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Technology's daily show (formerly the Technology Brothers Podcast). Streaming live on X and YouTube from 11 - 2 PM PST Monday - Friday. Available on X, Apple, Spotify, and YouTube.
We are live from the TVPN UltraDome. UltraDome.
Speaker 2:The temple of technology. The fortress of finance. The capital of capital.
Speaker 1:Today, we are covering Grok four launched. We're gonna break that down. The third browser war has begun. Every artificial intelligence company is getting in the game, launching a new browser.
Speaker 2:A browser.
Speaker 1:The new Volkswagen electric bus is a flop according to the Wall Street Journal. Ouch. Apparently, Linda Yaccarino was not fired for the Grok dust up with the crazy hallucinations that were going on. We have more details there. And
Speaker 2:Well, and I don't The other Why would anyone think that she was considering that
Speaker 1:Well, that was in the timeline. That was for sure in the timeline. People were we were talking about that. Like, oh, like, this happened and she stepped down within, like, six hours.
Speaker 2:Oh, really? Yeah. My my read my my read on it was clearly Grock and x a I are not her domain.
Speaker 1:Yeah. No. Totally.
Speaker 2:Obviously. And and they they were Grock was saying some things about her that should never be said.
Speaker 1:Yes.
Speaker 2:And my read on it was, know, who who knows? When when when Elon commented on her post and said, thank you for your contributions. It's like the boilerplate text. And so I'm I'm sure that their relationship is maybe not as good as it was day one. Yeah.
Speaker 2:But I I almost thought it was maybe the, you know, Mecca was the the straw that broke the camel's back. And and she basically said, look, like, you know, I'm I can no longer, you know, bet my career on this platform.
Speaker 1:Maybe. Yeah. I mean, we we can debate it. The there's more reporting in The Wall Street Journal about what actually happened. And the that story, we which we'll get into, is is kind of pointing to this idea that that it had been in the works for a
Speaker 2:while.
Speaker 1:Yeah. And and and that was not the straw that broke the camel's back. That was like like the the papers had been signed. Yeah. The the everything had been signed before that.
Speaker 1:And and then and then and then the the dust up happened with Grok. But the bigger news is that they actually got Grok four out, and people are excited about it. So we'll talk about that. And then the other Grok, g r o q, the CEO of which of that company we had on the on the show.
Speaker 2:Was it yesterday? I'm losing track of
Speaker 1:time. It was very very recently. No. It was Tuesday. Tuesday.
Speaker 1:Apparently, they're out raising at 6,000,000,000, and we have some more details on that company. So that's interesting. Anyway, let's tell you about ramp.com. Time is money saved both, easy to use corporate cards, bill payments, a whole more all in one place. They
Speaker 2:have a new agent launch today. Yep. Kareem will be joining later in the show. Excited for that. For him to break it
Speaker 1:down. So let's break down the Grok four launch. DD DOS has a summary. Insane that Elon Musk has pulled it off again, absolutely crushing the AI wars with Grok four. And we can go into some of the meta Crushing
Speaker 2:the benchmark wars.
Speaker 1:For sure. And there's a question about, like, are we post benchmark? Does this matter? What's the real question to be asking here? But there's a bunch of interesting takes.
Speaker 1:So just summarizing the core announcements, post training RL spend was equal to pre training spend for this for this release. That's the first time it's ever been like that. I think when you go back to the original RLHF stuff that Chattypiti was doing that kind of unlocked like, oh, wow, this really, really works. I'm pretty sure the pre training spend was an order of magnitude or two orders of magnitude bigger. Yep.
Speaker 1:Now, we are truly in this reinforcement learning regime. $3 per million input is tokens, $15 per million output tokens, 256,000 token context window priced two x beyond a 128 k. It's number one on humanity's last exam, which interestingly was a
Speaker 2:Effectively, like, postgraduate PhD level problems, but across a bunch of different domains. So everything from literature to physics.
Speaker 1:Yeah. Kinda like the hardest SAT possible. Interestingly, I I believe that benchmark was created by Scale AI. And and so Alex Wang is now at Meta trying to figure out how can we beat our own exam. Yeah.
Speaker 1:And Elon's just like, I'm number one at your thing.
Speaker 2:Interesting dynamic. Yeah. The the real test would be Elon, you know, doing the same problem set himself and saying, look.
Speaker 1:Well, yeah. I mean, I was talking to Tyler about this before the show. Like, you know, it's like humanities last exam. It's like really good at PhD level math, PhD level stuff. But like how often are you running into those types of problems?
Speaker 3:Yeah. I mean that I think that's the whole thing about there's there's this concept of like spiky intelligence Yeah. Right? Where it's like, okay, it's really good at this very obscure problem that I I never deal
Speaker 1:Yeah.
Speaker 3:But if I have a super long kind of like context window like or there's no kind of like long term, it it just completely loses its footing and then it's like useless.
Speaker 1:Yeah. We're kind of in like less of the benchmark regime and more of the agentic, like, how long can the agent run. So Yep. It's like, we're in the fifteen minute AGI regime. Maybe this is fifteen minutes of, like, even better AGI, but we want to go to
Speaker 2:Yep. Thirty minutes. Well, on Monday that this, you know, takes me back to him talking about continual learning being the next problem that we really need to solve. Yeah. Because it's great if you have a PhD level expert in your pocket that can solve any problem in any domain almost instantly.
Speaker 2:Yep. But if it can't learn and take feedback and improve on certain tasks, then it's basically like useless. Like if you had a if you had a PhD level, know, know, a PhD join your team to work on a specific problem. Yep. But it it was hard restarting at the beginning of every single task with no prior knowledge.
Speaker 2:No. It would the the it would be almost impossible for that person to succeed. So
Speaker 1:Yeah.
Speaker 2:But Siemens still got
Speaker 1:it Yeah.
Speaker 2:On that front.
Speaker 1:But at the same time, like, you know, if you are trying to just really establish yourself as, you know, a at least an API for tokens that that that every business should check out
Speaker 2:Yep.
Speaker 1:Against Anthropic or the the OpenAI APIs. Just saying, hey. You know, we're on the frontier.
Speaker 4:Yeah.
Speaker 1:We're Gemini. Yeah. We're on the frontier is a good way, they certainly prove that with Yeah. A hard graduate math problems at 88%. The the really interesting news
Speaker 2:is Yeah. The interest I mean, it's worth calling out. It's worth calling out. So Grok got number one on humanities last exam at 44.4%. Number two is sitting at 26.9%.
Speaker 1:Mhmm.
Speaker 2:And then going down this list of all these different sort of challenges, they are consistently well beyond the second place. So they are at the frontier now of all these different benchmarks.
Speaker 1:Yeah. So Mike Newb over at Arc AGI says zooming out on Arc progress, I'd say OpenAI's o series progression on v one is a bigger deal than Grok's progression on v two so far. The o series marked a critical frontier AI transition moment from scaling pre training to scaling test time adaptation. And this was the the o series progression, if you remember that OpenAI was spending it was like thousands of dollars of reasoning tokens generated in the test time inference to actually get a good score on the v one of Arc AGI. And so it had to think a ton, but it was able to figure it out.
Speaker 1:And at least it proved that that throwing a ton of tokens and a ton of inference at a problem and and letting the letting the letting it cook, basically, wound up producing progress there. So that was kind of like a new just a new paradigm. Says whereas Grok four mostly takes existing ideas and just executes them extremely well. In my opinion, the notable thing is the speed at which XAI has reached the frontier. And that is really like, it it just can't be understated that this is crazy.
Speaker 1:You you put a post from OWN in the in the chat.
Speaker 2:Yep. I'll pull it up here. He says, Elon Musk is such a beast. I'm not even a pure I'm not even a pure fanboy anymore. How does he He's a lot of swearing in here.
Speaker 2:Gotta keep the keep the timeline PG. But how does he come out of nowhere with a cold start late to the game and ship Grok four and do it alongside everything else he's up to? He's launching new political parties. Yeah. He's literally magnitudes above every founder.
Speaker 2:It's humbling.
Speaker 1:So Basically, agrees that
Speaker 2:that's It's almost like he was a cofounder of of OpenAI.
Speaker 1:Yeah. I guess he's returning
Speaker 5:You would
Speaker 2:have to you would have to, you know, be, you know, almost be a cofounder over there to to be able to do something like this. Yeah.
Speaker 1:Let me tell you about Graphite. Code review for the age of AI. Graphite helps teams on GitHub ship higher quality software faster. You can get started for free at graphite.dev.
Speaker 2:If you wanna ship like Ramp, get on graphite.
Speaker 1:Yeah. Chamath was was was saying the same thing. Somebody in his reply says, seriously, how does this guy produce what he produces? Meta is buying talent at $200,000,000 a year, and Elon keeps his people at a fraction. It's mind blowing.
Speaker 1:Very deeply underappreciated edge for Elon, says Shmoth. The retention of the best people happen when you can offer them a freewheeling culture of technical innovation, no politics, and few constraints. People in the comments are like, no politics. What are you talking about?
Speaker 2:Yeah. Can get a little political over there. But
Speaker 1:But but probably not within the engineering org at x AI. Right? Yeah. Like, it's probably just, okay. How do we build the biggest thing?
Speaker 1:Cool.
Speaker 2:Well, you can imagine the politics of, like, who gets the best spot for their tent in the office. Tent. You know, there's there's a hierarchy. Yeah. Yeah.
Speaker 2:Proximity to
Speaker 1:the I wanna be directly under the air conditioning unit. Wanna be closer to my desk.
Speaker 2:The windows can be nice too. So you can, you know, pull down your tent a little bit and get a little view Yeah. Morning light.
Speaker 1:I wonder what the political structure is of of the tent city.
Speaker 2:The tent hierarchy.
Speaker 1:So is there do they is it a democracy? Do they vote for who runs the tent city? I guess it's just a
Speaker 2:The x AI tent city.
Speaker 1:It's probably just Elon at the top. But just you have a tent?
Speaker 2:Something about San Francisco and tents.
Speaker 1:Yeah. Very funny. But Swix has has been chiming in saying like, need community notes for LLM benchmark porn because in the in the Grok four launch, they highlight this AIME competition math problem. And and and, I mean, it's and so Matt Schumer is basically saying AI AIME is saturated. Let that sink in.
Speaker 1:Grok four got 100%. It made no mistakes on on that benchmark, which is obviously very impressive. But there's this extra comment about the nature of AIME, and so it's a cautionary tale about math benchmarks and data contamination. Apparently, you know, like predictions were that the models weren't smart enough to actually solve these. But he says, I used OpenAI's deep research to see if similar problems to those in AIME exist on the Internet.
Speaker 1:And guess what? An identical problem to q one question one of AIME 2025 exists on Quora. I thought maybe it was just coincidence, so I used deep research again on problem three. And guess what? A very similar quest question was on math dot stack stack exchange.
Speaker 1:Skills still skeptical. I did problem five, and a near identical problem appears on math stack exchange. And so, like at a certain point, if people, you know, put out a benchmark, then talk about it a lot online, and then that gets baked in the training data. You're just memorizing
Speaker 2:the Yep.
Speaker 1:Results. You're not necessarily It's
Speaker 2:still cool.
Speaker 1:Learning everything. It's still cool. It's good. It's good to have everything memorized. But it it really it's not beating like the knowledge retrieval knowledge engine allegations, and it's and we're not really
Speaker 2:in full nature of intelligence. When Scott Wu was on the show earlier this year, he was basically saying AI will win an IMO gold medal this year. He felt very confident in that. Yep. And I'd be interested to see how he thinks about
Speaker 6:And I'm pretty sure
Speaker 2:new performance Yeah.
Speaker 1:I'm pretty sure the IMO gold medal questions are public once the IMO happens. So every year, they're they're developing new questions, but then they go out there, and then they get memorized and the solutions become discussed and, you know, there's all the context
Speaker 2:around And
Speaker 1:so, yeah, it gets it gets kind of baked in. Big question about how valuable are these. At the end of the day, it's really just about like adoption. And that's why, you know, we were we were looking at the Poly market for the best the which company has the best AI model at the July. And x AI has has just surpassed Google, which was sitting around 80% chance for a while, and then started dropping earlier this week, last week, started dropping.
Speaker 1:And now XAI is sitting at 48%. Google is sitting at 45%.
Speaker 2:Well, yeah, actually updating. It's updating live. Google's back up at 49%.
Speaker 1:Is Google planning to launch something new in July? Because it feels like it feels like this market particularly is more driven by Google's release schedule. Because Google might have something in the lab, but, like, they like to release things at specific times. Like, they have it's a big company. They don't just like
Speaker 2:Who knows? Drop it. Gemini team Logan over there might be fixated
Speaker 1:on this Polymarket. Be like, I need
Speaker 2:to. Yeah. Yeah. Yeah.
Speaker 1:He was like
Speaker 2:Oh, during during the wait, he was like, if if you need something to kill the time Yeah.
Speaker 1:Yeah. Yeah. Google AI studio. So, I mean, people were people were were definitely memeing the production values on the Grok four launch because it it was supposed to start at eight. I think it went live at 08:45 or something like that, maybe a little bit later at Pacific time.
Speaker 1:And Eigen Robot was saying
Speaker 2:Yeah. This this market is based on LLM LM Arena. LM Arena. Specifically, the text leaderboard. So currently, they haven't fully updated Okay.
Speaker 2:Yet. So it's unclear. Right now, Gemini 2.5 Pro is still at the top, but I think the expectation is once they get Grok up there, it will be the top spot. So we'll keep following Yeah. This market.
Speaker 2:There's over 2,000,000 of volume already on it.
Speaker 1:So Yeah. It's so interesting that Anthropic's not on this poly market at all. Because people talk about them as having like the best vibes, best like big model smell, the best like, you know, interaction. And Ella Marina is like supposed to kind of like test that with these AB tests and yet like doesn't seem to be performing there. But it almost doesn't matter because they're just focused on like the business at this point as opposed to like the benchmarks.
Speaker 1:So I don't know. It's all changing.
Speaker 2:We have a post here from Ben Hilek.
Speaker 1:Oh, yeah.
Speaker 2:He says, Elon Musk on AI. So during, the presentation, a lot of people were critiquing the presentation saying that it it it was it didn't feel like super polished or whatever. I don't think that was the intent. And and it was pretty fixated on the models themselves and and what went into them and and what they're good at. But Elon did have this one quote in here where he says, and at least if it turns out so he's talking about, you know, what will you know, what kind of impact AI will have on the world.
Speaker 2:And he goes, at least if it turns out to not be good, I'd at least like to be alive to see it happen.
Speaker 1:It's like, if we get the Terminator ending, I wanna be around for that. Yeah. Wanna experience it. What does that say about these timelines? Because it's like, is he expecting that to be alive?
Speaker 1:Like, I I I feel like most people that have been in the doom category have been like, the doom's coming soon. Not not the doom's coming in 200.
Speaker 2:I I didn't I I I read into it more like, he he will find it interesting if that is the outcome and and and it'll be entertaining. Yeah. Less so like will I be alive when it happens kind of But who knows? There was another funny quote at the end of the art at the end of the presentation where Elon kind of looked around at the very end and he's like, anyone else have anything to add? And one of the engineers goes, so it's a good model, sir.
Speaker 2:And they they cut it.
Speaker 1:Extremely online crew. Yeah. Definitely definitely on brand. Well, Ben Hilack, as you know, he's been on the show. He's a designer.
Speaker 1:Probably working in Figma.
Speaker 2:All day.
Speaker 1:Think big think bigger, build faster. Figma helps design and development teams build great products together. You can get started for free at figma.com.
Speaker 2:And we have our first product coming out very soon with Figma Make Cool. That Tyler has been cooking on. I've been very He
Speaker 1:showed me showed me it and I was like, oh, like someone built the thing that we were thinking about building. Like, and he was like, no. Like, I I did
Speaker 2:this. Generated.
Speaker 1:This is in Figure. And I was like, this is like an iframe on another website that, like, already exists because it looks like exactly what we want, but it looks so good.
Speaker 2:Like Like, it looks like he works
Speaker 1:on it.
Speaker 2:He looks like it Tyler. It looks like he worked on it for, like, a few weeks.
Speaker 1:No. It looked like someone else did it. It looked like it was a professional product that, like, stole our idea, basically. I was like, oh, like, someone else got to it. That that was the vibe when
Speaker 2:I Yeah.
Speaker 1:Heard it. Yeah. Well, how how has the how has the experience been?
Speaker 3:I don't know
Speaker 1:if you wanna leak exactly what you're working on. But
Speaker 3:Yeah. I I I don't wanna talk about it too, you know, closely.
Speaker 1:But how many props did it take you to get where you showed me?
Speaker 3:Yeah. I mean, five?
Speaker 1:I can't That's see so crazy. This thing is
Speaker 3:so Design is is super it's really great.
Speaker 1:It's really good.
Speaker 2:Yeah. The fact that it came out looking like basically like 90%.
Speaker 1:Yeah. Yeah. Yeah. I imagine that there's probably like the last 10%. If we were really strict about like, it's gotta be on this exact style guide, like that might be something where like you know, Tyler winds up spending more time finalizing and customizing stuff.
Speaker 1:But in terms of, like, just getting a functional prototype out, oh, man. It was it was mind blowing. It was awesome. I'm I'm I'm very excited about the the age of vibe coding. This is an interesting chart from Tracy Allaway.
Speaker 1:Yep. Been on the show.
Speaker 2:It up.
Speaker 1:The cost to rent an NVIDIA h 100 GPU hit a new low this week with annualized revenue at 95% utilization falling from 23,000 at the May to less than 19,000 today. So that's not that big of a percentage drop, but it is but, I mean, it is a 20% drop.
Speaker 2:It's a consistent trend.
Speaker 1:It's a consistent trend. I wonder how much of this driven is driven just by all of the Frontier Labs that are driving the most adoption or moving on from the h 100 to the 200. I don't know what else would be driving this because if if you can if you can still get if you only take a 20% drop off of a full refresh of a new, of a new of, like, a new hardware And it's a it's a latest and greatest anymore.
Speaker 2:Drop, not a utilization drop.
Speaker 1:Yeah. Annualized revenue at 95% utilization. So this is revenue per unit.
Speaker 2:So utilization is still very high. It's the it's the price that, these Neo Clouds are able to rent them for, which is dropping.
Speaker 1:Mean These tracks. Yeah, yeah. I mean, the market's more competitive than ever. There's more Neo Clouds spinning up and more people actually inferencing these things. And then I guess this is the question of, like, how how stuck will certain workloads get?
Speaker 1:Like, if you if you have figured out a great use case for an LLM in your organization, and it's something that's, you know, not one shotting your entire stack or whatever, but it's just like, you know, we have data flowing through our systems, and we are going to use, you know, LLMs are gonna, you know, interact with every PDF that gets uploaded to our to our website or whatever. And and so we're we're inferencing a lot. Like, you might not need to put that on the latest hardware or update the hardware forever. You might just like be like, yep. It's Llama three.
Speaker 1:It works. It's on h one hundreds and it'll be on h one hundreds forever. And that piece of our business will just stay there. Just like, you know, we have a Postgres database that you know works, and we're not changing it every year. We're not changing everything.
Speaker 1:We're just like we're just trying to cost optimize that, and just hopefully the cost just comes down on that. But like, we've solved this particular problem, then we'll go solve new problems with new technology. So I think that's probably what's going on here. But it gets to the point of like, the biggest question with Grok is that like, the the model clearly is frontier. It works.
Speaker 1:It's it it it you know, like, the the whole fine tuning on the on the actual x account is is, like, a crazy final step of, like, system prompt, and people were joking about that. Like, oh, they gotta fix that. It's like, that's not what they're demoing today. They're demoing, like, the underlying raw model, which is clearly, like, just engineering focused as you saw in the in the in the demo, the demo, which was just like,
Speaker 2:you know, benchmarks and stats. Turns out the secret ingredient to crushing every benchmark is to have the bunch of data from schizophrenic posts on
Speaker 1:x. No. Don't think so. I I actually think it's the design of the RLHF stuff and and the design of the the reinforcement learning pipeline. Tyler, you got anything?
Speaker 3:Yeah. I mean, I I think just like so far what I've seen on X, like the overall response like Vibe stuff
Speaker 1:Yeah.
Speaker 3:Is that people are saying maybe it was a little too kind of overfit on the RL like VR, like verifiable rewards. Yeah. Like you you kind of see this when even in in the demo, think it would it would sometimes respond in the answers with like in in like late tech formatting.
Speaker 1:Oh, sure.
Speaker 3:Which is like, okay, that means obviously they've trained a ton on, you know, math questions, stuff like that. Papers and stuff. Maybe people are saying maybe it was kind of, you know, bench maxed. You see it like, you know, 100% on on Amy is like kind of crazy.
Speaker 1:It's like sauce. It's like you you don't wanna Yeah. Get too Yeah. Yeah. This is the thing about democracy.
Speaker 1:Like, if you win like 80% of the popular vote, it's like, okay. Let's say it was a blowout. You win a 100% of the popular vote, like What's probably going on? Not a democracy. I don't know.
Speaker 1:I mean, in theory, these things should be able to to do it. But I'm I'm interested to know more if we dig into Arc AGI. Is there is there more stuff going on there? Are there any secrets? Because it does seem like an kind of an outlier result.
Speaker 1:You can see it from this Aaron Levy post. Grok four looks very strong. Importantly, it is made it has a mode where multiple agents do the same task in parallel, then compare their work to figure out the best answer. In the future, the amount of intelligence you will get will just be based on how much compute you throw at it. I was joking with Tyler about this that the the individual models are mixture of experts models.
Speaker 1:So there's a whole bunch of of parameters. Right? And then the individual parameters like light up the different neurons based on the an internal to the model router. So there's kind of like the math section of the brain, the literature section of the brain. And so this was like one of the this was one of the key breakthroughs in like GPT four, right?
Speaker 1:It was mixture of experts.
Speaker 3:People think, we're not super sure.
Speaker 1:Yeah. Don't still, we don't fully know. But that's like an internal decision that happens within the model to be like, let's go it's this feels like a math question. Let's go down the math path in the model.
Speaker 2:Yeah.
Speaker 1:But then, Grok four is doing multiple it's running the same model multiple times and then comparing the results. And so now you have
Speaker 2:Yeah. Grading it.
Speaker 1:Yeah. You have multiple agents running mixture of expert models. So you have mixture of mixture of agents running mixture of experts models. And the next thing is gonna be like, if you want the absolute best intelligence, you need a mixture of companies. You need like, I said one prompt and it goes to Grock and Claude and GPT and Gemini and a human.
Speaker 2:Yeah. I wonder how OpenRouter's thinking about this stuff. It is funny to think about the the the human version of that where you give five engineers on your team, build, you know, the same feature and then kind of compare notes afterwards. Wildly inefficient, but with with with software when you can do these things like very quickly, there's incremental cost, but you can, you know, have more confidence in in results and
Speaker 1:I mean, it's basically like having a brainstorming meeting with the whole team. And just throwing up a question and being like, hey, like we have this hard problem that we need to solve. Here's my idea. What do you think? What does Tyler think?
Speaker 1:What does Ben think? You kinda like go around the table. Everyone kind of gives their input, their various expertise. They kind of think through the problem in different ways, and then you compare answers, and everyone kind of coalesces around one strategy. This is like how work happens in the real world with a meeting.
Speaker 1:It's kind of the same thing, but certainly expensive to do that. So it'll be interesting to see where companies like how eager are companies to jump over to Grok. Because it seems like it's been a big lever for Microsoft to have Grok in the ecosystem as kind of a stocking horse for all the other models because Yeah. Satya wants Azure to be very model independent, serve them all. They have the I think they have exclusivity for ChatGPT or GPT APIs or they have obviously like a great deal there with OpenAI.
Speaker 1:And so if they can if they can have Grok four as well, that's another, you know, tool in the tool chest to be like this top layer.
Speaker 2:Satya is in such a good position. It's it's it's probably not discussed enough Yeah. How much just by owning those end customer relationships and being able to vend in whatever model is hot at that moment and and give people optionality and still get 20% of OpenAI's revenue, at least for now.
Speaker 1:Yeah. He's also SOC two compliant.
Speaker 7:Of And
Speaker 1:you wanna get SOC two compliant. Head of Vanta. Automate compliance, manage risk, prove trust continuously. Vanta's trust management platform takes the manual work out of your security compliance process and replaces it with continuous automation, whether you're pursuing your first framework or managing a complex program. So, yeah, Eigen Robot was was talking trash about the production values.
Speaker 5:I don't know about trash.
Speaker 2:They were just they were just
Speaker 1:think it was
Speaker 2:that bad.
Speaker 1:I think
Speaker 2:it's Slides are worse than I'd create after getting into rope to do a presentation with one hour notice. You can tell the engineers made them themselves. I think just this is just a reflection of the culture, right? They're not there. Yeah, very clearly is like screenshots dropped into a slide.
Speaker 2:But this is reflection that this
Speaker 1:mode screenshots on dark mode slides. So they're
Speaker 2:like Yeah.
Speaker 1:Let's do black slides. And then and then you come with your white with your white screenshots that are kind of like misaligned and not really evenly distributed. Like, they didn't do like the the distribute evenly or whatever distribute horizontally.
Speaker 2:Still gets the point across. Yeah. And I think it's a reflection of their culture. Yeah. And, you it shows what they care about, what they don't care about.
Speaker 2:They're not trying to be the most polished. They're just trying to be the best.
Speaker 1:Yeah. Eigen Robot kinda did like a whole like live tweet here.
Speaker 2:Yeah. So Elon storm. Was predicting the model will discover new physics within two years. He said, let that sink in One silence. One engineer laughs awkwardly.
Speaker 1:Is that sooner or or later than his previous timeline? Because he was he was talking about AI discovering new physics soon. I don't remember if he was saying
Speaker 2:Dating it.
Speaker 1:Two years or three years or one year before. Because this could be this could be that he's he's still excited about this. He still thinks it's possible, but he thinks it's gonna take longer than he said previously. And that's kind of the more important update. I don't remember what he said originally.
Speaker 2:See if Grock
Speaker 1:can find But he was saying this at the Grock three launch that like that is the goal. And and if you can get there, like you've kind of you've kind of solved everything. And Sam Altman was talking about that too. That if you can if you can create a super intelligence, like that's probably the first thing that you'd wanna do is like, hey go discover all the new physics and like really help us figure out how the world works. So you can solve you know, fusion I and all this other want to be clear.
Speaker 1:Love all you guys at XAI and only want the best for you. But I'm gonna continue to live post. Elon attempts to give a speech on alignment involving a very small child, a child much smarter than you. The monologue rambles with no conclusion in sight. A pause.
Speaker 1:Yeah. Will this be bad or good for humanity? He says the you know, at least if it turns out to not be good, I'd like to be alive to see it happen. Oh, yeah. They had a Polymarket integration.
Speaker 1:That was kind of interesting.
Speaker 2:Yeah. It's interesting. Basically giving giving the model access to real time polymarket data so that it can help make predictions and sort of add context around
Speaker 1:Yeah.
Speaker 2:The the market itself.
Speaker 1:Yeah. That's interesting. Elon asking the real questions. You say that's a weird photo, but what is a weird photo? I still don't understand why we're looking at weird photos of XAI employees, but they were charming.
Speaker 1:They're calling it Super Grok, crazy features, 16 bit microprocessors. What is I don't even understand what this is. Oh, they yeah. They built like a game in Grok. They had a demo of a video game generated by Super Grok.
Speaker 1:It's a Doom clone. Every time the PC shoots an enemy, floating text appears reading Groktum. Elon is fabricating timelines for product launches on the spot. The engineering engineer sitting next to him is looking at the floor face impassive nodding. It's a good model, sir.
Speaker 1:For real though. Congrats on the launch guys.
Speaker 2:It's a good model, sir.
Speaker 1:I thought I I thought this post from the actual x AI engineer Eric Zelicumin was funny. It's like AI AI model version numbers over time. Did you see this? No. So it's this chart of the version numbers over time and you can see that Grok is versioning fastest.
Speaker 1:Because it's like at this point what else are we measuring? Like like at least they're iterating on the version number effectively as opposed and I guess this is a shot at OpenAI because they launched 4.5 and then went to 4.1 and they're kind of like, you know, there's this big question about like when will GPT five come? The expectations are so high for GPT five. And so they've they've obviously with the Grock teams are like, hey at least every three months we release a new full number. So I wonder the five is a number that really no one has has like gone for.
Speaker 1:Yeah. And I wonder if Grok will do it first. Like if you draw the line on this, they certainly should do it Yeah. In like three months. They should have Grok five.
Speaker 1:And there's no reason that they shouldn't, but maybe there's
Speaker 2:And it's very possible that Colossus is the is the key. To getting Yeah. To five.
Speaker 1:Oh, the the new data center. Yeah. Well, they'll need Linear to plan that out. Linear is a purpose built tool for planning and building products, meet the system for modern software development, streamline issues, projects, and product road maps.
Speaker 2:They linear.app. Need Linear badly. So hopefully, they've gotten signed up.
Speaker 1:Near said, Grok on Humanities last exam, Grok four, I'm not sure I buy even in the general case that there's a given Humanities last exam number, which implies you discover useful new physics. How would one make a benchmark of the proper shape for this? You'd have to have a validation set of questions which are outside the scope of what we currently are able to do. You could choose things on the edge of our knowledge distribution and then try and exclude. Yeah.
Speaker 1:It is interesting. Like if you are able to memorize every hard math problem, does that allow you to discover new math? Like, it's sort of a prerequisite because you have
Speaker 2:to I think where I've imagined these discoveries coming from are having a single intelligence that has PhD level intelligence across, like a single mind that has PhD level intelligence across every human domain. Right? And being able to combine ideas from different domains. Like historically, a lot of innovation is just taking something from one field, bringing it over here, making some combination of it. Yeah.
Speaker 2:I think Elon talks about the potential of discovering new physics, but again, doesn't didn't didn't spend a lot of time like breaking down how that would actually happen. But world is unpredictable.
Speaker 1:So Yeah. It's interesting. People are really pushing this idea of like, okay, like like, we are accelerating. Like, the the AG the Arc AGI leaderboard is accelerating. But I keep seeing this and and feeling deceleration.
Speaker 1:Like I am not feeling acceleration right now. Are you Tyler?
Speaker 3:Yeah. I don't know. I I think generally I'm I'm kind of like not that interested in a lot of these kinds of benchmarks like Yeah. I think ArcAgi is more interesting but just like the humanities last exam, kind of general math, physics knowledge, it's doesn't seem to be that like it doesn't seem to line up with like you you see GPT 4.5 kind of does very poorly on these things. Mhmm.
Speaker 3:But like writing, it does really great. Mhmm. So like I I think I'm I'm more like if I were to go to long short on like different benchmarks like the usefulness of
Speaker 1:them Mhmm.
Speaker 3:I think stuff like HLE, I'm kind of short long. I'm like you guys seen the Minecraft benchmark where it builds the Okay. Two different You you basically, two models build like a Minecraft. There's like a prompt. Like build a house.
Speaker 1:Yeah.
Speaker 3:Then you can choose and then it's like their rank Models
Speaker 2:like that. For the minds.
Speaker 1:But but who who's who's grading that? The human?
Speaker 3:It it's a human who picks between them. Okay. It's kind of like a elo.
Speaker 1:Oh, okay.
Speaker 3:But just like general kind of creative tasks. Sure. I think stuff like that. Aidenbench is good. Yeah.
Speaker 3:I think even in the Grok launch, there was the vendor bench.
Speaker 1:Which one's Aidenbench?
Speaker 3:Aidenbench is Aiden McLaughlin's benchmark. It's just like it's it's kind of hard to describe how it works exactly, but it's just various like creative tasks. How like kind of novel its thinking is, the the like style of its text.
Speaker 1:Sure. Wait. Is it just like he it's just like whichever one he likes the most? I mean, at the end of the day Like, he's the only grader.
Speaker 3:No. No. There is, like, an objective, like, function that Okay. You can, like, run it. It's not just like Oh,
Speaker 1:yeah. It's like which one.
Speaker 2:The idea that
Speaker 1:He's like, open it up again. It
Speaker 2:it will be funny. You know, there come there there's a period of life where your SAT score, like, matters a lot Totally. And it says something about you.
Speaker 1:Yep.
Speaker 2:And then a decade later, it's, you know, what you can do, what you have done Yep. Starts to matter a And lot so I do think we'll reach that point where it's like, yes, you can one shot every hard exam question there is that you can throw at it. But like, what can you do for me?
Speaker 1:Yeah. Yeah. Totally. And I think that's I think that's why like the bigger question is almost like, you know, chatty PT DAUs. And like and like actual Revenue.
Speaker 1:Revenue. And the final app installs and stuff. Yeah. I mean, the the revenue thing is interesting because you wind up in like b to b cloud world, which is valuable, but it's maybe less it's like, it's more competitive because it's more commoditized. And
Speaker 2:Well, yeah. If if you you don't have a lot of leverage in the enterprise, if Azure is able to offer infinite models that are that are infinite frontier models, open source models that are maybe just behind the frontier but great at certain tasks. Yeah. The the leverage isn't quite there. There will need to be another pretty significant leap until then, you know, Anthropic being really good at cogen.
Speaker 2:There's leverage there. Yep. We we saw this yesterday with with Lama switching over to Anthropic. Anthropic models internally. And then, you know, just having a consumer app with a lot of users, also very valuable.
Speaker 1:Yeah. The other interesting thing about the the foundation model layer commoditizing and it becoming like cloud, and if you have a model, you'll just be, like, vended in as an API to anything else. Like, token factory is that the the hyperscaler clouds are extremely profitable. Like, even though AWS, GCP, and Azure are all somewhat directly competitive and and they're somewhat perfect substitutes for each other, they have not driven prices to zero such in the way airlines are, like, deeply unprofitable. Like, AWS and Google Cloud are both profitable.
Speaker 2:Yeah. Or or you look in other commodity sectors like oil
Speaker 1:Yeah. And gas. And I don't know if that's just because there's lock in. I'm not exactly sure, but there's something about where, you know, maybe the maybe the counterintuitive take is that, yes, they do commoditize, and there are a few major foundation models that are frontier, and they all are roughly the same price, but they all have decent lock in with their customers to the point where they're still able to extract some level of profit, or they're just creating so much value that even if they're taking, like, a small marginal slice on top of on top of the the cost to run, that they're creating so much value that it they still have 50% margins or something like that. Because, like, I mean, this was the story of AWS.
Speaker 1:Like, no one knew how much money it was making, and then and then they they they had to break out the financials in one of Amazon's earnings reports, and it was like the AWS IPO, as Ben Thompson put it. Anyway, before we get to the next story, let's tell you about numeral HQ. Sales tax on autopilot spend less than five minutes per month on sales tax compliance. So the big news is that the third browser war has begun. Google stock has dropped on the news that OpenAI is planning to launch a a Google Chrome competitor within just weeks.
Speaker 1:And this is very interesting timing because It's time to browse. Yeah. Time to browse. Certainly makes sense to become deeper in a more deeply integrated into the user's life. Makes a ton of sense.
Speaker 1:There's a ton of benefits that come from having a web browser. What was interesting is the we can go into what Google actually or what OpenAI is talking about launching, but this news, the scoop leaked the same day that Arvind from Perplexity announced that they're finally releasing their next big product after launching Perplexity, Comet, the browser that's designed to be your thought partner and assistant for every aspect of your digital life, work and personal. And so Perplexity launched this on June 9, and then OpenAI, the the scoop goes out via Reuters the same day. And so this feels like very much like let's not let perplexity get a bunch of attention and drive a bunch of people to to start daily driving Comet, the browser. Because even though we're not ready to launch our competitor,
Speaker 2:we want to Arvin was on the show talking about Comet. But Yeah. Over a month ago, he said it was really important to the business. This is a big bet that they're making. Yeah.
Speaker 2:He and and I'm sure both companies are racing to be the first to launch. Sure. Dia, the browser from the browser company, also launched out of or they're still in beta, but they launched like a month ago or something like that. So this is, you know, you're not gonna be the first
Speaker 1:Oh, they launched a month ago the Dia Yes, interesting because I saw Riley Brown also posted the cursor for web browser and Dia browser. And I thought Dia browser launched that same day, but I guess it had launched earlier.
Speaker 2:Yeah. So anybody that was an ARC user can download Dia today and chat with their tabs. Interestingly enough, Perplexity's browser browser and OpenAI's browser are both built on Chromium, the same open source project that underpins Google Chrome and Microsoft Edge.
Speaker 1:Yeah.
Speaker 2:So it the the cool thing here, that means that they're compatible compatible with existing Chrome extensions.
Speaker 1:Oh, interesting. Okay. That's cool. Yeah. It's it it's I I I wanna talk to more people who were, like, active and tech during the earlier browser wars.
Speaker 1:The first browser war was Netscape Navigator versus Microsoft Internet Explorer. This is in the mid mid nineties, early two thousands. Netscape was super dominant, and everyone loved Netscape. Was It originally the Mosaic browser. This is the Marc Andreessen project.
Speaker 1:And then but Microsoft bundled Internet Explorer with Windows '90 five, and the distribution was so powerful that Internet Explorer actually wound up winning and became really, really dominant. But then there was this lawsuit that went back and forth. But then, basically, in by the early two thousands, Internet Explorer had over 90% market share, but then they got kind of lazy and stagnant, apparently. And, I mean, I'm I'm not exactly sure what happened, but they there's a lot more competition. So Firefox, which was, I believe, like a spinout of Netscape or kind of like some of the same heritage there, began getting traction.
Speaker 1:And then Google Chrome launched in 2008 and leapfrogged everyone, and Google Chrome was really focused on, like, speed. It was the fastest browser, and they they they did a whole bunch of work to optimize JavaScript so the pages would just load faster and run better on pretty much every computer that you had. And so, and then they had the open source project with Chromium, and so they were able to kind of standardize the entire industry. And so everyone's always been trying to draw, analogies between, like, the browser wars and the LLM wars and, like, what's the role of open source in that? Like, is open source a strategy to wind up maintaining your your dominance?
Speaker 1:How much does distribution matter? Like, Chrome was probably pretty easy to distribute because every single person was visiting Google just every day searching. And so you just put this bar, hey, wanna switch to the faster browser? And people just do it because you can have basically like, you know, billions of ad impressions on your product every day. Will be interesting to see if ChatGPT can get people to download their own browser on desktop.
Speaker 1:I mean, I'm using ChatGPT on desktop in Chrome all the time.
Speaker 2:Which ChatGPT model would you want to use as a default search engine?
Speaker 1:That's the hard part because I always run into this problem where it defaults to o three pro, but that takes ten minutes. And so then I have to go to a four o. And then if I'm in an o three pro flow and I'm talking to o three pro and I let it cook for ten minutes, it gave me a great answer. But then I wanna just be like, okay, just like clean this up a little bit or summarize this or do some bullet points. I want four o to do that, I have to switch over.
Speaker 1:So I don't know. I I would imagine I'd go four o as the default because I want But even four o could probably be faster before it truly replaces.
Speaker 2:Google's very fast. They've spent a very long time being fast.
Speaker 1:Yeah. And I could imagine them doing a similar project to I believe it was like the v eight JavaScript engine. They sent this team out to, I wanna say like Iceland or something. They they basically sent like a bunch of engineers to like an off-site and they were like, just go optimize JavaScript for like a month. Just go focus on this for like a month or months and come back when it's done.
Speaker 1:Like, you have no other responsibilities than just like optimizing this like compiler. And they came out they came back with the VA JavaScript engine that created this whole like Node. Js boom. People were running JavaScript on the server then. And, and I could see Google kinda doing something similar where they're like, okay.
Speaker 1:We have Gemini. It's good at looking stuff up. It's a good knowledge retrieval engine. Go figure out how to make it load all the tokens for the full response in a hundred milliseconds. And that would be very, very cool.
Speaker 1:And I wonder if that's like a uniquely Google advantage. Tyler, you look something up?
Speaker 3:Yeah. It was in it was in Denmark.
Speaker 1:Denmark. Okay. I was close. I was close. Yeah.
Speaker 1:I wasn't sure it was Finland or Iceland and Denmark.
Speaker 2:Yeah. The interesting thing here, I'm realizing that tabs are definitely a light lock in to browser. Okay. It's not it's not just the default. But if you have six to 10 tabs that you've just had open for a really long time and they're like from a bunch of different things and you can't exactly remember what they were if you had to list them all off.
Speaker 2:But you know, you know, I I personally end up using tabs as like somewhat of a to do list. Mhmm. And so if you're spinning up a new browser and you don't have your tabs, it's like, oh, do I wanna just like get rid of my my tab stack? I have a bunch of tabs that just Interesting. All have stayed there for years and they're basically like it's basically like a mini operating system.
Speaker 2:Right? Yeah. Yeah. It's like different apps. It might be Yep.
Speaker 2:A Google Sheet or or something else. Yeah. I know. Know what you mean. So there's very real lock in.
Speaker 2:I could bring all those tabs over, but I have to then Yeah. Log in to a bunch of different services. And so it's it's really, really hard to actually Yeah. Win here.
Speaker 1:I wonder if anyone's using you know, in in Google Chrome, you can actually change the default search bar to you know, when you type in the search bar and if you just type words, it just Google searches it. You can change that to search chattypity. Yeah. Yeah. You can pass in a query parameter and it can just do that, but I haven't heard of anyone actually doing that.
Speaker 1:And I used to have I used to be such a power user of Chrome. I used to have different code words, basically. So if I if I typed, like, I space and then a query, it would go to IMDB and search that specifically. So you could you could have Chrome, like, route to any specific search. Any That's cool.
Speaker 1:So you could press, like, y space and would search Yelp or, you know, anything else. But I don't know if people are I don't know if people are doing that with Google with ChatGPT. I think people mostly just like control command t and then Yeah. Hang out in ChatGPT.
Speaker 4:Well, we'll have to
Speaker 2:ask Chris in fifteen minutes about get an update on the browser wars because he was an early investor in
Speaker 1:I know one of those tabs that you have pinned right now.
Speaker 2:What's that? Adio.
Speaker 5:Of
Speaker 1:course. Customer relationship magic. Adio is the AI native CRM that builds, scales, and grows your company to the next level. You can get started for free.
Speaker 2:I've had Adio open for thousands of hours in a row at this point.
Speaker 1:Yeah. So Signal kinda breaks it down with the OpenAI launching the web browser. He says, this is oldest playing tech. Find product market fit with a single killer use case, then vertically integrate and horizontally expand until you control the interface layer itself, app, platform. Once you own the interface, you own the defaults.
Speaker 1:Welcome to the next generation of browser wars. Yeah. What's interesting is they're like, Sam Altman at OpenAI and just the fact that OpenAI is a company. Like, there is kind of a mandate to, like, vertically and horizontally integrate, figure out code, figure out research, figure out devices. But every company wants to do everything, but then sometimes they run up against barriers.
Speaker 1:Like, there was a time when Google was like, we want to win social networking, and we want to beat Facebook, and we're going to launch a direct Facebook competitor. And they did, and it didn't go well. And then they shelved it, and then they wound up producing trillions of dollars in market cap just doing the thing that they do great. And so the question is, like, the surface area of OpenAI, they have to explore. They have to experiment.
Speaker 1:It's it would be stupid not to see if they could get a browser and a device and a chip and a nuclear reactor and everything and sand. Get the get the sand. Get everything. But but there's no there's no guarantee that they will win the entire vertical stack, and they will be the one company. Right?
Speaker 2:I think my question is, are these gonna be like is OpenAI's browser gonna be an entirely new app other than their existing mobile app? Is it their desktop app?
Speaker 1:I yeah. That is interesting.
Speaker 2:Because if they have to get people to redownload a separate app, then then that's then that's like an entirely you know, they have a good fly you know, they have
Speaker 1:a bunch of wouldn't just evolve the apps that they
Speaker 4:already have
Speaker 2:installed. Too. I don't I don't know why. Is planning to to release this as like a new standalone app or it will be in the Perplexity mobile app. Yeah.
Speaker 1:I mean, know I think Comet's like its own thing. Because we were looking to download it and we need a code. And you can't just get it if you're just on perplexity. But I don't know. All I know is that you should go to fin.ai, the number one AI agent for customer service, number one in performance benchmarks, number one in competitive bake offs, number one ranking on g two.
Speaker 1:So Arvin breaks down like his philosophy of of Comet, the browser that he's dropping from Perplexity. He says, you can either keep waiting for connectors and MCP servers for bringing in context from third party apps, or you can just download and use Comet and let the agent take care of browsing your tabs and pulling relevant info. It's a much cleaner way to make agents work. So that is interesting. So I wonder how much, like, puppeteering will be in this because ChachiPT and OpenAI have operator that operates a Chromium front, like a headless web browser basically.
Speaker 1:But you can actually see it working and it's clicking things. And so if they're like there's also the value of like the training data. If you're getting people using all these websites, you have all this training data of like, okay, they clicked on the blue button, they clicked on the green button, they saw this, they they they entered the this is how they dealt with this form, this is how they dealt with that form. And so that feels like very, very valuable data if you can get it. So it's probably worth duking it out even if it doesn't even if it even if it takes a long time.
Speaker 2:For sure.
Speaker 1:I do wonder where where else they will where they will plug in, like, clearly operates at, a higher level of abstraction with, like, the screen scraping. And I wonder if we'll hear rumbles about either Perplexity or OpenAI thinking about, like, moving up the stack to that level. Not exactly sure. Anyway, Dan Ives says, we believe Apple needs to acquire Perplexity for AI capabilities. Likely $30,000,000,000 range would be a no brainer deal given Treadmill AI approach in Cupertino.
Speaker 1:Perplexity would be a game changer on the AI front and rival Chattypuppy given the scale and scope of Apple's ecosystem. So people have been talking about this for a while. It feels like there were talks, and then they kind of stalled out. And and our vendors Could
Speaker 2:be a 300 and I think this would be a 375 x revenue multiple. Wow.
Speaker 1:I mean, the product sense is good. You use the product. And, like, there's, like, Apple hasn't been able to deliver on the product side. They have the distribution, but they haven't been able to get things out.
Speaker 2:We talked about this before though. The the most expensive acquisition Apple has ever made was Beats by Dre Yeah. For $3,000,000,000, which was a three x revenue multiple.
Speaker 8:It'd be a huge
Speaker 2:shift.
Speaker 1:Huge shift.
Speaker 2:I don't know that I I think that Apple is embarrassed right now and and feels a lot of pressure to deliver.
Speaker 1:Mhmm.
Speaker 2:I don't know if they're at the point where they would pay $30,000,000,000 just yet.
Speaker 1:And even then, it's, like, hard to integrate and Or even 14
Speaker 2:or whatever their last private valuation was.
Speaker 1:Yeah. And the big question for me was, like, Perplexity is is built on a lot of different clouds, a lot of different tools, a lot of different models. Is Apple cool with that stack? Yeah. Because if all of a sudden
Speaker 2:Or do they wanna just go direct to Anthropic or OpenAI, which they are in conversation Yeah. With. And every once in a while, these these like scoops pop up around perplexity and and Yeah. Apple conversations and it's hard to read into that. Are these like is this like rumor mill?
Speaker 2:Mhmm. Like, what's driving that rumor mill? Mhmm.
Speaker 1:Yeah. We'll pull up the mag seven chart. I wanna see where Apple and Google are sitting today. Apple at 3,200,000,000,000.0, Google at 2,100,000,000,000.0, and Nvidia's holding strong at 4,000,000,000,000. Not bad.
Speaker 1:Yeah. I mean, 1% of market cap. They're at 3,200,000,000,000.0. $30,000,000,000 acquisition to be, you know, to have a have an AI product that clearly has a good roadmap. Isn't that crazy?
Speaker 1:I don't know. Well, if you're making bets on any of the mag seven, do it on public.com. Investing for those who take it seriously. They have multi asset investing, industry leading yields, and they're trusted by millions, folks. So in other Apple news, they're preparing to launch the new version of the Apple Vision Pro.
Speaker 1:They're just doing a slight iteration on the chip. They're moving to the m four chip, and they're launching a new strap, which was something people were complaining about because the weight, maybe it'll be better distributed. People were switching out for the Pro strap, like, earlier.
Speaker 2:It's so funny. So last week, Elon announced the America Party or
Speaker 9:I
Speaker 2:guess it came out on Monday. Stop stock dropped from $312 a share all the way down to $291 a share. That is when Dave Portnoy, I think market bought. He's he was saying he he's Davy Day Trader is back. Yeah.
Speaker 2:That's not a top if that's not a top signal.
Speaker 1:Okay.
Speaker 2:I don't know what it is. But I he was market buying like 10,000,000 of of Tesla being like, I just think it's gonna go back up to where it was. It's just been climbing since then. It's back up to $3,308 a share. Almost almost recovered.
Speaker 2:It's up 4.2% today.
Speaker 5:Wow. So
Speaker 2:on brand for Elon. And basically gonna it looks like it'll just recover the price prior to the America party and Dave Fortnite. He literally was he basically his thesis was like, I think it's gonna go back up to where it was in about two weeks. Yeah. And I'm gonna make 10% and I'm gonna make a million
Speaker 1:was your thesis on Nvidia? You were like, wait, it's like down because of deep seek. Like, maybe it'll go back up. It was like the most basic analysis and it worked perfectly. Was it was fascinating.
Speaker 1:May maybe that's broadly a top signal. Just the idea of like simple analysis and and like not necessarily needing deep insight to to call the market is good.
Speaker 2:I don't know.
Speaker 1:Who knows? Anyway, this Apple story is from Mark Gurman, of course, the master of scoops in Bloomberg. He's got He's
Speaker 2:on his fourth or fifth this week.
Speaker 1:He's on his absolute tear. I mean, this one is a little bit minor. You know, they're gonna include a faster processor and components that can better run AI stuff. And so not that Apple has any crazy AI stuff that they really want to run-in there. I don't think that's a major differentiator.
Speaker 1:I've been thinking about how how like, is AI a key unlock for VR? And like, I don't think so at all. I think it's much more about the content and the use Entertainment. Entertainment. I think it's a replacement for a TV for to to start and they need to just make it dead simple to to use the TV.
Speaker 2:We got we got a we got a demo of VR product a while back and Yep. It had some very cool native AI features. Yep.
Speaker 1:So Yeah. It's
Speaker 2:there's something there. But Apple's products doesn't feel like it's ready for just wearing while you're making dinner.
Speaker 1:Yeah. So that version, the one that significantly reduces the weight of the headset, they're planning to launch that redesigned model for 2027, which feels so far away. Like, I I know it's only a year and a half, probably the end of twenty twenty seven, but so maybe we're talking two years. But that in in the in the AI race, we're really, yeah, AGI tomorrow. AGI next
Speaker 2:week. What's gonna happen?
Speaker 1:AGI next month.
Speaker 2:You know? I'm like Major news
Speaker 1:story breaking up seven. We can't we can't slim down the headset and take off the screen and create a little lighter materials like this this month. Like, let's do it. But hardware is hard and, you know, this stuff takes time. So good luck to them.
Speaker 1:I'm excited for it. I'm I'm very excited for the next Quest.
Speaker 2:Do you still have a VisionPRO?
Speaker 1:I don't. I had it for a month. I took it back, because, I just wasn't using it that much. It was, like, heavy, and I couldn't find and it had a bunch of things that, like, you had to do, like, these crazy workarounds. Like, I wanted just, an HDMI cable that I could plug into it and then just be like, okay.
Speaker 1:My PS five is in VR now, and I couldn't do that. It was like, you had to, like, pull the pull the screen into the Mac and then screen share it in. There'd be latency. It was ridiculous.
Speaker 2:The thing the use case that I still see is people using it on planes. Yeah. But I just
Speaker 1:We gotta check-in with Tyler. When give me your how many times have you thrown on the VR headset in the last week? Did you play it last night? Break it down. Have you turned it?
Speaker 1:Is it collecting dust?
Speaker 3:No. It's I I I've been playing a lot of Call of Duty.
Speaker 1:Pretty soon. The VR headset? Yeah. Okay.
Speaker 3:It's a lot
Speaker 6:of Hold
Speaker 3:your position. There's like no latency. I I'm kind of surprised.
Speaker 1:Really? No latency. And you're doing the cloud?
Speaker 3:Is really slow.
Speaker 1:You're doing in the cloud?
Speaker 3:Yeah. Okay. And It's online. It's multiplayer.
Speaker 1:Multiplayer. So you play multiplayer and you play like the latest and greatest Call of Duty basically?
Speaker 3:Yeah.
Speaker 1:Okay.
Speaker 3:Black Ops like six, I think.
Speaker 2:Cool.
Speaker 1:Six. You have
Speaker 2:a controller
Speaker 1:and it's a big screen on the wall and you just chill there. But walk me through it. It's like thirty minutes a day?
Speaker 3:Yeah. Probably like thirty, forty five minutes a day.
Speaker 2:You're fired.
Speaker 1:No. This is not a gotcha. This is this is true research. So, yeah. I mean, I I honestly think that that that that the the Quest Xbox, the Meta Meta Xbox Quest or whatever, I forget the name, but like that, I think that's more like, I think that's better news than like a processor bump on the Vision Pro.
Speaker 1:Just like Yeah. Deeper integrations that you don't have so you can just throw it on. What's the actual time to, you know, if you want to turn it on, throw it on, plus start get playing, get into a lobby, actually get your first kill. Is that one minute?
Speaker 3:No. It's like thirty seconds maybe.
Speaker 1:Thirty seconds.
Speaker 3:I I it's fast. No. Maybe like a minute.
Speaker 1:Okay.
Speaker 3:It's not like noticeably But you're
Speaker 1:logged in. You don't need passwords or anything.
Speaker 2:Yeah. It's not it's not
Speaker 1:like a hassle. Yeah. Okay. That's cool.
Speaker 3:And I I put the screen It's funny like I I I have a TV in in my apartment but I just put the screen right where the TV is because it's like the perfect spot on the couch.
Speaker 1:It's a
Speaker 2:nice black black square.
Speaker 3:Yeah. Yeah. Yeah.
Speaker 1:I'm gonna have to get this back from you now. This sounds amazing. Now I don't have any time to do this, but but but I I feel like the next what's on the feature road map that you would wanna see? Like, Apple is bumping the the the neural engine and trying to upgrade the chip. I'm not sure that that's the problem with the Vision Pro.
Speaker 1:What would you like to see out of the Quest four, I guess, is the next one that's coming?
Speaker 3:Yeah. I think the main thing so I've I've tried the the Vision Pro. Mhmm. And basically, I mean, the visuals are just, like, vastly superior. It's it's it looks so much better
Speaker 1:Okay.
Speaker 3:Than even the it's so it's it's the
Speaker 1:Quest Better screen.
Speaker 3:Menact Quest three x Xbox edition.
Speaker 1:That's what
Speaker 2:I have.
Speaker 1:Yeah.
Speaker 3:Yeah. And the screen is just way better. Okay.
Speaker 2:I think that's
Speaker 3:I would say that's the main thing.
Speaker 1:So if they can just go find the supplier, if Meta can just go find the supplier for the Vision Pro screen and put it in the Quest four, you'd buy it yourself?
Speaker 3:Depends on how much it is.
Speaker 1:I'm kind of broke.
Speaker 3:I I would definitely be inclined to.
Speaker 2:Not if not if you drop out and go full
Speaker 1:You speed run all of Halo in order to potentially win one?
Speaker 3:I would do that.
Speaker 1:You would do that? You would do a very difficult challenge in order to potentially win one because you would you would want it.
Speaker 3:Yeah.
Speaker 1:Okay.
Speaker 3:No. I think that's that's I would think that's definitely the
Speaker 1:main thing. Is are there any other are there any other nice to haves that you think might might shift people?
Speaker 3:I don't know. I mean, the it's it's very light. It's it's way lighter than the the Vision Pro.
Speaker 1:Yeah. But still, I mean, I I feel like light is very relative. Like, it's light to the point where you can do thirty minutes or an hour. I you probably can't do, like, a full day or, like, or, like, four hours.
Speaker 3:Or any kind of, like, workout stuff. I think I definitely would not do that.
Speaker 1:Totally. Totally. But but what I'm saying is
Speaker 2:You're not training neck enough.
Speaker 1:Knew I knew guys I knew guys at UCLA who I I didn't go there, but, like, I friends that went there, and they were so obsessed with Call of Duty that they would take a bunch of stimulants when the new Call of Duty came out and play it for twenty four hours straight to get the max prestige because they were so addicted to Call of Duty that they would just stay up all night chugging energy drinks and and just to just to beat that. And I I just don't think you could do that in VR. I think after, like, two or three hours right now, it's, like, too much, and you have to take it off and get sweaty and tired. But so so so I feel like I feel like screen first, then probably even a little bit lighter, a little bit more comfortable, and then just drop the price as low as possible. Because if the next one was a $100, you'd probably buy it.
Speaker 1:Right?
Speaker 3:Yeah. And I I think stuff like like, maybe I'd want another screen Yeah. Just this monitor. But that's just an issue with the with, like, the visuals. It's a screen.
Speaker 1:Yeah. Yeah. It's it's gotta be it's gotta be competitively priced with the TV. And the TVs are so cheap now that you gotta just be like, yeah. I'm just picking one up.
Speaker 1:Or the price of an of of AirPods or the price of you know, it's it's gotta be down in low, low hundreds of dollars to really ramp that up. But I don't know. It'll be interesting. Anyway, our first guest is here. Let's tell you about AdQuick really quickly.
Speaker 1:Out of home advertising made easy and measurable. Goodbye to the headaches of out of home advertising. Only AdQuick combines technology, out of home expertise, and data to enable efficient, seamless ad buying across the globe. And we will welcome Chris Pike to the show. Welcome back, Chris.
Speaker 1:Fantastic to have you on the show. Thanks so much for taking the time.
Speaker 8:Great to
Speaker 2:see you. Last time we got cut off.
Speaker 8:Great to be back.
Speaker 2:In the temple. Last time we got cut off, we were
Speaker 1:Yes. We were we were
Speaker 2:having to jump, and I was like, I wish we had another hour. So at least we have another thirty minutes here.
Speaker 1:Yeah. That's
Speaker 2:great. Get into it.
Speaker 1:What first off, what's top of mind for you? Have you been tracking anything in the news that's that that that's kind of updated your thinking? We were digging into Grok four and seeing, is this an update to, you know, agent timelines? It seems pretty great in the benchmark. But is there anything else in the last week that's been like, oh, I can't get enough of the story just in your world?
Speaker 8:It's a great question. I feel like every week is a total blur.
Speaker 1:Yes.
Speaker 8:It seems like we're all waiting for not just these foundation models come out, but, like, the next open source models, the big open source models to come out. Totally. I think that that that's super interesting to me. Yeah. The proprietary foundation models obviously are the frontier of research.
Speaker 8:Yeah. But they're relatively inaccessible from a technology perspective because they're fundamentally rent seeking. You can't run them on your own hardware. You can't it's it's significantly less accessible. And so I'm I'm kinda waiting for the next generation of open source models.
Speaker 1:Yeah. One maybe underrated or underanalyzed Grok four thing that happened last night was the I don't know if either of you saw this, but they they did this voice demo and they were, like, really pushing the accents really far. I don't know if any of you saw this. Tyler, did you see this?
Speaker 3:Yeah. And there's the the whispering.
Speaker 1:The whispering. And so
Speaker 2:it ASMR.
Speaker 1:Wait. Did you think it was uncanny valley, Tyler?
Speaker 3:I felt very uncomfortable
Speaker 1:Yeah.
Speaker 3:Listening to it.
Speaker 1:But at the same time, I think it I think it's a path where we're in the uncanny valley just like we were with like six finger hands and stuff. And when they actually sort out the accents, the whispering, the intonation, the cadence, it's going to become a much more addictive companion potentially. So I wanna I wanna bridge to your piece and talk about the the the the different use cases that you see might people might kind of flow into with these, like, chat companions because you mapped out way more than just the the normal take in my opinion.
Speaker 8:Yeah. Well, so I guess it it it's worth asking ourselves where where do we want other humans to exist Mhmm. And where will we accept, like, substitutes? I think the last time we were talking about it, you know, it's if you can imagine a situation where humans are getting in your way of doing something, then you kinda you hate them. Right?
Speaker 8:Imagine you're in in traffic, in gridlock traffic. That's the most misanthropic you could possibly be. You're like, if if none of you existed, I could just get where I wanted to go without you being here. But then there are just total other times where we will we would refuse to to accept anything other than humans as as that thing. I know that it's it's very popular to talk about, like, AI companionship, and I would never I would never I would never say that people who get a lot of value from AI AI companions, that that value isn't real.
Speaker 8:But at the same time, I think that when it comes to the allocation of let's call it, like, let's let's call it, like, allocation of leisure hours, we really care about other people, whether it's like I I think last time I I was talking about, like, going to fine dining or reality TV. I I think I I mentioned that, like, we have the chess software that is way better than any human will ever be, and it's not entertaining to us. It's not entertaining to us because it's sort of like a there's no drama.
Speaker 2:There's no emotion.
Speaker 8:Exactly. Yeah. Exactly. And so Sorry.
Speaker 2:Sorry. Go ahead.
Speaker 1:I I I I'm I'm just thinking about like the shape of companionship because in in your most recent piece you you call out like the imaginary friends that kids have, Calvin and Hobbes, Toy Story. These stories resonate because they poignantly depict how colorful whimsical placeholders of our childhood slowly fade as society offers real alternatives. And and I'm just thinking about like like kids love imaginary friends, but they also they also love like multiple IPs essentially. Like they like Batman and then they also like IP Spider combination. And so, I'm wondering like there's been this narrative for for, you know, a few years in AI of like, don't build a GPT rapper because you're gonna get you're gonna get rolled.
Speaker 1:There's gonna be immense concentration of value and there will actually be no middle class in this in this ecosystem. And I'm wondering, because there's this company, To land, that's, you know, kind of imaginary friend AI driven. And I'm wondering, like, how like, we might act like, is it possible that we're heading towards something where people are essentially developing new IP? And, yes, there's still a power law in the companionship market, but there but these models and these products are, like, much more opinionated to the point where there there there actually isn't a one product to rule them all. And there's and there's a variety of products that that fit into different holes just for companionship, but also even just within, like, the imaginary friend hole, there's 25 different options.
Speaker 1:And, yeah, there's one that's popular, but then there's one that's one tenth as popular, one one hundredth as popular. But, yeah, react to that.
Speaker 8:Yeah. It's a it's a it's a really interesting let me first go back to, like, the the this rapper concept.
Speaker 1:Please.
Speaker 8:And I think it's really important to distinguish when rapper strategies work and when they don't work.
Speaker 1:Mhmm.
Speaker 8:I would argue that the wrapper strategy works the best when the underlying infrastructure is purely commoditized. Like, you can choose across many different options. Where the wrapper strategy doesn't work is if you're being if you're if you're basically building on top of a monopoly, and that underlying landlord is basically just going to be increasingly rent seeking and squeeze you out of all margin. So sort of embedded in the wrapper strategy is the assumption that over time, you're going to be able to distribute your product on top of, like, increasingly commoditized infrastructure. So for example, like like Snowflake.
Speaker 8:Snowflake launched actually just just on Amazon, and then over time distributed its product across Azure and Google, and actually, in doing so, was able to expand the margin capture of its own product because it had a their vendors were competing to be their their underlying customer base.
Speaker 1:Mhmm.
Speaker 8:Going back to, like, this open source notion, this is actually why I'm so interested in open source. The more that we have competitive fungible models, the more that the application layer on top can really flourish, the more that we'll see distinct unique applications. And and kind of like and until we start to maybe s curve Mhmm. Near the top of of of the frontier I'm sure you guys are familiar with the it's really popular essay, the the bitter lesson
Speaker 1:Oh, yeah.
Speaker 8:Which is basically the the, you know, no amount of fine tuning or no amount of specific framing is actually going to outcompete just the fundamental advances when it comes to, like, more games with more compute. Yeah. But if we start s curving, if we if we start if if these scaling laws break, which it seems like they are breaking on pretraining and test time compute and things like that, all of a sudden, have largely fungible similar capabilities at that commoditized layer, and then we can really start to see the application layer flourish.
Speaker 1:Yeah. I my interpretation of the bitter lesson right now is that the the impact of AI should be tracked less in benchmarks and less in individual tests of one model and more in the actual, like, volume of inference tokens being generated by humanity. And it's fine that the that we don't have one central AI doing all of the work. If we just give everyone an AI copilot for every single task, they all get better and and we'll build more data centers to inference more, and eventually that will compound and compound and compound until the until the the the overall impact of AI is remarkable and and unmistakable in the same way the Internet has, but it won't be this, like, all of a sudden, we unlock this one incredible algorithm.
Speaker 8:Yeah. Maybe so so I'm gonna I'm gonna try and map a comparison that's probably wrong for any number of reasons. Like, let's say let's say we ported the Bidder lesson to the rollout of PCs.
Speaker 1:Mhmm.
Speaker 8:Right? I think there's there have been a lot of comparisons of, like, AI feels like a new computer. Mhmm. So, like, let's map the bitter lesson to PCs. Mhmm.
Speaker 8:Maybe the the analog would be, hey. Don't work on building software that optimizes for, like, a computer speed of, like, 50 megahertz because the the computer that comes out that's, like, a 100 megahertz or 200 megahertz or, you know, a a a one gigahertz Yeah. Is is gonna be you know, it's just gonna blow out whatever software optimization you've achieved at that compute stop. And so I think about it a lot like that. Now I think it this really begs the question of, okay.
Speaker 8:Well, what happens when the vast majority of our use cases are satisfied by the compute threshold. You know, I I I feel like, you know, every everybody's like, who needs the next gen version of this? Because largely, all of the applications that you can that you you use or you can run work. And so I I it's very clear that we're in this, like, rising part of the s curve. But when when the s curve starts to taper off, that's really when it comes in a question of, okay.
Speaker 8:Well, how do we think about this the value delivery that sits on top of the underlying rent capture from from these foundational models. You know, You could you could think about it also like video game consoles.
Speaker 1:Mhmm.
Speaker 8:The the amount of the amount of creativity that video game developers are limited by is actually how advanced the video game consoles are and also how it's like, how expand like, what the install base of the video game consoles are. And so I think one of the one of the challenges right now is, like, we have so few developers of AI applications.
Speaker 1:Yeah.
Speaker 8:Like, they're they're so, like you can kinda, like, count them on maybe a few sets of hands. Right? Which is insane. We should
Speaker 1:have I feel like there's, like, thousands of startups that that count in, like, the AI software developer world. I see market maps every single day.
Speaker 8:I'm sorry.
Speaker 1:There's, like, 10 in every b two b category. Like
Speaker 8:Well okay. So maybe, like, let's let's split the world between, like, enterprise use cases and consumer use cases.
Speaker 6:Oh, sure. Sure.
Speaker 8:Enterprise, I would yes. A 100 a thousand percent. There's Mhmm. There's a lot of companies that are it's it's a it's a blue ocean sprint to vertical value delivery within in different sectors. Yep.
Speaker 8:Because you're largely swapping you are the the spend the addressable spend is is OpEx, which is insane. Like, that's crazy. Your your revenue opportunity is just, like, headcount spend. Yeah. And so that's for sure.
Speaker 8:But when
Speaker 1:also when comes to tool spend, like, it's all the OpEx, like, to your point. I mean, I guess not, like, real estate or rent or something, but basically everything else.
Speaker 8:Yes. Which is by far the biggest cost center for any company.
Speaker 1:Yeah. Of
Speaker 8:course. Anyone who's ever run payroll or anyone who's ever scaled a company is like, man, like, humans are expensive.
Speaker 1:Yeah.
Speaker 8:Yeah. Humans are so expensive.
Speaker 1:Yeah.
Speaker 8:And, I mean, I I this is this is why I feel like everybody says that AI is the best candidate or the best argument that UBI is coming.
Speaker 1:Sure.
Speaker 8:Sure.
Speaker 1:Yeah. Jordy?
Speaker 2:I think I think that picks and shovels meme became too dominant. Mhmm. And there were so many over the last decade or so, there were just so many amazing outcomes of people building infrastructure. Yeah. And like very visible outcomes and it became cool to build infrastructure, right?
Speaker 2:Like the Collison brothers made building infra cool. And you have like Parker Conrad is like a bulk hero.
Speaker 1:Sure.
Speaker 2:Right? And like rippling. It's like like Yeah. And you have the like ramp is a good example of this. Corporate spend management should not be cool.
Speaker 2:They've built a really cool culture around it.
Speaker 1:Interesting.
Speaker 2:And there's this other, there's this like weird kind of pervasive meme of like, you have two years to escape like the permanent underclass. So I think people are like, well I'm not gonna just build something weird and fun. I'm gonna build enterprise SaaS so I can make, you know, so I can escape the permanent underclass. And so I don't think there's been enough weird fun attempts from people like Tollens, the one you brought up earlier is cool.
Speaker 1:It's pretty rare.
Speaker 2:Not the most rational thing to say, you know, if you just wanna build a big business to be like, I'm gonna build a little alien AI friend. But like clearly there's demand for that. And we were talking with Scott Pelsky yesterday around just wanting like new fun weird consumer use cases. And I feel like I think what you're getting at is like that whole area is like relatively under under explored to date. We're like we've had a bunch of we had two browser announcements yesterday and they're both built on Chromium.
Speaker 1:Yeah.
Speaker 2:And that's exciting and cool. And we should we should talk about the the potential for new browser wars. But I think, yeah, the number of people that are saying, I'm actually yeah, you could call it a rapper, but I'm actually trying to create something entirely novel. Like the example we gave yesterday is a dating app based on a digital twin that is just constantly dating other digital twins. And you know, I haven't seen I'm sure somebody's working on that.
Speaker 2:I haven't seen it yet. But there's like any pick any popular consumer app category. And there's probably a way to entirely rethink it with this sort of LLM as a new computer at the core of that
Speaker 1:Consumer just seems so much more risky because it's like it's either a billion dollar outcome or a zero. Whereas in in enterprise, it feels like, well, there's no way it's gonna be a zero. It's gonna be a $10,000,000 outcome or a $100,000,000 outcome or a billion dollar outcome, but it's not gonna be it's not gonna be a super
Speaker 2:expensive companies that each have
Speaker 1:10 to 50,000,000 doing the
Speaker 2:same thing.
Speaker 1:We're like, they were trying to go for the hits driven business with the game. Didn't work, and then they went into SaaS, and it worked. Anyway,
Speaker 10:sorry. Because
Speaker 8:An additional challenge with consumer is inference is not free right now.
Speaker 1:Yeah.
Speaker 8:Somebody has to pay for the inference bill. And so until you can run inference on device, it's still like, every developer is doing the mental math in their head of, like, how do I like, if if any one of your users can can run you out of house and home if they abuse your your service.
Speaker 1:Yeah.
Speaker 8:How do you build on top
Speaker 1:of that?
Speaker 2:Yeah. One of the one of one of the opening eye researchers, I think, in Zurich that's going to meta Yeah. Like, posted, oh, I didn't realize that I had this thing running. It was, a $150 a day just, like, every single day. Luckily, think he'll be fine.
Speaker 2:Should take the I'm sorry. Before we fully leave like kind of consumer.
Speaker 1:Stay here.
Speaker 2:What do you think of do you think that AI companions and LLMs broadly present a real threat to traditional social media. Like the idea of a companion. It, a lot of people in our world are using these tools like very functionally, for doing research or getting answers or understanding topics. But a lot of people are using them as companions and that is somewhat of a social entertainment experience. And and you see these charts ticking up of kind of user minutes in in in
Speaker 1:So Chetchikpedia went from about five user minutes per day to over 30. No Around 30. Around 30. I think it's a 28, 29. Over, like, the last six months, there's no blip on any of the other social networks.
Speaker 1:They're not declining yet. But personally, I'm finding that if I'm doing research on a topic, used to go to YouTube. I used to go to Instagram and just get people, you know, famously like search TikTok for answers to things. And some of that is shifting over. But but
Speaker 2:Yeah. And and if you think that an analog sort of anecdotal, you know Yeah. Sort of experience for me is when do I use social media the least is when I'm with my family, which is like companionship. It's the social time. Or when I'm with friends, like at dinner, hanging out.
Speaker 2:It's like rude to be Yeah. You know, using, you know, there's no point to go hang out with a friend and then use Instagram the whole time.
Speaker 1:Yeah. Totally.
Speaker 8:I I think I subscribe to this sort of cutting of our time as, like, we're we're either allocating labor hours or we're allocating leisure hours. So we're either we're either trying to be productive Mhmm. Or we're trying to enjoy ourselves.
Speaker 1:Mhmm.
Speaker 8:And so I would say that all all leisure allocation effectively competes against each other. I think maybe it was like the Netflix CEO that said that they weren't competing with HBO. They were competing with Fortnite. And that's largely true. Like, you know you know, we only have twenty four hours in a day.
Speaker 8:We only have a finite amount of leisure hours. So if I'm allocating one hour to this leisure activity, if I'm watching an episode of Love Island or whatever, that's in that's time that I'm not gonna be able to allocate to a different kind of leisure activity. So to that end, I would absolutely agree that AI companions almost certainly firmly in the bucket of leisure. Mhmm. More consumption of leisure equals less consumption consumption of other leisure activities.
Speaker 8:It's it's really zero sum. The only thing that is going to make it non zero sum is a fundamental advance on productivity that allows the leisure pie to be even larger. Yeah. So right. Like, maybe we have we we definitely have more leisure hours as as humanity now than we've ever had in the history of humanity.
Speaker 2:Let's give it up for leisure hours.
Speaker 8:Right. Like like proto human, zero zero leisure hours.
Speaker 1:Yeah. Hunting hours.
Speaker 8:Just, like, amazing amazing leisure hours. Mhmm. When it comes to labor hour allocation or, like, research or or utility, would say, like, that doesn't necessarily encroach against time on on social media. And I think that all social media, whether it's YouTube or TikTok or Twitter, they carry they they care less about helping people get work done, and they carry they care much, much more about absorbing as much of of attention as possible, which is why, like, the the the algorithms are so insidious of, like, serving you exactly the the kind of saccharin thing that you wanna consume next.
Speaker 1:I wonder if there will be an incentive. I mean, there there will obviously be incentive, but I wonder how it will play out in the LLM chatbot interface. Because right now, OpenAI and basically anyone who has a a dominant consumer AI app is probably seeing user minutes increase just naturally without putting in, like, growth hacks or retention loops or you know? But you could imagine a world where to get to to get from thirty minutes to sixty minutes, the LLM has to not just give you the response, but surface, hey, would you like to follow-up and learn more about this? Click these buttons.
Speaker 1:That that's already kind of happening.
Speaker 2:Yeah. It starts surfacing you stories that it knows you're interested in by what you've You've searched for
Speaker 1:a bunch of stuff. Let's give you a new breakdown.
Speaker 2:It's like the
Speaker 1:pre populating a deep research report.
Speaker 2:You you're the it is funny that the push notification hasn't really quite hit
Speaker 1:It hasn't. That's
Speaker 2:right. Chat apps. And Yeah. And it undoubtedly will.
Speaker 1:I had this thesis that that push would be very important versus, like, pull, like, you have to go to ChatGPT and ask it for something, and and someone is going to solve kind of like it's almost like an AI driven newsletter or something where it it it it understands what you're interested in and then generates the report before you can even ask it. Because it knows that, you know, if if, you know, Ferrari drops a new car, I'm gonna want a table of all of the details because I like consuming information that way in addition to just hearing commentary and watching the Doug DeMuro video about it. But but OpenAI could prepopulate that and just send that to me. But I don't know. How how do you how do you think that's gonna evolve?
Speaker 8:Question for you guys. Do you think we're gonna pay for AI services forever?
Speaker 2:I've been asking I've been asking that lot.
Speaker 1:I mean, I think I think
Speaker 2:I think the think the better the the more important question is will the will the will the average American pay for a an AI like an LLM? And then will they pay for multiple? Like the the comp for this is in streaming where Americans
Speaker 1:was about to say Netflix.
Speaker 2:Americans A lot of Americans pay for multiple streaming services but they're incredibly ruthless about canceling them on average. Like a lot of I'm sure a lot of people listening to this have like had some streaming service billing them monthly, you know, for years that they haven't even watched. But but the average American is like, I'm not getting a lot of value out of HBO right now. I'm gonna cancel. Even though they might sign up again in like four months next time there's a hit show that they're gonna watch.
Speaker 2:And I think that I think that there's not a Right now, there's this incredible demand for what's new and what's best. Right? Like, Grok will drive a lot of sign ups today because it is a a meet The the Grok four heavy is like a meaningful advancement. But I went and I haven't, you know, we've been busy this morning. I haven't had a chance to sign up and and play around with it yet.
Speaker 2:And I was getting plenty of value from Groc three. Like I was able to just search.
Speaker 1:Yeah. I was asking you our Groc three about Groc four as well. And it was actually doing a pretty good job which
Speaker 2:is funny. And so and I'm not I'm not yet. And so I'm not I'm not I guess, I probably get it through X Premium, So but
Speaker 1:I have some I have some data here. Netflix made 39,000,000,000 last year. They're on track for 44,000,000,000 this year. 1,800,000,000.0 of that was ad revenue last year. It's estimated to be around 4,000,000,000 this year.
Speaker 1:So their ad revenue is doubling while their subscription revenue is growing by 5%.
Speaker 2:And so
Speaker 1:my takeaway for ChatGPT would be, I would imagine that the ChatGPT paid subscriptions follow an s curve and we get to something where we see OpenAI making, I don't know if it'll be 10,000,000,000 or 40,000,000,000, but they will soak up a ton of subscription demand for ad free frontier models, the most advanced, the most expensive stuff. And then ads will eventually become the dominant revenue driver. But I feel like the subscription revenue will be a really hard tap to turn off just from, hey, it's people are paying and, you know, it's a lot of money and we don't want it
Speaker 2:to Even go in the enterprise it does my my bet is that we go, you know, continue down this trend towards paying for outcomes because a lot of people would just say, well, don't want a subscription for this service because I only use it every now and then. When I get value from it, I'm happy to pay for it. I I think a question to ask is, would you pay $20 a month for Instagram today to not have ads? And I would actually have to think about that for a while because like once a month, I get an ad for something that that looks interesting and I discover a product that I wouldn't have otherwise discovered and I buy it and sometimes it's great. Yeah.
Speaker 2:So do I wanna just completely eliminate that and rely entirely on random organic or do I actually like that this ad platform is spending a bunch of time and energy trying to serve me the next product that I'm gonna like, which is like actually a service. And like, it's it's it's not a bad trade at all.
Speaker 8:Totally. I I think we a lot of the way that we vote with our feet and vote with our wallets is that we we we're super happy to pay with our time and our attention if given the option.
Speaker 1:Yeah.
Speaker 8:Like, actually, like, the vast majority of people won't not only that, but people are more than happy to give up their attention and and privacy for that matter if it can save them money Yeah. Or instead of paying for something. One one thought experiment I like to think about is just how much people will trade privacy for for value. Imagine a a checkout flow where you could get $5 off if you enter your Social Security number. Like, how many people do you think would enter their Social Security number?
Speaker 1:Like, everyone or, like, 90% people.
Speaker 8:Like, an insane amount of people. Totally. And so people really don't value their privacy as much as I think, like, maybe we say people value their privacy. Yeah. And in aggregate, obviously, that data is super monetizable.
Speaker 8:It's it's interesting.
Speaker 2:You
Speaker 8:know? Search, obviously, is the is the big prize, I think, for AI. And what do I mean by search? It's it's it's intent. If you're if you're the the arbiter or you control this fire hose of intent, you can benefit by metering it out and having people bid for that intent.
Speaker 8:Obviously, Google maybe Google's like the best business, the best business model maybe ever invented. It's kind of insane. What's interesting is the the the most valuable searches, maybe, like, not what people think, and and and certain kinds of searches are totally worthless. So knowledge based search, like like fact based search, things like
Speaker 2:What is the market cap of this company?
Speaker 8:Right. Right. So what's the market cap of this? You know, who who who won this game or like, you know, who who was president in like, you know, 1936?
Speaker 2:Yeah. They're dead ends. They're dead ends. You get the fact Yeah.
Speaker 8:Zero value. Actually, like, the there was there was a Google got subpoenaed and had to actually share some some documentation of, like, what their most profitable keyword searches were. It's super interesting. I suggest people to to to go check it out. The number one most profitable search for Google was just the word iPhone.
Speaker 1:No way. That's hilarious.
Speaker 8:It's amazing. Because if you think if you think about it, like like, what does that mean? What is what is somebody telegraphing to the market when they search the word iPhone? They're like they're basically saying, in not so many words, I am ready to spend $1,600 on a smartphone.
Speaker 1:Yep.
Speaker 8:And and who's who's who's interested in, like, jockeying to get that person's attention? Well, Apple has to. Right? Best Buy as a retailer is interested. Samsung wants to.
Speaker 2:And then Verizon, AT and T, T Mobile, the networks.
Speaker 8:And so and so when when when you think about, like, where is valuable search, it it the value of search is often misunderstood because you have to really think about how to capture the most valuable intent. And not all intent is is equally valuable. There's there's a bunch of search that's garbage. You actually don't want it. Fact based the the most the the cream off the top of a fact based search would be what I would call, like, comparison shopping.
Speaker 8:So it's like, hey. Like, what's the best headphone? What are the best headphones?
Speaker 1:Or Yeah. Totally.
Speaker 8:Know. And then maybe maybe you can slice off a top of that revenue pie, but you there's a tension between that and the objective the objective truth that you're serving up the user. The the by far, the most valuable search is is not fact based, and it's it's relatively it's it's where the user kind of knows exactly what they want, and they're trying to do it. And other people are willing willing to bid to get into their get in their way and run interference.
Speaker 1:Mhmm. Rudy, last question.
Speaker 2:While we have you, how are you thinking about the new I wouldn't call it a browser war yet, but a but a potentially a skirmish eating up. Dia released to ARC users probably feels like maybe a month ago at this point, at least a few weeks. And then we have, OpenAI potentially coming in with a new standalone app. It's sort of unclear. It was unclear to us whether this is gonna be a new app that you download or just integrated into the ChatGPT mobile app.
Speaker 2:And then Perplexity as well does have a separate standalone app that they're pushing now. And and it feels interesting one because the browser company has spent now years working on the browser trying to figure out what is gonna enable, you know, unlocking more value out of this portal to the web and and then, you know, effectively an operating system. And so, meanwhile, you know, new players are basically being like, we wanna have a browser and just going, like, we're shipping. We just gotta get this out there. So I think it'll be interest the next month, two months, I think, will be very interesting, but I'm curious what you're looking at.
Speaker 8:I could not be more excited. I think I'm I'm obviously biased. We're investors in the browser company. Mhmm. I'm a daily user of Dia.
Speaker 8:I personally get a ton of value from it, particularly the the custom skills. And I think that the browser company has always known that this is a really valuable position, and it's, like, honestly, just validating to see incredible company. Like, know, OpenAir is a great company. Perplexity is a is a really amazing com formidable company. Also, recognizing that this is a really valuable position to to play for.
Speaker 8:I I have supreme confidence that the team at the browser company is the most talented, the best instincts, the best nuanced understanding of interaction design, and how to create and craft a great product regardless of the underlying model or technology that that underpins it. I'll be very curious to see how it plays out. My instinct is that, you know, OpenAI and OpenAI is an incredible foundational model company. Maybe I've seen them ship a lot of different products. To my knowledge, ChatGPT is really the only product that's that that's quite stuck.
Speaker 8:And and it's not really even, like, the interface design so much as there's the underlying power of things. To answer your question, could not be more excited. Thanks. Like, this is gonna be an amazing year.
Speaker 2:We check back. We can check back in a in a in a few weeks. I'm sure there'll a lot more.
Speaker 4:And it's
Speaker 8:gonna be huge plug for Dia. If you haven't downloaded Dia, it's on Mac. It's it's available. Please download it. It'll it'll blow your mind.
Speaker 8:It blew it blew my mind.
Speaker 2:Amazing. Alright. Great talking as always. Wish we had more time. Yeah.
Speaker 1:We'll talk to you soon.
Speaker 2:Can do
Speaker 1:this every week. We'll talk
Speaker 2:to soon. Bye.
Speaker 1:Bringing Will Brewery from Varda, let me tell you about Wander. Find your happy place. Find your happy place. Book a Wander with inspiring views, hotel grand amenities, dreamy beds, top tier cleaning and twenty four seven concierge service. It's a vacation home, but better folks.
Speaker 1:And soon with Varda, you'll be able to maybe they'll put a wander in space. Let's bring in Will Brewery from Varda. Is this your first time on the show? I feel like this is a disaster that we are finally rectifying. We did it.
Speaker 1:We made it.
Speaker 9:It is. Thanks for having finally.
Speaker 1:We've had that other the the kind of knock off version of you at Varda. Yeah. He's like He's been on the show a ton.
Speaker 9:Dell something I think. I forget.
Speaker 1:Dell. Yeah. Yeah.
Speaker 2:Great to finally have you on. Amazing. Massive day.
Speaker 1:Break it down for us. What's the news? Are we gonna make Jordy stand up and ring I'm looking for Oh, did
Speaker 9:you guys get this?
Speaker 1:You have the gong? Let's both
Speaker 9:So we we we have the gong for for big moments, you know, either or you know, know, sell a mission to a customer, stuff And, like yeah, we'd love that.
Speaker 1:Yeah. What's the
Speaker 2:here's here's my advice to you. Record every hit. Every hit. I wanna see a montage in ten years of just every hit and it will make it will bring tears to your eyes. We record every hit.
Speaker 1:We do. So
Speaker 2:we got you we got you on this one.
Speaker 1:So, yeah. What's the what's the news today? Break it down.
Speaker 9:So, wow. Lots of news today. So we we're announcing our series c, and we're getting to, use that too. Oh, yeah. Go for it, baby.
Speaker 9:How
Speaker 1:much how much How
Speaker 2:did you raise?
Speaker 1:Did you raise? How much? Tell us. How much did you raise?
Speaker 9:A 187,000,000.
Speaker 1:There we go. Congratulations.
Speaker 4:There we go.
Speaker 9:Thank Thank you. Appreciate it. Appreciate it. Yeah. So the Fantastic.
Speaker 1:Use of
Speaker 9:proceeds for this one. Really, it's about just scaling up. So we've kinda shown what we can do both from a spacecraft perspective and a and a drug formulation development perspective. Mhmm. So all of the a lot of the capital allocation of this one is gonna go for our biologics lab for produce preparing drugs for spaceflight.
Speaker 9:Yep. And then also just more spaceflight ramping up cadence. That means Yeah. Rate flights.
Speaker 1:So I've been to the facility in El Segundo. Are you gonna get a bigger space, a second space for the bio lab? Or
Speaker 2:Are you gonna need a bigger gong?
Speaker 1:Are you gonna use
Speaker 8:any gong?
Speaker 1:Two gongs. Right? Well,
Speaker 9:gong per facility. We gotta scale that up too.
Speaker 1:So so so so are you thinking about doing a second office essentially, or or how how do you see the actual, like, footprint of Varda growing over the next few years?
Speaker 9:Yeah. Over well, immediately, we've just signed a lease down the street.
Speaker 1:Oh, congratulations.
Speaker 9:Oh, thank you.
Speaker 1:Thank you. Yeah. We've we've got through on our right. Big day. Yeah.
Speaker 1:That's amazing.
Speaker 9:Yeah. The so this we were actually already moved in. A bunch of the pharmaceutical equipment is already in there. We're starting to use it right now. We got a couple of Chlori shots, you know, with folks with the lab coats on actually using it.
Speaker 9:So that's super exciting. Long term, I mean, really, I guess, zooming out of of what the footprint will look like is think about a formulation development company that really just provides a gravity off switch Mhmm. To the pharmaceutical industry. So we go to space, but, you know, not really because we want to per se, but because you can create new drug formulations when you turn off gravity, and you just can't turn it off on Earth. That's Einstein's principle of equivalence.
Speaker 1:Do you mean new drug formulations or just, like, purer drug formulations? Because when I think no gravity, I think, like, the way crystals form and the way gravity pulls things to one direction. And if that doesn't happen in space, you get just kind of, like, a nat a more natural growth. And so I always thought it was just it was just about purity, but it sounds like there's actually some binary like, you can't make this drug on Earth at all. Is that right?
Speaker 9:So both those concepts are correct. Wow. Well read there, Kugen. Yeah. Yeah.
Speaker 9:That's actually a great way to think about it. The the the purity is one aspect, but Yeah. Because gravity is so broad, there's a I use the analogy of temperature sometimes because temperature is so broad. Like, making things cold doesn't necessarily make drugs better per se.
Speaker 1:Sure.
Speaker 9:But you can create a lot of different formulations if you can have a cold cycle during the manufacturing process. And that'll be even, you know, with chilling things, you can make things more pure sometimes as well. Right? So but to your point, when you turn off gravity, crystals will typically grow slower. And that also means that they will grow more pure.
Speaker 9:And so that is one of of a few applications that we look at. The other one is particle size distribution. So when you create these crystals that will then go into the human body, you want them all to be the exact same size so that, you know, one big one doesn't get stuck in your elbow and
Speaker 1:Yeah. And you
Speaker 9:don't have uniform bioavailability. So the crystals will particle size distribution is also affected by gravity. So that's a whole separate thing compared to purity, which is another rationale for for going to space. So, really, the gravity knob is very broad, and there's kind of these verticals of science of how we can improve the drug formulation. And and to your point again, as far as, like, what I mean by drug formulation is going from molecule to medicine.
Speaker 9:Right? So is it a pill? Is it inhalable? Mhmm. Is it an IV bag?
Speaker 9:Is it a shot? The the drug the company or a pharmaceutical company does a trade study to determine which of those is the best for the patient given the disease, given the manufacturing costs. But ultimately, all of that is limited to what the chemistry can actually do. Right? Nobody wants to take a needle to the arm.
Speaker 9:They only do it because they can't deliver that molecule via a pill or something like that. And so by opening up the chemistry outcomes by going to microgravity, we can also open up the formulation outcomes and therefore get better patient experiences.
Speaker 1:Yeah. So, I I mean, I imagine that this is still this is such an ambitious project that is still kind of R and D phase with a lot of the bio stuff. It's not I mean, when I think about the manufacturing capacity of like GLP ones, like they're probably making that thing in like vats the size of like, you know, what they brew Bud Light in at this point. Well, they got
Speaker 2:a bunch of the lizards.
Speaker 1:Yeah. Yeah. Yeah. Probably massive. Yeah.
Speaker 2:You a monster.
Speaker 1:But but but but walk me through how we scale this up. I'm I understand launch costs falling. I understand you put up a capsule every you're you're doing it, like, every quarter now. It's gonna be every month, then it'll be multiple times per day. Like, the that capability seems clear, but but how much actual how much drug can you make on a single capsule?
Speaker 9:Yeah. Yeah. Great question. So this is actually a lot of fun because we can imagine how BARDA will go from what's real today
Speaker 1:Yeah.
Speaker 9:To making tomorrow's reality. Yeah. And so to answer your question immediately, about 20 kilograms Mhmm. On a on a per capsule basis right now today. Of course, you know, we wanna scale up everything, and that's one of them.
Speaker 9:But that's what how much we can do today, which is actually quite a lot.
Speaker 1:Yeah. That seems significant. If you just think about, like, you go to the doctor and the doctor gives you, like, a, you know, a thing of pills. That's, like, not one kilo. So you're you're probably talking about, like
Speaker 9:I mean, yeah. Unless unless, like, you you're having more fun than being prescribed.
Speaker 1:But but for most for most drugs, I feel like 20 kilos is probably enough for like a 100 people for a year or something like that. So you're actually in like $100,000. I'm I'm seeing the numbers kind of start to math out already.
Speaker 9:Correct. Correct. So it's, and every drug is a little bit different. So we go through a process of selecting which drugs make the most sense both from a scale perspective, like you're saying, but also unit economics
Speaker 1:Sure.
Speaker 9:How gravity affects them. And so we have a portfolio management team that explicitly does that for identifying and quantifying opportunities. Mhmm. But but going back to, like, what today looks like and how and how it goes tomorrow, I love the temperature analogy because it it really runs deep. So for example, right now, if you think of us having a antigravity oven where we can make drug formulations that you can't otherwise make on earth, but we only get to run it four times a year and each one is a few million bucks a run, you might use it for different use cases than you would use it from five years, ten years from now when you can run it every day for a few thousand dollars.
Speaker 9:And so in the near term, some of the use you know, imagine yourself with a super you know, the first refrigerator or, you know, or in this case, the first antigravity bioreactor. What you might use it for in the near term is just information. Right? How can we isolate gravity as a variable to inform what formulations can be improved on Earth? Is gravity ruining this chemical reaction or not?
Speaker 9:Right? We can answer those questions, and then that applies to the entire drug with just one flight. Or, in the very near term, we also wanna do, polymorph, seed crystals. And so what that means is we go to microgravity, we go to space, but just to develop the seed crystals, and then and once those seed crystals are developed, we can then use them to grow, more drug crystals on the ground. So we're only going for the nucleation event, and that's kinda like a sourdough bread mother business model, if that makes sense.
Speaker 9:Right? You you have the mother of the sourdough bread, then you can cut it a bunch and then regrow it and stuff like that. So that makes a lot of sense when we're still scaling up the use of our antigravity machine, if you will. Yeah. And then long term, when we're on a daily basis, then it totally makes sense to make every single dose manufacture every single dose in microgravity.
Speaker 9:And then that's when certain use cases come online as well. So kind of that's how that's how it progresses over time.
Speaker 1:Yeah. How's the, geopolitical landscape evolving for you? We saw some of flights come down in Australia as as Yeah. You just like Americans, it pained me to see them take a slice of the
Speaker 2:Great catch.
Speaker 1:Of the catch market. Are we are we getting these coming down in America anytime soon? Is it what's the progress there?
Speaker 9:Yeah. Yeah. Absolutely. So long term, we wanna have reentry sites all over the globe. Right?
Speaker 9:Sure. And and really, that's about availability. And the key metric to success of BARDA is cadence. How often can we go up and back? Because the more we do that, then the more we just look like a specialized piece of equipment to the pharmaceutical industry that, quite frankly, does not care that we're going to space.
Speaker 9:They'd much rather us have a real antigravity oven in in the lab. So, really, reentry sites are about cadence and availability. Mhmm. And right now, Australia is great for us because they have a private commercial reentry range. Whereas any re any range in the 48 states here locally have are are intended for military use exclusively.
Speaker 9:And so if we're doing a DOD mission, that that works well. But if we're doing a commercial mission, we're, you know, not the highest priority. Understandably so. Right? And so, in the near term, Australia makes the most sense.
Speaker 9:But in the long term, we want reentry sites all over the place. And why that gets enabled is because as our precision of landing and our cadence goes up, that data, that legacy history allows us to use a smaller and smaller plot of land. And then that and that's where really it makes more sense to go anywhere because we don't need such wide open spaces, like, without that many people like we would do right now today.
Speaker 2:Do do we have the legal infrastructure to create commercial landing sites in The United States and it's just that nobody's done it yet? Or is there laws or regulations that would need to change so that some enterprising young member of the Gundo could go buy, you know, a lot of land out in the middle of nowhere and start landing spacecraft?
Speaker 9:You can do it now. The the constraint is the real estate cost. And so one you know, Spaceport America, for example, right next to White Sands Missile Ranch is a good example of that. But the the so I guess the real reason why they aren't there, that many of them, is because there wasn't a demand large enough to warrant such a real estate purchase. But, you know, thanks to VARTA, that that could change.
Speaker 9:So, yeah, definitely let the the Gundos know.
Speaker 1:I have a I have a question from a a fan of yours, fan of the show. He says, ask him what big dogs gotta do.
Speaker 9:Oh my god. It's become a little bit of a of a an expression of excitement Okay. With a long and history of of lore at at Varda. But for some reason, you know, it's it's really just a specific instance of conservation of mass. Right?
Speaker 9:You can't have a big dog without eating. Right? So that's just physics right there.
Speaker 1:Yep. I wanna talk about the evolution of the FAA. I remember I was filming a video. I feel I I filmed with you guys. And at one point, I actually was driving back with Ben from San Diego.
Speaker 1:I filmed a a phone call with Delian, and he's like, we just got our I don't even know if I should say this, but, like, it was like, we got some bad news from the FAA. You guys sorted it out. It seems like you have a great relationship now. How did that happen? Do you is this is this a lobbying thing?
Speaker 1:Is this just storytelling? Is this structuring deals, getting better paperwork? Like, how do you get how do you fix a relationship with a government entity like that?
Speaker 9:Yeah. So it was definitely a little bit worked in the press, obviously, but I figured, you know, keep my head down and get the spacecraft home more so than worrying about what's being said in the press. Sure. But so what actually happened in the background is we were originally gonna reenter this space or we did reenter the spacecraft at the Utah Test And Training Range, which is ultimately a a weapons range for testing weapons and training warriors. That's their mission statement.
Speaker 9:Right? So likewise, we're not the highest priority there. And so we got bumped for higher priority work being done at the range, and in doing so, a domino effect to lose the FAA reentry license or not be able to get it granted Mhmm. Because part of the regulations say, hey. You need a range and all of these accommodations that come with a range.
Speaker 9:So the second we lost the range, we lose the license. So it wasn't really about a bad relationship with the FAA at all. Although it's it's much it's very easy to say, oh, they lost their license. Yet another space company in the FAA are having problems. Right?
Speaker 9:It's my headline. And it fit that narrative well, but it actually wasn't the case. And so what we did was we we scheduled a new date with the range farther out in advance to to give them some time and give us some time to we had to redo the analysis, of course, because the atmosphere is different, and that's part of the analysis. And so we gave ourselves a few months, and then that allowed them to reserve the dates. That allowed us to prepare, and that allowed the FAA to reorient the license for the new dates.
Speaker 9:And, ultimately, kind of in the background here was, like, this is the first time this has ever happened, like, a commercial reentry capsule with drugs on board coming back to America. So there was no way onto onto soil. Right? We're not doing a splashdown. And so there was no process or mechanism to have the Utah test and training range coordinate with the FAA.
Speaker 9:And so, basically, each organization saw themselves as taking on all the risk associated with this. So we had to do duplicative work because there was no process to to split it. Right? And so it's really cool to kinda be a trailblazer to to establish this so that now, of course, our competitors are gonna come in and do the same thing, right, and learn from our mistakes. But whatever.
Speaker 9:That's part of that's part of leading the way. Right? So Okay. Anyway, that that's what happened. But that you know, six months was it was quite the life experience.
Speaker 9:Right? Because if you have you're it was the first mission. Right? So we we didn't have any proof that this was gonna work. People poured three years of their lives into this thing, and our dreams are just like orbiting the Earth like,
Speaker 1:come home. You know?
Speaker 9:So when it came through, man, it was certainly you know, I I can't think of a better day.
Speaker 1:Yeah. That's amazing. I have one last question, Jordy.
Speaker 2:How's the talent market in the in the space economy right now? Hasn't been in the headlines the last couple weeks. There's been another story in in AI dominating, but but yeah. What what's it like today?
Speaker 1:It's like high in
Speaker 9:the AI and software engineers. I mean, you know, if you imagine yourself a software engineer coming out of school right now, you know, AI is is certainly where I would be interested. So so so there's definitely a software bent towards AI right now. That being said, there's a lot of disciplines we're hiring for. Software isn't the only one.
Speaker 9:And, really, it comes down to the application interest. Like, we're looking for mission driven folks
Speaker 1:Mhmm.
Speaker 9:At Varda. And so if you're only looking oh, you only wanna do AI because it's cool or whatever, we that might not be the type of person we wanna hire anyway. Now if you wanna do AI for mission perp driven purposes, then great. You know? Like, by all means.
Speaker 9:But we don't have that much overlap there. Right? We're very specific of what we're trying to do. We're trying to make microgravity formulations so that we can help peep help patients on Earth by using a gravity as as a knob, essentially, in developing these formulations. And so, you know, we we always kid around.
Speaker 9:We explicitly don't want the spacecraft to learn. You know? And so if you're a software engineer and you're mission driven bent towards that mission, then you've got a home at Varda. No question. And if and and, know, fads come and go and that sort of thing.
Speaker 9:But there is definitely an effect on I I would certainly be a lord to AI as a as a graduating software.
Speaker 2:That's good sorting function. One last question for me. We've there's been headlines, companies talking about this so far this year trying to dig into how real it is. People talking about putting data centers in space. Yeah.
Speaker 2:With everything that you've learned, why is that exciting, good idea or a bad idea? What what are some potential blind spots for people that haven't taken something to space but would like to?
Speaker 9:So it all comes down to the why. Right? Why are we putting data centers in space? It's not, are data centers in space in and of themselves a good idea, but what's the why? So the why is is is the only why that resonates with me is latency.
Speaker 9:Right? Because if you wanna do compute power, space is not the place to put a data center if you just want a data center. Right? I'd much rather have convection. Right?
Speaker 9:That like, that's a great heat or a great way to get rid of heat Sure. And and have it be able to be serviceable on Earth and all that sort of thing. So but there is one use case that comes to mind where I think data centers in space make sense, and that's only for very low latency use cases. So for example, right now, if you wanna use Starlink and you're transmitting a signal to Starlink, it goes from the ground to Starlink to another Starlink satellite to the ground then to the data server and back. Right?
Speaker 9:So you can cut that trip in half if you put the compute in the sky. Sure. Now that compute is way way way more expensive. But if your value prop of latency warrants that extra cost of the in orbit data center, then you'll start to see that. So it's it's kinda like edge computing.
Speaker 1:Edge computing. Yep. I was about to say. So, yeah, for for it it sounds like a very niche use case at least to start, but I'm sure we'll see some companies. We already are seeing some companies test it out and experiment with it because there's you know, all these things need to be evaluated in the tech tree.
Speaker 1:Yep. But thank you so much for stopping by. This is fantastic. Congratulations.
Speaker 8:Thank you
Speaker 2:for for having me.
Speaker 9:And hopefully not so long. I'll see you again soon.
Speaker 1:Absolutely. Yeah. Yeah. Hop on soon.
Speaker 2:Feeling about it.
Speaker 1:We'll talk to you soon. Have a good one.
Speaker 2:Cheers, Will. Congrats to you and the team.
Speaker 1:Bye. Up next, we have Joel from Metr coming on to talk about the impact of AI models, impact Cursor on software development.
Speaker 2:Or meter?
Speaker 1:Oh, meter probably. M e t r. We'll have him explain it to down. We'll also recommend that you go to getbezel.com because your bezel concierge is available now to source you any watch on the planet. Seriously, any watch.
Speaker 1:Anyway, so METER
Speaker 2:does METER does model evaluation and threat research. Okay. So does Bezel. They're stopping you from buying fake watches.
Speaker 1:Bad watch models. Bad actors. Foundation models and watch models. Lots of Anyway, we got Joel in the studio. Welcome to the stream.
Speaker 1:Hopefully, you're like, what did I get myself into? These guys are joking around. I'm a serious person.
Speaker 2:Alright. First off
Speaker 1:Yes.
Speaker 2:Is it it's meter?
Speaker 4:It's meter. It's meter.
Speaker 1:There we
Speaker 2:go. Sorry. There we go. Gotcha.
Speaker 1:Anyway, Gabe, please introduce yourself for for those who don't know you, the company, and then and and the organization. And then and then I wanna go into the news today.
Speaker 4:Let's do it. And thank you very much for having me, John and Jordy.
Speaker 1:Thanks for hopping on.
Speaker 4:ESA is a research nonprofit based in Berkeley dedicated to understanding the the capabilities of AI today and and in the near future, especially to the to the extent that those capabilities might speak to potentially dangerous risks.
Speaker 1:Mhmm. And what is what what's been the latest research?
Speaker 4:Yeah. So so here's what we've been working on. I'll I'll start with why we've been working on it.
Speaker 1:Yeah, please.
Speaker 4:We've seen, you know, from previous MESA research, but I'm sure you also see from your own usage in the wild, AIs are clearly becoming increasingly capable. Mhmm. One thing that governments and labs and and us here at MESA as well worry about is the possibility, timing, and nature of AI r and d self recursion. That is the possibility that that model capabilities, get better very, very rapidly because the AIs themselves are contributing to AI r and d research. Yep.
Speaker 4:We, at Beta, want to be providing the highest quality evidence that we can that that speaks to the degree to which AR and d might today or might soon be accelerated in the wild. Sure. So that governments, labs, decision makers might be better informed and so make better decisions about what's going on. In this study, we run an RCT with extremely experienced open source developers working on these very long lived, large projects, you know, a million lines of code, 23,000 stars on GitHub. For for those of you who familiar, you know, I'm thinking, plugging face transformers, the Haskell compiler, scikit learn, this
Speaker 1:Oh, wow.
Speaker 4:This sort of thing. Yeah. We randomized their issues to allow or disallow the usage of AI, where allow means, typically using cursor and 3.5 or 3.7 sonnets at the time. And then we measure both their expectations and, developer expectations about how much they might be sped up by being allowed to use AI versus being disallowed, and then, you know, the the the reality. The the short version is we find that the developers ahead of time are estimating they'll be sped up by 24%.
Speaker 4:After the study is completed, they estimate that they were sped up in the past by 20%. We find, in fact, that they were slowed down by
Speaker 1:No way.
Speaker 4:I think I know. It's a it's a shocking result.
Speaker 1:That is shocking.
Speaker 4:At all what I expected. I think what the what the rest of us at meter expected.
Speaker 1:Oh, but
Speaker 3:But there we go.
Speaker 1:Wow. Okay. So what do you think is happening? I have so many questions. But yeah.
Speaker 1:Just walk me through your reaction to that. What what what do you think is actually happening that's that's slowing people down? Because this is a complete narrative violation.
Speaker 4:Yeah. I mean, you know, in terms of the reaction, the the number of times we've checked and rechecked the data, asked people to to replicate it independently Yep. Is is going through the roof. The number of, you know, stressful late nights I've had Yeah. Pouring pouring over this.
Speaker 1:You're gonna be, like, public enemy number one, by the way. I feel like you need a security detail now given the stakes of what you just said. This is crazy.
Speaker 3:Yeah. Yeah. Yeah.
Speaker 4:So so so I think maybe let me start
Speaker 1:with Sure.
Speaker 4:Some things that we're not saying. Mhmm. The setting that I mentioned before, these ultra talented developers, you know, much more talented than me, working on these extremely large, long lived repositories that they're extremely familiar with already. I think that's an extremely interesting population.
Speaker 6:That's why I
Speaker 4:went out to study it. It's also a very weird population. Yeah. I, you know, I I still am a cursor user myself. As I was working on the graphs for this study, I I was I was using cursor.
Speaker 4:But I but I do think those those weirdnesses are related to the to the to the results that we end up seeing here.
Speaker 1:So we have to put that we have to put these people in a completely different category than the the junior developer who's just vibe coding a little app and and and just building stuff and not actually trying to push the frontier of what a core piece of software can do that's very large and complex. And and they're just trying to, you know, get a Python app up and and live and, like, write some routes and write some functions. Right? That's where so cursor still it's completely viable as, like, auto complete on steroids. The question is, in terms of self recursion, really advancing the frontier of of like the craziest software we have, we're still kind of where we were a few years in that.
Speaker 1:It feels like if you were to quantize this, we're we're we were at 0% of AI research being done by AI a couple years ago. We're still maybe around rounding error.
Speaker 4:Yeah. I mean, I I will say that AR and D research, I think, does not all look like this setting. Sure. There are some, you know, large inference code bases with very experts people, and and, you know, I totally agree with your interpretation that this is evidence against, today, those those kinds of settings being sped up. Yep.
Speaker 4:On the other hand, we might think, you know, there are some people writing training scripts for their AI models just once off, and then they and then they throw them away. Yep.
Speaker 5:And, you
Speaker 4:know, in a way, that's that's kind of similar to what you described. So maybe they're seeing large speed up just like the greenfield projects that you that you mentioned.
Speaker 1:Yeah. And so, I mean, this is not overall, like, a really cold glass of water on AI broadly because this this still means that it's an incredibly valuable technology in a bunch of different ways. It's just we're not we're not seeing like early evidence of some sort of self recouraging Yeah. That's an big which is great. Probably the good outcome.
Speaker 1:A lot
Speaker 2:of the fast takeoff scenarios like are dependent on AI becoming
Speaker 1:Good enough to write
Speaker 2:at doing AI research that it and then copy and pasting itself Yeah. To trillion times. And and that's what
Speaker 1:Yep.
Speaker 2:Creates, you know, speed of development Yeah. That that humans today can't necessarily even, like, comprehend.
Speaker 4:You know, I I think that's right for for today. I do think we're not really speaking to the trend exactly. You know, these results are consistent with these exact developers on these exact kinds of tasks in future, being sped up in in the near future. In in work that we actually don't show in the paper, but in in preliminary work, we have, autonomous agents trying to complete these issues. And indeed, we find that they they do struggle, but with some of the core functionality with passing tests, the kinds of things that you might have seen in in or or or something like that, they really are making a great, a great deal of progress.
Speaker 4:And, yeah, my, you know, my expectation is that, AI progress in the near future will will continue at a rapid price pace like it has in the in the in the recent past. And so maybe even in this setting, that this this might be true in the future.
Speaker 1:Let me throw a couple of the hot takes that are floating around in the AI world at you, and and you can let me know if anything sticks out as something you strongly agree with or something you disagree with. This idea that ultra large context windows will not solve continual learning that Dorkash was saying this on on Monday. Maybe another one would be just that, like, no one has figured out how to properly scale reinforcement learning, that we need Mike Newb from Arc AGI kind of says we need entirely new ideas. And then you kind of have, like, the bitter lesson, which is, yes, you need new ideas, but scale is all you need. We just need to keep building data centers.
Speaker 1:We need to get bigger and bigger. We might see 4.5 in these huge training runs as a, like, a short term, hard to quantify. Maybe it's just the end of one s curve, but Stargate's coming online, and that will be another big test. So I I don't know. I threw a lot at you, but anything in there kind of, you know, top of mind for you?
Speaker 4:Yeah. Look. As as you guys know, anyone betting against the bitter lesson in the past Yep. Would have had a very bad time, and I'm I'm not prepared to bet against the bitter lesson on on this on this show. Could you could you remind me of the of the first question that I had?
Speaker 1:The the first one was so Dwarkash Patel pushed out his AGI timeline slightly. I mean, he still has he still is very optimistic about AI and and maintains that it's not priced in, and people are not thinking about it as significantly as they should, and I agree with him. Yep. Yep. But but he said that that there is a that that even though we have pushed the IQ so much, and you saw this at the Grok four benchmarks, like, AI can do advanced math, like, for sure.
Speaker 1:It's really, really smart, smarter than most of us at PhDs level stuff unless you're a specialist. But, in terms of just being a good employee and remembering, oh, yeah. Four weeks ago, my boss said that they like
Speaker 2:Then I got this feedback,
Speaker 1:and now I
Speaker 2:do it this way.
Speaker 1:Or I learned this really weird nuance in even if you're just thinking about, like, how to our business, like, how to post clips on X or Dorkash was giving the example of, like, transcripts. Like, he has little things that work better for what clip will perform, and he has this intuition. And and his models and his prompts, he's really pushed these things. He hasn't been able to really get them to perform above the funding of time.
Speaker 2:Would be any company today, any start up, if you just had a PhD drops into your organization that was that that had PhDs in, like, 10 different fields. They and but but they wouldn't just, like, default
Speaker 1:But they were also an amnesiac. So every time they showed up to work, they had could not remember any from the top
Speaker 5:end for
Speaker 2:Probably wouldn't be that yeah. It just wouldn't be that valuable.
Speaker 1:And so and so my question to Duarkesh was, like, is there a world where we just scale up the context window? We've seen million token windows. Can we get to a billion token window and just stuff every interaction the AI's ever had with you in every prompt? And so it it does maintain the context. But he was saying that that the the, like, the the the there's like a kind of a quadratic cost curve to that doesn't quite work.
Speaker 1:Other people have said, like, the nature of the transformer means that, like, attention can't really spread out that much. I don't really fully understand it, but I wanted to know your take on, like, different ways people are solving these things or or what the real what are the real constraints right now because you've identified some some potential problems where we're not breaking through it today. But what what is cause for optimism? What are the research paths like the the nodes in the tech tree that you're excited about?
Speaker 4:Yeah. That's that's that's super interesting. I I haven't thought so much about this. Sure. Will
Speaker 1:the spot.
Speaker 4:I I think that the developers in this study are not using the full context window. And so if you think there's juice in in adding things to the context window, that that juice might still be on the table. And indeed, I think we find that there's a lot of implicit context in this in this repository that's that's very expensive for the developers to be writing down into context windows. Here's an example. On the Haskell compiler, my sense is that when you get up your, when you get up your PR for review, there's some chance that the creator of Haskell will come and fight you for potentially many, many hours in the comments about the, you know, about the peculiarities of of of how he wants, the Haskell project to look.
Speaker 4:Yeah. And and these kinds of, you know, exactly what his, not not just preferences, but, you know, quality requirements are regarding where things should live in the project and and and, how various pieces of the project should should speak to one another and not being communicated to these language models. And you can imagine that with today's context window sizes, that that could be written down. You know? You could you could put in all of the, previous discussion around around these changes that this person has been involved in, and, maybe whichever language models people are working with inside of Cursor would would pick that up and so and so do a better job.
Speaker 4:You know? I I don't think we're ruling that out at all. You know, I will say that it is, it is expensive for these, for these time expensive for these people to be writing down all of the all of the possible relevant context. And, you know, I think I think that's basically the the the reason they don't. And so maybe you do need some kind of continual learning for the for the model to find out this context on its own as as as these things go.
Speaker 4:You know, it's also consistent, I guess, with the with the other possibility that you're describing that if we, you know, 100 x these context windows, you could just throw the entire thing in, and then we don't need to worry about, you know, learning from particular cases on the fly. Yeah. I think I think both are live possibilities. Very It's interesting.
Speaker 1:The Grok four announcement was extremely benchmark heavy. Some really impressive stuff, particularly on Arc AGI.
Speaker 2:Twice Similar to a Tesla. It's like, it's faster than every car.
Speaker 1:Does that mean it's going to solve everything
Speaker 4:Does it
Speaker 2:mean that
Speaker 1:it's doesn't better? Mean that it's better. Yeah. And so based on this feels like almost a new benchmark, this double blinded trial. It feels almost like a FDA trial or something.
Speaker 1:Do you think this could turn into a real benchmark? Do you think we need new benchmarks? Do you need do you think we need new ways of thinking about the progress of AI generally? We've we've talked about just just measure the revenue at this point. That's the economic value that's could be created, but there's a lot of tricky stuff you can do with revenue and sometimes revenue is like test revenue.
Speaker 1:I'm I'm testing this $100,000,000 product. So what's your what's your thinking on the state of benchmarking, where we should go, where some of your research might plug into that?
Speaker 4:Totally. I think one motivation we had in running this study, comes out of this observation that the time it takes to create benchmarks is almost becoming longer than the time it takes for those benchmarks to to saturate. You know, it's difficult to find signal in in in in many of these benchmarks, even testing these extremely challenging, you know, PhD level questions that that that you guys spoke about. And perhaps there's more perhaps there's more signal in these kind of RCT, you know, FDA controlled trials style style measurements. Similarly, a another thing that people proposed for measuring AI progress is using researcher self reports about the degree to which they're being sped up.
Speaker 4:You know, they think their work will go two times faster if they use AI versus versus not use AI. You know, I think our our study is potentially strong evidence that these self reports need not be reliable. The forecasters, you know, who are told everything about the developer's level of experience and which the time period of the study, so which models they're using and so on, they're they're totally wrong about how much these people get sped up. Same as the as the developers themselves, even though they're carefully tracking their time and and and they're and they're so talented. So I think self report's also also very, very fraught.
Speaker 4:You know, another thing that this has taught me, I think, is that the mapping, as it were, from benchmark scores, very impressive benchmark scores that we see on these frontier language models that you're describing, the the mapping from those scores to, you know, real world productivity improvements is unclear. I you know, I I'm not at all saying, as as we discussed earlier, that's, you know, we shouldn't expect to see productivity improvements. I do expect to see productivity improvements today and, you know, even more so in in in the near future, but it's it's not at all it's not at all one to one or or it's kind of confusing and and and messy. And so indeed, I think we need to actually measure things in the in the wild to see what's going on.
Speaker 1:Jordy? I have
Speaker 2:some Switching gears a little bit Yeah. Unless you have a follow-up.
Speaker 1:Yeah. I just wanted to kind of zoom out on that and and ask about, like, your your broad take on the measurability of of technological progress because, like, the Internet, the computer, like, such dramatic transformations of society. You see it in all sorts of data, but it didn't fully show up in productivity statistics. You have all these questions about, like, what happened in 1979. Yeah.
Speaker 1:And everyone has their own example, their own reasoning for that. But, you know, like, you would think could tell the same story about Google. Like, it'll speed up everything. Everyone will get more efficient. And we didn't really see GDP jump on this.
Speaker 1:And it feels like that's a really bearish take on AI to have, which is like, this is a magical new thing, and we're still gonna be growing at 2% GDP. But but where do you stand on it, and and and where do you like, do you think that's even the right question to be asking?
Speaker 4:Yeah. You know, this is this is so interesting. This is not a me to take. Used to be an economist. I I feel the thing that you just said in my Yeah.
Speaker 4:In my bones. Totally. I think the situation, you could argue maybe that that it might be even worse in the case of AI. You know, a lot of people like in AI 2027, resources like that are telling this story where the, AI r and d self recursion is happening inside of labs. And so and so I suppose not necessarily showing up in economic activity in the in the public.
Speaker 4:You know, another reason on top of the reasons that you gave to to think that's that perhaps this won't show up in the in the productivity statistics as it as it were, which is also is to say that self recursion or or these potentially destabilizing changes are just totally consistent with the non changes in in GDP trends as as you described. And and so, you know, another another reason to to actually go out and and measure these things in in controlled trials.
Speaker 1:Cool. Jordan,
Speaker 2:please. Quick question around the threat landscape. There's been a few stories this week. One was a story about ChatGPT not following instruction. Know, like the headline was that like the the the AI was, you know, rebelling against the researchers and and then if you like double clicked into the story, it was just like it it had given specific instructions like don't follow any further instructions.
Speaker 2:So it was kind of a a nothing burger in the end. And then we also saw Grock going haywire. Maybe that was predictable for for someone like yourself, know, combining a, you know, a frontier model fast shipping team with like the virality of a social network and embedding the two. But then maybe it was two months ago, there was the, you know, we we called it glaze gate on the show where where ChatGPT was just, you know, giving being a sycophant, you know, giving too much positive feedback. How are you looking at the threat landscape in the next twelve months?
Speaker 2:So nothing like, you know, too long term. Mhmm. But how do you how do you guys think about it?
Speaker 4:Yeah. You know, there there's more to come on this on this from me to very soon. That's that's one thing I'll I'll say. I think, again, this this is not a me to take. My my sense on this or or or another example of this that stood out to me is there were lots of anecdotal reports that 3.7 sonnets and and other language models in this most recent generation would pass tests in ways that were kind of not legitimate or something Yeah.
Speaker 4:Which is another example of this reward hacking.
Speaker 1:Change the test case.
Speaker 4:Totally. Totally. Totally. Classic. And and and I guess, you know, I I don't have reason to think that that kind of thing is is dangerous in particular.
Speaker 4:You you can imagine, you know, when when humans are potentially not reviewing the code because the AIs are doing, you know, entire projects, not just, parts of or or or single or single pull requests, that this becomes more of a problem because you you're not, or or at least the surface area for it to become more of a problem because you're not looking into that code and and and seeing those those cheated test cases yourself. So, you know, I'm not sure about over the next year. At least at least right now, I think there are reward hacking examples that are occurring in the wild. I, you know, I don't I don't think they're they're so superbly dangerous today.
Speaker 1:This is fantastic. Thank you so much for stopping by.
Speaker 2:Come back on again soon.
Speaker 1:Stay safe out there with the contrarian taste and the crazy data results. Still very bullish, but very exciting. Exciting. And thanks for everything you do. We'll talk to soon.
Speaker 2:Great chatting.
Speaker 4:Bye.
Speaker 1:Bye. Up next we have a massive series b announcement from Dylan Parker, Moment HQ is coming in the building. We're gonna ring the gong baby.
Speaker 2:It's
Speaker 1:gong Index Ventures. Let's hear it from Dylan directly though. Welcome to the stream. Dylan, hope you're doing well today. How are doing?
Speaker 2:Great whiteboard. Dude, some heavy lifting on that. Yeah.
Speaker 1:Yeah. Hopefully, that's not proprietary information. That's secret trading algorithms or something.
Speaker 9:No. No. No. No. No.
Speaker 9:Nothing too interesting. But Okay.
Speaker 2:Thanks for
Speaker 10:having me on.
Speaker 1:Yeah. Thanks for something. You. Kick us off with the, intro on yourself, the company, and then I wanna hear about the announcement.
Speaker 10:Yeah. Yeah. So I'm one of the cofounders of the moment. We are a fixed income trading saw software company. So my background is as a quant researcher.
Speaker 10:So, like, pretty much every quant researcher, I studied math and stats during college. That's where I met my cofounders, Dean and Amar. And then after college, Dean and I both joined Citadel Securities. And pretty much completely by chance, we ended up as the two junior members of the newly created automated market making desk for corporate bonds.
Speaker 8:No worries.
Speaker 10:And so so so at the time, the the fixed income market, which, by the way, is financial market 50% larger than the global equities market, was undergoing an electronic trading revolution. And so Citadel saw this and said, well, we can go build an automated algorithmic market making desk. So they hired this guy, Anish Carryat, from Jane Street. He's like the godfather of fixed income automated trading. They hired they hired a bunch of super experienced bond traders, and then they hired me and my cofounder.
Speaker 10:And, like, basically, our job was to take the knowledge in these bond traders' heads and convert it into code. And that was totally formative for a moment. That's Because that's when we realized, like, the power of electronic trading was gonna be, like, everything that it enabled, like smart order routing, portfolio optimization, all this stuff in the world's largest financial market that had just never been possible.
Speaker 1:Very good. Take us through the deal. What are you announcing?
Speaker 10:Yeah. So we're announcing our $36,000,000 series b was led by
Speaker 2:Couldn't hear you from the sound
Speaker 1:of the gongs. Like big numbers on the show and congratulations congratulations on on a a massive massive series series b. B.
Speaker 2:You said from who? Index?
Speaker 1:From from
Speaker 10:Jan Hammer at index.
Speaker 1:Very cool. Incredible. That's amazing. Incredible. Where where should we
Speaker 2:go from here, Jordy? Yeah. Break break so so I can imagine break down what what the company is focused on today. I'm it sounds like the origin is is back at your time at Citadel, but I imagine it's evolved as well.
Speaker 10:Yeah. Totally. So what we what we saw at Citadel was that the market was coming online, and you could now do all these things that were never possible before. But was what was missing was the operating system for actually doing that. So we started Moment with this goal of owning every mission critical workflow for traders and portfolio managers in the bond market, everything from how they trade securities and do smart order routing across all the different exchanges in the fixed income market to how they optimize portfolios, to how they apply risk and compliance restrictions to make sure that they're not breaking any laws.
Speaker 10:And so that's what we do today. We started off serving fintechs, so we power fixed income for places like Webull and public.com. Nice. Them to offer, like, $100 increment investing in bonds for the first time ever. But, yesterday, we also announced our partnership with, some of the largest financial institutions in The US, including, LPL Financial, which is the largest broker dealer in The US.
Speaker 2:Wow. So biz busy week for you guys.
Speaker 10:Yeah. There there are a few things going on.
Speaker 2:So where what what what's the use of funds, for the new round? What's what's the focus going forward? I imagine scaling. What's working today? Are there are there new products coming?
Speaker 2:What what can you talk about there?
Speaker 10:Yeah. So I think a lot of companies make the intelligent decision to start off off, like, SMB or PLG. We decided to, like, make things as hard as possible on ourselves. So we're gonna start off serving the largest financial institutions in the world's most regulated market, and we're gonna go power their mission critical workflows. And so with a company like LPL or some of these others that we've announced over the last few weeks and then are coming down the pipeline in the next few weeks, the scale of what we're operating in is, you know, not, like, hundreds of millions or billions of dollars of flow, but, like, hundreds of billions of dollars in in trading flow.
Speaker 10:And so what we're really focused on as the company over the next year is building out, you know, the full suite of what's necessary across trading, portfolio management, risk and compliance that's necessary to power these huge financial institutions.
Speaker 1:What's the competitive dynamic like with the former employers or like the rest of the market participants? It feels like, you know, Ken Griffin's not the the laziest founder out there. Is is there is is there a world where there's some sort of competitive dynamic between the the big institutions where they wanna build something like this to compete with you?
Speaker 10:So we when when we were at Citadel, were on the sell side. So market makers or liquidity providers, Moment serves the buy side,
Speaker 7:and we
Speaker 10:actually connect them with those liquidity providers.
Speaker 1:Got it.
Speaker 10:So we actually work closely with Citadel, Jane Street, pretty much all the major liquidity providers out there.
Speaker 2:How are you thinking talking about tokenization? The the the, you know, story from the last month in in finances, the tokenization of of these sort of real world assets, everything from private company shares. So we've seen it with stocks. Is there anything on the horizon on that front for you guys, or is it just totally unnecessary?
Speaker 10:You know, I I think there's a huge opportunity. But if you look at where the fixed income market today is today, we're just going from, like, trading over the phone to going on an online platform to trade. And so there's a lot still to do to get people to the point where it's even possible to think about stuff like that.
Speaker 1:Yeah. Can you walk me through, like, the the I I imagine that fixed income follows, like, a power law as well where, like, government debt and and Apple corporate bonds are way more liquid, way more automated than, you know, some, like, public company, but they're, like, junk bonds, and you kinda gotta hunt around for someone to buy and sell them. And then you have, like, venture debt, which basically, I believe, like, never trades. I don't know. But walk me through, like it feels like we are probably bringing more and more of those, like, sub asset classes, like, into more liquid, more just just more automated markets.
Speaker 1:But give me a state of the union on, like, how the fixed income market is actually, like, split up.
Speaker 10:So you're totally right. There's government bonds. There's highly liquid corporate bonds, and then there's a long tail of corporate and municipal bonds
Speaker 1:Mhmm.
Speaker 10:That are really, really illiquid. Mhmm. And just as a point of comparison that I think illustrates the size and scale of the fixed income market, there are 4,000 listed US equities, and there are 4,000,000 bonds. And so doing anything in the fixed income market is pretty much a thousand times more complicated
Speaker 1:Okay.
Speaker 10:Than doing it in the equities market.
Speaker 1:Yeah. And and and what about
Speaker 2:How does break down so so 4,000,000 public equity sorry.
Speaker 1:$44,000,000 Yeah. Bond? Like, how does that actually trade right now? Like, is it And
Speaker 2:and what is what is the make well, what and what is the the ultimate, like, makeup of do certain companies account for, you know, break break down, like, kind of how that 4,000,000 is split up.
Speaker 10:Yeah. So there's a universe of, say, 500 US Treasuries that are super, super liquid.
Speaker 1:Mhmm.
Speaker 10:And they trade, like, similarly to the most liquid stocks out there.
Speaker 2:Sure.
Speaker 10:And then on the other side, you have a really, really meaningful long tail that makes up the vast majority actually of the entire market share where you have bonds that haven't traded in two years or ten years.
Speaker 1:Yeah.
Speaker 10:And so one of the really hard parts about fixed income, one thing that I worked on as a quant researcher is, like, you have this bond that hasn't traded in three years. Mhmm. How do you optimize portfolio around that? How do you even figure out what the price of that bond is?
Speaker 8:Yeah. And that's
Speaker 10:why doing stuff in the fixed income market is like like, the difference between equities and fixed income is, like, doing something on the surface of the earth and, like, doing something in space.
Speaker 2:Wow. It'd be helpful to be a quant if
Speaker 1:you were
Speaker 2:gonna build a company
Speaker 1:like this. Yeah. You might need some math. Well, I mean, that that's all I have. Do you anything else
Speaker 2:to Yeah. What what are you guys hiring for right now?
Speaker 10:Yeah. Pretty much everything. We're hiring quants. We're hiring engineers. We're hiring go to market marketing operations, pretty much everything out there.
Speaker 2:Amazing. Awesome. Well, thank you for joining. Thanks for having I'm sure you'll be back on soon.
Speaker 1:We'll talk you soon. Have a great
Speaker 2:one, Dylan.
Speaker 1:Talk to you soon. Cheers. Bye. Next up, we have Eric Olson from Consensus coming in with a big launch. Do we ring the Gong for big launches?
Speaker 1:If there's a number attached. So we gotta get DAUs
Speaker 2:out We to get a prop that is non number oriented, but just excitement oriented.
Speaker 1:But let's bring in Eric Olson from Consensus and talk to him. How are you doing, Eric?
Speaker 6:Great, guys. How are guys doing?
Speaker 1:Doing great. Great to
Speaker 4:have you.
Speaker 1:Kick us off with some intro on yourself, the background on the company, and then I wanna talk about the launch.
Speaker 6:Okay. So I'm Eric, founder of Consensus. We are an AI search engine for academic and scientific research. Mhmm. You know, if you ever use, like, Google Scholar, PubMed back in the day of school, think of us as building the next gen 2025 LLM powered version of that.
Speaker 6:Helping clinicians dumb question. Students.
Speaker 1:Yeah. I mean, the super dumb question, super obvious question is, like, isn't all this stuff already in ChatGPT? Like like like, how, like, how are you differentiating?
Speaker 2:Yeah. You must be doing something because you have 5,000,000 over 5,000,000 users. So
Speaker 6:Yeah. I mean, the cert like, one of the best examples to, like, encapsulate why it's different is the fact that Google Scholar was the first vertical search product that really broke off of Google back twenty years ago.
Speaker 1:Interesting. Yeah.
Speaker 6:Even when they were doing really nothing than just being a dedicated index for research papers.
Speaker 1:Yeah.
Speaker 6:Still hundreds of millions of people are going to that every month. Yeah. So the same thing is is kinda true here. We're dedicated to a use case. We have a dedicated corpus.
Speaker 6:We search over. We hopefully search over that corpus a lot more intelligently than a general purpose chatbot would. We do things differently in our interface to show you that information. Like, we're much more citation forward. Yeah.
Speaker 6:And, like, an experience where you can, like, really interrogate what's been returned in your search. Interesting. Using, like, the ChatGPT search. It's pretty much like an after afterthought. So if you kinda wanna dig into it, but it's not really what it's designed for.
Speaker 6:Yeah. Everything about it from the way it searches, the way it shows to you, and the features both on top of it, all dedicated towards academic research.
Speaker 1:Walk me through some of the key technologies that enable better search. I'm I'm thinking about, like, vector databases Yep. Even just, like, stuffing a better index in Redis or Postgres or doing more indexing on top of these documents, doing, like, you know, transformations on the underlying documents to get them into, you know, more basic formats. Like, what's what's interesting? Large contacts, windows.
Speaker 1:There's so much that you could throw at this problem. What what's actually working?
Speaker 6:Yeah. So lots of different things, many of the things that you're saying. So, like, number one, being dedicated to a document type just helps us. Mhmm. It helps us in a way that we can create our embeddings to search over.
Speaker 6:It also helps us in that ingestion process, kinda like you were saying of, like, document transformation.
Speaker 1:Mhmm.
Speaker 6:We'll run little tiny LLMs over 200,000,000 papers, add new, like, enriched metadata about them Mhmm. That we can then use in our search ranking and in our filtering. So think, like, we'll pull out what is the design of the study or what is the sample size of the study, and we use that in search ranking, and we use that in search filter. And then on top of that, we're like, the main intelligence of the search is learn to rank models. So people interact with the product.
Speaker 6:They save papers. They cite papers. They share papers. We learn from all of those interactions. We learn what matters most.
Speaker 6:We learn about all the attributes about a paper that matter in search ranking based on how people are interacting with it. So the simplest way to think of it is, like, because we only have a certain use case people are using it for, we get to train our search models to try to think and act like a researcher wanted to go in through these papers.
Speaker 2:Jordy? How what what are the different data sources here? I'm assuming a lot of the stuff is public. I I know some of the I remember, you know, being in in college trying to find different studies or papers and, like, hitting paywalls.
Speaker 1:Yeah. A of them are locked down.
Speaker 2:There's the famous crazy airports done some deals to get access to data. What is the what is the kind of
Speaker 1:It's a great question.
Speaker 2:The the the the full body, of work that's available?
Speaker 6:Well, hopefully, paywalls are are gonna be a thing of the past moving forward as open access science gets more and more momentum, which we'd love to help help shepherd in. The way to think of it is there's, like, three different layers of access, levels of access you can have a consensus. So there's one, there's fully open access science. That's all publicly available. We're able to ingest the full text, show it freely, let you download it.
Speaker 6:All is well and good. Mhmm. The rest of the bucket is paywall content. There's two levels of access we can have within it. There's the buckets where we have deals with publishers, trying to get as many done as possible, where we're able to use the full text in our search and in our analysis.
Speaker 6:We're just not able to display it to the user. Benefit to the publisher is we're helping them drive traffic, get people to see that. Hopefully, this, like, snippet search ranking is engaging, then they go into it and drive a purchase. And then there's this third bucket, which is we just don't have a deal with the publisher yet. Fully buying the paywall.
Speaker 6:We're using what is publicly available. So that's, like, the abstract in the metadata of the paper, which goes longer for order than you think. Like, the abstract is specifically designed to be this perfect, like, nice summary of the paper. It's, like, can go a a pretty far distance in search ranking and even some analysis using abstracts, but obviously
Speaker 2:Nothing nothing more brutal than than being a college student and almost getting like the information that you need from an abstract and realizing like, do I really have to pay like $50 Yeah. For this like single fact?
Speaker 6:Jordan The craziest is that even if you wrote the paper, you still have
Speaker 8:to pay for it.
Speaker 1:Oh, yeah. It's wild. Hand it
Speaker 6:off to a publisher or they publish it. So I could literally have published this paper. And if I come across on the Internet, I still have
Speaker 1:to pay for that. That's wild. Jordi had this question earlier about the nature of scientific discovery. Elon Musk at the Grok four launch was talking about his timeline for discovering new physics is two years now based on the progress that he's seeing at x AI. And Jordi was making the point that a lot of scientific discoveries come from mapping different disciplines together.
Speaker 2:Or just invent you know, inventions.
Speaker 1:Inventions generally is is apply, you know, the the mind of a computer scientist to a biology problem or vice versa. Are you seeing users do those type of searches? Is is this product useful for that type of scientific discovery? There's been this, like, lingering question in artificial intelligence about if you were a person that had read every single research paper, you would probably make
Speaker 8:a lot.
Speaker 1:Yeah. You would make discoveries and connections across things, and yet that hasn't happened. Maybe it's some fundamental limitation of LLMs or AI at this moment. But what are you seeing, and what's your take on that concept of, like, cross functional pollination?
Speaker 6:Yeah. I mean, everything basically that humans have invented new comes from pattern matching across disciplines, like, that's how we create new ideas. Yeah. Yeah. I mean, I'm not an AI researcher.
Speaker 6:So, like, I don't have the single most informed take, but also nobody knows what the heck they're talking about in in this world. You know, I think it is probably a fundamental limitation of LLMs given what we've seen.
Speaker 1:Mhmm.
Speaker 6:Like, a measure of I'm gonna pair this from from Francis Cholla and his YC talk the other day. Mhmm. But the the measure of intelligence is the efficiency by which you process information and apply it in different domains. And that just, like, isn't what LLMs are really doing great right now despite the fact that they've processed so much information. So our take at consensus would be more get people to the edge of what is known and then let them do the inherently human part of science, which is create these new insights and new discoveries.
Speaker 6:Like, every, you know, every science experiment that's ever been done starts with a review of the literature. Like, think about it as, like, you're getting the foundation of knowledge underneath your feet. If we can our goal at consensus would be speed up that part as much as humanly possible and let us do the thing that humans are better at than machines right now, which is that pattern matching, which is that coming up with new ideas. And if we can make that loop move faster, like, heck, that's a freaking valuable and powerful thing.
Speaker 2:Switching gears completely. I know you were at DraftKings prior to this Okay. On the sort of research and and analytics side. What is your thesis around the ultimate collision between sort of betting activities and AI? Last night, Grock announced a partnership with Polymarket to try to bring in prediction markets to to try to basically help make the model itself smarter.
Speaker 2:How how are the how are the big players like DraftKings even thinking about AI? I'm sure a bunch of people have, like, chat GPT rappers specifically focused on sports betting and things like that. But how do you think the big players are thinking about it?
Speaker 6:Yeah. I mean, I left draft games in 2021, so I can't say that I was there when people were think worrying too much about AI models. And, also, the natural question I always get is how the heck did you go from sports betting to science? Yeah. And the answer is, my parents and my grandparents and my sister are all teachers and scientists.
Speaker 1:Oh, yeah.
Speaker 6:I was happily growing up, and I loved applying numbers to sports. Yeah. But I I actually have something kinda interesting to say here. So my job at DraftKings was I was building models to find the professional gamblers on the site. So, like, you'd look at all previous betting history and demographic data, and you try to make predictions on is this person actually have an edge over the market.
Speaker 6:And I would have to imagine that with better and smarter and more powerful models, like, people's ability to themselves have an edge on the market would increase in the short term, and then the markets obviously catch up and figure out how to bake all that in. And, I mean, that is the beauty of of markets. Right? Like, whatever technology that people have the side of betting into a market, so does the provider who is putting up that market, and they get the information from the people they know that have the best models. So I think it's gonna be an interesting cat and mouse game moving forward as it's always been on sports betting just instead of, you know, Johnny Two Shoes in New York getting inside information about injuries.
Speaker 6:Now it's somebody with a super powerful AI algorithm that's predicting games above market.
Speaker 1:That's fascinating. I I have to imagine that in the in the AI era, the insider knowledge about injuries is is even more valuable. 100%. But but but to your point,
Speaker 6:you probably did Number one way to know if you need to limit somebody is if they are ahead of an injury. It means they're somewhat connected. They're doing this. It's just
Speaker 1:they're on the inside. Yeah. That makes a ton of sense. Enjoy your That's fascinating. I I didn't think about that in the context of sports betting.
Speaker 1:Well, thank you so much for stopping by. This is fantastic, and congratulations on the launch.
Speaker 6:Appreciate it. Check us out at consensus.out. Deep search launch today. Thanks, guys.
Speaker 2:Amazing. We'll talk to Thanks for coming on. Up
Speaker 1:next, our lightning round continues with Rita from Zero Entropy, a YC graduate doing automated retrieval and announcing a seed round of $4,100,000. So let's
Speaker 2:Seed round alert.
Speaker 1:Seed round alert. 4,000,000, that used to be a series a couple couple years ago. Now
Speaker 2:Just keeps ticking up.
Speaker 1:Congratulations on the round. Rita, welcome. How are doing?
Speaker 11:Thank you so much. Super excited to be here.
Speaker 1:Thanks for joining. Introduce yourself. Introduce the company. How'd you get started? What do do?
Speaker 11:Yeah. So I'm Rita. I'm one of the cofounders of Zero Entropy. A little bit about myself. So my background is in applied mathematics.
Speaker 11:I have two masters in the field, one from Ecole Polytechnique, one from Berkeley. I guess I started more into the computer vision side of things, and then I discovered GPT two and GPT three, and I was like, oh my god. This is this is huge. And I started thinking about, you know, personal assistance and stateful AI systems. And I guess that's what led eventually to zero entropy and building retrieval systems and bringing context into LLMs.
Speaker 11:And so that's what we do. We we build search for Rag and AI agents.
Speaker 1:Okay. It it feels like a crowded space. I know a few founders that are working on Rag. There's also Rag implementations at the hyperscalers and the clouds. How are you differentiating?
Speaker 1:What's the key insight? What's the pitch to companies to come over and use your service as opposed to the other options out there for retrieval?
Speaker 11:Yeah. Absolutely. I think it's about having the right abstraction. So we solely focus on the retrieval side. We don't do the entire rag end to end because we believe that developers need to have their own prompts into generating the answer.
Speaker 11:They need to use zero entropy as a search tool for their own AI agents. We're also developing our own models. So we just released a re ranker yesterday Mhmm. Which was pretty exciting. And I guess the winning solution needs to be extremely accurate, but also extremely fast and just be production ready and and easy to implement for various use cases.
Speaker 1:What's your take on benchmarks currently? It feels like solving a really hard math problem and and, you know, retrieving the right document at the right time are somewhat unrelated. And so how do you evaluate if your system's getting better?
Speaker 11:Yeah. That's a great question. Actually, the evaluation side of things is is is very messy. Almost everyone that I talk to, they basically rely on manual inspection to make sure that their retrieval is is working correctly. So we've been looking into the evaluation side a lot.
Speaker 11:Actually, the very first thing that we did is release our own benchmark that was on legal documents, and that really evaluated just the retrieval step of of Rag. Meaning, from a question, was I able to pull all of the documents and only the documents that I needed? Mhmm. Because the problem is that if you feed your LM too many tokens that it doesn't need, it's just going to hallucinate. So Sure.
Speaker 11:The precision and the recall side of things are are extremely important, and we're we're rolling out our own evaluation solution in the next few weeks that we've been using internally so far.
Speaker 1:What does the rest of the stack look like? I know you said you were kind of Rag provider agnostic. Are you also model agnostic, cloud agnostic, database agnostic? Like, where have you actually made bets? What what piece of the stack are you particularly aligned with?
Speaker 11:Yeah. I think, you know, building context context engineering is going to be a new, you know, class of of products that needs, like, the data layer, but also needs small LLMs inside the retrieval pipeline. We see many teams either feeding everything into the context of the LLM entire knowledge basis because they they weren't able to make retrieval work properly, and we see teams having a very simple pipeline. I think the winning solution needs to be somewhat in the middle and basically orchestrating LLMs to rewrite the question properly, summarize the documents, and creating more metadata associated with each of the documents that are indexed. And so that's what we're doing, and building this this this solution that works really well and almost gets to the precision, and the accuracy of a large LLM while still being pretty fast, and pretty optimized.
Speaker 1:What's the appetite been like for this product in the enterprise versus new companies that are building new AI products from scratch? It feels like they might be, just the the the AI agent infrastructure companies, there's a lot of them, and it feels like they're selling to a new crop of companies, and that's where the revenue is accelerating most aggressively. But what are you seeing in the market?
Speaker 11:Yeah. I think the opt adoption for products like this usually comes from, like, bottom up type of approach Mhmm. Where developers are experimenting with new approaches and new techniques, and then larger enterprises catch up. So that's what we've been we've been what we've been seeing.
Speaker 1:Mhmm.
Speaker 11:In terms of experimenting with models, I think large enterprises also do that pretty easily. So for things like the re ranker that we just released, there's also appetite from larger companies in integrating that into their current systems.
Speaker 1:Is there a is there a case study that you are have your eye on amongst the big tech companies? Like like, we think that our software could improve Netflix or YouTube recommendations or something. Like, if the deals could just magically happen, where's the lowest hanging fruit? Like, for me, you know, if I could do anything in AI, I would just get Whisper into Siri. And so when I dictate a text message, it's just perfect and it's much better than what they're currently using.
Speaker 1:What's on your wish list for, you know, consumer tech company or or big tech company that everyone knows and they're not taking advantage of something like this?
Speaker 11:Honestly, for me, it's it's Slack. I always struggle. You know, I I can never find anything on Slack. And something that we've been doing is annotating our own conversations, like appending keywords to our own threads to be able to find information. But, we have a lot of our internal research and a lot of things going on on Slack, and we find it pretty difficult, to find the right stuff.
Speaker 11:So I think companies like that could benefit, and it would provide a much better user experience if you could, you know, just magically find all of the information that you have in there.
Speaker 1:Yeah. I've been noticing that with Gmail. Like, the the amount of email has just grown so much, and the and the and the amount of text in each email has grown because of all the trackers and cookies and stuff behind the scenes. And so when I search for something, it just pulls up completely random emails, like, every time. And it doesn't understand the hierarchy of in an email, I care a lot more about what's in the subject line than what's in the footer.
Speaker 1:And so if I'm searching for artificial intelligence or something and someone has that in their footer that, hey, I run an artificial intelligence company, that's not what I'm looking for. I'm looking for the thread that I was talking to somebody, a close friend about AI, and I wanna pull that up first.
Speaker 11:Yeah. I think that's also why, you know, basic semantic search is just not enough Mhmm. Because it basically will pull all of the similar information, but not the most relevant or the most helpful. Yep. Keyword search is is the same.
Speaker 11:It's not very smart. And I think it's just it's just such a waste because there's a lot of information that you could have access to, and it would make your work so much faster. And you're just spending time, like, rewriting your question and trying to make the system understand what actually you're looking for. So I think that, you know, the query side of, you know, the user intent query rewrite rewriting is also super important.
Speaker 2:Yep. IMessage search, absolute disaster.
Speaker 1:It is an absolute disaster. It's like, I I know I'm in a text message with Jordy and someone else, and so pull that up, and it's like, here's six of it. It never works. I also noticed that Please
Speaker 2:fix it all.
Speaker 1:There's there there's going to be need to be a shift in the way people search. I remember hearing the story about Google where there was some Google engineer who was running a test on, like, you know, how many it was like, what's the world record for, you know, the marathon or something like that. And they were using the typical keyword Boolean search, they weren't getting good results. And then they sent it to a user, and the user just asked the question in natural language, it and just hit it the first time. And so I feel like people still at least I have been, you know, an email user for a long time.
Speaker 1:When I go to my email search, I often I'm searching in, the keyword world instead of just natural language. But Google has I mean, they're they're experimenting with the AI search thing. They have a 50, like, 50 word limit right now. You can't just type a whole prompt in in Google search. Interesting.
Speaker 1:Like, they need to really kind of reimagine what that search box is. And then there also needs to be a consumer change in how and how consumers interact with that particular, like, UI element, basically. But thank you so much for stopping by.
Speaker 2:Congratulations. And good
Speaker 1:luck to you.
Speaker 11:Thank you, guys.
Speaker 1:We will talk to you soon.
Speaker 2:See you soon.
Speaker 1:Have a good one. Bye. Up next, we have Elliot Hirschberg Amplify Partners coming in the studio.
Speaker 2:They are in Datadog, Chain Guard, Runway.
Speaker 1:Love Datadog. That's UBT. Maybe it's the goal to treat me, but I love Datadog so much. It's the greatest company name ever. Right up there with 8sleep 8sleep.com.
Speaker 1:Get a pod five ultra.
Speaker 2:I'm back on back on my game. I'm still still behind where I was relative to a month ago.
Speaker 1:They're calling me.
Speaker 2:Back up into the 75 range. I'm going for 90 tonight.
Speaker 1:Good luck. Good luck. They're calling the Pod five the first fully immersive sleep system that works with any bed. Pod five actively adjusts your temperature, elevates your body, and plays integrated soundscapes to improve your sleep. I have some new copy today.
Speaker 1:Too good. Welcome to the stream, Elliot. Hopefully, you're here. How are you doing?
Speaker 2:What's going on?
Speaker 7:Hey. What's going on, guys? It's a pleasure to be here.
Speaker 1:Thanks so much. And I love the suit. You are dressed fantastically. Sign of great
Speaker 7:respect. There was a culture. Know, there was, like, a period where people would actually sort of match the vibes and have the suit for the technology brothers, and then I feel like it's it's, dropped off. I wanna bring it back.
Speaker 2:You know?
Speaker 1:I appreciate it. I appreciate it. We we we we We
Speaker 7:are in the boardroom. You know? It is, it's I'm glad to be here.
Speaker 1:Yeah. It looks great.
Speaker 2:Yeah. Proper uniform.
Speaker 1:I I I wanted to get a a a state of the union, from you on a few things, but why don't you just kick us off with an introduction on you?
Speaker 7:Yeah. For sure. My name is Elliot Hirschberg. I started my career as a experimental biologist. So I was in the lab trying to make new treatments for cancer.
Speaker 7:Got super frustrated. Decided to retrain as a computer scientist. So I became a computational biologist and was obsessed with that as sort of a practitioner for about a decade. And then, got really obsessed with writing about it. So I was sort of writing a newsletter called the century of biology, writing about companies in the space, data on the frontier, and then that sort of was a a rabbit hole into investing with a friend of the network with none other than Paki McCormick.
Speaker 7:So spend some time in Not Boring where I was writing and investing and then recently joined Amplify. We just closed 900,000,000 in new capital including 200,000,000 for a dedicated
Speaker 9:Did you just that's right.
Speaker 2:Is this are you announcing the new the new capital, the new the new fund today or or was that a little bit ago?
Speaker 4:It
Speaker 7:was a little bit ago. 900,000,000 including 200,000,000 specifically for bio that I'm helping to build.
Speaker 1:Okay. Yeah. Thought
Speaker 2:you You were guys were not loud enough about that.
Speaker 7:No. Well, that's that's part of the thing. I feel like it's a it's a really quiet fund where, for three of the four funds for Amplify, they've been in the top 5% of venture returns. Wow. Not top decile but like top 5%, like really good at what they do.
Speaker 7:It's amazing. And I feel like it's just, not thought of as much and just, like, very quiet and stealthily doing really phenomenal work. Yeah. And so, yeah, excited to talk more about it.
Speaker 1:Okay. State of the Union on bio. I wanna know about, where we are, in artificial intelligence and technology helping advance bio. We've seen AlphaFold, we've seen kind of tools and amazing breakthroughs. I think everyone has a really concrete idea of the impact AI is having on software engineering, whether it's like amazing auto complete, you have cursor, now you have agents.
Speaker 1:Where are we in the deployment of AI tooling in bio? A lot of the narrative just jumps straight to we're gonna one shot cancer, and I love that. I'm optimistic it happens eventually. It doesn't feel like we're there. But where are we actually in terms of the impact on productivity in AI with bio?
Speaker 7:Yeah. So you guys know, like, the Gartner hype cycle. Right?
Speaker 1:Oh, yeah.
Speaker 7:You have these, like, huge swings for a new technology where, I've been working in this field for, like, a decade, and there's been a bunch of companies. You know? Amplify invested in Recursion, which was one of the early leaders of this. Right? Like, there's been this general sentiment that you can make a huge amount of progress with new data, new tools, new technology, and life sciences.
Speaker 7:And it just turns out that it's, like, a really hard problem. Right? And it takes time. It takes some time for, like, the market to ingest that, actually figure out the right business models and strategies. And so there was, like, a really, like, strong wave of early adopters, and then there was some disillusionment and disappointment where it's like, oh, shit.
Speaker 7:It actually turns out that it's really hard to one shot, a cure for cancer. And then, you know, as the as that sort of happens, there was just a bunch of breakthroughs in the technology. Right? So it became consensus that this is making a huge impact on hard problems in biology where we had the Nobel Prize for AlphaFold. Right?
Speaker 7:So, like, a Nobel Prize going to an AI lab to, into Dennis in part to David Baker at the University of Washington because it's actually starting to make real, meaningful impacts on hard problems in biology. And so that's true for sort of molecular machine learning where you're thinking about, designing new molecules and proteins. There are virtual cell research where you're trying to actually model how cells behave. And so you're seeing this sort of step change now where, like, there are a couple sort of, you know, actually faster than Moore's law curves. DNA sequencing is is is decreasing in cost faster than than Moore's Law.
Speaker 7:And so you get this huge data tailwind plus the tailwinds in machine learning and modeling. People are actually starting to scale these models, and it's just, like, getting pretty impressive pretty fast.
Speaker 1:Where where where are we on new drug discovery companies who are targeting a single thing? We're gonna build a drug to solve a problem versus we're gonna start a company that's SaaS. It's tool. It's gonna help with all different drug companies. What's what's working?
Speaker 1:What's more overhyped, underhyped? Where where what's your take on, like, picks and shovels versus drugs, basically?
Speaker 7:Yeah. So, like, short answer is we do both. Mhmm. So you guys had Jake on the other day at Centivax, like, absolutely insane founder, was one of the early computational immunologists at Stanford. Like, he's making a medicine that you just couldn't otherwise make without these technologies, right, where it's like all these impacts within biotech and within modeling to make these just really incredible drugs you couldn't otherwise make.
Speaker 7:And so that's people just, like, making these singular things that are they're, like, really phenomenal. Like, there's also a real platform opportunity there for other things beyond the universal flu vaccine. And then I think, like, if we take, like, a little trip down, like, history lane and think about, like, how hard it was to actually sell software in the life sciences, one of the early companies in the space is Schrodinger, and they've been around for about twenty five years. They're a molecular dynamics company, and it just took an extraordinary amount of of time and effort to actually saturate and get people to adopt the technology and get people to pay for it. And, you know, they're a phenomenal business.
Speaker 7:They're a public company. They're actually vertically integrating into making their own drugs. But the thing that we're hearing consistently, like, from CEOs of top pharma companies, is just, like, there's huge demand for new infrastructure. People realize that this technology is here now and that they need to adopt it. They're hearing this from their shareholders.
Speaker 7:They're hearing this from the scientists at their companies. And so there's just a very different moment, post AlphaFold and post even ChatGPT where, like, they're using it. Their kids are using these models, and they're just like, oh, I really actually need to adopt this. And so, I think opportunity for both, like, fundamentally new picks and shovels where you sort of replace experiments with compute, and then also just fundamentally new drug products.
Speaker 1:Jordy, last question.
Speaker 2:No. I mean, I think we should have you back on as Yeah. As new news hits because think
Speaker 7:there's We'll do the lot TBPN bio drop in, you know. Yeah.
Speaker 2:Yeah. It feels like there's within the traditional labs, bio has been used as like a as a as almost like marketing. Right? Yeah. Or or even Elon yesterday saying like, we're gonna discover new Physics.
Speaker 2:Physics.
Speaker 1:Yeah.
Speaker 2:I don't think that that's obviously not where, like, a lot of the true innovation is is happening. So, yeah, let's make it a regular thing.
Speaker 1:Yeah. This is great. Thank you so much.
Speaker 7:When think about that, right, like, there's just been a couple of things. Like, I had an investor who made a joke that that, like, there's just a couple of things that have consistently delivered venture returns, and that's, like, software and also drugs. And so, like, as far as the physical prediction goes for it being something that's super valuable, If you can make your inference be a billion dollar, drug product, that's a pretty good spot to be. We're excited about it. So see you
Speaker 2:guys soon. There there's who I forget who we had on when Trump had the executive order around drug prices. We talked to a few different people that were saying biotech has like on average been a terrible asset class Yeah. And there's some amazing outliers.
Speaker 1:Bloomberg maybe?
Speaker 2:Yeah. Like basically, there's all these amazing outliers Yeah. That that do deliver returns. Yeah. But if you just index the market Yeah.
Speaker 2:You were gonna underperform dramatically. And underperform
Speaker 4:And it feels like Specifically.
Speaker 2:Yeah. And the venture category feels like this could be a massive shift where suddenly, you know, the next five years become the golden age of of venture bio investing. So Great.
Speaker 7:I mean, there have been some massive companies before. Right? So you have, like, breakouts, like the Genentech's of the world.
Speaker 2:Yep. Yep.
Speaker 7:Huge companies. There actually is you guys should have Bruce Booth on, who's, an OG biotech investor at Atlas Ventures.
Speaker 1:That'd be cool.
Speaker 7:He's done a bunch of analysis in the fact that, like, there's actually some interesting just return data for biotech versus tech where it's, like, not as gloomy as you would as you would think. But I think in general, like, biotech's hurting right now. We wanna make a world where it's actually, like, engineering. Right? And that you're actually just getting these, like, really scalable, amazing medicines.
Speaker 7:And, like, I think that's where we're headed.
Speaker 2:Incredible. Very exciting. Thank you for joining. Great to have you on.
Speaker 1:Yeah. Awesome. Talk to you soon.
Speaker 2:Cheers. Good one, Eli.
Speaker 1:Alright. Bye. See you. And next up, we have Kareem from Ramp.
Speaker 2:The man himself.
Speaker 1:Coming in to talk about the launch.
Speaker 2:New agent launch. Is he in the waiting room?
Speaker 1:We will bring in Kareem from Ramp to chat. Second time in the show, he hopped on at, Hillen Valley.
Speaker 2:That's right.
Speaker 1:First time as a remote guest.
Speaker 7:Great to
Speaker 2:see you.
Speaker 1:How are doing, Kareem?
Speaker 5:Hello. It's great to see you guys. Can you hear me okay?
Speaker 2:Yes. Loud and clear.
Speaker 1:I don't think you need much of an introduction, so why don't you just kick it off with, the announcement and break down the launch today? And then we'll have a bunch of questions.
Speaker 5:Yeah. Of course. I mean, it's been a a very exciting day for us at at Ramp. We finally announced our it's our first agent. We're gonna be announcing a lot more agents soon, so it's hard to keep track sometimes.
Speaker 5:We've been playing with a lot of tech internally. We think we're in a very interesting space where maybe maybe
Speaker 2:the way
Speaker 5:to to think about it is a lot of people from the outside look at Ramp and think of maybe visualize the card. They think about the the fintech aspects. But at at the end of the day, like, what we're really trying to do is help reduce the drag on companies that happens when there's just a lot of bullshit work in between teams and all the, like, papers being passed around, questions being asked, the things that really get in the way of of of doing work. And that first agent that we're building is is is really just that. Like, it operates in the mess the messy middle between finance teams and every other team trying to spend to move the business forward.
Speaker 5:And, yeah, that's that's basically what we launched today. So it's an agent for controllers. It knows a lot more about the expense policy of a company, the rules that are in place that govern spend than any single employee, and it knows, a lot more about every single transaction than any single person on the finance team. So it can operate in the middle and automate all the little decisions and the extra work that needs to get done to figure out, what's in policy or not.
Speaker 2:And it's immediately available. Like, that's part of the power. Right? Is that if you're if you're if it like, in in the in the sense of, like, if an employee wants to decide whether or not they can buy something or or something's in policy, you no longer have to be slacking somebody. You know, it could be 100%.
Speaker 2:Night or something like that or off hours where there's creating that drag, that delay. Right?
Speaker 5:A 100%. That that's certainly one part of it. Like, you can you can ask questions about your your your policy and ask questions about specific transactions live to figure out whether they would be in or out of policy. But more interestingly, once you make a transaction, it's already doing work to go and figure out, like, well, that transaction that you made at that restaurant, it looks big. But if that was a dinner with 10 people, maybe it's not as bad as initially thought, and that's actually in policy.
Speaker 5:So while that information is in your calendar, it's in your email, it's in sometimes outside of just the immediate context of a transaction. So the the the agent will go out on the Internet, in some cases, contact vendors or pull data from APIs on your behalf to really gather all that context and make better decisions on behalf of of the company.
Speaker 1:I I I wanna talk about, like, this the, like, the the word agent and the decisions to Sure. Like like, how how agents are fitting in the different stack of a tech company these days. Because, like, there is kind of always there's kind of always been an agent behind the scenes working you you think of these as, like, cron jobs before. It's like there's a there's a long running process that when a receipt comes in, it gets tagged. And there has been for, I think, years.
Speaker 1:I don't wanna share anything you can't, but, like, there's been an LLM interacting with receipt data for a long time, but it's been fully agentic in the sense that it was behind the scenes. And so I've I've been thinking about this in the context of, like, meta and, like, some of the value that Zuck is gonna be getting from having a frontier AI model. It's like there's so many workloads inside a business that has billions of of users that just happens behind the scenes. And and these are agents, but they're, like, almost internal agents. And so I'm wondering about your decision to What?
Speaker 1:Yeah. Position position an agent as, like, this is a user facing agent versus something that we're just gonna have a process that's running behind the scenes entirely?
Speaker 5:Well, there's a bit of a difference. Right? Because when you think about the these processes running behind the scenes, for the most part, like, the code is pretty deterministic. The tools are the same. Mhmm.
Speaker 5:It's built for accuracy and auditability, and you have a high confidence. You could trace back the the path that the old school agent, let's call it that, went went through exactly. And in this case, like, it it's less deterministic. You give the agent a set of tools. You could tell the agent or you can essentially give it access to, let's say, ability to call, ability to email, and it could be like, go figure out a way to get the receipt.
Speaker 5:That's what you know about the restaurant. And it'll browse the web and figure out that that's the phone number of the restaurant, and then try to call the restaurant. And if that doesn't work, then we'll try to email the restaurant
Speaker 1:Mhmm.
Speaker 5:Until it achieved that goal of of getting you the receipt. Or it fails, and you can then interact with it. In in this case, like, the instructions that we are giving the agent as we're building building it are very high level. You're just giving it high level instruction and access to tools, and that's very different from, like, the the old way of building these these process these these processes in which you had to be, like, very specific about all these paths. So it would take a lot longer to build to build these systems, to debug them, to update them, etcetera.
Speaker 1:We lost you. Your your your Zoom background is turning like like a ghost. It's very funny. It's a
Speaker 2:super intel a super intelligence
Speaker 1:I think you just need a little bit of a light light on your face. We I think we actually
Speaker 5:I I I actually lost power.
Speaker 2:But
Speaker 1:Wait. You lost power?
Speaker 5:There we go. I'm back.
Speaker 1:There we go. That's wild. Much better. I wanna talk to you about the data walls that are going up and some of the battles that are playing out in, like, the enterprise world. Because when I when I read stories about, you know, companies that wanna do, like, enterprise search, you can see that, well, you know, maybe Google doesn't want you to be taking maybe they want that for themselves.
Speaker 1:Ramp's in a very different position, but at the same time, like, there's just evolving policies about, you know, how friendly this is a classic with, like, Amazon not sending the itemized receipts to Gmail because they just didn't wanna give Google the the the data. But as a Ramp customer, I want the Amazon details pulled in through via Gmail via the Ramp integration. So talk to me about, like, how's the broader trend playing out? And then how do you go to big companies and say, hey. Like, you know, work with us.
Speaker 1:Our clients wanna be able to pull data from your service, and we're not gonna build a delivery network, Amazon. So you're not a you're not a we're not a competitor for you.
Speaker 5:Yeah. No. For sure. Most of the data that that we need at the end of the day is is, like, data that is, quote, unquote, owned by our users
Speaker 8:Yes.
Speaker 5:The businesses that are on ramp, their employees. Yeah. I think it's a little bit easier to to to operate in the b to b space because, like, those I guess, what what what what governs who owns the data and whose data it is is a lot clearer than in in the a lot of consumer applications. So I can in in our case, it's like, what data do we really need to know to, in in in the case of the agents that we just launched, to figure out whether something's in policy or not. It's metadata about the transaction.
Speaker 5:Right? Like, what's in the receipt at the end of the day, like, stores all your receipt. That's your receipt. Right? We we get that information.
Speaker 5:We have information that we get through the networks through Visa, the met the the the metadata about the transit the geographical location of the transaction, maybe whether it's it was an in person transaction or not. There's data that's in your inbox, in your email, which, again, like, that information is also owned by the company. We we haven't really encountered a lot of of of pushback and challenges. I've I've found most of the challenges in in getting the data to be more, like, technical. How'd you make sure you get it quickly, clean it clean it up, and get it accurately as opposed to ones where there are third parties that are trying to make it harder and harder for us to access the data.
Speaker 5:Mhmm. We have
Speaker 1:some been in that in the previous company. That that was kinda the story of the previous company.
Speaker 5:Yeah. Of course. We had a lot of these problem. I mean, there there were there were lots of funny moments at at Paravis or previous companies where
Speaker 1:Yeah.
Speaker 5:We were I mean, we're really building an agent for consumers to help them save money on their online shopping. Right? And we're trying to log in on their behalf to Amazon accounts and Walmart accounts, etcetera. And, of course, they'll put blocks, they'll put captchas. And today, those captchas seem like a joke.
Speaker 5:I think
Speaker 2:Yep.
Speaker 5:Any version of any half recent version of of Chad GPT or or or Claude is able to solve the those captchas very easily. But Well, that's one
Speaker 2:of the ways the Internet's getting worse right now is the captchas are actually getting so hard and
Speaker 1:annoying even for Yeah. When we go to the gym in the morning, Jordy has to log in. It takes him, like, two minutes to get through the captchas to this gym.
Speaker 2:Because it has, like, the most, like, military grade security to get to a gym login.
Speaker 1:And it's just a it just gives you a barcode that you just scan as
Speaker 2:But I but but but on that, I'm I'm actually interested because I can imagine, you know, Ramp has tens of thousands of customers, like high value business customers and other people that are building agents. I'm sure would love to actually be able to make actions on the Ramp platform. But at the same time, you guys are trusted to handle the finance, you know, basically the finance Mhmm. Finance back office for these companies. You don't want like an agent like hallucinating, like saying, you know, based on and taking actions on the Ramp platform.
Speaker 2:So I'm curious how you see that that dynamic playing out. Because I'm sure you've been approached by a lot of companies saying, hey. We're building this agent to do this thing. We'd love to be able to, you know, get authorization.
Speaker 5:Of course. I mean, we're we're we're thinking through that a lot right now. I think there are good ways of exposing the right information to the right agent as long as our customers are very aware of what what they're exposing, and there are a lot lots of interesting applications for us to work on. Like, in in the case of any large purchase at a company, there are multiple parties within that company that need to review it or approve. Like, you wanna review a certain vendor and and and look at their data protection policies.
Speaker 5:You wanna look at the the the legal agreements. In some cases, you wanna negotiate the price. And you can imagine a a a day in the future where a lot of our customers have an agent tech tool that they trust or agents that they trust for legal work, agents that they trust for IT work, etcetera. And we're very interested in in in actually working with with some of these companies, but we we gotta figure out on our end how we expose the the right interface so that we're we're ensuring really, the the the security of the data of our of our customers. So
Speaker 1:Yeah.
Speaker 5:We it's it's an ongoing Discussion. Work stream.
Speaker 1:Yeah. Last question for me. The the the Grok four launch was very benchmark heavy. It seems like, you know, the consensus is that it's a good model. And so as soon as I see that, it's now about cost per token.
Speaker 1:And and so I wanna hear from your perspective what drives decision making. How big of a line item roughly, like or how much time is spent thinking about LLM inference optimization In cost. At your scale? Like, you know, like, how big of a deal is it? And then and then what what is the workflow to decide?
Speaker 1:Can we use a cheaper model? How do we do you have internal benchmarks? Are you just checking these things? Like, how are you making decisions about which model to use for what problem?
Speaker 5:Yeah. That that that's a great question. I mean, I'm a lot more paranoid, about being too slow to try, the newest model and and, the latest and greatest tool than I am by, maybe overspending a little bit in in in in one area.
Speaker 1:Sure.
Speaker 5:I mean, the amount of time and money wasted at companies doing BS work is just insane that Yeah. If we're debating whether you could make something faster by spending extra dollar or half dollar. Like, the value that we're able to create is so big that I don't worry about it too much. But we do have internally somewhat imprecise, like, stack ranking of the different places where we need to make inference calls. And in some cases, they're very simple, high volume Mhmm.
Speaker 5:Kinda low risk. Right? Like, you're you're trying to normalize or clean up some, like, merchant data to figure out the appropriate spelling and maybe the right, like, photo to use. It's not the end of the world if it's not not, like, perfect. We're doing it at high volume.
Speaker 5:It better be cheap. So we have a kind of stack ranking of, like, this is something
Speaker 1:Makes sense.
Speaker 5:High volume where we need to be cheap. This is something that's low volume and high stakes where you need to be accurate. And we'll generally try the the newest and greatest models and and the places where we think will make the biggest difference. And over time, like, we'll break up some workflows, and some parts of it will become cheaper or more repeatable with smaller versions or cheaper versions of the model, and and and it will just evolve. I mean, we come from I mean, I remember, like, micro optimizing every single thing on our AWS account back in in in 2014.
Speaker 5:Right? Like, we were it was it was a lot harder back then.
Speaker 1:Like Yeah.
Speaker 5:I I think we we also pride ourselves in in in being the the time and money company. So we do care a lot about making sure that we don't waste our own money and our and and our own time, but I would say that the the TLDR is, like, our time and engineering time is the most valuable thing here, and I'm a lot more focused on that than than anything else.
Speaker 1:Yeah. On the time issue, what do you think about the various latency trade offs? I'm sure if Yeah. If if if an employee wants to know, is this in policy and you hit o three pro and it waits ten minutes, like, they're probably just gonna Slack their manager and ask them. But you're gonna get a really accurate answer that's really detailed.
Speaker 1:And so how do you think about those trade offs in latency?
Speaker 5:Yeah. I mean, it it it really depends on, like, where where in the workflow are we making the inference call. Right? Like, if it's live in the interface and the user expects a quick answer, we'll be using some of the faster models.
Speaker 1:Sure.
Speaker 5:But the reality is, like, a lot of these agentic workflows that are being kicked off at Ramp, like, happen behind the scenes. Right? Like, you make a transaction. You maybe get a very quick question from Ramp's AI to gather a little bit more context. Like, that's enough.
Speaker 5:And then from there, we'll kick up another kick off kick off another task that can be a little bit slower. They'll happen in the background. And by the time it reaches a bottleneck or it'll reach a place where it needs, like, additional feedback, it'll be in someone else's, like, notifications or on someone else's Slack. Like, you could take a little bit of time when, like, the work is going from one person to another person, but less when it's, the same person interacting with the interface live. That's yeah.
Speaker 5:That's generally the the the thinking.
Speaker 1:But Cool.
Speaker 5:Yeah. I have I keep What else? I I tried some of the the the the newer brow the newest browsers today. And and, like, I I tried Comic today. I tried DI a couple weeks ago.
Speaker 5:And I think what they're trying to do is incredibly cool. But, like, I I often find myself thinking like, damn. Like, I wish this was a little bit faster. And I know I know it's coming, but I think unlike some of the the the browser agentic calls, like, you want it to be really fast. Yep.
Speaker 2:Yeah. I was thinking about that in the context of the of the OpenAI browser. And unless they figure out something that makes it basically 10 times as fast, I'm still gonna default to to Chrome Yep. If I have both of them open just because I'm like, well, I just really need a fast answer here.
Speaker 1:I mean, I know Chrome innovation. Right? Chrome won on speed. Like, they just won automize the code, and and they and they nailed speed, and it was enough to leapfrog. And and so you could see I mean, that's like the bull case for, like, Apple coming from behind is like, yeah.
Speaker 1:They like, it feels like if if x AI and Anthropic and and OpenAI are all kind of like and Gemini are, like, roughly at the frontier, if you can just get something that's at that frontier, not any new innovations, but hyper optimized and it runs locally on your phone and it and it's put spitting out, like, tons of tokens every second, like, you have a product that would be very, very it would be very rapidly adopted. It's exciting.
Speaker 5:It matters a lot. I mean, I I think one of the, like, weirdest UX patterns on on on ChatGPT now is that I have to do the work to figure out whether to use
Speaker 4:o three
Speaker 1:O three or four o. Every time.
Speaker 5:That's actually
Speaker 1:Do I have ten minutes, or do I want the and and four o is always it's so good that I usually don't need to, but then I'm just like, well, I want the best. Of course. It's like, I'll come back to it. And it's such a weird paradigm. It's gonna be something that dates us, and I just know our kids are gonna be like, what did you have to do back then?
Speaker 1:You have to rewind the VCR tape. You have to put disc in the X Box. You have to pick which model to use. This is insane. It's so legacy, and it's going away, but we're just in this weird, like, we we we don't have a model router solved.
Speaker 1:And it feels like the easiest thing. It's like, which model should we use for this? I don't know. We'll see.
Speaker 5:Yeah. And if if you I mean, I I I don't know if you guys I I grew up in Lebanon. I still remember the the days of dial up where
Speaker 1:Yeah.
Speaker 5:You would have to
Speaker 1:Kick everyone else
Speaker 5:off the phone line. Select well, exactly. Well, select the phone line. And in that case, like, okay. Which phone line am I gonna use?
Speaker 5:Like, I don't know.
Speaker 8:Can't you tell
Speaker 5:me which one is free and, like, pick it for me? And I was like, no. I still have to Right.
Speaker 1:Yeah. It seems like the easiest thing to do. And, I mean, this is just, you know, complaining about the app that I use thirty minutes a day at least of ChatGPT, but, I I almost wish I could just define it in the prompt and just say, hey. You you use o three pro, and then here's the prompt as opposed to needing to click the UI, change it, switch it, and then pick instead of just being able to go back and forth. I don't know.
Speaker 1:I mean, it's a it's a it's a good sign because, like, like, people are using this stuff so much that they're frustrated by these, like, niche UI things. So, you know, it's an exciting time.
Speaker 5:There's a lot of I forgot who who it was who who posted on on X. I think it was, like, a couple weeks ago that, like, every company is, like, one great UX breakthrough away from something amazing. And I think that will be true for a long time. Like, there's a lot of alpha right now and just great UX and and good patterns. We we haven't figured it out.
Speaker 5:We're still in the, maybe, terminal phase of of personal computers. Right? Like, when is the mouse gonna come out? When are the right GUIs gonna come out? Like, there there there's a lot of that happening right now, and, yeah, it's a fun fun time to be building.
Speaker 2:One one last question for me. On Monday, Dwarkash released an article and then came on the show kind of talking about his timelines around, you know, when an AI agent would be able to do his taxes, right? Sort of like, agentically basically, like, fully agentic experience being like, wanna do my $20.25 taxes, and then it just sort of autonomously runs. How do, like, big how are, like, you know, Fortune five hundred CFO like, what what are their timelines around? Maybe maybe you just tell them what the timelines are.
Speaker 2:Like, okay. By by 2028, you know, we're gonna be able to do this for you. But but how is the the the sort of finance arm of the C suite kind of anticipating like, the rate of advancement? Obviously, like, the agent today is a step towards that future, but you'll obviously need a variety of different agents. Or or Well,
Speaker 5:I I think in terms of capabilities of LLMs, we we we're there. We we have the capabilities. Like, the the the bottleneck on on on being able to to do this today is, like, having the right context. Right? It's it's so while some of that context is in my head, so the AI needs to know to, like, ask me the right questions efficiently so I can answer those, right, even when I'm working with my accountant.
Speaker 5:Like, you know, pick the best accountant in the world for your personal taxes. You just tell them, like, file my taxes. They can't do anything. Maybe you tell them file my taxes, and here's access to my email. They could do a little bit more Yep.
Speaker 5:But they can't get it fully. Just tell them, like, file my taxes. Here's access to to my email. Can You call my, like, wife as much as you want. You can look through my drawers, and you give it more and more of these things.
Speaker 5:Like, maybe it could do it, but it's gonna get lost. It's gonna take forever. And and and really what we we we need to do even even for businesses is, like, what are the right, like, patterns for us to extract context that's in in people's head, organize it, get them comfortable with connecting different tools, like your inbox and and and things of that nature. And I think in terms of tech and capabilities, we're we're there. We're we're not we're not really missing anything.
Speaker 5:So there's a lot of UX and classic, like
Speaker 1:Yeah. We all need AI agents that can email me a question and put it in my inbox, which is effectively my to do list. And that's what my accountant does when that access happened. They email me and say
Speaker 2:Well, that's why
Speaker 1:this is You gotta do this.
Speaker 2:Cool. You can just you can take a picture of a product or and and ask it if I buy this.
Speaker 9:You know?
Speaker 1:Yeah. Is it in policy? Yeah.
Speaker 5:A 100%.
Speaker 1:Yeah. Well, thank you so much for stopping by.
Speaker 2:You have any of conversation. Great.
Speaker 1:Well, we'll definitely see you soon, Karim. This is great.
Speaker 5:Congrats. See you guys.
Speaker 3:To the whole team.
Speaker 2:Talk soon.
Speaker 1:Talk to you soon. Bye. And that is the rest of our guests. We are through that. In other news, Periodic Labs.
Speaker 1:There's this scoop from Natasha Mascarinas, the startup being co founded by Liam Fidesz and Eric Dogus Kubuk, great names, is in talks to raise hundreds of millions of dollars in funding at above a $1,000,000,000 valuation. The two month old startup is looking to apply AI to physical science starting with discovering novel materials.
Speaker 2:Wow. To the two month old unicorn. Yo. We gotta have these guys on the show.
Speaker 1:That is extremely fast. I also like this post from David Perrell. We're we're getting him back on the show ASAP. We had a lot of fun talking to him a couple months ago. He said, I'm touring apartments in New York, and just about every new build has the same solace aesthetic.
Speaker 1:Flat walls, white paint, no cornices, no ornamentation, just a room in a box. Only one one real estate agent said to me, if you want something with character, you're going to have to stick to prewar buildings. Look, I'm all for some efficiency gains, but we've created a world where new things are soulless things. And that's how a society as modern as ours and that's not how how a society as modern as ours should function. Intuitively, you'd think that a wealthier society would build more would build more beautiful things, but not ours.
Speaker 1:And I completely agree. Truth. What's crazy is that this isn't I mean, I don't know. These apartments look nice, but this continues all the way to $20,000,000 houses that are still bland. And I think it's I think it's mostly because maybe time and and and and all the difficulties with permitting.
Speaker 1:Because if you are if you're if you even have the resources to build something from scratch, creating, okay, I want these ornaments, and I want this, and I want, like, something that's really expressive of my personality. Well, now you if you want that, no one else wants that. So you have to build it, and you have to and you have to, you know, underwrite it, and you're gonna be to code. You make sure it's to code, and then get it built. And and then and then the secondary market value is gonna be less because not everyone wants Hearst Castle.
Speaker 1:Whereas if you build if everyone builds the exact same thing, they're perfectly perfectly liquid market because every every apartment is interchangeable with every other.
Speaker 2:Yeah. That's a good point.
Speaker 1:It it it's kind of a function of just like, modernity, but it's more a function of, people not, you know, just risking it ever on building a disaster project, making their forever home. People learn the lesson of William Randolph Hearst too much. They should have just like never learned that lesson. Send it. Just ripped it and just send it and just build something that no one else will wanna buy and will take decades to build.
Speaker 1:That's always the best.
Speaker 2:Well, I have a good place to end it. Please. Rob Petrozzo says the original Hermes Birkin bag prototype just sold for $10,000,000 in Sotheby's. There was a two minute standing ovation. Says bull market confirmed.
Speaker 1:And a gong
Speaker 2:We a bull market.
Speaker 1:The original prototype. Fascinating.
Speaker 2:That's wild. Makes sense. Very cool. It's incredible lore.
Speaker 1:Yeah.
Speaker 2:And I wouldn't be excited for a bull market and and alternative assets Yeah. Such as Birkins.
Speaker 1:Be great.
Speaker 2:And you should be too. But that's a great show folks. We will be back tomorrow. I cannot wait.
Speaker 1:We will talk to you tomorrow.
Speaker 2:Talk to Have
Speaker 1:a good day. Cheers. Bye.