At the risk of doxxing myself, I have an advanced degree in Applied Mathematics. I have authored and contributed to multiple published papers, and hold a US patent all related to the use of machine learning in robotics and digital signal processing. I am currently employed as a supervising engineer by at a prominent tech company. For pseudonymity's sake I am not going to say which, but it is a name that you would recognize. I say this not to brag, but to establish some context for the following.
Imagine that you are someone who is deeply interested in space flight. You spend hours of your day thinking seriously about Orbital Mechanics and the implications of Relativity. One day you hear about a community devoted to discussing space travel and are excited at the prospect of participating. But when you get there what you find is a Star Trek fan-forum that is far more interested in talking about the Heisenberg compensators on fictional warp-drives than they are Hohmann transfers, thrust to ISP curves, or the effects on low-gravity on human physiology. That has essentially been my experience trying to discuss "Artificial Intelligence" with the rationalist community.
However at the behest of users such as @ArjinFerman and @07mk, and because X/Grok is once again in the news, I am going to take another stab at this.
Are "AI assistants" like Grok, Claude, Gemini, and DeepSeek intelligent?
I would say no, and in this post I am going to try to explain why, but to do so requires a discussion of what I think "intelligence" is and how LLMs work.
What is Intelligence
People have been philosophizing on the nature of intelligence for millennia, but for the purposes of our exercise (and my work) "intelligence" is a combination of perceptivity and reactivity. That is to say, the ability to perceive or take in new and/or changing information combined with the ability to change state based on that information. Both are necessary, and neither is sufficient on it's own. This is why Mathematicians and Computer Scientists often emphasize the use of terms like "Machine Learning" over "Artificial Intelligence" as an algorithms' behavior is almost never both.
If this definition feels unintuitive, consider it in the context of the following example. What I am saying is that an orangutan who waits until the Zookeeper is absent to use a tool to force the lock on it's enclosure is more "intelligent" than the insect that repeatedly throws itself against your kitchen window in an attempt to get outside. While they share an identical goal (to get outside) but the orangutan has demonstrated the ability to both perceive obstacles (IE the lock and the Zookeeper), and react dynamically to them in a way that the insect has not. Now obviously these qualities exist on a spectrum (try to swat a fly and it will react) but the combination of these two parameters define an axis along which we can work to evaluate both animals and algorithms, and as any good PM will tell you, the first step to solving any practical engineering problem is to identify your parameters.
Now the most common arguments for AI assistants like Grok being intelligent tend to be some variation on "Grok answered my question, ergo Grok is intelligent." or "Look at this paragraph Claude wrote, do you think you could do better?" but when evaluated against the above parameters, the ability to form grammatically correct sentences and the ability to answer questions are both orthogonal to it. An orangutan and a moth may be equally incapable of writing a Substack, but I don't expect anyone here to seriously argue that they are equally intelligent. By the same token a pocket calculator can answer questions, "what is the square root of 529?" being one example of such, but we don't typically think of pocket calculators as being "intelligent" do we?
To me, these sorts of arguments betray a significant anthropomorphic bias. That bias being the assumption that anything that a human finds complex or difficult must be computationally complex and vice versa. The truth is often the inverse. This bias leads people who do not have a background in a math or computer science to have completely unrealistic impressions of what sort of things are easy or difficult for a machine to do. For example, vector and matrix operations are a reasonably simple thing for a computer that a lot of human students struggle with. Meanwhile bipedal locomotion is something most humans do without even thinking, despite it being more computationally complex and prone to error than computing a cross product.
Speaking of vector operations, let's talk about how LLMs work...
What are LLMs
LLM stands for "Large Language Model". These models are a subset of artificial neural network that uses "Deep Learning" (essentially a fancy marketing buzzword for the combination of looping regression analysis with back-propagation) to encode a semantic token such as the word "cat" as a n-dimensional vector representing that token's relationship to the rest of the tokens in the training data. Now in actual practice these tokens can be anything, an image, an audio-clip, or a snippet of computer code, but for the purposes of this discussion I am going to assume that we are working with words/text. This process is referred to as "embedding" and what it does in effect is turn the word "cat" into something that a computer (or grad-student) can perform mathematical operations on. Any operation you might perform on a vector (addition, subtraction, transformation, matrix multiplication, etc...) can now be done on "cat".
Now because these vectors represent the relationship of the tokens to each other, words (and combinations of words) that have similar meanings will have vectors that are directionally aligned with each other. This has all sorts of interesting implications. For instance you can compute the dot product of two embedded vectors to determine whether their words are are synonyms, antonyms, or unrelated. This also allows you to do fun things like approximate the vector "cat" using the sum of the vectors "carnivorous" "quadruped" "mammal" and "feline", or subtract the vector "legs" from the vector "reptile" to find an approximation for the vector "snake". Please keep this concept of "directionality" in mind as it is important to understanding how LLMs behave, and it will come up later.
It should come as no surprise that some of the pioneers of this methodology in were also the brains behind Google Translate. You can basically take the embedded vector for "cat" from your English language model and pass it to your Spanish language model to find the vector "gato". Furthermore because all you are really doing is summing and comparing vectors you can do things like sum the vector "gato" in the Spanish model with the vector for the diminutive "-ito" and then pass it back to the English model to find the vector "kitten".
Now if what I am describing does not sound like an LLM to you, that is likely because most publicly available "LLMs" are not just an LLM. They are an LLM plus an additional interface layer that sits between the user and the actual language model. An LLM on its own is little more than a tool that turns words into math, but you can combine it with a second algorithm to do things like take in a block of text and do some distribution analysis to compute the most probable next word. This is essentially what is happening under the hood when you type a prompt into GPT or your assistant of choice.
Our Villain Lorem Epsom, and the Hallucination Problem
I've linked the YouTube video Badness = 0 a few times in prior discussions of AI as I find it to be both a solid introduction to LLMs for the lay-person, and an entertaining illustration of how anthropomorphic bias can cripple the discussion of "alignment". In it the author (who is a professor of Computer Science at Carnegie Mellon) posits a semi-demonic figure (akin to Scott Alexander's Moloch) named Lorem Epsom. The name is a play on the term Lorem Ipsom and represents the prioritization of appearance over all else. When it comes to writing, Lorem Epsom doesn't care about anything except filling the page with text that looks correct. Lorem Epsom is the kind of guy who, if you tell him that he made a mistake in the math, is liable interpret that as a personal attack. The ideas of "accuracy" "logic" "rigor" and "objective reality" are things that Lorem Epsom has heard of but that do not concern Lorem Epsom. It is very possible that you have had to deal with someone like Lorem Epsom in your life (I know I have), now think back and ask yourself how did that go?
I bring up Lorem Epsom because I think that understanding him provides some insight into why certain sorts of people are so easily fooled/taken in by AI Assistants like Claude and Grok. As discussed in the section above on "What is Intelligence", the assumption that the ability to fill a page with text is indicates the ability to perceive and react to a changing situation is an example of anthropomorphic bias. I think that a lot of people assume that because they are posing their question to a computer, they expect the answer they get to be something analogous to what they would get from a pocket calculator rather than from Lorem Epsom.
Sometime circa 2014 I kicked off a heated dispute in the comment section of a LessWrong post by asking EY why a paperclip maximizing AI that was capable of self-modification wouldn't just modify the number of paperclips in its memory. I was accused by him others and a number of others of missing the point, but I think they missed mine. The assumption that an Artificial Intelligence would not only have a notion of "truth", but assign value to it is another example of anthropomorphic bias. If you asked Lorem Epsom to maximize the number of paperclips, and he could theoretically "make" a billion-trillion paperclips simply by manipulating a few bits, why wouldn't he? It's so much more easier than cutting and bending wire.
In order to align an AI to care about truth and accuracy you first need a means of assessing and encoding truth and it turns out that this is a very difficult problem within the context of LLMs, bordering on mathematically impossible. Do you recall how LLMs encode meaning as a direction in n-dimensional space? I told you it was going to come up again.
Directionally speaking we may be able to determine that "true" is an antonym of "false" by computing their dot product. But this is not the same thing as being able to evaluate whether a statement is true or false. As an example "Mary has 2 children", "Mary has 4 children", and "Mary has 1024 children" may as well be identical statements from the perspective of an LLM. Mary has a number of children. That number is a power of 2. Now if the folks programming the interface layer were clever they might have it do something like estimate the most probable number of children based on the training data, but the number simply can not matter to the LLM the way it might matter to Mary, or to someone trying to figure out how many pizzas they ought to order for the family reunion because the "directionality" of one positive integer isn't all that different from any another. (This is why LLMs have such difficulty counting if you were wondering)
In addition to difficulty with numbers there is the more fundamental issue that directionality does not encode reality. The directionality of the statement "Donald Trump is the 47th President of the United States", would be identical regardless of whether Donald Trump won or lost the 2024 election. Directionally speaking there is no difference between a "real" court case and a "fictitious" court case with identical details.
The idea that there is a ineffable difference between true statements and false statements, or between hallucination and imagination is wholly human conceit. Simply put, a LLM that doesn't "hallucinate" doesn't generate text or images at all. It's literally just a search engine with extra steps.
What does this have to do with intelligence?
Recall that I characterized intelligence as a combination of perceptivity and and the ability to react/adapt. "AI assistants" as currently implemented struggle with both. This is partially because LLMs as currently implemented are largely static objects. They are neither able to take in new information, nor discard old. The information they have at time of embedding is the information they have. This imposes substantial loads on the context window of the interface layer, as any ability to "perceive" and subsequently "react" must happen within it's boundaries. Increasing the size of the window is non trivial as the relationship between the size of the window and the amount of memory and the number of FLOPS required is a hyperbolic curve. This is why we saw a sudden flurry of development following the release of Nvidia's multimodal framework and it's mostly been marginal improvements since. The last significant development being June of last year when the folks at Deepseek came up with some clever math to substantially reduce the size of the key value cache, but multiplicative reductions are no match for exponential growth.
This limited context window, coupled with the human tendency to anthropomorphize things is why AI Assistants sometimes appear "oblivious" or "naive" to the uninitiated. and why they seem to "double down" on mistakes. They can not perceive something that they have not been explicitly prompted to even if it is present in their training data. This limited context window is also why if you actually try to play a game of chess with Chat GPT it will forget the board-state and how pieces move after a few turns and promptly lose to a computer program written in 1976. Unlike a human player (or an Atari 2600 for that matter) your AI assistant can't just look at the board (or a representation of the board) and pick a move. This IMO places them solidly on the "insect" side of the perceptivity + reactivity spectrum.
Now there are some who have suggested that the context window problem can be solved by making the whole model less static by continuously updating and re-embedding tokens as the model runs, but I am skeptical that this would result in the sort of gains that AI boosters like Sam Altman claim. Not only would it be computationally prohibitive to do at scale, what experiments there have been (or at least that I am aware of) with self-updating language models, have quickly spun away into nonsense for reasons described in the section on Lorem Epsom., as barring some novel breakthrough in the embedding/tokenization process there is no real way to keep hallucinations and spurious inputs from rapidly overtaking the everything else.
It is already widely acknowledged amongst AI researchers and developers that the LLM-based architecture being pushed by OpenAI and DeepSeek is particularly ill-suited for any application where accuracy and/or autonomy are core concerns, and it seems to me that this unlikely to change without a complete ground-up redesign from first principles.
In conclusion, it is for the reasons above and many others that I do not believe that "AI Assistants" like Grok, Claude, and Gemini represent a viable path towards a "True AGI" along the lines of Skynet or Mr. Data, and if asked "which is smarter, Grok, Claude, Gemini, or an orangutan?" I am going to pick the orangutan every time.
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Notes -
(Not the original commenter, but wanted to jump in). I don't think anything in their comment above implied that they were talking about linear or simpler statistics, that's your own projection, and I think it does you a disservice. Similarly, I find it somewhat suspect to directly compare brains to LLMs. I don't think you did so explicitly, but you certainly did so implicitly, even despite your caveat. There's an argument to be made that Hebbsian learning in neurons and the brain as a whole isn't similar enough to the mechanisms powering LLMs for the same paradigms to apply, although I think I do appreciate the point I think you are trying to make which is that human cause and effect is still (fancy) statistical learning on some level.
After all, MLPs and the different layers and deep learning techniques are inspired by brain neurons, but the actual mechanics are different scales entirely despite a few overlapping principles. It seems to me the overlapping principles are not enough to make that jump by themselves. I'd be curious if you'd expand somewhere on that, because you definitely know more than me there, but I don't think I'm incorrect in summarizing the state of the research? Brains are pretty amazing, after all, and of course I could pick out a bunch of facts about it but one that is striking is that LLMs use ~about the same amount of energy for one inference as the brain does in an entire day (.3 kWh, though figures vary for the inferences, it's still a gap of approximately that magnitude IIRC). On that level and others (e.g. neurons are more sparse, recurrent, asynchronous, and dynamic overall whereas LLMs use often fully connected denser layers for the MLPs... though Mixture of Experts and attention vs feed-forward components makes comparison tricky even ignoring the chemistry) it seems pretty obvious that the approach is probably weaker than the human one, so your prior that they are more or less the same is a little puzzling, despite how overall enlightening your comment is to what you're trying to get at.
I personally continue to think that the majority of the 'difference' comes from structure. I did actually mention a little bit of it in my comment, but with how little anyone has discussed neural network principles it didn't seem worthwhile to talk about it in any more detail and I didn't want to bother typing out some layman's definition. There's the lack of memory, which I talked about a little bit in my comment, LLM's lack of self-directed learning, the temporal nature of weight re-adjustment is different, and as you pointed out their inputs are less rich than that of humans to start with. Plus your point about attention, though I'm not quite sure how I'd summarize that. While it's quite possible that we can get human-level thinking out of a different computational base, we're n=1 here on human development, so it sort of feels similar in a few ways to the debate over whether you can have significant numbers of equal complexity non-carbon based life forms on other planets. And smarter cephalopod brains share enough structural similarities while not achieving anything too special that I don't think it's very helpful. I might be wrong about that last point, though.
Why not? If we take multi-layer perceptrons seriously, then what is the value of saying that all they learn is mere "just statistical co-occurrence"? It's only co-occurrence in the sense that arbitrary nonlinear relationships between token frequencies may be broken down into such, but I don't see an argument against the power of this representation. I do genuinely believe that people who attack ML as statistics are ignorant of higher-order statistics, and for basically tribal reasons. I don't intend to take it charitably until they clarify why they use that word with clearly dismissive connotations, because their reasoning around «directionality» or whatever seems to suggest very vague understanding of how LLMs work.
What is that argument then? Actually, scratch that, yes mechanisms are obviously different, but what is the argument that biological ones are better for the implicit purpose of general intelligence? For all I know, backpropagation-based systems are categorically superior learners; Hinton, who started from the desire to understand brains and assumed that backprop is a mere crutch to approximate Hebbian learning, became an AI doomer around the same time he arrived at this suspicion. Now I don't know if Hinton is an authority in OP's book…
I don't know how you define "one inference" or do this calculation. So let's take Step-3, since it's the newest model, presumably close to the frontier in scale and capacity and their partial tech report is very focused on inference efficiency; in a year or two models of that scale will be on par with today's GPT-5. We can assume that Google has better numbers internally (certainly Google can achieve better numbers if they care). They report 4000 TGS (Tokens/GPU/second) on a small deployment cluster of H800s. That's 250 GPU-seconds per million tokens, for a 350W TDP GPU, or 24W. OK, presumably human brain is "efficient", 20Wh. (There's prefill too, but that only makes the situation worse for humans because GPUs can parallelize prefill, whereas humans read linearly.) Can a human produce 1 million tokens (≈700K words) of sensible output in 72 minutes? Even if we run some multi-agent system that does multiple drafts, heavy reasoning chains of thought (which is honestly a fair condition since these are numbers for high batch size)? Just how much handicap do we have to give AI to even the playing field? And H800s were already handicapped due to export controls. Blackwells are 3-4x better. In a year, the West gets Vera Rubins and better TPUs, with OOM better numbers again. In months, DeepSeek shows V4 with a 3-4x better efficiency again… Token costs are dropping like a stone. Google has served 1 quadrillion tokens over the last month. How much would that cost in human labor?
We could account for full node or datacenter power draw (1.5-2x difference) but that'd be unfair, since we're comparing to brains, and making it fair would be devastating to humans (reminder that humans have bodies that, ideally, also need temperature controlled environments and fancy logistics, so an individual employed human consumes like 1KWh at least even at standby, eg chatting by the water cooler).
And remember, GPUs/TPUs are computation devices agnostic to specific network values, they have to shuffle weights, cache and activations across the memory hierarchy. The brain is an ultimate compute-in-memory system. If we were to burn an LLM into silicon, with kernels optimized for this case (it'd admittedly require major redesigns of, well, everything)… it'd probably drop the cost another 1-2 OOMs. I don't think much about it because it's not economically incentivized at this stage given the costs and processes of FPGAs but it's worth keeping in mind.
I don't see how that is obvious at all. Yes an individual neuron is very complex, such that a microcolumn is comparable to a decently large FFN (impossible to compare directly), and it's very efficient. But ultimately there are only so many neurons in a brain, and they cannot all work in parallel; and spiking nature of biological networks, even though energetically efficient, is forced by slow signal propagation and inability to maintain state. As I've shown above, LLMs scale very well due to the parallelism afforded by GPUs, efficiency increases (to a point) with deployment cluster size. Modern LLMs have like 1:30 sparsity (Kimi K2), with higher memory bandwidth this may be pushed to 1:100 or beyond. There are different ways to make systems sparse, and even if the neuromorphic way is better, it doesn't allow the next steps – disaggregating operations to maximize utilization (similar problems arise with some cleverer Transformer variants, by the way, they fail to scale to high batch sizes). It seems to me that the technocapital has, unsurprisingly, arrived at an overall better solution.
Self-directed learning is a spook, it's a matter of training objective and environment design, not really worth worrying about. Just 1-2 iterations of AR-Zero can solve that even within LLM paradigm.
Aesthetically I don't like the fact that LLMs are static. Cheap hacky solutions abound, eg I like the idea of cartridges of trainable cache. Going beyond that we may improve on continual training and unlearning; over the last 2 years we see that major labs have perfected pushing the same base model through 3-5 significant revisions and it largely works, they do acquire new knowledge and skills and aren't too confused about the timeline. There are multiple papers promising a better way, not yet implemented. It's not a complete answer, of course. Economics get in the way of abandoning the pretrain-finetune paradigm, by the time you start having trouble with model utility it's time to shift to another architecture. I do hope we get real continual, lifelong learning. Economics aside, this will be legitimately hard, even though pretraining with batch = 1 works, there is a real problem of the loss of plasticity. Sutton of all people is working on this.
But I admit that my aesthetic sense is not very important. LLMs aren't humans. They don't need to be humans. Human form of learning and intelligence is intrinsically tied to what we are, solitary mobile embodied agents scavenging for scarce calories over decades. LLMs are crystallized data systems with lifecycle measured in months, optimized for one-to-many inference on electronics. I don't believe these massive differences are very relevant to defining and quantifying intelligence in the abstract.
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