Bank of France Urges Direct EU Oversight of Crypto Firms

TheCryptoTimesPublicado a 2025-10-09Actualizado a 2025-10-09

The Bank of France is urging the EU’s top markets regulator to step in and directly oversee major crypto firms operating across the bloc, highlighting growing concerns over the industry’s oversight.

In a speech delivered on Thursday, François Villeroy de Galhau, Governor of the Bank of France, urged that oversight powers for crypto businesses be transferred to the European Securities and Markets Authority (ESMA) to ensure consistent enforcement of rules across all member states.

As reported by Bloomberg, Villeroy criticized the current regulatory framework under the EU’s Markets in Crypto-Assets (MiCA) regulation, which allows crypto companies to obtain licenses from individual member states and use them to operate across the entire 27-nation bloc through a “passporting” system. 

He argued that this system may undermine uniformity in regulatory standards and expose the EU to risks in the event of market stress.

“This framework would benefit from the much stricter regulation of the multi-issuance of the same stablecoin within and outside the European Union to reduce arbitrage risks in times of stress,” Villeroy stated.

His remarks come amid rising concerns about stablecoin regulations, particularly those issued by companies operating both inside and outside the EU. The European Central Bank has also been pushing for a ban on jointly issued stablecoins within the bloc and other jurisdictions.

Circle Internet Group Inc., the largest stablecoin issuer in Europe, currently uses the multi-issuance model to support its $76 billion dollar-pegged token, USDC, within the EU. 

The company received an electronic money license in France last year under the existing MiCA framework, which permits stablecoin providers to maintain a local reserve in one EU state while issuing similar tokens internationally.

Pushback against proposed EU stablecoin rules

However, several groups that represent crypto and payment companies don’t agree with the possible new rules being discussed. On October 6, these groups sent a letter to the European Commission saying that if the EU changes its rules about how stablecoins are issued in multiple places, it could make it harder for these companies to do business.

They said that the current system, which allows global stablecoins to work across countries, is important because these stablecoins make up almost all of the market. Changing the rules could cause Europe to fall behind other parts of the world.

So far, the European Commission hasn’t replied to the letter. This shows there is a clear disagreement between the regulators who want stricter control and the companies who want clearer rules and to stay competitive.

Also Read: Bullish Teams Up with Deutsche Bank for Crypto-Fiat Integration 


Mobile Only Image

Lecturas Relacionadas

Three Years Later: Looking Back at My Predictions About ChatGPT in 2023

Three Years Later: Revisiting My 2023 Predictions on ChatGPT In March 2023, shortly after ChatGPT's launch, I made 20 predictions about its future. Now, in mid-2026, I've used AI agents to fact-check each one against the latest data. Overall, most major directional forecasts were correct, with only one outright error (incorrectly stating GPT-4 had 100 trillion parameters). Key successes included predicting that RAG and retrieval architectures would become the standard for handling knowledge and hallucinations, that natural language interfaces (LUI) would create a massive new industry layer beyond the models themselves, and that China would develop viable large language models, significantly closing the performance gap with Western counterparts within about three years. Predictions about the absence of mass unemployment, the rise of a new "robot network" for agent communication, and ChatGPT not possessing consciousness also held true in their core arguments. However, the "devil was in the details." Errors frequently involved specific numbers, timelines, or overlooking distributional effects. I tended to overestimate the speed of adoption (e.g., for agent networks) while underestimating the ultimate scale of capabilities or costs (e.g., AI winning IMO gold without tools, or the extreme capital required for frontier models). Other misjudgments included: underestimating how AI would reinforce, not dissolve, information filter bubbles; incorrectly assuming AI-generated content would easily circumvent copyright (it has instead triggered record-breaking settlements); and misidentifying where value would be captured (it accrued overwhelmingly to the compute layer, like Nvidia, not just the application or model layers). Key lessons from reviewing these predictions are: 1) Directional and mechanistic insights are far more reliable than precise numbers or absolute statements. 2) There's a consistent bias to overestimate short-term speed but underestimate long-term magnitude. 3) Errors often lie in missing distributional impacts within a generally correct aggregate trend. 4) Predictions phrased with nuance and caveats aged the best. 5) Some fundamental debates (e.g., on machine consciousness or the ultimate value chain) remain unresolved even after three years. This exercise is less about scoring the past and more about establishing rules for clearer thinking about the next three years of AI.

marsbitHace 3 hora(s)

Three Years Later: Looking Back at My Predictions About ChatGPT in 2023

marsbitHace 3 hora(s)

Three Years Later: Looking Back on My 2023 Predictions for ChatGPT

Looking Back After Three Years: Revisiting My 2023 Predictions on ChatGPT In March 2023, shortly after ChatGPT's debut and before GPT-4's release, I made over twenty predictions about AI's future based on limited information and intuition. Now, in May 2026, I revisited those forecasts using an AI-driven analysis with 41 Opus 4.8 agents to cross-reference them with the latest data. The assessment used symbols: ✅ Correct, 🟢 Mostly Correct, 🟡 Partially Correct, ❌ Incorrect. Overall, the directional judgments held up well, with only one major factual error regarding GPT-4's rumored parameter size (incorrectly cited as 100T). However, nuances and degrees of accuracy revealed more. **What Was Largely Correct:** Predictions about mechanisms and directions proved accurate. The rise of RAG (Retrieval-Augmented Generation) as the standard architecture for combating AI hallucination was confirmed, as was the transformative potential of LUI (Language User Interface) in creating a new industry layer atop GUIs. The emergence of "robot networks" (agent-to-agent communication protocols) and China's rapid catch-up in developing capable large models (closing the performance gap with top models to ~2.7%) were also on point. The analysis affirmed that LLMs lack consciousness and that the Turing Test merely measures perceived intelligence. **What Was Off Target:** Errors often involved specific numbers, over-optimistic timelines, or misjudged distributions. The prediction that value would primarily accrue to the application layer was half-right but missed NVIDIA's dominance as the profitable infrastructure layer. Forecasts about AI circumventing copyright issues and fostering a "global common ground" by averaging human viewpoints were incorrect; instead, major copyright settlements occurred and AI personalization is increasing. Estimates for model training costs ("$5-10 billion cap") were significantly off, underestimating frontier costs and overestimating replication costs. The notion that LLMs could never do complex math without tools was disproven by later models winning IMO gold. **Key Patterns from the Review:** 1. **Direction over precision:** Judgments about mechanisms and trends were more reliable than specific numbers or definitive statements. 2. **Timing bias:** There was a tendency to overestimate short-term speed but underestimate long-term magnitude and transformation. 3. **The distribution blind spot:** Aggregate-level correctness often masked uneven impacts (e.g., on young professionals' employment). 4. **The value of qualifiers:** Predictions framed with caution (e.g., "reportedly," "for now," "prototype in 2-3 years") aged better. 5. **Some debates continue:** Issues like the nature of "emergent abilities" or machine consciousness remain unresolved. This three-year review highlights that while seeing the big picture is crucial, humility regarding specifics, timelines, and disparate impacts is essential for future forecasting.

链捕手Hace 6 hora(s)

Three Years Later: Looking Back on My 2023 Predictions for ChatGPT

链捕手Hace 6 hora(s)

AI Bubble Warning: AI Investments Are Negative Returns for Most Tech Giants

The article issues a stark warning about a potential AI investment bubble. It notes that while the AI boom shares similarities with the TMT bubble of the late 1990s, its scale is vastly larger, currently driving 93% of U.S. GDP growth. Major hyperscale cloud providers like Microsoft, Alphabet, Amazon, Meta, and Oracle are planning to invest trillions in AI data centers over the coming years. However, calculations based on analyst projections for 2025-2030 reveal a concerning math problem: expected capital expenditure growth far outpaces projected revenue growth. Even under an extremely optimistic scenario of zero costs, the implied return on investment for most of these tech giants (except Amazon) is deeply negative. This suggests that the current trajectory could lead to one of history's largest shareholder value destruction events. The piece outlines two potential escapes: AI generating vastly more revenue than currently anticipated—a near-impossible task—or a significant cutback in the planned investment splurge. The latter scenario could trigger a domino effect, severely impacting the entire tech supply chain (from Nvidia to TSMC), potentially pushing the U.S. economy into recession, and causing a major stock market downturn. The author suggests upcoming high-profile IPOs by companies like OpenAI and Anthropic might represent a transfer of risk from early investors to public market participants. While the peak of the hype cycle might sustain investment through 2026, the fundamental financial dilemma remains unresolved, setting the stage for a potential market correction in 2027 or 2028, similar to the years following Alan Greenspan's "irrational exuberance" warning.

marsbitHace 7 hora(s)

AI Bubble Warning: AI Investments Are Negative Returns for Most Tech Giants

marsbitHace 7 hora(s)

From Tokens to Machine Labor: AI is Shifting from Tool to "Worker"

The article "From Token to Machine Labor: AI is Evolving from Tool to 'Worker'" argues that the business model for AI is shifting beyond simply selling computational resources (tokens, GPU hours) or model access. Instead, a new "machine labor market" is emerging, where the core economic transaction is the purchase of economically useful work directly performed by software. The central thesis is that AI pricing will evolve through four stages: 1) raw tokens, 2) standardized LLM capabilities (e.g., text generation), 3) industry-specific labor markets (e.g., legal review, radiology), and finally 4) a programmable results market where tasks like resolving a support ticket are bid on and priced based on outcome. In this future, buyers will care less about *which* model or GPU completes a task and more about whether the work meets specified standards for accuracy, latency, and cost. This transition reframes the impact of AI on human labor. Rather than simple replacement, it suggests a re-coordination where machines handle standardized, verifiable work, freeing humans for roles involving oversight, context management, responsibility, and final judgment. In some cases, this "last 1%" of human input becomes more valuable as it enables the other 99% to be automated. Furthermore, as AI reduces the cost of work, demand may expand, creating larger markets (e.g., 24/7 customer service) rather than just cheaper versions of existing ones. The article concludes that while infrastructure (GPUs, models, tokens) remains crucial upstream, the market is converging on a simpler, tradeable unit: machine labor that can be defined, measured, priced, and procured based on contractible specifications.

marsbitHace 7 hora(s)

From Tokens to Machine Labor: AI is Shifting from Tool to "Worker"

marsbitHace 7 hora(s)

Xiaomi MiMo's 99% Price Cut is Not Marketing! Luo Fuli Posts on X to Refute Critics

The price of Xiaomi's MiMo-V2.5 series API has been permanently reduced by up to 99%, specifically for the "Input (Cache Hit)" cost, which covers users re-reading historical context in long conversations. MiMo's head, Luo Fuli, published a detailed technical blog to clarify that this drastic price cut stems from genuine engineering breakthroughs, not a marketing stunt or a simple price war. The core of the achievement lies in six key engineering optimizations. First, the model architecture adopts a Hybrid Sliding Window Attention (SWA), reducing the memory footprint (KVCache) to 1/7th of a traditional model. Second, a dual-pool memory management system actually utilizes these savings, allowing a single GPU to handle over 5 times more concurrent users. Third, an upgraded prefix caching mechanism achieves a cache hit rate of 93-95% for repeated reads, meaning most such requests bypass GPU computation entirely. Fourth, a self-developed distributed cache (GCache) utilizes idle SSD space on existing GPU servers, eliminating additional storage costs. Fifth, an intelligent scheduling system (LLM-Router) efficiently routes requests to maximize cache reuse and performance. Sixth, Multi-Token Prediction (MTP) accelerates the model's text generation ("output") side. Together, these systemic optimizations dramatically lower the real computational cost per request, enabling the 99% price reduction for cached inputs while reportedly maintaining positive gross margins. Luo Fuli's disclosure aims to shift the narrative from "price war" to a demonstration of substantive AI engineering progress.

marsbitHace 9 hora(s)

Xiaomi MiMo's 99% Price Cut is Not Marketing! Luo Fuli Posts on X to Refute Critics

marsbitHace 9 hora(s)

Trading

Spot
Futuros

Artículos destacados

Qué es $BANK

Banco AI: Un Paso Revolucionario en el Futuro de la Banca Introducción En una era marcada por avances rápidos en tecnología, Banco AI se sitúa en la intersección de la inteligencia artificial (IA) y los servicios bancarios. Este proyecto innovador busca redefinir el panorama financiero, mejorando la eficiencia operativa, las medidas de seguridad y las experiencias del cliente a través del poder de la IA. Al embarcarnos en esta exploración de Banco AI, profundizaremos en lo que implica el proyecto, sus dinámicas operativas, su contexto histórico y hitos significativos. ¿Qué es Banco AI? En su esencia, Banco AI representa una iniciativa transformadora destinada a integrar la inteligencia artificial en varias operaciones bancarias. Este proyecto aprovecha las capacidades de la IA para automatizar procesos, mejorar los protocolos de gestión de riesgos y mejorar la interacción con los clientes a través de servicios personalizados. Los objetivos principales de Banco AI incluyen: Automatización de Funciones Bancarias: Al aprovechar las tecnologías de IA, Banco AI tiene como objetivo automatizar tareas rutinarias, reduciendo la carga sobre los recursos humanos y mejorando la eficiencia. Mejora en la Gestión de Riesgos: El proyecto utiliza algoritmos de IA para predecir e identificar riesgos, fortaleciendo así las medidas de seguridad contra fraudes y otras amenazas. Personalización de Servicios Bancarios: Banco AI se centra en ofrecer productos y servicios financieros a medida al analizar datos y comportamientos de los clientes. Mejoramiento de la Experiencia del Cliente: La implementación de soluciones impulsadas por IA, como chatbots y asistentes virtuales, tiene como objetivo proporcionar a los usuarios interacciones más humanas, revolucionando la forma en que los clientes se relacionan con los bancos. Con estos objetivos, Banco AI se posiciona como un jugador crucial para hacer que la banca sea más eficiente, segura y centrada en el usuario. ¿Quién es el Creador de Banco AI? Los detalles sobre el creador de Banco AI siguen siendo desconocidos. Como tal, no se ha identificado a ninguna persona u organización específica en la información disponible. El anonimato que rodea el inicio del proyecto plantea preguntas, pero no resta valor a su ambiciosa visión y objetivos. ¿Quiénes Son los Inversores de Banco AI? Al igual que con el creador del proyecto, no se ha divulgado información específica sobre los inversores u organizaciones que apoyan a Banco AI. Sin esta información, es un desafío delinear el respaldo financiero y el apoyo institucional que podrían estar impulsando el proyecto hacia adelante. No obstante, la importancia de contar con una sólida base de inversión es fundamental para sostener el desarrollo en un campo tan innovador. ¿Cómo Funciona Banco AI? Banco AI opera en múltiples frentes innovadores, centrándose en factores únicos que lo diferencian de los marcos bancarios tradicionales. A continuación, se presentan las características operativas clave: Automatización: Al aplicar algoritmos de aprendizaje automático, Banco AI automatiza varios procesos manuales dentro de los bancos. Esto resulta en la reducción de costos operativos y permite que los trabajadores humanos redirijan sus esfuerzos hacia actividades más estratégicas. Gestión Avanzada de Riesgos: La integración de la IA en las prácticas de gestión de riesgos equipa a los bancos con herramientas para predecir con precisión amenazas potenciales como el fraude, garantizando que la información y los activos de los clientes permanezcan seguros. Recomendaciones Financieras Personalizadas: A través del aprendizaje continuo a partir de las interacciones con los clientes, los sistemas de IA desarrollan una comprensión matizada de las necesidades del usuario, lo que les permite ofrecer consejos adaptados sobre decisiones financieras. Interacciones Mejoradas con los Clientes: Al utilizar chatbots y asistentes virtuales impulsados por IA, Banco AI permite una experiencia más atractiva para el cliente, permitiendo a los usuarios resolver sus consultas rápidamente, reduciendo así los tiempos de espera y mejorando los niveles de satisfacción. En conjunto, estas características operativas posicionan a Banco AI como un pionero en el sector bancario, estableciendo nuevos parámetros para la entrega de servicios y la excelencia operativa. Línea de Tiempo de Banco AI Entender la trayectoria de Banco AI requiere mirar su contexto histórico. A continuación, se presenta una línea de tiempo que destaca hitos y desarrollos importantes: Inicios de 2010: La conceptualización de la integración de la IA en los servicios bancarios comenzó a ganar atención a medida que las instituciones bancarias reconocieron los posibles beneficios. 2018: Se produjo un aumento notable en la implementación de tecnologías de IA cuando los bancos comenzaron a utilizar herramientas de IA como chatbots para el servicio al cliente básico y sistemas de gestión de riesgos para mejorar la seguridad. 2023: La sofisticación de la IA continuó avanzando, con la introducción de IA generativa para tareas más complejas como el procesamiento de documentos y análisis de inversiones en tiempo real. Este año marcó un salto significativo en las capacidades que la tecnología de IA otorgó a los bancos. 2024-Estatus Actual: A partir de este año, Banco AI se encuentra en una trayectoria ascendente, con investigaciones y desarrollos en curso que pronto mejorarán las capacidades en las operaciones bancarias. La continua exploración de las aplicaciones de IA sugiere emocionantes desarrollos aún por venir. Puntos Clave Sobre Banco AI Integración de la IA en la Banca: Banco AI se centra en adoptar inteligencia artificial para optimizar los procesos bancarios y mejorar la experiencia del usuario. Enfoque en Automatización y Gestión de Riesgos: El proyecto enfatiza fuertemente estas áreas, con el objetivo de desplazar la carga de tareas rutinarias mientras mejora los marcos de seguridad a través de análisis predictivos. Soluciones Bancarias Personalizadas: Al aprovechar los datos de los clientes, Banco AI permite servicios bancarios adaptados a las necesidades individuales de los usuarios. Compromiso con el Desarrollo: Banco AI se mantiene comprometido con esfuerzos de investigación y desarrollo continuos, asegurando su adaptabilidad y relevancia continua a medida que la tecnología sigue evolucionando. Conclusión En resumen, Banco AI ejemplifica un paso crucial hacia adelante en la industria bancaria, aprovechando la inteligencia artificial para redefinir los paradigmas operativos, mejorar la seguridad y promover la satisfacción del cliente. A pesar de las lagunas en la información sobre el creador y los inversores, los objetivos claros y los mecanismos funcionales de Banco AI proporcionan una sólida base para su evolución continua. A medida que la tecnología de IA sigue avanzando y fusionándose con el sector bancario, Banco AI está bien posicionado para impactar significativamente el futuro de los servicios financieros, mejorando la forma en que entendemos e interactuamos con la banca.

143 Vistas totalesPublicado en 2024.04.06Actualizado en 2024.12.03

Qué es $BANK

Cómo comprar BANK

¡Bienvenido a HTX.com! Hemos hecho que comprar Lorenzo Protocol (BANK) sea simple y conveniente. Sigue nuestra guía paso a paso para iniciar tu viaje de criptos.Paso 1: crea tu cuenta HTXUtiliza tu correo electrónico o número de teléfono para registrarte y obtener una cuenta gratuita en HTX. Experimenta un proceso de registro sin complicaciones y desbloquea todas las funciones.Obtener mi cuentaPaso 2: ve a Comprar cripto y elige tu método de pagoTarjeta de crédito/débito: usa tu Visa o Mastercard para comprar Lorenzo Protocol (BANK) al instante.Saldo: utiliza fondos del saldo de tu cuenta HTX para tradear sin problemas.Terceros: hemos agregado métodos de pago populares como Google Pay y Apple Pay para mejorar la comodidad.P2P: tradear directamente con otros usuarios en HTX.Over-the-Counter (OTC): ofrecemos servicios personalizados y tipos de cambio competitivos para los traders.Paso 3: guarda tu Lorenzo Protocol (BANK)Después de comprar tu Lorenzo Protocol (BANK), guárdalo en tu cuenta HTX. Alternativamente, puedes enviarlo a otro lugar mediante transferencia blockchain o utilizarlo para tradear otras criptomonedas.Paso 4: tradear Lorenzo Protocol (BANK)Tradear fácilmente con Lorenzo Protocol (BANK) en HTX's mercado spot. Simplemente accede a tu cuenta, selecciona tu par de trading, ejecuta tus trades y monitorea en tiempo real. Ofrecemos una experiencia fácil de usar tanto para principiantes como para traders experimentados.

768 Vistas totalesPublicado en 2025.05.09Actualizado en 2026.05.19

Cómo comprar BANK

Discusiones

Bienvenido a la comunidad de HTX. Aquí puedes mantenerte informado sobre los últimos desarrollos de la plataforma y acceder a análisis profesionales del mercado. A continuación se presentan las opiniones de los usuarios sobre el precio de BANK (BANK).

活动图片