I think it's largely true: the narrow AI "arsenal" currently being developed often comes up with results that seem to be transferable between fields. For example, there is a recent paper that applies the same novel strategy both for image understanding and natural language sentence parsing, both with success. Although you often need lots of tinkering to get state-of-art results, producing the same quality just using a general method without any parameters seems to make a good paper.
And while the problem of how to build an AGI is not directly solved by these, we certainly get closer to it using them. (You still need a module to recognize/imagine/process visual data, unless the solution is something really abstract like AIXI...)
I know people have talked about this in the past, but now seems like an important time for some practical brainstorming here. Hypothetical: the recent $15mm Series A funding of Vicarious by Good Ventures and Founders Fund sets off a wave of $450mm in funded AGI projects of approximately the same scope, over the next ten years. Let's estimate a third of that goes to paying for man-years of actual, low-level, basic AGI capabilities research. That's about 1500 man-years. Anything which can show something resembling progress can easily secure another few hundred man-years to continue making progress.
Now, if this scenario comes to pass, it seems like one of the worst-case scenarios -- if AGI is possible today, that's a lot of highly incentivized, funded research to make it happen, without strong safety incentives. It seems to depend on VCs realizing the high potential impact of an AGI project, and of the companies having access to good researchers.
The Hacker News thread suggests that some people (VCs included) probably already realize the high potential impact, without much consideration for safety:
Is there any way to reverse this trend in public perception? Is there any way to reduce the number of capable researchers? Are there any other angles of attack for this problem?
I'll admit to being very scared.