2026.04.13

8 min read

The act of pump­ing im­mense, dis­pro­por­tion­ate re­sources — money, crowd­sourced math, an­a­lyt­ics, op­ti­miza­tion, min-max­ing, pop­u­lar opin­ion ag­gre­ga­tion, etc. — into a pre­vi­ously ca­sual or com­plex, lay­ered ac­tiv­ity to force­fully ex­tract and squeeze out the purest, most con­cen­trated dopamine hit, with no re­gard for any­thing ex­cept dopamine.

Origin

One late evening while chat­ting on Discord, I coined the term “dopamine frack­ing” to de­scribe a phe­nom­e­non that has be­come in­creas­ingly preva­lent in on­line cul­ture — a con­cept which I pre­vi­ously strug­gled to ex­press. It’s a metaphor, be­cause just like in ac­tual frack­ing, it is im­mensely harm­ful to the long-term health and sus­tain­abil­ity of any­thing it is ap­plied to, but in the short term, it can yield a very in­tense and con­cen­trated hit of dopamine (or oil).

I briefly called this “sloptimization” — a term which was prob­a­bly coined by AI bros to de­scribe the process of op­ti­miz­ing AI mod­els to pass bench­marks, but it does­n’t quite cap­ture the de­struc­tive na­ture of the prac­tice. I guess you could say that a close al­ter­na­tive would be “commodification,” “over-consumption,” or “industrialization” of the hu­man ex­pe­ri­ence, but… all of these words sound more like ster­ile eco­nomic terms and don’t ac­tu­ally sig­nify how ut­terly dev­as­tat­ing this has been to cul­ture, cre­ativ­ity, and con­nec­tion. I feel like “dopamine frack­ing” cre­ates a much more gut­tural, vis­ceral, dis­gust­ing im­age of an oil rig in your brain, or worse, in things you love and cher­ish.

Commodifying the Human Experience

I was in­spired to come up with this af­ter watch­ing a few of Metta Beshay’s won­der­ful videos about drugs in the con­text of their orig­i­nal cul­tural sig­nif­i­cance. He cov­ers a lot of dif­fer­ent sub­stances and their his­to­ries, and I highly rec­om­mend go­ing to his chan­nel in­stead of lis­ten­ing to me (an id­iot) talk about it. In short, there’s a rea­son why cer­tain drugs were used in cer­tain cul­tures for thou­sands of years, but be­came much more ne­far­i­ous and de­struc­tive when they were taken out of that con­text. That rea­son is the in­dus­tri­al­iza­tion and cul­tural era­sure by the Enterprising Capitalist™️.

The same thing has been hap­pen­ing to so much of our cul­ture, hob­bies, and even re­la­tion­ships. For all in­tents and pur­poses, an enor­mous num­ber of peo­ple live on­line. The con­stant search for the next big thing, the next big hit of dopamine, has led to a cul­ture of over­con­sump­tion and ad­dic­tion. Whether it’s com­mu­ni­ties be­com­ing too pop­u­lar, mu­sic be­com­ing too cliché, videos be­com­ing too “MrBeast-y,” movies be­com­ing too Marvel, web­sites be­com­ing too flat — all that mat­ters is the dopamine hit. And the long-term con­se­quences are ig­nored. Not out of mal­ice, but be­cause it feels as ad­dic­tive as a com­mod­i­fied drug, and peo­ple are sim­ply try­ing to get their next hit.

I’m not say­ing that the things I listed lack merit or ef­fort: an im­mense amount of work un­doubt­edly goes into any movie, song, or video if it’s made by a per­son or team and not by AI. But at a cer­tain thresh­old, if every­thing con­verges on a sin­gle point, there’s quite lit­er­ally no room for any­thing else in zero di­men­sions.

The Strawberry Example

Perhaps my takes are a lit­tle too on­line, so let’s look at a more re­lat­able ex­am­ple: straw­ber­ries. Strawberries are de­li­cious, and they have a very com­plex fla­vor pro­file. They have hun­dreds, if not thou­sands, of strains, and for every sin­gle in­di­vid­ual straw­berry, there are thou­sands of unique com­pounds that con­tribute to its fla­vor. There are white ones, red ones, some are white on the in­side, some are red, some are sour, some are sweet, some are a lit­tle bit­ter, some are very aro­matic, some are very juicy, some are very firm, some are very soft. Even if the dif­fer­ences within a sin­gle bushel of straw­ber­ries are nigh im­per­cep­ti­ble, the ex­pe­ri­ence of eat­ing one is com­plex and lay­ered. And each and every one of the straw­ber­ries you put in a cake, blend into a smoothie, or eat on its own is, in a way, a beau­ti­fully im­per­fect, unique, ana­log ex­pe­ri­ence. You might not no­tice it, you might not care, but it’s there, and it mat­ters — even if just that tiny bit.

But if you were to de­com­pose a straw­berry, ex­tract the aro­matic com­pound that smells most like a straw­berry, an­a­lyze its for­mula, de­vise a way to syn­the­size it, and make it com­mer­cially vi­able, you could put that in every food as a sub­sti­tute for the metic­u­lous work of col­lect­ing good straw­ber­ries and the com­plex palate one has. It would be much cheaper to man­u­fac­ture, and it would give you a very con­cen­trated hit of straw­berry fla­vor. Most peo­ple would­n’t be able to tell much of a dif­fer­ence, and it would prob­a­bly still be de­li­cious. If you’re not greedy.

In fact, this is ex­actly what hap­pens in the food in­dus­try. They ex­tract the com­pound that gives straw­ber­ries their fla­vor and put it in every­thing from cheap candy to ex­pen­sive desserts.

But it would also com­pletely erase every­thing else about the ex­pe­ri­ence of eat­ing a straw­berry. The tex­ture, the juici­ness, the com­plex­ity of the fla­vor, the im­per­fec­tions, the joy of find­ing a par­tic­u­larly good one, the cos­mic hor­ror of eat­ing a wormy one, the nos­tal­gia of hav­ing your grand­ma’s straw­berry jam with dozens of in­di­vid­u­ally unique straw­ber­ries in it. All of that is lost and con­densed into a sin­gle, pure hit of straw­berry fla­vor. Tasty? Maybe. But it’s not a straw­berry any­more. It’s just a chem­i­cal that kind of tastes like a straw­berry. Soon enough, you for­get what one ac­tu­ally tastes like. Or worse, you pre­fer the chem­i­cals. Or even worse, you can’t even find real straw­ber­ries any­more be­cause the mar­ket is flooded with syn­thetic re­place­ments. Or even worser, the real ones have long gone ex­tinct be­cause no one wanted to grow them any­more when the syn­thetic ver­sion was cheaper and more con­ve­nient. And whoop-dee-doo, you’ve erased about 500 in­di­vid­ual hu­man ex­pe­ri­ences and re­placed them with a sin­gle, shared one. And that’s just straw­ber­ries.

This is what dopamine frack­ing does to cul­ture, hob­bies, and even re­la­tion­ships, which are so much more com­plex be­cause they are so deeply ab­stract. It ex­tracts the most con­cen­trated hit of dopamine and puts it in every­thing, while eras­ing all the com­plex­ity, nu­ance, and beauty that made it spe­cial in the first place. And the more we do it, the more we for­get what the orig­i­nal ex­pe­ri­ence was like, and the more we pre­fer the syn­thetic ver­sion, and the worse off we are. It’s a vi­cious cy­cle that leads to a ho­mog­e­nized, com­mod­i­fied cul­ture that is de­void of mean­ing and con­nec­tion.

Remember that SpongeBob episode where they made Krabby Patties out of goo? Yeah. That.

Conclusion

The worst part about it? This was so in­cred­i­bly easy and con­ve­nient to ig­nore for such a long time. Optimization was seen as a good thing, and the idea of “solving” some­thing was seen as a pos­i­tive. I def­i­nitely par­tic­i­pated in it, and I’m sure you or some­one you know has, too. After all, who does­n’t want to solve things? Who does­n’t want to op­ti­mize? But the more this hap­pens, the more we see just how de­struc­tive, dev­as­tat­ing, and un­sus­tain­able liv­ing like this is.

I’ve been grad­u­ally turn­ing off dopamine frack­ing in my life: delet­ing chan­nels and feeds that in­fu­ri­ate me or milk my trig­gers (positive or neg­a­tive), unin­stalling apps, and set­ting bound­aries on what I will and won’t en­gage with and con­sume. Becoming aware of this con­cept has made it eas­ier to nav­i­gate the world. And it’s be­com­ing eas­ier and eas­ier for me to sim­ply stop a video and close a tab when I sense that it’s just try­ing to give me a hit of dopamine. It’s so im­mensely lib­er­at­ing to be able to do that.

I don’t have any so­lu­tions. But aware­ness is the first step, and while it feels triv­ial com­pared to ac­tu­ally do­ing some­thing about it, it’s still a step in the right di­rec­tion. I hope that peo­ple can start talk­ing about this, even if not us­ing the term “dopamine frack­ing” — I can rec­og­nize that it’s a lit­tle ec­cen­tric, but hey, we call short-form sludge “brain rot,” so why not?

Written by a hu­man.

Since v1.1 you can con­fig­ure Music Decoy to launch an­other app when the ▷ Play but­ton is pressed.

To do that, run the fol­low­ing com­mand in the Terminal (example for Spotify):

* When you press the ▶ Play key on your key­board and there is no other app play­ing au­dio

* When end­ing a call, which causes the blue­tooth head­set to switch from call mode to mu­sic mode

There is a dae­mon called rcd (short for Remote Control Daemon) that is re­spon­si­ble for han­dling me­dia keys.

When a play event oc­curs, rcd checks if there is an app that is cur­rently play­ing au­dio. If there is, it sends the play com­mand to that app. If there is­n’t, it launches the sys­tem Music app.

There is a way to dis­able that dae­mon but it also dis­ables the abil­ity to con­trol me­dia play­back with the key­board.

Based on this StackExchange an­swer, there are a few dif­fer­ent ways to achieve the same ef­fect:

no­Tunes which lis­tens for launched apps and kills Music as soon as it is launched

Problem: it does use a tiny bit of CPU in the back­ground al­though check­ing for launched apps is very lit­tle work

The app has no Dock icon and no menubar icon so to quit it you’d need to do one of the fol­low­ing:

* Launch Activity Monitor, find Music Decoy and press the ❌ but­ton at the top

* Run the fol­low­ing com­mand in the Terminal: kil­lall ‘Music Decoy’

22 May 2026

John Laurenson

Getty Images

Social me­dia plat­forms used to be about com­mu­ni­ca­tion be­tween friends — now many are in­creas­ingly short video en­ter­tain­ment hubs. The busi­ness model is to in­crease the time peo­ple spend on their apps and in­crease ad rev­enue. But is there al­ready a con­sumer back­lash?

Aurélia fixes her­self a cof­fee, sits down in her beau­ti­ful gar­den not far from Paris and goes on Instagram “to re­lax.” First up: “a guy I like a lot who does in­te­rior de­sign. He’s in Venice at the mo­ment.” She’s into in­te­rior de­sign, and has even just had two bird draw­ings by the 19th Century English de­signer William Morris tat­tooed on her arms. She scrolls down. Two kit­tens hav­ing a fight. “I love an­i­mals so I get a lot of an­i­mals. That’s how it works, so­cial me­dia. You click on ba­nanas and they give you ba­nanas.”

There are ads too — al­though they look just like the other posts — for a ro­bot-vac­uum cleaner, a diet and bed linen (with Morris-inspired de­signs). But no friends. She has 198 on Instagram but she says “it’s com­pletely changed. I prac­ti­cally don’t see any friends’ posts any­more.” She’s pretty much given up post­ing her­self. “I don’t think any­one sees them any­more any­way.”

While there re­main com­mit­ted so­cial, am­a­teur posters on Instagram and es­pe­cially Facebook, the switch from com­mu­ni­cat­ing with peo­ple you know to scrolling through pro­fes­sion­ally made con­tent from peo­ple you don’t, is even more pro­nounced among young users.

Kylian, 16, is in vo­ca­tional train­ing to be­come a chef. He’s on TikTok and Youtube a lot, he says. “I like look­ing at videos more than pho­tos or mes­sages. I watch videos made by peo­ple I don’t know. I don’t post at all. I’m a rather shy per­son. I stay in my bub­ble. I watch and that’s all. I keep my re­ac­tions to my­self.”

“I spend a lot of time scrolling through videos made by con­tent-cre­ators,” says Lucie, also 16. “They’re more in­ter­est­ing than the posts of peo­ple I know.” She does­n’t post ex­cept some­times “stories” which dis­ap­pear af­ter 24 hours.

Whether it’s TikTok, Snapchat, Facebook and Instagram, we are a long way from the “digital town square” of per­sonal in­ter­ac­tion that so­cial me­dia was even just a few years ago.

John Laurenson

In France, an­nual of­fi­cial Barometre du nu­merique 2026 shows 49% of so­cial me­dia users are “active only oc­ca­sion­ally”. In the UK, an Ofcom re­port  pub­lished in April showed a year-on-year drop of users who ac­tively post from 61% to 49%. In the US, a Morning Consult sur­vey of June last year found 28% re­ported post­ing less of­ten than the pre­vi­ous year. Just 33% now post daily com­pared to 57% who use it for en­ter­tain­ment daily. The gap is a lot wider still for Gen Z — 18% ac­tive for 74% pas­sive.

Vanessa Lalo, a Paris-based clin­i­cal psy­chol­o­gist spe­cial­is­ing in on-line be­hav­iour, says “users have be­come more con­scious that the traces you leave (on so­cial me­dia) stay there for­ever and some no longer want to main­tain so­cial me­dia re­la­tions that can be su­per­fi­cial. Some don’t want the ex­po­sure to crit­i­cism that might be a risk when you post or the feel­ing that their post will seem poor along­side all the pro­fes­sional con­tent”.

However, Lalo adds, peo­ple haven’t stopped post­ing, rather they are post­ing dif­fer­ent things and in dif­fer­ent places. “On TikTok, for ex­am­ple, young peo­ple pub­lish a lot of con­tent but it’s more funny par­o­dies and remixes of ex­ist­ing ma­te­r­ial. The goal is to make peo­ple laugh, not to tell peo­ple about their lives.”

That still hap­pens, she says, but it’s moved from so­cial me­dia plat­forms like Instagram and Facebook to mes­sag­ing sites like WhatsApp. There’s also been a move to­wards pri­vate groups on Instagram and Snapchat. “These are much more in­ti­mate places where you’re not bom­barded with ads and con­tent made by in­flu­encers,” she says.

“What we’re see­ing is so­cial me­dia split­ting in two,” says so­cial me­dia con­sul­tant Matt Navarra, au­thor of the Geekout Newsletter. “Big plat­forms like Instagram and TikTok are be­com­ing more about en­ter­tain­ment and dis­cov­ery. WhatsApp is be­com­ing the place peo­ple go to ac­tu­ally be so­cial. The catch is, those kinds of spaces are harder for com­pa­nies to make money from.”

Small busi­ness own­ers are be­ing pushed to be­come pre­sen­ters, ed­i­tors, trend spot­ters and con­tent cre­ators, on top of ac­tu­ally run­ning the busi­ness — Matt Navarra

It was TikTok that helped to pi­o­neer an al­go­rithm that fig­ures out from the mo­ment you start scrolling what you like, and then fills your feed with ma­te­r­ial cal­cu­lated to keep you on the app for the longest pos­si­ble time.

Now, says Matt Navarra, “Meta has built what it calls an AI sys­tem for un­con­nected con­tent rec­om­men­da­tions on Face­book and Instagram, which ba­si­cally means, they’re in­creas­ingly show­ing you stuff from peo­ple you don’t fol­low be­cause the ma­chine thinks you’re go­ing to like it. It’s not bi­ased to­wards, is it a pro­fes­sional cre­ator? Is it a brand? Is it a friend? If they can see that you’ve en­gaged with a friend a lot, you might see a lot more of their con­tent. It’s just that who you are friends with, who you fol­low, has be­come ir­rel­e­vant in a way.”

This all means that small busi­nesses, that have long used so­cial me­dia for free pro­mo­tion have to up their game.

“There’s a real op­por­tu­nity for some small busi­nesses,” says Matt Navarra. “A bak­ery, florist, sa­lon or lo­cal café can still break through if they have a good story, strong vi­su­als or be­hind-the-scenes con­tent peo­ple want to watch. But it also means the job has changed. Small busi­ness own­ers are be­ing pushed to be­come pre­sen­ters, ed­i­tors, trend spot­ters and con­tent cre­ators, on top of ac­tu­ally run­ning the busi­ness.”

The so­cial plat­forms con­tinue to be mon­e­tised pre­dom­i­nantly by ad rev­enue. That is still the core busi­ness model. And ad rev­enue con­tin­ues to grow — Matt Navarra

Social me­dia is evolv­ing into some­thing pas­sive like tele­vi­sion, al­beit tele­vi­sion that adapts as you zap. Or rather which knows you so well that it does­n’t seem to mat­ter that much that it’s taken the re­mote con­trol. You give the plat­form in­for­ma­tion about your­self that it uses for com­mer­cial gain and, in re­turn, it gives you con­tent tai­lored to please you for free.

The tran­si­tion from truly so­cial me­dia to en­ter­tain­ment plat­form does seem to be pay­ing off. “The so­cial plat­forms con­tinue to be mon­e­tised pre­dom­i­nantly by ad rev­enue. That is still the core busi­ness model. And ad rev­enue con­tin­ues to grow,” says Matt Navarra. Global so­cial me­dia ad rev­enue is ex­pected to reach $317 bil­lion (£236bn) in 2026, up from $277 bil­lion (£206bn) last year. Meta is the biggest win­ner. Its ad sales al­ready in­creased 22% year-on-year in 2025. Ad sales are ex­pected to hit $243 bil­lion (£181bn) this year, enough to over­take Google for the first time.

AI pow­ered dig­i­tal ad tar­get­ing is be­com­ing ever more ef­fec­tive and pre­cise. “The so­cial plat­forms al­low com­pa­nies to put ads amongst the con­tent that you’re scrolling through. Every third or fourth scroll is an ad. And they are the world’s best ad tar­get­ing en­gines. They know so much about your in­ter­ests be­cause of what you’ve looked at, liked, en­gaged with, what you’ve cho­sen to fol­low, the time you’ve spent in cer­tain ar­eas of the app, things like that,” Navarra says.

“So ad­ver­tis­ers will go in and say: ‘I want to place an ad next to peo­ple in the UK who are be­tween thirty and sixty years old and who are in­ter­ested in DIY’ and the so­cial plat­forms will have that in­for­ma­tion and will place the ads ac­cord­ingly.”

The price will de­pend on the num­ber of im­pres­sions (clicks) the ad­ver­tiser wants and how tight the cri­te­ria are. It costs more to place ads in the so­cial me­dia feeds of peo­ple who buy horses than peo­ple who buy ice-cream.

More like this:

Might there be a back­lash com­ing? Don’t many peo­ple go on to so­cial me­dia to see how friends are re­act­ing to their posts or com­ments be­fore set­tling down to scroll through pro­fes­sion­ally made con­tent?

Meanwhile, for those who miss what are fast be­com­ing the old days when so­cial me­dia en­abled you to share a bit of your life, a joke or a point of view with peo­ple you more-or-less knew, there are tools within plat­forms, says Matt Navarra, that al­low you to choose to see mainly friends and fam­ily con­tent. “People can flick to a feed that gives them that,” he says. “But most peo­ple don’t.”

–

For more on busi­ness and be­yond, fol­low us on LinkedIn.

MiMo-V2.5-Pro-UltraSpeed: Pushing 1T-Parameter Model Generation Speed to 1000 TPS

1. Xiaomi MiMo-V2.5-Pro-UltraSpeed: Speed is the Ultimate Edge

From the first roar­ing racer of the com­bus­tion age to the sonic boom that shat­tered the sound bar­rier, hu­man­i­ty’s hunger for speed is writ­ten into our very DNA. The speed of AI rea­son­ing is no dif­fer­ent — it de­fines the bound­aries of in­tel­li­gence it­self. When a model is fast enough, it ceases to be a tool you wait on and be­comes an ex­ten­sion of your own think­ing: re­spond­ing in real time, it­er­at­ing in an in­stant, col­lab­o­rat­ing with­out fric­tion.

Today, we are thrilled to re­lease Xiaomi MiMo-V2.5-Pro-UltraSpeed in col­lab­o­ra­tion with TileRT, break­ing the 1000 to­kens/​s de­code speed on a 1-trillion-parameter model for the first time!

2. Limited-Time Access · Application-Based

The MiMo-V2.5-Pro-UltraSpeed API launches si­mul­ta­ne­ously at a lim­ited-time pro­mo­tional price — 3× the cost of MiMo-V2.5-Pro, but de­liv­er­ing ap­prox­i­mately 10× the gen­er­a­tion speed! 3× the price, 10× the out­put ex­pe­ri­ence. (API only; Token Plan not sup­ported.)

Due to lim­ited high-speed in­fer­ence re­sources, MiMo-V2.5-Pro-UltraSpeed will be avail­able through an ap­pli­ca­tion-based, lim­ited-time win­dow. Approved users can ac­cess the API dur­ing the trial pe­riod, avail­able only from June 9 to June 23, 2026, 23:59 (Beijing Time, UTC+8 / 08:59 PDT).

How to Apply

API plat­form: plat­form.xi­aomim­imo.com/​ul­tra­speed. Trial slots are lim­ited — sub­mis­sion does not guar­an­tee ap­proval. We will pri­or­i­tize en­ter­prises and pro­fes­sional de­vel­op­ers with gen­uine busi­ness needs. For stan­dard model ac­cess, please fol­low the MiMo-V2.5 model se­ries. For in-depth busi­ness part­ner­ships for the UltraSpeed model, con­tact busi­ness-mimo@xi­aomi.com.

Chat Experience (Free During Trial)

Approved users will re­ceive free Chat ac­cess valid within the two-week win­dow. Entry point: ul­tra­speed.xi­aomim­imo.com

To en­sure qual­ity and fair­ness un­der re­source con­straints, the fol­low­ing rules ap­ply: each ac­count may en­ter the queue up to 10 times per day; each ses­sion is capped at 30 min­utes; ses­sions idle for more than 5 min­utes will be au­to­mat­i­cally re­leased.

3. 1000 to­kens/​s: Not Just Fast, But a Paradigm Shift

At the tril­lion-pa­ra­me­ter (1T) scale, break­ing 1000 tps is far more than a faster type­writer — it fun­da­men­tally dis­rupts AI ap­pli­ca­tion par­a­digms.

First, speed it­self be­gins to trans­mute into in­tel­li­gence. Previously, when fac­ing a hard prob­lem, you could only “wait for one an­swer and pray it’s cor­rect.” Now, within the same wall-clock time, the model can run dozens of rea­son­ing paths in par­al­lel (Best-of-N / Tree Search), au­to­mat­i­cally ver­i­fy­ing and self-cor­rect­ing in the back­ground — us­ing raw speed to gen­er­ate depth of thought, di­rectly el­e­vat­ing rea­son­ing qual­ity.

Second, it com­pletely un­leashes the pro­duc­tiv­ity ceil­ing of Coding Agents. Before, hav­ing AI write code meant de­vel­op­ers painfully wait­ing in front of screens, bot­tle­necked by in­fer­ence la­tency. At 1000 tps, code gen­er­a­tion speed and pro­duc­tion ef­fi­ciency un­dergo a par­a­digm-level ac­cel­er­a­tion.

Most im­por­tantly, tril­lion-pa­ra­me­ter mod­els can now en­ter real-time de­ci­sion loops. Millisecond-level “think-respond” cy­cles al­low 1T flag­ship mod­els to seam­lessly plug into time-crit­i­cal sce­nar­ios — high-fre­quency quan­ti­ta­tive trad­ing sig­nal gen­er­a­tion, in­stant anti-fraud in­ter­cep­tion, in­tel­li­gent bid­ding, and real-time in­ter­ac­tive di­a­logue. And when this power is brought to sur­gi­cal as­sis­tance and med­ical imag­ing analy­sis in life-or-death sit­u­a­tions, AI speed is no longer just a met­ric of ef­fi­ciency — it be­comes a chip in the race against death. On the op­er­at­ing table, every sec­ond AI saves in com­plet­ing le­sion analy­sis and risk pre­dic­tion gives the sur­geon one more de­gree of free­dom. This deep­ens our con­vic­tion that the ul­ti­mate sig­nif­i­cance of speed is not merely boost­ing pro­duc­tiv­ity, but en­abling tech­nol­ogy to help hu­man­ity live bet­ter.

4. Extreme Model-System Codesign

Achieving 1000+ to­kens/​s gen­er­a­tion speed with a 1T flag­ship model is not the break­through of a sin­gle tech­nique — it is the prod­uct of deep col­lab­o­ra­tion and ex­treme Codesign be­tween the MiMo model team and the TileRT sys­tem team. The in­dus­try’s cur­rent ap­proach to sim­i­lar ex­treme speeds typ­i­cally re­lies on spe­cial­ized hard­ware — Cerebras’s Wafer-Scale in­te­gra­tion or Groq’s pure on-chip SRAM cus­tom ar­chi­tec­ture. We chose a dif­fer­ent path: achiev­ing even more im­pres­sive in­fer­ence speed on com­mod­ity GPUs through model-sys­tem code­sign alone.

On the model side, we ap­plied FP4 quan­ti­za­tion tar­get­ing the band­width bot­tle­neck of com­mod­ity hard­ware, dra­mat­i­cally shrink­ing model size and re­duc­ing mem­ory-ac­cess over­head; si­mul­ta­ne­ously, we in­tro­duced DFlash, an ef­fi­cient spec­u­la­tive de­cod­ing method based on block-level masked par­al­lel pre­dic­tion, sub­stan­tially in­creas­ing the ac­cepted to­ken length per ver­i­fi­ca­tion step. On the sys­tem side, TileRT per­fectly adapts to the dy­namic char­ac­ter­is­tics of these al­go­rithms, de­liv­er­ing a tai­lor-made com­pi­la­tion en­gine and com­pute ker­nels op­ti­mized specif­i­cally for the novel quan­ti­za­tion and spec­u­la­tive de­cod­ing pipeline. Through this ex­treme Codesign, we achieved 1000+ to­kens/​s out­put from a 1T model us­ing just a sin­gle stan­dard 8-GPU com­mod­ity node.

3.1 FP4 Quantization

At the tril­lion-pa­ra­me­ter (1T) scale, tra­di­tional 8-bit (FP8 / INT8) or even 16-bit in­fer­ence im­poses pro­hib­i­tive mem­ory foot­print and band­width pres­sure. Reducing pa­ra­me­ter bit-width di­rectly con­tributes to de­cod­ing speed. We there­fore adopt the widely val­i­dated, vir­tu­ally loss­less FP4 (MXFP4) quan­ti­za­tion for­mat[1].

However, naively ap­ply­ing FP4 across the en­tire model causes degra­da­tion in com­plex rea­son­ing, logic, and code gen­er­a­tion. Given the MoE (Mixture of Experts) ar­chi­tec­ture of Xiaomi MiMo-V2.5-Pro — where Experts con­sti­tute the vast ma­jor­ity of pa­ra­me­ters and ex­hibit the high­est tol­er­ance to quan­ti­za­tion — we se­lec­tively quan­tize only the MoE Experts to FP4 while pre­serv­ing orig­i­nal pre­ci­sion for all other mod­ules. Through FP4 QAT (Quantization-Aware Training), we dra­mat­i­cally re­duce model size and max­i­mize hard­ware band­width uti­liza­tion while keep­ing the mod­el’s over­all ca­pa­bil­ity es­sen­tially on par with the orig­i­nal, as shown be­low:

3.2 DFlash Speculative Decoding

Traditional Speculative Decoding re­lies on a small draft model to “guess” sub­se­quent to­kens, which the large model then ver­i­fies. This trans­forms au­tore­gres­sive gen­er­a­tion (1 to­ken per for­ward pass) into par­al­lel multi-to­ken gen­er­a­tion, with re­jec­tion sam­pling dur­ing ver­i­fi­ca­tion en­sur­ing loss­less out­put qual­ity. However, its bot­tle­neck lies in the draft mod­el’s qual­ity de­ter­min­ing the ac­cep­tance rate, while a stronger draft model in­curs higher com­pute over­head — a fun­da­men­tal ten­sion.

To break this dead­lock, we adopt DFlash, an in­no­v­a­tive block-level masked par­al­lel pre­dic­tion method from the re­search com­mu­nity[2]: the draft model fills an en­tire block of masked po­si­tions in a sin­gle for­ward pass, fun­da­men­tally elim­i­nat­ing the se­r­ial con­straint of “autoregressive draft­ing.”

We de­ployed this ap­proach on MiMo-V2.5-Pro with cus­tom op­ti­miza­tions tai­lored for tril­lion-scale MoE and long-con­text sce­nar­ios. Using the Muon sec­ond-or­der op­ti­mizer and model self-dis­til­la­tion, we en­sure that com­pact mask blocks still de­liver ideal ac­cep­tance rates while com­press­ing draft-stage over­head to near its the­o­ret­i­cal min­i­mum:

The draft model ex­clu­sively uses Sliding Window Attention (SWA), nat­u­rally align­ing with the SWA de­sign of the MiMo-V2 se­ries. This elim­i­nates de­pen­dency on com­plete pre­fixes, re­duc­ing per-pre­dic­tion com­pute from con­text-length-lin­ear to con­stant.

During train­ing, mask-sig­nal sam­pling is pushed down to GPU-local shards, en­abling a sin­gle se­quence to pro­duce tens of thou­sands of in­de­pen­dent train­ing sig­nals cov­er­ing di­verse con­text po­si­tions in one step — align­ing with the long-con­text ca­pa­bil­ity of the MiMo-V2 se­ries while avoid­ing cross-de­vice com­mu­ni­ca­tion over­head.

In terms of re­sults, our par­al­lel-pre­dic­tion spec­u­la­tive de­cod­ing achieves sig­nif­i­cant ac­cep­tance-length im­prove­ments across high-value agent and cod­ing sce­nar­ios, mean­ing the large model can con­firm more con­tent “in one breath” per ver­i­fi­ca­tion round. Furthermore, we limit block size to 8 to re­duce ver­i­fi­ca­tion over­head and in­crease con­cur­rency, al­low­ing high ac­cep­tance lengths to trans­late di­rectly into high in­fer­ence through­put:

In the Coding sce­nario, we achieve an av­er­age ac­cep­tance length of 6.30, with some sam­ples reach­ing a max­i­mum of 7.14 — mean­ing 6 – 7 out of the 8 draft to­kens per ver­i­fi­ca­tion round are ac­cepted. The draft model re­mains light­weight while push­ing ac­cep­tance rates to lev­els that de­liver real end-to-end gains. We also ob­serve that in more se­man­ti­cally di­ver­gent, higher-un­cer­tainty gen­eral con­ver­sa­tion sce­nar­ios, cur­rent ac­cep­tance rates are not yet high. We are con­tin­u­ously op­ti­miz­ing the al­go­rithm to ex­plore higher gen­er­al­iza­tion ceil­ings.

3.3 TileRT Ultra-Low-Latency Inference Kernels / System

If MiMo’s al­go­rith­mic in­no­va­tions un­shackle the band­width con­straints of hun­dred-bil­lion and tril­lion-pa­ra­me­ter mod­els, then the TileRT in­fer­ence sys­tem squeezes every last drop of phys­i­cal po­ten­tial from com­mod­ity GPUs down to the mi­crosec­ond level.

At 1000 to­kens/​s op­er­at­ing fre­quency, each op­er­a­tor’s life­cy­cle is com­pressed to mi­crosec­onds, and the “operator bound­aries” of tra­di­tional in­fer­ence sys­tems be­come the core bot­tle­neck — every op­er­a­tor launch, hard­ware syn­chro­niza­tion, and global mem­ory round-trip frac­tures the ex­e­cu­tion flow at the mi­crosec­ond scale, ex­pos­ing vis­i­ble “Execution Gaps.”

TileRT’s Paradigm-Level Execution Model Revolution

As the foun­da­tional in­fra­struc­ture for ul­tra-low-la­tency in­fer­ence, TileRT in­tro­duces an en­tirely new ex­e­cu­tion model that elim­i­nates ex­e­cu­tion gaps from op­er­a­tor bound­aries at their root:

Persistent Engine Kernel: Completely dis­cards the tra­di­tional per-op­er­a­tor launch par­a­digm, keep­ing the en­tire com­pute pipeline per­sis­tently res­i­dent and flow­ing within the GPU. This en­ables full-pipeline con­tin­u­ous prefetch­ing — while the cur­rent Tile is still com­put­ing on Tensor Cores, sub­se­quent data is al­ready flow­ing through the mem­ory hi­er­ar­chy, achiev­ing ex­treme over­lap be­tween data move­ment and com­pu­ta­tion.

Warp Specialization (Heterogeneous Pipeline Collaboration): At the Tile level, com­mu­ni­ca­tion, data move­ment, and ten­sor com­pu­ta­tion are phys­i­cally de­com­posed with finer gran­u­lar­ity. Breaking the ho­mo­ge­neous lock-step ex­e­cu­tion model, dif­fer­ent Warps (thread groups) and even het­ero­ge­neous ex­e­cu­tion do­mains across the en­tire GPU op­er­ate in­de­pen­dently yet in pre­cise co­or­di­na­tion — trans­form­ing the GPU into a con­tin­u­ously flow­ing, pre­cisely or­ches­trated het­ero­ge­neous ex­e­cu­tion sys­tem.

Microsecond-Scale Hardware-Software Deep Convergence (Codesign)

When the un­der­ly­ing ex­e­cu­tion model pushes hard­ware per­for­mance to its lim­its, pure run­time op­ti­miza­tion be­gins to hit phys­i­cal bound­aries. Building on this foun­da­tion, the TileRT sys­tem team and Xiaomi’s MiMo team en­gaged in deep tech­ni­cal co-cre­ation, break­ing down tra­di­tional soft­ware layer bound­aries. To per­fectly align model be­hav­ior with this ul­tra-low-la­tency ex­e­cu­tion pipeline, the model layer ul­ti­mately adopted a mixed FP4 quan­ti­za­tion strat­egy for MoE Experts and de­ployed SWA-aligned DFlash spec­u­la­tive de­cod­ing on the tril­lion-pa­ra­me­ter ar­chi­tec­ture. TileRT tightly cou­ples with these al­go­rith­mic char­ac­ter­is­tics and quan­ti­za­tion schemes, de­liv­er­ing cus­tom-built com­pi­la­tion en­gines and com­pute ker­nels. Both teams made pro­found joint en­gi­neer­ing trade­offs based on hard­ware physics, en­sur­ing ex­e­cu­tion pres­sure closes smoothly within hard­ware bound­aries.

The birth of 1000 to­kens/​s is no co­in­ci­dence of point op­ti­miza­tions. It is the in­evitable re­sult of world-class sys­tem in­fra­struc­ture and ex­treme al­go­rith­mic mod­els deeply con­verg­ing to­ward each other, co-evolv­ing as one.

TileRT is a fron­tier sys­tems ar­chi­tec­ture team fo­cused on next-gen­er­a­tion AI in­fra­struc­ture and ul­tra-low-la­tency in­fer­ence. The team is ded­i­cated to en­abling mil­lisec­ond-level real-time re­sponse for fron­tier large mod­els in pro­duc­tion en­vi­ron­ments, break­ing tra­di­tional stor­age-com­pute bar­ri­ers with an en­tirely new run­time ar­chi­tec­ture. The team has con­ceived and im­ple­mented a par­a­digm-level ex­e­cu­tion model. Through full-stack break­throughs in per­sis­tent ker­nels, tile pipelines, and het­ero­ge­neous col­lab­o­ra­tion, TileRT achieves ex­treme com­pute uti­liza­tion within com­plex het­ero­ge­neous ecosys­tems. As a core in­fra­struc­ture en­abler, the team ac­tively part­ners with in­dus­try-lead­ing col­lab­o­ra­tors on hard­ware-soft­ware code­sign, build­ing the high-per­for­mance com­pute foun­da­tion for the era of au­tonomous in­tel­li­gence that craves “ultimate speed.” For more TileRT tech­ni­cal de­tails: tilert.ai/​blog/​break­ing-1000-tps.html

5. More Demos

6. Open Source & Outlook

We have open-sourced the MiMo-V2.5-Pro-FP4-DFlash check­point on HuggingFace, in­clud­ing FP4 quan­tized weights and DFlash model pa­ra­me­ters. Community us­age and feed­back are wel­come: hug­ging­face.co/​Xi­aomiM­iMo/​MiMo-V2.5-Pro-FP4-DFlash

UltraSpeed sup­port for MiMo-V2.5 is on the way — stay tuned.

MiMo × TileRT — ex­treme model-sys­tem code­sign, de­liv­er­ing 1000 tps out­put speed for tril­lion-pa­ra­me­ter mod­els.

Almost 30 years ago a farm­ing fam­ily deeded land to the City of Taylor, Texas, on the con­di­tion the city use it for a pub­lic park. For the nom­i­nal fee of $10, the farm­ers granted the 87 acres to a pub­lic trust in 1999. Taylor sold it to Blueprint, a data cen­ter de­vel­oper, for $10 mil­lion in 2025. Now the land that was sup­posed to be­long to the com­mu­nity will be­come a 135,000 square foot data cen­ter.

Pamela Griffin and her fam­ily have owned homes near that land for gen­er­a­tions. Griffin and her broth­ers and sis­ters played base­ball on it, camped out on it, and then watched as their chil­dren and their chil­dren’s chil­dren did the same. Now a data cen­ter will be there, just 500 feet from Griffin’s home, nes­tled be­tween a power sub­sta­tion and the nearby rail­road tracks.

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[ TL;DR: As of 3 June 2026, we have iden­ti­fied more than 450 im­ages bear­ing signs of ma­nip­u­la­tion in ver­i­fi­ca­tion data ad­ver­tised by Thermo Fisher Scientific in its on­line pri­mary an­ti­bod­ies cat­a­log (+1 by Abcam). See the full repos­i­tory of prob­lem­atic im­ages, cu­rated by my­self and Sholto David, here:

Zenodo — Problematic im­ages in ven­dor an­ti­body ver­i­fi­ca­tion data

You are wel­come to con­tribute new find­ings at this Google form.

This blog post was orig­i­nal posted on 28 May 2026 and has not been edited to up­date counts since that date. There is an up­date cov­er­ing Thermo Fisher’s re­sponse at the bot­tom of this post. ]

A week and a half ago, while look­ing for trust­wor­thy data demon­strat­ing a cell line’s de­fi­ciency in the pro­tein p53, Sholto David came across the fol­low­ing im­age of a Western blot in Thermo Fisher Scientific’s on­line an­ti­bod­ies cat­a­log:

This im­age is sup­posed to demon­strate that the an­ti­body be­ing sold works as in­tended. It is la­beled as “Advanced Verification” data on Thermo Fisher’s site and its cap­tion im­plies that the data was pro­duced in­ter­nally (other im­ages in the cat­a­log that have not been pro­duced in­ter­nally are la­beled un­der “Published Figures”).

This Western blot ap­pears to be fab­ri­cated. As an­no­tated by Sholto, sev­eral of the bands in the im­age are iden­ti­cal af­ter flip­ping and ro­ta­tion:

Shortly af­ter, Johan Duchêne no­ticed a sim­i­larly sus­pi­cious im­age of an­other anti-p53 an­ti­body in Thermo Fisher’s cat­a­log. I de­cided to go look­ing my­self and quickly turned up ten more sus­pi­cious im­ages on eight other an­ti­body prod­ucts of­fered by Thermo Fisher.

Sholto and I have now doc­u­mented more than 100 im­ages pro­vided as ver­i­fi­ca­tion data in Thermo Fisher’s an­ti­body cat­a­log that have ap­par­ently been ma­nip­u­lated. You can see all of them at this Zenodo repos­i­tory, which we’ll try to up­date reg­u­larly. This repos­i­tory also con­tains a hand­ful of in­stances that are less sug­ges­tive of ma­nip­u­la­tion, but the data is still prob­lem­atic (e.g., the same im­age be­ing pre­sented as ver­i­fi­ca­tion data for two dif­fer­ent an­ti­bod­ies).

Here are some high­lights:

Some im­ages are sim­i­lar to the ex­am­ple that started this ex­cur­sion and also fea­ture bands that are un­usu­ally sim­i­lar to one an­other.

Many im­ages, if you ad­just the con­trast, fea­ture con­spic­u­ous “brushstrokes”, sug­gest­ing that part of the im­age has been painted over in a pro­gram like Photoshop.

Other im­ages fea­ture repet­i­tive blocks of back­ground noise, sug­gest­ing that parts of the im­age were copy-pasted over each other. They might also fea­ture sud­den un­ex­pected dis­con­ti­nu­ities in the pat­tern of back­ground noise.

In one in­stance, I thought I had stum­bled across an­other one of these in­stances of du­pli­cated blocks of back­ground noise…

…only to dis­cover that dozens of an­ti­bod­ies for sale from Thermo Fisher pre­sent a ver­i­fi­ca­tion Western blot that fea­tures this ex­act back­ground pat­tern, just with min­i­mal ed­its such that the sin­gle band is po­si­tioned where one would ex­pect to see the pro­tein of in­ter­est.

At the time of writ­ing, we’ve doc­u­mented 50 in­stances of this back­ground pat­tern ap­pear­ing in ver­i­fi­ca­tion data on Thermo Fisher’s site, but this is far from an ex­haus­tive list. “Similar im­age” searches us­ing Google Lens, Bing Images or DuckDuckGo be­tray hun­dreds more that we have yet to doc­u­ment.

Antibodies are near-ubiq­ui­tous but no­to­ri­ously fickle lab­o­ra­tory reagents in bio­med­ical re­search. For many ap­pli­ca­tions, it is ab­solutely cru­cial that the an­ti­bod­ies that you use are se­lec­tive (i.e., the an­ti­body binds strongly to the tar­get pro­tein) and spe­cific (i.e., the an­ti­body binds to the pro­tein of in­ter­est and lit­tle else). Commercially-available an­ti­bod­ies of­ten fail to meet these cri­te­ria. Members of YCharOS, an in­de­pen­dent an­ti­body val­i­da­tion ini­tia­tive, es­ti­mated in 2024 that “more than 50% of all an­ti­bod­ies failed in one or more ap­pli­ca­tions”. Antibodies that don’t work as in­tended can de­lay ex­per­i­ments by weeks and non-spe­cific an­ti­bod­ies are a mas­sive source of ir­re­pro­ducibil­ity in the bio­med­ical lit­er­a­ture. To learn more, check out Johan’s September 2025 talk in which he de­tails his ex­pe­ri­ence with a study pub­lished us­ing a non-spe­cific an­ti­body.

Antibody ven­dors like Thermo Fisher (probably the largest lab­o­ra­tory reagent sup­plier in the world) put ver­i­fi­ca­tion data in their cat­a­logs to demon­strate to sci­en­tists that the prod­uct works as in­tended. While signs of ma­nip­u­la­tion in this ver­i­fi­ca­tion data don’t nec­es­sar­ily im­ply that the an­ti­bod­ies in ques­tion don’t work as ad­ver­tised, with­out re­li­able ver­i­fi­ca­tion data avail­able, sci­en­tists will have no way of know­ing un­til they have ac­tu­ally pur­chased the an­ti­body. And an­ti­bod­ies are not cheap; at Thermo Fisher, a sin­gle vial con­tain­ing a 0.1 mL aliquot of an­ti­body so­lu­tion typ­i­cally costs 400 to 500 USD.

We cre­ated our repos­i­tory of prob­lem­atic im­ages in ven­dor an­ti­body cat­a­logs A) to raise aware­ness among work­ing bio­med­ical sci­en­tists that the an­ti­body ver­i­fi­ca­tion data they see in a ven­dor’s cat­a­log may be un­re­li­able and B) to en­cour­age oth­ers to look for and re­port prob­lem­atic ven­dor-pro­vided an­ti­body ver­i­fi­ca­tion data (not lim­ited to just Thermo Fisher). If you spot any­thing, feel free to fill out this Google form so that it might be added to the spread­sheet and repos­i­tory.

A part­ing mes­sage: al­ways val­i­date your an­ti­bod­ies!

UPDATE 8 June 2026: Thermo Fisher has re­leased a galling 15-point re­sponse to our ob­ser­va­tions. The most im­por­tant part (in my as­sess­ment) is quoted be­low (emphasis mine):

6. Did Thermo Fisher ma­nip­u­late or fab­ri­cate an­ti­body data?No. The Company fully stands by the data and un­der­ly­ing sci­ence. At Thermo Fisher Scientific, as the world leader in serv­ing sci­ence, sci­en­tific in­tegrity is a core value. The Company takes an­ti­body val­i­da­tion, speci­ficity and ac­cu­rate prod­uct doc­u­men­ta­tion se­ri­ously, and is com­mit­ted to the trans­par­ent and eth­i­cal gen­er­a­tion, analy­sis and pre­sen­ta­tion of sci­en­tific data. In the process of prepar­ing an­ti­body im­ages for pub­li­ca­tion on its web­site, some im­ages may have been ad­justed to clar­ify for pre­sen­ta­tion pur­poses — not to al­ter or mis­rep­re­sent the un­der­ly­ing ex­per­i­men­tal re­sults. Thermo Fisher rec­og­nizes, how­ever, that im­age ad­just­ments of any kind can raise ques­tions about data in­tegrity, which is why mov­ing for­ward, where an orig­i­nal im­age is not pre­sent or avail­able, the Company will en­sure that web­site users are in­formed that an­ti­body im­ages may have been op­ti­mized for pre­sen­ta­tion and clar­ity on the web­site.

6. Did Thermo Fisher ma­nip­u­late or fab­ri­cate an­ti­body data?

No. The Company fully stands by the data and un­der­ly­ing sci­ence. At Thermo Fisher Scientific, as the world leader in serv­ing sci­ence, sci­en­tific in­tegrity is a core value. The Company takes an­ti­body val­i­da­tion, speci­ficity and ac­cu­rate prod­uct doc­u­men­ta­tion se­ri­ously, and is com­mit­ted to the trans­par­ent and eth­i­cal gen­er­a­tion, analy­sis and pre­sen­ta­tion of sci­en­tific data. In the process of prepar­ing an­ti­body im­ages for pub­li­ca­tion on its web­site, some im­ages may have been ad­justed to clar­ify for pre­sen­ta­tion pur­poses — not to al­ter or mis­rep­re­sent the un­der­ly­ing ex­per­i­men­tal re­sults. Thermo Fisher rec­og­nizes, how­ever, that im­age ad­just­ments of any kind can raise ques­tions about data in­tegrity, which is why mov­ing for­ward, where an orig­i­nal im­age is not pre­sent or avail­able, the Company will en­sure that web­site users are in­formed that an­ti­body im­ages may have been op­ti­mized for pre­sen­ta­tion and clar­ity on the web­site.

The phrase “antibody im­ages may have been op­ti­mized for pre­sen­ta­tion and clar­ity on the web­site” is re­peated on this FAQ page six times. I en­cour­age read­ers to pe­ruse the im­ages col­lected in our Zenodo repos­i­tory and de­cide what could and could not char­i­ta­bly be de­scribed as “optimization for pre­sen­ta­tion and clar­ity”.

An un­ex­pected de­vel­op­ment over the past few weeks is xAI’s new part­ner­ships with Anthropic and Google, pro­vid­ing them with a huge amount of ca­pac­ity. It’s worth re­mem­ber­ing that xAI is now part of SpaceX, af­ter the two merged back in February - so the rev­enue from these deals flows straight into the en­tity about to go pub­lic. While much has been made of the po­ten­tial fi­nan­cial en­gi­neer­ing given SpaceX’s up­com­ing IPO, I think there’s a bit more to this than just pure ac­count­ing tricks.

Anthropic was in a se­ri­ous bind

If you use Claude prod­ucts much, you’ll be (very, prob­a­bly) aware that Anthropic has had se­ri­ous ca­pac­ity prob­lems, es­pe­cially early af­ter­noon on­wards in Europe and in the morn­ings in the US (this is when de­mand seems to be high­est as both European users and the Americas are both at work, fight­ing for ca­pac­ity). I’ve writ­ten about this com­pute crunch be­fore a few times - the com­ing crunch, whether it’s here yet, and what comes next.

This re­sulted in Anthropic hav­ing to in­tro­duce new peak hour re­stric­tions on their sub­scrip­tions, with us­age be­tween 5am–11am PT / 1pm–7pm GMT us­ing more of your us­age limit - with the aim of smooth­ing de­mand be­tween peak hours and off peak hours where they had more ca­pac­ity avail­able.

However, there is only so much de­mand shift­ing you can do when de­mand is grow­ing as fast as Anthropic’s. At some point you end up hav­ing to ra­tion users fur­ther, which def­i­nitely is far from ideal when you have both Google and OpenAI breath­ing down your neck for cus­tomers.

xAI to the res­cue?

At the start of May, xAI an­nounced a part­ner­ship with Anthropic to pro­vide ac­cess to their (older) Colossus 1 dat­a­cen­tre in Memphis. This al­lowed Anthropic to re­verse the us­age limit re­stric­tions on their sub­scrip­tions, and in gen­eral while sta­bil­ity of Anthropic ser­vices still leaves a lot to be de­sired, the peak time crunch has abated (for now, at least).

The fees in­volved are enor­mous, ramp­ing to $1.25bn/month for 300MW of ca­pac­ity - ap­prox­i­mately 220k GPUs.

Last week, Google an­nounced a sim­i­lar part­ner­ship - $920mn/month for 110k GPUs[1]. It’s im­por­tant to note that both agree­ments have can­cel­la­tion clauses - al­low­ing ei­ther party to can­cel with 90 days’ no­tice af­ter an ini­tial lock-in pe­riod.

If you take this on face value, this is a lu­di­crously prof­itable deal for xAI:

While this does­n’t in­clude opex[2] and de­pre­ci­a­tion, if the deals con­tinue for 18 months, xAI re­coups all the capex they spent and still has many hun­dreds of MW of GPUs avail­able. With the gi­ant com­pute short­ages likely to per­sist into the medium term, even older H100s are likely to be ex­tremely use­ful even 18 months out.

The case against

It’s im­por­tant to note there are cer­tainly some red flags with the deal. Firstly, Elon Musk and OpenAI were/​are locked in a bit­ter le­gal bat­tle, and the Anthropic deal could be mo­ti­vated to add pres­sure to OpenAI more than com­mer­cial re­al­ity.

And Google is a ma­jor share­holder in SpaceX, so they cer­tainly have in­cen­tive to juice the val­u­a­tion of the IPO.

While I’m sure there is some de­gree (potentially a lot!) of truth in these view­points, it’s im­por­tant to note that huge vol­umes of GPUs are in enor­mously short sup­ply.

One of the un­told sto­ries of this capex boom in dat­a­cen­tres is just how be­hind all of them are. Even OpenAI’s flag­ship Stargate UAE dat­a­cen­tre - be­ing built in a ju­ris­dic­tion that is renowned for a lais­sez-faire at­ti­tude to build­ing reg­u­la­tions - is now un­der di­rect threat from the cur­rent Iran con­flict, with Iranian drones hav­ing al­ready hit other UAE dat­a­cen­tres.

In com­par­i­son, SpaceX/xAI are in­cred­i­ble at build­ing dat­a­cen­tres on time. The orig­i­nal Colossus 1 dat­a­cen­tre was built in 122 days. Musk’s em­pire does have a huge ad­van­tage in re­ally un­der­stand­ing how to plan, build and ex­e­cute enor­mous in­fra­struc­ture pro­jects quickly. While the hy­per­scalers no doubt have the ex­pe­ri­ence to do this, they were built with far less ur­gency - with typ­i­cal pro­ject ex­e­cu­tion tak­ing many years. Given the capex only re­ally started to ramp up in the last cou­ple of years, many of these pro­jects are still years away.

This gives xAI a se­ri­ous com­pet­i­tive ad­van­tage that should­n’t in my opin­ion just be hand waved away.

But what about Grok?

There is no doubt this leaves Grok in an odd spot, with a lot of the dat­a­cen­tre ca­pac­ity that was des­tined for Grok train­ing and in­fer­ence now be­ing leased to a di­rect com­peti­tor.

While it’s fool­ish to write off any model provider, it cer­tainly looks like a se­ri­ous re­treat from Grok vy­ing to be a fron­tier class lab. But, per­haps, they over-spec­i­fied their dat­a­cen­tre ca­pac­ity - there is no doubt that in­fer­ence de­mand for Grok mod­els is likely to be se­ri­ously be­hind pro­jec­tions, leav­ing a bunch of spare ca­pac­ity which might as well be mon­e­tised while the train­ing lot­tery con­tin­ues? It’s hard to say and the xAI & Cursor deal mud­dies the wa­ter even fur­ther.

As such, I think all three things are true to some de­gree. There’s no doubt some level of fi­nan­cial en­gi­neer­ing go­ing on. There’s also an enor­mous com­pute short­age. And it seems to me SpaceX/xAI does have a real com­pet­i­tive ad­van­tage in dat­a­cen­tre build­out.

It’s just the mag­ni­tude of how true each of these are is go­ing to de­fine the suc­cess or fail­ure of the biggest IPO in North American his­tory.

Either way, the more I look at it, the more xAI is start­ing to re­sem­ble a dat­a­cen­tre REIT with a fron­tier lab at­tached, rather than the other way around.

I sus­pect that these are likely to be GB200s given the pric­ing, vs the mostly H100/H200 for Anthropic, but this is spec­u­la­tion on my part. ↩︎

I sus­pect that these are likely to be GB200s given the pric­ing, vs the mostly H100/H200 for Anthropic, but this is spec­u­la­tion on my part. ↩︎

Power is the ob­vi­ous big opex line, but at this scale it’s al­most a round­ing er­ror. 300MW run­ning flat out is roughly 300,000 kW × 8,760 hours, or about 2.6 bil­lion kWh a year. Tennessee has some of the cheap­est in­dus­trial elec­tric­ity in the US at around 6 cents/​kWh, so buy­ing it off the grid would cost some­where around $160mn a year. Colossus ac­tu­ally runs largely on its own on-site gas tur­bines, which comes out even cheaper: at a sim­ple-cy­cle heat rate of ~10,000 Btu/kWh and Henry Hub gas at ~$3.50/MMBtu, the fuel bill is only around $90mn a year. Either way, set against the ~$15bn a year Anthropic is pay­ing for that 300MW, power is no more than about 1% of rev­enue. The deal value ut­terly dwarfs the run­ning costs. ↩︎

Power is the ob­vi­ous big opex line, but at this scale it’s al­most a round­ing er­ror. 300MW run­ning flat out is roughly 300,000 kW × 8,760 hours, or about 2.6 bil­lion kWh a year. Tennessee has some of the cheap­est in­dus­trial elec­tric­ity in the US at around 6 cents/​kWh, so buy­ing it off the grid would cost some­where around $160mn a year. Colossus ac­tu­ally runs largely on its own on-site gas tur­bines, which comes out even cheaper: at a sim­ple-cy­cle heat rate of ~10,000 Btu/kWh and Henry Hub gas at ~$3.50/MMBtu, the fuel bill is only around $90mn a year. Either way, set against the ~$15bn a year Anthropic is pay­ing for that 300MW, power is no more than about 1% of rev­enue. The deal value ut­terly dwarfs the run­ning costs. ↩︎

A Declaration of the Independence of Cyberspace

Your Secret Right to Cash

A Declaration of the Independence of Cyberspace

Your Secret Right to Cash

A Declaration of the Independence of Cyberspace

Your Secret Right to Cash

A Declaration of the Independence of Cyberspace

Your Secret Right to Cash