Speaker 1GPT-6 Astra is here, and if you feel like people's first impressions are a little strange, you're not alone. This is supposed to be this crazy advanced model from OpenAI. It's a totally new pre-training base. It was so powerful they had to keep it under wraps for a little while. It's supposed to answer Anthropic's fable and mythos. And certainly some of the stuff that's showing up on social, these incredible one-shot games and 3D models and things like that, are really impressive, at least visually. But then in some other and more basic areas, people aren't necessarily finding it all that much better. So what is the story of Astra? The challenge is that GPT-6 Astra is not an efficiency AI model. In other words, it is not about doing what you currently do better. It is through and through an opportunity AI model that is going to challenge you to think differently about what you can do. And not only is it bringing a new capability set, with its computer use capabilities, it's also bringing along a new default interaction pattern, where increasingly we will not be sitting there clicking around and typing to our computer, but instead we'll be ambiently talking to it as it does all the things that we used to do. In short, the changes that it represents are immense, but not easy to package up in a weekend of testing. So let's try to figure out together the right way to look at Astra, and maybe help you figure out where to begin. 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, Robots and Pencils, 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. And to learn more about sponsoring the show, send us a note at sponsors at ai-daily-brief.ai. Two other quick things before we get into this very big episode. First is a last call for our new cohorts of our Executive Agent Leadership Program and our Executive Catch-Up Program. Both of those are kicking off this week. Although you are not too late to join right now, you can find the information to that linked off the top of ai-daily-brief.ai. And of course, you can also find linked at the top, the new Multiplayer AI Sprint for Teams. This is the latest AIDB free program following AIDB New Years and Claw Camp, Agent OS and the Summer Adventure. And this one is the first one that's designed specifically, not just for individuals, but for teams to come together to use agents together, which I believe this shift from single player to multiplayer AI is going to be one of the biggest trends defining the business of AI. So, if you're interested in learning more about this program, best, most successful AI using companies this fall. You can find the Sprint, which is again, completely free, linked from the main website or at multiplayerai.ai. Welcome back to the AI Daily Brief. Today, we are talking about one of the most significant and confounding model releases for a very long time, certainly for all of 2026. Now, inevitably, as soon as I planned a trip for Labor Day weekend and my birthday, you just knew that we were going to get a bunch of important model releases right as I was on my way wheels up and OpenAI did not disappoint. Now, Astra is one we have been waiting for for some time. It is intended much more than the GPT-5-6 class ever was to be an answer to Anthropix Mythos class models. Unlike the jump between something like 5.5 and 5.6, Astra started as a fundamentally different training run. And while the model has nominally been ready for some time now, Astra is of the new generation of models, which now operate in the paradigm of having to be delayed somewhat before their release based on safety and security considerations. Indeed, in his announcement, Sam Altman said, It took us some extra time to ensure that we could meet the safety and alignment standards required for this capability level, but we think you will find it worth the wait. Now, the rollout was a little bit messy. The model was announced on Thursday, but availability was limited to partners enrolled in the cybersecurity-focused daybreak program, and a lot of people, myself included, were very antsy to get our hands on the thing that had been so long anticipated. Tebow from OpenAI promised that for everyday paid subscribers didn't have access to Astra, they would give them one banked reset, and Altman promised to begin the broad rollout to their API and ChatGPT subscribers as soon as possible, and by Friday night, the new models were in everyone's hands. The announcement video for Astra went absolutely mega viral on X. At the time of recording on Tuesday morning, it has been viewed more than 132 million times and saved more than 100,000 times. The video is three minutes of people sitting in or pacing around a room with a big projection of a screen on the wall, talking to an open laptop, and watching Astra do things for them. The use cases are split between business, personal productivity, and recreation. One person is listing an item on eBay. One person is doing a contract review. One person is building a presentation. One person is making a game. They also show those same people, untethered from old pesky things like browser windows and mouses, doing multiple things at once. While in the foreground, they are working on their main productivity task, in the background, they are working on some administrative or simple task like ordering food or booking a tennis court. And just from that video alone, you could tell that there was kind of going to be something different about this Astra model. The first takes quickly started rolling in, and some parts were kind of expected. Dan Shipper from Every summed up, it's a big upgrade from 5.6 Sol, with some frustrating habits that keep it from matching Fable at the top end. Dan said that it was the best writing model he'd tried, being very easy to steer and producing very little slop. Good at computer use. Clearly a major priority for this model launch, with Dan saying that Astra can quote, go for hours at a time using complicated apps to get work done, hearkening to something that would be all over X all weekend. And which, by the way, is a good reason to potentially switch to the video version of this if you're just listening. Dan also pointed out that Astra was incredibly good at making 3D games and visualizations. But he said, Astra also had a tendency to overcomplicate things. For example, when he asked for a simple interface, he got extra labels, extra buttons. And features all over the place. He suggested that it quote, doesn't quite match Fable's ability to intuitively understand your prompt and do something delightful without overcomplicating. Now on the one hand, it's not all that surprising that a new release from OpenAI, even one that is meant to catapult them ahead of Anthropic and Fable, had aspects where people who were dedicated users preferred Fable more. At the same time, reading the Every Vibe check, after seeing a lot of other people's first experiments with it, felt very much like trying to judge a new thing with old criteria that didn't quite fit. There was something almost discordant about the capability set and the tools and tests people were using to evaluate it. Certainly an even more dramatic example of this came when the Artificial Analysis Intelligence Index scores came out. Initially, Astra scored a 61 on the Intelligence Index. That gave it the same score as GPT-56 Sol and left it a full five points behind Fable 5.1. It also meant that MetaMuse Spark, which was also released at the time, was one point ahead of GPT-6 Astra. And for some, this was evidence of exactly the idea that I just mentioned, that the tests that we're using to judge old models don't really reflect what their impact is likely to be. Now when it comes to artificial analysis specifically, the index is fairly skewed towards benchmarks that reward, for example, memorization of facts. They have fewer tests that require advanced coding, and even fewer still that are about advanced computer use. Unless you think that this is just a problem for the people who don't get a great score on the Intelligence Index, the AI team actually rushed out their update to the index over the weekend, announcing their new index version 4.2. The update includes more emphasis on agentic tasks with their AI briefcase test, plus a different structure and weighting on many of the existing tests. In that new version 4.2 iteration, GPT-6 Astra was still behind Fable 5.1, but was ahead of everything else. 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. 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Well, in just a minute, we're going to look at the benchmarks that OpenAI chose to highlight and some of the unique and different things that people share that they were doing with Astra over the weekend. But to not bury the lead, GPT-6 Astra is not an efficiency AI model. This is an opportunity AI model. In other words, GPT-6 Astra is not about doing what you currently do better. It's about expanding what you can do. That makes Astra extremely interesting, potentially extremely valuable, but also challenging. And part of what we're going to do in today's episode is look at some comparable past examples of this type of opportunity AI model release to see if and how we figured out what made those models different and how long it took to create them. So let's get started. And how long it took. But first, let's go back to some other benchmarks, specifically to OpenAI and what they chose to highlight. Even if one takes lab benchmarks with a grain of salt, where they choose to put their focus can still tell us something about how the OpenAI team sees the unique and differentiated value of Astra relative to other models. As has become the norm, OpenAI now shows benchmarks not just as a list of comparative performance numbers, but on charts that can map performance versus cost, building efficiency right there into the core analysis. On coding benchmark TerminalBench 4.0, the model scored 57.6%, which beat Fable 5.1 at 55.8% and represented a large jump from GPT-56 Sol at 37.3%. On DeepSwee, Astra scored 74.1%, which was slightly higher than Fable 5.1 at 73.7%. Now, interestingly, for both of those benchmarks, Astra achieved its best score on higher extra settings and actually declined a little bit with effort set to max. This suggests that higher effort could mean overthinking and getting sidetracked. And importantly, OpenAI chose to highlight that not only did Astra score higher, but they did it a lot more cheaply than, for example, Fable 5.1. Now, on AutomationBench, which measures computer use, the scores were off the chart. Astra scored 41.1%, which was much higher than Fable 5.1 at 31.4%, and GPT-56 Sol at 18.1%. And honestly, if you had to pick just one thing that OpenAI really wanted you to know about Astra, it is how transformatively different at using computers it is. In the announcement post, they call it explicitly the world's best computer use model, claiming that Astra marks a new frontier in the speed, accuracy, and safety of computer use. OpenAI also emphasized a big increase in capabilities for science and math. On TerminalBench Science, Astra scored 64.6%, beating Fable 5.1 at 52.6%, and absolutely demolishing GPT-56 Sol's score of 22.4%. And on FrontierMath Tier 4, Astra achieved a near-perfect score of 97.6%, another jump from Fable 5.1's score of 90.2%. In cybersecurity, advanced computer use seems to have led to a big breakthrough in capabilities as well. Astra scored 100% on ExploitBench across all effort levels, suggesting it can build and execute exploits with little difficulty. Using an internal benchmark of recently disclosed vulnerabilities, Astra scored 39% compared to just 5.5% for GPT-56 Sol. Now, in addition to benchmarks, the GPT-6 Astra announcement post also highlighted some use cases and it's very clear that OpenAI is not just pitching this as a better way to write documents and do research. They point to game development, circuit board printing, car transmission design, and again, many other things that are very much outside the classic set of use cases that these sort of model announcement posts focus on. And this was reflected in the types of experiments that were going on viral on X over the weekend. OpenAI's Thomas Ricard wrote, Astra is very good at 3D modeling, and then showed a walkthrough of how he built a demo house walkthrough for their blog post. Arita's Peter Gostev used Astra in Blender to recreate a famous Apple announcement video of Craig Federighi doing some weird parkour moves. Now, Blender, which is a 3D creation program, was all over Astra-related posts. Alex Oliver showed its capability and persistence by building a photorealistic bat in Blender, saying, it just kept going until I ran out of tokens. Yunfan Yi modeled the house tour use case that had been on the OpenAI blog post, writing, Give it a Zillow listing. It can 3D model the house based on the listing photos and create a cool promotional video. This video was created in one shot, and there are still some wrong details, but I'm sure it would be much better if I asked it to polish further. Duncan Trussell built a creepy VHS-looking version of Backrooms, sharing something that looks like an old VHS, but actually was an AI-generated video, just was all built in Blender. Duncan went on, I wonder how it is at rigging 3D characters and animating them. I'll try that next. He followed up, Yeah, it works. It did it. No problem. Rigged a 3D character, put it in the Backrooms, and made it walk around. If you've never messed with Blender, this might not seem like a big deal, but rigging sucks. I've tried this with other versions of GPT, and it never really works. This did it perfectly in one prompt. Ashbytes writes, I used GPT-6 Astra to create a 3D website that pulls apart a Tesla Model X into 334 modeled pieces. Google-applied AI PM Baron Roth wrote, Astra helped me win an argument I had with my fiancé on whether glasses drive faster right-side up or upside down, after dishwashing by simulating the physics of the water molecules and nearby air humidity. Now, many wondered whether this is an advisable use case or not, with Michael Briggs responding, One small step for science, one giant leap towards sleeping on the couch. As you might expect from what you've heard so far, lots of people also tested Astra by building games. Ethan Malek wrote, I asked GPT-6 Astra to turn Zork, the classic 1977 text adventure, into a full 3D action-adventure game. It kept the original plot and puzzles, added fight scenes, and built all the games. I asked GPT-6 Astra to turn Zork, the classic 1977 text adventure, into a full 3D action-adventure game. It kept the original plot and puzzles, added fight scenes, and built all the games. I asked GPT-6 Astra to turn Zork, the classic 1977 text adventure, all of the characters and environment directly in 3JS. Daria Anutmaz wrote, One of the games I absolutely loved playing in the mid-90s was Doom. Now, more than 30 years later, I've created my own Doom-inspired game in just a few hours with Astra, and I keep adding new features, enemies, and levels that are so enjoyable to play and have almost no bugs I can notice, even in first versions. Building it brought back so many great memories from those early days of gaming and was so much fun. It's beyond remarkable to think that something that once required a legendary team and extraordinary engineering can now be recreated, experimented with, and expanded in hours by the next generation. Someone like me, who has no software engineering expertise, Theo built a game called Fishslop, writing, GPT-6 Astra is world-class at Blender and three-dimensional reasoning. This was a one-shot game it created all running in browser. Now, when people assumed that this must be just absolutely eating through tokens, Theo responded, Fishslop would have been under $30 to generate, wouldn't have even noticed the dent on the $200 sub. Entrepreneur Anshu found something similar, sharing a game that they created and adding, Dude, GPT-6 Astra is some kind of true-to-life game. It's a game that you can play with your friends and family, a turbo-AGI machine god for 3D games. It one-shot this in 45 minutes for hardly a couple percentage of my quota. The AI Battle account built Sonic and found that on max settings, Astra was able to build it in 53 minutes using 4% of weekly usage on a Pro X5 account, while Astra on medium took 25 minutes and used 1% of the weekly usage. Even Sam Altman weighed in on this use case, saying, It is obviously trivial relative to everything else, but the fact that Astra can make me whatever fun little game I can imagine and I can be playing it a few minutes later is so cool. Others took this 3D capability in a learning direction. Andraganin wrote, Yesterday I built a cell model. Today it showed me how Ozempic works. I asked it to explain the mechanism, and one hour later, this. The video that Andrew shares is an interactive 3D model that shows exactly what Ozempic does. Higgsfield, head of product, combined Astra and Higgsfield to build an overlay for watching soccer that tracks players, maps passes, and follows possession, all in real time. And a manual from Scenario said that GPT-6 Astra helped him build something that he wished he'd had as a kid. He writes, Input just an image or an idea in a few words and get a fully buildable LEGO set, 100% customized to your prompt or image, using official LEGO parts you can order online. Emanuel AOF writes, I asked GPT-6 Astra to help me understand my own ankle pain. It gave me a full interactive 3D atlas, bones, ligaments, tendons, real motion axes, sliders for plantar flexion and inversion, and a live readout of what each ligament is doing. One session. Ethan Malek writes, The fact that Astra is incredibly good at visually pleasing 3D work, like Blender, gives it a perception edge over Fable in the war for social media attention. It's harder to judge other kinds of outputs, but the visual stuff comes through. And of course, Ethan is right, this stuff is really cool. But how much does it matter to the average person? If you knew that this sort of 3D design was what Astra was really good at, would you even have something that you could think of to use it for? And indeed, when people were sharing some of their more generic use cases, it wasn't all sunshine and rainbows. A-16Z's Martin Cassato wrote, The new models are amazing, but it seems coding has saturated? There's clearly a meaningful step in computer use, but I don't notice a meaningful step in coding for the work I'm doing. Maybe a skill issue. But others report something similar. Armin Ronancia wrote, I don't know where Astra learned Python programming, but when it's one step removed from normal code, it writes weird Python slop. The unit tests it writes are absolutely horrific. Stripes Chris Puckett wrote, Astra thoughts. Absolutely blew me away with taking some shader animation work to an incredible level. Absolutely crapped the bed the sloppiest slop to ever slop on front-end design. Dan Driss wrote, GPT-6 Astra has one pretty big problem, front-end and UI design. Astra is absurdly good at spatial reasoning, 3D, computer use, math, agents, coding, but ask it to make a beautiful website and Claude can still look noticeably better. This might genuinely be the biggest reason I'd still use Claude. Which is not to say that's been everyone's experience. Claire Vo from the How I AI podcast described in her long review how she had been trying to build an architecturally complex product intelligence app that aggregates and analyzes data across platforms. She said, Fable did insane things with the architecture. 5.6 just got stuck on quality of insights. Astra one-shotted it. This, she says, has been my experience this entire model. It's been so hard to do these very specific tasks. I've tried them over and over again for six months. Then Astra one-shotted it. And yet still, what comes through in the much more full-length version of the How I AI podcast is that it's been so hard to do these very specific reviews like the one from Claire or like Allie K. Miller's review is that in addition to these 3D capabilities, the really transformative thing around Astra seems to be in the realm of computer use. Claire, describing herself as a computer use maxer, said that Astra has taken her computer use to the next level. She described things like managing complex web UI or automating lead routing workflow in CRM and said this kind of thing just typing and thinking takes forever. Before Astra, I didn't feel like computer use could navigate these nodes as well. Now, however, she said, I'm just hands off my computer all the time now and pointed out how different a mental paradigm this type of interacting with computers really represents. Allie Miller agreed, writing, Computer use and browser use are just incredible. I'm confident that pretty much any stable workflow done on a computer can be at least partially done by AI. It's just so, so good. Imagine you were a creepy boss and recorded your screen for seven straight days. What would you have AI do that you've been doing? Can you go mouse free for a day? Now, there's way more to get into and we are just barely beginning to pull back the curtain on this model. But I want to close out on this idea of why I'm describing Astra as both significant and confounding all at the same time. When you think about capabilities of advanced AI, LLMs like GPT 3.5 and then 4 hit pretty directly down the line of an extremely core set of things that lots and lots of people do. Looking up answers to questions, researching, writing things. These sort of tasks and queries make up a big part of our daily experience, especially in the work world. And what's more, the interaction pattern of effectively just sending text messages to a bot was something that most people were at that point really comfortable with. But even as those core writing and research type use cases of LLMs have attracted legions of users, we've had a few major jumps outside of what people normally do as well. The first was image generation. Now, this is a little bit borderline because obviously even before things like mid-journey and stable diffusion, and later nano-banana and GPT image, people were interacting with images. But still, before it was really easy to generate them, interacting with images or at least creating images remained a more specialized domain. If you look at the big generative AI releases from November 2022 as a proxy, it's not surprising that ChatGPT was so quickly so much more ubiquitous than mid-journey's version 4, which also came out that month. There are simply more people and more established use cases for the things that ChatGPT could do than at that time at least for the things that mid-journey 4 could do. Over time though, as the AI capability set has changed, the way that people use generative images has increased and expanded and made it a major mainstream use of this technology as well. The second time I think we had a major jump outside of what the average person, call it knowledge worker, does was with the introduction of advanced AI coding. Now, what makes this interesting is that obviously for some people, a significant number and class of AI early adopters, coding was, in fact, the major substance of what they do in a day-to-day way. But for the rest of us who would quickly become vibe coders, it was a little bit different. We hadn't had the ability to interact with code as a way to build things and solve our own problems and accomplish our work up until a set of models and tools made it viable to do so without having to know the underlying coding languages. In many ways, you can chart the long history of 2026 back to the end of November 2025, when we got Cloud Opus 4.5, which was followed quickly after by GPT-5.2, and which together people realized over the next month or so, represented a total step change in their ability to do significant quality work with AI coding. At this point now, 10 months on from that, many of us previously non-coders have integrated AI coding into our work streams in various ways, but that's still a work in progress. It hasn't yet translated all the way across the entire span of knowledge work, even though for those of us who have fully embraced it, it feels like some version of that is inevitable. All of this 3D modeling and design stuff feels like a third age. It's not just a 3D modeling and design stuff. It's a 3D modeling and design stuff. In other words, a set of capabilities unlocked by some new advance in AI that gives people who previously had no way to interact with that capability set, the opportunity to now do so. The question will be, what, if any, use cases will become normalized for that for people whose work at this moment doesn't normally include 3D modeling or design? Will these use cases remain entirely novelty? Or will some make the jump, like AI and agentic coding has made the jump, to just a thing that now, a much broader version of AI and agentic coding has made the jump, to just a thing that now, a much broader version of AI and agentic coding has made the jump, to just a thing that now, a much broader set of knowledge workers actually use in a regular way? The other pattern of change that I see with Astra comes in the interaction patterns that it models. What I mean by that is that sometimes, the big shift isn't just in capabilities, but in how we interact to leverage those capabilities. When Nanobanana, aka Gemini 2.5 Flash Image was originally released back in August of 2025, it wasn't notable specifically because it was massively better at making photorealistic images, or images in particular styles. What made it so powerful was that it was able to make images in a way that was not necessarily possible to make in any other way. So instead of having to regenerate an entire image from scratch to change one detail, you could instead prompt it to fix just one specific part. And it turns out that that one small change in the interaction pattern unlocks a tremendous number of use cases. Throughout 2026, coding has also been witness to a set of rapid shifts in the interaction pattern as well. Now some of this obviously has been capability changes as well, but what has been the big watchword for AI and agentic coding this summer? It's been all about loops. And shifting from thinking of prompting a coding agent to do a thing, to instead setting up a loop structure in which it can manage itself and continuously progress towards a goal in a way that it can evaluate its own progress and continue on until it actually achieves that goal. That is a totally different type of interaction pattern that will change what we can do with and how much we can get out of AI, and that we are in the middle of trying to do. And it feels to me like even if 3D modeling and design stays more niche than either image generation or coding as a default part of all knowledge work, OpenAI at least is making a very strong argument that computer use capabilities are changing and will change how we think about interacting with computers. I don't think it's an accident at all that the entire three-minute launch video that has been seen 130 million times is people completely hands-free, sitting in a chair, pacing around, verbalizing and externally processing their way through a variety of different types of work, all with their voice as the AI manages the interface. Now this sort of interaction pattern has long been a part of sci-fi. There's a reason that everyone references Tony Stark's Jarvis. And we've had baby steps towards this with developments in voice mode. Anyone who's done pretty much any of the free training programs that I've released this year has heard me squawk endlessly about anchoring the way that you interact by using voice mode. But it's pretty clear that we're still early days. And I think the upshot of all of this is that Astra is not a model that I think we should expect to fully understand or take anywhere near full advantage of in the immediate term. While there's going to be lots of debates around whether this is now AGI, as for example OpenAI's Greg Brockman says that he believes GPT-6 Astra represents, the real work of the next few months is going to be to figure out not where Astra replaces GPT-5-6 Sol or Fable 5 for the everyday sort of things we do. And I think that's a model that we should expect to fully understand we do, but in a much bigger and more core way, what new opportunities it unlocks for us. One place to keep an eye on that is what's coming out from OpenAI staffers themselves. Thibault, who leads product over there, said, for example, "Astra was probably our biggest competitive advantage while it wasn't generally available. Since we've had it, our productivity jumped so much that we shifted some of our plans six months ahead and we'll ship them at dev day instead of mid-next year. Was it just better agent decoding or was it something else entirely?" That is the question that faces us, and we're going to have to take a lot more time to figure it out. For now, though, that is going to do it for today's AI Daily Brief, one where, even though we've gone wildly long, I feel like we are just barely scratching the surface. Appreciate you listening or watching as always, and until next time, peace. Thank you.
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