Published on November 7, 2025 4:15 AM GMT

(Cross-posted from my Substack; written as part of the Halfhaven virtual blogging camp.)

Many speculate about the possibility of an AI bubble by talking about past progress, the economy, OpenAI, Nvidia, and so on. But I don’t see many people looking under the hood to examine whether the actual technology itself looks like it’s going to continue to grow or flatline. Many now realize LLMs may be a dead end, but optimism persists that one clever tweak of the formula might get us to superintelligence. But I’ve been looking into the details of this AI stuff more lately, and it seems to me that there’s a deeper problem: self-supervised learning itself.

Here’s how supervised learning with gradient descent works, by my understanding:

1. Give the neural network some input, and it returns some output.
2. We score how “bad” the output is.
3. We update the model’s weights in directions that would have produced less bad output, making it less bad next time

This works great when you can judge badness reliably. AlphaGo Zero used a cleverly-designed oracle to evaluate its outputs, essentially comparing the move the model thought was the best with the real best move. But modern LLMs work differently. We have them complete a snippet of training data, and compare their output with the real completion. This is called self-supervised learning. By training the model this way, we minimize loss with respect to the training data, thereby creating an AI model that’s really good at predicting the next token of any snippet of training data, and hopefully other similar data.

By doing this, we create a model which tries to remember all patterns present in the data, however arbitrary. Common patterns get prioritized because they help minimize loss more, but the only way to minimize loss is to learn as many patterns as you can. That will include some patterns humans care about, and many more we do not.

Self-supervised learning is not a blind memorizer. It does abstract and generalize. But it abstracts indiscriminately.

Here’s the problem. Let’s say I want to train an AI model that can beat any human at chess. I train it on the history of all recorded chess games, including amateur games, master games, and grandmaster games. Feed it some number of opening moves and have it predict the next move. We update the model using self-supervised learning based on accuracy.

Training my AI model this way, it would learn to play well. It would also learn to play poorly. It would learn the playstyle of every player in the data. It would learn to use the King’s Indian Defense if the game was played in the ’60s, but probably not if the game was in the ‘90s. It would learn what I wanted, and orders of magnitude more that I didn’t care about.

The history of all recorded chess games is several gigabytes, but Stockfish, including the heuristics it uses to evaluate moves, can fit in 3–4 MB. This is at least a 1000x difference between the information we care about (some winning strategy) and the total information in the training data.

Keep in mind that when chess officials wrote down the moves for a chess game, they were implicitly throwing away most of the data for us, like whether the pieces were made of wood or plastic, or whether so-and-so happened to cough before making a move. Not all datasets are this refined to exactly what we want the AI to learn. If you were unlucky enough to have to learn chess from videos of chess matches, the ratio of noise to important data would be like 1,000,000x or 1,000,000,000x. Yet even in the case of chess notation data, most of the information is not worth holding on to.

Now expand this from chess to every domain. Most patterns in most data will be worthless. Most patterns in reality itself are worthless. Humans discard almost all the data we perceive. Our intelligence involves discrimination. Models trained by self-supervised learning like LLMs, on the other hand, try to stuff as much of reality into their weights as possible. An LLM might know a lot about chess, since there’s a lot of chess-specific training data, but only a small amount of what it knows will be about winning chess. That’s why it’s sometimes hard to get peak performance out of an LLM. It won’t necessarily give you the best moves it can unless you tell it to pretend it’s Magnus Carlsen. It knows how to play chess kinda well, but also kinda poorly, and it doesn’t know which one you want unless you specify.

A 7-year-old child given an addition problem learns from it, but given a calculus problem, they simply ignore it. They won’t try desperately to memorize shapes of symbols they don’t understand. We remember what matters and discard the rest.

What matters depends on context and values. The wood grain pattern on my hardwood living room floor is irrelevant if I’m having a conversation about politics, but critical if I’m painting a picture of the room. It takes judgement to know what to focus on. The ability to focus is how we make sense of a very complex world. If remembering everything relevant were easy, then evolution would have let us do so. Instead, we’re forced to remember based on what we think is important.

Human intelligence is neither specialized to a single domain, nor fully general, like reality-stuffing LLMs. Human intelligence is something else. Call it specializable intelligence. We’re specialized in our ability to tactically learn new information based on our existing knowledge and values.

Some imagine superintelligence as a magical system that could play chess for the first time at a grandmaster level, having only seen the rules, deducing winning strategies through pure, brilliant logic. This is impossible. Chess is computationally irreducible. Many games must be played, whether in reality or in some mental simulation of games (or sub-game patterns). Existing knowledge of Go or checkers or “general strategy” will not really help. You can’t have an AI model that’s just good at everything. Not without a computer the size of the universe. What you want is an AI that can get good at things as needed. A specializable intelligence.

There is a tradeoff between a fully general intelligence and a specialized intelligence. The “no free lunch” theorem states that for any AI model, improvements on one class of problems come with worse performance on other classes of problems. You either stay general, or specialize in some areas at the cost of others.

This implies that, for fixed compute, a general intelligence will perform worse at the things we care about than a specialized intelligence could. Much worse, given just how much we don’t care about. Our goal should be specializable intelligence which can learn new things as needed, as well as some fundamentals humans care about often, like language, vision, logic, “common knowledge”, and so on. Creating general superintelligence would require literally astronomical compute, but specializable superintelligence would be far cheaper.^[1]

Reality-stuffed general models that don’t discriminate what they learn we will never lead to superintelligent AI. Whatever superintelligence we achieve will not be general with respect to its training data. The chess example before was a contrived one. Keep in mind that we have a lot of good data for chess, and that chess is much less computationally complex than many tasks we care about.^[2]An LLM might conceivably play chess well by overfitting to chess, but it won’t have similar performance on novel games similar to chess, and it will be helpless at more complex tasks.

Here are some approaches to AI that I’d guess can’t get us to superintelligent AI:

1. Just increasing compute. Diminishing returns (in useful capabilities) will set in. Loss may decrease predictably, but scaling laws measure the wrong objective.
2. Higher quality data. This will help, practically speaking, but most of the information in even really high quality data is going to be worthless/discardable. Imagine you cleaned up a chess dataset. You only included grandmaster games, for example. That’s still way more data than the Stockfish heuristics. Preparing “good” data is equivalent to extracting patterns you care about from that data, which in the limit requires the intelligence you’re trying to create.
3. Synthetic data. This boils off some noise from the original dataset, essentially creating a higher quality dataset with hopefully less information you don’t care about. Hopefully. But that’s all you’re doing.
4. Curriculum learning. When you heard about that 7-year-old who learned from the addition problem but ignored the calculus problem, you might have thought the solution to this whole problem was to order the data such that harder information comes after prerequisite easier data. This won’t work because the model is still being evaluated on completing the trianing data, so it still has to memorize whatever patterns are in the data, even ones we don’t care about. Maybe it’ll learn more quickly, but it’s what it’s learning that’s the problem. It may also lead to more unified internal world models, which is good, but not great if those world models are of things we don’t even care about.
5. Using another smaller LLM as an evaluator. Using a small model to judge how good or bad the output of a larger model-in-training is based on some metric humans care about won’t work, because it’s limited by the intelligence of the smaller model.
6. RLHF (reinforcement learning from human feedback): The model is already stupid by the time you apply RLHF. It’s constrained by the abstractions already learned.
7. Transformers and “attention”: Paying attention to different parts of a sentence when processing a token, and only paying attention to certain patterns humans care about in the data, both use the word “attention”, but they have nothing to do with each other. The model will still be penalized if it fails to predict the next token in the training data, which is a task that inherently requires memorizing a bunch of information humans don’t care about. Any architecture trained with respect to this goal will fail to scale to superintelligent AI. You might think that LLMs are already kind of specializable, because they can do “in-context learning” without any weight updates. But models think with their weights. The depth of thinking you can do in a domain without any learned patterns in the weights is limited. The whole point of the weights is to store abstractions so you can reason with them later. Depriving the model of the ability to do this makes it much stupider.
8. Neuro-inspired models with Hebbian learning. (Hebbian = “neurons that fire together wire together”, basically if neuron A firing leads to neuron B firing, the connection between the two is strengthened, as in the human brain). Even with more sophisticated stuff like spike-timing-dependent plasticity, the problem is that Hebbian learning reinforces whichever thought patterns already occur, but doesn’t teach the model to care about certain things.
9. Growing neural networks, making them larger as they train. If you’re using self-supervised learning, you’re still growing an idiot. I think this will make internal world models more unified as in the case of better training data ordering, but will not make the models care about only the patterns we want them to care about.
10. Meta-learning. Using an outer loop based on gradient descent or evolution or something, and an inner loop based on gradient descent. I read one paper where the model did expensive evolution in the outer loop to set up the initial conditions for learning. They then had the evolved models learn using gradient descent on some task. The models that learned better were then selected for the next generation of evolution. The hope was that you could evolve a model that’s predisposed to be good at learning arbitrary tasks. But it seems wasteful to me to do expensive evolution to set up the initial state of a network only to bowl over that network with backpropagation. Gradient descent minimizing loss with respect to training data will create a reality-stuffed model, regardless of the initial conditions. So you’re essentially evolving good initial conditions for an idiot.
11. Predictive coding: I haven’t looked into this much, but it seems like minimizing surprise is pretty similar to minimizing loss with respect to training data. Same problem: learning a bunch of patterns humans don’t care about.
12. Anything that improves “grokking”. The transition from memorization to understanding the underlying patterns in data is important, but this is true whether you’re trying to learn important things, like “how English works” or “how to win at chess”, or you’re trying to learn unimportant things, like “how terrible chess players tended to make mistakes in the ’70s”. Grokking is a sign that abstraction is happening, but it’s not sufficient for discriminatory intelligence.
13. Manually encoding human knowledge. E.g. putting human knowledge of words and phonemes into the model. The bitter lesson is still bitter.
14. Online learning. This is necessary, but not sufficient for superintelligence. A general, reality-stuffing model with online learning will be trying to cram way too much information to be as smart as we want it to be.

I don’t know what approaches could be more promising. Evolution of neuro-inspired models could work. We have at least one working example, at least: us. Evolution gave humans basic architecture and values that tell us what information we “should” pay attention to and care about. Then, during our lifetimes, Hebbian learning lets us learn specific knowledge in accordance with these values. Unfortunately, evolution is just very expensive. Is there a cheaper way forward? Probably, but I have no idea what it is.

One thing to keep in mind is that any more promising approach will necessarily lose the loss minimization game. Yet currently, “conventional approaches” are a gold standard to which other more experimental approaches are compared. If a new method can’t predict the next token of training data better than the conventional approach, it’s reported as a failure — or perhaps as “only slightly better than” the conventional approach, to satisfy the publication demands of academia.

This heuristic cannot stand. We don’t want general loss minimization with respect to training data. We want capability. Performance on novel games could be a valid benchmark. It could could also be used during training. You’d first create specializable intelligence that can learn arbitrary games, then teach it specific games like “speaking English”.

Novel games could also be used to operationalize the claim that useful capabilities will plateau even as loss continues to decrease. Specifically, I’d predict that performance on computationally complex novel games (at least as complex as chess) will barely improve as newer self-supervised models are released and continue to improve at traditional benchmarks. Novel games are a good benchmark because they prevent cheating if the training data happened to contain similar problems. A sufficiently novel game is unlike anything in the training data.

Self-supervised learning can only create general models, which are limited in their capability in any domain by trying to succeed in every possible domain. The trillion dollar bet on self-supervised models will not pay off, because these general models will continue to fail exactly where we need them the most — on novel, difficult problems.

1. François Chollet also pointed out the weakness of general intelligence, citing the “no free lunch” theorem, but he went too far, missing the specializability of human intelligence. It’s true that humans are specialized for a certain environment. Infants are born with certain reflexes, and certain knowledge. For example, the fusiform face area of the brain specialized for recognizing human faces. But even though we are partly specialized, we are also specializable. Give us any task and enough time, and we’ll outperform a random actor. For example, psychologists created objects called greebles that share a similar number of constraints as human faces, but look totally alien. They then trained some humans to become experts at recognizing greebles, and found they could reliably tell them apart, and found they used a holistic approach when viewing them rather than looking at their individual parts. In short, as long as we can extract patterns from data, and use those patterns to further refine our search for more patterns, we can do anything.

2. https://en.wikipedia.org/wiki/Moravec%27s_paradox