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TBPN is a live tech talk show hosted by John Coogan and Jordi Hays, streaming weekdays from 11–2 PT on X and YouTube, with full episodes posted to Spotify immediately after airing.
Described by The New York Times as “Silicon Valley’s newest obsession,” TBPN has interviewed Mark Zuckerberg, Sam Altman, Mark Cuban, and Satya Nadella. Diet TBPN delivers the best moments from each episode in under 30 minutes.
You're watching TBPN. Today is
Speaker 2:Wednesday, 06/10/2026. We are live from the TBPN UltraDome. The Temple Of Technology, the fortress of finance, capital of capital. Let me tell you about ramp.com, baby. Time is money.
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Speaker 1:And what else?
Speaker 2:Two major stories, bunch of major stories, but the big one, Facebook got a movie, another movie. Actually, a third movie, if you count the documentary. There's been The Social Network, The Social Dilemma, and now The Social Reckoning. Only two of those are are movies. The Social Dilemma is a documentary, but it's very interesting.
Speaker 2:And the Ananthropic got a fable, and there's been a ton of reaction. Ben Thompson was writing about it to the rollout there, The Wall Street Journal. They're like, the announcement should have been like, the really good model launches. And people are amazed with what it can do, but they they put the title Anthropic puts curbs on AI models. So the well, I'm sure you've seen this on the timeline, but we'll take you through some of the some of the some of the debates over how restricted the model is in certain areas.
Speaker 2:Bio is a big one. Cyber is another one. There's a bunch of funny examples. There's a bunch of reasonable arguments for doing this type of stuff. So we'll go through all of it.
Speaker 1:Can we talk about I who's joining the show today?
Speaker 2:Absolutely. We got a bunch of great guests.
Speaker 1:What a lineup.
Speaker 2:What a lineup. Hey Clicky founder Farza Majeed is joining. You've probably seen his viral demo last week. We're very excited to talk to him about computer use, potential consumer AI applications, what he's doing to build hands free AI voice assistance. The CEO of Snowflake is coming on.
Speaker 2:CEO of Cloudflare is coming on. Vinod Khosla from Khosla Ventures is coming on. And then Brett Taylor. Brett Taylor didn't even make the cutoff on today's lineup. What's going on
Speaker 1:here? Brutal.
Speaker 3:And of
Speaker 1:course we have Markie Wagner
Speaker 2:Markie Wagner.
Speaker 1:Coming out Founder. Announcing emerging from stealth with her new company.
Speaker 2:We're very excited.
Speaker 1:She's fantastic.
Speaker 2:So I want to kick it off with the the Social Network sequel. I want to watch the trailer for The Social Reckoning because Jeremy Strong is playing Mark Zuckerberg. I've heard people say, hey, he looks he looks like Zuck, like the styling is there.
Speaker 1:Henry says no no Doug from semi analysis.
Speaker 2:But we gotta get him on.
Speaker 1:We should we should get Doug
Speaker 2:Let's hit him up.
Speaker 1:Hit up Doug.
Speaker 2:Let's get him on.
Speaker 1:And see if he's available.
Speaker 2:So Not
Speaker 1:that we have any time, but we'll make time. We'll we'll
Speaker 2:make time for Doug anytime. So the the one critique is Jeremy Strong looks a little bit older than Mark Zuckerberg did in 2011.
Speaker 1:Calling him unk? Or
Speaker 2:It just it just it just doesn't it it it I think it'll color the movie a little bit in the sense that the the film is telling the story of this, like, very difficult whistleblower situation. And and it's different when it looks like a, you know, a mature adult versus someone who was young and running running into these problems for the first time.
Speaker 1:Anyway Matthew Prince. What? He is joining.
Speaker 2:Yeah. He's joining. We yeah. We got the Cloudflare
Speaker 1:12:30 Pacific.
Speaker 2:12:30. Get ready. We're gonna go through Cloudflare. They have exciting news. I think it's public already, but I don't wanna blow them up if it's gonna be announced later today.
Speaker 2:But anyway, I want to I want to react to the the trailer. So let's pull up the trailer.
Speaker 4:Listen, before I go on, I want to make something clear.
Speaker 2:This is how it starts. Okay.
Speaker 4:I have a hunch you're not a fan of Facebook, but I am. I am here to help Facebook, not hurt it. Okay?
Speaker 5:Alright. You send me a message. What would you like to talk about?
Speaker 4:The chairman gavels a session to order. You'll read your opening statement, which we'll skip past for now.
Speaker 6:That's a
Speaker 4:separate session. And we'll move to witness questioning.
Speaker 5:Spell your name and state your current occupation for the record.
Speaker 2:So is it not m a r k
Speaker 5:c u K, e r b e r g.
Speaker 2:Let's see.
Speaker 5:And your occupation?
Speaker 2:Zuckerberg. I'm a professional Co produced and directed by Aaron Sridhar.
Speaker 4:Groups where 30% of content hits multiple risk factors.
Speaker 2:Hang on. I don't piece to the social
Speaker 4:Are you a tech reporter? Ish. Ish? Is this
Speaker 5:Mike counting on the next round
Speaker 2:of congressional testimony, make a likable mark. Jeff Horwitz.
Speaker 7:I'm happy to lend a hand, but I think you're
Speaker 2:doing He wrote broken code. He was journalist at the Wall Street Journal. He is a journalist at the Wall Street company
Speaker 5:and Broken
Speaker 2:a number of global news stories. Including the Facebook files.
Speaker 5:The fire hose of bad information, you are injecting it to the air supply.
Speaker 2:The builder?
Speaker 4:Becoming jet power.
Speaker 5:I'm a free speech absolutist. I'm not the one who's lying. And I'm not stopping them
Speaker 2:from seeing someone who is. It's not a bad Zuck impression. He's got the voice tone pretty down.
Speaker 4:Worse as a result of time spent on the platform. Senior leadership knows and is doing nothing.
Speaker 5:I know there are easier enemies to make. The mafia would be an easier enemy to make.
Speaker 4:So what would you need?
Speaker 5:To stand up the story? The internal documents.
Speaker 4:This is a material violation of my NDA.
Speaker 5:We're twice as big as the biggest country on Earth. We're not frightened of Congress. We're post government around here.
Speaker 2:Please. Twice as big as the biggest country on earth.
Speaker 7:We
Speaker 4:have a hundred and two hours to get
Speaker 3:From a
Speaker 1:from a user standpoint?
Speaker 2:Oh, okay. Users.
Speaker 8:I don't wanna be made an example of by a guy with unlimited resources.
Speaker 9:Harm, I promise you, is imminent. Enough.
Speaker 5:People around here understand that when I say no, that's the end of the debate. I'm not two years out of a dorm room anymore, Charlie. Look around.
Speaker 2:Man, if they were trying to go as inspirational as the social network, they really missed the mark there. What happened?
Speaker 1:Yeah. So that their intention was to try to fire up the next generation of entrepreneurs that to fire them.
Speaker 2:Seems like they missed
Speaker 1:the mark. Yeah.
Speaker 2:To inspire them. Uplift them.
Speaker 1:You could build any business out of your dorm room. Yep.
Speaker 2:Yep. And also, if they if the goal of this film is to really really take the viewer inside TBD and meta super intelligence and what's going on with Nat Friedman, Daniel Gross, Alex Wang, like Yeah. Feels like they just dropped the ball potentially. It's sort of crazy.
Speaker 1:Yeah. Like, I didn't see, like, anyone that even resembled Jan Lecun.
Speaker 2:That is crazy to not to not highlight that and what's going on over there. The launch of Llama the Llama four moment, that could have been the crescendo. Yeah. Behemoth. Behemoth and all the drama around
Speaker 1:epic of that.
Speaker 2:You want drama. You want Hollywood drama. You want Oscar bait. Llama four behemoth. That story is gonna get put butts in seats.
Speaker 2:That's right.
Speaker 1:No. More seriously, it's hard for me to it's gonna be really hard to not look at Jeremy Strong and and
Speaker 2:OC Kendall. Kendall. Yeah. Because you're succession guy. So this is the sequel to The Social Network instead of chronicling the birth of Facebook.
Speaker 2:It's the story based on the 2021 Facebook leak by whistleblower Francis Haugen. It's going to be dramatic. It's not going to make people like Facebook more and it's probably going to make Americans even more distrustful of tech. What's interesting here is that if you stop a random tech person on the street and ask them like what is the social reckoning about, they might say Cambridge Analytica because that was another drama moment and I I was sort of like, is it Cambridge Analytica? Is the it the Haugen thing?
Speaker 2:And a lot of people don't even remember what the whistleblower story was. Little refresher there. It was an internal document leak. The Facebook files showed that Facebook internal employees were aware of harmful societal effects from its platforms yet persisted in prioritizing profit over addressing these harms. Now Nikita Beer chimed in, who now works at X, a rival platform to Threads, and said, Zuck makes a lot of mistakes, but this isn't one of them.
Speaker 2:Meta literally had multiple teams of $1,000,000 per year engineers working on teen mental health, and they had the agency to override big product decisions. They're probably a thorn in Nikita's side because he's trying to maximize click through rates, maximize attention, and he's getting pushback from, hey, there's guardrails here. And he says the story this movie is is about is actually a product manager who didn't get a promotion. And so that's the pushback
Speaker 1:Yeah. Only from thing the only thing
Speaker 2:What's that?
Speaker 1:And and the only thing with that argument is like you could make the same argument is like the cigarette company has million dollar like doctors and researchers focused on making sure cigarettes are as healthy as possible. Yeah. Yeah.
Speaker 2:Yeah. And and especially with the new fertility data, I think that there's there's a new cycle brewing of like exactly how bad is social media. I still don't really buy the addiction thing just because Yeah. I'm with like addiction defined in nicotine, which is like extremely addictive. It's chemical, and like you have cravings.
Speaker 2:Like if you forget your phone, like do you have the same cravings? It's sort of different. And but there is like a very serious discussion going on around social media and its effects and Yes. This fits The right
Speaker 1:thing is is like very real. Because I was at I was at an event last night they required everybody to put their phones in these sort of like locked bags, so you could go outside at any point And you felt sick. I didn't feel sick. But I noticed probably like 20 times throughout the night Yeah. I was thinking like I was thinking of going for my phone.
Speaker 1:Yeah. I didn't start having like physical withdrawal symptoms.
Speaker 2:Yeah. But the craving was there?
Speaker 1:Yeah. Just started thinking about it. Was like phone noise. Right?
Speaker 2:Phone noise. Yeah. This is real. This is real.
Speaker 1:I was having some phone noise.
Speaker 2:Yeah. Yeah. Yeah. Yeah. Okay.
Speaker 2:So yeah. That's legitimate. So the question that I had really quickly is like, what is the impact on Meta? Like, we can pull up the stock chart. It's a 1 and a half trillion dollar company.
Speaker 2:They're trying to raise more equity for AI build out, hire people. Like, is this good, bad? What does it look like over the short, medium, long term? So short term, think this was amazing timing. They really got lucky because as we will talk about, like the fact that this trailer dropped the same time as Anthropics Fable five was incredibly fortuitous because Fable just took over the timeline and no one was really talking about this in tech.
Speaker 2:Tech insiders won't really see or talk about the social reckoning this week. Case in point, I put it second in the newsletter when I wrote it up. Medium term, I think Jeremy Strong is gonna drag Mark into, like, the bad group of AI leaders while he's on his Oscar tour. Like, there was a version of of of Facebook strategy or meta strategy that's just like, hey. We're like Amazon.
Speaker 2:We're like Microsoft. Like, yeah. We have bets in AI, but we're not like we're not deciding the frontier. Obviously, Mark has made the decision to hire some of the greatest researchers, invest very aggressively. And this puts him sort of at the center of the conversation around, well, what will the effect of AI be on our society, on kids?
Speaker 2:Should kids be talking to LLMs? Psychosis. All these different things will come up, just as we saw four O psychosis and then Claude Code psychosis a little bit pop up and people are talking about that. Like, you're you're you're priming the pumps for the next wave of, like, oh, bunch of people went into the meta app and, like, they'd had a bad time and now we gotta talk about that. So I wouldn't be surprised to
Speaker 1:see that conclusion. What do you think is gonna be more annoyed about this week? The fact that SpaceX is gonna be valued meaningfully higher
Speaker 2:Yeah.
Speaker 1:Than Meta or this trailer? It's actually a tough one. I don't know. It's actually a tough one.
Speaker 2:I mean, I think the real one is the Fable five. Because, like, if if Meta was using a ton of anthropic models and the new model comes out and says you specifically can't use it in MSL, the most important initiative in the company. Like, that's like a pretty rough thing where you're like, oh, I've been working with this company to develop my models for my family of apps.
Speaker 1:And now I can't use they them do, you know, Meta is spending billions of dollars a year Yeah. With Anthropic. Yeah. They have been for a while.
Speaker 2:Oh, ARR,
Speaker 1:but yeah. Yeah. Yeah. Yeah. Will spend billions of dollars this And the question is, like, if Anthropic does allow Meta to use their models for Meta's AI research, what does it say about what Anthropic thinks about Meta's potential in AI?
Speaker 2:They'll secretly love ads this whole time. No. I don't know.
Speaker 1:No. To me to me, it it would it would be them not not feeling like they had a The AGI. Ability to get to the frontier.
Speaker 2:Totally. And then and and that's an ace that's up their sleeve at any time. They can always just take the guardrails off and be like, hey, more more more AI, more intelligence on demand for everyone. So I think the fallout of the medium term, Jeremy Strong's gonna go on this press tour for the Oscar and he's gonna have a bunch of like really emotional pithy sound bites and then also be like just viral because it's funny to see him doing this impression. He's very good at, like, drawing.
Speaker 2:He's already gone viral for his Mark Zuckerberg impression in the past. So I wouldn't be surprised to see the next version of a Bernie Sanders press release about AI highlight Dario, Sam, and Zuck instead of what has historically been the order, which is Sam, Dario and Demis. So it's always based on like perceived industry power and negativity around personal brand. So they have to have a scary quote and then they also have to have a lot of power in the industry. Zuck's power in the industry is rising.
Speaker 2:And also with this movie, like, there's more ways to to to take shots at him. Like, oh, look at what he did with the Facebook files. Remind yourself of that because a lot of people don't really remember the details. They're going to after they see this movie. Then you can then you can say, well, this guy's also building crazy data centers and look at how he handled this.
Speaker 2:If he handles the AI thing like that, it's gonna be bad. Right? So that so that's like a little bit of a risk in the medium term. Long term, I don't think the social reckoning is gonna matter all that much for Meta. People will complain about Meta's data centers.
Speaker 1:Why did
Speaker 2:they not get
Speaker 1:Jeremy Strong like a wig?
Speaker 2:They did. Or they they because also he is remember playing an older
Speaker 1:when he
Speaker 2:had the Caesar haircut?
Speaker 1:Yeah. So it's
Speaker 2:So a different people will complain about all this stuff, but they'll do it on Meta's family of apps. I don't think there'll be churn. And certainly, advertisers won't pull out. It's impossible to pull out of Meta because it just drives it just drives up the r the ROAS, the the return on ad spend for smaller companies. And a bunch of small businesses will just jump in and say, oh, great.
Speaker 2:Like, you know, some big company pulled out and is boycotting Meta. That's great. Ridge Wallet's gonna jump in or somebody else is gonna jump in.
Speaker 1:Yeah. People have advertisers have tried to boycott.
Speaker 2:Yeah. You can on Facebook. It's impossible. So the business is just too strong. And so and I also don't think Mark will be singled out by a regulatory hammer should it come down hard.
Speaker 2:Like if there's a data center ban, I don't think it's going to be uniquely focused on meta. Only Dario and Sam have like true scapegoat risk because they run pure play labs and have been so noisy about AI and they have the biggest revenues in the category. There might be data center regulation, but it won't unfairly target Meta. So anyway, that's my Social Reckoning take. Let's move on to Fable which launched yesterday.
Speaker 2:And the model seems like incredibly impressive. I I've been seeing these like vibe coded games that look really really good and for some of those I haven't seen those like like the vibe coded games hit really well on social media because you take a video of them and you show the example of what it built, you share the time Yeah. And people just sort of believe it. It could be embellished, but in general, these feel like pretty solid. Of course, like making a great game is a great mechanic and there just needs to be multiple levels and like a single demo of like a forest putting in is not quite there, but really, really useful.
Speaker 2:And I'm sure we'll see a bunch more examples of like games as memes, simulators and these things are going to get easier to build. That's really exciting. Of course, there's a bunch of debate on the timeline yesterday and it's bleeding into today around the latest model, Fable five. The first mythos class model that both seems remarkably good at long horizon tasks like software development and knowledge work, but rejects requests related to biology, cybersecurity and frontier LLM development. Interestingly, I haven't seen anyone share anyone share rejections around anything else.
Speaker 2:Like did did they remember to reject like build me a nuke? Like I haven't seen anyone try that and it'd be very funny if it was like, oh yeah, just we didn't get around to that or there are so many other things. But I think a lot of those other other like things that you should reject, queries that you should reject have been ironed out in previous iterations. Yeah. So going back, you know, the say a slur was a big one for a while or say something rude or
Speaker 1:And it's a different political statement. Pausing pausing a chat, basically just like shutting down a conversation versus like switching you to a less performant model.
Speaker 2:Yeah. And it creates this like screenshot that went viral pretty much continuously yesterday. And so this aligns with Anthropic's focus on safety. But as many people have pointed out, it's also just good business. You don't want competitors using your products to directly create competitors, and you also don't want financial liability or negative headlines from bad actors using your models to for nefarious purposes.
Speaker 2:Ben Thompson called it true alignment. The take safety seriously culture aligns with business value creation, which is very, very rare. Oftentimes the like be good or your culture limits what you can do and actually hurts your business, but it's something that you do in favor of brand like Apple would probably be, would probably be more profitable if they were using like diesel generators for all of their data all of centers. They went clean energy because they wanted to have an environmental brand. And over the long term, it's helped them.
Speaker 2:But in the short term, it's been rough. Of course, inexpensive. So Ben Thompson writes, What is so fascinating about Anthropic, however, is that while I'm sure some executives of the company are thinking this way, I also totally believe that the employee base broadly also happen to believe that they are doing the right thing. It's fascinating to observe. Me, the rational business analyst sees a hard nosed but understandable decision cut off would be competitors, anthropic employees and advocates.
Speaker 2:The true believers see a regrettable but understandable safety decision that ensures that responsible and thoughtful people themselves of course will be the ones guiding our AI AGI future. This is true alignment and it's an incredible accomplishment. Facebook has tussled with this a bunch and we already talked about that. But to be clear, the Fable five rejection threshold really does feel way too low from what people are saying. Tons of examples on the timeline of a biologist just saying hi to the model and getting kicked down to Opus.
Speaker 2:I saw you shared someone just said cyber with the devil horns, the purple devil horn emoji, and it's like we can't do it. We're not going further. Of course, like bringing down like a broad hammer, you can always dial it back over time. But every rejection is this implicit invitation to hop on the phone with an anthropic sales rep and get on the Mythos enterprise plan. That's where the real dollars are too.
Speaker 2:The timeline is unhappy because the idea of democratizing science, technology, all of this is very alluring. But the pool of dollars available from all the biohackers in the world probably isn't close to the budgets available from big pharma. And so you, again, you're in this like rational business analyst situation and you fail to see how this is that damaging, except to like the hacker community. The real tricky part is how AI Frontier AI research is handled. Instead of outright rejecting the query and bumping the user down to Opus, the model appears to answer but quietly gives a degraded answer.
Speaker 2:And this was disclosed in the model card which is interesting. So this is again reasonable disclosed product. While they're paying for it. Which is a different which is a different path than Yeah. Bio and cyber.
Speaker 2:So if you if you go LLM frontier research devil horns, it will actually give you an answer apparently. It won't bump you down immediately. But so it doesn't disclose that, which is odd. Outright rejecting requests for AI research and just saying, hey, user, sorry this model doesn't work for that type project. Please share please use another model or contact sales if you want help with this, would have been much more in line with the bio and security and cybersecurity strategies.
Speaker 2:And they and then they also could not have disclosed and it's also possible that they just didn't need to disclose this at all. Like, they could have just released a model that was intentionally nerfed on AI research. It would have shown up in the benchmarks because people would have benchmarked it on some sort of AI research bench and been like, oh, weird. It's really good at all these other things but it's bad at LLM research. And maybe that would have been a bit of a brand hit maybe, but users might never know that the model was intentionally degraded around this category of work.
Speaker 2:So that leaves this third more worrying position, intentional degradation without disclosure in the model card. There's no evidence of this, but it's possible that other workflows might be nerfed and there's no law or even convention around disclosure. Again, maybe good business, but a weird situation to be in. So probably bullish for evals if you're building a business on top of a big lab. You can imagine like a legal AI company who want to be really sure that the models they're using aren't degrading unexpectedly and not telling them.
Speaker 2:It's different if you're like, hey, I've been a bio researcher for a while. I'm using this and I know that this model was never intended for me. Or or I've been using it, but what you don't want is like I'm using it and now it's giving, now it's leading me astray in my work and it's also not telling me that it's going to lead me astray, which would be like sort of an odd outcome that I think they'll probably address in the near future.
Speaker 1:Anyway. Yeah. That aspect triggered Dean Ball. What He he said, My last observation re anthropic secret sabotage safety policy is that it undermines actually good safety policy. How?
Speaker 1:First, it is very plausible to describe this as anti competitive behavior. Even if you're maximally sympathetic to Anthropic here, you must admit this and it behavior being justified in the name of AI safety. If you believe, as I and many Anthropic staff do, that it may end up being critically important to relax antitrust enforcement so that the Frontier Labs can cooperate and collaborate on some areas of AI safety, Anthropic just undermined the case for that in a large way. Overall, this massively and profoundly raises the status of the argument that AI safety has been hyped to justify monopolistic behavior by labs. I continue to believe that AI safety is a real and serious issue that is growing in importance rather than diminishing.
Speaker 1:If you agree with me, this incident is a setback, maybe a serious one. And third, says, as I have observed elsewhere, Anthropics official corporate policy is structurally identical to the fact pattern alleged against them by the Department of War. I still think DOW acted both falsely and wrongly in that fight, but it is no longer possible to defend Anthropic with a full throat after this incident. This raises the case for heavier handed regulations. Anthropic is making awfully good case here that their product ought to be treated as utilities and thus their alignment practices should
Speaker 2:Oh, be a matter counter carrier thing.
Speaker 1:Public policy rather than private property. I am starkly opposed to this sort of state power grab but Anthropic is doing more to justify it than anyone else. Thus significant damage has been done to community and entire approach to AI governance. It was done unilaterally by Anthropic likely motivated largely by self interest and justified within the internal psychology of the firm through the lens of safety. Suspect this is fixable in the economic and legal senses for Anthropic, but I fear that trust has just been broken and the goodwill extinguished will take very much time to repair.
Speaker 2:And just to level set on Deembal, he wrote the AI action plan, but then also came out very publicly in support of Anthropic during the conflict with the Department of War, saying that the Department of War is completely overstepping by pushing towards supply chain risk designation, putting pressure on Anthropic for not wanting to work with the government in that particular way. Obviously there's a whole bunch of new data that's been released and stuff and that conversation has evolved. But he's not is he I he doesn't strike me as some like crazy hater. Anyway, let me tell you about Railway. Railway is the all in one intelligent cloud provider, user favorite agent to deploy web app servers databases and more while Railway automatically takes care of scaling monitoring and security.
Speaker 2:Doug O'Laughlin was also sort of mixed results on Fable. He says, when it works, it's brilliant, but the unilateral guardrails make me frustrated beyond all belief. He has a folder of health information with like a hundred days of Aura Health data. This is a very logical thing that people would do with Codex or Cloud Code, gather up all your health data. I actually talked to a very, very prominent person in tech about how they got into vibe coding and CLIs.
Speaker 2:And one of the first things they did was reanalyze some WHOOP data, reanalyze some health data and he correctly detected sleep apnea and went and got it treated. Like that is it's not the cure for cancer but that's like incredibly awesome and like very, very good and exactly what you want to see. And who knows, maybe the thing that you detect early could lead to an increased risk of cancer in the future. And that would be a really, really big win for our society. And so a little bit tricky there.
Speaker 2:He says there's a private investment in the life sciences tool space. Guess what? It's not safe. Doing some code scanning for vulnerabilities, not safe. I get the safety but it feels incredibly out of touch for a group of a few 100 a few thousand people making all making total comp in the millions, telling me what is and isn't safe.
Speaker 2:Dario worried about inequality, I think he has to realize that he is the inequality and the unilateral gatekeeping feels whack as hell. I don't like it. Yeah.
Speaker 1:People are people are very
Speaker 2:And like Doug O'Laughlin is extremely optimistic about Anthropic. Like he he he recognizes the power of the models in the business and has been a very very strong supporter. I I feel like a strong user. He was one of the first people to admit to Claude code psychosis and he's unhappy with the current situation. But these things are very tunable.
Speaker 2:I'm optimistic with more disclosures and more fine tuning and and a and a smoother smoother path. I think we can get to the good outcome here. Cremieux. Last one,
Speaker 1:Cremieux says, tell me about It's the powerhouse of the cell. Right? It's chat The
Speaker 2:chat pause is rough. Yeah. It's a it's a screenshot.
Speaker 1:But maybe maybe the harness is just saying like, sorry, if you don't know that the mitochondria is a powerhouse of the cell like
Speaker 2:It's not worth my time. I don't even want your I mean, that's the thing with like the good
Speaker 1:You know, there are dumb questions.
Speaker 2:Yeah. I mean, are expensive questions and all of these screenshots are clearly from like $100, $200 a month plans. And I'm sure the GPUs are on fire as they always are with any new model launch from a frontier company. And it can make rational business sense. And so when everything aligns, when you're like, yeah, we're maybe being a little bit too safe, but it's actually good for our business, it's very easy to say yes to that stuff.
Speaker 2:The big question is like, where do you get tested where something there's a hard decision and it doesn't align with your business interest. That's always tough. Right?
Speaker 1:What what's going on? Was there the the new data retention policies as well that also has to do with, like, does it trying to
Speaker 2:This one was funny because I assume that every tech company stores everything forever, basically. I didn't realize that they
Speaker 1:not for these like enterprise use cases.
Speaker 2:I know that the enterprises said that they wouldn't train on your data. But like when I go into any chat app, I expect to have my data from a year ago still there. Like I want in fact, one of my main critiques and frustrations with these apps is that when I go to them, want to be able to instead of hitting the search bar, just go into the chat box and say, hey, remember a couple months ago we were talking about CrowdStrike and I had you pull up some data? Can you just go refresh that, get the original thing? And a lot of times these apps are like, oh, like I don't really have access to all your chats, but like the chats are clearly saved.
Speaker 2:But so I I I understand people were upset about this, but it didn't fully clock for me. Why? I I understand the training thing like the but
Speaker 1:Yeah. And they're very they're very explicit they're not using the data to train.
Speaker 2:They're not. Okay. Oh, well then that's good. So the the steel man I believe was that you need to hold it for thirty days because companies, probably international companies, competitors will set up a ton of shell corporations and send out, like, pseudo random queries over random times, over random accounts, through VPNs and they will triangulate something that is useful to distill or learn from the model. And so by keeping it all, you can now run analyses and look at, wait, is there a pattern where 25 different accounts that seem completely unrelated all seem to be triangulating the same question?
Speaker 2:That seems reasonable to me. But stay out of my data. I'm built different. I'm not distilling anything. I don't know.
Speaker 2:Anyway.
Speaker 1:I think it's time.
Speaker 2:Let me tell you about Shopify. Shopify is the commerce platform that lets you grow with your business. It grows with your business and lets you sell in seconds online, in store, on mobile, social, on marketplaces, and now with AI agents. And speaking of AI agents, we have a founder
Speaker 5:of the company. I don't know if we should call it
Speaker 2:an AI agent. What are we calling it, Farza? Is this are you creating a new category? Introduce yourself. Tell us what you're building.
Speaker 7:What's going on, guys? Farza here. What's happening on? No. Of course.
Speaker 7:Farza here working on Hey Clicky. Yeah. Man, what is it? It started as an AI teacher
Speaker 2:Yeah.
Speaker 7:And I guess now it's an AI that where you can essentially talk to your computer and it does whatever you wanted to do from like
Speaker 2:It's personal super intelligence and you got there before the big man. I love it.
Speaker 7:Somehow. I feel like the big man doesn't even know what they got sometimes, you know? It takes the little guy to figure it out.
Speaker 2:I love it. I love it. I'm always rooting for the little So, like like reintroduce the product because you went viral last week but it feels like you've been working on this for a while. What's the nature of the team, the structure, the funding? Like what's actually building this?
Speaker 2:What's adoption been like? Like where is the product today?
Speaker 7:Yeah. I started eight weeks ago. I just wanted to build a an AI that could Yeah. Teach could teach me DaVinci basically while I'm like actually using DaVinci. And I thought it was
Speaker 2:And this is DaVinci Resolve, the color and video editing suite that's free from Yes. Blackmagic Design? Got it.
Speaker 7:Exactly. Yes. So like I DaVinci Resolve complicated program
Speaker 2:Yep.
Speaker 7:One hour YouTube videos weren't in it, let me just build an AI that can help teach it to me while I could talk to my screen and I could learn. I've been there. And put it out and I thought it was really a really bad idea. Mhmm. One of my friends just had to post it.
Speaker 7:So I posted it and it goes really big Mhmm. About eight weeks ago. Mhmm. After that, I didn't keep working on it. But then the cool thing was I saw all these people, you know you know how when you put something new out there, have all this emergent behavior kinda start happening?
Speaker 7:Yeah. So when you let a human being talk to their screen, what do they start doing? Turns out they start doing a lot of crazy things all the way from watching anime with clicky, all the way down to like learning like blender with clicky.
Speaker 6:That's so
Speaker 7:easy to just talk to your screen. And that's kind of where it started. Yeah. About eight weeks ago.
Speaker 2:Okay.
Speaker 7:So it started it was yeah. Go ahead. Go ahead.
Speaker 2:I I I just want to hear about like the un hobbling path because I imagine that there's demand for, you know, like these chat interfaces have existed for years where you open an app and you you know, do the voice mode and then you have it read it back to you. And there's some that are more polished than others. But when I think about using a computer, think rearranging Windows, I want anime over here and DaVinci Resolve over here and the YouTube video over here. And I want to be able to puppeteer the mouse and the keyboard have full context so I need to be taking screenshots every minute, every second, 30 times a second, 144 hertz. Like what are we doing there?
Speaker 2:How are you getting the data in? And then what's actually under the hood because you need to process the voice actually produce an output. And it's surprising to see a harness like break out of this so quickly.
Speaker 7:Yeah. Yeah. It's actually so simple. We use g p t real time upfront Mhmm. To give you like like the first layer to give you like the really quick answer.
Speaker 2:Okay.
Speaker 7:But then if you want like a deeper kind of like a thought process over the image, you know, if you're in DaVinci Resolve, in some complicated software, we now use Fable five actually Really? By default. Yeah. So now Fable five is like absolutely mind blowing in terms of how precise it is on screen understanding.
Speaker 2:Under the
Speaker 7:So we use that now by default when you wanna like understand something on your screen. But yeah, only actually screenshot when you press a button. Okay.
Speaker 2:I don't
Speaker 7:wanna press the button.
Speaker 10:You won't
Speaker 2:even screenshot.
Speaker 7:But it's Yeah. But it's really crazy because we can still detect the program you're on and stuff and that's already really helpful. Sure. Like if I if I know you're on Notion, for example Yeah. And I you're there for like ten minutes, I can just ask you and be like, hey, what are you doing?
Speaker 7:Like, can I help you? And I think it's that sort of new modality that's like been really exciting because that's something that's just not possible today with any chat interface.
Speaker 1:Yeah. It's more like a like a co worker walking up, like like a co worker just like being like present and like kind of like like sometimes you don't need help until you're asked. Right? Or you don't describe it. Yeah.
Speaker 7:Yeah. I kinda describe it as like having this 23 year old intern with like a decent like a like a new grad that's always watching over my shoulder and and just pretty much like seeing patterns in what I'm doing and then it's and then just tapping me and saying, can I do that for you? If I had that, of course, that would be awesome. But it'd be cool if everybody had that.
Speaker 2:A lot of these frontier models are expensive. How are you thinking about the business model? Because it it seems like you've built something useful. People should just pay 10% on top of whatever the metered rate is. But at the same time, in consumer, prosumer subscriptions, flat pricing is very popular.
Speaker 2:But that introduces financial risk to you. You have to understand your costs and how they change. What do you think the business model will be as you grow the business?
Speaker 7:Yeah. I mean, believe it or not, like people are very open to paying large amounts of money like normal consumers, like large amounts of money
Speaker 2:Yeah.
Speaker 7:For for like more for having better access to these models
Speaker 6:Sure.
Speaker 7:Through different interfaces. So right now, we just charge a straight up $20 a month. Okay. But even then, there's a limit. You know, you get like for $20 a month, get a 150 agents on Clicky.
Speaker 11:Sure.
Speaker 7:But even that's very specific because once you pass that number, that's when we start losing money. Yeah. So everything's done in such a way where it's pretty cost effective. Also, like, just so you guys know, like, just calling, like, Sonnet or Opus or Fable, it's kinda cheap overall. Mhmm.
Speaker 7:The expensive part is, like, agentic work. That's really expensive. Yep.
Speaker 2:Like, just
Speaker 7:to give you an idea, on GBD 5.5, if you tell Codex right now to like go and click add to cart in Amazon, that costs 25¢ on by just API. Yeah. And I know because I'm seeing the cost myself. Yeah. And so there's a lot of tricks to kind of reduce that.
Speaker 7:But no, overall, there's so many tricks to making agents cheaper, more efficient, changing the model based on their quest
Speaker 1:catch it. Are did you haven't you have raised for this or you haven't?
Speaker 7:In the process.
Speaker 1:In the process. There we go.
Speaker 3:There we go.
Speaker 7:If you're out there.
Speaker 1:Yeah. But but if you're in the process, that means like you didn't launch this and be like, oh, I'm down to be losing like $5,000 a day Sure. You know, on you know, like, you're you've been focused on the economics probably earlier than you would have been if you were
Speaker 2:That's really
Speaker 1:had raised money out the gates.
Speaker 7:I mean, yeah. I mean, I'm I'm never the type of guy that wants to be losing thousands of dollars a day. So we can stop it early. But no, mean, that's the thing about AI. Like, know, if you're I built a lot of social apps in my life and for those costs technically don't matter based on the number of users coming in.
Speaker 7:But in my case, for every, like, 10 new people I add, I'm kinda losing or losing money or making money depending on who what they're doing. So Yeah. I kinda gotta gotta think about it. So, yeah, it's kind of AI kinda accidentally making everybody better at business, because it's kind of required.
Speaker 2:So is, I I'm I'm thinking about the hilarious, hilarious, like like, trade off in having an agent that applies a coupon code but winds up spending more money applying the coupon code than you save. Because it's like, oh, yeah. We put in this. It saved you 24¢, but the API call is 25¢. But how how do you how how important to you will, like, creating an ensemble of models that sort of maximize the Pareto frontier be as you build this business?
Speaker 2:Because I'm imagining that any day now we're going to get somewhat useful on device models on Mac from the new Siri models, the on device models. Those will probably be very limited in what they can do but useful for some things and that's free for you. Then you'll have open source legacy models, GPT-four class stuff and then you might only call out to a Fable five on demand. So do you need to build your own router or is there a tool that you'll be using? Like how important will that be from the interactions that you're seeing?
Speaker 7:That's a great question. You're pretty much asking, like, what's the harness? And we just built our own. Okay. Because technically, like, this is all so early where there's nothing to use off the shelf.
Speaker 7:Yeah. You have to think deeply about it yourself then build it yourself. So for example, let's just say, you know, you're on Clicky right now and you're saying, hey, Clicky. I want you to actually research the latest in the Iran war
Speaker 12:and put
Speaker 7:it in my in a Notion doc for me when you're done.
Speaker 2:Sure.
Speaker 7:What now? Like, should it hit Opus? Should it hit Codex? Should it hit it's actually depend. And so, like, we can take all those requests today and just route them ourselves.
Speaker 7:So, yeah, we we use, like, we use, like, four models right now underneath the hood.
Speaker 2:And and do you have a model that's doing the routing?
Speaker 7:So GPT real time actually does the routing.
Speaker 2:Does the routing? I think that How interesting.
Speaker 7:Yeah. That's something that we that's something that we figured out then. I think not even the OpenAI team really knew and they hit us up about, which is That's a great router. Yeah. It's so good at, like because it's so good at tool calling.
Speaker 3:Yeah.
Speaker 7:That means it's actually really good at writing requests. So for example, we have this tool call called Call Fable five. Mhmm. So if the user asks something that, like, is a heavier pixel task, we call Fable five. And if it's, like, more of, like, agentic work, we call we call GBD 5.5.
Speaker 7:And so, it's kinda all being done in the background. But that's the kind of magic of the product right now where it's like, most people who use our product have never used an agent before. They don't even know what codec is.
Speaker 2:Yeah. And
Speaker 7:so, to them it's magical where it's like, oh, I'm just talking to my computer and it's doing the thing. That's that's awesome. That's they want.
Speaker 1:How do you how do you think about the tension between, I think, you being a generational media talent Mhmm. And then, like, startups? Because I actually feel like this is our first time meeting, but I've seen your videos over many years at this point and like, you're just really really really really good at it. So much so that as like somebody who's in media, I'm like, well, I kinda want you to just do that. Podcaster.
Speaker 1:Normally normally it's the exact opposite where it's like, hey, like, you shouldn't do this podcast. You should just work on your company, you know, or whatever whatever the thing is. Yeah. And and they obviously feed into each other really well. Mhmm.
Speaker 1:Like you have Yeah. An edge. You can get attention for free, you know, just investing a few hours, making a video, whatever. It's very, very powerful skill set. But how do you think about that tension?
Speaker 7:You know, it's funny like I've been making videos for like fifteen plus years and I Overnight success.
Speaker 2:Second time. Yeah.
Speaker 10:I always just
Speaker 7:see this as a as like a I think I get to do for fun on the side while I get to do the main thing which is like actually build stuff for people. At the end of the day, like, I am painfully familiar with how bad of a business like making videos is, and making movies is, and making music is. Like, I know how bad of a business is like firsthand. And so I know, but I know more than anybody the power of getting in front of a billion people every single month. Mhmm.
Speaker 7:When there's a good engine underneath, a good business engine engine underneath it, that you that that's that's powering something else. And so, I fully intend to do that. So if I have this media ability, I'm glad I got it. I think Yeah.
Speaker 1:But it's like a
Speaker 2:way to do
Speaker 1:it's like sales.
Speaker 2:Yeah. Sales.
Speaker 1:It's like Really sales. Sales.
Speaker 2:You gotta be good at sales. WWC, some movement on interoperability in iOS still feels like the clicky iOS app is probably on the longer term and will be stuck in the walled garden for a while. But what I'm interested in is that do you see just a really, really solid market? Because there's no DaVinci Resolve iOS app that I'm aware of and certainly people don't there are there are like prosumer sort of professional tools that really only exist on a desktop or a laptop. And so are you interested in like going and hacking on crazy workarounds to build something that like maybe makes people a little upset in Cupertino but gets you into that more casual mobile space?
Speaker 2:Or do you think that you just want to focus on desktop, prosumer, you know, desktop apps?
Speaker 7:No. I want it all. Want when you talk to your computer, whether this is your computer Yeah. Or your computer is this computer.
Speaker 2:Let's go.
Speaker 7:I wanna be an interaction layer on top of it. Yeah. That being said though, I have no I have no interest in necessarily being like OS level controller. For example, like, kinda like what Apple is doing right now. There's no real interest.
Speaker 7:For us, most of our customers and most of our users are essentially connecting, like, 15 integrations to Clicky from g Suite to Notrek and to Dropbox Sure. And doing work with it. So I'm a lot more interested in, that reality where talk to what if I didn't talk to my phone in the morning and just say, hey, like, look through all my emails and send me send me a brief, you know, when you're done. Yeah. And just I can just talk to my phone and do that.
Speaker 7:I'm not sure that stuff, which is stuff I don't think Apple is gonna do personally. They're not gonna make, connect your connect these fifteen fifteen places to my Apple account and do work with it. I just don't
Speaker 13:think they're gonna do
Speaker 2:How do you think the health of like open ecosystems in desktop software is broadly? Because there's two ways that you could like update an Excel file or there's actually probably a bunch. But you can you can, you know, puppeteer the mouse, move over, click, type the cell, save, you know, or you can just go edit the underlying CSV file in like raw text and then just refresh the front end. And I imagine one's way cheaper for you so you want to like lean into getting the MCP servers, the APIs, the file writing like the CLI interaction down. But do you think that there will be companies that lean into that or companies that fight that because they want you to stay, you know, mouse and keyboard at all times?
Speaker 7:That's a good question. I don't think so because at the at the end of the day, what Clicky is doing in the background, just so you guys know how it works, we literally took the Rust binary that's in Codex and we package it with our with our app.
Speaker 2:Okay.
Speaker 7:So that when you actually call a click with the agent, you're just calling a sub spawn of codex. Yeah. And so this is on purpose. Like, I just wanna use the best the best model and the best kind of thing possible. Yeah.
Speaker 7:And so no. I don't think that's gonna end up happening. In fact, it's better. For example, if you ask me clicky right now
Speaker 2:Yeah.
Speaker 7:Hey hey, like, how do I actually, like, add a formula to this Google Sheet here?
Speaker 2:Sure.
Speaker 7:There's two answers there. One answer is let me show you. The second answer is I can show you, but do want me to do that for you?
Speaker 2:Sure.
Speaker 7:And I think that's where we're gonna start going with computers. Yeah. Where you're just gonna start your computer is just gonna say, can I just do this for you? Like, I see you doing it. Like, I can just do this.
Speaker 7:So that's what we're kinda gonna look.
Speaker 2:Well, good luck with the fundraise. I'm sure you'll be back on the show soon. Let us know when it closes.
Speaker 1:Yeah. It's exciting for this. Yeah. I love watching you you win and and have fun in the process.
Speaker 2:Yeah. It's great time. Meet you. Congratulations. We'll talk to you soon.
Speaker 2:Have a Thanks. One.
Speaker 1:Later.
Speaker 2:Let me tell you about MongoDB. What's the only thing faster than the AI market? Your business on MongoDB. Don't just build AI. Own the data platform.
Speaker 2:That powers it. And up next, we have Trent from SideTalk. He's a creator and cofounder of SideTalk. He's in the waiting room. We'll bring him in the team.
Speaker 2:And after them, He'll turn Nick's fandom into one of the Internet's most Woah. Recognizable All in my mind. Media brands. How are you doing? Take your spirit.
Speaker 2:How is New York? How is it going?
Speaker 13:You know, it's crazy out here. I'm not gonna lie.
Speaker 2:Okay.
Speaker 13:I was about to do this interview from my apartment and then I realized, hold on. Yeah. Has anyone done a TBPN interview live from the streets of New York?
Speaker 2:You gotta go to the sidewalks. This is the founder of Side Talk.
Speaker 1:This is amazing. Amazing.
Speaker 2:Know you. Take us through your media empire. Take us through your strategy. Break it down for us.
Speaker 13:Okay. So I have a little company called Sad Talk. You might see the logo and pardon the potential loss of voice from this Knicks crowd. But, you know, we kinda pretty much run around New York City and let people do and say whatever they want into our microphone. We created the Bing Bong trend years ago.
Speaker 13:We created these Knicks videos. Unfortunately, there's little bit of chaos outside of Madison Square Garden due to us. Mhmm. But, yeah, we're pretty much let people say what they want in New York, and it's it's pretty fun and entertaining.
Speaker 2:What's the secret to a good street interview?
Speaker 13:You gotta keep it shareable and engaging. It sounds obvious, but, like, why would you wanna watch an interview of someone talking about something boring or low energy or anything like that? What would you want to send to your friends? And that's kind of the difference.
Speaker 1:So you guys people about like language model diffusion and like that. Always.
Speaker 13:Always. Always. Always.
Speaker 7:That's what we love talking about.
Speaker 2:Token prices and that. Yep. Yeah.
Speaker 1:Yeah. Gotcha. The people wanna see. We're gonna start
Speaker 13:talking about that outside the Knicks game type thing.
Speaker 2:Yeah. Where where so I I understand that a lot of the, you know, man on the street interview format these days, it it it can be very prepped. It can be there's a PR person that's pitching some successful business person for the what do you do for a living. It looks candid but in fact it's staged. Do you ever participate in that or is everything candid?
Speaker 2:Is that part of the brand?
Speaker 13:We're a 100% natural on the streets. Don't know what you're gonna expect. Okay. Yes, sir. Organic.
Speaker 2:But it's
Speaker 13:been really cool too.
Speaker 10:Yeah. Yeah. Please. Bye.
Speaker 2:Then my my follow-up is like then if you aren't prepping and understand who you're gonna be interacting with like how do you vet like where is the bar? If you see somebody walking and they have a baseball hat on and they probably don't want to be bothered, is there some sort of social contract where you shouldn't like the the is there a doom scenario where everyone in New York is getting asked what do you do for a living 15 times when they walk to go get their morning coffee?
Speaker 13:Yeah. Honestly, it's it's a bit of an epidemic going on. I actually am kinda scared that I'm gonna get pulled up on right now and ask what I do for a living or what song I'm listening to.
Speaker 3:Yeah. But Oh
Speaker 2:yeah. That's another one.
Speaker 13:The the people the people of New York, I think they distinctify side talk a little bit from that. Mhmm. They know we're not there to to ask them something like, I don't know, so so cookie cutter like that.
Speaker 2:Sure.
Speaker 13:And I don't know. Now people come to us when they see the microphone, which is great. And, yeah. We we we just go with the flow.
Speaker 2:How do you think about monetization? Short form monetization, notoriously hard, but you have some merch. Break break down, like, the business side of the business.
Speaker 13:Yeah. It's actually very interesting. You would think that you would that, you know, I would have made a dollar off in TikTok by now.
Speaker 6:I don't
Speaker 13:think I've ever gotten paid for TikTok or Instagram, literally, not a dime, which is which is fine. It's cool. I want TikTok pulled through. But now it's cool. We do a lot of branded work
Speaker 2:Okay.
Speaker 13:For companies. So we've worked with everyone from the NFL to Netflix, Nike, Google, Amazon, creating a lot of content for them. So they'll come to us with a product or an event that they wanna highlight, and they say, hey, you know, you know how to get clicks and views, and Yep. We kind of pretty much apply our formula to that and then create a really good video for them.
Speaker 2:Okay. I I have like a hot take and I want you to walk me through whether you agree or disagree. I think you should just do a mid roll ad in a three minute Instagram reel because I see the branded integrations. It's like, oh, there was this example on Subway takes where someone's take was Android's better than iPhone. It went massively viral on rAndroid.
Speaker 2:And then the second and then Google was like, oh, we'd love to work with you on another take that's the same thing but it's branded. And those sponsored takes, often don't go as viral. It's hard to get branded content to go viral. But in some of these longer three minute videos, if I'm locked in after two minutes, I would sit through a ten second mid roll of like, hey, this was brought to you by this. Thanks for sponsoring this video.
Speaker 2:And then it's just all organic content around it. Does that work? Is that already happening? Are you thinking about that? What's the deal?
Speaker 13:Definitely. We one thing we really like doing is kind of like a natural integration. Mhmm. So we just did something with Nike, which is really cool, where they hit us up for these Knicks colored shoes.
Speaker 2:Okay.
Speaker 13:You know, okay. Who better to get word out about these Knicks colored boots than side talk. So we had people wear them in the episode. We were talking about them. We had to react to them.
Speaker 13:I think that feels authentic because that's kind of something we would do anyway. Mhmm. It it did really well.
Speaker 2:That's good.
Speaker 1:Predictions for tonight.
Speaker 13:We know if you're losing me by the way.
Speaker 2:A little bit. Yeah. It's a little patchy.
Speaker 1:We are losing you, but the energy is still there. Predictions for tonight.
Speaker 13:Nixon five.
Speaker 1:Nixon five?
Speaker 10:What's the plan for
Speaker 2:covering the game? Is this any isn't
Speaker 1:it in your Isn't it kind of in your best interest for it to be all seven games? Like, you know, more more
Speaker 2:More content.
Speaker 1:Yeah. More attention. You kinda want, you know, you we we obviously want your Knicks to win, but you don't want them to win too fast. Got
Speaker 13:a win in seven, I would like seven. If we can guarantee seven, that would be great. But listen, we need to get the win more than anything.
Speaker 2:Okay. What what the process of a shoot day? How early are you getting there? Who are you bringing with you? How big is the team?
Speaker 13:Definitely. It depends on what we're doing. We do such a wide variety of stuff. We can randomly go to a hot dog eating contest in Coney Island. We can go highlight a random character in Brooklyn.
Speaker 13:We can go obviously film these Knicks games. It was pretty dependent on what's going on, but with the Knicks games, we kinda show up in, like, the third quarter as it's ending. Unfortunately, we can't bring our equipment into the stadium, so we look. You just have to stand there and watch the game on our ESPN app and hope that the Knicks win or we hear, you know, someone just scored, something like that. And then the chaos erupts.
Speaker 13:We go into the chaos. We're there for, like, hours and hours at a time. And at the end of the day, people see, you know, fifty five seconds, but we're out shooting for, like, eight hours throughout these playoff games.
Speaker 2:Got it. Wild. Well, thank you so much for coming on. We appreciate you. Good luck.
Speaker 1:Yeah. We will we'll feature some of your videos in in tomorrow's episode from tonight. Good luck out there. Have fun. Be safe.
Speaker 2:Yeah. We'll talk to
Speaker 3:you soon.
Speaker 1:I'm hoping for I'm hoping for Knicks in seven personally. Let's do it. But can't guarantee it yet. So have have fun out there. Thank you for thank you for this walk and talk.
Speaker 13:Thank you, guys. Appreciate you.
Speaker 1:Yeah. Great to hang, dude.
Speaker 2:Appreciate it. Thank you. Let me tell you about CrowdStrike. Your business is AI. Their business is securing it.
Speaker 2:CrowdStrike secures AI and stops breaches. We will be joined by the CEO of Snowflake in just a few minutes. Thank you for tuning in. We need to move on to the story.
Speaker 1:Cannon says, the cashier at Home Depot just asked if I want to round up to support the SpaceX IPO.
Speaker 2:SpaceX IPO is gonna be big Friday. We might have a surprise guest. It's gonna be fun. Also, there's this crazy meta story going on. Not meta, the company, but, like, story about a story behind Ty Morse.
Speaker 2:He's going all out trying to get an Elon interview, and it's been fascinating watching him build the craziest set in podcasting history. We're rooting for you, Ty. Good luck. Hopefully, you land it. It is the quiet period, so it might be difficult, but I think that there's a plan to extend the effort and everyone's rooting for it to happen.
Speaker 2:It's been viral on X many many times. Also, Bloomberg's reporting that the SpaceX IPO is four x oversubscribed. Do they know that already? Does do do you get that information at this point? That's really good news for the market, for this for the overall.
Speaker 2:There was an interesting article. I mean, we can go through some of this. I think this was in The Economist SpaceX talking about, where is it? Can the market swallow SpaceX, Anthropic and OpenAI? Watch out for indigestion.
Speaker 2:But they talk about the float, the free float. This was very interesting. So obviously the company might be worth a trillion dollars. How much of that trillion dollars is actually actively being traded? Like sure Fidelity might at any point in time have a price at which they're willing to buy more and a price at which they're willing to buy to sell their stake.
Speaker 2:But founders are often locked up. Founders often want to maintain control. Employees are locked up for a certain amount time. Certain investors might be locked up unless you're Bill Gurley. Don't try to lock him up.
Speaker 2:Nobody can lock him up. He's a wild man. But the float is important because if it's a trillion dollar company and the CEO passed away a generation ago and there's all financial managers in, it's only owned by hedge funds. This was the story of Take Two before Strauss Zelnick came in. Take Two, the makers of Grand Theft Auto, It was entirely held by shareholders, by financial investors.
Speaker 2:And they were unhappy with the management team and the management team didn't have any equity. And so he was able to raise his hand and say like, I'll run this thing. And they were like, absolutely. Thank you. And they let him take over the company without really putting anything up.
Speaker 2:He didn't need to do a hostile takeover like bring a bunch of capital to bear to get control of that company and become the CEO. And it's been a fantastic success for him and fantastic success for Take Two shareholders who stuck along stuck around for the Strauss Zelnick era. But the float matters. And Microsoft, it's 100% floated. The free float is 100% because the founders have moved on and divested and they're not locked up.
Speaker 2:At Apple, it's like 99%. Broadcom is also at 98%. NVIDIA at 96%. Amazon is at 91%. Only Jeff Bezos is considered sort of off the table.
Speaker 2:Alphabet at 90%. Tesla at 89. And Meta is notably, of the big tech companies, the lowest free float at something like 88%, 86%. And the reason for that is because Mark Zuckerberg has a lot of control. If he sells his stake, he loses control.
Speaker 2:So no one's expecting him to sell even if the stock goes way up or whatever. Like, he's going to maintain his position because he wants to run that company. Now, SpaceX is in an interesting, interesting place. So 13% of Meta's shares are owned by Mark Zuckerberg. SpaceX plans to release locked up shares in a series of tranches if its IPO issues $75,000,000,000 of shares valuing the firm at its hoped for $1,800,000,000,000 valuation, the initial free float because if you buy the IPO, you're not locked up.
Speaker 2:You can sell the next day if you want. Technically, this means
Speaker 1:it's not like the whether whether somebody's like buying through, you know, investing through like JPM Yeah. Morgan Stanley Goldman. Goldman, all these pieces. Like, technically, you can go and sell. Yeah.
Speaker 1:They just might restrict you from other IPOs. So you would like And and this happens like on all the different apps, you know, Robinhood, public, etcetera. If you're buying into an IPO, they they basically are asking you nicely, do not sell. Don't don't flip don't flip here. Yeah.
Speaker 1:But people can. Yeah. And so with how many retail dollars are flowing into this, I expect a lot of people that are buying buying, you know you know, basically buying the IPO
Speaker 2:Yeah.
Speaker 1:To start trading almost immediately.
Speaker 2:Exactly. But So it's importantly, it's only 4% of the free float. So only 4% of that $1,800,000,000,000 is a lot of money, but it's only 4% will be really free trading. And then of course during the IPO process you're vetting investors and you're trying to get the people that will hold forever. And Elon has done a fantastic job of that with the indices.
Speaker 2:So he's gotten the NASDAQ, the S and P and a few others to like commit. So although NASDAQ has already shortened the seasoning period before index inclusion to fifteen trading days, the Russell slashed its waiting time to five days. Most share indices wait firms in proportion to the value only of the shares released for public trading. And this is important because people look at the S and P five hundred and say, wait, S and P five hundred, a $2,000,000,000,000 company coming into that, your weight, if you're just buying the S and P five hundred is going to be more than 1%. It might be in the single digit percent.
Speaker 2:That's a lot. And we've been talking about the S and P four ninety nine for the bears. Right? In fact, the initial weight in the S and P 500 will be around point 1% because it's just that 4% free float and then it increases over time as the shares get locked up. We actually have a chart here of how the lockups work and it takes basically
Speaker 1:Yeah. There's
Speaker 2:whole year, almost two, for everything to get unlocked and it's also triggered based on share price appreciation. So if the shares trade up 30% or more, then more shares become unlocked and the road to a 100% lock up ending happens slowly. And so just a little bit of an interesting deep dive into how the SpaceX IPO will fare. Anything else to talk about on that story before Apparently, we move
Speaker 1:senator Warren Yes. Has urged the SEC to halt SpaceX's IPO citing governance risks, Elon Musk's control, and potential foreign, especially Chinese investment concerns. She also highlighted SpaceX role as a US defense contractor. Has never met a business that she liked. I think except maybe I don't know.
Speaker 1:Large financial institutions.
Speaker 2:Who knows? Think she
Speaker 1:she likes those.
Speaker 2:Well, we have our next guest in the waiting room, Sridhar Ramaswamy from Snowflake. He is the CEO. We're very pleased to be joined by him. Welcome to show. Thank you so much for taking the time.
Speaker 2:How are you doing?
Speaker 9:I'm well. Thank you for having me.
Speaker 2:Can imagine you're doing well. The the company is doing fantastically. I am interested to hear no. I mean, obviously, there's been an emotional roller coaster, but I'm more interested in the data that you're seeing that shows acceleration, so much promise, so much opportunity in this particular business.
Speaker 9:Yeah. Data is the foundation for insights. I worked in a data company. I worked in the search ads team at Google, where great data was the flywheel that made it into a great business. That's what we aspire to do with each and every one of our customers.
Speaker 9:AI has done a couple of things. It's made the act of bringing data into Snowflake doing just a whole lot easier. Pipelines that used to take weeks to set up or migrations that used to take years now have dropped to days and months, which is pretty remarkable. But people also realize how much more power they can get by having great conversational access to their data. And now with things like Snowflake co work, how we can actually solve pretty meaningful business problems in an interactive way, it's that it's that loop that is compounding for us that drives a lot of our growth.
Speaker 2:Is has there been any movement for Snowflake? Whenever I hear the story of a a a data lake, a properly managed data warehouse as an entrepreneur, even in a small business, I think that sounds amazing. That sounds like the dream. Then I see the companies that, you know, actually have the resources to onboard be, you know, immensely large, and obviously, that's fantastic for your business. Yep.
Speaker 2:But I'm interested in in what the future looks like. Is there a world where a 10 person start up with AI agents and and these pipelines that can be built much faster can actually set up their and then not have to go through a cumbersome migration in the future.
Speaker 9:Yeah. We are seeing that happen in front of our eyes with Snowflake's own data team. Yeah. You know, they, like with every other company, used years long. You ask them for a feature, they'd be like, Here, take a ticket.
Speaker 9:Come back, you know, in a few quarters. What they've been able to do with Agentic AI is grind through those kinds of backlogs very quickly. Absolutely. Data and agents are going to make it much easier to set up these things and have them evolve as they go along. I'll give you examples from the public.
Speaker 9:We rewrote our, both our support teams and our SRE team's software. These are the folks that take care of different kinds of problems that we get into an, and the net result of that by agents investigating the problem. And if you see enough instances of a problem that you have not seen, you go figure it out, go write a tool for it, it's now in a self healing loop. I increasingly imagine that more and more operations get into that kind of a mindset where there are very competent agents, sets of recipes you can call them skills, there'll be some other name tomorrow that take care of problems, but it's it is a self learning loop. That's what AI makes possible.
Speaker 2:Got it. Where are we in the path to, like, fully automated migrations, fully automated new integrations? It feels like every new model release were like, this is the one. This is the one. The nontechnical CEO is gonna be doing the job now.
Speaker 2:He's just gonna say, hey. We we have two systems. Go fix it. Go integrate it. Get it into Snowflake.
Speaker 2:It's gonna happen. And then reality sets in and there's not just token costs and cost of that to to delegating to AI, but also just the real world of of other decisions that need to be made that might be outside of the context. So what are the the the remaining challenges that come up when you're building a new integration or or working on a migration?
Speaker 9:Yeah. I think the timelines are definitely shrinking Mhmm. When it comes to legacy migrating off of legacy platforms, the on prem ones. Yeah. We used to think of them, the biggest ones, as running into, say, like, three odd years.
Speaker 9:And these strike terror into every company's heart because it's three years of, like, really anxious development work and waiting for results and hoping things don't go wrong. To give you very, concrete numbers, we now feel a lot more confident that we can tackle really tough migrations and have them finish in a couple quarters. That's a big deal. It's going from 12 down to one or two. And the migration team, which work on top of agentic harnesses that we create, are confident that the technical parts of these problems will be solved by the end of the year.
Speaker 9:But there is the change management. There is all of the other applications that are running on the old system that you have to validate on the new system and ensure that there is business continuity. That kind of change management is still going to be nontrivial. But the technical parts, the act of, doing the migration or writing new software, these are the things that AI is absolutely driving down.
Speaker 2:What is the biggest
Speaker 9:By the way, I actually go check out every feature my, team puts out using our coding agents. Mhmm. That's already a thing. And, honestly, it's not that hard if you have a modestly technical background.
Speaker 2:Yeah. Yeah. No. That makes sense. What is the biggest lesson that you or the company have taken forward from Frank Slootman's era?
Speaker 9:Frank was always about amazing go to market execution. Yeah. And we remember that. Okay. And, in fact, our sales teams
Speaker 2:Yeah.
Speaker 9:Are incredibly well versed now in our products and in AI as a whole because we created products that could work for them. Mhmm. And having an effective go to market motion is the real strength of Snowflake. Again, to make this super concrete, the number of, use cases these are new basically within customers, that our account executives won, went up by some 75%, year on year. That's the power of sort of using AI to get work done faster.
Speaker 9:And, that is a tradition that will that that will continue. What we have added on focus that comes from a product first person like me. AI in sort of a generic way or about worry about things like what, you know, what can it do. But I think sometimes what we forget is if you have a well built system that uses, like, the latest it's actually an incredible delight. All kinds of information that you would want.
Speaker 9:I was at a conference recently. Every piece of information that I wanted to know about a customer that I was having a conversation with, I would look up even live if I didn't know that. I would do it in front of them and show them the power of Snowflake's AI. A big part of our success over the past couple of years has been internalizing this. AI is a different way to think about product and product delivery, and the companies that are going to succeed are the ones that truly internalize that.
Speaker 9:That there is a big chasm between how we did it with web pages and clicking on things to these agentic flows that can truly solve problems.
Speaker 2:Then how is the sales the role of the sales rep changing, the sales associate changing? Are you looking for any different skills or are you doubling down on what worked in the pre AgenTik AI era?
Speaker 9:First of all, a lot more familiarity with Snowflake is a must.
Speaker 2:Sure.
Speaker 9:Because these folks now have access to products that show off what Snowflake is about. Yeah. I expect every sales rep to have co work on their phone and be able to show off the actual impact of having a product like that with pretty access to information to each and every one of our customers. When it comes to our solution engineers, these are the technical presales people. Used to be that we'd have six demos total for all of Snowflake, and maybe they would do a little bit of customization and say, hey, here's what Snowflake can do.
Speaker 9:Now pretty much all of them are capable of creating a custom demo tailor made for a specific customer in a vertical, in a way that makes sense to them, that will really illustrate the power of Snowflake. It's that kind of empathy that they can use the technology that we create to drive. I would say it's changed in a really, really big way because a lot more possible, but the bar is also a lot higher in terms of what you're expected to bring to the table.
Speaker 2:Can you walk me through the partnership with Amazon Web Services? I I I'm sure you compete in some categories, but how did this come together? What does it mean for the future of your business?
Speaker 9:Yeah. We work with the hyperscalers. AWS is the biggest partner. Yeah. And, you know, we work with them on a number of different levels.
Speaker 9:We work on product integration.
Speaker 2:Sure.
Speaker 9:You know, buy a lot of capacity from them, a lot of GPUs that our inference team runs runs models on. But where we really shine is in joint go to market. AWS is an incredibly customer first company. And if a customer says, hey. I want AWS plus Snowflake, they lean into it.
Speaker 9:They bring us. And we really focus on how do we solve problems jointly, for customers. And there's a deep, deep relationship both at the account level going all the way up to the CEO level that ensures that problems that come up in practice get solved very quickly, it's it's the kind of partnership that one only dreams of. It's incredibly effective in practice.
Speaker 1:What's been your what's been your strategy for trying to predict AI progress? Because we have people on the show every single day, somebody from a lab, somebody that's an investor, customers of the labs, things like that. Everyone has a different opinion. You have to weight them differently based on their incentives. But you're running a massive company that's leveraging these tools, but at the same time, you wanna understand what the capability will be one year out, two years out, and it feels harder than ever to actually be able to predict that.
Speaker 9:I think this is a is a really good question and one that to deal with. I don't think the software industry as a whole has internalized that this precious commodity that we all loved creating by hand, which
Speaker 6:is
Speaker 9:software, the cost of creating new software is going to keep going down. I tell people that the best analogy that I can give for them is to go back to, let's say, the 2 thousands when the cost of creating and distributing new content truly dropped precipitously. And you have to work through what is the implication of something like this. For example, I think simply getting deployed into an enterprise and being part of a workflow there is no longer guarantee that things will going to things are going to keep continuing. Because switching is also easier, I I would say that's the first thing.
Speaker 9:Every every company that's in the world of creating software as a big part of their business needs to think through what is the durable value that they are creating. I think it's a really hard question to internalize, and so we spend a lot of time just thinking about it. The second one, and this is this gets to the heart of your question, and I honestly just have a which is that this is a bad time to be making two and three year plans. Any time things are getting better or worse by 30% every month, all the human ability to predict these things, which are exponential in nature, is, is, you know, it just doesn't it doesn't really work. I joke to people that at this, point in time, we should treat coding agents the same way we treat traffic.
Speaker 9:Any person is always going to go get, look at Google Maps first before they go somewhere because it's an all seeing agents are sort of becoming like that when it comes to when it comes to software. And the more you have that open mentality that more things might be possible with a new model release than was possible before, the more you'll be pleasantly surprised and you'll be ready to take advantage of it. And so I stress that childlike quality in figuring out what is possible. Obviously, you know, there are nuances to how do you put in work in such a way that even if the model got better, you'll still be able to take advantage of what you have done before. There are practical problems like that.
Speaker 9:But the biggest thing that I ask for is that open mindedness to what is possible today because a lot different from what was possible, say, a month ago.
Speaker 2:Are you positioning Snowflake as a company that's in the token path or in the token stream? I don't know if you've heard this phrase that's sort of new coinage for a position in the AI value chain. And it feels like you're firmly in token stream, in the token path. But I'm wondering about what you think about that category. Is that narrowing you?
Speaker 2:Is that valuable to communicate to customers and investors? What do you think of the phrase like in the token path?
Speaker 9:It's a useful way to think about how value is going to get created. Mhmm. Remember, back in the web era, we talked about, you know, number of visits, number of unique visitors.
Speaker 2:Mhmm.
Speaker 9:These were the things, are weekly active versus monthly active. These were the abstractions that we used, to ground the numbers that various people would report in a reality. You can think of the token path as existing along this path that is using the power and knowledge that these models have, and creating value for customers. And, both in the data where our coding agent called Coco comes into play, where we want everyone that wants to get value from data to be using it to create all of the artifacts in that path, but also in the consumption path, where, our product is Snowflake Cowork, where I want every employee in every company to be able to access the valuable data that is in Snowflake, but also in other applications via things like MCP, to be using co work. And so we very much think about can we be in this path of coding up the things that are going to drive consumption and creating value, but also in the path of how does an end user query enterprise data, take an action on that data, and also creating value.
Speaker 9:So we very much think about this.
Speaker 2:So my my my last question is, you said it's a bad time to be making two and three year plans. How far are you looking into the future? Do you have a one year plan, a three month plan? What
Speaker 1:Well, as a public company,
Speaker 9:you you
Speaker 2:do have have to have forecast. But but but more qualitatively, like, what are you gunning for this year or over whatever time horizon you think is appropriate at this point in time?
Speaker 9:Our overall strategy is very clear. It's just what I described. Yeah. Anytime anyone wants to do something with data, I want them to use, agent tools that are created by Snowflake Mhmm. Cocoa, for example
Speaker 7:Yeah.
Speaker 9:To be able to do that. We wanna have the best data platform in the world so that when and whenever someone thinks, hey, I need a historical view of this, or I need an OLTP database to do this, they think of Snowflake. They can use any coding agent, not just our own, to be able to, to be able to do that. Similarly, we think a lot about how can we make sure that we sell co work to CROs and demonstrate what we have done internally within Snowflake, just transform our own sales organization. So we think a lot about how do we take our customers through the same journey that we have been through in terms of how can AI make a difference to the business.
Speaker 9:There'll be a lot of details along the way, but that's the path that we are headed in, AI leveraging the full power of data.
Speaker 2:Well, congratulations. Thank you so much for taking the time to
Speaker 1:and Great
Speaker 2:to meet you.
Speaker 1:Congrats to the team on all the momentum.
Speaker 2:Yes. Stay strong in the token pass.
Speaker 9:Thank you, guys.
Speaker 2:We'll talk to you soon. Have a good day. Goodbye. Let me tell you about agents meet the canvas. Your AI agents can now create and modify your Figma files with design system context.
Speaker 2:Yes. Apologies for
Speaker 1:technical issues. A lot of people said, is this AI?
Speaker 2:It's not AI.
Speaker 1:It's just AI. There was some type of clip.
Speaker 2:We I I've never seen that exact technical failure before where it cuts out entirely. We've heard scratchy audio. We've heard degraded video. It was the video was coming through crystal clear, but the audio was cutting in and out a little bit. But I think we got a lot of good stuff.
Speaker 2:Coco, they should have called the AI agent Frosty. Snowflake? Frosty. Probably intellectual Frosty the agent. The jolly happy coder.
Speaker 2:Coder. Yeah. Something. Anyway, Citadel. Citadel Securities, former former employer of mine when I was an intern, wrote a post on tokenomics.
Speaker 2:They're one of the most significant hedge funds and they just dropped tokenomics. I have it here. I have a second printout. Tyler, you want to bring that over to me? It's not what you expected is what they say.
Speaker 2:So we've argued for some time that agentic and complex workflows delivered by frontier models would be expensive to run constrained by physical bottlenecks and vulnerable to unrealistic expectations of frictionless deployment cost. That judgment now looks less contrarian than it did when we set it out in February. They're taking a victory lap over at Citadel Securities. Amazon has now removed its token leaderboard. Microsoft has canceled clawed code subscriptions, and there have been multiple reports of unexpectedly large token bills.
Speaker 2:The salient point is that even the most powerful technologies must pass through the prosaic discipline of cost curves, capacity restraint constraints and marginal returns. Adoption is therefore becoming less about what frontier models can do in principle and more about the price and scarcity of the inputs required to make AI operational at scale. Compute power cooling, memory bandwidth and inference budgets are real and binding constraints. They go into economic theory talking about the the the three functions that prices provide. So they signal scarcity, create incentives for substitution, and ration scarce resources toward their highest value uses.
Speaker 2:This is the stuff that goes viral on TikTok. We're we're deep in it. You heard of the keeping the viewer watching. This is the most important thing. You read the Citadel Securities macro strategy report.
Speaker 2:But they do say they ration scarce capacity toward the areas where marginal productivity and AI justifies the marginal cost of using it. They're talking about ROI maxing. For the economy at large, simpler models may be more cost effective. Productivity augmenting pathway until physical constraints are eased. We hence see growing signs of a bifurcation in frontier versus everyday AI usage.
Speaker 2:They actually shared some data here around token expenditure index dropping, the log growth rate declining. And so folks are pulling back a little bit and shifting towards cheaper models as they figure out a new workflow. Something gets unlocked, and then they say, hey, we got to actually make this ROI positive. So that's the latest from Citadel. Let's see what Amazon Web Services is saying.
Speaker 2:They're black belting on the timeline. They said more AI generated code doesn't make your team faster. It might actually slow you down.
Speaker 1:Crazy This is such from AWS.
Speaker 2:Would have expected it from like some AI thought leader, podcaster, Twitter person.
Speaker 1:AI bear.
Speaker 2:But, yeah. It's funny coming from the AWS cloud gold check.
Speaker 1:It's funny because it's also like, if you were to write this, I think a lot of people would write this as more AI generated code doesn't necessarily make your team faster. No. Per No. More AI generated code doesn't make your team faster, which is just like factually incorrect for like a lot of teams. Yeah.
Speaker 1:Yeah. Right? That's true. We're actually moving faster than ever. Also, okay.
Speaker 1:Read the next post in this thread.
Speaker 2:It's like
Speaker 1:very clearly AI generated. Okay. The real bottleneck was never writing code. It's releasing it, debugging it, and keeping it running well. So when Honeycomb c Honeycomb IO
Speaker 2:CTO.
Speaker 1:CTO charity majors set a productivity target, she didn't chase 10 x. She chose two x and built from there.
Speaker 2:You think this is AI? Because the choose two x comma and ampersand built from there
Speaker 1:I think it was human edited but like that that
Speaker 2:Syntax Syntax. I agree. I agree. Her team also skipped the mandates and built a set of AI values instead. Every AI output has to have human owner.
Speaker 2:If you don't want
Speaker 1:your name on it, it's probably not good work.
Speaker 2:Yeah. That's reasonable. Quality first, quantity second. So they had a podcast about it. Coming from AWS, that's a wild statement.
Speaker 2:This is this is very funny. But, yes. Also funny with the backdrop of of a $500,000,000 budget.
Speaker 1:Well, yeah. And it's funny because Amazon is of course one of the largest investors in both OpenAI and Anthropic to Yeah. Leading coding model provider.
Speaker 2:Hey, they didn't say more AI generated posts don't make your social media team faster. Entirely possible it sped them up. They certainly got 14,000 likes on this. This is a banger post from a corporate account. Rare to see those numbers put up on the timeline from a corporate account.
Speaker 2:Congrats to the team over there putting up some fun numbers. Sridharam Krishnan, the don't know how to differentiate them. He's a kerniejackson.com investor adviser to bending spoons, not the former a 16 z a 16 z GP who worked for the government for a little bit. Sridhar Ramaswamy not many Silicon Valley vCs took our Bending Spoons intro in 2022. These vCs thought it was beneath them to be associated with the company.
Speaker 2:From 2024 onward, many clamored for an introduction to sell their portfolio companies. Bending Spoons is going public. So Bending Spoons will be a public company, and they own a number of of, you know, historical companies. They own AOL and have done a bunch of other stuff. Eric Souffert broke down the
Speaker 1:They literally own America Online.
Speaker 2:They own America Online. It must be electric. I can't get into my mobile email. Just going skip this Brutal. And move on.
Speaker 2:But you can Eric Souffert over at mobile dev memo wrote a breakdown of the Souffinator, wrote a breakdown of bending spoons filing to go public. The Italian roll up operator that owns among many other properties AOL, they own Eventbrite and Vimeo. Don't they also own Evernote, or am I getting that mixed up? Someone own Evernote, and the Evernote employee growth chart looks like a fast takeoff. Like, post 2020 COVID, they went from like a thousand employees to like almost 6,000 employees or they tripled or doubled the workforce.
Speaker 2:And Benning Spoons, you know, known as sort of like the the operational excellent, almost like a private equity firm, private like, they're, you know, they're gonna do cuts. They have done a few cuts, but like the the actual headcount has like had held fairly stable and I think the business has gotten to a much better place. So interesting to see there's a time and a place for being aggressive, expanding when there's a big moment and the market's up, and then there's also a time to consolidate and focus on operational efficiency.
Speaker 1:Why be your partner. Why is this guy so obsessed with GTA, John?
Speaker 2:Have you ever played GTA?
Speaker 1:Any GTA? Maybe like
Speaker 2:GTA three, GTA four?
Speaker 1:Maybe like for like two minutes.
Speaker 2:Two minutes?
Speaker 1:And I'd be and I was like, wow, you
Speaker 2:just Loser. What a loser. Never gamed in his life.
Speaker 1:I couldn't get into it. I couldn't get into it.
Speaker 2:Yeah. You're a rust guy. You're you're a
Speaker 1:Everything like I I just even as a child I was like Wait. Everything that you can do in this game you can just go do real life minus
Speaker 2:No.
Speaker 6:You can't.
Speaker 2:It's all crimes. Wait.
Speaker 1:Jordy turned it down.
Speaker 2:He turned it down.
Speaker 1:There Yeah. Maybe I'm pure of heart. I don't enjoy. Yeah. Like, like crap.
Speaker 2:They were gonna let me play for like fifteen hours, ten hours, maybe five hours, but I turned it down.
Speaker 1:They were like wanted to pay me to play.
Speaker 2:Yeah. Wanted to pay you to play. Strauss said actually. I I would love for you to play. I I was a video game tester.
Speaker 2:I got paid to play video games. Best job. Play video games all day?
Speaker 1:They they got you hooked. Yeah. They they they got you hooked.
Speaker 2:No. Was hooked. I was elite. So yeah. You go play the games while it's in development.
Speaker 2:You file bug reports. Quality assurance tester. But your basically your job is to play video games all day. Worked with a guy who was super chill and like he was just like, this is my career. Like I'm is I'm happy with this.
Speaker 2:Like I'm I'm I'm a video game tester. And then I I checked in with him a couple years later and I was like, hey man, like how's the video game testing business going? And he was like, you know, I actually had a shift in my career. I'm a DVD tester now. It's even easier.
Speaker 2:So he did, I just sit there and watch the full movie and then tell the tell the people like, yep, like there were no audio bugs. Or there was no there were no drop frames in the whole movie. I sat there I watched the whole movie and it looked fine. Thumbs up. And then every once
Speaker 9:in a
Speaker 2:while he'd be like, oh I was in the menus of the DVD and when I went to behind the scenes footage it wouldn't play the correct video. Had the title for, you know, interview with the director and it actually played interview with the cinematographer so you got to fix that. Like these things happen like UI bugs pop up in DVD menus. But what a wild job, DVD tester. It's a good one.
Speaker 2:Good one. Anyway, this fellow is obsessed with GTA six and he's doing anything and everything to understand whether or not GTA six will be released on time. There's two methods here if you want to know GTA six is going to released on time. You can just shake down Strauss Zelnick for the truth or you can do what he did which is he set up acoustics around the rock star headquarters to measure audio levels near a meeting room and he discovered that his equipment had disappeared and when he returned to check on the latest data. But he didn't stop there.
Speaker 2:He started camping outside, monitoring traffic and tracking the number of cars and their estimated value. Oh, there's an executive in town. That means something for the GTA six release date. Oh, there's more cars coming in, probably a lot of people working late nights getting it out, burning the midnight oil. And so he believes such data can be used to speculate on how many executives may be spending time at the HQ as opposed to regular employees and an increase in both traffic and high value vehicles could indicate Trailer three is coming.
Speaker 2:He's also considering an even more insane idea, which is to measure the amount of oxygen in the area to monitor how many people are around, he did it. He started monitoring the oxygen around Rockstar North, and he believes trailer three is coming soon after noticing that oxygen levels dropped to about 20.3%. We actually need to use basis points this time from twenty point three to twenty point o four after five p. M, suggesting increased oxygen consumption in the area after normal working hours. He's also monitoring the number of cigarette butts.
Speaker 2:I thought it was just a GTA six fan. I'm excited. Everyone's excited. Maybe it's a rock star or a take two shareholder. You had a different theory.
Speaker 2:What was your theory on his motivations?
Speaker 1:I don't believe that people are gonna go to these lengths unless they're making money on it.
Speaker 2:Okay. And how would he make money?
Speaker 1:There's a lot of like prediction markets with, you know, plenty of volume.
Speaker 2:Yeah. Could be trading the stock directly but could also be betting on a prediction market that's
Speaker 1:Oh, yeah. If there is a yeah. Theoretically, if there were to be another delay, I'm sure the stock would
Speaker 2:Yeah.
Speaker 1:Probably react to that.
Speaker 2:Yeah. Absolutely. It is very very fun story.
Speaker 1:A hedge fund should hire this guy to do True. Other types of
Speaker 2:True.
Speaker 1:Detective work.
Speaker 2:Yeah. It's almost I it almost feels like it's going over the line, maybe illegal. I don't know. Like if I I I would be very frustrated if someone was standing outside of our UltraDome with a
Speaker 1:Measuring the air.
Speaker 2:Measuring everything to see if we're gonna interview Matthew Prince yet next. Well, we are. We are. Because he's in the waiting room and we're bringing him in to the TBPN UltraDome. Welcome to the show.
Speaker 2:Thank you so much for taking the time. How are you doing? Gentlemen, good to see you. I'm doing well. Great to see you.
Speaker 1:Great to see you.
Speaker 2:What's new in your world? You made some acquisitions. You made an acquisition. Take us through it.
Speaker 14:Yeah. So we we bought a a a company called Void Zero, which, makes Veat, which is one of the most popular developer, platforms that's there. It's just an incredible team. Evan Yu, who's the founder, is just a just a first class human being, someone who who, our team is super excited to work with. The team that he's assembled, is is just great.
Speaker 14:And I think that this is increasingly becoming the platform that, is being used to power a lot of the, agents that are running around around the Internet, and, and a lot of those agents are running on Cloudflare. And so we think it's just a really natural combination.
Speaker 2:How simple is the synergy? Is it you'll funnel those 130,000,000 users who download Veets every month? I think it's a 130,000,000 weekly downloads. Weekly. Wow.
Speaker 2:Yeah. That's a lot. Into, you know, preferring Cloudflare, defaulting to Cloudflare? Or is there more synergy under the hood around developer integration, company integration? Like, how are you thinking about this playing out?
Speaker 14:Yeah. We continue we we plan to continue to leave it as an open source project and support it and invest in it that way. We wanna integrate it closely with Cloudflare's developer platform and make sure that Cloudflare is the best place to run any sort of Vite project that that you that you have. But it'll work in any any of the different platforms as well. And so we just really wanted to make sure that Evan and his team have the support to make sure they could continue to to really invest in in what was in what what's just an incredible platform.
Speaker 14:And and we think that that is gonna drive more developers to Cloudflare Cloudflare's Workers platform as well.
Speaker 2:What are the headaches for developers these days? I I know everyone's concerned about token costs and token budgets. Sometimes that doesn't show up for the developer. It's more like the CEO that's worried about it. But is uptime more difficult to maintain?
Speaker 2:We've been seeing screenshots of different uptime tracker status pages that have more and more red and yellow on them. Like, what's at the top of the stack and and how are you helping?
Speaker 14:Yeah. You know, I think that that you need a different architecture than than we've than we've had to build sort of the last generation of applications for what's coming with with agents. If you imagine, you know, there are about a 100,000,000 knowledge workers in The United States. If all of those knowledge workers had one agent which was which was working on their behalf, that would that would and and each of those was each of those agents was running in a container, a traditional container like something you get at a AWS or a a Google Cloud. The amount of just CPU resources that would be needed to to run just those those agents, assuming they've just had one agent per person, is about 50% of the total CPU capacity that's generated, you know, by all the different CPU manufacturers that are out there today.
Speaker 14:And that's just The United States knowledge workers. If you take that to the world, then it's several times, you know, 30 or 40 times the capacity of of GPUs and and CPUs that that are existing today. And so what we really think is that as these agents are creating code, you need a different platform for it. And and Cloudflare was built Cloudflare Workers was built not on on containers as much, but on something called isolates, which is a much more efficient technology. And so what we're seeing is that as people are building these agents, as they're using them, it's just a much more natural place to be running them.
Speaker 14:And it's I think why you're seeing more and more of the big AI labs have Clefler as the preferred target for where where their code gets run. You know, OpenAI released a project Yeah. For their enterprise users a little while ago that, again, is is targeting us. And and we wanna make sure that with with things like Veat as as first class citizens on Cloudflare that we can help power that future. Because, again, it's not gonna be kind of the same system that we that we built with the hyperscalers.
Speaker 14:It's going to be something different. And, again, think that we have a really good shot of of building that different thing.
Speaker 2:So can you help me understand more about the problems of, like, CPU bottlenecks and then maybe some of the solutions? I'm just thinking about, like, I I I would think bandwidth would be an issue, obviously, CPU load. But is there a world where we get to some sort of convention around maybe it's the robots. Txt or just the user agent and when Google bot shows up or any other AI system, AI agent shows up, you're just delivering a it's almost an MCP server, but you're just delivering something that looks more like a JSON package, something that's a little lighter, little less CPU intensive. Is there a path there to optimization?
Speaker 2:It feels like we're in the era of, like, squeeze every ounce of performance out of everything. Yeah. But what's actually going to happen here? Where where oh, like, are we just screwed or is there an opportunity?
Speaker 14:No. I you know, think so. I think two two different different problems. So the first is if you if you're as as as you ask these these different AI systems to perform tasks on your behalf, what what has to happen behind the scenes, especially those tasks get complicated, is that they need to be coordinated in in some ways. If you say plan a vacation for me or something like that, what what goes on behind the scenes is that there there's coordination there, and the best way to be make that as efficient as possible, what what agents are really good and and and and these various LLMs are really good at is actually writing code.
Speaker 14:And so that code needs somewhere to live. And the problem is that if that lives in a container, then you've got to bring in an operating system. You've got to bring in all all the tooling and everything else. And that's actually extremely heavyweight. And so that's the first place that you've got both a CPU and a GPU bottleneck that's there.
Speaker 14:And so with with something like Cloudflare Workers and isolates, you it's just a much lighter weight system. And so that means that you can have more agents running on the same CPU infrastructure and and be able to provide that. And, again, I think that's why a lot of these next generation, tools are actually built using our our platform. You mentioned something else, which is, you know, as as these agents go out and interact with the rest of the web, they they you wanna make sure that that is done in the most most efficient way. And so if they're pulling down, you know, all of the HTML from a from a web page, if and and it's and those web pages are designed kind of to be consumed by by humans, there's just a lot of cruft on that that isn't isn't necessarily as important.
Speaker 14:And so some of the things that we're doing are, you know, for those those customers of ours that wanna make sure their content is consumed by by agents, wanna make sure it's as as accessible as possible, We are automatically converting things into markdown, which is a much simpler Sure. System that that saves you a ton of tokens. It saves you a ton of processing. It means that your context window can be can you can fit more useful information into a into a context window. So I think there's a ton of optimizations, and we're helping both on the developer side as well as on the content side, making sure that we can have have these agents be as powerful as possible and and get as much done as possible.
Speaker 2:John Gruber, total victory. You know he invented markdown?
Speaker 1:I did not. Grubernator.
Speaker 12:Yeah. No way.
Speaker 2:Yeah. Isn't that amazing?
Speaker 1:Yeah. Anyway I mean, it's it's one of these
Speaker 14:things that just, you know, it it it turned out it was it was ahead of its time, but it's it's such a key for making sure that we can take information and make it, you know, as compact as possible.
Speaker 2:John Gruber created the the the format for God. It's a funny funny world we live in.
Speaker 1:Talk about it was last week you guys announced that the threshold had been passed around agent versus human traffic. Talk about that moment. Did it happen sooner or later than you expected?
Speaker 13:Much sooner.
Speaker 1:Much sooner.
Speaker 14:Yeah. I mean, was I the at end of last year, so end of of twenty twenty five, I said that I I thought that we would pass he it it bought traffic, and and that's that's, you know, across the board. So that's, like, Google's crawler, but also, you know, the new agents which are which are coming out. I thought would would pass human traffic by the end of twenty twenty seven. About three months ago, I revised that based on the traffic that we were seeing to say that it would actually be in the first half of twenty twenty seven.
Speaker 14:And so the fact that it actually happened in the first half of twenty twenty six is just is just it's just been extraordinary, and it just shows how quickly this this is is growing. And and the and the real key here is that I think that if, you know, you or I as humans were researching to go buy a digital camera or something, we might visit five websites and do, you know, a little bit of research and some price comparison. You know, you just watch as you use these agents. They have boundless attention to be able to just go to maybe 5,000 websites to find exactly what what you're you're looking for, the best price, the best delivery, the best service, and everything that's that's there. And so that's just driving an enormous amount of consumption of of of the Internet.
Speaker 14:At the same time, the other thing that's happening is that for a long time, since about 2015, the the web has kind of plateaued. There were not there were there were more websites that were being shut down than were being created during that time. In the last eighteen months, though, we're we're back to the the web growing at a rate which is is exponential. And and what and and it looks sort of similar to what what was happening back in the early two thousands in terms of in terms of growth of the web. And so I think you're seeing both more sort of consumption of what's online, but you're also seeing more things online as it as it goes forward.
Speaker 14:And so in both of those directions, you know, that's that's that's gonna continue to just drive, you know, more and more use of of the of the Internet. And and I wouldn't be surprised if, you know, going going, you know, forward, say, five years, that that bot traffic will be a thousand times human traffic online. And we've and we've gotta make sure that we make the Internet work for for that new future.
Speaker 2:Yeah. Like every year we're just gonna add another nine to the nine nine 99.999% of Internet traffic is bots. Yep. Do you do you know what the baseline was? Like pre AI, were we at like 1% bot traffic?
Speaker 2:I mean, I've said No.
Speaker 11:Was was more it
Speaker 14:was more than that. It was about 20% Okay. For for us for a long time. So it yes. And it's you know, Google was the was the largest.
Speaker 14:And then and then obviously, there's a lot of of malicious things that that run around online. But that was that was around what what it was for and it was pretty stable over, you know, the history of Cloudflare at least, so where where we could measure it. So so since 2010, you know, it was always kind of around that 20% range. Mhmm. And then and then it's and then it's grown.
Speaker 14:You know, it's it's in the last few years, it started growing steadily, then it really accelerated in the first half of of twenty twenty six.
Speaker 2:I wanna talk about the inference stack, I guess. Like, we're seeing two things play out, sort of a bifurcation, like a WWDC, Apple's launching on device inference that's it's not going to be frontier, but it's going to be usable for sure on your phone, on a computer. They can obviously go to the private cloud as well. And then you have like the new NVL 72 models. There's stuff in between, a Cerebras chip, fast but expensive.
Speaker 2:Then there's everything from, oh, it runs on commodity hardware, but it's still pretty big. You need a real desktop for it. And I'm wondering about how you see Cloudflare fitting into that. It would be a logical extension to Cloudflare workers to have inference on the edge, inference in different places. Like, where do you see yourself offering inference, if at all?
Speaker 14:Yeah. Well, we we we it's it's actually kind of funny. Yeah. Back in 2022, we we issued a a press release that there was a graphics chips company that we were gonna partner with in order to put GPUs at the edge edge of our network.
Speaker 2:Yeah.
Speaker 14:And that graphics chip company
Speaker 2:turned Little into bit of Nvidia. Nvidia. Yeah.
Speaker 14:And and and what was what was amazing was at the time, it was just crickets, like nobody cared.
Speaker 2:Sure.
Speaker 14:And but but we we had we had and and so we we sort of did a little of it, but we hadn't really Yeah. Rolled it out broadly. And and what was funny was and then you fast forward two years later, and all of a sudden everyone cared. And so we we basically just reissued the exact same press release.
Speaker 2:Mhmm.
Speaker 14:And and and now, you know, that's become that's become a pretty pretty important part of our our our business. And so today, you can run inference at the edge of Cloudflare. Yep. And because of the fact that we're, you know, in over 300 cities worldwide, you know, we're within within milliseconds of the vast majority of the world's population, we become a very natural place for inference to happen. I my my working assumption has always been that about 50% of the inference that happens will be on device, whether that's your your phone or your or your laptop or or whatever.
Speaker 14:But then there needs to be some standard protocol where your phone or your laptop or whatever that local thing is can hand those either long running tasks or larger tasks off next to the network, and so that would be to us. And then if if for some reason you need something that's more than that, then it could handle it back to, you know, some centralized data center with, you know, with with more more capacity than than we may have. And I think that that that's what that's what we're increasingly seeing. I think my my assumption was a little bit probably off. I think actually, I think less is gonna happen on device today because I think more and more of the tasks are going to be these long running Yeah.
Speaker 14:Task where it's not gonna be just, you know, what what's, you know, what's the, you know, what's the temperature in in New York today. Yeah. Instead, it's gonna be something like, help help me plan a a vacation, go you know, take into account all of these different things, shop for, you know, different hotels in different places, plan all the plan all the, the the travel between between the different locations. Here are the criteria I have. And that might be something that takes, you know, maybe, you know, certainly not not gonna be seconds.
Speaker 14:It may might be, you know, minutes or hours or days in some cases to run-in that case.
Speaker 1:I feel like you're gonna have long running agents just for Park City snow forecasting and reporting, tracking like individual I already do. Individual runs, you know. Maybe a network of maybe a network of drones that are identifying what's tracked out
Speaker 2:Satellite photos, planet lapsed.
Speaker 1:Optimizing your routes on the mountain.
Speaker 2:A lot of stuff.
Speaker 14:Exactly that's that's that's exactly right. Hopefully hopefully it snows this year unlike last year's.
Speaker 1:Yeah. So
Speaker 2:what are the decisions when you're doing inference on the edge? Because it's sort of a hard pitch to say, I'm going to shave off three hundred milliseconds, six hundred milliseconds, when you're talking about a twenty minute workflow or even a ten second workflow, you're getting me a 3% boost. Is that gonna make the difference? But are you optimistic that there will be sort of like a in between state where there will be inference that happens and needs to happen within the request loop under a second and then the the latency matters?
Speaker 14:Yeah. Well, I think I think there are there are sort of three different
Speaker 13:Yeah.
Speaker 14:Buckets why why people are choosing to run inference tasks on on Cloudflare. I think I think the first bucket is exactly what you said. It's just it's latency. And so especially for those things that have human and computer interaction where where, you know, any delay feels like it's it's it it hurts you that people are are trying to make sure that they can get the best best performance possible.
Speaker 2:For voice models, I mean, it feels like that has to yeah. Voice models have gotta be local. Yeah. I love that.
Speaker 7:Yeah. And so and
Speaker 14:so that's and so, you know, again, there there's a case for that. I think the second case is that a lot of times for either privacy or regulatory reasons, people wanna keep things as close to where they physically are Sure. As possible. And so, you you know, I think that a lot of especially in Europe and otherwise, of places think that, you know, they made a mistake with the Internet kind of one point o and two point o of having everything go back to Ashburn, Virginia, everything go back to The United States. And so I think that there's there's a real kind of sovereign desire to keep things local, and that's important.
Speaker 14:And I think the third thing is we we actually can be oftentimes much more cost effective because of the fact that, you know, we we are this we are a network and and because of how we're interconnected with networks around the world, bandwidth for us is effectively free. And so it's very easy for us to get things onto our network. And because of where we deploy, systems, we often are in places where we don't have to pay for the space or the power, which allows us to then pass those savings on to customers. So it can be significantly more cost effective to use inference with us than it can be in some other other places. That's somewhat counterintuitive, but but but it's sort of the nature of what we've what we've built and kind of the secret to what's allowed Cloudflare to to to to deliver as much as we have over time.
Speaker 2:There there's a ton of ton of debate over data centers, how they get built, how big they should be, what we need, the pushback, regulation, etcetera. Is any of this affecting your business? Are you thinking about how you position your footprint in the real world? How are you processing the evolving discussion around data center construction?
Speaker 14:Yeah. I mean, so so first of all, I mean, we're we're gonna need more data centers. I think a lot of a lot of the discussion is is somewhat silliness. I mean, the the water consumption I mean, these are closed loop systems. I mean, a a golf course uses more water than than probably all the data centers in The United States over over the course of over a year.
Speaker 14:Yeah. So, you know, I I think that I think, you know, there there's there's there's a lot of kinda silly concerns that are there. And but but there are but there are real concerns as well. We've gotta make sure that there are efficient ways to get power to these things, that it's not taking power from from other other systems that the grid can support support that. And so I think those are all all all good considerations.
Speaker 14:For Cloudflare ourselves, you know, it it we're we're we're a little bit different where in in the case of, you know, if you're a a AWS or a Google or, you know, a SpaceX, you're building one big facility or a handful of very big facilities and putting a ton of machines in that that one facility. Cloudflare is different. Have a ton of machines, but we spread them massively all around the world. And oftentimes, we we wanna go into the places where networks come together. And so those those can be, you know, some of the oldest data centers in the world.
Speaker 14:And in any of those facilities, we may not have we may we may have only hundreds of machines. But over collectively around the world, we've got, you know, what is effectively much larger than any individual data center which is which is out there. So I think we're less less impacted from, you know, the new builds of data centers. I think we're much more impacted by how do we find our way into the existing data centers, and then how do we make sure that the equipment that we're deploying is as power efficient as possible because we're often given sort of, here's the envelope that you have to fit your fit your your equipment in, and we're often guests of, you know, whoever the local ISP is or or or whoever, you know, partner is that we're working within in the, you know, the the thousands of buildings that we're in all around the world.
Speaker 1:What's new with Italy?
Speaker 2:Yeah.
Speaker 14:Italy and Spain. Like it's it's it's it's
Speaker 2:Wait. So you're fighting for Italy. You're summer plans.
Speaker 1:You're summer need world
Speaker 2:peace here.
Speaker 1:Come on. Our limited duty. Unlimited.
Speaker 9:I I
Speaker 14:I know no no Ibiza for me or or Brutal.
Speaker 2:In the
Speaker 14:Multi Coast. No. It's Brutal.
Speaker 7:It's you know, I
Speaker 14:think it's it's interesting. It's been actually kind of puzzling for us. So all of this stems back. So so for those who don't know, in in both Italy and in Spain, there there's been pretty aggressive tactics to come after either me personally or or Cloudflare. And it's all been driven largely by the football leagues, the the soccer league in those in those places.
Speaker 14:And what they're concerned about is is piracy. The the thing that's been ironic about it is, like, we don't like piracy on our network either. We don't make any money off of it. It's it takes bandwidth. It steals resources.
Speaker 14:So we have a whole team that's working all the time to shut shut the pirates down. And and yet, you know, the pirates are clever. They find ways to to use our our system because, you know, huge percentage of the Internet sits behind Cloudflare. You know, there's gonna be stuff from time to time that we don't we don't capture. It takes us some time to catch.
Speaker 14:With leagues everywhere else in the world, so the NBA and NFL and and MLB and and the Premier League in in The UK and and others, we work really closely with them to if they identify a pirate stream for us to, you know, pull that down. Because, again, it costs us money. We don't like it. But for whatever reason, La Liga in in in in Spain and and and the the league in Italy have have decided that instead of instead of partnering with us to take this down, they they would sue us instead. And so so again, yes, it it it does cramp some of my summer plans.
Speaker 2:I wanna talk about IPOs, life as a public company. There's a few entrepreneurs that are trying to go public this year. You had a very successful IPO. The stock is up more than a 100,000 basis points since you went public. Fantastic run.
Speaker 2:But advice for
Speaker 1:We're putting everything in
Speaker 2:even better. I mean, this stock's up a thousand percent. It's fantastic. But a 100,000 basis points sounds even better.
Speaker 14:Basis points a
Speaker 2:lot. 100 more 110,000 basis points. Who's counting? But anyway, what was the what was your process like? We were talking about the level of float lockups.
Speaker 2:Like what were the hard decisions? Were there things that we're talking about now with these big IPOs that weren't even on the top 10 of your to do list? What is important to a successful IPO and then running a public company?
Speaker 14:Yeah. I think, you know, there are handful of things that that that that are, you interesting lessons for us. So first of all, like, I love being a public company. It's, you know, I I think that it's interesting. You know, the the the in in jurisdictions where, yeah, you don't have no fault divorce, spousal homicides are much higher, which is I think kind of an analogy to the difference between private market investors and public market investors.
Speaker 14:It's really hard to fire your VC. It's hard for your VCs to fire you. And so as a result, like, actually, it's kind of dysfunctional. Whereas public market, like, I love our investors because if I do something stupid, they call me up and they say, that was stupid. I sold your stock.
Speaker 14:And then we and we have a conversation, and sometimes they say, oh, actually, that makes sense now, and I bought it back. Right? But I think you can have actually a much more honest, much more real And that's felt actually just a lot healthier than what you see in the third Yeah.
Speaker 1:It's so true. I mean, every every everybody that's angel invested in any number of meaningful companies is gonna have portfolio companies where you've fully given up on the company and you just try to be try to be nice and yeah, help if but you at some point, you're just totally disassociated with the investment and you're just like and and that's that's that's the reality. But yeah. So it's it's very healthy to be like, yep. I'm disassociated, like, moving on.
Speaker 1:Moving on. And and and
Speaker 14:in both directions. Right? I mean, it's just a better it's a it's a better relationship. And and and so I I, like, I loved the process of going public. I think the thing that we did, like, it was a it was an opportunity for us to really kind of retell our story.
Speaker 14:Like, you don't get to reinvent yourself very often, but the process of writing the s one was incredibly just an opportunity to sit down and and really do it. And and we, you know, really dug in and and told the story in a in a way that to this day, we still refer to parts of that that that document to sort of explain what is Cloudflare and and how do we work. I think that the most clever thing that we that we did, and this was advice that I got from from Ryan Smith from Qualtrics, was he said, you know, he's like, what are you doing about friends and family?
Speaker 5:And I'm like, we're not gonna
Speaker 14:do it. They because you can take 5% of a of a IPO and allocate it to friends and family. And I didn't wanna, like, get my aunt, like, over if it if it if the stock went down or something, I didn't wanna have explain that over Thanksgiving Yeah. Thanksgiving dinner. He said, no.
Speaker 14:No. No. You're thinking about it wrong. Think about the people who if they, you know, if you they owed you a favor, that they could make a meaningful difference in the future of of Cloudflare and then offer them to the ability to invest in in the IPO.
Speaker 9:Yeah. And I was like, is
Speaker 14:that, like, is that legal? He's like, check with your lawyers, check with the bankers, but the answer is yes. And I was like, some people are gonna have, like, conflicts. He said, it doesn't matter. Like, even if just the fact you offered it, even if they can't do it will will mean that, you know, that that that that they'll they'll always remember that super fondly.
Speaker 14:And it was incredibly good advice. And we made this crazy list of all of these, you know, people who, you know, who were like, you know, at some point, that might be an important relationship for us. And, like, 75% of them said yes, and and they all made, you know, a ton of money as a as a result of it. And it was it was it was one of those kind of Now
Speaker 1:your aunt doesn't talk to you anymore.
Speaker 14:No. My well, yeah, my aunt doesn't talk
Speaker 9:to me.
Speaker 2:Say, I missed out on a 100,000 basis point basis point move. How could you? Yeah.
Speaker 14:I I I am always comforted that we actually so we we priced it. Yeah. $15.15 dollars a share and it went up to and and it that's actually the other thing that's interesting. Everyone like Bill Bill Gurley and I have fights about this from from time to time about IPOs. Like, you have a lot of control over how much the pop is gonna be.
Speaker 14:And we we sat with bankers and we were like, listen. We want it to go up about 20%.
Speaker 2:Yeah.
Speaker 14:And and and they were like, okay. If you want it go up 20%, you price it right here. So we priced it right at that point, and it closed the first day at 18, which is exactly up 20%.
Speaker 2:Okay. Okay. But just to steel man a little bit, is that is that a function of of your business? Not to like be rude or anything, but there are some businesses that are just like meme stocks out of the gate and like they're in some weird thing and there's some froth and like they have less control or is it always controllable?
Speaker 1:Well, and and part of that was like, did you think the fair value of your business was 18% or 20% higher than where you priced it and so you wanted like you basically wanted the market to to sort of reprice and you didn't care about not.
Speaker 14:Yeah. I mean and they if we had priced it at like 13, it would have gone up more. Yeah. But but the problem again, you like, you you wanna you wanna play for the long term here. And so my my again, my fight with Bill Yeah.
Speaker 14:Is, you know, he he says, you know, that you should you should do, you know, some sort of system where you're where you actually, you know, price it at whatever the top price is. The problem with that is if you look at all the companies that that have done that, so the Airbnb's and and, and others that they basically you know, that that's that's great for your private market investors. That's great for the VCs. That's great for any of the the the founders that are taking money off the table. But you gotta keep running the company.
Speaker 14:Like, the IPO isn't the end. It might be the end for the the VCs, but it's not the end for the operators of the company. And you wanna be able to kinda build that relationship and build that over time. And so I think, you know, we worked incredibly hard to be very sort of choosy on who are kind of the who we gave allocations to, and we we we went through every single name on the allocation list. And and and I think as a result of doing that, you know, if you look at, like, who our top 10 shareholders are, it's been incredibly stable, over over time.
Speaker 14:And they're and they're they're folks who give us again great advice and and and and have been really just really, really great to work with. So I I I I'm sure that there are times you don't have it, but I was I was I was surprised at how the banks have a really good sense of if you price it here, here's what's gonna happen.
Speaker 2:Mhmm.
Speaker 14:And and in our case, was it was, you know, to the to the penny.
Speaker 1:Last question. How are you thinking about long term planning as a CEO when we're on these exponentials, you know, the the example of like agent versus human web traffic, like Yeah. You have you had like more data than almost anyone on the planet and your prediction was still off your original prediction was still off by twenty eighteen months, twenty four months, something in that range. So how are you thinking about overall business planning when model capability is increasing and and you're just seeing sort of exponentials everywhere?
Speaker 14:Yeah. I mean, I think a couple of different things. So one, I think we're we're trying to make bets on on people who who can who can really function in in really dynamic environments. And and that for us has been somewhat different than I I think a lot of other people. A lot of other people have cut back, for example, on kind of early career hiring.
Speaker 14:We we you know, Cluffer's about, like, 5,000 employees, but a little bit less than that now. But we hired 1,111 interns this this summer. They're straight out straight out of college. And they're like, they're just they're just killing it because they they are so native to to these tools. And and, again, I think we've always thought of our job is training the interns, but this year, like, the interns are helping train a lot of us in how to how to how to do these things, you know, really well.
Speaker 14:So I think staying super dynamic. And then, you know, from our perspective, just making sure that we've got optionality, going forward. So, you know, it's it's it has gotten tougher, to get equipment, today. Memory prices are are through the roof. You know, things are harder, but we've we we just have a team that's always constantly trying to figure out how how to to refine that.
Speaker 14:And then, generally, I think we're we're trying to be a, you know, big adopters of AI, not just in developer land, but also across the entire the entire business. So we built something called Cloudflare OS, which allows, you know, anyone on our finance team, legal team, you know, accounting team, finance yeah. Any anyone that's out there at the company to be able to have access to to tools and and and and really figure out how how their job gets done. The clever thing that we did to seed it was we actually set up this email address. And initially, we told the team that it was this magic AI model.
Speaker 14:And if they just wrote to the email address then and ask it, you know, I I have this part of my job that I need to get done. How do I get that that done? It would you know, sometimes it would ask more questions, but it would it would actually give you kind of a response back. What what we didn't tell people was actually it was a team team of humans behind the scenes that was initially receiving these these emails, and then they were using AI systems to kind of flush things out. But what they were really doing was recording all of the kind of key jobs to be done within the organization because, you mean, you have to if you're gonna have AI systems that can help facilitate this, you actually have to record what are the steps that people are are doing as as as a part of that.
Speaker 14:And so as a result of that, we've been able to see this this Cloudflare OS now with the ability to do things like if you're on the sales team and you have to do, like, an account plan, you know, you can literally just type slash account plan and then describe what it is that you wanna do, and it will output that. And that's made that's made our team so much more productive. And and again, I think it's giving us the flexibility to be able to respond to, you know, whatever comes next.
Speaker 1:Very cool. Final final question. Are we gonna are we gonna see an exponential increase in the number of interns this summer? We're gonna see ten ten thousand interns? I don't know.
Speaker 14:I mean, I I I we've got to find a place to put them all. And so it's mean, our office in Austin, like we've it's a big space in Austin. We've run out of space there because we have so many interns there. It's I just I just think that there it's I think a lot of companies are doing this wrong where they're saying, like, we're we're not gonna bet on on on the new people coming out of school because, you know, AI is gonna replace them. I mean, these these kids know how to use AI better than anyone else.
Speaker 14:So we're gonna bet incredibly heavily on that, and it's, and it's so far, it's paying off. I mean, these the like, I think the person who eventually replaces me might might literally be one of the interns to today because they're just so so so smart.
Speaker 2:It's happened it's happened before. I mean, I don't know if Satya Nadella was an intern, but he has that famous video of him as a product manager demoing Excel. And yeah. Oh, he company man.
Speaker 14:I remember I mean, I I was on a bunch of of calls with Satya Yeah. Before he was before he was CEO. And he was he was just a product manager, you know, at at So I think just if you you can find really great people and and be able to
Speaker 2:move on.
Speaker 1:That's a That's a There's a GOAT
Speaker 7:out there.
Speaker 2:Yeah. You're the GOAT as well. Yeah. Yeah. You know, whenever you put up a 100,000 basis point move in your stock over just a couple years, you're in you're in contention for GOAT, I I say.
Speaker 2:Always a fantastic time.
Speaker 1:Thanks for jumping on.
Speaker 14:Good to see you guys.
Speaker 2:Nice to
Speaker 7:talk to soon. And by the way,
Speaker 14:I haven't talked to you since the the
Speaker 2:acquisition Thank of the you. I appreciate it. We'll talk to you soon. Have a good one. Let me tell you about console.com.
Speaker 2:Console automates I builds AI agents that automate 70% of IT, HR and finance support giving employees instant resolution for access requests and password resets. We will be joined by Vinod Khosla And while we
Speaker 1:just a few minutes. While we wait, Mad's summer vacation is looking good. Let's see what's on the schedule.
Speaker 2:What is on
Speaker 1:6AM, he's waking up on the wrong side of the bed. Okay. 7AM, he's split between judging a book by its cover and beating a dead horse.
Speaker 2:Okay.
Speaker 1:8AM, this is where it gets spicy, he's gonna poke the bear.
Speaker 2:Gonna poke the bear.
Speaker 1:And shortly after that, he's skating on thin ice right into walking on eggshells. Wow. And at 10AM he decides, alright, it's time to address the elephant in the room. Okay. At eleven gets a bit dark, he's gonna cry over spilled milk.
Speaker 1:Yeah. But at noon he's letting the cat out of the bag.
Speaker 3:That's exciting.
Speaker 1:And that's where it really ramps up. He's starting at one to bark up the wrong tree and again, but at the same time holding his horses.
Speaker 2:He's double bucked.
Speaker 13:But then
Speaker 1:at 2PM, he's gonna add fuel to the fire.
Speaker 2:That's fantastic.
Speaker 1:And I can't really think of a more perfect summer day, John.
Speaker 2:You know you know there's a you can share your calendar with someone else and you can share it privately. So you can just see that I'm booked but you don't actually know if I'm having a meeting with someone. Maybe I'm interviewing Vinod Khosla, you won't see that. But you could build a plug in that changes the book to just these things. That would be delightful.
Speaker 2:The next Google April fools
Speaker 1:Super intelligence.
Speaker 2:I would that.
Speaker 1:Yeah. Somebody just can only see that you're letting the cat out of the bag Yeah. That's that's pretty exciting.
Speaker 2:Yeah.
Speaker 1:It's kind of mysterious.
Speaker 2:Okay. Don't interrupt him. It's an important meeting. He's letting the cat out of the bag. But hey, he's just beating a dead horse.
Speaker 2:Maybe I should go grab coffee with him, see if he wants to spin out on that.
Speaker 1:There's a website. Is big.
Speaker 2:Joe Weisenthal.
Speaker 1:Turning Joe Weisenthal's tweets into full articles.
Speaker 2:This is crazy because there's a there's a live show that turns his tweets into like minutes of just back and forth conversation and banter. But somebody made a version of our show that's just web pages. So they scrape every Joe Wiesenthal tweet and they turn it into a full SEO optimized art This is great. It it is great and Joe looks great in this photo. He's looking good.
Speaker 2:He's looking quizzical. He's having fun. Well, let's bring in our next guest, Vinod Khosla, the founder and managing director of Khosla Ventures. Vinod, thank you so much for taking the time. How are you doing?
Speaker 10:I'm doing great. Great to be here.
Speaker 2:Fantastic to have you. I want to kick it off with some of the recent deal making that you've been doing. Your outlook on artificial intelligence is obviously front and center. But what has been the most recent fundraise that you've done? Why did it get you excited?
Speaker 10:Well, obviously, there's a lot hits on yesterday or today. We are working a lot and doing a lot of new things That I'm pretty excited about is a company like Pramana. Mhmm. But more importantly, the broad category of order formalization, which isn't talked about much. But humans aren't very good at talking to AI in driving what they precisely want.
Speaker 10:And that's where auto formalization becomes important, and companies like Fana become very important.
Speaker 2:How did you get into auto formalization? And I guess the big better question is, as an investor, you have to consider, is auto formalization on the direct path for the other labs? How do you think about competition in this area? Investors are going back and forth, and it's been fascinating to see things that appear to be directly in the path of the big labs. They're still growing revenues 2,000,000,003 billion $4,000,000,000 We're seeing $60,000,000,000 acquisitions.
Speaker 2:Everything seems to be going well up and down the stack. But why is there a particularly big opportunity right now?
Speaker 10:Well, the labs, they will do more and more over time, but I still see the opposite being very broad for venture capital and fund nodes. In terms of nodes are coming up with great ideas.
Speaker 2:Mhmm.
Speaker 10:One of example looks at the tag board and says it's not very precise because it's written by humans. You can make it into a machine checkable language. Mhmm. Mathematicians turn all their problems in this specific language. It is hard for humans to understand.
Speaker 10:Auto formalization allows that kind of formalization so computers can operate on it precisely, not problemistically. And that's a really important thing. Mhmm. If you look at today's world, AI systems have been awesome and the rate of capability development would be awesome. But they have some pretty big gaping holes.
Speaker 10:They still hallucinate. I don't think hallucination is going away anytime soon. Yeah. So the reliability is low. Humans aren't great at specifying what they want, the specification problem.
Speaker 10:Mhmm. And then verification of what are you getting the right answers? Or are they hallucinate? Those are all large interesting problems which are open for entrepreneurs. Things can be built in certain domains
Speaker 2:Mhmm.
Speaker 10:Where you build a lot of value and a lot of capability that the general models don't do.
Speaker 2:How are you thinking about applications of this technology in particular? It's been so hard to predict the original GPT. No one was really expecting a knowledge retrieval service. It just sort of went viral when ChatGPT launched. Coding agents, some people had predicted those, but it was sort of an unexpected emergent capability.
Speaker 2:Do you expect that auto formalization as the next critical frontier of AI just makes all the existing applications better? Or is it actually going to unlock new applications?
Speaker 10:I think it will allow humans to use AI where it wasn't previously possible. Mhmm. If you want to know what your bank account is, you can't have a hallucination. If you have a medical problem, you can't have a hallucination. So auto formalization will significantly enhance existing models in domains like tax law.
Speaker 10:You know, you can have lots of tax startups, but they can't formalize.
Speaker 2:If you
Speaker 10:take tax law, it's a very specific thing. And that's an area Pramana is initially to say, can you formalize the tax code so computers can work on it precisely and not be subject to probabilistic answers or likely answers? So it's very, very important in certain domains to have precision, reliability, verification, and that's where auto formalization can significantly enhance the applications and still leverage all the power of LLabs.
Speaker 2:Mhmm. Well, we're excited to talk to the founder of Pramana in just a minute. We've been having a few technical errors, we will end this here and hopefully we can have you back on the show soon to go deeper. Thank you so much for taking the time to chat with us today, Vinod. It's been
Speaker 1:a Yeah. Thanks for jumping on. It's an honor.
Speaker 2:Have a great rest of your day.
Speaker 10:Great to be here. Cheers.
Speaker 2:Thank you. Goodbye. Let me tell you about public.com investing for those who take it seriously. Stocks, options, bonds, crypto, treasuries and more with great customer service.
Speaker 1:Have Unfortunate technical difficulties. I couldn't really hear anything personally. No. But fortunately, the founder Is coming on. Discussing.
Speaker 1:He's just
Speaker 2:a few minutes.
Speaker 1:Right now. So we're gonna hear it again and again. Sorry about that.
Speaker 2:Nikesh Arora is on the timeline. Quote tweeting, I like that he's you don't think of him as like a big poster. Obviously, 73
Speaker 1:followers posters.
Speaker 2:He's successful. But he said, congratulating prime minister Narendra Modi on becoming India's longest serving elected prime minister. April of leadership earned the trust earned through the trust of 1,400,000,000 people across three democratic mandates from lifting 250,000,000 people out of poverty to making India the world's fastest growing major economy. PM Modi's tenure has been nothing short of transformational. Look forward to a continued US India partnership.
Speaker 2:Love that. Yo Gesh says, bro went from stopping global ransomware attacks at Palo Alto Networks to doing math calculations on PM Modi's calendar milestones like it's his final exam.
Speaker 1:Just in quotes too. It's great. And Nick actually appreciates it.
Speaker 2:There's been debate over beverage media. Have you seen this? This is a very I saw this eight likes but I think this is an interesting post worth discussing. So Vassa, I don't know exactly this account before but beverage media doesn't have the good nuts to call out celebrity brands objectively because the economy is fanboy of fanboy ing in CPG. Celebrity founders sell tickets to events but they don't always move the industry forward.
Speaker 2:I think he's probably subtly subtweeting the maybe Tom Brady launch of the of the the nuts, which is a reference to the coconut water brand that's Tom Brady launched. But this does happen all the time. There's a lot of celebrity brands. We've had some celebrities on the show to talk about their brands. It's fun.
Speaker 2:Sometimes it works, sometimes it doesn't. That's the fun part about interviewing a celebrity about their brand. You kind of get to clock it and be like, no. Like, know, this person's really onto something.
Speaker 1:I think Wyatt they they named the brand that is they ran fully ran out of other names. Oh, really? Literally the last possible name that they could choose.
Speaker 2:We're not getting an invite. Tom Brady will not be coming to our event and selling tickets. But it's interesting because yeah, BevNet reports on CPG brands. They're one of the insider media, sort of the tech crunch of CPG, at least in beverages. They also have Nosh for food.
Speaker 2:And there's a number of other CPG media and critics. But if you got to have one of those celebrity founders at your event to sell tickets, you get into this weird, you get sort of audience captured, I don't know, celeb captured. You just get raptured aura of the celebrities and you have to stay out till two a. M. Partying with them.
Speaker 2:It's craziness out there. But beverage media, I think it's just interesting in the backdrop of more a more and more celebrity brands. Although it does feel like we're at peak celebrity brand at this point. And I feel like the more modern celebrity strategy is just invest in companies that have a number of good VCs on the cap table. Be happy with that.
Speaker 2:You can still
Speaker 1:Disagree.
Speaker 2:Or a farm. Yeah. Okay. Disagree. Explain.
Speaker 1:Think celebrity celebrity brands have always been a thing. They're just way more in your face because now celebrities have their own channels that they can communicate.
Speaker 2:I'd say they're less in my face. I I say we're on the decline. Top celebrity brand.
Speaker 1:No. I'm quoting John Feeney. No. I think it's just getting it's getting easier and easier to start a business. I think you'll see more celebrity brands.
Speaker 1:I don't necessarily think you'll see more breakout
Speaker 2:brands. Yeah. Okay. So your claim is is quantity is increasing. I think
Speaker 1:it's probably getting harder.
Speaker 2:Quality is decreasing.
Speaker 1:Quality More distribution. Yeah. Yeah. Greater greater I mean, there's gonna be some breakout celebrity brands. Yep.
Speaker 1:There's gonna be big exits. Yep. There's gonna be as many failures as ever. Right? I mean, my favorite example was like Travis Scott, a few years ago, launched a RTD, like, canned cocktail thing.
Speaker 1:Shut down, I think, within a year. Like Yeah. Maybe maybe maybe more. And so I I think you're just gonna see more and more and more.
Speaker 2:Would you put the Johnny Knoxville Neo Cloud in the in the bucket of celebrity brands?
Speaker 1:That is something I would like to see. Less Yeah.
Speaker 10:Less C
Speaker 2:K Y two k.
Speaker 1:Less beverage, more neo clouds.
Speaker 2:More
Speaker 1:neo. I wanna see Johnny Knoxville levered up.
Speaker 6:Yeah.
Speaker 1:Right? Yeah. I wanna see him
Speaker 2:Racking GPUs himself.
Speaker 7:Yeah. I want
Speaker 2:him like operationally involved too.
Speaker 1:Not just like Not just a pretty face.
Speaker 2:Endorsement.
Speaker 1:Yeah. Not just a pretty face. So anyways, I think more on the horizon and it's not you know, a lot of I I don't know. I feel like some people don't don't like this but I think it's fine.
Speaker 2:It's fine. It's fine. You heard it here first. Jordy Hayes. It's fine.
Speaker 2:You know what else is fine?
Speaker 1:Get used
Speaker 2:to The New York Stock Exchange. Than Wanna change the world, raise capital at the New York Stock Exchange. Our next guest might be there soon with Primana Labs. We have Ranjan, who's the cofounder in the waiting room. Let's bring him into the TBPN UltraDome.
Speaker 2:Welcome to the show. How are you doing? Thank you so much for taking the time.
Speaker 12:Doing great, John. Shadi.
Speaker 2:Pleased to be here. First time on the show. Please introduce the company. We heard a little bit about it from Vinod, but I wanna hear it from you.
Speaker 1:Yeah. Bravo by the way of getting being able to get Vinod to just like personally pitch Yeah. Your company for fifteen minutes on It's on Internet a it's a good, it's a good sales
Speaker 2:It's a sales.
Speaker 1:Hack. Good pitch.
Speaker 12:Thank you. Thank you so much. At Pramana, we we try to make AI more provably accurate. The current frontier of AI focuses on recall, which is more along the lines of how do you make more answers come up. Yeah.
Speaker 12:But we are trying to focus on very specific mission critical domains like tax, legal, health care, and governance, where a wrong answer could be catastrophic. Mhmm. You wouldn't want AI diagnosing you wrong and ending up in a catastrophic situation. So that's where we focus on. And the technology underneath it is formal verification.
Speaker 12:Yeah. I would like you guys to dive a little bit deeper and I can get more technical as it is.
Speaker 2:Yeah. I I I'm just wondering about the actual, like, rollout of this. Like, people are using AI tools for health care now. They've been using WebMD. People have been misdiagnosed with cancer for by random internet posts for like decades.
Speaker 2:You go to WebMD, you're like I have a headache and it's like you got brain cancer and then you freak out, go to the doctor and they're like what are you talking about? Take an Advil. But so like who exactly is is not adopting AI right now because of the hallucination problem?
Speaker 12:One, two major examples currently are like big four firms
Speaker 1:Okay.
Speaker 12:Which are tackling tax and legal cases. You would have heard about the Australian government suing one of the big fours Mhmm. Stating that there is a hallucination which ended up happening. And AI can actually make a plus on the fly. Yeah.
Speaker 12:If it wants to suit a particular situation, it can make up loss on the flight Mhmm. Which can lead to catastrophic situations. And another thing which we have noticed is also that insurance providers major insurance providers of US have also moved away from insuring AI outputs. So there is a very specific class they have introduced to say that we will not ensure if AI is behind whatever you have said. So those are some things which have come out specific to domains like tax, legal, and health care.
Speaker 12:Yeah. For health care, we can even see the models themselves typically stating that you need to go to a doctor to verify. We will we will be absolved of the responsibility.
Speaker 5:Yeah. How much Yeah.
Speaker 1:Imagine your CPA being like, great. I just I just finished your your return. It's 99% chance it's accurate.
Speaker 2:I mean
Speaker 1:There's 99% chance.
Speaker 2:The steel man here is like that is That's how I my feel about my lawyer and my like I love I love the people I work with but like humans do hallucinate and so if you give me something that's doesn't even need to be superhuman, something that's gonna just sit alongside that person, I'm pretty happy to buy, but I understand that I would buy more if I could be more sure. And so I understand that. What I'm what I'm confused about is like how are you thinking about building new foundation model, new LLM, new AI technology that bakes formalization into the actual workflow versus like we were talking to Doctor. Carr from Palantir last week about the idea of like you've built this ontology, you have your ground truth, you have your system of record and then you can have the LLMs hallucinate all over the place because at a certain point they're going to run into the ontology and that's going to be the formal, the formalization process. Like is there something is it is an extension of that two systems running side by side checking each other?
Speaker 2:Or is it more of an entirely new system?
Speaker 12:So it would be a couple of systems interlocking with each other. Mhmm. We actually have four layers in the stack. Mhmm. I can go through an example of how it happens.
Speaker 12:If you end up asking whether a particular transaction is taxable in Illinois Yeah. You have the first thing which we do is actually a fair bit offline. For tax specifically, we are formalizing The US tax code. Mhmm. So we are taking a very formal domain, but expressed in English today, we are converting that into lean, which is a proving language.
Speaker 12:Interesting. When we so when we do that, we get it ratified by experts. So our work is deeply involved in formalizing specified domains and ensuring that we get it ratified by the experts themselves too. In that case, we you we do use a lot of LLMs because the current knowledge base is already not formalized. It is present in English.
Speaker 12:And once we have the whole knowledge base, whenever a question pops up, we convert it into a series of constraints. Mhmm. You will notice Vinod also talking about how humans are not great at specifications. So we ensure that we ask the right set of questions so that ultimately we can give you a reliable answer. Post that, we have a solver and prover working in tandem to ultimately give you an answer along with the proof of correctness.
Speaker 12:Mhmm. So the key difference between an ontology based approach and a lean based approach is that a lean based approach is intrinsically verifiable. Your answer comes with a proof of correctness. The proof is something which a mathematician trusts. That's supposed to give you more confidence in a way.
Speaker 12:And like you suggested, you're right in saying that even, like, best humans might make mistakes. But if you actually have a mathematical proof encoded in Lean, you can choose to trust. And that's the frontier which we are aiming for, and it involves, like, fair bit of work between formal formal verification experts who are a deep part of who we are at Pramala Labs.
Speaker 2:Interesting.
Speaker 12:Working along with CPAs, frontier CPAs, frontier lawyers, and frontier doctors.
Speaker 2:Yeah. How are are models getting better at interacting with lean? I've seen a lot of progress in frontier math and the different really hard problems are getting solved. Sometimes they don't use lean and that's sort of a flex. Sometimes they do use Lean and they perform even better then.
Speaker 2:But is that getting just table stakes now? Because I think your average white collar worker doesn't know how to interact with Lean.
Speaker 12:Absolutely. So the models are getting better at proving with lean. Mhmm. But we are building more foundation models along the lines of formalization in the first place. If you say something in English, how do you convert that into a construct in lean?
Speaker 12:That's where our focus is on. Mhmm. If you think about it, lean is a system which has been around for the last twelve years. Mhmm. It has taken humanity around nine years to formalize a significant portion of math.
Speaker 12:We are taking that same technology, and we are we are trying to blitz through tax, legal, and health care. That's right. Where we are identifying what is the core knowledge base
Speaker 2:Yeah.
Speaker 12:And then we are converting it into lean, where you can reason on top of it provably.
Speaker 2:That's very cool.
Speaker 1:What were you doing before this?
Speaker 12:I was a machine learning engineer at Google, primarily focusing on Google Maps. Interestingly, I dropped arrows over there because, I was focusing on getting addresses and phone numbers accurately in Google Maps. Sure. If you think about it, Google Maps is like a very, very messy real world domain. Mhmm.
Speaker 12:And we have been able to make it trustable. A lot of people do trust Google Maps to a very large extent. Yeah. I intend to bring the same level of rigor to multiple other domains along the way. Yes.
Speaker 2:Very cool. Well, congratulations. Thank you so much for taking the time to come on the to
Speaker 1:meet you.
Speaker 2:We'll talk to you soon. Have a good one.
Speaker 8:Thank you so much.
Speaker 5:Yeah. Cheers.
Speaker 2:Let me tell you about Codex. Codex is a powerful work for workspace for getting work done with AI agents whether you're writing code, analyzing data, creating content or automating business workflows. Codex helps you move projects forward from start to finish.
Speaker 1:That's right.
Speaker 2:So there's a bitter feud going on.
Speaker 1:There was a there was a good comment over on the X Chat Yes. He said, we're on the topic of celebrity brands. He said, flip it on its head, Jordy. Brands by average Joe's like a painter or the owner of a lawn care company instead of celebrities. I think there could actually be something here.
Speaker 1:I think he's joking. But imagine as a brand, you're trying to differentiate. You hire an actor
Speaker 2:Yep.
Speaker 1:That's just playing the role of an average Joe. Hey, this is average Joe electrolytes.
Speaker 2:Right? Yeah.
Speaker 1:I'm not a I'm not a I'm not a professional athlete. I'm just a normal guy. Yeah. I'm just painting houses. Yeah.
Speaker 1:Right? Yeah. I'm just painting houses. I get a little dehydrated. I take my average Joe Relatable.
Speaker 1:It's relatable.
Speaker 2:I like it. I like it.
Speaker 1:The average Joe I do brand meta. Steve is calling it.
Speaker 2:I enjoy it as an idea. I enjoy it as a joke. I do think that there is a version of this, which is if you walk through an marijuana or Whole Foods and you pick up random CPG products and you look on the back, there's often what's called like the love language or whatever. So it's like a couple paragraphs of text. A good example is is it Dots Pretzels or there's some sort of like pretzel twist that's very popular right now.
Speaker 2:It's a little spicy. It has the the name of the founder and it and it tells the story of like an average Joe. The Buzzball, for example. That is an average Joe brand. It was not a celebrity brand.
Speaker 1:Disagree. Explain. There's nothing average about that founder. One of the most elite entrepreneurs.
Speaker 2:After she started the company.
Speaker 1:I'm gonna make the case that she was always elite. Always good. The buzz the buzz ball woke it up.
Speaker 2:Maybe. Maybe. But there are a lot of brands
Speaker 9:Yeah.
Speaker 2:That their whole origin story is like I was just a normal person. I had a problem for myself. I was going for, you know, olive oil and I smashed the bottle so I made a squeezy bottle. Like there's all these stories that aren't celebrity brands at least at the start and they have, you know, rags to riches stories that are told sort of quietly on the back of the packaging and then slowly over time eventually Unilever buys the company and takes that off
Speaker 1:I just like thinking of it as like a from the marketing lens a brand that is like doesn't have this like heartwarming story. Yeah. It was just like, yeah, we just hired a guy to be like, yeah.
Speaker 5:Just be
Speaker 2:the guy. Just be the
Speaker 7:average Just
Speaker 2:be an elite snowboarder like Nima. Starts to Salt and Stone, you know. He wasn't a celebrity before, although he was a fantastic team boarder.
Speaker 1:Anyway. Before we bring in our next guest, Markie, let's put pull up this video. Somebody put up fake tech ads. Going on here?
Speaker 2:I don't I love ads, but I hate fraud. So I don't know how to feel about this. This is gonna be peculiar.
Speaker 7:I don't
Speaker 1:think there's anything wrong here.
Speaker 2:With fake tech ads? That's real ad space. That could have been sold, Jordy. You're destroying shareholder value by running fake ads. We would never do that.
Speaker 2:We did do that early in the days. If you go back to the archive, Doctor. Squatch had that feel. Oh, Vince Vaughn. Vince Vaughn running a
Speaker 1:We have the video here.
Speaker 2:Okay. No. No. No. I know exactly where they're going with this.
Speaker 2:We put the q in q one one seven seven seven. This is hilarious. What if Texas is upside down?
Speaker 1:I mean, this is this is actually how this is how actually how normal people view
Speaker 2:What if the drizzler was purple? Firefly.
Speaker 1:You pay it.
Speaker 2:We pay you. You pay us, we pay you. Fireflow. Wow. What does this say?
Speaker 1:Dennis can tell you.
Speaker 2:Dennis can tell you. Brain
Speaker 1:I like ziplink is now
Speaker 2:They put up a lot of these. Wow. They really went hard. Who is behind this? This has to be a tech insider or someone making
Speaker 1:ziplink is now frugal. The cloud based online safety you know and love now in the palm of your hand.
Speaker 2:They put up so many of these. This is such a great stunt. Is this a if this is a if this is a launching campaign, a launch campaign for a tech brand marketing firm, it's genius. And whoever's behind this should actually be calling every tech company and saying
Speaker 13:What if
Speaker 2:forks not look like these. We know how to make good ads.
Speaker 1:What if forks were spoons?
Speaker 2:What if forks were spoons?
Speaker 13:Go to
Speaker 1:cutlery.ai.
Speaker 2:What's people been asking that question recently? Anyway, very fun. Let me tell you about Cisco. Where is Cisco? Cisco.
Speaker 2:Critical infrastructure for the AI era. Unlock seamless real time experiences and new value with Cisco. Our next guest is Markie Wagner from Poetic. She's the founder and CEO
Speaker 1:Future hall of
Speaker 2:prolific mafia player. What's your favorite role in mafia?
Speaker 8:Oh, mafia. I think who doesn't love to be fascist in Mafia? I you know, you always gotta hope you're getting the fascist card but Who is the fascist I in
Speaker 2:thought it was just Mafia Townsperson Oh,
Speaker 8:I'm the secret head larc.
Speaker 2:You're secret head larc.
Speaker 9:Yes. Yes.
Speaker 8:Yeah. I mean, mafia is always one of mafia. Yeah. You want single time.
Speaker 2:It's always power. It's it's less empowering to be the townsperson because you feel like you're just getting killed. You have no superpowers. But running angel or sheriff, that can be fun. But then if you get killed off early, it's the end.
Speaker 2:I'm just thinking of it because the the latest episode of Mafia episode two from Founders Fund dropped and you're backed by Founders Fund. But since this is your first time on the show, why don't you introduce yourself? Tell us how you wound up in a position to raise money from Founders Fund.
Speaker 8:Cool. Yeah. So I'm the CEO of and wait. I think I'm okay. Yeah.
Speaker 8:So I'm the CEO and founder of Poetic. Yeah. And Poetic's an AI system that will learn and execute super complicated processes Mhmm. In some of the biggest companies in the world at over 99% accuracy which is Okay. Which is quite hard.
Speaker 2:Yeah. So what is the process for obtaining and documenting the workflow? This feels like you could do something very database driven. You could build an ontology. You could just wait and maybe the models get better and stop hallucinating.
Speaker 2:There's been debates over the various approaches like what did you pick and why?
Speaker 1:Wait, before we get into that, like, talk more about the kinds of workflows Yeah. Where act like, you know, we we caught up a few weeks ago and and you were saying like, some of the customers that you're talking to, even if even if somebody came through and they were like, our AI system is 98.5% accurate, that that would actually create, you know, hundreds or thousands of hours of like issues in an organization. And it would actually like rolling that kind of system out would have sort of like negative enterprise value.
Speaker 2:That's just a pitch problem. If you're pitching a software like that, you just gotta tell someone, our system has 9,000 basis points of accuracy. Yeah. Is the goal.
Speaker 1:Put it in basis points.
Speaker 5:Put it in
Speaker 2:basis points also.
Speaker 1:Anyways, talk talk more about the problem before we talk about the solution.
Speaker 8:Yeah. So I think the problem is, you know, one of things you've seen is, you know, obviously AI is incredible at writing code Mhmm. And it has really crushed that. But, you know, a lot of the main processes that are at the heart of these giant businesses have remained pretty untouched by AI. Mhmm.
Speaker 8:And the reason why is that the rules that govern them, you know, the 10,000 secret rules, they live in people's heads.
Speaker 4:Mhmm.
Speaker 8:Right? And these rules need to be followed every single time. You know, we work on things like anti money laundering and underwriting and fraud investigations where every single step matters. And to actually get from where we are now to those running on machines, you need two things. One is a system that can learn the process.
Speaker 8:Not the 100 pages of written down stuff, but the 10,000 rules that live in people's head that they've never written down before. So that's part one. And then you have to be able to run them with many nines of accuracy. Not, you know I was talking with one CEO, he's like, know, 80% on an eval is great, but in underwriting, it's unusable.
Speaker 2:Mhmm.
Speaker 8:And I think that's the u for a lot of these biz processes that are at the heart of these, like, really big and old businesses. So you gotta do two things. You got a system that can learn all the rules and then run them with nines. And if you have that, you can get from where we are now to this version of the future that I think everybody's really excited about, but you need to build that bridge.
Speaker 2:Mhmm. Not everyone's excited about it. AI has very low approval rate, But tech leaders are certainly excited about it. But so why have you started with such big companies? Like, I feel like you've been you've been, you know, at the heart of the startup ecosystem for so long.
Speaker 2:If you came on here and said, oh, we have so many, you know, my friends' companies on board. These are the named customers SoFi, Chime, AIG, like these are large institutions at this point. Why start at the top? Feels harder.
Speaker 8:Yeah. I think our view was when you're building something like this, if you build something that's less powerful, it's hard to sometimes make it more powerful. Mhmm. Like, lot of these drag and drop tools, they were simpler, they were easier, they could do simpler things faster, but they just couldn't handle super complex things. Mhmm.
Speaker 8:And so, you kind of get kneecapped in terms of what you can represent and build. And the view is, hey, AI is gonna be doing a lot of this, like, writing of the software. You'd be in a better place optimizing for the power of the thing. Mhmm. Can it express and run these five hour processes require those nines and then everything else becomes really easy actually.
Speaker 8:Mhmm. And I don't think that people are going to be having a platform for the easy stuff and the hard stuff. I think they're just gonna be running all of their processes in one spot.
Speaker 2:Mhmm. I mean, you mentioned like tens of thousands of hours of information stuck in people's heads. Like what is data collection? What does data collection look like? Conversations?
Speaker 2:Like it feels like, yeah, like is it the McKinsey model or is it the self serve software model? Like where do you want to sit?
Speaker 8:Yeah. I mean, what's interesting is it's evolved over time, but the place that it has ended up going is looking closer to data labeling. So what happens is a big com company will say, here's my biggest process. It's a 100 pages of documentation, and you know that that's only 20% of it. So the question is like, how do you get that other 80?
Speaker 8:Mhmm. Well, what we'll do is we'll take that operating procedure, we'll generate the AI operating procedure, it's written in step by step English. What our system does is turn that into code under the hood, and you run it. Mhmm. And when you run it, and you put it in front of those experts, they have a ton of opinions.
Speaker 8:They have a lot of feedback to say, you you forgot that the threshold's actually a million, not 10,000. Right? All these little things
Speaker 2:Sure.
Speaker 8:Which get merged back into that document, and you're sort of doing that more and more automatically so that it looks almost like training a program or something like that, rather than sort of like long calls or or process mining in, like, the the normal sense. And so, more and more, it looks like people giving feedback into the system directly and that updates the rules that are written in it.
Speaker 1:Yeah. And so you're using AI models to generate that code that is then deterministic, which gets you the reliability that these companies need. Correct?
Speaker 8:Yep. Yep. Exactly. So the, you know, the source of truth is that AI operating procedure. It's English, but what are some of those turn into code.
Speaker 8:So when it runs, if the if the world's the same as yesterday, it's just going to run as code. Great. Nothing to see here. But if something changes, like the column name changes from month to month, or the save button moves, only then will AI go in, repair it, look at what the English goal was, and then update it. And so if you do that, you can get the best of, hey, it's code, it's precise when it runs.
Speaker 8:But if something changes, instead of breaking, it will actually just kick back and recover.
Speaker 2:Mhmm.
Speaker 8:And that's important because a lot of this work, code couldn't do alone, and agents couldn't do alone. So code is very static. Right? It's like very brittle. So even one small date change or something could break the whole thing.
Speaker 8:And so that's one side of it. Agents on the other hand are pretty improvisational and they think step by step and, you know, when you're figuring out what to do as you go, eventually you're going make a mistake. This is something that's kind of in the middle where it's code of things the same, AI is there to test, heal, recover if things are different, and that's how you can kind of get those nines.
Speaker 2:So are you hiring forward deployed engineers? Like, what is the role of engineering in your organization at this point?
Speaker 8:Yeah. So we do we hire tons of forward deployed engineers from all the best spots, you know, whether it's, you know, a place like Palantir or Mhmm. You know, even like, retool or scale and all these other sort of new places that have their own versions of forward deployed. And, yeah, I think it's interesting. It has over time looked less like extremely just only focusing on engineering, like hard engineering.
Speaker 8:It requires being able to change how people operate. Right?
Speaker 2:Yeah.
Speaker 8:And I can write the best code now, but even if you have the most incredible piece of software, you still have to change how the business organizes around it. And so, the people who understand like business and can think about, hey, like, what is the best possible fraud process going to look like? And, you know, what how should we reorganize the business around this new kind of thing? Mhmm. And they understand AI are the ones that are just, like, totally crushing it.
Speaker 2:Yeah. That feels like an entirely new skill set, like, much more people driven, but also, like
Speaker 8:Yeah.
Speaker 2:Forward thinking in technology. I don't know. Mhmm.
Speaker 11:It's a
Speaker 2:it's a it's an interesting new
Speaker 1:How has enterprise sentiment around just AI changed during the course of building the company? Right? Because like every, you know, it is I would say it's a roller coaster in some ways. It's like going up Yeah. Up into the right.
Speaker 1:Right? There's generally more excitement about the potential but at the same time there's these of like period troughs of disillusionment. And you guys are coming out of stealth at a time when again, I think companies are more excited than ever but at the same understand the overall shortcomings and and we had Karp on the show last week and you know, he was just saying like a lot of these deploy codes coming in, they're just trying to like they're they're they're trying to deploy their their goal is to deploy AI, but they don't fully understand all of this like business process under under the hood. And he makes some good points.
Speaker 8:Yeah. I think sentiment has evolved quite a bit. You know, I think it was extremely exciting earlier in the year. And then, you know, around the board meeting times, more and more CEOs would come to me and say, hey, like, you know, how do I get ROI here? And and, you know, this is what your other customers are seeing in these domains.
Speaker 8:And so, I think now people have realized, like, you cannot just throw an agent at a problem and expect to see the result that you want. Right? For an agent to to do a useful bit of work, it needs to learn how to do that work and run it, like, quite accurately. And that knowledge transfer between how's the work done today and getting it written down enough to where AI can run it, it's just hard. And it requires, you know, we sort of jokingly call it the great migration internally.
Speaker 8:Like, you have to go and migrate these, like, tremendous amounts of rules into something that AI can touch and improve and involve. And if AI can't touch it, it's not going to be able to help it. Mhmm. And so, you know, I think deployment is really important because in yeah, until that transition happens, it's going to be hard to just throw tokens to to see better outcomes.
Speaker 2:Give us some backstory on what you were doing before, how you wound up here.
Speaker 8:Yeah. So I got my start. I got extremely into AI in middle school after reading too much sci fi, you know. A lot of Dune. A lot of mainly Dune.
Speaker 2:Is that the message in Dune? I thought Dune was the you
Speaker 13:know?
Speaker 2:I guess, yeah.
Speaker 8:Yeah. You wanna avoid the Dune outcome.
Speaker 2:Well, I
Speaker 8:just felt like it was gonna be really important during my life, Yeah. You know, and you know, you read and you watch sci fi, you know, the biggest difference between the future and today, it's like mostly machines thinking. Like that is sort of the main a d sort of b c moment
Speaker 2:In hard sci In soft sci fi, it's like time travel and faster than light speed and like aliens. But I moon
Speaker 1:is made of cheese.
Speaker 2:That's the type of sci fi I'm trying to read.
Speaker 10:That's the
Speaker 2:white pill. So then walk me through the consulting work that you did and how this like evolved out of that. Was there like a clear moment where you're like I'm stopping that and starting a company or is this an evolution or a change of structure?
Speaker 8:Yeah. So I was initially got my start in research. So I was
Speaker 6:at I
Speaker 8:was at Stanford working in AI at like Waymo and Google. Yeah. And then, you know, one day I sort of realized that, you know, all the things I built like, you know, didn't didn't matter too much. And that was Friday and I ended up dropping out on Monday. And the idea was like, hey, you know, we've had software for decades.
Speaker 8:What are people still doing and and why? And I felt like I didn't really understand anything about how the world works. So Mhmm. To your point, started consulting and Yeah. The idea was like, let's just go into some of the oldest companies around and understand, like, what his software is still not touched and and what And happened when you do that, and you start doing some of the work yourself, and like going into, you know, I went to North Carolina, and you're people who've done this stuff for like decades, you realize that a huge amount of the work is really just operating procedures documents
Speaker 2:Mhmm.
Speaker 8:And people are just following them. Mhmm. And that this class of work everywhere, and you know, whether it's underwriting and claims and insurance, or onboarding customers and fraud and banks, it's just still sort of done by by people and software hasn't been able to go there. The reason why is because the second you write all this code to do that process, something will change. Right?
Speaker 8:A button will move, a column will change, or maybe even the the process changes. You want yearly instead of monthly transactions. And when you, you know, automate and see that happen enough, you you realize like, hey, there's a missing kind of material here that can flex but still have guarantees. And I really just waited for the models to get better. I knew some of the researchers and said, the models of today are not it.
Speaker 8:Please let me know when they get good enough to get stuff. And I truly just waited. So
Speaker 2:That's great. Well, congratulations on the round. How much did you raise? I wanna hit the gong.
Speaker 8:Oh, yeah. So you raised $50,000,000. Congratulations.
Speaker 1:And thanks so much. Who besides FF is in?
Speaker 8:Yeah. Kleiner Perkins Kleiner Perkins. First Harmonic, Genius Ventures Cool. All all participated in the round.
Speaker 1:Round of applause for Genius. Love Greg and Ben and Adam.
Speaker 8:We love Genius.
Speaker 2:It's great news. Well, have a great rest of your day. So great rest
Speaker 1:of your have you on the show. Congratulations on coming out into into the world. Yeah. And I'm sure you'll be back on very soon.
Speaker 2:We'll talk to you soon, Mark.
Speaker 8:Thank you. Appreciate it.
Speaker 7:You all.
Speaker 1:Cheers. Goodbye.
Speaker 2:Let's see. Canva AI. We got to talk about the dog. The dog walker and the dentist. The bitter feud between a dog walker and a dentist over who owns the beach.
Speaker 2:A lakefront owner likened his neighbor's shoreline walks to a home invasion in a dispute that could be headed for the Wisconsin Supreme Court. Who you got, Jordy? Knowing nothing about this story, are you going dentist or dog walker? Pure Vibes.
Speaker 1:Pure Vibes?
Speaker 2:Flip a coin. Which one I don't know. Who you got? You don't know. It's your job now.
Speaker 1:Is the dentist a day trader?
Speaker 2:See, it's your job to pick someone wildly and then defend it throughout this article. Recent trial in Shorewood, Wisconsin had all the trappings of a minor legal dispute, disgruntled neighbor. A defendant representing himself who called his own father as a character witness. You love that. Hey, dad.
Speaker 2:Tell them I'm a good person. 130, 100 and, $313 were at stake. $313. That's 31300¢, if you're paying attention. If it, but an if academic and devoted dog walker Paul Florsheim gets his way, the case will go all the way up to the Supreme Court, Wisconsin Supreme Court that is, and reshape the contours of shoreline access to one of the Great Lakes.
Speaker 2:It started when Florsheim started walking his two dogs past the Lake Michigan property of dentist Daniel Domagala, locally known for the time he spends in a tiki style boathouse and deck that doubles as a surveillance post. From there, Domagala monitors traffic and sets off alarms to scare walkers, swimmers, and kayakers away. He's got an air horn. He's got an air horn. He sets it off when somebody comes Woah.
Speaker 2:It's crazy. Florsheim repeatedly ignored signs outside of the dentist's house that said private property behind the sign, only water access behind this beyond this point. Demagala kept calling the cops and the village eventually issued a trespassing citation rather than pay the fine and walk away Florsheim dug in at stake is what right people in Wisconsin have to take a shoreline stroll. It's high stakes stuff, and we'll be following this case closely, of course.
Speaker 1:We have One of the most important stories in technology.
Speaker 2:Importantly, we have Brett Taylor from Sierra here in the waiting room. Welcome to the show, Brett.
Speaker 7:How you doing?
Speaker 3:Doing great. How are you?
Speaker 2:You going dentist or dog walker? We won't make you take a side. That's a tough question.
Speaker 1:That's a tough question.
Speaker 2:We're gonna ask you the real tough questions. How's business going?
Speaker 3:Business is great. We just crossed 200,000,000 in ARR. We were Great news. I was hoping to get a gong out of that.
Speaker 2:Of course. Of course.
Speaker 1:That revenue revenue gongs are always the best gong.
Speaker 2:Yeah. Yeah. I mean, fundraising is easy.
Speaker 3:Yeah. And today we announced we're a certified FedRAMP high, which really simply put means now federal government agencies have access to Sierra, which I think is really exciting. If you look at, you know, the conundrum of the federal government, it's like we want greater services for our citizens but we have a really big national debt. I think AI is gonna be a huge benefit to, you know, programs whether it's, you know, getting a passport or Medicare. So this is the first sort of the door opening so we can serve these agencies which I'm really excited about.
Speaker 2:Interesting. I don't think of like you're breaking my brain because I don't think of like getting a driver's license to the DMV as like a customer service interaction. But is that where we're going where more and more of these like like
Speaker 1:an address change on like an ID. Like something Yeah. Like, you know, if if Calling the tax department. Calling IRS. There's a lot of these.
Speaker 2:So, yeah, I mean, what
Speaker 3:all these government services. Got Medicare, you've got Medicaid, you've got the Department of Veterans Affairs, you've got, you know, immigration, like, you know, the passport agency. All of them run large call centers, but more importantly, people depend on them. You know, people depend on them. You have, you know, someone on Medicare or you have a vet who depends on Department of Veterans Affairs for their health care.
Speaker 3:It's a really big both payer provider. If you look at, you know, what we do for, you know, folks like Cigna and Blue Cross Blue Shield and you squint, well, it's almost that entire stack in a in a federal agency. And, you know, for good reason, there's just a very high bar for cloud technologies when you're serving the federal government and this is the main certification that kinda opens the door there. We're already working with a few agencies, none that I can fortunately talk about today. We work with FINRA, the regulator.
Speaker 3:Mhmm. So we we've always been really passionate about, we want the best technology to be available even in regulated industries. And so it's a big step for us. We're really excited about it. I hope hope we can contribute to the quality of life as US citizens, you know, and do so in a way that's a lot more cost effective.
Speaker 2:Yeah. I'm I it's interesting. Like, would you would you is the is the biggest low low hanging fruit just taking some of these agencies and making them available twenty four seven? I feel like one of the most frustrating things that I run into is like, call from nine to five. And I'm like, I have a job.
Speaker 2:Yeah.
Speaker 1:The thing like the difference between interacting with like a public utility versus like waste management. Right? Like waste man like waste management. Right? Like Yeah.
Speaker 1:You know, maybe it's not perfect, but like it's pretty fast. Like you can get stuff done. Like it's efficient. And then like the trying to do the same exact thing with like a public utility, you know, usually you I I I always personally estimate like, okay, if it would take me like, you know, ten minutes with waste management, it'll take me like thirty minutes with a public utility. Sure.
Speaker 1:And so like bringing bringing that there's there's no reason that those two things can't be at parity, but it is entirely it is almost entirely a a technology and like process Mhmm. Problem.
Speaker 3:Right? A 100%. And there's a couple of factors that I think go into why it's so hard to have great experience with some of these government programs. You know, first, a lot of people do need to call things on the phone and it's only been the past couple of years that we could digitize a phone call with AI agents. So we've essentially digitized the last remaining analog channel and everything you said is true, could it be twenty four seven.
Speaker 3:As importantly, it could be multilingual. If you imagine staffing up a big call center and you wanna represent all the languages in this country from Spanish, Mandarin, Tagalog, you know, like go down the list, really really expensive to do. Now you can do it at scale. Each new language, the marginal cost is effectively zero. And then the other part of it is for good reason, as I said, there's just a a really high compliance bar for technologies to be available.
Speaker 3:And as a consequence, if you look at, you know, what waste management or just pick any private company, they can go off and get the best of breed solution for any given technology problem they have. That is not something all federal agencies can do. And so what I'm really proud of is that CIRA is the best of breed solution in customer experience. We power everyone from, you know, Singtel to Rocket Mortgage on our platform. And now because we have this certification, federal agencies have access to the best of breed solution in this space as well.
Speaker 3:And so I'm really hopeful. I think AI if you look at some of the parts of the economy, the governments, both state and federal, the healthcare industry which has gotten progressively less productive over the past decade, it's one of the few parts of the economy that has, I think AI is incredibly important because these are the parts of the economy that need productivity. You know, we actually want to have great service as citizens and reduce our spending. AI is the answer. So we're really passionate about it.
Speaker 3:We're patriots here and we love our technology. And so I'm just excited that we can apply it in a in a in a great way.
Speaker 2:Talk about pilot programs and conversion to real customer. Is that timeline a KPI for you? Is that something that you monitor very closely? I have to imagine that it's speeding up. But whenever you shake the hand of a CEO and they say, Let's do this, or someone in the federal government perhaps, an agency lead and they say, I'm ready.
Speaker 2:I'm I'm bought in on the vision. The demo looked good. But then there's obviously an implementation time. Does that look What like did it look like a year ago? Where is it going in the future?
Speaker 3:We track it and we measure it in days not weeks.
Speaker 6:And
Speaker 3:so, you know, some of our favorite like Nordstrom, know, from, you know, concept to a 100% of their phone calls was thirty five days.
Speaker 2:That's good. Said three thousand days is good, then we're back to, oh, you're game in the stats.
Speaker 3:What's the thing about that too is we started out at 1% of their phone calls in I think four weeks and went to a 100% in just a week and that's because of the Nordstrom team. Yeah. You know, actually in Fiserv, which is an amazing, you know, financial technology company for banks, just broke the record. So I don't know if I'm at liberty to show the number of days Yeah. But That's they have an amazing president named Divya.
Speaker 3:She was just top down, like, we're gonna do this and broke the record. So we have a leaderboard internally about how quickly when a customer talks to us can we go live and start delivering value. For what it's worth, I think it's really relevant in this world. You can Financial Times, Wall Street Journal, it's all, you know, token maxing. Yeah.
Speaker 3:What value are we getting from the tokens? Like, is kind of in the headlines right now. We don't really have that issue with our clients because, like, our thing is, like, let's start answering the phone as quickly as possible, start driving those operating expense savings as quickly as possible, but as importantly, that you can actually improve sales, improve CSAT at the same time. Our you know, as you know, we've been really pioneering this idea of outcomes based pricing with the idea being you only pay Sierra when we successfully resolve a call or successfully make a sale. It's it sounds like a small business model thing, but it's big because we don't get paid until it's live.
Speaker 3:So Yeah. It really aligns like all the incentives. Like, we don't care about the sales process. We care about the go live process.
Speaker 2:Sure. And I
Speaker 3:think it really changes, I would say, almost the social dynamics between vendor and customer where we really become a partner because we have the same incentives as our clients. And I think it's a really big shift and you know how much I'm an advocate for it, but I actually think a lot of the, you know, token maxing issues would go away if more companies were just focused on outcomes.
Speaker 1:We're How are you tracking the popularity of the message talk to a human? Or the statement.
Speaker 2:No. No. When I pick up the phone I say talk to an agent, agent, and then they put me on the phone with an AI agent. It's great.
Speaker 3:Yeah. No. Thank you for that. You're on my side. Yeah.
Speaker 3:So we actually have a name for it. We call it greeting acceptance, which is basically what percentage of the time do people get past the greeting and actually give the AI agent a shot. It's going up. People in general I think the problem was we had ten, fifteen years of bots that were really bad. And so we're just you're just, like, paying down this debt because if you were talking to an AI more than three years ago, it sucked.
Speaker 3:Like, let's just be blunt about it. Now you can talk to an AI and it's conversational, it's multilingual, it has access to systems. I mean, the AI agents of today are just, like, a completely dead it's like horse and carriage versus flying car. Right? They're just very different technologies.
Speaker 3:The problem is in the first five seconds of a call, you're wondering, is this one of the crappy ones or is this like one of the good ones? And so we have a lot of techniques on our platform to help people with that. I think the real key is making sure the first greeting is personalized, is engaging, is empathetic. And I think the trend is in the right direction. I think over time I mean, don't know if you have this experience.
Speaker 3:If I have to call a restaurant to make a reservation, I'm like, I'm I'm going to a different place. Like, if you don't have OpenTable or Resi or whatever, I'm out. I think we're gonna get to the point where people demand an AI because they're not gonna wanna wait on hold, you know? And what's also interesting is these AI agents, once they're properly configured, are faster for me as a consumer. They have access to my information.
Speaker 3:They can get the things done quickly. We're not a 100% there and as I said, we're just paying down the debt of bad tech that we've had for the past ten years. But I'm hopeful Yes. In three or four years it will be the norm.
Speaker 1:When did it become popular to to get the, like, there's a thirty minute wait, like press 1 if you want a callback. Because I felt like that wasn't a thing when I was a kid, but now that feels like a pretty meaningful innovation at some point in the last Yeah. Ten years where it actually is really convenient to not be like, okay, like, I'm on I'm just gonna Some
Speaker 2:guy has a patent on that and
Speaker 1:has I'm I'm I'm sure, but like that was like the last great innovation Yeah. In customer service. Yeah.
Speaker 2:It was.
Speaker 3:Well, I think it's going in a really good direction. I mean, I think, you know, not having to wait on hold. We should never have to wait on hold again. Like, that is gonna be a thing of the past.
Speaker 2:But the GPUs are on fire. It's like, we'll call you back when the GPUs call like, cool off. We're load balancing right now.
Speaker 3:Please wait on hold. Yeah.
Speaker 2:Maybe. Imagine that world. That is a bizarre outcome. I mean on that on that note, are are are there any are there any like how how close are we to thinking about like a six for voice models or we were talking to Matthew Prince from Cloudflare about edge computing for voice models. That feels more relevant than, yes, if you're going to cook for an hour on some deep research project, go do it in the on the NBL 72.
Speaker 2:But how are you actually at the point where you're starting to think about that optimization or is that more a few years away?
Speaker 3:I think it's probably not a few years away, but it's definitely in the future. But I'm excited for it. I actually agree with your intuition that, you know, we're entering a world where first, the frontier models are actually separated in my opinion. GPT five five, the recent releases from Anthropic, I mean, you can just see it like Yeah. The gap between the best models like GPT five five and the the others, the open source models is growing not shrinking, which is probably not something we would have predicted a year ago or at least, like, certainly not what I saw in X and and the press.
Speaker 3:But we've reached sufficient intelligence for a lot of different domains. Like, you don't need, you know, a multi billion dollar multi billion parameter account model to do a simple classification or, you know, just do basic, you know, transcription. So as a consequence, I'm hopeful that over the next few years we'll end up with a constellation of models with really purpose built ones. And as you said, just given the power of the hardware in our pockets right now, I think edge will be a part of it. There's just no doubt.
Speaker 3:And it and it just stands to reason there might be creative ways to, you know, maybe convert what you're doing into tokens, you know, so that you have, like, lower bandwidth connection if you're doing voice. Could there be opportunities there? There also could be some privacy benefits to that. I I, know, I have a strong intuition, though no knowledge, but just intuition. That's gotta be something Apple's thinking about just given their their posture on privacy.
Speaker 3:So I'm very bullish on the frontier models like, you know, OpenAI and Anthropic have, you know, really shown the the strength of the research groups, but not at the expense of all the specialized models. I think we're in a in a world where, you know, just as we become more sophisticated in deploying AI, we're going to have a constellation of models with just different capabilities. So I'm excited for it, but it's not now. It's it's in the the short term future.
Speaker 2:Yeah. Yeah. That makes sense.
Speaker 1:Did you guys have to build in guardrails to prevent people from access? Every every like month there's a post that goes viral of somebody that's like figured out like some endpoint that Chipotle they can get to get like
Speaker 2:cold? Like
Speaker 1:frontier model
Speaker 2:in Chipotle code. Somebody figured out how to wire up. Well, they took some sort of like, open code fork of like codex agent, coding model, but it's wired up to Chipotle's customer service in the back end. This is a huge risk because you could run up a big token bill that way. Is this is this We actually a tough
Speaker 3:we have guardrails on it and, you know, it's it's just like any other thing. It's, you know, sort of you're always in a race, the good guy, the white hat versus the black hat to, you know, handle it. You know, in practice, I think it's more like digital graffiti, think nowadays. Like in the early days, you'd see a post online of a chatbot saying something goofy like, oh my gosh, now people are like, yeah, you you fooled with it, you know, like, come on, you know. So I think first, I think it's it's probably jailbreaking I think is a really interesting area of AI security, but probably more around the areas of cyber security and bio and things like that.
Speaker 3:I think this type of thing, you you it's why people work with places like Sierra to good guardrails. I also think like, you know, social media has sort of become numb to it at this point, you know. It's like, oh, great. You got a chatbot to say something silly again, you know. Yeah.
Speaker 3:I I think the bigger issue will be just as these frontier models become more capable, the guardrails around using them need to be more and more effective. And I think, you know, as you think about kind of the mission of OpenAI ensuring AGI's benefits humanity, you really need to make sure that those guardrails are effective against jailbreaking, which is not you you you know, it's not like possible to make perfect by the way, but just because these models are sort of capable under the hood, which is why there's been such a interesting, you know, topic around cybersecurity and others. That's probably the area I think more about though, you know, the digital graffiti is gonna continue to exist as well.
Speaker 1:Yeah. That's a good good term.
Speaker 2:I have you ever heard of $0.00 2¢?
Speaker 3:No. Tell me.
Speaker 2:Famous customer service nightmare call where someone called Verizon. They were getting billed at 2¢ and they were and the and the actual rate was supposed to be 0.002¢. So they were off by two orders of magnitude. They're talking to this customer service agent. We'll play it on the show after we wrap.
Speaker 2:But they're talking to this customer service agent and the customer service agent just actually doesn't know the difference between a decimal cent and an actual cent. And it the frustration that just ensues is like turned into this massively viral video. There's whole websites about it. Verizonmath.blogspot.com from 2006 and it's like this whole deep dive. I'm sure you've heard about
Speaker 3:all these different the good part is a super intelligent AI probably will know.
Speaker 2:Yeah. I think it will. It's some ground truth things, some code and there's there's opportunities here.
Speaker 1:The level intelligence for
Speaker 2:The raw audio for this call is twenty seven minutes. The guy went back and forth and it's hilarious. Cut downs we'll have to play. Yeah, he had unlimited data plan in The US and recently crossed the border to Canada. Prior to crossing the border, he called customer service to find out what rates he'd be paying.
Speaker 2:The data rate he was quoted was 0.002¢ per kilobyte and then he got billed at $0.002 per kilobyte. So it was a 100 times more and he couldn't get out of his quagmires, Kafka esque customer service interaction. Anyway, thank you so much for taking the time to come on the show. It's always a pleasure.
Speaker 1:Always a pleasure. Progress. Incredible progress. Looking forward to the next revenue gong.
Speaker 2:Yep. Come back anytime.
Speaker 3:I'll come back.
Speaker 1:See you. Accelerating.
Speaker 3:See you.
Speaker 2:Talk to you soon. Goodbye.
Speaker 1:Well, folks. Here's gonna
Speaker 2:make some sense.
Speaker 1:We're gonna now play this twenty seven minute video.
Speaker 2:No. We can play this one. This is a this is a just a little bit of this. Yeah. Yeah.
Speaker 2:Yeah. This is the one. The the three the three minute version. YouTube math fail. I wanna hear a little bit about this.
Speaker 2:See if it holds up or if see see if I'm washed on at this point. I'm old and remembering a a seventies video. Not this one. It's the $0.00 2¢. This is exciting news from Canva.
Speaker 2:We can talk about it later. But you can turn your chat GPT images into fully editable fully editable Canva designs with magic layers without ever leaving the chat. Great integration there. Apple Intelligence has some more updates too. We can run through later in the week.
Speaker 2:But let's play the $0.00 2¢ at least a few minutes. Because I
Speaker 3:wanna hear if
Speaker 6:it holds kilobyte usage that was done while in Canada, $0.002.
Speaker 11:Do you recognize that there's there's actually
Speaker 6:0.002¢.
Speaker 11:Yes. Do you recognize there's a difference between those two numbers?
Speaker 6:No. $0.002 and 0.002¢.
Speaker 11:Yes. Is there a difference between
Speaker 6:They're both the same if you if you look at them on paper wise.
Speaker 11:No. They're not, actually.
Speaker 6:So if you take 0.002
Speaker 11:Cents. Remember, it's cents.
Speaker 6:Times $35,896.71 dollars 79¢.
Speaker 11:No. That would be 71¢. How much should I be charged?
Speaker 6:By by the way this is calculated? $71
Speaker 2:I like these guys crashing out over $70.
Speaker 6:2 tenths of a penny per kilobyte.
Speaker 11:Two tenths of hold on. Hold on. 2 tenths of a penny would be 0.2¢. You quoted me 0.002¢.
Speaker 2:I think this guy also might be entirely wrong. He might have just been quoted the wrong number.
Speaker 6:0.002, sir.
Speaker 11:0.002 what?
Speaker 1:What? Cents per kilobyte.
Speaker 11:So you just said it was 0.2
Speaker 2:They they quoted it back to him.
Speaker 11:And then you also said it's 0.002¢. Those are two completely different numbers. They're 100 fold different.
Speaker 6:Okay. George, hold on one second for Okay? This
Speaker 2:guy, so dedicated.
Speaker 15:This is Andrea. I'm a manager on the floor. How can I help you today?
Speaker 2:So now
Speaker 11:the manager's involved. Do you recognize that there's a difference between $1 and $1?
Speaker 2:Coming out
Speaker 4:of the gate. Definitely.
Speaker 2:Definitely. Do
Speaker 11:you there's a difference between half a dollar and half a
Speaker 4:cent? Definitely.
Speaker 11:Then do you therefore recognize there's a difference between $0.002 and point $0.00 2¢.
Speaker 10:No. And
Speaker 15:I I mean, I'm trying to get what you're saying here, but it's just not
Speaker 11:And we're talking about cents. Right?
Speaker 15:Right. Point zero zero two. And if we multiply that by the amount of
Speaker 4:kilobyte usage that you have
Speaker 11:Three five eight nine three.
Speaker 15:Three five eight nine three. That comes out to what
Speaker 4:you paid $71.79.
Speaker 11:Cents. You never did the conversion from cents to dollars.
Speaker 2:I don't know. I'm not a I'm not a mathematician.
Speaker 11:To cents. Right. Times my 35,893. It's is a number, but it's still in cents.
Speaker 15:We're not quoting point $0.02.
Speaker 4:We're quoting point $0.00 2¢.
Speaker 11:Oh, god. Honestly. Well,
Speaker 15:I mean, it's obviously a difference of opinion.
Speaker 11:It's not opinion. Okay. Well, you know what? I'm gonna post this recording on my blog.
Speaker 2:Oh,
Speaker 11:that's fine.
Speaker 1:Okay.
Speaker 11:That's what I'm gonna do. And and and then you guys all at Verizon can learn.
Speaker 2:Yeah. What a funny interaction.
Speaker 1:Incredible. Deep Well, now now that you get a PhD level mathematician's
Speaker 2:Somebody's gonna run this on an on an AI lab. I was quoted point I was quoted I was quoted 5¢ per million tokens, not $5 per million tokens. And confuse someone, get a hundred hundred x discount on your on your bill, your token bill. Andoril has a space something. What was it?
Speaker 2:Space observation network. These are ground based satellites or ground based telescopes that monitor space. And look at this. Captured by Andoril's network of 400 telescopes deployed around the globe, the second stage of the Falcon Heavy launch of ViaSat three F3, performing a routine thrust event. This produced a spiral shaped plume effect, a nominal part of operations for a successful launch of ViaSat's latest satellite.
Speaker 2:Thought this was a cool image. And we learned in the process that they have 400 telescopes all over the world. Anyway, we can dig into all of this more later, more tomorrow. We'll be back at 11AM Pacific. Thank you for tuning in.
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Speaker 1:Or evening of your entire life.
Speaker 2:And we'll talk to you later. Folks.
Speaker 1:Good luck.