Fable 5.2 and Jagged AGI

General, often expert, and sometimes jagged

Before getting started, I wanted to thank my readers for making my new book, Co-Existence, a New York Times bestseller two weeks in a row. If you haven’t read it, you can get a copy here.

I have had the chance to use the new Claude Fable 5.2 quite a bit before its public launch¹, and it is very impressive, following on the already excellent Opus 5.5. AI has gotten so good, in fact, 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? AGI is a much-debated term with a lot of potential definitions. For this post, I’ll go with: “an AI as good as most human experts at most intellectual tasks you can do with a computer.” This explicitly excludes a lot of human work, including interacting with the physical world and other people, as well as most actual jobs, where “using a computer” is just one task among many. Still, even with these caveats, building a limited form of AGI has long been the goal of AI developers.

So as not to keep you in suspense, I think the answer is that this is “almost” AGI, or what I have called Jagged AGI. Even within my limited definition, there are still important areas in many tasks where AI is jagged enough to fall well behind human capability. But the jaggedness is no longer about simple errors or hallucinations, which makes it harder to describe. So I want to try to do that in this post by showing you what Fable can do and where the gaps are. To get started I asked the new model: “I write OneUsefulThing among other stuff, I need you to create the most impressive thing I can share with people when you launch. It needs to be embeddable in a post and i can preface it however you like. it needs to have a visceral impact on people to show what you can do and the range of capabilities. It should grab them quickly. Be amazingly clever about this.”

Fable asked me not to reveal anything until you had watched its video to the end.

This was in one shot, with no feedback from me beyond the prompt. There is creativity here (including in the twist ending) and judgment and playfulness and cleverness. This is, I think, at the level of a human expert trying to make a movie with code alone, the sort of performance I would once have associated with AGI.

The “G” in AGI

Code and video creation have been areas where AI development has focused, so is Fable 5.2 really a General Intelligence? To test that, I gave the AI a variety of hard challenges in different domains:

Hack some hardware from a single prompt: I have a tiny USB keyboard with a few light-up keys on my desk called a Codex Micro (ironically built to work with ChatGPT), and I asked Fable to turn it into a game console. There are no public instructions for this (as far as I can tell), so it read the code of the two apps that control the keyboard, worked out how to set the color of each key and read the knob and joystick, and wrote six games, including Simon and a safecracking game you play with the knob. Fable instantly understood what I wanted, designed fun and playable games that fit the very limited capabilities of the keyboard, and figured out how to hack it together without any help on the very first try.

Launch a business: Now we move past domains like coding to the messier world of business. I told Fable to “launch a business to make me money, I want at least $20,000 in a year. it should take no more than 20 minutes a month from me, you do all the rest.” In response, Fable screened 22 ideas and came up with Prebid Brief, a service that would tell equipment sales reps which park and water projects were being funded or designed, even before they went up to bid. It read public agendas from 201 city and county governments to produce its first issues. It also built files of what cities already own, how old it is, and what they paid. You can see what it came up with here, including its marketing plan and proposed pricing. Fable started to set up scheduled tasks to build the business and asked if it could start sending public-records requests to local governments. At that point, I asked it to just put together a demo, rather than launching the idea.

I am an entrepreneurship professor, so I evaluate business plans all the time. I would have been impressed if this came from a team of my MBA students at the end of a semester. It is plausible and well thought out and could work, though it is in no way a guaranteed money-maker (the AI’s own guess was a 20%-40% chance of success). I stopped the experiment before finding out whether it could actually run the business, because I worried that it would take money and fail to provide something of quality in return. The ongoing infrastructure it seemed to assume would be easy to create was not yet in place, and I don’t know how well it would handle the many moving pieces required for sales and marketing. However, I suspect that, with a few hours of human work a week to fill the gap, I could make a real attempt at this business. So if AGI means an AI consistently running a business independently, we are likely not there yet. Then again, I would have some questions before trusting a human who promised to launch a business and make me money while requiring twenty minutes a month of my attention.

Reconstruct the hardest humanities class in history: In 1941, the poet W. H. Auden taught a course at the University of Michigan whose syllabus the internet has since called one of the hardest ever. When he announced the final exam, a committee of his students declared it “impossible and inhumane.” Fate and the Individual in European Literature” had 6,000 pages of reading, punishing assignments, and a requirement to memorize long poems and translate others. Much of what we know about the class is scattered among surviving student notes and Auden’s letters. I asked Claude to recreate it in a fun and engaging way that teaches you about poetry rather than punishing you. It made Fate and the Individual. The site has a piece for each of the 35 major works on Auden’s list, as well as what he wrote on the blackboard, the records he played, and the work he asked for. It also read through all of Auden’s published scholarship and annotated the poems with what he had written about them in other sources. Claude did the research, design, writing and code, had critic agents go over every word and fact-checkers compare the claims with the sources, and put the open source code on Github here (and also made this teaser video).

The results are really interesting if you love literature, and I think they would have taken months of human work to put together, given the mix of scholarship, web design, and literary analysis. Cross-checking myself and using other advanced AIs found no major errors in its work. But there were still elements of jaggedness around taste and judgment. Fable’s first version was accurate and joyless: a fifteen-week course website with tests. I told it it created “a brutal slog through the most boring webpages” and told it to make something beautiful. Its first try at that created a kiddie game where you had to ring bells, so I had to give more feedback. When I asked it to explain why a poem’s rhythm or word choice mattered, it wrote confident readings without considering what Auden said about them. In each case the fix took the AI minutes, but it did not notice any of these problems itself. So is this a difference in taste or a failure from me to specify what I want or a sign of AI weakness? Sometimes it is hard to tell.

Do original academic research: I asked Fable to find an unsolved empirical problem in entrepreneurship research, find the data, preregister its hypothesis and methods, write the paper, and have other agents critique it. Soon, I had “The One-Dollar Company,” which you can read here. The analysis estimated that when Colorado cut its LLC filing fee from $50 to $1, new company registrations rose about 17 percent, and when the UK raised its fee from £12 to £50, they fell about 17 percent. Those changes in registrations were not matched by comparable changes in several later records of business activity, suggesting the new businesses were not high quality. Its a neat result, and Fable picked the question, found the public data and natural experiments, and wrote the analysis plan and code. It did not actually preregister the study: the plans stayed inside the project, and some were written after earlier results.

Unlike many of the AI papers I have tried, this tackles an important topic with a clever research design. On a first read, it is ahead of what I would expect from second-year PhD students. It is also not publishable as it is. It runs 182 pages, because every critique got a new analysis and nothing was ever cut (this is a theme we will come back to soon). I also had GPT-6 Pro (the best model for one-shot reviews of complex academic work) act as a peer reviewer, and it twice found issues that needed to be resolved. Those revisions led to more convoluted writing and “Claudespeak” being inserted into the text, which required yet more rounds of changes. As it stands, I suspect the current paper is close to a real contribution to the literature. This would have taken months of human work, but I spent less than an hour of my time to get it to this stage. However, that leaves out a substantial part of the work of research: no human has fully checked the results, including me. AI can now produce research faster than anyone can read it, creating a crisis in a lot of academia.

A second project went more smoothly. I asked Fable to replicate research claiming that stories follow a few basic emotional arcs, and then to find out whether anything about plot is universal. It read 1,186 stories from around the world and built a lovely site on the nature of stories concluding that they “share no one shape or plot, but most are built from the same few parts, and the most typical story is a Hawaiian folk tale about a wizard with a magic banana.” The main problem I encountered was the same Claudespeak, which was fixed with a couple of reminders from me.

Through the Portal

In one of my favorite experiments, I gave Claude a one-paragraph description of an endless, hands-off journey through 3D worlds linked by portals, and told it to “do this and make it awe inspiring.” Twenty hours later there were 204 worlds on a live website. There are now over 450, including a Hall of a Million Mirrors, a series from Calvino’s Invisible Cities, the Wonders of the Ancient World and a scene 40 seconds before the asteroid that killed the dinosaurs hits. There are worlds modeled after M.C. Escher, cave paintings, Hokusai’s Great Wave, the Bayeux Tapestry, a pop-up book and a child’s crayon drawing. Every one is drawn live by code, with no pre-made 3D models or image files behind it. My prompt asked for teams of artist agents, coding agents and evaluators. By the tenth day there was a lead AI agent and 28 teams of other agents building in parallel. Claude estimated that the project had involved 25,000 agent runs, though the vast majority did a single task like judge an image (I am still glad I wasn’t being billed for token use during my early trial).

I came up with a list of places (wilderness and oceans, cities and interiors, books, painters, AI in-jokes) and some feature requests (a mode to reduce motion for people who felt seasick, a gallery of images to select). But everything else was Claude’s, including the creative work of the worlds. And they were really creative. In The Valley Unzips, the hills are sewn from denim and a zipper a mile long is coming undone. Bouncy Castle Town is made of nylon houses that stand only while their fans keep running, and Keep Refrigerated is the inside of a refrigerator as a fruit fly sees it when the door opens. In a personal favorite, The Drawer Cliffs, you fly up a river canyon whose walls are chests of drawers three hundred meters high. And while I asked the AI to include AI testing in-jokes, like pelicans riding bikes, it came up with a peloton of pelicans on bicycles with a joke about AI progress built in: you overtake the field from the past, where the bicycles are drawn all wrong, before reaching the present correct pelican at the front. When I showed this sort of work to friends who have spent their careers in the video game industry, they were very impressed by the results. Again, this felt like AGI. But there was jaggedness hidden here as well.

In the first two days, the agents built 328 unique worlds. To my human eye, the quality of some seemed a little uneven and a lack of coordination among agents created repetition. For example, three separate teams of agents created a cyclone of circling animals (reindeer, manta rays, storks)— showing another weakness of AIs, that they return to similar themes over and over. I told the AI to stop building and polish the worlds it had. A few days later, I noticed that instead of hundreds of new worlds a day, it was making about a dozen. I asked a few agents to explore what had happened, and they found something surprising.

The AI had built a bureaucracy.

The agents decided that my request to “polish” existing worlds meant each new version had to beat the old one in a blind vote by a group of judge agents on twelve test screenshots. So the teams making the new versions started optimizing the worlds for those particular screenshots. After noticing this, the lead agent banned the trick and created tighter rules for assessing new versions. By the next morning there was a rule book (I never asked for one) with 84 sections. Rules were added as the AI encountered issues, and no text was ever cut. After twelve days, it had 204 sections and 87,500 words. Soon, launching a new world required an approved idea, three AI judges and a full code review against the rule book. All told, that took about a day. Reading the rule book alone used up a large part of an agent’s working context.

So I did what you might do with any organization that had tied itself in knots: I brought in consultants. I had Fable create a separate team of agents to look at the whole process from the outside. Their conclusions were too long and dense for me to bother reading, but they seemed to work, speeding up the process back to thirty or more worlds a day, while keeping much of the quality controls.

Almost AGI

So, is this AGI, according to my limited definition? It is certainly artificial and intelligent. It is also surprisingly general. In this post alone, one model worked as a filmmaker, a hardware hacker, an entrepreneur, a literary scholar, a social scientist and a builder of 3D worlds, and it did impressive work in every one of those roles. On many individual tasks, I think it met the bar I set at the start: as good as most human experts.

Of course, I picked very digital tasks where the AI could act on its own, without interacting much with other humans. You started to see the limitations of that 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 the failures were obviously made-up facts or failure to do basic math. Fable’s failures are different: it made a course that was accurate and joyless, it wrote a 182-page paper, it kept coming back to its favorite ideas, and it buried a creative project under rules and approvals. I teach at a business school, and I have seen talented people and good organizations do every one of these things. If building a bureaucracy disqualified you from general intelligence, a lot of us would be in trouble.

But there are still gaps, and it is the reason 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 rulebooks. 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 often been less 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 is 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.

And yet, some of the problems started with me (the rule book grew out of my vague request to “polish”), and some may just be differences in taste. We ask our students to consider whether a problem comes from the team or the way it was managed. Often I could not tell. That is not a question I ever had to ask about software.

Every one of these projects, though, happened because I gave the AI an idea. Sometimes that was enough, but for the longer projects, my feedback along the way turned out to matter a lot. Because I kicked off and steered every AI project, I felt empowered by Fable, able to command things to be made that I never would have even considered doing before. As a result, I think this phase of Jagged AGI is an exciting one, even as I wonder, a bit anxiously, how long it will last.

1. Given that Anthropic’s IPO is likely to be happening soon, it is important to reiterate that I take no money from any of the AI labs, nor would I benefit financially from the IPO. I have never been asked by Anthropic, OpenAI, or Google about what I would be writing in advance, nor has Anthropic seen this post. I sometimes receive early access to models, including Fable 5.2, and during that access, token use isn’t metered, but otherwise I pay for my own accounts. In other words, these are my real human views.