# Fable 5.2 Is the Best AI Ever. So Why Am I Not Switching Yet? — Transcript (2026-11-10)

https://aidailybrief.ai/e/2026-11-10 · Listen: https://pod.link/1680633614

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[00:00:00] As the Anthropic IPO gets closer, we have officially gotten a new flagship model. The company dropped Fable 5.2 and Mythos 5.2 yesterday. and a lot of the first reactions are exploring the fact that in some ways it is clearly the most powerful model we've ever had But on the other, it's powerful in ways that don't make it abundantly clear exactly when and where most people will actually use it How much is this specific to Fable 5.2 versus just the reality of new models at the capability level that we're at?

That plus everything else about Fable 5 too is what we'll be looking at on today's episode

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. All right, friends, quick announcements before we dive in. First of all, thank you to today's sponsors, KPMG, CData, Granola, and Blitzy To get an ad free version of the show, go to patreon.com/aidailybrief, or you can subscribe on Apple Podcasts.

And if you wanna learn more about sponsoring the show, send us a note at sponsors at [00:01:00] aidailybrief.ai. The website is also where you can find out about everything going on in the ecosystem

We've got a new training program coming up with Superintelligent

focused on AI champions, i.e. the key pillars of organizational and enterprise AI, who help not only do the AI projects, but bring AI across the organization

And you can also find a link to How We Got Here, my new longer form historical show

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but let's go see if this new model from Anthropic has any implications for how I do that show or this show or anything else that I'm working on

Welcome back to the AI Daily Brief.

Well, friends, you knew it was coming. no matter how much people have loved Opus 5.5, and as we'll talk about today, they continue to love Opus 5.5. You [00:02:00] always knew that in the run-up to the Anthropic IPO, We were going to get a new Fable model



that model plus its mythos equivalent has arrived and on the one hand, people are finding it unbelievably easy to describe as the best model that has ever existed And yet on the other hand, people are in certain circumstances struggling to identify what it's actually going to be useful for



to give you just a little preview of the nature of the discourse

even Professor Ethan Mollick decided that the best way to frame his exploration of the model was about asking whether it was officially AGI

So what we're going to do today is first rip through the benchmarks pretty quickly. Second, look at some people's first impressions and the types of things they're using it for

Third, dig into the deep dive analyses to see where it's actually going to fit in our model stacks. And finally, end on a few tips for how to use it best

Now, in terms of those benchmarks, simply put, Fable 5.2 lifts the bar on what AI can do Across every category, Fable is head and shoulders above anything that's come [00:03:00] before it. In coding, 

Fable 5.2 was four points ahead of GPT-6 Astra in second place and three and a half points ahead of Opus 5-5 on Frontier Code

On agentic scientific research It was a full six points ahead of previous leader GPT-6 Astra on Terminal Bench Science 0.1



On cybersecurity, they focused on Mythos

and in each case, Mythos 5.2 was about 10 full percentage points ahead of the previous leader

And on top of all that, Anthropic also included a bunch of non-standard benchmarks for emerging capabilities that we're not used to seeing For example, on robotics, Fable 5.2 scored 64.9% on RoboDojo Sim, adding 25 points to Opus 5-5's score

Which itself was a breakout result compared to other models

In protein design, Mythos 5.2 scored 85.1%, which was six points higher than Mythos 5.1

Now, Now, the one place where there was some complication was agentic knowledge work

On a briefcase, Fable 5.2 was about five points ahead of Fable 5.1, Opus 5.5, and Gemini 4 Carbon. However, its ELO [00:04:00] score

was actually lower than Opus 5.5's, meaning that it completed more tasks But wasn't necessarily better than its smaller stablemate

That said, one of the most notable results

was on the Remote Labor Index, which uses real-world contractor tasks

Where Fable 5 2 scored a 41.3%, almost doubling Opus 5 5 score

Obsolete author Garrison Lovely called this by far the most alarming score

Garrison writes, " when the when the Remote Labor Index was introduced a year ago, the best AI agent could only complete two point five percent of the projects." Opus 55 had the best score at 21%, and Fable 5 2 just doubled it, hitting 41

This is still a leading indicator of labor automation, as these projects are better defined than what you'd get at a typical white-collar job. But the fact that one model generation can do twice as many projects end to end is a reminder of how fast change can come during exponential growth

Now, unsurprisingly then, Fable 5.2 is also the new leader on the Artificial Analysis Intelligence Index. Its score of 65

Puts it five points ahead of Gemini Carbon, six points ahead of Opus [00:05:00] 5.5, and eight points ahead of GPT-6 Astra

Artificial analysis actually tested Fable 5 2 on three different settings: max, extra high, and high, with both extra high and high scoring 62



and Fable 5.2 now occupying the three top spots with three different effort settings. Artificial analysis said that gains were driven by improvements in science, knowledge, and coding scores, while less progress was made in agentic knowledge work and terminal coding.

Artificial analysis wrote, " Claude 5.2 improves on Fable 5.1 across capabilities, but sometimes only to Opus 5.5 levels."

They pointed out some of those same areas of knowledge work where it wasn't all that differentiated compared to Opus five five

Now on costs Anthropic has cut the per token prices for Fable 5.2 by 20%. Input tokens are now eight dollars per million compared to 10 for Fable 5.1, and output tokens are forty dollars per million compared to 50

Subscribers are still limited to using half of their token allowance on Fable. But Anthropic is handing out a banked Fable reset for this week to encourage a little more experimentation.

That said, Fable is still an extremely expensive and [00:06:00] token-hungry model. on max settings it costs $26.54 per task across the AA benchmark suite. That's more than twice as much as Fable 5.1 2.6 times as much as Opus 5.5 and more than four times as much as GPT-6 Astro with effort settings knocked down to extra high, cost per task dropped to 1483, which is still forty percent more than Opus 5.5 on max settings reducing effort down to high results in nine dollars and eight cents per task, which is 10% cheaper than Opus 5.5 on Macs

Now, Anthropic does say across their release docs that medium should be the default setting, that high is enough for everyday coding, and extra should be reserved for long-running agentic work with measurable outcomes. And max effort, they wrote, is for finding the ceiling and not recommended for everyday use.

And at least one early tester, Matt Schumer, was absolutely convinced that not only medium, but in fact low, was enough for the vast majority of tasks

Meaning that this cost efficiency profile might look a little bit different in practice than it seems at first glance



Now, one way to get a sense of a new model is to see which [00:07:00] particular use cases



or broader triumphs from the testing period, 

the company finds worth mentioning in their announcement

Anthropic mentioned that 90% of code changes are now written by Claude under human supervision, which reflects the number that we'd heard before andisn't particularly surprising Where Fable 5.2 has been added to that mix to fix stubborn bugs, optimize performance, and to, quote, " "Complete Complete high-value software projects that would otherwise have been too slow or too costly to attempt."

They gave the example of a migration of a critical production service from Python to Rust to improve its efficiency, reliability, and start time. Essentially, and this might be good guidance for the rest of us, Anthropic is reserving Fable for ambitious tasks that other models can't handle

They found that the model, quote, " Exercises strong engineering judgment and can produce high-quality results even from ambiguous requests." The showcase demonstration of this was an experiment where Fable 5.2 was tasked with designing a prototype operating system from scratch. Anthropic wrote, " Starting with an empty folder, the model coordinated over one thousand sub-agents working around the clock for eight days, writing, testing, and reviewing code.

[00:08:00] It produced a working operating system that boots on a laptop with its own core that runs the machine, drivers for the disk, keyboard and network, permanent file storage, Windows, a login screen, an installer, and built-in Claude functionality." One of the funniest comments I saw about this came from AI entrepreneur and YouTuber Theo, who wrote, " Love how Fable 5 2 literally built an operating system and my immediate response is, 'Yeah, and?'

Crazy how quickly we get desensitized to things

Now over in science, Anthropic announced multiple breakthroughs using Fable 5.2

They used the model to solve two open problems in theoretical physics concerning black holes. similar to recent math breakthroughs from OpenAI, these solutions were largely about solving calculations that were too laborious for a human scientist, even with traditional computational techniques

In In biology, Fable 5.2 used its protein-solving ability to design a new way to predict the structure of a protein shell used in gene therapy called a capsid. Anthropic wrote, " In our tests, it accurately modeled 28 of the 34 capsids it attempted.

Biologists could use it to design capsids faster and more cheaply." In math, Fable 5.2 fully [00:09:00] formalized Fermat's last theorem into a machine-readable proof. This took 30 hours using 1,000 agents, and by way of comparison, Opus 5.5 was only capable of getting 26% of the way through the proof, taking 18 hours before it stopped making progress

The choice to focus on many of these scientific discoveries is, I do not believe, accidental. Chubby on X wrote, while we're all focused on Claude Fable 5.2's coding abilities, its work in science and engineering has received far less attention." He then goes on to list some of these accomplishments and a bunch of others that Anthropic shared, and concluded, " So yeah, this is nuts, and at the same time, that's why we need to accelerate.

AI helps us solve problems in every scientific field And yet we are still in 2026, and this is still the beginning. Fable V2 shows us where we are heading, and 2027 is going to be nuts



one final demo worth mentioning

Fable's robotics demo showed the model piloting a pair of robotic arms

Writing, "Fable 5.2 shows substantially improved skills in controlling physical robots, even on tasks it has never explicitly been trained to do." They noted that Fable [00:10:00] 5.2 outperformed most models that are specifically designed to drive robotic arms

suggesting that the bitter lesson applies to advanced robotics as well



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So what were some of the first impression, so so what were some of the first impressions from the community?



perhaps not surprisingly given the origin story of Mythos many people found even the commercially available version, PayPal 5.2, to be extremely good with security issues Leander Builds wrote, " Fable 5.2 in Medium just discovered more than 10 severe security vulnerabilities in my project without asking for it.

These issues were created by Opus 5.5 before. If you haven't done it yet, highly recommend doing an audit with Fable 5.2."

AI entrepreneur Pavel Hurin actually [00:14:00] has a bug hunt bench

which tests different models for bugs in real work repos



He planted 105 bugs across the two repos And the previous best models mostly found between 40 and 45. all of the big models that you probably use cluster somewhere in that area, with the only exception actually being Sonnet 55 Max that recently jumped up and found 51 bugs Fibel 5.2 found another seven for a total of 58 bugs

And when it comes to the cost, Pavel wrote Fable 5 II got there with less than half of Sonnet's turns for less money



now, of now, of course, this being social media

There were a bunch of Blender-based 3D rendering tests

3D AI explorer Stefan Baskovic

ran the same Castle prompt that he had run on Opus 5.5 back in September

And while Fable 5.2 did take longer and cost a bit more than Opus 5.5, even to an external lay observer, the difference is very clear With Fable V2 having radically more detail

Maybe the most impressive video demo came from Riley Brown



it's a 60-second ad for Fable 5 too

That uses some incredible [00:15:00] zoomed-in visuals, artistic shots, realistic lighting and physics that change

And in a single shot done live on stream, created this entire thing

Even Claude Code creator Boris Cherny said, "Whoa." Now, Now, if you haven't listened to it yet, you should go listen to my episode about why agentic video is real and what you should be experimenting with. and certainly Fable 5.2 just adds more oomph to that argument

Still for some, the watchword for this release

was ambition. Ali K. Miller wrote, " I was an early tester of Fable 5 too, and I had it, one, build an entire water safety map of 3,100 cities in the US based on official government data with two demo videos. Two, build a new identity and create and run an entire ADHD-themed YouTube channel that can make money for me.

Three, build psychological profiles of my stalkers to determine if any of them were the same person."

Four, build a hilarious 3D hotdog kiosk game as a command center to manage my to-do list. Five, fill out 31 different personality tests on my behalf and provide an analysis over time Allie's conclusion, " I trusted it more than any other model I've tested. [00:16:00] Unbelievable at handling complexity.

Made some weirdly obvious errors like colors and units on the water map or overlapping text on a slide that secondary agents also didn't catch. Importantly, there is no such thing as a needle in a haystack anymore with this model, and I still feel like I could be more ambitious."

Now that sense of not even knowing how to use all that power came out in Every's vibe check as well

Their Their review was titled Fable 5 2 is a paradigm shift if you know what to do with it

The piece tells the story of how one of the every team built a working video editor

that combined, they said Adobe Premiere's editing capabilities, After Effects titles and visual effects, and Ableton's music mixing with an AI agent that can run all of it

" It's hard to overstate," they write, "what a big deal this is for building software. With Opus 5.5, you're still in the loop, checking its work as you go. Fable 5 and 5.1 could take a big task and run with it for hours or days. But Fable 5.2 can take the whole product." Hand it everything you want, and it keeps building, testing, and refining until it has something you'll use every day.

This is [00:17:00] the first model we've tested that lets professionals build production-grade tools of their own without ever reading the underlying code When our first round of early access ended in October, several of us put our most ambitious projects on hold until it came back because we expected a better first pass with less steering

Now, one of the things that I said when Fable V initially came out was very similar to this



while previous models I had felt allowed me to start a bunch of different projects, Fable 5 just actually got them done in complete form

I got the new version of the AI Daily Brief website and extraction pipeline running in the 48 hours I had access to Fable 5 before it got shut down, and basically didn't make any updates during the shutdown period

Now, Now, Dan Shipper and the Every team are actually resurfacing the narrative of people building their own professional software for the work they wanna do



This is something that people have been talking about with these agentic models For more than a year

and was of course the narrative at the heart of the SaaSpocalypse on Wall Street earlier in Q1 and Q2

now mostly the conclusion that people came to Was that the difficulty in terms of time, cost, and context switching [00:18:00] of maintaining your own software, even if you could build it

just wasn't really going to be worth it over using existing SaaS solutions, especially as they evolved along with these new capabilities as well

At least from their early testing, the team at Every is unsure that that still applies. And yet at the same time, they also got at another big theme that came across a number of the different reviews, which is the jaggedness or spikiness, as they put it, of what the model can do Dan said, " It's kind of nuts, but it's a spiky kind of nuts.

There's a certain kind of task where it's absolutely going to blow your mind and be unlike anything else you've ever tried before. And for everything else, it's just sort of normal. You should actually use Opus 5.5."

This This was a major theme of Ethan Mollick as well

whose post about Fable 5-2 was called Fable 5-2 and Jagged AGI

He writes, " AI has gotten so good that it is worth asking if Anthropic's newest models, and the inevitable competing models OpenAI and Google will soon release, cross the boundary into being an artificial general intelligence."

Later on, after sharing a bunch of examples



Ethan wrote

You could you could start to see the limitations of the very digital tasks I [00:19:00] picked when I felt uncomfortable letting Fable reach out to real customers. And there were the failures. A couple of years ago, the jagged frontier meant that failures were obviously made up facts or failure to do basic math.

Fable's failures were different. It made a course that was accurate and joyless. It wrote a one hundred and eighty-two-page paper. It kept coming back to its favorite ideas, and it buried a creative project under rules and approvals

Now he did add, " I teach at a business school, and I have seen talented people in good organizations do every one of these things. If building a bureaucracy disqualified you fromgeneral intelligence, a lot of us would be in trouble." But he continues, "There are still gaps, and it's the reason that my answer is that this is almost AGI.



Most critically, the AI did not notice these problems itself. It did not see that the course was a slog or that it had tied itself in knots with its own rule books. This was not for lack of checking. Fable put critics, fact-checkers, or judges on nearly every project, but those checks found errors and added fixes.

None of them asked whether the work was too long, too repetitive, or no fun



That all fell on me, and if I hadn't watched and taken action, the results would have been less [00:20:00] impressive, though not exactly a failure either. Knowing when your own work is not good enough is a large part of what makes someone an expert, and it's the part Fable does least reliably

I would still not trust it to run a business on its own, even though its plan would have impressed me coming from a team of my MBA students

Now Now in my tests I found something similar



Basically that while it can do impressive things, it didn't really seem to have a good sense

of what would appeal to people



I ran a version of a prompt that Ethan had used in two different places. one in the normal Claude tab And one in my Claude Code instance that has access to a bunch of different tools and systems and skills the gist of the prompt is basically asking it to create the most impressive thing that can share about its capabilities

And the version that it created In the regular Claude interface Was, it felt to me, pretty wildly underwhelming

It decided that the best approach was to create things that were hard to make but easy to check, and to put it on a website where people could check them itself The three things were a sentence that correctly counts every one of its own letters, a piece of music that harmonized with itself played backwards, [00:21:00] And a QR code encoded from scratch



to the extent that these things are particularly difficult

Most normal people, myself included, don't really understand why they're difficult, which makes the intellectual and emotional impact of them much, much lower

Also, some of the capabilities don't feel that far away from things we've had before, like the QR code



the insistence that people check it themselves kind of feels like an old lingering idea of AI from a couple of years ago. And overall, it just didn't really have a sense of what would be impressive to humans

now the version that was created In the coding harness was much more interesting

It was a three-minute film

that it pointed out had not been made from a recording. Fable wrote, " "The The picture is calculated frame by frame in your browser. The score is synthesized from sine waves while you watch. The narration is the one thing borrowed, a text-to-speech voice, and the only file this page loads."

every letter here, this sentence included, is drawn with the film's own alphabet. The source holds no fonts, no images, no audio samples, and no libraries

And it basically is a really [00:22:00] cool show off of all the different things that you can turn code into Now what was fascinating pretty much the same prompt that Ethan had used. It was shocking

how similar this output was to what Ethan got and shared on his blog

Right down to a little twist at the end



where in the case of Ethan's site If you take the first letter of every sentence in the script, it spells out the name of his blog, One Useful Thing

Whereas on my test, it did the same thing



It spelled out "Be amazingly clever about this," which was the last line of the prompt

Now these are interesting results, but not particularly useful

And so far, at least with my tests

The model's seeming inability to know what is interesting to humans And what's valuable to us is a real detriment to the type of content work that I might apply it to I asked it to propose an idea for the AI Daily Brief that could be built around a website and use original research and data in some interesting way

I said this is in part a test to see what you can do compared to other AI models, so be intentional about the episode idea being impressive. Once you have the idea, we're going to have you execute it. The [00:23:00] first idea that it pitched was, in a phrase, unfathomably boring and irrelevant. It was called Announced Is Not Available, and the pitch was, " Time every major AI launch since ChatGPT, from the day it was announced to the day each kind of customer could use it, and publish the result as a site."

The argument it says, "AI coverage runs on announcement dates, but people and companies live on availability dates, and I couldn't find anyone who has measured the distance between them

He pointed to September's GPT-6 Astra launch that Sam Altman actually apologized for because paying subscribers couldn't have it right away And a couple of others Now I had to explain why this was a very boring idea, which basically came down to the fact that one, no one cares about, when a model from 2024 was announced and when it got access.

In fact, people pretty much only care about the models that are available to them right this second. And what's more, with a very few exceptions, like GPT-6 Astra that he mentioned, at this point, the labs that are actually driving the conversation, which for now is pretty much just Anthropic and OpenAI, although Google might be back soon

aren't really having those sort [00:24:00] of delays between when they're announced and when you get access to them

Overall, what we got is an idea that while finding an actual factual gap in the coverage, had no ability to judge how utterly irrelevant the episode would be if I actually did it Now it did have two backup ideas. What America's 25 biggest employers told investors about AI and headcount last quarter

And every day-to-day AI prediction that has come due graded with each forecaster's average miss. Both of those are much better ideas for episodes the second about the biggest employers on earnings reports talking about AI was less interesting only because going through and aggregating and organizing public earning statements is something that models have been able to do for a couple of years now at this point.

and so even if Fable-5-2 had done it better in some way

It wouldn't necessarily have been an actual net new capability. the everyday to day AI prediction was a bit more interesting because while it still had that same flavor of aggregating and organizing lots of data

It involved a bit more thoughtfulness and subjectivity given that it had to grade things in advance

I had to do that third idea rating forecasters and forecasts. [00:25:00] And it actually was pretty interesting

But not necessarily, again, in a shocking new capability way. And I'm fairly certain that if I had Opus 5-5 do that same task It would do roughly as good a job

So what then are people going to use this for?



I think continuing a pattern we've seen all year, this is a model that really asks us to push ourselves in terms of what we can do. in other words, opportunity AI, not efficiency AI. Not to just improve what we already do, but to do things that weren't possible before 

that was the thesis of my video episode from a couple of days ago. and I think what Riley Brown demonstrated is proof positive that Fable V2 takes that to the next level I also think that while I am sometimes critical of people sharing 3D renderings and game demos as a way to determine whether a model is good, given how few people have that use case

The updates in game development

And particularly what people seem to be describing as the ability for Fable 5 to not just to create

The homunculus of games that look good

but don't actually play or don't play well. Instead, it seems like Fable V2 can actually do game mechanics

Sholto Douglas from [00:26:00] Anthropic wrote, " Fable 5.2 is the first model that passes my bar for seriously fun and beautiful games."



Matt Schumer in his post about Fable 5.2

Found something similar. having it create a Harry Potter themed Fortnite style battle royale game That you can play on Spawn right now And which when I was testing it, other people actually were playing



now just like advances in agentic coding weren't just about how existing coders did their work



And advances in agentic video, I believe, are not just about existing video editors doing their work differently, but all of us thinking about how we could use video in a way that we never have before

I wonder if the capability to build complete games actually changes what we use them for

and the different types of people that leverage them for personal or even professional reasons. For the first time

It seems like the capability set is at a point where it's worth asking

Instead of making a deck or a website, should you make a game? This is something that I'll wanna explore a little bit more in the future because I think even compared to video, it's gonna require stretching our ideas a bit more



Now, now for completeness, as you go out and decide how much [00:27:00] time to invest in experimenting with Fable 5 too, the single most common critique that I'm seeing

is that for the vast majority of uses, it's not only not better than Opus 5.5, it's going to be kind of more annoying Engineering manager Gagam wrote, " "I've I've never really liked Fable models. There's not much point to them. They're stubborn, weighed down by guardrail filters, have poor taste, and are expensive, token hungry, and in some areas worse than the Opus lineup.

This one is no exception. In my view, the new Fable 5.2 makes no sense, and I think I understand why they called this release 5.2 rather than 5.5. A revolutionary leap like the jump from Opus 5 to 5.5 just didn't happen."

Theo again echoed the sentiment saying, " It's been under an hour and I'm already struggling with should I use Opus 5.5 or Fable 5.2?"



and and yet even within this, I am certainly seeing people start to give their tips for the best ways to use it. Daisy Holman responded to Theo and said, " My advice, use Opus 5-5 for more specific tasks where you're already pretty sure you know what you want, and use Fable 5-2 for more creative tasks when you're not entirely sure what you want."

Now that [00:28:00] hasn't exactly been my experience yet, but it is worth experimenting with



The The team from every once again emphasized

that this is a model you give big end-to-end projects that you're going to leave and go away from



and and that seems to be what many of the folks from Anthropic were saying as well



Alex Albert from Anthropic said, " On big open-ended work, it feels like a major step above Opus 5.5," and then gave some tips for getting the most out of it, like ask what's wrong with your idea before it builds anything. It's great at brainstorming and will push back

He also said to tell it upfront where it needs your approval, such as deleting things And be clear that the rest is its call. to start on medium or high effort and save extra high for the hardest problems. And most importantly, to give it a whole project instead of a next step and let it run for hours

And remember, as Simon Willison points out, whenever Anthropic releases a new model, it updates their prompting guide One example from Fable 5.2 is that when you're using it to code, to tell it that it owns the code base Anthropic wrote, " Claude Fable-52 responds closely to what the prompt says about who owns the code it works on.

In Anthropic's testing on small [00:29:00] coding tasks, when a prompt said nothing about ownership or presented the code base as someone else's, the model tended to add to the existing structure even when that repeated existing code. It also tended to report nearby problems instead of fixing them.

When the prompt says the code base is the model's own, the model often merges repeated code and makes related fixes without being asked."

Ultimately, we're at the point Where a new model release cannot be digested in hours or even days, or in some cases even weeks. Even with Opus 5.5, it took three or four weeks for people to really understand to start to understand what made it different and what it could do differently, even if they had a glimpse and a sense right from the beginning that it might be one of those types of models



it seems

Like there may be something like that in Fable 5 too, but it might take some time and experimentation to figure out what it is. I will certainly be doing a lot more experimenting and will share along the way. For now though, that is gonna do it for today's AI Daily Brief. Appreciate you listening or watching as always, and until next time, peace. 

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