I think there is a difference between “using AI” and “enthusing over AI”, especially when you have a reasonably large following.
There are also different degrees of wrongness—e.g. you might believe it is wrong to eat any factory-farmed meat, but they would probably think it is more wrong to publicly enthuse over factory-farmed meat, and even worse to work at a factory-farm. Those seem like coherent and reasonable stances to me.
ToSummarise
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Valuing a $45 scarf gift more than a $55 coat makes sense to me, if you assume that the recipient actually wants to use the gift instead of just reselling it.
A $55 coat would be virtually useless to me—I already have coats worth a lot more than that, so the $55 coat would either be donated or just sit in my closet unused. A $45 scarf on the other hand, is probably nicer than most or all of the scarves I currently own. So it may actually get regular use (ignoring for now that I am not a scarf person).
Preferring the new gamble to the old gamble is undefensible, though.
Not really. I think news is just one of those areas that is extremely hard to do well. I still think The Economist is better than the average mainstream news org, but I have downweighted it considerably after this.
Strong agree. I think pretty much all treaties where a party has to give something up start out as some high-level agreement on a common goal, with details left to work out during the actual negotiations.
Take free trade agreements for example. Two (or more) countries start from a high-level agreement in principle that they want to increase trade between their countries. But each side will have certain industries or domestic standards that they wish to protect, which are incompatible with the other side’s goal of increased market access. Each will go in with an idea of what they are willing to give up, and each will have their own “red lines”. Neither side knows going in what the final rules will be. During the negotiation process, information is shared, problems are identified, solutions are proposed, and compromises are made. Sometimes it turns out after several rounds that the two sides’ red lines are fundamentally incompatible, and negotiations fail. But treaties where countries agree to give up something significant never start with all the details ironed out.
A related objection I often hear is “China will never agree to that” or “the US will never agree to that”. We don’t know that. They may not like a particular proposal, but they may still agree to it if they get a bunch of other concessions in the treaty. We have no idea what China or the US’s red lines are yet, so such objections are grossly premature.
Yeah, I think the phrase is genuinely ambiguous and, from the article I linked, it sounds like it could refer to something like AGI. It just seems to be highly contextual and I would hesitate to reach that conclusion based solely on a machine translation. Unfortunately this just seems to be an area where misunderstandings (in both directions) will be common.
The relevant phrase from what you cite is: 通用人工智能, and has been in Chinese AI plans since 2017.
This article (No, the 2017 New Generation AI Development Plan did not include a goal of building AGI ) makes a compelling argument that the better translation for the phrase isn’t AGI, but more “general-purpose artificial intelligence”.
And if you think about it, there’s nothing in the phrase “artificial general intelligence” that inherently implies AI that is better than humans at all cognitive tasks or anything. It just implies an AI that is generally capable across multiple domains.
I agree with the general thrust of your post, but not about LVTs. I think the real reasons why land and property taxes are so unpopular with the public are:
It’s a wealth transfer from retirees to working people. The most powerful opponents to LVTs are not the churches and community groups you mention. It’s the retirees, who are asset-rich and income poor, and who hold disproportionate political power. They oppose land taxes not because it undermines their “sociopolitical autonomy”, but because replacing income taxes (which they don’t pay much of) with land taxes would represent a financial loss to them, just like cutting pension entitlements would.
Transitional issues. It’s one thing to start out with a system where the govt owns all the land and charges ground rent to everyone (sort of like Singapore). But enacting a LVT from a starting point where most land is held privately is a whole other beast. Since that land would have been purchased at prices that did not incorporate such a tax, a new LVT—especially if imposed at rates high enough to replace income taxes—would represent a massive windfall loss to all existing landowners. And, like retirees, existing landowners hold disproportionate political power.
So I think the popular resistance to LVTs are explained largely with standard, self-interested economic reasons—not the sociopolitical autonomy reasons you suggest.
Caplan’s book has all kinds of flaws, one of which is that he talks about how “it’s almost all signalling” throughout the book. But when he actually does the math, he conflates it with ability bias, which grossly inflates his signalling estimates (from 8-35% to 33%-80%).
I also think Caplan is wrong on the conformity point and he offers virtually no evidence for it—at least in his book. As far as I recall, he only points to employers not taking online credentials seriously, but that can be equally explained by employers valuing conscientiousness (online credentials are easier to get) and still don’t really understand online credentials that well.
I suspect a failure mode here could be coming from the ivory tower with talking points without being responsive to the constraints and skills and options of the lawmakers (etc.) on the receiving end. That too will need to be a dialogue, perhaps a ‘tennis’ of sorts.
I used to work in policy and agree this is a likely failure mode. Especially in AI where there is so much uncertainty, having someone put their neck out to get it on a policy agenda at all can already be a success—even if they don’t go as far as you might like on highlighting the scale or urgency of the issue.
First, comparative advantage tells you that some human labor will remain valuable in some configuration, but nothing about the wages, number of jobs, or the distribution of gains. You can have comparative advantage and still have massive displacement, wage collapse, and concentration of returns to capital.
Agree 100%. I think this quote is really all you need to rebut the standard economist “but comparative advantage!” argument.
Most economists have shockingly little to say about distribution of gains and market power—a lot of their models simply assume perfect competition, such that market power doesn’t exist. It seems to be an enormous blind spot
Would be interested in hearing more about your upcoming policy document, as I’ve been working on a policy paper myself that touches on some of the same issues you raise. Who is your paper pitched at?
Agree with most of the points in this post. I would add “market power” to your list of important assumptions that often go unaddressed. Most economic models have a background assumption perfect or near-perfect competition, unless they are explicitly trying to model monopolies/oligopolies or monopsonies. Yet I expect AI is far more likely than previous technologies to concentrate market power (especially on the hardware side).
There is so much uncertainty in trying to quantify the impacts of working on AI capabilities while donating to charity that I think consequentialist moral reasoning is very likely to go astray and get hijacked by motivated reasoning.
I personally am very sympathetic to consequentialism but think this is a situation where people should plae more weight on virtue ethics or deontology.