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Technology's daily show (formerly the Technology Brothers Podcast). Streaming live on X and YouTube from 11 - 2 PM PST Monday - Friday. Available on X, Apple, Spotify, and YouTube.
You're watching TVPN.
Speaker 2:Today is Thursday, 09/25/2025. We are live from the TVPN UltraDome, the temple of technology
Speaker 1:The fortress of finance.
Speaker 2:The capital of capital. It's great to be to everyone in the chat. Good morning to Gold Rock or Gav, John, Techno Chief, of course. The post of the day is Alex Heath in sources. I printed it.
Speaker 2:He says, scoops, he's He says, OpenAI is planning to bring ads to Chet GPT. Also, in a message to employees, Sam Altman says he wants 250 gigawatts of compute by 2033. He calls OpenAI's team behind Stargate a core bet like research and robotics. Doing this right will cost trillions, he says. Well, hopefully, he can save time and money with ramp.com.
Speaker 2:Time is money saved both. Easy use corporate cards, bill payments, accounting, and a whole lot more all in one place.
Speaker 1:There we go.
Speaker 2:But I was thinking about what Sam Altman's projections have to say about the the the the the build out of AI. We're in pretty crazy times. Certainly, lots of people are comping it to 1999. Lots of people are saying 1996, if they're bullish. I don't know.
Speaker 2:The exact year matters a lot. I mean, even 1999, I feel like there's
Speaker 1:still And people are saying this time is
Speaker 2:months, you know. But, you know, the technology is even more magical than the Internet potentially, and of course, the Internet already exists. So it's easier to scale revenues as we've seen. And so my my take on this was the AI deals are getting crazier. Obviously, this week, we saw NVIDIA is going to invest a $100,000,000,000 in OpenAI, which in turn will be used to buy NVIDIA GPUs.
Speaker 2:But we aren't fully in 1999 mode even though a lot of people are saying that because big tech just has so much cash to fund the build out, And that wasn't quite what was happening in the .com boom. In the .com boom, there was a lot more leverage. The price to earnings ratios were crazy, 10x higher on median. And so we're not in the crazy, crazy debt era. There's a little bit of that going on with Oracle, but it's
Speaker 1:not Yeah.
Speaker 2:Sure. Huge. The debt to debt to equity ratios aren't insane yet.
Speaker 1:Yep. Yep. Certainly, players like Meta have tapped the debt markets. Right? They did an offering with Yeah.
Speaker 1:BlueOut. But it is a small you know, it's a it's a some minority percentage of their earnings.
Speaker 2:Exactly. Exactly. Yeah. Yeah. It's it it's not
Speaker 1:went out this week as well for a $15,000,000,000
Speaker 2:Convertible debt offering?
Speaker 1:Yeah, debt offering. I'm sure that'll get filled quickly again because it feels relatively low risk even though they're forecasting well beyond what a traditional public company would be doing.
Speaker 2:Yep. But the nature of the deals is getting more unique. It's different. I mean, we're seeing letters of intent now, which is just a very fuzzy
Speaker 1:Or investments being announced that are still in discussion.
Speaker 2:Exactly. Right? Exactly.
Speaker 1:This NVIDIA OpenAI investment, if you read the print at the bottom of the
Speaker 2:It was a nonbinding letter of intent.
Speaker 1:Yeah. They're basically saying, we're talking about this. Exactly. This is like the
Speaker 2:But we haven't paperwork
Speaker 1:for the deal.
Speaker 2:Usually, you go the opposite direction. You get the leaks. Then you get the actual announcement once the deal is fully signed. The deal signs, and then the press tour starts, but now we're putting the press tour a little bit earlier. And I keep coming back to YC Demo Day because at YC Demo Day, the letter of intent is often pulled out for companies that don't quite have the ARR or the DAUs to justify whatever they're trying to fundraise.
Speaker 2:And I wanted to revisit a story from YC demo day, my favorite YC letter of intent story, probably Boom Supersonic. Extremely, we've had Blake on the show multiple times, an extremely ambitious project to bring back commercial supersonic aviation, basically bring back the Concorde. Boom was one of the first hard tech companies to go through YC. There were a few at the time. This was the Sam Altman era again, and they didn't really fit the current mold.
Speaker 2:They couldn't show a graph of DAUs or ARR, obviously, because they were they hadn't even built planes yet. They were building, like, plans to build planes. There's only so much you can do in three months.
Speaker 1:It was a founder bet.
Speaker 2:Yeah. It was a founder bet. And Blake also
Speaker 1:as a product manager at Groupon. Yeah. Exactly. Got what it takes to take us back to hypersonic.
Speaker 2:Yeah. Yeah. So it wasn't like And I I
Speaker 1:it sounds it sounds like I'm joking, but I'm totally not when you see Blake's level of conviction and his inability to not go supersonic.
Speaker 2:Yeah. But it wasn't like he was, like, former Lockheed and was spinning out some technology that immediately could just, like, piggyback on the shoulders of giants immediately.
Speaker 1:SpaceX spin out.
Speaker 2:He had yeah. Exactly. He had to really build stuff from scratch and put the puzzle pieces together. And one of those puzzle pieces was a massive LOI. And so he stood on stage and said, you know, you're gonna think this is a sci fi project.
Speaker 2:You think this is a moonshot project. But the one thing that I can tell you is that I have an LOI. It's nonbinding, but it's for $2,000,000,000. And so that was what went on the screens, I think, around there. And he had gone to Virgin, talked to probably Richard Branson, and Virgin had optioned 10 airplanes in a nonbinding LOI, and the deal value was roughly $2,000,000,000 and so if it was fully exercised.
Speaker 2:Now And I think
Speaker 1:I think people generally believe this is the kind of business that's like, you build it, there will be demand. Exactly. There will demand from the airlines. There will be demand from consumers.
Speaker 2:But having the LOI means something.
Speaker 1:It means something.
Speaker 2:It shows that you can go in that room and convince them that if you build it, they'll And buy then that leads to investors being saying, okay. Well, I will back you. And so Booms had ups and downs. They had a down round. A lot of YC folks came back in, invested in the company again just to keep it going.
Speaker 2:And Blake's still at it. He's getting regulatory approvals, which we saw, and building planes one at a time. He's actually flying. And as loose as nonbinding LOIs can be, they still help pull forward the future and can be a useful tool in making everyone around the table feel like there's mutual buy in because everyone's at least said, if this happens, I'm in. If that happens, I'm in.
Speaker 2:And if everyone commits and if the capital partners commit and the customers commit and everyone commits, then the thing should actually happen. And so, the boom story for me underscores how unclear the path of technological development can be. Sam Altman is now guiding toward 250 gigawatts of compute at OpenAI by 2033, which is a staggering number. We saw a post which we'll get into. A single gigawatt data center is many football fields long, visible from space.
Speaker 2:It basically terraforms the Earth. And while many people are writing off Sam's projection as merely attempting to win the contest of saying the biggest number, His projections aren't entirely new to the world of AI compute forecasting. Leopold Ossenbrenner predicted a single training run cluster around a 100 gigawatts coming online by 2030. So three years earlier, one third the size. Just two years after and then Leopold predicted in 2028 that there would be the first 10 gigawatt cluster.
Speaker 2:250 gigawatts by 2033 lines up with that, but, wow, America will look dramatically different if that comes to fruition. In 2023, for reference, two years ago, The US was producing an average power in total for everything, not just AI, of 485 gigawatts. And so Sam is saying that just OpenAI, if everything else stayed the same, would be one third of all power consumption in America for that entire year in 2033. Now, obviously, everything else is growing, and there are other labs. And you would imagine that Amazon and Anthropic keep up, and Google and DeepMind keep up.
Speaker 2:And so it will be an entirely new chapter of the economic story.
Speaker 1:What are some of Elon's promises again or or or goals?
Speaker 2:Yeah. I was talking to Tyler about this because I so I I mapped out the power projections. This is straight lines on a log graph, of course. Leopold is the taller bars here. Jordy, you can see this.
Speaker 2:And Leopold is projecting logarithmic growth, which is exponential growth. This is every tick on here is an order of magnitude bigger for gigawatts. And you can see Leopold is projecting a straight line on a log graph, basically, exponential growth continuously based on, you know, how he's mapping out the few dates that he gave. He didn't give projections for every single year. Sam is actually it's so funny because he's the the Sam story is framed as, like, 250 gigawatts by 2033.
Speaker 2:This is so insane, but he's actually short of sort of guiding towards deceleration. Like, you can see Sam's Sam's darker bars here.
Speaker 1:He's being humble.
Speaker 2:He's being humble relative
Speaker 1:He he doesn't wanna, you know, over Yeah.
Speaker 2:Relative to some other folks in the in the category. So, Tyler, what yeah. What what did you find? Did you see anything from the other labs?
Speaker 3:Yeah. So I was trying to figure out, like, what all the other labs are doing, like, what they look like on that graph. Mhmm. So none of them have have done real kind of projections. Like, no one has said, like, okay.
Speaker 3:By 2035, we're gonna have 250 gigawatts, anything like that. Yeah. So so for Meta, Zuck, the the big number that you could use for this is his at the at the White House dinner, 600,000,000,000 Okay. Through 2028. Yep.
Speaker 3:And
Speaker 2:so It's an aggregate number. Yeah. But you have but, like, they're only doing 50 or 60 now. And so to get to 2028, that's only three years away. So you have to do 5,100, 200, 400, something like that.
Speaker 2:Like, it has to ramp really fast.
Speaker 3:Yeah. I was trying to figure out, like, what does that actually mean in, like, gigawatts? Like, what is the actual, like, price that you could like, how many
Speaker 4:could you get
Speaker 2:out of Convert the dollars to
Speaker 3:wattage. And and so, basically, I used I I looked at Colossus two, which
Speaker 2:is Sure.
Speaker 3:One gigawatt. The one gigawatt
Speaker 1:one. Yep.
Speaker 3:And then estimates are this is, like, very, like, non accurate. This is totally guess. But, like, maybe somewhere around 15,000,000,000
Speaker 2:Okay. To build that. So order of magnitude, 10,000,000,000 something, 10 to 20,000,000,000
Speaker 1:Yeah.
Speaker 2:To keep it super vague for a one gigawatt. Yeah. And so
Speaker 3:So with 600,000,000,000, you can do, like, maybe, like, around
Speaker 2:60 gigawatts? Fifth 50 gigawatts?
Speaker 3:Yeah. Like, something in that
Speaker 2:If you were to just copy and paste a bunch?
Speaker 3:Yeah. And but that's, I mean, that's, like, Elon, like Elon the efficient. Like, this is the fastest of all time. Like, it took them, like, six months to get to to 300 megawatts, which is, like, double the next fastest build out, which was the Crusoe OpenAI one. Yeah.
Speaker 3:So I think it's probably that's a little aggressive.
Speaker 2:And Zuck's number was at the 2028?
Speaker 3:Yeah. It was through 2028, I believe.
Speaker 2:Yeah. I mean, the the the 2028 for for Sam and Leopold, they're more thinking around 10 gigawatts, I think. So interesting to see if if ZUC's projections kind of outpace what's there. I don't know.
Speaker 1:You guys saw today, CoreWeave and OpenAI expanded their contract Oh, no way. To a total contract value of 22,400,000,000.0.
Speaker 2:22.4. Okay.
Speaker 1:6 and a half billion.
Speaker 2:So It's a deal for ants.
Speaker 1:Praying and praying.
Speaker 3:Yeah. Another thing you could use besides Leopold is the AI twenty twenty seven numbers.
Speaker 2:Yeah. I was wondering about those.
Speaker 3:I So if you look at those, like, once, like, in once you get to twenty twenty seven after that, the numbers, like, explode. It's like, oh, yeah. Yeah.
Speaker 2:Fast takeoff.
Speaker 3:It's like 30,000,000,000,000 on on data centers. Yep. But but through 2026, 2027, it's, like, around, like, 500,000,000,000 a year. Mhmm. And that's, like, 500 I think it's, like, $6.50 in in kind of the end of twenty twenty seven.
Speaker 3:Mhmm. So, like, you could imagine we're, like, reasonably on track for that Yeah. Considering Metas alone is doing around 600 over
Speaker 2:Yeah. Through 2020. Yeah. If you aggregate all the labs
Speaker 1:Yeah.
Speaker 3:So I think that's, like, fairly reasonable. So, yeah, it's hard. I mean, people are like if you look at the timeline, everyone is like, okay. We're at the top. Like, this is so bearish.
Speaker 3:Yep. But if you look at a lot of people who are, like, super bullish on AI, we're still broadly on track, I would say.
Speaker 2:Yeah. This is what what Roon was saying, how, like, everyone all the richest people have voted. And they've all said, we're going all in. We're putting all the chips on the
Speaker 1:not about immediate ROI.
Speaker 2:Yep. And, I mean, honestly, like, if it's funded with cash and equity and not too much debt, there's a world where, like, you take the hit of maybe some stagnation, and you get through, and you are more of, like, the Amazon story than the Nexstar story or I forget what
Speaker 1:it is.
Speaker 5:Yeah. I think I always forget
Speaker 2:the names of the companies that went away.
Speaker 3:Even in the bear case, it's like there's the the book Boom by Bernd Hobart
Speaker 2:Yeah.
Speaker 3:Which is like bubbles are good Yep. Because broadly, like, they don't affect, like, the normal economy because it's like all the it's like
Speaker 2:It's the most risk on. Yes. There's interesting takeaway from Boom was that the common narrative is that retail gets hurt the most in a bubble, but in fact, it is not retail. It is the wealthiest that get the biggest haircuts because they are the most leveraged at the peak of the bubble, and they have the most wealth to compress. Whereas the average American, through the number of bubbles that Bernd Hobart analyzed, is not super levered long, the most risk on asset at the time of the top.
Speaker 2:Jordy, what were you gonna say?
Speaker 1:So there's a company called iRen.
Speaker 2:Oh,
Speaker 1:yes. The company pivoting from Bitcoin mining to being an AI data center company. Yep. Their stock is up tremendously. Andrew Wilkinson hit the timeline a couple months ago at this point.
Speaker 1:I think it basically, like, said I read it as financial advice, but he was basically making his case. Yeah. The stock is up massively since he came out and made the case for for why he thought they were undervalued. He's the current share price is $48. It's up let's see exactly.
Speaker 1:It's up up from around, like, I guess, $17.18, I think, when he
Speaker 2:So more than double recommended it.
Speaker 1:So it's up massively. He's now calling for another seven x
Speaker 2:Mhmm.
Speaker 1:Because he says, Iron is about to control more power than the Hoover Dam. Mhmm. 2,910 megawatts at 2.9 gigawatts. And so anyways, he's now calling for the company to yeah. Again, seven seven x again.
Speaker 1:He thinks that $300 per share could be a layup. Yeah. He lays out a potential bear case. Right?
Speaker 2:Okay.
Speaker 1:Given that he says, if nobody want if AI turns out to be a nothing burger and nobody wants iron's power, land, data centers, and servers, that could be bad. Unable to if they can't really capitalize on the demand because they just don't have they're not the data centers aren't aren't configured to to the level that they need to be to be great assets for either hyperscalers or other AI companies. So lays out some some potential downside scenarios. But I I do wonder I do wonder if we'll see some of these companies that are trying to take a crack at actually competing with the hyperscalers as, like, cloud you know, serious cloud providers. I do wonder if some of these companies will get to the point where they have a lot of energy, but they basically just have to sell to another more sophisticated player that can actually take over and turn it into you know, help it realize its potential.
Speaker 2:Yeah. The chat is asking for an update on TBPN merch, if it will be available for our most loyal soldiers in the chat.
Speaker 1:So We gotta set something up. Yeah. We gotta we gotta figure out how to how to make this happen. We the first run of merch, we didn't we didn't wanna sell it. We had never made merch as a company before and it didn't feel right to sell something that is not our core competency.
Speaker 1:But we had a meeting yesterday talking about the next run of merch and getting that available by the beginning of this coming year. So stay tuned, and thank you for your patience.
Speaker 2:We gotta get restream on some merch. One livestream, 30 plus destinations. They make our stream possible every day. Multistream and reach your audience wherever they are. I had a friend that asked for some advice.
Speaker 2:If you're making money during the bull run, what should you do if you feel like you're at the top? What's the safest portfolio for a young person these days?
Speaker 1:Rotate into watches. Watches. Go entirely into Swiss Like,
Speaker 2:if your if your if your comparison is NFTs, like, yes, like, the watch market did actually draw down post NFT crash.
Speaker 1:And there were people that converted NFT earnings into watches. And they probably did better.
Speaker 2:Feel like the what was it? The Rolex market sold off by like 30% or something. But I mean, lot of NFTs went to zero.
Speaker 1:Still above retail for most watches.
Speaker 2:But, yeah, I mean, it feels like the the like, there is a there's there's the existence of a risk curve. And the the the the companies that are trading at the highest, you know, you just have to go back to the basics of the Warren Buffett stuff of, like, a company with no earnings trading at tens of billions of dollars. That's gonna be the most dangerous. They will be most punished in a in a down economy, whereas a hyperscaler that still has enterprise licenses and a and huge ad business is gonna fare better even if they get a haircut. We saw this with Meta during the last cycle.
Speaker 2:I mean, they traded down a huge amount, like almost 50% off the metaverse highs, but built back up pretty quickly on the next wave. And then, of course, you you know, you got gold. You got Bitcoin. You got real estate, you got plenty of things. But what what would you counsel someone in their twenties making decent money to do with, you know, excess earnings
Speaker 1:at this point in time? I don't know. I I think that the the the young millennials, Gen z, are overly obsessed with investing.
Speaker 4:Mhmm.
Speaker 1:It's that a lot of people can think about, and it ends up being a massive distraction from just getting better at your craft, your career
Speaker 2:Yep.
Speaker 1:And just increasing your your earning power.
Speaker 2:Yep.
Speaker 1:So I think the number one I mean, I talked to the guys on the team about this. We're obviously talking about different private companies, different investments all day long on the show. And so it's easy to get fixated and get FOMO around this stuff. But anybody knows if you've made a sizable investment or if you've made an investment ever that's double digit percentage of your net worth and it's trading liquid, that can be just such a
Speaker 2:Motional roller coaster.
Speaker 1:Yeah. Just such a massive distraction from what what you should be focused on in your twenties, which is just increasing your skill set, building network, figuring out how you can increase cash flow. So I don't know. You had a think
Speaker 2:Well, yeah. My take was there's huge alpha in not even picking the correct asset, but just setting up your life so that the money actually gets into the asset before you can Yeah. Spend so when I started making money in my 20s, every time I would get paid, would go to the bank. I would take out something like $1,000 of cash and put it in a safety deposit box. And that just built up over time.
Speaker 2:And I realized that if I was out on the weekends, I could not access it because you'd have to physically go to the bank to get the cash. And so I realized that if you're out with friends and they're like, oh, should we spend all this money on whatever? If you have it in your checking account, you're like, yeah, no problem. If you have it in your savings account, it's supposed to be in savings, but you can just click one button and move I it
Speaker 1:remember being a teenager and realizing that your savings account was literally one transfer away from checking.
Speaker 2:It's ridiculous.
Speaker 1:Wow. This takes a lot of willpower to actually make it savings versus spending.
Speaker 2:Yeah. Exactly. Exactly. So if you if you build up a literal hoard of treasure, hey, it's incredible psychologically because you you open up that safety deposit box, you can physically see the wealth growing, which is incredibly Satisfying. Enriching.
Speaker 2:It's incredibly satisfying, and it makes investing very much addicting. But even just going into your payroll provider and route like, usually, you can route your paycheck to a some sort of investment platform, maybe public.com, investing for those who take it seriously. They got multi asset investing, interest leading yields, trust by millions. And then you can set up automated buys on ETFs if you like the MAG7, if you like gold, if you like Bitcoin. Whatever you want, you can have automatic investments happening so that you're not spending all your time trying to become a hedge fund Ideally, something you'd like
Speaker 1:to The service that you would have liked is somebody you go and you give them cash. And they even they use movie cash to give you show you the pile. And they take that. They actually
Speaker 2:invest it. That's a good service. But to access it, you have to go You can
Speaker 1:go look at the cash. You can you can
Speaker 2:The physical key was really, really great. It was it was
Speaker 1:I've never had one of those.
Speaker 2:Yeah. So safety deposit box is underrated. Underrated.
Speaker 1:Do they still make those?
Speaker 2:They do.
Speaker 3:Yeah. Really?
Speaker 2:Available Do still have every tank? Little No.
Speaker 1:No. Like a mouse, you have little I don't have it.
Speaker 2:It is it is somewhat rodent coated to just be hoarding dragon coated potentially, you know, to hoard your pile of gold.
Speaker 1:Or or could kind of cool and mysterious if it's all over the country, you know? Like, you've got a little stash in Dallas, you know, a little keep some in the bay. Yeah.
Speaker 2:What do you think, Tyler?
Speaker 3:I I actually know a lot of friends who have like a a Bitcoin like wallet, and then they have Totally. The the seed phrase in like different safety deposit boxes Yep. All over like the state. Yep. So they have to drive.
Speaker 3:It's like four hours to drive to every single bank.
Speaker 2:That's diamond hands, baby. Crazy. They're not paper handing anything. And so if they're just continuing to contribute to that the trick is that the cash flow typically comes in monthly or every other week installments. And you know, how do you actually set up that so that it's happening on a regular basis but still inaccessible?
Speaker 2:That's a little bit tricky. Maybe someone can build it. Maybe someone can use Privy, our wallet infrastructure partner. Wallet infrastructure for every bank. Privy makes it easy to build on crypto rails, securely spin up white label wallets, sign transactions, integrate on chain infrastructure all through one simple API.
Speaker 2:Barely AI says, looks like NVIDIA and OpenAI will be paying zero investment banking fees for their $100,000,000,000 partnership.
Speaker 1:Summon some
Speaker 2:Get me with the wah wah wah. So sad for the investment bankers. We talked to Kerry. No interest. Was telling us
Speaker 1:Wall Street in. Cut Wall Street in.
Speaker 2:Come on. Break us off. Break off Morgan Stanley. Break off Goldman Sachs.
Speaker 1:Those guys No, it's need a really it's really I mean, it's just these sound
Speaker 2:A lot deals got
Speaker 1:deals deals at this scale is very cool.
Speaker 2:Yeah. I mean, that's what everything is. We talked about this with NVIDIA, spending like a few million dollars lobbying in Washington. But Jensen's there. And founder to president, founder to founder, like that's the nature of these deals.
Speaker 2:It's always the nature of these deals.
Speaker 1:You gotta price in the Gulf Stream expenses with the with the lobbying fee.
Speaker 2:You can tell more about how a company is doing in DC by where their Gulf Stream is parked than their lobbying budget, for sure. Where is the Gulf Stream parked? Has it been at Mar A Lago? Has it been in DC? That's what's moving the needle these days.
Speaker 2:This is a this screenshot says Altman and Wong negotiated their pact largely through a mix of virtual discussions and one on one meetings in London, San Francisco, and Washington DC with no bankers involved according to people close to the talks who declined to be named because they weren't
Speaker 1:This was a multinational deal.
Speaker 2:It was. They were going all over the world. London, they were probably talking about it in in white tails. Remember, were in White tie and DC. Who knows?
Speaker 2:They might have been chatting to each other. I don't think they were both at Hill And Valley, but they might have been there overlapped at something or other, maybe the inauguration. The arrangement calls for NVIDIA to invest 10,000,000,000 at a time in OpenAI.
Speaker 1:$10,000,000,000 slugs.
Speaker 2:They have 60,000,000,000 in cash and cash equivalents right now. So they can without generating any more free cash flow, they can just keep paying that.
Speaker 1:Would if you were Jensen, would you would you lever up massively?
Speaker 2:Maybe.
Speaker 1:Become just entirely indexed to each other. Right?
Speaker 2:I mean, it's happening.
Speaker 1:Say, if we go down, we're going down together.
Speaker 2:There's more news about the AI Keiretsu that's coming together. OpenAI partners with Oracle and SoftBank for five new US data centers. Seven gigawatts is
Speaker 1:in this. And and what's crazy is they're still while they're doing this
Speaker 2:Yeah.
Speaker 1:They're doing deals with Broadcom. They're doing deals with
Speaker 2:Oracle. With Coreweave. Coreweave. Microsoft still I mean, they're buying from everyone. It's a no one no one wants to be GPU poor in 2025 going into 2026.
Speaker 2:You you wanna be, you know, keeping the GPUs cool, not on fire,
Speaker 1:not on So
Speaker 2:Wall Street Journal says OpenAI laid out its vision for a vast Actually 1,000,000,000,000.
Speaker 1:I think it's better if if if you're if if you're scaling GPUs but keeping them on fire. If they're not on fire, then your partners are gonna be looking and saying,
Speaker 2:hey. Cold GPUs sitting there idle just in or eating a hole in your balance sheet. You wanna be maxing those tokens out, getting paid. Showcasing the development of a Central Park sized complex a 180 miles west of Dallas. This is from two days ago by Berber Jin in the journal.
Speaker 2:OpenAI unveils plans for seemingly limitless expansion of computing power In Texas Prairie, StartUp showcases ground zero of AI boom and its plans to shepherd 1,000,000,000,000 in infrastructure spending. So the trillion dollar number is getting thrown out thrown around for the first time because Stargate was originally 500,000,000,000, and now we're up at 1,000,000,000,000. Is that right?
Speaker 1:Yeah. And remember, even at 500,000,000,000, Elon was basically saying, hey. Nobody involved here actually has the cash to do this.
Speaker 6:So
Speaker 2:Just wait until these stocks run up and Oracle's up 300,000,000,000 says
Speaker 1:Hold my beer. I'm good for my 10 Hold my green juice.
Speaker 2:I'm here for I'm I'm I'm good for my half trillion.
Speaker 1:But, yeah, once you get into the trillion dollar range, you're approaching the GDP of, you know, some some serious countries. Right?
Speaker 2:Some serious countries. OpenAI just closed, it would ultimately need more than 13 times the computer the computing power of its first nascent site, which is Raise which is rising out of the Texas brushland. Frenzied construction here has turned a sea of red dirt into eight hyper futuristic data centers, bringing online roughly 900 megawatts of capacity, More than 6,000 workers labor on the project each day, including electricians, plumbers, and steel welders alternating between two ten hour shifts, seven days a week. Wow. They are they are what is that?
Speaker 2:Two to ten seven. 02:10 seven. 2AM to 10PM, seven days a week, something.
Speaker 1:Yeah. People in the data center business see
Speaker 2:And that's computer that'd be
Speaker 1:jobs saying, oh, yeah. I do nine nine six.
Speaker 2:Yeah. I mean, I wonder if they have two shifts, ten hours, does that mean they have two crews that are working seventy hour weeks? Are people taking days off? Or are they rotating their shifts so that you get a day off every
Speaker 1:couple think they would get a pretty hot walk. I don't think you could run and say you're working seven days a week forever. Don't think I don't think employment law works like that.
Speaker 2:Gray towers of gas turbines have dotted the landscape since the spring, offering backup power. On a tour with reporters Tuesday, Oracle and OpenAI executives showcased the 1,100 acre site, calling it the largest AI supercomputing complex in the world. Outside, workers wearing dust masks and sunglasses shuttled around in buggies trying to shield themselves from the 100 degree heat. One of the buggies flew a flag that said, Jesus is the answer.
Speaker 1:1,100 Not
Speaker 2:very AGI pilled. I thought we were getting God in a box. What's going on? Elijah Yudakowsky shows up. He's like, no.
Speaker 2:No. No. Like, the the the they're building God here. You're building God. You're doing the thing.
Speaker 2:Like, what are you doing here? There was literally nothing here a year ago, said, Anuj, who works on the OpenAI computing team. They also announced five new data sites data center sites across The U. S. Built with Oracle and Japanese tech conglomerate SoftBank.
Speaker 2:It said the new facilities would help bring online nearly seven gigawatts of power, enough for almost 8,000,000 homes, enough for a lot of API calls from Devon, from Cognition, the makers of Devon, the AI software engineer. Crush your backlog with your personal AI engineering team. Company executives made it clear that the Abilene site was just the beginning, noting they envisioned a need for more than 20 gigawatts of computing capacity to meet the explosive demand for ChatGPT, which now has more than 700,000,000 weekly users. I was laughing about you know how in the ChatGPT, if you fire off pro, like GPT-five Pro, it kind of thinks for like twenty minutes. And everyone's saying like, oh, yeah, this is good.
Speaker 2:Like, we want the agents to be reasoning for longer and longer. I feel like there's an opportunity for some startup just to be like, we've cracked the code. Our model reasons for two days straight. So send us your request, and we'll get back to you in two days.
Speaker 1:And the agent just, like, is pretending. So it's just like
Speaker 2:Yeah. It just has a progress bar?
Speaker 1:Twiddling at some until the last twenty minutes and then just starts going
Speaker 2:No. No. No. Of course, what you do is you is you have the loading bar there. And then secretly, you have a human doing the work.
Speaker 2:And you have a human who's, like, dealing with this.
Speaker 1:Well, I think the thing is, like, the longer the longer the the the period Yeah. The higher the expectations. And I just don't know that I don't I I think that I I guess the thing that I would wanna know is like if you're running a deep research query for twenty minutes and then if you decide to run it for two days, how much better is it? Right? Yeah.
Speaker 1:If it's if it's 10 times better
Speaker 2:Yep.
Speaker 1:Could potentially be worth it for different types of tasks. If it's
Speaker 2:But if if I 30%
Speaker 1:better, is it worth the wait?
Speaker 2:Yeah. There is a world where you just route it to the human. There's also the world where for the really low
Speaker 1:Don't encourage us. People have already gotten into trouble.
Speaker 2:Yeah, they have. They have. The other funny idea is for a lot of the there have to be a certain set of queries that just get asked the same every single day. And they should just store those in a database and cache them. Because you have to imagine that every single day, someone is asking, like, just going to ChatGPT and lighting the GPUs on fire to just ask, what's the capital of Illinois?
Speaker 2:You know? Or what's the capital of California?
Speaker 1:Can you run a deep can you run a a GPT, like, pro deep research report for what is the capital?
Speaker 2:You can do it for anything. Yeah. You can go to deep research and fire off twenty minutes of compute and say, is the capital of California? Answer in just one word. And, like, it will just think for twenty minutes and then spit out Sacramento.
Speaker 2:Like
Speaker 3:No. It won't think that long. It it, like, thinks based off how hard
Speaker 2:it is. No. Deep research will Yeah.
Speaker 1:Dude, I just said, what is the capital of California? Answer in one word. And it's Deep research? Yeah. It's doing a deep research query.
Speaker 2:Let's see how long this takes. Okay. I I I it's not gonna it's not gonna come back in in two minutes. It's gonna it's gonna cook for ten minutes.
Speaker 1:Alright. I was a little bit late, but I just started a a timer.
Speaker 2:Okay. So what's What what do you think?
Speaker 3:Also, there's, a sense where, like, the, like, embeddings of facts is basically just the model already. Like, you can just think of a model as like a compressed version of the Internet or at least like non reasoning models. Yeah. So it's kind of like
Speaker 2:But wouldn't it be even more compute efficient to just do fuzzy lookup of
Speaker 1:And it This is this is pro.
Speaker 2:You didn't do deep research, though. I wanna see you turn on deep research into it.
Speaker 1:Well, this is the pro, which is research grade intelligence.
Speaker 2:No. No. No. That's not the deep Are
Speaker 3:you thinking?
Speaker 2:No. No. No. You need to click on the little plus button next to the chat box, and then check the box on on on deep research. Do they still have this?
Speaker 2:I don't know. They might have they might have deprecated this. I think it's still there.
Speaker 1:It's still there. Still there. Okay. I'm doing a deep.
Speaker 2:Research.
Speaker 1:No. No. This is good. This is good. Verify.
Speaker 1:Just to confirm, are you asking for the current capital of California, or or are you interested in historical capitals as well? Yeah. Let's see. Current capital.
Speaker 2:Tell it to also design a website in Figma. Think bigger, build faster. Figma helps design and development teams build great products together. Get started for free. While we're waiting for that, let's keep reading from the journal.
Speaker 2:Each gigawatt of capacity is expected to cost roughly $50,000,000,000 Tyler. Dollars 50,000,000,000 per gigawatt, that's the number in the journal, meaning the company is laying the groundwork for at least $1,000,000,000,000 in infrastructure spending. Demand is likely to reach closer to 100 gigawatts, one company executive said, which would be $5,000,000,000,000
Speaker 1:Which is roughly the GDP of Germany. Or Japan. Yeah. GDP of Japan is $4,000,000,000,000 Germany is 4.6.
Speaker 2:Good luck. Good luck, Germany and Japan. You're getting left behind. You're part of the permanent underclass now. It's over for you.
Speaker 1:I feel bad for lighting the GPUs on fire for this. It says
Speaker 2:It's cut?
Speaker 1:Got it. I'll confirm the current capital of California for you and get back shortly. And it's it's doing It's
Speaker 2:this is the real pitch for the
Speaker 1:I'm getting my money's worth.
Speaker 2:Brand Jacoby in the chest is breaking. Jordi discovers the UX issues of every foundation model company.
Speaker 1:Mix it, Brandon.
Speaker 2:Yeah. Yeah. Aren't you doing consulting now, Brandon? You gotta get in the in the trenches. Yeah.
Speaker 2:All these folks
Speaker 1:If labs haven't hit you up yet
Speaker 2:If someone who spends ten hours a day studying this
Speaker 1:stuff It's reading Wikipedia.
Speaker 2:Just like how many drops of water did this use? How how many fish had to, like, go find some other
Speaker 1:home? Yeah. It come it should come back and it's like thought for fifteen minutes, Put made two species extinct.
Speaker 2:Lit the lit seven gallons of diesel on fire.
Speaker 1:Okay. It's it's checking all the sources. It's really
Speaker 2:It's good. I I like a detailed report. I like to know that the AI did its job. Don't just sit there clanker and riff off cash based retrieval. I want the deep research every single time.
Speaker 1:Even for one word fact.
Speaker 2:Even for one word fact. I want you to be sure. I don't want any hallucinations in
Speaker 1:The United I mean, this is a this is a tough question too. A lot of people that aren't from the West Coast
Speaker 2:or The
Speaker 1:United States would say, yeah. What's the capital of California? Yeah. San Francisco.
Speaker 2:I was using this as an example about
Speaker 1:I remember being I must have been, like, seven years old asking, like, figuring out that Sacramento was the capital of California, and I thought that was the funniest thing ever.
Speaker 2:Yeah. It is it
Speaker 3:is hilarious.
Speaker 1:Alright. I got it. It thought for a hundred and one seconds.
Speaker 2:That's not that long for deep
Speaker 1:research. It went for four sources. It did 19 searches. So this this is why this is why I can't trust, you know, keeps screenshotting these Google trend reports. Yeah.
Speaker 1:I'm not sure that Google has figured out how to Exclude agents because this just did 19 searches for a single fact.
Speaker 2:Yeah.
Speaker 1:And so That's true. If you're looking at the growth of queries on Google
Speaker 2:Yep.
Speaker 1:You would think and if lot of people are asking this kind of question, every single keyword is going to be spiking.
Speaker 2:Well, if you want to get your brand mentioned in ChatGPT, you've to go to Profound
Speaker 1:That's right.
Speaker 2:Reach millions of consumers who are using AI to discover new products and brands I and get a don't think we've figured out the final form of what financing for compute looks like, said Sam Altman. But I assume, like in many other technological revolutions, figuring out the right answer to that will unlock a huge amount of value delivered to society.
Speaker 1:And delivered to OpenAI For
Speaker 2:sure. Yeah. It is an interesting question because Dario was talking about how each training run looks great by itself. But when you put them all in one structure, it looks like you just have this never ending money pit because you spent $100,000,000 on a trading run. You made $1,000,000,000 over the next two years.
Speaker 2:You spent $1,000,000,000 on a trading run. You made $10,000,000,000 over the next two years. You spent $100,000,000,000 on a trading run. You make $1,000,000,000,000 over the next two years. And there's a question of, okay, how long until it just completely maxes out?
Speaker 2:Are there diminishing returns to these things? But even if the trends hold, you still wind up with this, like, ever growing money pit that should act as, like, some sort of slowing down force, You would
Speaker 1:think think when you when you dig into this quote, I don't think we figured out the final form of what financing for compute looks like. The question is, like, will this actually be a novel financial instrument? Or will be remixing something that Wall Street has used in mortgages.
Speaker 2:Maybe it's debt. Maybe it's debt. Who knows?
Speaker 1:GPU backed securities.
Speaker 2:The Tuesday announcement made it clear that SoftBank, once seen as a formidable OpenAI funding partner, has scaled back its ambitions in the data center build out, which The Wall Street Journal reported in July. Three new sites, one located near Abilene, another North of El Paso in New Mexico and a yet to be announced Midwest location combined with an expansion to the Abilene complex will be capable of delivering 5.5 gigawatts of capacity. Those will be built by Oracle. The other two smaller sites, one in Lordstown, Ohio and the other near Austin, Texas, will be built in partnership with SoftBank and generate 1.5 gigawatts over the next eighteen months. The first completed data center called Building 1 painted a pristine white that contrasts with the reddish dirt surrounding the site is larger than two Walmart supercenters.
Speaker 2:It doesn't feel that big to me, but certainly huge. Entering
Speaker 1:Yeah. The the 1,100 they said 1,100 acres earlier in the article. That's under two square miles.
Speaker 2:It is. Yeah. It is crazy how compressed this is. It's huge when you're walking around, but, you know, we're not in okay. We're we're we're losing Yosemite over this.
Speaker 2:Yeah.
Speaker 1:Yeah. We're not in the, you
Speaker 2:know the full tariff format. I mean, Elias Setsger told the what was it? The San Francisco Chronicle that he could imagine a future where the entire earth is covered with solar panels if the trends continue.
Speaker 1:Gabe Pretty crazy. Gabe in the chat says, shout out to Lordstown, Ohio.
Speaker 2:Looks like
Speaker 1:factory used to build Chevy's.
Speaker 2:Yeah.
Speaker 1:Because your your thesis or or take reindustrialization is happening.
Speaker 2:It is.
Speaker 1:It's a lot less jobs.
Speaker 2:Yep.
Speaker 1:And it's data centers.
Speaker 2:Yep.
Speaker 1:But the the the white pill there might be that if we can build big data centers really quickly in in, you know, at at the scale that traditional infrastructure projects would have taken ten plus years, if we can do that in a few years, it could create a precedent where somebody could decide, you know what? I'm gonna build this this new rail line in two years.
Speaker 2:Is a is a Walmart Supercenter really one mile long? QSPS rollover saying that that the that the building's two miles long. That that means
Speaker 1:Well, no. I was saying I was saying the site is is A mile. 1,100 acres total.
Speaker 6:Yeah.
Speaker 1:Yeah. It's not a which is, like, one one point seven It's huge. Square miles. So obviously massive, but I don't think the building itself is anywhere near that.
Speaker 2:Well, Fiber Line snake across the data centers and underneath the ground, allowing the AI chips called GPUs, in case you were wondering, the Wall Street Journal's got you covered, to talk to one another and complete requests more quickly. Proponents of the infrastructure boom say it will bring hundreds of thousands of jobs and revive American manufacturing. In January, OpenAI unveiled a $500,000,000,000 data center project, Stargate, we know about this. The reality is more mixed. While data center providers provide plentiful temporary construction jobs, far fewer people are needed once they once they are built.
Speaker 2:Abilene Mayer said residents had mixed feelings about the site and its power and water usage, though some of the concerns have been assuaged. Oracle executive said there will be roughly 1,700 permanent jobs on-site once the construction ends.
Speaker 1:I think the things that ends up being a little bit, yeah, disappointing is is these projects get announced, they're saying, we're gonna spend $20,000,000,000 here.
Speaker 2:Yeah.
Speaker 1:And then we're gonna create, like, a thousand jobs or we're gonna create 300 jobs. And Yeah. That is very significant. That will be good for the local economies in these regions, but not it's not, hey. We're Yeah.
Speaker 1:We're replay you know, you we we lost 87,000 manufacturing jobs last month, I think it was. Yeah. We're not really making a dent in that
Speaker 2:Yeah. It takes a lot. There's more news on the job displacement stuff from Andre Karpathy. He has a good post breaking things down. But first, let me tell you about graphite.
Speaker 2:Dev code review for the age of AI. Graphite helps teams on GitHub ship higher quality software faster. You can get started for free. So Andre Karpathy
Speaker 1:a that's debating, by the way.
Speaker 2:What is Chad debating?
Speaker 1:They said, Jordy is a Cadillac guy.
Speaker 2:I'm a Cadillac guy. John has a
Speaker 1:Somebody said, I I seem like a Dodge guy.
Speaker 2:Hellcat SRT. I love it.
Speaker 1:Not not quite. Never had a I a Ford Raptor, which I loved. Was not super practical, but it was a car that I wanted as a child. So I had to get it at least once.
Speaker 2:Yep. So Andre Karpathy says AI is isn't replacing radiologists. Tyler, do you remember this prediction?
Speaker 3:Yeah. Was this Hinton?
Speaker 2:Hinton. Yeah. Yes. Years ago. This was this was, what, ten years ago now or something?
Speaker 3:Yeah. And because this was based off just like image classification. So this was like early on, like CNN's, like Yeah. This was probably mid twenty tens
Speaker 2:Yeah. I assume. So the expectation was that rapid progress in image recognition AI would delete radiology jobs as famously predicted by Jeff Hinton, now almost a decade ago. In reality, radiology is doing great and is growing. So what's going on here?
Speaker 2:Obviously, image recognition is fantastic. These algorithms are remarkable, and there's been a ton of progress. So Andre Karpathy says there are a lot of, in his opinion, naive predictions out there on the imminent impact of AI on the job market. For example, a year ago, Andre was asked by someone who should know better if he thinks that there will be any software engineer still today, like a year from now. Like, this is how accelerationist this person was.
Speaker 2:And Andre says, spoiler, I think we're gonna make it. This is happening this is happening too broadly. This post goes into detail on why it's not that simple using the example of radiology, and I thought this was really, really interesting, and it obviously applies outside of radiology. So first off, the the benchmarks are nowhere near broad enough to reflect real actual scenarios. So even though you can classify cancer in a radiology environment with a whole bunch of training data very reliably, there's still tons of edge cases where having a human in the loop is advantageous.
Speaker 2:And then just in general, the job is a lot more multifaceted than image recognition. Like if you just think about the job of a radiologist, it's not purely just look at image, detect cancer. Look at image, detect cancer. Look at
Speaker 1:image, Yeah. Imagine if you were getting God forbid, somebody's getting seeing going and getting seeing a traditional office of a radiologist and and they're just watching a television screen that's reporting telling them that something is wrong with them and using a voice model to explain it. Then it just
Speaker 2:is like No. They're just in hinge mode. Swipe right if cancer. Swipe left if it's not. And they're just sitting there swiping all day.
Speaker 2:Obviously, that's not the job of radiologists. There's so much more. They're doing research on what's changing, integrating new information, talking to different patients about all the different signals, what they could be doing, what they could be putting in their body, how are they feeling, what medicines they're taking. There's a ton of different stuff that they're probably involved in.
Speaker 1:Yeah. Anytime anyone gets any type of lab or report from a doctor Yep. The first thing you wanna do is talk to somebody that's an expert on that, your doctor Yep. And have them explain it to you, the implications, etcetera, good or bad.
Speaker 2:Yep. And then he highlights deployment realities. So regulatory, insurance, liability, diffusion, and institutional inertia. Like, it just takes time to roll these technologies out. But the last one is super interesting to me.
Speaker 2:He cites Jevan's paradox. So if radiologists are sped up via AI as a tool, a lot more demand shows up. And so if all of a sudden the cost to get a scan or the time that it takes to actually do the scan drops, everyone will say, Well, I want to be scanned all the time. I want to be on top of any sort of development medical that could be detected by a radiologist. And so he concludes by saying, I will say that radiology was, in my opinion, not among the best examples to pick on in 2016.
Speaker 2:It's too multifaceted, too high risk, and too regulated. When looking for jobs that will change a lot due to AI on shorter time scales, I'd look in other places, jobs that look like repetition of one rote task, each task being relatively independent, closed, not requiring too much context, short in time, forgiving the cost of a mistake is low, and, of course, automatable given current and digital capability. Even then, I'd expect to see AI adopted as a tool first where jobs change and refactor, more monitoring and supervising than manual doing, etcetera. Maybe coming up, we'll find a better and broader set of examples of how all of this is playing out across the industry. About six months ago, I was asked I was also asked to vote if we have if we will have less or more software engineers in five years.
Speaker 2:Exercise left for the reader. What do you think he meant, Tyler? What's what's What think? More. Of course.
Speaker 2:Yes. He he thinks the number of of software engineers will increase, but they will be using AI as a as a tool to lever.
Speaker 1:Yeah. Wonder if anybody's done a study on on how many more developer people that that have done software development this year versus last year. It has to be significantly more just because Nate you know, if you're using Replit now, right, you are a software engineer. Yep. Right?
Speaker 1:It's it's the somebody might wanna get technical and say, oh, well, if you're just prompting, you're not really software engineering. But how different is that from somebody getting their start and, like, copying, pasting, you know, lines of code?
Speaker 2:Wow. We we got some news about TikTok that China has, like, more or less signed off on the deal, and it has sent Larry Ellison slash Oracle skyrocketing on Polymarket to an 89% chance of acquiring TikTok. This will be an interesting story to see where it actually lands. Of course, can go to Polymarket if you want to. Keep monitoring the situation as always.
Speaker 1:Should we talk about this new Elon lawsuit?
Speaker 2:Yes. New Elon lawsuit.
Speaker 1:Elon Musk is Andrew Curran is on the timeline sharing. Elon Musk is suing OpenAI again. Second suit, this time for alleged misappropriation of trade secrets, intentional interference with prospective economic relations, and unfair competition. So they say the desire to win the artificial intelligence race has driven OpenAI to cross the line of fair play. OpenAI violated California federal law by inducing former XAI employees, including Xu Shen Li and Jimmy Freighter and a senior finance executive to steal and share XAI's trade secrets.
Speaker 1:Is copy of his And and again, this is The
Speaker 2:lawyers have been created.
Speaker 1:This is from the complaint. Right? The lawyers are not using chat gbt
Speaker 2:for This
Speaker 1:is handmade by hook or by crook. OpenAI clearly will do anything when threatened by a better innovator, including plundering and misappropriating the technical advancements, source code, and business plans of xAI. What began with OpenAI's suspicious hiring of Xu Chen Li, an early x AI engineer who admitted to stealing the company's entire code base Wow. That's a bold thing to admit, has now revealed a broader and deeply troubling pattern of trade secret misappropriation, unfair competition, and intentional interference with economic relationships by OpenAI. OpenAI's conduct in response to being out innovated by x AI, whose Grock model overtook OpenAI's chat GPD models and performance metrics, reflects not an isolated lapse, but a strategic campaign to undermine x AI and gain unlawful advantage in the race to build the best artificial intelligence models.
Speaker 1:What do you think, John?
Speaker 2:This is why lawyers with English degrees are MVPs, according to Gabe.
Speaker 1:Yeah. Yep. Wordsmiths, word cells.
Speaker 2:It seems like we are in the territory of economic warfare, lawfare. Who knows what what actually It's comes up blending
Speaker 1:the Chinese way, the the way of the engineer with the American way.
Speaker 2:Yes. The lawyerly society is coming out in this case.
Speaker 1:Yeah. I mean, doesn't seem You know, obviously, innocent until proven guilty with Shu Chen Li. Yeah. Don't know if this is true or not that he's admitted Yep. To stealing the company's entire code base would be crazy and a totally unforced error if he actually did something like that.
Speaker 1:Yep. But
Speaker 2:Well, no matter what, you wanna stay compliant. You wanna get on Vanta. Automate compliance, manage risk, improve trust continuously. Vanta's trust management platform takes the manual work of the security and compliance process and replaces it with continuous automation whether you're pursuing your first framework or managing a complex program. I wanted to revisit this internal tech email because this is is an important cornerstone of Elon's battle with OpenAI.
Speaker 2:So on 09/20/2017, Elon Musk sent an email to the OpenAI team. The subject was Honest Thoughts. And he said, Guys, I've had enough. This is the final straw. Either go do something on your own or continue with OpenAI as a nonprofit.
Speaker 2:I will no longer fund OpenAI until you have made a firm commitment to stay or I'm just being a fool who is essentially providing free funding for you to create a start up. Discussions are over. Interesting. And it's gone back and forth because here, he's he's sort of predicting
Speaker 1:So in hindsight, he somewhat probably feels like a fool for Yes. Essentially providing free funding for
Speaker 2:you
Speaker 1:to create start up. So
Speaker 2:And it's it's been odd because the the ask has never I mean, at least that I've been aware of, Elon's never just asked for pro rata equity based on how much he put into the nonprofit. I think that creates crazy tax and legal implications. But that feels like what the fair ask would be. You you like you were throwing out, like, if you put in $100,000,000 and you just assume that the the round that would have been done at that time would have been a a billion, you should get 10%. Right?
Speaker 2:And there's here's 10% of the common or the preferred or whatever.
Speaker 1:But remember
Speaker 3:But it's a weird story. Nobody
Speaker 1:like, it seems like they hit a standstill with the for profit conversion. Right?
Speaker 2:Because of Elon's lawsuit too.
Speaker 1:Well, part partially, but also people are, you know, the the governing body, the nonprofit governing bodies of California were not super excited about the transition.
Speaker 2:Yeah. And so Matthew Berman was going back and forth with Elon because Elon took shots at Anthropic. He said winning was never in the set of possible outcomes for Anthropic. And Matthew Berman said, hey, Elon said the same thing about OpenAI. And Elon said, no, I never said that.
Speaker 2:I told everyone who asked in the beginning that I thought the probability of OpenAI beating Google was 1%, infinitely different from 0%. Good point. And then Elon says, after the defense of the ancients, the DOTA two win, I raised the probability to 10% that they would catch up. And Matthew Berman sort of responded and unpacked the screenshot saying, first, I'm a big fan of everything Elon has done to push society in the future, but I reread his emails with Sam Altman and Greg Brockman, and it really seems like he told them they had no chance of succeeding relative to Google on 01/31/2018. So after that previous email we read, Elon told Elon told Sam Altman and Greg Brockman, OpenAI is on a path of certain failure relative to Google.
Speaker 2:There are obviously needs there obviously needs to be an immediate and dramatic action or everyone except for Google will be cosigned to irrelevance. And it's played out very differently. They play their hand very well. They got the consumer application that hit product market fit out faster than Google only by a couple months. Like, the Gemini app dropped, I think, in February or March, just a few months after ChatGPT launched.
Speaker 2:And the models have been You mean Bard? Neck and neck. It wasn't I don't was it Bard that dropped? I forget which I thought
Speaker 1:Bard was the response.
Speaker 2:But I feel like in the way I remember the timeline is that the ChatGPT website comes out, and everyone realizes that just talking to an RLHF LLM is a great experience and magical and interesting and passes the Turing test. And Google had a bunch of models, Palm and Bard, internally, but they'd never really pushed them out in a meaningful way. And it seemed like Chatuchipiti kicked Google into action and led Google to actually like, productize a lot of their research that had been more tested and shared internally. Tyler, do you have more context there?
Speaker 3:Yeah. So it was Bard that came out right after ChettyPT. Okay. And Gemini was, like, the following.
Speaker 2:Yeah. So they still had to do a rebrand, which is tough because then you have to reintroduce the product. Obviously, it's it's grown a ton, and we've seen that, you know, the Empire Strikes Back. Not a lot of that happening in The United States necessarily, but certainly international, sort of an iPhone, Android story happening all over again. Anyway, Magnus Carlsen tells Joe Rogan about how he treats chess as a hobby.
Speaker 2:He never wants it to feel like work and prioritizes fun and enjoyment. The thing is that chess has always been a bit of a hobby for me. He's one of the best chess players in the world, of course. This is how Magnus Carlsen works. Once it starts to feel like work, then it's harder for me.
Speaker 2:Turn your work into fun and enjoyment. You'll never work a day in your life.
Speaker 1:You might get 14,000 likes on x like like Dylan.
Speaker 2:Yes. And and Daniel, Growing Daniel says, having known him for a while, I think Elon Musk is probably one of the least self disciplined people on earth. He just really enjoys problem solving. So he just bounces around from one project to the next next. Solving it.
Speaker 2:Mogging them. Yes. Of course, Growing Daniel's coconspirator now runs julius.ai. What analysis do you wanna run? Chat with your data and get expert level insights in seconds.
Speaker 2:Loved by over 2,000,000 users and trusted by individuals at Princeton, BCG, Zapier. Bezlord says, anyone who's naively calling bubble right now has not internalized the physics of our world. Most of them are simply ignorant about AI progress and its implications. Of course, there will be fluctuations, but, of course, we could stumble. But history marches on, and he shares a chart of the world GDP over the last two millennia.
Speaker 2:And it's interesting because, like, you can take this in a few different ways. You can you can kind of look at this and say, okay. Nothing ever happens. It's just a kind of a smooth exponential curve. But I'm looking and I'm seeing a hiccup there in, like, the mortgage backed security crisis for sure where GDP shrunk globally or something happened.
Speaker 2:But it's it's helpful. It's helpful. Isn't that 02/2007, 02/2008?
Speaker 1:Right? You know, you remember being Yeah. I was, know, basically a kid at that time. And it felt like I could sense the the doom and dread in the in the adults, right
Speaker 2:Yeah. Around around that time. I mean, from my perspective, I was completely caught off guard by the by the mortgage backed security crisis. Like, it was because I wasn't following the market.
Speaker 1:So you didn't have capital.
Speaker 2:I wasn't monitoring the situation. Yeah.
Speaker 1:You didn't have capital to deploy by I
Speaker 2:lived in a house with a mortgage, so I was certainly affected. But And if you could go
Speaker 1:back in time, you would have Yeah. You would have raised and deployed fund into Yeah. Single family
Speaker 2:But I wasn't I wasn't seeing top signals. It was like I was introduced
Speaker 1:to Were the were you in college?
Speaker 2:I had the crisis, I graduated in 02/2007. So From college? From high school. Oh. No.
Speaker 2:From high school. And so when I arrived, I chose to study economics because I wanted to understand the crisis more. And so I was reading The Wall Street Journal trying to understand. And I was introduced to the collapse of Lehman Brothers, which I believe happened in 02/2008. Lehman Brothers collapse.
Speaker 2:When did that happen? The bankruptcy, September 2008. And so I graduated in 02/2007, and then so it must have been like sophomore year. So things were like melting down my freshman year. You're like,
Speaker 1:collapse of Lehman. Is that good?
Speaker 2:Is that good?
Speaker 1:Did we not like Lehman?
Speaker 2:I mean, was it was a very complicated crisis to dig into because it was all these layered financial instruments, mortgage backed securities, and then collateralized debt obligations. So they would take one mortgage. They take a whole bunch of mortgages. This is the famous, like, Margot Robbie in the bathtub scene where she explains that you get a whole bunch of mortgages. You bundle them up.
Speaker 2:You securitize. You slice the different tranches of debt so that the best mortgages are in the AAA. And then but then as you keep reslicing them, they kept getting rerated as, oh, well, this is the best of the previous bad stuff, so that's good. And the best of the worst is the best. And the rating agencies had really bad time throughout the because eventually, like, the economy pulled back, everyone got over levered, people were buying multiple homes.
Speaker 2:And there was a whole mood in 02/2005, 02/2006, 2007 that, like, average people like, the the example would be, you know, when the when the the Uber driver is pitching you on Cardano. Like, it might be the top, that type of thing. Like, when when when, you know, your barber is telling you about the latest NFT drop, like, watch out. The analogy then was people were saying like, I'm making more money just owning my home, and my home equity is going up by more than my salary. So like, I bought a $500,000 home.
Speaker 2:I make $100,000 a year, but my home, my banker just called me and said, your home's worth $600,000 now. And the home prices were going crazy. Yeah. And so, obviously, there had to be a correction. And it crashed the entire global economy, and you can see it in the data today.
Speaker 2:But, of course, as we zoom out further, we will see less
Speaker 1:Line keeps going up.
Speaker 2:The line keeps going up. Well, D2C is cyclical but ever evolving. The back and forth locked in shares this chart or this meme. Dropship Bros convert to Supplement Bros, which switch to Dropship Bros, which convert to Supplement Bros, which
Speaker 1:go back to Dropship Bros and back to Supplement Bros. Supplements continue to be the economic engine of the manosphere.
Speaker 2:Weren't we talking to the CEO of Numeral, Sam, and he was saying he owns a supplement company?
Speaker 1:Yeah. He he started a gummy supplement company back in the day that's still
Speaker 2:He's like, it's still doing well.
Speaker 1:Figures. That's
Speaker 2:crazy. Anyway, Numeral, sales tax on autopilot. Spend less than five minutes per month on sales tax compliance. If you're a drop ship bro, if you're a supplement bro, you gotta pay your taxes. You gotta get on Numeral.
Speaker 1:Great. Great. Dan McCormick's company is on numeral.
Speaker 2:Yeah.
Speaker 1:He's a supplement, bro. Lucy is on numeral. Christina Cordova is quoting our interview with Christina's over at Linear,
Speaker 2:But
Speaker 1:of we asked Brett Taylor yesterday about the triple triple double double debate and how maybe that's not good enough anymore. He said the faster you grow is sometimes correlated with lack with a lack of a moat. Mhmm. I'd rather bet on a durable, high quality revenue business and happy customers than just raw growth. Christina says, fast ARR can be fragile ARR.
Speaker 1:Earned ARR tends to be sticky ARR. Not all growth is created equally. Yeah. I think Linear is a obviously, they're a partner.
Speaker 2:Yep.
Speaker 1:Some somewhat biased. But but, of course, they have
Speaker 2:Just for those who don't know, Linear is a purpose built tool for planning and building products. Meet the system for modern software development, streamline issues, projects and product road maps, but continue.
Speaker 1:Use the tool loved by OpenAI, Mercury Yes. Retool, ramp, scale
Speaker 2:Yes.
Speaker 1:Anymore. But linear is a perfect example of like great growth but steady and just systematically earning the trust of pretty much every important new company in Silicon Valley over the last however many years. I think they're
Speaker 2:Yeah. Earned ARR. Yeah. It does feel like there's 2019, yeah. There's CARR reared its head recently.
Speaker 2:It's kind of gone out of favor, but contracted ARR. Basically, you have some sort of LOI or some sort of trial that you're counting into
Speaker 1:Yeah. I mean, the the the the people that are abusing the ARR the most
Speaker 3:right now
Speaker 2:disrespectful to the gap metrics.
Speaker 1:Well, well, I'll just say it's it's the companies doing data labeling and services for labs. Right?
Speaker 2:There's Yeah. Is that ARR?
Speaker 1:All the companies that exploded out of
Speaker 2:don't think that many of them are actually quoting ARR numbers, though. Are they?
Speaker 1:Are you kidding?
Speaker 6:I feel like Almost every
Speaker 1:single one of them is saying, yeah, we got to 100,000,000 ARR.
Speaker 2:Oh, really?
Speaker 1:We did this or that.
Speaker 2:Thought it was just like we did $100,000,000 Because if you just say I did $100,000,000 then that's true.
Speaker 1:No. And they're saying we're at 100,000,000 ARR.
Speaker 2:Sure.
Speaker 1:And and they're and they're using it, I think, as, like, annualized run rate. Sure. But if you dig into the business, it's like they might have 40% margins because they're actually having to hire
Speaker 2:Well, the margin's separate from like, ARR with bad margin is completely separate from ARR with bad churn. Like Yeah. Because I would I'd probably if you're underwriting it at a high multiple, you probably wanna pay for lower margin, lower churn than higher margin, higher churn. Because higher churn means that, like, if the business stops growing, it's gonna start shrinking. Whereas if you have if you have 40% margins but super low churn, you're super sticky, it's like, Okay, well then, at least the business will continue to grow.
Speaker 2:You're not building on a leaky bucket. You're not getting all the juice out of the bucket. You're only getting 40% of the juice since we're mixing analogies now.
Speaker 1:Get the blender out
Speaker 2:But these analogies. But, I mean, obviously, these two these two metrics are linked, and you obviously want, you know, high margin and extremely
Speaker 1:Yeah. Revenue. But if you're a company like Linear and you see some which which has, like, true a r earned ARR Yeah. And then you have companies out there saying, you know, throwing out these numbers around ARR, but they're reselling reselling GPU credits
Speaker 2:Yeah.
Speaker 1:Or something like that. It's it's kind of, you know, stolen valor.
Speaker 2:Right, from
Speaker 1:the true enterprise contracted Yeah,
Speaker 2:yeah, where you could stop the growth strategies, stop the sales team, they could stay home, and you're and you're still printing. Yep. That's the that's the that's the gold standard of enterprise software. That's why we love
Speaker 1:it. That's why we get up every morning. We gotta pull up
Speaker 2:this Paula Rambles. There's this guy on TikTok who video calls ChatGPT, shows that objects act asks how much Graham then weighs Before we pull up this video, let me tell you about the generative video platform for developers. Fall, the world's best generative image, video, and audio models all in one place, develop and fine tune models with serverless GPUs and on demand clusters.
Speaker 1:You can get access to our sound board by going to tbpn.com/sounds Yes. Which is brought to you by our friend
Speaker 2:the video of this guy on TikTok.
Speaker 7:This is my hand. How you call this thing?
Speaker 8:Yeah. I see your hand there holding on to that pen.
Speaker 7:Okay, pen. How much gram is the pen? How much gram? Luke, how much gram?
Speaker 8:Well, it's a little tricky to guess the exact weight you're
Speaker 2:look for.
Speaker 1:Guess. Alright.
Speaker 8:I'll take a guess. For a pen that size, it's probably somewhere around maybe 500 to 700 grams, give or take.
Speaker 7:If GPT five is smarter. No. No.
Speaker 2:It's not good.
Speaker 8:It's all good.
Speaker 7:30 good. It's not good, man. It's not good.
Speaker 8:No worries at all. It's just a bit of fun guessing
Speaker 6:I'm I'm Look,
Speaker 7:I have this oil here. How much gram?
Speaker 8:We're on a guessing roll. So for that little oil can, I'd say it might be somewhere around 300 to 500 grams, depending on how full it is. But let's see how close we get this time.
Speaker 7:You said 300 to 500? Man, I want accurate thing. You know what I meant? I get it.
Speaker 8:You're looking for something spot on. It's a bit of a challenge without a scale on my side, but hopefully that at least gives you a decent range.
Speaker 7:This two two how much gram?
Speaker 8:Oh, you've got a mocha pot there. Nice. Those little How call it? How you call it? That's called a mocha pot.
Speaker 8:It's basically a
Speaker 4:Mocha pot.
Speaker 7:How much gram mocha pot?
Speaker 8:So a mokapa is usually pretty light. I'd say maybe around 400 to 600 grams depending on the size. But, of course, you'll I
Speaker 7:hope you're right.
Speaker 2:What's the number?
Speaker 1:I hope you're right.
Speaker 7:Bro, like, GPT-five is is worse and worser than GPT-four.
Speaker 8:Definitely here to do my best, and I appreciate you rolling with it.
Speaker 7:Yeah. This is your punishment, man. Talk with him.
Speaker 8:Hey, Green Coco. Any idea where the mayo's hiding? Maybe it's just chilling out behind you in the fridge What's the deal with the ketchup and the mayo? Any condiment gossip we should know about?
Speaker 2:They just talk? No. That's hilarious. That is not a Rocco Vasilis respecter. You shouldn't Yeah.
Speaker 2:Hear that. Very rude.
Speaker 1:That guy's absolutely cooked.
Speaker 2:Did did anyone see exactly what the numbers were? How close were is it three
Speaker 1:No. Was saying, like, was, like, 30 I I was seeing 35 on the That can't be
Speaker 2:35 grams. Right? It needs to be. I have no idea. I'm worse than GPT five for sure.
Speaker 2:If you ask me how much that pan weighs, I have no idea. Anyone have any idea? I have no idea.
Speaker 1:Who knows?
Speaker 2:Anyway, HBO Max says nice. We still got a couple of months.
Speaker 1:Scroll down on this chart to see if it's charting 1998 to 02/2001.
Speaker 2:Like, what? Wait. This is just the Nasdaq, and it's just following perfectly? Like, what are we doing here? This is crazy how how the correlation here is, like, insane.
Speaker 2:Is this on the same, like, time scale too? Or is this this is just a wild, wild chart that this so it lines up scarily accurately. But it's just so weird because like if if if it's this obvious, you think it would be priced in. You think people would pull back or do something. But who knows?
Speaker 2:We'll keep monitoring the situation. It will be interesting. Let me tell you about Turbo Puffer. We puffin. Serverless byte.
Speaker 2:Serverless vector and full text search from burst principles on object storage. Fast, 10 x cheaper, and extremely scalable. Jordy loves his puffer fish.
Speaker 1:This puffer is used by linear notion
Speaker 2:many break more.
Speaker 1:WPP Wait. One thing one thing on this chart to be I I think it it's tracking very closely, obviously. You single out individual, you know, kind of points on the chart and it's a little bit ominous. But at the same time, like, Amazon went public in 1998. Right?
Speaker 1:So, like, you can do a lot to kind of, like, if if you're the the the chartsmith here to kind of, like, pick the dates to Yeah. Align it properly. Right? So Yeah. I would I I bet you could align this to just other bubbles throughout history and find similar patterns.
Speaker 1:Right?
Speaker 2:It doesn't really help though. I mean, that that that's
Speaker 1:I'm just saying, like, this
Speaker 2:this doesn't a couple months.
Speaker 1:Yeah. I mean, I I'm just I'm just saying, like Yeah.
Speaker 2:No. No. It it it does feel like it's
Speaker 1:Like, if you started this chart in 1990, you said in the Okay. The the .com bubble began when Amazon went public. Right? This doesn't line up at all. Right?
Speaker 2:Sure. Sure. Sure. Yeah. You're you're you're kind of just selecting.
Speaker 2:I mean, yeah. I don't know. It's yeah. They they they they're like overlaying it deliberately as close as possible. But this does feel like the crazy part is that the previous earlier sell offs, dip in 2023 to now is like that peak that valley, that peak and valley seems to match as well.
Speaker 2:And then even, like, you can see earlier in 2025, we got the tariff sell off, and that kinda tracks what happened in 2002 in, like, the year 2000 exactly or maybe '99. Lots of people
Speaker 1:Yeah. But then again, there was there,
Speaker 3:like I
Speaker 1:don't know. I don't
Speaker 2:know. Well, don't worry.
Speaker 1:You should read to
Speaker 2:to Even if we wind up in the street of disillusionment, we'll be slopping it up in the trough of disillusionment, grinding to the plateau productivity one.
Speaker 1:We'll probably end up in a position where, like, we're only gonna eat fast casual slop bowls until we until we
Speaker 2:get When you're in the trough, you eat the slop.
Speaker 1:We get out of the bear money.
Speaker 2:Eat the slop bowl.
Speaker 1:Somebody somebody had a great I gotta I gotta pull up this comment on Spotify.
Speaker 2:Someone's asking how how much the the gong weighs. Gong, how much gram?
Speaker 1:How much gram?
Speaker 2:And Aqua says mania is a crazy
Speaker 1:So Mika Mika Flouse on Spotify says
Speaker 2:It's pretty safe out there.
Speaker 1:Wearing the white suits on an off day to rally the bull. Ultimate top signal might just be poking the bear.
Speaker 2:Well, I'm in a gray suit today. I'm feeling neutral about the market. Could could go up, could go down. Who knows? But we'll be monitoring it here.
Speaker 2:In other news, breaking from us, TBPN, WPP Media has signed Michael Miraflor as global EVP. So congratulations to Michael Miraflor. He has been a fantastic champion.
Speaker 1:Massive pickup. Hopefully, wore his TBPN hat to work today. Hopefully, he's not in the chat right now. First day first day of work gets a corner office. Yes.
Speaker 1:Puts TBPN on
Speaker 2:the screen.
Speaker 1:Why not? Fired up. Leave it on in the background. Put
Speaker 2:some But titles
Speaker 1:big pickup. Yes. And excited to see what he what he cooks on.
Speaker 2:Always enjoyed his commentary and excited to see more of it.
Speaker 1:Paul Enright says in 2021, Mitchell Green from Lead Edge Capital. Of course, Mitchell's a friend of the show. We gotta have him back on again.
Speaker 2:I texted him this morning.
Speaker 1:Get him on soon. He said said to Paul, this year will be remembered for the GPs that sold stock and distributed capital to LPs versus the ones that raised funds called capital and made new investments. Was a wonderful observation. I will never forget. Now, it feels similar in some ways.
Speaker 1:Why not both? Why not distribute capital, sell some positions, and raise some new funds?
Speaker 2:Yeah. I don't know the dynamics of that, but that does feel like the ultimate move is to raise a massive fund at the top. Don't deploy it. Sell a bunch of positions. Get out of the other stuff.
Speaker 2:Basically, just be sitting on a massive cash pile when you reach the trough of disillusionment, and then monetize the the plateau of productivity.
Speaker 1:Deploy into the trough. Into the trough. Well, you saw this chart yesterday. Somehow, like, one of Andreessen slides from a fundraising debt.
Speaker 2:Newcomer, baby. Leaf master. Oh, yeah. He got two So
Speaker 1:a '16 z returned 15,000,000,000 Yeah. Or or or generated 15,000,000,000 in returns, including their recycled fees Yep. And carry in 2021. Yep. That was, you know, biggest year on record by
Speaker 2:Huge.
Speaker 1:Quite a lot. Huge return.
Speaker 2:That's gotta be a lot of Coinbase. And who else got out there?
Speaker 1:Solana, Avalanche. Right? Some of these big
Speaker 2:lot of stuff.
Speaker 1:I don't know.
Speaker 2:Yeah. But Coinbase was a huge position.
Speaker 1:Yeah.
Speaker 2:And, I mean, they deployed in all these different rounds. And then I believe Coinbase went out and was probably distributed at something close to a 100,000,000,000 market cap. So something something there. Andrew Curran has more updates on Elon Musk's reuniting with the Trump administration. Andrew says their friendship is slowly being reforged.
Speaker 2:OpenAI, Anthropic, and Google already have similar agreements in place. OpenAI and Anthropic charge a $1 access fee. Google charges 47¢ because its president is the forty seventh president, right? Isn't that it? That's interesting.
Speaker 2:That's cute. Elon is charging the government 42¢ for access to Grok. This is a Hitchhiker's Guide reference.
Speaker 1:Have you read Finally, a competition for who can say the smallest number.
Speaker 2:It's been a lot
Speaker 7:of who
Speaker 1:can say the biggest number.
Speaker 2:You wanna be on the on the barbell, the extreme ends, charging the least and raising the most. Apparently, that's how business works.
Speaker 1:Most CapEx, least.
Speaker 2:Have you seen The Hitchhiker's Guide to the Galaxy? Have you?
Speaker 3:No. I thought it was just a book.
Speaker 2:They they adapted it into a movie. The movie's pretty good, but the book's probably better. Have you read the book?
Speaker 1:I did read the book at one point. Yeah. The book's great.
Speaker 2:But 42, of course, is the answer to the universe, all things, everything. It's a it's a fun fun reference. The Trump administration will offer artificial intelligence models from Elon Musk's XAI to federal agencies through a new partnership under the agreement with the GSA, which oversees technology procurement. Agencies will get access to models such as Grok4 and a new FAST version called Grok4 FAST. The deal follows a similar agreement with the other folks in the space.
Speaker 2:Automating state government processes is one of the most promising applications for technology, fueling the battle to be the most popular tool for federal workers. All four of the AI companies also each have $200,000,000 contracts with the Defense Department. Sort of interesting that
Speaker 1:All four?
Speaker 2:Yeah. So the government clearly wants an oligopoly here and is like, we're not gonna really pick winners, which is crazy because Dario has been very outspoken about the Trump administration. And Elon's obviously been fully in the Trump administration's camp. Google has been a little bit quieter and has leaned left in the past. OpenAI, Sam Altman was kinda stayed out of politics, but then has been at dinners and was at the was at the inauguration.
Speaker 2:And so you would expect based on, like, the news of how close the Foundation Model Lab CEOs are to Trump, like, just how many photo ops there have been. How many how close have they been? You would expect a massive contract for Elon and a very small contract for Anthropic, and yet they all got $200,000,000 equally. It's interesting. Just like they're clearly focused on having balance amongst the AI foundation.
Speaker 1:I want to see the usage in the government broadly and in the DOD.
Speaker 2:What are people using? I mean
Speaker 1:Everybody gets a $200,000,000 contract. It's unlikely that they're going to be super evenly leveraged. Right?
Speaker 2:Yeah. I wonder if they would use them for specific things. Mean, you would hope that they're Claude Code over there for something, and they're using ChatGPT for knowledge retrieval and stuff. And then they're using Gemini for the things that Gemini is great. Nano Banana.
Speaker 2:I guess the government is just, hey, you don't need to come in for DMV photos anymore.
Speaker 1:$200,000,000 for great memes.
Speaker 2:Gray beams?
Speaker 1:Great memes.
Speaker 2:Oh, great memes. Yes. Exactly. But you would hope that you'd hope that the government's using AI all over the place and they need an actual contract to make sure that it's provisioned and hosted in the proper way. Anyway, fin dot a I, the number one AI agent for customer service, number one in performance benchmarks, number one in competitive bake offs, number one ranking on g two.
Speaker 2:You can start a free trial. Lulu is chiming in about Rick Rubin. She says, I mean, just look at him. Can't media train this. Austin says, the Beastie Boys, Ed Sheeran, Lady Gaga, Red Hot Chili Peppers, The Strokes.
Speaker 2:Rick Rubin has worked with all of them.
Speaker 1:Why is no one talking about Rick Rubin?
Speaker 2:Why is no one talking about Rick Rubin? He's worked with all of them and is considered one of the greatest producers of all time. His process to creativity can be described as looking for clues. He has such a unique aesthetic with that massive
Speaker 1:Always has the red the the blue blockers on.
Speaker 2:Those are blue blockers?
Speaker 1:Yeah. Oh. He's very I feel
Speaker 2:like blue blockers are normally orange orange, but I guess those are just a dark orange that I'm seeing.
Speaker 1:Dark shade. Yeah.
Speaker 2:Okay. Yeah. Well, what was else is going on?
Speaker 1:Scott, I'll just Your post here. Were some accusations. Accusations earlier that somebody was doing a paid post.
Speaker 2:The post was deleted. So it was Mike Isaac at the New York Times picked up a device called a brick, which is a little NFC chip that they see you've everyone's seen this on Instagram, right, where it's supposed to, like,
Speaker 4:brick
Speaker 2:so your that you don't be distracted. And so you have the willpower just once to attach this to the back of your phone. It kind of magnetically attaches and it puts your phone in a do not disturb, don't show me any of the addicting social media apps, turn off everything, let me focus.
Speaker 1:Let me cook.
Speaker 2:Let me cook. Let me lock in. And so apparently, according to the original post, Mike Isaac picked one up off of an Instagram ad and said he actually loves it. It's helping him write. Of course, he writes for The New York Times.
Speaker 2:He needs to lock in and write a lot. He can't be distracted. He's also a prolific poster. Can't be caught on the timeline mid article when he's on deadline.
Speaker 1:That's right.
Speaker 2:So he posted about it. And a
Speaker 1:Another poster. Another poster
Speaker 2:accused him of doing a sponsored post without disclosing
Speaker 1:it. Undisclosed ad.
Speaker 2:And if you know The New York Times' standards and who Mike Isaac is, he's published a book that got turned into a movie or a TV show. Like, he's probably not doing spawn con for some d to c, like, you know, electronics company, the undisclosed. It's just, like, so much risk to so little reward. Like like, the post that of him saying, like, oh, I got this brick, and it's great. Like, it didn't get that many views.
Speaker 2:It's not like brick would be like, oh, yeah. We're gonna sell 10,000 of these things off of this. Let's pay Mike Isaac $100,000 to post this organic content, this fake content.
Speaker 1:He just he likes technology.
Speaker 2:Yeah. Likes People like to post things that they test out.
Speaker 1:Covers consumer tech. Not
Speaker 2:not everything is spawn con.
Speaker 1:But I thought your post was hilarious. You said it's time to come clean responding to this allegation. John said, you need to admit that Brick paid you to dunk on this organic post to drive more impressions. This post is brought to you by Brick.
Speaker 2:I do think there's something funny where you could potentially run some sort of, like, anti astroturfing campaign as a brand where you pay a bunch of of influencers to accuse organic UGC of being paid, and it drums up way more attention. And and so I was riffing on that. But on this show, we don't do undisclosed partnerships. We do disclosed partnerships. That's right.
Speaker 2:It's like our partnership with Adio. Customer relationship magic. Adio is the AI native CRM that builds scales and grows your company to the next level. Get started for free. When we read an ad, you know we're getting paid for that.
Speaker 2:You know we're not doing it for free. We're not doing it for the love of the game. We're doing it
Speaker 1:for the Well, we are doing it for the love of the game.
Speaker 2:We do love enterprise SaaS.
Speaker 1:Everybody loves SaaS more than us.
Speaker 2:That's true. That's true. Let's keep going. There is a lot of stuff going on. This was a really funny post because it gave me a total jump scare.
Speaker 2:I was talking to Brandon about it earlier. Ben Podgursky, sounds like a podcaster's name potentially. Ben says, this is correct, but we should take it to the natural conclusion. He was I guess he was quote tweeting Roon, which we'll get into. But, he says, like the Habsburgs of old, The USA should look for synergistic m and a, not through immigration, but with entire polities.
Speaker 2:In this case, the obvious answer is merge South Korea in as the fifty first to fifty seventh states. It's
Speaker 7:like Obvious.
Speaker 1:Totally obvious.
Speaker 2:The obvious answer to me. When people talk about, you know, growing The United States, they talk about Greenland. They talk about Canada, Mexico, Puerto Rico, the Fiji, like, The US Virgin Islands. Like, there's so many different territories that are, like, more closely aligned with The United States than just going all the way over to Asia and just picking up South Korea. But Ben makes the argument.
Speaker 2:He says merging South Korea in would resolve a wide range of defense, industrial, trade, and demographic crises. South Korea would not have to hem and haw about building their own nuclear shield. They'd be an inviolate part of The United States from The US from day one would have would have massive shipbuilding capacity, vastly improving national defense, and US companies could friction free contribute in the one spot where we genuinely have a technical contribution, small nuclear power systems. The US's relatively healthy demographics could gradually backfill South Korea's catastrophic demographics. Maybe we could even teach them how to have kids again.
Speaker 2:The free flow of labor would allow Korean workers to train US workers without insane visa shenanigans. South Korean would be neither, is neither unduly liberal or or conservative relative to The United States.
Speaker 1:Small couple I like the thought exercise. Couple small potential problems here would place The United States directly on having a shared border with North Korea and be right across the Yellow Sea from China.
Speaker 2:It is such a wild move. But he lays out a thoughtful case.
Speaker 1:It's a cool idea.
Speaker 2:I mean, maybe it's no coincidence that the most respectful TBPN derivative show inspired show is from Korea.
Speaker 1:That's right. Those guys know That says something. Do it. They drink Also, love have you ever had bimbimbap?
Speaker 2:No. I don't know what that is.
Speaker 1:Popular dish in South Korea. It's like rice and protein. It's
Speaker 2:you ever been to South Korea? I have not. I've never been there. Have you?
Speaker 1:I haven't.
Speaker 2:Have you have you ever traveled anywhere in the world?
Speaker 1:Yeah. Where?
Speaker 3:I've been to Ireland.
Speaker 2:Oh, okay. Jobs finished. Moving on. Ireland. World traveler.
Speaker 1:Fuck of the Irish.
Speaker 2:So this is all kicked off by Arun Post. He said, correct me if I'm wrong, but it seems like the theme of the Dan Wang book, who we had on the show, and the general elite consensus now is that industrial process is a technology that lives in the heads of people and that it was a mistake to let so much, quote, unquote, low value industry be offshored due to the tacit loss of process capital. TSMC Arizona, which makes the most complex and valuable industrial production in the world, was a massive success. This was a huge surprise to me. I did not expect the TSMC build out.
Speaker 2:Everyone was saying, like, it's impossible to airlift TSMC, but they wound up producing four nanometer chips at great yields on par with Taiwan merely years after striking ground for the first time. This involved a generous federal subsidy and importing thousands of the great Taiwanese semi, semiconductor experts despite unions trying to quell tyrant Taiwanese immigration and some culture clashes. In The United States, acqui hires of whole teams with process knowledge in their heads is very common. Zuck acquiring some of the greatest talent from other AI labs for massive numbers is just one example of this. Also seen in the full self driving wars between Uber and Google, which was interesting because that is, of course, about the Anthony Lewandowski case that ended in a lawsuit that landed Anthony Lewandowski, I believe, in jail for a little bit, but then he was pardoned, and so he got out.
Speaker 2:But this, it was more, I feel like it was more than just process knowledge in that case because it was specific patents. And I'm wondering how much, how much of a line you can draw between knowing a specific algorithm, knowing a specific, something patentable, an actual process, power process, process knowledge, but, it's certainly an interesting analogy to to dig into. So Tesla plus Apple plus Big Pharma acquires industrial process companies all the time. America is very capital rich, able to levy literally hundreds of billions of dollars for machine intelligence CapEx. We can afford to acquire whole groups of foreign talent for prices that are unheard of to them in their home countries.
Speaker 2:TLDR, aqua hiring foreign process knowledge for massive sums should be one of the primary goals of any reindustrialization effort. Special visa categories should be made for to scoop up whole teams of Sen Gens Best. The raids on the LG battery plants in Georgia are the exact opposite of what we need. Ability to tolerate new arrivals is a technical edge of American capital to be able to assimilate foreign knowledge into domestic industrial processes at a scale nobody else can count. I was I was hearing a story about how the I think what was it?
Speaker 2:The Manus team relocated to Singapore. And there's a world where they relocate to San Francisco.
Speaker 1:Set up in a benchmark office.
Speaker 6:Maybe. I mean
Speaker 1:A little satellite office.
Speaker 2:Yeah. I mean, there's there's there's something there. I don't know how how important their process knowledge is, but certainly, it worked for TSMC. I don't know how many more of those projects need to be done. Taiwan seems to be in a uniquely precarious position, whereas a lot of the South Korea tensions seem to be a lot lower.
Speaker 2:Like, there's less geopolitical risk in South in South Korea. So there's less of a we need to move SK Hynix to America. But, Tyler, do you have any thoughts on this post? I'm sure you saw it. What'd you think?
Speaker 3:Yeah. I think it makes a lot of sense. I think, politically, it might be hard to do this. But I've seen a lot of similar arguments just for, like, normal AI researchers in China. Yeah.
Speaker 3:We should basically have some kind of, like, visa that just, like, imports them, like, instantly. And then it's like the I I assume the salaries over there are not comparable to, like, MSL Yeah. Level, like, $100,000,000.
Speaker 2:Totally. This is the real Makes a lot of sense. This is the real AI paper clipping. It's not that you get turned into a paper clip. It's that you get operation paper clipped into a different country, and we need to avoid our best getting paperclip to a different country, and we need to be potentially paper clipping other national champions.
Speaker 2:I wonder if there's something to do in in AI in not just AI, but in the power around solar because we've talked to Casey Hammer about this. It feels like the vast majority of the cheapest solar panels come from China. We're maybe not we don't have a national champion there yet. We're not catching up. But fortunately, we have a guest who might be able to get us up to speed on this.
Speaker 2:We have Deleon Asperuhov from Founders Fund. He is back from paternity leave. He's in the restroom waiting room, and he will be joining us in the TVPN Ultra Dome in just a minute. Let's bring Delien to ask who we have.
Speaker 1:How are doing? Yes.
Speaker 2:Good to see you. What up, boys?
Speaker 6:Daddy's home. Daddy's home, baby.
Speaker 2:Oh, there we go.
Speaker 4:You got my twag.
Speaker 2:I love that. How how is it being a a father of multiple now?
Speaker 6:You know, I feel like you blow up your life once and then after that, you kinda you guys should figure it out where it's just like, yeah, got the infrastructure and that's why God gave you multiple hands is, you know, one baby per hand.
Speaker 2:Yes. But everything's good. Everyone's happy and healthy. Everyone's back to normal?
Speaker 6:Oh, yeah. Oh, yeah. I mean, she's still, like, you know, of month old, so, yeah, it still requires, you know, some work. But, yeah, in the grand scheme of things, happy, healthy, very grateful that, you know, all things pretty smooth.
Speaker 1:Yeah. Well, once once once she gets to two months, you know, it's it's cake.
Speaker 2:You know?
Speaker 6:Put her
Speaker 2:to work. She can come on she can come on TVPN. We've gotten you through. We've we've had three members of your household live on the show. We need the fourth.
Speaker 6:It's true.
Speaker 2:Well, actually Not letting your brother too. So we
Speaker 6:That's true. Five.
Speaker 2:The Asperuhov clan is dominating
Speaker 6:10 Asperuhovs on TVCN
Speaker 2:by the end
Speaker 6:of a decade.
Speaker 2:Yeah. What your parents up to next week? We'll get them on. Yeah. Really quickly, I I wanna get a general update on what's going on in your world, but I would be interested to hear your response to this this debate that was going on on this on the timeline yesterday from Roon about TSMC Arizona being a good a good example of airlifting industrial process from one place to another.
Speaker 2:He kind of ties it to the acquisition of whole teams at MSL, America having being capital rich, being able to actually afford to go and say, here's a $100,000,000 offer to some great researcher come to America potentially. And it gets into the obviously, there's a whole bunch of immigration stuff going on and cultural stuff. But did you have a reaction to that? Are there other industries where it seems like we need sort of a TSMC Arizona like project where we're just going to an international company and saying, do what you do over there here?
Speaker 6:Yeah. I feel like a lot of the reason people are discussing this so much on the timeline lately is because of Dan Wang's, new book, Breakneck, where, you know, breaks down basically the, like at least his argument is that, you know, China is the engineering society. We're the, you know, sort of lawyer society, and everything basically, you know, sort of stems from there, which I think is probably a little bit you know, it it I like a lot of parts of his book. That actually particular argument is probably one of my, like, less favorite, you know, sort of parts. Partially, because, look.
Speaker 6:There are things that China is obviously, you know, sort of very good at. I think I it was, you know, hearing your last guest talking about, like, you know, mass production of solar panels. They're obviously, like, phenomenal at that type of, like, commodity cost curve, relatively complex, but not deeply complex type of manufacturing. Right? Like, they've stepped up from, like, you know, the early days of Shanghai being, you know, sort of toys and knickknacks, etcetera.
Speaker 6:Obviously, now into, like, you know, mass consumer electronics, unit tree is obviously, like, by far the best, you know, sort of humanoid robotics company, and then obviously in solar panels. But there's, you know, I think some limitations to their approach. Like, you know, when I think about it from, like, the aerospace perspective, look, a Falcon nine rocket landed for the first time a decade ago now. The Chinese basically, like, stole the IP and planes that clearly have something that is, like, the equivalent to a Falcon nine, and they're still basically doing the, like, early days of, like, you know, Starhopper, which was, you know, back or grasshopper, I mean, which is, the SpaceX project in, like, 02/2013, I wanna say. Mhmm.
Speaker 6:They was just doing the suborbital little, like, hop ups and downs to practice for Falcon nine. And so I think I do think they, like, have this limitation when it comes to, like, deep, deep systems engineering and requiring some creativity that they, like, miss out on, and I think that's why you've seen them partially succeed in semis. Right? They're good at the, like, you know, sort of last generation of semis and, you know, sort of reassuring that to China. But I don't think you've really seen any progress on them, you know, sort of, you know, domesticating cutting edge, you know, sort of semis.
Speaker 6:TSMC Arizona, you know, is at least a very subscale version, you know, of, you know, being of being able to do actually some amount of cutting edge domestically. And I do think it, you know, sort of shows that there's just some breakdowns in Dan Wang's argument where, like, we are able to import that type of, like, everybody calls it, like, industrial process or process knowledge Yeah. In The United States and even develop it ourselves. Right? The other area that obviously, like, we do quite well on, you know, it's not the perfect company, but, like, look, like, the the, you 99% of commercial airliners in the world are still developed by the West.
Speaker 6:Right? You know, Boeing and Airbus and continue to be, and it's not obvious that China has really been able to displace that. You know, the only other areas that I think about actively that you need to be you know, sort of reshorred some amount of it is like, hey. If we want to, you know, to compete against China and Taiwan, it is gonna be almost certainly like a mass scale production, you know, sort of game more so than anything else. And so when you think about, like, mass scale drone manufacturing, mass scale metal cutting, mass scale, like, fighter jet production, that's something that, like, we're obviously behind on even, the munitions.
Speaker 6:Right? Like, you know, Ukraine has shown that, like, you know, artillery is back to being relevant again. It was not relevant at all in any of the, like, Middle East conflicts. But now with, like, but now with, like, continuous, you know, Internet coverage and continuous, basically, you know, spy satellites, you know, you could actually justify, basically, just, like, you know, anytime that your, you know, enemy makes a move, you just artillery shell the crap out of them, and that's actually, like, back to being, you know, sort
Speaker 4:of relevant on the
Speaker 6:battlefield. And they are far better. They have these, like, you know, lights off, you know, artillery shell production facilities that basically just, are twenty four seven fully roboticized, you know, produce, you know, way more than The United States can. And so I don't know if those are gonna be necessarily, like, the equivalent of TSMC where you're, like, taking somebody's preexisting industry and like dropping it here and that like there's not an obvious place to go import from basically Mhmm. For that type of stuff.
Speaker 6:I think we kind of saw that on our own and obviously Founders Fund were making some investments, you know, in that space. Obviously, you know Is that
Speaker 1:true too that in in China, like, yes, there there's flagship lights out factories that are truly cutting edge, the kind of thing we just don't really have here in The United States, but yet still the vast majority of manufacturing in China is just a lot of labor, like, you know, assembling cheap components?
Speaker 6:It shifted. I think that's like a, you know, sort of decade out or a decade old sort of, you know, sort of thinking where it's like, yeah. The reason that China won for a long time was because of just, the labor arm and cheap cost of labor. When you talk to people
Speaker 1:But but I'm just saying, like, with with the truce, like, we can't buy the like, when you talk about China's advantage of just being able to produce overwhelming mass, right, across every different sector, there's certain key areas where they are just light years ahead, and then it's they're still getting this massive benefit of even the components that get made that go to the lights out factory to be assembled, you know, but with robotics.
Speaker 6:Like, there's Yeah.
Speaker 4:I mean, when I
Speaker 6:think about, like, their advantages in, like, your drone manufacturing, it's mostly due to automated facilities. Right? Like, it's not because they, like, you know, have a bunch of, like, you know, sort of mass assembly, super precise, small hand, you know, sort of laborers or something like that. It actually is because when you think about, like, the PCB boards, largely, basically, fully automated. The, like, you know, propellers that they make, largely fully automated.
Speaker 6:The battery lines, largely fully automated. And so, yeah, I I just like, I when I think about, like, the edge that they have in drones, yeah, there's maybe some amount that's, you know, sort of this, like, assembly edge, but, like, I'd put that at, like, five to 10% of the edge. Like, 90 of the edge is that they, like, have such deep process knowledge across all these individual, like, different, you know, basically, like, subcomponent sectors and a lot those
Speaker 1:co location.
Speaker 4:You know, are fully. Say again?
Speaker 1:Co like, co location effectively.
Speaker 6:Yeah. And the collocation is just, you know, sort of wild. Right? This is also there's sometimes the problem with, like, this stuff in America when we, like, try and reimport it. It's like, you know, the CHIPS Act, one of the biggest battles was, like, the, you know, the size of funding required support from a broad set of senators and congressmen.
Speaker 6:But then by default, they want, you know, sort of jobs everywhere, and so it's been more difficult to fully, you know, sort of centralize all of the, like, you know, reshoring of semis in The United States into a, you know, sort of single location. Like, you know, Arizona is definitely you know, it's been impressive, but, like, ideally, it's not just the, like, fab. At some point, you wanna do, like, lithography there and a bunch of other parts of semis centralized there. But, like, in The United States, we actually kind of have a bit of a chips hub. Obviously, still some amount in Silicon Valley.
Speaker 6:Austin's become, like, a, you know, sort of really big area. Ideally, those would all be in, you know, sort of the same place rather than so spread out. And so it is harder given that we're not like China that's willing to sort of like put their finger on a scale to a single, you know, city and just be like, you are going to become like the guitar producing city and everything is going to be, you know, sort of based there. Mhmm. We don't we don't do that.
Speaker 1:Need a new guitar executive order.
Speaker 2:It does feel like we're starting to do that with the neo clouds, with some of the big data center build outs, Abilene, Texas. Like, we're we're marshaling capital
Speaker 1:by individual deal makers going and talking to these local governments and figuring out Yeah. Can we do this here? Okay. No. I'm gonna go across the state line over here.
Speaker 1:I'll do it over here. Sure.
Speaker 6:They're chasing Yeah. We're just much less top down coordinated. But, like, at the same time, I think you're starting to see, like, the faults and the brakes in that system, you know, in China. And Dan Wang talks about this user as well where it's like, look. You know, they clearly still excel in certain areas, but they've also, mean, like, think about it even over the course of time that I've been a founder's fund.
Speaker 6:In 02/2021, like, the number of unicorns being minted in China was starting to approach, you know, The United States, and there's, like, some real risk and fear that it's going to supersede. Now four and a half years later, you know, the Chinese venture capital ecosystem is effectively, you know, totally negligible. And so, you know, I do think that they've, you know, done a decent job on, like, you know, sort of prioritizing the industries that matter the most, but also their, like, capital markets are, like, you know, sort of way worse than they were, you know, sort of four and a half years ago. Mhmm. You know, they have reimported a bunch of, you know, sort of Chinese nationals from The United States back as scientists into China, you know, pretty good, but they've also, like, lost, you know, something on the order of, I forget what the number was, like, 25,000 Chinese millionaires, like, are, you know, exporting their capital.
Speaker 2:They lost the Manus team. The Manus team's over in Singapore now. So they might be working on the Benchmark office soon.
Speaker 1:Yeah. Well, speaking speaking of Benchmark, I thought it was interesting. Actually, Bill Gurley posted earlier game recognized game, and it was a quote from the CEO of Xiaomi who said, we bought three Tesla Model Ys for disassembly and research inside Xiaomi earlier this year. What a great vehicle. And of course, they're announcing like effectively a copy cap.
Speaker 1:But it's insane to just say
Speaker 2:Game recognized game. It's game copies game.
Speaker 1:Yeah. Just it's saying saying the quiet part out loud is wild. But
Speaker 6:Yeah. Recognizing the hype. Since I was last I was last on was when I have been poking the benchmark bear literally, basically, like, since, like, you know, week or two after joining Founders Fund. And I remember early on, I got a call from, like, one you know, one of the partners at FF being, hey, like, look, just so you know, you're definitely making some enemies over there. And I was, like, okay.
Speaker 6:And then they were, but I wouldn't necessarily like, it doesn't feel right to tell you to, like, take these things down because, like, at the end of the day, we are a founders fund, and you are critiquing them for firing founders, and that is our whole ethos. So just know that what you're doing is definitely creating enemies and, you know, is creating risk. So, anyways, I've been poking the bear for, like, five and a half years. And then Five and so glorious to have, like, finally an official response from Chetan, and it was just, like it was an alley oop. It was the perfect response.
Speaker 6:It was, how dare you not basically, like, you know you know, control, you know, sort of a company's choice and, you know, sort of, you know, capital provider. Like, why are you letting them take Chinese capital? And I was like, oh my god. This is perfect. This is like the exactly.
Speaker 6:Don't even realize how wrong you are because you don't even realize that what you're saying is why aren't you, the venture investor, telling the founder to run their fucking company and that's your critique. And I remember reading it and I was technically supposed to be on this, like, one week, like, Twitter or whatever, like, you know, detox. But then, you know
Speaker 1:You're on on Twitter being like, I'm supposed to be on a detox right now, but I'm about to go on a bender.
Speaker 6:I know. I was like, I got, like, five texts from friends being like, how have you not responded to this yet? And I read it, and I was like, oh my god. Yeah. Fuck this detox, bro.
Speaker 6:Like, definitely fucking going in. And it was just like, it is, I think, either my top or second best day on Twitter in terms of joy provided for me, like like, a true, like, existential, like, identity level, like Yeah. As much as, like, a child's birth. I mean, like, is a level of joy.
Speaker 2:Zero user regretted seconds. Like, the least regret of
Speaker 1:You would compare it to, yeah, holding your daughter. Stop it, Jordan.
Speaker 6:It is, like, it is, yeah, I think roughly equivalent.
Speaker 2:No. The only other day Yeah.
Speaker 6:That I've had that was that good was the, like, the day after the FTX blowup, and I was the first one to really find and screenshot the Sequoia blog post about him playing League of Legends and getting that out. And it was just that was just that was incredible. It was just it was it was like, I got to break news. Yeah. I got to, like, poke fun at a competitor.
Speaker 6:I forced them to, like, have a response to all their LPs about it. They had to, like, take down that blog post. It was just like, ugh. That was, like, truly.
Speaker 2:Yeah. Weren't you also better than SPF at at at League of Legends?
Speaker 6:Okay. I mean, like, AOC was better than AOC. Yeah. At, like, League of Legends. So, like, the bar is very low.
Speaker 6:I just like
Speaker 1:But at least you you at least you gotta respect some of some of the the the highly convicted investments in Anthropic, Robin Hood, you know. He he was a gambler, but, you know.
Speaker 6:Yeah. Dude, he was a good gambler. I mean, like, he just, you know, played the line a little too hard, but, like, yeah, there's a world where, like, yeah, I forget who was telling me this. Somebody that was basically responsible for, like, brokering the, like, FTX, like, bankruptcy, etcetera. So, like, you know, Wall Street, whatever, you know, sort of type.
Speaker 6:And they basically were like, yeah. Like, if he had just lasted another, like, six months, the book, like, would have been totally fine. He wouldn't have got liquidated. And today, if he just held these investments that he made from, like, back then, he would be richer than Elon. Like, he would actually just be the richest person on the planet.
Speaker 2:Wait. What?
Speaker 6:And so it's just it's wild to think that he was, like, on a knife's edge.
Speaker 1:Richer than Elon. I don't
Speaker 2:how how
Speaker 1:is he putting up?
Speaker 2:I I haven't seen that actual number, I mean, I guess it makes sense with like the various positions he had. Yeah.
Speaker 6:I think it was like assuming that FTX continued to go. It's ever his Yeah. And to one. Possible scenario. There were like I mean, again, like these some
Speaker 4:of those, you know, moves were
Speaker 6:just like well, like it's like the anthropic position. I forget. It's, like, now, like, $25,000,000,000.
Speaker 2:Mean, the guy was insane. Yeah. I I don't know if you remember, but he went on Tyler Cowen's show, conversation with Tyler, and Tyler asks him, would you flip a coin where there was a 50% chance of humanity being completely wiped out or 50% chance that you double the prosperity of humanity, but it's fifty one forty nine, the good outcome. And he was like, I would flip it a 100 times in a row. And it's like, that's insane.
Speaker 2:Like, that's not how these things work. But he was like, the expected value is positive, so I must take the the EV positive bet. And it's like, no. No. No.
Speaker 2:No. No. Please do not flip that coin. Like, you're gonna wipe us all out. It's like 99%
Speaker 6:of best part of SPF having to go to jail is Dustin Moskovitz finally shutting the fuck up about EA. Thank god.
Speaker 2:You know? You you you
Speaker 6:can stop talking about new only net EV positive things.
Speaker 2:Yeah. It's wild. Well, speaking of net EV positive things, is it no longer net EV positive to invest in a company that's growing revenue triple, triple, double, double, double? Because I have a lot of friends that have been doing that. They previously, they were saying, I'm about to IPO my company, but now they're thinking of shutting down because Delian's a According to the best venture capitalist
Speaker 1:Delian's a fan of zero zero zero zero zero zero zero x.
Speaker 2:But, yes, I want your reaction to the state of the software markets. It's sort of an Ev, take a look.
Speaker 1:Have you have you done a pure SaaS deal this year?
Speaker 6:I did just do like a like basically pure vertical AI SaaS Let's see state.
Speaker 2:Oh, even knew that. Couldn't resist. You had to do one. He had to do
Speaker 6:Can't help myself.
Speaker 1:SaaS. Underrated. Criminally underrated.
Speaker 2:That's true.
Speaker 1:You literally talk to every VC. It's like you you just go go invest in the foundation model layer. Go invest in hard tech. Leave this leave the vertical SaaS for for me.
Speaker 2:That's great. Yeah. But, yeah, overall thoughts on the triple, triple, double, double, double being being, like, out of out of, like, out of date with how fast the foundation model companies are growing. I had this interesting thought that it's like, you could be running a janitorial company. And if you have a foundation model as a client and they're growing 10 x, you're gonna be cleaning 10 times as many toilets.
Speaker 2:Your revenue is gonna be 10 x ing because they're 10 x ing. And if you just, like, are a barnacle on the side of the massive whale, you're gonna see massive growth. And I wonder if, like, that like, what what that means because
Speaker 1:We're investing in the barnacle economy.
Speaker 2:The barnacle economy. But in general, like, you know, what what what are your thoughts?
Speaker 6:Yeah. I think with this stuff all the time from, like, the, you know, sort of space perspective too where, like, you know, you can get diluted into this world where it's, the space economy is growing. But if it's only a bunch of other space startups, like, you know, trading revenue back and forth, you can, like, prop that up for, like, quite some time, trading a dollar back and forth, but at some point, it needs to connect into, like, the rest of the ecosystem. Right? And so, you know, I think there was, you know, some, you know, memes and jokes going around, you know, sort of yesterday about the, like, NVIDIA, Oracle, OpenAI, whatever, 100,000,000,000 investment, etcetera.
Speaker 6:And it's literally just, like, the same dollar effectively going, you know, sort of in circles over and over again. Yes. And that can, you know, sort of work, but, like, that can also blow up. And we just and I think we talked about this in maybe even in the last interview. It's like, you have this crazy dynamic where, you know, the percent of GDP being invested in this category is, like, you know, the equivalent of, like, at the railroad bubble, etcetera.
Speaker 6:Yeah. But a lot of the companies that are doing this have, like, these cash flows from other very large businesses that are driving towards this. Right? You know, Zuckerberg, I think, gave the quote where he's like, look. I would prefer to, like, you know, waste hundreds of billions of dollars Misspend
Speaker 1:a couple 100,000,000,000.
Speaker 6:Yeah. Spend a couple 100,000,000,000 rather than lose the machine god race. And so it's just, like, the level of it's just it's like nothing we've ever seen before in terms of, like, the super cycle of, you know, sort of capital going into this. Maybe that has the ROI, maybe it doesn't. When I think about it from, like, the investor perspective, I mostly think about it from twofold, which is, like, how you should analyze any business, which is basically, like, quality of revenue and, like, you know you know, in terms of, like, durability of that revenue and then, you know, the competition, e g, the margins, you know, sort of on that revenue.
Speaker 6:And so I'll maybe provide, like, one example that I think is very strong and one that I think is, like, a little weaker. You guys have had, you know, sort of Melissa, you know, John, know, sort of wife on for Cybernetic Labs. I think of that as a great example where, you know, she has really great revenue growth, and I believe it to be very durable in that most of her revenue growth is coming from things that are totally decoupled Yes. From the, like, insanity of, like, you know, NVIDIA, OpenAI, etcetera, circles. Like, it literally is janitorial companies, right, effectively, HVAC, plumbers, etcetera.
Speaker 6:Those people's revenue, their growth, etcetera, like, is completely, like, economically decoupled. And so in some ways, then Melissa's revenue is completely economically decoupled from the entire AI hype wave. And so when I think about, like, oh, okay. Let's say, imagine a world where all of a sudden, like, you know, the mag seven decide to, like, you know, reduce deployment into data centers and, like, foundation model training by a 100 x years or next year. Which companies are affected by that?
Speaker 6:Okay. Well, like, CoreWeave, I imagine, is, like, going to be affected by that. Oracle, definitely going to be affected by that. OpenAI, Anthropic, definitely going to be, you know, sort of affected by that.
Speaker 1:Yeah. You look at you look at how many companies are now indexed to OpenAI. Right? You have Broadcom, Oracle, Soft Bank, Core Weave. Like, these are
Speaker 2:There's a lot of them.
Speaker 1:Yeah. A lot
Speaker 3:of them.
Speaker 6:And I think as an investor, you have to take a little bit of, like, a bifurcated strategy where it's like it's I think it's a generational index. And this is you know, if there's one thing that I admire about Peter the most is he just does such a great job of even though he has lots of biases, particular, you know, preferences, etcetera, on one sector or another, founder or another, he's very willing to vary, if you like, soberly analyze the just, like, broad macro world and identify, hey. Even if, like, I don't love, you know, whatever boring SaaS software, etcetera, or, like, the AI stuff, you know, you know, may not be real, still be willing to, like, analyze it from a purely investment perspective and, like, take it on. And, obviously, we've, you know, done decent user checks into this field. Whereas, like, I literally, like, find my biases so strong that I can't even, like I can't even motivate myself to even spend time on it, you know, to, like, you know, analyze it as an investor.
Speaker 6:So that's where I think about the, you know, sort of, you know, durability perspective, and then there's, like, the margin, you know, sort of perspective, which is, like, how much, you know, competition is there. Right? You're seeing, obviously, in these foundation models where just, like, the token cost continues go down, the margin never seems to really be improving. And a part of it like, there's a bunch of, you know, companies that in capital that's going into this. And, you know, some of the, like, let's say, like, AI for, you know, whatever, you know, coding, it has maybe a bit of a similar dynamic where it is, like, you know, a a bunch of companies that are working on it, the foundation model's working on it, etcetera.
Speaker 6:I don't think there's gonna be, like, 15 AI for plumber, you know, sort of companies. Right? And so this is where Melissa both has this, like, great durability and great margins. And so I think you have to take this kind of bifurcated approach as an investor, which is both, like, invest into the, like, index effectively. Now our approach has generally been invest mostly into the thing driving the index, e g OpenAI.
Speaker 6:Mhmm. And then you're, like, you know, sort of counter strategy is invest in things that are totally decoupled from the index and, like, super far away, and then basically, like, nothing, you know, that much in, like, the, you know, sort of messy middle in between. And to go back to, like, the, you know, whatever, you know, the three three two or whatever it was, I do think that in both categories, yeah, I do think there's some merit to it where it's like, the growth rates and EV growth in these companies just do look very fundamentally different, and the bar is definitely higher. It's just been like I've been like, it's been very remarkable to watch Melissa and her company grow at a rate that just like yeah. It's I I just like I you know, it if you had told me, you know, write a distribution of where you think Melissa's revenue growth is going to be, like, when she, like, started the company, this is in, like, the ninety ninth percentile.
Speaker 6:You know? And and I think it just speaks to how much of a wave this can be, and I think Peter talks about this where it's like the Internet wave happened, but then it still took, like, twenty years to get integrate Internet integrated into the rest of the economy. I think you're to see the same thing, you know, sort of here where, like, there might be not as bad of, like, an AI bubble crash, but the, like, implementation period still may take might be more accelerated because just generally society is moving faster, but it may take ten years. And it's companies like Melissa that are getting it integrated into, the broader broader society.
Speaker 2:Have you seen any companies where they've done a good job? They're still in founder mode. They're they're they they've grown like a base of business that then when the AI story came, they could actually act they could act on that in a way that it wasn't like they're a public company, and they need to go through this massive transformation of how they bill, but they can just add AI on top, and then they're actually growing new quality revenue. Are you are you feeling that at all? What are you pointing to?
Speaker 6:Let me figure this out. Sorry. I'm having to
Speaker 2:Oh, yes. Yes. Yes. Ramp.
Speaker 6:Okay. There we go.
Speaker 2:There we go. Yes. Yes.
Speaker 1:There we go.
Speaker 6:That is really hard.
Speaker 2:Com. There you go.
Speaker 6:Ramp.com. Yeah. Great company that has done a, you know, great job of, look. They obviously, like, were growing very well before any of the you know, AI boom. Yep.
Speaker 6:But, like, I just remember this very particular I think they've announced this now, I'm not, like, revealing anything. Right? They have their, like, you know, AI agent, you know, basically product. But I do remember the board meeting where Kareem and crew presented, like, the vision for it. And I was like, oh, this is, like, what a very competent but, like, larger startup that is still fast moving, knows how to adopt things, does in this world where it's basically like, yeah.
Speaker 6:Like, look. We have a bunch of people that obviously use what is a very, like, beautiful interface that automates a lot of, like, you know, finance work, but it still requires people to go in, set up these rules engines, do some amount of tagging, etcetera. We're just going to, like, basically start to offer for, you know, some of our beta customers that are gonna try this out. We're just gonna, like, effectively screen record what they're doing and start training AIs on that. Mhmm.
Speaker 6:And it's like, I just can't imagine, like, both if you're a net new company being like, I'm gonna make AI agents for the CFO suite. It's like, well, but there's, a whole set of things you have to do. You have to have, like, the corporate card. You have to have travel expenses. You have to have this expense policy, accounting, integrations into the ERPs, integrations into, like, their tax it's like all these systems that you need to build that are, like, more web two point o or whatever, you know, sort of systems.
Speaker 6:Yep. And only when you have that and you have a ton of usage on it, like, can you then actually start to train the, like, AI agents on top of that usage. Right? And so I do really I've admired that a lot in ramp, and so that's probably the one portfolio company that I've seen the strongest in. And then the second is this other portfolio company of mine, Sword Health, that, you know, had been doing this, basically, you know, AI sort of physical, you know, sort of therapist, but most of it was that, basically, you had these, like, sensors that were strapped on your body.
Speaker 6:You would go through your physical therapy routine, and then they, like, PT would basically then review your movement data, etcetera, and, like, you message back and forth with you and adjust it. That was a very obvious area where, like, they literally had years and years of training data sets on how their digital PTs were basically interfacing with patients. They basically, like, significantly up the ratio. Forget the exact number, but, like, my guess would be something on the order of, like I remember when the company started, it was, like, 12 patients per PT. I remember by, like, 2022, it was, like, one to a 100, and I think now it's something on the order of, like, one to a thousand.
Speaker 6:And it is largely, like, the, like, you know, AI, you know, sort of wave that has enabled them to do that, and especially because it's, this text back and forth literally with, like, you know, sort of physical therapist. But, again, if you were starting from scratch, being like, I'm gonna make AI agents for physical therapy, it's like, kinda hard to do, like, just that. The physical sensors, the distribution with, like, Fortune 500 health systems, etcetera.
Speaker 1:Yeah. Yeah. We had a company on, I a couple days ago called Filevine that raised $400,000,000. They were, for ten years, creating just workflows for different
Speaker 2:law for law firms.
Speaker 1:SaaS for law firms. They have all the workflows, time tracking, conflict checks, etcetera, document storage. Now they can just add AI in a bunch places. And maybe they don't have their own foundation model, but, like, maybe Claude or OpenAI models become good enough. Right?
Speaker 1:Yeah.
Speaker 2:Give us an update on on space, defense tech, stuff that's happening in DC. It seems like there's a ton of deals there, but they're two orders of magnitude smaller than the AI sometimes. It's like a billion dollar deal for Palantir in The UK. And people are like, I I just heard about a $100,000,000,000 deal over here from the video, but there's some amazing stuff happening. What's been top of mind for you in space or defense or DC generally?
Speaker 6:Yeah. I mean, the you know, sort of big macro story for the next five days is it seems like we're very likely headed a, you know, sort of government shutdown. I think we've, you know, talked about this in some of the prior, you know, sort of appearances. Yeah. There's gonna be a lot of people furloughed, RIP.
Speaker 6:Yeah. You know, we've talked about this in prior, I think.
Speaker 1:Can you imagine if we had a a precedent like that in the private markets where, like, companies just routinely, like Yeah. We're out of out of cash. We're talking with the board. But Everyone's furloughed. Everyone's furloughed.
Speaker 1:Just probably it'll be, like, five days, maybe, like, forty days,
Speaker 2:you know, whatever.
Speaker 6:Imagine if it's close to yeah. CGPing coming out and be like, oh, yeah. Sorry. Like, we couldn't, like, you know, with my finance minister agree on a budget, and so we're shutting down the CGP for, like you know? What?
Speaker 6:It's just like it's so funny to me that, like, they so badly want to assert America, and our system still has us regularly shutting down for extended periods of time, unable to find the most basic facts of, like, what should we be even, like, prioritizing and allocating towards, and we still kick their ass. We have the best capital markets. We have the best nation, the best freedom, the best fucking technology in almost every single area.
Speaker 1:If you're a career politician, it must feel so good to shut the government down for
Speaker 6:my god. I just what I feel bad is, like, now that I we're so like, Varda, so deeply integrated in all these government programs, like, some amount of back pay. But, but, yeah, the reason we're marching towards it is, like, look. We've gotten into this world where, like you know, I think it's something like four of the last twenty years, we've actually passed, like, our budget on time. So the fiscal year for the government basically ends, you know, basically September every year.
Speaker 6:We effectively for many, many of the last twenty years, we never end up, you know, basically actually figuring out what the budget should be, so we go into these continuing resolutions. But when you're in that state and you're doing everything so last minute, it means much more regularly we have shutdowns, so shutdowns are happening, you know, sort of more often. And so this year, it's the these are Dems and Republicans going back and forth on, like, you know, basically, how much should be based just off the big beautiful bill, you know, how much, you know, should the, you know, sort of Dems have to, you know, sort of fold on, you know, some of their asks around you know, a lot of it is, like, health care spending, you know, basically related.
Speaker 2:Mhmm.
Speaker 6:And at least right now, the, like, tone in DC is, like, there isn't an obvious, like, you know you know, going to be a path forward over the next week or so. And then speaker Johnson isn't even necessarily, like, releasing a schedule on when he's gonna call everybody back to even, you know, sort of, you know, fix it. So it's an interesting place where, you know, CRs and shutdowns generally favor incumbents. Right? And so but what's interesting, though, this year is, like, now a bunch of the net new tech companies are kind of the incumbents.
Speaker 6:Right? And so, you know, if you were enrolled in '20, whatever, '18, government shutdown pretty painful. If you're, like, enrolled in 2025, yeah, it's I mean, you'd prefer for, you know, budgets to keep going, but, like, you can actually probably even continue to beat plan, etcetera, even with, you know, sort of government shutdown. And so mostly what it makes it painful for is some of the, like, you know, next gen, you know, companies that have been starting, like, in the last year or two really, you know, sort of painful. So at least as somebody that's closer to an incumbent now rather than a two year old company, I don't mind the government shutdown, you know, so that much.
Speaker 6:If anything, it kinda gives me, like, more room to, like, you know, get away from some of the, you know, sort of seed series a, series b companies. So, Mr. Politician, shut down away and we'll keep flying capsules.
Speaker 1:It's also if the government shuts down, then if it restarts, it'd be a bullish catalyst. Send us to new all time highs.
Speaker 2:Close us out with a white pill. What's the what's the best new development happening in defense tech space? Any positive news aside from
Speaker 1:the government stuff? The shutdown's kinda disappointing. Will you be buying the UniTree IPO?
Speaker 6:That's depressing. You know, I'll I'll Wait.
Speaker 1:No. Here here's what's depressing is it's going out at 7,000,000,000. That's low If they're the lead if they're the leader
Speaker 2:Yeah.
Speaker 1:And the and the market says this is a $7,000,000,000 company and we have companies that are worth 40,000,000,000. If you look at Tesla, what what premium does does Optimus give to Tesla? We're clearly valuing the potential of humanoids more than the at least the the Hong Kong Stock Exchange.
Speaker 6:This is kinda related to, like, how I think you could use unit tree for peace, where if I were to provide, like, you know, sort of the analogy, what Apple is to Foxconn, you ideally need some US company to be to unit tree, where it's like both the US government and the CCP are not happy with the Apple Foxconn relationship. Right? The US government is not happy that Apple does so much of its manufacturing with our primary adversary and would like them to ideally push to relocate more of that to more allied nations like, you know, Vietnam, India, etcetera, which they're starting to do and do more of that in The United States. The CCP is not particularly happy that Foxconn puts in all this work, technology, process, etcetera, ships out these iPhones, and then that just ships a ton of profit dollars over to the largest company that's owned by their adversary. And so both sides are super unhappy with the relationship, and yet the relationship has now persisted for, like, over twenty plus years.
Speaker 6:Right? And so I think there is the downside case of Unitree succeeds, you know, a lot in China and just makes, like, humanoid, like, you know, soldiers that, like, end up, you know, invading Taiwan. That's, like, the downside scenario. The upside scenario is Unitree becomes a contract manufacturer, but, like, an American company has way better foundation model, AI controls, you know, sort of robot, whatever operating system in design and uses Unitree basically as, like, the manufacturer. And then you basically get into this, like, unhappy wedding on both sides in a way that actually makes it even less likely to basically try to actually
Speaker 1:Only only downside is a sci fi scenario where there's a backdoor into the, you know, millions of humanoids that get deployed into The US.
Speaker 2:And Yeah. I
Speaker 1:mean Yeah. But but my iPhone's not gonna get up at night and stab me in the chest.
Speaker 2:You know?
Speaker 6:I mean, it could explode, and it's in your fucking you know? I mean, like, look. Like, what you you thought the Hamas fucking, like, you know, full of pagers were bad? Let me fucking tell you. There's, like, a little bomb in each of these, and, like, you can explode, like, just government officials.
Speaker 6:You know? Oof. Could be could be tough.
Speaker 2:Yeah. I asked for a white pill. That was
Speaker 1:And I got a black pill.
Speaker 2:Close, but we'll get you next time, Dalian. Thank you so much for hopping on, taking the time.
Speaker 1:To you. Congratulations. Congrats on the little one.
Speaker 2:Will talk to you soon.
Speaker 6:Later, boys.
Speaker 2:Heard him talk about Eight Sleep. You can enjoy an Eight Sleep if you go to 8sleep.com. Get a pod five, a five year warranty, thirty night risk free trial, free returns, free shipping.
Speaker 1:How did we do last night, Joe?
Speaker 2:I got cooked. Got a 78.
Speaker 1:I got a 70
Speaker 2:I got a 73, so you beat me.
Speaker 1:Okay. I got a 73. There we
Speaker 2:go. I'm back. Our next guest is from Flock Safety, a founder's fund portfolio company, actually. We have Garrett Langley. I'm very excited to meet him from the Rooster And Waiting Room.
Speaker 2:Garrett, how are doing?
Speaker 1:What's happening?
Speaker 2:I'll let you take the intro.
Speaker 1:Welcome to the show.
Speaker 4:Good. How are you?
Speaker 1:Doing great. Been we've been super excited to have you on first time. Why don't you give a quick introduction for anybody that's been living under a rock?
Speaker 4:Yeah. Or hiding under a rock, maybe. But yeah. Garrett Langley started a company called Flock Safety with a couple friends eight years ago, and we catch bad guys. We will help local law enforcement make just around 700,000 arrests across the country this year.
Speaker 4:All violent, nonviolent crimes.
Speaker 1:What was the, what year how quickly what was the time to first arrest?
Speaker 4:Yeah. Just under sixty days. So we were in y yeah. We were in YC and it was horrible because we had built this product. We had a couple neighborhoods using it and we're like, demo day is gonna be a bust.
Speaker 4:Because, like, yeah, we built a camera, but, like, what else? And I kid you not, the week before, this guy broke into someone's home, stole a nice road bike, DeKalb County, Georgia made the arrest. And so we went on demo day. We had one slide. It was this mugshot.
Speaker 4:And we were like, we build cameras that do this. Please invest.
Speaker 1:Wow. And it went We already had the mugshot. Yeah. Yeah. That Yeah.
Speaker 1:That Yeah. Than like, you know, we got 2,000 MRR. MRRs. Yeah. Yeah.
Speaker 1:Real world real world results. That's great and feels more, you know, more critical than ever with everything going on in the world. What is what have you guys been up to lately?
Speaker 4:Yeah. I mean, so we're in live in just over, like, 6,000 cities now. So Wow. Pretty well deployed, in building a lot. So we got into the drone business earlier this year, kind of at the end of last year, early this year.
Speaker 4:We've now now got drones flying all over the country. Just kinda launched that also for the private sector. So it might surprise you that private inter enterprise spends north of $30,000,000,000 a year on unarmed guards. I think drones are a pretty good alternative. They're cheaper.
Speaker 4:They're more reliable. And so now, like, if you're
Speaker 1:And and unarmed guards are are effectively just providing, a deterrence. Right? They're not meant to engage, but they're just trying to show that there's that you're not, you know, you're not alone, basically. Mhmm. Right?
Speaker 1:And so Yeah. So a drone can can play a similar role and potentially even a better role if it can follow a bad actor or something along those lines.
Speaker 4:Yeah. I know. Exactly. So when you think about, like, a retail example, which if you're in the Bay Area, this happens a lot. Stolen car car gets stolen.
Speaker 4:They drive it to a Home Depot, to Target. We know that car is stolen. Today, we just notify 911. And, you know, depending on what city you're in, a couple minutes or twenty, thirty minutes later, 911 shows up. Now with our drone, that drone can get automatically dispatched off a rooftop out of a box, completely remotely, kinda teleoperated.
Speaker 4:You've now got live video streaming to local law enforcement inside their vehicle. This is the vehicle. This is the person. They're going into the store. They're leaving the store, whatever it may be.
Speaker 4:And so it's kind of the way to think about it is we just treat drones like a camera that can fly. So we're pretty good at building cameras, and now these cameras can fly and chase bad guys.
Speaker 2:Can you give me some, timelines on when, you know, the average American would be seeing just drones flying around, what scale we're gonna see that? Are you bullish or excited about humanoid robots or wheeled robots? We've seen what Starship Technologies has done. There's a whole bunch of companies that are putting, you know, more, like, simpler robots on the on the ground. Like, what what's the actual path, and and what kind of timelines are you kind of operating against?
Speaker 4:So, I mean, the the good news is that these drones fly high. So we fly 400 feet up in the air.
Speaker 2:Okay.
Speaker 4:So if you're not looking up, you're not gonna hear it. You're not gonna see it.
Speaker 2:Wow.
Speaker 4:The other thing is our drones can cover up to, like, thirty thirty square miles effectively. Yeah. And so the sheer number of drones you need actually isn't that many.
Speaker 2:Sure.
Speaker 4:So if I think about a county near me, it's the largest county in Georgia. I think it's just over 500 square miles. They're gonna get 20 drones to cover the entire county.
Speaker 2:Wow.
Speaker 4:And that's a a couple million people. Like, you're never gonna see these things.
Speaker 2:So you would probably wind up either partnering or building yourself, like, something that looks kinda like a cell phone tower, has charging infrastructure. Maybe there's a human that goes out there and fixes parts of his Does
Speaker 1:it even need to look like that, or can you just put it on a roof of, a Walmart? Put
Speaker 2:it a roof.
Speaker 4:Even better. Fire department. Fire departments are legally required to be ECA deployed in us. There's a federal regulation of the density of fire departments, which makes them a perfect place to also put drones.
Speaker 2:For sure.
Speaker 4:So you put them on top of a fire department. Mhmm. They're fully autonomous. They live up there. It's got HVAC.
Speaker 4:It's pretty cozy place to live. It's a drone. Yep. And then someone clicks a button from a computer just like you guys are on
Speaker 2:Yep.
Speaker 4:Drone flies. And then it can track the car, locks in, tracks the car, the human is locked in. You can do multiple drones to one operator. So even in a county like the one I'm describing with 20 drones, you know, if I need three or four operators at all times Mhmm. But, like, they're covering 500 square miles, like, insane coverage.
Speaker 2:Yeah. Talk to me about some of the other robots. Like Yeah. Robotic dogs, wheeled four wheeled robots, humanoid robots. Like, when does this
Speaker 1:And and before that, like, talk about autonomy timelines. Because if you're flying 400 feet in the air Yeah. At what point like, there's not a lot else going on up there. I imagine you could build these to be pretty fully autonomous pretty fast.
Speaker 4:Yeah. I mean, the the autonomy is already there. The issue is the FAA regulation. It's like right now Yeah. We are legally required to have a human click a button that says launch drone.
Speaker 2:Yeah.
Speaker 4:And then they need to have a part one zero seven license that says they can fly,
Speaker 6:and they're Like an actual video game.
Speaker 1:That's a pilot slice.
Speaker 4:It's a it's a easier version. It's maybe twenty or thirty hours of, you know, studying.
Speaker 1:Okay. Specifically for commercial drones. Yeah.
Speaker 4:Yeah. And I think it's good, but the autonomy is there. And the FAA has done a great job over the last few years of kinda trying to catch up. They were a little slow for a while. They're catching up now.
Speaker 4:I mean, they're making better decisions. Like, they're now allowing us to fly multiple drones with one operator. It used to be it had to be one to one. It used to be able to you had to be able to see the drone. Now you don't have to be able see the drone.
Speaker 4:So in the case of that big county, that person sitting downtown, they're flying a drone 50 miles away. Yeah. And I think that makes a lot of sense. So the autonomy is there. It can autonomously track the car, stick to it, so you can avoid a high speed pursuit.
Speaker 4:But the FAA is gonna make sure that it's safe, and I agree. Like, safety is the most important feature we sell.
Speaker 1:Yeah. How much of a
Speaker 4:It's a big thing.
Speaker 1:It feels like high speed chases are, like, definitely a bug, not not a feature. Right? Because it creates an entirely new danger. Right?
Speaker 2:Of of,
Speaker 1:hey. Let's get we've got one guy that stole a car. Let's get six cops
Speaker 2:driving out
Speaker 4:of mile. Yeah. Super fast.
Speaker 2:What can go wrong? Yeah. It's probably thrilling, though.
Speaker 4:Yeah. I think it's, like, probably one of the highlights of the job if you're, like, an adrenaline junkie because, you're driving 90 miles an hour trying to, like, chase someone. Yeah. No. It's definitely a bug, and I think most elected officials would agree Yeah.
Speaker 4:As they've started to ban it, which creates all types of new conflicts. Mhmm. So I think high speed pursuits are a really good one. I think the other thing that we've been surprised by is the reduction in service calls, and let me try to expand on that. Please.
Speaker 4:When you call 911, the average response time might be thirty minutes.
Speaker 2:Mhmm.
Speaker 4:And a part of the problem is it's just slower to drive. So we'll get there faster because of that because we don't have to wait in traffic. The other issue is that a lot of times, you'll call 911 and say, hey. These two guys are in a street fight. And then twenty minutes later, those guys are gone.
Speaker 4:We still had to send an officer. They still had to look around. Maybe they go into the gas station and grab a Gatorade because they're thirsty. Mhmm. And all of sudden, like, we've wasted hours of this officer's day.
Speaker 4:So one of the beautiful parts of the drone is actually it gets there faster and it can also remove that call for service from the backlog so that for actual critical incidents, we get humans there faster as well. I think the other one that I'm pretty excited about is we have a number of colleges that we work with that are using drones to help escort females at night back home. Mhmm. So you can call 911 and be like, hey. I don't feel safe.
Speaker 4:Like, it's really dark. I just left this party. And you don't feel great, I think, as, a 19 year old maybe doing that because, you know, now I'm gonna get in back patrol car.
Speaker 2:Yeah.
Speaker 4:Now in a couple of colleges we work with, they're pushing this out. Like, hey, you can always call 911 and then a police drone will escort you home. Woah. And we'll have a video we'll have eyes on you so that anything social looks suspicious, like, we're on top of it. And I think there's just a ton of use cases that we're just undercovering now, to make drones just a more bigger part of our daily life.
Speaker 2:If you run the the, like, economic calculation on the 30 square miles for one drone and there's cost to service that and batteries versus do some sort of deal where every stoplight has a camera on it and you have sort of coverage, three sixty cameras on every single corner. Is there an economic calculation here that makes sense where the drone's more efficient, or is it just more flexible? Like, what are all the trade offs that a city or you think of?
Speaker 4:Yeah. So the the way we think about it is, there's a certain cost per citizen Mhmm. That a city is comfortable paying to eliminate crime. Mhmm. And we do think you can eliminate it.
Speaker 4:Like, you can't eliminate emotional crime, like crimes of hate, crimes of passion, but a capitalistic crime. Like, there was a really funny No Jumper podcast a couple couple weeks ago Yeah. Where the guy was like, those effing flockers, they're making it too hard to commit crime in San Francisco. And I'm like, that that that's the goal. Because, like, that guy knows
Speaker 1:On a podcast. On a podcast.
Speaker 2:Wow. He called you flockers. That's hilarious. That's That's
Speaker 1:so freaking It's just about, like, it's it's about making it so that
Speaker 2:You know you've
Speaker 1:probably got somebody could still do it, but it's not economically viable.
Speaker 2:Random podcast. That's amazing.
Speaker 1:Yes. That's product market fit. Getting called out on no jumper for for stopping crime.
Speaker 4:For stopping crime. So when we look at that though and, like, you know, the the the probably the most deployed city we have is spending about $22 a citizen per year with Flock. I think that's pretty reasonable. That's not actually it's not that much money. It's a lot for that that city, but, I mean, as an individual, I believe that city is gonna solve every single crime that happens.
Speaker 4:I don't think that's too much of a burden. But I do think it's about you know, you think about building a cake. You gotta have different layers. You don't want just all cake, all icing. You want cameras.
Speaker 4:You want cameras that fly. You also need software. You need trailers because there's a more of a deterrent. So I don't think it's a one size fit all fits all. But for us right now, we kinda see that deployments where you're gonna cover your perimeter with cameras that track cars.
Speaker 4:You're gonna have that kind of PTZs, penciled zooms for your major intersections, they're and gonna use drones to kinda cover the whole city or county as an overlay.
Speaker 1:How how do you Back to humanoids. Sorry. Well,
Speaker 3:yeah. I know. We're gonna
Speaker 2:get there. Okay. Yeah. I I really wanna know because cost per citizen is probably really expensive. We're seeing stuff from unitary.
Speaker 2:There's companies in America. Optimus is far away. I just give me your human o Sorry.
Speaker 4:So my so my so my human o take is, like, as it stands today, they're just way too expensive. Mhmm. So if you think about, like, an average retailer, some of them make a lot of money. Right? If you think about, a big box retailer, they might they might generate a couple million in EBITDA per location.
Speaker 4:But then you go to do you kinda go down market to, you know, an Ulta, Sephora, a Dollar General? Their box profit's just, like, not very high. They have a lot of locations. Yeah. But their individual stores are actually not very profitable.
Speaker 4:They can't spend more than a couple thousand dollars a year per store in safety. I just I don't see a world where other humanoids or or dogs work there, and we're seeing that unfold as well where bigger footprint locations just have both more assets and more dollars. In my conversations with retailers in particular, the kind of two wheeled approaches have been laughed off. They get stuck in a corner.
Speaker 2:Yeah.
Speaker 4:They get kicked over. The dogs are seeing more efficacy. Mhmm. And I gotta imagine, like, a biped, like, humanoid is gonna be the most effective because that just sounds really scary.
Speaker 1:Yeah. Well, now it And you see this you can't kick you can't kick them over. They
Speaker 2:come right back up. It's it's crazy. Talk
Speaker 4:about a scary situation. You're trying to rob a place. You try to kick it over, and it just stands back up. It's just looking at you. Just like, what
Speaker 1:It's gonna be like a dark dark web poo down. Yeah. Dark web tutorials of, like, how to get it
Speaker 2:Get around. Stop. Yeah.
Speaker 1:Like All
Speaker 2:of a sudden, the the the bippers would bring, like
Speaker 1:It's so sci fi. EMF. Like, you have to cut these cables on the back. And
Speaker 2:It's gonna be wild. Sorry, Jordy. You you had
Speaker 1:another question. Yeah. I I was just curious about just like how you think about product prioritization because I imagine when you get in working with these cities and police departments and they start using your products and getting value out of them, they just start coming to you with like more problems Yeah. Both like hardware, software. But it feels like the drone opportunity is big enough that at some you know, it's it's probably hard to that you have too many opportunities then you could probably pursue all at once.
Speaker 4:Yeah. I mean, I think we took the company from one SKU to eight SKUs in the last twelve months that all have clear line of sights and nine figures of ARR. Great. Amazing. Yeah.
Speaker 4:I agree. And, like, pipeline yeah. The pipeline is there. The ARR is growth. Like, it's kinda wild.
Speaker 4:This will be our first quarter, I think, where, like, our our core business, like, won't be the biggest product line, which is kinda weird. Again, that's a good thing Yeah. For us because we're not decelerating on that side. It's just other products are really attractive. Mhmm.
Speaker 4:But I think it is you're right. Like, our bigger concern is the market adoption, not our ability to build. Mhmm. I mean, this is a kind of enterprise y type buyer where they're just only gonna adopt things so quickly. So even, like, on the AI front, we have a lot of ideas.
Speaker 4:We've built a lot of interesting products. They're just they're really nervous to adopt Mhmm. Too quickly. And I think the example I always give is maybe you're trying to book a hotel and you you call the hotel and you realize you're talking to an AI agent, but you're kinda like, I don't know. This is better than the alternative because I'm just trying to book a hotel.
Speaker 4:I don't know, man. I think when you, like, call 911 and and you're in the middle of a violent situation, I think there's something warming about knowing there's another human on their side trying to help you.
Speaker 2:Yeah.
Speaker 4:Enough to say they can't be augmented with AI, but I think that's, like, a much more challenging societal question Yeah. Of what do we want humans to do and what do we want AI to do. Yeah. And we are being conscious to not make that decision for the hundreds of millions of Americans we help keep safe.
Speaker 2:Yeah. Just as a taxpayer, I would imagine that I would want the person to pick up, but I'd love for the calls to be transcribed and then AI to find patterns between what's happening and insights and analytics and all that.
Speaker 1:Yeah. There's also something strange Yeah. Like, if if you if somebody's calling 911 to report, like, a drunk somebody that's obviously drunk driving, it's like you're taking up bandwidth from somebody that is a human that could be going elsewhere. It's like for, you know, maybe it's like you should there should be no dial times is a clanker immediately picks up and says, what's the problem? And then routes it either to a human or if it's less like, you know, if it's not like immediate, you know, some violent act is taking place Yeah.
Speaker 1:It can actually be solved by a voice, you know, voice model.
Speaker 4:And I answered right approach. But, yeah, I think so to answer your question, like, we're we're pretty focused on doing three things for our customers, Solving more crime, whereas right now, it's about a 40% clearance rate for for killing someone. So you got almost 50 chance of getting away with homicide. Wow. That should be zero.
Speaker 4:You should every single person that kills someone should go to jail. Nonviolent crime is even worse. We wanna solve cases faster. It takes way too long to solve crime. Mhmm.
Speaker 4:And we wanna do more with less.
Speaker 2:Like Yeah.
Speaker 4:Most people I don't know if y'all remember, our grandparents, our parents, it was an admirable job to go into policing, to fire.
Speaker 2:Yeah.
Speaker 4:This generation does not view it that way, which means 80 plus percent of police departments are understaffed against their budget. It's not getting better, and technology's gonna fill the gap.
Speaker 2:Yeah. People have compared you to, like, the Anderol or Palantir, kind of, like, I don't know, American dynamism companies or whatever.
Speaker 1:People have compared you to good
Speaker 2:businesses. Yeah. But but I'm interested about we talked to Shyam Sankar, CTO of Palantir, about some of the some of the difficulties of deploying software into governments. And he gave the example that one time they they built a beautiful piece of software, exquisite system, and they went to deploy it. And the the the the team on the other side of the government that was firing it up was trying to run it on a computer that had, like, you know, a 128 megs of RAM instead of, like, a gig.
Speaker 2:And so that that was kind of the dawn of the forward deployed engineer. And I'm wondering if you, if you have any unique solves on how to deploy systems, how to deploy software. Yeah. If you like the forward deployed model or something else, like, what's working for you and actually Yeah. Getting your solutions into the field?
Speaker 4:So really similar example. I remember distinctly, we were, like, launching this product and the designer has a beautiful, you know, MacBook and this HD retina display. It's beautiful. Right? Super expensive setup.
Speaker 4:And she's designed this, like, super slick thing. And I was like, great, Mariana. Like, you should go, like, field test this. Like, this looks dope. Like, let's launch it.
Speaker 2:Yeah.
Speaker 4:And she gets into a patrol car and it's a Panasonic Toughbook.
Speaker 2:Yep. It's a
Speaker 4:13 inch screen. It's twelve eighty by ten twenty four resolution. And he's using it while he's driving 40 miles an hour. And I'm like, this the product's not gonna work. Like like, we gotta go back.
Speaker 4:But that that is literally our we've got, I don't know, hundreds of thousands of DAUs using this product now, and they're very happy. Yeah. But that's their normal use case. They're driving. Yep.
Speaker 4:They're on a Toughbook. Like, it's a tiny screen.
Speaker 2:Yep.
Speaker 6:It's low res. It's old.
Speaker 4:And so, like, you're pretty much treating it almost more like a mobile app, but, like, with a distracted teenager as your customer. And that's been pretty difficult because we wanna build these, like, super high fidelity. You're just sitting at a desk all day doing your job, and, like, our customers are in the field all day. So that one's been pretty pretty tough. And then I also think the other burden we've done a good job of going around is just IT in general.
Speaker 2:Yeah.
Speaker 4:Like, I I city IT is really tough to work with. They're really tough to kinda just, like, get get going. So we tend to build everything we can to avoid IT. Mhmm. There's some pros and cons to that, but so far, I think it's mostly pros.
Speaker 2:How how does that actually work? I imagine Yeah. Is it just like you're looking for, like, user licensing or, like, OAuth or something? Like, how how do you at some point, you have to plug into the IT systems a little bit, I imagine. Like
Speaker 4:Well, is it is it so as a good example, historically, everything in our world is on prem. Mhmm. Like, we're one of the first cloud only solutions in this market.
Speaker 2:Yeah.
Speaker 4:And early on, that was painful because people are used to buying a piece of software. They install a desktop application. Installation requires IT support. Mhmm. And so building everything in the cloud, which for this know, for y'all, it's like, well, obviously
Speaker 2:Yeah.
Speaker 4:I'd say a very it was a very contrarian take seven years ago. Mhmm. And now it's moving. But, I mean, we have a customer that just a few years ago moved off of paper records. They had filing cabinets.
Speaker 4:Like, imagine if you report a crime, like, hold on. We gotta go find that file. Wow. And that this is a big city. This is a city with millions of people in it.
Speaker 4:Yeah. Just got off of paper, and and I think in Maryland, in 2023, the cloud became legal. Mhmm. Legalized cloud. Is
Speaker 2:we're gonna protest for legalizing the cloud. Yes. We love the cloud.
Speaker 1:If it, if you there's any cities that have still banned cloud, we will.
Speaker 2:And we and we hate paper. We are we are the the show is presented by RAMP, of course, and we are strong in favor of ending paper and going to the Agreed.
Speaker 4:But literally, we didn't do business in Maryland until 2023 because it was illegal to be in the cloud.
Speaker 2:Yeah. Did you ever get a a test environment with a Toughbook and a car that a designer could actually go and and and drive around the parking lot of your office or anything like that?
Speaker 4:No. We make all of our employees go do ride alongs.
Speaker 6:Okay.
Speaker 2:Yeah.
Speaker 4:It's the it's so much fun. Yeah. Because then you get to see the whole product in action. You get to meet the customer. You get to go catch some bad guys.
Speaker 4:Like, it's a pretty fun way to spend eight hours.
Speaker 2:Yeah. How are you thinking about data privacy where data lives? There's obviously so many advantages to being cloud based, but then you have some citizens who might say, wait. Well, like, why is my image with this private company? I I'm a taxpayer.
Speaker 2:I I I don't have a vote at your board meeting. I have a vote at my city council. How how do you grapple with all that? What's what what what's the mood on the ground in various cities? What are the dividing lines?
Speaker 2:Yeah.
Speaker 4:I'm glad I'm glad you mentioned cities. So our general stance is we don't write the rules. We create the levers for local politics to dictate. Mhmm. And so data retention is the easiest one.
Speaker 4:Mhmm. In some cities we work with, data retention is seven days. Mhmm. And every single data we capture is purged up to within seven days. Mhmm.
Speaker 4:But then in some cities, like Dallas, it's a year. In New Jersey, it's five years. A state legislation for five years.
Speaker 2:Wow.
Speaker 4:I don't care. I live in Atlanta. Yeah. I think we're, like, at thirty or sixty days here in Atlanta. Yeah.
Speaker 4:And that's up to the local politicians to decide, like, what makes sense for them. The other thing that we do that's pretty unique is every single action in our system is audited in perpetuity. So whether you're downloading, you're searching, you're doing something, that's stored.
Speaker 2:Yep.
Speaker 4:So the the city manager, the internal affairs bureau, those groups can actually say, hey, has anything nefarious been done? Yeah. So that's a that's a big concern of people. So those two things tend to be a pretty pretty good levers for letting local politics dictate versus, to your point, no one elected me to police chief of America.
Speaker 1:Yep.
Speaker 4:I just built a camera at my house.
Speaker 2:I wanna pitch you Great line. A startup idea that Blake Scholl, the founder of Boom Supersonics Boom.
Speaker 4:Yeah. He
Speaker 2:was thinking about doing this. Have you heard this story? He was thinking about building something before he started Boom, probably right around the time you were starting Flock. Yeah. He wanted to make a smart stoplight that would have a camera on it, and it would see, hey.
Speaker 2:There's no cars here. Why do I have a red light? Switch them. Feels like it would improve traffic flow. It's a it's a, you know, a situation we've all been in.
Speaker 2:You're sitting there at the red light. There's no one coming the other way. Flip the switch. Why is that is that a good idea? Is that something you could do?
Speaker 2:Is that something any company could do? Like, what's the market structure of the stoplight industry? Do you need to do some sort of private equity roll up? Walk me through your your if you were putting on a VC, how how would you interpret that pitch?
Speaker 4:My my my pushback on that pitch is to show me the incentive, I'll show you the behavior. Who who's getting promoted? Who's making more money if we fix traffic? Mhmm. I don't think anyone.
Speaker 2:Opportunity cost of the workers. It's all diffuse in the economy.
Speaker 1:No. But this is the don't but think a Yeah. Don't you think a mayor could run on, hey, we have a lot of congestion here. We have a techno you'd have to do a study or something to show that that you could increase that reduce congestion if you had smarter traffic lights. Yeah.
Speaker 1:Feels like some Mayors don't.
Speaker 4:Mayors get elected on two things.
Speaker 2:Yeah.
Speaker 4:Well paved roads and low crime. Mhmm. That's it. That's the only things that matter. Mhmm.
Speaker 4:Like so, I mean, I I think the product idea makes a ton of sense.
Speaker 2:Yeah.
Speaker 4:But I just go back to, like, one of the only reasons why because you mentioned Andrew
Speaker 2:Yeah.
Speaker 4:When we were pivoting the business into local government, Trey Stevens was like, do not go sell to police. It's a horrible market. We tried it at Palantir. It's not gonna work. Mhmm.
Speaker 4:It's not gonna work. I was like, oh, Like, I'm a little bullish. He was an invest he's an investor in the company. It's kinda it's not work. And what I didn't realize is systems like Palantir are too many steps removed away from solving crime at a local level.
Speaker 4:And, like, Flockhead coincidentally built a product that solved crime right away. Mhmm. And so for a police department, crime is equivalent to revenue. So we weren't helping them save money. We're helping them generate revenue.
Speaker 4:And, like, that does get a sergeant lieutenant, a police chief promoted. Like, it's a mayor reelected. And and that's that that would be my pitch to Blake. It's like, we gotta find a way to make this relevant to a mayor.
Speaker 2:Yeah.
Speaker 4:And, like, just a little bit less traffic isn't gonna move the needle Yeah. And have them move off from having no technology and no cost structure.
Speaker 2:Well well, he's he's busy building supersonic jets. He's got a good thing going for now. But maybe maybe that's the next thing he works on. I have a second startup to pitch you based on your answer to that question. Automated road paving startup.
Speaker 2:Go to the mayors, and I say, paving gets you paid and gets you in the job. Would that work? Is that a good idea? How would that play out if you thought that process through? Advice would you give to a road paving entrepreneur?
Speaker 1:Yeah, there was a YC company doing the little road
Speaker 2:I remember this. Yeah. Remember this. But, yeah, yeah, yeah. Walk me through your your thought process.
Speaker 2:So
Speaker 4:I actually think that's a great idea. Mhmm. We're working on something tangential to that. Mhmm. So one of the biggest issues is that to do what you're describing, you have to know where the potholes are.
Speaker 4:Mhmm. And cities have no idea where potholes are. Yeah. And they rely on you calling 311, which I'm sure you both have done many times. Okay.
Speaker 4:Said, hey, I'm just calling you to report a pothole.
Speaker 2:Yep.
Speaker 4:No. You don't. And that that also cost the city on average like $8 per call on a 311. Woah. So it's a really inefficient system.
Speaker 2:Okay.
Speaker 4:And so what we've been able to do is train our cameras to look for potholes. And so now we can actually report back general road road conditions.
Speaker 2:Yep.
Speaker 4:Cool. And that is the beginning of the mule to say, hey, now we can actually be intelligent about where we should send people to go repave. Yep. And I think an automated robot that does the paving would be even better. Yeah.
Speaker 4:But, yeah, I love that idea. I think that I'm I'm in. That's amazing. $25,000 check.
Speaker 2:Fantastic. Well, you know where to reach him. He is, of course.
Speaker 1:Oh, last last Yeah. Did you one of your reactions, we had a guy named Riley on yesterday who made a find my parking cops. Did you see that?
Speaker 4:Oh, that that park was awesome.
Speaker 2:Yeah. Yeah. He's a little he
Speaker 4:down or did he back up?
Speaker 1:It got shut down. He's very was impressed with San Francisco's reaction time on that. They shut it down like Yeah. Very quickly. But it's good fun.
Speaker 4:Yeah. I'm a fan of it. I think most people don't pay for parking because most of the time, don't get a ticket. So
Speaker 2:Yeah. Well, thank you so much for hopping on the show. Good to hang out.
Speaker 1:Great to get the update.
Speaker 2:Yeah. And congratulations on the progress. And thank you for everything that you do.
Speaker 1:Yeah. Come back on anytime.
Speaker 2:Yeah. We'd love to see
Speaker 4:what's to
Speaker 2:Have a great rest of your day. Me turn it over.
Speaker 1:$10.09 figure businesses.
Speaker 2:Yeah. If you want to reach people in cities, you gotta go to adquick.com. Out of home advertising made easy and measurable. Say goodbye to the headaches of out of home advertising. Only add quick combines technology.
Speaker 2:Out of home expertise and data to enable efficient seamless ad buying across Before the
Speaker 1:our next guest joins, we did need to acknowledge that Mark Leonard has stepped down from Constellation Software.
Speaker 2:Sad news for health reasons.
Speaker 1:We hope he's okay. Interesting timing. Basically, called the top on
Speaker 2:Yeah. It was we talked to Carrie. No interest on the show. You can go pull that up if you wanna hear our conversation with him about these private equity software, enterprise software roll ups. Mark Leonard was mixed on AI, sort of unclear on on how it would affect the business long term, but the market did not like it.
Speaker 2:And the stock fell off, the stock has fallen again on this very sad news. So we are, of course, sending him our best. The health reasons that he is stepping down for are unspecified at this time. So but please send your thoughts and prayers
Speaker 1:to Thoughts and prayers.
Speaker 2:Mark Leonard. And we have our next guest in the restroom waiting room, Tom with the TVPN Ultra Dome.
Speaker 1:There he is. Suited up.
Speaker 2:Hey, Tom. How are doing? Hello.
Speaker 1:I'm doing well.
Speaker 3:How are
Speaker 7:you guys doing? It's been
Speaker 2:too long. Yeah. We've seen you before in the TV panel. Welcome back. Pleasure.
Speaker 2:Thank you. The news. Right. Give us the give us the 140 characters in the company. Refresh everyone and then give us the news.
Speaker 7:Absolutely. So I'm Matan, CEO at Factory. At Factory, our mission is to bring autonomy to software engineering.
Speaker 2:Mhmm.
Speaker 7:What that means more concretely, we have built droids, which are autonomous software development agents. And more importantly, I'm here to tell you that they are the number one agent in the world.
Speaker 2:Got
Speaker 7:it. As we just released today in the terminal bench benchmark, which evaluates tools like Cloud Code, OpenAI's Codec CLI
Speaker 2:Mhmm.
Speaker 7:Factory is number one. Congratulations. Number three and also number five. Pick any model you want. Factory is still number one.
Speaker 7:No matter how you slice it, our droids are simply the best agents in the world.
Speaker 2:So What's excited to share that. What's the secret? What got you there? Is it pre training, post training, a bunch of RL stuff? Is it a data flywheel?
Speaker 2:Like, what's the what's driving the growth?
Speaker 7:Yeah. Great question. So I think one of the biggest things is that most, if not all, of the agents out there are built for one model in particular. So, you know, Claude code is built to work with the Claude models. OpenAI's codex is built for the GPT models.
Speaker 7:There are some other tools out there that out there that aren't from the research labs that are focused on really, like, fine tuning their agent harness for one given model for any given step. But what we've done with our droids is make them fully model agnostic, which actually makes them more performant in the long run. It's kind of similar to if you were a human engineer and you only, let's say, studied one coding language, you would actually be a weaker engineer than if you studied all of them. It's somewhat analogous there in terms of how we've built these droids that now allows them to be the most performant with any model that you that you put in under the hood.
Speaker 2:How are you viewing the market and the customer landscape? Automated software engineering, that feels niched down from years ago, which was just the the the transformer based LLMs can write code. But as we actually dig in, we see that the needs of a Fortune 100 customer may be different than a mid market company, which is different than a start up, which is different than an SMB, which is different than a solo vibe coder, solopreneur. Where are you seeing opportunity in the market? How bifurcated is the market?
Speaker 2:It seems like you've wanted to be very general on the model side. Are you also trying to be general on the customer size side?
Speaker 7:Yeah. Great question. I mean, I think the the thing that a lot of the other tools have missed is the fact that the further you go into the large enterprise, the less overlap there is between software development and coding. Mhmm. So for example, if you're a solo developer, your software development is basically just coding.
Speaker 7:You're not really doing code review. You're not doing that much in the way of documentation or testing or design docs. And so where we focus and why we call Droids software development agents as opposed to coding agents is that we focus on that whole end to end software development life cycle, which is really where a lot of enterprises are missing. Like, there's study there's the famous study that came out from MIT that said something along the lines of 95% of AI adoption efforts are failing in the enterprise.
Speaker 4:I remember.
Speaker 7:Yeah. Exactly. And so, I mean, it it it kinda makes sense. Like, if you think about software development as
Speaker 1:a as a pipeline Sorry. I I You guys I love hitting you with the surprise sound effect. We'll get some more positive.
Speaker 2:We're not bearish on you. It's just No. No. No. No.
Speaker 2:Was good.
Speaker 7:It was perfect. It was probably just too good. But, you know, software development is a pipeline. Right? And if you focus on only one part of that pipeline Yeah.
Speaker 7:Namely code generation Yeah. And you expand that I used to be a physicist. I don't know if you guys are big on fluid mechanics, but opening one part of a pipe and doing nothing to the others, you don't actually increase the throughput.
Speaker 2:Sure.
Speaker 7:You just create new bottlenecks. Interesting. And so the reason why a lot of these efforts are failing for software development is they're focusing on just coding, but then that just punts the problem down to testing or code review or documentation. And that's why a lot of the other tools out there aren't seeing kind of the adoption and the real business outcomes that we are with, the droids.
Speaker 2:How do you see coding agents fitting into the consumer world? I this might not be a business at all, but I noticed that the you know, I've seen incredible results just from asking a question that you could hit deep research with. But, if I ask it to Claude code or Codex, it can build an entire HTML web page with JavaScript widgets and bar charts, and it just produces it instantiates the information in a much cooler, just interesting way. And I'm wondering how you're seeing crossover from advances in automated AI coding into a consumer world where they might not even know that they asked for code, but they got code. It's already happening a little bit when you hit o three Pro.
Speaker 2:It'll write some code to give you the answer. You don't even see it unless you unfurl the reasoning tokens. But how do you see that playing out in, like, kind of the mid to long term?
Speaker 7:Great question. I think this is something that, people aren't yet fully aware of, but this is actually the very first statement of our announcement, which is the best agents for software development are becoming the best agents for everything. And the reality is because basically every problem can be broken down into some sort of software development problem, and you can actually take it from Anthropic themselves. Literally yesterday, Alex Albert, the guy who runs DevRel at Anthropic, tweets out, you know, the best coding model will be the best model for many types of knowledge work. Code is how computers operate.
Speaker 7:Anything you do on a computer can be done through code. And this is really why factory being the number one software development agent is really going to then lead to a lot of other tasks, and we're already seeing this in the enterprise. So we sell explicitly to developers, but already PMs, designers, even people on the operation side, so, like, finance, biz ops, that sort of thing. Yeah. We find that they're, like, sneaking their way in even though they didn't actually, you know, put any budget towards it, and they're starting to use droids kind of, behind the back of some of the VPs of engineering.
Speaker 7:And this is really something that we've expected because of this fact that that code is really the language that computers speak. And if you want a computer to do something for you, you will eventually, through some means, need code.
Speaker 1:Couple couple questions. What what models are you guys getting the most leverage out of today? Is it a mix? Are you are you focused on You you said earlier, I think you're focused on a variety being trying to be model agnostic, but I'd be curious where who who you're paying out on the back end.
Speaker 7:Yeah. So right now, we achieved the number one score across the board on terminal bench with Claude
Speaker 2:Opus. Mhmm.
Speaker 7:But, again, we are, you know, really focused on having the the model agnostic stance because org by org, they might have different preferences. Also, what the best model is depends, you know, what day it is. You know? Tomorrow, there could be another new best, and it's important that we have that ability to quickly swap them in. So that's one thing that I think is important.
Speaker 7:It's also even task by task. There are certain models that are better, and we wanna make sure that the user has that familiarity, with the model so that they can go in and, you know, swap it out if there's a task that they're doing that GPT five might be better for or Grok might be better
Speaker 2:for. Mhmm.
Speaker 1:You forgot to mention you raised some new money today. Give us the news. Got a gong here.
Speaker 7:That is right. What's We have raised $50,000,000 from Oh. NEA, JPMorgan, and Nvidia.
Speaker 1:JP Morgan.
Speaker 2:Jamie Diamond's getting in?
Speaker 1:He couldn't help he couldn't help himself.
Speaker 7:Himself. Couldn't help himself.
Speaker 2:I love it. Congratulations.
Speaker 1:That's our deposits. Everybody the world's deposits at
Speaker 2:Talk to me about last question. We'll let you get back to your busy day. Talk to me about the branding. It feels like we just got the idea of an agent recently. Now you're kind of pitching droids.
Speaker 2:It's clearly your term for
Speaker 1:Droids you're looking for.
Speaker 2:Yeah. Yeah.
Speaker 1:People always talk about, you know, being we're not
Speaker 2:the droids. Much of that is, like, a differentiated brand that you want to be specific to your company versus, like, a new coinage that you want to describe a different way of working that you'd actually like to see other companies adopt? And if you were seeing the entire industry standardized around that particular piece of language, you would be happy? Or would you be like, hey. They stole our brand?
Speaker 7:Yeah. I mean, I think, generally, like, agent as a term is here to stay. Yep. But the point is, as a kind of general purpose term, it's often synonymous with, like, poor quality or, like, a while loop wrapped around LLM calls. Yeah.
Speaker 7:And I think what we wanna do is to make good on our promise to customers, which is giving them that best agentic experience, and we do that through droids. At the end of the day, when you have a cold and your, you know, your nose is running, you don't want a tissue. You want a Kleenex. Similarly with software development, you want the droids to come in there. Right?
Speaker 2:Okay. I love it. I love it. Well, congratulations on the round. Thank you so much for hopping by.
Speaker 3:Talk to you progress.
Speaker 7:Thank you
Speaker 1:guys for having me.
Speaker 2:Thanks. Talk to you soon. Let me tell you about Bezel. Get bezel.com. Your Bezel Concierge is available now to source you any watch on the planet.
Speaker 2:Seriously. Any watch. And we have our next guest
Speaker 1:in Also, room waiting guess we gotta wait to talk about this. Two new launches. One from Meta. Yes. The Alexander announced Vibes, which is a Meta AI app for short form AI generated videos.
Speaker 2:Okay. Yeah. YouTube launched something with AI generated shorts leveraging v o three recently. What else?
Speaker 1:And then there's a new chatty p t product called Pulse. But Pulse.
Speaker 2:We will Let's
Speaker 1:dig into that. Get into it with
Speaker 2:With our
Speaker 1:next over at Invisible.
Speaker 2:Welcome to the TV, Andrew. I'm Francis. How are you doing?
Speaker 1:What's happening? Welcome to the show.
Speaker 5:Doing great. Excited to be here.
Speaker 2:Thanks so much for hopping on the show. Kick us off with an introduction on you, the company. Any news you gotta share? What's new in your world?
Speaker 5:Well, as you saw on Bloomberg, we've raised a $100,000,000 this year,
Speaker 1:which is a big There
Speaker 5:you go.
Speaker 6:Whoo. 2,000,000,000. Valuation.
Speaker 5:Yeah. Crazy thing that when you saw Meta acquiring scale, they had raised $1,800,000,000 and we had only deployed 6,000,000. We scaled profitably to a 134,000,000 of revenue last year.
Speaker 1:That is wild. I remember I heard about you guys. I forget what context I heard about you guys for the first time. But even this was probably two years ago. Even back then, I was like, you guys were somewhat under the radar, I felt like.
Speaker 1:But I heard some of your revenue numbers back then. I was like, what? How have I not heard about this company? But, yeah, we wanted to Invisible for not much longer. Yeah.
Speaker 1:No longer Now
Speaker 2:very visible.
Speaker 5:Now we're visible.
Speaker 2:By the new domain, you're visible.
Speaker 1:Visible technology.
Speaker 2:Yes. Yes.
Speaker 1:Give a give us an an I mean, it'd be helpful to hear, like, where where you guys have are have an edge in in the market broadly. And then I I yeah. I wanna kind of, yeah, understand
Speaker 2:Yep. Yeah. Problem set core, like, what you're actually replacing, where customers are getting most value, anything like that would be super helpful.
Speaker 5:So ten years ago when we founded the company, there was a question, which is if there's an app for everything, why isn't everything perfect yet? And Salesforce was the first SaaS company in 1999. For the last twenty five years, every enterprise software company has been a SaaS company. Mhmm. And this has actually put customers in a pretty absurd situation where if the customer wants a cake, Silicon Valley will not sell them a cake.
Speaker 5:They will sell them tools to make a cake, and the customer then has to hire a systems integrator like Accenture to stitch together all these SaaS applications into an end to end solution that actually works for the business. And so, that is what we were disrupting. And Palantir was not public yet, so they weren't a well understood comp. We were kind of like an ugly duckling because we were talking about AI services. Mhmm.
Speaker 5:And, and so it's just a fundamentally disruptive business model.
Speaker 1:So you were you were competing initially with someone like an Accenture to integrate existing software systems. Is that right?
Speaker 5:Yeah. We build custom AI applications for enterprises and governments. And so you can think of this very different than SaaS, like a triangle. We have the horizontal platform, which is very powerful, but you have field engineers and field CTOs and a forward deployed motion that builds custom AI applications that actually solve the problem for the enterprise and the government. And our insight was that even though every, you know, solution would be custom, the horizontal platform would allow you to sort of build infinitely customizable software over time.
Speaker 2:That makes sense. We've been seeing Mark Benioff and Doctor Karp, going at it over various contracts, Salesforce, which you already mentioned, Palantir, which you mentioned. Yeah. Are are you are you in that knockout, drag out fight? Have you found a differentiator around industry or size of problem, or or are you just going in the the arena?
Speaker 5:So Salesforce was founded in 1999. Palantir was founded in 02/2003, so way before the Gen AI era. So we were in a sense like the Gen AI native, way of doing this. We hired Matt Fitzpatrick who previously ran McKinsey's Quantum Black Labs, and, he's now our CEO. And, after years of being capital efficient, this round sort of enables us to go into a pre IPO motion.
Speaker 5:And, yeah, if you're a public markets investor, you don't really have that many options for buying AI today. Mhmm. So and this is generally true for the enterprises, the customers themselves. If you wanna build custom AI, you know, solutions, Palantir is like a data integration and decision making support company. Mhmm.
Speaker 5:So they really they really focus on that. But if you have a problem in anywhere else in your business, how do you build a custom, you know, solution with world class field engineers? It's a very different type of engineer, very different type of go to market person, very different type of business that is familiar building custom solutions on the enterprise. You know, enterprises do not want their data to leave their systems. So they they need their they need all the solutions to be on prem and containerized, we have the ability to build, in that way.
Speaker 2:Do you find do do you think that there's a shift in the AI era to large enterprises doing more of these sort of, like, semi custom solutions? Is that's gonna be a continuing trend? Because it feels like the last era was very much, like, rip out the old custom on prem solutions and go to some sort of, like, one size fits all cloud solution.
Speaker 5:Exactly. Yes. And this is a reversal. We're going all the way the other way. And I think that's because enterprises ultimately need proprietary and custom solutions.
Speaker 5:So they in the SaaS era, like I said, they were stitching together things, and they had to do that with some combination of, like, their internal engineering function and systems integrators. And systems integrators are not tech companies. They're gonna overcharge, under deliver. You're gonna pay them by the hour, so their incentive is to bill you as many hours as possible without getting fired, and that's not really aligned with efficiency. And so in in this era, I think, you know, enterprises are realizing it's gonna be very, very difficult for them to build world class engineering teams and to build a platform like the one we have, where, we can take the best models, latest models, which we train.
Speaker 5:We have an AI training business, and deploy them in the enterprise, to actually solve the business use cases that they that they have.
Speaker 1:Yeah. So so how much should we read into you guys transitioning away from training? Is that a business that is
Speaker 5:We're not transitioning away. We are fully committed to training. So actually, the the move of Meta acquiring scale basically cleared the field. And so it's an it's inherently oligopolistic market structure where there are a few players that have invested as much as we've invested over as many years as we've invested. And so we have, like, 20,000 experts, and that number is scaling very quickly.
Speaker 5:We can hire, like, a thousand experts a week. So when you're asking a model a question about, the chemical properties of silver or the the history of Sweden or whatever you're asking a model, we have PhDs, masters, experts in every subject that are training these models and there's, you know, evals and and and lots of, you know, inputs into making these models great.
Speaker 1:Got it. So leveraging the experience on the training side in order to help the To deploy the enterprise. Got it.
Speaker 3:Got it.
Speaker 4:Exactly. And then
Speaker 1:what Yeah. And and then and then the the business model for for the deployment side is I'm just gonna repeat it back to you so I so I understand is there's some period that looks more like services, but it becomes a piece of durable software that they can use for a long It's great model.
Speaker 5:Long Exactly. Yes.
Speaker 1:Yeah. Yes. Got it. Cool.
Speaker 5:AI AI services is, exactly that way.
Speaker 2:Yeah. What yeah.
Speaker 5:And that that puts us in an investing mode. We literally invest in our customers.
Speaker 2:So Mhmm.
Speaker 5:We put, you know, field engineers, field CTOs in with our clients, and there's an investment period to build the solution. But once the solution's in place, that is very durable revenue.
Speaker 1:And so you're you're like a nightmare for someone like an Accenture, some of these big consulting companies that don't really have the true expertise around training these models and you get to and and have a lot of organizational bloat. Is that is that right?
Speaker 5:Yes. So, Matt, who, Matt, who's our new CEO, we bonded over ancient Greek and Roman history, and he calls it the battle of Thermopylae because even if you even Accenture has, like, a million people worldwide and has some huge headcount. If there's three if the pass is only yay wide and there's 300 people at the pass and we're always winning at the pass, then we're gonna win every battle. In other words, if you know, I believe our technology is superior dramatically superior already to traditional systems integrators. So if you're a buyer and you're hearing Accenture's pitch and Invisible's pitch, which one are you gonna pick?
Speaker 5:The battle of Thermopylae, Spartans win.
Speaker 1:It's fantastic. Spartans are coming coming for it.
Speaker 2:What a what a market. This is just a fantastic market.
Speaker 1:Awesome. Well Congratulations. Congrats on all the progress.
Speaker 6:Great to
Speaker 5:be on the show. Big fan.
Speaker 2:Appreciate it. Thanks
Speaker 1:for What's the what's the you you said prepare prepare to prepare to go public. What what's the what timeline are we thinking about? Is that is that multiple years out? Is it is it
Speaker 5:We we think in terms of of, you know, how many hundreds of millions in revenue do you need to have before the story is just obvious to a public markets investor? So we we really wanna build a, you know, business for all all markets. So the intrinsic value of the business, the ability to survive, you know, in any condition is key, and then we'll take
Speaker 3:it Awesome.
Speaker 1:Alright. We'll come back on anytime. This is fun.
Speaker 2:We'd love to ring the bell to you.
Speaker 5:Great talk, Jason. Thanks.
Speaker 2:Thank you. Up next, we have Jacob from or sorry. David from Juice Box coming into the studio.
Speaker 1:Juice Box.
Speaker 2:Restream waiting room. Bring him in. Juicebox has some fundraising news. The gong's already warmed up. How you doing?
Speaker 2:Welcome.
Speaker 9:Thank you. Thanks for having me on.
Speaker 1:Take good day today.
Speaker 2:Introduce yourself. Tell
Speaker 1:Pick's kind of a good day to drop a cinematic launch video.
Speaker 2:This is a good day for it. But take us through it. Introduce yourself, the company, and the news.
Speaker 9:Yeah. I'm David, co founder of JuiceBox. JuiceBox is an AI recruiting platform. We help our customers win the talent war, and we do that by helping them find and engage the best talent. And, yeah, today, we're announcing our $30,000,000 series a and previously unannounced $6,000,000 seat.
Speaker 1:Boom. And then a few few more talents. Few more talents. There we go. There we go.
Speaker 1:Congrats. Big day. Okay. AI winning recruiting wars. What does that what does that mean?
Speaker 2:How many how long until you're out of business because no one has a job anymore? If I thought AI was gonna get rid
Speaker 1:of it No. No. No. Question. Are you working more with early stage, growth stage, public companies, all the above?
Speaker 1:High skill. Where are you kind of focused right now? Because I saw, I admit, I only saw half the launch video so you had me up until whatever 50%.
Speaker 9:Nice. Did you see the the part with the Zac impression?
Speaker 1:I did see the Zac impression. Yeah. Made made
Speaker 9:it through the important part. Yeah. Yeah. We we work with all kinds of companies ranging from, you know, founders doing their first hire all the way to kind of growth stage companies, a few Fortune 500 customers as well. Some of those companies are Perplexity, Ramp, Quora, and and a bunch more.
Speaker 9:I think the common thread for where we're able to add the most value is if the right person for the role is actually really hard to find. And that can be because they have a specific skill set, a specific set of experiences, something that makes them unique and uniquely positioned to really excel in that role.
Speaker 2:I think of hiring as kind of like some sort of chain of events with different point solutions. There's actually finding talent, job platforms, LinkedIn, Indeed. Then there's the you might want to send someone a test or have them fill out a form or process resumes. You need an applicant tracking system. I've seen other point solutions for send a video and then ask some questions and then process the video and let the let the user or the hiring manager kind of review videos.
Speaker 2:There's a whole bunch of different things. Do you see yourself as kind of a a cross cross journey platform? Are you focused on a particular landing zone? Like, where's the product position today?
Speaker 9:Yeah. It's a great question. I think of the the recruiting space as really having two different ways in which you could add value. One is finding net new candidates, people that you wouldn't have discovered or wouldn't have gotten into your pipeline in the first place, and then everything that's carrying through the pipeline and optimizing that pipeline. And the latter is usually about, like, saving time and optimizing conversion rates, And the former is about making sure the right talent is speaking to you in the first place.
Speaker 9:We're focused on that part. And so our goal is to help companies discover talent that they wouldn't otherwise. And we do that by combining a bunch of different data sources and then using large language models to run the search and surface those profiles for you. And so if you think about what a, like, recruiter does today on, say, LinkedIn recruiter or, a typical search solution, they set a bunch of filters and then start reviewing profiles one by one. It's super manual, requires a lot of thought still to like think of what could make that profile a fit.
Speaker 9:And that's exactly the process that we're able to do with with LLMs.
Speaker 1:So is this a direct replacement for LinkedIn Recruiter? Are you are you trying to trying to get people to just be able to
Speaker 2:Or do you wanna, like, puppeteer LinkedIn with a screen recorder or API or something like
Speaker 1:widget don't if they'd like
Speaker 2:like that. I don't know. It depends. I mean, there's been some companies that have done it successfully. Some of them got acquired by LinkedIn.
Speaker 2:But I don't know.
Speaker 9:Yeah. No puppeteering of LinkedIn. Most of our customers are existing LinkedIn customers. Sure. Our goal is to help you find net new talent and do so in a more efficient way.
Speaker 9:So it's really about kind of enhancing the workflow of the recruiter and enabling them to do that search that would just be too manual or too time consuming to do otherwise. Like, looking through 50,000 software engineering profiles is just not something that's feasible. But for an LLM, it's very feasible and can do it in a couple minutes.
Speaker 2:So there are I mean, are different pools of talent for I mean, we do media video production here. We might want to hire somebody who doesn't really have a LinkedIn presence, but they might have an Instagram account or they might have a personal website where they've done some, they put up their show reels or what they've worked on. Do you have crawlers that could go out and find those profiles on the Internet? Like, how could I think about the surface area of you going out and finding me candidates and sourcing?
Speaker 9:That that's right. So we look at a a bunch of different data sources for different types of roles. So Mhmm. I'll give two examples. One on the engineering side, you know, GitHub profiles, a ton of value on those, especially where, you know, traditional LinkedIn profiles or resumes might be a bit more sparse, especially for for engineers who don't put a ton of info on there, GitHub's can be really rich and a really interesting data source.
Speaker 9:Meanwhile, on the sales side, often what we see is, you know, customers want to find sellers who have experience selling it to specific type of buyer persona. And so there, it's actually a bit more about the company data. You know, what type of companies does their current company sell into? What type of a platform are they selling? Is it a SaaS product?
Speaker 9:Is it a usage based product and more? And so all of that information we enrich and we try to aggregate upfront so we can make those decisions for you and help surface the right profiles there.
Speaker 1:There's another they put an example. They have a tag on their site called likely to switch, which would be like you have round of layoffs, Oh, Sure. Funding or vesting timelines. You know, people don't really think about that. You can see like, okay, this person's been here for four years.
Speaker 1:They're probably getting a refresher but they're vested out. That's pretty cool. What do you think do you have a a strong thesis yourself around employment over the next, you know, ten years? I'm assuming, you know, Sequoia leading this round. Think that says
Speaker 2:Sequoia confirmed, not AGI pilled. Confirmed.
Speaker 1:No. But it says that Okay. Your timeline. That we're still going to have talent wars in a decade. Right?
Speaker 1:That's what that tells me. But how do you think about it?
Speaker 9:Yeah. I I think if you're AGI pilled, it actually means hiring the right talent is even more important. And the reason for that is that if you think of, like, the output of a given person is usually constrained by, like, their time, their previous knowledge and context, and then whatever tooling they're using. And so if the tooling that they're using is getting, like, exponentially better, they use LLMs to automate a bunch of the things they'd otherwise do. They're much more productive than they would usually do, And that's all a multiple of the person or, like, the initial person and their kind of raw capacity.
Speaker 9:Having the right person in that role becomes even more important. Because if AI basically 10 x's whatever you do, you wanna have the highest baseline that you can add that 10 x to. And so it gets even more important to have the right talent on your team. And if you think of that in, like, the context of a software engineer, if a 10 x software engineer becomes a 100 x, you wanna have as many 10 x software engineers as you can. And so my view is that the the race for talent will become more and more competitive as AI gets better and better because having the right people on your team has that outsized impact.
Speaker 9:And I think we're already seeing some of that in, like, you know, the the warfare talent and the AI labs. But I think more and more of that will trickle down into the rest of the economy as well.
Speaker 2:Sure. What do you think about outcome based pricing? Famously used in the recruiting industry, Most human recruiters, even if they work for a firm, they might get one month salary as a bonus if they place someone successfully versus LinkedIn, which is a subscription software, completely not outcome based. What model do you think will dominate in the future? And what are you doing?
Speaker 9:Yeah. It's it's a really good question. We so we have, like, a per seat pricing, normal SaaS model. But then we also have an agent product, which is priced per role that the agent works on. And so it's not quite outcome based, but it's more like usage based.
Speaker 9:How many roles are you deploying the agent for? Over time, we wanna get closer and closer to outcome based pricing as as is possible. You know, if you think of recruiting agencies, pretty common to do, like, 20 to 25% fee of first year salary, which unlike a dollar value is is much much more than one could probably charge for a SaaS subscription. And so there's definitely a lot of things that are attractive there. At at the same time, I think the market's maybe not quite there yet or not quite ready to pay for software on, like, say, percentage of higher model.
Speaker 9:Though, you know, I hope we'll be able to push in that direction over over time as well.
Speaker 2:Fantastic. Well, congratulations on Super cool. Amazing
Speaker 1:round. Really insane progress. Yeah. And on the new round.
Speaker 2:We will talk
Speaker 4:to soon.
Speaker 9:Having me on.
Speaker 2:Cheers to your day. Well, for coming on. Samo launched a new feature today called Pulse. It's in ChatGPT, initially available to pro subscribers. That's the $200 a month tier.
Speaker 2:Right? Yep. Pulse works for you overnight and keeps thinking about your interests, your connected data, your recent chats, and more. Every morning, you get a custom generate custom generated set of stuff you might be interested in. It performs super well if you tell ChatGPT more about what's important to you.
Speaker 2:In a regular chat, you could mention, I like to go visit Bora Bora someday, or my kid is six months old and I'm interested in developmental milestones. And in the future, you might get useful updates. Think of treating ChatGPT like a super confident personal assistant. Sometimes you ask for things you need in the moment, but if you share general preferences, it will do a good job for you proactively. This also points to what I believe is the future of ChatGPT, a shift from being all reactive to being significantly proactive.
Speaker 2:I completely agree with this concept. And extremely personalized. This is an early look and right now only available to pro subscribers. We will work hard to improve the quality over time and find a way to bring it to plus subscribers too. That sounds compute intensive.
Speaker 2:That sounds like they're gonna be running essentially an o three pro level query, a ChatGPT five Pro level query every night asking, based on what's going on in the news, based on what this person searched for, based on all the chats recently, let's summarize and put together some sort of dossier for them and service that. So that's basically one big fire up the GPUs every single night. You gotta get the customers to pay for it, at least in the short term. But Yeah. What do think, Tyler?
Speaker 2:You think you'll use this?
Speaker 3:It's pretty interesting. I I I'm wondering, is this the thing that Seymour was talking about above the compute intensive thing they're rolling out?
Speaker 2:It's he said he said this week, we will be launching a couple compute intensive things. Some of those will probably be announced at Dev Day. This is one that he's just announcing this week.
Speaker 1:Mean, this feels like it's it's it's effectively doing something like, you know, just kind of
Speaker 2:Hanging out in the background.
Speaker 1:Hanging out in the background, you know, somewhat actively, somewhat passively Yep. Just gauging, you know, what your interests are. I can see this as like almost like having a Google alert set up.
Speaker 2:Omjad from Replit says, reminds me of Google now. But how do I get started? I don't see it in the app. So it's obviously rolling rolling out slowly.
Speaker 3:Yeah. You can definitely see like an agent being built into this.
Speaker 2:Like Yep.
Speaker 3:That seems very natural.
Speaker 2:Totally. And I feel like ChatGPT was already starting to sort of surface this concept of like, hey, do you want me to do this every week for you? Do you want me to do this every month for you? But I haven't actually set up any of those cron jobs and had it stick around and become part of my workflow. But I like the idea of it.
Speaker 2:And I think that that the big thing is just, like, for retention, if every time you open the ChatGPT app in the morning, instead of just an empty box, you're greeted with, like, here's some interesting stuff.
Speaker 1:Or you're getting a push notification.
Speaker 2:Or a push notification. That's gonna be huge for retention. Or it's like,
Speaker 1:hey, this product launch, do you wanna buy this? Yeah. Wanna buy this? Just give me Let's pull up this video from Alexander Wang. Let's pull it up.
Speaker 1:Just share vibes, a new feed in the Meta AI app. Mhmm. Let's see if they beat the the slop allegations. I think it's powered by Midjourney, so it looks pretty cool.
Speaker 2:Fantastic model. Looks pretty big. Yeah. I wonder how this is actually gonna so separate app is what he's saying?
Speaker 1:It's a new feed in the Meta AI app.
Speaker 2:Okay. In the Meta AI app. So not not you would expect this to be baked directly in Instagram. That's probably too much compute out of the gate to just drop it in as a filter or something or button in Instagram because if you have a billion people pushing that button, you're just gonna get get swamped. I think this beats the slop allegations.
Speaker 2:There's some cool stuff you could do with this. I mean, video generation looks good. Looks looks v o three level to me. I don't know. Obviously, these were curated and selected.
Speaker 2:But
Speaker 1:We'll have to get in the app. Tyler, before we do the show tomorrow, please use the app for, like, I don't know, like, twelve or fourteen hours, something like that. Just straight. No no break. Just
Speaker 2:in. Just lock in.
Speaker 1:Max Max Conrad in the chat says, I'm tired of hearing about fundraisers without business metrics, top line, bottom line value created for customers.
Speaker 2:Sorry, Max.
Speaker 1:Good point. We noted wanna push people harder to say, you wanna come on, you gotta give us some
Speaker 2:Gotta give us some juice. Some
Speaker 1:And if you don't got numbers to share other than the headline
Speaker 2:We should have we
Speaker 1:should have a very
Speaker 4:small
Speaker 1:call Credit to on juice. I think they've been
Speaker 2:actually crushing it. Raising money. I mean, is a market where money flows very freely. Because as a business, you're ready to sign a check for $200,000 to hire someone as a software engineer.
Speaker 1:150,000
Speaker 2:You're like, yeah, I'll pay $10,000 to get the job done, dollars 20,000. So the money flows quite freely. And that's why there's so many executive recruiters, software engineering recruiters that make a ton of money just working basically for themselves, just as talent agents, moving things around. Yeah. Well, Meek Mill and Elizabeth Holmes are going back and forth on X.
Speaker 2:Yeah. Elizabeth says, Thanks, Meek. Currently serving a hundred and thirty seven month sentence in federal prison. Would love to work with you on reform. Here's some legislation I have drafted.
Speaker 2:Your story inspired me. And Meek Mill says, let me check your story out. And Will Brown says, Elizabeth Holmes, this year is our year. Elizabeth Holmes, we can never forget this, Tiger. Meek Mill.
Speaker 1:Used to times like this to rhyme like this. I know. Focus on it too. It's a great one. Yeah.
Speaker 1:I think they're thinking about different aspects of reform.
Speaker 2:Well, we don't we
Speaker 1:don't Maybe Mel would want YC arrest, where she has to lock in and just repeatedly build the the device forever.
Speaker 2:I like that. We don't rap, we do sing.
Speaker 1:Your happy place. Find your happy place.
Speaker 2:Tyler missed it.
Speaker 1:I I got me a second to kind of like Yeah. You wanna try out there. Find happy place. Find your happy place.
Speaker 2:Book of wonders, conspiring views, hotel grade amenities, dreamy beds, top tier cleaning, and twenty four seven concierge service. It's an application.
Speaker 1:But We got breaking news. Trump signs order, to approve the TikTok deal
Speaker 2:and
Speaker 1:avoid The US ban. The chat was on it way before we were. And let's see. TikTok is poised to be spun off into a separate US entity to comply with the 2024 law requiring the China parent based parent company ByteDance to divest or face a US ban.
Speaker 2:Mhmm.
Speaker 1:Trump said, I had a very good talk with President Xi. A lot of respect for him. Hopefully, he has a lot of respect for me too. And we talked about TikTok and other things. We talked but we talked about TikTok and he gave us the go ahead.
Speaker 1:Under the deal, a group of US investors, including Oracle and Silver Lake, are set to take a majority stake in the new TikTok entity. Let's give it up for Silver Lake. Good to see them throwing some size around as well as Oracle. ByteDance will maintain less than 20% in equity to comply with the divest or ban law. Vice President Vance said the company will be valued around $14,000,000,000 Feels Woah.
Speaker 1:Fairly low.
Speaker 2:Pretty low. Just The US business? I don't know. I felt like I mean, we talked to Sean Frank, and it sounds like they were losing a lot of money on TikTok shop and some of the I mean, the growth had probably plateaued with
Speaker 1:Well, they were deals just losing money in general.
Speaker 2:Yeah. But you gotta imagine that that monetizes pretty well. It's such a big network, so much time on-site. But, yeah, it's it's being it's trading like a Snapchat or a Pinterest, basically. Like a non meta property, a non Google, YouTube property.
Speaker 2:Oh, well. Yeah.
Speaker 1:It's crazy. Well, keep checking in on it. So TikTok valued less than perplexity.
Speaker 2:Okay.
Speaker 1:Honestly, now I can see why Arvin was trying to get a bid in. True. Yeah. Everyone's like, oh, we got around.
Speaker 2:Much bigger, but yeah. Already in basically. 20
Speaker 1:Brett Adcock should've should've taken a crack.
Speaker 2:He could've he
Speaker 1:could've absorbed easily absorbed
Speaker 2:of his market cap.
Speaker 1:Easily absorb and could've.
Speaker 2:True. There's
Speaker 1:lot of that could have absorbed
Speaker 2:I'd like to see I'd like to see Palantir pick them up. Run run the database.
Speaker 1:$1,414,000,000,000 is crazy.
Speaker 2:That is crazy loud.
Speaker 1:Crazy low. That's gonna the
Speaker 2:the I I I'm surprised the CCP said yes to this.
Speaker 1:That feels Yeah. What's the what's the catch?
Speaker 2:I mean
Speaker 1:This feels like it's worth, like,
Speaker 2:a It 100 feels like 40 there's other five chips on the table that are being traded around. Whether it's, like, rare earths or, you know, some sort of, you know, deal on tariffs or this is such a multi pronged deal between different countries and stakeholders. The UN thing just happened, and so there's a whole bunch of different pieces. And the art of the deal is not a single iterated, you know, price a single asset in the moment type thing. Could Truth Social have picked it up?
Speaker 2:Truth Social's right around there too.
Speaker 1:No. They've
Speaker 2:They've fallen. They're in the single digits, still a unicorn. Right? Our president is still a unicorn tech founder,
Speaker 1:I believe. Yep. 4,700,000,000.0.
Speaker 2:There you go. He's a unicorn founder, folks.
Speaker 1:When's earnings on November 7? So Tune in. We gotta we gotta lock
Speaker 2:in for that one. Think I don't think BJT gives a speech. He should.
Speaker 1:That would be crazy.
Speaker 2:What you got, Tyler?
Speaker 3:Do we know I I think there are rumors that, like, the the TikTok algorithm would still, like, be governed by the Chinese, but they would run on, like, American hardware or something.
Speaker 6:Do we
Speaker 2:We're gonna do the inference.
Speaker 1:Yeah.
Speaker 2:We're gonna do the inference. They'll do the training. We'll do the inference.
Speaker 3:It's like the algorithm with Chinese characteristics.
Speaker 2:Yes. But I am somewhat bullish on the idea of, like, doing some post training on top of their algorithm or doing some secondary, like, post inference. You see the ranking of the feed. You're doing analytics on that and seeing, like, okay, like, there's still some, like, propaganda stuff here.
Speaker 3:We're not going Does that confirm that that is what's going to happen, though?
Speaker 2:I don't know. I don't know if that was confirmed. But there are it's not a total black pill if the training still happens there because you can do so much post training, I think, and monitoring. And even just having the data, you can start running analytics and understanding like what type of content is showing in what context. Like, are there actual like, are certain trends or keywords or is there certain censorship of certain ideas or promotion of certain ideas?
Speaker 2:At least if you have access to the data of what's being served, what TikTok users are watching, you can then understand and then you can apply pressure or post training or anything else. Anyway, in other news, the Lucy nicotine vending machine has arrived at Palantir. This was a journey. I worked with Eliano on this. And John says, Palantir has a climate controlled Lucy vending machine for their employees in their office and you're bearish.
Speaker 2:It's a lot of fun. So we'll be sending out a few more of those, hopefully getting one in the TBP and Ultradome soon.
Speaker 1:Last
Speaker 2:thing was the Financial Times has an article about America's biggest corporations keep talking about AI but struggle to explain the upsides. We'll dig into this another day, but it's they analyzed hundreds of filings that suggest S and P 500 constituents are clearer about the technology's risks than its benefits. And another one of these data points of Fortune 500 companies being reticent to adopt AI, I'm not sure if it's just blanket bearish for Fortune 500 companies. If you're a big enterprise and you're not able to figure out a way to deliver, you know, positive ROI when a magical new technology comes along, it might not be a good sign for you. Jordy, what else do you wanna talk about today?
Speaker 1:Yeah. I was just confused. I was trying to dig in and see. I didn't see a 16 z in the announcement anywhere.
Speaker 2:Oh, yeah. They were supposed Right? To Maybe they pulled out or something. I don't know. I like the idea of the Ben and Mark show just being hard coded into
Speaker 1:the They
Speaker 2:got Rick Rubin on there. Show me some Rick Rubin TikToks, some Tornburg, little Chris Dixon in there, some KB. This would be a good TikTok algorithm.
Speaker 1:They still could be in it, but we'll see. Well But we can ask. We it's it's out.
Speaker 2:Hope the thumb is on the scale regardless of their ownership position.
Speaker 1:Putting thumbs on scales.
Speaker 2:I'm a big fan of thumbs on scales.
Speaker 1:Anyways, without further ado, we I think I gotta get on with Taipei.
Speaker 2:I think we do.
Speaker 1:I think we do. We will see you folks tomorrow. Thank you for tuning in.
Speaker 2:Thank you for tuning in.
Speaker 1:Leave us a review on your favorite podcast app And if you're listening we love you.
Speaker 2:We will talk to you soon.
Speaker 1:See tomorrow. Goodbye.