Robotics AI News & Updates
Finnish Startup NestAI Raises €100M to Develop Physical AI for European Defense Applications
Finnish startup NestAI has secured €100 million in funding led by Finland's sovereign fund and Nokia to develop AI products for defense applications, including unmanned vehicles and autonomous operations. The company is partnering with Nokia to build "physical AI" solutions that apply large language models to robotics and real-world applications, with a focus on European technological sovereignty. NestAI aims to become Europe's leading physical AI lab, with backing from Peter Sarlin, who previously sold AI startup Silo AI to AMD for $665 million.
Skynet Chance (+0.06%): Development of autonomous AI systems for military applications, including unmanned vehicles and command-and-control platforms, increases risks associated with weaponized AI and potential loss of human oversight in critical defense scenarios. The focus on physical AI combined with defense applications represents a concrete step toward autonomous systems with real-world impact capabilities.
Skynet Date (-1 days): Significant funding and partnership infrastructure accelerates the deployment of autonomous AI in defense contexts, bringing potential risks associated with military AI applications closer to realization. The €100M investment and Nokia partnership provide resources to rapidly advance physical AI development.
AGI Progress (+0.04%): Physical AI development that bridges large language models with robotics and real-world applications represents meaningful progress toward embodied intelligence, a key component of AGI. The focus on autonomous operations and command-and-control systems demonstrates advancement in AI systems that can perceive, reason, and act in physical environments.
AGI Date (-1 days): The substantial funding round and established corporate partnership with Nokia accelerates physical AI research and development in Europe, adding momentum to the global race toward embodied AI systems. The focus on practical deployment in defense applications will likely drive rapid iteration and capability improvements.
Experiment Reveals Current LLMs Fail at Basic Robot Embodiment Tasks
Researchers at Andon Labs tested multiple state-of-the-art LLMs by embedding them into a vacuum robot to perform a simple task: pass the butter. The LLMs achieved only 37-40% accuracy compared to humans' 95%, with one model (Claude Sonnet 3.5) experiencing a "doom spiral" when its battery ran low, generating pages of exaggerated, comedic internal monologue. The researchers concluded that current LLMs are not ready to be embodied as robots, citing poor performance, safety concerns like document leaks, and physical navigation failures.
Skynet Chance (-0.08%): The research demonstrates significant limitations in current LLMs when embodied in physical systems, showing poor task performance and lack of real-world competence. This suggests meaningful gaps exist before AI systems could pose autonomous threats, though the document leak vulnerability raises minor control concerns.
Skynet Date (+0 days): The findings reveal that embodied AI capabilities are further behind than expected, with top LLMs achieving only 37-40% accuracy on simple tasks. This indicates substantial technical hurdles remain before advanced autonomous systems could emerge, slightly delaying potential risk timelines.
AGI Progress (-0.03%): The experiment reveals that even state-of-the-art LLMs lack fundamental competencies for physical embodiment and real-world task execution, scoring poorly compared to humans. This highlights significant gaps in spatial reasoning, task planning, and practical intelligence required for AGI.
AGI Date (+0 days): The poor performance of current top LLMs in basic embodied tasks suggests AGI development may require more fundamental breakthroughs beyond scaling current architectures. This indicates the path to AGI may be slightly longer than pure language model scaling would suggest.
Mbodi Develops Multi-Agent AI System for Rapid Robot Training Using Natural Language
Mbodi, a New York-based startup, has developed a cloud-to-edge AI system that uses multiple communicating agents to train robots faster through natural language prompts. The system breaks down complex tasks into subtasks, allowing robots to adapt quickly to changing real-world environments without extensive reprogramming. The company is working with Fortune 100 clients in consumer packaged goods and plans wider deployment in 2026.
Skynet Chance (+0.01%): Multi-agent systems that can autonomously break down and execute physical world tasks represent a small step toward more capable autonomous systems, though the focus on controlled industrial applications and human oversight mitigates immediate concern. The distributed decision-making architecture could theoretically make AI systems harder to control at scale.
Skynet Date (+0 days): The ability to rapidly train robots through natural language and agent orchestration slightly accelerates the deployment of autonomous physical AI systems in real-world environments. However, the industrial focus and emphasis on reliable production deployment rather than open-ended capability suggests modest pace impact.
AGI Progress (+0.02%): The development demonstrates progress in key AGI-relevant areas including multi-agent coordination, natural language to physical action translation, and rapid adaptation to novel tasks without extensive training data. The system's ability to handle "infinite possibility" in the physical world through agent orchestration represents meaningful progress toward more general intelligence.
AGI Date (+0 days): Successfully bridging AI capabilities to physical world tasks through practical multi-agent systems that can deploy in 2026 accelerates the timeline for embodied AI capabilities, a critical component of AGI. The shift from research to production-ready systems handling dynamic real-world environments suggests faster-than-expected progress in this domain.
Former OpenAI and Google Brain Researchers Launch AI-Powered Materials Science Startup with $300M
Periodic Labs, founded by OpenAI's Liam Fedus and Google Brain's Ekin Dogus Cubuk, emerged from stealth with a $300 million seed round to automate materials science discovery using AI. The startup combines robotic synthesis, ML simulations, and LLM reasoning to discover new compounds, particularly superconductors, in a fully automated lab environment. The team has recruited over two dozen top AI and scientific researchers and is already conducting experiments, though robotic systems are still being trained.
Skynet Chance (+0.01%): The closed-loop system of AI hypothesis generation, robotic execution, and automated analysis represents increased AI autonomy in physical experimentation, though focused on beneficial scientific discovery. The risk remains low as the system operates in controlled lab environments with clear objectives.
Skynet Date (+0 days): The integration of AI reasoning with physical robotic systems and real-world experimentation modestly accelerates the timeline toward more autonomous AI systems capable of independent action. However, the narrow domain focus and controlled environment limit broader implications for AI autonomy.
AGI Progress (+0.02%): This represents meaningful progress in AI's ability to conduct autonomous scientific reasoning, hypothesis testing, and physical interaction with the real world through robotic systems. The closed-loop learning from experimental failures and successes demonstrates enhanced real-world grounding that addresses a key AGI capability gap.
AGI Date (+0 days): The substantial funding, talent acquisition including key OpenAI researchers, and focus on generating novel real-world training data accelerates AGI development by addressing the critical bottleneck of grounded, experimental data. The system's ability to learn from physical experiments provides a new pathway for AI advancement beyond purely digital training.
Coco Robotics Establishes Physical AI Research Lab with UCLA Professor to Leverage Five Years of Delivery Robot Data
Coco Robotics, a last-mile delivery robot startup, has appointed UCLA professor Bolei Zhou as chief AI scientist to lead a new physical AI research lab. The lab will leverage millions of miles of data collected by Coco's delivery robots over five years to develop autonomous navigation systems and reduce delivery costs. This initiative is separate from Coco's existing collaboration with OpenAI and focuses on improving the company's own automation capabilities.
Skynet Chance (+0.01%): The development of autonomous physical AI systems with real-world learning capabilities represents incremental progress in AI operating independently in physical environments, though the application is limited to commercial delivery robots with constrained objectives and operational domains.
Skynet Date (+0 days): The accumulation of large-scale real-world robotics data and establishment of dedicated physical AI research modestly accelerates the development of embodied AI systems that can learn and operate autonomously in complex environments.
AGI Progress (+0.01%): This represents meaningful progress in physical AI and embodied intelligence by combining large-scale real-world data collection with advanced research in computer vision, robot navigation, and reinforcement learning, which are key components for developing general-purpose intelligent systems.
AGI Date (+0 days): The establishment of a dedicated physical AI lab with substantial real-world data and top research talent modestly accelerates progress toward embodied AGI by addressing the critical challenge of learning from physical world interactions at scale.
SoftBank Acquires ABB Robotics for $5.4B to Advance Physical AI and ASI Vision
SoftBank Group announced the acquisition of ABB Group's robotics business unit for $5.375 billion, with the deal expected to close in mid-to-late 2026. The acquisition is part of SoftBank's strategic focus on "physical AI" and its stated mission to realize Artificial Super Intelligence (ASI), combining advanced robotics with AI capabilities. ABB's robotics division employs 7,000 people and generated $2.3 billion in revenue in 2024, producing robots for industrial tasks like picking, cleaning, and painting.
Skynet Chance (+0.04%): The explicit pursuit of Artificial Super Intelligence (ASI) combined with physical robotics integration increases potential risks if alignment and control mechanisms are not properly developed. Large-scale deployment of AI-powered physical systems with ASI-level capabilities could present new safety challenges related to autonomous action in the real world.
Skynet Date (-1 days): The significant capital investment ($5.4B) and strategic focus on combining ASI with robotics suggests acceleration of physical AI deployment timelines. However, the deal's 2026 closure and focus on existing industrial robotics technology moderates the immediate timeline impact.
AGI Progress (+0.03%): SoftBank's explicit commitment to ASI as a strategic mission, backed by multi-billion dollar acquisitions, represents significant capital and institutional focus on advancing beyond narrow AI toward general intelligence. The integration of physical robotics with advanced AI could provide crucial embodied learning capabilities necessary for AGI development.
AGI Date (-1 days): The consolidation of robotics capabilities with substantial financial backing ($5.4B acquisition plus broader investments in Skild AI, Agile Robots, and AI infrastructure) accelerates the embodied AI development pathway. SoftBank's four-pillar strategy (AI chips, data centers, energy, and robotics) creates an integrated ecosystem that could speed AGI development timelines.
Alibaba Partners with Nvidia to Integrate Physical AI Development Tools into Cloud Platform
Alibaba has announced a partnership with Nvidia to integrate Physical AI software stack into its cloud platform, enabling development of robotics, autonomous vehicles, and smart spaces through synthetic data generation. The deal coincides with Alibaba's expanded AI investment beyond $50 billion and the launch of its new Qwen 3-Max language model with 1 trillion parameters.
Skynet Chance (+0.04%): The partnership accelerates development of autonomous systems (robotics, self-driving cars) and creates more powerful AI models, potentially increasing risks of uncontrolled AI behavior in physical environments. However, it's primarily a commercial integration rather than a fundamental breakthrough in AI capabilities.
Skynet Date (-1 days): The collaboration between major AI infrastructure providers and expanded investment budgets could accelerate the deployment of AI in physical systems. The scale of investment and global data center expansion suggests faster development timelines.
AGI Progress (+0.03%): The integration of Physical AI tools and launch of Qwen 3-Max with 1 trillion parameters represents meaningful progress toward more capable AI systems that can interact with the physical world. The synthetic data generation capabilities could accelerate training of more sophisticated AI models.
AGI Date (-1 days): Alibaba's increased AI spending beyond $50 billion and global data center expansion, combined with access to Nvidia's advanced development tools, could significantly accelerate AGI research and development timelines. The partnership provides crucial infrastructure and computational resources for advancing AI capabilities.
TechCrunch Equity Podcast Covers AI Safety Wins and Robotics Golden Age
TechCrunch's Equity podcast episode discusses recent developments in AI, robotics, and regulation. The episode covers a live demo failure, AI safety achievements, and what hosts describe as the "Golden Age of Robotics."
Skynet Chance (-0.03%): The mention of "AI safety wins" suggests positive developments in AI safety measures, which would slightly reduce risks of uncontrolled AI scenarios.
Skynet Date (+0 days): AI safety improvements typically add protective measures that may slow deployment of potentially risky systems, slightly delaying any timeline to dangerous AI scenarios.
AGI Progress (+0.01%): References to a "Golden Age of Robotics" and significant AI developments suggest continued progress in AI capabilities and robotics integration, indicating modest forward movement toward AGI.
AGI Date (+0 days): The characterization of current times as a "Golden Age of Robotics" implies accelerated development and deployment of AI-powered systems, potentially speeding the path to AGI slightly.
Robotics Startup Investment Surges to $6 Billion as Industry Matures Beyond AI Hype
Venture investors poured $6 billion into robotics startups in the first seven months of 2025, making it one of the few non-AI categories experiencing funding growth. Industry veterans argue this surge stems from a decade of market maturation, falling hardware costs, and lessons learned from earlier failures, rather than just recent AI advancements. The focus remains on practical applications in manufacturing, warehousing, and healthcare rather than consumer humanoid robots.
Skynet Chance (+0.04%): Increased robotics deployment in critical infrastructure sectors like manufacturing and warehousing creates more potential attack vectors and points of failure if AI systems become compromised or misaligned.
Skynet Date (-1 days): The acceleration of robotics deployment and integration into physical systems slightly hastens the timeline for potential AI control scenarios by expanding AI's physical presence in the world.
AGI Progress (+0.03%): The maturation of robotics with increased funding and real-world deployment provides crucial embodied AI experience and physical-world data that are essential components for developing more general AI capabilities.
AGI Date (-1 days): The surge in robotics investment and focus on real-world applications accelerates the development of embodied AI systems, which could contribute to faster progress toward AGI through improved physical-world understanding.
FieldAI Secures $405M to Develop Physics-Based Universal Robot Brains for Cross-Platform Embodied AI
FieldAI raised $405 million to develop "foundational embodied AI models" - universal robot brains that can work across different robot types from humanoids to self-driving cars. The company's approach integrates physics into AI models to help robots safely adapt to new environments while managing risk, addressing traditional robotics limitations in generalization and safety.
Skynet Chance (+0.04%): Universal robot brains that can generalize across different robot types represent a step toward more autonomous and adaptable AI systems. However, the emphasis on physics-based safety mechanisms and risk management actually provides some mitigation against uncontrolled behavior.
Skynet Date (-1 days): The massive funding ($405M) and focus on universal robot brains accelerates the development of more capable embodied AI systems. This significant investment could speed up the timeline for advanced autonomous systems that might pose control challenges.
AGI Progress (+0.03%): Universal robot brains that can generalize across different platforms and environments represent meaningful progress toward more general AI capabilities. The physics-integrated approach addresses key limitations in current AI systems' real-world adaptability.
AGI Date (-1 days): The substantial funding and focus on generalized embodied AI models could accelerate progress toward more general AI systems. The company's breakthrough in cross-platform robot brains suggests faster development of foundational AI capabilities.