“The danger is never that robots disobey, but that they obey perfectly.”
At the convergence of frontier research breakthroughs, billions in capital, and rising geopolitical tensions lies a dream for a new physical world. After the LLM wave, robotics is seen as the next exponential growth domain.0Chinese manufacturing is viewed as an existential threat to the US, adding to incentives. And, though robotics is the hardest domain of AI1, multiple new AI strategies now offer clear paths to Embodied General Intelligence (EGI).2
Informed by conversations with frontier researchers, intuitions gained at Optimus and Dyna2.5, and my own syntheses, I predict inference-controlled robots will comprise half the world’s GDP by 2045. This scenario illustrates how. — Read More
Monthly Archives: November 2025
Google DeepMind is using Gemini to train agents inside Goat Simulator 3
Google DeepMind has built a new video-game-playing agent called SIMA 2 that can navigate and solve problems in a wide range of 3D virtual worlds. The company claims it’s a big step toward more general-purpose agents and better real-world robots.
Google DeepMind first demoed SIMA (which stands for “scalable instructable multiworld agent”) last year. But SIMA 2 has been built on top of Gemini, the firm’s flagship large language model, which gives the agent a huge boost in capability.
The researchers claim that SIMA 2 can carry out a range of more complex tasks inside virtual worlds, figure out how to solve certain challenges by itself, and chat with its users. It can also improve itself by tackling harder tasks multiple times and learning through trial and error. — Read More
Disrupting the first reported AI-orchestrated cyber espionage campaign
We recently argued that an inflection point had been reached in cybersecurity: a point at which AI models had become genuinely useful for cybersecurity operations, both for good and for ill. This was based on systematic evaluations showing cyber capabilities doubling in six months; we’d also been tracking real-world cyberattacks, observing how malicious actors were using AI capabilities. While we predicted these capabilities would continue to evolve, what has stood out to us is how quickly they have done so at scale.
In mid-September 2025, we detected suspicious activity that later investigation determined to be a highly sophisticated espionage campaign. The attackers used AI’s “agentic” capabilities to an unprecedented degree—using AI not just as an advisor, but to execute the cyberattacks themselves. — Read More
Read the Report
Russia’s first AI-powered robot walked on stage to triumphant music, took a few steps, and then immediately faceplanted
Russia’s first domestically produced artificial intelligence-powered humanoid robot faceplanted during its first public demonstration this week, underscoring the challenges Russia faces in competing with more established leaders in AI and robotics like the U.S. and China.
The robot, named AIdol, was unveiled during a tech showcase at the Yarovit Hall Congress Center in Moscow on Monday. As the machine walked onto the stage accompanied by two handlers to “Gonna Fly Now,” the theme from the 1976 film Rocky, it waved to the audience before taking a few steps, losing its balance, and toppling over. — Read More
Advancing Precision Mental Health: Integrating Neuroimaging, AI, and Therapeutic Innovations
Baidu just dropped an open-source multimodal AI that it claims beats GPT-5 and Gemini
Baidu Inc., China’s largest search engine company, released a new artificial intelligence model on Monday that its developers claim outperforms competitors from Google and OpenAI on several vision-related benchmarks despite using a fraction of the computing resources typically required for such systems.
The model, dubbed ERNIE-4.5-VL-28B-A3B-Thinking, is the latest salvo in an escalating competition among technology companies to build AI systems that can understand and reason about images, videos, and documents alongside traditional text — capabilities increasingly critical for enterprise applications ranging from automated document processing to industrial quality control.
What sets Baidu’s release apart is its efficiency: the model activates just 3 billion parameters during operation while maintaining 28 billion total parameters through a sophisticated routing architecture. According to documentation released with the model, this design allows it to match or exceed the performance of much larger competing systems on tasks involving document understanding, chart analysis, and visual reasoning while consuming significantly less computational power and memory. — Read More
Common Ground between AI 2027 & AI as Normal Technology
AI 2027 and AI as Normal Technology were both published in April of this year. Both were read much more widely than we, their authors, expected.
Some of us (Eli, Thomas, Daniel, the authors of AI 2027) expect AI to radically transform the world within the next decade, up to and including such sci-fi-sounding possibilities as superintelligence, nanofactories, and Dyson swarms. Progress will be continuous, but it will accelerate rapidly around the time that AIs automate AI research.
Others (Sayash and Arvind, the authors of AI as Normal Technology) think that the effects of AI will be much more, well, normal. Yes, we can expect economic growth, but it will be the gradual, year-on-year improvement that accompanied technological innovations like electricity or the internet, not a radical break in the arc of human history.
These are substantial disagreements, which have been partially hashed out here and here.
Nevertheless, we’ve found that all of us have more in common than you might expect. — Read More
From Words to Worlds: Spatial Intelligence is AI’s Next Frontier
In 1950, when computing was little more than automated arithmetic and simple logic, Alan Turing asked a question that still reverberates today: can machines think? It took remarkable imagination to see what he saw: that intelligence might someday be built rather than born. That insight later launched a relentless scientific quest called Artificial Intelligence (AI). Twenty-five years into my own career in AI, I still find myself inspired by Turing’s vision. But how close are we? The answer isn’t simple.
Today, leading AI technology such as large language models (LLMs) have begun to transform how we access and work with abstract knowledge. Yet they remain wordsmiths in the dark; eloquent but inexperienced, knowledgeable but ungrounded. Spatial intelligence will transform how we create and interact with real and virtual worlds—revolutionizing storytelling, creativity, robotics, scientific discovery, and beyond. This is AI’s next frontier. — Read More
Google says new cloud-based “Private AI Compute” is just as secure as local processing
Google’s current mission is to weave generative AI into as many products as it can, getting everyone accustomed to, and maybe even dependent on, working with confabulatory robots. That means it needs to feed the bots a lot of your data, and that’s getting easier with the company’s new Private AI Compute. Google claims its new secure cloud environment will power better AI experiences without sacrificing your privacy.
The pitch sounds a lot like Apple’s Private Cloud Compute. Google’s Private AI Compute runs on “one seamless Google stack” powered by the company’s custom Tensor Processing Units (TPUs). These chips have integrated secure elements, and the new system allows devices to connect directly to the protected space via an encrypted link.
Google’s TPUs rely on an AMD-based Trusted Execution Environment (TEE) that encrypts and isolates memory from the host. Theoretically, that means no one else—not even Google itself—can access your data. Google says independent analysis by NCC Group shows that Private AI Compute meets its strict privacy guidelines. — Read More
The Transactional Graph-Enhanced LLM: A Definitive Guide to Read/Write Chatbots for Relational Data
The integration of Large Language Models (LLMs) with enterprise relational databases has been largely confined to read-only Retrieval-Augmented Generation (RAG) systems. This paper transcends that limitation, presenting a comprehensive architectural framework for building conversational AI agents capable of both reading and writing to a relational database via a Knowledge Graph (KG) intermediary. We will dissect the core architectural challenge, evaluate multiple design patterns — including KG as a cache, KG as a source of truth, and a sophisticated Command Query Responsibility Segregation (CQRS) pattern. This document provides an exhaustive, production-ready guide, complete with data modeling strategies, detailed prompt engineering for both query and command generation, Mermaid architecture diagrams, and best practices for security, validation, and transaction management. This is the blueprint for creating the next generation of truly interactive, data-manipulating chatbots. — Read More
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