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TBPN is a live tech talk show hosted by John Coogan and Jordi Hays, streaming weekdays from 11–2 PT on X and YouTube, with full episodes posted to Spotify immediately after airing.
Described by The New York Times as “Silicon Valley’s newest obsession,” TBPN has interviewed Mark Zuckerberg, Sam Altman, Mark Cuban, and Satya Nadella. Diet TBPN delivers the best moments from each episode in under 30 minutes.
You're watching TVPN. Today is Friday. It's the day before Valentine's Day twenty twenty six.
Speaker 2:That's right.
Speaker 1:We're live from the TVPN UltraDome, Temple of Technology.
Speaker 2:We should have mentioned Valentine's Day. Yeah. Earlier. Two months ago, and then again, month ago. Most of And then again.
Speaker 1:Audience is prepped for sure. But we have some we have some ideas, some recommendations if you're looking for advice this Valentine's Day. We, of course, live from the TV panel, Temple of Technology, the fortunes of finance, the capital capital. And here's an idea for Valentine's Day. Ramp.com.
Speaker 1:This Valentine's Day, show her you care about your future together by putting all of your couples spending on Ramp because nothing says I will provide for our family like pulling out a ramp card. Knows you take you handle business.
Speaker 2:That's right. Anyway Handling business.
Speaker 1:The big news, Anthropic has raised $30,000,000,000 at 380,000,000,000 post money valuation. We've all seen the revenue chart. 10 x growth four years in a row, 100,000,000, a billion, now 14,000,000,000. Will they do a 100? That's the question.
Speaker 1:Will they be at a $100,000,000,000 revenue run rate by the end of the year? They're growing on track to hit that, which is crazy and completely unprecedented. But, again, they're going after all of SaaS. They're going after all of software. They're going after all of labor, all of white collar work.
Speaker 2:All in your job specifically.
Speaker 1:Yeah. It's not looking good for you. No. We're joking.
Speaker 2:Never doom.
Speaker 1:Never doom. There's plenty of opportunity. There are plenty of good potential outcomes. Dario has been on Dwarkash Patel today, and he did something else with Ross Douthat, and so there's a number of places where you can go to hear his latest takes on the good ending and what he's guiding towards. It'd be interesting to follow.
Speaker 1:The question is, what happens to the companies that are currently under pressure with the anthropic narrative? They have to answer this question of, is anthropic just going to steamroll you? What is your real source of strength?
Speaker 2:Yeah. Just Anthropic, but the labs Yeah. Every YC company Yeah. That is building an AI native, you know, any company that is slapping AI native on their website. Yeah.
Speaker 2:Everyone's going after the opportunity. So we coined a phrase. We we decided to coin something.
Speaker 3:Yes.
Speaker 2:It was time to coin out.
Speaker 1:And before we tell you the coinage, let me tell you about Cisco. This Valentine's Day, give her the gift of enterprise grade networking this Valentine's Day, so your home WiFi never drops during movie night, because nothing kills the mood like buffering during a rom com. Get on Cisco.
Speaker 2:Head over to cisco.com. So yeah, we decided, so how do you what is a phrase that you can generally apply to businesses that can survive and then hopefully thrive Yeah. In during this moment in time. And
Speaker 1:I Intelligence think there's is too cheap to meter.
Speaker 2:Yeah. So so the the the question, you know, earning cycle last couple weeks, every CEO has gone on. If basically, like, you had to answer the question, like, what talk about the threat of AI Yeah. If you just had to answer the question Yeah. Like, basically, the companies that were just the entire earnings call was just about generally about AI Yeah.
Speaker 2:You know, if you're like a core weave or something like that Yep. That's a little bit more straightforward. But if you have to answer the question, do you have a durable moat right now with AI Progress, your stock is probably gonna sell off. But but, you know, either way, kind of however you answer it. But there's a second question, is like, are you a true beneficiary?
Speaker 2:So like, do you have a durable moat? And then are you a true beneficiary? So we decided to coin the phrase unsloppable. Unsloppable. Unsloppable.
Speaker 2:So these are companies that we'll get into that have some type of moat in an era where it feels like more code could be written in the next twelve months
Speaker 3:Mhmm.
Speaker 2:Than in all of human history. Yeah. I was kinda running the numbers.
Speaker 4:Seems But
Speaker 1:most is specifically not okay. You're a company that has just spent ten years writing a bunch of lines of code, and it would take a startup a lot of time and money, and they would have to
Speaker 2:hire a lot
Speaker 1:of engineers and write a lot of code to create a copy of what you have. Yeah.
Speaker 4:It's
Speaker 2:like, no. So like to rebuild Salesforce as a platform Yep. You would have to spend billions of historically, you would have had to spend billions of dollars Yep. Hiring thousands of software engineers Yep. To, you know, piece by piece build out all of the functionality.
Speaker 2:At Salesforce, of course, you could build vertical solutions and get some amount of traction. But in general, the idea was there was some, effectively, just an engineering mode, and that there was a lot of code that you'd have to write to effectively compete. So I talked about in the newsletter today
Speaker 1:Set the table first. What's going on in the market?
Speaker 2:Yeah. So software has undergone the largest non recessionary 12 drawdown in over thirty years. That's minus 34%, wiping out 2,000,000,000,000 of market cap from the peak.
Speaker 4:This is
Speaker 1:And JP.
Speaker 2:JPMorgan as of a couple days ago. AI threat sparks historic software stock crash. Goldman Sachs warns of newspaper like decline.
Speaker 1:I love the newspaper. What's wrong with newspapers?
Speaker 2:Still got it. No. Still got it. And then as of yesterday, over the prior eight trading sessions, more than 20% of the S and P 500 had a drawdown of 7% or more in a single session according to the compound.
Speaker 1:That's a lot. Quickly, before we continue, let me tell you about the New York Stock Exchange. This Valentine's Day, I recommend flying to Manhattan, take her to the floor of the New York Stock Exchange, IPO your company, and ring the opening bell together.
Speaker 2:Great.
Speaker 1:Your love just went public.
Speaker 2:Good one, John. So Yeah. Yeah. So continuing. So I wrote, Everything was great when we were disrupting manual workflows, but as we enter the software singularity, we are having the uncomfortable experience of disrupting ourselves.
Speaker 2:Assume the marginal cost of software development goes to zero. If you are a software company where your moat was that a competitor would have to spend $1,000,000,000 to hire a bunch of software engineers to write millions of lines of code to create a product, and you have no other moats, it's going to be rough. Thankfully, there are moats that are unaffected by coding agents and effectively zero cost software development. Peter Thiel, PT, outlined four key sources of monopoly power in zero to one back in 2014. These are proprietary technology, network effects, economies of scale, and brand, or you can think of as trust.
Speaker 2:Most of these still hold, proprietary technology by itself is no longer sufficient as a moat.
Speaker 1:In some cases, if you have a patent to a GLP-one drug, that is a proprietary technology that will give you pricing power probably for as long as the patent holds. And there are patents on certain pieces of technology that even if they can be cloned or rederived from first principles with your million geniuses in the data center, the first person to patent it gets to reap that value. And that's just the way
Speaker 2:our And the issue with software, how many I I I bunch of designer friends of mine have, a design patent on a specific kind of Can enforce it. Workflow. Yeah. And it's cool to say that you have a patent, but it's not
Speaker 4:Yeah.
Speaker 1:Proprietary technology can just be, Okay, we have a big software system. But oftentimes, it's more like we have proprietary, like something that's regulated, something that's a cornered resource, something that's scarce and will remain scarce. But yes, if your proprietary technology is just you're the only person with this particular Python script, that's Yeah. That that's probably going away. But network effects aren't.
Speaker 1:And some of the economies of scale, some of the liquidity on these platforms is going to be durable. You can vibe code a I was talking to Dara Khoshishari at Uber about this. You can vibe code a pickup app that, you know, looks like Uber, has a map, lets you click the button, accepts payment. But if there's no one on the other side of that network to actually come and pick you up, your your Uber Uber clone is dead in the water.
Speaker 2:Now Yeah. Or if a customer if somebody does pick it up and the customer has a terrible experience, do you have the do you have the resources to actually make it right?
Speaker 4:Yeah.
Speaker 1:But Uber works because they spend a bunch of money getting to scale, and cloning that scale is difficult. Now, the whole self driving car thing is separate because you bring Unclankable. Yeah. We're working on that one. That'll happen.
Speaker 1:But there is there are a set of businesses that will have to contend with the clankerfication of the economy. But that's
Speaker 2:the Yeah, becoming unsloppable means two things. First, your business actually has to drive its economic power from a moat that is unsloppable. And second, you need to clearly communicate that to shareholders. Right now, if the market thinks you're just a bunch of lines of code, you're cooked. Tech companies, we think of as unsloppable.
Speaker 2:You have hardware, NVIDIA, AMD, Intel, Cisco, Broadcom, SK Hynix, Western Digital, data centers, so neo clouds, things like CoreWeave, Lambda, Social networks, YouTube, Instagram, X, LinkedIn, even thinking Roblox, They can be not just they have the network, and they can be a beneficiary of AI because if it's easier to make games, lot more people will make games. Maybe you'll get more usage. Marketplaces, Airbnb, Uber, DoorDash. IP holders, Disney, Netflix, Warner Brothers. I think if you have a lot of IP right now and the cost to produce great content drops dramatically, you're gonna benefit from that.
Speaker 2:And then platforms, things like YouTube and Spotify as well. I said it's been an incredibly rough couple of weeks for public market CEOs. Really disheartening on the show. CEO's been putting up some great quarters. And then they're trading down between 720%.
Speaker 2:There are two main questions everyone wants to know, even if they already sold your stock to buy Atoms. One, do you have a durable moat in the software singularity? Two, are you a true beneficiary of AI? Many CEOs are still struggling to answer, number one, because it doesn't really matter what you say. Just having to answer the question equals a sell off.
Speaker 2:And two, this one can really only be answered in the numbers. You aren't an immediate AI beneficiary if revenue is not accelerating. Also, separately, there are a bunch of crazy things happening in the broader economy right now. I think Besson Besson went on CNBC at 4AM. They brought out the big dog.
Speaker 2:Mhmm. I haven't been able to catch it yet.
Speaker 1:It was 7AM Eastern. Right?
Speaker 2:Yeah. 4AM Pacific.
Speaker 1:4AM Pacific.
Speaker 2:Very, very early for us.
Speaker 1:It's
Speaker 2:possible to be unsloppable but not an obvious beneficiary, but you'll still likely sell off as the market digests and interrogates the actual real world impacts of coating agents. Some industries will be more resistant to change. Other industries will be revealed to have a secret source of market power that was underappreciated in the before times.
Speaker 1:And what I was thinking was Nielsen, this company, it was you know, in in one way, Nielsen ratings, Nielsen data. A lot of consumer packaged goods companies use this. I'm sure Mattina is looking at how is this Yerba Mate selling in this store. And you go to Nielsen, you pay them, and they give you data. And it just feels like an interface to sales data, but they have this whole network and you just have to pay for it.
Speaker 1:And it's not really something that you can just spin up. I don't know. What do you
Speaker 5:I mean, isn't that kind of like what what Simile is doing? We had them on yesterday. They're like trying maybe Yeah. You can kind of do like polls or something about how market will
Speaker 1:That's more for or like that's Yeah. More for future. Yeah. Prediction. The bigger one is like is like Like I would don't ever know
Speaker 2:Simile would actually want that data to update their models.
Speaker 1:Yeah. Like you wanna know, okay, what stores are actually turning my product? Which store should I be doing promos in? Which store should I be doubling down on running advertising in? Or what chains are working should I push more into, or even just, hey, I need to go to one chain and say, I'm working, like Target's working, so Walmart should carry me.
Speaker 1:They're not really going to accept just a simulation of that data. They're gonna wanna know that an independent rating agency sort of rated it. Gold Rock. Oh, you did.
Speaker 2:Just bought unsloppable.com.
Speaker 1:Okay. Great. Quickly. Before we move on, let me tell you about AppLovin. This Valentine's Day, use AppLovin's axon.ai to serve her hyper targeted ads for exactly the jewelry she wants.
Speaker 1:That way, she'll be extra excited when she unwraps presents on the big day.
Speaker 2:Great call. Capping off the newsletter, I said a lot of the software market feels like the office equipment and imaging sector in the '90s. So companies like Sharp, Canon, Panasonic, revenue was still up and to the right, but widespread adoption of the internet emails and PDFs was on the horizon. Even today, you'll still find a fax machine in every doctor's office, and many of the giants of that era are still around. But if you stayed in those names, you would have missed out on generational gains by simply being long PDF.
Speaker 1:Got it.
Speaker 2:Had to go long PDF. So yeah, if you look at these companies, Panasonic's still a massive company. Yeah. And they've obviously adapted over time. But but, you know, it's a
Speaker 1:It's a shift from growth stock to value stock. Investors are less willing to pay for earnings that might come ten years out because they're worried about those or twenty years out. Instead, they're asking, what will my return on invested capital be this year? What will the dividend be this year? How much cash will you give me back if I invest for a one year time horizon or shorter or longer?
Speaker 1:Let me tell you about Turbo Puffer. This holiday, Valentine's Day, here's an idea. Turbo Puffer. Store vector embeddings of every romantic moment you've shared in Turbo Puffer so you can do semantic search for that for that time in Paris and actually find it. Serverless vector and full text search.
Speaker 1:It's built from first principles in object storage. It's fast, 10 x cheaper, and it's extremely scalable. So no matter how many memories you're cramming in Turbo Puffer, you're good to go. You're good to go this Valentine's Day. Just do it.
Speaker 1:Anyway, it will be interesting. I think that there will be a reckoning around who is able to reveal a true moat and help people help understand what their source of strength is, whether it's liquidity on their platform, the network effect, the IP, if they have real IP that's defensible. But just having a big bag of code right now is a little bit of a wait since you're seeing so many companies that are saying, well, I haven't we we we our software engineers aren't even writing the code anymore. Yes. We're advancing our products.
Speaker 1:But so many companies are are going all
Speaker 6:in.
Speaker 2:It's also it's it's pretty wild how long it's taken for the public markets to react to this this kind of one shot in concept or the zero marginal cost code Yeah. You know, coding concept where we were having these same conversations in q one of last year Mhmm. Being like, what are the implications when you can just put in the prompt box build me x y z tool? Right? And it took a while for the models to make progress.
Speaker 2:But even a year ago, it was pretty obvious that you would get to some point where you could one shot Yeah. A big platform. Of course, reliability is still a concern. Yep. Right?
Speaker 2:Security is still a concern. Yep. There's a lot of businesses where the the potential risks of using, like, a vibe coded product, like, far outweigh the the the cost of just paying for the product and having something that's reliable, trustworthy, battle tested.
Speaker 1:Yep.
Speaker 2:And but Yeah.
Speaker 1:I mean, there's still a ton of questions about how quickly disruption happens, how quickly market structures change. Some things go from monopolies to oligopolies. Some oligopolies are gonna go to perfectly competitive. It's certainly a bull market for YC companies who can vibe code something that's as good as a public company SaaS product and then go to those customers and say
Speaker 2:Maybe not as good, but as feature complete.
Speaker 1:As feature complete or at least can compete a little bit faster and say, hey, I'll come in with an offer that's 10x cheaper and move you over. And that's just going to create some pricing pressure. The question is, what's the rate limiting factor? Is diffusion a real factor? Is adoption a real factor?
Speaker 1:Or do you need forward deployed engineers to go help companies transform with agentic coding? Or do you or can you or will this happen inside companies and they'll be building their own platforms? Or they want just a cheaper product from a new third party that has a different business model that's maybe more consumption based and something where if it goes down on a Saturday, they don't need to even fire off a prompt? How long until these Vibe coded systems are like self healing in the way an enterprise platform is and has, like Yeah. Proper SLA.
Speaker 1:What do think, Tyler?
Speaker 5:I I just have a question. Like, I'm I'm curious what do you guys think about this. It's so it's like, if the market is just catching up now to to, like, coding models being very good and vibe coding all this stuff, and they're basically, like, a year late. Mhmm. In one year, what do you think, like, is gonna be the thing that they're, like like, do you think they'll still be late?
Speaker 5:Is it gonna be, like, okay, actual, like, white collar work is you actually can't automate a lot of this stuff and only in in in a year that they're actually gonna, like, catch up to this?
Speaker 1:Yeah. That's a good question. The next next thing. Don't know.
Speaker 2:Tyler, if I knew the answer, we'd be on Wall Street.
Speaker 1:I think we'll I think we'll be talking about it over the next couple months. We'll need to see like glimmers of demos. I mean
Speaker 2:Yeah. The the one thing is is like coding never Well, felt so Here here's the thing. So I have an answer. In in coding has been a white is a white collar job Mhmm. But has always felt a lot less fake than most white collar jobs.
Speaker 7:Mhmm.
Speaker 2:Like, there's a lot of jobs Mhmm. Like email jobs, laptop jobs Mhmm. Where there's like six people on a call Mhmm. For an hour Mhmm. And like one person is doing, like, really doing the work Yeah.
Speaker 2:And the rest of them are just saying like, nothing from my end. Thanks. Right? And that's like their entire day. Mhmm.
Speaker 2:Whereas coding, like the best engineers were actually just like grinding all day long Mhmm. Putting in the hours, just shipping. Right? And so I think what's interesting like, as as some of these, like, more broad knowledge work tasks get more easy to automate, do those people just like, they're still gonna be doing meetings? Like, at some point, these companies I mean, to date, the the AI the AI job loss has just been primarily from companies, I would say, still processing the Twitter acquisition
Speaker 1:Yeah.
Speaker 2:And saying
Speaker 1:Yeah, yeah.
Speaker 2:Hey, we just we need 50% fewer people here.
Speaker 1:Yeah. This sell off is much more related to business model competition pricing pressure than automation and job loss, in my opinion. It's much more that there will just be more competition in enterprise software markets, and so you assume that margins will fall. That that's my read on this. I do have another example, but I will tell you about Gusto first, the unified platform for payroll benefits and HR built to evolve with modern small and medium sized businesses.
Speaker 1:So, my answer is the the unclankerable, company. So, right now, are industries think about, think about mining. Like, I have a piece of land. There is gold in the dirt. There's another company that comes, and their specialization is finding where the gold deposits are on my land.
Speaker 1:There's a third company that shows up with tractors and people that dig the gold out. Then there's a fourth company that takes the raw ore and refines it into gold. There's a fifth company that is a platform for for selling that gold onto the market. Right? So you like five different layers of the supply chain to get the gold into the market from the ground.
Speaker 1:Let's just use that. It could be oil. It could be any mineral. Does robotic labor too cheap to meter change the value of the land? Probably not.
Speaker 1:But if you have a robotic digging machine that can show up and dig the the ore out of the ground, dig the gold out of the ground at a lower cost. Well, the company that's been set up where their moat was they employed all the best miners, and they had systems to know who's good, train them, make sure that they're doing it safely, train them on the tools, make sure that they have the right equipment to dig the ore reliably, work in shifts. All of that becomes attackable. If you're like, well, all I have to do to start a company that competes in the gold mining business is place an order with a bunch of humanoid robots and go to the guy who has the land and say, I want to dig the land, and I will give you a little bit more than what the other team that's using a bunch of human labor and a bunch of unautomated systems. So I would say that that's probably the next thing that the market would be processing.
Speaker 1:And the ride hailing platforms dealing with the advent of the self driving car is probably one of these, like, clankerification narratives, but that will come to a whole host of industries. The question is just on a five year time line, on a ten year time line, when will it be real and then when will the market price it accordingly? Because a lot of the pressure that you're seeing in the market is not showing up in the financials. Like, the companies are still growing. They're still producing cash.
Speaker 1:The business hasn't changed, but the perception of the future of the business has changed, and the perception of the future of the market structure has changed. And so that might be the next thing if we're just to play out AI broadly. Anyway, Phantom Cash. Fund your wallet without exchanges or middlemen and spend with the Phantom card. Let's also pull up the linear lineup and take you through who's coming on the show today.
Speaker 1:Linear is the system for modern software development. 70% of enterprise workspace on linear are using agents. It's Friday. We have a lighter show, but we got some great guests. We got Martin Scrella coming on to talk about take a little victory lap about the quantum computing thing.
Speaker 1:Conor Hayes, head of the Reds. We hung out with him at Metaconnect. We're very excited to talk to him about the progress
Speaker 2:Brother of Hayes.
Speaker 1:Platform. Then Alex is coming on from DDN, and Brett Aycock from Figures coming on to talk about humanoid robots. They launched a new one today. So
Speaker 2:Finally. On the show. Gonna be an interesting one. Clavicular has also been
Speaker 1:In the news? Oh yes, what happened?
Speaker 2:Let's see here. Streamer Braden Peters to host boxing match billed as test of physical dominance. The Valentine's Day livestream pits two figures from the male self improvement Internet against each other. Braden Peters, the livestreamer known as Clavicular, announced Thursday that he will host a boxing match on his kick.com channel this Saturday evening, February fourteenth at 7PM Eastern directly opposite Valentine's Day festivities nationwide. The bout will feature two personalities from the online male aesthetics community, a figure known as ASU Frat Leader, an Arizona State University fraternity member who gained attention for his broad shouldered build, and a creator who goes by Androgenic, a fitness influencer focused on hormone optimization and physical appearance.
Speaker 2:Promotional materials build the event as a championship of skeletal frame superiority, essentially a contest to determine which man is more physically imposing. The announcement posted to X by the Kick affiliated account Kick Champ has drawn thousands of engagements and spawned a wave of commentary from users who noted the scheduling choice with amusement, characterizing the event as a deliberate alternative to the holiday. The matchup represents the latest example of niche internet subcultures, in this case, communities organized around male physical self improvement and body image optimization crossing over into live entertainment. Mr. Peters, who has built his following around content related to physique and social dynamics, appears to be positioning himself as a promoter within the space.
Speaker 2:No official venue has been announced. The event is expected to stream exclusively on kick.com. So we'll be interested to see how how the news kind of reacts to the event Yeah. Over the weekend. Certainly, this story has gone mainstream.
Speaker 1:You know, it's funny that so if you're if you haven't been following these, we've we've been taking these, like, viral kick clip posts and turning them into professionally written articles just as a joke. But Plavicular actually has a profile in the New York Times, and it's written like that. And so I think I think our joke is, like, over because it's hit the mainstream. Joe Bernstein wrote something that sounds exactly like something that we were joking about. Braden Peters, known as Clavicular, has emerged as a beacon for a group of narcissistic status obsessed men.
Speaker 1:He wants to take his fixation with Luxmaxing mainstream. It's a it's a wild piece. Clavicular is a six foot two. He weighs a hundred and eighty pounds and has a thirty one inch waist. His bichromial width, basically the span of the clavicle, from which the 20 year old streamer gets his name is nineteen point five inches.
Speaker 1:He has a mid face ratio which is derived by dividing the distance from pupil to the mouth by the distance between the pupils of 1.07. His chin to philtrum ratio is 2.6. According to Clavicular, these calculations make him handsome, just not as handsome as actor Matt Bomer. And then it goes on to explain the whole Luxmaxing phenomenon. And it was very funny watching this happen because Clavicular streams so much that he livestreamed the interview with Joe Bernstein.
Speaker 1:But of course, normally when you do an interview with a mainstream media journalist who's rating profile, it's like under embargo and you don't know it's coming until it and like you don't even really talk about it and the chat is confused. This is a very popular a very popular trend on social media these days and and The New York Times is breaking it down. But anyway, there there there's there's plenty more there. Let me tell you about public.com, investing for those that take it seriously.
Speaker 2:Stocks, options, bonds, crypto, treasuries, and more with great customer service. Really, really wild time on the Internet.
Speaker 1:Anyway, let's let's let's go back to the Anthropic round. Matt Slotnick says LOL at the jockeying behind the scenes to land on this wording, quote, we have raised $30,000,000,000 in series g funding led by GIC and CO2, valuing Anthropic at 380,000,000,000 post money. The round was co led by D. E. Shaw Ventures, Dragoneer, Founders Fund, Iconic, and MGX.
Speaker 1:Lots of folks getting in. A huge part of this raise is Claude Coats, says Boris Cherny, who is the creator of Claude Coats over at Anthropic. Weekly active users doubled since January. People who've never written a line of code are building with it, humbled to work on this every day with our team. That is remarkable growth at this scale, doubling.
Speaker 2:Kenneth having some humble humble pie or maybe maybe Yeah. Just how early it is. He says still less annual revenue than AirPods. AirPods, last I checked, were a $20,000,000,000 revenue business.
Speaker 1:22,000,000,000 in 2024. That's a massive business. But Anthropic will be there in, what, a week or two? They just broke 20 top 20 in the App Store, and now they're in the top 10. They're number seven.
Speaker 1:The the consumer app, Claude, by Anthropic is climbing in the charts. ChatGPT is number one for free apps. Google Gemini is number two, and free cash is number three. Threads is number four. And I wonder I wonder how much this is driven by momentum still.
Speaker 1:But Definitely driven by momentum. Right? Download. So like, it does so so There's
Speaker 2:so many people that have never there's so many people that have never tried Claude Still. Hadn't heard of it until recently. And again, the we know they're putting a lot of paid spend
Speaker 4:Yeah.
Speaker 2:Behind the the anti ads campaign.
Speaker 1:Sure.
Speaker 2:So that's going to be a factor. Metacritic Capital was pretty funny. Back in March 2024, he said, I continue to be puzzled by Anthropix's $18,000,000,000 valuation.
Speaker 1:Yeah.
Speaker 2:And then followed up and said, market is so stupid sometimes. Have no words. But of course, the market the market was right on this one so far.
Speaker 1:This is
Speaker 2:You can just
Speaker 5:see Well, they were are are we sure that he was saying it was overvalued? You could have been saying it's under
Speaker 6:No.
Speaker 1:I think he was saying undervalued.
Speaker 4:Okay. Yeah. Yeah.
Speaker 1:Yeah. Yeah. I I I think that's why he's taking the victory lap is because he was at the time, a year ago, he was like, why is Anthropic so, like, has such a low valuation based on the market?
Speaker 2:This is almost two years ago.
Speaker 1:Oh, this is yeah. This is almost two years ago.
Speaker 2:So I don't actually know.
Speaker 1:I don't know which way Metacritic was going. Cell phone? Ask Martin Scrawley who's coming on the show and
Speaker 2:Oh, he he responded. He said the puzzle meant I didn't understand why it was worth only 18,000,000,000. So
Speaker 1:Always vague posts so that you can take either direction. You never push yourself into a corner. Let me tell you about Gemini three Pro. Google's most intelligent model yet, state of the art reasoning, next level vibe coding, and deep multimodal understanding. I'm glad this chart is now public because it is bananas.
Speaker 1:It is ridiculous. It should not exist, says Bruno f, the founder of Magna Digital. $5,000,000,000 in tokens managed. Interesting. Yeah.
Speaker 1:What? Crazy, crazy.
Speaker 2:Just another pod guy says Salesforce invests in Anthropic colorized. I think when Mark, was on, he said they have about a point of anthropic going into this round, if I remember correctly. Again. What is this? It's a
Speaker 1:horse giving money to a car. The car goes and buys a rocket launcher. The car blows up the barn, and the horse is sad. That does feel like an apt analogy. It's very
Speaker 2:And Anthropic has been all over legacy media.
Speaker 1:Yeah. First, let me tell you about CrowdStrike. Your business is AI. Their business is securing it. CrowdStrike secures AI and stops breaches.
Speaker 1:Also, fantastic Valentine's Day gift. This from this extraordinary piece in the New Yorker last summer while Mark Zuckerberg was conducting hiring raids on other labs, Shalto Douglas, the anthropic engineer and TVPN guest, told me, this journalist, Gideon Lewis Krausz, that a number of his colleagues, quote, could have taken a $50,000,000 paycheck, but the vast majority of them hadn't even bothered to respond. Eviction. They are early at a three hundred and fifty billion dollar company and are clearly very optimistic. But It is funny to just
Speaker 2:Daniel money mug. Daniel says, so wait, Claude has seat based pricing. Does this mean they're disrupting themselves too?
Speaker 1:That's right.
Speaker 2:Of course, a lot of the concern has been around the seat based model.
Speaker 1:Team plan.
Speaker 2:But it even feels like that is less Yeah. Of that is less of a factor than just the overall threat of zero marginal cost software.
Speaker 1:Yeah. Why Why does Claude have seat based pricing? It's essentially a consumption based product. But psychologically, if I'm rolling out Claude to a company and I set up seats for a team, I know that there's individual rate limits so no one no one individual is gonna, like, blow me up, basically. That's the idea.
Speaker 1:But this this goes into, like, some people are posting, like, you're getting a job, you should ask what your
Speaker 2:team is. Not it it it this is Claude. This is not the API. This is not Claude. But,
Speaker 1:you know, when you fire up Claude code, like, you can integrate your Claude account. And so, like, this essentially gives you credits to write code as well. And so the the yeah. There's this new meme of, like, if you're going into a tech company, like, ask what your token budget will be. Like, what's your inference budget?
Speaker 1:And so, I mean, these can clearly skyrocket pretty quickly. There's debates over, you know, oh, should I let my employees use like the fast mode or the regular mode or pro? Like, is the work that they're doing really that valuable if they're spending thousands a month? If they're spending tens of thousands a month? Like, at certain point, I need to make sure that they're not being wasteful.
Speaker 1:And so I think the seat based plan still achieves a little bit of that psychological security for managers. And then there's also an interesting there's probably a pretty bimodal distribution in the value that or the actual cost associated with these plans. I would imagine that there's a portion of pro users that use 100% of their inference budget every month, and they cap out and they're frustrated. They might have a second plan or they might go down to a free plan or or or limit their usage. And then there's a whole bunch of folks who just have a seat and never use it, or they use basically, like very little inference or they're just asking things that can be answered by a free tier essentially.
Speaker 1:But they just like let it ride.
Speaker 2:Zach in the chat says we have a 150 corporate Cloud users purchased by seat, 50% maxed out in week one because of Excel.
Speaker 1:There you go.
Speaker 2:Good data point.
Speaker 1:Let me tell you about Restream. One livestream, 30 plus destinations. If you want a multistream, go to restream.com.
Speaker 2:Dan Primax says, working on newsletter, it may be shorter to list the VC firms not in the new anthropic round.
Speaker 1:It's a party round.
Speaker 2:Josh says, got some logo sniping, it seems. Pay no attention to what price we paid.
Speaker 1:Swear it was early. Yeah. No. This is the the this is a very, very good point. Like, if you are a venture capitalist and you say I mean, used to be if you were an AI VC and you had AI as a thesis and you weren't in one of the labs, that was sort of a red flag for your brand.
Speaker 1:It would be rough to move forward, raise the next round just from a logo perspective. And now with AI becoming such a megatrend, it's hard to imagine being a really enduring venture capital firm without one of these logos on the site, especially as like this is sort of train leaving the station since there's IPO rumors. And you probably want to grab at least one of the big labs logos. Many the firms have sniped all three at this point. Sequoia, Founders Fund, CO2's in a bunch, Andreessen's in multiple, I think.
Speaker 1:There's a variety of funds that have built stakes of various sizes in all the different labs and
Speaker 2:Yeah. It seems like Josh Kushner is one of the one of the few that has remained deeply loyal.
Speaker 1:Yeah. Yeah. There's there's there's
Speaker 2:a Yeah. We do recognize we just had some technical difficulties, but it seems like we're back on. We are so back.
Speaker 1:Let me tell you about Console. Console builds AI agents that automate 70% of IT, HR, and finance support, giving employees instant resolution for access requests and password resets. Slow Ventures is taking the other side of the all in on AI bet. They said, congrats to everyone who figured out that foundation models are infrastructure plays, not start ups. Now let's talk about what happens when the picks and shovels phase ends and we're back to building actual products.
Speaker 1:And Will Minitis says, Nightmarish degrees of coke.
Speaker 2:Yeah. Sam Sam was always super bearish on the labs.
Speaker 1:He thought they would come out time. Was that what it was?
Speaker 2:Yeah. So he wouldn't have pricing power? Effectively. Yeah. Effectively, he said, you know, open source models are gonna get really good.
Speaker 2:Yeah. He was right. They have gotten really good. Yeah. But I think maybe missed that the labs would turn into product companies and stop just being Yeah.
Speaker 2:You know, guys.
Speaker 1:It is sort of interesting. Like, if you if you wound back the clock and you were like, my job is just to invest well in tech startup booms from 2005 to 2025. There's one world where you're like, okay. I'm gonna go hunt for the Airbnb, the Stripe, the YC companies, the Coinbases, all the, like, application layer companies, the the Instagrams, the Twitters, all of these different companies. But there's a different side where you're like, I'm gonna buy, like, Broadcom, Cisco, NVIDIA, AMD, and still do really well and maybe even better depending on when you got in, when you got out.
Speaker 1:But, it's a, yeah, it's a very, like, just because it's an infrastructure play, even if that's true, that doesn't mean that it's not a good investment for an investor. There is a little bit of, like, purist Yeah. Vibes from, like, a venture capitalist should or certain funds have strategies, and so they say, I'm just going to
Speaker 2:sit Yes. Out Slow as a seed Yeah. Series A, but leans
Speaker 1:Focus. More
Speaker 2:Yep. And so by the time you're looking at some of these deals, investing at $5.10 Yeah. You know, $20.30, 40,000,000,000
Speaker 1:Yeah. That could start to be rough. Yeah. And there's a lot of there's a lot of funds who have expanded and will invest in anything. Like, you're a mining company.
Speaker 1:Great. Let's do it. You're, you know, you're you're buying Bitcoin on the balance sheet. It's like, okay. Let's do it.
Speaker 1:There were a lot of funds that expanded what it meant to just be an asset manager. And there were some funds that that stayed very focused and, you know, we'll we'll we'll see, but interesting. Highlights from the Dario Amade interview on Dwarkash Patel, Jacob Rintamaki, a friend of the show, has a quote here. All my lawyers never want me to say the word monopoly, Dario. Dario says, I don't think that's true.
Speaker 1:I mean, I feel like we're in an economics class. Dorcas says, do you know the Tyler Cowen quote? We never stop talking about economics. And Dario says, we never stop talking about economics. So no, I don't think this field's gonna be a monopoly.
Speaker 1:All my lawyers never want me to say the word monopoly, but I don't think this field's gonna be a monopoly. You do get industries in which there are a small number of players, not one, but a small number of players. And so that feels like the like where things are going with both the expressed viewpoints of the VC firms investing in multiple labs, that there's a variety of strategies to deploy intelligence, whether it's the best model and get deployment and traction, whether it's on the infrastructure side. I do wonder how many more changes there will be in the horse race. It feels like there's a new hot model every couple weeks, and then someone fires back, and then they go back and forth and back and forth.
Speaker 1:And with all the all the flow between the labs talent wise, it feels very hard to corner the market. And it doesn't feel like anyone can patent the transformer or anything like that, which would be a completely different scenario. Can you imagine if Google just had the patent and they were like, we actually filed a cease and desist against OpenAI and Anthropic. They're not allowed to use transformer based architectures anymore. Like, we invented it and we patented it and you can't have
Speaker 2:It's ours.
Speaker 1:I don't know. But, yes, we live in a world where, the little tweaks, the little strategies that go into advancing the models and creating these these improvements do not seem to be intellectual property. They seem much more like economies of scale and and process power of being able to to train at ever larger ever larger scales, marshal ever larger chunks of capital, and do whatever it takes to get to the frontier and stay there. Let me tell you about Vanta. Automate compliance and security, Vanta is the leading AI trust management platform.
Speaker 2:Why do play this? Because of the stream. We're having issues again. Oh, no. We're working to get it back up.
Speaker 2:If you can hear us, markets now see a 30% probability of a Fed rate cut by April, more than 80% of easing by June. Over on Calci, we're still seeing the Fed decision in March, 93%.
Speaker 1:Say maintains rate, no cut. So a cut would be a wild card at 7%, 9% for any sort of cut. So strong GDP growth, strong job numbers.
Speaker 2:Yeah.
Speaker 1:You know, stay the course would be the the logical thing, but we will continue to follow it. Let me tell you about Railway. Railway is the all in one intelligent cloud provider. Use your favorite agent to deploy web app servers, databases, and more. Well, Railway takes automatically takes care of scaling, monitoring, and security.
Speaker 1:Let's play this timeless clip of George Hotz and Nah.
Speaker 7:We don't believe in stealth. I'm a really open guy. You are pretty open. I mean I tell you everything I'm doing. Come on.
Speaker 7:Here's what I say. Here's what I say. I'm gonna tell you what I'm doing, and you could try to compete, but I'll still crush you. We're no. We don't believe in stuff.
Speaker 7:I'm really
Speaker 1:It's so funny. Also, I don't know why that person cut that to be so widescreen. It looks very cinematic, but I like the quote here. You think the you think the let the eggs you think the think the eggs I lay are valuable? I am the golden goose.
Speaker 1:Meanwhile I'm thinking thinking people will steal your ideas if you share them is a sign of low IQ. And I agree we are in the era of agency and actually going and executing on the idea is the difficult thing. You need to be charisma maxing.
Speaker 2:I can And there's do a still lot a of lot of secrets to every business.
Speaker 1:Yes, they're all.
Speaker 2:And CEOs can do one hundred hours of podcasts and tell you a lot about what they're doing without telling you Yeah. The one or two things No. That are actually important. It's very easy for somebody to come in and try to fast follow-up. Yeah.
Speaker 2:Ultimately, just like kind of get it entirely wrong even though it it looks like Yeah. The right.
Speaker 1:And I think there was a huge incentive. I mean, going back to the SaaS apocalypse, there was incentive for a long time for companies that where their moat was not software to say, we're a software company. We need to hire the best software engineers. Look at our open source projects. Focus on all the cool tech that we're building when really it was a marketplace or really it was a liquidity provider or really it was a network effect.
Speaker 1:And there if you're a network effects business, it can be sort of boring and honestly anticompetitive to just be like, look, we can do nothing and win. No one wants to say no one wants to hear a CEO say that. Yeah. But we're gonna find out who can do nothing and win because we'll see it show up in the margins over the next Yeah. Couple of financial months.
Speaker 2:Meanwhile, over on LinkedIn, George Hotz is posting. And Reid says, George Hotz is the only thing keeping my LinkedIn feed good. He says, Hello, participant. You are building the machine that will eat you. You think your fake money will keep you safe?
Speaker 2:It won't. You think your social climbing friendships will keep you safe? They won't. The only choice is to stop. Tell your friends.
Speaker 2:Tell your neighbors. If you keep feeding this machine, it will eat you. The proposed revolutions will not be enough. A global scale nuclear conflict might, but even then, I'm not sure. The problem was never AI itself.
Speaker 2:It's the collapse of trust in society. Apps and phones have snuck between every crevice of people, and they are run by psychopaths. The AI will be a further wedge, just another lever to manipulate you. You will not be able to stand up to it and you will be discarded the second you don't serve it. Like layoffs, you will die atomized and alone and you won't understand that you did this yourself.
Speaker 2:Brutal. Nice little white pill. Nice little Friday white pill.
Speaker 1:He's such a white piller. Well, here's a white pill. First, Figma. Figma make isn't your average vibe coding tool. It lives in Figma, so outputs look good, feel real, and stay connected to how teams build, create code back prototypes and apps fast.
Speaker 1:Hey. But here is the real white pill. For just $33,000,000, you can have a private home on a remote resort in Utah. Can you guess where it is?
Speaker 2:Park City?
Speaker 1:Nope. It's at the Amman Luxury Resort. It's in the Wall Street Journal. The Residence is the first to hit the market at Amman. In in remote Southern Utah, Amman Geary Resort, a crown jewel in the Amman hospitality company's portfolio.
Speaker 2:They're doing residences?
Speaker 1:They're doing residences. Global they have a portfolio of global hotels and residences. They're listing the first private home for $33,000,000 located just over the Utah border from the small town of Page, Arizona, the hotel currently features 34 guest suites starting at 5,000 per night and 10 tented pavilions at its Camp Cerica, providing a temporary escape for travelers. But the newly built house on nine acres can be purchased outright. Designed by Los Angeles based firm, Masa Studio, the roughly 12,000 square foot residence has six bedrooms and comes fully furnished.
Speaker 1:It's the first of 12 planned private homes, which will be about half a mile from the resort.
Speaker 2:OOTP says, but does it have a bunkie?
Speaker 1:Does it have a bunkie a bunk bed?
Speaker 2:No. Oh, a bunker? Bunker. Bunker.
Speaker 1:Oh, we're gonna get into bunkers. There's a whole piece in the journal about how to secure a mega mansion. I know you've been asking. We have the answers. Until it's sold, the home is available to rent for $45,000 per night.
Speaker 1:Before we continue, let me tell you about Century. Century shows developers what's broken. It helps it helps them fix it fast. That's why a 150,000 organizations use it to keep their apps working. So residents have been part residences have been part of the Amangiri vision since the resort opened in 2009.
Speaker 1:The decision to offer private private residences now was spurred by the success of the 2020 tinted camp launch, rising demand globally for hotel branded residences and a sense that the property was ready to take that step While future residences will share a cohesive aesthetic, each will be designed to respond to the unique contours of the specific site. In Page, the median sale price was $610,000 in August. There are currently around a half a dozen listings above 33,000,000, though, all clustered further north near ski resorts. Amand Ghiri buyer interest has been strong, he said, particularly among Amand loyalists and North American clients. Additional residential plots priced between 5,000,000 and 12,500,000.0 are under contract.
Speaker 1:And so let's get into Lambda. Lambda is the superintelligence cloud building AI supercomputers for training and inference that scale from one GPU to hundreds of thousands. The mega rich are turning their mansions into impenetrable fortresses, and we're gonna tell you how to do it for yourself. Anxiety over high profile violence has the wealthy spending big on armed security, bunkers, a bunkie, and even moats. They're building moats.
Speaker 1:I haven't heard of an alligator in the moat or a shark in the moat, but people are in fact building moats.
Speaker 2:Being an alligator salesman, I feel like is unsloppable. Think it could be clankable. But still at the moment unsloppable.
Speaker 1:But you gotta build the you gotta build the humanoid robot that can go in the water to wrestle the alligator. And that might be
Speaker 2:well, as
Speaker 1:Brad Eckock, is it waterproof? Is it waterproof? Can it go wrestle an alligator or not? Because I don't want it just to do my dishes and do the laundry. I want it wrestling alligators in my moat.
Speaker 1:So British music producer Alex Grant was living in an under construction mega mansion in Los Angeles. One morning, shortly after 9AM, an intruder armed burst into the home. He said, Grant said, he came in and we had a tussle. He was formerly known as Alex DeKid. Grant managed to call his manager who phoned the police.
Speaker 1:Soon officers and helicopters helicopters were were on on the the scene. Scene. He briefly considered abandoning the project after the 2017 break in, but ultimately finished the 24,000 square foot home, which has eight pools, a car elevator, and a nightclub. Wow. But he doubled down on security features, installing a guardhouse, tall gates, and security system with retina scanners that alert the homeowner to movement in the home.
Speaker 1:Later, I found out he had these knives on him, Grant said, who recently listed the mansion and a neighboring house for 85,000,000 after moving to New York. In an era of high profile violence, including the suspected abduction of Savannah Guthrie's mother from her Arizona home just over a week ago, the wealthy are investing heavily in their personal security, particularly when it comes to their homes. Security measures once reserved for presidents and royalty, safe rooms, biometric access controls, laser powered perimeter defenses, these are now mainstream items in luxury homes. Executive protection teams and armed guards patrol gated enclaves and suburban estates, while tech startups are rolling out predictive threat detection systems built for the ultra wealthy. The shift reflects a hardening view among the affluent.
Speaker 1:Traditional policing and communal safety are no longer enough. No security. So security is being privatized and customized. The new emphasis is reflected in sales data. Roughly 45% of luxury homes in 2025 included a reference to privacy or security, up from 38% the year earlier.
Speaker 1:So break ins at the homes of celebrities and professional athletes have been putting the wealthy on edge. A group of Chilean nationals was indicted last year for stealing items worth more than $2,000,000 from sports stars, including Kansas City chief players, Travis Kelce and Patrick Mahomes.
Speaker 2:Travis Kelce This had something to do with the visa process with Chile where you could very easily get a tourist visa. Oh. So there was these, like, base the allegedly, there were teams that would be permanently based in The US. Yeah. And then they would be running kind of the operations.
Speaker 2:They'd be in the kind of war room.
Speaker 6:Mhmm.
Speaker 2:And then basically tourists would come Come. For two weeks
Speaker 3:Yeah.
Speaker 2:Hit a bunch of houses and then bounce.
Speaker 1:Bounce. Wow.
Speaker 2:And those were the only people that were actually exposed to or exposed meaning they were like carrying out the different ops.
Speaker 1:Well, the Miami Dolphins player, Tua Tagovail Viola. Viola. I might be mispronouncing that. Said he hired personal security to monitor his house while he's on the road. He says, let that be known.
Speaker 1:They're armed. So if you try to go inside my house, think twice. The homes like celebrities like Brad Pitt and Nicole Kidman have also been broken into. Miami real estate agent Danny Hertzberg of Cold War Banker said he began noticing an increase in emphasis on security in 2020 when high profile executives were migrating from New York to Miami during the early days of the COVID pandemic. Private jet tracking websites have also been an issue.
Speaker 1:They sent chills through the through the high net worth community. Corporations are taking note. Companies offering personal security benefits for CEOs increased by 10% according to Goldman Sachs. One entrepreneur capitalizing on this growth is David Weiderhorn who got into real estate after selling a tech company in 2017. I wonder what he sold.
Speaker 1:He recently built a heavily secured home in Scottsdale, Arizona. And he, in early December, Widerhorn walked through the 8,600 square foot property pointing out 32 casino grade AI powered facial and vehicle recognition cameras. There's also a laser intrusion detection system around the perimeter. Pausing at a steel double gate in front of the house, he warned that the security system kicks in even before visitors reach the front door, which is fast which is fashioned out of three inch solid three inch thick solid steel and has 13 deadbolts. He said even the landscape was designed as a deterrent.
Speaker 1:Cacti? Sour orange trees. There are sour orange trees with four inch spikes in concrete planters on the edge of the property. And just beyond those trees, separating the house and the street, a moat. Gators.
Speaker 1:A moat. Gators. If you try and run through that bush, it will be a a bad day for you, he said. Should anyone get past the trees, lasers will detect motion and the system will call the police. Inside the house, three ear piercing alarms will go off.
Speaker 1:And this is an interesting thing. The fireplace surround, like around the fireplace in the great room, it will change colors. It's made out of crystallo quartzite, and it can change colors. So it'll turn red. So you're sitting there, and if there's anything detected on the property, your fireplace turns red above the TV to show you that something's going on.
Speaker 1:Very interesting. That's a visual cue. The home's most fortified feature lies behind a wood paneled wall, a reinforced concrete safe room with 2,000 pound door and an air filtration system built to US Army Corps of Engineers standards. Widerhorn declined to share specifics but said it cost more than $10,000,000 to build the house. About $1,000,000 was spent on bullet resistant smart glass, and the front entry security features cost more than $1,000,000.
Speaker 1:In Las Vegas, clients of luxury design firm Blue Herons are spending between a 100,000 and $1,500,000 on security features, including safe rooms and bunkers. Blue Heron is now working on new ways to incorporate architecture with security, such as exterior window shades that could be closed with the touch of a button to protect the home's occupants. In Surfside, Florida, the developer of the Delmore, a planned 37 unit ultra high end condominium project designed by Zaha Zaha Hadid Architects, And with units priced at up to 200,000,000, has tapped a Washington DC based security firm to design the building security.
Speaker 2:The 200,000,000 condo.
Speaker 1:Yeah. That is crazy. But, I mean, I guess from a security perspective, if you're in some massive building, you're sort of, like, diffusing the cost. There's more people that might notice something. There's more security guards.
Speaker 1:It's almost like a gated community in one building.
Speaker 2:I'm just
Speaker 1:Yeah. Layers of access.
Speaker 2:Purely thinking you're you're effectively looking at a a $100,000,000 a floor. Right? That's crazy. A couple floors, maybe maybe a few. Yeah.
Speaker 2:It's it's up there for a condo.
Speaker 1:That is huge. The firm is working to integrate technology like biometric access, facial recognition, and iris scanning into the design of the project. For instance, when a resident or visitor pulls into the building's parking garage, their car will be scanned for license plate recognition, but facial recognition may may also identify the car's occupants and their level of approval to access the the building. That, in turn, triggers the security system to allow the person to unlock only the doors and elevators that they are permitted to pass through. Meanwhile, an AI powered security system will track movements captured on camera throughout the building looking for anomalies.
Speaker 1:Hertzberg said he recently had a client fly in a security consultant to evaluate a roughly $50,000,000 house he had put on under contract. The consultant looked into the viability of installing a complex camera and laser system that could sense any movement on the perimeter of the property, including the water. So lots of interesting stuff. Let me tell you about Cognition. They're the makers of Devon, the AI software engineer.
Speaker 1:Crush your backlog with your personal AI engineering team.
Speaker 2:If you go further down, they talk about San Francisco tech entrepreneur Kevin Hart said he and his high net worth peers in California are increasingly focused on security. Kevin, of course, has a home security startup. Hart said he co founded his own security company, Soron, in 2024 after being spooked by an attempted break in at his home in San Francisco. The person first rang the doorbell before making his way around the house, trying some of the doors and windows. When he couldn't gain access, he went to Hertz's next door neighbor's home, where he tried to push through the front door.
Speaker 2:He was arrested by police. That could have been us, Hertz said. The Soron system, which has only been launched in beta across a few homes in in the Bay Area
Speaker 1:Mhmm.
Speaker 2:Will differ from other security systems, and that it includes deterrent strategies not only response. For instance, if it senses an intruder, it could include a feature that automatically triggers sounds, such as dogs barking or police sirens coming closer. Just the sound of dogs barking feels like a great feature. Just OTP in the chat was saying, do none of these people have golden German Shepherds?
Speaker 1:Yeah. German Shepherds. Fun fact about German Shepherds, you can like, a purebred dog might be like single digit thousands, but there are companies out there that will train a German Shepherd for, like, the military, basically, and then also train them to be pets. So they're they have that level of training, and then you can get up in, a $4,050,000 dollar range for dog, which is hilarious. Dog as much as a car.
Speaker 1:But dogs are typically
Speaker 2:It's lot of money, but it's a lot of dogs.
Speaker 1:It's a lot of dog. It's the GT three RS of dogs, truth truthfully. But when whenever you look at the list of, like, what what what's the most likely thing to eliminate, you know, home home intrusion risk, like dogs are always at the top. Quickly, let me tell you about another great Valentine's Day gift. MongoDB choose a database built for flexibility and scale with best in class embedding models and re rankers.
Speaker 1:MongoDB has what you need to build what's next. And without further ado, we have Martin Scrawley in the restream waiting room. Let's bring in Martin to the TV panel. Martin, good to see you again. How are you doing?
Speaker 7:Technology Brothers. How are you?
Speaker 1:It's great to see you.
Speaker 6:How are you?
Speaker 2:It's great to see you. Excellent.
Speaker 1:Are you gearing up for the weekend? Are you excited?
Speaker 7:Caffeinated, ready to do more
Speaker 1:work. Fantastic.
Speaker 2:Locked in.
Speaker 1:The great lock in.
Speaker 2:What's your daily caffeine stack? Yeah. You know, we talked with Huberman about this. You're the natural You
Speaker 1:micro dose micro doser or do you like do four hundred milligrams and then coast?
Speaker 7:I do I do coffee Mhmm. Several coffees and then just, like, keep taking drinking this all day long.
Speaker 1:And it's So, like, four hours a lot
Speaker 7:of fun.
Speaker 2:Yeah. Something like that. Five.
Speaker 1:Okay. Five hour energy. Oh, wait. How many hours are they? How many hours are they doing these days?
Speaker 1:Four or five? It's a lot of energy. Five. Anyway, what what are you seeing in the market? Give us the update on just how you're processing the last week of chaos, whether you wanna talk about software or quantum computing.
Speaker 1:What's going on? What's worth following?
Speaker 7:Yeah. So so I have this new potential product. My product ties this. I've been tweeting it for now for free. But it's basically this something nobody's ever done before, the VC investors, which is I'm using my network and some heuristics, maybe even some AI to guess, kind of what positions people took in rounds.
Speaker 7:Obviously, for some cases, I know exactly what the cap table is. Yeah. But in other cases, I don't. So I have this, like, list of of gains or or or investors, and it it's very interesting. So, you know, I started with, like, obviously, the the joke one, which is FTX, where we have 36,000,000,000 today.
Speaker 7:Sure. You know, putting in, I what I guess was $300,000 in any sphere, which, of course, is cursor at a four point four four point four, you know, million dollar pre pre pre money.
Speaker 1:Wait. Wait. Is that really the free money?
Speaker 2:That's still insane because in that era 10. Getting getting into a great company of Yeah. Four. Like, if somebody was pitching you a company of four, it was almost bearish
Speaker 1:Like, oh, they're because was like,
Speaker 3:yeah.
Speaker 2:They didn't have the comp they didn't they didn't talk to anyone smart. Was like, hey, you guys are really smart. You can price it 10.
Speaker 7:I'm guessing and have have, like, various heuristics. And obviously, like, I'd call somebody like you guys and say, actually, I think you want to talk to this guy, or that number might have to go up a little bit, etcetera. But that's better than nothing. And right now, at Crunchbase and PitchBook and stuff, there's you just there's nothing. And so it's a lot of fun.
Speaker 7:And so that $300,000 investment, they raised 400,000. So my guess was was elevated to 300. I think I can look at it's actually in the bankruptcy document. So eventually, we'll get the exact number, but that's a $1,200,000,000 position in today's money. Obviously, the bankruptcy estate lawyer is just like, oh, what the fuck is this?
Speaker 7:AnySphere. It sounds like zero.
Speaker 2:Yeah. Does. It does when you say AnySphere in in the context of FTX, it sounds like we're a blockchain company looking to do to build a live multiplayer game. And you guys start to glaze over a little bit.
Speaker 1:Like, how many NFTs did n n sphere drop? Like, not quite.
Speaker 7:Anthropic. They dropped 32,000,000,000 now. I'm sure his last had the
Speaker 4:Yeah.
Speaker 7:He posted a little thing about that. Thrive is the big mystery player because nobody really sure how much money they they sunk into OpenAI, but they also did AnySphere.
Speaker 2:Mhmm. Yes.
Speaker 7:You know? So huge, huge gains from Thrive. They were in couple later rounds of scale and and and some other companies. So big, big numbers there. Probably one of the more interesting ones is is Reid Hoffman.
Speaker 7:$5,050,000 $50,000,000 first check-in OpenAI with Kozlov. Maybe 25.
Speaker 1:Okay.
Speaker 7:25 or 50. And that, you know, worth many billions. And then Jan Jan Talin, the Yeah. EA CEO of Co founder of Skype. Of Altruism.
Speaker 7:Yeah. Skype guy. 100,000,000 turns to 11,000,000,000 in anthropic. First check Wow. With Reid Hoffman.
Speaker 7:So 11 bill. So a lot of fun to look through these and see, like, you know, you you can sort of calculate the returns. And, of course, VC fund returns eventually either go public, or you can find them somewhere, or, like like, oftentimes state pension funds and stuff do that. So Yeah. Anthropic, obviously, biggest
Speaker 2:when It you as you break this down, it's so funny that Crunchbase never, tried to roll out even something that was generally accurate. It is very fascinating information. And especially now where Dan Primak was joking, it's easier to list like, who's not in Anthropic at this point from kind of the big name funds. And so that just makes this kind of information, like, more interesting because, yeah, it's cool that you're in a company and and almost anybody, if they work hard enough, can get some exposure to these names. You know, maybe maybe it's like via SPV or an SPV and an SPV.
Speaker 2:But still, this is the information that like is actually like super fascinating.
Speaker 7:The other interesting one is Dustin Moskowitz, who who's 25,000,000 in Anthropic as part of the Effective Altruism Mafia, was able to make $4,000,000,000, which I think offsets his losses from starting Asana, but I'm not sure.
Speaker 1:She didn't. That's ridiculous. There's no losses from founding
Speaker 2:Well, that that's that that the Come on. The anthropic position would be worth two x what Asana is.
Speaker 1:Yes. Yes. Which is crazy. But he doesn't he's not sitting on losses. Oh, you think he bought
Speaker 7:it at the time? Well, we know he bought huge amounts of Asana with his with cash.
Speaker 1:So Okay. Okay. So maybe He's out
Speaker 7:of mind. Maybe. Maybe. I I doubt it. But, you
Speaker 1:know Yeah.
Speaker 7:Well your point.
Speaker 1:He's doing he's doing well. So the lesson for folks is just get a get a small check-in the next anthropic?
Speaker 7:Get get get a Get a big Try to network on ineffective altruism. I think that that seems to be the
Speaker 1:Yeah. What was the alpha from EA? Like, what yeah. Do you have a postmortem?
Speaker 7:There's a lot of smart people that have no other things to do, so their social setting is, like, replaced by this sort of like religion or anything like that and Yeah. You know, this this cult. And if you're in a a cult of really smart people, it's prob probably something that will come with it.
Speaker 1:Do you think it's still a cult, or do you think it's it's like b to b SaaS now?
Speaker 7:I think it's changed a lot. It's like b to b SaaS and and I think like the the new cult is
Speaker 1:Cult's a new sass. You're welcome. The water's warm. Come in. It's amazing.
Speaker 1:We're automating workflows. We're delivering enterprise value.
Speaker 2:Hiring consultants.
Speaker 1:We will we will we will forget about all the earlier stuff. We will welcome you into improving the economy, raising GDP. This is what we stand for in this cult.
Speaker 7:May maybe the new cult is the the AI agent, you know, website or whatever the you know, or whatever is next in that world where the AI
Speaker 2:entities What's your personal so I wrote in the newsletter today, like, somewhat of a joke, but a more serious topic, become unsloppable. The idea of there are still real moats that exist. And the historical moat of just we had a bunch of smart people working on building this software for a long time. So if you want to compete with us, have to also spend a lot of money and a lot of time hiring a bunch of software engineers. That's going away.
Speaker 2:Yet, you're building what is a seat based pricing tool. And I expect you to do very well with it just because I think in the future, will want great access to data to make different decisions. And maybe they're working with agents as well. So I can see that
Speaker 7:Yeah, the agents need the data.
Speaker 2:But what's your personal philosophy? Because you're clearly not if you were just caught up in the kind of like fear based marketing of the labs, you might not be building, you know, seat based SaaS tool.
Speaker 7:Yeah. I mean, data's data's often firewalled. You know, there's there's a, you know, we we have a guy that just talks to every exchange in the world. And, you know, the amount of times he has to pull his hair out because, you know, some exchange in Asia wants to meet yet again, you know, before before signing the deal. And and, you know, there's no self checkout.
Speaker 7:There's no agent. You know, it's you you have to the protocol is sit down meeting. And every time you go to enterprise SaaS company and it says talk to sales, you know, It's it's sort of like, what does AI do at that point? So I feel like, you know, there's you know, you also have this trend where, you know, why would you put huge amounts of data into the model the model should call out and, you know, compressing the world's information into some parameters and weights. It's just not a wise use of parameter space.
Speaker 7:And I think everybody's been saying this in AI, and and so the problem is, okay. Shrink the whole Internet, but what happens when stuff leaves the Internet? You know, there's a stock that Bloomberg doesn't have in its portfolio. You guys weren't born yet, I think, but the, it was called a Webvan.
Speaker 1:Oh, yeah.
Speaker 2:No. We know Webvan.
Speaker 1:OG Nordens. My family used Home Grocer, which got acquired by Webvan. And I think the Home Grocer founders probably got liquidity before Webvan crashed, so I think they wound up doing very well.
Speaker 7:They killed it.
Speaker 4:We need
Speaker 1:check-in with them. But yeah.
Speaker 7:Yeah. They so so Webvan is not on Bloomberg, for example
Speaker 1:Yeah.
Speaker 7:Even though it's, you know, supposed to be this great tool. And it's certainly not, you know, on the web. You know, lots of data gets, like, deleted from Google. And there just isn't this, like, rich tabular data available. So tabular data, I think, is gonna actually thrive in the AI world because, you know, it's just not gonna be in the models.
Speaker 7:Or if it is, the models get get tired after a while. It's not gonna give you 3,649, you know, SaaS companies with this market cap. It's gonna say, here's the top 200, and don't ask me about the next, you know, thirty thirty two hundred because that's just not what a what LMs are really good at. But the the Frontier technology has sold off very, very hard in the last month or two, and and there's lot of speculation in the quant community as to what's happening. So there there's some funds that ran really well with with this, frontier tech.
Speaker 7:So I think that includes quantum computing, but also includes nuclear, drones, you know, space, you know, all the stuff that's sort of, you know, next generation things, that aren't here yet. And this was, like, the hottest sector last year. And anybody who didn't have exposure to this underperformed. And there are some quant firms, I think, that that were very, very, very overexposed to this. And then New Year started in in the hedge fund and and quant world.
Speaker 7:January 1 is like a brand new page. Like, nothing nothing matters from last year. Everything you forget everything. And so this factor just, you know, flips in reverse. And part of the reason was was the the calendar, I think.
Speaker 7:And now the quantum stocks looks like like dog poo poo, and they've gone down a lot. And, you know, nobody knows what to make of anything. But in the private world, you know, numbers are still you know, there's still big valuations, still lots of, you know, big up rounds so far. So that disconnect will be really interesting as time goes on. There's you know, last time we talked about photonic computing, there's a new company, Thiel Fellow, young guy.
Speaker 7:You know, I'm telling you right now, these young guys think that, you know, I'm this old dog that can't learn new tricks. I'm gonna teach all of you young bucks, $22.25. You come into my space, I'm I'm gonna I'm gonna show you. You have to talk to me still.
Speaker 1:That's amazing.
Speaker 7:But anyway, Olix is is what it's called. And he's raised $2.20 or $2.50 to do photonic computing for for AI, which, you know, I think is you know, that's the second company or third company now that's come out and said, we're we're gonna do it. And he's gonna do it with SRAM Yeah. Interestingly. So he's got SRAM on board.
Speaker 1:Yeah.
Speaker 7:And, you know, so it's like Grok plus in essence.
Speaker 1:Okay.
Speaker 7:And I think it's a really good idea, but, you know, execution does does does matter.
Speaker 1:What do you think about biological computing? We talked to a fellow who built a neuron in the lab, and it was way over my head.
Speaker 2:Feed some protein, sugar.
Speaker 1:Sugar. So we like the protein part of the interview, but didn't get much further than that.
Speaker 7:Yeah. I mean, look. That's that's how we do it. So I don't see why not. You know, I think that it's a spiking neural network.
Speaker 7:Right? So, it's a little different from from the software neural network. But I don't see why you couldn't do it. I think the reading the output is kind of difficult. In photonics, you have to use like a almost like a camera, you know.
Speaker 7:You wouldn't use a camera, but you're using a camera like sensor. And, you know, that that sort of is your is your readout. What's your readout here? Well, probably in the body or the brain, we're using like calcium levels or like other things like that as well as synaptic firing. But if you wanna have really good control of that, I don't I don't think we know yet how that works.
Speaker 7:But, of course, they've gotten, these brains in a vat to play pong
Speaker 1:Yeah.
Speaker 7:And do other things like that. So, I mean, it's certainly possible. And, you know, I I was thinking about this with my with my girl who, is in the space about, you know, potentially, do we buy a pig farm? And we buy pig farm. Pigs are really interesting.
Speaker 7:They they're obviously the pork part, you know, gets sold to to meat companies. But what's interesting is different parts of the pig are are biological drugs. So there's adrenocorticotropin hormone, which is sold for a huge price. And then the pig's lungs also make a surfactant that's sold for respiratory disease. And then finally, the brain we're gonna keep and grow that in a separate, you know, separate container.
Speaker 7:We're gonna rent it out to Sam Altman at the end.
Speaker 1:Slop. Slop of the trial. Literal slop. You will be literally feeding slop to pigs. Yeah.
Speaker 1:Play the pig noise 25 times. Talk about, the the what's happening in small caps in or sort of like the long tail of the market as a reaction to the AI boom. I I I texted a friend who's
Speaker 2:laughing at and twenty years from now, your your child will say, my my father made his
Speaker 1:Money in pig farming.
Speaker 2:His money in pigs.
Speaker 1:It's great. I love it. Yeah. I I I texted I texted my friend saying like, you know, look, everyone is talking about a chip bottleneck. There's this massive AI build out going on.
Speaker 1:Like, have you looked at TSMC? And he was like, oh, like, it's, it's, like, too big to have, like, some breakout move. Like, I'm not interested. Like, call me when you're talking about, you know, a $4,000,000,000 company that's, like, deeper in the supply chain. I talked to one person that was like, they found they were excited about Anderol.
Speaker 1:They found some tiny supplier to Anderol, they were like, this is a proxy. What what companies are actually interesting? How do people think about those, like, long tail, early smaller cap companies that are still, like, properly indexed to the correct narrative around AI?
Speaker 7:Yeah. I think they're all they're all scams. I mean, it's it's an unfortunate, you know, situation. And this is why, you know, actually, Joe Loncil, you know, I think I talked did I talk about this last time? You know, he he gave a talk with the SEC commissioner, he basically said, why can I buy TBPN coin?
Speaker 7:No such thing, by the way. Yeah. You. Or that triples TBTBPN coin or whatever Yeah. Coin you make up on the spot.
Speaker 7:I can I can put millions in it, lose all my money? There's no investor protections. But if I try to buy Andoril
Speaker 1:Yeah.
Speaker 7:God forbid, you know, you know, you know, you guys blow the whistle, and Matt Grimm stops everyone from from buying yet and so forth. But the hi, Matt. But the in all seriousness, I I think that that's that's something we have to fix. I mean, because you end up having people chasing kind of really low quality companies. There's companies that just change their name to AI and hope that somebody buys them.
Speaker 7:Yeah. Same thing with with Quantum and and other things like that. And Mhmm. I feel like two things should happen. First, we should let people buy privates.
Speaker 7:Mhmm. But but but two, more private should go public. And I think, like, demystifying and and making it less scary, like, if I started to convince Replit to go public Mhmm. Because there's a drug company called Replimune. Yeah.
Speaker 7:And it would be the same ticker. It's like, you can't go public without them. So you have to buy Replimune
Speaker 1:Oh, okay. To invest the
Speaker 7:drug and you can go public. And Elon had to buy, United Steel, because they had X for, like, a hundred years.
Speaker 6:Oh, yeah. And,
Speaker 7:you know, now X is available. So he just waited for them to get bought out by somebody else. Perfect timing.
Speaker 1:But That's crazy.
Speaker 7:In all seriousness, you know, going public is the best thing ever. It's the freest, cheapest capital of all time. Mhmm. We obviously have seen down rounds from privates
Speaker 1:Yeah.
Speaker 7:In publics. But, you know, that's mostly for, like, boring SaaS. Once you have AI Mhmm. You know, you're gonna have, you know, a a million times revenue. Obviously, the the difference between the two is hard to say.
Speaker 7:But Yeah. I think I think if a company like Replit went public, like, you'd be surprised at the valuation you could get. I think you can there's enough demand out there that I think some of these guys should start going public.
Speaker 1:Did you have the same read on a lot of people that Michael Grimes going back to Morgan Stanley was incredibly
Speaker 7:bullish for
Speaker 1:late stage.
Speaker 7:Really big deal. Yes. That's that's that's
Speaker 1:of course. Incredibly bullish for late stage tech and the IPO window being firmly open.
Speaker 7:I think so. I think I think you're gonna also see other people from DC, you know
Speaker 1:Rotate in.
Speaker 7:Rotate back and, you know, it it was a really great thing for for these people to actually truly make a sacrifice because, you know, I don't I don't think there's that much upside in DC. And, you know, it's it's it's it's an amazing thing for them to come, you know, do something good for America. And then, now you have, you know, people like, you know, folks like, Anthropic and and OpenAI where their capital needs are larger than the private space, to be frank. And Mhmm. You know, I think that the ability for them to raise, you know, 100,000,000,000 or 200,000,000,000 or 300,000,000,000, it it the markets could realistically support that, whereas I think the private markets are really starting to stretch.
Speaker 7:Like you said, I mean, that anthropic list of investors, the exclusive
Speaker 1:Yeah.
Speaker 7:Syndicate was, you know, virtually a long list of every big fund.
Speaker 1:Yeah. Jordy, what else?
Speaker 2:What? Eleven
Speaker 7:Labs. Eleven billion. Amazing. Numerology.
Speaker 1:Do you use the product?
Speaker 7:So we we so my my company, our first six to nine months, I think, we spent trying to make a better Eleven Labs.
Speaker 1:Oh, yeah.
Speaker 7:Or or or compete. Yeah. Truth be told, we we couldn't make an equal. So Yeah. Couldn't make a better one.
Speaker 7:But it was sort of my my my fast lesson in software, which is, you know, the only thing that matters is is sales. You know, product and second most important in engineering is like last. But, you know, if you don't if you if you don't have distribution, you don't try to sell the product
Speaker 2:Mhmm.
Speaker 7:It's not gonna sell itself. And it and it's a sober lesson of those guys, like, very aggressive. For the longest time, if you did a one word or two word sentence in 11 Labs, it wouldn't output it at all. There were hallucinations or all this stuff. They just pushed you know, they fixed all that stuff, of But, you know, they pushed really, really hard on sales, and and that sort of fixes everything.
Speaker 7:And and I think that, you know, it's some of the best VCs I've ever talked to said, you know, when are gonna launch your product? And I said, oh, it's not ready. They said, just launch it. Just launch it. Just launch it.
Speaker 7:And, you know, get off the uncomfortable, like, stage fright and just start selling, and you'll get more feedback and so forth. So they, I think, they took that to heart really early on and just, you know, they didn't have better technology necessarily than other guys. I think they just sort of, you know, realized, okay, who needs to buy this stuff? Let's go build build the infrastructure around that. Just incredible success.
Speaker 7:I mean, I tip my hat to them.
Speaker 1:Yeah. How do you think about the moat that comes not from software engineering because generating code is cheaper, soon to be free, but training spend. So if if I spend if I spend a $100,000,000 employing a bunch of great software engineers for four years and built some elaborate soft software system and you can just vibe code it for two orders of magnitude less cost in tokens, you clearly have an advantage against me. But if it's gonna cost you a $100,000,000 to do the training run that I did for a $100,000,000, is that a durable moat?
Speaker 7:I doubt it. You know, I think it's I think it's product and sales and brand and things like that. I mean, it's trad business. So there's gonna be a lot of people replicating products and then they fail and they're gonna wonder why. Yeah.
Speaker 7:And, you know, it's it's the rest of the business, you know. There's there's that's you're talking about 20% of your your organization. I mean Yeah. You really have to get the rest of the organization excited about product. And and I think you're gonna find one interesting thing that'll happen probably is that folks from embedded entrenched industries like certain manufacturing and certain materials businesses, things like that, they're gonna spin out themselves and say, I'm gonna solve the problem that's been plaguing my industry, but I'm
Speaker 1:not a programmer.
Speaker 7:It's like that that I'm not a rapper YouTube. Sure. You know?
Speaker 1:And Yeah.
Speaker 7:And I'm starting a software company, but I'm not a programmer. Yep. But I know that our whole oil industry has had this huge well software problem. I'm build the well software. And I think startups like that are actually going to not only create tons of wealth for themselves, but they're going to actually help the economy.
Speaker 7:And that's where, just like the internet helped GDP, that sort of solution is where you're going to see GDP needle move. And it's going to be it's a wonderful time to be like the nerdiest, best guy in, say, equity research or something like that. Because you know you might have an inkling that, like, a rival might have an inkling that, oh, know, finance is going to be changed by AI. I'm going to try to point my my apparatus at this and and figure it out. But if you're the guy that's like, I know everything about, you know, this type of little narrow thing, Mhmm.
Speaker 7:You're gonna really crush it because, you know, you you really know what the problems are. So people coming out of industry Yeah. There's a rival of ours called Rogo. Rogo is a AI company focused on finance, old Wall Street guys, they have a much, much better chance of succeeding because they're they did the job. They know they know what what to do.
Speaker 7:And I think you're gonna see so many people come out of the S and P 500 that just said, oh, I was working at Eaton or or Fluor or, like, companies that are just, big, you know, Pulte Homes or whatever. Sure. And they and all of a sudden, you know, they they're they're starting software companies that solve the key problems in that industry.
Speaker 1:Yes. So maybe the only
Speaker 7:maybe more but
Speaker 1:fewer computer science background founders.
Speaker 7:Yeah. Definitely. I mean, so many of these problems can be solved, I think, without knowing every single data structure and and things like that. I mean, obviously, you know, there's there's gonna be people it's gonna be a barbell. Right?
Speaker 7:Like, there's gonna be people who still need to know how to make an FPGA and program an FPGA.
Speaker 3:Yeah.
Speaker 7:And and, you know, when Elon said that, you know, he he sort of set a lot of people on fire over the last few weeks when he said that you're gonna see AI write assembler or even machine level code, you know, compiled assembler. And, you know, that's that's a pretty wacky, you know, idea. And think about wacky ideas from Elon as they tend to be right. So it's definitely, you know, one of these things that, you know, is kinda mind blowing that, you know, if you think about AI safety Mhmm. You know you know, tell the tell the program, know, give me a program that does SaaS for oil wells.
Speaker 7:Cool. Here it is. But by the way, you know, in the in the compiled assembly, which you can't read because you don't you don't speak binary Mhmm. You know, there's this thing that says, you know, I I'm I'm taking 5% of the revenue and sending it in crypto to my I
Speaker 1:like that that's your AI doom scenario. Just just slight slight fraud.
Speaker 7:Clipping 5% off.
Speaker 1:Yeah. Just clipping a grift to
Speaker 2:grift stuff. How do you do you expect layoffs on Wall Street? Because because with with all of the broad fear right now among white collar workers, I'm not seeing the layoffs that are explicitly, you know, hey. You were doing this thing for the company, and now we're just running this agent to to do that. And so we'll see you later.
Speaker 2:We are seeing, hey. You were doing this thing. Now AI can help you do it a lot better. So our expectations are gonna rise. We are gonna expect you to do more and be more productive, but you still have your job.
Speaker 2:What what what are you kind of hearing from people at different finance firms about how they're adopting AI and how they're how they're feeling about job security?
Speaker 7:I think in general, you know, one of the things you learn in founder school after your fourth or fifth time is is that, you know, you're supposed to hate firing people, and and you're supposed to learn to like it over time. Nobody likes it. You know, it's the worst thing ever. And and the funny thing is, like, if if you become more productive at work, the company doesn't say, oh, yeah. Well, let's get rid you and save whatever amount of money.
Speaker 7:Because they're already making money with you employed. So the fact that you're becoming more productive means that, you know, whatever the the margins were, they're probably improving. Could they improve even further by getting rid of you? Maybe. But I think there's this, like, slow atrophy maybe, but I I think in general, we as humans want to employ other humans, and we we kind of want to be productive.
Speaker 7:I mean, nobody wants to needlessly employ people, but I think that there is this idea of, okay, machine can do your job. We we have this at my my office all the time. And, you know, I say, Chris, you know, a to do your job. Good news. The you know, you don't have to do it anymore, but there's a new thing you have to do now.
Speaker 7:Yeah. And, you know, our company just got twice as efficient. It's wonderful. And if the day came where there's literally nothing for Chris to do, then, you know, maybe we, you know, that that would be, you know, that could be the day that made sense.
Speaker 2:But But but the thing is there's always there's always an incremental thing for a company to do. Yeah. Almost no start no startup founder has ever thought, great. I
Speaker 1:I'm out of ideas.
Speaker 2:I did it. I built I built the six products that that our customers really need, and there's just no other way for me to expand the opportunity set.
Speaker 1:It's time to
Speaker 2:kick back. Yeah. Mean, yeah, those businesses die, though.
Speaker 1:Yeah. They
Speaker 2:do. You're either building Yeah.
Speaker 7:I mean I mean, you're gonna I mean, if that person has a couple more hours a day now, it's great. You know, go meet with, you know, some potential recruits. Go meet with some potential customers. I mean, there's there's always something you can do. And I think that's gonna happen.
Speaker 7:In finance, adoption of AI has been very slow. And it's probably gonna stay that way. Finance people are really stuck in their ways, which is a good thing and a bad thing if you're selling software to them. Once they get stuck in your way, you're very you're very happy. But, you know, the you know, there's there tends to be a heavy dose of contrarianism in certain industries.
Speaker 7:And I'd say, you know, across the S and P 500, there's this sort of like, you know, technology. We'll use it eventually. And that eventually takes time. And that's why the first people to adopt this stuff in great ways is not only because it works really well, but also because they're used to doing it as developers. Developers love AI, and they've embraced it, you know, very quickly.
Speaker 7:Virtually all programmers now use AI. There were a couple of holdouts even at our our company, but they eventually just gave up. And I think you're gonna see the same thing in other industries. Finance is tough because, like, there's this mystical idea that the trader is this, like, random, you know, far end of the bell curve, like, super talented person that just knows, has this, like, weird zen kind of ability to tell what stocks are gonna go up and down. And then the other under the barbell are the quants.
Speaker 7:And the quants sort of feel like AI is not not not good enough, but many of them under the what I've heard is many, many quants are are getting new ideas from AI and also implementing them with AI. Mhmm. So I do think that Yeah.
Speaker 2:If you work at hedge fund now, you can just ask, you know, your favorite LLM, how should I hedge AI and just implement exactly that, and you're guaranteed to to outperform. No.
Speaker 1:I'm kidding.
Speaker 7:It's a little bit scary for some quant funds because you you do have to wonder if if, you know, the thing you've been doing for twenty years that's your profit centers is gonna possibly be done by somebody else. You know, that that is a little worrisome. And then, you know, eventually, and certainly, there are firms. I I can name them, but you can just guess the big the big sort of institutions on the street that they're increasingly thinking about and and even in some cases deploying transformers to do analysis. And I don't see why you know, the hard part about being Warren Buffett was discipline.
Speaker 7:Right? Is is saying no to so many things.
Speaker 1:Mhmm.
Speaker 7:And if if you can put that in the prompt or put that in the, you know, in in whatever context that you just say, listen, I really only want the best, you know, the highest quality companies, the best returns. Say no to everything else. And, you know, I I I don't see how that's, you know, impossible. You just copy the Buffett, you know, strategy Mhmm. You might be better off.
Speaker 7:A lot of the mistakes in investing come from overdoing it in in things that, you know, in FOMO and things like that and resisting that FOMO and saying, you know, I'm just gonna sort of buy buy these types of companies and do my thing. So I I do think, like, investing as a whole is sort of gonna start changing a little bit. Mhmm.
Speaker 1:Lightning There's I have I have four questions.
Speaker 2:I've got one, and then there's a lot of performative AI usage happening right now. The people that are ordering a new Mac Mini on DoorDash. Ordering 10 Mac Minis on DoorDash. We were joking around. My girl just did this.
Speaker 2:Like But who are you looking to for founder style roles? Like what do you think truly the most like like the best founders are doing like ideally like the most important working on the most important thing at the company which could be a recruit, could be a customer, could be getting a raise done. It could be going going on a long walk and and just thinking about the business. But there's like heavy amount of delegation and ideally they're like delegating to people that are using a bunch of AI. But like do you have any sort of like internal fear around am I using this stuff as efficiently as I should be myself?
Speaker 2:Are you looking to anyone and saying like, okay, they're actually really tapped in? Because just buying a Mac mini and setting it up and, you know, having it running and texting it, you know, here and there is not necessarily, qualify you as, like, actually tapped in.
Speaker 7:Yeah. I wonder if there's a way to, you know, to to to not annoyingly reach out to customers with AI. And I think that, you know, we all get that email. Like, I just hit block on all of them. The, hey, I noticed that you're doing this.
Speaker 7:So now I'm gonna, you know, I'm I'm I'm, you know, I'm a vendor that's offering you that. And I think that's, you know, gonna result in not too many sales. But I do think that these things have some yield, and I wonder if there's a really good way. That's sort of in the back of my mind worries me. And then I I at this point, I wonder if LLMs can do can can replace recruiters.
Speaker 7:Right? Where they say, who's who is the best at, you know, time series tick programming or something like that? And LM says, John Smith here, Dave Smith there. You know, they're also named Smith for some reason, and Will Smith there. And I think those, you know, at some point, that might happen.
Speaker 7:Of course, some of that's just human knowledge where people whisper amongst each other that, you know, oh, you know, this is the best person at at this kind of investing. But I think that, you know, because we're all, like, blogging and, like, putting stuff out there, you know, we may also just be able to, you know, ask that person who's the best person. So I feel like there's definitely new ways, creative ways to use AI that that, you know, people are coming up with all the time that are really surprising and shocking. You know, they're all like secret sauce, I think, for for most people. But, you know, James Watson was our secret sauce for a while.
Speaker 2:Yeah. On the recruiting front, I I met a guy a couple years ago who, like, only his entire recruiting business for years had been work Brazilian fintech engineers. Like, had just done a decade and all he did was help companies hire the best Brazilian engineers that liked working on financial services companies. Mhmm. And, like, he had carved out a great business.
Speaker 2:And I do wonder I I don't know that identifying who the great ones are, it's like maybe you did a certain number of years at at NewBank, and then you popped over here, and then you popped back, and like, you can probably pick up some of that stuff. But then what is the value of just, like, actually having like, how durable is the personal relationship with those people that you've placed over a long enough period of time? And can you continue to basically extract rent because, like, they will respond to your text and not the, like, you know, millionth, you know, AI text that that that is constantly kind of chasing them. Anyways, lightning lightning round, John.
Speaker 1:I'll just do one. Neolabs, bullish, bearish? What do you think?
Speaker 7:I'd say bullish. You know, it is the contrarian view, I think, because I'm especially looking forward to JT, Jerry Turex
Speaker 1:Yeah.
Speaker 7:Neil Lab. I think these are really smart people.
Speaker 1:Are they product companies, or are they research companies that will get acquired in?
Speaker 7:I think they're gonna all have a crisis and have to figure it out.
Speaker 2:Yeah. So here's here's the here's They'll do it. Like, the bear the the the kind of bearish take on Neolabs. The the from an investment standpoint, I get it because I I would say, really, really elite smart team, there's somewhat of a capped downside. If you invest $50,000,000, you could probably get 50 of worth of, you know, some one other lab that's actually working down the line out if it doesn't work out.
Speaker 2:But these people were, like, working on something like, you know, hey, these LLMs aren't really learning in real time. They're just kind of like in a certain state. And I'm gonna leave this lab and go work on that problem. Meanwhile, the lab is still working on that problem, and we've also seen as different labs have different advancements, the other labs can like quickly just catch up. Right?
Speaker 2:So like one person has a breakthrough, and so my question is like if a NeoLab raises a $100,000,000 and, like, actually has a breakthrough, then they just have the problem of, like, are we actually gonna be able to sell this better than the labs that will probably figure out how to do this than the big labs that'll figure out how to do this in the next, maybe two months later, but they have, you know, a million customers already. So, like, that's the bear case for me is, like, even if you have this, like, breakthrough, you don't have the, like, sales distribution.
Speaker 7:Yeah. No. I mean, the the bear case is what do these people know about business? You know, and they're they're starting a business. Right?
Speaker 7:Yeah. So it's it's kind of a scary thing. But I think that, you know, look at biotech and some other industries, you know, this is pretty common. And ultimately, I think they're they're doing the hard part. You know, the easy part is you get a bunch of good looking guys like you guys and, you know, you you get you get them to start selling positioning product.
Speaker 7:But I think the hard part is, yeah, how do you do continual learning? How do you do a new form of AGI? It's a little past most of our pay grades. And I think, you know, there probably will be a crisis where, like, the real ones will be separated from the fake ones, but that's just human nature anyway. Like, there'll be some funding crunch, and then somebody has to like emerge with that dog in them and say, no, I'm gonna raise another 200,000,000.
Speaker 7:Gonna come out with something tonight, and we're gonna do it. And with that type of, you know, crazy, you know, person where and then there's gonna be folks like, I don't wanna name a certain AI company that folded, but I'll throw in one that did, which was was the guys who made Heypie inflection. Yeah. They sort of, you know, had that outcome that you're talking about. And, you know, but there'll be people like like that see that, you know, valley of death and say, no, we have to finish this.
Speaker 7:And I think that probably the one of the biggest things that people have to remember, but they don't because they don't care, is that the the investors' money is sacred. And Mhmm. If you if you're just thinking about it as, oh, what's the worst that happens? You know, I I lie down and Sequoia loses their money and this and that. You know, I take that really seriously, and everyone should.
Speaker 7:And, you know, I think that the handful that do, you know, will see their their runway dwindling and saying, we really gotta do a product here and tough it out and figure it out. And I think those will be the future leaders.
Speaker 1:Well, thank you so much for taking the time.
Speaker 4:Well said.
Speaker 1:Top on the stream. Always a great time chatting with you.
Speaker 2:Always a pleasure.
Speaker 1:Have a great weekend.
Speaker 4:Good to
Speaker 1:see you. Enjoy
Speaker 2:the building. In. Enjoy the caffeine. Enjoy your fifth five hour energy.
Speaker 1:Tell you about Plaid. Plaid powers the apps you use to spend, save, borrow, and invest, securely connecting bank accounts to move money, fight fraud, and improve lending now with AI. We have Connor Hayes, the head of Threads in the Mister Hayes. Studio. While he comes in, I'm gonna tell you about Okta.
Speaker 1:Where's Okta? Okta Okta helps you I assign every AI agent a trusted identity so you get
Speaker 4:the power of
Speaker 1:AI without the risk. Secure every agent. Secure any agent. The vanguards.
Speaker 4:The vanguards are out today.
Speaker 1:Yeah. Oh, they're out today.
Speaker 4:Well, out. They've been out. Yeah. But they're out here.
Speaker 2:Oh, they're out. On your behalf.
Speaker 1:Yeah. Months ago. Months ago.
Speaker 4:I have to ask you guys Yeah. Before we start.
Speaker 1:Please.
Speaker 4:How are you recovering from Clav getting brutally bombed
Speaker 7:by the ASU
Speaker 2:frat leader? I just wanna know. Dude, it rocked it rocked men everywhere. Rocked my world. Every I mean, I think the reason that that resonated is every everyone's experienced that.
Speaker 2:Right? I get
Speaker 4:that I get that every day. Get that every day. John Kugen has never been predictable.
Speaker 1:Think it was predictable. Yeah. I wasn't that surprised.
Speaker 2:But we did this morning, sauna. True. The whole team was in the sauna after our workout this morning and this guy must have been an ex bodybuilder. It was just ridiculous.
Speaker 1:It was one of the widest bats ever seen. You see the shine That's you
Speaker 4:need the meta vanguards on
Speaker 1:so Yes. You can
Speaker 4:capture that.
Speaker 6:But I think
Speaker 2:people That's that's a feature that could be a hit is you is it basically like You got a real potential frame mogs. Video model that reduces if the somebody's coming up to frame mog you Yes. It just kind of reduces them down.
Speaker 4:It's like haptic feedback that sends you out of the frame.
Speaker 1:I mean, those are some frame mogs right there. Frames. Right? They're they're fantastic. How is life?
Speaker 1:What what is the day to day like for you?
Speaker 4:The day to day changes a lot. Yeah. We're doing, you know, threads. I think a format like that only works if you are at the center of cultural relevance. Yeah.
Speaker 4:And so that brings us into a lot of stuff that's going on in the world. Sure. We were, like, all over Super Bowl last week. Mhmm. I'm here this week because we're doing a bunch of stuff with NBA for the Sure.
Speaker 4:All Star Game. Yeah. So that's been really I mean, that's fun. It's work, but it's fun. And then the rest of the day is like Yeah.
Speaker 4:How do we make the feed better? Yeah. Are we doing on, you know, content understanding? Are models good enough to, like, you know, do something like the deer algo feature that we just launched? Yeah.
Speaker 4:Let's
Speaker 1:talk about that. First, Super Bowl NBA. Like, obviously, we all know that social media content production is power law driven at this point. There's a few creators that take it really seriously, and they have expert teams. Obviously, threads is, like, a little lower barrier to entry than a polished two, you know, hour long YouTube video or something like that.
Speaker 1:But are are you shaking hands and kissing babies to get people on the platform? Is that what that is?
Speaker 4:I have shaken a lot of hands, actually, though.
Speaker 2:We Yeah. So we have
Speaker 4:a bunch of program It's actually one of the benefits of doing this at Meta is Yeah. We have such an infrastructure of working with creators and partners. And I think we talked about this actually when I saw you guys in September, like, the big learning for us was the people that rock at Instagram don't necessarily succeed on threads out of the box. Yeah. It's kind of a it's a very different format.
Speaker 2:The really good at making pictures and videos, and now you basically need to be really good at captions that can stand
Speaker 4:Right.
Speaker 2:On their own. Yeah.
Speaker 4:It's wit. It's like insight. It's things like But if if you're good at that, the to your point, John, the barrier is so low. Like Yeah. I I was on a flight here two days ago, and I think I fired off like 15 posts on the flight Yeah.
Speaker 4:Replying to people and whatever. And I'm not sending clips to a production team and having them edit it. So that's the beauty, I think, of the format.
Speaker 1:Yeah. How is the AI spam revolution keeping you up at night? Or is there does Meta have strong infrastructure there where you can kind of just, like, out of the box identify things yeah.
Speaker 4:I mean, we like I think as a company have made some good decisions in the last decade of taking these things that are like basically infrastructure that you would need for any service you build, ads, financial services Yeah. Yeah. Integrity, detection and monitoring. And we build central teams out that do that for the company Mhmm. And then build it in such a way that it can be applied to any app.
Speaker 4:Yeah. So we just benefit from all the work that the central teams do. We have some folks inside threads as well. But maybe what you mean, though, is like the agent like agents coming into I social
Speaker 1:guess a somewhat tangential question is just like when gbt3 dropped, it was like, okay, like, it can write text. And then the surprise to me was deep research agentic coding. I had sort of priced in, like, it's gonna be able to write a couple sentences. And yet, I find myself when I'm on a short text based platform not following AI accounts. Like, I'm more likely to go to a fully AI product when I want something that's more like a Wikipedia page and a
Speaker 4:deeper A deep utility.
Speaker 1:A utility. But when I'm actually scrolling a feed of short news items and posts and commentary and hot takes, I it's not even that I'm, like, anti AI. I would I would never follow someone who is using AI to post. It's like, no. Like, the no one's actually solved that piece of the puzzle.
Speaker 1:There's still some human element that's encoded
Speaker 7:For sure.
Speaker 1:In, you know, 16 words that are hilarious based on this moment and this experience and this audience. And that's just stuck around a lot longer than I thought it would.
Speaker 4:Well, that's like the I don't I don't know how much you guys talk about this on here, but I think like the word of 2026 in AI is gonna be taste.
Speaker 1:Okay.
Speaker 4:And that's what you're getting at. Yeah. It's like a model can produce output. Yeah. But taste is the thing that differentiates good from great.
Speaker 4:Even on modeling. Right? Yeah. You can have all the data in the world and inject it into a pre training run.
Speaker 1:Yeah.
Speaker 4:But actually, the best labs are the ones that have people with taste that can hand select golden sets of, like, what is the best response for this thing, or what's the best image aesthetic for this thing. Mhmm. I think it's the same with, like, text based posts. It has to be real time. It has to have a bunch of cultural understanding.
Speaker 4:I do think models will get really good at that at some point. My the thing that I'm most excited about though is like AI assistive in the creative process. Yeah. So like Yeah. If you're an NBA creator, half the stuff that you do is just clipping content and being like, did you see that Victor Wimbanyama dunk?
Speaker 4:Yep. Half of it is like weighing in on it and having an analysis that like comes from your point of view and feels native to you. That first half, if we can like automate for people and make super easy to do because they have a workflow that's like Yeah. Watch all the NBA games, give me the content that I should be posting, and then I'll add on my little flavor on top of it, like that would
Speaker 7:be amazing.
Speaker 2:Also fact Yeah. Because timing timing with this stuff is so important. I mean, we we is obviously a big part of our business is like, if you're getting to stories, you know, later than everyone else, it's just way less. It's becomes goes from interesting to not interesting at all. Totally.
Speaker 2:And so I think for creators that wanna build an account like that, any type of tool that allows them to be Mhmm. Faster in that process is
Speaker 4:is crazy though. Like, I don't You know, have you guys had Geo Rainbolt on here?
Speaker 2:We Not yet. I would
Speaker 1:love to
Speaker 2:have him on. Okay. Great. I'm worried he's gonna he's gonna he's gonna dox us. Oh, yeah.
Speaker 2:Let me talk real fast.
Speaker 1:We've given out so many little teasers with images behind He
Speaker 4:I think I've I'm I meet and love a lot of creators. He is like the most impressive content creator I've ever seen. Incredible. But he I saw an interview with him recently and he's like, oh, yeah. When I was like a teenager, I had a Steph Curry fan page.
Speaker 4:I think it's still up and it had like 50,000 followers. Wow. Yeah. And you meet all these kids that are like in their twenties. They were raised in a a version of the world where Instagram was at the center of the universe.
Speaker 5:Yep.
Speaker 4:And they create like fan accounts that get super huge. Yeah. And then it's like, then what do you do with it? I guess you get really fucking good at geo guessing. Yeah.
Speaker 2:Mean, I like, we we we have, some people on the team that are super early in their careers. Maybe this is their first job. And when we talk about and and a lot of the work is like selecting content, editing it, you know, distributing it on the right platform. And it feels like like very much like manual labor, like you're watching content. Mhmm.
Speaker 2:And we've stressed continuously that, it's actually, it's very important training for doing almost anything because it's like developing taste, it's like developing consistency, speed, being organized, you know, being able to get like immediate feedback on the work that you're doing. Like the feedback loop is super tight. And so we've consistently said like, hey, we don't expect you to be doing this in five years, but like for now, take it extremely seriously because if you can get really good at this one thing, you might be able to apply it in, you know, a bunch of other domains. Yeah.
Speaker 4:Yeah. It's kinda crazy. It's like today's mail room, basically. Like
Speaker 2:No. Really is. We went in this we went in the CAA mail room. When we were on we we were doing a tour of the building and we were like, hey, can we see it? And it it felt like exactly the mail room out
Speaker 3:of Yeah.
Speaker 2:Like the same mail room as like, you know, thirty years ago.
Speaker 4:I was at their event last night for All Star, and they were the most excited I've ever seen agents about TVPN being on the CIA roster. Like That's great. Ear to ear smiles when I brought you guys on That
Speaker 1:Rainbowl story is funny. The first social media account that I ever got to somewhat upscale was an Instagram for my dog that I got to, like, 20,000 followers. That's pretty good. I I I'm gonna get in trouble here because I used a bot to to automatically follow anyone who liked the page or leave a like No. On I will eventually, the bot got shut down, but it already, like, went up.
Speaker 4:What's the account?
Speaker 1:Get banned. I don't care. I I don't wanna post any more photos of my dog. I was bored at the time. But but it was an interesting thing of, how do you solve the cold start problem?
Speaker 1:And I'm wondering about, you know, now there's a lot of platforms where I feel like there is an audition process. Yeah. You can go on to a completely blank account and if you bring a banger Yeah. Some heat Yeah. Like, the algorithm will will audition you with, like, 500 random people Yeah.
Speaker 1:And be like, retention was really great. Yeah. Let's show this to more people, show this to more people. Yeah. Is that the way threads is set up right now?
Speaker 4:We do a bit of that. Okay. Yeah. Exactly. It's like you you basically take any piece of content on the platform, you sample it to some people.
Speaker 4:Yep. And then you very quickly try to understand, did this do well in this sample? I the smaller the sample, though, the wider the error bars are.
Speaker 3:Sure. So you
Speaker 4:have to keep auditioning. Yeah. Yeah.
Speaker 2:So it's like cycles of auditions?
Speaker 4:Cycles of auditions. Yeah. And then, you know, some people fail the audition. But
Speaker 2:Of course. What happens to those posts?
Speaker 4:What happens to those posts?
Speaker 2:Just don't end up getting served to many people or they have to get served later because No.
Speaker 4:Well, we also we have a really tight window of eligibility for recommendations. Mhmm. Like, want the app to feel very real time. So something you posted three days ago won't be eligible to be recommended to someone who doesn't follow you in the app. Yeah.
Speaker 4:Yeah. So it all has to happen very, very fast. Okay. Okay. The bet on on threads, the this is like a talk track that I give to every creator that's like, what do I do?
Speaker 4:It's reply to people. Yeah. The feed loves that. The feed kinda loves reply guys. And it's just not just replies though, like, are you driving a conversation?
Speaker 4:Sure. The best actually, he was at our event yesterday. Draymond Green, number one example. If you wanna go look at someone's replies on threads. He I asked him if he searches his name and he's like, no, man.
Speaker 4:You just show me haters. His feed is just people being like, Draymond is the worst. I I hate him on the Warriors. And he'll be like
Speaker 1:I disagree.
Speaker 4:I looked at your profile picture. You should talk to your mom about how ugly you are. My God, Dream Bond. He gets a lot of joy out of it. That's his brand and character.
Speaker 4:And when he does that, it shows the world. I'm on threads. I'm doing something that's true to me.
Speaker 1:Yeah.
Speaker 4:I think the people who don't do as well are the ones who kinda just, it's not organic. It doesn't feel like them. It doesn't have personality, and replies are, like, a good way to get that out
Speaker 2:What it what's what's Thread's relationship like with the rest of the app ecosystem? Early on, you guys opened the floodgates, brought a bunch of people in. I'm sure you looked at, like, retention, who's actually staying here, how do we get more people like But, like, what does that relationship look like? Are you, like because I'll see, like, a meta a pop up for, like, meta Ray Bans. Right?
Speaker 2:Right? When I open the app Mhmm. And then maybe I scroll a few times, and then there's, like, some threads content that's pushing Yeah. You Yeah. In Instagram.
Speaker 4:Yeah. I mean, we we, we promote the app, the content from the app in Yeah. Facebook and Instagram quite a Mhmm. I mean, I'm sure anybody watching this who uses Instagram has probably seen some of that. So that's that's the main point of integration that we have.
Speaker 4:When we built threads, there was a bunch of, like, foundational decisions that we had to make in the beginning, which were like, you know, what app what app binary do we build on top of? Like, we actually just took Instagram and
Speaker 1:Oh, yeah.
Speaker 4:We're like because on day one of threads when it was like a very thin app, it was like 300 megs or something in the app store because we just had the IG code base. I mean, we've like made it more efficient from then. But it's like what namespace do you use? Like, we we mirror the Instagram namespace. We you can have a threads only account, but you can only have a threads only account that isn't a name that's on Instagram, you know.
Speaker 4:Like, we made So there's a lot of natural tie ins to Instagram because of that. We're we were backed by them effectively in the beginning. But now, like, a lot of our users come from that integration in the Facebook app. Mhmm. We we and you can sign up for threads from Facebook without an Instagram account.
Speaker 4:Like, we're trying to make it stand on its own independent of the IG history without, like, disrespecting the fact that that's, like, the best marketing channel you could ever ask for. Yeah. So we we try to own setup.
Speaker 1:Talk about collabs. I was scrolling this Instagram creator. Have you ever seen the the let him cook guy? Have you seen this guy? No.
Speaker 1:He does this incredible thing where
Speaker 7:What shame.
Speaker 1:He'll bring, the video and he'll be like, you can't cook an f one driver. And he this song plays, and he goes in, and it transitions from, like, a tire to the road. And it's, like, this amazing editor. And I was scrolling, and I just, and one of them is just him, doing the same, like, motion graphics effect, this amazing edit. And Adam Messeri is sitting there with him.
Speaker 1:And it's very clear that he, like, collabed on this and they have the shared namespace on them. And I've and I've seen there's a bunch of different ways, but that feels like an interesting, you know, it's very popular in podcasting. You have a big guest on. Yeah. A bunch of their audience comes to yours.
Speaker 1:What does that look like on threads for someone who's trying to sort of network their way to broad account growth?
Speaker 4:Yep. We do a bunch of this. I'll give you a couple examples. Yeah. It's like yesterday, I actually put up, it's like kind of mortifying video because I did a training session with Lethal Shooter, the the NBA shooting coach.
Speaker 4:Cool. Yeah. I thought I was gonna crush, by the way.
Speaker 1:I was like You don't want to edit the video. I I can't edit out the misses.
Speaker 4:I walked onto this basketball court. I was like, I am going to be Yeah. The greatest shooter of all time. And it was so humbling and But like, we did it we did that thing. He was amazing.
Speaker 4:But that's like, you know, I put some content up, he'll repost it. He has a bunch of fans from Instagram that are on threads and like, he's actually really good on threads. He he's his mentality is very like, oh my god. There were so many one liners who were just screaming at me the whole time. But it's very much like
Speaker 2:Just emotes.
Speaker 7:You can only
Speaker 4:be great at a thing like shooting a basketball Mhmm. If you are centered as a human. Mhmm. And he posts like motivational quotes like that on threads and stuff and people love it. Mhmm.
Speaker 4:So that's one thing where it's like, not only is he doing well on the platform, but I do something with him and show everybody there like, this is We also then have a bunch of like more homegrown talent where it's less like, take someone who's huge on IG and bring them to threads. Yeah. There's a guy
Speaker 2:There's always there's always been alpha just getting being one of the first
Speaker 4:One of the first.
Speaker 2:10,000,000 Yeah. Users, but then taking it more seriously than any of the anyone else.
Speaker 4:That's this guy YoRush on threads. He's like they call him the mayor of NBA threads. Mhmm. He was just like at home. He's an NBA fan.
Speaker 4:Threads came out and he just started posting and people liked it and, like, he was with us yesterday at this thing we did in LA. Him and his wife are here for the weekend. They're coming to a bunch of events with us. Like, you know, we want people like that to I think it's really important if you have a content app to have homegrown talent too. Yeah.
Speaker 1:You
Speaker 4:can't just be transitioning people from other places. Like, you need to show everyone on the app that you could be successful here too if you just like do the right things and reach the right audience.
Speaker 1:Yeah.
Speaker 2:How What's your what's your philosophy around creator payouts? How do you think creator payouts on on other platforms have worked well? Clearly, they work well. On YouTube, our our point of view is like making a great YouTube video takes an insane amount of work. It's in the incentive of the YouTube platform to pay people Yeah.
Speaker 2:Because so they can quit, you know, quit their job or put more resources to it or buy gear. All these things where our our I I haven't felt like creator payouts have made x a better platform at all Yeah. Because it incentivizes people to just like churn out kind of like low quality content that might rage bait people into engaging, but isn't actually making the platform better. Yeah. I have
Speaker 4:I have pretty strong opinions on this and a bunch of I I agree with the way that you just positioned that. Like, the way that at least right now I'm thinking about this on threads is like, I wanna be in the business of directing traffic to the places where you make money in like a sustainable way. I don't know. We've tried different versions of this at Meta. X has obviously had their version.
Speaker 4:I've never seen in an in an app like Threads a sustainable creator creator payout product work well over time.
Speaker 3:Yeah.
Speaker 4:YouTube works well, you know, and then you have like the Substacks and Patreons of the world that are like more subscription based. Podcasts like getting subscribers and traffic to your podcast is a thing that you can monetize and know run ads and have sponsors like you guys do. And so we have been focused on that by, you know, we did this like pretty simple thing where we worked with Spotify to do like rich previews of podcasts. You can also pin That's cool. The podcast your podcast link on your profile.
Speaker 4:I would love to do stuff like then you can subscribe on Spotify Yeah. From the feed and things like that. But the whole Yeah.
Speaker 2:The re the reason that that philosophy, like, I think is smart is that's what we're seeing across the entire Internet. Because you're gonna have different ways to make money. Yeah. Different
Speaker 4:creators will.
Speaker 2:You can't just get paid for views because not every view is equal. Correct. Otherwise, like, the the kid running, you know, there's kids running meme accounts that are posting, like, funny, maybe controversial, edgy content on Instagram getting, like, a billion views a year, but it's it it actually has zero value.
Speaker 4:It's like the videos of the kids that take the fake turds and put them in like Burger King bathrooms. Have you not seen this? Oh my god. I'm like, what are we doing here, guys? Now you all all the viewers will have to look that It's like the most horrible content.
Speaker 4:It's you're right. It's the the incentive there is like, how do I do something funny? I actually like, I'm very that whole like prank video space to me is just this like insane like I Yeah. I guess you could have imagined it coming ten years ago, but whenever I see one, I'm like, how did we get here?
Speaker 1:I think it blew up on YouTube like years ago. I
Speaker 2:Yeah. Prank videos were were the
Speaker 4:That was the original HGTV.
Speaker 2:I used to have a few prank videos, you know, my holster if I go to a friend's house and say, let's pull up YouTube. Let's go. But but going going back to it, it's like, yeah, if you can be a place that helps people have an audience and build a business, that is what every successful content creator has done. They they're not just relying on views. Mhmm.
Speaker 2:Even for us on X with creator payouts, you know, generating hundreds of millions of views in the last year, like the x creator payout is like such a rounding error that I wouldn't be mad if it went away. Right? Mhmm.
Speaker 4:I think it's You end up It's funny because when you talk to people it's like there's people like you guys who hundreds of millions of views, rounding error, you wanna be mad if it goes away. The people that tend to care the most about it are the ones who get paid out like $80 a year. Mhmm. And I and I do, I actually sympathize with that because it's like if this is a side hustle for you to your point before, you're you wanna buy a new camera, you want better gear, finding ways to get people enough money to sustain the thing that they're doing and give themselves more attempts to make it big or build a bigger audience Yeah. I think is great.
Speaker 4:We just wanna do that by pushing people to the places where where you're monetizing more efficiently.
Speaker 3:Sure.
Speaker 1:Yeah. Are you seeing SpawnCon happen natively on the platform like on Instagram where I mean, I see a ton of influencers who are like, get ready with me and this outfit's brought to you by or And that's been a backbone for a whole variety. I I have a friend who's been working with Figs for a long time and she'll talk about Figs clothing and it works really well on Instagram. Have you seen that flywheel start on threads?
Speaker 4:It's interesting that you asked that. I mean, we've we have had some of these I would say there it it's more like memes that everyone in the app participates in for a few days Yeah. But not like categories like that. Mean, like, Get Ready With Me. It's like Alex Earle was on Yeah.
Speaker 4:Dancing With The Stars because she did Get Ready With Me Yeah. Videos five years ago. Yeah. That's like, don't think we've seen equivalence on threads, but we had this Yeah. Do you guys know the, like Sorry.
Speaker 4:I'm just bringing up memes on the show, but, like, hey.
Speaker 2:That's what we say.
Speaker 4:You with a meme.
Speaker 1:Brit vlogging.
Speaker 4:So you guys know the I hate gay Halloween thing? I mean, that was like big on Threads. Okay. It's just Wait. Wait.
Speaker 4:I I I actually do
Speaker 1:think I saw one thing.
Speaker 4:It was just people being like, know, there was actually one that I laughed at the other day. Like, I hate gay Halloween. What do you mean? I'm like, you're going as the grass from the Bad Bunny halftime show. Yeah.
Speaker 4:Yeah.
Speaker 7:There was
Speaker 1:like two All these like very obscure Exactly. Niche references
Speaker 4:that are think that's really what Threads is good at is like the niche humor.
Speaker 2:That is a great Halloween outfit. We were during the Super Bowl, we were I was just sitting there zooming in on on the grass. Grass? Because you could see there was like coordinators that would be like right up in the face Yeah. Of the grass just like yelling them like,
Speaker 4:get to the right. I thought they were gonna do something but then I found out it was because they had limitations on the number of carts you can roll out onto
Speaker 7:the field.
Speaker 1:So they had to add people
Speaker 4:The way that they were able to do the set was to have humans walk on and off because there's like a restriction on the number of carts
Speaker 1:Okay.
Speaker 4:You can put on the field. I like it.
Speaker 1:It's of finds a way.
Speaker 4:Yeah. It's amazing.
Speaker 2:How how big how big is the team? How do you think about scaling the team? What does it look like to go to to go to Zoc and say, need an I need a 500 more.
Speaker 4:Yeah. I've never made that ask. We're we're relatively small Yeah. Compared to the other apps inside Meta, like, by orders of magnitude. But, we are growing this year.
Speaker 4:We're investing in, two things. One is, like, just relevance, making the content ecosystem better and stronger and the personalization of the feed. The Dior algo thing is like a part of that.
Speaker 1:Question for that.
Speaker 4:And then the other one is just like making sure that we can grow sustainably. Like, these promotions that we have in Facebook and Instagram are awesome. I think that we will have them for a very long time. But we also wanna make sure that people are turning to threads without having to see a promotion. Mhmm.
Speaker 4:There's a bunch of just like basic work to do well there that I think other companies have done really well over the years. Even just like SEO and Mhmm. Getting yourself like if someone searches Super Bowl halftime show, I want Threads content to come up on a search engine there. And so those are the two categories where we're growing, but it's still a pretty small team.
Speaker 1:Yeah. Can I wanna know more about Dear Algo and I wanna share my experience? You can tell me if this is just me being weird or if this is actually a trend. Like, there was a time when a social network would be all things to all people. So if I liked sports and tech and cars, I would get all three of those sort of mashed together.
Speaker 1:I could maybe go into certain communities. Now I feel like I have different apps and different platforms. Like for real time tech news, I go to X. But then if I'm watching a video essay or a car review, that's on YouTube. My my Instagram is much more timely, much more funny, more reels.
Speaker 1:And then all my podcast players for, like, the conversations that aren't very visual. Yeah. And so I have all these different platforms that I'm wondering about, like, is there is there a future where someone's using threads for one interest of theirs, and then Instagram for a different interest of theirs, and there's kind of two separate communities, and they're sort of intentionally steering it that way.
Speaker 4:I think it's possible.
Speaker 1:Okay.
Speaker 4:Like, there is kinda to my point before about what content works well on the app and not. Yeah. Like, I would say in a in a category as broad as sports Yeah. You probably always will have two types of content. It's like, show me the super cut of like Kenneth Walker Yeah.
Speaker 4:In the Super Bowl, and then show me, you know, Mina Kimes talking about his free agency or something like that.
Speaker 7:It's breaking news.
Speaker 4:Threads is gonna be really good at the the latter. I think Instagram will be really good at the former. Mhmm. One of the ways that a lot of these apps think about how to get to the point that you just talked about which was like which is like is like, how can we get the user to tell us what they wanna see without asking them? Mhmm.
Speaker 4:So that's like, what do you search for? What do you dwell on? What do you share with other people? What do you like? All these signals and like, our job is to figure out which signals are signal and which ones are noise.
Speaker 4:I actually think it's possible for an app like Threads to be multiple things for people, but probably not everything. Yeah. Like, I don't I don't want Threads to be a video app. That wouldn't make sense.
Speaker 2:Yeah.
Speaker 4:We have, like, a lot of investment in short form video at Instagram and on Facebook.
Speaker 1:Been like, that's something we've not really
Speaker 4:I don't need to do that. Like, but I I think that there's a space for the text format that's really big. And and Mhmm. My biggest takeaway from the last few years of threads, which when we first started it, I think it we very much saw growing the app as we need to pull people from other services to grow. I've been really pleasantly surprised at how we've grown the category.
Speaker 4:Mhmm. There's a lot of people that use threads that never used x or similar platform in the And so that's the thing that I've been really focused on is like, why is that happening? What are those people doing? And a lot of it is like these niche interests. Dating threads is really big, actually.
Speaker 4:It's like people like singles go on threads and make a post and put it into the dating threads community and they're like
Speaker 1:Interesting.
Speaker 4:Hey, I'm looking for love. But then we also book threads. There's like a like crocheting community. Like, I tend to spend my time on sports and pop culture and stuff like that, but the there are these very niche communities that we actually built like a community's product so that you can find those people and kinda make your app about that.
Speaker 1:So how does Dear Algo work? Is it plain text or buttons? Yeah. UI? How can someone actually customize
Speaker 4:We just say we just sort of built it off of what we so there was this viral moment like a year ago Yeah. Where people were writing Dear Algo, show me more tech content or whatever. Yeah. Dear Algo, introduce me to people who are into these things. Yep.
Speaker 4:And that I mean, to say that it didn't work would maybe be incorrect, but it's like the system wasn't architected for that to be like a strong signal. Totally. Yeah. Of course, if you write about a thing and you like a bunch of content about it, maybe the algorithm will pick up. You wanna see more of that.
Speaker 4:Yeah. But it wasn't working with like high intent. So now, if you just go type Dear Algo into a post on threads, it like tags itself blue. You can say, you know, the other day, actually, because I'm a Patriots fan, I was like, stop showing me NFL content. Yeah.
Speaker 4:And for three days, I got nothing about the NFL and my beat was amazing. Like, I don't even know if the Seahawks parade happened. Like, it didn't cross my timeline. But then you could you can also say, show me more of something. So, actually, I think I made one the other day that was like, show me more real grass people from the halftime show, not AI generated ones.
Speaker 4:And that worked. Like, the reason why we're able to do it is because content understanding and like topic trees have just gotten so much better with LLMs. Like, five years ago, we might have had you as dog sports cars. Yeah. Now, it's like this specific model of this car which is associated with this brand Yeah.
Speaker 4:Which is made in this country. It's like
Speaker 1:a million parameters that no human can Exactly. But it's way better.
Speaker 4:Yeah.
Speaker 1:Just like ad targeting. Love it.
Speaker 4:You will get like a rejection if you if you say like show me more murder, we will be like, we can't do that. If you say show me more of something that's like so niche that we don't have enough content Sure. We'll tell you like, hey, there's not enough content for this. And then we actually tell you in the feed Yeah. When you see something that's because of the request that you made, it'll be like marked as such Yeah.
Speaker 3:So that
Speaker 4:you know what you're
Speaker 1:very cool.
Speaker 2:It's fun.
Speaker 4:You guys should try it.
Speaker 1:Yeah. I love it. I love it. Yeah. I've been waiting for the plain text interface to the
Speaker 4:Jordy could make his first threads post today, maybe, by trying it. I will do that. I checked
Speaker 2:your I haven't I haven't done one. No. I gotta Alright. I'm gonna get there.
Speaker 4:Brother don't mean to shame you on the on the live stream. No. That doesn't crosspost thing.
Speaker 2:Today, I'll be on there. JordyHayes on Thread. Find me there.
Speaker 4:Actually, made my first x post in three years today
Speaker 1:Yeah.
Speaker 4:Because you guys tagged me on there. Amazing. And I wanted to route people to
Speaker 1:Okay.
Speaker 2:There you go. Always be selling.
Speaker 4:Yeah. Exactly.
Speaker 2:Be selling. Yeah. Well, the app the app looks absolutely beautiful.
Speaker 4:Thank you.
Speaker 2:I love
Speaker 1:three in the app store too. Polish.
Speaker 4:Anywhere from two to three. I wanna get that number one.
Speaker 7:Do you
Speaker 2:do you do you like wake up and check the app store charts?
Speaker 4:No. That is not a thing I do. I wake up and I look at like six dashboards.
Speaker 1:Also, I mean, just to be clear, in
Speaker 2:Well, it might help if you put a big monitor in the office that just has your app store position. Yeah. Yeah. That'll
Speaker 4:make
Speaker 2:it Usually, when you put things up like that, it tends to, like That's
Speaker 4:a great tip. I'm sure my team will really enjoy that.
Speaker 1:Anyway, dude, it's great
Speaker 3:to hang.
Speaker 4:Yeah. You guys for having me.
Speaker 7:Thank you.
Speaker 1:Yeah. Thanks so much.
Speaker 4:Thank you guys.
Speaker 1:We'll talk to you soon. You heard Martin talk about it but now you're gonna hear me talk about it. Eleven Labs, build intelligent real time conversational agents, reimagine human technology interaction with Eleven Labs. And I'm also gonna tell you about fin dot a I, the number one AI agent for customer service. If you want AI to handle your customer support, go to fin.ai.
Speaker 1:And
Speaker 2:Up next.
Speaker 1:Up next, Alex Buzzari. He's the co founder and CEO of DDN. He's in the restroom waiting room and now he's in
Speaker 2:the TV panel. What's happening?
Speaker 1:How are you doing, Alex? Good to meet you.
Speaker 3:Hey. How are you guys doing?
Speaker 2:We're doing great. I expected we expected you to suit Magus.
Speaker 1:Yes. We certainly have. What's the background on the on the suit? Have you always been to fashion? Is it
Speaker 3:been into fashion. I'm sure you guys appreciate it because you're definitely not like everybody else.
Speaker 1:Yes. Yes.
Speaker 2:Well, now, we'll we'll hit you up after the show for some Taylor recommendations. But, very very excited to meet.
Speaker 1:Yeah. Well, first time on the show, please give us an introduction.
Speaker 3:So CEO, cofounder of DDN. DDN solves all the data problems associated with AI implementation
Speaker 7:Mhmm.
Speaker 3:For enterprises, sovereigns, so nations, countries, large scale deployments, Mhmm. NVIDIA uses us internally for everything they do. Elon, large, Rock on 200,000 GPUs is powered by DDN, hundreds and hundreds of deployments like that. So that's what we do. We solve the problems of AI, and we help organizations monetize AI.
Speaker 3:Because it's great to invest, but if you don't monetize, what's the point?
Speaker 1:What what's your background? How did you get into this business? How long have you
Speaker 2:been in
Speaker 3:this Been in technology forever. Yeah. Born in France. Came to The US in my early twenties, went to school here. Mhmm.
Speaker 3:Loved it. And then just did a bunch of technology companies. Mhmm. This one, my partner and I started about twenty some years ago.
Speaker 6:Wow.
Speaker 3:Wow. At the time, we were solving the problems
Speaker 1:high success. Computing.
Speaker 3:Yeah. So high performance computing is basically government labs, academia, trying to solve complex technology problems. I mean, those guys were our customers. We ended up powering 60 out of the 100 fastest supercomputers in the world, in every country, basically, three letter agencies
Speaker 1:Yeah.
Speaker 3:Department of defense, department of energy. And then this little thing called AI started to happen. And so NVIDIA came to us and tapped us on the shoulder, and they said, well, we're trying to stand up a reference architecture. That was eight years ago. And they said, we have all the pieces.
Speaker 3:We don't have the data. And so we became part of that architecture, and video became our customer. And, you know, here we are eight years later. AI is booming as as you know, as you see. I mean, it's expanding, exploding in every aspect, every industry.
Speaker 3:And and that's been the journey. And the journey is super exciting.
Speaker 2:Walk us through obviously, you're quite bullish on AI and implementing it across every possible industry. But how did you personal personally kind of process the different evolutions and paradigms from, you know, the transformer architecture to all the different steps that we've had since then? Sure. Sure. Sure.
Speaker 3:Great. And that's a great question. I mean, look. When NVIDIA came to us eight years ago, I mean, honestly, I don't think anybody realized how quickly it was going to grow and evolve. Mhmm.
Speaker 3:And so we walked away from that first meeting saying, well, we need to develop a radically different architecture, and that architecture for AI to be successful needs to connect edge. So edge devices, think of that as autonomous cars. Think of it as sensor data, robots in factories that move things around. So it has to connect the edge to the data center where the data is getting processed, analyzed. That's where a lot of the NVIDIA infrastructure is being deployed and then multi cloud.
Speaker 3:And so the evolution really was, as NVIDIA and other companies have been deploying faster and faster GPUs, the resulting factor is that there's a scarcity in the number of GPUs available in the world, scarcity in power. There's not enough power in the world, and there's not enough data center footprint in the world. So our technology has basically evolved to adapt to these limitations. People are spending organizations are spending millions, tens of millions, hundreds of millions. We have customers who are spending tens of billions in building out infrastructure.
Speaker 3:Well, if that infrastructure is not productive and is not delivering value, then it's wasted, and the ROI just doesn't work out. So we've evolved our software stack, call it the data plane
Speaker 1:Mhmm.
Speaker 3:To ensure that these infrastructures are running in the most effective way possible, irrespective of what power shortages might be or data center footprint shortages might be or the number of GPUs that are available. So so that's really been our evolution. It's been, you know, lock and step. A lot of it guided by NVIDIA. I mean, our engineers and their engineers interact on a daily basis across all aspects of NVIDIA's engineering.
Speaker 3:And the primary problem is, how do you make it easier for enterprises to implement AI in their environment in a nondisruptive way? Mhmm. I mean, in essence, you're dealing with CIOs who are like, well, I don't wanna have any glitches because if I have glitches, I'm gonna get fired, and line of business people who are saying, hey. I want to benefit from AI in developing better, more compelling, more competitive products and services. So you have this tension, which means you have to make it easy for them to deploy in their environment.
Speaker 3:You have to make it risk free. And so with NVIDIA and others, we've developed these integrated solutions that are industry specific that can be deployed and make it easy for enterprises to bring in AI into into their environment and benefit from it. So so it's really that. I think we're moving from an early adopter phase
Speaker 1:Mhmm.
Speaker 3:Which is a handful of organizations are benefiting from AI, you know, the hyperscalers, the, you know, chat GPTs of the world, the Grox of the world, into one where the industrialization of AI is underway. And I think that's one of the most compelling things that is happening out there. But but for that to take place
Speaker 2:It doesn't
Speaker 3:easy easy.
Speaker 2:Latest earnings cycle, I think everyone was shocked by some of the the CapEx numbers that were coming out from the hyperscalers. Was that surprising to you?
Speaker 3:Or Not not not really. Because, again, we're we're very, very close to the center of the universe, which is Jensen. And and and if you look at it, I mean, the CapEx
Speaker 2:Jensen Jensen was saying, like, in q four of last year, he was throwing out numbers that implied that the hyperscalers would be raising their their CapEx projections massively. So it shouldn't have been that much of a surprise. But when Jensen was first saying it, he's obviously a salesman. And Yep. Like, you know, it it it felt yeah.
Speaker 2:Of course, it feels a lot more real once once they're throwing out, you know
Speaker 3:Well, I mean, look, I mean, if you if you think about it, the hyperscalers have a software suite, which they're monetizing across a very broad population. Hundreds of millions of users, billions of users. And so you look at that CapEx and you align it with how much they're charging and how sticky the offering is, you just gotta do it. Because if you don't one hyperscaler will emerge, I think, as a leader. I mean, just like the Google search engine.
Speaker 3:There will be one leader, and then there will be a number of others who will have market share, but they won't be the leading market provider. And I think everybody has come to the conclusion that you have to invest very, very heavily because without massive infrastructure deployments, you cannot train the model at at the level of complexity that is required at the real time elements that are required in order to deliver outcomes to organizations and consumers. So so I think it's really that. Everybody is racing to be the market share leader in this newly created space. I mean, Google is doing it.
Speaker 3:Yeah. OCI is doing it. Microsoft is doing it. Meta is doing it.
Speaker 2:Yeah.
Speaker 3:There will be one leader.
Speaker 2:What Getting a little bit more specific. Soft software engineers have done an excellent job adopting creating and adopting bunch of AI tools. What are maybe some under discussed areas that you're seeing AI adopted and real usage growth that that the kind of broader tech community is less focused on because these are, you know, maybe companies or industries that aren't, you know, typically at the center of of the conversation.
Speaker 3:Sure. I mean, look, the the places where we see significant financial services because the ROI pencils out beautifully. I mean, it's a no brainer. The better your models are, the more complex you can run those model, the faster you can get outcomes, the more differentiation you create, and so the better return to your shareholders.
Speaker 2:So And so that's, like, companies that are doing trading or
Speaker 3:Oh, think hedge funds, high frequency traders. We have some very large customers in that space. Those are very technical organizations. Typically, the people in these organizations have come from the world of high performance computing, so they understand the benefits of it. Yep.
Speaker 3:And so, yeah, that's that's one bucket, which I think will continue to expand. Secondary is life sciences. Anything having to do with drug discovery, bringing a new drug to market, genomics. The costs associated with bringing a new drug to market are staggering. It's billions of dollars.
Speaker 3:It's years and years of development. And so in the end, if you find yourself with a drug which is being rejected by the FDA, well, you have a problem. So AI, gives them the ability to better triangulate what should an optimized drug be to cure a specific disease and how do you increase the likelihood of that drug getting accepted
Speaker 2:So the the cost will still be extreme because of, you know, animal studies, human trials, all the different steps. But you you we could enter a world where you have a higher success rate for drugs that are entering Exactly.
Speaker 3:I mean, it's it's higher success rate. It's better predictability. And it's also as the omniverse, digital twin starts to happen. I mean, the ability to run what if scenarios that you don't have to do in the real world. So if you're bringing a new drug to market and you're saying, well, there's, like, eight different ways I could do this, but I'm not sure which one is going to be the best outcome.
Speaker 3:You run simulation in the Omniverse with synthetic data, and then the responses come back saying, well, if you combine parts of the first one and the third one and the fifth one, combine them together, likelihood of success will be higher, the drug will be better. So I think increasingly, we're seeing that the omniverse and synthetic data is coming into the mix. Autonomous driving, clearly, because, well, if you have driverless cars on the road, you have to collect data in real time. This is cameras, audio, this, that, the other. You have to continuously reprocess the model at very low scale.
Speaker 3:So many car manufacturers are our customers in that space. Manufacturing is starting to happen. Again, factory floor automation, retail, just how do you optimize inventory in various types of retail organizations. And then the other really big ones is sovereign sovereign AI. I think with what the Trump administration has done, they've created lots of concerns worldwide in terms of, you know, autonomy and risk, and if The US is no longer there to just bankroll you, so you have to protect yourselves in some ways.
Speaker 3:So we're we're involved in lots Have
Speaker 2:you have you spent much time in in France over the last few years? There was obviously a little tiff between The US tech community and Macron last week around. He came out with an announcement that was kind of intentionally misinterpreted little bit, and then he was By
Speaker 1:who, Juri?
Speaker 2:Maybe by me. But he was putting out charts of showing foreign investment. We've heard that a number of labs have been excited about the energy availability in France and look to capitalize on that, but kind of just kept running into different blockers kind of from a regulatory standpoint or a general speed standpoint. But what's your kind of take on on France's progress around sovereign AI and just kind of catalyzing the industry locally?
Speaker 3:So, look, I mean, one one of the one of the organizations in France that's very, very active in this space is a company called Mistral. Mistral is our customer, so we're deployed in their infrastructure. I think look. At the end of the day, lots of super, super smart people in Europe in general, you know, France, Germany, UK, all of it. But the scale of investments isn't just not at the same level as as The US and China.
Speaker 3:I mean, so today, it continues to be a two horse race. I think it's US and China. And The Middle East is starting to deploy massive resources into it. I mean, Kingdom Of Saudi Arabia, I mean, we're involved in those infrastructure build outs, so sovereign AI. The good thing there is that the cost of energy is very low.
Speaker 3:Land availability is very, very significant. And there's a desire to really step up what KSA is doing. Likewise, in UAE. And while there are geopolitical tensions between the two, but I think both have aspirations to set themselves up on the world stage as being the third player. Yeah.
Speaker 3:Europe, I think, will will will be there. No question. But Europe, I mean, all these countries have centuries of history, and so getting things done quickly, easily is not quite there. Whereas in The Middle East, well, you have one decision maker. And if that decision maker says go, it goes Yeah.
Speaker 3:With infinite resources. I mean, right, trillions of dollars under management in in both places. So making a multi $100,000,000,000 investment is nothing. Straightforward. Yeah.
Speaker 2:When you look at where DDN is spending money on software today, how do you think that'll change over the next five years? We've been covering the SaaS pocalypse. It seems like a number of great companies today could be comparable to the sort of office equipment and imaging companies of like the nineties where they're they had the revenues were really high, but then the Internet and email and the PDF came along, and suddenly people just needed less fax machines and and all that kind of thing.
Speaker 3:Sure. I mean, look, I think the market is overreacting in some ways, as it sometimes does. So, you know, somebody makes an announcement, and then everybody freaks out. Oh my god. Oh my god.
Speaker 3:This whole industry is gonna get commoditized. Everybody's gonna go out of business. ServiceNow is gonna go out of business. My god. My god.
Speaker 3:I think you have the forward thinking organizations in SaaS who are adopting and integrating AI into their offering, and I think those will do well, provided that they do it at very high velocity. And then you have the ones who will be more traditional in their thinking and and in the way they operate, and I think those will go by the wayside. I mean, just look at IBM. IBM is a perfect example. Look at Intel.
Speaker 3:I mean, these are companies that had everything to to succeed. I mean, why is it that NVIDIA is where they are and Intel is not? Well, because the velocity of execution and the ability to adopt something that is happening that is completely different from what it was before was not quite there in the culture of the organization. So so I think the way to look at it is which organizations have leaders who are embracing the change, not fighting it, and who are going to integrate it into their portfolio and forge the right alliances. I mean, you could say the same thing about GSIs, Accenture, Deloitte, all of these organizations.
Speaker 3:I mean, Accenture has what? 750,000 employees? How many of those are going to be relevant in this AI enabled world? Well, Accenture has to completely transform the way they operate and the value that they deliver. Otherwise, it will just go like that.
Speaker 3:So you need really forward looking visionary leadership that will force the change. Because if you stay in your comfort zone and you're saying, oh, I have a great business, like you said, the top line is steady, bottom line is fine, you will get whacked. That that's just
Speaker 6:the way it is. Mhmm. I
Speaker 3:mean, look, this at which AI is is operating Mhmm. It's Jensen's feet.
Speaker 1:Yeah.
Speaker 3:It's pulling the whole industry at a velocity which very few can follow. The speed at which he is turning the GPUs, the integration of the software stack and the ecosystem into the GPU enablement. The open source approach he's taking, I don't know if you saw this CES keynote, is basically developing turnkey integrated software stacks and is open sourcing them in order to accelerate adoption industry by industry. I mean, what he did with Mercedes, it's okay. Here's an open source software stack.
Speaker 3:It's kind of the opposite of what Elon was doing at Tesla, which is a closed architecture. It's like I'm opening it up because if I if I open it up, I will accelerate the adoption of AI Mhmm. By the automotive industry, and they will buy more of my GPUs. So I will put 5,000 engineers on this for many, many years, and then I will put it out there. I mean, it's a matter of, really.
Speaker 3:What? Celebating adoption.
Speaker 2:Yeah. When when you think about kind of forecasting forecasting and planning for your business, are you more scared of a chip bottleneck or energy bottleneck when we talk to different I
Speaker 1:would also like to put in research idea bottleneck and energy There's sort of four categories that people are worried about progress halting on, chip, sort
Speaker 2:of And to date, it's been obviously oscillating between chips
Speaker 1:Data. Energy and Energy.
Speaker 2:Yeah. I'd love to know.
Speaker 3:So look. The the ability to process I mean, think of AI as you have models, you need to train the models, then you need to layer analytics on top of it. Mhmm. And then the most important part, creates value, is inference.
Speaker 1:Mhmm.
Speaker 3:From that data, you need to get value. So, you know, I always say we are to data what NVIDIA is to compute. And and in order to do AI successfully, you need to combine the two together. So what that means is an infrastructure can only deliver benefits if it is cost effective. And and and in order to accelerate the adoption of AI, you need to make it cost effective.
Speaker 3:These shortages will continue, I think, for the next several years. And so you have to say, given the limitations that I have, how do I make these AI workloads more effective and have the ROI pencil out across industries? I mean, I was at NVIDIA yesterday, actually, and and and we were talking about that. How do we accelerate the adoption of AI by enterprises? Well, by packaging turnkey solution that optimize outcomes.
Speaker 3:How do you make sure that these agentic AI organizations that are providing services and, unfortunately, these services are very consumptive of tokens, which means many of these companies are now upside down. They're losing money because what they're charging to their customers does not tie into what is costing them because they're relying on AI. So I think the cost reduction and the compression in terms of tokens required to perform a certain task is really what it is. So I think it's a software play. Mhmm.
Speaker 3:The underlying infrastructure, eventually, it will happen. I mean, SSD shortages and NAND shortages, I mean, we deploy our data plane on top of storage, SSDs, hard drives, and so on. Well, over the last few months, the cost of SSDs has tripled. Yeah. So it's significant, and it's not available on top of it.
Speaker 3:Now Yeah. We happen to have an architecture where we can tie into SSDs or hard drives and so
Speaker 1:on. Sure.
Speaker 3:So our customers are able to do the same, if not better, with less money. But, I mean, these are issues, but but these are transient issues. I think, eventually, the problem that needs to be solved is how do you ensure that a task that is performed for a consumer or an enterprise is cost effective for the organization that are delivering that service. And it's really that. I mean, that's what we're very focused on.
Speaker 3:That's what our partners are very focused on. That's why many of our interactions with NVIDIA revolve around this. How do we make sure that we make it easier to deploy, easier to integrate, and lower the cost? Lower the cost of power, lower the cost of building data centers, compress the velocity. I mean, look.
Speaker 3:What Elon did with his data center, and and we were involved every step of the way. I mean, he built out the data center in four, four and a half months, which was unheard of.
Speaker 2:Yeah. When you when you heard when when you and you and the team heard the initial timelines that they were planning around,
Speaker 3:did you believe I said it's completely I said it's completely mad because we had done probably more than a 100 large data center deployment, and I had never seen it done in less than three years.
Speaker 4:Yeah.
Speaker 3:And when the x team first came to us and said, oh, we're gonna do it in four, four and a half months, I said that this is just ludicrous. How is that even possible? But see, the way he did it, instead of hiring people who are experts in building data centers, and all of them would have said, it's impossible. Mental block. Right?
Speaker 1:Mhmm.
Speaker 3:If you've never seen it done in less than three years and somebody tells you four and a half months, he goes, it's impossible. This is stupid. So what he did is he hired very, very smart people who were very good at connecting the dog outside of the box, and he said, okay. I want this done in four and a half months. Figure out how to do it.
Speaker 3:And and they did it. I mean, we were there with them Christmas, New Year's, weekends, '20 four seven. I mean, there were mattresses in the hallways. I mean, everybody was sleeping there. He was just working to get it done, and he got it done.
Speaker 3:Now it was extremely painful, but he got it done. And so you go, okay. So the new benchmark now is not three years. It can be done in four, four and a half months.
Speaker 2:Have any other have you seen any other, either Neolabs or or labs or hyperscalers be able to replicate that kind of timeline? Like, once he set the bar?
Speaker 3:China. They are very, very good. I mean, we are so behind. We are so behind. Mhmm.
Speaker 3:I mean, they've developed models. I mean, I was looking at what they're doing in data centers. I mean, the first one is the cost metric. I mean, in The US, the cost metric is 10 to $15 per kilowatt to build a data center. In China, they're able to do it for between a third and a fifth of that.
Speaker 3:Why can't we do it? Because they're looking at it in a very optimized manner. What what Elon did, he did the first one in four months, Lots of issues. Then he did another three. And lessons learned from the first one, he applied to the next three.
Speaker 3:By the fourth one, he's like, okay. I got this. And then he did the next 32. And and the Chinese are doing it the same way. They're not looking at each one of these as a one off.
Speaker 3:They're saying, we really have to focus on optimizing, optimizing, optimizing, and then we replicate. And and that's something we need to do better in The US for sure. For sure. For sure. How do we lower the cost to build a data center, and how do we compress the time to build a data center?
Speaker 3:And and China is way ahead of us right now. They're just way ahead. It's reality.
Speaker 1:Well, hopefully, more Colossus data centers coming online soon.
Speaker 3:I know. I know. But but, I mean, it needs to be done. I think the good thing is people are realizing that China is very good at certain things.
Speaker 1:Yeah.
Speaker 3:And and instead of saying, well, no. We're just gonna ignore them. They say, okay. How how do we learn? I mean, I had a meeting with one of our large customers from The Middle East, and we're actually going through the design architectures from China looking at how they do it.
Speaker 3:And we're like, okay. How do we apply that to doing it in The Middle East in a very modular manner? Mhmm. And it's really remarkable. I mean, again, we're not talking about 30% cost improvement or or timeline compression.
Speaker 3:It's it's when when you say it's three to five times, the economics associated with that are huge.
Speaker 1:Yeah. Terrific.
Speaker 3:Yeah. That's Yeah.
Speaker 1:That makes a ton of sense. That's yeah. Wow. Thank you so much for coming on the show and bringing Yeah.
Speaker 2:Really, really enjoyed it.
Speaker 6:Appreciate it.
Speaker 1:Yeah. Welcome to the next
Speaker 2:Great to meet you, Alex.
Speaker 1:Chat. We'll talk to
Speaker 2:you soon.
Speaker 3:Thank you.
Speaker 6:Have a good
Speaker 1:rest of your weekend. Let me tell you about Labelbox reinforcement learning environments, voice, robotics, evals, and expert human data. Labelbox is the data factory behind the world's leading AI teams. Let me also tell you about vibe.co, where d to c brands, b to b startups, and AI companies advertise on streaming TV, pick channels, target audiences, measure sales just like on Meta. Up next, we have Brett Adcock.
Speaker 1:He's the founder and CEO figure and about 12 other companies, serial entrepreneur with a massive release today. Brett, how are you doing? Welcome to the show. How are you doing?
Speaker 6:Yeah. Thanks, guys. Thanks for having me. I'm great.
Speaker 1:I think most people will be familiar, but break us break it down. Where is Figure now and give us the news today?
Speaker 6:Yeah. So we, well, several months ago, we unveiled Figure three, our third generation humanoid robot. Yeah. This morning, we actually gave you a sneak peek at a future road map item we've been working on for about over three years
Speaker 1:Okay.
Speaker 6:Which is our our newest generation hand. Yeah. I think we we've we've been working on this project basically since the beginning. Our generation one was, like, this tendon based hand that we designed in 2022. Sure.
Speaker 6:Had tons of problems with it, and we've basically been working on trying to reach human like, how do we approach human parity in terms of
Speaker 1:Dexterity.
Speaker 6:Hand dexterity Yeah. Sensors.
Speaker 2:Yeah. Does does does anything else about the robot really even matter if the hands aren't, like, human level capable?
Speaker 6:Think, like, one thing we're realizing is, like, more and more if we wanna, like, learn from humans, we need to, like, look and do human like things. Mhmm. So even, like, from a from a visual perspective, like, having the right kinematics of the hands so that we can do human like stuff. Mhmm. Meaning, like, if a human folds, like, you know, socks or towels a certain way, like, we need to really understand how to be able to do that on the robot.
Speaker 6:Mhmm. And so if if we truly wanna do, like, full general purpose work in a home across the whole world at billion unit levels, we have to start approaching, like, human, like, level dexterity in the
Speaker 2:There's somebody going for a trolley in the background.
Speaker 1:Yeah. Is that? Is that just a walk cycle? Is that scripted? Is is that did he decide did the robot independently decide to walk behind you right now?
Speaker 1:What's going on?
Speaker 6:We have we literally have hundreds of robots here on our campus in California. They're they're everywhere. They're all over the place.
Speaker 1:Okay. Yeah. Why why jump straight to full dexterity humanoid form factor? Why not wheels? Why not pincher grabber more incremental?
Speaker 1:You know, we've seen Amazon acquire that that robotics company just to sort of move packages around. There is a logical chain of events that you could do more incrementally, but you're going for the moonshot straight away, it feels like. What informed that decision?
Speaker 6:Listen. We have, like, a a very deep respect to trying to do, human level work in the world without changing the world too much. Mhmm. And if, like, we, as humans, built a whole world around our the way we look and feel, like, the way we, like, move around the world. So we, like, you know, we use tools, doors, like stairs.
Speaker 6:Like like so, like, you know, we've, like, built the world so that human body can interact with. Mhmm. The ultimate form factor for this is a is a human. Mhmm. You know?
Speaker 6:Like, if you start, like, removing, like, the ability to, like, have legs or fingers or the different stuff, you're just gonna do less of what humans do in the world. So our view is that we wanna go out and basically do everything a human can. Mhmm. That that approach is basically a human form
Speaker 3:Mhmm.
Speaker 6:In the limit. So we went after the hardest problem here, which is, like, how do you design humanoid hardware? How do we design neural networks now to work on that hardware? It's a really difficult problem, but it's, like, super tractable. This is a problem that will be solved in our lifetime.
Speaker 6:Yeah. In the coming years and decade, we will see, like, millions of humanoids, out in the world doing all kinds of things.
Speaker 2:What Weird. What, what is the what's your bar for to get to the point where you're selling you're selling a robot that somebody can buy and put in their home and start doing tasks? Because it is there's there's a lot of you know, I'm sure you're testing this stuff constantly and you're able to do think tasks like laundry or moving dishes from a sink, cleaning dishes, etcetera. And yet the bar is an individual just saying, well, I can just do this myself. It's quick.
Speaker 2:So that's one. You have to overcome that. It has to be so good, so consistent. But what do you think is the bar? There's obviously companies like One X that are pushing hard to get robots into home.
Speaker 2:You guys are pushing
Speaker 1:fires tell operations. Pushing hard too.
Speaker 2:Elon is obviously adapting his Fremont facility, right, to be able to make these at scale. But I think everybody's sitting around being like, okay, once I can hit buy on one of these things
Speaker 1:Yeah.
Speaker 2:The the the the amount of pressure that the first company that kind of comes out if you don't count UniTree, the first American company to come out with a robot. The pressure to actually deliver real value when people are like, hey. I just spent $30 on this, $40, $50. The pressure is gonna be immense. What is the bar for you?
Speaker 6:Yeah. I would say, like, the thing that we that really matters here in the world is getting to a spot where you have a humanoid robot that can go off and do, like, many minutes and then hours and days of work fully autonomously with neural networks. Like, that's the bar. Mhmm. And if I think you if you if you look at, like, who's doing that today, there's not a single group out there that can recreate the video we did two years ago Mhmm.
Speaker 6:Which is basically we had a figure one just moving Keurig around with a couple hands for, like, a minute or two. I was done with Neural Nest. We were just standing in place. It was uncut. It was a few minutes long, and I haven't seen a single company in the world able to do that today.
Speaker 6:So we can, like, pretend that we're, like, teleoperating robots and be super silly and act like that's gonna work. It's not gonna work. We have to deploy neural nets at scale to robots that can be fully general purpose over a long period of time without any human intervention. Mhmm. So for Figure, I think we're just, like, by far and away the the best example of being able to do this today.
Speaker 6:And we're still, like we still have so much more to go in order to be able to put it into a home, like, for days and days and be, like, extremely useful in that respect. So right now, we're able to do, like, pockets of this work really well. Like, we're able to do, like clean up the home, do like, we can fold laundry. We can do dishes. Like, this stuff is being done with neural nets, like, fully end to end.
Speaker 6:Mhmm. And and a lot of times, like, doing it pretty high performance. So my view is, like, we will only launch a product here at Figure into the Home when we're really ready. I think it's the I think the world will only accept the product into the home when it's really ready too. Nobody's gonna deal with, like, silly tele operating the room in the home, things like this.
Speaker 2:Yeah. The the other thing is, like, we've we've seen with we've seen with a number of of hardware like the Humane Pen, you had the Rabbit R1. Like people people might be willing to try like a digital product like a couple times even if a lot of people try it. If they have a bad experience, they won't come back. Some people might try it again.
Speaker 1:Chuchipizi got better.
Speaker 2:Whereas with hardware, it really feels like if you launch a hardware product and it doesn't deliver real utility Yeah. You'd lose like a one. Like, it basically kills the company. Yeah. So the bar is just so high.
Speaker 1:Yeah. So
Speaker 2:So yeah, time timelines, You guys have the benefit of being private even though you have a a valuation, you know, somewhere around the range of of Ford. You have time to to figure this stuff out. How like, what are the timelines that you're setting internally? What are, you know, what are you rallying the team around?
Speaker 6:Yeah. We're we're we're working, like, kind of two two paths. How do we ship robots and industrial workforce as fast as possible? Mhmm. That track is pedal to the metal, like, every single day.
Speaker 6:Mhmm. We have many different customers there fully signed up, ready to go, and we're excited to you know, we had robots at BMW last year. We have, like, more robots going into commercial customers this year. The second is we wanna solve a general purpose robot, like solve general robotics in a home. Mhmm.
Speaker 6:And the best I I one of our top goals is, like, be able to drop a robot of ours into an unseen home this year and do, like, full general purpose end to end work. Mhmm. And it's extremely tough. I think we can go do it, but we're working, like, day and night to go get there. Mhmm.
Speaker 6:And, yeah, it's a it's it's like it's it's basically how do we design it's it's the closest thing to, like, AGI for, like, fit like, for physical world. Right? Like, how do we get something that can, like like, have common sense in a home that you can talk with, that can understand things? Maybe you can teach us something on the fly and can watch you, and then ultimately be able to carry out those tasks at high performance all throughout the day. So my my hope is we can make material progress on this this year.
Speaker 6:Like, our goal is, like, working day and night to just try to solve this. To the extent we can hit this goal of, like, being able to do full end to end work, there's other, like, barriers of, like, privacy and safety and other things that are really hard that we're also paralyzing. Mhmm. But my hope is by the end of this year, we're making considerable progress towards this, being able to, like, show, like, some some crazy insane things with these robots in, you know, in these type of environments. Mhmm.
Speaker 6:But this is, like, this is a separate track to, like, the commercial side. Like, we're already out, like and we've already been out being able to do this. We're gonna go out even larger this year in 2026. They'll deploy robots at scale. This is important for us to get, like, a real operational readiness.
Speaker 6:Like, how do we make sure robo like, how do we make sure we can run robots at scale really well here at Figure?
Speaker 2:Yeah. How Yeah. And it's all I mean, it's it's I'd say, like, much more straightforward to have a robot in a setting where you have trained professionals probably wearing hard hats that can be kind of monitoring the robots from far away. You're not dealing with the safety risk of like a robot falling on a dog or on a kid or any of the other challenges in the home. When what what's your timeline to a robot, a humanoid being able to bench two plates?
Speaker 2:Is that a is that an interesting is that an interesting problem to solve?
Speaker 1:It's the only problem
Speaker 2:that's because interesting to for us for us, that we're very fascinated on when when that'll happen.
Speaker 6:Yeah. Is it bench or squat? What do we wanna do here?
Speaker 2:I mean, squat is probably the overall compound lift. Squat, I feel like, is pretty
Speaker 1:Thousand pound club, ideally, but we'll take just bench press if that's what you got.
Speaker 6:We I mean, we should if we can if we can bench press that, we should be worth at least twice a four. Right?
Speaker 4:Okay. Yeah. Yeah.
Speaker 2:Yeah. Mean, I agree.
Speaker 1:I 100%. The tickets to the bodybuilding competition.
Speaker 2:No. But I I feel like I feel like, even as silly as that sounds, you know Use
Speaker 1:an interesting benchmark. Yeah.
Speaker 2:I think the the
Speaker 1:I mean, over in China, they're doing robot Olympics. They're they're doing marathons. They're doing all sorts of stuff. You know? It is a tactile.
Speaker 6:Yeah. Okay. Can we be real for a minute on all this stuff?
Speaker 4:Yes.
Speaker 6:Like, let's just, let's okay. Let's be real serious. The what really matters, I think, for us as humans is we look at the distribution of what humans do that's, like, useful, and we try to do as much of that as possible. Yeah. These things where we run, like, marathons or we do backflips or we do karate moves or we, like, try to deadlift 300 pounds, they're not in the main part or the fat part of the distribution.
Speaker 6:Yeah. They don't matter. And if you really wanna size those and do those, you're gonna build a really expensive and heavy and unsafe robot that's hard to manufacture.
Speaker 1:Yeah.
Speaker 6:You're gonna build, like, a super duty truck. And, like, nobody like, no people want the $1,020,000 dollar humanoid that can do general purpose work. Right? That's what we want. So if you're trying to size a robot to do those kind of things, like silly things
Speaker 4:Mhmm.
Speaker 6:I think of, like, those, like, gymnastics and other stuff. Yeah. You're gonna build a very specialized robot that is, like that can do, like, a very small percentage of what normal humans do every single day.
Speaker 1:Yeah.
Speaker 6:So our goal is to build a general purpose robot to do majority of what humans can do out there. We wanna do, like, laundry and dishes and be a companion. I wanna ship robots at scale and a building level into the workforce to do logistics and health care and build buildings and, you know, build build data centers. Like, that's the stuff we wanna do. I don't need to do backflips to do any of that work.
Speaker 6:I want other robots building other robots. So I think, like, I don't know. I mean, in part I I think of, like and you look at the silly stuff out there, it's not only not important for the road map. It makes the hardware extremely, like, heavy and hard and expensive, and all that causes more problems. Mhmm.
Speaker 6:So, like, none of that matters in our mind, I figure. We you know, every once in a while, we'll put a robot on a DJ stage with dead mouse and stuff for fun, but, like, we're we're definitely not trying to design a robot to be great at that. Yeah. We wanna be great at, like, the the the things I do every day and you guys probably do every day. Yeah.
Speaker 6:I mean, you guys are probably dead lifting 300 pounds. But, like, at least for me, every day, I'm trying to, like, you know, just do normal Yeah. Like, normal practical stuff that billions of people today are are doing that we can help offset.
Speaker 2:How do you think let's say we get the iPhone moment for humanoids, hopefully in the next few years, and that it's piece of hardware that has real utility that a lot of people are buying. How do you think the kind of form factor evolves? Do robots over time look do they follow the iPhone path and that they get thinner, lighter, that kind of thing? Is there like, what what are you what have you been learning so far that, maybe people kind of misunderstand about the form factor long term?
Speaker 6:Yeah. The long term form factor is more and more approaching the average human in terms of, like, the range of motion, payloads, and speeds, and, like, what you can do. If you're too short or too tall, you're just like you're not in the right habitat for, like, interacting with the the human world that well. So in an extreme case of three foot tall, it's, like, really hard to get most things out of the cupboard or get into the get into the, you know, the sink or reach over a table. These they actually become really practically hard to go do.
Speaker 6:So it's gonna be, like, an average human size overall. I think we're in, like, the pre I I'll make an argument here that we're in the pre iPhone stage. We're the flip phone stage for for humanoids. Mhmm. And I think we we know this better name, but we've been building them like crazy.
Speaker 6:We have our third generation out in three years. We've walked three generations in three years. I think what you'll see here is that we're trying to find this ideal product, like, product fit, like, long term where that's headed, and we're, like, learning every year where that's going. It certainly means being more human like in our minds so we can unlock more percentage of this distribution we just talked about earlier. I think I'll make a I'll make a statement that's pretty pretty bold is that when you look at, like, figure one to figure two to figure three, we've had to, like, step up in performance, they're just better and better every year.
Speaker 6:When we head to figure four here, it'll be the largest step that we've ever made by, like but by a long shot. It'll be the first time that we feel that we probably hit, like, iPhone one Mhmm. Level humanoid, where it's just like, this is the right place to be in. And then this will, like this will, you know, this will go extremely far to a point where it saturates at some point in the future, you know, maybe ten more years. But we feel, like, relatively early.
Speaker 6:It's an extremely difficult piece of technology. It's obviously early in this, so it's gotta be, like, earlier than phones. Right? We're not, like, we're not there yet. So but I think the the iPhone one moment will happen with Figure four, and it'll just be it's just an unbelievable machine.
Speaker 6:And I I I never would have suspected we'd able to make that big of a move. Like, you know, with Figure three shipped, I'm like, this is it. This is, like, the best it'll ever get. And then, you know, the more we learned and ran it and the more we developed neural nets here with Helix, the more we really understood better about, like, what the hardware should, like, should look like and and be like. And the more we got folks in the room together with us and said, how do we radically redesign the head, the the hands, the the kinematic systems, like, all of it from scratch.
Speaker 6:And I think you're you're gonna see something I mean, this stuff is just gonna get crazier and crazier in capabilities.
Speaker 2:What are your goals around, consistency? So for for example, a robot that, can unload my dishwasher if one out of a 100 times it it, like, breaks a plate into, you know, 200 pieces. Maybe that's not that big of a deal if it can, like, you know, pick them all up easily and I'm not home and I don't see that, you know, you're exploding the the plate. But what what level of, like, consistency do do you guys need to get to before you're you're you'd be at a point where, you know, you can sell one of these things?
Speaker 6:I would say, like, we probably need something pretty high. I think it would suck pretty bad if you're at my house and dropped, the number one mom coffee cup. You're getting your you're getting your ass booted. Right? Like Yeah.
Speaker 6:You know, like, I think you gotta be especially around safety and stuff, like, this seems to be super high performance. So we, like, we watch folks that are trying to, like, teleoperate and ship early when the product doesn't even work, and it's just it's just silly. They're all gonna die. The you gotta ship something really high quality, and that that is just, a super hard thing to do. So we we I figure we'll ship into the home when we're ready.
Speaker 6:We're not ready right now. We're trying to get like, you know, I'm here till midnight every night, seven days a week, trying to get more ready with my team. I hope we get like, we hit some place where we're getting really, really close this year is what I really hope.
Speaker 1:How are you
Speaker 6:pressing But you're right.
Speaker 1:Yeah. How are you pressing the the Waymo story of Teleopt? Because I was completely on board with the Elon pitch for straight shot to FSD, collect a bunch of data from the cars that are on the road, train the big neural network and FSD. I mean, we talked to Alex Roy who drove without touching the steering wheel all the way from LA to New York. Like, it clearly works.
Speaker 1:At the same time, a lot of people in San Francisco hop in a Waymo, and they're like, that works too. And so it was a bit of a narrative violation where a lot of people were saying, like, the Waymo teleop model will never scale, and it feels like it's scaling. So how is it is there a world where both approaches work, or the or or are they fundamentally, like, different industries?
Speaker 6:I think what I'm seeing in the space here, and it's been, like, a pretty big shock, is, like, everybody in the humanoid space is just teleoperating the robot with a human in the back, and they're putting out a video out and not being very explicit. It's very different than this. It'd be, like, the most analogy for your Waymo Tesla of, like, if your Waymo is being driven by some dude in Kentucky not with neural nets. Waymo has neural networks. The way they went about with the sensor suite to go do it is maybe harder to scale than cameras.
Speaker 6:The situation happening in humanoids is, like, there's a large percentage of the companies out there that are, like have a dude in the back, like Mhmm. That are, like, teleoperating with the robot in real time. And then, like, you know, we're try we we've done everything we've ever put out publicly. It's always been with neural nets on, like, you know, stuff we've done. We've never teleoperated that's you know, in that in that case, like, those any of those videos.
Speaker 6:So it's just like the the self driving stuff's not the greatest analogy. Mhmm. You're definitely not gonna be able to human teleoperate in people's homes, and the latencies will be terrible. The data coming back will be terrible, train neural nets. It's just not enough data.
Speaker 6:So there's just a bunch of problems with that story, and it's it's not gonna work. If it would work fast and we can get product market fit and get out earlier, we would do it. Just like it's just dead end
Speaker 1:Yeah.
Speaker 6:Completely. You really wanna solve, like, for real neural nets, real autonomy from from the get go. Mhmm.
Speaker 2:How big of a bottleneck is data? What are you guys doing to solve it? Like, is there hardware breakthroughs that you guys are looking to achieve? Or or do you feel like the obviously, you have the new hand, which sounds like it's it's a it's a step up. But are kind of the key bottlenecks?
Speaker 6:We just unveiled Helix two about three weeks ago. It's a robot that can basically do fully end to end whole body work. We did it in unloading the dishwasher and rerunning it. That was basically the whole stack there was basically neural nets basically all the way down the stack. The the only reason why it could do that now or, like, say, go from there to do laundry is just a data problem.
Speaker 6:Mhmm. Like, we need just more data to cover the distribution of those new tasks, and then the robot can do it at this point. So we we feel like the, like, the longest pole in the tent prior to, like, extremely high rate manufacturing is how do we acquire data at, like, a really high clip. And so we're spending a lot of time on that. That gets you to a point where, like, you know, acquiring data through teleoperation is just not gonna even be close.
Speaker 6:You have to embrace, like, learning from humans at scale. That's kind of figures, like, you know, core models that comes to Helix for neural nets. And I I would say if we could snap our fingers and have enough of the right data today, you would have a general purpose sci fi future of robots in our office right now that we'd be able put anywhere. Like, we have it. It's just we are just extremely data constrained.
Speaker 6:It's not as simple as just, like, going out and getting random data. It's, like, it's gotta be the right type of data to match the observations and action spaces of the models well. But we now know what that data is. We are acquiring that data like crazy here at Figure. We'll spend 9 figures of capital on acquiring data like this in 2026.
Speaker 6:So it's a huge focus for us. We think it's by far and away the biggest bottleneck to get to general robotics.
Speaker 1:Yeah. How do you think about China in the context of the race for humanoid robots? Obviously, there's competition from humanoid robot makers there, but there's also a bunch of great parts suppliers at all levels of the supply chain that might be useful to build American humanoid robotics companies. How does that puzzle play out?
Speaker 6:I mean, listen. I think, like, as it relates to competition, I think what's extremely important is seeing robots that can do, like, human like work with neural networks that's useful. Mhmm. We haven't seen any of that out of China today. They they really don't have any
Speaker 1:hands hardware, but they're still behind on software.
Speaker 6:Well, I would say, like, you probably don't have good enough hardware if you're not able to do the software really well. Mhmm. They don't have enough compute in a lot of cases to run, like, things like Helix onboard. UniStreet, for example, has a very tiny computer where you can run, like, very small reinforcement learning controllers to do, like, open loop replay of stuff. Mhmm.
Speaker 6:They wouldn't be able to run Helix on a board of hardware like that. They don't have any real human like hands, like five fingers. Mhmm. So you're really missing a couple of big parts of the story. Beyond that, I think they've been, you know, great in existing kind of industrial robotics and existing consumer electronics last, like, several decades.
Speaker 6:I think those are playing a big part of the ecosystem supply chain for humanoids that are important in some cases. But, you know but listen. We we basically design almost everything internally here at Figure. We don't we don't go buy we don't go buy designs from from China or elsewhere. We do it all here internally.
Speaker 6:We even manufacture the robots next door in our campus here. We have a we have a figure three robot now coming off the line every three hours, and I think we'll we'll be at every half an hour here in the coming, you know, few months. So we're like, know, we're like, things are coming out at a pretty high clip. But I think today, like, if you look at, like, who's doing the best human like work with neural nets over long time horizons, it's it's not China. And I think we can I think it we're we feel at least a few years ahead of anything we're seeing out of there?
Speaker 1:What's compute like? You I mean, you're mentioning you're working so intently on neural nets. You have to train those. Is it are you at a point where a training run is run on a massive cluster? It costs 9 figures or something like that, or is it more data collection at this point?
Speaker 1:And then the actual training run is pretty tight.
Speaker 6:Yeah. We spent, like, hundreds of millions of dollars on compute that is
Speaker 1:Sounder mode. There we go.
Speaker 6:There we go. We spent yeah. A bunch that went live already. So our next giant, like, step up is going in April like, April 1. Yeah.
Speaker 6:That's for training Helix models that we're doing here internally, which are quite large
Speaker 1:Yeah.
Speaker 6:And long runs. And then separately, we do all our inference onboard on two GPUs in the torso of the robot. Sure. So we can run-in, like, cases where we don't have a network. Mhmm.
Speaker 6:We can run at much faster speeds, and we can put all those models fully onboard the system. So all of our robots now are kind of they're they're running off brains that are all on robot. Yeah. So they don't need any outside network to be able to do work.
Speaker 1:So talk about, like, input and output. Is it is is the is the network sort of taking in voice input and trying to translate that into plain text actions that then get transformed into motor actions? What is, like, the reward function for a humanoid robot?
Speaker 6:Yeah. We're taking in we're basically taking in the, like, the the, like, the the instructions through text or speech, like, what should I be doing? Yeah. We're taking in vision from the cameras
Speaker 7:Sure.
Speaker 6:And the current state of the robot. Like, where's where is it? Like, you know, what what is what is the body doing? Like, what does the sensors look like? And then we're basically processing that onboard with Helix two.
Speaker 1:Yeah.
Speaker 6:Helix two is an outputting, like, basic trajectories of where the like, what the motor should be doing. Sure. Basically, figuring out, like, where where do I put torque at in every single joint Mhmm. To produce to move my body in a certain way. Mhmm.
Speaker 6:And I think, you know, honestly, one of the hardest problems we've had last two years is two years ago, we were basically doing just, like, you know, coffee work and other type of stuff on tabletops, and it was really it was unbelievable. It was, like, the first time we're like, man, neural nets on humanoids work. We spent the last two years trying to leave the tabletop. Mhmm. How do we walk around with neural nets fully end to end?
Speaker 6:And it's extremely it sounds kind of sounds like a you know, not maybe not the hardest problem in the world. It's it was a bit like some of the hardest problem in the world for
Speaker 4:us.
Speaker 6:Mhmm. How do we get the whole body, like 30 plus joints, all running at, like, say, 200 times a second and doing the right things with camera frames and a prompt coming in. And that's what we did with Helix two unveil three weeks ago is we had all that done. So it's the first time in three and a half years where we feel like we have the right technical stack to actually scale Mhmm. Which we didn't have last, like, several years.
Speaker 6:Yeah. We were running at BMW last year, we're like, man, this is going great. We're learning a lot, but it's not the tech stack I wanna scale. And we have that now with Helix two, so we're really excited to hit the gas on hey. We're gonna hit the gas on this in 2026.
Speaker 1:Congratulations. Yeah.
Speaker 2:It's great to get the update.
Speaker 1:Yeah. This is awesome.
Speaker 2:I know you said the the bench press is silly and things like that, but I think it's to us No. It makes perfect sense. Even even the bar. Even just repping out the bar, I'd be pretty excited about.
Speaker 1:Of a meme.
Speaker 2:I also think It's a serious company. It's a serious company, but even serious companies can have fun. Yeah. I'm also looking I'm looking forward to the moment that that you get a a figure robot surfing at Jaws too. Maybe maybe Are
Speaker 1:they waterproof? Can they swim?
Speaker 6:You guys, we got we we got a we got a gym here.
Speaker 1:Okay.
Speaker 6:You guys you guys you guys swing by.
Speaker 1:Okay.
Speaker 6:And let's get let's get the figure three and you guys in the gym. Let's let's see let's see, like, let's see how you guys are all let's see how we're
Speaker 1:Matching up. Yeah.
Speaker 6:We'll Yeah. Figure the
Speaker 1:matchup. Thank you. Awesome. Well, have a great rest of your day.
Speaker 2:Yeah. Great to meet you.
Speaker 1:We'll talk to you soon, Brad.
Speaker 6:Cheers. Nice to meet
Speaker 2:you, guys.
Speaker 1:Let me tell you about Graphite. Code review for the age of AI. Graphite helps teams on GitHub ship higher quality software faster. Cheers. And I will also tell you about Shopify.
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Speaker 2:There's a company that launched yesterday.
Speaker 1:Yes. What company?
Speaker 2:OpenClaw for Slack. I don't know what it's called. But they said they launched three hours ago and they just hit 1,000,000 ARR just now. So they made $350 in three hours, which is Sick. This is 1,000,000 of ARR.
Speaker 1:We've done it. We've done it.
Speaker 2:They did it. Final four. They did it.
Speaker 1:I saw a fake post that was like, Meta acquires Open Claw for a billion dollars or something. And I really had to fact check it because I was like, this seems so possible in this day and age.
Speaker 2:I think they they have that team with Manus.
Speaker 1:Yeah. Yeah. I think the Manus team is is in a good spot to sort of bring some of those functionality to bear.
Speaker 2:Ring apparently has terminated its partnership with Flock. Their Super Bowl ad did not go as planned.
Speaker 1:The
Speaker 2:the doorbell company ran an ad during the Super Bowl that Yeah. Added a search party feature that uses AI to help locate lost pets. People
Speaker 1:Sounds amazing.
Speaker 2:Quickly realize that maybe it could be tracking things other than pets. But anyways, they probably are the the Super Bowl loser. I was I was in the end the worst blowback of any company that I that I've seen.
Speaker 1:This this Searcy Puffs is so good. VCs love to be like, yeah. Hedge fund guys may be smarter, but at least I make less money. Good stuff. Goldman Sachs CEO, David Solomon, we're going to see potentially some very, very large IPOs unprecedented in size this year.
Speaker 1:He's he's agreeing that it's about to rain. It's about to rain. Oh, one of the IPOs that could be going out is cohere, Aiden Gomez. We gotta get him on the show. I'm such a big Aiden Gomez fan.
Speaker 1:$240,000,000 year set stage for IPO. This is, this is in TechCrunch. Of course, Aiden Gomez, a Death Grips fan, you know he's a good time. You know he likes the Death Grips.
Speaker 2:SeizGong moment. Airbnb, according to Sar and according to Chesky, has generated 19,000,000,000 in cash flow since going public.
Speaker 1:And Not gonna vibe code that. Not gonna vibe code a house. You're not gonna vibe code a basement that you can sleep on the couch on. I met my cofounders for my first company on Airbnb.
Speaker 2:I didn't know that.
Speaker 1:Yeah. Moved to Silicon Valley, get into YC, need to find somewhere to stay. I was staying with, like, a friend who was sort of, you know, not doing a start up, so way different lifestyle, and, and searched on a service that was actually, the most vibe coded software. But in 2012, it was a mashup between, Craigslist and Google Maps because Craigslist didn't have a Google Maps feature. So if you were looking for housing, you had to just guess where the places were.
Speaker 1:Insane. So it was called Padmapper. Someone took they scraped Craigslist and then put it on a Google Map, and so you could click and be like, oh, that's near me. That sounds like a good place. But Padmapper, Craigslist had given Padmapper a a cease and desist.
Speaker 1:We don't want you scraping us because Yeah.
Speaker 2:They're guess what? They were I mean, single marketplace created in Silicon Valley Yeah. Was immediately scraping Craigslist.
Speaker 1:Totally. Totally.
Speaker 2:They've fought back aggressively.
Speaker 1:Back, and they said, hey. We're gonna get around to doing Google Maps on Craigslist. And so Padmapper, get out of here. So when I when my my cofounder and I opened up Padmapper, the only data that was still flowing to Padmapper was Airbnb. Because Airbnb, of course, is a marketplace.
Speaker 1:It doesn't matter if if someone else is driving traffic. They love that. They were all over SEO. They wanted other people to flow in. So we didn't know this, but our future friends and cofounders had a large place in Sunnyvale that they had an extra room, and they'd thrown that on Airbnb.
Speaker 1:That showed up on Padmapper. We go over, take a tour. We're like, this place is sick. It was a disaster. All the toilets were broken.
Speaker 1:They were like, it's like the social network. It's got a pool. It's got a jacuzzi. We're gonna be hanging out. It's gonna be the best summer ever.
Speaker 1:The pool and jacuzzi filled with algae, like, truly filled with algae. We spent the entire summer being like, we're smart guys. We can beat the algae. Let's go get one gallon of bleach. Pour it in.
Speaker 1:We're like
Speaker 2:We're gonna need We're gonna need 100 gallons.
Speaker 1:Thousands of gallons.
Speaker 2:It's gonna the it's gonna just we're gonna be swimming in bleach.
Speaker 1:Yeah. No. It it was like there's nothing you could do, and then we were like there's like a filtration system, but that was super clogged, we were like empty out the filtration system, try and take it all apart. But like everyone who was like an expert was like, yeah, you just have to drain this and like declare like pool bankruptcy bankruptcy basically. Basically.
Speaker 1:Brutal. Brutal. Anyway, it was
Speaker 2:a good Well, New York Post says, have an AI girlfriend or boyfriend? Now there's a bar for you. There's a Hell's Kitchen establishment that has been redesigned for those who have AI partners so they can bring along their phone for romantic evening. Very very dystopian, her moment, but not entirely unsurprising that that this bar is pivoting to AI. Like, we mean, it's a good
Speaker 5:day to launch. Right? Because four o is is Four o is deprecated today.
Speaker 1:Deprecated today. Is it still is it still available? Or is or do they stop it at at at when the clock's I still see it. Midnight.
Speaker 2:It's still on my
Speaker 5:my chat, WPT.
Speaker 1:Well, it'll be interesting to see what the community does because I I did see some posts about people being like, I'm recreating four o. I'm fine tuning some, you know, Chinese model. Kimmy could be potentially fine tuned on four o outputs and paid for and distributed. Like, there are other ways for those folks to get what they want, essentially.
Speaker 2:Well, it is Valentine's Day weekend. But before we go, Tyler, we did have a recommendation for you
Speaker 1:Oh, yeah.
Speaker 2:This weekend. We You mentioned that you've been seeing a lovely lady, and we
Speaker 1:thought This was supposed to be abstract.
Speaker 2:We thought.
Speaker 1:This was supposed to be a recommendation for the audience.
Speaker 2:Yeah. Well, there's
Speaker 1:Now it's directed.
Speaker 2:Girl that that maybe I can't believe you're doing this. Maybe Tyler likes and we were just saying go surprise. Tell her, hey, tomorrow. Just have a bag ready.
Speaker 1:This is so out of pocket. Continue.
Speaker 2:Have a bag ready. We're gonna go do an overnight trip. Yeah. Find a nice
Speaker 1:Nice hotel.
Speaker 2:Check-in.
Speaker 1:Staycation, basically. You're not getting on a flight. You're just going somewhere local but somewhere nice.
Speaker 2:And so, yeah, somewhere nice.
Speaker 1:Yeah, the beach.
Speaker 2:Easy easy to set Check-in to the hotel. Yeah. Maybe get her kind of a spa day. Yeah. She goes to the spa, you sit down and she doesn't know this but you actually booked her an eight hour spa like a full day thing.
Speaker 2:You sit down Time
Speaker 1:to lock in.
Speaker 2:Time to lock in
Speaker 3:on
Speaker 5:some cheeky pine. Have dorchestre Oreo.
Speaker 1:Yes. Yeah. Yeah. You got
Speaker 7:a lot of
Speaker 2:stuff So lock in and then just start getting Guinness on room service.
Speaker 4:Yes.
Speaker 2:21 now.
Speaker 1:Pint for pint.
Speaker 2:And go go every time every time Drink
Speaker 1:every time AI is mentioned, you take you
Speaker 4:Yeah.
Speaker 2:Or every time John takes a sip, take a sip.
Speaker 1:Drink a whole beer.
Speaker 2:Yeah. And you basically are gonna have Yeah. 20
Speaker 1:She four comes back eight hours later from her eight hour spa treatment and is like, what were you doing?
Speaker 2:And you can just catch her up to speed on Yeah. On everything you watch. I think I think they really appreciate that.
Speaker 1:But you say, You haven't you haven't listened to Dwarra Keshe, Ilya? That one hits like a ton of bricks on Valentine's Day.
Speaker 2:Yeah.
Speaker 1:Ask ask your ask your spouses, ask your girlfriends, your boyfriends, have they listened to Ilya on DoorDash. Are their timelines up to date? If not, that's the best Valentine's Day gift you can
Speaker 4:get them.
Speaker 2:I agree.
Speaker 1:Up to date understanding of what's coming.
Speaker 2:We hope you all have a wonderful weekend. We love you. Yes. Thank you for hanging out with us this week. Yeah.
Speaker 1:And We we will be back Tuesday. Monday is a holiday.
Speaker 3:It is?
Speaker 1:We're off.
Speaker 2:Yeah. Really?
Speaker 1:Yeah. Yeah. Market's closed.
Speaker 2:I did not know that.
Speaker 1:Yeah. I'm learning this for the first time.
Speaker 2:I'm learning this for the first time.
Speaker 1:Yes. We we we experimented with with streaming on holidays and it was Gonna miss it. There was there was not a lot of news. So we'll be back Tuesday then. 11AM Pacific.
Speaker 1:We'll see you then. Love you. Nice
Speaker 5:work, brothers. I'll see you on the next one.