Comment by qoez
4 hours ago
To be fair we've gone from barely capable at writing code to what we have today in barely 3 years. I wouldn't put money on the counter bet that they won't get beyond this hump.
4 hours ago
To be fair we've gone from barely capable at writing code to what we have today in barely 3 years. I wouldn't put money on the counter bet that they won't get beyond this hump.
I wouldn’t assume that progress is linear. Maybe I’m wrong and it will be! But I’m reminded of early developments with self driving cars, it felt like manual driving would be a thing of the past within a decade. But progress has been much slower than that.
> But I’m reminded of early developments with self driving cars, it felt like manual driving would be a thing of the past within a decade. But progress has been much slower than that.
I think that's exactly the standpoint of Yann LeCun.
Let me clarify your illogic for you:
> The progress using narrowly intelligent algorithms to drive cars in a generalizable way was not as fast as the early gains in places the narrow algorithms could solve, thus the progress in using generally intelligent algorithms for any other field must also slow down.
Man, is this how 99% of the population works? They have a heuristic for anything that sounds computer sciencey and apply it for anything and everything that sounds similar without bothering to check if their heuristic actually applies?
No need to be rude! Besides OP just said:
> I’m reminded of
They were drawing a comparison, not saying "x will definitely happen because y"
No need to be such a dick, it reflects poorly on you.
You committed your own fallacy of applying a sloppy heuristic to understanding the person you were responding to without bothering to check if it applies. Spectacular. The comical levels of arrogance that infect over-hyped tech marketing boondoggles (one area among many in which GenAI is clearly similar to self-driving cars) is one of the reasons nobody trusts the hype anymore.
The breathless astroturf articles, the brain-dead talking head conversations, the endless flood of obvious misinformation about industry uptake, the nauseating LinkedIn puff-pieces about PREPARING YOUR ENTERPRISE OR ELSE... it's a hostile information economy at best, and its being driven by the same people who brought you bullshit like self-driving, crypto and the Metaverse... for the exact same reasons (to get rich).
There's no epistemic credibility left in the tech industry in general. Maybe the Dario's or whoevers of the world believed what they were talking about at one point, but I don't believe a single word I read from Anthropic or OpenAI for the same reason I don't believe anything I read from Russian state media. I know it's being produced for reasons that have nothing to do with the truth.
As someone who interacts with the technology on a daily basis at a non-trivial level... I'm not seeing anything that would cure my skepticism about GenAI. The information economy surrounding it is broken. The fundamentals don't line up. The hype about "nearby capabilities" is identical to the hype of generations after generations of hype in the AI space (dating at least back to Turing). It smells like a bad bet.
The product and UX of the tool is distinct from the raw ability of the tool.
Claude code has great UX.
It is hands down the most user hostile UX I put up with. The fact that I put up with it speaks volumes about the value proposition that the underlying models bring to the table.
I’m in the process of dumping codex for more open harnesses that don’t add problematic new features every week. Because Anthropic insists that I have to use their harness with the subscription, eventually I’m either going to wrap `claude -p` to get away from the crappy TUI or else I’ll just unsubscribe and use other models. Their bad UX has alienated me as a paying customer.
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No, the tooling I make has better UX very quickly. It is built for their financial goals, not for the end user.
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agreed. the thing i reach for most is its ability to coordinate my agent graph, e.g. seeing what sub-agents are doing, messaging agents, adding new capacity. claude code really is a delight to use here
Claude Code is better than Codex UX. But as per the sibling response it's more a developer tool made by developers for developers. It does have a lot of niceties. Codex/openai on the other hand is more clearly trying to reach mass-market and it's a UX shit show because of it. Consumer UX is its own thing. So the original point is that raw ability of the tool doesn't magically solve ergonomics.
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The current approach of the big companies is to keep increasing the number of parameters (there was a report that recent Claude models are on the 4T parameter scale). Even recent SOTA open weight models are in the 1T-2T range.
This is partly due to the multiplicative scaling factor -- the number of tokens; the number of transformer layers; the number of features; etc.
I suspect that for models to have a step upgrade either/both of 1) a new/improved architecture model; and 2) better training of smaller parameter models are needed. I say the latter as I think that with the larger weights there is a lot of unused/redundant parameters.
If we’re learning anything from the past 15 years it’s probably not to extrapolate AI progress in a straight line.
https://finance.yahoo.com/sectors/healthcare/articles/godfat...
Progress is spiky and not guaranteed. The capital costs have also ballooned to the point where the rate has to level out
> The capital costs have also ballooned to the point where the rate has to level out
That's a pretty bold use of "has to". None of the financing behind AI seems sane, yet it continues to defy rational expectations. Reminds me of the comment "the market can remain irrational longer than you can stay solvent". It's not the exact meaning but has the same feeling. The circular financing going on does not help anything either
I dont think the quality has gotten that much better. Take claude 4.5, the probability of getting usable outputs was less likely than with opus 5.5, and the usuable outputs weren't great.
I think newer models are more likely to produce the usuable output, almost always, but its still not great. The frequency in which you get usable results leads one to believe that quality is improving.. No usability is improving but the result is still meh.
Also put an llm in an environment without an lsp or compiler to check against and let me know how capable you think they are on their own. The "loop" is doing a lot of heavy lifting here.
Time doesn't solve bad designs. We still have lots of buffer overflows in C.
We don't see any indication that the LLM companies are slowing down and go for better code.
Instead it just looks like a cash grab until the AI bubble bursts.