Maintained by Epic Games, Lore is de­signed for un­prece­dented scal­a­bil­ity of both data and teams. It’s op­ti­mized for pro­jects—in­clud­ing games and en­ter­tain­ment—that com­bine code with large bi­nary as­sets, and caters for the needs of de­vel­op­ers and artists alike.

Key find­ings

74%

con­sumers say the in­ter­net feels less hu­man than 10 years ago

40 min

av­er­age time be­fore con­sumers ex­pe­ri­ence “bot fa­tigue”

61%

con­sumers can’t name a brand us­ing AI well in its mes­sag­ing

16.6

av­er­age weekly hours en­ter­prise teams spend im­prov­ing AI vis­i­bil­ity

Brands have been chas­ing AI vis­i­bil­ity for two years. You’ve spent time and bud­get on it, yet your au­di­ence can’t name a sin­gle com­pany they think is do­ing it well. The brands build­ing for the next phase treat their web­site as the place where AI gets clean data and hu­mans get some­thing worth their time.

A less hu­man web costs you read­ers.

Your au­di­ence can sense when a ma­chine is talk­ing to them. Most are check­ing out be­fore they’ve de­cided whether they care. Bot fa­tigue sets in when the in­ter­net stops feel­ing hon­est. The small mo­ments that used to make the web worth vis­it­ing are dis­ap­pear­ing.

The AI web

7/10

con­sumers say the in­ter­net feels less hu­man than it did 10 years ago

Feels less hu­man

Still hu­man

40 min

the av­er­age time to “bot fa­tigue,” when in­ter­ac­tions start to feel syn­thetic

Can your con­tent in­fra­struc­ture mea­sure this shift and re­spond to it? We’ll cover how en­ter­prise teams re­struc­ture con­tent for AI dis­cov­ery with­out los­ing what feels hu­man in our up­com­ing we­bi­nar.

What is AI brand vis­i­bil­ity?

AI brand vis­i­bil­ity is how of­ten a brand ap­pears in an­swers gen­er­ated by AI en­gines like ChatGPT, Perplexity, Claude, and Gemini. It’s a dif­fer­ent prob­lem from search en­gine vis­i­bil­ity, which mea­sures rank­ings on re­sult pages. A brand can rank at the top of Google and not ap­pear in­side ChatGPT at all. As of 2026, no sin­gle dash­board tracks AI brand vis­i­bil­ity across every en­gine, and the cat­e­gory has no es­tab­lished leader.

Nobody has won AI brand vis­i­bil­ity yet.

Every an­swer in our con­sumer sur­vey pointed the same way: Nobody has done it well yet. Brands have spent the past year fund­ing AI strat­egy, but con­sumers can’t point to a sin­gle com­pany they think is get­ting it right.

The cat­e­gory has no in­cum­bent, and no tem­plate to copy. The brand that builds that recog­ni­tion first gets to de­fine the stan­dard.

Brand vis­i­bil­ity

61%

of con­sumers can’t name a brand that uses AI well in its mes­sag­ing

16%

say no brand is us­ing AI well at all

60%

say AI in a brand’s mes­sag­ing is a turnoff, not a fea­ture

“No cus­tomer or user wakes up and says, ‘I hope I get to talk to a chat bot or an AI agent to­day.’ Human-centered de­sign is truer to­day with ar­ti­fi­cial in­tel­li­gence. Ironically, the an­swer is us­ing AI to be more hu­man.”— Brian Solis, Head of Global Innovation, ServiceNow

“No cus­tomer or user wakes up and says, ‘I hope I get to talk to a chat bot or an AI agent to­day.’ Human-centered de­sign is truer to­day with ar­ti­fi­cial in­tel­li­gence. Ironically, the an­swer is us­ing AI to be more hu­man.”

Build for both au­di­ences at once.

AI needs to find the con­tent and a per­son needs a rea­son to stay once they ar­rive. The sec­ond part is harder, and most en­ter­prises are still guess­ing at it. The brands worth watch­ing are bet­ting that “staying” comes from giv­ing peo­ple some­thing to do: in­ter­ac­tive con­tent, dy­namic ex­pe­ri­ences, the small mo­ments a flat AI sum­mary can’t de­liver.

The web­site is the only place where both jobs run to­gether. AI gets struc­tured con­tent it can cite, and the reader gets some­thing worth their time. That’s the foun­da­tion you get on WordPress VIP.

The guide for build­ing that dual-pur­pose site is in Future-Proof Your Brand for the AI-Native Web, a frame­work for prepar­ing your web plat­form.

How en­ter­prises are mea­sur­ing AI brand vis­i­bil­ity

The cat­e­gory is barely two years old and the toolset is still set­tling. No sin­gle dash­board tracks every AI sur­face. No shared de­f­i­n­i­tion of “good” ex­ists yet. Pricing across the cat­e­gory swings from free to six fig­ures de­pend­ing on cov­er­age and cus­tomiza­tion. What en­ter­prises are us­ing right now sorts into five cat­e­gories, with real tools in­side each.

This is a snap­shot since the spe­cific prod­ucts will shift in the next 12 months. The cat­e­gories will out­last them, which is why the sec­tion is or­ga­nized around what the tools do.

This is the newest cat­e­gory, built specif­i­cally to track how of­ten a brand ap­pears in ChatGPT, Perplexity, Claude, and Gemini an­swers. These tools sim­u­late queries at scale and sur­face ci­ta­tion fre­quency and sen­ti­ment over time.

Tools in this cat­e­gory: Profound, BrightEdge, brand­vis­i­bil­ity.ai, Tryevergreen, and a hand­ful of smaller com­peti­tors that emerged in late 2025.

Best for: Teams that need to con­nect AI vis­i­bil­ity to busi­ness out­comes. AI ci­ta­tions are top-of-fun­nel. This cat­e­gory mea­sures what those ci­ta­tions turn into. The brands that fig­ure out which AI-referred vis­i­tors con­vert can de­fend their AI strat­egy spend.

Watch for: Pricing mod­els are still set­tling. Most plat­forms re­quire four to six weeks of data col­lec­tion be­fore bench­marks are mean­ing­ful. Sample-based query sim­u­la­tion has gaps, and tools that promise “complete cov­er­age” of every AI an­swer are over­stat­ing their method­ol­ogy.

These are the es­tab­lished SEO plat­forms that ex­tended into AI track­ing start­ing in 2024. These tools layer AI ci­ta­tion data on top of tra­di­tional search met­rics, which makes them use­ful for teams al­ready run­ning SEO work­flows.

Tools in this cat­e­gory: Similarweb (AI Intelligence), Semrush (AI Toolkit), Ahrefs (Brand Radar).

Best for: SEO teams that want AI vis­i­bil­ity data with­out a new ven­dor re­la­tion­ship. The in­te­gra­tion with ex­ist­ing search re­port­ing is the main value; it lets a team see or­ganic and AI traf­fic in the same view.

Watch for: AI cov­er­age in this cat­e­gory is gen­er­ally nar­rower than in ded­i­cated AI ci­ta­tion plat­forms. The tools were built for search and are still catch­ing up on the AI side. AI num­bers here have to be treated as di­rec­tional.

In this cat­e­gory: the an­a­lyt­ics plat­forms that de­tect and seg­ment traf­fic ar­riv­ing from AI en­gines. These are the ci­ta­tion mon­i­tor­ing tools that tell a brand it’s be­ing men­tioned. This cat­e­gory tells a brand what hap­pens af­ter.

Tools in this cat­e­gory: Parse.ly (part of the WordPress VIP prod­uct fam­ily), Plausible, Fathom Analytics, and most en­ter­prise an­a­lyt­ics plat­forms (Google Analytics 4) with cus­tom seg­men­ta­tion.

Best for: Teams that need to con­nect AI vis­i­bil­ity to busi­ness out­comes. AI ci­ta­tions are top-of-fun­nel. This cat­e­gory mea­sures what those ci­ta­tions turn into. The brands that fig­ure out which AI-referred vis­i­tors con­vert can de­fend their AI strat­egy spend.

Watch for: AI re­fer­rer de­tec­tion still varies by plat­form. Some AI en­gines pass clean re­fer­rer head­ers, oth­ers rely on UTM tag­ging. Coordination be­tween con­tent and an­a­lyt­ics teams is usu­ally re­quired to get clean data.

Broader brand mon­i­tor­ing plat­forms that added AI sur­face track­ing to ex­ist­ing so­cial lis­ten­ing and PR mon­i­tor­ing ca­pa­bil­i­ties. These cover AI en­gines as one in­put along­side so­cial and tra­di­tional me­dia men­tions.

Tools in this cat­e­gory: Brandwatch, Talkwalker, Meltwater.

Best for: Communications and PR teams that al­ready use these plat­forms for cri­sis mon­i­tor­ing and share-of-voice track­ing. The AI cov­er­age is an ex­ten­sion of an ex­ist­ing work­flow.

Watch for: AI cov­er­age in this cat­e­gory tends to be lighter than in ded­i­cated AI ci­ta­tion tools. Useful for a 30,000-foot view, less use­ful for gran­u­lar ci­ta­tion analy­sis.

This is what en­ter­prises with en­gi­neer­ing ca­pac­ity are build­ing them­selves. These so­lu­tions use LLM APIs to query AI en­gines on a sched­ule and sur­face re­sults in a dash­board the team con­trols. Pew Research Center’s work with WordPress VIP, cov­ered in Chapter 2, is one ex­am­ple of this ap­proach.

Best for: Enterprises with en­gi­neer­ing re­sources who want to de­fine their own queries and con­trol their own data. Ideal when the brand’s AI vis­i­bil­ity strat­egy de­pends on niche or in­dus­try-spe­cific queries that off-the-shelf tools don’t cover well.

Watch for: Maintenance bur­den. LLM API ac­cess is now sta­ble, though pric­ing and rate lim­its change fre­quently. Custom dash­boards re­quire on­go­ing en­gi­neer­ing at­ten­tion to keep cur­rent.

AI brand vis­i­bil­ity tools at a glance

How to choose

Match the tool cat­e­gory to the ques­tion the team needs to an­swer:

“Are we be­ing cited?” Use an AI ci­ta­tion mon­i­tor­ing plat­form.

“Are we be­ing cited rel­a­tive to our search per­for­mance?” Use search an­a­lyt­ics with AI over­lays.

“What hap­pens af­ter we’re cited?” Use web an­a­lyt­ics with AI re­fer­ral track­ing.

“How does AI fit into our broader brand sen­ti­ment?” Use a brand in­tel­li­gence plat­form.

“We need to track some­thing none of the above can an­swer.” Build a cus­tom so­lu­tion.

Most en­ter­prises use two cat­e­gories to­gether. The most com­mon com­bi­na­tion is a tool from the AI ci­ta­tion mon­i­tor­ing cat­e­gory to know whether the brand shows up, and a tool from the web an­a­lyt­ics cat­e­gory to know what that vis­i­bil­ity is worth. The brands that fig­ured this out first are the ones whose 2027 AI vis­i­bil­ity bud­gets won’t be re-lit­i­gated in bud­get meet­ings.

Continue read­ing

Chapter 2

Brands chase AI vis­i­bil­ity. Consumers chase the source.

Chapter 3

Consumers are wary of gate­keep­ing. More than mar­keters are.

Chapter 4

The web­site is still the de­fault trust layer.

Chapter 5

The next web­site does­n’t look like a web­site.

FAQs about AI brand vis­i­bil­ity

Bot fa­tigue is the point at which on­line in­ter­ac­tions start to feel syn­thetic. WordPress VIP’s 2026 sur­vey of 1,200 U.S. con­sumers found the av­er­age per­son hits bot fa­tigue in about 40 min­utes. The broader pat­tern: 74% of con­sumers say the in­ter­net feels less hu­man than it did 10 years ago, which is the con­sumer-mood shift dri­ving most of what brands are now try­ing to solve in their AI strat­egy.

Not yet. The cat­e­gory is too new and the mea­sure­ment tools are too im­ma­ture. Platforms cite dif­fer­ent sources for dif­fer­ent queries, the ci­ta­tions change as mod­els up­date, and the met­rics en­ter­prise teams use to track AI vis­i­bil­ity aren’t stan­dard­ized across ven­dors. What’s clear is that no brand has built a durable AI pres­ence. The brand that de­fines what “AI brand vis­i­bil­ity done well” looks like will be the one that fig­ures out the mea­sure­ment layer be­fore the rest of the mar­ket does.

The web­site has two jobs now and they have to run on the same foun­da­tion. AI en­gines need struc­tured con­tent they can find and cite ac­cu­rately. Human vis­i­tors need a rea­son to stay once they click through from an AI sum­mary. The brands solv­ing for both are treat­ing the web­site as the place where AI ex­tracts data and a per­son has an ex­pe­ri­ence worth their time. This is the cen­tral ar­gu­ment of WordPress VIP’s 2026 State of the Open Web re­port.

Z ai’s GLM-5.2 is the new lead­ing open weights model on the Artificial Analysis Intelligence Index scor­ing 51 and it sits on the Pareto fron­tier of Intelligence vs Cost per Task

GLM-5.2 is the same size as GLM-5.1 (744B to­tal / 40B ac­tive pa­ra­me­ters) but scores 11 points higher on the Intelligence Index v4.1, plac­ing ahead of MiniMax-M3 (44) and DeepSeek V4 Pro (max, 44). On the first-party API it is priced in line with GLM-5.1 at $1.4/$4.4/$0.26 per 1M in­put/​out­put/​cache hit to­kens

Key re­sults:

➤ GLM-5.2 is the lead­ing open weights model on the Intelligence Index v4.1. At 51, it leads MiniMax-M3 (44), DeepSeek V4 Pro (max, 44) and Kimi K2.6 (43)

➤ Improvements across most eval­u­a­tions, par­tic­u­larly sci­en­tific rea­son­ing: GLM-5.2 gains over GLM-5.1 on most eval­u­a­tions, led by sci­en­tific rea­son­ing on CritPt (+16 points to 21%) and HLE (+12 points to 40%), along­side AA-LCR (+9 points to 71%), tau3 bank­ing (+15 points to 27%) and SciCode (+7 points to 50%). TerminalBench v2.1 also im­proves (+16 points to 78%) and GPQA Diamond gains 3 points to 89%

➤ Leading open weights model on GDPval-AA v2 and com­pet­i­tive with pro­pri­etary mod­els: GLM-5.2 scores 1524 on GDPval-AA v2, ahead of MiniMax-M3 (1418) and DeepSeek V4 Pro (max, 1328). This im­pres­sive re­sult places GLM-5.2 in-line with pro­pri­etary mod­els in­clud­ing GPT-5.5 (xhigh rea­son­ing). GDPval-AA v2 builds on the orig­i­nal GDPval-AA by baselin­ing Elo to hu­man per­for­mance at 1000, in­tro­duc­ing a ro­tat­ing panel of fron­tier-model judges, and rais­ing the turn limit from 100 to 250 for longer-hori­zon agent tra­jec­to­ries

➤ GLM-5.2 uses more out­put to­kens per task than other lead­ing open weights mod­els: the model uses 43k out­put to­kens per Intelligence Index task, up from GLM-5.1 (26k) and above MiniMax-M3 (24k), Kimi K2.6 (35k) and DeepSeek V4 Pro (max, 37k)

➤ On the Intelligence vs. Cost per Task Pareto Frontier: GLM-5.2 is on the Pareto fron­tier of the Intelligence vs Cost per Task chart, with the low­est cost per task among mod­els at its in­tel­li­gence level. GLM-5.2 costs ~$0.46 per task, com­pared to GLM-5.1 ($0.25), Kimi K2.6 ($0.31), MiniMax-M3 ($0.18) and DeepSeek V4 Pro (max, $0.05)

Additional Model Details:

➤ License: MIT

➤ Size: 744B to­tal pa­ra­me­ters, 40B ac­tive pa­ra­me­ters, equiv­a­lent to GLM-5.1

➤ Context win­dow: 1M to­kens, up from 200K on GLM-5.1

➤ Pricing: $1.4/$0.26/$4.4 per 1M in­put/​cache hit/​out­put to­kens

➤ Availability: Alongside Z ai’s first-party API, GLM-5.2 is avail­able across third-party providers in­clud­ing DeepInfra, Novita, Nebius, Parasail, Siliconflow, GMI Cloud, Baseten, and Fireworks

GLM-5.2 leads all open weights mod­els on GDPval-AA v2, our pri­mary met­ric for real-world agen­tic per­for­mance. At 1524 it places ahead of MiniMax-M3 (1418) and DeepSeek V4 Pro (max, 1328), and is ef­fec­tively level with GPT-5.5 (xhigh, 1514). We vi­su­ally in­spected GLM-5.2′s out­puts across a range of GDPval-AA tasks. We have at­tached a se­lec­tion be­low.

GLM-5.2 scores 4 on the AA-Omniscience Index, up from GLM-5.1 (2). The gain comes from both higher ac­cu­racy (25.1% vs 24.2%) and a lower hal­lu­ci­na­tion rate (28.1% vs 29.4%), with at­tempt rate flat at 47%.

GLM-5.2 uses 43k out­put to­kens per Intelligence Index task, of which 37k is rea­son­ing. This is up from GLM-5.1 (26k) and higher than open weights peers MiniMax-M3 (24k) and Kimi K2.6 (35k), plac­ing it among the less to­ken-ef­fi­cient open weights mod­els at its in­tel­li­gence level. GLM-5.2 sits off the most at­trac­tive quad­rant on the Intelligence vs Output Tokens chart.

Breakdown of the in­di­vid­ual eval­u­a­tions in the Artificial Analysis Intelligence Index v4.1.

Compare GLM-5.2 with other lead­ing mod­els at: https://​ar­ti­fi­cial­analy­sis.ai/​mod­els/​glm-5 – 2

Last year Christopher Reynolds started to worry that his space tele­scope was go­ing to be killed.

The mis­sion had started tak­ing shape nine years ear­lier, a bil­lion-dol­lar or­bit­ing ob­ser­va­tory that would look back in time into the early uni­verse to study the first black holes, the for­ma­tion of galax­ies, and more. Eight teams of re­searchers pitched NASA their ideas; Reynolds, an as­tronomer at the University of Maryland, was part of a group that wanted to de­ploy a new tech­nol­ogy: x-ray mir­rors made of sin­gle-crys­tal sil­i­con. It sounded promis­ing enough that in October 2024 Reynolds’s group got a $5-million grant from the agency to re­fine the idea—the Advanced X-ray Imaging Satellite, or AXIS. The sci­en­tists teamed up with space­craft builders at the nasa Goddard Space Flight Center. “Everything seemed to be go­ing pretty well,” Reynolds says. “And then we started to get hit by pro­gram­matic chaos.”

Last June the bud­get hawks in the Department of Government Efficiency (DOGE) pushed NASA into of­fer­ing a broad pack­age of buy­outs, paid leave and early re­tire­ment. Over the next few weeks nearly 4,000 NASA em­ploy­ees—about a fifth of the work­force—took the deal. Reynolds’s AXIS team lost 20 peo­ple. The en­gi­neer de­sign­ing the heaters to keep the x-ray mir­ror at a con­stant tem­per­a­ture: gone. The lead pro­ject man­ager: gone. William Zhang, the as­tro­physi­cist who in­vented the tele­scope’s mir­ror tech­nol­ogy: gone. “We were lit­er­ally left with their PowerPoints, try­ing to fig­ure out what they’d done and where we were with as­pects of the de­sign,” Reynolds says.

On sup­port­ing sci­ence jour­nal­ism

If you’re en­joy­ing this ar­ti­cle, con­sider sup­port­ing our award-win­ning jour­nal­ism by sub­scrib­ing. By pur­chas­ing a sub­scrip­tion you are help­ing to en­sure the fu­ture of im­pact­ful sto­ries about the dis­cov­er­ies and ideas shap­ing our world to­day.

Around the same time President Donald Trump’s bud­get pro­posal came out—with mas­sive cuts to sci­ence fund­ing. In the U.S., pri­vate money funds vast amounts of sci­en­tific de­vel­op­ment re­search, and phil­an­thropy con­tributes a bit, but some­thing like 40 per­cent of all the fund­ing for ba­sic, blue-sky, ex­ploratory re­search comes from the fed­eral gov­ern­ment. The pro­gram that would have funded AXIS was ze­roed out en­tirely.

That was just the re­quest, Reynolds fig­ured at the time; Congress still has to do the ac­tual ap­pro­pri­a­tion. “In any nor­mal year, that’s what would have hap­pened,” he says. “But the cen­ter lead­er­ship started quite quickly align­ing their pri­or­i­ties to the pres­i­den­t’s bud­get re­quest.”

Goddard re­as­signed en­gi­neers to pro­jects that would be funded if Congress ap­proved the bud­get as writ­ten. Reynolds’s team lost its sys­tems en­gi­neers, which in turn de­layed shar­ing of AXIS’s pro­posed de­sign with Goddard’s cost an­a­lysts and sched­ule spe­cial­ists. “We got our very first cost es­ti­mate in the mid­dle of September 2025,” Reynolds says. “We were 10 per­cent over bud­get.” He started try­ing to find things to cut. But then, in October, the fed­eral gov­ern­ment shut down. “The whole cen­ter just stopped,” he says. “Everything stopped.”

When the shut­down ended in mid-No­vem­ber, Reynolds’s team had just two weeks to get on bud­get. It failed. The plan the group sub­mit­ted would cost too much and take too long. “Our last hope was that NASA head­quar­ters would un­der­stand what had gone on and give us some lee­way,” Reynolds says. NASA did not. After nearly 10 years of work, AXIS was dead.

Now, Reynolds says, he’s fine, mostly. He’s a tenured pro­fes­sor and has other re­search to work on. “The jobs that are lost are the fu­ture jobs,” he says. “And there’s an en­tire field of study in which U.S. lead­er­ship is at stake.” The hard­est part, though, is how it hap­pened. DOGE’s cuts sliced through American re­search grants like a thresher, “but this was much murkier,” Reynolds says. “We were never can­celed. We were just starved to death.”

Countless sci­en­tists around the coun­try are go­ing through the same thing. Thousands of fed­eral grants have been frozen or can­celed, with per­haps 2,600 still in limbo—about $1.4 bil­lion worth. The National Science Foundation and the National Institutes of Health are award­ing three quar­ters of their usual num­ber of grants. Fewer peo­ple are en­ter­ing grad­u­ate pro­grams. Nearly 95,000 sci­en­tists have left fed­eral gov­ern­ment em­ploy­ment. The NIH used to is­sue as many as 850 “Notices of Funding Opportunity” every year—re­quests for pro­pos­als that sought spe­cific kinds of re­search. In 2025 the agency is­sued 120. By mid-March of 2026, the NIH had sent 14.

What’s go­ing on is noth­ing short of a gen­er­a­tional change in how the U.S. or­ga­nizes its sci­en­tific en­ter­prise. More than that, sci­ence feels dif­fer­ent. Its pur­pose, its ex­is­ten­tial vibe, seems to have shifted. The cul­tural sta­tus of the peo­ple who do it has changed. And they don’t un­der­stand why.

The pre­vail­ing emo­tions among sci­en­tists right now are rage and shock. A sur­vey con­ducted by sci­ence news web­site STAT found that more than half of re­searchers with grants from the NIH—once a re­li­able source of $40 bil­lion a year—re­ported some level of dis­rup­tion to their fund­ing: a to­tal freeze, a de­lay in dis­burse­ment or a re­duc­tion in amount. And 81 per­cent of re­searchers in tenure-track po­si­tions said they were con­cerned that fund­ing dis­rup­tions could af­fect their pro­duc­tiv­ity enough to jeop­ar­dize their chances of get­ting tenure.

Now, to be sure, the end prod­uct of sci­ence is sup­posed to be sci­ence, not grants or tenure. Applying for highly com­pet­i­tive grants with lim­ited fund­ing is what sci­en­tists have al­ways had to do to carry out the sci­ence—a flawed process with few al­ter­na­tives. But ar­bi­trary can­cel­la­tions and de­layed dis­burse­ments are un­prece­dented. And jus­ti­fy­ing them on the ba­sis of pol­i­tics—pro­hibit­ing, for in­stance, grants that in­clude lan­guage ref­er­enc­ing di­ver­sity, eq­uity and in­clu­sion (DEI)—was un­heard of un­til now.

When Jenna Norton, a pro­gram di­rec­tor at the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDKD), first got to the NIH 12 years ago, she wanted to in­crease re­search into the so­cial de­ter­mi­nants of health—struc­tural racism in home-loan prac­tices meant that non­white peo­ple got iced out of home own­er­ship and gen­er­a­tional wealth, which forced them to live in neigh­bor­hoods closer to toxic sites such as fac­to­ries and high­ways, with­out side­walks and ameni­ties. “It’s a chal­leng­ing field to quan­tify, but we’re get­ting to a place in sci­ence where we can start ask­ing these ques­tions,” Norton says. Now the topic is ver­boten in U.S. grants. “That whole line of re­search has been shut off and cen­sored be­cause some peo­ple find the words ‘structural racism’ of­fen­sive.”

Mari Fouz (illustration); Getty Images (photographs fea­tured in il­lus­tra­tion)

Political op­er­a­tives at the NIH passed around lists of words that grants weren’t al­lowed to use—in ei­ther ap­pli­ca­tions or ex­ist­ing, funded pro­jects. Program man­agers across the NIH and the NSF were told to ask af­fected re­searchers whether they’d care to change the lan­guage in their re­search de­scrip­tions or risk los­ing their fund­ing. Some re­searchers whose grants Norton man­aged at the NIDDKD called her to say they wanted to pre­emp­tively change the lan­guage in their grant ap­pli­ca­tions—be­fore they’d been dinged. Norton com­plained so much that she was placed on ad­min­is­tra­tive leave, al­though she has since been re­in­stated.

Of course, not all lost sci­ence had ob­vi­ous po­lit­i­cal im­pli­ca­tions. As Reynolds, the AXIS lead re­searcher, puts it, “whether there are black holes at a red­shift of 10 or not is not a par­ti­san is­sue.”

These kinds of ob­sta­cles are a new ex­pe­ri­ence for most re­searchers. Getting into a ca­reer in sci­ence was al­ready hard—stu­dents of­ten un­der­take in­tel­lec­tu­ally tax­ing and phys­i­cally gru­el­ing aca­d­e­mic work last­ing years longer than most peo­ple spend in school, with lim­ited re­mu­ner­a­tion. The peo­ple who do it tend to be mis­sion-dri­ven: they want to help oth­ers, learn some­thing about the uni­verse or in­vent some­thing new. If they con­sider the po­lit­i­cal im­pli­ca­tions, it’s be­cause they’re in­trin­sic to the work. “It’s not just that peo­ple feel their ca­reer is un­der at­tack,” says one long­time pub­lic health re­searcher. “They feel they per­son­ally are un­der at­tack.”

DEI as­so­ci­a­tions aren’t the only top­ics that get cap­tured by the new po­lit­i­cal fil­ters. Now, for the first time, grant re­cip­i­ents aren’t al­lowed to sub­con­tract to col­lab­o­ra­tors on pro­jects over­seas. “That’s ob­vi­ously a prob­lem when you study nasty dis­eases such as Lassa fever and Ebola, be­cause they’re not in this coun­try,” says Kristian Andersen, an evo­lu­tion­ary bi­ol­o­gist at Scripps Research in La Jolla, Calif. “That’s my whole ca­reer. This is why I came to the United States.”

Most years, when Andersen ad­ver­tises a post­doc­toral re­search op­por­tu­nity in his lab­o­ra­tory, he gets up to 200 ap­pli­cants with per­haps a third of them from Europe. This year he had 100 ap­pli­cants and none from Europe. Typically his lab would ap­ply for two or three so-called cen­ter grants every year. This past year there were none in vi­rol­ogy, im­munol­ogy or vi­ral im­munol­ogy to ap­ply for. So what’s next? Andersen, who’s Danish, says that “for peo­ple like my­self, I think the best op­tion is prob­a­bly to leave and do sci­ence else­where.” And he is­n’t the only one think­ing of get­ting out. Of about 1,650 sci­en­tists who re­sponded to a poll by the jour­nal Nature, 75 per­cent said they were con­sid­er­ing it.

“The most pas­sion­ate and cre­ative sci­en­tists are very in­tu­itive and very dri­ven by emo­tion and cu­rios­ity,” says Gregory Feist, a psy­chol­o­gist at San José State University who stud­ies sci­en­tists. “Until Trump, they’d been able to keep po­lit­i­cal ques­tions out of mind.” Their work was, if not above pol­i­tics, at least out­side it—es­sen­tial to every­one, re­gard­less of where they were on the po­lit­i­cal spec­trum.

Now they see things dif­fer­ently. “The big eye-opener for me this past year is how quickly things can change,” a NASA cli­mate sci­en­tist says. This shock at the ease with which the gov­ern­ment can rewrite the sys­tem came up in mul­ti­ple in­ter­views. “Is your grant go­ing to be frozen? Is it go­ing to be ter­mi­nated? Is it go­ing to be re­in­stated? Is it go­ing to be de­layed be­cause you’re re­quired to change the word­ing?” asks Scott Delaney, a for­mer Harvard University epi­demi­ol­o­gist who co-cre­ated the watch­dog group Grant Witness. “The re­al­ity is, be­cause of what hap­pened and what’s hap­pen­ing now, the trust be­tween re­searchers and the fed­eral gov­ern­ment is com­pletely bro­ken.”

Without that trust, the en­tire sys­tem could blow apart. “Laboratories are go­ing to close. Trainees are go­ing to go to other coun­tries or pur­sue non­science ca­reers,” says Carole LaBonne, a de­vel­op­men­tal bi­ol­o­gist at Northwestern University. “This com­pact that has ex­isted since World War II, that made the U.S. the suc­cess­ful, pros­per­ous na­tion that it is, is be­ing dis­man­tled.”

What broke the com­pact? Several re­searchers iden­ti­fied the re­sponse to the COVID pan­demic as a flash point. Public health guid­ance flailed ini­tially on ques­tions of mask­ing, school clo­sures and front­line drugs. It also pro­duced a good vac­cine in un­der a year, an un­heard-of suc­cess. Ultimately around a mil­lion peo­ple died of the dis­ease within the first two years.

The ex­pe­ri­ence dam­aged trust in sci­ence and sci­en­tists. It’s still high—the num­ber of peo­ple say­ing they have a lot of trust in sci­ence has hov­ered around 77 per­cent for years. But it was 10 points higher be­fore COVID, and it now splits hard along lines of po­lit­i­cal af­fil­i­a­tion. “Especially in the U.S. and with so­cial me­dia, all of a sud­den every­body was an ex­pert on COVID. So much of it was just bull­shit,” Andersen says. “And then at some point bull­shit was all that was left.”

That helps to ex­plain how a non­sci­en­tist such as Robert F. Kennedy, Jr., known for un­ortho­dox and un­proven ideas about health and med­i­cine, be­came leader of the Department of Health and Human Services with over­sight of the NIH. But it does­n’t ex­plain how Elon Musk, an in­dus­tri­al­ist and the rich­est hu­man to ever live, got the power to ex­cise so much of the coun­try’s re­search. It does­n’t ex­plain why the for­mer con­ser­v­a­tive think tanker Russell Vought could use con­trol of the wonk­ish Office of Management and Budget to zero out re­search fund­ing.

“I would like to see more peo­ple speak­ing up, but the fact is, mostly peo­ple don’t.” —Kristian Andersen Scripps Research

“I would like to see more peo­ple speak­ing up, but the fact is, mostly peo­ple don’t.” —Kristian Andersen Scripps Research

There’s a strain of an­tipa­thy to uni­ver­si­ties and aca­d­e­mic truth-seek­ing in far-right con­ser­vatism, cer­tainly. But other than burn-it-all-down ni­hilism or anti-in­tel­lec­tu­al­ism, why nuke the so­cial con­tract be­tween gov­ern­ment and sci­ence? One pos­si­bil­ity is that the deal was al­ready dy­ing.

In the first half of the 20th cen­tury, busi­ness­peo­ple, pol­i­cy­mak­ers and sci­en­tists try­ing to fig­ure out how airy aca­d­e­mic re­search got turned into use­ful stuff came up with what’s now called the lin­ear model of in­no­va­tion, a the­o­ret­i­cal (and con­tested) se­quence that went from fund­ing to ba­sic re­search to ap­plied re­search to the de­vel­op­ment of a tech­nol­ogy or prod­uct. The best-known cod­i­fi­ca­tion of the model came to­ward the end of World War II in a re­port called Science: The Endless Frontier, by Vannevar Bush, an en­gi­neer who had headed the wartime Office of Scientific Research and Development. Bush un­der­stood that ap­plied sci­ence had won the war for the Allies—not only the atomic bomb but also radar, peni­cillin, food preser­va­tion, cryp­tog­ra­phy, and so on. Nerds saved free­dom’s ba­con, but Bush and oth­ers had had a hell of a time get­ting that nascent sci­en­tific po­ten­tial onto the bat­tle­field. So Bush pro­posed putting all of U.S. sci­ence on re­tainer.

Basic re­search, Bush wrote, was “performed with­out thought of prac­ti­cal ends” and “creates the fund from which the prac­ti­cal ap­pli­ca­tions of knowl­edge must be drawn.” So he pro­posed a vast ex­pan­sion of the state’s ca­pac­ity to do sci­ence, via fund­ing man­aged by agen­cies such as the NSF and the NIH. The gov­ern­ment would give tax dol­lars to sci­en­tists so they could cast around in the dark do­ing ba­sic re­search. Irregularly, some of that work would lead to new drugs or com­mu­ni­ca­tions satel­lites or op­ti­mized food crops. Not every dol­lar of gov­ern­ment sup­port for sci­ence would re­sult in a block­buster drug or a bil­lion-dol­lar tech­nol­ogy, but a ma­jor­ity of block­buster drugs and bil­lion-dol­lar tech­nolo­gies would de­rive from gov­ern­ment sup­port. So the gov­ern­ment promised to fund a lot. And in re­turn, the sci­en­tists promised to jump through the gov­ern­men­t’s hoops and re­spond to an oc­ca­sional Bat-Signal. “That’s the hand­shake be­tween sci­ence and the mar­ket,” says Benjamin Jones, an econ­o­mist at Northwestern, who stud­ies in­no­va­tion.

It sounds like a busi­ness- and de­fense-minded strat­egy. But as in­no­va­tion re­searcher Benoît Godin points out, even though Bush agreed with busi­ness in­ter­ests about the fact that re­search and the train­ing of sci­en­tists led to in­dus­trial progress, his ra­tio­nale was ex­plic­itly so­cial. “Without sci­en­tific progress the na­tional health would de­te­ri­o­rate; with­out sci­en­tific progress we could not hope for im­prove­ment in our stan­dard of liv­ing or for an in­creased num­ber of jobs for our cit­i­zens; and with­out sci­en­tific progress we could not have main­tained our lib­er­ties against tyranny,” Bush wrote.

In fact, by the 1960s mil­i­tary and in­dus­trial in­ter­ests had mostly lost pa­tience with the ivory-tower ex­ploratory side of the equa­tion. The lead­ers of American cap­i­tal and fi­nance cer­tainly wanted to goose sci­en­tific and tech­ni­cal in­no­va­tion, but they thought the real prob­lem was where the money went and how much was avail­able. Banks did­n’t want to risk loans to iffy tech start-ups with no col­lat­eral. But a spe­cial kind of in­vestor—a ven­ture in­vestor—would bring high-risk dol­lars to re­search in re­turn for par­tial own­er­ship of the com­pany do­ing it.

That ap­proach seemed to stall out, too. In 1977 William Casey, fu­ture di­rec­tor of the Central Intelligence Agency, wrote a re­port for the U.S. Small Business Administration ar­gu­ing that it was be­cause ven­ture cap­i­tal did­n’t have ac­cess to enough money. His new model for in­no­va­tion, says M. R. Sauter, a his­to­rian of tech­nol­ogy at the University of Maryland, brought to the cen­ter not ba­sic re­search or even ap­plied en­gi­neer­ing but, sim­ply, money—and the in­vestors who had it. Casey’s re­port rec­om­mended chang­ing the reg­u­la­tions in the Employee Retirement Income Security Act of 1974 so that in­sti­tu­tional cap­i­tal, like re­tire­ment funds, could en­ter the riskier ven­ture game. In 1979 Congress did just that.

And in 1980 Congress passed the Bayh-Dole Act, mov­ing own­er­ship of the re­sults of gov­ern­ment-funded uni­ver­sity re­search from the gov­ern­ment to the uni­ver­si­ties. Now a block­buster new drug or search al­go­rithm could be a wind­fall for a uni­ver­sity, and uni­ver­sity ad­min­is­tra­tions had com­mon cause with ven­ture in­vestors. More ba­sic dis­cov­er­ies started get­ting turned into dol­lars. But the al­liance shifted the em­pha­sis from state ca­pac­ity to fi­nan­cial out­comes.

Today the most in­flu­en­tial pri­vate-sec­tor de­vel­op­ers of tech­nol­ogy are in Silicon Valley, and their per­spec­tive on in­no­va­tion is that it should move fast, dis­rupt mar­kets and make money. That per­spec­tive is in­flu­enc­ing gov­ern­ment fi­nanc­ing of sci­ence more than ever be­fore. “Right now the [Trump] ad­min­is­tra­tion is very de­struc­tive and is chang­ing its mind all the time. It has this dim­mer view of sci­ence and also sort of wants to win in tech­nol­ogy,” says Jones, the Northwestern econ­o­mist. “That is fu­eled some­what by the dis­rup­tive ori­en­ta­tion of suc­cess­ful peo­ple in Silicon Valley who are hav­ing an in­flu­ence.”

“I think that per­spec­tive is flat-out wrong,” Jones adds.

For most of this cen­tury pretty much every met­ric of sci­en­tific pro­duc­tiv­ity—new re­sults, new dis­cov­er­ies and new in­ven­tions—has ap­peared to be down. This idea is con­tro­ver­sial, and the data are dif­fi­cult to mea­sure, but that’s aca­d­e­mic be­cause this nom­i­nal down­turn opened the in­sti­tu­tions of sci­ence to crit­i­cism that it was sci­en­tists who were fail­ing to honor the bar­gain. Maybe it’s no sur­prise that the whole thing has turned into what Arizona State University so­ci­ol­o­gist Edward Hackett calls “academic cap­i­tal­ism.” Today’s in­vestors and pol­i­cy­mak­ers think all re­search should be eco­nom­i­cally rel­e­vant and as­sist in the ac­cu­mu­la­tion of cap­i­tal. A “knowledge-based econ­omy,” says Lancaster University so­ci­ol­o­gist Bob Jessop, wants all sci­en­tists to be en­tre­pre­neurs. Which all sounds fa­mil­iar.

This view might be why the newly re­con­sti­tuted President’s Council of Advisors on Science and Technology in­cludes just one sci­en­tist, a physi­cist. The other 12 mem­bers are Silicon Valley lu­mi­nar­ies such as ven­ture cap­i­tal­ist Marc Andreessen and Jensen Huang, CEO of com­puter chip­maker Nvidia. And in March, Trump nom­i­nated ven­ture cap­i­tal in­vestor Jim O’Neill as di­rec­tor of the NSF. Companies that work on ar­ti­fi­cial in­tel­li­gence, the hot tech of the mo­ment, tout the abil­ity of their prod­ucts to take over the la­bor of do­ing sci­ence, from an­a­lyz­ing data to for­mu­lat­ing hy­pothe­ses. “GPT-5.2 is kind of al­ready in­tel­li­gent enough to be a soft col­lab­o­ra­tor in many sci­en­tific in­quiries,” says Sébastien Bubeck, a com­puter sci­en­tist at OpenAI.

That’s not the world sci­en­tists want, but it’s the one they’ve got. The prob­lem is, sub­ject­ing sci­ence to po­lit­i­cal taste tests and a more com­mer­cial mind­set al­most cer­tainly means fewer world-chang­ing re­sults. No one can ever know when noodling around with Gila mon­ster saliva will yield anti-obe­sity GLP-1 drugs. And putting politi­cos atop the pyra­mid of grant eval­u­a­tions, sci­en­tists say, will be a dis­as­ter. Researchers who man­age to get grants to study health out­comes on the con­di­tion that they ig­nore the ef­fects of vari­ables such as so­cioe­co­nomic sta­tus, gen­der and eth­nic­ity won’t even be able to pub­lish their find­ings, be­cause peer re­view­ers, an NSF di­rec­tor says, “are not go­ing to sud­denly in­dulge this fan­tasy.” They’re go­ing to de­mand that stud­ies fac­tor in rel­e­vant vari­ables.

Last year a team of econ­o­mists imag­ined what this new fu­ture might look like by cre­at­ing an al­ter­na­tive past. In 2025 the NIH cut the amount of grant money awarded by more than 40 per­cent com­pared with years prior. What if, the team mem­bers asked, the NIH re­search bud­get had been 40 per­cent smaller for the past few decades? Grants in the bot­tom 40 per­cent of the pri­or­ity queue, they rea­soned, would­n’t have been funded. The team tracked those grants to their out­comes—re­search that never hap­pened in this par­al­lel uni­verse—and found that some­thing like half of all drugs sim­ply would­n’t ex­ist to­day. The lost ther­a­pies in­clude ima­tinib, the first real treat­ment for chronic myeloid leukemia, and the lung can­cer drug er­lotinib.

What are sci­en­tists sup­posed to do about all this? “I would like to see more peo­ple speak­ing up, but the fact is, mostly peo­ple don’t,” Andersen says. “They don’t want to be a tar­get of the fed­eral gov­ern­ment. Having been in that, and still be­ing in that, [I can say] it’s not very pleas­ant.”

Like many other sci­en­tists, Andersen ex­presses dis­ap­point­ment in what he sees as a fail­ure of the in­sti­tu­tions of sci­ence—the na­tional acad­e­mies, the American Association for the Advancement of Science, uni­ver­si­ties—to mount a louder op­po­si­tion. “We have seen none of that, es­pe­cially from the acad­e­mies,” he says.

Some sci­en­tists try to just keep their heads down and keep work­ing. Others know they can’t. “In pub­lic health, we have a proud his­tory of or­ga­niz­ing, right? We were cam­paign­ers,” says Gregg Gonsalves, an epi­demi­ol­o­gist and pol­icy pro­fes­sor at Yale University. By the 21st cen­tury that had changed. “We were told it was not im­por­tant, that what mat­tered was the num­ber of grants and pub­li­ca­tions you had. ‘Forget all the so­cial and po­lit­i­cal things; those are in­ci­den­tal.’ Turns out they were not. They’re core to it.”

Gonsalves, who was in­volved in the fight to care for peo­ple with HIV and AIDS in the 1980s, says that sci­en­tists now have an­other job: “bearing wit­ness and putting ev­i­dence on the table. It may not be per­sua­sive to Russell Vought or Marco Rubio, but it is for the dossier, for the truth and rec­on­cil­i­a­tion com­mis­sion, for the Nuremberg tri­als that come af­ter,” he says. “Keep the re­ceipts. Write down what you see. Tell them what they did. We’re very good at doc­u­ment­ing how X leads to Y.”

That’s the thing about gen­er­a­tional shifts. There’s al­ways a next gen­er­a­tion af­ter this one.

We’re in the era of tril­lion-dol­lar com­pa­nies, but that does­n’t mean you should leave $5 on the table!

Take, for ex­am­ple, Photobucket. In case you’ve never heard of it, it is the Imgur-equivalent of ages ago. I was us­ing it as a kid to up­load im­ages there and link them on var­i­ous fo­rums. A nice, sim­ple web­site that just did its job.

So what’s my old pal Photobucket do­ing now?

Well, re­cently, I was go­ing through my old ac­counts and started clean­ing them up. Deleting what needs to be deleted, restor­ing pass­words to what was no longer work­ing, things like that1…

And that’s how I re­mem­bered about Photobucket!

Now, mind you, that was af­ter I’d al­ready found my old Imgur ac­count with hun­dreds of old (and nos­tal­gic!) screen­shots that I then backed up safely. So I was re­ally ex­cited! This Photobucket ac­count must have been… damn… even older! Who knows what rem­nants of my child­hood I’ll find there?

And so, I logged in ex­pec­tantly. Ready to be amazed! Prepared to drop a nos­tal­gia tear! Excited to fi­nally…

Wait, WHAT?!!

You shared them. We pro­tected them.

…

AGAINST WHAT?!! Because it sure as hell is­n’t against cor­po­rate greed! More like:

You shared them. We pay­walled them. (And you should thank us!)

You shared them. We pay­walled them. (And you should thank us!)

So I now have to pay for the im­ages I up­loaded on a (previously) free ser­vice?!! No way, I’m not do­ing that! Take my im­ages and keep them on your servers un­til you go bank­rupt! I won’t en­dorse such be­hav­ior by giv­ing you money.

I’m no fool!

…

But… I mean it’s just $5… Surely my child­hood mem­o­ries must be worth more than that, right? And I guess it’s kinda nice they did­n’t delete them… So, okay, I’ll give this some thought and…

$5… PER MONTH?!!

So this unas­sum­ing claim:

It’s time to re­live them for just $5.

It’s time to re­live them for just $5.

from the sweet-sweet Photobucket Inc. was miss­ing just a tiny lit­tle de­tail… a foot­note… some legalese, as you might say… That it’s a MONTHLY SUBSCRIPTION, MAYBE?!!

THE. BLATANT. GREED!

So they’re hop­ing that I’m cu­ri­ous, I pay for this crap, and then for­get about my MONTHLY SUBSCRIPTION? Oh no, no, no. That’s al­most evil. Paywalling my child­hood mem­o­ries in or­der to trap me into ac­ci­den­tally sub­scrib­ing to your use­less ser­vice? Hoping I’d then can­cel it af­ter maybe a few years, when I fi­nally no­tice an odd ex­pense on my card?

NO.

Just no. I won’t stand for this. Childhood mem­o­ries be damned!

Aw man… I re­ally won­der what’s in there… I mean it’s just a sub­scrip­tion… I pay the $5, I down­load my im­ages, I can­cel the sub­scrip­tion, and I’m outta there. May God be my wit­ness that it was­n’t an easy call, that I fought for my dig­nity!

Ok, let’s do this quickly. I want to min­i­mize the pain.

Enter the card de­tails… click on Pay by Card… wait a bit…

And I’m in!

Finally! Really won­der­ing what’s in here! Let’s take a… look……

Oh… No…

IS IT FREAKING EMPTY?!!

…

I think I hate my­self now. I’m such a fool!!! That’s what I get for go­ing along with these preda­tory tac­tics… Should’ve known this would hap­pen. I guess I was us­ing a dif­fer­ent, even older Photobucket ac­count as a kid, or some­thing.

…

Wait.

Photobucket. You must have known I don’t have any im­ages on my ac­count. AND YOU STILL MADE ME PAY?!! Reclaim your mem­o­ries my ass.

That’s it, I’m done.

I knew I for­got some­thing!

Okay. Now I’m done.

Editor’s note

As I was writ­ing and re­liv­ing this beau­ti­ful ex­pe­ri­ence, I no­ticed a lit­tle foot­note on the pay­ments page:

Did I no­tice this in time to re­quest the re­fund? Of course I did­n’t. Those 5 beau­ti­ful dol­lars are for­ever gone.

But maybe this helps some­one… Yay to the move­ment against cor­po­rate greed!… 🍻

Update

This post ended up be­ing pretty pop­u­lar on Hacker News with lots of fun dis­cus­sions. Two things worth men­tion­ing:

Just to be ex­tra clear, I don’t be­lieve Photobucket in­ten­tion­ally deleted all the im­ages I up­loaded and then asked for $5/mo just be­cause they hate me. While I do re­mem­ber up­load­ing things there as a kid, I bet I was us­ing an even older ac­count.

Just to be ex­tra clear, I don’t be­lieve Photobucket in­ten­tion­ally deleted all the im­ages I up­loaded and then asked for $5/mo just be­cause they hate me. While I do re­mem­ber up­load­ing things there as a kid, I bet I was us­ing an even older ac­count.

It was sug­gested that I re­quest a charge­back from my card. Apparently debit cards also al­low them, did­n’t know this! I might give it a shot, who knows, maybe I’ll end up pay­ing $0 for this story!

It was sug­gested that I re­quest a charge­back from my card. Apparently debit cards also al­low them, did­n’t know this! I might give it a shot, who knows, maybe I’ll end up pay­ing $0 for this story!

…

Ok, no, pay­ing $0 won’t hap­pen. Apparently us­ing Vercel to host a per­sonal blog might not be the best idea. I’m nearly reach­ing the limit of Edge Requests in just 2 hours af­ter the post. According to trust­wor­thy Claude, the site will go down if I sur­pass it?…

So I’ll prob­a­bly end up do­ing this2:

No men­tion of any re­funds though, I checked! :P

I was bored. Also, go­ing through a fi­nite list of things and com­plet­ing them one at a time feels sat­is­fy­ing… even pro­duc­tive! However, feels is the cor­rect verb here. ↩

I was bored. Also, go­ing through a fi­nite list of things and com­plet­ing them one at a time feels sat­is­fy­ing… even pro­duc­tive! However, feels is the cor­rect verb here. ↩

And mi­grate to Cloudflare Pages or some­thing, when I have some time to spare. ↩

And mi­grate to Cloudflare Pages or some­thing, when I have some time to spare. ↩

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FileZilla is still awe­some af­ter 24 years (Paul Jacobson)

17 hours ago · Tech · 0 com­ments

19

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Adding a Town Square (Kev Quirk)

18 hours ago · Life · 1 com­ment

14

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Adding a Town Square (Kev Quirk)

18 hours ago · Life · 1 com­ment

14

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Why Does Trump Want the Save America Act? The Answer Should Worry Us. (Balkinization)

8 hours ago · Politics · 0 com­ments

6

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Why Does Trump Want the Save America Act? The Answer Should Worry Us. (Balkinization)

8 hours ago · Politics · 0 com­ments

6

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Caps lock is use­less and I wish I could re­move it (opulence piledrive)

20 hours ago · Tech · 9 com­ments

14

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Caps lock is use­less and I wish I could re­move it (opulence piledrive)

20 hours ago · Tech · 9 com­ments

14

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Bubbles men­tioned in the Installer Newsletter by The Verge. (@gurupanguji)

1 day ago · Tech · 1 com­ment

23

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Bubbles men­tioned in the Installer Newsletter by The Verge. (@gurupanguji)

1 day ago · Tech · 1 com­ment

23

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Appreciation for the small web (jola.dev)

2 days ago · Tech · 0 com­ments

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Appreciation for the small web (jola.dev)

2 days ago · Tech · 0 com­ments

43

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Reproducing a String Theory Vacuum in Rust (Made By Nathan)

56 min­utes ago · Tech · 0 com­ments

1

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Reproducing a String Theory Vacuum in Rust (Made By Nathan)

56 min­utes ago · Tech · 0 com­ments

1

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I missed Anil Dash’s lovely es­say cov­er­ing some of the his­tory of Markdown (jagibson.org)

6 hours ago · Tech · 0 com­ments

3

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I missed Anil Dash’s lovely es­say cov­er­ing some of the his­tory of Markdown (jagibson.org)

6 hours ago · Tech · 0 com­ments

3

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Nobody clicks your share but­tons (Ankur Sethi)

2 days ago · Tech · 2 com­ments

33

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Nobody clicks your share but­tons (Ankur Sethi)

2 days ago · Tech · 2 com­ments

33

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Check out my fa­vorite lo­cal plant (Laura Michet)

1 hour ago · Life · 0 com­ments

1

GrapheneOS Discussion Forum

Please en­able JS and dis­able any ad blocker

Despite the fact that AI in­creas­ingly dom­i­nates our econ­omy (it’s a hot IPO sum­mer and we’re all just along for the ride), most Americans are not par­tic­u­larly op­ti­mistic about the tech­nol­o­gy’s long-term im­pact on the coun­try, a new study from Pew Research re­veals.

In fact, al­though a whole lot of Americans in­creas­ingly use AI in their daily lives, most of them have neu­tral to neg­a­tive views about it, the re­search re­veals.

Only 16% of Americans think that AI’s im­pact on so­ci­ety dur­ing the next 20 years will be pos­i­tive, Pew says, while around 40% say that it will have a neg­a­tive im­pact.

A vast ma­jor­ity of peo­ple (67%) don’t be­lieve that the U.S. gov­ern­ment will do any­thing to mean­ing­fully reg­u­late AI. A sim­i­larly skep­ti­cal co­hort (59%) don’t trust com­pa­nies to de­velop it safely.

Young peo­ple — that is, those peo­ple un­der 30 — are the ones with the most neg­a­tive feel­ings about AI. Pew says that only 14% of this co­hort be­lieve the tech will have a pos­i­tive im­pact on so­ci­ety.

On top of all this, a vast ma­jor­ity of Americans — nearly two-thirds — also think that AI’s de­vel­op­ment is oc­cur­ring too quickly.

Despite all of the skep­ti­cism, a whole lot of Americans also re­port us­ing AI in their daily lives on an in­creas­ingly reg­u­lar ba­sis. About a quar­ter of Americans say they use AI chat­bots on a daily ba­sis. Those who do are typ­i­cally us­ing the chat­bots for re­search pur­poses or for work, Pew says.

A vast ma­jor­ity of peo­ple us­ing AI are us­ing ChatGPT. Pew writes that 44% of U.S. adults now say they use OpenAI’s chat­bot, a fig­ure that’s more than dou­bled since 2023.

The next most pop­u­lar chat­bot is Gemini (24%), fol­lowed by Copilot (17%) and Meta AI (14%), with Grok (8%), Claude (6%), and Character.ai (3%) lag­ging be­hind.

There is a bit of a gen­der di­vide. While chat­bot use is grow­ing for both men and women, men still use AI more and are more en­thu­si­as­tic about it, while women are more skep­ti­cal, Pew says. Men are more likely to say they use AI chat­bots in their daily lives (27% ver­sus 20% for women) and while equal shares of men and women re­port us­ing ChatGPT, men more com­monly re­port us­age of other brands, such as Copilot and Grok.

The re­port also high­lights how AI is chang­ing the ways Americans con­sume in­for­ma­tion. Six in 10 sur­vey re­spon­dents told Pew that they rou­tinely read AI-generated in­ter­net sum­maries (indeed, on Google, they’re pretty much un­avoid­able). A much smaller num­ber re­port us­ing AI to get in­for­ma­tion on fit­ness and di­et­ing.

There are also still a whole lot of peo­ple — about half of the coun­try — that say they do not use AI in their daily lives. The peo­ple who do not use AI tend to be older, while those un­der 50 are more likely to say that they use it. Nearly 75% of Americans aged 65 or older say that they never use AI chat­bots.

Those peo­ple who don’t use chat­bots say they don’t be­cause they’re not in­ter­ested in them, and add that they have no in­ten­tion of us­ing them in the fu­ture.

When you pur­chase through links in our ar­ti­cles, we may earn a small com­mis­sion. This does­n’t af­fect our ed­i­to­r­ial in­de­pen­dence.

Lucas is a se­nior writer at TechCrunch, where he cov­ers ar­ti­fi­cial in­tel­li­gence, con­sumer tech, and star­tups. He pre­vi­ously cov­ered AI and cy­ber­se­cu­rity at Gizmodo.

You can con­tact Lucas by email­ing lu­cas.ropek@techcrunch.com.

View Bio

A few days back I wrote a piece called “AI en­thu­si­asts are in a race against time, AI skep­tics are in a race against en­tropy.”

I have notes on a whole pile of AI-related top­ics that I’d like to cover in depth: AI man­dates, com­mu­ni­ca­tion norms, code re­view, AI art, and more. Unfortunately, I got too many in­ter­est­ing re­sponses to my last piece, and now I have to ad­dress those be­fore I can move on to other top­ics. 😉

There were two types of in­ter­est­ing re­sponses: the first on the tech­ni­cal mer­its, the sec­ond on eth­i­cal grounds. I will re­spond to each of these sep­a­rately. Let’s take the tech­ni­cal side first, be­cause it’s eas­ier.

Somehow, a sub­set of read­ers came away be­liev­ing I was telling every­one to ditch code re­view and push their shit­ti­est code straight into pro­duc­tion with­out read­ing it, right now, tout suite.1

That is not what I am do­ing. That is not what I think you should do. But I did not pick that ex­am­ple at ran­dom, and I will tell you why.

It’s easy to for­get, but for most of 2025, the idea that AI-generated code was slop and might al­ways be slop was not only a rea­son­able po­si­tion to hold, it was the de­fault, main­stream po­si­tion.2

That ques­tion was an­swered de­ci­sively last November. Ever since Opus 4.5 came out, AI has been able to gen­er­ate code that is ap­prox­i­mately as good as that of the me­dian soft­ware en­gi­neer, at least for com­mon pat­terns, and much faster and more cheaply. I came out of a book hole and re­al­ized this in January, and over the first few months of 2026, it seemed like every­one around me was hav­ing a sim­i­lar re­al­iza­tion.

But many saw it com­ing much sooner.

The pop­u­lar nar­ra­tive holds that Opus 4.5 was what changed. But Opus 4.5 was more like the tip­ping point. Agentic har­nesses (the code that wraps the LLM in a loop with tools) be­came a real thing in mid 2025, with pre­cur­sors build­ing back to late 2024. Tool use, func­tion call­ing, MCPs…all of this wave was build­ing over the course of 2025, and crested into real gen­eral pur­pose us­abil­ity at the end of the year.

That’s what the en­thu­si­asts were try­ing to tell us last year. Not only “this is com­ing”, but “this is com­ing faster than you think.”

As it turns out, they were right.

As you may know, I come from the re­li­a­bil­ity side of the house. The com­pli­ment I will pay to my­self and my peo­ple is that we do not strug­gle to adapt to new re­al­i­ties. As soon as a prob­lem is real and in front of us, we ad­just smoothly, even ea­gerly, thanks to an un­whole­some zest for lap­ping up dis­gust­ing tech­ni­cal messes (and the camp­fire tales we get to tell later).

The un-com­pli­ment I will pay my­self and my peo­ple is that we some­times strug­gle to ac­cept that progress is real, that the con­tin­ued ex­is­tence of bugs and edge cases does not di­min­ish the fact that huge swaths of prob­lem space do get more-or-less solved over time, to the point they can be taken for granted by most peo­ple.3

The speed at which code went from to­tal crap to “ah damn, that’s not bad” is what I have in the back of my mind, as en­thu­si­asts are telling us that har­ness en­gi­neer­ing and AI val­i­da­tion is real, it’s al­ready here, and it’s get­ting bet­ter as­ton­ish­ingly fast.

Holding out for “I’ll be­lieve it when I see it” was for­giv­able the first time, but much less so the sec­ond time. This is what it feels like to be on the in­side of an ex­po­nen­tial change curve, turns out.4

I want to pause here and be very clear about what I think is hap­pen­ing. Then I’m go­ing to tell you what specif­i­cally I am ex­cited about, and why.

You are un­der no oblig­a­tion to join me there. But there are way too many sweep­ing state­ments out there right now about “it was never X” — “it was al­ways Y” — “the fu­ture be­longs to xyzzy” 🤮 — and I want to be crys­tal clear how con­di­tional and spe­cific and con­tex­tual my claims are.

What hap­pened in 2025 was this: the eco­nom­ics of code pro­duc­tion were turned up­side down. Instead of be­ing very hard, time-con­sum­ing, and ex­pen­sive to gen­er­ate code, it be­came ef­fec­tively free and in­stant. Lines of code went from be­ing trea­sured, reused, cared for and care­fully cu­rated, to be­ing dis­pos­able and re­gen­er­a­ble, prac­ti­cally overnight.

For most of com­put­ing his­tory, the pri­mary way peo­ple have learned to un­der­stand soft­ware is by writ­ing the code. Once you’ve achieved some mas­tery, read­ing and dis­cussing code gets you most of the way there. (I might ar­gue that soft­ware en­gi­neers have al­ways re­lied far too heav­ily on the code in­stead of sense­mak­ing the sys­tem through ob­serv­abil­ity.)

Many great soft­ware en­gi­neers hold that true prod­uct of every (good) soft­ware en­gi­neer­ing team has al­ways been a shared un­der­stand­ing of the soft­ware we own. That it gets stored as cache state in our frag­ile lit­tle meat brains, fre­quently flushed to disk, de­ployed to pro­duc­tion, com­mit­ted to github, but our minds are where mean­ing has al­ways lived.

Is it any won­der that soft­ware has al­ways been such a fiercely col­lec­tivist en­deavor, ex­quis­itely sen­si­tive to re­la­tion­ship dy­nam­ics and man­ners and ques­tions of fair­ness and emo­tional va­lence? It’s ex­actly what you’d ex­pect when part of your brain lives in other peo­ple’s brains, and your col­lec­tive in­ter­de­pen­dence is sky high.

It’s some­thing that I love about this in­dus­try. But there’s no deny­ing that minds have been a poor con­tainer for cer­tain as­pects of the soft­ware de­vel­op­ment model. We are for­get­ful, dis­tractible, im­pa­tient. We are bad at spot­ting small de­tails, we grow ha­bit­u­ated to rep­e­ti­tion. Worst of all, the model in our heads di­verges mas­sively and per­pet­u­ally from the world our users in­ter­act with.

Anyway, SREs have never quite bought that ex­pla­na­tion. To us, it’s clear that the true prod­uct of every (good) soft­ware en­gi­neer­ing team is pro­duc­tion.

Only prod is prod. Test in prod, or live a lie.

(This is all back­story. I am get­ting to the point, I promise.)

We is­sued our AI man­date last August.5 I had seen enough to know that this was hap­pen­ing, and it was time to do the re­spon­si­ble thing. Honeycomb is a de­v­tools com­pany, and peo­ple come to us to help with hard prob­lems on the fore­front of tech­nol­ogy. I was all in on AI, but I can’t say I was su­per ex­cited about it, in my heart of hearts.6

Then I found Chad Fowler’s writ­ings on Phoenix Architectures.

If you don’t know what I’m talk­ing about, you should hon­estly stop read­ing my shit right now and go read his. Chad is the guy who coined the term “immutable in­fra­struc­ture” in 2013. His best-known es­say is “Relocating Rigor”, be­cause Martin Fowler7 men­tioned it re­cap­ping a Thoughtworks meetup on the fu­ture of soft­ware. I replied with “Production Is Where the Rigor Goes”, com­plain­ing that they did­n’t talk about pro­duc­tion enough.

When I wrote that, I think “Relocating Rigor” was the only piece I had read. But soon I found the rest of it, and af­ter read­ing two or three es­says, it just clicked. I knew ex­actly what he was talk­ing about. I could pre­dict the rest of what he was go­ing to say. And then, reader…then I got ex­cited.

I am go­ing to give you a small sam­ple of Chad quotes, just enough to get the gist. Here’s one from “The Death and Rebirth of Programming”.

Immutable in­fra­struc­ture. Stateless ser­vices. Containers. Blue-green de­ploy­ments. Infrastructure as code.These ideas all share a com­mon premise: never fix a run­ning thing. Replace it.AI pushes this premise be­yond in­fra­struc­ture and into ap­pli­ca­tion code it­self. When rewrit­ing is cheap, edit­ing in place be­comes risky. Mutation ac­cu­mu­lates en­tropy. Replacement re­sets it.

Immutable in­fra­struc­ture. Stateless ser­vices. Containers. Blue-green de­ploy­ments. Infrastructure as code.

These ideas all share a com­mon premise: never fix a run­ning thing. Replace it.

AI pushes this premise be­yond in­fra­struc­ture and into ap­pli­ca­tion code it­self. When rewrit­ing is cheap, edit­ing in place be­comes risky. Mutation ac­cu­mu­lates en­tropy. Replacement re­sets it.

Another fa­vorite: “The Deletion Test”.

Here’s a sim­ple test you can ap­ply to any soft­ware sys­tem you work on:Imag­ine delet­ing the en­tire im­ple­men­ta­tion.Most en­gi­neers ex­pe­ri­ence dele­tion as ex­is­ten­tial. Code feels like the thing. It’s what we write, re­view, ver­sion, de­ploy, and de­bug. Losing it feels like los­ing the sys­tem it­self.When peo­ple say, “We can’t just throw the code away,” what they usu­ally mean is some­thing more pre­cise:We don’t know ex­actly what be­hav­ior is re­quired.We don’t know which fail­ures are un­ac­cept­able.We don’t know what in­vari­ants must al­ways hold.We don’t know how to tell if a new ver­sion is cor­rect.We don’t know which bugs are in­ten­tional fixes for for­got­ten edge cases.Those are not code prob­lems. They are eval­u­a­tion prob­lems.Code be­comes pre­cious when it is the only place knowl­edge lives.

Here’s a sim­ple test you can ap­ply to any soft­ware sys­tem you work on:

Imagine delet­ing the en­tire im­ple­men­ta­tion.

Most en­gi­neers ex­pe­ri­ence dele­tion as ex­is­ten­tial. Code feels like the thing. It’s what we write, re­view, ver­sion, de­ploy, and de­bug. Losing it feels like los­ing the sys­tem it­self.

When peo­ple say, “We can’t just throw the code away,” what they usu­ally mean is some­thing more pre­cise:

We don’t know ex­actly what be­hav­ior is re­quired.

We don’t know ex­actly what be­hav­ior is re­quired.

We don’t know which fail­ures are un­ac­cept­able.

We don’t know which fail­ures are un­ac­cept­able.

We don’t know what in­vari­ants must al­ways hold.

We don’t know what in­vari­ants must al­ways hold.

We don’t know how to tell if a new ver­sion is cor­rect.

We don’t know how to tell if a new ver­sion is cor­rect.

We don’t know which bugs are in­ten­tional fixes for for­got­ten edge cases.

We don’t know which bugs are in­ten­tional fixes for for­got­ten edge cases.

Those are not code prob­lems. They are eval­u­a­tion prob­lems.

Code be­comes pre­cious when it is the only place knowl­edge lives.

and,

For most of soft­ware his­tory, treat­ing code as durable was rea­son­able.We treated code as per­ma­nent be­cause the la­bor to pro­duce it was the bot­tle­neck. Rewriting was ex­pen­sive. Re-validation was risky. Implementations ac­cu­mu­lated mean­ing over time. Structure, tests, com­ments, bug fixes, and tribal knowl­edge fused into some­thing you learned not to dis­turb.That made sense when pro­duc­tion was the con­straint.When re­gen­er­a­tion is easy, code stops be­ing an as­set and starts act­ing as a cache: a ma­te­ri­al­ized view of un­der­stand­ing that is use­ful while cur­rent, dis­pos­able when stale.

For most of soft­ware his­tory, treat­ing code as durable was rea­son­able.

We treated code as per­ma­nent be­cause the la­bor to pro­duce it was the bot­tle­neck. Rewriting was ex­pen­sive. Re-validation was risky. Implementations ac­cu­mu­lated mean­ing over time. Structure, tests, com­ments, bug fixes, and tribal knowl­edge fused into some­thing you learned not to dis­turb.

That made sense when pro­duc­tion was the con­straint.

When re­gen­er­a­tion is easy, code stops be­ing an as­set and starts act­ing as a cache: a ma­te­ri­al­ized view of un­der­stand­ing that is use­ful while cur­rent, dis­pos­able when stale.

“A ma­te­ri­al­ized view of un­der­stand­ing that is use­ful while cur­rent, dis­pos­able when stale.” I think that might have been the ex­act line that made it click in my head.

I am just barely old enough that my first job ti­tle was “System Administrator”. I was a teenager, work­ing at the uni­ver­sity, with root on every ma­chine in the days be­fore they learned they should def­i­nitely not do that.8

I lived through the shift from hand­crafted server pets to im­mutable in­fra­struc­ture cat­tle. I did­n’t re­ally un­der­stand what was hap­pen­ing at the time, but I’ve con­tem­plated it a lot in re­cent years. I wrote this in the fi­nal chap­ter of “Observability Engineering”, 2nd edi­tion (available for down­load as of Wednesday, June 17th!):

The shift from hand­crafted servers to im­mutable in­fra­struc­ture taught us that mu­ta­bil­ity is the sworn en­emy of un­der­stand­ing. Any ar­ti­fact that is edited in place cre­ates drift. Drift is what makes sys­tems im­pos­si­ble to main­tain.Our abil­ity to kill and re­gen­er­ate in­fra­struc­ture com­po­nents is the rea­son we trust it. At Honeycomb, we kill the old­est Kafka node off via cron every Tuesday. That’s why we are con­fi­dent in our boot­strap­ping and bal­anc­ing processes: every­thing is re­peat­able, the data can be re­gen­er­ated, the com­mit­ments live else­where.The fact that we can­not re­gen­er­ate our code in the same way is a sign that we do not un­der­stand it. We do not know which com­mit­ments we have made, we do not know which de­pen­den­cies will break. We find them by break­ing them, mostly.

The shift from hand­crafted servers to im­mutable in­fra­struc­ture taught us that mu­ta­bil­ity is the sworn en­emy of un­der­stand­ing. Any ar­ti­fact that is edited in place cre­ates drift. Drift is what makes sys­tems im­pos­si­ble to main­tain.

Our abil­ity to kill and re­gen­er­ate in­fra­struc­ture com­po­nents is the rea­son we trust it. At Honeycomb, we kill the old­est Kafka node off via cron every Tuesday. That’s why we are con­fi­dent in our boot­strap­ping and bal­anc­ing processes: every­thing is re­peat­able, the data can be re­gen­er­ated, the com­mit­ments live else­where.

The fact that we can­not re­gen­er­ate our code in the same way is a sign that we do not un­der­stand it. We do not know which com­mit­ments we have made, we do not know which de­pen­den­cies will break. We find them by break­ing them, mostly.

Think of all the years of your work­ing life you have wasted on painful mi­gra­tions and rewrites. Think of re­plac­ing load-bear­ing legacy code. Think of all the stran­gler figs.

Lines of code have been do­ing too much. The code has been the bun­dled up repos­i­tory of de­vel­oper in­tent, user ex­pec­ta­tions, im­plicit and ex­plicit be­hav­iors, the only fos­silized com­pos­ite record we have of bugs gone by. It’s too much!

And look at all the do­mains that have been ne­glected due to the tow­er­ing, all-con­sum­ing ex­pense of main­tain­ing and mu­tat­ing lines of code. Where are the ar­ti­facts I can re­view and dis­cuss to un­der­stand how our ar­chi­tec­ture is evolv­ing? Where are our ar­chi­tec­ture ar­ti­facts, pe­riod? What if we could dis­cuss and con­verge on an ar­chi­tec­ture di­a­gram, and the code could be re­gen­er­ated from changes to the ar­chi­tec­ture, in­stead of the ar­chi­tec­ture be­ing kinda-sorta in­ferred from the code?

I am not as­sert­ing that all code will even­tu­ally be AI-generated to spec, by­pass­ing hu­man un­der­stand­ing. The fea­si­bil­ity of this whole en­deavor hangs on the ques­tion of what a spec is, or what a spec could be. Anyone who has ever done a painful data­base mi­gra­tion should have learned some god­damn hu­mil­ity about our abil­ity to ex­tract and for­mal­ize users’ ex­pec­ta­tions in a re­playable, au­to­mate-able way.

But I think that every step we can take in that di­rec­tion will be good for us.

The tools to do this don’t ex­ist yet, but many of the ideas do ex­ist. Most come from op­er­a­tions and QA, two do­mains that soft­ware en­gi­neer­ing has his­tor­i­cally been rather snob­bish about.

Those tests and tech­niques are not about test­ing for cor­rect­ness or what ought to be hap­pen­ing, they are about ob­serv­ing and en­cod­ing what is hap­pen­ing. Behavioral tests, char­ac­ter­i­za­tion tests, cap­ture/​re­play, traf­fic split­ters. Observability (the good kind).

Having non­de­ter­min­is­tic code in pro­duc­tion is fi­nally forc­ing us to do the things we should have done all along. Instrumenting with traces. Tests and evals in pro­duc­tion. Production is not what hap­pens af­ter de­vel­op­ment is over, pro­duc­tion is a stage of de­vel­op­ment.

Human brains are not good at val­i­da­tion. The nit­pick­i­ness, the rep­e­ti­tion. This is the worst thing to be cling­ing to, y’all. There are so many bet­ter things for us to want to pre­serve and as­sert for our­selves in the pro­duc­tion and main­te­nance of soft­ware. We are never go­ing to beat the ma­chine when it comes to val­i­da­tion — we are lit­er­ally the weak­est link!

My mon­ey’s on hu­mans for a good long time when it comes to cre­ativ­ity, in­spi­ra­tion, leaps of logic, and a lot of other things, but PLEASE do not rest your killer ar­gu­ment for hu­mans in soft­ware on us be­ing the best qual­ity gate. OMG. 🙈

Alright. I’m al­most done here. Just one more thing.

I think what many en­gi­neers have found so alien­at­ing and ter­ri­fy­ing about the last two years of AI dis­course has been the way so many promi­nent AI voices ap­pear to be glee­fully de­clar­ing that soft­ware is no longer an en­gi­neer­ing prob­lem. “SaaS is dead!” “Making AI great at cod­ing was the strat­egy that un­locks every­thing else”, and so on. Even Adam Jacob, one of my dear­est friends and some­one who is rarely wrong about tech­nol­ogy, seems to an­tic­i­pate a blood­bath of soft­ware jobs.9

If 2025 was the year of vibe cod­ing, where AI got as good at gen­er­at­ing lines of code as the me­dian soft­ware en­gi­neer, and the range of pos­si­ble fu­tures of­ten felt desta­bi­liz­ingly, im­pos­si­bly wide open, I feel like 2026 is shap­ing up to be a re­turn to dis­ci­pline.

The knowl­edge in our heads is un­avail­able to AI un­til we en­code it into the sys­tem, af­ter all. The re­turns on those in­vest­ments will be mas­sive and non­lin­ear. We might ar­gue that they al­ways would have paid for them­selves in the long run. But now every CEO in ex­is­tence is chomp­ing at the bit to get some of those AI cook­ies, so let’s give it to them. Discipline first, cook­ies sec­ond.

The share of soft­ware en­gi­neer­ing teams that work in short, fast feed­back loops (the car­di­nal sign of dis­ci­pline in my book) is, and al­ways has been, ap­pallingly small. Five per­cent, maybe? Definitely less than 10%. AI tool­ing brings this more within reach than ever be­fore. Or it can. It could. The dis­con­tin­u­ous re­turns on in­vest­ment in en­gi­neer­ing dis­ci­pline are real enough that it just might hap­pen.

I am not wor­ried, at least in the near term, about AI cre­at­ing mas­sive, dis­con­tin­u­ous re­turns on in­vest­ment in the ab­sence of en­gi­neer­ing dis­ci­pline. (Many will try, and it will be en­ter­tain­ing to watch.)

But value is backed by dura­bil­ity, not dis­pos­abil­ity, and I don’t see that chang­ing. Bits are cheap and fast and gov­erned by the rules of logic and lan­guage, but any­thing with value must ul­ti­mately re­solve with phys­i­cal sys­tems: per­sis­tence on the one side, user ex­pe­ri­ence on the other.

People do not want to wake up every day and log in to Slack and find the but­tons and menus all sub­tly moved around. People do not want fi­nan­cial trans­ac­tions that com­plete most of the time. Determinism is not go­ing any­where, my friends.

AI is not magic. This is still en­gi­neer­ing. As Adam says, “it’s still tech­nol­ogy, and tech­nol­ogy needs tech­nol­o­gists.” And I for one am look­ing for­ward to learn­ing new and in­ter­est­ing en­gi­neer­ing prob­lems, re­view­ing dif­fer­ent kinds of ar­ti­facts.

And never do­ing an­other sticky, picky, two year long API rewrite or stran­gler fig mi­gra­tion, ever, ever again.

~charity

P.S. Thanks to every­one who read a draft and gave me feed­back: Dave Williams, Chad Fowler, Adam Jacob, Mark Ferlatte, Austin Parker, Erwin van der Koogh, Ankur Bhatt.

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I was not try­ing to be neu­tral or even-handed in my last piece, only to give a base­line of cour­tesy to every­one. But I think it’s re­veal­ing how many times I was ac­cused of be­ing “so overly hard on skep­tics”, by skep­tics, and “so overly hard on en­thu­si­asts”, by en­thu­si­asts, and some­times sim­ply “It’s sad how some peo­ple can’t ac­cept re­al­ity” with no in­di­ca­tion which side they meant. Lord.

2

Fred Hebert and I gave the clos­ing keynote at SRECon in March of 2025 where we told SREs they should get to know AI, maybe even try vibe cod­ing (pause for laughs), be­cause oth­er­wise their cri­tiques would­n’t land as well.

Seriously, that was our big pitch. Learn AI so that you can com­plain more ef­fec­tively.

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Infrastructure, for ex­am­ple. I think this is true of a lot of en­gi­neers, btw. I just think it’s re­ally re­ally true of the type of en­gi­neer that signs up to be an SRE. Technological pes­simism and ADHD, our two most defin­ing traits.

4

There is a seg­ment of AI en­thu­si­asts who be­lieve we are en­ter­ing an era of eter­nal ex­po­nen­tial growth, in which the ma­chines be­gin to build bet­ter and bet­ter ma­chines, in ways we can­not un­der­stand.

I think those peo­ple are bad at math. The only thing we know for cer­tain about ex­po­nen­tial growth is that it will end. It al­ways does. ei­ther in an S curve or a crash. (For a good time, google Heinz van Foerster and “our great-great grand­chil­dren will be squeezed to death.”)

I def­i­nitely think we will use ma­chines to build the ma­chines — duh, we al­ready are — but that’s about re­cur­sion and spe­cial­iza­tion. I think the ex­po­nen­tial curve we are on the in­side of now was cre­ated by sloshy free money chas­ing high re­turns, plus the prop­er­ties of soft­ware as a func­tion of lan­guage and logic, plus the biggest dis­cov­er­ies al­ways hap­pen in the early days of a tech­nol­ogy boom, be­cause low hang­ing fruit gets picked first.

My per­sonal sense — and keep in mind that I am no kind of ex­pert on AI — is that the ex­po­nen­tial ad­vance­ment in AI mod­els lev­eled out a while ago, and gains are be­com­ing harder to earn and more in­cre­men­tal in na­ture. I may turn out to be very wrong, of course. But even if there were no more AI in­no­va­tions mov­ing for­wards, the past year has un­leashed enough pent-up force to rad­i­cally re­shape the soft­ware in­dus­try as we know it. Like a pig in a python, we will be deal­ing with the con­se­quences for a long time to come.

5