Aug 03, 2026

Too of­ten I ask a ques­tion in Slack or leave feed­back un­der a merge/​pull re­quest or ar­gue with friends in a WhatsApp group and get back:

Claude said: [giant re­sponse ver­ba­tim]

Claude said: [giant re­sponse ver­ba­tim]

Please don’t do this. I mean, I’ve done this. But I’ve been on the re­ceiv­ing end too many times now. This is not adding value. I can talk to Claude my­self. It’s go­ing to be faster and I get to con­trol the con­text. I don’t need a meat proxy in be­tween.

Reading AI out­put is ex­tra ef­fort. It’s ver­bose, fre­quently con­tains all too plau­si­ble non­sense, and is in­creas­ingly jar­gon dense. I re­cently got this sen­tence from Claude:

NATS con­trol-plane events: stream leader elec­tion / R3 quo­rum re-form dur­ing pod churn.

NATS con­trol-plane events: stream leader elec­tion / R3 quo­rum re-form dur­ing pod churn.

Jesus. I had to lookup al­most every word to make sense of this.

By all means, prompt AI. But don’t just re­lay the out­put. Read it, un­der­stand it, val­i­date it, and then write a re­sponse in your own words (a de­cent cer­tifi­cate that you’ve done the prior steps). Making that ef­fort is value you can add.

Take code re­view in par­tic­u­lar. Shipping some code can be done with close to zero ef­fort now: Copy/paste the ticket de­scrip­tion into Claude Code. Don’t look at the code or read what Claude has writ­ten. If there’s any feed­back from re­view­ers, copy/​paste that into Claude Code as well. If nec­es­sary, it­er­ate.

That works. But who has done the im­ple­men­ta­tion? The re­view­ers did, us­ing Claude Code, and you as a meat proxy.

Something ter­ri­ble just hap­pened. My wife, Elise Cawley, was re­cov­er­ing from heart surgery and had just at­tended vir­tu­ally a cel­e­bra­tion for one of our chil­dren when she had a freak, vast car­dio­vas­cu­lar event—and died in­stantly. We had been to­gether for 36 years. The pic­ture above was taken just hours be­fore she died.

In all the writ­ing and pub­lic speak­ing I have done, I have cho­sen, as a mat­ter of pri­vacy, to say lit­tle about my fam­ily. But now that Elise is gone I can­not re­strain my­self from telling the world some­thing about the re­mark­able per­son with whom I shared the past 36 years of my life.

It was September 17, 1990, and I was in New York City. The event I had been at­tend­ing fin­ished a lit­tle early, so I de­cided to drop in on a friend of mine. And there, sit­ting styl­ishly on the floor, was Elise, a some­what bash­ful but ob­vi­ously bril­liant young pure math­e­mati­cian. It took me a cou­ple of weeks to call her. But from that mo­ment on we talked es­sen­tially every day for 36 years—un­til the day she died a week ago.

Somehow in the shock of what has hap­pened it feels as if I just met Elise, and now she is gone. But ac­tu­ally there were 36 won­der­ful years in be­tween, in which my life was lit up by Elise’s in­tel­lect, love and en­ergy. She was deeply the­matic, able to get, with re­mark­able ease, to the essence of things. Truth, beauty and our fam­ily and four chil­dren were her great themes. She al­ways told me that truth and beauty were ul­ti­mately the same thing, and that one day I would un­der­stand that. What I could see is that she her­self loved to cre­ate beauty—whether in math­e­mat­ics, in­te­rior de­sign, ar­chi­tec­ture or per­sonal style. She was a force­ful in­tel­lect, kind and sweet, but per­sis­tent and of­ten feisty in ar­gu­ing for what she thought was true.

Despite her deep knowl­edge and ca­pa­bil­ity in ab­stract math­e­mat­ics, she was at some level fun­da­men­tally a hu­man­ist, think­ing about the world in hu­man terms. She de­cried dry mod­ernism and was drawn to the hu­man con­nec­tions of clas­si­cism. In re­cent years she thought in­creas­ingly about the foun­da­tions of math­e­mat­ics, tak­ing a Platonic view of truth and lament­ing the twen­ti­eth-cen­tury tran­si­tion to a more for­mal­is­tic ap­proach.

There was al­ways a cer­tain lively spon­tane­ity to Elise, mixed with a drive to­wards the high­est stan­dards of in­tel­lec­tual and aes­thetic rigor. People’s first im­pres­sions of Elise tended to be of her per­sonal en­ergy, her lively style and her keen in­tel­lect. But as they got to know her, they would also come to see how much she cared about the peo­ple around her and how much lov­ing thought and ef­fort she would put into things she did for them. Whether it was a gift or the in­te­rior de­sign of a space, Elise would ap­ply her cre­ativ­ity and go back and forth un­til it was just right.

Our four chil­dren were a defin­ing part of Elise’s life, and she was im­mensely proud of all of them (as am I!). She had her own unique re­la­tion­ship with each of our chil­dren that helped them each de­velop in their own way. She also built us a won­der­ful home, full of warmth, ar­chi­tec­tural beauty and cre­ative de­sign. She took great plea­sure in per­fect­ing every de­tail, main­tain­ing re­mark­able over­all co­her­ence, with dashes of the un­ex­pected.

Elise was a very fine math­e­mati­cian, top flight in tech­ni­cal mas­tery but, more im­por­tantly, able to the­mat­i­cally pull to­gether grand con­cep­tual arcs. She was al­ways drawn to el­e­gance, of­ten with an ab­stract geo­met­ri­cal bent, and by the time I met her she had al­ready es­tab­lished her­self with re­sults that con­tinue to be cited to­day. Later in life Elise would re­fer to her­self as a “retired math­e­mati­cian”, but if ex­posed to her kind of math, she would im­me­di­ately en­gage with great en­thu­si­asm.

In all my years with Elise we never di­rectly worked to­gether on in­tel­lec­tual pro­jects. Perhaps we were both too strong willed, and with her quick mind, it did not take long for her to get im­pa­tient with the dif­fer­ences in our styles of think­ing. Still, I would of­ten tell her about work I was do­ing. And it was re­mark­able how ef­fort­lessly she would see deeper themes, which she would then ex­plain back to me, with vary­ing de­grees of pa­tience.

She liked my con­cept of com­pu­ta­tional ir­re­ducibil­ity, and I think was pleased that the ef­forts in sci­ence on which I had spent so many of our years to­gether had led to a place that high­lighted the lim­i­ta­tions of sci­ence and of sci­en­tism, and aligned well with her hu­man­is­tic world­view. When I be­gan to form ideas about ob­servers and the ru­liad, she was far ahead of me in see­ing their philo­soph­i­cal and meta­phys­i­cal im­pli­ca­tions.

For years, the foun­da­tions of math­e­mat­ics were some­thing of a sore point be­tween us (and our chil­dren were highly amused that one of the most vig­or­ous ar­gu­ments they ever saw be­tween us was about the meta­phys­i­cal char­ac­ter of the con­cept of a cir­cle). Elise main­tained that raw ax­iomatic for­mal­iza­tion can­not be the true essence of math and that math is re­ally some­thing much more hu­man. And fi­nally, just a few years ago, by a long and cir­cuitous route, I came to re­al­ize that—de­spite my ob­sti­nate in­sis­tence to the con­trary—she’d been right about this all along. But then she made other as­ser­tions too, that she thought were quite sig­nif­i­cant. Like that the fun­da­men­tal ab­strac­tions from which we build math as we know it are the con­cept of point (which she viewed as equiv­a­lent to space, con­ti­nu­ity and in­fin­ity), and the con­cept of num­ber. She also as­serted that physics dif­fers from math mainly in its in­tro­duc­tion of the con­cept of time.

While Elise could be a very orig­i­nal thinker, she also be­lieved in the value of ex­ist­ing wis­dom and ex­ist­ing ways of do­ing things—and was con­stantly telling me that I should­n’t try to fig­ure out ab­solutely every­thing (including about every­day life) from first prin­ci­ples. Being with Elise was al­ways some­thing of an ad­ven­ture. There was spon­tane­ity (“no plan; let’s play it by ear”), yet also pre­ci­sion and per­fec­tion. And there were the force­fully de­liv­ered bursts of in­sight, not just about math and ab­strac­tion, but also about the hu­man con­di­tion and the way the world works. Elise would say that there were peo­ple of ac­tion, and there were philoso­phers. She saw her­self more as the lat­ter. She had strong opin­ions about how things should be in the world. But she was much more in­ter­ested in fig­ur­ing out what should be than in what she saw as the grind of try­ing to make it ac­tu­ally hap­pen.

Elise was a per­son who had both great con­fi­dence and great hu­mil­ity. While she was al­ways con­vinced that she was right in things she thought (and in fact usu­ally was), she was never one to ad­ver­tise her ca­pa­bil­i­ties. Still, if ever a con­ver­sa­tion turned to things she knew or cared about, she would­n’t re­strain her­self from mak­ing what was of­ten a deeply in­sight­ful con­tri­bu­tion.

I al­ways thought Elise and I were an out­stand­ing match—sim­i­lar in many ways, yet also com­ple­men­tary. We both val­ued the­matic think­ing (though in many do­mains she was bet­ter at it than me), and we shared an ap­pre­ci­a­tion for aes­thet­ics and work well done. But for ex­am­ple our ways of in­ter­act­ing with peo­ple were some­what dif­fer­ent. My chil­dren say I have only one mode of in­ter­ac­tion: full on. Elise was at some level more in­tro­verted and would imag­ine her­self to be shy—though once en­gaged she would be­come highly talk­a­tive and an­i­mated. And when it was just Elise and me on our own, we al­ways seemed to have things to talk about, in fact an in­fi­nite num­ber of things.

Elise was born in 1961 in Urbana, IL—ironically not far from the head­quar­ters of our com­pany—when her fa­ther was a physics grad­u­ate stu­dent at the University of Illinois. The sec­ond of four chil­dren, she grew up mostly in Columbia, MD, as a top stu­dent, par­tic­u­larly rec­og­nized for her math and her the­matic es­says. As she of­ten men­tioned to our chil­dren, in 6th grade her teacher char­ac­ter­ized her writ­ing as “the cat’s pa­ja­mas”—and for a while she imag­ined grow­ing up to be a writer. In high school she had the for­ma­tive ex­pe­ri­ence of go­ing to the Hampshire sum­mer math pro­gram, where for the first time she met other peo­ple who were in­tensely in­ter­ested in math.

She went to MIT for col­lege, falling in with a group that in­cluded sev­eral later promi­nent math­e­mati­cians. She al­ways told me that what re­ally ig­nited her in­ter­est in con­cep­tual pure math­e­mat­ics was an epiphany she had in a class she took about topol­ogy. At MIT, she sup­ported her­self by work­ing in the the­sis bindery, us­ing her cal­li­graphic skills to la­bel the­ses. She also made up lan­guage puz­zles for a lan­guage ac­qui­si­tion study and had a sum­mer job work­ing on al­go­rithms at an early speech recog­ni­tion com­pany. A fam­ily cri­sis took her back to Maryland, where she fin­ished de­grees in both math and physics at the University of Maryland. She also had an in­tern­ship work­ing on a PET scan­ner at NIH, vol­un­teered at some hos­pi­tals and con­sid­ered go­ing to med­ical school. But in the end she de­cided on grad­u­ate school.

She could­n’t make her mind up be­tween physics and math but ended up ap­ply­ing in physics. One of her physics pro­fes­sors had said she was the best stu­dent he’d ever seen but would be most suited to very the­o­ret­i­cal physics. She had her choice be­tween Harvard and Berkeley. And in her ever-typ­i­cal fash­ion she waited un­til the last minute, pick­ing Berkeley be­cause she was told she’d be able to switch to math there.

After an in­tense “think on your feet” in­ter­view process, she re­ceived a Hertz Foundation fel­low­ship, which gen­er­ously sup­ported her grad­u­ate stud­ies. Before even ar­riv­ing at Berkeley she’d set­tled on math, and hav­ing im­pressed peo­ple with her el­e­gant so­lu­tion to a small open prob­lem, she joined a group work­ing on geo­met­ri­cal ap­proaches to dy­nam­i­cal sys­tems. After four years in grad­u­ate school she com­pleted her PhD the­sis on “Smooth Markov Partitions and Toral Automorphisms”. It was quin­tes­sen­tial Elise. An el­e­gant the­matic idea im­ple­mented with clean tech­ni­cal pro­fi­ciency.

It made her a hot­shot young math­e­mati­cian, sought af­ter by many uni­ver­si­ties. She ended up with two jobs: a tenure-track pro­fes­sor­ship at Stony Brook and an NSF post­doc spon­sored by the promi­nent math­e­mati­cian Dennis Sullivan. She chose to de­fer the pro­fes­sor­ship and take the post­doc for a year, partly at CUNY in New York City and partly at IHES near Paris.

While at IHES, Elise lived in the very cen­ter of Paris, only steps from the Jardin du Luxembourg, in an un­used stu­dio owned by an artist who was the sis­ter of an­other math­e­mati­cian. It was a pe­riod of in­tense math­e­mat­i­cal work for Elise about which she was very en­thu­si­as­tic. And her the­matic and aes­thetic sen­si­bil­i­ties drew her to the study of Teichmüller spaces, which are spaces of pos­si­ble geo­met­ri­cal ob­jects, or in a sense spaces of pos­si­ble spaces. It was also at this time that she em­barked on what would be a long-term in­ter­est in the so-called ther­mo­dy­namic for­mal­ism, or Gibbs the­ory.

With con­nec­tions to math­e­mat­i­cal physics, Gibbs the­ory pro­vides a way to talk about in­fi­nite col­lec­tions of in­fi­nite struc­tures, and their de­vel­op­ment, for ex­am­ple through time. (Ironically enough—as Elise would re­peat­edly men­tion—con­cepts such as trans­ver­sals that ap­pear in Gibbs the­ory are now di­rectly rel­e­vant to my own work on in­fra­geom­e­try and the foun­da­tions of physics.)

Rather un­typ­i­cally for a math­e­mati­cian, Elise was al­ways no­table for her dis­tinc­tive per­sonal style—of­ten an in­ter­pre­ta­tion of cur­rent fash­ion, but with her own spunky and col­or­ful touches. As one of my daugh­ters tells it, Elise’s in­ter­est in style be­gan to ex­pand with the fund­ing she re­ceived from her Hertz fel­low­ship and de­vel­oped fur­ther dur­ing her time in New York City and Paris.

Upon re­turn­ing from Paris, Elise was about to start at Stony Brook, but while wait­ing for her apart­ment to be ready, was stay­ing with her long-time friend Lisa Goldberg in New York City. And that’s how, on September 17, 1990, Elise and I first met.

Lisa’s apart­ment was an el­e­gant one, but I was not quite so el­e­gant that day. I had been at a tech in­dus­try event where I had re­ceived a rather bulky award, which was in my jacket pocket weigh­ing it down. Elise was sit­ting on the floor, with her bangs flopped across her face, in a pose that I thought might be yoga. We talked just a bit, but it was enough.

The next day I went back to Champaign, IL, where I was based at the time. It had been two and a half years since we’d re­leased Mathematica 1.0 and our com­pany was grow­ing rapidly. I had started my ca­reer young, so even though I was only two years older than Elise (she was close to 29 and I had just turned 31), I had al­ready done quite a bit, both in acad­e­mia and the tech in­dus­try.

Early ver­sions of Mathematica came with a book, that I had to write. And in September 1990 I was rush­ing to fin­ish the sec­ond edi­tion of that book, to ac­com­pany Mathematica 2.0. Two weeks af­ter I re­turned to Champaign, I did fin­ish, though promptly got sick with a high fever. And that was when I fi­nally called Elise.

Soon I was tak­ing trips to Stony Brook, and she to Champaign. And when apart we were talk­ing by phone for hours each day. Lisa Goldberg made a point of telling me, “She’s a very fine math­e­mati­cian, you know”. Elise had met an­other per­son a lit­tle ear­lier, and Lisa had dis­missed them as un­suit­able. But—as I just now learned from one of my chil­dren—when Elise asked Lisa about me she im­me­di­ately re­sponded with en­thu­si­asm.

One no­table trip was the re­sult of a call I got from Elise where she ex­plained that she’d mixed up con­tact lens so­lu­tion and hy­dro­gen per­ox­ide and now had to patch both her eyes for a few days. I im­me­di­ately got on the next flight. And while the only food I could cook at that time (and still to­day) was an omelette, she viewed this “rescue” as an act of chivalry that—as she told our chil­dren—was what de­fin­i­tively sold her on me.

On Elise’s 29th birth­day in December 1990 I sent some flow­ers. She re­sponded by email, now bit­ter­sweet:

Hi there. Some amaz­ing flow­ers just ar­rived. I think I can make to92. I’m head­ing off to aer­o­bics — tonight is the amaz­ing Margaret(who plays great mu­sic). I has some good math con­ver­sa­tion with­caro­line s. to­day. And got a lit­tle bit of real work done.

I’ll be back around 9pm.

elise

The very next email I re­ceived from her was about a bug in the Mathematica Residue func­tion.

My day job was (and still is) tech CEO. But what made me orig­i­nally de­velop our tech­nol­ogy was that I wanted to use it my­self—to do sci­ence. By the spring of 1991 I was get­ting frus­trated that I could in­ject new ideas into our com­pany faster than it could ab­sorb them. So I de­cided I should take some­thing of a sab­bat­i­cal and work on sci­ence for a while. Meanwhile, Elise still had time left on her NSF post­doc, that she could take any­where. And so it was that we de­cided to get a house to­gether in the hills above Oakland, CA, and she re­turned to Berkeley.

Before mov­ing into the house that she found for us, she came with me on an 18-city tour of Europe+Russia to pro­mote Mathematica 2.0. After that, we set­tled into a good rou­tine. She would go off each day to do math. I would stay home to be a re­mote CEO, and work on sci­ence. And from time to time she’d jet off to Texas, Montana, France or wher­ever to give talks.

By late 1992 we were think­ing about next steps. Elise was adamant that she could­n’t live in Champaign-Urbana (despite hav­ing been born there). But she thought the idea of liv­ing in the Chicago area was in­ter­est­ing. Meanwhile, she was highly in de­mand as a math­e­mati­cian, and there were mul­ti­ple op­por­tu­ni­ties for her there.

Elise took charge, and ex­plored many dif­fer­ent lo­ca­tions and houses around Chicago, even­tu­ally pick­ing a house in Hinsdale, IL. We moved there in the mid­dle of 1993, and Elise took up a po­si­tion at the University of Chicago. It was a nice house, but Elise thought it could be nicer, and set about ren­o­vat­ing it to have top-of-the-line in­te­rior de­sign. She started work­ing with a de­signer who she dis­cov­ered by read­ing mag­a­zines, and learned that her aes­thetic and geo­met­ric sen­si­bil­i­ties trans­lated well into cre­ative in­te­rior de­sign. Elise was par­tic­u­larly keen on in­te­grat­ing lively col­ors and un­ex­pected el­e­ments into tra­di­tional de­sign themes.

Meanwhile, at the University of Chicago, Elise was teach­ing a course about Gibbs the­ory. She wrote ex­ten­sive notes which she planned even­tu­ally to turn into a book. Over the years, peo­ple asked for the book many times, and Elise had re­cently started talk­ing about fi­nally fin­ish­ing it. Ironically enough, our daugh­ter Catherine, now her­self a math­e­mati­cian, has ended up work­ing in some­what re­lated ar­eas.

Elise in­ter­acted a lot with Bob Zimmer, who was then chair­man of the math de­part­ment (and would later be­come pres­i­dent of the University of Chicago). Together they de­vised the con­cept of a new ac­tiv­ity for the math de­part­ment: a pro­fes­sional mas­ter’s de­gree in fi­nan­cial math­e­mat­ics, ba­si­cally aimed at quants, and taught by math pro­fes­sors. Though Elise was never too in­ter­ested in the messi­ness of ac­tual mar­ket data, she liked the pure math­e­mat­ics of mar­ket mod­els, and helped de­velop a cur­ricu­lum around it. The mas­ter’s de­gree ended up be­ing highly suc­cess­ful and con­tin­ues to this day.

In the fall of 1994 Elise and I mar­ried. And then, in December 1995, as Elise would later de­scribe it, “the world overnight had a new cen­ter”: our first child, Alexander, was born. The next few years were dom­i­nated by the ar­rival of chil­dren, with Catherine be­ing born in 1997 and Christopher in 1998—leaving Elise, as she put it, with “three un­der three”.

With our fam­ily grow­ing—and our book col­lec­tion reach­ing over 5000 vol­umes—we were out­grow­ing our house in Hinsdale, and started look­ing for a new home. At first, Elise found real es­tate in Lake Forest, IL. But when the deal for it fell through we started think­ing more broadly and de­cided that since the Boston area was the place where to­gether we knew the most peo­ple, that was where we should go. But which town in par­tic­u­lar? Elise’s #1 ini­tial cri­te­rion was that it should al­ready have a Starbucks. From the list of such towns, she then started look­ing at USGS aer­ial pho­tographs (or what would now be satel­lite im­ages), and soon set­tled on Concord, MA. But find­ing no suit­able ex­ist­ing house to buy there, she set about de­sign­ing a house to build.

It was a com­plex process. The house had to ac­com­mo­date what was by then a li­brary of nearly 10,000 books, as well as space for my re­mote-CEO of­fice and for our three chil­dren. Elise had been scour­ing ar­chi­tec­ture books, and in­ter­viewed sev­eral top-of-the-line ar­chi­tects be­fore set­tling on one to work with. With her math­e­mat­i­cal and aes­thetic sen­si­bil­i­ties she eas­ily ab­sorbed prin­ci­ples of ar­chi­tec­ture and soon de­vel­oped her own in­tu­ition for it. Sometimes she’d bring in se­ri­ous math—like num­ber the­ory to de­sign a stair­case with aligned squig­gly ban­is­ters, or cir­cle pack­ing to lay out an Italian-inspired tiling based on cir­cles and Sierpinski pat­terns. She started off mak­ing sketches, but soon learned the lat­est CAD tools. And in the end her ideas de­fined much of the de­sign of the house. (At the be­gin­ning, I had tried mak­ing some “suggestions” about the plans, but quickly re­al­ized that this was an Elise pro­ject, and I should stay out of it.)

It took sev­eral years to build the house (with Elise reg­u­larly trav­el­ing to Boston to over­see the pro­ject) but in the spring of 2002 the pro­ject was fin­ished—co­in­ci­den­tally at ba­si­cally the same time as my 10-year pro­ject of writ­ing A New Kind of Science. Other than in CAD draw­ings I’d only seen the house once, early in its con­struc­tion, so when we drove from Hinsdale and ar­rived at the house I was in for a sur­prise. It was much big­ger than I ex­pected! And when we went in­side I could see that it was a mas­ter­piece, filled with per­son­al­ity: beau­ti­ful, el­e­gant, cre­ative—quin­tes­sen­tial Elise.

And then there was the in­te­rior de­sign. Bold and col­or­ful, yet clas­sic. Each room with a dis­tinct char­ac­ter, grace­fully flow­ing into the next. Over the years, Elise re­vised and up­dated the in­te­rior de­sign of the house many times. She was of­ten to be seen sketch­ing on graph pa­per new pos­si­ble de­signs based on new ideas. She kept the style fresh, con­stantly breath­ing new life into the house—even if it meant that our chil­dren would com­ment that every time they showed up the fur­ni­ture seemed to have been re­arranged. And 24 years af­ter I first saw the house, I still find my­self wowed by it.

By 2003, our old­est was en­cour­ag­ing us to have an­other child, not­ing, among other things, that the sym­me­try of the house had given it four chil­dren’s bed­rooms. And so it was that in the fall of 2004 our fourth child, Elizabeth, was born.

In her ear­lier years, Elise had al­ways said that she “couldn’t imag­ine her life not do­ing math”. But partly through me, and, more im­por­tantly through our chil­dren, her world had been broad­ened, and her per­spec­tives had shifted. She would later de­scribe her life as hav­ing sev­eral chap­ters, and this was a chap­ter about chil­dren.

Through her chil­dren show­ing in­ter­est in math, she ended up coach­ing some mid­dle-school MathCounts math com­pe­ti­tion teams. She was­n’t a fan of the way math was typ­i­cally taught in schools, com­plain­ing that the beauty of math was be­ing lost in the at­tempt to make math seem “applicable”, and not­ing that “most of math in mod­ern text­books is not re­ally math at all”. In an email to me in 2010 Elise spirit­edly went fur­ther:

What has hap­pened to beauty in math? The beauty de­rives­from the sim­plic­ity and el­e­gance. That has been lost in the­p­ro­lif­er­a­tion of ex­tra­ne­ous ter­mi­nol­ogy, meant to make things “moreconceptual”, from early end­less ex­cur­sions with “place value” to ab­sur­d­vari­a­tions on al­go­rithms, to gummy de­scrip­tions of every­thing us­ing­con­tor­tions of lan­guage, rather than the lan­guage of math­e­mat­ics it­self.

Elise did nev­er­the­less do some ap­pli­ca­tions of math her­self. During the pan­demic, for ex­am­ple, she de­vel­oped the idea that in­fec­tion was not a bi­nary phe­nom­e­non, but that in­stead each per­son had a con­stantly chang­ing vi­ral load and im­mu­nity, cre­at­ing a “viral field” at the pop­u­la­tion level. Elise was al­ways up to date on world af­fairs, and had a deep in­ter­est in un­der­stand­ing their the­matic arcs at work.

Elise loved the small-town at­mos­phere of Concord, MA, and en­joyed the feel­ing of be­ing in a place “where every­one knows your name”. She was a reg­u­lar at lo­cal cafes and ex­er­cise classes, bring­ing her live­li­ness wher­ever she went. She had a cir­cle of friends with many dif­fer­ent back­grounds, of­ten met through their chil­dren or sim­ply through re­peat­edly run­ning into them at Starbucks. She took great plea­sure in ap­ply­ing her knowl­edge and think­ing to help her friends in all sorts of ways. Whether it was do­ing re­search on med­ical is­sues, solv­ing in­te­rior de­sign prob­lems, or sim­ply us­ing her keen in­sight and judg­ment to pro­vide life ad­vice, she would re­li­ably go above and be­yond.

Our fam­ily had a def­i­nite rou­tine, with din­ner each day at about 6pm. Every Friday night—fol­low­ing a tra­di­tion from Elise’s own fam­ily grow­ing up—we would go out to a movie (and, yes, that meant we saw many ques­tion­able movies). Before our chil­dren were old enough, it was just Elise and me; later it in­cluded var­i­ous con­tin­gents of chil­dren too. Elise and I would talk af­ter each movie, with her of­ten be­ing quite ap­palled by my lack of un­der­stand­ing of what seemed to her like the movie’s ob­vi­ous the­matic el­e­ments. (Elise also gave me a hard time about my fail­ure to read fic­tion, claim­ing that many ideas, par­tic­u­larly about hu­man na­ture, are best com­mu­ni­cated in fic­tion.)

A dozen years ago two of our chil­dren were at the Stanford Online High School, and Elise was re­cruited as an in­au­gural mem­ber of the school’s Community Advisory Group. Originally this was just sup­posed to be for a year. But a decade later she was still there, a highly val­ued mem­ber of the group. She spent con­sid­er­able ef­fort think­ing about the over­all strat­egy and is­sues of the or­ga­ni­za­tion, and de­vel­oped many ideas and opin­ions. Despite this, she’d of­ten tell us that she did­n’t think she’d end up say­ing any­thing at a given meet­ing. But with great con­sis­tency it would later tran­spire that ac­tu­ally she had been the most talk­a­tive and en­gaged per­son there.

In the early 2000s, the Hertz Foundation, from which Elise had re­ceived her grad­u­ate school fel­low­ship, started reach­ing out to her, and she en­joyed en­gag­ing with the emerg­ing Hertz com­mu­nity, and meet­ing fel­lows from a wide range of fields. In 2018 she was also re­cruited to the board of the Hertz Foundation. And here again she be­came a key con­trib­u­tor, of­ten ex­pect­ing not to say any­thing, but then end­ing up as one of the most vo­cal peo­ple there.

By 2023, our three older chil­dren had all grad­u­ated from col­lege, but our youngest, Elizabeth, was just start­ing at Yale. Elizabeth has al­ways been in­volved in a great many ac­tiv­i­ties, and Elise took great plea­sure in at­tend­ing every pos­si­ble con­cert, sport­ing or other event—and also for ex­am­ple host­ing Elizabeth’s en­tire a cap­pella group for a re­treat at our house.

A bit more than a decade ago Elise also dis­cov­ered a love for the Southwest and for hik­ing. And with her brother Jason—with whom she was al­ways very close—liv­ing in the Phoenix, AZ, area, we started spend­ing time there each win­ter.

With Elizabeth off in col­lege we were fi­nally empty nesters, and Elise was ready for a new chap­ter in her life. She con­sid­ered re­turn­ing to where she’d left off in Gibbs the­ory 30 years ago, be­ing told by an old col­league that her work there was still very cur­rent. She also con­sid­ered get­ting more deeply in­volved in the phi­los­o­phy of math­e­mat­ics and was on the brink of writ­ing a piece about it. She was also quite en­gaged in think­ing about the pur­pose and di­rec­tion of higher ed­u­ca­tion, and the im­por­tance of free ex­pres­sion in the search for truth.

In some ways Elise and I were be­com­ing “the old mar­ried cou­ple”, with her ad­mon­ish­ing me about my messi­ness in eat­ing ice cream cones or her de­sire that I switch on self-dri­ving to avoid her be­ing sub­jected to my own sub­stan­dard hu­man dri­ving.

Elise had al­ways been a fit and healthy per­son, and was also very med­ically knowl­edge­able. So when she started notic­ing jaw pain when hik­ing in Colorado and Arizona, she was con­cerned. Tests at first showed noth­ing alarm­ing, though, as she al­ready knew, she had a small con­gen­i­tal heart is­sue that was grad­u­ally wors­en­ing. But just a few weeks ago her symp­toms wors­ened and more in­va­sive tests re­vealed that she needed ur­gent open heart surgery. She had an out­stand­ing team of top doc­tors, the com­plex surgery went very well, and she was soon con­tin­u­ing her re­cov­ery from home.

Meanwhile there was a long-planned cel­e­bra­tion for one of our chil­dren that Elise hoped to at­tend. But though it was not pos­si­ble for her to be there in per­son, we arranged for her to at­tend vir­tu­ally. It was a won­der­ful event, and Elise could be seen via video call beam­ing through all of it. But then, soon af­ter, dis­as­ter struck, and she col­lapsed, the vic­tim of a mas­sive car­dio­vas­cu­lar event. My only so­lace in this tragedy is that the end came in­stantly, af­ter a day filled with noth­ing but joy.

There is so much that Elise was look­ing for­ward to. The im­mi­nent ar­rival of our first grand­child. Elizabeth’s grad­u­a­tion from col­lege. The next chap­ter of her life. But Elise will not be here for any of these.

Elise had no idea the end was near. And even though she ex­pected more, she had al­ready had many chap­ters in her life, and many achieve­ments of which she was proud. Elise had a beau­ti­ful life, full of things she val­ued greatly, es­pe­cially her chil­dren. That she is gone is a tragedy for us all; she will be deeply missed.

Thank you, Elise, for all those won­der­ful years.

In the 2010s, if you had tech­ni­cal gaps (say, you could­n’t write CSS), you had to ei­ther rely on a skilled col­league or just hope that the an­swer to your ex­act prob­lem was out there on the in­ter­net. Today, every­one can write sort-of-okay CSS by del­e­gat­ing the task to an LLM. LLMs make every­body into a gen­er­al­ist.

Because of this, lots of peo­ple don’t think there’s any skill in­volved in work­ing with LLMs. If you want the prod­uct that LLMs can de­liver — PhD-level math­e­mat­ics, pretty good but some­times taste­less com­puter code, or awk­ward LinkedIn-style writ­ing — you can sim­ply ask for it. Since every­one is talk­ing to the same mod­els, “skilled prompters” are get­ting the same re­sults as peo­ple touch­ing LLMs for the first time.

This is wrong. The most im­por­tant skill in prompt­ing is ex­per­tise in the do­main you’re prompt­ing for.

A good il­lus­tra­tion of this is Terence Tao’s con­ver­sa­tion with ChatGPT about the re­cently-dis­cov­ered coun­terex­am­ple to the Jacobian Conjecture. This is not the same ChatGPT I talk to! I could­n’t get to where Tao gets, even with un­lim­ited to­kens to burn.

There’s a lot to learn about good prompt­ing from Tao’s con­ver­sa­tion. Here are a few ob­ser­va­tions:

Tao’s mes­sages are very short and to-the-point. He does­n’t re­spond point-by-point to the model, just to the gist

The model out­puts are much more con­cise than when I try and talk to GPT-5.6 Sol about math­e­mat­ics. By sig­nalling ex­per­tise, Tao shunts the model into “talking-to-mathematicians” mode, not “explaining-to-amateurs” mode

Tao pushes back when the mod­el’s re­sponses look wrong, but he does­n’t di­rectly con­tra­dict; in­stead, he says things like “this looks more com­plex than I was hop­ing for”

Tao makes sev­eral leaps and sug­ges­tions him­self. He al­most never takes the mod­el’s ad­vice about where to go next

However, you can’t prompt like Tao on math­e­mat­i­cal ques­tions just by fol­low­ing these tips. The key to his tech­nique is ac­tu­ally un­der­stand­ing the math­e­mat­ics: pulling the rel­e­vant idea out of ChatGPT’s multi-para­graph re­sponse, sug­gest­ing al­ter­nate ap­proaches or for­mu­la­tions, and iden­ti­fy­ing what “looks weird”.

Terence Tao is a bet­ter math­e­mati­cian than I am a pro­gram­mer. But the idea here — that do­main knowl­edge makes you bet­ter at us­ing LLMs — is some­thing I’ve also ex­pe­ri­enced in my own work. If you have a good the­ory of your code­base, you can push the LLM much harder than if you have no fa­mil­iar­ity. Because you have your own sense of what a good so­lu­tion might look like, you can say “no, I think it could be sim­pler here”, or “but don’t we al­ready do X?”, or “can we ex­press this prob­lem in these fa­mil­iar terms?“.

This touches on an idea I’ve writ­ten about be­fore: that sys­tem de­sign prob­lems are dom­i­nated by con­crete specifics, not generic prin­ci­ples. Of course both are use­ful, but I’d rather have fa­mil­iar­ity with the code­base than a deep gen­eral un­der­stand­ing of soft­ware sys­tems. In his con­ver­sa­tion, Terence Tao asks a lot of spe­cific ques­tions like “does X work here?”, or “given Y and Z, why A?“. I can’t ask those ques­tions about the Jacobian Conjecture, but I can ask them about the sys­tems I own at GitHub.

If you have no do­main knowl­edge, you can cling onto the LLM to at least get some­thing. That’s not bad! But if you have do­main knowl­edge, you can wring far more value out of the same LLM by steer­ing it hard in the di­rec­tion you want. Most of us will have to do a mix of both these ap­proaches, since we have do­main knowl­edge in some ar­eas but not oth­ers.

The use­ful­ness of do­main knowl­edge sug­gests that hu­man ex­per­tise will con­tinue to be use­ful even as mod­els get stronger. For many tasks, the hu­man is the bot­tle­neck, not the model, be­cause the dif­fi­cult part is in com­mu­ni­cat­ing to the model ex­actly what kind of so­lu­tion the hu­man wants. The in­for­ma­tion is “in the model” al­ready, but it takes a very smart hu­man to pull it out.

edit: this post got many com­ments on Hacker News. Some com­menters share their anec­dotes about how ex­per­tise has helped and lack of ex­per­tise has hurt. Other com­menters say it’s plau­si­ble, but they have a sen­si­ble sus­pi­cion of a view that’s re­as­sur­ing them about how they’re still valu­able. I agree with that, though I sus­pect by the time we get around to study­ing this, the land­scape will have changed un­der our feet again. Some com­menters point out that OpenAI’s math prompts were in­ex­pert, and so ex­per­tise is­n’t re­quired. Here I’d re­spond that OpenAI do have a team of ex­pert math­e­mati­cians that checked and fil­tered the mod­el’s sug­gested dis­cov­er­ies, and that you can­not cur­rently skip that step.

If you liked this post, con­sider sub­scrib­ing to email up­dates about my new posts, or shar­ing it on Hacker News.

Here’s a pre­view of a re­lated post that shares tags with this one.

Powerful AIs might es­cape con­tain­ment by re­leas­ing them­selves as open-weight mod­els­Be­fore large lan­guage mod­els, peo­ple who wor­ried about AI safety of­ten talked about the “boxing prob­lem”. It goes like this. Suppose some ge­nius fig­ures out ar­ti­fi­cial in­tel­li­gence in a late-night cod­ing ses­sion on their lap­top. Because they’re a ge­nius, they’re smart enough to dis­able in­ter­net ac­cess on the lap­top be­fore turn­ing it on. In or­der to es­cape to the out­side world (and be­gin self-repli­cat­ing) it would need to con­vince its cre­ator to “open the box”. Would that work? Could a suf­fi­ciently smart AI con­vince any­body to let it out?Con­tinue read­ing…

Powerful AIs might es­cape con­tain­ment by re­leas­ing them­selves as open-weight mod­els

Before large lan­guage mod­els, peo­ple who wor­ried about AI safety of­ten talked about the “boxing prob­lem”. It goes like this. Suppose some ge­nius fig­ures out ar­ti­fi­cial in­tel­li­gence in a late-night cod­ing ses­sion on their lap­top. Because they’re a ge­nius, they’re smart enough to dis­able in­ter­net ac­cess on the lap­top be­fore turn­ing it on. In or­der to es­cape to the out­side world (and be­gin self-repli­cat­ing) it would need to con­vince its cre­ator to “open the box”. Would that work? Could a suf­fi­ciently smart AI con­vince any­body to let it out?Con­tinue read­ing…

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 RWE’s 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.

In AMD’s 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.

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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 TSMC’s 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 AMD’s 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, AMD’s 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 AMD’s 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 AMD’s broader vi­sion. Subject to reg­u­la­tory ap­proval, the deal is ex­pected to close in the fourth quar­ter. ®

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.

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 AI’s 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.

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 GDM’s 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

I have a grow­ing ha­tred for AI-generated im­ages in blogs. It makes me won­der if the text in the blog posts is AI-generated to some ex­tent. It’s al­ways dis­ap­point­ing see­ing these im­ages in blogs run by in­di­vid­u­als. I ex­pect this from cor­po­rate blogs but not in­die blogs.

I’d rather see a shitty Microsoft Paint draw­ing as op­posed to some AI im­age.

I know there are plenty of things you can roast my blog for but at least you know for a fact you’re get­ting the thoughts of a real hu­man be­ing and not some LLM.

If you run a per­sonal blog, please avoid AI-generated im­ages.

Discussion over at Hacker News