18 August 2026

Gendlin gave the name “felt sense” to the unclear, pre-verbal sense of “something”—the inner knowledge or awareness that has not been consciously thought or verbalized—as that “something” is experienced in the body. It is not the same as an emotion. This bodily felt “something” may be an awareness of a situation or an old hurt, or of something that is “coming”—perhaps an idea or insight. Crucial to the concept, as defined by Gendlin, is that it is unclear and vague, and it is always more than any attempt to express it verbally. Gendlin also described it as “sensing an implicit complexity, a wholistic sense of what one is working on”.

Agosto | ¿Por qué tenemos tanto miedo de sentir?

¿Por qué uno tiene que reprimir tanto sus emociones?

¿Por qué no podemos ser intensos? ¿Por qué sentimos que somos demasiado cuando sentimos mucho?

Si te gusta alguien, ¿por qué da tanto miedo decirlo?

¿Por qué me da miedo decirte que te extraño?

Que ya no recuerdo tu aroma, pero que quisiera volver a tenerte cerca para recordarlo. Que quisiera volver a ver esos ojos de cerca, con ese brillo tan particular que tienen cuando hablas de algo que te apasiona. Escucharte hablar de todo y de nada. Volver a sentir tus besos, tus manos. Quisiera simplemente estar existiendo a tu lado, sin hacer nada extraordinario, solamente estar.

Te extraño.

Y me duele admitirlo.

Pero lo más complejo de todo no es extrañarte. Lo más complejo es sentir que no puedo decírtelo.

Porque entonces aparecen todas las dudas.

Tal vez dejaste de sentir lo mismo de la noche a la mañana.

Tal vez te arrepentiste.

Tal vez prefieres que seamos amigos.

Tal vez ya no me piensas de la misma manera en la que yo te pienso.

Tal vez para ti aquello que pasó fue solamente un momento y para mí se convirtió en algo mucho más difícil de acomodar.

Tal vez…

Y ese es el problema.

Que no sé.

No sé qué piensas. No sé qué sientes. No sé si me extrañas. No sé si alguna vez aparezco en tus pensamientos de la misma forma en la que tú apareces en los míos.

Y quizá por eso me da miedo preguntarlo.

Porque mientras no sepa, todavía puedo imaginar cualquier respuesta.

Puedo pensar que tal vez sí me extrañas. Que tal vez también tienes ganas de verme. Que tal vez cuando escuchas alguna canción, recuerdas algo de nosotros. Que tal vez, en algún momento del día, también te preguntas qué estoy haciendo.

Pero si pregunto, existe la posibilidad de que la respuesta sea otra.

Y creo que eso es lo que realmente me asusta.

No extrañarte.

No sentir.

No ser intensa.

Me asusta descubrir que todo esto que todavía siento está ocurriendo solamente de mi lado.

Y qué extraño es eso de querer decirle a alguien “te extraño” y, al mismo tiempo, tener tanto miedo de descubrir que quizá tú ya no tienes ganas de escuchar esas palabras.

Sequestered in absentia, notes of wander beckoned notice like the blooded claim of an overtly familiar bite. Too tarnished to maintain an omniscience, her need began The Unraveling. Each tug. Every touch. All sensation became a ripened prayer to yearning's phantom dismemberment, and the obligatory recycling mandated when learning to walk on two broken feet.

© 2026 AnOublietteofThought. All rights reserved.

—

I’m working on a book of erotic poetry. I thought I'd pen some pieces to wander along beside it and wet the appetite. Those who know my writing side well know that I loathe creating titles for poetry. Titles for books are quite exciting, on the otherhand. I already know what this one will be.

hello guess what? I’m on the train again. This time brightly illuminated pale fields of wheat and pine tree forests are zipping by along with a handful of red houses haphazardly scattered like raisins in a müsli

It’s always nice to come home. I felt disoriented when I woke up this morning in the apartment. A disappointment settling inside as I learned where I was.

I think mornings can be harsh, because there’s always a brief moment when you have a clean slate, and as the state is loaded back into the brain, the disappointments and failures briefly forgotten, are reexperienced.

Of course there are good things loaded with the context too, like waking up for the first time next to someone you love etc

Life itself is indeed like a müsli, but sometimes there’s shit in it.

Il existe des regards sous lesquels nous nous corrigeons avant même d'avoir parlé.

Ils n'ont pas besoin d'être hostiles. Une hésitation suffit, un sourcil qui se lève, une présence qui recule à peine. Alors nous baissons la voix, nous rangeons un désir, nous choisissons une version de nous plus facile à recevoir. Nous apprenons à nous présenter comme on prépare une chambre pour un invité difficile, en retirant tout ce qui pourrait encombrer son regard. À la longue, ce regard n'a même plus besoin d'être là. Nous l'emportons avec nous. Nous rangeons la chambre pour personne.

Et puis il existe le regard heureux. Celui qui ne nous embellit pas. Il se réjouit simplement de nous trouver là, et il ne demande pas que nous soyons achevés pour prendre plaisir à notre présence. C'est ce qui le rend si convaincant. La bienveillance est une décision, et nous le sentons. La joie de nous voir n'en est pas une. On peut choisir d'être patient avec quelqu'un. On ne choisit pas d'être content qu'il existe.

Sous ce regard, quelque chose en nous cesse de rentrer le ventre. La voix que nous avions baissée reprend son volume. Ce qui riait trop fort, hésitait trop longtemps ou rêvait de travers retrouve le droit de circuler dans la pièce. Nous n'avons plus à déposer devant nous, comme une carte de visite, une version commode de ce que nous sommes.

Quelqu'un nous accueille.

Pas avec une grande déclaration. Pas nécessairement avec des promesses. L'accueil véritable fait peu de bruit. Il ressemble à une porte qui reste ouverte alors que nous pensions devoir repartir. À une chaise que personne ne retire au moment où nous nous asseyons. À un regard qui ne se détourne pas lorsque notre belle composition se défait un peu.

Être accueilli, c'est pouvoir apparaître sans passer d'abord par l'atelier des retouches. C'est montrer une joie trop vive, une peur mal coiffée, une idée encore bancale, et découvrir que rien ne s'effondre. L'autre est toujours là. Il ne nous confond pas avec notre maladresse du jour. Nos aveux ne deviendront pas, plus tard, des marteaux. Notre vulnérabilité n'est pas versée au dossier à charge.

Il accueille et il observe.

Observer quelqu'un est une forme rare de générosité. Cela demande de ne pas courir vers une conclusion, de ne pas expliquer à sa place ce qu'il ressent. Expliquer quelqu'un est si souvent une manière polie de le ranger. Celui qui observe regarde ce qui naît, ce qui hésite, ce qui revient. Il laisse la personne se révéler à son rythme, comme une couleur monte dans une toile encore humide.

Et surtout, il peut le faire avec gaieté.

L'accueil n'a pas besoin du visage sévère des salles d'attente. La gravité de celui qui écoute risque même de confirmer la crainte de celui qui parle : si l'autre prend cet air, c'est que la chose devait être grave. On peut recevoir quelqu'un avec un sourire, un verre posé sur la table, une plaisanterie qui rend l'air plus léger. On peut trouver charmant qu'un être humain ne tienne pas tout entier dans la première définition venue.

Cette gaieté n'efface pas la profondeur. Elle lui donne de l'oxygène. Elle dit : ce que tu es n'est pas une catastrophe. Je peux te regarder sans convoquer aussitôt les pompiers, les juges et les architectes. Tu peux être étrange ici. Tu peux être magnifique et parfois franchement pénible. Tu peux te tromper sans être expulsé du paysage.

Alors quelque chose se détend.

La personne cesse de consacrer toute son énergie à protéger son droit de rester. Elle n'a plus à séduire à chaque phrase ni à préparer sa défense avant même d'avoir ouvert la bouche. Ce qui se tenait caché peut enfin sortir, s'étirer, respirer, regarder autour de soi.

Et c'est seulement là que le changement commence.

Car on ne fait pas grandir quelqu'un en lui répétant qu'il devrait déjà être différent. On obtient parfois une obéissance, une imitation convaincante, une amélioration de façade. Mais la transformation profonde ne pousse pas sous la menace de perdre l'amour. Elle pousse lorsque l'être comprend qu'il peut se transformer sans devoir d'abord se renier.

Ce qui est accueilli n'est pas condamné à rester identique. C'est même l'inverse : on n'est libre de devenir autre que lorsqu'on a le droit de rester tel qu'on est. Ce qui est accueilli n'a plus besoin de s'agripper à sa forme actuelle pour survivre. Il peut bouger. Il peut lâcher une vieille défense sans craindre de disparaître avec elle, essayer, rater, recommencer.

Ce regard ne devient pas pour autant celui d'un sculpteur. Il ne corrige pas chaque angle, il ne polit pas chaque rugosité, et il ne signe pas ce qui pousse. Il offre une présence assez vaste pour que nous puissions nous y rencontrer, et assez libre pour que nous n'en devenions pas la propriété.

Accueillir ne signifie pas tout accepter. On peut poser une limite sans retirer son regard, refuser un geste sans effacer la personne, dire non sans faire de ce non un exil. Un non prononcé sans retrait suppose même que l'autre peut l'entendre sans s'effondrer. C'est encore une forme de confiance. L'accueil véritable ne consiste pas à applaudir tout ce qui apparaît. Il consiste à ne pas confondre l'amour avec une récompense distribuée aux versions bien élevées.

Peu à peu, ce regard reçu devient le nôtre. Nous l'emportons avec nous, comme nous avions emporté l'autre. Nous apprenons à nous offrir cette même hospitalité, nous cessons de nous jeter dehors à la première faute. Nous devenons pour nous ce lieu où rien n'est forcé de mentir pour avoir le droit de rester.

C'est sans doute cela, aimer quelqu'un.

Lui préparer un espace assez doux pour qu'il ose y être vrai, une chambre où il n'ait rien à ranger avant d'entrer. L'observer avec curiosité, parfois avec amusement, toujours avec respect. Ne pas lui demander de devenir meilleur avant de mériter notre présence.

A more typical win.

I won this game yesterday evening when my opponent playing White resigned the game after my 33rd move (33...Rxc3), giving me the win by default. Checkmate was clearly only two moves away at this point and there was nothing he could do to avoid it.

This game began on 27 July and ended yesterday, so he and I moved at a pretty good pace. Even though we were playing with a 2 days per move time control, we kept at it; sometimes taking the full 2 days, other times moving several times in a day.

The position of pieces at game's end is shown above, and our full move record below:

1. e4 a6 2. Nf3 Nc6 3. Be2 e5 4. a3 Nf6 5. Nc3 d5 6. h3 Be7 7. O-O O-O 8. d4 Nxd4 9. Nxd4 exd4 10. Qxd4 c6 11. e5 Ne4 12. Nxe4 dxe4 13. Qxd8 Rxd8 14. b4 b5 15. Be3 h6 16. f3 exf3 17. Bxf3 Bb7 18. c3 Rd3 19. Bd4 a5 20. Rfe1 Rc8 21. Bg4 Rd8 22. Re4 c5 23. bxc5 Bxe4 24. e6 fxe6 25. Bxe6+ Kh8 26. Bg4 Bg5 27. Be2 Be3+ 28. Kh2 Bf4+ 29. Kg1 Rd2 30. Kf1 Rd8xd4 31. Kf2 Rd4d3 32. Rg1 Bg3+ 33. Kf1 Rxc3

And the adventure continues.

Thanks Brits!

Thanks to the British, we now have data that should shut down American Football, but it won’t.

When you are poor and athletic, nothing brings the promise of a better life like two-a-day football practice in high school. Like musicians, athletes are dangled in front of wealth, but rarely ever experience it. We’ve known for a while about CTE. Now we have numbers. (Again, thanks to the British.) We put kids in meat grinders here in the USA, tell them they’ll be rich. I have met kids wanting to play football to buy their parents a house. What do you think the odds are of that?

(Google says…)

“How likely is a highschooler in Texas in football to make enough in football to buy parents a house with football money?”

To buy a house, a player generally needs to secure a massive Name, Image, and Likeness (NIL) deal or reach the NFL. 

• The High School Population: There are roughly 170,000 high school football players in Texas at any given time.
• The College Jump: Only about 7% to 9.5% of high school players make it to any college roster, and only about 3% make it to a Division I FBS school, where the major money resides.
• The NFL Jump: Nationwide, out of more than 1 million high school players, only about 250–300 get drafted into the NFL each year. That is a 0.025% chance (roughly 1 in 4,000) of going from high school to the NFL. 

Because Texas produces a disproportionate amount of elite talent, a Texas player's individual odds might be slightly higher than the national average, but they still face a fraction of a percent chance.

What’s the deal with brain damage in America?

In the USA, we do not think much about brain damage, even if you are rich! If you start out poor, we bless you with wealth for taking on such brain damage. Not that you can enjoy it.

I am reminded of Romans and gladiators. I know this news this morning from Britain will fall on deaf ears because folks like football. They like music too! But that doesn’t mean they like the players involved. So athletes and musicians get taken advantage of with lottery-type career systems and concussions.

Are “earning” and “winning” the same thing? What about the losers? Didn’t they earn a living too, doing the same thing? Okay, who out there does not deserve to live?

A musician or athlete does not “earn” a living. They “win” it, often taking it from someone else. Folks do not “work” their way up, no matter how hard they work. It’s a lie. I am not saying they do not work. Many work circles around me. But to get the good life, luck is involved. .5 percent make it to NFL who start out in high school. Now we know 1 in 4 in the NFL get CTE. All that work and luck just to wind up brain damaged. CTE is the second form of brain damage these kids incur too. The first was joining football to begin with, those kind of odds. But they sure do! Also, they’re in high school. Not impressionable or anything ..

Let’s talk about lottery for a moment…

“What are popular thoughts against lottery systems, Google?”

(Google says..)

Popular thoughts against state-run financial lottery systems focus on their mathematical design, economic impact on low-income groups, and targeting of vulnerable communities. 

Mathematical Odds

• Extremely low chances: Winning a major jackpot like Powerball has odds of roughly 1 in 292 million.
• Negative expected return: Payouts average only about 60% of total revenue, meaning habitual players lose a vast majority of their spent money over time. 

Economic and Regressive Impact

• Regressive structure: Lower-income households spend a significantly higher percentage of their disposable income on tickets compared to wealthier individuals.
• Opportunity cost: Money spent on lottery tickets diverts funds from practical savings, investments, or basic household needs. 

Social and Demographic Concerns

• Targeted marketing: Critics note that ticket retailers and advertisements are often concentrated in lower-income and minority neighborhoods. 
• Exploitation of hope: Advocacy groups argue that state-sponsored gambling preys on people looking for financial escape from systemic poverty. 

Government Reliance

• Flawed revenue source: Critics argue that using lotteries to fund public services like education allows governments to avoid raising standard, progressive taxes or providing consistent budgetary support.

Who fixes this?

“Targeting vulnerable communities..” I see it says that. The flawed revenue source is good too. Instead of taxing millionaires and billionaires for civic upgrades and fair policy, we’ll just have a lottery. The plebs can do it themselves. At least they’re right about us doing it ourselves. But we have to get rid of team owners.

I want nice things too. But I also don’t want your kids to be brain damaged so I can watch a damn game.

One thing you get when folks think you’re rich is suck ups. Sycophants. Many people daydream about having folks concerned about their life, achievements, aspirations. Many folks want a team to work on. Many folks like money. Me! I am folks!

I don’t feel I am “owed” any of that stuff. I don’t even really like the sycophant thing since I quit social media and realized how fake everything is outside of it, (inside too, wooboy). I don’t feel like I have “earned” it, either. I am kinda’ lazy, do whatever art projects I want. I love my life, don’t get me wrong, but it isn’t because I am making bank. I am making decisions. For me, that’s just as good.

What do you want money for?

I create mainly because it gives me things to do, think about. That’s what a lot of folks want money for. Guess I found a softer way on accident. (It totally was not on accident, fyi. My reason for music is simply getting to do it. Happy place.)

Music and NFL fans, your sign.

Seriously NFL fans, the burden is on your shoulder pads . Music fans too. Do not reward slimy team owners with more money for 1 in 4 cases of CTE. That’s gladiator numbers. Do not reward greedy entertainment companies and managers using micro-targeting for pop-stars practically vacuuming the money and attention from your very life. That’s brain damaged too. So, I know you won’t do it. I just don’t see it getting better for these kids in high school until you do.

Kelly Kintner

The Horse’s Mouth magazine.

Bahkan ribuan bait kalimat takkan sanggup menampung bahagiaku saat jalan kita bersimpangan. Terima kasih sudah hadir dan menetap, menjadi pendengar bagi tiap keluh, penopang di masa sukar, serta kawan berani untuk menjelajah hal baru dan tumbuh bersama.

Jadilah temanku selamanya melintasi waktu yang terus berjalan.

This is a work still in progress. It began with Google AI describing me as a “self‑styled publication architect”, as though the term were a vanity label rather than a serious, highly developed craft.

Part I emerged from that irritation; a definition of the role. Then a friend observed: “Well, you don’t actually say what you do.” Part II arrived quickly after that.

And even as I clicked publish on Part II, the tension I know persists between writer and architect began needling its way into my thinking. More accurately, a tension that lives within me began demanding expression.

So here is my draft Part III.

The Publication Architect: Part III
The Essayist and the Architect

The Dual Movement Behind the Work

Most writing is understood as a single act: a person sits down, thinks, and produces sentences. But the strongest writing rarely emerges from a single movement. It arises from the tension between two distinct ways of seeing. One is perceptual. The other is structural. One concerns the world. The other concerns the reader. One discovers. The other designs.

These two movements are the Essayist and the Publication Architect.

They are not competing identities. They are complementary disciplines. The work only becomes fully itself when both are present.

The Essayist: The Surface of Attention

The Essayist begins with perception. S/he stands somewhere, sees something, and pays attention to it. The stance matters. The vantage matters. The Essayist writes from a position in the world, not from abstraction. S/he notices patterns, tensions, contradictions, small details that reveal larger truths. S/he is concerned with experience, with interpretation, with the movement of thought as it unfolds.

The Essayist’s work is exploratory. It begins with curiosity. It asks: What is happening here? What does this mean? What does this reveal? The Essayist does not begin with structure. S/he begins with encounter. The writing is shaped by the world as s/he sees it.

But perception alone is not enough.

The Publication Architect:
The Structure Beneath the Surface

The Publication Architect begins not with the world but with the reader. S/he asks: What does the reader understand? What do they believe? What do they need? What path will allow them to see what I have seen? The concern is not expression but construction. Not discovery but design.

The Architect works beneath the surface of the essay. S/he shapes the route through which understanding will unfold. S/he decides what is revealed first, what is held back, what is repeated, what is contrasted, what is resolved. S/he builds the architecture through which meaning can accumulate.

Where the Essayist perceives, the Architect arranges. Where the Essayist explores, the Architect guides. Where the Essayist sees, the Architect shows.

These are different disciplines. Yet they must coexist.

The Tension Between Them The Essayist wants freedom. The Architect wants order.

The Essayist wants to follow the movement of thought. The Architect wants to shape the movement of understanding.

The Essayist wants to write what s/he sees. The Architect wants the reader to see it.

If either discipline dominates, the work collapses.

An essay without architecture drifts. It may contain insight, but the insight does not land. It lacks force. It lacks clarity. It lacks the sequence through which understanding becomes inevitable.

Architecture without essayism is sterile. It may be clear, but it is empty. It lacks perception. It lacks encounter. It lacks the human movement that gives writing its purpose.

The work requires both.

The Dual Identity of the Writer

To write with clarity and force, the writer must inhabit both roles. S/he must be Essayist and Architect simultaneously. S/he must see the world and design the reader’s path through it. S/he must hold perception in one hand and structure in the other.

This dual identity is not a technique. It is a stance — a way of thinking. It recognises that understanding does not emerge from content alone, but from the relationship between content and structure, between perception and design, between what is seen and how it is revealed. As I have said before: revision is epistemology.

The Essayist provides the material. The Architect provides the form. Meaning arises from their tension.

Where the Work Truly Happens

The strongest writing is built in the space between these two movements. It is built in the moment where the Essayist’s perception meets the Architect’s discipline. It is built in the recognition that seeing is not enough; the reader must be guided toward seeing. It is built in the understanding that clarity is not a property of ideas but of structure.

The writer therefore moves continually between the two roles. S/he perceives, then designs. S/he discovers, then arranges. S/he writes, then rebuilds. S/he stands somewhere, then constructs the path that allows the reader to stand there too.

This is the dual movement behind the work.

Conclusion

The Essayist and the Publication Architect are not separate identities. They are two aspects of the same discipline. One sees. One shapes. One encounters. One constructs. The work becomes fully itself only when both movements are present.

The Essayist gives the writing its voice. The Architect gives the writing its structure. Together they give the writing its meaning.

This is the tension that defines the craft. This is the dual movement that makes the work possible. This is where the writer stands

David Marshall Skerries

I should’ve had a better day today. I went to the Apple Park gym, and I worked out on a business trip, which I usually never do. I texted friends, Did work, ate good food etc. But I still find myself hurting. And it’s strange because I don’t even see E In my mind, it’s More just a memory of hurting, like that is something I’m supposed to do like that is my obligation. It’s like I have this compulsion to feel pain. I think about how I see a Mazda and I don’t even think about her anymore. That used to be our big inside joke. I think about how she has a boyfriend again. I think about somehow running into her at the airport or seeing her in traffic in her car, even though I know those two things are virtually impossible. And I don’t miss her, and I fully understand that this was a part of my life that is passed. I don’t want it impact either, and I find myself in the shower, almost rehashing old arguments. I think about how for Christmas I got her the childhood stuffed animal that she couldn’t find anymore, and how that was something she used against me when we broke up the first time. She got mad at me for spending too much money on her and giving more thoughtful presence than she did, and I know it came from a place of guilt and hurt, and that wasn’t really the problem, but I also think about how behind my back she told our mutual friend about how it felt like I was trying to replace parts of her, or how everything was white because I was washing the color out of her life. And then I think about how she recorded me when she entered my house with three other people to take stuff back and really hit me at my lowest and make it as cruel as possible. I think about when I said at work and I listen to the recording taking notes because I tried to figure out what I did wrong to justify her still blaming me. I went through everything and I kept crying and I kept hurting, and I had to fight to argue about how it was not OK to even just record me let alone the rest of it, and how at the end of the day it circled back and she said she didn’t regret it and she was happy that she recorded me even though it hurt me so badly. And I just remember my hope breaking and all of the potential and the potential future that I had seen just wash away in that parking lot as I had that call. I think about how hollow I felt walking back to my car. It felt like it hadn’t set in yet like I was in shock. How is this the same person that helped me move into my new house. How is this the person that wanted to marry me, and the person that brought me to their Thanksgiving with their family, where they threw me a surprise birthday party. How is this the person that gave me back a bag depressants that I had given her, with the exception of the few ones she decided to keep. And it’s one of those things where I have given myself the closure I have learned is necessary. I don’t sit here, hoping that she feels remorse or that anything has changed or anything at all from her anymore. And I’m grateful for the experience, but it’s still fucking hurts. And I wish it was a little bit more gentle, but maybe that’s what I needed and I told myself, and I still do that. It was necessary for things to go so nuclear and for things to hurt me so incredibly badly because even after they went nuclear and she did all those things I tried to apologize and get her back. And I needed to learn that lesson sooner than later and so I’m incredibly grateful for this pain. But fuck.

I remember when I was first dealing with a breakup I was watching a lot of different videos about it and someone described grief like glitter exploding. You can clean a lot of it up, but no matter what you will always find little pieces of glitter tucked away. And I wonder how much more glitter is left for me to find. I think about how she gave me a list of food places to visit in San Jose, and that pops up in my head whenever I try to decide where to go for a nice meal. I think about the insecurity that I had because of the level of luxury that she came from. And I know that she did not really do anything bad with that, but I was always just preemptively afraid of judgment or shame for not going to really fancy places or understanding what to do in those situations. Hell, I played golf for the first time at a country club with their family. And I know that it’s not a thing about wealth because I know that I am incredibly wealthy compared to my peers due to the privilege that I have, and additionally the amount of wealth that I come from because of my dad‘s hard work and success. And I know that comparatively she did not have those things when she was not with her parents. But at the same time, I still feel a sting whenever I go to these nice places and I see people bringing their girlfriends. And I feel like it’s a competition of something I am not really willing to do. I don’t go to these food place with myself and I was not raised. To spend money like that, it always felt like a waste of money. But I would worry about how I am supposed to pay for things like this when I already feel like cringing at the thought of spending that much money. And I think it’s such a weird thing about how I got in trouble for spending too much money on her, but at the same time that being my fear in a different lens.

I think it’s just really hurts to have all of this hope and potential in this person in a relationship that I had put so much into, and then have it backstab me. And additionally, I have blame to give myself, because there were a lot of signs that I should have noticed and a lot of unhealthy patterns like the codependency and lack of sticking to my boundaries with things. And I think this is a lesson that I needed to learn, and I think about that line about how sometimes you need to go through it to give advice. And I think I needed to give myself this advice and truly learn from it in a way that words were not able to teach me ahead of time.

I just wonder how long this glitter will haunt me for.

I’ve always wanted to understand electronics a bit more. Mostly so I can repair them. We’ve been using a battery powered fan because we have no power to our house (intentionally, see this post here for details if you want), and the fan recently died. So I took it apart to see if I could figure anything out. With a volt meter I could see that the power was coming in under the battery connections at 20.8 volts. Inside there was a little circuit board connect to the switch with two wires going from the battery to that thing, and then from that switch board up to the motor. I tested up at the motor and there was nothing going from the switch to the motor.

The internet was just about useless for help. I couldn’t find a replacement switch + circuit board even though I had the part number. All the online sources were not giving me much to go on to repair it myself. And used broken fans to try and salvage parts were almost the same price as a new functioning one. It’s pretty lame living in a throw away culture.

So I ended up just cutting that switch out and hard wiring from battery to fan motor. I felt wires as it was running and nothing felt hot so it didn’t seem like any major problem. The biggest issue I have with it is that it is now on full blast when the battery is plugged in, where we like running it at about 30% speed for quietness and longer battery life. But it’s been so warm we wanted to run it and have been.

Just a warning to everyone out there who may try something like this. It turns out that the battery does not have a low voltage cutoff built into it. I ran the fan last night and it ran until it completely killed the battery. The battery was so low that it wasn’t recognized by the charger and wouldn’t charge. I found out that you can jump two batteries together by putting a wire from positive to positive and negative to negative and revive the dead battery to the point of being able to charge it again.

Here are a couple videos on the idea:

video one battery repair

video two battery repair

If you do this be careful. I noticed that the wires I used got warm. It’s better to copy the method used on video two. Touch the wires on and off to pulse some power to it rather than just hook it up and let it sit. It was hard to get wires in there, so I had to use too small of wires for the job. Doing this is risky and if you just left it there the wires could start on fire. So I only hooked it up for about 5 seconds and could feel it getting a little warm. I think it’s also smart to copy the method in video one where he’s using the same kind of battery to jump the dead one if you can. When I pushed the button on the battery to see how much power was left, it still did not register – the battery was still pretty low. But when I plugged it into the charger it worked! So now it’s out there charging.

If I use the fan anymore I’ll be monitoring the battery level more regularly. If you run batteries to very low voltage (beyond what it is built to do) you can do permanent damage. I may have shortened the lifespan by doing all this. However it’s an older battery and overall I’ve learned a lot through this.

If you think your battery is dead because it was over-drained for whatever reason you may be able to revive it without any specialized “smart charger.” One reason would be if you left a very low battery uncharged somewhere for too long and then came back to it thinking it was completely dead. This trick might give it a second life.

Hope this helps, have a great one!

CHAPTER 5 MY SAVIOR

“Are you okay?” Jaxon asks, his deep, steady voice cutting through the silence. I stare at him, feeling like time’s frozen. I don’t even know how long I stand there, just looking. Finally, I realize I’ve been staring too long and snap out of it. Man, rude much? I think to myself, annoyed. Ugh, whatever. It can’t be that bad, right? “Yeah, I’m fine,” I reply, trying to sound casual, but Jaxon’s eyes narrow as if he doesn’t believe me. He studies me for a moment, then says, “So, what made them go after you?” There’s a strange pity in his voice that makes my stomach turn. I shrug, trying to brush it off. “It’s nothing.” “Nothing, huh?” he repeats, voice calm but with that hint of skepticism. “Okay. Well, when you want to tell the truth, I’ll listen.” He turns to walk away. “Wait,” I call out desperately, trying to stop him. “That’s it? You’re just leaving now?” He pauses, glancing back at me. “Yeah, I did my part. You’re safe. Just stay out of trouble, okay?” He keeps walking, then stops in his tracks. His eyes meet mine again. “Like I said, when you want to tell me the truth, just come find me.” I stand there, processing what he said. I think for a moment, then ask, “Where can I find you?” He keeps walking, heading down the hall. Without missing a beat, he replies, “Room 456.” And before I can say anything else, he turns the corner and is gone. I pull out my phone, realizing I need to get back to class. It feels like forever before I finally gather my stuff and leave. I head down the corridor, my mind still spinning. When I finally step outside, I see Gracie standing right by the door, waiting for me. She’s so pretty—way more than me. Her features are softer, her eyes brighter, and her smile somehow makes everything seem lighter. She’s like a prettier, more radiant version of me. “Hey, kid,” I say, trying to hide how exhausted I am. “How was it?” Gracie looks up at me, her eyes filled with amazement. I suddenly realize she had a way better day than I did. “It was a good day,” she says cheerfully, her voice full of excitement. “What about you?” I force a big smile, the biggest I can muster. “It was fine. But I’m glad you had fun.” We walk in silence through the white corridors, passing many doors—doors that lead beyond the walls of this place, to a world that’s waiting for us.

ISABELLLA G. ROBERTS

She is eighty-five, she lives in the United States, and every morning a small tabletop device with a swivelling head and a soft glowing base tells her it is time to take her medication. It suggests she stretch. It asks how she slept. It is called ElliQ, it is made by the Israeli company Intuition Robotics, and according to the opinion piece that opened with her story in the Korea Times on 19 August 2026, it helps her stay independent.

The author of that piece was Suh Chung-ha, a former Korean ambassador to Singapore and Hungary who now runs the ASEM Global Ageing Center in Seoul. His argument was not that the robot is bad. His argument was subtler and considerably more uncomfortable: that the same broad technological shift that produced her helpful companion is quietly reshaping her access to healthcare, long-term care, credit, insurance and public services, and that it is doing so using systems trained on data which encode decades of assumptions about what old people are worth.

Here is the thing that should keep you awake. There are two artificial intelligence systems in this woman's life. One of them sits on her side table, greets her by name, and was designed with people like her explicitly in mind. The other one has never been in her house, has no name, no face and no voice, and it is scoring her.

She chose the first. The second chose her.

Two Machines, One Household

The asymmetry is almost perfectly clean, and it is worth stating precisely because it is the structural fact from which nearly everything else in this story follows.

The assistive machine is opted into. It arrives through a screening process, in the New York State case run by county and locality-based Area Agencies on Aging. It is visible: it occupies physical space, it announces itself, it is marketed at older people as a product for older people. If she hates it, she can unplug it and someone will come and take it away.

The discriminatory machine has none of these properties. She did not opt into the underwriting model that prices her travel insurance, the credit decisioning system that assessed her application for a modest overdraft extension, or the triage algorithm that sorted her referral. She cannot see them. She was not consulted in their design, and neither was anyone remotely like her. She cannot unplug them. In most cases she will never learn that a model was involved at all; she will simply receive an outcome, in a letter or on a screen, expressed in the passive voice.

Assistive AI for older people is a market with a sales pitch. Decisional AI about older people is infrastructure with a legal department.

Public debate about AI and ageing has been overwhelmingly captured by the first category. Companion robots photograph well. They allow governments with ageing populations and inadequate care workforces to point at something tangible. Meanwhile the systems that will actually determine whether an eighty-five-year-old can borrow money, buy cover for a trip to see her grandchildren, or get onto a surgical waiting list are being deployed with almost no public conversation about age at all.

Ninety-Five Per Cent of What, Exactly

Start with the good machine, because the claims made for it deserve the same scepticism we would apply to anything else.

In August 2023 the New York State Office for the Aging announced that its ElliQ rollout had produced a 95 per cent reduction in loneliness among participants. The figure travelled fast, and has been repeated in trade press, vendor materials and policy briefings ever since. That 2023 release reported that more than 800 older New Yorkers were in the programme, that users interacted with the device more than thirty times a day, six days a week, and that over 75 per cent of those interactions related to social, physical or mental wellbeing. Greg Olsen, the agency's director, said the results were “truly exceeding our expectations”.

Thirty interactions a day is not a device gathering dust.

Now read what the same agency published in February 2026. Its year-three project update, covering the programme year from June 2024 to May 2025, reports that 94 per cent of clients say they feel less lonely, up from 93 per cent the year before, that 97 per cent report feeling better overall and 79 per cent feel more connected to the world around them, with customer satisfaction at 4.6 out of 5. The average client is 75. As of May 2025, 834 older adults had joined. More than 3,500 have applied.

Four applicants for every place.

Look at what happened to the sentence. In 2023 the claim was a 95 per cent reduction in loneliness: a delta, a measured change, phrasing that implies an instrument, a baseline and a follow-up. In 2026 it is 94 per cent of clients who say they feel less lonely. That is not a reduction. It is a self-report, and the agency now presents it as one.

The wording did not soften by accident.

Neither version was ever a clinical finding. Both are programme metrics derived from participants who were screened in, who wanted the device, and who were asked how they felt. There is no control group in those numbers. There is no validated loneliness instrument named alongside them in the public materials, no randomisation, no blinding, and no comparison against the obvious alternative intervention, which is a person visiting.

Note too how little the figure moves. Ninety-three per cent, then 94, across a self-selected cohort that grew by hundreds in between. Effect estimates wander when the sample changes; satisfaction scores do not. The 4.6 out of 5 sitting in the same document is from the same family of measurement.

The wider literature is more honest and less exciting. A systematic review and meta-analysis by Lihui Pu, Wendy Moyle, Cindy Jones and Michael Todorovic, published in The Gerontologist, screened more than two thousand articles and found thirteen from eleven randomised controlled trials, nine of which entered the meta-analysis. Social robots appeared to have positive effects on agitation, anxiety and quality of life, but the meta-analysis found no statistical significance. The authors flagged a relatively high risk of bias in allocation concealment and blinding, and concluded that firm conclusions were limited by the shortage of high-quality studies. A later meta-analysis published in the Journal of the American Medical Directors Association, focused on residents of long-term care facilities and drawing on eight trials, did report significant reductions in depression and loneliness with large effect sizes.

So: promising, contested, and nowhere near the confidence implied by a round 95.

None of this means ElliQ is useless. It plainly is not; the engagement figures describe a device in near-constant use, and 834 people have one while thousands wait. It means that the most widely circulated statistic about AI and older people is a self-reported satisfaction figure from a self-selected group, and that we have collectively decided to treat it as proof of concept for an entire policy direction.

The Awkward Protected Characteristic

Now the harder half, and it requires conceding something that age-discrimination advocates often skate past.

Age is a genuinely strange thing to build fairness engineering around. Race and sex, as legal categories, are treated as characteristics that should almost never bear on how you are priced or assessed. Age is not treated that way, and not merely out of prejudice. Age correlates with mortality and morbidity in ways that are real, measurable and actuarially load-bearing. An insurer pricing life cover without reference to age is not being fair; it is being incompetent.

The law recognises this explicitly, and the contrast is sharper than most people realise.

When the Equality Act 2010 extended the ban on age discrimination to the provision of goods, facilities and services in April 2012, it carved out financial services. Schedule 3 permits providers to use age in connection with a financial service, provided that any risk assessment involving age is carried out by reference to information relevant to the assessment and from a source on which it is reasonable to rely. Insurers and banks can price by age. They just have to be able to show the evidence is relevant and the source reasonable.

Compare this with what happened to sex. In the Test-Achats case the Court of Justice of the European Union struck down Article 5(2) of the Gender Directive, the provision that had allowed insurers to differentiate by gender, ruling it incompatible with the principle of equal treatment and invalid from 21 December 2012. The wording of the age exception is close to identical to the gender exception the court destroyed. It survives anyway.

Age, in other words, occupies a legal position no other protected characteristic holds: formally protected, substantively negotiable, with a standing statutory permission to price on it as long as you can point at a table.

There is a second awkwardness. Age is continuous, not categorical. Most fairness metrics in machine learning are built for discrete groups: compare outcomes for group A against group B, measure the gap, minimise it. Continuous attributes require binning, and binning is a modelling choice that quietly determines what you will find. Is the relevant comparison 65 and over against under 65? 80 and over against everyone else? Each decade separately? A model can look admirably fair across coarse bands and be brutal at the eighty-fifth birthday.

That the technical community is working on this is not in doubt. A paper posted to arXiv on 7 April 2026 by Fernando López, Paula Delgado-Santos, Pablo Gómez, David Solans and Jordi Luque examined demographics-agnostic training for bias mitigation in wake-up word detection, evaluating fairness across sex, age and accent, and reported that one technique reduced predictive disparity by 83.65 per cent for age. Note what that result implies about the baseline. A voice interface, the very modality most often proposed as the accessible option for people who struggle with screens, had an age disparity large enough that removing four fifths of it counted as a headline.

Where the Actuarial Defence Runs Out

Take the actuarial argument seriously and it still only covers a fraction of the territory.

It works for life insurance, where the outcome predicted is death and age is causally implicated in death. It works, with more strain, for annuities and some health cover. It does not work for the vast and expanding class of decisions where age enters not as a causal variable but as a learned correlation with something the model was never asked to think about.

Consider hiring. In August 2023 the United States Equal Employment Opportunity Commission settled with the tutoring company iTutorGroup for $365,000, in what was widely described as its first settlement involving an AI-driven hiring tool. The company's application software had been configured to automatically reject female applicants aged 55 and over and male applicants aged 60 and over. More than 200 qualified applicants were rejected on that basis. The discrimination came to light in an almost novelistic way: an applicant submitted two applications identical in every respect except the date of birth, and only the younger one got an interview. The consent decree included five years of EEOC monitoring and an injunction against requesting applicants' birth dates.

There is no actuarial defence for that. It was a hard filter, and it was illegal.

The more consequential case is messier. In Mobley v. Workday, Derek Mobley alleges that the applicant screening tools supplied by Workday systematically disadvantaged older job seekers; he says he submitted more than a hundred applications through the platform and was rejected every time. Judge Rita Lin of the United States District Court for the Northern District of California dismissed his intentional discrimination claim but allowed the disparate impact allegation to proceed, and on 16 May 2025 granted preliminary certification of a nationwide collective action under the Age Discrimination in Employment Act, covering applicants aged 40 and over denied recommendations through the platform since 24 September 2020. The court had earlier accepted the theory that an AI vendor could be directly liable for employment discrimination as an “agent” of the employer, and a March 2026 ruling rejected Workday's argument that the ADEA does not cover job applicants.

Two rulings since have sharpened it, in opposite directions. On 28 May 2026 the court held that AI bias-testing data can be protected from discovery by attorney-client privilege, shielding Workday's own testing material while accepting that it had probative value as evidence of disparate impact, on the basis that counsel had been substantively involved in curating it. The most useful evidence for establishing whether a screening model disadvantages older applicants is the vendor's own bias testing, and a vendor that routes that testing through its lawyers may be able to keep it from the people the model rejected.

Then on 22 June 2026 the court granted in part and denied in part Workday's motion to dismiss. It declined to dismiss an Americans with Disabilities Act claim built on a proxy-discrimination theory, the allegation being that the screening tools inferred health status. It declined to dismiss claims under California's Fair Employment and Housing Act, finding sufficient allegations that Workday designed, developed, maintained and controlled the tools from its California headquarters. It did dismiss a Title VII race-based disparate impact claim brought by a plaintiff who had not sought authorisation to add it, and struck a newly asserted theory that Workday was itself the direct employer. The allegations remain allegations; the case is unresolved.

What makes Mobley the important one is that nobody claims a birth date field was set to reject anyone. The claim is that a model, trained on which past applicants got hired, learned the shape of the people who tend to get hired, and that this shape has an age.

That is not actuarial risk. That is a machine reproducing a hiring market's existing prejudice at industrial throughput and calling it a recommendation.

The Ghost in the Feature Set

The standard corporate response is to remove age from the model. Anyone who has worked on this knows why that fails, but the mechanism deserves spelling out, because it is where the eighty-five-year-old actually gets caught.

Age is one of the most redundantly encoded attributes in consumer data. It leaks through everything.

The landmark demonstration of how much can be inferred from almost nothing is the study by Tobias Berg, Valentin Burg, Ana Gombović and Manju Puri, published in the Review of Financial Studies, which analysed over 250,000 purchases at a German e-commerce firm. Their finding was that a handful of trivially available “digital footprint” variables matched the predictive power of a credit bureau score for consumer default. The variables were not financial. They included the device type and operating system the customer was using, characteristics of their email address, the channel through which they arrived at the site, and the time of day the order was placed.

Every one is age-correlated. Operating system and device age track purchasing power and upgrade behaviour, which skew by generation. Email domain is a near-fossil record: certain providers cluster heavily among people who set up an address in a particular decade and never changed it. Whether you arrived via a search engine, a price-comparison site or by typing the address directly is a behavioural signature that varies sharply with digital fluency. Time of day correlates with employment status and with sleep patterns that shift with age.

Add the signals a modern web session captures without asking: typing speed and correction rate, scroll behaviour, time per form field, zoom level, whether accessibility settings are enabled, session length, abandonment patterns, whether the customer switched to the telephone halfway through.

A model given these features and told to predict default, or churn, or fraud, or claim frequency, will find age whether or not you have deleted the birth date column. It will not label the pattern “age”. It will simply learn that a slow-typing user on an old Android device who zoomed the page, took eleven minutes over a form and then rang the call centre belongs to a cluster with a particular outcome rate. The cluster is old people. The model does not know this and does not need to.

This is proxy discrimination, and age is the characteristic most vulnerable to it, because unlike race or sex it is continuously written into behaviour rather than occasionally into a form field. You can decline to state your sex. You cannot decline to type at the speed you type.

It is also, since June 2026, a theory a federal court has agreed to hear. The proxy-discrimination claim that survived Workday's motion to dismiss is this argument made in a courtroom rather than a conference paper: that a system can sort people by a protected characteristic it was never given and could not name if asked.

A World Where They Were Not There

There is a deeper problem underneath the proxy problem, and it is the one the World Health Organization identified with unusual bluntness in February 2022 in its policy brief “Ageism in artificial intelligence for health”.

The brief made a point that is easy to nod along to and hard to fully absorb: the datasets used to train AI models frequently exclude older people, who often sit within a minority subset for technologies not explicitly designed as gerontechnology. It set out eight considerations, among them participatory design of AI by and with older people, age-diverse data science teams, age-inclusive data collection, investment in digital infrastructure and digital literacy for older people and their carers, rights for older people to consent and to contest, and governance frameworks with teeth.

Under-representation in training data is not neutral. This is the part that gets lost.

If a population is thinly represented in the data, the model has less signal about them, so its predictions for them are less accurate and typically more conservative. Less accurate prediction for a group means more errors in both directions, but the consequences of those errors are asymmetric. A false negative for an older applicant means a declined loan or a rejected application. A false positive means an accepted risk. Institutions tune thresholds to avoid the second kind of error, so noisier estimates for a group systematically produce more refusals for that group. Uncertainty gets priced as risk.

Now compound it across time. Because older people were less present in digital life during the decades when the training corpora were accumulating, they generated fewer digital records. Because they generated fewer records, models are worse at assessing them. Because models are worse at assessing them, they are more often refused or steered towards manual, slower, more expensive channels. Because they are pushed off the digital rails, they generate still fewer records. This is the thin-file problem, and for older people it runs in the opposite direction from the intuitive one: a person can have fifty years of impeccable financial history and still be functionally invisible to a model that mostly reads behavioural exhaust from the last eighteen months.

The past is not a neutral training set. It is a record of who was allowed to participate.

The Recursive Trap

Here is where the digital divide stops being a story about access and becomes a story about power, and it is the thread that runs directly back to the woman with the robot on her side table.

The numbers are not ambiguous. Ofcom's Adults' Media Use and Attitudes report, published on 2 April 2026, found that 6 per cent of UK adults still have no home internet access, and that 83 per cent of that group are aged 65 or over, with 66 per cent aged 75 and above. Among those offline, 68 per cent said they were not interested or felt no need, 38 per cent found it too complicated and 25 per cent cited cost. Age UK reported in July 2025 that 2.4 million older people, nearly one in five, use the internet less than once a month or not at all, that 920,000 had reduced their internet use in the previous twelve months, and that 4.3 million, a third, do not use a smartphone. Exclusion was higher among older Black people at 32 per cent and older Asian people at 26 per cent, and among older people living alone at 30 per cent. Caroline Abrahams, Charity Director at Age UK, has warned repeatedly that people who cannot or will not go online must still be able to reach services offline.

In the United States, Pew Research Center reported in January 2026, drawing on a survey conducted between February and June 2025, that 78 per cent of adults aged 65 and over own a smartphone against 97 per cent of adults under 50, that 70 per cent have home broadband, and that 14 per cent are online almost constantly compared with 63 per cent of those aged 18 to 29.

Now put those two facts side by side.

Fact one: older people are the population most likely to be scored by systems they cannot see, and most likely to be misscored because of thin data and proxy leakage. Fact two: older people are the population least equipped to use the machinery that exists for challenging an automated decision.

Because that machinery is digital. All of it. The right of appeal lives behind a login. The “why was I declined?” explanation is a link in an email. The subject access request is a web form. The complaint goes to a chatbot that triages before a human sees it. The regulator's guidance is a PDF. The decision notice arrives in an app.

Article 22 of the General Data Protection Regulation is supposed to be the backstop. It gives people the right not to be subject to a decision based solely on automated processing that produces legal or similarly significant effects, and where such decisions are permitted, Article 22(3) requires safeguards including the right to obtain human intervention, to express a point of view and to contest the decision. Legal scholars have criticised the provision for years, principally over the word “solely”, which invites institutions to insert a nominal human who rubber-stamps the model output, and over the vagueness of what counts as meaningful information about the logic involved.

But there is a failure mode more basic than any of those doctrinal complaints. A right you have to go online to exercise is not a right for someone who is not online. It is a courtesy extended to people who can already reach it.

This is the recursive trap. The people most likely to be wrongly assessed by an algorithm are, by the same underlying cause, the people least able to find out that an algorithm assessed them, least able to obtain an explanation, and least able to appeal. Digital exclusion does not merely sit alongside algorithmic harm. It is the mechanism that makes algorithmic harm unaccountable. The error and the inability to contest the error have a common origin, and that common origin is age.

What Seoul Cannot Fix by Declaration

On 2 September 2026, the ASEM Global Ageing Center will convene the 6th ASEM Forum on the Human Rights of Older Persons in Seoul, under the theme “Artificial Intelligence and the Human Rights of Older Persons: Toward Age-inclusive AI Transformation”, bringing together experts from Asia and Europe. Suh's Korea Times piece was, transparently, a curtain-raiser for it.

The forum arrives at a moment of unusual institutional motion. On 3 April 2025 the UN Human Rights Council adopted by consensus a decision to establish an intergovernmental working group to begin drafting a legally binding convention on the human rights of older persons, after more than a decade of stalled effort in the open-ended working group on ageing. The new body held an organisational meeting in Geneva in February 2026, convened its first substantive session from 13 to 17 July 2026, and will hold its second session from 26 to 30 October 2026 at the Palais des Nations in Geneva, with the early sessions devoted to purpose, general principles and scope before any drafting of articles. The timetable beyond that is now known: a discussion of an outline of the convention's main elements is expected in July 2027, and a first zero draft around October 2027. The International Telecommunication Union published its own report in July 2026 on artificial intelligence and ageing, examining the unequal distribution of AI benefits across age, gender and region.

This is real progress and it will take years. An outline of main elements in mid-2027, a zero draft in late 2027, and then the negotiation of text that states have to agree, sign and ratify before it binds anyone. The woman in the opening paragraph is eighty-five now.

Meanwhile, the regulation that already exists treats age oddly. The EU AI Act prohibits, under Article 5(1)(b), AI systems that exploit vulnerabilities due to age, disability or specific socio-economic circumstances in order to materially distort behaviour, with those prohibitions applicable from 2 February 2025. That is a genuine protection against the payday-lender-targeting-the-cognitively-impaired scenario. It is not a protection against underwriting. Credit scoring and life and health insurance pricing sit in the high-risk category under Annex III, which brings documentation, data governance and human oversight duties, but lawful risk assessment carried out for legitimate purposes with proportionate safeguards falls outside the Article 5 prohibition. Which is to say: the Act catches predation and regulates underwriting, but it does not question whether age-based underwriting is itself the problem, because European law has already decided that it is not.

The American position is different and, in its way, weaker. The Age Discrimination in Employment Act of 1967 covers employment and does so reasonably robustly, as Mobley is testing. Outside employment, there is no federal equivalent of the Equality Act's general services provision. The Equal Credit Opportunity Act does prohibit age discrimination in credit transactions, and Regulation B is on its face protective: a creditor may use age as a predictive variable in an empirically derived, demonstrably and statistically sound scoring system, but only provided the age of an elderly applicant is not assigned a negative factor or value, and applicants aged 62 and over must be treated at least as favourably as those under 62. Read that carefully and you can see exactly where it fails. The rule polices the explicit age variable. It says nothing about a model that has never seen a date of birth and has inferred one from an operating system, a typing cadence and an email domain. You cannot audit for a negative factor assigned to elderly applicants if the model does not know which applicants are elderly, and neither, formally, does the institution running it.

Conference declarations do not touch any of this. The gap between what Seoul will resolve and what a German e-commerce model does with an old Android handset is the whole subject.

Four Things That Would Actually Change the Arithmetic

Strip away the declaratory language and there are perhaps four interventions that would materially alter the position of the woman in the opening scene. None of them are technically hard. All of them are expensive, which is why they have not happened.

The first is a legally enforceable right to a non-digital channel. Not a helpline that exists at the discretion of the provider, not a branch that survives until the next cost review, but a statutory obligation on any organisation providing an essential service to maintain a route to a competent human being that requires no internet connection, no smartphone and no app. Age UK has been arguing this for years, most pointedly in its work on the collapse of high street banking, where the shift to online-only services has left people who do not trust or cannot use digital channels struggling to manage their own money. Offline access is currently a courtesy. It needs to be a licence condition.

The second is age-disaggregated auditing as a compliance requirement rather than a research exercise. Institutions deploying models in credit, insurance, employment and healthcare triage should be required to publish outcome rates by fine-grained age band, not by a single crude over-65 bucket that conceals everything interesting, and to do so alongside approval rates, appeal rates and appeal success rates. You cannot regulate a disparity that nobody has to measure.

The third is co-design that is not decorative. The WHO brief called for participatory design by and with older people and for age-diverse data science teams, which sounds like boilerplate until you consider how few people building consumer risk models have ever watched an eighty-five-year-old complete an online form.

The fourth is human review that is genuinely reachable and genuinely empowered: a named person, contactable by telephone, with the authority and the information to overturn a model, and a duty to record why. Article 22 gestures at this. Nobody has made it real.

The Grandchild That Isn't

Return, finally, to the room with the robot in it, because there is a question underneath the discrimination question that the fairness metrics cannot reach.

Sherry Turkle, the MIT professor who has spent decades studying what happens to people around machines, argued in Alone Together that sociable technology “will always disappoint because it promises what it cannot deliver. It promises friendship but can only deliver performances.” Her worry was never that robots do things for us. It was that they do things to us: that we attach to what we nurture, and that outsourcing the nurturing dissolves the attachment.

Recent research suggests this is not merely philosophical. A study posted to arXiv on 26 February 2026 by Tianqi Song, Black Sun, Jingshu Li, Han Li, Chi-Lan Yang, Yijia Xu and Yi-Chieh Lee examined AI-generated influencers on Chinese short-video platforms that adopt kinship personas, presenting themselves as virtual grandchildren, using visual and conversational cues to enact family roles. Through social media analysis and interviews with older adults, the researchers found that these relationships met real informational and emotional needs, and also identified risks including emotional displacement and unequal emotional investment.

Unequal emotional investment. That phrase should be read slowly. It describes a relationship in which one party gives everything and the other party is a product roadmap.

The care-substitution risk is not that a family decides to buy a robot instead of visiting. It is subtler and more institutional. It is that a care system under fiscal pressure, looking at a 95 per cent loneliness reduction figure and a device that costs a fraction of a support worker's hourly rate, makes an entirely rational commissioning decision. Nobody need intend the substitution for it to happen. It is a budget line, not a betrayal.

The Bill Arrives in the Post

So here she is, eighty-five years old, at the intersection of both machines.

The one on her side table knows her medication schedule, notices when she has not moved for a while, and asks about her day. It has probably improved her life; the engagement data suggests she uses it constantly. It was designed for her, sold to a state agency on her behalf, and screened to her through a public programme.

The other one does not know she exists as a person. It knows a vector: an operating system four versions behind, a form completion time in the ninety-ninth percentile, an email domain that has not been fashionable since the Clinton administration, a preference for the telephone channel, a session at three in the afternoon. It has never been told her age. It does not need to be told her age. It has learned the residue that age leaves on everything a person touches, and it has priced it.

When it declines her, or loads her premium, or drops her below a referral threshold, the letter will not say why. If she wants to know why, she will be directed to a portal. If she cannot use the portal, she will be offered a chatbot. If the chatbot cannot help, she will be given a number that leads to a menu. And at the end of that process, if she reaches it, a human being will look at a screen showing the model's output and a confidence score, and will decline to overturn it, because overturning it requires a reason and the reason is buried in a feature interaction that nobody at the institution can articulate either.

And if anyone ever tested the model for age bias, the test may be privileged.

Suh's article carried the line that no one should be considered too old for AI. It is a good line. But the more precise formulation of the problem is that nobody is too old for AI, because AI does not require your participation. It requires only your data exhaust, and it will make decisions about your money, your health and your access to public life whether or not you have ever touched a keyboard.

The robot on the side table is the part of this she agreed to. Everything else is happening to her, in rooms she will never see, in a language nobody will translate, on the basis of a life lived mostly before the data started being collected.

Sources and References

1. Suh Chung-ha (2026) “No one should be too old for AI,” The Korea Times, 19 August 2026. Available at: https://www.koreatimes.co.kr/opinion/20260819/no-one-should-be-too-old-for-ai
2. World Health Organization (2022) Ageism in artificial intelligence for health: WHO policy brief, WHO, 9 February 2022, ISBN 978-92-4-004079-3. Available at: https://www.who.int/publications/i/item/9789240040793
3. New York State Office for the Aging (2023) “NYSOFA's Rollout of AI Companion Robot ElliQ Shows 95% Reduction in Loneliness,” press release, 1 August 2023. Available at: https://aging.ny.gov/news/nysofas-rollout-ai-companion-robot-elliq-shows-95-reduction-loneliness
4. New York State Office for the Aging (2026) Transforming Care for Older Adults Across the State of New York: ElliQ Project Update, February 2026. Available at: https://aging.ny.gov/system/files/documents/2026/02/nysofa-elliq-project-update-2026.pdf
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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

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