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Today we are catching up on the most important recent AI news and talking about the calm before the AGI Storm. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. Alright friends, quick announcements before we dive in. First of all, thank you to today's sponsors, KPMG Blitzy, Robots in Pencils and Assembly. To get an ad free version of the show, go to patreon.com aidaily brief or you can subscribe on Apple Podcasts. To learn more about sponsoring the show, send us a Note@ SponsorsIDailyBrief AI now, one other quick announcement. For those of you who are looking for ways to get your team up to speed with building custom agents and agent teams, we are launching our second cohort of Enterprise Claw. The program is led by New Fargas Bar, who you've seen on this show numerous times, including last week, and you can find out more@EnterpriseClaw AI. I expect it will fill up quite quickly. So again, if you want to check it out, it's EnterpriseClaw AI. Now as for us here today I am back from traveling. We had a great spring break with the kids, got to see some giraffes and fireworks out our window at Disney, and my fears that some crazy thing would happen in AI that demanded that I pull my head up from the parks and put it back into AI land did not materialize. And yet, although there was not any one huge massive story like some new model coming out, the last week did have many stories that fell along the themes of our episode from yesterday, which was the six Questions Shaping AI. Even more than that though, I think when you take the sum total of the news from the last week or so, there is a very distinct picture emerging. I'm calling it the calm before the AGI Storm. And what it feels like to me is that even in the quiet moments for AI, the big labs are all jostling and positioning for a very different and fast moving future. You almost have that feeling of the electric charge that's in the air before a thunderstorm. So let's talk about the most important stories from last week and why they all add up to something that is even bigger than they might at first seem, as they are Wont to do. OpenAI was in the news throughout the week and the company actually began the week on a pretty high note as they closed a record breaking fundraising round. Now this is the round we've been hearing about for a while with the company having already announced the first 110 billion of the funding back in February. You might remember that that round was sourced from Amazon, Nvidia and SoftBank. But they have added an additional 12 billion to the round, this time from largely financial rather than strategic investors, meaning that the total size of the round is 122 billion, closed at an $852 billion valuation. Now, we had heard that the additional number was going to be around 10 billion, so the fact that it was 12 is a good sign that investor demand is still strong. For the first time, OpenAI took capital from individual investors through wealth management channels. That accounted for about 3 billion of this overall total. OpenAI also announced that their stock would be included in multiple ETFs managed by ARK Invest. Alongside the raise, OpenAI disclosed that they're now generating 2 billion in revenue per month, up from around 1.6 billion at the end of last year. The company said that they are currently growing revenue at four times the pace of the companies that defined the Internet and mobile eras, including Google and Meta. That said, the picture wasn't all rosy, although the fundraising was strong, Bloomberg reports that OpenAI stock is struggling to find buyers in secondary markets. Ken Smythe, who operates secondary marketplace Next Round Capital, said he's seen hundreds of millions of dollars worth of OpenAI stock come to market in recent weeks with no one biting. He said we literally couldn't find anyone in our pool of hundreds of institutional investors to take these shares. Meanwhile, he added that his buyers have suggested that they have about $2 billion in cash register ready to deploy into Anthropic. Now some are suggesting that the lack of demand for OpenAI and the apparent interest in Anthropic is simply about the gulf in valuations. Anthropic last raised at 380 billion, but shares are currently changing hands at up to a $600 billion implied valuation and for many that still seems cheap compared to the official $852 billion number for OpenAI. Adam Crawley of Augment Capital said, it's just better risk reward. Right now people are betting that Anthropic's valuation will catch up with OpenAI's, but if you buy OpenAI shares it's less clear what the return will be in the near term. Now this market dynamic has huge implications as the two companies race to go public. It is very important to note that it is not a one to one comparison between public markets and private market for secondaries, but it still isn't a great sign if you are on the OpenAI team that there is some amount of exhaustion in those secondary markets. The dynamic could also mean that OpenAI doesn't really have a choice to keep raising money from private markets and has to go to IPO anyways. We'll come back to IPO discussions because that would be a big part of OpenAI's story later in the week. Before that, though, we got upheaval in the C suite as there were several executive shuffles. CEO of AGI Deployment Fiji Simo announced that she would be taking several weeks of medical leave. Simo suffers from a chronic neuroimmune condition and had experienced a relapse shortly before beginning at OpenAI. In a memo to staff, Simo said she had deferred medical tests and new treatments in order to commit full time to her role. But after having caught up on some of those tests recently, she said, it's now clear that I've pushed a little too far and I really need to try new interventions to stabilize my health. With her taking a step back, President Greg Brockman will run the product organization, while Chief Strategy Officer Jason Kwan, Chief Financial Officer Sarah Fryer and Chief Revenue Officer Denise Dresser will take over the business and operations functions. In addition to this, there were several other leadership changes. Brad Lightcap will be stepping down as COO to begin a new role focused on special projects, which it sounds like will include leading the effort to form joint ventures with private equity firms as part of this larger sales and consulting play that we've been tracking. In addition to her function as Chief Revenue Officer, Denise Dresser will take on the COO role and Chief Marketing Officer Katie Rausch has stepped down to focus on her cancer recovery. She will return in a more narrow role as her health allows, with former META Chief Marketing Officer Gary Briggs filling in for roush until OpenAI can find a permanent replacement. Now this to me very much does not read as the type of leadership shakeup we've seen from other labs. As they really try to get their ship in order, it just kind of feels like a bunch of things that have really bad timing. Still, an executive reshuffle is basically the last thing that OpenAI needs this year as the competition with Anthropic heats up and they shift their focus to preparing to ipo. Indeed, on that front, over the weekend we got reporting that IPO strategy is a controversial topic among the leadership team. The information reported that Sam Altman and CFO Sarah Fryer are at odds over IPO timing and spending. Altman reportedly wants to take the company public in the fourth quarter, with reports suggesting he may even push to IPO ahead of anthropic who are targeting October to go public now. This is exactly what we talked about in our prediction post, where admittedly I said that I thought that when push came to shove, no one would actually go public in 2026, but that if that changed, it would be because OpenAI wanted to get out ahead of anthropic. CFO Sarah Fryer, for her part, apparently doesn't believe that OpenAI will be ready this year due to the procedural and organizational work required ahead of the ipo. Information sources also said that Fryer is concerned about the risks from OpenAI spending commitments. Reportedly, she has expressed skepticism that OpenAI will need to pour so much money into data centers, and also apparently has doubts about OpenAI's revenue growth's ability to support those spending commitments. According to the most recent figures, Altman is committed to spend 600 billion on infrastructure over the next five years, with OpenAI's forecast suggesting that they will burn 200 billion before turning a profit sometime towards the end of the decade. The information sources also suggest that this has gone beyond just a simple disagreement, suggesting that Altman has excluded Fryer from some conversations about financial plans, including a recent conversation about data center spending with a leading investor. The sources commented that her absence was noticeable and awkward, given that she was present for previous discussions. Part of what makes this notable is that is that Fryer was explicitly brought in to have these types of tough conversations and bolster investor confidence. She had previously taken on a similar role at Square, getting the company's books in order and shepherding them to a successful IPO back in 2015. One source said that Fryer has a hard job saying, quote, she's working with a founder with big ambitions who wants to push the envelope as hard as he can on spend. I think it's always worth taking these stories about executive disagreements with a grain of salt. The information sourcing is generally good, but it sounds like we're talking about a single missed meeting here, and there are about a million reasons why Friar might have not been at that meeting that don't come back to some Machiavellian psychodrama. That said, to the extent that the meta story that we're exploring is the ratcheting up of stakes and the feeling of anticipation as the acceleration gets closer, this does feel like it could be an example of that growing intensity. Now, of course, the other big piece of OpenAI news from last week was their acquisition of tech talk show tbpn. Now, many of you listening to this show have probably come across TVPN but for those who haven't, the show is a video podcast in the format of a daily talk show. They stream for three hours a day and have become a very desirable place for tech executives, startup founders and other commentators to show up talking about whatever the most recent news is. Part of why the story got so much traction is that it came hot on the heels of Fiji Simo mandating the cutting down of side quests, and to some seemed like exactly that. Despite the fact that it was reportedly Simo herself who was pushing for the acquisition, reactions were wildly varied. Any take that you have, you can probably go find someone who had it and who had it publicly on X There was a lot of confusion, especially among more traditional commentators. Wharton professor Paul Neri wrote, OpenAI acquiring TVPN makes zero sense to me an M&A professor New York Times reporter Mike Isaac argued that this was the culmination of tech's frustration with mainstream media coverage, a divide that has been growing for years now. He wrote, the OpenAI buying TVPN story is to me, the biggest proof point yet of CEO frustration with mainstream media coverage of tech at a time when consumers are growing increasingly skeptical of the effects of AI on society. I see this as a marketing expense, and indeed that's what a lot of people thought about this, that this was a way to try to present a better face to OpenAI for the world. Slow Ventures investor Jack Raines writes, everyone putting out the gigabrain future of marketing and media or whatever takes on TVPN and trying to apply that to other media brands is completely missing the point. This isn't a pattern matching thing. They aren't buying distribution. Sam's Twitter has bigger distribution than any tech media platform. The real takeaway is that if you build the right relationships with the right parties that find your skillset useful, they'll pay a gargantuan premium to work with you, same as ML researchers copping fat compensation packages. One thing that's interesting, though about this is that if the goal really is to bring John and Jordi, the founders and hosts of TPPN, closer into the OpenAI fold, the deal isn't really structured to do that. Indeed, they wrote very strict and clear editorial independence into the terms leading to attention pointed out by Simon Smith. He writes, here's the TBPN issue for me from a focused narrative standpoint, either it maximally supports OpenAI's current focus on productivity, in which case it can't have full editorial independence, or it has full editorial independence, in which case it's a Side quest. Basically what Simon is arguing is that it can't both actually stay editorially independent and at arm's length if it is supporting this new, more focused OpenAI. There are also a few like Robert Scoble, who thought that this was some big new play for the future of media. He writes, what does TVPN have A great library of the biggest AI thinkers. It's been interviewing the top CEOs every day for more than a year. That data set gives TVPN and OpenAI a huge dataset to train new models to do new kinds of journalism and create a 24 hour a day TV channel that's almost wholly AI generated or at very minimum AI produced. And frankly I don't think this has anything to do with it. I think all it comes down to is this. For more than a year now, OpenAI has felt the sting of being the company most associated with a technology that is, to say the least, very publicly controversial. And for a couple years the challenges inside the company have made coverage even more difficult. Meanwhile, they look over at TBPN and it seems to have good media juju. And so the logic is buy em and see if you can't get some of that lightning in a bottle for yourself. Ultimately I think it's a tough play. I think it creates some challenges for tppn. On the one hand, I don't think that people care overly who owns different media properties. But at the same time, at the very least, I don't think OpenAI direct competitors like Anthropic are gonna be particularly keen to break news with them anymore. To the extent that this is Vgsimo and OpenAI wanting to leverage the host talent outside of the show because of their marketing instincts, which is part of the logic that she shared internally. I think that could certainly make sense for OpenAI, but also might not be best for the TBPN audience. But I think that the single biggest challenge is that TBPN indexes extremely highly in a demographic that I don't think is OpenAI's main problem. Tbpn has become the preferred insider conversation space for tech, but at least so far hasn't broken super far outside of tech circles. I think going forward, not just OpenAI but every AI's company's biggest challenge from a communications perspective is not going to be winning the narrative battle inside tech. It's going to be with everyone else. At the same time, ultimately we are in uncharted territory and you kind of just have to make big moves and see how they work. All right. Folks, quick pause. Here's the uncomfortable truth. If your enterprise AI strategy is we bought some tools, you don't actually have a strategy. KPMG took the harder route and became their own client zero. They embedded AI in agents across the enterprise. How work gets done, how teams collaborate, how decisions move not as a tech initiative, but as a total operating model shift. And here's the real unlock that shift raised the ceiling on what people could do. Humans stayed firmly at the center while AI reduced friction, surfaced insight, and accelerated momentum. The outcome was a more capable, more empowered workforce. If you want to understand what that actually looks like in the real world, go to www.kpmg.us AI. That's www.kpmg.usa AI with the emergence of AI code generation in 2022, Nvidia master inventor and Harvard engineer Sid Pareshi took a contrarian stance. Inference, time, compute and agent orchestration, not pre training, would be the key to unlocking high quality AI driven software development in the enterprise. He believed the real breakthrough wasn't in how fast AI could generate code, but in how deeply it could reason to build enterprise grade applications. While the rest of the world focused on co pilots, he architected something fundamentally different. Blitzi, the first autonomous software development platform leveraging Thousands of agents that is purpose built for enterprise scale. Code bases. Fortune 500 leaders are unlocking 5x engineering velocity and delivering months of engineering work in a matter of days with Blitzi. Transform the way you develop software. Discover how@blitzi.com that's B L I T Z Y.com Today's episode is brought to you by Robots and Pencils, a company that is growing fast. Their work as a high growth AWS and databricks partner means that they're looking for elite talent ready to create real impact at Velocity. Their teams are made up of AI native engineers, strategists and designers who love solving hard problems and pushing how AI shows up in real products. They move quickly using roboworks, their agentic acceleration platform so teams can deliver meaningful outcomes in weeks, not months. They don't build big teams, they build high impact, nimble ones. The people there are wicked smart with patents, published research and work that's helped shaped entire categories. They work in velocity pods and studios that stay focused and move with intent. If you're ready for career defining, work with peers who challenge you and have your back, Robots and Pencils is the place. Explore open roles@rootsandpencils.com careers that's robotsandpencils.com careers. You've heard me talk about Assembly AI and their insanely accurate voice AI models, but they just shipped something big. Universal 3 Pro is a first of its kind class of speech language model that lets you prompt speech recognition with your own domain context and vocabulary instead of fixing transcripts and post processing. It's more flexible than traditional ASR and more deterministic than LLMs, so you get accurate output at the source and can capture the emotion behind human speech that transcripts often miss, all without custom models or post processing hacks. And to celebrate the launch, they're making it free to try for all of February. If you're building anything with voice, this one's worth a look. Head to AssemblyAI.com freeoffer to check it out. Moving away from OpenAI, though, their chief competitor Anthropic had one hell of a week themselves. On Tuesday, an update to Claude code included 512,000 lines of source code for the platform. Anthropic quickly removed the code, but not fast enough for the Internet, where people began hosting the code across various platforms. Anthropic's lawyers spent the following day taking down over 8,000 GitHub repos using copyright claims. But as it turned out, most of these repos weren't infringing and were actually forks of publicly released versions of Claude code. Anthropic's Boris Czerny later apologized for the mistake and retracted all but one of the takedown notices. Anthropic blamed the code release on human error and did not imply a security breach. The leak allowed us to learn about a few unreleased features that may be planned for Claude code. Perhaps most notably, the repo included an always on agent called Kairos, which allows Claude code to work in the background and send periodic updates to a user's phone. Kairos includes a dream mode, which allows it to autonomously consolidate memory across sessions, and it can also work in a proactive mode, allowing it to take initiative and make progress without needing instructions. On the other end of the spectrum of seriousness, the code included a virtual pet feature called Buddies. The Tamagotchi like features used duck avatars, and the source code implied that Anthropic hoped to generate what they called sustained Twitter buzz from the feature. Others took a deep dive into the code and found that Claude code is way more complex than it appears. It contains five different compaction strategies to compress context, dozens of tools, caching optimizations for sub agents, and highly configurable system prompts. Yuchen Jin wrote, one thing is clear from reading the code, harness engineering is hard and deeply non trivial. I think more AI rapper startups will try to win on product and harness first, gain distribution and then post train their own models later. Like what Cursor is doing. Still, somehow this wasn't the most negative news for Anthropic on the week. Early in the week, complaints around usage limits reached a fever pitch, with many claiming they were smoking through their usage in hours or even minutes. Some even claimed they were capped out after their first prompt of the session when using Opus. Many suspected this was a bug in the way that prompt caching was handled and the chatter grew so loud that Anthropic was forced to investigate. On Thursday, Claude co developer Lydia Halley delivered the results, posting peak hour limits are tighter and million token context sessions got bigger. That's most of what you're feeling. We fixed a few bugs along the way, but none were overcharging you. We also rolled out efficiency fixes and added popups in product to help avoid large prompt cache misses. Halley suggested that users should switch to Sonnet, lower effort levels and start fresh sessions instead of resuming after an hour. Alex Volkov, host of the Thursday AI Podcast, represented the voices of many when he found this response deeply unsatisfying. He said, oh, Anthropic's official response to everyone burning through their sessions is you're holding it wrong. Come on. I'm sure that this won't go well with the thousands of folks who experienced a significant decrease in their ability to use their Pro and Max plans and are canceling in favor of other solutions now. To cap it all off, on Friday Anthropic announced changes to charge more for people using Claude to manage their openclaw. As of Saturday, users will no longer be able to use their subscription for third party tools. Now, to be clear, you can still use CLAUDE models to drive openclaw, but you have to do it on a paper token basis via the API. OpenClaw creator Peter Steinberger said he had tried to talk OpenClaw out of this move, but only managed to delay the decision for a week. Author Daniel Jeffries suggested that this is about more than just the competitive dynamic between openclaw and Anthropic's CLAUDE code. He writes, I agree that this is a dumb move, but it was easily foreseeable. I've been saying for a year and a half that all of our subscriptions are heavily subsidized and that the agent economy will be freaking incredible, but freaking expensive too. All of those posts inevitably filled up with for now and AI will be able to do the job for pennies in your dreams. Grade school math says otherwise. The agent economy is not cheap. Anyone who thinks we will be running super intelligent agents around the clock on the most expensive chips ever made, chips that depreciate to worthless in three years while running in data centers on nuclear power is not doing the math. Things do get cheaper over time, but the key is over time the best models are more and more costly to build and run and will be for a long time barring some kind of revolutionary architecture that replaces the transformer with something much more memory efficient or wetware style chips that sip power, or both. Intelligence going up into the right keeps eating the bleeding edge of the best chips in memory as fast as we can make them. Older tasks will get cheaper and easier and on device models will be able to do cool things, but those machines are not cheap either. Unless you think 4Mac studios networked together is cheap. This smashes the AI does all the jobs theory to little bitty pieces. Good. It's terrible PR for the industry to keep babbling on about it anyway. True intelligence will be like paying for a full time salary to people and then the calculation of whether it's just cheaper and less error prone to throw more people at the problem comes into play. It's usually not cheaper to use the machine. All this is not to say that agents are not incredible and valuable, but the subsidy era is coming to an end. It always was and everything just happens faster in the age of AI. I think this is a super super salient point and one that we are going to be watching much more closely because if Daniel is right and that if we're actually about to see what it really costs to run these highly intelligent models and that that cost looks a lot closer to human salaries than we think it does. Obviously that has pretty dramatic implications for the whole jobs conversation. On the other end of the spectrum, Google actually increased its open source capabilities with the release of Gemma 4. The new model is the latest in Google's open source family and represents a significant jump in capabilities. Google claims the model family delivers state of the art performance across four different sizes. The lineup includes 2B and 4B models for small edge devices as well as a 26 billion mixture of experts model and a 31 billion dense model. The 31B model is currently ranked at number three on the Arena AI text leaderboard for open source models behind Kimik 2.5 thinking and Zai's GLM5. The models are optimized to deliver strong coding and agentic performance. With Google writing, this new level of intelligence per parameter means achieving frontier level capabilities with significantly less hardware overhead. The models are built on the same architecture that underpins Gemini models, meaning you can expect a similar feature set now. This is the first Western open source model competing at this level in years and could have big implications. Greg Eisenberg wrote thinking about Google's Gemma 4 and what it means a few months ago running something this capable locally meant serious hardware and serious trade offs on quality. Now it runs on your laptop, works offline on your phone, speaks 140 languages natively, 256k context window, costs nothing. LOL performs better than models 20x its size and you can swap it in as your model in Claude code, Cursor, Hermes or openclaw right now. Okay, here we go. It's a good time too, because in China, Alibaba continues its shift away from open Source Alibaba released three proprietary models in three days, culminating in the release of Quin 3.6 plus on Thursday. Even though this is closed, it's still in the good enough performance plus better cost camp. For example, it lags Opus 4.5 by a few points on Sweep Bench. Verified has full multimodal capabilities and utilizes a million token context window, but cost is massively reduced at around 1/8 of Opus. This release reinforces Alibaba's new strategy of proprietary models to capture more revenue from their models. Last month, three senior researchers, including QEN's team lead, stepped away from Alibaba, and a week later CEO Eddie Wu announced that he would take personal leadership of the AI division with a new focus on revenue maximization. Early returns seemed to validate the strategy. The Quen team announced on Friday that their new model was ranked number one on OpenRouter and had become the first model ever to serve a trillion tokens on release day. And of course, as a result of their shift to proprietary models, 100% of those token sales flow to Alibaba's bottom line. Meanwhile, China's tech giants are ramping up GPU deployments ahead of the new Deep SEQ model. The information reports that deep seq v4 is expected to finally arrive over the next few weeks. The hotly anticipated release could also be a watershed moment for China's semiconductor industry and their quest for self sufficiency. Sources said that orders are pouring in for new Huawei chips to serve the new model. Each of the tech giants have ordered hundreds of thousands of the unit, with the chip expected to begin mass production this month. Part of the reason the deepseaq model was delayed for this long was last minute optimizations to run on Huawei hardware. Deepseek has spent the last few months working directly with Huawei, including developing two variant models designed specifically for the chips. Staying in model release world for a moment, Microsoft is also keeping their superintelligence dream alive. On Thursday, Microsoft released three new models built for transcription, voice and image generation. None of the models are particularly notable except insofar as they demonstrate that Microsoft is back in the model training game. The last model from Microsoft was Mai1 preview, which was showcased last August but never publicly released. This range of models won't make a splash externally, but Microsoft plans to deploy them under the hood as a cost cutting measure in products like Microsoft Teams, which uses voice recognition and transcription at scale Last month, AI CEO Mustafa Suleyman was taken off commercial AI projects so he could focus solely on model training, with these small models seeming to be the first fruits of that change. While the current status is modest, Solyman has big ambitions. In an interview with Bloomberg, he said, we must deliver the absolute frontier, certainly by 2027. The objective is to really get to state of the art. Microsoft is putting resources behind the effort, standing up a training cluster of Nvidia GB200 Blackwell chips in October, Sullivan said. From there, we're ramping up over the next 12 to 18 months to get to Frontier scale computer. Separately, it seems that Copilot sales are back on track. On Thursday, commercial CEO Judson Altoff told staff that they had hit sales goals for the first quarter. He didn't disclose a number, but said leadership had set some pretty big, audacious goals. At the beginning of the year, Microsoft disclosed that only 3% of Office365 subscribers had purchased the $30 a month copilot add on. And while new bundles have changed the way the Copilot is sold, improving sales are still a positive sign. Microsoft has also folded anthropic models into their offering, now pitching Copilot as a way to access all the best models through their secure platform. Copilot sales, however, will remain the major focus, with Altaf telling staff, we're in a dogfight right now each and every day at the face of every single customer. On the compute and data center side, the Iran war and the associated energy shock is putting data center plans on hold as AI joins the front lines. Last Wednesday, the Iranian Revolutionary Guard declared that 18 US tech companies are now legitimate targets, their words for retaliation. The list included Nvidia, Apple, Microsoft and Google, with IRGC comms linking tech companies to AI enhanced targeting deployed in the war, an IRGC Telegram account declared. From now on, for every assassination, an American company will be destroyed. We already saw three Amazon data centers in Bahrain in the UAE hit by drones in the opening salvos of the war, so this threat could make further construction in the Middle east unviable in a less direct way. The energy shock is spurring a rethink on data center locations Bloomberg reports that projects across Asia are being scrutinized through a new lens. Asia is far more dependent on energy imports than Europe or the Americas, so that region is first to come into question. According to a Deloitte report from February, 800 billion in data projects are planned across Asia by the end of the decade. But obviously these changes could have pretty big implications for that in the US the limitation is in energy supply, but rather energy infrastructure. Bloomberg reports that more than half of US data centers are expected to face delays or cancellation due to a lack of electrical equipment. The infrastructure isn't in place and components like transformers, switchgear and batteries are in short supply. Now, electrical infrastructure is a relatively small part of data center projects, representing just 10% of total cost. But domestic supply is failing to keep pace and imports present a new set of supply chain challenges. Andrew Likens, the energy and infrastructure lead for data center developer Crusoe, said, if one piece of your supply chain is delayed, then your whole project can't deliver. It's a pretty wild puzzle at the moment. Now heading into this week, it feels like we're on the verge of the next set of models. Many people commented on what some thought was a new image model from OpenAI, which appeared on Arena AI over the weekend. In fact, there were three models codenamed masking tape, gaffer tape and packing tape, each of which showed strong world knowledge and text rendering. Some think this might be a first look at the forthcoming Spud model, which will be the first OpenAI LLM with native multimodal training. Now, for now it's just speculation and we don't know exactly what the model will be but but there is a lot of excitement and anticipation around it and the word from inside the labs, which dovetails with what we heard about the anthropic Mythos model, is that the next set of models that we're going to get are going to represent a major shift. In fact, the changes are enough that on Monday morning, just as I began recording, OpenAI dropped what is effectively a new social contract thought starter, which I imagine will be a big part of our show tomorrow the first line says it all. As we move towards superintelligence, incremental policy updates won't be enough. So friends, like I said, those are the most important stories from the last week or so. But it feels like we are on the verge of something much bigger. For now, that's going to do it for today's AI Daily brief. Appreciate you listening or watching as always. Until next time, peace.