As I made my way home from fitness dance class, I saw a man falling haplessly on the paving stones outside the main entrance to his apartment building.

— are you OK?, I asked

— yes but the PIN code doesn’t work, he said, meaning to the door

— Do you need help getting up? I asked

— I live here, he responded now slowly getting on his feet unsteadily

He’d dropped his pizza, box lay upside down on the ground. And the plastic containers of sauce were spattered on his wallet and his phone which he’d also dropped.

He looked about to fall again, I asked

— Can I pick your stuff up for you?

— No, he replied, but you can hold the door for me.

He managed to gather his stuff, but I took the pizza and handed it to him

— this still looks edible, I said encouragingly

One hand on the door frame, he took the pizza in his hand and I saw then that his arm was incredibly muscular.

— take care now, I said as we parted ways

And with thoughts of the ruined pizza on my mind I went home

I am thinking about it still.

Cardinals vs Guardians.

We've finished our lunch at home, the wife and I. She's now on her post lunch nap, and I've found a baseball game to follow: the Cleveland Guardians playing the St. Louis Cardinals. The teams are tied as they play through the middle innings, the score now is 1 to 1 in the top of the 6th inning.

And the adventure continues.

'What is your home?' A stranger asks.

Home (for you, my love)

Home?

No. Not what I once named it. Not walls, nor roads remembered by the body’s tired return.

Home has slipped its geography. It no longer answers to maps.

Listen, I will tell you, my friend, of a home with no address, no door, no fixed sky...

only a mind.

The mind.

Yours.

Where I wander like a pilgrim without sleep, touching the edges of your thoughts as if they were holy cloth.

I left a place once called home; a source, perhaps, a well I drank from without ever being quenched.

What is a home if the heart refuses it? If it does not loosen there, does not lay down its armor, does not breathe?

No—

Home is not where a man hangs his hat.

It is where he loses himself entirely.

And mine... mine is not here.

Not fully.

It is cleaved. like light through glass, like a prayer spoken in two languages—

here, and there, and in the terrible distance between.

You...

You are my home.

I have driven whole nights through the dark of myself to reach you,

whispering your name like a rhythm against the wheel, like a vow I could not break if I tried.

I would come to you in the hour when breath is deepest, when the world forgets itself—

not to wake you, but to feel you there, to exist in the same quiet as your dreaming body.

That would be enough. God— that would be everything.

There:

in that imagined room, in that borrowed closeness,

I am unafraid.

My demons do not follow. My doubts cannot cross the threshold.

There is only the heat of being known, the slow unraveling of all I pretend to be, the dangerous relief of becoming myself in the presence of you.

Amber-eyed, ocean-removed, twelve hundred leagues of absence and still

you are nearer to me than my own hands.

What is this place we make without touching?

What is this fire that asks nothing and takes everything?

I live there in the thought of you, in the shape of your name inside my mouth, in the quiet confession of wanting.

And one day—

if the world is merciful, or cruel enough

here and there will collapse into one,

and I will stand beside you with nothing left to lose,

and say, at last,

not as metaphor, not as longing—

but as truth:

I am home.

---

#poetry #wyst

It’s been a while since I wanted to start a blog. Years really. I kept telling myself that I’m not ready, no one will care, I’m too busy etc. It really is just standard stuff when it comes to starting something new or when you put yourself out there. You make up any excuse just so you can delay the whole thing until you either forget about it or you just don’t care about it anymore. Pretty neat defence mechanism.

You try to justify the whole delay so you can plan out everything in advance, everything can be perfect so you don’t make a mistake. It doesn’t work like that. I should know this by now that I’m 34 years old. Year by year I feel like I lie less to myself but it still happens daily. At least I’m aware. That is something I guess.

Okay so like I said I’m a 34 year old guy. I was born in Hungary but I moved to England in 2014 when I was 23. To this day I don’t know if that decision was good or bad. Probably never will. Because of this, English is my second language and that means I’ll make mistakes. This was another excuse I liked to tell myself. I mean my English is not perfect but I can convey my thoughts pretty well I feel like and I hope it adds some uniqueness to my posts. I don’t want to run all my stuff through an AI or spellchecker. I’ll obviously try to minimise mistakes especially spelling ones but I don’t want to sound like a robot. I honestly despise this whole new era of “everything is AI”.

The biggest thing that helped me get started was when I realised I don’t have to share this blog with anyone. No one needs to know who I am. It doesn’t matter if anyone reads it or not. I just like writing. I always have. I wrote very basic stories when I was a kid. Okay I admit they were heavily mimicking existing ones. I remember one that was basically Robinson Crusoe but written by a 12 year old.

I really started rambling here. I didn’t think I will write about that Robinson story, I honestly even forgot about it until 2 minutes ago. It is funny how much stuff comes to surface when you are trying to organise your thoughts so you can put them down in a readable fashion.

I have loads of interests and I like taking walks whilst I think about a lot of stuff. I used to have a car but I sold it. I walk to and from work too. I really don’t want to get lazy and I hate driving. I’ll write posts just about anything I think. My plan is to write at least one post per week. (I refuse to call my work an article because it feels pretentious.) I might even write multiple a day. Who knows? I just want to get going.

Without trying to give you the whole list, below is the stuff I like the most from the top of my head. This doesn’t mean I’ll only write about these but perhaps it gives you an idea of what kind of guy I am.

1. Guitar – Especially Rock and Roll, Blues, Hard Rock, Metal (Been playing since 2007.)

2. Football and Formula 1 – Favourite teams: Arsenal and Ferrari. Pain. I know.

3. Books – Andy Weir is my favourite author.

4. Films – Mainly horror, action and science fiction. I have a newfound love for old black and white Japanese films. I like the Human Condition trilogy, okay?

5. Philosophy – I was always interested and last year I’ve found stoicism which is probably the one I read the most.

Obviously I like ton of other stuff too. Gaming, cooking, hanging out with people, whatever. You get the gist. I really don’t know why I’m trying to make this into a list.

Anyway I think it is time for me to say goodbye and I hope, future me will be very happy that I started this blog.

Thanks,

Blip-A

I found a moth inside my elevator. I scooped it up with my hands shaped like a bowl and brought it out to my balcony. Then I started imagining what it would tell its moth friends afterward. Like, how she (yes, I am calling her SHE) suddenly entered this brightly lit moving box and got trapped there, no water, no food, and every now and then a giant would appear, absolutely terrifying her.

Until one day or some hours, she cannot really precise, but it felt like an eternity, a giant with long hair and a weird looking white horse (that's Livi in case you missed the ref) showed up, grabbed her with giant hands, and everything went dark again. She was sure that was the end. But then the hands opened, and there she was, at the highest height she's ever been in life, she was back outside, but outside this time was so enormous, she could see all the buildings and the city from above, all this happening as if she’d been teleported to freedom. Her moth friends would probably call the whole thing an abduction.

She’d be invited onto moth podcasts to share her testimony. The hater moths would say, “Fake. She just wants attention, next thing you know, she’s auditioning for Too Hot to Handle”, etc. Eventually, she’d write a book compiling testimonies from other moths who claim to have been abducted, trying to find patterns. Some would say, “My giant had short hair.” Others: “Mine was bald.” Some would insist there was no giant at all, just a huge transparent glass thing, and at the bottom, something that looked like a piece of Spar flyers. Other moths would never swallow the theory of the giant jar with Spar flyers at the bottom. “This is obviously a marketing move from Spar!” they would say.

Damn it's so hard to be a believable moth.

/Apr26

I recently watched the seventh season, second episode of Star Trek: DS9, Shadows and Symbols. The character Benny Russell (played by Avery Brooks) is in a psychiatric room writing his story on the walls. He does this because the doctors refuse to give him paper.

A psychiatrist, Dr. Wykoff (played by Casey Biggs) offers Benny a paint roller to erase his writings so he can be “cured” of his delusions. I won’t spoil any more so go watch. After watching that episode it gave me an idea.

Inside my home I have blue, white, and yellow walls. What color wall would I choose? Or would I write on all of them? Unfortunately, white and yellow walls are too bright even in low lighting. Blue walls are easier on my eyes and still bright enough when there’s not enough light.

However, all of this doesn’t matter. The real question is: how long can my kids and I write on the walls before my wife goes berserk and makes me clean and repaint them?

#writing #blue #ds9 #startrek #walls #white #yellow

#shuacantikharem

Sialan kan Wonwoo jadi kepikiran.

Kalo dibilang apa Wonwoo nyesel nyium bibir Joshua karena sekarang dia jadi buronan di kalangan temen-temennya sendiri (dan entah berapa juta manusia di luar sana yang Wonwoo nggak kenal tapi sama keselnya karena bibir Joshua udah direbut cowok anonim), jawabannya tentu aja enggak ya gaes yaaaaa ☝️

Wonwoo NGGAK AKAN pernah nyesel karena KAPAN LAGI BISA NYIUM BIBIR JOSHUA HONG WOI, MAU DUNIA KEBELAH KEK BODO AMAT YANG PENTING DIA UDAH NGERASAIN BIBIRNYA JOSHUA JISOO HONG‼️‼️‼️‼️

(eit nggak usah ngiri☝️)

Cuma, yeah, tetep aja Wonwoo kepikiran. Kalo reaksi temen-temennya aja udah radikal begitu, apakah bakal ada ekstrimis-ekstrimis lain yang siap nyulik Jeon Wonwoo pas tau dirinya lah perebut ciuman Joshua, terus Wonwoo dihanyutkan ke sungai Gangga? Ato, worse, ditunjuk jadi duta MBG?? 😨 (ih najis)

Dikernyitkannya dahi, auto hidung bangirnya ikut mengerut. Wonwoo berjalan memasuki perpustakaan di area pusat kampus seperti tiap sore dengan kedua lengan melipat di dada. Parasnya kelewat serius buat isi kepalanya yang random saat ini. Kayaknya better Wonwoo agak jaga jarak sama Joshua deh. Nerapin beberapa rules personal yang ketat. Jangan deket-deket biar nggak khilaf ciuman lagi. Jangan berduaan doang di ruang sepi. Jangan—

“Ikh...”

...Yaelah. Langsung muncul itu Joshua-nya depan mata. Baru juga mau dijauhin bjirrrrr. KENAPA SIH??!! SEGITU PENGENNYA SEMESTA INI COMBLANGIN WONWOO SAMA JOSHUA, HAH???!!! YAUDAH DEH KALO MAKSA MAH!!!

Wonwoo menghampirinya. Tapi Joshua juga nggak nyadarin kedatangan Wonwoo sih. Dia tengah sibuk berjinjit sambil ngulurin lengan setinggi mungkin, berusaha menggapai salah satu buku tebal di rak paling atas. Wonwoo diem aja ngeliatin dia dari koridor. Kayak biasa, perpustakaan di jam bubaran kampus gini udah tergolong lengang. Hampir nggak ada orang lain di sekitar mereka. Mungkin ada 1-2 orang yang ngumpet, tapi nggak tau deh lagi pada ngumpet di mana tepatnya.

Joshua berusaha jinjit lebih tinggi lagi. Suatu pemandangan yang separo bikin Wonwoo pengen ketawa soalnya Joshua lucuuuuuu bangettt, separonya lagi kesian pengen bantuin. Padahal beda tinggi badan Wonwoo sama Joshua juga nggak jauh-jauh banget, tapi mayanlah, selisih tinggi itu berperan besar dalam situasi kayak gini. Sementara itu, Joshua udah gemeter sebadan-badan, berusaha mengerahkan seluruh inci tingginya biar tangannya nyampe ke buku itu. “Dikit, uh, lagi...,” gumamnya tanpa sadar.

Alangkah kagetnya Joshua pas ada tangan lain menjulur santai, mengambil buku yang dia maksud tanpa kesulitan sama sekali. Arah pandangnya berputar dari lengan ke wajah orang itu yang lagi dongak kayak dia sebelumnya. Jeon Wonwoo. Lengkap dengan kacamata bingkai hitamnya dan wajah serius nan ganteng yang akhir-akhir ini menghantui pikiran Joshua. Salting, Joshua pun perlahan berbalik badan, menatap Wonwoo yang masih berkutat sama buku di rak atas dan membiarkan degup jantung nggak beraturan dalam dada serta rona merah melalap kedua pipinya.

Joshua menelisik satu-persatu fakta: mereka berduaan (lagi) + semburat jingga dari celah jendela jatuh menerangi perpustakaan sore itu + lorong rak di pojokan yang sunyi sepi + jarak tubuh mereka terlalu dekat + Wonwoo tetep seganteng pas nyium dia waktu itu. Deg degan, Joshua lalu memejamkan mata dan mengangkat sedikit dagunya.

Posisi Joshua yang seperti itulah yang Wonwoo temui saat dia akhirnya menunduk, berniat memberikan buku yang baru dia ambilkan. Namun, niat tersebut sirna seketika. Joshua dalam kukungannya jelas menantikan sesuatu, meminta sesuatu dari Wonwoo dengan tindakannya. Degukan ludah membuat jakun Wonwoo naik-turun. Dia yakin dia tau apa yang Joshua minta darinya, tetapi dia nggak berani ngambil kesimpulan segitu cepetnya.

Masa sih...? Masa cowok secantik ini—makhluk seindah, sesempurna, se-enggak nyata ini—nungguin ciuman dari Wonwoo?

Detik berlalu, meleleh menjadi menit. Nggak kunjung datang sentuhan yang diharapkan, Joshua (dengan penuh tanda tanya) perlahan membuka sedikit celah mata, mencari tau di mana kah keberadaan Wonwoo. Rupanya dia masih ada di hadapannya, masih mengukung Joshua, memojokkannya ke rak buku, tapi sekarang dia menatap Joshua lekat-lekat. Tatap mereka bersirobok dan, spontan, Joshua merasa malu. “Ah, ini, mm,” terbata-bata, sembari mukanya begitu merah bagai tomat kematengan. “A-aku enggak—”

“Mejemin mata gitu maksudnya apaan nih?” seloroh Wonwoo, sengaja. Sumpah deh, Joshua Hong itu kenapa bisa begitu gampangnya mancing sisi jail Wonwoo sih? Minta digodain banget?? “Lo nungguin gue ngapain?”

Makin dan makin kebakar aja pipi Joshua. “Eng-enggak kok, nggak gitu...,” balasnya dalam gumaman rendah, saking lembutnya sampe hampir nggak kedengeran andaikan perpustakaan lagi nggak sesepi itu. “Cuma...muka kamu deket banget, aku kan jadi keinget...lagi...”

...Sumpah.

Cantik. Cantiknya pake banget. Cantiknya nggak ngotak. Wonwoo harap Joshua sadar sepenuhnya kalo dia tuh cantik luar biasa dan bahwa dia berhak banget dipuja-puji, disembah bak ratu berlian pemilik hati para budak cinta. Joshua, sumpah lah...

“Terus, emm, jadi aku mikir apa kamu nggak mau—”

Wonwoo majuin kepala buat nutup mulut Joshua pake bibirnya. Refleks, juga dengan sentakan napas, Joshua mejamin mata lagi. Ciuman itu ringan. Hanya bibir ketemu bibir buat beberapa detik. Suara kecupan lah yang tertinggal kala kedua bibir dipisahkan paksa.

Bagai terhipnotis, Wonwoo mengelusi bibir atas Joshua. Lembut. Merah delima. Sedikit lengket, mungkin sisa lip balm yang masih menempel. Mata yang sayu. Pipi yang merona. Bener-bener secantik—bahkan jauh lebih cantik—di foto-foto majalah itu. Ibu jari Wonwoo turun ke bibir bawah Joshua, menekannya sedikit hingga terbuka, memperlihatkan geligi dan sekelebat ujung lidahnya. Turun lagi hingga membelai rahang dan menangkup dagu. Bisikan yang semakin rendah, semakin berat.

“Cantik...”

Dagu Joshua diangkat. Tangan Wonwoo yang lowong bertumpu pada rak di belakang Joshua. Nggak bisa menahan diri, Wonwoo kembali mencium bibir manis itu. Alih-alih Wonwoo merundukkan badan sedemikian rupa, kini Joshua lah yang harus menegakkan lehernya agar bisa mencapai bibir cowok itu. Dia pasrah, membiarkan Wonwoo terus menerus memberikan kecupan-kecupan kecil pada bibirnya. Sesekali, tautan bibir mereka sedikit lama, sedikit nggak rela harus terlepas meski sedetik kemudian akan langsung terpaut lagi.

Hati Wonwoo bagai melambung ke atas awan. Joshua Hong yang diidamkan cowok dan cewek sekampus kini berada di bawahnya, dengan bibir begitu penurut mengikuti gerak bibirnya. Wonwoo melepaskan ciuman dengan napas agak memburu, berniat memberikan kesempatan pada Joshua untuk menenangkan diri. Mungkin dia kelewat tergesa-gesa. Mungkin Joshua overwhelmed dan butuh time out untuk mengambil napas.

Di luar dugaan, Joshua malah menaikkan kacamata Wonwoo ke rambutnya, merangkulkan kedua lengannya ke leher Wonwoo dan menarik bagian belakang kepala cowok itu untuk menyatukan bibir mereka kembali. Kali ini bukan lagi kecupan naif yang mereka bagi, melainkan segala yang selama ini dibendung baik oleh Wonwoo maupun oleh Joshua. Bibir Joshua mencumbuinya, secara aktif mengajak Wonwoo untuk melepaskan segala hasrat yang dimilikinya. Ciuman demi ciuman yang mereka bagi semakin panas. Tangan Wonwoo menemukan pinggang Joshua, merangkulnya erat dengan harapan menghapus memori akan Seungcheol di sana. Tangannya yang lain menelusuri punggung Joshua melalui bahan kemejanya yang halus. Bagian depan tubuh mereka menempel nggak kalah lekat dari sepasang bibir.

“Mmh,” suara-suara geraman tertahan menemani bunyi cumbuan yang basah. Di satu momen, Wonwoo menggigit perlahan bibir Joshua, berbagi helaan napas bersama, sebelum memasukkan lidahnya ke celah yang tercipta. “Hng!” Joshua mendesah agak kencang, tapi untungnya lidah Wonwoo keburu menemukan lidahnya dan berhasil membungkam keributan tersebut. Decakan terdengar. Peluh menitik di kening Wonwoo. Kaki Joshua hampir nggak tahan untuk mengalungi pinggul Wonwoo, mengundang cowok itu untuk mencumbuinya terus seperti ini di sudut terpencil perpustakaan sampai malam turun.

“Uhuk, uhuk!”

Suara batuk seseorang. Bagai disiram air dingin, Wonwoo langsung melepas Joshua, hampir-hampir melompat mundur menjauhinya. Segera diturunkannya kacamata agar indra penglihatannya kembali. Dia memandangi Joshua—bibir bengkak dan basah, mata sayu, wajah memerah, serta napas memburu—lalu meneguk ludah. Dia. Dia yang udah bikin Joshua kayak gini. Jeon Wonwoo.

Tapi,

nggak di sini juga anjir. Kalo ada yang liat, gimana? Terus kalo sampe kesebar rumor kalo dia lah cowok yang udah nyium Joshua, gimana? Minimal digebukin, lebih mungkin digantung terbalik di pohon beringin di halaman belakang kampus. Screw that, nggak peduli nasib dirinya deh, tapi nasib Joshua? Wonwoo nggak mau kalo nama Joshua jadi jelek gegara ulahnya. Dia suka Joshua. Suka banget. Cinta. Karena cinta, makanya—

“Ah, Wonu—”

—sebelum Joshua sempet ngomong apapun, Wonwoo udah berbalik dan pergi (sambil doa nggak ada yang nyadar akan jendolan di celananya, amen), meninggalkan Joshua yang berusaha menenangkan dirinya sendirian sambil menyentuh bibirnya, masih terlena oleh ciuman bergairah dari cowok itu.

Terhalang oleh rak-rak buku, Joshua nggak sadar sama sekali kalo ada orang lain yang merhatiin mereka sejak bercumbu tadi. Orang lain yang menyeringai jahil karena suatu rencana udah terangkai manis di dalam kepalanya. Orang lain yang juga merupakan 'musuh' Joshua Hong akhir-akhir ini.

Instead of only criticizing “AI” (when in fact, the commercial LLM services are really the main issue), here is a more optimistic list of things I support 💪 (followed by a list of bad smells 🦨 in AI):

💪 Smarter machine learning models that do more with less: less data, less energy, less waste.

💪 Building models that are better, not just bigger: reliable, effective, and resource-conscious.

💪 Ethical innovation: training AI without exploiting creators or trampling intellectual property rights.

💪 Practical AI use cases that truly help people and society, not just corporate bottom lines.

💪 Sustainable business models that support fair, circular industries instead of endless extraction.

💪 Respect for language and culture – preserve diversity, don’t erase it.

...therefore, I stand against:

🦨 Bloated generative AI systems with bottomless appetites for data, energy, and water.

🦨 The expanding footprint of data centers swallowing land and resources.

🦨 Predatory tactics to grab training data at the expense of human rights.

🦨 Turning AI into a tool for surveillance capitalism and exploitation.

🦨 Pretending to care about AI safety while dodging real accountability.

🦨 Systems that funnel power to a few tech giants, making the rest of us renters in their digital empires.

🦨 Human suffering in AI’s hidden labor force – those forced to filter the internet’s worst as cheap, disposable labor (usually in the Global South).

🦨 Schemes to dodge taxes and skirt regulations, while claiming to build the future.

🦨 Generative AI services aren’t tools – they’re just content repositories, trained on a vast and murky pool of internet data. But the internet is a mess: full of errors, bias, satire, and outright lies. These systems can’t tell truth from fiction, and they strip away context and source credibility. There’s no metadata to distinguish fact from sarcasm or disinformation. It all looks the same to an AI. That’s a disaster waiting to happen.

🧠 The most sustainable, creative, and ethical model isn’t an algorithm. It’s the human brain. If you want art, writing, or ideas, hire a human being. You’ll get quality and originality, not a regurgitated mashup from a statistical prediction machine.

The right place for AI is in support – statistical prediction, maintenance, and optimization. That's proper tools. But generative AI services won’t help us work less or better. They’ll push us to go faster, sacrificing quality, creating stress, and robbing us of agency. To build a future centered on humans, we must focus on human well-being – not just on making tech billionaires richer.

(btw, I have nothing against skunks, the icon just represents “bad smells” 😀)

När internet började bli tillgängligt för en bredare publik under 1990-talet uppstod ett behov av enklare sätt att publicera innehåll. Tidiga webbplatser var ofta statiska och krävde teknisk kunskap för att uppdateras, men gradvis växte idéer fram om mer personliga och kontinuerligt uppdaterade sidor. Ur detta föddes bloggarna – en blandning av dagbok, publiceringsplattform och offentlig röst, där individer kunde dela tankar, länkar och berättelser i ett löpande flöde.

Samtidigt uppstod ett praktiskt problem: hur skulle man hålla koll på alla dessa uppdateringar utan att behöva besöka varje sida manuellt? Lösningen blev RSS, ett standardiserat sätt att distribuera innehåll automatiskt till läsare. Med hjälp av RSS kunde användare prenumerera på sina favoritbloggar och få nya inlägg samlade på ett ställe, vilket gjorde internet både mer överskådligt och mer levande. Tillsammans lade bloggar och RSS grunden för ett mer dynamiskt, användardrivet nät – långt innan sociala medier tog över scenen.

Under tidigt 2000-tal var bloggar själva ryggraden i det sociala internet. Plattformar som Tumblr, Blogger och WordPress gjorde det enkelt för vem som helst att publicera tankar, guider och dagboksinlägg. RSS, via format som RSS och Atom, blev ett slags distributionslager ovanpå detta: istället för att besöka varje blogg kunde man samla allt i en läsare och få uppdateringar i realtid. Det var en ganska decentraliserad och användarkontrollerad modell.

Sedan kom sociala medier och förändrade spelplanen. Plattformar som Facebook, Twitter och senare Instagram tog över mycket av det som bloggar tidigare stod för. Det blev enklare och snabbare att publicera kortare innehåll, och algoritmer började styra vad vi ser istället för kronologiska flöden. I den miljön tappade RSS sin synlighet, inte för att tekniken slutade fungera, utan för att den inte passade in i affärsmodellen hos de stora plattformarna.

Men det betyder inte att bloggar och RSS försvunnit. Snarare har de blivit mer nischade och ibland mer professionella. Nyhetsbrevstjänster som Substack och Ghost bygger i praktiken vidare på samma idéer: direkt relation mellan skribent och läsare, utan mellanhänder. Många av dessa erbjuder fortfarande RSS-flöden, även om de inte alltid lyfts fram lika tydligt.

Samtidigt finns det en tyst renässans för RSS bland mer tekniskt intresserade användare. Verktyg som Feedly och Inoreader används för att återta kontrollen över informationsflödet i en tid där algoritmer ofta upplevs som brusiga eller manipulativa. I en värld av “doomscrolling” blir RSS nästan ett motgift: du väljer själv vad du vill följa, och inget annat.

Bloggandet i sig har också förändrats snarare än minskat. Mycket av det som tidigare hade varit blogginlägg dyker idag upp som långa trådar på sociala medier, videor på YouTube eller poddar. Formen har skiftat, men drivkraften att publicera och dela perspektiv är densamma.

Så frågan är inte riktigt om bloggar och RSS är på väg bort, utan om de har slutat vara mainstream. De har gått från att vara standard för alla till att bli verktyg för de som aktivt väljer ett mer öppet och kontrollerat internet. Och just därför finns det något nästan tidlöst i dem. När pendeln svänger bort från centraliserade plattformar brukar intresset för öppna standarder och egna publiceringsytor komma tillbaka.

Det dyker också upp nya tjänster för att följa bloggar så som Blogflock. Så än är nog inte bloggar och RSS utdöda.

Det har också kommit mer nischade bloggplattformar. Nouw är en svensk sådan, den växte fram i en tid när bloggandet redan hade blivit etablerat, men höll på att förändras. Den lanserades 2015 som en vidareutveckling och omprofilering av det tidigare communityt Nattstad, med ambitionen att skapa något mer än bara ett tekniskt verktyg för att skriva inlägg.

Till skillnad från klassiska bloggplattformar fungerade Nouw inte bara som en plats där man publicerar texter, utan också som ett slags digitalt magasin. Bloggarna blev en del av ett större nätverk där innehåll kunde lyftas fram, kurateras och nå en bredare publik. Det gjorde att plattformen fick drag av både socialt nätverk och mediekanal, snarare än enbart ett publiceringsverktyg.

Framtiden för bloggar och RSS är svår att spika fast, men mycket pekar på att de inte försvinner utan snarare fortsätter leva i nya former. I takt med att fler tröttnar på algoritmstyrda flöden och centraliserade plattformar kan intresset för öppnare lösningar öka igen, där användaren själv styr vad som konsumeras. Tekniker som RSS finns redan på plats och används fortfarande bakom kulisserna i många tjänster, även när det inte märks utåt. Samtidigt kan nya sätt att publicera innehåll – som nyhetsbrev, poddar och egna plattformar – fortsätta sudda ut gränsen för vad en “blogg” egentligen är. Kanske blir framtidens blogg mindre synlig som begrepp, men desto mer närvarande som idé: en direkt kanal mellan skapare och läsare, utan att någon annan bestämmer vad som ska nå fram.

I did an over two hour leg workout with a ton of drop sets and failure and I feel good. I do believe that I have a life worth living and I would like to experience it and I’m grateful for all of the additional chances that I get to be appreciative for what I have.

She often shares pictures and videos of her daughter. The baby is 8 months old. I get the impression that she is more entertained by the baby than gently loving her. She is learning to love, to love herself by loving her daughter. The baby is filling the mother's lack of love. She gave birth to a girl rather than a boy because the girl is the healer for the mother.

Resilience. One word that can determine whether you survive or not. One word that can determine whether you pick up and keep going or gradually fade into the background, no longer relevant to the word around you.

I was reminded about what it means to be resilient recently when I was not selected for a job role, despite being one of the two finalists. I gave it my all, I had great conversations with my interviewers, and I felt good coming out of the final round of interviews. Then I started to notice the signs. Follow up wasn’t as forthcoming as I expected it to be despite how enthusiastic the organization was about me. I was told to expect feedback as of a certain date, it didn’t come. Then I was going to receive it by a slightly later date. It came. I was a strong candidate, the decision was hard, but I wasn’t selected. Someone that was closer to where the organization is headquartered was. Someone that wouldn’t require relocation. I lost the opportunity because my situation was harder to deal with logistically for this organization that what the other candidate’s situation was.

The anger set in, as did the frustration, the disappointment, and the questions about what I could have done differently. Rather than getting the chance to make a positive impact within an organization, I was shown the exit. I had little explanation as to why and a lingering feeling that I wasn’t selected because someone didn’t want to deal with the logistics involved with me taking the role.

The response to this kind of situation could becoming a defining moment in my professional and personal life. Either I choose to double down in my current role and excel where I am or I disengage, become bitter, and resent that I wasn’t going to be where I wanted to. I made a conscious decision to choose the former. I chose resilience. No organization is perfect; the organization I work in today is far from perfect. Yet if I choose to be resilient, I choose to engage more and choose to find opportunity in times of setback when I know I can make the organization better.

I refuse to let the decision made by someone else define my outlook, my attitude, or whether or I am happy or not. I choose to be resilient. I chose to move forward.

In January 2026, Kristalina Georgieva, the Managing Director of the International Monetary Fund, stood before an audience at the World Economic Forum in Davos and offered a statistic that landed with the quiet brutality of a footnote in a corporate restructuring memo. The number of translators and interpreters at the IMF, she said, had dropped from 200 to 50. The cause was not a budget crisis or a policy realignment. It was technology. The fund had simply decided that machines could handle most of the work that humans used to do.

Georgieva presented the figure as evidence of a broader transformation. Forty per cent of global jobs, she argued, would be transformed or eliminated by artificial intelligence, with that figure climbing to 60 per cent in advanced economies. But it was the specificity of the translation example that stuck. This was not a hypothetical projection or an economist's forecast. It was a headcount. Real people, with real expertise in the precise rendering of financial policy across languages and cultures, had been replaced by systems that could approximate their output at a fraction of the cost.

The IMF is not alone. Across the global translation industry, now valued at an estimated 31.70 billion US dollars according to Slator's 2025 Language Industry Market Report, a similar pattern is playing out. Large language models and neural machine translation systems have not simply made human translators obsolete. They have restructured the profession from the inside, converting skilled practitioners into quality controllers for text they did not write. The question this raises is not whether AI can translate. It demonstrably can, often to a standard that passes casual inspection. The question is what happens to a profession, and to the cultural knowledge it carries, when the market decides that “good enough” is good enough.

The Numbers Behind the Quiet Collapse

A 2024 survey conducted by the United Kingdom's Society of Authors, which polled 787 of its 12,500 members, found that 36 per cent of translators had already lost work to generative AI. Forty-three per cent reported a decrease in income as a direct result of the technology. Over three-quarters, some 77 per cent, believed that generative AI would negatively affect their future earnings. Eighty-six per cent expressed concern that the use of generative AI devalues human-made creative work. These are not projections. They are reports from working professionals describing what has already happened to their livelihoods.

The income data from individual translators is more granular and more alarming. Brian Merchant, writing in his newsletter Blood in the Machine, documented cases across the profession in mid-2025. One technical translator with 15 years of experience reported earning just 8,000 euros in 2025, down from six figures in previous years. A French-English translator based in Quebec described a 60 per cent income decline in 2024, with projections suggesting an 80 per cent drop from peak earnings by the end of 2025. An Italian-English translator in Rome reported that work requests had ceased entirely for the month of June 2025, after years of working 50 to 60 hours per week. An English-Portuguese translator documented that post-editing rates had collapsed from 0.04 euros to 0.02 euros per source word, halving the already modest compensation for correcting machine output.

In the United States, Andy Benzo, president of the American Translators Association, told CNN in January 2026 that many translators were leaving the profession entirely. Benzo noted that the risks of using AI translation in “high-stakes” fields remain “humongous,” yet the exodus continues regardless. Ian Giles, chair of the Translators Association at the UK's Society of Authors, confirmed the same pattern, noting that translators were seeking retraining “because translation isn't generating the income it previously did.” The exits are not dramatic. There are no picket lines or public protests. People are simply disappearing from a profession that can no longer sustain them.

The scale of this workforce is not trivial. There are approximately 640,000 professional translators globally, and three out of four are freelancers. It is this freelance majority that has borne the brunt of the disruption, lacking the institutional protections and guaranteed workloads that might have cushioned the blow.

A study published in 2025 by Carl Benedikt Frey and Pedro Llanos-Paredes at the Oxford Martin School quantified the scale of displacement with unusual precision. Analysing variation in Google Translate adoption across 695 local labour markets in the United States, the researchers found that a one percentage point increase in the use of Google Translate corresponded to a 0.71 percentage point reduction in translator employment growth. The cumulative effect, they estimated, amounted to more than 28,000 fewer translator positions created over the period from 2010 to 2023. And that figure captures only the impact of a single, relatively crude machine translation tool that preceded the large language model era. The arrival of systems like GPT-4, Claude, and Gemini has accelerated the process enormously, because these models do not just translate. They handle idiomatic expression, register, and contextual nuance at a level that earlier statistical systems could not approach.

In July 2025, Microsoft researchers published a study examining which occupations were most exposed to generative AI capabilities. Translators and interpreters ranked first on the list, with 98 per cent of their work activities overlapping with tasks that AI systems could perform with relatively high completion rates. The study analysed 200,000 real-world conversations between users and Microsoft's Copilot system to arrive at its rankings. The researchers were careful to note that high exposure does not automatically mean elimination. But the practical effect has been unmistakable. Employers have used the availability of AI translation as justification for cutting rates, reducing headcounts, and restructuring workflows around machine output.

From Translator to Post-Editor

The restructuring of translation work follows a pattern that is becoming familiar across AI-affected professions. The human does not vanish. Instead, they are repositioned downstream in the production process, tasked with reviewing and correcting output that a machine generated in seconds. In the translation industry, this workflow is known as Machine Translation Post-Editing, or MTPE, and it has rapidly become the dominant model for commercial translation work.

According to Slator's 2025 survey of the language industry, 60 per cent of all respondents were using machine translation, with adoption reaching 80 per cent among language service providers. Among those using machine translation or large language models, between 90 and 98 per cent performed some level of post-editing on AI-generated content. Eighty-four per cent of language service integrators reported that clients had specifically requested human editing services to review AI-generated translations. The human, in other words, has not been removed from the process. But the nature of their involvement has been fundamentally altered. They are no longer creating. They are correcting.

The compensation reflects this downgrade. Post-editing rates typically fall between 50 and 70 per cent of standard translation rates, with some agencies offering as little as 25 per cent of what a full human translation would command. Industry data from 2025 indicates that MTPE work commands between 0.05 and 0.15 US dollars per word, compared with 0.15 to 0.30 dollars per word for standard human translation. One translator documented by Equal Times, an international labour news platform, described pre-translated segments paying just 30 to 50 per cent of original rates, while fully automated platforms paid up to seven times less than standard. The economic logic is straightforward. If the machine does 80 per cent of the work, the reasoning goes, then the human should be paid for only 20 per cent. What this calculation ignores is that post-editing often requires comparable time and cognitive effort to translation from scratch, because the translator must not only identify errors but also understand the systematic patterns of how the AI fails and where its confidence is misplaced.

The workflow itself has been transformed in ways that strip autonomy from the translator. Texts no longer arrive as clean source documents to be rendered thoughtfully into a target language. They arrive pre-segmented, with machine-generated suggestions already populating each segment. The translator's task becomes one of triage: deciding which suggestions are acceptable, which need modification, and which must be discarded entirely. Automated platforms distribute this work via alerts that give translators minutes or even seconds to claim individual segments, creating a piecework dynamic more reminiscent of a fulfilment warehouse than a skilled profession. Some platforms threaten automatic disconnection for translators who dispute corrections imposed by quality-assurance algorithms.

Jean-Jacques, a 30-year veteran translator quoted by Equal Times, described the shift bluntly. “It's not really a matter of translating anymore,” he said, “but revising and correcting the segments proposed by the machine.” Another translator, identified as Alina, captured the paradox at the heart of the arrangement. “AI is both a tool and a threat,” she said. “We ourselves are teaching it how to translate, how to improve.” Each correction a post-editor makes feeds back into the training data that will make the next generation of AI translation marginally better, and the human's role marginally less essential.

This dynamic, in which skilled workers are conscripted into training their own replacements, is not unique to translation. It has appeared in content moderation, coding, and legal document review. But in translation, the irony is particularly sharp, because the expertise being extracted is precisely the kind that AI systems struggle most to develop on their own: cultural sensitivity, tonal awareness, and the ability to navigate the space between what a text says and what it means.

What Machines Cannot Feel

The case for human translation has always rested on something more than accuracy. It rests on the claim that translation is an interpretive act, a creative negotiation between two linguistic and cultural systems that requires not just knowledge but judgement. Jhumpa Lahiri, the Pulitzer Prize-winning novelist who has written extensively about translation, describes the process as “a radical act of reshaping text and self.” In her essay collection Translating Myself and Others, published by Princeton University Press in 2022, Lahiri argues that “a translator restores the meaning of a text by means of an elaborate, alchemical process that requires imagination, ingenuity, and freedom.”

This is not the language of quality assurance. It is the language of craft, of a practice that involves the translator's full intellectual and emotional engagement with a text. Emily Wilson, the first woman to translate Homer's Odyssey into English, has spoken repeatedly about the impossibility of separating linguistic from cultural knowledge in translation. The hardest part of translation, she has argued, is not understanding the original but “figuring out how to create it entirely from scratch in a totally different language and culture.” Wilson's translation of the Odyssey was widely praised precisely because it made choices that no algorithm would make: tonal decisions, rhythmic choices, and interpretive framings that reflected not just the Greek text but Wilson's own understanding of what the poem means to contemporary English-speaking readers.

Gregory Rabassa's English translation of Gabriel Garcia Marquez's One Hundred Years of Solitude is perhaps the most celebrated example of translation as creative achievement. Marquez himself reportedly said that he considered the English translation a work of art in its own right, a remarkable statement from an author about a rendering of his own novel. Edith Grossman, the acclaimed translator of both Marquez and Cervantes, described Rabassa as “the godfather of us all,” crediting him with introducing Latin American literature to the English-speaking world in a way that preserved not just meaning but spirit.

These examples belong to the domain of literary translation, which remains relatively insulated from AI disruption. Literary commissions have continued to flow to human translators, in part because publishers recognise that the qualities that make a literary translation valuable are precisely the qualities that machines lack. But the insulation is narrower than it appears. The vast majority of professional translation work is not literary. It is commercial, legal, technical, medical, and administrative. And it is in these domains that the restructuring has been most severe, not because the cultural stakes are lower, but because the market has decided they are.

Consider the translation of a medical consent form from English into Tagalog for a Filipino patient in a London hospital. The document is not literary. It will never win a prize. But the accuracy of its translation has direct consequences for a person's understanding of what is being done to their body. A machine translation might render the words correctly while missing the pragmatic force of the language: the way a particular phrasing might sound reassuring or threatening, the cultural assumptions embedded in notions of consent, the difference between informing someone and making them feel informed. These are not edge cases. They are the bread and butter of professional translation, and they are the first tasks being handed to machines.

Or consider immigration proceedings, where a mistranslation can determine whether an asylum seeker's testimony is deemed credible. The translator in that context is not merely converting words. They are mediating between legal systems, cultural frameworks of narrative and evidence, and the emotional register of a person recounting traumatic experiences. The difference between “I was afraid” and “I feared for my life” is not a matter of synonymy. It is a matter of legal consequence, and navigating it requires the kind of situated cultural judgement that no statistical model possesses.

The Hybrid Illusion

The industry's preferred narrative for this transition is “human-AI collaboration.” The framing suggests a partnership: the machine handles the heavy lifting, and the human provides the finishing touch. But the power dynamics of this arrangement are radically asymmetric. The machine sets the terms. The human adjusts.

This is not collaboration in any meaningful sense. It is supervision, and it is supervision of a peculiarly degrading kind, because the supervisor is being paid less than they would earn if they were simply doing the work themselves. The translator who once sat with a source text and crafted a target text from scratch, making hundreds of micro-decisions about register, idiom, rhythm, and cultural resonance, now sits with a machine-generated draft and decides, sentence by sentence, whether it is wrong enough to fix.

The cognitive experience of post-editing is qualitatively different from translation. Several translators have described it as more fatiguing and less satisfying than original translation work. The machine's output creates a kind of gravitational pull. Even when the translator knows a better rendering exists, the effort required to override the machine's suggestion and compose something from scratch can feel disproportionate to the compensation. Over time, this produces a phenomenon that linguists and labour researchers have begun to call “anchoring,” in which the translator's own instincts are gradually subordinated to the machine's defaults. The result is not a blend of human and machine intelligence. It is machine intelligence with a human stamp of approval.

A 2025 survey of translators found that a majority, some 66 per cent, acknowledged that MTPE can be useful but still requires substantial human intervention. Roughly half of respondents refused to offer discounts for post-editing work, arguing that the effort required is routinely underestimated by clients and agencies. Among those who did discount, the most common reduction fell between 10 and 30 per cent, far less than the 50 to 75 per cent cuts that many agencies impose unilaterally.

Rosa, a translator quoted by Equal Times, described the economic logic with characteristic directness. “Profit is the only thing that matters,” she said, “and translation has become like a commodity that they extract from us at the lowest possible price.” The commodity metaphor is precise. What was once a craft, defined by the individual translator's knowledge, taste, and cultural fluency, has been reframed as a raw material to be processed at industrial scale.

The Structural Incapacity Argument

There is a version of this story in which what is happening to translators is tragic but temporary, a painful adjustment period that will eventually stabilise as the technology matures and the market finds a new equilibrium. In this version, AI translation will continue to improve until the quality gap between machine and human output narrows to insignificance, at which point the remaining human translators will occupy a small, highly specialised niche: literary translation, diplomatic interpreting, and other domains where the stakes are too high for automation.

But this narrative assumes that the qualities human translators bring are merely a matter of degree, that machines are doing a slightly worse version of the same thing, and that incremental improvement will close the gap. There is a competing argument, advanced by translators, linguists, and cognitive scientists, that the gap is not quantitative but structural. That what human translators do when they translate with cultural sensitivity and emotional intelligence is not a more refined version of pattern matching. It is a fundamentally different cognitive operation.

A study published in Nature's Humanities and Social Sciences Communications in 2026, examining AI performance in literary autobiography translation, found that while AI models could produce grammatically correct and largely accurate translations, they consistently failed to capture the emotional texture and cultural specificity of the original texts. The researchers concluded that human translators brought interpretive capacities that were not simply absent from AI systems but categorically different in kind. AI models could identify the surface layer of meaning but failed to recognise cultural allusions and deeper emotional context, elements that are essential not just to literature but to any communication that carries weight beyond its literal content.

This distinction matters because it determines whether human translators are a temporary patch or a permanent necessity. If translation is ultimately a pattern-matching problem, then machines will eventually solve it. If it is an interpretive problem, requiring the kind of embodied cultural knowledge that comes from living inside a language and its associated worldview, then machines will not solve it, regardless of how much training data they consume. The patterns they learn are drawn from existing translations, which means they can only reproduce what human translators have already created. They cannot originate the kind of interpretive leap that makes a translation feel alive.

Poetry, with its reliance on rhythm, rhyme, and figurative language, remains a particularly formidable challenge. A machine can translate the denotative content of a poem. It cannot translate its music. It cannot decide, as Emily Wilson did with the Odyssey, that the opening word of an epic should be “Tell me” rather than “Sing to me,” and understand the cascade of interpretive consequences that follows from that single choice.

The Market Does Not Care About Craft

The structural incapacity argument, however compelling, runs into a problem that is not technological but economic. The market for translation services is not optimised for craft. It is optimised for throughput, cost reduction, and acceptable quality at scale. And by this measure, AI translation is already good enough for the vast majority of commercial applications. The Slator survey found that while 72 per cent of respondents cited accuracy concerns with machine translation and 68 per cent cited quality concerns, adoption continued to accelerate regardless. Trust grew slowly, but adoption grew fast. The concerns are real. They are also, from a procurement perspective, manageable.

This is the uncomfortable truth at the centre of the translation crisis. The question is not whether AI can match human translators in quality. It demonstrably cannot, particularly in contexts requiring cultural nuance, tonal sensitivity, or interpretive judgement. The question is whether the market values those qualities enough to pay for them. And the evidence, from rate compression to headcount reduction to the restructuring of workflows around machine output, suggests that it does not.

The AI-enabled translation services market, valued at 5.18 billion US dollars in 2025 according to Precedence Research, is projected to reach 50.69 billion by 2035, expanding at a compound annual growth rate of 25.62 per cent. These are not numbers that suggest a market hedging its bets. They describe an industry that has made a decisive bet on automation, with human involvement reduced to the minimum necessary to maintain an acceptable error rate. Software platforms already dominate the market, holding nearly 73 per cent of 2025 revenue, and they are growing faster than any other component as enterprises embed AI-driven localisation into core workflows.

The parallel to other creative and knowledge-work professions is instructive. Journalism, graphic design, customer service, and legal research have all experienced similar dynamics: AI systems that produce output of variable but often adequate quality, followed by a restructuring of human roles around review, correction, and oversight rather than creation. In each case, the same rhetorical move occurs. The technology is presented as a tool that augments human capability. In practice, it becomes a ceiling that constrains it. The human is not empowered. The human is made cheaper.

What Gets Lost When Languages Lose Their Interpreters

The consequences of this restructuring extend beyond the economic fortunes of individual translators. Languages are not neutral containers for information. They are living systems of meaning, shaped by history, geography, power, and culture. A translator who has spent decades working between English and Arabic, or Mandarin and Portuguese, or Hindi and German, carries within them a form of knowledge that is not reducible to a bilingual dictionary or a statistical model trained on parallel corpora.

The Frey and Llanos-Paredes study at Oxford Martin documented an additional finding that received less attention than the employment data but may be more consequential in the long term. Areas with robust Google Translate usage saw job postings demanding Spanish fluency grow by about 1.4 percentage points less than in other regions, with similar declines of roughly 1.3 and 0.8 percentage points for Chinese and German respectively, and measurable dampening even for Japanese and French. The adoption of machine translation, in other words, is not just replacing translators. It is reducing the perceived value of knowing another language at all.

This is a feedback loop with serious cultural implications. As machine translation becomes more capable and more widely adopted, the incentive to invest in human language skills diminishes. Fewer people pursue translation as a career. Fewer organisations invest in in-house linguistic expertise. The pool of human knowledge about how languages relate to one another, how cultural contexts shape meaning, and how texts function differently across linguistic boundaries gradually shrinks. And the AI systems that replace this knowledge are trained on the output of the very translators they displace, creating a closed loop in which the training data grows stale as the human source of fresh interpretive insight dries up.

Ian Giles, in his capacity as chair of the Translators Association, has raised precisely this concern, questioning whether “the demand for subtlety and craft from enough readers and publishers” will “save highly skilled individuals from becoming mere AI post-editors.” The word “mere” carries the weight of the entire argument. It acknowledges that the role of post-editor exists. It questions whether the role is sufficient to sustain the expertise it depends upon.

The problem is compounded by the pipeline effect. If experienced translators leave the profession and aspiring translators are deterred by collapsing incomes, the next generation of human translators simply will not exist in sufficient numbers. The craft knowledge that takes years to develop, the intuitive feel for how a sentence should land in a target language, the awareness of cultural registers that no textbook teaches, is not the kind of knowledge that can be stored in a database and retrieved on demand. It lives in people. When those people leave, it leaves with them.

The Canary and the Coal Mine

Professional translators have long occupied a peculiar position in the knowledge economy. Their work is invisible when done well. A reader who encounters a beautifully translated novel does not think about the translator. A patient who reads a clearly rendered medical document in their own language does not consider the person who bridged the linguistic gap. This invisibility made translators vulnerable long before AI arrived. It meant that their expertise could be devalued without anyone noticing, because the beneficiaries of their work rarely understood what it involved.

What is happening to translators now is therefore not just a story about one profession. It is a preview of what happens when AI is deployed not to eliminate human workers but to restructure their role in ways that extract their expertise while diminishing their authority, autonomy, and compensation. The translator who becomes a post-editor is still needed. But the nature of the need has changed. They are needed not for what they can create but for what they can catch. Not for their vision but for their vigilance.

Georgieva's statistic from Davos, those 150 translators who lost their positions at the IMF, represents one institution's calculation that the cultural and interpretive knowledge those individuals carried was worth less than the cost savings achieved by replacing them with technology. That calculation is now being replicated across every sector that relies on translation, from international law to pharmaceutical regulation to immigration services. In each case, the logic is the same. The machine produces output that is adequate for most purposes. The remaining humans clean up whatever the machine gets wrong. And the expertise that once defined the profession gradually atrophies, because there is no economic incentive to develop it and no structural pathway through which it can be transmitted to the next generation.

The question, then, is not whether AI translation will continue to improve. It will. And it is not whether human translators will survive in some form. They will, at least for a while, as post-editors and quality reviewers and specialists in the narrow domains where machine output remains unreliable. The question is whether a society that systematically devalues the ability to translate with feeling, with cultural awareness, with the full depth of human interpretive intelligence, will eventually discover that it has lost something it cannot rebuild. Not because the technology failed, but because the market decided that what translators knew was not worth preserving.

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References and Sources

1. CNN. “Meet the translation professionals losing their jobs to AI.” CNN Business, 23 January 2026. https://www.cnn.com/2026/01/23/tech/translation-language-jobs-ai-automation-intl

2. TIME. “The IMF's Kristalina Georgieva on the AI 'Tsunami' Hitting Jobs.” TIME, January 2026. https://time.com/collections/davos-2026/7339218/ai-trade-global-economy-kristalina-georgieva-imf/

3. Slator. “Five Ways AI Reshaped the Translation Industry in 2025.” Slator, 2025. https://slator.com/five-ways-ai-reshaped-translation-industry-2025/

4. Slator. “Slator 2025 Language Industry Market Report.” Slator, 2025. https://slator.com/slator-2025-language-industry-market-report/

5. Society of Authors. “SoA survey reveals a third of translators and quarter of illustrators losing work to AI.” Society of Authors, April 2024. https://europeanwriterscouncil.eu/soa-survey-uk-ai-2024/

6. Merchant, Brian. “AI Killed My Job: Translators.” Blood in the Machine, 2025. https://www.bloodinthemachine.com/p/ai-killed-my-job-translators

7. Equal Times. “Artificial intelligence, dehumanisation and precarious work: translators on the frontline of tech-induced job degradation.” Equal Times, 2025. https://www.equaltimes.org/artificial-intelligence?lang=en

8. Frey, Carl Benedikt and Llanos-Paredes, Pedro. “Lost in Translation: Artificial Intelligence and the Demand for Foreign Language Skills.” Oxford Martin School, March 2025. https://www.oxfordmartin.ox.ac.uk/publications/lost-in-translation-artificial-intelligence-and-the-demand-for-foreign-language-skills

9. CEPR. “Lost in translation: AI's impact on translators and foreign language skills.” CEPR VoxEU, 2025. https://cepr.org/voxeu/columns/lost-translation-ais-impact-translators-and-foreign-language-skills

10. Fortune. “Microsoft researchers have revealed the 40 jobs most exposed to AI.” Fortune, July 2025. https://fortune.com/article/what-are-the-jobs-most-exposed-to-ai-microsoft-research/

11. CNBC. “These 10 jobs are the least AI-safe, according to new Microsoft report.” CNBC, 5 August 2025. https://www.cnbc.com/2025/08/05/these-10-jobs-are-the-least-ai-safe-according-to-new-microsoft-report.html

12. Precedence Research. “AI Enabled Translation Services Market Size 2025 to 2035.” Precedence Research, 2025. https://www.precedenceresearch.com/ai-enabled-translation-services-market

13. Lahiri, Jhumpa. Translating Myself and Others. Princeton University Press, 2022. https://press.princeton.edu/books/hardcover/9780691231167/translating-myself-and-others

14. Princeton University. “Jhumpa Lahiri champions the writerly art of translation.” Princeton University News, 4 September 2020. https://www.princeton.edu/news/2020/09/04/jhumpa-lahiri-champions-writerly-art-translation

15. Wilson, Emily. Conversations with Tyler, Episode 63. “Emily Wilson on Translations and Language.” https://conversationswithtyler.com/episodes/emily-wilson/

16. Nature. “Exploring AI's performance in literary autobiography translation: how closely do AI models match human translation.” Humanities and Social Sciences Communications, 2026. https://www.nature.com/articles/s41599-026-06630-4

17. Washington Post. “AI is taking on live translations. But jobs and meaning are getting lost.” Washington Post, 26 September 2025. https://www.washingtonpost.com/business/2025/09/26/ai-translation-jobs/

18. The Bookseller. “A third of translators report losing work to generative AI systems, SoA survey reveals.” The Bookseller, 2024. https://www.thebookseller.com/news/a-third-of-translators-report-losing-work-to-generative-ai-systems-soa-survey-reveals

19. World Economic Forum. “Putting a figure on it: Davos 2026 in numbers.” WEF, January 2026. https://www.weforum.org/stories/2026/01/davos-2026-in-numbers/

20. GTS Translation. “The State of Machine Translation Post-Editing (MTPE) in 2025: What Translators Think.” GTS Blog, 7 April 2025. https://blog.gts-translation.com/2025/04/07/the-state-of-machine-translation-post-editing-mtpe-in-2025-what-translators-think/

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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

人の行動は、すべて外的な事象に対する反応、もしくは体調の変化など内的な要因に対する反応によって生まれるものだと考えている。 つまり、純粋な能動的な行動というものは人間には存在しない。

火事や地震が起きたとき、身体が自動的に防衛反応を示すように、あらゆる行動は何かしらの刺激に対する応答である。それらは日常の中で小さく、視覚的に分かりにくくなっているだけで、本質的にはすべて、受けたものに対するカウンターだ。

部屋が汚いから掃除をする。 お腹が空いたから食事を作る。 体が冷えたから服を着込む。 これらはすべて、能動的に見えて実際には受動的な反応である。

努力という言葉がある。 努力は能動的な行動ではなく、それができること自体が才能だ、という意見がある。 自分も概ねその意見には賛成だが、どちらかというと、行動回数というのは「事象に反応するスイッチが入る回数」だと考えている。

会社でバリバリ働いている人は、一見すると主体的に努力しているように見える。 しかし、人間の行動をすべて受けたものへの反応と捉えるなら、それは例えば、貧しい生活への危機感に対する応答とも言える。 つまり、その人が能動的に動いているのではなく、状況に対して反応しているだけと解釈できる。 では、不幸な人間だけが行動するのかというと、そうではない。 「大切な人に美味しいものを食べさせたい」とか、「愛する人が病気なら治療費を出したい」といったように、人が動く理由は無数にある。

要するに、人は「受け取った刺激の回数」に応じて行動する。 そして、その刺激に関心を持つかどうかが個性になる。

どれだけ感受性があるか。 どのような刺激に反応するか。 それに対する応答のパターンをどれだけ持っているか。 それらが人の違いを形作っている。

ここまで考えると、人を動かすには「どれだけ刺激を与えるか」という話になる。 ただし個性がある以上、何に反応するかは人それぞれであり、特定することは難しい。 だからこそ、多様な刺激を、繰り返し与えるしかない。

しかし人は経験的に「つらいことが含まれているもの」には手を出さなくなる。 そのため、自力では到達できない領域が多く存在する。

そこで他人の存在が必要になる。 人は、他人からの刺激を待っている。

ただし、他人が自分に刺激を与える明確な理由は基本的にない。 だから、それは頻繁には起こらない。

ではどうするか。 自分が他人に刺激を与えれば、結果としてそれが自分にも返ってくるのではないか。

そう考えると、他人から刺激を受けたいなら、自分が先に与えるしかないという結論に至る。 これは、自分が動くための一つの理由になる。

人間の行動がすべて外的要因への反応の連続であるならば、 その中であえて自分が他者に刺激を与えにいくという行為は、どこか矛盾を含んでいるようにも感じる。 それでも、その矛盾が結果として自分を動かす理由になるのであれば、ここまで考えた意味はあったのだと思う。

ここで一度、思考を止める。 次は「では、何を相手に与えるべきか」を考えたい。

In Summary: * Listening now to the Diamondbacks Sports Network for the Pregame Show ahead of tonight's game between the Arizona Diamondbacks and the Baltimore Orioles. I'll stay with this station for the radio call of the game. When it ends I'll wrap up the night prayers and head to bed.

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. Details of that regimen are linked to my link tree, which is linked to my profile page here.

Starting Ash Wednesday, 2026, I've added this daily prayer as part of the Prayer Crusade Preceding the 2026 SSPX Episcopal Consecrations.

Health Metrics: * bw= 233.9 lbs. * bp= 157/93 (61)

Exercise: * morning stretches, balance exercises, kegel pelvic floor exercises, half squats, calf raises, wall push-ups

Diet: * 07:00 – 1 banana, coffeecake * 09:25 – snack on cheese * 11:45 – meat oaf, white bread and butter, fresh mango * 16:40 – 1 fresh apple * 17:00 – 1 dish of ice cream

Activities, Chores, etc.: * 05:00 – listen to local news talk radio * 06:30 – bank accounts activity monitored. * 07:00 – read, write, pray, follow news reports from various sources, surf the socials, nap. * 11:45 to 14:15 – watch old game shows and eat lunch at home with Sylvia * 15:30 – listening to The Jack Riccardi Show * 16:30 – listening to sports talk on ESPN 620 AM, Phoenix, AZ

Chess: * 09:40 – moved in all pending CC games

Diamondbacks vs Orioles.

My MLB game of choice tonight will be the Arizona Diamondbacks vs the Baltimore Orioles. With its start time of 5:35 PM CDT, I've got about 3 hours before I'll need to find a radio station to bring me the call of the game. That's enough time for me to squeeze in a post-lunch nap, I think.

And the adventure continues.