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Fair question, but no, I don’t think OpenAI have hit a brick wall. GPT-3 was June 2020 and GPT-4 March 2023, so even if the next leap took the same time to train up (obviously it’s not that simple) we wouldn’t expect a similar leap in performance for another couple of years. On top of that, the GPU supply chain is creating short-term bottlenecks for training runs. We might see glimpses of true next-gen performance from competitors before then, but I expect most of the buzz for the next 18 months or so to be dominated by increasingly agential models and better multimodal capabilities. There’s also the long-delayed rollout of ChatGPT’s voice upgrade, which is a bigger deal both technically and in terms of social effects than most people realise.
Zooming out, AI now benefits from a forcing economy in a way that was never true of previous AI summers. Outside of specialist applications, there wasn’t much money to be made in AI until comparatively recently, especially for generalist systems like LLMs. But in the wake of ChatGPT you have real AI revenue streams, and every nerdy 18 year old wants to study machine learning (some of them will even get jobs). While we might have a short-term AI bubble as Capex grows out of all proportion to revenue run rates, it’ll be a temporary blip. There’s still gold in them hills, and we’re only scratching the surface of what’s possible in terms of AI products even using existing tech. Most big non-tech firms are still figuring out their AI strategy and paying OpenAI and Microsoft service fees for dumb off-the-shelf products. A lot of the real commercial impact of AI in the short-term is consequently going to come from last-mile products that invest time and energy in tailoring the better open-source models to specific business use cases.
Zooming out even more… look, humans aren’t that smart. We’re the dumbest possible species capable of building an industrial civilisation. Our intelligence is limited by a bunch of very contingent factors like caloric consumption, the size of the birth canal, and the fact we’re layering a System 2 architecture onto a 600 million year old foundation. Even if these constraints didn’t apply, evolution is just not that great of a search algorithm in design space. Take eusociality in insects for example. This is an incredibly successful strategy, with roughly three quarters of insect biomass today coming from eusocial species. But evolution stumbled across eusociality pretty late, only really getting going around 150 million years ago (compared to 400 million years for insects in general). It’s not because it requires large brains, but because evolution is just a crappy blind algorithm for finding optimal equilibria and human ingenuity can do a lot better. Nor is there any reason to think that anatomically modern humans constitute some kind of upper bound on intelligence; the massive intelligence differentials just among humans provide good evidence of that.
So to summarise: OpenAI is going about as fast as we might reasonably expect, the economic fundamentals of AI development have shifted in a way that is likely to accelerate long-term pace, and the goal we’re reaching for isn’t even that hard.
Is there any good theory basis for this claim? It seems to me just as likely that "intelligence" is more like large-scale Bayesian inference, and that for a given quantity of sensory input the possible predictive performance is quite bounded, and potentially even grows logarithmically such that billions of times more input data may only marginally improve the output.
But I will admit I'm somewhat spit balling here and not familiar with the existing literature.
The “dumbest possible species” claim is mostly a soundbite and truism, but the basic idea would be (1) that we see increasing encephalisation (especially in the neocortex) and increasing behavioural sophistication in the Hominins all the way up to Homo sapiens and Homo neanderthalensis, and (2) a small minority of the very smartest humans in very recent history (the last 1000 years out of the 300,000 or so of our species) were required to make the necessary move from agrarian societies to industrial society. Of course they were building on indispensable social, political, and economic foundations, but if you drop the IQ of Europe by 1SD for the second millennium AD I think it’s unlikely we’d get the Industrial Revolution at all.
Regarding the idea of Bayesian limits to intelligence, that applies well to cases where the dimensionality is fairly constrained, notably perception. The space of cognition (“possible good ideas”) by contrast is much more open-ended, and applies at multiple levels of scale and abstraction (because we need heuristics to deal with any large scale system). I don’t see any reason to think we’re even close to “topping out” in cognition, and the outsize contribution of the smartest humans compared to merely very smart humans provide some evidence in this regard.
I will admit that the emergence of AI may finally give some interesting answers and maybe closure to philosophical questions about how introspective and abstract philosophy and mathematics are. As much as (some) math claims to be proof pulled from the ontological ether, can the concept of, say, prime numbers be explained to an intelligence with no real-world sensory inputs? Does the notion of counting make sense in an absence of things to count?
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