How do you define intelligence? I encounter that kind of sentiment all too often, and I have to assume we go by wildly different understanding of what that might entail.
My definition is that I can be much less precise with AI the more intelligent it is. It can extract the intent from my fuzzy description of the problem. Which means I can offload some of the thinking effort.
It wasn't possible a couple years ago. I used to make fun of people who were trying to get ChatGPT to think about the problem when all it could do was write code from the pseudocode you provide.
But now I can say: "Look at the latest log and make a plan to fix". And it takes it from there.
I'm not sure if you are aware as to the extent certain processes and functions are being anthropomorphized.
These systems are not "intelligent" if you follow the dictionary definition. Hence the question posed to get a better understanding of how it is being used in this context.
They also do not "extract intent". There is for sure some intent behind your input to the service. What follows is a predictive text that uses your input, together with a LLM trained on a corpus with similar relations, that ultimately gives you a series of words.
That isn't to say a service like this cannot be useful. But I'm often wondering if the people who rely on these, and are particularly enthused by them, are actually aware that the terms they used are in fact anthropomorphized. I start by giving the benefit of the doubt, but it rarely lasts. 'Reasoning', 'agent', 'skill' 'hallucinate', 'know', 'think', 'train', 'learn', 'understand', 'harness', 'attention', 'context', 'prompt'.
I think this is the best and most useful way to measure model intelligence. In my experience it's what really sets apart the capable models from the best. A small model can be RL trained to be extremely good at programming or narrow problem solving for its size (eg 5.6 Luna, DS4 Flash, Qwen 3.6 27B), but even Luna is IME comparatively awful at understanding intent and making good decisions with limited guidance.
If you read Opus 5's output, it is beyond the comprehension of virtually all engineers and developers. That is what I mean by intelligence. Math, science, and engineering are all contained in one model. We may be experts in one field. The model is an expert in everything that humans know.
I'd have to ask for you to be more specific, otherwise, to take your answer at face value, it comes across as a contradiction.
> [Opus 5's output] is beyond the comprehension of virtually all engineers and developers
That would make it pretty bad? The key defining quality of good software, is clarity, and the ability to simplify a complex problem to the point of it seeming trivial.
> Math, science, and engineering are all contained in one model. We may be experts in one field. The model is an expert in everything that humans know.
The bar here should absolutely be to judge this against the expert level within each domain. I have time and time come across LLM output being woefully underwhelming in every single request where I am an expert. For all areas that I am not, it sure seems plausible. It is far more likely than not, that it is equally inadequate in the areas I lack the necessary knowledge to tell.
If the AI is being subpar in every field and category compared to an expert in said respective field, then, what a strange gauge of a tool's usefulness. Are we attributing higher value because a single model is "attempting to solve all knowledge and fields at the same time", why is that of any importance, or excuse?
We should not define "intelligence" as how effectively it can convince a non-expert of something being plausible. That sounds like the absolute worst tradeoff. You'd have to waste the experts time in filtering and refuting incorrect postulations that are cheep to generate. The perfect storm for bullshit asymmetry.
That seems awfully self deprecating. Surely, you expect better of yourself in at least some area, than the average competency of humans across all areas?
Careful, you may have a bit of psychosis. They are very, very far from incomprehensible, and also very far from the top at least of my field. The best in my field are produce far higher quality results, and I think that's true for all fields. It's just an incredibly good 85% quality machine that experts all use because they can guide it to be up to their quality faster than doing it themselves.
You could take that even further, to the actual danger of reliance of these tools when you lack the expert knowledge. That is, when you assume it took you 100%, but missed the 15% it got very wrong, or perhaps even worse: subtly wrong. This compounds with the next similar task, and either you've made the actual experts quit their job as it has become to babysit LLM output, or you end up with an unusable mess, deleted production databases, etc.
I don't think that's because of its "intelligence". It speaks obtuse techbro-ese: stringing together words that sound smart to obscure the simplicity of the thing it's describing. In many ways it's the opposite of intelligence.
Opus 5 and Fable 5 in particular suffer from this issue at worse level than most models in the same class.
I think the idea is packing more information into fewer words, but the result is a word salad that is somehow simultaneously very dense in adjectives and adverbs, and still way too verbose.
If you read the many, many complaints about opus 5 on anthropic forums, the sentiment is that opus 5 output is poor and people are back to 4.8 and 4.6.
You may want to re-evaluate and compare to the older models.
I'm using Sonnet 5 on a large porting project and it's good. I switched from Opus 5 to Sonnet 5 on a project of another customer and I didn't notice a decrease in quality. I concede that it's very difficult to assess a difference in quality unless one uses both models on the same task and carefully compare the code, not the output in the terminal. I really don't have the time and the tokens for that. Anyway, Sonnet is still doing a good job.
