Last week I launched a pro­ject called Dark Hours, which was a web­site util­ity to give you an idea of what could be seen in the sky that night.

A de­vel­oper who cre­ated an­other web app called DarkHours.app replied to my com­ment on Bluesky yes­ter­day to show me how sim­i­lar the pro­ject was to his, in­clud­ing the name. The thread is here.

I told him I’d sig­nif­i­cantly dif­fer­en­ti­ate the fea­ture­set, change the name, and write up a blog post to show peo­ple his pro­ject.

About an hour later, once it be­came clear to me that the web app I had launched us­ing Claude was strik­ingly sim­i­lar to his open source pro­ject, even re­pro­duc­ing a bug he had later fixed, it was clear that there was­n’t any­thing to do ex­cept to redi­rect the do­main di­rectly to him and kill any plans I had to launch an iOS app for the pro­ject.

I’d like to give credit where credit is due. I think he’s made a won­der­ful open source app and if you liked the fea­tures that were in­cluded in what I launched, please use the ac­tual ver­sion of the pro­ject. That’s what I’ll be do­ing.

I’d also like to apol­o­gize for my ir­re­spon­si­ble use of AI to build such a thing. While I had gen­uinely never seen DarkHours.app be­fore yes­ter­day, I was care­less in re­ly­ing on AI to gen­er­ate the pro­ject with­out do­ing the work to un­der­stand whether it closely re­sem­bled an ex­ist­ing pro­ject. That’s on me, and I am re­spon­si­ble for what I pub­lished.

Going for­ward, I won’t be us­ing AI in this way to cre­ate any more web stuff, and I do not use it to any­thing close to this ex­tent on iOS soft­ware. I do ask ques­tions, de­bug is­sues, things like that, but I do not cre­ate apps with Claude for iOS.

Many en­gi­neers I know use gen­er­a­tive AI for many func­tions, like build­ing PoCs, in­ter­nal tools or dash­boards, or even learn­ing new stuff. I per­son­ally find the style used by LLMs to ex­plain things dif­fi­cult to fol­low. It’s just too sim­plis­tic and de­pend­ing on the num­ber of emo­jis used, a bit an­noy­ing too.

While I was an­a­lyz­ing new AI bot­tle­necks that might slow down data cen­ter buildup, I re­al­ized there are many as­pects of chip pro­duc­tion that I do not know. Surfing the web, I asked my­self what if there would be a game to get you through the process of build­ing a chip at a fab? For sure learn­ing this way will stick, since you can map con­cepts with ob­jects within the game. This is when I de­cided to try it, and it ac­tu­ally turned out re­ally well.

The flow

Instead of just ask­ing AI to ex­plain a topic, I use the fol­low­ing flow:

In plan mode (using CC, or OpenCode) I ask a model to build the foun­da­tional knowl­edge for X topic.

I ask it to re­view the ac­cu­racy of the knowl­edge base it built in the pre­vi­ous step.

I pro­ceed ask­ing it to build a sim­u­la­tion of that topic in a low-poly, Rollercoaster Tycoon-like an­i­ma­tion. I add some UX el­e­ments as well, like the page needs to be vis­i­ble on both large and small screens, have con­trols to stop the flow when­ever I want etc.

I then push it to a new repo and en­able GitHub Pages for it.

The re­sult

What you get is a beau­ti­ful an­i­ma­tion that is 100% ac­cu­rate and free of hal­lu­ci­na­tions. For me, this method works a lot bet­ter than just read­ing end­less ma­te­ri­als that I find on Google, or try­ing to di­gest a bul­leted list that is spat by a lan­guage model.

I’ve done this specif­i­cally for learn­ing chip build­ing and launch it un­der this web­site: ChipTycoon. You get to fol­low a cart from the mo­ment when sand is col­lected, to the mo­ment when a chip is fi­nal­ized and de­liv­ered to a data cen­ter.

Visually, you can fol­low the cart and see how it changes too. Since it’s low-poly, the de­tails might be miss­ing, but it’s still a good in­di­ca­tor for show­ing how the prod­uct changes once it goes through the many steps re­quired in the man­u­fac­tur­ing process.

How to im­prove it fur­ther

Let’s say that the low-poly de­sign re­quires to much im­mag­i­na­tion to ac­tu­ally vi­su­al­ize what hap­pened to the quartz sand pile af­ter it left the fur­nace. To trans­form this into a more re­al­is­tic rep­re­sen­ta­tion, you can use my skill for trans­form­ing pic­tures into 3d ob­jects, and map the re­sult­ing ob­jects to your sim­u­la­tion. This way you get more ac­cu­rate de­sign.

Also, you can add chal­lenges to your sim­u­la­tion too. Trying to an­swer ques­tions about a pre­vi­ous step in the chip man­u­fac­tur­ing process will help you re­tain the knowl­edge tremen­dously. Add in­tu­itive puz­zles too that will help you learn even bet­ter.

Check out what other pages I cre­ated:

How rocket en­gines are made

How LLMs work

How F1 en­gines are built

How an EUV ma­chine is built

ⓘ Microsoft

A new re­port shows Windows 11′s built-in Weather app can con­sume more than 1 GB of RAM. By com­par­i­son, Apple’s na­tive Weather app on ma­cOS uses roughly five times less mem­ory un­der sim­i­lar con­di­tions.

Microsoft has been work­ing to make Windows 11 more ef­fi­cient on PCs with lim­ited RAM, but one of its own built-in ap­pli­ca­tions ap­pears to be work­ing against that goal. According to tests pub­lished by Windows Latest, the op­er­at­ing sys­tem’s Weather app can con­sume more than 1 GB of mem­ory de­spite per­form­ing a rel­a­tively sim­ple task.

Windows Latest re­ports that the app ex­ceeded 1.2 GB of RAM while dis­play­ing a weather fore­cast, with no in­ten­sive in­ter­ac­tion from the user. Wccftech ob­served sim­i­lar be­hav­ior, not­ing that mem­ory us­age typ­i­cally starts at around 1 GB, drops to roughly 500 – 600 MB when idle, and can climb to 1.5 – 1.6 GB dur­ing ba­sic ac­tions such as zoom­ing or nav­i­gat­ing the in­ter­face. On a PC equipped with 8 GB of RAM, that means the ap­pli­ca­tion alone may oc­cupy nearly 20% of the sys­tem’s mem­ory.

By com­par­i­son, Apple’s na­tive Weather app on ma­cOS re­port­edly uses less than 250 MB of RAM un­der sim­i­lar con­di­tions, giv­ing Microsoft’s im­ple­men­ta­tion a mem­ory foot­print roughly five times larger.

According to Windows Latest, the high mem­ory con­sump­tion is due to the fact that Weather is not a fully na­tive Windows ap­pli­ca­tion. Instead, it is es­sen­tially an MSN Weather web app built on Microsoft’s WebView2 frame­work. Task Manager shows mul­ti­ple Chromium-based sub­processes run­ning si­mul­ta­ne­ously, which con­tributes to the un­usu­ally high RAM us­age.

The is­sue is un­likely to af­fect high-end PCs with 32 GB or more of RAM, but it could have a no­tice­able im­pact on en­try-level sys­tems. On com­put­ers with 8 GB or even 16 GB of mem­ory, launch­ing the Weather app may in­crease mem­ory pres­sure enough for Windows to rely more heav­ily on the page file, po­ten­tially mak­ing the sys­tem feel less re­spon­sive.

The ap­pli­ca­tion also in­cludes ad­ver­tis­ing within its in­ter­face. According to Windows Latest, spon­sored con­tent is em­bed­ded di­rectly into the fore­cast feed, ap­pear­ing along­side weather cards in a sim­i­lar vi­sual style. While Microsoft li­censes weather data from mul­ti­ple providers — in­clud­ing Foreca, the European Centre for Medium-Range Weather Forecasts (ECMWF), and other re­gional me­te­o­ro­log­i­cal ser­vices — the pres­ence of ads in a built-in Windows ap­pli­ca­tion has at­tracted crit­i­cism.

The find­ings also ap­pear to con­tra­dict Microsoft’s re­cent ef­forts to im­prove Windows 11′s ef­fi­ciency. The com­pany has been up­dat­ing sev­eral built-in ap­pli­ca­tions and has re­peat­edly said it wants the op­er­at­ing sys­tem to per­form bet­ter on lower-end hard­ware. Microsoft ex­ec­u­tive Rudy Huyn has also stated that the com­pany in­tends to de­velop more fully na­tive Windows ap­pli­ca­tions in the fu­ture, al­though it re­mains un­clear whether MSN-branded ap­pli­ca­tions such as Weather will even­tu­ally be re­built us­ing WinUI.

Andrew Sozinow - Tech Writer - 71 ar­ti­cles pub­lished on Notebookcheck since 2024

I’ve been fas­ci­nated by com­put­ers, elec­tron­ics and mod­ern tech­nol­ogy since child­hood. I started writ­ing IT-news at high school and have been do­ing it con­tin­u­ously for more than 10 years. During this time, I have worked for many me­dia out­lets, and now I am a news ed­i­tor at 3DNews. Sometimes I also write smart­phone re­views. In July 2024, I de­cided to try my hand at writ­ing news on Notebookcheck. When I’m not work­ing, I like to play videogames, do puz­zles, and travel.

Andrew Sozinov, 2026 – 08- 9 (Update: 2026 – 08- 9)

In her up­com­ing book “The Rise and Fall of the Artificial State,” Jill Lepore warns that tech com­pa­nies are in­creas­ingly re­plac­ing the func­tions of de­mo­c­ra­tic gov­ern­ment. This shift, she said, marks “a re­turn to tyranny and mys­ti­fi­ca­tion in the form of rule by al­go­rithms, cor­po­ra­tions, ma­chines.”

On the lat­est episode of TechCrunch’s Equity pod­cast, I spoke to Lepore — a Harvard his­to­rian and New Yorker staff writer who re­cently won a Pulitzer Prize for her his­tory of the U.S. Constitution — about the evo­lu­tion of what she de­scribed as “the idea that we should live un­der an ar­ti­fi­cial state or gov­ern­ment by ma­chines.”

“I’m not an anti-tech­nol­o­gist,” Lepore in­sisted. Instead, she said, “My beef is the ways in which pri­vate cor­po­ra­tions have in­creas­ingly taken on the func­tions of the state.”

While Lepore’s book ex­am­ines tech­no­cratic philoso­phies that go back cen­turies, she ar­gued that many of Silicon Valley’s “charismatic or not-so-charis­matic lead­ers” — es­pe­cially Elon Musk — seem to be ush­er­ing in a fu­ture pulled from mis­read pulp sci­ence fic­tion and comic books.

“But what’s funny about Musk is, the stuff he likes ac­tu­ally com­pletely de­feats and de­fies all of his po­lit­i­cal be­liefs,” she said.

Our con­ver­sa­tion also cov­ered Apple’s fa­mous “1984” Macintosh ad, why it’s “bananas” to call Twitter a dig­i­tal town hall, and the cur­rent data cen­ter back­lash. Keep read­ing for high­lights, edited for length and clar­ity.

So you’ve prob­a­bly had to do this a lot al­ready, but can you ex­plain what you mean by the “artificial state”?

By the ar­ti­fi­cial state, I mean a kind of state that is re­plac­ing the lib­eral de­mo­c­ra­tic na­tion-state in the United States and around the world. It’s both a real thing, a con­struct, but it’s also an idea.

And so, in this book “The Rise and Fall of the Artificial State,” I trace the rise of the idea that we should live un­der an ar­ti­fi­cial state or gov­ern­ment by ma­chines. I also trace the no­tion that this is an in­evitable fail­ure, that the ar­ti­fi­cial state can­not sur­vive, and I trace that idea through sci­ence fic­tion.

At one point, you say the rise of the ar­ti­fi­cial state marks the end of cen­turies of democ­racy and equal rights, and it’s “a re­turn to tyranny and mys­ti­fi­ca­tion in the form of rule by al­go­rithms, cor­po­ra­tions, ma­chines.” Can you just say a lit­tle bit more about why you see it in such stark terms?

Yeah, I do have a pretty neg­a­tive view of it, and I think it’s im­por­tant to dis­tin­guish the ar­ti­fi­cial state from tech­nol­ogy it­self or modes of tech­nol­ogy. I’m not an anti-tech­nol­o­gist. I’m mar­ried to a com­puter sci­en­tist. I’m re­ally ex­cited about all kinds of in­tel­lec­tual rev­o­lu­tions that we’re in the midst of right now.

That’s not my beef, right? My beef is the ways in which pri­vate cor­po­ra­tions have in­creas­ingly taken on the func­tions of the state. No one con­sented to that. This has been a kind of grad­ual, largely ac­ci­den­tal trans­for­ma­tion of how many na­tion-states around the world work — it’s hap­pened first in the United States.

I think of­ten these in­no­va­tions in bring­ing new tech­nolo­gies to the op­er­a­tions of gov­ern­ment have been ex­tremely well in­ten­tioned; they orig­i­nate with an in­ter­est in ef­fi­ciency and speed and cheap­ness. And then, I think, only in the last 20, 25 years or so have these de­ci­sions been pur­pose­ful and de­lib­er­ate as a kind of usurpa­tion of the role of the na­tion-state.

And that’s not my spec­u­la­tion. You hear a lot of a lot of very promi­nent tech en­tre­pre­neurs talk about want­ing to move be­yond the era of the na­tion-state. […] A lot of fu­tur­ists in the ’90s were lib­er­tar­i­ans, and they had a spe­cific in­ter­est in us­ing the ad­vance of the in­ter­net and the suc­ces­sive in­no­va­tions that fol­lowed as a means to erad­i­cate the na­tion-state.

You talk about, on the one hand, the tech­nolo­gies them­selves, and then also the philoso­phies be­hind them, the role the cor­po­ra­tion has in­creas­ingly played. I’m cu­ri­ous to what ex­tent we can sep­a­rate them. Can we ac­tu­ally have a ver­sion of the in­ter­net and of so­cial me­dia that does­n’t nec­es­sar­ily lead to this fu­ture that it seems like we’re [currently] hurtling to­wards?

Absolutely. I’m a his­to­rian. I’m not a tech writer. I’m not a tech jour­nal­ist. I’m not a com­puter sci­en­tist. I’m a his­to­rian, and I’m chiefly a po­lit­i­cal his­to­rian, though I’m also a lit­er­ary his­to­rian. And so, one of the things that I’m re­ally in­ter­ested in un­rav­el­ing for read­ers in this book is all the what-ifs, all the al­ter­na­tives, the paths along the road that were not taken and why.

There was, of course, a re­ally avid dis­cus­sion in the 1990s about what the in­ter­net should look like when it was opened up, and what we ended up with, the 1996 Telecommunications Act — I think, a lot of peo­ple would say [that] just was a mis­take, not an act of sin­is­ter in­tent, right?

But it was a prod­uct of a par­tic­u­lar po­lit­i­cal mo­ment, re­ally was deeply in­flu­enced by Newt Gingrich and his Contract with America, and it’s been very dif­fi­cult to re­visit. I think it’s worth think­ing about what were the al­ter­na­tives that were in play at the time.

And you could say the same thing about the per­sonal com­puter. So, to the de­gree that we can lo­cate an ori­gin point for the promise that bet­ter com­puter tech­nol­ogy would make for bet­ter democ­ra­cies, I think the mo­ment you would first look to would be January 1984, that Super Bowl ad that Apple ran for the re­lease of the Macintosh, with the the sort of George Orwell, 1984 [theme]. Apple was re­ally big on the idea that main­frame com­put­ers rep­re­sented to­tal­i­tar­i­an­ism. They were try­ing to dis­man­tle the gi­ant gray IBM ma­chines, as in rep­re­sent­ing them in that ad as a to­tal­i­tar­ian state. And the lithe, beau­ti­ful, quick, adorable, per­sonal Macintosh would be the ax that would de­stroy that ma­chine and would usher in a new era in which “1984 would not be ‘1984.’”

That was clever ad­ver­tis­ing. I doubt that any­body at Apple re­ally be­lieved the per­sonal com­puter was go­ing to be an in­stru­ment of per­sonal lib­er­a­tion. I mean, it was go­ing to make pos­si­ble a lot of cool things. I re­mem­ber when I got my first Macintosh — it cer­tainly was­n’t 1984, but it was re­ally cool, it was re­ally fun, it was re­ally ex­cit­ing, I did a lot of things on it. It would never oc­cur to me that it was im­prov­ing my ca­pac­ity for cit­i­zen­ship or my abil­ity to func­tion bet­ter in civil so­ci­ety. It was a cool tool.

But if you wind the reel for­ward in time, down to 2026 — stops along the way in­clude the 2016 elec­tion when Facebook News, in re­sponse to its crit­ics, es­tab­lishes a Supreme Court. You get to last year, when Anthropic hired a moral philoso­pher to write a con­sti­tu­tion. You get to re­cently, when Sam Altman was on Joe Rogan and said [in re­sponse to a ques­tion from Rogan], “Oh, an AI pres­i­dent would be a great idea.”

In some ways, they’re silly ex­am­ples. But you see the ways in which these cor­po­ra­tions, these tech com­pa­nies from Silicon Valley, and es­pe­cially their charis­matic or not-so-charis­matic lead­ers, are just tak­ing on the trap­pings of the na­tion-state and the func­tions of democ­racy.

They’re not peo­ple with a so­phis­ti­cated po­lit­i­cal phi­los­o­phy, but it’s like a car­toon ver­sion of that 1984 Macintosh ad, ex­cept that it takes it­self so se­ri­ously. And these com­pa­nies have so much power.

But that said, the book does­n’t be­gin in 1984. I just think that’s a good ex­am­ple of our mod­ern era and the way a fun ad­ver­tis­ing cam­paign turns into a kind of delu­sional fan­tasy on the part of peo­ple like Sam Altman.

You [also] talk about the promise of the quote-un­quote “Twitter rev­o­lu­tion,” and this idea that it would bring democ­racy every­where. I can’t help but let that color the way I [react] when Sam Altman or some other AI CEO now says that AI is go­ing to bring all these in­cred­i­ble gifts — and there­fore, if you stand in the way, you’re stand­ing in the way of progress, in the way of his­tory.

To what ex­tent should we just dis­miss all these claims out-of-hand, or are there ways that it might come true?

I mean, Twitter is ac­tu­ally a good ex­am­ple, right? When it was launched, when Jack Dorsey started it, it did­n’t an­nounce it­self as, “We’re go­ing to save hu­man­ity, we’re go­ing to res­cue hu­man civ­i­liza­tion from ex­tinc­tion.” It was kind of a goof, and I think peo­ple that used Twitter re­ally early on were like, “You know what? It was ac­tu­ally re­ally fun.” It was like, “I made a tuna fish sand­wich to­day. What did you have for lunch?” Twitter as a com­pany did not launch it­self on a stage say­ing, “We’re here to save democ­racy.”

And re­ally, what hap­pened was that politi­cians, elected of­fi­cials be­gan us­ing Twitter in ways that en­hanced their po­lit­i­cal power, in ways that am­pli­fied their mes­sages, in ways that al­lowed them to reach a younger au­di­ence, in ways that al­lowed them to have a con­stant con­nec­tion with an au­di­ence. Politicians and po­lit­i­cal cam­paigns re­ally kind of con­vinced Twitter — at least in­so­far as I see them, I don’t have an in­side ac­count of the com­pany — but some­what be­grudg­ingly, Twitter came around to like, “Twitter’s got­ten so big, and peo­ple post about pol­i­tics so of­ten that it’s al­most like Twitter is a town hall.”

By the time you get to, I think it’s 2012 — many years into Twitter’s fairly short his­tory — they pub­lish this thing called the Twitter Politics and Elections Handbook, which is re­ally a guide for po­lit­i­cal can­di­dates and elected of­fi­cials and how to most ef­fec­tively use Twitter. And then they be­gin the roll­out of, “It’s a town hall in your pocket, and it’s im­prov­ing our democ­ra­cies be­cause we’re restor­ing the de­funct New England town meet­ing,” and that’s all just ba­nanas.

Objectively, noth­ing could be fur­ther from the truth. At that time, one in five Americans had a Twitter ac­count. Most peo­ple who had Twitter ac­counts had never used them, and above 90% of all tweets about pol­i­tics were posted by fewer than 10% of the peo­ple that did use Twitter all the time. There was no way in which Twitter was a rep­re­sen­ta­tion of the elec­torate. Twitter was a rep­re­sen­ta­tion of the most ex­treme, po­lit­i­cally ac­tive, hy­per-par­ti­san among Americans, who were fol­low­ing pol­i­tics re­ally avidly. Looking at it now, we can see, “Well, that’s re­ally just a dis­tor­tion ma­chine. And if politi­cians are us­ing it to gauge the elec­torate, they’re get­ting re­ally bad in­for­ma­tion.”

Again, you can say Twitter was not try­ing to par­tic­i­pate in the ar­ti­fi­cial state or un­der­mine democ­racy. Twitter is try­ing to do busi­ness and get more users and sell more what­ever. But it had these un­in­tended con­se­quences that then it sort of set­tles into and be­comes com­fort­able with.

I want to talk a lit­tle bit more about the struc­ture of the book. Like you said, it starts with this his­tory of tech­nol­ogy, his­tory of ideas, and the sec­ond half is about sci­ence fic­tion. Can you say more about how that struc­ture came to you and why you wanted to ad­dress things that way?

I be­came re­ally in­ter­ested, on the one hand, in how of­ten sci­ence fic­tion sto­ries pre­dict the ar­rival of what I then came to call the ar­ti­fi­cial state, and so I re­ally wanted to iden­tify a lit­er­ary tra­di­tion that I think of as the para­ble of the ar­ti­fi­cial state, in which ma­chines get more and more so­phis­ti­cated, they take over more and more of the func­tions of hu­mans, in­clud­ing the func­tions of gov­ern­ment, and even­tu­ally they come to rule the hu­mans, and then maybe they de­stroy all the hu­mans be­cause they don’t re­ally need them any­more.

Maybe they just en­slave them, it kind of de­pends. Are we in “The Terminator” or are we in “Battlestar Galactica”? There’s dif­fer­ent ver­sions, and these sto­ries go way back. They go back to the 1850s and the early decades of ru­mi­na­tion about the con­se­quences of in­dus­tri­al­ism.

I think a lot of peo­ple — this is cer­tainly true of my stu­dents, my un­der­grad­u­ates — re­ally be­lieve that tech­no­log­i­cal change equals progress. And not only that, but the only kind of progress is tech­no­log­i­cal change. That’s a nov­elty in hu­man his­tory. That’s an in­tel­lec­tual in­ven­tion of the 19th cen­tury, and it is partly be­cause tech­no­log­i­cal change was ac­cel­er­at­ing right at the time that Charles Darwin was de­vis­ing and then pub­lish­ing his the­ory of evo­lu­tion.

So there’s kind of a weird mar­riage be­tween evo­lu­tion as progress and tech­no­log­i­cal change as progress, and what drops out of that are all other, ear­lier no­tions of progress, which chiefly in­volve moral progress — like, things are get­ting bet­ter be­cause peo­ple are be­com­ing bet­ter, or things are get­ting bet­ter be­cause peo­ple are more free.

There are a lot of other ways we might think about progress, but what dom­i­nates to­day is this 19th-century no­tion of tech­no­log­i­cal progress as the only kind of progress, and there­fore all tech­no­log­i­cal change is progress, as op­posed to — ob­jec­tively, it’s only progress if things are get­ting bet­ter.

But in any event, that con­flu­ence in the 19th cen­tury of the idea of tech­no­log­i­cal progress and the idea of evo­lu­tion meant that peo­ple who were think­ing clearly were like, “Well, if the ma­chines keep get­ting bet­ter and faster and able to do more things — not just la­bor, but maybe talk or think or move around — what if they evolve to be­come bet­ter at every­thing than we are? Not just bet­ter at run­ning a loom, not just faster at mov­ing through time and space like a rail­road car?” And with that grew an in­cred­i­ble anx­i­ety that found form in sci­ence fic­tion again and again and again and again and again.

My fa­vorite one of these sto­ries was pub­lished, I think, in 1909 by E. M. Forster, right around when he was writ­ing “A Room with a View.” He wrote this story called “The Machine Stops,” which could be sub­ti­tled, “The Room Without a View.” He imag­ines a near fu­ture in which every­body just lives in these rooms, these lit­tle cells. You never see other peo­ple be­cause every­thing you need comes right to your room. It’s like DoorDash, your food is de­liv­ered, you have a screen where you can com­mu­ni­cate with other peo­ple. All your needs are met.

The thing that peo­ple fear most is the nat­ural world. No one wants to ever see the sun, it’s a lit­tle “Matrix”-y, and they all wor­ship the ma­chine that or­ga­nizes their lives and brings to them in their cubby-like rooms all the things that they need. The story is about, “Humans have be­come es­sen­tially slaves of the ma­chine, which is stronger, more pow­er­ful, and has more ca­pac­ity than hu­mans do, and hu­mans have lost what ca­pac­ity they had.” And then the cli­max of the story is when the ma­chine stops.

If read­ers were to go look at that story, it feels like it could be writ­ten to­day, ex­cept that it’s less sci­ence fic­tion-y to­day than it is the di­ary of a very un­happy YouTuber.

You con­nect that thread to some of the folks run­ning com­pa­nies and ar­guably run­ning as­pects of our gov­ern­ment to­day, like Elon Musk. Essentially, you sug­gest that they’re very bad sci­ence fic­tion read­ers. They read a lot of warn­ing sto­ries, or at least am­biva­lent sto­ries, as if they were man­u­als for the fu­ture.

This is some­thing I wres­tle with a reader of sci­ence fic­tion — some­one who loves Isaac Asimov, for ex­am­ple. I think it’s true that when Musk or Altman is just un­am­bigu­ously be­ing like, “Yes, this story is a tem­plate for what I should do with my com­pany,” that’s bonkers. But there is [also] this tech­no­cratic lib­er­tar­ian thread in sci­ence fic­tion that they are pick­ing up on. It’s not some­thing that they’re mak­ing up out of whole cloth, right?

Although weirdly, that’s Heinlein. That’s not Asimov, that’s not Douglas Adams.

Sure, there is that thread in sci­ence fic­tion. I don’t know, I guess [Jeff] Bezos is a big Robert Heinlein fan. You could say, “Okay, that lines up well. They’re read­ing it lit­er­ally, but at least they’re get­ting the po­lit­i­cal mes­sage that any ra­tio­nal per­son could find within that lit­er­ary work.”

But what’s funny about Musk is, the stuff he likes ac­tu­ally com­pletely de­feats and de­fies all of his po­lit­i­cal be­liefs.

You also say, re­peat­edly, that the ar­ti­fi­cial state in its cur­rent form is in­com­plete and doomed to fail­ure. Why is it doomed to fail­ure?

This is some­thing that’s fore­seen in all the sci­ence fic­tion that I dis­cuss.

It’s not an Asimov story, but it’s one of Asimov’s [favorite] sto­ries from his boy­hood [“The Man Who Awoke” by Laurence Manning] about a fu­ture in which the foresters have de­feated the wasters. […] The war that the fu­ture hu­mans had was be­tween the wasters, who just fig­ured you could just use every­thing up and waste it, and the foresters, who re­ally be­lieved in — we would call re­for­esta­tion and rewil­d­ing.

That’s gen­er­ally the ten­sion in these sto­ries. It’s be­tween the ar­ti­fi­cial state and the nat­ural world. To erect an ar­ti­fi­cial state and rule hu­mans within it, you must alien­ate them from the nat­ural world be­cause you are de­stroy­ing it. The ar­ti­fi­cial state will de­stroy the nat­ural world, and yet it needs the re­sources of the nat­ural world to run.

So, it is doomed in the sense that there is not a pos­si­bil­ity that the nat­ural world, a hab­it­able planet — hab­it­able for hu­mans — can sur­vive the full con­struc­tion and re­liance on the de­vices of the ar­ti­fi­cial state. That’s how the sci­ence fic­tion works, in any event.

Like you said, you’re a his­to­rian, not a politi­cian or a fu­tur­ist. But what do you think the de­feat of the ar­ti­fi­cial state looks like? Is it ba­si­cally just dis­man­tling all these com­pa­nies, tear­ing down the data cen­ters? Or is there a fu­ture that’s more about bring­ing it un­der con­trol?

I mean, I don’t have a play­book here, ex­cept for the rec­om­men­da­tion that we live in a democ­racy where de­ci­sions have to be made in con­sul­ta­tion with the gov­erned, and these de­ci­sions are not pop­u­lar.

You see this in all the lit­tle data cen­ter crises, town to town, county to county, state to state —  which are partly a con­se­quence of the de­cline of lo­cal news­pa­pers and the de­struc­tion of jour­nal­ism that has been one of the many con­se­quences of so­cial me­dia, and in the case of [Mark] Zuckerberg, I think a some­what in­ten­tional con­se­quence.

What you see is a lot of peo­ple show up at these town meet­ings and say, “We don’t even have hous­ing. We don’t have health­care. We don’t have jobs. Who said we’re build­ing this data cen­ter? I need to know a lot more about it. I need to know what its en­ergy costs are go­ing to be. Tell me about the wa­ter con­sump­tion. Are there go­ing to be jobs? Are the jobs go­ing to be long last­ing? Are they just go­ing to be for six months? What’s go­ing to hap­pen to the egrets that live in this area?” Whatever it is that peo­ple want to know.

More and more, you see peo­ple are — like in the Salt Lake ex­am­ple, where well over 70% of the peo­ple re­ally were op­posed to this data cen­ter, and their rep­re­sen­ta­tives sup­ported it. That’s not rep­re­sent­ing the peo­ple. I think there are po­lit­i­cal costs, and we’ll be­gin to see those at elec­tions.

Or maybe we won’t. Enough of de­mo­c­ra­tic func­tion­ing has to be in­tact for peo­ple to ac­tu­ally be able to re­spond to malfea­sance on the part of their rep­re­sen­ta­tives.

Part of your think­ing about [the ar­ti­fi­cial state] started with this great piece you wrote more than a decade ago for The New Yorker, about Clayton Christensen, cri­tiquing his idea of the in­no­va­tor’s dilemma and dis­rup­tive in­no­va­tion — which is very closely as­so­ci­ated with TechCrunch, be­cause we have a big con­fer­ence called Disrupt.

Ten years on, how do you feel about that idea of dis­rup­tive in­no­va­tion?

I stand by every­thing in that piece. [At the time, Lepore wrote, “Disruptive in­no­va­tion is a the­ory about why busi­nesses fail. It’s not more than that. It does­n’t ex­plain change. It’s not a law of na­ture.” Christensen re­sponded that Lepore broke “all the rules of schol­ar­ship that she ac­cused me of break­ing.”]

I reread it last sum­mer when I was work­ing on this book. What I would say here is, try­ing to be a peace­able hu­man be­ing, I think it re­ally is a prob­lem that his­to­ri­ans have not en­gaged with these ideas. One of the rea­sons I wrote that ar­ti­cle about dis­rup­tive in­no­va­tion — which was not an idea of mine, it was an as­sign­ment […] — was be­cause I just felt like, “Disruptive in­no­va­tion is a the­ory of his­tory. It’s a the­ory of his­tor­i­cal change, and it’s based on ev­i­dence from the archives.” And I just thought, as a his­to­rian, it makes no sense. His use of ev­i­dence is com­pletely un­ac­cept­able by any proper un­der­stand­ing of his­tor­i­cal method. Its ar­gu­ment is in con­ver­sa­tion with no mean­ing­ful un­der­stand­ing of how change hap­pens.

So I went and re­did the re­search, and it just did not stand up at all. I felt like I had to write it. And I wish that I felt like there were more en­gage­ment, in the years since, of aca­d­e­mic his­to­ri­ans think­ing through the na­ture of change — which are ques­tions that gen­uinely and au­then­ti­cally in­ter­est peo­ple who are in­volved in de­vel­op­ing new tech­nolo­gies.

People re­ally want to think [about], “What is this? What am I do­ing? What are go­ing to be the con­se­quences? Is there any­thing I could learn from his­tory? What hap­pened when the au­to­mo­bile re­placed the horse? What hap­pened to the law? How did we end up with dri­ver’s li­censes? How did we end up with traf­fic law? We did­n’t have traf­fic rules be­fore the au­to­mo­bile. We did­n’t have cer­tain kinds of in­sur­ance sys­tems. We did­n’t have dri­ver’s tests. How did those things emerge? How did [we de­velop] those guardrails on a tech­nol­ogy that was tremen­dously ex­cit­ing, im­proved peo­ple’s lives in many many ways, ut­terly changed the land­scape, rev­o­lu­tion­ized tort law? Maybe I should think about that.”

I just wish that his­to­ri­ans were more in con­ver­sa­tion with tech­nol­o­gists over these years, and with en­tre­pre­neurs. Not just be­cause we can stand around and say, “You know, I have a lec­ture to of­fer you on his­tory,” but I think there’s a real con­ver­sa­tion to be had.

All of which is just to say, thanks for hav­ing me on.

When you pur­chase through links in our ar­ti­cles, we may earn a small com­mis­sion. This does­n’t af­fect our ed­i­to­r­ial in­de­pen­dence.

Tom Stanton has spent years chas­ing a num­ber that grav­ity it­self seemed to for­bid. On a quiet field some­where in the UK, a 40-kilogram mass dropped a short dis­tance, spun a car­bon-fiber arm past 2,300 rev­o­lu­tions per minute, and sent a 4-gram pro­jec­tile into the air at 776 miles per hour. That is nine miles per hour past the speed of sound. For the first time, a purely grav­ity-pow­ered tre­buchet crossed the bar­rier.

Medieval en­gi­neers cre­ated these ma­chines to fling huge stones at cas­tle walls. The ba­sic idea is to hoist a large heavy weight, let it fall down, and then use the lever and sling to redi­rect that en­ergy into a much lighter pro­jec­tile. Unfortunately, physics gets in the way. A weight in free fall ac­cel­er­ates at a max­i­mum of 9.81 me­ters per sec­ond squared; drop one from 2 me­ters and it smacks into the ground at around 6 me­ters per sec­ond. No mat­ter how heavy you make the weight, the speed re­mains con­stant, and a typ­i­cal arm is like a car with a bike chain stuck in first gear, with lots of torque at first but just enough to get it mov­ing by the end.

Stanton de­vised a cre­ative en­gi­neer­ing so­lu­tion to the speed prob­lem. The coun­ter­weight is sus­pended from a pul­ley sys­tem with a 3:1 ra­tio, as a large di­am­e­ter at the be­gin­ning pro­pels the arm with plenty of force, and as it winds down, the smaller di­am­e­ter end pro­vides a sig­nif­i­cant jump in ro­ta­tional speed. In the end, the drum was re­designed so that weight could be lifted up to 1.9 me­ters, stor­ing more power in the sys­tem than pre­vi­ous it­er­a­tions.

The arm has to be both in­cred­i­bly light and su­per rigid. Carbon fiber was re­ally the only op­tion be­cause it is­n’t heavy enough to weigh down the en­tire sys­tem while yet be­ing able to with­stand pun­ish­ment. Stanton used his home­made CNC mill to carve the piece, keep­ing the dust un­der con­trol with a HEPA vac­uum and a good spray of wa­ter. It weighs only 116 grams. Stress tests and nu­mer­ous de­fec­tive 3D printed pro­to­types re­vealed that it would buckle un­der the sling’s pres­sure, so he mod­i­fied the de­sign to com­pen­sate, re­moved ma­te­r­ial from the ten­sioned side, tough­ened up the op­po­site side, and added some ex­tra brac­ing for good mea­sure. Aluminum hubs con­nect the arm to a short counter-arm, keep­ing the spin­ning bit as bal­anced as pos­si­ble.

The pro­jec­tile now starts near the axle, wrapped firmly in a sling that un­wraps at just the right mo­ment. The en­tire sys­tem is me­chan­i­cally re­leased, us­ing a spring-loaded catch that opens af­ter a cer­tain num­ber of rope ro­ta­tions. The re­lease win­dow is only a few mil­lisec­onds long, which is plenty of time to com­plete the task. Early it­er­a­tions failed un­der stress, but the fi­nal pin-and-loop struc­ture held up.

Testing was care­fully in­creased, and he be­gan with a 10 kg weight. The mod­i­fied aero­dy­namic arm reached a re­spectable 1248 rpm and launched at 394 mph with an in­cred­i­ble 43.7% ef­fi­ciency. Twenty and thirty kilo­grams passed thru with­out a hitch. 40 kg, on the other hand, sped the arm to 2336 rpm and the mis­sile to a blis­ter­ing 716 mph, falling only 51 mph short of the magic bar­rier. The ma­chine even­tu­ally snapped, and the clasp shat­tered be­neath the weight of 50 kg. Stanton re­duced the pro­jec­tile weight to ap­prox­i­mately 4 grams, changed the drum ta­per once more, and re­turned to the test area with the 40 kilo­gram setup.

The fi­nal test is ob­vi­ously the most im­por­tant, since the arm spun up to 2342 rpm. Tip speed reached a stag­ger­ing 274 mph. The high-speed footage was truly eye-open­ing, as the mis­sile trav­eled 1.94 me­ters in 5.6 mil­lisec­onds. Crunching those sta­tis­tics yields 346.4 me­ters per sec­ond, or a more than re­spectable 776 mph. To top it all off, there was a loud crack fol­lowed by a pleas­ant echo, con­firm­ing the sonic boom to every­one within earshot. Not one as­pect of the ma­chine came close to fail­ing, or so we’d like to think.

Anthony “Bingy” Arillotta waited years to be­come a made man in the Genovese crime fam­ily, and when at last the call came in August 2003, he fol­lowed di­rec­tions to the let­ter. According to sworn tes­ti­mony, Arillotta was sum­moned to a steak house in the Bronx, where he was made to hand over his cell­phone, beeper, and jew­elry be­fore be­ing dri­ven to an apart­ment build­ing. When he got there, he was taken to a small bath­room and strip-searched for elec­tronic de­vices. For his big meet­ing with the boss, he was given a bathrobe to wear.

Until re­cently, only spies and crim­i­nals had to worry this ob­ses­sively about their pri­vate state­ments be­ing picked up by elec­tronic equip­ment. But soon, the av­er­age per­son might need to de­ploy sur­veil­lance coun­ter­mea­sures. The next time you con­duct a del­i­cate bit of of­fice diplo­macy or share a ro­man­tic or fi­nan­cial se­cret with a friend over drinks, a sen­sor built into some­one’s glasses, neck­lace, or lapel pin might be watch­ing you and lis­ten­ing.

In March, the tech start-up Deveillance an­nounced the de­vel­op­ment of Spectre I, a hockey-puck-shaped de­vice that pur­ports to pre­vent oth­ers from record­ing you (no strip search re­quired). The com­pany was founded by Aida Baradari, a re­cent col­lege grad­u­ate who was wor­ried by the surge in peo­ple wear­ing AI-enabled recorders. These wear­ables can be used as a silent note­taker, a per­sonal as­sis­tant, or even a ther­a­pist of sorts. That tech­nol­ogy is­n’t yet main­stream, but it may be soon. Apple—the com­pany with the largest per­sonal-tech ecosys­tem in the world—is ru­mored to be de­vel­op­ing an AI pin or pen­dant that would serve as an iPhone’s con­stant eyes and ears; many other prod­ucts of this type are on the way. AI ac­ces­sories could one day be as wide­spread as AirPods.

New sur­veil­lance tech­nolo­gies tend to breed new coun­ter­mea­sures, which lead, in turn, to more so­phis­ti­cated sur­veil­lance. During the Second World War, af­ter Germany op­er­a­tional­ized radar, the Royal Air Force be­gan drop­ping thin strips of met­al­lized pa­per cut to a spe­cific size that res­onated with the radar, swamp­ing German screens with phan­tom echoes that were in­dis­tin­guish­able from real air­craft. Some his­to­ri­ans have ar­gued that the en­su­ing radar arms race was more con­se­quen­tial to the war’s out­come than the Manhattan Project.

For decades, crude jam­mers have been sold to peo­ple who hope to avoid be­ing recorded. Early ver­sions blasted loud, un­pleas­ant white noise to con­ceal voices. More re­cently, com­pa­nies have made mod­els that emit a steady stream of ul­tra­sonic sound at in­audi­ble fre­quen­cies, ex­ploit­ing a quirk of mi­cro­phone hard­ware that con­verts those high fre­quen­cies into noise. In 2020, a team at the University of Chicago led by Yuxin Chen re­ported that it had mounted 23 ul­tra­sonic trans­duc­ers on a sin­gle bracelet, such that jam­ming sig­nals could be sent in all di­rec­tions in­stead of be­ing fo­cused on a sin­gle tar­get.

Read: The most re­viled tech CEO in New York con­fronts his haters

But even high-tech jam­mers have a hard time fend­ing off to­day’s AI wear­ables. The most ad­vanced pins, pen­dants, and glasses use speech-re­cov­ery al­go­rithms to strip away un­wanted noise, whether it orig­i­nates from every­day sources—such as the clink­ing of glasses in a crowded bar—or from an ul­tra­sonic jam­mer. This task the al­go­rithms per­form is quite dif­fi­cult: In that crowded bar, a mi­cro­phone on a per­son’s lapel will in­ter­cept sound vi­bra­tions from many dif­fer­ent sources at once. It will pick up a bar­tender call­ing out a drink or­der, mu­sic em­a­nat­ing from a speaker, bursts of laugh­ter com­ing from nearby ta­bles—and all of these sounds ric­o­chet off of walls and other ob­jects, cre­at­ing yet more noise. The hu­man body solves this “cocktail party prob­lem” with­out us notic­ing: Our ears serve as dual mi­cro­phones, and our brain can use the tim­ing and in­ten­sity dif­fer­ences be­tween them, along with lay­ered pro­cess­ing in the au­di­tory cor­tex, to iso­late the voice of a per­son who is sit­ting across from us.

DeLiang Wang, a com­puter sci­en­tist at Ohio State University, has spent decades train­ing neural net­works to ac­com­plish that same goal, for the pur­pose of im­prov­ing hear­ing aids. By feed­ing the net­works hun­dreds of hours of recorded hu­man voices, he has taught them to rec­og­nize the fre­quen­cies and rhythms of speech. The mod­els build an in­ter­nal rep­re­sen­ta­tion of “speech-ness,” and when they en­counter a noisy record­ing, they fo­cus on the parts that match the pat­terns they have learned and then sup­press every­thing else. The most ad­vanced tech­nolo­gies can now in­fer miss­ing syl­la­bles in the way that a reader fills in a redacted word from con­text, al­low­ing them to re­con­struct speech that was­n’t cleanly cap­tured in the first place.

Big tech com­pa­nies are try­ing to do this too. Microsoft has been run­ning an an­nual Deep Noise Suppression Challenge since 2020 to ad­vance the field. (Their in-house team is try­ing to make Teams meet­ings less ex­cru­ci­at­ing.) Other com­pa­nies are work­ing on noise can­cel­la­tion for cell­phone calls and pod­cast soft­ware. This sort of re­search is meant to im­prove the lives of nor­mal users of tech­nol­ogy—as­sum­ing that we pod­cast lis­ten­ers count as nor­mal—but every ad­vance in de-nois­ing can also be used to help an AI as­sis­tant re­cover speech from a jammed record­ing.

Defeating these al­go­rithms may re­quire a dif­fer­ent coun­ter­sur­veil­lance ap­proach al­to­gether. Finn Brunton, a his­to­rian at UC Davis and the co-au­thor of Obfuscation: A User’s Guide for Privacy and Protest, told me that one of the best ways is to iden­tify the data that a de­vice is try­ing to col­lect, and then sup­ply it with a junk ver­sion. The Berlin-based artist Adam Harvey used this strat­egy when he de­vel­oped makeup and cloth­ing that frus­trate fa­cial-recog­ni­tion al­go­rithms. Daniel Howe and Helen Nissenbaum did some­thing sim­i­lar with a browser plug-in called TrackMeNot: Rather than con­ceal­ing a user’s Google searches, the ex­ten­sion con­tin­u­ally runs its own ran­dom­ized de­coy queries in the back­ground, so that what­ever a user ac­tu­ally searched for be­comes lost in a sea of false leads.

People have tried this tech­nique in the realm of au­dio too. Woodrow Hartzog, a law pro­fes­sor at Boston University who stud­ies pri­vacy and sur­veil­lance, told me that early in his le­gal ca­reer, he worked with de­fense at­tor­neys who wor­ried that their jail­house con­ver­sa­tions with clients would be recorded. To fight back, they played “babble tapes”—au­dio files lay­ered with 40 tracks of voices in dif­fer­ent ac­cents—in the back­ground.

In 2023, a team led by Ming Gao, now a re­searcher at Nanjing University, used hu­man voices to de­feat speech-re­cov­ery al­go­rithms in a dif­fer­ent way. Its jam­mer, called MicFrozen, is worn by a speaker who does­n’t want to be recorded. It lis­tens as they talk and then gen­er­ates a real-time stream of ul­tra­sonic “anti-speech” tuned to the speak­er’s voice, much like the noise-can­cel­la­tion tech­nol­ogy in your head­phones. The de­vice then sends out an­other layer of coun­ter­feit speech-shaped sound to mis­lead any al­go­rithm that tries to re­con­struct what was lost.

Baradari, whose com­pany is work­ing on the Spectre I de­vice, would­n’t tell me ex­actly how her jam­mer’s sig­nals work, but she said that they, too, re­sem­ble speech. The launch video for Spectre I claims that the de­vice will also be able to de­tect the pres­ence of nearby mi­cro­phones. When I asked Baradari how it will do that, she clar­i­fied that her team is still “working on that part right now.”

However ef­fec­tive Spectre I turns out to be, it won’t be the end of the record­ing arms race. More ca­pa­ble AI mod­els may even­tu­ally de­ploy some new lis­ten­ing tricks of their own. They may by­pass recorded au­dio al­to­gether. In Stanley Kubrick’s 2001: A Space Odyssey, when two as­tro­nauts re­treat to a sound­proofed pod to dis­cuss dis­con­nect­ing HAL 9000, the ship’s com­puter sim­ply reads their lips through the port­hole. A wear­able pow­ered by a model that’s been trained on enough con­ver­sa­tion footage could, in prin­ci­ple, do the same. In the­ory, it could also stare at a glass of wa­ter be­tween two peo­ple and re­cover their speech from vi­bra­tions on the liq­uid’s sur­face.

AI wear­ables may al­ways have an edge over coun­ter­mea­sures. After all, they’re us­ing a tech­nol­ogy that is a prod­uct of the en­tire speech-pro­cess­ing in­dus­try, which takes in bil­lions of dol­lars in in­vest­ments—not just for AI as­sis­tants but also for hear­ing aids, smart speak­ers, and tele­con­fer­enc­ing tools. Meanwhile, only a few aca­d­e­mics and small com­pa­nies are de­fend­ing us from these tech­nolo­gies. “The thing about cat-and-mouse games is that we know how they usu­ally end up for the mouse,” Hartzog said. “And in this case, the cat in­cludes some of the most pow­er­ful cor­po­ra­tions to ever ex­ist.”

The Mafia knows what it’s like to be a mouse. By the time Arillotta, the as­pir­ing made man, was told to put on the bathrobe, crim­i­nal or­ga­ni­za­tions had been en­gaged in sur­veil­lance arms races of their own for decades. After law en­force­ment started bug­ging their phones, bosses would con­duct busi­ness in per­son. Sometimes, they’d use a safe house or a ve­hi­cle, but those could be bugged, too, and so sen­si­tive in­for­ma­tion might have been com­mu­ni­cated only dur­ing a walk-and-talk. Eventually, crime fam­i­lies turned to burner phones, and then de­vices with en­cryp­tion. But here, again, they fell prey to the cat.

In 2018, the FBI be­gan se­cretly run­ning Anom, its own en­crypted-phone com­pany. Through in­for­mants, it sold 12,000 de­vices with a spe­cial Anom mes­sag­ing app. Members of Mafia fam­i­lies, mo­tor­cy­cle gangs, and other crim­i­nal or­ga­ni­za­tions treated the phones as a sta­tus sym­bol, and used them to ne­go­ti­ate drug deals, laun­der money, and par­tic­i­pate in all man­ner of other il­le­gal ac­tiv­ity. But the se­cu­rity that they of­fered was a ruse: Every mes­sage that they sent was be­ing in­ter­cepted by the feds.

Taxi and am­bu­lance dri­vers are less likely than work­ers in al­most any other job to die of Alzheimer’s dis­ease. That was the sur­pris­ing re­sult of a 2024 study ex­am­in­ing the death cer­tifi­cates of nearly 9 mil­lion peo­ple in the U.S.

These find­ings stopped me in my tracks be­cause those two jobs rely on the same thing as my own work: maps.

I have spent more than two decades star­ing at maps. Not pa­per maps on a wall, but dig­i­tal ones with mul­ti­ple lay­ers: flood bound­aries draped over cen­sus blocks, car crash hot spots plot­ted against road geom­e­try, and satel­lite read­ings of rain­fall stitched across river basins. Much of my work as a civil and en­vi­ron­men­tal en­gi­neer is done through GIS — that is, ge­o­graphic in­for­ma­tion sys­tems. Engineers like me hold sev­eral spa­tial re­la­tion­ships in their minds at once, rea­son­ing about where things sit rel­a­tive to one an­other across scales rang­ing from a city block to a whole wa­ter­shed.

I al­ways as­sumed that spa­tial rea­son­ing across map lay­ers was purely pro­fes­sional. But that study on taxi and am­bu­lance dri­vers made me won­der whether all that men­tal work might be do­ing some­thing good to the brain.

Taxi dri­ver brains

Of the 9 mil­lion death cer­tifi­cates from January 2020 to December 2022 that re­searchers ex­am­ined, taxi and am­bu­lance dri­vers had the low­est risk of dy­ing from Alzheimer’s dis­ease out of 443 oc­cu­pa­tions. After ad­just­ing for age, sex, race, eth­nic­ity and ed­u­ca­tion, roughly 1 in 100 taxi and am­bu­lance dri­vers died of Alzheimer’s, com­pared with 1 in 60 peo­ple over­all.

This pat­tern did not ex­tend to other dri­ving jobs. The re­searchers con­cluded that the key to re­duc­ing the risk of Alzheimer’s was not dri­ving it­self but con­tin­u­ous real-time nav­i­ga­tion: the con­stant work of lo­cat­ing your­self in space, track­ing a des­ti­na­tion and up­dat­ing a men­tal map as con­di­tions change. Drivers whose jobs re­lied on fixed or pre­de­ter­mined routes, like bus dri­vers and air­craft pi­lots, did­n’t seem to ex­pe­ri­ence a sim­i­lar ad­van­tage.

Researchers be­lieve the as­so­ci­a­tion be­tween nav­i­ga­tion-heavy work and lower Alzheimer’s risk cen­ters on the hip­pocam­pus, a part of the brain that gov­erns mem­ory and spa­tial nav­i­ga­tion. It’s one of the first brain re­gions that Alzheimer’s dam­ages: Problems with spa­tial nav­i­ga­tion and ori­en­ta­tion are among the ear­li­est signs of the dis­ease, some­times sur­fac­ing be­fore ob­vi­ous mem­ory loss.

In one land­mark 2000 study, neu­ro­sci­en­tists com­pared the brains of li­censed London taxi dri­vers with those of peo­ple who did not drive cabs. Their find­ings pro­vided the first ev­i­dence via struc­tural imag­ing that re­gions of the adult brain can mea­sur­ably change un­der sus­tained nav­i­ga­tional de­mand. To earn a li­cense, London cab­bies must mem­o­rize more than 25,000 streets within a 6-mile ra­dius of Charing Cross, a chal­lenge known as “The Knowledge” that takes three to four years.

The re­searchers found that London taxi dri­vers had mea­sur­ably more gray mat­ter in the pos­te­rior hip­pocam­pus, a brain area tied to stor­ing large-scale spa­tial maps. That vol­ume tracked with ex­pe­ri­ence: The longer some­one had dri­ven, the larger that part of the brain. The change was built through prac­tice, not in­her­ited. While peo­ple who are good at nav­i­ga­tion might grav­i­tate to this kind of job, the job it­self does have an im­pact on the brain.

Together, these two stud­ies make a co­her­ent case: Work that in­ten­sively ex­er­cises the hip­pocam­pus may re­shape it, and that re­shap­ing may pro­tect against one of the most feared dis­eases of ag­ing.

Where map spe­cial­ists fit in

Cartographers, ur­ban plan­ners and geospa­tial an­a­lysts spend their work­ing days in sus­tained spa­tial rea­son­ing. A ge­o­graphic in­for­ma­tion sys­tem spe­cial­ist might use a com­puter to over­lap pop­u­la­tion data on flood ex­po­sure maps to find who is at risk, or read satel­lite im­agery to map land cover af­ter a wild­fire. Researchers jug­gle sev­eral lay­ers of data, co­or­di­nate sys­tems and scales at once.

Does spa­tial rea­son­ing through a screen en­gage the hip­pocam­pus the way mov­ing through a real city does?

While a taxi dri­ver nav­i­gates from in­side the scene at street level, GIS re­searchers pic­ture space from above as a map — what cog­ni­tive sci­en­tists call al­lo­cen­tric rea­son­ing. But these two per­spec­tives over­lap in the brain: The hip­pocam­pus also builds maps from out­side view­points, not just from a nav­i­ga­tor’s own po­si­tion.

Research on cog­ni­tive maps points to­ward the same con­clu­sion as the study on taxi dri­vers. In 2023, re­searchers ran a ma­chine learn­ing model on more than 22,500 peo­ple in a na­tional dataset and were able to pre­dict which ZIP codes had higher rates of Alzheimer’s with 84% ac­cu­racy based on how com­plex the en­vi­ron­ment was. Those liv­ing in spa­tially com­plex sur­round­ings — the kind that force ac­tive map build­ing, such as dense street net­works with nu­mer­ous in­ter­sec­tions, di­verse points of in­ter­est and land­marks, and mul­ti­ple path op­tions — were less likely to de­velop Alzheimer’s.

A fol­low-up study tied geospa­tial com­plex­ity in one’s en­vi­ron­ment to greater vol­ume in the brain’s spa­tial nav­i­ga­tion re­gions. The re­searchers hy­poth­e­sized that rou­tinely build­ing cog­ni­tive maps ex­er­cises the very cir­cuitry that Alzheimer’s at­tacks first. Repeatedly en­gag­ing the cog­ni­tive sys­tems in­volved in spa­tial nav­i­ga­tion could help de­lay symp­toms of dis­ease.

However, these two stud­ies fo­cus on where peo­ple live, not the work peo­ple do. Whether spa­tial rea­son­ing through a screen ex­er­cises the same cir­cuitry as real-life city nav­i­ga­tion re­mains untested.

Protecting your brain

The im­pli­ca­tions of whether sus­tained spa­tial rea­son­ing pro­tects the brain reach be­yond map­mak­ers and cab dri­vers. Studies have re­peat­edly found a link be­tween men­tally com­plex oc­cu­pa­tions and de­layed cog­ni­tive de­cline and lower de­men­tia risk, even af­ter ac­count­ing for ed­u­ca­tion.

Research on cog­ni­tive re­serve — the brain’s ca­pac­ity to con­tinue func­tion­ing de­spite dis­ease — can help ex­plain why two peo­ple with a sim­i­lar dis­ease bur­den can show markedly dif­fer­ent lev­els of cog­ni­tive im­pair­ment. If de­mand­ing spa­tial think­ing pro­tects the brain, then how so­ci­eties de­sign school­ing, pro­fes­sional train­ing and re­tire­ment all be­come ques­tions of brain health.

Spatial rea­son­ing can be trained, and train­ing op­por­tu­ni­ties are al­ready wide­spread. GIS and re­mote sens­ing in­struc­tion are avail­able through ge­og­ra­phy, en­gi­neer­ing, pub­lic health and en­vi­ron­men­tal sci­ence pro­grams world­wide.

If sus­tained en­gage­ment with spa­tial rea­son­ing can help the brain strengthen the cir­cuitry that Alzheimer’s at­tacks first, its value reaches well be­yond the tech­ni­cal skills it builds.

Bet 601

Duration 11 years (02011 – 02022)

STAKES $1,000

will go to Bletchly Park Trust if Keith wins, or The Internet Archive if Haughey wins.

Keith’s Argument

“Cool URIs don’t change” wrote Tim Berners-Lee in 01999, but link rot is the en­tropy of the web. The prob­a­bil­ity of a web doc­u­ment sur­viv­ing in its orig­i­nal lo­ca­tion de­creases greatly over time. I sus­pect that even a rel­a­tively short time pe­riod (eleven years) is too long for a re­source to sur­vive.

I would love to be proven wrong.

Haughey’s Argument

Though much of the web is ephemeral in na­ture, now that we have sur­passed the 20 year mark since the web was cre­ated and gone through sev­eral booms and busts, tech­nol­ogy and strate­gies have ma­tured to the point where keep­ing a site go­ing with a sta­ble URI sys­tem is within reach of any­one with mod­er­ate tech­no­log­i­cal knowl­edge. My old­est sites are go­ing on 13 years old at the time of this bet and the orig­i­nal URL scheme still func­tions via 301 redi­rects to a fi­nal for­mat we se­lected about six years ago.

Detailed Terms

On February 22nd, 2022 from 00:01 UTC un­til 23:59 UTC,entering the char­ac­ters http://​www.long­bets.org/​601 into the ad­dress bar of a web browser or com­mand line tool (like curl)ORus­ing a web browser to fol­low a hy­per­link that points to http://​www.long­bets.org/​601­MUS­Tre­turn an HTML doc­u­ment that still con­tains the fol­low­ing text: “The orig­i­nal URL for this pre­dic­tion (www.long­bets.org/​601) will no longer be avail­able in eleven years.”

A 301 redi­rect from www.long­bets.org/​601 to a dif­fer­ent URL con­tain­ing that text would also ful­fill those con­di­tions.

If those con­di­tions are met, Matt wins.

If those con­di­tions aren’t met, Jeremy wins.

Bet 601

Duration 11 years (02011 – 02022)

STAKES $1,000

will go to Bletchly Park Trust if Keith wins, or The Internet Archive if Haughey wins.

Keith’s Argument

“Cool URIs don’t change” wrote Tim Berners-Lee in 01999, but link rot is the en­tropy of the web. The prob­a­bil­ity of a web doc­u­ment sur­viv­ing in its orig­i­nal lo­ca­tion de­creases greatly over time. I sus­pect that even a rel­a­tively short time pe­riod (eleven years) is too long for a re­source to sur­vive.

I would love to be proven wrong.

Haughey’s Argument

Though much of the web is ephemeral in na­ture, now that we have sur­passed the 20 year mark since the web was cre­ated and gone through sev­eral booms and busts, tech­nol­ogy and strate­gies have ma­tured to the point where keep­ing a site go­ing with a sta­ble URI sys­tem is within reach of any­one with mod­er­ate tech­no­log­i­cal knowl­edge. My old­est sites are go­ing on 13 years old at the time of this bet and the orig­i­nal URL scheme still func­tions via 301 redi­rects to a fi­nal for­mat we se­lected about six years ago.

Detailed Terms

On February 22nd, 2022 from 00:01 UTC un­til 23:59 UTC,entering the char­ac­ters http://​www.long­bets.org/​601 into the ad­dress bar of a web browser or com­mand line tool (like curl)ORus­ing a web browser to fol­low a hy­per­link that points to http://​www.long­bets.org/​601­MUS­Tre­turn an HTML doc­u­ment that still con­tains the fol­low­ing text: “The orig­i­nal URL for this pre­dic­tion (www.long­bets.org/​601) will no longer be avail­able in eleven years.”

A 301 redi­rect from www.long­bets.org/​601 to a dif­fer­ent URL con­tain­ing that text would also ful­fill those con­di­tions.

If those con­di­tions are met, Matt wins.

If those con­di­tions aren’t met, Jeremy wins.