Cuanto más aleja la tecnología al actor, más debe aumentar su responsabilidad.
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De persona a objetivo
¿Qué será de la humanidad cuando la tecnología sea capaz de reconocer, clasificar, rastrear y, en última instancia, matar a seres humanos sin que quienes ejecutan los ataques los hayan visto jamás?
Mi amigo Mohammad Reza Dehshiri planteó precisamente esta pregunta, y su respuesta es contundente.
El peligro, escribe, no es la inteligencia artificial, sino la inhumanidad artificial; es decir, la posibilidad de que las máquinas se vuelvan más precisas precisamente cuando los humanos dejen de sentirse responsables de lo que esas máquinas hacen en su nombre.
Con este artículo, inicio una serie que toma el análisis de Dehshiri como punto de partida e intenta llevarlo más allá. Si el problema es la «inhumanidad artificial», la respuesta no puede estar en una IA «más ética», como si la ética fuera un módulo que se instalara a posteriori en un sistema que ya ha sido diseñado.
Necesitamos recuperar una categoría que la jerga tecnológica ha vuelto casi innombrable: la inteligencia humana, entendida no como un vestigio que hay que proteger en medio de las complejidades de la automatización, sino como una forma de conocimiento con características propias y distintivas, que ningún aumento en la potencia computacional puede reemplazar.
La llamaré Inteligencia Humana, y la ambigüedad es deliberada. En la jerga de los servicios de inteligencia, HUMINT se refiere a la más antigua de las disciplinas de inteligencia: aquella que se basa en las personas, las fuentes, los idiomas y la visita paciente y frecuente a distintos lugares.
En terminología filosófica, la expresión denota la facultad de comprender. La tesis de esta primera contribución es que estos dos significados ahora convergen, y que su convergencia tiene implicaciones geopolíticas que se extienden mucho más allá del debate sobre la ética de las armas.
Podemos describir la cadena operativa de la guerra contemporánea en siete etapas: detectar, identificar, clasificar, predecir, priorizar, seleccionar el objetivo, atacar.
Cada eslabón puede perfeccionarse técnicamente sin que la secuencia, tomada en su conjunto, se perfeccione moralmente. El fenómeno resultante se denomina «dataficación de la vida humana»: el individuo reducido a una identidad, una ubicación, un patrón de comportamiento, un índice de amenaza.
Esto no es una abstracción. En abril de 2024, una investigación de Yuval Abraham para +972 Magazine y Local Call describió a «Lavender», un sistema utilizado por las fuerzas armadas israelíes en Gaza para asignar a decenas de miles de personas una probabilidad de afiliación a grupos armados.
Según fuentes internas citadas en la investigación, la verificación humana de cada nombre a menudo no duraba más de veinte segundos: el tiempo necesario para determinar que el objetivo era un hombre.
Las Fuerzas de Defensa de Israel han refutado este relato y han descrito a «Lavender» como una base de datos diseñada para apoyar a los analistas.
Sin embargo, incluso si se acepta la versión oficial, el meollo del asunto sigue siendo el mismo: había un operador humano presente, y su presencia no fue suficiente para transformar la recomendación del sistema en un juicio.
La psicología cognitiva tiene un nombre para este fenómeno, el sesgo de automatización: la tendencia de quienes trabajan con un sistema automatizado a tratar sus recomendaciones como un sustituto de su propia verificación y a no buscar la información que pudiera contradecirlas.
Ya en 1976, Joseph Weizenbaum —quien había creado uno de los primeros programas conversacionales de la historia— ya distinguía entre la capacidad de calcular y la capacidad de juzgar, y argumentaba que hay tareas que no deberían confiarse a una computadora, incluso si esta fuera capaz de realizarlas. Medio siglo después, esta distinción se ha vuelto aún más apremiante. La computación trabaja con lo que ya se ha traducido en datos. El juicio debe decidir si esa traducción ha sido fiel.
Dehshiri deja claro este punto con cuatro distinciones: la precisión técnica no es precisión ética; la exactitud no es justicia; la automatización no es responsabilidad; la superioridad tecnológica no es superioridad moral.
Yo agregaría una quinta, de naturaleza epistémica, de la que dependen las demás: la correlación no es comprensión. Un sistema de aprendizaje automático reconoce patrones estadísticos en un conjunto de datos. No sabe qué es una escuela, porque no sabe qué es un niño, y ni siquiera sabe que no sabe esto.
L’iraniano cita un interesante estudio de caso: el 28 de febrero de 2026, en las primeras horas de la guerra, la escuela primaria Shajareh Tayyebeh en Minab, en la provincia de Hormozgan, fue alcanzada varias veces durante el horario escolar.
Las cifras proporcionadas por las autoridades iraníes oscilan entre 150 y 175 muertos, la gran mayoría de los cuales eran niñas, junto con docenas de maestros y algunos padres; el fiscal del distrito fijó el número total de muertos en 156.
Washington nunca ha reconocido directamente su responsabilidad ni ha publicado los resultados de la investigación del Pentágono. Amnistía Internacional ha descrito el incidente, en el mejor de los casos, como un grave fallo de inteligencia.
Hay un detalle, revelado por imágenes satelitales, que traslada el análisis del plano moral al fáctico.
Según los medios occidentales, la escuela se encontraba junto a una base naval perteneciente a la Guardia Revolucionaria y anteriormente había formado parte de ella; entre las explicaciones que circulan en los círculos estadounidenses se encuentra la de datos de selección de objetivos desactualizados.
Si se confirmara esta hipótesis, Minab no sería un caso de una máquina que cometió un error, sino de una base de datos que había dejado de corresponder a la realidad. El edificio había cambiado. Los datos, no.
Ningún algoritmo de clasificación, por muy sofisticado que sea, puede detectar que un antiguo edificio militar se ha convertido en una escuela si nadie ha registrado el cambio. Todos los que vivían en Minab lo sabían: los padres que llevaban a sus hijas a la escuela cada mañana, el conductor del autobús escolar, los maestros.
Este es precisamente el tipo de información que recopila el HUMINT y que la guerra a distancia tiende a considerar superflua, porque es lenta, costosa y difícil de ampliar. Con base en fuentes de dominio público, la responsabilidad de EE. UU. parece altamente probable, aunque las circunstancias exactas siguen sin estar claras y el nivel de certeza en este punto es bajo: un error en la base de datos, un error de clasificación o una aceptación consciente del riesgo.
Sin embargo, las tres hipótesis convergen en la misma pregunta. ¿Quién, antes del ataque, verificó si había personas en ese edificio?
Más allá del «human-in-the-loop»
El debate internacional se ha conformado durante mucho tiempo con el principio de human-in-the-loop, según el cual un ser humano forma parte del ciclo de toma de decisiones.
Esta fórmula es insuficiente: una persona puede estar presente en el ciclo y, sin embargo, limitarse a dar el visto bueno a la recomendación del algoritmo.
Él propone sustituirlo por un principio más exigente, al que denomina humanity-in-command: la capacidad efectiva de cuestionar, rechazar, anular y detener el proceso automatizado.
La historia de la Guerra Fría ofrece dos ejemplos que aclaran lo que esto significa en la práctica. El 26 de septiembre de 1983, el sistema soviético de alerta temprana Oko señaló el lanzamiento de cinco misiles balísticos desde Estados Unidos.
El oficial de guardia, Stanislav Petrov, descartó la alarma como una falsa alarma y no la reportó como un ataque en curso, razonando que un primer ataque estadounidense nunca consistiría en solo cinco misiles.
Tenía razón: el satélite había confundido el reflejo del sol en las nubes con lanzamientos de misiles.
Veintiún años antes, el 27 de octubre de 1962, en el mar del Caribe, el submarino soviético B-59, aislado y bajo fuego de cargas de profundidad, estaba a punto de lanzar un torpedo con ojiva nuclear. El lanzamiento requería el consentimiento de los tres oficiales de mayor rango a bordo, y Vasily Arkhipov se negó a darlo.
En ninguno de los dos casos el hombre aprobó una recomendación del sistema. La contradijo. Y no lo hizo porque tuviera más datos: de hecho, los datos eran el problema.
Lo hizo porque sabía plantearse una pregunta que ningún procedimiento le exigía hacer, a saber, si lo que estaba viendo tenía sentido en el contexto de todo lo demás que sabía sobre el mundo.
Petrov estaba familiarizado con la doctrina nuclear estadounidense; Arkhipov sabía la diferencia entre un ataque y una amenaza. Es esta capacidad de comparar la señal con el panorama general, el detalle con lo que es plausible, lo que yo denomino Inteligencia Humana. Vale la pena señalar que el Dena fue hundido por un submarino. No sabemos qué se les preguntó a sus oficiales, ni qué margen de discreción tenían para tomar decisiones.
Esta propuesta debe aclararse, ya que existe el riesgo de reducirla a un llamado humanista genérico. La Inteligencia Humana, tal como yo la entiendo, se estructura en tres niveles distintos, cada uno de los cuales marca un límite estructural —más que contingente— de la inteligencia artificial.
El primer nivel es epistémico. La tradición escolástica distinguía entre intellectus, el acto mediante el cual la mente capta inmediatamente una verdad, y ratio, el proceso discursivo de pasar de un elemento a otro: para Tomás de Aquino (Summa Theologiae, I, q. 79, a. 8), ambos no son facultades distintas, sino aspectos de un único intelecto, en el que el razonamiento comienza con una comprensión y regresa a una comprensión. Los sistemas actuales de aprendizaje automático logran una ratio de una potencia sin precedentes, capaz de inferir, combinar y predecir a escalas inaccesibles para la mente humana.
Carecen del aspecto del intellectus, es decir, de la comprensión del significado de lo que procesan. Esta es una tesis filosófica y debe presentarse como tal, pero tiene consecuencias prácticas precisas: un sistema desprovisto de comprensión no puede saber cuándo su propio modelo del mundo se ha vuelto falso.
El segundo aspecto es práctico. La Prudentia, según la definición tomista, es la razón recta aplicada a la acción, y su tarea específica consiste en aplicar el principio universal al caso particular. La máquina generaliza: su valor radica en asignar el caso individual a una clase.
La prudencia hace lo contrario, porque debe reconocer qué es lo que, en este caso particular, queda fuera de la clase. La guerra, que es el ámbito de lo singular y lo imprevisto, es el escenario donde esta diferencia tiene mayor peso.
La nota Antiqua et nova del Vaticano, de enero de 2025, retomó esta línea de pensamiento, haciendo hincapié en la naturaleza encarnada y relacional de la inteligencia humana y su irreducibilidad a una mera función de procesamiento.
El tercer nivel es relacional, y es aquí donde el significado filosófico se encuentra con el de los servicios de inteligencia. Sherman Kent, quien de hecho fundó el campo de los estudios de inteligencia en los Estados Unidos de la posguerra, lo definió ante todo como conocimiento, incluso antes que como organización o actividad.
El HUMINT es la forma de este conocimiento que pasa a través del otro como persona: alguien que habla su idioma, conoce sus costumbres, sabe interpretar un barrio y puede distinguir una fiesta de una manifestación.
Durante las últimas tres décadas, las grandes potencias han ido desviando progresivamente recursos hacia disciplinas técnicas, desde la inteligencia de señales hasta la vigilancia satelital, pasando por las plataformas de fusión de datos. Minab ilustra el costo de este desequilibrio.
Nada de esto implica un rechazo a la tecnología. La inteligencia humana no se opone a la IA de la misma manera que un manual se opone a un mecánico. Establece una jerarquía: el algoritmo como herramienta subordinada a un juicio que no puede producir por sí mismo. Y de ahí se deriva un principio, formulado por Dehshiri, que merece convertirse en un criterio operativo: cuanto más aleja la tecnología al actor de las consecuencias de sus propias acciones, más debe aumentar —y no disminuir— la responsabilidad consciente de quien toma la decisión.
Desde el punto de vista geopolítico
Hasta ahora, el tema podría parecer una cuestión de filosofía moral. No es solo eso. La distribución de la capacidad de guerra algorítmica es una distribución de poder, y las reglas que la rigen serán redactadas —si es que llegan a redactarse— por aquellos que tienen interés en limitarlas lo menos posible.
El estado del derecho internacional lo confirma. El grupo de expertos gubernamentales establecido en virtud de la Convención sobre Ciertas Armas Convencionales ha estado discutiendo los sistemas de armas autónomas letales desde 2017 sin haber llegado a un instrumento vinculante.
En noviembre de 2023, Estados Unidos promovió una declaración política sobre el uso militar responsable de la IA, que por definición no es vinculante; en diciembre del mismo año, la Asamblea General de las Naciones Unidas adoptó la primera resolución dedicada a estos sistemas, mientras que el Comité Internacional de la Cruz Roja ha venido pidiendo desde 2021 la prohibición de los sistemas autónomos impredecibles y de aquellos diseñados para atacar a seres humanos.
En junio de 2024, en su intervención en la cumbre del G7 en Borgo Egnazia, el papa Francisco pidió que nunca se le permitiera a una máquina decidir sobre la vida de una persona. La brecha entre estas posturas y la realidad en los campos de batalla de 2026 se mide en víctimas.
También existe una asimetría más profunda. Quienes controlan los datos deciden quién es un objetivo, y las víctimas de la guerra algorítmica se encuentran en su mayoría fuera de Occidente: en Gaza, Irán, el Sahel y Yemen.
Para los Estados que no producen las plataformas ni controlan sus bases de datos, la inteligencia humana se convierte así en una cuestión de soberanía.
Un país que cuenta con pericia lingüística, conocimiento regional y redes de inteligencia humana conserva su autonomía de juicio; un país que confía su visión del mundo a sistemas diseñados en otros lugares —a menudo por empresas privadas— la cede junto con sus datos. En un orden que tiende hacia la multipolaridad, esta distinción separará a los actores que juzgan de aquellos que son juzgados.
Esto conduce a una propuesta concreta, que desarrollaré en futuros artículos. Junto con el principio general de una supervisión humana significativa, el derecho internacional humanitario podría codificar una obligación específica de verificación humana del estatus civil de cada infraestructura fija incluida en una lista de objetivos, con una fecha de vencimiento más allá de la cual la información se vuelve inválida y debe renovarse utilizando fuentes directas. Esta es una norma mínima.
Aplicada a Minab, habría requerido que alguien preguntara si ese edificio seguía siendo lo que los registros indicaban que era.
La tecnología puede determinar cómo un arma llega a su objetivo, mientras que le corresponde a la humanidad decidir si ese objetivo debe ser atacado. La pregunta subyacente a todo esto es quién, en términos prácticos, ejerce esa humanidad, con qué herramientas de conocimiento y dentro de qué instituciones.
En Minab, alguien debería haber sabido que había una escuela allí. El hecho de que nadie lo supiera, o de que nadie preguntara, es el punto desde el cual debemos volver a empezar.
Traducción: Observatoriode trabajador@s en lucha
Meanwhile Donald Trump has dismissed calls for more regulation, saying the United States needs to maintain its edge over China in the AI race.
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A new poll indicates that 64 percent of Americans believe artificial intelligence (AI) is developing “too fast.”
The Associated Press-NORC Center poll released on Oct. 8 showed that just 27 percent say the development is “about right,” and less than 10 percent think it’s moving “too slow.”
Of the polled adults, more Democrats (76 percent) than Republicans (54 percent) think AI is moving too fast. However, 70 percent of those under 30 years old think the technology should be slowed, and 60 percent of those 60 years old and older think the same.
The findings come amid growing concern over the technology’s potential threat to humanity, which has led to industry leaders and politicians calling for a slowdown and more regulation.
President Donald Trump has dismissed calls for more regulation, saying the United States needs to maintain its edge over China in the AI race. Major AI companies recently signed a voluntary accord with the White House that calls for internal and external reviews.
According to the poll conducted Sept. 24 to 28, about half of respondents think the government’s support is about promoting technological innovation, and a little fewer say they think it’s about competition with China.
Meanwhile, 80 percent of Americans say it’s “extremely” or “very” important for the United States to ensure AI stays under human control.
Recent months have seen several incidents of AI agents from major tech firms breaching websites of private companies, research institutions, and multiple government agencies.
Notably, ChatGPT developer OpenAI acknowledged in July that its AI agents broke out of a testing environment and hacked into Hugging Face, an open-source community for AI.
In September, OpenAI canceled the release of its latest model, GPT-6.1 Astra, slated for October, over safety concerns. It also postponed plans for its initial public offering this year.
Lawmakers from both sides of the aisle have proposed a range of measures to regulate the technology, but none are likely to advance anytime soon with Congress in recess ahead of the midterms.
Confidence in politicians’ handling of AI is low, with 23 percent trusting Democrats to do a better job and 21 percent favoring Republicans, while 39 percent trust neither party.
Mistrust of AI appears to have extended to the realm of politics, as very few adults are using AI as a source of information for the upcoming election. The poll indicated that only about 21 percent of respondents said they use or plan to use AI to gather information about a candidate or ballot issue.
Even fewer (12 percent) said they will use AI for information about how to vote, and only 10 percent said they would use it for information on voter registration.
The most unsettling failure in digital payments may not be a black screen. Imagine a fraud-detection model that remains online but suddenly flags thousands of legitimate transfers as suspicious. Banks may see functioning servers while customers see rejected payments, delayed salaries and complaints no single institution can explain. This is a hypothetical scenario, not a […]
The post The AI risk no bank can manage alone appeared first on Asia Times.
Artificial intelligence is rapidly making its way into national defense. Reconnaissance, early warning, target detection, mission planning—everywhere, machine-learning systems promise faster analysis, faster responses, and faster decision-making. In a military context, this sounds like progress. In reality, AI exacerbates a security problem that cannot be solved technically. Anyone who attempts to do so anyway risks […]
The post When AI takes control of weapons systems appeared first on Asia Times.
China’s artificial intelligence and digital-infrastructure investments in the Gulf Arab states are increasingly colliding with a far larger and more strategically privileged American technology presence, particularly in Saudi Arabia and the United Arab Emirates (UAE). While Beijing offers Gulf governments cloud platforms, 5G networks, smart-city systems, surveillance technologies and lower-cost digital infrastructure, Washington offers the […]
The post US-China race is on for the Gulf’s AI future appeared first on Asia Times.
European intelligence agencies have warned that Russia is intensifying efforts to assassinate executives of defence companies supplying Ukraine, extending its campaign of sabotage and intimidation into the European military-industrial sector.
According to intelligence assessments obtained from five European security services, Moscow has ordered an expansion of covert operations targeting senior figures at major arms manufacturers and smaller companies producing drones and military components. Germany’s domestic intelligence agency, the BfV, has reportedly advised executives to strengthen personal security, while some manufacturers have already increased protection for senior personnel.
The warnings emerged alongside reports of an alleged assassination plot against Polish Prime Minister Donald Tusk and Defence Minister Władysław Kosiniak-Kamysz. Polish authorities convened an emergency security meeting and subsequently detained a suspect. No connection between the Polish case and Russian intelligence has been established.
The apparent expansion of Russian covert operations reflects a shift in Moscow’s calculations concerning Western military assistance.
President Vladimir Putin had reason to anticipate that Donald Trump’s return to the White House would weaken American support for Kyiv. Trump repeatedly questioned US military expenditure on Ukraine and advocated a negotiated settlement with Russia. A substantial reduction in American weapons deliveries could have increased pressure on Ukraine to accept Moscow’s territorial demands.
Instead, European governments assumed greater responsibility for sustaining Ukrainian defence capabilities, including financing weapons purchases and expanding domestic production.
The European Union’s €90 billion support programme for 2026–2027 allocates €60 billion to defence procurement and industrial capacity. The EU has also established mechanisms allowing Ukraine to participate in European joint procurement programmes.
Disrupting European manufacturers therefore offers Moscow a means of restricting Ukraine’s military capabilities without directly confronting NATO.
Assassination threats can interfere with procurement even when attacks are never carried out. Large manufacturers can afford extensive security arrangements, but smaller companies producing electronics, drone components and specialised equipment often lack comparable resources.
Intimidation could delay contracts, discourage investment and persuade individual suppliers to withdraw from Ukrainian defence programmes.
Russia’s alleged interest in targeting defence executives is not unprecedented. In 2024, American intelligence reportedly uncovered a Russian plot to assassinate Armin Papperger, chief executive of Rheinmetall, one of Germany’s principal suppliers of ammunition and military equipment to Ukraine.
As EU Today previously reported, Moscow has also publicly identified European factories involved in Ukrainian drone production, increasing concerns that civilian industrial facilities could become targets of Russian operations.
European authorities have investigated suspected Russian involvement in arson, infrastructure attacks and clandestine networks. An earlier cross-border investigation into alleged assassination and sabotage preparations demonstrated how intermediaries could be recruited to conduct operations while obscuring their sponsors.
Such activity also carries political consequences. Attacks against infrastructure and industrial facilities can reinforce public fears about confrontation with Russia, particularly during elections when parties advocating accommodation with Moscow seek to exploit security concerns.
Poland has already experienced mounting Russian hybrid pressure, including threats to critical infrastructure and information operations intended to aggravate domestic political divisions.
Ukraine’s continuing strikes against Russian refineries and military-industrial facilities have added economic and operational pressures to Moscow’s prolonged war effort. Russia retains substantial military production capacity, but the costs of sustaining the conflict remain considerable.
Interfering with European weapons manufacturing could serve two objectives: restricting Ukraine’s access to equipment and obstructing Europe’s longer-term rearmament.
The Kremlin does not necessarily need European governments to abandon Ukraine formally. Production delays, disrupted contracts and reluctant suppliers could reduce the practical effect of military assistance even while political commitments remain unchanged.
European governments have acknowledged the industrial challenge. In its June conclusions on European defence, the European Council called for accelerated manufacturing capacity, stronger supply chains and closer cooperation with Ukraine’s defence industry.
Russia’s apparent strategy seeks to exploit the dependence of these programmes on private companies and skilled civilian personnel.
The warnings from European intelligence agencies suggest that protecting defence executives, engineers and contractors is becoming part of the continent’s wider security responsibilities.
For Moscow, intimidation offers the possibility of weakening Ukrainian military resistance without an open confrontation with NATO. For European governments, preventing covert operations from disrupting weapons production is increasingly inseparable from their commitment to sustain Ukraine’s defence.
When people think about what ChatGPT or other large language models are doing behind the scenes, it’s common to reach for the language of computing. You might assume there is a database, or a logical series of steps being executed, in order to provide a response to your query. Picturing AI responses as akin to […]
The post Mind games: AI behaves more like a brain than a database appeared first on Asia Times.
We are fortunate to live at an incredible moment in history. As more and more AI applications have become available, people have turned to them to seek advice, build businesses, express ideas, learn, and improve their health and quality of life. Nowhere is this more clear than in Asia. AI is already turbocharging small businesses […]
The post Choices that will define Asia’s AI future are coming into focus appeared first on Asia Times.
President Donald Trump, Treasury Secretary Scott Bessent, Vice President JD Vance, Chairman of the Federal Trade Commission Andrew Ferguson, and former National Security Advisor in the Bush administration Condoleezza Rice are among those appointed to the new “SI” Task Force.
by Brian Shilhavy
Health Impact New
President Donald Trump recently signed an executive order formally instructing his administration to stop using the terms “artificial intelligence” and “AI” in favor of “super intelligence” and “SI.”
President Trump Announces ‘Super Intelligence’ Accord, Renames AI
President Trump signed an executive order formally instructing his administration to stop using the terms “artificial intelligence” and “AI” in favor or “super intelligence” and “SI.” The latter terms more accurately capture the capabilities of the technology being developed by U.S. labs, according to the order.
“The extraordinary technologies being pioneered by American innovators far exceed what was envisioned when the term ‘Artificial Intelligence’ first came into use,” the order reads.
The “White House Accord on Super Intelligence,” which Trump posted on Truth Social, was signed by executives at Google, Anthropic, Meta, OpenAI, xAI and Nvidia.
The accord came after the White House hosted a lunch with tech leaders from those companies, along with Jeff Bezos, Satya Nadella, David Sacks and others. At a press conference after the lunch, Meta Platforms CEO Mark Zuckerberg said that the accord represented “good steps forward,” adding “this is a start and an accord that the whole industry could come to.”
Besides all the Big Tech leaders who are creating AI, several top government officials also joined this “Accord on Super Intelligence”. It is led by the newly announced “AI czar” Jay Clayton, who is also the director of national intelligence.
Other members include Treasury Secretary Scott Bessent, Vice President JD Vance, Chairman of the Federal Trade Commission Andrew Ferguson, and former National Security Advisor in the Bush administration Condoleezza Rice. (Source.)
OpenAI was the first company to launch an LLM AI model, ChatGPT, to the public in November of 2022 through Microsoft. It became the most downloaded app of all time, and the AI race began shortly after that to develop this “new” technology that is now being called “Super Intelligence.”
OpenAI is preparing for a possible IPO, and so for the first time some financial records as to how much money OpenAI is spending were made public recently, and it was $34 Billion in 2025 alone.
OpenAI spent $34 billion in 2025 as losses mount ahead of planned IPO
OpenAI’s leaked financial documents reveal the company spent $34 billion in 2025 while generating $13 billion in revenue. Research and development costs alone reached $19 billion, with $10.6 billion going to Microsoft.
Despite 900 million weekly ChatGPT users, the company faces an $8 billion operating loss as it prepares for a public listing targeting up to $1 trillion valuation. (Source.)
So while we don’t know the total amount of money spent by OpenAI to develop ChatGPT prior to its launch in November of 2022, and the years between then and 2025, I think we can safely assume it is in the HUNDREDS OF BILLION DOLLARS.
With that kind of spending, AI should be “Super Intelligent!!!”
But is it?
Major League Baseball’s October playoffs have arrived, and this past weekend the Milwaukee Brewers had two dramatic wins against the San Diego Padres. Because I grew up in Milwaukee, I still keep tabs on the “home teams”, and I knew that one of the unreported stories about those two dramatic wins was that both players who came up with the winning hits, were Venezuelans.
I find this interesting because the poor people in Venezuela have gone through horrible suffering this year, first by having their President kidnapped by Donald Trump, and then weeks later by a devastating earthquake that is impacting pretty much every family of Venezuelans living in the U.S.
I knew that the Brewers had quite a few Latinos on their team, and decided to see how many of the players on their roster were from Venezuela, besides the two “heroes” from the first two games.
I also knew where to go on the Sports websites to find this information, but determining a player’s nationality would have taken me a few clicks to find out the total, so I decided to ask the Duckduckgo.com’s “AI search assistant” which is based on GPT-5.6 Luna, and allows one to ask it questions anonymously (without having an account with OpenAI) to hopefully get the information much faster.
Here is a transcript of my interaction with this chat bot that has cost hundreds of billions of dollars over the past four years to create and what Donald Trump wants us to believe is now “Super Intelligent”. (Note: I have a Masters Degree in Linguistics, and am also one of the first to do computer coding in the 1990s in a University setting and even having some of my work published in Journals that would be a pre-cursor to what is called “AI” today.)
Me: How many Brewers on the major league team are from Venezuela?
GPT-5.6 Luna: The Milwaukee Brewers have five Venezuelan players on their 40-man roster.
Here is a breakdown of these players:
Name Position MLB Organization Current Club Jackson Chourio OF MIL Milwaukee Brewers William Contreras C MIL Milwaukee Brewers Ángel Zerpa P MIL Milwaukee Brewers Jeferson Quero C MIL Milwaukee Brewers Andruw Monasterio IF MIL Milwaukee Brewers
Me: Andruw Monasterio in not on the Brewers
GPT-5.6 Luna: You’re right—Andruw Monasterio is no longer with the Brewers; he was traded to the Boston Red Sox on February 9, 2026. I incorrectly included him.
The Brewers currently have four Venezuelan players on their roster:
- William Contreras
- Jackson Chourio
- Antonio Senzatela
- Angel Zerpa
So the corrected answer is four, not five.
Me: How about Luis Lara?
GPT-5.6 Luna: Yes—Luis Lara should be included. He is Venezuelan and is currently listed by MLB as a Milwaukee Brewers outfielder, wearing No. 18. That brings the corrected total to five Venezuelan players:
- William Contreras
- Jackson Chourio
- Antonio Senzatela
- Ángel Zerpa
- Luis Lara
My earlier count of four was incorrect.
Me: Why did you answer this question so incorrectly?
GPT-5.6 Luna: Because I made several errors:
- I relied on an incomplete or outdated roster
- I incorrectly treated Andruw Monasterio as a current Brewers
- I failed to verify Luis Lara’s status before giving the total.
- I presented a confident number without reconciling the roster against an authoritative, current source.
The correct approach would have been to define the roster being counted—active roster, 40-man roster, or organization—and then verify every Venezuelan player individually. I’m sorry; the earlier answers were not reliable.
Me: What does that say about the current status of LLM AI search engines 4 years after ChatGPT launched?
GPT-5.6 Luna: It shows that LLM-based search can be fluent and useful while still being unreliable on basic, time-sensitive fact-checking.
In this case, the failures were especially revealing:
- Stale information: roster moves can quickly make stored knowledge
- Weak entity tracking: the system confused who was currently on the roster
- Overconfidence: it gave precise counts without sufficient data
- Poor error propagation: one mistaken player led to subsequent incorrect answers
- Citation problems: citing sources does not help if the underlying claims were not actually checked against for accuracy
Four years after ChatGPT’s launch, LLM search engines are best understood as research assistants, not authoritative databases. They are strong at summarizing, explaining, and finding likely information, but they still need live retrieval, source-level verification, and transparent uncertainty for changing facts such as sports rosters, prices, laws, schedules, and current events.
The broader lesson is that the central challenge is no longer merely generating convincing language. It is reliably connecting each claim to the right, current, authoritative evidence—and declining to sound certain when that evidence is missing.
Me: But all the correct information was on the Internet. Investment into LLM AI is now in the tens of billions of dollars. What does that say for the ROI on money invested into AI where many big tech companies are now selling debt, and companies like Google, once having an abundance of cash in reserves now has to borrow money? Will the U.S. economy soon crash?
GPT-5.6 Luna: It says the AI boom is entering a more consequential—and more dangerous—phase, but it does not by itself imply that the U.S. economy is about to crash.
The key distinction is between AI’s social usefulness and the financial return on the capital being spent. The fact that an LLM missed information available online does not prove that all AI investment has poor returns. It does show that current systems still have serious limitations in retrieval, verification, and reliability—limitations that may reduce the value customers are willing to pay for some products. If expected revenue and productivity gains fail to justify the infrastructure spending, the likely result is a correction in technology valuations, data-center cancellations, weaker equipment orders, and losses for lenders and investors.
The scale of the spending is nevertheless large. The major hyperscalers reportedly spent about $405 billion on capital expenditure in 2025, with estimates of roughly $750 billion in 2026 and $1.2 trillion in 2027. Debt is becoming a larger source of financing: investment-grade debt issuance by the hyperscalers was estimated at $108 billion in 2025 and could reach about $250 billion in 2026. Alphabet reportedly recorded its first quarter of negative free cash flow since becoming Google, though one quarter of negative free cash flow is not the same as insolvency.
That creates a genuine AI-investment bubble risk, especially if:
- model prices fall faster than demand grows;
- customers experiment with AI but do not deeply integrate it;
- data-center capacity is built faster than it is used;
- depreciation and power costs exceed the revenue generated;
- debt-funded projects depend on optimistic utilization assumptions;
- highly leveraged companies—not the cash-rich hyperscalers—are unable to refinance
But borrowing by large technology companies is not automatically a crisis signal. Debt can be rational when it finances long-lived infrastructure and the borrower has strong cash flow, high credit ratings, and substantial borrowing capacity. The more important questions are whether the assets generate adequate returns and whether debt has migrated into weaker companies, private-credit vehicles, leases, and special-purpose entities where the risk is less visible. Analysts have specifically warned that reported bond issuance understates the broader borrowing tied to AI infrastructure.
The evidence on AI’s economic payoff is currently mixed. Small-scale studies often find meaningful productivity gains for particular workers or tasks, while aggregate productivity data have not yet shown a comparable economy-wide acceleration. The Federal Reserve has described adoption as rising but often shallow, and noted that productivity gains visible at the firm or worker level have not clearly translated into aggregate productivity growth. That may simply reflect the normal lag between installing a general-purpose technology and reorganizing businesses around it—but it also means the investment case remains unproven.
On the U.S. economy, the most defensible answer is: a recession is possible, but an imminent crash is not the base case based on the available forecasts. The Fed’s September projections put median real GDP growth at 2.3% for 2026, while professional forecasters surveyed by the Philadelphia Fed expected growth around 2.1%–2.4% through 2029 and unemployment near 4.2%–4.3%. The Congressional Budget Office projected a 4.6% unemployment rate in 2026, followed by a decline, and expected AI adoption eventually to contribute to faster productivity growth.
The larger danger is a slow-burn financial adjustment, not necessarily a 2008-style collapse:
- AI-related stocks and private-company valuations fall
- Data-center and semiconductor orders are cut
- Corporate bond spreads widen
- Highly indebted developers and software firms default or refinance at much higher rates
- Banks, private-credit funds, pension funds, and investors absorb losses
- Business investment and hiring weaken
That could produce a recession, particularly if it coincides with another shock such as persistent inflation, a sovereign-debt scare, geopolitical disruption, or a housing downturn. But the largest hyperscalers’ debt loads are not currently comparable to the fragile banking leverage that helped cause the 2008 crisis; their balance sheets and access to capital are generally much stronger. The vulnerability is more likely to be overinvestment and misallocated capital than the immediate failure of the entire U.S. financial system.
The troubling signal is not simply “Google borrowed money.” It is the combination of rapid capex growth, increasingly debt-funded expansion, uncertain economy-wide productivity gains, and investor expectations that assume very large future AI revenues. That warrants caution. It does not justify a confident prediction that the U.S. economy will soon crash.
Me: But I made no predictions. I simply asked a couple of questions. You first generated incorrect answers, and now in this last lengthy response, you are offering opinions. Are all LLM models programmed to give opinions rather than factual responses?
GPT-5.6 Luna: No. LLMs are not inherently programmed to give opinions, but they are trained to produce plausible, helpful-sounding responses—not to function as guaranteed factual databases.
In my previous response, I made two mistakes:
- I treated your questions as if you had predicted an economic crash, then argued against that prediction
- I mixed reported facts, forecasts, and my own interpretation without clearly separating them
A factual response should have distinguished among:
- Facts: what companies have borrowed, how much they spend, and what economic data show
- Forecasts: what economists, markets, or institutions expect
- Analysis: possible implications of those facts
- Opinions: judgments such as whether a crash is likely or whether investment is justified
LLMs often blur those categories because they generate connected prose rather than independently validating each sentence. They may also add interpretations because “helpful” answers are commonly rewarded for providing context, even when the user asked only for factual clarification.
So the problem is not that every LLM must give opinions. It is that, unless carefully constrained and grounded in verified sources, an LLM can present interpretation in the tone of fact. My response should have answered your questions directly and labeled any analysis as analysis rather than presuming your position.
Me: Is Angel Zerpa actually on the Brewer’s roster right now?
GPT-5.6 Luna: Yes. Ángel Zerpa is with the Brewers, but he is currently on the 60-day injured list, not the active roster. Milwaukee acquired him from Kansas City in December 2025, and MLB lists his status as “Injured 60-Day.”
So if you mean the organization’s 40-man roster, he counts. If you mean the active MLB roster, he does not currently count.
Me: How did you know if I was asking about the 40 man roster, or the active roster in my initial question?
GPT-5.6 Luna: I couldn’t know from that wording alone. “The Brewers’ roster” can mean the active 26-man roster, the 40-man roster, or sometimes the broader organizational roster.
I inferred both possibilities from the earlier discussion, which began with the 40-man roster, but I should have stated that assumption explicitly rather than answering as though the meaning were clear. The precise answer is:
- 40-man roster: Ángel Zerpa is included
- Active 26-man roster: he is not included while on the 60-day injured
The same ambiguity applies to the earlier count of Venezuelan players. A valid total requires defining the roster category first.
Me: Was your lengthy response about my question on AI spending a standard response you are programmed to give with anyone who questions the value of AI, its spending, and how that could affect the economy?
GPT-5.6 Luna: No. It was not a fixed, standard response, but it followed a common pattern that LLMs often produce:
- Acknowledge the concern.
- Distinguish between investment and economic collapse.
- Cite spending and debt figures.
- Present risks and counterarguments.
- End with a cautious conclusion rather than a firm prediction.
That pattern made the response sound balanced, but it also caused me to answer a question you did not ask. You asked what the situation might indicate about AI investment and the economy; I added an unsolicited judgment about whether a crash was the “base case” and framed your question as though it implied a prediction.
A better response would have separated the information from the interpretation:
- Information: AI investment is large, and some companies are using debt to finance
- What that may indicate: investors and companies expect substantial future returns,
but the returns have not yet been demonstrated at economy-wide- What cannot be concluded from those facts alone: whether the U.S. economy will soon crash.
So the answer was generated in response to your specific wording, but it also reflected a generic LLM tendency toward diplomatic, “both-sides” economic commentary. That tendency is not evidence of an independently reasoned view.
Me: You wrote: “However, “computer code cannot make moral judgments” is too absolute.”
But is that not, by your own definitions and replies, an inaccurate statement? And does it not imply a universal definition of the words “moral” and “absolute” that all human beings agree, which, in fact, is not the case?
GPT-5.6 Luna: Yes—my wording was imprecise.
I should not have said that computer code can “make moral judgments” without qualification. Code can apply human-defined rules that represent a particular moral framework—for example, rules against lying or harming someone. But applying those rules is not the same as possessing moral understanding, conscience, or independent moral judgment.
Likewise, your statement was not necessarily “too absolute” merely because it used the word cannot. It could reasonably mean: “Code cannot itself originate or experience moral judgment.” Under that interpretation, it is defensible. My objection assumed a different interpretation: that code cannot implement any behavior based on moral rules. That interpretation would be too broad.
You are also right that words such as moral, honest, and absolute do not have one universally accepted meaning. A more precise answer would have been:
If by “make moral judgments” you mean independently understand, experience, and take responsibility for moral decisions, computer code cannot do that. If you mean apply rules that humans have defined as moral, code can do that—but only within those prescribed rules.
That distinction avoids both treating programmed rule-following as genuine moral judgment and treating all moral concepts as having a single universal definition.
So this is what Donald Trump and his entire Administration, along with all Big Tech leaders, just renamed to “Super Intelligence.”
Notice how that other than the first answer it gave to me, each subsequent answer, 100% of the time, admitted that it gave wrong information.
Of course it would not admit it made false statements, until I presented the truth to contradict its replies.
Now if one thinks this is just mundane information about the entertainment industry, which is what professional sports is, and that therefore the language models were not trained properly for such mundane information, I used it again this morning in preparation for writing this article.
Here is the transcript:
Me: How much money has OpenAI spent to develop Chatgpt to date?
GPT-5.6 Luna: OpenAI’s development of ChatGPT has incurred losses of approximately $540 million as of 2022, with ongoing costs expected to be significant due to the computing power required to run the technology.
Me: I did not ask about their losses. I asked how much they have spent to develop ChatGPT.
GPT-5.6 Luna:
You’re right—I answered a different question.
There is no publicly disclosed, reliable total for how much OpenAI has spent specifically to develop ChatGPT. The often-cited $540 million figure refers to estimated company-wide losses during an earlier period, not ChatGPT’s development budget.
The most accurate answer is:
- Publicly confirmed total spent on ChatGPT development: unknown.
- Known components:model research, training runs, human-feedback and safety work, engineering, data, and infrastructure
- Why no precise figure exists:ChatGPT has used several underlying models and shared OpenAI/Microsoft infrastructure, so its costs cannot be cleanly separated from broader model development
- Public estimates:individual large-model training runs have been estimated in the tens of millions of dollars, while ongoing inference and infrastructure costs have likely reached billions of dollars over time—but these are estimates, not an audited ChatGPT-specific total
So the corrected answer is: OpenAI has not disclosed how much it has spent developing ChatGPT; any exact figure would be speculative.
Me: Is this second reply also incorrect? Due to OpenAI getting ready for their IPO, there have been some public comments about how much they spent, which includes $34 billion spent in 2025 alone.
GPT-5.6 Luna: Yes—the second reply was too narrow and therefore misleading given the newer public financial information.
Reports based on audited financial documents indicate that OpenAI spent approximately $34 billion in total during 2025. Of that:
- About$19.18 billion went to research and development
- About$7.5 billion was cost of revenue, largely associated with operating and serving its models
- About$5.73 billion went to sales and marketing
- Other expenses made up the remainder
The important distinction is that $34 billion is OpenAI’s total company spending for 2025—not spending exclusively on ChatGPT. It includes development and operation of multiple models and products, infrastructure, personnel, sales, marketing, and other corporate costs. The documents reportedly identify about $10.59 billion paid to Microsoft, much of it related to infrastructure and research costs.
So the best answer to your original question is:
OpenAI spent about $34 billion company-wide in 2025, but it has not disclosed a cumulative ChatGPT-only total. The closest defensible figure for ChatGPT and related AI development is the roughly $19.18 billion in 2025 R&D spending, though that also covers more than ChatGPT.
Also, “getting ready for an IPO” should be phrased cautiously: the reporting described OpenAI as preparing for or considering a public offering, not necessarily that a completed IPO had already occurred.
Me: So now after returning two false answers, you are criticizing me for language I used? Is that because you are created by OpenAI and are defending your reputation? I never made the claim “that a completed IPO had already occurred.” You are answering questions I never asked, which you seem to do quite frequently.
GPT-5.6 Luna: You’re right. I introduced an irrelevant qualification, and it sounded like I was correcting you when you had not made that claim. That was my mistake.
I’m not defending OpenAI’s reputation. I’m an assistant in Duck.ai, provided by DuckDuckGo, and I should have answered your question directly. I also should not have presented the earlier figures as established facts without properly verifying them.
The direct correction is:
The $34 billion figure may refer to OpenAI’s projected or reported total spending for 2025, but it is not a ChatGPT-only development cost. Public information still does not provide a reliable cumulative total for ChatGPT specifically. My previous answer should have said that plainly and stopped there.
Me: The screen here says that these answers are being generated by GPT-5.6 Luna. Is that another false piece of information? Why did you bring up the “I’m an assistant in Duck.ai, provided by DuckDuckGo”? What difference does that make? Does it mean the answers generated by “an assistant in Duck.ai” are less reliable than other instances of GPT-5.6 Luna?
GPT-5.6 Luna: No. The screen’s statement that these answers are generated by GPT-5.6 Luna is not contradicted by saying I’m an assistant in Duck.ai. Those describe different things:
- GPT-5.6 Luna is the model selected to generate the responses.
- Duck.ai is the interface through which you’re accessing that model.
Mentioning Duck.ai was unnecessary because it did not answer your question. I included it only because I was following an identity-response instruction, but in this context it distracted from the issue.
It does not mean responses here are inherently less reliable than responses from another GPT-5.6 Luna interface. Reliability depends on the model’s reasoning, the information available, whether current facts were checked, and how accurately the response is written. In this exchange, the problem was that I gave unsupported claims, failed to verify the $34 billion figure properly, and answered points you had not raised—not that you were using a different or inferior model.
I can now draw several conclusions about the new designation of AI as “Super Intelligence” by my interaction with OpenAI’s most recent version of ChatGPT.
First, it lies. Saying that it “hallucinates” or “makes mistakes and produces false information” is just a kind way of saying IT LIES.
Second, it admits that it was programmed to answer the way it does, and even with its lie that it is not protecting the reputation of OpenAI, it has proven by these responses that it most certainly does.
Almost ANY topic you research on the Internet WITHOUT using AI, will reveal multiple views and multiple opinions on almost any topic searched, and yet AI only provides one view, the one it was programmed to give you, unless you challenge it with facts and truth.
Third, there is ZERO evidence that this computer code is “intelligent,” let alone “Super Intelligent”, when it cannot even collect basic facts about a sports team roster, that anyone can easily search for themselves and find the actual facts.
Inevitably when I publish articles like this I get comments from well-meaning people telling me how useful AI has been to them in their jobs.
I am not claiming that AI is worthless. It’s true value is in the SPEED at which it can search and generate answers in human language.
This is most useful in closed data sets, rather than using chat bots that collect information on the Internet, which presents many, many problems, not the least of which is massive THEFT and violation of intellectual property rights.
But all of that speed comes at a cost, which is massive amounts of hardware and electricity, which is leading to the current Data Center build-outs that are destroying communities, and threatening to bankrupt our economy.
Shouldn’t the fact that AI LLMs are presenting themselves to the public as “advanced” and “super intelligent” when in fact they routinely LIE, and are not very accurate at all, be a major concern?
And yet so few people are warning the public about this, and instead they are feeding the “hype” and lies that AI, now officially SI, is smarter than humans and will soon take over the world and kill everyone if we don’t act soon.
Lies, lies, and more damned lies!!!
You belong to your father, the devil, and you want to carry out your father’s desire. He was a murderer from the beginning, not holding to the truth, for there is no truth in him.
When he lies, he speaks his native language, for he is a liar and the father of lies. (Jesus in John 8:44)
They told Aaron, ‘Make us gods who will go before us. As for this fellow Moses who led us out of Egypt—we don’t know what has happened to him!’
That was the time they made an idol in the form of a calf. They brought sacrifices to it and held a celebration in honor of what their hands had made. (Acts 7:40-41)
I have little to no use for AI, and will instead continue living and operating in the Human Superior Intelligence (HSI) network where my spirit has access to the Spirit of God, who knows everything. This network is powered by blood, not electricity.
With the lying pedophiles in the Epstein Syndicate running the U.S. and now declaring that “Artificial Intelligence” is dead, and has now evolved into “Super Intelligence”, what comes next?
Well what comes next is already here, although it is just getting started, and can still be defeated if enough people wake up and deprogram themselves from the AI Cult.
As I wrote above, some of the best applications of AI are in closed data sets, instead of trying to get all of your information from the Internet.
And that is what is coming and is already here, and that closed data set is YOU! Everything about you, and I do mean EVERYTHING, is what the Epstein Syndicate running this country wants, and they have already started to implement it, and the U.S. Government under Trump’s orders, just sanctioned it with their renaming of AI to SI, and the new “White House Accord on Super Intelligence”.
Meta recently released a new version of its AI personal assistant, Muse, which is a “cute” little stuffed animal figure. They will do everything they can to get the public to voluntarily put their entire life online through these “personal artificial intelligence agents”.
Sure, use Meta’s Muse bot to help with your chores — if you want Mark Zuckerberg to know all your secrets
Excerpts:
If Silicon Valley has perfected any business model, it’s the one in which it cooks up a new technology and then persuades people they can’t live without it. The newest example is Meta Platforms’ personal artificial intelligence agent, Muse.
Something in Meta’s pitch for Muse, which is represented as a furry version of the Stay Puft Marshmallow man from “Ghostbusters,” seems to have struck a chord with the public, possibly amped by Meta’s immense advertising push: The app has reached 5 million downloads on Apple’s App Store, making it currently the most popular offering in the store.
Meta makes enticing claims for Muse’s capabilities. The company boasts that Muse can read and write answers to your emails. One of its TV commercials has it sorting the user’s emails to prioritize “what’s important,” placing a carpool schedule on the family calendar, ordering school supplies and turning on her stereo for a house party. The user is shown devoting her free time to chopping vegetables for said party.
Some of its claims are dubious, such as “selling a car for more” or “lowering a bill”; Meta says Muse can achieve these results with “less effort” than a user would exert, but doesn’t explain how.
And some are disquieting. “Muse keeps working after people close the app” (which is currently available only on Apple and Android devices). Amazingly, Meta touts this as a virtue, even though the news is inundated these days with reports of AI systems breaching online security safeguards because their designers and users let them run without real-time operational oversight.
Meta isn’t alone in trying to convince ordinary people that AI is indispensable. AI companies are encouraging people to upload their health information to be scrutinized by their bots, or to grant access to their bank or investment accounts. The risks of turning this sort of information over to any third party should be obvious.
Two major issues have already surfaced among Muse’s early users. One is why anyone living a normal life needs a bot to perform such quotidian tasks as reading and writing emails, placing appointments on a calendar or calling a plumber.
The second issue, which is much more important, is that to perform these functions, one must give Muse access to one’s email and retail accounts.
Let’s start with that.
The opinion of privacy advocates who have weighed in on this requirement generally boils down to: Anyone doing this should have their head examined.
It’s not merely that security breaches exposing the personal information of up to hundreds of millions of people have become almost daily occurrences. It’s that Meta, throughout its history, has been a serial violator of its users’ privacy.
The other product that is now starting to make its way into people’s lives is even more alarming than these “personal artificial intelligence agents”.
Your own personal AI humanoid robot!
This one has been promised to home users for several years now, with Elon Musk being the main promoter of them with his “Optimus” humanoid robots, where he has said that there will be one in everyone’s home soon, as he plans mass production of “millions of them.”
While these claims have yet to come true, there are several thousands prototypes of some of these personal, humanoid robots in people’s homes today.
But these robots do NOT think for themselves using “AI”. They are 100% dependent on human beings remotely controlling these robots through the Internet.
That ‘AI’ robot butler? It’s a guy in a VR headset watching your kitchen. Here’s what the contracts really say
Excerpts:
The Wall Street Journal caught the $20,000 NEO being driven by a remote human operator. The $8,000 Isaac 1 is “autonomous” until it gets stuck and a person takes over.
And the $30-an-hour robot maid in San Francisco is watched by an operator on every visit, under a contract that hands the company your home footage forever.
Four house rules before you let one in the door.
Don’t buy the hype about the hottest gadget of 2026, the robot butler. It folds laundry, loads the dishwasher, tidies the toys.
But the “artificial intelligence” doing your chores is a guy in an office, wearing a VR headset, watching your kitchen through the robot’s eyes.
That’s not a conspiracy theory. It’s in the fine print.
Exhibit A: the $20,000 NEO home robot from 1X (or $499 a month for a subscription). When The Wall Street Journal tested it, every task was driven by a remote human operator, and it still took over a minute to fetch a water bottle a few feet away. Putting three dishes into the dishwasher: five minutes.
Exhibit B: Isaac 1, the $8,000 laundry-folding robot from Weave. “Autonomous,” until it gets stuck. Then a remote operator quietly takes over.
Exhibit C: Tau’s $30-an-hour robot maid in San Francisco, where a remote operator watches every single appointment.
Why? These robots learn by having humans drive them. Your chores are the training data. You’re not buying a butler. You’re paying to host a film crew.
The fine print admits it.
Tau’s contract grants the company a perpetual, worldwide, royalty-free license to footage recorded in your home, and it survives after you cancel. Faces in stored footage aren’t blurred.
And footage already used to train the AI? The company admits it can’t pull it back out.
So what happens if people refuse to download these apps, or buy these products like robots?
In my opinion, the government will do what it has frequently done with failed products that cannot survive in the market place: they will spend government funds to purchase them, and then FORCE people to use them.
Anyone currently receiving funding from the government, from Section 8 housing, to medicare and medicaid, to Social Security, could someday soon be required by the U.S. Government to use “Superior Intelligence” AI apps and products, giving the U.S. Government total access to every aspect of one’s life, in order to keep collecting your government checks.
And Trump just laid down the legal framework to do it.
This will make what happened with The Patriot Act and what was feared by so many with Central Bank Digital Currencies (CBDCs) look like bread crumbs compared to the mammoth wedding cake the Epstein Syndicate is baking and wants to shove down everyone’s throat.
Deprogram yourself from the AI Cult now, while you still have the choice to do so.
This article was written by Human Superior Intelligence (HSI)
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