[datsci_est_2015]

I’ve seen this style of take so much that I’m dying for someone to name a logical fallacy for it, like “appeal to progress” or something.

Step away from LLMs for a second and recognize that “Yesterday it was X, so today it must be X+1” is such a naive take and obviously something that humans so easily fall into a trap of believing (see: flying cars).

[Gareth321]

In finance we say "past performance does not guarantee future returns." Not because we don't believe that, statistically, returns will continue to grow at x rate, but because there is a chance that they won't. The reality bias is actually in favour of these getting better faster, but there is a chance they do not.

[aspenmartin]

this is true because markets are generally efficient. It's very hard to find predictive signals. That is a completely different space than what we're talking about here. Performance is incredibly predictable through scaling laws that continue to hold even at the largest scales we've built

[Gareth321]

I agree this is a new space and prediction volatility is much higher. We have evidence going back to at least 2019 that improvements have been exponential (https://metr.org/blog/2025-03-19-measuring-ai-ability-to-com...). The benchmarks are all over the place because improvements don't happen in a straight line. Even composites aren't that useful because the last 10% improvement can require more effort than the first 90%.

To be frank, from what I can see, even if all progress stopped right now, it would take 1-2 decades to fully operationalise the existing potential of LLMs. There would be massive economic and social change. But progress is not stopping, and in some measurements, continues to improve exponentially. I really think this is incredibly transformative. Moreso than anything humanity has ever experienced. In the last year, OpenAI and potentially Claude have been working on recursive self-improvement. Meaning these models are designing better versions of themselves. This means we have effectively entered the singularity.

[aspenmartin]

I agree with all of this -- the one nit I'll say is that scaling laws (e.g. Chinchilla -- classic paper on this that still holds) are based on next-token log loss on an evaluation set for pretraining, and follow (empirically) very consistent powerlaw relationships with compute / data (there is an ideal mixture of compute + data, and the thing you scale is the compute at this ideal mixture). So that's all I mean by performance -- we do also have as you observe benchmark performance trends (which are measured on the final model, after post-training, RL stages etc). These follow less predictable relationships, but it's the pretraining loss that dominates anyway.

I agree with all of this though

[andrewflnr]

Even more insane than assuming the trend will continue is assuming it will not continue. We don't know for sure (especially not by pure reason), but the weight of probability sure seems to lean one direction.

[mikkupikku]

Logical fallacies are vastly overrated. Unless the conversation is formal logic in the first place, "logical fallacies" are just a way to apply quick pattern matching to dismiss people without spending time on more substantive responses. In this case, both you and the other are speculating about the near future of a thing, neither of you knows.

[datsci_est_2015]

Hard to make a more substantive response when the OP’s entire comment was a one-sentence logical fallacy. I’m not cherry-picking here.

> In this case, both you and the other are speculating about the near future of a thing, neither of you knows.

One of us is making a much grander claim than the other:

  - LLMs have limitless potential for growth; because they are not capable of something today does not mean they won’t be capable of it tomorrow
  - LLMs have fundamental limitations due to their underlying architecture and therefore are not limitless in capability

[fenomas]

The post you replied to was:

> We went from 2 + 7 = 11 to "solved a frontier math problem" in 3 years, yet people don't think this will improve?

All that says is that the speaker thinks models will improve past where they are today. Not that it's a logical certainty (the first thing you jumped on them for), and certainly not anything about "limitless potential for growth" (which nobody even mentioned). With replies like this, invoking fallacies and attacking claims nobody made, you're adding a lot of heat and very little light here (and a few other threads on the page).

[datsci_est_2015]

> All that says is that the speaker thinks models will improve past where they are today. Not that it's a logical certainty

Exceedingly generous interpretation in my opinion. I tend to interpret rhetorical questions of that form as “it’s so obvious that I shouldn’t even have to ask it”.

[fenomas]

> generous interpretation

The term of art for that is steelmanning, and HN tries to foster a culture of it. Please check the guidelines link in the footer and ctrl+f "strongest".

[mikkupikku]

Better put than I could have.

[graemep]

OK, its not a logical fallacy, its a false assumption.

The belief in the inevitability of progress is a bad assumption. Especially if you assume a particular technology will keep advancing.

[mikkupikku]

We won't know if his assumption is false until time passes and moves future speculation into the empirical present.

[graemep]

A possibility is not a fact. Assuming a possibility will happen is not justified. Therefore it is false as an assumption, even if it is true it is a possiblity.

[mikkupikku]

I genuinely have no idea what you're on about. One guy expressed his belief about how the future will play out, and another disagreed. Time will be the judge of it, not either of us.

[aspenmartin]

Hmm...the sun comes up today is a pretty good bet that the sun comes up tomorrow.

We have robust scaling laws that continue to hold at the largest scales. It is absolutely a very safe bet that more compute + more training + algorithmic improvements will certainly improve performance it's not like we're rolling a 1 trillion dollar die.

[famouswaffles]

Well if people give the exact same 'reasons' why it could not do x task in the past that it did manage to do then it is tiring seeing the same nonsense again. The reason here does not even make much sense. This result is not easily verifiable math.

[torginus]

Yeah, and even if we accept that models are improving in every possible way, going from this to 'AI is exponential, singularity etc.' is just as large a leap.

[tim333]

The comment doesn't say it must be X+1. It implies it will improve which I would say is a pretty safe bet.

[botro]

How about 'slippery incline'?

[gf000]

https://xkcd.com/605/