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TBPN is a live tech talk show hosted by John Coogan and Jordi Hays, streaming weekdays from 11–2 PT on X and YouTube, with full episodes posted to Spotify immediately after airing.
Described by The New York Times as “Silicon Valley’s newest obsession,” TBPN has interviewed Mark Zuckerberg, Sam Altman, Mark Cuban, and Satya Nadella. Diet TBPN delivers the best moments from each episode in under 30 minutes.
Well, on the front of The Wall Street Journal today, this is how you know this is the whole AI twenty twenty seven Washington waking up. The AI stories are making it to the front page, the the 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 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 gonna get into it.
Speaker 1: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 the frontier would have collapsed and there would be, you know, perfect commoditization or they would have fallen so far behind.
Speaker 2:It'll it'll just go, it's over. We're so back. It's over.
Speaker 1: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 dot ai. It was officially released June 13, so it's taken a couple weeks for it to really break through to the front page of The Wall Street Journal. But they're seeing 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.
Speaker 1: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.
Speaker 1: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 Xipu AI, also known as Z dot 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 5.2 is open weight. You can just download it, run it anywhere. You don't need to go to an API.
Speaker 1: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 and run on hardware operated by anybody and can be modified and used without supervision. Scary stuff.
Speaker 1: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. Unfettered. Oh, that's We
Speaker 2:completely out of names for new NeoLabs.
Speaker 1:That's a good NeoLabs.
Speaker 2:Yeah. Unfettered intelligence.
Speaker 1: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 Italo over there. Absolutely cooking at OpenRouter.
Speaker 1: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 Anthropix Clod Opus 4.8 model, which was released in May. When given further instructions, 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.
Speaker 1: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.
Speaker 1: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 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.
Speaker 1: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 four point zero to o one, o three minutei, o three, OPUS four, GPT five, five point two, OPUS 4.6, GPT 5.4, GPT 5.5, showing a, you know, linear trend in this ELO, which Right. Is a
Speaker 2:says, GLM 5.2 sounds too much like a gray market peptide that you're taking.
Speaker 1:It actually does. It does sound a lot like that. And and then you can see the red line are the 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?
Speaker 3:Yes. So this is by Casey. I I think it's how you pronounce it. The Center for AI Standards and Innovation. They they have this way to calculate, like, Elov model.
Speaker 3:It's basically approximation of a bunch of different benchmarks. Mhmm. Some of those, like, are proprietary. Like, they that they're not open. So it's actually hard to run these.
Speaker 3:Also, I I was basically trying to bench, like, all the recent models since this was published. Yeah. Think it was, I I wanna say, May 1.
Speaker 1:Yeah. It'd be great to throw 5.6 Soul, Mytho Fables. And Fable. All all it would be great to just continue this chart because it's an interesting trend.
Speaker 3:So a lot of those benchmarks aren't actually public, so it's very hard to
Speaker 1:estimate.
Speaker 3:But I I tried I I got you you can look at, like, some of the benchmarks that that are public Mhmm. That you can reference.
Speaker 2:You can
Speaker 3:kinda match them up to previous models. Mhmm. 5.2 looks like it it it is like a big step up from the, like, Chinese
Speaker 1:The trend.
Speaker 3:Trend line. Right? I think the the group of benchmarks that were chosen for this Elo definitely accentuate the the gap between US and Chinese labs. Mhmm. I I think there's a bunch of other, like, groups, like Epoch AI has done Yeah.
Speaker 3:A chart. They basically seen a a relatively stable gap between closed source and open source models
Speaker 1:Yeah.
Speaker 3:Since, like, 2023, like, a long time.
Speaker 1:Yeah. And perhaps at this point, the the discussions should be more centered around cost per task more than cost per token.
Speaker 3:Yes. Yeah. Definitely. Because e even, like, you know, new models, a lot of times when when they come out, like, okay, maybe the Mhmm. The token price is actually the exact same Mhmm.
Speaker 3:But the token efficiency is much better. Mhmm. Because then when you do a lot of these tasks, it's like it's it's not the the the price for tokens, price per
Speaker 1:Yep.
Speaker 3:Per, you know Yep. Something completed. Yeah. And then you actually see it go
Speaker 1:down. 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 nonviable for any real enterprise use case and probably not even viable if you're trying to be a nefarious hacker or
Speaker 3:something. Yes. Most people are saying, like, 5.2 is very token hungry. Right? So it uses a lot of tokens.
Speaker 3:Mhmm. So maybe it like, it definitely is much cheaper than the Frontier models.
Speaker 1:On a per token basis.
Speaker 3:On a per token basis.
Speaker 1:But on the per task basis Exactly. It might be more expensive.
Speaker 3:Yeah. I I mean, on that that's still it's generally not. Okay. But on on specific tasks Yeah. Yeah.
Speaker 3:You can get you know, if you have low thinking models Yeah. Low thinking mode Mhmm. On the Closed Source ones, you can Well,
Speaker 1:let's revisit John Ludwig's post from 2024, May 2024. This is pre DeepSeek 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, intensity 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.
Speaker 1:The impulse to release open source models makes sense as a free marketing strategy and as a path to commoditize your compliments, 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 a really fast takeoff in ARR around those models, it showed that, hey, they're able to develop these models and that might help them cut their costs in the long term.
Speaker 1: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 DeepSeaks launch at the start of twenty twenty five, and the game theory became way more complicated. So George Hots sort of summed this up nicely.
Speaker 1: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 to train models away for free. They love to see deflationary economics in The US. It is not it 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.
Speaker 1: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.
Speaker 1:And there's been this clip that's resurfacing from Dario Amadeh 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.
Speaker 1: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, it's 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.
Speaker 1: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 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.
Speaker 1: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 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?
Speaker 1:How are the reviews?
Speaker 3:Yeah. So I I think so far, one of the main things is like people are saying it's, oh, it's distilled. Right? This is this been a big thing with a lot of these open source models Yep. Especially the Chinese ones.
Speaker 3: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. Mhmm. It certainly seems like there there there's some, you know I think it aspects of of anthropic models
Speaker 1:Didn't anthropic openly accuse Alibaba of distilling A number of these
Speaker 3:these labs.
Speaker 1:Yeah. And there's also been a big, professionalization of the gray market where a whole bunch of different sort of individual groups will connect a whole bunch of different entities and users Subscriptions. And script 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?
Speaker 1:I don't understand.
Speaker 3:Yeah. I mean, 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 with with Yeah. Resource models. Yeah.
Speaker 3:You're kind of, like, distilling, but it's not really, like is this really kind of distilling? I don't know. Yeah. But so so if you are like if you're convinced that these are like super distilled, the only reason that they're good is is because they're just, you know, basically taking the closed sourced
Speaker 2:Yeah.
Speaker 3:Like labs.
Speaker 1: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
Speaker 3:Yeah.
Speaker 1: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.
Speaker 3:Yeah. And so so I think it's safe to say that, like, we've generally seen that distilled models Mhmm. Generally will generalize worse. Right? Mhmm.
Speaker 3:So you'll see really good benchmarks Yep. Maybe they're benchmarked, maybe they're not. But even if they're not, like, directly benchmarked, you you still find that they generally
Speaker 2:Yeah.
Speaker 1:They're kind of accidentally benchmarking.
Speaker 3:Yeah. Yeah. So you should always so I I think initially, you should just be a little bit suspicious of these super high benchmark scores.
Speaker 1:Yeah. But they lack that big model je ne sais quoi.
Speaker 3:Yeah. And this is like anecdotally reinforced. A bunch of people have been saying, you know, for coding Yeah. These models are really great. GLM, it's a very good model, you know, for creative writing or or something like Okay.
Speaker 3:This where you'd imagine it's a bit harder to to kind of benchmark this.
Speaker 1:I wonder hey. Have 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
Speaker 3:Yeah. Also, I I think, you know, even if that's true, like, the model is open source. You can kind of just fine tune it to, like Sure. Not that
Speaker 1:talk about.
Speaker 3:Maybe it's a bit harder than that, but Yeah.
Speaker 2:I think
Speaker 3:you can kind of get around, like, that kind of stuff.
Speaker 1:Okay. Yeah. So we talked about the token hunger and the API price. And in general, I mean, 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.
Speaker 3:Yes. I I think, like, what we've seen
Speaker 2:is Yeah.
Speaker 1:That's the job
Speaker 3:You know, a marginal IQ point of the models Yeah. Is, like, extremely expensive. Front end models are are getting very expensive. Yeah. People have to cut back.
Speaker 3:Yeah. They're they're talking maxing. This is, like Sure. 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.
Speaker 3:There's like the frontier ones
Speaker 1:Mhmm.
Speaker 3:And they're they're using coding agents. They need the best thing. If you're doing cyber, like, you just need the 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.
Speaker 3:Yeah. And then there's the second class, which is, like, these very small, very fast, very cheap
Speaker 1:Yeah.
Speaker 3: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. Yeah. I I think in the middle, it's hard to actually figure out what is the the real use case. Mhmm.
Speaker 3:Maybe it's like hobbyists using these coding agents and and they don't wanna pay the the super expensive tokens of the Closed Source Labs. You you see this on OpenRudder where, like, what are the top models by by token, like, usage? It's these very small models. It's like Yeah. You know, DeepSeek flash.
Speaker 1: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.
Speaker 3:Yeah.
Speaker 1:It can just do standard OCR.
Speaker 2:That'd be my preference.
Speaker 1:Yeah. You want you want super intelligence overseeing your expenses, most likely. But no, you use the right tool for the job, that's clearly what's happening on
Speaker 3:Yeah. Red 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.
Speaker 3:I think they are, like, very good.
Speaker 1:Yeah. Yeah.
Speaker 3:Yeah. 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 think we can't really fully dismiss these labs.
Speaker 1:Yeah. It throws a little bit of a wrench in the monetization potential, like how long can you monetize a new frontier model. That's more tricky. And then, the other one is just like, if you're going to keep a model behind KYC or behind an approval for specific companies, like the government has been sort of edging towards and moving towards, it gets a little bit tricky if all of a sudden you just wait three months and, Oh, I was waiting to get approved for this one for like GPT-seven or whatever, but by the time I the government got back to me, my company got access to GLM-six and it's close enough. And so that just throws another wrench that I think the government will have to figure out how it puzzles together with the rest of the strategy, which has been, yeah, back and forth as always.
Speaker 2: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 scarcest commodity. And they have a picture here of a Google Gemini bicycle,
Speaker 1:which
Speaker 2:looks fantastic.
Speaker 1:What does that have to do with Meta, though?
Speaker 2:I think that was just the best Just like Gemini picture. Google has put limits on Meta's use of its Gemini AI models after the social media giant, Sopmore computing capacity than the rival tech group could provide in the latest evidence of the infrastructure constraints facing even the world's largest AI providers. Google told Meta around March that it could not provide all 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 don't understand how this is possible. Yeah.
Speaker 2:So one
Speaker 1:Google spent $200,000,000,000 on CapEx.
Speaker 2:Okay. Of course, around this time, token maxing was becoming a A lot of every company in the world, at least every tech company in the world, of going a little bit crazy from a spending standpoint. I could see Meta going and wanting to basically buy a bunch of capacity and then being told, hey, we can't fulfill that. Yeah. But I'm wondering, is it worth reading
Speaker 1:I mean, it sounds extremely bullish for Google. Like, if they're
Speaker 2:asking Yeah. That fast this tracks with what they talk about on earnings calls.
Speaker 1:Yeah. Yeah. Yeah, Google Cloud
Speaker 2:acceleration creates You great do have to wonder, like, could distillation be part of this story? That Could that be a factor here? I have no idea.
Speaker 1:I don't know. ZeroHedge said Meta puts limits on Claude and Kodak's fearing distillation, the information.
Speaker 2:But so this story is different. This is Meta telling its own employees, don't use Clod and Kodak in certain parts certain parts of our business because we don't to want accidentally do distillation Oh. Is what Meta is saying. Interesting. So that's different.
Speaker 2:I was wondering, is Google thinking like, woah, that's a lot of
Speaker 1:Yeah. Yeah.
Speaker 2:Cool it. 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.
Speaker 1:Interesting.
Speaker 2:Very interesting. On the topic of Meta Yeah. Meta shared this morning
Speaker 3:What they do?
Speaker 2:A new milestone. It is a mind reader.
Speaker 1:Mind reader?
Speaker 2:Non invasive brain detects decoder research, brain to QWERTY v two, building on V1, which was published today in Nature, brain to QWERTY V2 is the highest performing end to end pipeline capable of real time sentence decoding from raw brain signals. It advances beyond character level performance to decoding words and semantics enabling accuracy for overall communication. So if you thought Instagram was listening to you
Speaker 1:It's gonna
Speaker 2:be really You thought it was listening to your conversations. Now you can have a new conspiracy at home, which is that they might be just listening to your thoughts.
Speaker 1:Do you know the device? Said it's a noninvasive device. I just shared an image of this device. I want you to tell me, do you consider this noninvasive or invasive? Look at this image of the magneto and salafa and salafa graphy device.
Speaker 2:No. You gotta go high you need to scroll up a little bit cause you can't even see the whole thing here. Scroll on.
Speaker 1:It's non invasive.
Speaker 2:Because it looks like the device could potentially carry on for like a whole half
Speaker 1:of a month. Really does seem like it's a just put yourself in this in this room sized device. Now, of course, this
Speaker 2:I'm is shrink credit giving here. Non invasive? Non invasive.
Speaker 1:Okay.
Speaker 2:As long as he
Speaker 1:You're you're putting this thing on? You're daily driving this thing. I don't
Speaker 2:know if I'm ready to
Speaker 3:daily it.
Speaker 2:I don't know if I'm ready to daily it. Yeah. Will be a cool demo. Yeah. Like, this will actually when when when you can just walk in, sit down in a chair and Yeah.
Speaker 2:See your thoughts on a screen.
Speaker 1:No. We were debating it earlier. My buddy Rob Pave'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.
Speaker 1: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 com more competition. And then he predicts that in 2030, telepathy will be commonplace, which is a very aggressive prediction in my in in my estimation. How Terry Semmel fumbled Yahoo's Facebook Facebook deal. How much is Facebook worth?
Speaker 1:5,000,000,000, 10,000,000,000, 15,000,000,000, whatever the number? It's probably a lot more than the 1,000,000,000 that Yahoo could have bought it for a year ago. As Yahoo continues its soul searching, here's an unpleasant rendition of Semmel's catastrophic decision courtesy of Wired. When Yahoo came calling with a bid of $1,000,000,000 in cash, the pressure became too much. Zuck relented in July 2006.
Speaker 1:He was just like eighteen 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.
Speaker 1: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. Its stock price tumbled 22% overnight.
Speaker 1:Terry Semmel, Yahoo's CEO at the time, reacted by cutting his offer from 1,000,000,000 to 800,000,000. He just took 20% off, but Zuckerberg, who had been warned about Semmel's reputation for last minute renegotiations, walked away. And that's probably reasonable. I mean, if they're cutting the price there, you have to imagine that as it gets papered, you get cut down again, then the earn out, you get cut down again, and all of a sudden, you're walking away with barely anything. But two months later, Semmel reissued the original $1,000,000,000 bid.
Speaker 1: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 $1,000,000,000 from
Speaker 2:Wired. Legendary.
Speaker 1:Legendary. It's so interesting to imagine the road not traveled there because the the 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 the dominant position in social networking? What do
Speaker 2:you think? I think Yahoo should make another offer.
Speaker 1:Mhmm.
Speaker 2:I would like to see Yahoo make another bid. Hey.
Speaker 1:That is trading down. Just keeps going.
Speaker 2:If it continues
Speaker 1:99.99 percent might be able to pick it
Speaker 2:at this trend.
Speaker 1:Anyway
Speaker 2:makers are profiting off AI at the expense of just about everyone
Speaker 1:on the cover of the business and finance section today.
Speaker 2:We are witnessing an extraordinary transfer of cash from the providers of and perhaps one day AI users to memory chip makers. Take us away, John.
Speaker 1:Yeah. The explosive growth in Micron Technology's 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 The Korea with Korea's Samsung Electronics and Sam 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.
Speaker 1: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 is 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 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.
Speaker 1:But for Micron, customers paid $18,000,000,000 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 MacBooks more than 15%, closer to home for me than memory I bought on amazon.com a year ago to build a super quiet computer. I hate fan noise. Good good color commentary here.
Speaker 1: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.
Speaker 1: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 $1,000,000,000,000 club.
Speaker 2:Tyler, how many trillion dollar companies are there in Europe out of curiosity?
Speaker 3:I'm gonna go with zero.
Speaker 2:That's
Speaker 1:true. 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 businesses.
Speaker 2:Who's building the andoril of theme parks?
Speaker 1:It does seem like a
Speaker 2:Could there not be an opportunity to create a a net new theme park business with with modern a modern technology stack?
Speaker 1:It's very expensive. Everything needs to be like, the modern technology stack in Oh. Parks is expensive.
Speaker 2:You don't believe in the theme park capital markets.
Speaker 1: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 twenty years. And so, you'll go
Speaker 2:It seems like an absolutely brutal business Yeah. 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 niche Yes. Instantly available.
Speaker 1: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 you have that like barbell strategy where Thrive is buying a stake in the San Francisco Giants, a baseball team that should
Speaker 2:face the NBA
Speaker 3:team
Speaker 2:Yeah. To Vegas. But at the same time, that the same time, John
Speaker 1:it's there is an
Speaker 2:came out, there's more sports betting volume than all sales of movie tickets Mhmm. Theaters, theme parks, and like a couple other of these IRL categories.
Speaker 1:Up or down?
Speaker 2:Yeah. Less. Lower? Less. Like And and and the the stat was like volume.
Speaker 1:Yeah.
Speaker 2:Yeah. And so it's not exactly like proxy for like revenue
Speaker 1:Mhmm.
Speaker 2:But still meaningful. Raised a $135,000,000 series a for $80.90. They got sale they got Salesforce Ventures. They got Wunderco. They got Kraft, and they got Launch.
Speaker 1:It's the besties.
Speaker 2:They got the besties.
Speaker 1:They got the besties together.
Speaker 2:You think Friedberg be in.
Speaker 1:That's the production board.
Speaker 2:Oh, the production board.
Speaker 1:Yeah. In reals. No. Friedberg's fun.
Speaker 2:Yeah. Great.
Speaker 1:So, yeah, you actually have all three of the other besties. And we will see you tomorrow.
Speaker 3:Boeing Flashback.
Speaker 1:Goodbye.