Asking this openly, even on HN, is going to get a lot of unqualified answers. So I'll preface that I have a recent PhD in deep RL and pretty well versed on the

Asking this openly, even on HN, is going to get a lot of unqualified answers. So I'll preface that I have a recent PhD in deep RL and pretty well versed on the cutting edge develops of ML. I think your question has two angles. First, do LLMs have potential for AGI? I think that's an emphatic no. There is nothing special about this generative model vs. say something like sampling from a mixture of Gaussians. Much better generations and super impressive it works, sure, but there is no mechanism for it to improve itself let alone change its "prime directive". See Sam Altman's claim that RL with human feedback being where most are the gains are now. At a higher level, there is the concern that the pace at which we are going that we can extrapolate that AGI is around the corner. My take on this is that basically everything made possible in the last 20 years is because of GPUs and better tooling. In specific, the recent hype we see is because of how democratized things are getting. We have kids using ChatGPT to do homework. This is very disruptive from a societal perspective but we are essentially at the height of the rate at which this technology is being adopted. The growth rate will decelerate and it will stop being news once society has learned to adapt to the new technology. However, from a technical perspective, these concerns are like looking at children playing legos and being concerned they will build the next nuclear bomb. Hypothetically feasible but there are so many fundamental gaps that clearly separate the two that the real concerns would be when we see Manhattan projects being successful. From an outsider's perspective, Deep Mind and AlphaZero or OpenAI and LLMs seem like project Manhattans but the fact these companies have spent billions with no returns yet should say a lot about the utility of these models.