I don’t agree. I think they clearly lack general intelligence, which is why all the AI companies can think of is sourcing more and more niche domain data to plug more and more holes. When you get enough of that stuff, you can get a convincing illusion of general intelligence, but there is always another car wash test coming that shows it isn’t real.

What you’re doing is the classic No True Scotsman fallacy.

“Isn’t real” is a tell for that. It’s the same as saying it’s not a “true” intelligence.

Define what you mean by “true”, then. But for me, what I’m interested in is functional capability, not some mysterious ineffable quality that only humans can have. And in terms of functional capability, current models are certainly better at software development than you are. You’re just in denial.

Perhaps I was unclear: the part that isn't real is the generalization. The models appear to generalize because they're fitted to so many discrete tasks that it almost doesn't appear to matter. But then it leaks, and the failure modes reveal no coherent model or process that generated the failure. The labs only have one answer for this, which is more duct tape.

> current models are certainly better at software development than you are. You’re just in denial.

I have no ego in this. It could be true. Wikipedia is also "smarter" than I am; it "knows" so many more concepts than I could ever. But regardless, I think the state of slopcoded messes like Claude Code shows that the models are missing something.

Although I agree with you, in fairness, there are some lively controversies in the world of cognitive science and philosophy of mind about whether this meaningfully differs from human thinking at sufficient scale.

The general idea is that the building blocks of "coherent models" and "processes" within the churning of the human intellect are also, in so many words, prior art and existing concepts, and so, while the human mind is not a text model, a sufficiently large and sensorily multimodal neural net would not be too different. Neural nets are, after all, inspired by what we understand of human cognition -- they'd say.

It's quite trivial to show that an LLM doesn't have underlying intent, and that it can only emit direct textual convolutions of its training and not combine tokens in truly novel ways. This is the very thesis of the world-model folks, e.g. LeCun et al, that LLMs are a general intelligence dead-end because they lack any inner concept of the world around them, and do not reason from that.

Furthermore, LLMs clearly do not "reason", despite the marketing around this term; their "chains of thought" are the nothing more than the result of having been trained on explicitly verbalised multi-step processes. There are many cases where the putative result arrived at in the <think>chain of thought</think> does not match the result emitted.

Whether they are "better" at software development than I am greatly depends on whether one is asking them to retrace worn technology paths that are well-represented in their training--in effect, to copy prior art--or to do something in quite obscure technology, or something quite novel altogether. (However, I will happily concede that most everyday business programming involves neither.)

Still, if LLMs were actually intelligent, let alone superhuman in the sense you suggest, then we would expect major scientific breakthroughs to be raining from the sky. If, say, an Einstein, could transform physics with only the knowledge gleaned from a human's feeble capacity to retain the literature of the time, I'd expect LLMs, who retain orders of magnitude more information with far greater fidelity and precision, to have offered at least a small slither of evidence of their superhuman capabilities.

I would also expect the objective progress and capabilities of this galaxy brain to be accelerating, not substantially slowing down as it has. GPT-2 to GPT-3 was truly a quantum leap, GPT-3 to GPT-4 was a substantial jump, GPT-4 to GPT-5 was meh, 5+ is basically unimportant, and so it goes for the other models. There are, of course, holes plugged and benchmarks where these evolutions have been, in various niche ways, consequential, but in the plainspoken meaning of model capability, the low-hanging fruit of pretraining was clearly exhausted quite some time ago. The carnival has been running on "agentic" / MCP / RAG / tool-use fumes since. This is moderately impressive and adds quite a bit of runway, but intelligence it is not.

You're working with mystical definitions of words like "intelligence" and "reasoning", where there's some ineffable quality that you can't define that makes those properties difficult to achieve.

I'm not a mystic, so what I care about is functional behavior. Functionally speaking, models perform the functional equivalent of reasoning. Functionally speaking, they exhibit intelligence. Is it a perfect equivalent of human intelligence? Obviously not, but so what? The ball is in your court if you want to try to nail down the mystical quality you think isn't being achieved.

One hardly needs to soar to the exalted heights of "mysticism" or any sort of "ineffable qualities" to come into contact with the limitations of LLMs.

1) Models do not perform the functional equivalent of reasoning at all. When we reason, we don't simply babble out textual derivations of prior examples of "reasoning" to which we have been exposed, arrive at a conclusion, then occasionally state an altogether different conclusion while pointing at the largely irrelevant reasoning to substantiate it.

2) Models have real-world, not-at-all-mystical functional constraints that are directly relevant to the production of everyday economic work in which one attempts to involve them. Their inability to extrapolate or maintain clear mental models leads to staggering, head-scratching mistakes that even a very feeble and developmentally awry human intelligence would not make.

A basic, if well-worn example that was widely discussed in the last year or two:

https://medium.com/@JerryCuomo/why-ai-gets-the-strawberry-qu...

However, this is emblematic of a much larger idea: the LLM doesn't have any idea what a letter is or what you're asking it to do. This isn't a question of "ineffable qualia"; when it doesn't know what something is at any essential level, it can't competently solve problems related to it. One bumps up against this in everyday programming and all the time.

Also, what is "mystical" about my demand for the kind of scientific progress--no, forget that, any scientific progress--that a functional superhuman intelligence would yield? I am not a "mystic", either; I want functional results, show me the functionality.