Nick Vasiloglou, VP of Research at Relational AI, analyzes the key trends from NeurIPS 2025, highlighting the most impactful and under-the-radar developments for industry professionals. The discussion covers the rise of data markets through real-time attribution, the sophisticated engineering behind capable small language models (SLMs), the explosion of AI for science, and the shift towards post-training models with real-world tools.
A detailed overview of building ChatGPT Workspace Agents, covering the process from conversational setup to deployment. The session demonstrates creating a meeting preparation agent and a software review agent, highlighting features like tool integration, skills, memory, and enterprise-level admin controls.
Andrej Karpathy discusses the shift from 'vibe coding' to 'agentic engineering,' explaining why LLMs should be treated as 'ghosts'—jagged, statistical entities—rather than animals. He delves into the Software 3.0 paradigm, the limits of verifiability, and why human understanding remains the ultimate bottleneck in an age of outsourced thinking.
Demis Hassabis, CEO of Google DeepMind, outlines the path to AGI, which he predicts by 2030. He discusses the profound impact of AI on science, particularly in revolutionizing drug discovery with systems like AlphaFold, and posits that AI will enable new forms of simulation-based science. Hassabis also delves into the philosophical underpinnings of his work, viewing information as the universe's most fundamental quantity and advocating for developing AGI as a powerful tool before tackling the deeper questions of consciousness.
This podcast episode explores two X moonshot projects aimed at revolutionizing computer chips. Project Positron focused on creating specialized chips for real-time AI inference, acting as 'brains for robots'. Project Bodger took a meta-approach, using AI and inverse design to automate the chip design process itself, aiming to overcome the limitations of Moore's Law.
Demis Hassabis, CEO of Google DeepMind, outlines the remaining challenges on the path to AGI, including memory, continual learning, and true reasoning. He discusses how learnings from AlphaGo are shaping agent development, the strategic importance of powerful small models like Gemma, and his vision for AI as the ultimate tool for scientific discovery, offering a framework for identifying breakthrough opportunities and advice for founders building in the age of AI.