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Introducing Muse Glimmer: An Open Agentic Model That Runs on Your Device

research.meta.ai

Today, we’re in­tro­duc­ing Muse Glimmer, the next model from Meta Superintelligence Labs, and open sourc­ing the model weights un­der a per­mis­sive Apache 2.0 li­cense.

Muse Glimmer is a 30-billion-parameter model op­ti­mized for al­ways-on lo­cal agent work­flows. It’s small enough to run on a Mac or PC with a sin­gle con­sumer GPU, en­abling use cases that range from lo­cal agents and func­tion call­ing, to lo­cal cod­ing, and LLM-as-a-judge eval­u­a­tion. Muse Glimmer de­liv­ers strong per­for­mance on key agen­tic use cases and bench­marks com­pared with lead­ing mod­els in its size cat­e­gory.

Foundation mod­els have achieved re­mark­able ca­pa­bil­i­ties across rea­son­ing, code gen­er­a­tion, and tool use — yet most de­ploy­ments still de­pend on cloud in­fra­struc­ture and net­work ac­cess. Running mod­els lo­cally en­ables you to use AI any­where, any­time, with or with­out an in­ter­net con­nec­tion. This is in­creas­ingly vi­able: the open source com­mu­nity has shown that smaller mod­els, when trained ef­fec­tively, can ap­proach fron­tier-level per­for­mance on tar­geted tasks. Muse Glimmer is op­ti­mized for these lo­cal use cases.

Keeping with our long tra­di­tion of shar­ing fun­da­men­tal AI re­search, we’re re­leas­ing Muse Glimmer open weights to­day on Hugging Face, along with de­vel­oper doc­u­men­ta­tion to help you start build­ing and run­ning your own agents. Muse Glimmer is built to work with the tools de­vel­op­ers al­ready use. Optimized in­te­gra­tions on llama.cpp, MLX, and ExecuTorch will land in the com­ing days, so you can go from down­load to work­ing agent in min­utes.

How We Trained Muse Glimmer

An agent that man­ages your sched­ule, drafts your mes­sages, or­ga­nizes your files, and learns how you work needs deep ac­cess to per­sonal con­text. It also needs sev­eral ca­pa­bil­i­ties work­ing in con­cert: long-hori­zon ex­e­cu­tion, pre­cise tool call­ing, mul­ti­modal un­der­stand­ing, long-con­text mem­ory, and in­struc­tion fol­low­ing.

We de­signed Muse Glimmer to bal­ance ca­pa­bil­ity against the mem­ory and com­pute con­straints of lo­cal hard­ware. This re­quired a com­pact ar­chi­tec­ture, a novel dis­til­la­tion recipe that trans­fers agen­tic rea­son­ing from a much larger teacher model, and in­fer­ence op­ti­miza­tions — in­clud­ing quan­ti­za­tion — to meet la­tency ex­pec­ta­tions. We achieved this in the fol­low­ing phases:

Pre-Training. We trained Muse Glimmer on Muse Spark’s out­puts us­ing logit dis­til­la­tion, lever­ag­ing a sim­i­lar data mix as the teacher.

Mid-Training. We trained the model on longer-con­text, more agent-heavy data with richer rea­son­ing traces, along­side or­ganic data.

Post-Training. We com­bined su­per­vised fine-tun­ing with a mix of on-pol­icy dis­til­la­tion and re­in­force­ment learn­ing across gen­eral, rea­son­ing, cod­ing, and agen­tic do­mains.

Muse Glimmer was eval­u­ated un­der the stan­dards set out in Meta’s Advanced AI Scaling Framework and as­sessed for open-weight re­lease across all rel­e­vant cat­e­gories.

Built for Agents: What Muse Glimmer Can Do

Building ef­fec­tive agents re­quires key ca­pa­bil­i­ties work­ing to­gether to achieve the user’s goals. Muse Glimmer is trained and eval­u­ated across each of the fol­low­ing:

End-to-end Agentic Task Completion. Muse Glimmer achieves strong suc­cess rates on full-task bench­marks in­clud­ing DeepSearch QA, MCP-Atlas, 𝛕-Bench and SWE-Bench, which mea­sure its abil­ity to work within scaf­folds, write and de­bug code, and re­solve multi-turn re­quests from start to fin­ish.

Reliable Tool Use. The model han­dles a wide range of func­tion calls, in­vok­ing tools with pre­cise schemas through­out ex­tended work­flows.

Multi-Step Reasoning. Muse Glimmer chains rea­son­ing over long hori­zons, sus­tain­ing co­her­ent plans across com­plex, ex­tended work­flows.

Failure Recovery. When a tool call fails or re­turns an un­ex­pected re­sult, the model is trained to di­ag­nose the er­ror and retry rather than halt.

Multimodal Input and Reasoning. Through a ded­i­cated per­cep­tion en­coder, the model ac­cepts in­ter­leaved text and im­ages. This en­ables agents to in­ter­pret screen­shots, charts, and doc­u­ments along­side con­ver­sa­tion.

Scaffold Compatibility. Muse Glimmer works across OpenClaw and other agen­tic or­ches­tra­tion pat­terns.

Controllable Effort. Muse Glimmer sup­ports dif­fer­ent rea­son­ing strengths to se­lect the right bal­ance be­tween qual­ity and speed.

Multilingual. Muse Glimmer is trained on data from more than 100 lan­guages.

Performance

We eval­u­ated Muse Glimmer across a broad range of bench­marks to as­sess the di­verse ca­pa­bil­i­ties re­quired for ef­fec­tive au­tonomous agent be­hav­ior. Compared with Gemma4 – 31B and Qwen3.6 – 27B, Muse Glimmer per­forms strongly for its size class on sev­eral widely used LLM bench­marks.

For more de­tail about our eval­u­a­tions, see our re­port.

Optimized for Local Deployments

A lo­cal agent is truly use­ful if it’s fast enough to feel re­spon­sive. An agent that takes min­utes to re­ply or plan its next step breaks the flow of real work. We ap­plied two op­ti­miza­tions to make Muse Glimmer run at prac­ti­cal speeds on con­sumer hard­ware with­out sac­ri­fic­ing qual­ity.

Fitting the Model on Your Device.

At full pre­ci­sion, a 30-billion pa­ra­me­ter model would re­quire over 55 GB of mem­ory — far more than any con­sumer GPU of­fers. We use quan­ti­za­tion tech­niques to com­press the mod­el’s weights to ap­prox­i­mately 4-bit pre­ci­sion, shrink­ing the lan­guage model to un­der 20 GB. This leaves enough head­room for the mod­el’s work­ing mem­ory (its KV cache”), the per­cep­tion en­coder for im­age un­der­stand­ing, and the spec­u­la­tive de­cod­ing drafter to run si­mul­ta­ne­ously within a 24 GB or 32 GB en­ve­lope. We val­i­dated that this com­pres­sion in­tro­duces min­i­mal to no degra­da­tion on agen­tic tasks.

Faster Generation Through Speculative Decoding.

Language mod­els nor­mally gen­er­ate text one to­ken at a time, which can feel slow dur­ing long rea­son­ing chains or multi-step tool calls. Muse Glimmer ships with a light­weight drafter” model based on DFlash — a small com­pan­ion net­work that pro­poses en­tire blocks of to­kens at once. The main model then ver­i­fies these pro­pos­als in par­al­lel, ac­cept­ing cor­rect to­kens and cor­rect­ing wrong ones. This tech­nique lets Muse Glimmer gen­er­ate text sig­nif­i­cantly faster than stan­dard to­ken-by-to­ken gen­er­a­tion while pro­duc­ing iden­ti­cal out­put qual­ity. We pro­vide quan­tized drafter ver­sions to in­cur a smaller mem­ory over­head in the re­lease.

The Result:

We mea­sure the speed of our K-Quant-17GB model along­side the quan­tized DFlash drafter on MacBook M4-Max, M5-Max and on a RTX-5090. The model is fast enough for fluid con­ver­sa­tion and real-time agent in­ter­ac­tion, all run­ning en­tirely on your de­vice.

Get Started With Muse Glimmer Today

Muse Glimmer is avail­able now, and you can down­load the weights on Hugging Face. In the com­ing days, run it lo­cally through part­ners like Ollama, LM Studio, and Unsloth, de­ploy it with edge frame­works in­clud­ing llama.cpp, ExecuTorch, and MLX, serve it at scale with vLLM and SGLang, or get started quickly through part­ners like Together AI, Fireworks AI, and OpenRouter. You can even cus­tomize it for your use case by lever­ag­ing PyTorch’s TorchTitan train­ing fea­ture to tune the model fur­ther.

We’re also work­ing with our part­ners in­clud­ing AMD, Arm, Dell, Intel, and NVIDIA to op­ti­mize per­for­mance across de­vices. In ad­di­tion, we’re re­leas­ing doc­u­men­ta­tion so de­vel­op­ers have the re­sources they need to get started and build re­spon­si­bly with Muse Glimmer. This in­cludes guid­ance on set­ting up cus­tom scaf­folds, so it’s even eas­ier to start build­ing and de­ploy­ing per­sonal agents on day one. You can learn more and find re­sources to build on Meta’s AI Developer Center.

This work builds on Meta’s long track record of open AI re­search, ex­tend­ing it into agen­tic AI and giv­ing de­vel­op­ers ac­cess to lo­cal agen­tic ca­pa­bil­i­ties. As al­ways, we wel­come feed­back from the com­mu­nity and can’t wait to see what de­vel­op­ers build with this open weights model.

Download the Model on Hugging Face Developer Documentation

Client Challenge

www.lemonde.fr

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Trump administration to pay German firm to halt US wind projects

www.bbc.com

2 days ago

Rorey Bosotti

Getty Images

German en­ergy com­pany RWE has said it will aban­don its off­shore wind pro­jects in the US af­ter reach­ing a $1.2bn (£892m) pay­out deal with President Donald Trump’s Department of the Interior (DoI).

RWE said that it will now rein­vest the sum into con­ven­tional gas pro­jects, in­clud­ing $900m (£669m) in a liq­ue­fied nat­ural gas (LNG) ex­port ter­mi­nal pro­ject in Louisiana.

After care­ful con­sid­er­a­tion, it was de­ter­mined there is no path for­ward to per­mit these pro­jects in the US for the fore­see­able fu­ture,” the com­pany said in a state­ment.

Trump has de­rided wind power for years and of­ten uses his rally speeches to rail against ugly” tur­bines.

RWE said it has agreed to re­lin­quish its leases off the California and Louisiana coasts as well as in the New York Bight.

Overall, the German firm plans to in­vest ap­prox­i­mately €17bn (£14.5bn; $19.6bn) in the US over the next six years to grow its gen­er­a­tion ca­pac­ity”.

Interior Secretary Doug Burgum said in a state­ment posted on X that Americans de­serve an en­ergy sys­tem built on com­mon sense and not one de­pen­dent on costly sub­si­dies”.

We wel­come RWEs agree­ment and vol­un­tary in­vest­ment in pro­jects that strengthen our na­tion’s en­ergy se­cu­rity,” he added.

The deal is the lat­est the Trump ad­min­is­tra­tion has reached this year as Trump, a vo­cal sup­porter of the fos­sil fuel in­dus­try, con­tin­ues his push to halt off­shore wind pro­jects.

Days af­ter his re­turn to of­fice, he said we’re not go­ing to do the wind thing” and called them big, ugly wind­mills” that were dan­ger­ous to wildlife.

And this week, he said that any coun­try with wind­mills is a loser”.

In March 2026, the DoI reached a deal with TotalEnergies putting an end to the French com­pa­ny’s off­shore wind pro­jects in the US.

Instead, the firm agreed to reroute in­vest­ment to build a LNG plant in Texas and to de­velop upstream con­ven­tional oil” in the Gulf of Mexico.

The ad­min­is­tra­tion signed a sim­i­lar $129m (£96m) agree­ment with Charlotte-based Duke Energy last month in ex­change for the ter­mi­na­tion of the com­pa­ny’s off­shore wind lease in the Carolina Long Bay area.

AMD acquires AI chip startup Taalas to boost inference performance by etching models into silicon

www.theregister.com

In AMDs lat­est bid to up­set Nvidia’s dom­i­nance in AI hard­ware, the House of Zen has ac­quired AI chip com­pany Taalas, which bakes model weights di­rectly into sil­i­con in a process that promises to boost in­fer­ence per­for­mance by an or­der of mag­ni­tude or more.

The deal, an­nounced at mar­ket close on Thursday, ap­pears to be framed in much the same con­text as Nvidia’s $20 bil­lion li­cens­ing deal with Groq last December: make high-per­for­mance premium” in­fer­ence ser­vices prized for AI agents, like code as­sis­tants, faster and cheaper to run. AMD did­n’t dis­close the terms of the deal, but from what we un­der­stand, this is an ac­tual ac­qui­si­tion rather than an ac­qui­hire.

Founded in 2023 and based in Toronto, Taalas’ ap­proach to in­fer­ence is rad­i­cally dif­fer­ent from con­ven­tional GPUs or the dataflow ar­chi­tec­tures that un­der­pin Groq LPUs or Cerebras’ wafer­scale ac­cel­er­a­tors.

REG AD

A model-spe­cific in­te­grated cir­cuit

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The star­tup’s chips don’t rely on HBM to store the model weights but rather etch them di­rectly into the sil­i­con. In a sense, Taalas’ chips are re­ally model-spe­cific in­te­grated cir­cuits or MSICs.

Perhaps more im­por­tantly, Taalas’ tech is­n’t just con­cep­tual. In February, the startup re­vealed its first test chip fabbed on TSMCs 6nm process tech, which it called the HC1. Initial bench­marks saw the chip serve Meta’s Llama 3.1 8B at a blis­ter­ing 16,960 to­kens a sec­ond — when an­nounced last February, that was 48x faster than Nvidia’s GPUs and 8.5x faster than Cerebras’ ac­cel­er­a­tors.

While Llama 3.1 is an­cient by to­day’s stan­dards, hav­ing made its de­but all the way back in mid 2024, the ret­i­cle-sized chip was re­ally in­tended to prove the con­cept.

Taalas has been in­cred­i­bly se­cre­tive about how its chips ac­tu­ally work, but we know its proces­sors are com­prised of two main re­gions: the mask-ROM re­call fab­ric where model weights are etched, and the SRAM re­call fab­ric where KV caches and fine-tun­ing adapters are stored.

For its sec­ond-gen HC2 chip due out this sum­mer, Taalas aims to boost pa­ra­me­ter count to 20 bil­lion pa­ra­me­ters. That might not sound like much, but just like with GPUs for larger mod­els, weights are sim­ply dis­trib­uted across mul­ti­ple ac­cel­er­a­tors us­ing pipeline par­al­lelism.

At 20 bil­lion pa­ra­me­ters per chip, you’d need just 50 ac­cel­er­a­tors to sup­port a tril­lion-pa­ra­me­ter model, and AMD just so hap­pens to have a rack-scale com­pute plat­form and in-house sys­tem de­sign team that can com­fort­ably ac­com­mo­date that.

That’s quite a bit more space and power ef­fi­cient than Nvidia’s re­cently un­veiled LPX sys­tems, which would need a few dozen GPUs and at least 2,000 Groq LPUs to serve the same model.

From what we un­der­stand, AMD in­tends to pair its Instinct-based Helios racks with chips based on Taalas’ tech, which im­plies a dis­ag­gre­gated ar­chi­tec­ture where com­pute-heavy prompt pro­cess­ing is done on GPUs while to­ken gen­er­a­tion is of­floaded to Taalas-based ac­cel­er­a­tors.

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It’s also pos­si­ble that AMD could adopt a sort of tick-tock ca­dence in which cus­tomers ini­tially de­ploy and val­i­date mod­els on Instinct ac­cel­er­a­tors and, once they’re sat­is­fied with them, tran­si­tion to Taalas ac­cel­er­a­tors. We can only spec­u­late at this point, but here’s what AMDs SVP of AI, Vamsi Boppana, had to say about it in a canned state­ment:

AMD is build­ing a full-stack AI plat­form that gives cus­tomers the flex­i­bil­ity to de­ploy the right com­pute so­lu­tions for every AI work­load.”

You bet­ter re­ally love that model

While the tech is blaz­ing fast, if you had­n’t al­ready fig­ured it out, it comes with a pretty sub­stan­tial down­side. Once the chips are de­ployed you’re stuck with that model. Any change big­ger than some­thing like a LoRA adapter is go­ing to re­quire a re-spin of the chips, which is not only ex­pen­sive but time-con­sum­ing.

Nearly four years into the AI boom, new mod­els are rolling out on a nearly monthly ba­sis. In or­der to ben­e­fit from Taalas’ tech, AMDs cus­tomers are go­ing to have to be re­ally sure about their choice of mod­els, which will be eas­ier for some than oth­ers.

However, if the startup is to be be­lieved, the sit­u­a­tion is­n’t quite as bad as it sounds. While new mod­els will re­quire a re-spin, it does­n’t re­quire start­ing over from scratch. Instead, just two lay­ers of metal need to be changed, which is a lot cheaper and less time-con­sum­ing.

With that said, we strongly sus­pect this tech will largely be de­ployed by AI model devs, their in­fra­struc­ture providers, and a hand­ful of in­fer­ence providers. In an in­ter­view with our sib­ling site The Next Platform in February, the com­pany sug­gested that etch­ing a mod­el’s weights into sil­i­con is 100x less ex­pen­sive than train­ing a fron­tier model.

AMD is cer­tainly in a po­si­tion to ne­go­ti­ate those deals. OpenAI, Anthropic, and Meta are all ma­jor Instinct cus­tomers. Given the close work­ing re­la­tion­ship be­tween the model houses and the chip de­signer, it would­n’t be sur­pris­ing to see a GPT or Claude de­ployed on a com­bi­na­tion of Taalas and in­stinct ac­cel­er­a­tors.

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The tech also has im­pli­ca­tions for model de­vel­op­ment. One of the ways de­vel­op­ers have cut down on hal­lu­ci­na­tions is by trad­ing time for ac­cu­racy. The tech­nique, called test-time scal­ing, is quite sim­ple in prac­tice, and in­volves al­low­ing a model to think” for longer be­fore re­spond­ing.

One draw­back of test-time scal­ing is that it con­sumes sub­stan­tially more to­kens, which makes it ex­pen­sive, and means users have to wait longer for the chat­bot, code as­sis­tant, or agent to re­spond. If AMDs Taalas buy can drive down the cost per to­ken and boost out­put speeds by 10x or 20x, model devs may opt to ex­tend the rea­son­ing time even fur­ther.

In any case, we may not have to wait long to see just how Taalas fits into AMDs broader vi­sion. Subject to reg­u­la­tory ap­proval, the deal is ex­pected to close in the fourth quar­ter. ®

Google Search Is Dying. What Comes Next Is Worse

thewalrus.ca

It turns out a lot of peo­ple ask Google when the sun will set. It’s a good fact to know. Getting a ball­park on twi­light can help you plan a bike ride, bar­be­cue, or a per­fectly timed promposal. The oc­cur­rence lasts only be­tween two and a half and four min­utes, and it hap­pens dy­nam­i­cally de­pend­ing on your prox­im­ity to the equa­tor, mak­ing it a tricky event to keep tabs on.

Recently, sun­set chasers have no­ticed that Google’s new AI sum­maries started in­vent­ing times. I had the pro­jec­tor set up out­side and was wait­ing for the sun to set,” wrote one Facebook user in Colorado Springs, but to my sur­prise I was sim­ply liv­ing in the past. AI in­formed me the sun­set had al­ready hap­pened.” It’s a small er­ror, even comic. But these episodes point to a big­ger prob­lem: the world’s dom­i­nant search en­gine now strug­gles over ba­sic facts. The com­pany that built its rep­u­ta­tion re­turn­ing the right an­swer has, as one tech ex­pert ob­served, lost its edge.”

Still, we keep googling be­cause we keep ex­pect­ing the in­ter­net to know things. That stub­born hope un­der­pins al­most every ar­gu­ment about in­for­ma­tion ac­cess. When search wors­ens, we grum­ble that Google has been en­shit­ti­fied. When AI hal­lu­ci­nates, we say the model is sloppy. When mis­in­for­ma­tion spreads, we re­as­sure our­selves the truth is still out there,” wait­ing pa­tiently for us to string to­gether a bet­ter query. Even as the web is pol­luted by AI slop, we cling to the be­lief it’s a skills prob­lem and share search tips.

But what if the truth” is harder to find on­line be­cause the in­fra­struc­ture that once stored it is break­ing down? Some of that is wear and tear. Link rot erases pages every day. Key sec­tions of the United States Constitution briefly dis­ap­peared from the Library of Congress web­site be­cause of a cod­ing er­ror. But by in­ter­pos­ing an er­ror-prone AI be­tween us and an orig­i­nal source, Google has en­sured that if an un­der­ly­ing page ex­ists, it can be­come prac­ti­cally undis­cov­er­able. Meanwhile, pol­lu­tion is now mov­ing up­stream: 404 Media re­ports com­pa­nies are plant­ing con­tent on Reddit to in­flu­ence the an­swers gen­er­ated by AI search, con­t­a­m­i­nat­ing the pub­lic record from which those sys­tems draw their sum­maries.

The cor­pus is col­laps­ing in real time. This de­graded dig­i­tal space should force a broader un­der­stand­ing of cul­tural sov­er­eignty. Right now, the de­bate is framed largely as a fight over whether for­eign mega plat­forms like Netflix, YouTube, or Spotify should sup­port Canadian con­tent. That charged con­ver­sa­tion side­steps the deeper ques­tion: Who pre­serves and con­trols ac­cess to our cul­tural record—not just in the pre­sent but for gen­er­a­tions af­ter?

Search can no longer pre­tend to be a neu­tral gate­way to a sta­ble body of knowl­edge. While the web has al­ways been or­ga­nized around in­ter­me­di­aries that shape what sur­vives on­line and who sees it, the in­ter­net’s archival func­tion is to­day break­ing down un­der re­lent­less pres­sure from stake­hold­ers with very dif­fer­ent—and of­ten con­flict­ing—pri­or­i­ties. At least book ban­ning hap­pens in your face: it has vil­lains, school board meet­ings, sen­sa­tional head­lines. Digital era­sure is more in­sid­i­ous and, in some ways, more dev­as­tat­ing. A banned book can still be found. A scrubbed web­page can dis­ap­pear so com­pletely few peo­ple would ever re­al­ize it was there.

Consider what hap­pened to FiveThirtyEight, an American web­site that fo­cused on opin­ion poll analy­sis, pol­i­tics, eco­nom­ics, and sports blog­ging. The Walt Disney Company had owned the blog through its sub­sidiaries for more than a decade. Its founder, Nate Silver, left in 2023, and by March 2025, the re­main­ing staff had been laid off. Once Disney con­cluded the site was no longer an ac­tive as­set—no new jour­nal­ism and no mean­ing­ful ad­ver­tis­ing rev­enue—it straight up deleted the en­tire archive.

The re­trieval cri­sis has reached even Wikipedia, one of the world’s most sig­nif­i­cant vol­un­teer-run pub­lic knowl­edge re­sources. For years, search en­gines sent bil­lions of view­ers to its pages. But now, AI sys­tems scrape and in­gest Wikipedia’s con­tent di­rectly when pre­sent­ing their re­sults, elim­i­nat­ing the need for users to click through. Wikipedia has be­come the in­fra­struc­ture of its own demise: dwin­dling traf­fic means at­ten­tion and do­na­tions no longer re­li­ably flow back to the en­cy­clo­pe­dia to keep it alive.

What has been hap­pen­ing to the Internet Archive is just as alarm­ing. A non-profit dig­i­tal li­brary, it col­lects and makes avail­able ma­te­ri­als that might oth­er­wise van­ish—from au­dio record­ings to his­tor­i­cal doc­u­ments to books. It is best known for the Wayback Machine, a ser­vice that has cap­tured hun­dreds of bil­lions of snap­shots of the web at dif­fer­ent points in time. Journalists, re­searchers, his­to­ri­ans, lawyers, and the pub­lic use it to re­cover deleted pages, ver­ify past state­ments, and doc­u­ment changes to on­line con­tent. The Wayback Machine is the clos­est thing the web has to a fail-safe backup mem­ory.

The over­all pro­ject, how­ever, is not only buck­ling un­der the en­gi­neer­ing strain of in­dex­ing and stor­ing an ever-grow­ing repos­i­tory of in­for­ma­tion but it is also be­ing bat­tered by cy­ber­at­tacks and costly lit­i­ga­tion. After pub­lish­ers suc­cess­fully sued the Internet Archive over its dig­i­tal lend­ing pro­gram, call­ing it unau­tho­rized copy­ing, news or­ga­ni­za­tions started block­ing the Wayback Machine’s crawlers out of fear that archived pages can pro­vide AI com­pa­nies with an in­di­rect source of copy­righted ma­te­r­ial. Each new re­stric­tion lim­its the archive’s abil­ity to act as a com­pre­hen­sive back­stop.

Even the in­creas­ing use of ephemeral for­mats like Instagram Stories and WhatsApp sta­tus up­dates means that large por­tions of cul­tural, so­cial, and po­lit­i­cal com­mu­ni­ca­tion are never con­served in the first place. As a so­ci­ety, we can prob­a­bly sur­vive bad search re­sults and come up with an­other way to sched­ule a sun­set make-out ses­sion. But we can’t as­pire to sov­er­eignty if we can’t re­tain and re­trieve our col­lec­tive mem­ory.

For now, you can still use Google to find out how other gov­ern­ments are build­ing al­ter­na­tives to it. France has come to treat for­eign plat­forms as a threat to both tech­no­log­i­cal sov­er­eignty and na­tional cy­ber­se­cu­rity. In a pre­scient late 2018 move, the coun­try’s na­tional as­sem­bly and the min­istry of the armed forces be­gan adopt­ing Qwant, a French ser­vice hosted in Europe. Qwant op­er­ates on a dif­fer­ent model: it does­n’t store search data, sell per­sonal in­for­ma­tion, or use tar­geted ad­ver­tis­ing based on your brows­ing his­tory. The European Parliament, with its 720 elected mem­bers as well as thou­sands of ad­min­is­tra­tive staff, also made the switch, re­flect­ing the grow­ing be­lief that in­for­ma­tion re­trieval is a strate­gic as­set in its own right.

Every small breakup with Big Tech cracks open the door wider on what’s pos­si­ble. France now re­quires civil ser­vants to use Tchap, a home­grown mes­sag­ing app re­plac­ing for­eign plat­forms such as WhatsApp and Signal in gov­ern­ment com­mu­ni­ca­tions. European gov­ern­ments have fol­lowed suit on the push away from Silicon Valley, swap­ping out Microsoft prod­ucts with open-source al­ter­na­tives across hun­dreds of thou­sands of work­sta­tions. The European Commission just an­nounced it would join W Social, a new in­de­pen­dent so­cial site cre­ated by a Swedish start-up. As Denmark’s dig­i­tal­iza­tion min­is­ter, Caroline Stage Olsen, put it: Far too much pub­lic dig­i­tal in­fra­struc­ture is to­day tied up with very few for­eign sup­pli­ers. This makes us vul­ner­a­ble.”

That fight­ing mood is push­ing some coun­tries to go even fur­ther. In a rul­ing that could be­come a land­mark prece­dent, a German court re­cently held Google li­able for false state­ments gen­er­ated by its AI overview fea­ture. The case arose af­ter Google’s AI wrongly linked two pub­lish­ing com­pa­nies to scammy busi­ness prac­tices. Because the search en­gine ex­tracts and rewrites in­for­ma­tion in its own words, the court rea­soned, it is do­ing more than im­par­tially point­ing users to­ward the pub­lic record. It’s au­thor­ing an en­tirely new layer of con­tent—and with that comes ed­i­to­r­ial re­spon­si­bil­ity. If for­eign tech­nol­ogy firms can no longer claim to be a pas­sive con­duit for in­for­ma­tion, gov­ern­ments could have greater lat­i­tude in reg­u­lat­ing them.

Google is ap­peal­ing the de­ci­sion, but the point has been made: look­ing af­ter the in­for­ma­tion econ­omy can no longer be a side pro­ject for un­der­funded in­sti­tu­tions. Wikipedia is as mirac­u­lous as the Wayback Machine is in­dis­pens­able. Still, we need to stop pre­tend­ing vol­un­teerism alone can hold up the pub­lic in­ter­net. We un­der­stand this duty more clearly in other do­mains. After all, roads aren’t gov­erned by ride-shar­ing com­pa­nies; pay­ment apps don’t set mon­e­tary pol­icy; cloud ser­vice providers don’t dic­tate na­tional se­cu­rity frame­works (at least not yet). Yet we have been shock­ingly ca­sual about gov­ern­ing our dig­i­tal de­pen­den­cies.

It’s hard to imag­ine a coun­try that had its AI strat­egy blessed by Google’s chief econ­o­mist choos­ing to stop rout­ing the every­day pol­icy work of the state through an ad­ver­tis­ing com­pany. Switching over to a pri­vacy-pre­serv­ing de­fault would help Canada re­duce un­nec­es­sary data leak­age and sig­nal that pub­lic in­sti­tu­tions treat search as an in­fra­struc­ture key to our dig­i­tal sov­er­eignty rather than a free” con­sumer ser­vice.

Information has eco­nomic value; even when in­di­vid­ual searches look mun­dane, the ag­gre­gate pat­tern can sur­face quite sen­si­tive in­sights about a so­ci­ety and its cit­i­zens. If gov­ern­ments want to get se­ri­ous about ac­tu­ally gov­ern­ing the in­tan­gi­ble econ­omy, they must first gov­ern the con­di­tions that make in­for­ma­tion durable and dis­cov­er­able. We would­n’t be start­ing en­tirely from scratch. We have pieces of this pub­lic-in­ter­est ar­chi­tec­ture kick­ing around: Library and Archives Canada, which pre­serves fed­eral web records and other his­tor­i­cal in­for­ma­tion, and the Internet Archive Canada, which works in tan­dem with the main or­ga­ni­za­tion and hosts data back­ups at Canadian uni­ver­si­ties.

Canada has also built this kind of dig­i­tal in­fra­struc­ture be­fore. CANARIE—originally, the Canadian Network for the Advancement of Research, Industry and Education—helped cre­ate a na­tional re­search and ed­u­ca­tion net­work for uni­ver­si­ties, re­searchers, and in­no­va­tors. In the 1990s, SchoolNet and the Community Access Program brought schools and li­braries on­line way be­fore con­nec­tiv­ity was taken for granted. The Public Knowledge Project at Simon Fraser University gave the world Open Journal Systems, help­ing thou­sands of schol­arly jour­nals pub­lish out­side the grip of ma­jor com­mer­cial plat­forms, be­gin­ning in 2002. MapleMusic built an on­line mar­ket for Canadian mu­sic be­fore stream­ing swal­lowed cul­tural dis­cov­ery. These pro­jects shared a premise we should re­cover: Canada does not have to wait for pri­vate plat­forms to or­ga­nize and con­trol our dig­i­tal lives.

The sun is set­ting on the old bar­gain with Google: give us your cu­rios­ity and we will give you the world. What rises next could be stranger and much more syn­thetic: an in­ter­net ran­domly re­mem­bered by ma­chines and mon­e­tized by in­ter­me­di­aries. Or it could be some­thing stur­dier: a pub­lic in­ter­net treated as in­fra­struc­ture, with his­tor­i­cal mem­ory pre­served not be­cause it is prof­itable but be­cause it is ours.

The fu­ture of pub­lic knowl­edge will de­pend not only on what ex­ists but on who has the ini­tia­tive and in­cen­tives to pre­serve it. Even this ar­ti­cle won’t be on­line for­ever. Should it?

Discovery Loop — Continuous Exploration

www.discoveryloop.com

Continuous Exploration

Automating dis­cov­ery to ac­cel­er­ate sci­ence and en­gi­neer­ing for the world.

Scientific dis­cov­ery is bot­tle­necked.

The sci­en­tific method is one of the great­est tools hu­man­ity has ever de­vised, yet ex­e­cu­tion en­tails repet­i­tive ex­per­i­men­tal loops that are hard to scale with to­day’s man­ual ef­forts: you pro­pose an ex­per­i­ment, im­ple­ment and run it, ex­am­ine the re­sults, then it­er­ate to re­fine your ap­proach.

Historically, sci­en­tific progress has re­lied on these se­quen­tial hu­man it­er­a­tions. In many do­mains, this process re­mains in­cred­i­bly slow and la­bor-in­ten­sive.

01 — The Approach

Automating the ex­per­i­men­tal loop.

At Discovery Loop, we are build­ing sys­tems to au­to­mate these en­tire ex­per­i­men­tal loops. By uti­liz­ing fron­tier AI mod­els and large-scale com­pu­ta­tional in­fra­struc­ture, our sys­tems will be able to rapidly pro­pose, run, and learn from eval­u­a­tions.

This ap­proach al­lows for the par­al­lel ex­e­cu­tion of thou­sands of ex­per­i­ments, dras­ti­cally com­press­ing it­er­a­tion time and dri­ving up the quan­tity and qual­ity of sci­en­tific and en­gi­neer­ing out­put.

Start with Machine Learning

We will ini­tially fo­cus on au­tomat­ing the process of ma­chine learn­ing re­search and en­gi­neer­ing.

Act as Our Own First Customer

We will use these au­to­mated ML ca­pa­bil­i­ties to rapidly op­ti­mize our own tech­nol­ogy stack be­fore ex­pand­ing to other do­mains.

Grand Challenges

We be­lieve our ap­proach will be able to solve any learn­ing loop with mea­sur­able out­comes within the do­mains of sci­ence and en­gi­neer­ing. Ultimately, we are build­ing sys­tems ca­pa­ble of tak­ing on National Academy of Engineering (NAE) Grand Challenges—such as en­gi­neer­ing bet­ter med­i­cines, ad­vanc­ing health in­for­mat­ics, mak­ing so­lar en­ergy eco­nom­i­cal, pro­vid­ing ac­cess to clean wa­ter, se­cur­ing cy­ber­space, and en­gi­neer­ing the tools of sci­en­tific dis­cov­ery.

02 — Mission

Our mis­sion is straight­for­ward: we are build­ing AI so­lu­tions that can au­to­mat­i­cally solve im­por­tant prob­lems in ma­chine learn­ing, sci­ence, and en­gi­neer­ing. By ad­vanc­ing the pace at which we con­duct en­gi­neer­ing and sci­en­tific dis­cov­ery, we can bring the ben­e­fits of sci­ence and tech­nol­ogy to the world much faster. Ultimately, our goal is to build AI sys­tems that act as a deeply pos­i­tive, em­pow­er­ing force for hu­man­ity, de­liv­er­ing tech­nol­ogy so­lu­tions that im­prove peo­ple’s lives on a global scale.

03 — The Team

The brain trust.

Our found­ing team — Jeff Dean, Sanjay Ghemawat, Quoc Le, and Oriol Vinyals — has a shared his­tory of deep friend­ship and decades of close and im­pact­ful col­lab­o­ra­tion.

From left Oriol Vinyals  ·  Sanjay Ghemawat  ·  Jeff Dean  ·  Quoc Le

Collectively, we rep­re­sent three of the most-cited re­searchers in ar­ti­fi­cial in­tel­li­gence and two of the most-cited re­searchers in dis­trib­uted sys­tems.

Between us, we have pi­o­neered mas­sive scale com­put­ing and led the cre­ation of crit­i­cal in­fra­struc­ture, prod­ucts, and foun­da­tional AI ad­vances that the world re­lies on, in­clud­ing mul­ti­ple gen­er­a­tions of Google Search, Google Ads, Google News, Google Translate, Google File System, MapReduce, BigTable, Spanner, TensorFlow, Pathways, TPUs, AlphaChip, AlphaStar, AlphaCode, AlphaFold, Gemini, model dis­til­la­tion, mix­ture-of-ex­perts model ar­chi­tec­tures, word2vec, se­quence-to-se­quence mod­els, chain of thought rea­son­ing, neural ar­chi­tec­ture search, and mul­ti­ple gen­er­a­tions of Large Language Models (LLMs) among oth­ers.

Our rel­a­tive ad­van­tage is­n’t just our tech­ni­cal abil­ity; it is the un­prece­dented scale of the sys­tems we have pre­vi­ously built. We pos­sess true full-stack depth that spans chips, hard­ware in­fra­struc­ture, soft­ware in­fra­struc­ture, ML mod­els, and prod­ucts.

04 — What’s Next

Imagine a fu­ture where a hand­ful of peo­ple can con­duct sci­en­tific re­search and en­gi­neer­ing tasks much more rapidly, and with higher qual­ity, than mas­sive teams of sci­en­tists and en­gi­neers do to­day. By au­tomat­ing the loops of dis­cov­ery, the world will be able to make much more rapid ad­vances across count­less fields of sci­ence.

We are build­ing a lean, in-per­son team to ex­e­cute this trans­for­ma­tive vi­sion.

"Code was never the hard part" is an insult to all programmers

blog.senko.net

The soft­ware de­vel­op­ment pro­fes­sion is in the midst of up­heaval. Nobody knows how the AI rev­o­lu­tion will play out in the end, but it is clear many as­pects of work and life will be trans­formed—in­clud­ing pro­gram­ming.

One of the com­ments I hear of­ten lately boils down to LLMs may be good at cod­ing, but soft­ware was never the hard part” and coding is easy, it’s fig­ur­ing out what to code that’s hard”.

I be­lieve that’s a gross in­sult to all pro­gram­mers every­where.

If cod­ing is easy…

If cod­ing is easy, how come pro­gram­mers were in high de­mand, and have de­manded large salaries for years (even be­fore ZIRP)? Why was there so much stress, over­work and burnout even be­fore AI started churn­ing out 5000-line PRs? Why did com­pa­nies seek 10x ninja rock­star coders and sub­ject them to leet­code in­ter­views—surely, a ju­nior fresh out of col­lege could churn out some­thing if it’s so easy?

If cod­ing is easy, why do we have doorstop­pers like Clean Code and The Pragmatic Programmer? Is The Art of Computer Programming a light sum­mer read? Is SICP a cof­fee-table book? Why do we have boot­camps or even whole col­lege de­grees ded­i­cated to it?

If cod­ing is easy, was Carmack just at the right place at the right time? Why do we con­sider Fabrice Bellard a ge­nius?

If cod­ing is easy, why are peo­ple an­gry at AI (or any­one else) copy­ing their code? Why do they act like they’ve poured their sweat, soul, and co­pi­ous amounts of time into some­thing so triv­ial?

If cod­ing is easy, why do many now feel like their iden­tity and pro­fes­sional pur­pose are be­ing stripped away from them?

If cod­ing is easy, why is soft­ware so damn buggy?

If fig­ur­ing out what to build is the hard part…

If de­cid­ing what to build is the hard part, why do so many prod­uct man­agers seem clue­less? Why aren’t there rig­or­ous 10-step in­ter­views for them? Why aren’t they get­ting paid more than the de­vel­op­ers?

If de­cid­ing what to build is the hard part, why aren’t mar­ket re­searchers, us­abil­ity ex­perts and—hell, cus­tomer suc­cess—con­sid­ered rock­stars in a soft­ware com­pany? If understanding the cus­tomer” is harder, why are busi­ness an­a­lysts looked down on as pen­cil push­ers?

If im­ple­men­ta­tion is easy and find­ing de­mand is harder, why are pro­gram­mers up­set when the sales­peo­ple promise a new fea­ture to a cus­tomer to close the sale? They’ve found a gen­uine de­mand, some­thing peo­ple will pay for!

If cod­ing is easy, why does­n’t every­one just build ten vari­a­tions of a thing and see which pans out?

Another cliché com­ment is most work in soft­ware de­vel­op­ment is talk­ing to stake­hold­ers, un­der­stand­ing the cus­tomer’s needs, and hav­ing clar­ity on the pri­or­i­ties”.

I have met many pro­gram­mers through­out my ca­reer, and very few of them want to talk to stake­hold­ers, much less cus­tomers (exceptions are free­lancers and founders, es­pe­cially of soft­ware de­vel­op­ment shops). And, having clar­ity on the pri­or­i­ties” boils down to just tell me what to do and don’t switch it up every two days”.

Some soft­ware de­vel­op­ers do say I don’t write code, I solve cus­tomer’s prob­lems”. But then they turn around and start to opine on mon­ads, mem­ory safety, and DRY prin­ci­ples, while their un­der­stand­ing of the cus­tomer is a made-up user per­sona”, and they think affordance” is the money your par­ents used to give you on week­ends so you could go out and have a good time.

Yet oth­ers will say Software de­vel­op­ment is the­ory build­ing”. Programs are ac­tu­ally proofs (as in, math­e­mat­i­cal proofs). Every com­mit should tell a story. And solv­ing a cus­tomer’s prob­lem by FTPing a PHP file is a car­di­nal sin.

I don’t mean to im­ply there are no de­vel­op­ers that si­mul­ta­ne­ously care deeply about the craft of soft­ware de­vel­op­ment and re­ally em­pathize with the cus­tomer. I do be­lieve they might want to see a pro­fes­sional about a split per­son­al­ity dis­or­der, tho. (post-scriptum edit: this sen­tence was way over­board; I meant it as tongue-in-cheek very few peo­ple can do that” and I do ac­tu­ally ad­vo­cate for that in the next sec­tion. mea culpa)

What is im­por­tant?

I do be­lieve that talk­ing to users, un­der­stand­ing their ex­pe­ri­ence, em­pathiz­ing with them, solv­ing cus­tomers’ prob­lems and hav­ing all the stake­hold­ers on the same page is crit­i­cal to the suc­cess of a soft­ware pro­ject.

I also be­lieve that cre­at­ing good code is a craft that re­quires skill, pa­tience, at­ten­tion to de­tail, ex­pe­ri­ence and wis­dom, and that it will con­tinue to be rel­e­vant in the times ahead.

¿Por qué no los dos?

To the ex­tent that we can pull it off, I think we should aim for both. A deep un­der­stand­ing of the sys­tem we’re build­ing, to­gether with a deep un­der­stand­ing of why we’re build­ing it.

Loudly pro­claim­ing that code is easy” or, at the op­po­site end, code is art, a cre­ative hu­man ex­pres­sion that can­not be au­to­mated”, is just bury­ing our heads in the sand.

It’s cope. And you don’t want cope, you want to thrive.

By this, I don’t mean jump on the LLM band­wagon.” I don’t mean become a man­ager of fleets of AI agents.” I also don’t mean AI-generated code is stolen slop garbage, fight it with tooth and nail, the bub­ble will pop soon enough any­ways.”

But do rec­og­nize we’re in the mid­dle of an in­dus­try-wide tec­tonic change. We need to fig­ure out how to adapt. We need to un­der­stand what is likely to change and what never changes.

What does­n’t change?

Software will be get­ting more com­plex. Software will al­ways need main­te­nance: bit-rot is a fact of life. So is en­tropy. Technology (hardware and soft­ware) will move for­ward, for bet­ter or worse. The tower (skyscraper?) of ab­strac­tions grows ever higher.

Users will al­ways want more and be pre­pared to spend less. They still won’t know how to re­lay their needs and wants. Worse, they still won’t know ex­actly what they want. The dis­con­nect be­tween the cus­tomers (who ac­tu­ally pay for the soft­ware) and users (who use it) will still be here, as will the ten­sion be­tween the needs of the busi­ness and the needs of its cus­tomers.

Also: there will never be a short­age of snake oil sales­men. Tech du jour comes and goes (I’m still wait­ing for the new VR re­nais­sance!)

What changes?

Programmers have been in the busi­ness of dis­rupt­ing our own in­dus­try since the be­gin­ning. Nobody uses punch-cards any more. Very few peo­ple need to code in as­sem­bly, or COBOL. Those decades spent fight­ing mem­ory bugs in C or C++, with the scars to prove it, are worth­less in the age of Rust, Go, Python and JavaScript.

I’m old enough to ap­pre­ci­ate val­grind or re­mem­ber mysql_re­al_es­cape_string() from the PHP4 era—stuff I’ll never again need in my life. And that was­n’t even so long ago! I nar­rowly missed the dBase, Clipper, HyperCard and Access era, tech­nolo­gies which I can still spot op­er­at­ing in shops, cafes, or a dusty, once beige and now golden-brown, midi-tower still hap­pily run­ning some be­spoke biz so­lu­tion (backups? what back­ups?)

How do we thrive?

Accept that change hap­pens. Be equal parts cu­ri­ous and crit­i­cal about the new stuff.

Understand there’s a lot of hype and try to dis­crim­i­nate be­tween hot air and what re­ally works (and to what ex­tent). Also be aware of ever-shift­ing goal­posts: stand back and look at the past year, or five, and as­sess the ve­loc­ity of change (technical, eco­nomic, so­ci­etal).

Your role and your re­spon­si­bil­i­ties will be chang­ing. Be will­ing to in­vest time and en­ergy into bet­ter un­der­stand­ing fields or roles ad­ja­cent to yours.

If you’re a se­nior de­vel­oper, don’t just find so­lace in deep­en­ing your ex­per­tise. Learn about user ex­pe­ri­ence, cus­tomer in­ter­views, or busi­ness strate­gies for the com­pa­nies in your do­main. It will help you gain a bet­ter ap­pre­ci­a­tion of all the work done to put a piece of soft­ware into users’ hands, whether or not you’ll ac­tu­ally ever have to do any of those other bits.

If you’re just start­ing or are ju­nior in your role: in­vest in deep­en­ing your un­der­stand­ing of how soft­ware works. Understanding point­ers, re­cur­sion, or mem­ory hi­er­ar­chy will help you even if you’re a JavaScript de­vel­oper. Understanding net­work pro­to­cols and how HTTP works will be use­ful even if you’re build­ing WordPress plu­g­ins. Do leet­code and learn about al­go­rithms and data struc­tures even if you don’t need to. Don’t be afraid to ask why and how ex­actly.

For in­spi­ra­tion, here are a few books and other re­sources that might be help­ful:

Structure and Interpretation of Computer Programs (PDF)

Cracking the Coding Interview

The Mythical Man-Month

Working Backwards

Team Topologies

7 Powers

The Soul of a New Machine

Obviously Awesome

The Design of Everyday Things

Don’t Make Me Think

Continuous Discovery Habits

The Mom Test

One more thing

Whoever you are, don’t out­source your un­der­stand­ing, judge­ment, em­pa­thy and taste to AI. Don’t ab­di­cate your re­spon­si­bil­ity. Don’t be a meat proxy.

PS. Lots of in­ter­est­ing and in­sight­ful com­ments over at Hacker News and Lobsters—the post re­ally hit a nerve. Fascinating how many dif­fer­ent ex­pe­ri­ences peo­ple have and the vary­ing de­f­i­n­i­tions of cod­ing, pro­gram­ming, de­vel­op­ment and en­gi­neer­ing they use.

How I use LLMs to learn complex topics · Laurentiu Raducu

laurentiugabriel.github.io

Many en­gi­neers I know use gen­er­a­tive AI for many func­tions, like build­ing PoCs, in­ter­nal tools or dash­boards, or even learn­ing new stuff. I per­son­ally find the style used by LLMs to ex­plain things dif­fi­cult to fol­low. It’s just too sim­plis­tic and de­pend­ing on the num­ber of emo­jis used, a bit an­noy­ing too.

While I was an­a­lyz­ing new AI bot­tle­necks that might slow down data cen­ter buildup, I re­al­ized there are many as­pects of chip pro­duc­tion that I do not know. Surfing the web, I asked my­self what if there would be a game to get you through the process of build­ing a chip at a fab? For sure learn­ing this way will stick, since you can map con­cepts with ob­jects within the game. This is when I de­cided to try it, and it ac­tu­ally turned out re­ally well.

The flow

Instead of just ask­ing AI to ex­plain a topic, I use the fol­low­ing flow:

In plan mode (using CC, or OpenCode) I ask a model to build the foun­da­tional knowl­edge for X topic.

I ask it to re­view the ac­cu­racy of the knowl­edge base it built in the pre­vi­ous step.

I pro­ceed ask­ing it to build a sim­u­la­tion of that topic in a low-poly, Rollercoaster Tycoon-like an­i­ma­tion. I add some UX el­e­ments as well, like the page needs to be vis­i­ble on both large and small screens, have con­trols to stop the flow when­ever I want etc.

I then push it to a new repo and en­able GitHub Pages for it.

The re­sult

What you get is a beau­ti­ful an­i­ma­tion that is 100% ac­cu­rate and free of hal­lu­ci­na­tions. For me, this method works a lot bet­ter than just read­ing end­less ma­te­ri­als that I find on Google, or try­ing to di­gest a bul­leted list that is spat by a lan­guage model.

I’ve done this specif­i­cally for learn­ing chip build­ing and launch it un­der this web­site: ChipTycoon. You get to fol­low a cart from the mo­ment when sand is col­lected, to the mo­ment when a chip is fi­nal­ized and de­liv­ered to a data cen­ter.

Visually, you can fol­low the cart and see how it changes too. Since it’s low-poly, the de­tails might be miss­ing, but it’s still a good in­di­ca­tor for show­ing how the prod­uct changes once it goes through the many steps re­quired in the man­u­fac­tur­ing process.

How to im­prove it fur­ther

Let’s say that the low-poly de­sign re­quires to much im­mag­i­na­tion to ac­tu­ally vi­su­al­ize what hap­pened to the quartz sand pile af­ter it left the fur­nace. To trans­form this into a more re­al­is­tic rep­re­sen­ta­tion, you can use my skill for trans­form­ing pic­tures into 3d ob­jects, and map the re­sult­ing ob­jects to your sim­u­la­tion. This way you get more ac­cu­rate de­sign.

Also, you can add chal­lenges to your sim­u­la­tion too. Trying to an­swer ques­tions about a pre­vi­ous step in the chip man­u­fac­tur­ing process will help you re­tain the knowl­edge tremen­dously. Add in­tu­itive puz­zles too that will help you learn even bet­ter.

Check out what other pages I cre­ated:

How rocket en­gines are made

How LLMs work

How F1 en­gines are built

How an EUV ma­chine is built

After read­ing the feed­back on the HN post, I de­cided to cre­ate an awe­some Skill.MD file for cre­at­ing cool an­i­ma­tions to learn com­plex top­ics.

Why Is Everyone In Tech So Sad?

www.noemamag.com

Credits

Aaron Horwath is the di­rec­tor of AI op­er­a­tions at a cre­ative tech­nol­ogy com­pany where he is fo­cused on im­ple­ment­ing AI in a way that sup­ports both hu­mans and busi­ness.

On a re­cent morn­ing com­mute, I sat on a train in one of those awk­ward four-per­son con­fig­u­ra­tions with a shared table. Across from me sat a typ­i­cal com­muter: early 30s, slacks, dress shirt, dirty white sneak­ers, hair a lit­tle di­sheveled, AirPods in, bask­ing in the glow of an open MacBook.

For over half an hour, I lis­tened to this young man as he was on a call ex­plain­ing, in painfully mo­not­one de­tail, EBITDAs, mar­gin ex­pan­sion op­por­tu­ni­ties, cost struc­tures, ARR, etc. On and on he went un­til the screech­ing of the train’s brakes sig­naled our ar­rival at the fi­nal sta­tion. But as every­one else around us be­gan shuf­fling to dis­em­bark, I watched the man be­gin dig­ging fran­ti­cally through a leather bag at his side. Intriguing! I won­dered what he’d pull out. A copy of Atomic Habits”? A framed por­trait of Gary V? A Mac mini run­ning OpenClaw?

None of the above. Instead, he took out two long knit­ting nee­dles. Between them dan­gled a mound of pink yarn. He ex­plained to me that he was mak­ing a win­ter hat for a niece. And for the first time that morn­ing, I no­ticed a glint of pride and ex­cite­ment in his eyes.

The hat was a pro­ject born from a de­sire, as he put it, to do some­thing.

Among my Knowledge Worker peers, I am hear­ing this more and more of­ten: peo­ple who want to pick up pot­tery, paint­ing, cro­chet or other old-timey ana­log hob­bies. In cof­fee shops and in the low-lit cor­ners of bars, pro­fes­sion­als are shar­ing dreams of disappearing” or living on a farm some­where” or going off the grid.” These aren’t fan­tas­ti­cal day­dreams; they are vi­sions of es­cape shared in a tone that be­trays an un­der­ly­ing ex­is­ten­tial angst, a fun­da­men­tal doubt about work and ca­reerism in­spired by a seem­ingly in­creas­ingly com­mon ex­pe­ri­ence: wak­ing up one morn­ing at an ex­is­ten­tial precipice, struck with a sud­den sense that knowl­edge work is, and per­haps al­ways has been, point­less. Perhaps you too have felt such a feel­ing, a ris­ing tide of over­whelm­ing, in­de­scrib­able melan­choly, slowly threat­en­ing to en­velop you in your Ikea of­fice chair.

Among my Knowledge Worker peers, I am hear­ing this more and more of­ten: peo­ple who want to pick up pot­tery, paint­ing, cro­chet or other old-timey ana­log hob­bies.”

Among my Knowledge Worker peers, I am hear­ing this more and more of­ten: peo­ple who want to pick up pot­tery, paint­ing, cro­chet or other old-timey ana­log hob­bies.”

This dis­il­lu­sion­ment is in­fec­tious. One per­son speaks of it and oth­ers be­gin nod­ding: They too have felt it, the drop in mo­ti­va­tion, the sense of be­ing dis­tant from their work, the lack of sleep, the con­cerns about the fu­ture. These con­ver­sa­tions in­evitably turn to fun­da­men­tal ques­tions about ca­reers: What the fuck are we ac­tu­ally do­ing? What the fuck is the point of all of this?

We are, it’s true, liv­ing through a time of dis­rup­tion. But what seems to dif­fer­en­ti­ate this pe­riod from those of the past is the na­ture of the angst it­self. Yes, AI is threat­en­ing jobs and dis­rupt­ing in­dus­tries. But Knowledge Workers have faced re­ces­sions, out­sourc­ing, new tech­nol­ogy and au­toma­tions of many kinds be­fore. Recent grad­u­ates al­ways worry about break­ing into the job mar­ket. Millennial pro­fes­sion­als have nav­i­gated eco­nomic un­cer­tainty their en­tire ca­reers. What’s dif­fer­ent about this mo­ment is that the ques­tions are not just eco­nomic but ex­is­ten­tial, the kind of ques­tions that cause high-earn­ing tech­ni­cal pro­fes­sion­als to con­tem­plate throw­ing it all away to start a goat farm in Washington or be­come a surf in­struc­tor in Costa Rica.

It strikes me as sig­nif­i­cant that the peo­ple who are usu­ally the most in­su­lated from eco­nomic up­heaval, and seem to be well-po­si­tioned to ride out AIs near-term im­pacts — highly paid ex­ec­u­tives and se­nior pro­fes­sion­als with decades of in­sti­tu­tional knowl­edge — are also un­cer­tain about the fu­ture of their ca­reers amid the rapid change nearly every in­dus­try is un­der­go­ing.

Some will say: good, fuck em. In the 2010s, Knowledge Workers told every­one to learn to code while they sipped kom­bucha and played Xbox in bean­bag chairs. Then, Knowledge Workers sat in­side dur­ing the pan­demic while front­line work­ers risked their health to make Amazon and Uber Eats de­liv­er­ies. Now, those same Knowledge Workers are build­ing AI that threat­ens to elim­i­nate work for hu­mans across in­dus­tries and make a very few peo­ple wealthy be­yond imag­i­na­tion. And these same ass­holes want pity now?

To that I say: fair enough. But this sweep­ing dis­en­chant­ment begs a fas­ci­nat­ing (or ter­ri­fy­ing or sad) set of ques­tions: What is this angst plagu­ing Knowledge Workers? And what hap­pens to a so­ci­ety and its in­dus­tries if an en­tire class of work­ers loses faith in their ca­reers overnight?

Workism: Praise Thee

In 2019, Derek Thompson wrote in The Atlantic about American Workism” where he de­scribed a trend among Knowledge Workers, par­tic­u­larly in the U.S., of in­creas­ingly seek­ing ful­fill­ment, com­mu­nity and a sense of mean­ing from work that pre­vi­ous gen­er­a­tions had gar­nered from re­li­gion. As Thompson wrote, Workism is emotional — even spir­i­tual. The best-ed­u­cated and high­est-earn­ing Americans, who can have what­ever they want, have cho­sen the of­fice for the same rea­son that de­vout Christians at­tend church on Sundays: It’s where they feel most them­selves.”

People have long held ca­reers from which they’ve de­rived a deep sense of mean­ing. Traditionally, we’ve re­ferred to those ca­reers as vo­ca­tions: a call­ing, a way of life. More than a job: a pur­pose.

A vo­ca­tion em­pha­sizes skills, val­ues and the de­sire to con­tribute some­thing ben­e­fi­cial. Vocations have tra­di­tion­ally re­ferred to ca­reers that are chal­leng­ing, so­cially im­pact­ful, of­ten re­ward­ing in ways other than fi­nan­cial. These are your teach­ers, nurses, fire­fight­ers, so­cial work­ers, para­medics or even ser­vice providers with di­rect con­nec­tions to the com­mu­ni­ties and cus­tomers they serve, like me­chan­ics, plumbers or elec­tri­cians. Even on bad days, deep down, most of these folks find their work re­ward­ing in im­por­tant and in­tan­gi­ble ways.

But those aren’t the ca­reers young peo­ple have ded­i­cated their lives to. Instead, grad­u­ates are over­whelm­ingly tak­ing jobs in fi­nance, con­sult­ing or tech­nol­ogy. There’s no doubt that get­ting these jobs is com­pet­i­tive, and that they are de­mand­ing, com­plex and re­quire nav­i­gat­ing lay­ers of pol­i­tics and bu­reau­cracy, a ton of ass-kiss­ing, long work hours, heavy cog­ni­tive work­loads, ad­vanced skillsets and the emo­tional bur­den of a near-con­stant threat of lay­offs. But so much of the work lacks any al­tru­is­tic up­side.

Perhaps you too have felt such a feel­ing, a ris­ing tide of over­whelm­ing, in­de­scrib­able melan­choly, slowly threat­en­ing to en­velop you in your Ikea of­fice chair.”

Perhaps you too have felt such a feel­ing, a ris­ing tide of over­whelm­ing, in­de­scrib­able melan­choly, slowly threat­en­ing to en­velop you in your Ikea of­fice chair.”

In Bullshit Jobs,” David Graeber cat­a­loged peo­ple who ad­mit­ted their jobs serve no mean­ing­ful func­tion. Slide decks built for pro­jects that will never launch. Heated de­bates over the minute de­tails of soft­ware fea­tures no­body asked for. Agonizing over ad­min­is­tra­tive processes to help money move from one rich per­son to an­other. Optimizing every word of an ad no one will no­tice for a ser­vice no one needs. Resting and vest­ing — when en­gi­neers and other highly paid work­ers get to sit around and wait for their stock to vest — and pro­mo­tion-dri­ven de­vel­op­ment, where de­vel­op­ers ig­nore what’s good in fa­vor of what ap­pears to be good when they’re up for a pro­mo­tion.

The al­tru­ism in these ca­reers, then, is hard to find. So how does any­one do it with­out los­ing their mind?

That’s the true beauty of Workism: It is man­u­fac­tured to dis­tract peo­ple from the hole in their souls that a vo­ca­tion would oth­er­wise fill. It is the opi­ate of com­muters in quar­ter zips. And it works. It keeps tal­ented peo­ple show­ing up at the of­fice to ar­gue over re­ports and strat­egy doc­u­ments and re­brand­ings with the se­ri­ous­ness of pe­di­atric heart surgery.

But Workism has a weak­ness. Like re­li­gion, it re­lies on faith’s tri­umph over logic. What would hap­pen, then, if some­thing threat­ened that faith? A par­a­digm shift that broke the spell of Workism? A sort of en­light­en­ment that caused Knowledge Workers to start ask­ing tough ques­tions of their or­ga­ni­za­tions and them­selves. And what would hap­pen if Knowledge Workers awoke from the spell of Workism with no fi­nan­cially vi­able al­ter­na­tive?

Well, it seems AI might of­fer us the plea­sure of find­ing out.

Popping The Workism Bubble

Knowledge work has al­ways been in­her­ently ab­stract. Plenty of these jobs, par­tic­u­larly at larger or­ga­ni­za­tions, are struc­tured like Russian nest­ing dolls: roles de­signed to sup­port other roles, which sup­port still other roles, layer af­ter layer, un­til it’s no longer clear where there’s a solid cen­ter to be found. It’s easy to see how, to a plumber, a car­pen­ter or a line cook, work of this na­ture can ap­pear like ex­actly what David Graeber called it: bull­shit.

Knowledge work’s one sav­ing grace, un­til re­cently, was that it was still ex­e­cuted by hu­mans. We were needed. It was flesh-and-blood hu­mans who sat down to work through a chal­lenge, built the slide deck, wrote the cus­tomer re­sponse and de­vel­oped the strat­egy. Even if it was ex­is­ten­tially mean­ing­less, there was hu­man thought, col­lab­o­ra­tive work and cre­ativ­ity poured into that work, giv­ing it life.

Now, AI agents are in­creas­ingly ex­e­cut­ing much of that work for Knowledge Workers. It is com­mon for peo­ple re­spon­si­ble for in­te­grat­ing these tools into their or­ga­ni­za­tions, my­self in­cluded, to de­scribe the fu­ture of work as one in which all hu­mans will es­sen­tially be man­agers of armies of AI agents. That seems pretty great. Let the soft­ware com­pile the re­ports, chase down the data, for­mat the deck, draft the first pass of doc­u­men­ta­tion and han­dle the dozens of small, repet­i­tive tasks that used to qui­etly eat an af­ter­noon. But in many cases, em­ploy­ees un­der pres­sure from lead­ers to pro­duce more are us­ing AI agents for far more than grunt work: for­mu­lat­ing com­plete busi­ness strate­gies, gen­er­at­ing full mar­ket­ing cam­paigns, build­ing en­tire web­sites, draft­ing strat­egy for whole di­vi­sions of an or­ga­ni­za­tion. From a sin­gle email to an en­tire cor­po­rate strat­egy, the out­puts of in­di­vid­u­als, teams and or­ga­ni­za­tions are in­creas­ingly gen­er­ated in an in­stant by AI.

But does this power — this ad­di­tional level of ab­strac­tion — take peo­ple too far, in some sense, from their work? Does some­thing feel … off … about hav­ing some­one, or some­thing, else ex­e­cute nearly all the work, even if the end prod­uct did­n’t feel very mean­ing­ful to be­gin with?

Debord’s Spectacle: I Don’t Wanna Do This Anymore

In so­ci­eties where mod­ern con­di­tions of pro­duc­tion pre­vail, life is pre­sented as an im­mense ac­cu­mu­la­tion of spec­ta­cles. Everything that was di­rectly lived has re­ceded into a rep­re­sen­ta­tion.”

That’s the open­ing para­graph of Guy Debord’s The Society of the Spectacle.” I will fail to sum­ma­rize the book ad­e­quately. It is si­mul­ta­ne­ously ex­cit­ing and im­pen­e­tra­ble. But the main thrust of Debord’s ar­gu­ment is this: Spectacle is a fea­ture of late cap­i­tal­ism in which life, rather than be­ing di­rectly lived, is per­pet­u­ally me­di­ated. There is al­ways some­thing be­tween us and the thing we are sup­posed to be ex­pe­ri­enc­ing. I don’t talk to my mom; I text her. I don’t travel; I watch other peo­ple travel on YouTube. I don’t have sex; I watch porn. In late cap­i­tal­ism, the medium re­places the ex­pe­ri­ence.

Workism is a mi­cro­cosm of the larger spec­ta­cle: a world where ap­pear­ing busy, im­por­tant and uniquely knowl­edge­able is as valu­able as ac­tu­ally be­ing any of those things, and where work needs only to ap­pear im­pact­ful rather than ac­tu­ally be im­pact­ful. As Debord writes in Thesis 12: The spec­ta­cle pre­sents it­self as a vast, in­ac­ces­si­ble re­al­ity that can never be ques­tioned. Its sole mes­sage is: What ap­pears is good; what is good ap­pears.’” This is Workism’s most con­vinc­ing ar­gu­ment: The work must be im­por­tant be­cause, well, we’re all here, aren’t we? Signing in. Staying late. Every week.

The ques­tions to­day are not just eco­nomic but ex­is­ten­tial.”

The ques­tions to­day are not just eco­nomic but ex­is­ten­tial.”

Adding AI to the spec­ta­cle feels ex­is­ten­tially daunt­ing be­cause it moves us even fur­ther from the work we do, and its value. I don’t build the pitch that wins the client; I write the query that tells the AI to write it, and then I check the work af­ter­ward. I don’t gather the ma­te­ri­als and write the in­dus­try newslet­ter; my agent does it. Before, that work might’ve felt cheap and un­sat­is­fy­ing, deep down, but it was still ours. Now AI is be­ing forced on or­ga­ni­za­tions in ways that call the value of the en­tire en­ter­prise of Workism it­self into ques­tion.

And this is where things get par­tic­u­larly in­ter­est­ing. I don’t think Debord imag­ined some­thing so seis­mi­cally par­a­digm-shift­ing that it could ab­stract work to an ex­tent that it would shake the work­ing class, or, in the case of Knowledge Workers, enough to rup­ture a spec­ta­cle like Workism. But with the in­tro­duc­tion of AI, it is as if, over the past 30 years, we have been slowly tak­ing steps away from the di­rect ex­pe­ri­ence of life and work, and AI risks push­ing us far enough that the il­lu­sion be­comes en­tirely vis­i­ble.

Organizations find them­selves in a pickle. They want to in­te­grate AI into their op­er­a­tions, as do their share­hold­ers. And they will. In the short term, the po­ten­tial ef­fi­ciency gains are too good to pass up. And, done right, it can of­fer ben­e­fits to both busi­nesses and em­ploy­ees. But what makes many ex­ec­u­tives most ex­cited about AI — less col­lab­o­ra­tion, fewer peo­ple — risks dis­man­tling the struc­tures that hold the very or­ga­ni­za­tions they lead to­gether.

What if the en­light­en­ment from Workism, iron­i­cally, is de­liv­ered by Workism’s most rev­o­lu­tion­ary prod­uct? And what does that mean for the em­ploy­ees who have spent their ca­reers pray­ing at the Workism al­tar?

What’s At Stake: The Value Of The Messy Middle

To un­der­stand why AI threat­ens to kill Workism specif­i­cally, we have to un­der­stand what has kept faith in Workism alive.

In Thompson’s ar­ti­cle, he states that one thing peo­ple look for in Knowledge Work jobs is com­mu­nity: to spend time with like-minded peo­ple with sim­i­lar in­ter­ests, to col­lab­o­rate with oth­ers to solve prob­lems. Relationships are the foun­da­tion of the hu­man work­place, and Knowledge Work’s re­deem­ing value. None of us will make it to Knowledge Work’s pearly gates, but at least we will make our false jour­ney to­gether.

Ironically, it’s a grow­ing dream of ex­ec­u­tives that an or­ga­ni­za­tion of em­ploy­ees armed with a swarm of agents and pow­er­ful LLMs no longer need to col­lab­o­rate with one an­other to gather in­for­ma­tion, ideate or ex­e­cute a pro­ject. In their vi­sion for the fu­ture, work goes from a messy ex­pe­ri­ence of learn­ing and ex­plor­ing to some­thing more akin to as­sem­bly-line pro­duc­tion. As Debord wrote back in 1967, This pro­le­tariat is be­ing ob­jec­tively re­in­forced by the vir­tual elim­i­na­tion of the peas­antry and by the in­creas­ing de­gree to which the service’ sec­tors and in­tel­lec­tual pro­fes­sions are be­ing sub­jected to fac­tory-like work­ing con­di­tions.”

But some­times messy in­ef­fi­ciency is a fea­ture, not a bug. In a re­cent episode of Bill Simmons’s pod­cast, the writer Chuck Klosterman ar­gued with Bill about the role of tech­nol­ogy in sports, par­tic­u­larly when it comes to ref­er­ee­ing. Consider ten­nis. The days of Johnny Mac blow­ing up at a ref­eree over a bad line call are over. With Hawk-Eye tech­nol­ogy, the judg­ment is right 100% of the time. In the NBA, the chal­lenge sys­tem now helps to en­sure that bad calls do not change a game. The MLB has in­te­grated an au­to­mated ball-strike sys­tem. These tech­nolo­gies have been im­ple­mented with very dif­fer­ent lev­els of suc­cess, but the goals are the same: Get of­fi­ci­at­ing right more of­ten — maybe al­ways. Who could com­plain? It’s so ef­fi­cient!

What would hap­pen if Knowledge Workers awoke from the spell of Workism with no fi­nan­cially vi­able al­ter­na­tive? It seems AI might of­fer us the plea­sure of find­ing out.”

What would hap­pen if Knowledge Workers awoke from the spell of Workism with no fi­nan­cially vi­able al­ter­na­tive? It seems AI might of­fer us the plea­sure of find­ing out.”

Klosterman ar­gues that bad calls are a nat­ural and fun part of games. In fact, bad calls have cre­ated iconic sports mo­ments that peo­ple still talk about. Sports are hu­man con­structs and the messi­ness of hu­man er­ror, whether by player or ref­eree, is part of them. Getting calls right 100% of the time may be ob­jec­tively bet­ter, but it is sub­jec­tively less in­ter­est­ing and en­ter­tain­ing. And en­ter­tain­ment is the goal of sport.

Knowledge work is strik­ingly sim­i­lar. Sure, an AI that can pro­duce in min­utes a spot-on pro­ject that would have taken hours is cool. But the rate of slide deck cre­ation is­n’t what is go­ing to re­tain tal­ent. Employees over­whelm­ingly choose to re­main or leave their roles be­cause of their col­leagues and/​or bosses, and the qual­ity of the ex­pe­ri­ence of the messy mid­dle a work­place of­fers. Those are the el­e­ments that make work fun—and valu­able.

But not every­one feels that way. In my ex­pe­ri­ence, there are two broad cat­e­gories of Knowledge Workers. The first are out­come-first work­ers. Their fo­cus is on the busi­ness, on win­ning, on ef­fi­ciency. Human needs and faults and emo­tions are an ob­sta­cle to be over­come.

The sec­ond group has an ex­pe­ri­ence-first per­spec­tive. These work­ers love the messy mid­dle. They value the jour­ney. They want their or­ga­ni­za­tions to per­form well but as a nat­ural con­se­quence of col­lab­o­ra­tion, de­bate and shared strug­gle with peo­ple they ac­tu­ally like. Many ex­pe­ri­ence-first peo­ple are artists out­side of work. Photographers, di­rec­tors, screen­writ­ers, sculp­tors, painters, po­ets, writ­ers. Many are ac­tive vol­un­teers. For these peo­ple, to be pulled away from their eco­nom­i­cally un­vi­able pas­sions re­quires some­thing in re­turn: a com­pany ex­pe­ri­ence with free­dom for ex­plo­ration, cre­ativ­ity, prob­lem solv­ing and cool col­leagues.

Research sug­gests this group needs that en­vi­ron­ment to do their best work. Harvard Business School’s Teresa Amabile spent decades study­ing what pro­duces gen­uinely cre­ative, high-qual­ity out­put. Her Intrinsic Motivation Principle de­ter­mines that peo­ple do their most cre­ative and in­no­v­a­tive work when mo­ti­vated by the work it­self — the in­ter­est, the chal­lenge, the en­joy­ment — not by out­comes or met­rics. The en­vi­ron­ments that kill cre­ativ­ity are po­lit­i­cal, risk-averse and re­lent­lessly out­come-fo­cused. The en­vi­ron­ments that stim­u­late it are col­lab­o­ra­tive, idea-dri­ven and free. The messy mid­dle, in other words, is­n’t in­ef­fi­cient. It’s the con­di­tion un­der which gen­uinely valu­able work gets pro­duced.

Workism is a mi­cro­cosm of the larger spec­ta­cle: a world where ap­pear­ing busy, im­por­tant and uniquely knowl­edge­able is as valu­able as ac­tu­ally be­ing any of those things.”

Workism is a mi­cro­cosm of the larger spec­ta­cle: a world where ap­pear­ing busy, im­por­tant and uniquely knowl­edge­able is as valu­able as ac­tu­ally be­ing any of those things.”

It’s im­por­tant to note that the im­pact of re­mov­ing the messy mid­dle from the work ex­pe­ri­ence of these two groups is asym­met­ri­cal: For out­come-first peo­ple, it is a vic­tory. For ex­pe­ri­ence-first peo­ple, it un­der­mines the foun­da­tion of work.

As I’m writ­ing this es­say, big com­pa­nies are on a lay­off ben­der. Thousands of peo­ple are be­ing fired all over the place. Executives are of­ten claim­ing these head­count re­duc­tions are a re­sult of AI. They aren’t. At the mo­ment, AI au­toma­tion is not cre­at­ing any­where near the in­creased ef­fi­ciency nec­es­sary to jus­tify hun­dreds of thou­sands of lost jobs.

We’ve been through waves of hir­ing and fir­ing a thou­sand times be­fore. But this time might be dif­fer­ent. In the past, there was al­ways a fresh co­hort of work­ers wait­ing to be brought in. But what if tal­ent stops com­ing back? What if the AI trans­for­ma­tion gone wrong makes top tal­ent — par­tic­u­larly those who are ex­pe­ri­ence-first by na­ture — lose faith in Workism en masse? What if, when com­pa­nies go to re­plen­ish head­counts, tal­ent freed from the Workism spec­ta­cle has turned to dif­fer­ent ver­sions of their lives? What if their side pro­jects sud­denly be­came prof­itable? The tal­ent pool in­creas­ingly does­n’t own homes and does­n’t plan on hav­ing kids, so it has per­haps never been eas­ier to make a ca­reer pivot to­ward some­thing that is gen­uinely sat­is­fy­ing in ways cor­po­rate life is­n’t.

What would Debord say? Well, he had se­ri­ous doubts about the pos­si­bil­ity of leav­ing the spec­ta­cle be­hind.

Post-Workism: Escaping The Inescapable

As Debord puts it, Complacent ac­cep­tance of the sta­tus quo may also co­ex­ist with purely spec­tac­u­lar re­bel­lious­ness—dis­sat­is­fac­tion it­self be­comes a com­mod­ity as soon as the econ­omy of abun­dance de­vel­ops the ca­pac­ity to process that par­tic­u­lar raw ma­te­r­ial.”

At some point, in other words, the spec­ta­cle will put eco­nomic pres­sure on you that you’ll need to meet. In to­day’s world, that might mean start­ing a YouTube chan­nel and a Substack about how you aban­doned your tech ca­reer to start a horse res­cue farm, ul­ti­mately turn­ing your­self and your life into a com­mod­ity that feeds the larger so­ci­etal spec­ta­cle. Or it might mean start­ing a com­pany of your own, thereby sud­denly need­ing to cre­ate a spec­ta­cle at­trac­tive to po­ten­tial em­ploy­ees. Either way, you are doomed to exit one ver­sion of the spec­ta­cle only to be ab­sorbed into an­other one. That’s the logic of the spec­ta­cle: It re­ab­sorbs even the dis­sat­is­fied in or­der to sus­tain it­self.

Debord be­lieved es­cape from the spec­ta­cle was im­pos­si­ble. He went on to dis­solve his own move­ment rather than watch it be­come in­sti­tu­tion­al­ized, and then drank him­self to death in the coun­try­side.

Some will cer­tainly leave Knowledge Work — the most dis­il­lu­sioned, the most ar­tis­ti­cally tal­ented or mo­ti­vated, the most ex­is­ten­tially sen­si­tive to a sud­den mo­ment of en­light­en­ment, the fi­nan­cially able. But many more will re­main, ei­ther by choice or ne­ces­sity, to face a work ex­pe­ri­ence where the hu­man­ity of work is erod­ing away. Are those who chose to re­main, or who sim­ply can­not leave, doomed to spend their ca­reers ex­is­ten­tially dis­traught?

The en­vi­ron­ments that kill cre­ativ­ity are po­lit­i­cal, risk-averse and re­lent­lessly out­come-fo­cused. The en­vi­ron­ments that stim­u­late it are col­lab­o­ra­tive, idea-dri­ven and free.”

The en­vi­ron­ments that kill cre­ativ­ity are po­lit­i­cal, risk-averse and re­lent­lessly out­come-fo­cused. The en­vi­ron­ments that stim­u­late it are col­lab­o­ra­tive, idea-dri­ven and free.”

Debord also be­lieved the spec­ta­cle could be un­der­mined through de­lib­er­ate acts: hi­jack­ing spec­tac­u­lar im­ages and turn­ing them against them­selves, drift­ing through ur­ban space in ways that re­sist its ge­og­ra­phy and con­struct­ing mo­ments of gen­uine, un­medi­ated ex­pe­ri­ence that the spec­ta­cle can­not me­tab­o­lize. At work, that might mean jump­ing on a call to talk through a prob­lem with a col­league in­stead of query­ing an agent for the an­swer. Or ex­e­cut­ing work the old-fash­ioned way — me­an­der­ing, ex­ploratory — with the un­der­stand­ing that it may be slower but might pro­duce some­thing more gen­uinely hu­man. Or sim­ply de­cid­ing that cer­tain work is best left to hu­man hands en­tirely. Anything that pre­serves the el­e­ments of work that have made it worth do­ing in the first place.

But per­haps the most pow­er­ful ac­tion of all is sim­ply to main­tain an aware­ness (and re­mind oth­ers) that Workism is a spec­ta­cle. For most em­ploy­ees, we owe it to each other to re­mind one an­other that this work is not, in the grand scheme of things, all that im­por­tant. It is il­lu­sory. No one has ever died over a spread­sheet. The world does not wait with bated breath for prod­uct launches. A mar­ket­ing cam­paign will not change the world. Almost no one will re­mem­ber all the work we do. But they might re­mem­ber the types of peo­ple we were, the re­la­tion­ships we had and how we treated the peo­ple we worked with.

And once freed from the false sat­is­fac­tion of be­liev­ing we are chang­ing the world with our day jobs, per­haps we will be in­spired to fill that hole with some­thing real — true al­tru­ism, not its ar­ti­fi­cial Workism sub­sti­tute — by ac­tu­ally try­ing to im­pact real peo­ple, in our com­mu­ni­ties, in real ways.

Even if it’s just one knit­ted hat at a time.

The next chapter of our AI momentum

blog.google

Editor’s note: Today, Google and Alphabet CEO Sundar Pichai shared some changes with Google DeepMind teams, in­clud­ing new roles for Demis Hassabis and Koray Kavukcuoglu. Below are the mes­sages Sundar and Demis sent to em­ploy­ees.

Message from Sundar Pichai

We’ve made ex­tra­or­di­nary progress to de­liver on our full AI stack. We’ve got amaz­ing tal­ent, world-class com­pute, and prod­ucts that bring AI to more peo­ple than any other com­pany. And you saw the in­cred­i­ble mo­men­tum at earn­ings across all our busi­nesses, in­clud­ing Search, YouTube, and Cloud. Our Gemini mod­els are in high de­mand among de­vel­op­ers and busi­nesses, and the Gemini app reached 950M+ monthly users. Meanwhile, our AI re­search con­tin­ues to drive field-defin­ing break­throughs (like last week’s Gemini Robotics ad­vances).

We have to ac­cel­er­ate all this work and stay fo­cused on the AI fron­tier. At the same time, there’s never been a more im­por­tant mo­ment to shape the fu­ture of AGI and sci­ence. Today Demis, Koray and I are shar­ing a few changes to our Google DeepMind teams that will en­able us to do both.

AGI and sci­ence: Demis has de­scribed us as stand­ing in the foothills of the sin­gu­lar­ity, and has been spend­ing a lot of his time en­gag­ing ex­ter­nally. He and I have been long dis­cussing a role that al­lows him to put his full at­ten­tion on ac­tively shap­ing the fu­ture of AGI. It’s work that is vi­tally im­por­tant to Alphabet and hu­man­ity, and I can’t imag­ine a bet­ter per­son than Demis to do it. So, mov­ing for­ward, Demis will be­come the Chair of GDM and Chief Scientist of Alphabet, while con­tin­u­ing to lead Isomorphic Labs. He’ll re­main closely con­nected to Koray, Josh, and our GDM teams, ad­vis­ing across mod­els and re­search. I’m so ex­cited for Demis — this is truly his life’s work and pur­pose. You can read Demis’s note to GDM be­low.

Google DeepMind: We are build­ing strong mo­men­tum: Flash is in high de­mand, our Cyber model is live, and Gemma mod­els have sur­passed 900M+ down­loads. We are com­mit­ted to be­ing at the fron­tier, and are su­per fo­cused on the ar­eas where we need to im­prove. I’m re­ally ex­cited for our up­com­ing model re­leases and the progress we’re see­ing. We have to con­tinue to move fast and with clear pur­pose here. Koray, the cur­rent Chief Technology Officer of GDM and our Chief AI Architect, will step up as SVP of Google DeepMind, re­port­ing to me. He will over­see Gemini model de­vel­op­ment, Frontier AI re­search, and the Gemini app and de­vel­oper teams. Koray has been at DeepMind since its early days, and over his 13 years there, he has started our deep learn­ing team and led the way on break­throughs like WaveNet and DQN. I look for­ward to see­ing him lead GDM into this next chap­ter.

Lastly, af­ter an in­cred­i­ble 27-year run, Jeff Dean is at a mo­ment where he wants to try some­thing new, and we’re ex­cited to sup­port him in that. Jeff and Google Senior Fellow Sanjay Ghemawat are launch­ing an in­de­pen­dent pub­lic ben­e­fit cor­po­ra­tion to ac­cel­er­ate dis­cov­er­ies in ML, sci­ence, and en­gi­neer­ing. Jeff and Sanjay helped to drive some of the most sig­nif­i­cant tech­nol­ogy tran­si­tions, from our early search in­fra­struc­ture to the neural net­works that helped cre­ate the mod­ern AI era. On a per­sonal note, it’s been a priv­i­lege to work along­side Jeff and Sanjay, and I wish them all the best! We’ll con­tinue to work with them as a found­ing in­vestor and Cloud part­ner, and col­lab­o­rate on a re­search frame­work for ML sys­tems and re­lated in­fra­struc­ture ad­vances.

We are at a dy­namic mo­ment with so much op­por­tu­nity ahead. With to­day’s changes we’re go­ing to keep dri­ving our mo­men­tum. Onwards!

-Sundar

Message from Demis Hassabis

Hi Team

We have ar­rived at a piv­otal mo­ment in hu­man his­tory. I’ve been work­ing to­wards AGI my whole life and now, like many of you, I feel it is close at hand. It’s crit­i­cal that we col­lec­tively get the next steps right to en­sure this all goes well for hu­man­ity and we usher in an in­cred­i­ble new age of dis­cov­ery and won­der.

With this back­drop, I’ve de­cided that now is the right time for me to hand over my day-to-day op­er­a­tional re­spon­si­bil­i­ties at GDM, so that I have the time and space to fo­cus on the big pic­ture and help in­flu­ence what is to come to the best of my abil­ity. I will be tak­ing on a new strate­gic role as Chair of GDM and Chief Scientist of Alphabet, and I’m ex­cited to an­nounce that Koray will be step­ping up to lead GDM as SVP of Google DeepMind, in ad­di­tion to his role as Chief AI Architect of Google.

Koray and I have been work­ing to­gether for over 13 years, since the early days of DeepMind. He is one of the world’s fore­most AI ex­perts and has been cham­pi­oning GDMs mis­sion from day one. I have to­tal con­fi­dence in Koray, Josh, and the rest of the GDM exec team as they con­tinue to spear­head the lat­est AI de­vel­op­ments across Google. The Gemini mod­els are in good hands with Koray and the leads, as they have been for a while, and I’m ex­cited about the great progress we’re mak­ing with our new mod­els in­clud­ing Gemini 4.

In my new role, I will con­tinue to work closely with Sundar on strate­gic and global AGI mat­ters, and to ad­vise Koray, Josh, and the GDM leads, from our awe­some new London Platform 37 of­fices. As part of this tran­si­tion, I’ll also be lean­ing into my role at Isomorphic, where we are mak­ing ex­tremely rapid and promis­ing progress, to ac­cel­er­ate our mis­sion there even faster. As you’ve heard me say many times, I’ve al­ways be­lieved the No.1 ap­pli­ca­tion of AI should be to im­prove hu­man health. It’s time for AI to prove its un­equiv­o­cal value to the world, and what bet­ter way to demon­strate that than to help fi­nally cure dis­eases like can­cer.

We’ve built a unique cul­ture at GDM that has served us very well. I want to thank each and every one of you for your bril­liance, ded­i­ca­tion, and ef­fort that make GDM the huge suc­cess it is to­day. We should all be ex­tremely proud of the amaz­ing things we’ve achieved so far. We’ve be­come the AI en­gine room of Google, with Gemini de­liv­er­ing help­ful ex­pe­ri­ences every­where in­clud­ing AI Mode and AI Overviews, the Gemini App rock­et­ing to over 950M monthly users, and our fun­da­men­tal and sci­en­tific re­search con­tin­ues to lead the world. I’m very ex­cited for our next chap­ter and the best is yet to come!

As a busi­ness we are in an in­cred­i­bly strong po­si­tion. We are the only com­pany that has the full stack and we’re world-class at every layer from in­fra­struc­ture to cloud to fron­tier mod­els to AI-first ap­pli­ca­tions. We have all the in­gre­di­ents to lead from here, and I firmly be­lieve we will.

Best

Demis

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