Speaker 4:And if you watch the reels that do do go viral, you you see there's, like, ways that we've engaged and beta the videos and this is what we'll keep doing to, probably a billion views a month is what probably
Speaker 2:How how long does it take to figure out if an intern is cracked? Is it, like, an hour or two hours? How much time do you need?
Speaker 4:For me, personally? Me personally, probably like ten minutes, but for anybody watching probably would take like one one or two weeks.
Speaker 2:There we go. There we go. How do you guys think about how do you guys think about product marketing? Obviously, you're just going viral everywhere getting all this attention. How do you make sure that it that it I'm shaking his head Doesn't think about it's not about the product.
Speaker 2:It's about the attention.
Speaker 4:Go viral.
Speaker 2:Yeah. Yeah. But but but how do
Speaker 10:side of the street, you know, you, make some UGC videos, make some Twitter posts, you know, you could sell anything. You know, in 2025, product doesn't matter. You know, I could jack off off the side of a building, sell some videos of it for $20 each, make $2,000,000,000,000.
Speaker 1:It's crazy.
Speaker 2:2,000,000,000,000. That that's intense. How do you guys think about burn? Is it on your mind is it on your mind at all?
Speaker 4:I don't know if you saw the last tweet, but as of literally, like, two days ago, we're still we're still cash flow positive. We're still fucking profitable.
Speaker 1:We're still profitable.
Speaker 2:Let's give it up for the profit.
Speaker 1:Let's hear it. Let's hear it.
Speaker 2:It sounds So
Speaker 1:you're charging for the products and people are paying. Are they at all satisfied, or do they feel, like they got scammed?
Speaker 4:Satisfied, bro. Like, the product works. You're either using as a consumer and it's working because, like like, you're passing your interviews and or if it doesn't work, you're not gonna complain to me because I'm a go right to your employer and tell them, yo, guess who's complaining about using the product? Like, I'll get you blacklisted if you complain really slow. Where
Speaker 2:how are you thinking about how do you how are you guys thinking about product evolutions? What do you wanna add to the product? Obviously, you wanna help people cheat on everything. Where where are you gonna help people cheat next?
Speaker 4:We don't care about like, the product is going to be led by the virality of the content. Mhmm. We have video ideas right now that we're gonna try to push for different use cases. We're gonna see which ones go consistently the most viral. If you can make something go go more viral, then, like, you can just build the technology after you have all the attention.
Speaker 4:So we'll figure out the exact use cases and exact niches we're gonna quintuple down on once once these guys get to work.
Speaker 1:What what, what formats on Instagram Reels are, like, the most modern in terms of, consistently viral? Like, you mentioned, like, man on the street interviews, what do do for a living? That's always been a fertile ground. What about, I see a lot of those, like, mobile game ads that look like, you know, you're fighting down some sort of bridge, then you go into the game. It's actually just match three.
Speaker 1:What what what are the different formats that you like to pull from?
Speaker 4:Every week, there's two new ones. And at any point, there's probably 10 to 20 viral trends that is happening.
Speaker 1:And these
Speaker 4:cycles so quick, you need to keep your finger on the pulse. These things will, like, expire immediately. You need to be on the ball and, like, if I if I told you right now, by the time people watch this on YouTube, like, would all been expired.
Speaker 1:Well, they were live. So so give us give us the latest and greatest. Like, what's going viral today?
Speaker 9:Well, right now, we got 10,000,000 views using a a Snapchat format,
Speaker 2:which
Speaker 9:viral for, like, the last three years, to be honest.
Speaker 4:Okay.
Speaker 9:And and I I I think that, like, we just have to get people who continuously scroll TikTok, like, six hours a day.
Speaker 1:Yeah. But what's the what's the actual format that you use? Like, describe the video. What is the hook? Like, break it down for me like you're explaining the, like, the art behind the viral format.
Speaker 4:There's a caption. It starts with a face. Usually, a a handsome dude or a pretty girl is saying, damn. This interview started with the interviewer started with the hard questions. I should have been a CS major, not a business major.
Speaker 4:That's in game debate because people are saying, like, bro, like, CS is way harder than business. Then it turns around interviewer asks, like like, hey. How are you doing? Why should we hire you? And then this guy uses Cluey to generate a response, but he can't fucking read the response.
Speaker 4:So he reads it hello artistically, like, oh, I revel in detail. And and then that's that is, like, another conversation point. Like, people are cooking on the guy because he he can't read properly. The guy is like a doing a really dumb interview using three.
Speaker 1:That's great.
Speaker 8:That's great.
Speaker 2:How are you guys using AI generated content internally? I know a lot of these the videos that you guys are creating are just typical social media vertical video. Do you have an intern that's just generating, basically copy and pasting making
Speaker 1:Do roll the v o three or any tools relevant? Anything clicking?
Speaker 4:Not not yet. I think there's still like a 10% left before they cross the uncanny valley.
Speaker 1:Mhmm.
Speaker 4:And the biggest thing is that people need to think your video is real. Mhmm. That like like, that is a difference between a hundred k views and 10,000,000 views if people think it is real.
Speaker 2:Yeah. Literally AI CEO is bearish on AI.
Speaker 1:What about yeah. What?
Speaker 4:Google needs, like, 10 more Chinese researchers to, like, figure it out. And once once they push out the latest update, then then then v o three will be there. But right now, we need real people.
Speaker 1:Yeah. Yeah. Yeah. Well, I I mean, what about just using AI as, like, stock footage replacement? Not not as the lead in for the video, not the entire video, but just, like, sprinkled in to illustrate a point, you know, an establishing shot of, like, a building, a helicopter pulling into a building.
Speaker 1:Like, that that historically has been kind of something that you would reach to, you know, Adobe Stock video for. V o three feels like it's there, but are you not drawing on that at all yet?
Speaker 4:If there's a viral format when we need it, maybe we'll use it. But right now, like, it's it's really brain dead to go viral on Instagram.
Speaker 2:Yeah.
Speaker 4:Formats are not hard. You don't need a helicopter. You need a guy, a camera, a really shitty camera. You need
Speaker 1:a computer. That's it. I mean, what about, like, those those kind of, like, AI mashups, like Harry Potter Balenciaga or the, the kangaroo with the plane ticket getting on the plane? Like like, AI content can go viral when it's really, when it's, like, inspired almost by a human. It's not it's not entirely AI generated, but it's using the tools effectively to create something that's, like, still catchy.
Speaker 1:Do you think you'll be using any of that anytime soon?
Speaker 4:Pro probably very soon. We're scaling up. Like, what you see right now is probably about less than 1% of what the size we will be by the end of this year. Like, we are profitable. We're not trying to be profitable.
Speaker 4:We just keep making so much money. We can't help it. So we're really scaling this shit up. I'm not even trolling you. 1,000 creators are gonna be shipping out content.
Speaker 4:We're doing a complete Internet takeover.
Speaker 1:Okay. So I love it. So so so so why in house? Why why like, like, why do they even have to be employees? What couldn't you turn this into, a multilevel marketing scheme or something, a pyramid scheme?
Speaker 4:Or, what we're gonna do. That's exactly
Speaker 1:Oh, that's what you're gonna do. Okay. MLM. MLM. MLM.
Speaker 1:AI. Hey. How are worried MLM.
Speaker 2:That you could be infiltrated by journalists. I'm sure they're circling the house right
Speaker 3:now.
Speaker 1:Pieces are gonna come. You know? We're we're doing a softball interview right now.
Speaker 2:But mean,
Speaker 1:the the person is brave
Speaker 2:enough to try to do a hit piece on the Cluelly Army.
Speaker 1:Oh. It's gonna be it's gonna
Speaker 4:be I bet they're dying too. Look. Four more eyeballs is better. There's no companies that ever died from a founder being too controversial. You got Deal fucking infiltrating with genuine spies, and they're still doing fine, bro.
Speaker 4:You got all the workers 17 guys. They're they're they're still kicking like no company ever dies from being too controversial. You die because you don't make enough fucking money.
Speaker 1:Yeah. Yeah. Yeah. Yeah. Speaking of making money, what what's the pricing model right now?
Speaker 1:Are you doing anything on price discrimination? Is there a super high tier if you get a whale? What does a clearly whale look like? Can I spend $2,000 a month on this service?
Speaker 2:Yeah. You should add a tipping feature too.
Speaker 1:Yeah. People
Speaker 2:should people should be able to tip you guys if if they have a good experience.
Speaker 1:They get the job. Like, really financialization, pay as you go, high interest rate loans. Just really push it. Make a sports gambling in there maybe. Just throw it all in.
Speaker 4:Yeah. I mean, it's $20 a month per consumer, a hundred dollars a year, and and our our top line revenue is really being driven up by enterprise.
Speaker 3:Mhmm.
Speaker 4:And you're have to talk to sales team to get a custom quote, but, you know, like, there's a
Speaker 2:lot Wait. Are you are you serious? What what what are what are the is that more on the sales side? What who who are the enterprise?
Speaker 1:So you sell the SDRs?
Speaker 4:You guys laugh because you think I can't sell enterprise because I'm No.
Speaker 2:I I I don't believe
Speaker 6:it. I trust.
Speaker 4:Like, these 20 these Fortune 500 CEOs, like, are, like, 35 year old dudes who sit there scrolling through Twitter laughing at my post. Yeah. Yeah. Yeah. Yeah.
Speaker 4:No. No. It seems it seems legit. It makes sense.
Speaker 2:No. I I I believe it.
Speaker 1:But but, I mean, you're you're not going even higher tier? Like, what with the $2,000 a month Cluely Vision?
Speaker 4:For for consumer, there's a lot more we can do with more compute. But right now, we're like to be honest, I didn't expect to grow this fast. The engine team is quite small. I'm, like, spending a lot of time trying to hire Sure. More more competent engineers.
Speaker 4:We have a lot of backlog tasks that we need to fill out, especially for this last contract that we signed. So we're full time focusing on the one big guy that we got right now. Mhmm. And after that, the Cool. That that that then we'll we'll try and scale this up.
Speaker 4:But right now, we're focused on the one one big client that we signed.
Speaker 2:Yeah. Talk about your compensation strategy. The people wanna know. You you said you can raise infinite capital and you're so confident. I believe you.
Speaker 2:But but I'm curious to to get some more insight there.
Speaker 4:Bro, I feel like it's so retarded to be a company. Sorry.
Speaker 7:Am I allowed
Speaker 4:to say that?
Speaker 10:You're not allowed.
Speaker 1:No. This is a family friendly show.
Speaker 4:It's very stupid to be a company.
Speaker 1:I try
Speaker 4:to race to the bottom to see how little you can pay your employees. Bro, if I'm making hella money, we're all making hella money. Like like, it's I'm trying to pay them more to see if, man, like, maybe tomorrow we'll start being, like, cash flow negative. Fuck. Make a fucking pussy, bro.
Speaker 4:Like, I I would like to pay these guys what they're worth, and the output is fucking insane. We did 10,000,000 UGC views in what, like, eight days.
Speaker 1:Like Mhmm.
Speaker 4:Like, you don't see this sort of traction in any company, and you don't see killers like this in any company unless you're paying these motherfuckers, like, what they're worth, bro. Like, I don't wanna Like, one thirty five
Speaker 2:out contracts. Maxed out contracts.
Speaker 3:Exactly. Exactly.
Speaker 1:Yeah. What about devices? I mean, seemed like this would be a natural fit for some sort of AI wearable or other platform. Is there an app coming, or are you interested in what's happening with Johnny Ive and OpenAI? What's what's your take on the device
Speaker 4:We're very interested in the hardware space. We've got, like, a million things cooking on hardware. We got people in the garage right now working on shit you don't you don't even know about, bro. Like like like, we're we're bringing manufacturing back to America, and it all starts at the Cluelly Garage.
Speaker 2:Cluelly Garage. I love to see it. Nobody you know, they doubt it, but you guys are reindustrializing America. You guys really are
Speaker 7:the It
Speaker 4:was hard way to come there. They're working on brain chips down there.
Speaker 1:Yeah. Brain chips. Brain chips. That's the future.
Speaker 2:There we go.
Speaker 1:There we
Speaker 2:The new Neuralink. Yeah. I mean, I I you know, there's a world in the future where you guys actually just roll up Neuralink and OpenAI. For the fluently umbrella. Yep.
Speaker 2:Right? Definitely. It's possible.
Speaker 4:Excited to offer acquisitions for for both of those companies. It's in the road map.
Speaker 2:It's on the road map. Alright. This has been a lot of fun. It's been great. I'm excited for you guys.
Speaker 2:It is And I have no doubt that you'll go from, you know, 10,000,000 views a week. 10,000,000 views a week to a hundred, and I'm excited to see you guys hit that billion view mark very soon. So keep it up. We are all very entertained and rooting for you.
Speaker 4:Shish. Shish. Shish.
Speaker 1:Shish. I love the energy. Thanks, man. We appreciate you joining.
Speaker 2:Better guys. We'll talk to fun.
Speaker 1:Bye. You know what he needs? He needs linear. He needs linear to manage all his interns.
Speaker 2:Sounds chaotic in there. Yep. He needs to use the platform purpose built to design and build the best products on earth. Oh, you knew I bet they already use Linear, to
Speaker 8:be Meet
Speaker 1:the system for modern software development, streamline issues, projects, and product road maps. Go to Linear and get started today. I wanted to go through the the Sarah Guo piece because we are having Sean on because he is throwing the AI engineer world's fair, and he's gonna be joining with some some other folks breaking down what's going there. We can we can kind of revisit this Sarah Guo post, which we talked about later, possible topics for her keynote. Apparently, she completely revised everything, but I really liked that she wanted to talk about AI native UX, not just chat skins, vertical AI surge.
Speaker 1:That was something we were discussing earlier. Multimodal frontier video, three d audio, retrieval plus long term memory, synthetic data flywheels, obviously super important. The SIM to real robotics push, closing the autonomy gap, robust agentic workflows, cell scale, digital twins, and programmable biology, compute geopolitics, world models for zero shot planning in RL environments that actually generalized generalized. She has mapped out the, like, the the the true surface area, but more recently, she, authored a post, an article on x, directly on x. You can read the full thing at her, at her x page, Cerinormous, on taste.
Speaker 1:And I thought this is an interesting post. Lulu, quote tweeted, and she says, Stripe returns errors in plain English. Quote, that card number doesn't look right, not error underscore invalid parameter because developers debug at 2AM. Spotify's shuffle isn't random. It avoids playing the same artist twice in five songs.
Speaker 1:True random fields feels broken. Engineered random Point point. Feels right. Notion's drag handle appears only on hover. Six dots arranged in two columns, not three lines, not always visible.
Speaker 1:Because permanence is clutter and six dots whisper grab me while three lines shout, I'm a menu. I love it. This is taste. The relentless, almost painful ability to know what should exist, what shouldn't, and where quality matters. It's the difference between shipping a product and shipping a point of view.
Speaker 1:The best founders understand that taste is a competitive advantage that compounds. It runs deeper than pixels. It's in your code base, your culture, your cap table. And I'm just thinking about the Clue Lead taste, which is just like the most maximal possible. Just maxing everything.
Speaker 1:He's he's max maxing.
Speaker 2:He's max maxing.
Speaker 1:It it's he's such a character. I love that we can have him on and then have someone, you know, like, you know, public company CEO on the same
Speaker 2:We got range.
Speaker 1:We got range.
Speaker 4:Range. It's fun.
Speaker 2:Yeah. Think of it like we absolutely wild. He We should probably text the families and say, you know, don't play this in front of the kids.
Speaker 4:Yeah. Seriously.
Speaker 2:But he knows, you know, the level, you know, he he even in every word he's thinking of how do I get the max amount of attention out of each incremental word Totally. Not just each incremental post.
Speaker 1:He he knows what he's doing even on the I
Speaker 2:am like systemically offending people
Speaker 4:Yeah. Yeah.
Speaker 2:In multiple ways in a sentence. Yeah. Yeah. Yeah. At the same time, like he's entitled to be his own personality.
Speaker 4:Yeah.
Speaker 2:And and I do think, you know, not to get completely sidetracked now that we're onto the next But I do think, you know, an enterprising young journalist might want to take a crack at Roy just because it will turn into a feud. Yep. It will probably be good for both of
Speaker 1:them.
Speaker 3:It will be very
Speaker 1:I would recommend. It's WWE.
Speaker 2:It's WWE Paywall the article.
Speaker 1:He's become a little bit of a heel of tech. Like, he's somebody that people love to hate.
Speaker 2:No, it's like, how do you take the YC playbook and just run the opposite of it? Yeah. Like, it is the inverse of the YC playbook, is be loud before you're confident in your business model and confident in
Speaker 1:your product. The question is just like Is actually getting Yeah, it can't all be style. There needs to be some substance. You have to actually build a product that people will love. You have to make something people want.
Speaker 1:And so we're not seeing that side right now, obviously, at least not in in public interviews. But I hope that he can turn it off incrementally, and and eventually, you probably need to turn it off 90% of the time. Because, yes, attention is really, really important right now, but at a certain point, you have to just deliver on the on on the core metrics and the value prop. Or else someone else will come along and and and offer something with with yeah. They won't have all the all the style, but they will have
Speaker 2:the sizes. I I I think there's a venture capital firm out there that even now would give him an income. Just see Oh, totally. In that interview.
Speaker 4:Yeah. Yeah.
Speaker 2:See that and be like, I'll give this I'll give this team another 10 mil Yeah. Just figure it out. To go keep figuring it out. Right?
Speaker 1:I mean, attention is incredibly valuable and
Speaker 2:But he's also but he's also being smart and that he's figured that he basically has the world almost convinced that he's going to burn through every dollar he has in the next two months.
Speaker 1:Oh, totally.
Speaker 2:Which is pretty but behind the scenes, he's like, well, I'm actually making money.
Speaker 1:Yeah. Mean, Avi Shiffman at Friend was going through a similar thing where there was that big story about, oh, he raised like a $2,000,000 seed round and he bought a $1,000,000 domain. And everyone was like, oh, he's so wasteful. But then, of course, he would like finance the domain. So it was really just adding like an incremental like 30 k of burn per month.
Speaker 1:And it wasn't really like, he didn't take that whole hit upfront, and then he was able to kind of build the business.
Speaker 2:Yeah. He's making a category bet on AI companionship. Yep. And having friend.com is a great is will lead to increased marketing efficiency over time Yeah. As you search the scale.
Speaker 2:So Well Anyways, we should go back to this completely
Speaker 1:You know what he should do? He should get a he should get a watch on Bezel. He's gonna go at bezel.com.
Speaker 2:Sucks. That's how you express.
Speaker 1:Your bezel concierge is available now to source you any watch on the planet, seriously any watch. So go to getbezel.com. So Sarah goes on to say, think of it like running a restaurant.
Speaker 4:It's so funny comparing the taste thing to this.
Speaker 1:It's like the most serious post. It was the most silly, just silly hype train over there. Think of it like running a restaurant. Anyone can follow recipes, source ingredients, and serve food, but the difference between a forgettable meal and a Michelin star isn't just technique. It's the chef's palate, their ability to know when something needs more acid, when a dish has one element too many, when to stop plating.
Speaker 1:Software is the same, but products aren't just feature complete, they're composed. And we see this with, like, a lot of the best software products. The software is more like art than than science in terms of, like, the the design and how you're how you're interacting with the user. So Sarah says it's easy to say, hard to do. Everyone claims to have taste now.
Speaker 1:It becomes the new, product market fit, a term so overused its lost meeting. Founders drop. We're taste driven in pitch meetings. VCs nod knowingly. Nobody defines it.
Speaker 1:Most companies confuse taste with aesthetics. They hire a design agency, pick a nice font, and call it done. But real taste runs deeper in the error message, the loading sites, the the loading states, the features you killed because they were merely good, not essential. Real taste hurts. It's saying no to features that would triple your TAM.
Speaker 1:It's spending a week per of it's spending a week perfecting an interaction that users will barely notice consciously. It's choosing the harder technical path because the UX is 101010% better. If your taste doesn't cost you something, it's not taste. It's preference. The pain compounds daily.
Speaker 1:A Fortune 500 prospect wants a demo tomorrow. Do you ship a half baked feature or the half ass collateral or lose the deal? The entire AI landscape reshuffles every three weeks. Do you chase every new model or trust your vision? Your competitor just shipped something flashy.
Speaker 1:Do you match it or hold your ground? Anyway, we'll have to have Sarah on the, on the show to recap her talk. But right now, I believe we have Sean coming in to the studio to tell us about the AI Engineering World's Fair. We're very excited to have him join the show. Thanks so much for hopping on.
Speaker 1:I wish I could be there. I have the biggest FOMO I've ever had with any tech conference because the lineup seemed absolutely fantastic. Unfortunately, I have a large family and and a studio that is covered in junk, and we're moving in. So I appreciate you taking this remotely, and we'll have to do the next one in person. But thanks so much for joining today.
Speaker 1:How are you doing?
Speaker 3:Hey, guys. Glad to be back. It's funny because, like, your setup looks so great on camera. I'm just imagining the mess that it's off off camera.
Speaker 1:Oh, yeah. Yeah. Yeah. Yeah. It's definitely a work in progress, but part of the brand is, of course, just showing the nice parts.
Speaker 1:Yeah. But but but but congratulations. This is many years in the making. Can you give us a little bit of the the history, the plan, and then I wanna go into some of the hot topics that you've been discussing today.
Speaker 3:Yeah. I guess this is the fourth conference we've done Mhmm. Done, and I started this basically right after the sort of ChatGPT moment and talking with enough developers and understanding that the people who can wield LM APIs are gonna be way more powerful than people who just chat with products. And this is actually gonna widen a lot because they can basically wield serverless intelligence. Mhmm.
Speaker 3:And so I coined you know, I sort of popularized the term AI engineer. Andrej Karpathy sort of endorsed it. He said that he does believe that you can get very far without ever training anything, which is a big thing for him to say because Yeah. I think status quo at the time was, you know, it's high status to trade models
Speaker 4:Totally.
Speaker 3:Research scientists. But now it's actually consensus now that you want to work on wrappers rather than models.
Speaker 1:Yep.
Speaker 3:And there are many, many multibillion dollar companies that have spoken at AI engineer that, reflect that fact. Yeah. And yeah. So, like, you know, I guess And
Speaker 1:there was a big shift there where where there was there was a moment where, fun like, the vibe had shifted a little bit to, like, the application layer, but there was still the idea that if you were going to build an AI legal startup, you were going to train an AI legal foundation model. And now it's moved all to post training, all to RL, and how you're how you're prompting and how you're integrating and ultimately, like, the user interface. And so, yeah, it makes a ton of sense, and I'm sure there's a ton of companies that are beneficiaries of that boom.
Speaker 3:Yeah. And it's not even companies, but it's more just, like, customers.
Speaker 1:Sure. Like
Speaker 3:because the Foundation Model Labs are never going to work directly with, like, the your health care system. Like, it's gonna be, like, a bridge that
Speaker 1:Yep.
Speaker 3:That comes up and and does that. And they're not the foundation model labs are not gonna work with directly with lawyers. It's gonna be Harvey. You know? I think so that that that directly led led into the rise of vertical AI enabled SaaS, and, I mean, that's consensus now.
Speaker 3:Yeah. But, yeah, I'm sorry. I that's a Slack thing that
Speaker 1:Yeah.
Speaker 3:Yeah. Slacked me out. But by the way, the conference is still going on. We just had the morning keynotes. Cool.
Speaker 3:I have my chat with Greg Brockman later today.
Speaker 1:Fantastic.
Speaker 3:And he doesn't know this, but I can I might as well just share this? But, like, we have a nice little cameo from Jensen Huang coming by.
Speaker 2:Amazing. No way. That's great.
Speaker 3:Yeah. That's We're trying to level up. The the cat the keynotes this morning were fantastic. Like, the Microsoft, you know, they they, like, really went on. They're they're going so hard after the AI audience.
Speaker 1:We were just reading about that in the information. Platform. Platform. Platform. Platform, says Satya.
Speaker 3:Yeah. Yeah. I I think they see this as their chance to overtake AWS.
Speaker 1:That's great.
Speaker 3:Wow. But, AWS is also, you know, also sponsored. They've been, like and have a very strong presence with us. So, like, we just want to be the vendor neutral plat place. Right?
Speaker 3:Like, all the big clouds, all the big labs Yeah. Work with us. We have the the MCP team here with the entire steering committee presenting as well. And we just want to be for developers. Like, this is where you this is, like, kind of the trade show.
Speaker 3:You come to hire people, learn about what's new, and upscale.
Speaker 1:I have a couple more questions. How long do you have exactly? Five minutes?
Speaker 3:Or do you I can I
Speaker 4:can go till, like, one 01:30? So
Speaker 1:Okay. Great. Yeah. So We had we had
Speaker 3:a guest, and we we I I moved into a different day.
Speaker 1:Okay. First, perfect. So, I wanna know about the mix of attendees. How many, how many folks are are trying to start venture backable application layer AI companies versus is there a new trend of someone who's building more of a vibe coded, almost lifestyle type, AI driven business? Are there folks from either companies that have established themselves and are now trying to bolt on AI, or are there lots of folks that are, working for for large companies and just wanna stay ahead and become AI engineers?
Speaker 1:What's kind of the shape of the audience, if you can characterize it at all?
Speaker 3:Yeah. So we we do surveys, but we don't we don't know specifics Sure. To to to to that, like, high level granularity. I would say about 50% are are people working at medium to large sized companies and trying to upscale. And then the others are smaller companies and and, you know, the our most popular title is, like, founder or CXO of, like, a smaller start up.
Speaker 3:Yep. And those are those are venture backable. And I think mostly that's just a function of us being in San Francisco. Yeah. Because we we we will have that startup bias.
Speaker 3:I do think that one thing I I kinda don't really care about this whole lifestyle versus venture backed thing because, for example, I have a TinyTeams track that is speaking this year. TinyTeams is something that I'm trying to push as an idea Mhmm. Of companies that have more millions in ARR than employees. Mhmm. Right?
Speaker 3:So your your revenue efficiency is so high because, obviously, if you pay each employee less than a million dollars, you're probably profitable. Yep. And, therefore, you don't actually need the venture money except to point to marketing. And that's your choice. You can be profitable.
Speaker 3:I have a six person team making more than more than $40,000,000. And, yeah, I mean, it's it's absurd. Like, the the amount of leverage you can get with with agents and also building AI products for other people to use, you're where you're just kind of passing through or or slapping a margin on top of the tokens that you resell from the big model labs. I think that really makes a lot of sense.
Speaker 1:Totally. Has the narrative of, like, oh, if you're building an AI, like, you're gonna become, obsolete by Google or or or open AI is gonna steamroll you in their next dev day keynote. Has that narrative dissipated? And what if so, what's driving it? Is it kind of the pretraining scaling law wall that we're kind of seeing with GPT 4.5?
Speaker 3:Are you guys still I I don't know if he was still talking about that. I mean, everyone, you know
Speaker 1:Everyone's moved on.
Speaker 3:Yeah. We've moved on to a different time. Right? Like, yeah, I think I think most people have agreed. Yeah.
Speaker 3:Like, there are still new pretrains happening, especially with the open source model that OpenAI is working on.
Speaker 1:Sure.
Speaker 3:But yeah. I mean, I think we've just, like we've seen it come and go enough times. So for example, we have OpenAI launching ChatTPG Codex, which is just head on a direct Devon competitor.
Speaker 1:Yep.
Speaker 3:You know, Devon's not worried because they, you know, have been doing this for two years, and they are much more polished in terms of the integrations, and they have different things that their customers already like. And so I think it it's just like everyone is gonna need their version of a thing. Yep. And so this is the sort of house store brand version of what Chargedee Codex could be, and it's it's not competitive just because the the ocean is so huge for software engineering, and Devin has at least established the category by being first there. And I think you can see similar versions of that across across the the domain.
Speaker 3:Yeah. I haven't I haven't really seen anything there yet. Although, it's not to say that it doesn't happen. It does happen.
Speaker 4:Yep.
Speaker 3:For example, with the first wave for g p t three startups like jazz Jasper. Yeah. But it I mean yeah. So far, there's no fear that, in fact, that people are very excited to beat the Foundation Model Labs. Like, this is where the engineers meet the lab people, and the lab people train them on how to use LLMs.
Speaker 3:And I I think that's that's a perfectly harmonious relationship, to be honest. I I haven't seen any
Speaker 2:What was your reaction to the the news around Anthropic and Windsurf yesterday? Yeah.
Speaker 1:A little
Speaker 3:bit awkward in
Speaker 2:drama on the timeline. But So yeah.
Speaker 1:Yeah. Yeah. Give us a give us a walk through for those who might not be familiar of what happened, and then I'd love your your analysis.
Speaker 3:Yeah. So the history of this is that Windsurf is an independent company that basically kind of followed Cursor's footsteps and launched an AI agentic They've done very, very well for themselves in a very short amount of time. I think they launched we're the first podcast that they launched with in October or November. And and then they there's rumors that they got acquired by OpenAI for $3,000,000,000. Those are rumors that are unconfirmed by both sides.
Speaker 3:I've talked to both sides.
Speaker 2:Yeah. It's it's in I've been fascinated how everybody just takes it as fact.
Speaker 1:I take it as fact. But He wore two polos.
Speaker 2:Nobody yeah. He wore two polos. But, again, at no point, every every news outlet, legacy news outlets have been reporting it as fact Yeah. Yeah. Even though no side has verified it and nobody said it closed or anything along those lines.
Speaker 3:Yeah. The the only thing I can say is I Windsurf is speaking tonight right right before Greg Brockman goes on, and there's a reason.
Speaker 1:Exciting.
Speaker 3:We're not, like, we're not dropping a ton of alpha, but I'm just saying, like, I'm not I that's all I can say. I'm that's all I'm allowed to say.
Speaker 1:Yes. Of course.
Speaker 3:And I and I think that so there's there's there's a there's a relationship there
Speaker 7:Mhmm.
Speaker 3:As there is a fact that both Cursor and Windsurf had benefited a lot from their relationship with Anthropic because Quad three point five and three point seven have, for whatever it's worth, been regarded as the best coding models. Yep. I'll open and they all disagree. Gemini will disagree, but whatever. Like, the community has voted.
Speaker 3:Yeah. So so overnight, I think, like, two days ago, one day ago, Anthropic cut off the first party access to Cloud to for Claude to Windsurf. This is their top model, and now they just don't have access as a as a first party tool. You can, for example, still bring your API key and use your accounts on Windsurf, but it's right? You can't just like like, Windsurf, I'm gonna pay you $20.
Speaker 3:Use your account. Whatever. I you know, I just don't wanna worry about the the the the rate limits and stuff. That's gone. Windsurf.
Speaker 3:They just woke up overnight, and and that's gone. So a lot of people are very upset. This is a a very big no no. If you're, like, aiming to be any sort of, like, credible LLM API provider
Speaker 1:Yep.
Speaker 3:To just cut off a a a access. Google hasn't done that even though, you know, if you take the the sort of rumors of the acquisition on face value. But I I think this does lead credence that I think Anthropic at least thinks that Windsurf is just a competitor product now. So I think, like, insofar as you're putting odds on whether the acquisition actually closes, the odds have moved up.
Speaker 1:Interesting.
Speaker 2:Yeah. The other the other factor that's interesting is how much does this benefit Cursor if you're a developer who loves various Claude models, and now maybe you have a reason to go spin up.
Speaker 3:You know market share of Cursor versus Windsurf?
Speaker 2:I don't.
Speaker 3:Like, estimation estimations?
Speaker 1:What is that?
Speaker 3:I I think the numbers I've seen I I I saw it on on my timeline on Twitter. I didn't save it, and it's it's so fast. But someone please look this up. It's something like Windsurf is 5% of Cursor.
Speaker 1:Wow. Because based on the based on the market caps, I would assume that Windsurf's now 30%.
Speaker 3:I know. Of Cursor. So the the thing that you don't see there is Cursor has entirely developed its business on the IDE. Sure. With Windsurf used to be Codium, which used to be, like, this GPU service GPU company that has significant LLM inference for enterprises.
Speaker 3:Like, they they spent the last four years doing that.
Speaker 1:Yeah.
Speaker 3:Yeah. So they're buying that team, that revenue, that product, as well as Windserve.
Speaker 1:Oh, interesting. Yeah. That makes a lot of sense. What how do you think that the agentic coding market is shaping up? We've been kind of talking about maybe it's three different markets.
Speaker 1:Like, I can go to GPT. Like, I I can go to o three, and I can ask it for a question, and it will just write code and write Python and execute it. And I don't even have to tell it to write code. It's just if I ask a question that requires code, it will write some code. Then, of course, they OpenAI now has Codex, and then they also potentially have Windsurf or or or, you know, you can think about the IDEs, as a different, as a different, entry point into that market.
Speaker 1:Are we seeing, like, a true permanent bifurcation between, synchronous and asynchronous, AI coding usage, or do you think these all blend together at some point?
Speaker 3:Yeah. I think that, actually, you're just catching up on something that has already been a thing. Mhmm. That was the divergence, and, actually, they're converging. Mhmm.
Speaker 3:So they they all like, it already started that you had the synchronous ones like Cursor Windsurf, and then you had asynchronous ones like Devin and Factory AI, for example. Yeah. Yeah. And they were all sort of, like, inner what we call the developer inner loop, which is hands on keyboard coding
Speaker 1:Yep.
Speaker 3:And developer outer loop, which is PR review and all that kind of stuff, or or, like, file an issue, make a PR, that kind of stuff. Yep. So those are merging. The explicit goal of co codecs, suite agents, is something I'm gonna talk to Greg about later today Mhmm. Is that they will merge those paradigms just that they're merging the reasoning models and the the sort of instant thinking models with GPT five.
Speaker 3:Mhmm. And I I I that totally makes sense. It's technically really hard to do because one's in one's on your machine and one's one's cloud. And, also, there's just different set of user experience paradigms. Is my video freezing, by the way?
Speaker 1:It is. It is. But you
Speaker 2:look great. You look great.
Speaker 3:I see you guys great.
Speaker 2:How how seriously do you take Google's coding agent, Jules? Do you think it's just an experiment that they're putting out there? Do you think they'll actually invest in it and try to get real adoption?
Speaker 3:You're trying to get me in trouble.
Speaker 2:Actually, you're Not conflicted. You're conflicted. I'm sure I'm sure Google has has is a part of
Speaker 3:Google's great.
Speaker 2:Things. Products
Speaker 3:wise, they have had hits like LM that then have failed to continue the momentum for whatever that
Speaker 1:Sure.
Speaker 3:That. And I think that is I think that most people are extremely unfair to NobelkLM. I think they
Speaker 4:have shit
Speaker 3:really well. It's just that you'll never repeat the initial wave of excitement that they
Speaker 1:had. Totally.
Speaker 3:Just never will. Like Yeah.
Speaker 1:And, I mean, Notebook LM seems interesting because that feels like a a case study in if you're a startup and you're fast following an innovation that actually came out of a hyperscaler, that's where you turn into a bullet point on the next dev day or something like that. Because the Notebook LM, it felt like there there was this amazing tremendous momentum online. Everyone was excited. And then it was kind of faltering, and it was like, oh, there's no app. So maybe I'll build the app, and I'm sure some people built Notebook LM apps or Notebook LM spin offs.
Speaker 1:And then it just took it just took six months, and then there now there's an app. And now and I'm sure they will continue to iterate on this. And so yeah. Yeah. I I I know to some degree.
Speaker 1:But but it I I think the lesson is probably, like, yeah, there's probably some narrow window where you can cash grab as kind of like a cynical, like, rip off startup. But, like, realistically, if you if you found out about the concept of turning, a deep research report into an audio product from NotebookLM, like, you might be just behind the ball. And so, like like like, you don't you don't have the right to own that market long term as opposed to, you know, I think dev and cognition, like, they have more of a right and and and factory. They have more right to, continue to fight with Google's Juul's product because they were really there earlier. They they issued the original hype and and and brokered a whole bunch of enterprise contracts and, like, kind of got a couple years down the road before.
Speaker 1:So they're not like a fast follower to Big Tech. They're actually, Big Tech is maybe fast following them.
Speaker 3:Yes. I I think I think they just boils down to execution and everything. Right?
Speaker 1:Yeah. Of course.
Speaker 3:And I think, like, that's that's one of the reasons why we've actually somewhat brought it now from just engineering to we've added AI product management and AI design. Okay. And I think that really good this is this is product management. This is just straight up. Can you keep up the momentum?
Speaker 3:Can you listen to your users? Can you come up with creative new stuff that keeps the momentum going? And some some teams can and some teams cannot. Yeah. The only thing I can say with regards to Google and, like, the fate of jewels is the initial the founding PM of of Left, and she's speaking here with her own start up.
Speaker 3:Oh, yeah. It's a bit like, the the it's really hard to keep employees of people who, like, start interesting products for Epic Lab because they will get any number any amount of money thrown at them. Yeah. Open it up.
Speaker 2:That's million dollars. 1 million seed round, 10,000,000 of secondary if you quit your job right now. Probably not that extreme. I I wanted to ask you about
Speaker 3:Pretty cheap.
Speaker 2:Git. Yeah. It's a I wanted to ask you about GitHub.
Speaker 4:How much
Speaker 2:do you think Satya cares about, you know, GitHub? Do you think he's, you know, really pushing
Speaker 1:Yeah. Is that an important wedge into the AI coding market? Because, obviously, they were first with GitHub Copilot, And you could imagine that that it's just a phenomenal distribution channel. And when you look at the the value of and the way Microsoft executed around Teams rolling into you you're already using Outlook. Now you're using Teams instead of Slack.
Speaker 1:You could imagine really great adoption, maybe not at the most cutting edge companies that are hyper online, but we could see one of those charts where we're like, oh, wow. Like, Microsoft's really crushing it in in developer tooling.
Speaker 3:I mean, absolutely. I I think it's extra extremely core. What would you put GitHub Copilot revenue at right now? Yeah. If you wanna if you had to hazard a guess.
Speaker 2:500.
Speaker 1:Yeah. 500,000,000? Yeah. Yeah. It's ready
Speaker 4:to nail it. You nailed it?
Speaker 2:Well, that's
Speaker 3:Like, standalone, that is a publicly listed company.
Speaker 1:Wow. Yeah. Yeah. Yeah. That's that's that's
Speaker 2:the perfect Here's the thing. Here. Mean, here's the here's the Right? Here's the challenge is that no one is talking about GitHub Copilot.
Speaker 7:Is that a challenge?
Speaker 3:It's inside of Microsoft, so Yeah.
Speaker 1:I mean, no one's talking about No.
Speaker 2:I know, but you know, nobody's saying like, look what, you know, there nobody's sitting there being like, I'm blown away by Copilot. Right?
Speaker 3:I I think I think I think that people who live in the Microsoft stack are because that is what they have access to and, like, their their company agreements are. I think on Twitter, there's a novelty bias. Right? People people always want the new thing. They wanna they wanna support the little guy.
Speaker 3:It it is
Speaker 2:I support the current thing.
Speaker 3:You wanna support the current thing. Yeah. But okay. Here's the mental framework I wanna leave you guys with on on that stuff is data gravity and the moat is is a thing. Like, data has attracts the compute, attracts the money, attracts stickiness.
Speaker 3:You like, once once my is one place, everything else moves towards that data. And, if my code, my code is the the most valuable form of data I have, that's that's the most, expensive, data to acquire. So if my code already lives with you, then it's much easier for my coding agents to also live with you. And and so so they they have a home turf advantage. I think the GitHub acquisition back in the day was one of the smartest and most underpriced positions in in developer tools history.
Speaker 3:So, like, I think I think you should you should take that point of view. So, that that moat is so strong. Like, any like, try try to be like a no name random dev tool startup that's that comes up and says, hey. Give me access to your code base. Like like, I need to run agents on it.
Speaker 3:They're like, no. Like, I I I'm already doing it.
Speaker 2:What what happened with Codex yesterday? There there was one user that was reporting that With their
Speaker 1:Private repo information was leaking from one to another or
Speaker 2:user error? Did you did you track that at all? Now you're probably getting busy. No.
Speaker 3:I I was I was running the conference. Yeah. Yeah. It doesn't. And individual things are going to happen.
Speaker 1:The scope is
Speaker 3:gonna happen. I think GitHub's reputation is is going to last it through that.
Speaker 1:Think Totally.
Speaker 3:It has to be really egregious for for that kind of stuff to slip through.
Speaker 2:Yeah. Yeah.
Speaker 3:Yeah. I I I mean, I think, like, more broadly, I think that there's a standard stack of what we are calling what Andrej Karpathy is calling the LMOS, right
Speaker 1:Mhmm.
Speaker 3:That I think you guys are kind of going and learning about. Yeah. But I guess it's pretty well established by anyone who's who works in software agents, coding agents, which is you want a sandbox. You want a browsing environment. You want branching.
Speaker 3:You want fine tuning on your code base. Like, there there's just a standard stack of things that Cloud Code, Codex, Jules, Cognition, Factory, and anyone else in this field, they're all converging on the same thing, and it's just who does it best and who reaches who who solves it for their customers best. I think that's that's gonna be the the name of the game for now.
Speaker 1:Makes a ton of sense. Do we have to roll over to the next person?
Speaker 3:No. Because I I I Okay.
Speaker 1:You're good.
Speaker 3:He has a
Speaker 1:Yeah. I mean, I guess my question on data gravity is, it it seems like, if we compare the evolution of video generation models, Google has a really amazing cornered resource in YouTube because that data is so big. It's so many tokens. Hasn't been it's growing, and it also hasn't been, like, exfiltrated to the public web. But it feels like GitHub, although it is a massive dataset, it's not nearly as big.
Speaker 1:I think it would I I think somebody clocked it, like, a hundred million tokens or something. It's it's it's just not it's just No.
Speaker 3:That's true.
Speaker 1:Is it? Way bigger? Well, public repos because I I I don't think that they can train on run private repos. Right? Or can they?
Speaker 3:Who? GitHub? GitHub.
Speaker 1:GitHub. GitHub.
Speaker 3:Don't think they will let themselves.
Speaker 1:Yeah. They wouldn't.
Speaker 3:Yeah. I don't know. Hundred millions just sounds way too low. And that's my that's my gut reaction.
Speaker 1:Sure. Sure. Sure.
Speaker 3:Keep going. Keep going.
Speaker 1:So so so the question is, like, how durable is the data moat at encoding versus, in video generation? Purely, I'm just thinking about the the raw dataset size of YouTube has to be orders of magnitude larger than GitHub. And so I would imagine that it's it's Microsoft probably has a less durable advantage versus Google's, like, kind of march down the video generation pathway if it's truly Yeah. Restricted by data.
Speaker 3:Not all data is comparable like that. Yeah. So you're you're comparing verifiable data to unverifiable data. So Sure. Code can compile, and if it runs it if it runs, you you can do more code like that.
Speaker 3:And that creates the RL loop that Mhmm. Lets you generate synthetic data for for more code. Mhmm. With videos, you have what you have, and you can train on that. And so and you're technically not even allowed supposed to train on that, but who knows what Google is doing behind the scenes?
Speaker 1:Well, I think Google can train on public YouTube videos. Right?
Speaker 3:I I don't know.
Speaker 1:I I imagine that, like I I've posted videos on YouTube. I imagine that I've opted in at some point.
Speaker 3:I think it would was, like, a big fuss with MKBHD and all the other guys Yeah. Picking up a fuss about this. Do remember?
Speaker 1:Sure.
Speaker 3:Yeah. So I'm just I don't know. I'm not a liar, but, like, I'm sure Yeah. Is is it a completely untested clause that just has to go to court?
Speaker 2:Yeah. I just wanna say if to Google, if you remove John's rate limits on v o three, you can train on our back catalog. Full permission. Please.
Speaker 3:Yeah. I I I think I think there is that diversity of of opinion. Right? Like, do you wanna just, like, give yourself to the machine? Mhmm.
Speaker 3:Or do you want to keep your keep your data to yourself? And it's it's like there's no one between. Like, you're either one or the other. It's it's a very strange dichotomy. Like, that it's not a spec like, many things in life are a spectrum.
Speaker 3:This one is not. Like, you you you run into someone, they're either maxi privacy maxi or they're, like, an AI maxi.
Speaker 1:Yeah. Yeah. Yeah. No. That makes sense.
Speaker 1:What what else is on the cutting edge of of of debates that are kind of raging at the conference, this year versus prior years? We've we've lived through, like, the P Doom debate. We've we've we've we've we've lived through, like, the Leopold Aschenbrenner era. We've we've shifted to the geopolitics debates. But what's kind of on the frontier of, like, hottest topics to discuss?
Speaker 3:First of all, Leopold was right. And I don't know if people know that he was on Ilya's team when the the the the sort of board drama happened, and, like, he was, like, directly connected to it. Anyway Okay. He we we haven't lived through it. We are living through it.
Speaker 3:It is happening. It is directly leading to the geopolitics because he was foresee foreseeing that, and he goes exactly right.
Speaker 1:Yeah. But the the way I would the way I would, I would kind of characterize at least my takeaway was that his piece, was a, was a little bit of a pivot from the paper clipping doom and a shift to geopolitical competition in AI. Is that a is that a mischaracterization?
Speaker 3:No. Nailed it. Okay. But, like, he was right.
Speaker 1:Yeah. No. No. I I I completely agree. I completely agree.
Speaker 1:I'm just wondering if if if it's like the consensus is that he was right, then the book is closed. We're not debating that anymore. What are we debating? What's more water?
Speaker 3:Are the debates? Yes. Yeah. How to do great I AIPming. Sure.
Speaker 3:How to run a tiny team. Yep. Have a robotics track for the first time that is Okay. Like, Tesla Optimus is is speaking physical intelligence. Cool.
Speaker 3:Waymo Waymo just overtook Lyft. I I I Yeah.
Speaker 1:Yeah. I saw that.
Speaker 3:That already. Voice is the hottest thing in in in terms of multi modalities. Yeah. Like, everyone's sort of building with voice because I think it's, like, finally good enough. Yep.
Speaker 3:And I think maybe the last thing I will highlight to you is we are also emphasizing security for the first time. Security is, like, kind of a boring topic. It's nobody really wants to talk about, like, how to secure your system, but, like, they actually do now because they they have real money running through their their product. So there's all that. And then that is, like, roughly important equal in size to the excitement about MCP.
Speaker 3:And so we have an entire MCP track with the Anthropic team here
Speaker 1:Very cool.
Speaker 3:Because it's because they're nice enough to to to come by, and that fills up the the whole ballroom that we have. So Are
Speaker 1:we gonna get payments in MCP? Is there a sub track for putting stablecoins in there or something like that?
Speaker 3:What do think? There there's nothing in the official spec, but we have a number of people, and we have a speaker talking about the sort of MCP economy that's being enabled. I do think that remote MCPs and authorization give you the foundation for basically just remote agents that you can buy and hire. True. And that that's that's, like, that's effectively what it is.
Speaker 3:It's just that we're still in that stage where there's still these things are still trivial enough that you can actually just write your own. So you have to overcome that build versus buy for this to actually kick off. Okay. And I would just say like, I'm not at all a crypto person, but I would say that the crypto people have been ahead of us here, and they haven't found that much yet. You know?
Speaker 1:No. Makes sense.
Speaker 3:But they're but they're actively hunting. And if anyone finds it first, it'll be them. It's it's a circle's presenting here. Solana presented in my previous conference. And, they're all they're all on it.
Speaker 3:Stripe is also on it with with their stablecoin thing. Something's gonna happen
Speaker 1:here. Bridge. Yeah. In robotics, are we getting to a point where we're starting to see an ecosystem of companies pop up like we've seen? No.
Speaker 1:It's just it's just
Speaker 3:What what do you like, yeah. So they're all vertically integrated. Sure. They're all building every their their stack. I don't see, like, any horizontalness
Speaker 1:Nothing.
Speaker 4:If that
Speaker 3:if that's what you're going for.
Speaker 1:That's exactly what I was asking.
Speaker 3:Yeah. Yeah. It's so weird. I think, you know, there's just so much custom needs that you you have to sort of reinvent the universe every time. The one, thing that that does reuse bots is CloudChef, which which is, you know, a very young company, but I I ran across them when I tried their food.
Speaker 3:So they're they're a kitchen robot. They do demonstration learning from a single shot from, let's say, like, a Michelin chef, and you just give them you give them the ingredients, and then and you will and it would just flawlessly just kind of repeat that that cooking for you, and you can hire it for $12 an hour. So it's meant to directly replace human labor, which is very expensive and very labor intensive and unreliable. Let's let's put it that way. Sure.
Speaker 3:And they they use they can they can live on top of any other robot arm or Tesla Optimus substrate, which is good. Okay. Because it's more about the the robotics sort of framework than it is about the individual hardware. And I think, like, that is the first time I've seen that happen. I'm, like, relatively new to this, but, like, this is first time I'm like I'm like, oh, okay.
Speaker 3:Like, he's actually pretty confident he transfers across any any system as long as they have the minimum required set of device drivers, basically. And I'm like, yeah. That's cool. Like yeah. Yeah.
Speaker 3:So he's he's speaking tomorrow.
Speaker 1:What about other pieces of the robotics data stack? Are we thinking about, like, data brokerage or or kind of like a Scale AI or maybe Scale AI actually working to generate more robotics data? Anything on, the sim to real gap? I saw some semi analysis date summary of the paper that was pretty cool.
Speaker 3:So I would say that as an and as a sort of industry practical conference, those are just in the domain of research right now.
Speaker 1:It's in research. Yeah.
Speaker 3:Yeah. It's it's like there's there's a lot of papers out there. Yep. Jim Fan had a fantastic talk at Sequoia Ascent that I I recommend everyone watch if you haven't seen it about the physical Turing test. Yeah.
Speaker 3:How you need to just do a lot more simulation. And there's a there's a lot of work on being done on this. I think Google Genie is the other one that I would rec recommend people to. There's, like, a really interesting tie in between, like, the generative video world and the robotics world that
Speaker 1:Yep.
Speaker 3:You wouldn't necessarily expect expect until you, like, spend some time in that. But we just haven't focused on it because people cannot get jobs in it. Like, I I want people to get jobs at my conference. That's, like, almost the whole reason of That's the whole point. I bring the companies.
Speaker 3:I bring the engineers. They meet. They they fall in love with each each other. That's, like to me, that's, like, the most fulfilling thing.
Speaker 2:Can you give a a high level update on the hiring market today? Yeah. What what is fact? What's fact? What's fiction?
Speaker 2:Yeah. You know, it's one of those things people love to, you know, talk about how bad things are. And yet every company that I know is like, can't find people. And maybe there's a talent kind of gap there. But but what's your read?
Speaker 3:Yeah. I mean, I I I think both are right, which is weird. This is actually the topic of my conversation with Greg Brockman later today because I I half the people I I meet at my own conference are worried for their own jobs. Right? They do ask about this.
Speaker 3:It's not like they're not they're they're blind to it, but they're just like, I'm here to see what else I I need to do or how how how does my skill set need to change for the future. And nobody knows. You know? Everyone says the trite thing, which is like, oh, I'm going from I write the code to I manage things that write the code. So I become an engineering manager.
Speaker 3:But, like, what does that mean in practice? How much knowledge do you need to supervise an agent? You actually kinda need a lot when it goes when it goes off track. So, like, it that's super unknown. I do think that the juniors, the the the, like, fresh out of college students, they're a little bit cooked unless they're unless they're unless they're good.
Speaker 3:So it really amplifies skill issue. It really Yeah. Will amplify a skill issue.
Speaker 1:Yeah.
Speaker 2:Yeah. It's a hard thing to to to, you know, try to tell somebody. You just need to be Better. Five times smarter
Speaker 1:than you are.
Speaker 2:And then the job and then you'll have like a bunch of offers. Yeah. But I also think I also think that the the solution here and and we we just hired an intern who already shipped a new product. He he started Monday. He just shipped something relatively simple, but it's cool.
Speaker 2:It's a guest directory. He shipped he just sent us a v one of that product that he built last Thursday. We saw it and we were like, this is super cool. Why don't you start on And it's that one kind of interaction that can get your foot in the door that allows you to develop skills and build relationships that, you know, hopefully we, you know, we work with him for a long time. But even, you know, I'm sure people are already seeing him at TBPN and and we'll probably try to poach him Yeah.
Speaker 2:Before the end of the summer. So all
Speaker 1:you need is is like a changing shape of the software engineering employment because, like, I feel like like the vibe coders can come into organizations that might not have a full engineering department and then have an impact because of the way the tools work. But, it's certainly it's certainly a lot of change very, very quickly.
Speaker 3:I would I I would say that's not the consensus yet that they hire a a, like, chief vibe code officer Yeah. Because, like, humans are still very much needed to to patch the gaps that the models are not good at, and it's really painful when they do go wrong, and they do go wrong. You know? I think, like, all the hypes that you see on Twitter fails to admit, like, what happens, like, one month, two months, three months after? Yeah.
Speaker 3:That's honestly what you get paid to do as a software engineer, make maintainable software, not Yeah. Being filled all the time.
Speaker 1:I've even seen people repurpose old old projects and say, I've I've I've coded this in twenty four hours since and everyone's like, wait. Now this was published in 2019. Like, you spent months on this, and you're just hyping this up. That's misinformation.
Speaker 3:Yeah. I I I can't govern I I I pay attention to what people say there. But I do care what people report for themselves that they've that they've done at serious companies. So one thing I would highlight there is Booking.com did about ten company years worth of migrations in three months with with their sort of automated code migration that that they that they did with Sourcegraph. So we did a talk with them at my conference in New York in February, which if you wanna look look that up.
Speaker 3:So, like, those that company has been around for, like, fourteen, fifteen years. That's that's a serious code base. Right?
Speaker 1:Yeah. Totally.
Speaker 3:They're just reporting their success. They got nothing to to gain from selling you on it. They're just they're just happy about it, and they wanna teach others. Yeah. So you wanna look for those.
Speaker 3:They're they're not trying to sell you anything. They're just looking at reporting, like, their progress at a real company. Yeah. And it's that's really what I try to optimize for for the for the conference. It's it's hard because not everyone's incentivized to do it.
Speaker 1:Yeah. Of
Speaker 3:course. You just have to create an environment where they get something out of it by meeting their peers at at a thing like this.
Speaker 1:Well, give us the plug. Where can people watch your talk with Greg Brockman later today?
Speaker 3:Yeah. It's on YouTube. YouTube K.@ai.engineerFantastic... And I'm what would you ask him? Mean, you're having him on eventually.
Speaker 3:So, you know, what what's, like, the the the way that you would open the conversation?
Speaker 1:Oh, that's a good question. For Greg, there's a lot. But, I mean, the question that I feel like is at the top top of mind in, like, Ben Thompson world this week is, the shift from training to inference and how and how workloads are are are shifting. Yeah. This was in the context of an NVIDIA earnings, but Greg obviously has intimate knowledge.
Speaker 1:And so, OpenAI is obviously going to do larger and larger training runs, but they're also doing tons and tons of inference. How is that shifting? And then what I wanna know, and I don't know how much he can speak to this, is is is, like, where how will if we get to a world where we're in 80 inference, 99% inference, something really extreme, does that change the type of data centers we're building? Does that change the chips that we're demanding? Are we moving to ASICs?
Speaker 1:Are we moving to FPGAs, something like that to, like, speed up the actual inference workloads. At what point do we actually bake these models down if they're deemed, like, good enough and then we're, like, orchestrating them? If we hit a plateau, that might make sense. At the same time, if there's a lot of promise on algorithmic progress and we're expecting, like, oh, yeah. We're gonna leave the transformer behind eventually.
Speaker 1:Well, then, yeah, you don't wanna bake all that down. So I think that that's, like it's a little bit more of, a semis question, but it it is an interesting question for him as well as he's, like, seeing the workloads.
Speaker 3:It is very relevant. He he does have a role to play in Stargate that I'm not super clear about. I'm gonna ask him about. Yeah. And, yeah, I mean, I I I I I would say that nobody is betting on the end of transformers
Speaker 4:Oh, yeah. Except for
Speaker 3:a very small very small number of people that are just experimental. Yeah. And it's it's really about just scaling the RL.
Speaker 1:Yeah. I mean, there is this interesting thing that's happening. I believe images and ChatGPT seems to be using a number of different algorithms combined. So there's a little bit of diffusion in there potentially. There's some there's some transformer based stuff.
Speaker 1:I don't know if you have a if you're pushing back on that, but that's I'm not.
Speaker 3:I there there's a there's a strong hypothesis based on the hints that they have dropped from the people that worked on it.
Speaker 1:Yeah. That there that there's multiple algorithms at work. Right?
Speaker 3:It's a it's a it's a diffusion head
Speaker 1:Yep.
Speaker 3:With the transformer backbone. Yep. The rest of it is just regular four o. Yep. And that's so good at chat and has has decent world knowledge.
Speaker 3:Yep. There's not, like, a ton of complexity there beyond that that we'd know of.
Speaker 1:Yeah. And so what I'm interested in is, like, we saw the demo from from Google on diffusion, text based diffusion, 900 a second. I've heard good reviews about that. I've heard kind of mixed reviews on that. Maybe it's not a path.
Speaker 1:But, what does what does the future of an LLM look like if we're if we're applying the same path that we've seen in images where we're seeing an ensemble of models come together to create a better like, even more multimodal, multi multi Yeah. Like, multi architectures, because it feels like we're moving we're moving further and further away from the single big transformer being the answer to everything. And and so what does that mean? Are we going closer or farther away from the single big transformer that is, like, the the god in the weights?
Speaker 3:Yeah. This is where I can offer a little bit of coaching for, for your audience, and you you guys have talked to a lot people. I I don't
Speaker 1:know Please.
Speaker 3:Rad on this is where people usually say the term mixture of experts, and they're wrong to use that. Okay. But I just and sometimes they say mixture of ages, mixture of experts.
Speaker 1:They're they're Sure.
Speaker 3:They're incorrect. Anyway, we have talked with a lot of the the Frontier lab researchers. Yeah. No none of them believe that that is the the way forward. That is that is an optimization for the current thing, which is
Speaker 1:Which which one? A mixture of experts or
Speaker 3:a like a Or or multiple Mixture of architectures.
Speaker 1:That's of architectures.
Speaker 3:Let okay. Yeah. So experiments, for example, with Jamba with Yeah. From 08/21 in Israel, where they mix, for example, like a Mamba layered with with transformers.
Speaker 7:Sure.
Speaker 3:That seems to be promising.
Speaker 4:But even Gong Shazir, a
Speaker 3:correct character was just used to scaling out transformers. Sure. I I I asked a variant of the question you just stated to Noam Brown in an upcoming podcast that we did. We recorded it. We haven't released it.
Speaker 3:He's the same way. He's like, anything you're trying to do fancy around mixing a weird architectures is just not gonna scale. Just scale the the the the basic thing. Yep. And he's pretty strongly convicted in that.
Speaker 3:I I know people I can name at Gemini who is also pretty strongly convicted in that. I always suspect suspect otherwise. Obviously, like, you can't shut this shut down anything because, like, it might be true. I'm just telling you what the people working on this would say because I
Speaker 4:Okay. Well, I mean, that's AI The last the last
Speaker 2:thing I wanted to kinda cover, I I would wanna get a sense of, you know, prediction markets are pricing in that Google continues to dominate on benchmarks. How much is Yeah. OpenAI even gonna how much do they even care about being at the top of benchmarks? Is it is it purely an ego thing, or is it just about, you know, usability and and value for users and
Speaker 3:Yeah.
Speaker 2:Code quality and things like that?
Speaker 3:It's usability and value. You you I think you need to notice that the benchmarks that Google talks about at Google IO are no longer the benchmarks that OpenAI measures itself on. Mhmm. And, you know, I wanna I wanna stay friends with them. But, like, there are some that are more sus than others.
Speaker 1:Sure.
Speaker 3:And I think, like, at the end of the day, the the the customer will win. The only one that that has not that's clearly been caught out kind of lying, kind of, like, being whatever like, underperforming. I gotta say it. It's Apple. Mhmm.
Speaker 3:When they launched Apple Intelligence, we were all very excited about the Apple Intelligence paper, which is which is beautiful documents that had no evals apart from their own internal evals. So they were not accountable the standard set of evals that everyone else has. Right? So step one is you should you should try to hold yourself to some public benchmark. I I flawed.
Speaker 3:Just do it. You know? Mhmm. But and then so so so Apple did not do that. And and then, like, now there's a question of, like, are you holding yourself to a benchmark that can be gamed?
Speaker 3:And there's been accusations of Ella Marina being gamed, and, unfortunately, that's what Google has put all its chips on. They may do that for Gemini three, but for 2.5, that's what they did. And probably because they spent the last year doing it.
Speaker 1:It's all revenue from here on out. Like, that is the benchmark.
Speaker 2:Is the benchmark.
Speaker 1:GitHub Copilot, 500,000,000. Size gone for GitHub.
Speaker 3:Revenue is also tricky.
Speaker 1:Yeah. You guys know how
Speaker 3:like, Gemini I don't know if you know. Gemini gives a billion tokens a day per human.
Speaker 4:What? Wait.
Speaker 3:Your personal API token.
Speaker 1:Okay.
Speaker 3:I'm kidding you. You can you can you can you can set up a camera right now, run Gemini two point five flash on it, run like a frame a second
Speaker 9:Yeah.
Speaker 3:And ask you to do whatever you want for free. Wow. It's absurd. So Let's see.
Speaker 1:They are for Google. Thank you. Intelligence is too cheap to meter. We love
Speaker 3:Also, okay. Like, that chart that you saw at Google IO where their their tokens went like this.
Speaker 1:Yep. Now you
Speaker 3:know why? It's free.
Speaker 1:Yeah. Yeah. Yeah. Yeah. Yeah.
Speaker 1:Yeah. I
Speaker 3:mean, it's they also have a very, very good model. Don't get me wrong. But I'm just like, revenue is a short term play. Like, you're trying to maximize revenue now, you might cut off cut off yourself from getting the largest dataset in the world, which is the the sort of early adopter humans.
Speaker 1:Yep. That
Speaker 3:are gonna give you their data because they're like, train on me, daddy, and I I I'm you're giving to me for free. Like, whatever. Train on me.
Speaker 1:Yeah.
Speaker 3:And and that's a that's a deal that lots of people think it is the theme that we we talked about is lots of people are going to make.
Speaker 4:Yeah.
Speaker 3:Is free. Why? They they can eat your data. Codex is free. Why?
Speaker 3:Yep. So I I talked about this in in one of my recent posts. Like, the the the marginal cost of software has is is has gone down to zero. Mhmm. And, like, can it go negative?
Speaker 3:Will I start paying you for you for me for you to use me? Absolutely. Because then you'd become my labeler. Yeah. You're you're a labeler I can never pay for.
Speaker 3:You know, usually, have to, like, hire someone like The Philippines or something. We maxed them out. So now I got I gotta I gotta pay someone in Silicon Valley to label my my software. So I might as well just give you my coding agent for free, or, like, even honestly just pay you for good feedback on my coding agents. Mhmm.
Speaker 3:And why should that why should it stop at, like, $20 in a month when, like, there are people in New York who I've heard, by the way, get they're, like, form they've had they hire former bankers. They hire former hedge fund guys paying them 500 k a year to label.
Speaker 1:It's wild. Well, thank you so much for hopping on. Good luck with the rest of the conference.
Speaker 2:Let's make this a a regular thing.
Speaker 1:This is fantastic conversation.
Speaker 2:Thank you for taking the time. We really appreciate it. And we'll be there next year
Speaker 1:Yeah.
Speaker 2:Whether you like it or not.
Speaker 3:Fantastic. You're invited. For sure.
Speaker 1:We'll talk to you soon. Thanks so
Speaker 2:much for hopping. Thanks, Sean. Bye. Cheers.
Speaker 1:Fantastic. Well, we gotta tell you about Wander. Find your happy place. Find your happy place. Book a Wander with inspiring views, hotel grade amenities, dreaming beds, top tier cleaning, and twenty four seven concierge service.
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Speaker 2:I want the TBPN army to clear out every single one of all of some Block
Speaker 3:a clearing order inbound.
Speaker 1:This is an interesting one. Oscar has joined the Fortune five hundred for the first time. I know what you're thinking, and I agree. What? Only now.
Speaker 1:And Josh Kochner says, I'm deeply proud of the tenacity and persistence of the Oscar team. We've been humbled many times from down 95% from our IPO to now entering the Fortune five hundred. I am excited for all that is ahead of us. Wow. Congratulations to Josh Kushner.
Speaker 1:That is a phenomenal run. Amazing. And somebody else was quote tweeting this saying that there's only two people who have incubated a Fortune 500 company who are actively investing, and it's Palantir with Peter Thiel and Founders Fund and Josh Kushner with Thrive and and Oscar. So what a phenomenal run. It is rare that the incubations are starting to work, and the incubations will continue until morality improves.
Speaker 1:They will continue. Until the global economy is worth is growing at 10% a year. Anyway, every person who works in tech needs at least non five nontech friends to interact with closely on a weekly basis so they can understand how the general public actually thinks, says Kit Volta. You posted this from a different account. It was a copy pasta.
Speaker 1:Somebody copied the whole thing. So I found the original. You shared
Speaker 2:I shared it.
Speaker 1:You shared the fake one, the rip off.
Speaker 2:I shared a rip off.
Speaker 1:Yeah. You shared a rip off. And it got community noted, and I found the original one right here.
Speaker 2:Wait. So somebody just stole it?
Speaker 1:Yeah. Yeah. They didn't even screenshot it and put it in Bangor.
Speaker 2:That is
Speaker 1:They actually But 20 k on this post. The repost has 15 k likes. And so it got but it got community noted. This is a copy paste. I you feel like that could be, that could be, like, automated in Axe where it's like Yeah.
Speaker 1:This is the exact same text as has been posted elsewhere. Yeah. You should just know that you should maybe go to the source on this if somebody's posting the exact same thing. Except, you know, Josh Diamond would get that every day because he's constantly boasting, good morning. We are gonna win.
Speaker 1:And so he would have the note every single day. JT, Jira tickets says web design. You mean digital physiognomy? Very funny. Oh, you put another one in here from JT.
Speaker 1:Guy who is burnt out from six figures, five hours a week, big tech job. You gotta just hit the hit the hit the roof and just, get some sun. You know? Anyway, Pavel has a good one. I don't think Waymo can work in New York City because during rush hour, you need to break the law to get anywhere, and I'm not sure how you could get away with embedding that in software.
Speaker 1:And IB says, Travis would have gotten it done. Never too late.
Speaker 2:Somebody says full self driving gladly speeds for me.
Speaker 1:Yeah.
Speaker 2:I thought this was good from Will. Yeah. Two thick scoops. Okay. Sounds good.
Speaker 2:Thank you. And it's Jersey Mike's order pickup at 12:48. Dutch government collapses over migration dispute.
Speaker 1:Why is this?
Speaker 2:This is a Gmail.
Speaker 1:Oh, it's just rolling out multiple emails or something.
Speaker 2:This is Apple AI summary.
Speaker 1:Oh, yeah. It's an Apple AI Apple intelligence is the summaries are so good. It's genius. 75,000 likes, that's lot of attention on that. Gotta upgrade it, You're missing out on entertainment.
Speaker 2:Would pay to help me produce Apple intelligent viral bangers.
Speaker 1:Yeah. I wanna wrap up, but we gotta say congratulations to Jesse Michaels. He got on Joe Rogan. He said it was the honor of a lifetime to sit down with a goat himself, Joe Rogan.
Speaker 2:Many people have called him a young Rogan.
Speaker 1:I've called him that. He is fantastic. He's been on a fantastic run with American Alchemy, his podcast. He's climbing the charts. If you're into aliens, AI apocalypses, Bob Lazar, nuclear weapons, secret antigravity research, go check out the Jesse Michaels, Joe Rogan experience episode.
Speaker 1:It's episode 2,331. Rogan's putting up big numbers. He's given us a run, but we're catching up. We're catching up.
Speaker 2:Wild. Other years, lastly, I just wanted to cover this quickly. AMD has acquired Brium
Speaker 4:Oh, okay.
Speaker 2:Reported today Interesting. To help reduce NVIDIA's market dominance when it comes to AI hardware.
Speaker 1:Talking to George Hots, and and we're we're gonna have him on the show, but, he's been he's been back in AMD. We got Dylan Patel coming on the show Friday. We'll ask him more about AMD, and they're they're planning to unseat CUDA as the dominant AI platform.
Speaker 2:Yep. And
Speaker 1:we have other news. We are officially we have doubled the size of the show. Once again, Jordy, hit that gong. Hit the real Hit the real gong for me. Grabbed the real gong.
Speaker 1:We're going to gong cam. We got 64,000 followers on X. Thank you for everyone who's been around. Oh, there we go. That's great.
Speaker 1:Thank you for watching. Thank you for supporting us. It's been a fantastic journey.
Speaker 2:It is a pleasure
Speaker 1:doing business. Got a whole crew in the studio. We got a whole bunch more cameras here. We're growing every day.
Speaker 2:Damn. We got the studio cam too.
Speaker 1:The studio cams What's cooking? Thank you for watching. Thanks for
Speaker 2:Live star
Speaker 1:Live star review. Apple Podcasts and Spotify.
Speaker 2:I'm actually on Apple Podcasts.
Speaker 1:Yeah. I feel like we're doing low heavy It doesn't really make sense. But if you're on Apple and you're listening and you haven't left us a a review, we'd love a review. So thank you so much. And we'll see you tomorrow.
Speaker 1:We have an awesome day. We have a we have a bit of an AI day coming together with folks from OpenAI and Anthropic coming on. It should be a great show. So stay tuned. We'll see you tomorrow, and have a great rest of your Wednesday.
Speaker 1:Goodbye. Cheers.