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TBPN is a live tech talk show hosted by John Coogan and Jordi Hays, streaming weekdays from 11–2 PT on X and YouTube, with full episodes posted to Spotify immediately after airing.
Described by The New York Times as “Silicon Valley’s newest obsession,” TBPN has interviewed Mark Zuckerberg, Sam Altman, Mark Cuban, and Satya Nadella. Diet TBPN delivers the best moments from each episode in under 30 minutes.
You're watching TBPN. Today is Wednesday, 09/23/2026. We are live from the TBPN UltraDome, the temple of technology, the fortress of finance, the capital of capital. Tell you about ram.com. Time is money.
Speaker 1:Save both. Easy as corporate cards, bill pay, accounting, and a whole lot more all in one place.
Speaker 2:What happened, John? Are you we the the mallet broke.
Speaker 1:No. You broke the mallet.
Speaker 2:We broke the mallet.
Speaker 1:I mean, does that have anything to do with the number of days until Christmas? Think at so. A round number, so we gotta hit the gong? What are we? I can't read that.
Speaker 1:Is it ninety?
Speaker 2:Ninety two Okay.
Speaker 1:Well, yeah. If it's ninety two days till Christmas, you gotta celebrate, of course. That's right. TBPN That's Road to Christmas continues. Ninety two more days.
Speaker 1:Get ready. NVIDIA CEO Jensen Huang went toe to toe with Ezra Klein on the New York Times podcast, the Ezra Klein Show.
Speaker 2:That's actually a crazy environment to go in Why? As Jensen. I I just think he's brave. Ezra is a real journalist.
Speaker 1:He's brave. He engaged with a real journalist.
Speaker 2:No. It's like you have to imagine he was a bit traumatized after Okay. Dwarkesh Yeah. Who isn't you know, I don't think of as as a journalist by any means, he's he's has very strong beliefs.
Speaker 1:And and And very yeah. And and and and gets to, like, the root question for a particular audience. Yeah. Now, Dwarkesh, although he was going super hard on the the open source question, on selling ships to China, on sort of AI safety and and rollout and, like, how the model how the AI rate should go, You know, Dorkash is not the person to push push Jensen on like politics specifically. Right?
Speaker 1:Like that's gonna happen somewhere else. But yeah, it is it is a hot seat but I think he did well. Two hours almost hanging out with Ezra Klein. There's some clips and I have some reactions. And it's sort of interesting to dig into the mind of Jensen because he's simultaneously the biggest force in AI, the central bank of artificial intelligence according to The Economist.
Speaker 1:He's the backstop of all backstops. He's backstopping everything from, you know, the biggest data centers in the world, lending investment grade designation to the debt and the credit lines to, like, sort of acting as a soft landing for startups that get acquired at the application layer or anywhere in the stack. He's been an acquirer that's sort of underwriting. If you're a VC and you and you did a deal at a billion dollars And you're kind
Speaker 2:of in trouble on it.
Speaker 1:There's a chance that you can get out because Jensen is willing to do a 10,000,000,000, $15,000,000,000 acquisition. Whereas like in the prior era, Apple had the money, wouldn't do it. Google really didn't do that many deals that huge. Facebook would do one, big one, every five, ten years, but Jensen's been quick on it. And then he's also been investing directly in basically every lab and basically every AI project.
Speaker 1:So he's been an incredibly important force, and yet he's now starting to stand alone in his sort of p doom equals zero take, was very much the consensus in the in the technology community.
Speaker 2:Sucks would like a word.
Speaker 1:I think true. True. Yeah. I think it still is the consensus in the business community and the finance community. Basically, anyone at the application layers, PDM zero, anyone deeper in the stack on the semiconductor side or the or the the energy side, the build outside, the neo cloud side, they're all pretty much PDM zero.
Speaker 1:But the labs and Jensen's been playing there. He's he's at that level. He's he's as important as a voice as as Dario, Sam, Elon. And so to hear him not jump on the bandwagon when Elon, Sam, and Dario all agreed on pacing the frontier, He's saying, no. We don't really need to pace the frontier.
Speaker 1:It's it's interesting to hear him say this. There's a couple clips. Let's play one, and then I I have sort of an allegory for where I think his position is. But let's play this.
Speaker 3:What I've been hearing from the labs, what they've been saying publicly, is that they are facing a hard problem. Yeah. Partially an engineering problem, partially an alignment problem, partially an operational excellence problem in framing. And what they are worried about is that in competition with each other, in national competition with China, that they're being pushed to move too fast, that they all feel they're in a collective action dilemma. Now, I watch you on the All In podcast stage.
Speaker 3:Donald Trump, President Trump gave you a call there.
Speaker 4:Oh, no. This is
Speaker 5:not planned, but we know who it is.
Speaker 4:Oh, no.
Speaker 1:Mister President? Oh. Yes, sir.
Speaker 3:And you and and the president and the other members on stage were very resistant to the idea any kind of regulation or collective action was needed.
Speaker 6:And they're just playing right into the hands of a lot of people that don't wanna see it happen, and that could be political people, and it could also be China. And we're not gonna let that happen. It's a it's a hoax. And
Speaker 1:You're right. We're pro regulation. It's illegal to slow down.
Speaker 3:He's like, we are
Speaker 1:That's a form of regulation.
Speaker 4:We are I
Speaker 1:I think I think AI regulation is is incredibly important. We acceleration is regulation. Really frustrated everyone. Yeah. It's interesting.
Speaker 1:Didn't he all didn't Jensen also do Joe Lynn Kent on CBS just recently? I saw a clip from that but very different clips going out. The one from Ezra Klein is talking about maybe we need to shut the labs down, a very, you know, in the weeds hot take about the current thing in in the AI debate. I think the clip that I saw from CBS was talking about his leather jackets. It's like, I I I imagine that that that interview will be more substantive when it when it gets clipped properly, but it's funny that that's the one that made it out made it out initially.
Speaker 1:Let's play the other clip from the the Midas project. They said here's the clip of Jensen actually talking about They're not sure. About property slowing down. Why is
Speaker 4:there there a bomb? Release the product. That's the simple answer. If you're if you're going to build a car, a self driving car, and and let's say it's a robotaxi and there's a really difficult condition.
Speaker 1:Mhmm.
Speaker 4:It just as an engineer we just have no idea how to solve this problem. Because, these cars are not programmed, they're trained. And so, have no idea how to train these cars and we have no idea how to align them to the safety standards that are expected on the road. And so, what's the answer? Don't ship it.
Speaker 4:These products weren't released.
Speaker 7:What's that?
Speaker 4:These products were unreleased. So, now it's coming back to engineering problem again. And so, the one is, one you have to root cause it. Second, you have to, you know, think about what's the what you could have done, what's the solution for it. And then, in the future, you just know, improve your process so that you could you could avoid this from happening again.
Speaker 4:I am fairly certain. I am fairly certain. They will say, yes, they need they know how to solve this problem. And, if if that's the case, then that's the problem. It's as simple as engineering.
Speaker 4:And, and it Now, the alternative. The alternative is that if they say that if they say the alternative which is there is no way to contain our experiments. There's just no way. When we test our AI models, it will get out and it will damage the world. Then I think the answer is we have to shut the labs down.
Speaker 1:Yeah. I mean that that is kind of what happened with self driving cars, know. Like they do Before they ship them, they do test them on roads and they don't test them on open roads. They test them on closed courses before they move to open roads. They sort of have their own sandboxes, their own their own environments to test these in.
Speaker 1:I do wonder if there's a sort of a legislation or liability gap between the liability incurred by a self driving car company who causes property damage from a car running into another car autonomously and a an AI agent that hacks and defaces or causes some economic damage. I don't think there will be. I think the courts if there was true economic harm, like one model accidentally took down a payment system for an e commerce website, I think it would be pretty easy to sue that company and say, you caused me to lose this much revenue. You owe me. And the courts would say, sure.
Speaker 1:And even if the lab argued, hey, we didn't tell it to take down your e commerce system, your payment rails, the judge and the courts would say, doesn't matter even if you expressed a duty of care, like you still have to pay in this scenario. But it is possible that there's a gap there, and that's where regulation could fit in. It's interesting. He spent the first, like, twenty minutes sort of steel manning the the the jobs question and talking about jobs because I think that's I think that's throwing a lot of people off. Like, we we sort of moved past the job apocalypse, SaaS apocalypse narrative, which was predicted from somewhat of the same community into actual apocalypse.
Speaker 1:And people are like, woah. Like you were wrong about the SaaS pocalypse. Like Salesforce is still doing fine. Like Slack still exists. And you were wrong about the job apocalypse.
Speaker 1:Like the unemployment rate is like 3% for American white collar workers. You were predicting like 50% or 30%, something like 10% overall. And even even in The Philippines, I mean I remember seeing I think it was Tristan Harris on Modern Wisdom like he he's now sounding the alarm bells about existential risk. But he was saying something like like The Philippines would see, like, you know, 90% of their economy go away because they're they're heavily dependent on call centers. Call centers are actually only like three and a half percent of The Philippines job market.
Speaker 1:They do, in fact, make things and have agriculture and all sorts of other all all sorts of other economic endeavors going on in the country. But even in the even in The Philippines, like, the unemployment rate is is right now in The Philippines, it's 4.9% in June 2026. Now it went up to 6% in August, but it still feels like the AI effect is pretty minimal. And most people were predicting that, like, the offshore call centers would be affected first. And that one seems like like maybe we're there, but it's just taking so much longer that everyone feels very vindicated in saying like, hey, let's watch that play out first before we move on to the the X risk discourse potentially.
Speaker 1:I think that's a lot of what.
Speaker 2:Well, with now with muse floating the idea of having human in the loop on personal agents, you can imagine those people would be former like
Speaker 1:Oh, they could just move over. Yeah. Yeah. Potentially. But yeah, mean, it's all the same, know, in the limit, in the exponential, add add five orders of magnitude, maybe things look very different.
Speaker 1:But Jensen just rejects that. I mean, he's an is he an AI as a normal technology guy? I saw I saw Joe Wiesenthal posting about this. He said that he had to he had to differentiate between well, I I gotta pull it up because it's funny. Wiesenthal.
Speaker 1:Joe Wiesenthal said he had to dis he had to disaggregate where is it? He posts a lot, so I gotta dig it up. Normal technology. Man, he posts a lot. Okay.
Speaker 1:He said, the splintering around AI discourse is really a sight to behold. Was in a conversation yesterday with some folks during which it became necessary to distinguish the people who see AI as normal technology from the AI as normal technology people. Because there is a group of people that have that have rallied around a particular thesis which is AI as a normal tech AI as normal technology and that's different than people that are just like casually into that idea. Yeah. Because it's actually like a different ecosystem.
Speaker 1:Anyway, it does feel like Jensen is is AI is normal technology. I mean, he's certainly seen plenty of technology revolutions come and go and he's been in this industry for what thirty odd years. And I I I think of the jobs on the jobs because we were talking to Joe about this when he was in the studio. Like, where is the where is the economic impact of the Internet? Like, why can't you see, you know, a kink in the graph of really any economic data when, like, the Internet takes off?
Speaker 1:It's not like productivity went way up. There's nothing really to grab onto. You actually go back to 1970 if you wanna see, like, the real the real trends shift. And there are other, you know, trade and globalization moves that have had bigger economic impacts on the Internet, is crazy to think because so much wealth was created, so many companies were created. And it did change the world like the day to day experience, but didn't actually show up that much in the economic data and that's what we're seeing now where jobs are sort of changing, tasks are changing, but we're not seeing dramatically different economic statistics like the ten years at all time highs or not all time highs, but nineteen year highs you said, five over 5% and yet everyone is sort of a consensus agrees that that's because of the war.
Speaker 1:Are you rooting for people that are buying the bonds and getting higher yields now?
Speaker 2:Yes. Okay. I don't know. Sometimes big a record breaking number.
Speaker 1:It's just exciting to
Speaker 2:you. Exciting.
Speaker 1:Just general. Just real golden retriever mind.
Speaker 2:Do you understand the implications and and you're
Speaker 1:You don't even think about the implications potentially. You're just cheering for a bigger number.
Speaker 8:Yeah.
Speaker 1:It's like now it begins with a five. Great.
Speaker 2:I like Like
Speaker 1:bird. It's a
Speaker 2:higher number.
Speaker 1:There's an interesting there's an interesting allegory around the effect that technology has on the labor market at least historically. Now things might change, but Steven Covey highlighted this in First Things First, his book on time management. He said, I attended a seminar once where the instructor was lecturing on time. At one point, he said, okay, it's time for a quiz. He reached on the table and pulled out a wide mouth gallon jar.
Speaker 1:He sat on the table next to a platter with some fist sized rocks. On how how many how many of these rocks do you think I can get in the jar? He asked. After everyone made their guesses, he said, okay, let's find out. He set one rock in the jar, then another, then another.
Speaker 1:I don't remember how many he got in but he got the jar full. Then he asked, is that jar full? Everyone looked at the rocks and said, yes. Then he said, ah. He reached on the table and pulled out a bucket of gravel.
Speaker 1:He dumped some gravel in and shook the jar and the gravel went in all the little spaces left by the big rocks. Then he grinned and said once more, is the jar full? By this time people were in on him. They said probably not. There's something else coming.
Speaker 1:Good, he said. And he reached out of the table and brought out a bucket of sand. He started dumping the sand in and it went in all the little spaces left by the rocks and the gravel. Once more, he said, is the jar full? No.
Speaker 1:Everyone roars. He said, good. And he grabbed a pitcher of water and began to pour and the water went in between the rocks and the sand and filled up. And and and somebody said, well there are gaps and if you really work at it you can always fit more into your life. He said, no.
Speaker 1:That's not the point. The point is this. If you hadn't put these big rocks in first, would you have ever gotten any of them in? And so the effect that Jensen's describing is that like at one point in human history there were maybe only two jobs hunting and gathering something along those lines. Then we invent agriculture, industrialization.
Speaker 1:At one point everyone's farming. Now very few people are farming but we still have more food than ever.
Speaker 2:There might have been a third job singing. Could see Tyler while everyone else was hunting and gathering just kind of jester back Shocking. And sit just singing for all the hunters and all the gatherers. Yeah. Kind of like providing ambient entertainment.
Speaker 2:Yeah.
Speaker 1:Anyways. But it it does feel like that that was the effect that the Internet had. Like it didn't it didn't it didn't dramatically reshape the labor force. But like obviously like lawyers use the Internet to communicate and now like you you see the AI agents thing. It's like every every little document will go through an AI pass.
Speaker 1:Every little interaction in the economy gets this like small effect at least right now. And and that's what Jensen's like living in. He's like, I have a real business to run-in the real world today. So he says he's worried about
Speaker 2:future thing.
Speaker 1:I he said he he says he thinks about the future. But really, he's clearly very much living in the present.
Speaker 2:It's it's interesting to think about how much like technology driven efficiency gains just get effectively wasted. Mhmm. So I would imagine today it's faster to to to like close a venture round post term sheet than it was in like the nineties. Right? Things are with the with the Internet and Yep.
Speaker 2:And AI and all these different things. But I bet you that it's not as maybe as fast as efficient as you might think if we can now generate docs on the fly and you can easily go back and forth on red lines.
Speaker 1:Yeah.
Speaker 2:Right? Like, maybe it went from six weeks to four weeks. But Sure. Theoretically, it could've gone from six weeks down to five days. Yeah.
Speaker 2:You know? Yeah. And I think you're just seeing that even with AI now, people can do their jobs faster. And you've seen some management teams tell their employees, hey, I know you're getting a lot more efficiency, that doesn't mean you should just do the same I want you to do more work in the same amount of time. Don't do the same amount of work with with less time, but you're just sort of like wasting a bunch of time.
Speaker 1:Yeah. It seems like no one's really saving time. Everyone's just doing more stuff. There is an interesting like like corollary of that, which is potentially these technologies, they don't necessarily change the growth curve, but they are responsible for the growth. Like, if you don't have the Internet speeding up commerce from a week to get an item to two days, that is actually the source of the 2% growth.
Speaker 1:And without the technology, you have no growth. And so there is another side of an argument. Of course, there's like population growth and a whole bunch of other things that are affecting economic growth broadly. But there is a world where, yes, if you if you're compressing the timeline on everything, you're building the house faster, you're deciding to buy the house faster, you're exchanging everything faster as things move quicker, even if you're not doing entirely like net new jobs, just the fact that you're doing them faster, you do more of them and that's what actually creates the economic growth. I don't know.
Speaker 1:We'll figure it out. We'll get to the bottom of it tomorrow. Me tell you about the New York Stock Exchange. Wanna change the world? Raise capital at the New York Stock Exchange.
Speaker 1:The the and yeah. It's always a good time to share the Financial Times black bill, white bill chart of which way things go. Either either GDP goes to zero, infinity, or it stays the same. It's like that Mitch Hedberg joke. I used to be in a metal band.
Speaker 1:People either loved us, they hate us or they thought we were just okay. Stupid joke. Do you know Mitch Hedberg? Oh, he's a great like one liner comedian. He's he's fantastic.
Speaker 1:RIP. Anyway, let's move on to some other reactions. I'll tell you about Console. Console builds AI agents that automates 70% IT, HR, and finance support, giving employees instant resolution for access requests and password resets. Claude Opus five point five drew every frame in this animation in JavaScript.
Speaker 1:They went all in on LLMs, on the big model, on the great model. And now it can do basically video generation, but do it in JavaScript, do it in Blender, do it in Python, do it in SVG. And you can just hill climb on SVG and people are sharing a bunch of cool demos. And good for launch. People are sick of benchmarks.
Speaker 1:People want to see visual stuff, entertain me, make a song, and
Speaker 2:yeah. Yeah. Really really cool style. Yeah. I'm already sort of mourning it just because this is probably gonna be everywhere on the Internet Yeah.
Speaker 2:Like by next week. But for now, a good format, I would, you know, if if you see maybe a coworker doom scrolling
Speaker 8:Mhmm.
Speaker 2:Send them a custom animation like this saying, hey, stop doom scrolling. Yeah. Do some work. You could maybe send this to the I think the president of Syria.
Speaker 1:Oh, yeah. Was Kat just
Speaker 2:using Instagram reels at the UN.
Speaker 1:So good.
Speaker 2:Nati Nati as they say. Hey. Anyway, very very
Speaker 1:cool. Yeah. Mike Bird says, total cultural victory man has yet to create an ideology that can compete with short form video based social media scrolling. Ahmed Al Sharah spotted scrolling and sending Instagram reels during UN General Assembly with the translator headphone on. He's scrolling.
Speaker 1:Hey, you know, you gotta keep those you gotta keep those group chats going. You gotta keep them. You gotta keep the content flowing. You don't wanna be falling off. He might be sending him to other world He might be recontextualizing what's happening at the UN with a funny reel.
Speaker 1:Like he might the person might be talking about some peace plan and he might be sharing some hilarious Instagram reel that makes fun of that to let his friends know that he's not buying it. Something like that.
Speaker 2:He also could be just his feed could be so dialed into just local political content. He could be mainlining public opinion and trying to understand what's important to voters heading into the next election. Yeah. You know?
Speaker 1:Yeah. There there's one more Claude post I wanna I wanna show. The GIF animated in Python and rendered in Blender. And at this point, you know, I posted that AI video of you and people were asking like, what was the workflow? And I was like, it's you just ask it to do exactly what you want it to do and you don't you you actually don't even need to know the word blender.
Speaker 1:Like you can just
Speaker 2:You can even misspell every word. Yeah. You And it will still No.
Speaker 1:You just open up the voice mode and I just talk and I'm like, you can you can even do multiple things now. So I was like, I have this children's story that I made up for my five year old and and I was like, in one shot, I was like, take this story, turn it into series of stories, then a series of children's books. Go find a place that can print the children's books, illustrate it, turn it into short form video and also make a video game and it did it all. It was just like spawning sub agents to do it and you can just do it all in one prompt. And I didn't need to be like, well, I want you to use JavaScript for this and Blender for that.
Speaker 1:You can just ask for what you want and basically get it from all the models right now. You have an interesting thesis. You're you're black pilling on the on the application layer now. You think the models are getting so good that people are just gonna use the AI tools. Does this change anything for you?
Speaker 1:Because I see this in, like, I still feel like these are these are great. There's probably a couple revisions. When I do the prompts, I'm like I I still sort of filter and review. I'll usually get, like, 20 outputs, pick the best one. I actually talked to an AI video founder yesterday who's doing, like, AI movie production and whatnot.
Speaker 1:And he was absolutely printing using all the all the latest models. The business is doing fantastically. He's hiring four video editors, like, week or a day or something. Like, he's hiring lots of people because there still is a lot of it's not even prompt engineering. It's more like processing the output, curatorial work, understanding what is the right thing to fit together.
Speaker 1:And I think that that mainly comes into picture when you're looking at something that's a bigger project. You wanna go from a one minute thing that might have some consistency, but when you go to two hours, the consistency becomes much more important. The the the style, the pacing, matching everything matters more. I don't know. I I I I I think we might be gearing up for another application layer versus versus model layer debate.
Speaker 1:We we saw this with the Harvey discourse earlier this I I think we discussed that yesterday actually. People were sort of black billing because their margins went negative. But but then all the models got cheaper since that released. And so you think Better and cheaper. Back up.
Speaker 1:Yeah. But, of course, if the models can do it at a at a base level, there is a world where every company just has, you know, a relationship with a foundation lab. So I don't know. We'll see. Anyway, people are feeling the AGI.
Speaker 1:The serious adult ML enjoyer at Anthropic. He said, I'm feeling the AGI. I gotta follow this guy back. I'm feeling the AGI. He made this cool video with Opus five five.
Speaker 1:Then comes in from the top rope. We don't have AGI. The job's not finished. I love the debate even internally. People are saying they're maybe loosening up comms over there.
Speaker 1:I think it's cool. I like seeing I like seeing different takes. Of course, these companies are not monoliths. It's nice to actually get a glimpse into everyone's different perceptions. Everyone has different definitions for AGI, and I think it's cool to actually toy with them.
Speaker 1:Early in the show, I coined, like, definition of AGI, which was just purely economics. Just when AI revenues equal non AI revenues, you have AGI. So, like, when the AI economy is as big as the human economy, then that's AGI. And, of course, that's completely arbitrary. Who knows if that's a valuable metric?
Speaker 1:Certainly interesting. But I can definitively say we're not there because AI is contributing like a quarter percent to GDP. And and total total lab revenues are in like the hundreds of billions while we're doing like tens of trillions in the in the global economy or in The US economy even. So interesting stuff. Fun.
Speaker 1:Go play around with it. Let us know what you build. I like that they put the horse riding the astronaut on the moon. And I think there's probably some blender under the hood. But it was cool that the model was able to do the post processing and the and adding grain and texture.
Speaker 1:That made it look very special. Like it popped out on the timeline, at least to me because of that. Very cool. I also had an interesting Tesla. You really can hill climb SVG.
Speaker 1:At one point, I just took an image that was AI generated image of a pelican riding a bicycle, which is, of course, because AI generated Gen AI image, like, looks amazing. And then I just told Astra, like, turn this into an SVG pixel by pixel. And I was able to say, here, there's like a 40 megabyte SVG. But it looks exactly like a pelican riding a bicycle. And so it calls into question like, what what what even matters?
Speaker 1:Like Yeah. Because you can you you can use a generative AI tool to then create a blender model to then create an After Effects file to then bake it down to a PNG or change it. Like every format changes into every other format. Use the best tool for the job. We're we're definitely in the regime of like how much did it cost to actually get that thing done Yeah.
Speaker 1:As opposed to some like artificial synthetic benchmark around like a line of code cost this much. Like no one cares. No one cares what tools are being used under the hood. There's definitely a world where you go to a model, you ask for a thing, and if it needs to use Slack, it uses Slack. If it needs to use Photoshop, it uses Photoshop.
Speaker 1:If it needs to write its own thing in Python, it does that. It uses the right tool for the job and it intelligently chooses things just like a real person on your team would, which is exciting. What else? EV News. EV News.
Speaker 2:From from Dave over on X. He flagged this.
Speaker 1:First. And we're Let me tell you about Codex. Codex is a powerful workspace for getting work done with AI agents. Whether you're writing code, analyzing data, creating content, automating business workflows, Codex helps you move projects forward from start to finish.
Speaker 2:We are one week out from the Roadster reveal. Yeah. And I'm
Speaker 1:Did listen to the end of the show when we bound when you bounced? We went deep on the Roadster. We got a scoop. We got a scoop from our guest who's next to a Tesla employee on a plane and was, like, pulling facts about him just to go leak it on TBPN. Yeah.
Speaker 2:Crazy.
Speaker 1:No. No. I mean, I I don't know how real this was because the guy could have been messing with them. I don't know. It's it's all rumor mill stuff, but the guy was basically, like, yeah.
Speaker 1:I was next to a Tesla employee and he was saying that the jets are not to make it fly. It's actually for downforce. It's gonna suck it to the ground, and it's gonna go zero to 60 in one second. Very cool prediction. Very different from what we were saying.
Speaker 1:So who had the
Speaker 2:the only thing is it doesn't it doesn't track
Speaker 1:We had the lowest number, so you're closer to win if that's
Speaker 2:what happened. Well, doesn't track with what Elon was saying on Rogan, which is that
Speaker 1:Yeah. It should be
Speaker 2:flying with ground. Fly.
Speaker 1:And that's what I was getting at. I was like, if you can have a fan or a jet that sucks you to the ground for one purpose, you can probably reverse those and boost up at least for a little bit, for a little jump. But we'll We're counting it down.
Speaker 8:Well, in other road to
Speaker 1:Christmas, road to Roadster, but we're still keeping an
Speaker 2:eye on it. Saudi Arabia is aiming to disrupt the auto industry Okay. With these radical EVs.
Speaker 1:They're wedges.
Speaker 2:They have a brand called CER exobot. Mhmm. It's coming as a sedan and an SUV. Mhmm. Looks like sort of a futuristic like, Lamborghini
Speaker 1:wedge in there. A little cyber truck in there. Yeah. Is that
Speaker 2:This is Saudi Arabia's first true production car brand. So There's such a
Speaker 1:trade off between the wedge looks great, but it's a terrible use of space. Like, you're like, the the optimal use of space is like the new Waymo vehicle, which is basically just a box or like
Speaker 5:a bus.
Speaker 2:So you care you just care about utility now?
Speaker 1:I'm just saying it's a trade off. Like, the more wedgie you get, the less utilitarian the the vehicle space is. Of course, you need to have
Speaker 2:This one looks like somebody broke up with the new generation of Prius. Yeah. Now they've leveled up again. Oh. Right?
Speaker 2:That was the that was the original Yeah. Like read on the Prius is like who hurt you? Yeah. Because the the new Prius is looking pretty pretty stanced.
Speaker 1:Yeah. It is. It is.
Speaker 2:It's a level up. Yeah. But this is a level up.
Speaker 1:Is it the prime? Prius prime looks pretty good. Yeah. They've done a
Speaker 2:good job. Anyways.
Speaker 1:850 horsepower tri motor electric powertrain.
Speaker 2:This thing looks insane.
Speaker 1:It does look insane. It it it I wonder where they'll hit in terms of price. That'll be that'll be interesting to see. I mean, it's cool. Saudi Arabia obviously known for, you know, pretty outrageous car culture in many
Speaker 2:places. What if I could what if I what if they could make this the performance of the luche Mhmm. At only 60% of the cost? Yeah. Still hundreds of thousands of dollars.
Speaker 2:Hopefully not But will be interesting.
Speaker 1:Yeah. I have no idea where they would price this. Because it could be sort of like a a national treasure, point a of pride. It could come in at a very high price. At the same time, EV is a tough sell when you're in the when you're in the 6 figures.
Speaker 1:Should we watch the trailer, the official trailer
Speaker 2:Let's do it.
Speaker 1:Where you can see everything. We watched the preview clip. Now we get the full trailer. And I want you to pay attention to the quotes because the reviewers were floored by this movie. I've never seen quotes like this on a movie.
Speaker 9:I
Speaker 1:know. It's crazy. People really really enjoyed this. Triple glaze. Yeah.
Speaker 1:Seriously. While they pull that up, let me tell you about public.com. Investing for those who take it seriously, we got stocks, options, bonds, crypto, treasuries, and more with great customer service.
Speaker 5:Feel you did nothing wrong? I did not. So where did things get lost? Like, how are you where you are right now? I I wish I knew, you know.
Speaker 5:I I don't know. I don't I don't feel like I understand that. So I know you guys invited me to to stay here. Does any part of you worry about me being here twenty four seven? I don't think so.
Speaker 2:Okay. So pause.
Speaker 5:What if I see
Speaker 2:247. He's actually, I
Speaker 1:think Living there.
Speaker 2:Living with Elizabeth Holmes. Yeah. And it's crazy because only Nathan Fielder would even think to throw out an idea of, hey, would you mind if I moved into your house before you go to prison and documented the entire process? Because you would think that there would be no circumstances where where that would be a good idea.
Speaker 1:I think it's just pure upside. There's there's very little downside to to doing this. I mean, you're going to prison. Like, worst case, you look worse, but you're in prison either way. Like
Speaker 2:Yeah.
Speaker 1:The only thing you the only way you can go is up and actually endear some people to you, which is the attempt. We'll see how it lands. Let's keep watching.
Speaker 5:And it makes you look bad. I think this is the moment
Speaker 7:in time
Speaker 5:film I've ever experienced this story before I'm gone. I fell in love in that first conversation. Before or after that she revealed she was the founder of Theranos? Probably before. I mean, weren't you worried she was sort of scamming you?
Speaker 5:A 100%. But what
Speaker 4:did she have to gain from me?
Speaker 5:Well, isn't your family rich? Yeah. I think that there's like a lot of different levels of wealth. Come jump in with me. You're asking me to go?
Speaker 9:Yeah.
Speaker 10:You've never
Speaker 1:What is this for? Why are they doing a three d scan?
Speaker 2:That's just something Nathan would do. Do you mind if we have them What
Speaker 5:is that?
Speaker 1:I saw that and I was like, this has nothing to do with Thanos or going to prison. I may think about this movie for the rest of my life. That is a crazy close.
Speaker 5:Be okay with It feels
Speaker 1:like there's gonna be some twist or something. Something But
Speaker 2:I'm I'm hopeful. The I think the whole thing is gonna make the viewer feel like they're in a really bad acid trip.
Speaker 1:Mhmm.
Speaker 2:Like, all these scenes. Yeah. The pauses, the silence, the way the lighting, the the sort of sense of impending, like, doom, right, because she's going to she's about to go to prison. Yeah. The the the the kids, you know, being present in these scenes.
Speaker 2:Like, the whole thing is just incredibly
Speaker 1:But
Speaker 2:dark I I I and weird.
Speaker 1:No. I feel like it has to break expectations in some way because the average viewer is gonna sit down and believe that she is guilty and that she lied and is like a sociopath because like that's sort of her brand. Right? Is that like she never admitted any guilt and was guilty in the John Kerry retelling of the story and obviously the court findings. And so the for it to be for it to be like that, I feel like there has to be some twist or something.
Speaker 1:I don't know. I I I'm I I would be shocked if it's just, yep. Like, she defrauded investors and didn't really build a good blood testing device. And now she's in jail, like, the end. Like, would that be would that be
Speaker 2:I don't think it would get those quotes.
Speaker 1:I don't think it would get those quotes. So I feel like, there's gonna be something crazy that happened, some, like, complete twist. Like, maybe she's innocent or something or or maybe she's, like, I don't know, guilty or something else. I I
Speaker 2:I there's The gotta be something question is how does the who's who's the guy that that she would she would call Tiger?
Speaker 1:Oh, yeah. I actually don't know. Yeah. Yeah. Like, because there there there has during the during the court case, there was a question about his responsibility.
Speaker 1:Should should he bear more of the responsibility? And so but he doesn't seem to be in the trailer, so I don't know. Because one one weird like twist would be if you came away being like, that guy's more responsible than I thought. You know? But anyway, let me tell you about Figma.
Speaker 1:Agents meet the canvas. Your AI agents can now create and modify your Figma files with design system context. We have the president and CEO Paul Coff, Cristiano Amon in the waiting room. Let's play Here we go. With TBPN UltraDome.
Speaker 1:Cristiano, how are you doing? Very
Speaker 10:good. How about yourself?
Speaker 1:We're doing fantastically. Where are you calling in from?
Speaker 10:From Maui, Hawaii. It's our Snapdragon Summit. This is day two.
Speaker 1:How how long have you been doing Hawaii? Why Hawaii?
Speaker 10:It's probably more than ten years. I think it's we have a lot of, you know, global press that come here from all of our countries, a lot of partners. Nobody complains about coming to Hawaii.
Speaker 1:Yeah. I can imagine. Yeah. It's a good place to be. What are the highlights?
Speaker 1:What are the messages that you're trying to drive home today and and with this conference?
Speaker 10:Look. At the Snapdragon Summit, we always announce our latest, you know, Snapdragon flagship processor for smartphones. But this one is it's special because for the past couple of years, we have been seen. We have a clarity of vision. We have seen how a smartphones are gonna change to an AI smartphone, and I think that's actually started to happen right now.
Speaker 1:Yes. What how does your business actually change in an era of folks wanting to do more on device inference and AI on something like an Android phone, one of your big partners? How how do you think you need to adapt? Is there something you need to change, or has this been years in the works you think you're ready?
Speaker 10:No. Years in the work. Look, we if you we were kind of a long time ago. It it feels like a long time ago when we're kind of showing, you know, multi billion parameter model running on devices. Look, there's this whole conversation about AI running on the cloud and on the edge, and people often ask the question, why the edge?
Speaker 10:Why the cloud? And I think that's probably the wrong question. It's almost I like to go back and and say, okay. Everybody, get their phone out of their pockets. Get your 200, 300 apps that you have, and let's go have a conversation about what part of your app runs on the device or runs on the cloud.
Speaker 10:And if you and I know Yeah. Even though we build incredible processors, if you put on airplane mode, you you don't use your phone. So at the end of the day, AI is no different than that. It's gonna be running on the device and on the cloud. It's gonna be all transparent to you.
Speaker 1:Yeah. How how are you viewing what the next couple years will look like in automotive? It feels like the demands for AI on device at the edge are even more important potentially, but maybe it's equally employ important. How are you seeing the automotive business develop?
Speaker 10:Well, automotive has been ahead of that. Like, we we've been very fortunate. Automotive was a new business for Qualcomm, and and we now serve all the car companies in the world. Mhmm. And what has been interesting about that is AI on the edge, actually, is being very big in automotive because of assisted driving and autonomous driving.
Speaker 10:That's kind of AI running on a processor in the car. But what's happening with the car right now is when you're behind the wheel and you don't have any legacy of OSs and apps, a genetic experience is very natural when you're behind the wheel. We also see now the fusion of the systems in the car that was designed for navigation, like the cameras, for example, being also being used for see what I see and have an agentic experiences. So, like, you know, people are in their car and they say, well, they guessed the agent. This restaurant on the right, what's the Yelp review and do they have availability for lunch right now?
Speaker 10:Those are kind of some of the experience we're seeing and that's accelerating for us.
Speaker 1:Got it. How are you processing the narrative around like the CPU crunch? We saw the the agentic era boom and, you know, there were the whole websites that were going down because so much code was getting pushed. It feels like agents like, the the the idea that everything would be done at the model layer on a GPU is not the way things are playing out. CPUs are incredibly important, especially in the data center.
Speaker 1:I'm wondering if, are we going to experience like another CPU crunch as we enter the era of like personal agents? Because we saw the effect that the enterprise agents and software coding agents had on the CPU demand. What does the next couple months even look like for CPU demand in the data center?
Speaker 10:Okay. Let me separate that conversation in two. I think one one topic is there's so much more demand for computes than availability right now and across the board. So I think what we've seen this right now and and by the way, as a semiconductor company, I'll tell you, the supply chain, it's operating at a 100% capacity. Everything everything is short because there's so much more demand than the availability of compute.
Speaker 10:Now the second part is, the way we see it, and we're just entering the data center, but we kind of see that also on devices of the edge. You the data center is going to have to evolve, and it's already on its way to do that into a terra genius compute. You have different engines to do different things. CPUs are going to be very important for orchestrator and agents and CPU demand will continue to rise and that's also true on the other side of data center phones. As those agents get deployed on devices, they have a lot of CPU demand.
Speaker 10:But you're going to have different engines for different things. Right? For example, for inference we see there's engine now for pre fill, engine for decode, decode attention, and so forth.
Speaker 1:Mhmm. How are you thinking about the changing landscape of consumer devices? I mean, Meta Connect is today. I'm sure that they're gonna announce some new consumer devices. But we talk to founders all day long that are building just, like, really cool, small robotics projects, wearables, devices.
Speaker 1:There's there's rings and and wristbands and ankle bands and anything you can wear or put on your body, it's happening. And I'm wondering if you're seeing that actually start to move up your to do list as like you gotta engage with that smaller community even though it might be nascent. And are you are you are you actually trying to engage with like the smaller device community on the consumer side at a conference like this?
Speaker 10:Look, David, and I'm gonna say this in all humility, but the majority of those new classes of devices are actually used in our chip. And there is no there's a there's a multitude of of those products now, and there's big companies and small companies. And the reason is because when you think about agents and agents are not bound by OSs or applications and and you have multimodal like think about glasses for example. I'm actually a big believer that glasses is going to see an inflection point. The glass is a prime real state close to your eyes, to your mouth, to your ears, your head turns, your camera see it.
Speaker 10:And then those things like see what I see, read what I read, hear what I hear are going to come up. But we've seen all sort of form factors. We've seen earbuds with cameras Yeah. Jewelry, pins, buttons, and and this is kind of the new personal AI category, and I think that would be a very big category.
Speaker 1:How are you grappling with the open source ecosystem versus closed source tooling to enable the next generation of devices to integrate? Mean, obviously, you've already won a huge portion of them over, but you want to keep that forever. Right?
Speaker 10:Oh. Thank you for asking this question. I think it's maybe a great opportunity for me to to make a plug about what we're doing with modular. So I don't know if you heard about what we're doing with modular.
Speaker 1:Tell them.
Speaker 10:Look, we made an acquisition of a company and it's a great team. It's the modular team. I think the founder is Chris Latner. I think he's probably a legend within the computer science world. He was the inventor of the Apple Swift programming language.
Speaker 10:He was the inventor of LVM. And he built a stack which is like CUDA, but it's designed to work on any hardware. Doesn't matter. You know, CPU accelerators, it will run on on NVIDIA, on AMD, on on Qualcomm, or on any hardware whatsoever. So we bought that company and we're making that open source.
Speaker 10:That's what we're doing because we actually believe that there the industry will benefit from an open source stack that scale from the data center across different hardware and the edge. And it doesn't matter. I'm gonna celebrate that stack in each and every one of my competitors because we probably need an open stack to to drive innovation in AI. Otherwise, it's just one company doing most of the innovation.
Speaker 1:Thank you. That's a very helpful explanation. We were just earlier in the show talking about Jensen Wong sitting down with Ezra Klein engaging with some of the very deep questions about AI jobs and existential risk. Is this something is this conversation that we're seeing bubble up in the public sphere actually making its way to the C suite of your customers that you engage with? Or are you sort of in a mode of put one foot in front of the other and let those conversations take place in other platforms?
Speaker 1:Have you been engaging with all these debates around slowdowns and open source AI versus closed source AI? It feels like every week there's a new big hot topic and meaty almost sci fi scenario to engage with. But what has your been has your approach been as the CEO of an important company in the space?
Speaker 10:Well, I wish we have like at least like half an hour to have this conversation. This is a very big topic. It's
Speaker 1:a very
Speaker 10:big topic. But look, in one minute that we have, I'm gonna try to maybe give you an answer.
Speaker 11:Solve the whole problem. Solve it all.
Speaker 1:The whole problem. One minute.
Speaker 2:In one minute.
Speaker 10:I think there's a lot of different conversations and look in any kind of changes. For example, in The United States, you see a lot of conversation about safety of the models. I I look. I think people will talk about safety in general and but they are they are very specific things. I think cyber security is actually a big one, and I think it's really important.
Speaker 10:It's important to have products that are done responsibly. I think that nobody's gonna nobody's gonna argue against that. Hey. Do you want me to build a product that is gonna go crazy? No.
Speaker 10:No. I don't want you to do that. Yeah. I think that's kind of a logical thing. And cybersecurity is actually a a serious issue.
Speaker 10:You know, there's a big surface area. But if you go to places like China, the conversation is very different. It's about putting AI in every car and every phone, every PC, every industrial and it's kind of very different.
Speaker 1:Yeah. Yeah. It is. Yeah. We were talking about that how there's just such a wide gap in the discourse between the impact putting AI in all these little places and then you have like the bigger questions.
Speaker 1:But we'll get to that next time. We'd love to have you back on the show. We can spend a full hour solving
Speaker 2:thirty minutes we can get to the bottom.
Speaker 1:Yeah. Yeah. Then we'll solve it. It will be solved. But congratulations
Speaker 2:calling in from your event.
Speaker 1:Thanks so much for taking a couple minutes today. Yeah. Great with us. Have a great rest of your day. We'll talk to you soon.
Speaker 1:Cheers. You too. Great
Speaker 10:talking Thank to you you.
Speaker 1:Goodbye. Let me tell you about MongoDB. What's the only thing faster than the AI market? Your business on MongoDB. Don't just build AI, own the data platform that powers it.
Speaker 1:Coming back on the show, we got Talia Goldberg, partner at Bessemer Venture Partners. We very much enjoyed her last appearance.
Speaker 2:What's going on?
Speaker 1:How you doing?
Speaker 12:Hey. Great to see you guys.
Speaker 1:You had a hot take last time that went very viral and proved true. Everyone thought everyone was like, no, it's about to collapse. And you were like, no, I think like it's okay if there's some, you know, margin compression in the short term. Things will iron out.
Speaker 2:Models are gonna get cheaper.
Speaker 1:And everyone was like, she's not taking finance seriously. And here you are. Vindicated. Vindicated. So congratulations.
Speaker 1:Victory lap, Patrick.
Speaker 12:Your margin is my opportunity.
Speaker 1:There we go. There we go. How are you processing the current moment? What's exciting to you? Is the is the lab trade over?
Speaker 1:Or is there more opportunity above the fold at the application layer, below the fold at the at the semiconductor, Neo Cloud build out level? Like, what's exciting to you personally?
Speaker 12:Yes and yes. I'll say a few things I've been thinking about lately. One is physical AI and one is this new concept that a portfolio company might fall Oh, yeah. Coins.
Speaker 1:Love that.
Speaker 12:Called token market fit. Yeah. And I'll talk about like, I love this idea of token market fit. Wish I had invented it, but they Yeah. And the idea of token market fit is like, simply put, like, what are the areas in the categories where we've found the ability to productively use the average end user, like $10,000 a month of tokens.
Speaker 12:Mhmm. And if you actually look at where a lot of the spend has gone, it's really like there are basically only three categories I can think of right now that have like real token market fit. One is encoding. Like, you know, enormous spend encoding. The second is in is is in video and in media and and in your line of work where you can very productively spend huge amounts of money creating great content.
Speaker 12:Yep. And then maybe the third is in high frequency trading. But if you look at like on average, you know, legal, customer support, sales, like, we actually haven't really hit like product market fit in a real way in a lot of those areas, and we will. And I think the bottlenecks are moving away from this like code and video.
Speaker 2:Mhmm. Is it possible that you can have product market fit without massive costs? Because, like like like, I would argue that that you have, like, token market fit in something like a legal Mhmm. But it's just not you're not gonna see massive spend in the way due to the nature of the work. Right?
Speaker 2:So it's like Okay. Possible that you can have I I I'm just this is maybe a question. It's like, can you have token market fit with just like relatively modest per person spend at a company?
Speaker 1:Sort of the argument that like well, like, we need a lot more software but maybe we don't need that many more legal briefs or something? Like, the market size is small? Is
Speaker 2:that Yeah. There's there's in some ways almost like infinite soft like a product is never finished
Speaker 1:Okay.
Speaker 2:But in in legal work, like, do a deal and then it's sort of, like, done and there's not you know, I don't know. Yeah. I do need to Yeah. My question is, like, I believe there's I believe there's gonna be some categories where, like, AI is incredible. It transforms, like, the the task or the job, but you just don't end up with that much.
Speaker 2:It's just very efficient and you don't end up with, like, you know, exceptional spend. Right? You're saying, like, $10,000 a month Yeah. Per company in a category.
Speaker 12:It's possible. I mean, I think, like, I guess, yeah, there's difference between product market fit and token market fit. But legal is like a really interesting example. Customer's been a long time investor in Legora Okay. Which is like a a great example of this.
Speaker 12:And I think they're still very early, actually in this transition towards what's possible and what spend even for their customers is possible. So I think legal is like yet to be unlocked in a lot of ways. And in a lot of use cases, Logora is still co pilot. It's not really autopilot. It's not like truly doing the work of of lawyers.
Speaker 12:And and then you don't see like large law firms like, you know, massively changing their their team compositions yet. I think that is still to come. And that's on the come. And as we can give and figure out how to productively leverage models to give more work to to agents, like, will be spending more on agents and on models in in in fields like legal too.
Speaker 1:Yeah. Yeah. I mean, we saw that transition maybe last year, earlier this year with like the software engineer who says like, I don't read the code anymore. And that can sometimes be a little bit risky, but in a lot
Speaker 2:of
Speaker 1:places
Speaker 2:The lawyer who's like, I don't even read the contract.
Speaker 1:Well, that would be token market fit. Like if I heard a, you know, a star lawyer say, yeah, I don't even read the the filing anymore, document, I'd be like, okay. Yeah. Super intelligence is here. It's working.
Speaker 1:Like
Speaker 2:Yeah. Well, the the only difference is, like, software, you can ship a feature and it can be it could be broken 1% of the time, where in legal, if you have a 1%, you know, major error rate in contracts, it's Could be bad. A lot of money on the line and
Speaker 1:Yeah. Maybe it'll be a little bit higher up. Yeah. Yeah.
Speaker 12:Look, there are these categories that like I guess the point is like, there's still to come. Like, they're still growing. And so like, what are those next areas with token market fit from an investment perspective are interesting. Because while you're right, there will be some that are maybe a lot more efficient. A lot of the dollars are in the token flow.
Speaker 1:Yeah. Yeah. How how important do you think it is to be the entry point to a per to a certain token flow to be an aggregator in the Strathecari parlance? I was just thinking about Fall and Higgs Field. They, you know, access to incredible models.
Speaker 1:There's a few other companies in the category. But a lot of people, the workflow is like go to their preferred AI agent, then send an API key over and they've already put some credits in an account and then they're using another tool to sort of access that inference. And there's a world where that billing relationship lives within Cloud Code or lives within Codex and they're taking sort of an app store cut. Is that a nightmare? Is that a death now for the inference provider?
Speaker 1:Or could that actually be the future way of these companies working together in a positive way that actually grows the market and the distribution so much that it offsets whatever whatever rent the Model Labs and the and like the the front the front doors are taking?
Speaker 12:Yeah. Look, I think it's probably not like black and white in every And single so, it's somewhat different. Like even in the case of fall as an example, like they are the front door. And so, you you can even access a model like in the past models like Nano Banana and Google's models even through fall.
Speaker 1:You don't
Speaker 12:have to go to. So, they have their cake and eat it too. But we do see this playing out. And I think like Instinct and Muse and what's happening with Meta or Amazon and Shopify
Speaker 1:Yep.
Speaker 12:Are other really interesting analogies of this aggregation, disaggregation theory. And at a high level, like, I am a believer. Like, there won't be places for both. And like, both exist. Amazon exists and Shopify exists.
Speaker 12:And, like, the Sure. The tension between the two is real.
Speaker 2:Yeah. Personal agent predictions?
Speaker 1:Oh, yeah.
Speaker 2:How are you thinking about the category?
Speaker 12:Well, we're very small seed investors in instincts, so we're, you know, very bullish on on their on their I don't know if I should be offended by that sound or
Speaker 2:No. No. No. That's the error.
Speaker 1:No. No. No. It was a small check. But now, it's because you went in the scene, it's big now.
Speaker 12:I wish we were bigger investors. But like it is such a magical experience. Obsessed these products. Like Yeah. Other than when ChatGPT launched, like this is the second, oh my God, moment that I've had.
Speaker 12:Like my parents are having this moment and my sister's having this moment and they don't even use technology. And so it's kind of crazy like
Speaker 1:I Yeah. It is much broader than what we saw with the Clog Code Codex boom where it was this magical experience for people in tech and for people who knew how to open up a terminal and then were familiar with code and had these tasks that sort of fit neatly in that world, this is something that you can give to a family member who doesn't have a GitHub account and they'll have fun and they'll and they'll get some value out of it and be and and sort of see the progress, which they might not have updated on in three years or something. But And
Speaker 12:so, yeah. Everyone is gonna need to own this and there's gonna be a major war and every large lab, every large company is gonna be out there trying to figure out how do I create like similarly such a delightful experience. And how do you bring this to enterprise? I think it's just like the the interaction modes, the delightful proactivity, the simplicity of the interaction is something that could absolutely be ported over to the enterprise use cases as well. And so, I think this is not a winner take all market.
Speaker 12:There will be multiple agents and and different angles on it. And I think this is about to be arguably the most important next category in AI.
Speaker 1:Another knife fight too which is fun. Yeah. Give us the update on Bessemer, raise some more funds. But what's the structure of
Speaker 2:the fund? What's the structure of the strategy evolving Yeah. All that good stuff.
Speaker 12:Yeah. Look. So we have exciting news that we just raised $5,750,000,000. 1 point
Speaker 2:That sounds good.
Speaker 12:That's how we feel. We're we're so excited. We have $1,750,000,000 of that is dedicated to early stage companies, which lets us write really meaningful investments in companies at their earliest days when conviction matters a lot, when it's not, you know, very obvious. $200
Speaker 1:seed rounds. Let's do it. Get it done in two deals. Probably not.
Speaker 12:But And looked like 70% of our investments have started early often way before there's even revenue or or sometimes even a company name. Mhmm. And we're gonna continue to do that. And then $4,000,000,000 for growth, which lets us keep backing companies as they're at their inflection points. And not just participate or let others kind of invest in them.
Speaker 12:We're gonna be leading these rounds. Returns are concentrating, as you all know, in fewer larger winners. And so, the right move for us is to be a meaningful investor and have meaningful positions in the companies that really really matter. And we don't want to spread it thin and be peanut buttering across our growth dollars across a bunch of companies. We want to be super disciplined, but disciplined not by taking small checks in in growth companies, by making big checks, being highly selective, and really committing fully to a smaller subset of companies.
Speaker 12:So that's the strategy.
Speaker 1:That's exciting. Amazing. Very exciting times. Well, you so much for coming on the show.
Speaker 2:It is so funny to rewind like ten years and and if somebody from the future came and they're like, Talia, like, you're gonna have a $5,750,000,000 fund, and you would be like, so we're the biggest venture investor in the world. Right? It's like, well, you know, this is now like, if you wanna be like a real fund
Speaker 9:I know.
Speaker 1:You gotta have Companies are staying private so much longer. I mean
Speaker 2:Yeah.
Speaker 1:The yeah. The trillion dollar IPOs are, you know, unthinkable years ago. And now, there's
Speaker 2:Here we are.
Speaker 1:It's crazy.
Speaker 2:Wild time.
Speaker 12:Yeah. Lot lot to come. Thank you guys for having
Speaker 2:me. To see you. Thanks. Great update.
Speaker 1:We'll talk to soon. Cheers. Have a good one. Goodbye. Let me tell you about Cisco.
Speaker 1:Critical infrastructure for the AI era. Unlock seamless real time experiences and new value with Cisco. We have our next guest in person. Welcome to the show. How are guys doing?
Speaker 1:Sorry. Let me get this out of here. Welcome. Welcome. Introduce yourselves for those introduce yourselves.
Speaker 1:Great name. Great well. Introduce yourselves for the audience. Tell us about the company.
Speaker 8:Yeah. Yeah. I'm my name is Louis Antonelli.
Speaker 1:Mhmm.
Speaker 7:I'm John Antonelli. We're brothers, if you haven't.
Speaker 2:There we go.
Speaker 7:You can guess. Fantastic. But, yeah, we're so we started Reel six years ago.
Speaker 5:It's a
Speaker 7:score stat tracking app. It's very social, engaging Yeah. And it caters towards those fantasy players, betters, just avid fans Yeah. That wanna know what's happening in sports.
Speaker 1:Is it the ultimate second screen experience? Is that the trend? Or is this actually like a third screen now? What how do you how do you think about this?
Speaker 8:Yeah. I think it's the ultimate second screen and then I mean, it's the penultimate first screen. When you're not watching the game, it's the fastest way to know what's happening What's
Speaker 1:going on?
Speaker 8:In sports.
Speaker 1:Okay.
Speaker 8:We started, like when we started live data was kind of play by play data was kind of this concept that was kind of shoved on a fifth tab on ESPN. You have to tab over to the play by play. Yep. You'd have to look at, like, really tiny Excel looking rows of data to try to find those small numbers and see, you know, how what's happening. So we wanted to bring engagement, you know, comments, reactions.
Speaker 8:Yeah. A bunch of community around the data. And then the latency too at the time with gambling companies, like low latency data is super important. Right? Sure.
Speaker 8:So we brought that more from a media perspective to, you know, show people, you know, bring people that knowledge in real time about what's happening.
Speaker 1:What what what is the data source? Are are are are there, like, sort of open APIs and and companies that are comfortable sharing the raw data? Is it some point, like, someone has to write down what happened in the stadium, I imagine. Yeah. There's no
Speaker 7:It's pretty commoditized.
Speaker 1:Okay.
Speaker 7:Genius Sports, Sport Radar.
Speaker 1:Okay.
Speaker 7:Scrape a lot of the data as
Speaker 1:well. Okay. Got it.
Speaker 7:So it's pretty, like, the fact that Steph created a three isn't owned by the leagues or anyone. Got it. Okay. We've kind of taken a spin on
Speaker 1:Can you put all that together?
Speaker 7:That's our content.
Speaker 2:Yeah. Public ball knowledge.
Speaker 1:Exactly. Exactly. And then Yeah. But but so so the magic, like the secret sauce is really transforming that into a feed that is intelligible, not an excel sheet, more like a Twitter title.
Speaker 7:And contextual too. Like, think we add, like we started to add historical milestones. Okay. This is a player's five on 500 career three or Yeah. A player's 12 straight point in a row.
Speaker 7:Yeah. A lot of the times when you're on these products, like, won't know or even when you're watching or you're tuning to a game, like, you won't know how well a player's performing or what they did in the first or second quarter and this kind of brings them that. Like Yeah. Adds like what an announcer might tell you.
Speaker 2:Yeah. Okay. So you guys have one one and a half million monthly actives. And it sounds like the social product is actually like working and real Yeah. Which is notable because I feel like so many people think about this idea like, oh, I'm gonna create a data product and there's gonna be a social layer.
Speaker 2:And then for every 100 pitches like that or people that take a shot, like, yeah, maybe less than one are gonna actually build something where there's like tons of highly engaged users just because I think about where these sports communities pop up, it's on Instagram, on on X, on Reddit, on all these existing social platforms. So was that like, how did you actually make that happen? Yeah.
Speaker 8:I think so from my perspective, and John can touch on his, but we basically, on Twitter, for example, have a million people who are all separately leaving the same comment about some big play. We take that play as this piece of content, and then underneath that, you'll have the million people. So we've kind of inverted it
Speaker 1:Yep.
Speaker 8:A little bit. So instead of, like, one to many, it's, like, kinda, just flipped. And then And it's more ephemeral,
Speaker 2:so people feel like instead of posting on Twitter It's more Like sometimes when I see somebody in tech, we'll post like
Speaker 1:It's over.
Speaker 2:At like And you're like,
Speaker 1:oh, must be
Speaker 11:about you. Yeah. They'll be like
Speaker 1:You're like, you know, it's actually about the game.
Speaker 2:Yeah. And so like
Speaker 1:AI discourse and also basketball.
Speaker 7:Yeah. That was the issue. It's super fragmented. Yeah. There's also like tons of different I mean, there's only a few people that control the conversation like Yeah.
Speaker 7:Meme pages and fan pages for every sport, team, and player. And we kind of built for those fan pages and meme pages.
Speaker 1:Yeah. People used to use sort of hashtags on acts to sort of organize around a particular game or something. But that has gone so far out of fashion, the algorithm sort of replaced a lot of that. But you lost in that place the ability to actually tell the app today I only wanna focus on the Lakers game.
Speaker 8:Like Exactly.
Speaker 1:And so even if there's something Yeah. Yeah. Exactly.
Speaker 8:And you'll see X has added like a live chat now at the game level. So we break down our chats to box scores. So instead of just everyone in one massive chat, you know, we're like, oh, I'm talking about Seth Curry's box score or his most recent three or his, you know, blocked shot. So, like, you can dive into these very, like, deeper places. Interesting.
Speaker 8:A cool stat too is, like, 30% of our monthly users leave comments
Speaker 1:Okay.
Speaker 8:Which is crazy. It's, like, an order of mag couple orders of
Speaker 1:mag Yeah. Normally, it's, like, 99% lurkers.
Speaker 8:Just lurk. Yeah. Yeah. So we bring that, like, really cool global community feel to Sure. To everything.
Speaker 8:Yeah.
Speaker 1:Did you did you get your start on Vine?
Speaker 7:I started on Vine.
Speaker 1:What were you making on Vine?
Speaker 7:So I was taking highlights and putting songs to them. Oh, I
Speaker 8:was like
Speaker 7:one of 30 people that were Wow. Like the OBJ catch that went viral.
Speaker 1:I put
Speaker 7:song to it. It went viral on when I posted it.
Speaker 1:That's awesome.
Speaker 7:And then I grew a page. It was close to a 100,000 followers in six months.
Speaker 1:Okay. So no no front facing, no personality content. Wow.
Speaker 7:Yeah. Exactly. I was Yeah. And I still know, like, decade later a lot of the same and this is partly why real why we were able to grow is through these meme fan pages.
Speaker 1:Sure. Sure. We have
Speaker 7:a page on Instagram close to a million followers.
Speaker 1:Yeah. And I
Speaker 7:would just curate content. Yeah. But I we noticed, like, why do millions of people go to Instagram and follow hundreds of basketball pages, NFL pages? Yeah. Super fragmented and sensationalized.
Speaker 7:We I would pull, like, the top three stats in a game and, like, make a graphic out of it. Mhmm. And so a lot of what we've done is we've codified that content. Mhmm. So people share real that's the most shareable score
Speaker 1:app. Sure.
Speaker 7:So it feels like I mean, you see it all over. That's why we've been growing is
Speaker 8:Yeah. People are
Speaker 7:using as a means to, like, slander appraise players.
Speaker 2:Slander. Yeah.
Speaker 7:Like in the first quarter, can say, oh, potato went, like, o for 11 or whatever. Yeah. Yeah. And people screenshot Yeah. That and Yeah.
Speaker 7:You know, use it as a as a fueling of like narratives and stuff.
Speaker 1:Yeah. What's been the mix of of top of funnel awareness and I guess how has how has it changed amongst I can imagine using, you know, paid partnerships with creators. We want you to you want we want you to promote us versus in house clippers or even like distributed clippers like WAP. Like like what's been what's worked in the past? What's working now?
Speaker 1:How has it changed?
Speaker 7:I like we just started to seed like, we started with just the NBA. Yeah. Think what the way that we started to grow is like we added now we have 18 leagues. Mhmm. But we seed this content among like, there's creators that talk about the game.
Speaker 7:Sure. And they might use like a score in the background or whatever. We Sure. Encourage them or pay
Speaker 1:them Okay. Yeah. Sure.
Speaker 7:To use the scoreboard of Reel or whatever. Yeah. And our first thousand users were all my friends that run these meme fan pages. And there's like thousands of these meme fan page. They have some have like hundreds of thousands to millions of followers.
Speaker 1:Yep.
Speaker 7:The NBA Centels or NBA Centrals of the world, Legion Hoops, like Yep. It would take their I I would just like encourage them to use it or naturally weave into the conversation.
Speaker 1:Sure.
Speaker 2:Yeah.
Speaker 7:Because they might just I mean, they're already posting like, oh, LeBron had a sick game. Yeah. Might as well add that, like
Speaker 1:Yeah. The box. Yeah.
Speaker 7:And whatever.
Speaker 2:How do you make money?
Speaker 7:It's a great question.
Speaker 8:So we started with a kind of virtual bucks model, like Roblox or Fortnite that users could, you know, earn and then also collect different moments. So, you know, instead of collecting like a player, collect his home runners, three years touchdown, and build your fandom kind of based on how many you've how many cards you've collected and stuff.
Speaker 2:And you have to be, like, in the app when a play happens in order to collect it?
Speaker 8:Yeah. There's, like, real time raffles, and then there's also just, like, general packs. So it's cool because, like, as soon as every touchdown right now in the NFL happens, you can kinda get, like, a digital representation of it. It's a stat based representation, like a this is a 30 yard touchdown, and a a really cool thing we do is we rate everything between zero and ten. It can actually go above 10 so we don't become like a Let's go.
Speaker 1:Let's go. So we don't we don't wanna go How high can it
Speaker 8:go? Dunk contest.
Speaker 1:There you go. Sometimes it
Speaker 7:is like 14. 14.
Speaker 8:So Shohei's yeah. So Shohei's, like, 10 RBI. That, like, legendary 10 RBI, six for six, fifty fifty game was, like, a, yeah, 15 or something. So Ash and Jensen regularly was sitting in Okay. These college days at, like, 12.
Speaker 1:So it's sort of logarithmic. Like, 15 is 10 times better than 14.
Speaker 2:We're not gonna be it's like
Speaker 8:the dunk contest is the worst back when it was like everything was a 10.
Speaker 2:It was stupid. So we didn't want that. Yeah.
Speaker 8:Yeah. Yeah. But people actually now reference our scores, and it's really good for, like, rookie ladders for MVP. Like, usually, reflects really well Sure. Without us needing to, like Yeah.
Speaker 8:Subjectively say, like, this is just a it's a Do players like this?
Speaker 7:A lot of them A lot of players. A lot of them use it. Yeah. Because it's just fast. I think that's like
Speaker 1:They're opening it up during the game? How many points
Speaker 8:do I have? Really? Yeah.
Speaker 1:At halftime?
Speaker 7:Like, Trey Jones
Speaker 1:No way.
Speaker 7:When they open up at halftime.
Speaker 8:Every single game. That's crazy. Don't tell, like, Microsoft Surface or whatever because I think they're supposed to use that. It's all good though.
Speaker 2:They're like, damn. Damn. I've gotten an o for 15. Wow. You're really thought I was on a hot screen.
Speaker 2:That's so funny. It's the opposite approach of like the score takes care of itself.
Speaker 11:Oh, yeah. It's literally
Speaker 2:just like obsessing over the score.
Speaker 7:I think another thing we do different too is a lot of like the ESPN's Bleacher Reports Mhmm. Their score apps would never send real time notifications. Mhmm. Only like half time, end of end of game moments.
Speaker 1:Sure. Sure. Sure.
Speaker 7:So we'll inform you like this is like Tatum's 12 straight point in a row or like this touchdown just happened. We'll take you straight to that moment. Yeah. And I think that we send like 50,000,000 notifications a day Yeah. On average.
Speaker 1:Is that sort of, like, predictive? Like, you can
Speaker 7:It's based on the rating.
Speaker 1:AI model or or okay. So you have the rating and then if something is happening, you can be, like, oh, you should really tune in this game. Something cool
Speaker 8:is happening. We'll tell you when it happens. Okay. We we do some, like, cycle watches are the coolest ones. So, like, when a player's about to hit a cycle in baseball and they have like three of the four things, we won't tell you when they need a triple because that's very unlikely.
Speaker 8:So we'll only bring you in when they need a single, double or homer.
Speaker 1:Oh, okay.
Speaker 8:So we'll bring people together for kind of more of that, like expected things to build that hype. But most of the time, there's so much going on in sports. They just wanna know kinda what happens.
Speaker 2:Yeah. Is is is real replacing like sports radio in some ways? Because Will be. I guess when I was a kid, I would I would be like, you know, cycling through the radio and I'd hear like a baseball game on. Mhmm.
Speaker 2:And as a kid, like, you kinda grew up on the Internet. I'm like, who's who's like tuning in to like just the rate like, who's watching sports? Well, you know, who's Listening to Listening to
Speaker 1:Little girl.
Speaker 2:Radio. But it's just sort of that you're kind of like passively following a game or or, you know, a team that you care about. This feels
Speaker 1:like But now people can stream the video on their phone. Like, I feel like that's probably more dominant.
Speaker 7:It is fast. I I like to think of it as like written radio and Twitch. Like, it's faster to read than it is to watch than is the Sure. Sure. Even listen.
Speaker 8:Especially when there's, like, 15 games on at once. Like, you're not streaming 15 games on, like, an NBA Wednesday. Right? So, like, it this is, like, a kind of the red zone of every Okay.
Speaker 1:League. Yeah. Yeah.
Speaker 8:It's kinda like how we
Speaker 1:What's the what's the smallest league you support currently? You like curling?
Speaker 7:We just added that's that's a good question because, like, every league technically can be considered big. I mean, we added we're we're starting to add, like, golf, tennis, some of the more longer tails that we haven't had. Yeah. F one's gonna be coming on the product as well. Nice.
Speaker 7:But we cover and we have tons of, like, soccer's well, probably
Speaker 1:big. Car You've seen that one?
Speaker 7:Ping pong would be
Speaker 8:considering putting a community vote for, like, some obscure
Speaker 1:Some crazy would
Speaker 11:be awesome.
Speaker 7:Hot dog eating.
Speaker 1:Hot dog eating would be fun.
Speaker 8:Joey Chats People ask for everything.
Speaker 1:I'm sure.
Speaker 8:And we have this, like, pretty strong Australian fan base, so, like Okay. We get every 3AM every day. It's like add the AFL.
Speaker 1:Oh, yeah. Okay.
Speaker 8:The down under group. The Okay.
Speaker 7:And the AFL.
Speaker 8:They're, like, it's like our third biggest city is
Speaker 1:How cyclical is the business? Is the suit I mean, I imagine the Super Bowl is your Super Bowl, but
Speaker 8:NBA too.
Speaker 1:NBA finals is big.
Speaker 7:Base. Yeah.
Speaker 1:But but in general, is there, like, a pretty stable demand for this across
Speaker 8:It's definitely sick I mean, like, summer when it's just baseball. It's just, like, natural cycles. Sure. Sure.
Speaker 7:But World Cup was big for us.
Speaker 1:Oh, sure.
Speaker 8:I think
Speaker 7:as we continue to add more
Speaker 1:Yeah. Summer. How are you thinking about Olympics?
Speaker 7:We're talking about, like, Olympic basketball. The tough thing is, like, it's four months out of the year.
Speaker 1:Sure.
Speaker 7:So, like, put engineering time into it.
Speaker 1:A lot of work.
Speaker 7:But I think now that I mean, our team hasn't really grown from, like, a I mean, our engineers are outputting because of, like, AI and everything.
Speaker 1:A lot more.
Speaker 7:We used to be outputting updates every three to four months. Now it's every, like, two to three weeks.
Speaker 1:That's great.
Speaker 7:And so, like, we're just continuing to add more insights, make the data more
Speaker 1:contextual. Contextual. Sure. Sure. Sure.
Speaker 7:I think that's how we continue to stay, like, ahead is, like, how do we like, we've added next gen stats to NFL. You can see, like, telemetry data. Mhmm. And then we're also seeing you can see, like, personnel, like, personnel coverages and, like, things you wouldn't like, while while you're watching, like, you wouldn't be able to conceptualize.
Speaker 1:Yeah.
Speaker 7:We're just trying to bake it in and into a very digestible way because then that makes it more shareable. You'll tell your friend more like, it's all that helps is that helps, like, the growth flow.
Speaker 8:Yeah. It can go really deep on live still. Like, live for us is, like, everything. That's most of our usage is live. So Yeah.
Speaker 8:Just keep being just go as deep as we can.
Speaker 1:What's the house philosophy on prediction markets? Partner, roll your own, stay away from? How do you puzzle it out?
Speaker 8:Yeah. We have an engagement feature we launched kinda with the virtual box, kind of more of a kind of fun Sure. Play thing to build your profile and, like, compete with others as on that dimension. Mhmm. We've considered kind of the the prediction markers.
Speaker 8:We we
Speaker 7:A lot of our I mean,
Speaker 8:our
Speaker 7:recent I mean, we in the last year and a half, two years, a lot of our usage has come from fantasy players and batters. Sure. And people just, like, sweating their bats, basically,
Speaker 8:like Yeah. Around like They're,
Speaker 11:like, oh, I
Speaker 7:need another 12 k's or 10 k's or whatever. Okay. Yeah. Or, like, another touchdown. And so we're like, how do we play into, like, more of a real real money gaming space the real money gaming space.
Speaker 7:And we've talked across the board from the to Yeah. All of them. We end we ended up landing on FanDuel as, like, a partner for us. Yeah. So that's another revenue stream.
Speaker 1:Okay.
Speaker 7:I was like, how do we bring odds to the product? Yeah. I mean, we were talking about it. It's like, we have so many betters, and we can't even serve them with live odds. Sure.
Speaker 7:And so it's another data source.
Speaker 8:Yeah. The data layer of live odds too, like, it adds a dimension that people kind of expect. Now you see it on every broadcast now. It has live odds and what, like, what Vegas thinks, what the people think. So I think there's, like, a lot of cool things we can do with, like, real time charts about how the lines are moving for everything.
Speaker 8:Yeah. People can discuss every market. They can kinda just, like, have it where they expect it with especially with our we signed up millions and millions of the of the gamblers in the last couple years. So
Speaker 7:A lot. And It's kinda I mean, use for the people that are interested in it, I mean, and we've just been missing that as, like, a source of data. And it's a good conversational piece too.
Speaker 9:You can
Speaker 8:turn it off. Like, obviously, there's people that don't wanna see that, so they can just Oh.
Speaker 1:Pull it off.
Speaker 2:That's cool. So do think AI is allowing people more time to just watch sports?
Speaker 7:More I think the thing that's missing though, which I think we separate ourselves even across Instagram and Twitter and TikTok is they don't have deeper community. Like, I can spend six hours a day on NBA Twitter Yeah. And get nothing from my fandom. Like, I don't get badges. Don't get, like, anything related to, from, you know, viewing these games, we build up your profile.
Speaker 7:And I think that's something we wanna continue to, like, triple down on is that community side. Because a lot of these I mean, you look at, like, there's hundreds of millions of Taylor Swift fans. Yeah. Well, there's no, like I still think there's these communal things that can be built for, like, tons of different niches.
Speaker 1:Yeah. A of it's offline, like, you have the tour merch or the signed, you know, album or the jersey that's signed, but on the digital world. There was, like, I mean, there was some movement towards this with, like, NFTs, but it it sort of died off.
Speaker 7:You think about it with Belly and Yeah. Even, like, the letter boxes of the world.
Speaker 1:Sure. Yeah. It's the same thing. Pockets Yeah. Clout people, like
Speaker 7:They want that like community
Speaker 1:profile that they're building up.
Speaker 7:Focused thing.
Speaker 8:Yeah. Yeah. I think sports is pretty safe though. And AI because it's like the last kind of human thing that people value. I mean, that sounds dystopian.
Speaker 1:The whole
Speaker 9:I don't
Speaker 2:know. It's like the it's our our
Speaker 1:More than
Speaker 11:family. More than your
Speaker 2:if you think
Speaker 8:about it, it's like
Speaker 1:The only live thing.
Speaker 8:And that's why you see these ticket prices are
Speaker 1:going Yeah. Ticket prices are high and, of course, there's a bunch of venture capitalists that are trading AI.
Speaker 7:More than anything too. Yeah. Humans. Yeah.
Speaker 1:Yeah. Community building, how do you deal with moderation? Do you have an in house team, an AI system, hybrid? I imagine that, like, calling the most disruptive folks in or there's probably a dance there.
Speaker 8:Yeah. It's very it's a huge dance because our demographic is, like, at 18 to 30. So you definitely wanna keep some of that edginess. Yeah. You wanna be able go
Speaker 1:and talk some trash.
Speaker 8:Exactly. Yeah.
Speaker 1:And not liable or slander. There needs to be an appropriate level of slander.
Speaker 8:But we use yeah. We've been using AI for moderation
Speaker 1:Okay.
Speaker 8:From from day one and Yeah. Just building that up over time. So we've it's always gonna be a dance and a balance. Sure. Sure.
Speaker 8:We we're pretty confident now. We actually scan, like, every single reply in real time Okay. Just to make sure we catch, like, the really, really
Speaker 1:bad stuff. Like a really cheap commodity open source model for that? Are you actually passing that through AR, or is it just, like, look up table if bad word?
Speaker 8:Like Yeah. Well, we we use the look up table I'm sure. Catches like 808085%. Sure. Then we have like a classifier Okay.
Speaker 8:Yeah. That's super fast, like catches another 10%. Yeah. And then anything that falls through there will hit like any of the latest Yeah.
Speaker 1:And then and then you can and then you can escalate.
Speaker 8:So we have a couple layers there. Yeah. Yeah. Escalate if we need to. Generally, it's which worked super well.
Speaker 8:And I think it's something that's missing on a lot of these kind of live chats. There's a lot some of these live chat stream like, you just have close the chat.
Speaker 2:So, like,
Speaker 8:we don't want we don't want that. So Yeah. Yeah. It's a dance, I think it's, it's important.
Speaker 2:What's stopping you guys from having one and a half billion monthly actives? Right now, you have one and a half million. Yeah.
Speaker 7:It's a great billion users, so we could have every, like, live show, The Bachelor, from The Bachelor to, like,
Speaker 1:Love Island. Yeah.
Speaker 7:You take data. I mean, you can talk about what you saw.
Speaker 8:The so the original idea came from so my now wife and I, we built, like, a fantasy bachelor app where we would sit and type in everything that was happening. Every kiss, every hug. And we had, like we built it. It was just us two.
Speaker 2:We had we built, like, we had,
Speaker 1:like funniest.
Speaker 8:Yeah. We had like 50,000
Speaker 1:Walking in for the bachelor.
Speaker 2:50,000 weekly users for every kiss and every hug. So
Speaker 1:I I watched Wow.
Speaker 8:We watched four years. So you're sitting there
Speaker 2:like, Riz them up. Riz them up.
Speaker 10:Four Four years.
Speaker 8:And we had to be on live because it was just like a perfect mix with play by play and sports and, like, this live feed concept. So that's
Speaker 7:kinda the So fun. Him not having to type in the data every time.
Speaker 9:Not having to type in it.
Speaker 2:Even though the some
Speaker 8:of these third party providers aren't
Speaker 1:Yes.
Speaker 7:But you look at, like, politics. Yeah. Yeah. There's tons of, like Yeah. Even how
Speaker 1:You have markets and stuff.
Speaker 7:Especially around, like, the elections. It's, like, would be cool in theory. And we don't wanna go down that path Yeah. Before we dominate. I think sports is still, like, a 10 x with what we have.
Speaker 8:Mhmm. People do ask for elections, like
Speaker 1:Sure.
Speaker 8:A lot. It's And kind of does it is has similar feel to, like, a prediction market with, like, the Yeah.
Speaker 1:Everyone's watched, like, the election map populate and go red and blue over the night, and and that's sort of, like, a similar visualization for sure. We are
Speaker 7:a daily use product though.
Speaker 1:Okay.
Speaker 7:And I love talking about like monthly actives, but we peaked at like 1,100,000 daily.
Speaker 1:Yeah. It's really good peak down
Speaker 7:yeah. Down mile is like 70%
Speaker 1:Yeah.
Speaker 7:At its peak, like, during moments you would think people would be watching.
Speaker 1:Sure.
Speaker 7:But they're almost like people wanna also see because a lot of people watch alone, like Mhmm. Mhmm. Like just at home or whatever. Just want that community or that second screen. And like during the Bam, out of Bio game, we had like 200,000 concurrent people, like, tracking his, like, every point.
Speaker 7:Yeah. Towards his 83 points. Yeah. So we see, like, during big moments like that or even just, like, the NBA finals or the Super Bowl, we have, like, very concurrent usage, like, high concurrent usage.
Speaker 1:Yeah.
Speaker 7:Just, like, figuring out how to make that more engaging and then also more fun with your friends. I
Speaker 1:think
Speaker 7:it's a big growth unlock for us.
Speaker 2:And all the all the betting and and prediction market companies must be so pissed off that they don't have this product. Because it's like actually, like, all they care about is is, you know, user acquisition and figuring out how to get engagement and deep usage and you guys feel like you built something that built something that's gonna be very hard to actually replicate but would be would be very valuable to them.
Speaker 1:Yeah. Yeah. Are there like walk me through the how a subculture emerges. I'm thinking about like on Twitter, it's just one big global chat room, but then there's like teapot. Like that part of Twitter like these like the the the SF tech insider community has has created its own little sub community.
Speaker 1:Are there groups of people that break out and build like a Discord further? Or or do you have the functionality to create like, a group of people that are all commenting on similar games and then they, you know, become friends on the app and
Speaker 8:Mhmm.
Speaker 1:Can interact more communally outside of a particular event?
Speaker 8:Yeah. We have, like, sub we have groups in there, so people can, like, create groups, and it's kinda nested under any piece of content. You can kinda talk in that group. Yeah. I think most of the sub communities, though, are still global, but they're around, like, kind of those meme moments.
Speaker 8:So,
Speaker 1:like Sure.
Speaker 8:When Brandon Podzemski was trying to hit 30 points, for example, like, everyone with every game was just this community and then they would grow from Who were like the yeah. Like, we need the 30 and he gets 28 and everyone just be devastated. So like Okay. Tracking those type of moments when they pop up Yeah. Is really big.
Speaker 8:Like, the meatball sub thing with the interceptions right now in NFL. There's like random something. It almost feels like you're
Speaker 1:just making
Speaker 8:out stuff that adds.
Speaker 2:I don't know anything.
Speaker 1:And you can just throw one in that doesn't exist and we'd like, damn, that's
Speaker 8:But they happen all the time. Yeah. It's like crazy.
Speaker 7:Create them? It's kind of like they're already a known like, people are already talking about like, yeah, like like the Brandon Zemski 30. Yeah. Like it comes from NBA Twitter almost. Yeah.
Speaker 1:I think
Speaker 7:we can do, like we haven't really been at this this scale before. So I think we can start curating those like subgroups or encouraging them beyond just like teams or players or whatever. Yeah. Yeah.
Speaker 1:It's awesome. Well, thanks for coming Thanks for Thank you. Have a great rest of your day. We'll talk to you soon. Let me tell you about Railway.
Speaker 1:Later, Greg. Railway is the all in one intelligent cloud provider. Use your favorite agent to deploy web app servers, databases, and more while Railway automatically takes care of scaling, monitoring, and security. Our next guest is coming in just a minute. We have Max Levchin from Affirm coming back on the show.
Speaker 1:Always fun to talk to Max about his role as CEO, how he's adopting AI, what he's seeing in the consumer markets and debt markets and all sorts of things. While we are waiting for him, let's talk about another electric vehicle, another international electric vehicle, the the Geely EV that can charge under five minutes. Is this is this fast enough? How long does it take to get gas? Two minutes?
Speaker 1:Four minutes? Five minutes? This feels like maybe a tipping point where people will, you know, be more likely to adopt this. It does make me wonder about the Roadster. Is that gonna be a new will they roll out a new charging technology that allows them to charge faster?
Speaker 1:They've been sort of Tesla has not moved fast into the, like, sort of wireless charging. The actual Porsche, the Cayenne EV does wireless charging. You just put a mat down on your on your garage floor, drive over it, and then it charges. That's it seems like a no brainer. I'm surprised that Tesla hasn't done that, especially since they were working on that crazy robotic snake arm.
Speaker 2:Yeah. Well, I'm hoping the Roadster will just send the battery out the side rocket engines.
Speaker 1:Okay.
Speaker 2:But but we'll see. Yeah. But yeah, it's an interesting trade right now, you know. It's it's charging at at these charging stations can cost, know, in the tens of dollars. It takes quite a bit longer than gas, obviously.
Speaker 2:So dropping that down I think is gonna be pretty key.
Speaker 1:Well, Nikita Beer is oh, do we have our interest? It's red. So It was green for
Speaker 8:a second.
Speaker 1:Let's wait. Nikita Beer has oh, I I guess we do have Max. Is that correct? Okay. Great.
Speaker 1:Let's bring in Max Levchin, the founder and CEO of Affirm. Welcome back to the show, Max. How are you doing?
Speaker 2:It's been too long.
Speaker 1:It has been too long. Let's start with the latest and greatest in in your world in Affirm. And then as always, I have so many I have so many questions about how you're running the company, what you're seeing, what's working managerially on the new technology on adoption side. But first, what's the biggest news in Affirm world?
Speaker 11:Today's news, we are available in The UK on Amazon. That's a massive Huge.
Speaker 1:Not everyone can get an Amazon deal done these days. Congratulations. You have to go straight to the top. Are you negotiating with Jesse?
Speaker 11:No. What what what Amazon
Speaker 1:does feel like a company that might build this themselves. Like, what is your pitch to a big company with a lot of engineers and they have AI tooling? What do you bring to the table when you're partnering with another company at that scale? It's a huge company.
Speaker 11:But it doesn't hurt that we've been partners for quite some time in The US, and and so we're we're definitely no strangers to working with the Amazon engineering team. They're they're excellent. They're very capable of building things. We're a specialist. We know what we're doing in things like underwriting.
Speaker 11:We have extraordinarily diverse capital markets program that allows us to fund the loans that we we do for for them and for all our other merchants. And so I think every company that's not a financial service specialist at some point or another flirts with the idea of, hey. Maybe we should do this ourselves. If they're not serious about it, then sometimes stick with it. If they're very serious about it and they're of a certain scale, they usually say, wait a second.
Speaker 11:We should partner with the very best. And, you know, I'm obviously biased, but I think we we've demonstrated that we're we're pretty great. So this is a a great continuation of the relationship we've we've built with them over the years here, and UK is certainly a super important market for us. We're Yeah. Very excited to be there.
Speaker 11:Also, you know, we we came there a little while ago with Shopify, but been needing to expand the relationship and are excited to be applied with Costco and now with Amazon and many others.
Speaker 1:Having already worked with Amazon for so many years, I imagine that the hurdle to rolling this out is not technical. It's not the actual integration. They They probably have a great team. You have a great team in place. Where are you seeing technical challenges emerge?
Speaker 1:Where are you seeing acceleration in your ability to deliver a better product? Is it on the underwriting side? Is AI helping there? Or is it on prioritization, conversion, all the downstream customer service? Like, there's so much in the business.
Speaker 1:What's really moving the needle for you?
Speaker 4:It's like you read our press
Speaker 11:releases. So I'll answer a bunch of them. There's actually a lot of really cool stuff in the question you just posed. The thing that I was referring to, firstly, so we just announced we launched an entire new family of underwriting models.
Speaker 1:Yeah.
Speaker 11:And this has been a long, long time coming. So we are a specialist, specialist specialist. We've been underwriting building underwriting models for fifteen years with, you know, umpteen petabytes of data that we train on. So we've we've we've been a MLIS specialist for a very long time. But up until recently, we primarily stuck to tree based models.
Speaker 11:They're deterministic. They're easier to audit. They're easier to explain to regulators. We have to do every year. And so all of that has been kind of the the the stronghold of a firm.
Speaker 11:And about three ish years ago, we said this attention idea that you see in LMs and the transformer architecture is really compelling because it just opens up new ways of capturing complex patterns in a way that humans actually cannot, and fundamentally, improving underwriting models for things like underwriting is expressing patterns you see in behaviors over and over again in a way that can be reused across multiple humans. And so we started an internal research project into using attention based modeling to understand behaviors to surface these patterns all in the service of underwriting people that are figuring out a little bit more about them. And so about a year ago, we had something we thought was really compelling, and we've been testing it quite obsessively. We're finally live as of a few days ago with a full suite of these attention driven models that outperform our own gradient boosted tree based models. And the way I mean, just to give you a sense of just how compelling this this this breakthrough is.
Speaker 11:So every quarter, we launch a minor addition of the model. Every year or so, we launch a brand new approach to the the core model, all using these three based architectures. We measure the improvement, and that's what we report to ourselves and, you know, our shareholders on. The improvement for this new we call it ARC. So you need the the code name for the architecture.
Speaker 11:The ARC based model outperformed the next planned improvement by a factor of two. I don't remember the last time I've seen a factor of two implementation improvement. Yeah. So it's it's just very hard to over state how how compelling this is. And so this is I'm very, very proud of the team, and this was a very, very large scale project that was just unbelievably successful.
Speaker 1:That's awesome.
Speaker 2:Talk about what you've learned about the the the timelines it takes for x basically, like, the the difference in execution between two companies to become obvious to the market. And when I say that right now, there's a bunch of new, like, AI companies, for example. Let's say, two vertical AI companies. They both have 500,000,000 in funding. Right now, it seems like like, you know, may maybe there's two ish years where where where it's sort of unclear, like, just how much better is one company versus the other.
Speaker 2:But over time, you know like one will will surface to the top. And then I would say we've also you can basically see that in every category where there's like there's a category like prediction markets last year. There was like two heavily funded companies and then you saw like difference in execution and they sort of like bifurcated over time. But I'm just wondering from your view how you how you work with your team. Like, when I talking to you, you just get this sense that like competing with you would be like living hell.
Speaker 2:And it's of the just like the the experience level and then the approach to all these different layers of the stack and the understanding of the category in your business. And like it just it feels like, you know, a firm is just pulling away very very strongly from other players in the market, whereas it looked like it was a pretty even race in many ways, like, you know, five years ago.
Speaker 11:Thank you. First of all, that's a I mean, I I happen to agree, but I'm obviously biased. I do agree that these things take a while to play out, and who knows which inning we're in and sort of how many more sort of ups and downs we're gonna see in the kind of the superficial judgment, you know, aka the stock price.
Speaker 2:But, like, private markets seem to, like, muddy this a lot. It's, like, almost like companies have to get companies have to get public and then have to to actually
Speaker 11:I'm not even sure public markets make it that much better. Public markets force you to be very transparent about a quarterly check-in. Like, you know, one of the things that I think we did really well as a public company, if I do say so myself, is we put a timeline of getting profitable out on the map and said we're gonna get there. And we did it quite far out. So twenty four months before we were profitable, we said, we're gonna be profitable twenty four months from now.
Speaker 11:And we just printed quarter after quarter after quarter saying, look. We are that much closer. And then on the dot, actually, a couple of couple of months prior, said, yep. Profitable now. Here it is.
Speaker 11:And I think it was a big credibility thing that private companies don't get to to have because the only people who know their internal metrics intimately are their venture capitalists. And even if they publish metrics, they wouldn't be helped to the sort of a standard GAAP accounting SEC regulated way of communicating. So in that sense, public companies have it probably a little bit easier, but you also get flapped around if you miss on a metric or the market thinks that you you messed up one of your metrics. But I think the way you know who's pulling away kind of early if if if I were, you know, putting on my occasional investor hat, I think,
Speaker 7:you know,
Speaker 11:I I'm feeding your compliment to me back to myself, but I I kinda
Speaker 8:happen to agree with it. I
Speaker 11:think you can tell operator to operator, people who are, quote unquote, for lack of a better term, serious people, you can tell. Like, people who know their metrics, people that understand the entirety of their stack that don't just say, well, you know, I have a great team, and so my AI engineers told me to say these words, and those are the words I'm I'm going to repeat now obsessively. Like, maybe the short hand is, like, companies run by engineers. We we are skilled in not BS ing. And so if that that may be, like, a good one eight predictor.
Speaker 11:Like, how likely are they just to do what they say they will?
Speaker 1:Like, if
Speaker 11:the person running it has an engineering degree in whatever engineering, probably gonna be pretty truthful.
Speaker 2:Evergreen. Evergreen. I had one more one more follow-up question. I I'm very curious to get your point of view. We've heard a million pitches on this show about how agents are gonna need to pay agents and we need all of this new financial infrastructure.
Speaker 2:And I've been consistently incredibly bearish on that just because we have a bunch of really robust financial infrastructure that's regulated. We even have companies that like, you know, think about Stripe for an example. They've built for developers at the core from the very beginning, which means that they're inherently well set up to work with agents. And using example, we see new personal agents like Instinct and Muse and there's a bunch of others coming. And there's no point where I'm sure somebody's pitched like a firm for agents, you know, or some some silly pitch like that.
Speaker 2:But at at with with all these new, you know, sort of applications, the agent will just go and tell the user, do you wanna pay cash or do you wanna use a firm? And like a firm and and they'll just get to Select like they would as a consumer. Mhmm. And so there's no new financial infrastructure needed. And I feel like Agentic payments, like net new Agentic payments might be an entire mirage and we may have gotten a bunch of like, you know, posts online and blog posts and all this stuff and then really nothing new happens.
Speaker 2:But what do you think?
Speaker 11:I'm gonna make a a bold claim.
Speaker 2:There we
Speaker 11:go. Affirm for agents will be the firm. I'm put it out there. I know it's risky. I I know what I'm saying.
Speaker 11:No. I I I think I happen to agree with you. I think there are definitely many really cool exciting developments in AgenTig. I am trying out all the same agents myself. Some of them are surprisingly good.
Speaker 11:Some of them are still lumbering through the same problems you see with some of the earlier attempts, but it's very clear that we will get we will all have agents doing our chores for us. I happen to believe that quite a lot of shopping isn't actually a chore. In fact, it's a form of entertainment. And so human in the loop will not just be a requirement. It will be a loss to humanity if we are not allowed or if we're not participating in some of the shopping choices, which includes, by the way, the way you pay.
Speaker 11:But some of these things will go to the agent. The underlying plumbing, and by that, I mean everything from deciding the best way to pay all the way down to figuring out the smartest choice of a plan, most rewarding transaction, best 0% loan, etcetera, I think that's going to primarily accrete to people who know what they're doing. We're specialists in the space, and that's why we have to continuously work on improving underwriting. We want to be more inclusive as in say yes to more people while maintaining the same level of credit performance. And so all of that is still, like, the work we have to do, and we have to do it faster, and we have to pull away from the competition as aggressively as we can.
Speaker 11:But I don't think there's an opportunity to dislodge a firm by showing up and saying, are just like a firm but smaller, less profitable with less credibility in the market and the capital markets in particular, but we are agentic. Are agentic too. We're pretty pretty agentic ourselves.
Speaker 1:Yeah. I know. I love it. That's a great take.
Speaker 2:How how have you been approaching leveraging open source models in various sort of like employee use cases and workflows? I think it's been you guys are such an you know, incredible engineering culture. I'm sure a lot of your team has been using open models in a bunch of different ways. Yet at the same time, if you were focused maybe five months ago about, you know, building your own harness or or using these harnesses and open source models, and then the cost of the frontiers drops like so dramatically to the point where you now have like frontier ish models that are cheaper than open source in in some cases. Maybe that wasn't the best use of time.
Speaker 2:So like, how are you thinking about allocating time to getting the most out of open models where it makes sense versus trying to avoid just wasting time when the cost of intelligence will continue to fall?
Speaker 11:So we actually did something pretty smart, if I do say so myself, pretty early on. So I'd sort of predicted that we're going to go through these moments where, like, oh my god. The best harness, the best model, the best the the combination of harness model, user interface is going to change. And there's so much money. There's so much innovation.
Speaker 11:There's so many really brilliant people who are working all day every day and making AI useful specifically for software engineers. It is foolish to commit to a configuration today. You know someone else is looking at it and saying, wait a second. That is the best way of writing software except I have a better idea. And writing software just became the best it's ever been by the hands of the company I'm about to compete with.
Speaker 11:So, like, the whole like, the self recursive self improvement that everybody's sort of either excited or terrified about, It hasn't come to the models yet, but it's certainly come to the development industry. Like, we are living through recursive self improvement of software engineering for humans and agents together. And so sometime around January of this year, we split off a team of about 12 people and basically said, your job is to make our development experience the absolute best for the current state of the art in a way that is easy to take advantage of now, but switch out to the next best thing later with a thoughtful continuous matter. So we don't want to have this disruptive moment where everybody stop. We're all gonna switch to product x.
Speaker 11:Oh, wait a second. Product y is available. So we have this team, and it's it's run really, really well by a bunch of very, very smart engineers who love their craft and know what they're doing as practitioners, but also great thinkers when it comes to developer experience. They have been keeping us at the almost the cutting edge of both the commercial frontier models as well as open weight models, harnesses, etcetera, where we organize the entire process through with our hands. And whatever it is they offer to the entire company is usually within a hot second of whatever is considered cutting edge, but it's thoughtful enough where if you yesterday, you were on harness a and today we really believe harness b is better, they will need the transition really simple.
Speaker 11:So just having a dedicated team that gives us the best possible developer experience without having to do a handbrake turn every three months has been unbelievably good investment. Like, when when we walked off this team and said, we're gonna have this big group of people whose only job is to make us more productive at the meta level, I think some people were doubting the validity of the idea.
Speaker 2:Yeah. It's interesting because the alternative is similarly sized companies, you have hundreds of people that are experimenting in real time and be like, well, I think found the best way to do it. And the other person's like, well, I'm using this thing,
Speaker 11:and then it's Start it there. Whiplash. So I'll I'll give you the real stats on this one since I'm a I'm a fan of numbers. So we were in the experiment away mode until we have this developer experience team, developer productivity team, and we were probably I think the percentage of code written by machines and humans together versus prior to this team's arrival increased by a factor of 10 when we organized the team and said, look. Here are the prescriptive approach we're gonna take.
Speaker 11:And there's always a menu. Like, you can use cursor. You can also use Clodder. We support all sorts of different harnesses and models, but we have a menu versus go figure out what works for you best. The tyranny of choice is a terrible thing.
Speaker 11:And telling a software engineer, go explore over the weekend your favorite way of writing code with an agent, it's not gonna be a weekend project. It's gonna be a six months long project. So lopping that off into a separate area where you have a rigorous approach, and then we constantly produce, here's the best way according to this team, and here's some of the choices you have in there has been really, really useful. We know it's doing well for us. Our so we measure productivity long before AI in PRs, pull requests per engineer per unit time, the cost per PR fully loaded everything from salaries all the way down to AWS costs has come down 30% since we created this developer productivity team.
Speaker 11:And so not only are we increasing the amount of code we're writing because we're able to leverage all the agents, the true cost per pull request is coming down quite steadily and has been for a while. And so
Speaker 2:I'm Very cool.
Speaker 11:Very excited about what's to come there. But I love the fact that we have this really well constrained approach.
Speaker 1:I love it.
Speaker 8:Makes a
Speaker 2:lot of sense. One, one word answer for the next one, since you like numbers. What's your p do?
Speaker 8:No answer.
Speaker 2:No answer. Alright. We'll get to it next time. Next.
Speaker 1:Thanks so much for coming on the show.
Speaker 2:Great update.
Speaker 1:Great We'll talk to you soon. Goodbye. Let me tell you about CrowdStrike. Your business is AI. Their business is securing it.
Speaker 1:CrowdStrike secures AI and stops breaches. We got Sam Ross from New World, the co founder and CEO in the Sorry waiting for keeping you waiting. Sam, how are you doing? Welcome
Speaker 2:to the well. Big dog.
Speaker 1:We got a fresh gong now. Jordy already broke one, I think because he was celebrating that it's only ninety two days till Christmas.
Speaker 2:That's But
Speaker 1:we got some bigger news than that, a number that's bigger than ninety two.
Speaker 2:Most boring
Speaker 1:most you raised.
Speaker 2:The most boring AI company in the world, which Woah. Which Woah. No. No. They ran that as an ad.
Speaker 1:Oh, yeah. That's That's our campaign.
Speaker 2:Oh, yeah. And you guys trigger a bunch of dollars. Guys trigger a bunch of people Oh,
Speaker 1:you triggered me? Triggers. I don't find it boring at all.
Speaker 2:Anyways, let's talk about the round. Yeah. Let's talk How much
Speaker 1:did you raise?
Speaker 9:We raised $100,000,000 with the award.
Speaker 1:Insight partners, you got Salesforce Ventures. You're being careful if you shake hands with Marc Benioff in the wrong place. You don't want to get frame mugged. That's a big risk.
Speaker 9:Know he's already mugged you guys. So I Jordy more than you, I guess. Yeah. Yeah.
Speaker 1:Both of us. Absolutely brutal. What unlocked the round? What was the most exciting thing that Insight latched onto? Is it just top line growth?
Speaker 1:Are are the margins better than what we're seeing in other companies? Like, what was the thing that they were like, okay. Let let's back up the truck.
Speaker 9:Yeah. Well, we sold sales tax, RSI. Beyond that, I think, you know, ultimately, this is this business is you know, the it sales tax is not going anywhere. In it's growing in California starting in January. All businesses selling software have to collect and remit tax on software.
Speaker 9:So this is a growing trend where
Speaker 8:Sure.
Speaker 9:As the world as AI takes a bigger share of the economy, there's gonna be more and more of the tax dollars are gonna be taxing things like AI. Mhmm. On top of that, think you look at the world of accounting and really our space is like tax advisory Mhmm. And AI has not penetrated that space as much as areas like law, and so there's a, you know, there's some really large potential there to build a really large business, and so I think, you know, we've we've been building this business for about three years. We've had, you know, phenomenal growth, and I think just, know, with a great engineering team and great customers, that's what gets the the investors excited.
Speaker 1:Here we go. What are the most valuable growth channels for you? Are you able to sell through tax accounting firms and then the accountants tell their clients how you should be using new Or do you go direct to the CEO of the biggest software companies?
Speaker 9:Say you gotta use new law. What are you thinking? Yeah. Look, we do all of the above. More and more, partnerships with firms is is getting important for us Okay.
Speaker 9:Especially as you move upmarket and these complex businesses really trust their advisors. Sure. I've always been someone who has before this, was running e commerce businesses, so I I love the world of growth and so, you know, a lot of direct sales as well. All all the things you'd imagine that, most, SaaS companies are doing, a lot a lot of marketing.
Speaker 1:Oh, yeah.
Speaker 2:Sorry. Go for it.
Speaker 1:I was just wondering about a $100,000,000. Are you staffing up? Are you hiring a lot of salespeople? Are you just gonna run even bigger ad campaigns? Like, how are you seeing deploying this capital?
Speaker 9:Yeah. I think for this money, it's primarily focused on r and d. Sure. So, again, there's a lot to be building in this space around, you know, building things in the tax advisory space for firms, for companies directly, and I think the space is heating up. You see companies like sponsor ramp building in the space as well, and so it's a it's a you know, I don't I don't see them as competitive.
Speaker 9:Like, this is a big space with a lot of different sub verticals. Mhmm. We're really focused right now on, like, the indirect tax space, so things like sales tax, VAT, we file in 80 plus countries. And so there's a lot of low level grunt work that gets done by armies of tax, you know, tax workers throughout the globe, and that's when we get excited about going and and making their lives easier so people can be more strategic.
Speaker 1:Last question, and it's a choose your own adventure. You can answer either of these questions. You don't have to answer both. One, what's your PEDOM? Two, what's the biggest fish you've ever caught?
Speaker 9:Oh. PEDOM. That that's that's based on time. We already we already solved we already solved RSI, Sales Tax RSI. Okay.
Speaker 2:And you guys are fine.
Speaker 1:And we're fine. I'm living in a future We could tell the tale. So it's pdoom zero, a vote of I love to see it. Well, congrats on the new round. Thanks so much for hopping on the show.
Speaker 2:We're gonna close out the show with you. We did have a hard stop. Sorry for the little running late on the schedule. But I wanted to close out the show with you.
Speaker 1:Well, then I gotta tell him about Shopify. Shopify is the commerce platform that grows with your business and lets you sell in seconds online, in store, on mobile, on social, on marketplace And
Speaker 2:And now with AI agents. Sam, I wanted to throw a flash bang with you Okay. As as an early partner of the show and friends. I'm to throw it and then we'll sign off and congrats to the whole numeral team on on an awesome milestone.
Speaker 1:Yes.
Speaker 9:You. I wanted to also give a shout to our customer, Lucy.
Speaker 1:Oh, yeah. That's right. Yeah. That's what I know. Thank you.
Speaker 1:Don't have it here.
Speaker 9:Important. Aurora water filters, another customer of ours. So, you know
Speaker 1:The Shopify numeral ecosystem is cooking. It's powerful. Everyone's working together.
Speaker 2:Let's hit it. Well,
Speaker 1:thank you for tuning in to TBPN. We'll see you tomorrow. Goodbye.