This is a linkpost for https://aiimpacts.org/reinterpreting-ai-and-compute/
Some arguments saying that the recent evidence about the speed at which compute has been increasing and has been responsible for rapid progress in machine learning, might mean that we should be less worried about short timelines, not more.
[...] Overall, it seems pretty common to interpret the OpenAI data as evidence that we should expect extremely capable systems sooner than we otherwise would.
However, I think it’s important to note that the data can also easily be interpreted in the opposite direction. The opposite interpretation goes like this:
1. If we were previously underestimating the rate at which computing power was increasing, this means we were overestimating the returns on it.
2. In addition, if we were previously underestimating the rate at which computing power was increasing, this means that we were overestimating how sustainable its growth is.
3. Let’s suppose, as the original post does, that increasing computing power is currently one of the main drivers of progress in creating more capable systems. Then — barring any major changes to the status quo — it seems like we should expect progress to slow down pretty soon and we should expect to be underwhelmed by how far along we are when the slowdown hits.