This seems correct and a valid point to keep in mind—but it cuts both ways. It makes sense to reduce your credence when you recognize that expert judgment here is less informed than you originally thought. But by the same token, you should probably reduce your credence in your own forecasts being correct, at least to the extent that they involve inside view arguments like, “deep learning will not scale up all the way because it’s missing xyz.” The correct response in this case will depend on how much your views depend on inside view arguments about deep learning, of course. But I suspect that at least for a lot of people the correct response is to become more agnostic about any timeline forecast, their own included, rather than to think that since the experts aren’t so reliable here, therefore I should just trust my own judgement.
This was my initial reaction, that suspiciousness of existing forecasts can justify very wide error bars but not certainty in >50 year timelines. But then I realized I didn’t understand what probability OP gave to <50 years timelines, which is why I asked a clarifying question first.