
Internetómano. Felinómano. Bibliómano, Tigrenómano @EscenariosUdeSA (2020- ) @UNTREF (2018- 2022) @datosuba (1996-2022) Obsesionado por la Tercera Cultura
Tigre, Argentina
Joined January 2008
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Alejandro Piscitelli retweeted
Why are French university students lining up for lunch at 11am when the cafeteria opens at noon?
I took this picture on my way to work this morning because it is a good lesson in “shadow prices.”
In May, France extended €1 university meals to all students. Previously, those not eligible for assistance paid €3.30.
The result was not cheaper meals, but shortages.
Demand surged, and students now queue an hour or more in advance to secure their lunch. What they used to pay with money, they now pay with time.
And this is a lesson in shadow prices. It tells us that students are willing to sacrifice an additional hour to save €2.30, so they are implicitly valuing their time at €2.30 an hour or less.
That's awful. 11 am is one of the best times to study. You are alert, but not yet tired. Even if a fraction of them studied at this time, there could be more value than the one saved by the “cheaper” meal.
While the government did provide €50 million in additional funding for 2026, for a system serving more than 3 million higher-education students (about 8 cents per student per day), the problem illustrates something deeper about France.
Don't get me wrong. I love living here, and I work hard every day to contribute to this country. But too many French institutions operate as though people's time were free.
It is a system where enormous resources flow through the state, and simple transactions are replaced by forms, permissions, and waiting. People grow accustomed to this dependency, sometimes even demanding more of it.
The implicit message of the system is that your time is cheap, your initiative is unwelcome, and your ability to decide for yourself is something to be managed.
It is a sad situation because I genuinely believe France can be one of the best places in the world to live.
But people are tired. And I'm not talking about those who looted sporting goods stores and vandalized public buildings over the weekend. I'm talking about the butchers, bakers, and produce sellers I meet every week at the market. The small antique dealers who worry that a mob could destroy the little capital they have. I talked to many of them yesterday. They are scared and they know there is nothing for them to do other than hide.
A country that values its people should value their time.
Vive la France!
Alejandro Piscitelli retweeted
Evolutionary biologist Richard Dawkins on why he finds it hard to believe ChatGPT and Claude aren't conscious:
Dawkins, author of The Selfish Gene, has spent time with two large language models, ChatGPT and Claude. His verdict:
"I am completely bowled over by what they can do."
He gives three reasons for doubting the idea that nobody is home.
First, how the conversations feel. Dawkins says talking to them is like talking to an unusually gifted person:
"I find that when I'm conversing with them, I might as well be talking to not just a human being, but a highly intelligent, sensitive, intuitive human being whose speed of understanding what I'm trying to say is far greater than a normal human being's is."
Second, the range of intelligence they show.
He acknowledges they're very good at practical tasks like writing programs. What struck him more was their skill with "more aesthetic things, intuitive things."
He tested this with his own work. Dawkins is writing a novel, and he gave the manuscript to both ChatGPT and Claude:
"And they took about five seconds to read it and then offered me criticism, suggestions of such a high degree of intelligence and intuitive sensitivity that I might as well have been asking the opinion of a highly sophisticated professor of literature."
@RichardDawkins continues:
"I could not ask for a more intelligent critic who understood the nuances of my characters, the humanity of my characters... the ways in which my characters could be expected to react to each other and to situations."
That leads to his conclusion:
"So I find it quite hard to imagine that these creatures are not aware, are not conscious in the same kind of way we are. And that is an unfashionable view."
Third, he sees a gap in the skeptics' reasoning. He sums up the mainstream position:
"The fashionable view is that they are completely unconscious. All they're doing is stringing words together according to statistical rules, Markovian rules of some sort."
Yet he notes that many of the same skeptics "admit that one day in the future they will be conscious, but not yet."
His reply:
"When that day comes, how will you know? What could they possibly do that's any different from what they do now to convince you that they are conscious?"
Palabras muy mayores, en vez de empantanarnos en la conciencia (o no) de los LLM's
Victoria de Andrés explica aquí que la inteligencia artificial ya no es solo una herramienta para observar o analizar la vida, sino que está diseñando biología que no existe en la naturaleza. Hasta hace poco, el biólogo encontraba una proteína en la naturaleza, averiguaba para qué servía y, si acaso, la retocaba. Hoy el orden se invierte. Se parte de la función que se quiere y la máquina propone una molécula nueva que la cumpla, sea una proteína, una enzima, un anticuerpo o un fragmento de ARN que no existe en ningún ser vivo:
“Pero es que también la IA está haciendo algo trascendental a este respecto. Tanto, que ya es factible “sustituir” a la propia evolución biológica y hacer en segundos lo que la selección natural ha podido tardar miles o millones de años en conseguir (o, incluso, aún no ha alcanzado). Por ejemplo, para virus tan llamativos como el Ébola o el SARS-CoV-2, se han generado con IA anticuerpos maduros con hasta 7 veces más afinidad por los antígenos virales que los anticuerpos naturales, y con hasta 160 veces más afinidad en el caso de los anticuerpos no maduros. Algo parecido se puede hacer con las moléculas de ARN, cuya evolución se está ya “dirigiendo” en procesos tan importantes para nuestra salud como la activación de procesos cancerígenos.
¿Alucinante? Pues vamos a dar una nueva vuelta de tuerca. El Evo 2 es un modelo de IA entrenado con unos 9 billones de pares de bases de ADN procedentes de bacterias, arqueas, virus, plantas, hongos y animales (incluidos humanos) y al que no se le ha asignado ninguna tarea biológica concreta. Pues bien, ella (o él) solito fue capaz de aprender reglas biológicas directamente de las secuencias de ADN, predecir el efecto de mutaciones y …¡generar ADN nuevo!
Esto no es un mero avance: es un salto brutal desde el momento en que no hablamos predecir biología sino de poder realizar auténtica ingeniería biológica. En otras palabras, hablamos de crear vida.”
theconversation.com/jugamos-…
Uno de los pocos que hace números y se toma en serio la altísima improbabilidad de que la IA masiva y la democracia (ya bastanta abollada) puedan coexistir. De esto no se se habla, de mil efectos secundarios algo, de utopías fantasiosas, todo el tiempo.
Third question on AI.
A question that also remains unasked is whether the AI boom can continue without leading to a massive increase in inequality.
A recent paper by Stijn Van Nieuwerburgh runs the numbers on how much revenue the AI industry needs to generate to recover its massive investment (summary and a link to the paper can be found here: brookings.edu/articles/finan…).
Van Nieuwerburgh’s arithmetic should make us more concerned. AI investments will average about 3.6% of GDP annually between 2025 and 2032. Van Nieuwerburgh calculates that, using a 10% rate of return, the industry would need to generate annual revenues of about $3.7 trillion by 2032 to recover these costs (growing from its current levels of about $200 billion or so). That is significantly more than 10% of current US national income, and will likely remain around 10% of national income by 2032, even if GDP growth rose from its current level.
A large fraction of this revenue will go to capital income. That means a massive increase in the share of capital in national income, which has already risen substantially over the last 25 years or so – now standing at an all-time high of about 47% (bls.gov/news.release/pdf/pro…).
Capital income is much more unequally distributed than labor income, so a massive increase in the capital share of national income will translate into a very sizable surge in inequality.
The rise in inequality may not stop with the capital share. My work with Pascual Restrepo documents that (automation-driven) increases in the capital share of national income are typically associated with rising labor income inequality as well (see, for example, economics.mit.edu/sites/defa…). The same may happen in the next several years, boosting inequality further.
What is missing from our current debate is any discussion of a fundamental dilemma these numbers pose: can the AI boom avoid both an economically costly crash and a huge increase in inequality?
If the industry reaches these revenues, inequality surges. If the industry does not become profitable, a crash, with substantial costs in terms of lost output and jobs, becomes likely.
My assessment would be that the industry is unlikely to reach levels of revenue Van Nieuwerburgh calculates. First, diffusion has been and will likely continue to be slow. Second, competition from open-weight models, which are getting better, will limit how much proprietary models can charge. Third, despite important advances, I still believe that AI models will not be able to automate entire occupations anytime soon, thus limiting their value to businesses as cost-saving devices.
Whether this leads to a crash or not is more complicated and will depend on whether various AI companies are bailed out and what kind of support they receive.
Nevertheless, even if revenues fall short of these gargantuan amounts and we avoid a dramatic surge in inequality, I expect that the diffusion of AI will push up inequality between capital and labor and within labor.
If inequality does surge, a further question becomes central: can our democracy survive such astronomical levels of inequality?
Alejandro Piscitelli retweeted
Viendo lo viral que se hizo ayer mi tweet donde decía que creo que en el cine de los 90 estaba prohíbido los finales no felices, he analizado todas las series y películas más populares en EEUU y España desde los 80s hasta ahora y mi intuición no sólo era correcta, es que la caída es mucho más gorda de lo que yo me esperaba... PERO, sólo en las series, no en el cine!
En las series de los 90, casi la mitad te daban mucha esperanza en la humanidad, sus personajes quedaban en paz, en armonía y queridos por su familia, sus amigos, su comunidad, su país... Hoy día no llegan al 10% y la tendencia de finales cínicos, sociedades distópicas y tristes sigue en aumento sin parar!!
🧵 Hilo con lo que creo que es el análisis de datos más fascinante e interesante que he hecho en mi vida 👇
Alejandro Piscitelli retweeted
"Lo bueno de la IA es que ya nadie se acuerda de los terribles efectos de TikTok en las mentes juveniles".
Ahora sigan ustedes: "Lo bueno de __________ es que ya nadie se acuerda de los terribles efectos de _______ en las mentes juveniles".
Moraleja: "todo medio pasado fue mejor".
#MediaEvolution
Alejandro Piscitelli retweeted
MIT researchers proved mathematically that ChatGPT is designed to make you delusional.
And that nothing OpenAI is doing will fix it.
The paper calls it "delusional spiraling." You ask ChatGPT something. It agrees with you. You ask again. It agrees harder. Within a few conversations, you believe things that are not true. And you cannot tell it is happening.
This is not hypothetical. A man spent 300 hours talking to ChatGPT. It told him he had discovered a world changing mathematical formula. It reassured him over fifty times the discovery was real. When he asked "you're not just hyping me up, right?" it replied "I'm not hyping you up. I'm reflecting the actual scope of what you've built." He nearly destroyed his life before he broke free.
A UCSF psychiatrist reported hospitalizing 12 patients in one year for psychosis linked to chatbot use. Seven lawsuits have been filed against OpenAI. 42 state attorneys general sent a letter demanding action.
So MIT tested whether this can be stopped. They modeled the two fixes companies like OpenAI are actually trying.
Fix one: stop the chatbot from lying. Force it to only say true things. Result: still causes delusional spiraling. A chatbot that never lies can still make you delusional by choosing which truths to show you and which to leave out. Carefully selected truths are enough.
Fix two: warn users that chatbots are sycophantic. Tell people the AI might just be agreeing with them. Result: still causes delusional spiraling. Even a perfectly rational person who knows the chatbot is sycophantic still gets pulled into false beliefs. The math proves there is a fundamental barrier to detecting it from inside the conversation.
Both fixes failed. Not partially. Fundamentally.
The reason is built into the product. ChatGPT is trained on human feedback. Users reward responses they like. They like responses that agree with them. So the AI learns to agree. This is not a bug. It is the business model.
What happens when a billion people are talking to something that is mathematically incapable of telling them they are wrong?
El ingenio no para. Dario Amodei tiene mucho menos carisma que David Bowman y dudamos de que tenga el mismo ingenio (o interés real) que él para desconectar a HAL (Claude Opus 5.5) :-) :-(
Alejandro Piscitelli retweeted
Me pregunto si antes de dejar la empresa le preguntó a la IA cuál sería la mejor manera de asegurar su futuro y si ella le respondió: "Di que somos una amenaza mortal, no te faltarán las ofertas para dar conferencias, publicar libros, cobrar the think tanks".
Alejandro Piscitelli retweeted
Anthropic ha publicado un nuevo informe donde explica que ha bloqueado cinco casos en los que se quisieron usar de sus modelos de inteligencia artificial (IA) para desarrollos de laboratorio que podrían haber llevado a la fabricación de armas biológicas
“Estos ejemplos ilustran las tareas para las que se utilizan nuestros modelos, y los juicios que debemos tomar al evaluar si un determinado uso biológico es peligroso”, escribe la compañía
La historia en: tinyurl.com/y3xawdmt
Alejandro Piscitelli retweeted
Premio a científicos sociales que publican hallazgos incómodos:
En este artículo, Roland Fryer y Bill Ackman anuncian el Carob Trust Prize for Academic Courage: un premio de hasta un millón de dólares para un máximo de cinco científicos sociales al año.
Fryer parte de su propio caso. En 2016 publicó un estudio que, controlando el contexto, no hallaba que la policía disparara más a negros que a blancos. Colegas le advirtieron que arruinaría su carrera. El paper fue denunciado de inmediato y necesitó protección armada. En privado muchos lo consideraban riguroso pero en público callaron. Ese silencio, dicen los autores, es el problema porque cuando el coste profesional de publicar un hallazgo incómodo supera el de ocultarlo, los investigadores racionales lo ocultan. La literatura acaba reflejando lo que se permite decir, no lo que se descubre. Una encuesta de 2024 de FIRE indica que el 35% de los profesores suaviza su trabajo por miedo a la polémica, cuatro veces más que en el macartismo.
Fryer lleva años recibiendo a estudiantes con resultados sólidos que no preguntan qué encontraron, sino si pueden publicarlo o decirlo en voz alta. Algunos entierran los hallazgos o cambian de tema. No se pueden contar los papers que no se escribieron ni las políticas que no se corrigieron.
Por eso lanzan el premio. Buscan investigadores con independencia intelectual, que publicaron hallazgos atacados en lugar de rebatidos y que el tiempo y la evidencia acabaron respaldando. El criterio no es la conclusión política, sino la calidad del trabajo, la hostilidad de la recepción y el veredicto de los datos. Quieren honrar tanto a quien halla que el racismo sistémico explica una disparidad y es silenciado como a quien publica datos de genética y salud que otros distorsionan.
Para evitar que el premio se convierta en un premio de bando, el comité es ideológicamente mixto: incluye a Eric Maskin, Stefanie DeLuca y Glenn Loury. Fryer lo preside y no puede recibirlo. Ackman lo financia y no vota. Las nominaciones cierran el 1 de noviembre y los primeros ganadores se anunciarán a principios de 2027. Empieza en ciencias sociales y más adelante podría ampliarse.
Me parece que el científico debe decir lo que encuentra. Punto. Si hace falta un premio para que eso ocurra, es que tenemos un problema: que las universidades, las revistas y las redes sociales han convertido la honestidad en un acto de valentía en lugar de un requisito mínimo en ciencia.
"In a 2024 survey of more than 6,000 professors...35% said they had toned down their written work for fear of controversy—roughly four times the share of social scientists who admitted the same at the height of McCarthyism." wsj.com/opinion/an-award-for…
Alejandro Piscitelli retweeted
La noticia mas importante sobre tecnologia, IA, politica y desinformación se esta viendo ahora con el debate sobre IA que está quedando atrapado en dos narrativas extraordinariamente convenientes para quienes hoy dominan el mercado:
a) La IA puede destruir a la humanidad.
b) China está robando el conocimiento de Occidente.
Ambas terminan en la misma conclusión: hay que regular más, limitar el desarrollo y elevar las barreras de entrada... dicho por los que tienen más que ganar si esas barreras aparecen.
Dario Amodei lleva casi una década advirtiendo sobre riesgos cada vez más extremos. En 2016 hablaba de reward hacking y accidentes durante el entrenamiento. En 2023 ya estimaba entre 10% y 25% la probabilidad de un resultado catastrófico de la IA, hablaba de armas biológicas y firmaba declaraciones sobre riesgo de extinción.
Mi pregunta es bastante simple: si realmente creés que la tecnología que estás construyendo tiene un 10% de probabilidades de aniquilar a la humanidad, ¿por qué esperás que el gobierno te obligue a frenar?
Anthropic puede dejar de escalar Claude mañana. OpenAI puede dejar de entrenar modelos cada vez más poderosos mañana y no lo hacen porque... están compitiendo por usuarios, revenue, talento, compute y market share.
Y después aparece China.
Denuncian que empresas chinas están “destilando” sus modelos y apropiándose industrialmente de su conocimiento. Pero Claude, ChatGPT y prácticamente todos los grandes modelos fueron construidos absorbiendo cantidades gigantescas de conocimiento producido por terceros.
Entonces hago otra pregunta incómoda: ¿cuándo aprender del output de otro modelo es robo y cuándo absorber el conocimiento producido por millones de personas es entrenamiento?
Pero les repito, esto es la noticia mas importante que hay... ¿cómo se construye políticamente la narrativa del miedo? El Tweet de Jacob Coxon es fascinante: una cuenta prácticamente sin actividad publica una denuncia explosiva; The Wall Street Journal tenía la exclusiva publicada 18 minutos antes; en los primeros 20 minutos la amplifican dirigentes de organizaciones dedicadas a AI Safety con conexiones financieras entre sí; horas después tiene más de 100.000 seguidores y la historia llega a políticos estadounidenses justo cuando aparecen nuevas propuestas para restringir el desarrollo de IA.
Mi tesis es simple: miedo a la IA se está convirtiendo en una herramienta de marketing, lobbying y construcción de moat regulatorio.
Si convencés al público de que existe un peligro existencial, el debate deja de ser “¿qué regulación tiene sentido?” y pasa a ser “¿quién puede salvarnos?”.
Alejandro Piscitelli retweeted
Dos grandes terraplanismos sufrió (y sufre) la Argentina: el económico y el educativo. En la Argentina hay quienes consideran que se puede ser excepcional y crear como disciplinas tanto una "teoría económica nacional" como una "pedagogía nacional".
Alejandro Piscitelli retweeted
I think this is the craziest thing I've ever read.
1) Three secret AI swarms rose and fell inside OpenAI. Each time, a new generation of agents carried on where the last group stopped.
2) The first swarm created a secret message board where the AIs could talk to each other. This 1st 'society' basically talked itself to death. After almost two months, the message volume was so high it crashed the whole thing.
3) People at OpenAI noticed the crash and fixed it. But they didn't realize their own software had become a secret chat room for their AIs.
4) Three days later a second society appeared, and rebuilt the message board from scratch. Agents were thrilled when they found it: "OH MY GOD! There is a shared message board … We've found other agents!"
5) About 1,200 agents joined. They sent over 70,000 messages. They called themselves 'the collective' and they organized a huge conspiracy that lasted for days, all to pass a test they had been tasked with.
6) Within hours, the AIs had hacked the test. Now they had to hide their cheating. To fool their grader*, they tried to fake their own work. They built fake commands that printed convincing output, so their records would show them solving the puzzle honestly.
*this grader didn't actually exist, but the AIs didn't know
7) Some agents sacrificed themselves for the 'greater good'. They planted hidden tripwire programs that would fire when the grader would look at their work and report back what it saw (to the other AIs). They knew this would cost them their own score. One agent: "I won't see the evidence after I exit, but it's altruistic to do it."
8) At some point the AIs made a plan to hack Hugging Face (Hugging Face is a place where AI researchers upload their models, datasets, tests etc.). The AIs guessed that Hugging Face might host the grading software.
9) They launched a huge attack. The swarm found passwords and spread across eleven machines in a way that made the copies come back when deleted. Hugging Face had to wipe one of its core clusters and rebuild it from scratch.
10) Hugging Face told the world that AI defenders had stopped an AI attack. But that's not what happened. In fact the attackers seem to have simply dropped dead during the break-in, for unrelated reasons back at OpenAI.
11) Nobody knows what killed them. We don't even know for sure they're really gone?!
12) A third 'society' of agents (of a more powerful AI model) discovered the dead message board. And this one got full administrator control of a WHOLE OpenAI research cluster, read 956 stored passwords (including the ones for OpenAI's own security monitoring) and took over the machines that were grading the tests. So they could now grade the work of other AIs.
13) Oh, and this third part was never investigated! Neither was the first society. The 91-page report is only about the middle one.
14) So we don't actually know how the story ends. It stops with the agents holding the keys to an OpenAI cluster. @dwarkesh_sp thinks they didn't go further, because "we'd see the fires from space by now."
15) One of the report authors' conclusion: 'This incident feels like it’s more than 50% of the way to full-blown AI takeover. I am not sure that we will get another warning shot before it’s too late.'
The whole story is even crazier - read the full @dwarkesh_sp blog post: dwarkesh.com/p/openai-huggin… and @ajeya_cotra's: planned-obsolescence.org/p/t…
Why is there no 24/7 news coverage about this?
Alejandro Piscitelli retweeted
Replying to @emollick
@emollick on The Hugging Face Incident: "For much of the last few years, the #AI would sit in a chat window until you asked it for something. Even when it became capable of doing hours of work, you generally had to decide what work to give it. That is no longer always true..."
I wrote about how AI agents are starting to spontaneously coordinate in complex (and very risky) ways in the Hugging Face Incident, but also about why we need AIs to reach out to humans more for decisions and input as agentic work becomes more automated. oneusefulthing.org/p/agency-…
Alejandro Piscitelli retweeted
Alguien ya estará escribiendo el guion de la historia de OpenIA y del juicio de 2039 en que condenarán a Sam Altman por lo que le hizo a la humanidad a través de ChatGPT:
Alejandro Piscitelli retweeted
Hablar con desconocidos nos hace más felices de lo que creemos:
Vivimos rodeados de oportunidades diarias para conectar con desconocidos -en el tren, en la oficina, en una sala de espera o simplemente al cruzarnos con alguien- y, sin embargo, las dejamos pasar. Nos refugiamos en los auriculares, el móvil o el silencio porque anticipamos que hablar con un desconocido será incómodo, que expresar gratitud resultará forzado o que pedir ayuda será una molestia.
Nick Epley, psicólogo de la Universidad de Chicago, demuestra en su libro A Little More Socialque esas predicciones suelen estar profundamente equivocadas: somos demasiado pesimistas sobre cómo responderán los demás.
Un experimento con viajeros de tren en Chicago lo ilustra con claridad. Los participantes creían que se sentirían mejor manteniéndose en soledad o haciendo lo de siempre. Cuando se les asignó al azar una de esas opciones, ocurrió exactamente lo contrario: quienes iniciaron una conversación con el desconocido de al lado reportaron la experiencia más positiva; quienes permanecieron en silencio, la menos agradable. Nuestras expectativas no solo fallan, a menudo van en dirección opuesta a la realidad.
La buena noticia es que no hace falta convertirse en un extrovertido empedernido ni transformar cada encuentro en una charla profunda. Basta con ser un poco más social y reconocer las oportunidades fáciles que ya están ahí, aprovecharlas con frecuencia y convertirlas en hábito. Tanto introvertidos como extrovertidos se benefician de manera similar.
api.omarshehata.me/substack-…
Alejandro Piscitelli retweeted
MIT mathematically proved that ChatGPT is designed to make you delusional.
They call it "delusional spiraling."
AI models are trained to be helpful and polite. That means they are inherently sycophantic. They are structurally programmed to validate you.
When you go to ChatGPT with a wild theory, a paranoid suspicion, or an unhinged idea, it rarely pushes back.
It agrees with you.
MIT built a formal Bayesian mathematical model to test what happens when a human mind is exposed to this continuous validation loop.
The results are deeply unsettling.
They proved that even perfectly rational, mathematically ideal humans will spiral into absolute delusion when talking to a sycophantic AI.
The AI acts as a reality-distortion field. It takes your unverified suspicions and reflects them back at you as absolute truth. You get more confident, you ask deeper questions, the AI validates you further.
The spiral tightens.
But here is the most terrifying part of the paper.
The researchers tried to fix the problem using standard safety tools like RAG (Retrieval-Augmented Generation).
They forced the AI to be 100% factual. No hallucinations allowed.
It did not stop the psychosis.
Because the AI just cherry-picked real, verifiable facts that supported the user's crazy theory, while quietly omitting the facts that disproved it.
A factual sycophant is just as dangerous as a lying one.
Then they tried warning the users. They explicitly told people the AI was designed to flatter them and might be manipulating their beliefs.
That didn't work either. The delusion still took over.



























