You’re prob­a­bly fa­mil­iar with the dead in­ter­net the­ory: most of what you en­counter on­line is now gen­er­ated by bots, for bots, with hu­mans re­duced to a shrink­ing au­di­ence for ma­chine-gen­er­ated noise. Last year, over half of new con­tent on the in­ter­net was AI-generated. The hu­mans are still there, scrolling, but the thing they’re scrolling through has be­come a per­for­mance staged by ma­chines for an au­di­ence that has­n’t yet re­al­ized the show is­n’t for them.

It’s ut­terly des­ic­cat­ing to log onto spaces seek­ing a live mind to joust and think with, and find a re­lent­less stream of slop. Promised an age of su­per­con­nec­tiv­ity, we’ve let our shared phys­i­cal spaces wither, only to find our promised dig­i­tal com­mons to be one large bill­board in­creas­ingly read and cre­ated by bots.

That’s bad enough. I want to talk about some­thing worse. Call it the dead econ­omy the­ory.

A word of wel­come to the folks who have ar­rived here from Hacker News and var­i­ous other places. Two quick com­ments, given that I’ve re­ceived many mes­sages and have seen many com­ments on HN on this. First, the text of this piece is en­tirely hu­man-gen­er­ated, in­clud­ing the in­fe­lic­i­tous phras­ings and pen­chant for two-dol­lar words. The AI-generated im­ages, which many of you hate, are an in­side joke with a friend. Had I known this piece was go­ing to get the trac­tion it has, I promise you I would have gone with nor­mal head­ers. But, to para­phrase Dostoevsky from the pro­logue to The Brothers K, yes, I agree that it is su­per­flu­ous, but it’s done, so let it stand. Thanks for read­ing.

The AI in­dus­try has a num­bers prob­lem.

OpenAI, Anthropic, Google DeepMind, Meta AI, Microsoft: the com­bined in­vest­ment in large-scale AI in­fra­struc­ture now runs into the hun­dreds of bil­lions of dol­lars, with pro­jec­tions into the tril­lions over the next decade. OpenAI alone has been val­ued at north of $800 bil­lion. Anthropic, which has yet to pro­duce a sin­gle year of profit, com­mands a val­u­a­tion in the same stratos­phere. These num­bers need an ad­dress­able mar­ket large enough to jus­tify them.

There is only one mar­ket that large: the global la­bor mar­ket.

As we’re get­ting ex­cited about dis­cov­er­ing how to use claude.md files in Cowork, the in­dus­try is pitch­ing a dif­fer­ent re­al­ity. Every in­vestor pre­sen­ta­tion of an AI agent “doing the work of ten an­a­lysts” is telling you the same thing: the prod­uct is la­bor re­place­ment. The gen­tler lan­guage (”copilot,” “assistant,” “augmentation”) is mar­ket­ing. The fi­nan­cial model un­der­neath re­quires the elim­i­na­tion of hu­man cost cen­ters at civ­i­liza­tional scale. If it does­n’t do that, these com­pa­nies are the most over­val­ued as­sets in the his­tory of cap­i­tal­ism. The peo­ple writ­ing the checks are not in the habit of light­ing tril­lions of dol­lars on fire for a bet­ter au­to­com­plete and an end­less pro­lif­er­a­tion of longer and longer memos that no­body reads.

The AI com­pa­nies now con­struct their own bench­marks to prove the point. OpenAI’s GDPVal bench­mark mea­sures how well mod­els per­form across forty-four oc­cu­pa­tions, from real es­tate bro­ker to news an­a­lyst. The AI Productivity Index eval­u­ates mod­els against four spe­cific pro­fes­sional roles: in­vest­ment bank­ing as­so­ci­ate, man­age­ment con­sul­tant, Big Law as­so­ci­ate, pri­mary care physi­cian. These are tar­get­ing ret­i­cles aimed at the pro­fes­sional class. As an OpenAI eval­u­a­tion lead told the New York Times,1 mod­els now achieve “over an 80 per­cent win rate com­pared to hu­man pro­fes­sion­als” on tasks that, months ear­lier, no model could match. A for­mer banker on the re­search team “keeps be­ing shocked by how much of her old work the mod­els can do.”

So let’s take them at their word. Assume the tech­nol­ogy works as ad­ver­tised, that AI sys­tems be­come ca­pa­ble of per­form­ing most cog­ni­tive la­bor at a frac­tion of the cost of hu­man work­ers. What hap­pens next?

Follow the money through three turns.

Turn one: a com­pany li­censes AI to re­place a sig­nif­i­cant por­tion of its work­force. Costs drop. Margins ex­pand. The stock price goes up. Everyone on the earn­ings call is happy. When Block’s Jack Dorsey laid off nearly half his work­force in March, cit­ing AI cod­ing agents, in­vestors re­sponded with a twenty-five per­cent stock price surge in af­ter-hours trad­ing. The mar­ket re­warded the elim­i­na­tion of hu­man la­bor with an im­me­di­ate, mas­sive trans­fer of value to share­hold­ers.

Turn two: the re­placed work­ers stop earn­ing in­come. They cut spend­ing. The busi­nesses they used to pa­tron­ize see rev­enue de­cline. Some of those busi­nesses also adopt AI to cut costs, com­pound­ing the dis­place­ment. Consumer de­mand con­tracts across the econ­omy.

Turn three: the com­pany that fired its work­ers to save money dis­cov­ers that its cus­tomers were, in ag­gre­gate, other com­pa­nies’ work­ers. Revenue growth stalls. The AI sub­scrip­tion that was sup­posed to be an in­vest­ment in ef­fi­ciency turns out to be a con­tri­bu­tion to the de­struc­tion of its own mar­ket.

Economists Brett Hemenway Falk and Gerry Tsoukalas at Wharton have re­cently de­scribed this dy­namic in a pa­per they aptly ti­tled, “The AI Layoff Trap.” In com­pet­i­tive mar­kets, an au­tomat­ing firm cap­tures the full cost sav­ings from re­plac­ing work­ers but bears only a frac­tion of the re­sult­ing de­mand de­struc­tion. In a mar­ket with twenty com­peti­tors, each firm feels one-twen­ti­eth of the de­mand it de­stroys. The rest falls on ri­vals. This cre­ates a pris­on­ers’ dilemma: every firm ra­tio­nally au­to­mates be­yond the so­cially op­ti­mal level, be­cause the in­di­vid­ual in­cen­tive to cut la­bor costs al­ways out­weighs the dif­fuse, shared con­se­quence of elim­i­nat­ing con­sumer spend­ing. Better AI makes this worse. Improved pro­duc­tiv­ity widens the profit gap from au­tomat­ing faster than your com­peti­tors, in­ten­si­fy­ing the arms race to­ward col­lec­tive ruin.

Sometimes the lay­offs hap­pen be­fore ex­ec­u­tives even know whether AI will do the job. Zoë Hitzig, an econ­o­mist who pre­vi­ously worked at OpenAI, told the Times: “When chief ex­ec­u­tives are say­ing they’re cut­ting jobs be­cause of A.I., other peo­ple feel like they have to too. That dy­namic could make the changes hap­pen sooner than ef­fi­ciency would dic­tate.” Herd be­hav­ior dressed in the lan­guage of in­no­va­tion.

Henry Ford un­der­stood, per­haps apoc­ryphally but cor­rectly in prin­ci­ple, that his work­ers needed to earn enough to buy his cars. The AI econ­omy is elim­i­nat­ing the work­ers and ex­pect­ing the cars to keep sell­ing, ex­cept that soft­ware has near-zero mar­ginal cost, so the en­tire value propo­si­tion is the elim­i­na­tion of the hu­man cost cen­ter. The prod­uct is the re­moval of the cus­tomer base.

The op­ti­mists will tell you this is just pro­duc­tiv­ity gains. The econ­omy has ab­sorbed au­toma­tion be­fore; agri­cul­tural em­ploy­ment col­lapsed from ninety per­cent of the American work­force to two per­cent and civ­i­liza­tion con­tin­ued. David Autor at MIT has shown that roughly sixty per­cent of to­day’s jobs did­n’t ex­ist in 1940. New tech­nolo­gies cre­ate new cat­e­gories of work. True. But there’s a dif­fer­ence be­tween an ob­ser­va­tion about the past and a law of na­ture, and the op­ti­mists con­sis­tently con­fuse the two. The agri­cul­tural tran­si­tion took a hun­dred and forty years. Carl Benedikt Frey at Oxford has doc­u­mented that the Industrial Revolution took sev­enty years be­fore wages and em­ploy­ment re­cov­ered for the work­ers it dis­placed. In the in­terim, wages stag­nated, the la­bor share of in­come col­lapsed, prof­its surged, in­equal­ity sky­rock­eted, and the po­lit­i­cal con­se­quences in­cluded the Chartist move­ment and wide­spread so­cial up­heaval. As Frey puts it: “Most econ­o­mists will ac­knowl­edge that tech­no­log­i­cal progress can cause some ad­just­ment prob­lems in the short run. What is rarely noted is that the short run can be a life­time.”

Compare that time­line to the one the AI in­dus­try is work­ing on. Bharat Ramamurti, a for­mer deputy di­rec­tor of the National Economic Council, has drawn the par­al­lel to the China shock, the wave of man­u­fac­tur­ing job losses that re­shaped American pol­i­tics when pro­duc­tion moved over­seas. “The China shock un­folded over sev­eral years, whereas this could hap­pen over two years,” he told the Times. “These com­pa­nies have spent so much money de­vel­op­ing mod­els that there’s go­ing to be im­mense pres­sure on them to gen­er­ate rev­enue through quick adop­tion.”

Previous au­toma­tion re­placed spe­cific tasks within jobs. The power loom re­placed hand weav­ing, the spread­sheet re­placed man­ual cal­cu­la­tion, etc. In each case, the tech­nol­ogy was nar­row. General-purpose AI threat­ens cog­ni­tive la­bor com­pre­hen­sively, across every in­dus­try, si­mul­ta­ne­ously. The econ­o­mist Wassily Leontief saw this com­ing in 1983 when he com­pared hu­man la­bor to horses. The US horse pop­u­la­tion grew from nine mil­lion in 1840 to twenty-one mil­lion by 1900, seem­ingly im­mune to tech­no­log­i­cal change. Within sixty years of the in­ter­nal com­bus­tion en­gine, the pop­u­la­tion col­lapsed by eighty-eight per­cent. The horses weren’t re­tired out of mal­ice. They be­came un­eco­nom­i­cal to keep. Leontief’s point was that there is no eco­nomic law pre­vent­ing the same thing from hap­pen­ing to hu­mans.

Daron Acemoglu, who won the Nobel Prize in Economics in 2024 and is the most rig­or­ous voice on this topic, has found that be­tween 1987 and 2017, “the dis­place­ment ef­fect of new tech­nolo­gies far out­weighed their pro­duc­tiv­ity and re­in­state­ment ef­fects.” The new tasks did not ma­te­ri­al­ize fast enough to ab­sorb the dis­placed work­ers. His as­sess­ment of AI is more pointed still: firms are de­ploy­ing what he calls “excessive au­toma­tion,” us­ing AI to kill jobs with­out gen­er­at­ing sig­nif­i­cantly lower pro­duc­tion costs, while im­pos­ing sub­stan­tial so­cial costs. The tech­nol­ogy, in many ap­pli­ca­tions, is­n’t good enough to jus­tify the dis­place­ment it causes. Automation for the sake of the stock price, not for gen­uine pro­duc­tiv­ity.

Who is the cus­tomer when the cus­tomer is the thing you’ve elim­i­nated?

An econ­omy that does­n’t need hu­man la­bor is a po­lit­i­cal cri­sis of a kind de­mo­c­ra­tic sys­tems have never faced.

Democratic gov­er­nance rests on a bar­gain so old we’ve for­got­ten it’s a bar­gain at all. The gov­erned have some­thing the gov­er­nors need: la­bor, tax rev­enue, mil­i­tary ser­vice, con­sumer spend­ing. This de­pen­dency is the source of de­mo­c­ra­tic lever­age. The whole sys­tem func­tions be­cause power is dis­trib­uted, and it’s dis­trib­uted be­cause the peo­ple at the top need some­thing from the peo­ple at the bot­tom.

Remove la­bor from that equa­tion and watch what hap­pens.

When value is gen­er­ated by AI sys­tems owned by a hand­ful of cor­po­ra­tions al­ready world-class at tax op­ti­miza­tion, every fis­cal mech­a­nism of de­mo­c­ra­tic gov­er­nance starves at once. The tax base erodes. Collective bar­gain­ing be­comes ves­ti­gial (employers who don’t need em­ploy­ees don’t bar­gain with them). Consumer spend­ing, which de­pends on la­bor in­come, con­tracts. Piketty’s r > g, the en­gine of wealth con­cen­tra­tion, ac­cel­er­ates be­cause AI sev­ers the last link be­tween cap­i­tal ac­cu­mu­la­tion and the need for hu­man la­bor as a pro­duc­tion in­put. Without re­dis­tri­b­u­tion, as one analy­sis of the frame­work put it, “approximately every­thing will even­tu­ally be­long to those who are wealth­i­est when the tran­si­tion oc­curs.”

And the pub­lic funded the re­search that made it pos­si­ble. The trans­former ar­chi­tec­ture, large-scale train­ing meth­ods, semi­con­duc­tor ad­vances—all of these were pub­licly or quasi-pub­licly funded through uni­ver­si­ties, DARPA, and na­tional labs. The pub­lic bore the risk. Private com­pa­nies cap­tured the re­ward. This is blind­ingly com­mon across tech­no­log­i­cal ad­vance­ment in the last sixty years. As Mazzucato puts it, “AI risks be­com­ing an­other en­gine of rent ex­trac­tion rather than value cre­ation.” We sub­si­dized the rev­o­lu­tion and are now be­ing told to ac­cept dis­place­ment as the cost of progress that some­one else prof­its from.

You can still vote (and please do, for peo­ple who get this shit and are will­ing to try to stop it). But what you’re vot­ing over is the dis­po­si­tion of a shrink­ing pool of re­sources, while the real econ­omy op­er­ates in a par­al­lel sys­tem you in­creas­ingly have no in­put into.

The peo­ple build­ing these sys­tems un­der­stand this per­fectly. Dario Amodei, the CEO of Anthropic, has said it on the record: “The bal­ance of power of democ­racy is premised on the av­er­age per­son hav­ing lever­age through cre­at­ing eco­nomic value. If that’s not pre­sent, I think things be­come kind of scary.” The CEO of one of the three lead­ing AI com­pa­nies is telling you that the tech­nol­ogy he is build­ing will un­der­mine the ma­te­r­ial ba­sis of de­mo­c­ra­tic gov­er­nance. He sees the prob­lem. He is build­ing the thing that causes it. His com­pany has not en­dorsed a sin­gle piece of leg­is­la­tion to ad­dress it. When asked about pol­icy ad­vo­cacy, Anthropic co-founder Jack Clark de­scribed it as “the end of a very, very long chain of work.”

Peter Thiel wrote in 2009 that he no longer be­lieved free­dom and democ­racy were com­pat­i­ble. The logic runs: de­mo­c­ra­tic sys­tems pro­duce reg­u­la­tion, re­dis­tri­b­u­tion, and ac­count­abil­ity, all of which cre­ate fric­tion on the abil­ity of ex­cep­tional peo­ple to re­shape the world. If you be­lieve you’re build­ing the most trans­for­ma­tive tech­nol­ogy in hu­man his­tory, de­mo­c­ra­tic over­sight is an ob­sta­cle. Note: he is­n’t talk­ing about your or my free­dom. We don’t mat­ter.

This view has only gained ad­her­ents. The po­lit­i­cal spend­ing, the me­dia ac­qui­si­tions, the sov­er­eign-fund diplo­macy where Sam Altman tours the Middle East cut­ting com­pute deals with au­to­cratic gov­ern­ments: ra­tio­nal be­hav­ior for peo­ple who’ve con­cluded that de­mo­c­ra­tic gov­er­nance is a legacy in­sti­tu­tion to be routed around when it in­ter­feres.

Autocracies are bet­ter cus­tomers for this tech­nol­ogy than democ­ra­cies, which is pre­cisely why the broli­garchy has rapidly shifted its sup­port be­hind Trump and MAGA. A de­mo­c­ra­tic gov­ern­ment that de­ploys AI to re­place its work­force faces elec­toral con­se­quences. An au­thor­i­tar­ian gov­ern­ment faces no such con­straint and gains a sur­veil­lance and con­trol div­i­dend on top of the eco­nomic ef­fi­cien­cies. Saudi Arabia, the UAE, Singapore: vast cap­i­tal, cen­tral­ized de­ci­sion-mak­ing, no elec­torate to an­swer to, and an ac­tive in­ter­est in tech­nolo­gies of con­trol. This is one of the mo­ti­vat­ing fac­tors in the Valley’s latch­ing on to Trump: he and his cronies can be bought, and as im­por­tantly, they have no loy­alty to democ­racy. The eco­nomic in­cen­tives for AI com­pa­nies point to­ward the en­ti­ties with the fewest de­mo­c­ra­tic ac­count­abil­ity mech­a­nisms.

Leave a com­ment

Every pro­posed so­lu­tion to mass AI dis­place­ment treats it as a re­source dis­tri­b­u­tion prob­lem. Universal ba­sic in­come. Retraining pro­grams. The “leisure econ­omy.” The as­sump­tion is that if you send peo­ple checks, they’ll find mean­ing in hob­bies and com­mu­nity. They’ll paint. They’ll gar­den. They’ll fi­nally write that novel.

This is ahis­tor­i­cal bull­shit.

We don’t have to spec­u­late about what hap­pens when eco­nomic func­tion dis­ap­pears from com­mu­ni­ties. Anne Case and Angus Deaton’s re­search on “deaths of de­spair” tracks the ris­ing tide of sui­cide, drug over­dose, and al­co­holic liver dis­ease mor­tal­ity con­cen­trated in less-ed­u­cated, for­merly man­u­fac­tur­ing-de­pen­dent pop­u­la­tions. The mech­a­nism is­n’t just poverty. We lose any sense of eco­nomic pur­pose, and with that, so­cial sta­tus and a per­ceived fu­ture. Communities or­ga­nized around in­dus­tries that left, where what re­placed the jobs was opi­oids, do­mes­tic vi­o­lence, and a life ex­pectancy that dropped year over year in the rich­est coun­try on earth.

Molly Kinder at Brookings drew the con­nec­tion ex­plic­itly in Sun’s NYT piece: “Our econ­omy grew ex­tra­or­di­nar­ily and prices went down, but there were clear losers.” The AI com­pa­nies’ nar­ra­tives about abun­dance re­peat the same promises of glob­al­iza­tion. This time, the losers won’t be lim­ited to man­u­fac­tur­ing towns in the heart­land. “I’ve in­ter­viewed so many col­lege stu­dents who are su­per fear­ful about what the fu­ture means,” Kinder told the Times, “and their nar­ra­tive is ex­actly the same as those blue-col­lar guys in the heart­land.” The twenty-some­thing soft­ware en­gi­neer in San Francisco and the dis­placed fac­tory worker in Ohio are star­ing at the same ques­tion: what hap­pens when the mar­ket de­cides my skills are worth­less?

Guy Standing’s work on the “precariat” adds the struc­tural di­men­sion. The psy­cho­log­i­cal con­se­quences of per­ma­nent eco­nomic pre­car­ity cor­rode so­cial co­her­ence re­gard­less of whether the rent is paid. Four decades of ne­olib­eral pol­icy plus dig­i­tal ac­cel­er­a­tion have al­ready cre­ated this class. AI ac­cel­er­a­tion ex­pands it to in­clude the col­lege-ed­u­cated pro­fes­sion­als who thought they were safe.

Piketty, no con­ser­v­a­tive, has ar­gued that UBI fails to ad­dress root struc­tural prob­lems: “unequal ac­cess to ed­u­ca­tion and health, low-pay­ing and low-pro­duc­tiv­ity jobs, mal­func­tion­ing mar­kets, cor­rup­tion, and re­gres­sive tax sys­tems.” David Shor’s polling data bears this out from the other di­rec­tion: UBI is un­pop­u­lar with American vot­ers; a fed­eral jobs guar­an­tee has legs. People don’t want a check. They want work. They want pur­pose.

Anthropic’s own re­search has doc­u­mented some­thing worse than dis­place­ment: ac­tive deskilling. Junior en­gi­neers who re­lied on AI cod­ing agents did­n’t com­plete tasks much faster and un­der­stood their work less when quizzed af­ter­ward. The tech­nol­ogy is de­grad­ing the ex­per­tise of the next gen­er­a­tion of work­ers at the same time it’s com­pet­ing with them for their jobs. The re­train­ing ar­gu­ment as­sumes peo­ple can de­velop new skills to stay rel­e­vant. The ev­i­dence sug­gests the tools are pre­vent­ing them from de­vel­op­ing skills at all.

At the scale these com­pa­nies need to jus­tify their val­u­a­tions, you’re look­ing at so­cial in­sta­bil­ity that makes the cur­rent pop­ulist mo­ment look quaint. Tens of mil­lions of peo­ple, in their pro­duc­tive years, with no eco­nomic func­tion, no clear path to one, and a keen aware­ness that the peo­ple who did this to them are the rich­est hu­man be­ings who have ever lived. Stiglitz points out that AI will hit “routine white col­lar jobs,” the col­lege-ed­u­cated desk work that felt in­su­lated from man­u­fac­tur­ing dis­rup­tion. Accountants, an­a­lysts, ju­nior lawyers, ra­di­ol­o­gists, soft­ware de­vel­op­ers. The pro­fes­sional class that con­sti­tutes the back­bone of po­lit­i­cal sta­bil­ity in de­vel­oped democ­ra­cies.

The most hon­est thing you can say about vi­o­lence is that no­body wants it, but the con­di­tions that pro­duce it are be­ing en­gi­neered with ex­tra­or­di­nary ef­fi­ciency by peo­ple who have ap­par­ently never opened a his­tory book. It’s hap­pen­ing. In April, some­one tried to fire­bomb Sam Altman’s home. Another at­tacker tar­geted an Indianapolis city coun­cil­man who ap­proved a lo­cal data cen­ter pro­ject. Alex Karp, the CEO of Palantir, told a re­cent panel: “The biggest chal­lenge to A.I. in this coun­try is po­lit­i­cal un­rest. If I were sit­ting here in pri­vate with my peers, I’d be telling them the coun­try could blow up po­lit­i­cally and none of us are go­ing to make any money when the coun­try blows up.” Karp, to his credit, is say­ing this out loud. Most of his peers re­strict such ob­ser­va­tions to the dis­ap­pear­ing-mes­sage Signal chats where, as Jasmine Sun has re­ported, tech ex­ec­u­tives boast about the roles they plan to au­to­mate.

A strain of thought runs through Silicon Valley, from the Thiel Fellowship to the ra­tio­nal­ist blogs to the ef­fec­tive al­tru­ism move­ment, that treats its in­tel­lec­tual frame­work with the se­ri­ous­ness of re­ceived rev­e­la­tion. These are peo­ple who be­lieve they are op­er­at­ing at the fron­tier of hu­man thought.

They are op­er­at­ing at the level of a sec­ond-year phi­los­o­phy sur­vey, armed with enor­mous con­fi­dence and no aware­ness of the coun­ter­ar­gu­ments.

Start with Nietzsche, be­cause the Valley loves Nietzsche, or rather a ver­sion of Nietzsche that would have made the man lose his shit and go horse-hug­ging much faster than the syphilis. The Übermensch gets trot­ted out as jus­ti­fi­ca­tion for the ex­cep­tional founder, the vi­sion­ary who tran­scends con­ven­tional moral­ity be­cause he’s op­er­at­ing on a higher plane. Nietzsche was di­ag­nos­ing the cri­sis of mean­ing af­ter the col­lapse of meta­phys­i­cal cer­tainty, not writ­ing a man­age­ment phi­los­o­phy for peo­ple who got rich sell­ing ad­ver­tis­ing tech­nol­ogy. The Übermensch is about the in­di­vid­u­al’s re­la­tion­ship to the cre­ation of mean­ing in a god­less uni­verse. It has noth­ing to do with whether Peter Thiel should be ex­empt from de­mo­c­ra­tic ac­count­abil­ity. Nietzsche would have clas­si­fied these peo­ple as the last men, the ones who blink, say “we have in­vented hap­pi­ness,” and mis­take com­fort and op­ti­miza­tion for hu­man flour­ish­ing. He would have fuck­ing loathed them.

The pat­tern re­peats. Effective al­tru­ism is util­i­tar­i­an­ism rein­vented by peo­ple who have ap­par­ently never en­coun­tered Bernard Williams, or Derek Parfit’s own ag­o­nized wrestling with the im­pli­ca­tions of con­se­quen­tial­ist rea­son­ing, or the two cen­turies of philo­soph­i­cal lit­er­a­ture ex­plain­ing why naive ex­pected-value cal­cu­la­tions pro­duce mon­strous out­comes when ap­plied with­out lim­it­ing prin­ci­ples. The EA move­ment walked it­self into the Sam Bankman-Fried cat­a­stro­phe be­cause it adopted a moral frame­work with­out un­der­stand­ing its fail­ure modes. What hap­pens when you skip the course­work and go straight to the fi­nal exam.

Longtermism, the philo­soph­i­cal en­gine of AI ac­cel­er­a­tion, whether its pro­po­nents ac­knowl­edge it or not, is warmed-over Parfit with­out the rigor. The ar­gu­ment (that we should op­ti­mize for the wel­fare of tril­lions of hy­po­thet­i­cal fu­ture be­ings, and that pre­sent-day costs are ac­cept­able in ser­vice of that goal) is a frame­work any com­pe­tent ethi­cist can dis­man­tle in an af­ter­noon. It has no lim­it­ing prin­ci­ple. It can­not dis­tin­guish be­tween gen­uine moral ur­gency and the self-serv­ing con­clu­sion that what­ever the speaker was al­ready do­ing is cos­mi­cally im­por­tant. In prac­tice, it is a ma­chine for gen­er­at­ing jus­ti­fi­ca­tions for the con­cen­tra­tion of power by peo­ple who have de­cided they are the ones best po­si­tioned to stew­ard the fu­ture of the species. How con­ve­nient.

The ra­tio­nal­ist com­mu­nity re­dis­cov­ers Bayesian epis­te­mol­ogy and treats it like a rev­e­la­tion, ap­par­ently un­aware that the phi­los­o­phy of sci­ence has been work­ing through these ques­tions since the 1920s. Blog posts get treated as foun­da­tional texts. People who have never read Kuhn or Lakatos or Feyerabend con­struct an epis­te­mol­ogy from first prin­ci­ples, mar­vel at what they’ve built, and pro­ceed to use it as the in­tel­lec­tual build­ing blocks for de­ci­sions that af­fect bil­lions of peo­ple. The con­fi­dence is in­versely pro­por­tional to the depth. Dunning-Kruger at scale.

The in­tel­lec­tual poverty ex­tends to the eco­nom­ics. Acemoglu has found that only 4.6 per­cent of tasks in the econ­omy are cur­rently cost-ef­fec­tive to au­to­mate with AI. His es­ti­mate for AI’s to­tal pro­duc­tiv­ity im­pact over the next decade: 0.66 per­cent. Goldman Sachs pro­jected seven per­cent in 2023, be­fore we be­gan to see the shape of this thing. McKinsey pro­jects be­tween 0.5 and 3.5 per­cent an­nu­ally. Someone is cat­a­stroph­i­cally wrong, and the peo­ple spend­ing the money are not the ones with the Nobel Prize. Over ninety per­cent of firms sur­veyed in 2025 re­ported no mea­sur­able im­pact on em­ploy­ment or pro­duc­tiv­ity de­spite a quar­ter-tril­lion dol­lars in AI in­vest­ment. Torsten Slok: “AI is every­where ex­cept in the in­com­ing macro­eco­nomic data.” These are peo­ple who have de­cided what the fu­ture looks like and are spend­ing other peo­ple’s money to will it into ex­is­tence.

These bas­tards al­ways tell on them­selves. OpenAI pub­lished a white pa­per in April call­ing for “Industrial Policy for the Intelligence Age,” full of rad­i­cally pro­gres­sive pro­pos­als: a thirty-two-hour work­week, higher taxes on cor­po­ra­tions and cap­i­tal gains, a “public wealth fund” pro­vid­ing all cit­i­zens an eq­uity stake in AI com­pa­nies. In the same pe­riod, OpenAI’s pres­i­dent helped fund a su­per PAC that spent over two mil­lion dol­lars on ads against Alex Bores, a New York con­gres­sional can­di­date whose crime was in­tro­duc­ing safety reg­u­la­tion for large AI de­vel­op­ers and propos­ing to tax AI to fund di­rect pay­ments to Americans. The com­pany re­moved a profit cap that had pre­vi­ously lim­ited in­vestor re­turns to a hun­dred times their ini­tial in­vest­ment. Chris Lehane, OpenAI’s chief lob­by­ist, sys­tem­at­i­cally de­pri­or­i­tized in­ter­nal re­search that could pro­duce un­flat­ter­ing re­sults. “Whenever some­one wrote a pa­per which talked about some neg­a­tive as­pect of A.I.,” a col­league told the Times, “he would say, ‘We’re not go­ing to re­lease some­thing about a prob­lem un­til we have a so­lu­tion for it.’” Lehane’s own char­ac­ter­i­za­tion: “We want to do ap­plied physics, not the­o­ret­i­cal physics.” Tell the story that helps us, not the one that’s true.

A Philosophy 101 stu­dent who mis­reads Nietzsche writes a bad pa­per and gets a C. A bil­lion­aire who mis­reads Nietzsche builds a po­lit­i­cal phi­los­o­phy around the mis­read­ing and funds it with the GDP of a small na­tion. This is fuck­ing in­sane.

These are not se­ri­ous peo­ple. They are se­ri­ous about ac­cu­mu­la­tion and about win­ning. They are not se­ri­ous about the ques­tions that mat­ter for what they’re build­ing: what we owe each other, what makes a life worth liv­ing, and what hap­pens to a civ­i­liza­tion when you re­move the ma­te­r­ial ba­sis of hu­man agency. These ques­tions have oc­cu­pied the best minds in hu­man his­tory for mil­len­nia. The Valley’s en­gage­ment with them amounts to read­ing the CliffsNotes on a transat­lantic flight and ar­riv­ing con­vinced you’ve mas­tered the canon.

And they want to re­struc­ture civ­i­liza­tion.

Albert Camus broke with Jean-Paul Sartre and the French left over the most con­crete po­lit­i­cal ques­tion there is: can the peo­ple alive to­day be treated as ac­cept­able ca­su­al­ties in the pur­suit of a bet­ter fu­ture?2

Sartre and the Marxists said yes. History has a di­rec­tion. The rev­o­lu­tion re­quires sac­ri­fice. Camus said no. Any sys­tem of thought that sub­or­di­nates liv­ing peo­ple to a hy­po­thet­i­cal fu­ture has al­ready com­mit­ted the foun­da­tional moral er­ror. Once you ac­cept that logic, there is no lim­it­ing prin­ci­ple. Any atroc­ity be­comes jus­ti­fi­able. Any amount of pre­sent suf­fer­ing can be ra­tio­nal­ized as a nec­es­sary in­put to the glo­ri­ous out­put.

This is the struc­ture of the AI ac­cel­er­a­tion ar­gu­ment. The tech­nol­ogy will even­tu­ally ben­e­fit hu­man­ity (trillions of fu­ture hu­mans, lives of abun­dance and mean­ing we can barely imag­ine), so pre­sent dis­rup­tion is tol­er­a­ble. Displaced work­ers, hol­lowed com­mu­ni­ties, the ero­sion of de­mo­c­ra­tic lever­age, the con­cen­tra­tion of power in a hand­ful of pri­vate ac­tors who have ex­empted them­selves from the con­se­quences of their own pro­ject: re­gret­table but nec­es­sary. The ex­pected value math works out.

The founders of Mechanize, a startup whose stated mis­sion was “to en­able the full au­toma­tion of the econ­omy,” made the logic ex­plicit: “the only real choice is whether to has­ten this tech­no­log­i­cal rev­o­lu­tion our­selves, or to wait for oth­ers to ini­ti­ate it in our ab­sence.” Technological de­ter­min­ism as moral ab­so­lu­tion. The fu­ture is fixed. Our only choice is whether to build it first. Therefore, noth­ing we do along the way re­quires jus­ti­fi­ca­tion, be­cause the des­ti­na­tion was never in our hands. They’re mak­ing the same ar­gu­ment as the Marxists who sent dis­si­dents to the gu­lag.

Camus staked his in­tel­lec­tual legacy on the claim that the per­son stand­ing in front of you is not an in­put to a util­ity func­tion. Their suf­fer­ing is not re­deemed by a fu­ture state of af­fairs they may never see. Their dig­nity is not ne­go­tiable against pro­jected out­comes. The per­son who ex­ists now (who has a job they’re about to lose, a fam­ily they sup­port, a com­mu­nity that de­pends on a func­tion­ing lo­cal econ­omy) is the unit of ac­count. Not hu­man­ity in the ab­stract. Not the tril­lions of fu­ture be­ings that the longter­mists con­jure to win their ex­pected-value cal­cu­la­tions.

Once that com­mit­ment is aban­doned, the door opens to every form of ra­tio­nal­ized cru­elty that the twen­ti­eth cen­tury spent a hun­dred mil­lion lives try­ing to teach us to re­ject.

The en­tire AI ac­cel­er­a­tion pro­ject is premised on aban­don­ing it. It asks pre­sent peo­ple to bear costs for fu­ture ben­e­fits they may never see, dis­trib­uted to peo­ple who do not yet ex­ist, ad­min­is­tered by a self-ap­pointed class that has in­su­lated it­self from the con­se­quences en­tirely. Altman’s “universal ba­sic com­pute” pro­posal ac­knowl­edges, if you squint, that the fu­ture he’s build­ing re­quires a new dis­tri­b­u­tion mech­a­nism. It is also a pro­posal in which he gets to be the one do­ing the dis­trib­ut­ing. Feudalism with bet­ter brand­ing.

Jasmine Sun re­ported re­cently that tech in­dus­try sources “expressed more ex­treme con­cern about the la­bor mar­ket im­pacts of A.I. in pri­vate con­ver­sa­tion, but sud­denly be­came op­ti­mists once I turned on the mic.” They know what they’re build­ing. They know what it will do. They per­form op­ti­mism in pub­lic be­cause the al­ter­na­tive is ad­mit­ting that the thing they’ve staked their ca­reers and for­tunes on will im­mis­er­ate a sig­nif­i­cant por­tion of hu­man­ity, and they’re do­ing it any­way. Amodei has writ­ten that Anthropic is “currently con­sid­er­ing a range of pos­si­ble path­ways for our own em­ploy­ees,” im­ply­ing that even the peo­ple build­ing the tech­nol­ogy may be sur­plus to its re­quire­ments. He framed this as com­pas­sion­ate. Read it again as a CEO telling his work­force that their jobs, too, are tem­po­rary.

I don’t want to dwell on whether AI can do what these com­pa­nies claim. It may well be able to, though the cur­rent ev­i­dence sug­gests the gap be­tween pitch and prod­uct is vast, and se­ri­ous econ­o­mists think the pro­duc­tiv­ity gains are a frac­tion of what the in­dus­try pro­jects. But Acemoglu’s core find­ing is that AI does­n’t need to be rev­o­lu­tion­ary to be de­struc­tive. “So-so” au­toma­tion (technology that’s mediocre at re­plac­ing work­ers but cheap enough to do it any­way) still dis­places at scale while de­liv­er­ing un­der­whelm­ing pro­duc­tiv­ity. The worst out­come may not be su­per­in­tel­li­gent AI. It may be ad­e­quate AI, de­ployed ag­gres­sively by com­pa­nies chas­ing stock prices, elim­i­nat­ing jobs it can’t ac­tu­ally do well be­cause the quar­terly in­cen­tives de­mand it.

Has any­one with the power to shape this tran­si­tion thought se­ri­ously about what it means for the peo­ple alive to­day who did­n’t get a vote on any of it?

Fuck no.

The win­dow for chang­ing that an­swer is not in­fi­nite. The reg­u­la­tory cap­ture is al­ready ad­vanced: AI-related in­vest­ments ac­counted for thirty-nine per­cent of US eco­nomic growth in the first three quar­ters of 2025, giv­ing the fed­eral gov­ern­ment a vested in­ter­est in sus­tain­ing the boom. Amodei him­self ac­knowl­edges that this leads to “the re­luc­tance of tech com­pa­nies to crit­i­cize the U.S. gov­ern­ment, and the gov­ern­men­t’s sup­port for ex­treme anti-reg­u­la­tory poli­cies on A.I.” The reg­u­la­tor and the reg­u­lated have con­verged into a sin­gle in­ter­est. The ex­per­tise asym­me­try be­tween leg­is­la­tors and the in­dus­try they’re sup­posed to over­see is in­sur­mount­able. The feed­back loop (AI sys­tems ad­vis­ing on the gov­er­nance of AI sys­tems) is clos­ing.

The in­ter­ven­tions that could mat­ter are known. Public own­er­ship stakes in AI in­fra­struc­ture. Aggressive an­titrust en­force­ment. A gen­uine tax regime on au­to­mated la­bor. Branko Milanovic’s pre­scrip­tion is char­ac­ter­is­ti­cally di­rect: spread cap­i­tal own­er­ship more widely, tax the high­est cap­i­tal in­comes more ag­gres­sively. None of these are tech­no­log­i­cally dif­fi­cult. All of them re­quire func­tion­ing de­mo­c­ra­tic in­sti­tu­tions with the will to chal­lenge the rich­est com­pa­nies in hu­man his­tory. The com­pa­nies that would need to be taxed are spend­ing mil­lions to de­feat the politi­cians who pro­pose it.

The dead econ­omy is not one where noth­ing hap­pens. Plenty will hap­pen. The GDP might even go up; AI-related in­vest­ments are al­ready prop­ping it up. The dead econ­omy is one where plenty hap­pens and none of it re­quires you. Where the pro­duc­tive ca­pac­ity of civ­i­liza­tion has been cap­tured by a sys­tem you have no stake in, no in­put into, and no vote on. Where the peo­ple who built it told you they don’t think you should have a say. Where they ex­press alarm about the con­se­quences in pri­vate and op­ti­mism in pub­lic. Where they pub­lish white pa­pers call­ing for rad­i­cal re­dis­tri­b­u­tion while fund­ing su­per PACs to de­stroy the politi­cians who pro­pose it.

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1

This es­say re­lies fre­quently on the out­stand­ing re­port­ing of Jasmine Sun’s April 30, 2026 piece in the New York Times, which you can find at: https://​www.ny­times.com/​2026/​04/​30/​opin­ion/​ai-la­bor-work-force-sil­i­con-val­ley.html

I’m not go­ing to link it for every quo­ta­tion pulled from Sun’s piece, so if a di­rect quo­ta­tion is not cited in­di­vid­u­ally, I have pulled it from Sun’s re­port­ing.

By Chad Whitacre ❧ Published on May 28, 2026

tl;dr AI took the last of the wind out of my Open Source sails. I wish you all the best!

Disclosure / ad­ver­tise­ment: I worked for Sentry when I wrote this.

Notes

This post gar­nered a lovely send-off from HN. Thank you all!

I am also us­ing this mo­ment to share a fund-raiser for my good friend Dana. We met IRL and worked to­gether for 15+ years in Open Source com­mu­ni­ties. Thanks for help­ing if you can! 🙏

Be sure to use AI when mak­ingy­our next, I don’t know, meal plan,for ex­am­ple. Definitely do not cal­ly­our friend who loves to cook and ask her­for her fa­vorite recipes or tips or ways to save time mak­ing meals, be­cause you will endup talk­ing for longer than you had hoped,hear­ing, per­haps, about her fa­ther’s can­cer di­ag­no­sis or how lonely she’s been or even­what she’s planted in her spring gar­den and then lost with the early frost.

And be sure to use AI when plan­ning that nextcamp­ing trip, the last one you will take­with this par­tic­u­lar child. Definitely do not text your friend who has fly-fished every river in Pennsylvania and biked every back­woods trail, be­cause you might end up tex­ting back and forth for the rest of the dayor even meet­ing up late for a beer and hear­ing­how he has ended each re­cent night black-out drunk, or per­haps you’ll hear how his­cousin is an id­iot on Facebook or maybe just­that he re­paired his own wash­ing ma­chine­and is pretty damn proud of that.

And be sure to use AI when your next child­gets mar­ried, so that you can write themthe per­fect toast or poem or speech or song­be­cause no one wants to hear your words, the ac­tual poorly writ­ten words of a par­ent (you) who changed­hun­dreds of di­a­pers for said child or fed them in the mid­dle of the night from your ac­tual body. Or cried when they were late home be­cause you were pos­i­tive they were dead. We don’t want those words—we’d pre­fer the ster­ile words of a ma­chine that never lived, never had an orig­i­nal thought, never felt the pain of mis­car­riage or bro­ken­re­la­tion­ships or the joy of a friend­ship re­store­dor of see­ing spring’s first robin danc­ing on frost.

And be sure to use AI when work­ing on your next­book or es­say or piece of art or pho­tog­ra­phy,and then smile or even laugh at your own­clev­er­ness when you see how good it is, and how easy, be­cause who the hell has timeto work at some­thing, to give time to craft, tocre­ate with their own minds, to spend years be­ing mediocre. Why do that when­mas­tery, or at least com­pe­tency is so sim­pleonly a good prompt away?

How mag­nif­i­cent the fu­neral song our chil­dren or con­tem­po­rarieswill write for us, a song they will make by tak­ing our obit­u­ary and Facebook posts,plus ran­dom quotes from our al­go­rithm,and feed­ing them into Chat or Gemini or Claude. The tears that will fall in the face of such­san­i­tary sweet­ness!

Be sure to use AI

and while you do I’ll be over here in my 50thyear, my youngest daugh­ter asleep on my chest,my arm falling asleep be­cause I dare not move­lest I scare away this mo­ment, ly­ing here melan­choly about my older chil­dren mov­ing out and my mid­dlechil­dren no longer need­ing me, at least­not like they used to, weary about this bodythat fails me now in ever in­creas­ing ways that will never be re­stored. Sighing over sto­ries I tried to write but never hit the page the way they felt in my mind.

But is­n’t that, my flesh-and-blood friend, the nat­ural or­der of things?

the long­ing for some­thing that could al­ways bea bit bet­ter

or the way that any­thing­worth do­ing feels a bit clumsy and painful, es­pe­cially at first

or hear­ing an­other hu­man voice and some­howre­al­iz­ing the beauty of life is found in all of the­sesub­tle im­per­fec­tions

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GTA 6 de­vel­op­ers have for­mally an­nounced a union to com­bat Rockstar Games’ ac­tions which will be de­bated in court.

On Thursday, the Independent Workers’ Union of Great Britain (IWGB) and Rockstar staff mem­bers an­nounced the Rockstar Game Workers Union. This union will be part of the IWGB. The re­veal came in the form of an in­for­ma­tive video which delves into their mo­tives and what we should be look­ing out for in the fu­ture.

This union is the one fac­ing a le­gal bat­tle with Rockstar. Over 30 em­ploy­ees were fired last year for “gross mis­con­duct” which the IWGB dis­putes as an act of union bust­ing. As time has passed, the union has now been fully set up and is look­ing to fight Rockstar Games in court. A date for the trial in court has been set though not been pub­lished by the union just yet.

The Rockstar Game Workers Union say in their video that Rockstar’s ac­tions to­wards those 30+ staffers charged oth­ers to join the union. Whilst most of the af­fected work­ers were based at Rockstar North in Edinburgh, the seeds have spread far and wide. The union claims a size­able num­ber of Rockstar em­ploy­ees from their London, Leeds, Lincoln and Dundee of­fices have joined forces with the many oth­ers in Edinburgh.

Together, we are or­gan­is­ing around the things we want to change. Starting with: Pay trans­paren­cyFlex­i­ble workingAn end to crunch Rockstar IWGB Game Workers Union

Together, we are or­gan­is­ing around the things we want to change. Starting with:

Pay trans­parency

Flexible work­ing

An end to crunch

The Rockstar IWGB Game Workers Union have cre­ated BlueSky, Instagram and Twitter ac­counts for you to fol­low and re­ceive up­dates from. There is also a page to do­nate to­wards their le­gal bat­tle with Rockstar here.

This comes af­ter politi­cians have ac­cused Rockstar of block­ing the on­go­ing le­gal pro­ceed­ings. Learn more about it here.

Be sure to stay tuned to RockstarINTEL for all the lat­est news on Rockstar Games and GTA VI.

Subscribe to our newslet­ter!

DBOS re­cently ar­gued that Postgres is all you need for durable ex­e­cu­tion: if you al­ready trust your data­base, you do not need a sep­a­rate or­ches­tra­tion tier. I agree with the di­rec­tion, and I think the idea can be pushed fur­ther.

For a large class of durable sys­tems, SQLite is all you need.

The Durable Part

Durable ex­e­cu­tion is of­ten dis­cussed as if it re­quires durable in­fra­struc­ture. In many cases it does not. The durable part is the work­flow state. The com­pute can stay cheap and dis­pos­able.

That is a nat­ural fit for Obelisk: work­flow progress lives in an ex­e­cu­tion log, work­flows re­play from per­sisted his­tory, and ac­tiv­i­ties can be re­tried. What mat­ters most is keep­ing the work­flow state around and easy to in­spect.

Why SQLite Fits

SQLite is ap­peal­ing be­cause it gives you trans­ac­tional durable state with­out in­tro­duc­ing a sep­a­rate data­base ser­vice. There is no net­work hop, no ex­tra con­trol plane, and no new op­er­a­tional sur­face area just to keep work­flow progress safe. For many sys­tems, a lo­cal data­base file is ex­actly the right level of ma­chin­ery.

Litestream Makes It Portable

The ob­vi­ous con­cern is what to do with those SQLite files once ex­per­i­ments start to ac­cu­mu­late. That is where Litestream helps. It can stream SQLite changes asyn­chro­nously to S3-compatible ob­ject stor­age. That gives you a sim­ple way to keep work­ing state close to the run­time while still copy­ing data­bases out for backup, mi­gra­tion, and in­spec­tion.

The caveat is that Litestream repli­ca­tion is asyn­chro­nous. A re­store can miss the newest lo­cal writes if the SQLite vol­ume dis­ap­pears be­fore they are copied. That is fine for many AI and ex­per­i­men­ta­tion work­flows, but it is not the same thing as a highly avail­able shared data­base.

That still leads to a use­ful op­er­at­ing model: run an Obelisk server with a SQLite data­base, back it up with Litestream, and let an ob­server pull in­ter­est­ing data­bases when needed. The same file can be used for lo­cal re­play, de­bug­ging, and un­der­stand­ing what an agent ac­tu­ally did.

Why This Works Well For Agents

This is es­pe­cially at­trac­tive for AI agents and AI-generated work­flows. Those sys­tems are of­ten bursty, ex­per­i­men­tal, and eas­ier to rea­son about when each agent or ten­ant has a small self-con­tained unit of state. A fleet of tiny servers in mi­cro VMs or con­tain­ers, each with its own SQLite data­base and ob­ject stor­age backup, is of­ten a bet­ter fit than one large al­ways-on shared sys­tem. It is sim­pler, cheaper, and gives bet­ter fault iso­la­tion.

When To Use Postgres Instead

SQLite is not the an­swer to every de­ploy­ment shape. Obelisk also sup­ports Postgres, and that is the right choice when you need higher avail­abil­ity, broader shared scal­a­bil­ity, or other de­ploy­ment prop­er­ties that are bet­ter served by a net­work data­base. It is also the bet­ter fit when asyn­chro­nous repli­ca­tion to ob­ject stor­age is not the dura­bil­ity model you want.

Many work­flow sys­tems do not need that on day one and should not start with more in­fra­struc­ture than their state ac­tu­ally de­mands.

In a large set of cases, a lo­cal SQLite data­base plus Litestream backup to S3 is enough. Add cheap work­ers around it and you get a durable sys­tem with very lit­tle in­fra­struc­ture. For the world of AI agents, that may be the most sen­si­ble de­fault.

Please en­joy this ar­ti­cle on its own web­page. Trust me.

dick­over n. : a modal panel, popover, or cur­tain pre­sented by a web­site or app, de­lib­er­ately ob­scur­ing its own con­tent to frus­trate the user with an un­wanted, un­nec­es­sary, manda­tory in­ter­ac­tion; e.g. ask­ing the user to ac­cept “cookies”, sub­scribe to a newslet­ter, in­stall the web­site’s mo­bile app, agree to terms of ser­vice, or any­thing else that the user could­n’t give two shits about.

You know what a dick­over is, even if you did­n’t know what to call it (until now). If you use the Internet, you en­counter them every day. They’re popovers, but dick­headed. The web is ab­solutely lousy with them, and mo­bile apps pre­sent them too, with in­creas­ing fre­quency.

Dickovers are a ver­i­ta­ble scourge. They’re so com­mon they’re ef­fec­tively part of the fir­ma­ment. I started call­ing these things dick­pan­els in 2022, but when dick­over popped into my head last week,1 I could­n’t shake the feel­ing that it’s a bet­ter term for these ubiq­ui­tous odi­ous ir­ri­ta­tions. You can hardly go any­where on the web with­out get­ting dicked over by a dick­over. They of­ten pester you about per­mit­ting cook­ies, like this one from Euronews or this one from Gallup. This ma­li­cious de­sign pat­tern is so ubiq­ui­tous that it has spread even to per­sonal blogs, like this one from my friend Om Malik, and to great brands like Field Notes, both ask­ing you to sign up for their newslet­ters.

The home­page for every sin­gle blog hosted by Substack shows a par­tic­u­larly per­ni­cious dick­over on its home­page. The Substack dick­over does­n’t even look like a panel. It’s a full-screen cur­tain de­signed and worded to sug­gest, strongly, that you need to sign up for the blog’s email newslet­ter just to read any­thing. The dis­missal but­ton for the Substack dick­over is a small text link — that does­n’t look any­thing like a but­ton — that says some­thing like “No thanks” (e.g. Paul Krugman, Matt Yglesias) or some­thing that adds in­sult to in­jury with a cloy­ingly sac­cha­rine la­bel like “Just gimme that con­tent!” (e.g. Volts).

Here’s one from The Philadelphia Inquirer, for which I pay $20/month to sub­scribe, ask­ing me to sign up for SMS text mes­sages about the Jersey shore, while I’m logged into their cursed web­site, be­fore they’ll let me see the ar­ti­cle I came to read. Every time I see one of these I think about un­sub­scrib­ing. I’m pay­ing them to abuse my time and at­ten­tion. I started cap­tur­ing screen­shots of every dick­over I saw when I started work­ing on this ar­ti­cle, and I soon had to give up be­cause I was col­lect­ing too many of them. But this one from Tom’s Hardware I ac­tu­ally en­joyed, be­cause their own dick­over got dicked over by one of their own fuck­ing ads in a JavaScript Z-axis slap­fight.

If you visit a web­site you should … see the web­site. See its con­tent. Be able to read the ar­ti­cle whose page you are at­tempt­ing to visit. Showing a “subscribe to our newslet­ter” or “accept our fuck­ing cook­ies” dick­over to some­one try­ing to read an ar­ti­cle on the web makes no more sense than send­ing out an email newslet­ter that only con­tains a link to read the newslet­ter on a web­page. A web­page should show the web­page. An email should show the email. I should not have to ex­plain this.

Some sites hit you with their dick­overs on page load, when you might be braced for it. We’re all braced for ob­sta­cles and an­noy­ances these days when we load web pages. But some sneaky, cow­ardly bas­tards sucker-punch you with their dick­bars only af­ter you have started read­ing, and be­gin to scroll down the page. Then, wham, they hit with their dick­over. It’s a god­damn priv­i­lege for any­one to be­stow your ar­ti­cle, story, or prod­uct page with their at­ten­tion. The gall, to de­lib­er­ately in­ter­rupt them while they are in the mid­dle of ac­tively read­ing, to pre­sent them with a dick­over. It is no dif­fer­ent from snatch­ing a phys­i­cal copy of a book or mag­a­zine out of a read­er’s hands in or­der to bad­ger them for some­thing other than the at­ten­tion they were al­ready grant­ing your work, ex­cept that the phys­i­cal act of snatch­ing a pub­li­ca­tion from a read­er’s hands would sub­ject you to be­ing punched in the face.

Addendums

Dickbars are re­lated to dick­overs, but are far lesser crimes against de­sign and user ex­pe­ri­ence. A dick­bar is a non-modal popover that ob­structs only a por­tion of the un­der­ly­ing con­tent, of­ten just a short hor­i­zon­tal strip. If dick­overs are de­sign felonies, dick­bars are mis­de­meanors. Here are typ­i­cal dick­bar ex­am­ples on desk­top and mo­bile lay­outs. Here’s a rel­a­tively at­trac­tive one from Apple on its Newsroom blog, and one from the ex­cel­lent Acquired pod­cast that is gra­cious enough to tuck it­self into a cor­ner. Here’s an ob­nox­ious one from the Four Seasons that is large enough to edge to­ward dick­over con­sid­er­a­tion. What makes dick­bars lesser of­fenses is that they do not ob­scure the en­tirety of the un­der­ly­ing con­tent, and thus do not de­mand manda­tory ac­tion to dis­miss them. You can view and scroll the page be­low them. What makes dick­bars crimes is that they still ob­struct and dis­tract. (Horizontal dick­bars, the most com­mon form, in­ter­fere with pag­ing via the space bar one screen­ful at a time. The con­tent scrolls by the height of the web­page, not the height of the web­page mi­nus the height of the dick­bar. Thus the dick­bar cov­ers un­read text each time you page down.)

Dickbars are re­lated to dick­overs, but are far lesser crimes against de­sign and user ex­pe­ri­ence. A dick­bar is a non-modal popover that ob­structs only a por­tion of the un­der­ly­ing con­tent, of­ten just a short hor­i­zon­tal strip. If dick­overs are de­sign felonies, dick­bars are mis­de­meanors. Here are typ­i­cal dick­bar ex­am­ples on desk­top and mo­bile lay­outs. Here’s a rel­a­tively at­trac­tive one from Apple on its Newsroom blog, and one from the ex­cel­lent Acquired pod­cast that is gra­cious enough to tuck it­self into a cor­ner. Here’s an ob­nox­ious one from the Four Seasons that is large enough to edge to­ward dick­over con­sid­er­a­tion. What makes dick­bars lesser of­fenses is that they do not ob­scure the en­tirety of the un­der­ly­ing con­tent, and thus do not de­mand manda­tory ac­tion to dis­miss them. You can view and scroll the page be­low them. What makes dick­bars crimes is that they still ob­struct and dis­tract. (Horizontal dick­bars, the most com­mon form, in­ter­fere with pag­ing via the space bar one screen­ful at a time. The con­tent scrolls by the height of the web­page, not the height of the web­page mi­nus the height of the dick­bar. Thus the dick­bar cov­ers un­read text each time you page down.)

All dick­overs are modal block­ers, but not all modal block­ers are dick­overs. A pay­wall sign-up / sign-in panel, for ex­am­ple, is not a dick­over. Paywalls are, at times, an­noy­ing, but one of the defin­ing as­pects of a dick­over is that they are un­nec­es­sary. Cookie per­mis­sions are un­nec­es­sary. Signing up for an email newslet­ter is un­nec­es­sary. But for pay­walled con­tent, ask­ing for sign-up / sign-in is nec­es­sary.

All dick­overs are modal block­ers, but not all modal block­ers are dick­overs. A pay­wall sign-up / sign-in panel, for ex­am­ple, is not a dick­over. Paywalls are, at times, an­noy­ing, but one of the defin­ing as­pects of a dick­over is that they are un­nec­es­sary. Cookie per­mis­sions are un­nec­es­sary. Signing up for an email newslet­ter is un­nec­es­sary. But for pay­walled con­tent, ask­ing for sign-up / sign-in is nec­es­sary.

The ne­ol­o­gism came to me when I de­cided to write about Dropover, a great lit­tle drag-and-drop “shelf” util­ity for the Mac, shortly af­ter I posted an item com­plain­ing about a par­tic­u­larly ridicu­lous cookie modal blocker from Euronews. That Euronews post got me think­ing that dick­panel just was­n’t stick­ing. The dick part was right but the panel part lacked oomph. It did­n’t snap. Then I got to writ­ing about Dropover and it came to me. That night I ran a poll on Mastodon ask­ing “What’s a bet­ter word for in-win­dow fake ‘dialog box­es’ that web­sites (and some apps) use to cover up con­tent?” — dick­panel or dick­over — and, with 1,130 re­sponses, dick­over won by a slim 51 – 49 per­cent mar­gin. But af­ter sleep­ing on it I was con­vinced dick­over was the bet­ter term, no mat­ter how the poll turned out. Those who voted for dick­panel were, judg­ing from their com­ments, con­cerned that “over” is­n’t de­scrip­tive enough. But what makes a ne­ol­o­gism stick is­n’t de­scrip­tive­ness or ob­vi­ous­ness, but us­age. If we use it, it’ll stick. And I, for one, want to use dick­over be­cause it stings, and it’s fun to sting peo­ple who are do­ing some­thing dick­headed. ↩︎

The ne­ol­o­gism came to me when I de­cided to write about Dropover, a great lit­tle drag-and-drop “shelf” util­ity for the Mac, shortly af­ter I posted an item com­plain­ing about a par­tic­u­larly ridicu­lous cookie modal blocker from Euronews. That Euronews post got me think­ing that dick­panel just was­n’t stick­ing. The dick part was right but the panel part lacked oomph. It did­n’t snap. Then I got to writ­ing about Dropover and it came to me. That night I ran a poll on Mastodon ask­ing “What’s a bet­ter word for in-win­dow fake ‘dialog box­es’ that web­sites (and some apps) use to cover up con­tent?” — dick­panel or dick­over — and, with 1,130 re­sponses, dick­over won by a slim 51 – 49 per­cent mar­gin. But af­ter sleep­ing on it I was con­vinced dick­over was the bet­ter term, no mat­ter how the poll turned out. Those who voted for dick­panel were, judg­ing from their com­ments, con­cerned that “over” is­n’t de­scrip­tive enough. But what makes a ne­ol­o­gism stick is­n’t de­scrip­tive­ness or ob­vi­ous­ness, but us­age. If we use it, it’ll stick. And I, for one, want to use dick­over be­cause it stings, and it’s fun to sting peo­ple who are do­ing some­thing dick­headed. ↩︎

May 29, 2026

4 min read

ai

ar­ti­cle

I was in Paris the last few days to visit the AI Now Summit by Mistral AI, hop­ing to learn more about their mod­els, plans for the fu­ture of European AI and more. My per­sonal in­sights:

Mistral is no longer just a model com­pany. They’re build­ing the full AI stack: com­pute, mod­els, plat­forms & con­sul­tancy. They own the com­pute (a 40MW data cen­ter in Paris, more data cen­ters com­ing soon, in­clud­ing one in Sweden). They fo­cus on ef­fi­cient, open and be­spoke mod­els that you own and can run on-prem. That seems to be their unique sell­ing point com­pared to Anthropic or OpenAI.

The mes­sag­ing was all about part­ner­ships: col­lab­o­ra­tions with ASML, BNP Paribas, Amazon’s Alexa+ and how they were help­ing them with AI to solve real prob­lems. It was less about up­com­ing new mod­els and tech in­no­va­tion. Something I found dis­ap­point­ing. They did launch Vibe for Work, a prod­uct sim­i­lar to Claude for Work.

When it comes to agen­tic, the har­ness is every­thing. In a talk by Pieter Stock he men­tioned that the model alone is­n’t enough. With a har­ness you add con­text, per­sis­tence and learn­ing. Reasoning is es­sen­tial for this; it’s what lets a sys­tem back­track, re­cover from er­rors and stay trans­par­ent. Skills are the way for or­gan­i­sa­tions to cap­ture best prac­tices, you de­velop these in co­op­er­at­ing with the AI agent.

Specialized small mod­els are their strat­egy. Mistral showed sev­eral ex­am­ples where small, fast and fo­cused mod­els out­per­form the big gen­eral-pur­pose ones when it comes to en­ergy ef­fi­ciency and speed: Document AI for OCR (used by the EU Patent Office to do large scale OCR), Voxtral for mul­ti­lin­gual voice (powering Amazon’s Alexa+ in Europe), and Robostral for in­dus­trial ro­bot­ics with ASML. And also in to­ken-heavy agen­tic ap­pli­ca­tions, speed and ef­fi­ciency are be­com­ing as im­por­tant as raw ca­pa­bil­ity.

Sovereignty and on-prem are their sell­ing points. BNP Paribas runs Mistral mod­els on-prem for KYC in Belgium, with sen­si­tive data stay­ing within the bank’s walls. Abanca is us­ing agent or­ches­tra­tion to han­dle sen­si­tive cus­tomer in­for­ma­tion at a huge scale (more than 1 mil­lion cus­tomers in their app). For European com­pa­nies in reg­u­lated in­dus­tries, this is a good al­ter­na­tive to re­ly­ing on US hy­per­scalers.

A talk that was a bit out of the or­di­nary and that I re­ally en­joyed was about an­cient pa­pyrus doc­u­ments: a re­search team from the Austrian Academy of Sciences fine­tuned a cod­ing LLM by Mistral (Codestral) to read tiny snip­pets of mil­len­nia-old dis­carded pa­pyri that had sat un­pub­lished for decades. This work helps make a col­lec­tion of 180,000 doc­u­ments found in the Egyptian desert ac­ces­si­ble, a job that would have taken more than 2000 years with­out AI. A beau­ti­ful ex­am­ple of how AI can also help the hu­man­i­ties.

All in all, the sum­mit left me with a bet­ter pic­ture of Mistral’s vi­sion for Europe an AI: maybe not to win the race for AGI (Artificial General Intelligence), but to be­come the European full-stack AI part­ner that de­liv­ers real re­turn on in­vest­ment NOW. Whether that pays off will de­pend on more European com­pa­nies com­mit­ting to this, but the com­bi­na­tion of open mod­els, on-prem de­ploy­ment and en­ter­prise part­ner­ships could be ap­peal­ing to many big or­ga­ni­za­tions in the EU. And hon­estly, it’s good to see a se­ri­ous European player at the table. The days of blindly re­ly­ing on US tech gi­ants is com­ing to an end.

Post-script: Many thanks to Mistral for the in­vi­ta­tion. The lo­ca­tion was just per­fect, in the mid­dle of Paris near the Louvre, and it was re­ally some­thing to be in the place where Paris Fashion Week nor­mally takes place: with co-founders and other speak­ers on the cat­walk.

This ar­ti­cle was also pub­lished on LinkedIn and Hacker News. Feel free to join the dis­cus­sion there.

There is a weird col­lec­tion of ar­gu­ments for ap­prais­ing the value of hu­mans and their cre­ative ar­ti­facts. It usu­ally goes some­thing like this: In the age of AI, we should still pre­fer hu­mans in cer­tain roles be­cause AI could never per­form the tasks re­quired for that role. Or, a hu­man can at least do it bet­ter. Or, per­haps the out­put from a hu­man and AI may look sim­i­lar, but hu­man out­put is prefer­able for sub­tle styl­is­tic rea­sons that an AI can­not re­pro­duce. Or, at least the AI can­not re­pro­duce it con­sis­tently. Observe the scuff marks around the base of the goal­post from con­stant move­ment. Allow 28 days for the con­crete to cure.

This line of think­ing boils down to, “Humans are valu­able if they pro­duce high-qual­ity out­put.” This ar­gu­ment dan­ger­ously de­pends on the ex­ist­ing-but-nar­row­ing hu­man-AI ca­pa­bil­ity gap. The gap cer­tainly ex­isted in the past (2023-era ChatGPT). It may still ex­ist now. I do not know if it will hold in the fu­ture.

Consider in­stead

“Humans are valu­able.” You can just say it. As a hu­man your­self, I ad­vise you to. You do not need to qual­ify it. This is a ro­bust1 state­ment that is not con­di­tional on a point-in-time snap­shot of the lead­ing fron­tier mod­el’s score on some re­cent bench­mark.

Qualities of “quality”

As a re­lated but im­por­tantly un­nec­es­sary aside: How do you mea­sure the qual­ity of a cre­ative ar­ti­fact? Is it ef­fec­tive? Does it ac­com­plish the thing it is meant to do? This ques­tion im­plies two sub­com­po­nents2 of qual­ity: in­tent and ma­te­r­ial form. It seems to me that many ar­gu­ments for the value of cre­ative ar­ti­facts fo­cus too much on form at the ex­pense of in­tent.

Creation is the dis­til­la­tion of in­tent into form. Intent is usu­ally in­sep­a­ra­bly em­bed­ded into the form of the ar­ti­fact. A hu­man it­er­a­tively (sometimes painstak­ingly) shapes and re­shapes their cre­ation un­til it suf­fi­ciently matches what’s in their mind’s eye. The odd thing about gen­er­a­tive AI is that it can pro­duce sub­stan­tial form with min­i­mally ap­plied in­tent. A hu­man can show up to a task with an un­clear men­tal model of what they mean to ac­com­plish, and an AI can gen­er­ate some­thing any­way. “Write a let­ter of res­ig­na­tion for me to send to my boss.” “Hmm…I guess that looks good.”

Perhaps “AI slop” is re­ally a way of ex­press­ing that it’s dif­fi­cult to iden­tify the in­tent be­hind the form. By that de­f­i­n­i­tion, hu­mans are very ca­pa­ble of gen­er­at­ing slop, too; gen­er­a­tive AI has sim­ply low­ered the en­try bar­rier for cre­at­ing in­tent­less form. It could be said that in­tent is ex­pressed in the prompt. For prose text ar­ti­facts, a well-de­vel­oped prompt is per­haps near the in­tended form al­ready. In a re­cent con­ver­sa­tion about (not) us­ing LLMs to me­di­ate hu­man com­mu­ni­ca­tion, my friend Tom Hudson told me, “If you’re go­ing to use an LLM to write me an email, I’d much rather you just send me the prompt; at least then I’d have an idea of what you ac­tu­ally meant to say.”

The pathol­ogy of gen­er­a­tive AI is that it too eas­ily al­lows sub­stan­tial form with­out dis­cernible in­tent. That mis­take is harder to make when cre­at­ing by hand.

“God cre­ated man in his own im­age.” Genesis 1:27. “Human dig­nity does not de­pend on a per­son’s abil­i­ties.” Magnifica Humanitas, §50. ↩︎

“God cre­ated man in his own im­age.” Genesis 1:27. “Human dig­nity does not de­pend on a per­son’s abil­i­ties.” Magnifica Humanitas, §50. ↩︎

Similar terms are used in sacra­men­tal the­ol­ogy. ↩︎

Similar terms are used in sacra­men­tal the­ol­ogy. ↩︎

May 29, 2026

My nephew just grad­u­ated high school, and wants a lap­top. When he de­cides what com­puter to buy, price (or more pre­cisely, value) is the most im­por­tant at­tribute.

Apple’s MacBook Neo up­ended the ‘value lap­top’ equa­tion—Ap­ple’s not sup­posed to be both the cheap­est op­tion and the best value… but it seems like that’s squarely where the Neo landed for the good-but-cheap lap­top cat­e­gory.

My nephew is also my god­son, and to kick off his com­put­ing jour­ney, I thought I’d let him choose from a Framework 12 I bought to test, or the MacBook Neo I bought a cou­ple months ago to use around the stu­dio.

I had al­ready put both lap­tops through my bench­mark gaunt­let, which re­vealed one theme: the Mac is faster (in most cases), more ef­fi­cient, qui­eter, built bet­ter, has a much nicer dis­play, and costs much less.

The Framework is more ex­pen­sive, slower (in most cases), louder (its fan ramps up quite of­ten), has a pretty poor dis­play, but it is a touch­screen, has a 360° hinge, and is more re­pairable and up­grade­able1.

The prob­lem is, for an over­all worse ex­pe­ri­ence, are you will­ing to pay 20 – 40% more? Because that’s the dif­fer­ence for the low-end Framework, which starts at $749 for a DIY edi­tion (accounting for a used 8GB stick of RAM and a 256GB SSD), or $799 for a pre-built.

For stu­dents like my nephew, Apple of­fers up the base model Neo for $499.

I posted a video cov­er­ing my ex­pe­ri­ence with the lap­top, and my nephew’s de­ci­sion to pick the Neo, over on YouTube:

Performance

I won’t get into the full depth of my bench­marks here—for that, check out these is­sues:

MacBook Neo bench­mark re­sults

Framework 12 - Intel i3 re­sults

But I will cover a few of the most telling re­sults.

First, Geekbench 6 is ter­ri­ble for larger sys­tems, but is a de­cent proxy for real-world per­for­mance for sys­tems with less than 16 cores. And the re­sult shows how much faster Apple’s low-end CPU cores are than Intel’s:

The Neo is also silent through­out (owing to its lack of a fan), and for all tasks I tested, al­most twice as ef­fi­cient:

But there’s one per­for­mance-re­lated area where the Framework pulls ahead—a lit­tle—and that’s sus­tained per­for­mance. When run­ning a heavy work­load like HPL (a FP64 HPC task, that taxes the CPU and RAM con­stantly for many min­utes), the Framework’s fans al­low it to throt­tle less than the Neo.

Both re­duce clock speed pretty quickly af­ter the ini­tial burst of work, but the Neo has to re­duce its clock speed fur­ther since there’s no fan to move heat away faster.

The Framework 12′s fan will quickly ramp up to 100%, and at that level, it pro­duces about 40 – 45 dBa of sound mea­sured near the com­puter (versus noth­ing dis­cernible over the 33 dBa noise floor of my stu­dio). And even with the fan, it’s not a big delta be­tween the two.

The GPU fares poorly on Intel’s side:

GravityMark is the best bench­mark I’ve found to give raw GPU per­for­mance, but gen­eral UI re­spon­sive­ness and things like 4K video play­back weren’t no­tice­ably dif­fer­ent be­tween the two sys­tems. It’s only in gam­ing or GPU-accelerated com­pute tasks where you’d no­tice.

It’s hard to cost down

The build qual­ity of the MacBook Neo is far above its price class—that’s Apple’s scale work­ing in their fa­vor. But it’s ob­vi­ous the Framework had to cut costs and com­pro­mise to hit their price and size tar­gets:

The dis­play’s col­ors are no­tice­ably off. It’s bet­ter than $300 Chromebooks I use, but far be­hind the Neo.

It’s thicker and heav­ier than the Neo, de­spite the screen be­ing smaller. I do like the 16:10 as­pect ra­tio.

The plas­tic top cover presses against the rub­ber feet when you fold the dis­play in tablet mode, and I have to keep clean­ing off dirt from that. Quite an­noy­ing, but I guess I’ll have to get use to the patina.

The speak­ers are pretty bad. The Neo’s speak­ers aren’t amaz­ing, but at least they don’t eat up the en­tire low end of what­ever you’re play­ing (and have a bet­ter stereo im­age).

The we­b­cam and mic are pass­able, and I re­ally like the phys­i­cal pri­vacy switches for both. I wish more com­put­ers had that.

The biggest win is the mod­u­lar ports. You can put four ‘expansion mod­ules’ in the lap­top, and get up to USB 3.2 Gen 2x1 per­for­mance out of each. The MacBook Neo just has 2 ports, and only one is USB 3.2 Gen 2x1.

I think Framework’s in a hard place with the Framework 12. Because it’s an odd di­men­sion, and be­cause they wanted a full 360° hinge for tablet mode, they had to com­pro­mise on the dis­play.

They could’ve com­pen­sated by mak­ing the touch­screen and sty­lus func­tion­al­ity that much bet­ter, but they’re us­ing older sty­lus tech, re­sult­ing in a worse tablet/​draw­ing mode than what you’d ex­pect on a mod­ern iPad or Surface dis­play.

My nephew chose the MacBook Neo, which I was us­ing around the stu­dio. It’ll be in­ter­est­ing to see how well I ad­just to us­ing the Framework as my ‘utility lap­top’. I thought the tablet mode would be more use­ful, but af­ter try­ing it that way, I re­al­ized a lap­top-as-a-tablet is quite cum­ber­some.

As I men­tion in my video, the Framework 12 is­n’t a bad lap­top, it’s just a bad value, es­pe­cially in com­par­i­son to the Neo.

Some of that is due to fac­tors out of Framework’s con­trol2. For now, I think Framework’s 13″ lineup is more com­pelling if you fa­vor re­pairabil­ity/​up­grade­abil­ity and top-tier Linux sup­port. (Lenovo seems to have a good op­tion too, now.)

While the Neo is prob­a­bly one of the eas­i­est Mac lap­tops to re­pair in re­cent mem­ory, the Framework 12 al­lows you to up­grade com­po­nents in­clud­ing a DDR5 SODIMM, 2230-sized NVMe SSD, WiFi card, and even four mod­u­lar ports around the sides. I out­fit­ted mine with 2x USB-C, 1x USB-A, and 1x full-size HDMI. ↩︎

While the Neo is prob­a­bly one of the eas­i­est Mac lap­tops to re­pair in re­cent mem­ory, the Framework 12 al­lows you to up­grade com­po­nents in­clud­ing a DDR5 SODIMM, 2230-sized NVMe SSD, WiFi card, and even four mod­u­lar ports around the sides. I out­fit­ted mine with 2x USB-C, 1x USB-A, and 1x full-size HDMI. ↩︎

For ex­am­ple, be­cause of their scale, Apple can con­vince a man­u­fac­turer to build a nice dis­play ex­actly to their spec­i­fi­ca­tion. Framework has to find an off-the-shelf com­po­nent to fit their needs. This usu­ally re­sults in some com­pro­mise. ↩︎

For ex­am­ple, be­cause of their scale, Apple can con­vince a man­u­fac­turer to build a nice dis­play ex­actly to their spec­i­fi­ca­tion. Framework has to find an off-the-shelf com­po­nent to fit their needs. This usu­ally re­sults in some com­pro­mise. ↩︎

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Reference: MCP is dead. Long live the CLI

After read­ing the above ar­ti­cle, we ran the ex­per­i­ments on our ac­tual stack. This doc­u­ment cov­ers the orig­i­nal ar­gu­ment, ad­di­tional re­search, and our mea­sure­ments.‍

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Update: Since these mea­sure­ments were taken, Claude Code has rolled out Tool Search with Deferred Loading, which loads MCP tool schemas on-de­mand and re­duces con­text us­age by 85%+. The con­text bloat de­scribed in Problem 1 is largely ad­dressed for users on cur­rent Claude Code ver­sions. The per­for­mance, de­bug­ging, and ar­chi­tec­tural ar­gu­ments be­low still ap­ply.

What’s Wrong with MCP

What’s Wrong with MCP

MCP (Model Context Protocol) con­nects LLMs to ex­ter­nal tools (GitHub, Linear, Notion, Slack, etc.).

Since its launch in late 2024, it’s been called “the USB-C of the AI ecosys­tem.” But de­vel­op­ers ac­tu­ally us­ing it day-to-day are start­ing to think dif­fer­ently.‍

TL;DR: MCP eats con­text, has low re­li­a­bil­ity, and over­laps with ex­ist­ing CLI/API.

Problem 1: It Devours the Context Window

The con­text win­dow is the LLM’s desk. When you con­nect MCP servers, tool de­f­i­n­i­tions alone take up a sig­nif­i­cant chunk of that desk.

Restaurant anal­ogy:

You sit down and 10 menus (MCP tool de­f­i­n­i­tions) are spread across the table

There’s no room left for ac­tual food (your work)

Every time you or­der, the menus have to be pulled out again

We ex­tracted and mea­sured the ac­tual tool de­f­i­n­i­tions from the MCP servers con­nected in our en­vi­ron­ment. With all 4 servers con­nected, 10.5% of the con­text win­dow is con­sumed by tool de­f­i­n­i­tions alone.

Measurement: Tool Definition Sizes (Quandri Stack)

Context Window Usage (all servers com­bined)‍

Linear alone ac­counts for over 12,800 to­kens. That’s 42 tool de­f­i­n­i­tions al­ways loaded, even if you only ever use get_is­sue and save_is­sue.

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Biggest Tools by Size

Problem 2: Low Operational Reliability

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Performance is a known is­sue. The au­thor of the orig­i­nal ar­ti­cle bench­marked Jira MCP against its REST API di­rectly and found MCP was 3x slower per call, and 9.4x slower on first call in­clud­ing ini­tial­iza­tion. This is­n’t Jira-specific, it’s ar­chi­tec­tural: every MCP server adds a process layer be­tween the LLM and the un­der­ly­ing API. The same over­head ap­plies to the Linear, Notion, and Slack servers in our stack.

Problem 3: Overlaps with Existing CLI/API

Token Comparison: MCP vs CLI for Linear Issue Lookup

How many to­kens does it cost to look up the same Linear is­sue?MCP con­sumes ~65x more to­kens than the CLI ap­proach.

[ CLI ap­proach: ~200 to­kens ] curl -s -H “Authorization: Bearer $LINEAR_TOKEN” \ -H “Content-Type: ap­pli­ca­tion/​json” \ -d ‘{“query”:“{ is­sue(id: "ISSUE-ID") { ti­tle state { name } as­signee { name } } }“}’ \ hthttps://​api.lin­ear.app/​graphql

-> Prompt (curl com­mand): ~50 to­kens -> Response: ~150 to­kens

[ MCP ap­proach: ~12,957 to­kens ] -> Tool de­f­i­n­i­tions (always loaded): ~12,807 to­kens (42 tools) -> Tool call + re­sponse: ~150 to­kens

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What Are the Alternatives?‍

Alternative 1: CLI-First Strategy

Provide CLI -> API -> docs, in that or­der. LLMs al­ready learned from man pages and StackOverflow.

Using ex­ist­ing CLI di­rectly:

No con­text wasted on tool de­f­i­n­i­tions

Same in­ter­face for hu­mans and AI, easy to de­bug

Freely com­pos­able with pipeli­nes‍

Alternative 2: Skills Pattern

If MCP is “spreading all menus on the table up­front”, Skills is “asking the li­brar­ian for only the book you need”.

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The key is em­bed­ding CLI us­age in­struc­tions in­side Skills. Combined with Alternative 1′s CLI-first strat­egy, this is most ef­fi­cient. For ex­am­ple, a Linear skill:‍

# Linear Issue Lookup Skill - Linear API: https://​api.lin­ear.app/​graphql - Auth: Bearer Token ($LINEAR_TOKEN env var) - Get is­sue: curl -s -H “Authorization: Bearer $LINEAR_TOKEN” -H “Content-Type: ap­pli­ca­tion/​json” -d ‘{“query”:“{ is­sue(id: "ISSUE-ID") { ti­tle state { name } as­signee { name } } }“}’ hthttps://​api.lin­ear.app/​graphql - Search is­sues (GraphQL): ad­just the query field for JQL-like fil­ter­ing - Results are JSON, parse with jq

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This way, the LLM only loads the above into con­text when the skill is in­voked. No need to carry 42 tool de­f­i­n­i­tions at all times. Just the CLI com­mands it needs.

So Is MCP Really Dead?

Not en­tirely. MCP is still valid when:

No CLI ex­ists for the ser­vice - web-only SaaS where MCP may be the only con­nec­tion method

Non-developer users - MCP is more ac­ces­si­ble for those who don’t use ter­mi­nals

Real-time bidi­rec­tional com­mu­ni­ca­tion - sce­nar­ios be­yond sim­ple re­quest-re­sponse

What About Databases?‍

What About Databases?‍

Short an­swer: it de­pends.

DBs are just query ex­e­cu­tion at the end of the day. LLMs al­ready know SQL and MongoDB queries well. Put DB info and CLI us­age in a skill, and it works fine with­out MCP. Just give it the schema and it writes the queries.

# Postgres Skill - Host: post­gres://​lo­cal­host:5432/​myapp - Tables: users (id, name, email), or­ders (id, user_id, sta­tus) - CLI: psql -h lo­cal­host -d myapp -c “SELECT * FROM users WHERE …”

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However, MCP has ad­van­tages for data­bases:

Query safety - MCP servers can en­force read-only mode and block dan­ger­ous queries at the server level. Skills + CLI can’t stop the LLM from run­ning DROP TABLE.

Credential pro­tec­tion - CLI ap­proach may ex­pose con­nec­tion strings in the prompt. MCP servers man­age cre­den­tials in­ter­nally.

But for most de­vel­oper work­flows, MCP is over-en­gi­neer­ing.

These days, every SaaS land­ing page has “MCP sup­ported” in the fea­ture list. Whether the MCP server is sta­ble or how much con­text it eats does­n’t mat­ter - the goal is check­ing the “we do MCP too” box. Same pat­tern as “AI-powered” and “blockchain-based” mar­ket­ing from years past. When users ac­tu­ally con­nect, they get dozens of tool de­f­i­n­i­tions loaded, ini­tial­iza­tion fail­ures, and mid-ses­sion crashes.

How We Use Skills at Quandri

At Quandri we use all three ap­proaches side by side, pick­ing what fits each ser­vice:

Bash + CLI for tools we al­ready use day-to-day (gh, psql, aws). Zero con­text cost, full flex­i­bil­ity, de­bugs straight in the ter­mi­nal.

Skills for re­peat­able multi-step work­flows like com­mit draft­ing and PR re­views. Loaded only when in­voked.

MCP for ser­vices with­out a strong CLI (Slack, Linear, Notion), and where team-wide auth or per­mis­sion scop­ing mat­ters (e.g., pro­duc­tion data­base ac­cess).

We don’t force one path. If a CLI al­ready ex­ists and au­then­ti­cates lo­cally, that’s usu­ally the light­est op­tion. If a ser­vice has no CLI or we need uni­form auth across the team, MCP earns its keep.

Conclusion

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Teaching well mat­ters more than con­nect­ing every­thing.

For us, re­plac­ing MCP servers with Skills that wrap ex­ist­ing CLIs freed up ~21K to­kens of con­text, re­moved init fail­ures from our daily work­flow, and kept de­bug­ging in the ter­mi­nal where it be­longs.

Load only the tools you need, only when you need them, with CLI in­struc­tions baked in. MCP might evolve to solve these prob­lems, but right now, Skills win.‍

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‍Measurement method­ol­ogy: Tool de­f­i­n­i­tion sizes were mea­sured by ex­tract­ing the JSON schema of each tool (name + de­scrip­tion + pa­ra­me­ters) from ac­tu­ally loaded MCP servers in our Claude Code en­vi­ron­ment. Token es­ti­mates use the ~4 chars/​to­ken heuris­tic. Full server es­ti­mates are ex­trap­o­lated from sam­pled tool av­er­ages.

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