The AI Model Tier List

It used to be that when it came to advanced AI models, all that anyone cared about was who was in the lead. Was the model from Anthropic or OpenAI or Google the best one out there? And was it better enough that it meant that I needed to switch right away? These days, things are getting a lot more sophisticated. Not only have all of these models reached a certain critical threshold where they can just do a lot more than any of those models used to be able to do, the sheer volume at which we are using AI on both individual, small team, and enterprise levels has created a moment where people and companies are thinking not only about capabilities, but also model efficiency and how they put together complete model architectures or model stacks that can allow for the right tasks to find the right models. Today, we're looking at a few ways in which that new moment is showing up in the numbers, as well as analyzing a popular AI YouTuber's AI model tier list. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. All right, friends, quick announcements before we dive in. First of all, thank you to today's sponsors, KPMG, Rackspace, Blitzy, and HyperAgent. To get an ad-free version of the show, go to patreon.com slash ai daily brief, or you can subscribe on Apple Podcasts. If you want to learn more about sponsoring the show, send us a note at sponsors at ai daily brief.ai. While you're at ai daily brief.ai, you can find out what else is going on in the community. Superintelligence next round of agent training programs for executives is kicking off at the beginning of September, and there's a link to register for those. And this week on Wednesday, we have a free webinar and hands-on lab, Agentic Loops for Knowledge Workers, which will try to take a thing that has been very buzzy and hypey in developer circles and make it relevant for all of you non-developers. Again, you can find all of that at ai daily brief.ai. The sub-theme that's going to run through both the headlines and the main episode today is about the growing place of open models in the overall model stack. And that is certainly the subtext of our first story, which is Hugging Face apparently courting acquisition partners. Business Insider reports that Hugging Face is seeking a $13 billion exit. Sources say they've engaged an investment bank to field offers, but no deal has been reached as of yet. The company's last round came all the way back in 2023 at a valuation of $4.5 billion. That round saw participation from Google, Amazon, NVIDIA, Intel, and Salesforce. Since then, the platform has, of course, only grown in prominence. It started off as a place for developers and researchers and enthusiasts to explore open models that, while, of course, they were interesting and important in a variety of different ways, weren't really in the consideration set for professional or business type of users. Over the past year, of course, the gap between open models and Frontier has closed, with open models crossing critical thresholds that allow them to be integrated into serious business workflows. In and around that change, Hugging Face has become a critical piece of infrastructure, hosting the latest model drops that can dramatically change how AI work gets done. AI commentator Rowan Paul wrote, Hugging Face now hosts more than 2 million models, 1.5 million datasets, and 1.5 million AI apps. A buyer would be acquiring the distribution layer around those assets, plus the workflow that helps developers find an artifact, judge whether it is safe, and put it into production. As open models multiply, that coordination layer becomes harder to replace. Both Stripe's purchase of OpenRouter and this new interest in Hugging Face look like a bet on persistent model fragmentation as the future. AI testing catalog writes, to be honest, for NVIDIA, it would make a lot of sense. And Jun Song expands, if NVIDIA acquires Hugging Face and actually taps into that data, they could, increasingly, drop an open-weight model that beats China before the end of the year. Certainly, it is the case that one of the under-followed NVIDIA products is their Nemotron series of models. But if you are paying attention, you certainly get the sense that NVIDIA is getting more and more serious about open models as a major piece of the competitive stack, which could make this type of deal pretty interesting. Adding some further heft to that idea, on Thursday, independent tech journalist Eric Newcomer reported that Poolside had accepted what amounted to a partial acquisition deal from NVIDIA. NVIDIA will pay $6 billion for a licensing deal to access Poolside's technology, alongside a billion-dollar equity investment at a $12 billion valuation. Poolside was founded in 2023 by a former GitHub CTO to train open-source foundation models geared towards software development. As part of the deal, NVIDIA will hire over 100 Poolside engineers away from the company to work on future iterations of their, yep, exactly, Nemotron models. Sources said that this is the bulk of Poolside's engineering team, but according to the letter sent to Poolside investors, this is not an acquisition and it is not an acqui-hire. A key distinction is that, unlike other huge acqui-hire deals in recent years, the founders and key leaders will remain at Poolside and will continue operating the startup with a focus on unspecified research projects. Sources said the plan was to staff up the Nemotron team for an attempt to build the world's most powerful open models, to rival Chinese labs like DeepSeek and Moonshot, specifically. In that same letter to shareholders, Poolside's founders wrote that the deal was controlled by a few, but one built by many out in the open. Elie Bacausch of Prime Intellect wrote, Wow, this is kind of a shock. From what I understand, NVIDIA bought the model factory part of Poolside and a lot of employees, researchers, got offers from NVIDIA. Founders staying at Poolside is unusual. Wondering if they will just become a neocloud-slash-compute provider, since I don't see any mention of PIC, Poolside Infrastructure Company, here. The Wall Street Journal reports that the deal came together in a hurry over recent weeks as a result of a busted fundraising round. Poolside founders wrote to shareholders, At the end of last year, we had a six-week window in which to raise $2 billion to pay for a 40,000 GB300 cluster coming online in January. We didn't close it in time and we lost the cluster. They said they dusted themselves off and got back to work, but quickly realized that they would run out of compute and capital as soon as next year. In their view, NVIDIA was the perfect partner to carry on the work of building a frontier open coding model. Now, just to add further heft to the idea that NVIDIA is going deeper on model training, last week the information reported that the company is taking part in the latest fundraising round for G2. NVIDIA is going deeper on model training. Last week, the information reported labeling startup Mercor, and notably NVIDIA used Mercor for reinforcement learning on their last two Nemotron models. On Sunday night, the information added reporting that NVIDIA is also participating in a new fundraising round for Perplexity. The round would value Perplexity at $30 billion, a 50% markup from their last fundraising round almost a year ago. Sources said that NVIDIA was initially interested in a licensing deal that would allow them to hire some staff, but are settling for a normal equity investment. Now, I think the chattering classes in the AI world are going to have a lot to say about this one. I would expect we'll hear more about it. But the point is that it's very clear that across all of these deals, NVIDIA is putting serious consideration into research, talent, training data, and the app layer as they look at the growing importance of the open source frontier. Now back to NVIDIA's core business. The information again reports that NVIDIA has begun notifying customers that the price for top-end Grace Black and Vera Rubin chips will increase by as much as 17%. The change applies to chips already ordered and set to be delivered next year. The price for a full 72 chip rack of up to $8 million, adding $5 billion to the cost of building a gigawatt of compute. Writes the information, it isn't clear whether cloud providers that buy NVIDIA chips will eat some of the price hikes or pass the cost to customers that rent the chips. One person with knowledge of the price hike said cloud providers will almost certainly need to pass on the increases to their customers. Bloomberg suggests the price increase stems from the spiraling cost of memory. NVIDIA already trimmed the amount of memory to be included on some Vera Rubin systems, but that hasn't made them immune to cost pressures. Overall, it seems like further confirmation that companies are positioning for a memory shortage that will stretch deep into next year or even longer. Now, speaking of positioning to deal with AIflation, Alibaba has raised $10 billion in a record-breaking share sale. The secondary share sale was executed on Friday at the market close, completing the largest offering of its kind in the Hong Kong market. Shares were down as much as 10% on Monday morning, their largest intraday drop since April of last year. The sale suggests that China is ramping up their AI build-out and starting to pull capital from every available source. Vaser and Ling, the managing director at Union, Banker Privé, noted this is a departure from Alibaba's tight management of share supply, asking, Big shorter Michael Burry was outspoken on Alibaba following the US tech giants into the AI CapEx wars. In a Substack post, he wrote, It is impressive as a disruptive force and I believe this will continue. But I cannot bless share issuance. This is a new paradigm again for Alibaba, and its return on invested capital will continue to fall. Elsewhere in the Chinese markets, a massive IPO marked the beginning of the humanoid robot hype cycle. Unitree Robotics went public on Wednesday on the Shanghai Stock Exchange, raising $900 million and debuting with a market cap of $9 billion. It appears that the offering was severely underpriced, with the stock surging more than 460% on the first day of trading. Bloomberg intelligence analyst Ian Ma said, Now, the information does note that a huge day one pop isn't all that unusual for Chinese IPOs. In fact, this is now the fourth IPO this year that rose by more than 400% on day one. A range of regulatory guardrails help boost day one performance mechanically by limiting selling, but the Chinese market also features smaller companies going public with much larger returns, which is a little bit different than the scenario here in the US. Lastly today, a bit of a narrative violation, Dr. Dre, at least, isn't worried about AI taking over the music industry. In a profile in the New York Times, the rap legend and his longtime producer, Jimmy Iovine, said that they believe that AI is good for music. Said Iovine, I'm very pro-AI in music creation. I don't see the downside at all. There will be some crappy music. There's crappy music now. In the studio, when gifted people have AI, they're going to make better records. At the same time, Iovine acknowledged, the AI companies have the worst public relations in the history of the world. I don't know the history of the world, but let's just put it this way, they have terrible communication skills. That's why everybody is all up in arms. Dre agreed completely, adding, I don't see it as a threat. I think the only people that see it as a threat are the people who have trouble creating. I had a discussion with a few people a few days ago. They were against AI, and I'm like, okay, you sound like the person who would have been against the drum machine when it came out. Or synthesizers, right? It's a new tool for creativity. Some people are afraid of learning new things. I'm embracing it. I can't wait to see what's going to happen with this. Dre said that he is extensively using AI in his work, particularly to see how the model might do it differently, kind of the musical equivalent of brainstorming. Ivy noted that Timbaland is also making use of the tools, commenting, there's a lot of closet AI producers out there. Dre added, that's a good way to put it. They're using it. They just don't want to admit it. I think it's a super interesting interview, particularly because music to me has always provided some of the best reason to not be concerned about AI infringing on creativity. If you're interested in that discussion, go dig up the interview I did with Rick Rubin from last year, where we get into why he as well views AI simply as a tool in the next generation of things that great musicians are going to use. For now though, that's going to do it for today's headlines. Next up, the main episode. A new study from KPMG in the University of Texas at Austin found that when people work with AI, similar skills don't guarantee similar outcomes. Researchers studied more than 500 early career professionals and found that the best performers consistently amplified the value of AI by guiding, evaluating, and refining its outputs. These top performers, called AI amplifiers, weren't defined by what they knew alone, but by how they worked with AI. Learn more about what separates AI amplifiers from everyone else at kpmg.com slash US slash AI amplifiers. One of the more interesting shifts in enterprise AI right now is how quickly the conversation is moving towards infrastructure and operations. As AI moves into core workflows, regulated data environments, and agentic systems, enterprises need governed infrastructure, and inference that can operate reliably day-to-day with clear operational accountability built in from the start. As those systems scale, the operating model increasingly becomes part of the AI strategy itself. Rackspace technology is the operator of the full enterprise AI stack, from agents to infrastructure, across private cloud, hybrid cloud, and edge environments. Rackspace builds and operates governed AI infrastructure, inference, and production AI systems for organizations where sovereignty, compliance, and uptime are non-negotiable. Therefore, deployed engineers stay embedded beyond deployment to help operate and manage the infrastructure. To learn more about where enterprise AI runs and outcomes scale, go to rackspace.com. Every AI coding tool on the market does the same thing first. It starts writing code. Blitzy does the opposite. Before writing a single line, Blitzy spends days reverse engineering your entire code base. Thousands of agents ingest millions of lines, mapping every dependency, every undocumented constraint, every architectural decision made over the last decade. The result is a dynamic knowledge graph that understands your software the way a principal engineer would after 30 years of experience. Other tools guess at context with grep searches and markdown files. Blitzy never guesses. It builds true understanding first, then delivers over 80% of entire software epics autonomously. Validated, end-to-end tested, production-grade pull requests. That's why Fortune 500 engineering teams trust Blitzy with the code bases that matter most. See for yourself at blitzy.com. That's B-L-I-T-Z-Y dot com. This episode of the AI Daily Brief is brought to you by HyperAgent, where you run fleets of agents your team can manage together. New users get to know you. Get $1,000 in inference. Forget local agents and chat workflows waiting on your laptop to be prompted. HyperAgent deploys always-on agents in the cloud, doing real work across the tools your team already uses. Marketing's agent turns competitor moves into landing pages. Sales' agent enriches leads, drafts emails, and updates the CRM. Ops' agent chases the paperwork and tracks the budget. Every agent has access to shared context and follows your rules about scope and approvals. It's time you add agents that feel like teammates. Hire yours at HyperAgent, built by the team at Airtable. Claim your $1,000. Claim your $1,000 in inference at HyperAgent.com slash AI Daily Brief. Welcome back to the AI Daily Brief. One very common kind of content that you see on social media these days is the tier list. Even back since before social media became a thing, people have always loved lists. It's why there's a billboard and a Forbes list and so many other examples. But on the internet, especially in the short-form video era, we really, really love putting things together. into tier lists. In other words, ranking them on a sort of grading A, B, C, D type of scale with the very top being S-tier, which, depending on who you ask, stands for either supreme or superior or just nothing and just S-tier and you just know what S-tier means. Over the weekend, AI entrepreneur and content creator Theo put together an AI model tier list. And as they do, it generated a ton of discussion. At the top of the list, he had Fable 5 in S-tier, GPT-56 Sol was in A, Kimi K3, DeepSeek V4 Flash, and GPT-56 Luna were in B, Grok 4.6 and MuseSpark 1.2 were in C, then below, yes, MuseSpark 1.2, down in D-tier were Opus 5, Sonnet 5, GLM 5.3, GPT-56 Terra, and Cursor slash SpaceX AI's Composer 2.5, DeepSeek V4 Pro was in F-tier, and down in their own sad tier below F, called the Google tier, was Gemini 3.7 Flash and Gemini 3.1 Pro. Now, we're going to explore this idea of a model tier list today, not just because it's fun to debate, although it is, but because one of the main things that's happening right now is a diversification of our model stacks. This is certainly happening on an individual level, and increasingly, it is happening on a business level, where organizations aren't simply picking one model or another, but building an infrastructure that can move between models based on different needs and different tasks. There is even a category of businesses that are made to do exactly this, the router companies, the best known of which, OpenRouter, was just acquired by Stripe for $7,000, and even mainstream media is picking up on the idea that the AI model war is no longer just about the pure state of the art, although, of course, they're doing it in a very incomplete kind of way. You might have seen this chart from the Financial Times flying around social media this weekend. The header of the chart is Anthropic's best model, Fable 5, has drawn limited sales, and it shows that across business spend on Anthropic, Opus 4.8 remains by far the most dominant model. In the last few weeks, as Opus 5 has come online, it has also outpaced Fable 5. In fact, at the moment, Sonnet 4.6 and Fable 5 are at pretty common levels. Now, for some folks, this is very surprising. Investor Dan Robinson wrote, This is pretty surprising to me and makes me rethink some assumptions. Are so many enterprise use cases really saturated by Opus? I can't really imagine not wanting frontier intelligence even for simpler tasks. Now, his comment section reflects a lot of the discourse about this chart that's flown around X and other places, which is to say that it's confidently sure that businesses in general are making a very conscious decision not to buy Fable because it's too expensive. It's also to say that they're making a very conscious decision not to buy Fable because it's too expensive. Without either, A, understanding the context of where this data comes from, or B, having any real experience with what AI in the enterprise actually involves. This data comes from the Ramp AI Index and was shared by Ramp's lead economist, Eric Harazian, about two weeks ago. Now, the Ramp team is great, and the work they do putting out economic analysis of AI is really good and incredibly valuable to the industry. But with this one, it was pretty clear to me that they had missed the analysis. When Eric introduced the chart, he added the summary statement, and said, It was so powerful it was briefly banned, and yet businesses don't think it's worth the price. Except, I don't think that businesses making a conscious decision that Fable 5 isn't worth the price has very much to do with this at all. It certainly might be a part of it, but one thing that was completely missed in the diagnosis was the fact that Fable 5 has a 30-day data retention policy. It was part of the provisional safeguards that came with it when the model came back online after being shut down by the government. That, all on its own, is enough for a huge number of enterprises to say absolutely not. There's just no way that causing all sorts of serious infosec and data concerns justifies upgrading to the next model when the models that don't have that data retention policy are still quite powerful. And if you need evidence that this is in fact a big part of this, just look at how aggressively in the past week OpenAI have been pushing their zero data retention policies for frontier models. Now to Aaron Ramp's credit, he actually came back later and said, A lot of replies from employees who say they aren't allowed to use Fable because Anthropic is required to retain prompts for 30 days for years. And that's not the only thing here. As Simon Smith points out, this data not only comes from Ramp, which is an extremely tech-forward company that only other pretty extremely tech-forward companies are interacting with, but comes specifically from a token and spend management product that users are using to try to minimize costs. Simon writes, Ramp data overall suffers from selection bias, and this data suffers from it even more so. This is from their token and spend management product, so users are predisposed to, Fable simply isn't cost-effective for most tasks. There is also the startup world blind spot showing through here, where the idea that it's shocking that enterprises in general haven't adopted a model that's just a few months old kind of misses the glacial pace at which most enterprises move. Shocking though it may be, I hear from people every single day who are still using GBT 5.2 and other models from nine months ago because that's what their companies give them access to. Which is not to say that the leading indicators don't suggest that enterprises are in fact getting more model fluent and building more complete model stacks. This week, for example, the information profiled AT&T and reported on their attempt to use open source models to cut down on their AI bills. AT&T's plan, according to Vice President of Data Science Mark Austin, is to hold spending with open AI in anthropic flat over coming years and slowly supplant that use with open models. The company has around 100,000 staff and has embedded AI into workflows across every department, ranging from coding, and financial analysis to HR and customer support. The vast majority of AT&T's AI use is internal, and the company claims that they are already using open models to service 40% of employees' AI queries. They plan to ratchet that percentage up to between 60% and 70% over the coming years. Austin said that he's found that open models are just as good or better than previous generation models from Anthropic or OpenAI, which were already up to the task. AT&T still uses frontier models for advanced tasks like generating code, but for simpler use cases like summarizing a PR, AT&T is now using an open model. Austin said we expect that to just keep getting better going forward. By the way, it's worth noting that the models that AT&T is using include NVIDIA's Nemotron, as well as open models from Meta and Google. AT&T is also making extensive use of model routers to drive further savings. For AI coding, Austin said that the use of a router has decreased cost by as much as 56%, while quality only fell 2%. Now, when it comes to competition with China, although at the moment they're not using any Chinese models, they are analyzing the risks of including them in the mix. And one thing which could change how they view that equation is that, Austin noted that switching to open models allowed them to host part of the service in their own data center stocked with NVIDIA and AMD chips, which was often cheaper than renting compute from cloud providers. The point being that thinking about what different models are good for compared to one another is in fact more than a vanity exercise and will be something that enterprises do more of, even if the Financial Times is just grabbing a chart that they can use to reinforce their pre-existing narratives. Now, back to Theo's list. Theo didn't only publish the list, he put a companion video with it. And he said, And to give a few of the highlights from that before we get into the takes, let's talk first about GPT-56 Sol at A-tier and Fable-5 at S-tier. On 56 Sol, he says, It's capable of things I never thought AI would ever be able to do. It's unbelievable what you could do. It's my default model I use for most things most of the time, but it's not the most intelligent model I use. It's still not my favorite for writing important code I actually hope to merge. Now, on Fable, he writes, Fable knows more than any model I've interacted with. Unbelievably thoughtful. He notes that it still trips over things, and touches things that it shouldn't sometimes, that it takes unnecessary shortcuts, and occasionally loses track of what it's doing. He calls Fable-5 a genius that needs to be tamed, whereas 56 Sol is a slightly dumber robot that does exactly what you tell it. Interestingly, even though he rated Fable-5 as the only S-tier above GPT-56 Sol's A-tier, he said, If I had to pick, I would pick Sol. It's the model I default to. I would miss Sol more than Fable. But Fable is the best model. It's the model that writes code I want to merge. It's the model I trust to double-check work from, It's the model I talk to about hard, deep things, with things I want to build or areas I want to explore. Fable-5 is the next generation. 56 Sol is an unbelievable model that feels next generation, while still being built on the last generation of tech. Fable is that genius at the company that no one wants to work with, but no one wants to fire because they're the smartest person there. If you learn how to work with them, it's incredible. Now, what's super interesting about this, is that this is pretty similar to my experience right now. On any given day, at any given moment, I am jockeying between these two. And for many tasks, I initiate the task in both of them, and after a little bit of back and forth, decide which one I want to hone in on, which tends to be, but is not always, Fable. To some extent, though, what's way more interesting than the A and S tier is how he ranks the other models. Because the other models aren't trying to compete with 56 Sol and Fable-5. They are meant to do different things. A really great example of this is that Luna, which is presented as the least capable of the three GPT-56 models, he has ranked a couple tiers ahead of the theoretically balanced middle Terra model. Of Luna at the B tier, he says, "It's not there because of coding, but because it is, in his words, 'smart, fast, and good at a bunch of random stuff.'" Luna, he says, "is probably my most used models by sheer calls to it. Not because I'm doing code with it, but I'm doing a bunch of other stuff with my code." The things that he's referring to are things like categorizing code, pulling from GitHub, reading content. Basically, he doesn't trust it with things that aren't reversible. Now, meanwhile, of Terra, he writes, "fits in such a weird place. A lot of these numbers can be gotten for much cheaper with Luna. I'd rather use Sol on high because it's going to be much faster because it generates fewer tokens. I have never chosen Terra for anything, and I would be surprised if many people do. It makes sense on a pricing chart, but doesn't make sense in reality for me." And I think what's interesting and what this reflects is that because we are just now coming into this model stack and complex model architecture type of moment, where companies are realistically thinking about different models for different tasks, we're starting to get more conscientious trade-offs in model design, with companies actually competing, not just at the state-of-the-art, but for various types of performance efficiencies based on what they hope people will do with their model. And as that transition happens, it's likely to me that you see a lot of models fall in kind of an uncanny middle, where they are neither frontier state-of-the-art models worth the premium that they cost, but are also not the most efficient or fast models for other types of use cases. For example, although Theo likes Kimmy K3, he reminded people in his video that it's not as cheap as people seem to think, that just because it's open weight doesn't mean it's cheaper, and that in fact, it costs slightly more than SOL on extra high, given that SOL does more with fewer tokens. Now, in terms of other people's responses, you get the impression that a lot of folks are just shilling for their personal favorite, and given that a lot of this analysis comes from X, as you might imagine, one of the most common commentaries was that Grok 4.6 needed to be higher. And yet, one other strand of analysis came from Noah, who said, I have zero understanding of how people develop opinions about ModelNow ever since Sonnet 4.5, to be honest. They're all fantastic, bro. Feddy's intern writes, One of the reasons I hope we reach AGI is that I'm tired of these model connoisseurs opining nonstop about subtle pseudo-differences between the models. This is starting to look like arguing about your favorite color or your favorite Pokemon. In a few years, we will laugh about all this. Even Theo, in his video, says, Whether you're using expensive best-in-class stuff like Fable or surprisingly cheap and effective stuff like DeepSeek V4 Flash, it's kind of hard to go wrong. I don't think a tier list is the best way to compare models right now because there's so many axes to compare on. Task capability, cost, token efficiency, speed, etc. And in many ways, what becomes more interesting than the tier list is the combined composition. And an interesting source of data for what that composition might look like and how it's changing comes from Vercel. Vercel CEO Guillermo Rauch recently showed how the balance of open-weight models versus closed-weight models had shifted on their AI Gateway product. In terms of the share of tokens used, on June 24th, a couple of months ago, closed-model tokens represented around 72%, while open-model tokens represented around 28%. Two months later, that ratio has largely flipped, with closed at 38% and open up to 62%. Now, if anything, the Vercel Gateway data is going to be even more heavily biased towards developers as it is specifically positioned as an AI routing tool for developers. And yet to some, it still shows where the winds might be blowing. Investor Gavin Baker shared the chart and said, more data that open-source AI is taking share from open AI and Anthropic. Super impressive given that the sum of open AI and Anthropic accelerated in July. So net token and AI infra demand accelerated even more than the acceleration we saw at the frontier. In Gavin's estimation, open-source AI taking share is positive for AI infrastructure demand as it lowers margins at the model layer and an open-source token costs just as much compute to produce as a frontier token. Nothing about open-source AI inference is free. Gavin predicts, most likely end state, in my opinion, is that closed frontier tokens are 60 to 90% of economic value, but only 15 to 25% of tokens. Doubling down on the conclusion, investor Daniel Newman adds two very important points here from Gavin. One, open-source models will be the highest utilization and consumption over closed-source. Two, frontier will still realize most of the economics because premium intelligence commands an economic premium. MIT's Christian Catalini thinks that it will actually split in three different ways. The first spend category is cheap generalist, which is the commodity open-weight models. On the other end of the spectrum is the state-of-the-art generalist, i.e. the tokens from the closed labs. And then in the middle in the category he's adding is what he calls the state-of-the-art specialists, those that combine open-weights with enterprise proprietary context. Now, while obviously Microsoft's models are not open-weights, this is the type of thesis that Microsoft seems to be pursuing with their Microsoft Foundry product, which allows companies to use their own data to post-train and build on the base of their MAI models. Although I think there's a lot of reasonable debate to be had around just how common that will be across all enterprises. What's clear is that we don't live in a world anymore where the only thing that matters is what's the best model. Increasingly, it will be important to understand where different models fit for different reasons. And even enterprises that, yes, move more slowly and stay a little bit more connected to a single ecosystem are probably going to want to set up environments where small groups of users can test various approaches to look for these new types of efficiencies. But does this mean that the days of getting excited about the latest state-of-the-art model release are gone? We'll have to see. A lot of chatter that Fable 5.1 is coming shortly, although it appears that OpenAI's Astra has been delayed until September, so we'll have a chance to find out soon. For now, that's going to do it for today's AI Daily Brief. Appreciate you listening or watching, as always. And until next time, peace.