> why it's so easy to underestimate long-term progress and overestimate short-term progress

I dunno, I think that's mostly post-hoc rationalization. There are equally many cases where long-term progress has been overestimated after some early breakthroughs: think space travel after the moon landing, supersonic flight after the concorde, fusion energy after the H-bomb, and AI after the ENIAC. Turing himself guesstimated that human-level AI would arrive in the year 2000. The only constant is that the further into the future you go, the harder it is to predict.

I think that for a lot of examples, the differentiating factor is infrastructure rather than science.

The current wave of AI needed fast, efficient computing power in massive data centres powered by a large electricity grid. The textiles industry in England needed coal mining, international shipping, tree trunks from the Baltic region, cordage from Manilla, and enclosure plus the associated legal change plus a bunch of displaced and desperate peasantry. Mobile phones took portable radio transmitters, miniaturised electronics, free space on the spectrum, population density high enough to make a network of towers economically viable, the internet backbone and power grid to connect those towers to, and economies of scale provided by a global shipping industry.

Long term progress seems to often be a dance where a boom in infrastructure unlocks new scientific inquiry, then science progresses to the point where it enables new infrastructure, then the growth of that new infrastructure unlocks new science, and repeat. There's also lag time based on bringing new researchers into a field and throwing greater funding into more labs, where the infrastructure is R&D itself.

There is also an adoption curve. The people that grew up without it wont use it as much as children that grew up with it and knowing how to use it. My sister is an admin in a private school (Not in USA) and the owner of the school is someone willing to adopt new tech very quickly. So he got all the school admin subscriptions for chatgpt. At the time my sister used to complain a lot about being over worked and having to bring work home everyday.

2 years later my sister uses it for almost everything and despite her duties increasing she says she gets a lot more done rarely has to bring work home. And in the past they had an English major specially to go over all correspondences to make sure there were no grammatical or language mistakes that person was assigned a different role as she was no longer needed. I think as newer generations used to using LLM for things start getting into the work force and higher roles the real effect of LLM will be felt more broadly as currently apart from early adopters the number of people that use LLM for all the things that they can be used for is still not that high.

GPT3 is when the mass started to get exposed to this tech, it felt like a revolution.

Got 3.5 felt like things were improving super super fast and created that feeling the near feature will be unbelievable.

Got to 4/o series, it felt things had improved but users weren't as thrilled as with the leap to 3.5

You can call that bias, but clearly version 5 improvements displays an even greater slow down, that's 2 long years since gp4.

For context:

- gpt 3 got out in 2020

- gpt 3.5 in 2022

- gpt 4 in 2023

- gpt 4o and clique, 2024

After 3.5 things slowed down, in term of impact at least. Larger context window, multi-modality, mixture of experts, and more efficienc: all great, significant features, but all pale compared to the impact made by RLHF already 4 years ago.

The more general pattern is “slowly at first, then all at once.”

It almost universally describes complex systems.

Your threshold theory is basically Amara's Law with better psychological scaffolding. Roy Amara nailed the what ("we tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run") [1] but you're articulating the why better than most academic treatments. The invisible-to-researchers phase followed by the sudden usefulness cascade is exactly how these transitions feel from the inside.

This reminds me of the CPU wars circa 2003-2005. Intel spent years squeezing marginal gains out of Pentium 4's NetBurst architecture, each increment more desperate than the last. From 2003 to 2005, Intel shifted development away from NetBurst to focus on the cooler-running Pentium M microarchitecture [2]. The whole industry was convinced we'd hit a fundamental wall. Then boom, Intel released dual-core processors under the Pentium D brand in May 2005 [2] and suddenly we're living in a different computational universe.

But teh multi-core transition wasn't sudden at all. IBM shipped the POWER4 in 2001, the first non-embedded microprocessor with two cores on a single die [3]. Sun had been preaching parallelism since the 90s. It was only "sudden" to those of us who weren't paying attention to the right signals.

Which brings us to the $7 trillion question: where exactly are we on the transformer S-curve? Are we approaching what Richard Foster calls the "performance plateau" in "Innovation: The Attacker's Advantage" [4], where each new model delivers diminishing returns? Or are we still in that deceptive middle phase where progress feels linear but is actually exponential?

The pattern-matching pessimist in me sees all the classic late-stage S-curve symptoms. The shift from breakthrough capabilities to benchmark gaming. The pivot from "holy shit it can write poetry" to "GPT-4.5-turbo-ultra is 3% better on MMLU." The telltale sign of technological maturity: when the marketing department works harder than the R&D team.

But the timeline compression with AI is unprecedented. What took CPUs 30 years to cycle through, transformers have done in 5. Maybe software cycles are inherently faster than hardware. Or maybe we've just gotten better at S-curve jumping (OpenAI and Anthropic aren't waiting for the current curve to flatten before exploring the next paradigm).

As for whether capital can override S-curve dynamics... Christ, one can dream.. IBM torched approximately $5 billion on Watson Health acquisitions alone (Truven, Phytel, Explorys, Merge) [5]. Google poured resources into Google+ before shutting it down in April 2019 due to low usage and security issues [6]. The sailing ship effect (coined by W.H. Ward in 1967, where new technology accelerates innovation in incumbent technology)[7] si real, but you can't venture-capital your way past physics.

I think we can predict all this capital pouring in to AI might actually accelerate S-curve maturation rather than extend it. All that GPU capacity, all those researchers, all that parallel experimentation? We're speedrunning the entire innovation cycle, which means we might hit the plateau faster too.

You're spot on about the perception divide imo. The overhyped folks are still living in 2022's "holy shit ChatGPT" moment, while the skeptics have fast-forwarded to 2025's "is that all there is?" Both groups are right, just operating on different timescales. It's Schrödinger's S-curve where we things feel simultaneously revolutionary and disappointing, depending on which part of the elephant you're touching.

The real question I have is whether we're approaching the limits of the current S-curve (we probably are), but whether there's another curve waiting in the wings. I'm not a researcher in this space nor do I follow the AI research beat to weigh in but hopefully someone in the thread can? With CPUs, we knew dual-core was coming because the single-core wall was obvious. With transformers, the next paradigm is anyone's guess. And that uncertainty, more than any technical limitation, might be what makes this moment feel so damn weird.

References: [1] "Amara's Law" https://en.wikipedia.org/wiki/Roy_Amara [2] "Pentium 4" https://en.wikipedia.org/wiki/Pentium_4 [3] "POWER4" https://en.wikipedia.org/wiki/POWER4 [4] Innovation: The Attacker's Advantage - https://annas-archive.org/md5/3f97655a56ed893624b22ae3094116... [5] IBM Watson Slate piece - https://slate.com/technology/2022/01/ibm-watson-health-failu... [6] "Expediting changes to Google+" - https://blog.google/technology/safety-security/expediting-ch... [7] "Sailing ship effect" https://en.wikipedia.org/wiki/Sailing_ship_effect.

I'd love to know more about how OpenAI (or Alec Radford et al.) even decided GPT-1 was worth investing more into. At a glance the output is barely distinguishable from Markov chains. If in 2018 you told me that scaling the algorithm up 100-1000x would lead to computers talking to people/coding/reasoning/beating the IMO I'd tell you to take your meds.

GPT-2 was the first wake-up call - one that a lot of people slept through.

Even within ML circles, there was a lot of skepticism or dismissive attitudes about GPT-2 - despite it being quite good at NLP/NLU.

I applaud those who had the foresight to call it out as a breakthrough back in 2019.

What you're saying isn't necessarily mutually exclusive to what gp said.

GPT-2 was the most impressive leap in terms of whatever LLMs pass off as cognitive abilities, but GPT 3.5 to 4 was actually the point at which it became a useful tool (I'm assuming to programmers in particular).

GPT-2: Really convincing stochastic parrot

GPT-4: Can one-shot ffmpeg commands

That’s true, but not contradictory.

I disagree. Some things are hard to Google, because you can't frame the question right. For example you know context and a poor explanation of what you are after. Googling will take you nowhere, LLMs will give you the right answer 95% of the time.

Once you get an answer, it is easy enough to verify it.

Modern ChatGPT will (typically on its own; always if you instruct it to) provide inline links to back up its answers. You can click on those if it seems dubious or if it's important, or trust it if it seems reasonably true and/or doesn't matter much.

The fact that it provides those relevant links is what allows it to replace Google for a lot of purposes.

This was true before it could use search. Now the worst use-case is for life advice because it will contradict itself a 100 times over while sounding confident each time on life-altering decisions.

Most of the value I got from google was just becoming aware that something exists. LLM-s do far better in this regard. Once I know something exists it's usually easy enough to use traditional search to find official documentation or a more reputable source.

The most useful feature of LLMs is giving sources (with URL preferably). It can cut through a lot of SEO crap, and you still get to factcheck just like with a Google search.

They outperform asking humans, unless you are asking an expert. On average

It covers 99% of my use cases. And it is googling behind the scenes in ways I would never think to query and far faster.

When I need to cite a court case, well the truth is I'll still use GPT or a similar LLM, but I'll scrutinize it more and at the bare minimum make sure the case exists and is about the topic presented, before trying to corroborate the legal strategy with a new context window, different LLM, google, reddit, and different lawyer. At least I'm no longer relying on my own understanding, and what 1 lawyer procedurally generates for me.

It doesn't replace legitimate source funding but LLM vs the top Google results is no contest which is more about Google or the current state of the web than the LLMs at this point.

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Disagree. You have to try really hard and go very niche and deep for it to get some fact wrong. In fact I'll ask you to provide examples: use GPT 5 with thinking and search disabled and get it to give you inaccurate facts for non niche, non deep topics.

Non niche meaning: something that is taught at undergraduate level and relatively popular.

Non deep meaning you aren't going so deep as to confuse even humans. Like solving an extremely hard integral.

Edit: probably a bad idea because this sort of "challenge" works only statistically not anecdotally. Still interesting to find out.

ChatGPT was a proper product, but as an engine, GPT-3 (davinci-001) has been my favorite all the way until 4.1 or so. It's absolutely raw and they didn't even guardrail it.

3.5 was like Jenny from customer service. davinci-001 was like Jenny the dreamer trying to make ends meet by scriptwriting, who was constantly flagged for racist opinions.

Both of these had an IQ of around 70 or so, so the customer service training made it a little more useful. But I mourn the loss of the "completion" way of interacting with AI vs "instruct" or "response".

Unfortunately with all the money in AI, we'll just see companies develop things that "pass all benchmarks", resulting in more creations like GPT-5. Grok at least seems to be on a slightly different route.

This was my experience as well. 3.5 was the point where stackoverflow essentially became obsolete in my workflow.

I preferred o3 for coding and analysis tasks, but appreciated 4o as a “companion model” for brainstorming creative ideas while taking long walks. Wasn’t crazy about the sycophancy but it was a decent conceptual field for playing with ideas. Steve Jobs once described the PC as a “bicycle for the mind.” This is how I feel when using models like 4o for meandering reflection and speculation.

For json generation (and most API things) you should be using “structured outputs”

Nah that rings a bell. 4o for me was the beginning of the end - a lot faster, but very useless for my purposes [1][2]. 4 was a very rocky model even before 4o, but shortly after the 4o launch it updated to be so much worse, and I cancelled my subscription.

[1] I’m not saying it was a useless model for everyone, just for me.

[2] I primarily used LLMs as divergent thinking machines for programming. In my experience, they all start out great at this, then eventually get overtrained and are terrible at this. Grok 3 when it came out had this same magic; it’s long gone now.

Not crazy. 4o was a hallucination machine. 4o had better “vibes” and was really good at synthesizing information in useful ways, but GPT-4 Turbo was a bigger model with better world knowledge.

Shows that they can't solve the fundamental problems as the technology, while amusing and with some utility, is also a dead end if we are going after cognition.

Its strange how Claude achieves similar performance without reasoning tokens.

Did you try advanced voice mode? Apparently it got a big upgrade during gpt 5 release - it may solve what you are looking for.

Yeah, I'd love something where you pronounce a word and it critiques your pronunciation in detail. Maybe it could give you little exercises for each sound, critiquing it, guiding you to doing it well.

If I were any good at ML I'd make it myself.