Comment by gejose
1 day ago
I believe Gary Marcus is quite well known for terrible AI predictions. He's not in any way an expert in the field. Some of his predictions from 2022 [1]
> In 2029, AI will not be able to watch a movie and tell you accurately what is going on (what I called the comprehension challenge in The New Yorker, in 2014). Who are the characters? What are their conflicts and motivations? etc.
> In 2029, AI will not be able to read a novel and reliably answer questions about plot, character, conflicts, motivations, etc. Key will be going beyond the literal text, as Davis and I explain in Rebooting AI.
> In 2029, AI will not be able to work as a competent cook in an arbitrary kitchen (extending Steve Wozniak’s cup of coffee benchmark).
> In 2029, AI will not be able to reliably construct bug-free code of more than 10,000 lines from natural language specification or by interactions with a non-expert user. [Gluing together code from existing libraries doesn’t count.]
> In 2029, AI will not be able to take arbitrary proofs from the mathematical literature written in natural language and convert them into a symbolic form suitable for symbolic verification.
Many of these have already been achieved, and it's only early 2026.
[1]https://garymarcus.substack.com/p/dear-elon-musk-here-are-fi...
Which ones are you claiming have already been achieved?
My understanding of the current scorecard is that he's still technically correct, though I agree with you there is velocity heading towards some of these things being proven wrong by 2029.
For example, in the recent thread about LLMs and solving an Erdos problem I remember reading in the comments that it was confirmed there were multiple LLMs involved as well as an expert mathematician who was deciding what context to shuttle between them and helping formulate things.
Similarly, I've not yet heard of any non-expert Software Engineers creating 10,000+ lines of non-glue code that is bug-free. Even expert Engineers at Cloud Flare failed to create a bug-free OAuth library with Claude at the helm because some things are just extremely difficult to create without bugs even with experts in the loop.
The bug-free code one feels unfalsifiable to me. How do you prove that 10,000 lines of code is bug-free, and then there's a million caveats about what a bug actually is and how we define one.
The second claim about novels seems obviously achieved to me. I just pasted a random obscure novel from project gutenberg into a file and asked claude questions about the characters, and then asked about the motivations of a random side-character. It gave a good answer, I'd recommend trying it yourself.
I agree with you but I'd point out that unless you've read the book it's difficult to know if the answer you got was accurate or it just kinda made it up. In my experience it makes stuff up.
Like, it behaves as if any answer is better than no answer.
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I've done similar loose experiments using a 19th century French novel I have read (L'Agonie by Jean Lombard), and which is quite obscure. There is no English translation of the novel published.
Using Gemini Notebooks I've found it passable at summarizing chapters, listing characters, major theme and so on, and it can do this in English.
"What is the symbolism of the Black Stone?"
> In the sources, the Black Stone (referred to as the Pierre-Noire) serves as the central religious icon of the Syrian Sun cult brought to Rome by the Emperor Elagabalus. Its symbolism is multifaceted, ranging from fertility and virility to a mystical pursuit of universal unity.
> It represents the perennity of the Sun, which fertilizes the world, causing "germs to rise" and spreading them through the atmosphere. It is viewed as the "definitive form" of divinity, intended to absorb and replace the "transitory forms" of all other Roman, Greek, Egyptian, and Persian gods, including the Christian "Kreistos". > Virility and Phallic Symbolism > > The sources explicitly characterize the Black Stone as a gigantic sacred phallus. It is described as: • An icon of virility and the "organ of generation" • A "unisexual icon" that materializes the generative force of nature • A representation of "virility in activity," which is why it is often paraded and elevated in an "orgasmic" or "colossal adoration"
> The Androgyne and Universal Unity - Beyond simple fertility, the philosopher Atillius explains a more complex, "mad" metaphysical project associated with the stone. It symbolizes "Life One" (Vie Une) and the return to a unisexual state of perfection. • The Androgyne: Atillius believes that by pursuing "the male sex by the male sex," the cult "inutilizes" the female sex to eventually create the Androgyne—a self-sufficient being containing both sexes • Unity: The stone signifies the fusion of all generative forces into a single Unity, reversing the "separation of the sexes" which is viewed as a state of unhappiness and impotence. • Marriage of Moon and Sun: The ritual marriage of the goddess Astaroth (representing the Moon and the female principle) to the Black Stone (representing the Sun and the male principle) symbolizes the merging of the Orient and Occident into this unified life principle > > Destruction of the Symbol - The Black Stone ultimately becomes a symbol of Oriental pollution and decadence to the Roman populace. During the final rebellion against Elagabalus, the stone is torn from its temple on the Palatine, defiled with filth, and broken into pieces to ensure that its "signification of Life" would never again dominate Rome.
This is all accurate to the book, even teasing out a couple themes that were only subconsciously present to me.
The NotebookLM version gives citations with links to the original text to support all these assertions, which largely are coherent with that purpose.
The input is raw images of a book scan! Imperfect as it is it still blows my mind. Not that long ago any kind of semantic search or analysis was a very hard AI problem.
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1 and 2 have been achieved.
4 is close, the interface needs some work to allow nontechnical people use it. (claude code)
I strongly disagree. I’ve yet to find an AI that can reliably summarise emails, let alone understand nuance or sarcasm. And I just asked ChatGPT 5.2 to describe an Instagram image. It didn’t even get the easily OCR-able text correct. Plus it completely failed to mention anything sports or stadium related. But it was looking at a cliche baseball photo taken by an fan inside the stadium.
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I dispute 1 & 2 more than 4.
1) Is it actually watching a movie frame by frame or just searching about it and then giving you the answer?
2) Again can it handle very long novels, context windows are limited and it can easily miss something. Where is the proof for this?
4 is probably solved
4) This is more on predictor because this is easy to game. you can create some gibberish code with LLM today that is 10k lines long without issues. Even a non-technical user can do
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> In 2029, AI will not be able to read a novel and reliably answer questions about plot, character, conflicts, motivations, etc. Key will be going beyond the literal text, as Davis and I explain in Rebooting AI.
Can AI actually do this? This looks like a nice benchmark for complex language processing, since a complete novel takes up a whole lot of context (consider War and Peace or The Count of Monte Cristo). Of course the movie variety is even more challenging since it involves especially complex multi-modal input. You could easily extend it to making sense of a whole TV series.
Yes. I am a novelist and I noticed a step change in what was possible here around Claude Sonnet 3.7 in terms of being able to analyze my own unpublished work for theme, implicit motivations, subtext, etc -- without having any pre-digested analysis of the work in its training data.
How do you get a novel sized file into Claude? I've tried, and it always complains it's too long.
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Yes they can. The size of many codebases is much larger and LLMs can handle those.
Consider also that they can generate summaries and tackle the novel piecemeal, just like a human would.
Re: movies. Get YouTube premium and ask YouTube to summarize a 2hr video for you.
Novel is different from a codebase. In code you can have a relationship between files and most files can be ignored depending on what you're doing. But for a novel, its a sequential thing, in most cases A leads to B and B leads to C and so on.
> Re: movies. Get YouTube premium and ask YouTube to summarize a 2hr video for you.
This is different from watching a movie. Can it tell what suit actor was wearing? Can it tell what the actor's face looked like? Summarising and watching are too different things.
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No human reads a novel and evaluates it as a whole. It's a story and the readers perception changes over the course of reading the book. Current AI can certainly do that.
> It's a story and the readers perception changes over the course of reading the book.
You're referring to casual reading, but writers and people who have an interest and motivation to read deeply review, analyze, and summarize books under lenses and reflect on them; for technique as much as themes, messages, how well they capture a milieu, etc. So that's quite a bit more than "no human"!
>Can AI actually do this? This looks like a nice benchmark for complex language processing, since a complete novel takes up a whole lot of context (consider War and Peace or The Count of Monte Cristo)
Yes, you just break the book down by chapters or whatever conveniently fits in the context window to produce summaries such that all of the chapter summaries can fit in one context window.
You could also do something with a multi-pass strategy where you come up with a collection of ideas on the first pass and then look back with search to refine and prove/disprove them.
Of course for novels which existed before the time of training an LLM will already contain trained information about so having it "read" classic works like The Count of Monte Cristo and answer questions about it would be a bit of an unfair pass of the test because models will be expected to have been trained on large volumes of existing text analysis on that book.
>reliably answer questions about plot, character, conflicts, motivations
LLMs can already do this automatically with my code in a sizable project (you know what I mean), it seems pretty simple to get them to do it with a book.
> Yes, you just break the book down by chapters or whatever conveniently fits in the context window to produce summaries such that all of the chapter summaries can fit in one context window.
I've done that a few month ago and in fact doing just this will miss cross-chapter informations (say something is said in chapter 1, that doesn't appears to be important but reveals itself crucial later on, like "Chekhov's gun").
Maybe doing that iteratively several time would solve the problem, I run out of time and didn't try but the straightforward workflow you're describing doesn't work so I think it's fair to say this challenge isn't solve. (It works better with non-fiction though, because the prose is usually drier and straight to the point).
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Which ones of those have been achieved in your opinion?
I think the arbitrary proofs from mathematical literature is probably the most solved one. Research into IMO problems, and Lean formalization work have been pretty successful.
Then, probably reading a novel and answering questions is the next most successful.
Reliably constructing 10k bug free lines is probably the least successful. AI tends to produce more bugs than human programmers and I have yet to meet a programmer who can reliably produce less than 1 bug per 10k lines.
Formalizing an arbitrary proof is incredibly hard. For one thing, you need to make sure that you've got at least a correct formal statement for all the prereqs you're relying on, or the whole thing becomes pointless. Many areas of math ouside of the very "cleanest" fields (meaning e.g. algebra, logic, combinatorics etc.) have not seen much success in formalizing existing theory developments.
> Reliably constructing 10k bug free lines is probably the least successful.
You imperatively need to try Claude Code, because it absolutely does that.
I have seen many people try to use Claude Code and get LOTS of bugs. Show me any > 10k project you have made with it and I will put the effort in to find one bug free of charge.
I'm pretty sure it can do all of those except for the one which requires a physical body (in the kitchen) and the one that humans can't do reliably either (construct 10000 loc bug-free).
> Many of these have already been achieved, and it's only early 2026.
I'm quite sure people who made those (now laughable) predictions will tell you none of these has been achieved, because AI isn't doing this "reliably" or "bug-free."
Defending your predictions is like running an insurance company. You always win.
Besides being a cook which is more of a robotics problem all of the rest are accomplished to the point of being arguable about how reliably LLMs can perform these tasks, the arguments being between the enthusiast and naysayer camps.
The keyword being "reliably" and what your threshold is for that. And what "bug free" means. Groups of expert humans struggle to write 10k lines of "bug free" code in the absolutist sense of perfection, even code with formal proofs can have "bugs" if you consider the specification not matching the actual needs of reality.
All but the robotics one are demonstrable in 2026 at least.
In my opinion, contrary to other comments here I think AI can do all of the above already except being a kitchen cook.
Just earlier today I asked it to give me a summary of a show I was watching until a particular episode in a particular season without spoiling the rest of it and it did a great job.
You know that almost every show as summaries of episodes available online?
How do you find them?
This comment or something very close always appears alongside a Gary Marcus post.
And why not? Is there any reason for this comment to not appear?
If Bill Gates made a predication about computing, no matter what the predication says, you can bet that 640K memory quote would be mentioned in the comment section (even he didn't actually say that).
becuase
- it's tiresome
- and the only less useful than making predictions is making predictions about predictions.
I think it’s for good reason. I’m a bit at a loss as to why every time this guy rages into the ether of his blog it’s considered newsworthy. Celebrity driven tech news is just so tiresome. Marcus was surpassed by others in the field and now he’s basically a professional heckler on a university payroll. I wish people could just be happy for the success of others instead of fuming about how so and so is a billionaire and they are not.
Which is fortunate, considering how asinine it is in 2026 to expect that none of the items listed will be accomplished in the next 3.9 years.