Thank you, GradientDissenter. I am pleased and honored that you though to invite me, andin the normal course of events would have jumped at the invitation. I think I could add some value to the conversation about X-risks.
Unfortunately, I am recovering from major surgery and don't have the physical stamina to do a conference yet. If your event had been scheduled even a month later I think I would be able to give you a different answer.
If you run any future events of this kind I would be very interested in attending.
My name is Eric S. Raymond, and I approve this message. :-)
I've been applying this strategy consciously for a long time, working the polymathy, looking for ways to apply insights across fields. And I in fact did become famous and respected this way, by applying the toolkits of cultural anthropology, evo-psych, and libertarian economics to software engineering.
The "riches" part has eluded me, however.
Maybe I'm stating what is now obvious, but this applies to shortages of other outputs as well.
Think of potential producers for a good as a portfolio of options, one or more of which is exercised when you need to actually have it. You want to have multiple options available to reduce your risk that no producer will be able to deliver
When the cost of transportation is high, production has to be local to you, so it is more likely that all your options have correlated risks. As the cost of transport drops it gets possible to pick up options from producers further away, so the risks de-correlate. Trade networks get larger.
But that's not all good. Transport disruption is always a risk in trade, even when it's very local. But as trade networks get larger and the transport network has more moving parts, coordination issues increase.
Also, you're trading decorrelation of risk for the possibility of becoming critically dependent on suppliers far away from you. Which brings us back to my original post.
Corrected, thanks for spotting that.
I got the 40% from one of Peter Zeihan's talks. I don't know where he got it from, but I have yet to catch him in a clear error on statistics I know how to check.
I should have said 40% of the feedstock for exported fertilizer. I also should have explicitly noted that because a lot of wheat is still produced locally to where it's consumed, the famine risk is limited to places that are dependent on wheat imports.
Unfortunately there are plenty of those. And they are generally places that are poor by other measures as well, making coping with economic shocks more fraught.
Your argument that 3X current phosphate prices is tolerable seems sound. On the other hand, anything that has to be mined has inelastic supply because the cost of the capital equipment - and in many countries the cost of the regulatory approvals - is so high.
Those are interesting figures and I thank you for them, but I think they underpredict the impact.
First, there's no reason to believe we'll keep as much as a quarter of Russian and Ukrainian wheat production. Peter Zeihan says Ukraine will be a net wheat importer for some years, and I've found him to be pretty careful and non-alarmist about claims like that.
Second, don't forget the phosphate problem. As long as Russia can't sell its phosphate because it's been sanctioned out of SWIFT etc., 40% of the normal feedstock for fertilizers is out of play. This is going to push up prices and hit productivity seriously in a lot of wheat-growing areas that aren't in the war zone. (As usual, the US will be far less impacted than most other places - we have large domestic phosphate reserves.)
Let me try to ballpark this based on your figures. I'll steelman the case for low impact by choosing a low guess for the decrease in wheat yield due to not having phosphates, let's say 40% . I know this is low because a lot of growing areas have tired soil that barely produces at all without fertilizers. Multiply this with the lost volume of Russian phosphate exports and it looks like world wheat production takes a 16% hit. Add that to the 8% from the lost wheat exports and we're down 24%.
That's best case. It's more likely the impact is worse, producing a century-scale shock.
The reason it happens this way is that in a multi-agent economy with reliable trade, reserves against disruption get interpreted as wasteful and competed out. Investors seek the higher returns from companies that cut that below-the-line cost and plow the savings into capital goods or other things that can increase future revenues.
For example, Just In Time delivery swept the business world because investors and managers optimizing for profits began to see warehouses full of inputs as places that were costing money, not making money. JIT eliminates that inventory, but it relies on the assumption that you can always collect your inputs from suppliers just before you need them.
This was continuous with a century-long trend of vertically-integrated manufacturing dis-aggregating into contract networks, which made total sense as long as transaction costs kept dropping.
Another way to look at it is this: As long as communications and transport are getting cheaper and more reliable, profit maximizers will try to use them to replace more expensive goods, including storage space. Since food is perishable, the advantage of not buffering it in warehouses is exceptionally high. Which is fine until the cost gradients reverse and JITting your food from overseas gets expensive, at which point you can be in a heap of trouble.
Is efficiency-driven supercriticality the only reason for interconnected failures at all scales? No. Is it an important driver, perhaps the single most important one under modern conditions? Evidence points to yes. Worked example follows: embedded electronics for automobiles.
This is an example of how even ordinary fluctuations in demand for non-perishable goods (something much less disruptive than a war) can cause cascade failures in a system that's supercritical due to insufficient buffering. In 2020, automobile manufacturers reacted to a COVID-induced collapse in demand for their product by letting their contracts with the chip fabs making their car electronics go un-renewed - assuming that once demand picked up they could buy the chips again at the same prices.
When they went back beginning in later 2021, they discovered that (a) the chip fabs had converted the idled capacity to higher-margin products, and (b) they hadn't warehoused enough parts to keep making cars until they could buy line capacity. The consequences rippled down the chain towards consumers as a bunch of availability and price shocks, nearly wrecking several manufacturers and damaging a legion of related service businesses.
Took them a lot more money to get production reestablished than it would have to keep their piece of the fab capacity running through the slump. The recovery isn't done yet, and business schools are already treating this as a textbook case of the management stupids
Of course the word "might" is doing a lot of work here! Because there is no guaranteed happy solution, the best we can do is steer away from futures we absolutely know we we do not want to be in, like a grinding totalitarianism rationalized by "We're saving you from the looming threat of killer AIs!"
" At least with the current system, corporations are able to test models before release". The history of proprietary software does not inspire any confidence at all that this will be done adequately, or even at all; in a fight between time-to-market and software quality, getting their firstest almost always wins. It's not reasonable to expect this to change simply because some people have strong opinions about AI risk.