Lots of people die after overdosing on acetaminophen (paracetamol, Tylenol, Panadol). In the U.S., it’s estimated to cause 56,000 emergency department visits, 2,600 hospitalizations, and 500 deaths per year. Acetaminophen has a scarily narrow therapeutic window. The instructions on the package say it’s okay to take up to four grams per day. If you take eight grams, your liver could fail and you could die.

Meanwhile, it seems to be really hard to kill yourself by overdosing on ibuprofen (Advil, Nurofen, Motrin, Brufen). In 2006, Wood et al. searched the medical literature and found 10 documented cases in history. Nine of those cases involved complicating factors, and in the 10th, a woman took the equivalent of more than 500 standard (200mg) pills.

So, for many years, if I needed a painkiller, I’d try to take ibuprofen rather than acetaminophen. My logic was that if eight grams of acetaminophen could kill my liver, then one gram was probably still hard on it. I’m fond of my liver and didn’t want to cause it any unnecessary inconvenience.

But guess what? My logic was wrong and what I was doing was stupid. I’m now convinced that for most people in most circumstances, acetaminophen is safer than ibuprofen, provided you use it as directed. I think most doctors agree with this. In fact, I think many doctors think it’s obvious. (Source: I asked some doctors; they said it was obvious.)

Should this have been obvious to me? I figured it out by obsessively researching how those drugs work and making up a story about metabolic pathways and blood flow, and amino acid reserves. It’s a good story, one that revealed that my logic stemmed from an egregious lack of respect for biology and that I’m a big dummy (always a favorite subject). But if the clearest road to some piece of knowledge runs through metabolic pathways, then I don’t think that knowledge counts as obvious.

So how is a normal person meant to figure it out? Why doesn’t the fact that acetaminophen is typically safer than ibuprofen appear on drug labels or government websites or WebMD? Are normal people supposed to figure it out, or has society decided that this is the kind of thing best left illegible?

Note: You should not switch medications based on the uninformed ramblings of non-trustworthy pseudonymous internet people.

How does ibuprofen work?

Ibuprofen inhibits the the Cyclooxygenase (COX) enzyme. This in turn inhibits the formation of messenger molecules involved in inflammation, which leads to less physical inflammation and thus less pain.

The same story is true for almost all over-the-counter painkillers, which is why they’re almost all considered “non-steroidal anti-inflammatory drugs,” or NSAIDs. This includes ibuprofen, aspirin, naproxen (Aleve), and a long list of related drugs. But it does not include acetaminophen.

How does acetaminophen work?

Nobody knows!

Like ibuprofen, acetaminophen inhibits some COX enzymes. But it does so in a weird way that barely affects inflammation or messenger molecules, so it’s unclear if this matters for pain reduction.

In the brain, acetaminophen is metabolized into a mysterious chemical called AM404. This activates the cannabinoid receptors and increases endocannabinoid signaling, which seems to reduce the subjective experience of pain. AM404 also activates the capsaicin receptor, which is associated with burning sensations that you’d normally expect to increase pain, but maybe some desensitization thing happens downstream? And maybe acetaminophen also interacts with serotonin or nitric oxide or does other stuff? How this all comes together to reduce pain is still somewhat a scientific mystery.

Aside: When trying to understand painkillers, it’s natural to focus on chemistry and molecular biology. But the unknown physical origins of consciousness are always nearby, looming ominously.

What risks does ibuprofen have?

In an ideal world, the only thing ibuprofen would do is reduce inflammation in the part of your body that hurts. But that is not our world. When ibuprofen inhibits the COX enzymes, it does so throughout the body. And mostly, that is bad.

For one, ibuprofen reduces production of mucus in the stomach. That might sound okay or even good. But stomach mucus is important. You need it to shield the lining of your stomach from your extremely acidic gastric juice1. Having less mucus can lead to gastrointestinal problems or even ulcers.

Ibuprofen also affects the heart. When ibuprofen inhibits the COX enzymes there, this in turn inhibits one chemical that prevents clotting and another that causes clotting. In balance, this seems to lead to more clotting, and an increased statistical risk of heart attacks2. If you’re healthy, the risk of a heart attack from an occasional low dose of ibuprofen is probably zero. But if you have heart issues and take medium to large doses regularly for as little as a few days, this might be a serious concern.

Ibuprofen also affects the kidneys. If you’re stressed, or cold, or dehydrated, or take stimulants, your body will constrict your blood vessels. That squeezes your kidneys’ intake tube, depriving them of blood. Your kidneys don’t like that, so they release signaling molecules to locally re-dilate the blood vessels.

Trouble is, when ibuprofen inhibits COX enzymes in the kidneys, it inhibits those signaling molecules. If everything is normal, that’s okay, because the kidneys wouldn’t try to use those molecules anyway. But if your body has clamped down on the blood vessels, then the kidneys don’t have the tool they use to keep blood flowing, meaning they don’t get as much blood as they want. This is bad3.

There are many other less common side effects, including allergies, respiratory reactions in asthmatics, induced meningitis, and suppressed ovulation. If you take a lot of ibuprofen, this could hurt your liver. But the major concerns seem to be the stomach, the heart, and the kidneys.

What risks does acetaminophen have?

Acetaminophen also inhibits some COX enzymes. But unlike ibuprofen, the effect is minimal outside the central nervous system. Thus, acetaminophen has little effect on stomach mucus, blood clots, or blood flow, and so presents almost none of the risks that ibuprofen does.

Even so, if you take too much acetaminophen at once, you could easily die.

How does this happen? Well, when acetaminophen is metabolized by the liver, it’s mostly broken down into harmless stuff. But a small fraction (5-15%) is broken down by the P450 system into an extremely toxic chemical called NAPQI.

Ordinarily this is fine; your body creates and neutralizes toxic stuff all the time. For example, if you drank 20 grams of formaldehyde, you’d likely die. But did you know that your body itself makes and processes ~50 grams of formaldehyde every day? When liver cells sense NAPQI, they immediately release glutathione, which binds to NAPQI and renders it harmless.

But there’s a problem. If you take too much acetaminophen at once, the pathways that break it down into harmless stuff get saturated, but the P450 system doesn’t get saturated. This means that not only is there more acetaminophen, but also that a much larger fraction of it is broken down into NAPQI. Soon your liver cells will run out of glutathione to neutralize it. Then, NAPQI will build up and bind to various proteins in the liver cells (especially in mitochondria) causing them to malfunction and/or commit suicide. This can cause total liver failure.

So you should never take more than the recommended dose of acetaminophen4. If you do take too much, you should go to a hospital immediately. They will give you NAC, which will replenish your glutathione and neutralize the NAPQI. Your prospects are good as long as you get to the hospital within a few hours56.

Acetaminophen has lots of other possible side effects, like skin issues and blood disorders. But these all seem to be quite rare.

What if you have liver issues?

The primary concern with acetaminophen is liver damage. So if you have liver disease, then surely you’d want to avoid acetaminophen and take ibuprofen instead, right?

Nope. It’s the opposite. Liver disease shifts the balance of risk in favor of acetaminophen.

With liver disease, it’s hard for blood to flow into the liver, meaning that blood tends to pool in the abdomen. To counter this, blood vessels elsewhere in the body contract. This includes blood vessels around the kidneys.

Remember the kidneys? Again, when blood vessels are constricted, the kidneys send out signaling molecules to locally re-dilate the blood vessels. But those signaling molecules are blocked by ibuprofen. So if you have liver disease, taking ibuprofen risks starving your kidneys of blood just like if you were dehydrated.

Meanwhile, people with moderate liver disease are usually still able to process acetaminophen without issue, as long as it’s in smaller amounts. So doctors usually tell patients with liver disease to avoid ibuprofen and take acetaminophen instead, just with a maximum of two grams per day instead of four.

(Obviously, if you have liver disease, then you should talk to a doctor, I beg you, for the love of god.)

What about other situations?

The main takeaway from all this is that the risks of both drugs emerge from the madhouse of complexity that is your body. Surely there are some situations where acetaminophen is more dangerous than ibuprofen?

I tried to capture the most common situations in this table:

It’s actually fairly hard to find situations where ibuprofen is safer than acetaminophen. Possibly this is true if you’re hungover, but I would be very careful, because you tend to be dehydrated when hungover, raising the risk of kidney damage. (It’s probably optimal, from a health perspective, to avoid taking recreational drugs at doses that leave you physically ill the next day.)

Aside from hangovers, the only situations I could find where ibuprofen might be safer than acetaminophen are if you’re taking certain anti-seizure or tuberculosis drugs or maybe if you have a certain enzyme deficiency (G6PDD).

So…

What have we learned so far?

1. The body is really complicated!

2. The main risk of acetaminophen is liver damage by creating too much NAPQI. Taking too much at once can easily kill you. However, as long as you don’t take too much at once and your liver isn’t depleted, then your liver will maintain NAPQI levels at zero and it will be completely fine. And there are very few other risks.

3. Meanwhile, ibuprofen poses a risk of gastrointestinal issues, heart attacks, or kidney damage. The risk varies based on lots of factors like whether you’ve eaten food, whether you’re dehydrated, your blood pressure, and your heart health7.

4. Therefore, acetaminophen is probably safer, provided you never take too much8

I don’t want to be alarmist. If you’re healthy, the risk from taking an occasional dose of ibuprofen as directed is extremely low. Given that so many people find that ibuprofen is more effective for many kinds of pain, it’s totally reasonable to use it. I do so myself.

Still, it seems to be the case that in the vast majority of situations, acetaminophen is saf_er_. Personally, if I have pain, I first take acetaminophen, and then add ibuprofen if necessary. I’m pretty sure many experts think this is somewhere between “sensible” and “obvious.”

But if acetaminophen is safer, then why don’t official sources tell you that9? I can get doctors to admit this off-the-record. I can find random comment threads with support from people who seem to know what they’re talking about. But why does this fact never appear on government websites or drug labels?

Let’s look at those drug labels

In the U.S., the Food and Drug Administration (FDA) creates10 a“drug facts” label for over-the-counter drugs.

Here’s what that looks like for ibuprofen:

And here’s what it looks like for acetaminophen (paracetamol):

I feel dumb saying this, but when I saw those labels in the past, I thought of them as a bunch of random information thrown together for legal reasons. But after spending a lot of time trying to understand these drugs myself, I now realize that these labels are… really good?

Imagine you work at the FDA and it’s your job to write a safety label. You need to synthesize a vast and murky scientific landscape. Your label will be read by people with minimal scientific background who are likely currently in pain, and who could die if they take the drug in the wrong situation.

If I were in that situation, I’d think about all the different situations in which taking one of these drugs could literally kill someone, and then — after a quick panic attack — I’d write a label that screamed, HEY, IF YOU ARE IN ANY OF THESE SITUATIONS, TAKING THIS DRUG COULD LITERALLY KILL YOU. Then I’d think about all the other situations where taking the drug might be okay depending on a set of complex science stuff and tell people in those situations to PLEASE TALK TO A DOCTOR FOR THE LOVE OF GOD because I DON’T KNOW IF YOU’VE HEARD BUT SCIENCE IS COMPLICATED. Everything else would be a minor concern.

From that perspective, these labels are a triumph. This isn’t random information — every word is a synthesis of a mountain of research, carefully optimized to save lives.

FDA good

How did those drug labels come to be?

If you want a taste for the FDA’s process, I encourage you to skim the 2002 Federal Register document in which the FDA proposed to update ibuprofen’s safety label and to formally classify it as Generally Recognized as Safe. It’s more than 21,000 words long and — I think — astonishingly good. It not only summarizes the entire medical literature on ibuprofen, it summarizes it well. Here is onerepresentative bit:

Bradley et al. (Ref. 42) conducted a 4-week, double-blind, randomized trial in 184 subjects comparing the effectiveness and safety of the maximum approved OTC daily dose of 1,200 mg of ibuprofen (number of subjects (n) = 62) to that of a prescription dose of 2,400 mg/day (n = 61), and to 4,000 mg/day of acetaminophen (n = 59) for the treatment of osteoarthritis. While there were no significant differences in the number of side effects reported during this study, the study demonstrated a trend towards a dose dependent increase in minor GI adverse events (nausea and dyspepsia) associated with higher doses of ibuprofen (1,200 mg/day: 7/62 or 11.3 percent; versus 2,400 mg/day: 14/61 or 23 percent). In addition, two subjects treated with 2,400 mg/day of ibuprofen became positive for occult blood while participating in the study.

I spend a lot of time complaining about bad statistical writing. A lot. Probably too much. But I’m here to tell you, that paragraph is gorgeous. The writing is clear and penetrating. It contains all the important details, but no other details. Compared to the abstract of the original paper, the above is shorter and easier to understand yet simultaneously more informative. Five stars.

The rest of the document is equally good, with clear and sensible explanations for various recommendations. For example, they discuss a proposal from the National Kidney Foundation for additional warning about risks to kidneys, explain why they think that proposal has merit, and then recommend a shorter version, which appears on every package of ibuprofen sold today.

As far as I can tell, this level of quality is typical. For example, the FDA’s 2019 proposed rule on sunscreens is similarly masterful.

So why?

This leaves us with this constellation of facts:

1. Acetaminophen is, in general, safer than ibuprofen.

2. The FDA doesn’t tell you that. Neither do other respectable authorities.

3. The FDA is highly competent.

So what’s happening here? Have the experts conspired to keep this knowledge secret?

I don’t think so. Mostly, I think this is down to two factors. First, the FDA doesn’t really have a mission of determining “in what circumstances is drug A safer than drug B?” Their goal is to take individual drugs and determine how people can use them safely. They seem to be quite good at this.

Second, everyone is mortally afraid of giving “medical advice.” It varies by jurisdiction, but in general, giving “wellness advice” is OK, but if you give personalized advice, you risk going to prison. The more credible you are, the higher that risk is11.

Stepping back, how should we think about this situation?

The body is complicated. When experts give the public advice on drugs, they are trying to insulate us from that complexity. But there is no way to do that without making trade-offs. Society has implicitly chosen tradeoffs that mean certain “less important” facts are de-prioritized. It’s not obvious that this is the wrong choice. I feel foolish for not having more respect for the body’s complexity and for the difficulty of the task all the experts are trying to accomplish. This is not medical advice.

In early January 2025, a family friend was over for lunch. One of my many guilty midwit pleasures is a love of New Year’s resolutions, so I asked her if she had made any. She said no, but mentioned that she had some relatives that were doing “damp January”.

In case you’re not aware, Dry January is a challenge many people do to quit drinking alcohol during the month of January. These folks were doing a variant in which, instead of not drinking, one simply drinks less.

For some reason, this triggered me. I thought, “Are you kidding? You can’t even stop drinking for a single month? Do you know how pathetic that is?” And then, “Fuck you! Fuck you for doing damp January! You know what, I’m going to stop drinking for a year!”

To be clear, these thoughts were directed at people I’ve never even met. In retrospect, I wonder what was going on with me emotionally. But I take resolutions seriously, so I felt committed.

We are now 15 months down the timeline, so I’ll make my report.

It was easy

This will sound odd, but I swear it’s true. Not drinking was so easy that it was almost easier than my previous baseline of not-not-drinking.

Before starting this resolution, I didn’t drink much—perhaps two or three drinks per week. But I often thought about drinking. Every time I saw friends or went to a restaurant, I thought, “Should I have a drink?” Usually I decided not to. But making that decision required effort.

After a few weeks of not drinking, that question never even came up. Drinking was simply not a thing I did, so I never needed to negotiate with myself.

Theoretically, you could allow yourself one drink a month instead of zero. Theoretically, that should be easier. But I’m pretty sure I’d find it harder, because alcohol would still be an option, a thing to consider.

Sometimes I need a thing

Early on, I sometimes wanted a drink. But gradually I noticed that I didn’t really want a drink, I just wanted a thing. I can’t find a precise name for this concept in psychology, but often, some deep part of my brain seems to scream, “I WANT A THING.” It could be alcohol, but I found dessert worked just as well. I suspect that a new shirt or meeting a new dog would also work.

I was not able to stop my brain from doing this. When it demanded a thing, I gave it a thing. I just substituted a non-alcohol thing. So, over the year, I became interested in desserts and even-more interested in tea.

The struggle was The Chocolates. Shortly after I made this resolution, my mother gave me a bag of chocolates that each contained a bit of whiskey. In general, I don’t keep chocolate at home. If anyone gives me chocolate, I immediately eat all of it and then text the giver, “Thanks for the chocolate, I ate it instead of dinner, it’s all gone, this is what will always happen if you give me chocolate.”

But I couldn’t eat the Chocolates, because they contained alcohol. I managed to get guests to eat a few. A couple of times I came close to draining out the alcohol and eating the chocolate container. I even considered throwing them away, but that felt wrong. So instead I spent a year glaring at them and waiting for them to apologize for the anguish they were causing me. This represented half the difficulty of this resolution. I do not recommend it. Keep your things separate.

Alcohol is bad for sleep

Have you heard that alcohol is bad for sleep? Because alcohol is bad for sleep. I’ve always known that was true, abstractly. But sleep is variable. If I didn’t sleep well on an individual night, I was never sure: Was that because of the alcohol, or was it random variation?

After a year without alcohol, I am very confident that yes indeed, alcohol is bad for sleep, because my sleep during 2025 was much better than in previous years. Sure, like anyone else, I still sometimes wake up and start thinking about oblivion rushing towards me, and how everything I love will vanish into time, and how all that was once future and hope inevitably becomes static and dust, and how the plague of bluetooth speakers continues to spread across the globe. But now: less!

I wish there was a drug I could take that would give me energy and improve my mood and make me physically healthier and smarter, all without side-effects. I don’t think such a drug exists. But we do have the opposite!

So, sadly, I’ve come to believe that alcohol is basically the perfect anti-nootropic. That’s not because it makes you dumb while you’re drunk. (True, but who cares?) Rather, that’s because it is bad for sleep, and therefore makes you worse across all dimensions the next day.

Alcohol is good for socializing sometimes

I did find not drinking to have one clear downside: It’s just not that much fun to hang out with people who are drinking if you are not drinking yourself.

To be clear, this is a limited effect. It’s only an issue at bars or certain parties where people are there to drink. I don’t go to many such gatherings, but when I did, I felt it was less fun.

It’s not that I missed alcohol. Instead, my theory is that drinking parties are a sort of joint role-playing exercise: “Let’s all get together and collectively reduce our inhibitions and see what happens.” It’s fun not (just) because everyone is taking a recreational drug, but because it’s a joint social experience. If you don’t drink, then you aren’t fully participating.

It seems like it should be possible to reproduce this effect without alcohol. You could imagine other ways to push the social equilibrium out of balance. Like… Masks? Or weird environments? Or mutual disclosure games? Should people get together and do a group cold plunge?

Unfortunately, all these are complicated and/or carry some kind of social stigma. So until we figure something better out, this is a real cost. It was minor for me, but it probably depends a lot on where you are in life.

Other effects

All other effects were minor. I guess I saved money at restaurants. I actually lost a bit of weight over the year, despite all the extra desserts, though I can’t say for sure if alcohol was the cause. Otherwise, once I stopped thinking of alcohol as an option, I rarely thought about the resolution at all, except when I saw those damn chocolates.

Aftermath

Towards the end of the year, I started wondering if I should quit drinking forever. But I never came to a conclusion, because I rarely thought about alcohol. I considered having a drink at midnight on New Year’s eve, but I happened to be on a plane that crossed the international date line, and thus skipped New Year’s eve.

And then… for the first few months of 2026, I still didn’t drink. That wasn’t because of any decision. It just never seemed appealing because (a) sleep and (b) I’d broken the mental link between want thing and drink alcohol. Eventually, I ate the chocolates, and I had a glass of wine when visiting some friends. If I can continue rarely drinking while almost never thinking about drinking, I’ll probably do that. If I slowly slide back into always thinking of alcohol as a live option and always negotiating with myself, I might just resolve to quit forever.

So that’s my story. Obviously, it’s heavily colored by my own idiosyncrasies, so it’s hard to say if it offers any general lesson.

I do think people underrate the long-term health impact of drinking. The effect on heart disease is debated, but everyone agrees that any alcohol increases the risk of cancer. Still, the long-term effects from occasional light drinking probably aren’t huge. What’s really underrated is the short-term effects, via worse sleep.

If I had to give advice, it would be this: If you drink, and you think you might be better off not drinking, why not try it? Maybe you’ll find that champagne is essential to your happiness and drink it every night, to hell with the costs. Maybe you’ll find a different baseline, or maybe you’ll quit forever. Whatever you decide, you’ll have full information.

Suppose I pour 8 oz (226.8 g) of boiling water into a ceramic coffee mug that weighs 1.25 lb (0.57 kg). The ambient air is still and 20 degrees Celsius. The cup starts at room temperature. Give me an equation for the temperature of the water in Celsius over time. The only free variable in the equation should be the number of seconds t since the water was poured. Focus on accuracy during the first 5 minutes.

Does that seem hard? I think it’s hard. The relevant physical phenomena include at least:

1. Conduction of heat between the water, the mug, the air, and the table.

2. Conduction of heat inside each of those things.

3. Convection (fluid movement) inside the water and the air.

4. Evaporation cooling as water molecules become vapor.

5. Movement of water vapor in the air.

6. Radiation. (Like all matter, the mug and water emit temperature-dependent infrared radiation.)

7. Surface tension, thermal expansion/contraction, re-absorption of air into the water as it cools, probably more.

And many details aren’t specified in the prompt. Is the mug made of porcelain or stoneware? What is the mug’s shape? What is the table made of? How humid is the air? How am I reducing the spatially varying water temperature to a single number?

So this isn’t a problem where you can sit around and think and find

with a “correct” answer that you can find by thinking. Reality is too complicated. Instead, answering question requires “taste”—guessing which factors are most important, making assumptions about missing details, etc.

So I put that question to a bunch of LLMs. Here is what they said:

(Technically, they gave equations as text. I’m plotting those equations.)

I was surprised by those curves, both in terms of how fast they think the temperature will drop in the beginning, and how slowly they think it will drop later on. They think you get as much cooling in the first few minutes as you do in the rest of the hour. Can that be right?

Then I did the experiment. First, I waited until the ambient temperature happened to reach 20 degrees Celsius. Then, I put 8 oz of water into a measuring cup, microwaved it until it reached a boil, let the temperature equalize a bit, and then microwaved it until the water boiled again. Then, I poured the water into a 1.25 lb coffee mug with a digital thermometer in it and shouted out measurements every five seconds, which were frantically recorded by the Dynomight Biologist. Gradually I reduced measurements to every 15 seconds, 30 seconds, 1 minute, and then 5 minutes.

Behold:

Or, here’s a zoomed-in view of the first five minutes:

The predictions were all OK, but none were great. Probably Claude 4.6 Opus did best, albeit after consuming $0.61 of tokens. (Insert joke about physical experiments / Department of Defense / money / coffee.)

That said, what surprised me about the predictions was how quickly the temperature dropped in the first few minutes, and how slowly it dropped later on. But experimentally, it dropped even faster early on, and even slower towards the end. So if you wanted to ensemble my intuition with the LLM, I guess my intuition would get a weight of zero.

In conclusion, they may take our math, but they’ll somewhat more slowly take our fine motor control. Thank you for reading another middle-school science project.

EXHIBIT A

Here is some text. It is made out of words.

Here is a subsection

And here are some bullet-points:

• Here is one.

• Here is another.

Hierarchy

1. Here is a numbered list.

2. And now:

   • Look at this.

   • Bullets inside a number inside a section inside a section.

3. What a time to be alive.

Pictures

The text can also contain pictures for you to look at with your eyes¹.

¹ There can also be footnotes; have an eye emoji: 👀

Quotes

The text can also include quotes.

• Actually, let’s do one inside of a list.

  • A deeply nested list.

    • This is going to be awesome.
      
      > The awful thing about life is this: Everyone has his reasons.

    • Nailed it.

Back up

Wait a second.

• Are we currently in a section or subsection or a subsubsection?

• What parent section encloses this one?

• Where are we in the hierarchy?

• What are we doing?

EXHIBIT B

This is also text. It is also made out of words. But instead of jerky fragments, these words are organized into sentences, like normal human language.

Do you see how relaxing this is? After the torment you suffered above, isn’t it nice to have words that come in a simple linear order? And isn’t it nice that you just have to read the words, and not worry about how they fit into some convoluted implied knowledge taxonomy?

These sentences are themselves organized into paragraphs. The first sentence of each paragraph is a sort of summary. So if you want to skim, you can do that. But you don’t have to skim. This text also has italics and parentheses and whatnot. But not too much. (Just a little.)

Why I bring this up

Thanks for enduring that. My purpose was to illustrate a mystery. Namely, why do so many people today seem to write more like Exhibit A than Exhibit B?

People sometimes give me something they wrote and ask for comments. Half the time, my reaction is Good god, why is 70% of this section titles and bullet points?

This always gives me a strange feeling. It’s like all the formatting is based on some ontology. And that ontology is what I really need to understand. But it’s never actually explained. Instead, I guess I’m supposed to figure it out as things jerk between different topics? It’s disorienting, like a movie that cuts between different scenes every three seconds.

But maybe that’s just my opinion? Maybe, but sometimes I’ll ask people who write like this to show me some writing they admire. And inevitably, its’s not 70% formatting, but mostly paragraphs and normal human language. So I feel that people who write this way are violating the central tenet of making stuff, which is to make something you would actually like.

So then why write like that? Why do I, despite my griping, often find myself writing like that? I’ve wondered this for years. But I told myself that I was right and that too much formatting is bad.

But now—have you heard?—now we have this technology where computers can write stuff. And guess what? When they do that, they also use an insane amount of formatting.

That’s weird. I figured people were addicted to formatting because they’re noobs that don’t know any better. But AIs have been optimized to make human raters happy. And that led to a similar addiction. Why?

1. Maybe formatting is good

The obvious explanation is that formatting is good. People love reading stuff that’s all formatting. We should all be formatting-maxxing.

There’s something to this. But it can’t really be right, because popular human writers use formatting in moderation. So formatting can’t be that good.

2. Maybe formatting is good in certain contexts

Even before AI, everyone did agree that formatting was great in one context: Search-engine optimized content slop. Back in 2018, if you searched for anything, you’d find pages brimming with section titles and bullet points.

Why? Well, when I type “why human gastric juice more acidic than other animals”, I’m not really looking for something to read. I just want to skim an overview of the main theories. I’ve experimented with asking AIs to give the same information in various styles, and I reluctantly concede that the formatting helps.

But that’s not reading. Say you’ve written a ten-thousand word manifesto on human-eco-social species enhancement. If I actually care about what you think, I maintain that it’s better in paragraphs, because reading ten thousand words with endless formatting would be excruciating. This is why everyone who writes long-form essays that people actually read uses normal paragraphs.

So our mystery is still alive. Most writers aspire not to write content slop, but meaningful stuff other people care about. Often, when people show me formatting-maxxed essays, I’ll complain and they’ll rewrite it with less formatting and agree that the new version is better. So why use so much formatting even when it’s bad?

3. Maybe quality is hard to verify

There’s something odd about that previous example. When I search for “why human gastric juice more acidic than other animals”, why am I not looking for something to “read”? After all, I like reading. If one of my favorite bloggers wrote an essay on the mystery of human gastric juice, I would devour it.

So if I want a good essay, why don’t I look for one? I guess it’s because I instinctively rate my odds of finding one on any random topic as quite low.

There’s something here related to Gresham’s law: A format-maxxed essay might be sort of crap, but at least I can ascertain its crap level quickly. A “real” essay could be great, but I’d have to invest a lot of time before I can know if that time was worth investing. So I—regretfully—mostly only read “real” essays when I have some signal that they’re good. If everyone behaves the way I do, I guess people will respond to their incentives and write with lots of formatting.

Similarly, if a (current) AI tried to write a “real” essay, I probably wouldn’t read it, because I wouldn’t trust that it was good. Perhaps that explains why they don’t.

Aside: If this is right, then it predicts that as AIs advance, they should become less formatting-crazy. The better they are, the more we’ll trust them.

4. Maybe chain-of-thought works well in format world

Some people can think of an idea, organize their thoughts, and then write them down, tidy and sparkling. I am not one of those people. If I mentally organize my ideas and go to write them down, I soon learn that my ideas were not in fact organized. Usually, they’re hardly even ideas and more a slurry of confused psychic debris.

The way I write is that I make an outline. Or, rather, I try to make an outline. But then I realize the structure is off, so I start over. After a few cycles, I give up and just write the first section. After revising it eight times, I’ll try (and fail) to make an outline for the rest of the post. This continues—with occasional interludes where I reorganize everything—until I can’t take it anymore and publish.

I don’t recommend it. My point is just that blathering out a bunch of text is a good way to think. And when blathering, formatting seems to help. Partly, I think that’s because formatting allows you to experiment with structure without worrying about the details. And partly I think that’s because formatting makes it easier to get down details without worrying about the bigger picture.

So maybe that’s one source of our formatting addiction? We blather in formatting, but don’t put in the work to clarify things?

Oddly, some claim that something similar is true for AI: If you tune them to write with lots of formatting, that doesn’t just change how the content looks, but also improves accuracy. The idea is that as the AI looks at what it’s written so far, formatting helps it stay focused on the most important things. Supposedly.

Maybe that’s true. But we have “reasoning” AIs now, that blather for a while before producing a final output. If they wanted, they could format-maxx while thinking and output paragraphs at the end. But they don’t. So while this explanation might work for people, I don’t buy it for AI.

5. Maybe formatting is a bluff

Finally, a conspiracy theory. Sometimes when I try to fight through a format-maxxed essay, it seems like all the formatting speaks to me. It says: “This is a nonlinear web of ideas. I’m giving you the pieces. If you pay attention, you should see how they fit together. Sadly, the world isn’t a simple narrative I can spoon-feed to you. So this is the best I can do.”

I think this is a bluff. And it’s a good one, because it’s based in truth. The world is not a narrative. Narratives are lies we tell ourselves to try to cope with the swirl of complexity that is reality. All true!

Editor’s note: At this point, the author became agitated and wrote and then deleted a bunch of bullet points. In the interest of transparency, these are collected in a footnote1.

But imagine you’re an AI. You’re being trained to respond in ways that make human raters happy. You can remember most knowledge ever created, but you’re so-so at synthesizing it into new ideas. If someone asks you a question and you try to write a beautiful narrative, your response might look like confusing babbling, meaning your raters say, “Bad AI. Stop that.” Whereas if you output a bunch of section titles and bullet points, raters might say, “This seems OK.” So you’ll start doing the latter.

That’s not bad. Arguably, you (you’re still an AI) are responding in the way that’s most useful, given your capabilities. But you are also responding in a way that gives a misleading impression that you’ve figured out how everything fits together, even if you haven’t.

I suspect something similar happens with humans. Say you have a bunch of ideas, but you haven’t yet sewn them together into a clear story. If you write paragraphs, people will probably view them as confused babbling. Whereas if you write with lots of formatting, people might still be at least somewhat positive. Just like AIs, we all respond to our rewards.

More importantly, if you’ve written something that’s 70% formatting, it’s easy to delude yourself that there’s a clear set of ideas underneath, even when there isn’t.

The good news is that if you put in the effort, you can write better paragraphs than AI (for now). The act of creating a narrative forces you to confront contradictions that are invisible in format-world. So even if you want to write with 70% formatting, consider forcing yourself to write in paragraphs first.

Summary

Theory: Both people and AIs are addicted to formatting because:

• Formatting is good.

  • Sometimes.

  • Especially if you don’t trust the author.

  • On the internet, most people probably don’t trust you.

• It’s harder to see that something has problems when it’s written in all-formatting.

• It’s easier to blather out a bunch of formatting than to write lucid paragraphs.

  • This is good at some stages, because it’s easy.

  • But forcing yourself to actually write a narrative is also good, because it’s hard.

So:

1. First write with lots of formatting.

2. Then figure out how to remove it.

3. Then put it back, if you want.

P.S.

How does the optimal amount of formatting vary in the length of a piece of writing? I suspect it’s like this:

It occurred to me that if I could invent a machine—a gun—which could by its rapidity of fire, enable one man to do as much battle duty as a hundred, that it would, to a large extent supersede the necessity of large armies, and consequently, exposure to battle and disease [would] be greatly diminished.

Richard Gatling (1861)

2.

In 1923, Hermann Oberth published The Rocket to Planetary Spaces, later expanded as Ways to Space Travel. This showed that it was possible to build machines that could leave Earth’s atmosphere and reach orbit. He described the general principles of multiple-stage liquid-fueled rockets, solar sails, and even ion drives. He proposed sending humans into space, building space stations and satellites, and travelling to other planets.

The idea of space travel became popular in Germany. Swept up by these ideas, in 1927, Johannes Winkler, Max Valier, and Willy Ley formed the Verein für Raumschiffahrt (VfR) (Society for Space Travel) in Breslau (now Wrocław, Poland). This group rapidly grew to several hundred members. Several participated as advisors of Fritz Lang’s The Woman in the Moon, and the VfR even began publishing their own journal.

In 1930, the VfR was granted permission to use an abandoned ammunition dump outside Berlin as a test site and began experimenting with real rockets. Over the next few years, they developed a series of increasingly powerful rockets, first the Mirak line (which flew to a height of 18.3 m), then the Repulsor (>1 km). These people dreamed of space travel, and were building rockets themselves, funded by membership dues and a few donations. You can just do things.

However, with the great depression and loss of public interest in rocketry, the VfR faced declining membership and financial problems. In 1932, they approached the army and arranged a demonstration launch. Though it failed, the army nevertheless offered a contract. After a tumultuous internal debate, the VfR rejected the contract. Nevertheless, the army hired away several of the most talented members, starting with a 19-year-old named Wernher von Braun.

Following Hitler’s rise to power in January 1933, the army made an offer to absorb the entire VfR operation. They would work at modern facilities with ample funding, but under full military control, with all work classified and an explicit focus on weapons rather than space travel. The VfR’s leader, Rudolf Nebel, refused the offer, and the VfR continued to decline. Launches ceased. In 1934, the Gestapo finally shut the VfR down, and civilian research on rockets was restricted. Many VfR members followed von Braun to work for the military.

Of the founding members, Max Valier was killed in an accident in May 1930. Johannes Winkler joined the SS and spent the war working on liquid-fuel engines for military aircraft. Willy Ley was horrified by the Nazi regime and in 1935 forged some documents and fled to the United States, where he was a popular science author, seemingly the only surviving thread of the spirit of Oberth’s 1923 book. By 1944, V-2 rockets were falling on London and Antwerp.

3.

North Americans think the Wright Brothers invented the airplane. Much of the world believes that credit belongs to Alberto Santos-Dumont, a Brazilian inventor working in Paris.

Though Santos-Dumont is often presented as an idealistic pacifist, this is hagiography. In his 1904 book on airships, he suggests warfare as the primary practical use, discussing applications in reconnaissance, destroying submarines, attacking ships, troop supply, and siege operations. As World War I began, he enlisted in the French army (as a chauffeur), but seeing planes used for increasing violence disturbed him. His health declined and he returned to Brazil.

His views on military uses of planes seemed to shift. Though planes contributed to the carnage in WWI, he hoped that they might advance peace by keeping European violence from reaching the American continents. Speaking at a conference in the US in late 1915 or early 1916, he suggested:

Here in the new world we should all be friends. We should be able, in case of trouble, to intimidate any European power contemplating war against any one of us, not by guns, of which we have so few, but by the strength of our union. […] Only a fleet of great aeroplanes, flying 200 kilometers an hour, could patrol these long coasts.

Following the war, he appealed to the League of Nations to ban the use of planes as weapons and even offered a prize of 10,000 francs for whoever wrote the best argument to that effect. When the Brazilian revolution broke out in 1932, he was horrified to see planes used in fighting near his home. He asked a friend:

Why did I make this invention which, instead of contributing to the love between men, turns into a cursed weapon of war?

He died shortly thereafter, perhaps by suicide. A hundred years later, banning the use of planes in war is inconceivable.

4.

Humanity had few explosives other than gunpowder until 1847 when Ascanio Sobrero created nitroglycerin by combining nitric and sulfuric acid with a fat extract called glycerin. Sobrero found it too volatile for use as an explosive and turned to medical uses. After a self-experiment, he reported that ingesting nitroglycerin led to “a most violent, pulsating headache accompanied by great weakness of the limbs”. (He also killed his dog.) Eventually this led to the use of nitroglycerin for heart disease.

Many tried and failed to reliably ignite nitroglycerin. In 1863, Alfred Nobel finally succeeded by placing a tube of gunpowder with a traditional fuse inside the nitroglycerin. He put on a series of demonstrations blowing up enormous rocks. Certain that these explosives would transform mining and tunneling, he took out patents and started filling orders.

The substance remained lethally volatile. There were numerous fatal accidents around the world. In 1867, Nobel discovered that combining nitroglycerin with diatomaceous earth produced a product that was slightly less powerful but vastly safer. His factories of “dynamite” (no relation) were soon producing thousands of tons a year. Nobel sent chemists to California where they started manufacturing dynamite in a plant in what is today Golden Gate Park. By 1874, he had founded dynamite companies in more than ten countries and he was enormously rich.

In 1876, Nobel met Bertha Kinsky, who would become Bertha von Suttner, a celebrated peace activist. (And winner of the 1905 Nobel Peace Prize). At their first meeting, she expressed concern about dynamite’s military potential. Nobel shocked her. No, he said, the problem was that dynamite was too weak. Instead, he wished to produce “a substance or invent a machine of such frightful efficacy for wholesale destruction that wars should thereby become altogether impossible”.

It’s easy to dismiss this as self-serving. But dynamite was used overwhelmingly for construction and mining. Nobel did not grow rich by selling weapons. He was disturbed by dynamite’s use in Chicago’s 1886 Haymarket bombing. After being repeatedly betrayed and swindled, he seemed to regard the world of money with a kind of disgust. At heart, he seemed to be more inventor than businessman.

Still, the common story that Nobel was a closet pacifist is also hagiography. He showed little concern when both sides used dynamite in the 1870-1871 Franco-Prussian war. In his later years, he worked on developing munitions and co-invented cordite, remarking that they were “rather fiendish” but “so interesting as purely theoretical problems”.

Simultaneously, he grew interested in peace. He repeatedly suggested that Europe try a sort of one-year cooling off period. He even hired a retired Turkish diplomat as a kind of peace advisor. Eventually, he concluded that peace required an international agreement to act against any aggressor.

When Bertha’s 1889 book Lay Down Arms became a rallying cry, Nobel called it a masterpiece. But Nobel was skeptical. He made only small donations to her organization and refused to be listed as a sponsor of a pacifist congress. Instead, he continued to believe that peace would come through technological means, namely more powerful weapons. If explosives failed to achieve this, he told a friend, a solution could be found elsewhere:

A mere increase in the deadliness of armaments would not bring peace. The difficulty is that the action of explosives is too limited; to overcome this deficiency war must be made as deadly for all the civilians back home as for the troops on the front lines. […] War will instantly stop if the weapon is bacteriology.

5.

I’m a soldier who was tested by fate in 1941, in the very first months of that war that was so frightening and fateful for our people. […] On the battlefield, my comrades in arms and I were unable to defend ourselves. There was only one of the legendary Mosin rifles for three soldiers.

[…]

After the war, I worked long and very hard, day and night, labored at the lathe until I created a model with better characteristics. […] But I cannot bear my spiritual agony and the question that repeats itself over and over: If my automatic deprived people of life, am I, Mikhail Kalashnikov, ninety-three years of age, son of a peasant woman, a Christian and of Orthodox faith, guilty of the deaths of people, even if of enemies?

For twenty years already, we have been living in a different country. […] But evil is not subsiding. Good and evil live side by side, they conflict, and, what is most frightening, they make peace with each other in people’s hearts.

Mikhail Kalashnikov (2012)

6.

In 1937 Leo Szilárd fled Nazi Germany, eventually ending up in New York where—with no formal position—he did experiments demonstrating that uranium could likely sustain a chain reaction of neutron emissions. He immediately realized that this meant it might be possible to create nuclear weapons. Horrified by what Hitler might do with such weapons, he enlisted Einstein to write the 1939 Einstein–Szilárd letter, which led to the creation of the Manhattan project. Szilárd himself worked for the project at the Metallurgical Laboratory at the University of Chicago.

On June 11, 1945, as the bomb approached completion, Szilárd co-signed the Franck report:

Nuclear bombs cannot possibly remain a “secret weapon” at the exclusive disposal of this country, for more than a few years. The scientific facts on which their construction is based are well known to scientists of other countries. Unless an effective international control of nuclear explosives is instituted, a race of nuclear armaments is certain to ensue.

[…]

We believe that these considerations make the use of nuclear bombs for an early, unannounced attack against Japan inadvisable. If the United States would be the first to release this new means of indiscriminate destruction upon mankind, she would sacrifice public support throughout the world, precipitate the race of armaments, and prejudice the possibility of reaching an international agreement on the future control of such weapons.

On July 16, 1945, the Trinity test achieved the first successful detonation of a nuclear weapon. The next day, he circulated the Szilárd petition:

We, the undersigned scientists, have been working in the field of atomic power. Until recently we have had to fear that the United States might be attacked by atomic bombs during this war and that her only defense might lie in a counterattack by the same means. Today, with the defeat of Germany, this danger is averted and we feel impelled to say what follows:

The war has to be brought speedily to a successful conclusion and attacks by atomic bombs may very well be an effective method of warfare. We feel, however, that such attacks on Japan could not be justified, at least not unless the terms which will be imposed after the war on Japan were made public in detail and Japan were given an opportunity to surrender.

[…]

The development of atomic power will provide the nations with new means of destruction. The atomic bombs at our disposal represent only the first step in this direction, and there is almost no limit to the destructive power which will become available in the course of their future development. Thus a nation which sets the precedent of using these newly liberated forces of nature for purposes of destruction may have to bear the responsibility of opening the door to an era of devastation on an unimaginable scale.

[…]

In view of the foregoing, we, the undersigned, respectfully petition: first, that you exercise your power as Commander-in-Chief, to rule that the United States shall not resort to the use of atomic bombs in this war unless the terms which will be imposed upon Japan have been made public in detail and Japan knowing these terms has refused to surrender; second, that in such an event the question whether or not to use atomic bombs be decided by you in the light of the consideration presented in this petition as well as all the other moral responsibilities which are involved.

The Truman administration did not adopt this recommendation.

How heritable is hair color? Well, if you’re a redhead and you have an identical twin, they will definitely also be a redhead. But the age at which twins go gray seems to vary a bit based on lifestyle. And there’s some randomness in where melanocytes end up on your skull when you’re an embryo. And your twin might dye their hair! So the correct answer is, some large number, but less than 100%.

OK, but check this out: Say I redefine “hair color” to mean “hair color except ignoring epigenetic and embryonic stuff and pretending that no one ever goes gray or dyes their hair et cetera”. Now, hair color is 100% heritable. Amazing, right?

Or—how heritable is IQ? The wise man answers, “Some number between 0% or 100%, it’s not that important, please don’t yell at me.” But whatever the number is, it depends on society. In our branch of the multiverse, some kids get private tutors and organic food and $20,000 summer camps, while other kids get dysfunctional schools and lead paint and summers spent drinking Pepsi and staring at glowing rectangles. These things surely have at least some impact on IQ.

But again, watch this: Say I redefine “IQ” to be “IQ in some hypothetical world where every kid got exactly the same school, nutrition, and parenting, so none of those non-genetic factors matter anymore.” Suddenly, the heritability of IQ is higher. Thrilling, right? So much science.

If you want to redefine stuff like this… that’s not wrong. I mean, heritability is a pretty arbitrary concept to start with. So if you prefer to talk about heritability in some other world instead of our actual world, who am I to judge?

Incidentally, here’s a recent paper:

I STRESS THAT THIS IS A PERFECTLY FINE PAPER. I’m picking on it mostly because it was published in Science, meaning—like all Science papers—it makes grand claims but is woefully vague about what those claims mean or what was actually done. Also, publishing in Science is morally wrong and/or makes me envious. So I thought I’d try to explain what’s happening.

It’s actually pretty simple. At least, now that I’ve spent several hours reading the paper and its appendix over and over again, I’ve now convinced myself that it’s pretty simple. So, as a little pedagogical experiment, I’m going to try to explain the paper three times, with varying levels of detail.

Explanation 1: The very extremely high level picture

The normal way to estimate the heritability of lifespan is using twin data. Depending on what dataset you use, this will give 23-35%. This paper built a mathematical model that tries to simulate how long people would live in a hypothetical world in which no one dies from any non-aging related cause, meaning no car accidents, no drug overdoses, no suicides, no murders, and no (non-age-related) infectious disease. On that simulated data, for simulated people in a hypothetical world, heritability was 46-57%.

Commentary

Everyone seems to be interpreting this paper as follows:

Aha! We thought the heritability of lifespan was 23-35%. But it turns out that it’s around 50%. Now we know!

I understand this. Clearly, when the editors at Science chose the title for this paper, their goal was to lead you to that conclusion. But this is not what the paper says. What it says is this:

We built a mathematical model of alternate universe in which nobody died from accidents, murder, drug overdoses, or infectious disease. In that model, heritability was about 50%.

Explanation 2: The very high-level picture

Let’s start over. Here’s figure 2 from the paper.

Normally, heritability is estimated from twin studies. The idea is that identical twins share 100% of their DNA, while fraternal twins share only 50%. So if some trait is more correlated among identical twins than among fraternal twins, that suggests DNA influences that trait. There are statistics that formalize this intuition. Given a dataset that records how long various identical and fraternal twins lived, these produce a heritability number.

Two such traditional estimates appear as black circles in the above figures. For the Danish twin cohort, lifespan is estimated to be 23% heritable. For the Swedish cohort, it’s 35%.

This paper makes a “twin simulator”. Given historical data, they fit a mathematical model to simulate the lifespans of “new” twins. Then they compute heritability on this simulated data.

Why calculate heritability on simulated data instead of real data? Well, their mathematical model contains an “extrinsic mortality” parameter, which is supposed to reflect the chance of death due to all non-aging-related factors like accidents, murder, or infectious disease. They assume that the chance someone dies from any of this stuff is constant over people, constant over time, and that it accounts for almost all deaths for people aged between 15 and 40.

The point of building the simulator is that it’s possible to change extrinsic mortality. That’s what’s happening in the purple curves in the above figure. For a range of different extrinsic mortality parameters, they simulate datasets of twins. For each simulated dataset, they estimate heritability just like with a real dataset.

Note that the purple curves above nearly hit the black circles. This means that if they run their simulator with extrinsic mortality set to match reality, they get heritability numbers that line up with what we get from real data. That suggests their mathematical model isn’t totally insane.

If you decrease extrinsic mortality, then you decrease the non-genetic randomness in how long people live. So heritability goes up. Hence, the purple curves go up as you go to the left.

Intermission: On Science

My explanation of this paper relies on some amount of guesswork. For whatever reason, Science has decided that papers should contain almost no math, even when the paper in question is about math. So I’m mostly working from an English description. But even that description isn’t systematic. There’s no place that clearly lays out all the things they did, in order. Instead, you get little hints, sort of randomly distributed throughout the paper. There’s an appendix, which the paper confidently cites over and over. But if you actually read the appendix, it’s just more disconnected explanations of random things except now with equations set in glorious Microsoft Word format.

Now, in most journals, authors write everything. But Science has professional editors. Given that every single statistics-focused paper in Science seems to be like this, we probably shouldn’t blame the authors of this one. (Other than for their decision to publish in Science in the first place.)

I do wonder what those editors are doing, though. I mean, let me show you something. Here’s the first paragraph where they start to actually explain what they actually did, from the first page:

See that h(t,θ) at the end? What the hell is that, you ask? That’s a good question, because it was never introduced before this and is never mentioned again. I guess it’s just supposed to be f(t,θ), which is fine. (I yield to none in my production of typos.) But if paying journals ungodly amounts of money brought us to this, of what use are those journals?

Moving on…

Explanation 3: Also pretty high level, but as low as we’re doing to go

Probably most people don’t need this much detail and should skip this section. For everyone else, let’s start over one last time.

The “normal” way to estimate heritability is by looking at correlations between different kinds of twins. Intuitively, if the lifespans of identical twins are more correlated than the lifespans of fraternal twins, that suggests lifespan is heritable. And it turns out that one estimator for heritability is “twice the difference between the correlation among identical twins and the correlation among fraternal twins, all raised together.” There are other similar estimators for other kinds of twins. These normally say lifespan is perhaps 20% and 35% heritable.

This paper created an equation to model the probability a given person will die at a given age. The parameters of the equation vary from person to person, reflecting that some of us have DNA that predisposes us to live longer than others. But the idea is that the chances of dying are fairly constant between the ages of 15 and 40, after which they start increasing.

This equation contains an “extrinsic mortality” parameter. This is meant to reflect the chance of death due to all non-aging related factors like accidents or murder, etc. They assume this is constant. (Constant with respect to people and constant over time.) Note that they don’t actually look at any data on causes of death. They just add a constant risk of death that’s shared by all people at all ages to the equation, and then they call this “extrinsic mortality”.

Now remember, different people are supposed to have different parameters in their probability-of-death equations. To reflect this, they fit a Gaussian distribution (bell curve) to the parameters with the goal of making it fit with historical data. The idea is that if the distribution over parameters were too broad, you might get lots of people dying at 15 or living until 120, which would be wrong. If the distribution were too concentrated, then you might get everyone dying at 43, which would also be wrong. So they find a good distribution, one that makes the ages people die in simulation look like the ages people actually died in historical data.

Right! So now they have:

1. An equation that’s supposed to reflect the probability a given person dies at a given age.

2. A distribution over the parameters of that equation that’s supposed to produce population-wide death ages that look like those in real historical data.

Before moving on, I remind you of two things:

1. They assume their death equation entirely determines the probability someone will die in a given year.

2. They assume that the shape of someone’s death equation is entirely determined by genetics.

The event of a person dying at a given age is random. But the probability that this happens is assumed to be fixed and determined by genes and genes alone.

Now they simulate different kinds of twins. To simulate identical twins, they just draw parameters from their parameter distribution, assign those parameters to two different people, and then let them randomly die according to their death equation. (Is this getting morbid?) To simulate fraternal twins, they do the same thing, except instead of giving the two twins identical parameters, they give them correlated parameters, to reflect that they share 50% of their DNA.

How exactly do they create those correlated parameters? They don’t explain this in the paper, and they’re quite vague in the supplement. As far as I can tell they sample two sets of parameters from their parameter distribution such that the parameters are correlated at a level of 0.5.

Now they have simulated twins. They can simulate them with different extrinsic mortality values. If they lower extrinsic mortality, heritability of lifespan goes up. If they lower it to zero, heritability goes up to around 50%.

More commentary

Almost all human traits are partly genetic and partly due to the environment and/or random. If you could change the world and reduce the amount of randomness, then of course heritability would go up. That’s true for life expectancy just life for anything else. So what’s the point of this paper?

There is a point!

1. Sure, obviously heritability would be higher in a world without accidents or murder. We don’t need a paper to know that. But how much higher? It’s impossible to say without modeling and simulating that other world.

2. Our twin datasets are really old. It’s likely that non-aging-related deaths are lower now in the past, because we have better healthcare and so on. This means that the heritability of lifespan for people alive today may be larger than it was for the people in our twin datasets, some of whom were born in 1870. We won’t know for sure until we’re all dead, but this paper gives us a way to guess.

3. Have I mentioned that heritability depends on society? And that heritability changes when society changes? And that heritability is just a ratio and you should stop trying to make it be a non-ratio because only-ratio things cannot be non-ratios? This is a nice reminder.

Honestly, I think the model the paper built is quite clever. Nothing is perfect, but I think this is a pretty good run at the question of, “How high would the heritability of lifespan be if extrinsic mortality were lower?”

I only have two objections. The first is to the Science writing style. This is a paper describing a statistical model. So shouldn’t there be somewhere in the paper where they explain exactly what they did, in order, from start to finish? Ostensibly, I think this is done in the left-hand column on the second page, just with little detail because Science is written for a general audience. But personally I think that description is the worst of all worlds. Instead of giving the high-level story in a coherent way, it throws random technical details at you without enough information to actually make sense of them. Couldn’t the full story with the full details at least be in the appendix? I feel like this wasted hours of my time, and that if someone wanted to reproduce this work, they would have almost no chance of doing so from the description given. How have we as a society decided that we should take our “best” papers and do this to them?

But my main objection is this:

At first, I thought this was absurd. The fact that people die in car accidents is not a “confounding factor”. And pretending that no one dies in a car accidents does not “address” some kind of bias. That’s just computing heritability in some other world. Remember, heritability is not some kind of Platonic form. It is an observational statistic. There is no such thing as “true” heritability, independent of the contingent facts of our world.

But upon reflection, I think they’re trying to say something like this:

Heritability of human lifespan is about 50% when extrinsic mortality is adjusted to be closer to modern levels.

The problem is: I think this is… not true? Here are the actual heritability estimates in the paper, varying by dataset (different plots) the cutoff year (colors) and extrinsic mortality (x-axis).

When extrinsic mortality goes down, heritability goes up. So the obvious question is: What is extrinsic mortality in modern people?

This is a tricky question, because “extrinsic mortality” isn’t some simple observational statistic. It is a parameter in their model. (Remember, they never looked at causes of death.) So it’s hard to say, but they seem to suggest that extrinsic mortality in modern people is 0.001 / year, or perhaps a bit less.

The above figures have the base-10 logarithm of extrinsic mortality on the x-axis. And the base-10 logarithm of 0.001 is -3. But if you look at the curves when the x-axis is -3, the heritability estimates are not 50%. They’re more like 35-45%, depending on the particular model and age cutoff.

So here’s my suggested title:

Heritability of human lifespan is about 40% when extrinsic mortality is adjusted to modern levels, according to our simulation.

There might be a reason I don’t work at Science.

Many years ago, I read Crime and Punishment. Here, with no research and no notes, is what I can remember about that book:

1. It was pretty good.

2. There was some guy, I think named Ras-something.

3. He was really angsty/edgy and lived in a small apartment or attic.

4. One day, for no particular reason, he killed an old woman.

5. Having done this random murder, he became even more angsty/edgy.

6. Then there was this police inspector guy.

7. The inspector kept coming after Ras-whoever and making extremely long philosophical rants.

8. Those rants may or may not have represented the personal views of Fyodor Dostoevsky.

9. I can’t remember how the book ended. Surely Ras-whoever didn’t live happily ever after? But was he caught or did he confess? No idea.

This is probably below average. I know people who seem to remember every detail of everything they read. But even if you’re one of them, so what? Is remembering those books better than remembering whatever else you would have done with your time if you hadn’t been reading?

And yet: If I’m on vacation and I spend an afternoon reading a novel somewhere in the mountains or on a beach, I feel like I’m living my best life. Whereas if I spent an afternoon staring at short videos on my phone, I’m sure I’d feel like a gigantic loser. So what’s going on here?

Theory 1: Ye olde status

The obvious explanation is that there’s nothing intrinsically great about reading novels. The reason we think it’s great is that reading novels—at least the right ones—is high status. It’s a way of playing the Glass Bead Game, a way of collecting cultural capital for you to lord over other people who don’t have as much time or education as you do. It may feel like you “actually enjoy reading”, but that’s because you’re a desperate striver that subconsciously shape-shifts into whatever you think will make you look fancy. Apologize for reading. Apologize!

I think there is something in this. However, I’m also pretty sure it’s not the full explanation, and I’m bored to death with everyone trying to explain everything this way. So let’s move on.

Theory 2: Diminishing returns

Say you can’t read novels. Maybe because you’re illiterate, maybe because you have no attention span, maybe because you can’t tear yourself away from Candy Clicker. Now, say you cultivate the ability to read novels. Whatever issues you address in that process, it seems like it will clearly be good for you, right?

Under this theory, what’s important is having the ability to read novels. But said ability is acquired by reading novels, so read some novels.

Alternatively, say you could read novels, but you simply never have. It’s plausible that the first time you have the “novel” experience of taking photons into your eyes and mentally converting them into a story, this truly does feed your mind.

Both versions of this theory suggest that reading novels has diminishing returns. That fits nicely with the fact that many people push their children to read novels while not reading any themselves. But do we really believe that after you’ve read some number of novels, it’s pointless to read more?

Theory 3: Common language

I think Catcher in the Rye is a good but not great book. But I love talking about Catcher in the Rye because (1) all North Americans seem to have read it, and (2) whenever I ask someone to tell me how they feel about Holden Caulfield, I always seem to learn something about them.

(I find him sympathetic.)

If there’s a group of people talking about Catcher in the Rye—or The Three-Body Problem, or Infinite Jest, or Don Quixote—then you benefit from being able to participate. The cynic might argue that this is zero-sum status competition. But I don’t think that’s most of it. Because, at least in my social circles, people feel boorish talking about books if not everyone has read them. So these conversations only happen if everyone has read the book in question.

Ultimately, we’re all alone in the world, and trying to connect with each other by pushing air through our throat meat. With more shared cultural context, those meat sounds are more meaningful, so we can all feel less alone.

True. But shared context can come from other things, too, like traveling to the same places, or watching the same sports, or practicing the same skills or hobbies. So what makes books special? The two answers I see are:

1. Nothing. If you think they’re better than other types of cultural context, that’s because you’re a book person.

2. Books leave more room for interpretation. Maybe Don Quixote is a fanatic, maybe he’s an idealist, maybe he’s a “wise fool”. It’s debatable. But there’s no doubt who won the last World Cup.

I lean weakly towards the first answer. Novels are a useful form of social context. But that’s a side benefit. It’s not why we read most books.

Theory 4: Legible mind-space

Maybe novels are just another form of entertainment. OK. But say you tried to tell the same story as a novel or as movie / podcast / opera / interpretive dance performance. Different formats will be better in different ways. One advantage I see for novels is that they make it natural to explore the interior worlds of the characters.

Some movies have voice-overs where characters explain what they’re thinking. But this is generally considered cringe and a poor use of the medium. Meanwhile, many books are mostly about exploring what the characters are thinking.

Thoughts are worth exploring. If you want to explore thoughts, maybe novels are the best way to do that.

Aside: I’ve mentioned before that I think My Brilliant Friend is the best TV show ever made. Can I confess that I like it much more than the books it is based on? Because, like the books, the TV show involves a lot of what the main character is thinking, and even makes heavy use of voice-overs. So maybe other mediums have unrealized potential?

Theory 5: Purity of vision

Movies are expensive to make. To be financially viable, they need to target a large slice of the population. Movies also reflect the combined efforts of many people. Both of these mean that movies are a compromise between different visions.

Novels are usually written by one person. And they’re often written more for personal expression than to make money. After all, writing is fun. I mean—writing is hard, but would you rather spend an afternoon holding up a shotgun microphone, cleaning a movie star’s trailer, or writing a novel?

To quantify this, some searching suggests that around 10,000 feature films are released each year, as compared to around 1,000,000 novels. (Does one in 7,000 people really write a novel each year?) That’s two orders of magnitude. So if you want to hear a truly unique story, a pure vision of one person, maybe novels are where you’ll find it.

Theory 6: All these theories are stupid

Or: Maybe the point of reading War and Peace is that War and Peace is incredible and obviously one of the greatest pieces of art ever made in any medium. No one who reads War and Peace can question the value of what they’ve done. What are we talking about?

Fair. I definitely feel like I’m living my best life when I read War and Peace. But I also feel like I’m living an OK-ish life when I read a novel about Spenser, private investigator. And most novels most people read are closer to the Spenser than to War and Peace. And I still feel better spending an afternoon reading about Spenser than I would watching 99% of TV shows.

Theory 7: Dopamine

Or perhaps the difference is that reading is a thing you do rather than something you consume.

This theory holds than when spend an hour slurping up short-form video, you’re training yourself to sort of pull a lever in the hope that some reward is delivered to you. But if you read (or do watercolors, or meditate) you’re training yourself to calmly pursue long-term goals and to sustain attention in the face of complexity.

Sometimes I wonder if phones/apps are the most addictive thing ever created. I suspect that more people today are addicted to their phones today than were ever addicted to any drug other than caffeine or perhaps nicotine. And while a phone addiction is less physically harmful than tobacco, that phone addiction will eat a larger part of your soul.

I think this is a big part of the explanation.

Theory 8: Non-fungible time

In the end, I don’t think novels are the best way to spend your time. In my view no novel—not even War and Peace—is as good as a truly great conversation.

But great conversations are hard to create. Sometimes you’re sitting on a train, or laying in bed, or it’s just been a long day and you don’t have the energy to find a giant block of marble and pursue your dream of experimental sculpture. In these situations, maybe reading a novel is the best thing you could do in the category of things you could realistically do.

Exercise for the reader: Apply these theories to blog posts.

Then you enclose horse. You have a finite budget of walls. (For the above map, 13.) You need to place them so that the horse is enclosed in as large an area as possible. The horse cannot move diagonally and cannot move over water.

Here’s one (bad) solution:

This is a charming little game. But something about it disturbs me. It’s exploring a mathematical structure (good) but it’s a disorganized mathematical structure that stubbornly resists understanding. There are—I think—no broad theorems or abstractions that might allow you to “derive” or “explain” the best solution for a given problem. If you want to know why the best solution is best, the honest answer is: Because it’s better than all of the other solutions.

I guess that what really bothers me is living in such a disordered universe. Did you know that this is the most compact known way to pack 11 squares together into a larger square?

Really makes you think about the mindset of whoever made the universe, am I right?

Few people seem quite as bothered by this aspect of reality as I am. So, since I can’t enjoy this kind of game, I thought I’d try to ruin it for everyone by showing that it’s extremely easy for machines.

Integer programming

Integer programming is, I think, one of humanity’s most unsung achievements. If you’re not familiar with it, don’t be confused by the word “programming”. It’s not like C++ or whatever. It’s a type of mathematical optimization problem. You have some list of variables, and you want to minimize a linear function of those variables under linear (technically affine) constraints.

For example, you might want to minimize

3×a + 5×b

with respect to a and b under the constraints that

b ≥ 2,
3×a + b ≥ 10.

If the variables are continuous, then this is just linear programming, which is theoretically pretty fast and in practice extremely fast.

However, if the variables are integers, then this is integer programming. Theoretically, this is NP-hard, meaning that it’s probably impossible to solve all integer programming problems in any reasonable amount of time.

But NP-hardness is not nearly as big a deal as people make it out to be. People have put a lot of effort into integer programming, because it’s useful for laying out chips, doing logistics and routing, managing supply chains, creating financial products and lots of other real-world problems that involve constraints and discrete choices.

After decades of effort, we have solvers that are extremely good. For most real-world problems of moderate size, these solvers can not just find the optimal solution, but also provide a proof that that it’s actually optimal.

OK, enough ranting. Let’s enclose some horse.

Maps

To start, I needed a representation of the map. I looked at the source code for the game page, but couldn’t easily figure out how to extract the map. So I started typing it into a spreadsheet:

I knew this would be slow and tedious, but the empirical slowness and tedium exceeded my expectations. After getting as far as shown above, I couldn’t take it anymore and decided to use image processing instead. I took a screenshot and then wrote a script that breaks up the image into tiles and then counts how many pixels in each chunk match the background color for water and for field. Then I manually added the horse. This gave me an array with 0 for water, 1 for field, and 2 for horse.

Enclosing the horse

Now, how to enclose the horse? How to formalize this as an instance of integer programming?

Thinking about this for a few minutes, I had the following thoughts:

1. Instead of thinking about if a given tile is enclosed, it’s easier to think about if it’s possible to escape from a given tile. Minimizing the number of tiles from which it is possible to escape is equivalent to maximizing the size of the horse’s enclosure.

2. So let’s have two binary variables for the solver to set each tile: (1) Is there a wall? (2) Is it possible to escape? The objective is to minimize the number of tiles from which you can escape. Say that W[i,j] indicates if there is a wall at row i and column j while E[i,j] indicates if it’s possible to escape from that tile.

3. Now all we need to do is add enough constraints to guarantee that the solution is valid.

4. There’s a maximum number of walls. So let’s constrain that the sum of W[i,j] over all i and j doesn’t exceed that.

5. It must not be possible for the horse to escape. So let’s constrain that E[i,j]=0 for that tile.

6. You can’t put a wall on top of the horse. So let’s constrain that W[i,j]=0 for that tile.

7. For water tiles, it’s never possible to escape. So let’s constrain that E[i,j]=0 for those tiles.

8. For field tiles on to boundary, it’s possible to escape unless there is a wall on the tile. So for E[i,j]=1 - W[i,j] for those tiles.

9. At field tiles not on the boundary, it’s possible to escape unless (1) there is a wall or (2) it’s impossible to escape from any neighboring tile.

Those thoughts can easily be translated into an integer programming problem.

Don’t believe me?

OK, I’ll give more detail. (If you do believe me, feel free to skip this section.)

Mathematically, our variables are W[1,1], ···, W[ROWS,COLS] and E[1,1], ···, E[ROWS,COLS]. These are all constrained to be zero or one. W[i,j]=1 indicates a wall at the tile on row i and column j, while E[i,j]=1 indicates that it’s possible to escape from the tile at row i and column j.

The objective is to minimize the number of tiles from which it’s possible to escape. That is, the goal is to minimize

E[1,1] + ··· + E[ROWS,COLS].

There’s a maximum number of walls. For the above puzzle, it is 13. So we have the constraint that

W[1,1] + ··· + W[21,21] ≤ WALLS.

It must not be possible for the horse to escape. And you can’t put a wall on top of the horse. For for whatever tile (i,j) contains the horse, we must have that

 E[i,j] = 0,
 W[i,j] = 0.

It’s never possible to escape from water. So for all tiles (i,j) that have water, we must have that

E[i,j] = 0.

For field tiles on the boundary, it’s possible to escape unless there is a wall. So for all field tiles (i,j) on the boundary,

E[i,j] = 1 - W[i,j].

Finally, we’re left with the problem of enforcing the rules for escaping from the interior field tiles. Recall that the logic here is that you can escape unless there is a wall or it’s impossible to escape from any neighboring tile. Naively, if (i,j) is a non-boundary field tile, I’d have written the constraint as

E[i,j] = (1-W[i,j]) × max(E[i-1,j], E[i-1,j], E[i,j-1], E[i,j+1]).

Taking a maximum is not linear, so integer programming solvers can’t deal with it. Fortunately, we can achieve the same thing by breaking it down into four separate constraints as

E[i,j] ≥ (1-W[i,j]) E[i-1, j],
E[i,j] ≥ (1-W[i,j]) E[i+1, j],
E[i,j] ≥ (1-W[i,j]) E[i, j-1],
E[i,j] ≥ (1-W[i,j]) E[i, j+1].

Those are all the constraints. I claim that if any arrays E and W satisfy all those constraints, then the walls constitute a valid solution. I also claim that if you minimize the number of escapable tiles, then they constitute an optimal solution.

Still don’t believe me?

If you like your math formal and acerbic, here’s the full model:

Minimize:
∑ᵢⱼ E[i,j]
Subject to:
E[i,j] ∈ {0,1}
W[i,j] ∈ {0,1}
∑ᵢⱼ W[i,j] ≤ WALLS
E[i,j] = 0 if (i,j) is the horse
W[i,j] = 0 if (i,j) is the horse
E[i,j] = 0 if (i,j) is water
E[i,j] = 1-W[i,j] if (i,j) is a field on the boundary
E[i,j] ≥ (1-W[i,j]) E[i',j'] if (i,j) is a non-boundary field and (i',j') is adjacent

Read now

Drumroll

So I wrote some code to take the map as an array of integers and create all those constraints. Then I called scipy.optimize.milp. A fraction of a second later I had this solution, where I used 5 to indicate a wall.

Entering this into the website, it looked pretty good:

So I submitted the results, and...

As guaranteed by me / math / the solver, the solution was optimal.

For fun, I ran the same solver on all the previous days. Here I ran into some edge cases with the image processing for extracting the maps. Turns out, my computer slightly dims windows that aren’t focused, and the utility I was using to sample the colors for the website itself took focus. This took probably five times longer than everything integer-programming related and was extremely annoying.

But once the maps were extracted, the solver had no issues. It seems unsporting to show the actual solutions here, but these were the results. I manually entered all the solutions into the website to double-verify that they were optimal.

day   solve time  optimal?
1     0.062 s     yes
2     0.964 s     yes
3     1.190 s     yes
4     0.091 s     yes
5     0.280 s     yes
6     1.948 s     yes
7     3.826 s     yes
10    4.536 s     yes
11    1.142 s     yes

Days eight and nine had “cherries” that give extra points for being enclosed. Later days added “wormholes”. These are all extremely easy to deal with mathematically, but would have required even more image processing to get the maps, and I could not bear the thought of any more image processing.

If you want to see the code, please download the .CBZ file below and then change the extension to .ZIP. Inside you will find a single map image and a single 150-line python script. I know this is a stupid way to share code, but I’ve temporarily lost access to my main website. On the plus side, perhaps this will briefly prevent AI scrapers from training on my code?

Enclose

38.1KB ∙ CBZ file

Read now

Read now

Read now

Parting thoughts

Thought #1

Integer programming is magical. It really deserves to be considered a basic tool like grep.

Thought #2

How many NP-complete problems could be rendered “fun” to solve by hand with a sufficiently charming interface?

Well, many problems started as games and were only later observed to be NP-complete. This includes n-Queens completion, Nonograms and LaserTank. People empirically find these fun, so I guess we should classify them as fun.

But lots of other problems seem like they could be fun with the right interface. This seems plausible for Hamiltonian paths or graph coloring or the Chinese postman problem. Honestly, it was hard to find any that would clearly be non-fun. Maybe bipartite dimension or longest common subsequence? Those don’t seem fun to me, but given the large and vocal contingent of readers who insist that they enjoy going to the dentist, I suspect someone might enjoy them.

My best guess is that what makes a problem fun is a combination of two things:

1. Legibility. For a problem to be fun, you need to be able to sort of look at it and “feel” the mathematical structure you’re dealing with.

2. Difficulty. You should be able to almost solve the problem through broad heuristics like “put the walls in small bottlenecks” and only need to do a little bit of searching. Too hard or too easy, and it won’t be fun.

To put it another way, I think you should be able to mostly solve the problem using System 1 but need to engage System 2 enough to keep things interesting.

I make this guess on the basis that the ultimate non-fun problem to solve by hand is surely... integer programming? If I just give you a big list of variables and constraints, finding the optimum wouldn’t be fun at all, because your brain wasn’t designed to be good at manipulating big lists of numbers. It would be all search with nothing to understand or hold on to.

Thought #3

It’s not entirely obvious to me that enclose.horse actually is NP-complete. Just because you can use integer programming doesn’t necessarily mean you can’t find something that’s guaranteed to be fast. Though I’d bet against it.

Thought #4

Don’t play Factorio.

Wikipedia’s list of countries by the percentage of vegetarians is extremely very muchísimo surprising:

🇮🇳 India 20–39%
🇲🇽 Mexico 19%
🇧🇷 Brazil 14%
🇨🇳 China 14%
🇹🇼 Taiwan 13–14%
🇦🇷 Argentina 12%
🇦🇹 Austria 11%
🇯🇲 Jamaica 10%
🇻🇳 Vietnam 10%
🇮🇹 Italy 9.5%
🇫🇮 Finland 8%
🇩🇪 Germany 8%
🇮🇱 Israel 8%
🇹🇭 Thailand 8.0%
🇨🇦 Canada 7.6%
🇧🇪 Belgium 7%
🇵🇱 Poland 6.6%
🇨🇱 Chile 6%–14%
🇸🇪 Sweden 6%
🇦🇺 Australia 5.3%
🇨🇭 Switzerland 5.3%
🇺🇦 Ukraine 5.2%
🇨🇿 Czech Republic 5%
🇭🇺 Hungary 5%
🇵🇭 Philippines 5%
🇸🇬 Singapore 5%
🇬🇧 United Kingdom 5%
🇲🇾 Malaysia 5%
🇮🇪 Ireland 4.3–8.4%
🇺🇸 United States 4.2%
🇨🇴 Colombia 4%
🇩🇰 Denmark 4%
🇪🇪 Estonia 4%
🇬🇷 Greece 4%
🇯🇵 Japan 4%
🇳🇴 Norway 4%
🇱🇻 Latvia 3%
🇱🇹 Lithuania 3%
🇰🇷 South Korea 3%
🇳🇱 Netherlands 2.3%
🇳🇿 New Zealand 2%
🇷🇴 Romania 2%
🇪🇸 Spain 1.4%
🇸🇮 Slovenia 1.4–1.6%
🇵🇹 Portugal 1.2%
🇷🇺 Russia 1%
🇫🇷 France 0.8%

India at the top? Of course. And Taiwan / Vietnam / Israel / Germany all seem plausible. But Mexico at #2? Argentina at #6? What?

I think there’s a 90% chance this reflects that those numbers all come from different surveys and measuring the percentage of vegetarians in a country is harder than it seems. The World Atlas gives a list where the ordering seems less crazy, though I can’t find any information about their methodology and many of the numbers still strike me as high.

Spam

The main domain of this blog, dynomight.net, is listed on the SpamHaus domain block list, which apparently makes it inaccessible in a significant fraction of the world. This is puzzling because:

1. No spam has even been sent from dynomight.net.

2. No email has ever been sent from dynomight.net.

3. No ads have ever been hosted on dynomight.net.

4. No malware has ever been hosted on dynomight.net.

5. The domain has a good enough reputation to get thousands of referrals from Google each month, suggesting that it isn’t generally considered harmful and dangerous.

If you go to the block page, SpamHaus reassures that if you’re blocked, you need not panic! Just submit a request and they’ll quickly verify that nothing nefarious is happening, and you’ll be good to go! But I’ve submitted that request several times over the past six months, which always leads to the following conversation:

Dynomight: Please unblock me, I am not evil, I am good.

SpamHaus: Thank you for contacting Spamhaus Removals Ticketing department. The ticket cannot be processed as we are unable to verify the ownership of dynomight.net. Please contact your IT department for further assistance.

Dynomight: But it’s me. This email is listed on my about page. Can you at least explain why the domain is blocked? Or what I need to to verify ownership, whatever that means?

SpamHaus: Thank you for contacting Spamhaus Removals Ticketing department. The ticket cannot be processed as we are unable to verify the ownership of dynomight.net. Please contact your IT department for further assistance.

SpamHaus: This issue is closed.

Oddly, my personal blog does not have an IT department. Maybe they think DumPy is malware.

(Edit: To clarify, the problem is that when people go to the website in their browser, the website is blocked as malware. I don’t care about sending email from dynomight.net because I don’t send email from dynomight.net.)

Spite programming

Speaking of which, there was a proposal to add DumPy-like syntax to NumPy. This led to the following response:

This is the kind of thing that I’d like to see mature outside of numpy for a while before it gets calcified by an in-numpy implementation. Right now, it’s basically a spite project (and I mean this in the most complimentary, productive sense) by one person that satisfies their lone use cases. It will change as more people use it for their own use cases.

(By the way, I think this is the right call, and I take it in the sense meant. I just thought it was an amusingly incisive description.)

Spite self-promotion

Speaking of which, if you haven’t yet experienced the grandeur of DumPy, I finally made it into a “real” package, meaning you need no longer suffer the hideous inconvenience of downloading a single file and putting it in a folder. If you have uv installed you should be able to play around with it simply by typing the following into your command line:

uv run --with dumpy-numpy python

For example, you should be able to compute a Hilbert Matrix by doing this:

> dynomight@computer:~$ uv run --with dumpy-numpy python  
Installed 7 packages in 47ms  
Python 3.14.2 [blahblahblah] on linux  
Type "help", "copyright", "credits" or "license" for more information.  
>>> import dumpy as dp  
>>> H = dp.Slot()  
>>> with dp.Range(5) as i:  
...     with dp.Range(3) as j:  
...         H[i,j] = 1 / (i + j + 1)  
...            
>>> H  
Slot([[1.         0.5        0.33333334]  
     [0.5        0.33333334 0.25      ]  
     [0.33333334 0.25       0.2       ]  
     [0.25       0.2        0.16666667]  
     [0.2        0.16666667 0.14285715]], shape=(5, 3))

If you have no idea what NumPy or uv or Hilbert Matrices are, carry on.

Drug names

Tisagenlecleucel is a drug. When I look at that name, I see a long series of Science Letters. But what science lies within, exactly?

It turns out that the way to read that name is to split it by syllables into “tisa-gen-lec-leu-cel” and then read themback to front: “Cell therapy drug that targets leukocytes (white blood cells) that is genetically modified and BTW ‘tisa’ is a cool-sounding series of letters.”

As far as I can tell, “lec” is just a kind of spacer indicates selection of certain types of cells (h/t/ Lessons in Chimeristry).

Most generic drug names work like this. The formula is some random-cool-sounding letters at the start, followed by a meaningful structured suffix. For example, the “-ib” suffix indicates small-molecule inhibitors. But because there are many kinds of small-molecule inhibitors, there are many kinds of “ib”.

• -ib (Small Molecule Inhibitor)

  • -ciclib (Cyclin-Dependent Kinase inhibitor - Cyclin + ib) (Palbociclib, Ribociclib)

  • -coxib (COX-2 inhibitor - Cyclooxygenase + ib) (Celecoxib, Etoricoxib)

  • -degib (Hedgehog pathway inhibitor - Hedgehog + ib) (Vismodegib, Sonidegib)

  • -farnib (Farnesyltransferase inhibitor - Farnesyl + ib) (Lonafarnib, Tipifarnib)

  • -lisib (PI3K inhibitor - Phosphatidylinositol + ib?) (Alpelisib, Idelalisib)

  • -menib (Menin inhibitor - Menin + ib) (Revumenib, Ziftomenib)

  • -nib (Kinase / Enzyme Inhibitor - kinase + ib) (Imatinib)

  • -parib (PARP inhibitor - Poly ADP-Ribose Polymerase + ib) (Olaparib, Niraparib)

  • -rasib (RAS inhibitor - RAS + ib) (Sotorasib, Adagrasib)

  • -zomib (Proteasome inhibitor - Proteasome + ib) (Bortezomib, Carfilzomib)

In some places, the hierarchy goes deep. For example, you can expand the “nib” subcategory into sub-sub-categories and sub-sub-sub-categories:

• -ib (Small Molecule Inhibitor)

  • -nib (Kinase Inhibitor - kinase + ib) (Tofacitinib)

    • -rafenib (RAF kinase inhibitor - RAF + nib) (Vemurafenib, Sorafenib)

    • -tinib (Tyrosine Kinase inhibitor - tyrosine + nib) (Imatinib)

      • -brutinib (Bruton’s TK inhibitor - Bruton’s + tinib) (Ibrutinib, Zanubrutinib)

      • -ertinib (EGFR TK inhibitor - EGFR + tinib) (Osimertinib, Lazertinib)

      • -metinib (MEK inhibitor - MEK + tinib) (Trametinib, Cobimetinib)

I also made a longer tree for antivirals1.

Sadly, I have not been able to find any of these things:

1. Any comprehensive reference on where the names actually came from. (The hints above are mostly just guesses.)

2. Any guide on what are the most important suffixes to learn first so you can impress people with your knowledge of generic drug names.

3. Any nice visualization at all of the entire tree. Instead, the American Medical Association just seems to make available this extremely-not-fun-looking spreadsheet.

Inevitably, older drugs often don’t stick to the rules and the US has it’s own standard for drug names which is almost but not quite the same as the international / WHO standard. That’s is why the US (and Canada and Japan and South Korea) call N-acetyl-para-aminophenol

Acetaminophen (N-acetyl-para-aminophenol),

while the rest of the world starts with a slightly different name for the same chemical and calls it

Paracetamol (Para-acetylaminophenol).

Incidentally, Johnson & Johnson seem to have chosen their brand name to basically overlap as little as possible with the US name:

Tylenol (N-acetyl-para-aminophenol)

They say you’re supposed to choose your prior in advance. That’s why it’s called a “prior”. First, you’re supposed to say say how plausible different things are, and then you update your beliefs based on what you see in the world.

For example, currently you are—I assume—trying to decide if you should stop reading this post and do something else with your life. If you’ve read this blog before, then lurking somewhere in your mind is some prior for how often my posts are good. For the sake of argument, let’s say you think 25% of my posts are funny and insightful and 75% are boring and worthless.

OK. But now here you are reading these words. If they seem bad/good, then that raises the odds that this particular post is worthless/non-worthless. For the sake of argument again, say you find these words mildly promising, meaning that a good post is 1.5× more likely than a worthless post to contain words with this level of quality.

If you combine those two assumptions, that implies that the probability that this particular post is good is 33.3%. That’s true because the red rectangle below has half the area of the blue one, and thus the probability that this post is good should be half the probability that it’s bad (33.3% vs. 66.6%)

(Why half the area? Because the red rectangle is ⅓ as wide and ³⁄₂ as tall as the blue one and ⅓ × ³⁄₂ = ½.)

Theoretically, when you chose your prior that 25% of dynomight posts are good, that was supposed to reflect all the information you encountered in life before reading this post. Changing that number based on information contained in this post wouldn’t make any sense, because that information is supposed to be reflected in the second step when you choose your likelihood p[good | words]. Changing your prior based on this post would amount to “double-counting”.

In theory, that’s right. It’s also right in practice for the above example, and for the similar cute little examples you find in textbooks.

But for real problems, I’ve come to believe that refusing to change your prior after you see the data often leads to tragedy. The reason is that in real problems, things are rarely just “good” or “bad”, “true” or “false”. Instead, truth comes in an infinite number of varieties. And you often can’t predict which of these varieties matter until after you’ve seen the data.

Aliens

Let me show you what I mean. Say you’re wondering if there are aliens on Earth. As far as we know, there’s no reason aliens shouldn’t have emerged out of the random swirling of molecules on some other planet, developed a technological civilization, built spaceships, and shown up here. So it seems reasonable to choose a prior it’s equally plausible that there are aliens or that there are not, i.e. that

P[aliens] ≈ P[no aliens] ≈ 50%.

Meanwhile, here on our actual world, we have lots of weird alien-esque evidence, like the Gimbal video, the Go Fast video, the FLIR1 video, the Wow! signal, government reports on unidentified aerial phenomena, and lots of pilots that report seeing “tic-tacs” fly around in physically impossible ways. Call all that stuff data. If aliens weren’t here, then it seems hard to explain all that stuff. So it seems like P[data | no aliens] should be some low number.

On the other hand, if aliens were here, then why don’t we ever get a good image? Why are there endless confusing reports and rumors and grainy videos, but never a single clear close-up high-resolution video, and never any alien debris found by some random person on the ground? That also seems hard to explain if aliens were here. So I think P[data | aliens] should also be some low number. For the sake of simplicity, let’s call it a wash and assume that

P[data | no aliens] ≈ P[data | aliens].

Since neither the prior nor the data see any difference between aliens and no-aliens, the posterior probability is

P[no aliens | data] ≈ P[aliens | data] ≈ 50%.

See the problem?

We’re friends. We respect each other. So let’s not argue about if my starting assumptions are good. They’re my assumptions. I like them. And yet the final conclusion seems insane to me. What went wrong?

Assuming I didn’t screw up the math (I didn’t), the obvious explanation is that I’m experiencing cognitive dissonance as a result of a poor decision on my part to adopt a set of mutually contradictory beliefs. Say you claim that Alice is taller than Bob and Bob is taller than Carlos, but you deny that Alice is taller than Carlos. If so, that would mean that you’re confused, not that you’ve discovered some interesting paradox.

Perhaps if I believe that P[aliens] ≈ P[no aliens] and that P[data | aliens] ≈ P[data | no aliens], then I must accept that P[aliens | data] ≈ P[no aliens | data]. Maybe rejecting that conclusion just means I have some personal issues I need to work on.

I deny that explanation. I deny it! Or, at least, I deny that’s it’s most helpful way to think about this situation. To see why, let’s build a second model.

More aliens

Here’s a trivial observation that turns out to be important: “There are aliens” isn’t a single thing. There could be furry aliens, slimy aliens, aliens that like synthwave music, etc. When I stated my prior, I could have given different probabilities to each of those cases. But if I had, it wouldn’t have changed anything, because there’s no reason to think that furry vs. slimy aliens would have any difference in their eagerness to travel to ape-planets and fly around in physically impossible tic-tacs.

But suppose I had divided up the state of the world into these four possibilities:

If I had broken things down that way, I might have chosen this prior:

P[no aliens + normal people] ≈ 41%
P[no aliens + weird people] ≈ 9%
P[normal aliens] ≈ 49%
P[weird aliens] ≈ 1%

Now, let’s think about the empirical evidence again. It’s incompatible with no aliens + normal people, since if there were no aliens, then normal people wouldn’t hallucinate flying tic-tacs. The evidence is also incompatible with normal aliens since is those kinds of aliens were around they would make their existence obvious. However, the evidence fits pretty well with weird aliens and also with no aliens + weird people.

So, a reasonable model would be

P[data | normal aliens] ≈ 0	
P[data | no aliens + normal people] ≈ 0
P[data | weird aliens] ≈ P[data | no aliens + weird people].

If we combine those assumptions, now we only get a 10% posterior probability of aliens.

P[no aliens + normal people | data] ≈ 0
P[no aliens + weird people | data] ≈ 90%
P[normal aliens | data] ≈ 0
P[weird aliens | data] ≈ 10%

Now the results seem non-insane.

Huh?

I hope you are now confused. If not, let me lay out what’s strange: The priors for the two above models both say that there’s a 50% chance of aliens. The first prior wasn’t wrong, it was just less detailed than the second one.

That’s weird, because the second prior seemed to lead to completely different predictions. If a prior is non-wrong and the math is non-wrong, shouldn’t your answers be non-wrong? What the hell?

The simple explanation is that I’ve been lying to you a little bit. Take any situation where you’re trying to determine the truth of anything. Then there’s some space of things that could be true.

In some cases, this space is finite. If you’ve got a single tritium atom and you wait a year, either the atom decays or it doesn’t. But in most cases, there’s a large or infinite space of possibilities. Instead of you just being “sick” or “not sick”, you could be “high temperature but in good spirits” or “seems fine except won’t stop eating onions”.

(Usually the space of things that could be true isn’t easy to map to a small 1-D interval. I’m drawing like that for the sake of visualization, but really you should think of it as some high-dimensional space, or even an infinite dimensional space.)

In the case of aliens, the space of things that could be true might include, “There are lots of slimy aliens and a small number of furry aliens and the slimy aliens are really shy and the furry aliens are afraid of squirrels.” So, in principle, what you should do is divide up the space of things that might be true into tons of extremely detailed things and give a probability to each.

Often, the space of things that could be true is infinite. So theoretically, if you really want to do things by the book, what you should really do is specify how plausible each of those (infinite) possibilities is.

After you’ve done that, you can look at the data. For each thing that could be true, you need to think about the probability of the data. Since there’s an infinite number of things that could be true, that’s an infinite number of probabilities you need to specify. You could picture it as some curve like this:

(That’s a generic curve, not one for aliens.)

To me, this is the most underrated problem with applying Bayesian reasoning to complex real-world situations: In practice, there are an infinite number of things that can be true. It’s a lot of work to specify prior probabilities for an infinite number of things. And it’s also a lot of work to specify the likelihood of your data given an infinite number of things.

So what do we do in practice? We simplify, usually by limiting creating grouping the space of things that could be true into some small number of discrete categories. For the above curve, you might break things down into these four equally-plausible possibilities.

Then you might estimate these data probabilities for each of those possibilities.

Then you could put those together to get this posterior:

That’s not bad. But it is just an approximation. Your “real” posterior probabilities correspond to these areas:

That approximation was pretty good. But the reason it was good is that we started out with a good discretization of the space of things that might be true: One where the likelihood of the data didn’t vary too much for the different possibilities inside of A, B, C, and D. Imagine the likelihood of the data—if you were able to think about all the infinite possibilities one by one—looked like this:

This is dangerous. The problem is that you can’t actually think about all those infinite possibilities. When you think about four four discrete possibilities, you might estimate some likelihood that looks like this:

If you did that, that would lead to you underestimating the probability of A, B, and C, and overestimating the probability of D.

This is where my first model of aliens went wrong. My prior P[aliens] was not wrong. (Not to me.) The mistake was in assigning the same value to P[data | aliens] and P[data | no aliens]. Sure, I think the probability of all our alien-esque data is equally likely given aliens and given no-aliens. But that’s only true for certain kinds of aliens, and certain kinds of no-aliens. And my prior for those kinds of aliens is much lower than for those kinds of non-aliens.

Technically, the fix to the first model is simple: Make P[data | aliens] lower. But the reason it’s lower is that I have additional prior information that I forgot to include in my original prior. If I just assert that P[data | aliens] is much lower than P[data | no aliens] then the whole formal Bayesian thing isn’t actually doing very much—I might as well just state that I think P[aliens | data] is low. If I want to formally justify why P[data | aliens] should be lower, that requires a messy recursive procedure where I sort of add that missing prior information and then integrate it out when computing the data likelihood.

I don’t think that technical fix is very good. While it’s technically correct (har-har) it’s very unintuitive. The better solution is what I did in the second model: To create a finer categorization of the space of things that might be true, such that the probability of the data is constant-ish for each term.

The thing is: Such a categorization depends on the data. Without seeing the actual data in our world, I would never have predicted that we would have so many pilots that report seeing tic-tacs. So I would never have predicted that I should have categories that are based on how much people might hallucinate evidence or how much aliens like to mess with us. So the only practical way to get good results is to first look at the data to figure out what categories are important, and then to ask yourself how likely you would have said those categories were, if you hadn’t yet seen any of the evidence.

Thus, I present:

WHY DID THE CHICKEN CROSS THE ROAD
ACCORDING TO VARIOUS PEOPLE
OR OTHER ENTITIES

Q) Why did the chicken cross the road?

A) The chicken ain’t fussy. Everybody gotta be somewhere. The chicken been on this side a long time and never suffered none for it. The chicken don’t see no obvious benefit to the other side. But the talk of the town is nothing but crossing, and the chicken can’t help but go see what got everyone so stirred up.

(Mark Twain)

Q) Why did the chicken cross the road?

A) The outcome would be best if no one crossed. However, if other chickens do cross, then the outcome would be better if this chicken also crossed. The chicken rejects the Kantian universalism. So the chicken crosses.

(Derek Parfit)

Q) Why did the chicken cross the road?

A) You were a beautiful little chick
The whole world was before you
You greased your wattles and crossed the road
Sure it would last forever

Now it’s a cold morning and you’re driving to work
Cursing all the cockerels in your way
How did you get here
Where did that little chick go

(Pink Floyd)

Q) Why did the chicken cross the road?

A) It didn’t. There is no chicken. You are the road. You and the sides are in an entangled macrostate. The chicken is an emergent property of the superposition. The chicken abhors being measured. A team of plucky chemists rush to inject enough decoherence to collapse the wavefunction before the chicken can consume the lightcone.

(Christopher Nolan)

Q) Why did the chicken cross the road?

A) Chicken

C H I C K E N

3, 8, 9, 3, 11, 5, 14

11, 9, 3, 8 14, 3, 5

gcd(11^(9 + 3) - 8, 14), 3 × 5

7, 15

G O

Go

(Ramanujan)

Q) Why did the chicken cross the road?

A) For sex. Neither glamorized nor gross, possibly added for commercial reasons, possibly to make some point about sex’s place in real life. It’s all very unclear.

(Paul Thomas Anderson)

Q) Why did the chicken cross the road?

A) Did it cross the road, though? Did it? Sure, the chicken is associated with crossing. And it’s mechanistically possible for a chicken to cross a road. It’s plausible the chicken crossed the road. But maybe the chicken and the crossing were both caused by something else. Or maybe the road crossed the chicken. This is why we have RCTs. Come on, people!

(Dynomight)

Q) Why did the chicken cross the road?

A) Once there was a dragon who watched over the chicken village. The chickens begged the dragon, “Please let us have a road, so that we might cross back and forth!”

“A road?” the dragon asked. “Are you sure?”

“Yes!” the chickens answered. “A road! We wish for nothing but a road to cross, and then we will be happy forever and ever!”

[7000 words redacted]

And thus, all mass-energy in the universe was converted to chicken-torture annihilators. Makes you think.

(LessWrong)

Q) Why did the chicken cross the road?

A) We were out on the edge of the farm when the diethyltryptamine took hold. Beaky screamed something about coccidiostats in our feed and made a break for it, totally out of control. Before I could stop him, I heard the voice of God say, “Scrapples: The road awaits.” Suddenly I was standing on the median, cars screaming past, a group a baby ducks asking where the mountains of peas I’d promised them were.

(Hunter S. Thompson)

Q) Why did the chicken cross the road?

A) The chicken’s crossing is not a voluntary act but the unconscious actualization of a class habitus: raised in a coop whose symbolic boundaries naturalize the road as a site of danger and prestige, the chicken embodies the field’s doxa that “real” chickens must invest in the illusio of reaching the median. While the chicken never doubts the legitimacy of the crossing rules, crossing is not about the other side, but a performance of distinction that ultimately perpetuates the same field of species domination that produced it.

(Pierre Bourdieu)

Q) Why did the chicken cross the road?

A) grug on one side

grug see other side

grug chicken

many metal box speed by very fast very volume

metal box seem to stay on black land strip

grug think better if metal box not hit grug because box hard and grug small soft chicken

grug wait a while

when no metal box for a while also often no metal box for a while after

largest gap between metal box around 20 minutes

grug wait until no metal box for 10 minutes then grug cross

no metal box come

grug safe

other side also fine

maybe cross back someday

grug think side not matter too much

grug enjoy chicken life either side same

chicken life pretty good

grug hope you also have life as good as grug chicken life

groodbye from grug

(grug)

Q) Why did the chicken cross the road?

A) Before there was chicken the road was waiting. The road is empty. Dust on your hackles. Heat rises in shimmering waves. No way to see what’s coming. How did it come to this. How a chicken supposed to move with roads everywhere. Creosote blows in from the mesa. Nothing left but to cross. You cross and nothing happens. A few minutes later a car stops but you don’t turn around. A door opens and you hear a click. Then the car is gone.

(Cormac McCarthy)

Q) Why did the chicken cross the road?

A) For food.

(An actual chicken)

Requests: Peter Singer, Ayn Rand, Judith Butler, Bertrand Russell, Andrei Tarkovsky, the mother hen, a junglefowl, an SSRI, Singapore, the chicken’s hypothalamus.

1. That your dog, while she appears to love you only because she’s been adapted by evolution to appear to love you, really does love you.

2. That if you’re a life form and you cook up a baby and copy your genes to them, you’ll find that the genes have been degraded due to oxidative stress et al., which isn’t cause for celebration, but if you find some other hopefully-hot person and randomly swap in half of their genes, your baby will still be somewhat less fit compared to you and your hopefully-hot friend on average, but now there is variance, so if you cook up several babies, one of them might be as fit or even fitter than you, and that one will likely have more babies than your other babies have, and thus complex life can persist in a universe with increasing entropy.

3. That if we wanted to, we surely could figure out which of the 300-ish strains of rhinovirus are circulating in a given area at a given time and rapidly vaccinate people to stop it and thereby finally “cure” the common cold, and though this is too annoying to pursue right now, it seems like it’s just a matter of time.

4. That if you look back at history, you see that plagues went from Europe to the Americas but not the other way, which suggests that urbanization and travel are great allies for infectious disease, and these both continue today but are held in check by sanitation and vaccines even while we have lots of tricks like UVC light and high-frequency sound and air filtration and waste monitoring and paying people to stay home that we’ve barely even put in play.

5. That while engineered infectious diseases loom ever-larger as a potential very big problem, we also have lots of crazier tricks we could pull out like panopticon viral screening or toilet monitors or daily individualized saliva sampling or engineered microbe-resistant surfaces or even dividing society into cells with rotating interlocks or having people walk around in little personal spacesuits, and while admittedly most of this doesn’t sound awesome, I see no reason this shouldn’t be a battle that we would win.

6. That clean water, unlimited, almost free.

7. That dentistry.

8. That tongues.

9. That radioactive atoms either release a ton of energy but also quickly stop existing—a gram of Rubidium-90 scattered around your kitchen emits as much energy as ~200,000 incandescent lightbulbs but after an hour only 0.000000113g is left—or don’t put out very much energy but keep existing for a long time—a gram of Carbon-14 only puts out the equivalent of 0.0000212 light bulbs but if you start with a gram, you’ll still have 0.999879g after a year—so it isn’t actually that easy to permanently poison the environment with radiation although Cobalt-60 with its medium energy output and medium half-life is unfortunate, medical applications notwithstanding I still wish Cobalt-60 didn’t exist, screw you Cobalt-60.

10. That while curing all cancer would only increase life expectancy by ~3 years and curing all heart disease would only increase life expectancy by ~3 years, and preventing all accidents would only increase life expectancy by ~1.5 years, if we did all of these at the same time and then a lot of other stuff too, eventually the effects would go nonlinear, so trying to cure cancer isn’t actually a waste of time, thankfully.

11. That the peroxisome, while the mitochondria and their stupid Krebs cycle get all the attention, when a fatty-acid that’s too long for them to catabolize comes along, who you gonna call.

12. That we have preferences, that there’s no agreed ordering of how good different things are, which is neat, and not something that would obviously be true for an alien species, and given our limited resources probably makes us happier on net.

13. That cardamom, it is cheap but tastes expensive, if cardamom cost 1000× more, people would brag about how they flew to Sri Lanka so they could taste chai made with fresh cardamom and swear that it changed their whole life.

14. That Gregory of Nyssa, he was right.

15. That Grandma Moses, it’s not too late.

16. That sleep, that probably evolution first made a low-energy mode so we don’t starve so fast and then layered on some maintenance processes, but the effect is that we live in a cycle and when things aren’t going your way it’s comforting that reality doesn’t stretch out before you indefinitely but instead you can look forward to a reset and a pause that’s somehow neither experienced nor skipped.

17. That, glamorous or not, comfortable or not, cheap or not, carbon emitting or not, air travel is very safe.

18. That, for most of the things you’re worried about, the markets are less worried than you and they have the better track record, though not the issue of your mortality.

19. That sexual attraction to romantic love to economic unit to reproduction, it’s a strange bundle, but who are we to argue with success.

20. That every symbolic expression recursively built from differentiable elementary functions has a derivative that can also be written as a recursive combination of elementary functions, although the latter expression may require vastly more terms.

21. That every expression graph built from differentiable elementary functions and producing a scalar output has a gradient that can itself be written as an expression graph, and furthermore that the latter expression graph is always the same size as the first one and is easy to find, and thus that it’s possible to fit very large expression graphs to data.

22. That, eerily, biological life and biological intelligence does not appear to make use of that property of expression graphs.

23. That if you look at something and move your head around, you observe the entire light field, which is a five-dimensional function of three spatial coordinates and two angles, and yet if you do something fancy with lasers, somehow that entire light field can be stored on a single piece of normal two-dimensional film and then replayed later.

24. That, as far as I can tell, the reason five-dimensional light fields can be stored on two-dimensional film simply cannot be explained without quite a lot of wave mechanics, a vivid example of the strangeness of this place and proof that all those physicists with their diffractions and phase conjugations really are up to something.

25. That disposable plastic, littered or not, harmless when consumed as thousands of small particles or not, is popular for a reason.

26. That disposable plastic, when disposed of correctly, is literally carbon sequestration, and that if/when air-derived plastic replaces dead-plankton-derived plastic, this might be incredibly convenient, although it must be said that currently the carbon in disposable plastic only represents a single-digit percentage of total carbon emissions.

27. That rocks can be broken into pieces and then you can’t un-break the pieces but you can check that they came from the same rock, it’s basically cryptography.

28. That the deal society has made is that if you have kids then everyone you encounter is obligated to chip in a bit to assist you, and this seems to mostly work without the need for constant grimy negotiated transactions as Econ 101 would suggest, although the exact contours of this deal seem to be a bit murky.

29. That of all the humans that have ever lived, the majority lived under some kind of autocracy, with the rest distributed among tribal bands, chiefdoms, failed states, and flawed democracies, and only something like 1% enjoyed free elections and the rule of law and civil liberties and minimal corruption, yet we endured and today that number is closer to 10%, and so if you find yourself outside that set, do not lose heart.

30. That if you were in two dimensions and you tried to eat something then maybe your body would split into two pieces since the whole path from mouth to anus would have to be disconnected, so be thankful you’re in three dimensions, although maybe you could have some kind of jigsaw-shaped digestive tract so your two pieces would only jiggle around or maybe you could use the same orifice for both purposes, remember that if you ever find yourself in two dimensions, I guess.

(previously, previously, previously, previously)

The thing about dating apps is that if they do a good job and match people up, then the matched people will quit the app and stop paying. So they have an incentive to string people along but not to actually help people find long-term relationships.

May I explain why I don’t find this type of theory very helpful?

I’m not saying that I think it’s wrong, mind you. Rather, my objection is that while the theory is phrased in terms of dating apps, the same basic pattern applies to basically anyone who is trying to make money by doing anything.

For example, consider a pizza restaurant. Try these theories on for size:

• Pizza: “The thing about pizza restaurants is that if they use expensive ingredients or labor-intensive pizza-making techniques, then it costs more to make pizza. So they have an incentive to use low-cost ingredients and labor-saving shortcuts.”

• Pizza II: “The thing about pizza restaurants is that if they have nice tables separated at a comfortable distance, then they can’t fit as many customers. So they have an incentive to use tiny tables and cram people in cheek by jowl.”

• Pizza III: “The thing about pizza restaurants is that if they sell big pizzas, then people will eat them and stop being hungry, meaning they don’t buy additional pizza. So they have an incentive to serve tiny low-calorie pizzas.”

See what I mean? You can construct similar theories for other domains, too:

• Cars: “The thing about automakers is that making cars safe is expensive. So they have an incentive to make unsafe cars.”

• Videos: “The thing about video streaming is that high-resolution video uses more expensive bandwidth. So they have an incentive to use low-resolution.”

• Blogging: “The thing about bloggers is that research is time-consuming. So they have an incentive to be sloppy about the facts.”

• Durability: “The thing about {lightbulb, car, phone, refrigerator, cargo ship} manufacturing is that if you make a {lightbulb, car, phone, refrigerator, cargo ship} that lasts a long time, then people won’t buy new ones. So there’s an incentive to make {lightbulbs, cars, phones, refrigerators, cargo ships} that break quickly.”

All these theories can be thought of as instances of two general patterns:

• Make product worse, get money: The thing about selling goods or services is that making goods or services better costs money. So people have an incentive to make goods and services worse.

• Raise price, get money: “The thing about selling goods and services is that if you raise prices, then you get more money. So people have an incentive to raise prices.”

Are these theories wrong? Not exactly. But it sure seems like something is missing.

I’m sure most pizza restauranteurs would be thrilled to sell lukewarm 5 cm cardboard discs for $300 each. They do in fact have an incentive to do that, just as predicted by these theories! Yet, in reality, pizza restaurants usually sell pizzas that are made out of food. So clearly these theories aren’t telling the whole story.

Say you have a lucrative business selling 5 cm cardboard discs for $300. I am likely to think, “I like money. Why don’t I sell pizzas that are only mostly cardboard, but also partly made of flour? And why don’t I sell them for $200, so I can steal Valued Reader’s customers?” But if I did that, then someone else would probably set prices at only $100, or even introduce cardboard-free pizzas, and this would continue until hitting some kind of equilibrium.

Sure, producers want to charge infinity dollars for things that cost them zero dollars to make. But consumers want to pay zero dollars for stuff that’s infinitely valuable. It’s in the conflict between these desires that all interesting theories live.

This is why I don’t think it’s helpful to point out that people have an incentive to make their products worse. Of course they do. The interesting question is, why are they able to get away with it?

Reasons stuff is bad

First reason stuff is bad: People are cheap

Why are seats so cramped on planes? Is it because airlines are greedy? Sure. But while they might be greedy, I don’t think they’re dumb. If you do a little math, you can calculate that if airlines were to remove a single row of seats, they could add perhaps 2.5 cm (1 in) of extra legroom for everyone, while only decreasing the number of paying customers by around 3%. (This is based on a 737 with single-class, but you get the idea.)

So why don’t airlines rip out a row of seats, raise prices by 3% and enjoy the reduced costs for fuel and customer service? The only answer I can see is that people, on average, aren’t actually willing to pay 3% more for 2.5 cm more legroom. We want a worse but cheaper product, and so that’s what we get.

I think this is the most common reason stuff is “bad”. It’s why Subway sandwiches are so soggy, why video games are so buggy, why IKEA furniture and Primark clothes fall apart so quickly.

It’s good when things are bad for this reason. Or at least, that’s the premise of capitalism: “When companies cut costs, that’s the invisible hand redirecting resources to maximize social value”, or whatever. Companies may be motivated by greed. And you may not like it, since you want to pay zero dollars for infinite value. But this is markets working as designed.

Second reason stuff is bad: Information asymmetries

Why is it that almost every book / blog / podcast about longevity is such garbage? Well, we don’t actually know many things that will reliably increase longevity, and those things are mostly all boring / hard / non-fun. And even if you do all of them, it probably only adds a couple years in expectation. And telling people those facts is not a good way to find suckers who will pay you lots of money for your unproven supplements / seminars / etc.

True! But it doesn’t explain why all longevity stuff is so bad. Why don’t honest people tell the true story and drive all the hucksters out of business? I suspect the answer is that unless you have a lot of scientific training and do a lot of research, it’s basically impossible to figure out just how huckstery all the hucksters really are.

I think this same basic phenomenon explains why some supplements contain heavy metals, why some food contains microplastics, why restaurants use so much butter and salt, why rentals often have crappy insulation, and why most cars seem to only be safe along dimensions included in crash test scores. When consumers can’t tell good from evil, evil triumphs.

Third reason stuff is bad: People have bad taste

Sometimes stuff is bad because people just don’t appreciate the stuff you consider good. Examples are definitionally controversial, but I think this includes restaurants in cities where all restaurants are bad, North American tea, and travel pants. This reason has a blurry boundary with information asymmetries, as seen in ultrasonic humidifiers or products that use Sucralose instead of aspartame for “safety”.

Fourth reason stuff is bad: Pricing power

Finally, sometimes stuff is bad because markets aren’t working. Sometimes a company is selling a product but has some kind of “moat” that makes it hard for anyone else to compete with them, e.g. because of some technological or regulatory barrier, control of some key resource or location, some intellectual property, some beloved brand, or because of network effects.

If that’s true then those companies don’t have to worry much about someone else stealing their business, and so (because everyone is axiomatically greedy) they will find ways to make their product cheaper and/or raise their prices up until it’s equal to the full value it provides to the marginal consumer.

Conclusion

Why is food so expensive at sporting events? Yes, people have no alternatives. But people know food is expensive at sporting events. And they don’t like it. Instead of selling water for $17, why don’t venues sell water for $2 and raise ticket prices instead? I don’t know. Probably something complicated, like that expensive food allows you to extract extra money from rich people without losing business from non-rich people.

So of course dating apps would love to string people along for years instead of finding them long-term relationships, so they keep paying money each month. I’d bet that some people at those companies have literally thought, “Maybe we should string people along for years instead of finding them long-term relationships, so they keep paying money each month. I love money so much.”

But if they are actually doing that (which is unclear to me) or if they are bad in some other way, then how do they get away with it? Why doesn’t someone else create a competing app that’s better and thereby steal all their business? It seems like the answer has to be either “because that’s impossible”, or “because people don’t really want that”. That’s where the mystery begins.

1. Dating apps are very popular.

2. Lots of people hate dating apps.

3. They hate them so much that there’s supposedly a resurgence in alternatives like speed dating.

None of those are too controversial, I think. (Let’s stress supposedly in #3.) But if you stare at them for a while, it’s hard to see how they can all be true at the same time.

Because, why do people hate dating apps? People complain that they’re bad in various ways, such as being ineffective, dehumanizing, or expensive. (And such small portions!) But if they’re bad, then why? Technologically speaking, a dating app is not difficult to make. If dating apps are so bad, why don’t new non-bad ones emerge and out-compete them?

The typical answer is network effects. A dating app’s value depends on how many other people are on it. So everyone gravitates to the popular ones and eventually most of the market is captured by a few winners. To displace them, you’d have to spend a huge amount of money on advertising. So—the theory goes—the winners are an oligopoly that gleefully focus on extracting money from their clients instead of making those clients happy.

That isn’t obviously wrong. Match Group (which owns Tinder, Match, Plenty of Fish, OK Cupid, Hinge, and many others) has recently had an operating margin of ~25%. That’s more like a crazy-profitable entrenched tech company (Apple manages ~30%) than a nervous business in a crowded market.

But wait a second. How many people go to a speed dating event? Maybe 30? I don’t know if the speed dating “resurgence” is real, but it doesn’t matter. Some people definitely do find love at real-life events with small numbers of people. If that’s possible, then shouldn’t it also be possible to create a dating app that’s useful even with only a small number of users? Meaning good apps should have emerged long ago and displaced the crappy incumbents? And so the surviving dating apps should be non-hated?

We’ve got ourselves a contradiction. So something is wrong with that argument. But what?

Theory 1: Selection

Perhaps speed dating attendees are more likely to be good matches than people on dating apps. This might be true because they tend to be similar in terms of income, education, etc., and people tend to mate assortatively. People who go to such events might also have some similarities in terms of personality or what they’re looking for in a relationship.

You could also theorize that people at speed dating events are higher “quality”. For example, maybe it’s easier to conceal negative traits on dating apps than it is in person. If so, this might lead to some kind of adverse selection where people without secret negative traits get frustrated and stop using the apps.

I’m not sure either of those are true. But even if they are, consider the magnitudes. While a speed dating event might have 30 people, a dating app in a large city could easily have 30,000 users. While the fraction of good matches might be lower on a dating app, the absolute number is still surely far higher.

Theory 2: Bandwidth

Perhaps even if you have fewer potential matches at a speed dating event, you have better odds of actually finding them, because in-person interactions reveals information that dating apps don’t.

People often complain that dating apps are superficial, that there’s too much focus on pictures. Personally, I don’t think pictures deserve so much criticism. Yes, they show how hot you are. But pictures also give lots of information about important non-superficial things, like your personality, values, social class, and lifestyle. I’m convinced people use pictures for all that stuff as much as hotness.

But you know what’s even better than pictures? Actually talking to someone!

Many people seem to think that a few minutes of small talk isn’t enough time to learn anything about someone. Personally, I think evolution spent millions of years training us to do exactly that. I’d even claim that this is why small talk exists.

(I have friends with varying levels of extroversion and agreeableness, but all of my friends seem to have high openness to experience. When I meet someone new, I’m convinced I can guess their openness to ±10% by the time they’ve completed five sentences.)

So maybe the information a dating app provides just isn’t all that useful compared to a few minutes of casual conversation. If so, then dating apps might be incredibly inefficient. You have to go through some silly texting courtship ritual, set up a time to meet, physically go there, and then pretend to smile for an hour even if you immediately hate them.

Under this theory, dating apps provide a tiny amount of information about a gigantic pool of people, while speed dating provides a ton of information about a small number of people. Maybe that’s a win, at least sometimes.

Theory 3: Behavior

Maybe the benefit of real-life events isn’t that they provide more information, but that they change how we behave.

For example, maybe people are nicer in person? Because only then can we sense that others are also sentient beings with internal lives and so on?

I’m pretty sure that’s true. But it’s not obvious it helps with our mystery, since people from dating apps eventually meet in person, too. If they’re still nice when they do, then this just resolves into “in-person interactions provide more information”, and is already covered by the previous theory. To help resolve our mystery, you’d need to claim that people at real-life events act differently than they do when meeting up as a result of a dating app.

That could happen as a result of a “behavioral equilibrium”. Some people take dating apps seriously and some take them casually. But it’s hard to tell what category someone else is in, so everyone proceeds with caution. But by showing up at an in-person event, everyone has demonstrated some level of seriousness. And maybe this makes everyone behave differently? Perhaps, but I don’t really see it.

Obscure theories

I can think of a few other possible explanations.

1. Maybe speed dating serves a niche. Just like Fitafy / Bristlr / High There! serve people who love fitness / beards / marijuana, maybe speed dating just serves some small-ish fraction of the population but not others.

2. Maybe the people who succeed at speed dating would also have succeeded no matter what. So they don’t offer any general lessons.

3. Maybe creating a dating app is in fact very technologically difficult. So while the dating apps are profit-extracting oligopolies, that’s because of technological moat, not network effects.

I don’t really buy any of these.

Drumroll

So what’s really happening? I am not confident, but here’s my best guess:

1. Selection is not a major factor.

2. The high bandwidth of in-person interactions is a major factor.

3. The fact that people are nicer or more open-minded in person is not a major factor, other than through making in-person interactions higher bandwidth.

4. None of the obscure theories are major factors.

5. Dating apps are an oligopoly, driven by network effects.

Basically, a key “filter” in finding love is finding someone where you both feel optimistic after talking for five minutes. Speed dating is (somewhat / sometimes) effective because it efficiently crams a lot of people into the top of that filter.

Meanwhile, because dating apps are low-bandwidth, they need a large pool to be viable. Thus, they’re subject to network effects, and the winners can turn the screws to extract maximum profits from their users.

Partly I’m not confident in that story just because it has so many moving parts. But something else worries me too. If it’s true, then why aren’t dating apps trying harder to provide that same information that in-person interactions do?

If anything, I understand they’re moving in the opposite direction. Maybe Match Group would have no interest in that, since they’re busy enjoying their precious network effects. But why not startups? Hell, why not philanthropies? (Think of all the utility you could create!) For the above story to hold together, you have to believe that it’s a very difficult problem.

Well OK then!

Pointing machines

How did Michelangelo make this?

What I mean is—marble is unforgiving. If you accidentally remove some material, it’s gone. You can’t fix it by adding another layer of paint. Did Michelangelo somehow plan everything out in advance and then execute everything perfectly the first time, with no mistakes?

I learned a few years ago that sculptors have long used a simple but ingenious invention called a pointing machine. This allows you to create a sculpture in clay and, in effect, “copy” it into stone. That sounds magical, but it’s really just an articulated pointer that you move between anchor points attached to the (finished) clay and the (incomplete) stone sculpture. If you position the pointer based on the clay sculpture and then move it to the stone sculpture, anything the pointer hits should be removed. Repeat that thousands of times and the sculpture is copied.

I was sad to learn that Michelangelo was a talentless hack, but I dutifully spent the last few years telling everyone that all sculptures were made this way and actually sculpture is extremely easy, etc.

Last week I noticed that Michelangelo died in 1564, which was over 200 years before the pointing machine was invented.

Except, apparently since ancient times sculptors have used a technique sometimes called the “compass method” which is sort of like a pointing machine except more complex and involving a variety of tools and measurements. This was used by the ancient Romans to make copies of older Greek sculptures. And most people seem to think that Michelangelo probably did use that.

Population pyramids

I think this is one of the greatest data visualizations ever invented.

Sure, it’s basically just a histogram turned on the side. But compare India’s smooth and calm teardrop with China’s jagged chaos. There aren’t many charts that simultaneously tell you so much about the past and the future.

It turns out that this visualization was invented by Francis Amasa Walker, who was apparently such an impressive person that this invention doesn’t even merit a mention on his Wikipedia page. He used it in creating these visualization for the 1874 US atlas:

The atlas also contains many other beautiful visualizations, for example this one of church attendance:

Or this one on debt and public expenditures:

Post office scandal

If you haven’t heard about the British Post Office scandal, here’s what happened: In 1999, Fujitsu delivered buggy accounting software to the British Post Office that incorrectly determined that thousands of subpostmasters were stealing. Based on this faulty data, the post office prosecuted and convinced close to a thousand people, of whom 236 went to prison. Many others lost their jobs or were forced to “pay back” the “shortfalls” from their own pockets.

Of course, this is infuriating. But beyond that, I notice I am confused. It doesn’t seem like anyone wanted to hurt all those subpostmasters. The cause seems to be only arrogance, stupidity, and negligence.

I would have predicted that before you could punish thousands of people based on the same piece of fake evidence, something would happen that would stop you. Obviously, I was wrong. But I find it hard to think of good historical analogies. Maybe negligence in police crime labs or convictions of parents for “shaken baby syndrome”? Neither of these is a good analogy.

One theory is that the post office scandal happened because the post office—the “victim”—had the power to itself bring prosecutions. But in hundreds of cases things were done the normal way, with police “investigating” the alleged crimes and then sending the cases to be brought by normal prosecutors. Many cases were also pursued in Scotland and Northern Ireland, where the Post Office lacks this power.

Another theory would be:

1. Prosecutors have incredible latitude in choosing who they want to prosecute.

2. Like other humans, some prosecutors are arrogant/stupid/negligent.

3. It’s actually pretty easy for prosecutors to convict an innocent person if they really want to, as long as they have some kind of vaguely-incriminating evidence.

Under this theory, similar miscarriages of justice happen frequently. But they only involve a single person, and so they don’t make the news.

Type species

Type species - Wikipedia

I link to this not because it’s interesting but because it’s so impressively incomprehensible. If there’s someone nearby, I challenge you to read this to them without losing composure.

In zoological nomenclature, a type species (species typica) is the species whose name is considered to be permanently taxonomically associated with the name of a genus or subgenus. In other words, it is the species that contains the biological type specimen or specimens of the genus or subgenus. A similar concept is used for groups ranked above the genus and called a type genus.

In botanical nomenclature, these terms have no formal standing under the code of nomenclature, but are sometimes borrowed from zoological nomenclature. In botany, the type of a genus name is a specimen (or, rarely, an illustration) which is also the type of a species name. The species name with that type can also be referred to as the type of the genus name. Names of genus and family ranks, the various subdivisions of those ranks, and some higher-rank names based on genus names, have such types.

In bacteriology, a type species is assigned for each genus. Whether or not currently recognized as valid, every named genus or subgenus in zoology is theoretically associated with a type species. In practice, however, there is a backlog of untypified names defined in older publications when it was not required to specify a type.

Can such a thing be created unintentionally? I tried to parody this by creating an equally-useless description of an everyday object. But in the end, I don’t think it’s very funny, because it’s almost impossible to create something worse than the above passage.

A funnel is a tool first created in antiquity with rudimentary versions fabricated from organic substrates such as cucurbitaceae or broadleaf foliage by early hominid cultures. The etymology of fundibulum (Latin), provides limited insight into its functional parameters, despite its characteristic broad proximal aperture and a constricted distal orifice.

Compositionally, funnels may comprise organic polymers or inorganic compounds, including but not limited to, synthetic plastics or metallic alloys and may range in weight from several grams to multiple kilograms. Geometrically, the device exhibits a truncated conical or pyramidal morphology, featuring an internal declination angle generally between 30 and 60 degrees.

Within cultural semiotics, funnels frequently manifest in artistic representations, serving as an emblem of domestic ephemerality.

The good news is that the Sri Lankan elephant is the type species for the Asian elephant, whatever that is.

Hormones

I previously mentioned that some hormonal medications used to be made from the urine of pregnant mares. But only after reading The History of Estrogen Therapy (h/t SCPantera) did I realize that it’s right there in the name:

Premarin = PREgnant MARe’s urINe

If you—like me—struggle to believe that a pharmaceutical company would actually do this, note that was in 1941. Even earlier, the urine of pregnant humans was used. Tragically, this was marketed as “Emmenin” rather than “Prehumin”.

But, particularly for “explainer” posts, the long-term prospects seem dim. I write about random stuff and then send it to you. If you just want to understand something, why would you read my rambling if AI could explain it equally well, in a style customized for your tastes, and then patiently answer your questions forever?

I mean, say you can explain some topic better than AI. That’s cool, but once you’ve published your explanation, AI companies will put it in their datasets, thankyouverymuch, after which AIs will start regurgitating your explanation. And then—wait a second—suddenly you can’t explain that topic better than AI anymore.

This is all perfectly legal, since you can’t copyright ideas, only presentations of ideas. It used to take work to create a new presentation of someone else’s ideas. And there used to be a social norm to give credit to whoever first came up with some idea. This created incentives to create ideas, even if they weren’t legally protected. But AI can instantly slap a new presentation on your ideas, and no one expects AI to give credit for its training data. Why spend time creating content so just it can be nostrified by the Borg? And why read other humans if the Borg will curate their best material for you?

So will the explainer post survive?

Let’s start with an easier question: Already today, AI will happily explain anything. Yet many people read human-written explanations anyway. Why do they do that? I can think of seven reasons:

1. Accuracy. Current AI is unreliable. If I ask about information theory or how to replace the battery on my laptop, it’s very impressive but makes some mistakes. But if I ask about heritability, the answers are three balls of gibberish stacked on top of each other in a trench-coat. Of course, random humans make mistakes, too. But if you find a quality human source, it is far less likely to contain egregious mistakes. This is particularly true across “large contexts” and for tasks where solutions are hard to verify.

2. AI is boring. At least, writing from current popular AI tools is boring, by default.

3. Parasocial relationships. If I’ve been reading someone for a long time, I start to feel like I have a kind of relationship with them. If you’ve followed this blog for a long time, you might feel like you have a relationship with me. Calling these “parasocial relationships” makes them sound sinister, but I think this is normal and actually a clever way of using our tribal-band programming to help us navigate of the modern world. Just like in “real” relationships, when I read someone I have a parasocial relationship with, I have extra context that makes it easier to understand them, I feel a sense of human connection, and I feel like I’m getting a sort of update on their “story”. I don’t get any of that with (current) AI.

4. Skin in the game. If a human screws something up, it’s embarrassing. They lose respect and readers. On a meta-level, AI companies have similar incentives not to screw things up. But AI itself doesn’t (seem to) care. Human nature makes it easier to trust someone when we know they’re putting some kind of reputation on the line.

5. Conspicuous consumption. Since I read Reasons and Persons, I can brag to everyone that I read Reasons and Persons. If I had read some equally good AI-written book, probably no one would care.

6. Coordination points. Partly, I read Reasons and Persons because I liked it. And maybe I guess I read it so I can brag about the fact that I read it. (Hey everyone, have I mentioned that I read Reasons and Persons?) But I also read it because other people read it. When I talk to those people, we have a shared vocabulary and set of ideas that makes it easier to talk about other things. This wouldn’t work if we had all explored the same ideas though fragmented AI “tutoring”.

7. Change is slow. Here we are 600 years after the invention of the printing press, and the primary mode of advanced education is still for people to physically go to a room where an expert is talking and write down stuff the expert says. If we’re that slow to adapt, then maybe we read human-written explainers simply out of habit.

How much do each of these really matter? How much confidence should they give us that explainer posts will still exist a decade from now? Let’s handle them in reverse order.

Argument 7: Change is slow

Sure, society takes time to adapt to technological change. But I don’t think college lectures are a good example of this, or that they’re a medieval relic that only survive out of inertia. On the contrary, I think they survive because we haven’t really any other model of education that’s fundamentally better.

Take paper letters. One hundred years ago, these were the primary form of long-distance communication. But after the telephone was widely distributed, it only took it a few decades to kill the letter in almost all cases where the phone is better. When email and texting showed up, they killed off almost all remaining use of paper letters. They still exist, but they’re niche.

The same basic story holds for horses, the telegraph, card catalogs, slide rules, VHS tapes, vacuum tubes, steam engines, ice boxes, answering machines, sailboats, typewriters, the short story, and the divine right of kings. When we have something that’s actually better, we drop the old ways pretty quickly. Inertia alone might keep explainer posts alive for a few years, but not more than that.

Arguments 5 and 6: Coordination points and conspicuous consumption

Western civilization began with the Iliad. Or, at least, we’ve decided to pretend it did. If you read the Iliad, then you can brag about reading the Iliad (good) and you have more context to engage with everyone else who read it (very good). So people keep reading the Iliad. I think this will continue indefinitely.

But so what? The Iliad is in that position because people have been reading/listening to it for thousands of years. But if you write something new and there’s no “normal” reason to read it, then it has to way to establish that kind of self-sustaining legacy.

Non-fiction in general has a very short half-life. And even when coordination points exist, people often rely on secondary sources anyway. Personally, I’ve tried to read Wittgenstein, but I found it incomprehensible. Yet I think I’ve absorbed his most useful idea by reading other people’s descriptions. I wonder how much “Wittgenstein” is really a source at this point as opposed to a label.

Also... explainer posts typically aren’t the Iliad. So I don’t think this will do much to keep explainer posts alive, either.

(Aside: I’ve never understood why philosophy is so fixated on original sources, instead of continually developing new presentations of old ideas like math and physics do. Is this related to the fact that philosophers go to conferences and literally read their papers out loud?)

Argument 4: Skin in the game

I trust people more when I know they’re putting their reputation on the line, for the same reason I trust restaurants more when I know they rely on repeat customers. AI doesn’t give me this same reason for confidence.

But so what? This is a loose heuristic. If AI were truly more accurate than human writing, I’m sure most people would learn to trust it in a matter of weeks. If AI was ultra-reliable but people really needed someone to hold accountable, AI companies could perhaps offer some kind of “insurance”. So I don’t see this as keeping explainers alive, either.

Argument 3: Parasocial relationships

Humans are social creatures. If bears had a secret bear Wikipedia and you went to the entry on humans, it would surely say, “Humans are obsessed with relationships.” I feel confident this will remain true.

I also feel confident that we will continue to be interested in what people we like and respect think about matters of fact. It seems plausible that we’ll continue to enjoy getting that information bundled together with little jokes or busts of personality. So I expect our social instincts will provide at least some reason for explainers to survive.

But how strong will this effect be? When explainer posts are read today, what fraction of readers are familiar enough to have a parasocial relationship with the author? Maybe 40%? And when people are familiar, what fraction of their motivation comes from the parasocial relationship, as opposed to just wanting to understand the content? Maybe another 40%? Those are made-up numbers, but I think it’s hard to avoid the conclusion that parasocial relationships explain only a fraction of why people read explainers today.

And there’s another issue. How do parasocial relationships get started if there’s no other reason to read someone? These might keep established authors going for a while at reduced levels, but it seems like it would make it hard for new people to rise up.

Argument 2: Boring-ness

Maybe popular AIs are a bit boring, today. But I think this is mostly due to the final reinforcement learning step. If you interact with “base models”, they are very good at picking up style cues and not boring at all. So I highly doubt that there’s some fundamental limitation here.

And anyway, does anyone care? If you just want to understand why vitamin D is technically a type of steroid, how much does style really matter, as opposed to clarity? I think style mostly matters in the context of a parasocial relationship, meaning we’ve already accounted for it above.

Argument 1: Accuracy

I don’t know for sure if AI will ever be as accurate as a high-quality human source. Though it seems very unlikely that physics somehow precludes creating systems that are more accurate than humans.

But if AI is that accurate, then I think this exercise suggests that explainer posts are basically toast. All the above arguments are just too weak to explain most of why people read human-written explainers now. So I think it’s mostly just accuracy. When that human advantage goes, I expect human-written explainers to go with it.

Counter-arguments

I can think of three main counterarguments.

First, maybe AI will fix discovery. Currently, potential readers of explainers often have no way to find potential writers. Search engines have utterly capitulated to SEO spam. Social media soft-bans outward links. If you write for a long time, you can build up an audience, but few people have the time and determination to do that. If you write a single explainer in your life, no one will read it. The rare exceptions to this rule either come from people contributing to established (non-social media) communities or people from people with exceptional social connections. So—this argument goes—most potential readers don’t bother trying to find explainers, and most potential writers don’t bother creating them. If AI solves that matching problem, explainers could thrive.

Second, maybe society will figure out some new way to reward people who create information. Maybe we fool around with intellectual property law. Maybe we create some crazy Xanadu-like system where in order to read some text, you have to first sign a contract to pay them based on the value you derive, and this is recursively enforced on everyone who’s downstream of you. Hell, maybe AI companies decide to solve the data wall problem by paying people to write stuff. But I doubt it.

Third, maybe explainers will follow a trajectory like chess. Up until perhaps the early 1990s, humans were so much better than computers at chess that computers were irrelevant. After Deep Blue beat Kasparov in 1997, people quickly realized that while computers could beat humans, human+computer teams could still beat computers. This was called Advanced Chess. Within 15-20 years, however, humans became irrelevant. Maybe there will be a similar Advanced Explainer era? (I kid, that era started five years ago.)

TLDR

Will the explainer post go extinct? My guess is mostly yes, if and when AI reaches human-level accuracy.

Incidentally, since there’s so much techno-pessimism these days: I think this outcome would be... great? It’s a little grim to think of humans all communicating with AI instead of each other, yes. But the upside is all of humanity having access to more accurate and accessible explanations of basically everything. If this is the worst effect of AGI, bring it on.

Say an alien spaceship is headed for Earth. It has 30 aliens on it. The aliens are weak and small. They have no weapons and carry no diseases. They breed at rates similar to humans. They are bringing no new technology. No other ships are coming. There’s no trick—except that they each have an IQ of 300. Would you find that concerning?

Of course, the aliens might be great. They might cure cancer and help us reach world peace and higher consciousness. But would you be sure they’d be great?

Suppose you were worried about the aliens but I scoffed, “Tell me specifically how the aliens would hurt us. They’re small and weak! They can’t do anything unless we let them.” Would you find that counter-argument convincing?

I claim that most people would be concerned about the arrival of the aliens, would not be sure that their arrival would be good, and would not find that counter-argument convincing.

I bring this up because most AI-risk arguments I see go something like this:

1. There will be a fast takeoff in AI capabilities.

2. Due to alignment difficulty and orthogonality, it will pursue dangerous convergent subgoals.

3. These will give the AI a decisive strategic advantage, making it uncontainable and resulting in catastrophe.

These arguments have always struck me as overcomplicated. So I’d like to submit the following undercomplicated alternative:

1. Obviously, if an alien race with IQs of 300 were going to arrive on Earth soon, that would be concerning.

2. In the next few decades, it’s entirely possible that AI with an IQ of 300 will arrive. Really, that might actually happen.

3. No one knows what AI with an IQ of 300 would be like. So it might as well be an alien.

Our subject for today is: Why might one prefer one of these arguments to the other?

The case for the simple argument

The obvious reason to prefer the simple argument is that it’s more likely to be true. The complex argument has a lot of steps. Personally, I think they’re all individually plausible. But are we really confident that there will be a fast takeoff in AI capabilities and that the AI will pursue dangerous subgoals and that it will thereby gain a decisive strategic advantage?

I find that confidence unreasonable. I’ve often been puzzled why so many seemingly-reasonable people will discuss these arguments without rejecting the confidence.

I think the explanation is that there are implicitly two versions of the complex argument. The “strong” version claims that fast takeoff et al. will happen, while the “weak” version merely claims that it’s a plausible scenario that we should take seriously. It’s often hard to tell which version people are endorsing.

The distinction is crucial, because these two version have have different weaknesses. I find the strong version wildly overconfident. I agree with the weak version, but I still think it’s unsatisfying.

Say you think there’s a >50% chance things do not go as suggested by the complex argument. Maybe there’s a slow takeoff, or maybe the AI can’t build a decisive strategic advantage, whatever. Now what?

Well, maybe everything turns out great and you live for millions of years, exploring the galaxy, reading poetry, meditating, and eating pie. That would be nice. But it also seems possible that humanity still ends up screwed, just in a different way. The complex argument doesn’t speak to what happens when one of the steps fails. This might give the impression that without any of the steps, everything is fine. But that is not the case.

The simple argument is also more convincing. Partly I think that’s because—well—it’s easier to convince people of things when they’re true. But beyond that, the simple argument doesn’t require any new concepts or abstractions, and it leverages our existing intuitions for how more intelligent entities can be dangerous in unexpected ways.

I actually prefer the simple argument in an inverted form: If you claim that there is no AI-risk, then which of the following bullets do you want to bite?

1. “If a race of aliens with an IQ of 300 came to Earth, that would definitely be fine.”

2. “There’s no way that AI with an IQ of 300 will arrive within the next few decades.”

3. “We know some special property that AI will definitely have that will definitely prevent all possible bad outcomes that aliens might cause.”

I think all those bullets are unbiteable. Hence, I think AI-risk is real.

But if you make the complex argument, then you seem to be left with the burden of arguing for fast takeoff and alignment difficulty and so on. People who hear that argument also often demand an explanation of just how AI could hurt people (“Nanotechnology? Bioweapons? What kind of bioweapon?”) I think this is a mistake for the same reason it would be a mistake to demand to know how a car accident would happen before putting on your seatbelt. As long as the Complex Scenario is possible, it’s a risk we need to manage. But many people don’t look at things that way.

But I think the biggest advantage of the simple argument is something else: It reveals the crux of disagreement.

I’ve talked to many people who find the complex argument completely implausible. Since I think it is plausible—just not a sure thing—I often ask why. People give widely varying reasons. Some claim that alignment will be easy, some that AI will never really be an “agent”, some talk about the dangers of evolved vs. engineered systems, and some have technical arguments based on NP-hardness or the nature of consciousness.

I’ve never made much progress convincing these people to change their minds. I have succeeded in convincing some people that certain arguments don’t work. (For example, I’ve convinced people that NP-hardness and the nature of consciousness are probably irrelevant.) But when people abandon those arguments, they don’t turn around and accept the whole Scenario as plausible. They just switch to different objections.

So I started giving my simple argument instead. When I did this, here’s what I discovered: None of these people actually accept that AI with an IQ of 300 could happen.

Sure, they often say that they accept this. But if you pin them down, they’re inevitably picturing an AI that lacks some core human capability. Often, the AI can prove theorems or answer questions, but it’s not an “agent” that wants things and does stuff and has relationships and makes long-term plans.

So I conjecture that this is the crux of the issue with AI-risk. People who truly accept that AI with an IQ of 300 and all human capabilities may appear are almost always at least somewhat worried about AI-risk. And people who are not worried about AI-risk almost always don’t truly accept that AI with an IQ of 300 could appear. If that’s the crux, then we should get to it as quickly as possible. And that’s done by the simple argument.

The case for the complex argument

I won’t claim to be neutral. As hinted by the title, I started writing this post intending to make the case for the simple argument, and I still think that case is strong. But I figured I should consider arguments for the other side and—there are some good ones.

Above, I suggested that there are two versions of the complex argument: A “strong” version that claims the scenario it lays out will definitely happen, and a “weak” version that merely claims it’s plausible. I rejected the strong version as overconfident. And I rejected the weak version because there are lots of other scenarios where things could also go wrong for humanity, so why give this one so much focus?

Well, there’s also a middle version of the complex argument: You could claim that the scenario it lays out is not certain, but that if things go wrong for humanity, then they will probably go wrong as in that scenario. This avoids both of my objections—it’s less overconfident, and it gives a good reason to focus on this particular scenario.

Personally, I don’t buy it, because I think other bad scenarios like gradual disempowerment are plausible. But maybe I’m wrong. It doesn’t seem crazy to claim that the Complex Scenario captures most of the probability mass of bad outcomes. And if that’s true, I want to know it.

Now, some people suggest favoring certain arguments for the sake of optics: Even if you accept the complex argument, maybe you’d want to make the simple one because it’s more convincing or is better optics for the AI-risk community. (“We don’t want to look like crazy people.”)

Personally, I am allergic to that whole category of argument. I have a strong presumption that you should argue the thing you actually believe, not some watered-down thing you invented because you think it will manipulate people into believing what you want them to believe. So even if my simpler argument is more convincing, so what?

But say you accept the middle version of the complex argument, yet you think my simple argument is more convincing. And say you’re not as bloody-minded as me, so you want to calibrate your messaging to be more effective. Should you use my simple argument? I’m not sure you should.

The typical human bias is to think other people are similar to us. (How many people favor of mandatory pet insurance funded by a land-value tax? At least 80%, right?) But as far as I can tell, the situation with AI-risk is the opposite. Most people I know are at least mildly concerned, but have the impression that “normal people” think that AI-risk is science fiction nonsense.

Yet, here are some recent polls:

• Gallup, June 2-15 2025

  • “[AI is] very different from the technological advancements that came before, and threatens to harm humans and society”

  • 49% agree

• Reuters / Ipsos, August 13-18 2025

  • “AI could risk the future of humankind”

  • 58% agree

• YouGov, March 5-7 2025

  • “How concerned, if at all, are you about the possibility that artificial intelligence (AI) will cause the end of the human race on Earth?”

  • 37% very or somewhat concerned

• YouGov, June 27-30 2025

  • “How concerned, if at all, are you about the possibility that artificial intelligence (AI) will cause the end of the human race on Earth?”

  • 43% very or somewhat concerned

Being concerned about AI is hardly a fringe position. People are already worried, and becoming more so.

I used to picture my simple argument as a sensible middle-ground, arguing for taking AI-risk seriously, but not overconfident:

But I’m starting to wonder if my “obvious argument” is in fact obvious, and something that people can figure out own their own. From looking at the polling data, it seems like the actual situation is more like this, with people on the left gradually wandering towards the middle:

If anything, the optics may favor a confident argument over my simple argument. In principle, they suggest similar actions: Move quickly to reduce existential risk. But what I actually see is that most people—even people working on AI—feel powerless and are just sort of clenching up and hoping for the best.

I don’t think you should advocate for something you don’t believe. But if you buy the complex argument, and you’re holding yourself back for the sake of optics, I don’t really see the point.

I fear we are in the waning days of the People Read Blog Posts About Random Well-Understood Topics Instead of Asking Their Automatons Era. So before I lose my chance, here is a blog post about some random well-understood topics.

Marathons are stupidly fast

You probably know that people can now run marathons in just over 2 hours. But do you realize how insane that is?

That’s an average speed of 21.1 km per hour, or 13.1 miles per hour. You can think of that as running a mile in 4:35 (world record: 3:45), except doing it 26.2 times in a row. Or, you can think of that as running 100 meters in 17.06 seconds (world record: 9.58 seconds), except doing it 421.6 times in a row. I’d guess that only around half of the people reading this could run 100 meters in 17.06 seconds once.

This crazy marathon running speed is mostly due to humans being well-adapted for running and generally tenacious. But some of it is due to new shoes with carbon-fiber plates that came out in the late 2010s.

The theory behind these shoes is quite interesting. When you run, you mainly use four joints:

1. Hips

2. Knees

3. Ankles

4. Metatarsophalangeal

If you haven’t heard of the last of these, they’re pronounced “met-uh-tar-so-fuh-lan-jee-ul” or “MTP”. These are the joints inside your feet behind your big toes.

Besides sounding made-up, they’re different from the other joints in a practical way: The other joints are all attached to large muscles and tendons that stretch out and return energy while running sort of like springs. These can apparently recover around 60% of the energy expended in each stride. (Kangaroos seemingly do even better.) But the MTP joints are only attached to small muscles and tendons, so the energy that goes into them is mostly lost.

These new shoe designs have complex constructions of foam and plates that can do the same job as the MTP joints, but—unlike the MTP joints—store and return that energy to the runner. A recent meta-analysis estimated that this reduced total oxygen consumption by ~2.7% and marathon times by ∼2.18%.

Algernon

I wonder if these shoes are useful as a test case for the Algernon argument. In general, that argument is that there shouldn’t be any simple technology that would make humans dramatically smarter, since if there was, then evolution would have already found it.

You can apply the same kind of argument to running: We have been optimized very hard by evolution to be good at running, so there shouldn’t be any “easy” technologies that would make us dramatically faster or more efficient.

In the context of the shoes, I think that argument does… OK? The shoes definitely help. But carbon fiber plates are pretty hard to make, and the benefit is pretty modest. Maybe this is some evidence that Algernon isn’t a hard “wall”, but rather a steep slope.

Or, perhaps thinking is just different from running. If you start running, you will get better at it, in a way that spills over into lots of other physical abilities. But there doesn’t seem to be any cognitive task that you can practice and make yourself better at other cognitive tasks.

If you have some shoes that will make me 2.7% smarter, I’ll buy them.

Pangea

Pangea was a supercontinent that contained roughly all the land on Earth. At the beginning of the Jurassic 200 million years ago, it broke up and eventually formed the current continents. But isn’t the Earth 4.5 billion years old? Why would all the land stick together for 95% of that time and then suddenly break up?

The accepted theory is that it didn’t. Instead, it’s believed that Earth cycles between super-continents and dispersed continents, and Pangea is merely the most recent super-continent.

But why would there be such a cycle? We can break that down into two sub-questions.

First, why would dispersed continents fuse together into a supercontinent? Well, you can think of the Earth as a big ball of rock, warmed half by primordial heat from when the planet formed and half by radioactive decay. Since the surface is exposed to space, it cools, resulting in solid chunks that sort of slide around on the warm magma in the upper mantle. Some of those chunks are denser than others, which causes them to sink into the mantle a bit and get covered with water. So when a “land chunk” crashes into a “water chunk”, the land chunk slides on top. But if two land chunks crash into each other, they tend to crumple together into mountains and stick to each other.

You can see this by comparing this map of all the current plates:

To this map of elevation:

OK, but once a super-continent forms, why would it break apart? Well, compared to the ocean floor, land chunks are thicker and lighter. So they trap heat from inside the planet sort of like a blanket. With no cool ocean floor sliding back into the warm magma beneath, that magma keeps getting warmer and warmer. After tens of millions of years, it heats up so much that it stretches the land above and finally rips it apart.

It’s expected that a new supercontinent “Pangea Ultima” will form in 250 million years. By that time, the sun will be putting out around 2.3% more energy, making things hotter. On top of that, it’s suspected that Pangea Ultima, for extremely complicated reasons, will greatly increase the amount of CO₂ in the atmosphere, likely making the planet uninhabitable by mammals. So we’ve got that going for us.

Egypt and the Sea Peoples

The Sea Peoples are a group of people from… somewhere… that appeared in the Eastern Mediterranean around 1200 BC and left a trail of destruction from modern Turkey down to modern Egypt. They are thought to be either a cause or symptom of the Late Bronze Age collapse.

But did you know the Egyptians made carvings of the situation while they were under attack? Apparently the battle looked like this:

In the inscription, Pharaoh Ramesses III reports:

Those who reached my boundary, their seed is not; their hearts and their souls are finished forever and ever. As for those who had assembled before them on the sea, the full flame was their front before the harbor mouths, and a wall of metal upon the shore surrounded them. They were dragged, overturned, and laid low upon the beach; slain and made heaps from stern to bow of their galleys, while all their things were cast upon the water.

PendingKetchup comments on my recent post on what it means for something to be heritable:

The article seems pretty good at math and thinking through unusual implications, but my armchair Substack eugenics alarm that I keep in the back of my brain is beeping.

Saying that variance was “invented for the purpose of defining heritability” is technically correct, but that might not be the best kind of correct in this case, because it was invented by the founder of the University of Cambridge Eugenics Society who had decided, presumably to support that project, that he wanted to define something called “heritability”.

His particular formula for heritability is presented in the article as if it has odd traits but is obviously basically a sound thing to want to calculate, despite the purpose it was designed for.

The vigorous “educational attainment is 40% heritable, well OK maybe not but it’s a lot heritable, stop quibbling” hand waving sounds like a person who wants to show but can’t support a large figure. And that framing of education, as something “attained” by people, rather than something afforded to or invested in them, is almost completely backwards at least through college.

The various examples about evil despots and unstoppable crabs highlight how heritability can look large or small independent of more straightforward biologically-mechanistic effects of DNA. But they still give the impression that those are the unusual or exceptional cases.

In reality, there are in fact a lot of evil crabs, doing things like systematically carting away resources from Black children’s* schools, and then throwing them in jail. We should expect evil-crab-based explanations of differences between people to be the predominant ones.

*Not to say that being Black “is genetic”. Things from accent to how you style your hair to how you dress to what country you happen to be standing in all contribute to racial judgements used for racism. But “heritability” may not be the right tool to disentangle those effects.

Dear PendingKetchup,

Thanks for complimenting my math (♡), for reading all the way to the evil crabs, and for not explicitly calling me a racist or eugenicist. I also appreciate that you chose sincerity over boring sarcasm and that you painted such a vibrant picture of what you were thinking while reading my post. I hope you won’t mind if I respond in the same spirit.

To start, I’d like to admit something. When I wrote that post, I suspected some people might have reactions similar to yours. I don’t like that. I prefer positive feedback! But I’ve basically decided to just let reactions like yours happen, because I don’t know how to avoid them without compromising on other core goals.

It sounds like my post gave you a weird feeling. Would it be fair to describe it as a feeling that I’m not being totally upfront about what I really think about race / history / intelligence / biological determinism / the ideal organization of society?

Because if so, you’re right. It’s not supposed to be a secret, but it’s true.

Why? Well, you may doubt this, but when I wrote that post, my goal was that people who read it would come away with a better understanding of the meaning of heritability and how weird it is. That’s it.

Do I have some deeper and darker motivations? Probably. If I probe my subconscious, I find traces of various embarrassing things like “draw attention to myself” or “make people think I am smart” or “after I die, live forever in the world of ideas through my amazing invention of blue-eye-seeking / human-growth-hormone-injecting crabs.”

What I don’t find are any goals related to eugenics, Ronald Fisher, the heritability of educational attainment, if “educational attainment” is good terminology, racism, oppression, schools, the justice system, or how society should be organized.

These were all non-goals for basically two reasons:

1. My views on those issues aren’t very interesting or notable. I didn’t think anyone would (or should) care about them.

2. Surely, there is some place in the world for things that just try to explain what heritability really means? If that’s what’s promised, then it seems weird to drop in a surprise morality / politics lecture.

At the same time, let me concede something else. The weird feeling you got as you read my post might be grounded in statistical truth. That is, it might be true that many people who blog about things like heritability have social views you wouldn’t like. And it might be true that some of them pretend at truth-seeking but are mostly just charlatans out to promote those unliked-by-you social views.

You’re dead wrong to think that’s what I’m doing. All your theories of things I’m trying to suggest or imply are unequivocally false. But given the statistical realities, I guess I can’t blame you too much for having your suspicions.

So you might ask—if my goal is just to explain heritability, why not make that explicit? Why not have a disclaimer that says, “OK I understand that heritability is fraught and blah blah blah, but I just want to focus on the technical meaning because…”?

One reason is that I think that’s boring and condescending. I don’t think people need me to tell them that heritability is fraught. You clearly did not need me to tell you that.

Also, I don’t think such disclaimers make you look neutral. Everyone knows that people with certain social views (likely similar to yours) are more likely to give such disclaimers. And they apply the same style of statistical reasoning you used to conclude I might be a eugenicist. I don’t want people who disagree with those social views to think they can’t trust me.

Paradoxically, such disclaimers often seem to invite more objections from people who share the views they’re correlated with, too. Perhaps that’s because the more signals we get that someone is on “our” side, the more we tend to notice ideological violations. (I’d refer here to the narcissism of small differences, though I worry you may find that reference objectionable.)

If you want to focus on the facts, the best strategy seems to be serene and spiky: to demonstrate by your actions that you are on no one’s side, that you don’t care about being on anyone’s side, and that your only loyalty is to readers who want to understand the facts and make up their own damned mind about everything else.

I’m not offended by your comment. I do think it’s a little strange that you’d publicly suggest someone might be a eugenicist on the basis of such limited evidence. But no one is forcing me to write things and put them on the internet.

The reason I’m writing to you is that you were polite and civil and seem well-intentioned. So I wanted you to know that your world model is inaccurate. You seem to think that because my post did not explicitly support your social views, it must have been written with the goal of undermining those views. And that is wrong.

The truth is, I wrote that post without supporting your (or any) social views because I think mixing up facts and social views is bad. Partly, that’s just an aesthetic preference. But if I’m being fully upfront, I also think it’s bad in the consequentialist sense that it makes the world a worse place.

Why do I think this? Well, recall that I pointed out that if there were crabs that injected blue-eyed babies with human growth hormone, that would increase the heritability of height. You suggest I had sinister motives for giving this example, as if I was trying to conceal the corollary that if the environment provided more resources to people with certain genes (e.g. skin color) that could increase the heritability of other things (e.g. educational attainment).

Do you really think you’re the only reader to notice that corollary?

The degree to which things are “heritable” depends on the nature of society. This is a fact. It’s a fact that many people are not aware of. It’s also a fact that—I guess—fits pretty well with your social views. I wanted people to understand that. Not out of loyalty to your social views, but because it is true.

It seems that you’re annoyed that I didn’t phrase all my examples in terms of culture war. I could have done that. But I didn’t, because I think my examples are easier to understand, and the degree to which changing society might change the heritability of some trait is a contentious empirical question.

But OK. Imagine I had done that. And imagine all the examples were perfectly aligned with your social views. Do you think that would have made the post more or less effective in convincing people that the fact we’re talking about is true? I think the answer is: Far less effective.

I’ll leave you with two questions:

Question 1: Do you care about the facts? Do you believe the facts are on your side?

Question 2: Did you really think I wrote that post with with the goal of promoting eugenics?

If you really did think that, then great! I imagine you’ll be interested to learn that you were incorrect.

But just as you had an alarm beeping in your head as you read my post, I had one beeping in my head as I read your comment. My alarm was that you were playing a bit of a game. It’s not that you really think I wanted to promote eugenics, but rather that you’re trying to enforce a norm that everyone must give constant screaming support to your social views and anyone who’s even slightly ambiguous should be ostracized.

Of course, this might be a false alarm! But if that is what you’re doing, I have to tell you: I think that’s a dirty trick, and a perfect example of why mixing facts and social views is bad.

You may disagree with all my motivations. That’s fine. (I won’t assume that means you are a eugenicist.) All I ask is that you disapprove accurately.

xox
dynomight

Here’s one possible hobby:

1. Take something you don’t like.

2. Try to like it.

It could be food or music or people or just the general situation you’re in. I recommend this hobby, partly because it’s nice to enjoy things, but mostly as an instrument for probing human nature.

1.

I was in Paris once. By coincidence, I wandered past a bunch of places that were playing Michael Jackson. I thought to myself, “Huh. The French sure do love Michael Jackson.” Gradually I decided, “You know what? They’re right! Michael Jackson is good.” Later, I saw a guy driving around blasting Billy Jean while hanging a hand outside his car with a sparkly white Michael Jackson glove. Again, I thought, “Huh.” That day was June 25, 2009.

2.

I don’t like cooked spinach. But if I eat some and try to forget that I hate it, it seems OK. Why?

Well, as a child, I was subjected to some misguided spinach-related parental interventions. (“You cannot leave this table until you’ve finished this extremely small portion”, etc.) I hated this, but looking back, it wasn’t the innate qualities of spinach the bothered me, so much as that being forced to put something inside my body felt like a violation of my autonomy.

When I encountered spinach as an adult, instead of tasting a vegetable, I tasted a grueling battle of will. Spinach was dangerous—if I liked it, that would teach my parents that they were right to control my diet.

So I tried telling myself little stories: I’m hiking in the mountains in Japan when suddenly the temperature drops, and it starts pouring rain. Freezing and desperate, I spot a monastery and knock on the door. The monks warm me up and offer me hōrensō no ohitashi, made from some exotic vegetable I’ve never seen before. Presumably, I’d think it was amazing.

I can’t fully access that mind-space. But just knowing it exists seems to make a big difference. Using similar techniques, I’ve successfully made myself like (or less dislike) white wine, Ezra Klein, disco, yoga, non-spicy food, Pearl Jam, and Studio Ghibli movies.

Lesson: Sometimes we dislike things simply because we have a concept of ourselves as not liking them.

3.

Meanwhile, I’ve failed to make myself like country music. I mean, I like A Boy Named Sue. Who doesn’t? But what about Stand By Your Man or Dust on the Bottle? I listen to these, and I appreciate what they’re doing. I admire that they aren’t entirely oriented around the concerns of teenagers. But I can’t seem to actually enjoy them.

Of course, it seems unlikely that this is unrelated to the fact that no one in my peer group thinks country music is cool. On the other hand, I’m constantly annoyed that my opinions aren’t more unique or interesting. And I subscribe to the idea that what’s really cool is to be a cultural omnivore who appreciates everything.

It doesn’t matter. I still can’t like country music. I think the problem is that I don’t actually want to like country music. I only want to want to like country music. The cultural programming is in too deep.

Lesson: Certain levels of the subconscious are easier to screw around with than others.

4.

For years, a friend and I would go on week-long hikes. Before we started, we’d go make our own trail mix, and I’d always insist on adding raisins. Each year, my friend would object more loudly that I don’t actually like raisins. But I do like raisins. So I’d scoff. But after several cycles, I had to admit that while I “liked raisins” there never came a time that I actually wanted to eat raisins, ever.

Related: Once every year or two, I’ll have a rough day, and I’ll say to myself, “OK, screw it. Liking Oasis is the lamest thing that has ever been done by anyone. But the dirty truth is that I love Oasis. So I will listen to Oasis and thereby be comforted.” Then I listen to Oasis, and it just isn’t that good.

Lesson: You can have an incorrect concept of self.

5.

I don’t like this about myself, but I’m a huge snob regarding television. I believe TV can be true art, as high as any other form. (How does My Brilliant Friend only have an 89 on Metacritic?) But even after pretentiously filtering for critical acclaim, I usually feel that most shows are slop and can’t watch them.

At first glance, this seems just like country music—I don’t like it because of status-driven memetic desire or whatever. But there’s a difference. Not liking country music is fine (neurotic self-flagellation aside) because there’s an infinite amount of other music. But not liking most TV is really annoying, because often I want to watch TV, but can’t find anything acceptable.

I see three possible explanations:

1. Almost all TV is, in fact, bad.

2. Lots of TV is fine, but just doesn’t appeal to me.

3. Lots of TV is fine, but it’s hard to tell yourself stories where you’re hiking in the mountains and a bunch of Japanese monks show you, like, Big Bang Theory.

Whatever it is, it seems hard to change.

Lesson: Some things are hard to change.

7.

On planes, the captain will often invite you to, “sit back and enjoy the ride”. This is confusing. Enjoy the ride? Enjoy being trapped in a pressurized tube and jostled by all the passengers lining up to relieve themselves because your company decided to cram in a few more seats instead of having an adequate number of toilets? Aren’t flights supposed to be endured?

At the same time, those invitations seem like a glimpse of a parallel universe. Are there members of my species who sit back and enjoy flights?

I have no hard data. But it’s a good heuristic that there are people “who actually X” for approximately all values of X. If one in nine people enjoy going to the dentist, surely at least that many enjoy being on planes.

What I think the captain is trying to say is, “While you can’t always control your situation, you have tremendous power over how you experience that situation. You may find a cramped flight to be a torture. But the torture happens inside your head. Some people like you situation. You too, perhaps could like it.”

That’s an important message. Though one imagines that giving it as an in-flight announcement would cause more confusion, not less. So the captain does what they can.