Javier Aszerman

un poco y un poco, otro stoner de Traslapiedra

[Brian Roemmele @BrianRoemmele]

The Great Inversion: AI Is Quietly Draining the Public Internet of Thought

Something profound is happening to the Internet, and almost no one is naming it clearly.

I have been cataloging it of the last 4 years.

For decades, the web’s most valuable resource wasn’t bandwidth or servers. It was the messy, public give-and-take of human questions and answers.

I was a Top Writer on Quora for nearly a decade as well as a massive pre-crazy Wikipedia contributor — I lived in the space of that give and take.

Someone would hit a wall, open a forum, type out the problem, and a conversation would form. Ideas were stress-tested in the open. Nuance appeared. Dead ends were marked. Better questions got refined in real time. That entire process — the friction, the disagreement, the collective thinking — was left behind as searchable, linkable, durable public knowledge.

That flow has reversed.

The same questions, the same moments of curiosity or confusion, are now more routed into private AI interfaces. A person opens a chat window, types the prompt, receives the output, and the entire exchange vanishes into a closed ecosystem.

The assumption we must make (because it is true) is that the AI company stores both the prompt and the response. The thinking that once would have become part of the shared human record is instead locked inside a corporate training silo.

This is not a minor shift in user behavior. It is an inversion of the Internet’s original knowledge architecture.

Public discourse is being systematically stripped of its higher-signal material. As the internet’s data gets more and more noisy in a very really way it is growing quiet.

The questions that used to generate the richest threads the ones that forced people to clarify assumptions, admit uncertainty, or synthesize across domains are the ones most efficiently answered by AI. What remains on the open web is increasingly the residual: lower-effort posts, performative arguments, recycled takes, and the questions that still feel too social or too ambiguous for a model.

AI then trains on that thinning residue, while the better thinking is captured privately and never returns to the commons.

The result is a strange feedback loop. The public Internet becomes less thoughtful precisely because the thoughtful interactions have been privatized. And the models, trained more and more on what is left behind, risk becoming increasingly fluent in the lesser exchanges that now dominate the surface.

This is not the usual complaint about “AI is making people dumber” or “forums are dying.” Those are symptoms. The deeper phenomenon is structural: the migration of human cognitive surplus from a public knowledge commons into private, non-reciprocal data reservoirs.

The Internet is no longer primarily a place where thinking is externalized and shared. It is becoming a place where thinking is extracted and contained.

The novelty of this observation is that it reframes the problem away from content moderation, engagement algorithms, or even AI capability. The core issue is architectural. We have inverted the direction of knowledge flow. What once leaked outward into a shared substrate now drains inward into silos that do not, by default, give back.

The long-term consequences are massive under-discussed as not noticed.

How does a civilization maintain a high-quality public epistemic environment when its best questions are no longer asked in public? What happens to the training data of future models when the richest human reasoning is locked behind commercial walls? And what cultural muscles atrophy when the habit of thinking out loud, in front of others, becomes rarer?

This is the silent inversion of the Internet. The data once destined for the commons is now shunted into AI. The thoughtful exchanges are disappearing from the surface, and the models are learning from what remains.

That is the novel dynamic. Everything else is downstream of it.

[Massimo @Rainmaker1973]

On August 17, 2026, Singapore officially unveiled the world’s first independently managed biological data center prototype, integrating 16 million living human neurons with traditional silicon hardware.

The project, hosted at the National University of Singapore Medicine, is a strategic collaboration between the university, digital infrastructure developer DayOne Data Centers, and bio-computing startup Cortical Labs. This infrastructure marks a shift from purely digital architecture to wetware, combining synthetic biology with computational processing power.

The system departs completely from traditional server farms based solely on microchips. The prototype is housed in a single standard rack that contains 20 CL1 biological computing units. Each unit holds approximately 800,000 neurons cultivated in a laboratory from human stem cells. These living neurons sit on silicon chips equipped with electrodes, where electrical impulses from the digital system stimulate the cells, causing them to alter their synaptic connections.

The neural activity is then transmitted back and translated into computational data by a proprietary operating system named biOS. Because these are living cells, the servers require a literal life-support system, which is why technicians feed the neurons every three days with a cocktail of sugars, micronutrients, and pH buffers, while specialized mixers regulate oxygen, nitrogen, and carbon dioxide levels.

The transition toward biological processing aims to solve two of the biggest challenges in the artificial intelligence era, which are energy consumption and learning efficiency. A single CL1 biological unit consumes about 25 to 30 watts, compared to the hundreds of watts required by a conventional silicon data center chip. Consequently, the entire biological rack demands only between 800 and 1,000 total watts, whereas traditional infrastructures consume tens of kilowatts for AI workloads.

Furthermore, the technology offers high data flexibility, delivering strong efficiency with small datasets and unpredictable scenarios without needing the massive data volumes required to train traditional models. This approach drastically lowers environmental impact and intense cooling requirements, aligning with Singapore’s Green Data Center Roadmap.

The prototype does not aim to replace the entire global cloud infrastructure immediately, but focuses instead on specific tasks where biology outperforms current computer architecture. A primary field is advanced biomedical modeling, which uses the neurons to better understand the development of neurodegenerative diseases directly on the integrated hardware-software platform.

Another application involves accelerating pharmaceutical research to test the efficacy of new drugs and molecules, which drastically cuts laboratory testing timelines. Finally, the technology enables the development of predictive AI algorithms that leverage the natural ability of the human brain to adapt to contextual changes without requiring massive computational recycling. This initiative is part of DayOne’s long-term plan to expand next-generation computing capabilities across Asia and Europe, while construction of their first local AI-ready traditional data center, named SG1, is set for completion in early 2027.

[Ricoteros De Alma @RicoterosDAlma]

Aura?
Aura tiene el pogo de “Ya nadie va a escuchar tu remera”, una mezcla de adrenalina, felicidad desbordante y locura que ingresan por el oído, te inundan el alma y te desbordan por cada rincón de tu cuerpo. Larga vida a Patricio Rey.

[Brian Roemmele @BrianRoemmele]

This 1965 papers gets quoted by every AI doomer about the AI “risks”. But the paper actually stated human survival itself depends on building the ultraintelligent machine fast and early with no central control.

https://t.co/8BgVUa6jvk

[SergioOpina @sergioopina_]

Un día como hoy, 37 años atrás, Technotronic irrumpieron como exponentes primigenios del hip house.

El 18 de agosto de 1989, ‘Pump Up The Jam’ comportó su sencillo debut e hizo las veces de carta de presentación del elepé homónimo.

[Brian Roemmele @BrianRoemmele]

Most AI that has been made to be safe will struggle with this first principle image based question.

It is the safety layer that causes radical failure across every vector.

I use 1000s of tests to prove that “safety” and “constitutional AI” is lobotomized.

[Wines of Argentina @winesofarg]

Argentina’s #PinotNoir scene has quietly turned into one of the most exciting cool-climate terroir experiments in the Southern Hemisphere 🇦🇷.

Winemakers across #Patagonia, #UcoValley, and #BuenosAires are embracing minimal oak, whole-cluster fermentation, lower alcohol, and site-specific micro-vinifications.

🔭 If you love high-acid, mineral-driven, site-expressive Pinot Noir, it’s time to update your radar: https://t.co/3CBeIfT4en

#ArgentineWine #TheWineForNow

[Javier Aszerman @javitoelcapo]

@alelahitte Obvio!

[Ale Lahitte @alelahitte]

¿Pero cómo vas a dejar de tomar vino porque empezaste el gimnasio?
Yo voy al gimnasio para poder seguir tomando vino...
Todo mal hacen.

[Brian Roemmele @BrianRoemmele]

1963, Moon Travel.

An interesting film.

[Fabricio Portelli @FabriPortelli]

Pinot Noir, la uva que conquista paladares en la tierra del Malbec: 23 vinos para entender su potencial - Infobae https://t.co/oxORapG9bl

[Rock History Live! @KTrain939913]

Random Song of the Day

Let There Be Rock
AC/DC
live -1978

[Alexander Delarge 655321 @AlexDelarge6553]

This! Is what its about! Feel it?

[P.S. I Love ME @ps_ilove_me]

The most predictable object in physics becomes completely unpredictable the moment you add a second joint.

It's called a "double pendulum."

This is why scientists still cannot predict the double pendulum exactly.

A double pendulum with initial conditions measured to a trillionth of a degree will still become completely unpredictable within ten seconds.

Think about what that sentence actually means. We have the full equations. Newton wrote them. Lagrange refined them. Every physics undergraduate can derive them on a napkin. There's no missing physics, no hidden variable, no quantum weirdness. Just two rods, two joints, gravity. That's it.

And yet the system laughs at prediction.

The reason lives in something called the Lyapunov exponent, a number that measures how fast two nearly-identical starting positions diverge from each other over time.

For a double pendulum, that exponent sits around 3 to 5 per second, and in some experiments as high as 7.9. Translation: "any tiny uncertainty in your starting angle doubles roughly every fifth of a second. After one second, your error has grown by a factor of thousands. After five seconds, by a factor of billions. After ten, the two pendulums have nothing in common except the laws they obey."

The deeper trap is philosophical.

Classical mechanics was supposed to be the temple of determinism. Laplace famously claimed that a sufficiently powerful intelligence knowing every particle's position and velocity could predict the entire future of the universe. The double pendulum quietly destroys that dream using two sticks and a hinge. Determinism holds in theory, but predictability fails because measurements are never exact.

You cannot know an angle to infinite decimal places. The universe doesn't hand you those digits. So even in a fully deterministic system, the future becomes practically unknowable the moment sensitivity to initial conditions outpaces your measurement resolution.

This is why weather forecasts fail past two weeks. Why heart arrhythmias resist prediction. Why financial models built on smooth curves get shredded by reality.

The double pendulum is the honest face of most complex systems in nature. Smooth predictability is the exception. Chaos is the default setting of anything with coupled nonlinear parts.

[Massimo @Rainmaker1973]

A reminder that most of what we worry about never actually happens

[Retro Gaming Memories @RetroMemoriesHQ]

Are you old enough to remember configuring AUTOEXEC.BAT and CONFIG.SYS?

[Javier Aszerman @javitoelcapo]

@BrianRoemmele @grok me podes indicar que ventaja tiene este modelo en palabras simples y algún caso de uso?

[Javier Aszerman @javitoelcapo]

@MatthewHyland_ @grok Analizando los post de las últimas semanas cuál es tu teoría de precio en 1 mes?

[Javier Aszerman @javitoelcapo]

@MatthewHyland_ @grok que es realmente todo esto? BTC sube o baja en la próximas semanas?

[Javier Aszerman @javitoelcapo]

@Thebestfigen @grok is AI generated?