It appears to be the extrapolation using exponential growth from current capacity using maximum likelihood to fit the growth rate. Whether you believe the date comes down to how well you think their generalized logical Qubit measures what they're trying to capture.
I think it's worth remembering that asking experts for timelines requiring more than 10 years often results in guessing 10 years, so I would tend to favor a data-based extrapolation over that.
Eliminating aging also has the potential for strong negative long-term effects. Both of the ones I'm worried about are actually extensions of your point about eliminating long-term value drift. No aging enables autocrats to stay in power indefinitely, as it is often the uncertainty of their death that leads to the failure of their regimes. Given that billions worldwide currently live under autocratic or authoritarian governments, this is a very real concern.
Another potentially major downside is the stagnation of research. If Kuhn is to be believed, a large part of scientific progress comes not from individuals changing their minds, but from outdated paradigms being displaced by more effective ones. This one is less certain, as it's possible that knowing they have indefinite futures may lead to selection for people who are willing to change their minds. Both of these are cases where progress probably *requires* value drift.
I suspect a major divide in the usefulness of academic publication is whether we're trying to establish specific empirical claims, or develop a philosophical framework. For the former, if you want to make STEM claims, it's difficult to get people to take you seriously without having published results. This is what MIRI is doing. Many other EA problems, such as disease mitigation and economic development already have a developed literature, meaning much of the problem right now is applying that literature to donating strategy. As EA becomes more prevalent and we begin targeting problems other than the low hanging fruit, we will push the boundary of what that literature has to say. Givewell is running into this problem already. While the discussions should advance beyond what is published, it makes sense to have a paper trail of evidence that various methods are as effective as they're claimed to be. While the discussion shouldn't be limited to academia, there should be an academic branch to EA, particularly in STEM.
It appears to be the extrapolation using exponential growth from current capacity using maximum likelihood to fit the growth rate. Whether you believe the date comes down to how well you think their generalized logical Qubit measures what they're trying to capture.
I think it's worth remembering that asking experts for timelines requiring more than 10 years often results in guessing 10 years, so I would tend to favor a data-based extrapolation over that.
Eliminating aging also has the potential for strong negative long-term effects. Both of the ones I'm worried about are actually extensions of your point about eliminating long-term value drift. No aging enables autocrats to stay in power indefinitely, as it is often the uncertainty of their death that leads to the failure of their regimes. Given that billions worldwide currently live under autocratic or authoritarian governments, this is a very real concern.
Another potentially major downside is the stagnation of research. If Kuhn is to be believed, a large part of scientific progress comes not from individuals changing their minds, but from outdated paradigms being displaced by more effective ones. This one is less certain, as it's possible that knowing they have indefinite futures may lead to selection for people who are willing to change their minds. Both of these are cases where progress probably *requires* value drift.
I suspect a major divide in the usefulness of academic publication is whether we're trying to establish specific empirical claims, or develop a philosophical framework. For the former, if you want to make STEM claims, it's difficult to get people to take you seriously without having published results. This is what MIRI is doing. Many other EA problems, such as disease mitigation and economic development already have a developed literature, meaning much of the problem right now is applying that literature to donating strategy. As EA becomes more prevalent and we begin targeting problems other than the low hanging fruit, we will push the boundary of what that literature has to say. Givewell is running into this problem already. While the discussions should advance beyond what is published, it makes sense to have a paper trail of evidence that various methods are as effective as they're claimed to be. While the discussion shouldn't be limited to academia, there should be an academic branch to EA, particularly in STEM.