A year ago, if you were talking about Frontier models, pretty much you were referring to a model from one of either OpenAI, Anthropic or Google. By a couple of months ago you were probably referring to a model just from either OpenAI or Anthropic. Now, however, things have changed over the past couple of months. Any conversation about model performance has to include a recognition of Chinese open weight models that are pushing the frontier of both efficiency and cost. And as of this week, SpaceX AI's Grok is back in the conversation. The just released Grok 4.6 is putting up benchmark numbers that put it in the category of a GPT 5.6 or a Fable 5 and doing so at a fraction of the cost. Although of course, as we know, AI in the benchmarks tends to be very different than AI in the real world. After some initial testing, while users are not ready to declare Grok4.6 a fable or GPT class model yet, they are ready to argue fairly definitively that Grok and SpaceX AI are back in the race. 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, Blitzi, hyperagent and Harbor. 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 at sponsorsdailybrief.AI and one other thing you should check out on aidailybrief AI. As you know, we've recently updated the website so now each episode has a full companion edition that includes all the key numbers, all the key quotes, all the key themes, each organized into different shareable cards that make it easy for you to find exactly the part that you want to share with someone else. We have now added an archive as well to hopefully make it easier to find previous episodes about a particular theme. It's organized on both an episode and a card basis and we'll be continuing to try to improve it as time goes on. Now with that out of the way, let's get to the headlines, which are all about big money, and into the change in the model landscape that's the subject of our main episode. Welcome back to the AI Daily Brief Headlines edition. All the daily AI news you need in around five minutes and the theme of today is Big Money. Cognition is seeking another funding round on the back of booming coding agent demand. Bloomberg reports that Cognition is in early talks with investors for new funding at a valuation of $40 billion. Cognition closed their last round just three months ago, raising a billion dollars at a $26 billion valuation. For those doing the quick math, that means that the company's valuation would be up almost 50% in a quarter, and the revenue figures seem to back it up. Sources familiar with the fundraising efforts said Cognition has doubled their revenue run rate to a billion dollars since they were last seeking funding. One source said that Cognition is seeking a billion dollars in this round, giving themselves a substantial increase in resources to address the current agent boom. The numbers also imply that the premium attached to coding agents is growing among venture investors. Cursor is one of the closest comps, and their last fundraising round in March saw them seeking a $50 billion valuation on 2 billion in annualized revenue. That round, of course, ended with SpaceX acquiring the company in a $60 billion all stock deal. And honestly, if Cognition has the ability to price their round at 40 billion, the SpaceX deal could start to look like a bargain. Many think that the path that cursor took with SpaceX feels inevitable for Cognition as well, writes Richard Wu. I wouldn't be surprised if within the next six to 12 months we see one of the hyperscalers preempt Cognition and offer to acquire them for 60 to 100 billion in stock. Given the success with SpaceX acquiring cursor, the boards of these companies will put pressure on them to make a move. Jeff Wu says Google should buy Cognition for 200 billion and make Scott Wu, CEO Sandeep from Cognition, responded, we aren't selling also 200 billion. The stock would move three times that in after hours alone. Next up, we have Lovable, who announced their $400 million Series C round at a $13.3 billion valuation. What's interesting is that you can clearly see how Lovable is evolving just in the way that they describe themselves in their fundraising announcement. In short, Lovable feels to me to be inching farther away from Claude Code and closer towards something like Shopify, they write. Lovable is building the software creation platform that gives those closest to a problem the power to solve it, a generational opportunity that spans billions of people all over the world. For most people, turning an idea into software once required so much capital, technical fluency and time that many ideas never came to life. Lovable's first chapter was about changing that. Since our Series B in December 2025, we've been building features people need to reach customers, manage day to day operations, and run software securely for many builders, the product they create with Lovable is becoming the business itself. User survey data shows us that nearly 8 in 10 are building a business or side project they hope to monetize, and more than one third of those are already earning revenue. In CEO CEO Antoine Osica's post, he absolutely emphasizes the same idea, saying that Lovable will create, quote, the most intuitive platform to build and run a business. If you are looking for a place to see the intersection of where what was once called vive coding meets the actual transformation of small and digital businesses, look no further than Lovable now moving into public markets, Businesses booming for the Neo Clouds as AI demand continues to rise this week saw Core Weave and Nebbys report earnings, both vastly outstripping analyst expectations. On Tuesday night, Core Weave reported that revenue had doubled over the past year to reach 2.6 billion for the quarter. At the same time, Cash Burn also doubled, now running at 5.7 billion per quarter. Still, the big story for investors was a line out the door for compute. Corey reported a $104 billion backlog in demand. In the footnotes, they added that the backlog had grown by 25 billion since they closed their books at the end of June. The story was the same for Nebias, who reported on Wednesday they recorded 454% revenue growth over the past year to reach 582 million. Their cash burn is also escalating rapidly, but like Corweave, Nebias has endless demand, with CEO Arkady Velos telling investors, demand for what we are building continues to be enormous. We could sell Today our entire 2027 capacity if we wanted. Supply is in fact so tight that Nevius is seeing huge profits on their available capacity. Earnings per share beat analysts forecasts by 83%, and Volos told investors that their auctions for Blackwell Compute, which began in Q2, cleared at 15% above their previous record price for Hopper. Compute Markets rewarded both stocks with core weave up 19% since reporting and Nebias gaining a staggering 34%. Analysts believe that Neo Clouds are some of the best indicators of marginal demand for AI as they service the overflow from the hyperscalers. And even during a quarter when token austerity came into vogue, demand is showing no signs of slowing. Meanwhile, the infrastructure boom also is coming to China as Tencent has tripled their capex. Tencent reported that they spent 7.8 billion on AI infrastructure in the past quarter, boosting their training and inference fleet. Now, of course, that spending is still relatively modest compared to the US hyperscalers where Meta had the slowest capex in their group and spent 31.9 billion in Q2. Still, there's a pretty clear attitude shift as the Chinese tech giants commit to scaling up their data center construction. During an earnings call on Wednesday, Chief Strategy Officer James Mitchell said, we are allocating a very substantial portion of new compute to our own models and applications. The company's revenue is growing at 11%, but free cash flow has dipped into the negative with incremental earnings going toward infrastructure. Tencent President Martin Lau said that Tencent could monetize their compute by selling to outside customers if they wanted to, but for now they're prioritizing their own needs. Basically just like model training. It seems like China's AI buildout and the narratives around it are three to six months behind the US as well. It is uncanny how closely this is. Following the narratives from the US in Q1, hyperscalers flipped to negative free cash flow folks like Zuckerberg appeasing the market by telling them that he could sell his compute but he doesn't want to. I'm not sure I think that US market participants have fully accounted for a Chinese capex boom and what it does for the larger global investment environment. Meanwhile, Samsung is seeing incredible efficiency gains from their use of AI in chip design. According to reports from a Korean outlet, the first three months of integrating CLAUDE code into the software stack have been an outstanding success. Development personnel have been able to cut down the time to complete complex tasks like system on chip verification from three months to two days. In one example, a second year engineer was able to complete a month long task in a single day. Now of course this report doesn't claim that CLAUDE code produced efficiency gains throughout the entire chip design process, but it does seem like an interesting example of the jagged frontier of AI adoption in the enterprise. CLAUDE code was able to make highly customized jobs more efficient and able to help a junior employee contribute way beyond their expertise. Lastly today, some reported updates coming to the Trump administration's model testing framework. Last Tuesday, Leading Frontier Labs were briefed on that framework. Although the rest of us didn't get to learn all the details, it was reported that the policy would cover only state of the art models, although we didn't know how exactly that was defined. What we did hear with a fair degree of confidence was that the policy wouldn't cover open models. Open source advocates were relieved at that decision, but there was also a contingent of China hawks who believed that this would leave a gap. On Wednesday, Wired reported that the administration has changed their mind. An official said that the White House is expected to expand the policy to cover open models in the coming months. The policy, they added, is aimed at ensuring that as soon as open models reach the same capabilities as Mythos or GPT 5.6, they're added to the safety testing framework. White House officials said the administration had hoped the policy would be one and done, but the exponential development of model capabilities had forced them to iterate. When it comes to the inclusion of open models, the thinking is that leaving them out of the framework could actually create a two tiered system that would be negative for those open models. Specifically, officials are concerned that the framework could be viewed as a stamp of approval, leaving enterprises hesitant to use open models if they don't receive the same testing. The concern then, is that leaving open models out could actually disincentivize US Labs from developing those open models, adding some evidence to the idea that the government is pro U.S. open models. Treasury Secretary Scott Bessen actually retweeted Mark Zuckerberg this week, saying we welcome Meta's release of Muse Glimmer. Another win for American innovation. Sustaining US Leadership in AI means advancing both open and closed weight models, ensuring the future is built on trusted foundations. Overall, it's still pretty clear that there's a lot of consternation around the administration policy. President Trump himself is reportedly insisting on keeping the framework voluntary, as he believes formal regulation will help China catch up. But by the same token, the safety focused faction of the administration also isn't satisfied and are reportedly still pushing for a more formal arrangement. Who the heck knows how that's all going to turn out? But still, this is a perfect segue to a broader discussion of the state of models. So for now, that's going to do it for today's headlines. Next up, the main episode. Hello everyone. One big change around AI is We've shifted our thinking from how we rank our pages to how do we become the source that AI trusts enough to answer with? At kpmg, they're seeing this firsthand AI generated results now surface answers directly, often without a single click. That's why they are increasingly focused on generative engine optimization or geostructuring content, so AI systems can retrieve it, understand it, and cite it as trusted authority. This is not just an SEO evolution, but a visibility mandate. And indeed, the GEO mandate from KPMG is simple. If AI is shaping decisions, your expertise needs to show up inside the answer. 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Investing involves risk, including possible loss of principle. Welcome back to the AI Daily Brief. The big news that we are covering today is the release of Grok 4.6, which is getting some pretty good reviews out of the gate. But what's interesting to me is not just the model itself, but what it says about the state of the AI race and how that's changing. Now the version of the AI race story that I am concerned with mostly here of course, the one that has to do not just with the achievement of some ill defined far flung goal like AGI or asi, but the practical impacts on where different labs are for what we get to do with AI at home and in our companies. I think CNBC's Deirdre Bossa summed up the vibes when she tweeted yesterday, what a difference a year makes. A year ago, Frontier basically meant the big three US closed labs, OpenAI, Anthropic and Google. Now a credible list includes XAI and multiple Chinese and open weight labs. And while we'll get into the implications for the leading labs in a minute, I think Nathan Lambert also gets at another part of the sentiment when he writes the vibe shifting from Anthropic is so far ahead to model competition back to all time highs took like four weeks. So let's talk Grok4.6 first. The release appears to put SpaceX AI squarely back in the Frontier model competition. Regular listeners will know that I take any release benchmarks not just with a grain of salt, but with an entire bowl full. But still, Grok's reported benchmarks are pretty hard to ignore on GDP val, which is the measure of how agentic AI performs on economically valuable tasks. SpaceX AI claims to have overtaken both GPT5.6 SOL and Fable 5 by a small amount, yes, but taken over them nonetheless. Coding performance is improving as well, with Grok scoring right between but in the range of 5.6Sol and Fable 5 on Cursorbench being a few points behind on both Deep Suite and Terminal benchmark. On the overall Artificial analysis Intelligence Index, Grok 4.6 jumped a full five points from Grok 4.5's 56 to achieve an overall score of 61. That puts it ahead of Kimik 3, tied with 5.6Sol, and just a point or two behind Fable 5 and Opus 5. What that means is that if this was a new model from either Anthropic or OpenAI, we'd probably be talking about how it's not quite state of the art and didn't push the Frontier forward. But but for SpaceX AI, who many had written out of the model race until fairly recently, this is a huge achievement, summed up by the broad sense that you can see across AI circles that we once again have three Frontier Labs in the race. Also, while SpaceX AI has massively improved Grok's performance from 4.5, it seems like they're still working from the same base model as Grok 4.5. Pricing remains the same at $2 per million input tokens and $6 per million output tokens, making it 60% cheaper than GPT5.6 SOL on a per token basis. Of course, as we know, comparing tokens to tokens is a seductive but ultimately fraught exercise given the massive differences in how many tokens different models might use to solve the same problem. But once again, artificial analysis is testing found that the model is pretty token efficient as well. It completed the Benchmark run at $0.84 per task, putting it in line with Kimik 3 and making it 32% cheaper than GPT5.6 SOL and 73% cheaper than Fable, writes investor Gavin Baker. Absolute Pareto dominance for Grok and Cursor even after the OpenAI price cuts. Now, in terms of reactions for the community, for many folks it was just gobsmacked at the achievement overall, Vittorio writes, so they just caught up in three years. How does Elon do it? Ben Davis writes, Grok 4.6 feels very good on first Tests, very fast and capable and cheap. But time will tell. As always, the cursor and SpaceX AI comeback is glorious to watch on Martin Casado from A16Z's highly technical tests. He found that it was strong. Pavel Huron writes, tried Grok 4.6 on my bugbench an hour after release 105 hidden bugs in 2 real repos judged blind. His conclusion looks like it may be my new default model. The best combination of time, value and cost. And yet some folks did not have that same experience. Mehul mohan writes, Grok 4.6 is not as good as GPT5.6 SOL in my 30 minutes of usage. It does incomplete work. Not incorrect, just incomplete. Maybe it's the Grok harness. Justin Schroeder writes, early vibes on Grok 4.6 are not great. It's fast and is willing to do security work. I've already seen multiple instances where it makes dangerous mistakes and later tries to cover up poor decisions. It even gets defensive. Unfortunately, we cannot trust it. Entrepreneur Timmy McKeegan writes Grok 4.6 is one of the most oddly behaved models I've seen so far. It produces many times the output tokens compared to Terra or any similar intelligence model. It is cheap and fast, but takes everything extremely seriously and always investigates unclear information. It values completeness above everything, including economics. The model seems to be designed to be economically viable, but acts differently now. When someone tried to clarify if this is a positive or a negative sign, Timmy kind of shrugged and said probably positive. Benjamin decracker tried to sum up Lots of people acting like Grok 4.6 just beat anthropic and OpenAI when really it didn't. The Grok 4.6 numbers show that XAI is not out of the race, but also not at the top. It's in the middle, topish against models that the competition is already getting ready to update. It shows that Grok still has a pulse, which is a good but different thing, he continues. Or in sports terms, they advance past a critical wild card game into the playoffs, but are mid rank against tough competition. They prove they can still hang not yet winning everything. And by the way, he clarified, this is not a slight against Grok 4.6, which looks solid, just a read of the actual rankings and situation. Now of course what Benjamin is referring to is the fact that 4.6is being compared against GPT 5.6 and Fable 5, when both of those models are at this point several months old. And pretty much the only reason we don't have updates of them is that we're now past the threshold where the US government is going to be involved in every big new model release. And so state of the art for us is very different from state of the art at those top labs. However, it sounds like Grok 4.6 is itself just a waypoint. Elon Musk tweeted, Grok 4.7 is significantly better than 4.6 and should be ready in 3 to 4 weeks. Initial training is complete and now we're adding a massive amount of SpaceX company data in supplemental training. This will be something special. In another Tweet he said Grok 4.7 will exceed all current models. That said, Anthropic is a great company and will probably release improved models soon. However, the SpaceX training corpus is so awesome and unique that I would be shocked if any model is better at real world engineering than 4.7. Capturing the zeitgeist of credulity around these claims, Chubby shared both those posts and said, I'm taking this seriously now. Grok 4.6 was the leap I've been hoping for. If the 10T model is still to come, then Elon's words can be taken seriously. It really could become the best model in general. Although of course anthropic already has Fable 5.5 ready and just waiting to be released. That much is clear. Nevertheless, the next few weeks will be exciting, and Xai has shown just how much potential they possess. Leo at synthwave DD adds, Xai have made an incredible comeback from the days of Grok 4 to 4.3, where they were trailing the Frontier by far. They're now arguably the third best lab in the world, behind only Anthropic and OpenAI. So where does this leave the rest of the field? Well, first of all, there's Google, the company that many feel Anthropic has now overtaken as the definitive third place when it comes to state of the art models. After last week's departure of DeepMind CEO Demis Hassabis and longtime product leader Jeff Dean, many are basically counting Google completely out of the Frontier AI race. The counterpoint, however, is that it appears that co founder Sergey Brin is back in the picture to spur a comeback for Gemini. Reuters reported that Brin has become a key cheerleader for Google's AI team in recent months, encouraging AI engineers to catch up in the AI race. He reportedly addressed the town hall after the release of Mythos, telling engineers it's time for Google to play catch up. Sergey had, of course, been out of the picture for several years after stepping down as president in 2019. However, he returned to frequent work at Google in 2023 and stepped into his involvement with the AI team in 2024, just as they were getting back on track ahead of the release of Gemini 2. During last week's news cycle, we had already heard that Google was relocating AI training out of the DeepMind office in London and back to the main campus in Mountain View. That relocation would conveniently allow Bryn to play a more active role, working day to day with key researchers. And of course, given what else we've heard about internal Google politics, one of the big benefits to having Sergey fully engaged is that presumably he's one of the few people that could effortlessly cut through that bureaucracy to get things done at Google, according to the Reuters report that came out on Wednesday. That has already begun, reuters writes. Brin has used the implicit power he holds as Google's co founder to push resource allocation towards specific areas such as recursive self improvement. And to some, this is a good enough reason, all on its own to not count Google out. Nick, the CS guy from Google, writes, don't mess with Sergey and definitely don't underestimate what he can do. Still, others think that Google is just temperamentally ill suited to this particular race. Computer science professor Pedro Domingos writes, hey Sundar, getting DeepMind to be an LLM lab is trying to shove a square peg into a round hole. You're destroying them and you'll still lose the race. Let them focus on AI beyond LLMs, which is what they're good at, and create a nimble new lab to run the LLM race. Now, when it comes to what models we can expect next, I think at this point broad sentiment is that it would not be enough to recapture momentum by releasing a competent Gemini 3.5 pro. At this point, we're already a couple months behind when we expected to get it, and just catching up I think would be seen as a failure. According to Leo and some other leakers I've seen, the reports are that teams are instead shifting to work on the scaled up Gemini 4, which while risky, I think does make sense in context. Now, as Deirdre pointed out in that tweet at the top of this show, the Top Model Labs question now has to necessarily include a bunch of entrants from China. And interestingly, just a few hours after Grok 4.6 launched, we got a significant leak out of China. Specifically, we got the benchmarks for the updated version of Deepseek v4 Pro, and they appear, on paper at least, to be very competitive. For example, these leaked benchmarks claim that the forthcoming model scored 87.9% on Terminalbench 2.1, putting it just 0.1% behind Fable and 1.1% behind GPT5.6 SOL. It also claims to beat Fable by 0.2% on Cyber Gym, the main cybersecurity benchmark. Now, as always, there's the risk that this is just benchmark maxing and actual performance will feel a little flat. And unfortunately, almost as soon as these leaks started appearing, other information came out suggesting that the model was more significantly behind than the benchmarks would have it seem. Artificial analysis benchmark run was pretty disappointing, with V4 Pro scoring just 53. That's only one point ahead of V4 Flash and trails behind Kimik 3 and Musepark 1.2. On the plus side, the model is pretty cheap, even after Deepseek delivered a substantial price increase this morning at $1.32 per million input and 396 per million output, it's about 1/12 the price of Fable and slightly cheaper than Musespark. And people's first impressions also aren't that great. Lucky Faraday writes, Deepseek V4 Pro is Benchmax slop. I had high hopes for this model, but it's complete trash. This was supposed to be a Fable level model and it can't even make a simple Minecraft clone. Even Deepseek V4 Flash did a better job. I know a Minecraft clone isn't a good test for a model, but come on, this is complete nonsense. And before the don't compare a less than $1 output model to Frontier model replies, they are the ones comparing themselves to the Frontier, not me. Still, others pointed out that when we're discussing models in the second half of 2026, it is less about raw performance alone and more about where they fit in the model stack. Dax from OpenCode says, DeepSeek is insanely good at inference, using about two times less GPU time. And Augustin Lebron writes, I'm sure Kimik3 and Grok 4.6 and DeepSeek V4 Pro are benchmarks more than Fable and GPT, but it doesn't matter. These models are an order of magnitude cheaper. As the Frontier proceeds, fewer and fewer people need the bleeding edge and need it less often. And at first glance, Ramp's latest AI index seems to provide some evidence of that. Ramp's lead economist Ara Kharazyan writes new from Ramp AI index disappointing adoption of Fable 5 We've heard several reasons from businesses, mainly Fable 5 is just too expensive a model so powerful it was briefly banned, and yet businesses don't think it's worth the price. Specifically, Ramp found that Fable 5 has made up only 6% of tokens that businesses purchased from Anthropic and represented only 11.4% of dollars spent on anthropic models. For Comparison, they write, OpenAI's GPT5.6 SOL comprises 25% of OpenAI tokens and 23% of spend. In fact, they say Fable 5 is less popular with businesses than GPT 5.6 overall. Ramp argues that quote with Fable 5 We found a new upper bound to how much businesses are willing to spend on AI. Here, more performance is not worth the price tag. To encourage business adoption of the latest models, the labs will need to prove performance beyond even what Fable 5 is able to achieve and simultaneously ensure that competitors aren't able to come reasonably close. That seems increasingly out of reach, especially as open source models catch up to being only a few months behind. However, I think that story is much less clear than they're letting on. First of all, as Simon Smith points out, ramp data overall suffers from selection bias and this data suffers from it even more. This data comes from their token and spend management product, meaning users are predisposed to focus on cost control. Fable simply isn't cost effective for most tasks. In other words, this is an extremely enfranchised set of users who are specifically using this in a product that is designed to manage spend and optimize spend away from models that are more powerful than you need, rather than being a general assessment across a wide cross section of businesses and business use cases. Still, to me, that isn't even the most damning thing, as perhaps one could argue that those companies in that type of spend management are a leading indicator of where others will get I think the bigger and more obvious issue is that Fable 5 still comes with a 30 day data retention policy and most businesses aren't willing to touch that with a 39 and a half foot pole. Indeed, Ara actually came back to Twitter and retweeted himself to add this incredibly important detail, saying 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 US government safety checks. Look, it is absolutely the case that the more sophisticated buyers get, the less they're just going to smash on the state of the art model at the highest effort level for every single prompt, but the data retention policy really makes this not a particularly clear comparison. Now, lurking behind everything we've discussed in today's show is the fact that Anthropic and OpenAI both have more advanced models more or less ready to go at this point that are being held back by a combination of government pressure, internal concern, or is simply the fact that because nothing else is caught up, they don't really have pressure to move things forward faster still, even if on the one hand we are seeing a slowdown in the speed with which Anthropic and OpenAI specifically are dropic models, I think it's pretty hard to look around the model landscape right now and not feel like we have increasingly more rather than less choice. Anyways, friends, some fun new treats to try for the weekend, but that is going to do it for today's AI Daily Brief. Appreciate you listening or watching as always and until next time, peace.
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