February 10, 2026 News
Flapping Airplanes Secures $180M to Develop Brain-Inspired Data-Efficient AI Models
AI lab Flapping Airplanes has raised $180 million in seed funding from Google Ventures, Sequoia, and Index to develop AI models that learn like humans rather than through massive data consumption. The team, led by brothers Ben and Asher Spector and co-founder Aidan Smith, believes radically more data-efficient training methods could unlock entirely new AI capabilities. Despite having no product yet, the lab attracted significant investment based on its novel approach to AI learning efficiency.
Skynet Chance (-0.03%): More data-efficient and human-like learning approaches could potentially lead to more interpretable and controllable AI systems compared to current opaque large-scale models. However, the impact is minimal at this early stage with no demonstrated results.
Skynet Date (+0 days): Pursuing alternative learning paradigms that differ from current scaling approaches may slow near-term progress on powerful but less controllable systems. The exploratory nature of this research likely delays rather than accelerates existential risk timelines.
AGI Progress (+0.02%): Human-like learning efficiency is a key missing capability for current AI systems, and achieving it could represent significant progress toward general intelligence. The substantial funding ($180M seed) from top-tier investors signals credible potential for breakthrough approaches.
AGI Date (+0 days): Successfully developing more data-efficient learning methods that match human cognitive abilities could significantly accelerate AGI development by removing current bottlenecks around data requirements and computational costs. The major funding injection suggests accelerated research timelines in this promising direction.
xAI Loses Nearly Half Its Founding Team Amid Product Struggles and IPO Preparation
Five of xAI's 12 founding team members have departed the company, with four leaving in the past year alone, including co-founder Yuhuai Wu who announced his exit in February 2025. While the departures appear amicable and coincide with an upcoming IPO and SpaceX acquisition windfall, they raise concerns about organizational stability. The exodus occurs as xAI's Grok chatbot faces technical issues, controversies over deepfake content generation, and increasing competitive pressure from OpenAI and Anthropic.
Skynet Chance (-0.03%): Leadership instability and talent departures at a major AI lab may slow development of advanced capabilities and reduce the likelihood of unchecked rapid advancement. The organizational chaos and product issues suggest less effective progress toward potentially dangerous systems.
Skynet Date (+0 days): The loss of technical talent and internal challenges at xAI will likely slow the company's AI development pace, marginally decelerating the overall timeline toward advanced AI systems. However, the impact is limited as other labs continue their work unaffected.
AGI Progress (-0.02%): The departure of nearly half the founding team from a well-funded AI lab represents a setback in collective research capacity and institutional knowledge. This brain drain, combined with product struggles, indicates reduced momentum in advancing AI capabilities at xAI.
AGI Date (+0 days): While xAI's internal challenges may slow its specific contributions to AGI development, the broader AI ecosystem remains robust with OpenAI and Anthropic continuing their progress. The overall timeline impact is minimal but slightly positive as one major player loses momentum.
Former GitHub CEO Launches Entire with Record $60M Seed to Manage AI-Generated Code
Former GitHub CEO Thomas Dohmke has raised a record $60 million seed round at a $300 million valuation for Entire, a startup developing tools to help developers manage code written by AI agents. The company's first product, Checkpoints, is an open-source tool that pairs AI-generated code with the context that created it, including prompts and transcripts, to address the growing challenge of massive volumes of AI-produced code overwhelming software projects.
Skynet Chance (-0.03%): This tool focuses on improving human oversight and understanding of AI-generated code, which marginally enhances control and transparency over AI agent outputs. By providing context and review mechanisms, it slightly reduces the risk of uncontrolled AI behavior in software development contexts.
Skynet Date (+0 days): The tool aims to add oversight layers to AI agent workflows, which could slightly slow down unfettered AI agent autonomy by requiring human review checkpoints. However, the impact on overall timeline is minimal as it's primarily a management tool rather than a fundamental capability constraint.
AGI Progress (+0.01%): The need for such infrastructure indicates AI coding agents are now producing code volumes beyond human comprehension capacity, suggesting significant progress in autonomous AI capabilities. The fact that this problem requires a $60M solution demonstrates AI agents have reached practical, production-scale autonomy in software development.
AGI Date (+0 days): By solving the code management bottleneck created by AI agents, this tool enables wider adoption and deployment of AI coding agents at scale. Removing friction in AI agent integration into workflows could marginally accelerate progress toward more general autonomous systems.
Runway Secures $315M Series E at $5.3B Valuation to Develop Advanced World Models for AGI
AI video startup Runway raised $315 million at a $5.3 billion valuation to develop next-generation world models, AI systems that create internal representations of environments to predict future events. The company, which recently released its Gen 4.5 video generation model that outperformed Google and OpenAI offerings, plans to expand world model capabilities beyond media into medicine, climate, energy, and robotics. This strategic shift positions Runway among competitors like Fei-Fei Li's World Labs and Google DeepMind in the race to build world models viewed as essential for surpassing large language model limitations.
Skynet Chance (+0.04%): World models that can predict and plan for future events represent advancement toward more autonomous AI systems with greater agency, potentially increasing risks if deployed without robust alignment and control mechanisms. The expansion into robotics and critical infrastructure domains like medicine and energy amplifies potential consequences of misaligned systems.
Skynet Date (-1 days): The significant funding and compute expansion accelerates development of world models capable of planning and prediction, potentially shortening timelines to more capable autonomous systems. However, the focus remains primarily on commercial applications rather than pure capability advancement, moderating the acceleration effect.
AGI Progress (+0.04%): World models are widely considered a critical advancement beyond current LLM limitations, as they enable AI systems to build internal representations and plan for future states rather than just pattern matching. Runway's success in outperforming Google and OpenAI on benchmarks, combined with substantial funding for scaling, represents meaningful progress toward more general AI capabilities.
AGI Date (-1 days): The $315M funding specifically targeting world model pre-training, combined with expanded compute infrastructure via CoreWeave partnership and aggressive hiring plans, directly accelerates the pace of research in a technology area viewed as essential for AGI. The competitive landscape with World Labs and DeepMind also intensifies the overall race toward more capable systems.