Big Technology Podcast - Kimi K3 & AI’s Price War, What Happened To Google?, OpenAI’s Partner Trouble

Episode Date: July 17, 2026

Ranjan Roy from Margins is back for our weekly discussion of the latest tech news. We cover: 1) Kimi K3's benchmark breaking results 2) How Kimi K3 fits alongside MuseSpark 1.1 and Grok 4.5 3) What are OpenAI and Anthropic's advantages today? 4) Is the price of frontier intelligence about to drop? 5) It's all about the product now 6) Satya Nadella's Reverse Information Paradox 7) What is happening at Google? 8) Is Google too focused on 'Flash' models 9) Apple's lawsuit vs. OpenAI 10) OpenAI's boneheaded espionage 11) Why does OpenAI struggle to maintain good relationships with partners? --- Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice. Want a discount for Big Technology on Substack + Discord? Here’s 25% off for the first year: https://www.bigtechnology.com/subscribe?coupon=0843016b Learn more about your ad choices. Visit megaphone.fm/adchoices

The walls are coming down for OpenAI and Anthropic as cheaper models and new open source competitors from China challenge their ability to make a profit. Google, meanwhile, delays its flagship model and seems to be spinning its wheels. And why can't Open AI keep its partners from hating it? That's coming up on a Big Technology Podcast Friday edition right after this. In the face of ongoing disruption and opportunity, TMT leaders need to deliver tangible results, not just ideas. When pace and performance matter most, PWC. combines market insights and deep sector experience with AI, cloud, and emerging tech to accelerate your transformation and drive measurable ROI from strategy to execution. PWC can help you anticipate

what's next, outpaced disruption, and compete. For more information, visit pwc.com. Welcome to Big Technology Podcast Friday edition where we break down the news in our traditional cool-headed and nuanced format. It's the Kimmy K3 episode. We're going to talk all about this new challenger from China, a 2.8 trillion parameter model that is taking on the frontier and beating basically every model except for Fable. So we'll get into the implications of what happens after that. And when they're a price war, is really getting underway, especially now that META and SpaceX have released cheaper models. We're also going to talk about the state of Google, what's going on there. Why is their latest model delayed?

And of course, Open AI, we didn't even get to it last week. By the time we recorded, it was too early because just a few hours later, Apple would announce they had sued Open AI. So of course, today we'll talk about the lawsuit. And more importantly, why Open AI cannot hang on to its partners, at least not for long. Joining us, as always on Fridays to do it is Ranjan Roy of margins. Ranjan, good to see you. Happy Kimmy K-3 day. Are you ready?

I sure am, because this is a massive week, a potentially earth-shaking week in the AI story with the entry of Kimmy K3 into the conversation. So let me just read the story from Bloomberg and then we can discuss it. Bloomberg says China's powerful new AI surprises investors fueling a tech route. A surprise breakthrough from Chinese AI startup moonshot rippled through global markets Friday sending AI and semiconductor stocks sharply lower as investors drew parallels with last year's deep seek moment. The catalyst was Moonshots, new Kimi K3 model, which the company said rivals the strongest offerings from Open AI and Anthropic, the launch was quickly dubbed the new Kimmy moment. This is an interesting quote from Vaisernling, who's a managing director at Union Banachair Privy. He said, people are worried that if

U.S. companies start using Chinese models more and Anthropic less, Anthropic will invest less. That means U.S. firms will lower the CAPEX and in the end, the chip demand will be affected. So basically here's the story. Open AI Ananthropic owned the frontier, but there's a set of models that were effectively, you know, 10, 15 months behind them that were basically jumping in and something that you could route like your lower intensity tasks to. All of a sudden, Moonshot, which has had basically released the very successful Kimi K2, comes out with this new massive 2.8 trillion parameter models, model KimiK3, And it is right there with GPT 5.6, Seoul and Opus 4.8 on almost all the benchmarks. In fact, it even beats Kimmy K-3 on something called Program Bench. It beats Fable 5 on Programme Bench.

It beats Fable 5 on SWE Marathon. And it's right there in league with all the other top models on all of the benchmarks that we look to to assess model quality. It's a big moment because it seems to show that China. as open source movement is not the 10 or 15 months behind US AI, but maybe four or five months at the very, at the most, maybe even less than that. And when that happens, you know, your, your rationale for going with, you know, the close more expensive model as opposed to one of these open AI model, sorry, one of these open source models gets less and less. And you start to wonder, is there actually a benefit in building frontier intelligence if you're

to be equaled this quickly. So that is my outlook on it. Ranjan, what do you think about this? I think this is a massive moment. I do think this is actually on par with the deep seek moment, because it's the same principle at work again. It's the idea that, you know, like as you said, that the frontier model, why invest in it? Why is it so important? Is that truly a moat and a competitive edge versus to actually build things at work? Is there a cheaper and better way? And this reminds us. And I mean, I've been saying this for a while, like, is our frontier models required for the majority of tasks that are going to be done and become agentified? And I don't think they are. And now having a very powerful model that's just a lot more affordable and is also open source,

I think is exactly where companies are going to go. I think let's hold off on terms of what it means for the overall U.S. versus China tech. I'm not going to call it a tech war, but, I think before getting there, I do think this is going to already, in the last few months, so many of the conversations I've been in have shifted to model interoperability, what is the best model for the best task, and now even more so realizing that actually why do I need Fable, why do I need 5.6, when I can actually have all these other options. And the model gets a bit more commoditized. and it becomes about the harness and the process and the data.

I think this is, I don't want to say it's like a transformational moment, but I think this is actually going to be a moment where even more so, the idea that frontier models are a massive moat really goes away. Well, here's a thing. So Kimmy is not massively cheaper than any of these other models. And it also doesn't really beat, you know, of the latest frontier models on these benches, on these benchmarks. So for instance, it doesn't really beat Fable. And in, you know, most benchmarks here, it beats 5.5 and 5.6 from

Open AI. It beats Opus 4.8, but it doesn't beat the others, right? It doesn't beat sort of the top-notch fable models. Okay. So it's not markedly better. It's also not that much, it's not cheaper than in Grox 4.5 model or Meta's Mew Spark 1.1. Those are cheaper and those are also competitive in some benchmarks with the, with the Opus 4.8 and the GPD 5.5s. All right. So go ahead. So go ahead. I'm just setting the table here, but go ahead. No, no, of course, of course. To me, the benchmark side, like the kind of like, what is that kind of a final delta between, you know, on the deep S-W-E benchmark or the

front. Like, to me, that really is less important than if it isn't the general quality range of a 4.8 or a 5.5, you're in business. So I think on that side, the fact that on some, it actually beat Fable on others, I love that we just actually threw in GROC 5.5. I guess they're still, he's still going for it. No, they are. They are. Grock 5.4.5 actually, you know, was competitive on coding benchmarks. It's apparently more token efficient and most importantly it's cheap. So this was also, since over the past eight days, we've seen meta and grok show up to the game, not in models that are better than let's say 4.8 and 5.5 and 5.5. Sorry, Opus 4.8 and GPD 5.5 and 5.6. But like you said, models that are almost as good that deliver this at 25 or 50% of the price. Well, so, but that's why I

actually, what I found most fascinating about the way that this has been related. least is, it's not massively cheaper, but it's cheaper. $3 per million token input, $15 output. It's 40% cheaper than GPT 5.6, 70% cheaper than Fable. And again, meta's already come in hard. GROC's coming hard as well. I think this is, what makes this even more of a significant moment is a Chinese company coming in and saying, we're not going in as like significantly cheaper. We're we're actually battling on quality and a little bit more affordable, and you have more control over it, which is why I think it's very different. Deep Seek's whole thing was at pure price.

Like, can we get something that's not even as good, but just in the general vicinity and far cheaper? Now this is the first time we're actually seeing no. On actual quality, it's competitive. again, is it better or not as good as Fable 5 or GPD 516? I think we'll see over time. But I mean, this is not dirt cheap stuff. This is not something you buy off TEMU. This is actually good quality and reasonably affordable.

And it's just making us realize that, like, again, to me, the front, and we can get into that idea of, like, is that going to actually completely distort the investments? cycle because Anthropic realizes Frontier is no longer the moat. So they're not going to invest. I think we should get into that. But I think it's a big moment because now more and more you're going to hear everyone talking about what's the most efficient and cost effective model for the task. And I'm already hearing it. But now it's, I think it's going to be far more. Whereas six months ago, it was like, how could you not do anything on the frontier model? It's obviously the best. Yeah. So there's a couple of important points here.

The first is that, you know, in the past, well, you might think, okay, I need the best version of intelligence, so you would bring in Anthropic or Open AIs forward deployed engineers to build something for your company with their best models. This model from Kimi is going to, from Moonshot, is going to be open weight. So what you could do is basically if you get the right people in, you could download the weights, you know, and sort of build it, build your application for yourself with it. although it is massive. So you need a lot of infrastructure to do this. Right. So that's going to be for like, let's say, the governments or the J.P. Morgan's of the world.

We've already seen Apple do a version of this with Gemini. Right. So the question is, does Apple go to Google or Open AI or Anthropic to do something like it did and building Apple intelligence on a really good model? And we've seen the progress they were able to make there. Or does it work with an open source model? The other side of it is you're going to get this model downloaded and put on all the clouds. And that's where the commoditization comes in, right? Because right now,

the pricing that you listed, that's the pricing that we get from Moonshot, right? Can these clouds find a way to deliver it even more efficiently? So giving people frontier intelligence at an even lower price. And that's when you really get into an interesting, interesting world where like the business on the API side, the business of Open AI and Anthropic was, we are going to to build a model that is that much better than anything else out there and we're going to charge you a premium to use it. So we will mark it up in a massive way. And when you have these two forces coming at it, you have the metas and the grocs coming in with comparable models at much cheaper. And then you have the open source Chinese models coming in and giving you effectively the same

performance as some of your better models at a price that's competitive. It does get you to a point where you start to ask from the API side, is their profit? Does this all become a commodity? And then let's also recognize the fact that like anthropic completely shifted its strategy to the enterprise and they did a very good job of that. Open AI, it hasn't really like become as dominant in that way, but they're certainly investing very heavily and they've made significant moves around enterprise. And what you just said right there, the companies, Like you and I are not going to be cranking out our own version of Kimmy K3 on my MacBook Pro. It's a pretty good MacBook Pro, but I'm not going to be running.

Yeah, exactly. So, but large enterprises will potentially, or as you said, the cloud services will be kind of bringing in their own offerings here. So I think Open AI story and their pivot gets hit harder than anyone else with this announcement. because suddenly why open AI becomes a much more salient question than it was just a week ago. Yeah, I want to read you an analysis from Gavin Baker, who's sort of, who's an investor, managing partner at Arsides Management. He's done the podcast circuit. But his analysis on this was actually excellent, and it kind of shows you where the challenge hits and where the benefit comes. So he says, Kimmy K3 may be an important inflection point for AI, potentially negative for Anthropic and OpenAI.

while being net positive for essentially every other company in the world. A world where there's only two to three dominant frontier labs with 90% inference margins is net negative for every other layer while being awesome for those two to three labs. Those labs would become monopsonies for power, data centers, semiconductors and hyposcalers and would obviously vertically integrate over time into all those layers while also completely subsuming the application software layers. Anything that lowers the margins and increases competition at the model layer is good for every other AI layer, power, semiconductors, hypers, hyperscalers, neoclods, and yes, even software.

I think that captures it. Yeah. And also monopsony is one of my favorite words. I still remember it from undergrant Econ is where there's one dominant buyer rather than dominant seller. And I think like it is the, I'm so curious, man, Anthropics S1. I just want to see it so badly. I'm sure they'll be able to tell reasonable.

story, but like, again, is it the story was 90% inference margins. Do the training, invest the money, your model becomes dominant, and then you make a ton of money. This cuts so directly into that. And I think this is why this makes the entire AI story completely new and brings in just a lot more opportunity and a lot more players. And like, I mean, the vibe shift on Twitter has been wild in terms of like, again, six to eight months ago, everyone who's just like clod and anthropic, there's unstoppable greatest thing in the world, everyone is now obsessed with what is the best model for the best task, Kimi3, harnesses, everything, which is good, competition is good. This is exciting. Yeah, more Baker. He says an open source model requires the exact same amount of compute to run

as a closed frontier model of a similar size in architecture. Kimmy K3 is roughly the same size as gpt 5.6 terra on a per token basis, which actually suggests that it's less computationally efficient. That being said, lower margin at the model layer. So going back to this question of like, will the open AI an Anthropic not be able to command those 90% margins? What happens? Baker says it's more margin at every part of the infrastructure layer and it's a godsend for software. This can happen either through open source models like K3 at the frontier or having vertically integrated models like meta, SpaceX, or Google at the frontier, which is exactly what we're talking about. We're seeing that. Google to come. He says both outcomes result in a lower margin at the

model layer and as vertically integrated model companies don't really care where the margin comes from. This is why it was so painful for Open AI Ananthropic when Google was right there with them from a model competitiveness perspective and my Grog 4.5 and Muse 1.1 were just as important as Kimmy K3, right? So this is all happening in conjunction at this week. So what do you think OpenAI and Anthropic need to do? I mean, I guess Open AI you make a speaker and physical devices or clouds or all these other business lines, but Anthropic, I mean, this is their business right now. This is inference margins. That's the entire game. So they start to feel even more under threat?

If you're either of those companies, what do you do? Okay, so I'm going to read Smore Baker because he talks a little bit about it. And then I'm going to give my own perspective. He goes, the reason Kimmy K3 is only potentially negative for Anthropic and Open AI is, one, the Claude and Chad CTPT products and harnesses may be more important than their models today.

And two, the hypothesis that they have much more advanced model checkpoints internally that are already being used for recursive self-improvement. In the latter scenario, reaching recursive self-improvement, even a few months ahead of the other labs, might be enough to cement a permanent lead. Okay, I'm going to tackle the second one first and then the first part. Okay. So basically there's a theory that they have like, you know, self-improving AI internally already and then therefore that will help them open up a gap, you know, far ahead of any other competitor. I don't believe that.

And I don't believe that that's actually, you know, defensible, given how far we've seen these models, the open source models and the competitor models start to catch. up or how quickly we've seen them catch up. This is from Ryan Greenblatt, who's a researcher. He says, I now expect an open-weight AI, which is straightforwardly mythos level at cyber, in like five months, supposing Kimmy and the others don't change their open-weight model policy. All right. So that is how close the open source world is to the frontier right now. And so any, like, you know, sizable tech advantage or model advantage or intelligence advantage,

I don't believe in. I don't believe in anymore. It was always treading this way. I mean, but this is where the whole AGI, and I like that we made it this far without actually saying AGI yet, but I mean, when I've spoken with people at these companies and spoken with others, like,

there still is this belief. And we debated this last week, like all the things that you can do about model efficiency and the right model for the right task, if you just get smarter and smarter, you can just subsume the need to even think about all of that and the model is just so good, it does everything. But I don't know. Like to me, it feels more and more like no one's talking about that now. Even hearing recursive self-improvement at these labs that's going to give them

some significant edge, I don't know. Is that significantly different or real if Kimmy's able to, or Moonshot is able to do this? I don't think it is. No. There's been no evidence that you could hoard that. That's the whole point. Right. So Nick Clegg, who is, you know, executive at Meta came on this show a couple years ago and basically said, I don't understand why any of these come, like where are the profits going to come from pursuing super intelligence since I don't think you'll be the only company that's going to have super intelligence when you get there. And I've never gotten a good answer in terms of what the response is on that front. So let's assume that intelligence is commoditized, right? That's sort of what this is all building to. If you look at what's happening with meta, with garage, with Kimmy K-3. As Baker put it, if you have two companies that have this, it works. If you have five companies that have this level of intelligence, it's a price war. It commoditizes.

So I think we should assume, and we've talked about this on the show, that intelligence is going to commoditize. Well, I mean, going back to Baker's point, I actually think it is interesting that chat GPT, I still believe the product in UI was. as important as the underlying model and intelligence. Again, like, I remember this is back in 2023, feeling the difference between typing something into chat GPT and it actually looking like it's thinking and kind of like streaming the text out

versus just getting like a chunked API response as a block of text felt more intelligent and AI. And we said this from all, like, OpenAI and ChatchipT was, is a great product. But it feels like both Anthropic and OpenAI have kind of been moving away from the product in the UI side of things. Again, like actually kind of, you know, bringing it all back down to just a command line experience only moved away from that chat chept, the new Mac app, they're removing more of the actual chat function. Like the chat experience. They're adding some of it back.

They're adding some of it back this week. Okay. No, because there's an outcry. But, like, they have kind of like forgone the entire UI battle versus and just focused on the model is going to be so smart. So they've given up and seeded some of that ground. And if I think that's the right point. It's the product. It's the hard.

I mean, okay, sorry. Of course I think that's the right point because I've always said it's surprising the model. Yeah, I know. I like as I was saying that out loud, I was like, oh, yeah, now it feels even more real. Are you team product now over model? I mean, my perspective was always that, and I guess my perspective was more like kind of getting to AGI is important, right? Because once you get there, the product experience is much better. And because they were able to improve the intelligence, they've been able to build better products.

Right. But if you're going to ask me today, are they going to compete based off of building the most intelligent model? or are they going to compete based off of the best product? I would have to say, who is they? Because this is where things get very interesting. If you assume that intelligence commoditizes, then does Open AI or Anthropic have that big of an advantage over anybody off the street who would take these type of models to build their own product that competes with them?

So basically, I think they're going to differentiate on product, But instead of it just being open AI and Anthropic competing to sort of corner this market on intelligence and everybody depending on them, now if intelligence is abundant and available to be accessed through multiple providers, it will come down to who builds the best product. And so it goes from a two-person race, right, or a two-company or a three-company race, Open AI Anthropic, maybe Google, to, Like now, in order to expect Open AI and Anthropic to win, you basically have to expect them to be the best AI product builders in the world. And that is a much tougher bet than expecting them to be the best intelligence builders in the world. But then do they even make sense as a business? Like the way these companies have structured their entire business is they have to win on intelligence.

They can make some good products and, you know, like they're used. usable and they got some good features. But like that's not the story. That's not the, I was just, I saw some like bank analyst note that was saying Anthropics should come out at $6 trillion. Like, I mean, come on. Like the absurdity of the story is all built around super intelligence or AGI at a minimum. Versus we make some pretty good products.

We're going to build a good vertically integrated company. We're going to be the next Google. That's not their story right now. That's not the way they're going to supposed to be coming out to market. That's why, to me, the most interesting part of this week is, I don't want to say it's the nail in the coffin, but like that story, I think we're both agreeing doesn't work as well as it did certainly three months ago and even last week. So I'm not sure unless Open AI gets a really good Johnny I've pit.

and then suddenly they become and launches a little bit of a neocloud business. I'm not sure what do you think there is going to be some kind of story other than first stage. Yeah, it's a much tougher story without being able to hoard intelligence, but it doesn't mean it's an impossible story for them. And I'll point you to two interviews I've done with folks at Anthropic over the past year that sort of shows the line for these companies to be able to make it work. Right. So last year, when I was with Dario, he confirmed that more than 50% of anthropic revenue was coming from the API.

This year, when I was with Boris Turney, who runs Claude Code, he would not confirm that. And in fact, he said that the Claude products have contributed meaningfully to the company's revenue. And the company's revenue has 10x pretty much since that time that I was with Dario last year. So there is an advantage of being that close to the intelligence that, like, you don't need to sort of guess on how it works or you can be, you can sync it into your products better than anything else, right? So you can sort of have that integration in a way that it's going to be harder for other people to do. And you know what's coming next, so which Anthropic has built off of. So that to me is the path here, is that there is still a way where you can be the developer of it. AI and then have a product sense that enables you to still be a massive company, which

Anthropic is effectively doing, even though the API is still important to it, it is really made, the company has really made a lot of headway with the products that it's selling. And we're going to have Paul Kodrowsky on next week. And basically the anticipation from him is he's an investor and analyst is that these companies will continue to go up market and try to, like remember there was cursor before there was cloud code Cloud Code is built to Anthropic has shown that they can build a tremendous business by doing it themselves and how many other areas are there for a company to build AI native products themselves for a company like Anthropic or OpenAI to build AI native products themselves

and then you know start to profit tremendously from that direction so I think that's still open for them Okay I will say Claudecote even though it's just in the command line is was and is an incredible product. So like, and the product was effectively the harness and like along with the model itself, but like the experience. So even in the command line, they did create something that was dramatically different or better than everything that was out there before. One thing that that actually brings to mind, though, is like, I mean, I've been seeing a lot more around. I'm sure most of our listeners saw the, you know, like Figma with a plug-in to Claude and then Claude Design comes in, cursor running a lot on anthropic models and then Claude Coe coming out.

Like at what point do other companies actually avoid anthropic where it becomes clear that especially if they are completely dependent on going upmarket, creating this suite of AI native products, which they're able to do because they're being fed all of the data of these companies that are plugging into these systems, at what point do people actually just say, no, like, sorry, we see what's happening. It becomes a lot easier when you have a model like Kimmy K3 that works just as well that you can customize. Yeah, but again, that's what I mean.

That's exactly what I'm saying. I mean that like, now with that option, if you have any fear that they're going to take everything that you're giving them because you're using their models and then recreate your business, you now have an option. And I think that's going to, that can slow things. Again, the speed at which they just completely replicated Figma and did it better, the speed at which cursor was replicated. And then they did a very good job probably better. I think people will start questioning that a bit more.

Yep. We should actually, we should actually, this is a good time to just bring in quickly, or maybe not quickly, this idea of the reverse information paradox that's not, Nadella wrote about this week. He said, you know, because Microsoft also wants to come in and offer this to its customers, basically like, we'll protect, we will let you develop AI and we won't take your stuff. He writes, in the age of AI, the buyer risks giving away knowledge just in order to use what they bought. You essentially pay for intelligence twice, once with money, and again, with something even more valuable. The proprietary knowledge you must reveal to make that intelligence

it's useful. The better you want the model to perform, the more knowledge you have to feed it. Over time, the information asymmetry becomes increasingly skewed. The seller learns more and more about you as you use what you purchased, while you learn very little about what the seller is learning in return, I think of this as the reverse information paradox. Wink, wink, don't buy directly from opening I and anthropic. Yeah, I think, actually, how do you feel about these Sotia X and LinkedIn posts? I'm curious. This is like his second kind of, do you think you write some? I'm always, yeah, probably with help, but yes. I mean, I'm always for, I'm always for more executive communication because, like, as a reporter,

it, like, helps me at least understand the mindset here. And we could definitely understand the mindset of Microsoft here. They seem furious. I mean, they really don't like the fact. And it's interesting. Like, didn't we talk about it last week or a couple weeks ago about how Open AI and Anthropic were like this single point of failure? because only they were getting the products right,

and they are the ones getting the products right, and they're the ones profiting. And what was going to happen? Everybody was going to come in and try to knock them down a peg. And we're watching that now. We're watching that with Meta. I mean, not just knock down a peg. I think this is like kind of fundamentally pushing against the entire.

I mean, I can tell you. So at writer where I work, like, we have our own foundation models. They're trained on synthetic data. And we have seen when you're not, and we will be ingesting all the data of the users, it does change the entire trajectory of how models the advances in the training. And I mean, I think Anthropic and Open AI are very open that all non-enterprise data, at least in theory, is not, is used for training.

And that is one of the biggest advantages. And it's kind of that flywheel. And I think Satya is certainly making it clear. Again, I can say this like a few months, maybe six months ago, everyone stopped caring about these companies training on your data. And now everyone is talking about it again. And it's great. Again, like the vibe shifts are so wild right now. Like it literally just how much the conversation changes.

But I mean, Sancho is completely right. Like it's completely right on this. Yeah. I mean, this is sort of this. By the way, this is like this is normal business cycle. Right? It's like companies get ahead. Oh, no, come on.

This stuff is compressed to like... But compressed, but compressed. I agree. Compressed. Like, things that would typically take years are now taking weeks. So it's very, very interesting. Okay. A couple more interesting things.

First of all, it is interesting that even with the restrictions, I don't think we should gloss over this point. China has been able to catch up the way they have. This is from a user named Matt on Twitter, who I'm sure is not a Chinese bot. This was my favorite. This was my favorite. Since his handle.

is Matt 503 EA 5SF 9Z5. Doesn't sound body anymore. But body, botty. But anyway, he wrote, How was Kimmy running on a bunch of 14 nanometer Huawei toasters beating SpaceX AI with multiple data centers of Blackwell chips? That was my favorite thing I saw, I think, on Twitter this week. But I think, again, like, what do you think this means for?

for the not just U.S. versus China, but also within even the U.S. Like actually going back to, you had made, you'd kind of read this at the very beginning around like this worry that anthropic invests because they believe frontier models are going to be the battle and that powers so much of the current trade or the U.S., and I don't want to say the entire U.S. economy, but the whole AI ecosystem. So it's technically bad if they don't believe that, which I find that ridiculous. I don't agree with that. Well, here's my perspective is, all right, let's say, okay, let's say Anthropic and Open AI go to zero.

But like the industry, whatever, achieves AGI or something close to it, right? So what happens is Amazon, Google, Microsoft, they buy all the data centers and they buy all the compute that these companies have. In fact, a lot of the computer these companies have are already effectively, you know, kind of paid for in conjunction with these companies. Right. Like Amazon. Not just paid. They are paid paid to these companies accounted as revenue that invested in by, come on. Let's not forget our circular financing.

No, you're right. Our circular financing was. All right. I'm Andy Jassy and I have, and I'm running Amazon. And all of a sudden, Anthropic goes to zero. and now, like, as a big shareholder, I get to collect and basically own at 100% these data centers. Or I don't have to meet, like, commitments to compute that I would previously for Anthropic. But my customers can use Kimmy K-17 and get AGI-like performance out of them.

And I just get paid by delivering the infrastructure. So that would encourage me to invest even more, you know, in AI infrastructure, even though Anthropic and Open AI aren't hoarding it anymore. It's rare that I will ever look at Amazon as the company that tells a good story about a competitive economy, but you just did, and I agree with it. I think that was a good Andy Jassy impression. I think that's like that that is like a very logical way that and I don't want to say likely, but logical way this can play out and would play out in this case. Yeah, it's one possibility. All right.

Let's talk about it quickly before we move on from the U.S. perspective, David Sacks, all in podcast host and erstwhile AIs are from the U.S. government, who was sort of instrumental in making sure that this fable ban happened when it happened. He writes, this is concerning. For the first time, a Chinese model, Kimmy K3 has taken number one on the front end code arena and is scoring at or near frontier on other benchmarks. Meanwhile, America is tying itself in knots. Politicians and bureaucrats are banning new data centers piling on state regulations and pushing for new federal agencies to pre-approve frontier models. This is how you lose the AI race.

The rest of the world won't play by our rules if we bog ourselves down. Permissionless innovation is how America won the internet and became the technological envy of the world. We can do it again with AI. I appreciate a lot of what David Sachs has to say, and I've invited him on this show. but this is like hilarious and so rich and ironic that he being part of the Trump administration, which did like the most interventionalist policy to ban Fable is now saying we need permissionless innovation to beat China. I mean, come on. And let's not forget like regulatory capture was kind of the entire business or that like that was this project Stargate and

whatever else that he helped coordinate. Maybe he was kind of resistant to a lot of this stuff early on, but, you know, Oracle, Stargate, all of these things, which, I mean, the Trump administration, I think, it is rich. It's ironic. It's, I don't, I agree that, or actually, do you think we are over-regulating at the current state and that that is a danger and competitively against China? Yeah, I think so. I mean...

Why? I don't think... Well, we had Stamos explained it to us at the summit. It wasn't like Fable had cyber capabilities beyond that weren't available, you know, from like Opus 4.8. But Fable wasn't, again, rich and ironic, the Fable ban felt as much political as it did... Yes. That's my point. Yeah.

You asked me if we're over-regulating. That is like a pure case of poorly thought out over-resulating. regulation. Okay, I guess I differentiate. I'm still thinking of like overregulation around actual safety concerns and like are we too concerned with like well thought out but potentially overly aggressive things around we should not release models purely from the safety side versus Dario's beefing with someone at the secretary or the defense department. Like to me, let's ignore that. Let's ignore no. The pure political. No, because I mean, that's the, That's not good in any situation.

I'm talking about, like, should the U.S. be versus China more of a leader in terms of we're going to have safe, equitable, regulated AI, like Europe? I mean, I don't know what, safe, equitable, regulated AI. I don't know. I don't even know what that means. I was trying to think of what, like, just smartly regulated AI, but it's not free for all. Anything goes purely permissible.

missionless innovation. Here's my perspective. If you have a model that is going to cause cybersecurity problems for companies, if it's released to the public right away, and not just companies, companies, academic institutions, governmental agencies, if you know, if you can see in your testing that it's going to cause these issues, if it's released to everybody right away, I would try to release it in a somewhat controlled way in the early going. and then release it to everyone.

Like I think the Fable initial launch of Fable made sense. Same with GPD 5.6. But I don't want the, I don't think the way the government has been involved recently has been smart because it has been largely political. Wait a B, so you're a glasswing guy. I'm a glass one guy. You're a glass one guy. I've always been for a glassman.

All right. All right. I'm a glass man. Hold on, but you just said that the rollout was good. So what, what don't you agree with? in terms of the rollout. The government banning the model for no reason.

Oh, yeah, I guess. Got about that? Yeah, yeah, yeah. Oh, that whole, I don't even remember last week, man. Yeah, I know, right. It's crazy. All right, let's take a break. I want to come back, talk a little bit about Google,

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We come to you amid a rapid deployment of AI models. Of course, we have the latest models coming out from Anthropic with Fable and OpenAI with 5.6. And meta with Mew Spark 1.1 and SpaceX with Grock 4.5. And China with Kimi K3 and Google with Google with dot, dot, dot. This is from Bloomberg. Google Gemini launched delayed as tech falls short of internal goals. What's going on at this company?

Apple Links Google is months behind schedule on delivering Gemini 3.5 Pro. It's most powerful flagship AI model because the company has been taking time to try to improve its capabilities, particularly encoding. The delay has been a source of frustration for Google engineers, AI researchers and managers, many of whom are concerned the company risks losing an edge in the market as rivals, Anthropic and OpenAI produced models that exceed Google's capabilities. Google has multiple layers of stakeholders involved in preparing models for release,

working to weave AI across a vast product portfolio, including search, maps, and YouTube, which can cause delays. Both OpenAI and meta platforms recently released new models that further outpace Google's current offerings in AI for writing code. Late last month, Google updated the data being used to train jazz. Gemini in an attempt to improve these skills, but the results were disappointing. Share slipped as much as 3.2% on Thursday. Oh, this is, this is, I mean, we've known that Google has been behind for a while,

but this is like getting to the point of embarrassing. What do you think about this? See, I actually, I was just looking up, Gemini 3 was launched November 18th, 2025. Let's not forget, there was a few months where everyone's like, Like they're back. We were like they're back. Yeah, they were back. And they were back. Yes. Okay. They were back. And like it was on par with everything else. And it just stopped. And I genuinely wonder what's going on because they caught up. They felt maybe they could even be ahead and they have distribution like no other. And suddenly like Gemini itself, even the standard consumer version, like has just kind of got.

a little bit, maybe not worse, but everything else is getting so much better, you can feel the difference. Nothing in the last seven or eight months feels like it's significantly improved. Though, except for AI overviews, I have found myself again, and I always feel very basic using them more and more and actually doing a Google search and following on with the, and maybe they're just going to go all in on that and make a ton of money. but in terms of like the actual frontier battles, they feel like they're out. Yeah.

So here's a theory. I mean, you know, the world's perspective on AI seems to seem to shift last year, January or February, when Deepseek came out, right? And there was a perspective that if you can deliver intelligence that was on par with the intelligence that existed then, which was pretty smart, at a cheaper price, more people would use your mom. models and you would be like the beneficiary of the Jevin's Paradox.

And for Google, which had, you know, not just a model, but cloud services to sell, it figured maybe if I bundle the smaller flash models with the cloud services, I will enable people to do more. Then the world shifted. And the bigger models began to do this coding autonomously starting in December, January, this year, right? So a year later, and a big company moves slowly. Right? So Google just took a long time to catch up and this idea perhaps that, you know, a big model is all you need. And we see the problem of developing these big models. Maybe it didn't really, you know, catch on within Google. And, you know, instead of training these, these like, unifying to train these big models. It might have just decided smaller flash models is going to be the way to sort of make our, make the most out of this. And it's also how.

helpful for our products, which could use these sort of smaller purpose-built AIs to enhance what they're doing. Actually, you can even feel that big company under, like in the same article, it talked about Google founder, co-founder Sergey Brin and others were advocating for Google to move faster to seize opportunities in AI coding, but slowed by competing factions, two former employees said, both cloud computing unit, Google, cloud. Research Lab, Google, DeepMind, and the team behind the Android operating system, we're all building AI coding tools.

So you can picture, I guess I can, I mean, that's a mess. And like, again, you have developers who have the opportunity to build their developer tools. They're going to do it. But to try to do that in a unified fashion, that just feels, that feels like old school Google. not the lean fighting machine that Sundar just somehow reorged into. This is back to like Google chat hangout, whatever the product names, whenever they all just were ridiculous and kind of like stacked on top of each other. This feels like that, Google.

Yeah, there's some crazy stuff in here in this Bloomberg story. Efforts to win that coding have also been up against some engineers at Google with the more purest hands who believe what all important code should be human written to adhere to Google standards. I mean, obviously you don't want AI to write like the core Google software, but to have these purists who are like it must be handwritten where like nobody's writing handwritten code anymore

is sort of where you get into trouble. Yeah, that actually, I mean, they're a giant organization. It is funny though, because remember Sundar, I think said like 95% percent of code is written by AI? 75%. 75%. Okay. I guess the AI holdouts are still hand crafting their code, but yeah.

Do you think they're going to come back? Of course, they'll come back. Yeah, they'll come back. John F4 is going to blow us all away. Google, don't we know? Like, Google will inevitably come back. So they, I mean, they have the talent and they have the compute. But the one thing that I can't understand here is,

for the life of me. If you're Google, you should never run out of compute. Right. The fact that they've run out of compute, by the way, they're licensing a lot of their compute in their cloud business where maybe, I mean, the cloud business is doing great, but maybe that should be going to your AI development if you think this is the most important technology in history or one of them. And Sundar certainly does, comparing it to fire. So I don't get how you run. Yeah, but that's like a perfect. That just seems like poor management. No, no, but that's a perfect example of like Google Cloud I can't. I mean, it was one of the fastest growing businesses the last 15 years. It's gigantic. It's run very separately from the rest of Google. So like that idea that you can reallocate resources without any massive complexity in internal politics. I can only imagine how difficult to that would be. And that's a perfect example of like trying to move stuff around to where it's most effectively allocated is got to be difficult. Yeah, but that's your job, right?

By not running a company like this, that is your job. Come on, Sundar. You got to make decisions. McKinseyify this once again. Let's reorg. They'll see it. I mean, they've shown that when they get the whole company focused on a goal, they can accomplish it.

But, I mean, it is crazy watching them to go, watching them go from unfocused to focused and effective to whatever this is now. It's not good. I'll say, I'll give them credit that still on like multimodality image and video, they still kind of own it right now and everyone kind of puts them far in a way like I do I really never hear that much about chat GBT image two or any of these others like out of the big players they still kind of own multi-modality so they're still doing good there but the rest of it they're definitely something is up right okay we can't leave today without talking about what's going on

with Apple and Open AI so if you listen to this show you've already heard that Apple sued Open AI for stealing trade secrets from it. And if you've done any of the reading or if you've watched any of the coverage, you know that this is the most boneheaded corporate espionage attempt maybe in history where the Apple employees on Apple issued laptops were discussing plans to exfiltrate Apple data, to bring Apple parts into interviews, to, you know, access through a bug. But like Apple's roadmap and future plans, and then Apple caught them red-handed. So I don't know.

Like, I'm just going to turn to you quickly on that, Ron John. Like, this is, do you agree with me that this is like one of the like legitimately dumbest moments in corporate espionage history? I mean, if you are going to an employer's whose goal is super intelligence, don't do something this dumb. I mean, come on. Like, I still cannot believe. Yes. I would firmly agree this is one of the dumbest corporate espionage things I've ever seen. Even though the Uber-Wa-Mo stuff back in the day had a little bit more like cloak and dagger elements to it.

Like this, it's literally like you're on Slack or whatever other chat. Just, hey, how do we steal information from our employer? Right. But you're not just on Slack. You're on Apple's, you know, just like for being figured. On Apple's computers. Stupid. Yeah. Yeah. Anyway, Apple, go ahead.

Do you think, like, these are career technologists. Like, how do you end up here or thinking this way? Seriously. Some people are really smart in some ways and really not smart in other ways. Okay. You know what? The one bright side I'll say is it's probably clear they had not stolen information or participate in corporate espionage prior to this. So that's what I'll give them.

That's the good side. I mean, when you make a career and potentially company ruining moment like blunder, and you start this blunder with LOL, I think you're just like completely out of your death. Right. Like one of the people at Open AI apparently on the chat said like, LOL, I found all the network files. He's like, oh, so stupid. Come on, guys. Up your espionage.

So the latest is that Apple has now. sent dozens of Open AI employees legal letters asking them to to preserve their documents. And Apple has also said that they only found the tip of the iceberg. And the AI lab said that while it had, open AI had said, well, taken the allegation seriously, it was not aware of any evidence that the complaint has merit. So that's where it goes next is Apple is going to take Open AI into discovery. You would imagine not settle and just get about as much information as it can. and this is probably going to look much worse when all said and done.

Well, I mean, 40 employees is a lot. I think O'Manyi has probably got like 7 or 8,000. Oh, they have 400 from Apple. But if 10% of those employees were engaged in something like this, and that's the tip of the iceberg, that's actually, that's wild. That's wild. Like if it's 400 from Apple and 40 have already been, you know, like directly receiving some kind of communication around this.

This is, this is going to get fun. This is going to get very fun. Not for Open AI. Not for Open AI. And if your Open AI, you have to think like, what, what am I doing that makes my partners, my enemies? Elon Musk, you know, founder of Open AI now and about enemy. Dario, Dario.

And early Open AI employee, now an Open AI enemy. Microsoft, biggest. funder of OpenAI for a long time, at least, and the champion of this company. Now, we just read what Sautia said, an open AI enemy. Apple, a partner with OpenAI to build ChatchipT into Apple intelligence, now an OpenAI enemy. I'll just say it's one thing. I'm going to turn to you. In tech, you need friends to win.

You can't do it yourself. You need friends. Look at Apple and Google. They should be enemies. Google helped Apple save its business. by putting Gemini into Siri to a degree. I don't want to overstate things. Open AI's loss of these friends over time

is going to add up to something really bad. Who knows what that exactly is, but there's going to come a time where Open AI is going to need Satya, or it will need John Turnus, or it will need Elon, and they won't be there for them. So I keep thinking right now,

who do you think, is it Tim Cook, or is it Ternus, who's going to be the one. Could this be like Tim Cook's final act, just the head of Sam Altman in his head, or is this Ternus coming in like killer Ternus? Who's going to be spearheading this effort? Great question.

So it's got to be Ternus, right? Because Cook is going to step down. This case will like last for a long time after he leaves. And from what I would have guessed is that Cook brought this to Ternus and said, John, we got a problem. John, we got a problem. A number of your former employees, including people that reported to you, stole our shit. Oh, wait, do they report to Ternus even?

At least one reported to Ternus, but he did. He ran hardware engineering. These people are stealing from hardware engineering. Oh, man. Oh, he's going to come strong. Oh, he's coming strong on this one. And Cook probably said, we want to do this. And the thing is, this may take.

up a lot of energy as you get your as you get going. But ultimately it's sort of up to you in terms of whether we should sue open AI. And Ternus probably looked at him and said, Timmy boy, sick the lawyers on these assholes. The betrayal. The betrayal. All right. This is, okay, this is now becoming one of my favorite stories of that to see what happened. Forget, forget, forget Kimmy K-3 in the entire.

future of the AI ecosystem and economy. I just want to see what Ternis is going to do to Open AI right now. I just want to be clear. I'm not the one saying that Open AI folks are assholes. I'm just saying that that's probably what John Ternis would have said. I think John Ternis doesn't swear. John Ternus definitely swears. He's a very polite, upstanding citizen.

No. Do you think, okay, we'll end on this. do you think that there's any of the folks at the top ranks of these companies who doesn't curse during the day? No, but no, this is a good. There's so much stress involved in these jobs. You almost need swear words as a way to let off some steam. But I would like to say as the parent of a seven-year-old child, I do feel, and as someone who has sworn many, many times in my life proudly, I still feel really weird. how normalized it's become with adults swearing in like very public communications and forums,

obviously the precedent and others. And like it's just become very normalized and it's weird to me. Like they're still there's still bad words. We should hold them. And Ternis should be dropping F bombs left and right, but not in front of the kids. That's all I'm asking. Let's take a moment to reprimand John Ternus. Yeah, for swearing in front of kids. Apparently, you're not. The fact that an imagined fan fiction version of you swore on this show is deeply upsetting to us and our listeners. And you should really think twice before using that sort of language. I think the good thing, the good thing if I am to swear on this show, I think the population of seven-year-olds listening to the big technology podcast is one of our smaller, if not-existent demographics. It's definitely not non-existent.

That's why I try to keep it as clean as I can because I know that. parents play it in the car. And honestly, I commend those parents. You know, we're here, you know, in the interest of that. As an educational endeavor to make sure that the youth of the world knows what the AI industry is going to look like when they grow up. We're here. And not to mess with John Turnus. Do not mess with John Turnus. One of those lessons is, yeah, stay out of the bad side of John Turner's because you never know what he'll do. It's going to be a running joke that will just have bad John. Well, I think I know so little about his personality that I can only create extended fan fiction

around it. So, sorry, John. Get ready. Okay. Well, this is a new thread for us, and it's a new meme. So we're going to run with it. I guess that's it for this week. That's it.

Give me K3 episode ending with. a meditation on language, as we typically do here on Big Technology Podcast. Let me leave you with that to think about it. Thank you, Ron John. Great to see you as always. And thanks to all of you listeners and viewers. We'll see you next time on Big Technology Podcast. She's got a breakaway.

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