As you implied above, pessimism is driven only secondarily by timelines. If things in 2028 don’t look much different than they do now, that’s evidence for longer timelines (maybe a little longer, maybe a lot). But it’s inherently not much evidence about how dangerous superintelligence will be when it does arrive. If the situation is basically the same, then our state of knowledge is basically the same.
So what would be good evidence that worrying about alignment was unnecessary? The obvious one is if we get superintelligence and nothing very bad happens, despite the alignment problem remaining unsolved. But that’s like pulling the trigger to see if the gun is loaded. Prior to superintelligence, personally I’d be more optimistic if we saw AI progress requiring even more increasing compute than the current trend—if the first superintelligences were very reliant on massive pools of tightly integrated compute, and had very limited inference capacity, that would make us less vulnerable and give us more time to adapt to them. Also, if we saw a slowdown in algorithmic progress despite widespread deployment of increasingly capable coding software, that would be a very encouraging sign that recursive self-improvement might happen slowly.
As you implied above, pessimism is driven only secondarily by timelines. If things in 2028 don’t look much different than they do now, that’s evidence for longer timelines (maybe a little longer, maybe a lot). But it’s inherently not much evidence about how dangerous superintelligence will be when it does arrive. If the situation is basically the same, then our state of knowledge is basically the same.
So what would be good evidence that worrying about alignment was unnecessary? The obvious one is if we get superintelligence and nothing very bad happens, despite the alignment problem remaining unsolved. But that’s like pulling the trigger to see if the gun is loaded. Prior to superintelligence, personally I’d be more optimistic if we saw AI progress requiring even more increasing compute than the current trend—if the first superintelligences were very reliant on massive pools of tightly integrated compute, and had very limited inference capacity, that would make us less vulnerable and give us more time to adapt to them. Also, if we saw a slowdown in algorithmic progress despite widespread deployment of increasingly capable coding software, that would be a very encouraging sign that recursive self-improvement might happen slowly.