whoa there, pard­ner!

Your re­quest has been blocked due to a net­work pol­icy.

Try log­ging in or cre­at­ing an ac­count here to get back to brows­ing.

If you’re run­ning a script or ap­pli­ca­tion, please reg­is­ter or sign in with your de­vel­oper cre­den­tials here. Additionally make sure your User-Agent is not empty and is some­thing unique and de­scrip­tive and try again. if you’re sup­ply­ing an al­ter­nate User-Agent string, try chang­ing back to de­fault as that can some­times re­sult in a block.

You can read Reddit’s Terms of Service here.

If you think that we’ve in­cor­rectly blocked you or you would like to dis­cuss eas­ier ways to get the data you want, please file a ticket here.

When con­tact­ing us, please in­clude your Reddit ac­count along with the fol­low­ing code:

019f7a54-eab6 – 74ed-ae43 – 09b2c9b3615c

I’m su­per ex­cited to share tran­scribe.cpp to­day.

tran­scribe.cpp is a ggml based tran­scrip­tion li­brary which sup­ports all the lat­est tran­scrip­tion mod­els. Every model pub­lished un­der the handy-com­puter HF org has been nu­mer­i­cally val­i­dated and WER tested to match the ref­er­ence im­ple­men­ta­tion. It’s ac­cel­er­ated every­where.

I’m the au­thor and main­tainer of Handy. This li­brary grew from the pains of dis­trib­ut­ing a cross-plat­form speech-to-text ap­pli­ca­tion to many peo­ple.

This is a v0.1.0 li­brary which means that there are some rough edges which I can­not dis­cover alone! Please re­port them, and let’s fix them to­gether!

Motivation

Let me say this. I think dis­trib­ut­ing a cross-plat­form ap­pli­ca­tion with the cur­rent ASR in­fer­ence stack is ter­ri­ble.

You’ve ba­si­cally got whis­per.cpp and ONNX. That’s it. You could roll MLX in for Apple de­vices, but now you’ve to sup­port two dif­fer­ent en­gines and port mod­els to each. I’ve been a fan of ONNX for get­ting model sup­port into Handy quickly, but so much per­for­mance is left on the table with CPU only.

There are a few ran­dom li­braries out there which claim to sup­port a lot of mod­els, but they have un­known au­thors, and un­known test­ing, as far as I’ve seen. They leave me with more ques­tions than an­swers.

When will they stop main­tain­ing this li­brary? Has the cre­ator thought about bind­ings so you can ac­tu­ally use it in a real desk­top or mo­bile app? Is this ef­fec­tively demo code? Have they bench­marked it? Is it faster than ONNX?

And this is what led to tran­scribe.cpp. As Handy’s main­tainer I needed a li­brary I could trust. Where I could down­load a file and run in­fer­ence on it. Where I can know that the in­fer­ence com­ing from the model in the en­gine is as good as the ref­er­ence im­ple­men­ta­tion. The in­fer­ence should run on the GPU for the best per­for­mance. It should be triv­ially em­bed­d­a­ble in Handy, it can­not be a huge py­torch lib. It must be some­thing that works on Mac, Windows, and Linux. And ggml seemed like by far the best way for­ward. It has a strong com­mu­nity, and a great dis­tri­b­u­tion story.

So what do you get?

You get a fast and ac­cu­rate in­fer­ence en­gine with wide rang­ing model sup­port.

Support for 16 ASR Families (60+ mod­els) with more com­ing

Acceleration via Vulkan, Metal, CUDA, and TinyBLAS

Every model has been nu­mer­i­cally ver­i­fied and WER tested

Support for Streaming Transcription

Support for Batch Transcription

More or less drop in whis­per.cpp re­place­ment

Maintainer sup­ported bind­ings in 4 Languages

Python Javascript/Typescript Rust ObjC/Swift

Python

Javascript/Typescript

Rust

ObjC/Swift

Wide Model Support

We in­tend to sup­port as many state-of-the-art tran­scrip­tion mod­els as pos­si­ble. As of to­day, we sup­port most of the mod­ern tran­scrip­tion mod­els that are pub­licly avail­able. There are a few miss­ing still, but they will be added soon.

Acceleration Support

One of my top goals was to run any ASR model I wanted on Vulkan. In my opin­ion this is the floor for any ap­pli­ca­tion ship­ping lo­cal in­fer­ence. For every model we sup­port, there is a cor­re­spond­ing bench­mark run from a Ryzen 4750U (CPU + Vulkan) on Fedora as well as on my M4 Max.

Numerically Verified

I also wanted to make sure that in­fer­ence in tran­scribe.cpp is ac­cu­rate and as close to the ref­er­ence im­ple­men­ta­tion as pos­si­ble. This largely came from a huge de­gree of un­cer­tainty of in­fer­ence ac­cu­racy when us­ing .onnx mod­els I found on Hugging Face. In or­der to en­sure the in­fer­ence we do is cor­rect we nu­mer­i­cally val­i­date every model ver­sus the ref­er­ence. On top of nu­mer­i­cal val­i­da­tion, we run full WER sweeps to make sure that what­ever the ref­er­ence is out­putting, we out­put the same thing. That means every model has run through thou­sands of ut­ter­ances and is very close or same as the ref­er­ence. And the re­sults of this data are pub­lished in the tran­scribe.cpp repo as well as with each model on Hugging Face.

Drop In whis­per.cpp re­place­ment

tran­scribe.cpp is more or less a drop in sup­port for whis­per.cpp. The main rea­son for this is: Handy used whis­per.cpp and I needed to ship an up­date with tran­scribe.cpp which would re­place it. I needed to keep some com­pat­i­bil­ity with the very pop­u­lar .bin files which run in whis­per.cpp and shipped with Handy. tran­scribe.cpp can run them. There are some flags and fea­tures in whis­per.cpp which we do not sup­port yet. But I think for the vast ma­jor­ity of use cases our whis­per im­ple­men­ta­tion is solid and can re­place whis­per.cpp while hav­ing about equal per­for­mance.

Real Distribution

Language bind­ings were on my mind to be­gin with. While this li­brary is writ­ten in C/C++, I needed bind­ings in Rust. And I also knew that in or­der for us to dis­trib­ute lo­cal tran­scrip­tion as widely as pos­si­ble, it re­quires at min­i­mum de­cent first-party sup­port of bind­ings. I’ve cho­sen 4 lan­guages that I think are fairly rep­re­sen­ta­tive of where peo­ple will use the li­brary. I wel­come oth­ers to con­tribute bind­ings di­rectly to the pro­ject as well, as­sum­ing that they are will­ing to take on the main­te­nance bur­den of do­ing so.

And of course, at the end of the day, a lot of the de­ci­sions were dri­ven by Handy. As a re­sult of Handy be­ing pop­u­lar, I in­tend to main­tain this li­brary, just as I’ve done my best to main­tain Handy. I in­tend to be some­one who con­tin­ues to main­tain open source pro­jects and con­tribute to the ecosys­tem where I can.

This li­brary never would have ex­isted with­out Handy be­cause I would­n’t have had the prob­lem of try­ing to sup­port a bunch of dif­fer­ent ASR mod­els. I would have never learned all the use cases that peo­ple have for ASR. I’ve done my best to cover the ones that I hear about the most. Certainly, there are cases in the li­brary that are not cur­rently han­dled. If there are things that I missed, you are free to con­tribute to the li­brary!

Making Local Speech to Text More Accessible

tran­scribe.cpp is aimed squarely at mak­ing lo­cally run ASR eas­ier. We know that tran­scrip­tion can run ex­tremely ac­cu­rately on most de­vices, and there should be no need to send your voice to a cloud ser­vice. An RK3566 can run mod­els via tran­scribe.cpp faster than real time on its ane­mic CPU. Faster than real time tran­scrip­tion with SOTA mod­els runs in a hand­ful of watts. It’s not a hope or a dream, it’s a fact.

I think as we look for­ward to the fu­ture, more in­fer­ence will start hap­pen­ing lo­cally for one rea­son or the other. This brings the dis­tri­b­u­tion story front and cen­ter. In or­der to have more ap­pli­ca­tions run­ning in­fer­ence lo­cally, we need to make run­ning in­fer­ence eas­ier. Certainly tran­scribe.cpp does not solve this on the whole, and there is a long way to go, but I hope it’s a small step for­ward. I’ve cer­tainly learned a lot.

Gratitude

I am ex­tremely thank­ful for all the folks who have sup­ported this pro­ject.

First and fore­most is to Mozilla AI, their BiR pro­gram, and Davide from Mozilla AI. This pro­ject was largely a prob­lem in my head that I came to them with, and they de­cided to sup­port me in solv­ing the prob­lem. At the time tran­scribe.cpp was­n’t even a con­crete idea, I was just ex­plor­ing how to solve ac­cel­er­ated dis­tri­b­u­tion in Handy. So a huge thanks to them, their sup­port, and help­ing to bring this pro­ject into ex­is­tence.

ggml. This pro­ject would­n’t be pos­si­ble with­out ggml and all of the con­trib­u­tors to it. Thank you all so much for the work you’ve done. I think ggml re­ally does amaz­ing work in help­ing to make dis­trib­ut­ing lo­cal in­fer­ence ap­pli­ca­tions easy and pos­si­ble.

Modal has also been a crit­i­cal help for me. I reached out to them, and they gave me cred­its. These cred­its are put to­wards do­ing the WER test­ing and en­sur­ing the li­brary works well on CUDA. It is an im­mense help be­ing able to ver­ify the cor­rect­ness of the work.

Blacksmith helps to power some of the CI/CD for tran­scribe.cpp. Again I reached out to them and they im­me­di­ately re­sponded with cred­its. Of course CI/CD is crit­i­cal for mak­ing sure every­thing put out has been tested to at least some de­gree.

Hugging Face both for be­ing a pil­lar in the lo­cal AI com­mu­nity, as well as pro­vid­ing the handy-com­puter org pri­vate stor­age, so I could up­load mod­els at my own will.

AI Assisted?

Yes ab­solutely. I don’t think it’s pos­si­ble for a sin­gle in­di­vid­ual to write an en­gine from scratch of this size us­ing ggml in a hand­ful of months with­out out­side as­sis­tance. Were any of the words here writ­ten us­ing AI? Nope. They came from my mouth or my fin­gers.

New York City mayor Zohran Mamdani is hav­ing a very busy week. Just a day af­ter an­nounc­ing a “click-to-cancel” rule aimed at com­pa­nies like Adobe, Mamdani is crack­ing down on “deceptive land­lord prac­tices,” in­clud­ing us­ing AI-generated and AI-edited im­ages de­signed to make prop­er­ties look more ap­peal­ing.

Mamdani and his team re­leased a “Rental Ripoff Report” to­day, and in it, the ad­min­is­tra­tion out­lines rec­om­men­da­tions to re­quire land­lords and re­al­tors to dis­close the use of AI to al­ter their list­ings, in­clud­ing any im­agery.

Alongside mea­sures like rec­og­niz­ing ten­ant unions and ex­pand­ing ten­ants’ bar­gain­ing rights, the re­port also says that land­lords should “disclose when rental list­ings have been al­tered us­ing ar­ti­fi­cial in­tel­li­gence or other dig­i­tal tools.”

Misleading AI-generated and AI-edited im­ages in real es­tate list­ings are an in­creas­ingly se­ri­ous prob­lem, far be­yond just New York City. While the re­sults are oc­ca­sion­ally funny, if not ter­ri­fy­ing, there’s noth­ing funny about ten­ants be­ing de­ceived when re­al­ity does not match pho­tos on a list­ing. This is es­pe­cially trou­ble­some for ten­ants who have to sign a lease re­motely, such as when mov­ing some­where for a new job.

“At Rental Ripoff Hearings across the five bor­oughs, we heard from thou­sands of New Yorkers liv­ing with mold that was never treated, pests that were never ad­dressed and fees that were never ex­plained. Listening was only the first step. This re­port turns those sto­ries into con­crete ac­tion. From re­quir­ing dis­clo­sure of AI-altered list­ings to bring­ing our code en­force­ment sys­tems into the 21st cen­tury and fi­nally rec­og­niz­ing ten­ant unions, we are mak­ing it clear that every New Yorker de­serves a safe home — and every land­lord who re­fuses to pro­vide one will be held ac­count­able,” Mayor Mamdani says, em­pha­sis added.

“… these poli­cies are rooted in real ex­pe­ri­ences and ad­dress real con­cerns,” adds Leila Bozorg, Deputy Mayor for Housing and Planning.

“The Rental Ripoff Hearings and to­day’s re­port are writ­ing a new chap­ter in ten­ant power in New York City. Governing is a part­ner­ship. By bring­ing ten­ants’ voices di­rectly into pol­icy and tak­ing un­prece­dented steps to fa­cil­i­tate ten­ant or­ga­niz­ing across the city, we are show­ing what gov­ern­ing with New Yorkers looks like,” says Cea Weaver, Director, Mayor’s Office to Protect Tenants.

After Mayor Mamdani es­tab­lished the Rental Ripoff Hearings dur­ing his first week in of­fice, he met with 2,400 New Yorkers across each bor­ough to hear about the is­sues a wide range of peo­ple faced. Safety and liv­ing con­di­tions were a ma­jor fo­cus, as were de­cep­tive prac­tices by land­lords.

Image cred­it­sPhoto cour­tesy of Mayor Mamdani’s of­fice.

Moonshine Micro — Voice Interfaces for Microcontrollers

Moonshine Voice is an open source AI toolkit for de­vel­op­ers build­ing real-time voice agents and ap­pli­ca­tions. Moonshine Micro is a ver­sion de­signed specif­i­cally for em­bed­ded sys­tem proces­sors like mi­cro­con­trollers and DSPs, and uses the Raspberry Pi RP2350, which re­tails for just 80 cents, as its ref­er­ence plat­form. It in­cludes voice-ac­tiv­ity de­tec­tion, com­mand recog­ni­tion, and neural speech syn­the­sis and can run in as lit­tle as 470 KB of RAM.

You can see a full walk­through in the video be­low:

The mem­ory and com­pute re­quire­ments are de­signed to fit re­source-con­strained sys­tems. Figures be­low are for the RP2350 demo; the de­tailed mem­ory bud­get breaks each one down:

Notes:

Flash is .text + .rodata mea­sured with arm-none-eabi-size on the de­fault moon­shine_mi­cro_e­cho firmware (includes the em­bed­ded neural voice pack); SRAM is .bss + heap + stacks.

*VAD, STT, and neural TTS run se­quen­tially and time-share one ~384 KiB TFLM arena, so SRAM is not ad­di­tive — ~468 KiB is the to­tal RAM pro­vi­sioned on the 520 KiB RP2350 (wifi_hardware ~491 KiB).

A MAC is one mul­ti­ply-ac­cu­mu­late; MMAC/s = mil­lions per sec­ond dur­ing the ac­tive (non-idle) stage.

The code is re­leased un­der the per­mis­sive MIT License, suit­able for com­mer­cial ap­pli­ca­tions.

There’s a com­plete end-to-end ex­am­ple show­ing how to set up a wifi con­nec­tion on a mi­cro­con­troller us­ing voice on an RP2350 MCU.

The VAD, STT, and TTS li­braries can be used in­de­pen­dently of each other, re­ly­ing on the in­cluded TensorFlow Lite Micro li­brary for the neural com­pu­ta­tions.

Documentation

Voice Activity Detection

Speech to Text

Custom Word Recognition

Neural Text to Speech

Wifi Setup Example

License

This code, apart from the source in third-party/, is li­censed un­der the MIT License — see LICENSE in this di­rec­tory (also at the repos­i­tory root).

The SpellingCNN and TinyVadCNN mod­els in mod­els/ are re­leased un­der the MIT License.

The code in third-party/ is li­censed ac­cord­ing to the terms of the open source pro­jects it orig­i­nates from, with de­tails in a LICENSE file in each sub­folder.

I’ve been run­ning Kimi K3 along­side Claude on my nor­mal cod­ing work, and for all prac­ti­cal pur­poses I can’t tell them apart. Same tasks, same qual­ity of out­put, and near iden­ti­cal to­ken counts to get there. I ex­pected an open model to be slop­pier or to grind through more to­kens on the way to the same an­swer, and nei­ther turned out to be true.

The prices are nowhere near each other. K3’s API runs $3 per mil­lion in­put to­kens and $15 per mil­lion out­put. Claude’s top model costs $10 and $50 for the same units. The sub­scrip­tion side is even more lop­sided. Kimi’s paid plans start at $19 a month, and the $39 cod­ing tier is far more gen­er­ous than any­thing Claude sells any­where near that price. Claude’s plans are me­tered tightly enough that a nor­mal day of agent work can chew through the al­lowance be­fore lunch.

Then there’s the fine print. Claude could­n’t sus­tain Fable ac­cess on the twenty dol­lar plan, so they turned it off, and the plan qui­etly falls back to Opus. When the head­line model on your plan can be switched off be­cause the eco­nom­ics don’t work, the plan was never re­ally sell­ing you the head­line model. Kimi’s tiers don’t come with that as­ter­isk.

Step back and the big­ger story is what an un­mit­i­gated fail­ure US AI pol­icy has been. The ad­min­is­tra­tion held Fable back, and what fi­nally shipped is a hin­dered ver­sion that re­fuses whole cat­e­gories of work. Meanwhile a fron­tier qual­ity model with none of those re­stric­tions is a down­load away, re­leased by a Chinese lab the US gov­ern­ment has no abil­ity to reg­u­late. Whatever the the­ory be­hind gat­ing American mod­els was, it plainly was­n’t thought through, be­cause the only peo­ple the gates con­strain are American cus­tomers. Semgrep found GLM 5.2 beat­ing Claude on their cy­ber bench­marks for ex­actly this rea­son. The re­stricted model de­clines the work and the open one just does it.

And it is­n’t only Kimi. GLM 5.2 came out un­der an MIT li­cense, beats the lat­est Opus re­lease on real work while not even claim­ing to be fron­tier, and costs a frac­tion of it. OpenAI got pushed through the same gov­ern­ment gaunt­let with GPT-5.6 but came out the other side able to put their flag­ship on the twenty dol­lar plan. Whatever you think of OpenAI, they have run­way here that Anthropic clearly does­n’t.

I think I can see where this goes. The gov­ern­ment will try to reg­u­late AI and open source in par­tic­u­lar, and it will run the play­book it ran for the auto in­dus­try. Decades of sub­si­dies, bailouts, and pro­tec­tive tar­iffs pro­duced American car­mak­ers that sell trucks at home and barely reg­is­ter any­where else in the world. I ex­pect the cur­rent ad­min­is­tra­tion to reach for the same tools here. Public pri­vate part­ner­ships prop­ping up do­mes­tic mod­els that only get used in­side the US and can’t com­pete in­ter­na­tion­ally. That’s a sad fu­ture where America is the one coun­try with­out ac­cess to the best mod­els at the best prices, buy­ing mod­els deeply tied to the cor­rupt Trump ad­min­is­tra­tion that are nei­ther the high­est qual­ity nor the cheap­est. Until then, at least, I can’t come up with a rea­son to keep pay­ing for Claude.

Several times in my life I’ve tried to break in with a new so­cial group. I ran into a cool com­mu­nity I wanted to join, or I moved to a new city and wanted to make friends there, or I just wanted to broaden my hori­zons.

Pretty quickly I learned that the best and fastest way to join a group is to or­ga­nize events where we do the group’s core ac­tiv­ity. In every group I’ve ever en­coun­tered, there is far more de­mand for so­cial events and things to do than there is sup­ply. Getting peo­ple to come is like giv­ing away ice cream at the beach. You can make friends by show­ing up from the out­side and con­sis­tently at­tend­ing other peo­ple’s events, but it’s a lot eas­ier and faster to make friends if you’re also or­ga­niz­ing your own stuff and invit­ing the peo­ple you’re hop­ing to get closer to, or help­ing with some­one else’s events. I’ve found this true every­where from niche on­line fan­fic­tion dis­cus­sion groups to van­guard in­tel­lec­tual move­ments di­rect­ing hun­dreds of mil­lions of dol­lars an­nu­ally.

Lots of peo­ple have a sort of con­sumer at­ti­tude to­wards their com­mu­ni­ties, where they take every­thing for granted. I saw things this way when I was young. A so­cial scene is an au­to­matic fea­ture of the world that ap­pears on its own, like a wild blue­berry bush. It starts sprout­ing par­ties and din­ners and con­fer­ences and read­ing groups as nat­u­rally as the bush sprouts berries.

Surprisingly, it turns out things don’t ac­tu­ally work that way. In fact, events hap­pen when some­one puts in the leg­work to or­ga­nize them. And one of the most re­li­able laws of the uni­verse is that, if some­thing takes a lit­tle bit of leg­work, then most peo­ple just won’t do it. A scene’s lead­ers are mostly the peo­ple who ac­tu­ally bother to put in the work.

The work of or­ga­niz­ing is un­der­ap­pre­ci­ated by many peo­ple. But the other or­ga­niz­ers are very at­tuned to this, and ab­solutely will no­tice who else is pick­ing up the bur­dens.

I’ve come to be­lieve that part of to­day’s prob­lem of so­cial alien­ation is a prob­lem of too many free rid­ers. Lots of peo­ple want to con­sume so­cial fab­ric, but our so­cial scripts telling peo­ple to pro­duce so­cial fab­ric have largely fallen by the way­side. I don’t know how to solve this un­der­sup­ply at the scale of so­ci­ety. But you can solve it at the scale of your own com­mu­nity by just sup­ply­ing it.

From 19 July, large com­pa­nies across the EU are pro­hib­ited from de­stroy­ing un­sold clothes, cloth­ing ac­ces­sories and footwear. Medium-sized com­pa­nies will be sub­ject to the same rules from 2030.

The mea­sure, in­tro­duced un­der the Ecodesign for Sustainable Products Regulation (ESPR), aims to pre­vent the waste of valu­able prod­ucts and the re­sources used to make them.

When new, us­able goods are dis­carded, the raw ma­te­ri­als, wa­ter, en­ergy and labour in­vested in their pro­duc­tion are lost, while their dis­posal gen­er­ates avoid­able green­house gas emis­sions. By en­cour­ag­ing reuse, re­pair and more re­source-ef­fi­cient busi­ness prac­tices, the new rules sup­port the tran­si­tion to a more cir­cu­lar and com­pet­i­tive European econ­omy.

What the new rules mean for com­pa­nies

Under the new rules, busi­nesses must pri­ori­tise keep­ing prod­ucts in use by sell­ing them (including through dis­counts or al­ter­na­tive mar­kets), do­nat­ing them to char­i­ties or so­cial en­ter­prises, or prepar­ing them for reuse (repairing, re­fur­bish­ing or re­man­u­fac­tur­ing).

Destruction will be al­lowed only un­der spec­i­fied cir­cum­stances and must be car­ried out in ac­cor­dance with the waste treat­ment hi­er­ar­chy, giv­ing pri­or­ity to re­cy­cling.

When the ban does not ap­ply

Companies may only de­stroy un­sold clothes and shoes in lim­ited cases, such as when items are un­safe or dam­aged, coun­ter­feit or in­fring­ing in­tel­lec­tual prop­erty rights, or are re­jected by char­i­ties or do­na­tion schemes.

To pre­vent mis­use, busi­nesses re­ly­ing on these ex­emp­tions must pro­vide proof (e.g. doc­u­ments or test re­sults) and pub­lish an­nual re­ports on what they have dis­carded.

How the rules will be en­forced

National au­thor­i­ties will check com­pli­ance and can im­pose fines for vi­o­la­tions. Companies must keep records for five years to al­low in­spec­tions.

To re­duce pa­per­work, busi­nesses will use ex­ist­ing cus­toms and lo­gis­tics codes when re­port­ing. Small and mi­cro-busi­nesses are ex­empt from these re­quire­ments.

Background

The Ecodesign for Sustainable Products Regulation (ESPR), which came into force in 2024, sets EU-wide rules to make prod­ucts more durable, re­pairable, re­cy­clable and re­source-ef­fi­cient.

The ban on de­stroy­ing un­sold tex­tiles is one of the first con­crete mea­sures un­der the ESPR. Textiles are the first prod­uct group sub­ject to this ban due to the neg­a­tive en­vi­ron­men­tal im­pacts of cur­rent busi­ness mod­els, which of­ten lead to the de­struc­tion of un­sold goods.

According to the European Environment Agency, an es­ti­mated 4 – 9% of all tex­tile prod­ucts put on the mar­ket in Europe are de­stroyed be­fore use, amount­ing to be­tween 264,000 and 594,000 tonnes of tex­tiles de­stroyed each year.

The Commission de­vel­oped the rules af­ter wide con­sul­ta­tion with busi­nesses, NGOs and ex­perts to en­sure they work in prac­tice with­out cre­at­ing un­nec­es­sary red tape.

More in­for­ma­tion

Ecodesign for Sustainable Products Regulation | European Commission

EEA brief­ing - The de­struc­tion of re­turned and un­sold tex­tiles in Europe’s cir­cu­lar econ­omy | European Environment Agency

New EU rules to stop the de­struc­tion of un­sold clothes and shoes (February 2026) | European Commission

Commission Delegated Regulation set­ting out dero­ga­tions from the pro­hi­bi­tion of de­struc­tion of un­sold con­sumer prod­ucts | EUR-Lex

Commission Implementing Regulation on the de­tails and for­mat for the dis­clo­sure of in­for­ma­tion on dis­carded un­sold con­sumer prod­ucts | EUR-Lex

Note: This has been cross-posted to my com­pa­ny’s blog, in case you think there is some use in shar­ing with some­one in a for­mat that looks more au­thor­i­ta­tive. Link here.

I strongly be­lieve there are en­tire com­pa­nies right now un­der heavy AI psy­chosis and it’s im­pos­si­ble to have ra­tio­nal con­ver­sa­tions with them about it. I can’t name any spe­cific peo­ple be­cause they in­clude per­sonal friends I deeply re­spect, but I worry about how this plays out. — Mitchell Hashimoto, of HashiCorp and Ghostty fame

I strongly be­lieve there are en­tire com­pa­nies right now un­der heavy AI psy­chosis and it’s im­pos­si­ble to have ra­tio­nal con­ver­sa­tions with them about it. I can’t name any spe­cific peo­ple be­cause they in­clude per­sonal friends I deeply re­spect, but I worry about how this plays out.

– Mitchell Hashimoto, of HashiCorp and Ghostty fame

Over the past year, I’ve run point on all of our com­pa­ny’s sales, led the tech­ni­cal com­po­nents of all but two of our en­gage­ments, and over the life­time of this blog have had some­thing like 300 catchups with pro­fes­sion­als from around the world. This has ranged from peo­ple on the ground in niche ser­vice in­dus­tries to ex­ec­u­tives at Fortune 500 com­pa­nies1. Because of this, I’ve had a front-row view to our col­lec­tive in­sti­tu­tions across both the pri­vate and pub­lic sec­tor un­der­go­ing breath-tak­ing mass psy­chosis. This es­say is an at­tempt to de­scribe the bizarre dy­nam­ics that are cur­rently at play, as I am in the rare po­si­tion where my well­be­ing is not con­tin­gent on pay­ing lip ser­vice to mad­ness, and to re­as­sure the peo­ple try­ing to sur­vive amidst all of this that they are not crazy.

The re­al­ity is thus: the peo­ple in charge ei­ther have no plan, or see no path for­wards other than keep­ing their heads down. Not at banks, not at hos­pi­tals, not in our gov­ern­ment in­sti­tu­tions. The world’s or­gan­i­sa­tions have been cap­tured by peo­ple in the throes of froth­ing ex­cite­ment, and saner peo­ple who now live in a state of con­stant com­min­gled fear and frus­tra­tion.

I. AI Investments Are Generally Total Failures

Reading this while work­ing for a di­vi­sion that piv­oted to pro­vide in­ter­faces for agen­tic work­flows, only to dis­cover that only ten users had ever touched the prod­ucts we made for agents, only to pivot again to sup­port for agen­tic work­flows, which has a lot of com­pe­ti­tion be­cause every com­pany has to do some­thing agen­tic now and there’s only like four things you can do in that space, is brac­ing. — An ed­i­tor of this es­say

Reading this while work­ing for a di­vi­sion that piv­oted to pro­vide in­ter­faces for agen­tic work­flows, only to dis­cover that only ten users had ever touched the prod­ucts we made for agents, only to pivot again to sup­port for agen­tic work­flows, which has a lot of com­pe­ti­tion be­cause every com­pany has to do some­thing agen­tic now and there’s only like four things you can do in that space, is brac­ing.

– An ed­i­tor of this es­say

Are com­pa­nies ac­tu­ally see­ing mas­sive pro­duc­tiv­ity gains from their AI adop­tion? Does any of this sor­did af­fair make sense?

This should be an easy ques­tion, but it is sur­pris­ingly hard to get a straight an­swer to it. Executives that tell the press that their com­pany has gone in­sane will quickly find them­selves re­moved from their po­si­tions. Employees who are hon­est will find them­selves fired in short-or­der, or “randomly” se­lected for a round of lay­offs. In fact, it is in the in­ter­ests of al­most every ac­tor in the space — boards, ex­ec­u­tives, em­ploy­ees, ven­dors, con­sul­tants — to ob­fus­cate and mis­rep­re­sent the suc­cess rate of AI pro­jects. Many pub­licly traded com­pa­nies are putting out an­nounce­ments about their AI pro­duc­tiv­ity gains when I know for a fact that the busi­nesses have done noth­ing other than pur­chase Copilot li­censes and de­clare vic­tory.

Yet we need to know if these pro­jects are pan­ning out — if the to­tal fo­cus on AI as a core tenet of busi­ness strat­egy is suc­ceed­ing at a rea­son­able rate, then a dis­cus­sion about the rel­a­tive risk and re­ward is war­ranted.

Unfortunately, we live in a dark time­line. All of the AI pro­jects we have ob­served as a team are fail­ing. Every sin­gle one — we have seen 0% suc­cess in a year and a half, not only amongst pro­jects we have been asked to par­tic­i­pate in2, but even within pro­jects that we have ob­served in pass­ing while do­ing to­tally un­re­lated work. Even if you grant that AI tool­ing ac­cel­er­ates spe­cific work­loads, the method and scale of the cur­rent in­vest­ments is sense­less. Frequently the fail­ure is not re­lated to AI it­self, but rather that com­pa­nies are ter­mi­nally bad at run­ning soft­ware pro­jects ef­fec­tively, and as I have re­marked pre­vi­ously, AI pro­jects are sub­ject to all the fail­ure modes of nor­mal pro­jects plus you can get every­thing right and then still fail be­cause of the method’s nov­elty. Very few com­pa­nies are so good at ship­ping soft­ware that they can af­ford the ex­tra risk pro­file.

Often enough, though, it’s an ac­tual fail­ure in what LLMs can ac­com­plish. The most com­mon ver­sion of this, be­ing rolled out across busi­nesses around the world, is the in­ter­nally-fac­ing chat­bot, or for the more dar­ing com­pany, the cus­tomer-fac­ing chat­bot. The story is al­ways the same. For the for­mer, I’ve never seen sub­stan­tial in­ter­nal up­take from in­side a busi­ness. Employees don’t use in­ter­nal chat­bots be­cause com­pa­nies tend to have low-qual­ity doc­u­men­ta­tion and an LLM is not psy­chic — it can only know things that have been writ­ten down and made ac­ces­si­ble. For the lat­ter cus­tomer-fac­ing ap­pli­ca­tions, I have rarely had a pleas­ant ex­pe­ri­ence as a con­sumer, with per­haps the ex­cep­tion of live tran­scrip­tion dur­ing med­ical ap­point­ments — hardly some­thing worth piv­ot­ing an en­tire or­gan­i­sa­tion around. In both cases, pro­ject lead­ers are very care­ful to avoid track­ing ba­sic met­rics, such as whether the tools are be­ing used at all, or they track met­rics that are eas­ily gamed.

For ex­am­ple, my last con­sumer in­ter­ac­tion was at­tempt­ing to get help from Mitsubishi fol­low­ing an au­to­mo­tive fail­ure, where a very po­lite ro­bot asked me to de­scribe the prob­lem and that I’d re­ceive a call back as soon as some­one was avail­able. This was the sin­gle most com­pe­tent im­ple­men­ta­tion of such a pro­ject I’ve seen in the wild, in that the voice was nat­ural sound­ing, re­sponded quickly, was clearly “live” in pro­duc­tion, and promised a swift res­o­lu­tion.

That was six months ago, and I did not, in fact, get a call back.

When Mitsubishi did not call me back, what hap­pened? Did that re­quest just go into the void, show­ing one less in­ci­dent for the year? Does it ap­pear that the phone bot re­solved my query with­out the need for hu­man in­ter­ven­tion? All we know is that it did­n’t show up as an er­ror, or I’d have re­ceived a call. I’m sure it looks great in all sorts of ways ex­cept the one that mat­ters, which is that I was plan­ning to buy a car and de­cided not to buy an­other one of theirs.

For this rea­son, our team has quickly learned while on an en­gage­ment not to ask any­thing about on­go­ing AI pro­jects in any con­text — by the time that pro­ject has started, it is too late for the man­age­ment team, and in­ter­ven­tion is not pos­si­ble un­til a cri­sis point is in­evitably reached. There is no con­ceiv­able pos­i­tive out­come. The fail­ure rate is so high that even ba­sic in­quiry leaves us in an un­ten­able po­si­tion. Any co­her­ent ques­tion about how it’s go­ing, what the goal is, who is us­ing it, con­sti­tutes an in­ad­ver­tent at­tack on the chain of com­mand re­spon­si­ble for the work be­cause there are no good an­swers to any­thing. Even in rare cases where my in­ter­locu­tor has stated that things are go­ing well (usually while the pro­ject is still mid-flight and fail­ure has not had a chance to man­i­fest), it is gen­er­ally ob­vi­ous that they are doomed, but at least in these cases I can sim­ply agree and then go home to scream into a pil­low for six hours straight3.

All of this is to say that I am very con­fi­dent that al­most every re­port at a com­pany about “massive AI pro­duc­tiv­ity gains” is un­true as a mat­ter of brute fact. Even if some com­pa­nies are see­ing clear gains, this is the ex­cep­tion, not the norm. With that as­sump­tion in place, we can talk about the dy­nam­ics at play, and how it has be­come im­pos­si­ble for many or­gan­i­sa­tions to stay fo­cused on things that ac­tu­ally mat­ter to their long-term (or even short-term) health.

II. Heretics Will Be Shot

It has be­come out­right dan­ger­ous to even raise the pos­si­bil­ity that AI might not be the so­lu­tion to a prob­lem, let alone be the sole fo­cus of a com­pa­ny’s en­tire strat­egy.

In every suf­fi­ciently large busi­ness we have ob­served (say, with 500+ em­ploy­ees), we have noted that con­tin­ued ad­vance­ment, and in­creas­ingly con­tin­ued em­ploy­ment, has started to re­quire re­peated pro­fes­sions of be­lief in the trans­for­ma­tive power of AI for said busi­ness. I am not talk­ing about pro­vid­ing ideas about how to use AI in the busi­ness — I mean re­li­gious pro­fes­sion, de­c­la­ra­tions of faith. Overwhelmingly these state­ments are made by non-tech­ni­cians, though it is not un­com­mon for tech­ni­cians to emit de­ranged state­ments to curry favour.

There have been sev­eral oc­ca­sions where I have seen some­one, apro­pos of noth­ing, blurt out al­most word-for-word “AI is chang­ing every­thing”, only to con­cede mo­ments later that their or­gan­i­sa­tion does not cur­rently use LLMs for any­thing, and in­deed, that they can­not name a sin­gle thing that has changed other than they get some use out of ChatGPT (frequently the free-tier). In one ex­treme case, I have seen an ex­ec­u­tive con­fess that they had never even used ChatGPT or any AI tool in their life, im­me­di­ately af­ter pro­duc­ing a tech­ni­cal strat­egy for an or­gan­i­sa­tion with $2B+ in rev­enue which was en­tirely cen­tered around AI.

Initially these state­ments were so ab­surd on their face that I thought it was some cyn­i­cal ploy to achieve thought leader sta­tus, and there are cer­tainly some peo­ple do­ing this — I have had it ad­mit­ted to me. But the broader re­al­ity is so much worse: peo­ple who have no back­ground in the tech­nol­ogy at all ac­tu­ally be­lieve what they are say­ing. As a gen­eral rule you should avoid get­ting into busi­ness with a liar, but if you must, you can at least rea­son with them even if only in pri­vate. A true be­liever is much more threat­en­ing be­cause they are im­per­vi­ous to even in­duce­ment by self-in­ter­est.

The turn­ing point in my be­lief was watch­ing some­one with a spec­tac­u­lar amount of money on the line fire their high­est per­form­ers be­cause they were achiev­ing that per­for­mance with­out LLMs. When an em­ployer pub­licly talks about AI in­no­va­tion, we have to ask our­selves if they’re sim­ply try­ing to ma­nip­u­late the mar­ket or cus­tomers. When they pri­vately com­mit to strate­gies like this with their own money at stake, with no at­tempt to com­mu­ni­cate that strat­egy to ex­ter­nal clients, I can only as­sume they re­ally mean what they’re say­ing.

A while ago, I wrote “Contra Ptacek’s Terrible Article On AI”, which was fo­cused on the fact that many of Ptacek’s points in his own es­say “My AI Skeptic Friends Are All Nuts” were in­ter­nally in­con­sis­tent4. But on the crux of the mat­ter, we are ac­tu­ally in to­tal agree­ment, be­cause he opens his es­say with this:

Tech ex­ecs are man­dat­ing LLM adop­tion. That’s bad strat­egy.

Tech ex­ecs are man­dat­ing LLM adop­tion. That’s bad strat­egy.

Which is to say that we can side­step ar­gu­ments about the pre­cise util­ity of LLMs en­tirely and we’re left in a very sim­ple place — it is en­tirely ob­vi­ous to both my­self and Ptacek, two peo­ple that are com­ing at this from fairly op­posed views, that peo­ple are be­ing re­ally, re­ally stu­pid about this, and that or­gan­i­sa­tions are de­mand­ing bizarre work­flow con­straints from their spe­cial­ist staff.5

These man­dates have led to ex­tremely strange places. Several of my peers now “AI-wash” their work, mean­ing that even when they can per­fectly com­pe­tently ex­e­cute on their jobs to the sat­is­fac­tion of their man­age­ment teams, said man­agers are un­happy if the en­gi­neers haven’t used AI in the work… so now they’re ly­ing about us­ing LLMs even in con­texts where their pro­fes­sional judge­ment is that they aren’t the ap­pro­pri­ate tool. They just do the work, the same way they have for decades, and say Claude did it. Others are be­ing mea­sured on their AI bills with “token leader­boards”, where higher is bet­ter be­cause I have ev­i­dently fallen into the pocket of Hell where the demons tor­ment me by do­ing elab­o­rate im­pres­sions of ab­solute fuck­ing mo­rons, so the peo­ple hired for their freak­ish abil­ity to per­form sys­tem op­ti­mi­sa­tion do the ob­vi­ous thing. They set the LLMs prompt­ing them­selves in a semi-plau­si­ble loop in case some­one in­spects the to­ken con­sump­tion and then they watch Netflix. Not a sin­gle one has been caught, even when their own as­sess­ment of the out­put is that it is­n’t suit­able for de­ploy­ment.

Checking out a par­al­lel copy of our Go repos­i­tory and telling the AI to rewrite the whole thing in Zig while I work on some­thing else just so I can keep my job. I hate this shit so much. My job has us­age track­ing and quo­tas. I don’t use it for ac­tual work, I just spin it up and dis­re­gard the out­put. — An ac­tual soft­ware en­gi­neer

Checking out a par­al­lel copy of our Go repos­i­tory and telling the AI to rewrite the whole thing in Zig while I work on some­thing else just so I can keep my job. I hate this shit so much. My job has us­age track­ing and quo­tas. I don’t use it for ac­tual work, I just spin it up and dis­re­gard the out­put.

– An ac­tual soft­ware en­gi­neer

In fact, the only peo­ple I know of to be fired over this whole thing are peo­ple that have ex­pressed vis­i­ble doubt about this or­gan­i­sa­tional strat­egy, which again, even Ptacek thinks is trans­par­ently dumb. The net re­sult is that every­one has learned very quickly to praise ex­ec­u­tives on their vi­sion­ary AI prowess, or they will be gunned down in the prover­bial streets.

III. AI Demos Are The Mind-Killer

Bless me, Father, for I have sinned. It has been ∞ days since my last con­fes­sion. I ac­cuse my­self of the fol­low­ing sins:

One of the main pieces of in­fra­struc­ture we de­ploy at our clients is an an­a­lyt­ics-fo­cused data­base called Snowflake — for a typ­i­cal busi­ness, the bill is tiny be­cause it’s a pay-as-you-go sit­u­a­tion and we can process all their data in one minute a day, you get a very hands-off de­ploy­ment, and in short it has many char­ac­ter­is­tics that are very pleas­ant for our work. One of the fea­tures in Snowflake that we don’t use is called Cortex.

Cortex is their AI chat­bot layer, with the abil­ity to plug into meta­data (for non-nerds, de­scrip­tions of your data, like what a col­umn in a spread­sheet means) and query a com­pa­ny’s data­base au­tonomously. In the­ory, you can ask a ques­tion like “What was our rev­enue for last week?” and it will spit out an an­swer.

It is not re­ally suit­able for pro­duc­tion us­age. From mem­ory, the last time I was given a pre­sen­ta­tion on it, by ac­tual Snowflake staff, they re­ported that ideal con­fig­u­ra­tion re­sults in some­thing like ~92% ac­cu­racy due to the com­plex­ity of data at a large busi­ness (see: prob­a­bly best-in-class for these tools, but imag­ine your CFO hav­ing one in every ten of their num­bers be out­right wrong) and there were se­ri­ous is­sues with man­ag­ing de­ploy­ments. Nonetheless, it can be used to pro­duce some very flashy demon­stra­tions.

On sev­eral oc­ca­sions, we’ve been ex­posed to folks that have been sort of luke­warm on our main of­fer­ings, but they re­ally, re­ally wanted to use AI to per­form a nat­ural lan­guage query on their data. And we thought “Okay, if you re­ally want to see it, maybe we can caveat this ap­pro­pri­ately and show you what it might look like.”

This was a ter­ri­ble mis­take. It back­fired in the most pre­dictable way imag­in­able — every luke­warm client that saw the chat­bot in ac­tion, even with us telling them that it was not go­ing to ac­com­plish what they wanted, wanted to buy it im­me­di­ately. Every other con­sid­er­a­tion, in­clud­ing mil­lions of dol­lars that we could plau­si­bly help them achieve by non-AI means, was swept aside. It was like a dark and ter­ri­ble force seized con­trol of their limbs, plunged their hands into their own chests, and pre­sented their still-beat­ing credit cards to us in grim sup­pli­ca­tion. We were so mor­ti­fied by the in­ex­plic­a­ble shift in en­ergy that we (wisely) de­clined to take the money and ended the sales process, and soon there­after re­moved Cortex from our list of demon­stra­tions. It would have been too ir­re­spon­si­ble to ex­ploit this gap in their rea­son­ing, and frankly, it was al­ready ir­re­spon­si­ble to have even run the demon­stra­tion — doc­tors don’t walk around show­ing off cool pills that they’d never pre­scribe.

Watching the to­tal 180°, that shift from ice-cold to red-hot buy­ing frenzy, was a deeply un­set­tling ex­pe­ri­ence. It was per­son­ally un­com­fort­able to see peo­ple that clearly did­n’t gel with us in­ter­per­son­ally sud­denly dy­ing to en­ter an on­go­ing re­la­tion­ship, but more broadly un­com­fort­able be­cause for a brief mo­ment I be­gan to un­der­stand what is hap­pen­ing in sales meet­ings around the world. There was no warn­ing I could have given that would have made them refuse to buy the damn thing — their ap­petite was as large as their bud­get could stretch, and some part of me won­ders if this is be­cause they knew that their rav­en­ous hunger would be pre­sent in their own cus­tomers. They’d just buy it from us, then pivot right to a larger com­pany and mind con­trol their lead­er­ship team un­til the buck fi­nally stops with the loser that needs to jus­tify the ex­pense. The main pro­tec­tion against this seems to be that the me­dian ven­dor is so bad at their jobs that we had pre­sented the first even some­what-work­ing prod­ucts these peo­ple had seen, and this in­cluded an ASX-listed com­pany that was al­ready brag­ging about their AI us­age. It took our team two hours to pro­duce some­thing that was frankly not that good — ba­si­cally just typ­ing text de­scrip­tions of data into a web browser — and it was still bet­ter than any­thing the leads had seen be­cause they had noth­ing to show for all the in­vest­ment.

In fact, we have been forced to opt out of every sale where the lead has ex­pressed any­thing be­yond the most fleet­ing cu­rios­ity in the use of AI in their busi­ness. I don’t mean that we’ve heard that they’re in­ter­ested in AI and elected to drop the con­tract on moral grounds. I mean that, over the course of the en­gage­ment, these peo­ple have ex­hib­ited a pat­tern of be­hav­ior that has made it near-im­pos­si­ble to sell to them with­out in­cur­ring rep­u­ta­tional and le­gal risk, and are fur­ther­more craft­ing man­age­ment en­vi­ron­ments that I can only de­scribe as cultish, in­ef­fec­tive, and “please dear God, do not let it be on earth as it is on LinkedIn”.

IV. Executives, Game Theory, and The Emperor’s Clothes

The good news is, CISOs are used to hav­ing to pro­tect the busi­ness from their hare-brained ini­tia­tives, and this one is­n’t re­ally that dif­fer­ent, ex­cept that there’s a cult-like at­mos­phere to it that you did­n’t see with, say, the cloud. It al­most does­n’t mat­ter whether you em­brace the ini­tia­tive or not; there’s work to be done to man­age the risk, so that’s what you do. From talk­ing to CISOs every­where, I would say most of them are qui­etly skep­ti­cal but afraid to speak up. — Career CISO and well-known speaker that asked to re­main anony­mous

The good news is, CISOs are used to hav­ing to pro­tect the busi­ness from their hare-brained ini­tia­tives, and this one is­n’t re­ally that dif­fer­ent, ex­cept that there’s a cult-like at­mos­phere to it that you did­n’t see with, say, the cloud. It al­most does­n’t mat­ter whether you em­brace the ini­tia­tive or not; there’s work to be done to man­age the risk, so that’s what you do. From talk­ing to CISOs every­where, I would say most of them are qui­etly skep­ti­cal but afraid to speak up.

– Career CISO and well-known speaker that asked to re­main anony­mous

Despite the sub­stan­tial preva­lence of true be­liev­ers, many of the peo­ple run­ning large AI ini­tia­tives, or mak­ing pub­lic state­ments about them, do not be­lieve what they are say­ing. There are “heads of AI” who read this blog, at com­pa­nies with $1B+ in an­nu­ally re­cur­ring rev­enue, who have writ­ten in to say they be­lieve their job is to­tally fraud­u­lent but it was the only pro­mo­tion path­way re­main­ing at the or­gan­i­sa­tion.

On a trip over­seas, I had the priv­i­lege of a meet­ing with one of the Fortune 500 ex­ec­u­tives men­tioned at the be­gin­ning of the post, who will re­main anony­mous so that they are not ex­e­cuted by fir­ing squad by their board. As we were chat­ting, it be­came clear that they were very switched-on and tech­ni­cally com­pe­tent, and they also hap­pened to be at a com­pany that had com­mit­ted to the usual bat­tery of ex­or­bi­tant claims about their re­cent in­no­va­tions — we’ve 100x’d our pro­duc­tiv­ity, AI is the fu­ture of every­thing, I am but a ves­sel for OpenAI to make love to my wife. You know, nor­mal things. But since I had them there with­out any mi­cro­phones around, I asked why this was be­ing re­peated with­out op­po­si­tion. Was it just sales fluff?

The an­swer was a lot more in­ter­est­ing. It was par­tially ridicu­lous sales ma­te­r­ial be­ing de­liv­ered to an eas­ily ex­citable au­di­ence, but this was not the dom­i­nant fac­tor con­strain­ing hon­esty. Executives at their cus­tomers were say­ing ab­surd things about achiev­ing 100x pro­duc­tiv­ity, and this meant that if any ex­ec­u­tive at the ven­dor said that these gains were not plau­si­ble, it would un­der­mine the cred­i­bil­ity of the cus­tomer’s ex­ec­u­tive, be per­ceived as an at­tack (or heresy), and pos­si­bly re­sult in an en­ter­prise con­tract can­cel­la­tion. And get­ting en­ter­prise con­tracts can­celled be­cause you wanted to opine on some­thing that does­n’t re­ally mat­ter to your or­gan­i­sa­tion’s mis­sion is a great way to get fired.

But this com­pany was also a ma­jor player, of the kind that signs enor­mous en­ter­prise con­tracts with other com­pa­nies. So pre­sum­ably there is an­other ven­dor that has sold to them, and their CEO is wor­ried that say­ing some­thing sane will con­tra­dict this ex­ec­u­tive, and very quickly we can see how we can have ex­ec­u­tives around the world ner­vously point­ing guns at each other, not want­ing to be shot first but also watch­ing every­thing grad­u­ally spi­ral out of con­trol6. This is to say that we’re fac­ing a co­or­di­na­tion prob­lem around ex­ec­u­tives be­ing hon­est around the AI gains they’ve wit­nessed — if they co-op­er­ate, they keep their jobs. If they de­fect, they will pos­si­bly be fired by their em­bar­rassed peers (who have now been im­plic­itly called liars, cow­ards, or in­com­pe­tents) and then re­placed with some­one that will toe the line any­way. If they could all ad­mit the truth at once there might be some hope, but there is no way to co­or­di­nate that event.

This sounds deeply con­cern­ing, but it is worth not­ing that it means that some ex­ec­u­tives who are emit­ting non­sen­si­cal state­ments are not as dull as they might seem at first — they’re in a fraught po­lit­i­cal en­vi­ron­ment, where they are sur­rounded by many peo­ple that are gun­ning for their roles, and sub­ject to the whims of a board that is un­der­go­ing sim­i­lar pres­sure. Against all the dic­tates of rea­son, I have pre­sented on nav­i­gat­ing AI hype to peo­ple on S&P 500 boards7 and they are in ex­actly the same sit­u­a­tion — the main com­ments I re­mem­ber from the ses­sion were board mem­bers ad­mit­ting they were skep­ti­cal, but ex­press­ing anx­i­ety that their po­si­tions were con­tin­gent on de­mand­ing AI in­vest­ment. One of them com­mented “investing this early seems like risk with­out much up­side”. About two years later, I can see now that their decade-old multi-bil­lion dol­lar or­gan­i­sa­tion is now branded as “AI-native”, what­ever the hell that means.

V. You Must Be This AI-Native To Ride

All of the above con­verges on the state that we find our­selves in now, where ef­fec­tive de­ci­sion­mak­ing has ground to a halt. Collectively, what started as a few peo­ple un­der­go­ing ei­ther desta­bil­is­ing psy­cho­log­i­cal events or be­ing caught up in hype has now re­sulted in an en­vi­ron­ment where lead­ers can­not speak hon­estly about their be­liefs on how best to guide or­gan­i­sa­tions, for fear of be­ing re­moved, cre­at­ing a sort of dis­trib­uted gov­ern­ment by as­sas­si­na­tion. This means that the least sen­si­ble rec­om­men­da­tions are go­ing to­tally un­chal­lenged, re­sult­ing in em­ploy­ees be­ing eval­u­ated on to­tally game­able met­rics such as “money spent on AI”, and those em­ploy­ees must play along to avoid be­ing ter­mi­nated. This has also cre­ated an in­sa­tiable ap­petite for pur­chas­ing “AI” so­lu­tions, which tar­get both true be­liev­ers that will be­lieve im­plau­si­ble claims, and also non-be­liev­ers that can­not de­cline the pur­chases with­out hav­ing their com­mit­ment to the cause com­ing into ques­tion.

This means that all of­fers that are sub­ject to in­ter­nal pol­i­tics at an ide­o­log­i­cally cap­tured or­gan­i­sa­tion must in­clude AI align­ment, even if the value propo­si­tion is patently am­bigu­ous. My as­sess­ment of the mar­ket so far is that a sub­stan­tial com­po­nent of the out­burst of AI pro­jects are ac­tu­ally non-AI pro­jects with an AI el­e­ment slapped on af­ter the fact to pass the pu­rity test.

For ex­am­ple, I re­cently wit­nessed an or­gan­i­sa­tion han­dling a data­base mi­gra­tion from an Oracle data­base to Snowflake — in­stead of han­dling the mi­gra­tion di­rectly, the ven­dor bolted on a pre­lim­i­nary phase which in­volved try­ing to get an LLM to au­to­mate the trans­la­tion of the Oracle-flavored SQL to Snowflake-flavored SQL. When the pro­ject failed (due to is­sues get­ting enough per­mis­sions to au­to­mate the work, not be­cause an LLM can’t do some­thing that easy), the ven­dor sim­ply started han­dling the trans­la­tion by hand but the com­pany billed it as an AI-driven suc­cess be­cause some in­con­se­quen­tial por­tion of the SQL had been trans­lated by AI be­fore be­ing pasted over.

What was ac­tu­ally pur­chased? A to­tally stan­dard data­base mi­gra­tion to help an ex­ec­u­tive meet the strate­gic de­liv­er­able of de­com­mis­sion­ing a sys­tem prior to li­cense re­newal. What was sold to their su­pe­ri­ors? “I al­lo­cated a sub­stan­tial per­cent­age of my bud­get to AI and it helped me ac­com­plish my man­date.” True AI pro­jects, of the kind that is dri­ven by an LLM as the sole mech­a­nism un­der­ly­ing it, where the pro­ject can clearly fail to de­liver spe­cific num­bers, are ac­tu­ally very rare. We mostly see them in the con­text of star­tups, and frankly we have stopped en­gag­ing with them be­cause we kept get­ting to the end of the sales con­ver­sa­tion and find­ing out they wanted us to build the prod­uct that they were mar­ket­ing as com­pleted.

However, some pro­jects sim­ply do not have an easy way to tack on the AI la­bel, or the per­son ad­vo­cat­ing for them ei­ther does not want to lie or has not un­der­stood that ly­ing has be­come nec­es­sary. In all cases, this ei­ther kills the re­quest for fund­ing out­right, or adds a per­va­sive and in­tractable drag on all com­mu­ni­ca­tions, as every re­quest must be worked and re-worked un­til it is “AI enough”. Failure to com­ply will ei­ther re­sult in de­nial or, in many cases, a de­mand from a true be­liever to know why the ex­tra work “can’t be done with AI”. Many com­pa­nies have ac­tively pub­li­cized that this is their new hir­ing pol­icy — when a mem­ber of staff re­quests ad­di­tional head­count, they must demon­strate that they have tried to use AI first. The part that’s be­ing left out is that if you say you used AI and still need the help, you will be la­belled “bad at AI” and po­ten­tially laid off.

The net re­sult of this is that al­most every large or­gan­i­sa­tion that I am aware of is no longer able to fo­cus on any­thing im­por­tant, un­less they are one of the (very) few or­gan­i­sa­tions where AI hap­pens to ad­dress their high­est pri­or­i­ties. They can­not buy sen­si­ble soft­ware, hire com­pe­tent tal­ent, com­mu­ni­cate hon­estly with ex­ec­u­tives about the state of pro­jects, or un­der­take any sort of sen­si­ble ini­tia­tive.

VI. Navigating AI Mania

An empti­ness falls through you As you re­al­ize what this means You’re start­ing to feel what I feel Now you’ve seen what I’ve seen — So Sick, Domesticated Incels

An empti­ness falls through you As you re­al­ize what this means You’re start­ing to feel what I feel Now you’ve seen what I’ve seen

– So Sick, Domesticated Incels

This is an un­for­tu­nate sit­u­a­tion to be in, but it will pass even­tu­ally. I’ve learned a lot about the la­tent in­san­ity that we have in­cul­cated in our lead­er­ship strata, and un­for­tu­nately those traits will per­sist long past the cur­rent bub­ble, merely await­ing an­other sim­i­lar re­ac­ti­va­tion trig­ger — and some or­gan­i­sa­tions will stay cap­tured un­til they have to­tally col­lapsed, in the way that not every­one has suc­cess­fully moved away from the dread­ful blockchain af­fair. That’s some­thing to write about for an­other time.

What I wanted to get to were some thoughts on sur­viv­ing the im­me­di­ate cri­sis, ei­ther by di­rectly mak­ing sys­temic im­prove­ments or by hold­ing onto your san­ity. I’ll start with the “making im­prove­ments” part, be­cause that’s the sit­u­a­tion I find my­self in the most fre­quently.

When You Have Another Objective

We’re go­ing to do a lot of suck­ing it up and smil­ing here. This sec­tion as­sumes that you are try­ing to achieve some goal that is­n’t re­pair­ing the or­gan­i­sa­tion’s manic stance, but ei­ther try­ing to course-cor­rect a spe­cific pro­ject (and pos­si­bly risk get­ting fired as ei­ther a leader or con­sul­tant) or achieve some to­tally un­re­lated goal.

Where pos­si­ble, when rais­ing is­sues, do not have con­ver­sa­tions about the state of AI pro­jects in group set­tings, as this cre­ates a dy­namic where each in­di­vid­ual mem­ber of the group is wor­ried about out­ing them­selves in front of their peers. Arrange for one-on-one set­tings. Make it clear that you are will­ing to coun­te­nance that the cur­rent AI en­vi­ron­ment is frothy, and that you will keep opin­ions uniden­ti­fi­able when rais­ing them else­where. Be ex­tremely aware that the most out­spo­ken peo­ple can be iden­ti­fied by their peers, so take care to avoid ex­pos­ing your sources by, e.g. di­rect quotes. In the event that only a small mi­nor­ity (say, one per­son in a group of six peo­ple) is will­ing to speak out, it might be worth giv­ing up and mov­ing on to a pa­tient that has bet­ter chances.

For on­go­ing pro­jects, an ef­fec­tive trick that I be­lieve I picked up from Secrets of Consulting is the anony­mous poll, where you can ask in­di­vid­u­als to rate their opin­ion of an AI pro­jec­t’s suc­cess chances on a scale of 1 to 10. The typ­i­cal split I have ob­served is half of those in­volved rat­ing the pro­ject at a 3/10 and oth­ers at around an 8/10 — a clear bi­modal split on a pro­ject that was al­ready three years late. Bringing this data to a CEO can be an ef­fec­tive method of point­ing out that some in­for­ma­tion is clearly be­ing hid­den from them on the state of the pro­ject.

Always in­volve peo­ple on the ground. The only source of data on whether pro­jects are suc­ceed­ing or the in­vest­ment is go­ing any­where are the peo­ple that use it for their day-to-day ac­tiv­ity. Care must be taken to bring them into the en­vi­ron­ment where they are treated with re­spect (all suf­fi­ciently large com­pa­nies have peo­ple that view sub­or­di­nates as not-quite-real-peo­ple). It is not un­com­mon to un­cover world­view-shak­ing in­for­ma­tion in short or­der — with one client, we un­cov­ered that staff were to­tally un­aware they had been given li­censes for AI tool­ing, which cast into doubt all pro­duc­tiv­ity claims.

Do not ques­tion the broad­est claims about AI. I can­not em­pha­size this enough. If some­one says “AI is chang­ing every­thing”, just let it pass if your goal is to fix an ob­ject-level prob­lem rather than chal­lenge the re­al­ity at the in­sti­tu­tion. The chal­lenge can only come af­ter you have gained the trust of the most se­nior per­son in­volved. Trust is gained over a meal in pri­vate where you as­suage their anx­i­eties, not by em­bar­rass­ing them in front of peers.

Remember that you do not know what state­ments have been emit­ted prior to en­ter­ing a room. There will some­times be peo­ple that have pub­licly com­mit­ted to state­ments like “I am 100x more pro­duc­tive than I was last year”, and some may even wish they had­n’t said that but are too em­bar­rassed to walk it back. In an untested room, com­mon sense like “LLMs should not be al­lowed to de­ploy code with­out hu­man re­view” can kill your chances to make an im­pact be­fore you’ve even started.

My prac­tice re­quires me to main­tain an hon­est re­la­tion­ship with my clients or the whole thing falls apart, so I can’t do this — but hon­estly, if you work in the fire ser­vice and need money to stop a puppy from catch­ing fire, just lie. It’s fine. History will for­give you. Add a $10,000 AI chat­bot to your pro­ject, ex­clu­sively dis­cuss that part in meet­ings, what­ever. Save that puppy.

When You’re Just Trying To Survive

This is for peo­ple that are just wait­ing for the bub­ble to burst and try­ing not to go nuts.

I have bad news — ac­cept that you are prob­a­bly not go­ing to mean­ing­fully push back on any of this. This is not a fea­ture of AI, it’s a fea­ture of dys­func­tional com­pa­nies.

If you feel like you’re go­ing ab­solutely nuts, con­sider switch­ing over to con­tract­ing. I’ve ad­vo­cated for con­tract­ing many times over full-time em­ploy­ment, but you’ll get paid a lot more and be left out of most in­ter­nal pol­i­tics. Also when you run into a re­ally in­tol­er­a­ble sit­u­a­tion, you’ll know that you’ve got a fixed end-date.

I do my best to limit my up­take of AI-related news, as it is pretty crazy-mak­ing and un­pro­duc­tive to con­sume. I no longer visit Hackernews, Reddit, or re­ally any­where where I am go­ing to be drip-fed non­sense, though I al­low my­self ex­cep­tions for very funny things like Apple su­ing OpenAI over al­leged cor­po­rate es­pi­onage. Consume ex­actly the amount you need to feel like you aren’t go­ing in­sane, then stop. Ditto for com­plain­ing with friends — and tell them that’s why you’re talk­ing about it, which buys a lot of tol­er­ance.

When some­one tells me they are us­ing AI for some­thing when they re­ally should­n’t be, I smile and nod as long as they are un­likely to get them­selves killed. Even fam­ily. Especially fam­ily.

When some­one asks me for my opin­ion of AI as a pro­gram­mer, I rec­om­mend say­ing “Oh, that stuff is pretty overblown” and then chang­ing the topic, un­less they are in a po­si­tion where their opin­ion might in­flu­ence some­thing im­por­tant. Non-programmers need this guid­ance the most.

If you’re be­ing asked to re­view huge vol­umes of ter­ri­ble AI code, just as­sume that the or­gan­i­sa­tion is go­ing to burn you out and fire you. You will not con­vince the per­son drown­ing you in 2000 line PRs to stop. Start look­ing for a new job as if you have al­ready been fired. I have seen this hap­pen many times now, and it al­ways plays out the same way — do the job search while you have en­ergy. Don’t worry if your speed drops or man­age­ment gets an­noyed at you. There is no way to avoid that, you can sim­ply choose whether it hap­pens now be­cause of your job search, or later be­cause you are too de­pressed to work any­more.

If your man­ager is re­spond­ing to you with clearly AI-generated text, use AI to re­spond to save your san­ity and then look for a new job. Many peo­ple as­sume they will get in trou­ble for be­ing that ob­vi­ously rude. You will not, this par­tic­u­lar be­hav­ior is ex­hib­ited only by true be­liev­ers, and they ac­tu­ally like that you’ve clearly not both­ered to en­gage with them. I know, it’s fuck­ing wild.

If you’re be­ing asked to max out on to­ken us­age, look for a new j — okay look, you get it, right? Go find a job that is­n’t go­ing to wrench re­al­ity from your ten­u­ous grasp. They do ex­ist, largely at com­pa­nies so small that they don’t turn up on job plat­forms. It might take months to find one, so start now.

Fight the good fight, and don’t let the bas­tards grind you down. Godspeed.

Also, and this is 100% true, Matt Mullenweg once asked me for cof­fee be­cause he read the AI piledrive es­say, and in con­text prob­a­bly en­joyed it, but had to can­cel be­cause he had­n’t re­al­ized he had a flight later the same day. I am will­ing to pay a com­pe­tent witch to hex him for this slight. ↩

Also, and this is 100% true, Matt Mullenweg once asked me for cof­fee be­cause he read the AI piledrive es­say, and in con­text prob­a­bly en­joyed it, but had to can­cel be­cause he had­n’t re­al­ized he had a flight later the same day. I am will­ing to pay a com­pe­tent witch to hex him for this slight. ↩

We have re­jected all AI im­ple­men­ta­tion work. It is ab­solutely a gi­gan­tic bub­ble and we have min­i­mized our ex­po­sure to it — every sin­gle one of our cur­rent con­tracts would be to­tally un­af­fected by OpenAI col­laps­ing, save for per­haps some sec­ond-or­der ef­fects such a re­ces­sion caus­ing a client to be­come un­able to pay us. And there’s noth­ing we can do to in­su­late our­selves from that any­way. ↩

We have re­jected all AI im­ple­men­ta­tion work. It is ab­solutely a gi­gan­tic bub­ble and we have min­i­mized our ex­po­sure to it — every sin­gle one of our cur­rent con­tracts would be to­tally un­af­fected by OpenAI col­laps­ing, save for per­haps some sec­ond-or­der ef­fects such a re­ces­sion caus­ing a client to be­come un­able to pay us. And there’s noth­ing we can do to in­su­late our­selves from that any­way. ↩

One of the most valu­able rules I’ve heard, from Gerry Weinberg, is that con­sult­ing is in­flu­enc­ing peo­ple at their re­quest. Unless some­one has in­di­cated that they want us to stick my nose in, usu­ally by ex­plic­itly say­ing they want guid­ance on gen­eral data strat­egy, we just let the pro­jects fail in peace. You can barely rec­og­nize me, I’m so calm these days. ↩

One of the most valu­able rules I’ve heard, from Gerry Weinberg, is that con­sult­ing is in­flu­enc­ing peo­ple at their re­quest. Unless some­one has in­di­cated that they want us to stick my nose in, usu­ally by ex­plic­itly say­ing they want guid­ance on gen­eral data strat­egy, we just let the pro­jects fail in peace. You can barely rec­og­nize me, I’m so calm these days. ↩

We have since kissed and made up in pri­vate, though I don’t think we’ve budged at all on the core points of our view­points. I main­tain that Thomas is a very tal­ented writer with a lot of good ad­vice who just hap­pened to blow it mas­sively that one time be­cause he takes Hackernews com­menters too se­ri­ously. We all have our weak­nesses. Mine is peo­ple telling me that “Scrum is good if you do it right”. ↩

We have since kissed and made up in pri­vate, though I don’t think we’ve budged at all on the core points of our view­points. I main­tain that Thomas is a very tal­ented writer with a lot of good ad­vice who just hap­pened to blow it mas­sively that one time be­cause he takes Hackernews com­menters too se­ri­ously. We all have our weak­nesses. Mine is peo­ple telling me that “Scrum is good if you do it right”. ↩

This is al­ways baf­fling to me as a mat­ter of be­ing a re­spon­si­ble adult. If I was some­how CEO at a hos­pi­tal or civil en­gi­neer­ing firm, I would not for a sec­ond think it’s my place to start man­dat­ing spe­cific pro­ce­dures or build­ing tech­niques with­out ex­plicit agree­ment from the pro­fes­sion­als on staff — how fuck­ing clue­less are the non-tech­ni­cians who have at­tended a few talks and are now mak­ing man­dates about how their ex­tremely ex­pen­sive pro­fes­sion­als are do­ing their jobs? ↩

This is al­ways baf­fling to me as a mat­ter of be­ing a re­spon­si­ble adult. If I was some­how CEO at a hos­pi­tal or civil en­gi­neer­ing firm, I would not for a sec­ond think it’s my place to start man­dat­ing spe­cific pro­ce­dures or build­ing tech­niques with­out ex­plicit agree­ment from the pro­fes­sion­als on staff — how fuck­ing clue­less are the non-tech­ni­cians who have at­tended a few talks and are now mak­ing man­dates about how their ex­tremely ex­pen­sive pro­fes­sion­als are do­ing their jobs? ↩

If you’re an ex­ec­u­tive, board mem­ber, or any­one in charge of an “AI pro­ject” that feels trapped, I would love to hear from you. I will file the se­r­ial num­bers off any sto­ries very care­fully, as I’ve done here and in every other ar­ti­cle. ↩

If you’re an ex­ec­u­tive, board mem­ber, or any­one in charge of an “AI pro­ject” that feels trapped, I would love to hear from you. I will file the se­r­ial num­bers off any sto­ries very care­fully, as I’ve done here and in every other ar­ti­cle. ↩

This sounds very fancy, but I think it was se­cretly one of those com­pul­sory pro­fes­sional de­vel­op­ment things and half the au­di­ence were just like, mak­ing din­ner. Truly, HR and pro­fes­sional bod­ies make vic­tims of us all. ↩

This sounds very fancy, but I think it was se­cretly one of those com­pul­sory pro­fes­sional de­vel­op­ment things and half the au­di­ence were just like, mak­ing din­ner. Truly, HR and pro­fes­sional bod­ies make vic­tims of us all. ↩

⭐️ A friendly lan­guage for build­ing type-safe, scal­able sys­tems!

Rust

92.9%

JavaScript

3.6%

Gleam

1.8%

HTML

0.4%

CSS

0.4%

Erlang

0.3%

Shell

0.3%

TypeScript

0.1%

Makefile

0.1%

Dockerfile

0.1%

Elixir

0.1%

PowerShell

0.1%

Other

0.2%

Clone this repos­i­tory

Use perma­link

HTTPS

https://​tan­gled.org/​gleam.run/​gleam https://​tan­gled.org/​did:plc:j5z­i6tqqabuqao5cjcg­b­j5qc

SSH

git@tan­gled.org:gleam.run/​gleam git@tan­gled.org:did:plc:j5zi6tqqabuqao5cjcgbj5qc

For self-hosted knots, clone URLs may dif­fer based on your setup.

Download tar.gz

Download .zip

Why have I been blocked?

This web­site is us­ing a se­cu­rity ser­vice to pro­tect it­self from on­line at­tacks. The ac­tion you just per­formed trig­gered the se­cu­rity so­lu­tion. There are sev­eral ac­tions that could trig­ger this block in­clud­ing sub­mit­ting a cer­tain word or phrase, a SQL com­mand or mal­formed data.

What can I do to re­solve this?

You can email the site owner to let them know you were blocked. Please in­clude what you were do­ing when this page came up and the Cloudflare Ray ID found at the bot­tom of this page.