OpenAI is now in­ter­nally test­ing ‘ads’ in­side ChatGPT that could re­de­fine the web econ­omy.

Up un­til now, the ChatGPT ex­pe­ri­ence has been com­pletely free.

While there are pre­mium plans and mod­els, you don’t see GPT sell you prod­ucts or show ads. On the other hand, Google Search has ads that in­flu­ence your buy­ing be­hav­iour.

As spot­ted by Tibor on X, ChatGPT Android app 1.2025.329 beta in­cludes new ref­er­ences to an “ads fea­ture” with “bazaar con­tent”, “search ad” and “search ads carousel.”

This move could dis­rupt the web econ­omy, as what most peo­ple don’t un­der­stand is that GPT likely knows more about users than Google.

For ex­am­ple, OpenAI could cre­ate per­son­alised ads on ChatGPT that pro­mote prod­ucts that you re­ally want to buy. It might also sneak in ads in the search ads, sim­i­lar to Google Search ads.

The leak sug­gests that ads will ini­tially be lim­ited to the search ex­pe­ri­ence only, but this may change in the fu­ture.

ChatGPT has roughly 800 mil­lion peo­ple us­ing it every week, up from 100 mil­lion weekly users in November 2023 and about 300 mil­lion weekly users in late 2024.

An OpenAI-backed study es­ti­mated 700 mil­lion users send­ing 18 bil­lion mes­sages per week by July 2025, which lines up with this growth, and other an­a­lysts now peg traf­fic at around 5–6 bil­lion vis­its per month.

GPT handles about 2.5 bil­lion prompts a day, and India has be­come the sin­gle biggest user base, ahead of the US.

ChatGPT has every­thing it needs for ads to suc­ceed. What do you think?

More than a decade ago, when I was ap­ply­ing to grad­u­ate school, I went through a pe­riod of deep un­cer­tainty. I had tried the pre­vi­ous year and had­n’t got­ten in any­where. I wanted to try again, but I had a lot go­ing against me.

I’d spent most of my un­der­grad build­ing a stu­dent job-por­tal startup and had­n’t bal­anced it well with aca­d­e­mics. My GPA needed ex­plain­ing. My GMAT score was just okay. I did­n’t come from a big-brand em­ployer. And there was no short­age of peo­ple with sim­i­lar or stronger pro­files ap­ply­ing to the same schools.

Even though I had learned a few things from the first round, the sec­ond at­tempt was still dif­fi­cult. There were mul­ti­ple points af­ter I sub­mit­ted ap­pli­ca­tions where I lost hope.

But dur­ing that stretch, a friend and col­league kept re­peat­ing one line to me:

“All it takes is for one to work out.”

He’d say it every time I spi­raled. And as much as it made me smile, a big part of me did­n’t fully be­lieve it. Still, it be­came a lit­tle maxim be­tween us. And even­tu­ally, he was right — that one did work out. And it changed my life.

I’ve thought about that fram­ing so many times since then.

You don’t need every job to choose you. You just need the one that’s the right fit.

You don’t need every house to ac­cept your of­fer. You just need the one that feels like home.

You don’t need every per­son to want to build a life with you. You just need the one.

You don’t need ten uni­ver­si­ties to say yes. You just need the one that opens the right door.

These processes — col­lege ad­mis­sions, job searches, home buy­ing, find­ing a part­ner — can be emo­tion­ally bru­tal. They can get you down in ways that feel per­sonal. But in those mo­ments, that truth can be ground­ing.

All it takes is for one to work out.

And that one is all you need.

Iceland has taken the rare step of treat­ing a cli­mate-linked ocean threat as a mat­ter of na­tional sur­vival, launch­ing a co­or­di­nated gov­ern­ment re­sponse to one of the most feared po­ten­tial tip­ping points in the cli­mate sys­tem.

Officials say the shift re­flects mount­ing ev­i­dence that a key Atlantic cur­rent sys­tem could be head­ing to­ward dan­ger­ous in­sta­bil­ity.

According to CNN, Iceland’s National Security Council for­mally la­belled the pos­si­ble col­lapse of the Atlantic Meridional Overturning Circulation (AMOC) a na­tional se­cu­rity risk in September — the first time the coun­try has ap­plied such a des­ig­na­tion to a cli­mate im­pact.

The move fol­lowed a gov­ern­ment brief­ing on new re­search that raised “grave con­cerns” about the sys­tem’s fu­ture sta­bil­ity.

Jóhann Páll Jóhannsson, Iceland’s min­is­ter for en­vi­ron­ment, en­ergy and cli­mate, said the risks ex­tend far be­yond weather.

“Our cli­mate, econ­omy and se­cu­rity are deeply tied to the sta­bil­ity of the ocean cur­rents around us,” he told CNN.

He later de­scribed the threat as “an ex­is­ten­tial threat,” warn­ing that a break­down could dis­rupt trans­port, dam­age in­fra­struc­ture and hit the coun­try’s fish­ing in­dus­try.

The AMOC — of­ten com­pared to a gi­ant con­veyor belt — car­ries warm wa­ter north­ward be­fore it cools and sinks, help­ing reg­u­late weather across the Atlantic basin.

CNN re­ported that sci­en­tists in­creas­ingly worry that warm­ing tem­per­a­tures and dis­rupted salin­ity lev­els are slow­ing the sys­tem.

Some stud­ies sug­gest a tip­ping point could be reached this cen­tury, though the ex­act time­line re­mains un­cer­tain.

Stefan Rahmstorf, an oceanog­ra­pher at Potsdam University, told CNN that a col­lapse “cannot be con­sid­ered a low like­li­hood risk any­more.”

The con­se­quences, he said, would be dra­matic: surg­ing sea lev­els along US and European coasts, ma­jor mon­soon dis­rup­tions across Africa and Asia, and a deep freeze across parts of Europe.

For Iceland, he said, the coun­try “would be close to the cen­ter of a se­ri­ous re­gional cool­ing,” with sea ice po­ten­tially sur­round­ing the is­land.

The se­cu­rity des­ig­na­tion means Iceland will now pur­sue a high-level, cross-gov­ern­ment ef­fort to analyse the threat and con­sider how to man­age or re­duce the con­se­quences. Jóhannsson said the de­ci­sion

“reflects the se­ri­ous­ness of the is­sue and en­sures that the mat­ter gets the at­ten­tion it de­serves.”

Rahmstorf praised Iceland’s stance, telling CNN that other na­tions should treat the risk with sim­i­lar ur­gency.

Jóhannsson said the coun­try is con­fronting a stark pos­si­bil­ity: “What we do know is that the cur­rent cli­mate might change so dras­ti­cally that it could be­come im­pos­si­ble for us to adapt… this is not just a sci­en­tific con­cern — it’s a mat­ter of na­tional sur­vival and se­cu­rity.”

In the in­ter­ests of clar­ity, I am a for­mer NASA en­gi­neer/​sci­en­tist with a PhD in space elec­tron­ics. I also worked at Google for 10 years, in var­i­ous parts of the com­pany in­clud­ing YouTube and the bit of Cloud re­spon­si­ble for de­ploy­ing AI ca­pac­ity, so I’m quite well placed to have an opin­ion here.

The short ver­sion: this is an ab­solutely ter­ri­ble idea, and re­ally makes zero sense what­so­ever. There are mul­ti­ple rea­sons for this, but they all amount to say­ing that the kind of elec­tron­ics needed to make a dat­a­cen­ter work, par­tic­u­larly a dat­a­cen­ter de­ploy­ing AI ca­pac­ity in the form of GPUs and TPUs, is ex­actly the op­po­site of what works in space. If you’ve not worked specif­i­cally in this area be­fore, I’ll cau­tion against mak­ing gut as­sump­tions, be­cause the re­al­ity of mak­ing space hard­ware ac­tu­ally func­tion in space is not nec­es­sar­ily in­tu­itively ob­vi­ous.

The first rea­son for do­ing this that seems to come up is abun­dant ac­cess to power in space. This re­ally is­n’t the case. You ba­si­cally have two op­tions: so­lar and nu­clear. Solar means de­ploy­ing a so­lar ar­ray with pho­to­voltaic cells — some­thing es­sen­tially equiv­a­lent to what I have on the roof of my house here in Ireland, just in space. It works, but it is­n’t some­how mag­i­cally bet­ter than in­stalling so­lar pan­els on the ground — you don’t lose that much power through the at­mos­phere, so in­tu­ition about the area needed trans­fers pretty well. The biggest so­lar ar­ray ever de­ployed in space is that of the International Space Station (ISS), which at peak can de­liver a bit over 200kW of power. It is im­por­tant to men­tion that it took sev­eral Shuttle flights and a lot of work to de­ploy this sys­tem — it mea­sures about 2500 square me­tres, over half the size of an American foot­ball field.

Taking the NVIDIA H200 as a ref­er­ence, the per-GPU-de­vice power re­quire­ments are on the or­der of 0.7kW per chip. These won’t work on their own, and power con­ver­sion is­n’t 100% ef­fi­cient, so in prac­tice 1kW per GPU might be a bet­ter base­line. A huge, ISS-sized, ar­ray could there­fore power roughly 200 GPUs. This sounds like a lot, but lets keep some per­spec­tive: OpenAI’s up­com­ing Norway dat­a­cen­ter is in­tend­ing to house 100,000 GPUs, prob­a­bly each more power hun­gry than the H200. To equal this ca­pac­ity, you’d need to launch 500 ISS-sized satel­lites. In con­trast, a sin­gle server rack (as sold by NVIDIA pre­con­fig­ured) will house 72 GPUs, so each mon­ster satel­lite is only equiv­a­lent to roughly three racks.

Nuclear won’t help. We are not talk­ing nu­clear re­ac­tors here — we are talk­ing about ra­dioiso­tope ther­mal gen­er­a­tors (RTGs), which typ­i­cally have a power out­put of about 50W - 150W. So not enough to even run a sin­gle GPU, even if you can per­suade some­one to give you a sub­crit­i­cal lump of plu­to­nium and not mind you hav­ing hun­dreds of chances to scat­ter it across a wide area when your launch ve­hi­cle ex­plo­sively self-dis­as­sem­bles.

I’ve seen quite a few com­ments about this con­cept where peo­ple are say­ing things like, “Well, space is cold, so that will make cool­ing re­ally easy, right?”

Really, re­ally no.

Cooling on Earth is rel­a­tively straight­for­ward. Air con­vec­tion works pretty well — blow air across a sur­face, par­tic­u­larly one de­signed to have a large sur­face area to vol­ume ra­tio like a heatsink, will trans­fer heat from the heatsink to the air quite ef­fec­tively. If you need more power den­sity than can be di­rectly cooled in this way (and higher power GPUs are def­i­nitely in that cat­e­gory), you can use liq­uid cool­ing to trans­fer heat from the chip to a larger ra­di­a­tor/​heatsink else­where. In dat­a­cen­ters on Earth, it is com­mon to set up cool­ing loops where ma­chines are cooled via chilled coolant (usually wa­ter) that is pumped around racks, with the heat ex­tracted and cold coolant re­turned to the loop. Typically the coolant is cooled via con­vec­tive cool­ing to the air, so one way or an­other this is how things work on Earth.

In space, there is no air. The en­vi­ron­ment is close enough to a hard, to­tal vac­uum as makes no prac­ti­cal dif­fer­ence, so con­vec­tion just does­n’t hap­pen. On the space en­gi­neer­ing side, we typ­i­cally think about ther­mal man­age­ment, not just cool­ing. Thing is, space does­n’t re­ally have a tem­per­a­ture as-such. Only ma­te­ri­als have a tem­per­a­ture. It may come as a sur­prise, but in the Earth-Moon sys­tem the av­er­age tem­per­a­ture of pretty much any­thing is ba­si­cally the same as the av­er­age tem­per­a­ture of Earth, be­cause this is why Earth has that par­tic­u­lar tem­per­a­ture. If a satel­lite is ro­tat­ing, a bit like a chicken on a ro­tis­serie, it will tend to­ward hav­ing a con­sis­tent tem­per­a­ture that’s roughly sim­i­lar to that of the Earth sur­face. If it is­n’t ro­tat­ing, the side point­ing away from the sun will tend to get pro­gres­sively colder, with a limit due to the cos­mic mi­crowave back­ground, around 4 Kelvin, just a lit­tle bit above ab­solute zero. On the sun­ward side, things can get a bit cooked, hit­ting hun­dreds of centi­grade. Thermal man­age­ment there­fore re­quires very care­ful de­sign, mak­ing sure that heat is care­fully di­rected where it needs to go. Because there is no con­vec­tion in a vac­uum, this can only be achieved by con­duc­tion, or via some kind of heat pump.

I’ve de­signed space hard­ware that has flown in space. In one par­tic­u­lar case, I de­signed a cam­era sys­tem that needed to be very small and light­weight, whilst still pro­vid­ing sci­ence-grade imag­ing ca­pa­bil­i­ties. Thermal man­age­ment was front and cen­tre in the de­sign process — it had to be, be­cause power is scarce in small space­craft, and ther­mal man­age­ment has to be achieved whilst keep­ing mass to a min­i­mum. So no heat pumps or fancy stuff for me — I went in the other di­rec­tion, de­sign­ing the sys­tem to draw a max­i­mum of about 1 watt at peak, drop­ping to around 10% of that when the cam­era was idle. All this elec­tri­cal power turns into heat, so if I can draw 1 watt only while cap­tur­ing an im­age, then turn the im­age sen­sor off as soon as the data is in RAM, I can halve the con­sump­tion, then when the im­age has been down­loaded to the flight com­puter I can turn the RAM off and drop the power down to a com­par­a­tive trickle. The only ther­mal man­age­ment needed was bolt­ing the edge of the board to the chas­sis so the in­ter­nal cop­per planes in the board could trans­fer any heat gen­er­ated.

Cooling even a sin­gle H200 will be an ab­solute night­mare. Clearly a heatsink and fan won’t do any­thing at all, but there is a liq­uid cooled H200 vari­ant. Let’s say this was used. This heat would need to be trans­ferred to a ra­di­a­tor panel — this is­n’t like the ra­di­a­tor in your car, no con­vec­tion, re­mem­ber? — which needs to ra­di­ate heat into space. Let’s as­sume that we can point this away from the sun.

The Active Thermal Control System (ATCS) on the ISS is an ex­am­ple of such a ther­mal con­trol sys­tem. This is a very com­plex sys­tem, us­ing an am­mo­nia cool­ing loop and a large ther­mal ra­di­a­tor panel sys­tem. It has a dis­si­pa­tion limit of 16kW, so roughly 16 H200 GPUs, a bit over the equiv­a­lent to a quar­ter of a ground-based rack. The ther­mal ra­di­a­tor panel sys­tem mea­sures 13.6m x 3.12 m, i.e., roughly 42.5 square me­tres. If we use 200kW as a base­line and as­sume all of that power will be fed to GPUs, we’d need a sys­tem 12.5 times big­ger, i.e., roughly 531 square me­tres, or about 2.6 times the size of the rel­e­vant so­lar ar­ray. This is now go­ing to be a very large satel­lite, dwarf­ing the ISS in area, all for the equiv­a­lent of three stan­dard server racks on Earth.

This is get­ting into my PhD work now. Assuming you can both power and cool your elec­tron­ics in space, you have the fur­ther prob­lem of ra­di­a­tion tol­er­ance.

The first ques­tion is where in space?

If you are in low Earth or­bit (LEO), you are in­side the in­ner ra­di­a­tion belt, where ra­di­a­tion dose is sim­i­lar to that ex­pe­ri­enced by high al­ti­tude air­craft — more than an air­liner, but not ter­ri­ble. Further out, in mid Earth or­bit (MEO), where the GPS satel­lites live, they are not pro­tected by the Van Allen belts — worse, this or­bit is lit­er­ally in­side them. Outside the belts, you are es­sen­tially in deep space (details vary with how close to the Sun you hap­pen to be, but the prin­ci­ples are sim­i­lar).

There are two main sources of ra­di­a­tion in space — from our own star, the Sun, and from deep space. This ba­si­cally in­volves charged par­ti­cles mov­ing at a sub­stan­tial per­cent­age of the speed of light, from elec­trons to the nu­clei of atoms with masses up to roughly that of oxy­gen. These can cause di­rect dam­age, by smash­ing into the ma­te­r­ial from which chips are made, or in­di­rectly, by trav­el­ling through the sil­i­con die with­out hit­ting any­thing but still leav­ing a trail of charge be­hind them.

The most com­mon con­se­qence of this hap­pen­ing is a sin­gle-event up­set (SEU), where a di­rect im­pact or (more com­monly) a par­ti­cle pass­ing through a tran­sis­tor briefly (approx 600 pi­cosec­onds) causes a pulse to hap­pen where it should­n’t have. If this causes a bit to be flipped, we call this a SEU. Other than dam­age to data, they don’t cause per­ma­nent dam­age.

Worse is sin­gle-event latch-up. This hap­pens when a pulse from a charged par­ti­cle causes a volt­age to go out­side the power rails pow­er­ing the chip, caus­ing a tran­sis­tor es­sen­tially to turn on and stay on in­def­i­nitely. I’ll skip the semi­con­duc­tor physics in­volved, but the short ver­sion is that if this hap­pens in a bad way, you can get a path­way con­nected be­tween the power rails that should­n’t be there, burn­ing out a gate per­ma­nently. This may or may not de­stroy the chip, but with­out mit­i­ga­tion it can make it un­us­able.

For longer du­ra­tion mis­sions, which would be the case with space based dat­a­cen­ters be­cause they would be so ex­pen­sive that they would have to fly for a long time in or­der to be eco­nom­i­cally vi­able, it’s also nec­es­sary to con­sider to­tal dose ef­fects. Over time, the per­for­mance of chips in space de­grades, be­cause re­peated par­ti­cle im­pacts make the tiny field-ef­fect tran­sis­tors switch more slowly and turn on and off less com­pletely. In prac­tice, this causes max­i­mum vi­able clock rates to de­cay over time, and for power con­sump­tion to in­crease. Though not the hard­est is­sue to deal with, this must still be mit­i­gated or you tend to run into a sit­u­a­tion where a chip that was work­ing fine at launch stops work­ing be­cause ei­ther the power sup­ply or cool­ing has be­come in­ad­e­quate, or the clock is run­ning faster than the chip can cope with. It’s there­fore nec­es­sary to have a clock gen­er­a­tor that can throt­tle down to a lower speed as needed — this can also be used to con­trol power con­sump­tion, so rather than a chip ceas­ing to func­tion it will just get slower.

The next FAQ is, can’t you just use shield­ing? No, not re­ally, or maybe up to a point. Some kinds of shield­ing can make the prob­lem worse — an im­pact to the shield can cause a shower of par­ti­cles that then cause mul­ti­ple im­pact at once, which is far harder to mit­i­gate. The very strongest cos­mic rays can go through an as­ton­ish­ing amount of solid lead — since mass is al­ways at a pre­mium, it’s rarely pos­si­ble to de­ploy sig­nif­i­cant amounts of shield­ing, so ra­di­a­tion tol­er­ance must be built into the sys­tem (this is of­ten de­scribed as Radiation Hardness By Design, RHBD).

GPUs and TPUs and the high band­width RAM they de­pend on are ab­solutely worst case for ra­di­a­tion tol­er­ance pur­poses. Small geom­e­try tran­sis­tors are in­her­ently much more prone both to SEUs and latch-up. The very large sil­i­con die area also makes the fre­quency of im­pacts higher, since that scales with area.

Chips gen­uinely de­signed to work in space are taped out with dif­fer­ent gate struc­tures and much larger geome­tries. The proces­sors that are typ­i­cally used have the per­for­mance of roughly a 20-year-old PowerPC from 2005. Bigger geome­tries are in­her­ently more tol­er­ant, both to SEUs and to­tal dose, and the dif­fer­ent gate topolo­gies are im­mune to latch up, whilst pro­vid­ing some de­gree of SEU mit­i­ga­tion via fine-grained re­dun­dancy at the cir­cuit level. Taping out a GPU or TPU with this kind of ap­proach is cer­tainly pos­si­ble, but the per­for­mance would be a tiny frac­tion of that of a cur­rent gen­er­a­tion Earth-based GPU/TPU.

There is a you-only-live-once (my ter­mi­nol­ogy!) ap­proach, where you launch the thing and hope for the best. This is com­mon­place in small cube­sats, and also why small cube­sats of­ten fail af­ter a few weeks on or­bit. Caveat emp­tor!

Most satel­lites com­mu­ni­cate with the ground via ra­dio. It is dif­fi­cult to get much more than about 1Gbps re­li­ably. There is some in­ter­est­ing work us­ing lasers to com­mu­ni­cate with satel­lites, but this de­pends on good at­mos­pheric con­di­tions to be fea­si­ble. Contrasting this with a typ­i­cal server rack on Earth, where 100Gbps rack-to-rack in­ter­con­nect would be con­sid­ered at the low end, and it’s easy to see that this is also a sig­nif­i­cant gap.

I sup­pose this is just about pos­si­ble if you re­ally want to do it, but I think I’ve demon­strated above that it would firstly be ex­tremely dif­fi­cult to achieve, dis­pro­por­tion­ately costly in com­par­i­son with Earth-based dat­a­cen­ters, and of­fer mediocre per­for­mance at best.

If you still think this is worth do­ing, good luck, space is hard. Myself, I think it’s a cat­a­stroph­i­cally bad idea, but you do you.

Fed up with tril­lion-dol­lar com­pa­nies ex­ploit­ing your data? Forced to use their ser­vices? Your data held for ran­som? Your data used to train their AI mod­els? Opt-outs for data col­lec­tion in­stead of opt-ins?

Join the move­ment to make com­pa­nies more like Clippy. Set your pro­file pic­ture to Clippy, make your voice heard.

Below is a video that ex­plains the Be Like Clippy move­ment. It’s a call to ac­tion for de­vel­op­ers, com­pa­nies, and users alike to em­brace a more open, trans­par­ent, and user-friendly ap­proach to tech­nol­ogy.

Americans have grown sour on one of the long­time key in­gre­di­ents of the American dream.

Almost two-thirds of reg­is­tered vot­ers say that a four-year col­lege de­gree is­n’t worth the cost, ac­cord­ing to a new NBC News poll, a dra­matic de­cline over the last decade.

Just 33% agree a four-year col­lege de­gree is “worth the cost be­cause peo­ple have a bet­ter chance to get a good job and earn more money over their life­time,” while 63% agree more with the con­cept that it’s “not worth the cost be­cause peo­ple of­ten grad­u­ate with­out spe­cific job skills and with a large amount of debt to pay off.”

In 2017, U. S. adults sur­veyed were vir­tu­ally split on the ques­tion — 49% said a de­gree was worth the cost and 47% said it was­n’t. When CNBC asked the same ques­tion in 2013 as part of its All American Economic Survey, 53% said a de­gree was worth it and 40% said it was not.

The eye-pop­ping shift over the last 12 years comes against the back­drop of sev­eral ma­jor trends shap­ing the job mar­ket and the ed­u­ca­tion world, from ex­plod­ing col­lege tu­ition prices to rapid changes in the mod­ern econ­omy — which seems once again poised for rad­i­cal trans­for­ma­tion along­side ad­vances in AI.

“It’s just re­mark­able to see at­ti­tudes on any is­sue shift this dra­mat­i­cally, and par­tic­u­larly on a cen­tral tenet of the American dream, which is a col­lege de­gree. Americans used to view a col­lege de­gree as as­pi­ra­tional — it pro­vided an op­por­tu­nity for a bet­ter life. And now that promise is re­ally in doubt,” said Democratic poll­ster Jeff Horwitt of Hart Research Associates, who con­ducted the poll along with the Republican poll­ster Bill McInturff of Public Opinion Strategies.

“What is re­ally sur­pris­ing about it is that every­body has moved. It’s not just peo­ple who don’t have a col­lege de­gree,” Horwitt added.

National data from the Bureau of Labor Statistics shows that those with ad­vanced de­grees earn more and have lower un­em­ploy­ment rates than those with lower lev­els of ed­u­ca­tion. That’s been true for years.

But what has shifted is the price of col­lege. While there have been some small de­clines in tu­ition prices over the last decade, when ad­justed for in­fla­tion, College Board data shows that the av­er­age, in­fla­tion-ad­justed cost of pub­lic four-year col­lege tu­ition for in-state stu­dents has dou­bled since 1995. Tuition at pri­vate, four-year col­leges is up 75% over the same pe­riod.

Poll re­spon­dents who spoke with NBC News all em­pha­sized those ris­ing costs as a ma­jor rea­son why the value of a four-year de­gree has been un­der­cut.

Jacob Kennedy, a 28-year-old server and bar­tender liv­ing in Detroit, told NBC News that while he be­lieves “an ed­u­cated pop­u­lace is the most im­por­tant thing for a coun­try to have,” if peo­ple can’t use those de­grees be­cause of the debt they’re car­ry­ing, it un­der­cuts the value.

Kennedy, who has a two-year de­gree, re­flected on “the num­ber of peo­ple who I’ve met work­ing in the ser­vice in­dus­try who have four-year de­grees and then within a year of grad­u­at­ing im­me­di­ately quit their ‘grown-up jobs’ to go back to the jobs they had.”

“The cost over­whelms the value,” he con­tin­ued. “You go to school with all that stu­dent debt — the jobs you get out of col­lege don’t pay that debt, so you have to go find some­thing else that can pay that debt.”

The 20-point de­cline over the last 12 years among those who say a de­gree is worth it — from 53% in 2013 to 33% now — is re­flected across vir­tu­ally every de­mo­graphic group. But the shift in sen­ti­ment is es­pe­cially strik­ing among Republicans.

In 2013, 55% of Republicans called a col­lege de­gree worth it, while 38% said it was­n’t worth it. In the new poll, just 22% of Republicans say the four-year de­gree is worth it, while 74% say it’s not.

Democrats have seen a sig­nif­i­cant shift too, but not to the same ex­tent — a de­cline from 61% who said a de­gree was worth it in 2013 to 47% this year.

Over the same pe­riod, the com­po­si­tion of both par­ties has changed, with the Republican Party gar­ner­ing new and deeper sup­port from vot­ers with­out col­lege de­grees, while the Democratic Party drew in more de­gree-hold­ers.

Remarkably, less than half of vot­ers with col­lege de­grees see those de­grees as worth the cost: 46% now, down from 63% in 2013.

Those with­out a col­lege de­gree were about split on the ques­tion in 2013. Now, 71% say a four-year de­gree is not worth the cost, while 26% say it is.

Preston Cooper, a se­nior fel­low at the right-lean­ing American Enterprise Institute, said enough cracks have pro­lif­er­ated un­der the long-stand­ing nar­ra­tive that a col­lege de­gree al­ways pays off to cre­ate a se­ri­ous rup­ture.

“Some peo­ple drop out, or some­times peo­ple end up with a de­gree that is not worth a whole lot in the la­bor mar­ket, and some­times peo­ple pay way too much for a de­gree rel­a­tive to the value of what that cre­den­tial is,” he said. “These cases have cre­ated enough ex­cep­tions to the rule that a bach­e­lor’s de­gree al­ways pays off, so that peo­ple are now more skep­ti­cal.”

The up­shot is that in­ter­est in tech­ni­cal, vo­ca­tional and two-year de­gree pro­grams has soared.

“I think stu­dents are more wary about tak­ing on the risk of a four-year or even a two-year de­gree,” he said. “They’re now more in­ter­ested in any path­way that can get them into the la­bor force more quickly.”

Josiah Garcia, a 24-year-old in Virginia, said he re­cently en­rolled in a pro­gram to re­ceive a four-year en­gi­neer­ing de­gree af­ter work­ing as an elec­tri­cian’s ap­pren­tice. He said he was mo­ti­vated to go back to school be­cause he saw the de­gree as hav­ing a di­rect ef­fect on his fu­ture earn­ing po­ten­tial.

But he added that he did­n’t feel that those who sought other de­grees in ar­eas like art or the­ater could say the same.

“A lot of my friends who went to school for art or dance did­n’t get the job they thought they could get af­ter grad­u­at­ing,” he said, ar­gu­ing that de­grees for “softer skills” should be cheaper than those in STEM fields.

Jessica Burns, a 38-year-old Iowa res­i­dent and bach­e­lor’s de­gree-holder who works for an in­sur­ance com­pany, told NBC News that for her, the worth of a four-year-de­gree largely de­pends on the cost.

She went to a com­mu­nity col­lege and then a state school to earn her de­gree, so she said she grad­u­ated with­out hav­ing to spend an “insane” amount of money.

But her hus­band went to a pri­vate col­lege for his de­gree, and she quipped: “We are go­ing to have stu­dent loan debt for him for­ever.”

Burns said she be­lieves a col­lege de­gree is “essential for a lot of jobs. You’re not go­ing to get an in­ter­view if you don’t have a four-year de­gree for a lot of jobs in my field.”

But she framed the value of de­grees more in terms of how so­ci­ety views them in­stead of in­trin­sic value.

“It’s not valu­able be­cause it’s brought a bunch of value added, it’s valu­able be­cause it’s the key to even get­ting in the door,” she said. “Our so­ci­ety needs to fig­ure out that if we value it, we need to make it af­ford­able.”

Burns said she be­lieves that a lot more peo­ple in her mil­len­nial gen­er­a­tion are “now sad­dled with a huge amount of debt, even as suc­cess­ful busi­ness pro­fes­sion­als,” which will in­flu­ence how her peers ap­proach pay­ing for col­lege for their chil­dren.

There has­n’t just been a de­cline in the cost-ben­e­fit analy­sis of a de­gree. Gallup polling also shows a marked de­cline in pub­lic con­fi­dence in higher ed­u­ca­tion over the last decade, al­beit with a slight in­crease over the last year.

“This is a po­lit­i­cal prob­lem. It’s also a real prob­lem for higher ed­u­ca­tion. Colleges and uni­ver­si­ties have lost that con­nec­tion they’ve had with a large swath of the American peo­ple based on af­ford­abil­ity,” Horwitt said. “They’re now seen as out of touch and not ac­ces­si­ble to many Americans.”

The NBC News poll sur­veyed 1,000 reg­is­tered vot­ers Oct. 24-28 via a mix of tele­phone in­ter­views and an on­line sur­vey sent via text mes­sage. The mar­gin of er­ror is plus or mi­nus 3.1 per­cent­age points.

Let’s rip the Band-Aid off im­me­di­ately: If your un­der­ly­ing busi­ness process is a mess, sprin­kling “AI dust” on it won’t turn it into gold. It will just speed up the rate at which you gen­er­ate garbage. In the world of Business IT, we get se­duced by the shiny new toy. Right now, that toy is Artificial Intelligence. Boardrooms are buzzing with buzz­words like LLMs, agen­tic work­flows, and gen­er­a­tive rea­son­ing. Executives are fran­ti­cally ask­ing, “What is our AI strat­egy?“Like every ma­jor tech­no­log­i­cal shift be­fore it—from the steam en­gine to the spread­sheet—AI does not in­her­ently make an or­ga­ni­za­tion smarter. AI, like any other tool, only makes faster.If you au­to­mate a stu­pid de­ci­sion, you just make stu­pid de­ci­sions at light speed. If you ap­ply an agen­tic AI work­flow to a bu­reau­cratic night­mare of an ap­proval chain, you haven’t fixed the bu­reau­cracy; you’ve just built a ro­bot that hates its job as much as your em­ploy­ees do.For decades, tra­di­tional soft­ware de­manded struc­ture. Rows, columns, booleans, and fixed fields. If data did­n’t fit the box, the com­puter could­n’t read it.Be­cause com­put­ers could­n’t han­dle the mess, hu­mans han­dled it (before AI). And hu­mans don’t al­ways fol­low a flow chart. These processes—like “handling a com­plex cus­tomer com­plaint” or “brainstorming a mar­ket­ing cam­paign”—are of­ten ad-hoc, in­tu­itive, and com­pletely un­doc­u­mented. They live in the heads of your se­nior staff, not in your SOPs.If you want to use AI to process un­struc­tured data, you must first bring struc­ture to the work­flow it­self. You need to im­prove your process de­sign to ac­count for the am­bi­gu­ity that AI han­dles.What is the trans­for­ma­tion? (What ex­actly is the hu­man—or now the AI—supposed to ex­tract or de­duce from that mess?)The Old Way: An an­a­lyst reads 50 con­tracts (unstructured), high­lights risks based on gut feel­ing (unstructured process), and sum­ma­rizes them in 3 days.The AI Way: An AI scans 50 con­tracts and ex­tracts spe­cific risk clauses based on de­fined pa­ra­me­ters in 3 min­utes.The process (Review Contracts -> Identify Risk -> Summarize) has­n’t changed, but it had to be rig­or­ously de­fined for the AI to work. The in­tel­li­gence (knowing what a “risk” ac­tu­ally means) still re­quires hu­man gov­er­nance. What has changed is the ve­loc­ity.Go back to the white­board. Map out your value chain—es­pe­cially the messy, hu­man-cen­tric parts in­volv­ing un­struc­tured data that you pre­vi­ously ig­nored. Find the bot­tle­necks. Identify the waste.Tech­nol­ogy changes.

The rules of busi­ness ef­fi­ciency do not.

It’s al­ways the process, stu­pid!

And that’s where ac­tual AI Tools are miss­ing that point, be­cause they weren’t build for that

Von der Idee zur App ohne eine Zeile Code zu schreiben

Vom Datengrab zur Goldmine - KI Einsatz mit schnellem ROI (Promptcast)

Wie man KI am schnell­sten gewinnbrin­gend ein­set­zen kann (Diesmal nur als Prompcast)

Vom Datengrab zur Goldmine - KI Einsatz mit schnellem ROI0:00/894.6184131×

Live long and pros­per 😉🖖

Silicon Valleys KI-Burggraben hat ein Leck — es heißt Open Source

Der Mythos der un­ein­nehm­baren Festung

In den Strategie-Etagen des Silicon Valley erzählt man sich gerne die Geschichte von den un­ein­nehm­baren Burggräben. Der KI-Wettlauf, so die Legende, sei ein Spiel für Giganten mit Budgets so groß wie Kleinstaaten. Nur eine Handvoll US Tech-Konzerne könne hier mit­spie­len, der Rest der Welt schaut

Was, wenn der lauteste Teilnehmer im Raum nicht zwangsläu­fig der führende ist?

For those un­fa­mil­iar, Zigtools was founded to sup­port the Zig com­mu­nity, es­pe­cially new­com­ers, by cre­at­ing ed­i­tor tool­ing such as ZLS, pro­vid­ing build­ing blocks for lan­guage servers writ­ten in Zig with lsp-kit, work­ing on tools like the Zigtools Playground, and con­tribut­ing to Zig ed­i­tor ex­ten­sions like vs­code-zig.

A cou­ple weeks ago, a Zig re­source called Zigbook was re­leased with a bold claim of “zero AI” and an orig­i­nal “project-based” struc­ture.

Unfortunately, even a cur­sory look at the non­sense chap­ter struc­ture, book con­tent, ex­am­ples, generic web­site, or post-back­lash is­sue-dis­abled repo re­veals that the book is wholly LLM slop and the pro­ject it­self is struc­tured like some sort of syco­phan­tic psy-op, with bot­ted ac­counts and fake re­ac­tions.

We’re leav­ing out all di­rect links to Zigbook to not give them any more SEO trac­tion.

We thought that the broad com­mu­nity back­lash would be the end of the pro­ject, but Zigbook per­se­vered, re­leas­ing just last week a brand new fea­ture, a “high-voltage beta” Zig play­ground.

As we at Zigtools have our own Zig play­ground (repo, web­site), our in­ter­est was im­me­di­ately piqued. The form and func­tion­al­ity looked pretty sim­i­lar and Zigbook even in­te­grated (in a non-func­tional man­ner) ZLS into their play­ground to pro­vide all the fancy ed­i­tor bells-and-whis­tles, like code com­ple­tions and goto de­f­i­n­i­tion.

Knowing Zigbook’s his­tory of de­cep­tion, we im­me­di­ately in­ves­ti­gated the WASM blobs. Unfortunately, the WASM blobs are byte-for-byte iden­ti­cal to ours. This can­not be a co­in­ci­dence given the two blobs (zig.wasm, a lightly mod­i­fied ver­sion of the Zig com­piler, and zls.wasm, ZLS with a mod­i­fied en­try point for WASI) are en­tirely cus­tom-made for the Zigtools Playground.

We archived the WASM files for your con­ve­nience, cour­tesy of the great Internet Archive:

We pro­ceeded to look at the JavaScript code, which we quickly de­ter­mined was sim­i­larly copied, but with LLM dis­tor­tions, likely to pre­vent the code from be­ing com­pletely iden­ti­cal. Still, cer­tain sec­tions were copied one-to-one, like the JavaScript worker data-pass­ing struc­ture and log­ging (original ZLS play­ground code, pla­gia­rized Zigbook code).

The fol­low­ing code from both files is iden­ti­cal:

try {

// @ts-ignore

const ex­it­Code = wasi.start(in­stance);

postMes­sage({

stderr: `\n\n–-\nexit with exit code ${exitCode}\n–-\n`,

} catch (err) {

postMes­sage({ stderr: `${err}` });

postMes­sage({

done: true,

on­mes­sage = (event) => {

if (event.data.run) {

run(event.data.run);

The \n\n–-\nexit with exit code ${exitCode}\n–-\n is per­haps the most ob­vi­ously copied string.

Funnily enough, de­spite copy­ing many parts of our code, Zigbook did­n’t copy the most im­por­tant part of the ZLS in­te­gra­tion code, the JavaScript ZLS API de­signed to work with the ZLS WASM bi­na­ry’s API. That JavaScript code is ab­solutely re­quired to in­ter­act with the ZLS bi­nary which they did pla­gia­rize. Zigbook ei­ther avoided copy­ing that JavaScript code be­cause they knew it would be too glar­ingly ob­vi­ous, be­cause they fun­da­men­tally do not un­der­stand how the Zigtools Playground works, or be­cause they plan to copy more of our code.

To be clear, copy­ing our code and WASM blobs is en­tirely per­mis­si­ble given that the play­ground and Zig are MIT li­censed. Unfortunately, Zigbook has not com­plied with the terms of the MIT li­cense at all, and seem­ingly claims the code and blobs as their own with­out cor­rectly re­pro­duc­ing the li­cense.

We sent Zigbook a neu­tral PR cor­rect­ing the li­cense vi­o­la­tions, but they quickly closed it and deleted the de­scrip­tion, seem­ingly to hide their mis­deeds.

The orig­i­nal de­scrip­tion (also avail­able in the “edits” drop­down of the orig­i­nal PR com­ment) is re­pro­duced be­low:

We (@zigtools) no­ticed you were us­ing code from the Zigtools Playground, in­clud­ing byte-by-byte copies of our WASM blobs and ex­cerpts of our JavaScript source code. This is a vi­o­la­tion of the MIT li­cense that the Zigtools Playground is li­censed un­der along­side a vi­o­la­tion of the Zig MIT li­cense (for the zig.wasm blob).The above copy­right no­tice and this per­mis­sion no­tice shall be in­cluded in

all copies or sub­stan­tial por­tions of the Software.

We’ve fixed this by adding the li­censes in ques­tion to your repos­i­tory. As your repos­i­tory does not in­clude a di­rect link to the *.wasm de­pen­den­cies, we’ve added a li­cense dis­claimer on the play­ground page as well that men­tions the li­censes.

Zigbook’s afore­men­tioned bad be­hav­ior and their con­tin­ued vi­o­la­tion of our li­cense and un­will­ing­ness to fix the vi­o­la­tion mo­ti­vated us to write this blog post.

It’s sad that our first blog post is about the pla­gia­rism of our coolest sub­pro­ject. We chal­lenged our­selves by cre­at­ing a WASM-based client-side play­ground to en­able of­fline us­age, code pri­vacy, and no server costs.

This in­ci­dent has mo­ti­vated us to in­vest more time into our play­ground and has gen­er­ated a cou­ple of ideas:

* We’d like to en­able mul­ti­file sup­port to al­low more com­plex Zig pro­jects to be run in the browser

* We’d like to col­lab­o­rate with fel­low Ziguanas to in­te­grate the play­ground into their ex­cel­lent Zig tu­to­ri­als, books, and blog­postsA per­fect ex­am­ple use­case would be en­abling folks to hop into Ziglings on­line with the play­groundThe Zig web­site it­self would be a great tar­get as well!

* A per­fect ex­am­ple use­case would be en­abling folks to hop into Ziglings on­line with the play­ground

* The Zig web­site it­self would be a great tar­get as well!

* We’d like to sup­port stack traces us­ing DWARF de­bug info which is not yet emit­ted by the self-hosted Zig com­piler

As Zig com­mu­nity mem­bers, we ad­vise all other mem­bers of the Zig com­mu­nity to steer clear of Zigbook.

If you’re look­ing to learn Zig, we strongly rec­om­mend look­ing at the ex­cel­lent of­fi­cial Zig learn page which con­tains ex­cel­lent re­sources from the pre­vi­ously men­tioned Ziglings to Karl Seguin’s Learning Zig.

We’re also us­ing this op­por­tu­nity to men­tion that we’re fundrais­ing to keep ZLS sus­tain­able for our only full-time main­tainer, Techatrix. We’d be thrilled if you’d be will­ing to give just $5 a month. You can check out our OpenCollective or GitHub Sponsors.

What can re­searchers do if they sus­pect that their man­u­scripts have been peer re­viewed us­ing ar­ti­fi­cial in­tel­li­gence (AI)? Dozens of aca­d­e­mics have raised con­cerns on so­cial me­dia about man­u­scripts and peer re­views sub­mit­ted to the or­ga­niz­ers of next year’s International Conference on Learning Representations (ICLR), an an­nual gath­er­ing of spe­cial­ists in ma­chine learn­ing. Among other things, they flagged hal­lu­ci­nated ci­ta­tions and sus­pi­ciously long and vague feed­back on their work.

Graham Neubig, an AI re­searcher at Carnegie Mellon University in Pittsburgh, Pennsylvania, was one of those who re­ceived peer re­views that seemed to have been pro­duced us­ing large lan­guage mod­els (LLMs). The re­ports, he says, were “very ver­bose with lots of bul­let points” and re­quested analy­ses that were not “the stan­dard sta­tis­ti­cal analy­ses that re­view­ers ask for in typ­i­cal AI or ma­chine-learn­ing pa­pers.”

But Neubig needed help prov­ing that the re­ports were AI-generated. So, he posted on X (formerly Twitter) and of­fered a re­ward for any­one who could scan all the con­fer­ence sub­mis­sions and their peer re­views for AI-generated text. The next day, he got a re­sponse from Max Spero, chief ex­ec­u­tive of Pangram Labs in New York City, which de­vel­ops tools to de­tect AI-generated text. Pangram screened all 19,490 stud­ies and 75,800 peer re­views sub­mit­ted for ICLR 2026, which will take place in Rio de Janeiro, Brazil, in April. Neubig and more than 11,000 other AI re­searchers will be at­tend­ing.

Pangram’s analy­sis re­vealed that around 21% of the ICLR peer re­views were fully AI-generated, and more than half con­tained signs of AI use. The find­ings were posted on­line by Pangram Labs. “People were sus­pi­cious, but they did­n’t have any con­crete proof,” says Spero. “Over the course of 12 hours, we wrote some code to parse out all of the text con­tent from these pa­per sub­mis­sions,” he adds.

The con­fer­ence or­ga­niz­ers say they will now use au­to­mated tools to as­sess whether sub­mis­sions and peer re­views breached poli­cies on us­ing AI in sub­mis­sions and peer re­views. This is the first time that the con­fer­ence has faced this is­sue at scale, says Bharath Hariharan, a com­puter sci­en­tist at Cornell University in Ithaca, New York, and se­nior pro­gramme chair for ICLR 2026. “After we go through all this process … that will give us a bet­ter no­tion of trust.”

The Pangram team used one of its own tools, which pre­dicts whether text is gen­er­ated or edited by LLMs. Pangram’s analy­sis flagged 15,899 peer re­views that were fully AI-generated. But it also iden­ti­fied many man­u­scripts that had been sub­mit­ted to the con­fer­ence with sus­pected cases of AI-generated text: 199 man­u­scripts (1%) were found to be fully AI-generated; 61% of sub­mis­sions were mostly hu­man-writ­ten; but 9% con­tained more than 50% AI-generated text.

Pangram de­scribed the model in a preprint1, which it sub­mit­ted to ICLR 2026. Of the four peer re­views re­ceived for the man­u­script, one was flagged as fully AI-generated and an­other as lightly AI-edited, the team’s analy­sis found.

AI is trans­form­ing peer re­view — and many sci­en­tists are wor­ried

For many re­searchers who re­ceived peer re­views for their sub­mis­sions to ICLR, the Pangram analy­sis con­firmed what they had sus­pected. Desmond Elliott, a com­puter sci­en­tist at the University of Copenhagen, says that one of three re­views he re­ceived seemed to have missed “the point of the pa­per”. His PhD stu­dent who led the work sus­pected that the re­view was gen­er­ated by LLMs, be­cause it men­tioned nu­mer­i­cal re­sults from the man­u­script that were in­cor­rect and con­tained odd ex­pres­sions.

When Pangram re­leased its find­ings, Elliott adds, “the first thing I did was I typed in the ti­tle of our pa­per be­cause I wanted to know whether my stu­den­t’s gut in­stinct was cor­rect”. The sus­pect peer re­view, which Pangram’s analy­sis flagged as fully AI-generated, gave the man­u­script the low­est rat­ing, leav­ing it “on the bor­der­line be­tween ac­cept and re­ject”, says Elliott. “It’s deeply frus­trat­ing”.