Open Source vs. Closed Source, Memory Chips Eat AI Profits, Comcast Restructures | Diet TBPN

Diet TBPN delivers the best of today’s TBPN episode in 30 minutes. TBPN is a live tech talk show hosted by John Coogan and Jordi Hays, streaming weekdays 11–2 PT on X and YouTube, with each episode posted to podcast platforms right after. Described by The New York Times as “Silicon Valley’s newest obsession,” the show has recently featured Mark Zuckerberg, Sam Altman, Mark Cuban, and Satya Nadella. 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/ Follow TBPN: https://TBPN.com https://x.com/tbpn https://open.spotify.com/show/2L6WMqY3GUPCGBD0dX6p00?si=674252d53acf4231 https://podcasts.apple.com/us/podcast/technology-brothers/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.

• 0:02John Coogan?

  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 world news section, not just the business and finance section, more and more. So the picture is about the heat wave, but the lead, 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 thought that we'd have a conclusion to the open source AI debate by now. Either they would, the frontier would have collapsed

• 0:45John Coogan?

  and there would be, you know, perfect commoditization, or they would have fallen so far behind.

• 0:51Jordi Hays?

  There will never be a conclusion. It'll, it'll just go, it's over. We're so back. It's over.

• 0:55John Coogan?

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

• 1:40John Coogan?

  released this month by China's Zepu 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. So unlike models from Anthropic or OpenAI, Zepu's GLM.

• 2:01John Coogan?

  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. 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.

• 2:34Jordi Hays?

  Unfettered intelligence.

• 2:35John Coogan?

  Unfettered. Ooh, that's a good-

• 2:37Jordi Hays?

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

• 2:41John Coogan?

  That's a good Neo Lab name, yeah.

• 2:43Jordi Hays?

  Unfettered intelligence.

• 2:44John Coogan?

  Unfettered intelligence is good. GLM 5.2 has ranked as one of the top 10 most-used AI models, according to data from Open Router, a company that provides access to more than 400 AI models. And what a fantastic business. Alex Atala over there, absolutely cooking at Open Router. It's such an exciting way to plug into the AI, the AI race, without actually needing to play the, play the benchmark game so much, be the, 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,

• 3:25John Coogan?

  Opus 4.8 and GLM 5.2 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, the, 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

• 4:07John Coogan?

  a stronger takeoff in the American closed-source AI industry. Now, this chart-

• 4:14John Coogan?

  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. 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, which is a blend.

• 4:51Jordi Hays?

  Ryan says GLM 5.2 sounds too much like a gray market peptide that you're taking.

• 4:56John 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? 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?

• 5:27Speaker 02

  Yeah, so this is by Casey. I think it's how you pronounce it, the Center for AI Standards and Innovation. They have this way to calculate like the Elo of model. It's basically 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.

• 5:47John 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.

• 5:55Speaker 02

  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? I think the group of benchmarks that were chosen for this Elo definitely accentuate the gap between US and Chinese labs. I think there's a bunch of other like groups, like Epoch AI has done a chart. They basically see a relatively stable gap between closed source and open source models since like 2023, like a long time.

• 6:33John Coogan?

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

• 6:41Speaker 02

  Yes, yeah, definitely, because even like, you know, new models, a lot of times when they come out 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, it's the price per,

• 6:54John Coogan?

  Yep.

• 6:55Speaker 02

  per, you know,

• 6:55John Coogan?

  Yep.

• 6:56Speaker 02

  something completed.

• 6:57John Coogan?

  Yeah.

• 6:57Speaker 02

  And then you actually see it go down.

• 6:58John 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.

• 7:14Speaker 02

  Yes, 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.

• 7:22John Coogan?

  On a per-token basis.

• 7:23Speaker 02

  On the per-token basis.

• 7:24John Coogan?

  But on the per-task basis,

• 7:26Speaker 02

  Exactly.

• 7:26John Coogan?

  it might be more expensive.

• 7:28Speaker 02

  Yeah, I mean, that's still, it's generally not.

• 7:30John Coogan?

  Okay.

• 7:30Speaker 02

  But on specific tasks,

• 7:31John Coogan?

  Yeah, yeah.

• 7:32Speaker 02

  you can get, you know, if you have low-thinking models,

• 7:33John Coogan?

  Yeah.

• 7:34Speaker 02

  low-thinking mode on the closed-source ones, you can see.

• 7:37John 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-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. The impulse to release open-source models makes sense as a free marketing strategy."

• 8:23John Coogan?

  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. It showed that, hey, they're able to develop these models and and that might help them cut their costs in the long term.

• 9:05John Coogan?

  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 launched 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 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

• 9:50John Coogan?

  to train models away for free. They love to see deflationary economics in the US. It is not, is 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. And so he likens what's happening in DC to sort of rearranging deck chairs on the Titanic. It's a very fun, fun piece. 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

• 10:34John Coogan?

  that's resurfacing from Dario Amadei 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 years for the frontier models for the bio risks, is sort of a bad transcription of what he was saying. 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

• 11:18John Coogan?

  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 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

• 12:03John Coogan?

  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 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, and you can tell us, like, what is the shape of this model? How are the reviews?

• 12:36Speaker 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. It certainly seems like there's some, you know, aspects of anthropic models.

• 12:55John Coogan?

  Didn't Anthropic openly accuse Alibaba of distilling?

• 13:00Speaker 02

  Yes, a number of these labs.

• 13:02John 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.

• 13:15Speaker 02

  Subscriptions.

• 13:15John Coogan?

  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.

• 13:40Speaker 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 open source models. Then you're kind of like distilling, but it's not really like, is this really, count as distilling? I don't know.

• 13:57John Coogan?

  Yeah.

• 13:58Speaker 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.

• 14:06John 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 in open source repos that have effectively been, completely been rewritten by closed source models.

• 14:40Speaker 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 benchmark scores, that maybe they're benchmarks, maybe they're not, but even if they're not like directly benchmarks, you still find that they generally.

• 14:55John Coogan?

  Yeah, they're kind of accidentally benchmarking.

• 14:57Speaker 02

  Yeah.

• 14:57John Coogan?

  Yeah.

• 14:57Speaker 02

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

• 15:02John Coogan?

  Yeah, but they lack that big model je ne sais quoi.

• 15:06Speaker 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 kind of benchmark this.

• 15:18John Coogan?

  I wonder, have people been testing it with the like Tiananmen Square bench? Like, does it reject that stuff? Or because it felt like that was something that was like widely misunderstood by American audiences, that in fact, that might not be the biggest deal for the CCP.

• 15:36Speaker 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,

• 15:41John Coogan?

  Sure.

• 15:41Speaker 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.

• 15:45John 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.

• 16:08Speaker 02

  Yes, I think like what we've seen is, you know, a marginal IQ point of the models is like extremely expensive. Frontier models are getting very expensive. People have to cut back, you know, they're token maxing, this is like massive bill on their balance sheet or whatever. 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.

• 16:38John Coogan?

  Yeah.

• 16:38Speaker 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 little agents using these very cheap models.

• 16:52John Coogan?

  Yeah.

• 16:52Speaker 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. You see this on open router where, like, what are the top models by, by token? Uh, like usage. It's these very small models. It's like, uh, you know, uh, DeepSeek Flash.

• 17:12John 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.

• 17:25Speaker 02

  Yeah.

• 17:25John Coogan?

  It can just do standard OCR.

• 17:27Jordi Hays?

  That'd be my preference.

• 17:29John 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.

• 17:38Speaker 02

  Yeah, but also I, 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.

• 17:49John Coogan?

  Yeah, yeah, yeah.

• 17:49Speaker 02

  Uh, 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, um, I, I think we, we can't really fully dismiss these, these labs.

• 18:02John Coogan?

  Yeah, it, it throws, it throws a little bit of a wrench in the, uh, like the monetization potential, like how long can you monetize a new frontier model? That's more tricky. And then, uh, the other one is just like, if you're going to, uh, 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

• 18:43John Coogan?

  got access to GLM-6 and it's close enough. And so, uh, that in, that, that just throws another wrench that I think the government will have to figure out how it puzzles together with the rest of the strategy, uh, which has been, yeah, back and forth as always.

• 19:00Jordi Hays?

  Google caps Meta's Gemini use as AI demand strains capacity in the financial times. 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.

• 19:20John Coogan?

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

• 19:23Jordi Hays?

  I think that was just the best Google Gemini picture. Google has put limits on Meta's use of its Gemini AI models after the social media giant sought 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.

• 19:54John Coogan?

  I don't understand how this is possible.

• 19:56Jordi Hays?

  Yeah, yeah. So, so one.

• 19:58John Coogan?

  Google spent $200 billion on CapEx.

• 20:00Jordi Hays?

  Okay, so, so, so of course, like, 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. You know, I could see Meta going and like wanting to basically buy a bunch of capacity and then being told like, hey, we, we can't fulfill that. But I'm, I'm wondering, is it worth reading?

• 20:24John Coogan?

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

• 20:26Jordi Hays?

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

• 20:30John Coogan?

  Yeah, yeah. Yeah, Google Cloud acceleration.

• 20:34Jordi Hays?

  You do have to wonder, like, could distillation be, be part of this, this story? Is, is that, could that be a factor here? I have, I have no idea.

• 20:44John Coogan?

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

• 20:49Jordi 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 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? 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.

• 23:30John Coogan?

  Four of the five were very, very like reasonable. You know, Anthropic's going to be bigger and you know, TSMC is going to face more competition. And then he predicts that in 2030, telepathy will be commonplace, which is a very aggressive prediction in my estimation. How Terry Semel fumbled Yahoo's Facebook deal. How much is Facebook worth? Five billion, 10 billion, 15 billion, whatever the number. It's probably a lot more than the one 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.

• 24:12John Coogan?

  When Yahoo came calling with a bid of $1 billion in cash, the pressure became too much. Zuckerberg leant it in July of 2006. He was just like 18 months into building the company, something like that. Verbally agreeing to sell Facebook to Yahoo. He said yes. He said he was going to sell Facebook to Yahoo, allegedly. Strategically, it seemed like a good match. Yahoo had hundreds of millions of users, but its foray into social networking was struggling. Facebook had cool tools and was looking for a mass audience. The timing, however, could not have been worse. In the days after Zuckerberg agreed to sell, Yahoo announced it was projecting slower sales and earnings growth and that it's that the launch of its new advertising platform would be delayed.

• 24:55John Coogan?

  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, 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 the 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

• 25:40John Coogan?

  from Wired.

• 25:41Jordi Hays?

  Legendary.

• 25:42John Coogan?

  Legendary. It's so interesting to imagine the road not traveled there because the dynamic, the way Facebook is built 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?

• 26:16Jordi Hays?

  I think Yahoo should make another offer. I would like to see Yahoo make another bid.

• 26:21John Coogan?

  Hey, that is trading down, just keeps going.

• 26:24Jordi Hays?

  If it continues at this trend.

• 26:26John Coogan?

  Ninety-nine point nine percent. Might be able to pick it up.

• 26:29Jordi Hays?

  At this trend.

• 26:30John Coogan?

  Anyway.

• 26:31Jordi Hays?

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

• 26:36John Coogan?

  This is on the cover of the business and finance section today.

• 26:39Jordi 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.

• 26:48John 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 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.

• 27:31John Coogan?

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

• 28:12John Coogan?

  Apple last week raised prices for MacBooks more than 15%. Closer to home for me, the memory I bought on amazon.com a year ago to build a super quiet computer. I hate fan noise. Good, 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.

• 28:55John 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.

• 29:16Jordi Hays?

  Tyler, how many trillion dollar companies are there in Europe out of curiosity?

• 29:22Jordi Hays?

  I'm gonna go with zero.

• 29:25Jordi Hays?

  That's true.

• 29:25John Coogan?

  NBC, Universal and Sky will separate the company's connectivity business from its film, theme park and streaming operations. Oh yeah, Universal Studios. Comcast plans to separate its media and connectivity businesses.

• 29:39Jordi Hays?

  Who's building the Andorol of theme parks?

• 29:42John Coogan?

  It does seem like a.

• 29:43Jordi Hays?

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

• 29:52John Coogan?

  It's very expensive. Everything needs to be like the modern technology stack in.

• 29:57Jordi Hays?

  Oh, it's expensive. You don't believe in the theme park capital markets?

• 30:01John Coogan?

  I don't know. I, 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.

• 30:12Jordi Hays?

  It seems like an absolutely brutal business.

• 30:14John Coogan?

  Yeah.

• 30:15Jordi Hays?

  That is probably harder today because at the time that a lot of these parks were built, like you didn't have like infinite online entertainment for every single sub.

• 30:26Jordi Hays?

  Niche instantly available.

• 30:28John Coogan?

  I mean, there's a whole bunch of trend pieces right now about how I R L experiences are seeing higher than ever pricing in the face of you could just watch the Knicks game on Tik Tok 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.

• 30:55Jordi Hays?

  Exploring the NBA team to Vegas. But at the same time, at the same time, John, there's also that stat that came out. There's more sports betting volume than all sales of movie tickets, theaters, theme parks, and like a couple other of these I R L categories.

• 31:13John Coogan?

  Is it up or down?

• 31:14Jordi Hays?

  Uh, less, yeah, less. Less like and and and the stat was like volume.

• 31:20John Coogan?

  Yeah.

• 31:21Jordi Hays?

  Uh, and so it's not exactly like a proxy for like revenue, but still meaningful. Chamath raised $135 million series A for 8090. They got sale. They got Salesforce Ventures. They got Wonderco. They got Kraft and they got Launch.

• 31:41John Coogan?

  It's the besties.

• 31:41Jordi Hays?

  They got the best.

• 31:42John Coogan?

  They got the besties together.

• 31:43Jordi Hays?

  You think Fried, Friedberg, Friedberg's got to be in.

• 31:47John Coogan?

  That's the production board.

• 31:48Jordi Hays?

  Oh, the production board.

• 31:49John Coogan?

  Yeah, no, it's Friedberg's fun. Yeah. So yeah, you actually have all three of the other besties and we will see you tomorrow.

• 31:55Jordi Hays?

  We flashback.

• 31:56John Coogan?

  Goodbye.