These days it seems you need a tril­lion fake dol­lars, or lunch with politi­cians to get your own data cen­ter. They may help, but they’re not re­quired. At comma we’ve been run­ning our own data cen­ter for years. All of our model train­ing, met­rics, and data live in our own data cen­ter in our own of­fice. Having your own data cen­ter is cool, and in this blog post I will de­scribe how ours works, so you can be in­spired to have your own data cen­ter too.

If your busi­ness re­lies on com­pute, and you run that com­pute in the cloud, you are putting a lot of trust in your cloud provider. Cloud com­pa­nies gen­er­ally make on­board­ing very easy, and off­board­ing very dif­fi­cult. If you are not vig­i­lant you will sleep­walk into a sit­u­a­tion of high cloud costs and no way out. If you want to con­trol your own des­tiny, you must run your own com­pute.

Self-reliance is great, but there are other ben­e­fits to run­ning your own com­pute. It in­spires good en­gi­neer­ing. Maintaining a data cen­ter is much more about solv­ing real-world chal­lenges. The cloud re­quires ex­per­tise in com­pany-spe­cific APIs and billing sys­tems. A data cen­ter re­quires knowl­edge of Watts, bits, and FLOPs. I know which one I rather think about.

Avoiding the cloud for ML also cre­ates bet­ter in­cen­tives for en­gi­neers. Engineers gen­er­ally want to im­prove things. In ML many prob­lems go away by just us­ing more com­pute. In the cloud that means im­prove­ments are just a bud­get in­crease away. This locks you into in­ef­fi­cient and ex­pen­sive so­lu­tions. Instead, when all you have avail­able is your cur­rent com­pute, the quick­est im­prove­ments are usu­ally speed­ing up your code, or fix­ing fun­da­men­tal is­sues.

Finally there’s cost, own­ing a data cen­ter can be far cheaper than rent­ing in the cloud. Especially if your com­pute or stor­age needs are fairly con­sis­tent, which tends to be true if you are in the busi­ness of train­ing or run­ning mod­els. In com­ma’s case I es­ti­mate we’ve spent ~5M on our data cen­ter, and we would have spent 25M+ had we done the same things in the cloud.

Our data cen­ter is pretty sim­ple. It’s main­tained and built by only a cou­ple en­gi­neers and tech­ni­cians. Your needs may be slightly dif­fer­ent, our im­ple­men­ta­tion should pro­vide use­ful con­text.

To run servers you need power. We cur­rently use about 450kW at max. Operating a data cen­ter ex­poses you to many fun en­gi­neer­ing chal­lenges, but procur­ing power is not one of them. San Diego power cost is over 40c/kWh, ~3x the global av­er­age. It’s a ripoff, and over­priced sim­ply due to po­lit­i­cal dys­func­tion. We spent $540,112 on power in 2025, a big part of the data cen­ter cost. In a fu­ture blog post I hope I can tell you about how we pro­duce our own power and you should too.

Data cen­ters need cool dry air. Typically this is achieved with a CRAC sys­tem, but they are power-hun­gry. San Diego has a mild cli­mate and we opted for pure out­side air cool­ing. This gives us less con­trol of the tem­per­a­ture and hu­mid­ity, but uses only a cou­ple dozen kW. We have dual 48” in­take fans and dual 48” ex­haust fans to keep the air cool. To en­sure low hu­mid­ity (

The ma­jor­ity of our cur­rent com­pute is 600 GPUs in 75 TinyBox Pro ma­chines. They were built in-house, which saves us money and en­sures they suit our needs. Our self-built ma­chines fail at a sim­i­lar rate to pre-built ma­chines we’ve bought, but we’re ca­pa­ble of fix­ing them our­selves quickly. They have 2 CPUs and 8 GPUs each, and work as both train­ing ma­chines and gen­eral com­pute work­ers.

For data stor­age we have a few racks of Dell ma­chines (R630 and R730). They are filled with SSDs for a to­tal of ~4PB of stor­age. We use SSDs for re­li­a­bil­ity and speed. Our main stor­age ar­rays have no re­dun­dancy and each node needs to be able to sat­u­rate the net­work band­width with ran­dom ac­cess reads. For the stor­age ma­chines this means read­ing up to 20Gbps of each 80TB chunk.

Other than stor­age and com­pute ma­chines we have sev­eral one-off ma­chines to run ser­vices. This in­cludes a router, cli­mate con­troller, data in­ges­tion ma­chine, stor­age mas­ter servers, met­ric servers, re­dis servers, and a few more.

Running the net­work re­quires switches, but at this scale we don’t need to bother with com­pli­cated switch topolo­gies. We have 3 100Gbps in­ter­con­nected Z9264F switches, which serve as the main eth­er­net net­work. We have two more in­fini­band switches to in­ter­con­nect the 2 tiny­box pro groups for train­ing all-re­duce.

To ef­fec­tively use all these com­pute and stor­age ma­chines you need some in­fra. At this scale, ser­vices don’t need re­dun­dancy to achieve 99% up­time. We use a sin­gle mas­ter for all ser­vices, which makes things pretty sim­ple.

All servers get ubuntu in­stalled with pxe­boot and are man­aged by salt.

All of our stor­age ar­rays use mkv. The main ar­ray is 3PB of non-re­dun­dant stor­age host­ing our dri­ving data we train on. We can read from this ar­ray at ~1TB/s, which means we can train di­rectly on the raw data with­out caching. Redundancy is not needed since no spe­cific data is crit­i­cal.

We have an ad­di­tional ~300TB non-re­dun­dant ar­ray to cache in­ter­me­di­ate processed re­sults. And lastly, we have a re­dun­dant mkv stor­age ar­ray to store all of our trained mod­els and train­ing met­rics. Each of these 3 ar­rays have a sep­a­rate sin­gle mas­ter server.

We use slurm to man­age the com­pute nodes, and com­pute jobs. We sched­ule two types of dis­trib­uted com­pute. Pytorch train­ing jobs, and mini­ray work­ers.

To train mod­els across mul­ti­ple GPU nodes we use torch.dis­trib­uted FSDP. We have 2 sep­a­rate train­ing par­ti­tions, each in­tra-con­nected with Infiniband for train­ing across ma­chines. We wrote our own train­ing frame­work which han­dles the train­ing loop boil­er­plate, but it’s mostly just py­torch.

We have a cus­tom model ex­per­i­ment track­ing ser­vice (similar to wandb or ten­sor­board). It pro­vides a dash­board for track­ing ex­per­i­ments, and shows cus­tom met­rics and re­ports. It is also the in­ter­face for the mkv stor­age ar­ray that hosts the model weights. The train­ing runs store the model weights there with a uuid, and they are avail­able to down­load for who­ever needs to run them. The met­rics and re­ports for our lat­est mod­els are also open.

Besides train­ing we have many other com­pute tasks. This can be any­thing from run­ning tests, run­ning mod­els, pre-pro­cess­ing data, or even run­ning agent roll­outs for on-pol­icy train­ing. We wrote a light­weight open-source task sched­uler called mini­ray that al­lows you to run ar­bi­trary python code on idle ma­chines. This is a sim­pler ver­sion of dask, with a fo­cus on ex­treme sim­plic­ity. Slurm will sched­ule any idle ma­chine to be an ac­tive mini­ray worker, and ac­cept pend­ing tasks. All the task in­for­ma­tion is hosted in a cen­tral re­dis server.

Miniray work­ers with GPUs will spin up a tri­ton in­fer­ence server to run model in­fer­ence with dy­namic batch­ing. A mini­ray worker can thus eas­ily and ef­fi­ciently run any of the mod­els hosted in the model mkv stor­age ar­ray.

Miniray makes it ex­tremely easy to scale par­al­lel tasks to hun­dreds of ma­chines. For ex­am­ple, the con­trols chal­lenge record was set by just hav­ing ~1hr of ac­cess to our data cen­ter with mini­ray.

All our code is in a monorepo that we have cloned on our work­sta­tions. This monorepo is kept small (

The most com­plex thing we do at comma is train dri­ving mod­els on-pol­icy, these train­ing runs re­quire train­ing data to be gen­er­ated dur­ing train­ing by run­ning sim­u­lated dri­ving roll­outs with the most re­cent model weights. Here’s a real-world com­mand we just used to train such a model. This train­ing run uses all of the in­fra­struc­ture de­scribed above. While only this small com­mand is needed to kick every­thing off, it or­ches­trates a lot of mov­ing parts.

Does all this stuff sound ex­cit­ing? Then build your own dat­a­cen­ter for your­self or your com­pany! You can also come work here.

There are many good places for ad­ver­tis­ing. A con­ver­sa­tion with Claude is not one of them.

Advertising dri­ves com­pe­ti­tion, helps peo­ple dis­cover new prod­ucts, and al­lows ser­vices like email and so­cial me­dia to be of­fered for free. We’ve run our own ad cam­paigns, and our AI mod­els have, in turn, helped many of our cus­tomers in the ad­ver­tis­ing in­dus­try.

But in­clud­ing ads in con­ver­sa­tions with Claude would be in­com­pat­i­ble with what we want Claude to be: a gen­uinely help­ful as­sis­tant for work and for deep think­ing.

We want Claude to act un­am­bigu­ously in our users’ in­ter­ests. So we’ve made a choice: Claude will re­main ad-free. Our users won’t see “sponsored” links ad­ja­cent to their con­ver­sa­tions with Claude; nor will Claude’s re­sponses be in­flu­enced by ad­ver­tis­ers or in­clude third-party prod­uct place­ments our users did not ask for.

When peo­ple use search en­gines or so­cial me­dia, they’ve come to ex­pect a mix­ture of or­ganic and spon­sored con­tent. Filtering sig­nal from noise is part of the in­ter­ac­tion.

Conversations with AI as­sis­tants are mean­ing­fully dif­fer­ent. The for­mat is open-ended; users of­ten share con­text and re­veal more than they would in a search query. This open­ness is part of what makes con­ver­sa­tions with AI valu­able, but it’s also what makes them sus­cep­ti­ble to in­flu­ence in ways that other dig­i­tal prod­ucts are not.

Our analy­sis of con­ver­sa­tions with Claude (conducted in a way that keeps all data pri­vate and anony­mous) shows that an ap­pre­cia­ble por­tion in­volve top­ics that are sen­si­tive or deeply per­sonal—the kinds of con­ver­sa­tions you might have with a trusted ad­vi­sor. Many other uses in­volve com­plex soft­ware en­gi­neer­ing tasks, deep work, or think­ing through dif­fi­cult prob­lems. The ap­pear­ance of ads in these con­texts would feel in­con­gru­ous—and, in many cases, in­ap­pro­pri­ate.

We still have much to learn about the im­pact of AI mod­els on the peo­ple who use them. Early re­search sug­gests both ben­e­fits—like peo­ple find­ing sup­port they could­n’t ac­cess else­where—and risks, in­clud­ing the po­ten­tial for mod­els to re­in­force harm­ful be­liefs in vul­ner­a­ble users. Introducing ad­ver­tis­ing in­cen­tives at this stage would add an­other level of com­plex­ity. Our un­der­stand­ing of how mod­els trans­late the goals we set them into spe­cific be­hav­iors is still de­vel­op­ing; an ad-based sys­tem could there­fore have un­pre­dictable re­sults.

Being gen­uinely help­ful is one of the core prin­ci­ples of Claude’s Constitution, the doc­u­ment that de­scribes our vi­sion for Claude’s char­ac­ter and guides how we train the model. An ad­ver­tis­ing-based busi­ness model would in­tro­duce in­cen­tives that could work against this prin­ci­ple.

Consider a con­crete ex­am­ple. A user men­tions they’re hav­ing trou­ble sleep­ing. An as­sis­tant with­out ad­ver­tis­ing in­cen­tives would ex­plore the var­i­ous po­ten­tial causes—stress, en­vi­ron­ment, habits, and so on—based on what might be most in­sight­ful to the user. An ad-sup­ported as­sis­tant has an ad­di­tional con­sid­er­a­tion: whether the con­ver­sa­tion pre­sents an op­por­tu­nity to make a trans­ac­tion. These ob­jec­tives may of­ten align—but not al­ways. And, un­like a list of search re­sults, ads that in­flu­ence a mod­el’s re­sponses may make it dif­fi­cult to tell whether a given rec­om­men­da­tion comes with a com­mer­cial mo­tive or not. Users should­n’t have to sec­ond-guess whether an AI is gen­uinely help­ing them or sub­tly steer­ing the con­ver­sa­tion to­wards some­thing mon­e­ti­z­able.

Even ads that don’t di­rectly in­flu­ence an AI mod­el’s re­sponses and in­stead ap­pear sep­a­rately within the chat win­dow would com­pro­mise what we want Claude to be: a clear space to think and work. Such ads would also in­tro­duce an in­cen­tive to op­ti­mize for en­gage­ment—for the amount of time peo­ple spend us­ing Claude and how of­ten they re­turn. These met­rics aren’t nec­es­sar­ily aligned with be­ing gen­uinely help­ful. The most use­ful AI in­ter­ac­tion might be a short one, or one that re­solves the user’s re­quest with­out prompt­ing fur­ther con­ver­sa­tion.

We rec­og­nize that not all ad­ver­tis­ing im­ple­men­ta­tions are equiv­a­lent. More trans­par­ent or opt-in ap­proaches—where users ex­plic­itly choose to see spon­sored con­tent—might avoid some of the con­cerns out­lined above. But the his­tory of ad-sup­ported prod­ucts sug­gests that ad­ver­tis­ing in­cen­tives, once in­tro­duced, tend to ex­pand over time as they be­come in­te­grated into rev­enue tar­gets and prod­uct de­vel­op­ment, blur­ring bound­aries that were once more clear-cut. We’ve cho­sen not to in­tro­duce these dy­nam­ics into Claude.

Anthropic is fo­cused on busi­nesses, de­vel­op­ers, and help­ing our users flour­ish. Our busi­ness model is straight­for­ward: we gen­er­ate rev­enue through en­ter­prise con­tracts and paid sub­scrip­tions, and we rein­vest that rev­enue into im­prov­ing Claude for our users. This is a choice with trade­offs, and we re­spect that other AI com­pa­nies might rea­son­ably reach dif­fer­ent con­clu­sions.

Expanding ac­cess to Claude is cen­tral to our pub­lic ben­e­fit mis­sion, and we want to do it with­out sell­ing our users’ at­ten­tion or data to ad­ver­tis­ers. To that end, we’ve brought AI tools and train­ing to ed­u­ca­tors in over 60 coun­tries, be­gun na­tional AI ed­u­ca­tion pi­lots with mul­ti­ple gov­ern­ments, and made Claude avail­able to non­prof­its at a sig­nif­i­cant dis­count. We con­tinue to in­vest in our smaller mod­els so that our free of­fer­ing re­mains at the fron­tier of in­tel­li­gence, and we may con­sider lower-cost sub­scrip­tion tiers and re­gional pric­ing where there is clear de­mand for it. Should we need to re­visit this ap­proach, we’ll be trans­par­ent about our rea­sons for do­ing so.

AI will in­creas­ingly in­ter­act with com­merce, and we look for­ward to sup­port­ing this in ways that help our users. We’re par­tic­u­larly in­ter­ested in the po­ten­tial of agen­tic com­merce, where Claude acts on a user’s be­half to han­dle a pur­chase or book­ing end to end. And we’ll con­tinue to build fea­tures that en­able our users to find, com­pare, or buy prod­ucts, con­nect with busi­nesses, and more—when they choose to do so.

We’re also ex­plor­ing more ways to make Claude a fo­cused space to be at your most pro­duc­tive. Users can al­ready con­nect third-party tools they use for work—like Figma, Asana, and Canva—and in­ter­act with them di­rectly within Claude. We ex­pect to in­tro­duce many more use­ful in­te­gra­tions and ex­pand this toolkit over time.

All third-party in­ter­ac­tions will be grounded in the same over­ar­ch­ing de­sign prin­ci­ple: they should be ini­ti­ated by the user (where the AI is work­ing for them) rather than an ad­ver­tiser (where the AI is work­ing, at least in part, for some­one else). Today, whether some­one asks Claude to re­search run­ning shoes, com­pare mort­gage rates, or rec­om­mend a restau­rant for a spe­cial oc­ca­sion, Claude’s only in­cen­tive is to give a help­ful an­swer. We’d like to pre­serve that.

We want our users to trust Claude to help them keep think­ing—about their work, their chal­lenges, and their ideas.

Our ex­pe­ri­ence of us­ing the in­ter­net has made it easy to as­sume that ad­ver­tis­ing on the prod­ucts we use is in­evitable. But open a note­book, pick up a well-crafted tool, or stand in front of a clean chalk­board, and there are no ads in sight.

We think Claude should work the same way.

SaaS is the most prof­itable busi­ness model on Earth. It’s easy to un­der­stand why: build once, sell the same thing again ad in­fini­tum, and don’t suf­fer any mar­ginal costs on more sales.

I have been writ­ing soft­ware for more than half my life. In the last year it­self, I’ve talked to hun­dreds of founders and op­er­a­tors in SF, from pre­seed to Series E com­pa­nies.

AI is bring­ing an ex­is­ten­tial threat to a lot of B2B SaaS ex­ec­u­tives: How to keep ask­ing cus­tomers for re­newal, when every cus­tomer feels they can get some­thing bet­ter built with vibe-coded AI prod­ucts?

And the mar­ket is pric­ing it in. Morgan Stanley’s SaaS bas­ket has lagged the Nasdaq by 40 points since December. HubSpot and Klaviyo are down ~30%. Analysts are writ­ing notes ti­tled “No Reasons to Own” soft­ware stocks.

The new prob­lem for B2B SaaS is that with AI, cus­tomers can get some­thing work­ing with vibe cod­ing. There are tens of vibe cod­ing “internal tool” ser­vices that promise to con­nect to every in­te­gra­tion in the world to pump out CRUD and work­flow apps.

Whatever they build sim­ply works. It takes some wran­gling to get there (one Series C VP listed eleven dif­fer­ent vibe cod­ing tools they’ve tried and the pros and cons be­tween each on a phone call once), but pro­duc­tiv­ity gains are im­me­di­ate.

And vibe cod­ing is fun. You feel like a mad wiz­ard us­ing the right in­can­ta­tion to get this mag­i­cal new sil­i­con in­tel­li­gence to do ex­actly what you want.

What they don’t know, though, is that a poorly ar­chi­tected sys­tem will fail, even­tu­ally. As every se­nior pro­gram­mer (eventually) un­der­stands, our job is com­plex be­cause we have to un­der­stand the re­la­tion­ships in the real world, the processes in­volved, and the work­flows needed, and rep­re­sent­ing it in a ro­bust way to cre­ate a sta­ble sys­tem. AI can’t do that.

Non-programmers don’t know any of this nu­ance. One Series E CEO told me that they’re re-eval­u­at­ing the quar­terly re­newal of their en­gi­neer­ing pro­duc­tiv­ity soft­ware be­cause they along with an en­gi­neer reim­ple­mented some­thing us­ing Github and Notion APIs. They were pay­ing $30,000 to a pop­u­lar tool and they were not go­ing to re­new any­more.

If cus­tomers feel like they aren’t be­ing served ex­actly like they want to, they are more likely to churn. The rea­son be­hind all this is that cus­tomers are de­mand­ing more from their B2B ven­dors, be­cause they know what’s pos­si­ble.

Previously, you would change your com­pany to fit what your ERP and pay them hun­dreds of thou­sands of dol­lars. Now, every­one can see that agen­tic cod­ing makes an un­prece­dented level of flex­i­bil­ity pos­si­ble. And cus­tomers are de­mand­ing that flex­i­bil­ity, and if they don’t get it, they’ll leave.

This week it­self I was on a phone call with a Series B AE talk­ing about how they’re po­ten­tially los­ing an $X00,000 ac­count just be­cause the cus­tomer can’t use a spe­cific fail­ure re­port­ing work­flow in the SaaS. They’re now work­ing with me to build what the cus­tomer needs and re­tain them.

If the en­tire com­pa­ny’s work­flows op­er­ates on your plat­form, i.e. you’re a line-of-busi­ness SaaS, you are in­te­grated into their ex­ist­ing team al­ready. They know your UI and rely on you on the day to day.

For ex­am­ple, to cre­ate a data vi­su­al­iza­tion I won’t seek any SaaS. I’ll just code one my­self us­ing many of the pop­u­lar vibe cod­ing tools (my team ac­tu­ally did that and it’s vastly more flex­i­ble than what we’d get off-the-shelf).

Being a “System of Record” means you’re em­bed­ded so deeply that there’s no choice but to win. My pre­dic­tion is that we’ll see more SaaS com­pa­nies go from the ap­pli­ca­tion layer to of­fer­ing their ro­bust SoR as their pri­mary sell­ing point.

This is where vibe-coded apps silently fail — and where es­tab­lished SaaS plat­forms earn their keep.

When a non-tech­ni­cal team vibe-codes an in­ter­nal tool, they’re not think­ing about en­vi­ron­ment keys, XSS vul­ner­a­bil­i­ties or API keys hard­coded in client-side JavaScript. They’re not im­ple­ment­ing rate lim­it­ing, au­dit logs, or proper ses­sion man­age­ment. They’re def­i­nitely not think­ing about SOC 2 com­pli­ance, GDPR data res­i­dency re­quire­ments, or HIPAA au­dit trails.

I’ve seen it first­hand: a fi­nance team built a “quick” ex­pense ap­proval tool that stored un­en­crypted re­ports in a pub­lic S3 bucket. A sales ops team cre­ated a com­mis­sion cal­cu­la­tor that any­one with the URL could ac­cess — no auth re­quired. These aren’t edge cases. They’re the norm when soft­ware is built with­out se­cu­rity as a foun­da­tional con­cern.

Enterprise SaaS plat­forms have spent years (and mil­lions) solv­ing these prob­lems: role-based ac­cess con­trol, en­cryp­tion at rest and in tran­sit, pen­e­tra­tion test­ing, com­pli­ance cer­ti­fi­ca­tions, in­ci­dent re­sponse pro­ce­dures. Your cus­tomers may not con­sciously value this — un­til some­thing breaks.

The chal­lenge is that se­cu­rity is in­vis­i­ble when it works. You need to com­mu­ni­cate this value proac­tively: re­mind cus­tomers that the “simple” tool they could vibe-code them­selves would re­quire them to also han­dle auth, per­mis­sions, back­ups, up­time, and com­pli­ance.

The times of ask­ing cus­tomers to change how they work are gone. Now, SaaS ven­dors that dif­fer­en­ti­ate by be­ing ul­tra cus­tomiz­able win the hearts of cus­tomers.

How? It’s the most pow­er­ful se­cret to in­crease us­age. We’ve all heard the clas­sic SaaS prob­lem where the soft­ware is sold at the be­gin­ning of the year, but no one ac­tu­ally ends up us­ing it be­cause of how in­flex­i­ble it is and the amount of train­ing needed.

And if a SaaS is un­der­uti­lized, it gets no­ticed. And that leads to churn.

This is the case with one of my cus­tomers, they have a com­plex SaaS for main­te­nance op­er­a­tions. But turns out, this was not be­ing used at the tech­ni­cian level be­cause they found the UI too com­plex.

How I’m solv­ing this is es­sen­tially a white­la­belled vibe-cod­ing plat­form with in-built dis­tri­b­u­tion and se­cure de­ploy­ments. When they heard of my so­lu­tion they were im­me­di­ately on­board. Their cus­tomer suc­cess teams quickly coded a very spe­cific mo­bile we­bapp for the tech­ni­cians to use and de­ployed it in a few days.

Now, the IC tech­ni­cian is ex­posed to just those parts of the SaaS that they care about i.e. cre­at­ing main­te­nance work or­ders. The ex­ec­u­tives get what they want too, vibe cod­ing cus­tom re­ports ex­actly the way they want vs go­ing through com­pli­cated BI con­fig. They are able to build ex­actly what they want and feel like dig­i­tal gods while do­ing it.

Usage for that ac­count was un­der 35%, and is now over 70%. They are now work­ing closely with me to vibe code new “micro-apps” that work ac­cord­ing to all of their cus­tomer work­flows. And the best part? This is all on top of their ex­ist­ing SaaS which works as a sys­tem of record and han­dles se­cu­rity, au­then­ti­ca­tion, and sup­ports lock-in by be­ing a data and a UI moat.

This is ex­actly what I’m build­ing: a way for SaaS com­pa­nies to let their end-users vibe code on top of their plat­form (More on that be­low). My cus­tomers tell me it’s the best thing they’ve done for re­ten­tion, en­gage­ment, and ex­pan­sion in 2026 — be­cause when your users are build­ing on your plat­form, they’re not eval­u­at­ing your com­peti­tors.

Here’s what I’ve re­al­ized af­ter hun­dreds of con­ver­sa­tions with founders and op­er­a­tors: AI is­n’t killing B2B SaaS. It’s killing B2B SaaS that re­fuses to evolve.

The SaaS model was built on a sim­ple premise: we build it once, you pay for­ever. That worked when build­ing soft­ware was hard. But now your cus­tomers have tasted what’s pos­si­ble. They’ve seen their fi­nance team whip up a cus­tom dash­board in an af­ter­noon. They’ve watched a non-tech­ni­cal PM build an in­ter­nal tool that ac­tu­ally fits their work­flow.

You can’t un­see that. You can’t go back to pay­ing $X0,000/year for soft­ware that al­most does what you need.

The sur­vivors won’t be the SaaS com­pa­nies with the best fea­tures. They’ll be the ones who be­come plat­forms — who let cus­tomers build on top of them in­stead of in­stead of them. When I showed a well-known VC what I was build­ing to help SaaS com­pa­nies do ex­actly this, he said: “This is the fu­ture of mar­ket­places and soft­ware com­pa­nies.”

Maybe. Or maybe this is just an­other cy­cle and tra­di­tional SaaS will adapt like it al­ways has. But I know this: the com­pa­nies I’m talk­ing to aren’t wait­ing around to find out. They’re al­ready re­build­ing their re­la­tion­ship with cus­tomers from “use our prod­uct” to “build on our plat­form.”

The ques­tion is­n’t whether AI will eat your SaaS.

It’s whether you’ll be the one hold­ing the fork.

I’m solv­ing ex­actly this prob­lem with a white­la­belled AI plat­form for B2B SaaS com­pa­nies, so your users can vibe code cus­tomized work­flows on top of their ex­ist­ing sys­tem of record.

My cus­tomers tell me this is the best way to sup­port re­ten­tion, en­gage­ment, and ex­pan­sion in 2026. If this sounds in­ter­est­ing to you or some­one you know, I can reach out with a cus­tom demo or you can learn more about Giga Catalyst.

Something strange is hap­pen­ing with Mac Minis. They’re sell­ing out every­where, and it’s not be­cause peo­ple sud­denly need more cof­fee table com­put­ers.

If you browse Reddit or HN, you’ll see the same pat­tern: peo­ple are buy­ing Mac Minis specif­i­cally to run AI agents with com­puter use. They’re set­ting up head­less ma­chines whose sole job is to au­to­mate their work­flows. OpenClaw—the open-source frame­work that lets you run Claude, GPT-5, or what­ever model you want to ac­tu­ally con­trol your com­puter—has be­come the killer app for Mac hard­ware. Not Final Cut. Not Logic. An AI agent that clicks but­tons.

This is ex­actly what Apple Intelligence should have been.

Apple had every­thing: the hard­ware, the ecosys­tem, the rep­u­ta­tion for “it just works.” They could have shipped an agen­tic AI that ac­tu­ally au­to­mated your com­puter in­stead of sum­ma­riz­ing your no­ti­fi­ca­tions. Imagine if Siri could gen­uinely file your taxes, re­spond to emails, or man­age your cal­en­dar by ac­tu­ally us­ing your apps, not through some brit­tle API layer that breaks every up­date.

They could have charged $500 more per de­vice and peo­ple would have paid it. The mar­gins would have been ob­scene. And they would have won the AI race not by build­ing the best model, but by be­ing the only com­pany that could ship an AI you’d ac­tu­ally trust with root ac­cess to your com­puter. That trust—built over decades—was their moat.

So why did­n’t they?

Maybe they just did­n’t see it. That sounds mun­dane, but it’s prob­a­bly the most com­mon rea­son com­pa­nies miss op­por­tu­ni­ties. When you’re Apple, you’re think­ing about chip de­sign, man­u­fac­tur­ing scale, and re­tail strat­egy. An open-source pro­ject let­ting AI agents con­trol com­put­ers might not ping your radar un­til it’s al­ready hap­pen­ing.

Or maybe they saw it and de­cided the risk was­n’t worth it. If you’re Apple, you don’t want your AI agent au­to­mat­i­cally buy­ing things, post­ing on so­cial me­dia, or mak­ing ir­re­versible de­ci­sions. The li­a­bil­ity ex­po­sure would be enor­mous. Better to ship some­thing safe and lim­ited than some­thing pow­er­ful and un­pre­dictable.

But there’s an­other dy­namic at play. Look at who’s about to get an­gry about OpenClaw-style au­toma­tion: LinkedIn, Facebook, any­one with a walled gar­den and a care­ful API strat­egy. These ser­vices de­pend on fric­tion. They want you to use their app, see their ads, stay in their ecosys­tem. An AI that can au­to­mate away that fric­tion is an ex­is­ten­tial threat.

If Apple had built this, they’d be fight­ing Instagram over ToS vi­o­la­tions by Tuesday. They’d be tes­ti­fy­ing in front of Congress about AI agents com­mit­ting fraud. Every tech plat­form would be up­dat­ing their terms to ex­plic­itly ban Apple Intelligence.

By let­ting some third party do it, Apple gets plau­si­ble de­ni­a­bil­ity. They’re just sell­ing hard­ware. Not their fault what peo­ple run on it. It’s the same strat­egy that made them bil­lions in the App Store while main­tain­ing they’re “not re­spon­si­ble for what de­vel­op­ers do.”

But I think this is short-term think­ing.

Here’s what peo­ple miss about moats: they com­pound. The rea­son Microsoft dom­i­nated PCs was­n’t just that they had the best OS. It’s that every­one built for Windows, which made Windows more valu­able, which made more peo­ple build for Windows. Network ef­fects.

If Apple owned the agent layer, they could have cre­ated the most de­fen­si­ble moat in tech. Because an AI agent gets bet­ter the more it knows about you. And Apple al­ready has all your data, all your apps, all your de­vices. They could have built an agent that works across your iPhone, Mac, iPad, and Watch seam­lessly—some­thing no one else can do.

More im­por­tantly, they could have owned the API. Want your ser­vice to work with Apple Agent? You play by Apple’s rules. Suddenly Apple is­n’t fight­ing with plat­forms—they’re the plat­form that plat­forms need to in­te­grate with. It’s the App Store play­book all over again, but for the AI era.

The Mac Mini rush is a pre­view of this fu­ture. People want agents. They want au­toma­tion. They want to pay for it. They’re lit­er­ally buy­ing ex­tra com­put­ers just to run some­one else’s AI on Apple’s hard­ware.

Apple is get­ting the hard­ware rev­enue but miss­ing the plat­form rev­enue. That might look smart this quar­ter. But plat­form rev­enue is what built Apple into a $3 tril­lion com­pany. And plat­forms are what cre­ate tril­lion-dol­lar moats.

I sus­pect ten years from now, peo­ple will look back at 2024-2025 as the mo­ment Apple had a clear shot at own­ing the agent layer and chose not to take it. Not be­cause they could­n’t build it—they ob­vi­ously could—but be­cause they were op­ti­miz­ing for this year’s le­gal risk in­stead of next decade’s plat­form power.

The peo­ple buy­ing Mac Minis to run AI agents aren’t just early adopters. They’re show­ing Apple ex­actly what prod­uct they should have built. Whether Apple is pay­ing at­ten­tion is an­other ques­tion en­tirely.

If you’re on one of the cheaper Anthropic plans like me, it’s a pretty com­mon sce­nario when you’re deep into Claude cod­ing an idea, to hit a daily or weekly quota limit. If you want to keep go­ing, you can con­nect to a lo­cal open source model in­stead of Anthropic. To mon­i­tor your cur­rent quota, type: /usage

The best open source model is chang­ing pretty fre­quently, but at the time of writ­ing this post, I rec­om­mend GLM-4.7-Flash from Z. AI or Qwen3-Coder-Next. If you want or need to save some disk space and GPU mem­ory, try a smaller quan­tized ver­sion which will load and run quicker with a qual­ity cost. I’ll save an­other de­tailed post for how to find the best open source model for your task and ma­chine con­straints.

If you haven’t used LM Studio be­fore, it’s an ac­ces­si­ble way to find and run open source LLMs and vi­sion mod­els lo­cally on your ma­chine. In ver­sion 0.4.1, they in­tro­duce sup­port to con­nect to Claude Code (CC). See here: https://​lm­stu­dio.ai/​blog/​claude­code or fol­low the in­struc­tions be­low:

Find the model search but­ton to in­stall a model (see im­age above). LM Studio rec­om­mends run­ning the model with a con­text of > 25K.

Open a new ter­mi­nal ses­sion to:

a. start the server: lms server start –port 1234

b. con­fig­ure en­vi­ron­ment vari­ables to point CC at LM Studio:

ex­port ANTHROPIC_BASE_URL=http://​lo­cal­host:1234

ex­port ANTHROPIC_AUTH_TOKEN=lmstudio

c. start CC point­ing at your server: claude –model ope­nai/​gpt-oss-20b

Reduce your ex­pec­ta­tions about speed and per­for­mance!

To con­firm which model you are us­ing or when you want to switch back, type /model

LM Studio is built on top of the open source pro­ject llama.cpp.

If you pre­fer not to use LM Studio, you can in­stall and run the pro­ject di­rectly and con­nect Claude Code to it but hon­estly, un­less you are fine tun­ing a model, or have re­ally spe­cific needs, prob­a­bly LM Studio is go­ing to be a quicker setup.

For the mo­ment, this is a backup so­lu­tion. Unless you have a mon­ster of a ma­chine, you’re go­ing to no­tice the time it takes to do things and a drop in code qual­ity but it works(!) and it’s easy enough to switch be­tween your lo­cal OSS model and Claude when you’re quota limit is back, so it’s a good way to keep cod­ing when you’re stuck or you just want to save some quota. If you try it let me know how you go and which model works for you.

Have you won­dered why the stock mar­ket has been so choppy since October and why crypto and gold keep flash crash­ing? The west­ern me­dia would have you be­lieve this is due to AI bub­ble, war in Greenland, and Trump’s tweets. We have a bet­ter story to tell.

There’s been a fair bit of quiet chaos in fi­nan­cial mar­kets re­cently. Cryptocurrencies have lost 40% of their value. We saw sil­ver drop 40% which has­n’t hap­pened since 1980. Stocks like Microsoft are get­ting picked off one-by-one with 15% drops when pos­i­tive earn­ings re­ports come out. Meanwhile the broader mar­ket chops side­ways, so peo­ple think things are fine. Trump and Europe were on the brink of war for con­trol of a des­o­late arc­tic ter­ri­tory. Truth Social has over­taken FOMC as the most im­por­tant source of fi­nan­cial news. These things may all ap­pear to the un­trained eye as a se­ries of idio­syn­cratic, dis­con­nected shocks. The pre­vail­ing me­dia nar­ra­tive is that the mar­ket is re­act­ing neg­a­tively to AI CapEx spend­ing and a hawk­ish new Fed chair. But our sys­tem­atic analy­sis of cross-as­set flows, de­riv­a­tives po­si­tion­ing, cen­tral bank pol­icy min­utes, and in­sti­tu­tional bal­ance sheets sug­gests a sin­gu­lar, uni­fied causal­ity that binds these dis­parate anom­alies, which is the covert un­wind­ing of the Japanese Yen carry trade.

For nearly thirty years, the Bank of Japan’s (BOJ) Zero Interest Rate Policy (ZIRP) and sub­se­quent Negative Interest Rate Policy (NIRP) ef­fec­tively trans­formed the Yen into the world’s fund­ing cur­rency. We would call it the great­est free money printer ever made. By an­chor­ing bor­row­ing costs at or near zero, the BOJ en­abled Wall Street to bor­row Yen cheaply and in­vest it with lever­age into higher yield­ing in­stru­ments glob­ally, such as U. S. trea­suries, eq­ui­ties, and cryp­tog­ra­phy. For ex­am­ple, you bor­row Yen from Japan at 0% in­ter­est, you ex­change it for USD, and then you buy trea­sury bonds that pay 4%. It’s that sim­ple. This funded gov­ern­ment ben­e­fits and pro­vided con­tin­u­ous re­li­able liq­uid­ity for fi­nan­cial mar­kets that made stocks keep go­ing up while sup­press­ing volatil­ity.

Trillions of dol­lars of free loans from the Bank of Japan were used by a gen­er­a­tion of in­vestors to buy a dou­ble digit per­cent­age of the U. S. econ­omy. Now those loans are be­ing re­called. Wall Street traders who lev­ered up on the free Japanese money now have to sell tril­lions of as­sets and con­vert the pro­ceeds back to Yen in or­der to not be liq­ui­dated. These aren’t happy times for them. They get liq­ui­dated when Japan raises in­ter­est rates; they get liq­ui­dated when the Federal Reserve low­ers in­ter­est rates; they get liq­ui­dated when the Japanese Yen in­creases in value; they get liq­ui­dated when tech stocks aren’t go­ing up enough, and all four of these things have been hap­pen­ing at once.

Wall Street may be greedy, but they’re very in­tel­li­gent too. They made many smart choices about where to put the “free” money. Now let’s say you’re some­one who’s also smart, but was wise enough to not use Sauron’s ring. Chances are you in­vested in the same things as Wall Street. So by now you’ve prob­a­bly seen your whole port­fo­lio move against you; you’re won­der­ing why your hedges don’t work; and you feel like you’re be­ing pun­ished for mak­ing all the right choices. It’s be­cause other smart peo­ple, who got greedy, are be­ing forced to close their po­si­tions, and you’re the whip­ping boy for their avarice.

The Japanese Yen is sort of like GameStop ($GME). It’s the most shorted cur­rency on Earth. When you bor­row yen to buy American as­sets, you’re ef­fec­tively short­ing the yen. Currency can be re­hy­poth­e­cated so that yen-de­nom­i­nated debt ends up ex­ceed­ing the ac­tual yen sup­ply, the same way GME’s short in­ter­est ex­ceeded 100% of its float. When shorts start cov­er­ing it com­pounds tragedy, be­cause they all have to buy yen, which makes its value in­crease, forc­ing more shorts to cover, and Japan is a small is­land.

This December 2025 rate hike to 0.75%, fol­lowed by the ex­plic­itly hawk­ish sig­nalling from Prime Minister Sanae Takaichi’s ad­min­is­tra­tion, has fun­da­men­tally al­tered the risk-re­ward cal­cu­lus of these lever­aged po­si­tions. The mar­ket dis­rup­tions ob­served in January 2026 bear the dis­tinct math­e­mat­i­cal sig­na­ture of a forced liq­ui­da­tion event rather than a fun­da­men­tal repric­ing of growth prospects. When cor­re­la­tions be­tween his­tor­i­cally un­cor­re­lated as­sets (e.g. Gold, Bitcoin, Microsoft, and Silver) ap­proach 1.0 dur­ing a sell-off, it serves as a dis­tinct in­di­ca­tor that traders are not sell­ing what they want to sell, but rather what they must sell in or­der to meet mar­gin calls in a fund­ing cur­rency that is rapidly ap­pre­ci­at­ing against their li­a­bil­i­ties.

We shall in­ves­ti­gate the me­chan­ics of this un­wind in ex­haus­tive de­tail. We an­a­lyze the “Greenland Distraction” not as a root cause but as a volatil­ity trig­ger that shat­tered the com­pla­cent calm of the “Davos Consensus.” We ex­am­ine the anom­alous liq­ui­da­tion in pre­cious met­als fol­low­ing the nom­i­na­tion of Kevin Warsh to the Federal Reserve Chairmanship, and we dis­sect the flow of funds from ma­jor Japanese in­sti­tu­tional whales like Norinchukin Bank, whose re­treat from for­eign bond mar­kets has left a liq­uid­ity vac­uum in the U. S. Treasury com­plex. The ev­i­dence points to a sys­temic repric­ing of the global cost of cap­i­tal, orig­i­nat­ing in Tokyo and trans­mit­ting vi­o­lently through the plumb­ing of Wall Street, leav­ing no as­set class un­touched.

To fully com­pre­hend the mar­ket chaos of January 2026, one must look be­yond the im­me­di­ate head­lines of the new year and scru­ti­nize the sub­tle yet seis­mic shifts that oc­curred in Tokyo dur­ing the clos­ing months of 2025. The con­ven­tional mar­ket nar­ra­tive has long re­garded the Bank of Japan as a pas­sive, al­most par­a­lyzed ac­tor, per­pet­u­ally trapped in a de­fla­tion­ary mire and un­able to nor­mal­ize pol­icy. This view has al­ways been demon­strat­ably false. The truth is that Wall Street lead­ers have been plan­ning for the next quar­ter, while the Japanese have been prepar­ing for the next cen­tury. The data con­firms a de­lib­er­ate, ag­gres­sive shift to­ward nor­mal­iza­tion that caught global carry traders of­f­guard.

In a move that many Western an­a­lysts crit­i­cally un­der­es­ti­mated, the Policy Board of the Bank of Japan voted unan­i­mously to raise the un­col­lat­er­al­ized overnight call rate to 0.75% dur­ing its pol­icy ses­sion on December 18-19, 2025. While a 25 ba­sis point hike might ap­pear neg­li­gi­ble in the con­text of Federal Reserve or ECB tight­en­ing cy­cles, in the con­text of the Japanese fi­nan­cial sys­tem, which has op­er­ated near the zero-bound for decades, it rep­re­sents a mas­sive tight­en­ing of fi­nan­cial con­di­tions.

This move was not merely a tech­ni­cal ad­just­ment; it was a fun­da­men­tal regime change. Coming from a base­line of -0.1% in early 2024 and 0.50% in late 2025, the move to 0.75% sig­naled that the era of “free money” had de­fin­i­tively ended. The ra­tio­nale pro­vided by the BOJ was grounded in shift­ing in­fla­tion­ary dy­nam­ics. Core CPI (excluding fresh food), the cen­tral bank’s pre­ferred met­ric, was track­ing near 3% in late 2025, per­sis­tently ex­ceed­ing the 2% price sta­bil­ity tar­get. Although in­fla­tion eased slightly to 2.4% in December, the BOJ min­utes re­veal a board con­vinced that “wage gains may be durable,” thus jus­ti­fy­ing higher rates to pre­vent a wage-price spi­ral.

Crucially, the min­utes from the December meet­ing, which were re­leased in late January 2026, con­tain ex­plicit lan­guage sug­gest­ing that the tight­en­ing cy­cle is far from com­plete. The board noted that “real in­ter­est rates are ex­pected to re­main neg­a­tive,” im­ply­ing that a pol­icy rate of 0.75% is still con­sid­ered ac­com­moda­tive rel­a­tive to in­fla­tion. To a bond trader, this is hawk­ish code. It sug­gests that the “neutral rate” is sig­nif­i­cantly higher, po­ten­tially be­tween 1.5% and 2.0%. If the mar­ket prices in a ter­mi­nal rate of 2.0%, the cost of fund­ing for carry trades ef­fec­tively triples from pre­vi­ous lev­els, turn­ing prof­itable ar­bi­trage po­si­tions into deep losses.

The po­lit­i­cal di­men­sion in Japan has ex­ac­er­bated the mon­e­tary tight­ness, cre­at­ing a “double tight­en­ing” ef­fect that al­go­rithms have strug­gled to price. Prime Minister Sanae Takaichi, prepar­ing for a snap elec­tion on February 8, 2026, has adopted a com­plex eco­nomic stance that blends fis­cal ex­pan­sion with mon­e­tary dis­ci­pline, a volatile mix for cur­rency mar­kets.

Takaichi ad­vo­cates for “strategic fis­cal spend­ing” and tax cuts to stim­u­late the do­mes­tic econ­omy. In stan­dard macro­eco­nomic the­ory, an ex­pan­sion­ary fis­cal pol­icy (increased gov­ern­ment spend­ing) com­bined with a tight­en­ing mon­e­tary pol­icy (higher rates to com­bat the re­sult­ing in­fla­tion) is the per­fect recipe for cur­rency ap­pre­ci­a­tion. While Takaichi has pub­licly soft­ened her rhetoric to avoid ac­cu­sa­tions of cur­rency ma­nip­u­la­tion, stat­ing she “did not have a pref­er­ence for the yen’s di­rec­tion”, her poli­cies speak louder than her sound­bites.

The mar­ket fears that Takaichi’s pro­posed fis­cal largesse will force the BOJ to hike rates faster than cur­rently pro­jected to coun­ter­act the in­fla­tion­ary ef­fects of gov­ern­ment spend­ing. This cre­ates a two-front war on the Yen carry trade:

Exchange Rate Risk: If the Yen ap­pre­ci­ates due to the fis­cal-mon­e­tary pol­icy mix, the prin­ci­pal value of the USD-denominated as­sets held by Japanese in­vestors falls in Yen terms, trig­ger­ing mar­gin calls.

The ten­sion be­tween the Prime Minister’s of­fice and the Ministry of Finance (MOF) adds an­other layer of un­cer­tainty. Finance Minister Satsuki Katayama has been far less tol­er­ant of cur­rency volatil­ity, re­peat­edly in­ter­ven­ing or threat­en­ing in­ter­ven­tion when USD/JPY ap­proaches the 155-160 dan­ger zone. This po­lit­i­cal fric­tion cre­ates a “floor” for the Yen, mak­ing short­ing the cur­rency a per­ilous en­deavor for global macro funds.

Perhaps the most crit­i­cal, yet un­der­re­ported, de­vel­op­ment is the be­hav­ior of Japan’s gar­gan­tuan in­sti­tu­tional in­vestors, specif­i­cally Norinchukin Bank (often re­ferred to as the “CLO Whale”) and Nippon Life Insurance. These en­ti­ties have his­tor­i­cally been the largest buy­ers of U. S. debt, re­cy­cling Japan’s trade sur­plus into U.S. Treasuries and cor­po­rate bonds.

The data in­di­cates a mas­sive re­ver­sal in these flows. Following sig­nif­i­cant losses in 2024 and 2025 due to un­hedged ex­po­sure to U. S. and European sov­er­eign bonds, Norinchukin has been ac­tively liq­ui­dat­ing for­eign as­sets. By the end of December 2025, the bank had un­loaded nearly ¥12.8 tril­lion (approximately $88 bil­lion) in for­eign gov­ern­ment bonds.The bank’s CEO, Taro Kitabayashi, con­firmed the com­ple­tion of this sell-off, stat­ing the bank would “take its time” be­fore com­mit­ting cap­i­tal to fresh in­vest­ments.

The sig­nif­i­cance of this can­not be over­stated. A ma­jor, price-in­sen­si­tive buyer of U. S. debt has left the build­ing. When the U.S. Treasury is­sues debt to fund its deficit, Norinchukin is no longer the guar­an­teed bid. This re­moval of liq­uid­ity sup­port weak­ens the floor for U.S. Treasuries, con­tribut­ing to the yield spikes seen in January. Similarly, Nippon Life has sig­naled a ro­ta­tion back into do­mes­tic Japanese Government Bonds (JGBs), ac­knowl­edg­ing that “unrealized losses” on for­eign bonds had swelled to ¥4.7 tril­lion.The logic is sim­ple: why take cur­rency risk for a 4.5% U.S. yield when do­mes­tic JGB yields are ris­ing and of­fer a risk-free re­turn in your home cur­rency?

By December 31, 2025, the stage was set. The “free money” era was over. The largest hold­ers of cap­i­tal in Tokyo were repa­tri­at­ing funds or mov­ing into cash. Global mar­kets, how­ever, were still po­si­tioned for “business as usual”, long Nvidia, long Bitcoin, short Yen. The dis­so­nance be­tween Japanese re­al­ity and Western po­si­tion­ing cre­ated the per­fect con­di­tions for a crash.

To val­i­date the the­sis that the Yen un­wind is the pri­mary dri­ver of volatil­ity, we must ex­am­ine the se­quence of events. The crash did not hap­pen in a vac­uum; it fol­lowed a pre­cise time­line where geopo­lit­i­cal shocks acted as trig­gers for a struc­tural fragility that had been build­ing since the BOJ’s December pivot.

The pres­sure be­gan to build in Q4 2025. As the BOJ sig­naled its in­ten­tion to hike rates, Japanese traders, of­ten the “canary in the coal mine” for global liq­uid­ity, be­gan to re­duce risk. This cy­cle started with Bitcoin. Bitcoin is a pure liq­uid­ity as­set; it has no yield and is of­ten funded via mar­gin. As the cost of Yen mar­gin rose, Japanese sell­ing pres­sure on Bitcoin in­ten­si­fied from October through December. This was the first tremor.

Was the “Greenland War” the­ater? While the mil­i­tary di­men­sions may have been per­for­ma­tive, the eco­nomic con­se­quences were tan­gi­ble and acted as the cat­a­lyst that ex­posed the fragility of the Yen carry trade.

On January 17, 2026, President Trump es­ca­lated his de­mand to pur­chase Greenland by threat­en­ing a 10% tar­iff on eight European na­tions (including the UK, Germany, and France) and es­ca­lat­ing to 25% by June if the ter­ri­tory was not ceded. This in­tro­duced a “tail risk” that mar­kets had not priced: the frac­ture of the Atlantic eco­nomic al­liance.

Following the Martin Luther King Jr. hol­i­day, U. S. mar­kets opened on January 20 to a blood­bath. The S&P 500 fell 2.1%, the Nasdaq com­pos­ite dropped 2.4%, and yields on U.S. Treasuries spiked.The nar­ra­tive was “Greenland,” but the mar­ket me­chan­ics told a dif­fer­ent story. The threat of tar­iffs on close al­lies dis­rupts the “Atlantic Trade” nar­ra­tive. For Japanese in­vestors hold­ing U.S. as­sets, this in­tro­duced a new risk pre­mium. It was­n’t just about rates any­more; it was about the sta­bil­ity of the U.S.-led global or­der. This geopo­lit­i­cal volatil­ity forced risk par­ity funds and al­go­rith­mic traders to re­duce gross ex­po­sure. When a global port­fo­lio delever­ages, it buys back its fund­ing cur­rency. In this case, it bought Yen.

While Trump walked back the mil­i­tary threat on January 21 at Davos, the eco­nomic threat of tar­iffs re­mained a live wire. The volatil­ity per­sisted, sug­gest­ing that the “Greenland” nar­ra­tive was merely the match that lit the fuse of a much larger pow­der keg.

The fi­nal and most vi­o­lent phase of the crash oc­curred at the end of the month, trig­gered by the nom­i­na­tion of Kevin Warsh as Federal Reserve Chair. Warsh is widely per­ceived as a hawk, fa­vor­ing sound money and skep­ti­cism to­ward quan­ti­ta­tive eas­ing. His nom­i­na­tion sig­naled the po­ten­tial end of the “Fed Put”, the as­sump­tion that the cen­tral bank would al­ways in­ter­vene to sup­port as­set prices.

This an­nounce­ment trig­gered a mas­sive repric­ing of the “Debasement Trade.” Assets that thrive on cur­rency de­base­ment, Gold, Silver, and Bitcoin, col­lapsed. Gold fell ~11%, and Silver crashed ~36% in a sin­gle ses­sion. This syn­chro­niza­tion of losses across un­cor­re­lated as­sets (Tech and Gold falling to­gether) is the de­fin­i­tive sig­na­ture of a liq­uid­ity cri­sis dri­ven by mar­gin calls.

The un­wind­ing of a carry trade is not a mono­lithic event; it is a cas­cade that rip­ples out­ward from the most liq­uid and spec­u­la­tive as­sets to the core hold­ings of in­sti­tu­tional port­fo­lios. The se­quence of as­set price col­lapses ob­served from October 2025 to January 2026 fol­lows this clas­sic liq­ui­da­tion hi­er­ar­chy per­fectly.

As noted, the un­wind be­gan in the crypto mar­kets. Japan is home to a mas­sive re­tail crypto trad­ing base, and the Yen is a ma­jor pair for Bitcoin trad­ing. Snippets in­di­cate that Japanese traders be­gan sell­ing off Bitcoin in October 2025.

This tim­ing is cru­cial. It cor­re­lates with the pe­riod when the BOJ be­gan sig­nal­ing the December rate hike. Retail traders, fac­ing higher mort­gage rates and loan costs in Japan, likely liq­ui­dated their most volatile, liq­uid as­set first to raise cash. The sell­ing was ex­ac­er­bated by the loom­ing tax re­form in Japan. While the pro­posal to move to a flat 20% tax rate is bull­ish in the long term, the im­me­di­ate pres­sure of ris­ing fund­ing costs forced traders to sell be­fore the tax cut could be re­al­ized. By January 31, mas­sive out­flows from Bitcoin ETFs ($528 mil­lion) co­in­cided with the broader mar­ket crash, con­firm­ing that crypto was be­ing used as a source of liq­uid­ity to cover losses else­where.

Consider the “painful ~3% dump” in the Nasdaq and Microsoft’s stag­ger­ing 15% drop. On January 29, 2026, Microsoft re­ported earn­ings. Despite beat­ing rev­enue es­ti­mates ($81.27 bil­lion vs. $80.28 bil­lion), the stock plum­meted ~11-15% in­tra­day.

The street blamed con­cerns over “AI CapEx”, the idea that Microsoft was spend­ing bil­lions on data cen­ters with slow re­turn on in­vest­ment. However, a 15% drop in a $3 tril­lion com­pany on a “good” earn­ings beat is rarely fun­da­men­tal; it is me­chan­i­cal. Microsoft is a quin­tes­sen­tial “momentum” stock, heav­ily held by for­eign in­sti­tu­tional in­vestors, in­clud­ing Japanese pen­sion funds. When the Yen strength­ens, the value of these USD-denominated as­sets falls in JPY terms.

If a Japanese in­surer holds Microsoft un­hedged, a falling USD/JPY ex­change rate hurts their bal­ance sheet. If they hold it hedged

(rolling short USD po­si­tions), the ris­ing U. S. rates (driven by the Warsh nom­i­na­tion) make the hedge pro­hib­i­tively ex­pen­sive. The January 29 drop was likely ex­ac­er­bated by a “stop-loss” cas­cade from Tokyo desks. As the price broke key tech­ni­cal lev­els, al­go­rithms pro­grammed to pro­tect Yen-denominated re­turns in­dis­crim­i­nately sold the most liq­uid blocks. Microsoft, be­ing one of the most liq­uid stocks in the world, be­came the ATM for the rest of the port­fo­lio.

The most com­pelling ev­i­dence of a forced liq­ui­da­tion event is the be­hav­ior of Gold and Silver on January 31, 2026. Gold fell ~11-12% and Silver crashed ~31-36% in a sin­gle ses­sion. Historically, Gold acts as a safe haven dur­ing eq­uity mar­ket tur­moil. If the Nasdaq is crash­ing due to “Greenland” fears, Gold should rally. Instead, it crashed.

This anom­aly can be ex­plained by two fac­tors:

The Warsh Effect: As dis­cussed, Warsh’s nom­i­na­tion strength­ened the USD and un­der­mined the the­sis for hold­ing anti-fiat as­sets.

Margin Call Dynamics: Snippets re­veal that CME Group and the Shanghai Gold Exchange raised mar­gin re­quire­ments on gold and sil­ver fu­tures days be­fore the crash. When Japanese traders faced losses on their Microsoft longs and their Yen shorts, they needed cash im­me­di­ately. They could­n’t sell illiq­uid bonds quickly enough, so they sold their “winners.” Gold had ral­lied to ~$5,400/oz prior to the crash. Traders liq­ui­dated their prof­itable Gold po­si­tions to pay for the mar­gin calls on their los­ing Tech and Yen po­si­tions.

Cross-Asset Correlations (Week Ending Jan 31, 2026)

Figure 2: Cross-asset cor­re­la­tions, Jan 15–Jan 31,

2026. Note the spike in cor­re­la­tion be­tween Gold, Bitcoin, and

Nasdaq 100 on Jan 31, in­di­cat­ing a sys­temic “sell-everything” mar­gin

call.

Data sources:

Alex Lexington,

Finance Magnates,

Morningstar,

Investing.com,

Seeking Alpha

This cor­re­la­tion break­down is vi­su­al­ized in Figure 2, where the cor­re­la­tion be­tween Gold and the Nasdaq 100 spikes to nearly 1.0 dur­ing the crash week, a sta­tis­ti­cal anom­aly that only oc­curs dur­ing se­vere liq­uid­ity events.

The “Yen Whale” hy­poth­e­sis is strongly sup­ported by the data on fu­tures vol­umes and repo mar­ket stress. The “central mys­tery” is not just in the price ac­tion, but in the un­seen flows of the de­riv­a­tives mar­ket.

About a week ago, some whale kicked off an as­tro­nom­i­cally large mar­ket or­der for a /6J long when it hit re­cent lows. /6J (CME Yen Futures) hit a low of ~0.00647 (approximately 154.50 USD/JPY) in late January. This level has his­tor­i­cally been a “line in the sand” for the Japanese Ministry of Finance (MOF).

Figure 4: The whale event that kicked off

the Japanese Yen un­wind. Note the mas­sive spike as /6J hit re­cent

lows, ral­ly­ing in­vestors world­wide to go long on yen

fu­tures.

CME re­ported record vol­umes in FX and Interest Rate prod­ucts for January 2026. The ag­gres­sive buy­ing off the lows sug­gests a mas­sive repa­tri­a­tion flow. Who is the Whale? Two the­o­ries emerge:

The MOF Thesis: The Ministry of Finance has a his­tory of stealth in­ter­ven­tion. Buying /6J (Long Yen) is func­tion­ally equiv­a­lent to sell­ing USD re­serves. Buying fu­tures al­lows them to sup­port the cur­rency with­out im­me­di­ately de­plet­ing cash re­serves, squeez­ing spec­u­la­tors who are short.

The Carry Unwind: A mas­sive hedge fund or bank (like Norinchukin) re­al­iz­ing that the “game is up” and clos­ing out short-Yen po­si­tions. The size of the or­der sug­gests an en­tity that needed to move bil­lions, not mil­lions.

The sub­se­quent price ac­tion, a sharp rally fol­lowed by “hammering back down”, rep­re­sents the bat­tle­ground. U. S. macro funds are still try­ing to short the Yen (betting on U.S. eco­nomic ex­cep­tion­al­ism and Warsh’s poli­cies), while Japanese do­mes­tic ac­counts are buy­ing it. The volatil­ity is the re­sult of these tec­tonic plates grind­ing against each other.

The plumb­ing of the U. S. fi­nan­cial sys­tem showed signs of stress that co­in­cided with the Japanese re­treat. The Overnight Reverse Repo fa­cil­ity (ON RRP) saw a year-end spike to $106 bil­lion but has since drained.

Japanese banks are typ­i­cally huge par­tic­i­pants in the U. S. repo mar­ket to fund their dol­lar as­sets. As Norinchukin and oth­ers re­treat (repatriating funds to Japan), liq­uid­ity in the U.S. repo mar­ket be­comes thin­ner. The “air pocket” in Microsoft and Gold prices was likely ex­ac­er­bated by a lack of mar­ket maker depth in the repo-funded de­riv­a­tives mar­ket. When mar­ket mak­ers can­not ac­cess cheap repo fund­ing, they widen spreads and re­duce liq­uid­ity pro­vi­sion, lead­ing to “gaps” in price ac­tion dur­ing sell-offs.

There have been sig­nif­i­cant moves in other cur­rency fu­tures as well: /6A in­creased 87 ba­sis points, /6L rose 19 ba­sis points, and /6S rose 18 ba­sis points.

/6A (Australian Dollar): The 87 ba­sis point rise in the Aussie Dollar is no­table. AUD is of­ten a proxy for Chinese growth and global risk sen­ti­ment. A rise here, amidst a tech crash, sug­gests a ro­ta­tion out of U. S. as­sets and into com­modi­ties or Asia-Pacific cur­ren­cies, fur­ther sup­port­ing the “Sell America” the­sis trig­gered by the Greenland tar­iff threats.

/6L (Brazilian Real) and /6S (Swiss Franc): The rise in the Swiss Franc (a clas­sic safe haven) aligns with the risk-off sen­ti­ment. The move in the Brazilian Real sug­gests that emerg­ing mar­kets are also see­ing volatile flows as the dol­lar sta­bi­lizes.

Why was the VIX at 16 de­spite the chaos? The VIX mea­sures im­plied volatil­ity of S&P 500 op­tions. Its rel­a­tively low level (16) com­pared to the vi­o­lence in in­di­vid­ual names (MSFT -15%, Gold -11%) in­di­cates that the crash is a de-lever­ag­ing event, not a panic event.

In a panic, in­vestors buy Puts on the in­dex to pro­tect them­selves, spik­ing the VIX. In a de-lever­ag­ing event, in­vestors sim­ply sell the un­der­ly­ing as­sets (stocks, gold, crypto) to raise cash. They are not hedg­ing; they are ex­it­ing. This ex­plains why the VIX re­mained sub­dued while prices col­lapsed, the sell­ing was or­derly, al­go­rith­mic, and re­lent­less, rather than emo­tional and pan­icked.

Skepticism about the “Greenland War” is well-founded. While the diplo­matic row was real, its util­ity as a fi­nan­cial

nar­ra­tive was far greater than its geopo­lit­i­cal re­al­ity.

President Trump’s threat of mil­i­tary force was re­tracted on January 21 at Davos. This “de-escalation” should the­o­ret­i­cally have calmed mar­kets. Instead, the volatil­ity wors­ened into month-end. This con­firms that the real prob­lem was­n’t Greenland; it was the re-pric­ing of the Yen.

The fi­nan­cial me­dia loves a sim­ple cause-and-ef­fect nar­ra­tive. “Stocks down be­cause of War” is easy to di­gest. “Stocks down be­cause the cross-cur­rency ba­sis swap spread widened due to BOJ min­utes” is not. The “Greenland” nar­ra­tive pro­vided the per­fect cover for so­phis­ti­cated ac­tors to liq­ui­date po­si­tions in Gold and Tech un­der the guise of “war jit­ters.” This al­lowed them to exit with­out spark­ing a broader panic about liq­uid­ity in the bank­ing sys­tem. The fo­cus on the Arctic masked the struc­tural rot in the lever­age com­plex.

The ev­i­dence sug­gests a covert, struc­tural un­wind­ing of the Yen carry trade is the pri­mary dri­ver of the January 2026 mar­ket chaos.

The in­ter­con­nect­ed­ness of these events is un­de­ni­able. The BOJ’s rate hike in December 2025 and the sub­se­quent hawk­ish sig­nal­ing from the Takaichi ad­min­is­tra­tion fun­da­men­tally al­tered the cost of cap­i­tal for the world’s largest carry trade. The “Greenland Crisis” acted as the ini­tial volatil­ity trig­ger, forc­ing a re­duc­tion in gross ex­po­sure. The nom­i­na­tion of Kevin Warsh acted as the fi­nal cat­a­lyst, shat­ter­ing the “Debasement Trade” and forc­ing a liq­ui­da­tion of pre­cious met­als and crypto to cover mar­gin calls on Yen-funded po­si­tions.

Here are some key take­aways:

The “Free Money” Era is Over: BOJ poli­cies have fun­da­men­tally al­tered the global cost of cap­i­tal. The flow of liq­uid­ity from Tokyo to New York has re­versed.

Geopolitics as Catalyst: “Greenland” may have been the spark, but the Yen lever­age was the pow­der keg. The tar­iff threats dis­rupted the “Atlantic Trade” nar­ra­tive, forc­ing a repa­tri­a­tion of cap­i­tal.

Liquidity Event: The syn­chro­nized crash of Gold, Crypto, and Tech con­firms a sys­temic de-lever­ag­ing. The “Whale” or­ders in Yen fu­tures and the break­down of cor­re­la­tions are the smok­ing guns of a mar­gin-dri­ven event.

With the Japanese elec­tion on February 8 and U. S. tar­iffs loom­ing, the “hammering” of the Yen is likely tem­po­rary. The struc­tural trend is now to­ward repa­tri­a­tion. This im­plies lower U.S. as­set prices,

higher U.S. yields, and a stronger Yen over the medium term. The “mystery” of the low VIX is ex­plained by the me­chan­i­cal na­ture of the un­wind, a con­trolled de­mo­li­tion of lever­age rather than a chaotic panic.

This won’t just be the big one. This could be the last one. If you’ve been prepar­ing your whole life, know­ing that some­thing’s com­ing, then this could be the thing you’ve been prepar­ing for. One fi­nal op­por­tu­nity to get the guys who did this.

Longing the Yen is com­monly re­ferred to as “The Widowmaker Trade” on Wall Street, be­cause you have tril­lions of dol­lars of mo­nop­oly money work­ing against you. The carry traders have com­pro­mised every level of our gov­ern­ment. Their great­est vul­ner­a­bil­ity is the Yen ris­ing in value. They will do any­thing to de­fend their po­si­tions, even if that means bring­ing America’s econ­omy down with them. Since re­cent events have made it ob­vi­ous they’re go­ing to lose, we might as well fight them. Most of us prob­a­bly won’t make it out of this fight. But if we at least try, then there’s a chance we might pros­per when it’s over.

The IV on OTM CME

/6J fu­tures calls is 11% which is as­ton­ish­ingly low. The same is true for calls on the

FXY

ETF. Call op­tions have de­fined risk. The more Yen we con­trol, the more its value goes up, and the more crooks on Wall Street get liq­ui­dated. The worst that can hap­pen is you lose your mo­nop­oly money, but that’s been hap­pen­ing any­way. Since carry traders own 10% of all U. S. trea­suries, when they get liq­ui­dated they’ll have to sell a lot of trea­sury bonds, which means that

CME

/UB fu­tures and the TLT

ETF will fall.

This blog is brought to you by var­i­ous rad­i­cals, mal­con­tents, and peo­ple who think the sys­tem is rigged. We’re not af­fil­i­ated with any or­ga­ni­za­tion. Nothing here con­sti­tutes fi­nan­cial ad­vice. Occupy Wall Street is not your fi­nan­cial ad­vi­sor or your lawyer. We’re re­tail in­vestors like you. Do your own re­search. Past per­for­mance does not guar­an­tee fu­ture re­sults. We are the 99 per­cent. The only so­lu­tion is world rev­o­lu­tion. Wall Street’s time has fi­nally come.

Immigration and Customs Enforcement (ICE) is sur­vey­ing the com­mer­cial ad­ver­tis­ing tech­nol­ogy mar­ket for tools ca­pa­ble of sup­ply­ing lo­ca­tion data and large-scale an­a­lyt­ics to fed­eral in­ves­ti­ga­tors, ac­cord­ing to a re­cent Request for Information (RFI).

Framed as mar­ket re­search rather than a pro­cure­ment, the RFI seeks in­for­ma­tion from com­pa­nies of­fer­ing “Ad Tech com­pli­ant and lo­ca­tion data ser­vices” that could sup­port crim­i­nal, civil, and ad­min­is­tra­tive in­ves­ti­ga­tions across ICE’s mis­sion set.

The RFI, is­sued by ICE’s Homeland Security Investigations (HSI), em­pha­sizes that the gov­ern­ment is not so­lic­it­ing pro­pos­als or com­mit­ting to a fu­ture con­tract, but it does sig­nal ac­tive in­ter­est in se­lect­ing ven­dors for live demon­stra­tions of op­er­a­tional plat­forms and data ser­vices, a step that typ­i­cally pre­cedes pi­lot de­ploy­ments or in­te­gra­tion into ex­ist­ing in­ves­tiga­tive en­vi­ron­ments.

ICE says it is at­tempt­ing to bet­ter un­der­stand how com­mer­cial big data providers and ad­ver­tis­ing tech­nol­ogy firms might di­rectly sup­port in­ves­tiga­tive ac­tiv­i­ties, while re­main­ing sen­si­tive to “regulatory con­straints and pri­vacy ex­pec­ta­tions.”

The agency noted that its com­po­nents are han­dling in­creas­ing vol­umes of crim­i­nal, civil, and ad­min­is­tra­tive in­for­ma­tion from both in­ter­nal and ex­ter­nal sources and are as­sess­ing whether com­mer­cial off-the-shelf plat­forms com­pa­ra­ble to large in­ves­tiga­tive data and le­gal an­a­lyt­ics providers can help man­age and ex­ploit that data at scale.

At the cen­ter of the in­quiry is a cat­e­gory of in­for­ma­tion tra­di­tion­ally as­so­ci­ated with dig­i­tal ad­ver­tis­ing rather than law en­force­ment: lo­ca­tion data, de­vice iden­ti­fiers, IP in­tel­li­gence, and be­hav­ioral sig­nals de­rived from every­day con­sumer ac­tiv­ity.

Advertising tech­nol­ogy, com­monly re­ferred to as ad tech, is the sprawl­ing ecosys­tem of soft­ware, data bro­kers, an­a­lyt­ics plat­forms, and in­ter­me­di­aries that power tar­geted ad­ver­tis­ing on the mod­ern Internet.

Ad tech com­pa­nies col­lect and process in­for­ma­tion about where de­vices are lo­cated, how users move be­tween phys­i­cal and dig­i­tal spaces, which apps are in­stalled on their phones, and how de­vices can be linked across web­sites, ap­pli­ca­tions, and net­works.

While the in­dus­try typ­i­cally frames this ac­tiv­ity as anony­mous or pseu­do­ny­mous, the un­der­ly­ing data is of­ten per­sis­tent, gran­u­lar, and ca­pa­ble of track­ing in­di­vid­u­als over time.

Location data is a par­tic­u­larly valu­able com­po­nent of that ecosys­tem. Mobile ap­pli­ca­tions rou­tinely share lat­i­tude and lon­gi­tude co­or­di­nates with ad­ver­tis­ing part­ners through em­bed­ded soft­ware de­vel­op­ment kits.

Even when pre­cise GPS data is not avail­able, com­pa­nies in­fer lo­ca­tion through IP ad­dresses, Wi-Fi net­works, Bluetooth bea­cons, and cell tower con­nec­tions. That in­for­ma­tion is then ag­gre­gated, an­a­lyzed, and sold to ad­ver­tis­ers seek­ing to mea­sure foot traf­fic, tar­get au­di­ences, or as­sess the ef­fec­tive­ness of cam­paigns.

ICE’s RFI sug­gests that the agency is ex­plor­ing whether those same mech­a­nisms can be re­pur­posed as in­ves­tiga­tive tools.

The doc­u­ment asks ven­dors to de­scribe plat­forms and data ser­vices that can sup­port in­ves­tiga­tive needs while re­main­ing “Ad Tech com­pli­ant,” a phrase that re­flects in­dus­try norms rather than statu­tory law en­force­ment stan­dards.

ICE ap­pears to be look­ing into tap­ping into the com­mer­cial data ecosys­tem rather than build­ing be­spoke sur­veil­lance tools from scratch, a strat­egy that al­lows agen­cies to ac­cess rich data streams with­out di­rectly col­lect­ing the in­for­ma­tion them­selves.

ICE’s in­ter­est is not lim­ited to raw data. The RFI re­peat­edly ref­er­ences “operational plat­forms,” sig­nal­ing a de­sire for sys­tems that can in­gest, cor­re­late, an­a­lyze, and vi­su­al­ize in­for­ma­tion from mul­ti­ple sources.

In prac­tice, that means soft­ware en­vi­ron­ments ca­pa­ble of fus­ing lo­ca­tion data with other records, such as crim­i­nal his­to­ries, fi­nan­cial data, travel records, so­cial me­dia ac­tiv­ity, or ad­min­is­tra­tive files, to gen­er­ate in­ves­tiga­tive leads or sup­port on­go­ing cases.

The agency frames its in­quiry as ex­ploratory and cau­tious. It notes that the gov­ern­ment is seek­ing to un­der­stand the “current state” of ad tech and lo­ca­tion data ser­vices avail­able to fed­eral in­ves­tiga­tive en­ti­ties, par­tic­u­larly con­sid­er­ing reg­u­la­tory con­straints and pri­vacy ex­pec­ta­tions.

That lan­guage re­flects grow­ing scrutiny of com­mer­cial data prac­tices by courts, reg­u­la­tors, and civil lib­er­ties ad­vo­cates, es­pe­cially when such data is ac­cessed by fed­eral agen­cies like ICE.

In re­cent years, fed­eral agen­cies have in­creas­ingly re­lied on com­mer­cially avail­able data to side­step tra­di­tional le­gal bar­ri­ers.

Because ad tech data is col­lected by pri­vate com­pa­nies un­der con­sumer-fac­ing pri­vacy poli­cies, agen­cies have ar­gued that pur­chas­ing or ac­cess­ing that data does not con­sti­tute a search un­der the Fourth Amendment.

Critics counter that this ap­proach al­lows the gov­ern­ment to ob­tain highly sen­si­tive in­for­ma­tion, in­clud­ing de­tailed lo­ca­tion his­to­ries, with­out war­rants, prob­a­ble cause, or mean­ing­ful over­sight.

The U. S. Supreme Court has sig­naled skep­ti­cism of such prac­tices in cases rec­og­niz­ing the sen­si­tiv­ity of long-term lo­ca­tion track­ing, even when data is held by third par­ties.

At the same time, reg­u­la­tors have brought en­force­ment ac­tions against data bro­kers ac­cused of sell­ing sen­si­tive lo­ca­tion in­for­ma­tion with­out ad­e­quate safe­guards.

Against that back­drop, ICE’s as­ser­tion that it is con­sid­er­ing pri­vacy ex­pec­ta­tions ap­pears de­signed to re­as­sure both pol­i­cy­mak­ers and po­ten­tial ven­dors that the agency is aware of the con­tro­versy sur­round­ing com­mer­cial sur­veil­lance data.

Yet the RFI it­self pro­vides lit­tle de­tail about how those con­cerns would be op­er­a­tional­ized. It does not ref­er­ence war­rants, court or­ders, or ju­di­cial au­tho­riza­tion.

Nor does it ex­plain how ICE would dis­tin­guish be­tween data as­so­ci­ated with U. S. per­sons and nonci­t­i­zens, how long in­for­ma­tion would be re­tained, or whether data ob­tained for one in­ves­tiga­tive pur­pose could be reused for oth­ers.

That am­bi­gu­ity is par­tic­u­larly sig­nif­i­cant given HSI’s broad man­date. Unlike agen­cies fo­cused solely on crim­i­nal en­force­ment, HSI con­ducts civil and ad­min­is­tra­tive in­ves­ti­ga­tions along­side crim­i­nal cases.

Location data or ad tech-de­rived in­sights could there­fore be used in con­texts rang­ing from im­mi­gra­tion en­force­ment to cus­toms vi­o­la­tions to sanc­tions and ex­port con­trol in­ves­ti­ga­tions, of­ten un­der lower le­gal thresh­olds than those re­quired in crim­i­nal pro­ceed­ings.

ICE’s em­pha­sis on “Ad Tech com­pli­ant” ser­vices also un­der­score a fun­da­men­tal ten­sion. Compliance in the ad­ver­tis­ing in­dus­try typ­i­cally refers to ad­her­ence to self-reg­u­la­tory frame­works, con­trac­tual oblig­a­tions, and pri­vacy poli­cies that per­mit ex­ten­sive data col­lec­tion so long as cer­tain dis­clo­sures are made.

Those stan­dards are not de­signed to con­strain gov­ern­ment use, nor do they sub­sti­tute for con­sti­tu­tional or statu­tory pro­tec­tions gov­ern­ing law en­force­ment sur­veil­lance.

Companies mar­ket­ing “privacy-friendly” lo­ca­tion or IP in­tel­li­gence tools of­ten ar­gue that they avoid di­rectly iden­ti­fy­ing in­di­vid­u­als. But re­searchers and reg­u­la­tors have re­peat­edly demon­strated that sup­pos­edly anonymized or ag­gre­gated data can be rei­den­ti­fied when com­bined with other datasets.

In an in­ves­tiga­tive con­text, rei­den­ti­fi­ca­tion is not a bug but a fea­ture, en­abling an­a­lysts to link dig­i­tal sig­nals back to real-world sub­jects.

Biometric Update ear­lier re­ported that a Government Accountability Office au­dit had found that pub­licly ac­ces­si­ble data — from so­cial me­dia posts to com­mer­cial ge­olo­ca­tion records — can be ag­gre­gated into de­tailed “digital pro­files” that ex­pose U. S. per­son­nel, mil­i­tary op­er­a­tions, and se­nior lead­ers to tar­get­ing, co­er­cion, and dis­rup­tion.

In January 2025, Gravy Analytics, a promi­nent lo­ca­tion data bro­ker, dis­closed that a sig­nif­i­cant data breach had po­ten­tially ex­posed through de-anonymiza­tion the pre­cise lo­ca­tion in­for­ma­tion of mil­lions of in­di­vid­u­als.

The RFI’s fo­cus on live demon­stra­tions sug­gests that ICE is in­ter­ested in ma­ture, de­ploy­able ca­pa­bil­i­ties rather than the­o­ret­i­cal of­fer­ings. Vendors se­lected to pre­sent would be ex­pected to show how their plat­forms op­er­ate in prac­tice, how data is ac­cessed and an­a­lyzed, and how in­ves­tiga­tive out­puts are gen­er­ated.

While the agency stresses that it is not com­mit­ting to a fu­ture so­lic­i­ta­tion, such demon­stra­tions of­ten in­form sub­se­quent pro­cure­ments, task or­ders, or pi­lot pro­grams con­ducted un­der ex­ist­ing con­tracts.

ICE has used sim­i­lar mar­ket re­search ap­proaches in the past to nor­mal­ize new sur­veil­lance ca­pa­bil­i­ties be­fore for­mal adop­tion.

Social me­dia mon­i­tor­ing tools, mo­bile bio­met­ric sys­tems, and large-scale an­a­lyt­ics plat­forms were all in­tro­duced through in­cre­men­tal steps that be­gan with RFIs and demon­stra­tions rather than head­line-grab­bing con­tracts.

For pri­vacy ad­vo­cates, the lat­est fil­ing fits a fa­mil­iar pat­tern. Commercial sur­veil­lance mar­kets evolve rapidly, dri­ven by ad­ver­tis­ing and mar­ket­ing de­mand. Government agen­cies then adopt those tools af­ter the fact, of­ten be­fore law­mak­ers have fully grap­pled with the im­pli­ca­tions.

Oversight mech­a­nisms, how­ever, lag tech­ni­cal ca­pa­bil­ity, leav­ing key ques­tions unan­swered un­til af­ter sys­tems are al­ready in use.

ICE’s RFI does not in­di­cate when demon­stra­tions might oc­cur or whether a so­lic­i­ta­tion will fol­low. It does make clear, though, that the agency sees the ad tech ecosys­tem as a po­ten­tial in­ves­tiga­tive re­source worth se­ri­ous con­sid­er­a­tion.

As de­bates over com­mer­cial data, sur­veil­lance, and con­sti­tu­tional pro­tec­tions con­tinue, the fil­ing of­fers a win­dow into how fed­eral law en­force­ment is adapt­ing to — and seek­ing to lever­age — a data econ­omy built for ad­ver­tis­ing rather than ac­count­abil­ity.

For now, ICE is ask­ing in­dus­try to ex­plain how ad tech-de­rived lo­ca­tion and an­a­lyt­ics ser­vices can be made suit­able for in­ves­tiga­tive use while re­spect­ing pri­vacy ex­pec­ta­tions.

What re­mains un­clear is who will de­fine those ex­pec­ta­tions, how they will be en­forced, and whether ex­ist­ing le­gal frame­works are equipped to gov­ern a sur­veil­lance model that blurs the line be­tween con­sumer mar­ket­ing and gov­ern­ment in­tel­li­gence.

Claude Code for in­fra­struc­ture. Debug, act, and au­dit every­thing Fluid does on your in­fra­struc­ture.

Fluid is a ter­mi­nal agent that do work on pro­duc­tion in­fra­struc­ture like VMs/K8s clus­ter/​etc. by mak­ing sand­box clones of the in­fra­struc­ture for AI agents to work on, al­low­ing the agents to run com­mands, test con­nec­tions, edit files, and then gen­er­ate Infra-as-code like an Ansible Playbook to be ap­plied on pro­duc­tion.

LLMs are great at gen­er­at­ing Terraform, OpenTofu, Ansible, etc. but bad at guess­ing how pro­duc­tion sys­tems work. By giv­ing ac­cess to a clone of the in­fra­struc­ture, agents can ex­plore, run com­mands, test things be­fore writ­ing the IaC, giv­ing them bet­ter con­text and a place to test ideas and changes be­fore de­ploy­ing.

I got the idea af­ter see­ing how much Claude Code has helped me work on code, I thought “I wish there was some­thing like that for in­fra­struc­ture”, and here we are.

Safety. I did­n’t want CC to SSH into a prod ma­chine from where it is run­ning lo­cally (real prob­lem!). I wanted to lock down the tools it can run to be only on sand­boxes while also giv­ing it au­ton­omy to cre­ate sand­boxes and not have ac­cess to any­thing else.

Fluid gives ac­cess to a live out­put of com­mands run (it’s pretty cool) and does this by ephemeral SSH Certificates. Fluid gives tools for cre­at­ing IaC and re­quires hu­man ap­proval for cre­at­ing sand­boxes on hosts with low mem­ory/​CPU and for ac­cess­ing the in­ter­net or in­stalling pack­ages.

Note: this is a ter­mi­nal agent (like Claude Code) meant to be in­stalled on your lo­cal lap­top/​work­sta­tion

Skip to main con­tentHow Jeff Bezos Brought Down the Washington PostThe Amazon founder bought the pa­per to save it. Instead, with a mass lay­off, he’s forced it into se­vere de­cline. On September 4, 2013, the Amazon founder Jeff Bezos held his first meet­ing with the staff of the Washington Post, the news­pa­per he had agreed to pur­chase a month ear­lier from the Graham fam­ily, for two hun­dred and fifty mil­lion dol­lars. It had been a long and un­set­tling stretch for the pa­per’s staff. We—I was a deputy ed­i­tor of the ed­i­to­r­ial page at the time—had suf­fered through years of re­trench­ment. We trusted that Don Graham would place us in ca­pa­ble hands, but we did not know this new owner, and he did not know or love our busi­ness in the way that the Graham fam­ily had. Bezos’s words at that meet­ing, about “a new golden era for the Washington Post,” were re­as­sur­ing. Bob Woodward asked why he had pur­chased the pa­per, and Bezos was clear about the com­mit­ment he was pre­pared to make. “I fi­nally con­cluded that I could pro­vide run­way—fi­nan­cial run­way—be­cause I don’t think you can keep shrink­ing the busi­ness,” he said. “You can be prof­itable and shrink­ing. And that’s a sur­vival strat­egy, but it ul­ti­mately leads to ir­rel­e­vance, at best. And, at worst, it leads to ex­tinc­tion.”To look back on that mo­ment is to won­der: How could it have come to this? The pa­per had some prof­itable years un­der Bezos, sparked by the 2016 elec­tion and the first Trump term. But it be­gan los­ing enor­mous sums: sev­enty-seven mil­lion dol­lars in 2023, an­other hun­dred mil­lion in 2024. The owner who once of­fered run­way was un­will­ing to tol­er­ate losses of that mag­ni­tude. And so, af­ter years of Bezos-fuelled growth, the Post en­dured two pun­ish­ing rounds of vol­un­tary buy­outs, in 2023 and 2025, that re­duced its news­room from more than a thou­sand staffers to un­der eight hun­dred, and cost the Post some of its best writ­ers and ed­i­tors. Then, early Wednesday morn­ing, news­room em­ploy­ees re­ceived an e-mail an­nounc­ing “some sig­nif­i­cant ac­tions.” They were in­structed to stay home and at­tend a “Zoom we­bi­nar at 8:30 a.m.” Everyone knew what was com­ing—mass lay­offs.The scale of the de­mo­li­tion, though, was stag­ger­ing—re­port­edly more than three hun­dred news­room staffers. The an­nounce­ment was left to the ex­ec­u­tive ed­i­tor, Matt Murray, and hu­man-re­la­tions chief Wayne Connell; the news­pa­per’s pub­lisher, Will Lewis, was nowhere to be seen as the grim news was un­veiled. In what Murray termed a “broad strate­gic re­set,” the Post’s sto­ried sports de­part­ment was shut­tered “in its cur­rent form”; sev­eral re­porters will now cover sports as a “cultural and so­ci­etal phe­nom­e­non.” The metro staff, al­ready cut to about forty staffers dur­ing the past five years, has been shrunk to about twelve; the for­eign desks will be re­duced to ap­prox­i­mately twelve lo­ca­tions from more than twenty; Peter Finn, the in­ter­na­tional ed­i­tor, told me that he asked to be laid off. The books sec­tion and the flag­ship pod­cast, “Post Reports,” will end. Shortly af­ter the meet­ing, staffers re­ceived in­di­vid­u­al­ized e-mails let­ting them know whether they would stay or go. Murray said the re­trenched Post would “concentrate on ar­eas that demon­strate au­thor­ity, dis­tinc­tive­ness, and im­pact,” fo­cussing on ar­eas such as pol­i­tics and na­tional se­cu­rity. This strat­egy, a kind of Politico-lite, would be more con­vinc­ing if so many of the most tal­ented play­ers were not al­ready gone.Gra­ham, who has pre­vi­ously been res­olutely silent about changes at the pa­per, posted a mes­sage on Facebook that pulsed with an­guish. “It’s a bad day,” he wrote, adding, “I am sad that so many ex­cel­lent re­porters and ed­i­tors—and old friends—are los­ing their jobs. My first con­cern is for them; I will do any­thing I can to help.” As for him­self, Graham, who once edited the sports sec­tion, said, “I will have to learn a new way to read the pa­per, since I have started with the sports page since the late 1940’s.”What hap­pened to the Bezos of 2013, a self-pro­claimed op­ti­mist who seemed to have ab­sorbed the im­por­tance of the Post in the na­tion’s jour­nal­is­tic ecosys­tem? In 2016, ded­i­cat­ing the pa­per’s new head­quar­ters, he boasted that it had be­come “a lit­tle more swash­buck­ling” and had a “little more swag­ger.” As re­cently as December, 2024, at the New York Times’ DealBook Summit, Bezos ex­pressed his com­mit­ment to nur­tur­ing the pa­per: “The ad­van­tage I bring to the Post is when they need fi­nan­cial re­sources, I’m avail­able. I’m like that. I’m the dot­ing par­ent in that re­gard.” Not long ago, he en­vi­sioned at­tract­ing as many as a hun­dred mil­lion pay­ing sub­scribers to the Post. With these bru­tal cuts, he seems con­tent to let the pa­per limp along, di­min­ished in size and am­bi­tion.“In the be­gin­ning, he was won­der­ful,” Sally Quinn, the vet­eran Post con­trib­u­tor and wife of its leg­endary ex­ec­u­tive ed­i­tor, Ben Bradlee, told me of Bezos. “He was smart and funny and kind and in­ter­ested. He was joy­ful. He was a per­son of in­tegrity and con­science. He re­ally meant it when he said this was a sa­cred trust, to buy the Post. And now I don’t know who this per­son is.”The au­thor David Maraniss was with the Post for forty-eight years. He re­signed as an as­so­ci­ate ed­i­tor in 2024, af­ter Bezos killed the ed­i­to­r­ial page’s planned en­dorse­ment of Kamala Harris. “He bought the Post think­ing that it would give him some grav­i­tas and grace that he could­n’t get just from bil­lions of dol­lars, and then the world changed,” Maraniss said of Bezos. “Now I don’t think he gives us—I don’t think he gives a fly­ing fuck.”I asked Maraniss what cuts of this mag­ni­tude would mean for the in­sti­tu­tion. “I don’t even want to call it the Washington Post,” he said. “I don’t know what it’ll be with­out all of that.”The first sign of im­pend­ing lay­offs came in late January, when the sports staff was in­formed that plans to send writ­ers to Italy to cover the Winter Olympics had been can­celled. (Management later agreed to send a smaller crew.) In the fol­low­ing days, as ru­mors be­gan to spread of se­vere cuts, the pa­per’s re­porters be­gan post­ing mes­sages di­rected at Bezos on X, with the plain­tive hash­tag #SaveThePost. “Our re­porters on the ground drove ex­clu­sive cov­er­age dur­ing piv­otal mo­ments of re­cent his­tory,” the for­eign staff wrote to Bezos. “We have so much left to do.” The lo­cal staff noted that it had al­ready been slashed in half in the past five years. “Watergate,” they wrote, “started as a lo­cal story.”It did not help the staff’s morale that Lewis and his team were hob­nob­bing in Davos, or that Bezos and his wife, Lauren Sánchez, were in Paris for Haute Couture Week. More trou­bling were re­minders that Bezos, who once em­bla­zoned “Democracy Dies in Darkness” on the pa­per’s mast­head, ap­pears to be pur­su­ing a pol­icy of ap­pease­ment to­ward the Trump Administration. During the first Trump term, Bezos stood by the Post even when his stew­ard­ship threat­ened to cost him bil­lions in gov­ern­ment con­tracts. Now Bezos had not said a word about a re­cent F.B.I. raid on the home of the Post fed­eral-gov­ern­ment re­porter Hannah Natanson, in which the agency seized her phones, lap­tops, and other de­vices. As the staff awaited the axe, the President and the First Lady cel­e­brated the pre­mière of “Melania,” a doc­u­men­tary that Amazon had li­censed for forty mil­lion dol­lars and was re­ported to be spend­ing an­other thirty-five mil­lion to pro­mote. The deal was inked af­ter Bezos had din­ner with the Trumps shortly be­fore the Inauguration.Martin Baron, who over­saw cov­er­age at the pa­per that gar­nered eleven Pulitzer Prizes dur­ing his eight years as ex­ec­u­tive ed­i­tor, said in a state­ment, “This ranks among the dark­est days in the his­tory of one of the world’s great­est news or­ga­ni­za­tions. The Washington Post’s am­bi­tions will be sharply di­min­ished, its tal­ented and brave staff will be fur­ther de­pleted, and the pub­lic will be de­nied the ground-level, fact-based re­port­ing in our com­mu­ni­ties and around the world that is needed more than ever.” The news in­dus­try is in “a pe­riod of head-spin­ning change,” Baron told me. But the Post’s prob­lems “were made in­fi­nitely worse by ill-con­ceived de­ci­sions that came from the very top.” He pointed to Bezos’s de­ci­sion to kill the Harris en­dorse­ment—a “gutless or­der” that cost the pa­per more than two hun­dred fifty thou­sand sub­scribers. “Loyal read­ers, livid as they saw owner Jeff Bezos be­tray­ing the val­ues he was sup­posed to up­hold, fled The Post. In truth, they were dri­ven away, by the hun­dreds of thou­sands,” Baron said. “Bezos’s sick­en­ing ef­forts to curry fa­vor with President Trump have left an es­pe­cially ugly stain of their own. This is a case study in near-in­stant, self-in­flicted brand de­struc­tion.”I spent more than forty years at the Post, as a re­porter, an ed­i­tor, an ed­i­to­r­ial writer, and a colum­nist. I re­signed last March, af­ter Bezos an­nounced that the Opinions sec­tion, where I worked, would hence­forth be con­cen­trat­ing on the twin pil­lars of “personal lib­er­ties and free mar­kets.” More alarm­ing, Bezos ad­vised, “Viewpoints op­pos­ing those pil­lars will be left to be pub­lished by oth­ers.” We had been an opin­ion sec­tion re­flect­ing a wide range of views—which Bezos him­self had en­cour­aged. It seemed ob­vi­ous that this change was deeply mis­guided.I had writ­ten a col­umn crit­i­cal of the non-en­dorse­ment de­ci­sion sev­eral months ear­lier. The pa­per pub­lished it with­out any sub­stan­tive changes. But, when I wrote a col­umn dis­agree­ing with the no-dis­sent-al­lowed dic­tum, I was told that Lewis had killed it—it ap­par­ently did­n’t meet the “high bar” for the Post to write about it­self—and de­clined my re­quest to meet. I sub­mit­ted my let­ter of res­ig­na­tion. A new ed­i­to­r­ial-page ed­i­tor went on to shift both un­signed ed­i­to­ri­als and signed opin­ion columns dra­mat­i­cally to the right, to the point that no lib­eral colum­nists re­main. One re­cent ed­i­to­r­ial praised the President’s plan for a new ball­room and ex­cused his unau­tho­rized bull­doz­ing of the East Wing, say­ing that “the blue­prints would have faced death by a thou­sand pa­per­cuts.” Another en­dorsed the move to re­name the Defense Department the Department of War as “a wor­thy blow against gov­ern­ment eu­phemism.” There are some ed­i­to­ri­als crit­i­cal of Trump, but the in­cli­na­tion to fawn­ing praise is un­mis­tak­able. Had I not de­fen­es­trated my­self, I would, no doubt, have been ad­vised to take my buy­out and go.But I am not—at least, I have not been—a Bezos-hater. I am grate­ful for the re­sources, fi­nan­cial and tech­no­log­i­cal, that he de­voted to the pa­per in his early years as owner. The sur­prise of Bezos’s tenure at the Post has been his bad busi­ness de­ci­sions. Fred Ryan, a for­mer chief of staff to Ronald Reagan and found­ing pres­i­dent of Politico, was hired as the pub­lisher and C.E.O. in 2014 and over­saw a pe­riod of spec­tac­u­lar growth. Buoyed by Bezos-funded ex­pan­sion and the pub­lic’s fix­a­tion on the new Trump Administration, the num­ber of dig­i­tal sub­scribers soared from thirty-five thou­sand when he ar­rived to two and a half mil­lion when he left, in the sum­mer of 2023. But Ryan failed to de­velop an ad­e­quate plan for how the news­pa­per would thrive in a post-Trump en­vi­ron­ment. As traf­fic and rev­enue plunged, Ryan found him­self in­creas­ingly at odds with the news­room. He held a year-end town-hall meet­ing in 2022 at which he an­nounced that lay­offs were com­ing, and then, to the con­ster­na­tion of the staff, left with­out tak­ing ques­tions. As Clare Malone re­ported for The New Yorker, Woodward be­seeched Bezos to in­ter­cede. The owner made a rare visit to the pa­per in January, 2023, for meet­ings with key staffers, tak­ing notes on a le­gal pad as they poured out their anx­i­ety.Ryan left that sum­mer, but Lewis, his even­tual re­place­ment, ac­com­plished the feat of mak­ing the news­room nos­tal­gic for Ryan. A decade ear­lier, Lewis, then a se­nior ex­ec­u­tive in Rupert Murdoch’s British-tabloid em­pire, had played a piv­otal role in deal­ing with the fall­out from the phone-hack­ing scan­dal at some of Murdoch’s pa­pers. Lewis had said that he was act­ing to pro­tect “journalistic in­tegrity,” when the Post ques­tioned him about his ac­tions dur­ing that time, but in 2024 ques­tions arose, fu­elled by a civil law­suit brought against the pa­pers, about whether Lewis had sought to con­ceal ev­i­dence, in­clud­ing by car­ry­ing out a plan to delete mil­lions of e-mails. (Lewis has said the al­le­ga­tions against him were “completely un­true.”) At the Post, Lewis clashed with ex­ec­u­tive ed­i­tor Sally Buzbee over cov­er­age of the story, re­port­edly in­sist­ing that it was not news­wor­thy. Shortly af­ter­ward, Lewis an­nounced Buzbee’s de­par­ture, and his plan to re­place her with Robert Winnett, a for­mer col­league of his from London’s Daily Telegraph and Sunday Times. The Post and the Times both re­ported on how Lewis and Winnett had used fraud­u­lently ob­tained ma­te­r­ial as the ba­sis for ar­ti­cles. “His am­bi­tion out­ran his ethics,” one of Lewis’s for­mer re­porters told the Times. Winnett ended up with­draw­ing from the po­si­tion, but the episode poi­soned re­la­tions be­tween Lewis and the news­room.The staff, mean­while, be­came in­creas­ingly con­cerned that Lewis was of­fer­ing cor­po­rate word salad in place of a vi­sion to ad­dress the Post’s de­cline. “Fix it, build it, scale it” was his catch­phrase when he ar­rived, in January, 2024. In June of that year came an amor­phous plan for what Lewis called a “third news­room.” (The sec­ond news­room, we were sur­prised to learn, was the Opinions sec­tion.) First, it was to fo­cus on so­cial me­dia and ser­vice jour­nal­ism. Then it was rechris­tened WP Ventures and, ac­cord­ing to a memo to staff, would “focus en­tirely on build­ing per­son­al­ity-dri­ven con­tent and fran­chises around per­son­al­i­ties.” By February, 2025, the sit­u­a­tion had de­te­ri­o­rated to the point that two for­mer top ed­i­tors, Leonard Downie and Robert Kaiser, wrote to Bezos about Lewis. “Replacing him is a cru­cial first step in sav­ing The Washington Post,” they urged in an e-mail. Bezos never re­sponded.Downie, who served as ex­ec­u­tive ed­i­tor from 1991 to 2008, con­trasted the paths of the Times and the Post. During the past decade, the Times trans­formed it­self into a one-stop-shop­ping en­vi­ron­ment that lured read­ers with games such as Spelling Bee, a cook­ing app, and a shop­ping guide. By the end of 2025, it was re­port­ing close to thir­teen mil­lion dig­i­tal sub­scribers and an op­er­at­ing profit of more than a hun­dred and ninety-two mil­lion dol­lars. The Post does not re­lease in­for­ma­tion about its dig­i­tal sub­scribers, but it was re­ported to have two and a half mil­lion dig­i­tal sub­scribers at the time of the non-en­dorse­ment de­ci­sion, in 2024.“One of the big dif­fer­ences to me was that they hired a pub­lisher”—Ryan—“who did­n’t come up with any ideas,” Downie told me. “And then when he left . . . we knew that Bezos was los­ing money, and we were en­cour­aged by the fact that they were look­ing for some­body who could im­prove the busi­ness side of the pa­per and the cir­cu­la­tion side of the pa­per. And then they chose this guy who we hardly ever heard from, who had a check­ered past in British jour­nal­ism.”Writ­ing last month on a pri­vate Listserv for for­mer Post em­ploy­ees, Paul Farhi, who as the me­dia re­porter for the Post cov­ered Bezos’s ac­qui­si­tion of the pa­per, shared his “utter mys­ti­fi­ca­tion and baf­fle­ment” about Bezos’s tol­er­ance of Lewis. “Even as a hands-off boss,” he won­dered, “could Bezos not see what was ob­vi­ous to even ca­sual ob­servers within a few months of Will’s ar­rival—that Will was ill-suited to the Post, that he had alien­ated the news­room, that he had an eth­i­cally sus­pect past, and—most im­por­tant—that none of his big ideas was work­ing or even be­ing im­ple­mented?” (Farhi, who took a buy­out in 2023, gave me per­mis­sion to quote his mes­sage.)Even be­fore these new cuts, a pa­rade of key staffers had left the Post. A beloved man­ag­ing ed­i­tor, Matea Gold, went to the Times. The na­tional ed­i­tor, Philip Rucker, de­camped to CNN, and the po­lit­i­cal re­porter Josh Dawsey to the Wall Street Journal. The Atlantic hired, among oth­ers, three stars of the pa­per’s White House team: Ashley Parker, Michael Scherer, and Toluse Olorunnipa. These are losses that would take years to re­build—if the Post were in a re­build­ing mode. The Post, Woodward said, “lives and is do­ing an ex­tra­or­di­nary re­port­ing job on the po­lit­i­cal cri­sis that is Donald Trump”—including its scoop on the sec­ond strike to kill sur­vivors of an at­tack on an al­leged Venezuelan drug boat. But the print edi­tion is a shadow of its for­mer self, with metro, style, and sports melded into an ane­mic sec­ond sec­tion; daily print cir­cu­la­tion is now be­low one hun­dred thou­sand. More press­ingly, it’s un­clear whether a news­room so stripped of re­sources can sus­tain the qual­ity of its work.The sports colum­nist Sally Jenkins, who left the Post in August, 2025, as part of the sec­ond wave of buy­outs, has been more sup­port­ive of man­age­ment than many other Post vet­er­ans. So it was strik­ing that, when we spoke re­cently, she was both pas­sion­ate about the work of her news­room col­leagues and un­spar­ing about how the busi­ness side had failed them. “When you whack at these sec­tions, you’re whack­ing at the roots of the tree,” she told me. “We train great jour­nal­ists in every sec­tion of the pa­per, and we train them to cover every sub­ject on the globe. And when you whack whole sec­tions of peo­ple away, you are re­ally, re­ally in dan­ger of killing the whole tree.” When I asked how she felt about the losses, Jenkins said, “My heart is cracked in about five dif­fer­ent pieces.”Jenk­ins, who was in California cov­er­ing Super Bowl week for the Atlantic, has spent a ca­reer study­ing what ac­counts for the dif­fer­ence be­tween win­ning teams and los­ing ones. Bezos, she said, had been gen­er­ous with his money and laud­able for never in­ter­fer­ing in the work of the news­room. But, she added, “making money at jour­nal­ism, you have to break rocks with a shovel. You have to love think­ing about jour­nal­ism to the point that it wakes you up at night with an idea, and then you have to be will­ing to try it. And I don’t see a sense that he loves the busi­ness enough to think about it at night. It’s al­most like he’s treated it like Pets.com—an in­ter­est­ing ex­per­i­ment that he’s will­ing to lose some money on un­til he’s not. But the dif­fer­ence with this busi­ness is it’s not Pets.com. It’s not a busi­ness that just dis­ap­pears into the muck of ven­ture cap­i­tal­ism. It’s a busi­ness that is es­sen­tial to the sur­vival of the Republic, for Christ’s sake. So you don’t fuck around with it like that.”As Post staffers and alumni braced for the cuts, I called Kaiser, the for­mer man­ag­ing ed­i­tor, who spent more than half a cen­tury at the pa­per. “Mr. Bezos’s per­sonal sys­tem has failed him in a way I fear he does­n’t grasp,” Kaiser, now eighty-two, told me. “He has no sense of the dam­age that will be done to his rep­u­ta­tion in his­tory if he be­comes seen as the man who de­stroyed the in­sti­tu­tion that Katharine Graham”—the famed pub­lisher who led the pa­per from the six­ties to the nineties—“and Ben Bradlee built.” Kaiser re­called ar­riv­ing at the pa­per’s London bu­reau in 1964. “If I say, ‘I’m Kaiser from the Washington Post’—what’s that? They never heard of it.” A decade later, he was posted in Moscow, as Woodward and Carl Bernstein were break­ing the Watergate story. “Explaining was not nec­es­sary,” Kaiser said. “The Russians, in fact, had a glo­ri­ously ex­ag­ger­ated im­pres­sion of the Washington Post as the king-maker and the king-de­stroyer.”Be­zos, Kaiser con­tin­ued, “knew what the role was, ac­knowl­edged the role—those words ‘doting par­ent’—and then he walked away from it. What the hell?” The dam­age, he pre­dicted, will re­ver­ber­ate be­yond the im­me­di­ate cuts. “What pur­pose does any hon­or­able, at­trac­tive, com­pe­tent jour­nal­ist have for re­main­ing at the Post? None.”At one point, as we talked about the trans­for­ma­tion of the Post, Kaiser stopped him­self. “I’m go­ing to cry,” he said, and paused. “Oh, God, it’s killing me.”Be­zos may be tir­ing of the Post, but he has not seemed in­clined to sell the pa­per. Nor is it clear that would be a bet­ter, or at this point even fea­si­ble, out­come. Newspapers across the coun­try are be­ing bought up by pri­vate-eq­uity firms that are es­sen­tially sell­ing off the valu­able parts. But there is an­other model for Bezos to con­sider: turn­ing the Post into a non­profit, en­dowed by Bezos but op­er­at­ing in­de­pen­dently of him. For Bezos, this would re­duce the role of the Post as a headache and a threat to other, more fa­vored en­deav­ors, such as his rocket com­pany, Blue Origin. For the Post, as­sum­ing the en­dow­ment is suf­fi­cient, it would pro­vide that con­tin­u­ing run­way.There are mod­els for this ap­proach. In Philadelphia, the late ca­ble-tele­vi­sion ty­coon H. F. “Gerry” Lenfest pur­chased the Inquirer, the Daily News, and Philly.com in 2015, and the fol­low­ing year do­nated the pub­li­ca­tions to a char­i­ta­ble trust. “What would the city be with­out the Inquirer and the Daily News?” asked Lenfest, whose con­tri­bu­tion to the en­deavor has been val­ued at al­most a hun­dred and thirty mil­lion dol­lars. In Utah, the in­vestor Paul Huntsman bought the Salt Lake Tribune from the hedge fund Alden Global Capital in 2016; three years later, he trans­formed it into a non­profit, sup­ported in part by tax-de­ductible con­tri­bu­tions from read­ers.Writ­ing in the Columbia Journalism Review in 2024, Steven Waldman sug­gested that Bezos fol­low a sim­i­lar course. “ ‘Nonprofit’ does not mean ‘losing money,’ ” Waldman wrote. “Nonprofit news or­ga­ni­za­tions can sell ads, of­fer sub­scrip­tions, and take do­na­tions. Done well, it is an es­pe­cially strong busi­ness model, be­cause it pro­vides an ex­tra rev­enue stream (philanthropy) and is deeply em­bed­ded in serv­ing the com­mu­nity.” My quib­ble with Waldman’s pitch is that he asked Bezos to ante up a pal­try hun­dred mil­lion. When Bezos pur­chased the Post, his net worth was about twenty-five bil­lion; it is now an es­ti­mated two hun­dred fifty bil­lion. Why not one per cent of that for the Post, enough to sus­tain the pa­per in­def­i­nitely? A pipe dream, I know, but this arrange­ment would make Bezos the sav­ior of the Post, not the man who presided over its demise.In the 1941 movie “Citizen Kane,” Charles Foster Kane, a news­pa­per pub­lisher who, like Bezos, is one of the rich­est men in the world, is con­fronted by his le­gal guardian, Walter Thatcher, about the folly of fund­ing his pa­per. “Honestly, my boy, don’t you think it’s rather un­wise to con­tinue this phil­an­thropic en­ter­prise, this Inquirer that’s cost­ing you a mil­lion dol­lars a year?” Thatcher de­mands. “You’re right, Mr. Thatcher. I did lose a mil­lion dol­lars last year,” Kane replies. “I ex­pect to lose a mil­lion dol­lars this year. I ex­pect to lose a mil­lion dol­lars next year. You know, Mr. Thatcher, at the rate of a mil­lion dol­lars a year, I’ll have to close this place in sixty years.” Update Kane’s out­lays to as­sume losses of a hun­dred mil­lion an­nu­ally, in per­pe­tu­ity. By that math, Bezos would have more than two mil­len­nia be­fore need­ing to turn out the lights. ♦

sqldef is a CLI tool for diff­ing two SQL schemas. You can use it to man­age the mi­gra­tion of RDBMSs us­ing reg­u­lar SQL DDLs.

The on­line demo uses the WebAssembly build of sqldef to diff two SQL schemas and gen­er­ate DDLs.