Diana Pamela Flores

Experta en #eCommerce, #Logística, #estrategia y #transformaciondigital, en múltiples industrias, países y compañías #Fortune100. Eternamente curiosa.

[Alan Daitch @AlanDaitch]

De las 253 millones de canciones que existen hoy en las plataformas, 120 millones tuvieron menos de 10 reproducciones el año pasado. 

Hoy, se suben más de 106.000 temas por día. El 88% de toda la música que existe tuvo menos de mil reproducciones en todo el 2025. Deezer dijo que el 44% de las canciones que le suben, casi la mitad, están hechas con IA.

Con los libros pasa lo mismo: Amazon tuvo que poner un tope de tres por día por autor. ¡Mil por año!

El problema es que la producción se disparó, pero el día sigue teniendo 24 horas: subió la cantidad de cosas que se hacen, pero no la cantidad de gente que las recibe.

En Estados Unidos, la cantidad de adultos que lee por placer cayó un 40% en veinte años y en 2023 tocó el nivel más bajo registrado.

Antes, el primer gran cuello de botella era hacer la cosa: grabar el disco, escribir las 300 páginas, bancarte el rodaje. Ahora, hacer cosas tiene cada vez menos fricción y el problema está en conseguir alguien que la consuma.

[Sergio Ferrero @calotonterias]

Una crema solar con SPF 30 reduce un 95-98% la capacidad de la piel para producir vitamina D₃, al bloquear la mayor parte de los rayos UVB.

Recuerda, además, que el sol no es sólo vitamina D. El bloqueo que realizan estas cremas tanto de los rayos UVB como de los UVA puede inhibir la producción de otros múltiples fotoproductos beneficiosos y necesarios (lumisterol, taquisterol, serotonina-melatonina, óxido nítrico, melanina...).

[Clarín @clarincom]

NORUEGA PROHIBIÓ EL USO DE LA IA A LOS NIÑOS: "LO MÁS IMPORTANTE ES QUE APRENDAN A LEER, ESCRIBIR Y HACER MATEMÁTICAS"

Lectura rápida
El gobierno noruego prohibió el uso de inteligencia artificial en los primeros grados escolares, una medida anunciada por el primer ministro Jonas Gahr Stoere que se implementará desde el inicio del nuevo año escolar, a fines de agosto. La decisión llega acompañada de un proyecto de ley para volver a financiar el uso de libros de papel en las aulas.

Cómo es la restricción por edades
Los alumnos de primero a séptimo grado, de entre 6 y 13 años, no podrán usar IA. Los de primero de secundaria, entre 14 y 16 años, podrán adoptarla con cautela y bajo supervisión docente. En la educación secundaria superior, entre 17 y 19 años, los estudiantes deberán aprender a usarla correctamente para llegar preparados a la educación superior y al mundo laboral.

No es el primer país en restringirla
China implementó medidas similares en mayo de 2025, prohibiendo a los alumnos de primaria usar herramientas de IA de forma independiente y permitiendo su uso solo en contextos supervisados.

Un giro en uno de los países más digitalizados de Europa
Noruega incorporó computadoras en las aulas desde los años 90 y tablets a partir de 2010, lo que redujo el uso de libros físicos y la escritura a mano. Ahora busca revertir esa tendencia justo cuando las grandes tecnológicas promueven la IA en la educación.

Parte de un plan más amplio
La medida se suma a otras decisiones del gobierno noruego, como la propuesta de abril para prohibir las redes sociales a menores de 16 años, en línea con lo que ya hizo Australia, con el objetivo de reducir el uso de dispositivos electrónicos entre niños y adolescentes.

Testimonios

"El uso de la IA aumenta el riesgo de que los niños pequeños se salten pasos importantes en su educación." — Jonas Gahr Stoere, primer ministro de Noruega.

(+) en Clarín: https://t.co/M5J8Bp9HPR

[Jaime Saavedra @JaimeSaavedra22]

Si la IA hace la tarea de tu hijo, el cerebro de tu hijo no participó en eso. Y ese uso de la IA es un problema. Un estudio de Stromberg, Lei y Wu (https://t.co/8QHcVapdup) siguió a 26,000 estudiantes de secundaria en China durante 30 meses. Muestran que el uso de IA elevó las notas de las tareas en 18% y redujo el tiempo que dedicaban a esas tareas en 30%. Al mismo tiempo, las notas en los exámenes a libro cerrado cayeron 20% luego de seis meses. Analizando un efecto de largo plazo, las notas en los exámenes de ingreso a la universidad cayeron hasta 24%. Como se ve en la figura, mas eficiencia para hacer las tareas, pero menos menos aprendizaje. Por otro lado, los estudiantes que usaron IA pero igualmente dedicaron el mismo tiempo que sus compañeros sin IA obtuvieron en los exámenes notas casi idénticas a las de quienes no usaron IA. Pero los que "externalizaron" su tarea completamente, terminando más rápido y posiblemente poniendo menos esfuerzo, que cualquier estudiante sin IA, solo sacaron notas altas en los ejercicios. La diferencia fue el esfuerzo cognitivo: si el cerebro hizo el trabajo, o simplemente observó cómo la IA lo hacía en su lugar.
La IA es una herramienta. Un bisturí en manos de un cirujano salva vidas. El mismo bisturí en las manos equivocadas hace daño. La misma IA que puede ser un tutor efectivo, desafiando al estudiante a pensar, explicar, luchar productivamente con el problema, se vuelve dañina si elimina el esfuerzo que el aprendizaje requiere. Esto no es grave si le pasa a pocos estudinates. Pero cerca del 80% de los estudiantes que usaban IA cayeron en lo que el estudio llama "tercerización de tareas" (homework outsourcing).

Esto conecta directamente con la clasificación que hacíamos con mis colegas Ezequiel Molina y Maria Barron, (https://t.co/ygsaXKPVfR) de tres grupos de estudiantes, los Empoderados por la IA, que la usan para pensar con más profundidad; los Dependientes de la IA, que la usan para evitar pensar; y los Excluidos de la IA, que no tienen acceso. Nos preocupaba que el grupo de los Dependientes pudiera crecer. Ya creció. Es, con mucho, el grupo más numeroso en este estudio. Completar una tarea no es lo mismo que aprender. El cerebro no construye conocimiento observando cómo trabaja la IA sino equivocándose, esforzándose y resolviendo las cosas por sí mismo. Ese esfuerzo cognitivo es esencial para el aprendizaje .

[Aakash Gupta @aakashgupta]

No child is born able to read. The brain ships with no reading region at all. It builds one, and the construction runs on the exact effort AI removes.

Learning to read physically repurposes a patch of visual cortex. A spot in the left fusiform gyrus starts out tuned to objects and faces. Through months of effortful decoding, a 6-year-old converts it into the visual word form area, the region every literate adult uses to recognize words on sight. Stanislas Dehaene mapped it and called it neuronal recycling. Pre-literate kids show no special response to letters there. It shows up only as they struggle to read.

The struggle is the build signal. When a child strains to sound out a word or hold a sum in working memory, focus chemicals like acetylcholine and norepinephrine flag that circuit as worth keeping. Effort is how the nervous system marks which synapses to strengthen. Low effort, no marker.

Errors carry the same signal. The brain learns from the gap between what it predicted and what turned out true. Each wrong guess followed by a correction releases the dopamine that drives the rewire. Fluent, instant output produces almost none of it.

The wiring locks in later, during deep sleep, when the circuits tagged that day get consolidated. Only the ones that fired hard enough to get tagged. A child who never strained tagged nothing to keep.

Hand that child a model that returns the sentence or the answer on demand, and the strain, the errors, and the prediction gap vanish at once. The worksheet looks finished. The cortex that should have rewired underneath it never fired.

The window is the urgent part. The tissue reading recycles is where childhood plasticity peaks, and ages 6 to 13 are when that repurposing is cheapest. Miss the reps then and the same wiring costs far more to build later, if it builds at all.

Norway is the country that already ran the opposite experiment. In 2016 they gave a tablet to every 5-year-old, went all in on screens in class, and watched the results for a decade. Now they're pulling AI out for ages 6 to 13 and funding paper books again. A government reading its own data ahead of the curve.

The biology is identical in every country. Norway just moved on it first. Watch how fast others follow.

[Jaynit @jaynitx]

In the 1920s, a Stanford psychologist tracked genius children for 50 years.

Malcolm Gladwell breaks down what he discovered:

Rich families → successful. Poor families → failures.

Not average. Failures. Genius-level IQs that produced nothing.

He spent 60 minutes at Microsoft explaining why we're wrong about success:

The psychologist was named Terman. He gave IQ tests to 250,000 California schoolchildren.

He identified the top 0.1%. Kids with IQs of 140 and above.

His hypothesis: these children would become the leaders of academia, industry, and politics.

He tracked them. And tracked them. For decades.

The results split into three groups:

The top 15% achieved real prominence. The middle group had average, moderately successful professional lives.

And the bottom group? By any measure, failures.

The difference wasn't personality. Wasn't habits. Wasn't work ethic.

It was simple: the successful geniuses came from wealthy households. The failures came from poor families.

Poverty is such a powerful constraint that it can reduce a one-in-a-billion brain to a lifetime of worse than mediocrity.

There's a concept called "capitalization rate."

It asks a simple question: what percentage of people who are capable of doing something actually end up doing that thing?

In inner city Memphis, only 1 in 6 kids with athletic scholarships actually go to college.

If our capitalization rate for sports in the inner city is 16%, imagine how low it must be for everything else.

Here's something stranger.

Gladwell read the birth dates of the 2007 Czech Junior Hockey Team:

January 3rd. January 3rd. January 12th. February 8th. February 10th. February 17th. February 20th. February 24th. March 5th. March 10th. March 26th...

11 of the 20 players were born in January, February, or March.

This isn't unique to the Czechs. Every elite hockey team in the world shows the same pattern. Every elite soccer team too.

Why?

The eligibility cutoff for youth leagues is January 1st.

When you're 10 years old, a kid born in January has 10 months of maturity on a kid born in October. That's 3 or 4 inches of height. The difference between clumsy and coordinated.

So we look at a group of 10 year olds, pick the "best" ones, give them special coaching, extra practice, more games.

We think we're identifying talent. We're just identifying the oldest.

Then we give the oldest more opportunities, and 10 years later they really are the best.

Self-fulfilling prophecy.

The capitalization rate for hockey talent born in the second half of the year? Close to zero.

We're leaving half of all potential hockey players on the table because of an arbitrary date on a calendar.

Kids born in the youngest cohort of their school class are 11% less likely to go to college.

11% of human potential squandered because we organize elementary school without reference to biological maturity.

Now here's the part about math.

Asian kids dramatically outperform Western kids in mathematics. The gap is enormous and consistent across decades of testing.

Some people say it's genetic. It's not.

It's attitudinal.

When Asian kids face a math problem, they believe effort will solve it.

When Western kids face a math problem, they believe the answer depends on innate ability they either have or don't.

Here's the proof.

The international math tests include a 120-question survey. It asks about study habits, parental support, attitudes.

It's so long most kids don't finish it.

A researcher named Erling Boe decided to rank countries by what percentage of survey questions their kids completed.

Then he compared it to the ranking of countries by math performance.

The correlation was 0.98.

In the history of social science, there has never been a correlation that high.

If you want to know how good a country is at math, you don't need to ask any math questions. Just make kids sit down and focus on a task for an extended period of time.

If they can do it, they're good at math.

Why do Asian cultures have this attitude?

Gladwell's theory: rice farming.

His European ancestors in medieval England worked about 1,000 hours a year. Dawn to noon, five days a week. Winters off. Lots of holidays.

A peasant in South China or Japan in the same period worked 3,000 hours a year.

Rice farming isn't just harder than wheat farming. It's a completely different relationship with work.

There's a Chinese proverb: "A man who works dawn to dusk 360 days a year will not go hungry."

His English ancestors would have said: "A man who works 175 days a year, dawn to 11, may or may not be hungry."

If your culture does that for a thousand years, it becomes part of your makeup.

When your kids sit down to face a calculus problem, that legacy of persistence translates perfectly.

Now consider distance running.

In Kenya, there are roughly a million schoolboys between 10 and 17 running 10 to 12 miles a day.

In the United States, that number is probably 5,000.

Our capitalization rate for distance running is less than 1%.

Kenya's is probably 95%.

The difference isn't genetic. The difference is what the culture values and where it spends its attention.

Here's the most fascinating finding.

30% of American entrepreneurs have been diagnosed with a profound learning disability.

Richard Branson is dyslexic. Charles Schwab is dyslexic. John Chambers can barely read his own email.

This isn't coincidence. Their entrepreneurialism is a direct function of their disability.

How do you succeed if you can't read or write from early childhood?

You learn to delegate. You become a great oral communicator. You become a problem solver because your entire life is one big problem. You learn to lead.

80% of dyslexic entrepreneurs were captain of a high school sports team. Versus 30% of non-dyslexic entrepreneurs.

By the time they enter the real world, they've spent their whole life practicing the four skills at the core of entrepreneurial success: delegation, oral communication, problem solving, and leadership.

Ask them what role dyslexia played in their success and they don't say it was an obstacle.

They say it's the reason they succeeded.

A disadvantage that became an advantage.

Here's what Gladwell wants you to understand:

When we see differences in success, our default explanation is differences in ability.

We forget how much poverty, stupidity, and attitude constrain what people can become.

We refuse to admit that our own arbitrary rules are leaving talent on the table.

We cling to naive beliefs that our meritocracies are fair.

The capitalization argument is liberating.

It says you don't look at a struggling group and conclude they're incapable. It says problems that look genetic or innate are often just failures of exploitation.

It says we can make a profound difference in how well people turn out.

If we choose to pay attention.

[Anish Moonka @anishmoonka]

Fred Rogers met with a child psychologist every week for 22 years to build his show. She shaped everything: every script, prop, and song. The whole point was to give a child's nervous system time to slow down. In 1984, a single regulatory decision ended all of it.

The psychologist was Dr. Margaret McFarland, who co-founded the Arsenal Family and Children's Center alongside Benjamin Spock and Erik Erikson. She and Rogers understood that the prefrontal cortex in children, the part of the brain that controls impulse, emotion, and attention, takes decades to fully develop. At the start of every episode, Rogers tied his sneakers and changed his sweater while children settled in. Those pauses were intentional, designed to help a child's nervous system shift into a calmer, more focused state.

What ended it had nothing to do with child development science. In 1984, Reagan's FCC chairman Mark Fowler abolished the advertising limits that had protected children's programming from commercial pressure. Toy companies moved within months. Between 1984 and 1985, cartoons tied to toy lines increased by 300%, from a handful of shows to more than 40 animated series. In almost every case, the toy was designed first. The cartoon was built to sell it.

Researchers later put numbers to what parents were already noticing. A 2011 study in Pediatrics from the University of Virginia tested 60 four-year-olds across three groups: one watching SpongeBob, which cuts scene every 11 seconds; one watching a slow PBS show, which cuts scene every 34 seconds; and one drawing. Nine minutes later, all three took tests on attention, impulse control, short-term memory, and problem-solving. The SpongeBob group scored significantly worse across every measure.

In the 1970s, children began watching television around age 4. Research from pediatrician Dimitri Christakis found that by 2009, the average age of first screen exposure had dropped to 4 months, as the content got faster and the audience got younger. Researchers separately found that each additional hour of daily screen time at ages 1 or 3 raised the risk of attention problems at age 7 by 9%.

[Diana ❤️🇺🇸🙏🐸 @DianaT192]

IN 1910, THE FLEXNER REPORT — FUNDED BY JOHN D. ROCKEFELLER AND ANDREW CARNEGIE — SHUT DOWN OVER 100 MEDICAL SCHOOLS IN AMERICA IN A SINGLE DECADE. HOMEOPATHY, NATUROPATHY, HERBAL MEDICINE, ELECTROTHERAPY — ALL ELIMINATED. NOT BECAUSE THEY DIDN'T WORK. BECAUSE THEY COULDN'T BE PATENTED. THE ENTIRE MODERN MEDICAL SYSTEM WAS DESIGNED FROM DAY ONE TO SELL PETROCHEMICAL DRUGS.
Before 1910, American medicine was diverse. Doctors practiced homeopathy, naturopathy, eclectic medicine, herbal medicine, hydrotherapy, and electrotherapy alongside conventional approaches. Patients had choices. Competition existed. Many of these modalities were effective and affordable.
Then Abraham Flexner — not a doctor, not a scientist, an educator — was hired by the Carnegie Foundation to evaluate every medical school in America. His report, published in 1910, recommended that only schools teaching pharmaceutical-based, laboratory-focused medicine should receive funding and accreditation.
Within a decade, over 100 medical schools were closed. The number dropped from 155 to 31. Homeopathic colleges: eliminated. Naturopathic schools: defunded. Eclectic medicine programs: shut down. Electrotherapy training: erased from the curriculum entirely.
Who funded this? The Rockefeller Foundation and the Carnegie Foundation — the same families who owned the pharmaceutical and petrochemical industries. John D. Rockefeller's father was literally a traveling snake oil salesman. His son built Standard Oil, then realized that petroleum byproducts could be patented as drugs. But first, the competition had to be destroyed.
The Flexner Report was not a scientific evaluation. It was a business strategy. Eliminate every form of medicine that uses natural, unpatentable substances. Eliminate every therapy based on energy, frequency, or the body's innate healing capacity. Replace them all with patentable synthetic molecules derived from petroleum.
It worked perfectly.
By 1930, American medicine was a pharmaceutical monopoly. Doctors were trained exclusively to diagnose diseases and prescribe patented drugs. Nutrition was removed from medical education. Energy medicine was labeled "quackery." Prevention became irrelevant — because healthy people are not customers.
Every doctor trained after 1920 was trained inside this system. Every medical textbook was funded by pharmaceutical companies. Every hospital was built on this model. The entire structure — from medical school to pharmacy to insurance — was designed as a delivery system for patentable molecules.
This is not conspiracy theory. The Flexner Report is a public document. The funding sources are documented. The closure of 124 medical schools is historical fact. The Rockefeller Foundation's simultaneous investment in pharmaceutical companies is public record.
You did not choose pharmaceutical medicine. It was chosen for you — in 1910 — by oil barons who needed customers for their petroleum byproducts.
Everything that was eliminated still works. Frequency medicine. Herbal medicine. Electrotherapy. They were not disproven. They were defunded. There is a difference.
🔔 The Flexner Report. 1910. The day medicine became a business. Read it yourself. Share this.
Source: QuantumMedicineNews

[Sama Hoole @SamaHoole]

The year is 1949.

The Nobel Prize in Medicine has just gone to the man who invented the lobotomy. Your doctor suggests one for your sister, who has not been herself since the baby came. It is the most celebrated advance in psychiatry of the age, and he is simply current. By the time the prize curdles into an embarrassment, close to twenty thousand Americans have had the operation, and proportionally more here in Britain.

The year is 1956.

Lay the baby down on his front, the doctor says. So does the most trusted childcare book ever written, the one on every new mother's shelf. On his back he might choke, the reasoning goes. Millions obey. The advice holds for nearly thirty years, long after the evidence has quietly turned, and a generation of cot deaths is counted before anyone thinks to roll the babies over.

The year is 1966.

A bestselling book informs your wife that menopause is a disease, that she is, in the author's word, a castrate, and that a small daily pill will keep her youthful and tolerable to live with. Her doctor agrees. The drug becomes one of the most prescribed in the country. Nobody mentions that the author sat on the payroll of the company that made it. That detail surfaces decades later, in the same year the landmark trial is halted early for raising rates of breast cancer, stroke and clots.

The year is 1979.

Your ulcer is caused by stress and sharp food, the doctor explains. Calm down, drink milk, take the antacid that happens to be the best-selling medicine on earth. Two Australians are about to prove that most ulcers are caused by a bacterium and cured by a fortnight of antibiotics. The profession laughs. One of them eventually drinks a beaker of the stuff to settle the matter. The establishment takes the better part of twenty years to stop laughing. The Nobel lands in 2005.

The year is 1985.

Butter is dangerous, the doctor says. Switch to margarine, it is modern, it is heart-healthy, the experts are united. The spread he nudges you toward is loaded with trans fats, which the next decade will identify as the genuinely dangerous one, and which will eventually be banned outright. The butter goes quietly back in the fridge. No correction is ever printed at the volume of the original warning.

The year is 1992.

There is a pyramid on the surgery wall, and the very same one in your grandchild's classroom. Bread, cereal, rice and pasta form the broad virtuous base, up to eleven servings a day. Fat is exiled to the tiny tip. The chart was reportedly held back a year while the relevant industries had their say. It is wrong at the bottom and wrong at the top.

Now it is today.

Your doctor has new guidelines, new studies, a fresh consensus, delivered with precisely the steady confidence of every guideline above. He believes it, and he has good reason to. So did every doctor in this thread. None of them were villains. Each was sincere, most were kind, and all were certain, reading from a map that somebody else had drawn and handed them. That is the part worth sitting with.

So when the man in the white coat tells you what to eat, what to fear, and what to swallow every morning for the rest of your life, you are allowed to ask. Who paid for the study. What the evidence says beneath the headline. What he was just as certain about thirty years ago, and where that advice sits now.

Then make up your own mind. Call it scepticism, or call it whatever your grandmother called it when she ignored the advert, kept the butter where it was, and lived to ninety-one.

It has outlasted every consensus on this list. It will outlast this one too.

[IT Guy @T3chFalcon]

The reason is quite hilarious 😂😂.

Microsoft put $50 billion into Anthropic.

FIFTY billion dollars.

they are a Project Glasswing partner. Fable 5 runs inside Azure. Microsoft sells Claude to its own enterprise customers through Microsoft 365 and GitHub Copilot.

and they won't let their own employees use it.

here's why.

under Anthropic's new Mythos-class data retention policy, every prompt you type and every response you get is stored for 30 days. automatically. no opt out.

if their safety classifiers flag anything in your session, anything, they keep it for up to two years.

you don't get told when that happens, what was flagged or who can see it.

Microsoft employees paste confidential contracts into these things. customer data. internal roadmaps. acquisition strategies. legal documents. source code.

all of it sitting on Anthropic's servers for 30 days minimum. flagged sessions for two years.

so the company that invested $50 billion looked at that policy and told its staff: actually hold on.

other Claude models still work internally. under Zero Data Retention rules. the normal ones are fine.

just not the most powerful one they helped fund.

and one more thing.

the Pentagon listed Anthropic as a supply chain risk in March and banned defense contractors from using its products.

Microsoft funds Anthropic. sells Anthropic's models. runs them on Azure. helped build the most powerful one.

won't let employees use it.

the Pentagon won't let defense contractors near it.

the safeguard that makes Fable 5 safe enough to release publicly is the same safeguard that lets Anthropic keep your data for two years.

the guardrail is a data retention policy.

but you can use it. it's in your browser right now. 🌚

have fun.

[Nav Toor @heynavtoor]

You have noticed it. ChatGPT feels dumber than it used to. Your prompts that worked six months ago produce worse results now. The writing sounds flatter. The ideas sound safer. The internet itself feels like it is shrinking. Every article reads the same. Every email sounds the same. Every answer sounds like it was written by the same voice.

You thought it was you. It is not you.

Researchers at Oxford and Cambridge published a paper in Nature proving what is happening. They call it Model Collapse.

Here is the mechanism in one sentence. AI trained on AI-generated data gets dumber every generation until it forgets what real human data looked like.

The internet is filling with AI-generated content. Blog posts. Articles. Reviews. Comments. Social media. AI companies scrape the internet to train the next generation of models. Which means the next generation of AI is being trained on the output of the current generation.

Each cycle loses information. Not randomly. It loses the rarest, most unusual, most creative parts first. The researchers call these the "tails of the distribution." The weird ideas. The unexpected perspectives. The things that made the internet feel human. Those disappear first.

What remains is the average. The safe. The expected. The bland.

Then the next generation trains on that. And loses more. And the next generation trains on that. And loses more. The researchers proved this is not a slow decline. Major degradation happens within just a few iterations. Even when some of the original human data is preserved.

They tested it on large language models. On image generators. On statistical models. The pattern was the same every time. The output converges toward a narrow, flattened version of reality that looks nothing like the original data.

The lead researcher put it plainly. "Large language models are like fire. A useful tool. But one that pollutes the environment."

The pollution is invisible. You cannot see which sentence on the internet was written by a human and which was written by AI. Neither can the AI that is about to train on it. And once the tails are gone, they do not come back. The damage is irreversible.

This is not a prediction anymore. It is a diagnosis.

The internet you grew up on was built by humans writing things no algorithm would have written. Strange, personal, imperfect, alive. That internet is being diluted. One generation of AI at a time. And the models trained on what remains are learning a smaller and smaller version of the world.

Model Collapse is not a technical problem. It is a cultural one. The thing that made the internet worth reading is the thing that disappears first.

[Virtud Mental @VirtudMental]

Manipulación de los rangos de referencia médicos:

1985: Glucosa en ayunas superior a 140 mg/dL = diabetes
2003: Se redujo a 126 mg/dL
De repente, 2 millones más de diabéticos

1985: Presión arterial superior a 160/100 = hipertensión
2017: Se redujo a 130/80
De repente, 30 millones más de "pacientes"

1985: Colesterol superior a 280 = preocupación
2004: Se redujo a 200
De repente, todo el mundo necesita estatinas

No están descubriendo enfermedades. Están creando pacientes cambiando las reglas del juego.

Bajar el umbral, expandir el mercado, vender más medicamentos.

[Tony Fadell @tfadell]

Nobel Prize winning economist Kenneth Arrow wrote about "learning by doing" decades ago. He knew that productivity and expertise improve through experience.

The messy, repetitive works is often where you learn the patterns that eventually become judgment. Knowledge can be taught, but judgement is built through lived experience.

The first draft you rewrite. The customer call you listen to. The bug you fix and fix again. The factory floor you walk.

Small decisions you make every day teach you judgement. And, judgement is the thing everyone wants from senior people in the workplace. If we automate away every entry-level task without replacing the learning loop, we are removing a part of the process that creates experts.

The goal should be to use AI to accelerate learning, remove friction, and give people better tools to build expertise faster.

https://t.co/MpFZzCk1An

Thanks @Fortune & @tbove4 for sharing this story. Link in the comments.

[Ihtesham Ali @ihtesham2005]

A computer scientist proved that "trust your gut" is terrible advice, because the gut decisions humans struggle with most are optimal stopping problems, and there is a precise formula that beats intuition every single time.

His name is Brian Christian, and together with a cognitive scientist named Tom Griffiths, he wrote a book that does something nobody had done before.

It takes the exact algorithms that run inside computers and uses them to solve the decisions that keep humans awake at night.

The book is called "Algorithms to Live By." The core idea is unsettling once you sit with it.

Every painful decision you have ever agonized over is something a computer already faces. When to stop searching. When to commit. When to try something new instead of sticking with what works.

Computer scientists have been grinding on these exact problems for sixty years. They solved most of them. Nobody bothered to translate the answers back into human language until now.

Start with the one that haunts everyone. When do you stop looking and commit.

You are hunting for an apartment. Every place you skip might have been the one. Every place you take might be worse than the next one you would have seen. There is no way to know, because you cannot see the future and you cannot go back.

This is called the optimal stopping problem, and it has a clean answer.

Look at the first 37% of your options and commit to nothing. No matter how good they are. You are not choosing during this phase. You are calibrating. You are learning what good actually looks like.

Then the moment you cross that 37% line, leap on the very first option that beats everything you have seen so far.

That is it. Look, then leap.

The strange part is the math does not care how big the pool is. Whether you have 10 candidates or 10 million, the number stays 37%. Your odds of landing the single best option do not collapse as the choices multiply. The formula holds.

It works for hiring. It works for apartments. It works, uncomfortably, for finding a partner. Spend the first chunk of your search learning, then be ready to commit fast when something clears the bar.

The second problem they solve is the one you fight every weekend. Do you go to your favorite restaurant again, or try the new place that might be better or might be a disaster.

Computer scientists call this explore versus exploit. Exploring means gathering new information. Exploiting means cashing in on what you already know is good.

The answer depends entirely on one thing. How much time you have left.

When you are new to a city, explore relentlessly. You have years ahead to enjoy whatever you discover. When it is your last night in town, go to the place you already love. There is no future left to spend the information in.

This is why trying new things feels riskier as you get older, and why it should. The window to benefit from a discovery is shrinking. The math quietly rearranges your priorities for you, whether you notice it or not.

Then comes the finding that should change how you treat yourself.

When you have already made a solid choice, going back to second-guess it almost always makes things worse. A computer running a good-enough decision does not loop back to torture itself. It moves on.

Humans do the opposite. We commit, then we reopen the case file at 2am and reargue it. The math says this is not diligence. It is self-sabotage dressed up as carefulness.

The deepest thing in the book is the reframe at the center of all of it.

A good decision is not one that produces a good outcome. The outcome involves luck you cannot control. A good decision is one that used the best possible process given what you actually knew at the time.

You do not get to control the result. You only get to control whether you ran the right algorithm.

Stop trusting your gut on problems that have a formula.

The formula was always there. You were just never handed it.

[How To Prompt @HowToPrompt__]

MIT's Nobel Prize-winning economist proved that AI is mathematically guaranteed to destroy human knowledge.

They published a massive NBER paper modeling the long-term impact of AI on human cognition.

And they found the most alarming conclusion in the AI literature so far.

It’s called "Knowledge Collapse."

Here is how human progress actually works.

When you struggle to solve a complex problem, you generate two things:

General knowledge about how the world works, and context-specific knowledge about your exact problem.

Normally, humans acquire both at the same time. You do the hard work to solve your specific problem, and in the process, you learn a general principle.

You share that principle. That is how human knowledge grows.

Then comes Agentic AI.

AI is incredibly good at giving you the exact, context-specific answer you need right now. It hands the solution to you on a silver platter.

So you stop doing the hard work.

And because you stop doing the work, you stop generating the "general knowledge" that society relies on.

Acemoglu calls it the "knowledge-collapse equilibrium."

When AI reaches a certain accuracy threshold, the incentive for humans to learn drops to zero.

Nobody verifies. Nobody explores. Nobody discovers new fundamental truths.

Society gets increasingly sophisticated automated outputs, while our actual capacity to generate new knowledge quietly erodes.

But here is the most terrifying finding in the paper.

Welfare is "non-monotone" to AI accuracy.

That means as AI gets more accurate, society actually gets worse off.

[John Doomer @jonathandoomer]

The Pope just dropped the theological patch notes for the entire AI industry:

“Your chatbot can generate a breakup text, a fake Van Gogh, and a VC deck about replacing nurses, but it has never held its mother’s hand in a hospital room, never felt shame, never prayed, never forgiven anyone, never had to live with what it said.”

It can imitate the soul.

It cannot grow one.

[Pope Leo XIV @Pontifex]

Artificial intelligences do not undergo experiences, do not possess a body, do not feel joy or pain, do not mature through relationships, and do not know from within what love, work, friendship or responsibility mean. Nor do they have a moral conscience, since they do not judge good and evil, grasp the ultimate meaning of situations, or bear responsibility for consequences. They may imitate or even simulate, but they do not understand what they produce, for they lack the affective, relational, and spiritual perspective through which human beings grow in wisdom. #MagnificaHumanitas

[Mark Ajzenstadt @mardehaym]

None of this is satire.

→ A company spent $500,000,000 on Claude in one month because nobody set usage limits

→ Uber ran leaderboards ranking engineers by how much AI they used, not what they shipped

→ Uber burned their entire 2026 budget by April. Their COO said he can’t connect any of it to consumer features

→ A CTO told Axios employees were using enterprise AI to check the weather

→ Microsoft canceled most Claude Code licenses because the token bill spiraled

→ Companies are now laying people off to pay the AI bill. Not because AI replaced the work. Because the bill replaced the headcount.

[Papa León XIV @Pontifex_es]

Las inteligencias artificiales no viven una experiencia, no poseen un cuerpo, no pasan por la alegría y el dolor, no maduran en las relaciones ni conocen desde dentro lo que significan el amor, el trabajo, la amistad y la responsabilidad. Tampoco tienen una conciencia moral: no juzgan el bien y el mal, no captan el sentido último de las situaciones ni asumen el peso de las consecuencias. Pueden imitar, pueden simular pero no conocen lo que producen, porque no residen en el horizonte afectivo, relacional y espiritual en el que el ser humano se hace sabio. #MagnificaHumanitas

[Alex Veremeyenko @alex_verem]

Let me trace the timeline here because nobody's connecting it.

Step 1: Scrape the entire internet. Every book, every article, every conversation, every piece of art, every forum post. Do it without asking. Do it without paying.

Step 2: Train a model on all of it. Call it "artificial intelligence."

Step 3: Go to BlackRock's Infrastructure Summit and announce: "We see a future where intelligence is a utility, like electricity or water, and people buy it from us on a meter."

Step 3 is where you sell people's own knowledge back to them. On a meter.

They took the collective output of human thought, compressed it into a model, and now they want to charge you by the token to access a version of what you and everyone you know already created.

One Reddit user put it perfectly: "They stole all this data from us, the people, our life's work, creativity, art, by devouring the internet and blowing through all copyright laws. Now they want to sell it back to us in the form of a utility."

Imagine if someone photocopied every book in the public library, burned the library down, and then opened a subscription service for the copies.

That's the metered intelligence business model.

And they're pitching it to infrastructure investors as though they invented water.