Regarding your first point, I think when people say that language models “don’t bring us closer to full code automation” they mean there’s no way of improving/upgrading language models such that they implement full code automation. I think it would be better to argue against that claim directly instead of bringing up language model’s productivity-boosting effects. There are many things that could potentially boost programmers’ productivity—better nootropics, say—but it seems overly broad to say that they all “bring us closer to full code automation”, even if it might be causally true that they reduce the time to automation in expectation.
The problem with arguing against that claim is that nobody knows whether transformers/scaling language models are sufficient for full code automation. To take your nootropics example, an analogy would be if nootropics were legal, did not have negative side effects, with a single company giving “beta access” (for now) to a new nootropic in unlimited amount at no cost to a market of tens of millions of users, that the data from using this nootropic was collected by the company to improve the product, that there actually were 100k peer-reviewed publications per year in the field of nootropics, where most of the innovation behind the tech came from a >100B-parameters model trained on open-source nootropic chemistry instructions. Would such advancements be evidence for something major we’re not certain about (e.g. high bandwidth brain computer interface) or just evidence for increased productivity that would be reinjected into more nootropic investments?
I think those advancements could be evidence for both, depending on the details of how the nootropics work, etc. But it still seems worth distinguishing the two things conceptually. My objection in both cases is that only a small part of the evidence for the first comes from the causal impact of the second: i.e. if Codex gave crazy huge productivity improvements, I would consider that evidence for full code automation coming soon, but that’s mostly because it suggests that Codex can likely be improved to the point of FCA, not because it will make OpenAI’s progammers more productive.
Regarding your first point, I think when people say that language models “don’t bring us closer to full code automation” they mean there’s no way of improving/upgrading language models such that they implement full code automation. I think it would be better to argue against that claim directly instead of bringing up language model’s productivity-boosting effects. There are many things that could potentially boost programmers’ productivity—better nootropics, say—but it seems overly broad to say that they all “bring us closer to full code automation”, even if it might be causally true that they reduce the time to automation in expectation.
The problem with arguing against that claim is that nobody knows whether transformers/scaling language models are sufficient for full code automation. To take your nootropics example, an analogy would be if nootropics were legal, did not have negative side effects, with a single company giving “beta access” (for now) to a new nootropic in unlimited amount at no cost to a market of tens of millions of users, that the data from using this nootropic was collected by the company to improve the product, that there actually were 100k peer-reviewed publications per year in the field of nootropics, where most of the innovation behind the tech came from a >100B-parameters model trained on open-source nootropic chemistry instructions. Would such advancements be evidence for something major we’re not certain about (e.g. high bandwidth brain computer interface) or just evidence for increased productivity that would be reinjected into more nootropic investments?
I think those advancements could be evidence for both, depending on the details of how the nootropics work, etc. But it still seems worth distinguishing the two things conceptually. My objection in both cases is that only a small part of the evidence for the first comes from the causal impact of the second: i.e. if Codex gave crazy huge productivity improvements, I would consider that evidence for full code automation coming soon, but that’s mostly because it suggests that Codex can likely be improved to the point of FCA, not because it will make OpenAI’s progammers more productive.