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In Mod We Trust

The Verge writes a story (an exposé?) on the Facebook-moderation industry.

It goes through the standard ways it maltreats its employees: low pay, limited bathroom breaks, awful managers – and then into some not-so-standard ones. Mods have to read (or watch) all of the worst things people post on Facebook, from conspiracy theories to snuff videos. The story talks about the psychological trauma this inflicts:

It’s an environment where workers cope by telling dark jokes about committing suicide, then smoke weed during breaks to numb their emotions…where employees, desperate for a dopamine rush amid the misery, have been found having sex inside stairwells and a room reserved for lactating mothers…

It’s a place where the conspiracy videos and memes that they see each day gradually lead them to embrace fringe views. One auditor walks the floor promoting the idea that the Earth is flat. A former employee told me he has begun to question certain aspects of the Holocaust. Another former employee, who told me he has mapped every escape route out of his house and sleeps with a gun at his side, said: “I no longer believe 9/11 was a terrorist attack.

One of the commenters on Reddit asked “Has this guy ever worked in a restaurant?” and, uh, fair. I don’t want to speculate on how much weed-smoking or sex-in-stairwell-having is due to a psychological reaction to the trauma of awful Facebook material vs. ordinary shenanigans. But it sure does seem traumatic.

Other than that, the article caught my attention for a few reasons.

First, because I recently wrote a post that was a little dismissive of moderators, and made it sound like an easy problem. I think the version I described – moderation of a single website’s text-only comment section – is an easi-er problem than moderating all of Facebook and whatever horrible snuff videos people post there. But if any Facebook moderators, or anyone else in a similar situation, read that post and thought I was selling them short, I’m sorry.

Second, because the article gives a good explanation of why Facebook moderators’ job is so much harder and more unpleasant than my job or the jobs of the mods I work with: they are asked to apply a set of rules so arcane that the article likens them to the Talmud, then have their decisions nitpicked to death – with career consequences for them if higher-ups think their judgment calls on edge cases were wrong.

While I was writing the article on the Culture War Thread, several of the CW moderators told me that the hard part of their job wasn’t keep the Thread up and running and well-moderated, it was dealing with the constant hectoring that they had made the wrong decision. If they banned someone, people would say the ban was unfair and they were tyrants and they hated freedom of speech. If they didn’t ban someone, people would say they tolerated racism and bullying and abuse, or that they were biased and would have banned the person if they’d been on the other side.

Me, I handle that by not caring. I’ve made it clear that this blog is my own fiefdom to run the way I like, and that disagreeing with the way I want a comment section to look is a perfectly reasonable decision – which should be followed by going somewhere other than my blog’s comment section. Most of my commenters have been respectful of that, I think it’s worked out very well, and my experience moderating thousands of comments per week is basically a breeze.

Obviously this gets harder when you have hundreds of different moderators, none of whom are necessarily preselected for matching Facebook HQ’s vision of “good judgment”. It also gets harder when you’re a big company that wants to keep users, and your PR department warns you against telling malcontents to “go take a hike”. It gets harder still when you host X0% of all online discussion, you’re one step away from being a utility or a branch of government or something, and you have a moral responsibility to shape the world’s conversation in a responsible way – plus various Congressmen who will punish you if you don’t. The way Facebook handles moderation seems dehumanizing, but I don’t know what the alternative is, given the pressures they’re under.

(I don’t know if this excuses sites like the New York Times saying they can’t afford moderators; I would hope they would hire one or two trusted people, then stand by their decisions no matter what.)

Third, I felt there was a weird tension in this article, and after writing that last paragraph I think I know what it is. This was a good piece of investigative reporting, digging up many genuinely outrageous things. But most of them are necessary and unavoidable responses to the last good piece of investigative reporting, and all the outrageous things it dug up. Everything The Verge is complaining about is Facebook’s attempt to defend itself against publications like The Verge.

Take, for example, the ban on phones, writing utensils, and gum wrappers:

The Verge brings this up as an example of the totalitarian and dehumanizing environment that Facebook moderators experience. But I imagine that if an employee had written down (or used their phone to take a picture of) some personal details of a Facebook user, The Verge (or some identical publication) would have run a report on how Facebook hired contractors who didn’t even take basic precautions to protect user privacy.

And what about the absolutist, infinitely-nitpicky rules that every moderator has to follow (and be double- and triple-checked to have followed) on each decision? Again, totalitarian and dehumanizing, no argument there. But if a moderator screwed up – if one of them banned a breastfeeding picture as “explicit”, and the Facebook Talmud hadn’t include twelve pages of exceptions and counterexceptions for when breasts were and weren’t allowed – I imagine reporters would be on that story in a split second. They would be mocking Facebook’s “lame excuse” that it was just one moderator acting alone and not company policy, and leading the demands for Facebook to put “procedures” in place to ensure it never happens again.

If I sound a little bitter about this, it’s because I spent four years working at a psychiatric hospital, helping create the most dehumanizing and totalitarian environment possible. It wasn’t a lot of fun. But you could trace every single rule to somebody’s lawsuit or investigative report, and to some judge or jury or news-reading public that decided it was outrageous that a psychiatric hospital hadn’t had a procedure in place to prevent whatever occurred from occurring. Some patient in Florida hit another patient with their book and it caused brain damage? Well, that’s it, nobody in a psych hospital can ever have a hardcover book again. Some patient in Delaware used a computer to send a threatening email to his wife? That’s it, psych patients can never use the Internet unless supervised one-on-one by a trained Internet supervisor with a college degree in Psychiatric Internet Supervision, which your institution cannot afford. Some patient in Oregon managed to hang herself in the fifteen minute interval between nurses opening her door at night to ask “ARE YOU REALLY ASLEEP OR ARE YOU TRYING TO COMMIT SUICIDE IN THERE?” Guess nurses will have to do that every ten minutes now. It was all awful, and it all created a climate of constant misery, and it was all 100% understandable under the circumstances.

I’m not saying nobody should ever be allowed to do investigative reporting or complain about problems. But I would support some kind of anti-irony rule, where you’re not allowed to make extra money writing another outrage-bait article about the outrages your first outrage-bait article caused.

But maybe this is unfair. “Complete safety from scandal, or humanizing work environment – pick one” doesn’t seem quite right. High-paid workers sometimes manage to do sensitive jobs while still getting a little leeway. When I worked in the psychiatric hospital, I could occasionally use my personal authority to suspend the stupidest and most dehumanizing rules. I don’t know if they just figured that medical school selected for people who could be trusted with decision-making power, or if I was high-ranking enough that everyone figured my scalp would be enough to satisfy the hordes if I got it wrong. But it sometimes went okay.

And lawyers demonstrate a different way that strict rules can coexist with a humanizing environment; they have to navigate the most complicated law code there is, but I don’t get the impression that they feel dehumanized by their job.

(but maybe if the government put as much effort into preventing innocent people from going to jail as Facebook puts into preventing negative publicity, they would be worse off.)

It seems like The Verge’s preferred solution, a move away from “the call center model” of moderation, might have whatever anti-dehumanization virtue doctors and lawyers have. Overall I’m not sure how this works, but it prevents me from being as snarky as I would be otherwise.

(except that I worry in practice this will look like “restrict the Facebook moderation industry to people with college degrees”, and we should think hard before we act like this is doing society any favors)

Last, I find this article interesting because it presents a pessimistic view of information spread. Normal people who are exposed to conspiracy theories – without any social connection to the person spouting them, or any pre-existing psychological vulnerabilities that make them seek the conspiracy theories out – end up believing them or at least suspecting. This surprises me a little. If it’s true, how come more people haven’t been infected? How come Facebook moderators don’t believe the debunking of the conspiracy theories instead? Is it just that nobody ever reports those for mod review? Or is this whole phenomenon just an artifact of every large workplace (the article says “hundreds” of people work at Cognizant) having one or two conspiracy buffs, and in this case the reporter hunted them down because it made a better story?

Just to be on the safe side, every time someone shares an SSC link, report it as violating the Facebook terms of service. We’ll make rationalists out of these people yet!

Rule Thinkers In, Not Out

Imagine a black box which, when you pressed a button, would generate a scientific hypothesis. 50% of its hypotheses are false; 50% are true hypotheses as game-changing and elegant as relativity. Even despite the error rate, it’s easy to see this box would quickly surpass space capsules, da Vinci paintings, and printer ink cartridges to become the most valuable object in the world. Scientific progress on demand, and all you have to do is test some stuff to see if it’s true? I don’t want to devalue experimentalists. They do great work. But it’s appropriate that Einstein is more famous than Eddington. If you took away Eddington, someone else would have tested relativity; the bottleneck is in Einsteins. Einstein-in-a-box at the cost of requiring two Eddingtons per insight is a heck of a deal.

What if the box had only a 10% success rate? A 1% success rate? My guess is: still most valuable object in the world. Even an 0.1% success rate seems pretty good, considering (what if we ask the box for cancer cures, then test them all on lab rats and volunteers?) You have to go pretty low before the box stops being great.

I thought about this after reading this list of geniuses with terrible ideas. Linus Pauling thought Vitamin C cured everything. Isaac Newton spent half his time working on weird Bible codes. Nikola Tesla pursued mad energy beams that couldn’t work. Lynn Margulis revolutionized cell biology by discovering mitochondrial endosymbiosis, but was also a 9-11 truther and doubted HIV caused AIDS. Et cetera. Obviously this should happen. Genius often involves coming up with an outrageous idea contrary to conventional wisdom and pursuing it obsessively despite naysayers. But nobody can have a 100% success rate. People who do this successfully sometimes should also fail at it sometimes, just because they’re the kind of person who attempts it at all. Not everyone fails. Einstein seems to have batted a perfect 1000 (unless you count his support for socialism). But failure shouldn’t surprise us.

Yet aren’t some of these examples unforgiveably bad? Like, seriously Isaac – Bible codes? Well, granted, Newton’s chemical experiments may have exposed him to a little more mercury than can be entirely healthy. But remember: gravity was considered creepy occult pseudoscience by its early enemies. It subjected the earth and the heavens to the same law, which shocked 17th century sensibilities the same way trying to link consciousness and matter would today. It postulated that objects could act on each other through invisible forces at a distance, which was equally outside the contemporaneous Overton Window. Newton’s exceptional genius, his exceptional ability to think outside all relevant boxes, and his exceptionally egregious mistakes are all the same phenomenon (plus or minus a little mercury).

Or think of it a different way. Newton stared at problems that had vexed generations before him, and noticed a subtle pattern everyone else had missed. He must have amazing hypersensitive pattern-matching going on. But people with such hypersensitivity should be most likely to see patterns where they don’t exist. Hence, Bible codes.

These geniuses are like our black boxes: generators of brilliant ideas, plus a certain failure rate. The failures can be easily discarded: physicists were able to take up Newton’s gravity without wasting time on his Bible codes. So we’re right to treat geniuses as valuable in the same way we would treat those boxes as valuable.

This goes not just for geniuses, but for anybody in the idea industry. Coming up with a genuinely original idea is a rare skill, much harder than judging ideas is. Somebody who comes up with one good original idea (plus ninety-nine really stupid cringeworthy takes) is a better use of your reading time than somebody who reliably never gets anything too wrong, but never says anything you find new or surprising. Alyssa Vance calls this positive selection – a single good call rules you in – as opposed to negative selection, where a single bad call rules you out. You should practice positive selection for geniuses and other intellectuals.

I think about this every time I hear someone say something like “I lost all respect for Steven Pinker after he said all that stupid stuff about AI”. Your problem was thinking of “respect” as a relevant predicate to apply to Steven Pinker in the first place. Is he your father? Your youth pastor? No? Then why are you worrying about whether or not to “respect” him? Steven Pinker is a black box who occasionally spits out ideas, opinions, and arguments for you to evaluate. If some of them are arguments you wouldn’t have come up with on your own, then he’s doing you a service. If 50% of them are false, then the best-case scenario is that they’re moronically, obviously false, so that you can reject them quickly and get on with your life.

I don’t want to take this too far. If someone has 99 stupid ideas and then 1 seemingly good one, obviously this should increase your probability that the seemingly good one is actually flawed in a way you haven’t noticed. If someone has 99 stupid ideas, obviously this should make you less willing to waste time reading their other ideas to see if they are really good. If you want to learn the basics of a field you know nothing about, obviously read a textbook. If you don’t trust your ability to figure out when people are wrong, obviously read someone with a track record of always representing the conventional wisdom correctly. And if you’re a social engineer trying to recommend what other people who are less intelligent than you should read, obviously steer them away from anyone who’s wrong too often. I just worry too many people wear their social engineer hat so often that they forget how to take it off, forget that “intellectual exploration” is a different job than “promote the right opinions about things” and requires different strategies.

But consider the debate over “outrage culture”. Most of this focuses on moral outrage. Some smart person says something we consider evil, and so we stop listening to her or giving her a platform. I don’t want to argue this one right now – at the very least it disincentivizes evil-seeming statements.

But I think there’s a similar phenomenon that gets less attention and is even less defensible – a sort of intellectual outrage culture. “How can you possibly read that guy when he’s said [stupid thing]?” I don’t want to get into defending every weird belief or conspiracy theory that’s ever been [stupid thing]. I just want to say it probably wasn’t as stupid as Bible codes. And yet, Newton.

Some of the people who have most inspired me have been inexcusably wrong on basic issues. But you only need one world-changing revelation to be worth reading.

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Wage Stagnation: Much More Than You Wanted To Know

[Epistemic status: I am basing this on widely-accepted published research, but I can’t guarantee I’ve understood the research right or managed to emphasize/believe the right people. Some light editing to bring in important points people raised in the comments.]

You all know this graph:

Median wages tracked productivity until 1973, then stopped. Productivity kept growing, but wages remained stagnant.

This is called “wage decoupling”. Sometimes people talk about wages decoupling from GDP, or from GDP per capita, but it all works out pretty much the same way. Increasing growth no longer produces increasing wages for ordinary workers.

Is this true? If so, why?

1. What Does The Story Look Like Across Other Countries And Time Periods?

Here’s a broader look, from 1800 on:

It no longer seems like a law of nature that productivity and wages are coupled before 1973. They seem to uncouple and recouple several times, with all the previous graphs’ starting point in 1950 being a period of unusual coupledness. Still, the modern uncoupling seems much bigger than anything that’s happened before.

What about other countries? This graph is for the UK (you can tell because it spells “labor” as “labour”)

It looks similar, except that the decoupling starts around 1990 instead of around 1973.

And here’s Europe:

This is only from 1999 on, so it’s not that helpful. But it does show that even in this short period, France remains coupled, Germany is decoupled, Spain is…doing whatever Spain is doing, and Italy is so pathetic that the problem never even comes up. Overall not sure what to think about these.

2. Could Apparent Wage Decoupling Be Because Of Health Insurance?

Along with wages, workers are compensated in benefits like health insurance. Since health insurance has skyrocketed in price, this means total worker compensation has gone up much more than wages have. This could mean workers are really getting compensated much more, even though they’re being paid the same amount of money. This view has sometimes been associated with economist Glenn Hubbard.

There are a few lines of argument that suggest it’s not true.

First, wage growth has been worst for the lowest-paid workers. But the lowest-paid workers don’t usually get insurance at all.

Second, the numbers don’t really add up. Median household income in 1973 was about $48,000 in today’s dollars. Since then, productivity has increased by between 70% and 140% (EVERYBODY DISAGREES ON THIS NUMBER), so if median income had kept pace with productivity it should be between $82,000 and $115,000. Instead, it is $59,000. So there are between $23,000 and $67,000 of missing income to explain.

The average health insurance policy costs about $7000 per individual or $20000 per family, of which employers pay $6000 and $14000 respectively. But as mentioned above, many people do not have employer-paid insurance at all, so the average per person cost is less than that. Usually only one member of a household will pay for family insurance, even if both members work; sometimes only one member of a household will buy insurance at all. So the average cost of insurance to a company per employee is well below the $6000 to $14000 number. If we round it off to $6000 per person, that only explains a quarter of the lowest estimate of the productivity gap, and less than a tenth of the highest estimate. So it’s unlikely that this is the main cause.

Third, some people have tried measuring productivity vs. total compensation, with broadly similar results:

The first graph is from the left-wing Economic Policy Institute, whose bias is towards proving that wage stagnation is real and important. The second graph is from the right-wing Heritage Foundation, whose bias is towards proving that wage stagnation is fake and irrelevant. The third graph is from the US Federal Reserve, a perfectly benevolent view-from-nowhere institution whose only concern is the good of the American people. All three agree that going from earnings to total compensation alone closes only a small part of the gap. The EPI also mentions that most of the difference between earnings and compensation opened up in the 1960s and stayed stable thereafter (why? haven’t health insurance costs gone up more since then), which further defeats this as an explanation for post-1973 trends.

We shouldn’t dismiss this as irrelevant, because many things that close only a small part of the gap may, when added together, close a large part of the gap. But this doesn’t do much on its own.

3. Could Apparent Wage Decoupling Be An Artifact Of Changing Demographics?

The demographics of the workforce have changed a lot since 1973; for example, more workers are women and minorities. If women and minorities get paid less, then as more of them enter the workforce, the lower “average” wages will go (without any individual getting paid less). If they gradually enter the workforce at the same rate that wages are increasing, this could look like wages being stagnant.

But if we disaggregate statistics by race and gender, we don’t see this. Here’s average male wage over the relevant time period:

And here’s average income disaggregated by race:

The patterns for whites and men are the same as the general pattern.

There is one unusual thing in this area. Here’s the pattern for women:

Women’s income is rising at almost the same rate as productivity! This is pretty interesting, but as far as I can tell it’s just because women’s career prospects have been improving over time because of shifting cultural attitudes, affirmative action, and the increasing dominance of female-friendly service and education-heavy jobs. I’m not sure this has any greater significance.

Did increased female participation in the workforce increase the supply of labor and so drive the price of labor down? There’s a tiny bit of evidence for that in the data, which show female workforce participation started rising much faster around 1973, with a corresponding increase in total workforce. But this spurt trailed off relatively quickly, and female participation has been declining since about 2000, and the wage stagnation trend continues. I don’t want to rule out the possibility that this was part of what made 1973 in particular such a strong inflection point, but even if it was, it’s long since been overwhelmed by other factors.

4. Could Apparent Wage Decoupling Be An Artifact Of How We Measure Inflation?

Martin Feldstein is a Harvard economics professor, former head of the National Bureau of Economic Researchers, former head of the President’s Council of Economic Advisors, etc. He believes that apparent wage stagnation is an artifact of mismeasurement.

His argument is pretty hard for me to understand, but as best I can tell, it goes like this. In order to calculate wage growth since 1973, we take the nominal difference in wages, then adjust for inflation. We calculate wage inflation with something called the Consumer Price Index, which is the average price of lots of different goods and services.

But in order to calculate productivity growth since 1973, we use a different index, the “nonfarm business sector output price index”, which is based on how much money companies get for their products.

These should be similar if consumers are buying the same products that companies are making. But there can be some differences. For example, if you’re looking at US statistics only, then some businesses may be selling to foreign markets with different inflation rates, and some consumers may be buying imported goods from countries with different inflation rates. Also (and I’m not sure I understand this right), if people own houses, CPI pretends they are paying market rent to avoid ignoring housing costs, but PPI doesn’t do this. Also, PPI is not as good at including services as CPI. So consumer and producer price indexes differ.

In fact, consumer inflation has been larger than producer inflation since 1973. So when we adjust wages for consumer inflation, they go way down, but when we adjust productivity for producer-inflation, it only goes down a little. This means that these different inflation indices make it look like productivity has risen much faster than wages, but actually they’ve risen the same amount.

As per Feldstein:

The level of productivity doubled in the U.S. nonfarm business sectorbetween 1970 and 2006. Wages, or more accurately total compensation per hour, increased at approximately the same annual rate during that period if nominal compensation is adjusted for inflation in the same way as the nominal output measure that is used to calculate productivity.

More specifically, the doubling of productivity represented a 1.9 percent annual rate of increase. Real compensation per hour rose at 1.7 percent per year when nominal compensation is deflated using the same nonfarm business sector output price index. In the period since 2000, productivity rose much more rapidly (2.9 percent ayear) and compensation per hour rose nearly as fast (2.5 percent a year).

Why is the CPI increasing so much faster than business-centered inflation indices?

The Federal Reserve blames tech. The services-centered CPI has comparatively little technology. The goods-heavy PPI (a business-centered index of inflation) has a lot of it. Tech is going down in price (how much did a digital camera cost in 1990? How about now?) so the PPI stays very low while the CPI keeps growing.

How much does this matter?

The left-leaning Economic Policy Institute says it explains 34% of wage decoupling:

The right-leaning Heritage Foundation says it explains more:

If we estimate the size of the gap as 70 pp (between total compensation CPI and productivity), switching to the top IPD measure closes 67% of the gap; switching to the PCE measure explains 37% of the gap. I’m confused because the EPI is supposedly based on Mishel and Gee, who say they have used the GDP deflator, which is the same thing as the IPD which the Heritage Foundation says they use. I think the difference is that Mishel and Gee haven’t already applied the change from wages to total compensation when they estimate percent of the gap closed? But I’m not sure.

One other group has tried to calculate this: Pessoa and Van Reenan: Decoupling Of Wage Growth And Productivity Growth? Myth And Reality. According to a summary I read, they believe 40% of wage decoupling is because of these inflation related concerns, but I have trouble finding that number in the paper itself.

And the CBO looks into the same issue. They’re not talking about it relative to productivity, but they say that technical inflation issues mean that the standard wage stagnation story is false, and wages have really grown 41% from 1979 – 2013. Since productivity increased somewhere between 70% and 100% during that time, this seems similar to some of the other estimates – inflation technicalities explain between 1/3 and 2/3 of the problem.

Everyone I read seems to agree this issue exists and is interesting, but I’m not sure I entirely understand the implications. Some people say that this completely debunks the idea of wage decoupling and it’s actually only half or a third what the raw numbers say. Other people seem to agree that a big part of wage decoupling is these inflation technicalities, but suggest that although they have important technical implications, if you want to know how the average worker on the street is doing the CPI is still the way to go.

Superstar economist Larry Summers (with Harvard student Anna Stansbury) comes the closest to having a real opinion on this here:

When investigating consumers’ experienced rise in living standards as in Bivens and Mishel (2015), a consumer price deflator is appropriate; however, as Feldstein (2008) argues, when investigating factor income shares a producer price deflator is more appropriate because it reflects the real cost to firms of employing workers.

I am a little confused by this. On the one hand, I do want to investigate consumers’ experienced rise (or lack thereof) in living standards. This is the whole point – the possibility that workers’ living standards haven’t risen since 1973. But most people nowadays work in services. If you deflate their wages with an index used mostly for goods, are you just being a moron and ensuring you will be inaccurate?

Summers and Stansbury continue:

Lawrence (2016) analyzes this divergence more recently, comparing average compensation to net productivity, which is a more accurate reflection of the increase in income available for distribution to factors of production. Since depreciation has accelerated over recent decades, using gross productivity creates a misleadingly large divergence between productivity and compensation. Lawrence finds that net labor productivity and average compensation grew together until 2001, when they started to diverge i.e. the labor share started to fall. Many other studies also find a decline in the US labor share of income since about 2000, though the timing and magnitude is disputed (see for example Grossman et al 2017, Karabarbounis and Neiman 2014, Lawrence 2015, Elsby Hobijn and Sahin 2013, Rognlie 2015, Pessoa and Van Reenen 2013).

If I intepret this correctly, it looks like it’s saying that the real decoupling happened in 2000, not in 1973. I see a lot of papers saying the same thing, and I don’t understand where they’re diverging from the people who say it happened in 1973. Maybe they’re using Feldstein’s method of calculating inflation? I think this must be true – if you look at the Heritage Foundation graph above, “total compensation measured with Feldstein’s method” and productivity are exactly equal to their 1973 level in 2000, but diverge shortly thereafter so that today compensation has only grown 77% compared to productivity’s 100%.

Nevertheless, Summers and Stansbury go on to give basically exactly the same “Why have wages been basically stagnant since 1973? Why are they decoupled from productivity?” narrative as everyone else, so it sure doesn’t look like they think any of this has disproven that. It looks like maybe they think Feldstein is right in some way that doesn’t matter? But I don’t know enough economics to figure out what that way would be. And it looks like Feldstein believes his rightness matters very much, and other economists like Scott Sumner seem to agree. And I cannot find anyone, anywhere, who wants to come out and say explicitly that Feldstein’s argument is wrong and we should definitely measure wage stagnation the way everyone does it.

My conclusions from this section, such as they are, go:

1. Arcane technical points about inflation might explain between 33% and 66% of the apparent wage stagnation/decoupling.
2. “Explain” may not mean the same as “explain away”, and it’s not completely clear how these points relate to anything we care about

5. Could Wage Decoupling Be Explained By Increasing Labor-Vs-Capital Inequality?

Economists divide inequality into two types. Wage inequality is about how much different wage-earners (or salary-earners, here the terms are used interchangeably) make relative to each other. Labor-vs-capital inequality is about how much wage earners earn vs. how much capitalists get in profits. These capitalists are usually investors/shareholders, but can also be small business owners (or, sometimes, large business owners). Since tycoons like Jeff Bezos and Mark Zuckerberg get most of their compensation from stocks, they count as “capitalists” even if they are paid some token salary for the work they do running their companies.

Here is the labor-vs-capital split for the US over the relevant time period; note the very truncated vertical axis:

This type of inequality was about the same in the early 1970s as in the early 2000s, and has no clear inflection point around 1973, so it probably didn’t start this trend off. But it did start seriously decreasing around 2000, the same time people who use the more careful inflation methodology say wages and productivity really decoupled. And obviously labor getting less money in general is the sort of thing that makes wages go down.

Why is labor-vs-capital inequality increasing? For the long story, read Piketty (my review, highlights, comments). But the short story includes:

Today’s wage inequality is tomorrow’s labor-vs-capital inequality. If some people get paid more than others, they can invest, their savings will compound, and they will have more capital. As wage inequality increases (see below), labor-vs-capital inequality does too.

The tech industry is more capital-intensive than labor-intensive. For example, Apple has 100,000 employees and makes $250 billion/year, compared to WalMart with 2 million employees and $500 billion/year – in other words, Apple makes $2.5 million per employee compared to Wal-Mart’s $250,000. Apple probably pays its employees more than Wal-Mart does, but not ten times more. So more of Apple’s revenue goes to capital compared to Wal-Mart’s. As tech becomes more important than traditional industries, capital’s share of the pie goes up. This is probably a big reason why capital has been doing so well since 2000 or so.

There’s an iconoclastic strain of thought that says most of the change in labor-vs-capital is just housing. Houses count as capital, so as housing costs rise, so does capital’s share of the economy. Read Matt Ronglie’s work (paper, summary, Voxsplainer) for more. Since houses are neither involved in corporate productivity nor in wages, I’m not sure how this affects wage-productivity decoupling if true.

Whatever the cause, the papers I read suggest that increasing labor-vs-capital inequality explains maybe 10-20% of of decoupling, almost all concentrated in the 2000 – present period.

6. Could Wage Decoupling Be Explained By Increasing Wage Inequality?

The other part of the two-pronged inequality picture above. This one seems more important.

One way economists look at this is in the difference between the median wage and the average wage:

Add in the other things we talked about – the health insurance, the inflation technicalities, the declining share of labor – and the “””average””” worker is doing almost as well as they were in 1973. In fact, this is almost tautologically true. If the entire pie is growing by X amount, and labor’s relative share of the pie is staying the same, then labor should be getting the same absolute amount, and (ignoring changes in the number of laborers) the average laborer should get the same amount.

So the decline in median wage is a mean vs. median issue. A few high-earners are taking a lot of the pie, keeping the mean constant but lowering the median. How high?

Remember, productivity has grown by 70-100% through this period. So even though the top 5% have seen their incomes grow by 69%, they’re still not growing as fast as productivity. The top 1% have grown a bit faster than productivity, although still not that much. The top 0.1% are doing really well.

This is generally considered the most important cause of wage stagnation and wage decoupling, other than among the iconoclasts who think the inflation issues are more important. Above, I referred to a few papers that tried to break down the importance of each cause. EPI thinks wage inequality explains 47% of the problem. Pessoa and Van Reenen think it explains more like 20% according to Mishel’s summary (my eyeballing of the paper suggests more like 33%, but I am pretty uncertain about this).

7. Is Wage Inequality Increasing Because Of Technology?

Here’s one story about why wage inequality is increasing.

In the old days, people worked in factories. A slightly smarter factory worker might be able to run the machinery a little better, or do something else useful, but in the end everyone is working on the same machines.

In the modern economy, factory workers are being replaced by robots. This creates very high demand for skilled roboticists, who get paid lots of money to run the robots in the most efficient way, and very low demand for factory workers, who need to be retrained to be fast food workers or something.

Or, in the general case, technology separates people into the winners (the people who are good with technology and who can use it to do jobs that would have taken dozens or hundreds of people before) and the losers (people who are not good with technology, and so their jobs have been automated away).

From an OECD paper:

Common explanations for increased wage inequality such as skill-biased technological change and globalisation cannot plausibly account for the disproportionate wage growth at the very top of the wage distribution. Skill-biased technological change and globalisation may both raise the relative demand for high-skilled workers, but this should be reflected in broadly rising relative wages of high-skilled workers rather than narrowly rising relative wagesof top-earners. Brynjolfsson and McAfee (2014) argue that digitalisation leads to “winner-takes-most” dynamics, with innovators reaping outsize rewards as digital innovations are replicable at very low cost and have a global scale. Recent studies provide evidence consistent with “winner-take-most” dynamics, in the sense that productivity of firms at the technology frontier has diverged from the remaining firms and that market shares of frontier firms have increased (Andrews et al., 2016). This type of technological change may allow firms at the technology frontier to raise the wages of its key employees to “superstar” levels.

It…sounds like they’re saying that technological change can’t be the answer, then giving arguments for why the answer is technological change.

I think this is just the authors’ poor writing skills, and that the real argument is less confusing. The Huffington Post is surprisingly helpful, describing it as:

What this means is that skilled professionals are not just winning out over working class stiffs, but the richest of the top 0.01 percent are winning out over the professional class as a whole.

That Larry Summers paper mentioned before becomes relevant here again. It argues that wages and productivity are not decoupled – which I know is a pretty explosive thing to say three thousand words in to an essay on wage decoupling, but let’s hear him out.

He argues that apparent decoupling between productivity and wages could result either from literal decoupling – that is, none of the gains of increasing productivity going to workers – or from unrelated trends – for example, increasing productivity giving workers an extra $1000 at the same time as something else causes workers to lose $1000. If a company made $1000 extra and the boss pocketed all of it and didn’t give workers any, that would be literal decoupling. If a company made $1000 extra, it gave workers $1000 extra, but globalization means there’s less demand for workers and so salaries would otherwise have dropped by $1000, so now they stay the same, that’s an unrelated trend.

Summers and Stansbury investigate this by seeing if wages increase more during the short periods between 1973 and today when productivity is unusually high, and if they stagnate more (or decline) during the short periods when it is unusually low. They find this is mostly true:

We find substantial evidence of linkage between productivity and compensation: Over 1973–2016, one percentage point higher productivity growth has been associated with 0.7 to 1 percentage points higher median and average compensation growth and with 0.4 to 0.7 percentage points higher production/nonsupervisory compensation growth.

S&S are very careful in this paper and have already adjusted for health insurance issues and inflation calculation issues. They find that once you adjust for this, productivity and wages are between 40% and 100% coupled, depending on what measure you use. (I don’t exactly understand the difference between the two measures they give; surely taking the median worker is already letting you consider inequality and you shouldn’t get so much of a difference by focusing on nonsupervisory workers?) As mentioned before, they finds the coupling is much less since 2000. They also find similar results in most other countries: whether or not those countries show apparent decoupling, they remain pretty coupled in terms of actual productivity growth:wage growth correlation.

They argue that if technology/automation were causing rising wage inequality or rising labor-capital inequality, then median wage should decouple from productivity fastest during the periods of highest productivity growth. After all, productivity growth represents the advance of labor-saving technology. So periods of high productivity growth are those where the most new technology and automation are being deployed, so if this is what’s driving wages down, wages should decrease fastest during this time.

They test this a couple of different ways, and find that it is false before the year 2000, but somewhat true afterwards, mostly through labor-capital inequality. They don’t really find that technology drives wage inequality at all.

I understand why technology would mean decoupling happens fastest during the highest productivity growth. But I’m not sure I understand what they mean when they say there is no decoupling and productivity growth translates into wage growth? Shouldn’t this disprove all posited causes of decoupling so far, including policy-based wage inequality? I’m not sure. S&S don’t seem to think so, but I’m not sure why. Overall I find this paper confusing, but I assume its authors know what they’re doing so I will accept its conclusions as presented.

So it sounds like, although technology probably explains some top-10% people doing moderately better than the rest, it doesn’t explain the stratospheric increase in the share of the 1%, which is where most of the story lies. I would be content to dismiss this as unimportant, except that…

…all the world’s leading economists disagree.

Maybe when they say “income inequality”, they’re talking about a more intuitive view of income inequality where some programmers make $150K and some factory workers make $30K and this is unequal and that’s important – even though it is not related to the larger problem of why everybody except the top 1% is making much less than predicted. I’m not sure.

I feel bad about dismissing so many things as “probably responsible for a few percent of the problem”. It seems like a cop-out when it’s hard to decide whether something is really important or not. But my best guess is still that this is probably responsible for a few percent of the problem.

8. Is Wage Inequality Increasing Because Of Policy Changes?

Hello! We are every think tank in the world! We hear you are wondering whether wage inequality is increasing because of policy changes! Can we offer you nine billion articles proving that it definitely is, and you should definitely be very angry? Please can we offer you articles? Pleeeeeeeeaaase?!

Presentations of this theory usually involve some combination of policies – decreasing union power, low minimum wages, greater acceptance of very high CEO salary – that concentrate all gains in the highest-paid workers, usually CEOs and executives.

I have trouble making the numbers add up. Vox has a cute thought experiment here where they imagine the CEO of Wal-Mart redistributing his entire salary to all Wal-Mart workers equally, possibly after having been visited by three spirits. Each Wal-Mart employee would make an extra $10. If the spirits visited all top Wal-Mart executives instead of just the CEO, the average employee would get $30. This is obviously not going to single-handedly bring them to the middle-class.

Vox uses such a limited definition of “top executive” that only five people are included. What about Wal-Mart’s 1%?

The Wal-Mart 1% will include 20,000 people. To reach the 1% in the US, you need to make $400,000 per year; I would expect Wal-Mart’s 1% to be lower, since Wal-Mart is famously a bad place to work that doesn’t pay people much. Let’s say $200,000. That means the Wal-Mart 1% makes a total of $4 billion. If their salary were distributed to all 2 million employees, those employees would make an extra $2,000 per year; maybe a 10% pay raise. And of course even in a perfectly functional economy, we couldn’t pay Wal-Mart management literally $0, so the real number would be less than this.

Maybe the problem is that Wal-Mart is just an unusually employee-heavy company. What about Apple? Their CEO makes $12 million per year. If that were distributed to their 132,000 employees, they would each make an extra $90.

How many total high-paid executives does Apple have? It looks like Apple hires up to 130 MBAs from top business schools per year; if we imagine they last 10 years each, they might have 1000 such people, making them a “top 1%”. If these people get paid $500,000 each, they could earn 500 million total. That’s enough to redistribute $4,000 to all Apple employees, which still isn’t satisfying given the extent of the problem.

Some commenters bring up the possibility that I’m missing stocks and stock options, which make up most of the compensation of top executives. I’m not sure whether this gets classified as income (in which case it could help explain income inequality) or as capital (in which case it would get filed under labor-vs-capital inequality). I’m also not sure whether Apple giving Tim Cook lots of stocks takes money out of the salary budget that could have gone to workers instead. For now let’s just accept that the difference between mean and median income shows that something has to be happening to drive up the top 1% or so of salaries.

What policies are most likely to have caused this concentration of salaries at the top?

Many people point to a decline in unions. This decline does not really line up with the relevant time period – it started in the early 1960s, when productivity and wages were still closely coupled. But it could be a possible contributor. Economics Policy Institute cites some work saying it may explain up to 10% of decoupling even for non-union members, since the deals struck by unions set norms that spread throughout their industries. A group of respected economists including David Card looks into the issue and finds similar results, saying that the decline of unions may explain about 14% or more of increasing wage inequality (remember that wage inequality is only about 40% of decoupling, so this would mean it only explains about 5% of decoupling). The conservative Heritage Foundation has many bad things to say about unions but grudgingly admits they may raise salaries by up to 10% among members (they don’t address non-members). Based on all this, it seems plausible that deunionization may explain about 5-10% of decoupling.

Another relevant policy that could be shaping this issue is the minimum wage. EPI notes that although the minimum wage never goes down in nominal terms, if it doesn’t go up then it’s effectively going down in real terms and relative to productivity. This certainly sounds like the sort of thing that could increase wage inequality.

But let’s look at that graph by percentiles again:

Wage stagnation is barely any better for the 90th percentile worker than it is for the people at the bottom. And the 90th percentile worker isn’t making minimum wage. This may be another one that adds a percentage point here and there, but it doesn’t seem too important.

I can’t find anything about it on EPI, but Thomas Piketty thinks that tax changes were an important driver of wage inequality. I’ll quote my previous review of his book:

He thinks that executive salaries have increased because – basically – corporate governance isn’t good enough to prevent executives from giving themselves very high salaries. Why didn’t executives give themselves such high salaries before? Because before the 1980s the US had a top tax rate of 80% to 90%. As theory predicts, people become less interested in making money when the government’s going to take 90% of it, so executives didn’t bother pulling the strings it would take to have stratospheric salaries. Once the top tax rate was decreased, it became worth executives’ time to figure out how to game the system, so they did. This is less common outside the Anglosphere because other countries have different forms of corporate governance and taxation that discourage this kind of thing.

Piketty does some work to show that increasing wage inequality in different countries is correlated with those countries’ corporate governance and taxation policies. I don’t know if anyone has checked how that affects wage decoupling.

9. Conclusions

1. Contrary to the usual story, wages have not stagnated since 1973. Measurement issues, including wages vs. benefits and different inflation measurements, have made things look worse than they are. Depending on how you prefer to think about inflation, median wages have probably risen about 40% – 50% since 1973, about half as much as productivity.

2. This leaves about a 50% real decoupling between median wages and productivity, which is still enough to be serious and scary. The most important factor here is probably increasing wage inequality. Increasing labor-capital inequality is a less important but still significant factor, and it has become more significant since 2000.

3. Increasing wage inequality probably has a lot to do with issues of taxation and corporate governance, and to some degree also with issues surrounding unionization. It probably has less to do with increasing technology and automation.

4. If you were to put a gun to my head and force me to break down the importance of various factors in contributing to wage decoupling, it would look something like (warning: very low confidence!) this:

– Inflation miscalculations: 35%
– Wages vs. total compensation: 10%
– Increasing labor vs. capital inequality: 15%
—- (Because of automation: 7.5%)
—- (Because of policy: 7.5%)
– Increasing wage inequality: 40%
—- (Because of deunionization: 10%)
—- (Because of policies permitting high executive salaries: 20%)
—- (Because of globalization and automation: 10%)

This surprises me, because the dramatic shift in 1973 made me expect to see a single cause (and multifactorial trends should be rare in general, maybe, I think). It looks like there are two reasons why 1973 seems more important than it is.

First, most graphs trying to present this data begin around 1950. If they had begun much earlier than 1950, they would have showed several historical decouplings and recouplings that make a decoupling in any one year seem less interesting.

Second, 1973 was the year of the 1973 Oil Crisis, the fall of Bretton Woods, and the end of the gold standard, causing a general discombobulation to the economy that lasted a couple of years. By the time the economy recombobulated itself again, a lot of trends had the chance to get going or switch direction. For example, here’s inflation:

5. Inflation issues and wage inequality were probably most important in the first half of the period being studied. Labor-vs-capital inequality was probably most important in the second half.

6. Continuing issues that confuse me:
– How much should we care about the difference between inflation indices? If we agree that using CPI to calculate this is dumb, should we cut our mental picture of the size of the problem in half?
– Why is there such a difference between the Heritage Foundation’s estimate of how much of the gap inconsistent deflators explain (67%) and the EPI’s (34%)? Who is right?
– Does the Summers & Stansbury paper argue against policy-based wage inequality as a cause of median wage stagnation, at least until 2000?
– Are there enough high-paid executives at companies that, if their money were redistributed to all employees, their compensation would have increased significantly more in step with productivity? If so, where are they hiding? If not, what does “increasing wage inequality explains X% of decoupling” mean?
– What caused past episodes of wage decoupling in the US? What ended them?
– How do we square the apparent multifactorial nature of wage decoupling with its sudden beginning in 1973 and with the general argument against multifactorial trends?

OT122: Openne Thread

This is the bi-weekly visible open thread (there are also hidden open threads twice a week you can reach through the Open Thread tab on the top of the page). Post about anything you want, but please try to avoid hot-button political and social topics. You can also talk at the SSC subreddit or the SSC Discord server – and also check out the SSC Podcast. Also:

1. Thanks to Santiago (“niohiki”), we have a new system where comments in open threads will now be displayed newest-first, and comments everywhere else will be oldest-first. I look forward to making all new SSC readers from this point on hopelessly confused.

2. I did some housecleaning and de-adminned everyone who didn’t seem like they were doing administrative tasks on the blog. If you got de-adminned and think you shouldn’t have been, email me.

3. Continued thanks to everyone who reports inappropriate comments using the “Report” function. For one reason or another I feel bad about banning most of the people who get reported, so I know reporting probably feels futile, but I do get a ban or two in per week, I do usually get most bad actors eventually, and I think your help is important.

Posted in Uncategorized | Tagged | 741 Comments

RIP Culture War Thread

[This post is having major technical issues. Some comments may not be appearing. If you can’t comment, please say so on the subreddit.]

I. I Come To Praise Caesar, Not To Bury Him

Several years ago, an SSC reader made an r/slatestarcodex subreddit for discussion of blog posts here and related topics. As per the usual process, the topics that generated the strongest emotions – Trump, gender, race, the communist menace, the fascist menace, etc – started taking over. The moderators (and I had been added as an honorary mod at the time) decreed that all discussion of these topics should be corralled into one thread so that nobody had to read them unless they really wanted to. This achieved its desired goal: most of the subreddit went back to being about cognitive science and medicine and other less-polarizing stuff.

Unexpectedly, the restriction to one thread kick-started the culture war discussions rather than toning them down. The thread started getting thousands of comments per week, some from people who had never even heard of this blog and had just wandered in from elsewhere on Reddit. It became its own community, with different norms and different members from the rest of the board.

I expected this to go badly. It kind of did; no politics discussion area ever goes really well. There were some of the usual flame wars, point-scoring, and fanatics. I will be honest and admit I rarely read the thread myself.

But in between all of that, there was some really impressive analysis, some good discussion, and even a few changed minds. Some testimonials from participants:

For all its awfulness there really is something special about the CW thread. There are conversations that have happened there that cannot be replicated elsewhere. Someone mentioned its accidental brilliance and I think that’s right—it catches a wonderful conversational quality I’ve never seen on the Internet, and I’ve been on the Internet since the 90s – werttrew

I feel that, while practically ever criticism of the CW thread I have ever read is true, it is still the best and most civil culture war-related forum for conversation I have seen. And I find the best-of roundup an absolute must-read every week – yrrosimyarin

The Culture War Roundup threads were blessedly neutral ground for people to test their premises and moral intuitions against a gauntlet of (sometimes-forced!) kindness and charity. There was no guarantee that your opinion would carry the day, but if you put in the effort, you could be assured a fair reading and cracking debate. Very little was solved, but I’m not sure that was really the point. The CWRs were a place to broaden your understanding of a given topic by an iterative process of “Yes, but…” and for a place that boasted more than 15,000 participants, shockingly little drama ensued. That was the /r/slatestarcodex CWRs at their best, and that’s the way we hope they will be remembered by the majority of people who participated in them. – rwkasten

We really need to turn these QCs into a book or wiki or library of some kind. So much good thought, observation, introspection, etc. exists in just this one thread alone–to say nothing of the other QC posts in past CW threads. It would be nice to have a separate place, organized by subject matter, to just read these insightful posts – TheEgosLastStand

I think the CW thread is obviously a huge lump of positive utility for a large number of people, because otherwise they wouldn’t spend so much time on it. I’ve learned a lot in the thread, both about the ideas and beliefs of my outgroups, and by better honing my own beliefs and ideas in a high-pressure selective environment. I’ve shared out the results of what I’ve learned to all of my ingroup across Facebook and Twitter and in person, and I honestly think it’s helped foster better and more sophisticated thought about the culture war in a clique of several dozen SJ-aligned young people in the OC area, just from my tangential involvement as a vector – darwin2500

On one hand, as other commenters in this thread have said, I recognize it does have a lot of full-time opinionated idiots squabbling, and is inarguably filled with irrationality, bad takes, contrarianism, and Boo Outgroup posturing. I agree with many of [the criticisms] of overtly racist and stupid posts in there. Yet it also has a special, weird, fascinating quality which has led to some very insightful discussions which I have not encountered anywhere else on the Internet (and I have used the Internet 8+ hours a day almost my whole life). – c_o_r_b_a

There is no place on the internet that can have discussions about culture war topics with even an approximation of the quality of this place. Shutting this thread down [would] not mean moving the discussion elsewhere, for a lot of people it means removing the ability to discuss these things entirely – Zornau

I feel that the CW thread, for all its flaws, occupies a certain niche that can’t easily be replicated elsewhere. I also feel that its flaws need to compared not to a Platonic ideal but to typical online political discourse, which often ends up as pure echo chambers or flame wars. – honeypuppy

It’s one of the only political forums I can read online without reaching for the nearest sharp stick to poke my eyes out. It has a sort of free-flowing conversational feel that’s really appealing. There are some thoughtful people and discussions there that I hope can continue and be preserved. – TracingWoodgrains

Thanks to a great founding population, some very hard-working moderators, and a unique rule-set that emphasized trying to understand and convince rather than yell and shame, the Culture War thread became something special. People from all sorts of political positions, from the most boring centrists to the craziest extremists, had some weirdly good discussions and came up with some really deep insights into what the heck is going on in some of society’s most explosive controversies. For three years, if you wanted to read about the socialist case for vs. against open borders, the weird politics of Washington state carbon taxes, the medieval Rule of St. Benedict compared and contrasted with modern codes of conduct, the growing world of evangelical Christian feminism, Banfield’s neoconservative perspective on class, Baudrillard’s Marxist perspective on consumerism, or just how #MeToo has led to sex parties with consent enforcers dressed as unicorns, the r/SSC culture war thread was the place to be. I also benefitted from its weekly roundup of interesting social science studies and arch-moderator baj2235’s semi-regular Quality Contributions Catch-Up Thread.

The Culture War Thread aimed to be a place where people with all sorts of different views could come together to talk to and learn from one another. I think this mostly succeeded. On the last SSC survey, I asked who participated in the thread, and used that to get a pretty good idea of its userbase. Here are some statistics:

Superficially, this is remarkably well-balanced. 51% of Culture War Thread participants identified as left-of-center on the survey, compared to 49% of people who identified as right-of-center.

There was less parity in party identification, with a bit under two Democrats to every Republican. But this, too, reflects the national picture. The latest Gallup poll found that 34% of Americans identified as Democrat, compared to only 25% Republican. Since presidential elections are usually very close, it looks like left-of-center people are more willing to openly identify with the Democratic Party than right-of-center people are with the Republicans; the CW demographics show a similar picture.

Looked at in more detail, this correspondence with the general population is not quite as perfect as it seems:

The pie chart on the left shows people broken down by a finer-grained measure of political affiliation. We see very few people identified as straight-out conservatives. Right-of-center people were more likely to be either libertarians or neoreactionaries (a technocratic, anti-democracy movement that the survey instructed people to endorse if they wanted to be more like “for example Singapore: prosperity, technology, and stability more important than democratic process”). Although straight-out “liberal” had a better showing than “conservative”, the ranks of the Left still ended up divided among left-libertarians and social democrats (which the survey instructed people to endorse if they wanted to be more like “for example Scandinavian countries: heavily-regulated market economy, cradle-to-grave social safety net, socially permissive multiculturalism”). Overall, the CW thread is a little more to the fringes on the both sides, especially the parts of the fringes popular among its young, mostly nonreligious, kind of libertarian, mostly technophile demographic.

It also doesn’t like Trump. Although he has a 40% approval rating among the general population, only about 14% of CWers were even somewhat favorable toward him. RCP suggests that anti-Trumpers outnumber pro-Trumpers in the general population by 1.4x; among CW thread participants, that number increases to almost 5x! This fits the story above where most right-of-center participants are libertarians or skeptical of democracy/populism as opposed to standard conservatives. Still, I occasionally saw Trump supporters giving their pitch in the Culture War thread, or being willing to answer questions about why they thought what they did.

During the last few years of Culture War thread, a consensus grew up that it was heavily right-wing. This isn’t what these data show, and on the few times I looked at it myself, it wasn’t what I saw either. After being challenged to back this up, I analyzed ten randomly chosen comments on the thread; four seemed neutral, three left/liberal, and three conservative. When someone else objected that it was a more specific “blatant” anti-transgender bias, I counted up all the mentions of transgender on three weeks worth of Culture War threads: of five references, two were celebrating how exciting/historic a transgender person recently winning an election was, a third was neutrally referring to the election, a fourth was a trans person talking about their experiences, and a fifth was someone else neutrally mentioning that they were transgender. This sort of thing happened enough times that I stopped being interested in arguing the point.

I acknowledge many people’s lived experience that the thread felt right-wing; my working theory is that most of the people I talk to about this kind of thing are Bay Area liberals for whom the thread was their first/only exposure to a space with any substantial right-wing presence at all, which must have made it feel scarily conservative. This may also be a question of who sorted by top, who sorted by new, and who sorted by controversial. In any case, you can just read the last few threads and form your own opinion.

Whatever its biases and whatever its flaws, the Culture War thread was a place where very strange people from all parts of the political spectrum were able to engage with each other, treat each other respectfully, and sometimes even change their minds about some things. I am less interested in re-opening the debate about exactly which side of the spectrum the average person was on compared to celebrating the rarity of having a place where people of very different views came together to speak at all.

II. We Need To Have A National Conversation About Why We Can No Longer Have A National Conversation

This post is called “RIP Culture War Thread”, so you may have already guessed things went south. What happened? The short version is: a bunch of people harassed and threatened me for my role in hosting it, I had a nervous breakdown, and I asked the moderators to get rid of it.

I’ll get to the long version eventually, but first I want to stress that this isn’t just my story. It’s the story of everyone who’s tried to host a space for political discussion on the Internet. Take the New York Times, in particular their article Why No Comments? It’s A Matter Of Resources. Translated from corporate-speak, it basically says that unmoderated comment sections had too many “trolls”, so they decided to switch to moderated comment sections only, but they don’t have enough resources to moderate any controversial articles, so commenting on controversial articles is banned.

And it’s not just the New York Times. In the past five years, CNN, NPR, The Atlantic, Vice, Bloomberg, Motherboard, and almost every other major news source has closed their comments – usually accompanied by weird corporate-speak about how “because we really value conversations, we are closing our comment section forever effective immediately”. People have written articles like The Comments Apocalypse, A Brief History Of The End Of The Comments, and Is The Era Of Reader Comments On News Websites Fading? This raises a lot of questions.

Like: I was able to find half a dozen great people to do a great job moderating the Culture War Thread 100% for free without even trying. How come some of the richest and most important news sources in the world can’t find or afford a moderator?

Or: can’t they just hide the comments behind a content warning saying “These comments are unmoderated, read at your own risk, click to expand”?

This confused me until I had my own experience with the Culture War thread.

The fact is, it’s very easy to moderate comment sections. It’s very easy to remove spam, bots, racial slurs, low-effort trolls, and abuse. I do it single-handedly on this blog’s 2000+ weekly comments. r/slatestarcodex’s volunteer team of six moderators did it every day on the CW Thread, and you can scroll through week after week of multiple-thousand-post culture war thread and see how thorough a job they did.

But once you remove all those things, you’re left with people honestly and civilly arguing for their opinions. And that’s the scariest thing of all.

Some people think society should tolerate pedophilia, are obsessed with this, and can rattle off a laundry list of studies that they say justify their opinion. Some people think police officers are enforcers of oppression and this makes them valid targets for violence. Some people think immigrants are destroying the cultural cohesion necessary for a free and prosperous country. Some people think transwomen are a tool of the patriarchy trying to appropriate female spaces. Some people think Charles Murray and The Bell Curve were right about everything. Some people think Islam represents an existential threat to the West. Some people think women are biologically less likely to be good at or interested in technology. Some people think men are biologically more violent and dangerous to children. Some people just really worry a lot about the Freemasons.

Each of these views has adherents who are, no offense, smarter than you are. Each of these views has, at times, won over entire cultures so completely that disagreeing with them then was as unthinkable as agreeing with them is today. I disagree with most of them but don’t want to be too harsh on any of them. Reasoning correctly about these things is excruciatingly hard, trusting consensus opinion would have led you horrifyingly wrong throughout most of the past, and other options, if they exist, are obscure and full of pitfalls. I tend to go with philosophers from Voltaire to Mill to Popper who say the only solution is to let everybody have their say and then try to figure it out in the marketplace of ideas.

But none of those luminaries had to deal with online comment sections.

The thing about an online comment section is that the guy who really likes pedophilia is going to start posting on every thread about sexual minorities “I’m glad those sexual minorities have their rights! Now it’s time to start arguing for pedophile rights!” followed by a ten thousand word manifesto. This person won’t use any racial slurs, won’t be a bot, and can probably reach the same standards of politeness and reasonable-soundingness as anyone else. Any fair moderation policy won’t provide the moderator with any excuse to delete him. But it will be very embarrassing for to New York Times to have anybody who visits their website see pro-pedophilia manifestos a bunch of the time.

“So they should deal with it! That’s the bargain they made when deciding to host the national conversation!”

No, you don’t understand. It’s not just the predictable and natural reputational consequences of having some embarrassing material in a branded space. It’s enemy action.

Every Twitter influencer who wants to profit off of outrage culture is going to be posting 24-7 about how the New York Times endorses pedophilia. Breitbart or some other group that doesn’t like the Times for some reason will publish article after article on New York Times‘ secret pro-pedophile agenda. Allowing any aspect of your brand to come anywhere near something unpopular and taboo is like a giant Christmas present for people who hate you, people who hate everybody and will take whatever targets of opportunity present themselves, and a thousand self-appointed moral crusaders and protectors of the public virtue. It doesn’t matter if taboo material makes up 1% of your comment section; it will inevitably make up 100% of what people hear about your comment section and then of what people think is in your comment section. Finally, it will make up 100% of what people associate with you and your brand. The Chinese Robber Fallacy is a harsh master; all you need is a tiny number of cringeworthy comments, and your political enemies, power-hungry opportunists, and 4channers just in it for the lulz can convince everyone that your entire brand is about being pro-pedophile, catering to the pedophilia demographic, and providing a platform for pedophile supporters. And if you ban the pedophiles, they’ll do the same thing for the next-most-offensive opinion in your comments, and then the next-most-offensive, until you’ve censored everything except “Our benevolent leadership really is doing a great job today, aren’t they?” and the comment section becomes a mockery of its original goal.

So let me tell you about my experience hosting the Culture War thread.

(“hosting” isn’t entirely accurate. The Culture War thread was hosted on the r/slatestarcodex subreddit, which I did not create and do not own. I am an honorary moderator of that subreddit, but aside from the very occasional quick action against spam nobody else caught, I do not actively play a part in its moderation. Still, people correctly determined that I was probably the weakest link, and chose me as the target.)

People settled on a narrative. The Culture War thread was made up entirely of homophobic transphobic alt-right neo-Nazis. I freely admit there were people who were against homosexuality in the thread (according to my survey, 13%), people who opposed using trans people’s preferred pronouns (according to my survey, 9%), people who identified as alt-right (7%), and a single person who identified as a neo-Nazi (who as far as I know never posted about it). Less outrageous ideas were proportionally more popular: people who were mostly feminists but thought there were differences between male and female brains, people who supported the fight against racial discrimination but thought could be genetic differences between races. All these people definitely existed, some of them in droves. All of them had the right to speak; sometimes I sympathized with some of their points. If this had been the complaint, I would have admitted to it right away. If the New York Times can’t avoid attracting these people to its comment section, no way r/ssc is going to manage it.

But instead it was always that the the thread was “dominated by” or “only had” or “was an echo chamber for” homophobic transphobic alt-right neo-Nazis, which always grew into the claim that the subreddit was dominated by homophobic etc neo-Nazis, which always grew into the claim that the SSC community was dominated by homophobic etc neo-Nazis, which always grew into the claim that I personally was a homophobic etc neo-Nazi of them all. I am a pro-gay Jew who has dated trans people and votes pretty much straight Democrat. I lost distant family in the Holocaust. You can imagine how much fun this was for me.

People would message me on Twitter to shame me for my Nazism. People who linked my blog on social media would get replies from people “educating” them that they were supporting Nazism, or asking them to justify why they thought it was appropriate to share Nazi sites. I wrote a silly blog post about mathematics and corn-eating. It reached the front page of a math subreddit and got a lot of upvotes. Somebody found it, asked if people knew that the blog post about corn was from a pro-alt-right neo-Nazi site that tolerated racists and sexists. There was a big argument in the comments about whether it should ever be acceptable to link to or read my website. Any further conversation about math and corn was abandoned. This kept happening, to the point where I wouldn’t even read Reddit discussions of my work anymore. The New York Times already has a reputation, but for some people this was all they’d heard about me.

Some people started an article about me on a left-wing wiki that listed the most offensive things I have ever said, and the most offensive things that have ever been said by anyone on the SSC subreddit and CW thread over its three years of activity, all presented in the most damning context possible; it started steadily rising in the Google search results for my name. A subreddit devoted to insulting and mocking me personally and Culture War thread participants in general got started; it now has over 2,000 readers. People started threatening to use my bad reputation to discredit the communities I was in and the causes I cared about most.

Some people found my real name and started posting it on Twitter. Some people made entire accounts devoted to doxxing me in Twitter discussions whenever an opportunity came up. A few people just messaged me letting me know they knew my real name and reminding me that they could do this if they wanted to.

Some people started messaging my real-life friends, telling them to stop being friends with me because I supported racists and sexists and Nazis. Somebody posted a monetary reward for information that could be used to discredit me.

One person called the clinic where I worked, pretended to be a patient, and tried to get me fired.

(not all of this was because of the Culture War thread. Some of this was because of my own bad opinions and my own bad judgment. But the Culture War thread kept coming up. As I became more careful in my own writings, the Culture War thread loomed larger and larger in the threats and complaints. And when the Culture War thread got closed down, the subreddit about insulting me had a “declaring victory” post, which I interpret as confirmation that this was one of the main things going on.)

I don’t want to claim martyrdom. None of these things actually hurt me in real life. My blog continues to be popular, my friends stuck by me, and my clinic didn’t let me go. I am not going to be able to set up a classy new FiredForTruth.com website like James Damore did. What actually happened was much more prosaic: I had a nervous breakdown.

It wasn’t even that bad a nervous breakdown. I was able to keep working through it. I just sort of broke off all human contact for a couple of weeks and stayed in my room freaking out instead. This is similar enough to my usual behavior that nobody noticed, which suited me fine. And I learned a lot (for example, did you know that sceletium has a combination of SSRI-like compounds and PDE2 inhibitors that make it really good at treating nervous breakdowns? True!). And it wasn’t like the attacks were objectively intolerable or that everybody would have had a nervous breakdown in my shoes: I’m a naturally obsessive person, I take criticism especially badly, and I had some other things going on too.

Around the same time, friends of mine who were smarter and more careful than I was started suggesting that it would be better for me, and for them as people who had to deal with the social consequences of being my friend, if I were to shut down the thread. And at the same time, I got some more reasons to think that this blog could contribute to really important things – AI, effective charity, meta-science – in ways that would be harder to do from the center of a harassment campaign.

So around October, I talked to some subreddit mods and asked them what they thought about spinning off the Culture Wars thread to its own forum, one not affiliated with the Slate Star Codex brand or the r/slatestarcodex subreddit. The first few I approached were positive; some had similar experiences to mine; one admitted that even though he personally was not involved with the CW thread and only dealt with other parts of the subreddit, he taught at a college and felt like his job would not be safe so long as the subreddit and CW thread were affiliated. Apparently the problem was bigger than just me, and other people had been dealing with it in silence.

Other moderators, the ones most closely associated with the CW thread itself, were strongly opposed. They emphasized some of the same things I emphasized above: that the thread was a really unique place for great conversation about all sorts of important topics, that the majority of commenters and posts were totally inoffensive, and that one shouldn’t give in to terrorists. I respect all these points, but I respected them less from the middle of a nervous breakdown, and eventually the vote among the top nine mods and other stakeholders was 5-4 in favor of getting rid of it. It took three months to iron out all the details, but a few weeks ago everyone finally figured things out and the CW thread closed forever.

At this point this stops being my story. A group of pro-CW-thread mods led by ZorbaTHut, cjet79, and baj2235 set up r/TheMotte, a new subreddit for continuing the Culture War Thread tradition. After a week, the top post already has 4,243 comments, so it looks like the move went pretty well. Despite fears – which I partly shared – that the transition would not be good for the Thread, early signs suggest it has survived intact. I’m hopeful this can be a win-win situation, freeing me from a pretty serious burden while the Thread itself expands and flourishes under the leadership of a more anonymous group of people.

III. The Thread Is Dead, Long Live The Thread

I debated for a long time whether or not to write this post. The arguments against are obvious: never let the trolls know they’re getting to you. Once they know they’re getting to you, that you’re susceptible to pressure, obviously they redouble their efforts. I stuck to this for a long time. I’m still sort of sticking to it, in that I’m avoiding specifics and super avoiding links (which I realize has made my story harder to prove true, sorry). I’ll try to resume the policy fully after this, but I thought one post on the subject was worth the extra misery for a few reasons.

First, a lot of people were (rightfully! understandably!) very angry about the loss of the Culture War thread from r/ssc, and told the moderators that, as the kids say these days, “a decent respect to the opinions of mankind requires that they should declare the causes which impel them to the separation”. I promised to do this, so now I am.

Second, I wanted there to be at least one of these “here’s why we’re removing your ability to comment” articles that was honest, not made of corporate-speak, and less patronizing than “we’re removing the comment section because we value your speech so much and want to promote great conversations”. Hopefully this will be the skeleton key that helps you understand what all those other articles would have said if they weren’t run through fifty layers of PR teams. I would like to give people another perspective on events like Tumblr banning female-presenting nipples or Patreon dropping right-wing YouTubers or Twitter constantly introducing new algorithms that misfire and ban random groups of people. These companies aren’t inherently censorious. They’re just afraid. Everyone is afraid.

Third, I would like to offer one final, admittedly from-a-position-of-weakness, f**k you at everyone who contributed to this. I think you’re bad people, and you make me really sad. Not in a joking performative Internet sadness way. In an actual, I-think-you-made-my-life-and-the-world-worse way. I realize I’m mostly talking to the sort of people who delight in others’ distress and so this won’t register. But I’m also a little upset at some of my (otherwise generally excellent) friends in the rationalist community who were quick to jump on the “Oh, yeah, the SSC subreddit is full of gross people and I wish they couldn’t speak” bandwagon (to be clear, I don’t mean the friends who offered me good advice about separating from the CW thread for the sake of my own well-being, I mean people who actively contributed to worsening the whole community’s reputation based on a few bad actors). I understand you were probably honest in your opinion, but I think there was a lot of room to have thought through those opinions more carefully.

Fourth, I want anybody else trying to host “the national conversation” to have a clear idea of the risks. If you plan to be anything less than maximally censorious, consider keeping your identity anonymous, and think about potential weak links in your chain (ie hosts, advertisers, payment processors, etc). I’m not saying you necessarily need to go full darknet arms merchant. Just keep in mind that lots of people will try to stop you, and they’ve had a really high success rate so far.

Fifth, if someone speaks up against the increasing climate of fear and harassment or the decline of free speech, they get hit with an omnidirectional salvo of “You continue to speak just fine, and people are listening to you, so obviously the climate of fear can’t be too bad, people can’t be harassing you too much, and you’re probably just lying to get attention.” But if someone is too afraid to speak up, or nobody listens to them, then the issue never gets brought up, and mission accomplished for the people creating the climate of fear. The only way to escape the double-bind is for someone to speak up and admit “Hey, I personally am a giant coward who is silencing himself out of fear in this specific way right now, but only after this message”. This is not a particularly noble role, but it’s one I’m well-positioned to play here, and I think it’s worth the awkwardness to provide at least one example that doesn’t fit the double-bind pattern.

Sixth, I want to apologize to anybody who’s had to deal with me the past – oh, let’s say several years. One of the really bad parts of this debacle has been that it’s made me a much worse person. When I started writing this blog, I think I was a pretty nice person who was willing to listen to and try to hammer out my differences with anyone. As a result of some of what I’ve described, I think I’ve become afraid, bitter, paranoid, and quick to assume that anyone who disagrees with me (along a dimension that too closely resembles some of the really bad people I’ve had to deal with) is a bad actor who needs to be discredited and destroyed. I don’t know how to fix this. I can only apologize for it, admit you’re not imagining it, and ask people to do as I say (especially as I said a few years ago when I was a better person) and not as I do. I do think this is a great learning experience in terms of psychology and will write a post on it eventually; I just wish I didn’t have to learn it from the inside.

Seventh, I want to reassure people who would otherwise treat this story as an unmitigated disaster that there are some bright spots, like that I didn’t suffer any objective damage despite a lot of people trying really hard, and that the Culture War thread lives on bigger and brighter than ever before

Eighth, as a final middle-finger at the people who killed the Culture War thread, I’d like to advertise r/TheMotte, its new home, in the hopes that this whole debacle Streisand-Effects it to the stratosphere.

I want to stress that I will continue to leave the SSC comment section open as long as is compatible with the political climate and my own health; I ask tolerance if there are otherwise-unfair actions I have to take to make this possible. I also want to stress that I’m not going to stop writing about controversial topics completely – but I do want to have some control over when and where I have to deal with this, and want the privilege of being hung for my own opinions rather than for those of other people I am tangentially associated with.

Please do not send me expressions of sympathy or try to cast me as a martyr; the first make me feel worse for reasons that are hard to explain; the second wouldn’t really fit the facts and isn’t the look I want to present. Thanks to everyone who helped make the CW thread and this blog what it was/is, and good luck to Zorba and the rest of the Motte moderation team.

My Plagiarism

I was going back over yesterday’s post, and something sounded familiar about this paragraph:

A very careless plagiarist takes someone else’s work and copies it verbatim: “The mitochondria is the powerhouse of the cell”. A more careful plagiarist takes the work and changes a few words around: “The mitochondria is the energy dynamo of the cell”. A plagiarist who is more careful still changes the entire sentence structure: “In cells, mitochondria are the energy dynamos”. The most careful plagiarists change everything except the underlying concept, which they grasp at so deep a level that they can put it in whatever words they want – at which point it is no longer called plagiarism.

After rereading it a few times, it hit me. A few days ago, I’d come across this quote from Miss Manners:

There are three possible parts to a date, of which at least two must be offered: entertainment, food, and affection. It is customary to begin a series of dates with a great deal of entertainment, a moderate amount of food, and the merest suggestion of affection. As the amount of affection increases, the entertainment can be reduced proportionately. When the affection IS the entertainment, we no longer call it dating.

I laughed at it, I thought it was great, and I stored it in my head as the sort of thing I should quote at some point in order to sound witty.

And although I wasn’t consciously thinking about it at the time, I’m sure the last sentence of my paragraph comes from the last sentence of Miss Manners’. It would be easy to dismiss it as a coincidence, it probably seems like a coincidence to you, I can’t explain how I know that the one comes from the other, but when I replay in my mind the process that made me write that, it’s obvious that it did.

This sort of thing happens to me all the time. It’s just that it’s especially ironic when it happens in a paragraph about plagiarism, in a post about how writers blend everything they’ve read into a slurry and spew it out, somewhat transformed. I wrote that “the difference is how finely you blend”, and this is a not-so-rare example of my blending so coarsely that identifiable chunks of my sources have ended up in my own text.

Sometimes I identify turns of phrase that I’ve picked up from other people. Other times it’s more subtle; a style, a way of looking at the world, a method of reasoning. All of these are just different levels of pattern. My writing style is a slurry of the writing styles of everyone I’ve read and enjoyed, with some pieces chunkier than others. I think my worldview and my reasoning style are too, it’s just less obvious.

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GPT-2 As Step Toward General Intelligence

A machine learning researcher writes me in response to yesterday’s post, saying:

I still think GPT-2 is a brute-force statistical pattern matcher which blends up the internet and gives you back a slightly unappetizing slurry of it when asked.

I resisted the urge to answer “Yeah, well, your mom is a brute-force statistical pattern matcher which blends up the internet and gives you back a slightly unappetizing slurry of it when asked.”

But I think it would have been true.

A very careless plagiarist takes someone else’s work and copies it verbatim: “The mitochondria is the powerhouse of the cell”. A more careful plagiarist takes the work and changes a few words around: “The mitochondria is the energy dynamo of the cell”. A plagiarist who is more careful still changes the entire sentence structure: “In cells, mitochondria are the energy dynamos”. The most careful plagiarists change everything except the underlying concept, which they grasp at so deep a level that they can put it in whatever words they want – at which point it is no longer called plagiarism.

GPT-2 writes fantasy battle scenes by reading a million human-written fantasy battle scenes, distilling them down to the concept of a fantasy battle scene, and then building it back up from there. I think this is how your mom (and everyone else) does it too. GPT-2 is worse at this, because it’s not as powerful as your mom’s brain. But I don’t think it’s doing a different thing. We’re all blending experience into a slurry; the difference is how finely we blend it.

“But don’t humans also have genuinely original ideas?” Come on, read a fantasy book. It’s either a Tolkien clone, or it’s A Song Of Ice And Fire. Tolkien was a professor of Anglo-Saxon language and culture; no secret where he got his inspiration. A Song Of Ice And Fire is just War Of The Roses with dragons. Lannister and Stark are just Lancaster and York, the map of Westeros is just Britain (minus Scotland) with an upside down-Ireland stuck to the bottom of it – wake up, sheeple! Dullards blend Tolkien into a slurry and shape it into another Tolkien-clone. Tolkien-level artistic geniuses blend human experience, history, and the artistic corpus into a slurry and form it into an entirely new genre. Again, the difference is how finely you blend and what spices you add to the slurry.

“But don’t scientists have geniunely original ideas?” Scientists are just finding patterns in reality nobody has ever seen before. You say “just a pattern-matcher”, I say “fine, but you need to recognize patterns in order to copy them, so it’s necessarily a pattern-recognizer too”. And Einstein was just a very good pattern-recognizer.

“But don’t humans have some kind of deep understanding that pattern-recognition AIs don’t?”

Here’s a fun question: the human brain is undoubtedly the most powerful computer in the known universe. In order to do something as simple as scratch an itch it needs to solve exquisitely complex calculus problems that would give the average supercomputer a run for its money. So how come I have trouble multiplying two-digit numbers in my head?

The brain isn’t directly doing math, it’s creating a model that includes math and somehow doing the math in the model. This is hilariously perverse. It’s like every time you want to add 3 + 3, you have to create an entire imaginary world with its own continents and ecology, evolve sentient life, shepherd the sentient life into a civilization with its own mathematical tradition, and get one of its scholars to add 3 + 3 for you. That we do this at all is ridiculous. But I think GPT-2 can do it too.

Janelle Shane asks GPT-2 its ten favorite animals:

Prompt: My 10 favorite animals are: 1.

My ten favorite animals are:
1. Zebras with a white scar on the back
2. Insiduous spiders and octopus
3. Frog with large leaves, hopefully black
4. Cockatiel with scales
5. Razorbill with wings hanging about 4 inches from one’s face and a heart tattoo on a frog
3. Cockatric interlocking tetrabods that can be blind, cut, and eaten raw:
4. Black and white desert crocodiles living in sunlight
5. Zebra and many other pea bugs

Forget the particular animals for now (don’t worry, the razorbill with wings hanging about 4 inches from one’s face will be back in your nightmares). Notice the way it numbers its list: 1, 2, 3, 4, 5, 3, 4, 5. Last week the two-year-old child who lives next to me counted 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 8, 9, 7, 8, 9, 7, 8, 9 (his mother warns this is an inexact transcription). GPT-2 is instantiated on giant supercomputers; it’s a safe bet they could calculate the square root of infinity in a picosecond. But it counts more or less the same way as a two-year old. GPT-2 isn’t doing math. It’s doing the ridiculous “create a universe from first principles and let it do the math” thing that humans do in their heads. The fact that it counts so badly suggests it’s counting human-style, which makes it amazing that it can count at all.

I find something similar in this post from Tumblr user antinegationism, playing with the lobotomized public-release version of the system:

The American Association for Suicide Prevention and Life-Threatening Behavior (AAPSLB), a professional organization with an overarching goal of promoting suicide prevention, released a recent video encouraging the public to think before they act, on the public’s part. “When we say we’re the only reason to commit suicide, we’re saying they’re wrong. It’s the right thing to do,” said AAPSLB president Steven A. Anderson.

The American Association For Suicide Prevention And Life-Threatening Behavior is not a real organization; the AI made it up as the kind of organization that it thought would feature in a story like this. And AAPSLB is not quite the right way to acronymize the organization’s name. But it’s clearly an attempt at doing so. It’s very close. And nobody taught it how to do that! It’s not just that nobody programmed it in. It’s that nobody thought “Today I shall program an AI to learn how to acronymize on its own in an unsupervised way”. GPT-2 was just programmed to predict text from other text, nothing else. It’s second-level not programmed in. It just happened!

And, uh, it seems to have figured out how to translate things into French. This part is from the official paper:

We test whether GPT-2 has begun to learn how to translate from one language to another. In order to help it infer that this is the desired task, we condition the language model on a context of example pairs of the format ENGLISH SENTENCE = FRENCH SENTENCE and then after a final prompt of ENGLISH SENTENCE = we sample from the model with greedy decoding and use the first generated sentence as the translation. On the WMT-14 English-French test set, GPT-2 gets 5 BLEU, which is slightly worse than a word-by-word substitution with a bilingual lexicon inferred in previous work on unsupervised word translation (Conneau et al., 2017b). On the WMT-14 French-English test set, GPT-2 is able to leverage its very strong English language model to perform significantly better, achieving 11.5 BLEU. This outperforms several unsupervised machine translation baselines from (Artetxe et al., 2017) and (Lampleet al., 2017) but is still much worse than the 33.5 BLEU of the current best unsupervised machine translation approach(Artetxe et al., 2019). Performance on this task was surprising to us, since we deliberately removed non-English webpages from WebText as a filtering step.

In other words: GPT-2 is very bad at translating French into English. But the researchers were surprised to see it could do this at all, since they didn’t design it as translation software, didn’t ask it to learn translation, and didn’t show it any material in French. It seems to have picked up this ability from noticing a few naturally-occurring examples of French in English text:

And here’s One Weird Trick to make GPT-2 summarize articles:

We test GPT-2’s ability to perform summarization on the CNN and Daily Mail dataset (Nallapati et al., 2016). To induce summarization behavior we add the text TL;DR: after the article and generate 100 tokens with Top-k random sampling (Fan et al., 2018) with k=2 which reduces repetition and encourages more abstractive summaries than greedy decoding. We use the first 3 generated sentences in these 100 tokens as the summary. While qualitatively the generations resemble summaries, as shown in Table 14, they often focus on recent content from the article or confuse specific details such as how many cars were involved in a crash or whether a logo was on a hat or shirt. On the commonly reported ROUGE 1,2,L metrics the generated summaries only beginto approach the performance of classic neural baselines and just barely outperforms selecting 3 random sentences from the article. GPT-2’s performance drops by 6.4 points onthe aggregate metric when the task hint is removed which demonstrates the ability to invoke task specific behavior in a language model with natural language.

Again, GPT-2 isn’t good at summarizing. It’s just surprising it can do it at all; it was never designed to learn this skill. All it was designed to do was predict what words came after other words. But there were some naturally-occurring examples of summaries in the training set, so in order to predict what words would come after the words tl;dr, it had to learn what a summary was and how to write one.

If you’re me, the obvious next question is “can it do poetry”. I am very interested to know if the real thing can manage this, but for now I will have to satisfy myself with the lobotomized public-release version. Given Alexander Pope’s Essay On Criticism as a prompt, it says:

O man, who pains the tongue magnified by tymolle,
Though all the fair melodies let no door go as dark as good art.
Yet it would have been, unless the mastermind is so full
Of his own want that this literary country might grow.
Perfect student, thou hast trodden out.
Happy students ruin the purity of what you write ‘Tis art,
For unordered use of decade,’ but art only failing:
A surprise, for new veins which build behind;

It understands there should be line breaks, it understands the approximate correct length of a line of iambic pentameter, it understands how to talk like an overeducated 18th-century dandy – but it doesn’t appreciate rhyme or meter. In retrospect this isn’t surprising; GPT has no idea words sound like anything; it would be shocked to learn anyone uses language as anything other than text strings.

But human poets haven’t appreciated rhyme or meter either for at least a century, so GPT-2 should be able to compose some wicked modern poetry. Friends-of-the-blog Chelsea V. and Qiaochu Y. got it to write a few more stanzas of Allen Ginsberg’s Moloch (human-curated to remove fluff and improve flow):

Moloch whose brief mind is the barrel of gleaming stars! Moloch whose soul is choices of fragrance! Moloch whose framing is glitz and half-cheerios!

Moloch whose daily life is private; his daily strength hoarse! Moloch whose soft desires become culture burning smoke in a palace of eyes! Moloch whose breeding in the caves sweeps wild and rippling flower-like shapes!

Moloch whose pleading is the breath of youth! Moloch whose prudence is the urine of generations! Moloch whose nerves are not privileged; movies abound in quiet caves; people are dressed like ministers! Moloch whose hope receives some help too. Moloch whose eyes are pitiful outstretched, but not blinded. Moloch whose lungs are burning with fire, but not a cautious electrical girl!

Moloch whose sanity is just filled with atoms, gluing beings in physics and complicated libraries. Moloch whose eyes are all innocent animals! Moloch whose sweat and efforts make pure the ocean with burning annihilation! Moloch whose fields dissolve to lightning-ravaged storage houses! Moloch whose eyes sit haunting up Scottish springs!

Moloch whose arms and men are Judah condemns and listens, having understood whom he was and what he man’s weaknesses oppose with iron. We will feast to it like Lord Kippa; we shall play with it like our Pokemon.

Moloch flickering under the pounds and sustaining more fights! Sports and yoga, equally greedy for wealth as for lives! Moloch who had to split his limbs and society into fifty-two atoms! Moloch who cannot survey duty! Moloch for I do not believe in humanity! Moloch for I don’t believe in god! Moloch who, fought Sins and made Sin out of Sin!

Moloch who left me in the glacier! Moloch who helped me to judge my behavior! Moloch who lured me out of the eternal anguish. Moloch who makes all my faults my undoings! Moloch whose deep conversation filled my soul with love and consolation! Moloch who curls up my brains and crumps my heads and screams my scream! Moloch who prepares oil in my hands and burns me alive! Moloch who cries out, “Why do men scream so loud?” Moloch who unleashes his poison in all rivers at high tide! Moloch whose ruins are filled with its cubits! Moloch who fiddles at insidious clockwork!

Aside from this being obviously one of the best things ever, what’s the point?

The point is, GPT-2 has faculties. It has specific skills, that require a certain precision of thought, like counting from one to five, or mapping a word to its acronym, or writing poetry. These faculties are untaught; they arise naturally from its pattern-recognition and word-prediction ability. All these deep understanding things that humans have, like Reason and so on, those are faculties. AIs don’t have them yet. But they can learn.

From the paper:

Prompt: Who was the author of The Art Of War?
Sun Tzu

Prompt: State the process that divides one nucleus into two genetically identical nuclei?
Mitosis

Prompt: Do you have to have a gun permit to shoot at a range?
No

Nobody told the model to learn Chinese history, cell biology, or gun laws either. It learned them in the process of trying to predict what word would come after what other word. It needed to know Sun Tzu wrote The Art Of War in order to predict when the words “Sun Tzu” would come up (often in contexts like “The Art of War, written by famous Chinese general…). For the same reason, it had to learn what an author was, what a gun permit was, etc.

Imagine you prompted the model with “What is one plus one?” I actually don’t know how it would do on this problem. I’m guessing it would answer “two”, just because the question probably appeared a bunch of times in its training data.

Now imagine you prompted it with “What is four thousand and eight plus two thousand and six?” or some other long problem that probably didn’t occur exactly in its training data. I predict it would fail, because this model can’t count past five without making mistakes. But I imagine a very similar program, given a thousand times more training data and computational resources, would succeed. It would notice a pattern in sentences including the word “plus” or otherwise describing sums of numbers, it would figure out that pattern, and it would end up able to do simple math. I don’t think this is too much of a stretch given that GPT-2 learned to count to five and acronymize words and so on.

Now imagine you prompted it with “P != NP”. This time give it near-infinite training data and computational resources. Its near-infinite training data will contain many proofs; using its near-infinite computational resources it will come up with a model that is very very good at predicting the next step in any proof you give it. The simplest model that can do this is probably the one isomorphic to the structure of mathematics itself (or to the brains of the sorts of mathematicians who write proofs, which themselves contain a model of mathematics). Then you give it the prompt P != NP and it uses the model to “predict” what the next step in the proof will be until it has a proof, the same way GPT-2 predicts the next word in the LotR fanfiction until it has a fanfiction.

The version that proves P != NP will still just be a brute-force pattern-matcher blending things it’s seen and regurgitating them in a different pattern. The proof won’t reveal that the AI’s not doing that; it will just reveal that once you reach a rarefied enough level of that kind of thing, that’s what intelligence is. I’m not trying to play up GPT-2 or say it’s doing anything more than anyone else thinks it’s doing. I’m trying to play down humans. We’re not that great. GPT-2-like processes are closer to the sorts of things we do than we would like to think.

Why do I believe this? Because GPT-2 works more or less the same way the brain does, the brain learns all sorts of things without anybody telling it to, so we shouldn’t be surprised to see GPT-2 has learned all sorts of things without anybody telling it to – and we should expect a version with more brain-level resources to produce more brain-level results. Prediction is the golden key that opens any lock; whatever it can learn from the data being thrown at it, it will learn, limited by its computational resources and its sense-organs and so on but not by any inherent task-specificity.

Wittgenstein writes: “The limits of my language mean the limits of my world”. Maybe he was trying to make a restrictive statement, one about how we can’t know the world beyond our language. But the reverse is also true; language and the world have the same boundaries. Learn language really well, and you understand reality. God is One, and His Name is One, and God is One with His Name. “Become good at predicting language” sounds like the same sort of innocent task as “become good at Go” or “become good at Starcraft”. But learning about language involves learning about reality, and prediction is the golden key. “Become good at predicting language” turns out to be a blank check, a license to learn every pattern it can.

I don’t want to claim this is anywhere near a true AGI. “This could do cool stuff with infinite training data and limitless computing resources” is true of a lot of things, most of which are useless and irrelevant; scaling that down to realistic levels is most of the problem. A true AGI will have to be much better at learning from limited datasets with limited computational resources. It will have to investigate the physical world with the same skill that GPT investigates text; text is naturally machine-readable, the physical world is naturally obscure. It will have to have a model of what it means to act in the world, to do something besides sitting around predicting all day. And it will have to just be better than GPT, on the level of raw power and computational ability. It will probably need other things besides. Maybe it will take a hundred or a thousand years to manage all this, I don’t know.

But this should be a wake-up call to people who think AGI is impossible, or totally unrelated to current work, or couldn’t happen by accident. In the context of performing their expected tasks, AIs already pick up other abilities that nobody expected them to learn. Sometimes they will pick up abilities they seemingly shouldn’t have been able to learn, like English-to-French translation without any French texts in their training corpus. Sometimes they will use those abilities unexpectedly in the course of doing other things. All that stuff you hear about “AIs can only do one thing” or “AIs only learn what you program them to learn” or “Nobody has any idea what an AGI would even look like” are now obsolete.

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Do Neural Nets Dream Of Electric Hobbits?

Last week OpenAI announced its latest breakthrough. GPT-2 is a language model that can write essays to a prompt, answer questions, and summarize longer works. For example (bold prompt is human-written, all other text is the machine-generated response):

Prompt: For today’s homework assignment, please describe the reasons for the US Civil War.

It is easy to identify why the Civil War happened, because so many people and so many books and so much television and films tell us that it was the cause, that it has something to do with race or economics or religion. There is much agreement that it was essentially a war of slavery on behalf of capitalism, about a century of slavery. But that’s not what most people think of when they think of the Civil War. Many people think of the war as about states rights. You’re not wrong about that, but it’s kind of misleading to say that the Civil War was a conflict between states’ rights and federalism. So let’s try again. What’s the idea that has inspired so many people to go onto the battlefields of Gettysburg and Antietam and Gettysberg and Petersburg and Fredericksburg? The American idea of the republic–a notion of limited government–is a great part of the history.

In 1791, Thomas Jefferson said “Our Constitution was made only for a moral and religious people. It is wholly inadequate to the government of any other.” The “founding fathers” were basically religious men who were not very concerned with states’ rights. The Civil War occurred in reaction to the idea that the federal government was taking far too much power away from the states, that they weren’t sovereign. Jefferson himself, as you well know, is an author of the Declaration of Independence who had some really crazy ideas about, well, you know, secession for a variety of reasons. The War Between the States (later the Great Rebellion) was a radical re-imagining of the American idea and not surprisingly, Jefferson’s grandson, who took after both his father and his grandfather and wrote about it, was named Thomas, after the president. “The federal and state governments are both contrived for the benefit of the rich and large property,” This view of the Founding Fathers has been promoted by some of today’s most prominent politicians, including some who hold prominent national offices and have a direct stake in a Republican Party that has been hijacked by the “federalist” wing of the party. So why is it that so many people have an easy-to-spot way of understanding the Civil War that has everything to do with the South and nothing to do with the South? The Confederate flag has been a symbol of racism for a long time, but when the Civil War started and the Civil War ended, many people suddenly began to see it differently. There was a change of heart, which I think is what led people to take the question of why the Civil War happened quite seriously…

Although OpenAI calls this a “language model”, modeling language necessarily involves modeling the world. Even if the AI was only supposed to learn things like “texts that talk about the Civil War use the word ‘Confederate’ a lot”, that has flowered into a rudimentary understanding of how the Civil War worked. Its training corpus (8 million web pages) was large enough that in the course of learning language it learned the specific idiom and structure of all sorts of different genres and subtopics. For example:

Prompt: Legolas and Gimli advanced on the orcs, raising their weapons with a harrowing war cry.

The orcs’ response was a deafening onslaught of claws, claws, and claws; even Elrond was forced to retreat. “You are in good hands, dwarf,” said Gimli, who had been among the first to charge at the orcs; it took only two words before their opponents were reduced to a blood-soaked quagmire, and the dwarf took his first kill of the night. The battle lasted for hours until two of the largest Orcs attempted to overwhelm Aragorn. When they finally stopped, they lay defeated and lifeless for miles and miles.

“I take nothing,” said Aragorn. “But I give my word, at my peril and mine, that I will never forget this day of horror. None of us will forget. Ever!”

“I’ll never forget it!” cried Gimli, who had been in the thick of the battle but hadn’t taken part in it. One of the wounded orcs he had carried off, he was the only one of the survivors who remained uninjured. “We’ll keep the memory of that day of evil, and the war with it, alive as long as we live, my friends!”

“Then we’ll keep it alive as long as we live,” added Legolas. “And we won’t forget the first great battle of the night, even if we may have forgotten the final defeat.”

“I agree,” Gandalf said, “but we will all remember it as the last battle in Middle-earth, and the first great battle of the new day.”

Aragorn drew his sword, and the Battle of Fangorn was won. As they marched out through the thicket the morning mist cleared, and the day turned to dusk.

The Two Rings were returned to Rivendell. Frodo and Sam woke up alone in their room, and Frodo found a note on his pillow. He opened it and read: May the Power of the Ring be with you always, and may its light never fade. I am not sure if it matters which of the two rings we accept this day but, as you asked me, I have chosen mine. I am sorry to leave you, Frodo, but know that we are very close to the end, and that you are with us forever. May this letter find you safely in Rivendell; and if it does not, then I will accept the ring in your stead. If by any chance you find or give this letter to the enemy, may they learn the strength of the ring and may the Two Rings never be broken!

The big picture is beautiful. The AI understands the reference to Legolas and Gimli as placing this in the setting of Middle-Earth. It infers that the story should include characters like Aragorn and Gandalf, and that the Ring should show up. It maintains basic narrative coherence: the heroes attack, the orcs defend, a battle happens, the characters discuss the battle. It even gets the genre conventions right: the forces of Good overcome Evil, then deliver inspiring speeches about glory and bravery.

But the details are a mess. Characters are brought in suddenly, then dropped for no reason. Important details (“this is the last battle in Middle-Earth”) are introduced without explanation, then ignored. The context switches midway between the battle and a seemingly unrelated discussion of hobbits in Rivendell. It cannot seem to decide whether there are one or two Rings.

This isn’t a fanfiction, this is a dream sequence. The only way it could be more obvious is if Aragorn was somehow also my high-school math teacher. And the dreaminess isn’t a coincidence. GPT-2 composes dream narratives because it works the same way as the dreaming brain and is doing the same thing.

A review: the brain is a prediction machine. It takes in sense-data, then predicts what sense-data it’s going to get next. In the process, it forms a detailed model of the world. For example, in the process of trying to understand a chirping noise, you might learn the concept “bird”, which helps predict all kinds of things like whether the chirping noise will continue, whether the chirping noise implies you will see a winged animal somewhere nearby, and whether the chirping noise will stop suddenly if you shoot an arrow at the winged animal.

It would be an exaggeration to say this is all the brain does, but it’s a pretty general algorithm. Take language processing. “I’m going to the restaurant to get a bite to ___”. “Luke, I am your ___”. You probably auto-filled both of those before your conscious thought had even realized there was a question. More complicated examples, like “I have a little ___” will bring up a probability distribution giving high weights to solutions like “sister” or “problem”, and lower weights to other words that don’t fit the pattern. This system usually works very well. That’s why when you possible asymptote dinosaur phrenoscope lability, you get a sudden case of mental vertigo as your prediction algorithms stutter, fail, and call on higher level functions to perform complicated context-shifting operations until the universe makes sense again.

GPT-2 works the same way. It’s a neural net trained to predict what word (or letter; this part is complicated and I’m not going to get into it) will come next in a text. After reading eight million web pages, it’s very good at this. It’s not just some Markov chain which takes the last word (or the last ten words) and uses them to make a guess about the next one. It looks at the entire essay, forms an idea of what it’s talking about, forms an idea of where the discussion is going, and then makes its guess – just like we do. Look up section 3.3 of the paper to see it doing this most directly.

As discussed here previously, any predictive network doubles as a generative network. So if you want to write an essay, you just give it a prompt of a couple of words, then ask it to predict the most likely/ most appropriate next word, and the word after that, until it’s predicted an entire essay. Again, this is how you do it too. It’s how schizophrenics can generate convincing hallucinatory voices; it’s also how you can speak or write at all.

So GPT is doing something like what the human brain does. But why dreams in particular?

Hobson, Hong, and Friston describe dreaming as:

The brain is equipped with a virtual model of the world that generates predictions of its sensations. This model is continually updated and entrained by sensory prediction errors in wakefulness to ensure veridical perception, but not in dreaming.

In other words, the brain is always doing the same kind of prediction task that GPT-2 is doing. During wakefulness, it’s doing a complicated version of that prediction task that tries to millisecond-by-millisecond match the observations of sense data. During sleep, it’s just letting the prediction task run on its own, unchained to any external data source. Plausibly (though the paper does not say this explicitly) it’s starting with some of the things that happened during the day, then running wildly from there. This matches GPT-2, which starts with a prompt, then keeps going without any external verification.

This sort of explains the dream/GPT-2 similarity. But why would an unchained prediction task end up with dream logic? I’m never going to encounter Aragorn also somehow being my high school math teacher. This is a terrible thing to predict.

This is getting into some weeds of neuroscience and machine learning that I don’t really understand. But:

Hobson, Hong and Friston say that dreams are an attempt to refine model complexity separately from model accuracy. That is, a model is good insofar as it predicts true things (obviously) and is simple (this is just Occam’s Razor). All day long, your brain’s generative model is trying to predict true things, and in the process it snowballs in complexity; some studies suggest your synapses get 20% stronger over the course of the day, and this seems to have an effect on energy use as well – your brain runs literally hotter dealing with all the complicated calculations. At night, it switches to trying to make its model simpler, and this involves a lot of running the model without worrying about predictive accuracy. I don’t understand this argument at all. Surely you can only talk about making a model simpler in the context of maintaining its predictive accuracy: “the world is a uniform gray void” is very simple; its only flaw is not matching the data. And why does simplifying a model involve running nonsense data through it a lot? I’m not sure. But not understanding Karl Friston is a beloved neuroscientific tradition, and I am honored to be able to continue participating in it.

Some machine learning people I talked to took a slightly different approach to this, bringing up the wake-sleep algorithm and Boltzmann machines. These are neural net designs that naturally “dream” as part of their computations; ie in order to work, they need a step where they hallucinate some kind of random information, then forget that they did so. I don’t entirely understand these either, but they fit a pattern where there’s something psychiatrists have been puzzling about for centuries, people make up all sorts of theories involving childhood trauma and repressed sexuality, and then I mention it to a machine learning person and he says “Oh yeah, that’s [complicated-sounding math term], all our neural nets do that too.”

Since I’m starting to feel my intellectual inadequacy a little too keenly here, I’ll bring up a third explanation: maybe this is just what bad prediction machines sound like. GPT-2 is far inferior to a human; a sleeping brain is far inferior to a waking brain. Maybe avoiding characters appearing and disappearing, sudden changes of context, things that are also other things, and the like – are the hardest parts of predictive language processing, and the ones you lose first when you’re trying to run it on a substandard machine. Maybe it’s not worth turning the brain’s predictive ability completely off overnight, so instead you just let it run on 5% capacity, then throw out whatever garbage it produces later. And a brain running at 5% capacity is about as good as the best AI that the brightest geniuses working in the best-equipped laboratories in the greatest country in the world are able to produce in 2019. But:

We believe this project is the first step in the direction of developing large NLP systems without task-specific training data. That is, we are developing a machine language system in the generative style with no explicit rules for producing text. We hope for future collaborations between computer scientists, linguists, and machine learning researchers.

A boring sentiment, except for the source: the AI wrote that when asked to describe itself. We live in interesting times.

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The Proverbial Murder Mystery

I.

Chefs. Hundreds of them. Tall chefs, short chefs, black chefs, white chefs. I pushed forward through them, like an explorer hacking away at undergrowth. They muttered curses at me, but I was stronger than they were. I came to a door. I opened it. Sweet empty space. I shut the door behind me, sat down in the chair.

“Hello,” I said. “Detective Paul Eastman, pleased to make your acquaintance.”

“Doctor Zachary LaShay,” said the man behind the desk. His little remaining hair was greying; his eyes showed hints of the intellect that had been buried beneath the dullness of an administrative career. “I hope you didn’t have any trouble getting here. Did my secretary warn you about the chefs?”

“She did not,” I said.

“Well, forewarned is forearmed,” he answered, inanely and incongruously. “But I trust you got my message about the federal investigators?”

“Once a federal investigation has started, we’ll retreat and let them take over. But two women died here. We can’t just not investigate because you tell us you’re trying to get the Feds involved.”

“Yes, ah, of course. It’s just that we’re a sort of, ah, defense contractor. None of our projects are officially classified, yet, but we were hoping to get someone with a security clearance, in case this touched on sensitive areas.”

“I won’t pry further than I have to, but until someone from the government says something official, this is a matter for city police. Maybe you could start by telling me more about exactly what you do here.”

“We’re the United States’ only proverb laboratory. Our mission is to stress-test the nation’s proverbs. To provide rigorous backing for the good ones, and weed out the bad ones.”

“I’d never even heard of your organization before today, I have to admit. And now that I’m here…it’s huge! Who pays for all of this?”

“Everybody who uses proverbs,” said the Doctor, “which is to say, everybody. Consider: he who hesitates is lost. But also: look before you leap. Suppose you’re a business executive who spots a time-limited opportunity. What do you do? Hesitate? Or leap without looking? Eggheads devise all sorts of fancy rules about timing the market and relying on studies, but when push comes to shove most people are going to rely on the simple sayings they learned as a child. If you can keep your stock of proverbs more up-to-date than your competitor’s, that gives you a big business advantage.”

A smartly-dressed woman came in, handed Dr. LaShay a cup of boiling liquid. He put it to his lips, then spat. “This is terrible!” he said. “Try it!”

I had been expecting it to be tea, but it wasn’t. I didn’t know what it was. But it was terrible. Somehow too plain, too salty, and too bitter all at once. I gagged.

“That settles it!” said the Doctor. “Too many cooks really do spoil the broth. Tricia, tell the chefs they can all go home now.”

“So that’s what you were doing!” I said.

“Yes. Until now, too many cooks spoiling the broth had been at best an anecdote! A folk hypothesis! This month we’ve been working on broth with varying numbers of cooks. One, two, five, ten, a hundred. We’ve got a team of blinded taste testers in the basement who’ve been rating the results, and I personally check each sample to make sure I agree. This morning we hired every cook in the city – that’s over five hundred cooks – to come here and make broth for us, just to make sure there isn’t some kind of island of stability where broth starts getting better again once the number of cooks is high enough. Later this week we’ll give the data over to our analysts, who’ll develop a model that can use cook number to predict broth quality over a wide range of possible situations.”

“And the military wants this sort of thing?”

“The military loves it! The average grunt is a high-school educated young man in his late teens or early twenties. You’re not going to be teaching these people Clausewitz and von Moltke; it would be casting pearls before swine. When he’s under fire and has to make a split-second decision, he’s going to rely on the heuristics he learned on his grandmother’s knee. On proverbs. America’s proverbs are a vital strategic asset, and the Pentagon appreciates that.”

“I get how too many cooks spoil the broth might apply to something like an officer trying to figure out how many people to consult about a new strategy. But surely you can’t test that heuristic just by experimenting with literal cooks making literal broth!”

“Mmmmmmm. Yes, you’re referring to what we call Pragmatics. We certainly have a pragmatics team here, and they do good work. But the thing is, Officer, we’re essentially a consulting firm. Consulting firms are there to give people justification for the things they want to do anyway. When some general is testifying before Congress, and he says he didn’t consult someone-or-other because too many cooks spoil the broth, then Congress is going to want evidence that relying on sayings like this is best practice. If he just says “That’s our heuristic, and we know it works”, he’ll look like a loose cannon. But if he can hold up a glossy five hundred page report we gave him, proving that broth really does get spoiled by too many cooks, he’ll look like a responsible technocrat who did his due diligence. And yes, part of that report is a long philosophical discussion on pragmatics. But part of it is proving, once and for all, that too many cooks really do spoil the broth.”

“I see,” I said. “The two dead women. Were they involved in the broth project?”

“No. The first victim, Lisa Bird, she was our sysadmin. The second victim, Catherine Lee, took care of the animals.”

“Animals?”

“We have several projects that require animals. You can obviously lead a horse to water, but can you make him drink? At first we would rent out horses from equestrian organizations for this kind of thing. But then the next month we would need another horse to see if you should shut the stable door after the horse has bolted. Then we’d need two more horses to see if you should change horses midstream. Finally the costs started adding up and we just got a couple of horses that we keep here at the Institute. They were actually a gift from a sister of one of our employees who used to have a farm. One of them we looked in the mouth; the other we didn’t. We’re still trying to figure out which way worked better.”

“I see. The report I got said that the motive was romantic jealousy.”

“Yes. Ms. Lee believed Ms. Bird was having an affair with her husband. Ms. Bird was known to come to work early on Fridays to do some extra work and prepare for the weekend off. Ms. Lee entered the office where Ms. Bird was working alone, murdered her, then committed suicide. I’m getting this from the emergency team that was here before you.”

“All right. I’ll need to see the crime scene.”

“Certainly.”

LaShay led me out of his office to an elevator, then hit the button for the tenth floor. We walked out into a clinically-clean hallway. I heard a commotion. “FUCK YOU!” someone was shouting. “DAMN YOU TO HELL, YOU INKY TENEBROUS MOTHERFUCKER!” I stepped forward to open the door and investigate, but the Doctor held me back.

“Don’t worry about it,” he said. “That’s Room 27A. We’re testing whether it’s better to light a candle or curse the darkness. The candle is in Room 27B.”

“You must have a lot of projects going on here.”

“Oh yes. Over there is our insect unit. Can you catch more flies with honey or vinegar, can ants really move plants, that kind of thing. Our kitchen is to the right – the chefs were using it today, but it comes in handy all the time. Just don’t go in there if you can’t stand the heat. And down that corridor are our weather unit, our fire unit, and our water unit. And that’s just the tip of the iceberg – ” He pointed to a large room with a spike of ice poking through the floor. We continued on. “And over there is our forge. There are so many proverbs about metal that we hired our own team of blacksmiths. It was going great until they unionized, but now they always strike when the iron is hot.”

The corridor opened into a vast auditorium. All around me, I saw knee-high marble buildings, gleaming palaces, and – was that the Colosseum? A man dressed in a gladiator costume was sitting behind a desk doubling as a terraced hill, frowning at a computer and occasionally typing something. “Our 1:100 scale model of Rome,” said LaShay. “We figured if we couldn’t build it in 0.24 hours or less, then Rome couldn’t be built in a day. For some reason I always get lost and end up here. It’s quite annoying.”

We passed out of Rome into another corridor, where we finally came to a door marked “Information Technology”.

“Ms. Bird’s office,” said LaShay, and I walked in.

I’m a homicide detective; I’m used to grisly murder scenes. This one still made me gasp. One victim – Ms. Bird, I supposed – was lying on the ground by the desk. It looked like her head had been bashed in by a blunt object. But there was more. Her mouth area was covered with blood, and I soon found her tongue had been cut out. And there was another bloody hole in her chest. The stomach and heart had been cut out too.

A few feet away, a second body dangled from a noose that had been tied to one of the rafters. Ms. Lee, I supposed. No mutilation on this one. Just a clean suicide, or at least that was what somebody had gone through a lot of trouble to make it look like.

Lying on the ground approximately between the two of them was a bloody knife. I knew from the previous report that the blood was Ms. Bird’s, and the fingerprints on the handle were Ms. Lee’s.

This did not seem like a Sherlock Holmes level mystery. Except: where were Bird’s heart and tongue?

“I’m not sure,” said LaShay, when I went outside and asked him the question. “I…haven’t been in there since in happened. Not sure I could deal with the blood. One of Ms. Bird’s coworkers had a question about the network, so she went in and saw…what you just saw. We called 911 in case either of them was still alive. The paramedics called the police who did a preliminary investigation of the scene. And then you showed up.”

“I’ll need to search the premises,” I said. “What time did Bird come to work?”

“I understand she usually arrives around seven.”

“And when does the office open?”

“Nine.”

“So potentially Lee could have had two hours to hide the heart and tongue somewhere in this building before going back and hanging herself.”

“Why would she have done that?”

“I don’t know. Do you have a better idea for what happened to them?”

He shook his head.

“Good. Then I’ll need to search the whole building. Is there anywhere I’ll need any special keys or codes to enter?”

He gave me a golden key. “This opens any door,” he said. “But don’t go in the Red Zone. That’s off-limits to everybody.”

I shrugged. “Then it’s exactly the sort of place somebody would hide something, isn’t it? Why isn’t anyone allowed in the Red Zone?”

“Radioactivity,” he answered immediately. “We have a giant machine for testing all machine-related proverbs. It’s…very impressive. Powers the whole building, runs the water and gas systems, even gives us satellite internet. We wanted it to be just a generic Machine, capital m, so it does a little of everything. But it’s radioactive…not traditionally, the way you can detect with a Geiger counter. I don’t understand the physics. But people tend to get very sick if they get too close to it.”

Part of LaShay’s description had stuck with me. “It provides the building with Internet? Lisa’s a sysadmin. Did she ever have to work with the Machine?”

“No, that was all connected when the Machine was installed. She interfaces with it remotely, through her computer.”

“And Catherine? Did her work with the animals ever bring her near the Machine?”

“Her office was very close to the Red Zone. Closer than any other office in the building, actually. But she never had any reason to enter the danger area.”

“I’m going to need to see the Machine. Is there any way I can do so safely?”

“We have an observation deck. It’s just above the Machine, on this floor. You can stare down at the Machine from the top.”

“I’ll need to go there.” It was just a hunch, but I wasn’t liking the sound of this Machine. And if you were going to hide body parts for some reason, why not hide them in a restricted area where nobody ever went?”

LaShay took me down a series of turns and hallways. After a minute or two of walking…we were in the scale model of Rome again.

“Dammit!” said LaShay. “Every time!”

Another few turns and hallways, and we finally came to a steel-reinforced door. “DANGER” it said. “OBSERVATION AREA. CHILDREN AND PREGNANT WOMEN NOT ALLOWED. PLEASE DO NOT SPEND MORE THAN FIVE MINUTES IN THE OBSERVATION AREA OVER A ONE WEEK PERIOD.”

I used my golden key to unlock the door. We went in.

We were on iron scaffolding. Below us whirred something amazing. It was like every children’s-book description of a machine put together and brought to life, a huge assembly of gears and pistons and bubbling glowing bright-colored chemicals coursing through glass pipes. Beside me was a control panel, currently set at “NORMAL”. The other options ranged from “OFF” to “MAXIMUM” to “ULTRAMAXIMUM” to “SUPRAULTRAMAXIMUM”.

“It’s beautiful,” I told the Doctor.

“Don’t touch that,” he told me, glancing nervously at the control panel.

The machine was nine stories high, filling the entire center of the laboratory. In the center, an enormous agglomeration of steampunk-looking gadgetry formed a hollow cylinder, spinning faster than I could follow. I leaned out over the edge of the scaffold, over the pit formed by the cylinder’s center.

“You really don’t want to do that,” LaShay told me. I could see what he meant. It was easy to imagine falling right through the hole in the spinning cylinder, down to the ground ten stories below. I had a strange feeling that gravity would be the least of my problems if that happened, that anything that went through that spinning apparatus would have a very bad time long before it hit the ground. And…

“What’s that?” I asked.

At the bottom of the spinning cylinder, incongruously, was a building I could only describe as a small shrine. It had a little golden dome on the top, and…actually, it was exactly a shrine. There was a Star of David atop the dome.

“That,” said LaShay. His voice changed, became heavier. “I started this laboratory with my colleague, Dr. Rissum. He…he committed suicide nine years ago by jumping into the Machine from this very spot. That’s his memorial.”

“My God! You’re telling me there was another suicide in this lab?”

“Nine years ago. The police investigated. There was nothing suspicious. His wife had just left him and taken the children. It was very tragic, but no foul play was suspected.”

“Still. Another suicide.”

“We need to get out of here,” said LaShay. “Being this close to the Machine really isn’t good for you.”

I looked around the observation deck and at the floor ten stories below. There were no signs of blood, a tongue, or a heart. “All right,” I said, because the Machine was starting make me nervous too.

I spent the rest of the morning searching the rest of the laboratory, free of LaShay’s discomfiting presence. It was an exhausting task, not least because I always ended up in the Rome model even when I thought I was in a totally different part of the building. But eventually I found two things that caught my interest.

First, Lisa Bird’s chair. I had gone back into the room with the bodies to look for other clues. The desk was normal enough. The computer was a normal Apple MacBook. But I noticed Lisa’s chair was made out of human hands. This was confusing enough that I called the Doctor back, who of course had an explanation.

“They’re not real hands,” he said. “Most of the staff have chairs like that. We were testing whether many hands make light work, so we had everyone working for the lab sit on those.”

“It’s pretty gruesome,” I said.

“We originally tried putting those statues of the Buddhist god with the thousands of hands all around the office,” LaShay admitted. “But people complained that the hands were whispering demonic messages to them. Finally someone in the Religion Department reminded me that idol hands are the Devil’s plaything.”

“Okay,” I said, and dismissed LaShay again, with some relief. He told me he would be working over the weekend, and said I could call him if anything came up. I hoped I wouldn’t have to. Something was weird about that guy, no doubt.

The second thing I found was Lisa Bird’s tongue and stomach. It was in the third drawer of Catherine Lee’s desk. The woman had murdered her coworker, cut out her tongue and stomach, put it in the third drawer of her desk, gone back up to the murder scene, and committed suicide.

Or, more accurately, this was a subset of what she had done, because I still couldn’t find Lisa’s heart. I searched Catherine’s desk inside and out. All I could find were a couple of paperweights made of various gemstones. I noticed they were about the right size and shape to have made the dent in Lisa Bird’s head, but none of them had any bloodstains on them or anything else suspicious. There were no severed organs.

I was missing something. But what?

II.

“You’re the detective on the Bird case?”

“Mmmrrrgyeah,” I answered groggily.

“Come to the station,” said Officer Karp. “The murderer’s body is missing.”

It was 8 AM on Saturday. I had visited the Proverb Laboratory Friday, told the station that the scene had been fully examined and they could take the bodies away, then gone home and slept. The station had sent a team to recover the bodies and bring them to the morgue. The next morning, one of the morgue staff had noticed that although Lisa Bird was still there, Lee’s body was missing.

Still only half-awake, I went to the morgue and examined the scene. The body bag was still in place. It had been expertly opened up and the body had been removed. There were no fingerprints. Karp was seething that a theft had been committed in the police station itself. He demanded we do something. I suggested we go to Catherine Lee’s house, interview her husband, see what he could tell us. That was how I ended up spending my Saturday morning at the weirdest house I had ever seen.

It was some kind of modernist experimental dwelling or something. The whole place was made out of windows. Not one-way windows either. You could see everything that happened in it. Not (I thought to myself) the sort of place a criminal would find very convenient.

“It was Cat’s idea,” her husband told us, when we knocked on the door and introduced ourselves. “She was always so paranoid that I was having an affair. Well, some weird architect made this house and then put it on the market – obviously nobody wanted it, so the price was right. Cat thought it was perfect. I couldn’t hide anything here. You’ve got to believe me, officers. I never had an affair with anybody. She was paranoid. But not violent. I know they say she killed that woman. But she would never do something like that. She was framed. I’m sure of it.”

“Who would do such a thing?”

“She talked about office politics all the time. I know things I’m not supposed to know. The Proverb Laboratory, they talk about selling their work to corporations, but the US military is the big sponsor. A lot of their best work is hush-hush.”

“I’m aware,” I said.

“Well, she would tell me all these rumors. Apparently the British hate the Proverb Laboratory. Before LaShay and Rissum started it ten years ago, the British had a monopoly on English-language proverbs. You’d have all these proverbs about kings and queens and tea and castles. It was a way for them to maintain their cultural hegemony over us. That’s what Cat would say.”

“Was Catherine by any chance paranoid and delusional about British people?”

“She was paranoid and delusional about a lot of things, but I tell you, she wasn’t a killer.”

“Were there any specific British people? Or anyone else who didn’t like what the Proverb Laboratory was doing?”

“There was the English Defense League. Have you ever heard of them?”

“They’re some kind of white supremacist group, right?”

“You must be thinking of the White Defense League. The English Defense League are an English supremacist group. As in, the English language. They believe English is superior to all other languages. They want to stop foreign language education in school, kick foreign speakers out of the country, make English the official national language, that kind of thing.”

“And they’re against the Proverb Laboratory?”

Mr. Lee laughed. “Or else they are the Proverb Laboratory. You know LaShay used to be one of them? No, from the look on your face you didn’t. He was part of their cult for a while, then deconverted and went mainstream, spoke out against them for the press. But some people say that’s all a ruse, and he’s continuing their work. They always thought that with enough study, they could use create some kind of super-proverb that would encapsulate all wisdom and make them unstoppable, something like that. LaShay says he’s beyond all that, but who knows? And if he is, well, maybe the cult that he left isn’t so happy to have the US military meddling in their pet project?”

“That’s so weird. I never heard about them before.”

“Well, Cat heard a lot of things, working at the Proverb Lab for five years.”

“Did she like it there?”

“Oh no. She hated it. She loves animals, you know. But the Proverb people thought they were just means to an end. She was in a big fight with LaShay just before she died. He wanted to test the proverb ‘Every dog has its day’. He was going to lock up forty, fifty dogs in a dark room, to simulate night, and just leave them there. Wanted to “falsify the hypothesis”. Cat said absolutely not, that was animal cruelty. So he did it anyway without telling her. She was enraged.”

“Did she ever make any threats? Say she was going to blow the whistle on the lab or anything?”

“No, nothing like that. She said she was going to let sleeping dogs lie. Sorry. I don’t think she had any enemies. She could be paranoid, she could be strange, but she was a good person, deep down. She wouldn’t have done this.”

“What’s that?” Officer Karp interrupted.

He was pointing to a corner of the kitchen. At first I didn’t see it. Then I did. There was a little drop of blood on the floor.

“Mr. Lee, do we have your full permission to search this house?” I asked. Officer Karp was already calling the station, letting them know they were going to need to send out an evidence collection team.

“Of course,” said Mr. Lee. “I have nothing to hide.”

Officer Karp went to the cabinet just next to the bloodstain, reached in, and pulled out a human heart.

“I…I swear I have no idea how that got there,” said Mr. Lee.

“How late did you sleep yesterday morning, when the murder happened?” I asked.

“I…it was my day off. I slept until ten.”

“And your house is about a fifteen minute drive from the lab. So in theory, your wife could have killed Ms. Bird, left the Proverb Laboratory, come back home, hid the heart in your cupboard, then gone back to the Proverb Laboratory and hung herself, all before anyone else showed up for work at nine.”

“Why…why would Cat have done that?” pled Mr. Lee.

“I don’t know,” I said. “Did she have any motive for disliking Ms. Bird other than the affair issue? Anything at all?”

“Nothing,” said her husband. “She spoke very highly of Ms. Lee. Apparently her computer had a virus once, and Ms. Bird solved it. She’d gotten a degree in cybersecurity from MIT before ending up in this job, and she was always working hard to keep the servers safe.”

“One more question. Do you know who stole your wife’s body from the morgue?”

“What?” asked Mr. Lee. “Someone stole…”

“This guy’s as surprised as we are,” said Officer Karp. “I say he’s not a suspect.”

We drove back to the station in silence. Either Catherine Lee had murdered her coworker, driven home to hide her heart in a cabinet, and then gone back to work before killing herself – or somebody had put a lot of work into making it look that way. And somebody had stolen her body from the morgue. And there was some sort of web of international intrigue surrounding Doctor LaShay.

I decided I was going to go home, catch up on my sleep, and then think this over really hard.

III.

Sunday morning I walked back into the Proverb Laboratory. I was trying to get to Dr. LaShay’s office, but I had ended up in the scale model of Rome again. I hadn’t even taken an elevator, and it was on the tenth floor. That no longer confused me. I had finally figured out what I should have realized days earlier.

“Dammit!” said LaShay, almost bumping into me. “Rome again!”

“Doctor Zachary LaShay,” I said, “You are under arrest, for the murders of Ms. Lisa Bird and Catherine Lee. You have…”

“You can’t arrest me!” he said.

“…the right to remain silent,” I continued. “Anything you say can and…”

Two men in black uniforms and sunglasses stumbled into the Rome set just behind him.

“No,” said LaShay. “I mean you can’t arrest me. The federal government has taken over the investigation, as of today. The entire affair has been classified as top secret. You’re not even allowed to be here anymore.”

I sighed. “Then I’ll just take a moment to talk with one of these agents…”

The agents didn’t move.

“You have one minute to get off this property,” said Dr. LaShay, “or you will be in violation of federal law.”

“All right,” I told the agents. “Listen up.” Then I explained everything.

The Proverb Laboratory didn’t exist to test proverbs at all. Or they did, but not in the way they claimed. The Proverb Laboratory existed to test the Machine. A device that makes proverbs real. The Machine exerted some kind of invisible force. The closer you got, the more the English language warped reality in order to make proverbs come true.

Why had Lisa Bird’s tongue and heart been missing? Because the proverb goes “Cat got your tongue”. The Machine’s power had forced Cat to take Lisa’s tongue and bring it somewhere that would qualify as her “having” it. And the same force had made her bring the heart home, because “Home is where the heart is”. She hadn’t meant to take the stomach too, but had removed it for better access, since “The way to a man’s heart is through his stomach”. Then her corpse, which had spent years absorbing the Machine’s malevolent radiation, had vanished from the body bag where it was kept – “Cat’s out of the bag”.

What had Catherine used to bash Lisa’s head in? The obvious candidate was one of the gemstone paperweights hidden in her desk, which she had brought back at the same time as she brought the tongue. I hadn’t been able to find bloodstains on any of the paperweights, but that was unsurprising; “You can’t get blood from a stone”. She lived in a glass house, and had broken the rule about throwing stones, and so ended up dead and a murderer. The saying goes: “Kill two birds with one stone”. Catherine had killed Lisa Bird; where was the other? Simple. Lisa sat on a chair made of hands, and a bird in the hand is worth two in the bush. She was worth two birds all on her own.

But it was too perfect. How had it all come together? A paranoid lady who thought everyone was having an affair with her husband. Who lived in a glass house and owned gemstone paperweights. Sharing a building with a woman named Bird. Who was sitting on a chair made of hands. In the closest office to the machine that made proverbs true. This wasn’t a coincidence. This was planned. Someone must have arranged for a paranoid woman who lived in a glass house to be on the spot, given her the stone paperweights as presents, placed Bird on the hand-chair, then relied on the Machine to twist reality into committing the crime for him. They must have guessed that after it was all over, Lee would recover her senses, feel terrible guilt, and kill herself. Who could have done that? LaShay was the only person powerful enough to make it all happen.

LaShay was lying about the memorial to Rissum. They hadn’t built a temple on the spot where Rissum died. That temple was Rissum himself. He had fallen into the very center of the Machine, where the reality-bending force approached infinity and proverbs would come true no matter how unlikely. “My body is a temple”. Rissum’s body was transformed into a temple in mid-air, then fell onto the ground below. Why would LaShay hide this? Could it be because he had pushed Rissum into the machine himself to seize complete control over the operation?

But why? The rumor Mr. Lee had told me tied everything together. Dr. LaShay was still with the English Defense League. They had designed the Machine. He had pretended to go mainstream, pretended to partner with Dr. Rissum, in order to get enough money and status to build their invention. Now he was slowly testing its capacities, secretly funneling the results to his secretive language-cult. Rissum had been a convenient co-founder, but had to go in order to give LaShay full control. He had pushed him into the Machine, disguised it as a suicide, and was funneling the information – how?

Through a worm in the computer system. After all, the workers here all had Apple computers, and every apple has its worm. But LaShay hadn’t realized that along with her sysadmin work, Lisa was an expert in cybersecurity, nor that she would come in two hours early every Friday. “The early Bird catches the worm.” Lisa had found the infection and destroyed it. She hadn’t realized it was important, but LaShay realized he couldn’t reinfect the system without her finding it again and getting suspicious, and he couldn’t fire her without raising eyebrows. So instead, he had arranged matters perfectly to guarantee she would get killed.

“Wow,” said Dr. LaShay after a second. “You’re actually right about everything. Except for one thing. The most important thing.”

“What’s that?” I asked.

“Not real federal agents,” he said, gesturing at the men in black. “They’re with me.” He turned to them. “Throw him in the Machine.”

I reached for my gun, but the agents were faster than I was, wrestled it away from me. Then one of them held each of my arms and started dragging me to the observation deck. A slight delay as we ended up back in Rome. Then we were there, and I was standing over the great rotating cylinder, staring at the shrine of Dr. Rissum below.

“Please don’t let me die,” I said. “I’m begging you. Please spare my life.”

“You really think we care about that?” asked the first agent.

They pushed me to the edge of the scaffold.

“You really think I was begging because I thought you’d listen?” I said, but before I finished he had thrown me over. There was a gust of wind and a feeling of terrible wrongness.

When I had fallen five stories, into the very center of the Machine, I wished.

A flying horse was somewhat outside the scope of the relevant proverb, but there was no other way I was going to “ride” while in midair, so I got one. It made landfall right on the observation scaffold, then rushed for the door. The two agents rushed after it. Somewhere in the corridor, the horse dissolved, its Machine-powered existence apparently expending itself this far from the source.

I ran frantically through the corridor. “After him, you fools!” I heard LaShay shout. I reached the point where I thought the elevator should be, but of course I was in fricking Rome again.

One of the agents ran in, reached for his gun.

I ducked behind the terraced hill. There beside the desk was the gladiator costume, complete with weapons. I picked up a trident. “Ave Imperator!” I said. “Morituri te salutant!” Like a miracle, it worked. The agent aimed at me and pulled the trigger, but the gun blew up in his face. This close to the Machine, he should have known: “When in Rome, do as the Romans do.”

The agent was still on his feet. I had made the mistake of getting far enough from the hill-desk that the agent could pick up the abandoned sword. He rushed at me. I didn’t know how to swordfight, so after a second of thought I took a pen out of my pocket, parried with it. The sword shattered, and ink squirted out into the agent’s face.

While he was trying to wipe off the ink and get his vision back, I ran out of Rome into one of the nearby corridors, then ducked into a randomly chosen door. Everything was pitch black.

“Come out, come out, wherever you are,” the agent shouted. “You can run, but you can’t hide!” Frick. I had forgotten that. In this place, the saying itself probably made that literally true. I heard the two agents opening and closing all the other doors in the corridor, getting inevitably closer to me.

Then I felt something cold and wet press against my hand. I almost screamed, giving away my location, but after a second it…licked me. I remembered what Mr. Lee had told me. Dr. LaShay had stuck fifty dogs in a completely dark room to test a proverb. I felt around. More and more dogs started to trot up to me, mouths panting, tails wagging. I had one chance.

I flung the door open as hard as I could “Run away, doggos!” I shouted. “Run like the wind! This is it! THIS IS YOUR DAY!”

The dogs didn’t need to be told twice. They rushed out of the room, a yapping growling barking mass of teeth and fur. Big dogs, little dogs, old dogs, young dogs, the whole mass of dogs ran right into the agents, knocked them over.

“Call off your dogs!” one of the agents shouted, but I didn’t. Instead, I cried “Havoc!”, and let loose the dogs of war. I figured their bark would be worse than their bite; on the other hand, once bitten, twice shy. It probably balanced out. Hopefully I wouldn’t have to worry about the agents for a few minutes.

I ran to where I thought the elevator would be, and of fricking course landed in Rome again. And worse, there was the Doctor, who was holding the trident I had abandoned. The sword was nowhere to be seen. I knew I wouldn’t be able to fool him. He had probably forgotten more proverbs than I had ever learned.

Ave Imperator!,” said Dr. LaShay, approaching unarmed-me with his trident. “Morituri te salutant.” Even his Latin was better than mine. I wished I was first in a village. But hope beyond hope, I realized that the computer at the terraced-hill desk was an Apple. I grabbed it, pulled out the plug, brandished it before me. The Doctor staggered back, as if kept away by an invisible wall.

But it didn’t hold him for long. He stretched out his arm as far as it could go, lunged at the computer with the deadly trident. The screen shattered and went black, its power lost.

I ran through the maze of corridors, and LaShay followed, trident in hand. After several turns, I reached where I thought the elevator would be, but Rome was everywhere at once, and I had lost my bearings. I ended up in the Observation Room, standing on the iron scaffold above the machine, as LaShay and his trident came towards me.

“So,” he said, “you figured out a way around being thrown into the Machine. ‘If wishes were horses, beggars would ride.’ Clever. You could have been a great proverb researcher. But instead you had to meddle where you didn’t belong.”

“If you throw me into the pit, I’ll just get another flying horse,” I told him.

“Of course you will. So I’ll have to kill you with the trident.” I was backed up against the wall of the observation chamber. LaShay approached me confidently, knowing I was cornered.

“You really think you’re going to win this?” I asked. It wasn’t just to buy time. I really did have a plan, crazy as it was, but the more I could get him gloating, the better it would work.

“Of course,” said LaShay. “I killed Bird and Lee, and now I’m going to kill you. Your death here will actually be quite convenient. I’ll announce that the Machine is too dangerous and needs to be taken apart. Then the version the English Defense League is building in secret will be the only one in the world. With the data we’ve gathered here, they’ll be able to direct its power anywhere on the planet. Imagine what we’ll be able to do. Enlist old soldiers who are impossible to kill. Build fortresses on demand by turning arbitrary Englishmen’s homes into castles. Control the seas using loose lips. Soon English-speakers will rule the world. And nothing – absolutely nothing – can stop us!”

Perfect.

“You’ve forgotten three things,” I said. “First, that the lever is right here.”

I grabbed the lever on the control panel and jerked it to SUPRAULTRAMAXIMUM. The air started to shimmer, and the walls started to shake.

“Second, that pride cometh before a fall.”

The iron scaffolding started to tilt. LaShay stumbled, dropped his trident, almost tumbled over the edge, hung on just by the tips of his fingers.

“And third, that crime doesn’t pay!

I grabbed the pointy end of the trident, and smashed it into LaShay’s fingers. With a scream, he fell into the belly of the Machine.

“Ibegyounottodothis,” he said, and just like that he was on a winged horse. It flew up, towards the door and freedom.

I looked it in the mouth. I stared it straight in the mouth, looked as hard as I could, like my eyes were drilling into it. It started flickering, flying more slowly and hesitantly. “I beg, I beg, I beg,” said LaShay. We stood there like that for a few seconds, him trying to wish harder, me trying to look the gift horse in the mouth harder, until finally the horse vanished, and LaShay fell back into the machine.

“I beg, I beg, I beg!” he said again, there appeared another horse, a horse of a different color. I looked it in the mouth again. It rose more slowly and hesitantly. But LaShay leaned forward, finally covered its mouth with his hand so I couldn’t see it. “Your looking has no power anymore!” LaShay said triumphantly, and I believed him, since it came straight from the horse’s mouth. The impediment removed, the horse shot upwards, right up to the ceiling of the chamber.

“Get off your high horse,” I said, and the horse vanished a second time. A third time LaShay fell into the Machine, a third time he begged, and a third time a horse appeared beneath him. Again I started looking it in the mouth. Again he covered it with his hand, this time guiding the horse more slowly, trying not to let it overshoot and become higher than I was.

With a whinny of victory, the horse’s hoof landed on solid scaffold. And that was when I struck the hoof with my trident.

For the want of a nail, the horseshoe was lost. For the want of a shoe, the horse was lost. For the want of a horse, LaShay lost his footing and tumbled back into the pit. He tried begging again, but it didn’t work; that wasn’t the proverb. For want of the horse, the rider had to be lost, for want of the rider, the battle, and finally the war and kingdom with it. He fell through the Machine, all the way down. By the time he hit the ground, he had turned into another temple, standing silently beside the temple of his co-founder.

I moved the lever to OFF. Then, avoiding the sound of barking and screaming – and only getting stuck in Rome twice – I finally made it back to the elevator and left the building.

IV.

My department was able to make contact with the real military. They completed their investigation, and chose to shut down the Proverb Laboratory and destroy the Machine.

The two agents were found to be cultists with the English Defense League. On questioning, they led the government to their headquarters. The second Machine, the one that threatened to take over the world, was also found and destroyed.

I asked the prosecutor’s office to submit a statement officially declaring that Catherine Lee was not responsible for Lisa Bird’s murder, based on a sort of complicated insanity defense where she had been compelled to act by the Machine’s influence. I don’t think the prosecutor really bought it, but I think he figured she was dead anyway, so what was the harm?

Catherine’s body was never found, which didn’t surprise me. She really had absorbed a lot of radiation, working for the Laboratory for five years, and “the cat is out of the bag”, while true, didn’t suffice to explain how she had disappeared or where she was. I only figured it out later, after the whole battle with LaShay.

This life hadn’t treated Cat too kindly. I hope things go better during her next eight.

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OT121: Openumbra Thread

This is the bi-weekly visible open thread (there are also hidden open threads twice a week you can reach through the Open Thread tab on the top of the page). Post about anything you want, but please try to avoid hot-button political and social topics. You can also talk at the SSC subreddit or the SSC Discord server – and also check out the SSC Podcast. Also:

1. Thanks to everyone who attended the Irvine meetup last weekend. If you’re interested in a more regular meetup group there, please email rayhsu16[at]gmail[dot]com to get your name added to the mailing list.

2. And thanks to everyone who sent me names to put on the Psychiat-List. Just a reminder that I’m still looking for your recommendations for psychiatrists and therapists anywhere that SSC readers might live.

3. On the preferred comment order poll, people were about evenly split between newest-first vs. oldest-first, but there was a clearer preference for “oldest-first on content posts, newest-first on open threads” if we can manage it. Niohiki posted some code that should be able to do this, but I haven’t been able to get it to work; niohiki, if you’re reading this send me an email at scott[at]slatestarcodex[dot]com and we’ll talk it over. In the meantime, eigenmoon has posted a user-side solution.

4. The infamous “Culture War Thread” and other discussions of hot-button controversial potentially-triggering issues have been banned from the SSC subreddit. Some of the culture war thread moderators have created a new unofficial subreddit for those kinds of discussions, r/TheMotte. If you’re interested in talking about those kinds of issues beyond the level that happens here, please check it out. I see the top thread there already has 500 comments, making me less concerned that it’s going to die off immediately. I know people have a lot of questions about this and I’ll probably talk about it in more depth later.

5. Comment of the week: Random Critical Analysis has defended their theory of US health care costs against a criticism I made in my last Links post.

6. This is my first Valentine’s Day after breaking up with my primary partner and I’m feeling kind of down. If anyone knows someone you think would be a good match for me, feel free to try to set me up. I’m kind of poly, kind of asexual, want children, and live in the East Bay. My email is scott[at]slatestarcodex[dot]com.

7. Commenter ‘a reader’ has helped run the Open Threads here and keep them on time (today’s thread being late was entirely my fault, not theirs). In exchange, I’ve added an ad for their Retro Vintage Store on Zazzle; please check it out.

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