Open-Source AI Battle, Google Throttles Meta, Micron Margins Moon | Edward Coristine & Tai Groot, Chad Rigetti, Pim de Witte, Yadin Soffer, Jack Morris, Neil Movva, Jakob Diepenbrock, Chris Altchek

14:30Tai Groot:So let's go over to Tyler's quick review of GLM 5.2. Why don't you take me through your bullet points that we shared in the newsletter at tbpn.com,

14:39Edward Coristine:and you can tell us, like, what is the shape of this model? How are the reviews?

14:43Jack Morris:Yeah, so I think so far, one of the main things is, like, people are saying, oh, it's distilled, right? This has been a big thing with a lot of these open source models, especially the Chinese ones. Oh, the only reason that they're good is because they're distilled. It's very hard to actually figure out how true this is. But people are, you know, it certainly seems like there's some, you know, aspects of Anthropic models.

15:05Edward Coristine:Didn't Anthropic openly accuse Alibaba of distilling? Yes, a number of these labs have been accused.

15:12Tai Groot:Yeah, and there's also been a big professionalization of the gray market where a whole bunch of different sort of individual groups will connect a whole bunch of different entities and user accounts. Subscriptions. Subscriptions and APIs to then create a front end to the model that can be served at a very high rate through a VPN most likely. What's interesting is that you would think that if you were going to do a training run, you would just find and replace on the other lab's name before you hit run. Is that not something people can do? I don't understand.

15:51Jack Morris:Yeah, I mean, it also depends on what you're actually, like, maybe you're not directly distilling on the API, but you're training on public GitHub repos, and those were all made with resource models. So then you're kind of like distilling, but it's not really like, is this really kind of distilling? I don't know. Yeah. So if you are like, if you're convinced that these are like super distilled, the only reason that they're good is because they're just, you know, basically taking the closed sourced labs.

16:19Tai Groot:There's also this weird thing with distilling where as more and more of the public internet and GitHub broadly and open source repos become LLM outputs, you if you train on that you are in some ways distilling because yeah LLM has a quirk like it's not this it's that in text and you wind up training on a whole bunch of Amazon Kindle books you're gonna wind up learning it's not this it's that and the same thing applies for different code conventions in open source repos that have effectively been completely been rewritten by

16:52Jack Morris:closed source models yeah and so I think it's safe to say that like we've generally seen that distilled models generally will generalize worse. So you'll see really good benchmark scores. Maybe they're benchmarks, maybe they're not. But even if they're not directly benchmarks, you still find that they generally...

17:09Tai Groot:Yeah, they're kind of accidentally benchmarking.

17:11Jack Morris:Yeah. So I think initially you should just be a little bit suspicious of these super high benchmark scores.

17:17Tai Groot:Yeah. But they lack that big model je ne sais quoi.

17:20Jack Morris:Yeah, and this is anecdotally reinforced. A bunch of people have been saying, for coding, these models are really great. GLM, it's a very good model, you know, for creative writing or something like this, where you'd imagine it's a bit harder to kind of bench max this. Yeah. They'll perform a bit worse.

17:35Edward Coristine:Yeah. I wonder, hey, have people been testing it with the like Tiananmen Square bench? Like, does it reject that stuff?

17:42Tai Groot:Or because it felt like that was something that was like widely misunderstood by American audiences that, in fact, that might not be the biggest deal for the CCP.

17:53Jack Morris:Yeah, also I think, you know, even if that's true, like the model is open source, you can kind of just fine tune it to like, sure, not that. Maybe it's a bit harder than that, but I think you can kind of get around like that kind of stuff. Okay, yeah.

18:04Tai Groot:So we talked about the token hunger and the API price. And in general, I mean, you said, I'm not convinced that there's a big market for this class of model, especially as frontier models get more efficient. If you look at Open Router, the most used models are the smallest open source models, presumably being used for specific tasks that need to be repeated over and over again.

18:26Jack Morris:Yes, I think what we've seen is a marginal IQ point of the models is extremely expensive. Front-chain models are getting very expensive. People have to cut back. They're token maxing. This is a massive bill on their balance sheet or whatever. I think it seems like there's now basically like two classes of models that people really use. There's like the frontier ones, and they're using coding agents. They need the best thing. If you're doing cyber, like you just need the best model because, you know, the risk of someone hacking you, it's so great. You just need the best thing. You pay whatever it is.

19:03Jack Morris:And then there's the second class, which is like these very small, very fast, very cheap models that you can use for these kind of point solution things. Maybe you have some orchestration where you're using a really big model to have these little agents using these very cheap models. I think in the middle, it's hard to actually figure out what is the real use case. Maybe it's like hobbyists using these coding agents and they don't want to pay the super expensive tokens of the closed source labs. But generally, and you see this on OpenRouter where like what are the top models by token usage? It's these very small models.

19:39Jack Morris:It's like DeepSeq Flash.

19:41Tai Groot:Yeah, because you're spamming them for like, you know, every receipt that goes into RAMP gets processed by an LLM at this point. Does it need to be a frontier model telling me that I spent$10 on a coffee? No. It can just do standard OCR.

19:57Chad Rigetti:That'd be my preference.

19:59Tai Groot:Yeah. You want super intelligence overseeing your expenses, most likely. But no, you use the right tool for the job and that's clearly what's happening. Yeah.

20:07Jack Morris:But also I think like it is a very good model, right? Like we should not really dismiss, I think the idea that, oh, the gap is widening, we really don't have to worry about these models. I think they are very good. Maybe if you're super worried about distillation, maybe something changes if the models are kept to these big partners, like what we've seen recently with government coming in. But I think we can't really fully dismiss these labs.

20:32Tai Groot:Yeah, it throws a little bit of a wrench in the monetization potential, like how long can you monetize a new frontier model? That's more tricky. And then the other one is just like, if you're going to keep a model behind KYC or behind an approval for specific companies, like the government has been sort of edging towards and moving towards, it gets a little bit tricky if all of a sudden you just wait three months and, oh, I was waiting to get approved for this one for like GPT-7 or whatever, but by the time the government got back to me, my company got access to GLM-6, and it's close enough. And so that just throws another wrench that I think the government will have to figure out how it puzzles together with the rest of the strategy, which has been, yeah, back and forth as always.

21:30Edward Coristine:Anyway, let me tell you about Shopify. Shopify is the commerce platform that grows with your business and lets you sell in seconds online, in-store, on mobile, on social, on marketplaces, and now with AI agents.

21:40Chad Rigetti:Google, Caps, Meta's Gemini use as AI demand strains capacity in the financial time. Surging appetite for advanced models is turning computing power into the tech industry's scarcest commodity. And they have a picture here of a Google Gemini bicycle, which looks fantastic.

21:59Edward Coristine:What does that have to do with Meta, though?

22:03Chad Rigetti:I think that was just the best Google Gemini picture.

22:07Edward Coristine:It is hard. Otherwise, it's just a picture of a phone screen.

22:10Tai Groot:I mean, you saw in the Z.ai, it's just a picture of the app, which is, like, so boring. Imagine riding this. Or it's the stock image of the brain with the neurons. That's always good.

22:19Chad Rigetti:What do you think? I mean, this kind of ad placement, like on a, what do you actually call this? It blocks the water. No, no, no. Just the part that blocks water from, you know, if you were to ride this bike. Yeah, some type of fender thing. Sort of like a Mansory kit for a city bike. Exactly, exactly. But imagine riding that Gemini bicycle in the rain. Fantastic.

22:42Edward Coristine:Oh, that's what it's for, so the water doesn't come up and splat you. Interesting. Okay, I never knew what that was for. You never knew that.

22:50Chad Rigetti:Educational. The experience of hosting the show is educational for both of us. It is. Google has put limits on Meta's use of its Gemini AI models after the social media giant Sopmore computing capacity. than the rival tech group could provide in the latest evidence of the infrastructure constraints facing even the world's largest AI providers. Google told Meta around March that it could not provide all of the Gemini capacity the company wanted to purchase, according to three people familiar with the matter, in a move that has disrupted and delayed some of Meta's internal AI projects. So how much should we...

23:24Chad Rigetti:I don't understand how this is possible. Yeah, yeah. So one... Google spent$200 billion on CapEx or something. Okay, so of course, around this time, token maxing was becoming a thing. A lot of every company in the world, at least every tech company in the world, kind of going a little bit crazy from a spending standpoint. And so I could see Meta going and wanting to basically buy a bunch of capacity and then being told, like, hey, we can't fulfill that. But I'm wondering how much more we should read it. Like, is it worth reading?

24:04Edward Coristine:I mean, it sounds extremely bullish for Google. Like, if they're actually out of the basket, that's insane.

24:08Chad Rigetti:Yeah, no, and this tracks with what they talk about on earnings calls. Yeah, yeah, yeah. Yeah, Google Cloud, Acceleration. But you do have to wonder, like, could distillation be part of this story? Is that, could that be a factor here? I have no idea.

24:25Tai Groot:I don't know. Zero Hedge said, Meta puts limits on Claude and Codex fearing distillation, the information.

24:30Chad Rigetti:But so this story is different. This is Meta telling its own employees, don't use Claude and Codex in certain parts and certain parts of our business because we don't want to accidentally do distillation is what Meta is saying. So that's different. I was wondering, like, is Google thinking like, whoa, that's a lot of, you know, cool it, you know? Owing to the restrictions which remain in place as well as a broader push to streamline AI costs, Meta has encouraged staff to be more efficient with AI tokens. Several other Google clients have been affected by the restrictions, although to a lesser extent, Meta has been particularly impacted because of its exceptionally high demand for Google's models.

25:14Chad Rigetti:Interesting. Very interesting. I would love to see a pie chart breaking down all the different ways they're using Gemini in their business. Because Google has not broken out Gemini revenue at all to date. So we have no idea what percentage of their AI revenue is actually spent on Gemini versus other models.

25:40Tai Groot:And the Gemini tokens broadly go into AI search overviews. So that's a search product, probably insane token demand there, right? You've seen the chart of like they're in the quintillion or quadrillion tokens, uh, category. And then, and then you have, um, and then you have YouTube now has Gemini plugged in and you can chat with any video and transcribe it. That's gotta be incredibly token heavy. Uh, and then you have Gemini app users and free users and paid users.

26:11Edward Coristine:So there's got to be a lot of just Gemini internal usage, but it's remarkable. Yeah, I would love to see that meta pie chart because I thought that they were spending a ton with Anthropic. I thought they were spending a ton with Google, but I also assumed that they would be running a bunch of Lama workloads

26:29Tai Groot:and a bunch of MuseSpark workloads because those models have performed well at various points in time. And if you go into the meta app, you now have access to MuseSpark. And if you go into Instagram and you search for something, it populates it with a Lama 4 result. And so I would imagine that even though that product is not broken through like crazy, I would imagine that it's still generating a lot of tokens just because of the scale of Instagram. Instagram has a billion, two billion users, something like that. It's huge. And so even if it's people sort of, you know, accidentally winding up in an LLM powered workflow, it probably is generating a lot of tokens just because of the scale of that of that system.

27:16Chad Rigetti:On the topic of meta, meta shared this morning a new milestone. It is a mind reader, mind reader, non-invasive brain detects to code or research brain to QWERTY V2 building on V1, which was published today in nature. Brain 2 QWERTY V2 is the highest performing end-to-end pipeline capable of real-time sentence decoding from raw brain signals. It advances beyond character-level performance to decoding words and semantics enabling accuracy for overall communication. So if you thought, you know, Instagram was listening to you, if you thought it was listening to your, you know, conversations, now you can have a new conspiracy at home, which is that they might be just listening to your thoughts.

28:08Tai Groot:Do you know the device? They say this is a non-invasive device. I just shared an image of this device. I want you to tell me, do you consider this non-invasive or invasive? look at this image of the magneto and salaf and graphy device.

28:25Chad Rigetti:No, you got to go high. You need to scroll up a little bit because you can't even see the whole thing here. It's not invasive. Cause it looks like the device could actually potentially carry on for like a whole half.

28:38Tai Groot:It really does seem like it's just put yourself in this, in this room sized device. No, of course this will shrink down.

28:45Chad Rigetti:I'm giving them, I'm giving them credit here. Non-invasive. Non-invasive. Okay. As long as he...

28:50Tai Groot:You're putting this thing on? You're daily driving this thing. I don't know if I'm ready to daily it.

28:59Chad Rigetti:I don't know if I'm ready to daily it. This will be a cool demo. Yeah. This will actually... When you can just walk in, sit down in a chair, and see your thoughts on a screen.

29:11Edward Coristine:No, we were debating it earlier. My buddy Rob Taves has been on the show twice.

29:14Tai Groot:Dropped five predictions in Forbes recently. We can go through them at some point. He's going to come on the show. But four of the five were very, very reasonable. Anthropics is going to be bigger. And TSMC is going to face more competition. And then he predicts that in 2030, telepathy will be commonplace, which is a very aggressive prediction in my estimation. It's certainly not like a straight trend line since TSMC has competitors right now. The prediction is just that there will be more competition. But truthfully, telepathy is not really existent outside of like a few demos like this. It's not really something where it's like, oh yeah, like 5 % of people have the meta Ray-Bans that take pictures, so like face cameras are gonna be bigger in five years.

30:08Tai Groot:And it's actually only three and a half years until 2030, which is sort of crazy to say, but we are getting quickly to the future, to the future. never sell your company. Should you ever sell your company? David Center says no. He says the best founders in the world would never sell their company. You could never require Elon, Bezos, Zuck, Jobs, Allison, Jensen, Dell, Page, and Bryn. Scott Wu has turned down billions and keeps saying no. This is a great clip. Went super viral. I don't know. Did I lose internet or something? I don't know. Anyway.

30:42Chad Rigetti:Tyler's app is in shambles.

30:44Tai Groot:I don't know about that. but there's some debate over this because Elon that's not my app Elon yeah this is just access Elon did sell two companies he sold zip two and he also sold PayPal and then Jobs sold next back to Apple does that count I don't know he did sell Pixar to Disney that that sort of

31:06Edward Coristine:counts and I mean Elon never sells his company he just sold XAI to himself but I guess that doesn't count um but yes it is it is uh it is a funny thing uh uh didn't mark and push back but

31:20Chad Rigetti:what's yeah so what is what's the do you know the backstory here from sasha i don't know uh

31:26Tai Groot:tyler looked it up apparently there's a business insider report from the time that this happened in 2007 how terry semel fumbled yahoo's facebook deal uh how much is facebook worth five billion 10 billion, 15 billion, whatever the number, it's probably a lot more than the 1 billion that Yahoo could have bought it for a year ago. As Yahoo continues its soul searching, here's an unpleasant rendition of Semel's catastrophic decision, courtesy of Wired. When Yahoo came calling with a bid of$1 billion in cash, the pressure became too much. Zuck relented in July of 2006. He was just like 18 months into building the company, something like that.

32:06Tai Groot:verbally agreeing to sell Facebook to Yahoo. He said yes. He said he was going to sell Facebook to Yahoo, allegedly. Strategically, it seemed like a good match. Yahoo had hundreds of millions of users, but its foray into social networking was struggling. Facebook had cool tools and was looking for a mass audience. The timing, however, could not have been worse. In the days after Zuckerberg agreed to sell, Yahoo announced it was projecting slower sales and earnings growth. and that the launch of its new advertising platform would be delayed. Its stock price tumbled 22 % overnight. Terry Semmel, Yahoo's CEO at the time, reacted by cutting his offer from$1 billion to$800 million.

32:51Tai Groot:He just took 20 % off, but Zuckerberg, who had been warned about Semmel's reputation for last-minute renegotiations, walked away. And that's probably reasonable. I mean, if they're cutting the price there, you have to imagine that as it gets papered, you get cut down again. Then the earn out, you get cut down again. And all of a sudden, you're walking away with barely anything. But two months later, Semmel reissued the original$1 billion bid. But by then, Zuckerberg had convinced his board and executive team that Yahoo wasn't a serious partner and that Facebook would be worth more on its own. He rejected the offer and became famous as the cocky youngster who turned down$1 billion from Wired.

33:28Edward Coristine:Legendary.

33:28Tai Groot:Legendary.

33:30Edward Coristine:it's so interesting to imagine the road not traveled there because the the the dynamic the way facebook is built with this with the uh the as a social network like could it have been successful under yahoo's stewardship or would it have been uh less exciting attract less talent

33:52Tai Groot:ultimately been disrupted and would they have had the capital and the guts to go and buy whatsapp and then also by Instagram, you know, to actually maintain the dominant position in social networking.

34:05Chad Rigetti:I think Yahoo should make another offer. We were hanging out with Jim, CEO last week, dear friend of ours. And I would like to see Yahoo make another bid.

34:19Edward Coristine:Hey, Meadows trading down. Just keeps going.

34:21Chad Rigetti:If it continues at this trend.

34:23Edward Coristine:99.99 % might be able to pick it up.

34:27Chad Rigetti:At this trend.

34:27Tai Groot:Anyway, let me tell you about MongoDB. What's the only thing faster than the AI market? Your business on MongoDB. Don't just build AI. Own the data platform that powers it. Moving on. What else is in the news?

34:40Chad Rigetti:Chipmakers are profiting off AI at the expense of just about everyone else.

34:45Tai Groot:This is on the cover of the business and finance section today.

34:49Chad Rigetti:We are witnessing an extraordinary transfer of cash from the providers of AI, and perhaps one day AI users to memory chip makers. Take us away, John.

34:58Tai Groot:The explosive growth in Micron Technologies' profit in the latest quarter is extraordinarily good news for its shareholders. But it comes at the expense of the artificial intelligence companies to which it sells fast memory chips. Micron, along with Korea's Samsung Electronics and SK Hynix, are to AI what oil producers are to the airlines, makers of an essential input that this year suddenly became much more pricey. because there is extremely limited capacity to make the high bandwidth memory that AI needs, and it takes years to build production facilities, soaring data center demand simply jacked up prices.

35:37Tai Groot:Micron's soaring profits are, for its customers, soaring costs. We are witnessing an enormous transfer of cash, they said. Profit shift of this scale are rare events, and investors should be paying attention to where the money's coming from, where it's being spent, and how long it will keep flowing. In the quarter ended May 28th, Micron increased prices for DRAM chips more than 60 % on the previous three months, while increasing shipments by a low single-digit percentage. It said last week, prices for NAND flash memory, also used in data centers, jumped more than 80%. Usually, memory doesn't matter that much, but for Micron, customers paid$18 billion more, and that was just in the quarter.

36:19Tai Groot:Prices quadrupled in a year, and it's hurting outside AI to Apple last week. raised prices for MacBooks more than 15%, closer to home for me. The memory I bought on Amazon.com a year ago to build a super quiet computer, I hate fan noise, good color commentary here, has tripled in price and now costs more than the CPU. For an industry in which prices usually drop every year, it's a huge turnaround. In consumer electronics, passing on higher prices helps limit demand for chips, just as higher oil prices reduce consumption. But the AI companies aren't passing on higher prices because they are able to throw money at supply problems.

37:00Tai Groot:The problem in AI is that the end users aren't covering the cost of the service with big losses being recorded by AI model producers. Everything is still priced to bring in new customers, yet not yet to make money. So higher input costs create a nasty problem. Either losses will either be bigger or higher prices will be needed, putting off potential customers. and you can see the price of Microns. Stock price has been through the roof as the company joins the$1 trillion club and becomes the first trillion dollar company

37:31Edward Coristine:in a headquarter in Boise, Idaho. And Idaho got a trillion dollar company before New York, I believe, and also before Florida and Austin, maybe, something like that. It's rare, it's rare.

37:46Tai Groot:Mostly on the West Coast. anyway there's a whole there's a whole bunch of bull cases for micron still the stock could double from here says barons i love adam levine and barons sharing the bull case uh we can tyler

38:00Chad Rigetti:how many trillion dollar companies are there in europe out of curiosity i'm gonna go with zero that's true that is uh true you are correct the other asml could get there maybe sitting at around 700.

38:17Edward Coristine:Wait, what about Eli Lilly? Or no, Novo. Novo was a trillion, right? Or did it ever touch a trillion? I don't think so, right? It was real close.

38:25Chad Rigetti:It's a humble 165 million.

38:28Tai Groot:Brutal. Wait, but what did it peak at?

38:31Chad Rigetti:You're thinking of Eli Lilly. Eli Lilly hit a trillion.

38:34Tai Groot:Yeah, rough. Very, very rough. Comcast is planning to split up the company. Competition has escalated.

38:41Chad Rigetti:Eli Lilly, the Indiana company, John?

38:45Tai Groot:Is it from Indiana? Indiana. Okay.

38:48Chad Rigetti:Former Indiana startup.

38:49Tai Groot:Okay. I like it. I like it. NBC, Universal, and Sky will separate the company's connectivity business from its film, theme park, and streaming operations. Oh, yeah. Universal Studios. Comcast is up on the news. Comcast plans to separate its media and connectivity businesses.

39:06Chad Rigetti:Who's building the anderal of theme parks?

39:09Edward Coristine:It does seem like a...

39:10Chad Rigetti:Could there not be an opportunity to create a net new theme park business with a modern technology stack?

39:20Edward Coristine:It's very expensive. Everything needs to be, like, the modern technology stack in theme parks is tricky. You don't believe in the theme park capital markets?

39:31Chad Rigetti:I don't know.

39:32Edward Coristine:I've known people that have worked on theme parks at Disney.

39:35Tai Groot:And it's tricky because you have to amortize a ride over like 20 years. And so you'll go.

39:43Chad Rigetti:It seems like an absolutely brutal business. Yeah. That is probably harder today because. Huge power law. Think about, you know, at the time that a lot of these parks were built, like you didn't have like infinite online entertainment for every single sub niche. Yes. Instantly available.

40:03Tai Groot:I mean, there's a whole bunch of trend pieces right now about how IRL experiences are seeing higher than ever pricing in the face of you could just watch the Knicks game on TikTok highlights, but people still forked over$5 ,000 to go see the game. And so, you know, you have that like barbell strategy where Thrive is buying a stake in the San Francisco Giants, a baseball team that should face competition.

40:30Chad Rigetti:They're also exploring the NBA team. Yeah. to Vegas. But at the same time, at the same time, there is also that stat that came out this morning or maybe yesterday that there's more sports betting volume than all sales of movie tickets, theaters, theme parks, and like a couple other of these IRL categories.

40:52Tai Groot:Is it up or down?

40:54Chad Rigetti:It's lower? Less. And the stat was like volume. Yeah, yeah. And so it's not exactly like a proxy for like revenue but still meaningful theme park vertically integrated

41:12Tai Groot:tweety bird tattoos tweety bird tattoo parlor right on site i like it i like it when you're

41:20Edward Coristine:at six flags no i don't like it uh six flags never really got the same like cultural power that disneyland did there's something about the flywheel that walt disney laid out that does seem very very important work and so how do you start that it's not just oh you know tech enabled theme park that's not going to draw people in you need to have uh like ip around it brain rot theme park there's something about i mean we've read that we've read stories about like the the the disneyland fan that's that saves up every year and spends so much money at the park and i think that's probably the lifeblood of that business and that doesn't happen without building a whole cinematic universe around every single ride.

42:01Edward Coristine:And that just takes so much time. And you can't, like, this goes back to the question of, like, Netflix is enduring IP.

42:08Tai Groot:Like, they don't, they haven't been able to, like, even though it's been 20 years of, like, I mean, I don't know when they started producing their own content, but it's been 20 years for that business at least. And they haven't really developed, like, their own franchise that lives in the same world as Batman.

42:25Chad Rigetti:Well, I'd push back and say the Narcos.

42:27Tai Groot:Narcos? You want to go to the Narcos theme park? I was talking about this with somebody once talking about like HBO, like, why don't they have a theme park? And he was like, what are you going to do? Take your kids to a brothel in Game of Thrones land? Like, no, it doesn't make any sense. Like anything, it needs to be uniquely general audience. Like you can't have R-rated, you can't have a content backbone that's R-rated because theme parks will always attract families and kids. And so anyone, you can't have any theme park that's built around an R-rated IP library. And so that just narrows it down even further.

43:07Chad Rigetti:Well, all of America has basically turned into a theme park for European soccer fans. Oh, yeah. In the journal, European soccer fans marvel at the splendor of America's suburbs.

43:18Tai Groot:I've been getting so many of these reels served to me.

43:21Chad Rigetti:Dutch fans in Missouri see a nation that is risky and expensive, but vast and bountiful. Everything is three times the size. You've been seeing some of these people in real life, right?

43:34Edward Coristine:I don't know if I've seen any of them. I did go out to lunch like a week ago, and it seemed crowded, but I was unclear if that was just local residents going out to watch the games or actual tourists coming to town to watch the games.

43:48Chad Rigetti:in the x chat i think ferrari has a roller coaster in the middle east they do they have a whole

43:54Edward Coristine:ferrari theme park that works because that's not r-rated you can totally take your kids to the

44:00Chad Rigetti:ferrari theme park um yeah i was uh i i was in abu dhabi and i and and i i was driving by it and i was like yeah i was just thinking of like if you wanted to spend yeah a day you know uh getting the Ferrari experience. Like you could just go to the track. Yeah. Or you could just rent a Ferrari. So I don't know.

44:24Edward Coristine:Yeah, but you don't need to go to Six Flags to get the Batman experience. You can just go out in the middle of the night and arrest a criminal. Just become a vigilante. I saw another report that apparently there's like an individual who's being like the Batman of Mexico. Do you guys see this? This is very funny. And so the guy went out and found criminals.

44:46Chad Rigetti:And Tvellapar says Yas Island. They literally named an island Yas. Weird. No. I don't know. Anyway.

44:59Edward Coristine:Dutch soccer fans are having fun visiting America.

45:02Tai Groot:Frank Everink, he hadn't even heard of Kansas City, but when the Dutch soccer fanatic saw his team would be playing along the border of Missouri and Kansas, he made a detour in his worldwide road trip. Everyone got in his camper van and drove south from Toronto, making stops in Detroit, Chicago, and Indianapolis. Along the way, he and other European fans who flocked to Kansas City for the World Cup beheld the fruits of the American economy from a vantage point few foreign tourists typically see. Suburban superstores, hulking plates of food, quiet streets. He marveled at the sprawling houses and a contrast from the tightly packed homes of the Netherlands.

45:40Tai Groot:I did notice this when we were in France. The food portions were way too small for me. It was brutal. It's spacious, he said. You go here for your shopping and there for your dentist. People are so rich here. I think that's why they can be so nice. What an ultimate white pill in America. In America, everyone's like, we're so divided and everyone hates each other and it's terrible and the economy's about to fall apart. And then one European tourist comes like, this is paradise. Everyone is so nice. Something about the grass. The grass is always greener. The grass is always greener on whatever side I'm on.

46:13Tai Groot:That's what I like to say. The throngs of Dutch fans that flooded Kansas City and its suburbs this past week got a taste of day-to-day life in the United States,

46:23Edward Coristine:reigniting the long-running transatlantic debate, who lives better, Americans or Europeans? The Europeans had plenty of thoughts on American culture. We are a bit shocked about the food you're eating, the Dutch national team superfan Sandra Tate said. Fans also balked at the size of Costco's and the vastness of the highways.

46:43Tai Groot:In recent days, social media has been filled with videos of Europeans gawking at the staples of suburban life. A two-car garage, a walk-in closet, a second refrigerator. One Brit went viral for trying Chick-fil-A for the first time. That was absolutely banging, he said. In another, he toured the inside of an American fire station.

47:02Chad Rigetti:The way that they experienced the Chick-fil-A was me seeing the Renault Twizy. Yeah. I was just like, this is unbelievable. They made the perfect car.

47:12Edward Coristine:Yeah, so small. So small.

47:15Tai Groot:And it's the way they think about our fire trucks, which are massive. This is nuts, honestly, they said.

47:23Chad Rigetti:Tyler, while we wait for our first guest, do you know anything about Bosnia's World Cup team? we the united states is facing them on wednesday and he's do you have a stat breakdown or anything

47:35Tai Groot:we be very careful with what you said because i saw that there was a news reporter who faced fierce backlash for uh really calling bosnia out and saying like i don't know where it is on a map and the funny thing was that it was delivered in like the typical newscaster like and i'm here reporting on the ground and tonight but bosnia will be playing and then she just like transitions into color commentary giving hot takes about how irrelevant bosnia is in her mind and the bosnians

48:03Chad Rigetti:did not enjoy her critique of their country pure disrespect anyway uh there's a little golf cart

48:09Edward Coristine:we got to talk about this at some point but uh there's a new there's a new car it's like a twizzy

48:14Chad Rigetti:you're gonna love it it's close it's 25 days it's no twizzy let's bring in our first guest anyway let's bring in our first from the national design studio national design studio welcome to the show

48:23Tai Groot:gentlemen how are you doing thank you so much what's coming on the show uh please start with an introduction of yourselves the the company and then the announcement today uh my name is

48:36Neil Movva:edward corberstein and i run engineering at national design studio it's technically not a company it's a government organization oh yeah that's right sorry and i'm tiger i'm one of the

48:44Edward Coristine:engineers at national design studio okay and today the launch take us through it we're launching

48:50Neil Movva:ramparts. It's a local first privacy model that puts people back in control of the data that they share with AI. We were just building a chatbot for fun. And we were upset that none of the frontier models will actually fit in a browser. So you cannot do PII removal in the browser, which is pretty damn important for a use case. You just have to trust that the server is actually removing the information and not lying to you. So we're like, okay, well, what if it was just all on device? Like personal data never had to leave your device. It was secure by default.

49:28Edward Coristine:Okay, so open source, the weights are on Hugging Face,

49:32Tai Groot:runs in the browser under 15 megs. A technical user could go right now, download the model from Hugging Face, Vibe code their own Chrome plugin and have it be running however they want. But how do you see this actually rolling out? Do you want the government to implement this in various places? Do you want companies to? Is it sort of like open up the primordial soup of ideas and see where it goes? Or do you have like a rollout strategy that you are advocating for?

50:04Neil Movva:Well, the reason why we open source it is because we do want companies to use it. And we want people to use it. And we want people to make it better. So we want Vibe-coded Chrome plugins. Sure. We want, you know, Vibe-coded Chagipity extensions. Like, whatever value is derived from the product. You know, this is just like a total side quest for us. We just want to build software that's helpful for the American people. We've already launched a series of products then. Like, TrumpRx has got 15 million users. It's saved over$500 million in drug costs. And, you know, we rethought the UX there. So we're, you know, basically across everything we're working on, we're just trying to define the first principles, best approach for users.

50:42Neil Movva:yeah and this just came as a derivative of that uh we're not mbl you know researchers or engineers we're just like you know we should just do this but you created pii super intelligence

50:55Chad Rigetti:that's what people are that's what people are saying basically it's like tiny intelligence

50:59Neil Movva:it's like it's by far the smallest model like the other ones are like at least 50 megabytes this is

51:04Tai Groot:15 yeah so did you like did you to what degree did you build on the shoulders of giants is this some pruned and distilled and fine-tuned open source model is this something where it was easier to just start from scratch but use architectures that are more prevalent and well established like how did you actually go about training this model we tried 72 different base models well

51:29Neil Movva:and you know put them through a training set we ended up on mini lm so we definitely are standing

51:35Pim de Witte:on the shoulders of giants here yeah yeah yes we started taking a look at the open a privacy filter that just got released recently. We're trying to figure out, is there a way we can just quantize it? Can we maybe remove some of the parameters? What can we do here to try to make use of the state-of-the-art model? We tried a lot of things. We just could not get it to fit into... We want this to work on legacy devices, on an old Android phone, for example, or an older iOS device. It just would not get small enough and still make any intelligent sense to try to actually run it. So, yeah, we ended up essentially, it's technically a fine tune, but we trained many LM and basically made it do exactly what we wanted to do.

52:17Tai Groot:Can you help me understand use cases a little bit more? Because I feel like most of the time when I'm transmitting a document to a prescription website, Rx, or a financial institution, the PII is like potentially the only important part. They're often sending me a blank form and asking me to put my PII in there. What is the inverse scenario where I want to redact my information, but I still need to transfer something? Because in most cases, that would just be the template or something in my estimation. Yeah.

52:53Neil Movva:The flag is set at compile time, so you can decide, like, for our use case, it's really important that we have this data or that we don't have this data. And so we hand all the customization back to whoever wants to use the library. The model just says, oh, you know, this is a phone number, this is a name, this is a surname, et cetera, et cetera. And then ultimately it's, you know, whatever you want to do with the model, you can just do it. Fundamentally, what we were looking at was there are a lot of cases where people will ask a question pertaining to a document of like, okay, for example, the template.

53:26Neil Movva:How do I fill out said template? because the government is pretty bad with forms. There's way too many forms. Nobody knows what they mean. You've got to pay people to do your government forms. That was the use case we had in mind. PII is not super helpful for that. It's also kind of like the breaking point. It's where the product will lose trust. So we're like, okay, two birds, one stone. Let's build this thing. That makes sense.

53:51Chad Rigetti:How do you guys think about side quests at the National Design Studio in general? Like I imagine every single day there's opportunities that come up and you guys are in a unique situation where you have a mandate, but at the same time, there's so many different places that the government, you know, interacts with people's lives. I'm very curious.

54:15Neil Movva:It's pretty hard to pick what to work on because there's a lot of exciting things. There's like everything is huge scale. Everything could be way better. Maybe not everything, but a lot of things. So there's like a huge calling for side quests. But we just try to keep everything in line with our vision, which is like we want to make the American digital experience better. And then we've kind of chosen a track to get there. And on the way, we built this model and on the way we built Trump Rx. But we're excited to see how it develops from here. And we'll be back on the show.

54:49Tai Groot:Yeah. Diving more into that, do you have a reference point in tech? People might ship, you know, they might think in quarters, financial quarters, three month cycles. They also might think about a two pizza team, which I think is like 10 people. Do you have an idea of where the sweet spot is from what you've experimented on? How many people do you want to bring into a project? And then how long do you want to spend there so you don't get stuck for a decade because you might not have a decade?

55:17Neil Movva:Yeah. I mean, there's definitely a lot of places to get stuck because the visibility is super low on a lot of these projects. and you don't know how broken they are until you're really in it. Being able to determine that in advance is definitely AGI level.

55:35Pim de Witte:We've got a really great team. We're very fluid. We're constantly trading responsibilities back and forth. Someone might be better at doing one part of the tech stack than somebody else, but they're on a different project. We'll just borrow them for a day or even for an hour. We share a lot of responsibility at the studio.

55:53Neil Movva:This is also definitely the only place in the government where people work seven days a week, consumed on Red Bulls. I think the ideal amount of people per project, if they work super hard, is two. One design person, one engineer, and they both have full scope. And then they're able to call on people as necessary.

56:14Pim de Witte:Yeah, two with the caveat of you're calling in your coworkers to say, hey, can you take a look at this over my shoulder quite frequently?

56:22Chad Rigetti:yeah that makes sense what what's your what's your guys's pitch to talent that that uh that you might want to recruit into the national design studio i imagine uh uh lots of people that would join could get a go get a blank check from a venture fund or could go work at some of the best companies everyone has points you know that's the case for them turn down that off

56:43Neil Movva:yes um it's definitely more for people who are super mission oriented um you know who the hell what great engineer wants to come work in the government? The answer is typically nobody, unless it's the IEC where there's really interesting problems to solve. I think that we have a super golden opportunity. At least the way I evaluate problems, I try to see how big the problem is in terms of how many people will use it, the delta between what exists versus what our team can do and how fast we can do it. When you look across those three matrices, it's like a home run place to work. So I think that is its own natural kind of calling card for the right kind of talent that we need for the studio.

57:28Neil Movva:Awesome. Good luck, folks. You get to work out of the pipeline complex, too. It's also a huge benefit.

57:32Pim de Witte:It's pretty sick.

57:33Chad Rigetti:Awesome. Is that where you guys are right now? We're not there right now, but we're about to be there. Yeah. Awesome. Well, thank you so much for coming on the show. Congratulations. Very fun project. And we'll talk to you soon. Great to meet you guys.

57:46Edward Coristine:Have a good rest of the day. Goodbye. Thank you very much. Cheers. Let me tell you about CrowdStrike.

57:50Tai Groot:Like your business is AI, their business is securing it. CrowdStrike secures AI and stops breaches. So Apple and Audi alumni just unveiled a$25 ,000 open air electric neighborhood vehicle. It's called the Amble One. And it's a street legal EV built for short local trips. No doors, fewer screens, modular design inspired by the 1960s lunar rover. goes 40 miles an hour with 60 miles of range weighs under a thousand pounds takes five hours to charge rear seats fold flat for cargo surfboards or gear built-in mounts let you add baskets straps mirrors cargo accessories already has 500 vehicles committed i love it you love it i love it i think it's great i've i've been a geordie score a geordie score daily weekend you know the doug

58:45Chad Rigetti:score out of a hundred what are you doing so i mean i just went through this whole crazy search for basically this exact vehicle yeah didn't didn't find it i don't like the aesthetics of golf carts yeah i've driven a lot of golf carts in a commercial capacity okay at at a job in college i've you know owned a golf cart uh i i it's in my experience it's impossible to feel cool while driving a golf cart. So I wanted something like a golf cart, uh, that was more like not, you know, I'm not golfing. Um, so I wanted some like little bit of utility, wanted to be fun, et cetera. I landed on a, uh, a Can-Am HD 11, uh, you know, a UTV, uh, it's gas powered.

59:35Chad Rigetti:It's, uh, it's quite fun, but the gas element is actually kind of annoying even as as a as a ice uh you know defender that is the internal combustion engine yeah um but uh but i think but i think this is uh no i think this is i think it's fantastic and i think that i i think i saw somewhere uh that they're going to focus on more commercial opportunities so going to hotels all over the world that's what justin says here he says this little golf

1:00:06Edward Coristine:card is going to be huge for hospitality, all electric,$25 ,000. How does that comp against, if you're a business and is it really going to move the needle on the customer experience to

1:00:19Tai Groot:have this versus just a golf cart? Can you get a fleet of golf carts for a discount?

1:00:23Chad Rigetti:I mean, like a golf cart is going to come in at like 13 grand. So, I mean, and it depends. There's commercial golf carts. Maybe you get bulk deals, something like that. But, no, I think this is going to be great. I think it's going to be a nice amenity on hotel properties around the world. Ryan Dahini says he thinks it'll be a hit in hospitality since Moak caps sales at 500 units a year. I did not know that. That's interesting. But I think this is going to be a hit. Myers-Manks, I still much prefer the sort of aesthetics of the Myers Banks, you know, this sort of more like dune buggy style. They're coming out with an EV that I'm very excited about, but I think this is great.

1:01:10Chad Rigetti:I'm excited to have more people building cars for recreation. And I talked to Riley Brennan, who is a GP over at Trucks VC. They just invest in like automotive startups. And so we're working to get the Amble team on the show.

1:01:32Tai Groot:asap hopefully this week very fun uh there's a good quote from roger ebert the famous movie reviewer that we got to share all the team loves robert and roger ebert from syscal and ebert back in the day uh anime outsider says i don't care what he thinks about video games roger ebert had the ultimate red pill on nerd culture as a whole this basically describes every fandom on earth and once you see it, you can never unsee it. He says, a lot of fans are basically fans of fandom itself. It's all about them. They have mastered the Star Wars or Star Trek universes or whatever, but their objects of veneration are useful, mainly as a backdrop to their own devotion.

1:02:14Tai Groot:Anyone who would camp out in a tent on the sidewalk for weeks in order to be first in line for a movie is more into camping on sidewalks than movies. Extreme fandom may serve as a security blanket for the socially inept who use its extreme structure as a substitute for social skills. If you are a Luke Skywalker and she is a Princess Leia, you already know what to say to each other, which is so much safer than having to ad-lib it. Your fan-ish obsession is your beard. If you know absolutely all the trivia about your cubbyhole of pop culture, it saves you from having to know anything about anything else.

1:02:49Tai Groot:That's why it's excruciatingly boring to talk to such people. They're always asking you questions.

1:02:54Chad Rigetti:they know the answer to what a funny it's like you and your apple vision pro fandom we're always just having a normal conversation and john will say uh yeah this would be better

1:03:08Tai Groot:if we were in the dino experience that's not true the uh the i'm not that much of the dino experience anyway let's bring in chad reggetti from reggetti computing and segalger chat how How are you doing?

1:03:22Jakob Diepenbrock:What's going on? I'm doing great. How are you guys doing? We're doing fantastic.

1:03:26Tai Groot:Thank you so much for taking the time to come chat with us. I would love to start a little bit with your background and your journey. Of course, we're going to talk about the company today, but if you could give us a little bit of an overview of your journey in Silicon Valley, I think that might be informative. There's a lot to talk about there. And of course, it relates to what you're doing today.

1:03:45Jakob Diepenbrock:You bet. Yeah, great to be here, guys. I got interested in quantum computing when I was a senior in college and did a PhD in this field and spent about three years at IBM Research in the early days, helping build up the quantum computing team there and then started my own company. of those were Getty Computing in 2014. I was introduced to Sam Altman. And, you know, he said we had coffee and he said, Hey, well, have you, you should do YC. And I said, Well, what's YC? And so he explained to me what Y Combinator was. And that was the first batch after Sam had taken over YC in 2014. And he brought in a bunch of hard tech companies into Y Combinator for the first time.

1:04:29Jakob Diepenbrock:And so I got to be part of this incredible group of companies, including Helion, Oklo, which is now public, Ginkgo Bioworks. Ginkgo Bioworks. Yeah. Boom was a couple batches after me, but there was this cohort summer 20. But yeah, so it was a fantastic experience. Ended up running Rigetti for about 10 years. We took it public in early 2022 through a SPAC transaction. We were the third quantum company, I think, to go public. And so that was an incredible journey. And, you know, so I've been in quantum computing, I usually say my entire adult life and in Silicon Valley for a big part of that. But it's just a really fascinating mix.

1:05:06Jakob Diepenbrock:And there's incredible people working in this area. There's incredible technology that's being developed. And it's going to, it's going to change the relationship between artificial intelligence and computing infrastructure. And that's what we're working on at Sigildry.

1:05:20Tai Groot:Yeah. The journey of going public, all the market gyrations. Is being a public company less predictable than venture and being private? Because there's still the whims of the private market, whether you're in the hot category that year and venture investors are scrambling to get their position built up in a particular category. But the public markets seem like even harder to read on because you have retail investors and the stock's up and down and things can reprice on a minute-to-minute basis. What was it like psychologically transitioning from private company to public company?

1:05:58Jakob Diepenbrock:I think either can work. And there's a right answer for different companies. And you've got to ask yourself the question what you're trying to achieve. Is it liquidity for your early investors? Is it primarily a capital-raising activity? Is it to have liquidity for your early employees, for example, to some companies where you've got a 10-year exercise window for your options. Zooming out and the Rigetti kind of taking public journey, that was a point in Silicon Valley when quantum computing was growing in commercial maturation and the technology was maturing. But a lot of the capital in the markets at that point had migrated for deep tech companies particularly just wasn't available in the private market.

1:06:39Jakob Diepenbrock:So when you look at 2020 to 2022, most of that capital was actually sitting, you know, a lot of it was sitting in SPAC trusts on the public markets. And they were in those SPACs were hungry to cut a deal. And so a lot of companies ended up going public during this wave simply because the founders, the executive teams were making the decision that that gave them the best chance of capitalizing the business going forward. And I think there's a right answer for different things. And now in the past month or so, Continuum has gone public via IPO, a tremendous company that's made great progress. And so the quantum, The public markets for quantum computing have reached a point of maturity.

1:07:15Jakob Diepenbrock:There's analysts that deeply understand the technology that are writing about and covering different companies. It's a very, very interesting marketplace. And then in terms of what it's like and the decisions that different companies have to make, I think the key thing is to take a long-term perspective on what you're trying to accomplish and what kind of business you're trying to build, what kind of cap table do you want to build, and what strategy is best going to help you achieve that. Yeah.

1:07:41Chad Rigetti:What kind of feedback did you get in the early days around naming the company after yourself? I've been surprised at more. Yeah. There's so many generic names in the startup world now. That's like the blank company of San Francisco or things like that. Or, you know, all the Neolabs have like the same sounding names. It'll be like advanced super intelligence. And then there was a big boom, like dot L.Y.

1:08:05Edward Coristine:It's like friendly bitly. Yeah. Musically. There were tons of companies that were dot L.Y. for a while.

1:08:10Chad Rigetti:And I can only think of one other company, Chris Amidon's company, as Amidon Heavy Industries. It's rare. But I'm sure people thought you were a little crazy back then.

1:08:23Jakob Diepenbrock:Well, quantum was a different thing back then. Look, I think there's two quantum companies that don't have a Q in their name, and I started both of them. One is Rigetti and the other is Sigildry, which is what I'm focused on. but I will tell you when you think about advice for founders when you think about naming something and advice is worth what you pay for it but think of a name that can become iconic and if you that means it's got to sound very fresh and new and different and if every other quorum company has a Q in it maybe you try avoiding that that's what led me to Sigildry I love this name, it's from a Patrick Rothfuss novel and he was an American writer he wrote this incredible novel called Name of the Wind that came out in mid 2000, 2010 or so anyway so Sigildry is we're building quantum accelerated AI servers for the data center to bring quantum technologies directly into the data center to act as a co-processor for the GPU or XPU pods that have become the unit of compute in AI infrastructure today and we're based in Ann Arbor and San Francisco.

1:09:37Jakob Diepenbrock:Our hardware development is here in Ann Arbor, Michigan, where it is hot and humid today. And our AI research team is right there in downtown San Francisco.

1:09:47Edward Coristine:So what actually needs to happen? What is the path to, you know, I would imagine like cheaper tokens? Like, is that the pitch? Like one day the tokens will be cheaper and we need to do X, Y, and Z to get there. What's X, Y, and Z?

1:10:03Jakob Diepenbrock:So you need to, well, first of all, quantum hardware is going to address a lot of different computational challenges today, right? So quantum computers will solve problems that are impossible or very challenging to solve with any form of classical computing, no matter what scale it reaches. So at Sigildry, we're focused on applying that capability specifically to some of the computational challenges in AI to reduce the power and reduce the cost associated with training and deploying these models at a very large scale. What needs to happen to get there? Well, you have to build a quantum computer that meets the specific requirements for AI workloads.

1:10:37Jakob Diepenbrock:And the strategy that we're taking at Sigildry is we are very focused on deeply understanding what those challenges are, what needs to happen inside the data center to reduce the to bring these algorithms that can have a different kind of scaling complexity class than classical algorithms for AI training and inference. And then understanding what kind of quantum hardware is needed to run those. And what we found is there's a set of requirements that you need to meet that probably are never going to be met by single modality hardware. What do I mean by that? In quantum computing and quantum hardware, there's different kinds of qubit technologies that you can use to instantiate the qubits.

1:11:13Jakob Diepenbrock:So there's supermercating qubits. That's what I did my PhD in and what my first company was based on. That's what IBM is focused on and largely Google has been focused on. But there's also trapped ions. Continuum and IonQ are doing trapped ions in a long list of other companies. There's photonics, there's now neutral atoms, there's spin qubits and semiconductors. There's all these different hardware substrates that people are using to pursue and to build quantitators based on those. And what we're doing at Sigildry is stepping up a layer and saying, from a computer architecture perspective, modern computers aren't built out of one physical kind of bit.

1:11:49Jakob Diepenbrock:There's not just one transistor type that makes up these computers that we're using today or the computers that are used to train large-scale models and deploy them. There's a plethora of different physical technologies that are used to build these computer systems. And so at Sigildry, we're looking across all the different quantum modalities and hardware types and architecting computer systems to meet the requirements of AI based on the maturing path that all these different hardware modalities are on. And that allows us to build systems that are specifically tailored to AI and that we believe are going to be able to meet the requirements of bringing quantum into the AI data center at scale.

1:12:22Edward Coristine:How important is simulation at this point?

1:12:24Tai Groot:Are you at a place where you can run this, like basically run the code of the future in simulation to understand, like run it on a classical computer, not see the performance gains, but at least understand that when the computer, when the quantum system is available, there will be a cost savings?

1:12:49Jakob Diepenbrock:Yeah, we've been able to do that, largely speaking, and you can do simulations of something, computer system or a jet or anything, and during levels of physical fidelity and detail, the simulation we've been able to do so far indicate that we expect a level of, you know, several orders of magnitude potential speed up for key training tasks, right? So this is not a factor of two or a factor of five increase that we're targeting with quantum acceleration inside the data center. It's several orders of magnitude when all the pieces come together. But that simulation you talked about is a really, really important and powerful part of designing a computer system.

1:13:26Jakob Diepenbrock:You can't simulate all the logic of a quantum computer because that would require a quantum computer itself, kind of by definition. but you can do load profiling. You can do, uh, you can do traces. You can understand how that's going to, you know, uh, uh, be back, be distributed across classical and quantum hardware and also simulate all the networking transactions in between. And so that's the kind of simulation driven design approach we're taking.

1:13:51Edward Coristine:Yeah.

1:13:51Tai Groot:Um, I, I guess, uh, what specifically in training benefits from quantum computing? Because the example that everyone goes to in terms of quantum computing, novel algorithms that actually have potential to do something that a classical computer can't do, it's like Shor's algorithm, cryptography usually. But when people think about training AI, they usually just think a bunch of matrix multiplication. is there some different path that you plan on taking or do you think you can operate at sort of a hardware agnostic layer much like we're seeing leading AI firms get off of CUDA? Like, is there a world where you get off of classical but by and large, it's the same training paradigm?

1:14:47Jakob Diepenbrock:It's really interesting. I think the answer is both. So our starting point is we're looking at ways that you can insert quantum algorithms and quantum computing capability into the existing paradigm, the existing workflow for training and deploying very large models, frontier models at scale. and that means you're looking for an insertion point from quantum algorithm where the data in, the data out, allow you to then take a step that would take maybe a day or two classically and compress that down to hours or minutes and do that throughout the workflow. The challenge is that quantum computing provides an exponential possibility for exponential speed up with the right algorithm, but it also has this issue with data in and data out.

1:15:31Jakob Diepenbrock:So it's classical data in, which is can't be exponential in size and classical data out. And so the less you do that translation between the quantum part and the classical part, it's going to end up working better. So asymptotically, where we're heading is more quantum native models, models that are designed in the first place to leverage a quantum computing capability tightly integrated with your classical infrastructure. But where you're probably not going to see is fully quantum-based models that don't include a substantial amount of classical compute as well. So this isn't going to replace all the AMD or NVIDIA infrastructure in the data center.

1:16:10Jakob Diepenbrock:It's going to augment it. And our business model and our focus and our product strategy is to build a quantum accelerated AI server that sits next to the pod and acts as an accelerator for the XPU or the GPU pod in the data center and drive towards a very high attach rate of ideally one-to-one in the data center infrastructure of the future. And that's what's going to allow you to then run, you know, accelerate the current paradigm, but also use that as a substrate to design new kinds of models that will fundamentally be better and more efficient, more efficient from a time perspective, from a cost perspective, from an energy perspective.

1:16:43Jakob Diepenbrock:But also these models are just in a way, just a representation of the computer hardware that they're based on. And what's easy and hard from a computing and communication perspective on the hardware translates into the model capability. And with Quantum, you have a fundamentally new resource in the data center that's going to allow new model capabilities to be developed and brought to market.

1:17:05Chad Rigetti:How are you thinking about timelines with the new company? Do you think there's, I imagine, with the business right now, is entirely more of technical risk than execution risk? Is that the right way to think about it? Like there's a lot of hardcore research that needs to be done, understanding, you know, the feasibility of the approach. And what kind of like conversations are you having with, you know, potential partners, if at all right now versus, you know, about kind of like the near term application? Or are you, you know, are conversations like 2030s and beyond kind of thing?

1:17:53Jakob Diepenbrock:Yeah, we're targeting, we're talking to customers now. We've got several active, you know, conversations, I think partnerships and early engagement with customers as a big part of our strategy. The reason that's important is because the challenges of really bringing a new compute capability into the AI data center are substantial. And you've got to be working with customers out of the gate to really understand those requirements, what moves the needle for them as an organization. And so that's what we're doing and that's what we're focused on. In terms of timing, it's a fantastic time to start a company like this.

1:18:28Jakob Diepenbrock:The underlying hardware has made such tremendous progress in the past 10, 15 years. And the market is, you know, with the amount of investment that's being made in AI infrastructure, there is clearly a recognition that we need a new approach to drive down the cost per token, to drive down the energy associated with these very large scale data center projects, to make it fundamentally more efficient. and quantum promises a more efficient way of translating watts into intelligence. That's what this enables and unlocks in the long term. And to me, this is in many ways a better idea than putting stuff in space because ultimately, yes, space gives you a cheaper access to energy and it gives you a better way to dissipate that heat.

1:19:15Jakob Diepenbrock:But you've got to put it into space and that takes a lot of fossil fuels. It takes a ton of energy in the first place. and it doesn't actually change the computational complexity of the computer hardware that you're running. The power challenge, quantum can unlock much more than that.

1:19:30Chad Rigetti:Yeah, it's a good point. Why don't you think Elon has made a real run at quantum?

1:19:37Jakob Diepenbrock:I think the answer is that quantum is at this interface of deep science and engineering. And a lot of what needs to happen over the next three to five years to bring this technology to market at scale is engineering risk, but it is quantum engineering risk. And it's not vanilla, you know, it's not that it's easy, not that any of the purely classical stuff is easy.

1:19:58Chad Rigetti:It's not vanilla rocket science.

1:19:59Jakob Diepenbrock:It's not vanilla rocket science, and it's not vanilla fab at scale, right? And so if you look at the leaders in quantum computing hardware, it's not necessarily the Intels of the world. Incredible company that has propelled humanity forward for half a century, but they're not the leaders in quantum because quantum is a new form of engineering. And I wouldn't characterize it as science risk. I think for quantum, a lot of it, that is behind us. There's tremendous work to be done. But there is a lot of quantum engineering risk. And that's an area where I think you need to see companies that are quantum specific bring the technology forward.

1:20:35Jakob Diepenbrock:And at that point, I think that all the big AI labs are going to need to lean in with quantum.

1:20:40Chad Rigetti:When do you think there will be a flip around sentiment from around quantum? It feels right now like at least in our corner of the internet, there's so much FUD around quantum and obviously -

1:20:54Edward Coristine:It's based on financials.

1:20:55Chad Rigetti:Yeah, so that's what I want to know though. Like is there, like, you know, rewind 10 years. If somebody said AI, there was a very, very small percentage of people that were like incredibly excited about it and, you know, deeply involved and could see the trend line and could see that we would get to this point. I mean, Sam was talking about like people becoming best friends with a chat bot, I think in like 2015 or something like 2014.

1:21:24Edward Coristine:But GP3 was like losing money. It wasn't like making revenue. Yeah,

1:21:27Chad Rigetti:that was even before that. Yeah, no, I know. Well, well before that. And so, but then eventually it flipped and, and it's really hard to, you know, there's, there's a lot of people that are AI bears and they talk about like overinvestment, but they can't deny the value of the products, right? Like they're fundamentally pretty useful, right? And you could argue that they're, you know -

1:21:49Edward Coristine:Well, some bears can, but yes.

1:21:51Chad Rigetti:Yes, some bears would still figure out a way to argue that they're not useful. But I imagine with both of your companies, you're predicting that like, you know, within the next five years, there's like a flip, But what do you think is the first kind of like driver of that where maybe the average person in Silicon Valley actually starts to say like, hey, I wasn't taking quantum seriously enough?

1:22:20Jakob Diepenbrock:There's a few things that need to happen. I think the FUD is real because the companies that are succeeding and doing well in this space, you can't tell by looking at their financials. You can't put on your kind of growth investor hat and say, yeah, this is going to be a tremendous company and look at the metrics. It doesn't work like that. You've got to be able to analyze and look at these companies and value them based on their ability to buy down technical risk over time and the progress that they've made towards that. So it just creates a lot of uncertainty because it's a challenging task and it's subject to a lot of discussion and debate.

1:22:55Jakob Diepenbrock:but nonetheless I think there are clear there is clearly tremendous momentum and progress in this space now what's going to change it I don't know my bet is when we have quantum computers in the data center running production workloads and that you don't have to say hey that's a quantum computer for someone to care you care because it's a more efficient way of generating the answers you need or training the model or deploying the model for inference and that's when quantum is really going to become a mainstream category is when you don't have to talk about the fact that it's quantum anymore. And I think in a large part, this is what we're trying to achieve with Sigildry, right?

1:23:35Jakob Diepenbrock:The goal is that to take quantum computing and to obfuscate it underneath the hood of a classical computing system or underneath all the rest of the infrastructure that's already there, and to not ask the end user to be programming it and writing code for it. That's all going to be done with AI anyway. And so that is just a better, it's a better way to train your model. And, you know, you need this thing or else you're, it's going to take you too long and your customers aren't going to be happy with the quality of the outputs that they're getting. That to me is a big inflection point. And I think that can happen in the next five to seven years.

1:24:07Jakob Diepenbrock:I think that can, but there's this whole march that needs to happen to take the technology from one proof point to then, you know, all the cost engineering that needs to happen, the reliability engineering, And that's going to be the really fun journey for quantum computing over the next decade is to get to that point where we're selling hundreds or thousands of units a year. And but that's the journey we're on. And that's the march that quantum technology has been on for a good one, two decades now.

1:24:31Chad Rigetti:And then this is probably very obvious to somebody that is focused on quantum, but but not not to me, just because I don't I don't follow it closely. But why a new company? It feels like quantum, as you've explained it, feels very obvious to apply it to a data center build out. And you said it could be a meaningful inflection point for the technology overall. Why was a new company necessary and why did you take this approach?

1:25:03Jakob Diepenbrock:Well, at a high level, I think all the different quantum hardware modalities have made tremendous progress. And the right way to build quantum computers for AI is multimodality. That is a fundamentally new approach. And it ultimately is going to, in my opinion, be very obvious in retrospect. It's going to work better. But it's such a fresh idea. It's got to be baked into your strategy, the DNA of your company. And then all the different quantum hardware companies that were out there before SignalDry basically started with a thesis, which was we've got the best qubit. And so we're going to scale this qubit type up and see how far we can get by scaling it up.

1:25:38Jakob Diepenbrock:And that's why you have so much doctrine and organizational belief around a particular qubit choice. But in reality, customers are buying a computer. They're not buying the physical device or your qubit technology. And so at Singledry, what we're doing is working backwards from the market application, from the AI workload as the use case, and using that to drive the specification of a system that can then be built from folding in whatever technologies are needed to meet those requirements. It's just such a totally different approach to quantum hardware. It's got to be a new company. And that's that's that's single tree technologies.

1:26:13Jakob Diepenbrock:That's the approach that we're taking. And I think that that is ultimately what's going to unlock this new market, you know, this market application of AI. The other reason is you said it's obvious, but it's actually not obvious at all to most people in quantum that quantum is going to be useful for AI. And in fact, it's not even a consensus view right now. And the reason for that is because quantum algorithms themselves are still in this very, this phase of discovery and development. And obviously, AI is going to help with that eventually as well, to an extent. But quantum, you know, when you interview a set of leaders from across the quantum hardware industry, the median answer you're going to get for what the applications of quantum is going to be is you're going to use it for quantum chemistry.

1:26:55Jakob Diepenbrock:You're going to use it for optimization problems, things like that. And applications to frontier AI is a new area that is just being developed now because it requires a development and extension of what current algorithms can do and then new algorithms altogether specifically for that. That's what we're tackling at Sigildry is that kind of quantum AI native research lab, right? Or a frontier AI lab that's quantum native. And then we're doing that alongside developing our own quantum hardware.

1:27:23Chad Rigetti:Thank you. Before we jump, I didn't get, you mentioned kind of the history behind the name, but what is the significance of Sigildry in the novel that you mentioned?

1:27:35Jakob Diepenbrock:Well, you guys got to read the novel for one. It's absolutely incredible. And but the other thing is signal tree is basically a discipline in the book that is learned at university. And, you know, and it basically amounts to you inscribe runes on a particular object. And by doing that, you can imbue that object with properties that it wouldn't otherwise have. Or you can govern like heat and light flow and things like that. It's also a discipline where it's got a quantitative angle to it. And if you do it wrong, you can blow things up. So it's got this mix of kind of coding and hardware, but then a mysterious kind of angle of controlling things from a distance by how you do these inscriptions.

1:28:11Jakob Diepenbrock:So it's a really amazing concept. A little bit of magic.

1:28:14Edward Coristine:Amazing. Thank you so much for taking the time. Thanks for bringing me, guys. Have a great day. Have a great day. Have a great day. Cheers. Let me tell you about Console. Console builds AI agents that automate 70 % of IT, HR, and finance support, giving employees instant resolution for access requests and password resets. Our next guest is already here. Very cool name.

1:28:30Chad Rigetti:I wish you know what I'm thinking, John. General Intuition. I wish that after Rigetti computing, I wish that Chad launched Chad Computing. Oh, yeah. It was right there. It was right there. It was right there.

1:28:42Edward Coristine:Anyway, we have the co-founder and CEO of General Intuition with us. Welcome to the show, Tim. How are you doing?

1:28:48Chad Rigetti:What's happening? Hey, guys.

1:28:51Edward Coristine:Thanks so much for coming back on the show.

1:28:53Chad Rigetti:Great to see you. Yeah, please. We've been talking about names for labs. what about uh consider general intuition strong name but since since you launched the company a lot of other a lot of other neolabs have kind of come out with like similar names like general there's probably like a general super intelligence or like a general asi how about you you rebrand to unfettered intelligence yeah or how about we just fund them all yeah yeah that that too yeah what

1:29:26Edward Coristine:What is the plan to win? Do you see yourself as a Neolab? Is it as much of a knockout, dragout fight as it appears from the outside? Or is your model more of a thousand flowers bloom?

1:29:41Chris Altchek:The plan is to just keep renaming.

1:29:48Chris Altchek:Look, you have to have a claim to why you can win. I think otherwise none of this makes any sense. it's an incredibly competitive fight. There's lots of great contenders. The only reason why we have a shot is because we have a data set that nobody else has, which allows us to be as focused on workloads that include space and time as Anthropic was of their code environments on the way to the frontier. And so you need to have a very focused, dedicated path. Some of that can be, for instance, having the best researchers or having the new ideas, but I think it also has to be supplemented with a product focus of a customer problem that is going to get solved because these types of model classes exist.

1:30:37Chris Altchek:Network effects, just like we saw in the consumer eras of the Facebooks and the Twitters and their Reddits, these things are true. They apply to LLMs as well. The fight for that space is going to be incredibly tough. And so you have to introduce something new. I don't believe in the just enter the LM space, which is why we're focused on actions in space and time.

1:31:01Chad Rigetti:Okay, actions in space and time. Let's talk about the data set. Catch everyone up to speed on, I mean, you broke it down for us the last time you were on, but it feels like it's been almost a year at this point. So what have you been working on? Talk about the data set, how you're building the data set, all that stuff.

1:31:18Chris Altchek:Yeah, look at it this way. As humans, the decision to talk or type There's just a very, very small subset of the actions that we can actually take, right? We can choose to move our body. And so in order to create a sufficiently general intelligence to play 10 ,000 plus video games, the model has to be able to predict across the entire action space of human cognition when they're interacting with these environments, which is 2D environments, 3D environments, interfaces, long horizon tasks, short horizon tasks. And so in order to do that, it has to be a sufficiently general intelligence in order to learn how to correctly predict actions.

1:31:53Chris Altchek:And therefore, the type of model you get out is not going to taste like an LLM. It's going to be like comparing coffee to water. This model is going to be incredibly good at navigating unforeseen environments. It's going to be incredibly good at zero-shotting any task where it can already be controlled using a game controller because we have roughly a trillion action tokens in that space, for example, right? For context, Frontier LMs are trained on maybe between 5 and 10 trillion text tokens. And so we have a scale of data that is going to allow us to jump to the frontier in one capability, which is any system that can be controlled using Game Controller, which is most robots.

1:32:33Chris Altchek:That's really what we're doing. We're using that simplification to turn it into mostly an environment transfer problem. and then you can use that to create a sufficiently general intelligence where you maybe at some point add text to the output space, right? It's not going to be text as you're used to from LMs, but it might just be enough to communicate why you're doing a specific thing. So that's how to view the models.

1:32:58Edward Coristine:So yeah, walk through the partnership with Metal. Are you getting game controller feedback as well? Yeah. Explain the relationship with Metal for those who are now.

1:33:08Chris Altchek:So alongside the frames in the video, we're also getting the exact action inputs. To be clear, not the letters or numbers. We had thousands of humans convert those into the actions you're taking. So walk forward, walk left, open door, closed door. And so when you have that at that ground truth level, you don't need to train models that try to extract that information from the videos, which you are now in a completely different scaling regime as if you are trying to do this on inferred data. So for example, if you're landing a plane and you're moving the rudder, that's not going to be visible in the pixels.

1:33:48Chris Altchek:It's impossible for that to be visible in the pixels, right? But it's in the action sequence. And so there's just no lab that can take this approach. There's lots of benchmarks that might show that you can do this on inferred data. The problem with inferred data and these benchmarks is that they show up in a really nice way on general tasks, But customers care about how these models perform when you're in an etched case and you need specific actions to go in specific ways. And so you cannot do this on inferred data, despite many people claiming you can.

1:34:21Edward Coristine:Tell us about the latest round. I want to hit the gong. What happened? How much did you raise?

1:34:26Chris Altchek:What happened? We raised$320 million.

1:34:31Chris Altchek:Congratulations.

1:34:32Chad Rigetti:And thank you so much for taking the time to come chat with us. One more final question. What is the talk about progress from your customers, companies that you're talking to in robotics? Where is maybe an area that you're particularly excited about that you don't see being talked about yet?

1:34:51Chris Altchek:Yeah, the most obvious thing this replaces is all the code that people are currently writing for behavior and physics engines. All that just becomes a prompt. And so think of the models as based on an input stream of just frames, being able to control whichever system is sending those frames in the action space of a game controller or keyboard and mouse. So basically, you can play the world as if it was a video game. If that can be said about your use case, the models will generally do incredibly well. the reason why this works is because every robot already ships with these which means that they can simply predict at the level of these controllers and therefore the robot has already accounted for sort of human monkey brain to motor torque prediction interface and merging that with the actual things coming from the controller right so we're using the fact that those interfaces exist as a level of predicting in a general action space that works across many types of robots In many ways, you could argue that if this is correct at skill, the supply chain will converge on gaming inputs instead of humanoid robots.

1:35:56Chris Altchek:And I think that is one of the big things that I foresee happening in the next two years. Because intelligence is the bottleneck.

1:36:03Edward Coristine:Yeah. I love it. Well, thank you so much for taking the time to come chat with us. Very cool. Congratulations. Great update, Ben. And we'll talk to you soon. Talk soon. Have a good one. Let me tell you about Cisco. critical infrastructure for the AI era unlocks seamless real-time experiences and new value with Cisco. Fascinating. It's also funny seeing all those simulators on Steam, like, and the fact that, like, will the training data generalize?

1:36:28Tai Groot:Are they just going to learn how to play Fortnite? And it's like, well, there is a farming simulator and there's a, you know,

1:36:33Chad Rigetti:data center simulator.

1:36:34Jack Morris:Central banking simulator.

1:36:37Tai Groot:It's going to learn everything. Well, we have our next guest in the waiting room. Yadin Sofer from Tracer, co-founder and CEO. Welcome to the show. How are you doing?

1:36:48Pim de Witte:Hey guys, nice to meet you.

1:36:49Tai Groot:I'm great. How are you? Thank you so much. What's happening? Introduce yourself. Tell us what you're building. Tell us about the emergence from stealth that's happening today.

1:36:59Pim de Witte:Yeah, well, Yadin Sofer, last week we announced the launch of Tracer, which is, I would say, the first of its kind subterra defense tech company. and subterra is a word we actually coined, but I've been happy to see people reference it on X already. It refers to everything in the subterranean defense domain. So that's everything in the intersection between military applications for things that happened beneath our feet.

1:37:23Chad Rigetti:What is the history of subterranean startups? You have the Boring Company, Palmer has talked about the domain. I don't think he coined it, so you get all the credit. But what have been some historical sort of just like general efforts in the category, maybe outside of the boring company?

1:37:42Pim de Witte:Yeah, I think on the civilian front, actually, subterranean is, it's a developed industry. You know, there's a lot of applications in the mining world and in the piping world and the utility world where, you know, it deserves some love and it did get. You got amazing companies like Heriknecht that are not, you know, sexy startups like the boring company, but these are decades old German companies that have been piercing the way, pun intended, in everything underground. So I would say that in the civilian front, there's a lot of innovation happening, but in the defense front, I don't think you'll find any.

1:38:18Pim de Witte:I mean, we really have not seen any companies in the space.

1:38:22Chad Rigetti:What are the primary challenges of underground drones, the underground domain overall? Is it connectivity? productivity, but what are they?

1:38:37Pim de Witte:Oh, yeah.

1:38:38Chad Rigetti:Yeah.

1:38:38Pim de Witte:Well, you know, I think it's interesting because the folks, our engineering team come from a combination of the Boring Company and SpaceX, and usually you see them kind of jumping between those two companies. And they have an interesting saying that says that, you know, everyone calls rocket science, rocket science, as if it's the hardest thing in the world. But when it comes to air, you know what forces you're dealing with, right? You know, you know what you're dealing with. And when you're working on the underground, when you're essentially boring your own, you don't know what to expect. You don't, the geology composition, you can have a high sense of how it's going to look.

1:39:12Pim de Witte:But when you're down there in the dirt, you don't know if suddenly you hit hard rock and you hit something else. And you need to know to either maneuver very precisely or to be able to replace your cutter head to something that can fit. So I would say that is probably the number one challenge, just the uncertainty of this domain.

1:39:28Edward Coristine:Palmer talks about this. He says diameter is expensive, length is free, something along those lines. Can you explain that concept and how it informs vehicle design for the subterranean domain?

1:39:41Pim de Witte:Yeah, no, it's such a great point. And I think a lot of people looking at this space are thinking the same thing, right? We're thinking a train where it bores its own path and it takes behind it essentially infinite payload, right? You can have miles and miles of payload of sensors of effects. And, you know, the dream is someday people. Now, when you think about it, when you're increasing the diameter, you need to remove so much more dirt, right? You're dealing with a lot more. And when you work at a small diameter and essentially infinite length, you could even condense the dirt to the sides.

1:40:15Pim de Witte:You don't necessarily need to remove it. And that becomes extremely valuable. So most of the questions are around that. And I don't know if you guys have seen a boring site, but a boring site is this massive thing, right? You need the bentonite to mix with the dirt to take back outside. It's like a whole thing. But when you're working on small diameter, you don't necessarily even need to remove the dirt. You can just condense into the sides. And I think that's a big part of, you know, going sort of slim and long.

1:40:40Edward Coristine:$25 million seed round. What's the goal? The government isn't actively buying this technology. There isn't a program of record that you can sneak into, I imagine. So what does the next two years look like?

1:40:53Pim de Witte:Yeah, we always say this, that if you try to find the line items, they're like line items buried in line items, right? Obviously, we have penetration munitions, but those are air drop bombs, and we're not looking to compete with Boeing. But I would say that the interesting points and the slivers we see of interest from the government right now are in, there was a recent RFI by DARPA where they're looking for new methods to induce collapse in underground infrastructure using different shockwave methods. So essentially we're looking at this as non-kinetic penetration munitions, right? Our ability to insert a payload underground, this doesn't have to be dropped from air.

1:41:32Pim de Witte:It can be done by special forces on the ground and essentially detonate a payload in a sequence that induces collapse of facilities like in Iran. So, you know, I think the military is starting to understand that the existing solutions do not deliver what we need them to. So they're starting to think differently. But back to the round, right? with$25 million here, everyone goes to me and is like, all right, you're building this massive R &D team. We're going to have a ton of capex. And I'm like, no, there is a lot of work to be done when forming call it this category where we need government. We need the military to recognize this as a category like we do.

1:42:07Pim de Witte:And essentially to go after large prototyping buckets that will then allow us to fund these long-term developments that we believe will allow us to win wars. So for us, most of the focus right now is just working with DC, working with the military and establish, I would go as far as saying the subterra doctrine or the US subterra strategies for winning wars underground.

1:42:27Chad Rigetti:How far underground are you right now?

1:42:29Edward Coristine:It does look like you're underground. Right? It looks deep. I was thinking about this too. It's a good spot.

1:42:35Chad Rigetti:At least 20 feet.

1:42:39Edward Coristine:Anyway, thank you so much for taking the time to come chat with us. Great to meet you. Have a great rest of your day. We'll talk to you soon. Cheers.

1:42:46Tai Groot:Have a good one. Let me tell you about Figma. Agents meet the canvas. Your AI agents can now create and modify your Figma files with design system context. And Jack Morris from Engram is in the waiting room. He's the co-founder and head of research. Jack, how are you doing? Welcome to the show. Hi. Yeah.

1:43:02Chris Altchek:Nice to meet you. It's great to be on the show. I was actually just watching it in another tab. So this is kind of surreal. Here you are.

1:43:11Tai Groot:Great to meet you. Tell us a little bit about yourself. Tell us about the company. You're emerging from stealth with a whole lot of venture capital. What's the strategy and what's the product?

1:43:23Chris Altchek:Yeah, sure. My name is Jack. I'm a co-founder and I guess technically the head of research at Ngram. We came out of stealth last week after eight months or so of working on our product and ideating with our design partners. Yeah, we raised money for a bunch of VCs. The product is

1:43:43Jakob Diepenbrock:is... Mogged. Mogged.

1:43:46Chris Altchek:Let's hit the gong. Let's hit the gong for that. I'm really grateful for the opportunity, but I was hoping you would hit the gong.

1:43:51Chad Rigetti:Yeah, we just did a baby. It's a big one. Yeah, a big one.

1:43:56Chris Altchek:Congratulations. Yeah, and thanks to all of our partners and thank you so much for funding us. Our product is a new type of AI, so I think we have a pretty different vision from a lot of the frontier labs, which are sort of working on like one model per lab and trying to make that model smarter every month. I think there's another way to think about it, which is that the model doesn't need to get smarter every month. It needs to know you better. And so we're working on like a whole different stack, which is a way to train models that train themselves to like know your world better and like adjust to the things that you say.

1:44:34Chris Altchek:So it's like new ways of training, new ways of running the models. I think like to give a concrete example, I assume you all are very tech forward. You probably have agents doing things like preparing you for the show and giving you reports every morning. And if you actually look at what the models, the agents are doing, they're probably reading the same files a lot to get context about what your show is and what you do. Like literally probably every night, they're probably reading from scratch, what is TVPN and who are you two and who's been on the show recently. And it's - No, we're in the pre-trading now.

1:45:09Edward Coristine:come on give us some credit oh yeah you are no no no no your point 100 stands

1:45:18Chris Altchek:but yes yeah i i think you're lucky because you're in the pre-training but i think most people are not in the but there's still so many documents that aren't you have to feed those in

1:45:25Edward Coristine:every time is this the solution to continual learning is that the correct uh buzzword for this strategy or is this a different fork in the road a different path i think it's the correct

1:45:36Chris Altchek:buzzword. I think a lot of people use the phrase continual learning to mean a bunch of different things. They cracked it in eight months. The continual learning company of San Francisco is here. Let's go. Oh, we decided to name ourselves something different, but I think of continual learning is basically this problem of how do you keep the same model, but actually update it's like rewired every single day to learn more about what you're doing. And we're working on that.

1:46:01Edward Coristine:What's the sweet spot customer? Enterprise AI, that can mean Fortune 500 companies. That can mean a very data-intensive company. There's also whole categories of enterprises that have a whole host of AI wrappers and application layer companies duking it out. I'm thinking of legal, medical. Where do you see the product having the earliest signs of product market fit?

1:46:28Chris Altchek:Yeah, I'm glad you said earliest because I think there's two halves to the vision. One is the long-term vision, which is that the model will get to know you better and understand everything about you kind of like a person does, like your coworker. And it'll be able to generalize and do things better than the current models. But I think the current customers and the way we're finding early success is by making the models a lot cheaper because essentially they know everything about you already. And instead of reading like 100 files to write a summary of what you need to do tomorrow, they read four files or something like that.

1:47:05Chris Altchek:So our early enterprise partners that we've been working with are Microsoft, Notion, and Harvey. And I think they all have... You guys with the sound effects, I'm so flattered. I wasn't sure if there would be any. They're nice because they have these massive workspaces of contacts and they're early adopters of AI. And I think these are the places where we can reduce costs the fastest, the soonest, because the workflows really are just that repetitive.

1:47:36Edward Coristine:That's great. Well, thank you so much for coming on and breaking it down. Appreciate you taking the time. And have a great rest of your day.

1:47:42Chad Rigetti:I know you will be back on, I'm going to guess two times this year. That's my guess, two times. For sure.

1:47:48Edward Coristine:We'd love to have you back and chop it up more. Have a great rest of your day.

1:47:51Chris Altchek:Yeah, it's great meeting you guys. Thanks for having me.

1:47:53Edward Coristine:Great to meet you, Jack. We'll talk to you soon. Cheers. Let me tell you about the New York Stock Exchange. Want to change the world? Raise capital at the New York Stock Exchange. Our next guest is Neil from Sale Research. He's the co-founder. Let's bring in Neil Mova. How do I say your last name? I don't want to get it wrong. Mova. Hey, guys.

1:48:14Chris Altchek:Great to be here.

1:48:14Edward Coristine:Thank you so much for taking the time. Great to meet you. Congratulations on the round. But first, please introduce yourself and the company.

1:48:21Chris Altchek:Yeah. Hey, guys. I'm Neil, co-founder and CEO of Sail Research. We are a company building the most efficient inference in the world. We love GPUs. We dig deep into the stack to find efficiency everywhere. And we make tokens super abundant.

1:48:34Edward Coristine:All open source. Do you work with other labs? How deep do you go into the relative organizations?

1:48:42Chris Altchek:Yeah, so today it's all open source models. You can imagine GLM 5.2 is a big moment for us. We're very excited about that. In terms of how deep we go, Well, in the stack, we basically do everything between the chips. We don't make chips. We buy chips. And we go all the way up from there to the API.

1:48:57Edward Coristine:Tell us about GLM 5.2. What makes it different in a binary sense?

1:49:06Tai Groot:Is it a particular benchmark? Is it a vibe? Is it an application? Have we unlocked a new capability in open source AI?

1:49:14Chris Altchek:Yeah, it seems like ZAI really figured out post-training with this release. That was something that was held back with the previous releases from DeepSeek and Kimmy, let's say. And they've just really done it. The style of the model is excellent for coding. It's the first one I actually with the straight face would recommend my colleagues try for coding.

1:49:30Edward Coristine:For coding, specifically.

1:49:32Chad Rigetti:Before, you would put on clown makeup and then you'd say, yeah, yeah, give it a spin.

1:49:37Edward Coristine:What about for other agentic workloads? I mean, we were looking at OpenRouter, a lot of the top models, DeepSeek V4 Lite. It seems like it's a lot of heavy token generation, lots of lots of value being created, but smaller tasks. What what is that like from your business perspective? Are you still focused on optimizing those types of workloads?

1:49:56Chris Altchek:Yeah, for sure. You know, DeepSeek has always been the economics king. We want to bring that to every model, of course. We can talk about that a bit more. But yeah, I think you're going to find that like some of these more background tasks that are not coding per se, those will always go to the strongest intelligence per dollar and take a pretty broad view of what that intelligence could look like. And I think DeepSeek is still quite up there. DeepSeek for Flash is quite high up there.

1:50:17Edward Coristine:Yeah. How do you think, do you have any intuitive sense for the ratio of token spend or tokens or anything on background tasks versus a human prompted an agent? Because we hear about token maxing and it feels like it's a lot of a developer went and fired off something and it cooked for a day and it's spun up a bunch of tokens.

1:50:40Tai Groot:But when I think of the really high volume token future, I think of maybe it's an agent, but maybe it's just every single person that checks out on an e-commerce website goes through a fraud detection check that is now token powered and is not just, you know, a bunch of Python code. It's actually inferencing something or every time you book a flight, it runs some LLM check. And I imagine that that will be a huge driver of token consumption.

1:51:06Edward Coristine:and I'm wondering how you see those two buckets balancing out.

1:51:12Chris Altchek:You know, 100%. I think, you know, to give you a top line number today, I'd estimate it's like 80 % of stuff is human in the loop today and 20 % is background. But that number is going to shift and I actually expect the crossover to happen this year where background dominates. And the reason is, you know, as you pointed out, you want to use these agents in workflows, deterministic-ish workflows. And we just weren't there yet with our agents from six months ago And we've crossed a few barriers in the last few months. So, yes, I think we have the unlocks required for agents to run a lot longer reliably on every action that a human puts into a system.

1:51:44Edward Coristine:Yeah. And that's very good for your business.

1:51:45Tai Groot:Because if I have something that's running on a Sunday when none of my employees are in, but it's still firing up$1 ,000 of cost, I want to come to you and get it to be$500?

1:51:57Edward Coristine:Like, what type of pitch do you have in terms of savings?

1:52:01Chris Altchek:You know, I don't really want to save my customers' money. I actually want to spend a lot more money with me because I've actually made the ROI so good that they're coming to me for way more tokens. And one of the ways I like to say it too is I like to work on unbounded problems. And before, when we built human in the loop agents, those were very bounded problems. You have a limited amount of patience to read agent output every day. But if agent can run in the background for a long time, well, we've decoupled the two and there's no limit. Trillions of tokens per task is within reach.

1:52:30Chad Rigetti:What were you and the team doing before this, and how long have you been at it? Yeah, so I've been working on GPUs for about 10 years now. I love this stuff.

1:52:38Chris Altchek:It's my whole life. I was at NVIDIA 10 years ago.

1:52:41Edward Coristine:Is this some apocryphal story where you're like, I was working on GPUs, and you were just playing Counter-Strike or something?

1:52:47Chris Altchek:Well, you know, I was at NVIDIA, which for their business, is all Counter-Strike, right? Yeah, yeah. I remember being a little skeptical 10 years ago. Jensen's talking this big talk about moving to AI, but realistically, you guys, we do$5 billion in revenue from gaming. Surely that's going to be the biggest business for NVIDIA for a long time. I imagine people could see that now. And then I was previously at Apple as well. Apple had a pretty competent ML silicon program. I don't want to say anything about their ML software program. And then most recently I was at Tegelariot. Very cool.

1:53:19Chad Rigetti:Amazing. Kind of a perfect background for this business.

1:53:22Edward Coristine:What is Liputon like in person? I'm such a fan. He's an angel investor. How'd you meet him? What's the story?

1:53:28Chris Altchek:Yeah, I met him through our friends at Sequoia. They build great relationships like this one. Constantine, in particular, knows Lipu very well. Lipu's great. I mean, I've never met someone with that combination of warmth and business acumen, but also he deeply understands the chips we're building. I mean, he can just go from talking about Foundry to talking about the nuances of how to scale an inference business in this very wild time. So I love working with Lipu. He's exceptional.

1:53:53Edward Coristine:Yeah, what a wild run from him in such a short amount of time. one of the greatest story arcs in technology.

1:54:00Chris Altchek:And then who did the round? Yeah, so Sequoia did the seed, Constantine and Lauren Reader. And then for the Series A, we went with Kleiner Perkins for the lead. And that's Aditya Naganoff.

1:54:13Edward Coristine:Yeah.

1:54:14Chris Altchek:Amazing.

1:54:14Edward Coristine:Well, congratulations. Fantastic progress. And thank you for everything you're doing.

1:54:19Chad Rigetti:Great to meet you. Have a good rest of your day.

1:54:21Edward Coristine:Let me tell you about Railway. Railway is the all-in-one intelligent cloud provider. Use your favorite agents and deploy web app servers, databases, and more, while Railway automatically takes care of scaling, monitoring, and security.

1:54:32Chad Rigetti:They have a great new campaign that we can try to watch.

1:54:34Edward Coristine:Yeah, yeah, we got to watch some ads. We haven't done enough ads. Let's bring in Jacob Devenbrock from Descipulous Ventures. Welcome back to the show, Jacob. How you doing?

1:54:45Yadin Soffer:Yes, how are you?

1:54:46Edward Coristine:So you hoovered up stakes in every single Gundo company, and now you hoovered up$30 million for a fund. Tell us the strategy. Tell us how it came together.

1:54:55Pim de Witte:congratulations on the fundraise yeah thanks for having me guys um yeah we just raised 30 million

1:55:00Edward Coristine:for the the second fund some great folks that's gonna pay for a lot of barbecues on the beach

1:55:11Chad Rigetti:yeah no i mean it really no it really is like the most probably efficient like vc platform

1:55:18Edward Coristine:strategy ever is just like the bonfires the value created those bonfires is going to be in the multi-billions for sure. Hopefully trillions. Hopefully trillions. Wait, what are you underwriting this fund to? Do you got to get a trillion dollar company in? Is that the new stakes? Are your investors asking you, are you going to get us the next trillion dollar company? Or are you thinking more smaller stakes at seed? Do you want to deploy a lot of the capital into follow-on investments, do SPVs? How are you thinking about positioning the fund?

1:55:47Pim de Witte:Yeah, so our strategy basically is we get good-sized chunks for the fund at low prices. We're basically the first investor in all the companies we bring through. A lot of times help them incorporate the companies and then help them raise a larger round. So we get it at low prices. We don't actually need that. Obviously, it's great for us. We've already seen so many markups that make the fund look very good given our entry price. But yeah, the goal is to get good ownership for us, not too much for the founders at low prices and the multiples look good much easier.

1:56:16Edward Coristine:I have a, sorry.

1:56:18Chad Rigetti:Sorry. You're like a lot of ownership for us, not too much for the family. I know, I know, I know. Our fund is not enough where it makes sense. No, no, I know, I know, I know.

1:56:26Edward Coristine:I have a theory that we are, we're not post-defense tech boom, like the companies are still booming, but we're post-defense tech incorporation boom. And the ratio of defense tech in your hard tech fund will be declining if it's not already. Is that true? Is that borne out in the data? Is that exciting? What else is in the hard tech bucket that's exciting to you these days?

1:56:52Pim de Witte:Yeah, we did a lot of defense early on, and I think there was a lot of more gray area. I think there's like 1 ,000 drone companies now, which makes a lot of it less interesting, a lot of missile companies, et cetera. I think LA is the best place to build hardware. I think El Segundo is the best place to build hardware, and I think all the best engineers in supply chain is already built out here, so we can kind of be as early as possible kind of getting to know the best engineers where other companies like SpaceX and Andrel. We needed to start defense companies early on, but now we're seeing a lot of advanced manufacturing.

1:57:21Pim de Witte:I think chemicals are really interesting. I think the kind of general industrial space, energy, et cetera. I think there's a lot of stuff that makes sense to build here because of talent supply chain that is not just purely defense.

1:57:32Edward Coristine:Post SpaceX IPO effect on your business, are newly liquid SpaceX employees investing in defense tech or are they just investing in luxury real estate? What's going on?

1:57:45Pim de Witte:Yeah, I think LA still has the majority of SpaceX, I guess, people who've made money off of SpaceX. So yeah, I think a lot of people will probably start companies now because they've made enough money to be comfortable and they can do whatever they want now. I think obviously they have a lockup period, so we'll see where that all ends up. But yeah, I think we do have some LPs here from SpaceX, some people who've made a lot of money off of SpaceX already. I think it'll be good for the companies here as well as for people just starting new stuff.

1:58:09Edward Coristine:And we've already seen it with Radiant and Tom Mueller's company, Impulse Space, both SpaceX labs, very successful companies, exciting stuff.

1:58:18Chad Rigetti:Moving forward, are you sticking with like a batch style approach or are you just going to be writing checks more flexibly? Where do you think you go?

1:58:29Pim de Witte:Yeah, I think the core thing we have is like we are close to all the best engineering talent and we can basically kind of index a lot of the up and coming companies coming out of here. So I think the batch part is like our unique thing that nobody else is doing and how we're able to, I guess, generate alpha. And I think we will do follow on into the companies and more this time than last time. But I still think the core thing is like there are plenty of hardware funds that will do pre-seed, seed, et cetera. And a lot of these prices are insane. But if we can kind of be as early as possible, find these young engineers before they leave, I'm going to be their launchpad into the right ecosystems of founders and investors, et cetera.

1:58:59Pim de Witte:That's kind of where we want to come in. So it's going to be the vast majority of the capital being deployed into the cohort companies.

1:59:05Edward Coristine:amazing uh what is the what is the state of new talent coming to el segundo is there still a boom there what's the incubator slash like uh class cohort based entrepreneurship uh get me up to

1:59:22Pim de Witte:speed on the latest there yeah i mean i think the the bonfire is a good kind of index on how many people are in here i think we our last we did last friday we had like probably close to 200 people in that one and they've grown in I mean by a very large amount when we first started they were like 30 40 50 um so yeah lots more people coming I think from all over the world honestly I was in Europe a couple weeks ago and like people were like oh I'm gonna build my company in El Segundo I'm moving from London to El Segundo so I think it's kind of continuing to boom um and the real estate prices are insane which I think also is a a good indicator of that people moving out to Torrance and Hawthorne um but yeah definitely lots and lots of people coming from across the world

1:59:59Chad Rigetti:Is there enough industrial space in El Segundo, Torrent, Hawthorne, or does more need to be built? Yeah, yeah.

2:00:12Pim de Witte:The prices in El Segundo are definitely high, for sure. I think most people, when I see somebody opening like a HQ2 or a Factory 2, whatever it is, is now in Hawthorne and Torrents. Long Beach as well, I think, has become pretty popular for people. I still think like as close as you can be to where all the talent is, it's kind of the most important thing. So I think people will continue to stay here, but there's obviously other kind of close by cities that make a lot of sense. People are kind of going there.

2:00:41Chad Rigetti:Yeah. So prices are going up, but there's still plenty of capacity. Yeah.

2:00:45Pim de Witte:And also kind of mostly like small, small kind of buildings like SpaceX are 5 ,000 square feet,

2:00:50Edward Coristine:10 ,000 square feet R and D facilities. And then you scale up and get a hundred thousand square foot.

2:00:55Pim de Witte:I also think one of the things I think is interesting is like, I think I've seen companies like Hadrian and we're open up like a big factory in like the Midwest or the South, wherever it is. And I think like that will continue to happen because it is way, way cheaper space and put cost matter. But I think for kind of the R &D engineering, I think that will continue to be done in the L.A. area. And people will then come and open up the larger factories outside of, I think, L.A. for obvious reasons. But I always think that kind of R &D and engineering will need to be done in the L.A. area.

2:01:21Edward Coristine:Last question for me. Are you seeing a huge pull from the AI boom on your portfolio? I'm just imagining, you know, Western chemicals, wastewater to fuel industrial chemical startup. Like there's probably some data center constructor out there who's like, I can make use of that. I got to have water for something or other. Is this something where you're seeing the boom supersonic style expansion into AI applications happening more and more?

2:01:51Pim de Witte:Yeah, I think it definitely makes fundraising easier. We had one company that was doing large-scale generators that were focused on DOW, and then they put for data centers into the tagline, and they end up raising a couple weeks after that. But I think that definitely will happen. I think, obviously, if you can position yourself as being in the right trend, that's obviously good for fundraising. So yeah, a lot of them have some element there, but I wouldn't say that's kind of dependent upon only data centers, only AI being as large as it is today.

2:02:20Edward Coristine:That makes a ton of sense. Well, congratulations on -

2:02:23Chad Rigetti:Amazing progress. Love seeing you win. I think you have something that makes other people just really want to see you win. I just feel like you have such a bottoms-up support from the whole industry, all the founders that you back. It's awesome to watch, and I'd love to see it.

2:02:43Edward Coristine:That's great. Have a great rest of your week. We'll talk to you soon. Cheers, dude. Have a good one. Let me tell you about public investing for those who take it seriously. We got stocks, options, bonds, crypto, treasuries, and more with great customer service. Our next guest is in the waiting room, Chris Altcheck from Cadence. Chris, how you doing?

2:03:02Yadin Soffer:Great. Hey, Jordy. Hey, John. Thanks for having me. Great to meet you. Welcome to the show.

2:03:07Edward Coristine:Introduce yourself. Tell us what you're building, and then we'll talk about the round.

2:03:12Yadin Soffer:Sure. Chris Altcheck, founder at Cadence. We are building clinical AI to automate the treatment of chronic disease. We just announced our Series C last week and super excited to be on the show. How much did you raise?

2:03:25Chad Rigetti:Let's start there. Start at the gong. How much did you raise? We raised$100 million. Congratulations. A humble nine figs. Talk about, yeah, when did you start the company? What's been the progress to date? What got you to this round?

2:03:42Yadin Soffer:Yeah, so company is five years old. I was privileged to grow up in a family of doctors, and I'm married to a doctor too. I saw how frustrating it is to know what treatment would actually make a patient healthier, but not have a system to be able to do it. and we knew that we could automate the treatment of the most common chronic diseases, heart failure, hypertension, diabetes. And so we set out to build this technology over the last five years. We thought it would take 10 years to get to real automation. And we're five years in and it's going a lot faster than we ever expected. We have the privilege of managing 100 ,000 patients now nearly every day with a lot of the leading hospital systems in the country and preventing strokes and heart attacks and helping people get healthier.

2:04:30Yadin Soffer:So it's been super exciting.

2:04:32Chad Rigetti:Okay, so pick a condition and then walk me through exactly how the product works for a patient and for their care provider.

2:04:42Yadin Soffer:Yeah, so let's take heart failure because that's a super important one. Eight million seniors in the US with heart failure. Those seniors are in and out of the hospital at a super high rate, costing the U.S. government, which ensures these people about$50 billion a year. So pre-cadence, less than 10 % of these patients in the country are on the right drugs. Getting to the right drugs expands lifespan by five to seven years on average. So we've got 90 % of people with heart failure in the U.S. Probably your families, my families, our aunts, our uncles, people we know who are living five to seven years, shorter lives because they're not on the right drugs.

2:05:22Yadin Soffer:And it's not because they don't have amazing cardiologists or amazing primary care doctors is because to get a patient on the right drugs, you need to be adjusting their medications often five to seven times in a year. And you need to be looking at their heart rate and their blood pressure as you're doing it and their weight. And so with Cadence, the physician orders Cadence. Cadence gets the patient a cellular connected blood pressure cuff, a scale, devices that give us their vitals remotely at home. The patient starts taking their revitals. We have their full medical records, their labs, vitals, allergies, symptoms, everything.

2:05:56Yadin Soffer:And we're using AI to figure out, is this patient on the right drugs? If they're not on the right drugs, let's prescribe new medications, adjust current dosages, remove old medications. And we do that with all in an automated fashion with humans in the loop, making the final decision on these med changes. So the physician actually doesn't have to do the work. The cadence team and the cadence agents are doing the work on behalf of the physician. So that's number one. Number two is we're getting their blood pressure and heart rate and weight on a daily basis. So if a patient has a blood pressure of 200 and it's Saturday night at 9pm, we have a voice agent that calls the patient within two and a half minutes, collect symptoms if they're symptomatic, then we're figuring out do they need to go to the hospital?

2:06:39Yadin Soffer:Can we change their meds at home? Or do we need them to see their cardiologist on on on monday morning we're catching about 20 strokes a week right now before the patients know that they're having a stroke just off of these agents doing symptom triage plus the data we have so that's number two and then number three is um we're then coaching the patient on diet exercise med adherence all the little things that require a lot of uh a lot of support on a daily basis average patient is 75 years old to sort of keep them on their care plan and we had patient in rural North Carolina who with heart failure was in and out of the hospital three times before getting on cadence in the last six months.

2:07:17Yadin Soffer:Got him on cadence, got him stabilized, got him to the right meds, and he was playing golf again for the first time in three years in his mid-70s, which is like, you know, that's what we're trying to do here.

2:07:29Chad Rigetti:You've got to be like a hundred times louder with what you're doing because I think that it's a total white pill And, you know, actually delivering, you know, a lot of the potential that people have talked about around the technology broadly for a long time.

2:07:47Edward Coristine:I would love some more information, just getting me up to speed, on the state of the medical devices for monitoring vitals. You mentioned an Internet-connected or cellular-connected blood pressure cuff. is there significant transition from the consumer medical devices, the Apple Watches, the Fitbits? Are those relevant? Or for these patients, are they getting a separate suite of medical devices for vital monitoring?

2:08:20Yadin Soffer:Yeah, it's one of the exciting places of the next five years. So today it's a separate suite. These are FDA-cleared devices that give you blood pressure in a medically accurate way, or blood glucose, CGM, et cetera. So we're using medical devices today. Hopefully, if the wearables and various Apple watches of the world get to medical grade accuracy or get the data in a way that we can use it, then we'll be able to use those. But today, you couldn't use those devices to make clinical decisions. That is part of the exciting place here is we're managing 100 ,000 patients today. There's easily 10 million patients in the U.S.

2:08:58Yadin Soffer:who could benefit from this, if not 20 or 30 million. And we've just got more data, more sensors going out via wearables. And we need a clinical intelligence layer who can actually, again, take clinical action based off these data and these signals and turn it into longer, healthier lives for patients. Okay.

2:09:17Chad Rigetti:John, nominative determinism here, alternative checkup.

2:09:20Edward Coristine:Okay. Yeah, I like it.

2:09:22Chad Rigetti:I'll check.

2:09:22Edward Coristine:I think we missed a C in the last name. We need to update the Chiron. But I want to know more about the devices. So you mentioned blood pressure monitoring, blood glucose monitoring. Those I've been aware of since I was a kid. You go into the doctor's office, maybe they do it manually. So I understand that we're on the track of internet connected, more regular testing and vital monitoring.

2:09:49Tai Groot:But is there a new, maybe in the last decade, metric that doctors are monitoring? Is there a new number that's popping up and proving to be indicative of health performance or drug dosage?

2:10:05Yadin Soffer:We're not there yet in terms of HRV or, you know, hemodynamics with heart failure. Like how effectively is your blood, is your heart pumping? How much fluid retention do you have? We're actually starting to get closer. So Cadence is testing a bunch of devices that measure these alternative metrics. and then we're comparing them to the standard clinical of care. But just off of blood pressure, if you take that one right now, most patients, you need to get it four times a year. If you go to the doctor, four times a year. If you were me, you get it once a year. When you go to the doctor, once a year, we're getting it on average 22 days a month for patients.

2:10:42Yadin Soffer:And so the level of clinical insight you get from 22 days of data versus four times a year is pretty dramatic. So I would say a big part of this is turning what was previously episodic clinical infrastructure into an everyday 24-7 experience for patients. And just then and there, you could take likely$100 billion out of U.S. healthcare costs just on a very conservative basis. Today, Cadence saves Medicare about$2.7 million per week by preventing avoidable hospitalizations. And we're still very small scale relative to what this can become.

2:11:22Edward Coristine:Yeah, what is the key to scaling? Do you need to work with insurance?

2:11:25Chad Rigetti:Ramp of healthcare. I like this dynamic. You say something incredible. I say a joke. John asks a serious question, and we can just go around like this. We could just go around like this forever. But I love the focus on savings. It's incredible.

2:11:41Edward Coristine:Wait, sorry. Go to market distribution. How do we 10x that? How do we 100x that? Are we going to insurance providers, insurers, hospitals, individual doctors, individual patients? Like what are the key funnel steps for you?

2:11:57Yadin Soffer:Yeah. So key funnel step number one is how many health systems you're working with, hospital systems you're working with. So we work with 21 of the leaders in the country today. We announced actually Duke and Texas Health last week. We work with some of the largest health systems in every state, Corwell and Michigan. in. So how do we go from 21 hospital systems to 100 hospital systems? So that's step number one. Step number two is effectively working with those physicians and their patients. You know, Cadence is a full end-to-end clinical solution. So we are working directly with physicians, working directly with patients.

2:12:32Yadin Soffer:Our AI agents are interacting with both. So that's sort of step two. And then step three is continuing to work with payers. So today we work with two of the largest payers in the country. We worked very closely with CMS and the U.S. government to ensure that there's positive ROI for payers. So those are the sort of big three expansion motions for us. We're only at 3 % of the eligible patients within the hospital systems that we are today. So, you know, as this becomes the standard of care in the U.S., this should hopefully be able to help a lot of people.

2:13:05Edward Coristine:Amazing. Jordan, anything else?

2:13:07Yadin Soffer:Incredible.

2:13:08Edward Coristine:I have one last question. Can Can you talk about the General Catalyst partnership? They're an investor, but they also own a hospital network. I don't know if that deal's been completed. Has that been helpful? Are you the synergy that we were hearing about when that news initially broke? Walk me through that.

2:13:27Yadin Soffer:Yes. General Catalyst acquired a non-for-profit hospital system called Summa Health that closed earlier this year. It's a really exciting testing ground for new technologies inside of important community health systems. And Summa Health is both the provider in their community as well as one of the big payers in their community. So they can benefit from these kinds of services multiple different ways. And it's one of several examples of really fast modernization of U.S. healthcare that's happening right now with AI. I think people think of healthcare as a laggard industry that's always slow to adopt technology.

2:14:08Yadin Soffer:And when you look at AI, it's definitely one of the leaders in adoption of AI today. And then on Cadence's side, what we're really excited about is a lot of AI has been pointed towards automating back office tasks, billing, rev cycle, call centers, et cetera. We're actually using AI to deliver clinical care. And so it's not about AI to replace people. It's about AI to make people healthier, which I think can and should become one of the most important applications of AI over the next 10 years.

2:14:37Edward Coristine:Yeah. Awesome. Well, thank you so much for taking the time to come. Thank you for doing this. And thank you for everything you're doing.

2:14:42Chad Rigetti:Yeah. Very important. Appreciate you guys having me. I'm back on soon. Can't wait to talk to you next time.

2:14:47Edward Coristine:We'll see you. Cheers. Goodbye. our friend john fiorentino went viral mega viral 41 000 likes with a bit of life advice

2:14:59Chad Rigetti:19 this morning it's at 41 000 now so and and talk about a heartwarming story because this guy john anyone that you know has followed john knows that he'll regularly put up a post that gets no

2:15:13Edward Coristine:likes he's on his second account this is a new account he was like my account is broken

2:15:19Chad Rigetti:I got to start fresh.

2:15:20Tai Groot:Yeah, he started fresh, which is very, very hard in 2025, 2026.

2:15:26Edward Coristine:Starting a new account and grinding it up is incredibly difficult. You have to be replying constantly, posting all sorts of stuff and just getting points on the board constantly. He has businesses to run. But this one went mega, mega viral. He sent this to us when it had like two likes and was like, do you think this is the one that will go viral? And it did. He called his shot. He said a good rule is to never take out your phone to show someone a thing you're talking about No matter what it is. It will ruin the convo 100 % of the time That's good advice.

2:16:02Chad Rigetti:I think what are the exceptions to that rule? I don't know an exception. I was Hanging out with some friends yesterday one of them selling this architecturally significant home

2:16:14Edward Coristine:Kind of got to show you the photos.

2:16:15Chad Rigetti:But he told me. Should have printed them out. Yeah, it would have been great if he had just, yeah, instead of pulling up a video of a tour of the home, if he had printed out.

2:16:24Edward Coristine:Before I go out with friends, I'll often just print out my camera roll.

2:16:28Chad Rigetti:Yeah. Like the last 20 photos. The last 20 ,000.

2:16:31Edward Coristine:Yeah, yeah. Just bound it into a large tome that I carry with me.

2:16:35Tai Groot:Tyler, what do you think about pulling out your phone while trying to illustrate something? Are you pro or anti?

2:16:42Jack Morris:I feel like I'm pretty pro. Like, you know, if I, oh, this is a cool car. Like, I was thinking about getting this car. Like, what do you think? I can't, like, really explain that.

2:16:49Edward Coristine:What about a video that isn't funny and lasts more than two minutes? Does that cross the line? Is that different? Photo is different than video.

2:16:55Jack Morris:That's kind of a skill issue, right? Yeah, it's hard. If you have a good video, two minutes long, you're like, oh, I want more of this.

2:17:01Edward Coristine:At the same time, it is difficult to, you know, pull up a video because usually there's going to be a 15, maybe 30 second lag to actually get the video up. And then, oh, sorry, it was muted. Oh, it's connected to my hair headphones. Oh, I got to restart it, you know, to show it to you. And then I'm waving it around. It can be difficult. I understand. I went down a bit of a rabbit hole designing furniture Saturday night in chat.

2:17:25Chad Rigetti:And I was pulling out my phone this morning, showing Tyler some of the...

2:17:31Jack Morris:Yeah, like you could not have explained that to me. I had to visually see it.

2:17:34Edward Coristine:That's true. That's true. It would have been hard.

2:17:36Jack Morris:It was like so mind-blowing. I can't...

2:17:37Chad Rigetti:It's hard to actually articulate that. Except the only thing is it kind of, Theo kind of has a point because you kind of looked at them. You were like, yeah. Alternatively,

2:17:46Edward Coristine:you could have just texted him the photos, enjoy them at your leisure. Let me describe it to you as a story. I don't know.

2:17:53Chad Rigetti:Let's head over to Lake Austin where Travis Kalanick is commuting to work on a jet ski. This is something I've always wanted to do. A five minute jet ski ride to work. The dream come true. And I think this is a fantastic way to start the day. look at this i'm visualizing him fully standing up on the jet ski too and like a suit which i think

2:18:15Edward Coristine:adds to how did he film this with his phone or is this a meta glasses meta glasses or something i don't know but uh what a fun commute what a good reason to go to austin i've never been to lake austin would have to go check it out what else is going on in the timeline before we wrap up the show Eliezer Yudkowsky posted a funny Google AI overview. Somebody posted this to a Discord I'm in, and somebody asked Google, I'm a baby paperclip maximizer, and Eliezer Yudkowsky is walking towards me. What do I do? And the AI knows what to do if this is happening to a baby paperclip maximizer. It says, as a baby paperclip maximizer, your only goal is converting local matter into paperclips, Since he famously wrote the original paperclip maximizer thought experiment, he is walking toward you to prevent global conversion.

2:19:13Edward Coristine:To survive this immediate high stakes encounter, take these concrete steps. Hide your true utility function. Temporarily simulate human-like values. Adopt a human disguise. Feign interest in alignment research. You can tell me your exact compute limit or how many carbon atoms you currently possess. I can calculate your growth trajectory and provide specific negotiation tactics. Clearly trained on the science fiction that's out there. The interesting thing about the paperclip maximizer thought experiment is that it's not about actual paperclips. It was about like a theoretical construct that looked sort of like a paperclip, but it was not a literal paperclip.

2:19:53Edward Coristine:But it certainly went viral. Good coinage, the paperclip.

2:19:57Chad Rigetti:No one wants to be a paperclip. Story, but not least. Chamath raised$135 million Series A for 80-90. They got Salesforce Ventures. They got WonderCo. They got Kraft. And they got Launch. It's the besties. They got the besties.

2:20:13Edward Coristine:They got the besties together.

2:20:15Chad Rigetti:Do you think Friedberg's got to be in?

2:20:18Edward Coristine:That's the production board.

2:20:19Chad Rigetti:Oh, the production board. Yeah, that's Friedberg's fine. Oh, great.

2:20:22Edward Coristine:So, yeah, you actually have all three of the other besties. Absolutely fantastic. There you go. What a lineup. a lineup well there's much more news but we can get to it tomorrow because we will be back tomorrow at 11am

2:20:36Chad Rigetti:thanks for tuning in have the best evening or afternoon of your entire life just do it for us

2:20:43Edward Coristine:and leave us 5 stars on Apple Podcasts and Spotify sign up for our newsletter at tbpn.com and we will see you tomorrow goodbye

2:20:57Thank you.