Can you quantify this rate of progress? Because someone always comes around and says there's been exponential progress in the past <short timeline> every time someone complains that models just aren't very good. Both can't be true
Both can be true, because the experience depends on the skill of the user. The article the other day here on HN that LLMs reward skill is my exact experience. If you are are good at what you are trying to use it for they can be a skill amplifier, and they are definitely getting much better rapidly for the work I do with them. At the same time people are complaining that they are getting dumber. Saying that both can't be true ignores the skill requirement to use them and the completely different perspectives of people using them.
Even if both aren't true, your evidence was people saying two opposing things. The truth (if there is a single objective truth on a given thing) has little bearing on whether or not different people agree on it.
> At the same time people are complaining that they are getting dumber.
I think this is due to rapidly rising expectations.
When LLMs first show they can do some new thing, we're excited at first. Then, we quickly start taking it for granted, and get upset whenever the LLM fails.
Just three years ago, LLMs could barely hold a conversation. Now, they're writing entire code bases and solving famous mathematical conjectures, but we still focus on whatever they can't do.
You could say the same when applied to games with high RNG and chance, such as Slay the Spire 2, and yet those with real skill do perform far better than those without. Those with skill can clear the highest difficulties more often than those with lower skill.
Something being "non-deterministic" is orthogonal to whether or not skill plays a role.
not equal, but probabisticly better. Best example: give the agent a tight spec and it will perform better compared with a spec that leaves room for interpretation. This is true for all models, more or less. (purely anecdotal of course)
I don’t think that’s fair to say, there are two principle sources of data that paint a fairly consistent picture
- one is scaling laws, where we found years ago that pretraining validation loss scales in an almost miraculously predictable way with data volume and compute. There are apparently theoretical bases for this that I don’t quite understand but this property alone is holding at every scale we’ve ever tested. There is not just “one” scaling law but the point is there are scaling laws and they continue to faithfully predict the performance gains we see
- one is benchmarks, which I always point to epoch capability index as a good summary of them in aggregate which makes it nice to plot on one curve the capability improvement over time
To me either one without the other is substantially weaker, the fact that theory and empirical measurements give you a very good scaling law on a more unintuitive quantity (pretraining validation loss) that’s only indirectly related to the downstream performance you care about, benchmarks (in aggregate) are more direct measures of downstream performance but are harder to nail down clean and well motivated “laws” from theory (as far as I can tell). Nevertheless we do in fact see a clear trend that is not slowing.
That doesn’t mean there aren’t a whole host of benchmark problems that don’t impact the numbers involved here (leakage from training data, fundamental flaws in the design, benchmaxxing) but they don’t change the larger story. These problems don’t plausibly explain the clean trends we see.
I'm very bullish on AI, but I still feel that we pretty much plateaued at Opus 4.6 and everything since then has been in the domain of "extremely verifiable and loopable tasks" (math), benchmarkmaxxing, and harness improvements. Which are fine to good things, but I think its very reasonable at this point to start asking questions about when we'll see progress in more general domains. The readability of AI output, for example, has nosedived as they've gotten more intelligent, which makes the frontier models difficult to use even for things like writing emails.
In that sense, the frontier models are going to quickly blaze past any semblance of usefulness to humans, while every once in a while we get a news drop like "GPT-7 solved some crazy math problem" or "it invented some new awesome drug"; meanwhile what most people will use will be smaller, more human-specialized models, maybe distilled from those frontier models, that take much longer to iterate on because they rely on large amounts of human feedback in the domain they're specialized for. In other words, useful progress will probably slow down and become more linear starting in Q4, bounded by the rate at which the humans paying for it say "yes this is a good react website".
(By the way: I earnestly do categorize "inventing a new drug" as non-useful AI progress, counter-intuitively. The drug industry has more ideas for drugs than they know what to do with; "useful progress" is, after the idea is made, validating that it works in humans and doesn't kill the human, and productionizing it. AI will help with this and does, but I have substantial doubt that we'll ever see the drug pipeline speed up to, like, a year from idea to prescription. That would be useful progress, which unfortunately many AI pilled hypermaxers conveniently forget. The invention of a promising new drug, or the solution to an arcane set theory problem, are cherries that, through the diligent labor of humans and AI, may become useful, but progress is rarely made by the lone intellect having an a-ha moment.)
I agree on Opus. I’ve had more luck with other models. In particular, Opus’s writing style makes one want to… blow their brains out. While it’s not hallucinating too much, and can troubleshoot certain issues extremely well, the comments it leaves are silky smooth and chock full of inscrutable phrases. And it’s a lot slower than it used to be.
Point being: it’s overall a worse experience even if the model is technically better at a lot of things.
Coding is an extremely verifiable and loopable task, like math (in fact, all of the math that these models has done has been through the lens of Lean, which is itself just coding). I am talking about their capabilities in tasks that are more general, the execution of which represent the vast majority of economic value generation in the world.
We are not even close to what AI slowdown looks like.
The whole business side of things are now building Agentic Layer for Business applications. All of this Agentic Layer needs to be build and its happening right now and still needs a little bit of time.
Anthropic and co have the biggest and centralized reinforcement loop on the planet: Millions of people telling them what is good and what not due to thumbs up/down.
And for sure when the businesses are building the agentic layer they might give direct feedback to them.
While in parallel LLMs get better, more generic and a LOT cheaper too.
Cheaper? For whom? As a solo practitioner, I can no longer afford the workloads I was getting for $20/mo in January. Now the same plan being utilized at the same level for the same work hits its limits within a few hours, and runs out of tokens in less than two days.
I mean the token prices in general as certain services were never really using a subscription.
I do run a claude subscripton right now though and since there capacity change, i hit the limit rarely in comparision to the past, but I don't think this will stay as it is.
Your issue, I believe, is that you seem to believe capabilities are measured along one axis. This is natural to believe because it is representative of how the models have evolved up to this point, and thus it is also what many AGI-pilled people believe.
Critically, you did not quote the most important part of my sentence: "useful progress will probably slow down and become more linear starting in Q4"; your omission of those words is why I believe you don't understand what I'm saying; you didn't find it important to make your point, so you omitted it, when actually it is critical to the entire assertion. You can read my third paragraph, if you wish, to understand why it is important, instead of just stopping at the first word you disagree with and hitting the "Submit Comment" button.
There is high rate of progress in specific domains, not high rate of progress in generalness. The models haven't gotten generally smarter, for things they didn't focus on the models are just as bad as a year ago.
I don't think that's correct. Increasing parameter count increases capabilities in all domains per the scaling laws. Models are larger than they were years ago, so capabilities in all domains must necessarily be better. This doesn't even account for better training data, which has also much improved.
This is an amazingly ignorant thing to say given the current pace of progress.