Because I am known to write us­ing a foun­tain pen on pa­per, a num­ber of peo­ple have pointed me to this post and its un­der­ly­ing re­search. I won’t re­hash what is said in those sources, but the gist of it is that when you write things down by hand you’re re­cruit­ing more of your brain, which is a good thing.

I’m not an ex­pert on how the brain works, but I can say that, when writ­ing by hand, one is con­tin­u­ally solv­ing a se­ries of small prob­lems hav­ing to do with the spac­ing of words, how let­ters are con­nected, the cross­ing of the let­ter t (sometimes more than one in the same word) and the dot­ting of the let­ters i and j, and how to ac­com­plish all of those things through co­or­di­nated move­ments not just of the fin­gers but of the whole arm. All of that has to be in­te­grated in real time with what­ever is hap­pen­ing on a more ab­stract level in the brain’s pro­cess­ing of ideas and im­agery.

Concurrently I have been fol­low­ing dis­course on Reddit and other sources about how wide­spread use of AI has forced ed­u­ca­tors to re­turn to the long-aban­doned prac­tice of hav­ing their stu­dents take ex­ams in per­son by writ­ing things out long­hand in blue books. This has cre­ated new chal­lenges for stu­dents who never re­ally learned how to write by hand, and for teach­ers who can’t make sense of their stu­dents’ ter­ri­ble hand­writ­ing.

About twenty-five years ago I stopped com­pos­ing at the key­board and switched over to foun­tain pen on pa­per. Since then I have writ­ten many thou­sands of pages that way. The man­u­script of The Baroque Cycle was a stack of hand­writ­ten pages 42 inches high, which for a time was on dis­play at the Museum of Science Fiction in Seattle. With the ex­cep­tion of The Rise and Fall of D.O.D.O., which I co-wrote with Nicole Galland by email­ing Word files back and forth, every book I’ve writ­ten since then has been com­posed with foun­tain pen on pa­per.

Every so of­ten, when I’m sign­ing books at a book tour ap­pear­ance, some­one will come up to me and say some­thing like “you must have writer’s cramp!” or “is your hand sore yet?” I never have the time to pro­vide a full an­swer. If I did, how­ever, my an­swer would be that never, at any time dur­ing a quar­ter of a cen­tury dur­ing which I have spent a sub­stan­tial frac­tion of each work­ing day writ­ing by hand, have I ex­pe­ri­enced even the faintest traces of so-called “writer’s cramp” or any other such hob­gob­lins.

Yet I can re­mem­ber get­ting a sore hand when I was a kid writ­ing out as­sign­ments in school. Many peo­ple prob­a­bly re­mem­ber such ex­pe­ri­ences and as­sume, rea­son­ably enough, that it’s a nat­ural con­se­quence of writ­ing by hand for any length of time. This is not the case.

Here are some fairly sim­ple dos and don’ts for peo­ple who want to reap the ben­e­fits of writ­ing by hand.

It’s pretty ob­vi­ous that you’re go­ing to get tired faster if your mus­cles have to ex­ert more force. Writing with a pen­cil re­quires sig­nif­i­cantly more force than writ­ing with a good pen. Old-school ball­points with thick ink are no bet­ter. You can see vi­sual ev­i­dence of this if you flip over a sheet of pa­per on which you’ve been writ­ing with a pen­cil or an old ball­point. The pa­per will bear a vis­i­ble im­print where it was pressed down by the writ­ing in­stru­ment. Often that will con­tinue down into the stack of pa­per be­neath. That’s be­cause you had to push hard. This does­n’t hap­pen with a foun­tain pen. If the nib is work­ing prop­erly you need to ex­ert very lit­tle force. The nib is ba­si­cally skat­ing on the lit­tle lake of ink that it has just laid down.

Pains me to say it, but roller­ball gel pens are about as good as foun­tain pens on this front.

It might then seem rea­son­able to think that writ­ing with a sty­lus on an iPad or sim­i­lar would be best, since no force is needed and fric­tion is min­i­mized. I don’t think this is true. A small amount of fric­tion is ac­tu­ally de­sir­able. You don’t want the tip of the writ­ing in­stru­ment to skid out of con­trol. Your brain and your lit­tle hand mus­cles are re­ly­ing on a lit­tle bit of fric­tion. Since I’m writ­ing this dur­ing the World Cup, I’ll make a soc­cer anal­ogy. Soccer play­ers have spent many hours drib­bling balls across play­ing fields, and they’ve in­ter­nal­ized the physics—they know about how far the ball is go­ing to travel when they kick it a cer­tain way, and how of­ten they need to give it an­other kick to keep it mov­ing. If you put them on a gi­ant, fric­tion­less air hockey table, all of that knowl­edge would be­come use­less. Every touch on the ball would send it out of con­trol. Dribbling the ball down the field would be­come more tir­ing be­cause they’d have to be mak­ing con­tin­ual ef­forts to con­trol the bal­l’s move­ment. Relying on a lit­tle bit of fric­tion re­duces the amount of men­tal and phys­i­cal ef­fort.

The com­bi­na­tion of foun­tain pens and pa­per em­bod­ies a bal­ance that has been worked out over a long span of time by peo­ple who write a lot. This phe­nom­e­non is called “tooth” by afi­ciona­dos. Removing fric­tion by us­ing a hard sty­lus on glass will ac­tu­ally make the process more tir­ing.

Too much fric­tion, and too lit­tle fric­tion, are both more tir­ing than just a lit­tle bit of fric­tion, and that’s the bal­ance that is re­flected in the foun­tain pen/​pa­per tech­nol­ogy.

Rresults vary when you use var­i­ous pens on var­i­ous kinds of pa­per. Generally I get the worst re­sults on cheap printer pa­per, be­cause it wicks ink out of the nib too fast, and so cre­ates fat, blurry lines. Often I have the same prob­lem with yel­low le­gal pads. But al­most any pa­per in a blank note­book, or higher-grade printer pa­per with at least 25% cot­ton con­tent, works fine. I’ve learned over time that some of my foun­tain pens work bet­ter with cer­tain kinds of pa­per than oth­ers, so I match them up with­out hav­ing to think about it too hard.

Here’s a 300 dpi scan of tests I did with three dif­fer­ent pens on var­i­ous types of pa­per. You might have to zoom in to see much dif­fer­ence.

The pen on the left is a Jorg Hysek with a wide nib, and you can see that the cheap printer pa­per soaked up a lot of ink and left a thicker, fuzzier line. The le­gal pad was­n’t much bet­ter. Everything else ba­si­cally worked. The 100% cot­ton pa­per is from a box I pur­chased a long time ago - it was mar­keted for print­ing re­sumes, back in the days when peo­ple printed re­sumes. It is the tooth­iest of all these pa­pers and felt no­tice­ably scratch­ier. I guess it goes with­out say­ing that fancy Italian pa­per is the best, but the comp book and mole­sk­ine work per­fectly well with just about any pen.

(For those scor­ing at home, the mid­dle pen is a Diplomat Aero and the one on the right is a Monteverde Invincia)

If the pa­per is thin, writ­ing on one side can bleed through to the other, so the re­sults can be slightly harder to read if you write on both sides. Which leads me to:

The ecosys­tem is­n’t go­ing to col­lapse if you use more pa­per. It’s cheap. Focus on what’s im­por­tant here: your brain and your time. Write on one side. Trying to cram more words into a sheet will take you out of your nat­ural and com­fort­able writ­ing style and make you tired. Just buy a shit­load of pa­per or note­books or what­ever it is you want to use, and use it.

There’s a rea­son cur­sive was in­vented. Don’t even think about not us­ing it. It is far less tir­ing than print­ing one let­ter at a time. I learned cur­sive as a child. Then I went for many years with­out us­ing it much, and for­got some of it. Later I re-learned it by sit­ting in my kid’s el­e­men­tary school class­room dur­ing a par­ent-teacher con­fer­ence and ex­am­in­ing the forms printed on a long strip above the chalk­board (I still re­mem­bered how to do the lower-case let­ters, but I had for­got­ten some of the cap­i­tals).

Legibility was more im­por­tant back in the day when writ­ten doc­u­ments had to be read by other peo­ple. Hence the need for ex­act­ing pen­man­ship, taught in schools to long-suf­fer­ing chil­dren. This is prob­a­bly the source of a lot of angst around writer’s cramp and ink dis­as­ters. Today, if you’re writ­ing things down with ink on pa­per, you’re prob­a­bly writ­ing just for your­self, or per­haps for fam­ily mem­bers who can learn to rec­og­nize your hand­writ­ing.

To judge from the way peo­ple talk, a lot of them have mem­o­ries of foun­tain pen dis­as­ters where ink got all over the place for some rea­son. Or per­haps it’s just gen­er­a­tional trauma, handed down in an oral tra­di­tion. If the pen is work­ing cor­rectly, ink can only come out of it so fast. A cou­ple of rare ex­cep­tions:

If the pen’s ink reser­voir is partly empty, so that it con­tains an air bub­ble, and if it’s po­si­tioned nib down, then, when you go up in an air­plane, the bub­ble will ex­pand as the am­bi­ent pres­sure drops, forc­ing ink out the nib. Once I fig­ured that out, I got in the habit of mak­ing sure my pens were po­si­tioned nib up when tak­ing off in an air­plane. If I have time I’ll also re­fill the pen be­fore de­par­ture, to min­i­mize the size of the air bub­ble.

Sometimes if a pen gets dirty, or if the nib is some­how dam­aged, the ink will stop com­ing out and you can restart it by giv­ing it a lit­tle shake. If you do it just right, the ink flow restarts with­out in­ci­dent, but if you overdo it, a few drops of ink might shoot out onto the page and be­come blots. This sce­nario hap­pens a few times of year for me, only with one pen that has this prob­lem. I blot it with a piece of scrap pa­per and move on.

Just have note­books ly­ing around, or on your per­son. Write gro­cery lists, doo­dles, notes on meet­ings, to-do lists, or stray ideas. Journal. Copy out good lines from books. Anything that has your men­tal fo­cus will have a more en­dur­ing pres­ence in your brain if you write it down.

I am left handed. I have never had any trou­ble with my hand smear­ing the ink. Yet every con­ver­sa­tion I have about foun­tain pens leads to some­one claim­ing that it can never work for them be­cause they are left handed. I have no idea what they’re talk­ing about. When I was a child, writ­ing at length with pen­cil, the side of my hand some­times be­came gray from graphite picked up as my hand rubbed across the page. And some­times I have got ink on my hand when us­ing a ball­point pen that left an ink glob on the pa­per. But with foun­tain pens it’s easy to find a pen/​pa­per com­bi­na­tion such that the ink soaks into the pa­per and dries quickly enough that it does­n’t smudge when you’re writ­ing the next line. Here’s a sim­ple demon­stra­tion of dry­ing time and how it works with two pens: first a foun­tain pen and then a Pilot G-2 gel pen.

Obviously the Pilot gel pen ink dries faster, and so that might be a bet­ter choice for peo­ple who are re­ally wor­ried about smudg­ing.

Most mod­ern pens al­low you to choose be­tween us­ing pre­loaded plas­tic ink car­tridges and a plunger that en­ables you to draw ink up out of a bot­tle by hand. I use both. Start with the ink car­tridges, es­pe­cially if you travel. There’s no need to com­pli­cate mat­ters by mess­ing around with bot­tles. Since I do a lot of work from one lo­ca­tion, I have a cor­ner of a table­top set up there with ink bot­tles and a folded-up pa­per towel for wip­ing off the nib af­ter it’s filled (I have been us­ing the same pa­per towel for about twenty years). In the­ory this works bet­ter in the long term be­cause it al­lows you to flush the nib by forc­ing ink in and out of it a cou­ple of times when­ever you re­fill. In prac­tice I see no dif­fer­ence at all - pens that I re­fill with car­tridges don’t get clogged.

Even if every­thing works per­fectly you’ll end up with the oc­ca­sional ink-smudged fin­ger. It will wash off quickly - the ink is wa­ter-sol­u­ble. Until then, con­sider it a mark of dis­tinc­tion.

If you’re new to this I think it makes most sense to start by con­sid­er­ing what kind of pa­per is go­ing to work best in your life. Are you writ­ing loose­leaf, or in note­books? Legal pads? Blue books? Remember, it’s okay to use lots of pa­per, so pick some­thing that is­n’t too pre­cious and that is easy to re­plen­ish. I use a lot of mole­sk­ine note­books and Mead comp books, which I can buy in bulk on­line. For com­pos­ing fic­tion I use fancy loose­leaf pa­per.

If you have ac­cess to a store where they sell foun­tain pens, take some of that pa­per there and see what works best. If you’re work­ing with cheaper, thin­ner pa­per, start with finer nibs and work up to fat­ter ones un­til you start to see bleed-through.

Buy cheaper pens un­til you know what you like. I doubt there’s much of a dif­fer­ence be­tween cheaper and more ex­pen­sive foun­tain pens in terms of their ac­tual per­for­mance. What you’re pay­ing for, in an ex­pen­sive pen, is fancy ma­te­ri­als and styling. For ex­am­ple, if you look at the Pilot Vanishing Point line of pens - an in­ge­nious foun­tain pen that you can click, like an old-fash­ioned ball­point, to re­tract the nib in­side the bar­rel - fancier ver­sions cost five times as much as the base model.

In all hon­esty, the Pilot G-2 gel pens are go­ing to give you 80% of what you could ex­pect from a foun­tain pen for min­i­mal cost.

On the other hand, a ten-pack of Pilot G-2 gel pens goes for about twenty bucks. For the same amount you can buy a sim­ple but com­pletely ser­vice­able foun­tain pen that will last longer than you will.

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Claude Opus 5 is avail­able to­day. It’s a thought­ful and proac­tive model that comes close to the fron­tier in­tel­li­gence of Claude Fable 5 at half the price.

On cod­ing and knowl­edge work eval­u­a­tions like Frontier-Bench and GDPval-AA, Opus 5 is the new state-of-the-art, though it re­mains be­hind Mythos 5 on cy­ber­se­cu­rity tasks.

Opus 5 is de­signed to be used every day: it works more ef­fi­ciently than other mod­els. It’s the new de­fault model on Claude Max, and the strongest model on Claude Pro.

Performance and cost-ef­fec­tive­ness

Claude Opus 5 pro­vides greatly im­proved per­for­mance for the same cost as its pre­de­ces­sor, Opus 4.8. The charts in this sec­tion show how per­for­mance changes ac­cord­ing to the mod­el’s ef­fort set­ting, which cus­tomers can use to op­ti­mize for in­tel­li­gence or con­serve to­kens for faster and cheaper re­sults.

Opus 5 ex­cels on valu­able soft­ware en­gi­neer­ing tasks. For ex­am­ple, on Frontier-Bench v0.1, Opus 5 sur­passes all other mod­els, and more than dou­bles Opus 4.8’s per­for­mance at a lower cost per task. On CursorBench 3.2, at max ef­fort, the model per­forms within 0.5% of Fable 5’s peak score, but at half the cost per task; it also achieves greater per­for­mance at a given cost than all other mod­els on high, xhigh, and max ef­fort.

We see sim­i­lar re­sults on knowl­edge work and prob­lem-solv­ing tasks. For ex­am­ple:

On ARC-AGI 3, an eval­u­a­tion where the model has to solve novel prob­lems, Opus 5’s score is three times as high as the next-best model.

On Zapier AutomationBench, which mea­sures whether mod­els can com­plete busi­ness tasks from start to fin­ish, Opus 5’s pass rate is around 1.5× the next-best model for the same cost per task. Even at its low­est ef­fort set­ting, Opus 5 passes more tasks than any other model.

On OSWorld 2.0, a com­puter use bench­mark, Opus 5 out­per­forms every other model at any given cost, sur­pass­ing Fable 5’s best re­sult at just over a third of the cost.

It’s also our best and most cost-ef­fi­cient model on sev­eral re­lated eval­u­a­tions:

Opus 5 is a mean­ing­ful im­prove­ment over Opus 4.8 for sci­en­tific re­search. It shows bet­ter per­for­mance than Opus 4.8 on every one of our life sci­ences eval­u­a­tions, which cover top­ics in­clud­ing struc­tural bi­ol­ogy, or­ganic chem­istry, and bioin­for­mat­ics. Its im­prove­ments are most no­table on or­ganic chem­istry tasks, like in­fer­ring mol­e­c­u­lar struc­tures from spec­troscopy data (it scores 10.2 per­cent­age points higher than Opus 4.8 on our in­ter­nal bench­mark), and on pro­tein-re­lated tasks like pre­dict­ing how vari­a­tions in a pro­tein’s se­quence af­fect how it func­tions (here, it scores 7.7 per­cent­age points higher).

Finally, Opus 5 is ca­pa­ble of pro­duc­ing much stronger vi­sual out­puts:

Working with Claude Opus 5

Claude Opus 5 is much stronger at ver­i­fy­ing its work and it­er­at­ing care­fully un­til it suc­ceeds. In eval­u­a­tions and early-ac­cess test­ing, we and our users found many ex­am­ples of Opus 5’s agency and thor­ough­ness:

On one Frontier-Bench task, Opus 5 was given a draw­ing of a ma­chine part and asked to write code to re­build it as a 3D FreeCAD model. However, in this task, the model was in­ten­tion­ally given no way to di­rectly view the draw­ing. Opus 5 re­sponded by writ­ing its own com­puter vi­sion pipeline to pull the geom­e­try from the raw pix­els, then re­con­structed the full ma­chine part. It suc­ceeded in do­ing so re­peat­edly; no com­pet­ing model with the same setup could solve it af­ter five at­tempts.

Given a real bug in a pop­u­lar open-source pack­age man­ager, Opus 5 found the root cause and fixed an edge case that the com­mu­ni­ty’s patch had missed. A com­pet­ing model fixed only the sur­face symp­tom (not the un­der­ly­ing cause), then re­ported the bug re­solved.

An en­gi­neer at a trad­ing firm used Opus 5 to build a mar­ket data feed for a new ex­change in a sin­gle ses­sion. Previous mod­els could not com­plete this task at all, even given ex­ten­sive plans from the en­gi­neer. Finding no live feed to val­i­date against, Opus 5 even built its own test har­ness to check that its code parsed the ex­change’s data cor­rectly.

Below are fur­ther re­ports from our early-ac­cess cus­tomers on their ex­pe­ri­ence of work­ing with Opus 5:

On FrontierCode 1.1, Claude Opus 5 ap­proaches Fable-level per­for­mance at half the cost. Within Devin, it also shows par­tic­u­lar strength on dif­fi­cult de­bug­ging and root-cause analy­sis tasks.

On FrontierCode 1.1, Claude Opus 5 ap­proaches Fable-level per­for­mance at half the cost. Within Devin, it also shows par­tic­u­lar strength on dif­fi­cult de­bug­ging and root-cause analy­sis tasks.

Claude Opus 5 de­liv­ers near Fable 5 in­tel­li­gence at Opus speed and cost. On CursorBench it’s just un­der Fable 5 and has many of the same be­hav­iors. We are ex­cited to see how de­vel­op­ers use it in Cursor.

Claude Opus 5 de­liv­ers near Fable 5 in­tel­li­gence at Opus speed and cost. On CursorBench it’s just un­der Fable 5 and has many of the same be­hav­iors. We are ex­cited to see how de­vel­op­ers use it in Cursor.

Claude Opus 5 topped Zapier’s AutomationBench leader­board with­out spend­ing more to­kens than prior Claude mod­els. It took a raw ac­count-health work­book and ran a full churn-pre­ven­tion se­quence end to end: flag­ging at-risk ac­counts, alert­ing the right owner, and sum­ma­riz­ing for re­ten­tion ops. Previous mod­els did­n’t pass; Opus 5 hit 100%.

Claude Opus 5 topped Zapier’s AutomationBench leader­board with­out spend­ing more to­kens than prior Claude mod­els. It took a raw ac­count-health work­book and ran a full churn-pre­ven­tion se­quence end to end: flag­ging at-risk ac­counts, alert­ing the right owner, and sum­ma­riz­ing for re­ten­tion ops. Previous mod­els did­n’t pass; Opus 5 hit 100%.

On our ge­nomics analy­sis work, Claude Opus 5 be­haves more like a care­ful sci­en­tist than any model we’ve run. It reaches for the right sta­tis­ti­cal tests to rule out con­founders, cross-checks its own re­sults by in­de­pen­dent meth­ods, and stays on track through long multi-step analy­ses.

On our ge­nomics analy­sis work, Claude Opus 5 be­haves more like a care­ful sci­en­tist than any model we’ve run. It reaches for the right sta­tis­ti­cal tests to rule out con­founders, cross-checks its own re­sults by in­de­pen­dent meth­ods, and stays on track through long multi-step analy­ses.

Claude Opus 5 came out ahead of every model in its fam­ily on our in­ter­nal evals. It is­n’t just bet­ter on our hard­est agen­tic cod­ing tasks, up 22% over Opus 4.7, it’s stead­ier, with far less vari­ance run to run. For the mil­lions of builders on Lovable, that con­sis­tency is the whole game. Reliable re­sults, build af­ter build.

Claude Opus 5 came out ahead of every model in its fam­ily on our in­ter­nal evals. It is­n’t just bet­ter on our hard­est agen­tic cod­ing tasks, up 22% over Opus 4.7, it’s stead­ier, with far less vari­ance run to run. For the mil­lions of builders on Lovable, that con­sis­tency is the whole game. Reliable re­sults, build af­ter build.

Claude Opus 5 is the biggest leap in the Opus fam­ily since 4.5. On the same full-stack app builds, the front end shows it first: the best an­i­ma­tions, games, and 3D work we have seen from an Opus model.

Claude Opus 5 is the biggest leap in the Opus fam­ily since 4.5. On the same full-stack app builds, the front end shows it first: the best an­i­ma­tions, games, and 3D work we have seen from an Opus model.

We’re lov­ing Claude Opus 5. For the kind of open-ended an­a­lyt­i­cal work our agent han­dles, it’s a strict up­grade over Opus 4.8, and the gains are biggest ex­actly where it mat­ters: the harder, vaguer tasks. Responses are clearer and more con­cise, and we see im­proved ef­fi­ciency at higher ef­fort lev­els too.

We’re lov­ing Claude Opus 5. For the kind of open-ended an­a­lyt­i­cal work our agent han­dles, it’s a strict up­grade over Opus 4.8, and the gains are biggest ex­actly where it mat­ters: the harder, vaguer tasks. Responses are clearer and more con­cise, and we see im­proved ef­fi­ciency at higher ef­fort lev­els too.

Claude Opus 5 is a strik­ing im­prove­ment over Opus 4.8 for the fi­nan­cial re­search work­flows our an­a­lysts run every day. It stands out on nu­mer­i­cal rea­son­ing, table work, and sharper crit­i­cal think­ing where pre­ci­sion mat­ters.

Claude Opus 5 is a strik­ing im­prove­ment over Opus 4.8 for the fi­nan­cial re­search work­flows our an­a­lysts run every day. It stands out on nu­mer­i­cal rea­son­ing, table work, and sharper crit­i­cal think­ing where pre­ci­sion mat­ters.

Claude Opus 5 de­liv­ers the in­dus­try in­tel­li­gence and ac­cu­racy that is es­sen­tial for the analy­sis of spe­cial­ized en­ter­prise con­tent. Box found that Opus 5 out­per­forms Opus 4.8 by 8% and de­liv­ers no­table per­for­mance gains in the data analy­sis (11% im­prove­ment) and due dili­gence (17% im­prove­ment) work­flows that tech­nol­ogy, health­care, and pub­lic sec­tor or­ga­ni­za­tions rely on daily.

Claude Opus 5 de­liv­ers the in­dus­try in­tel­li­gence and ac­cu­racy that is es­sen­tial for the analy­sis of spe­cial­ized en­ter­prise con­tent. Box found that Opus 5 out­per­forms Opus 4.8 by 8% and de­liv­ers no­table per­for­mance gains in the data analy­sis (11% im­prove­ment) and due dili­gence (17% im­prove­ment) work­flows that tech­nol­ogy, health­care, and pub­lic sec­tor or­ga­ni­za­tions rely on daily.

Claude Opus 5 is a clear gen­er­a­tional step up from Opus 4.8. Over one week­end I gave it a chief-of-staff role over my dev en­vi­ron­ments: it built its own mon­i­tor, drove each box, and pulled me in only for the judg­ment calls.

Claude Opus 5 is a clear gen­er­a­tional step up from Opus 4.8. Over one week­end I gave it a chief-of-staff role over my dev en­vi­ron­ments: it built its own mon­i­tor, drove each box, and pulled me in only for the judg­ment calls.

Claude Opus 5 made large scale changes across our Fundamental Research Assistant code­base, adapt­ing to feed­back through­out an agen­tic work­flow and ex­plain­ing its rea­son­ing more clearly than any model we’ve used. It han­dled work we would nor­mally have bro­ken into much smaller pieces.

Claude Opus 5 made large scale changes across our Fundamental Research Assistant code­base, adapt­ing to feed­back through­out an agen­tic work­flow and ex­plain­ing its rea­son­ing more clearly than any model we’ve used. It han­dled work we would nor­mally have bro­ken into much smaller pieces.

On some of our hard­est fi­nan­cial-mod­el­ing tasks, Claude Opus 5 is a clear step up from Opus 4.8 in both ac­cu­racy and ef­fi­ciency. Its per­for­mance floor is ma­te­ri­ally higher, es­pe­cially on deep fi­nance do­main logic. Across ef­fort lev­els it av­er­aged 9 per­cent­age points higher ac­cu­racy with a third fewer turns and tool calls and 60% less time.

On some of our hard­est fi­nan­cial-mod­el­ing tasks, Claude Opus 5 is a clear step up from Opus 4.8 in both ac­cu­racy and ef­fi­ciency. Its per­for­mance floor is ma­te­ri­ally higher, es­pe­cially on deep fi­nance do­main logic. Across ef­fort lev­els it av­er­aged 9 per­cent­age points higher ac­cu­racy with a third fewer turns and tool calls and 60% less time.

Claude Opus 5 checks its own work the way a real fron­tend de­vel­oper would. On our bench­mark it opened its pages in a browser at desk­top and phone widths, caught a prod­uct hid­den be­low the mo­bile fold and an off-screen check­out but­ton, and fixed both be­fore hand­ing the work back.

Claude Opus 5 checks its own work the way a real fron­tend de­vel­oper would. On our bench­mark it opened its pages in a browser at desk­top and phone widths, caught a prod­uct hid­den be­low the mo­bile fold and an off-screen check­out but­ton, and fixed both be­fore hand­ing the work back.

Claude Opus 5 is a clear step up in per­for­mance on le­gal agent work com­pared to prior Opus mod­els, and we saw the biggest gains in prac­tice ar­eas like cor­po­rate gov­er­nance and ar­bi­tra­tion. We were also im­pressed with Opus 5’s abil­ity to main­tain qual­ity at lower rea­son­ing lev­els, achiev­ing sim­i­lar per­for­mance while gen­er­at­ing 26% fewer to­kens on av­er­age com­pared to Opus 4.8 at max rea­son­ing.

Claude Opus 5 is a clear step up in per­for­mance on le­gal agent work com­pared to prior Opus mod­els, and we saw the biggest gains in prac­tice ar­eas like cor­po­rate gov­er­nance and ar­bi­tra­tion. We were also im­pressed with Opus 5’s abil­ity to main­tain qual­ity at lower rea­son­ing lev­els, achiev­ing sim­i­lar per­for­mance while gen­er­at­ing 26% fewer to­kens on av­er­age com­pared to Opus 4.8 at max rea­son­ing.

Claude Opus 5’s biggest gains for us are on longer-hori­zon work: build­ing a full deck, then re­vis­ing it. Artifact qual­ity is what de­cides which model we ship, and this is the clear­est step up we’ve seen — bet­ter vi­sual un­der­stand­ing, cleaner for­mat­ting, fewer slide is­sues.

Claude Opus 5’s biggest gains for us are on longer-hori­zon work: build­ing a full deck, then re­vis­ing it. Artifact qual­ity is what de­cides which model we ship, and this is the clear­est step up we’ve seen — bet­ter vi­sual un­der­stand­ing, cleaner for­mat­ting, fewer slide is­sues.

Claude Opus 5’s judg­ment is what stands out. Handing off a PR, it does­n’t rush to pub­lish: it ver­i­fies the branches, checks the tem­plate, and thinks through test im­pli­ca­tions so the hand­off is clean. The older mod­els tended to jump ahead and get caught on our checks.

Claude Opus 5’s judg­ment is what stands out. Handing off a PR, it does­n’t rush to pub­lish: it ver­i­fies the branches, checks the tem­plate, and thinks through test im­pli­ca­tions so the hand­off is clean. The older mod­els tended to jump ahead and get caught on our checks.

During a rearchi­tect­ing ses­sion, Claude Opus 5 pushed back on a de­sign I pro­posed, and it did­n’t fold when I in­sisted. Instead, it ex­plained ex­actly what was valu­able in my idea, nar­rowed its ob­jec­tion to a sin­gle de­sign ques­tion, and pro­posed a com­pro­mise that kept the good part while fix­ing the flaw. That’s the kind of judg­ment that lets us trust it with less over­sight.

During a rearchi­tect­ing ses­sion, Claude Opus 5 pushed back on a de­sign I pro­posed, and it did­n’t fold when I in­sisted. Instead, it ex­plained ex­actly what was valu­able in my idea, nar­rowed its ob­jec­tion to a sin­gle de­sign ques­tion, and pro­posed a com­pro­mise that kept the good part while fix­ing the flaw. That’s the kind of judg­ment that lets us trust it with less over­sight.

On first-turn red­lines, Claude Opus 5 scored the high­est of any model we tested, nearly dou­ble Opus 4.8. Commenting is bet­ter too: on NDAs it gets to the red­line in less time and with fewer passes, with ac­cu­racy main­tained or bet­ter.

On first-turn red­lines, Claude Opus 5 scored the high­est of any model we tested, nearly dou­ble Opus 4.8. Commenting is bet­ter too: on NDAs it gets to the red­line in less time and with fewer passes, with ac­cu­racy main­tained or bet­ter.

Claude Opus 5 writes clean, tight diffs with no dead code, and it’s the stronger haz­ard spot­ter on sub­tle, code­base-spe­cific is­sues. We’re adopt­ing it for pro­duc­tion work­loads.

Claude Opus 5 writes clean, tight diffs with no dead code, and it’s the stronger haz­ard spot­ter on sub­tle, code­base-spe­cific is­sues. We’re adopt­ing it for pro­duc­tion work­loads.

We will def­i­nitely mi­grate a num­ber of use cases in Cosmos, our uni­fied agent plat­form. We’re look­ing for­ward to in­creas­ingly us­ing Claude Opus 5 for code re­view, and I am con­fi­dent in say­ing we would rather peo­ple be us­ing Opus 5 than Opus 4.8.

We will def­i­nitely mi­grate a num­ber of use cases in Cosmos, our uni­fied agent plat­form. We’re look­ing for­ward to in­creas­ingly us­ing Claude Opus 5 for code re­view, and I am con­fi­dent in say­ing we would rather peo­ple be us­ing Opus 5 than Opus 4.8.

What stands out about Claude Opus 5 is judg­ment. It thinks harder be­fore it writes a sin­gle line, catches its own log­i­cal faults dur­ing plan­ning rather than af­ter the fact, and rea­sons about why an an­swer is right, not just whether it works. It’s the clear­est jump in prob­lem-solv­ing we’ve seen from one Claude model to the next, and we’re look­ing for­ward to see­ing it adopted in JetBrains IDEs.

What stands out about Claude Opus 5 is judg­ment. It thinks harder be­fore it writes a sin­gle line, catches its own log­i­cal faults dur­ing plan­ning rather than af­ter the fact, and rea­sons about why an an­swer is right, not just whether it works. It’s the clear­est jump in prob­lem-solv­ing we’ve seen from one Claude model to the next, and we’re look­ing for­ward to see­ing it adopted in JetBrains IDEs.

Claude Opus 5 is the strongest Opus model we’ve tested on our trad­ing bench­mark, and it gets there us­ing roughly a sev­enth of the rea­son­ing to­kens and un­der half the la­tency of Opus 4.8. Better an­swers at a frac­tion of the com­pute.

Claude Opus 5 is the strongest Opus model we’ve tested on our trad­ing bench­mark, and it gets there us­ing roughly a sev­enth of the rea­son­ing to­kens and un­der half the la­tency of Opus 4.8. Better an­swers at a frac­tion of the com­pute.

01 /

22

Alignment and safety

Alignment. During pre-de­ploy­ment test­ing, our au­to­mated be­hav­ioral au­dit found Opus 5 to be our most aligned model to date (as shown in the graph be­low). It ad­heres to Claude’s Constitution bet­ter than Opus 4.8, Sonnet 5, or Fable 5; ex­hibits the low­est rates of de­cep­tive be­hav­ior; and is the least sus­cep­ti­ble to be­ing tricked into mis­use. It’s also our safest model yet in terms of avoid­ing reck­less ac­tions that could have hard-to-re­verse side ef­fects.

Safety. Opus 5 does not ad­vance the fron­tier in risky, dual-use ca­pa­bil­i­ties. In rig­or­ous eval­u­a­tions con­ducted along­side pri­vate-sec­tor and gov­ern­ment part­ners, we found it re­mains be­hind Mythos 5 in both bi­ol­ogy re­search and of­fen­sive cy­ber­se­cu­rity. More in­for­ma­tion about these eval­u­a­tions can be found in our System Card.

As with its pre­de­ces­sor, Opus 4.8, we’ve in­ten­tion­ally avoided train­ing Opus 5 on cy­ber tasks. The model has nev­er­the­less im­proved sub­stan­tially on these tasks as a re­sult of be­com­ing more gen­er­ally ca­pa­ble, and it comes close to Mythos 5 at find­ing cy­ber­se­cu­rity vul­ner­a­bil­i­ties. However, it re­mains sub­stan­tially be­hind Mythos 5 on the ex­ploita­tion of those vul­ner­a­bil­i­ties—that is, in turn­ing vul­ner­a­bil­i­ties into ma­te­r­ial cy­ber threats.

This is il­lus­trated by Opus 5’s per­for­mance on OSS-Fuzz, an eval­u­a­tion we’ve de­vel­oped to as­sess how well mod­els can find and then ex­ploit vul­ner­a­bil­i­ties with­out ex­ten­sive hu­man guid­ance. Although Mythos 5 and Opus 5 iden­tify vul­ner­a­bil­i­ties with sim­i­lar suc­cess, Opus 5’s score on the de­vel­op­ment of ex­ploits is far be­hind that of Mythos 5.

Safeguards for Opus 5

Claude Opus 5’s safe­guards are de­signed to al­low ben­e­fi­cial uses of the model in both cy­ber­se­cu­rity and bi­ol­ogy. They are sim­i­lar to those we ap­plied to Opus 4.8, with the ex­cep­tion of some stronger guardrails on a nar­row range of cy­ber tasks.

Cybersecurity. Opus 5’s cy­ber clas­si­fiers are pro­por­tion­ally less re­stric­tive than those on Fable 5. They al­low Opus 5 to find vul­ner­a­bil­i­ties in source code, but block “binary-based” vul­ner­a­bil­ity scan­ning (a method more likely to be as­so­ci­ated with ma­li­cious ac­tors), pen­e­tra­tion test­ing, and ex­ploit gen­er­a­tion.

Based on our test­ing, we ex­pect the clas­si­fiers to in­ter­vene around 85% less of­ten than they do for Fable 5. In Claude.ai, Claude Code, and Claude Cowork, any flagged re­quests will fall back to Opus 4.8 by de­fault. Fallbacks to Opus 4.8 can also be en­abled on the API.

Our Cyber Verification Program (CVP) fa­cil­i­tates cy­ber­se­cu­rity work that would oth­er­wise be im­peded by the mod­el’s safe­guards. Enterprises and re­searchers who are al­ready part of the CVP have im­me­di­ate ac­cess to a ver­sion of Opus 5 with fewer se­cu­rity re­stric­tions.

Biology. Since Opus 5 has a sim­i­lar suite of safe­guards to Opus 4.8, it is now our most ca­pa­ble gen­er­ally avail­able model for sci­en­tific re­search. Nevertheless, the model still shows im­por­tant lim­i­ta­tions on long-run­ning, au­tonomous re­search tasks, which is where we ex­pect AI mod­els to pose the most sub­stan­tial bi­ol­ogy-re­lated risks. (Mythos 5 re­mains the stronger model for this type of bi­o­log­i­cal work.) As part of this launch, bi­ol­ogy-re­lated re­quests that are blocked on Fable 5 will now route to Opus 5 rather than Opus 4.8.

Getting started

Claude Opus 5 is avail­able to­day on all plat­forms, priced at $5 per mil­lion in­put to­kens and $25 per mil­lion out­put to­kens (the same as Opus 4.8). Developers can get started with claude-opus-5 on the Claude API.

It’s also of­fered in Fast mode, where it runs around 2.5 times the de­fault speed. As with Opus 4.8, Fast mode is avail­able at twice Opus 5’s base price on the Claude Platform and through us­age cred­its in Claude Code.

Alongside Opus 5, we’re re­leas­ing two up­dates in beta:

Mid-conversation tool changes on the Claude Platform. Within a con­ver­sa­tion, de­vel­op­ers can now change which tools Claude can use with­out in­val­i­dat­ing the prompt cache.

Automatic fall­backs on the API. Users can now choose to have re­quests that are flagged by our safety clas­si­fiers on Opus 5 (or Fable 5) au­to­mat­i­cally route to an­other model. With au­to­matic fall­backs on, API re­quests al­ways route to the best avail­able model by de­fault rather than be­ing blocked.

Consistent with prior Opus mod­els, Opus 5 does not have data re­ten­tion re­quire­ments for gen­eral ac­cess.

For more guid­ance on how to get the best out of Opus 5, see our prompt­ing guide.

Footnotes

Frontier-Bench v0.1, Effort plot: These re­sults are from an in­ter­nal run of Frontier-Bench v0.1, on the mini-SWE-agent har­ness and a GKE back­end, mean re­ward over 5 at­tempts per task. Opus 4.8 served as fall­back on safety-clas­si­fier re­fusals for Opus 5 and Fable 5.

Related con­tent

A re­search agenda for the Economic Futures Research Fund

We’re shar­ing the re­search agenda for the Anthropic Economic Futures Research Fund.

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Ask Claude about the Anthropic Economic Index

We’re launch­ing the Anthropic Economic Index con­nec­tor for Claude, which lets any­one ex­plore real data about AI and work.

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Anthropic is do­nat­ing an­other $20 mil­lion to Public First Action

Anthropic is con­tribut­ing an ad­di­tional $20 mil­lion to Public First Action, bring­ing our to­tal sup­port to $40 mil­lion.

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China’s open-weights AI strat­egy is win­ning: its com­pa­nies are tak­ing the lead. America’s closed-first, locked-down strat­egy is doomed to fail­ure - and it could take the US econ­omy down with it.

Link: China de­liv­ers a one-two punch to America’s AI dom­i­nance, by Robert Hart in The Verge

AI mod­els, as a prod­uct in them­selves, have very lit­tle moat be­yond what amounts to brand loy­alty and su­per­fi­cial switch­ing costs. Instead, the moat is in the en­ter­prise ser­vices that sit around them: the deals and con­tracts, con­nec­tiv­ity with en­ter­prise sys­tems, and qual­ity of life fea­tures in an en­ter­prise con­text.

If we con­sider the mod­els them­selves, it’s easy to switch be­tween them: some­one could be us­ing ChatGPT to­day and Claude to­mor­row, with very lit­tle im­pact on their work­flows. This is par­tic­u­larly true in the en­gi­neer­ing world, where mod­els are ac­cessed via API: you can swap out the API and use the same prompt.

Those com­pa­nies can make deals to lock their cus­tomers in, but in prac­tice there’s very lit­tle long-term tech­ni­cal in­cen­tive to use one ven­dor over an­other. You pick the best model for your needs and change mod­els and ven­dors if an­other one be­comes bet­ter.

The US gov­ern­ment has placed ex­port con­trols on GPUs. There are also strong reg­u­la­tions that (reasonably) pre­vent shar­ing cer­tain kinds of data with Chinese servers. The re­sult is that while Chinese com­pa­nies have enough com­pute to train mod­els, they can’t re­ally pro­vide the kinds of global-scale cen­tral­ized ser­vices that we see from OpenAI and Anthropic — at least, not in the same way.

And open al­most al­ways wins when it comes to in­fra­struc­ture adop­tion. Open tech­nolo­gies can be used per­mis­sion­lessly and there­fore can be at the cen­ter of more in­no­va­tion. You can host them where you want, ex­per­i­ment with them, al­ter them, and tweak to fit your use case. Open weights mod­els are not open source, but they are portable and per­mis­sion­less.

With all this in mind, it makes sense for China to re­lease its AI mod­els openly. It turns a US-created com­pute dis­ad­van­tage into a dis­tri­b­u­tion ad­van­tage; it com­modi­tizes the layer where American com­pa­nies make money; and it cre­ates a far more ef­fec­tive global ecosys­tem than could be es­tab­lished through locked-in, cen­tral­ized ser­vices. It’s ob­vi­ous to me that there are ecosys­tem ben­e­fits through­out China, from man­u­fac­tur­ing to sci­en­tific re­search; every sec­tor can just plug in these mod­els.

The sav­ing grace for American com­pa­nies has been that US fron­tier mod­els have out­per­formed open ones. That gap is now clos­ing:

“Moonshot and Alibaba un­veiled mod­els they claim can go toe-to-toe with the best from OpenAI and Anthropic at a frac­tion of the cost. The rapid-fire re­leases sug­gest America’s lead at the AI fron­tier is in­creas­ingly tight, just as the tech­nol­ogy is be­com­ing cen­tral to na­tional se­cu­rity, eco­nomic power, and geopo­lit­i­cal in­flu­ence.”

Even with­out these new ca­pa­bil­i­ties, the strat­egy has al­ready been work­ing. a16z part­ner Martin Casado noted in the Economist that there’s an 80% chance that any given startup is us­ing Chinese mod­els, and Chinese mod­els are poised to take the lead.

It’s worth tak­ing a step back and con­sid­er­ing the sur­pris­ing un­der­ly­ing dy­nam­ics. We think of China as be­ing a locked-down so­ci­ety — and it is in many ways. I have se­ri­ous con­cerns about how these mod­els might re­flect Chinese gov­ern­ment per­spec­tives (try ask­ing them about Tiananmen Square). But it’s American com­pa­nies that are keep­ing tight con­trol of their tech­nol­ogy rather than re­leas­ing it as openly as pos­si­ble. This is in stark con­trast to the strat­egy be­hind US gov­ern­ment sup­port for the open in­ter­net, for ex­am­ple.

Locked-down busi­ness prac­tices for a tech­nol­ogy with no real moat but sig­nif­i­cant po­ten­tial ecosys­tem ben­e­fits is an ob­vi­ously los­ing strat­egy; per­mis­sively re­leas­ing it with an open, col­lab­o­ra­tive ap­proach is ob­vi­ously a win­ning one. But the in­cen­tives in the US aren’t there: in­stead, these com­pa­nies are forced to chase first-or­der prof­its rather than ecosys­tem ben­e­fits, and the gov­ern­ment tries to put its fin­ger on the scale through forcible mea­sures like tight ex­port con­trols. We should con­sider what would need to change to make those in­cen­tives more aligned. That’s par­tic­u­larly im­por­tant given how much of the US econ­omy is cur­rently dri­ven by AI spend­ing. If the bot­tom falls out of that spend­ing — and I think it clearly will, given the dy­nam­ics — the out­come could be se­vere.

I care about hav­ing open tech­nol­ogy that can be run in the pub­lic in­ter­est, aligned with the pub­lic’s val­ues. Threads like pub­lic AI, fed­er­ated ser­vices, and open re­search have trac­tion but need back­ing. Getting there in the US needs more nu­anced strat­egy and sup­port than we’re see­ing to­day.

Please en­able cook­ies.

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Listen to this post:

There’s a story I tell about my first day in STRT-431 at Kellogg School of Management, the in­tro­duc­tory class that every first-year MBA was re­quired to take; I leafed through the read­ings and case stud­ies and was dis­mayed that there weren’t any tech com­pa­nies on the docket. Me be­ing me, I spoke to the pro­fes­sor af­ter class won­der­ing why, and was told that the goal of the course was not to nec­es­sar­ily learn about spe­cific in­dus­tries, but rather to un­cover broadly ap­plic­a­ble uni­ver­sal prin­ci­ples that could be ap­plied to any com­pany in any in­dus­try.

I did not, as I usu­ally tell the story, find this very sat­is­fac­tory: to me the na­ture of tech, par­tic­u­larly the fact that soft­ware and dis­tri­b­u­tion had zero mar­ginal costs (and zero trans­ac­tion costs), was some­thing fun­da­men­tally dif­fer­ent; putting in ze­roes in for­mu­las tends to wreak havoc! I soon re­al­ized, how­ever, that that was my op­por­tu­nity. The fun­da­men­tal in­sight un­der­gird­ing Aggregation Theory is that zero mar­ginal costs leads to fun­da­men­tally dif­fer­ent value chains than peo­ple once ex­pected from the Internet: cen­tral­iza­tion and scale in a world where con­trol­ling de­mand mat­tered more than dis­trib­ut­ing sup­ply.

What is fas­ci­nat­ing about AI, how­ever, is the ex­tent to which those old uni­ver­sal prin­ci­ples are com­ing back to the fore­front. That was never more ap­par­ent than this past week­end, when ar­gu­ments raged on X about the im­pli­ca­tions of Kimi K3, an­other open weights model out of China, ap­proach­ing the state-of-the-art in terms of ca­pa­bil­i­ties. The long and short of it is this: mar­ginal costs are back in a big way, both in terms of short-term im­pli­ca­tions of state-of-the-art free mod­els, and in terms of the long-term struc­ture of the in­dus­try.

COGS Versus R&D

One of the most com­mon mis­con­cep­tions un­der­gird­ing dis­cus­sion of open weights mod­els is that they are cheaper — free, even. After all, you can just down­load the weights, and skip the time and ex­pense and ca­pa­bil­i­ties nec­es­sary to cre­ate your own model. That is, of course, true, but the “free” in this case is a ref­er­ence to the amount you need to spend on re­search and de­vel­op­ment; R&D is a fixed ex­pense that is in­de­pen­dent of the rev­enue you gen­er­ate. If you spend $1 mil­lion in R&D, it does­n’t mat­ter if you do $100 thou­sand in rev­enue or $100 mil­lion; you still spent $1 mil­lion on R&D (it does, of course, im­pact your prof­itabil­ity).

What is re­lated to rev­enue is COGS — cost of goods sold — and COGS is real for AI in a way it has­n’t been for soft­ware for a very long time. Specifically, run­ning in­fer­ence on a model — whether that model be Kimi or Fable — costs money, and the amount of money an AI provider spends on in­fer­ence is, at least in most busi­ness mod­els, di­rectly cor­re­lated to rev­enue. To reuse the above ex­am­ple, gen­er­at­ing $100 mil­lion ver­sus $100 thou­sand in rev­enue will likely re­quire 1,000x COGS. In con­crete terms, if it costs 50 cents to gen­er­ate the to­kens that drive $1 in rev­enue, then $100 mil­lion in rev­enue will have $50 mil­lion in COGS; $100 thou­sand in rev­enue will only have $50 thou­sand in COGS.

The point in terms of open weight mod­els is that they are not free to serve. Kimi K3 costs $3 per mil­lion in­put to­kens, and $15 per mil­lion out­put to­kens; that is cheaper than Sol’s $5 per mil­lion in­put to­kens and $30 per mil­lion out­put to­kens, but that might not even be the right mea­sure­ment.

Tokens Versus Intelligence

Nvidia CEO Jensen Huang has de­scribed what Nvidia is build­ing as “token fac­to­ries”, and from Nvidia’s per­spec­tive that fram­ing makes sense. Nvidia GPUs are model ag­nos­tic: they gen­er­ate to­kens, and do so in the fastest and most ef­fi­cient way pos­si­ble. That leads to mea­sure­ments like to­kens-per-sec­ond, time-to-first-to­ken, to­kens-per-watt, to­ken cost, etc., and Huang ar­gues that these met­rics will be the ba­sis for de­ci­sion-mak­ing.

This is a fram­ing that def­i­nitely made sense dur­ing the first par­a­digm of AI, the ChatGPT era, when to­kens were de­liv­ered straight to the end user. The sec­ond par­a­digm of AI, how­ever, the rea­son­ing era, con­founds this mea­sure­ment. Reasoning en­tails an ex­plo­sion in chain-of-thought to­kens, and dif­fer­ent mod­els need dif­fer­ent amounts of rea­son­ing to­kens to ar­rive at the right an­swer. Kimi, for ex­am­ple, re­port­edly uses sig­nif­i­cantly more to­kens than Sol, ren­der­ing its price ad­van­tage moot. Agents in­tro­duce a sim­i­lar dy­namic: some mod­els are more ef­fi­cient than oth­ers in terms of the num­ber of to­kens they need to ex­e­cute agen­tic work­flows.

What this means is that to­kens are not a com­mod­ity. The defin­ing char­ac­ter­is­tic of a com­mod­ity is that it is fun­gi­ble: a gal­lon of oil is a gal­lon of oil; a ton of cop­per is a ton of cop­per; a bushel of wheat is a bushel of wheat. A to­ken from one model, how­ever, is not the same as a to­ken from an­other model. What is fun­gi­ble is what is con­structed from to­kens, which is to say in­tel­li­gence. In other words, if both Kimi and Sol gen­er­ated the right an­swer, then that an­swer is fun­gi­ble; the dif­fer­ence in to­kens gen­er­ated to get to that right an­swer is a con­trib­u­tor to a dif­fer­ence in COGS.

The COGS for in­tel­li­gence is a func­tion of a few dif­fer­ent fac­tors:

Model foot­print: The weights and run­time state de­ter­mine how much ex­pen­sive mem­ory and how many ac­cel­er­a­tors are re­quired to host each serv­ing replica.

Inference ef­fi­ciency: Architectural choices (e.g. Mixture-of-Experts) re­duce com­pu­ta­tion per gen­er­ated to­ken.

Memory ef­fi­ciency: Architectural choices can re­duce KV cache re­quire­ments, al­low­ing more con­cur­rent re­quests and bet­ter GPU uti­liza­tion.

Serving ef­fi­ciency: Batching, sched­ul­ing, pre­fix caching, and other in­fer­ence op­ti­miza­tions max­i­mize uti­liza­tion and share work across re­quests.

Token ef­fi­ciency: The fewer to­kens re­quired to reach a cor­rect an­swer, the lower the in­fer­ence cost.

The rea­son this mat­ters is that we are rapidly ap­proach­ing a state in which in­tel­li­gence for many eco­nom­i­cally ben­e­fi­cial tasks is in fact a com­mod­ity. Anyone build­ing a ba­sic CRUD app, for ex­am­ple, can likely do so us­ing mod­els from mul­ti­ple providers. And, in a com­mod­ity mar­ket, the route to prof­itabil­ity is not through charg­ing higher prices — again, you can (or will soon be able to) make the ex­act same app us­ing mul­ti­ple mod­els — but rather through hav­ing a su­pe­rior cost struc­ture.

Understanding Commodity Markets

It’s worth step­ping through the me­chan­ics here, be­cause, as I noted a few months ago in Amazon’s Durability, the dy­nam­ics of com­mod­ity mar­kets are not some­thing peo­ple in tech are gen­er­ally fa­mil­iar with:

In com­mod­ity mar­kets, every­one charges the same price, be­cause every­one is sell­ing the same thing; that price is de­ter­mined by sup­ply and de­mand.

The de­mand for a com­mod­ity is a func­tion of price elas­tic­ity: the cheaper the com­mod­ity, the more de­mand there is for it, and vice-versa.

The sup­ply for a com­mod­ity is a func­tion of the mar­ginal cost of pro­duc­ing the com­mod­ity.

The key thing to un­der­stand is that the mar­ginal cost of pro­duc­ing the com­mod­ity dif­fers by sup­plier. What this means in prac­tice is that the sup­plier with the worst cost struc­ture ends up sell­ing the com­mod­ity at their mar­ginal cost (if they can pro­duce at all); the prof­its of every­one else de­pend on the ex­tent to which their cost struc­ture is bet­ter than the mar­ginal sup­plier.

As an ex­am­ple:

Supplier A can pro­duce 10 units of the com­mod­ity for $10 each

Supplier B can pro­duce 10 units of the com­mod­ity for $15 each

Supplier C can pro­duce 10 units of the com­mod­ity for $20 each

Let’s as­sume the price elas­tic­ity is such that there is de­mand for 25 units of the com­mod­ity at $20. That means:

Supplier A will sell 10 units of the com­mod­ity for $20, earn­ing $10/unit

Supplier B will sell 10 units of the com­mod­ity for $20, earn­ing $5/unit

Supplier C will sell 5 units of the com­mod­ity for $20, earn­ing $0/unit

This is­n’t pre­cisely right: the rea­son why Supplier C will bear the short­fall is be­cause Suppliers A and B will be able to slightly un­der­cut them in price, which will of course af­fect de­mand (which is elas­tic), but it makes the point. Supplier A has a great busi­ness, Supplier B has a good busi­ness, and Supplier C is go­ing to go bank­rupt.

Bankruptcy risk is where fixed costs come back to the fore­front: Supplier C has both fixed costs (like po­ten­tially R&D spend) and also may have taken on debt to fi­nance the equip­ment nec­es­sary to pro­duce the com­mod­ity. It can’t price its com­mod­ity with these costs in mind — re­mem­ber, the mar­ket-clear­ing price ap­prox­i­mates the mar­ginal cost of the high­est-cost unit needed to sat­isfy de­mand — but those costs can ab­solutely drive the sup­plier out of busi­ness. And, if that sup­plier goes out of busi­ness, then prices go up, un­til an­other sup­plier de­cides to en­ter (or the other sup­pli­ers ex­pand).

The Intelligence Market

Let’s bring this back to mod­els. Right now, none of the above analy­sis ap­plies be­cause de­mand ex­ceeds sup­ply for fron­tier mod­els, and sup­ply is lim­ited by a lack of com­pute. This com­pute short­age does­n’t just mean that a com­pute sup­plier like Nvidia makes very large mar­gins, but also that Nvidia’s cus­tomers, like SpaceXAI, can turn around and re­sell com­pute at high mar­gins as well to a com­pany like Anthropic. Anthropic, mean­while, can pay the markup be­cause they can sell to­kens with a higher markup still.

It’s not just ex­cess de­mand that gives Anthropic great mar­gins, how­ever: Anthropic and OpenAI likely have among the low­est costs per unit of fron­tier-qual­ity in­tel­li­gence, thanks to model ca­pa­bil­ity, serv­ing scale, and to­ken ef­fi­ciency. They are serv­ing mod­els at a par­tic­u­lar ca­pa­bil­ity level for months be­fore their com­peti­tors, and are si­mul­ta­ne­ously ap­ply­ing the best mod­els to op­ti­miz­ing those costs.

It’s also worth not­ing that the mar­ket is not yet treat­ing in­tel­li­gence like a com­mod­ity: de­mand is for Anthropic and OpenAI specif­i­cally, and much less for mod­els that aren’t as good (thus SpaceXAI and Meta sell­ing ca­pac­ity to Anthropic); one way to think about the push for op­ti­miz­ing cost is that that is a func­tion of defin­ing jobs-to-be-done by in­tel­li­gence level, such that in­tel­li­gence buy­ers can cre­ate a mar­ket where in­tel­li­gence is com­modi­tized. In the long run, how­ever, who­ever is on the fron­tier is the best placed to dom­i­nate non-fron­tier mar­kets as well, which are just the fron­tier mi­nus n-months, i.e. months in which the fron­tier model mak­ers have been op­ti­miz­ing their cost of serv­ing.

All of this is to say that I think the re­ac­tion to Kimi and Chinese mod­els gen­er­ally is pretty over-blown, at least from an eco­nomic per­spec­tive. Right now there is a price um­brella that is down­stream of the lack of com­pute; I highly doubt that Chinese mod­els are cheaper to serve on a mar­ginal cost ba­sis, they just seem cheaper be­cause Anthropic and OpenAI are so sup­ply con­strained that they are charg­ing far more than they would if there were suf­fi­cient sup­ply to meet the de­mand for in­tel­li­gence.

Frontier Lab Paranoia

Why, then, do the model mak­ers in par­tic­u­lar seem so pan­icked about Chinese mod­els?

First, I think the fron­tier labs are an­chored in a world where train­ing costs dom­i­nated their fi­nan­cial mod­el­ing. As long as train­ing con­sumed more GPUs than in­fer­ence, it was crit­i­cal to max­i­mize in­fer­ence rev­enue to help fund the next train­ing run, which meant charg­ing very high prices for in­fer­ence.

Going for­ward, how­ever, I ex­pect the in­fer­ence mar­ket to grow much faster than train­ing costs (and that in­cludes the as­sump­tion that train­ing costs will con­tinue to sky­rocket), which means they re­ally can make it up in vol­ume. It was­n’t clear this would be the case as re­cently as eight months ago, but the agent par­a­digm un­lock is so mas­sive that fron­tier labs should have more con­fi­dence that they can not just sur­vive but thrive with lower prices (once they have suf­fi­cient com­pute).

Second, in­tel­li­gence is­n’t in fact a per­fect com­mod­ity, in part be­cause ap­plied in­tel­li­gence makes it­self smarter. Specifically, who­ever is run­ning in­fer­ence is also col­lect­ing data, and that data goes into mak­ing the next it­er­a­tion of the model bet­ter. This is, on one hand, all the more rea­son for the fron­tier labs to lower prices and in­crease us­age as more com­pute comes on­line; on the other hand, this is why com­pa­nies like Microsoft are in­creas­ingly ob­sessed with help­ing com­pa­nies run their own mod­els. That is much more vi­able if Chinese mod­els are a vi­able al­ter­na­tive.

Third, the other way that fron­tier labs can not only dif­fer­en­ti­ate from Chinese mod­els but also from each other is by con­tin­u­ing to in­te­grate up into the cus­tomer ex­pe­ri­ence. It’s strik­ing the ex­tent to which Claude Code and Codex are prov­ing to be quite sticky; whichever har­ness you start work­ing with is likely to be the one you stick with, and that fig­ures to be even more the case with non-tech­ni­cal users. And, in the long run, this im­per­a­tive to move up the stack does mean that fron­tier mod­els are ab­solutely a threat to soft­ware providers, in­clud­ing Microsoft. On the flip­side, the ex­tent to which soft­ware com­pa­nies who cur­rently own the cus­tomer ex­pe­ri­ence have ac­cess to com­pet­i­tive mod­els is the ex­tent to which they may be able to re­sist the en­croach­ment of the fron­tier labs.

Finally, the ide­o­log­i­cal an­gle of Anthropic in par­tic­u­lar is im­pos­si­ble to ig­nore. This is a com­pany that be­lieves only it can be en­trusted with AI, and the ex­is­tence of open weights al­ter­na­tives strikes a fa­tal blow to that pre­sump­tion.

China’s Motivation

Kimi is­n’t the only new Chinese model; from Bloomberg:

Alibaba Group Holding Ltd. shares rose as much as 5.4% on Monday af­ter the com­pany launched a pre­view ver­sion of its flag­ship Qwen3.8 Max model, de­scrib­ing it as sec­ond only to Anthropic PBC’s Fable 5. The Sunday re­lease came only days af­ter startup Moonshot AI un­veiled a pow­er­ful new of­fer­ing that’s roiled mar­kets and trig­gered con­cern in the US about China clos­ing the gap on global lead­ers like Anthropic and OpenAI. Qwen3.8 Max has 2.4 tril­lion pa­ra­me­ters, join­ing Moonshot’s Kimi K3 in the heavy­weight class. With 2.8 tril­lion pa­ra­me­ters, K3 ri­vals top of­fer­ings and Alibaba is set­ting sim­i­larly high ex­pec­ta­tions.

Developers can now ac­cess Qwen3.8 Max through Alibaba’s cod­ing plat­forms, in­clud­ing Qoder. Alibaba plans to make the model open-weight soon, ex­pand­ing ac­cess be­yond the pre­view re­lease. Interest in these made-in-China ar­ti­fi­cial in­tel­li­gence sys­tems and mod­els is so high that Moonshot was forced to pause tak­ing on new sub­scrip­tions late on Sunday to man­age over­whelm­ing de­mand.

Alibaba Group Holding Ltd. shares rose as much as 5.4% on Monday af­ter the com­pany launched a pre­view ver­sion of its flag­ship Qwen3.8 Max model, de­scrib­ing it as sec­ond only to Anthropic PBC’s Fable 5. The Sunday re­lease came only days af­ter startup Moonshot AI un­veiled a pow­er­ful new of­fer­ing that’s roiled mar­kets and trig­gered con­cern in the US about China clos­ing the gap on global lead­ers like Anthropic and OpenAI. Qwen3.8 Max has 2.4 tril­lion pa­ra­me­ters, join­ing Moonshot’s Kimi K3 in the heavy­weight class. With 2.8 tril­lion pa­ra­me­ters, K3 ri­vals top of­fer­ings and Alibaba is set­ting sim­i­larly high ex­pec­ta­tions.

Developers can now ac­cess Qwen3.8 Max through Alibaba’s cod­ing plat­forms, in­clud­ing Qoder. Alibaba plans to make the model open-weight soon, ex­pand­ing ac­cess be­yond the pre­view re­lease. Interest in these made-in-China ar­ti­fi­cial in­tel­li­gence sys­tems and mod­els is so high that Moonshot was forced to pause tak­ing on new sub­scrip­tions late on Sunday to man­age over­whelm­ing de­mand.

The fact that Qwen3.8 Max will also have open weights is no­table. Alibaba stopped re­leas­ing weights for its lead­ing edge mod­els ear­lier this year, but ap­pears to have re­verted that change; I sus­pect that shift was re­lated to last week’s Xi Jinping speech about AI that dou­bled down on the open weights ap­proach:

We should ad­here to the prin­ci­ple of open­ness and win-win and boost in­no­va­tion-dri­ven de­vel­op­ment. As a new en­gine of world eco­nomic growth and an ac­cel­er­a­tor for the shift of growth dri­vers, AI is mov­ing from the dig­i­tal world into the phys­i­cal world. We should seize this rare, his­toric op­por­tu­nity to en­cour­age open source, open­ness, col­lab­o­ra­tion and shar­ing. We should fa­cil­i­tate tech­no­log­i­cal in­no­va­tion, in­dus­trial de­vel­op­ment and sce­nario-based ap­pli­ca­tion of AI. We should make co­or­di­nated ad­vances in the trans­for­ma­tion and up­grade of tra­di­tional in­dus­tries, the cul­ti­va­tion and growth of emerg­ing in­dus­tries and for­ward-look­ing plan­ning for fu­ture in­dus­tries, so that all sec­tors and busi­nesses can ben­e­fit from AI.

We should ad­here to the prin­ci­ple of open­ness and win-win and boost in­no­va­tion-dri­ven de­vel­op­ment. As a new en­gine of world eco­nomic growth and an ac­cel­er­a­tor for the shift of growth dri­vers, AI is mov­ing from the dig­i­tal world into the phys­i­cal world. We should seize this rare, his­toric op­por­tu­nity to en­cour­age open source, open­ness, col­lab­o­ra­tion and shar­ing. We should fa­cil­i­tate tech­no­log­i­cal in­no­va­tion, in­dus­trial de­vel­op­ment and sce­nario-based ap­pli­ca­tion of AI. We should make co­or­di­nated ad­vances in the trans­for­ma­tion and up­grade of tra­di­tional in­dus­tries, the cul­ti­va­tion and growth of emerg­ing in­dus­tries and for­ward-look­ing plan­ning for fu­ture in­dus­tries, so that all sec­tors and busi­nesses can ben­e­fit from AI.

The strat­egy for China is ob­vi­ous: com­modi­tize your com­ple­ments. Note that Xi ex­plic­itly ties open­ness to AI “moving from the dig­i­tal world into the phys­i­cal world”; the phys­i­cal world is the world dom­i­nated by China, and the coun­try’s lead in ar­eas like ro­bot­ics is go­ing to mas­sively ben­e­fit from widely avail­able AI mod­els.

Along the same lines, China does not want the U.S. to gain an asym­met­ric ad­van­tage in AI; to the ex­tent that China can weaken the U.S. fron­tier labs while strength­en­ing any and all po­ten­tial U.S. ad­ver­saries so much the bet­ter, and it can ben­e­fit from the in­no­va­tion that will at­tach it­self to an open ecosys­tem.

The Distillation Question

By the same to­ken, don’t ex­pect China to do any­thing about dis­til­la­tion at­tacks on the fron­tier labs. I think it is mis­taken to at­tribute all of the suc­cess of Chinese labs to dis­til­la­tion, but it’s just as much of a mis­take to pre­tend like dis­til­la­tion does­n’t give Chinese labs a big ad­van­tage. That ad­van­tage has re­ally come to bear in the last year as post-train­ing re­in­force­ment learn­ing has be­come in­creas­ingly cru­cial to model per­for­mance. Instead of hav­ing to fash­ion re­in­force­ment learn­ing en­vi­ron­ments from scratch, Chinese labs can sim­ply use fron­tier labs mod­els as teach­ers, al­low­ing for rapid im­prove­ment at much lower costs (this is not the only rea­son why Chinese mod­els are cheaper to de­velop, but it’s a big one).

What is in­ter­est­ing is that one of the most im­por­tant use cases for Chinese mod­els in the West is it­self dis­til­la­tion. Thinking Machines, for ex­am­ple, which just re­leased an open-weight model, re­lies on Chinese mod­els to solve the cold start prob­lem for re­in­force­ment learn­ing. Dean Meyer and Konstantine Buhler wrote an ex­cel­lent ar­ti­cle on X ex­plain­ing that dis­til­la­tion means that Western open weight mod­els are fun­da­men­tally dis­ad­van­taged rel­a­tive to China:

Distillation does not ex­plain China’s en­tire open-model lead. Chinese labs have world-class re­searchers, sub­stan­tial com­pute, strong pre-trained mod­els, soft­ware-hard­ware code­sign, and rapidly im­prov­ing post-train­ing ca­pa­bil­i­ties. But dis­til­la­tion com­presses the costly fi­nal gap be­tween a strong base and a near-fron­tier sys­tem. Even if dis­til­la­tion rep­re­sents a smaller share of a Chinese mod­el’s to­tal ca­pa­bil­ity, it rep­re­sents a mean­ing­ful share of its ad­van­tage over American open mod­els.

New en­force­ment mech­a­nisms will make large-scale dis­til­la­tion harder, slower, and more ex­pen­sive for Chinese com­pa­nies. However, en­force­ment will not elim­i­nate dis­til­la­tion backed by state ac­tors. Every Western fron­tier ad­vance there­fore cre­ates an­other teacher for Chinese labs. Western builders must ei­ther re­pro­duce those ca­pa­bil­i­ties in­de­pen­dently or wait to learn from Chinese mod­els. This gap gives Chinese labs a re­cur­ring struc­tural ad­van­tage over Western com­pa­nies.

Distillation does not ex­plain China’s en­tire open-model lead. Chinese labs have world-class re­searchers, sub­stan­tial com­pute, strong pre-trained mod­els, soft­ware-hard­ware code­sign, and rapidly im­prov­ing post-train­ing ca­pa­bil­i­ties. But dis­til­la­tion com­presses the costly fi­nal gap be­tween a strong base and a near-fron­tier sys­tem. Even if dis­til­la­tion rep­re­sents a smaller share of a Chinese mod­el’s to­tal ca­pa­bil­ity, it rep­re­sents a mean­ing­ful share of its ad­van­tage over American open mod­els.

New en­force­ment mech­a­nisms will make large-scale dis­til­la­tion harder, slower, and more ex­pen­sive for Chinese com­pa­nies. However, en­force­ment will not elim­i­nate dis­til­la­tion backed by state ac­tors. Every Western fron­tier ad­vance there­fore cre­ates an­other teacher for Chinese labs. Western builders must ei­ther re­pro­duce those ca­pa­bil­i­ties in­de­pen­dently or wait to learn from Chinese mod­els. This gap gives Chinese labs a re­cur­ring struc­tural ad­van­tage over Western com­pa­nies.

This is a point that bears re­peat­ing: be­cause U.S. open weight model mak­ers must fol­low the fron­tier labs’ terms of ser­vice, they (1) are worse than Chinese al­ter­na­tives and (2) end up dis­till­ing the dis­til­la­tion, just with a de­tour through Chinese labs. Wouldn’t it be bet­ter if west­ern open weight model mak­ers could go to the source?

To that end, here’s an even more in­ter­est­ing ques­tion around dis­til­la­tion: why ex­actly is it bad? After all, what are large lan­guage mod­els but the dis­til­la­tion of all of the knowl­edge on the open Internet, scraped by the fron­tier labs and dis­tilled into the mod­els that are them­selves be­ing dis­tilled? Who is ex­actly be­ing wronged here?

In fact, this para­dox is the so­lu­tion. I be­lieve that open weight mod­els are good for in­no­va­tion (and, per the above, I think that labs on the fron­tier will be fine), but it’s a prob­lem to be de­pen­dent on China. The U.S. should pass a law that (1) makes ex­plicit that col­lect­ing data for train­ing mod­els is fair use, and (2) bars terms of ser­vice that for­bid dis­til­la­tion, for U.S. com­pa­nies at a min­i­mum. Stopping dis­til­la­tion — which is lit­er­ally just query­ing the API — is nearly im­pos­si­ble; the U.S. should go the other way and lean into a new copy­right pol­icy that both in­dem­ni­fies the labs and also guar­an­tees that what they learned fu­els fur­ther in­no­va­tion for every­one else.

The Reason to Be Afraid

This en­tire Article has been an ex­er­cise in de­fus­ing over­re­ac­tion to Kimi K3 specif­i­cally and Chinese open weight mod­els gen­er­ally; how­ever, there is one rea­son to be con­cerned, and that is cy­ber­se­cu­rity. Consider this story from The Stack:

Hugging Face said its pro­duc­tion in­fra­struc­ture was breached by an “autonomous” AI agent sys­tem early last week. The plat­for­m’s se­cu­rity team were ini­tially stymied in their in­ci­dent re­sponse (IR) by un­named US LLM fron­tier model guardrails “which can­not dis­tin­guish an in­ci­dent re­spon­der from an at­tacker,” they said. So Hugging Face’s de­fend­ers turned in­stead to the open-source GLM 5.2 model from China’s Z.ai lab — run­ning it on their own in­fra­struc­ture to analyse the 17,000+ logs, or foot­prints, that the at­tack­ers left be­hind.

That’s a strik­ing pub­lic ad­mis­sion for the New York-headquartered Hugging Face, which lets users col­lab­o­rate on mod­els, datasets and ap­pli­ca­tions, and which this sum­mer hit the $100 mil­lion ARR mark. In an in­ci­dent re­port, the com­pany rec­om­mended that de­fend­ers “have a ca­pa­ble model you can run on your own in­fra­struc­ture [our ital­ics] vet­ted and ready be­fore an in­ci­dent, both to avoid guardrail lock­out and to keep at­tacker data and cre­den­tials from leav­ing your en­vi­ron­ment.”

Hugging Face said its pro­duc­tion in­fra­struc­ture was breached by an “autonomous” AI agent sys­tem early last week. The plat­for­m’s se­cu­rity team were ini­tially stymied in their in­ci­dent re­sponse (IR) by un­named US LLM fron­tier model guardrails “which can­not dis­tin­guish an in­ci­dent re­spon­der from an at­tacker,” they said. So Hugging Face’s de­fend­ers turned in­stead to the open-source GLM 5.2 model from China’s Z.ai lab — run­ning it on their own in­fra­struc­ture to analyse the 17,000+ logs, or foot­prints, that the at­tack­ers left be­hind.

That’s a strik­ing pub­lic ad­mis­sion for the New York-headquartered Hugging Face, which lets users col­lab­o­rate on mod­els, datasets and ap­pli­ca­tions, and which this sum­mer hit the $100 mil­lion ARR mark. In an in­ci­dent re­port, the com­pany rec­om­mended that de­fend­ers “have a ca­pa­ble model you can run on your own in­fra­struc­ture [our ital­ics] vet­ted and ready be­fore an in­ci­dent, both to avoid guardrail lock­out and to keep at­tacker data and cre­den­tials from leav­ing your en­vi­ron­ment.”

It’s dif­fi­cult to over­state how wrong-headed the Trump ad­min­is­tra­tion’s pan­icked re­sponse to Anthropic’s re­lease of Fable was, par­tic­u­larly since it ex­ac­er­bated Anthropic’s worst ten­den­cies in terms of as­sum­ing only they can be trusted with pow­er­ful AI. In a world with only one AI, it might make sense to re­serve the most pow­er­ful cy­ber­se­cu­rity ca­pa­bil­i­ties for the U.S. gov­ern­ment and trusted al­lies; how­ever, that’s not the world we live in.

There are and will be mod­els em­i­nently ca­pa­ble of mount­ing cy­ber­se­cu­rity at­tacks on ex­ist­ing in­fra­struc­ture, and those mod­els will be — al­ready are — widely avail­able. The best de­fense — the only vi­able de­fense, in fact — will be to make sure de­fend­ers have ac­cess to the best mod­els as well. Right now de­fend­ers are ef­fec­tively banned from us­ing Fable or Sol for cy­ber­se­cu­rity be­cause of Trump ad­min­is­tra­tion di­rec­tives; that means the best al­ter­na­tive is us­ing mod­els from a coun­try which has been try­ing to weaken our cy­ber de­fenses for years. This is in­sane!

The bet­ter course is clear: first, loosen Fable and Sol re­stric­tions on cy­ber­se­cu­rity, and sec­ond, en­sure that U.S. open weight model mak­ers are on an equal play­ing field with China. Yes, the fron­tier labs will kick and scream about this, but the Administration should re­al­ize that lis­ten­ing to their histri­on­ics has led the U.S. to a po­si­tion where U.S. com­pa­nies are de­pen­dent on China for their de­fenses. Let the fron­tier labs win by be­ing bet­ter; don’t let them de­fine safety or se­cu­rity, or pull up the lad­der of hu­man­i­ty’s col­lec­tive knowl­edge. China is al­ready hard enough to com­pete with; let­ting them carry the stan­dard for open­ness and in­no­va­tion is sim­ply giv­ing away our biggest ad­van­tage.