If you’d like to make a trans­la­tion of this post, check out the in­struc­tions of how to do that here. Thank you so much for mak­ing this thing ac­ces­si­ble to more folks!

Wanna be the first(ish) to see my new stuff when it comes out? Sign up for my very low-vol­ume, max­i­mum once-a-month

newslet­ter!

If you have dis­pos­able in­come to dis­pose of, you can sup­port my free, open-source work on:

Vitamin D & Omega-3 have a larger ef­fect on de­pres­sion than an­ti­de­pres­sants

⏱ This post is over years old.

Proceed at own risk.

The “effect size” of the best an­ti­de­pres­sants on de­pres­sion, vs placebo, is around 0.4. (On av­er­age; some peo­ple re­spond much bet­ter or much worse.) This is like go­ing from a C to a C+.

In con­trast: the ef­fect size of 1500 mg/​day of “≥60% EPA” Omega-3 sup­ple­ments is a bit higher, around 0.6. This is like go­ing from a C to a B–. (With un­cer­tainty; at worst, Omega-3′s “only” on par with an­ti­de­pres­sants.)

But, much bet­ter: the ef­fect size of 4000 IU/day of Vitamin D is twice as high as an­ti­de­pres­sants’, around 1.0. This is like go­ing from a C to an B! (With un­cer­tainty; at worst, Vitamin D’s “only” on par with an­ti­de­pres­sants.) This works even for peo­ple who don’t have a Vitamin D in­suf­fi­ciency — but around half of American adults do.

Even if you’re al­ready tak­ing Vitamin D & Omega-3, dou­ble check your dose: it may still not be enough! The of­fi­cial rec­om­men­da­tions are all too low, and re­cent re­search sug­gests even the of­fi­cial max­i­mum safe dose for Vitamin D is too low.

I know the “yay sup­ple­ments” genre of writ­ing is full of sloppy re­search & grifters, and you should be skep­ti­cal of my claim of easy wins, of “$100 bills lay­ing on the side­walk”. But there is good sci­ence among the trash, and pol­icy is of­ten decades be­hind sci­ence in any field, not just health.

So, Vitamin D & Omega-3: pos­si­bly high re­ward, for low risk. That’s a pos­i­tive “expected value” bet! These sup­ple­ments are safe, cheap, over-the-counter, and have pos­i­tive side-ef­fects (on Covid & cog­ni­tion). As al­ways, “ask your doc­tor”, show them the peer-re­viewed pa­pers cited in this post.

Unless you have spe­cific rea­sons to not take Vitamin D & Omega-3 — kid­ney stones, blood thin­ners, etc — please try them, for at least a month! They could save your men­tal health. Maybe even your life.

In Alicetown, the av­er­age per­son has 4 younger cousins.

In Bobtown, the av­er­age per­son has 3 younger cousins.

Alright, not so sur­pris­ing. You may not even no­tice a dif­fer­ence.

In Alicetown, the av­er­age per­son has 4 limbs.

In Bobtown, the av­er­age per­son has 3 limbs.

It’s the same ab­solute dif­fer­ence (4 vs 3) and rel­a­tive dif­fer­ence (3/4). So what makes limbs more sur­pris­ing than cousins? Well, partly it’s more dra­matic & vis­i­ble, but also be­cause: we ex­pect high vari­a­tion in the num­ber of some­one’s younger cousins, but not their num­ber of limbs.

This is why sci­en­tists cal­cu­late an “effect size” or “standardized mean dif­fer­ence” (“mean” = av­er­age). We take the dif­fer­ence be­tween two groups, then di­vide by the to­tal amount of vari­a­tion, to ac­count for how sur­pris­ing a dif­fer­ence is.

Unfortunately for laypeo­ple, the ef­fect size is usu­ally just re­ported as a num­ber, like “+0.74” for spac­ing out your study­ing vs cram­ming, or “–0.776″ for sleep de­pri­va­tion on at­ten­tion.

But what’s that mean? How can we make these num­bers in­tu­itive?

Well, a com­mon way for data to be is a bell-shaped curve (also called a “normal dis­tri­b­u­tion”). And most of us are, alas, well-ac­quainted with the bell curve in school grades. (“grading on a curve”)

So: school grades give us a use­ful way to think about stan­dard­ized ef­fect sizes! We can now con­vert that num­ber into an ac­tual let­ter grade:

For ex­am­ple: spac­ing out your study­ing, rel­a­tive to cram­ming, will on av­er­age lift your test scores from a C to a B–. (effect size = +0.74) And short-term sleep de­pri­va­tion, rel­a­tive to healthy sleep, will on av­er­age tank your abil­ity to pay at­ten­tion from a C to a D+. (effect size: –0.776)

But it’s not lim­ited to just grades & aca­d­e­mic per­for­mance. Effect sizes can also help us un­der­stand any kind of dif­fer­ence be­tween groups, in ob­ser­va­tion or in ex­per­i­ments!

Let’s use our school grade anal­ogy, to in­ter­pret ef­fect sizes on men­tal health:

What’s an “F in men­tal health”? By de­f­i­n­i­tion of a bell curve, ~2.3% of peo­ple are be­low –2 sigma (an “F”). (See: this bell curve cal­cu­la­tor.) In Canada, ~2.6% of peo­ple had sui­ci­dal ideation in 2022, while in the US, it was ~4.9% in 2019. So, it’s not too far off to say: “F in men­tal health = lit­er­ally sui­ci­dal”. (Also, re­minder that ~4% is 1-in-25 peo­ple. You likely know some­one, or are some­one, who will feel sui­ci­dal this year. Please reach out to your friends & loved ones!)

What’s a “D in men­tal health”? ~16% of peo­ple are be­low –1 sigma (a “D”) on a bell curve. The Keyes 2002 study es­ti­mated that ~14.1% of adults meet the DSM-III cri­te­ria for a ma­jor de­pres­sive episode. So, D = Depressed.

What’s an av­er­age “C in men­tal health”? ~68% of peo­ple are within a sigma of av­er­age (a “C”) on a bell curve. Same above study found that 56.6 per­cent had mod­er­ate men­tal health. They were nei­ther “languishing” nor “flourishing”. I guess C = Could Be Worse.

What’s a “B in men­tal health”? ~16% of peo­ple are above +1 sigma (a “B”) on a bell curve. Same above study found that 17.2% of adults are “flourishing”. Good for them! B = Flourishing, life is good.

What’s an “A in men­tal health”? I don’t know who these freaks are. I ac­tu­ally could not find any sci­en­tific stud­ies on “the +2 sigma in well-be­ing”. In con­trast, there’s lots of re­search on sui­ci­dal ideation, the –2 sigma in well-be­ing. In the ab­sence of any ac­tual data, I’ll just say: A = AWESOME

So, if an in­ter­ven­tion is found to have an ef­fect size of +1.0, that’s like go­ing up a let­ter grade. If some­thing’s found to have an ef­fect size of -2.0, that’s like go­ing down two let­ter grades. And so on.

Okay, so how do we get peo­ples’ “mental health grades” up?

Let’s look at an­ti­de­pres­sants, Omega-3, and Vitamin D, in turn:

The good news is they work. The bad news is they don’t work as well as you’d think they may work.

Cipriani et al 2018 is a meta-analy­sis: a study that col­lects & com­bines lots of pre­vi­ous stud­ies (that pass some ba­sic cri­te­ria, to min­i­mize a garbage-in-garbage-out sit­u­a­tion). While meta-analy­ses aren’t per­fect, it’s usu­ally bet­ter for “science com­mu­ni­ca­tors” like me to cite meta-analy­ses over in­di­vid­ual stud­ies, to re­duce the chance I’m cherry-pick­ing.

Anyway: this meta-analy­sis an­a­lyzes 522 tri­als with 116,477 par­tic­i­pants. All 21 an­ti­de­pres­sants they stud­ied were bet­ter than placebo (a pill that con­tains no ac­tive med­i­cine). The most ef­fec­tive an­ti­de­pres­sant, Amitriptyline, had an “Odds Ratio” of 2.13, which con­verts to an ef­fect size of 0.417, which is “small-medium” ac­cord­ing to Cohen’s rec­om­men­da­tions. Or, by our school-let­ter-grade com­par­i­son: the best an­ti­de­pres­sant would take your men­tal health grade from an F to F+, or C to C+.

From Figure 3 of that pa­per, you can see that Amitriptyline has the high­est es­ti­mated ef­fect size, while the side ef­fects are no worse than placebo:

But hang on, only F to F+ on av­er­age? How does that square with peo­ple’s per­sonal ex­pe­ri­ence that an­ti­de­pres­sants have been life­sav­ing?

Well, first: the av­er­age per­son has around 1 tes­ti­cle.

The punch­line be­ing ~50% of peo­ple have 2 tes­ti­cles while ~50% of peo­ple have 0 tes­ti­cles, hence the av­er­age is “around 1”. Likewise, the av­er­age ef­fect for the best an­ti­de­pres­sant is 0.4 — but some peo­ple re­spond much bet­ter than that… and some re­spond much worse. (e.g. dif­fer­ent kinds of an­ti­de­pres­sant, dif­fer­ent kinds of de­pres­sion, dif­fer­ent kinds of peo­ple, etc. Note that this caveat also ap­plies to the Vitamin D & Omega-3 stud­ies, and all med­ical stud­ies.)

And, sec­ond: the be­lief that things will get bet­ter is a pow­er­ful thing. Unfortunately, the power of hope gets a bad name in med­i­cine: “placebo”.

When you take any med­i­cine, you don’t just get (effect of med­i­cine). You get (effect of med­i­cine + ef­fect of placebo + ef­fect of time).

The ef­fect of placebo + time: prob­a­bly around 0.9.

The ef­fect of placebo alone: Amazingly, de­spite re­searchers hav­ing used place­bos for decades, it’s only re­cently that we started test­ing “open-label” place­bos: place­bos where we just tell the pa­tient it’s a placebo. We then com­pare “getting placebo” to “getting noth­ing”. The ef­fect size of open placebo, on stuff rang­ing from pain to de­pres­sion, is around 0.43. (Spille et al 2023)

The ef­fect of time alone: Using the above two num­bers, I’d guessti­mate: 0.9 - 0.43 = 0.47. “Time” in­cludes both nat­ural heal­ing, and “regression to the mean”.

So, the in­di­vid­ual ef­fect of med­ica­tion, psy­cho­log­i­cal placebo, and time, are all around +0.4 each. And com­bined, they give you +1.20, or go­ing from F to D+ or C to B+. That’s why many peo­ple re­port an­ti­de­pres­sants be­ing life­sav­ing! (Again, on av­er­age; some peo­ple re­act much worse.)

“Wait, the im­prove­ment from an­ti­de­pres­sants is mostly placebo + time?” Yes, and this is widely known in psy­chi­a­try. I mean, they’re not yelling it from the rooftops, but it ain’t no se­cret. Decades ago, the in­fa­mous Kirsch & Sapirstein 1998 es­ti­mated that the im­prove­ment from an­ti­de­pres­sants is ~75% placebo + time. Even the most crit­i­cal re­sponse to Kirsch’s work, Fountoulakis & Möller 2011, still finds it’s mostly placebo + time.

But again, I think “placebo” is too dis­mis­sive a word for the power of hope. Hope is­n’t magic, but it’s some­thing, and mea­sur­ably so: around +0.4. I as­sert: the placebo ef­fect is­n’t a bug, it’s a fea­ture! It proves the con­nec­tion be­tween men­tal state & phys­i­cal health.

But any­way, for the rest of this ar­ti­cle, I’ll only be re­port­ing ef­fect sizes ver­sus placebo + time. Just re­mem­ber that the power of hope gives you an ex­tra +0.4 (like C to C+) for all in­ter­ven­tions.

Keep get­ting con­fused on which fat is what? Me too. So, here’s a crash course on var­i­ous fats:

Fatty acids are chains of car­bons & hy­dro­gens + two oxy­gens. They say “OOH” at one end, and “HHH” at the other end:

A sat­u­rated fatty acid is one where all the car­bons’ free spots are filled up with hy­dro­gens. (Hence, “saturated”) This makes the mol­e­cule stick straight out. This is why long sat­u­rated fatty acids — like those found in but­ter — tend to be solid at room tem­per­a­ture.

In con­trast, un­sat­u­rated fatty acids have at least one hy­dro­gen miss­ing. This causes them to have a dou­ble-bond “kink” in the mol­e­cule. This makes them not stick out, which is why un­sat­u­rated fats tend to be liq­uid at room tem­per­a­ture. Mono-unsaturated fatty acids (MUFAs) — like in olive oil — only have one kink. Poly-unsaturated fatty acids (PUFAs) — like in fatty fish — have two or more kinks. Let’s be ma­ture adults about this, please.

For com­plete­ness: trans fats are un­sat­u­rated fats whose “kink” is twisted around, caus­ing them to go straight. That is the worst sen­tence I’ve writ­ten all month. The twisted kink is caused by the hy­dro­gens be­ing on op­po­site sides, hence “trans”. (And yes, if they’re on the same side it’s “cis”. Latin was a mis­take.) The mol­e­cule be­ing straight is why trans fats — which mar­garine used to be full of — are solid at room tem­per­a­ture, de­spite be­ing an un­sat­u­rated fat.

It’s neat when­ever you can trace the his­tory of some­thing right down to its atoms! Margarine was first in­vented be­cause it’s cheaper, and is spread­able straight from the fridge, un­like but­ter. Margarine (used to be) made by tak­ing un­sat­u­rated veg­etable oils, which were cheaper than an­i­mal fats, then pump­ing a bunch of hy­dro­gens into it (hence, “hydrogenated oils”). If you com­pletely hy­dro­genate an oil, it be­comes a sat­u­rated fat. But they only par­tially hy­dro­genated those oils, lead­ing to trans fats, which were cheaper & a spread­able semi-solid at fridge tem­per­a­ture.

In the 1970s & 80s, the US Food & Drug Administration con­cluded that trans fats were not harm­ful to hu­mans, and nu­tri­tion­ists pro­moted mar­garine over but­ter, be­cause but­ter had “unhealthy” sat­u­rated fats. But in the early 1990s, sci­en­tists re­al­ized that trans fats were even worse for you than sat­u­rated fats. Only in the 2010′s, did most Western coun­tries start of­fi­cially ban­ning trans fats. Reminder: pol­icy is of­ten decades be­hind sci­ence.

I need to stop go­ing on in­fo­dump tan­gents. Anyway, Omega-3 is any fatty acid with its first kink at the 3rd car­bon from the Omega end (“HHH”), though it can have more kinks later down the chain. (And yes, Omega-6 has its first kink at the 6th car­bon, and Omega-9 has its first kink at the 9th car­bon. There’s noth­ing phys­i­cally pre­vent­ing Omega-4 or Omega-5′s from ex­ist­ing, but due to some quirk of evo­lu­tion, Omega-3, -6, and -9 are the ones bi­o­log­i­cal life uses most. As far as I can tell, there’s no spe­cific rea­son they’re all mul­ti­ples of 3. Probably just a co­in­ci­dence. There is a less com­mon Omega-7.)

Finally, there’s three main types of Omega-3: EPA (Eicosapentaenoic Acid), DHA (Docosahexaenoic Acid), and ALA (Alpha-Linolenic Acid). ALA is mostly found in plants like chia seeds & wal­nuts, while EPA & DHA mostly come from seafood, though there are al­gae-based ve­gan sources.

EPA & DHA are the fo­cus of this sec­tion. For bio-me­chan­i­cal rea­sons I don’t un­der­stand but I as­sume some­one else does: EPA is the one as­so­ci­ated with anti-in­flam­ma­tion, bet­ter brain health, and less de­pres­sion… while DHA is­n’t. (But DHA is still needed for other stuff, like your neu­rons’ cell walls, so don’t cut them out com­pletely!)

All the above info in a Venn (technically Euler) di­a­gram:

Okay, enough yap. Time for the ac­tual data:

Sublette et al 2011 is an older meta-analy­sis (15 tri­als with 916 par­tic­i­pants). It’s the only meta-analy­sis I could find that es­ti­mates the ac­tual “dose-response” curve, which shows: how much ef­fect, for how much treat­ment.

Why is dose-re­sponse im­por­tant? Because one prob­lem with many meta-analy­ses is they’ll do some­thing like: “Study 1 gave pa­tients 1 gram of med­i­cine and saw a +1 im­prove­ment in dis­ease, Study 2 gave 10 grams and saw +4 im­prove­ment, Study 3 gave 100 grams and saw neg­a­tive –5 im­prove­ment… the av­er­age of +1, +4, and –5 is zero… there­fore the med­i­cine’s ef­fect is zero.”

As men­tioned ear­lier, this is a mean­ing­less mean. That’s why we want to know the re­sponse at each dose.

Anyway, the Sublette meta-analy­sis gath­ered ran­dom­ized tri­als study­ing Omega-3 on de­pres­sion (vs placebo, of course) and got the fol­low­ing dose-re­sponse curve.⤵ Note that the hor­i­zon­tal axis is not just amount of to­tal Omega-3, but specif­i­cally the ex­tra amount of “unopposed” EPA, above the amount of DHA. Or in other words, “EPA mi­nus DHA”:

The top ef­fect size is around +0.558, which is like go­ing from an F to D–, or C to B–. You get this max­i­mum ef­fect around 1 to 2 grams of ex­tra EPA, and too much EPA gets worse re­sults. The meta-analy­sis finds that Omega-3 sup­ple­ments that are ~60% EPA (and the rest DHA) are op­ti­mal.

Is this in line with later meta-analy­ses? More or less! Liao et al 2019 also finds that ~1 gram of ≥60% EPA is best, but ac­tu­ally finds a higher ef­fect size: +1.03. Kelaiditis et al 2023 also finds 1 to 2g of ≥60% EPA is best, but found a lower ef­fect size of +0.43… which is still as good as the best an­ti­de­pres­sant! So, I’m tak­ing +0.558 as the me­dian es­ti­mate.

Let’s con­vert this to an ac­tion­able rec­om­men­da­tion: You want around 1 gram of EPA a day. So if your sup­ple­ments are 60% EPA, you need 1 gram ÷ 0.6 ~= 1.667 grams = 1667 mil­ligrams. Let’s round this down for con­ve­nience: get 1500 mg/​day of 60%-EPA Omega-3 sup­ple­ments.

In com­par­i­son, most of­fi­cial health or­ga­ni­za­tions rec­om­mend “250–500 mg com­bined EPA and DHA each day for healthy adults.” That is over three times too low, at least for op­ti­mal ef­fects on de­pres­sion. Which, as we cal­cu­lated above, is prob­a­bly around 1500 mg/​day. (The of­fi­cial safe dose is 5000 mg/​day)

Direct ef­fect on sui­cide: Finally, a (small) study di­rectly in­ves­ti­gat­ing the link be­tween sui­cide & Omega-3. Sublette et al 2006: “Low [DHA] and low Omega-3 pro­por­tions […] pre­dicted risk of sui­ci­dal be­hav­ior among de­pressed pa­tients over the 2-year pe­riod.” Though keep in mind this is a small study, and it’s ob­ser­va­tional not ex­per­i­men­tal. Also, weird that con­trary to the above stud­ies on de­pres­sion, DHA pre­dicted sui­cide but not EPA. Not sure what to make of that.

Bonus: Omega-3 may also boost cog­ni­tion? Shahinfar et al 2025: “Enhancement of global cog­ni­tive abil­i­ties was ob­served with in­creas­ing omega-3 dosage up to 1500 mg/​day. [effect size = 1.00, like go­ing from a grade of C to B!], fol­lowed by down­ward trend at higher doses.”

Ghaemi et al 2024 is a meta-analy­sis on Vitamin D on de­pres­sion (31 tri­als with 24,189 par­tic­i­pants).

Again, it ac­tu­ally es­ti­mates a dose-re­sponse curve! Below is Figure 1 + Table 2, show­ing the ef­fect of Vitamin D dosage on de­pres­sion vs placebo. The solid line is the av­er­age es­ti­mated ef­fect, dashed lines are 95% con­fi­dence in­ter­val. Note the ef­fect size is neg­a­tive in this fig­ure, be­cause they’re mea­sur­ing re­duc­tion in de­pres­sive symp­toms:

The up­per range of un­cer­tainty is low­est at 5000 IU (International Units) of Vitamin D a day, with an es­ti­mated ef­fect size of 1.82, with a 95% un­cer­tainty range, from 0.98 to 2.66. Let’s be pes­simistic, and take the low­est end: 0.98, like tak­ing your men­tal health from an F to D, or C to B.

Is this in line with ear­lier meta-analy­ses? Again, more or less! Mikola et al 2022 found a lower es­ti­mate: the ef­fect for ≥ 2000 IU/day is 0.407. Note that even this is still on par with the best an­ti­de­pres­sant! And Xie et al 2022 found a higher es­ti­mate: the ef­fect of > 2,800 IU/day is 1.23. So, I’ll take the me­dian es­ti­mate: around 0.98. (And I’m rec­om­mend­ing 4,000 IU/day, since that’s the “official” max safe dose. Though as we’ll see later, even the of­fi­cial max dose may be too low.)

Does this still work even if you’re al­ready tak­ing an­ti­de­pres­sants? Yup! Table 1 of the first meta-analy­sis, also shows that Vitamin D helps for both pa­tients us­ing an­ti­de­pres­sant med­ica­tion, and not. This is en­cour­ag­ing: it means you can stack both med­ica­tions & sup­ple­ments!

Does this still work even if you don’t have Vitamin D in­suf­fi­ciency? Yes, but ad­mit­tedly much less. That said, you prob­a­bly do have a Vitamin D in­suf­fi­ciency. Liu et al 2018 finds that a bit un­der half of American adults (41.4%) have in­suf­fi­cient Vitamin D blood lev­els. And Manios et al 2017 finds that over half of kids (52.5%) in Greece — frick­in’ sunny Greece! — are still Vitamin D in­suf­fi­cient.

Also, the “official” rec­om­men­da­tions are all too low:

So, if these three meta-analy­ses are right, then high doses — 2000 IU/day or more, pos­si­bly 4000 (official max dose) or higher — is op­ti­mal. But the of­fi­cial rec­om­men­da­tion for Vitamin D is 400–800 IU/day, sev­eral times too low.

And even the of­fi­cial max dose of 4000 IU/day may be too low! But McCullough et al 2019 gave over thou­sands of par­tic­i­pants 5,000 to 10,000 IU/day, for seven years, and there were zero cases of se­ri­ous side ef­fects. This matches later stud­ies like Billington et al 2020, a 3-year-long trial on hun­dreds of par­tic­i­pants, which found “the safety pro­file of vi­t­a­min D sup­ple­men­ta­tion is sim­i­lar for doses of 400, 4000, and 10,000 IU/day.” (Although 15 par­tic­i­pants got “mild hy­per­cal­cemia”, but “all cases re­solved on re­peat test­ing.” Either way, that’s a small cost for re­duc­ing the risk of ma­jor de­pres­sion & sui­cide.)

And it makes evo­lu­tion­ary sense that 10,000 IU a day should be safe. Your skin, ex­posed to the Sun’s ul­tra­vi­o­let rays, can syn­the­size up to (the equiv­a­lent of) 10,000 IU a day, be­fore plateau­ing out. Source is Vieth 1999: “Because vi­t­a­min D is po­ten­tially toxic, in­take of [1000 IU/day] has been avoided even though the weight of ev­i­dence shows that the cur­rently ac­cepted [limit] of [2000 IU/day] is too low by at least 5-fold.” And Papadimitriou 2017 re­views sev­eral pre­vi­ous stud­ies that find sta­tis­ti­cal er­rors be­hind of­fi­cial rec­om­men­da­tions; cor­rect­ing for these, adults should get 8000 IU/day.

So why are all the of­fi­cial sources still so para­noid about Vitamin D, and low­balling the rec­om­men­da­tions? Well, alas, of­fi­cial pol­icy is al­ways a few decades be­hind the sci­ence in any field. See: trans fats, open-la­bel place­bos, aerosol trans­mis­sion of Covid-19, every­thing about ed­u­ca­tional pol­icy. And be­cause some­thing some­thing in­cen­tives, it’s “rational” for gov­ern­ment/​in­sur­ers to be very risk-averse & slow to change (for bet­ter & worse).

Speaking of the Sun, why take sup­ple­ments in­stead of just get­ting Vitamin D from sun ex­po­sure? Well, skin can­cer. But also: be­cause Sun-Skin D varies greatly de­pend­ing on the sea­son, your lat­i­tude, and your skin type. There’s less ul­tra­vi­o­let rays from the Sun in win­ter/​fall, and at lat­i­tudes fur­ther from the equa­tor. And the darker your skin is, the less Vitamin D your skin makes for the same amount of Sun ex­po­sure. As ex­pected from the bio-physics of skin, Black adults have the high­est preva­lence of Vitamin D de­fi­ciency (82.1%!!), fol­lowed by Hispanic adults (62.9%). (But hey, at least Black adults have the low­est in­ci­dence of skin can­cer. You win some you lose some.) The point is: speak­ing as some­one with Southeast Asian skin, who’s cur­rently in Canada dur­ing win­ter… even if I stood out­side naked for hours, I’d get ap­prox­i­mately zero IU/day of Vitamin D from the Sun. Thus: sup­ple­ments.

Direct ef­fect on sui­cide: Finally, a meta-analy­sis di­rectly mea­sur­ing the ef­fect of Vitamin D on sui­ci­dal be­hav­iour. Yu et al 2025: “Vitamin D in pa­tients with [suicidal be­hav­iours] were sig­nif­i­cantly lower than in con­trols (standardized mean dif­fer­ence: –0.69, or a ‘medium’ dif­fer­ence)”. Reminder that this pa­per by it­self only mea­sures cor­re­la­tion, not cau­sa­tion — but com­bined with the above ex­per­i­ments of Vitamin D on de­pres­sion, I think it’s rea­son­able to guess it’s partly causal.

* Almost half of you have a Vitamin D in­suf­fi­ciency ac­cord­ing to the of­fi­cial rec­om­men­da­tion (800 IU/day).

* And those of­fi­cial rec­om­men­da­tions are way too low. The op­ti­mal amount of Vitamin D for de­pres­sion is prob­a­bly 4000 IU/day, with an ef­fect around twice that of the best an­ti­de­pres­sant.

* Even the of­fi­cial max­i­mum safe dose (4000 IU/day) is be­low what your body can pro­duce from the Sun in op­ti­mal con­di­tions (10,000 IU/day). Recent ran­dom­ized con­trolled tri­als con­firm that 10,000 IU/day is, in­deed, mostly safe.

* Reminder that of­fi­cial pol­icy is of­ten decades be­hind the sci­ence.

* Reminder that I’m not say­ing “take sup­ple­ments in­stead of an­ti­de­pres­sants”; in fact the above meta-analy­sis shows you can ef­fec­tively stack them!

Bonus: Vitamin D sup­ple­men­ta­tion was found in sev­eral ran­dom­ized con­trolled tri­als to re­duce mor­tal­ity from Covid-19, though much less than of­fi­cial treat­ments like Paxlovid. Vitamin D also prob­a­bly helps guard against in­fluenza too, though the ev­i­dence is small & early.

Scurvy is caused by a lack of Vitamin C. It’s a con­di­tion that causes your wounds to re-open up & teeth to fall out. Scurvy used to kill al­most half(!) of all sailors on ma­jor ex­pe­di­tions; it’s es­ti­mated mil­lions died. It can be cured by eat­ing lemons.

Rickets is mostly caused by a lack of Vitamin D. It’s a con­di­tion where kids’ bones go all soft and de­formed. During the Industrial Revolution, up to 80% of kids suf­fered from it. It can be pre­vented with cod liver oil.

Goiters is mostly caused by a lack of Iodine. It’s a con­di­tion where the thy­roid gland in your neck swells up painfully, to the size of an ap­ple. During WWI, a third of adult men had goi­ters. It can be pre­vented with iodized salt.

About 1 in 4 peo­ple are ex­pected to have clin­i­cal de­pres­sion some­time in their life. Depression is the #1 source of the global “burden from dis­ease” in the men­tal health cat­e­gory, and that cat­e­gory is the #6 bur­den of dis­ease in the world, above Alzheimer’s, malaria, and sex­u­ally trans­mit­ted in­fec­tions.

The ef­fec­tive al­tru­ists are all, “woah for just $3000 you can pre­vent a child’s death from malaria” — and that’s great! save them kids! — but where’s the fan­fare for the ac­cu­mu­lat­ing ev­i­dence that, “woah with cheap daily sup­ple­ments we can save mil­lions from sui­cide & de­pressed lives”?

Over and over again through­out his­tory, some hor­rific thing that caused mil­lions to suf­fer, turned out to be “yeah you were miss­ing this one mol­e­cule lol”. To be clear: not every­thing is gonna be that sim­ple, and men­tal health is not “just” chem­istry. Also, all the num­bers on this page have with large er­ror bars & un­cer­tainty, more re­search is needed.

But, as of right now, I feel I can at least con­fi­dently claim the fol­low­ing:

The first im­ages from the Meteosat Third Generation-Sounder satel­lite have been shared at the European Space Conference in Brussels, show­ing how the mis­sion will pro­vide data on tem­per­a­ture and hu­mid­ity, for more ac­cu­rate weather fore­cast­ing over Europe and north­ern Africa.

The im­ages from Meteosat Third Generation-Sounder (MTG-S) show a full-disc im­age of Earth as seen from geo­sta­tion­ary or­bit, about 36 000 km above Earth’s sur­face. These im­ages were cap­tured on 15 November 2025 by the satel­lite’s Infrared Sounder in­stru­ment. In the ‘temperature’ im­age (below), the Infrared Sounder used a long-wave in­frared chan­nel, which mea­sured Earth’s sur­face tem­per­a­ture as well as the tem­per­a­ture at the top of clouds. Dark red cor­re­sponds to high tem­per­a­tures, mainly on the warmer land sur­faces, while blue cor­re­sponds to lower tem­per­a­tures, typ­i­cally on the top of clouds.As would be ex­pected, most of the warmest (dark red) ar­eas in this im­age are on the con­ti­nents of Africa and South America. In the top-cen­tre of the im­age, the out­line of the coast of west­ern Africa is clearly vis­i­ble in dark red, with the Cape Verde penin­sula, home to Senegal’s cap­i­tal Dakar, vis­i­ble as among the warmest ar­eas in this im­age. In the bot­tom-right of the im­age, the west­ern coast of Namibia and South Africa are also vis­i­ble in red be­neath a swirl of cold cloud shown in blue, while the north­east coast of Brazil is vis­i­ble in dark red on the left of the im­age.

The ‘humidity’ im­age (below) was cap­tured us­ing the Infrared Sounder’s medium-wave in­frared chan­nel, which mea­sures hu­mid­ity in Earth’s at­mos­phere. Blue colours cor­re­spond to re­gions in the at­mos­phere with higher hu­mid­ity, while red colours cor­re­spond to lower hu­mid­ity in the at­mos­phere.The out­lines of land­masses are not vis­i­ble in this im­age. The ar­eas of least at­mos­pheric hu­mid­ity, shown in dark red, are seen ap­prox­i­mately over the Sahara Desert and the Middle East (top of im­age), while a large area of ‘dry’ at­mos­phere also cov­ers part of the South Atlantic Ocean (centre of im­age). Numerous patches of high hu­mid­ity are seen in dark blue over the east­ern part of the African con­ti­nent as well as in high and low lat­i­tudes.

Below we see a close-up from MTG-Sounder of the European con­ti­nent and part of north­ern Africa. Like the first im­age above, here we see heat from land sur­faces and tem­per­a­tures at the top of clouds. The heat from the African con­ti­nent is seen in red in the lower part of the im­age, while a dark blue weather front cov­ers Spain and Portugal. The Italian penin­sula is in the cen­tre of the im­age.

Temperatures over Europe and north­ern Africa by MTG-Sounder

And the an­i­ma­tion (below) uses data from the MTG-Sounder satel­lite to track the erup­tion of Ethiopia’s Hayli Gubbi vol­cano on 23 November 2025. The back­ground im­agery shows sur­face tem­per­a­ture changes while in­frared chan­nels high­light the de­vel­op­ing ash plume. The satel­lite’s timely ob­ser­va­tions en­able track­ing of the evolv­ing ash plume over time.

MTG is a world-class Earth ob­ser­va­tion mis­sion de­vel­oped by the European Space Agency (ESA) with European part­ners to ad­dress sci­en­tific and so­ci­etal chal­lenges. The mis­sion pro­vides game-chang­ing data for fore­cast­ing weather and air qual­ity over Europe.The satel­lite’s geo­sta­tion­ary po­si­tion above the equa­tor means it main­tains a fixed po­si­tion rel­a­tive to Earth, fol­low­ing the same area on the plan­et’s sur­face as we ro­tate. This en­ables it to pro­vide cov­er­age of Europe and part of north­ern Africa on a 15-minute re­peat cy­cle. It sup­plies new data on tem­per­a­ture and hu­mid­ity over Europe every 30 min­utes, sup­ply­ing me­te­o­rol­o­gists with a com­plete weather pic­ture of the re­gion and com­ple­ment­ing data on cloud for­ma­tion and light­ning from the MTG-Imager (MTG-I) satel­lite.

ESA’s Director of Earth Observation Programmes, Simonetta Cheli, said, “Seeing the first Infrared Sounder im­ages from the MTG-Sounder satel­lite re­ally brings this mis­sion and its po­ten­tial to life. We ex­pect data from this mis­sion to change the way we fore­cast se­vere storms over Europe — and this is very ex­cit­ing for com­mu­ni­ties and cit­i­zens, as well as for me­te­o­rol­o­gists and cli­ma­tol­o­gists. As ever, the out­stand­ing work done by our teams in col­lab­o­ra­tion with long-stand­ing part­ners, in­clud­ing Eumetsat, the European Commission and dozens of European in­dus­try teams, means we now have the abil­ity to pre­dict ex­treme weather events in more ac­cu­rate and timely ways than ever be­fore.”The Infrared Sounder in­stru­ment on board MTG-S is the first European hy­per­spec­tral sound­ing in­stru­ment in geo­sta­tion­ary or­bit. It is de­signed to gen­er­ate a com­pletely new type of data prod­uct. It uses in­ter­fer­o­met­ric tech­niques, which analyse minis­cule pat­terns in light waves, to cap­ture data on tem­per­a­ture and hu­mid­ity, as well as be­ing able to mea­sure wind and trace gases in the at­mos­phere. The data will even­tu­ally be used to gen­er­ate three-di­men­sional maps of the at­mos­phere, help­ing to im­prove the ac­cu­racy of weather fore­cast­ing, es­pe­cially for now­cast­ing rapidly evolv­ing storms.“It’s fan­tas­tic to see the first im­ages from this ground­break­ing mis­sion,” said James Champion, ESA’s MTG Project Manager. “This satel­lite has been 15 years in de­vel­op­ment and will rev­o­lu­tionise weather fore­cast­ing and es­pe­cially now­cast­ing. The abil­ity to ver­ti­cally pro­file the full Earth’s disk with a re­peat cy­cle of only 30 min­utes for Europe is an in­cred­i­ble ac­com­plish­ment!”

“I’m ex­cited that we can share these first im­ages from the Infrared Sounder, which show­case just a small se­lec­tion of the 1700 in­frared chan­nels con­tin­u­ously ac­quired by the in­stru­ment as it ob­serves Earth,” said Pieter Van den Braembussche, MTG System and Payload Manager at ESA. “By com­bin­ing all 1700 chan­nels, we will soon be able to gen­er­ate three di­men­sional maps of tem­per­a­ture, hu­mid­ity and even trace gases in the at­mos­phere. This ca­pa­bil­ity will of­fer a com­pletely new per­spec­tive on Earth’s at­mos­phere, not pre­vi­ously avail­able in Europe, and is ex­pected to help fore­cast­ers pre­dict se­vere storms ear­lier than is pos­si­ble to­day.”

The MTG mis­sion cur­rently has two satel­lites in or­bit: MTG-I and MTG-S. The sec­ond Imager will be launched later in 2026.MTG-S was launched on 1 July 2025. Thales Alenia Space is the prime con­trac­tor for the over­all MTG mis­sion, with OHB Systems re­spon­si­ble for the MTG-Sounder satel­lite. Mission con­trol and data dis­tri­b­u­tion are man­aged by Eumetsat.The MTG-S satel­lite also hosts the Copernicus Sentinel-4 mis­sion, which con­sists of an ul­tra­vi­o­let, vis­i­ble and near-in­frared (UVN) imag­ing spec­trom­e­ter. Sentinel-4 de­liv­ered its first im­ages last year.

Thank you for lik­ing

You have al­ready liked this page, you can only like it once!

The goal of this tracker is to de­tect sta­tis­ti­cally sig­nif­i­cant degra­da­tions in Claude Code with Opus 4.5 per­for­mance on SWE tasks. What you see is what you get: We bench­mark in Claude Code CLI with the SOTA model (currently Opus 4.5) di­rectly, no cus­tom har­nesses.

Shows if any time pe­riod has a sta­tis­ti­cally sig­nif­i­cant per­for­mance drop (p < 0.05).

Historical av­er­age pass rate used as ref­er­ence for de­tect­ing per­for­mance changes.

Percentage of bench­mark tasks passed in the most re­cent day’s eval­u­a­tions.

Aggregate pass rate over the last 7 days. Provides a more sta­ble mea­sure than daily re­sults.

Aggregate pass rate over the last 30 days. Best mea­sure of over­all sus­tained per­for­mance.

Daily bench­mark pass rates over the past 30 days. Hover over leg­end items for de­tails on each vi­sual el­e­ment.

Daily bench­mark pass rate show­ing the per­cent­age of tasks solved each day.

Historical av­er­age pass rate (58%) used as ref­er­ence for de­tect­ing per­for­mance changes.

Shaded re­gion around base­line (±14.0%). Changes within this band are not sta­tis­ti­cally sig­nif­i­cant (p ≥ 0.05).

95% con­fi­dence in­ter­val for each data point. Toggle check­box to show/​hide. Wider in­ter­vals in­di­cate more un­cer­tainty (fewer sam­ples).

Historical av­er­age pass rate (58%) used as ref­er­ence for de­tect­ing per­for­mance changes.

Shaded re­gion around base­line (±5.6%). Changes within this band are not sta­tis­ti­cally sig­nif­i­cant (p ≥ 0.05).

95% con­fi­dence in­ter­val for each data point. Toggle check­box to show/​hide. Wider in­ter­vals in­di­cate more un­cer­tainty (fewer sam­ples).

The goal of this tracker is to de­tect sta­tis­ti­cally sig­nif­i­cant degra­da­tions in Claude Code with Opus 4.5 per­for­mance on SWE tasks. We are an in­de­pen­dent third party with no af­fil­i­a­tion to fron­tier model providers.

Context: In September 2025, Anthropic pub­lished a

post­mortem on Claude degra­da­tions. We want to of­fer a re­source to de­tect such degra­da­tions in the fu­ture.

We run a daily eval­u­a­tion of Claude Code CLI on a cu­rated, con­t­a­m­i­na­tion-re­sis­tant sub­set of

SWE-Bench-Pro. We al­ways use the lat­est avail­able Claude Code re­lease and the SOTA model (currently Opus 4.5). Benchmarks run di­rectly in Claude Code with­out cus­tom har­nesses, so re­sults re­flect what ac­tual users can ex­pect. This al­lows us to de­tect degra­da­tion re­lated to both model changes and har­ness changes.

Each daily eval­u­a­tion runs on N=50 test in­stances, so daily vari­abil­ity is ex­pected. Weekly and monthly re­sults are ag­gre­gated for more re­li­able es­ti­mates.

We model tests as Bernoulli ran­dom vari­ables and com­pute 95% con­fi­dence in­ter­vals around daily, weekly, and monthly pass rates. Statistically sig­nif­i­cant dif­fer­ences in any of those time hori­zons are re­ported.

Get no­ti­fied when degra­da­tion is de­tected We’ll email you when we de­tect a sta­tis­ti­cally sig­nif­i­cant per­for­mance drop. Thanks for sub­scrib­ing! Check your email to con­firm. Something went wrong. Please try again.

Your browser does not sup­port the au­dio el­e­ment.

This con­tent is gen­er­ated by Google AI. Generative AI is ex­per­i­men­tal

In August, we pre­viewed Genie 3, a gen­eral-pur­pose world model ca­pa­ble of gen­er­at­ing di­verse, in­ter­ac­tive en­vi­ron­ments. Even in this early form, trusted testers were able to cre­ate an im­pres­sive range of fas­ci­nat­ing worlds and ex­pe­ri­ences, and un­cov­ered en­tirely new ways to use it. The next step is to broaden ac­cess through a ded­i­cated, in­ter­ac­tive pro­to­type fo­cused on im­mer­sive world cre­ation. Starting to­day, we’re rolling out ac­cess to Project Genie for Google AI Ultra sub­scribers in the U.S (18+). This ex­per­i­men­tal re­search pro­to­type lets users cre­ate, ex­plore and remix their own in­ter­ac­tive worlds.A world model sim­u­lates the dy­nam­ics of an en­vi­ron­ment, pre­dict­ing how they evolve and how ac­tions af­fect them. While Google DeepMind has a his­tory of agents for spe­cific en­vi­ron­ments like Chess or Go, build­ing AGI re­quires sys­tems that nav­i­gate the di­ver­sity of the real world.To meet this chal­lenge and sup­port our AGI mis­sion, we de­vel­oped Genie 3. Unlike ex­plorable ex­pe­ri­ences in sta­tic 3D snap­shots, Genie 3 gen­er­ates the path ahead in real time as you move and in­ter­act with the world. It sim­u­lates physics and in­ter­ac­tions for dy­namic worlds, while its break­through con­sis­tency en­ables the sim­u­la­tion of any real-world sce­nario — from ro­bot­ics and mod­el­ling an­i­ma­tion and fic­tion, to ex­plor­ing lo­ca­tions and his­tor­i­cal set­tings.Build­ing on our model re­search with trusted testers from across in­dus­tries and do­mains, we are tak­ing the next step with an ex­per­i­men­tal re­search pro­to­type: Project Genie.Project Genie is a pro­to­type web app pow­ered by Genie 3, Nano Banana Pro and Gemini, which al­lows users to ex­per­i­ment with the im­mer­sive ex­pe­ri­ences of our world model first­hand. The ex­pe­ri­ence is cen­tred on three core ca­pa­bil­i­ties:

Prompt with text and gen­er­ated or up­loaded im­ages to cre­ate a liv­ing, ex­pand­ing en­vi­ron­ment. Create your char­ac­ter, your world, and de­fine how you want to ex­plore it — from walk­ing to rid­ing, fly­ing to dri­ving, and any­thing be­yond.For more pre­cise con­trol, we have in­te­grated “World Sketching” with Nano Banana Pro. This al­lows you to pre­view what your world will look like and mod­ify your im­age to fine tune your world prior to jump­ing in. You can also de­fine your per­spec­tive for the char­ac­ter — such as first-per­son or third-per­son — giv­ing you con­trol over how you ex­pe­ri­ence the scene be­fore you en­ter.

Your world is a nav­i­ga­ble en­vi­ron­ment that’s wait­ing to be ex­plored. As you move, Project Genie gen­er­ates the path ahead in real time based on the ac­tions you take. You can also ad­just the cam­era as you tra­verse through the world.Remix ex­ist­ing worlds into new in­ter­pre­ta­tions, by build­ing on top of their prompts. You can also ex­plore cu­rated worlds in the gallery or in the for in­spi­ra­tion, or build on top of them. And once you’re done, you can down­load videos of your worlds and your ex­plo­rations.

Project Genie is an ex­per­i­men­tal re­search pro­to­type in Google Labs, pow­ered by Genie 3. As with all our work to­wards gen­eral AI sys­tems, our mis­sion is to build AI re­spon­si­bly to ben­e­fit hu­man­ity. Since Genie 3 is an early re­search model, there are a few known ar­eas for im­prove­ment:Gen­er­ated worlds might not look com­pletely true-to-life or al­ways ad­here closely to prompts or im­ages, or real-world physic­sChar­ac­ters can some­times be less con­trol­lable, or ex­pe­ri­ence higher la­tency in con­trolA few of the Genie 3 model ca­pa­bil­i­ties we an­nounced in August, such as prompt­able events that change the world as you ex­plore it, are not yet in­cluded in this pro­to­type. You can find more de­tails on model lim­i­ta­tions and fu­ture up­dates on how we’re im­prov­ing the ex­pe­ri­ence, here.Build­ing on the work we have been do­ing with trusted testers, we are ex­cited to share this pro­to­type with users of our most ad­vanced AI to bet­ter un­der­stand how peo­ple will use world mod­els in many ar­eas of both AI re­search and gen­er­a­tive me­dia.Ac­cess to Project Genie be­gins rolling out to­day to Google AI Ultra sub­scribers in the U.S. (18+), ex­pand­ing to more ter­ri­to­ries in due course. We look for­ward to see­ing the in­fi­nitely di­verse worlds they cre­ate, and in time, our goal is to make these ex­pe­ri­ences and tech­nol­ogy ac­ces­si­ble to more users.

The act­ing head of the US gov­ern­men­t’s top cy­ber­se­cu­rity agency re­port­edly up­loaded sen­si­tive gov­ern­ment files into a pub­lic ver­sion of ChatGPT, trig­ger­ing in­ter­nal se­cu­rity alerts and a fed­eral re­view.

A Politico in­ves­ti­ga­tion claims Madhu Gottumukkala, the in­terim di­rec­tor of the Cybersecurity and Infrastructure Security Agency, up­loaded con­tract­ing doc­u­ments marked “For Official Use Only” into ChatGPT last sum­mer.

The re­port says Gottumukkala re­quested a spe­cial ex­emp­tion to ac­cess ChatGPT, which is blocked for other Department of Homeland Security staff.

Cybersecurity mon­i­tor­ing sys­tems then re­port­edly flagged the up­loads in early August. That trig­gered a DHS-led dam­age as­sess­ment to de­ter­mine whether the in­for­ma­tion had been ex­posed.

Public ver­sions of ChatGPT share user in­puts with OpenAI, which raised con­cerns in­side the fed­eral gov­ern­ment about sen­si­tive data leav­ing in­ter­nal net­works.

CISA spokesper­son Marci McCarthy told Politico that Gottumukkala “was granted per­mis­sion to use ChatGPT with DHS con­trols in place,” adding that the use was “short-term and lim­ited.”

Gottumukkala has served as act­ing di­rec­tor since May, while the Senate has yet to con­firm Sean Plankey as per­ma­nent head of the agency.

The ChatGPT in­ci­dent fol­lows other re­ported is­sues dur­ing Gottumukkala’s tenure. Politico said he pre­vi­ously failed a coun­ter­in­tel­li­gence poly­graph re­quired for ac­cess to highly sen­si­tive in­tel­li­gence. During con­gres­sional tes­ti­mony last week, he re­jected that char­ac­ter­i­za­tion when ques­tioned.

The re­port lands as the ad­min­is­tra­tion of US President Donald Trump con­tin­ues to push AI adop­tion across fed­eral agen­cies.

Trump signed an ex­ec­u­tive or­der in December aimed at lim­it­ing state-level AI reg­u­la­tion, while the Pentagon has an­nounced an “AI-first” strat­egy to ex­pand the mil­i­tary’s use of ar­ti­fi­cial in­tel­li­gence.

A Waymo ro­b­o­t­axi struck a child near an el­e­men­tary school in Santa Monica on January 23, ac­cord­ing to the com­pany. Waymo told the National Highway Traffic Safety Administration (NHTSA) that the child — whose age and iden­tity are not cur­rently pub­lic — sus­tained mi­nor in­juries.

The NHTSA has opened an in­ves­ti­ga­tion into the ac­ci­dent, and Waymo said in a blog post that it “will co­op­er­ate fully with them through­out the process.” The National Transportation Safety Board said Thursday af­ter­noon that it has also opened an in­ves­ti­ga­tion in co­or­di­na­tion with the Santa Monica Police Department.

Waymo said its ro­b­o­t­axi struck the child at six miles per hour, af­ter brak­ing “hard” from around 17 miles per hour. The young pedes­trian “suddenly en­tered the road­way from be­hind a tall SUV, mov­ing di­rectly into our ve­hi­cle’s path,” the com­pany said in its blog post. Waymo said its ve­hi­cle “immediately de­tected the in­di­vid­ual as soon as they be­gan to emerge from be­hind the stopped ve­hi­cle.”

“Following con­tact, the pedes­trian stood up im­me­di­ately, walked to the side­walk, and we called 911. The ve­hi­cle re­mained stopped, moved to the side of the road, and stayed there un­til law en­force­ment cleared the ve­hi­cle to leave the scene,” Waymo wrote in the post.

News of the crash comes as Waymo faces dual in­ves­ti­ga­tions into its ro­b­o­t­axis il­le­gally pass­ing school buses. The NHTSA opened a probe into the prob­lem in October shortly af­ter the first re­port of the in­ci­dent in Atlanta, Georgia, and the NTSB opened its own in­ves­ti­ga­tion last week af­ter around 20 in­ci­dents were re­ported in Austin, Texas.

According to the NHTSA, the ac­ci­dent oc­curred “within two blocks” of the el­e­men­tary school “during nor­mal school drop off hours.” The safety reg­u­la­tor said “there were other chil­dren, a cross­ing guard, and sev­eral dou­ble-parked ve­hi­cles in the vicin­ity.”

The NHTSA’s Office of Defects Investigation is in­ves­ti­gat­ing “whether the Waymo AV ex­er­cised ap­pro­pri­ate cau­tion given, among other things, its prox­im­ity to the el­e­men­tary school dur­ing drop off hours, and the pres­ence of young pedes­tri­ans and other po­ten­tial vul­ner­a­ble road users.”

Waymo said in its blog post that its “peer-reviewed model” shows a “fully at­ten­tive hu­man dri­ver in this same sit­u­a­tion would have made con­tact with the pedes­trian at ap­prox­i­mately 14 mph.” The com­pany did not re­lease a spe­cific analy­sis of this crash.

This story has been up­dated to in­clude in­for­ma­tion about the National Transportation Safety Board’s in­ves­ti­ga­tion.

Two se­cu­rity pro­fes­sion­als who were ar­rested in 2019 af­ter per­form­ing an au­tho­rized se­cu­rity as­sess­ment of a county cour­t­house in Iowa will re­ceive $600,000 to set­tle a law­suit they brought al­leg­ing wrong­ful ar­rest and defama­tion.

The case was brought by Gary DeMercurio and Justin Wynn, two pen­e­tra­tion testers who at the time were em­ployed by Colorado-based se­cu­rity firm Coalfire Labs. The men had writ­ten au­tho­riza­tion from the Iowa Judicial Branch to con­duct “red-team” ex­er­cises, mean­ing at­tempted se­cu­rity breaches that mimic tech­niques used by crim­i­nal hack­ers or bur­glars.

The ob­jec­tive of such ex­er­cises is to test the re­silience of ex­ist­ing de­fenses us­ing the types of real-world at­tacks the de­fenses are de­signed to re­pel. The rules of en­gage­ment for this ex­er­cise ex­plic­itly per­mit­ted “physical at­tacks,” in­clud­ing “lockpicking,” against ju­di­cial branch build­ings so long as they did­n’t cause sig­nif­i­cant dam­age.

The event gal­va­nized se­cu­rity and law en­force­ment pro­fes­sion­als. Despite the le­git­i­macy of the work and the le­gal con­tract that au­tho­rized it, DeMercurio and Wynn were ar­rested on charges of felony third-de­gree bur­glary and spent 20 hours in jail, un­til they were re­leased on $100,000 bail ($50,000 for each). The charges were later re­duced to mis­de­meanor tres­pass­ing charges, but even then, Chad Leonard, sher­iff of Dallas County, where the cour­t­house was lo­cated, con­tin­ued to al­lege pub­licly that the men had acted il­le­gally and should be pros­e­cuted.

Reputational hits from these sorts of events can be fa­tal to a se­cu­rity pro­fes­sion­al’s ca­reer. And of course, the prospect of be­ing jailed for per­form­ing au­tho­rized se­cu­rity as­sess­ment is enough to get the at­ten­tion of any pen­e­tra­tion tester, not to men­tion the cus­tomers that hire them.

“This in­ci­dent did­n’t make any­one safer,” Wynn said in a state­ment. “It sent a chill­ing mes­sage to se­cu­rity pro­fes­sion­als na­tion­wide that help­ing [a] gov­ern­ment iden­tify real vul­ner­a­bil­i­ties can lead to ar­rest, pros­e­cu­tion, and pub­lic dis­grace. That un­der­mines pub­lic safety, not en­hances it.”

DeMercurio and Wynn’s en­gage­ment at the Dallas County Courthouse on September 11, 2019, had been rou­tine. A lit­tle af­ter mid­night, af­ter find­ing a side door to the cour­t­house un­locked, the men closed it and let it lock. They then slipped a makeshift tool through a crack in the door and tripped the lock­ing mech­a­nism. After gain­ing en­try, the pen­testers tripped an alarm alert­ing au­thor­i­ties.

The PlayStation 2’s li­brary is eas­ily among the best of any con­sole ever re­leased, and even if you were to nar­row down the list of games to the very best, you’d be left with dozens (more like hun­dreds) of in­cred­i­ble ti­tles.

But the PS2 hard­ware is get­ting a bit long in the tooth, and even though you can hook up the con­sole us­ing RGB com­po­nent ca­bles to a great up­scaler (or use other means) to get the best vi­su­als on a mod­ern 4k TV, em­u­la­tors have grown in pop­u­lar­ity with PCSX2 of­fer­ing gamers means to scale ti­tles to ren­der in­ter­nally at higher res­o­lu­tions, run with a more sta­ble frame rate and, even make use of tex­ture packs.

But do you know what’s bet­ter than an em­u­la­tor? Taking the ex­ist­ing Playstation 2 game and re­com­pil­ing it to run on a mod­ern plat­form (such as your Windows or Linux desk­top PC). That’s ex­actly what is be­ing worked on now with PS2Recomp, a sta­tic Recompiler & Runtime Tool.

To keep things sim­ple here, this will ba­si­cally take a Playstation 2 game (which would be de­signed around the PS2’s unique ar­chi­tec­ture such as the ‘Emotion Engine’ CPU that’s based around a MIP R5900) and con­vert it to na­tively run on what­ever plat­form you’re tar­get­ing.

In plain English, this is a tool and ob­vi­ously, would need to be used on dif­fer­ent games. In other words, it’s not just a ‘download and every game au­to­mat­i­cally runs’ ap­pli­ca­tion. But, it will give folks a tool to be able to de­com­pile the game and quite frankly, that’s ab­solutely in­cred­i­ble.

This is a great step­ping stone for some in­cred­i­ble re­mas­ters and com­mu­nity re­makes of games. There are al­ready HD Texture Packs avail­able for PS2 em­u­la­tors, as well as other ways to im­prove vi­su­als. But this would give even more free­dom and flex­i­bil­ity to do mod­ify and re­ally en­hance the games. That’s to say noth­ing of to­tally un­lock­ing the frame rates (and likely not break­ing physics or col­li­sion de­tec­tion which is a big prob­lem with em­u­lated ti­tles).

At a guess, too, the games would also run great even with much lower-end hard­ware than would be needed for em­u­la­tors. Recompilation ef­forts in the com­mu­nity cer­tainly aren’t new. Indeed, you can look to the N64 be­cause there have been sev­eral high-pro­file ex­am­ples of what these kind of pro­jects can achieve.

A few in­fa­mous ones would in­clude both in­clud­ing Mario 64 and Zelda. Indeed, there’s a fork of the Mario 64 pro­ject sup­port­ing RTX (ray trac­ing) for Nvidia own­ers. You can see an ex­am­ple of Mario 64 be­low:

Another ex­am­ple on the N64 is Zelda, where the pro­ject has a plethora of vi­sual and game­play en­hance­ments, and in the longer term again, they’re plan­ning to in­tro­duce Ray Tracing.

So, in the fu­ture we could be play­ing the likes of MGS2, Gran Turismo, God of War, Tekken 4, Shadow Hearts with ‘native’ PC ver­sions. This would al­low con­trollers to run (such as dual shock or Xbox con­trollers) and other fea­tures to be bun­dled in too (exactly as we see with the N64 ports).

So yes, cur­rently play­ing PS2 games on PC via em­u­la­tor is still ab­solutely fan­tas­tic, but na­tive ports would be the holy grail of game preser­va­tion.

The Playstation 2 ar­chi­tec­ture is ex­tremely unique, and as I men­tioned ear­lier in this ar­ti­cle fo­cused around a MIPS R5900 based CPU known as the Emotion Engine (operating a shade un­der 300MHz). This CPU was su­per unique, be­cause Sony im­ple­mented a num­ber of cus­tomized fea­tures in­clude two Vector Units de­signed to help ma­nip­u­late geom­e­try and per­form a bunch of other co-pro­cess­ing du­ties.

This was bun­dled with 32MB of mem­ory, and the GPU was known as the Graphics Synthesizer, run­ing at about 147MHz, and sport­ing 4MB of em­bed­ded DRAM. Sony’s de­sign was fas­ci­nat­ing for the time, and de­spite its proces­sor clocked sig­nif­i­cantly lower than ei­ther Nintendo’s GameCube or Microsoft’s Xbox, punched well above its weight class.

As a small up­date — I want to re­mind peo­ple that (as of the time I’m writ­ing this ar­ti­cle) the pro­ject is *NOT* fin­ished yet, and there is still work to do. But the fact that this is be­ing worked on is awe­some for those of us in­ter­ested in game preser­va­tion.

Writing about lay­offs and the tech mar­ket has been on my TODO for sev­eral years. Yesterday, the news of 16k Amazon lay­offs plus two LinkedIn posts on the same topic back-to-back en­cour­aged me to fi­nally write about it.

Disclaimer: I worked 5 years at Shopify. This is prob­a­bly why such posts come one af­ter an­other on my feed but Shopify is­n’t the point here, they are just a mi­cro piece of the whole fucked up sys­tem.

Tech job mar­ket is fun­da­men­tally bro­ken and we all point­ing fin­gers at AI.

But hav­ing spent al­most 2 decades in the in­dus­try, I think the rot goes much deeper than ChatGPT.

Truth to be told tech mar­ket has­n’t truly ‘improved’ since the 2008 fi­nan­cial cri­sis. It just mu­tated into some­thing evil.

After the 2008 mort­gage cri­sis, the eco­nomic regime sig­nif­i­cantly changed. Which was also around the time my in­ter­est in Finance be­gan and re­cently I started to build my own in­vest­ment tool you can read more about it here.

At that time time we en­tered an era of ex­ten­sive liq­uid­ity (cheap money). When in­ter­est rates are near zero, in­vestors de­mand growth above all else.

As a re­sult, tech com­pa­nies stopped build­ing for sus­tain­abil­ity and started build­ing for ex­po­nen­tial ex­pan­sion.

Here is a graph shows US Fed Interest Rates by years.

In tra­di­tional in­dus­tries like man­u­fac­tur­ing you don’t hire 500 fac­tory work­ers un­less you have a pro­duc­tion line that needs them. You don’t over-hire based on a guess.

But in Tech, the play­book is dif­fer­ent. Companies over-hire soft­ware en­gi­neers in­ten­tion­ally. To play the lot­tery. It is sim­i­lar to hav­ing slow and steady ETF in­vest­ments vs ac­tive in­vest­ing. No mat­ter how godly you are with ac­tive in­vest­ing sooner or later, you will in­vest on a loser. Same goes for busi­nesses.

In a fac­tory, “Work in Progress” (unfinished goods) is a li­a­bil­ity. You don’t want in­ven­tory sit­ting on the floor; you want it out the door.

In soft­ware, we con­vinced our­selves that “Work in Progress” (hiring en­gi­neers to work on pro­jects that haven’t shipped yet) is an as­set.

It is not. It is just ex­ces­sive in­ven­tory.

When the mar­ket turned, com­pa­nies re­al­ized they were ware­hous­ing tal­ent like un­sold prod­ucts. And just like un­sold in­ven­tory, when the stor­age costs get too high, you dump it.

Till ~2010, a lay­off was a sign of fail­ure. It meant the CEO messed up.

In 2024, a lay­off is a sig­nal of “discipline.” Companies lay off thou­sands, and their stock price jumps.

They are sig­nal­ing to Wall Street that they are will­ing to sac­ri­fice hu­man cap­i­tal to pro­tect mar­gins.

Big Tech com­pa­nies (think Google, Meta, or any hy­per-growth SaaS) op­er­ate on a two-tier sys­tem:

1. The Core: A fun­da­men­tal team work­ing on the ac­tual rev­enue-gen­er­at­ing prod­ucts (the search en­gine, the ad net­work, the check­out flow).

2. The Bets: Thousands of en­gi­neers hired to build par­al­lel prod­ucts, ex­per­i­men­tal fea­tures, or sim­ply to keep tal­ent away from com­peti­tors and po­ten­tially build some­thing that would move into “The Core” tier.

The com­pany knows that most of these side bets will fail. When the eco­nomic winds change, the ‘non-core’ staff be­comes im­me­di­ately re­place­able.

It’s a vi­cious cy­cle: Hire the best peo­ple you can find to hoard tal­ent, see what sticks, and lay off the rest when in­vestors want to see bet­ter mar­gins.

Most en­gi­neers (including me) spent months grind­ing LeetCode at least twice in their ca­reer, study­ing sys­tem de­sign, and pass­ing gru­el­ing 6-round in­ter­views to prove they are the “top 1%.”

Yet, once hired, they are of­ten placed on a non-es­sen­tial team where they be­come noth­ing more than a sta­tis­tic on a spread­sheet.

You jump through hoops to prove you are ex­cep­tional, only to be treated as dis­pos­able.

For a long time, Europe of­fered a counter-bal­ance. The pay was lower than Silicon Valley, but the trade-off was sta­bil­ity, stronger la­bor pro­tec­tions, and a slower, more sus­tain­able pace of work.

As American tech gi­ants ex­panded into Europe and as European uni­corns chased the same growth-at-all-costs play­books the in­cen­tives changed.

Leadership im­ported US-style com­pen­sa­tion mod­els, in­vestor ex­pec­ta­tions, and or­ga­ni­za­tional volatil­ity, but with­out im­port­ing US-level pay or up­side.

”On pa­per” Europe still has strong la­bor laws. In prac­tice, com­pa­nies learned to route around them: con­stant re­or­ga­ni­za­tions, “strategic re­fo­cus” lay­offs, per­for­mance-man­aged ex­its.

The re­sult is the worst of both worlds. European en­gi­neers now face US-level job in­se­cu­rity with European-level com­pen­sa­tion and lim­ited mo­bil­ity. The safety net has­n’t dis­ap­peared, but it’s be­ing slowly hol­lowed out.

And sev­er­ances… A small, one-time pay­ment is used to jus­tify years of be­low mar­ket com­pen­sa­tion, while of­fer­ing lit­tle real pro­tec­tion against sud­den dis­place­ment.

Europe just be­came a lower-cost ex­ten­sion of Silicon Valley.

Ultimately, this comes down to how com­pa­nies sig­nal value.

Traditional busi­nesses used to show their health through rev­enue, profit, and smart cap­i­tal in­vest­ment. Today, Tech com­pa­nies use lay­offs as a mar­ket­ing sig­nal to Wall Street. They cut costs not be­cause they are go­ing bank­rupt, but to show they can be “efficient.”

The more liq­uid­ity that was pumped into Tech, the harder this sit­u­a­tion be­came. As long as en­gi­neers are treated as spec­u­la­tive as­sets rather than hu­man cap­i­tal, the mar­ket will re­main bro­ken re­gard­less of how good AI gets.

The job mar­ket is not “tough” right now be­cause of AI. It is tough be­cause we are un­wind­ing 14 years of fi­nan­cial tox­i­c­ity.

The liq­uid­ity that flooded the tech sec­tor did­n’t just in­flate val­u­a­tions; it in­flated teams, egos, and ex­pec­ta­tions.

Until the in­dus­try re­learns how to build with scarcity rather than ex­cess, the “vicious cy­cle” of hire-and-dump will con­tinue re­gard­less of how good AI will get.

So you aren’t be­ing laid off be­cause your per­for­mance was bad; you are be­ing ef­fec­tively “liquidated” like a bad stock trade that you sell with a loss.

Here’s the story of a re­mark­able scan­dal from a few years ago.

In the South Pacific, just north of Australia, there is a small, im­pov­er­ished, and re­mote coun­try called Papua New Guinea. It’s a coun­try that I’ve al­ways found ab­solutely fas­ci­nat­ing. If there’s any out­post of true re­mote­ness in the world, I think it’s ei­ther in the outer moun­tains of Afghanistan, in the deep­est jun­gles of cen­tral Africa, or in the high­lands of Papua New Guinea. (PNG, we call it.) Here’s my fa­vorite fact: Papua New Guinea, with about 0.1 per­cent of the world’s pop­u­la­tion, hosts more than 10 per­cent of the world’s lan­guages. Two vil­lages, sep­a­rated per­haps only by a few miles, will speak lan­guages that are not mu­tu­ally in­tel­li­gi­ble. And if you go into rural PNG, far into rural PNG, you’ll find your­self in places that time for­got.

But here’s a ques­tion about Papua New Guinea: how many peo­ple live there?

The an­swer should be pretty sim­ple. National gov­ern­ments are sup­posed to pro­vide an­nual es­ti­mates for their pop­u­la­tions. And the PNG gov­ern­ment does just that. In 2022, it said that there were 9.4 mil­lion peo­ple in Papua New Guinea. So 9.4 mil­lion peo­ple was the of­fi­cial num­ber.

But how did the PNG gov­ern­ment reach that num­ber?

The PNG gov­ern­ment con­ducts a cen­sus about every ten years. When the PNG gov­ern­ment pro­vided its 2022 es­ti­mate, the pre­vi­ous cen­sus had been done in 2011. But that cen­sus was a dis­as­ter, and the PNG gov­ern­ment did­n’t con­sider its own find­ings cred­i­ble. So the PNG gov­ern­ment took the 2000 cen­sus, which found that the coun­try had 5.5 mil­lion peo­ple, and worked off of that one. So the 2022 pop­u­la­tion es­ti­mate was an ex­trap­o­la­tion from the 2000 cen­sus, and the num­ber that the PNG gov­ern­ment ar­rived at was 9.4 mil­lion.

But this, even the PNG gov­ern­ment would ad­mit, was a hazy guess.

About 80 per­cent of peo­ple in Papua New Guinea live in the coun­try­side. And this is not a coun­try­side of flat plains and paved roads: PNG is a coun­try of moun­tain high­lands and re­mote is­lands. Many places, prob­a­bly most places, don’t have roads lead­ing to them; and the roads that do ex­ist are al­most never paved. People speak dif­fer­ent lan­guages and have lit­tle trust in the cen­tral gov­ern­ment, which sim­ply is­n’t a force in most of the coun­try. So trav­el­ing across PNG is ex­tra­or­di­nar­ily treach­er­ous. It’s not a coun­try where you can send peo­ple to sur­vey the coun­try­side with much ease. And so the PNG gov­ern­ment re­ally had no idea how many peo­ple lived in the coun­try.

Late in 2022, word leaked of a re­port that the UN had com­mis­sioned. The re­port found that PNG’s pop­u­la­tion was not 9.4 mil­lion peo­ple, as the gov­ern­ment main­tained, but closer to 17 mil­lion peo­ple—roughly dou­ble the of­fi­cial num­ber. Researchers had used satel­lite im­agery and house­hold sur­veys to find that the pop­u­la­tion in rural ar­eas had been dra­mat­i­cally un­der­counted.

This was a huge em­bar­rass­ment for the PNG gov­ern­ment. It sug­gested, first of all, that they were com­pletely in­com­pe­tent and had no idea what was go­ing on in the coun­try that they claimed to gov­ern. And it also meant that all the eco­nomic sta­tis­tics about PNG—which pre­sented a fairly happy pic­ture—were en­tirely false. Papua New Guinea had been ranked as a “lower-middle in­come” coun­try, along with India and Egypt; but if the re­port was cor­rect then it was sim­ply a “lower-income” coun­try, like Afghanistan or Mali. Any eco­nomic progress that the gov­ern­ment could have cited was in­stantly wiped away.

But it was­n’t as though the gov­ern­ment could point to cen­sus fig­ures of its own. So the coun­try’s prime min­is­ter had to ad­mit that he did­n’t know what the pop­u­la­tion was: he did­n’t know, he said, whether the pop­u­la­tion is “17 mil­lion, or 13 mil­lion, or 10 mil­lion.” It ba­si­cally did­n’t mat­ter, he said, be­cause no mat­ter what the pop­u­la­tion was, “I can­not ad­e­quately ed­u­cate, pro­vide health cover, build in­fra­struc­tures and cre­ate the en­abling law and or­der en­vi­ron­ment” for the coun­try’s peo­ple to suc­ceed.

But in the end, the PNG gov­ern­ment won out. To pre­serve its dig­nity, it is­sued a gag or­der on the re­port, which has still never been re­leased. There was some ob­scure be­hind-the-scenes bu­reau­cratic wran­gling, and in 2023 the UN shelved the re­port and agreed with the PNG gov­ern­men­t’s ex­ist­ing es­ti­mate. And so to­day, PNG of­fi­cially has ap­prox­i­mately 10 mil­lion peo­ple, per­fectly in line with what had been es­ti­mated be­fore.

The truth, of course, is that we have no idea how many peo­ple live in Papua New Guinea.

Last week, some­one call­ing them­selves Bonesaw went vi­ral on Twitter for a post that claimed that China’s pop­u­la­tion num­bers were en­tirely fake. China, they said, had been ly­ing about its pop­u­la­tion for decades: it ac­tu­ally had only about 500 mil­lion peo­ple. In fact prac­ti­cally every non-West­ern coun­try had been ly­ing about its pop­u­la­tion. India’s num­bers were also badly ex­ag­ger­ated: the idea that there are 1.5 bil­lion Indians was ab­surd. The true pop­u­la­tion of the world, Bonesaw said, was sig­nif­i­cantly less than 1 bil­lion peo­ple.

This is ob­vi­ously an ex­tremely stu­pid idea. It’s pos­si­ble that Chinese pop­u­la­tion num­bers are mildly ex­ag­ger­ated, but the most cred­i­ble es­ti­mates—the ones ad­vanced by Yi Fuxian—are that the ex­ag­ger­a­tion is on the or­der of a few per­cent­age points. (It’s also worth not­ing that no rep­utable source has yet backed Yi Fuxian’s the­ory.) Actually fak­ing the ex­is­tence of bil­lions of peo­ple would re­quire a global con­spir­acy or­ders of mag­ni­tude more com­plex than any­thing in hu­man his­tory. Tens or hun­dreds of thou­sands of peo­ple, spread across every coun­try in the world, would have to be in on it. Local, re­gional, and na­tional gov­ern­ments would all have to be in­volved; also the UN, the World Bank, the IMF, every satel­lite com­pany, every NGO that does work in any of these places. Every elec­tion would have to be fake. Every gov­ern­ment data­base would have to be full of fake names. And all for what? To get one over on the dumb Westerners?

So we can dis­miss Bonesaw’s claim pretty eas­ily. But, as much as I hate to ad­mit it, his ar­gu­ment does have a ker­nel of truth. And that ker­nel of truth is this: we sim­ply have no idea how many peo­ple live in many of the world’s coun­tries.

This is not the case for most coun­tries, of course. In wealthy coun­tries, like Germany or Japan or Sweden, pop­u­la­tions are gen­er­ally trust­ing and bu­reau­cra­cies are gen­er­ally ca­pa­ble. Sweden, for its part, main­tains such an ac­cu­rate daily birth-and-death count of pop­u­la­tion num­bers that it no longer even needs to con­duct a cen­sus. And pop­u­la­tion num­bers are also not so much of a prob­lem in coun­tries like China, India, or Vietnam. These places might be poorer, but they have strong cen­tral gov­ern­ments that have a strong in­ter­est in know­ing what’s go­ing on in­side the coun­try. Population counts might be slightly over­stated in these places be­cause fer­til­ity is falling faster than ex­pected (which could be the case in a coun­try like India where fer­til­ity rates are falling quickly), or be­cause lo­cal of­fi­cials are ex­ag­ger­at­ing the num­ber of stu­dents in their schools to se­cure more ed­u­ca­tion sub­si­dies (that’s Yi Fuxian’s the­ory of pop­u­la­tion counts in China), or be­cause more peo­ple have em­i­grated than ex­pected (as was the case in Paraguay when a cen­sus re­vealed its pop­u­la­tion to be smaller than of­fi­cials ex­pected). But if the state is in full con­trol of a coun­try, it will want to know what’s go­ing on in­side that coun­try; and that starts with the sim­ple fact of know­ing how many peo­ple live there.

But “the state be­ing in full con­trol of a coun­try” is not a cri­te­rion that holds in much of the world. Which brings us to Nigeria.

Nigeria is a huge place. Officially, it’s a na­tion of 240 mil­lion peo­ple, which would make it the most pop­u­lous coun­try in Africa and the sixth most pop­u­lous coun­try in the world. And with­out a doubt, there are a lot of peo­ple in Nigeria. But we ac­tu­ally have no idea how many there are.

Like PNG, Nigeria is sup­posed to con­duct a cen­sus every 10 years. But in Nigeria, the cen­sus is a po­lit­i­cally fraught thing. Nigeria is not a nat­ural polity, and its on­go­ing unity as a sin­gle coun­try is frag­ile. And so Nigerian elites ex­pend enor­mous ef­fort to en­sure that Nigeria re­mains one coun­try. They have two im­por­tant tools at their dis­posal. The first is the rel­a­tive rep­re­sen­ta­tion of dif­fer­ent re­gions in the Nigerian state. And the sec­ond is the dis­tri­b­u­tion of Nigeria’s vast oil rev­enues. Both of these—how many seats a state is given in the Nigerian par­lia­ment, and how large a share of oil rev­enues it re­ceives—are de­ter­mined by its share of the pop­u­la­tion.

So lo­cal elites have a strong in­cen­tive to ex­ag­ger­ate the num­ber of peo­ple in their re­gion, in or­der to se­cure more oil rev­enue, while na­tional elites have a strong in­cen­tive to bal­ance pop­u­la­tions across states in or­der to main­tain the pre­car­i­ous bal­ance of power be­tween dif­fer­ent re­gions. And so the over­whelm­ing bias in Nigerian pop­u­la­tion counts is to­ward ex­tremely bla­tant fraud.

It’s long been the case that cen­suses in Nigeria are shoddy af­fairs. When Nigeria was a colony of Britain, its cen­suses were lim­ited to Lagos, a few town­ships, and a small num­ber of vil­lages: so the 1931 cen­sus for Nigeria yielded num­bers that were too low by as much as 75 per­cent. Once Nigeria be­came in­de­pen­dent, in 1960, the bias swung from un­der­es­ti­ma­tion to over­es­ti­ma­tion. Nigeria’s first cen­sus as an in­de­pen­dent state came in 1962, and it im­me­di­ately caused a po­lit­i­cal prob­lem: the rul­ing regime was dom­i­nated by north­ern elites, but the cen­sus found that south­ern Nigeria had more peo­ple. And so an­other cen­sus was or­dered the next year, which con­ve­niently found an ex­tra eight mil­lion peo­ple in the north. This pat­tern of brazenly false num­bers con­tin­ued for decades. The next cen­sus, in 1973, was such an ob­vi­ous fraud that the gov­ern­ment opted not even to pub­lish the re­sults. For eigh­teen years af­ter that there was no at­tempt even to con­duct a cen­sus. The next cen­sus, in 1991, was by far the most cred­i­ble, and it shocked many peo­ple by find­ing that the pop­u­la­tion was about 30 per­cent smaller than es­ti­mated. But even that one was rid­dled with fraud. Many states re­ported that every sin­gle house­hold had ex­actly nine peo­ple.

In 2006, Nigeria tried once again to count its pop­u­la­tion. And as luck would have it, it found that since the last cen­sus each state’s pro­por­tion of the na­tional pop­u­la­tion had re­mained ex­actly the same: so there was no need to change the com­po­si­tion of the Nigerian par­lia­ment or the dis­tri­b­u­tion of oil rev­enues. But this cen­sus was an ex­tremely rocky af­fair. The city of Lagos, for in­stance, re­jected the re­sults of the cen­sus, which it claimed un­der­counted its pop­u­la­tion in or­der to pre­serve north­ern power; so it con­ducted its own (technically il­le­gal) cen­sus and found that it had eight mil­lion more peo­ple than the na­tional cen­sus had reck­oned. And there was also a good deal of vi­o­lence that ac­com­pa­nied the cen­sus: about ten peo­ple were killed in clashes around the cen­sus, usu­ally in re­gions with sep­a­ratist ac­tiv­ity. The whole ex­pe­ri­ence was so dif­fi­cult that Nigeria has opted not to re­peat it. The 2006 cen­sus was the last time that Nigeria has tried to count how many peo­ple live in the coun­try.

So the Nigerian gov­ern­men­t’s fig­ure of 240 mil­lion peo­ple is, as is the case in Papua New Guinea, an ex­trap­o­la­tion from a long-ago cen­sus fig­ure. Is it cred­i­ble? Very few peo­ple think so. Even the head of Nigeria’s pop­u­la­tion com­mis­sion does­n’t be­lieve that the 2006 cen­sus was trust­wor­thy, and in­deed said that “no cen­sus has been cred­i­ble in Nigeria since 1816.” (Nigeria’s pres­i­dent fired him shortly there­after.) There are plenty of rea­sons to think that Nigeria’s pop­u­la­tion might be over­stated. It would ex­plain, for in­stance, why in so many ways there ap­pear to be tens of mil­lions of miss­ing Nigerians: why so few Nigerians have reg­is­tered for na­tional iden­ti­fi­ca­tion num­bers, or why Nigerian voter turnout is so much lower than voter turnout in nearby African na­tions (typically in the 20s or low 30s, com­pared to the 50s or 60s for Ghana, Cameroon, or Burkina Faso), or why SIM card reg­is­tra­tion is so low, or why Nigerian fer­til­ity rates have ap­par­ently been drop­ping so much faster than de­mog­ra­phers ex­pected.

None of this ev­i­dence is con­clu­sive, of course. (There are cred­i­ble third par­ties—like the Against Malaria Foundation—that be­lieve that Nigerian pop­u­la­tion counts might ac­tu­ally be un­der­stated.) But the cru­cial thing, as in Papua New Guinea, is that we don’t know how many peo­ple live in Nigeria. It might be that there are 240 mil­lion Nigerians, as the Nigerian gov­ern­ment claims; or that there are 260 mil­lion Nigerians; or that there are only 180 mil­lion. We don’t know. But we have plenty of rea­son to think that the of­fi­cial num­bers have lit­tle re­la­tion­ship to re­al­ity.

What about other coun­tries?

Nigeria is not the only poor coun­try with an ex­tremely patchy his­tory of cen­suses. Indeed we find that count­less poor na­tions with weak states have only the vaguest idea how many peo­ple they gov­ern. The Democratic Republic of the Congo, which by most es­ti­mates has the fourth-largest pop­u­la­tion in Africa, has not con­ducted a cen­sus since 1984. Neither South Sudan nor Eritrea, two of the newest states in Africa (one cre­ated in 2011 and the other in 1991), has con­ducted a cen­sus in their en­tire his­tory as in­de­pen­dent states. Afghanistan has not had one since 1979; Chad since 1991; Somalia since 1975.

The var­i­ous bod­ies that in­ter­est them­selves in na­tional pop­u­la­tions, from the World Bank to the CIA, re­li­ably pub­lish pop­u­la­tion num­bers for each of these coun­tries. But with­out ground­ing in trust­wor­thy cen­sus data, we sim­ply have no idea if the num­bers are real or not. Estimates for Eritrea’s pop­u­la­tion vary by a fac­tor of two. Afghanistan could have any­where be­tween 38 and 50 mil­lion peo­ple. Estimates for the DRC’s 2020 pop­u­la­tion range from 73 mil­lion to 104 mil­lion. How did the coun­try reach its of­fi­cial num­ber for that year, 94.9 mil­lion? We have no idea. “It is un­clear how the DRC na­tional sta­tis­ti­cal of­fice de­rived its es­ti­mate,” the U. S. Census Bureau said, “as there is no in­for­ma­tion in its 2020 sta­tis­ti­cal year­book.”

Many other coun­tries do con­duct more reg­u­lar cen­suses, but do a ter­ri­ble job of it. Enumerators are hired cheaply and do a bad job, or they quit halfway through, or they go un­paid and just refuse to sub­mit their data. An un­know­able num­ber sim­ply sub­mit fake num­bers. These are not, af­ter all, tech­ni­cal ex­perts or trained pro­fes­sion­als; they are ran­dom peo­ple sent into re­mote places, of­ten with ex­tremely poor in­fra­struc­ture, and charged with de­ter­min­ing how many peo­ple live there. It is ex­cep­tion­ally dif­fi­cult to do that and come out with an ac­cu­rate an­swer.

So even those coun­tries that do con­duct reg­u­lar or semi-reg­u­lar cen­suses of­ten ar­rive at in­ac­cu­rate re­sults. The most re­cent South African cen­sus, for in­stance, un­der­counted the pop­u­la­tion by as much as 31 per­cent—and that is one of the wealth­ier and bet­ter-run na­tions in Africa. In poorer and less func­tional coun­tries, sta­tis­ti­cal ca­pac­ity is of­ten just nonex­is­tent. Take, for in­stance, the tes­ti­mony of the for­mer di­rec­tor of Sudan’s sta­tis­ti­cal bu­reau, who said that the most ac­cu­rate cen­sus in Sudan’s his­tory was con­ducted in 1956, when the coun­try was still un­der British rule.

It should­n’t be new to any­one that pop­u­la­tion data in the poor world is bad. We’ve known about these prob­lems for a long time. And for an equally long time, we’ve had a pre­ferred so­lu­tion in mind. Technology can com­pen­sate for the de­te­ri­o­ra­tion of hu­man co­or­di­na­tion: we have satel­lites.

Satellites have two great ben­e­fits for count­ing pop­u­la­tions. First, satel­lites can see pretty much any part of the world from space, and so you en­tirely ob­vi­ate the lo­gis­ti­cal prob­lem of send­ing peo­ple into re­mote ar­eas: all you need is a small count of some por­tion of the area un­der study, which you can use to ground your es­ti­mates in some­thing like re­al­ity. And sec­ond, you don’t have to rely on lo­cal gov­ern­ments to ob­tain the data—so you can get away from the bad in­cen­tives of, say, Nigerian elites.

But satel­lite data can only tell us so much. A satel­lite can look at a house, but it can’t de­ter­mine whether three peo­ple live there, or six peo­ple, or eight peo­ple. And of­ten the prob­lem is worse than that. Sometimes a satel­lite can’t tell what’s a build­ing and what’s a fea­ture of the land­scape. Dense cities are a prob­lem; and so, by the way, are jun­gles—satel­lites can’t pen­e­trate thick for­est cover, and there are quite a few peo­ple around the world who still live in forests. (The “forest peo­ple” of cen­tral Africa, for in­stance, or a few mil­lion of the Adivasi in India.)

So guess­ing pop­u­la­tion num­bers from high-res­o­lu­tion satel­lite im­agery is an ex­tra­or­di­nar­ily dif­fi­cult prob­lem. The var­i­ous com­pa­nies that guess pop­u­la­tion num­bers from satel­lite im­agery—work­ing with groups like the World Health Organization that might be in­ter­ested in map­ping, say, malaria cases—take dif­fer­ent ap­proaches to tack­ling this prob­lem. And the dif­fer­ent ap­proaches they take can lead to wildly dif­fer­ent re­sults. For ex­am­ple: Meta and WorldPop both used satel­lite im­agery to pre­dict the pop­u­la­tion of the city of Bauchi, in north­east­ern Nigeria. But the num­bers that they reached were en­tirely dif­fer­ent, be­cause they take dif­fer­ent ap­proaches: Meta uses a deep learn­ing model to de­tect in­di­vid­ual build­ings in im­ages and then dis­trib­utes pop­u­la­tion pro­por­tion­ally across those struc­tures, while WorldPop feeds a ma­chine-learn­ing model with dozens of vari­ables (land cover, el­e­va­tion, road net­works, so on) and uses that to pre­dict pop­u­la­tion. Meta guessed that Bauchi has 127,000 chil­dren un­der the age of five; WorldPop says that it has 254,000, twice as many. So Meta’s es­ti­mate is about 50 per­cent lower than WorldPop’s. We see sim­i­lar dif­fer­ences in other re­gions. Meta says that Ganjuwa, also in north­east­ern Nigeria, has 76,000 chil­dren un­der the age of five; WorldPop says that it has 162,000.

And when we do have ground-truth data, we tend to find that satel­lite-based data does­n’t per­form much bet­ter. Last year, three Finnish sci­en­tists pub­lished a study in Nature look­ing at satel­lite-based pop­u­la­tion es­ti­mates for rural ar­eas that were cleared for the con­struc­tion of dams. This was a use­ful test for the satel­lite data, be­cause in re­set­tling the peo­ple of those ar­eas lo­cal of­fi­cials were re­quired to count the lo­cal pop­u­la­tion in a care­ful way (since re­set­tle­ment counts de­ter­mine com­pen­sa­tion pay­ments), and those counts could be com­pared to the satel­lite es­ti­mates. And again and again, the Finnish sci­en­tists found that the satel­lite data badly un­der­counted the num­ber of peo­ple who lived in these ar­eas. The European Commission’s GSH-POP satel­lite tool un­der­counted pop­u­la­tions by 84 per­cent; WorldPop, the best per­former, still un­der­es­ti­mated rural pop­u­la­tions by 53 per­cent. The pat­tern held world­wide, with par­tic­u­larly large dis­crep­an­cies in China, Brazil, Australia, Poland, and Colombia. Nor is it just rural ar­eas be­ing re­set­tled: WorldPop and Meta es­ti­mated slums in Nigeria and Kenya to be a third of their ac­tual size.

So satel­lite data is not a panacea. It might be that in the fu­ture the tools ad­vance to the point where they can pro­duce re­li­able es­ti­mates of hu­man pop­u­la­tions in ar­eas of ar­bi­trary size. But we are not re­ally close to that point.

Where does that leave us?

I don’t think there’s any rea­son to em­brace the sort of id­i­otic con­spir­acism of Bonesaw. We sim­ply have no rea­son to think that the num­ber of peo­ple in the world is dra­mat­i­cally dif­fer­ent from what of­fi­cial es­ti­mates in­di­cate; in­deed while there are spe­cific cases where the num­bers might be dra­mat­i­cally off, there’s just no rea­son to think that this is the case for every coun­try. There are many places, like per­haps Papua New Guinea, where pop­u­la­tion counts are prob­a­bly too low. The only thing that can be said with any re­li­a­bil­ity is that we sim­ply don’t know how many peo­ple live in these coun­tries.

Given that we don’t have much ev­i­dence of a sys­tem­atic bias in pop­u­la­tion counts—Nige­ria might over­count, but Sudan might un­der­count, and at scale these dif­fer­ences should can­cel out—the best we can do is as­sume that there is a sort of “law of large num­bers” for pop­u­la­tion counts: the more units we have un­der con­sid­er­a­tion, the more closely the num­bers should hew to re­al­ity. So pop­u­la­tion counts for in­di­vid­ual coun­tries, par­tic­u­larly in Africa, are prob­a­bly badly in­ac­cu­rate. It would­n’t be sur­pris­ing if the to­tal pop­u­la­tion for Africa is off-base by some amount. But we don’t have much rea­son to think that the global pop­u­la­tion is very dif­fer­ent from what we be­lieve it to be.

But it’s good to be re­minded that we know a lot less about the world than we think. Much of our think­ing about the world runs on a sta­tis­ti­cal ed­i­fice of ex­tra­or­di­nary com­plex­ity, in which raw num­bers—like pop­u­la­tion counts, but also many oth­ers—are only the most ba­sic in­puts. Thinking about the ac­tual con­struc­tion of these num­bers is im­por­tant, be­cause it en­cour­ages us to have a healthy de­gree of epis­temic hu­mil­ity about the world: we re­ally know much less than we think.