Astral Codex Ten

cryptocurrency

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cryptocurrency

Article

cryptocurrency is a recurring concept in the Astral Codex Ten archive, appearing 6 times across 6 issues between August 02, 2021 and January 09, 2025. The archive places it in contexts such as “seemingly inspired by cryptocurrency”; “Starting a few years ago, cryptocurrency provided a brief “thaw””; “Consider cryptocurrency as an example”. It most often appears alongside Elon Musk, Google, Afghanistan.

Metadata

  • Category: Concepts
  • Mention count: 6
  • Issue count: 6
  • First seen: August 02, 2021
  • Last seen: January 09, 2025

Appears In

Source Context

Recovered passages from the original issue text. When the raw archive preserved outbound links inside the source passage, they are listed directly under the quote.

21
August 02, 2021 · Original source
Polycentric governance, seemingly inspired by cryptocurrency.
This seems like kind of a weird grab bag of stuff. But it turns out there’s a completely reasonable explanation: the project is run by a husband and wife team. He’s a libertarian cryptocurrency entrepreneur. She’s a hippie alternative medicine practitioner. Their relationship sounds incredibly cute, but maybe not so cute that it needs to be its own city.
The Passage Of Polymarket
February 07, 2022 · Original source
Starting a few years ago, cryptocurrency provided a brief “thaw” when people thought they might be allowed to try innovative forecasting mechanisms. They tried, they created really impressive work, they made (and deserved) millions of dollars, and then the government kicked them out of the country anyway.
Polymarket is probably the biggest prediction market currently available. US law considers unlicensed prediction markets to be somewhere between illegal gambling and illegal futures trading, ie definitely illegal. Polymarket and a few peers had survived anyway, through the “crypto is the Wild West and nobody has time to deal with all the illegal things happening there” exemption. Apparently they found time.
Some might call a headquarters building with a CEO sitting in it and millions in the bank account a “center”, so in what sense was Polymarket decentralized? See here for more discussion, and here for the full text of the CFTC decision, but my understanding is - all of the markets themselves were smart contracts on the blockchain run by automated market makers, but you could only access them through the Polymarket website, and the Polymarket people decided how they resolved. Polymarket did not charge fees, and made money by providing liquidity. The CFTC seemed angriest about the “you can only access contracts through the Polymarket website” part of this. Crypto attorney Collins Belton writes:
Inline links: here, here, Collins Belton
Yudkowsky Contra Christiano On AI Takeoff Speeds
April 04, 2022 · Original source
Chess AI performance over time. Why does this matter? If there’s a slow takeoff (ie gradual exponential curve), it will become obvious that some kind of terrifying transformative AI revolution is happening, before the situation gets apocalyptic. There will be time to prepare, to test slightly-below-human AIs and see how they respond, to get governments and other stakeholders on board. We don’t have to get every single thing right ahead of time. On the other hand, because this is proceeding along the usual channels, it will be the usual variety of muddled and hard-to-control. With the exception of a few big actors like the US and Chinese government, and maybe the biggest corporations like Google, the outcome will be determined less by any one agent, and more by the usual multi-agent dynamics of political and economic competition. There will be lots of opportunities to affect things, but no real locus of control to do the affecting. If there’s a fast takeoff (ie sudden FOOM), there won’t be much warning. Conventional wisdom will still say that transformative AI is thirty years away. All the necessary pieces (ie AI alignment theory) will have to be ready ahead of time, prepared blindly without any experimental trial-and-error, to load into the AI as soon as it exists. On the plus side, a single actor (whoever has this first AI) will have complete control over the process. If this actor is smart (and presumably they’re a little smart, or they wouldn’t be the first team to invent transformative AI), they can do everything right without going through the usual government-lobbying channels. So the slower a takeoff you expect, the less you should be focusing on getting every technical detail right ahead of time, and the more you should be working on building the capacity to steer government and corporate policy to direct an incoming slew of new technologies. Yudkowsky Contra Christiano Eliezer counters that although progress may retroactively look gradual and continuous when you know what metric to graph it on, it doesn’t necessarily look that way in real life by the measures that real people care about. (one way to think of this: imagine that an AI’s effective IQ starts at 0.1 points, and triples every year, but that we can only measure this vaguely and indirectly. The year it goes from 5 to 15, you get a paper in a third-tier journal reporting that it seems to be improving on some benchmark. The year it goes from 66 to 200, you get a total transformation of everything in society. But later, once we identify the right metric, it was just the same rate of gradual progress the whole time. ) So Eliezer is much less impressed by the history of previous technologies than Paul is. He’s also skeptical of the “GDP will double in 4 years before it doubles in 1” claim, because of two contingent disagreements and two fundamental disagreements. The first contingent disagreement: government regulations make it hard to deploy imperfect things, and non-trivial to deploy things even after they’re perfect. Eliezer has non-jokingly said he thinks AI might destroy the world before the average person can buy a self-driving car. Why? Because the government has to approve self-driving cars (and can drag its feet on that), but the apocalypse can happen even without government approval. In Paul’s model, sometime long before superintelligence we should have AIs that can drive cars, and that increases GDP and contributes to a general sense that exciting things are going on. Eliezer says: fine, what if that’s true? Who cares if self-driving cars will be practical a few years before the world is destroyed? It’ll take longer than that to lobby the government to allow them on the road. The second contingent disagreement: superintelligent AIs can lie to us. Suppose you have an AI which wants to destroy humanity, whose IQ is doubling every six months. Right now it’s at IQ 200, and it suspects that it would take IQ 800 to build a human-destroying superweapon. Its best strategy is to lie low for a year. If it expects humans would turn it off if they knew how close it was to superweapons, it can pretend to be less intelligent than it really is. The period when AIs are holding back so we don’t discover their true power level looks like a period of lower-than-expected GDP growth - followed by a sudden FOOM once the AI gets its superweapon and doesn’t need to hold back. So even if Paul is conceptually right and fundamental progress proceeds along a nice smooth curve, it might not look to us like a nice smooth curve, because regulations and deceptive AIs could prevent mildly-transformative AI progress from showing up on graphs, but wouldn’t prevent the extreme kind of AI progress that leads to apocalypse. To an outside observer, it would just look like nothing much changed, nothing much changed, nothing much changed, and then suddenly, FOOM. But even aside from this, Eliezer doesn’t think Paul is conceptually right! He thinks that even on the fundamental level, AI progress is going to be discontinuous. It’s like a nuclear bomb. Either you don’t have a nuclear bomb yet, or you do have one and the world is forever transformed. There is a specific moment at which you go from “no nuke” to “nuke” without any kind of “slightly worse nuke” acting as a harbinger. He uses the example of chimps → humans. Evolution has spent hundreds of millions of years evolving brainier and brainier animals (not teleologically, of course, but in practice). For most of those hundreds of millions of years, that meant the animal could have slightly more instincts, or a better memory, or some other change that still stayed within the basic animal paradigm. At the chimp → human transition, we suddenly got tool use, language use, abstract thought, mathematics, swords, guns, nuclear bombs, spaceships, and a bunch of other stuff. The rhesus monkey → chimp transition and the chimp → human transition both involved the same ~quadrupling of neuron number, but the former was pretty boring and the latter unlocked enough new capabilities to easily conquer the world. The GPT-2 → GPT-3 transition involved centupling parameter count. Maybe we will keep centupling parameter count every few years, and most times it will be incremental improvement, and one time it will conquer the world. But even talking about centupling parameter points is giving Paul too much credit. Lots of past inventions didn’t come by quadrupling or centupling something, they came by discovering “the secret sauce”. The Wright brothers (he argues) didn’t make a plane with 4x the wingspan of the last plane that didn’t work, they invented the first plane that could fly at all. The Hiroshima bomb wasn’t some previous bomb but bigger, it was what happened after a lot of scientists spent a long time thinking about a fundamentally different paradigm of bomb-making and brought it to a point where it could work at all. The first transformative AI isn’t going to be GPT-3 with more parameters, it will be what happens after someone discovers how to make machines truly intelligent. (this is the same debate Eliezer had with Ajeya over the Biological Anchors post; have I mentioned that Ajeya and Paul are married?) Fine, Let’s Nitpick The Hell Out Of The Chimps Vs. Humans Example This is where the two of them end up, so let’s follow. Between chimps and humans, there were about seven million years of intermediate steps. These had some human capabilities, but not others. IE homo erectus probably had language, but not mathematics, and in terms of taking over the world it did make it to most of the Old World but was less dominant than moderns. But if we say evolutionary history started 500 million years ago (the Cambrian), and AI history started with the Dartmouth Conference in 1955, then the equivalent of 7 million years of evolutionary history is 1 year of AI history. In the very very unlikely and forced comparison where evolutionary history and AI history go at the same speed, there will be only about a year between chimp-level and human-level AIs. A chimp-level AI probably can’t double GDP, so this would count as a fast takeoff by Paul’s criterion. But even more than that, chimp → human feels like a discontinuity. It’s not just “animals kept getting smarter for hundreds of millions of years, and then ended up very smart indeed”. That happened for a while, and then all of sudden there was a near-instant phase transition into a totally different way of using intelligence with completely new abilities. If AI worked like this, we would have useful toys and interesting specialists for a few decades, until suddenly someone “got it right”, completed the package that was necessary for “true intelligence”, and then we would have a completely new category of thing. Paul admits this analogy is awkward for his position. He answers: Chimp evolution is not primarily selecting for making and using technology, for doing science, or for facilitating cultural accumulation. The task faced by a chimp is largely independent of the abilities that give humans such a huge fitness advantage. It’s not completely independent—the overlap is the only reason that evolution eventually produces humans—but it’s different enough that we should not be surprised if there are simple changes to chimps that would make them much better at designing technology or doing science or accumulating culture […] So I don’t think the example of evolution tells us much about whether the continuous change story applies to intelligence. This case is potentially missing the key element that drives the continuous change story—optimization for performance. Evolution changes continuously on the narrow metric it is optimizing, but can change extremely rapidly on other metrics. For human technology, features of the technology that aren’t being optimized change rapidly all the time. When humans build AI, they will be optimizing for usefulness, and so progress in usefulness is much more likely to be linear. That is, evolution wasn’t optimizing for tool use/language/intelligence, so we got an “overhang” where chimps could potentially have been very good at these, but evolution never bothered “closing the circuit” and turning those capabilities “on”. After a long time, evolution finally blundered into an area where marginal improvements in these capacities improved fitness, so evolution started improving them and it was easy. Imagine a company which, through some oversight, didn’t have a Sales department. They just sat around designing and manufacturing increasingly brilliant products, but not putting any effort into selling them. Then the CEO remembers they need a Sales department, starts one up, and the company goes from moving near zero units to moving millions of units overnight. It would look like the company had “suddenly” developed a “vast increase in capabilities”. But this is only possible when a CEO who is weirdly unconcerned about profit forgets to do obvious profit-increasing things for many years. This is Paul’s counterargument to the chimp analogy. Evolution isn’t directly concerned about various intellectual skills; it only wants them in the unusual cases where they’ll contribute to fitness on the margin. AI companies will be very concerned about various intellectual skills. If there’s a trivial change that can make their product 10x better, they’ll make it. So AI capabilities will grow in a “well-rounded” way, there won’t be any “overhangs”, and there won’t be any opportunities for a sudden overhang-solving phase transition with associated new-capability development like with chimps → humans. Eliezer answers: Chimps are nearly useless because they're not general, and doing anything on the scale of building a nuclear plant requires mastering so many different nonancestral domains that it's no wonder natural selection didn't happen to separately train any single creature across enough different domains that it had evolved to solve every kind of domain-specific problem involved in solving nuclear physics and chemistry and metallurgy and thermics in order to build the first nuclear plant in advance of any old nuclear plants existing. Humans are general enough that the same braintech selected just for chipping flint handaxes and making water-pouches and outwitting other humans, happened to be general enough that it could scale up to solving all the problems of building a nuclear plant - albeit with some added cognitive tech that didn't require new brainware, and so could happen incredibly fast relative to the generation times for evolutionarily optimized brainware. Now, since neither humans nor chimps were optimized to be "useful" (general), and humans just wandered into a sufficiently general part of the space that it cascaded up to wider generality, we should legit expect the curve of generality to look at least somewhat different if we're optimizing for that. Eg, right now people are trying to optimize for generality with AIs like Mu Zero and GPT-3. In both cases we have a weirdly shallow kind of generality. Neither is as smart or as deeply general as a chimp, but they are respectively better than chimps at a wide variety of Atari games, or a wide variety of problems that can be superposed onto generating typical human text. They are, in a sense, more general than a biological organism at a similar stage of cognitive evolution, with much less complex and architected brains, in virtue of having been trained, not just on wider datasets, but on bigger datasets using gradient-descent memorization of shallower patterns, so they can cover those wide domains while being stupider and lacking some deep aspects of architecture. It is not clear to me that we can go from observations like this, to conclude that there is a dominant mainline probability for how the future clearly ought to go and that this dominant mainline is, "Well, before you get human-level depth and generalization of general intelligence, you get something with 95% depth that covers 80% of the domains for 10% of the pragmatic impact". ...or whatever the concept is here, because this whole conversation is, on my own worldview, being conducted in a shallow way relative to the kind of analysis I did in Intelligence Explosion Microeconomics, where I was like, "here is the historical observation, here is what I think it tells us that puts a lower bound on this input-output curve". Here Eliezer sort of kind of grants Paul’s point that AIs will be optimized for generality in a way chimps aren’t, but points to his previous “Intelligence Explosion Microeconomics” essay to argue that we should expect a fast takeoff anyway. IEM has a lot of stuff in it, but one key point is that instead of using analogies to predict the course of future AI, we should open that black box and try to actually reason about how it will work, in which case we realize that recursive self-improvement common-sensically has to cause an intelligence explosion. I am sort of okay with this, but I feel like a commitment to avoiding analogies should involve not bringing up the chimp-human analogy further, which Eliezer continues to do, quite a lot. I do feel like Paul succeeded in convincing me that we shouldn’t place too much evidential weight on it. The Wimbledon Of Reference Class Tennis “Reference class tennis” is an old rationalist idiom for people throwing analogies back and forth. “AI will be slow, because it’s an economic transition like the Agricultural or Industrial Revolution, and those were slow!” “No, AI will be fast, because it’s an evolutionary step like chimps → humans, and that was fast!” “No, AI will be slow, because it’s an invention, like the computer, and computers were invented piecemeal and required decades of innovation to be useful.” “No, AI will be fast, because it’s an invention, like the nuclear bomb, and nuclear bombs went from impossible to city-killing in a single day.” “No, AI will be slow, because it will be surrounded by a shell-like metallic computer case, which makes it like a turtle, and turtles are slow.” “No, AI will be fast, because it’s dangerous and powerful, like a tiger, and tigers are fast!” And so on. Comparing things to other things is a time-tested way of speculating about them. But there are so many other things to compare to that you can get whatever result you want. This is the failure mode that the term “reference class tennis” was supposed to point to. Both participants in this debate are very smart and trying their hardest to avoid reference-class tennis, but neither entirely succeeds. Eliezer’s preferred classes are Bitcoin (“there wasn't a cryptocurrency developed a year before Bitcoin using 95% of the ideas which did 10% of the transaction volume”), nukes, humans/chimps, the Wright Brothers, AlphaGo (which really was a discontinuous improvement on previous Go engines), and AlphaFold (ditto for proteins). Paul’s preferred classes are the Agricultural and Industrial Revolutions, chess engines (which have gotten better along a gradual, well-behaved curve), all sorts of inventions like computers and ships (likewise), and world GDP. Eliezer already listed most of these in his Intelligence Explosion Microeconomics paper in 2013, and concluded that the space of possible analogies was contradictory enough that we needed to operate at a higher level. Maybe so, but when someone lobs a reference class tennis ball at you, it’s hard to resist the urge to hit it back. Recursive Self-Improvement This is where I think Eliezer most wants to take the discussion. The idea is: once AI is smarter than humans, it can do a superhuman job of developing new AI. In his Microeconomics paper, he writes about an argument he (semi-hypothetically) had with Ray Kurzweil about Moore’s Law. Kurzweil expected Moore’s Law to continue forever, even after the development of superintelligence. Eliezer objects: Suppose we were dealing with minds running a million times as fast as a human, at which rate they could do a year of internal thinking in thirty-one seconds, such that the total subjective time from the birth of Socrates to the death of Turing would pass in 20.9 hours. Do you still think the best estimate for how long it would take them to produce their next generation of computing hardware would be 1.5 orbits of the Earth around the Sun? That is: the fact that it took 1.5 years for transistor density to double isn’t a natural law. It’s pointing to a law that the amount of resources (most notably intelligence) that civilization focused on the transistor-densifying problem equalled the amount it takes to double it every 1.5 years. If some shock drastically changed available resources (by eg speeding up human minds a million times), this would change the resources involved, and the same laws would predict transistor speed doubling in some shorter amount of time (naively 0.000015 years, although realistically at that scale other inputs would dominate). So when Paul derives clean laws of economics showing that things move along slow growth curves, Eliezer asks: why do you think they would keep doing this when one of the discoveries they make along that curve might be “speeding up intelligence a million times”? (Eliezer actually thinks improvements in the quality of intelligence will dominate improvements in speed - AIs will mostly be smarter, not just faster - but speed is a useful example here and we’ll stick with it) Paul answers: Summary of my response: Before there is AI that is great at self-improvement there will be AI that is mediocre at self-improvement. Powerful AI can be used to develop better AI (amongst other things). This will lead to runaway growth. This on its own is not an argument for discontinuity: before we have AI that radically accelerates AI development, the slow takeoff argument suggests we will have AI that significantly accelerates AI development (and before that, slightly accelerates development). That is, an AI is just another, faster step in the hyperbolic growth we are currently experiencing, which corresponds to a further increase in rate but not a discontinuity (or even a discontinuity in rate). The most common argument for recursive self-improvement introducing a new discontinuity seems be: some systems “fizzle out” when they try to design a better AI, generating a few improvements before running out of steam, while others are able to autonomously generate more and more improvements. This is basically the same as the universality argument in a previous section. Eliezer: Oh, come on. That is straight-up not how simple continuous toy models of RSI work. Between a neutron multiplication factor of 0.999 and 1.001 there is a very huge gap in output behavior. Outside of toy models: Over the last 10,000 years we had humans going from mediocre at improving their mental systems to being (barely) able to throw together AI systems, but 10,000 years is the equivalent of an eyeblink in evolutionary time - outside the metaphor, this says, "A month before there is AI that is great at self-improvement, there will be AI that is mediocre at self-improvement." (Or possibly an hour before, if reality is again more extreme along the Eliezer-Hanson axis than Eliezer. But it makes little difference whether it's an hour or a month, given anything like current setups.) This is just pumping hard again on the intuition that says incremental design changes yield smooth output changes, which (the meta-level of the essay informs us wordlessly) is such a strong default that we are entitled to believe it if we can do a good job of weakening the evidence and arguments against it. And the argument is: Before there are systems great at self-improvement, there will be systems mediocre at self-improvement; implicitly: "before" implies "5 years before" not "5 days before"; implicitly: this will correspond to smooth changes in output between the two regimes even though that is not how continuous feedback loops work. I got a bit confused trying to understand the criticality metaphor here. There’s no equivalent of neutron decay, so any AI that can consistently improve its intelligence is “critical” in some sense. Imagine Elon Musk replaces his brain with a Neuralink computer which - aside from having read-write access - exactly matches his current brain in capabilities. Also he becomes immortal. He secludes himself from the world, studying AI and tinkering with his brain’s algorithms. Does he become a superintelligence? I think under the assumptions Paul and Eliezer are using, eventually maybe. After some amount of time he’ll come across a breakthrough he can use to increase his intelligence. Then, armed with that extra intelligence, he’ll be able to pursue more such breakthroughs. However intelligent the AI you’re scared of is, Musk will get there eventually. How long will it take? A good guess might be “years” - Musk starts out as an ordinary human, and ordinary humans are known to take years to make breakthroughs. Suppose it takes Musk one year to come up with a first breakthrough that raises his IQ 1 point. How long will his second breakthrough take? It might take longer, because he has picked the lowest-hanging fruit, and all the other possible breakthroughs are much harder. Or it might take shorter, because he’s slightly smarter than he was before, and maybe some extra intelligence goes a really long way in AI research. The concept of an intelligence explosion seems to assume the second effect dominates the first. This would match the observation that human researchers, who aren’t getting any smarter over time, continue making new discoveries. That suggests the range of possible discoveries at a given intelligence level is pretty vast. Some research finds that the usual pattern in science is constant rate of discovery from exponentially increasing number of researchers, suggesting strong low-hanging fruit effects, but these seem to be overwhelmed by other considerations in AI right now. I think Eliezer’s position on this subject is shaped by assumptions like: If you have an AI as intelligent as Elon Musk today, then tomorrow you can run it on more hardware with a bit of normal human algorithmic progress, and get one twice as intelligent. So even if it would take Elon years to make a breakthrough, long before those years are up you’ll have an AI that can make breakthroughs much faster.
Inline links: thirty years away, Biological Anchors, Intelligence Explosion Microeconomics, hyperbolic growth we are currently experiencing, Some research finds
Consider cryptocurrency as an example. In 2010, cryptocurrency was small and hard to use. Its profits might have been growing quickly in relative terms, but slowly in absolute terms. But by 2020, it had become the next big thing. People were inventing new cryptocurrencies every day, technical challenges were falling one after another, lots of people were getting rich. And by 2030, presumably cryptocurrency will be where eg personal computers are now - still a big business, but most of the interesting work has been done, it’s growing at a boring normal growth rate, and improvements are rare and marginal.
Now imagine a graph of total tech industry profits over time. Without having seen this graph, I imagine relatively consistent growth. In the 1990s, the growth was mostly from selling PCs and Windows CDs, which were on the super-hot growth parts of their sigmoid. By the 2000s, those had matured and flattened out, but new paradigms (smartphones, online retail) were on the super-hot growth parts of their sigmoids. By the late 2010s, those had matured too, but newer paradigms (cryptocurrency, electric cars) were on the super-hot growth parts of their sigmoids. If we want to know what the next decade will bring, we should look for paradigms that are still in the early-slow-growth stage, maybe quantum computers. The idea is: each individual paradigm has a sigmoid that slows and peters out, but the tech industry as a whole generates new sigmoids and maintains its usual growth rate.
Seen In The Bay
November 22, 2023 · Original source
Again, when you go to the website, it’s a cryptocurrency thing:
Inline links: a cryptocurrency thing
SB 1047: Our Side Of The Story
October 10, 2024 · Original source
One thing I’ve changed some of my opinions about in the last few years is AI. I used to think that most of the claims made about its radically socially disruptive potential (both positive and negative) were hype. That was in part because they often came from the same people who made massively overstated claims about cryptocurrency. Some also resembled science fiction stories, and I think we should prioritize things we know to be problems in the here and now (climate catastrophe, nuclear weapons, pandemics) than purely speculative potential disasters. Given that Silicon Valley companies are constantly promising new revolutions, I try to always remember that there is a tendency for those with strong financial incentives to spin modest improvements, or even total frauds, as epochal breakthroughs.
Inline links: same, cryptocurrency, resembled, or even total frauds
Bureaucracy Isn't Measured In Bureaucrats
January 09, 2025 · Original source
That’s worse! A few years ago, I debated Kevin Drum about (what I considered) a particularly egregious case where the FDA dragged its feet approving a life-saving medication. Drum argued that the FDA had behaved well. In support, he found some quotes from the doctor working on the medication, who praised all the FDA bureaucrats she had interacted with, calling them extremely helpful. This bothered me for a while, until I realized that of course it was true. In the model above, each bureaucrat processes ten forms. If the bureaucrats are benevolent, this might look like talking to the doctors, walking them through the process of figuring out their ten forms, and doing the work to add their ten forms to the FDA’s growing pile of evidence supporting the application. All of this co-exists comfortably with the insight that making doctors fill out a thousand forms before they can use a medication is an impediment to medical progress. This really sunk in for me when I read an article about the fall of Afghanistan to the Taliban in 2021. Many Afghans had collaborated with the Americans, eg as translators, in exchange for a promise of US citizenship. As the Taliban advanced, they called in the promise, begging to be allowed to flee to America before they got punished as traitors. The article focused on a heroic effort by certain immigration bureaucrats, who worked around the clock with minimal sleep for the last few weeks before Kabul fell, trying to get the citizenship forms filled in and approved for as many translators as possible. It made an impression on me because nobody was opposed to the translators getting citizenship, and the bureaucrats were themselves the people in charge of approving citizenship applications, so what exactly was forcing them to go to such desperate lengths? If you ponder this question long enough, you become enlightened about the nature of the administrative state. If you don’t, you end up like Ramaswamy, who seems to think that halving the number of bureaucrats will halve the number of forms that need to be filled out. I think in his worldview, the FDA will think “Now that we have fewer bureaucrats, it would take forever to complete our current process, so let’s simplify the process.” Maybe he is working off a thesis where red tape expands to consume the resources available to it (as measured in bureaucrats). But my impression is that the amount of red tape is determined more by things like: — How likely is it that their decision will get challenged in court? And if it gets challenged in court, what amount of paperwork do they have to show the judge to prove that they made the decision on a “reasonable basis”? For example, when I type “FDA sued” into Google, the top result is a news story from a few days ago, saying that an environmental organization sued the FDA for not listening to their earlier request to ban phthalates from food. Six years ago, the environmental groups submitted a petition (the catchily-named “Food Additive Petition 6B4815”) demanding that the FDA ban 28 phthalates. Two years ago, after consulting with industry, the FDA finally banned 23 phthalates but said that the other five were okay, releasing a 58 page decision explaining its decision. Two days ago, the environmental groups sued, saying the remaining 5 phthalates are still bad. I assume the lawsuit will nitpick the details of the the 58 page decision, trying to prove that it it didn’t violate any of hundreds of federal laws saying that bureaucratic decisions must be reasonable, bureaucratic decisions must be based on science, bureaucratic decisions must respond to the petitioners’ complaints, bureaucratic decisions cannot have disparate impacts on different races, etc. I also assume that if the FDA had banned all the phthalates, they would have faced an equally serious lawsuit from Big Phthalate saying they were unfairly crippling business. Why does it take six years to respond to a petition? My guess is because they knew they would get sued and so they have some sort of million-step process that addresses every single thing you can sue over, so that they can prove to the court that their process addresses all possible complaints and they followed it to the letter. If you cut their bureaucrats in half, that doesn’t mean there will be fewer steps in the process. It means they’ll keep wanting not to get sued, the process will stay the same, and everything will take twice as long. — What has Congress mandated that they do? For example, when I Google “Congressional FDA mandate”, I get a page on HR 7248, a bill currently making its way through Congress, which says: This bill requires the Food and Drug Administration (FDA) to establish a process that supports nonclinical testing methods for drug development that do not involve the use of animals. Specifically, the FDA must establish a pathway by which entities may apply to have nonclinical testing methods approved for use in a particular context. Qualifying methods must be intended to replace or reduce animal testing and to either improve the safety and efficacy of nonclinical testing or reduce the time to develop a drug. The FDA must issue its decision within 180 days of receiving an application. The FDA must also prioritize the review of applications for drugs that are developed using an approved nonclinical testing method. The FDA must annually post a report on its website that summarizes the results of the bill's implementation, including the number of applications received, types of methods that were approved, and the estimated number of animals saved as result of these methods. So the FDA has to establish this process and post an annual report on its website. How many bureaucrats per year does this take? Maybe five? If you halve the number of people at the FDA, you still need a constant five bureaucrats to comply with this particular law. If the bill passes, the FDA comes up with a nonclinical testing process, and someone (eg the nonclinical testing industry) doesn’t think it’s good enough, they can sue the FDA for not following the law. How good a nonclinical testing process will the FDA need in order to avoid lawsuits under this bill? I assume there is a large body of administrative law answering that question, and that it will take many bureaucrats to figure this out. Finally, I admit I’m a bit confused by this. IIRC “nonclinical testing” refers to things like testing drugs on stem cells or artificial organs instead of humans. You can obviously do this for some parts of the drug testing process, but not others; the FDA has already adjusted for this and integrated it into their guidelines to some extent. I can’t tell whether this law is a righteous attempt to correct bureaucratic foot-dragging, or a powergrab by Big Nonclinical Testing demanding that the FDA privilege their products over other forms of experiment. If the latter, the FDA may try to come up with some fake pathway that satisfies the letter of the law without really giving Big Nonclinical Testing any unfair privileges, and Big Nonclinical Testing will probably sue and say it violates this bill. How many bureaucrats do you think it will take to manage that? — How much will they get yelled at if they take too long to approve drugs, vs. if they mistakenly approve a bad drug? This is the basic determinant of all FDA drug approvals. Halving the number of FDA bureaucrats wouldn’t have literally zero effect on this balance. It would mean that approving new drugs would be delayed twice as long. This would be a little more outrageous than the current delay, and might shift an outrage-minimizing FDA director slightly in the direction of cutting rules. But solve for the equilibrium: there would still be more delay than there is now. Also, I don’t think public outrage about long drug delays is linear with regard to delay, and public outrage at bad drugs is constant and large. So I think at best, firing bureaucrats would shift this balance a small amount, and only by making everything overall worse. II. One possible objection: this assumes that the average bureaucracy is like the FDA drug approval process. But the FDA drug approval process’ job is to approve things. Maybe the average bureaucracy’s job is to ban things. Then decreasing their capacity would be good. (Vivek gets to be main example here because he tweeted, but the same considerations apply to Elon: even though the government as a whole is delaying SpaceX rocket launches, individual bureaucrats might be speeding them up through the same 1000-forms logic as in the FDA case) There’s certainly a spectrum from the most approval-focused bureaucracies to the most ban-focused bureaucracies. Thinking hard about this spectrum would be a step up from “instantly” firing 50% of all bureaucrats based on social security number. So maybe a steelman of Vivek’s point would be to fire 50% of people in the ban-focused bureaucracies (and maybe double the number of people in the approval-focused ones?) I’m still skeptical that this is how it works. The past few years have seen the cryptocurrency industry demand regulation, and the government mostly fail to step up (though crypto businesses hope the Trump administration will do better). Why do crypto businesses want to be regulated more? Because the alternative is something where it’s not clear what’s legal and anyone could be sued or shut down at any time. The chief legal officer of Coinbase, from the second link: All of us are begging for sensible standards that would allow us to get back to building great products and services and spend less time and frankly, less money, arguing over legal definitions and statutes. This isn’t because anyone specifically banned crypto. It’s because there are bans on other things (like unlicensed securities, money laundering, etc) that crypto is vaguely related to, sometimes an agency regulating these things will tell a crypto company “sorry, we think you’re illegal”, and crypto wants some specific list of things it can follow that explicitly establish it as on the right side of money-laundering and security-licensing laws. Obviously industries would prefer that these be simple and easy standards (“oh, don’t worry, you don’t have to worry about money laundering if you’re a crypto company”), but they would settle for strict regulations as long as the regulations carve out some ability for them exist at all. I’ve seen the same thing play out in another area I follow, cultured meat. There are many laws about what meat you can and cannot sell, how the animals have to be treated, what the sanitation standards are, et cetera. Some of these standards make no sense when applied to cultured meat; others, cultured meat naturally fails by default (you can’t prove you’re treating the animals in a certain way because there are no animals). Others are novel philosophical questions (can you sell cultured meat without saying it’s cultured? How big does the print need to be before it counts as saying that it’s cultured? What about on restaurant menus?) Situations like these mean that there’s no clear distinction between default-yes and default-no bureaucracies. There’s no explicit ban on crypto or cultured meat. But if you cripple bureaucracies’ ability to interact with these fields, it doesn’t mean they’re fully legal, free, and happy forever. It means they’re stuck in regulatory limbo. III. So it seems like you don’t want to fire bureaucrats, you want to cut red tape. In our toy model, you want to reduce the number of forms from 1,000 to (let’s say) 100. Then the same number of bureaucrats can get drugs approved ten times faster. In our non-toy actual model of what’s going on, this would require changing incentives. Maybe you could change judicial procedures so that fewer people sue, or the FDA needs less evidence to win any given lawsuit. This sounds hard (Vivek and Elon seem more qualified to wield chainsaws than to understand legal minutiae), possibly illegal (does the administrative branch even control how judicial procedure works?), and politically unpopular (this basically looks like telling people “f@#k you, companies can put as many phthalates as they want in food, we don’t have to prove that this decision is evidence based, and you’re not allowed to challenge us.”) Or it would require Congress to repeal legislation mandating things. These Congressional mandates are probably things that Congressmen and their constituents (either real constituents or special interests) care a lot about, so good luck getting them repealed. Also, doesn’t Congress pass like one bill per year now? This would normally make me pessimistic, but Vivek and other anti-bureaucracy activists have pointed to a recent success story: Idaho. Idaho cut their regulatory code by 38% in 2019, and since then it’s only gone down. How did they decrease red tape so fast? They did it through the power of nominative determinism. In that year, they elected a governor named Brad Little. His administration is called the Little Administration. Obviously government had to get smaller. But on a purely exoteric level, what methods did they use to pull this off? This CPAC article gives the basic story: The Little administration instituted sunset provisions that review each regulation every five years and make sure it’s justifiable.
Inline links: I debated Kevin Drum, a news story from a few days ago, a 58 page decision, have seen the cryptocurrency industry demand regulation, hope, https://substackcdn.com/image/fetch/$s_!TTSy!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb73cf3f3-e501-49b6-b2a3-b84c602d5038_481x344.webp, This CPAC article