Dan Shipper, CEO of Every, spent a week testing Anthropic's Fable 5, a Mythos-class model that scored 91/100 on Every's senior-engineer benchmark, compared with 63 for Opus 4.8 and 62 for GPT-5.5. The video explains what a Mythos-class model is and how to get the most out of it, with demonstrations including an infinite 3D library built from a single prompt, a Heidegger lecture site, a growth bet found in customer data, and a cleared GitHub backlog. It also covers where the model falls short (it is slow and expensive), who it suits best - high-level agentic builders working on research, complex judgment, and large projects - and what it means for the future of work after automation. # Video Identity Observations ## Creator Self-Description (Explicit Claims) - **Named creator with role**: "My name is Dan Shure, I'm the co-founder and CEO of Every." - **Channel/company framing**: "Every is the only subscription you need to stay at the edge of AI. You can kind of think of us as like an AI lab for the future of work." - **Stated methodology**: "We spend all of our time testing new models, using them to do our work, from programming to writing to design to business building to decision making. We use them hands-on and we tell you about what works and what doesn't for real use cases." - **Subscription pitch**: "We've got like 10,000 paying subscribers... every.to/subscribe" and roughly "100,000 free subscribers." ## Claimed Scope - **Model reviews / "vibe checks" on launch day**: "Fable 5 launches today. Here's your day zero vibe check." - **Benchmarking work**: References an internal "senior engineer benchmark" scored out of 100, comparing Fable, Opus 4.8, and GPT 5.5. - **Multi-domain testing**: Claims roughly "seven or so people testing it internally over the last week, everyone from programmers to writers to editors to marketers." - **Own products referenced**: an app called "Proof" ("agent-native markdown editor") and articles ("after automation," "the eight levels of AI adoption"). ## Intended Audience - **AI-interested professionals and early adopters**: "This is a place for you to see how this thing might fit into your work and into your life and in a realistic way." - **Technical users and vibe coders**: advice segmented by adoption level ("if you're at like a seven or eight on the scale... you're orchestrating multiple agents"). - **Viewers experiencing AI anxiety**: "hopefully this can help alleviate if you're feeling a little bit of AI psychosis." ## Observable Presentation Patterns (Demonstrated) - **Demo-driven format**: opens with a hands-on showcase (Borges-inspired 3D library built "in a single prompt"), then a mini-site built from Hubert Dreyfus lectures, then survey-data analysis, then GitHub issue fixes. - **Balanced evaluation tone**: pairs strengths ("sustained autonomous execution") with weaknesses ("It didn't actually do substantially better at writing than Opus 4.8"; "very slow... very expensive... extremely token-hungry"). - **Metaphor-based explanation**: the "warp drive" analogy is used to characterize appropriate vs. inappropriate use cases. - **Cross-promotion habit**: repeatedly directs viewers to Every's articles, subscription, prior video, and team write-ups. - **Humor/personality markers**: opens with "never make any major life decisions within 30 days of a meditation retreat, a psychedelic experience, or your first encounter with a frontier model." ## Notes on Evidence Limits - No curated metadata was provided; identity observations rest solely on title and transcript. - Claims about Anthropic internals ("talking to people internally and at Anthropic, there's nothing special about it architecturally") are creator assertions, not independently verifiable from this material. # Delivery Observation: "We Tested Anthropic's Fable 5 for a Week" ## Voice & Tone - **Conversational, hype-adjacent but hedged**: Enthusiastic delivery ("It's just genuinely the most powerful coding model I've ever used by far") tempered with caveats ("It's very slow. It's very expensive. It's extremely token-hungry."). - **Direct audience address**: Frequently speaks to "you" — "Here's your day zero vibe check," "if you're like me, you might be excited, but you're also like a little scared." - **Humor and irony as connective tissue**: Opens with a mock-serious disclaimer — "remember to never make any major life decisions within 30 days of a meditation retreat, a psychedelic experience, or your first encounter with a frontier model. Cheers." - **Coining playful terminology**: Repeatedly uses "AI psychosis" ("hopefully this can help alleviate if you're feeling a little bit of AI psychosis") and references "vibe check," "vibe coded slop," "vibe coder." ## Vocabulary & Recurring Phrases - **"Warp drive" as the central metaphor**, established early and reused at least four times: "It's it's like a warp drive," "this is this is why it's a it's a warp drive," "go use your new warp drive." - **"One prompt" / "one shot"** to emphasize effortlessness: - "I made this entire thing in a single prompt" - "It just made it in one shot in 3 or 4 hours" - "Like this is from one prompt." - **"Meaty"** for describing suitable tasks: "you have to you have to have big meaty things to give it," "you've got big meaty problems." - **Repetition for emphasis** as a spoken habit: "And it just went boom boom boom boom boom boom," "I would definitely definitely definitely try it." - **Self-interruptions and restarts** throughout the transcript ("It's it's it's amazing. Like it it just figures stuff out"), suggesting unscripted or lightly scripted delivery. ## Teaching & Structural Habits - **Demo-first structure**: Opens with a concrete artifact (the Library of Babel 3D experience) before any framing, then says "let me show you" and displays the original prompt. - **Explicit signposting**: "But first," "So, let's get into it," "Okay, so first example," "So far, I've I've shown you like a a lot of cool one-shotted demos." - **Benchmark-by-comparison teaching**: Explains capability via scores — "The best model score is a 63 out of 100, which is Opus 4.8... Fable scored a 91 on this benchmark." - **Extended analogy teaching**: A multi-sentence Star Wars warp-drive explanation, including an inserted quoted exchange ("> Punch it. > Punch it.") before drawing the lesson. - **Numbered/typology framing**: References "the eight levels of AI adoption" and segments the audience into persona buckets (vibe coders, knowledge workers, technical people). ## Communication Patterns - **Balanced evaluation**: Pairs strengths with limitations — strong at "sustained autonomous execution," but writing "didn't actually do substantially better than Opus 4.8" and "for my day-to-day, it's a bit overkill even for me." - **Citing internal/testing provenance**: "As far as I can tell from talking to people internally and at Anthropic," "We probably had seven or so people testing it internally over the last week." - **Embedded promotional calls-to-action**, placed mid-content rather than only at the end: "If you have not subscribed, you should subscribe every.to/subscribe," plus repeated plugs for "after automation" and the written vibe check. - **Emotional acknowledgment of the audience**: "I think it's normal to be sad or angry or weirded out by that" — validating viewer anxiety before reframing change as "an opportunity." ## Uncertainty Notes - Word-repetition fragments ("it it it," "I I think") appear pervasively in the transcript; this may reflect transcription artifacts rather than the speaker's actual delivery style. - Persona or trait inferences (e.g., whether he is genuinely enthusiastic) are not assessed — only surface delivery features above are observable. # Video Position Record: "We Tested Anthropic's Fable 5 for a Week" **Speaker:** Dan Shure, co-founder and CEO of Every (self-identified in transcript) ## Central Claim Fable 5, a new "Mythos class" model from Anthropic, is exceptionally powerful for **sustained autonomous execution** — described as a "warp drive" — but is slow, expensive, and not suited for everyone or for quick back-and-forth collaboration. ## Demo Evidence Cited - **Library of Babel 3D game:** Built in a single prompt; the model read Borges's story, ran autonomously for 3–4 hours, "looping itself and checking its work," and produced an accurate game with the book's mathematics correct. Shure had early access ~4 days prior. - **Hubert Dreyfus lecture mini-site:** Prompted vaguely ("go grab the lectures and turn it into a mini site"); the model found the lectures, wrote summaries, built a synced transcript player. Cited as evidence of "taste and attention to detail" (font choices, drop caps — "not the Claude purple highlight vibe slop thing"). - **Survey analysis:** Fed thousands of Every subscriber survey responses; output ("You have a conversion merchandising problem... falsifiable bet: ship pricing transparency and a trial offer") was, per Shure, more interesting than what his team produced in weeks. - **Proof app GitHub backlog:** Model triaged issues and wrote merged Rust fixes at scale. ## Benchmark Claims - **Senior engineer benchmark** (Every's internal test): Opus 4.8 scored 63/100; GPT 5.5 scored 62; **Fable scored 91** — "the same score as a human engineer with just one prompt." Shure: "I knew this benchmark was going to get saturated, but I thought it would happen in like 6 months." ## Model Characteristics (as characterized by Shure) - **Positioning:** Largest Anthropic model tier (Haiku < Sonnet < Opus < Mythos). Per Shure's conversations with people at Anthropic: "nothing special about it architecturally... it's just bigger and better." - **Safeguards:** Strict restrictions on cyber and bio use; Shure states this is what makes Anthropic comfortable releasing it. Also notes a leak ~1.5 months prior where "Anthropic said it was too dangerous to even release it." - **Cost:** $10/M input, $50/M output tokens — about twice Opus. - **Strengths:** Autonomous long-running work; judgment (may push back rather than eagerly comply — contrasted with prior Claude models as "a little try hard"); large-context research. - **Weaknesses:** Writing not substantially better than Opus 4.8 — sentences "dense... pretty literary"; not for copywriting; slow and overkill for daily-driver use. Shure personally still uses GPT 5.5 in Codex. - **Pro tip:** Lower reasoning settings for basic questions; "that's how people inside of Anthropic use it." ## Who Should Use It - Best for those at level 7–8 of Every's "eight levels of AI adoption" (multi-agent orchestrators, usually technical); vibe coders should try but be cost-cautious; typical knowledge workers will find it "overkill." - Internal testing (~7 people): "pretty high spread of who liked it"; about half of Every's team is at the level where the problems it solves arise. - Stated tension: "Using it is a skill." ## Broader Interpretive Claims - Automation creates more human work (referencing his piece "After Automation"); Fable **raises the floor for non-experts and the ceiling for experts** (vibe coder makes a one-shot game; expert makes a AAA game alone). - Emotional framing: normal to be "sad or angry or weirded out"; also an opportunity. Cost will drop — "within the next 6 months to a year, everyone will be able to have this." - Caveat offered early: "Never make any major life decisions within 30 days of a meditation retreat, a psychedelic experience, or your first encounter with a frontier model." ## Embedded Promotions - Every subscription (every.to/subscribe) for full vibe check; his article/video "After Automation." # Speaker Attribution Record ## Video **Title:** We Tested Anthropic's Fable 5 for a Week **Video ID:** 01a0f15a-8372-75c7-9eda-b571126e6cfb ## Speakers | Speaker | Role | Evidence | |---|---|---| | **Dan Shure** | **Creator / host** | Self-identified: "My name is Dan Shure, I'm the co-founder and CEO of Every." Single-narrator monologue throughout. | **No guests, interviewees, or other speakers** appear in the transcript. ## Provenance & Sources Referenced ### Products / Models Discussed - **Anthropic's Fable 5** ("Mythos class model") — subject of the review; claims of pre-release access ("I got this model a little bit ahead of time," prompt "from 4 days ago or so"). - Other models referenced: **Opus 4.8**, **Opus 4.7**, **GPT 5.5**, Haiku, Sonnet, Mythos. ### Claims Attributed to Third Parties - Leaked model report: "When it leaked a month and a half ago, Anthropic said it was too dangerous to even release it." *(third-party claim, unverified in source)* - Internal Anthropic practices: "As far as I can tell from talking to people internally and at Anthropic, there's nothing special about it architecturally" — attributed to **unnamed internal sources**. - Anthropic safeguard policy: cyber/biological restrictions — attributed to Anthropic. ### Creative / Literary Sources Referenced - **Jorge Luis Borges, "The Library of Babel"** — basis of the demo 3D project. - **Hubert Dreyfus** — lectures on Heidegger (2007 audio) converted into a mini-site demo. - **Star Wars / warp drive analogy** (with quoted dialogue: "Punch it.") — pop-culture reference. ### Internal (Every) Materials Cited - "Senior engineer benchmark" (custom; scores cited: Fable 91, Opus 4.8 63, GPT 5.5 62). - Survey data from Every subscribers (~100,000 free; ~10,000 paying claimed). - Every's app **Proof** (agent-native markdown editor; GitHub issues). - Articles: **"After Automation"**, **"The Eight Levels of AI Adoption"**. ## Promotional Segments — Flagged - **Subscription promotion (Every):** "We've got like 10,000 paying subscribers. If you have not subscribed, you should subscribe every.to/subscribe." - **Cross-promotion:** Repeated pushes for the "After Automation" article/video and the Every "vibe check" write-up. - **Affiliation disclosure:** Speaker is CEO/co-founder of Every, the outlet running the tests — inherent promotional context. ## Availability / Limitations - **Curated metadata:** Not supplied; no comment availability info present in source. - **Note:** Model names/versions and benchmark figures are as stated by the creator and not independently verifiable from this transcript. This is the infinite library of Babel from the Borges story. It contains all of the books in the universe because books are just strings. If you look, you can even go into bookmarks and I can click one of my articles, after automation, and it finds it in the library. It's truly infinite and look, I could go up the stairs. I can like look down. I can look up. This seems like it took a long time to make, right? Wrong. I made this entire thing in a single prompt with Fable 5, the new model from Anthropic. Like like literally, let me show you. So, this is a prompt. It's from 4 days ago or so. I got this model a little bit ahead of time. Read Jorge Luis Borges's The Library of Babel. And then plan and execute end-to-end a browser playable 3D game in which the player is dropped in blah blah blah blah blah. Loop until it's done. Just wrote that, press enter, and it just went off and read the story, and then it just ran and ran and ran. You can see it's like looping itself and it's checking its work. After 3 or 4 hours or so, done. We've got hexagonal galleries stacked endlessly. We've got 20 shelves, five per side. As one would expect, it's accurate to the book. It's got the mathematics right. It says, "The part I'm proudest of." This is crazy. It just made it in one shot in 3 or 4 hours running on its own. Fable 5 launches today. Here's your day zero vibe check. But first, remember to never make any major life decisions within 30 days of a meditation retreat, a psychedelic experience, or your first encounter with a frontier model. Cheers. So, before we get into it, you're probably wondering, "How's this video even out?" My name is Dan Shure, I'm the co-founder and CEO of Every. Every is the only subscription you need to stay at the edge of AI. You can kind of think of us as like an AI lab for the future of work. We spend all of our time testing new models, using them to do our work, from programming to writing to design to business building to decision making. We use them hands-on and we tell you about what works and what doesn't for real use cases. And I'm incredibly excited to be doing this because the first encounter with any new model could be crazy, but Fable, which is a Mythos class model, I think is like is a particularly big moment. It's like the most hyped model. When it leaked a month and a half ago, Anthropic said it was too dangerous to even release it. And now it's out. And I have a feeling that if you're like me, you might be excited, but you're also like a little scared. Because we've been using this model for about a week now, we get to pull back the curtain a little bit and show you what it's like to have live with this model a little bit more. It does change things, but hopefully this can help alleviate if you're feeling a little bit of AI psychosis. I'm sure that that's going to be going around on X and YouTube and and the news and all that kind of stuff. This is a place for you to see how this thing might fit into your work and into your life and in a realistic way. So, let's get into it. Okay, so Fable is a Mythos class model. Mythos is a model from Anthropic. It's the largest model that they make. There's Haiku, Sonnet, Opus, and then Mythos. As far as I can tell from talking to people internally and at Anthropic, there's nothing special about it architecturally. It's basically the same thing as their other models, it's just bigger and better. In order to make it safe to release, they put pretty strict safeguards on it, so you can't use it for anything cyber related, you can't use it for anything biological related. That's what makes Anthropic comfortable releasing it to the general public. It's pretty expensive. It's $10 per million input tokens and $50 per million output tokens, which is about twice the cost of Opus. So, it's a lot, but it is just genuinely the most powerful coding model I've ever used by far. To give you a sense, we have a senior engineer benchmark, which basically tests the model on its ability to act like a human senior engineer. We give it a vibe coded slop production code base, a real production code base, and we ask it, "If you're going to rewrite this from first principles, how would you do it?" And then we see how it does. We score it out of 100. The best model score is a 63 out of 100, which is Opus 4.8, which came out like 2 weeks ago. And right behind that is GPT 5.5, which is a 62 out of 100. Fable scored a 91 on this benchmark. 91 out of 100. That's the same score as a human engineer with just a just just one prompt. That's That's it's crazy. I like I I knew that this benchmark was going to get saturated, but I thought it would happen in like 6 months. Look at this view of it when we break it down by what it's good at versus other models. This is Opus 4.7. The you know, the orange stuff is what it what it does versus what it's going to do. You know, pretty spiky, not that great. GPT 5.5 like we're starting to fill out the the hexagon a little bit. This is just like, oh yeah, it just did it. If I try to like break down for you, okay, what is it really good at? Because it's not good for everything. I think it's fantastic at sustained autonomous execution. Like for example, the way to work with this model is to give it a task and then leave. Go do something else. Let it go for 3 or 4 hours. Set it up overnight. It's it's amazing. Like it it just figures stuff out and it just does good work. It has good taste. It has good attention to detail. There's all these like little details that it does pretty well that I'll show you. That's that's really impressive even with a not very well specified prompt. Um it it it has it has more judgment. I think previous Claude models you be like, oh do this thing and be like, oh my god, yes, I'm going to do it. I'm going to do it. And then purple accents purple accents. It was like a little try hard to be honest. And this model it feels like it it's going to go do it and it's going to think it through and think about how to do it well. And if it doesn't think it's it can do it well, it'll it might say something to you, which is which is really helpful. And it's also just incredibly good at like using a lot of context. Like doing a bunch of research, digging into data, giving you a bunch of things from the data that that that you wouldn't have known beforehand. I'm going to go through a bunch of really specific examples of exactly what this is and how it works. But the if I step back and I think about in particular for programming, like what is this model? It's it's like a warp drive. Um you know, like in Star Wars they you know, you want to you want to like jump across the galaxy. You like you know, you punch out you punch the coordinates into the computer. The computer does some calculations to see like, okay, we don't want to we don't want to like go through a star or whatever. Then you punch it and then like the stars blur into blue and you're like \>\> Punch it. \>\> Punch it. \>\> Just punch it, man. Just punch it. Go. \>\> You don't get there instantly with a warp drive. You're going across the galaxy, but it it takes you like, you know, a couple hours or a couple days, which is pretty good because it used to take you like a year or two, or maybe 20 or 100 years. But with a warp drive, you get there in a couple hours or a couple days. And that's kind of like what this model is like. You can specify a destination for a big trip, and it just like it compresses what normally would have been like years or months into like hours or days. But also like it's not really that good for getting around town, you know? You wouldn't use a warp drive to get around town. You need like You need more control. You need more feedback back and forth between you and the vehicle. And the same thing is true for this model. If you're using it for like true collaboration or quick questions or things that need tight back and forth, I don't think it's that good for that. I mean, it's very slow. It's very expensive. It's extremely token-hungry. One thing that's a sort of a pro tip that's not intuitive for this model is you can set it to lower reasoning levels, like, you know, instead of max or extra high, you can put it to a medium or low for more basic questions. That's not something that was very intuitive to me, but that's how people inside of Anthropic use it. So, if you want to use it a lot and you're trying for more of the like everyday thing, you can do this. But I just I I think it's I think it's a warp drive. It's really good for those big things, but you have to you have to have big meaty things to give it. I want to show you some examples of some of the things that we saw in testing this. It'll tell you some of the properties of this model and give maybe give you some inspiration for where you might want to use it. Okay, so first example. There's this philosopher I love, his name is uh Hubert Dreyfus. He wrote a book called What What Computers Can't Do in the '70s, which was like a famous explanation of why AI at the time was not going to work. Really like him. He's really into Heidegger, the a philosopher. And he has these lectures on Heidegger from like 2007. But they're kind of hard to follow. Their audio recording's not not that good. So, I just told Fable, "Hey, can you like go get his go get his lectures and turn it into a little mini site for me so that they're easier to consume?" That's literally all I did. I didn't even link it to the lectures. I just said, "Go grab the lectures." I didn't know where they were. It went and grabbed the lectures. It wrote like Look at it. It wrote like a little summary. Why does this matter? These are audio lectures. It broke it down into a table of contents. And then it has this like player experience where, you know, I can press play and it'll play him. And And it highlights what he's saying as he's saying it. Like it synced the the the playback to the text. And it has this like player thing. And it's got like, you know, minus 15 and plus 15. And, you know, you can do 1x or 2x and you can follow it or not follow it. Like this is from one prompt. And that's what I mean when I talk about this model having really exceptional taste and attention to detail. There's a lot of different dimensions along which this is happening. Look at the font choices. This is all caps. There's a little bit more font weight on here. It does the drop cap here. These are not like the defaults that you would expect. It's not like the Claude purple highlight vibe slop thing. I assume some of this stuff will look sloppier and at some point cuz everyone's going to be using this model and using to do stuff. But for now, it really looks good. And the things that it builds feel much more thoughtful. These are things Obviously, you could have built with a model before, but now they're just available to you almost for free. And I I think that that's like a good test of a model is Does it get you to just like try a bunch of new stuff because now stuff that used to be hard is easy. And so it just opens up a whole realm of things. That's 100% true with this model. So, this is my first one. This is the the kind of taste and and detail example. Another thing that it does, which I think is so cool, is it's so good at using its context and doing research at every We paid subscription. We've got like 10,000 paying subscribers. If you have not subscribed, you should subscribe every.to/subscribe. Uh you'll get a text version of this vibe check if you want more detailed insights. So, check that out. So, at every we got maybe 100,000 free subscribers. And we do surveys. So, we want to understand What do people think about us? We have a bunch of recent survey data. So, we fed it into this model. And look what it did. Like, a team of us have been looking at this data for weeks now with AI, and we've not come out with anything this interesting or succinct. This is from like hundreds and hundreds and hundreds of survey responses, maybe in the thousands of survey responses. The punchline at the top. You have a conversion merchandising problem. Your free-to-paid conversion ratio is lower than it should be. Okay. Next, the falsifiable bet. If we ship pricing transparency and a trial offer, I think it's going to go up. Like, that is something that I would expect a really, really good growth person to do with a lot of time and thought and research. It requires it to think across so many different dimensions. It has to go look at all the data. It has to go look at all the survey responses. It has to go look at our analytics data. It has to then go through the site itself and put that all together in a way where it can tell you, "Here's the punchline, and here's what I would do next." in a way that I could just scan. We've not seen a model that was able to do this, and I think this applies to lots and lots of different challenges, whether you're using it for coding or knowledge work, or whatever you want to use it for. So far, I've I've shown you like a a lot of cool one-shotted demos and stuff like that. The other thing I did is we have this we have this app called Proof. It's a agent-native markdown editor. This is this is Proof. We We have a bunch of issues in the GitHub with Proof cuz agents can submit issues. So, as you're using it with your agent, it just submits a bunch of issues. A bunch of them come in every day. I just pointed it at our issues in GitHub, and I just said like, "Okay, I want you to take all the issues for the last couple weeks and close any that aren't relevant and write fixes for all the all the rust." And it just went boom boom boom boom boom boom. And actually wrote fixes that we merged. Again, other models can do this, but it's much more, "Okay, you go one at a time. You make sure it's doing well." You can't just be like, "Go do it." This is this is why it's a it's a it's a warp drive. It just speeds through the backlog of simple things like this in a way that would be impossible with other models. And now the question might be in your mind like, "Who should use this?" And I really don't think that this model, as it is right now, is for everyone. Again, it's slow, expensive. It's super powerful, but because of that it's not for everyone. We have this article on Every called the eight levels of AI adoption, which you should definitely read. You can actually throw it into your agent, and we'll put the link in the show notes. Your agent can just go through how you how you use AI, and it'll break it down into eight levels. Everything from at the bottom level, you're just using it essentially like a Google replacement to just like ask basic questions to the top level, it's like you're orchestrating many, many different agents where you're delegating work, and and it's working 24/7 and all that kind of stuff. That's the kind of that's the spectrum, and everyone falls on a different place in the spectrum. We probably had seven or so people testing it internally over the last week, everyone from programmers to writers to editors to marketers. What we found is there's actually like a pretty high spread of who liked it and who didn't. And it's not like anyone hated it, but if you're if you're not at a certain level of AI workflow, you're kind of like, I don't know what to use this for. Because you you don't you don't have a problem that's big enough where you need to speed through the galaxy. What we found is if you're at like a seven or eight on the scale, you know, you're you're using multiple agents, and you're orchestrating them, and all that kind of stuff, you've got big meaty problems where you're like, "Wow, this is crazy." And usually that's technical people. If you're non-technical, if you're a vibe coder, you you probably are watching this being like, "Holy I have so many projects I I want to do." As long as you can afford it. I'll say it again, this model is expensive, and it hogs a ton of tokens. for now, if you're vibe coding, I would be I would be careful with it, but I would definitely definitely definitely try it. And then if you're kind of like if you're a knowledge worker, and you're just using it to get your job done, uh unless you're a very advanced knowledge worker that's using it in in this sort of way, like you're you're orchestrating multiple agents together and delegating a lot of your work, it's going to feel like overkill. And and I think that's that's a really interesting thing is like what we're finding is yeah, yeah, you have this AI, but using it is a skill. You need to be exposed to problems and working at a level of expertise where the problems come up in order for it to be useful. Internally, like all of us are early adopters of AI. I'd say probably about maybe half of us are are at a level where we can see the problems that this thing solves and about half of us are still getting there. I think we will. I I actually think that this kind of workflow is going to be available to everyone sooner or it's going to be useful for everyone soon, but it just depends on your on your where you are on the adoption curve. And to get into a couple things that model doesn't do as well, it it didn't actually do substantially better at writing than Opus 4.8. So if you're using it for writing, we we found its sentences to be pretty dense, you know, like big blocks and they're pretty literary. So for some things it can be good for that and I I do think it's very good for thinking through writing issues, but for actually writing sentences for copywriting, it's probably not the thing you're going to want to use. If you're a Claude person, just use 4.8. If you're a GPT person, 5.5 is much better. I I personally prefer 5.5 and I still use 5.5 inside of Codex as my daily driver because most of the stuff I'm doing I want to go back and forth pretty quick and a model like this is going to is going to be overkill. So this this model like it increases my confidence with for big projects or maybe writing production code like that kind of stuff, but for my day-to-day, it's a bit overkill even for me. Finally, like let's talk a little bit about what is what is the meaning of Fable? We want to go past the all my god, it's going to change everything and to some extent that's actually right, but it's not going to change everything in the way that I think people imagine. I just wrote this piece called after automation which you should read which is about what is it what is work like after we've automated everything? It turns out automation actually creates a lot more human work. It's a very interesting paradox. I think the same is true here. What we're going to find is this model increases the floor of capability for non-experts, but it also raises the ceiling for experts. So a vibe coder might be able to make a one-shot video game and an expert might be able to make like a true AAA game just by themselves. And I think that I think that is so cool. Obviously, the fact that things are changing this much and and I think I think important for us to say this does change things. If you're someone who's used to typing code into your computer, this changes that a lot. And I think it's normal to be sad or angry or weirded out by that. And it also changes things. Even if you're using AI already, this this changes how you can expect to use it in the future. A lot of the skills, a lot of the things that you thought you might have to do are starting to change a bit because this model is so much more powerful. Change can be scary, but it's also an opportunity to be like, "Wow, what can I do now that I might be into that this now makes possible that I can just do? I don't need to ask permission. I don't need more money. I don't need anything." And and because this capability is out now, we can expect that even if even if it's too expensive for you to use right now, it's going to be pretty cheap soon. Like, let's say within the next 6 months to a year, everyone will be able to have this. And I think that's I think that is incre- incredible. So, if you like this video, you should really watch my video after automation, which talks about what happens when we automate everything, and read the article. You should also read our vibe check on every We go in-depth through every part of the testing that we did for this model, all the benchmarks, from coding to writing to knowledge work. We have takes from the entire team. We have a a bunch of people testing this from different perspectives and different walks of life and different ways that they like to use AI. But, if you're psyched about this, the thing I recommend most is go use your new warp drive. And let me know what you make.