The AI Model Tier List

0:00It 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 new moment where people and companies are thinking not only about capabilities, but 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.

0:40Today, 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.

1:00All 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 aidailybrief.ai. While you're at aidailybrief.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.

1:28And this week on Wednesday, we have a free webinar in 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 AIDailyBrief.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.

2:04Sources 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.

2:34Over 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.

3:07As 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 easily 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 Pneumotron 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.

3:45Adding 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 non-exclusive 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.

4:21Sources said that this is the bulk of poolside's engineering team, but according to the letter sent to poolside investors, quote, this is not an acquisition and it is not an acquihire. A key distinction is that unlike other huge acquihire 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 intended to create a future where AGI, quote, would not be a closed technology controlled by a few, but one built by many out in the open.

5:02Ali Bakaosh 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.

5:36We 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 data labeling startup Mercore, and notably NVIDIA used Mercore for reinforcement learning on their last two Nemotron models.

6:04On 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, so 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.

6:41Now 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 Verirubin 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 Vera Rubens is expected to reach$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.

7:16Bloomberg suggests the price increase stems from the spiraling cost of memory. NVIDIA already trimmed the amount of memory to be included on some Vera Rubens 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 AI-flation, 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.

7:45Shares 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 buildout and starting to pull capital from every available source. Vaser and Ling, the managing director at Union Bank Air Privé, noted this as a departure from Alibaba's tight management of share supply, asking, why not bonds? It tells me that they may need more funds than we expect for AI investments, and also that they may be rushing to be ahead of other companies. Big shorter Michael Burry was outspoken on Alibaba following the US tech giants into the AI CapEx Wars.

8:16In a Substack post, he wrote, Alibaba is making serious inroads in the commodity low-cost LLM bloodbath in the US. It is impressive as a disruptive force, and I believe this will continue. But I cannot bless share issuances. 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.

8:53Bloomberg intelligence analyst Ian Ma said, Unitree's debut surge signals strong appetite for China's embodied AI sector. IPO proceeds should accelerate AI development and commercialization. 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.

9:25Lastly 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 Iving, 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, Iving 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.

9:58They 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.

10:30Ivy 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 and the next generation of things that great musicians are going to use to create great music.

11:00For now though, that's going to do it for today's headlines. Next up, the main episode.

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14:05Welcome back to the AIDailyBrief. 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 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.

14:43Over 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 Soul 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.

15:36This 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, Open Router was just acquired by Stripe for$7 billion. 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.

16:07You might have seen this chart from the Financial Times flying around social media this weekend. The header of the chart is Anthropics 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?

16:41I 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 in 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 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.

17:13Now, the RAM 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 Ere introduced the chart, he added the summary statement, a model 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.

17:45It 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.

18:17Now 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 U.S. government safety checks. 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.

18:53This is from their token and spend management product, so users are predisposed to focus on cost control. 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.

19:32This 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 OpenAI in Anthropik 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.

20:08They 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 Open AI, 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.

20:39AT &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.

21:09The 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 to give a few of the highlights from that before we get into the takes, let's talk first about GPT-56Soul at A-tier and Fable 5 at S-tier. On 5-6Soul, he says, It's capable of things I never thought AI would ever be able to do.

21:41It'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 5-6-Soul is a slightly dumber robot that does exactly what you tell it.