Love

One cannot love anybody without turning away from oneself. However, the crucial question is whether this movement is prompted by the desire to turn toward a positive value, or whether the intention is a radical escape from oneself.

Scheler, M. (2023). Ressentiment (English Edition) [Kindle Android version]. Retrieved from Amazon.com. p38

Soy escritor, ensayista para ser exactos. La coherencia es algo que permite dotar de unidad al texto; mantener el hilo conductor del asunto tratado.

Escribir es apenas una parte de mí. No me da para vivir, pero me adorna, me abre puertas. Soy secretario de una fundación dedicada al estudio de la paz y el desarme. No se gana mucho dinero con estos temas. Son quizás los más importantes del mundo, pero en la práctica pagan mal, los sueldos son bajos y el trabajo puede hacernos personas acostumbradas a ir despacio, sin el atractivo de otras actividades más emocionantes.

Por eso quise escribir. Tengo tiempo, de sobra. He publicado una veintena de libros. Sobre la guerra, sobre la paz -por supuesto-, sobre los convenios de Ginebra, los derechos humanos en zonas de conflictos, y un largo etcétera.

Pero por la noche, créanme, soy el terror de la salsa. Canto y toco las maracas con la orquesta San Ramón, la mejor de la ciudad. Sonamos como el pecado, pura caña, guerra y más guerra: tiro la muleta y el bastón y te pongo a bailar el son.

Aunque me gusta ejercitar la atención, hay momentos en los que se agradece que la mente se balancee en una especie de hilo colgante.

No sé si esto tiene un nombre especial.

No es como el que se queda varado en el limbo, ni como aquellos de los que se dice que vieron pasar un ángel.

Tampoco debemos confundir esta experiencia con la mera distracción, como ocurre cuando nos atrapa una sucesión de imágenes o nos perdemos en la fiebre del sábado por la noche, al ritmo de lo que suene.

No sé si este hilo proviene de nuestro espacio interior, pero cuando regresamos tenemos una perspectiva más dulce de las cosas. Incluso inspiración.

El hilo del que hablo es un punto de extraña claridad, un momento mágico del que retornamos, como aquel que, en un instante, vio una chispa de la luz blanca que late en el corazón de las estrellas.

Soy campeón de yoyo y sé por cabeza propia que los que nos dedicamos a esto somos un poco supersticiosos y desconfiados. Personalmente, soy más desconfiado que otra cosa. Pero también sé que hay que ponerle un ojo a “ciertos detalles”.

Cuando nadie me ve, repaso cada centímetro de la cuerda, porque una vez el yoyo se me fue a la ventana.

-¡Ay qué desgracia, pero qué ocurrencia y ahora qué le digo a tu papá, ya no sé qué haceerr! -dijo mi madre.

Pero enseguida empezaron los triunfos, recuperé la autoestima y el pasado, pasado. El triunfo lo justifica todo.

Conocí a Laftan en Japón, en el campeonato 2023. Chocamos en un pasillo y dijo:

-Esto es una coincidencia significativa. Soy Laftan, Romina Laftan, y tú, ¿quién eres? -Soy Rufino. -Sígueme, Ruf -me dijo.

Y desde entonces no nos separamos: según ella, nos traería mala suerte. Por si acaso, pienso igual.

Si Laftan encuentra el reloj de mesa mirando a la pared, piensa que puede ser una señal de que “se le acaba el tiempo”. Ya me entienden.

Cada paso que da tiene un significado positivo o negativo y a veces ambos, porque a continuación matiza.

Es una leyenda. Varias veces campeona del mundo. En competición, incluso contra sus propios pronósticos, hace volar y danzar su yoyo de un modo nunca visto, y si lo hace a dos yoyos, es sublime. En la intimidad, es cariñosa y conversa con el yoyo. Confieso que al principio sentí celos.

Anoche, mientras hacíamos las maletas, se dió cuenta de que le faltaba la yoya a la que bautizó “Barbie”. Me miró espantada. Para reducir el drama, le dije:

-No me dirás que crees que eso significa que no irás a Chicago... -¿Tú lo sentiste también?

Ahora estamos en el aeropuerto para volar. Sangre fría, porque el vuelo se ha demorado, y esto va para largo. Aquí estamos.

Ella está intentando captar si esta demora “es un aviso”. Y yo, que soy desconfiado, no sé cómo, si el avión va tarde, podrá recibir en pista el adecuado mantenimiento. Así que lo mejor es permanecer cada uno en lo suyo, prácticamente sin mirarnos. Nos entendemos perfectamente, somos las dos mitades del yoyo.

You might not want ketchup on your chips after reading this…

Right then. Nineteen eighty-something, this was. I’m working at Sainsbury’s, aren’t I? Stacking shelves. Putting back all the stuff folk had grabbed off ’em during the day.

You’d traipse out the back to the warehouse, grab yourself a trolley piled up with whatever it might be spuds, bog rolls, anything, then wheel the thing out onto the shop floor and get it all up on them shelves quick as you like.

Then you’d go back and get another one. And do it again. And keep on doing it, hour after hour after bloody hour. And you couldn’t just sling it up there any old how neither. You had to dress it. Why a tin of beans needed trousering up, I never did work out.

The Grocery Manager, Maurice, he was called. Five foot two, near enough as wide as he was tall, with a face like a sheep’s arse that someone had taken the clippers to, he comes waddling over and goes: “Yow! Condiments aisle, next. Gerra move on.”

And he jabs his finger at this trolley loaded up with salts and sauces and great jars of beetroot. Heavy as sin, the thing was.

So off I went, good as gold. Keeping one eye on them beetroots the whole way, mind. I didn’t fancy one of them rolling off and turning me and half the shop floor the colour of a plum.

Made it to the pickles section. Got the pickled onions up there careful as you like, and yes, the beetroot and all. No incidents. Stacked the tubs of salt like I was building a little cathedral out of ’em. Lovely job.

Then. The ketchup.

Bottles of the stuff. Glass bottles. This was the Eighties, remember. Nobody gave a monkey’s about recycling back then. The cases — six bottles a go — were stacked two deep on my trolley.

I looked at ’em the way you’d look at a dog you didn’t quite trust. I didn’t fancy them going over neither. Nobody in their right mind wants to be paddling through a lake of sauce.

So I started stacking. Should’ve been simple enough. Get your scalpel out, slice the plastic off, slide the six-pack onto the shelf, next one, job done. Only some clever sod had mis-stacked ’em, hadn’t they.

So I had to lift a six-pack onto my trolley tray, get it ready to go up, and shift what was already on the shelf about a bit to make room.

CRASSHHHHHH!

SPPPPLLLLLAAAAATTTT!!!

The six-pack on my trolley tray made a break for it. Flung itself at the floor like it had somewhere better to be. And the floor caught it a treat. Caught it so well it smashed every last bottle to bits.

Red ketchup went everywhere. Across the floor. Up the shelving. All over my shoes. Started pooling under the trolley like something out of a horror film.

Maurice came barrelling round the corner, hit the sauce at full tilt, and sat himself down a good deal faster than he’d been planning to.

SQUELCH!!!

“AH BLOODY BOLLOCKS!!!”

Which — when you think about it — him sitting there in a spreading puddle of red… well. I’ll let you picture that one for yourselves.

“HOW the FUCK did that HAPPEN?”

“Well, you come running round the corner and—”

“NOT BLOODY ME, YOW DAFT SOD! The fucking sauce!”

“Ah. Right. Well, I had to shift everything about on the shelf and the pack slid off my tray.”

He picked himself up. Stood there dripping. Went stomping off down the aisle leaving a trail of red footprints behind him like some sort of angry, ketchup-soaked hobbit.

“Clean that UP!” 

Now then, I could eat nothing with ketchup on it for months after that. The SMELL. Oh fuck, I can smell it now just thinking about it. It was like somebody had brewed up rotten eggs and gone-off vinegar in a massive vat. And then squatted over it.

Honestly. It has got to be one of the worst smells I have ever had the misfortune of experiencing. Before or since.

Sixty-two mop buckets later, or thereabouts, I got rid of the evidence. Tipping them buckets of sauce-water down the drain out back was a whole other experience I could’ve done without, and all.

A hundred and sixty-two J-Cloths later, I’d got everywhere else sorted. And Nora — one of the women I worked with, lovely woman, built like a prop forward — put the finishing touches on it with about two gallons of air freshener. Sprayed it about like she was crop-dusting.

Ketchup is very nice. In small amounts. Squeezed out of a little sachet onto your burger. I do not recommend wading through it.

Or walking home with your shoes squelching sauce out of the soles with every step. Folk looking at you on the bus like you’ve murdered someone.

I got home and Mum had got tea on. Chips and fish fingers.

“Does anyone want the sauce—”

NO. NOT FOR A LONG TIME.

I broke up with her. I think she was anyway going to break up with me, but either way I broke up with her first. And it hurts really fucking bad.

I finally realized that I had to set so I’m kind of a boundary or have some concrete thing because otherwise I would never leave and it showed up in the form of her saying that even knowing how much it hurt me, she did not regret that I was recorded without my knowledge. And if she could, she would’ve wanted it to happen the same way again. I know that this is because she is probably hurt by something I said, but I went through the audio for the first time and everything I said I still standby and I do not think I said anything hurtful, so at most it was a miscommunication. They however did say stuff that I did not hear behind my back that was incredibly not OK and the fact that she does not recognize that even fundamentally recording someone in such a vulnerable state without their knowledge gave me enough to finally leave. It hurts a fucking lot, but at least I can have my head held high, because for once I broke up instead of sitting there and begging and hoping that things will change. That being said it’s still fucking hurts so much. So many little things remind me of her, and it hasn’t even been 12 hours. I find myself like at this weird in between of begging and coming to terms with it. I recognize that I need to stand on business here and fully end it. Even if she comes back and she says the right things, I think I need to be strong and begin to move on and heal. I have given her too many chances and she has said the right things before and her actions did not stay consistent with that. There are a lot of things that she has done and I need to recognize the fact that even if people can change, it is a gradual process and it will not happen nearly as fast as would be fair to me. An additionally there has been so many things that she has done that I’ve been incredibly hurtful and fucked up, and I just don’t think that she is currently at the emotional maturity level to be able to make up for those things, and so those Bridges have been burned, and that is it. It still fucking hurts me so much. It hurts that the person I love so much isn’t actually the person they are. I think I’ve gotten high on the fantasy and the hope of who they could be, and I ignored all of the many warning signs and issues. Partially because I haven’t had any relationships before, I think I just told myself that this is normal, and actually healthy. But this is not at all the love that I hoped for. And as much as I want to, I cannot love her into her changing. And I know that there will be plenty of other people out there that would treat me better and be better matches for me. I really wished that she was the one. But I think there’s a lot of different things that I missed because I blinded myself. One thing my sister mentioned was how having such a big intelligence gap in a relationship isn’t super fun, because while it is nice for the moment in the sense of me being able to teach her things and stuff, I would eventually get bored because she doesn’t have things that she can show me in the same way, and it would be a consistent relationship of me teaching her and not really too much the other way around. I think also in terms of maturity there is a big gap there. Also even like practically, she is not good with money and she also does not have any kind of a job lined up and is finishing a useless degree. Even in games she is a low rank, meaning whenever we would play together it would always be having to be me smurfing. I think it’s not that hard to find a partner that is willing to go to the gym with me, and I can even look for explicitly a gym partner if I want that. But I think more than anything else it’s just the emotional maturity. We kept having issues because her struggles with emotional regulation, and I had to walk her through so many different things like how to validate feelings, understanding how certain actions are perceived, or like how to react in certain situations, and there are just so many different issues there where it feels like I have to baby her or teach her simple stuff. There is going to be some partner out there that is smart, funny, successful, kind, open-minded, and most of all closer to me in terms of maturity. And I think it would be a little bit of the best for me to not right now try to search for that. Just focus on being happy again and healing.

Your future is your neighbor.

So I got sick. My throat started aching, my voice disappeared, and the fever was kicking in. Around 8 PM, pretty delirious, I texted my neighbor on the 2nd floor asking if she had any ibuprofen because I had run out. Five minutes later, I was medicated and pampered with freshly baked cake. At this point, we’re basically each other’s pharmacies. Medication is the main thing we usually trade, but honestly, the list is endless.

The other day, she asked me about my leg‑hair removal and where I get it done. So I told her I have the Philips laser machine at home and, by the way, why doesn’t she try it? Oh no, don’t waste money on a clinic; try my Lumea first. All of this exchanged casually in the hallway, as always.

Across the hall lives my neighbor with her 9 yo son and Juanita, a Colombian au pair who helps take care of him part time. Since she’s German and the boy's father is South American, having someone from Colombia at home helps keep the language alive. When they go skiing on vacation, the au pair takes the opportunity to visit her family, and I get to water the plants on their terrace. They always come back with the best vegan treats for me.

I only recently learned that my neighbor Evri does nails super well, and she loves doing it. I’m grateful, because doing my own nails was a disaster every time. Now I just pop over while the dogs go wild, and instead of spending out 60 euros at a studio, I treat us to whatever dinner she’s craving. Girls date, win-win.

It feels like a past life, but I still remember my first address in Vienna. I moved to the 8th district, freshly arrived from São Paulo. One day, my neighbor knocked on my door to say he’d noticed me moving in the week before, and wanted me to know he was there if I needed anything. Door 25, feel free to ring anytime. Sounds small, but just knowing someone nearby cared, even in the back of my mind, was comforting.

Truth is, I moved to Austria with nothing but two suitcases. I didn’t own a bowl. Or a fork. It was a full life reset: sixty square meters of pure, echoing emptiness. Not a single familiar object to warm the place up, everything that carried any emotional value stayed behind. A few weeks after I arrived COVID hit. So now I was in an almost empty apartment, suddenly working remotely, not knowing anyone but my cousins living in the 19th district and because I’m excellent at prioritizing in a crisis, what did I do? Yes, exactly that, I bought a Yamaha Clavinova. In a week when other people were panic buying toilet paper, I was panic buying a pianino.
Call it intuition or survival instinct, but that probably kept me from spiraling into a full blown depression in the months that followed. I'd play for hours every day.

Out of nowhere one day, my hallway neighbor, an old man between 80 and immortal, who had spent weeks avoiding any interaction with me, and at the peak of the paranoia even brought a complaint to the house management that I was leaving home “too much” (It was 1x a day, to run), and that by leaving my pair of shoes in the corridor I was risking to bring the virus to him, suddenly opens his door as soon as he heard me leaving.

He kept his distance to avoid any unwanted entanglement and asked if I was the one playing the piano in the “late afternoon.” I answered very dryly “Yes”, because I was fully prepared for the lecture. But instead, he told me it was very beautiful and asked what the “piece” was called. I told him it was “La valse d’Amélie” by Yann Tiersen.

That day, affection somehow won over fear. And because he had turned our differences into curiosity, and his cautiousness, for the first time, into some sort of sympathy, I took the chance to explain that I’d be running like a hamster around Hamerlingpark at night, not meeting anyone, just doing what I needed to keep my mental health intact. He smiled, and with a tiny hand wave, gave me the longest and most reassuring “Paaaasst” of my life.

What I am trying to say is that community resilience shapes individual resilience. And we can’t build a strong future in a socially or emotionally weak neighborhood. As much as we try to project our wildest plans and future accomplishments, the truth is: your future self isn’t some stranger, just a slightly evolved version of who you already are today.

/feb26

There is a particular conversational move that has become common in discussions about AI. Someone demonstrates a new capability, shares a use case, or describes how their workflow has changed, and a familiar response arrives. What about security? What about governance? What about the hallucination problem? What about my twenty years of experience? Each objection arrives wearing the costume of legitimate concern, and each one contains enough truth to feel reasonable in the moment. But taken together, they form something that looks less like careful analysis and more like a defence mechanism.

The pattern is whataboutism in its textbook form. The term originates from Cold War-era Soviet diplomacy, where officials would deflect criticism of human rights abuses by pointing to racial violence in America. The rhetorical structure was never designed to resolve the original issue. It existed to neutralise it. To shift the frame from “is this true” to “but what about that other thing,” and in doing so, to ensure that neither question ever gets properly answered. The AI version of this runs on similar fuel, though the people doing it are rarely aware they’re doing it at all.

The objections are correct and that is beside the point

The uncomfortable thing about AI whataboutism is that the concerns are mostly valid. AI security is genuinely underdeveloped, particularly around Model Context Protocol implementations, where the attack surface is wide and poorly understood. Governance frameworks in most organisations range from nonexistent to laughably outdated. Hallucinations remain a structural feature of large language models, a byproduct of how they generate text rather than a bug that some future update will fix. And twenty years of domain expertise does contain knowledge that no model can replicate, particularly the kind of tacit understanding that comes from watching things break in production over and over again until you develop an instinct for where the next failure will come from.

So all are true, but none is the point.

The point is that these objections are being deployed not as calls to action but as reasons for inaction. There is a significant difference between “AI has security vulnerabilities, so we need to build better guardrails while we adopt it” and “AI has security vulnerabilities, so we’ll wait.” The first is engineering. The second is avoidance dressed up as prudence.

Leon Festinger’s theory of cognitive dissonance, first published in 1957, describes exactly what’s happening. When a person holds a belief about themselves (I am an expert, my skills are valuable, my experience matters) and encounters information that threatens that belief (this technology can do significant parts of my job faster and cheaper than I can), the resulting psychological discomfort has to go somewhere. Festinger identified three common escape routes for that discomfort. You can avoid the contradictory information entirely, you can delegitimise its source, or you can minimise its importance by focusing on its flaws. AI whataboutism is all three at once, packaged as due diligence.

The sunk cost

Samuelson and Zeckhauser’s work on status quo bias adds another layer here that is worth sitting with. Their 1988 paper demonstrated that people disproportionately prefer the current state of affairs, even when alternatives are measurably better, and that this preference strengthens as the number of available options increases. The mechanism underneath isn’t stupidity or laziness. It is loss aversion applied to identity.

When you have spent fifteen or twenty years building expertise in a specific domain, that expertise becomes part of how you understand yourself. It is the thing that justifies your salary, your title, your seat at the table. The suggestion that a tool might compress the value of that expertise, or redistribute it, or make parts of it accessible to people who didn’t put in the same years and hard yards, triggers something that feels like an attack even when it isn’t one. The natural response is to find reasons why the tool can’t possibly do what it appears to be doing. And conveniently, AI provides an inexhaustible supply of such reasons, because it is, in fact, imperfect.

The trap is that imperfect doesn’t mean useless. Imperfect is the condition of every tool that has ever existed. The first commercial aircraft couldn’t fly in bad weather. The early internet went down constantly. Mobile phones in the 1990s weighed a kilogram and dropped calls in buildings. Nobody looked at any of those technologies and concluded that the smart move was to wait until they were perfect before learning how they worked.

Yet that is precisely the position many experienced professionals are taking with AI, and the whataboutism provides them with just enough intellectual cover to feel like they’re being rigorous and righteous rather than scared.

The velocity problem

What makes this particular round of technological change different from previous ones, and what makes the coping mechanisms around it more dangerous than usual, is the speed.

Previous disruptions gave people time to adjust. The internet took roughly a decade to move from novelty to necessity for most businesses. Cloud computing crept in over years, first as a weird thing Amazon was doing with spare server capacity, then gradually as the default. Even mobile took the better part of five years to go from “we should probably have an app” to “our mobile experience is our primary channel.”

AI is not operating on that timeline. The gap between GPT-3 and GPT-4 was measured in months. The capabilities that seemed like science fiction in 2023 are baseline features in 2026. Agentic systems that were theoretical eighteen months ago are shipping in production today. The window in which “wait and see” was a defensible strategy has already closed for most knowledge work, and many of the people deploying whataboutism as a delaying tactic are burning through competitive advantage while they debate whether the fire is hot enough to worry about.

This is where the coping mechanism becomes actively harmful rather than merely unproductive. If the pace of change were slower, there would be time for the concerns to be addressed sequentially. Fix the security model, then adopt. Build the governance framework, then deploy. But the pace doesn’t allow for sequential anything. The security model has to be built while adopting. The governance framework has to be designed while deploying. The two activities are not opposed to each other, and treating them as an either-or is itself a form of denial.

What experience is actually worth now

The most pernicious form of AI whataboutism is the appeal to experience, because it contains the highest concentration of legitimate truth mixed with self-serving reasoning.

Experience matters enormously. The question is which parts of it matter, and for what. The parts that involve pattern recognition accumulated over decades of watching projects succeed and fail, the ability to smell trouble before it shows up in a status report, the judgment to know when a technically correct answer is practically wrong, those parts matter more than ever in a world where AI can generate plausible output at speed. What AI cannot do is evaluate whether the output is appropriate for the specific context, the specific client, and the specific political dynamics of a given organisation. That evaluation requires exactly the kind of accumulated wisdom that experienced people possess.

But the parts of experience that involve doing the work that AI can now do faster, the manual production, the research grunt work, the first-draft generation, the template building, those parts are depreciating rapidly. And for many experienced professionals, the manual production was the majority of how they spent their time, which means the shift feels existential. AI is also moving up the value chain, much as Chinese manufacturing moved from cheap toys to highly complex electronics. This creates a kind of creeping dread that even our most valued, intangible skills will also eventually be under threat.

The whataboutism around experience is often an attempt to avoid this sorting exercise entirely. Rather than doing the difficult work of figuring out which parts of twenty years of expertise are now more valuable and which parts need to be released, it is easier to treat the entire bundle as sacred and dismiss the technology that requires the unbundling.

The way out is through the discomfort

Cognitive dissonance resolves in one of two directions. You can change your beliefs to match the new information, which is uncomfortable but productive. Or you can distort the information to match your existing beliefs, which is comfortable and eventually catastrophic. Whataboutism is the distortion path, and the longer you walk down it, the harder it becomes to turn around, because every objection you’ve raised becomes part of the identity you’re now defending.

The alternative isn’t to abandon caution. It is to be honest about the difference between caution that leads to better decisions and caution that functions as a socially acceptable way to avoid making decisions at all. Build the governance framework, but build it while experimenting, not instead of experimenting. Raise the security concerns, but raise them in the context of “how do we solve this”, rather than “this proves we should wait.” Lean on your experience, but do the honest accounting of which parts of that experience the world still needs and which parts you’re holding onto because letting go feels like losing a piece of yourself.

The concerns are all valid. The coping mechanisms aren’t.

The gospel is the heart of Christianity, proclaiming the transformative work of God through Jesus Christ to reconcile humanity with Himself. It is the message of hope, grace, and redemption for a broken world.

1. God’s Holiness and Humanity’s Sin

Core Truth: God is perfectly holy, while humanity is separated from Him by sin.

God is the Creator of all things, characterized by absolute holiness and righteousness. He designed humanity in His image for a relationship of love and obedience (Genesis 1:26-27; Isaiah 6:3). However, every person has sinned by rebelling against God’s perfect standard, choosing self over Him (Romans 3:23). This sin creates an unbridgeable chasm, separating us from God’s presence and leaving us under His righteous judgment (Isaiah 59:2). This is not an abstract theological problem — it is the weight behind every restless night, every unshakable guilt, and every quiet sense that something is deeply wrong. Without intervention, we are spiritually lost, incapable of restoring this relationship on our own.

• Key Scriptures:
  • Isaiah 6:3 – God’s holiness is exalted.
  • Genesis 1:26-27 – Humanity’s creation in God’s image.
  • Romans 3:23 – All have sinned and fall short of God’s glory.
  • Isaiah 59:2 – Sin separates us from God.

2. Jesus’ Perfect Life and Sacrificial Death

Core Truth: Jesus, fully God and fully man, bridges the gap through His sinless life and atoning death.

Because humanity cannot overcome sin, God sent His Son, Jesus Christ, to accomplish what we could not. Jesus lived a flawless life, fulfilling God’s law perfectly (2 Corinthians 5:21). In love, He willingly died on the cross, taking the punishment for our sins as our substitute (Romans 5:8). His death satisfied God’s justice (1 Peter 3:18), securing forgiveness and reconciliation for all whom God draws to Himself (John 6:37). This act of grace demonstrates God’s profound love and mercy, providing the only way to restore our relationship with Him. Whatever you have done, whatever has been done to you, the cross speaks into the deepest wounds and the darkest corners of your story.

• Key Scriptures:
  • 2 Corinthians 5:21 – Jesus became sin for us.
  • Romans 5:8 – God’s love is shown through Christ’s death.
  • 1 Peter 3:18 – Christ suffered once for sins to bring us to God.
  • John 6:37 – All the Father gives to Christ will come, and none will be cast out.

3. Salvation by Grace Through Faith

Core Truth: Salvation is a free gift received through faith in Jesus Christ alone.

The gospel declares that salvation cannot be earned through good deeds, moral effort, or religious rituals (Ephesians 2:8-9). It is God's gracious gift, offered freely to all who trust in Jesus as their Savior and Lord (John 3:16). Faith is more than intellectual agreement; it is a wholehearted reliance on Christ's finished work on the cross, marked by genuine repentance — a turning away from sin and toward God with a humble heart (Mark 1:15). By trusting in Him, we receive forgiveness of sins, eternal life, and a restored relationship with God (Acts 4:12). You do not need to clean yourself up first — Christ draws you to Himself and by His Spirit begins the work of making you new.

• Key Scriptures:
  • Ephesians 2:8-9 – Saved by grace through faith, not by works.
  • John 3:16 – Belief in Jesus brings eternal life.
  • Mark 1:15 – Repent and believe the good news.
  • Acts 4:12 – Salvation is found in no one else.

4. The Purpose of Salvation: Glorifying God

Core Truth: Salvation transforms our purpose, freeing us to live for God’s glory and enjoy Him forever.

Salvation is not merely about personal benefit but about redirecting our lives to honor God. As redeemed people, our ultimate purpose is to glorify God in all we do, reflecting His love, truth, and goodness (1 Corinthians 10:31). This new life involves growing in Christlikeness, pursuing holiness, and shining as a light to others (Matthew 5:16). Salvation reorients our desires, leading us to find true joy in knowing and serving God (Romans 12:1-2).

• Key Scriptures:
  • 1 Corinthians 10:31 – Do all for the glory of God.
  • Romans 12:1-2 – Offer your life as a living sacrifice.
  • Matthew 5:16 – Let your light shine to glorify God.

5. God’s Ongoing Work: Sanctification

Core Truth: God initiates, sustains, and will complete our salvation through a lifelong process.

Salvation is not just a one-time event but the beginning of a journey. The God who calls us to Himself provides the faith to believe and empowers us to grow in holiness—a process called sanctification (Philippians 1:6). Through the Holy Spirit, He transforms us to reflect Christ’s character, enabling us to turn from sin and embrace righteousness (Romans 8:29-30). This lifelong work assures believers of God’s faithfulness to keep and guide them to the end (John 10:27-28). On the days when your faith feels weak and your failures feel loud, remember that your standing before God rests on Christ's faithfulness, not yours.

• Key Scriptures:
  • Philippians 1:6 – God completes the work He begins in us.
  • Romans 8:29-30 – God’s plan is to conform us to Christ’s image.
  • John 10:27-28 – Christ’s sheep are eternally secure in His hand.

6. Sharing the Gospel: The Great Commission

Core Truth: Christians are called to share the good news as God’s ambassadors.

The gospel is not meant to be kept private but shared with the world. God invites every believer to participate in His mission by proclaiming the good news of Jesus (Matthew 28:19-20). As ambassadors, we represent Christ, sharing His message of reconciliation through our words, actions, and transformed lives (2 Corinthians 5:18-20). This calling, known as the Great Commission, is both a privilege and a responsibility, empowering us to bring the hope of the gospel to a broken world (Mark 16:15).

• Key Scriptures:
  • 2 Corinthians 5:18-20 – We are ambassadors for Christ.
  • Matthew 28:19-20 – Go and make disciples of all nations.
  • Mark 16:15 – Proclaim the gospel to all creation.

The Power and Promise of the Gospel

The gospel is “the power of God for salvation” (Romans 1:16), offering hope to all who believe. It reveals God’s character, addresses humanity’s deepest need, and provides a purpose that transcends this life. By embracing the gospel, we are not only reconciled with God but are also invited into His mission to redeem and restore the world. If you find yourself drawn to this message — stirred by a longing for God you cannot explain — take heart. That very desire is evidence of His work in you, for no one seeks God apart from His calling (John 6:44). Before the foundation of the world, He set His love on a people for Himself (Ephesians 1:4), and the hunger you feel is His Spirit drawing you home (John 6:65).

This living relationship with God is the very point of our existence, giving us both an unshakeable hope for tomorrow and a profound answer for how to live today. For tomorrow, it offers the certainty of eternal life—the unbreakable promise that we will be with Him forever in a place without sorrow or pain. This future hope gives us the courage to face hardship, knowing that our present struggles are temporary and our ultimate victory is secure.

For today, knowing God provides the strength to endure, the wisdom to navigate complexity, and a peace that anchors us in the midst of life's storms. Our purpose is no longer found in fleeting achievements but in a daily walk with Him, turning our work, our relationships, and even our challenges into acts of worship. He gives our present reality ultimate meaning, transforming our ordinary lives into an extraordinary offering of love, service, and unshakable joy.

If you are looking for a church — or looking to come back to one — here are some things worth considering: How to Find a Faithful Church. You were not meant to walk this road alone, and God ordinarily works through the gathering of His people to strengthen and sustain the faith He gives.

Finding a good church matters. The Christian life was never meant to be lived alone. God saves individuals, but He saves them into a body — a community of Christ's people who gather around His Word, receive His sacraments, and hold one another accountable in love. The church is not an optional add-on to the faith; it is the ordinary means God uses to grow and sustain His people.

But not every church that calls itself a church is faithful to what Scripture teaches. So how do you tell the difference? Here are some things to look for — and a few things to watch out for.

1. The Bible Is the Authority, Not the Pastor's Personality

A faithful church treats Scripture as the final word on all matters of faith and life. The preaching should open the Bible, explain what it says, and apply it to the congregation — not use a verse or two as a springboard for the pastor's opinions or motivational talks. Look for expository preaching that works through books of the Bible and lets the text set the agenda (2 Timothy 4:2). If the sermons could work just as well without the Bible, that is a problem.

2. The Gospel Is Clearly Proclaimed

The gospel — the good news that sinners are reconciled to God through the life, death, and resurrection of Jesus Christ — should be the heartbeat of everything the church does. Not moralism. Not self-help. Not vague spirituality. The cross and the empty tomb should shape the preaching, the prayers, the songs, and the sacraments. A church that drifts from the gospel has drifted from the point (1 Corinthians 15:3-4).

3. The Sacraments Are Practiced

Christ gave His church two sacraments: baptism and the Lord's Supper. A faithful church practices both regularly and takes them seriously as means of grace — not mere rituals or symbolic gestures, but real instruments through which God strengthens the faith of His people (Matthew 28:19; 1 Corinthians 11:23-26). If a church rarely celebrates the Lord's Supper or treats baptism casually, ask why.

4. There Is Real Church Government

The New Testament does not envision a church run by a single charismatic leader with no accountability. Faithful churches have a plurality of elders who shepherd the congregation, teach sound doctrine, and are themselves accountable to one another and to a broader body (Titus 1:5-9; Acts 20:28). Whether the structure is presbyterian, reformed baptist, or another form with meaningful elder oversight, the key is accountability. If one person makes all the decisions and answers to no one, be cautious.

5. Church Discipline Exists

This one may sound harsh, but it is actually a mark of love. A church that never confronts sin in its members is a church that does not care enough to protect them. Church discipline, practiced according to the pattern Jesus laid out in Matthew 18:15-17, is about restoration — calling wandering sheep back before they destroy themselves. A church that practices it carefully and humbly is a church that takes holiness and the wellbeing of its members seriously.

6. The Congregation Is Expected to Grow, Not Just Attend

A faithful church calls its members to more than showing up on Sundays. It provides opportunities for Bible study, prayer, and genuine fellowship. It expects its people to serve one another, to use their gifts, and to grow in their knowledge of God over time (Hebrews 10:24-25). If a church asks nothing of you beyond attendance and a check, it is not asking enough.

7. The Worship Is God-Centered, Not Experience-Centered

Worship is about God — who He is, what He has done, and what He has promised. A faithful church gathers to praise Him, hear from Him through His Word, respond in prayer and song, and receive His grace through the sacraments. The question to ask is not “did I enjoy the experience?” but “was God honored and His Word faithfully proclaimed?” (John 4:24). Be wary of churches where the atmosphere feels more like a concert or a performance than a congregation gathered before a holy God.

8. There Is a Confession or Statement of Faith

A church should be able to tell you what it believes — clearly, specifically, and in writing. Historic Reformed confessions like the Westminster Confession of Faith, the Heidelberg Catechism, or the London Baptist Confession provide a tested and proven framework that ties a church to the broader Christian tradition and guards against doctrinal drift. If a church cannot clearly articulate what it believes or dismisses creeds and confessions as unnecessary, there is little to hold it to the faith once delivered to the saints (Jude 1:3).

A Few Honest Cautions

No church is perfect. You will not find a congregation without flaws, frustrations, or people who sometimes let you down. That is not a reason to stay home — it is actually the point. The church is a gathering of sinners who are being sanctified together, not a collection of people who have arrived. If you wait for the perfect church, you will wait forever and miss what God intends to give you through the imperfect one down the road.

At the same time, do not settle for a church that makes you comfortable but leaves you unchallenged. A good church will step on your toes occasionally. It will tell you what you need to hear, not just what you want to hear. That is not a bug — it is a feature.

If you are coming from a church that hurt you, or if you have never been part of a church at all, the idea of walking through those doors can feel overwhelming. That is understandable. But God uses the ordinary, sometimes messy, gathering of His people to do extraordinary work in the lives of those who show up. Find a faithful church, commit to it, and let God do what He does best — shape you into the image of His Son alongside others who are on the same road.

Where to Start Looking

You may have noticed that I have purposefully avoided pointing toward a specific denomination or church, with the prayer that the Lord would use this page as a starting point for conversation between you and a local congregation. That said, if you are not sure where to begin, here are three confessionally Reformed Presbyterian denominations that hold to the Westminster Standards and take the marks of a faithful church seriously. Each has a church locator to help you find a congregation near you:

• Orthodox Presbyterian Church (OPC) — Find a Church
• Presbyterian Church in America (PCA) — Church Directory
• Associate Reformed Presbyterian Church (ARPC) — Find a Church
• Reformed Church in the US (RCUS) – Find a Church

These are not the only faithful churches out there, but they are a solid place to start. Visit, ask questions, sit under the preaching, and see if the marks described above are present. A good church will welcome your scrutiny — they have nothing to hide.

Você esta oficialmente proibida de me escrever qualquer coisa que tenha mais de 7 linhas.

Meu bem, hoje quando acordei eu pensei em você.

Acho que isso já virou habito, tem dias que antes mesmo de abrir os meus olhos eu já começo a lembrar dos seus.

Mas mesmo acordando pensando em você, mesmo já bolando planos de como falaria com você nas entrelinhas hoje, antes mesmo de sair da cama, eu NUNCA imaginaria que eu terminaria a noite trancado no banheiro do restaurante chorando.

Precisei ir para o andar de cima (já que lá só abre quando tem eventos) e me alojar lá, pois estava chorando de soluçar, o que definitivamente é algo inédito pra mim, e fiquei com medo de alguém escutar...

Então definitivamente, você esta proibida de me escrever qualquer coisa com mais de 7 linhas para o resto da vida.

Meu bem..

Enquanto digito isso ainda estou no restaurante, vim para o escritório e estou piscando freneticamente para impedir que as lagrimas voltem.

O que quero dizer com isso é que eu não tive tempo hábil pra conseguir processar tudo e escrever algo que faça mais sentido.

Mas não consigo esperar chegar em casa porque sei que por ligação conseguirei organizar e pensar menos ainda nas coisas.

O seu texto foi uma das coisas mais bonitas que ja li, senti seu amor em cada palavra e por isso chorei como se fosse aquele menino que voce conheceu.

Não vou me estender pois preciso arrumar as minhas coisas para ir embora.

Só queria te falar 3 coisas.

Te amo, amo com tudo que tenho.

Também não estou pronto para deixar a nossa casa.

Você citou o seu filme, eu ainda não assiste ele todo, mas andei dando uma olhada e tem um trecho dele que eu gostaria de citar:

“Katie, está cometendo um grande erro.

Olhe, se rejeita-lo agora… A grande missão da vida dele será encontrar a garota mais linda e perfeita do mundo e tentar esquecer você.

Ele vai se casar com essa mulher e passar o resto da vida com ela. E dirá a si mesmo que ela é perfeita e que ele tem que ser feliz. Mas ela não é você…”

Você não pode me prometer que vai ficar…

Mas eu também não posso te dizer que não irei te esperar, porque cada átomo do meu corpo grita que é exatamente isso que vai acontecer caso um dia eu “siga em frente”.

Ps: “E se precisar me despedir de você até o fim da vida, para nunca te dizer adeus, assim o farei.”, não fazia parte do poema, eu escrevi porque faz parte de mim, e se hoje de alguma forma isso faz parte de você também, então meu coração já se alegra.

Te amo, minha escritora.

Do seu menino chorão,

Nathan.

In February 2024, Reddit filed for an initial public offering and simultaneously announced a deal worth approximately $60 million per year granting Google access to its vast archive of user-generated conversations for the purpose of training artificial intelligence models. Reddit CEO Steve Huffman captured an emerging paradox of the digital age: “The source of artificial intelligence is actual intelligence. That's what you find on Reddit.” Within months, Reddit struck a similar arrangement with OpenAI, reportedly valued at around $70 million annually. In its IPO prospectus, Reddit disclosed that data licensing arrangements signed in January 2024 alone carried an aggregate contract value of $203 million over two to three years. The company's first earnings report as a public entity showed a 450 per cent year-over-year increase in non-advertising revenue, driven almost entirely by those licensing agreements.

Something strange had happened. A platform built on the unpaid contributions of millions of anonymous users had discovered that the messy, argumentative, sometimes brilliant, often profane corpus of human conversation it had accumulated over nearly two decades was now worth hundreds of millions of dollars. Not because advertisers wanted it. Because the machines needed it.

This is the paradox at the heart of artificial intelligence in 2026. AI systems can generate infinite synthetic content, flooding the internet with text, images, video and audio at a pace that dwarfs human output. Yet the data those systems need most, the human-created information that grounds their training and prevents their degradation, is becoming scarcer and more precious by the month. The implications for personal privacy and data rights are profound, unsettling, and largely unresolved.

The Photocopier Problem

In July 2024, a team of researchers led by Ilia Shumailov at the University of Oxford published a landmark paper in Nature demonstrating what happens when AI models train on the outputs of other AI models. The phenomenon, which the researchers termed “model collapse,” showed that large language models, variational autoencoders, and Gaussian mixture models all degrade when successive generations are trained on content produced by their predecessors. The tails of the original data distribution vanish first, eliminating rare events and minority perspectives. Eventually, the model's output bears little resemblance to the distribution of the real world it was supposed to represent.

Nicolas Papernot, an assistant professor of computer engineering at the University of Toronto and a co-author of the study, offered a vivid analogy. “A good analogy for this is when you take a photocopy of a piece of paper, and then you photocopy the photocopy,” he told the University of Toronto News. “Eventually, if you repeat that process many, many times, you will lose most of what was contained in that original piece of paper.” The research, published with collaborators from the Universities of Cambridge and Edinburgh and Imperial College London, found that training on AI-generated data not only degrades quality but further encodes the biases and errors already present in the training pipeline. Papernot warned that the findings “cast doubt on predictions that the current pace of development in LLM technology will continue unabated.” The paper received over 500 citations and an Altmetric attention score exceeding 3,600, reflecting the urgency with which the research community received its conclusions.

The timing of this discovery was particularly significant. By April 2025, a study by the SEO research firm Ahrefs, analysing 900,000 newly created web pages, found that 74 per cent contained AI-generated content. A separate analysis by the SEO firm Graphite, reported by Axios in May 2025, found that the share of newly published articles written by AI had reached approximately 52 per cent. Google search results containing AI-written pages climbed from 11 per cent in May 2024 to nearly 20 per cent by July 2025, according to an ongoing study by Originality.ai. An arXiv research paper from March 2025 estimated that at least 30 per cent of text on active web pages originates from AI-generated sources, with the actual proportion likely approaching 40 per cent. The internet is rapidly filling up with machine-generated text, and every drop of it threatens to contaminate the training pipelines of next-generation AI models.

This creates a vicious feedback loop. As AI-generated content proliferates, the proportion of authentic human-created data in any given web scrape declines. Models trained on this increasingly synthetic web produce outputs further removed from genuine human expression, which then get published and scraped again. Researchers at the International Conference on Learning Representations (ICLR) in 2025 found that this “strong model collapse” cannot generally be mitigated by simple data weighting adjustments. A separate paper at the International Conference on Machine Learning (ICML) in 2024 revealed that as synthetic data grows in training datasets, the traditional scaling laws that have driven AI progress begin to break down entirely.

The upshot is stark. The most valuable commodity in the AI economy is no longer processing power or algorithmic innovation. It is authentic, verified, human-generated data. And that realisation has set off a global scramble with enormous consequences for anyone who has ever posted, typed, spoken, or created anything online.

The Great Data Land Grab

The race to secure human data has produced a wave of licensing agreements that would have seemed improbable just a few years ago. The Associated Press was among the first major publishers to sign a deal with OpenAI in July 2023, granting access to its news archive dating back to 1985. Google struck its first AI content licensing agreement with the AP in January 2025. The Financial Times signed a content licensing deal with OpenAI in April 2024. News Corp agreed to a multi-year arrangement reportedly worth up to $250 million over five years. Conde Nast and Time also entered agreements. By early 2025, the wave had reached The Guardian, The Washington Post, Axios, and the Norwegian publisher Schibsted Media.

These deals represent a fundamental shift in the economics of content creation. For decades, digital publishers watched their revenues erode as platforms aggregated their content and captured the advertising value. Now, the same dynamic is playing out again, but with a new twist: the platforms are not just displaying human content to attract eyeballs. They are consuming it to build intelligence. And this time, at least some publishers are negotiating payment.

But the deals also expose a deeper asymmetry. The individuals who actually created the content receive nothing directly. The Reddit users whose posts are now worth $60 million a year to Google, the journalists whose reporting trains ChatGPT, the photographers whose images teach image generators to see, are not party to any of these agreements. Huffman himself acknowledged this tension in a 2024 interview with Fast Company: “As more content on the internet is written by machines, there's an increasing premium on content that comes from real people.” Reddit, he noted, has “nearly two decades of authentic conversation” and more than 16 billion comments. The premium is real. The compensation flows to the platform, not to the people who made the platform valuable.

Reddit has also aggressively defended its data from unauthorised extraction. After years of being “scraped every which way,” as Huffman put it, the company updated its robots.txt file in July 2024 to block all web crawlers except Google. Huffman publicly accused Microsoft of training its AI services on Reddit data “without telling us,” and named Anthropic and Perplexity as companies that had also trained their systems using Reddit content without permission. In late 2025, Reddit filed lawsuits against Perplexity AI and Anthropic. The company has since proposed a “dynamic pricing” model for its data, seeking compensation that increases as its content becomes more essential to AI-generated answers, rather than accepting fixed licensing fees.

This dynamic echoes a framework articulated by Shoshana Zuboff, the Harvard Business School professor emerita whose 2019 book The Age of Surveillance Capitalism described the extraction of human behavioural data as the defining feature of the digital economy. Zuboff argued that technology companies had claimed “human experience as free raw material for hidden commercial practices of extraction, prediction, and sales.” The AI training data economy takes this logic and intensifies it. Where surveillance capitalism extracted behavioural surplus from user interactions to predict future actions, the new data economy extracts the creative and intellectual output itself, using it not merely to predict behaviour but to replicate and replace the capabilities of its creators.

When Deletion Becomes Impossible

The rising value of human data collides directly with one of the foundational principles of modern privacy law: the right to be forgotten. Under Article 17 of the European Union's General Data Protection Regulation, individuals have the right to request the erasure of their personal data. California's landmark AB 1008, signed into law by Governor Gavin Newsom in September 2024 and effective from January 2025, went further still, amending the California Consumer Privacy Act to specify that personal information can exist in “abstract digital formats,” including “artificial intelligence systems that are capable of outputting personal information.” Under this law, consumers have the right to access, delete, correct, and restrict the sale of personal data contained within trained AI systems, including data encoded in tokens or model weights. California also passed SB 1223 alongside AB 1008, introducing neural data as a category of sensitive personal information subject to even stricter protections.

The problem is that complying with these rights is, at present, somewhere between extraordinarily difficult and functionally impossible. AI models do not store information in discrete, retrievable entries the way a database does. Once personal data has been absorbed into a model's parameters through the training process, it is distributed across billions of numerical weights in ways that cannot be straightforwardly traced or extracted. Personal data can appear in multiple layers of the AI stack: raw training datasets, tokenised text, embeddings, model checkpoints, and fine-tuned weights. As one expert quoted by MIT Technology Review observed, “You can assume that any large-scale web-scraped data always contains content that shouldn't be there.”

The European Data Protection Board acknowledged this challenge in a January 2025 technical report, stating that the right to erasure requires reversing the “memorisation of personal data by the model,” involving deletion of both “the personal data used as input for training” and “the influence of that data on the model.” The Board has made the right to erasure an enforcement priority for 2025, with 32 Data Protection Authorities across Europe participating in coordinated investigations.

The emerging field of “machine unlearning” attempts to address this gap, but the technology remains immature. Exact unlearning methods, such as the SISA framework, require partitioning training datasets and retraining from earlier checkpoints. Approximate methods aim to selectively remove the influence of specific data points without full retraining. But there is no universally accepted standard for verifying whether unlearning has been effective. As a November 2025 research paper from the Centre for Emerging Policy noted, machine unlearning methods “have been there for several years but have not been put into industry practice, which reflects the immaturity of this stream of methods.” Engineers acknowledge that the only truly reliable method of removing an individual's data from a model is to retrain it from scratch, a process costing millions of dollars and weeks of computation time for frontier models.

The practical reality in 2026 is that the right to erasure operates primarily at the input and output layers, not within the model itself. Companies can delete source training data and implement output filters to prevent models from generating specific personal information. But the influence of that data on the model's learned parameters persists. The Hamburg Data Protection Authority has argued that large language models do not store personal data in a way that triggers data protection obligations. Other authorities disagree sharply. The GDPR itself contains exceptions that further complicate compliance, allowing companies to deny erasure requests on grounds including archiving in the public interest and scientific research, providing potential justification for retaining training data even when individuals demand its removal.

For individuals, the implications are deeply concerning. The more valuable human data becomes, the greater the incentive for companies to acquire, retain, and resist deleting it. And the technical architecture of modern AI makes meaningful erasure a problem that legal frameworks have not yet solved.

Synthetic Abundance and Its Discontents

The mirror image of human data scarcity is synthetic data abundance. The synthetic data generation market, valued at approximately $400 million to $500 million in 2025 according to Mordor Intelligence and Grand View Research, is projected to reach between $2 billion and $9 billion by the end of the decade, with growth rates ranging from 25 to 46 per cent annually. In March 2025, NVIDIA acquired the synthetic data startup Gretel for more than $320 million, integrating its privacy-preserving data generation platform into its AI development tools. Gretel's technology allows organisations to generate realistic datasets that retain the statistical properties of real-world data while ensuring no actual personal information is disclosed.

The appeal of synthetic data for privacy is obvious. If AI models can be trained on data that was never derived from real individuals, many of the thorniest privacy and consent problems simply evaporate. The EU AI Act, fully applicable from 2 August 2026, explicitly establishes a hierarchy in which synthetic and anonymised data should be used before processing sensitive personal data. Article 10(5) specifies that providers of high-risk AI systems may only process special categories of personal data for bias detection and correction if the goal “cannot be effectively fulfilled by processing synthetic or anonymised data.”

Yet synthetic data brings its own considerable risks. The model collapse research demonstrates that over-reliance on synthetic training data degrades model quality over successive generations. Gartner has predicted that by 2027, 60 per cent of data and analytics leaders will face critical failures in managing synthetic data, risking AI governance, model accuracy, and compliance. Synthetic data may be privacy-preserving in principle, but it is not a substitute for the diversity, unpredictability, and grounding in lived experience that human-generated data provides.

The Epoch AI research group has documented the scale of the problem. The total effective stock of human-generated public text data amounts to roughly 300 trillion tokens, with an 80 per cent confidence interval suggesting this stock will be fully utilised for AI training sometime between 2028 and 2032. Pablo Villalobos, lead author of Epoch's study “Will we run out of data? Limits of LLM scaling based on human-generated data,” has acknowledged that “some relatively small but very high-quality sources have not been tapped yet,” including digitised documents in libraries, but warned that dwindling reserves “might not be enough” to postpone the issue significantly. OpenAI researchers have confirmed that during the development of GPT-4.5, a shortage of fresh data was more of a constraint than a lack of computing power.

The scarcity of human data and the abundance of synthetic data create a peculiar economic inversion. In the data broker market, valued at $303 billion to $333 billion in 2025, the average cost of personal data for an individual aged 18 to 25 is just $0.36, according to VPNCentral. For those over 55, it falls to $0.05. These figures reflect the commoditised value of personal data in the advertising economy. But in the AI training economy, the same human data takes on an entirely different character. It is not purchased per record from a broker. It is licensed in bulk, for millions of dollars, from platforms that aggregated it. The value of your data is simultaneously trivial and enormous. You are paid for neither.

The Question of Data Dignity

This asymmetry has revived interest in a concept first articulated by Jaron Lanier and E. Glen Weyl in their 2018 Harvard Business Review essay “A Blueprint for a Better Digital Society.” Lanier and Weyl proposed the idea of “data dignity,” arguing that data generated through interactions with digital systems constitutes a form of labour that should be compensated. They envisioned organisations called “mediators of individual data,” or MIDs, functioning as unions for data contributors. These MIDs would negotiate collectively with technology companies over access, usage, and royalties.

The concept remained largely theoretical until generative AI made the exploitation of human creative output visible at an industrial scale. Artists, writers, musicians, and photographers discovered that their work had been scraped from the internet and fed into training datasets without consent or compensation. Reddit users learned their posts were training chatbots. Authors found their books in the Books3 dataset. Photographers recognised their images in the outputs of image generators. The discovery was not that data had value. It was that the people who created it had been systematically excluded from capturing any of that value.

The “data as labour” framework has gained renewed academic attention. A paper published in Business Ethics Quarterly examined the labour analogy in depth, arguing that if data contributions are “characterised by asymmetric bargaining power of the kind found in the labour market, we should embrace proposals such as the creation of data unions and data strikes and similar collective actions by data contributors.” The American Economic Association has published research on the concept, arguing that treating data as capital “neglects users' roles in creating data, reducing incentives for users, distributing the gains from the data economy unequally, and stoking fears of automation.”

Yet the data dignity framework has its critics. The communications theorist Nick Couldry has suggested that paying people for their data may actually undermine rather than enhance human dignity, by “further commodifying our lives, treating us as mere labourers or passive resources to be mined.” If the solution to the exploitation of human data is to make that exploitation transactional, have we resolved the problem or merely normalised it?

The Regulatory Scramble

Legislators and regulators around the world are grappling with these questions, but responses remain fragmented and often contradictory.

The European Union's AI Act represents the most comprehensive legislative attempt to govern AI and data. Fully applicable from August 2026, it imposes strict requirements on data governance for high-risk AI systems, mandating that training data be relevant, representative, and accompanied by documentation of collection methods. Non-compliance carries penalties of up to 35 million euros or 7 per cent of global annual turnover. Transparency obligations for general-purpose AI model providers, including requirements to disclose copyrighted training data, took effect in August 2025.

In the United States, the landscape is more fractured. California's AB 1008 is the most ambitious state-level effort, explicitly extending privacy rights into AI model weights. Colorado's Algorithmic Accountability Law, effective February 2026, grants consumers rights to notice, explanation, correction, and appeal for high-risk AI decisions. But there is no federal data protection law. David Evan Harris, who teaches AI ethics at UC Berkeley, has described this gap as leaving Americans with “no standardised legal right to opt out of AI training.” Marietje Schaake, international policy director at Stanford's Cyber Policy Centre, has observed: “We have the GDPR in Europe, we have the CCPA in California, but there's still no federal data protection law in America.”

The 47th Global Privacy Assembly, held in Seoul in September 2025 and attended by over 140 authorities from more than 90 countries, adopted a resolution noting that “the public availability of personal data does not automatically imply a lawful basis for its processing” for AI training purposes. France's CNIL has been particularly active, publishing recommendations urging AI developers to incorporate privacy protection from the design stage.

In the United Kingdom, the approach has been characteristically principles-based. The Financial Conduct Authority confirmed in September 2025 that it would not introduce AI-specific regulations. The Competition and Markets Authority, armed with new powers under the Digital Markets, Competition and Consumers Act 2024, can now investigate breaches of consumer protection law directly and impose fines of up to 10 per cent of global turnover. Whether these powers will be used to address the extraction of personal data for AI training remains to be seen.

The Emerging Stratification of Data

The convergence of model collapse, data scarcity, privacy regulation, and the rising economic value of authentic human content is producing a new stratification of information. At the top sit curated, high-quality datasets licensed from publishers, platforms, and institutions. These command premium prices and form the foundation of frontier AI models. In the middle sits synthetic data, cheap and abundant but requiring careful curation to avoid degrading model performance. At the bottom sits the vast, unsorted mass of web-scraped content, increasingly contaminated by AI-generated material and of diminishing value for training purposes.

This hierarchy has implications for power as well as privacy. The organisations that control large repositories of authentic human data occupy a position of increasing strategic importance. Reddit understood this early, monetising its user base not through advertising alone but through the licensing of its conversational corpus. The question is whether the individuals whose contributions created that corpus will ever share in the value it generates.

Tamay Besiroglu, a co-author of the Epoch AI study on data depletion, compared the situation to “a literal gold rush” that depletes finite natural resources, warning that the AI field might face challenges in maintaining its current pace of progress once it drains the reserves of human-generated writing. If that projection proves correct, the organisations that have already secured exclusive access to high-quality human data will possess an advantage that is difficult to replicate.

For ordinary individuals, this future raises uncomfortable questions. Every social media post, every product review, every comment thread contributes to a collective resource that is being enclosed and monetised by corporations. The privacy frameworks designed to protect personal data were built for an era of databases and profiles, not for an era in which the very patterns of human thought and expression have become the raw material of a trillion-dollar industry.

What Authentic Expression Is Actually Worth

Stanford's Institute for Human-Centred Artificial Intelligence has proposed a shift from opt-out to opt-in data sharing, arguing that the default should be that data is not collected unless individuals affirmatively allow it. The precedent is instructive: when Apple introduced App Tracking Transparency in 2021, requiring apps to request permission before tracking users, industry estimates suggest that 80 to 90 per cent of people chose not to allow tracking. If a similar opt-in framework were applied to AI training data, the supply of available human data would contract dramatically, further increasing its scarcity value and the incentive to either circumvent consent mechanisms or develop viable synthetic alternatives.

Cisco's 2025 Data Privacy Benchmark Study found that 64 per cent of respondents worry about inadvertently sharing sensitive information with generative AI tools. That concern is not unfounded. A California lawsuit filed in 2025 accuses Google's Gemini of accessing users' private communications, alleging that a policy change gave the chatbot default access to private content such as emails and attachments, reversing a previous opt-in model. Technology companies, as Al Jazeera reported in November 2025, are “rarely fully transparent about the user data they collect and what they use it for.”

The tension between privacy and utility is not new, but AI has sharpened it beyond recognition. Privacy advocates argue that individuals should have meaningful control over how their data is used, including the right to withdraw it from AI training pipelines. AI developers counter that the technology cannot advance without access to diverse, representative human data, and that restricting access will entrench the dominance of companies that have already amassed large datasets. Both arguments contain truth, and neither resolves the fundamental question: in an economy where human creativity and expression have become the most valuable raw material for machine intelligence, who should decide how that material is used, and who should benefit from its exploitation?

The answer will not emerge from a single regulation, technology, or market mechanism. It will require a renegotiation of the relationship between individuals, platforms, and the AI systems that increasingly mediate our experience of the world. The data we generate is not merely a commodity to be bought and sold. It is an expression of who we are, how we think, and what we value. In the age of synthetic abundance, human data is not becoming less important. It is becoming more important, more contested, and more urgently in need of protection. The machines can generate infinite content. But they cannot generate meaning. That still comes from us. And until we collectively decide what that is worth, the value will continue to accrue to those who have the infrastructure to extract it.

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References & Sources

1. Shumailov, I. et al. (2024). “AI models collapse when trained on recursively generated data.” Nature, 631, 755-759. https://doi.org/10.1038/s41586-024-07566-y

2. University of Toronto. (2024). “Training AI on machine-generated text could lead to 'model collapse.'” https://www.utoronto.ca/news/training-ai-machine-generated-text-could-lead-model-collapse-researchers-warn

3. PBS NewsHour. (2024). “AI 'gold rush' for chatbot training data could run out of human-written text as early as 2026.” https://www.pbs.org/newshour/economy/ai-gold-rush-for-chatbot-training-data-could-run-out-of-human-written-text-as-early-as-2026

4. Villalobos, P. et al. (2022). “Will we run out of data? Limits of LLM scaling based on human-generated data.” arXiv:2211.04325.

5. Ahrefs. (2025). “74% of New Webpages Include AI Content.” https://ahrefs.com/blog/what-percentage-of-new-content-is-ai-generated/

6. Axios. (2025). “AI-written web pages haven't overwhelmed human-authored content, study finds.” https://www.axios.com/2025/10/14/ai-generated-writing-humans

7. Originality.ai. (2025). “Amount of AI Content in Google Search Results.” https://originality.ai/ai-content-in-google-search-results

8. Fast Company. (2024). “CEO Steve Huffman on Reddit's essential humanity in the AI era.” https://www.fastcompany.com/90997770/reddit-steve-huffman-interview-ai-ipo-2024

9. TechCrunch. (2024). “Reddit says it's made $203M so far licensing its data.” https://techcrunch.com/2024/02/22/reddit-says-its-made-203m-so-far-licensing-its-data/

10. Columbia Journalism Review. (2025). “Reddit Is Winning the AI Game.” https://www.cjr.org/analysis/reddit-winning-ai-licensing-deals-openai-google-gemini-answers-rsl.php

11. Digiday. (2024/2025). Timelines of publisher-AI licensing deals. https://digiday.com/media/

12. Zuboff, S. (2019). The Age of Surveillance Capitalism. PublicAffairs.

13. Securiti. (2024). “AB 1008: California's Move to Regulate AI and Personal Data.” https://securiti.ai/blog/california-ab-1008-to-regulate-ai-and-personal-data/

14. Future of Privacy Forum. (2025). “Do LLMs Contain Personal Information?” https://fpf.org/blog/do-llms-contain-personal-information-california-ab-1008-highlights-evolving-complex-techno-legal-debate/

15. European Data Protection Board. (2025). “Effective implementation of data subjects' rights.” https://www.edpb.europa.eu/system/files/2025-01/d2-ai-effective-implementation-of-data-subjects-rights_en.pdf

16. TechPolicy.Press. (2025). “The Right to Be Forgotten Is Dead: Data Lives Forever in AI.” https://www.techpolicy.press/the-right-to-be-forgotten-is-dead-data-lives-forever-in-ai/

17. CEP Project. (2025). “Machine Unlearning and the Right to be Forgotten Under Emerging Legal Frameworks.” https://cep-project.org/

18. Mordor Intelligence. (2025). “Synthetic Data Market Report.” https://www.mordorintelligence.com/industry-reports/synthetic-data-market

19. TechCrunch. (2025). “Nvidia reportedly acquires synthetic data startup Gretel.” https://techcrunch.com/2025/03/19/nvidia-reportedly-acquires-synthetic-data-startup-gretel/

20. EU AI Act. (2024). Regulation (EU) 2024/1689. https://artificialintelligenceact.eu/

21. Gartner. (2025). “Top Data & Analytics Predictions.” https://www.gartner.com/en/newsroom/press-releases/2025-06-17-gartner-announces-top-data-and-analytics-predictions

22. Lanier, J. and Weyl, E.G. (2018). “A Blueprint for a Better Digital Society.” Harvard Business Review.

23. Cambridge Core. (2024). “Is Data Labor?” Business Ethics Quarterly. https://www.cambridge.org/core/journals/business-ethics-quarterly/

24. Stanford HAI. (2025). “Privacy in an AI Era.” https://hai.stanford.edu/news/privacy-ai-era-how-do-we-protect-our-personal-information

25. Future of Privacy Forum. (2025). “GPA 2025: AI development and human oversight.” https://fpf.org/blog/gpa-2025-ai-development-and-human-oversight-of-decisions-involving-ai-systems-were-this-years-focus-for-global-privacy-regulators/

26. VPNCentral. (2025). “Data Brokering Statistics.” https://vpncentral.com/data-brokering-statistics/

27. Cisco. (2025). “2025 Data Privacy Benchmark Study.”

28. ICLR. (2025). “Strong Model Collapse.” https://proceedings.iclr.cc/

29. MIT Technology Review. (2025). “A major AI training data set contains millions of examples of personal data.” https://www.technologyreview.com/2025/07/18/1120466/

30. Al Jazeera. (2025). “Are tech companies using your private data to train AI models?” https://www.aljazeera.com/news/2025/11/24/

31. American Economic Association. (2018). “Should We Treat Data as Labor?” https://www.aeaweb.org/articles?id=10.1257/pandp.20181003

32. CNIL. (2025). “AI and GDPR: new recommendations.” https://www.cnil.fr/en/ai-and-gdpr-cnil-publishes-new-recommendations-support-responsible-innovation

33. WebProNews. (2025). “Reddit's Billion-Dollar Bet.” https://www.webpronews.com/

34. World Economic Forum. (2025). “Artificial intelligence and the growth of synthetic data.” https://www.weforum.org/stories/2025/10/ai-synthetic-data-strong-governance/

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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