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Rävens godnattsaga
from 
Sagor
En godnattsaga om en liten Räv.
Det var en gång en liten räv som hette Mårten. Han bodde tillsammans med sin pappa i en varm och rymlig lya under rötterna på den äldsta eken i hela skogen.
Eken var så gammal att ingen riktigt visste när den hade börjat växa. Dess stam var tjock och fårad, och grenarna sträckte sig över gläntan som skyddande armar. På våren slog små gröna blad ut på grenarna. På sommaren gav kronan sval skugga. På hösten regnade gyllene löv över marken, och på vintern vilade snön mjukt på de nakna grenarna.
Under eken hade Mårten och hans pappa gjort det hemtrevligt. Golvet var täckt av torr mossa. I ett hörn låg en hög med mjuka fjädrar som de hade hittat i skogen. På en liten hylla av trädrötter förvarade pappa Räv vackra stenar, borttappade nötter och andra märkvärdiga saker som Mårten brukade hitta under sina utflykter.
Där fanns en blå fjäder från en skata, ett snäckskal som någon hade burit hela vägen från havet och en rund sten som glittrade när månskenet föll på den.
Men det bästa i hela lyan var ändå sovplatsen längst in. Där kunde Mårten krypa tätt intill sin pappa och känna värmen från hans mjuka päls.
Varje kväll hade de samma rutiner.
Först borstade pappa Räv bort löv och barr ur Mårten päls. Sedan drack de några klunkar kallt vatten ur en liten skål av bark. Därefter brukade Mårten få välja en godnattsaga.
Ibland berättade pappa om flygande rävar som seglade över molnen. Ibland berättade han om ett hemligt rike under sjön, där fiskarna bar kronor av näckrosor. Ibland berättade han historier om när han själv var liten och trodde att månen var en stor ost som någon hade hängt upp på himlen.
Men en kväll hjälpte inga berättelser.
Mårten låg under sin filt av mjuka löv och vred sig från den ena sidan till den andra. Han lade svansen över nosen. Sedan lade han svansen under hakan. Han rullade ihop sig till en liten boll, men öppnade snart ögonen igen.
Pappa Räv låg bredvid och låtsades först sova. Han visste att Mårten ibland behövde lite tid för att komma till ro.
Men efter en stund hörde han en liten suck.
Sedan ännu en.
Till sist satte sig Mårten upp.
”Pappa?” viskade han.
”Ja, min lilla räv?”
”Sover du?”
Pappa Räv öppnade ett öga.
”Inte längre.”
Mårten tittade mot ingången till lyan. Utanför hade kvällshimlen blivit mörkblå. De sista solstrålarna hade försvunnit bakom bergen, och mellan trädstammarna låg skuggorna långa och djupa.
”Jag kan inte somna”, sa Mårten.
”Är det något som oroar dig?”
Mårten nickade.
”Skogen låter annorlunda på natten.”
Pappa Räv satte sig upp och lade svansen om honom.
”Hur låter den?”
Mårten spetsade öronen.
Utanför prasslade något bland löven.
”Där!” sa han. ”Hörde du?”
”Jag hörde.”
”Tänk om det är något stort?”
Pappa Räv lyssnade noga. Prasslet kom närmare. Det stannade precis utanför lyan.
Mårten höll andan.
Sedan dök en liten brun nos fram i öppningen. Bakom nosen kom ett runt huvud och två nyfikna ögon.
Det var igelkotten Iris.
På ryggen bar hon tre gula löv och en liten kvist.
”God kväll”, sa Iris. ”Jag hoppas att jag inte stör. Jag letar bara efter ett bra löv att ha som kudde.”
Mårten pustade ut.
”Det var du som prasslade.”
”Jag prasslar nästan alltid”, sa Iris. ”Det är svårt att vara tyst när löven fastnar på taggarna.”
Pappa Räv hjälpte henne att välja ett stort, torrt lönnlöv.
”Det här borde bli en utmärkt kudde”, sa han.
Iris tackade och vandrade vidare mot sin lilla håla under en buske.
Mårten lade sig ner igen.
”Det var bara Iris”, sa han.
”Ja”, svarade pappa. ”På dagen ser vi vem som gör ljuden. På natten hör vi ljuden först och får tänka efter.”
Mårten låg tyst en stund.
Då hördes ett djupt hoande från skogen.
”Hooo. Hooo.”
Mårten satte sig genast upp igen.
”Vad var det?”
”Det låter som ugglan Uno”, sa pappa.
”Men tänk om det inte är Uno?”
”Då kan vi gå ut och ta reda på det.”
Mårten spärrade upp ögonen.
”Gå ut? Nu?”
Pappa nickade.
”Ibland blir mörkret mindre skrämmande när man tittar närmare på det.”
Mårten var inte helt säker på att detta stämde. Mörkret såg väldigt stort ut från lyan. Men han litade på sin pappa.
Pappa Räv tog fram deras lilla lykta. Den var gjord av ett tomt nötskal, och inuti lyste tre vänliga eldflugor. Eldflugorna hette Glim, Gnist och Greta. De sov på dagarna och hjälpte gärna till som lykta om nätterna.
”Är ni vakna?” frågade pappa Räv.
Tre små ljus tändes inuti nötskalet.
”Vi är vakna”, pep Greta.
”Vart ska vi?” frågade Glim.
”På en liten nattpromenad”, sa pappa.
Gnist blinkade ivrigt.
”Nattpromenader är de bästa promenaderna.”
Mårten kröp ut ur lyan efter sin pappa.
Luften var kyligare än den varit på dagen. Gräset kittlade hans tassar, och små droppar av dagg glittrade i lyktans sken. Ovanför dem syntes de första stjärnorna.
Skogen var verkligen annorlunda på natten.
Men den var inte tom.
En nattfjäril fladdrade förbi dem som ett blekt löv. En snigel gled långsamt över en sten. Långt bort hoppade en hare genom ormbunkarna.
”Hooo”, hördes det igen.
Mårten gick lite närmare sin pappa.
”Ljudet kommer från den stora granen”, sa pappa.
De följde stigen mellan blåbärsriset. Ju längre de gick, desto mer hörde Mårten.
Bäcken porlade över stenarna.
Vinden susade genom trädtopparna.
En gren knarrade långsamt.
Små tassar sprang genom löven.
Allt lät starkare på natten, men när Mårten tittade ordentligt såg han att varje ljud hade en förklaring.
Vid den stora granen satt ugglan Uno på en gren. Hans runda ögon glimmade i mörkret.
”God kväll”, sa Uno.
”God kväll”, svarade pappa Räv.
”Var det du som hoade?” frågade Mårten.
Uno blinkade långsamt.
”Ja. Jag ropar för att höra om någon annan uggla är vaken.”
”Får du något svar?”
Alla lyssnade.
Från andra sidan skogen hördes ett svagt hoande.
”Hooo.”
Uno såg nöjd ut.
”Där är min syster Ulla. Nu vet jag att hon har det bra.”
Mårten tittade bort mot den mörka skogen.
”Så hoandet betyder inte att något farligt kommer?”
”Nej”, sa Uno. ”Det betyder oftast bara att en uggla har något att säga.”
Mårten tänkte på det. Det var svårt att vara rädd för ett ljud när man visste att det egentligen betydde: Är du vaken? Ja, jag är här.
De önskade Uno en god natt och fortsatte genom skogen.
Efter en stund kom de till bäcken. Månen hade stigit högre och speglade sig i vattnet. Men bäcken lät mycket högre än vanligt.
Vattnet kluckade, porlade och plaskade.
”Bäcken låter som om den pratar”, sa Mårten.
”Det gör den kanske”, svarade pappa.
De satte sig på en flat sten.
”Vad säger den?”
Pappa Räv lutade huvudet åt sidan.
”Jag tror att den sjunger godnattvisor för stenarna.”
Mårten lyssnade.
Vattnet rann över en rund sten med ett mjukt porlande. Sedan hoppade det ner från en liten kant och landade med ett försiktigt plask.
Porl, porl, plask.
Porl, porl, plask.
Det lät nästan som en sång.
”Kan stenar sova?” frågade Mårten.
”De ligger åtminstone väldigt stilla”, sa pappa.
Mårten fnissade.
De satt kvar en stund och lyssnade på bäckens godnattvisor.
Då såg Mårten något märkligt på andra sidan vattnet.
Ett litet blått ljus svävade mellan buskarna.
Sedan syntes ett till.
Och ett till.
”Pappa”, viskade Mårten. ”Vad är det där?”
Pappa Räv kisade.
”Det ser ut som fler eldflugor.”
Men Glim, Gnist och Greta började blinka oroligt inuti lyktan.
”De där känner vi inte”, sa Greta.
De blå ljusen rörde sig djupare in bland träden. De svävade långsamt fram och tillbaka, nästan som om de ville att någon skulle följa efter.
Mårten kände både rädsla och nyfikenhet.
”Ska vi gå tillbaka hem?” frågade han.
Pappa Räv tittade på honom.
”Vad tycker du?”
Mårten funderade. Han ville tillbaka till den varma lyan. Samtidigt ville han veta vad de blå ljusen var.
”Vi kan gå lite närmare”, sa han. ”Men bara om du går först.”
”Det gör jag.”
De hittade en smal plats där bäcken var grund och hoppade över på några stenar. Sedan följde de de blå ljusen.
Ljusen förde dem till en del av skogen där träden stod tätare. Här växte höga ormbunkar och mjuk mossa. Luften doftade av jord och svamp.
Plötsligt försvann ljusen.
Mårten stannade.
”Vart tog de vägen?”
Då började marken framför dem lysa.
Där, i en ring under en gammal bok, växte små svampar med blåskimrande hattar.
”Det var svamparna”, sa Mårten.
”Deras sken syntes mellan grenarna när vinden rörde dem”, sa pappa.
Mårten gick försiktigt närmare.
Svamparna lyste så svagt att de nästan såg ut som små stjärnor som fallit ner på marken.
Mitt i svampringen låg en liten mus och sov.
Hon hade huvudet på en kastanj och svansen virad runt kroppen.
”Det är Mimmi”, viskade Mårten.
Musen öppnade ena ögat.
”Hej”, mumlade hon sömnigt. ”Ni får gärna titta, men försök att inte stampa. Jag har precis hittat en perfekt sovplats.”
”Är du inte rädd för de lysande svamparna?” frågade Mårten.
Mimmi gäspade.
”Nej. De fungerar som nattlampor.”
Sedan somnade hon om.
Mårten tittade länge på det blå skenet.
Mörkret runt svamparna kändes inte längre lika tomt. Det var fullt av små saker som lyste.
De gick vidare.
Snart kom de till en glänta som Mårten aldrig hade sett förut. I mitten stod en liten damm. Vattnet var blankt och stilla, och runt dammen växte vita blommor som bara slog ut på natten.
På en sten satt grodan Göran och sjöng.
”Kvack, kvack, kvackeli-kvack.”
Runt honom satt flera små grodor i en halvcirkel.
”Vad gör ni?” frågade Mårten.
Göran bugade.
”Vi övar kvällskören.”
”Kvällskören?”
”Ja. De små grodorna ska lära sig skogens vaggvisor.”
De små grodorna tog ett djupt andetag.
”Kvack, kvack, kvack.”
Några sjöng för tidigt. En sjöng för sent. En mycket liten groda sjöng så högt att han föll baklänges ner i vattnet.
Plask!
Mårten började skratta.
Den lilla grodan kom upp igen med en näckros på huvudet.
”Det där var meningen”, sa han.
Göran harklade sig.
”Från början igen.”
Grodkören sjöng en långsam sång. Den handlade om månen, om vatten som vilade och om små grodyngel som sov tryggt bland vassen.
Sången var lite kvackig, men mycket vacker.
”Det där var en riktig vaggvisa”, sa Mårten när de sjungit klart.
”Tack”, sa Göran stolt. ”Vaggvisor behöver inte vara perfekta. De behöver bara få någon att känna sig trygg.”
Pappa Räv nickade.
”Det var klokt sagt.”
De tackade för sången och gick vidare.
Nu började Mårten känna sig trött. Hans tassar gick långsammare, och han gäspade så stort att öronen vek sig bakåt.
”Ska vi gå hem?” frågade pappa.
Mårten nickade.
Men just när de vände sig om hörde de ett svagt ljud från skogen.
Det lät nästan som gråt.
Mårten blev genast klarvaken.
”Hörde du?”
Pappa Räv nickade.
De följde ljudet till en tät buske. Där satt en liten harunge. Hon darrade och hade tårar i ögonen.
”Vad har hänt?” frågade pappa Räv mjukt.
Harungen snyftade.
”Jag heter Tova. Jag följde efter en nattfjäril och nu hittar jag inte hem.”
Mårten satte sig bredvid henne.
”Är ditt hem långt härifrån?”
”Vi bor vid den stora stenen som ser ut som ett sovande björnhuvud.”
Mårten hade sett stenen förut. Den låg nära hasselsnåret, ganska långt bort.
”Vi kan följa dig”, sa han.
Pappa Räv såg på Mårten.
”Orkar du gå så långt?”
Mårten tittade på Tova. Hon såg liten och ensam ut.
”Ja”, sa han. ”Jag kan vara trött senare.”
Så började de vandringen mot hasselsnåret.
Tova gick mellan Mårten och pappa Räv. Glim, Gnist och Greta lyste vägen. För att Tova inte skulle vara rädd berättade Mårten allt han hade lärt sig under natten.
Han berättade att prasslet i löven kunde vara Iris som letade efter en kudde.
Han berättade att ugglans hoande betydde att Uno pratade med sin syster.
Han berättade att bäcken sjöng godnattvisor för stenarna.
Han berättade om de lysande svamparna, om Mimmi som sov i svampringen och om grodorna som övade vaggvisor vid dammen.
Tova slutade darra.
”Natten verkar inte så farlig när du berättar om den”, sa hon.
Mårten blev lite förvånad. För bara en stund sedan hade han själv varit rädd.
”Natten är mest saker som gör sådant de brukar göra”, sa han. ”Fast man kan inte alltid se dem direkt.”
De gick över en kulle och genom ett område med högt gräs. Månen följde dem ovanför träden.
När vinden blåste rörde sig gräset i långa vågor.
”Titta”, sa pappa Räv. ”Det ser nästan ut som ett silverhav.”
Mårten föreställde sig att de vandrade på botten av ett hav. Grässtråna blev sjögräs. Nattfjärilarna blev fiskar. Månen blev ett stort pärlemorskal.
”Pappa”, sa Mårten, ”tror du att månen följer efter oss?”
”Det kan kännas så.”
”Varför gör den det?”
Pappa Räv tänkte efter.
”Kanske vill den se till att vi hittar hem.”
Tova tittade upp.
”Då följer den kanske alla som är ute på natten.”
”Det tror jag”, sa Mårten.
Till sist nådde de den stora stenen. Den såg verkligen ut som ett sovande björnhuvud. På andra sidan stenen satt två harar och väntade.
När de såg Tova rusade de fram.
”Där är du!” ropade hennes mamma.
Tova kastade sig i hennes famn.
”Jag följde en nattfjäril”, erkände hon. ”Sedan gick jag vilse. Men Mårten och hans pappa hjälpte mig.”
Hararna tackade dem många gånger.
Tovas pappa gav Mårten en liten påse med söta skogsbär.
”Till frukost”, sa han.
Mårten gäspade igen.
”Tack.”
Nu började vägen hem.
Den kändes mycket kortare, trots att Mårten var trött. Han visste var ljuden kom ifrån. Han kände igen stigarna. Och när en gren knarrade ovanför honom tittade han upp och såg att det bara var vinden som gungade den fram och tillbaka.
När de passerade dammen hade grodorna slutat sjunga. De små grodorna sov på näckrosbladen.
När de gick förbi de lysande svamparna sov Mimmi fortfarande med huvudet på kastanjen.
Vid bäcken fortsatte vattnet att porla sin sång.
Uno satt kvar i granen, men nu hade han fått sällskap av sin syster Ulla.
Iris låg hoprullad under busken med lönnlövet under huvudet.
Hela skogen vilade.
När Mårten och pappa Räv kom tillbaka till den gamla eken hade månen klättrat högt upp på himlen.
De kröp in i lyan.
Pappa Räv ställde tillbaka lyktan på hyllan.
”Tack för hjälpen”, sa han till eldflugorna.
”Tack för promenaden”, svarade Greta.
Glim, Gnist och Greta släckte sina ljus och somnade i nötskalet.
Mårten kröp ner på sovplatsen. Pappa borstade bort några barr ur hans päls och lade lövfilten över honom.
”Var skogen annorlunda än du trodde?” frågade pappa.
Mårten nickade sömnigt.
”Jag trodde att mörkret gömde farliga saker.”
”Och vad gömde det?”
Mårten räknade upp dem.
”Iris och hennes lövkudde. Uno och Ulla. Bäcken. De blå svamparna. Mimmi. Grodornas kör. Och Tova.”
”Det var ganska mycket.”
”Ja.”
Mårten låg tyst en stund.
”Pappa?”
”Ja?”
”Var du aldrig rädd när du var liten?”
Pappa Räv log.
”Jo, många gånger.”
”För mörkret också?”
”Särskilt för mörkret.”
Mårten öppnade ögonen.
”Men du verkar aldrig rädd nu.”
Pappa lade sig bredvid honom.
”Att vara modig betyder inte att man aldrig är rädd. Det betyder att man kan ta ett litet steg även när man är rädd. Ibland tar man steget själv. Ibland håller man någon i tassen.”
Mårten lade sin tass på pappas.
”Som i kväll?”
”Precis som i kväll.”
Utanför lyan blåste vinden genom ekens grenar. Löven rasslade mjukt, nästan som tusen små viskningar.
”Nu sjunger trädet också”, mumlade Mårten.
”Vad tror du att det sjunger?”
Mårten lyssnade.
”En sång om en liten räv som gick ut i mörkret.”
”Och vad hände med honom?”
”Han upptäckte att natten inte var tom.”
”Vad var den full av?”
Mårten gäspade.
”Vänner. Sånger. Små ljus. Och en pappa som följde med.”
Pappa Räv drog sin svans över Mårten som en varm filt.
”Det låter som en bra sång.”
”Kan du sjunga den?”
Pappa Räv hade inte den vackraste sångrösten i skogen. Den var varken klar som en fågels eller djup som en grodas. Men Mårten tyckte att det var den tryggaste rösten som fanns.
Pappa började sjunga mycket tyst:
”Sov nu, lilla tass,
natten vandrar varsamt.
Månen lyser över stig,
och jag stannar här hos dig.
Bäcken sjunger, träden ler,
stjärnor tänds och blir allt fler.
Blunda tryggt och vila så,
hemmet väntar där vi två.”
Mårten ögon blev tyngre.
”En vers till”, mumlade han.
Pappa fortsatte:
”Om en dröm tar dig långt bort,
över äng och över port,
följer jag ditt spår ändå,
vart än dina tassar gå.
Genom moln och månens sken,
över berg och under gren,
följer jag dig hela vägen,
hem till lyan under eken.”
Mårten andning blev långsam och jämn.
Men precis innan han somnade frågade han:
”Pappa?”
”Ja, min lilla räv?”
”Tänk om jag drömmer att jag går ända till månen?”
”Då följer jag efter.”
”Tänk om jag går vilse bland stjärnorna?”
”Då frågar vi månen om vägen.”
”Tänk om månen inte vet?”
”Då lyssnar vi efter bäckens godnattvisor.”
Mårten log med slutna ögon.
”Och grodornas vaggvisor?”
”Dem också.”
”Och om vi fortfarande inte hittar hem?”
Pappa Räv nosade honom mjukt på pannan.
”Då bygger vi en liten lya bland stjärnorna och väntar tills morgonen visar vägen.”
Mårten tass slappnade av i hans.
Snart sov den lilla räven djupt.
I drömmen vandrade han genom en skog där alla stjärnor hade fallit ner och lagt sig i mossan. Varje stjärna lyste som en liten lykta.
Han mötte Iris, som bar en krona av gula löv.
Han mötte Uno och Ulla, som flög över träden och ropade vänliga hälsningar till alla som var vakna.
Han såg Mimmi segla över dammen i ett nötskal.
Han hörde grodorna sjunga så vackert att näckrosorna började dansa.
Sedan kom Tova skuttande längs stigen.
”Månen har tappat bort sig”, sa hon.
Mårten tittade upp.
Himlen var mörk och tom.
”Då måste vi hitta den”, sa han.
De följde ett silverfärgat spår genom skogen. Det ledde över bäcken, förbi svampringen och uppför det högsta berget.
Där, bakom en stor sten, satt månen.
Den var mycket mindre på nära håll. Ungefär lika stor som en rund pumpa.
”Varför gömmer du dig?” frågade Mårten.
”Jag tror att jag har glömt hur man lyser”, sa månen sorgset.
Mårten satte sig bredvid den.
”Kanske behöver du höra en sång.”
Alla djuren samlades runt månen.
Bäcken sjöng sina godnattvisor.
Grodorna sjöng sina vaggvisor.
Ugglorna hoade mjukt.
Vinden susade genom träden.
Men månen började fortfarande inte lysa.
Då hördes steg bakom Mårten.
Det var pappa Räv.
Han satte sig på andra sidan månen och började sjunga samma sång som i lyan.
”Sov nu, lilla tass,
natten vandrar varsamt.”
Sakta började månen glöda.
Först som en eldfluga.
Sedan som de blå svamparna.
Sedan starkare och starkare, tills hela berget badade i silverljus.
”Jag kom ihåg!” ropade månen.
Den steg upp på himlen igen och lyste över hela skogen.
Alla jublade.
Mårten vände sig mot sin pappa.
”Hur visste du vilken sång månen behövde?”
Pappa log.
”Alla behöver en sång som påminner dem om att de inte är ensamma.”
Sedan lyfte vinden Mårten försiktigt från marken. Den bar honom över träden, över bäcken och tillbaka mot den gamla eken.
När morgonen kom vaknade Mårten i lyan.
En smal solstråle letade sig in genom öppningen. Fåglarna sjöng, och utanför glittrade daggen i gräset.
Pappa Räv låg bredvid honom och sov fortfarande.
Mårten låg alldeles stilla en stund.
Sedan kröp han närmare och lade sin lilla svans över pappas tass.
Pappa öppnade ena ögat.
”God morgon.”
”God morgon”, sa Mårten.
”Sov du gott?”
Mårten nickade.
”Jag drömde att månen hade glömt hur man lyste.”
”Det låter besvärligt.”
”Men vi hjälpte den.”
”Vad bra.”
Mårten tittade mot ingången, där morgonsolen lyste varmt.
Skogen såg inte alls likadan ut som den hade gjort under natten. Nu kunde han tydligt se stigarna, träden och buskarna.
Men han visste att den mörka skogen fortfarande fanns där, gömd under dagsljuset.
Och han visste vad som väntade när kvällen kom.
Iris skulle prassla bland löven.
Uno och Ulla skulle ropa till varandra.
Mimmi skulle sova bland de lysande svamparna.
Grodorna skulle öva sina sånger.
Bäcken skulle sjunga för stenarna.
Och hemma under eken skulle pappa Räv lägga sin svans över Mårten och berätta en saga.
Mårten var fortfarande inte säker på att han aldrig mer skulle bli rädd för mörkret.
Men det gjorde inget.
För nu visste han att rädsla kunde bli mindre om man lyssnade noga, tittade närmare och höll någon man älskade i tassen.
Och framför allt visste han att hur långt bort han än vandrade, genom mörka skogar, över höga berg eller ända upp bland stjärnorna, skulle hans pappa alltid hjälpa honom att hitta hem.
Another girls trip
from An Open Letter
Today I went to a friend‘s birthday party, and I was talking about how I wanted to go to Six Flags waterpark. One of the girls there was constantly shitting on it and saying how there’s no point driving that far for it, and I was just kind of saying I enjoy it. I was asking A if she was interested, and she was saying that she was. The other girl started talking about how she wanted to go to a different one, and then said that it would be fun to go as a girls thing. It just directly feels like such a slap in the face to say that because it excludes me. It feels like intentionally trying to set up a situation or social dynamic where it is implied that I am not allowed. That shit hurts.
Surveillance Pricing: Why AI Charges You More Than Your Neighbour
from 
SmarterArticles
Consider the moment you do not see. It is an ordinary Tuesday evening, and you open a grocery app to order the week's essentials. Nappies, milk, bread, the brand of coffee you always buy, the painkillers a household runs through unnoticed. You add the items, glance at the total, tap to confirm. The total seems about right. You have nothing to compare it against, because there is nothing to compare it against. The price you see is the only price you will ever see. You do not know, and have no way of finding out, that the shopper in the next postcode, ordering the identical basket from the identical store at the identical minute, has been quoted a figure several pounds lower. You do not know that a piece of software has looked at what it can infer about you, your past behaviour, your location, the predictability of your needs, the apparent absence of alternatives, and concluded that you, specifically, will pay a little more. No negotiation, no notice. There was only a number, presented as if it were the number, and you accepted it because the entire architecture of shopping has trained you to assume that a price is a fact about a product rather than a judgement about you.
This is not a thought experiment. In December 2025, a joint investigation by Consumer Reports, the Groundwork Collaborative and More Perfect Union pulled back the curtain on exactly this practice, running inside Instacart, the largest grocery delivery platform in the United States. The investigation found that roughly three-quarters of products checked were being offered to different customers at different prices, for the same item, from the same store, at the same time. The variations ran from a few pennies to more than two dollars per item. Extrapolated across a typical household's annual spend, the swing came to around 1,200 dollars a year. The engine behind it was an artificial intelligence pricing platform called Eversight, which Instacart had acquired in 2022, and which the company marketed to retailers as a way to lift sales and squeeze out incremental margin. Within days of the story being published, Instacart announced that, effective immediately, it was ending all item price tests on its platform. The lab, as one campaigner put it, had been closed only because someone finally switched on the lights.
The episode is not an aberration. It is a preview. The capacity to set a different price for every customer, calibrated to the maximum each will tolerate, has been the holy grail of commerce for as long as commerce has existed, and for almost all of that history it has been impossible at scale. What has changed is that the impossibility has dissolved. Cheap data, behavioural tracking and machine learning have made it not merely feasible but routine to estimate, in real time, how much a particular human being is likely to pay, and to charge them precisely that. The question this raises is not technical. The technology works. It is what it means to live in a market where the price is no longer a shared fact about the world but a private message addressed to you alone, written in a language you cannot read, by a system that knows things about you that you have not agreed to disclose and may not even know yourself.
The Oldest Dream in Retail
Economists have a name for what Instacart's software was reaching towards, and it is not new. They call it first-degree price discrimination, or perfect price discrimination, and it describes the seller's fantasy of charging each buyer exactly their maximum willingness to pay. The market trader who sizes up a customer's shoes and accent before naming a figure is practising a crude, intuitive version of it. The theory has been in textbooks for a century. What it has lacked, until very recently, is a mechanism. To charge everyone their personal maximum, a seller must somehow know everyone's personal maximum, and individual human beings have historically been quite good at concealing it. The posted price, the same number on the same shelf for everyone, emerged in part because sellers could not do better. It was a technological limit dressed up as a social norm.
The first sign that the limit might be lifting came in September 2000, when shoppers on Amazon noticed something strange. A man buying a DVD found that when he deleted the cookies from his browser, the price dropped. Amazon, it turned out, had been running an experiment in which the price of certain titles varied according to what the company could infer about the shopper from their browsing and purchase history. Loyal customers, the kind least likely to wander off, were in some cases being shown higher prices than newcomers. The discovery produced a wave of public fury, and Amazon retreated almost at once, insisting the variations had been a random test rather than deliberate profiling, and refunding the difference. The episode entered the folklore of e-commerce. The lesson the industry drew was not that personalised pricing was wrong. The lesson was that it must never again be visible.
For the better part of two decades the dream advanced quietly, in forms ordinary shoppers had been trained to accept. Airlines pioneered the art, charging fares that lurched with demand, with the day of the week, with how close the departure loomed, and, as many travellers suspected, with how many times a route had been searched from a given device. Ride-hailing apps normalised the idea that a price could surge in real time, rising when it rained or when a concert let out, framed as a neutral response to supply and demand rather than a calculation about the rider's desperation. Streaming services and online retailers learned to offer a discount to one customer that never materialised for another. Each of these was a step away from the posted price and towards the personalised one, and each was small enough, and dressed in enough economic respectability, that it provoked little sustained alarm. The frog, to borrow the old image, was being warmed by degrees.
What the Machine Sees
The leap from dynamic pricing, where the figure moves with the market, to surveillance pricing, where the figure moves with the customer, is a leap in kind and not merely degree. A surge fare is at least the same for everyone standing on the same wet pavement at the same moment. Surveillance pricing is the surge fare turned inward, aimed not at the conditions but at the person. The raw material it runs on is the vast, largely invisible economy of behavioural data that has accreted around every digital interaction we have.
In January 2025, the United States Federal Trade Commission published the initial findings of a study into precisely this market. Acting under its Section 6(b) authority, which lets it compel companies to hand over internal documents, the agency had sent orders to a clutch of intermediaries that sit, mostly unseen, between retailers and shoppers: Mastercard, Accenture, the pricing-software firm PROS, the personalisation company Bloomreach, the pricing optimiser Revionics and the consultancy McKinsey and Company. What the staff found, even in a preliminary cut, was a thriving and shadowy infrastructure for setting individualised prices. The intermediaries drew on a remarkable breadth of signals, both data volunteered by consumers and data inferred about them from first and third party sources. The behaviours that could be tracked and fed into a price ranged from the movements of a mouse across a webpage to the specific products a shopper abandoned, unpurchased, in an online basket. One example in the documents was a cosmetics company targeting promotions by a customer's skin type and skin tone. The intermediaries the FTC examined were, between them, working with at least 250 clients selling everything from groceries to clothing. The market for knowing what you will pay was already industrial in scale.
The Instacart investigation gave that abstraction a face. When Consumer Reports and its partners examined the patent filings that Instacart and Eversight had lodged from 2017 onward, they found the ambition spelled out in the dry language of intellectual property. The patents referenced setting prices using previous purchase history, buying behaviour, and characteristics such as age, gender, household size and household income. One metric flagged was whether a shopper was new to a brand or returning to it. The investigation also documented what it called phantom discounts, in which different customers were shown different inflated original prices for the same item, manufacturing the impression of a bargain where none existed. A box of premium saltine crackers, in one example, was presented with an original price of 5.93 dollars, 5.99 dollars or 6.69 dollars depending on the shopper, before a sale price of 3.99 dollars that was in fact the same for everyone. The discount was theatre. The variation was real.
Instacart denied that it currently used personal or demographic data to set prices, maintaining that customers were randomly assigned to pricing cohorts by product category and location rather than profiled as individuals. But the denial, even taken at face value, missed the point the industry's own analysts kept returning to. Phil Lempert, a grocery analyst who runs the site SupermarketGuru, put it plainly: once the technology is in place, even if a company is not profiling shoppers today, the capacity to start is a button-press away. The machinery of individualised pricing does not need to be aimed at you to be pointed in your direction. Its mere existence changes the relationship between buyer and seller, removing the floor of the posted price and replacing it with an open question about how much, in your case, the seller thinks it can get.
The Survey Nobody in Retail Wanted to Read
Defenders of personalised pricing tend to argue that consumers do not really mind, or that they accept it as the price of convenience, or that the discounts it enables for the price-sensitive outweigh the premiums it imposes on the rest. The data does not support this. As part of its investigation, Consumer Reports ran a nationally representative survey of 2,240 American adults in September 2025. Among those who had used Instacart in the previous year, 72 per cent did not want the company to charge different users different prices for any reason. Not for some reasons. Not unless the reasons were fair. For any reason at all. The aversion was close to universal, and it cut against the entire logic of the surveillance-pricing business.
This exposes the gap between what the practice does and what it claims to do. The economic defence of first-degree price discrimination holds that it can, in theory, expand the market, letting sellers profitably reach price-sensitive buyers who would otherwise be excluded while extracting more from those who can afford it. On a whiteboard this looks almost progressive, a kind of automated means-testing. In the world it works the other way around. The signals a machine-learning system finds most useful for estimating willingness to pay are precisely the signals that track vulnerability. A shopper in a food desert, with no rival supermarket within reach, has fewer alternatives, and the algorithm can learn to read that constraint and charge for it. A household ordering nappies and prescription items has predictable, inelastic needs, and inelasticity is exactly what a pricing model is built to exploit. The customer with limited mobility, least able to drive between shops, is least able to escape and therefore most worth charging more. The system does not optimise for fairness. It optimises for revenue, and the people with the least room to push back are the ones from whom there is the most to extract.
Lina Khan, who chaired the FTC from 2021 to 2025 and now teaches at Columbia Law School, framed the stakes in a sentence that has stuck to the debate. We are moving, she said, from a transparent market with public prices to an opaque world where we are alone against secret algorithms. The phrasing identifies the precise thing that is lost. It is not simply that some people pay more; markets have always produced unequal outcomes. It is that the mechanism becomes unknowable. In a market of posted prices, a high price is public information that competitors can undercut and shoppers can refuse. In a market of personalised prices, it is a private transaction between you and a model, invisible to everyone else, including the regulators, journalists and rival retailers who might otherwise discipline it. The discipline of the market depends on the price being a shared fact. Surveillance pricing dissolves the shared fact, and with it the discipline.
The Fairness Nobody Consented To
Set aside the question of whether you pay more or less. Ask instead the question the practice never lets you ask: what, exactly, is being used to decide. This is where personalised pricing stops being a story about money and becomes one about discrimination in the older and graver sense of the word.
A price built from inferred willingness to pay is a price built from a model of who you are, and the characteristics that feed such a model are not chosen for their moral acceptability. They are chosen because they predict. If income predicts willingness to pay, the model uses income, and if it can infer income from your postcode, your device, your browsing and the brands you buy, then it is charging you according to your wealth without ever asking your salary. If household size predicts inelastic demand, the model uses household size, which means a larger family, often a poorer one, may face systematically higher prices on essentials it cannot do without. The Instacart patents named age, gender, household size and income directly. Some are characteristics anti-discrimination law has spent a century learning to treat as illegitimate grounds for differential treatment. None is one an ordinary shopper would knowingly hand over as a reason to be charged more for milk.
The trouble is that the shopper never gets to decide. The whole design of surveillance pricing is that the grounds of differentiation are hidden. You cannot object to being priced on your gender if you do not know your gender is in the model. You cannot contest a markup based on the inference that you are housebound if you never learn the inference was made. The ordinary apparatus of fairness, the ability to know the reason for a decision and to challenge it, simply does not engage, because the reason is buried in a proprietary system and the decision arrives disguised as a fact of nature. A price, to the shopper, looks like something the world has handed down. It does not look like an accusation, a profile or a bet. But that is what, increasingly, it is.
This is the argument that Veena Dubal, professor of law at the University of California, Irvine, has developed across both the consumer and the labour sides of the same phenomenon. Writing in Governing magazine in April 2026, Dubal set out why AI should not be setting prices or wages, and why states needed to push back. The techniques now spreading through consumer pricing were pioneered on workers, in the ride-hailing and food-delivery platforms, where her earlier research documented what she named algorithmic wage discrimination: the practice of paying different workers different amounts for substantially the same work, with the wage personalised in real time according to dozens of behavioural signals invisible to the worker. The platform companies, she has observed, have been at the cutting edge of experimenting with ways to control people without it being obvious, and when those experiments work, they leach into other industries. Surveillance pricing is the consumer-facing twin of surveillance pay. Both rest on the same engine of behavioural inference. Both produce outcomes the affected person cannot predict, cannot explain and cannot contest.
The Revenue Nobody Mentions
Dubal's Governing piece adds a dimension that rarely surfaces in the consumer-protection framing: the state's own balance sheet. When algorithmic systems reclassify what would once have been straightforward taxable wages into a shifting patchwork of bonuses and incentives, calibrated worker by worker, the effect is not only to make individual incomes unpredictable. It is to erode the tax base on which public insurance depends. Dubal cites Connecticut, estimating that the state stands to lose around 60 million dollars a year in unemployment-insurance contributions as wages are restructured into forms that escape the payroll levy. The same opacity that lets a company extract a few extra pennies from a vulnerable shopper lets it shrink its obligations to the commons, and because the mechanism is granular and individualised, it is fiendishly hard for any tax authority to see, let alone challenge.
This is the quiet scandal beneath the loud one. The visible harm of surveillance pricing is the markup on your groceries. The invisible harm is what the same techniques do to the institutions that depend on legible, shared economic facts: tax systems, labour statistics, consumer-price indices, the apparatus by which a society measures and governs its own economy. An economy of personalised prices and wages becomes progressively harder to measure, because measurement assumes that prices and wages are public things. The spread of these tools from the gig platforms into healthcare, retail, logistics and customer service threatens not only individual fairness but the informational foundations of governance itself. An audit of 500 AI vendors her research points to found at least 20 at high risk of enabling surveillance-based pay, most already wired directly into employers' payroll and HR systems. The leach is well underway.
A Legal Framework That Is Mostly a Vacuum
If this sounds like the sort of thing the law would surely prohibit, the uncomfortable answer is that, for the most part, it does not. In April 2026, the legal-analysis service JD Supra carried a clear-eyed assessment, written by attorneys at the firm Holland and Knight. Their conclusion was blunt: there is no comprehensive federal statutory framework in the United States governing surveillance pricing. What exists instead is improvisation, the stretching of older authorities to cover a practice their drafters never imagined. Enforcement, where it happens at all, leans on Section 5 of the FTC Act, which prohibits unfair or deceptive practices, and on the agency's rule against unfair or deceptive fees. The authors noted that pricing-enforcement risk was no longer theoretical but an active priority. Yet active priority is not clear law. Section 5 was written to police deception in the abstract, not to answer whether a retailer may infer your income from your shopping habits and charge you accordingly, and the absence of a statute on that question leaves enormous room for argument, delay and retreat.
The patchwork that fills the federal vacuum is uneven and young, but filling fast. New York has moved fastest on disclosure, with a law requiring that when a price has been personalised using a consumer's data, the shopper must be told, in some versions with a stark warning that an algorithm set the price. The disclosure approach restores a measure of the visibility that surveillance pricing destroys, but it does not prohibit the practice, and a warning that everyone learns to ignore is a thin protection. The decisive shift in 2026 has been towards bans. By the spring, state lawmakers had introduced more than forty bills across at least twenty-four states to regulate personalised algorithmic pricing, outpacing the whole of 2025.
Maryland was the first to enact one, its governor, Wes Moore, signing the Protection From Predatory Pricing Act on 28 April 2026, effective 1 October. The statute, the first of its kind in the food sector, prohibits large food retailers and third-party delivery services from using personal data to set higher prices for particular consumers or classes. It carries penalties of up to 10,000 dollars per violation, rising to 25,000 dollars for repeat offenders, enforced by the state attorney general. It also carves out the practices the industry was most anxious to protect: loyalty schemes, voluntary membership discounts, genuine promotional offers and price differences attributable to objective costs such as shipping or tax.
Connecticut followed within weeks. Its bill, SB 4, passed the legislature on 4 May 2026, 141 to 6 in the House and 31 to 4 in the Senate, and Governor Ned Lamont signed it on 27 May as Public Act 26-64. Where Maryland's law reaches only the food sector, Connecticut's prohibits surveillance pricing, defined as setting a customised price for a consumer or group of consumers on the basis of personal data gathered through any technology, across retail generally and binding third-party delivery services. The act goes further still, establishing a state data-broker registry, a one-request mechanism for wiping a consumer's records across the whole industry, and a ban on the sale of precise geolocation data. Its provisions take effect on 1 October 2026, the same day as Maryland's.
The same season showed how easily such laws fail to arrive. Colorado's legislature passed the most ambitious measure of all, HB26-1210, which would have banned individualised price and wage setting based on surveillance data across every industry, on 8 May 2026. Governor Jared Polis vetoed it. His objection was not to the principle but to the reach: the bill, he wrote, took too broad an approach, capturing any technology that incidentally influences a price or wage amount rather than targeting unethical conduct, and would punish lower prices as readily as higher ones. The veto is an instructive counterpoint to the bills that passed: the friction these tools provoke reaches all the way to the governor's desk. California, meanwhile, kept moving: its surveillance-pricing bill cleared a key vote on 15 May 2026 and is still in progress.
Governing magazine's April 2026 reporting, and Dubal's argument within it, treated these state moves as the leading edge of a necessary legislative response rather than a settled solution. The pattern is familiar from the early history of data protection and of antitrust in the digital economy. The technology arrives at national, indeed global, scale. The law responds at the level of individual states, slowly, unevenly and with vigorous lobbying against every clause. The result, for now, is a map in which the legality of charging you a personalised price for a tin of beans depends substantially on which state line you happen to be standing behind, and in most of the country the answer remains that the practice is lawful, undisclosed and unmeasured.
What Europe Did Differently, and Did Not
Across the Atlantic, the legal starting point is different, though it is a mistake to imagine it amounts to a clean prohibition. The European Union confronted personalised pricing earlier and built a disclosure obligation into its consumer law through the Omnibus Directive of 2019, which took effect across member states in 2022. Under it, a trader must inform a consumer whenever the price they are being shown has been personalised on the basis of automated decision-making and profiling. The obligation is narrower than it sounds. It requires the seller to say that the price is personalised; it does not forbid the personalisation, and it does not require the seller to reveal what data went into it or how. A consumer told that a price has been tailored to them learns that they are being profiled without learning anything about the profile.
The heavier weapon in the European arsenal is data-protection law, and here the picture is genuinely contested. Article 22 of the General Data Protection Regulation gives individuals a right not to be subject to decisions based solely on automated processing that produce legal or similarly significant effects on them. Whether a personalised price counts as such a decision, and whether the regulation can therefore be read to require explicit consent before a shopper is priced by algorithm, is a question on which European lawyers have argued for years without settling. Some scholars contend that the GDPR, read seriously, enshrines something like a right to an impersonal price, a right to be quoted the same figure as everyone else unless you have genuinely agreed otherwise. Others regard that reading as aspirational. What is not in dispute is that European anti-discrimination law forbids using certain protected characteristics, of the kind that pricing models are perfectly capable of inferring, as the basis for differential treatment. The European framework, in other words, contains stronger raw materials than the American one, but it has not yet been assembled into a coherent answer to the specific harm. The United Kingdom, having left the EU before the Omnibus Directive bound it, is under no obligation to mirror even the disclosure rule, and its Competition and Markets Authority has approached the question through its broader work on online choice architecture and the manipulative design of digital interfaces rather than through a dedicated pricing statute.
The comparison yields a sober conclusion. No major jurisdiction has yet produced a settled, comprehensive answer to the question of when, if ever, a company may charge you a price calculated from a secret model of who you are. Europe has more tools and more disclosure. America has more enforcement appetite in some states and almost nothing in most. Everywhere, the technology is ahead of the law, and everywhere the burden of that gap falls on the individual shopper, who has neither the information to know what is happening nor the standing to do much when they find out.
The Asymmetry at the Heart of It
Strip the subject to its bones and what remains is an asymmetry of knowledge so steep that it makes a mockery of the idea of a transaction between equals. The seller knows the cost of the good, the price it shows you, the price it shows others, the model that produced your figure and the data that fed the model. You know the price it shows you. That is all. You cannot see the distribution of prices around you, the inputs, or the inference. You cannot even reliably tell whether personalisation is happening at all, because a personalised price and a non-personalised one look identical: both are just numbers on a screen. The market, classically conceived, was supposed to be an information system, aggregating dispersed knowledge into a public signal that coordinated the behaviour of strangers. Surveillance pricing inverts it. It turns the price from a signal the market sends to you into a signal the seller extracts from you, and does so silently, so that you go on reading the number as though it still carried its old public meaning.
This is why disclosure remedies, useful as they are, feel inadequate to the scale of the thing. Telling a shopper that their price has been personalised restores a sliver of the lost information, but it leaves the deeper asymmetry intact. It is rather like being told that a stranger has formed an opinion of your character without being told what the opinion is or what evidence it rests on. The grievance is not merely that the price was tailored. It is that it was tailored using a portrait of you that you did not sit for, that you cannot see and that may be wrong, unfair or built from characteristics you would never have agreed to be judged by. The ordinary person's intuition that there is something improper here is not naivety about how markets work. It is an accurate perception that a hard-won feature of how markets are supposed to work, the shared and public price, is being quietly removed, with nothing put in its place to protect them.
Switching the Lights Back On
What, then, is the ordinary person at the invisible checkout to do? Honesty requires admitting that individual self-defence is mostly futile. Clearing cookies, browsing privately, comparing prices across devices: these are the folk remedies of a simpler era of price discrimination, and against a system that fuses dozens of inferred signals they offer little. The man who deleted his cookies on Amazon in 2000 found a cheaper DVD because the discrimination then was crude. It is not crude now, and the burden of evading it cannot reasonably be placed on the shopper. A person should not have to conduct counter-surveillance against their grocer to be charged a fair price for bread.
The more honest answer is that this is a collective problem requiring collective tools, and the encouraging part of the story is that those tools are beginning, haltingly, to appear. The Instacart episode is the clearest demonstration of the mechanism that actually works, which is exposure. The company did not stop because the law compelled it. There was, in the relevant sense, no law to compel it. It stopped because an investigation made the practice visible, and visibility was intolerable to a business that depended on shoppers believing the price was the price. Lindsay Owens of the Groundwork Collaborative put the dynamic with precision when she said that once the curtain was pulled back, the company had no choice but to close the lab. Surveillance pricing is a practice that cannot survive being seen. That is its great vulnerability, and it points directly at the remedy.
The remedy has three reinforcing layers. The first is sunlight, the dogged work of investigators, researchers and regulators in dragging an invisible practice into view, because each exposure raises the reputational cost of doing it. The FTC study, the Consumer Reports investigation and the work of scholars like Dubal are instances of the same act: making the hidden price visible so it can be argued about. The second is disclosure as a legal default, the New York and European approach of requiring sellers to declare when a price has been personalised, imperfect but better than silence. The third, on which the rest depend, is substantive law of the kind Maryland and Connecticut have now enacted: rules that do not merely require disclosure but forbid the use of certain data and inferences to set the price of essentials, and give a public enforcer the teeth to make the prohibition real. Colorado's veto shows that this third layer is the hardest to lay, the one over which the fight is fiercest.
None of this will arrive quickly or cleanly, and the lobbying against every line of it will be intense, because the prize for the seller is enormous and the constituency for the shopper is diffuse. But the direction of travel is set by a simple fact that no amount of optimisation can engineer away. People do not want to be charged according to a secret estimate of how much they can be made to bear. The Consumer Reports survey found the objection close to unanimous, and it cut across every reason a company might offer. That near-universal refusal is the political bedrock on which any durable response will be built. The invisible price depends, in the end, on staying invisible. The work of the coming years, in legislatures and regulators and newsrooms alike, is to ensure that it cannot.
The next time you tap to confirm an order and the total looks about right, hold for a second the thought that you cannot verify it is right, because right has quietly stopped meaning the same thing for everyone. That second of doubt is not paranoia. It is the appropriate response of a citizen to a market that has learned to read them and has not asked permission. The price you see may be the price everyone sees. It may not. That you can no longer tell is the whole problem, and reclaiming the ability to tell is the whole of the answer.
References
- Consumer Reports. “Instacart's AI-Enabled Pricing Experiments May Be Inflating Your Grocery Bill, CR and Groundwork Collaborative Investigation Finds.” December 2025. https://www.consumerreports.org/money/questionable-business-practices/instacart-ai-pricing-experiment-inflating-grocery-bills-a1142182490/
- Consumer Reports. “New Report Exposes Instacart's Hidden Price Games.” Press release. December 2025. https://www.consumerreports.org/media-room/press-releases/2025/12/new-report-exposes-instacarts-hidden-price-games/
- Consumer Reports. “Instacart Stops AI Pricing Tests.” December 2025. https://www.consumerreports.org/money/questionable-business-practices/instacart-stops-ai-pricing-experiments-a1176475852/
- CBS News. “Instacart to end AI price tests for retailers following investigation.” 23 December 2025. https://www.cbsnews.com/news/instacart-ends-ai-price-testing-tool-eversight/
- Grocery Dive. “Instacart ends controversial price tests.” December 2025. https://www.grocerydive.com/news/instacart-ends-controversial-price-tests/808490/
- Veena Dubal. “AI Shouldn't Be Setting Prices or Wages. States Need to Push Back.” Governing. 20 April 2026. https://www.governing.com/workforce/ai-shouldnt-be-setting-prices-or-wages-states-need-to-push-back
- Veena Dubal. “On Algorithmic Wage Discrimination.” Columbia Law Review 123(7), 2023. https://columbialawreview.org/content/on-algorithmic-wage-discrimination/
- Christopher Armstrong, Benjamin Genn, Ashley Joyner Chavous and Kwamina Thomas Williford (Holland and Knight LLP). “Surveillance Pricing, AI Pricing Tools and the Push for Price Transparency.” JD Supra. 28 April 2026. https://www.jdsupra.com/legalnews/surveillance-pricing-ai-pricing-tools-8489430/
- Federal Trade Commission. “FTC Surveillance Pricing Study Indicates Wide Range of Personal Data Used to Set Individualized Consumer Prices.” January 2025. https://www.ftc.gov/news-events/news/press-releases/2025/01/ftc-surveillance-pricing-study-indicates-wide-range-personal-data-used-set-individualized-consumer
- Federal Trade Commission. “Issue Spotlight: The Rise of Surveillance Pricing.” January 2025. https://www.ftc.gov/system/files/ftc_gov/pdf/sp6b-issue-spotlight.pdf
- Office of Governor Wes Moore. “Governor Moore Signs Legislation to Protect Marylanders' Pocketbooks in Grocery Stores.” 28 April 2026. https://governor.maryland.gov/news/press-releases/governor-moore-signs-legislation-protect-marylanders-pocketbooks-grocery-stores-safeguard-voting
- Skadden, Arps, Slate, Meagher and Flom LLP. “Maryland Becomes the First State to Restrict Surveillance Pricing in the Food Industry.” May 2026. https://www.skadden.com/insights/publications/2026/05/maryland-becomes-the-first-state-to-restrict-surveillance-pricing
- Greenberg Traurig LLP. “Maryland Enacts Food-Sector Personalized Pricing Law.” May 2026. https://www.gtlaw.com/en/insights/2026/5/maryland-enacts-food-sector-personalized-pricing-law
- Future of Privacy Forum. “Third Time's the Charm: Connecticut Enacts Annual Privacy Update.” May 2026. https://fpf.org/blog/third-times-the-charm-connecticut-enacts-annual-privacy-update/
- Covington and Burling LLP. “Connecticut Enacts Omnibus Privacy Law.” Inside Privacy. May 2026. https://www.insideprivacy.com/state-privacy/connecticut-enacts-omnibus-privacy-law/
- Colorado Newsline. “Colorado bill to ban surveillance prices, wages vetoed by Gov. Polis.” May 2026. https://coloradonewsline.com/briefs/surveillance-pricing-bill-vetoed/
- CalMatters. “Why surveillance pricing bans are suddenly gaining traction this year (and not just in California).” May 2026. https://calmatters.org/economy/technology/2026/05/why-surveillance-pricing-bans-are-suddenly-gaining-traction-this-year-and-not-just-in-california/
- Covington and Burling LLP. “State Lawmakers Introduce New Wave of Personalized Algorithmic Pricing Bills.” Inside Privacy. 2026. https://www.insideprivacy.com/artificial-intelligence/state-lawmakers-introduce-new-wave-of-personalized-algorithmic-pricing-bills/
- European Parliament. “Personalised pricing.” Study, IPOL, 2022. https://www.europarl.europa.eu/RegData/etudes/STUD/2022/734008/IPOL_STU(2022)734008_EN.pdf
- BEUC (The European Consumer Organisation). “Price personalisation.” 2023. https://www.beuc.eu/sites/default/files/publications/BEUC-X-2023-097_Price_personalisation.pdf
- Frederik Zuiderveen Borgesius and others. “The GDPR enshrines the right to the impersonal price.” Computer Law and Security Review, 2022. https://www.sciencedirect.com/science/article/pii/S0267364922000085
- Washington Center for Equitable Growth. “How artificial intelligence uncouples hard work from fair wages through 'surveillance pay' practices, and how to fix it.” 2026. https://equitablegrowth.org/how-artificial-intelligence-uncouples-hard-work-from-fair-wages-through-surveillance-pay-practices-and-how-to-fix-it/
- OECD. “Personalised Pricing in the Digital Era.” DAF/COMP(2018)13. 2018. https://one.oecd.org/document/DAF/COMP/WD(2018)150/en/pdf
Tim Green UK-based Systems Theorist & Independent Technology Writer
Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.
His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.
ORCID: 0009-0002-0156-9795 Email: tim@smarterarticles.co.uk
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Saturday
from 
Roscoe's Story
In Summary: * No yard work today. Having spent 3 days back to back doing yard earlier in the week, these old bones are now refreshed and probably as recovered as they ever are. And I'm planning the next few days of yard work. Tomorrow I'll haul the green organics bin to the back yard and load it up with broken branches. The bigger branches I've stored back there in a staging area will be broken and cut up into smaller pieces to fit into the bin.
On Monday I'll start mowing again. I may mow out back, it really needs it back there, but I also want to lower the mower blade and run over the front yard again, making it look much more civilized. Huh. That's about two weeks worth of yard work I've just outlined there, given our heat and humidity and my age and general level of decrepitude. Oh well, we'll do our best.
Tonight I may watch the Saturday night Svengoolie. I've seen the movie he's showing tonight many times, but his schtick is always fun. At any rate, I'll wrap up the Saturday prayers and turn in at a reasonable time.
Prayers, etc.: * I have a daily prayer regimen I try to follow throughout the day from early morning, as soon as I roll out of bed, until head hits pillow at night.
Health Metrics: * bw= 235.90 lbs. * bp= 148/79 (70)
Exercise: * morning stretches, balance exercises, kegel pelvic floor exercises, half squats, calf raises, wall push-ups, BP breathing exercises, pilates
Diet: * 07:10 – 1 banana * 07:45 – pizza * 12:20 – bowl of home made beef and vegetables soup, white bread and butter * 13:45 – bowl of ice cream * 16:50 – crispy oatmeal dunkin' cookies, cup of cold milk
Activities, Chores, etc.: * 07:00 – bank accounts activity monitored. * 07:30 – read, write, pray, follow news reports from various sources, surf the socials, nap * 08:45 – Prayerfully reading the Propers of the Roman Catholic Mass of today, 27 June 2026, Sanctae Mariae Sabbato, according to the 1962 Ordo as found in Sanctifica * 09:20 – watching Saturday Morning Cartoons on MeTV Toons * 11:30 – Now listening to general sports talk on 105.3 The Fan, DFW's #1 Sports Station, ahead of this afternoon's Rangers / Blue Jays game. I'll stay with this station to hear the radio call of that game. * 17:25 – ... and the Rangers win this one, 7 to 4.
Chess: * 13:30 – moved in all pending CC games.
Day 17
from Out of Office
My dog went in for a check up today. I got the same information I have been getting… she has a heart tumor, will continue to have issues, and only has days to live.
She is doing okay, but my family and I have plans to go out of town. We are going to take her with us so she can still spend as much time with us as possible, but she looks so good right now too. I really hope the trip goes well and does not stress her out too much.
I finally got around to painting a little portrait of her. It feels so good to have her near, but I am still so scared knowing she won't be here for much longer.
That is all for now.
Thank you for your message. I am currently out of office with no set return date. I will get back to you when the time is right.
Day 16
from Out of Office
Today was also hard. I have no idea how I am doing this on top of everything going on. I received a rejection letter for a job opportunity, my dog is doing better but literally has days to live, and my situation is still pending. How do humans keep going during difficult times?
I feel like I am failing my family, friends, and not doing much to improve or be proactive, therefore also failing myself. I don’t mean to sound so hopeless, but I think today I am realizing that I may be struggling more than I realize. I have therapy next week, but that really only helps so much. We don't ever really get deep into other issues, since my situation pending tends to be the main focus of our time.
Anyway, I will just keep holding on as best I can.
Thank you for your message. I am currently out of office with no set return date. I will get back to you when the time is right.
making natural pigments
from Rooted and Growing in the Ozarks
A few years ago I had the wonderful pleasure of taking my children to an incredible summer camp specializing in sharing natural art and primitive skills. Cina Canada hosts these camps and epic classes at her beautiful studio south of Springfield, Wild Arts Learning Center. Check it out here – https://www.wildartslearning.com/
I recently felt very inspired by the beautiful red cabbages we harvested from the garden here at Sassafras Ranch, and decided to make an attempt at extracting the pigment from a cabbage on my own for the first time! The experiment went surprisingly well and i’m excited to pick up more alum and washing soda so I can try my hand at making more pigments!
These pigments can be used for many things once they have been extracted and dried. They can become watercolor, gouache, oil paint, ink, or crayons, and you can even mix them into natural wall paint! It does take quite a bit to color a gallon of paint, but it can certainly be done and where there is a will there is a way! From about a half of a red cabbage, I got around 10 grams of mineral pigment after it dried. It could have been more, but my experiment erupted over the jar and I lost some pigment in the process.
I won’t be doing a full on tutorial yet.. stay tuned for future posts… but here are some pictures of the process for now.
This is the cabbage water from simmering a half head of cabbage for about an hour. Very deep purple.
The chemical reaction! First I mixed alum into hot water and added it to the cabbage tea, then I mixed washing soda into hot water separately and added that and BAM!
This chemical reaction causes the solids to separate from the water and it can then be filtered through a cloth or coffee filter to let the water drip out and capture the pigment… which is light blue now!
I dried it in our electric dehydrator to speed up the process a bit.
And this was the end result! So cool. I’m stoked to do more!!
creating with love
from Rooted and Growing in the Ozarks
My life recently took another big turn as it seems to be doing so often for so many in this epic of time on the planet. I’m grateful for every moment, every lesson, every blessing that has come my way. i am fully with it, immersing myself in the love and abundance that surrounds us here in this beautiful place.
I have joined forces with my love to build our vision together, united, creating from our hearts into all the things we touch and this union feels like divine magic. Another ego death took place, a leap into the unknown, and it brought lessons about people and places i thought I knew. The lessons never end and I feel more aligned and grateful than ever before.
Rise up! Be everything you were meant to be! You have a purpose and when you get aligned to it, sometimes the shit seems to hit the fan but it’s all for the greater good. Have faith. Breathe. You are the light.
We have been very busy in the gardens. We finished harvesting the cabbage, celery, broccoli and cauliflower, which all turned out so great! Now we are harvesting garlic, onions, carrots, beets, peppers, and our first zucchinis and tomatoes! I have been obsessively planting, harvesting, and processing herbs into various forms of medicine this year. Things like yarrow, motherwort, mullein, monarda, mimosa, wild carrot seed, chamomile, thyme, oregano, and catnip to name a few… I love the abundance of this time!
I found this darling in a bag of donated plant pots for the nursery. So cute!
We finished the recent Summer Solstice issue of The Ozarks Agrarian News and got it out to our subscribers and into some local shops! I illustrate and help to compile, edit, print, assemble, and send these out 8 times a year, harmonizing with each season and cross quarter of the year and adapted to the Ozarks bio-region. If you are interested in subscribing, sharing content, or supporting us, let us know!