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

(02:21) - Open-Source AI Battle (14:33) - GLM-5.2 Review (21:38) - Google Throttles Meta (27:20) - 𝕏 Timeline Reactions (34:38) - Micron Margins Moon (38:48) - Comcast Splits in Two (43:04) - Europe Meets Suburbia (48:18) - Edward Gorberstein, head of engineering at the National Design Studio, discusses the launch of Ramparts, a local-first privacy model that allows users to control the data they share with AI by keeping personal information on their devices. He explains that existing AI models are too large to run in browsers, preventing in-browser PII removal, and emphasizes that Ramparts is open-source, with weights available on Hugging Face, enabling technical users to create custom applications. Gorberstein highlights the studio's mission to improve the American digital experience by developing user-centric software, citing previous successes like Trumper X, which saved users over $500 million in drug costs. (57:53) - 𝕏 Timeline Reactions (01:03:16) - Chad Rigetti, founder of Rigetti Computing, discusses his journey from developing quantum computing at IBM to establishing his own company, which went public in 2022. He highlights the importance of integrating quantum technologies into data centers to enhance AI capabilities, emphasizing the need for a multimodal approach to quantum hardware. Rigetti also addresses the challenges of transitioning from private to public markets and the significance of long-term strategic planning in the evolving quantum computing landscape. (01:28:42) - Pim de Witte, CEO of General Intuition, discusses the company's unique approach to AI development by leveraging extensive datasets of action-labeled video game footage to train models capable of spatial-temporal reasoning. He emphasizes the competitive nature of the AI industry and highlights General Intuition's distinct advantage: a proprietary dataset that enables their models to predict actions in both virtual and physical environments. Additionally, de Witte announces a recent $320 million funding round, bringing the company's valuation to $2.3 billion, which will support further advancements in their AI research and applications. (01:36:39) - Yadin Sofer, co-founder and CEO of Tracer, discusses the company's emergence from stealth with the launch of a subterranean defense technology firm. He highlights the challenges of underground operations, such as unpredictable geology, and emphasizes the importance of small-diameter, long-length designs for efficiency. Sofer also mentions Tracer's $25 million seed round aimed at collaborating with the military to establish a U. S. subterranean strategy for warfare. (01:42:52) - Jack Morris, co-founder and head of research at Engram, discusses the company's recent emergence from stealth with $98 million in funding from investors like General Catalyst, Kleiner Perkins, and Sequoia. Engram focuses on developing AI systems that enhance human intelligence by creating models capable of understanding users' unique contexts and workflows, thereby improving efficiency and reducing costs. Early enterprise partners include Microsoft, Notion, and Harvey, who benefit from these AI solutions that adapt to specific organizational needs. (01:48:03) - Neil Movva, co-founder and CEO of Sail Research, discusses his company's focus on building the most efficient inference systems for AI agents that operate autonomously over extended periods. He highlights their commitment to open-source models, such as GLM 5.2, and emphasizes the importance of optimizing the entire stack—from hardware to API—to enhance efficiency. Movva also notes the shift in AI workloads from human-in-the-loop tasks to background processes, predicting that background tasks will soon dominate, and underscores the need for infrastructure that supports long-running agents effectively. (01:54:34) - Jakob Diepenbrock, the 22-year-old General Partner of Discipulus Ventures, recently closed a $30 million fund targeting early-stage investments in defense-tech, energy, mining, manufacturing, and other critical industries. In the conversation, he discusses the firm's strategy of securing significant ownership in startups at low valuations by being the first investor, often assisting with company incorporation and subsequent fundraising. He highlights the advantages of El Segundo's robust engineering talent and supply chain infrastructure for hardware development, noting a shift from defense-focused investments to sectors like manufacturing, chemicals, industrials, space, and energy. (02:02:52) - Chris Altchek is the founder and CEO of Cadence, a health technology company that partners with major health systems to provide remote patient monitoring and management for chronic conditions. In the conversation, Altchek discusses Cadence's recent $100 million Series C funding, the company's rapid progress in automating chronic disease treatment, and the significant impact their technology has had on patient outcomes, including preventing strokes and heart attacks through real-time monitoring and intervention. (02:14:40) - 𝕏 Timeline Reactions TBPN is made possible by: Ramp - https://ramp.com Public - https://public.com Cisco - https://www.cisco.com Console - https://www.console.com CrowdStrike - https://www.crowdstrike.com Figma - https://www.figma.com MongoDB - https://www.mongodb.com NYSE - https://www.nyse.com Railway - https://railway.com Shopify - https://www.shopify.com Codex - http://openAI.com/codex Follow TBPN: https://TBPN.com https://x.com/tbpn https://open.spotify.com/show/2L6WMqY3GUPCGBD0dX6p00?si=674252d53acf4231 https://podcasts.apple.com/us/podcast/tbpn/id1772360235 https://www.youtube.com/@TBPNLive

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00John Coogan?

  You're watching TVPS. Today is Monday, June 29th, 2026. We're live from the TVP in Ultra Dome, the temple of technology, the fortress of finance.

• 0:10Jordi Hays?

  The capital of capital.

• 0:12John Coogan?

  Tell you about ramp.com. Time is money. Say both. Easy use corporate cards, bill payments, accounting, and a whole lot more.

• 0:19Jordi Hays?

  Lot more.

• 0:20John Coogan?

  All in one place. I'm gonna adjust my IEM. Well, on the front of the Wall Street Journal today, this is how you know this is the whole AI 2027, Washington waking up. The AI stories are making it to the front page, the the world news section, not just the business and finance section, more and more. So the the very front page of the Wall Street Journal, of course, the picture is about the heatwave, but the lead, the the story with the largest text is about artificial intelligence. China resets the AI race with the United States as security models mark gains. We're going to get into it. This is a fascinating debate because I

• 1:04John Coogan?

  thought that we'd have a conclusion to the open source AI debate by now. Either they would, the the frontier would have collapsed and there would be, you know, perfect commoditization, or they would have fallen so far behind.

• 1:18Jordi Hays?

  It'll it'll just go, it's over. We're so back. It's over.

• 1:22John Coogan?

  If you're in open source AI, that's exactly how it feels.

• 1:25Jordi Hays?

  Before we get into the story completely, Hill in the chat said, did you see the US National Design Studio open sourced a privacy model? We did.

• 1:34John Coogan?

  And we got them coming on the show today.

• 1:36Jordi Hays?

  In just 45 minutes.

• 1:38John Coogan?

  At 11:45.

• 1:39Jordi Hays?

  I are going to be talking about a first iteration on device PII redaction model that is far smaller than existing models.

• 1:49John Coogan?

  It's actually, it's actually tiny. It's 15 megs, which is, and you can do it in the browser.

• 1:54Jordi Hays?

  And we have Chad Rigetti is coming on to talk about a whole lot of quantum mumbo jumbo. We'll see what's going on there.

• 2:04John Coogan?

  And Pim's coming back on from General Intuition. And we got a bunch more founders coming on. Jacob, Deep and Brock were announcing a $30 million oversubscribed fund with tons of TBN guests already in the portfolio. The rest of the portfolio soon to be on the show, I'm sure. Anyway, open source AI. So the, the big, the big story is centered around GLM 5.2 from Z.ai. It was officially released June 13th. So it's taken a couple of weeks for it to really break through to the front page of the Wall Street Journal. But there's seen some strong performance on benchmarks, some positive reviews from developers. I have a whole review from Tyler we can go through in a little bit.

• 2:44John Coogan?

  But we're now entering another round of debates around open source AI. What can the model actually do? Is this a threat to national security? What are the geopolitical ramifications here? And so I'm sure this will be an ongoing conversation throughout this week, probably next week. We have some guests lined up to help contextualize it. But laying down the facts from the journal. Security researchers said that a new AI model released this month by China's ZPU AI, also known as Z.ai, can match the latest US models when it comes to finding security bugs. A development poised to reset the global tech race and pressure the White House in its overhaul of US AI policy.

• 3:25John Coogan?

  So unlike models from Anthropic or OpenAI, ZPU's GLM 5.2 is open weight. You can just download it, run it anywhere. You don't need to go to an API. You don't need to go to a private company and pay them. You can run it on your own server, provided you have the electricity and GPUs to do so. It is expensive to run, as we'll go into, but it is open weight. The Wall Street Journal says that means it can be downloaded, run on hardware, operated by anybody and can be modified and used without supervision. Scary stuff. Open weight models are ideal for users who want unfettered access to systems they control. But they're also ideal for hackers who want to run them in the shadows.

• 4:06Jordi Hays?

  Unfettered intelligence.

• 4:08John Coogan?

  Unfettered.

• 4:08Jordi Hays?

  We were completely out of, we were completely out of names. For new Neo Labs.

• 4:13John Coogan?

  That's a good Neo Lab name, yeah.

• 4:15Jordi Hays?

  Unfettered Intelligence.

• 4:16John Coogan?

  Unfettered Intelligence is good. GLM 5.2 has ranked as one of the top 10 most used AI models, according to data from OpenRouter, a company that provides access to more than 400 AI models. And what a fantastic business. Alex Atala over there, absolutely cooking at OpenRouter. It's such an exciting way to plug into the AI race without actually needing to play the benchmark game so much. Be the front door. Anyway, in some benchmarking tests, according to cybersecurity company SEMGrep, GLM 5.2 bested Anthropic's Claude Opus 4.8 model, which was released in May. When given further instructions, Opus 4.8 and GLM 5.2

• 5:01John Coogan?

  can match Mythos in bug-binding ability, according to researchers. So, prior to this launch, and there's a chart that we should pull up here about overall AI capability. We can talk to Tyler about what this chart actually means. But there was this narrative brewing that open-source AI was slowing down relative to the closed-source frontier. And I saw a lot of American AI fans sort of cheer for this. Hey, we have the capital markets, we have the data centers, we have the researchers, and so we are able to push the frontier at a different rate. And if we're actually growing at a faster rate in America, within the closed-source labs, that will compound and there will be a stronger takeoff

• 5:43John Coogan?

  in the American closed-source AI industry. Now, this chart sort of goes back and forth, and there's some debate over it. It's in the newsletter, you can go sign up at tbpn.com. While we're pulling that up, let me tell you about Codex. Codex is a powerful workspace for getting work done with AI agents. Whether you're writing code, analyzing data, creating content, or automating business workflows, Codex helps you move projects forward from start to finish. So. This chart, which we can pull up, shows progress from GPT-4O to 0.1, 0.3 mini, 0.3, OPUS 4, GPT-5, 5.2, OPUS 4.6, GPT-5.4, GPT-5.5, showing a linear trend in this Elo.

• 6:25Jordi Hays?

  Ryan says GLM 5.2 sounds too much like a gray market peptide.

• 6:31John Coogan?

  It actually does. It does sound a lot like that. And then you can see the red line are the Chinese models, which are also improving over time, but at a slightly lower rate. And so the question was, are they going to plateau while America's progress continues to advance? And this latest model, GLM 5.2, seems, it's very hard to apply it to this particular benchmark, because this Elo was, can you give us some background, Tyler, on where this chart came from, what this is demonstrating?

• 7:05Speaker 02

  Yeah, so this is by Casey. I think that's how you pronounce it, the Center for AI Standards and Innovation. They have this way to calculate, like the Elo of model is basically a kind of approximation of a bunch of different benchmarks. Some of those, like, are proprietary, like, they're not open, so it's actually hard to run these. Also, because I was basically trying to bench, like, all the recent models since this was published, I think it was, I want to say, May 1st.

• 7:30John Coogan?

  Yeah, it'd be great to throw 5.6, Sol, Methos, and Fable. It would be great to just continue this chart, because it's an interesting trend.

• 7:38Speaker 02

  Yeah, so a lot of those benchmarks are not actually public, so it's very hard to estimate. But I tried, I got, you can look at, like, some of the benchmarks that are public, that you can reference, you can kind of match them up to previous models. 5.2 looks like it is like a big step up from the, like, Chinese trend line, right? But even then, I think it's hard, like, I think the group of benchmarks that were chosen for this Elo, like, definitely

• 8:08Speaker 02

  Accentuate the gap between US and Chinese labs. I think there's much of other like groups like Epoch AI has done a chart. They basically a relatively stable gap between closed source and open source models.

• 8:21John Coogan?

  Yeah.

• 8:22Speaker 02

  Since like 2023, like a long time.

• 8:24John Coogan?

  Yeah. And perhaps at this point, the discussion should be more centered around cost per task more than cost per token.

• 8:33Speaker 02

  Yes, yeah, definitely. Because even like, you know, new models, a lot of times when they come on like, okay, maybe the token price is actually the exact same, but the token efficiency is much better. Then when you do a lot of these tasks, it's like, it's not the price for tokens, the price per, you know, something completed. And then you actually see it go down.

• 8:51John Coogan?

  And there's a lot of test time scaling laws where you can just throw a million dollars of compute at a particular problem and all the models do really well at it, but it's completely non-viable for any real enterprise use case and probably not even viable if you're trying to be a nefarious hacker or something.

• 9:07Speaker 02

  Yeah, most people are saying like 5.2 is very token hungry, right? So it uses a lot of tokens. So maybe it like, it definitely is much cheaper than the frontier models. It's.

• 9:16John Coogan?

  On a per-token basis.

• 9:17Speaker 02

  On the per-token basis.

• 9:18John Coogan?

  But on the per-task basis, it might be more expensive.

• 9:22Speaker 02

  Yeah, I mean, that's still, it's generally not, but on specific tasks, you can get, you know, if you have low-thinking models, low-thinking mode on the closed source ones, you can see.

• 9:31John Coogan?

  Well, let's revisit John Ludig's post from 2024, May 2024. This is pre-deep-seek, talking about his prediction about why the future of foundation models is closed source. He got a lot of pushback from this because a lot of people like open source models, but he laid out a thesis around closed data, closed source data, flywheels, exponential capex, intensivity of training. And he said, "Open source will have a home wherever smaller, less capable and configurable models are needed, enterprise workloads, for example. But the bulk of the value creation and capture in AI will happen using frontier capabilities.

• 10:13John Coogan?

  The impulse to release open source models makes sense as a free market." Strategy and as a path to commoditize your complements. But open source model providers will lose the capital expenditure war as open source ROI continues to decline. And that was the thesis around the time that the open source AI discussion was primarily driven by Mark Zuckerberg's work at Meta on the Lama family of models. The idea was that Meta would benefit from attracting talent. It was good marketing. It told the story that Meta has an AI story and has AI talent in house. Even if they weren't monetizing it and sharing, you know, a really fast takeoff in ARR around those models.

• 10:55John Coogan?

  It showed that, hey, they're able to develop these models and and that might help them cut their costs in the long term. Very interesting that that wound up being very different in 2026, looking at the news today, which we'll go into about them spending a lot on Gemini. There's been reports about them spending a lot with other closed source frontier labs that they should have commoditized with their open source plan. But nonetheless, that was the idea with Meta. But then China sort of woke up and the deep seeks and deep seeks launch at the start of 2025. And the game theory became way more complicated. So George Hatz sort of summed this up nicely. He has a take in AI will be massively deflationary, a post from just a few weeks ago.

• 11:38John Coogan?

  As to why China benefits from investing in open source more than American firms. He says, this explains why Chinese, the Chinese are giving the much more moderate resources to train models away for free. They love to see deflationary economics in the US. It is not as much less of a service based economy. And so if they can go and give away free tools that deflate the value of the service sector, that is an advantage to the Chinese economy in his formulation. He says, even if you don't regulatory capture the US government, nobody is getting a monopoly on AI. We don't live in a unipolar world anymore.

• 12:15John Coogan?

  And so he compares what's happening in, he likens what's happening in DC to sort of rearranging deck chairs on the Titanic. It's a very fun, fun piece. But so we're back to this discussion of what are the consequences and the impacts of open source models, particularly in the United States. And there's been this clip that's resurfacing from Dario Amade when he was testifying in front of Congress in 2023, and it's now recirculating, and it was reposted like he just said it, and he did not. So be clear about that. This is from three years ago. But some of his predictions were very prescient as of where the frontier is today. So he said, I'm very concerned about where things are going. If we talk about two to three

• 12:59John Coogan?

  years for the frontier models for the bio risks, is sort of a bad transcription of what he was saying. But he's talking about 2025, 2026. Remember, he was saying this in 2023. We're there now. I think the path that things are going in terms of the scaling of the open source models, I think it's going down a very dangerous path. And again, if the path continues, I think we could get to a very dangerous place. So he was worried about cybersecurity and bio risks being open sourced, and then not having a counterweight to that. Now, the good news is that we've talked to the CEOs of cybersecurity firms like CrowdStrike and Palo Alto Networks, and they've been working with

• 13:40John Coogan?

  Mythos and GPT 5.5 Cyber for months now to harden systems from LLM-driven attacks. And so there's still this gap between closed source and open source models, and that gap allows white hat hackers to implement fixes before black hat hackers have a chance to exploit easy bugs. There still will be a bigger discussion here, though, in DC over the next few months as the frontier models roll out, and the gap doesn't appear to be widening at the moment. So security stances must adjust. It's not a closed source is falling behind, so it's never going to be an issue. There will be this gap, and how the American cybersecurity industry, and eventually the biosecurity industry, implements, changes, and fixes before open source catches up, or

• 14:24John Coogan?

  commoditizes and makes that particular capability widely available, is going to continue to be important. 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 tbn.com, and you can tell us, like, what is the shape of this model? How are the reviews?

• 14:43Speaker 02

  Yeah, so I think so far, one of the main things is like, people are saying it's, oh, it's distilled, right? This has been a big thing with a lot of these open source models, especially 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,

• 15:03John Coogan?

  I think Anthropic

• 15:04Speaker 02

  of anthropic models.

• 15:05John Coogan?

  Didn't Anthropic openly accuse Alibaba of distilling?

• 15:10Speaker 02

  Yes, a number of these labs.

• 15:12John Coogan?

  Yeah, and there's also been a big, like, 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 and subscriptions and APIs to then create a front end to, like, the model that can be served at a very high rate through a VPN, most likely. What's interesting is that you'd think that if you were going to do a training run, you would just find and replace some of the other lab's name before you hit run? Is that not something people can do? I don't understand.

• 15:51Speaker 02

  Yeah, I mean, it also depends on what you're actually, like, maybe you're not directly distilling on the API, but, you know, you're training on, you know, public GitHub, you know, repos. And those were all used, those were all, you know, made with source models. Then you're kind of like distilling, but it's not really like, is this really counted as distilling? I don't know.

• 16:09John Coogan?

  Yeah.

• 16:09Speaker 02

  But 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, like, labs.

• 16:19John Coogan?

  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 an 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 and open source repos that have effectively been, completely been rewritten by closed source models.

• 16:53Speaker 02

  Yeah, and so I think it's safe to say that, like, we've generally seen that distilled models generally will generalize worse, right? So you'll see really good benchmarks, maybe they're benchmarks, maybe they're not. But even if they're not like directly benchmarks, you still find that they generally

• 17:09John Coogan?

  Yeah, they're kind of accidentally benchmarking.

• 17:11Speaker 02

  Yeah.

• 17:11John Coogan?

  Yeah.

• 17:12Speaker 02

  So you should always, so I think initially you should just be a little bit suspicious of these super high benchmark scores.

• 17:16John Coogan?

  Yeah. But they lack that big model je ne sais quoi.

• 17:20Speaker 02

  Yeah, and this is like anecdotally reinforced. A bunch of people have been saying, you know, 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 to kind of benchmark this.

• 17:33John Coogan?

  Yeah.

• 17:34Speaker 02

  They'll perform a bit worse.

• 17:35John Coogan?

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

• 17:53Speaker 02

  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.

• 17:58John Coogan?

  Sure.

• 17:58Speaker 02

  Not that, and maybe, maybe it's a bit harder than that, but I think you can kind of get around like that kind of stuff.

• 18:03John Coogan?

  Okay, yeah. 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.

• 18:26Speaker 02

  Yes, I think like what we've seen is, you know, like a marginal IQ point of the models is like extremely expensive. Frontier models are getting very expensive.

• 18:37John Coogan?

  Yeah.

• 18:37Speaker 02

  People have to cut back, you know, they're talking maximum, this is like massive bill in their balance sheet, whatever.

• 18:44Speaker 02

  I, I, I think, like, it seems like there's, there's now basically like two classes of models that, that people really use. There's like the frontier ones, and they're, they're using coding agents, they need the best thing. If you're doing cyber, like you, you just need the best model. Because, you know, the, the risk of, of someone hacking you, it's so great, you just need the best thing, you pay whatever it is.

• 19:02John Coogan?

  Yeah.

• 19:03Speaker 02

  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 using a really big model to, to have these like little agents using these very cheap models.

• 19:17John Coogan?

  Yeah.

• 19:18Speaker 02

  I, I think in the middle, it's. Hard to actually figure out what is the the real use case. Maybe it's like hobbyists using these coding agents and they don't want to pay the the super expensive tokens of the closed source labs. But generally, and you see this on open router where like what are the top models by by token? Like usage, it's these very small models. It's like, you know, DeepSeek Flash.

• 19:41John Coogan?

  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.

• 19:57Speaker 02

  That'd be my preference.

• 19:59John Coogan?

  Yeah, you want 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.

• 20:07Speaker 02

  But also, I I think like it is a very good model, right? Like we should not fully dismiss. I I think the the idea that, oh, the gap is widening. We we really don't have to worry about these these models. I think they are like very good.

• 20:18John Coogan?

  Yeah, yeah, yeah.

• 20:19Speaker 02

  And maybe if you're super worried about distillation, maybe something changes if if the models are are, you know, kept to these big partners, right? Like what we've seen recently with with government coming in, but. I I think we we can't really fully dismiss these these labs.

• 20:32John Coogan?

  Yeah, it it throws it throws a little bit of a wrench in the like 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 a 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 I the government got back to me, my company got access to GLM-6.

• 21:15John Coogan?

  And it's close enough. And so that in that that just throws another wrench that I think the government will have to.

• 21:24John Coogan?

  Figure out how it puzzles together with the rest of the strategy, which has been yeah, back and forth as always. Anyway, let me tell you about Shopify. Shopify is the commerce platform that grows your business and lets you sell in seconds, online, in store, on mobile, on social, on marketplaces, and now with AI agents.

• 21:39Jordi Hays?

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

• 21:59John Coogan?

  What does that have to do with with Meta, though?

• 22:03Jordi Hays?

  I think that was just the best Google Gemini picture.

• 22:06John Coogan?

  It is hard. Otherwise, it's just a picture of a phone screen you saw in the z.ai. It's just a picture of the app, which is like so boring.

• 22:14Jordi Hays?

  Imagine riding this.

• 22:16John Coogan?

  Or it's the stock image of the brain with the neurons. That's always good.

• 22:19Jordi Hays?

  What do you think? I mean, this this kind of ad placement, like on a, what do you actually call this? It blocks the water?

• 22:26John Coogan?

  City bike.

• 22:27Jordi Hays?

  No, no, no, that just the the the part that blocks water from, you know, if you were to ride this bike.

• 22:32John Coogan?

  Fender flare.

• 22:32Jordi Hays?

  Yeah, some type of fender thing.

• 22:35John Coogan?

  Sort of like a Mansory kit for a city bike.

• 22:37Jordi Hays?

  Exactly, exactly. But imagine riding that that Gemini bicycle in the rain. Fantastic.

• 22:41John Coogan?

  Oh, that's what it's for. So the water doesn't come up and splat you. Interesting. Okay, I never knew those four.

• 22:47Jordi Hays?

  You never knew that?

• 22:48John Coogan?

  No.

• 22:49Jordi Hays?

  This is.

• 22:50John Coogan?

  Educational.

• 22:50Jordi Hays?

  This, the experience of hosting the show is educational for both of us.

• 22:54John Coogan?

  It is.

• 22:55Jordi Hays?

  Google has put limits on Meta's use of its Gemini AI models after the social media giant stopped more 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.

• 23:23John Coogan?

  I don't understand how this is possible.

• 23:26Jordi Hays?

  Yeah, yeah. So so one.

• 23:27John Coogan?

  Google spent $200 billion on CapEx.

• 23:30Jordi Hays?

  Okay, so so so of course, like, this this. 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, you know, I could see Meta going and like 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 read, like, is it worth reading?

• 24:04John Coogan?

  I mean, it sounds extremely bullish for Google. Like if they're

• 24:06Jordi Hays?

  Yeah, no, this tracks with what they talk about on earnings calls.

• 24:11John Coogan?

  Yeah, yeah. Yeah, Google Cloud acceleration.

• 24:14Jordi Hays?

  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.

• 24:25John Coogan?

  I don't know. Zero Hedge said Meta puts limits on Claude and Kodak spearing distillation, the information.

• 24:30Jordi Hays?

  But, but, 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, we don't want to accidentally do distillation is what, is what Meta is saying. So that's, that's different. I was wondering, like, is Google thinking like, whoa, that's a lot of, you know, cool it, you know?

• 24:56Jordi Hays?

  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.

• 25:14John Coogan?

  Interesting.

• 25:16Jordi Hays?

  Very interesting. I would love to see a pie chart, like breaking down what all the different ways they're using Gemini in their business because Google has not broken out Gemini revenue.

• 25:31Jordi Hays?

  Like, at all today. So we have no idea what percentage of their AI revenue is like actually spent on Gemini versus other models.

• 25:40John Coogan?

  And like 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 category. And then, and then you have, and then you have YouTube now has Gemini plugged in and you can chat with any video and transcribe it. That's got to be incredibly token heavy. And then you have Gemini app users and free users and paid users. So there's, there's got to be a lot of just Gemini internal usage, but it's, it's remarkable. Yeah, I would love to see that meta pie chart of, because I thought that they were spending a ton with Anthropic. I thought they were

• 26:24John Coogan?

  spending a ton with Google, but I'd also assumed that they would be running a bunch of Llama workloads and a bunch of Mew Spark 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 Mew Spark. And if you go into Instagram and you search for something, it populates it with a, with a Llama, like Llama 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. Like Instagram has a billion, two billion users, something like that. It's huge. And so even if it's people sort of,

• 27:08John Coogan?

  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.

• 27:16Jordi Hays?

  On the topic of meta, meta shared this morning.

• 27:20John Coogan?

  What did you?

• 27:21Jordi Hays?

  A new milestone. It is a mind reader.

• 27:24John Coogan?

  Mind reader?

• 27:25Jordi Hays?

  Non-invasive brain text decoder research, Brain2QW2, building on V1, which was published today. In Nature, Brain2QW2 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.

• 27:55Jordi Hays?

  If you thought I was listening to your, you know, conversations, now you can have a, you know, new conspiracy at home, which is that they might be just listening to your thoughts.

• 28:08John Coogan?

  Do you know, do you know the device? They say it's a non-invasive device. I just shared an image of this device and I want you to tell me, do you consider this non-invasive or invasive? Look at this image of the Magneto and Selaf and Selaf graphy device.

• 28:25Jordi Hays?

  No, you gotta go, you need to scroll up a little bit, because you can't even see the whole thing here.

• 28:31John Coogan?

  It's not invasive.

• 28:34Jordi Hays?

  Because it looks like the device could actually potentially carry on for like a whole half of a football field.

• 28:39John Coogan?

  It really does seem like it's a, just put yourself in this, in this room-sized device. No, of course this will shrink down.

• 28:45Jordi Hays?

  I'm giving him, I'm giving him credit here. Non-invasive.

• 28:48John Coogan?

  Okay.

• 28:49Jordi Hays?

  As long as he...

• 28:50John Coogan?

  You're, you're putting this thing on? You're daily driving this thing?

• 28:53Jordi Hays?

  I don't know if I'm ready to daily it. I don't know if I'm ready to daily it.

• 29:00John Coogan?

  Yeah.

• 29:01Jordi Hays?

  This will be a cool demo.

• 29:03John Coogan?

  Yeah.

• 29:04Jordi Hays?

  Like, this, this will actually, when, when, when you can just walk in, sit down in a chair and see your thoughts on a screen.

• 29:11John Coogan?

  No, we were debating it earlier. My buddy Rob Tiff's been on the show twice, dropped five predictions in Forbes recently. We can go through them at some point. He's gonna come on the show. But four of the five were very, very, like, reasonable, you know, Anthropic's gonna be bigger and, you know, TSMC is gonna face more, more competition. And then he predicts that in 2030, telepathy will be commonplace, which is a very aggressive prediction in my, in my estimation. You know, it's certainly not like a, like a straight trend line, since like, you know, like, TSMC has competitors right now. The prediction is just that there will be more competition. But truthfully, telepathy is not really existent outside

• 29:56John Coogan?

  of like a few demos like this. It's not, it's not really something where it's like, oh yeah, like 5% of people have the meta ray bands that take pictures. So like, face cameras are gonna be bigger in five years. 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 Senna says, no. He says the best founders in the world would never sell their company. You could never acquire Elon, Bezos, Zuck, Jobs, Ellison, Jensen, Dell, Page and Brin. 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.

• 30:41Jordi Hays?

  Tyler's app is in shambles.

• 30:45John Coogan?

  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 X. Elon did sell two companies. He sold Zip2 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 sort of counts. And I mean Elon never sells his company. He just sold XAI to himself. But I guess that doesn't count. But yes, it is a funny thing. Didn't Mark?

• 31:19Jordi Hays?

  Yeah. So what is, what's the, do you know the backstory here from Sasha?

• 31:25John Coogan?

  I don't know. 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. How much is Facebook worth? 5 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.

• 32:07John Coogan?

  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.

• 32:21John Coogan?

  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 it's that the launch of its new advertising platform would be delayed. Its stock price tumbled 22% overnight. Terry Semel, Yahoo's CEO at the time, reacted by cutting his offer from $1 billion to $800 million. He just took 20% off, but Zuckerberg, who had been warned about Semel'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,

• 33:03John Coogan?

  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, Semel 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.

• 33:27Jordi Hays?

  Legendary.

• 33:28John Coogan?

  Legendary. It's so interesting to imagine the road not traveled there because the dynamic, the way Facebook is built with the, as a social network, like, could it have been successful under Yahoo's stewardship? Or would it have been less exciting, attract less talent, ultimately been disrupted? And would they have had the capital and the guts to go and buy WhatsApp and then also buy Instagram, you know, to actually maintain the dominant position in social networking?

• 34:05Jordi Hays?

  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.

• 34:16Jordi Hays?

  I would like to see Yahoo make another bid.

• 34:19John Coogan?

  Hey, Metas trading down, just keeps going.

• 34:21Jordi Hays?

  If it continues at this trend.

• 34:23John Coogan?

  99.99% might be able to pick it up.

• 34:27Jordi Hays?

  At this trend.

• 34:28John Coogan?

  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?

• 34:40Jordi Hays?

  Chipmakers are profiting off AI at the expense of just about everyone else.

• 34:45John Coogan?

  This is on the cover of the Business and Finance section today.

• 34:49Jordi Hays?

  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.

• 34:58John Coogan?

  Yeah, 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, with Korea 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. Micron's soaring profits are, for its customers, soaring costs. We are witnessing

• 35:42John Coogan?

  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 28, 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. Prices quadrupled in a year, and it's hurting outside AI too. Apple, last week, raised prices for

• 36:25John Coogan?

  MacBooks more than 15%, closer to home, 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. 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.

• 37:07John Coogan?

  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 Micron's stock price has been through the roof as the company joins the one trillion dollar club and becomes the first trillion dollar company in headquartered 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.

• 37:46Jordi Hays?

  Crazy, crazy, moggy.

• 37:47John Coogan?

  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 Barron's. I love Adam Levine and Barron's sharing the bull case.

• 37:59Jordi Hays?

  Tyler, how many trillion dollar companies are there in Europe, out of curiosity? I'm gonna go with zero. That's true. That is true. You are correct.

• 38:12John Coogan?

  The other.

• 38:13Jordi Hays?

  ASML could get there.

• 38:14John Coogan?

  Maybe.

• 38:15Jordi Hays?

  Sitting at around 700.

• 38:17John Coogan?

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

• 38:25Jordi Hays?

  It's a humble 165 million.

• 38:28John Coogan?

  Brutal. Wait, but what did it?

• 38:30Jordi Hays?

  You're thinking of Eli Lilly.

• 38:32John Coogan?

  Eli Lilly hit a trillion. Yeah. Rough. Very, very rough. Comcast is planning to split up the company. Competition is escalating.

• 38:41Jordi Hays?

  Eli Lilly, the Indiana company, John.

• 38:44John Coogan?

  Is it from Indiana?

• 38:46Jordi Hays?

  Indiana.

• 38:47John Coogan?

  Okay.

• 38:48Jordi Hays?

  Former Indiana startup.

• 38:49John Coogan?

  Okay. 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.

• 39:06Jordi Hays?

  Who's building the Andorol of theme parks?

• 39:09John Coogan?

  It does seem like a

• 39:10Jordi Hays?

  Could there not be an opportunity to create a, a net new theme park business with, with modern, a modern technology stack?

• 39:21John Coogan?

  It's very expensive. Everything needs to be like, the modern technology stack in theme parks is tricky.

• 39:27Jordi Hays?

  You don't believe in, in the cap, the theme park capital markets?

• 39:31John Coogan?

  I don't know. I, I, I know, I've known people that have worked on theme parks at Disney and it's tricky because you, you have to amortize a ride over like 20 years. And so you'll go

• 39:43Jordi Hays?

  It seems like an absolutely brutal business.

• 39:46John Coogan?

  Yeah.

• 39:46Jordi Hays?

  That is probably harder today because

• 39:49John Coogan?

  Huge power law.

• 39:49Jordi Hays?

  Think about, you know,

• 39:52Jordi Hays?

  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.

• 40:02John Coogan?

  Yes.

• 40:02Jordi Hays?

  Instantly available.

• 40:03John Coogan?

  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.

• 40:30Jordi Hays?

  They're also exploring the, the NBA team.

• 40:32John Coogan?

  Yeah.

• 40:32Jordi Hays?

  To Vegas. But at the same time, at the same time, John,

• 40:36John Coogan?

  I think I'm more in your camp that it's, there is maybe an opportunity.

• 40:37Jordi Hays?

  There's 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.

• 40:52John Coogan?

  Is it up or down?

• 40:54Jordi Hays?

  Less.

• 40:54John Coogan?

  It's lower?

• 40:55Jordi Hays?

  Less. Like, and, and, and the stat was like volume.

• 40:59John Coogan?

  Yeah.

• 41:00Jordi Hays?

  And so it's not exactly like a, a proxy for like revenue, but still meaningful.

• 41:08John Coogan?

  Theme park vertically integrated.

• 41:13John Coogan?

  Tweety Bird Tattoos. Tweety Bird Tattoo Parlour, right on site.

• 41:19Jordi Hays?

  I like it.

• 41:19John Coogan?

  Six Flags.

• 41:21Jordi Hays?

  I like it.

• 41:23John Coogan?

  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.

• 41:32Jordi Hays?

  It works.

• 41:33John Coogan?

  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 like IP around it.

• 41:41Jordi Hays?

  Brain rot theme park.

• 41:43John Coogan?

  There's something about, I mean, we've read, we've read stories about like 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. And that just takes so much time. And you can't, like, this, this goes back to the question of like Netflix is enduring IP. Like, they don't, they, 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.

• 42:25Jordi Hays?

  Well, I'd push back and say the Narcos.

• 42:27John Coogan?

  Narcos? You want to go to the Narcos theme park? I, 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, you can't have any theme park that's built around an R-rated like IP library. And so, that just narrows it down even further.

• 43:07Jordi Hays?

  Well, all of America's basically turned into a theme park for European soccer fans.

• 43:12John Coogan?

  Oh, yeah.

• 43:13Jordi Hays?

  In the journal, European soccer fans marvel at the splendor of America's suburbs.

• 43:18John Coogan?

  I've been getting so many of these reels serve to me.

• 43:22Jordi Hays?

  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?

• 43:34John Coogan?

  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.

• 43:48Jordi Hays?

  Gabe in the X chat, I think Ferrari has a roller coaster in the Middle East.

• 43:53John Coogan?

  No way.

• 43:53Jordi Hays?

  They do. They have a whole Ferrari theme park in Abu Dhabi.

• 43:56John Coogan?

  That works because that's not R rated. You can totally take your kids to the Ferrari theme park.

• 44:01Jordi Hays?

  Yeah, I was, I was in Abu Dhabi and I was driving by it and I was like, yeah, I was just thinking of like, if you wanted to spend a day, you know, getting the Ferrari experience, like you could just go to the track.

• 44:20John Coogan?

  Yeah.

• 44:20Jordi Hays?

  Or you could just rent a Ferrari. So I don't know.

• 44:24John Coogan?

  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.

• 44:36John Coogan?

  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.

• 44:47Jordi Hays?

  Tavella Pars says Yas Island. They literally named an island Yas.

• 44:54John Coogan?

  Weird.

• 44:55Jordi Hays?

  No.

• 44:56John Coogan?

  I don't know. Anyway.

• 44:59John Coogan?

  Dutch soccer fans are having fun visiting America. 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. Everink 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 marvelled at the sprawling houses in a contrast from the tightly packed homes of the Netherlands. I did notice this when we were in France, the food portions were way too small for

• 45:44John Coogan?

  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 was like, "This is paradise."

• 46:07Jordi Hays?

  Everyone is so nice.

• 46:08John Coogan?

  Something about the grass, the grass is always greener, right? The grass is always greener on whatever side I'm on. 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, 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 Tai said. Fans also balked at the size of Costco's and the vastness of the highways. 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.

• 46:53John Coogan?

  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.

• 47:02Jordi Hays?

  The way that they looked, they experienced at Chick-fil-A was me seeing the Renault Twizy.

• 47:08John Coogan?

  Yeah.

• 47:08Jordi Hays?

  I was just like, "This is unbelievable. This looks like they made the perfect car."

• 47:12John Coogan?

  Yeah, so small, so small.

• 47:16John Coogan?

  And it's the way they think about our, our fire trucks, but which are massive. This is nuts, honestly, they said.

• 47:23Jordi Hays?

  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 do you have a stat breakdown or anything?

• 47:35John Coogan?

  We, we, be very careful with what you said, because I saw that there was a news reporter who faced fierce backlash for 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 did not enjoy her critique of their country.

• 48:06Jordi Hays?

  Yeah, pure disrespect.

• 48:07John Coogan?

  Anyway, uh, there's a little golf cart. We got to talk about this at some point, but, uh, there's a new, there's a new car. It's like a Twizy, you're going to love it.

• 48:15Jordi Hays?

  Well, it's no Twizy.

• 48:16John Coogan?

  It's no Twizy.

• 48:17Jordi Hays?

  Bring in our first guest.

• 48:18John Coogan?

  Anyway, let's bring in our first.

• 48:20Jordi Hays?

  From the National Design Studio.

• 48:21John Coogan?

  National Design Studio, welcome to the show, gentlemen. How are you doing? Thank you so much.

• 48:28Jordi Hays?

  What's going on?

• 48:30John Coogan?

  Uh, please start with an introduction of yourselves, the, the company, and then the announcement today.

• 48:35Speaker 03

  Uh, my name is Edward Corvus, and I run engineering at National Science Studio. Technically not a company, it's a government organization.

• 48:41John Coogan?

  Oh, yeah. Sorry.

• 48:43Speaker 04

  And I'm Tiger, I'm one of the engineers at National Design Studio.

• 48:46John Coogan?

  Okay. And today, the launch, take us through it.

• 48:49Speaker 03

  We're launching Ramparts. It's a local first privacy model that puts people back in control of the data that they share with AI. Um, we were, we were just like, you know, kind of building a chatbot for fun. And we were, that's that, uh, none of the frontier models will actually fit in a browser. So you cannot do P I R removal in the browser. Which is pretty damn important for our use case. You just have to like, you know, trust that the server is actually moving the information and not lying to you. So we're like, okay, well, what if, what if it's just all on device? Like, personal data never had to leave your device. It was just, you know, secure by default.

• 49:28John Coogan?

  Okay, so, open source, the weights are on Hugging Face, 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, or is it sort of like, open up the primordial soup of ideas and see where it goes? Or do you have, do you have like a rollout strategy that you are advocating for?

• 50:04Speaker 03

  Well, the reason why we're open source 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.

• 50:12John Coogan?

  Sure.

• 50:13Speaker 03

  We want, we want, you know, vibe-coded chat GPT 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 TrumpRex 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 find the first principles, best approach for users.

• 50:42John Coogan?

  Yeah.

• 50:43Speaker 03

  And this just came as a derivative of that. We're not MBL, you know, researchers or engineers. We're just like, you know, we should just do this.

• 50:52Jordi Hays?

  But you created PII super intelligence. That's what people are, that's what people are saying online.

• 50:57John Coogan?

  Basically.

• 50:58Speaker 03

  It's like Tiny Intelligence. It's like, it's by far the smallest model. Like, the other ones are like, at least 15 megabytes, this is 15.

• 51:04John Coogan?

  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?

• 51:26Speaker 03

  We tried 72 different base models.

• 51:29John Coogan?

  Whoa.

• 51:30Speaker 03

  And, you know, put them through a training set. We ended up on Mini LM, so we definitely are standing on the shoulders of giants here.

• 51:36John Coogan?

  Yeah.

• 51:37Speaker 04

  Yeah, yes. We started taking a look at the OpenAI privacy filter that just got released recently.

• 51:41John Coogan?

  Yeah.

• 51:42Speaker 04

  We were trying to figure out, is there a way we can, you know, just quantize it? Can we maybe remove some of the parameters? Like, what can we do here to try to make use of, like, the state-of-the-art model? And, you know, we tried a lot of things. We just could not get it to fit into, you know, we want this to work on, like, legacy devices. Um, an old Android phone, for example, or, you know, an older iOS device. Um, and it just would not get small enough and still make any intelligent sense to try to actually run it. So, yeah, we ended up, uh, essentially, uh, it's technically a fine tune, but we were, we, we, we trained Mini LM and basically made it do exactly what we wanted to do.

• 52:18John Coogan?

  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, uh, the PII is like the, 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, in my estimation.

• 52:52Speaker 03

  Yeah, the, 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.

• 53:00John Coogan?

  Hmm.

• 53:01Speaker 03

  And so we hand all the customization back to whoever wants to use the library. Uh, the model just says, oh, you know, this is a phone number, this is a name, uh, this is a surname, et cetera, et cetera. And then ultimately, it's, it's, you know, whatever you want to do with the, the model, you can just do it.

• 53:18Speaker 03

  But eventually, 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. How do I fill out said template? Because the government is pretty bad with forms. There's like way too many forms. Nobody knows all they mean. Like, you've got to pay people to do your government forms. So like, that was a use case we had in mind. PII is like, not, not super helpful for that. And it's also kind of like the breaking point. It's where, you know, the product will lose trust. So we're like, okay, keep it on the down. Let's pull this thing.

• 53:49John Coogan?

  That makes sense.

• 53:51Jordi Hays?

  How do you guys, 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. You have a mandate, but at the same time, there's so many different places that the government, you know, you know, interacts with people's lives. I'm, I'm very curious.

• 54:14Speaker 03

  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, we, we just try to keep everything aligned with our vision, which is like, we want to make the American digital experience better. And then we've, 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 or X. But we're excited to see, to see what develops here and.

• 54:47John Coogan?

  Do you, uh, yeah. Diving more into that, do you have a reference point in, 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, 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.

• 55:16Speaker 03

  Yeah. I mean, there's definitely a lot of, a lot of places to get stuck because the, the visibility is super low on a lot of these projects. You don't know how broken they are until you're like, you're really in it.

• 55:27John Coogan?

  Sure.

• 55:28Speaker 03

  That, that, well, you know, being able to determine that in advance is like, is definitely, you know, AGI level.

• 55:35Speaker 04

  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.

• 55:53Speaker 03

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

• 56:14Speaker 04

  Yeah, the 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?

• 56:22Jordi Hays?

  Yeah. What's your guys' pitch to talent that you might want to recruit into the National Design Studio? I imagine lots of people that would join could get a blank check from a venture fund or could go work at some of the best companies.

• 56:40Speaker 03

  Everyone that has points.

• 56:41Speaker 04

  Turn down that off.

• 56:43Speaker 03

  It's definitely more for people who are super mission oriented. Who the hell, what great engineer wants to come work in the government? The answer is typically nobody, unless it's like the IC where there's really interesting problems to solve. So I think that we have like a super golden opportunity. At least the way I evaluate problems, I try to see how big the problem is in terms of like 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 we need for the studio.

• 57:27Jordi Hays?

  Awesome.

• 57:28Speaker 04

  Good luck, folks. It's also a huge benefit. It's pretty sick.

• 57:33Jordi Hays?

  Awesome. Is that where you guys are right now?

• 57:37Jordi Hays?

  We're not there right now, but we're about to be there.

• 57:39John Coogan?

  Yeah.

• 57:40Jordi Hays?

  Awesome.

• 57:40John Coogan?

  Well, thank you so much.

• 57:41Jordi Hays?

  Congratulations on the launch.

• 57:43John Coogan?

  Congratulations.

• 57:43Jordi Hays?

  Very fun project.

• 57:44John Coogan?

  And we'll talk to you soon.

• 57:45Jordi Hays?

  Great to meet you guys.

• 57:46John Coogan?

  Have a good rest of the day. Goodbye.

• 57:48Jordi Hays?

  Thank you very much. Cheers.

• 57:49John Coogan?

  Let me tell you about CrowdStrike. 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 Ambole 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 1,000 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.

• 58:34John Coogan?

  Already has 500 vehicles committed.

• 58:37Jordi Hays?

  I love it.

• 58:37John Coogan?

  You love it.

• 58:38Jordi Hays?

  I love it. I think it's great. I've been surprised.

• 58:41John Coogan?

  Give it a Jordy score.

• 58:42Jordi Hays?

  A Jordy score.

• 58:44John Coogan?

  Daily, weekend, you know, the Doug score out of 100. What are you doing?

• 58:47Jordi Hays?

  So, I mean, I just went through this whole crazy search for basically this exact vehicle.

• 58:52John Coogan?

  Yeah.

• 58:53Jordi Hays?

  Didn't, didn't find it.

• 58:54John Coogan?

  Okay.

• 58:54Jordi Hays?

  I don't like the aesthetics of golf carts.

• 58:58John Coogan?

  Yep.

• 58:58Jordi Hays?

  I've driven a lot of golf carts in a, in a commercial capacity.

• 59:01John Coogan?

  Okay.

• 59:02Jordi Hays?

  At, at, at a job in college.

• 59:04John Coogan?

  Yep.

• 59:04Jordi Hays?

  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.

• 59:16John Coogan?

  Yep.

• 59:17Jordi Hays?

  Uh, that was more like not, you know, I'm not golfing.

• 59:21John Coogan?

  Yep.

• 59:21Jordi Hays?

  So, I wanted some like little bit of utility, wanted to be fun, et cetera. I landed on a, a Can-Am HD11.

• 59:31John Coogan?

  Mm-hmm.

• 59:31Jordi Hays?

  Uh, you know, a UTV. Uh, it's gas-powered, 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 has the internal combustion engine.

• 59:48John Coogan?

  Yeah.

• 59:49Jordi Hays?

  But I think this is, no, I think this is fantastic. And I think that, I think I saw somewhere that they're going to focus on more commercial opportunities. So going to hotels all over the world.

• 1:00:04John Coogan?

  That's what Justin says here. Says this little golf cart 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 have this versus just a golf cart? Can you get a fleet of golf carts for a discount?

• 1:00:23Jordi Hays?

  I mean, like a golf cart is going to come in at like 13

• 1:00:28John Coogan?

  Okay, half price.

• 1:00:29Jordi Hays?

  Ish 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 in a nice amenity on hotel properties around the world. Ryan Dahini says, he thinks it'll be a hit in hospitality since Moke cap 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 much, I still much prefer the sort of aesthetics of the Myers Manks, you know, the 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.

• 1:01:10Jordi Hays?

  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 ASAP, hopefully this week.

• 1:01:33John Coogan?

  Very fun. There's a good quote from Roger Ebert, the famous movie reviewer that we got to share. All the team loves Roger Ebert, from Siskel and Ebert back in the day. 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. Anyone who would camp out in a tent on the sidewalk for weeks in order to be first in

• 1:02:18John Coogan?

  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 fanish 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. That's why it's excruciatingly boring to talk to such people. They're always asking you questions they know the answer to." What a funny-

• 1:02:58Jordi Hays?

  It's like you and your Apple Vision Pro fandom. We're always just having a normal conversation, and John will say, "Yeah, this would be better if we were in the Dino Experience."

• 1:03:10John Coogan?

  Isn't that true? The, uh, I'm not much of the Dino Experience. Anyway, let's bring in Chad Righetti from Righetti Computing and Segaldr. Chad, how are you doing?

• 1:03:22Speaker 05

  What's going on? I'm doing great. How are you guys doing?

• 1:03:25John Coogan?

  We're doing fantastic. 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.

• 1:03:45Speaker 05

  You bet. Yeah, great to be here, guys. I started, 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.

• 1:04:00Speaker 05

  You know, helping build up the quantum computing team there and then started my own company, those Righetti Computing in 2014. I was introduced to Sam Altman and, you know, he said, we had coffee and he said, hey, well, you should do YC. And I said, 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. And so I got to be part of this incredible group of companies, including Helion, Oclo, which is now public, Ginkgo BioWorks.

• 1:04:37John Coogan?

  Ginkgo BioWorks, yeah.

• 1:04:39Speaker 05

  Yeah, Boom was a couple batches after me, but there was this cohort, summer 20, but yeah, so anyways. It was a fantastic experience. Ended up running Righetti for about 10 years. We took it public in, in early 2022. Through a SPAC transaction, I was the third quantum company, I think, to go public. And so that was an incredible journey.

• 1:04:58Speaker 05

  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. And there's incredible people working in this area. There's incredible technologies being developed and it's gonna, it's gonna change, change the relationship between artificial intelligence and computing infrastructure. And that's what we're working on at Sigle Tree.

• 1:05:19John Coogan?

  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, you know, their position built up in a particular category. But the public market seemed 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?

• 1:05:59Speaker 05

  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?

• 1:06:11John Coogan?

  Sure.

• 1:06:12Speaker 05

  Is it to provide, you know, have liquidity for your early employees, for example, with some companies where you've got a 10-year exercise window for your options. And, you know, 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. 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, and those SPACs were hungry to cut a deal.

• 1:06:49John Coogan?

  Sure.

• 1:06:50Speaker 05

  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, past month or so, Continuum has gone public via IPO, a tremendous company that's made great progress. And so the quantum, you know, the public markets for quantum computing have reached a point of maturity. There's analysts that deeply understand the technology that are writing about and covering different companies. It's a, you know, 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.

• 1:07:33Speaker 05

  And what kind of business are you trying to build? What kind of cap table do you want to build? And what strategy best suits, you know, is best going to help you, help you achieve that?

• 1:07:40John Coogan?

  Yeah.

• 1:07:41Jordi Hays?

  What kind of feedback did you get in the early days around naming the company after yourself? I've been surprised at more.

• 1:07:48Speaker 05

  Yeah.

• 1:07:49Jordi Hays?

  There's so many generic names in the startup world now. It's like the blank company of San Francisco or things, things like that. Or, you know, all the Neo Labs have like the same sounding names. It'll be like Advanced Superintelligence and then...

• 1:08:03Speaker 05

  There was a big boom in like .LYs, like Friendly, Bitly.

• 1:08:06Jordi Hays?

  Yeah.

• 1:08:07Speaker 05

  Musically, there were tons of companies that were .LY for a while, but...

• 1:08:10Jordi Hays?

  And I only know one other, I can only think of one other company, Chris Amadon's company has Amadon Heavy Industries.

• 1:08:17Speaker 05

  Yeah.

• 1:08:18Jordi Hays?

  But I'm sure, I'm sure people thought you were a little crazy back then.

• 1:08:23Speaker 05

  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 Sigldry, which is what I 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 that means it's got to sound very fresh and new and different. And if every other quantum company has a Q in it, maybe you try avoiding that. And that's what led me to Sigldry. Sigldry, I love this name. It's from a Patrick Rothfuss

• 1:09:05Speaker 05

  novel. And he was an American writer. He wrote this incredible novel called Name of the Wind that came out in mid-2010 or so. Anyway, so Sigldry 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. 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.

• 1:09:47John Coogan?

  So what actually needs to happen, what is the path to, I would imagine, cheaper tokens? 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?

• 1:10:04Speaker 05

  Well, first of all, quantum hardware is going to address a lot of different computational challenges today, right? So quantum computers are able to solve problems that are impossible, are very challenging to solve with any form of classical computing, no matter what scale it reaches. So Sigldry, we're focused on applying that capability specifically to some of the computational challenges in AI to reduce the power and reduce the costs 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. And the strategy that we're taking at Sigridry is we are very focused on deeply understanding what those challenges are, what needs to happen inside the data center

• 1:10:45Speaker 05

  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, what do I mean by that? In quantum computing and quantum hardware, there's different kinds of qubit technologies that you can use to, to instantiate the qubits. So there's supermorphic 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 and a long list of other companies.

• 1:11:27Speaker 05

  There's photonics, there's now neutral atoms, there's spin qubits and semiquavers. There's all these different hardware substrates that people are using to pursue and to build quantum computers based on those. And what we're doing at Sigridry is stepping up a layer and saying, from a computer architecture perspective, you know, modern computers aren't built out of one, aren't built out of one physical kind of bit. 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 Sigridry, 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

• 1:12:08Speaker 05

  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, meet the requirements of bringing quantum into the AI data center at scale.

• 1:12:22John Coogan?

  How important is simulation at this point? Are you at a place where you can run this, like basically run the, 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?

• 1:12:49Speaker 05

  Yeah, we've been able to do that largely speaking, and you could do simulations of something, computer system or a jet or anything, and varying 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, you know, 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. You can't simulate all the, all the logic of a quantum computer because that would require a quantum computer itself, kind of by definition.

• 1:13:33Speaker 05

  But you can do load profiling, you can do, you can do traces, you can understand how that's gonna, you know, 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.

• 1:13:51John Coogan?

  Yeah.

• 1:13:53John Coogan?

  I guess, what specifically in training benefits from quantum computing? Because the example that everyone goes to in terms of quantum computing, you know, novel, 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

• 1:14:35John Coogan?

  seeing, you know, leading AI firms get off of CUDA? Like, is there a world where you get off of classical and, but, but by and large, it's the same training paradigm?

• 1:14:47Speaker 05

  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 that 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, you know, 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, you know, the possibility for exponential speed up with the right algorithm. But it also has this issue with data in and data out.

• 1:15:31Speaker 05

  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, that translation between the quantum part and the classical part, it's going to end up working better. So asymptotically, where we're, 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 not, where you're probably not going to see is fully quantum, you know, quantum based models that don't include a substantial amount of classical compute as well. So this isn't going to replace all the, you know, the AMD or Nvidia infrastructure in the data center. It's going to augment it. And our business model and our, our focus and our product strategy is to take the,

• 1:16:15Speaker 05

  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 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. 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

• 1:17:00Speaker 05

  that's going to allow new model capabilities to be developed and brought, and brought to market.

• 1:17:05Jordi Hays?

  How are you thinking about timelines with this new, with the new company? Is it, do you think there's, I imagine with the business right now is like entirely more of like technical risk than execution risk? Is that, is that the right way to think about it? Like, there's a lot of hardcore research that needs to be done, understanding the feasibility of the approach. And what kind of conversations are you having with potential partners? If at all, right now versus about kind of like the near term application?

• 1:17:47Jordi Hays?

  Or are you, are conversations like 2030s and beyond kind of thing?

• 1:17:52Speaker 05

  Yeah, we're targeting, we're talking to customers now. We've got several active conversations, I think partnerships and early engagement with customers is 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, 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. The underlying hardware has made such tremendous progress in the past 10, 15 years.

• 1:18:34Speaker 05

  And the market is, 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 lower, cheaper access to energy and it gives you a better way to dissipate that heat.

• 1:19:15Speaker 05

  But you gotta 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.

• 1:19:25Jordi Hays?

  Why don't?

• 1:19:26Speaker 05

  The power, the power challenge. Quantum can unlock much more than that.

• 1:19:29Jordi Hays?

  Yeah, it's, it's, it's a good point. Why, why don't you think Elon has, has made a, a real run at quantum?

• 1:19:36Speaker 05

  I think the answer is that quantum is at the, 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, not that it's easy, not that any of the purely classical stuff is easy.

• 1:19:57Jordi Hays?

  It's not vanilla rocket science.

• 1:19:59Speaker 05

  It's not vanilla rocket science and it's not vanilla fab at scale, right? And so if, if, if you look at the leaders in quantum computing hardware, it's not necessarily the Intels of the world, incredible company that has, you know, 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 that, that is behind us. There's tremendous, 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, uh, you know, companies that are quantum specific, bring the technology forward. And at that point, I think that all the big AI labs are going to need to lean in with quantum.

• 1:20:39Jordi Hays?

  Yeah. When, when do you think there will be a flip around sentiment, uh, from, uh, around, around quantum? It, it feels right now like, at least in our corner of the internet, there's so much fud around quantum and, uh, obviously.

• 1:20:54Speaker 05

  Based on financials, like.

• 1:20:56Jordi Hays?

  Yeah, yeah. So that's, that's what I want to know though. Like, is, is there like, 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 chatbot, I think in like 2015 or something like

• 1:21:23John Coogan?

  But GPT-3 was like losing money. It wasn't like making revenue.

• 1:21:27Jordi Hays?

  Yeah, that was even before that.

• 1:21:28John Coogan?

  Yeah, no, I know.

• 1:21:29Jordi Hays?

  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, and they talk about like over investment, 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,

• 1:21:49John Coogan?

  Well, some bears can, but yes.

• 1:21:51Jordi Hays?

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

• 1:22:19Speaker 05

  It, there's a few things that need to happen. I think the, the FUD is real because the companies that are succeeding and doing well in this space, you, 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, it doesn't work like that. You got to be able to analyze and, and look at these companies and value them based on their ability to buy down technical risk over, 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, you know, discussion and debate. But nonetheless, I think there are clear, there is clearly tremendous, you know, momentum of progress in this space.

• 1:23:01Speaker 05

  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, you know, the answers you need or, or, you know, 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, in a large part, This is what we're trying to achieve with Sigiltry, right? 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

• 1:23:45Speaker 05

  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, you know, the outputs they're getting. That to me is a big inflection point. And I think that can happen in the next five to seven years. 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, you know, 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

• 1:24:29Speaker 05

  on for good, you know, one, two decades now.

• 1:24:31Jordi Hays?

  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 like, why, why a new company, it feels like quantum, like as you've explained, it feels very obvious to apply it to data center build out and, and you said it could be like a meaningful inflection point for the technology overall. Why, why, why was a new company necessary and, and you know, why'd you take this approach?

• 1:25:02Speaker 05

  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 is, 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 Sigiltry 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. And that's why you have so much doctrine and like kind of organizational belief around a particular qubit choice.

• 1:25:45Speaker 05

  But in reality, you know, customers are buying a computer, they're not buying a, you know, the, the physical device or your, your qubit technology. And so at Sigldry, what we're doing is working backwards from the market application, from the AI workload as the 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 Sigldry Technologies. 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.

• 1:26:28Speaker 05

  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, you know, 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, 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.

• 1:27:10Speaker 05

  That's what we're tackling at Sigldry 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.

• 1:27:22John Coogan?

  Thank you.

• 1:27:24Jordi Hays?

  Before, before we jump, I didn't get, you, you mentioned kind of the, the history behind the name, but what is the significance of Sigldry in the novel that you mentioned?

• 1:27:34Speaker 05

  Well, you guys got to read the novel, novel for one. It's absolutely incredible. And, but the other thing is Sigldry 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, 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 this, these inscriptions. So it's really, it's a really amazing concept.

• 1:28:13Jordi Hays?

  A little bit of magic.

• 1:28:14John Coogan?

  Amazing. Thank you so much for taking the time.

• 1:28:16Jordi Hays?

  Thanks for breaking me down.

• 1:28:17Speaker 05

  Thanks, guys.

• 1:28:19John Coogan?

  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.

• 1:28:29Jordi Hays?

  Very cool name. I wish, you know what I'm thinking, John?

• 1:28:32John Coogan?

  General Intuition.

• 1:28:33Jordi Hays?

  I wish that after Rigetti Computing, I wish that Chad launched Chad Computer.

• 1:28:39John Coogan?

  Oh, yeah.

• 1:28:40Jordi Hays?

  It was right there.

• 1:28:41John Coogan?

  It was right there.

• 1:28:42Jordi Hays?

  It was right there.

• 1:28:43John Coogan?

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

• 1:28:48Jordi Hays?

  What's happening?

• 1:28:49Speaker 06

  Hey, guys.

• 1:28:50John Coogan?

  Thanks so much for coming back on the show.

• 1:28:53Jordi Hays?

  Great to see you.

• 1:28:54John Coogan?

  Yeah, please.

• 1:28:55Jordi Hays?

  We've been talking about names for labs. What about, consider, General Intuition, strong name, but since you launched the company, a lot of other Neo Labs 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 rebrand to unfettered intelligence?

• 1:29:19Speaker 06

  Yeah, or how about we just fund them all?

• 1:29:22Jordi Hays?

  Yeah, yeah, that, that too.

• 1:29:25John Coogan?

  Yeah, what is the plan to win? Do you see yourself as a Neo Lab? And do you see, is it as much of a knockout, drag-out fight as it appears from the outside? Or is your model more of a thousand flowers bloom?

• 1:29:41Speaker 06

  The plan is to just keep renaming.

• 1:29:43John Coogan?

  Okay.

• 1:29:44Speaker 06

  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 on 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

• 1:30:24Speaker 06

  or having the new ideas, but I think it also has to be supplemented with a product focus of some customer problem that is going to get solved because these types of model classes exist.

• 1:30:38Speaker 06

  Network effects, just like we saw in the consumer areas of the Facebooks and the Twitters and the Reddits, these things are true. They apply to LMs 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.

• 1:31:00Jordi Hays?

  Okay, actions in space and time. Let's talk about the data set. Catch everyone up to speed on, I mean, you know, 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, how you're building the data set, all that stuff.

• 1:31:18Speaker 06

  Yeah, look at it this way. As humans, the decision to talk or type is 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. And therefore, the type of model you get out is not going to taste like an LM. It's going to be like comparing coffee to water. This model is going to be

• 1:32:01Speaker 06

  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, right? 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, right? 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

• 1:32:44Speaker 06

  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.

• 1:32:58John Coogan?

  So yeah, walk through the partnership with Metal. Are you getting game controller feedback as well? When those, yeah, explain, explain the relationship with Metal for those who don't know.

• 1:33:09Speaker 06

  So alongside the frames in the video, we're also getting the exact action inputs, to be clear, not the letters or numbers, right? We can, we have, 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. It's impossible for that to be visible in the

• 1:33:49Speaker 06

  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 edge 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.

• 1:34:21John Coogan?

  Tell us about the latest round. I want to hit the gong. What happened?

• 1:34:25Speaker 06

  What happened? We raised $320 million.

• 1:34:31John Coogan?

  Congratulations. And thank you so much for taking the time to chat with us.

• 1:34:35Jordi Hays?

  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?

• 1:34:51Speaker 06

  Yeah, the most obvious thing this replaces is all the code that people are currently writing for behavior in physics engines. All that just becomes a prompt.

• 1:35:01Speaker 06

  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

• 1:35:45Speaker 06

  that works across many types of robots. In many ways, you could argue that if this is correct at scale, the supply chain will converge on gaming inputs instead of humanoid robots. And I think that is one of the big things that I foresee happening in the next two years. Because intelligence is the bottleneck.

• 1:36:02John Coogan?

  Yeah. 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. Let me tell you about Cisco. Critical infrastructure for the AI era. Unlock 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? 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... Data center simulator. Data center simulator. Happy Barra simulator.

• 1:36:35Speaker 02

  Central banking simulator.

• 1:36:36John Coogan?

  Central banking simulator, it's going to learn everything. Well, we have our next guest in the waiting room, Yadin Suffer from Tresor, co-founder and CEO. Welcome to the show. How are you doing?

• 1:36:48Speaker 07

  Hey, guys. Nice to meet you. I'm great. How are you?

• 1:36:50John Coogan?

  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.

• 1:36:59Speaker 07

  Yeah, well, Yadin Sofer, we last week, we announced the launch of Tracer, which is, I would say, the first of its kind sub-terror defense tech company. And sub-terror 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 happen beneath our feet.

• 1:37:22Jordi Hays?

  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?

• 1:37:42Speaker 07

  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 Herrichnech that are not, you know, sexy startups like the Boring Company, but these are decades-old German companies that have been, you know, 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. I mean, we really have not seen any companies in this space.

• 1:38:22Jordi Hays?

  What are the primary challenges of, you know, underground drones, the underground domain overall? Is it connectivity? But what are they?

• 1:38:37Speaker 07

  Yeah, 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, 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.

• 1:39:22Speaker 07

  So I would say that is probably the number one challenge, just the uncertainty of this domain.

• 1:39:28John Coogan?

  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?

• 1:39:40Speaker 07

  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 fours 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. 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've got this, I've seen a boring

• 1:40:23Speaker 07

  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.

• 1:40:40John Coogan?

  Twenty-five million dollar 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?

• 1:40:53Speaker 07

  Yeah, we always say this, that you know, 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, airdrop 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 Eric and be done by a special

• 1:41:35Speaker 07

  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? The twenty-five million dollars here, everyone goes to me and is like, all right, you're building this massive R and D team, you're gonna have a ton of CapEx. And I'm like, No, there is a lot of work to be done when for me call it this category where we need government, we need the military to recognize this as a category like we do. 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

• 1:42:20Speaker 07

  and establish, I would go as far as saying the subterra doctrine or the US subterra strategy for, you know, winning wars underground.

• 1:42:27Jordi Hays?

  How far underground are you right now?

• 1:42:29John Coogan?

  It does look like you're underground.

• 1:42:31Speaker 07

  Right, it looks deep. I was thinking about this too. It's a good spot.

• 1:42:35Jordi Hays?

  At least, at least twenty.

• 1:42:36Speaker 07

  It's just an abandoned restaurant, actually.

• 1:42:37Jordi Hays?

  At least twenty feet, at least twenty feet.

• 1:42:39John Coogan?

  Anyway, thank you so much for taking the time to come chat with us.

• 1:42:42Jordi Hays?

  Great to meet you.

• 1:42:43John Coogan?

  Have a great rest of your day. We'll talk to you soon.

• 1:42:45Jordi Hays?

  Cheers.

• 1:42:45John Coogan?

  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 co-founder and head of research. Jack, how are you doing? Welcome to the show.

• 1:43:01Speaker 08

  Hi, yeah, nice to meet you. It's great to be on the show. I was actually just watching it in another tabs, so this is kind of.

• 1:43:08Jordi Hays?

  Here you are.

• 1:43:09John Coogan?

  You're here.

• 1:43:11Jordi Hays?

  Great to meet you.

• 1:43:13John Coogan?

  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?

• 1:43:23Speaker 08

  Yeah, sure. My name is Jack. I'm a co-founder and I guess technically the head of research at Engram. 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.

• 1:43:43Jordi Hays?

  Mogs, mogs. Let's hit the gong, let's hit the gong for that.

• 1:43:48Speaker 08

  I'm really grateful for the opportunity, but I was hoping you would hit the gong.

• 1:43:51Jordi Hays?

  Yeah, we just did a baby, you know, baby, a big one.

• 1:43:56John Coogan?

  Congratulations.

• 1:43:58Speaker 08

  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 that train themselves to like know your world better and like adjust to the things that you say. 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 know, you all are very tech forward.

• 1:44:43Speaker 08

  You probably have agents doing things like preparing, preparing you for the show and like giving you reports every morning. And if you actually look at what the models, the agents are doing, they're probably like 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 like reading from scratch. What is TVPN and who are you to and you know, who's been on the show recently?

• 1:45:07John Coogan?

  No, we're in the pre-training now. Come on, give us some credit.

• 1:45:11Speaker 08

  Oh, yeah, you are in the pre-training.

• 1:45:14John Coogan?

  Your point 100% stance.

• 1:45:18John Coogan?

  But yes.

• 1:45:18Speaker 08

  Yeah, I think you're lucky because you're in the pre-training, but I think most people are not.

• 1:45:22John Coogan?

  But there's still so many documents that aren't, you have to feed those in every time. Is this the solution to continual learning? Is that the correct buzzword for this strategy? Or is this a different fork in the road, a different path?

• 1:45:35Speaker 08

  I think it's the correct buzzword. I think a lot of people use the phrase continual learning. They cracked it. They cracked it in eight months.

• 1:45:43John Coogan?

  The continual learning company of San Francisco is here. Let's go.

• 1:45:47Speaker 08

  Oh, we decided to name ourselves something different, but I think the, I think of continual learning is basically this problem of how do you keep the same model, but actually update it's like rewire it every single day to learn more about what you're doing. And we're working on that.

• 1:46:01John Coogan?

  What's the, 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, of AI wrappers and application layer companies duking it out. I'm thinking of legal, medical. Where, where do you see the product having the earliest signs of product market fit?

• 1:46:28Speaker 08

  Yeah, I'm glad you said earliest because I think like there's, 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, you know, coworker. And it'll be able to like generalize and do things better than the current models. But I think the current customers and like the way we're finding early success is by making the models a lot cheaper because like essentially they know everything about you already. And instead of reading like a hundred files to write a summary of what you need to do tomorrow, they read, you know, four files or something like that. So our early enterprise partners that we've been working with are Microsoft,

• 1:47:09Speaker 08

  Notion, and Harvey. And I think they all, you guys with the sound effects, I'm like so flattered. I wasn't sure if there would be any. They, they're nice because they have these like massive workspaces of context and like they're, you know, early adopters of AI. And I think these are the places where we can like reduce costs the fastest, soonest, because the workflows really are just that repetitive.

• 1:47:36John Coogan?

  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.

• 1:47:42Jordi Hays?

  I know you will be back on, I'm gonna guess, two times this year. That's my guess, two times.

• 1:47:48John Coogan?

  For sure. We'd love to have you back and shop it up more. Have a great rest of your day.

• 1:47:52Speaker 08

  Yeah, it's great meeting you guys. Thanks for having me.

• 1:47:53Jordi Hays?

  Yeah, great to meet you, Jack.

• 1:47:54John Coogan?

  We'll talk to you soon.

• 1:47:55Jordi Hays?

  Cheers.

• 1:47:56John Coogan?

  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 Movva. How do I say your last name? I don't want to get it wrong. Movva. Hey guys, good to be here. Thank you so much for taking the time. Movva. Great to meet you. Congratulations on the round, but first, please introduce yourself and the company.

• 1:48:21Speaker 09

  Yeah. Hey, guys. I'm Neil, co-founder and CEO of Sale 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.

• 1:48:34John Coogan?

  All open source? Do you work with other labs? Are you, how deep do you go into the relative organizations?

• 1:48:42Speaker 09

  Yeah, 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, you know, 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.

• 1:48:57John Coogan?

  Tell us about GLM 5.2. What makes it, what makes it like different in a binary sense? Is it, is it a particular benchmark? Is it a vibe? Is it an application? Have we unlocked a new capability in open source AI?

• 1:49:14Speaker 09

  Yeah, it seems like ZAI really figured out post-training with this release. That was something that was held back with the previous releases for DeepSeek and Kimi, 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 a straight face would recommend my colleagues try for coding.

• 1:49:30John Coogan?

  For coding specifically.

• 1:49:32Jordi Hays?

  And before you would put on clown makeup, and then you say, ah, yeah, give it, give it a spin.

• 1:49:37John Coogan?

  What about for other agentic workloads? I mean, we're 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?

• 1:49:56Speaker 09

  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.

• 1:50:17John Coogan?

  Yeah. How, 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?

• 1:50:31John Coogan?

  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. 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. And I'm wondering how you see those two buckets balancing out.

• 1:51:12Speaker 09

  You know, a hundred percent, I think, you know, to give you 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 just, we've crossed a few barriers in the last few months. So yes, I think we had the unlocks required for agents to run a lot longer reliably on every action that a human puts into a system.

• 1:51:43John Coogan?

  Yeah. And that's very good for your business because if I have something that's running on a Sunday when none of my employees are in, but it's still firing up a thousand dollars of cost. I want to come to you and get it to be $500. Like what, what, what type of pitch do you have in terms of savings?

• 1:52:01Speaker 09

  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.

• 1:52:11Speaker 09

  And you know, one of the ways I like to say it too is, um, you know, I like to work on unbounded problems. And before we built human in the loop agents, those were very bounded problems. You have a number of, limited amount of patients to read agent output every day. But if they 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.

• 1:52:30Jordi Hays?

  What were you and the team doing before this and, and how long have you been at it?

• 1:52:35Speaker 09

  Yeah. So I've been working on GPUs for about 10 years now. I love this stuff. It's my whole life.

• 1:52:41John Coogan?

  Is this some remarkable story where you're like, I was working on GPUs and you were just playing Counter-Strike or something?

• 1:52:47Speaker 09

  No, well, you know, I was in Nvidia, which for their business, it's all Counter-Strike, right?

• 1:52:51John Coogan?

  Yeah.

• 1:52:51Speaker 09

  You know, I remember being a little skeptical 10 years ago, like Jensen's talking, this big talk about moving to AI. But like realistically, you guys, we do $5 billion in revenue from gaming. That's surely that's going to be the biggest business for Nvidia for a long time. I imagine, well, I could see that now. And then I was previously at Apple as well. Apple had a pretty competent ML program, ML Silicon program. I won't say anything about their ML software program.

• 1:53:14John Coogan?

  Sure.

• 1:53:15Speaker 09

  And then most recently I was at Takeda AI.

• 1:53:18John Coogan?

  Very cool, very cool.

• 1:53:20Jordi Hays?

  Kind of a perfect background for this business.

• 1:53:22John Coogan?

  What is Liputan like in person? I'm such a fan. He's an angel investor. How'd you meet him? What's the story?

• 1:53:29Speaker 09

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

• 1:53:53John Coogan?

  Yeah, what a, what a wild run from him in such a short amount of time. One of the greatest story arcs in, in technology.

• 1:54:00Jordi Hays?

  And then who did, who did the round?

• 1:54:03Speaker 09

  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 so you can not get enough.

• 1:54:13John Coogan?

  Yeah.

• 1:54:13Jordi Hays?

  Amazing.

• 1:54:14John Coogan?

  Fantastic. Well, congratulations.

• 1:54:16Jordi Hays?

  Fantastic progress.

• 1:54:17John Coogan?

  We'll talk to you soon and thank you for everything you're doing.

• 1:54:18Jordi Hays?

  Great to meet you.

• 1:54:20John Coogan?

  Have a good rest of your day.

• 1:54:21Jordi Hays?

  Cheers.

• 1:54:21John Coogan?

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

• 1:54:32Jordi Hays?

  They have a great new campaign that we can try to watch.

• 1:54:34John Coogan?

  Yeah, yeah, we gotta watch some ads. We haven't done enough ads. Let's bring in Jacob Ethan-Brock from Discipulus Ventures. Welcome back to the show, Jacob. How you doing?

• 1:54:45Jakob Diepenbrock?

  Guys, how are you doing?

• 1:54:46John Coogan?

  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. Congratulations on the fundraiser.

• 1:54:57Jakob Diepenbrock?

  Yeah, thanks for having me, guys. Yeah, we just raised 30 million for the second fund with some great folks.

• 1:55:07John Coogan?

  That's gonna pay for a lot of barbecues on the beach.

• 1:55:11Jakob Diepenbrock?

  Yeah.

• 1:55:12Jordi Hays?

  No, it really is like the most probably efficient like VC platform strategy ever is just like the bonfires.

• 1:55:20John Coogan?

  The value created those bonfires is going to be in the multi billions for sure.

• 1:55:25Jordi Hays?

  Hopefully trillion.

• 1:55:26John Coogan?

  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 or your investors asking you? Are you going to get us the next trillion dollar company? Or do you, 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?

• 1:55:48Jakob Diepenbrock?

  Yeah, so our strategy basically is we get like good sized chunks for the fund at low prices. We're the first investor in all companies we bring through. A lot of them, 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 and I mean, we've already seen some of these markups that make the fund look very good given our entry price. But yeah, I mean, the goal is get good ownership for us, not too much for the founders at low prices. And the multiple look, look good much easier.

• 1:56:17John Coogan?

  I have a.

• 1:56:18Jordi Hays?

  Sorry. You're like a lot of ownership for us, not too much for the.

• 1:56:23Jordi Hays?

  I know, I know.

• 1:56:23Jakob Diepenbrock?

  Our fund is small enough where it makes sense.

• 1:56:25Jordi Hays?

  No, no, I know, I know.

• 1:56:26John Coogan?

  I have a, I have a theory that we are, we're not post defense tech boom. Like the companies are still booming, but we're, 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, what else is in the hard tech bucket that's exciting to you these days?

• 1:56:52Jakob Diepenbrock?

  Yeah, we did a lot of defense early on and I think there was a lot of more like gray area. I think there's like a thousand 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 and 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 the companies like SpaceX and General.

• 1:57:16John Coogan?

  Yep.

• 1:57:16Jakob Diepenbrock?

  We need to start defense companies early on, but now we're seeing a lot of manufacturing. I think chemicals are really interesting. I think the general industrial, space, energy, et cetera. And there's a lot of stuff that makes sense to build here because of talent and supply chain. It is not just purely defense.

• 1:57:32John Coogan?

  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?

• 1:57:45Jakob Diepenbrock?

  Yeah, I think LA has, still has the majority of SpaceX. I guess people who made money off of SpaceX.

• 1:57:51John Coogan?

  Yeah.

• 1:57:52Jakob Diepenbrock?

  So, yeah, I think a lot of people will probably start companies now because like they made enough money to be comfortable and they can do whatever they want now.

• 1:57:57John Coogan?

  Yeah.

• 1:57:58Jakob Diepenbrock?

  I think obviously they have like a lockup period to 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.

• 1:58:09John Coogan?

  And we've already seen with Radiant and Tom Mueller's company, Impulse Space, both SpaceX alums, very successful companies, exciting stuff.

• 1:58:18Jordi Hays?

  Moving forward, are you sticking with like, like a batch style approach? Are you just going to be writing checks more, more flexibly? Where do you think you go?

• 1:58:29Jakob Diepenbrock?

  Yeah, I think the core thing we have is like, we are close to all of 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 is how we're able to, I guess, generate alpha. And I think we will do follow on into the companies in 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. If we can kind of be as early as possible, find these young engineers before they leave and kind of be their launch pad into the right ecosystems of founders and investors, et cetera. That's kind of where we want to come in. So it's going to be vast majority of the capital being deployed into the coworker companies.

• 1:59:05Jordi Hays?

  Amazing.

• 1:59:06John Coogan?

  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 class cohort based entrepreneurship? Get me up to speed on the latest there.

• 1:59:24Jakob Diepenbrock?

  Yeah, I mean, I think the the bonfires are a good kind of index on how many people 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 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 of 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 continued to boom. Um, and the real estate prices are insane, which I think also is a good indicator that people moving out to Torrance and Hawthorne. Um, but yeah, definitely lots and lots of people coming from across the world.

• 2:00:00Jordi Hays?

  Is there enough industrial space in in, you know, El Segundo, Torrance, Hawthorne, like, or or does more need to be built?

• 2:00:11Jakob Diepenbrock?

  Yeah, yeah. Um, the prices in El Segundo are definitely high for sure. Um, I think most people, when I see somebody opening like a HQ2 or a factory to whatever it is, um, is now in Hawthorne and Torrance, Long Beach as well. I think this has kind of become pretty popular for people. Um, I I still think like as close as you can be to where all the talent is is 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.

• 2:00:41Jordi Hays?

  Yeah, so prices are going up, but there's still plenty of capacity.

• 2:00:44Jakob Diepenbrock?

  Yeah, and El Segundo is mostly like small, small kind of buildings like SpaceX.

• 2:00:48John Coogan?

  5,000 square feet, 10,000 square feet, R and D facilities, and then you set it up and get a 100,000 square foot warehouse.

• 2:00:55Jakob Diepenbrock?

  And I also think, one of the things I think is interesting is like, I think I've seen companies like Adrian and roll up and like a big factory in like the Midwest or the South, wherever it is. And I think like that will continue to happen because they're just way, way cheaper space and the cost matter. But I think for kind of the R and D engineering, I think that will continue to be done in the LA area. And people will then go and kind of open up the larger factories outside of, I think, LA for obvious reasons. But I I always think that kind of R and D engineering will need to be done in the LA area.

• 2:01:21John Coogan?

  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 chemicals startup. Like, there's probably some data center constructor out there who's like, I can make use of that. I gotta have water for something or other. Uh, is this something where you're seeing the boom supersonic style expansion into AI applications happening more and more?

• 2:01:52Jakob Diepenbrock?

  Yeah, I think it definitely makes fundraising easier. Like, we had one company that was doing like large-scale generators, were focused on DOW originally, and then they put like for data centers into the the tagline and they end up raising like a couple of weeks after that. Um, but I, I think that's, that definitely will happen. I, I think, obviously, like, if you can position yourself as being in the right trend, that's obviously good for fundraising. Um, so yeah, a lot of them have some elements there, but I wouldn't say like that's kind of dependent upon only data centers, only AI being as large as.

• 2:02:20John Coogan?

  That makes a ton of sense. Well, congratulations on.

• 2:02:23Jordi Hays?

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

• 2:02:43John Coogan?

  That's great. Have a great rest of your week. We'll talk to you soon.

• 2:02:46Jordi Hays?

  Cheers, dude.

• 2:02:47John Coogan?

  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 Altchek from Cadence. Chris, how you doing?

• 2:03:02Speaker 11

  Great. Hey, Jordy. Hey, John. Thanks for having me.

• 2:03:05Jordi Hays?

  Great to meet you. Welcome to the show.

• 2:03:07John Coogan?

  Introduce yourself. Tell us what you're building and then we'll talk about the round.

• 2:03:12Speaker 11

  Sure. Uh, Chris Altchek, founder at Cadence. We are building clinical AI to automate the treatment of chronic disease. Uh, we just announced our Series C last week and super excited to be on the show.

• 2:03:24John Coogan?

  How much did you raise?

• 2:03:25Jordi Hays?

  Start there. Start at the gong.

• 2:03:27Speaker 11

  We raised a hundred million dollars.

• 2:03:33Jordi Hays?

  Humble nine figs. Uh, talk about, yeah, when did you start the company? What's been the progress to date? What, what got you to this round?

• 2:03:42Speaker 11

  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're, we have the privilege of managing 100,000 patients now nearly every day with a lot of the leading hospital systems in the country.

• 2:04:27Speaker 11

  And preventing strokes and heart attacks and helping people get healthier. So it's been super exciting.

• 2:04:32Jordi Hays?

  Okay, so pick a condition and then walk me through exactly how the product works for a patient and for their, you know, care provider.

• 2:04:42Speaker 11

  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. Causing, costing the US 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 US. You know, 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. And it's not because they don't have amazing cardiologists or amazing primary care doctors.

• 2:05:25Speaker 11

  It's 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 vitals. We have their full medical records, their labs, vitals, allergies, symptoms, everything.

• 2:05:56Speaker 11

  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 9:00 PM, we have a voice agent that calls the patient within two and a half minutes, elect symptoms. If they're symptomatic, then we're figuring out, do they need to go to the hospital? Can we change their meds at home?

• 2:06:40Speaker 11

  Or do we need them to see their cardiologist 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, we're then coaching the patient on diet, exercise, med adherence, all the little things that require a lot of, a lot of support on a daily basis. Our average patient is 75 years old, to sort of keep them on their care plan. And we had a 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. 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

• 2:07:25Speaker 11

  is like, you know, that's, that's what we're trying to do here.

• 2:07:29Jordi Hays?

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

• 2:07:47John Coogan?

  I would, I would love some more information, just getting me up to speed on the state of the medical devices for monitoring vitals. You mentioned a, an internet connected or cellular connected blood pressure cuff. Are, is there, 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?

• 2:08:20Speaker 11

  Yeah, it's one of the exciting places of the next five years. So today it's a separate suite. These are FDA clear devices that give you blood pressure in a medically accurate way, or blood glucose, CGM, etc. 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 US who could benefit from this, if not 20 or 30 million. And we've just got more data,

• 2:09:03Speaker 11

  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.

• 2:09:16John Coogan?

  Okay.

• 2:09:17Jordi Hays?

  John, nominative determinism here, alternative checkup.

• 2:09:20John Coogan?

  Okay, yeah.

• 2:09:21Jordi Hays?

  I'll check.

• 2:09:22John Coogan?

  I think we missed a C in the last name. We need to update the Chyron. 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. But is there a new, maybe in the last decade, metric that doctors are monitoring? Is there a new, is there a new number that's popping up and proving to be indicative of health performance or drug dosage?

• 2:10:03Speaker 11

  We're not, we're not there yet.

• 2:10:07John Coogan?

  Okay.

• 2:10:07Speaker 11

  In terms of HRV, or hemodynamics with heart failure, like how effectively is your blood, is your blood, 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're you or 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. And so the level of clinical insight you get from, you know, 22 days of data versus four times a year

• 2:10:48Speaker 11

  is pretty dramatic. So I would say a big, a big part of this is, 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 US 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.

• 2:11:22John Coogan?

  Yeah. What is the key to scaling? Do you need to work?

• 2:11:24Jordi Hays?

  Ramp of healthcare.

• 2:11:27Jordi Hays?

  I like this dynamic. You say something incredible. I say a joke. John asks a serious question. And we could just go around like this. We could just go around like this forever. No, but I, but I love, I love the focus on, on savings. It's incredible.

• 2:11:41John Coogan?

  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?

• 2:11:56Speaker 11

  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. From, we announced actually Duke and Texas Health last week. We work with some of the largest health systems in every state, Orwell in Michigan. 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. 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.

• 2:12:40Speaker 11

  payers in the country. We worked very closely with CMS and the US government to ensure that there's positive ROI for payers. So those are the sort of big three expansion expansion motions for us. We're only at 3% of the eligible patients within the hospital health systems that we are today. So as this becomes the standard of care in the US, this should hopefully be able to help a lot of people.

• 2:13:05John Coogan?

  Amazing. Jordan, anything else?

• 2:13:07Jordi Hays?

  Incredible.

• 2:13:08John Coogan?

  I have one last question. 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? Is, are you the synergy that we were hearing about when that news initially broke? Walk me through that.

• 2:13:27Speaker 11

  Yes, so General Catalyst acquired a non-for-profit hospital system called Suma Health that closed earlier this year. It's a really exciting testing ground for new technologies inside of important community health systems. And Suma 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 US healthcare that's happening right now with AI. You know, I think people think of healthcare as a laggard industry that's always slow to adopt technology.

• 2:14:08Speaker 11

  And when you look at AI, it's definitely one of the leaders in adoption of AI today. And then on cadence aside, what we're really excited about is a lot of AI has been pointed towards automating back office tasks, billing, rev cycle, call centers, etc. 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.

• 2:14:37John Coogan?

  Yeah, awesome. Well, thank you so much for taking the time to come.

• 2:14:40Jordi Hays?

  Thank you for doing this.

• 2:14:41John Coogan?

  And thank you for everything you're doing.

• 2:14:42Jordi Hays?

  Yeah, very important work.

• 2:14:43Speaker 11

  Appreciate you guys having me. Thank you.

• 2:14:45Jordi Hays?

  Come back on soon.

• 2:14:46John Coogan?

  Can't wait to talk to you next time. We'll see you.

• 2:14:48Jordi Hays?

  Cheers.

• 2:14:49John Coogan?

  Goodbye.

• 2:14:51John Coogan?

  Our friend John Fiorentino went viral, mega viral, forty one thousand likes with a bit of life advice.

• 2:14:59Jordi Hays?

  Up from nineteen this morning.

• 2:15:01John Coogan?

  It's at forty one thousand now, so.

• 2:15:03Jordi Hays?

  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 likes.

• 2:15:14John Coogan?

  He's on his second account. This is a new account.

• 2:15:16Jordi Hays?

  He was like, my account is broken. I gotta start fresh.

• 2:15:20John Coogan?

  Yeah, he started fresh, uh, which is very, very hard in twenty twenty five, twenty twenty six, starting a new account and grinding it up is, 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, uh, but this one went mega, mega viral. He sent us, he sent this to us when it had like two likes and was like, do you think this is the one that'll go viral? And it did. He called a 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 one hundred percent of the time." I think that's good advice.

• 2:16:02Jordi Hays?

  I think.

• 2:16:03John Coogan?

  What are the exceptions to that rule?

• 2:16:05Jordi Hays?

  I don't know, an exception. I, I was, uh, hanging out with some friends yesterday, one of them selling this, uh, architecturally significant home.

• 2:16:13John Coogan?

  Kinda gotta show you the photos, but.

• 2:16:15Jordi Hays?

  He told me.

• 2:16:16John Coogan?

  Should've printed them out.

• 2:16:17Jordi Hays?

  Yeah, it would've been great if he had just, yeah, instead of pulling up a video of a tour of the home, if he had printed out.

• 2:16:24John Coogan?

  Before I go out with friends, I'll often just print out my camera roll.

• 2:16:27Jordi Hays?

  Yeah.

• 2:16:28John Coogan?

  Like the last twenty photos.

• 2:16:29Jordi Hays?

  Twenty, twenty thousand.

• 2:16:31John Coogan?

  Yeah, yeah, just bound it into a large tome that I carry with me. Tyler, what do you think about pulling out your phone while trying to illustrate something? Are you pro or anti?

• 2:16:41Speaker 02

  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.

• 2:16:49John Coogan?

  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.

• 2:16:55Speaker 02

  That's kind of a skill issue, right?

• 2:16:56John Coogan?

  Yeah, it's hard.

• 2:16:57Speaker 02

  If you have a good video, two minutes long, you're like, oh, I want more of that.

• 2:17:00John Coogan?

  At the same time, it is, it is difficult to, you know, pull up a video because there usually there's like 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 of headphones. Oh, I, I gotta restart it, you know, to show it to you and then I'm waving it around. It's pretty difficult. I understand.

• 2:17:18Jordi Hays?

  I got, I, I went down a bit of a rabbit hole designing, uh, designing

• 2:17:22John Coogan?

  Furniture.

• 2:17:22Jordi Hays?

  Furniture Saturday night in chat and, uh, I was pulling out my phone this morning, showing Tyler, showing Tyler some of it.

• 2:17:31Speaker 02

  Yeah, like you could not have explained that to me. I had to visually see it.

• 2:17:34John Coogan?

  That's true. That's true. It would have been hard.

• 2:17:36Speaker 02

  Because it was like so mind blowing. Like I can't, it's hard to actually like articulate.

• 2:17:39Jordi Hays?

  Except the only thing is it kinda, Theo kinda has a point because you kinda looked at them. You were like, eh.

• 2:17:45John Coogan?

  Yeah. Alternatively, 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.

• 2:17:53Jordi Hays?

  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.

• 2:18:05John Coogan?

  Dream come true.

• 2:18:06Jordi Hays?

  And, uh, 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 adds to.

• 2:18:16John Coogan?

  How did he film this? With his phone or is this a?

• 2:18:19Jordi Hays?

  Meta glasses.

• 2:18:21John Coogan?

  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 Udikowski 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 Udikowski 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

• 2:19:04John Coogan?

  paperclips. Since he famously wrote the original paperclip maximizer thought experiment, he is walking toward you to prevent global conversion, 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

• 2:19:45John Coogan?

  was about like a theoretical construct that looked sort of like a paperclip, but it was not a literal paperclip. And so, but it certainly went viral. Good coinage, the paperclip.

• 2:19:57Jordi Hays?

  Last.

• 2:19:58John Coogan?

  No one wants to be a paperclip.

• 2:19:59Jordi Hays?

  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.

• 2:20:12John Coogan?

  It's the besties.

• 2:20:13Jordi Hays?

  They got the besties.

• 2:20:13John Coogan?

  They got the besties together.

• 2:20:15Jordi Hays?

  Do you think Friedberg's gotta be in?

• 2:20:18John Coogan?

  That's the production board.

• 2:20:19Jordi Hays?

  Oh, the production board.

• 2:20:20John Coogan?

  Yeah, Friedberg's fun. So yeah, you actually have all three of the other besties.

• 2:20:25Jordi Hays?

  There you go. What a lineup. A lineup.

• 2:20:29John Coogan?

  Well, there's much more news, but we can get to it tomorrow because we will be back tomorrow at 11 a.m. Sure.

• 2:20:36Jordi Hays?

  That's right.

• 2:20:37John Coogan?

  Thanks for tuning in.

• 2:20:38Jordi Hays?

  I can't wait. Have the best evening or afternoon of your entire life. Just do it for us. Just do it for us.

• 2:20:43John Coogan?

  And leave us five stars on Apple Podcasts and Spotify. Sign up for our newsletter, tbpn.com. And we will see you tomorrow.

• 2:20:49Jakob Diepenbrock?

  Rolling flashback.

• 2:20:50John Coogan?

  Goodbye.