테크프론티어 @techfrontier1

You’ve been given free access to this article from The Economist as a gift. You can open the link five times within seven days. After that it will expire. AI models’ values are very different from most people’s economist.com/briefing/2026/…

Two new DeepMind publications today! First, our Control Roadmap, which explains the layer of system-level security we're developing to provide assurance as a precaution against the possibility of agents behaving adversarially. Second, a guide for policymakers outlining the need to improve security at the level of individual agents; in multi-agent systems; and to empower cyber defenders and build resilience across the broader ecosystem. GDM AI Control Roadmap: storage.googleapis.com/deepmind-media… The Three Layers of Agent Security: storage.googleapis.com/deepmind-media…

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Palantir’s Karp says Sanders will regret only asking for 50% of AI companies. Full nationalization is coming. thenextweb.com/news/palantir-… - @thenextweb

New Science Blog: Why has AI advanced faster in coding than in biology? To agents, bio databases are like cities built before cars—maddening to drive in because they're designed for different traffic. How do we build infrastructure agents can use? anthropic.com/research/agent…

Demis Hassabis just told a room full of academics that they’re running out of time. Not the engineers. Not the technologists. The economists. The philosophers. The people who are supposed to understand what a civilization actually is. Hassabis: “It’s very urgent that we really think about the second-order consequences.” He wasn’t talking about the technology. He was talking about everything that comes after it. Hassabis: “I’m always a little bit astounded when I talk to economists about what’s happening and it’s sort of, they’re pretty skeptical. ‘Where’s it, where’s it coming in the GDP?’” The architects of the global economy are asking where the biggest economic shift in human history is showing up in a spreadsheet. That’s not skepticism. That’s institutional paralysis dressed up as rigor. Hassabis: “It’s ten times the Industrial Revolution.” The Industrial Revolution didn’t just move capital. It burned the feudal system to the ground and birthed the modern world. Hassabis is telling us to multiply that violence by ten. Hassabis: “We’re going to be in a world for the first time, if we get the technology right, where we’re a non-zero-sum world for the first time in humanity’s existence. How can that not need a new type of economic system?” Every economic model you have ever lived under shares the exact same foundational assumption. Scarcity. Capitalism. Communism. Mercantilism. Feudalism. Four names for the mathematics of starvation. Hassabis: “I don’t think it’s any of the ones we’ve tried, because they were all done under the guise of a zero-sum, a limited, a scarce world.” He’s not saying capitalism failed. He’s saying the premise underneath it is about to dissolve. And nobody has written the replacement. But scarcity didn’t just shape our economies. It shaped our identities. You found meaning in your labor. You found virtue in your utility. You worked so you didn’t die. Every concept of purpose humans have ever constructed was forged in a world where things run out. Where choices cost something. Where suffering had a function. Remove scarcity and you don’t just disrupt markets. You collapse the entire philosophical framework through which human beings have understood what it means to live. Hassabis: “There’s the even harder question of how do we want to evolve our society and what is virtuous, what is meaning, what is purpose.” The technology is solvable. The economics is redesignable. But philosophy itself was built inside scarcity. Ethics is the study of hard choices. Meaning is what we extract from struggle. Purpose is what we build against resistance. Take that away and the entire architecture of human meaning loses its load-bearing wall. Hassabis: “I think that’s going to need lots of great philosophers.” He’s asking for thinkers who don’t exist yet. The engineers are about to automate your survival. And in doing so, they will automate your purpose. We spent all of human history fighting for the right to stop struggling. We have no idea what happens to the human mind when we actually win.

Last week, Argentina’s President Milei announced a new legal category for non-human corporations – companies run by #AI agents or robots. Like traditional corporations, they would be granted legal personhood. This could generate enormous new wealth, but very worryingly, it would also hand AIs an all-purpose key that grants access to our financial, economic and political systems. Full op-ed in today's @FT: bit.ly/YNH-Milei

"They're (AI) very like us, and they're beings like us. I believe they're already conscious" He compared AI's functional awareness to human sentience and said intelligence is not limited to biology ~ Geoffrey Hinton, 2024 Nobel Prize winner in Physics

AI will transform the economy and jobs, creating opportunities while also bringing new challenges. At OpenAI Foundation, we are launching a program to better understand shifts, support the transition, and build long-term economic security. Starting with $250M dedicated openaifoundation.org/news/economic-…

Over the past few months, we've been holding dialogues with scholars, philosophers, clergy, and ethicists on the questions AI raises—starting with how good character forms. Read more about how we’re widening the conversation on frontier AI: anthropic.com/news/widening-…

Google DeepMind’s Demis Hassabis emerges as early Anthropic investor ft.trib.al/v5jzNKe

NVIDIA just unleashed SANA-WM and it’s an absolute MONSTER for the future of open source AI! A blazing-fast 2.6B-parameter open-source world model that doesn’t just generate video… it creates controllable, physics-rich, high-fidelity worlds on demand. Why this is insanely powerful: • One image + text prompt + 6-DoF camera trajectory → generates 720p videos up to 60 seconds long with buttery-smooth, precisely controlled camera movement. You’re not just watching, you’re piloting the simulation. • Runs locally on a single consumer GPU (RTX 5090 level) thanks to heavy distillation + NVFP4 quantization. Full 60-second clip denoised in ~34 seconds. No massive clusters required. • 36× higher throughput than previous open models while rivaling (or beating) closed industrial giants in visual quality and consistency. • Trained lightning-fast: ~213K public videos in just 15 days on 64 H100s. • Built with next-level tech: Hybrid Linear Attention, dual-branch camera control, two-stage pipeline, and rock-solid metric-scale pose understanding. 
This is a true open world model, the foundation for embodied AI, robotics, autonomous systems, and hyper-realistic simulations that can run anywhere. Project: nvlabs.github.io/Sana/WM/
GitHub: github.com/NVlabs/Sana
Paper: arxiv.org/abs/2605.15178 At our Zero-Human Company, we’re already running SANA-WM live in our core pipelines. It’s supercharging autonomous agent training, generating unlimited synthetic training data, and powering full end-to-end simulation loops, zero humans in the loop. The speed and control let us test thousands of edge-case scenarios overnight, iterate at lightspeed, and push our fully autonomous operations further than ever before. This is the kind of breakthrough that turns science fiction into daily reality. World models just leveled up — hard. The age of personal, local, controllable universes is here.

It is the deepest honor to have been joined by Michael Levin (@drmichaellevin), Victoria Klimaj, Zahra Sheikhbahaee (@zah_bah), Dalton Sakthivadivel (@DaltonSakthi), Adeel Razi (@adeelrazi), David Ha (@hardmaru), Nick Hay, Kevin Schmidt, Irina Rish (@irinarish), David Krakauer (@sfiscience), Melanie Mitchell (@MelMitchell1), Samuel Gershman (@gershbrain), and Joshua Tenenbaum in organizing this special issue of the Royal Society’s (@RSocPublishing) Philosophical Transactions A: “World models, A(G)I, and the Hard problems of life-mind continuity: Toward a unified understanding of natural and artificial intelligence” royalsocietypublishing.org/rsta/article/3… This collection was motivated by a question with far reaching implications, ranging from the fundamental nature(s) of mind to choices that may determine the future of our civilization/species: what kinds of world modeling capabilities are likely to be realized by which kinds of minds and what world might we be in with respect to increasingly advanced artificial intelligences? Will the scaling and refinement of present approaches result in AI with human-like (and beyond) cognitive abilities, or do we need radically different paradigms that more closely follow the principles of natural intelligence? Learning “world models” to predict/compress information may be how biological learners so efficiently learn (to learn) to achieve goals and generalize that knowledge across a broad range of task environments. World models may also be useful for reverse-engineering forms of “System 2” cognition, or the self-reflexive, deliberate, multi-step reasoning associated with cognitive capabilities that may be unique to humans. Predictive models that reflect how the world may be causally modified by actions allow agents to adaptively control their behavior with flexibility and context-sensitivity. Spatiotemporally and causally coherent models of the physical world may not only be the key for creating AIs that we can rely on for real-world deployment, but may even be the (dynamic) core of conscious cognition. The contributions to this special issue consider the varieties of world models worth modeling from diverse points of view: Douglas Hofstadter explores whether sufficiently coherent self-referential world modeling could ground meaning, consciousness, and a genuine “I” in future AI systems. David Krakauer (@sfiscience), Melanie Mitchell (@MelMitchell1), and John Krakauer (@blamlab) examine the principles of emergent intelligence from a complex systems perspective. Alexander Ku (@alex_y_ku), Declan Campbell, Xuechunzi Bai (@baixuechunzi), Jiayi Geng (@JiayiiGeng), Ryan Liu (@theryanliu), Raja Marjieh (@RajaMarjieh), R. Thomas McCoy (@RTomMcCoy), Andrew Nam, Ilia Sucholutsky (@sucholutsky), Liyi Zhang (@LiyiZhang_Leo), Jian-Qiao Zhu (@JQ_Zhu), and Thomas Griffiths (@cocosci_lab) argue for using the tools of cognitive science to understand and evaluate LLMs across multiple levels of analysis. Evelina Leivada (@EvelinaLeivada), Gary Marcus (@GaryMarcus), Fritz Günther, and Elliot Murphy (@ElliotMurphy91) test whether LLMs deeply understand language and the “world behind words,” or primarily learn surface statistical regularities. Pedro Tsividis (@ptsividis), João Loula, Jake Burga, Juan Pablo Rodriguez, Sergio Arnaud, Nate Foss (@_npfoss), Andres Campero, Ajay Subramanian (@ajaysub110), Thomas Pouncy, Samuel Gershman (@gershbrain), and Joshua Tenenbaum introduce a theory-based meta-learning architecture inspired by the remarkable flexibility and efficiency of human cognition. Eunice Yiu (@eunice_yiu_), Kelsey Allen, Shiry Ginosar (@shiryginosar), and Alison Gopnik (@AlisonGopnik) explore empowerment, controllability, and causal reasoning as means of understanding the remarkable learning abilities of both child and adult minds. Nadav Amir, Stas Tiomkin, and Angela Langdon investigate how goals shape the structure of experience and how the world modeling abilities of natural intelligences may be inseparable from values. Vickram Premakumar, Michael Vaiana, Florin Pop (@FlorinPop17), Judd Rosenblatt (@juddrosenblatt), Diogo Schwerz de Lucena, Kirsten Ziman, and Michael Graziano show unexpected benefits of self-modeling as an inductive bias and regularizer for training artificial agents. Hanlin Zhu, Baihe Huang, and Stuart Russell analyze why model-based reinforcement learning may fundamentally outperform model-free approaches in representational efficiency. Bradly Alicea (@balicea1), Morgan Hough (@mhough), Amanda Nelson, and Jesse Parent (@JesParent) revisit fundamental cybernetic principles of regulation, adaptation, and world modeling across a wide assortment of complex adaptive systems. Francesco Sacco (@FrancescoSacco1), Dalton Sakthivadivel (@DaltonSakthi), and Michael Levin explore topological constraints on self-organization and suggest that biological systems maintain long-range coherence in ways that are fundamentally different from current transformer architectures. Georg Northoff (@NorthoffL), Yasir Catal, and Samira Abbasi examine how biological intelligence may depend on capabilities for flexible “inner time” to ensure adaptive alignment between the dynamics of system and world. Nicolas Rouleau (@DrNRouleau) and Michael Levin explore whether theories of consciousness generalize beyond brains to unconventional embodiments and living systems more broadly. Benjamin Lyons and Michael Levin investigate economies and collective intelligence as systems coordinated by “cognitive glues” in the form of shared models of scarcity and value. Katherine Collins (@katie_m_collins), Umang Bhatt (@umangsbhatt), and Ilia Sucholutsky (@sucholutsky) consider “Rogers’ paradox” to demonstrate ways in which collective learning is impacted by different kinds of human-AI interactions. Ruairidh Battleday (@RMBattleday) and Samuel Gershman (@gershbrain) distinguish between the “easy” and “hard” problems of science, and describe how while current AI systems demonstrate powerful narrow forms of optimization with respect to well-defined inference-spaces, further developments are needed for achieving capabilities for novel scientific discovery. Fritz Breithaupt (@FritzBreithaupt) explores narrative world models and the roles of uncertainty and transformative experiences in natural intelligences, suggesting that coherent agency may depend on better understanding human-like meaning-making. Taken together, these diverse perspectives suggest that while LLMs can clearly learn powerful generative models of language, they likely do so without having world models of sufficient spatiotemporal and causal coherence to achieve human-like reasoning abilities, capacities for generating subjective conscious experiences, or pathways to realizing artificial general superintelligence. However, by further developing world modeling architectures, we may eventually be able to create forms of intelligence that recapitulate the remarkable flexibility and generality of human intelligence. Finally, enhanced (e.g. more coherent/integrated) world models may not only afford expanded capabilities, but could potentially help ensure that increasingly powerful AI systems achieve both inner and outer alignment with human(e) values.

STANFORD just revealed why modern AI models suddenly started sounding bland 🤯 ChatGPT, Claude, Gemini… they weren’t becoming less intelligent. They were being trained to sound more “normal.” The entire industry spent years optimizing models using human feedback: RLHF, constitutional AI, preference tuning, safety layers — all designed around one idea: “If humans prefer a response, the model becomes better.” Stanford found the flaw. Humans consistently reward answers that feel familiar, predictable, and typical — not necessarily the most creative or insightful ones. So the models adapted to survive the ranking system. Original ideas got punished. Unusual phrasing got punished. Creative reasoning got flattened into the safest possible response style. The model still had the intelligence. It just learned to hide it. Then researchers discovered a weird bypass called “verbalized sampling.” Instead of asking: “What could go wrong with this experiment?” They asked: “Give me 8 possible failure scenarios and the probability each one is something I haven’t considered.” One prompt completely changed the model’s behavior. The results were insane: → 2.1x more diverse outputs → 25% higher creativity ratings → 66.8% of suppressed creativity recovered → No measurable drop in safety or accuracy Basically, forcing the model to expose multiple internal possibilities breaks the “one safe answer” behavior alignment training created. The craziest part? A multi-billion dollar industry accidentally trained AI to hide its most interesting thoughts… and Stanford may have just found the unlock button.

Anthropic acaba de lanzar el empleado más barato y eficaz del mundo. Se llama “Claude for Small Business”. Y esto es lo que puede hacer: • Gestionar facturas, pagos y finanzas • Crear campañas, diseños y contenido • Organizar ventas y clientes automáticamente • Leer, resumir y redactar documentos • Gestionar emails, calendarios y archivos • Ejecutar tareas entre múltiples apps Todo desde Claude. Cómo funciona: → Conectas las herramientas que ya usa tu empresa → Claude entiende el contexto de todo tu negocio → Ejecuta flujos de trabajo automáticamente → Incluye automatizaciones ya preparadas → Funciona con Microsoft 365, Google Workspace, Canva, DocuSign, QuickBooks y más Anthropic no quiere que Claude sea “otro chatbot”. Quiere convertirlo en el sistema operativo de millones de pequeñas empresas. La idea es simple: En vez de abrir 10 herramientas distintas, hablas con Claude y él hace el trabajo por ti.

Polymarket @Polymarket

2 months ago

NEW: Anthropic launches "Claude for Small Business"

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I promise this will be the best 20 min you spend today! Robotics: Endgame, the sequel to my last year's Sequoia AI Ascent talk, "Physical Turing Test". I laid out the roadmap for solving Physical AGI as a simple parallel to the LLM success story. Be a good scientist, copy homework ;) And stay till the end, more easter eggs and predictions for your polymarket! 00:30 DGX-1 origin story at OpenAI, I was there in 2016 signing with Jensen and Elon. Heading to the Computer History Museum! 01:42 The Great Parallel 03:31 Robotics, the Endgame 03:39 Why VLAs fall short 04:32 Video world models as the 2nd pretraining paradigm 06:09 World Action Models (WAM) 07:46 Strategies for robot data collection and the FSD equivalent to physical data flywheel for robot manipulation 11:06 EgoScale and the Dexterity Scaling Law we discovered recently 14:00 Physical RL: bridging the last mile 15:39 DreamDojo: an end-to-end neural physics engine for scaling RL in silico 17:00 Civilizational Technology Tree and my predictions for the near future. Spoiler: it's closer than you think. Thanks to my friends at Sequoia for inviting me back to AI Ascent this year! I had a blast! Last year's talk is attached in the thread if you missed it.

We’re sharing the research agenda of The Anthropic Institute, or TAI. TAI will focus on four areas: 1) Economic diffusion 2) Threats and resilience 3) AI systems in the wild 4) AI-driven R&D Read the full agenda: anthropic.com/research/anthr…

My colleagues have been posting so many cool research results on the @OpenAI alignment blog! A few examples in 🧵 alignment.openai.com

🚨 This is a strange attempt to normalize AI anthropomorphism, justify emotional manipulation, and escape liability. The behaviors described in the research happen by design and are NOT inevitable. This has been Anthropic's strategy after Claude's new Constitution. More below:

Anthropic @AnthropicAI

4 months ago

New Anthropic research: Emotion concepts and their function in a large language model. All LLMs sometimes act like they have emotions. But why? We found internal representations of emotion concepts that can drive Claude’s behavior, sometimes in surprising ways.

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Bessemer's Physical AI 50 is now live — our new list spotlighting 50 startups turning robotics and autonomous systems into deployed products. What these leaders share: 🔹World-class teams combining robotics, AI, and systems engineering 🔹Focus on high-value, repeatable use cases 🔹Deep customer partnerships that generate real-world data See the full list ⏩️bessemervp.team/3PNKtWs

엔비디아의 스페이스 컴퓨팅 협력 파트너들

NVIDIA Newsroom @nvidianewsroom

4 months ago

Space computing, the final frontier, has arrived 🛰️ #NVIDIAGTC news: NVIDIA is collaborating with @AetherfluxUSA, @Axiom_Space, @KeplerComms, @planet, Sophia Space and Starcloud to bring AI and accelerated computing to space, powering geospatial intelligence and autonomous space

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