Speaker 1Throughout the summer, the big thing we've been exploring at the AI Daily Brief is all about the move from a single model paradigm where you pick the best model overall, and that's the one you stick with, to a more complex model architecture where we are, both as individuals and as teams, able to navigate nimbly between different models and even different harnesses to get the most out of AI based on whatever particular use case we might have. And what's more, this summer, we got really clear on the fact that getting the most out of AI is not just a question of model or harness capability, but also a question of efficiency and cost, especially as we move to more complex agentic workloads. And so it's fitting that the beginning of September has been just a cavalcade of new models, from Fable 5.1 to GPT-6 Astra to the models that we're looking at today, including Muse Spark 1.3 and ChatGPT Images 2.5. All of these add up to way more diversity in the tools we have access to for you to design the perfect model for your actual life and work. 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, Blitzy, Section, 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. To learn more about sponsoring the show, send us a note at sponsors at ai-dailybrief.ai. And finally, if you haven't yet, you can check out our latest free self-directed training program. It is called the Multiplayer AI Sprint for Teams. And basically, the idea is to shepherd you through a process of figuring out how to build agents that don't just help you, but actually sit at the intersection of work that is shared across your teams. I'm pretty convinced that this is the next big paradigm for AI inside companies. And so I wanted to build a sprint that could help you guys fully embrace that. There's, of course, a link to that on the ai-dailybrief.ai website, but you can also find it at multiplayerai.ai. We kick off the day with a story that very easily could have been the main episode, given how much drama is surrounding it. On Tuesday, OpenAI published a solution to the Navier-Stokes problem, one of the seven problems selected for the Millennium Prize in the year 2000. The Wall Street Journal characterized these problems as the, quote, holy grail of math, and that's fairly accurate. Each Millennium Prize problem has a million-dollar reward attached, and only one has been solved in the 26 years since the prize was established. The other problems include the most famous unsolved problems in math, such as the Riemann hypothesis and P versus NP. You know, the things we all talk about when we get together for dinner. Now, for the purposes of this particular episode, I'm actually not going to get into the details of the problem itself, or debates around whether it has any significant real-world applications. I'll read OpenAI's description of the problem just to give you a flavor. They write, The Navier-Stokes equations use Newton's second law of motion, F equals ma, to describe how fluids move. Importantly, they treat a fluid as a fluid, and they use it to describe how a fluid moves. a continuous medium rather than tracking individual molecules. These equations are used for aircraft design, weather forecasting, and the study of blood flow. A fundamental open question for these dynamical equations has been whether the continuum approximation of the fluid can break down. Specifically, can the Navier-Stokes equations for a three-dimensional, incompressible fluid with constant density develop a singularity even when the motion starts smoothly? Here, a singularity means the dynamics lead to speeds in the fluid growing without bound within a finite amount of time. The development of a singularity would have to happen despite the presence of viscosity, which tends to smooth out motion. Because a real fluid cannot move infinitely fast, this would mark a breakdown in how the equations model the fluid. To continue modeling the system, one would then need to track the behavior of each particle individually. So that's the problem they're addressing here. And I think, again, for context, the important note is that this represents a huge step up from something like the Erdos problems that made news last year. OpenAI claims to have solved this problem, and they did so using an internal model that is significantly more capable than GPT-6 Astra. Noam-Brown said that the result cost several million dollars to find, and it seems to have taken a week or two. However, the big controversy surrounded exactly how OpenAI had arrived at this result. Shortly after the result was published, New York University professor Tristan Buckmaster published his version of the events. According to Buckmaster, he and an entropic employee named Levent Alpagi had been working on the Navier-Stokes problem together for more than a year. This was an outside project for Levent, and the pair had used a range of different AI models, including GPT-5-6 Sol in the Codex Harness. Crucially, Levent and Buckmaster did not find a solution to Navier-Stokes, but they did find novel solutions to related problems that could be viewed as a stepping stone to the Millennium Prize problem. They were also using extremely novel methodology that few in the mathematics world were pursuing. Rumors of their work spread through AI circles in recent weeks, incorrectly claiming that Anthropic had solved a Millennium Prize problem. Buckmaster says he reached out to OpenAI last week to clarify the situation. According to Buckmaster's telling, Sebastian Bubeck from OpenAI informed him on Sunday that they had solved Navier-Stokes and wanted to discuss publication. Buckmaster wrote, I asked whether the model had been trained on or had access to our sessions in Codex, into which we had been putting all of our drafts for the whole of the project. I was told the model did not look up user data. I asked again about training and I did not get an answer. He said he was offered two proposals, for either OpenAI to publish separately or Buckmaster to join the publication. He said he was offered two proposals, for either OpenAI to publish separately or Buckmaster to join the publication. If he agreed to remove Levin from the authorship because of his ties to Anthropic. Buckmaster declined both options and threatened to go public if OpenAI published. Continuing his account, Buckmaster wrote, The reply was, Why would you ruin your career? I replied that I am an academic and asked why he thought going public would ruin my career. The reply was, If you don't want me to be nice, then I don't have to be nice. OpenAI leaders responded with a series of statements. Sebastian Bubeck called the allegations false and inflammatory, then revealed part of his text message, which he claims contradicts Buckmaster's version of events. Sam Altman gave a series of explanations for how this went sideways, but claimed that OpenAI's approach was different than that of Buckmaster and Levin. The OpenAI account claimed, We, the researchers and the agents, did not see any of their work through any means until they released it publicly. In particular, no specific user data was accessed in order to solve this problem. While unlikely, we cannot rule out that de-identified data derived from the usage of our products helped improve our models. After Levin 's statement as quote-unquote coming clean, OpenAI chief research officer Mark Chen responded, Two things to distinguish. Did any human or agent look at user data as part of the Navier-Stokes effort? No. Do we use user feedback and de-identified data to improve chat GPT and codecs in a holistic way? Yes, and so does every LLM company. Now, as for the controversy, there are two distinct strains of conversation. Firstly, academics are up in arms over what they see as unethical behavior. Assistant Professor Talia Ringer of Illinois University wrote, Rushing to get a result after you hear someone else has a result is messed up. That is AI scooping culture and goes against every academic norm that exists in reasonable fields like mathematics. This is how AI culture rots entire fields. Thomas Wolfe, the co-founder of Hugging Face, suggested this might just be a preview of accelerated AI science, commenting, Hope this is not a glimpse of the future we'll get in science research, with these dominating players playing marketing games hurtful for the real scientific community. The second, and likely far more relevant criticism, at least for the AI Daily Brief audience, was questions of trust in OpenAI. From Buckmaster's account, we can assume they were using some consumer version of codecs, but it's unclear whether they agreed to share data to improve OpenAI's models. For some, it's a wake-up call for anyone who is using AI to work on proprietary tasks. Former DeepMind employee Susan Zhang wrote, Everyone getting sniped by the personal drama, but missed the more interesting unanswered question. Can these labs see all your work and scoop you when the stakes are high enough? Seeking clarification from OpenAI leaders, mathematician Tryon Zyloris asked, Important question. If I opt out from training then paste a trade secret using my paid subscription, do you de-identify my personal details but keep the trade secret and may add it to your training data? At the time of recording, that has not received a response. So taking a step back, there are a few reasons that this whole episode is having such resonance. First is honestly the voyeurism of it. People love drama. Fighting against that is like trying to fight the tides. But the question of ethics around advanced AI and what these companies do is a question that, while it has been present basically since the beginning of LLMs, has gotten a lot louder in consideration more recently. Especially as model leadership starts to consolidate around a couple of companies, it brings up a lot of uncomfortable questions for people. Now some of those questions go to economic incentives and where the AI companies ultimately land. One of the things that there is a lot more chatter about right now is questions of whether these companies will ultimately not view themselves just as selling the inputs to innovation, but also making more sense for the market. In other words, does it make more sense for Open AI and Anthropic to sell existing scientists and labs and companies the ability to do novel drug discovery? Or does it make more sense to do that drug discovery yourself and get the money on the other side of the patents? Given all the chatter that we had around whether Dario had actually said that Anthropic was going to be the only company in the world at some point, those questions feel a little bit more pertinent now than they might have in the past. And finally, the fact that the story reveals that Open AI has a using internally certainly captured some notice as well. Unfortunately, as is so often the case, no one looks particularly good coming out of this. The New York Times' Mike Isaac wrote, Not lost on me that the two labs asking the public to trust them as stewards of responsible AI leadership at existential stakes are having a slap fight on Twitter about who gets credit over a math problem. Like I said, this could have been a whole main episode, but since we're a little compressed on time, let's quickly rip through a couple of other stories before we get to our main, which is all about all sorts of new models that we haven't had a chance to talk about yet. First up, speaking about Anthropic, there is a new class action lawsuit against the company. filed on behalf of Claude Max subscribers, with the claim that Anthropic used deceptive marketing and opaque fine print to underserve customers. In particular, the lawsuit claims that the $100 a month 5x plan and the $200 a month 20x plan don't actually deliver 5 and 20 times the usage of a $20 a month pro plan. Plaintiffs allege the way 5-hour and weekly usage limits are calculated mean the actual usage is far lower than the advertised multiples. Now, usually this type of case wouldn't be all that interesting to me, but I think it's sort of representative of the type of thing that we're going to see a lot more of as Anthropic and OpenAI become increasingly interwoven with just the normal way of doing business. The lawyers running the case themselves noted that what made it compelling to them was how ubiquitous and necessary a top-tier AI subscription has become. They said that they were frequently hearing from workers who felt they needed to pay high-priced subscription costs to remain relevant in the job market, but felt they weren't getting what they were paid for. I'm not particularly sure I think this goes anywhere, but it is certainly representative of the level of scrutiny that the top AI labs are going to face going forward. Over in markets, it looks like it's not just OpenAI and Anthropic that are thinking about IPO. Eleven Labs has also hired a chief financial officer to help the startup head for a public listing. On Tuesday, Eleven Labs announced that Ethan Tandowski had joined the executive team, having most recently served as the CFO of Adyen, a Dutch fintech firm that went public in 2018. Eleven Labs co-founder Maddy Staniszewski said in a press release, Ethan brings a strong track record of scaling financial operations in high-growth environments and valuable experience as CFO of a public company. And that appears to be Eleven Labs' ambition as well. The information reports that they are beginning to explore a possible IPO. The company said that they are on track to reach $600 million in annualized revenue by the end of the year, up from $350 million at the end of last year. And sources said that the company has reached profitability and is now generating more than half of their revenue from large enterprise customers. Cognition's recently rumored fundraising round has completed. The company raised $2 billion in new funds, catapulting them to a $48 billion valuation. Cognition last raised funds in May at $26 billion, meaning they've almost doubled their valuation in three months. In that time period, Cognition has gone from a $492 million revenue run rate to almost $900 million at present. Beyond the numbers, the raise suggests that Cognition will continue to operate as an independent agent lab. Following SpaceX's acquisition of Cursor for $60 billion, there were rumors that they would pursue Cognition as well. CEO Scott Wu strongly denied the chatter at the time. And this fundraising round certainly gives Cognition more runway to continue building their coding agent Devon and pursuing their thesis. In an announcement post, they wrote, We're still at the dawn of the self-driving software era. In this next chapter, agents will become proactive by default, software will improve itself, and even resource allocation will become intelligent, as compute budgets self-allocate towards the highest-impact use cases. Human engineers will increasingly act as architects, setting goals and priorities while agents take on more of the work to achieve them. Cognition wrote that independence is core to this strategy, saying, We can choose and combine the models best suited to the work, including our own, rather than tie customers to one provider. You gotta think that after watching OpenAI cut off access to Cursor customers because of SpaceX's acquisition, Cognition sees the value of staying independent even more acutely. For now, though, that is going to do it for today's AI Daily Brief headlines. Next up, the main episode. 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Relative to other industries, AI certainly has less of a summer slowdown, but you can still feel the difference, man, when September hits. In the last nine days alone, we have gotten Fable 5.1, GPT-6 Astra, and the three models and one agent product that we're going to cover in today's episode. I hope you are as excited as I am because there is a lot of new stuff to check out. First up, last week, right as I was leaving for vacation, of course, we got a new model from Google. It still is not a Pro Series model, but it is notable how quickly Google is iterating on their smaller Flash Series models. The new Gemini 3.8 Flash comes just a few weeks after 3.7. The central claim from Google around 3.8 Flash is that it will work harder than 3.7. It's trained to call tools iteratively and perform more reasoning steps on complex tasks, yielding much better results. On the benchmarks, the model looks solid, if a little spiky. It scored 73.7% on Coding Benchmark DeepSwee, just a hair shy of Opus 5's score of 74%, and outperforming GPT-5.6 Sol by 1%. Terminal Bench was another story. The model scored 89.4% on version 2.1, in line with Opus and Sol. However, the scores tanked on version 4.0, falling to 19.1%, compared to, for example, Opus 5's 51.8%. On GDPVal, the scores were very middle of the road at 15.45%, around 300 points shy of Opus, and closer to Sonnet 5 and GPT-5.6 Terra. Artificial Analysis found the model was pretty solid on their benchmark run, scoring 59, slotting it in just behind GLM 5.3 in 7th place at the time of release, and only a few points off the frontier. However, this was the old formation of the Intelligence Index, the one that gave GPT-6 Astra a fairly low score, prompting Artificial Analysis to rush forward their new version of the intelligence index. But once AA updated their formula, 3.8 Flash slipped from 7th to 12th place, behind GPT-5.6 Terra and GLM 5.3 Flash. Now, the idea of this Artificial Analysis Intelligence Index update was to re-weight, re-prioritize, and add some new tests that better reflected the computer use and broader agentic paradigm, as opposed to just general knowledge tests, which are now pretty much table stakes and saturated. Gemini Flash remains the undisputed leader in speed, outputting around 20% more tokens per second than GPT-5.6 Terra. And if you're wondering what that is, we will get to that in just a moment, and almost four times faster than GLM 5.3 Flash. The question is, of course, which use cases require that much speed at the cost of trade-offs in performance. Maybe the biggest bright spot from their write-up was cost, with Artificial Analysis writing that 3.8 Flash was, quote, the cheapest we've measured at this level of intelligence. They continued, This is up 40% from Gemini 3.7 Flash despite unchanged per-token pricing, driven by a 30% increase in output tokens per task, and more turns on agentic evaluations. Unfortunately for Google, as we will see with the release of MewSpark 1.3 the following day, Google would very quickly lose their place on the Pareto frontier. Certainly, cost and efficiency is a big part of the pitch from Google. Announcing the new model, the Google AI account wrote, "While solving ambiguous and high-friction tasks is immensely helpful, it can also be expensive. Fortunately, 3.8 Flash features the usual effort controls, ensuring that the amount of thinking required to accomplish the task at hand is proportional to the token spend. Logan Kilpatrick from Google emphasized the speed at which Google is putting out these new versions, pointing out that it's just their third updated Flash model in six weeks. Now, when it came to user testing, people validated that it was really fast, but found a lot of performance lacking. Building the same sticky ball game in Kimmy K3 versus 3.8 Flash, Aditya from Intelligence AI wrote, Flash was insanely fast and used way fewer tokens, but the actual game was nowhere close. K3 had much better mechanics, movement, and overall game design. Flash clearly has the speed and efficiency part down, but the gap in what it can actually build is pretty big, wrote Ethan Malek. It is a very good Flash model, but not equivalent to a Frontier model. Others are more optimistic about what that speed could represent. In another head-to-head noclip, Pepe wrote, Opus 5 won, but Gemini 3.8 Flash was 39x faster. Opus 5 took 24 minutes, Gemini 3.8 Flash took 37 seconds. Opus is clearly more detailed and polished, no debate there, but getting a result this good in 37 seconds completely changes the trade-off. In the time Opus finished one run, Flash could theoretically finish around 39. At what point does speed matter more than the last bit of quality? And it will come, of course, as no surprise to anyone here, that as always, I think that the right way to look at these new models is not whether it's going to replace your daily driver, but instead whether there are specific use cases for which its particular set of trade-offs are the right fit. Is there something you're doing right now, where being able to do it 39 times in a row to iterate is likely to be better than just letting something like Opus do it once? Now, the bigger other model release was Meta's MuseSpark 1.3, and Meta Chief AI Officer Alexander Wang was not shy about promoting the progress that's been made. He wrote, This is our most capable model yet. Frontier performance almost too cheap to meter. Much stronger at agentic encoding with better usability. We think users will really notice the jump. Meta chose to compare their new model to GPT-56 Sol in Opus 5, and in that grouping, it was pretty competitive. Generally, it lagged a little behind on agentic benchmarks, but was a little ahead on coding. Spark 1.3 scored a 75.4 on DeepSwee, compared to 73 for 56 Sol and 74 for Opus 5. On Terminal Bench, it scored 88.8%, which tied it with 56 Sol and beat Opus 5 by a couple of points. Wang claimed that the model used 20% fewer tool calls and 25% fewer tokens compared to their previous Spark 1.2, while also producing a very clear jump on the benchmarks. A couple of days after the initial release, it also added a max effort setting that boosted performance even more. Now, the part of the release that really made everyone sit up and pay attention came when Artificial Analysis released their benchmark run. On max settings, Spark 1.3 scored a 68 on the Coding Agent Index, making it tied for first place with Opus 5. Now, worth noting that at the time testing for Fable 5.1 and GPT-6 Astra hadn't been completed, but still a pretty impressive result. On the overall intelligence index, Spark 1.3 on max settings scored 62, placing it in third place behind Fable 5.1 and Opus 5, tied with Fable 5 and a point ahead of GPT-5.6 Sol. Now, the assumption for many is that this model was absolutely benchmark maxed to achieve the maximum possible score. Certainly, that's what Semi-Analysis argued, writing, Gemini 3.8 Flash and MuseSpark 1.3 are two of the most clearly benchmarked models we've seen yet, despite being comparable to both GPT-6 and Fable 5.1 on Terminal Bench 2.1, their Terminal Bench 4.0 performance is markedly worse. How is this possible? All of the tasks, Terminal Bench 2.1 are fully public. Though Meta and Google would never trade on the tasks directly, they absolutely will buy data from RL environment startups that's designed to mimic TB 2.1 tasks as closely as possible. The net effect is the same. You'd typically expect improved TB 2.1 performance to generalize to other agentic tasks, but Gemini and Muse don't even generalize to TB 4.0. Ultimately, Semi-Analysis concludes, this is the fate of all good public benchmarks. TB 4.0 is no exception. It's only useful signal now because it was released ago. Since all the tasks are similarly public, it won't be long until it's hill-climbed by all the aspiring quote-unquote frontier labs. Alexander Wang actually responded to that one saying, we don't claim MuseSpark 1.3 is as strong as Astra or Fable 5.1, but it is significantly more cost-effective. Our future models will compete more directly with those models. It is worth also noting that after Artificial Analysis revised their index, no doubt in part because the original formula had Spark 1.3 outranking GPT-6 Astra, after the revision, Spark 1.3 remained in 5th place with a score of 48, which was slightly ahead of GPT-5.6 Sol and behind Fable 5, Opus 5, and Astra. And despite the big jump on the benchmarks, the model is still extremely cheap. AA found that the model spent 55 cents per task, which made it slightly cheaper than Gemini 3.8 Flash, 20% cheaper than GLM 5.3, and about a quarter of the cost of Opus 5. Summing up, Artificial Analysis wrote, MuseSpark 1.3 Extra High is the most cost-efficient model at its intelligence level. No model's scoring 59 or above costs less per task. Now, the first impressions on this one were pretty positive. SPAC89 wrote, I've been testing MuseSpark 1.3 Max, and honestly, it's insanely good and surprisingly efficient. Darada's Code writes, MuseSpark 1.3 is kind of ridiculous for a free small model on OpenCode. It also avoids some of the obvious AI design traps like purple gradients and all that. I want to push it into nastier edge cases next, but for the price, this thing is already very good. Now, on the topics that we were discussing in the lab, for some, Meta is a tough one. After writing about MuseSpark 1.3 being good and basically Opus 5 for cheaper, Zwin on Axe adds, there's a catch though. Meta may use your inputs and outputs for training, which is the whole reason the tier on OpenCode is called Contributor and the whole reason it's free. Certainly many people were excited to try the discounted OpenCode version. With Dax from OpenCode writing, Meta MuseSpark has dethroned DeepSeek as the most used model of the day. First time an American model tops this list. And yet, if MuseSpark 1.3 made some start to wonder, is Meta back? It was the launch of their new personal AI assistant Muse that garnered even more attention. On Tuesday, September 8th, Meta announced their long-promised personal agent called Muse. The product has been rumored to be in the works for months under the codename Hatch, with the basic pitch that we had heard being open claw for normal people with a bunch of usability improvements. Presenting the agent, Alexander Wang wrote, Today we're rolling out Muse, our new personal AI assistant. Muse is always, always on, wicked fast, can use a browser, connect to your apps, and is designed to be secure. Meta claims that Muse can do everything we've come to expect from personal agents. It can triage your inbox, organize your calendar, make bookings, or shop for you. It also has some of the more impressive features introduced in recent months, such as operating a separate virtual computer, which is the same way that Grokbot works. Meta also made a solid attempt, it seems, at dealing with the security nightmare associated with the earliest versions of personal agents. Wang again wrote, A big focus for us here was making sure it was safe to give Muse access to your inbox, calendar, and finances. Each Muse runs in its own secure VM, an isolated computer dedicated to you. A separate system, the Sentinel, checks every action before anything leaves the VM. Your Muse never sees your actual passwords or card numbers. Now, certainly reviews from inside Meta were glowing, with CTO Andrew Bosworth, aka Boz, writing, Very excited for the launch of Muse today. I've been using it internally for months and I am hard-pressed to think of any product that I've come to rely on more in such a short period of time. I have a lot of work to do, but I'm not sure I'll be able to do it. to my email, calendar, and credit cards. I use it to help me plan travel, pack for trips, research and make purchases, and sort through all the communication I get from my kids' school. This is a tool for everyone. You don't need to be an expert or even think about AI. You just talk to it from the app or from WhatsApp like your own personal assistant, except it can do lots of tasks in parallel at the same time. And of course, you'll soon be able to talk to it from your Meta glasses too. Jason Toff from Meta said, When I moved to California this summer, I unplugged my Mac Mini and Mac Studio, both running Clause locally and switched entirely to Muse. They're still unplugged. My favorite thing about Muse is how natural it feels. You talk to it like a person and it responds like a top-notch personal assistant. And even from the outside, early reviews are pretty positive. EAC spiritual guru Beth Jezos writes, Got to try this product early. It's very solid and quite feature-rich. As model intelligence is no longer the bottleneck for utility, context on your life is, and personal agents running on secure compute is the way. Write Signal, Muse has been a genuinely impressive product to play with. It has all the functionality of the iMessage agents in flight today, but also has all of the ingredients that will expose it to hundreds of millions, including a massive friend graph through Insta, increasingly rich context from email and other services which you connect, and more importantly, stuff like Facebook Marketplace. Marketplace in particular is an incredible distribution wedge. Millions of normal people could encounter Muse simply because an agent helps them try to find something, negotiate the price, and arrange pickup. Pretty good execution here from FB. Olivia Moore from A16Z said that she likes the rich library of connectors that are available in-app, thinking that the native connectors will be more reliable than browser use, and also said that she liked that she can set up goals connected to that data and attach artifacts to them to visualize progress. She worried that the UI was still too cluttered and there was a few too many things to do, but concluded this could be one of the first true mainstream consumer agents to get adoption. Still, Meta has some hills to climb when it comes to consumer trust. That same Olivia Moore wrote, In my opinion, Meta's distribution advantage cuts both ways here. Do I really want to give an agent my personal data and then set it loose on networks where all my friends are? One of the things that makes Meta interesting and worth paying attention to in the broader AI race is that they are the only company at their scale that is primarily focused on a consumer rather than a business use case. Now, obviously, this is all a little blurry, especially when you consider the legions of small businesses that use Meta products as their key communication channels. But ultimately, I think it's pretty uncontroversial to say that what Medicare about is consumers more than B2B. For a while, open AI looked like it was going after both, and nominally they still are. But of course, the pressure from Anthropic has meant that they have really had to focus a lot more resources on the B2B and work use cases of late. Especially as there are more and more questions about whether general consumers will ever really care about AI agents. These sort of experiments from Meta have significance that goes beyond just them. Does agentic shopping actually become a thing? Do people really like having a personal agent assistant to help them with daily things like booking travel? We're not really going to know until those things are available and broadly good enough that they actually do what they promise. And it feels like Meta is finally playing at the level where that promise might be real. Right Spox's Aaron Levy, personal assistant agents are going to be a very exciting AI category. It's the first time you can have high token volume agentic use cases that make sense for consumers. Lots of different approaches emerging right now and it's going to be hyper competitive because these agents will mediate a lot of consumer spend over time. But this certainly plays directly to Meta's strengths. Lots of compute required, can monetize with ads and commerce, software focused experiences so can distribute it at scale and so on. Sums up Y Combinator president Gary Tan. Harness wars are full on now and Muse is very impressive. Now I don't have a horse in the harness wars or the model wars, but I will certainly be rooting for this as a product category if for no other reason than people actually getting value out of a personal assistant agent might make them just a little less hostile to AI in the first place. Lastly, one more model release to talk about. Also on Tuesday, OpenAI released ChatGPT Images 2.5. This is the latest in the series of models that power built-in image generation in ChatGPT, a feature which still gets a ton of use. OpenAI says that users are generating more than 3 billion images a week and write that the new model will provide sharper details, more precise editing, and faster generation with a 50% reduction in latency. Alongside the model, OpenAI is releasing a new ChatGPT feature called Sketch, which as the name suggests, allows you to draw an input to help guide your image generation directly in the app. Users can add a text prompt to describe a particular style or provide additional details to guide the model output. The model comes in two variants, Flare, which is the fast version designed for quick iteration, and Sunburst, which is optimized for professional workflows that require better control across edits. And I think that that word control is really key here. In the same way that the big innovation and update of NanoBanana was more fine-grained control over the editing process, that seems to be a big change. OpenAI is going forward with this new model as well. Exultan Alamkulov, the head of product at Higgsfield, wrote, What impressed us most about GPT Image 2.5 Flare is how well it understands what not to change. You can make a meaningful edit without losing the character, composition, or visual identity of the original image. That's incredibly important for the way creators and teams actually work across film, UGC, and advertising. And when you combine that level of control with the speed, quality, and cost, Image 2.5 Flare really stands out. One example that you're seeing a lot of, of what the new better controls and image consistency can lead to is entire new genres like stop-motion animation that become viable for the first time. One of the interesting things that I increasingly feel is I think right now, in general, we under-appreciate the value of images, not just as a consumer differentiator, but actually as a business use case differentiator for OpenAI. As a for example, while in general I still like the aesthetics of Fable-created websites better than GPT-created websites, the fact that I can call upon GPT Image 2.5 Flare to generate aspects of the UI, or certain types of aesthetics, makes a pretty big difference, and leads me to use the integrated GPT models and image generation in Codex more often than I otherwise would. Point is, although this update feels routine, don't sleep on how significant it could be. So that is the new model story for now. Like I said, lots of exciting goodies to try out, and I'm sure there is more on the way. For now, 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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