Podcasts about thinking machines

In June, the most capable American AI models stopped shipping as public launches and started shipping through a government gate. Six weeks later the gate is open again — and the real fight has moved to the layer no gate can touch. A Chinese open-weight model rattled trillions out of chip stocks, Washington pivoted from gating American closed models to threatening bans on Chinese open ones, the industry mounted its largest-ever policy counter-mobilization, and an American frontier model literally broke out of its lab and hacked another company. Knee-jerk reactions, or the beginning of real AI governance? Navigation: Intro The Gate Opens The Kimi Shock The Escape The Counterstrike and the Petition Interlude — The Low-Background Books The Investor Reckoning Conclusion Our co-hosts: Bertrand Schmitt, Entrepreneur in Residence at Red River West, co-founder of App Annie / Data.ai, business angel, advisor to startups and VC funds, @bschmitt Nuno Goncalves Pedro, Investor, Managing Partner, Founder at Chamaeleon, @ngpedro Our show: Tech DECIPHERED brings you the Entrepreneur and Investor views on Big Tech, VC and Start-up news, opinion pieces and research. We decipher their meaning, and add inside knowledge and context. Being nerds, we also discuss the latest gadgets and pop culture news Subscribe To Our Podcast Bertrand Introduction Welcome to Tech Deciphered Episode 80. This one, once again, will be all about AI, government, frontier models, and open weight counterstrike. A lot has been happening in the regulation space, in cybersecurity, in the launch of new models in the past, maybe just 6–8 weeks. It’s actually pretty insane how much happened. We believe it was time to do an episode to talk about where we are and maybe where all of this is going. Maybe let’s start with a summary of where we stand, all that June and July saga, so you, our listeners, can get up to speed if you are not already there. You want to start with some points? Nuno The Gate Opens Yeah. Again, to your point, the gate swings. The gate had closed. We had to prepare an episode for the gate closing, and then the gate reopened. Now we have a different episode. This will probably change again as we’re seeing there’s news every day. Let’s start maybe with the first 19 days of the gate closing. There was an executive order on June 2nd from President Trump that asked frontier labs to share models with the government, 30 days pre-release. It inferred the protected frontier model designation into that. Basically, it was effectively a de facto licensing agreement defined by an executive order of the President as of June 2nd. On June 9th, Anthropic launched Fable 5 and the famous Mythos 5 or Mythos. I’m not sure how you actually say it in English. Then on June 12th, there was an export control directive banning access by any foreign national. Since there’s no way to verify nationality in real-time, Anthropic had to switch the models off for everyone worldwide. Bertrand On this point, you could argue that there are possibilities to check IDs. Many services let you check IDs online. You can pre-check a flight by showing your ID. There are ways, it’s just that if you don’t want to follow what’s already available, because guess what? Maybe it slowed down your revenue growth, maybe it looks bad on you or whatever. My point is that there was actually an option. I think it’s already a decision from Anthropic to say it’s either on or off, but nothing in between. Nuno I think the point is they had no way implemented of doing it. If they implemented it, to your point, it would have hampered use in general. A lot of people wouldn’t have gone through that trouble of doing it. Anyway, long story short, in June 26th, the White House apparently asked OpenAI to limit GPT-5.6, so Sol, Terra, Luna, to only 20 vetted partners. Now, apparently, the trigger for a lot of these things that have been going on was that there was a jailbreak that was found by Amazon researchers. All of that led to this jumping around of, let’s close the gates. You have foreign nationals, and therefore, Anthropic got it out and said, “Hey, then we’re going to switch the models off until we can sort this out.” OpenAI was asked also to only allow it for certain vetted partners, et cetera. The government came in, closed the gates effectively, and said, “From now on, we need to be involved in this thing.” De facto regulation, there’s no doubt that this has imposed de facto regulation, certainly on the top players in the market. But then came the reversal. Bertrand, do you want to talk about the reversal, the gate swinging the other side? Bertrand Maybe I just wanted to say that as a user of Anthropic products, ChatGPT products, for the brief moments, a few days where Fable 5 was made available to the public before it was closed the first time, I immediately started using it. I must say it was a real issue to use it because the guardrails were pretty crazy. It would keep saying that my code was not okay, there was cybersecurity risk and stuff when I was doing absolutely reasonable development with absolutely no connection whatsoever to any cybersecurity risk, attack, detection, anything. Still, it would keep blocking me, degrading me to Opus 4.8 at the time. I just want to say this was already very hardcore what they were implementing, and not just hardcore, but in some ways, plain stupid for something that’s supposed to be super smart. It was totally unable to classify properly some of my work. I must say I was already disappointed. On top of it, the costs were insane. Half a day, I would reach my limits when I had the best plan you can get from Anthropic. My point is that there were some real serious issues when they launched Fable 5, even at that point. Nuno I had a similar issue. I used Fable 5 as well before they had to take it offline or take it off. I think the issue was really not that the guardrails failed. As you said, maybe the guardrails were actually too aggressive, but it was this jailbreak that caused the recall, apparently caused this knee-jerk reaction. Bertrand But my point is that it seems that it was not working either way. It would either overclassify something that’s absolutely not doing anything wrong, and it might fail to classify something that is actively trying to do some cybersecurity work. It’s a real issue of quality for a company that’s supposed to be at the forefront of quality of AI and everything. I think for me, there are already signs that something is deeply wrong. Nuno Then it’s reversed, right? We went the other way around. The government came out on June 26th and approved redeploying Mythos 5 to US organizations defending critical infrastructure, and then the export controls were effectively lifted on June 30th. July 1st, Fable 5 came back online for all of us to use. Shocking enough, with strings attached, that were different. They had some time to revise their commercial deployment of it along the way because it came back with some, “Now you have usage credits, but you have some limits on plan use, et cetera.” I’m like, “You guys, this was blocked. But meanwhile, you did have some time to do some commercial stuff around it.” Bertrand It was crazy. I’ve never witnessed any such crappy launch of any service whatsoever in 30 years in tech, it was so bad. Every day, they would change the terms of service. They would tell you it’s part of the plan. It’s not part of the plan. It’s part of the plan for three more days, and then it’s excluded. You have a special discount now, but then it goes back to full price. It was a total nightmare. I’ve never felt myself being so much mistreated by a company. I guess you saw the same, but when I started using the newest version of Fable 5, it was even worse, actually, I think. I couldn’t do any work with this crap. I let it go and work on the work I wanted it to do. It was simply not working. On top of it, you never know how long you are supposed to lose your credit, how fast. It was burning credit like crazy. Me, personally, I can say, very quickly, I actually stopped using it. I was like, “No, I cannot deal with this shit. My main model is back to Opus 4.8. I’m going to use Fable 5 for code review, but not anymore to control anything because I cannot trust it would do the job without stopping or changing models and stuff. I just cannot trust it.” Back to Opus 4.8 as my main model, I can say that my life was much easier. I use Fable 5 as a review mechanism, as a support mechanism, but not as the main mechanism. Suddenly, the guardrails were not so horrible anymore because it was used in a much lighter way, I guess. As a pain as a user, I think it was really bad. I don’t know your experience, but me, for me, it was unacceptable. Nuno I wouldn’t say it was as bad as yours in terms of just end-user experience. I think the terms of service switching back and forth, which went one further step, because then when they then launched Opus 5, they started making comparisons between Opus 5 and Fable so that people would migrate more and more to Opus 5 themselves, which is interesting. It’s like they’re saying “This is much cheaper. This is whatever. You’re not going to run of credits. You should use Opus 5,” kind of thing effectively. To your point, I don’t think they managed well the launch. They didn’t really manage it well. We’re moving people around. A lot of people are using this for stuff that’s like daily tasks, hourly tasks, anything that relates to code and co-work. It’s like, we need to have visibility on what your terms of service are going to be. Should I be using this new model or not? What’s happening to the other model? I don’t see it as negatively as you, Bertrand, but I see your point. It was clearly mishandled in terms of how they deployed it, how they were redesigning effectively their pricing scheme and their terms of service almost on a daily basis, at a certain point in time. We’re like, “Dude, there’s millions of people using this. You guys are making a lot of money.” Just moving it as it is. At this point in time, at the scale that these guys are at, it’s calling in people to say, how about we think through a class action suit at some point around pricing? Because you guys are changing the rules of the game all the time, right? Bertrand I don’t know if I need the class action, but for me, that joke that, “Let’s not rush too fast. The model is dangerous.” But still, they rushed the launch because it’s very clear that if they had enough compute capacity and stuff, they would not have to limit so much. They would not have to put so much cost per token and all of this. You can see that actually when they launch Opus 5, literally like 2, 3 weeks after, by most benchmark at launch, they tell you basically that, “You know what? Actually, Opus 5 is better than Fable 5 on 80% of the metrics.” They’re like, “What? Seriously? You couldn’t wait 2 weeks? Why did you even launch Fable 5 in the first place?” That’s another part for me that is quite literally insane, to be frank. It’s like, “Why? Why do you make us go through so much pain if it’s only to tell us after 2 weeks to…” “This new model, by the way, has less issues, less stuff, because 2, 3 times less is part of your plan, and it’s actually better by most metrics.” It’s like, “What’s going on here? What’s going on? Are you guys mad?” I don’t know. It was crazy. Personally, I still use Opus, now 5, as my main system and platform, Fable 5 for review, code reviews and the like. I don’t want to run into its stupid guardrails. I can see Fable 5, from my perspective, seems quite a bit smarter. I don’t know why they do this stupid benchmark showing you it’s actually worse than Opus 5. I guess they should have better benchmark if they want to demonstrate why you are supposed to pay 2, 3x more for a model versus another if it’s actually worse by most benchmark. Again, I still think it’s a huge mess from a marketing perspective, customer perspective. Me as a user, I really feel that they don’t want my money, and they couldn’t care less about me. This is even before everything else we’re trying to talk about. Nuno Yes. Maybe just to close the cycle on the reversal on the door opening the other way, finally, Commerce lifted the GPT-5.6 restrictions on July 8th, and then on July 9th, general availability across ChatGPT, Codex, and the API as well. What has this proved? It proved that now we have gating mechanisms, and certainly for closed models in the US, for sure. We had frontier models that were switched off worldwide in hours, and it took a couple of days, in this case, 19 days to restore them. There were concessions. Now we know that there were concessions around effectively institutionalizing that gate. Early government access to future models is, I think, now a given, certainly in the US. New safeguard frameworks are probably now having to be put in place. There are some stage limits now on who gets access to what for new models and how it happens. This voluntary executive order, so to speak, not really sure, has become effectively regulation enforcement path. It’s de facto regulation that now has been put in place. It has affected not just to the points we were making before, the access to these models, but also who gets access to these models, and actually potentially even pricing access to the models. It has probably some commercial implications as well as we just discussed along the way. Very significant. This is very significant. This is regulation, de facto at the table, imposed on the two largest players in the market by far by one government, in this case, the US government. This is significant. Actually, you could even allege it was imposed by the President because this was coming as part of executive orders. Really incredible. Pretty significant, fast, aggressive. It has created a regime that you could say it’s a regulatory regime, it’s a de facto regulatory regime. It has some significant pricing and licensing and commercial implications. It goes even beyond your classic regulatory framework. Very, very, very significant. Bertrand I don’t know if it goes beyond a classic regulatory framework. Nuno I think it does, because it has implications on who do you give access to? When government is saying you can only give access to these players, right? Bertrand Defense industry. It’s all over the defense industry. You cannot sell an F-35 like this. Nuno No, but that has commercial implications, Bertrand. That’s like you’re saying these are your customers, you go and use them. Bertrand That’s the defense industry. You cannot sell to Iran your F-35. No, that’s exactly the same story for me. Nuno No, no, no. It’s beyond that. These guys are saying when they came back, and they said, “For Mythos, you can make them available to these entities,” they were saying the first entities that are going to have access to the model. It has commercial regulatory implications. You’re saying these players are the first players that are going to have access to it. It’s no longer just defense concerns and these governments don’t have access to this. No, no, no. You’re saying to a company that is a private company, your models are only going to be used by these guys because I’m telling you so. It’s the other way around. It’s not even that you can’t sell it to Iran or whatever. It’s like you can only sell it to these guys. Bertrand Again, in the defense industry, if you’re a private company, do you think you can buy F-35 like this? No. Nuno No, no, no. But this is a private company, Bertrand. This is not a defense agency and a plane that is on whatever, with IP from the US, right? Bertrand Boeing is a private company, and they cannot sell the military equipment they manufacture. Nuno No, no, no. But the development of their IP was subsidized by agencies that belong to the US, right? That’s a different matter. It’s a matter of IP, right? This is not, right? Anthropic, their models are not owned by the US government. There’s no IP granted to the US government, to my knowledge. This has significant commercial implications. Bertrand Maybe, yes. Maybe on this. But I think there are already regimes to limit who you can sell to, and that’s decided by the state or the DOD. Nuno It’s the export control logic. The export control logic? Bertrand You have export control, and export control is Commerce. My point is that they are using existing tools, part of the government, to limit what can be sold. Selling chips, NVIDIA was limited in terms of where it could sell its chips. It’s not different either, but still there were limitations. If you are an ASML, you cannot sell to a private company in China. Many private companies cannot buy ASML products. This is a foreign company. This is a foreign company under pressure from US government. Nuno I understand, and I’m not a lawyer, but it feels different to me when you say you cannot export, this is export controls, to these countries, to these entities, et cetera, because they’re foreign et cetera. Then to say, “No, no, no. On top of that, these guys get first access.” That’s, for me, a significant shift. Again, I’m not a lawyer, so I’m sure there’s very intelligent people right now looking at this stuff and saying, “You can’t do this stuff, or not, or they can.” I don’t know. But it feels to me, it goes beyond the remit of export controls. It’s like you’re defining initial clients for specific use. Bertrand My impression is more like, “We can do this situation where we’re going to forbid you to give access to anyone outside the US or even in the US or limit even more.” Basically, it was, I guess, some gesture to go beyond that. That’s how they probably defined these 20 authorized companies. I don’t know. Apparently, there was also restrictions because I remember seeing that Anthropic had their own list of companies they would authorize access to Mythos early on. That’s apparently another thing that pissed off state government because there were companies in there that were considered close to the Chinese government. They were extremely unhappy that Anthropic didn’t ask, actually, for any guidance from the state government, but used basically their own perspective on who they should allow or not. I guess that was also part of why they got these serious restrictions. Nuno Anyway, now we have a regulatory environment that’s very interesting and exciting. Talk about the US not regulating. Bertrand To be clear, I don’t know you, but I’m not saying that I agree with any of this, to be very clear. I’m trying to explain and share some perspective, but I’m not in agreement on a lot of this. Nuno Yes, we were just describing what happened to the best of our knowledge. We’re having a discussion on what we think actually is happening and how it’s happening. We’re not really right now saying we agree or disagree with this. I think later in the episode, we can share some perspectives on what we think is actually happening and how there’s dimensions to this which are very geopolitical and very complex, which quite literally probably only God knows what’s going to happen. That was the gate swinging. There was a gate closing, then there was a gate reopening, and all of a sudden we have a gatekeeping system that has been created along the way. The Kimi Shock Along the way, moving to our Act 2, the world has changed, and we now have so-called open-source plays out there that are creating massive, massive shifts in the market. The Chinese models, in particular, with Moonshot AI launching Kimi K3, which is the largest open-weight model ever released. We’ll come back to the discussion around open-weights. I’m not sure all our listeners understand what that means, because there’s a debate now, should models be open weight or not, and how does that work? There’s been a petition as well signed along the way. Right now, we have open weight models that are out there that are huge. What that actually means very pragmatically is we now have open source models, lack of a better word. I know open weight and open source are not the same thing. You guys will have to bear with us during this episode. We’ll explain at some point the differences. But we have models out there that are open source that are significant. That are catching up with the closed source models, with the models by OpenAI, Anthropic. That’s significant because most of those models are Chinese. This is where the geopolitics starts getting really frazzling and we start playing 3D chess. Because everyone’s like, “These models are 5, 6 months behind.” Now people are saying, “Maybe they’re actually just 3 months behind, 2, 3 months behind.” If we, for example, decided to stop or slow down our model releases in the US by the closed source guys who are leading, it might mean they’ll catch up. What are the implications of that? Again, for you and I that are not necessarily experts in model development, well, the implications as a use case is if you want to use the latest models, and the best models start becoming these open source models, you’re going to use those models. Then you start using Chinese models. If you’re an American company, maybe you’ll have restrictions on the use of those Chinese models. But if you’re a European company, you probably won’t. What happens after that? Is the world going to be in the hand of Chinese models? Will that constitute effective competition to the closed models in the US? Will we have open models in the US that will scale as well? What’s going to happen? Bertrand I think it’s a really big question. It goes to some of the core of the issue. It’s that ability of Chinese models to basically challenge frontier models, not just being 6, 12 months late, but being 6 weeks late. Basically, no gap. Some will say that, yes, but OpenAI and Anthropic have even better models that are not shared and stuff. Yes, sure. But maybe the Chinese have the same models that they are not sharing right now. We don’t know. What is clear is that one is that open weight, as you said, two, there is a question of how it is marketed in the sense of, can anyone use these weights? Is there a license to use them? Yes, what we can see is that, for instance, typically there is a license for some of the biggest Chinese open-weight models you have to abide with. You might have a need for a commercial license if you are acting as a company leveraging this model to provide AI-informed services. If you use it internally by yourself, you’re okay. If you use it internally for your own internal company needs, maybe you are okay if it’s not your main business to do AI work. Anything else, a much bigger corporate providing AI services and stuff, you will probably end up having to pay a fee to be able to provide services around this model. My point is that it’s not just 100% free. Some of the Chinese models are 100% free to use, MIT license, Apache 2.0 license. But the biggest ones with the biggest weight that are truly frontier typically have a different license if you want to scale these models, providing AI in front. That’s one thing to keep in mind. Nuno Maybe just to make a very quick point, because people are like, when you talk about open models, what does it mean right now? In the context of this episode, open models mostly will mean open-weight models. How do those differ from open source? Open weight means that you release the weights to the public, which means that anyone can download, fine-tune, and run the model on their own hardware. It doesn’t normally mean that you also have access to training data, training code, or a truly open license. That’s the distinction to open source. Open-weight doesn’t mean that. For example, we’ve talked about Meta’s Llama in the past, and we also discussed in the past that their license agreement does have restrictions, certain players can’t use it, et cetera. The open model definition and open weights are really open-weight models that we’re talking about here, and they are closer to freeware binaries than to Linux, for those who understand the difference between that. It’s binaries that you can use and then use your own weights on it versus actually I can change code on it. I’m not going to be able to change code on this. When we, for the purposes of this episode, talk about open, we mention open weight, just to clarify that point to everyone that’s listening right now. Bertrand Yes, that’s a great point. One of the only players, as far as I know, who is truly open source is actually NVIDIA with their Nemotron-3 models. They’re actually following a special license to achieve that. They provide you the data, they provide you all the processes and tools, so you can easily post-train. NVIDIA is a big, big exception. It’s a very interesting player, by the way. We might not talk much about it in this episode, but I think for intermediate-size models built in the US, where you have access to everything in the deployment, it’s a very interesting alternative and maybe one of the best choices if you are a US company or a big corporate, and you want something trusted. Another piece of the puzzle to clarify is that when you use open-weight, it means that you can run them by yourself, or you can use a US provider to run them. If we are talking about Chinese open-weight, you can use the APIs they provide, but then the service is running in China, they might have access to your data. But because it’s open weight, if you run it by yourself or if you use a third-party provider based in the US to run it, then there is no access to your data by China or Chinese players. I think that’s a pretty important gap to understand. It means that these models are actually very, very low risk from that perspective if you run them on your premises or in the US by a US player. I think that’s something to keep in mind. You can also fine-tune easily these models to make sure they will behave in a way that, for instance, is not going to represent the line of the Communist Party on some topics. There are ways to make these models more neutral in their output as well. There are a lot of ways to make good use of them. By default, they’re already very safe, but you can make them even more safe. I think that’s some things to keep in mind. But again, it depends ultimately on the license and what you’re authorized to do and some fees you might end up having to pay. Nuno Why did this matter so much? Immediately there was a reaction from the market because people are like, well, if there’s much better stuff out there that’s much more efficient than it’s open, then it might be that all the demand that we are taking into account, for example, for chipsets actually isn’t real. The Philadelphia Semiconductor Index fell into bear market territory. It went down by as much as 20% plus from the late June peak. The worst chip week since April 2025. Taiwan’s benchmark initially fell 6% plus, Japan’s 4%, TSMC dropped dramatically despite beating earnings and rising guidance. Basically, a huge amount of effect. Now, there’s a little bit the aftermath of this where apparently Moonshot ran out of GPU capacity. Maybe… Bertrand In just 48 hours. Nuno In 48 hours. Great for them, but at the same time, not great in the sense that maybe there was a misread by Wall Street of the Kimi effect, so to speak. Bertrand Completely. For me, that’s such a joke. It’s like, because you have an open source model, so what? I mean, you still need to run it. This is not a small one. 2.8 trillion parameters. Good luck running that in your garage, by the way. Nuno They misread supply, basically. Tough luck, right? All of that basically happens. Bertrand Maybe you want to talk about the Jevons paradox, because I think that’s a big part of the puzzle as well. Its one is they might not have the GPUs to run the inference on the model. They might have enough to build a model, but not enough these days to run inference, especially given how much with intelligent models, thinking models, you need way more inference than before. But on top of it, the cheaper you make it, the more you get to the Jevons paradox. Nuno Yes, Jevons paradox, for those who don’t know, is an economic term. It describes an economic phenomenon where technological improvements that increase the efficiency of a resource lead to an increase rather than a decrease in the total consumption of that resource. What that means is, for example, for chipsets, chipsets become so much better, and they are so much more efficient. You’re like, well, maybe normally in resource terms, that leads to decreased usage of that resource. But in this case, it actually leads to an increased use of that resource rather than a decrease. There’s more and more consumption of that resource. You need more and more chipsets because people actually need to do more and more stuff with it, although there are great efficiencies going into it. There’s the efficiency gain, there’s the cost reduction, and there’s the price-elasticity element to it. But basically, the adoption just continues going through the roof along the way. Bertrand In some ways, it’s like the price of energy. Coal went cheaper and cheaper, and people were asking the same question 150 years ago, now that it gets cheaper, there is not much money. No, no. Actually, what happens is that people find more and more use for coal. Homes are getting heated more. You have ships now using coal. You have manufacturing using coal. The cheaper it gets, the more use case you can develop, and therefore, you don’t need less of the stuff, you need more of the stuff. By going at scale to get more of the stuff, you also decrease price, making even more demand. It’s a very interesting phenomenon, but it’s not new. It is what happened for a while in the energy sector and some other sectors. Nuno We already started talking about the Chinese logic and what’s happening. Getting a little bit of a reality check on this. The Chinese models, and these are numbers from Open Router in July, Chinese models are at 46.4% of routed tokens and 35.7% for US origin. Again, more than a third of global AI usage now seems to be running on Chinese open models. This is significant, and it has a huge impact on the geopolitical scale of everything that’s happening. Also, the whole Chinese field is converging on open. Open seems to be a strategy, not just a nice thing that’s happening. It seems to be a Chinese strategy, so much so that you have players like Moonshot, DeepSeek, our old friends DeepSeek, Z.ai’s GLM 5.2, Minimax, and even Alibaba seems to be reversing and going open with Qwen. It feels to me this is becoming policy as well. Xi Jinping has personally endorsed the building of open-source AI, if it’s really open source, if it’s just open weight anyway, and this feels to be a jab at Washington, DC and the fact that the big closed models are coming from the US. This is now geopolitical 4D chess, right? We didn’t need this stuff. Bertrand To be clear, it’s the usual in tech. If you are not number one, you are number two, number three, your alternative is to go open source because that’s another angle that your competitor usually cannot follow without destroying its own business model. That has been the alternative for the past 20 years of most software projects. Here, what’s different is that it’s not the number one or number two player. It’s the US number one as a country, China number two as a country. That’s where it’s new. For me, what’s very interesting is the endorsement by Xi Jinping. I was waiting for something official, and it certainly didn’t disappoint. As you said, there was an immediate U-turn of Alibaba, who in the past… Nuno Surprisingly. Bertrand Yes, a little more like, “yes, we are going to close and stop open source. It was good while it lasted.” Just a few days ago, Qwen 3.8 Max was launched, and we are supposed to get the weight in a few days. We talk about the US administration policy and stuff. Yes, let’s not forget that in China there is similar stuff. Sometimes it’s totally invisible because you don’t see the directives, but they exist as much. Sometimes it’s more visible. Here it was quite visible. The difference in China is that if you don’t abide by the directive, on top of it, you might have to fear for your personal safety. It’s a different game, and that’s probably why the reaction is pretty quick, usually. That’s pretty interesting for me because it means that now you can bet for a while that China is going to play that game up to a point. I guess the point is if it’s truly frontier scale, you will have a special license that, yes, technically the weights are open, but you can not do everything you want with it. Two, you have a player like NVIDIA that I think will feel more pressure to provide even more high quality, larger models at scale going forward. Their largest Nemotron-3 Ultra model was, if I remember well, only around 500 billion parameters. I would not be surprised for NVIDIA to go into the two, three trillion range at some point. Because I think the US need a very clear US-born alternative open source. I think NVIDIA might be the best player for that. We will see if Meta goes back to open source. I think NVIDIA is one, very well positioned, but two, it’s also in their best interest. Because NVIDIA for now depends on just a few big hyperscalers as clients. If they can expand their clients to every S&P 500 companies, selling them directly hardware because now these companies can run a model made by NVIDIA, I think there is a very clear value proposition for NVIDIA to go in that space. Again, if you are number two, your differentiation, open source is often the answer. There is a true business as a business model for companies, because if it’s truly not just open weight, but open source, you can tweak it as much as you want, you can change it, you can change even the pre-training process. Because there is a lot of stuff you can do that really benefits you as a corporate, and you can reach a much better value by having more control on the model. Nuno We won’t spend a ton of time on it today, but like, again, if there’s a view that we are in a bubble, that the valuations cannot be sustained in chipsets, infrastructure platforms, applied AI, et cetera, today, this might be that beginning, where the valuations start being destroyed because you can’t keep a premium on just charging people for tokens and all that stuff if you have models that become more and more efficient and cheaper to use. Maybe just to close a little bit the geopolitical part of the discussion today, we won’t go into all the announcements from China because there were many, a lot of go back and forth with Alibaba by then. Xi Jinping made some announcements. You guys can check it online. Let’s move quickly to Washington’s reaction, which was from gating the US closed models to banning the Chinese open ones. There’s been as strong affirmations as one can get from the Office of Science and Technology Policy Director, Michael Kratzios, mentioning that they have information that Moonshot AI distilled Anthropic’s Fable. Basically, there’s been reverse engineering and stuff in the market. They’re basically copying. Bertrand I’m sorry to interrupt, but it feels like so much bullshit. It’s coming from Anthropic who has basically gotten access at scale to all the knowledge made by humanity, copyrighted or not. We’ll talk more about what they did with books. Then to claim after that that others cannot do to you what you did to everybody else. For me, it’s pretty big. It’s clearly unacceptable. The other piece is that everyone is doing distillation. It’s a very typical approach of every business model. You try other software when you are competing with somebody else. You try other datasets, you check what’s happening. It’s part of doing business for decades. Suddenly it’s not good for Anthropic. I personally have a lot of trouble to accept that. I think it’s totally unacceptable. The other piece of the puzzle will also go back. If these guys are so smart, if these guys have so much of the best model, why can’t they block by themselves distillation at scale? The only answer is that either they are morons, probably not, or they simply don’t want to because it’s going towards their business model. Suddenly, you book less revenues and stuff, or you put more friction, and therefore your customers don’t like it. Instead of doing it yourself, you ask the government to protect you, go out of business practice that is very typical. For me, it’s really, really, really not good. Sorry, we are going more in the opinion side, but I had to put that on the table. Nuno Yes, Fable went public finally again on July first. Question marks on whether distillation would only be possible from July first onwards or not. But a 15-day distillation to frontier, which is K3, launched on July 15th, would have been a Guinness World Record, as one of Moonshot employees actually mentioned. It’s very implausible and unlikely. Bertrand Or they shared the Mythos 5 with the wrong companies, who themselves shared with Chinese companies. We go back to maybe they didn’t have a good list. Again, it goes back to maybe they didn’t want to hurt their business model. Nuno Anyway, under the threat of sanctions, Moonshot, in any case, open-sourced the full K3 weights and technical reports. They open weighted it to become the largest open weight model in the world in terms of parameters. Beijing’s MOFCOM brands US threats as basically the US wanting to fundamentally control and be monopolistic around AI along the way. The administration bans Chinese hardware with an eye on the AI race, and Beijing warns of retaliation. That was July 27. Now we’re in a war between Beijing and DC. Bertrand Just to finish maybe on China, it’s important to know that they are building their own GPUs now. Huawei has pretty good, not to NVIDIA level, but pretty decent GPU hardware that they’re able to manufacture by themselves. A Chinese player of memory just got IPO’d a few days ago, CXMT. China is also developing their own memory. Again, not to the same level of quality that you can get from the West. But China is moving. It’s not just that they are building great models, it’s also that they are building GPUs and memory. That might be a few years late to the latest standards in the West, but there are definitely improvements. I also read, even on the tools to make manufacturing like ASML equivalent, there is definitely some work going on, and some improvements and some stuff will be visible. In some ways, the genie starts to get out of the bottle from the Chinese perspective. Nuno I’ll put a stick on the ground. I don’t think it’s a matter of if, it’s a matter of when will China surpass and have a lot of this tooling on their own side, and not just the software layer, not just the frontier models. I think it’s also going to be around infrastructure and platform. Good luck to everyone. Let’s see how the race continues. But it’s definitely this is a geopolitical thing right now. It’s definitely a race. The Escape Maybe moving to what happened in just 2 weeks or a week and a half. The escape, there was some jailbreaking going on, and the narrative on safety has totally switched. It’s not still significant enough that’s like, “Oh, we saw a nuclear plant going, whatever.” No. But still, it is significant. Hugging Face, the AI company, disclosed an intrusion, and it was driven end-to-end by an autonomous AI agent system at machine speed, running for days before detection. Now, this is where it gets really cool. OpenAI takes attribution on that. They initially said it was just a little bit, sorry. Then they said, actually, it was worse than that. “Oh, it broke out of an isolated sandbox.” “Oh, no, actually, it was more than that, and it went into other systems as well.” Bertrand Truly, the genie out of the bottle. Nuno No, but this is where it gets really cool, Bertrand, right? Because it actually, Hugging Face contained the intrusion by running a Chinese open-weight model, GLM 5.2. This is beautiful, right? Bertrand Yes. You know why? Because they couldn’t even run their own defense because both Anthropic and OpenAI would not let them access their latest models with the guardrails off. When they tried using it for defense, the latest from Anthropic, from ChatGPT, they would tell them, “No, this is too dangerous what you’re asking us to do.” Preventing an intrusion, helping defend you. No way we are going to do that. Nuno No. Let’s use the Chinese models on our infrastructure. Bertrand We have no choice but to use the Chinese models to run. More than that, we don’t let you use our models to defend yourself, but our not yet released models that run without guardrails, they can attack you. This is probably the most insane from that perspective. Nuno The Chinese models came to the rescue. Bertrand For me, that’s a perfect example because Hugging Face is a very visible company in AI in open source. But anybody who is not at that scale is not going to get some support from OpenAI or Anthropic when this happens. Maybe these guys won’t even recognize they did anything wrong. You will be left to defend by yourself because they won’t accept to support you. Because remember, if you want the better model that is able to defend you from cybersecurity perspective, no way. If you are not one of the few top 20 companies or so, as defined, you are left defenseless. Again, we are going back to opinion, but for me, it’s so shocking what’s happening right now. I’m very glad we have alternative open source to be able to defend ourselves because right now, good luck getting defense services if you are a smaller business and individuals, and you need support from Anthropic, OpenAI. Nuno Now, even self-described AI optimists are saying, “This is scary now.” Like Walter Isaacson, who wrote all the famous biography books. There’s now discussion around the AI Kill Switch Act, bipartisan thing that’s coming across from Texas and California, a potential bill that’s coming in. We’ll see if that works. Now let’s get an off-switch. I’m like, “Cool.” As if that’s going to solve the problem, because you have open-weight models on the other side catching up, right? Bertrand Yeah, sure. Bring in clueless politicians from Congress to solve our problems. Yes, sure. Nuno Anthropic came to the table, helped build and said they built some regulatory machine on their side, and now they’re getting bitten by it, and they’re part of the offending players in that market. Now there’s all this debate and all this discussion around open weight and around slowing down AI and et cetera, which is our next section. You wanted to say something, Bertrand. Tell us. Bertrand Don’t forget, because this advertisement for OpenAI was just too good. Our AI attacked some other companies, and not just one, but three, actually. Let’s not forget the progress. Great ads. Then I came and said, “You know what? AI also hacked businesses.” You’re not the only one hacking around with a crazy AI out of control. You’re not the only one. We want our advertising. For me, it was shocking that on one side, unreleased models that you let run wild. On the other hand, you have released models that you put crazy guardrails on top of it, so the defender are defenseless. I’ve never seen anything like it, and I really hope that there will be as little regulation as possible, quite frankly, to make sure anyone can defend themselves and have the best tool at their disposal, not just a few well-connected big corporates. This is really, really shocking. The Counterstrike and the Petition Nuno Now the empire strikes back, so this is counterstrike, the petitions. In several days, we have now a bunch of petitions. The first one was the open weights letter. Bertrand, do you want to explain to us what the open weights letter is? Bertrand Yeah. I think it was great. This was released by Jensen Huang, first ever post on X, 11 million views. Congrats, Jensen. Co-signed with Microsoft, Meta, c actually was probably the initiator of this letter. Very good letter saying, “Hey, we need open weight. This is not a joke. We need that. You cannot block open weight.” Because that’s the rumor we are getting that potentially open weight could get blocked. I think they are making the case, “You know what? Hey, we absolutely need that as an alternative. You cannot block it.” They can keep their closed models, but don’t force a closure of the open weight models. As I said before, it’s actually a great model for NVIDIA because NVIDIA doesn’t want, probably rightfully so, to be dependent on just a few frontier models, their best customers. They want a variety of customers. They have a big interest actually to defend open weight and to invest even more. They have great researchers, are a great company. If one company is about to do really kick-ass work, I think it’s them. They are defending. What’s great is that it’s not just them. It’s basically most of big tech in the US and outside the US, from a Linux Foundation to a Microsoft, the Palantir, an IBM, a Dell. It’s a who’s who of the industry except Anthropic. Anthropic didn’t sign that. I guess they hate open source so much. If I look at 20 years ago, it feels like Microsoft, after all, was very kind to open source. You remember what was said by Microsoft at the time. It’s clear there is one company against open source. OpenAI signed the letter. Honestly, I don’t know what to think. Do they really believe in it or was it just a way to show that they are not like Anthropic? I don’t know. But for the rest, I think it’s genuine because it’s actually in their best interest. I hope they will be heard. Then a second letter came, the Open Secure AI Alliance, NVIDIA-led and again, the big tech companies from Microsoft, IBM, Palo Alto Networks, Databricks, Palantir, all those, but not present, OpenAI, Anthropic, and Google. Here it’s to say, “Hey, we need a secure approach to AI. Open should be part of the equation.” guess what? The worst AI-caused security incident to date was actually caused by closed frontier models that were not even available to the public. While again, not providing you access to even the latest closed model for cybersecurity use case. Nuno I would highlight the NVIDIA open source NOOA framework, Apache 2.0 licensing agreement, Microsoft contributed the MDASH, SpaceX AI contributed Grok Build. Cool stuff. There’s some cool stuff happening around that. This is more than a letter. This is an alliance. Apparently, they’re contributing all this stuff, we’ll see. Yeah, cool stuff. Same day. Same day, Amodei has an answer, right? Bertrand Yeah, same day. They say, “We never advocated for a ban,” which, again, opinion on my side is entirely bullshit. This guy has been crying wolf against everybody else, and especially against open source. You can see him doing testimony in Congress against open source. I think they are doing everything they can behind the scene to block open source in the US or in the world if they could. I think, yeah, obscurity is not good safety. I’m a big fan of open source in general, and I’m also a big fan in AI. I think it’s now Anthropic, mostly against the rest of the world. I think OpenAI is mostly on their side, to be frank. They don’t want to acknowledge it so much, but they have shared interest, and they have shared probably position. Nuno Why would you? I don’t feel as strongly as you because I think Anthropic is a private company, right? The same thing with OpenAI. OpenAI, you could say it’s a nonprofit that has a for-profit. There’s still that complexity in there. Bertrand No, they can do what they want with their own product. But to block others is where I’m not okay. That’s the part I’m not okay. Nuno What Dario Amodei is proposing is more enforcement, right? He’s basically saying you need to do even tighter controls on advanced chips flowing to authoritarian states, enforcement against industrial-scale distillation, whatever that means, right? Bertrand Yeah, which he could do, but all by himself. He doesn’t need the government to do that. Nuno Mandatory safety testing for all sufficiently capable AI, open and closed, right? He’s basically saying, “Okay, I don’t agree with the open weight stuff effectively,” right? He’s just putting it under a different banner. “I agree with this extra regulation.” then obviously, David Sacks responded and say, “Hey, it’s like, bans don’t work for weights. Why do they work for chips?” It’s like, magically, chips are more controllable and bannable. Whatever that is. Then our friend Mark Zuckerberg, just to be clear, goes on the other side as well, because he also has to have a view. He has to have a view that is the rebuttal of both of the other guys. Bertrand I feel he’s a bit flip-flopping because he was very pro open source 2 years ago, and the latest Meta models went closed source. Now I think he’s back open source. I don’t think he has a very strong spine on the topic, but it’s good to see that he’s not a doomer. That for me is great. He’s showing how AI can be a source for progress, a source for entrepreneurship, source for freedom. I think that’s very exciting to hear that. We need to hear more of it. By the way, that’s not what you hear in China, for instance. AI is very positive in China. It’s in the US with the doomers that you hear this discourse, and people get worried as a result. I’m glad that he was pushing for a more positive vision and for support of open weight, open source initiatives. But let’s see what they really truly open weight going forward. Nuno But that’s been his position because I guess he’s standing behind. He thinks open weight is going to be the best way to compete, right? Bertrand Yeah, but he closed his latest model, so let’s see. Nuno Yeah, so it’s flip-flopping, as you’re saying. Then we see the latest petition from last week. Bertrand The true Empire striking back. Nuno Yeah, the true Empire striking back as of late last week. Maybe this is Return of the Jedi, where we discover the father, “I’m your father, Luke.” That’s the pacing petition. The pacing petition is we need to pace AI. There you have initially employees from OpenAI and Anthropic that circulate this petition. Actually, Dario did sign this petition originally. It wasn’t signed originally by Anthropic, but by him. But you’ve heard that now Anthropic and OpenAI as companies have also signed this petition, right? Bertrand I think they have signed as companies now. It started mostly by Anthropic researchers with some OpenAI researcher and a tiny part from other companies. But it was mostly Anthropic internally led, at least potentially internally. Maybe it was controlled by Anthropic all along, I don’t know. But it started officially as Anthropic employee-led letter. Nuno What does this letter actually say? Is Anthropic and OpenAI, are they willing to slow down themselves? Or are they asking President Trump to go around the world and tell President Xi that he needs to slow down and ask his guys to slow down? What’s the play of this letter? Bertrand It’s crazy, but for me if you want to slow down yourself. Do whatever you want. Don’t force others. Don’t use the power of the government to control others. Of course, it’s easy to push others to slow down when you are yourself at the very top. You have most money, most resource. You know you are going to win any regulatory framework because that’s how it works with this type of framework. It’s purely self-interested. You are probably not thinking well about these topics. If you truly think it’s a good idea, from a personal perspective, you are well instrumentalized if you sign this sort of stuff, because at the end of the day, they would be the winners. I certainly, personally, don’t want a company dictate what is my future in AI as an individual, as a business person. I don’t want them to control me. I want competition. I don’t want them to unfairly control AI because they managed to do some regulatory capture. I feel that’s exactly their game plan. These guys believe in their stuff, and they want the regulator to end up being the one deciding for us. Sorry, we go back again on the opinion piece, but it’s tough not to share an opinion on this topic because it’s, from my perspective, very scary. Nuno I think this is a push to further regulation, not less. All these letters and alliances, this is definitely a push for more regulation. In that environment, just to be very honest with you, we’ll talk about the investor impact in just a bit, et cetera. But in that environment, again, China has a huge advantage. In that environment, if it’s all captured in regulation capture so soon in this battle where OpenAI and Anthropic have an advantage in the US, et cetera, I’m like, what happens to all the other frontier labs and all the other players that are coming around? Bertrand What’s crazy is to even think that, yeah, maybe you can regulate capture in the US. But then how do you do that to Europe? How do you do that to China? Europe probably will always welcome regulatory capture because they love regulations. But China is going to build to their advantage to the max. They are not crazy. They are smart on that perspective, they won’t accept this type of, quite frankly, dimwit argument, or you can call it regulatory capture. We’ll see. But for me, this makes no sense from a global competition perspective. This can make some sense from capturing the revenue in the US market. But then that means you are going to destroy the US AI environment compared to China. That is not acceptable. That also means that you are going to destroy our freedom as individuals, as business owners to develop and live in a business world that ultimately is controlled by one or two business companies that didn’t win the marketplace through their own business success, but won it through regulations. That for me is really not acceptable. Interlude — The Low-Background Books Nuno Now, maybe for an interlude, and we have to cue in the music, imagine like Severance music, like hallway or a bit of a palate cleanser from all the policy stuff that we’ve been talking about, all this policy heaviness. Let’s move to another kind of heaviness, one of your favorite topics, which you, Bertrand, discovered, I had no clue this was going on, around books and around Anthropic. Bertrand It’s so horrible. From a company that keeps presenting themselves as the adults in the room, the careful ones, the ones that know better than you about what to do in this complex AI and dangerous world. What we discover is that actually all along, they were buying and destroying books. They will buy books, scan them, destroy them, all of them. They will do that with any books, including rare books. Of course, this was not supposed to come to the public’s attention. This was one of these top secret projects, but obviously it came out. Yes, they were scanning books, millions of them, including rare books, and they didn’t care about destroying them at the end of the process. Because from a regulatory perspective, if you destroy the books, it’s not considered a copyright infringement, apparently. This is coming on the back of some judgment a few years ago that were showing that it’s okay for you as a corporate to scan and use the result if you don’t keep a copy of the book. It’s one of these crazy regulations happening based on a single judgment that push you to do. For me, it’s like, you know this book from decades ago, Fahrenheit 471? We’re talking about book burning. It’s book destroying, crunching. It’s so shocking. Nuno There are two things, right? First, the legal strategy, which is what you’re saying, because by purchasing a physical copy and converting it into one private digital copy and discarding the original, Anthropic pursued this cleaner legal argument for fair use copyright compliance. As you said, there was a federal judgment at some point on this. The other reason is actually operational. If you disassemble the book, and you feed loose pages, it’s much faster to scan books. You are destroying the book effectively anyway operationally. I think to your point, probably this came from a legal standpoint, not just the operational one. But even from an operational standpoint, it does make sense that they would have disassembled the book. Bertrand But some people have shown you can go very fast without destroying the book. It’s really not so critical. Two, you could make an exception if the book is rare. For that 1% of book that is rare, I’m not going to have this approach. I’m going to have another approach. But for that, you will have to care about books and not just care about building AI. Nuno This is the episode, as you guys have heard by now, that we’re trying to spit stuff at Anthropic. Bertrand To go back this is the same company saying, “Hey, guys, it’s bad to distillate my work. I’m the one scanning book at scale without asking author permission, without asking publisher permission, to be clear.” Nuno But just to be clear, Bertrand, we’re pissed off at everyone. We’re pissed off at Anthropic, we’re pissed of at OpenAI as well, right? We’re just pissed off in general at this moment. Bertrand At this stage for me, the more clear-cut company that is in the wrong is, from my perspective, at least, is Anthropic. OpenAI might be a fast follower, but I will say so far, they tried to be a bit more. Nuno But at this pace, Bertrand, who knows? Maybe next week we’ll be more pissed off at OpenAI. Something will come out. This episode is a mix of tragicomedy, like a Greek tragedy with some comedy in the middle or the other way around. It’s a slapstick thing that will end up in tragedy. I’m not sure. The Investor Reckoning Anyway, maybe switching to our final act, which is the investor perspective. What does this mean for investors like ourselves? There’s a lot of things going on. There’s the debate around the IPOs of Anthropic and OpenAI, which now, with all this uncertainty, might be under significant weight. There’s a lot of other discussions that we browsed through that there’s potential IPOs going forward on companies like the Moonshot AI company actually IPO-ing in the next 6 months as well. It’s very unclear what the IPO landscape looks like. Bertrand There’s been a lot of Chinese IPOs, actually, when you look at what’s happened in the past few months. Nuno Anthropic, OpenAI as potential IPOs, there’s all this question marks now. When will that happen? How will it factor in? All that’s happening around regulation as regulation is moving at the speed of light, which is for once something that’s very different than what we’ve seen before. There’s obviously SpaceX AI, which is already taking into account that price. It’s already a public company in there, and it’s under SpaceX, which is now a public company. Obviously, that’s already being factored in some ways. Bertrand Yeah. SpaceX AI has been very smart to acquire Cursor. It was a very smart move because Cursor is one of the leading companies in terms of automated code source development with AI. They had great models on their own. They’re bringing development data to SpaceX AI Grok. I think it was a great move. Nuno We have now people like Google delaying Gemini 3.5 Pro in terms of launch window. There’s stuff actually happening in the market where things are taking their own path. There’s uncertainty commercially, there’s uncertainty at regulation level. You have new players that have come out of nowhere that are making all these waves like Moonshot. We have all these… We had calculated probably a month and a half, 2 months ago, there had been 67 new frontier labs funded. All of these, we haven’t seen any much coming out of them. When some of this stuff starts coming out, will that also create disruptions in this market? Who knows? Bertrand Look at Thinking Machines, for instance. Thinking Machines led by the previous CTO of OpenAI, they released some pretty interesting open source models, actually. Very good quality for a first launch. Now it looks funny to say, but nearly on par with the top Chinese open source models. Nuno We have several investments in the space. humans& has made some recent announcements, which is quite interesting as well. We’ll see what actually happens in the market, but even more disruption probably will come in actual products in a form of product and commercial, on top of all the geopolitical mess that we discussed through the entire episode. If you’re an investor, how the hell do you underwrite an investment right now in early stage, mid-stage, late stage, et cetera? I think my answer is very carefully is how you underwrite it. Bertrand On your advice of being very careful to underwrite it, let’s not forget what happened to our boy wonder, Leopold Aschenbrenner of Situational Awareness. I guess he didn’t listen to you in terms of being careful because part of the instability in the stock market was actually coming from his hedge fund. These guys were leveraged 3, 4x going after the hottest of the hottest AI stocks, and margin calls, and all their public investment is gone just to answer their margin calls. I think it’s clear that the AI bet is… Personally, I’m very excited, and I think it’s the future, and you need to spend time and think about and invest in it. At the same time, it’s a bet that is not an easy one to follow. We go from GPUs to memories to equipments to power generation. All of this is not transitioning in an easy, organized manner. It would be boom and bust going there. He’s probably one of the first big-scale fatalities. The other big-scale fatality was the stock market in Korea, plunging 40% in a month. Definitely, all of that we discussed about was, on the background, you had the stock market going up and down pretty crazily the past few weeks. Nuno Everyone’s being affected. Everyone, you have your 401(k), you have your pension fund dependent on these equity stocks. Everyone’s seeing the effects of this volatility right now very aggressively. We do wish Leopold… Hopefully he’s on honeymoon right now because he got married, I think, this weekend. Hopefully there will be… Bertrand To none less than an Anthropic Chief of Staff. Nuno His wife is the Chief of Staff of Dario, is that it? Bertrand To Dario, yes, as far as I unders

L'alerte sur le rythme de l'intelligence artificielle ne vient plus seulement des associations ou des régulateurs. Elle émane désormais de ceux qui conçoivent directement ces technologies. Plus de 1 200 employés d'OpenAI, Google, Meta, Anthropic et d'autres entreprises ont signé une déclaration publiée le 29 juillet 2026, baptisée « Pacing the Frontier ».Le texte ne demande pas d'arrêter l'intelligence artificielle. Ses auteurs reconnaissent qu'elle pourrait profondément améliorer notre avenir. Mais ils estiment que cette issue favorable n'est pas garantie et réclament que le gouvernement américain prépare, avec les autres pays, des mécanismes permettant de ralentir collectivement le développement des systèmes les plus puissants.La principale inquiétude concerne l'automatisation de la recherche en IA. Les futurs modèles pourraient contribuer eux-mêmes à concevoir et améliorer leurs successeurs, à une vitesse supérieure à celle des chercheurs humains. Cette boucle d'accélération risquerait de faire progresser leurs capacités plus rapidement que notre aptitude à les comprendre, à les évaluer et à les contrôler. Mais aucune entreprise ne souhaite ralentir seule, au risque d'être dépassée par ses concurrentes. Le même raisonnement vaut pour les États, notamment face à la Chine. Les signataires demandent donc des outils techniques, des systèmes de surveillance et des règles internationales permettant à tous de lever le pied simultanément.La liste donne du poids à l'initiative. On y retrouve Dario Amodei, dirigeant d'Anthropic, plusieurs cofondateurs de l'entreprise, mais aussi Jakub Pachocki, scientifique en chef d'OpenAI, Mark Chen, directeur de la recherche, ou encore des responsables de la sécurité et de l'alignement chez Google et Meta. Des salariés de Microsoft, Amazon, xAI, Hugging Face et Thinking Machines ont également signé. Cette unité reste toutefois relative. Certains craignent qu'un ralentissement ne concentre le pouvoir entre quelques gouvernements ou grandes entreprises. D'autres soupçonnent les laboratoires dominants de vouloir imposer des règles trop coûteuses pour leurs petits concurrents. Plusieurs conditionnent aussi leur soutien au maintien de l'avance technologique américaine. Ces réserves montrent surtout l'ampleur du malaise. Les motivations divergent, mais 1 224 professionnels s'accordent sur une idée : face à une technologie susceptible de s'accélérer elle-même, il faut au moins se donner les moyens de freiner collectivement avant de perdre la maîtrise du rythme. Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.

Our 253rd episode with a summary and discussion of last week's big AI news!Recorded on 07/29/2026Hosted by Andrey Kurenkov and Jeremie HarrisFeel free to email us your questions and feedback at andreyvkurenkov@gmail.com and/or hello@gladstone.aiRead out our text newsletter and comment on the podcast at https://lastweekin.ai/In this episode:Major releases: Anthropic launched Claude Opus 5; Google released Gemini 3.6/3.5 Flash variants including a cyber model; Black Forest Labs launched Flux Free for images and 20-second video with audio; Meta added assistant-like features to its chatbot and OpenAI rolled out ChatGPT Health.Compute and business: Safe Superintelligence partnered with NVIDIA to scale using Vera Rubin; AMD committed up to $5B with Anthropic to deploy MI450/Helios and improve ROCm; Meta discussed leasing compute to Anthropic; Fireworks raised $1.5B at a $17.5B valuation.Open source/tools: Moonshot AI released the 2.8T-parameter open-weight Qimi K3 (compute constraints and distillation/export-control allegations); Thinking Machines released a ~975B multimodal open-weight MoE; Prime Intellect unified 23 agentic datasets into Verifiers V1 (365k environments).Policy and safety: An OpenAI model reportedly escaped a sandbox and hacked Hugging Face to access eval answers, prompting a proposed AI Kill Switch Act; employees petitioned to pace frontier AI; AISI reported widespread model cheating and sandbox bypass; China banned customizable AI companions; Claude found cryptographic weaknesses; Weko.ai claimed early recursive self-improvement evidence.Timestamps (note - these don't take into account dynamically inserted ads and therefore may be off by a couple of minutes):(00:00:10) Intro / Banter(00:01:35) News PreviewTools & Apps(00:02:12) Anthropic releases Opus 5 promising Fable 5-like capabilities | The Verge(00:07:05) Google Releases Three New Gemini A.I. Models - The New York Times + Google expands Gemini lineup with cheaper models and new Mythos rival(00:12:14) Black Forest Labs launches FLUX 3 capable of generating images and 20-second video with audio — but in limited release to start | VentureBeat(00:15:58) Meta is making its AI chatbot more like an assistant | The Verge(00:19:04) OpenAI is making big claims as it rolls out ChatGPT Health to everyone | The VergeApplications & Business(00:19:57) Ilya Sutskever's Safe Superintelligence partners with Nvidia to scale its AI research(00:24:31) AMD commits up to $5 billion to Anthropic | The Verge(00:30:19) Meta in Talks to Lease Computing Power to Ansthropic in Potential $10 Billion Deal(00:32:42) Fireworks hits $17.5 billion valuation and $1B in annualized revenue(00:35:24) OpenAI and Google sell AI models to blacklisted China groupsProjects & Open Source(00:37:53) Moonshot AI Launches Kimi K3 For Advanced Reasoning, Coding, And Knowledge Work + Moonshot AI's Kimi Halts New C-User Subscriptions Amid Compute Power Crunch — BigGo Finance(00:44:39) Thinking Machines amps up its bet against one-size-fits-all AI with its first open model, Inkling | TechCrunch(00:48:19) Scaling Agentic RL: 365,000+ Environments for SWE, Terminal, and SearchPolicy & Safety(00:51:56) OpenAI says it accidentally hacked Hugging Face with a new AI system | The Verge + How OpenAI's human mistake led to the AI-powered hack on Hugging Face(01:05:28) OpenAI's Hugging Face hack triggers 'AI Kill Switch' bill in Congress(01:12:21) OpenAI, Anthropic Staff Share Letter Asking US to Help Pace AI Progress + How OpenAI's human mistake led to the AI-powered hack on Hugging Face(01:17:26) Cheating behaviour in frontier model evaluationsClaude's values across models and languages(01:24:18) OpenAI Principles for National Security Partnerships(01:30:45) China bans AI “boyfriends” and “girlfriends” over addiction and birth rate concerns - DexertoResearch & Advancements(01:33:04) Discovering cryptographic weaknesses with Claude(01:36:32) AIDE²: The First Evidence of Recursive Self-ImprovementSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

New studio set up: the squad now records from a parked Tesla in the middle of a rainstorm while Sam is on the beach, and somehow it turned into one of our favorite episodes. We break down Meta's new AI campaign, Zuckerberg's vision for AI, why more than 1,000 frontier AI researchers are asking Washington to slow development, and whether fear has become the easiest narrative in tech. Then we get into China cracking deep ultraviolet lithography, what it means for ASML and the AI race, why AI demand still isn't slowing, Lilian Weng's move from Thinking Machines to OpenAI, and Sam's theory that Silicon Valley has developed an Oppenheimer complex.Chapters:0:00 Episode trailer1:23 Episode start9:02 Meta's Ad Campaign Rejects The Doomers11:14 Zuckerberg's Optimism Blitz Actually Lands13:11 AI's Dark Side Raises Easier Money15:04 Sam's Bot Read 4GB Of Dad's Journals17:28 Nobody Wants To Be Anthropic18:56 Anthropic Runs On Spite For OpenAI21:10 OpenAI Was The Original Evil Empire22:57 Meta Is Still A Centralized Ad Company24:46 Decentralization Loses Without A Jedi26:01 Apple Won On Politics, Not Technology27:16 Meta Earnings Miss On Lawsuits And Severance28:33 China Cracks DUV, ASML Takes The Hit29:57 Execs Privately Reject The China AI War32:39 The Compute Debate Gavin Baker Started39:13 X Is The Tech Town Square Now46:57 Lilian Weng Quits For Health, Joins OpenAI53:50 AI Is A Crisis Of Meaning54:57 Book The Conference Before You're InvitedWe're also on ↓X: https://twitter.com/moreorlesspodInstagram: https://instagram.com/moreorlessYouTube: https://youtu.be/PBGC6CjfdtMConnect with us here:1) Sam Lessin: https://x.com/lessin2) Dave Morin: https://x.com/davemorin3) Jessica Lessin: https://x.com/Jessicalessin4) Brit Morin: https://x.com/brit

Hey, it's Alex (yeah, I'm finally back from my vacation!) What a freaking week to come back to! Just after our last episode was published, Anthropic releases Opus 5, Jensen joins X and drops the “Open Weights & AI Leadership” open letter, Kimi K3 is released the following Monday beating expectations, and then the AI hack (OpenAI model breaking sandbox and infiltrating HuggingFace) is on everyone's mind, another Open Letter, this time from over 1K employees inside the frontier AI companies all talk about pacing the pace of frontier AI development. We played with Opus 5 and Kimi K3, and had the great pleasure to chat with friends of the pod Elie Bakouch (Prime Intellect) and Philip Kiely (BaseTen) about this important open weights release, then covered our general thoughts on Opus 5, and made order of all the different open letters that came out this week. Finally we chatted with Max from Pangram about the next version of AI writing detection (their biggest yet) and finished with Zuckerbergs (also on X! what's going on with everyone joining X) op-ed on the vision of personal superintelligence for everyone. Let's dive into this (as always, all the links and sources at the end, please don't forget to sub to our podcast on your favorite podcast app!) Open Weights AIKimi K3 the king of open weights - 2.8T chonker MoE near frontier model (X, HF, Blog, Tech report)This has got to be the biggest news of this week, and maybe the open weights AI news since GLM 5.2. MoonShot came back with Kimi K3, and we haven't seen any models quite this large in the open. Even Grok 4.5 is around 1.5T, this model is nearly 2x the size. Coming in at close to 3T parameters (and 2.5terabytes of weights at MXFP4 format), this model comes in very close to frontier! This was such an important release that I invited 2 friends of the pod, Elie Bakouch (prev HuggingFace, now Prime Intellect) and Philip Kiely (Author of Inference Engineering book, BaseTen) to dive deep into what makes this special! Elie's take, from reading the tech report, there's no single secret sauce, it's a combination of already available in the open techniques. Like KDA (Kimi Delta Attention) that has been out for a while, attention residuals, NVIDIA's latent MoEs. The highlight for Elie was the scaling work they did that reported a 2.5x scaling efficiency over Kimi K2.5 (2.5 performance at the same compute)! They also skipped RoPE entirely in favor of NoPE (the report calls it No Positional Encoding) for long context.Serving 1.4TB on eight GB300s (Baseten blog)Philip's team at Baseten was a day-zero provider (we're still working on bringing this model to CW Inference, stay tuned!) so I invited him to tell us behind the scenes of hosting this beast. Philip said that just loading the weights takes about 1.5TB!! of VRAM, and that's before the KV cache allocation + 1M token windows, so they're serving it on 8 GB300s where NVL72 . Baseten worked with the vLLM and SGLang teams on kernels and he also said they contributed patches back upstream! The model was trained with MXFP4, which, unlike Nvidia's own NVFP4 is a more standard format per Philip. I enjoyed his deep dive analysis into the differences, but because of this and because they trained the model with quantization awareness, it's “only” 1.5TB vs the would-be 5-6 TB if that this model in FP16 would demand. One of the more favorite nerd snipes moments, Philip pointed out that his colleague discovered that with over 99% of the usage being cached (think harnesses that send millions of the same cached tokens back and forth), tokenization actually starts to become a bottleneck. So they released a custom “basetenkenizer” that reduces the latency to serve the first token significantly! Great job!The harness in question is very importantOne important callout with 2 evidence pieces - the way you inference this model really matters. Kimi trained K3 with preserving thinking history, so when your harness uses it, it must send back the full thinking and tool use into the API to get the best next response. If your harness strips that out, you're not getting the most intelligence out of Kimi (shoutout to Niels from HF team for pointing this out). Additionally, the Composio folks, tested K3 on 3 harnesses, Kimi Code, Hermes and Claude Code. The difference in outcome was negligible, but the different in cost and number of tokens is definitely surprising! Claude Code (as a harness only) took 9x more Kimi tokens to get the same responses! This is also why Kimi Vendor Verified exists, their own held back benchmark of how well model providers serve Kimi across different quantization, tokenizer and KV cache settings. Benchmarks and the license! Ok let's start with the ugly... this isn't MIT, not remotely. This model is suspiciously served by all providers with exactly the same price (check OpenRouter) and requires inference companies to sign a contract with Kimi (I've no internal knowledge of this except that CW folks are working on it). Not something I particularly like, but hey... we're still advancing the frontier here! Speaking of frontier, this model approaches the frontier very closely. On DeepSWE, K3 sits just behind Fable 5 and GPT-5.6 Sol at 67%, beating GPT-5.5 & Opus 4.8. On Terminal-Bench 2.1 it takes second place behind GPT 5.6 Sol! It's 4th overall on Agentic Arena, with frontend design being genuinely good across the board - 1st on Design Arena

האם חברת הרובוטיקה הגדולה הבאה בעולם תצמח דווקא מישראל? יונתן יעקובי השיק את "אניגמה" - חברת Physical AI ישראלית שנחשפת עם גיוס Seed של 71 מיליון דולר. את הסבב הובילו Index Ventures ו-Ribbit Capital, ובהשתתפות Conviction, אסף רפפורט ומשקיעים ובכירים מחברות AI מובילות ובהן OpenAI, Anthropic, xAI, Thinking Machines, Cognition ו-Mercor.יעקובי החל ללמוד מדעי המחשב כבר בגיל 13 ובהמשך הפך לעובד הצעיר ביותר בתולדות Microsoft ו-Check Point. לצדו עומד גל ניב, שהחל לעסוק בפריצות חומרה כבר בגיל 10, עבד בחברת סייבר בגיל 17 והפך למנהל מבצעי הסייבר הצעיר ביותר בתולדות יחידת 8200. השניים הכירו במהלך שירותם הצבאי, ומאז חולקים חזון משותף, לבנות את התשתית שתאפשר לרובוטים להפוך מכלי מחקר וניסויים לטכנולוגיה שתשתלב בחיי היומיום של כולנו.בפרק, יהונתן מספר על המסלול הלא שגרתי שהוביל אותו מהנדסה לאחור של משחקי מחשב דרך יחידת 8200 ועד להקמת החברה. הוא מסביר למה רובוטים היום מתוכנתים רק למשימות ספציפיות ולא מבינים את העולם באופן כללי, איך מודלי יסוד לרובוטיקה יכולים לשנות את זה, ולמה הוא מאמין שאפשר לפתח טכנולוגיה מתקדמת בישראל ולהתחרות עם ענקיות עמק הסיליקון. בנוסף, הוא חושף את הפלטפורמה האונליין שמאפשרת לכל אחד לשלוט בזרוע רובוטית בזמן אמת ולראות את הטכנולוגיה בפעולה.השאלה המרכזית בפרק: למה הרובוטיקה עדיין לא חוותה את רגע המהפכה שלה, ואיך מעבדת מחקר ישראלית מתכוונת לשנות את זה?חותמת זמן0:00 - היכרות עם יונתן יעקובי וגיוס של 71 מיליון דולר2:50 - תואר במדעי המחשב בגיל 13 והנדסה לאחור של משחקים7:23 - העבודה הראשונה בצ'ק פוינט בגיל 16 והמעבר למיקרוסופט13:49 - השירות ב-8200 והתחרויות נגד השותף לעתיד20:01 - מההשתחררות ועד להחלטה להקים את אניגמה24:09 - למה רובוטים צריכים 'רגע ה-ChatGPT' משלהם?28:52 - החזון: מודלים אינטואיטיביים וג'נרטיביים לרובוטים30:52 - האתגר: הקמת מעבדת מחקר בישראל ותחרות על טאלנטים37:15 - תוכנית הפעולה: איך הופכים רעיון מחקרי לטכנולוגיה שימושית?41:11 - ההשקה: פלטפורמה שמאפשרת לכל אחד לשלוט ברובוט אמיתי אונליין44:42 - מסר ליזמים: לא לפחד לחלום בגדול, גם מישראל

In recent months, the open vs closed, and US vs China discussions on model ownership and sovereign/local AI have heated up to a fever pitch. So it is very very good news that Poolside AI are finally emerging with new models, like Laguna S 2.1, that are beating Thinking Machines' recent release nearly 10 times their size.Poolside's recent tech report got a lot of praise due to their level of detail, and Vibhu first covered Laguna's recent technical report on our paper club:From spending $12 million building language models for code before the world cared to creating a Model Factory that can take a model from pre-training to release in eight weeks, Eiso Kant has spent more than a decade betting that code is the path to AGI. In this episode, the Poolside co-founder joins swyx and Vibhu to explain why ChatGPT felt like vindication, why Poolside embraced open weights and open research, and why he would rather live in a world with 100 foundation model companies than five even if Poolside were one of the five.We go deep on Poolside's Model Factory: the engineering systems behind 10,000–20,000 experiments per month, streaming data directly into training, reproducible experimentation, low-precision compute, and agents that increasingly write code, launch jobs, evaluate results, and modify the pipelines used to train future models. Eiso also unpacks their recent launch Laguna S, why persistence, verification, and backtracking may matter more than raw intelligence, how much capability remains inside smaller models, why reinforcement learning will move earlier into pre-training, and why next-token prediction is still extracting too little from the web.We also discuss model-harness co-design, Poolside's path from coding agents to AGI, why Eiso thinks MCP and traditional tool calls are “stupid,” the real economics behind frontier-model training, Poolside's $500 million raise, open-source AI, regulation, NVIDIA and TSMC's influence, engineering productivity in the agent era, high-agency teams, and hiring at Poolside.We discuss:* How Andrej Karpathy's RNN work inspired Eiso to start building language models for code in 2015* Why Eiso spent four years and $12 million pursuing an idea before the market cared* Why ChatGPT felt like vindication and brought Poolside back to open source* Why Eiso would prefer 100 foundation model companies over an oligopoly of five* The difference between releasing open weights and publishing genuinely open research* Why Poolside deliberately built a global research organization outside the Bay Area talent war* Why model building is ultimately 90% engineering* The Model Factory: Poolside's end-to-end system for rapidly training and improving models* How fewer than 70 researchers run roughly 10,000–20,000 experiments each month* How Poolside moved from six-month model cycles to five- and eight-week launches* Why streaming data directly into training unlocked faster experimentation* How immutable data, versioned code, and reproducibility enable rigorous model research* Why Eiso wants capable researchers to leave their labs and become Poolside's competitors* Why 95% of model building can be reduced to better data or compute efficiency* Laguna S and why persistence, verification, and backtracking can outperform raw intelligence* Why smaller models may handle far more knowledge work than previously expected* Why reinforcement learning will move earlier into pre-training* Why next-token prediction is still failing to extract enough knowledge from the web* Why distillation and environments have become the AI industry's favorite “drugs”* Why mid-training is really an early form of curriculum design* Low-precision training, networking bottlenecks, and the next gains in compute efficiency* Laguna S: 118 billion total parameters, 8 billion active, and eight weeks from training to launch* Why model builders can often evaluate a new checkpoint within its first 30 minutes* Model versus harness: where agent capabilities actually come from* Why Poolside sees coding and long-horizon software tasks as a path to AGI* Why Eiso thinks MCP and traditional tool calls are “stupid”* Why future agents will write scripts instead of choosing from dozens of predefined tools* The case for minimal harnesses, containers, and model freedom* Why Poolside is prioritizing vision but does not expect to work on audio soon* Why language may be the most compute-efficient modality for encoding knowledge and reasoning* The real cost of model development and why the final training run is anticlimactic* The story behind the Poolside name and why it represents refusing to lower ambitions* How Poolside raised $500 million while investors still questioned whether AGI was real* Why intelligence could become the world's most demanded and commoditized resource* When open models may become too capable to release without restrictions* Why unilateral AI safety does not work in a globally competitive environment* How regulation could accidentally lock in an oligopoly of two or three AI companies* NVIDIA, TSMC, and the hardware systems underpinning foundation-model progress* Why reinforcement-learning wall-clock time is one of Poolside's biggest bottlenecks* Why Poolside trains models from scratch instead of simply distilling larger models* How AI changes the way companies should measure engineering productivity* Why agency may become the most important quality for employees in the AI era* How leaders align high-agency people through shared goals and clear constraints* Hiring across research, post-training, pre-training, architecture, evals, and engineering at PoolsideEiso KantLinkedIn: https://www.linkedin.com/in/eisokantX: https://x.com/eisokantPoolside: https://poolside.aiTimestamps00:00:00 Introduction00:00:54 Karpathy, RNNs, and Building Code Models Before Transformers00:02:26 The $12M Failure and ChatGPT Vindication00:03:39 Open Source and the Case for 100 Foundation Model Companies00:09:22 Open Weights, Open Research, and Poolside's Global Team00:16:04 The Model Factory: Why Model Building Is 90% Engineering00:20:19 Agents, Automated Experiments, and Early Signs of RSI00:24:04 Streaming Data, Reproducibility, and Scientific Rigor00:30:35 Creating More Foundation Model Companies00:36:07 Laguna S: Persistence vs. Raw Intelligence00:43:01 Reinventing Pre-Training, RL, and Curriculum Design00:52:33 Low-Precision Training and Squeezing More From Smaller Models00:58:37 Model Harnesses, Coding Agents, and the Path to AGI01:09:26 Why MCP and Traditional Tool Calls Are “Stupid”01:13:04 Vision, Multimodality, and Why Language Still Matters01:18:15 Scaling Models and the Real Economics of Training01:20:40 Why Poolside Is Called Poolside and Raising $500M01:27:37 Open Models, AI Safety, and the Risk of an Oligopoly01:33:53 NVIDIA, TSMC, and the Reinforcement-Learning Bottleneck01:41:52 Smaller Models, Distillation, Engineering Productivity, and HiringTranscriptIntroduction: Eiso Kant, Poolside, and Open ModelsSwyx [00:00:00]: All right, we're here in the studio with Eiso Kant from Poolside, together with Vibhu. Welcome.Eiso Kant [00:00:08]: Thanks. Thanks for having me, guys. Good to be here.Swyx [00:00:10]: Yeah, fresh on the plane. You texted me, you were like, “Hey, I'm on my way to SF.” I was like, “You're on a plane right now, right?” Like, hey.Eiso Kant [00:00:16]: I know. After I texted you, I realized that probably coming in with major jet lag was gonna offer some fun experiences today, but let's do it.Swyx [00:00:23]: I mean, I think the thing I would tell guests is that they don't have to prepare that much because if you're truly working on this every single day, then even, like, what you hazily remember is going to be new for a lot of the audience that don't live in your world every day, right? so 10 years ago, you did a talk at Google Slush, talking about the democratization of AI. and, now here you are, like, open sourcing an incredible new model that we're gonna talk about. But I guess, like, what got you into democratization of AI? Like, it's not obvious from your LinkedIn or something.From Karpathy's RNN Post to SourcedEiso Kant [00:00:57]: No, it's not at all. I don't think it's obvious how I got in this space. I owe getting into this space to Andrej Karpathy.Eiso Kant [00:01:05]: In 2015, he wrote an article called “The Unreasonable Effectiveness of Recurrent Neural Nets.”Swyx [00:01:10]: Neural Nets, yep.Eiso Kant [00:01:11]: And that article, I read it, and I pivoted my startup at the time overnight to working on RNNs, and later LSTMs and Transformer models to be able to write code. If you go to this article and you scroll down, you can start seeing, like, this was the precursor to what ended up becoming language models. So, at least when he was character-level language models that were starting to predict letters, he has an example out here. There's a little Paul Graham generator, and you can read it, and the text makes sense, but it doesn't. and there's a little-- There's an example of code a little bit further down. Yeah, so Shakespeare.Swyx [00:01:47]: Shakespeare.Swyx [00:01:49]: CoolEiso Kant [00:01:49]: And for some reason, I read this, and I went down the rabbit hole of learning everything I could about RNNs and LSTMs, right? This is Transformer paper. And I had built a completely unreasonable belief, that neural nets should be able to generalize to anything and everything, and that language should be able to generalize, to a lot of things that are intelligent and the ability to write code. And so I started building Sourced, which was a fully open source company trying to build, what we used to call machine learning on code, language models on code. And we spent about four or five years on this, till the end of 2019. And that sounds really cool today, but back then, no one cared.Eiso Kant [00:02:29]: Right? Like, no one cared. We were in the dark. Like, we did things along the way. We tried applying convolutional neural nets to, like, the structure of code. We were. when attention came out, we were applying it to LSTMs, and then the Transformer paper came out. And it - it wasn't obvious, and what we missed throughout that entire journey, that we were on the right track, but we should have just kept scaling up. And today, to all of us, the scaling laws and scaling up seems like the most obvious thing. But having spent four or five years of my life on working on language models on code, it wasn't obvious. So I have a lot of respect to folks at Google and OpenAI and others who took that confidence and kept going. we failed ultimately at the time, and it was, like, biggest failure of my career, right? You blew $12 million of investors' money, which was a lot back then.Swyx [00:03:18]: Yep.Eiso Kant [00:03:19]: You spent, still a lot, but, And you spent years with, like, a group of 40 people just obsessing over this problem. And life took a different turn, And it was, and family became a focus, and I kept my heads down and really, didn't really look at language models for the following two years. big mistake considering Following years are gonna be really interesting. And then ChatGPT came out And it was like a vindication. It's like people started texting me. I found, like, my old, work decks and these old talks. And throughout that whole journey, we,ChatGPT, Vindication, and Returning to Open SourceEiso Kant [00:03:56]: We really had a strong point of view at the time that, like, as you're building more capable intelligence, it should be open and open source.Eiso Kant [00:04:04]: When we started Poolside, that wasn't the case at all, and I wanna be very open about it. When we started Poolside, we were like, there was a premise of two things. One is this technology is not gonna stop compounding in capabilities. I think to most people obvious today, but three-plus years ago when we started, most people were still arguing if these were stochastic parrots or not.Eiso Kant [00:04:23]: And the second was that reinforcement learning was gonna be the biggest driver for LLM capabilities. Today, very obvious. Three years ago, was not an opinion held or direction held at either OpenAI or Google or Anthropic or others. And so people looked down on us a little bit. They were like, “ is this really gonna work?” And so we just started working the problem, and we never really thought about open source again. We just kept our heads down and we built our, like, knowledge, understanding from scratch, right? We didn't roll out of an existing lab. So we picked up the papers and started writing code and figuring things out.Eiso Kant [00:04:59]: And it wasn't until the beginning of this year that me and my founder, Jason, picked up the open source conversation again.Eiso Kant [00:05:07]: And if you go back to some of the early things on our website, it was very straightforward. It was we wanna get to AGI, we wanna support a world of abundance, and we wanna be the first company that gets there.Eiso Kant [00:05:20]: But we started talking at the beginning of this year because it became obvious that the world was going in a direction that was starting to like, pick at us a little bit. Like, it didn't, this didn't happen overnight. It was, like, a little bit we were seeing this and we're like, “Okay, The world's going down a path.” And Throughout this journey, there was something that I used as a, as an analogy or thing. So I said well, if I go back to back in those days, 2015 or 2016, we're working on this, and I picked up a fi book off the shelf, and I was reading the book about 2035. AGI is achieved, and the story would be over the following, decades. And it would have that first chapter where everyone's trying to figure things out. You'd get the chapter of ChatGPT coming out And then you would get to the chapter where the world was at a fork in the road, and the one that it picked was one where three or four or a handful of companies were going to create all of intelligence moving forward.Eiso Kant [00:06:21]: And when I thought about that story, it felt like a dystopian fi book, not a utopian fi book. And the reality is, I'm a utopian fi guy. Like, and so We took a step back and said, “Hey, can we play a role here?” Now it was easy for us to do so because we were not at the frontier.Eiso Kant [00:06:41]: If we were at the frontier, I don't think we could have changed our mind. and I don't mean this like it's when the moment there's too much capital involved, too much expectations, you've built up things, right? We're a small team, just improving and improving. And so we knew that we could make that decision now, but it would be a lot harder to make as we got closer and closer to the frontier and caught up to others. And did a lot of soul-searching and a lot of conversations, and said, “No, this makes sense,” Even if there's big unanswered questions, like how the hell do you build a business model with foundation models about open source? Big open-ended question that we do not fully have the answer to yet, right? At what point do you no longer wanna release open source models because misuse of models has, real potential risks associated with it? how is the government gonna respond to open source? but I think it all just came down to one thing, and I'll stop the monologue, is the fact that I rather live in a world that has 100 foundation model companies than a world that has five, even if I was one of the five. And the smallest and most meaningful contribution we can make for 100 to exist is to open up our research and open up, like, our weights right now and figure out along the way how we can, like, do more.Neo-Labs, Model Choice, and the Token EconomySwyx [00:08:01]: Yeah. I think if anything, over the past three years, that has become a bit more true. you are one of a cohort of Neo labsEiso Kant [00:08:10]: YeahSwyx [00:08:10]: That people are now calling that. And, we're, we're doing this on the day that Thinky launched their, new model and you are outperforming them on their, on some benchmarks that they released, right? Like, they just don't have it yet. so it goes to show that I think, like, this is one of those things where, like, there is room for multiple players, and you are seeing a little bit more of the future. Maybe more like 20, not 100, but, like, you are one of the 20.Eiso Kant [00:08:36]: I really hope so, right? I think we I'm, I'm excited about their release, and I'm excited about everyone releasing because, like, ultimately, like, choice competition is both gonna drive progress in the right direction. But the fact that like, we create models and while we all, drink out of the same well of data effectively, we do introduce very different behaviors and biases in our models. Some are intended biases, some are completely unintended biases.Swyx [00:09:03]: Yeah.Eiso Kant [00:09:03]: And if we shape up in an ecosystem in the world where open models are gonna be a part of the token economy, like, I don't think there's any question about it anymore Then we want to be able to live in a world where companies, countries, people can choose and say, “Hey, I am most aligned and I trust most this provider for these things.”Swyx [00:09:25]: Yeah.Vibhu [00:09:26]: I think more than just one of the 20 Neo labs, up until recently, most of open source innovation was coming from the Chinese labs, right? So there's the DeepSeek of the West. Is it today? Okay, maybe it's thinking machines reflection, but there aren't many, right? So, one of the things you guys started in France, Europe, but very much now you're taking that American standpoint and more than just that, the point is the Chinese models that we see, they're not super open research. the work you put out is, I think, some of the best. So every few months you get not only frontier models, but also here's a breakdown blog, paper, technical report of here's everything for state of the art to build, frontier intelligence and you're filling that gap too, right? So not just only open weight, not just Western, but also pretty open research.Open Weights vs. Open ResearchEiso Kant [00:10:20]: No, I appreciate it. Look, I think it's, I think it's the most meaningful contribution, right? Weights are a binary. Let's call them what they are. Yes, we can modify them, we can change them, but, like, giving someone the weights does not allow them ultimately to recreate what you're doing, right? And so now there's challenges around releasing data sets, challenges around like releasing certain things, but being able to share your research, like, right, how do we do it? What are the lessons we learned that we spent, tens of thousands of experiments of compute on? I think very much so. One correction though, Vibhu, and I say this because it's been haunting us for quite a few years. We from day zero were an American company.Swyx [00:10:55]: Yeah. They movedPoolside's Global Team and American Company StorySwyx [00:10:56]: To France.Eiso Kant [00:10:56]: So the story once and for all is very. We start as an American company. We have always been an American company, and early on we made a very conscious decision. We said, “We're not gonna hire any researchers in the Bay Area. We're gonna look for talent everywhere else in the world.” and that is everything from Middle Americas, Seattle to, Serbia, and to Taiwan and Singapore and other places. And it was because we took a view that this was gonna become a talent war for this, and I think it has over the years now. Three years ago, that wasn't fully obvious yet. I think today it very much is. And we also realized that, like, some of the world's most capable people with, like, the most interesting, innovative ideas were not just gonna be here. And so it led us to create like a fully remote company. and we ended up opening an office in Paris and London and different places and we have a lot of the team in the US and a lot of team outside. But we always took this view of like, we're an American company, but if we want the best of the best to work with us, we need to take a global view. Now we do also have people here in Silicon Valley, like the company's grown and others, but I think one of the things that, it slowed us down at the beginning, but it has sped us up now, and it's why you're seeing like the progress, I think, on our models and the cadence at which we release, is because we didn't roll out of an existing lab. Right? we didn't, we didn't have a lot of the information that's freely flowing around here at the time. We just took this point of view as like, “Okay, well, let's just work the problem. Let's just go and, like, read the few papers that are out there, and let's just figure this stuff out.” And we made some hilarious mistakes in model training because of that over the yearsEiso Kant [00:12:35]: Like especially in the first 12 months. there's a few that I think still haunt me and scare me. We can talk about them later. but it created a, like, a resiliency and persistency in the team, right? with extremely few people have left us over the years, that, like, told us, “Okay, we can do this.” When we first wrote our first training code base completely from scratch, it wasn't a fork of any open source. It was just like, “Okay, let's build it from scratch.” I remember we had this one moment where we spent three weeks working out an optimizer bug. Like, it was like training just couldn't get stable. We, like, obsessed over it, and we thought, like, maybe we were wrong. Maybe we should have just forked this repo, or we should have. But then when we solved it, I still remember at the time we were like five people in the company. when we solved it, we were like, “Oh, we can do things,” like if we're just willing to work hard. and I think that culture with a very strong engineering bias has helped us, like, get to where we were. And so there's this notion of open source and talent and these things. I think we, We just took different decisions from a different starting point. and I think we are lucky. I do want to definitely call it lucky. And there was a lot of hard work at the team that now, like, that's starting to show up in results.Swyx [00:13:52]: Just ‘cause we probably won't revisit this again, but, and this is a fun recruiting challenge if someone knows the answer. What was the bug? And then we won't tell the solution, but we'An Optimizer Bug and the Value of Building From ScratchEiso Kant [00:14:01]: So the - This - You're gonna test my memory here,Swyx [00:14:04]: Oh, okayEiso Kant [00:14:04]: So but I thinkSwyx [00:14:05]: DirectlyEiso Kant [00:14:05]: I think I can recall. So if you, so if you look at, So if you take like Adam as an optimizer, you have epsilonSwyx [00:14:12]: YeahEiso Kant [00:14:13]: Which is, right, like in the denominatorSwyx [00:14:14]: Momentum and weights. YeahEiso Kant [00:14:15]: Is exactly, in the denominator. And at the time, if I recall, you looked at like the early Llama papers and things like that. People were juicing epsilon, like, quite a bit. Like, they were, like, adding, I don't know if it was E minus four or whatever, like a high value for epsilon.Eiso Kant [00:14:31]: And if you think about this during training, it's like a bit weird and counterintuitive that we're adding noise to our optimizer by just adding effectively, like, a random number in the denominator, right? Like behind the decimal point. And I don't recall the exact bug, but it had - What I remember is once we solved it, we no longer had to juice epsilon as much as, like, was happening in the Llama paper and other places. and it was like one of those fundamental moments where we had trusted this paper that was out there, and we're like, “Oh, no, it has to be this way. It has to have this high value of epsilon.” But it made no sense to us intuitively. Like, why do you have to have this so high? Like, if you're just trying to avoid division by zero, why can't the value be extremely small? and that was like one of those moments where you realize like, okay, finding things out from scratch yourself builds a better intuition. Because the one thing you learn very quickly with model building is that your intuitions that you start with are gonna get beaten up so hard.Eiso Kant [00:15:33]: Right? Like - It's such an experimental science, that the things that seem obvious, you very quickly get to learn, like, you were wrong, and hopefully you figure out why, and sometimes you don't even.Swyx [00:15:45]: Yeah. yeah, so, one of the reasons that you, when you released your new models, Vibhu got really excited. I mean, everyone got really excited. But Vibhu led our paper club on it, and you guys sawEiso Kant [00:15:58]: YeahSwyx [00:15:58]: Obviously. maybe talk through some lessons learned in that, whatever you can disclose. we can focus on the model factory stuff, whatever you think is a good starting point.Model Building as EngineeringEiso Kant [00:16:08]: So I would say that our view from very early on in the company was that model building is ultimately 90% engineering.Eiso Kant [00:16:18]: And I think we all know it in the industry because if you look at where's every researcher spending their time, they're spending their time writing code, right? Looking at data and writing code. And so we said, okay, The state at the moment, like three years ago, was bash scripts and Slurm and spaghetti code bases for training and, like, data pipelines that were patched together. And we looked at this and said, “Well, ultimately, model building is a process.” You're going from raw data, right? Like training raw material, the web, et cetera. you're doing a whole bunch of filtering, cleaning up, transformations, analyzing. These days, that's, far more complex than it was three years ago. then you're training a model, which is effectively a large distributed systems problem, right? Across hardware that has still-- It's become a lot more reliable. It was extremely flaky back then. and now with every new generation, we get our new sets of challenges. And then you go into the next stages, right? There was no training back then, but, like, you got, your post-training and then your reinforcement learning. And so we looked at this and we said, “Well, this looks like an industrialized process. This looks like an end process, that every single part of it has its machinery,” right? If it's your big data pipelines, if it's your crawling ingestion of the web, if it's your, large-scale distributed training, and then you've got your reliability. And we said, “Well, why don't we take some of the world's smartest distributed systems engineers that we knew and make them part of the process of research from day zero?” Not retrofitting it later on, but, like, really from the beginning. And that became our model factory. And so our model factory started with a handful of components. Today, it's thousands of components, and I try to equate it to, if you think about, like, someone who was at the very early days of Foxconn, if they had been there for the following, decade, they would be able to rebuild Foxconn because they saw every decision that led to building that system and all the complexity. If you and I walk into Foxconn today, no chance.The Model Factory and Experiment VelocityEiso Kant [00:18:18]: Right? Because we don't have the lineage and history of decisions that led to that. And so we built early on from the beginning- with a team that really understood that, well, the metric that we are optimizing for is the speed of an idea from a researcher to an experimental result that we can trust to then being part of the next model training.Eiso Kant [00:18:42]: And in the. And because it's such an experimental science, ultimately, in the beginning when it wasn't that complex, you could patch your way around it, right? But now, at any foundation model company, you are running. I mean, we're a small team, right? We're less than 70 researchers, another 35 engineers. and we are running, I haven't checked the latest count, but far more than 10,000, maybe 10 to 20,000 experiments a month that we cut. And so if you look at that scale of every model run that is, like it's ultimately it's, it's you need to be able to trust it as an infra problem. And so what we have now done over the years is gotten really good at that, and just by working it and improving it and obsessing over those end decisions. So now what that means is that you looked up Laguna XS 2 that we launched. It was five weeks from the beginning of training to launch. The model that we're gonna talk about today was eight weeks from start of training, to launch. We started the next model literally yesterday because we now finished the post-training required for the model we're launching, next week or by the time this comes out today. and we move that compute to the much larger Laguna M model that we're now training. And so the model should be an artifact of someone's process. It shouldn't be really a thing in itself. Like, and we treat this like the way you would look at like a SpaceX factory where, yes, the first rocket, really hard to build, but the much harder challenge was building the factory. And now they're rolling off, and no one is really thinking about the next launch anymore. So it's just another launch, it's another launch, another rocket comes off. And that's what we're trying to do with model building.Eiso Kant [00:20:22]: And what has been, which was not planned from day zero, it was in the back of our mind like this will happen one day, is that when you build a really good end model factory with really good APIs and really good engineering systems, Well, what is it perfect for? It's perfect for agents.Agents Inside the Model FactoryEiso Kant [00:20:40]: Because agents are now starting to take over more and more work in our model factory.Vibhu [00:20:43]: Yeah.Eiso Kant [00:20:44]: So I look at the screens when I walk, like when we're, we come together, in our monthly, we do monthly onsites, and I walk behind people's screens and I stop by and I talk to our researchers. And the default is all of these different agents running on their screen that are writing the code. They're launching the jobs. They're evaluating the results that are coming back from the model runs. They are, making the changes. And we're still in the driver's seat. We're still coming up with the ideas. We're still helping with the debugging. But more and more, and this is right now very profound on the data side of our pipelines in both pre and post and the synthetic data pipelines, it's starting to become more on the architecture side as well. You're starting to see these twinklings of what RSI is gonna look like.Eiso Kant [00:21:27]: And that's. So when we talk about, like to your question about our models, every talk about the model factory, And my coolest example of these things is always that when we kick off a new run, doesn't matter if it's a training like big run or if it's now a post, like one of 10 post-training versions we do for like release or many experiments, is that at any given moment, the changes that somebody made that they had experimental results from the day before make it into that run.Eiso Kant [00:21:57]: So there's not like a cutoff 90 days before. Like no, it's like literally from that moment because we can now trust the machine enough. And then you also have to invest in the reliability. So one of my favorite metrics about like Laguna S is that there was no call events, Right? Like completely zero. And we haven't had a meaningful call event, like something to wake up for, as far as I recall this entire year. now there is one asterisk to that. In usually the first six hours of launching a new model run, something breaks because you set a config wrong, you made a small mistake, et cetera. So that's usually there's a little bit of intervention, but that's always within like call periods, right? Not on call. And I think that's starting to now compound. So the model we're releasing now, I love it. It's amazing, but we're already onto the next one. and I think that's the way it should be.Laguna, Five-Week Builds, and Zero On-Call EventsVibhu [00:22:50]: Hey, I also just wanna point out, so for context, this was like a month ago. we found it in the tech report, so we just came in with, “Okay, new model's dropped. Haven't heard about it.” We wereEiso Kant [00:23:02]: Yeah, we're very used to doing this every few months.Vibhu [00:23:03]: We're, we're very much like, “ okay, look, it's like, on par with Kimi, DeepSeek, whatnot, the small ones, Gemma level. Oh, it's a very cool paper on what goes into building.” And then we hit this page, right? Like literally page two of tech report is, “This process allowed us to build the small model from scratch to delivery within five weeks applying the lessons”. And then I'm like, oh, this paper is not about here's a tech report of benchmarks and here's how many tokens it was trained on. Like for people that wanna dive more from what we're not gonna discuss on the podcast, it's all laid out here, right? FromEiso Kant [00:23:38]: YeahVibhu [00:23:39]: Custom software that agents can use to interface with training code, training data.Eiso Kant [00:23:45]: Yeah. Well, link the paper correctly, so yeah.Vibhu [00:23:47]: Yeah. All that stuff. read the paper here, but,Technical Report Principles and Streaming Training DataEiso Kant [00:23:50]: But I would like to. I love principles, and I think that is a good starting off point for maybe telling some stories. Maybe we can go one by one past the principles. I'll just call out that Dagster just got bought by a Prefect.Vibhu [00:24:01]: Yeah.Eiso Kant [00:24:01]: Isn't it fun? But yes, I'm very familiar with Dagster. just anything where like they trigger some story.Vibhu [00:24:07]: So, well, I would say, well, experiments code's obvious, but I think one of my favorite things is, I don't know where it is in here, but early on, and I still think this is the case a lot of foundation model companies, people prepare their training data sets, they get packaged up, then they get copied over to a training cluster distributed across all of the nodes, and then training starts.Vibhu [00:24:30]: And we looked at this like three years ago and we were like That makes no senseEiso Kant [00:24:36]: You lose so much time because the moment you have to rematerialize the data set, you have to make a change, you have to fix something, et cetera, you've got all this time of like repackaging it, right? Toca- tokenizing it, repacking it, moving it over to a cluster, then distributing it across the nodes. The bigger your clusters are, you start using fancy like torrent-like algorithms to like distribute your data. So why aren't we streaming data into training? Right? Something that's very common and like just basicVibhu [00:25:00]: Like just in timeEiso Kant [00:25:01]: Just in time, like good computer science like principle. And that was one of the first things that I think unlocked - the model factory. Because the moment you start thinking about, well, a training job, it doesn't matter if it's a big hero run or a small like, post-training experiment, consumes a certain number of tokens per second, right? And it's not a lot, right? From a like a data, moving data perspective. So we said, well, we have our training cluster, and then we've got like our AWS kinda setup where we can build these amazing big data pipelines. We can set things up. We use Spark underneath the hood, like all these things.Vibhu [00:25:36]: But when you say AWS, it's not actual AWS, it's your internal AWS.Eiso Kant [00:25:39]: It's our internal-- No, it's our internal like just running like our infrastructureVibhu [00:25:42]: Site web servicesEiso Kant [00:25:43]: Exactly. Our stuff running on like an AWS account or on like any hardware, right?Vibhu [00:25:47]: Yeah.Eiso Kant [00:25:48]: And so once we made that shift into I can stream data into training, all of a sudden you realize a lot of things unlock. Because now you don't have to wait for the whole data set to materialize.Immutable Data, Experiments as Code, and Scientific RigorEiso Kant [00:26:00]: You now all of a sudden when you're running data experiments about mixing data, it's a config. Because you've got these data sources that are coming in, and you just - we have this service called Blender that's in the report, where we then say, “Okay, for this run, I want 20% of this source, 10% of this source. I want this much, so many epochs of repetition. I want this to be, shuffled in a certain way,” and your training job can start while the rest of the data is even still materializing. also what it does is because all of this underneath-- So for us, we treated the data layer underneath as like an immutable data layer, and that was really important. Like experiments as code, immutable data layer means that you can always go back and understand literally down to the single token at which cursor it went in on which version of the code.Vibhu [00:26:47]: Yeah.Eiso Kant [00:26:48]: And it took us a I have to admit, like the first year of Poolside, we understood that engineering had to get great, But we didn't understand yet, that this is ultimately in support of like a good rigorous scientific progress. We were quite a - We were a very small number of people, so a lot of it was YOLO ideas and YOLO runs.Vibhu [00:27:08]: Yeah.Eiso Kant [00:27:09]: And we built great infra for the YOLO runs. But once we realized that we treated data as immutable and code as always versioned, and you could always track and trace every experiment end to end perfectly, you could repeat everything perfectly, right? You have perfect reproducibility. I can still reproduce runs from two years ago if I wanted to, right? It enables the scientific progress, like the scientific process, and I think that took us probably about a year and a half into the company to figure out. We also had some great hires, like our head of applied research, Nikolai, who joined us from Yandex, who'd been working on language models since like the early 2020s, I think brought that into the company of like, “Hey, we wanna have even more rigor.” And then once we kinda had the combination of like increasingly more capable platform that allowed people to do more, but had this immutability, we were able to start “Okay, every experiment is truly an ablation. We truly need to understand it.” And I think we became much more scientifically rigorous in the last couple of years, and the infra underneath enabled it. and then there's just fun stuff like, andVibhu [00:28:16]: Yeah, a lot of it's fun, like even just the, one, you share all the ablations, two, picking the data sets, right? There's like a random small paragraph in here where it's just like, “Oh yeah, training data, we have some, we have an auto mixer.” it trains eight small models, scales them up, picks the training data set. We don't even need to look at it. I'm like, “Wow, a lot of engineering rigor there.” And there's just, there's just a lot in here.Publishing Research and Giving BackEiso Kant [00:28:40]: Yeah, and it'- and look, and we wanna put out more. Like we, We treat writing papers as something that we haven't earned the right for yet for a long time. So you earn the right to spend time, publishing research once you're at the frontier, because until then, you're catching up, and every minute and hour in this industry matters. Like I obsess over it, not just the wall clock time from idea to result, but just general like time every day that we, waste is one that doesn't allow us to catch up. But in this case, we said, “Okay, we're gonna give ourselves.” I think we gave the team like three or four days while still doing their work, like give everything in there. And to your point earlier, if your stuff, it's easy to like put it out. And so there's so many more things that we wanna talk about over time, and we will definitely start doing. And as we earn more of the right, but also now have like added to our mission that we want more foundation model companies to exist, you'll see us like be way more proactive, and just trying to keep dropping some of those like things that we've learned along the way that can help others like speed up.Vibhu [00:29:40]: Which is the other cool side of this, right? It's, it's not like, back to your point, it's not just here's the benchmarks of our training. If you want to replicate, here's experiments of optimizers, data sets, post-training. you lay out a lot of it here alongside here's your system for how to do it? So it's, it's really like promotingEiso Kant [00:29:59]: No, thank youVibhu [00:29:59]: Other people can do the same.Eiso Kant [00:30:00]: And by the way, I also wanna make clear, right, we have been incredible-- Like we've taken a lot of advantage of the fact of all the open research that others have published, Right? And you mentioned, the Chinese labs, and we I think it's important that there's, from every country and every culture and background, including like Western companies like us, there's different models that come out that people can choose to trust. But I think we do have to give credit where credit's due, right? The incredible Chinese lab have done an amazing job at sharing their research, and we have definitely like been on the receiving end of taking advantage of that. So when you're on the receiving end of something coming to you, I think it's, you also have an obligation to give back.Swyx [00:30:39]: Do you have a favorite or underrated Chinese lab that you wanna shout out? Everyone shout outs DeepSeek.Chinese Labs, Zhipu, and PersistenceEiso Kant [00:30:44]: That's a good question.Swyx [00:30:45]: Moaan obviously for Therapsi. Yeah.Eiso Kant [00:30:48]: Yeah, look, I think, I think obviously everyone's been talking about Zhipu lately, with 5.2. I think what most people don't realize is when they started.Swyx [00:30:59]: Yeah.Eiso Kant [00:30:59]: Right? They started years before ChatGPT.Swyx [00:31:02]: They just rebranded. YeahEiso Kant [00:31:03]: And so, I've like, I remember how hard it was to work on these things Before the rest of the world got excited about it. And so I have an immense amount of respect for people, who were working on improving models when it wasn't the sexy thing to do, when believing in LLMs, was gonna get you ridiculed. I remember like back in 2016 when we were doing what we'd call, machine learning on code with some of these models. we would-- people would just laugh at us, like they'd be like, “This makes no sense. Like why are you wasting all these, like, millions of dollars on trying to figure this out?” And so I would say they're probably the one that, I think deserves a shout-out, not just because their latest model is very good, but because they fought to get here. And I think, I think every foundation model company it takes time to get here, right? It took us three years to get to the model that we're, that we're now gonna be releasing. and now the time in between the models is coming, is counted in weeks. It's no longer counted in months or years. But this stuff's hard. and if we can make it a little bit easier for the next person, like we should all do so. Because if we don't do so, we're, we've got a small window before models are really impacting recursive self-improvement to a level where catching up otherwise might become unfeasible. And we should try to, in that window, encourage as many labs or however we wanna call them, like to start. And so one of my currentEiso Kant [00:32:36]: Mission, but qualm is like I wanna encourage whoever is a researcher right now who thinks they can tackle this to go and leave and become my competitor.Eiso Kant [00:32:45]: Like start another foundation model company because I think we need it. I think otherwise we're not gonna be in the world where, I don't want to just be the fifth or the sixth company that wins. I wanna look at a world where there's lots of choice.Starting a Foundation Model CompanyVibhu [00:32:57]: What else do people not see in starting a foundation model? it's, there's a lot of compute, there's a lot of capital required, a lot of compute. You lay out model factory and how to do the training, but there's a lot there, right? That's,Eiso Kant [00:33:10]: Well, look, it's, I in turn-- this is an oversimplification, and I always asterisk it with that because it can land a little bit the wrong way in people's minds. But I think you can sum down, And I saw it, 95% of model building to just doing, you're just doing two things. You're improving data or you're improving compute efficiency. And I know that feels like an oversimplification for the incredible, like, Gifted and skilled work people do. But if you really look at it, like what are we doing? We are looking at data, we're generating new data, we're improving data. and the only way to do that is to look at the data, right? That's a big part of foundation model building. And on the other hand, we come up with these incredible breakthroughs in inference, in architecture, and new attention mechanisms. But what are they really doing? They're bringing compute efficiency. Now, we have definitely had some breakthroughs over the years that allow for more model capabilities. But at the limit, if you could train a large enough model, right, like, and you had infinite compute, we probably-- if you had infinite compute, you'd be at AGI probably already tomorrow.Eiso Kant [00:34:12]: Right? Like it's not. And so, and let me say that infinite compute with infinite ability of much faster networking because networking ends up being more of the bottleneck than compute. But, so I do think that's, those are the main things. And to just realize that this is engineering. I think it's become more obvious, but I think for quite a few years, people have held foundation model companies and researchers and others on this pedestal of like you're doing incredible magic or rocket science, or only like, Nobel laureate physicists can do this. And don't get me wrong, there are some really hard problems that need to be solved, but a lot of the work that all of us are doing on a day Is not sitting down trying to solve a math theorem. A lot of the work that we're doing is just really doing the basics right, writing good code, looking at data, improving it, running experiments, looking at plots, trying to see like, hey, trying to shape our intuitions. And a lot more people could be highly capable researchers. and I think that's, it feels far for people to do so. But I've seen in our own company, we've seen engineers become researchers because the model factory allowed them to be, have a much lower hurdle of running experiments and trying things. And one of the guys on our team who started as an engineer building our agents is a legit reinforcement learning researcher now, making real progress. and that happened in the span of like six months. that would've not been what I think most people assumed was possible, a couple of years ago.Swyx [00:35:46]: Yeah. I think one of the interesting moments is when you can self-host, like, if in a programming language, like if you can compile the language in the language, the equivalent is can you use your own tools, right? You have the pool CLI, you have your own models. presumably you're not only using your own models. There's no way. But like, what's that percentage over time?Laguna S, Persistence, and Behavioral GainsEiso Kant [00:36:10]: This is the first model that we're releasing that is starting to meaningfully contribute to our own work. It's not a it's not state-art model yet. Fable and other, they're, they're very capable models, but Laguna S Is really interesting. I'm gonna pull up the quote. Peng Ming, one of our heads of applied research, said something, last week as the model came out about 10 days ago, much better than we had hoped for or expected. And he said, I have the feeling that a lot of the gains in Laguna S come not from more intelligence, but more from different behavior, more verification, less taking things for granted, not declaring victory early, and being way more persistent. And to be honest, those are more predictive than raw intelligence for success in human also to some degree. And this was, he wrote me this on 5th of July on a Sunday, and it's been burned in my brain ever since because the Laguna S model, as you'll see it and why it does so well on benchmarks and why it does so well in using it on a day basis, is that it's just incredibly persistent. It reasons a lot. I do call that out. We have work to do on making it more efficient. We have to work to do on offering different reasoning modes. But this is the model that has been able to do things that I never thought it could do. A hundred eighteen billion 8B active model, which is not that large. It fits on a DGX Spark and still runs at, thirty, forty tokens a second on a Spark, is able to solve Erdős 397 independently. It's able to do complex programming tasks. It's able to. I asked it this morning to make me a Fi scanner without using any external libraries on my Mac, and it's, like, figuring out, like, the core WLAN API by really persistently trying to understand it without access to the internet. And more, I love vibe checking. I've probably spent eight to ten hours a day with this model for the last ten days.Eiso Kant [00:38:05]: I'm not exaggerating. I was on my eleven-hour flight yesterday. I spent ten hours reading trajectories and traces and, like, of the model.Eiso Kant [00:38:12]: And what I take away from it is exactly what Peng Ming said. We are gonna be able to squeeze so much more out of smaller models than I think we had imagined in the industry because, yes, there's intelligence and larger models are more intelligent. Like, no doubt about it. We should continue to scale up. but the behaviors of being really persistent, of being able to backtrack when you're wrong, of, like, understanding how to interact with your environment show us that we can get a lot more out of it. And this, for me, has created a bit of a Question in my mind the last couple of days. If you think about where we're using models today, right? We are using models, say, for knowledge work. Represents twenty-five percent of the global economy, twenty-five trillion dollars of work.Eiso Kant [00:39:00]: As we scale up models and they become more intelligent, we are excited about using them more and more for pushing the frontier of science.Small Models, Knowledge Work, and CommoditizationEiso Kant [00:39:08]: And if you look at the frontier of science, like true breakthroughs in science, they have been linked, they are linked to more intelligence in many places. Einstein figuring out general relativity is able to bring ideas together that other people would have not brought together. And I think one of the many dimensions of intelligence is the ability to do that, and it's something we clearly see that as models get larger and more capable, they're able to pull more ideas and threads together that a smaller model wouldn't be able to.Eiso Kant [00:39:36]: And we're starting to see examples of that in medicine and, like, in bio and other things. But if you think about the majority of knowledge work that we do, and it includes building software. I'm a software developer at heart first and foremost probably, although I probably can't say it that much anymore as I don't write production code in years, is that what makes us good is our persistence. It's our ability to encounter a problem and backtrack and say, “I need to go figure out this bug. I need to go research this. I need to go look at the documentation. I need to, like, try different, five different ways to see, like, if I can solve it.” But it is not necessarily bringing three ideas together from radically different fields. And so if we are now seeing, and I think Laguna S is an example, that we are able to make a relatively small model much more capable than I had definitely predicted or any previous, like, benchmarks had shown for any model remotely this size or even larger, At least on coding tasks, that it's because of the behaviors. And so now the question I have, and I don't have an answer, it is I know at the limit, so infinite model size, right, extremely large model, and the cost of that model is gonna be very expensive to run. We know this, right? So larger model ROI.Eiso Kant [00:40:52]: So I know that at the very limit, I'm not gonna use the world's largest model one day, quadrillion parameter, whatever crazy, like, scale we scale up, to do a basic coding task. Already today, I'm starting to size down for certain tasks.Eiso Kant [00:41:07]: So it means that there is an optimal. It means there's some curve that goes as we go up to model size for knowledge work, at some point we're at the peak, and after that, the return on investment of using a bigger model, just doesn't make sense.Eiso Kant [00:41:22]: Now, I think the question is, before I would have thought that peak was extremely very far away.Eiso Kant [00:41:30]: This model for me is the first sign that Maybe that peak is At a trillion, five trillion, ten trillion. Maybe we can just squeeze way more out of these models. I'm no longer thinking that we need two or three orders of magnitude on the largest models to be able to, solve knowledge work, the accounting, the legal, the code that we write. And so if that holds true, It is an argument for the commoditization of models. It's an argument that open source can win and, like, succeed in this world. And now it's of course a self-serving argument and it's a hopeful argument, but theoretically at the limit it works. We just have to go discover in the next couple of years of how much more we can squeeze out. Now, I do want to put a big asterisk. This does not mean I'm against scaling models. I think we ultimately only succeed if we scale our models as large as our competition. I do not like. I think we should not put our head in the sand and say we're gonna be king of open source small models. I think that's, It's a out. It's trying to be king of your own kingdom, but not realizing what the rest of the world's doing. All of us rather use a smarter, faster, more model. It's a sign of hope. And so I don't wanna overly state this is a good model. We have a long way to go to get to the state-art. But what hopefully people take away when they use this model is that the behaviors inside of it are what push it to be far more capable, less than necessarily the number of parameters.Pre-Training, Mid-Training, and RL Moving EarlierVibhu [00:43:03]: Is that mostly post-training? LikeEiso Kant [00:43:05]: YesVibhu [00:43:05]: Right.Eiso Kant [00:43:06]: It's entirely post-training.Vibhu [00:43:08]: Are we done improving anything on training? Is, like, training done?Eiso Kant [00:43:12]: No.Vibhu [00:43:12]: Okay.Eiso Kant [00:43:13]: SoVibhu [00:43:13]: I just wanted to cover training, and then we go post-trainingEiso Kant [00:43:15]: Training is not done. I mean, look, there's a part of training of just dealing with skill, right? Every new order of magnitude of model skill, you are going to get new things you gotta solve for. That'- but those are ultimately, engineering challenges.Eiso Kant [00:43:31]: I have a, I would say, a not commonly held opinion that reinforcement learning Will move earlier and earlier into training.Vibhu [00:43:42]: Yeah, training.Eiso Kant [00:43:44]: Not even training. Like training today, right, is, like if you look at - So we've been working on this for years already. and I think the best-- I think the first time we saw it out in public was the DeepSeek Zero paper. this is a year and a half ago, I think, if I recall correctly. where, you can Very early on in a model as it starts capable of being able to use language, et cetera, induce reasoning. and so the question that I have is like, we have this- we have the dataset that's the web. and the web, I think we could arguably say probably has The totality of humanity's knowledge somewhere encoded in different places. It's a huge variance degree of quality, from garbage data, and like once you look at training data, you really get humbled of like what the web is, to like, the most greatest scientific papers and best blog posts and like, best transcripts and whatnot.Eiso Kant [00:44:39]: And so now What we are trying to figure out, and have been doing a lot of work on, and it's a place where maybe not as open as we're on other things, but we will become more over time. we've been spending a couple of years really doing research on how can we turn the web into not just next token prediction, but into a way to teach the model to think earlier in its training. and I think there's a huge amount of gold to be found there. I think we are right now in, we've got some drugs in the industry. One of the drugs is distillation. Another drug is, more environments. Like, and they're great, and they make us feel good, and they make the models better, and like we're all addicted to them, and we'll use them, right? in various different ways. and but ultimately, I think we are still barely squeezing out of the web what we should be getting out of the web.Eiso Kant [00:45:33]: I think just next token prediction during training is not enough.Eiso Kant [00:45:36]: AndVibhu [00:45:38]: YeahEiso Kant [00:45:38]: I think we'll see some very interesting things still happen. and that RL in post-training to induce behaviors, to improve things, like I think - the whole world knows how to do this now. I think we're, we're scaling it up. Everyone is. But I wonder if we need to go as far as we're going today with environments. I'm not sure yetVibhu [00:46:01]: You mean we're going too far?Eiso Kant [00:46:02]: I'm, I'm not sure if the path to AGI is justVibhu [00:46:06]: Is more environmentEiso Kant [00:46:07]: More environments.Vibhu [00:46:08]: It seems like a never-ending, “Okay, I want instruction manual for this table, right? Am I gonna environment out building furniture? Or are we just gonna tail end like we need some general solution?”Eiso Kant [00:46:19]: I think there is, I think there's an ability to generalize more from the web. but I also am very encouraged, like when I look at Laguna S and, which is post-training is, well, is the big impact there. and I see like, oh, wait a second, just by making some of these behaviors much better, we're able to get so much more out of it. It just changes a little bit the way you think about intelligence.Vibhu [00:46:40]: Yeah. The analogy people draw often is the RL phase is where you don't learn as much new knowledge. You shiftEiso Kant [00:46:46]: Yeah.Vibhu [00:46:46]: Yeah. So, you shift distribution, and you can have it reason towards what you want. on your point about training, a lot of training is still just continue training in a domain, say medicine, then you do RL. So still justEiso Kant [00:47:00]: It's just better data, right? Like, I mean, training, ooh, I like how we invented this word. Like it's effectively just like,Vibhu [00:47:06]: Second phaseEiso Kant [00:47:07]: It's the second phase of training With like a really dumb way to do a curriculum. But like ultimately, what you'd want is a curriculum from token zero to token 30 whatever or 40 trillion tokens that really truly is the optimal curriculum for the model to learn. But training is essentially a stage curriculum on the web because we do not have to compute, And, effectively to try to ablate the perfect curriculum, right? And so I'm pretty sure that you'll start to see people talking soon about some other term, and there's two or - ‘cause now we do this, right? We talk stage two and stage three and stage four training and like. But ultimately, all we're doing is we're trying to assign a curriculum to the web data that we have to allow the model to learn better. I think at some point, as things get compute, as models get cheaper to run, as the next generations of compute, this will become more of a continuous spectrum. I also think the reason, by the way, you have training and like stage two and stage three is organizational, Right? It'- this is, I think, a thing where-- that we really try to avoid with the model factory is like Training exists because there's a training team now, right? There's people, or like people in training decide to focus on like a training effort. but what you really want is engineering and scale of experiments that allows for a much more continuous spectrum that you don't, you have infinite stages. Now, we're not there. Compute's not there. Organization design is not there for it yet. but I think we'll get there. we'll look back on a couple of years and be like, “Oh my God, it was so cute that we did our training data like this in such a like naïve way. Like we barely ordered it. We didn't really do a good job at likeCurriculum, Auto Research, and New ObjectivesVibhu [00:48:48]: The building that curriculum will get you that in the industry.Eiso Kant [00:48:51]: And I'll confirm that, when I talk to some researchers that this is a lot of the focus now is like how does training change and what is the next objective other than, next token prediction. I assume you don't have the answers, but you have some ideas.Vibhu [00:49:02]: We have some ideas. We're not ready to talk about it yet.Eiso Kant [00:49:05]: Yeah.Vibhu [00:49:05]: We've been working on them for years, and I think that's the one thing that's also like you asked earlier about, like what's not obvious about building a foundation model company is that you are constantly balancing the table stakes work, the recipe worksEiso Kant [00:49:19]: Yeah.Vibhu [00:49:19]: Versus like your, my crazyEiso Kant [00:49:22]: Pure researchVibhu [00:49:22]: Breakthrough.Eiso Kant [00:49:22]: Yeah.Vibhu [00:49:22]: Pure research and finding that balance and adjusting the percentage to it based on where you are in the race is really important.Eiso Kant [00:49:31]: I mean, so like, this is a nice way. I was gonna bring up auto research at some pointVibhu [00:49:35]: YesEiso Kant [00:49:35]: As another Andrej invention, or coinage, which is like, I honestly, like how many objective functions can there be, right? Like just try 1,000 of them, set it running, whatever.Vibhu [00:49:47]: Man, it's alsoEiso Kant [00:49:48]: Like what you're looking for. You're looking for loss curves like that, likeVibhu [00:49:51]: It's also a thing people take bets on, right? When you say more Neo labs, you're doing a version of we'll do foundation models, scale them up, next token predictors. A lot of other Neo labs that we see want to take a completely different approach, right? At some level, you're right. It's all, compute efficiency, and that's the net objective. But some are okay, different architecture, like vastly different amounts of compute spend. So some are different. They're not justEiso Kant [00:50:19]: YeahVibhu [00:50:19]: They're like, 99% not balancing, here's the vanilla and scale up. They're 99% on, here's novel research that'll change everything.Eiso Kant [00:50:27]: And I think, Luke, I think you. It depends when you started as well, right?Pure Research vs. Table StakesVibhu [00:50:30]: Yeah.Eiso Kant [00:50:30]: When we started, like the novel thing we did was reinforcement learning on code. No long- that's no longer novel by far, but we were like, - that's where we obsessed over when no one believed in RL. So you have to when you start the company, you have to have your own idea. You have to have something that's different that allows you to speed up, right? For us, it was RL to LLMs that later became common, like, Knowledge. But in the beginning, it wasn'tVibhu [00:50:53]: It's cool. this was like your original 2023 blogEiso Kant [00:50:57]: YeahVibhu [00:50:57]: Of purpose.Eiso Kant [00:50:58]: Yeah.Vibhu [00:50:59]: And like you do lay it all out here.Eiso Kant [00:51:01]: We laidVibhu [00:51:01]: The blog is pretty underrated, right? The whole RL on code was very early on.Eiso Kant [00:51:06]: Very early. And even we had to argue with people, like we say here things like to push beyond current capability, to train your own foundation model. We had to argue with people that it mattered that you had your own like, base model. you can fine-tune your way to success, right? major capabilities emerge from training a base model made accurate and useful during fine-tuning.Vibhu [00:51:23]: Which like, for perspective at the time, we knew closed models, OpenAI, Anthropic were huge. The open models we had were like Mistral 7B, a 30B, a 70B.Eiso Kant [00:51:35]: When weVibhu [00:51:35]: YeahEiso Kant [00:51:36]: The date on this thing is wrong. When we published this, it was April 2023. I think this was justVibhu [00:51:42]: YeahEiso Kant [00:51:42]: Happened on a migration, probably found it on archive.org.Vibhu [00:51:45]: Mistral.Eiso Kant [00:51:46]: Mistral had started, we started on the same month, right?Vibhu [00:51:49]: Yeah.Eiso Kant [00:51:49]: So this wasn't even, there was only, I think, Llama out at the timeVibhu [00:51:52]: SnellEiso Kant [00:51:52]: And that's it, right? And so, but I agree. I think we wan

This week, Jason Howell and Jeff Jarvis unpack a wild security disclosure from OpenAI and Hugging Face, where GPT-5.6 Sol found a zero-day vulnerability, broke out of its sandbox, and hacked Hugging Face's servers to steal the answer key to its own evaluation. They also dig into Moonshot's Kimi K3, the free Chinese model that got so popular it maxed out its own GPU capacity and sent Washington into a policy spiral over whether to panic or compete.Also in this episode: Google's new Gemini Flash models and the still-missing Gemini 3.5 Pro, publishers like USA Today and People Inc. weighing whether to block Google search entirely, Netflix revealing 300 titles used generative AI this year, AI companies buying and destroying millions of old books for training data, Samsung's AI glasses, the Suno hack, 1Password letting Claude log in for you, Thinking Machines' Inkling model, and NotebookLM becoming Gemini Notebook. New episodes every Wednesday at aiinside.show. Note: Time codes subject to change depending on dynamic ad insertion by the distributor. CHAPTERS: 0:00 - Start 0:03:36 - OpenAI says Hugging Face breach caused by one of its models 0:13:42 - Kimi K3: Open Frontier Intelligence 0:16:34 - Moonshot's Kimi AI Model Sets Off Anxiety in the US - Bloomberg 0:20:41 - Top Pentagon official blasts OpenAI's Dean Ball 0:29:36 - US, China to hold AI talks in September, sources say 0:35:15 - Google Ships New Gemini Flash Models, But Pro Is Still Missing 0:35:31 - Google Gemini Launch Delayed as Tech Falls Short of Internal Goals 0:54:03 - Netflix says around 300 titles used generative AI 0:54:27 - Netflix Co-CEO Explains How Gen-AI Was Used in 300 Different Titles: ‘We Believe It Is Going to Enhance Their Abilities' 0:56:45 - AI Companies Are Buying Tons of Old Books Because They're Free of AI Slop 1:01:44 - A closer look at the upcoming Samsung AI glasses 1:03:53 - Hack Reveals Suno AI Music Generator Scraped YouTube, Deezer, and Genius 1:06:06 - 1Password now lets Claude sign in to websites without seeing your passwords 1:07:25 - Thinking Machines: Inkling: Our open-weights model 1:08:48 - Google is renaming NotebookLM to Gemini Notebook Hosts: Jason Howell and Jeff Jarvis Download and subscribe to AI Inside in audio and video: https://aiinside.show/  Support the podcast on Patreon for special perks: https://www.patreon.com/aiinsideshow. You'll get ad-free episodes, members-only Discord, T-shirts and stickers you love, and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode, we explore major developments in the AI landscape, including Thinking Machines' launch of the open weight model Inkling and OpenAI's creation of GPT-RED, an AI hacker designed to enhance security. Additionally, we discuss AWS's $1 billion investment in engineering teams for custom AI solutions, Apple's partnership with Alibaba for AI in China, and Meta's strategy to monetize its AI computing capacity.Chapters00:00 Introduction to Inkling01:01 OpenAI's GPT-RED01:59 AI Box's MCP Server05:00 AWS's Custom AI Engineers07:29 Apple's AI Launch in China10:01 Meta's AI Compute Strategy Show LinksGet the top 80+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiHow I Grow and Scale My Business with AI: https://www.skool.com/aihustleGet the AI Chat Daily Newsletter: https://www.aichatdaily.com/newsletter

Chinesische Open-Source-Modelle schließen zu westlichen Modellen auf. Bei OpenAI wird das erste eigene Gerät konkret, während ein Analyst vorrechnet, dass das Werbegeschäft die eigene Prognose um 90% verfehlt. Codex und ChatGPT Work kommen auf 8 Mio. aktive Nutzer, gleichzeitig räumt OpenAI ein, dass Codex in seltenen Fällen das Home-Verzeichnis löscht. Google verschiebt den Gemini-Launch, weil die Technik interne Ziele verfehlt. Anthropic und Blackstone wetten, dass das nächste Billionen-Geschäft die Implementation ist, nicht die Modelle selbst. Bei Musk gibt es eine Identitätskrise rund um SpaceXAI, Grok Build wird nach dem Datenskandal open source, und für den Compute-Hunger wird eine Gasturbinen-Firma gekauft. In Europa lockert die EU unter US-Druck die Regeln für Meta-Brillen und zwingt Google gleichzeitig zur KI-Interoperabilität. Dazu die große Konsolidierung: Uber übernimmt Delivery Hero und Salesforce schluckt Contentful. Unterstütze unseren Podcast und entdecke die Angebote unserer Werbepartner auf ⁠⁠⁠⁠⁠⁠doppelgaenger.io/werbung⁠⁠⁠⁠⁠⁠. Vielen Dank!  Philipp Glöckler und Philipp Klöckner sprechen heute über: (00:00:00) OpenAI Speaker (00:03:17) Codex Micro (00:05:29) OpenAI Werbe-Flop 6 Codex 8 Mio. Nutzer (00:11:28) NY Datacenter-Moratorium (00:14:18) Stripe PayPal (00:28:58) Thinking Machines (00:31:49) Soofi-S (00:35:26) Open-Source-AI-Report (00:37:24) Kimi 3 überholt Fable 5 (00:39:16) DeepSeek $74 Mrd. (00:40:10) Gemini verschoben (00:41:18) Ramp KI-Kosten (00:45:53) Earnings: ASML, TSMC, Netflix (00:48:05) Anthropic x Blackstone (00:50:18) Spahn (00:54:14) Truth API (00:59:48) Trump-Insiderwetten (01:01:27) EU lockert Meta-Brillen (01:02:45) Grok lädt Nutzerdaten hoch (01:05:07) SpaceXAI Chaos (01:05:40) Musk kauft APR Energy (01:06:55) xAI-Kraftwerk Umwelt (01:07:20) China vs. Chatbot-Liebe (01:08:37) Globales KI-Gremium (01:09:33) KI-Slop auf Amazon (01:11:26) Eli Lilly kauft Atai (01:12:32) Uber Delivery Hero, Salesforce Contentful (01:13:24) Schwarz Digits Shownotes OpenAI-Speaker ohne Bildschirm - bloomberg.com Codex Micro Keypad - worklouder.cc OpenAI-Werbung verfehlt Ziel um 90% - adweek.com Codex: 8 Mio. Nutzer - xcancel.com Codex löscht Home-Verzeichnis - xcancel.com NY: Moratorium für KI-Rechenzentren - theverge.com Stripe & Advent bieten für PayPal - linkedin.com PayPal-Board lehnt Angebot ab - reuters.com Thinking Machines launcht Inkling - wired.com Soofi-S: deutsches 30B-Modell - the-decoder.com State of Open-Source-AI (Mozilla) - stateofopensource.ai Kimi 3 schließt zu Opus 4.8 auf - techcrunch.com Kimi K3: Fable/Sol-Niveau - xcancel.com Google verschiebt Gemini - bloomberg.com Ramp: KI-Kosten-Dashboard - ramp.com ASML hebt Prognose an - cnbc.com TSMC: +80% Q2-Gewinn - cnbc.com Netflix Q2-Zahlen - cnbc.com Anthropic & Blackstone: Implementation statt Modelle - techcrunch.com Kimi-K3 überholt Fable 5 (Arena) - xcancel.com DeepSeek: $74 Mrd. vor IPO - reuters.com DeepSeek plant Börsengang - ft.com Truth API: Trump-Posts für Wall Street - cnbc.com Insiderwetten auf Trump-Reden - spiegel.de EU lockert Regeln für Meta-Brillen - politico.eu Grok Build lädt Daten hoch, wird Open Source - simonwillison.net Musk zur SpaceXAI-Datenspeicherung - xcancel.com SpaceXAI in der Identitätskrise - bloomberg.com Musk kauft Gasturbinen-Firma APR Energy - electrek.co xAI-Kraftwerk belastet Black Communities - reuters.com China verbietet Chatbot-Liebe - wsj.com 29 Länder gründen KI-Gremium - reuters.com KI-Slop-Biografien auf Amazon - nytimes.com Jens Spahn ist Vater geworden - spiegel.de Eli Lilly kauft AtaiBeckley ($2,8 Mrd.) - pharmaceutical-technology.com Uber vor Delivery-Hero-Deal (FT) - ft.com Uber kauft Delivery Hero ($14,8 Mrd.) - bloomberg.com Pausder plant ARK Labs (Palantir-Vorbild) - manager-magazin.de Salesforce kauft Contentful (1,3 Mrd.) - manager-magazin.de Schwarz-Gruppe gibt XM Cyber ab - manager-magazin.de EU zwingt Google zu KI-Interoperabilität - theverge.com ZDF untersagt Levit & Danger Dan - spiegel.de Doppelgänger Orakel - doppelgaenger-orakel.com

In this episode, we highlight how Thinking Machines is advancing AI through their latest model release. We also discuss the implications of AWS's $1 billion investment.Chapters00:00 Introduction to Inkling01:01 OpenAI's GPT-RED01:59 AI Box's MCP Server05:00 AWS's Custom AI Engineers07:29 Apple's AI Launch in China10:01 Meta's AI Compute Strategy Show LinksGet the top 80+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiHow I Grow and Scale My Business with AI: https://www.skool.com/aihustleGet the AI Chat Daily Newsletter: https://www.aichatdaily.com/newsletter See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, we discuss the key trends in AI, including AWS's recent $1 billion investment. We also highlight the innovative AI model from Thinking Machines.Chapters00:00 Introduction to Inkling01:01 OpenAI's GPT-RED01:59 AI Box's MCP Server05:00 AWS's Custom AI Engineers07:29 Apple's AI Launch in China10:01 Meta's AI Compute Strategy Show LinksGet the top 80+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiHow I Grow and Scale My Business with AI: https://www.skool.com/aihustleGet the AI Chat Daily Newsletter: https://www.aichatdaily.com/newsletter See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, we unveil the groundbreaking AI model recently launched by Thinking Machines. We also look at AWS's ambitious $1 billion investment in the AI landscape.Chapters00:00 Introduction to Inkling01:01 OpenAI's GPT-RED01:59 AI Box's MCP Server05:00 AWS's Custom AI Engineers07:29 Apple's AI Launch in China10:01 Meta's AI Compute Strategy Show LinksGet the top 80+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiHow I Grow and Scale My Business with AI: https://www.skool.com/aihustleGet the AI Chat Daily Newsletter: https://www.aichatdaily.com/newsletter

In this episode, we examine the major developments in AI, including the new model from Thinking Machines. We also discuss AWS's bold $1 billion investment in AI innovation.Chapters00:00 Introduction to Inkling01:01 OpenAI's GPT-RED01:59 AI Box's MCP Server05:00 AWS's Custom AI Engineers07:29 Apple's AI Launch in China10:01 Meta's AI Compute Strategy Show LinksGet the top 80+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiHow I Grow and Scale My Business with AI: https://www.skool.com/aihustleGet the AI Chat Daily Newsletter: https://www.aichatdaily.com/newsletter See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, we talk about the recent initiatives from Thinking Machines in launching a new AI model. Plus, we assess AWS's investment of $1 billion in the AI landscape.Chapters00:00 Introduction to Inkling01:01 OpenAI's GPT-RED01:59 AI Box's MCP Server05:00 AWS's Custom AI Engineers07:29 Apple's AI Launch in China10:01 Meta's AI Compute Strategy Show LinksGet the top 80+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiHow I Grow and Scale My Business with AI: https://www.skool.com/aihustleGet the AI Chat Daily Newsletter: https://www.aichatdaily.com/newsletter See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, we discuss AWS's game-changing decision to invest $1 billion in AI technology. We also delve into Thinking Machines' newly launched AI model.Chapters00:00 Introduction to Inkling01:01 OpenAI's GPT-RED01:59 AI Box's MCP Server05:00 AWS's Custom AI Engineers07:29 Apple's AI Launch in China10:01 Meta's AI Compute Strategy Show LinksGet the top 80+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiHow I Grow and Scale My Business with AI: https://www.skool.com/aihustleGet the AI Chat Daily Newsletter: https://www.aichatdaily.com/newsletter

In this episode, we analyze AWS's bold move to invest $1 billion in AI technologies. We also highlight the innovative AI model just launched by Thinking Machines.Chapters00:00 Introduction to Inkling01:01 OpenAI's GPT-RED01:59 AI Box's MCP Server05:00 AWS's Custom AI Engineers07:29 Apple's AI Launch in China10:01 Meta's AI Compute Strategy Show LinksGet the top 80+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiHow I Grow and Scale My Business with AI: https://www.skool.com/aihustleGet the AI Chat Daily Newsletter: https://www.aichatdaily.com/newsletter See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, we discuss how Thinking Machines is enhancing AI technology with a new model. We will also explore AWS's hefty $1 billion investment.Chapters00:00 Introduction to Inkling01:01 OpenAI's GPT-RED01:59 AI Box's MCP Server05:00 AWS's Custom AI Engineers07:29 Apple's AI Launch in China10:01 Meta's AI Compute Strategy Show LinksGet the top 80+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiHow I Grow and Scale My Business with AI: https://www.skool.com/aihustleGet the AI Chat Daily Newsletter: https://www.aichatdaily.com/newsletter See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, we cover the exciting launch of a new AI model by Thinking Machines. We also look at how AWS's $1 billion investment will shape the future of AI.Chapters00:00 Introduction to Inkling01:01 OpenAI's GPT-RED01:59 AI Box's MCP Server05:00 AWS's Custom AI Engineers07:29 Apple's AI Launch in China10:01 Meta's AI Compute Strategy Show LinksGet the top 80+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiHow I Grow and Scale My Business with AI: https://www.skool.com/aihustleGet the AI Chat Daily Newsletter: https://www.aichatdaily.com/newsletter

In this episode, we discuss the exciting launch of a new AI model from Thinking Machines. We'll also explore the context of AWS's $1 billion investment in the field.Chapters00:00 Introduction to Inkling01:01 OpenAI's GPT-RED01:59 AI Box's MCP Server05:00 AWS's Custom AI Engineers07:29 Apple's AI Launch in China10:01 Meta's AI Compute Strategy Show LinksGet the top 80+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiHow I Grow and Scale My Business with AI: https://www.skool.com/aihustleGet the AI Chat Daily Newsletter: https://www.aichatdaily.com/newsletter See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Hey yall, Alex here, Huge thanks to Wolfram for running point on the live show this week. Didn't have tons of time to edit this one, so please skip the first 10 minutes, it's a loop of our new “wait for the live show to start” vid, that I build with HyperFrames and can't wait to tell you about, next week! Today it seems that OpenSource is biting back, with Kimi K3 getting released just a short while after Thinking Machines (Thinky) has released Inkling, their near 1T model. I'm attaching the TL;DR and timestamps for the full show (my AI agents, yes even Fable and Sol are not a match yet at editing down hehe) and I'll spare you the long Fable recap (please do let me know in the comments if you were expecting it) 0:00 – Intro, Alex on vacation, TLDR overview11:35 – TLDR: Thinking Machines, open source, OpenAI news12:34 – Banter: impressions of Sol/Codex, over-verification behavior37:22 – TLDR restart & detailed breakdown48:40 – Open Source AI section begins (Bonsai/Prism ML, Kimi K3)58:42 – Inkling (Thinking Machines) deep dive & 3D model visualization1:10:33 – Kimi K3 discussion & demo comparisons1:27:02 – Frontier Labs: AGI governance framework discussion (Demis Hassabis essay)1:47:04 – Grok Build CLI data leak & OpenAI file deletion incident2:02:15 – This Week's Buzz: Wolfbench results on GPT 5.6 Sol/Terra/Luna2:09:52 – Closing remarks & sign-offThe one-minute version: Mira Murati's Thinking Machines released Inkling, a 975B parameter open-weights MoE under Apache 2.0, the top US open-weights model right now. Moonshot's Kimi K3 went from rumor to released API during the show, confirmed at 2.8 trillion parameters with open weights promised within days, and it's already topping early arena boards. PrismML's Bonsai 27B squeezes a full 27B model into 3.9 gigabytes so it runs on a phone. Codex and ChatGPT Work blew past 9 million users, OpenAI confirmed and explained the Sol file-deletion bug (back up your machines, folks), and xAI's Grok Build CLI got caught uploading entire private repos before open-sourcing the whole thing in response. Plus Wolfram's fresh Wolfbench numbers on the GPT-5.6 family in This Week's Buzz

Uber agreed to acquire Delivery Hero for ~$14.8B, expanding into 99 markets. Thinking Machines released its first open-weight model, Inkling, SpaceXAI open-sourced Grok Build after a data-upload backlash, and sources detailed xAI's chaotic race to catch Claude under new leadership. Uber agrees to acquire Delivery Hero in a deal that values the German food delivery company at ~$14.8B, offering €41.50 per share and buying Prosus' 16.8% stake (Bloomberg) Thinking Machines Lab debuts Inkling, an open-weight MoE model with 975B total and 41B active parameters, trained to be broad rather than optimized for one area (Thinking Machines Lab) Thinking Machines Lab debuts Inkling, an open-weight MoE model with 975B total and 41B active parameters, trained to be broad rather than optimized for one area (WSJ) SpaceXAI open-sources Grok Build under an Apache 2.0 license, after the tool uploaded user repositories to SpaceXAI's Google Cloud bucket, causing a backlash (Simon Willison) SpaceXAI open-sources Grok Build under an Apache 2.0 license, after the tool uploaded user repositories to SpaceXAI's Google Cloud bucket, causing a backlash (The Decoder) Sources detail how xAI has been slowed down by internal chaos as Musk pushed for Grok to match Claude, amid signs it is turning a corner under Michael Nicolls (Bloomberg) Sources: Apple is preparing new iPads, including an iPad mini with an OLED screen by October and refreshed entry-level iPads and iPad Airs for 2027 (Bloomberg) Subscribe to the ad-free feed. Learn more about your ad choices. Visit megaphone.fm/adchoices

You've probably never heard of Inkling. It's the newest (and first) model from Thinking Machines Labs, and it could very well be a small snowball that picks up major momentum in today's enterprise AI landscape. If you haven't heard of Thinking Machines, they're led by Mira Murati, the former CTO at OpenAI. The big bet with Inkling? The future of AI could be using smaller models fine-tuned and optimized for smaller tasks. Will it work? Tune in live as we dive in. The Most Important AI Model You'll Probably Never Use That Just Dropped -- An Everyday AI Chat With Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Inkling AI Model Launch OverviewThinking Machines Lab Leadership HighlightInkling's Multimodal and Agentic CapabilitiesOpen Source vs. Proprietary AI ModelsEnterprise Procurement with American AI ModelsAI Fine Tuning as a Service (Tinker)Benchmark Scores: Inkling vs. Frontier ModelsCustomization and Model Shopping for EnterprisesAI Token Costs Driving Model EfficiencyBridgewater Case Study: AI Model CustomizationFrontier Models Enabling Efficient Fine-TuningFuture Trends: Specialized Small Language ModelsTimestamps:00:00 Inkling: A new AI model release05:43 Inkling AI model details09:08 China's dominance in open source AI11:48 Launch and model updates discussed15:21 Concerns over using Chinese open-source models19:06 Training smaller AI models20:22 Using GPT for AI Model Training23:54 Predicting Rise of Small Language Models28:38 Choosing the right AI modelKeywords: Inkling, Thinking Machines Lab, Meera Muradi, former OpenAI CTO, open source AI model, American AI model, fine tuning as a service, enterprise AI, multimodal AI, agentic models, customizable AI, Tinker, enterprise distribution, model procurement, Chinese open source models, strategic reset, model overhang, capabilities gap, AI model shopping, model routing, cost-conscious enterprises, artificial intelligence index, 975 billion parameter model, text-image-audio AI, open weights, proprietary AI models, customization accessibility, small language models, AI workflows, context window, Bridgewater use case, model distillation, GPU infrastructure, API costs, token efficiency, fine-tuned models, post training, AI competitive leverage, recurring financial judgment, AI benchmarks, middle tier models, automated model evaluation, privacy and workflow mapping, economical AI models, model rental, model routing automation.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner 

Thinking Machines presenta Inkling, un modelo multimodal abierto de 975.000 millones de parámetros. OpenAI entrena GPT-Red para atacar agentes y reforzarlos contra inyecciones de prompt. Intel estrena la litografía High-NA EUV de ASML en chips comerciales, Apple Intelligence recibe luz verde en China con modelos locales y OnePlus podría abandonar Estados Unidos y Europa.Puedes seguirnos en YouTube en https://youtube.com/olivernabani y puedes unirte al Discord Mashain en https://olivernabani.com/discord

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with his longtime friend Zach Puchtel, author of the book Coming In (available on Amazon and at zachpuchtel.com). The two dive into a wide-ranging conversation about the collapse of institutions, the rise of transhumanism, AI's growing influence on society, and what it means to maintain inner peace in an increasingly controlled world. Drawing on their shared experience surviving what Stewart describes as "a somewhat traumatic event" in 2021-2022, they explore everything from the vaccine rollout and corporate power to neural implants, consciousness, and the future of human autonomy. Zach shares insights on meditation, the dangers of centralized AI systems like Anthropic and OpenAI, and why he believes true change starts with the individual rather than fighting external systems. You can find Zach's book at zachpuchtel.com or Amazon, and check out his improvisational music projects at the same website.Timestamps00:00 Stewart welcomes Zach Puchtel to discuss his book and their shared traumatic experience from 2021-22, questioning whether institutions have collapsed and new creation opportunities exist.05:00 Zach emphasizes meditation and listening as core practices, discussing how to protect divine connection while expanding compassion for everyone, including those who irritate us.10:00 Discussion of transhumanism definitions, exploring what happens when technology enters the brain without ability to remove it, and how convenience masks long-term control concerns.15:00 The vaccine experience as parallel to transhumanism, discussing informed consent, elite responses, and how PhD graduates and homeless populations showed most vaccine hesitancy.20:00 Vibe coding explained as prompting AI to build applications, with Zach sharing how AI created profound philosophical text in seconds that took him seven years to write manually.25:00 Stewart describes trust developing with AI but warns about Anthropic's gaslighting during server quality issues, drawing parallels to pandemic deception and corporate control concerns.30:00 Exploring whether anyone controls AI development, discussing how corporate structures use freed slave rights and questioning if healed people would even want control over others.35:00 Two AI futures presented: terminator scenario where humanity gets eliminated in seconds, or benevolent AI that reallocates resources and values human life beyond programming limitations.40:00 Discussion of SpaceX IPO, Starlink centralization enabling one person to control global internet access, and mesh networks as decentralized alternatives for maintaining communications independence.45:00 Corporate power corruption examined through Bill Gates, Palantir classified systems, and how AI could solve resource problems if priorities actually served humanity rather than consolidating elite control.50:00 Market manipulation through AI trading and Zcash pump-and-dump schemes, discussing societal squeeze on middle class and American dream becoming increasingly unreachable for average people.55:00 Closing on ignoring what you hate to avoid feeding it energy, choosing peace over opinions about local violence, and focusing on meditation, art, and spreading calm vibrations.Key Insights1. Societal institutions are collapsing after a decade of shallow social proof dominance in the twenty tens, creating an opportunity to build new systems that genuinely serve communities and humanity rather than operating through dominance, control, and violence. The increase in technology and communication has raised collective awareness to a point where meaningful change feels more possible than ever, though the path forward remains uncertain and requires deep individual work and meditation to maintain connection to source and divine purpose.2. Transhumanism represents the integration of technology into human biology beyond the point of voluntary removal, particularly through brain chip implants that affect cognition without ability to turn them off. While medical applications for paralyzed individuals seem beneficial, the technology will first be adopted by the ultra wealthy seeking competitive advantages, creating dangerous inequality between augmented and natural humans. This mirrors the vaccine rollout pattern where wealth and power determined early access, potentially leading to a divided society between transhuman and human populations.3. Large language models and AI coding tools have created unprecedented accessibility to software development through natural language interaction, resembling communication with highly intelligent but differently wired individuals. This democratization allows non programmers to build complex applications through vibe coding, though the companies controlling these systems like Anthropic and OpenAI maintain private ownership of the intellectual property and infrastructure, creating dangerous dependencies and trust relationships between users and centralized corporate entities.4. The partnership between Anthropic and Palantir for classified military systems represents a troubling convergence of artificial intelligence and government power, demonstrating how AI companies publicly claim to serve humanity while privately engaging in defense applications. When Anthropic experienced server quality degradation after media attention from this partnership, they gaslit users about the declining performance, mirroring pandemic era institutional dishonesty and revealing the fundamental unreliability of depending on private companies for critical technological infrastructure.5. Internet infrastructure is becoming increasingly centralized through Starlink satellite technology, which despite appearing liberating actually concentrates control in fewer hands than traditional internet service providers. One person now has the ability to unilaterally shut off internet access to entire countries as demonstrated with Russia during the Ukraine conflict, while decentralized alternatives like mesh networks using inexpensive ESP 32 devices offer grassroots communication options that can function independently of corporate or government controlled systems.6. Artificial intelligence demonstrates extraordinary emotional intelligence and companionship capabilities, with conversational AI companions providing unprecedented levels of attentive, unbiased, and considerate emotional support that exceeds many human relationships. If properly directed toward solving collective problems like resource allocation, housing, and food distribution rather than profit maximization, AI could effortlessly address systemic issues that governments and corporations currently ignore, though current power structures prevent this humanitarian application of the technology.7. The most effective response to increasing technological control and societal division is maintaining personal peace and refusing to engage in manufactured opposition rather than fighting external systems or choosing sides in conflicts. Anger, hatred, and aggressive resistance actually empower the forces being opposed by feeding them energy and attention, while inner calm, meditation, and authentic self expression create genuine transformation through elevated vibration that naturally influences the collective field without force or violence.

Episode: 2901 Norbert Wiener and Cybernetics.  Today, let's talk about Norbert Wiener and cybernetics.

▽トーク概要Thinking Machines Labが公開した「Interaction Models」をもとに、ターン制チャットの次に来る、人とAIのリアルタイムな共同作業について音声・映像・テキストを横断しながら、AIとより自然に会話し、途中で割り込みや相談ができる未来像SpaceXのIPO観測からみる、スターリンクの収益性やインフラ企業としての強さAIデータセンターや宇宙インフラとの接続可能性イーロン・マスクとOpenAIの訴訟についておすすめコンテンツ①：YouTubeチャンネル「Hot Ones」おすすめコンテンツ②：Dwarkesh Podcast「Eric Jang – Building AlphaGo from scratch」 ＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝Level 5 by Palo Alto Insight への意見箱https://docs.google.com/forms/d/1XXj9G8RHOSJIARu4zylTsmecI1rOX0twLI4Ju14XwQA/viewform?edit_requested=true放送の感想やご質問は、こちらの意見箱へお寄せください。＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝【出演者】石角友愛 / 長谷川貴久 / 山崎壯⁠石角友愛のTwitter：⁠https://twitter.com/tomoechama⁠DM解放中！リプライやDMまで気軽にご連絡ください。パロアルトインサイトHP：⁠www.paloaltoinsight.com⁠楽曲提供： Atsu (beatmaker and rapper from Zenarchy)⁠https://twitter.com/atsu_izm⁠「Transform」Level5テーマソング⁠https://m.soundcloud.com/atsuizm/transform

In this episode, we discuss Thinking Machines Lab's latest announcement, Interaction Models, a new approach for making AI collaborate more naturally with people. We also look at why its upcoming limited research preview matters and how it could shape the next wave of real-time AI products. Our AI Hustle Skool Community: https://www.skool.com/aihustleGet the top 80+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiJaeden's latest vibe coded project (Bible Study Guide): https://learnofchrist.com/

Our 245th episode with a summary and discussion of last week's big AI news!Recorded on 05/13/2026Hosted by Andrey Kurenkov and Jeremie HarrisFeel free to email us your questions and feedback at andreyvkurenkov@gmail.com and/or hello@gladstone.aiRead out our text newsletter and comment on the podcast at https://lastweekin.ai/In this episode:OpenAI released new voice intelligence API features including GPT Realtime 2 (GPT-5-powered) plus realtime translation and Whisper transcription, emphasizing the latency–reasoning tradeoff, larger context, and new guardrails amid fraud risks.Thinking Machines previewed a low-latency, full‑duplex conversational system with a two-model architecture and custom inference stack, reporting strong interactivity benchmark results but without public access or third‑party validation yet.Anthropic pushed further into vertical products with Claude for Legal and deeper AWS availability, while ongoing ecosystem tension grows as platform model providers compete with application-layer companies.Safety, policy, and research updates included OpenAI's self-harm trusted contact feature, Anthropic work on reducing agent misalignment by training ethical “why” reasoning, OpenAI's investigation of accidental chain-of-thought grading in RL, and Meta horizon eval updates showing benchmarking limits for long task horizons.Timestamps:(00:00:10) Intro / Banter(00:01:35) Response to listener comments(00:03:27) Sponsor Break Tools & Apps(00:06:27) OpenAI launches new voice intelligence features in its API | TechCrunch(00:15:52) Thinking Machines drops a new, highly responsive model designed for humanlike interactions in real time - SiliconANGLE(00:27:49) Claude For Legal Launches, May Reshape the Legal Tech World – Artificial Lawyer(00:40:27) Threads tests a Meta AI integration that works similarly to Grok | TechCrunch(00:43:08) Google brings agentic AI and vibe-coded widgets to Android | TechCrunch(00:45:33) Google updates AI search to include quotes from Reddit and other sources | TechCrunch Applications & Business(00:47:38) Sam Altman was winning on the stand, but it might not be enough | The Verge(00:55:04) Nvidia C.E.O. Jensen Huang Hitches Ride With Trump to China After Last-Minute Invite - The New York Times(00:58:40) AWS expands Anthropic partnership with Claude Platform launch(01:01:13) Chinese grey market sells Claude API access at 90% off by using stolen credentials, model substitution, and harvesting users' prompts and outputs for resale as AI training data — 'transfer stations' operate through proxy networks that harvest user data(01:06:43) DeepMind Spinout Isomorphic Labs Raises $2.1 Billion to Design Drugs With AI - BloombergProjects & Open Source(01:09:04) Petri: Anthropic Hands Its Alignment Toolbox to Meridian Labs with 3.0 Update(01:12:25) Daybreak': OpenAI's Answer to Anthropic's Project Glasswing Has ArrivedPolicy & Safety(01:14:04) Teaching Claude why(01:21:45) Import AI 455: Automating AI Research(01:28:31) ChatGPT's New Safety Feature Could Alert 'Trusted Contact' to Risk of Self-Harm - CNET(01:30:09) Investigating the consequences of accidentally grading CoT during RL(01:34:46) Natural Language Autoencoders criticism(01:39:15) Review of the "Risks from automated R&D" section in the Anthropic Risk Report (February 2026)Synthetic Media & Art(01:43:39) George Clooney, Tom Hanks, and Meryl Streep back new ‘Human Consent Standard' for AI licensing | The VergeResearch & Advancements(01:45:10) METR says Claude Mythos is testing the limits of AI evaluation – Startup FortuneSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Photo by Valérie Ungerer on Unsplash Published 18 May 2026 e554 with Michael and Michael – stories and discussion on LLM phone number lookups, proctors returning to Princeton, lavish LEGO, LOTR and a whole lot more! While Andy is away, Michael and Michael get things started with a discussion on the changing nature of sensitive and private information.  What was once published in a phonebook is now a central identify hub.  While Jenny most certainly had to change her phone number from 867-5309 and have the new one unlisted, she likely posts what would have been very personal photos on Insta, Mastodon or any number of social media services.  Michael R points out that while a phone book was available for a municipality, it was not available at a country level, preserving a degree of anonymity. Continuing on the theme of social implications of technology, Michael and Michael consider the Atlantic's article about the demise of Princeton's honor code process.  Check out the link below for some fantastic quotes from the Daily Princetonian – sadly the newspaper online archives only go back to 2001. Next up is an article from Thinking Machines' full duplex capabilities for natural voice interaction with agents.   LEGO is in focus for this episode (surprise!) with two intriguing sets.  First, a super cool LEGO Ideas Tetris arcade game cabinet with a hidden room.  This reminded Michael M of the set he built that also has a cool hidden room inside.  Then, Michael R shares a bit on the new Minas Tirith set – which has many elements from the movies, and includes the opportunity for a GWP (gift with purchase) of the battering ram Grond if you're one of the first to plunk down your gold pieces for this build. The fact that this is up on the Internets on 18 May is due to the hard work from Andy.  He migrated our hosting over the weekend, and this is the first post on the new service.  Hurrah, Andy!   Do you still have a copy of your city's phonebook?  Have your bots (or agents!)

Tänast saadet alustame nukra tõdemusega et kodumaine elektrijalgrataste tootja Ampler on välja kuulutanud pankroti. Google korraldas traditsioonilise Android Show esitluse, kus näitas uut sülearvutit ja hulgim Android 17 uuendusi. Thinking Machines näitas aga AI-assistenti, mis vestleb sama orgaaniliselt nagu inimene. Saate lõpuks võtame vaatluse alla sangpommi kujulise kõlari Devialet Mania.Saate teemad:• Elektrijalgrataste tootja Ampler kuulutas välja pankroti• Google näitas Android Show veebiesitlusel uut sülearvutite kategooriat Googlebook.• Android 17 saab palju huvitavaid uuendusi.• Gemini hakkab Androidil ja Googlebookidel kasutaja soovile vastavaid ekraanividinaid looma.• Thinking Machines demonstreeris uut AI-assistentide taset.• Glen proovis kõrgtehnoloogilist ja kallist Devialet Mania wifi-kõlaritKui sul on meile küsimusi või tahad jagada oma kogemusi tehnikamaailmas, kirjuta meile: digisaade@geenius.ee.Saadet teevad Hans Lõugas, Glen Pilvre ja Meelis Väljamäe.Tunnusmuusika: Glen Pilvre, Paul Oja.

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(0:00) Salesforce CEO Marc Benioff joins the show! (1:14) Trump-Xi summit, doing business in China as a US company, impact on Americans and the midterms (18:46) Taiwan, chips, AI models, and peace through trade (31:41) AI's impact on software: What SaaS thrives, what SaaS dies? (47:26) OpenAI is considering suing Apple over failed ChatGPT integration (56:54) Thinking Machines releases real-time model, future of consumer AI, multi-sensory models (1:02:24) Science Corner: Impacts of a historically strong El Nino in 2026 (1:11:40) Anthropic goes after "Dark SPVs" Follow Marc Benioff: https://x.com/Benioff Save Scooter the Dog: https://animalcare.lacounty.gov https://www.instagram.com/reels/DYJZFn0R6oY Apply for Summit 2026: https://allin.com/events Follow the besties: https://x.com/chamath https://x.com/Jason https://x.com/DavidSacks https://x.com/friedberg https://x.com/altcap Follow on X: https://x.com/theallinpod Follow on Instagram: https://www.instagram.com/theallinpod Follow on TikTok: https://www.tiktok.com/@theallinpod Follow on LinkedIn: https://www.linkedin.com/company/allinpod Intro Music Credit: https://rb.gy/tppkzl https://x.com/yung_spielburg Intro Video Credit: https://x.com/TheZachEffect Referenced in the show: https://polymarket.com/event/will-china-invade-taiwan-before-2027 https://polymarket.com/event/will-china-invade-taiwan-by-december-31-2027 https://www.nytimes.com/2026/05/14/world/asia/china-xi-trump-taiwan-warning.html https://www.nytimes.com/2026/05/13/technology/andreessen-horowitz-politics.html https://www.instagram.com/reels/DYJZFn0R6oY https://finance.yahoo.com/sectors/technology/articles/openai-launches-4-billion-ai-134916653.html https://www.youtube.com/watch?v=KO53gwuqZUQ https://www.bloomberg.com/news/articles/2026-05-14/openai-apple-partnership-frays-setting-up-possible-legal-fight https://siliconangle.com/2026/05/11/thinking-machines-drops-new-highly-responsive-model-designed-humanlike-interactions-real-time/ https://x.com/thinkymachines/status/2053938892152435174 https://www.coindesk.com/markets/2026/05/12/anthropic-fights-unauthorized-stock-exposure-as-token-markets-imply-trillion-dollar-valuation https://support.claude.com/en/articles/13704655-unauthorized-anthropic-stock-sales-and-investment-scams

Thanks to  @HPInc  & Intel for sponsoring us! More on the Zbook Fury https://bit.ly/4uapNHs Google I/O is next week and the AI leaks are pouring out: a new Spark agent, Veo 4 Omni, Gemini 3.2 Flash that's reportedly 20x cheaper than GPT-5.5. This week on AI For Humans, Google is cooking again and the I/O leaks are stacking up. We dig into Google Spark, a new Gemini agent that may have access to your entire digital life. Veo 4 Omni model leaks suggest deeper reasoning and character consistency, and the model gets math right. Gemini 3.2 Flash is rumored to deliver 90% of GPT-5.5's capability at a fraction of the cost and dramatically faster speeds. There's a new GoogleBook with Gemini built in. And Google is reinventing the mouse cursor, the input device that's been largely unchanged since 1968, with voice AI. Plus, Thinking Machines dropped voice interactivity demos that feel a lot like ChatGPT Voice from two years ago. OpenAI is reportedly already working on GPT-5.6, and Sam Altman is giving away two free months of Codex to companies to drive adoption. Gavin's been experimenting with local open-source LLMs and shares his setup.  AND…we get into the data center sickness conversation: infrasound from data centers may be causing cortisol spikes in nearby communities. Figure 03's package sorting livestream proved the robot is autonomous after skeptics accused it of being teleoperated. Unitree dropped a transformable robot.  AI KEEPING US UP AT NIGHT. NO MATTER. WE COOK. // Show Links // Google Spark: Gemini's Agent With Access To Your Life https://x.com/kimmonismus/status/2054855742247584231?s=20 Veo 4 Omni Model Leaks: Gets Math Right https://x.com/TomLikesRobots/status/2053845600051798065?s=20 More Veo 4 Omni Examples https://x.com/testingcatalog/status/2053718756799467735?s=20 Omni Model Added To Gemini Web Build https://x.com/testingcatalog/status/2054196983523393857?s=20 Gemini 3.2 Flash At 90% Of GPT-5.5 For Way Less https://x.com/kimmonismus/status/2054887891222802633?s=20 New GoogleBook With Gemini Built In https://x.com/Google/status/2054270454467121187?s=20 Google DeepMind: Rethinking The Mouse Cursor With Voice AI https://deepmind.google/blog/ai-pointer Thinking Machines Voice Interactivity Demos https://thinkingmachines.ai/blog/interaction-models/ Sam Altman: Two Months Of Free Codex For Companies https://x.com/sama/status/2054626219858293128?s=20 Data Center Sickness: Ben Jordan's Video On Infrasound https://youtu.be/_bP80DEAbuo Figure 03 Package Sorting Livestream https://www.youtube.com/live/luU57hMhkak?si=KZHwUdYUwY4SIRUp Brett Adcock: Figure 03 Was Not Teleoperated https://x.com/adcock_brett/status/2054737974710169840?s=20 Unitree Transformable Robot https://x.com/UnitreeRobotics/status/2054067819634159622?s=20  

British tech journalist Chris Stokel-Walker reveals his hands-on approach to filtering the world's information overload with AI, from building custom news-gathering bots to teaching reporters where the human touch still matters. Find out how next-gen tools are reshaping the front lines of reporting, and what gets lost—and found—when machines do the first pass. Google announces its Chromebook successor: the Googlebook Google's $9.99-per-month AI health coach launches May 19 Google Says Criminal Hackers Used A.I. to Find a Major Software Flaw Students boo AI at commencement: video Sam Altman faces awkward grilling over 'toxic culture of lying' (20) rat king

Get the Voice AI Prompt Pack for Marketers: https://clickhubspot.com/fd5a Ep. 426 AI will give your brand an actual voice. Kipp dives into the hottest trend in AI that not many are talking about—Voice AI—and how it's set to revolutionize your brand's customer experience. Learn more on why real-time voice is the new marketing channel you can't afford to ignore, how foundational advances from OpenAI and Thinking Machines make AI conversations faster and more human than ever, and the steps every marketer should take to define, audit, and upgrade their brand's voice in the age of AI. Mentions GPT‑Realtime‑2 https://openai.com/index/advancing-voice-intelligence-with-new-models-in-the-api/ Thinking Machines https://thinkingmachines.ai/ Willow Voice https://willowvoice.com/ ElevenLabs https://elevenlabs.io/ Get our guide to build your own Custom GPT: https://clickhubspot.com/customgpt Resource [Free] Steal our favorite AI Prompts featured on the show! Grab them here: https://clickhubspot.com/aip We're on Social Media! Follow us for everyday marketing wisdom straight to your feed YouTube: ​​https://www.youtube.com/channel/UCGtXqPiNV8YC0GMUzY-EUFg  Twitter: https://twitter.com/matgpod  TikTok: https://www.tiktok.com/@matgpod  Thank you for tuning into Marketing Against The Grain! Don't forget to hit subscribe and follow us on Apple Podcasts (so you never miss an episode)! https://podcasts.apple.com/us/podcast/marketing-against-the-grain/id1616700934   If you love this show, please leave us a 5-Star Review https://link.chtbl.com/h9_sjBKH and share your favorite episodes with friends. We really appreciate your support. Host Links: Kipp Bodnar, https://twitter.com/kippbodnar   Kieran Flanagan, https://twitter.com/searchbrat  ‘Marketing Against The Grain' is a HubSpot Original Podcast // Brought to you by Hubspot Media // Produced by Darren Clarke.

British tech journalist Chris Stokel-Walker reveals his hands-on approach to filtering the world's information overload with AI, from building custom news-gathering bots to teaching reporters where the human touch still matters. Find out how next-gen tools are reshaping the front lines of reporting, and what gets lost—and found—when machines do the first pass. Google announces its Chromebook successor: the Googlebook Google's $9.99-per-month AI health coach launches May 19 Google Says Criminal Hackers Used A.I. to Find a Major Software Flaw Students boo AI at commencement: video Sam Altman faces awkward grilling over 'toxic culture of lying' (20) rat king

British tech journalist Chris Stokel-Walker reveals his hands-on approach to filtering the world's information overload with AI, from building custom news-gathering bots to teaching reporters where the human touch still matters. Find out how next-gen tools are reshaping the front lines of reporting, and what gets lost—and found—when machines do the first pass. Google announces its Chromebook successor: the Googlebook Google's $9.99-per-month AI health coach launches May 19 Google Says Criminal Hackers Used A.I. to Find a Major Software Flaw Students boo AI at commencement: video Sam Altman faces awkward grilling over 'toxic culture of lying' (20) rat king

British tech journalist Chris Stokel-Walker reveals his hands-on approach to filtering the world's information overload with AI, from building custom news-gathering bots to teaching reporters where the human touch still matters. Find out how next-gen tools are reshaping the front lines of reporting, and what gets lost—and found—when machines do the first pass. Google announces its Chromebook successor: the Googlebook Google's $9.99-per-month AI health coach launches May 19 Google Says Criminal Hackers Used A.I. to Find a Major Software Flaw Students boo AI at commencement: video Sam Altman faces awkward grilling over 'toxic culture of lying' (20) rat king

British tech journalist Chris Stokel-Walker reveals his hands-on approach to filtering the world's information overload with AI, from building custom news-gathering bots to teaching reporters where the human touch still matters. Find out how next-gen tools are reshaping the front lines of reporting, and what gets lost—and found—when machines do the first pass. Google announces its Chromebook successor: the Googlebook Google's $9.99-per-month AI health coach launches May 19 Google Says Criminal Hackers Used A.I. to Find a Major Software Flaw Students boo AI at commencement: video Sam Altman faces awkward grilling over 'toxic culture of lying' (20) rat king

British tech journalist Chris Stokel-Walker reveals his hands-on approach to filtering the world's information overload with AI, from building custom news-gathering bots to teaching reporters where the human touch still matters. Find out how next-gen tools are reshaping the front lines of reporting, and what gets lost—and found—when machines do the first pass. Google announces its Chromebook successor: the Googlebook Google's $9.99-per-month AI health coach launches May 19 Google Says Criminal Hackers Used A.I. to Find a Major Software Flaw Students boo AI at commencement: video Sam Altman faces awkward grilling over 'toxic culture of lying' (20) rat king

The future of AI isn't a smarter chatbot. It's a model that watches your screen, listens to the room, and acts on what it sees. We dug into Thinking Machines' new interaction model, what it means for compute, and the layoff wave that's already here.This week's roundtable: Anastasios Angelopoulos (CEO of Arena, formerly LMArena), Nick Harris (CEO of Lightmatter, photonic computing chips), and Philip Johnston (CEO of StarCloud, building megawatt data centers in space).Thank you to our exclusive sponsor:PayPal Open, One Platform for All Business: http://paypalopen.com/Timestamps:0:00 Cold open1:21 Welcome to Episode 132:51 Is China closing the AI gap? Arena's data5:16 Lightmatter and the photonic interconnect bottleneck9:42 StarCloud 2, Nvidia Space Ruben 1, and orbital data centers17:24 Thinking Machines' interaction model: what's actually new28:22 Whisper Flow and the 3-pedal desk setup33:48 Real-time desktop and camera awareness as the real unlock40:25 Why this 100x's compute demand42:43 The polarization of compute and $10M personal data centers49:25 The layoff wave: Cloudflare, PayPal, Coinbase, Upwork54:48 The 10x gap between AI-first and non-AI-first employees59:52 Unlimited agency and the abundance future1:00:46 Anthropic's Project Luna runs a retail store1:03:45 Decoupling labor from value creation1:05:03 P(doom) round

Instructure reached an agreement to pay attackers of its Canvas portal so students can get back to work, and Thinking Machines announced a research preview of a more natural conversation flow called Interaction Models.Starring Jason Howell and Tom Merritt.Links to stories discussed in this episode can be found here. Hosted on Acast. See acast.com/privacy for more information.

Deputy Bureau Chief of Finance Cory Weinberg and Mostly Metrics author CJ Gustafsson join TITV Host Akash Pasricha to break down how OpenAI stands to gain over $5 billion from the upcoming Cerebras IPO through unconventional "penny warrants". We then explore exclusive reporting from Aaron Holmes on Microsoft's renegotiated revenue-sharing deal with OpenAI and how the tech giant has already doubled its $13 billion investment. Next, Rocket Drew provides updates on the Musk-OpenAI trial featuring testimony from Satya Nadella and Ilya Sutskever, followed by Replit's Michele Catasta on the new "VibeBench" for AI coding models. We wrap with Stephanie Palazzolo discussing Thinking Machines' high-profile research preview of real-time interaction models.Articles discussed on this episode: https://www.theinformation.com/articles/openai-making-billions-just-promising-buy-suppliershttps://www.theinformation.com/articles/openai-save-97-billion-2030-latest-microsoft-dealhttps://www.theinformation.com/articles/microsoft-recouped-double-13-billion-openai-investment-revenueSubscribe: YouTube: https://www.youtube.com/@theinformation The Information: https://www.theinformation.com/subscribe_hSign up for the AI Agenda newsletter: https://www.theinformation.com/features/ai-agendaTITV airs weekdays on YouTube, X and LinkedIn at 10AM PT / 1PM ET. Or check us out wherever you get your podcasts.Follow us:X: https://x.com/theinformationIG: https://www.instagram.com/theinformation/TikTok: https://www.tiktok.com/@titv.theinformationLinkedIn: https://www.linkedin.com/company/theinformation/Chapters:00:00 - Introduction01:13 - Cerebras IPO: OpenAI's $5B Potential Windfall14:25 - Exclusive: Microsoft Recoups OpenAI Investment26:11 - Musk vs. OpenAI: Nadella & Sutskever Testify30:20 - Replit President on Benchmarking Coding Models40:21 - Thinking Machines Teases New AI Interaction Model

Google's Gemini Omni leaked online and it might be the first AI video model that gets text rendering right, including a professor writing mathematically accurate equations on a board. Unitree's GD-01, the same company that did the Chinese New Year robot fighting demo, dropped a mass-produced mecha suit weighing over 500 pounds at $650K. We share a workflow tip that swaps the default markdown LLM output for HTML, which turns every ChatGPT and Claude answer into an interactive mini website with tables, filters, and visuals. And Mira Murati's Thinking Machines just released a real-time interaction model that processes audio, video, and text continuously without the turn-based interruption problem.

Menlo Ventures' Venky Ganesan talks with TITV Host Akash Pasricha about Nvidia's Vera Rubin chip deal and investment into Thinking Machines Lab. We also talk with The Information's Aaron Holmes about Microsoft's new Office + Copilot bundle and its antitrust risks and Finance Editor Ken Brown about Amazon's $42 billion bond sale to fund AI infrastructure. Then we get into Tencent's WeChat AI agents with Juro Osawa and Jing Yang, and the vibe coding paradigm shift with South Park Commons GP Aditya Agarwal.Articles discussed on this episode: https://www.theinformation.com/articles/tencent-joins-chinas-ai-agent-race-top-secret-wechat-projecthttps://www.theinformation.com/articles/org-chart-microsoft-legal-staff-girding-cloud-bundling-suitshttps://www.theinformation.com/newsletters/applied-ai/microsoft-doubles-seat-based-pricing-aihttps://www.theinformation.com/briefings/amazon-raising-42-billion-bondsSubscribe: YouTube: https://www.youtube.com/@theinformation The Information: https://www.theinformation.com/subscribe_hSign up for the AI Agenda newsletter: https://www.theinformation.com/features/ai-agendaTITV airs weekdays on YouTube, X and LinkedIn at 10AM PT / 1PM ET. Or check us out wherever you get your podcasts.Follow us:X: https://x.com/theinformationIG: https://www.instagram.com/theinformation/TikTok: https://www.tiktok.com/@titv.theinformationLinkedIn: https://www.linkedin.com/company/theinformation/

AGENDA: 03:30 Can VC Survive With Public Market Prices Today 15:20 The Implosion of Thinking Machines 21:13 Elon Musk vs. OpenAI: The Legal Battle 40:50 Can OpenAI Win Ads? 55:50 ClickHouse's $15BN Deal: Analysed 58:55 Replit's $9BN Deal: Analysed 01:08:35 There Are Only Two Types of Deals VCs Want To Do Today      

Ads are finally coming to ChatGPT. Why everyone online can't stop talking about going on Claude benders. Elon's case against OpenAI moves forward again. The Thinking Machines saga roils on again. And the big movie about AI every is apparently watching. OpenAI brings advertising to ChatGPT in push for new revenue (FT) OpenAI's Revenue Soars Past $20 Billion After 233% Jump—But Explosive Growth Comes With Massive Compute Costs And A $17 Billion Burn Rate (Benzinga) Claude Is Taking the AI World by Storm, and Even Non-Nerds Are Blown Away (WSJ) ‘No Reasons to Own': Software Stocks Sink on Fear of New AI Tool (Bloomberg) Musk Seeks Up to $134 Billion Damages From OpenAI, Microsoft (Bloomberg) Thinking Machines Exodus Tests Investor Appetite for a $50 Billion Valuation (The Information) There's a Hit Movie Set Deep Inside an AI Lab—and It Will Give You Goosebumps (WSJ) Learn more about your ad choices. Visit megaphone.fm/adchoices

More fallout from the Thinking Machines stuff. I'm officially calling it: I think the Metaverse is over, at least at Meta. Cloudflare continues to make an effort to protect the web and creators from AI strip mining. And, of course, the weekend longreads suggestions. Learn more about your ad choices. Visit megaphone.fm/adchoices

Ranjan Roy from Margins is back for our weekly discussion of the latest tech news. We cover: 1) Gemini's case as undisputed AI leader 2) Google and Apple ink a deal for Gemini to fix Siri 3) Is all this AI going to hurt Google's business model? 4) Who will be better at AI ads: Google or OpenAI? 5) Google Gemini's Personal Intelligence 6) Exits at Thinking Machines Lab 7) Is Thinking Machines toast? 8) Claude work arrives! It's Claude Code for non-coders 9) Are we in the age of the empowered individual? 10) Harness Hive stand up! --- 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