Our CEO, Dr. @bengoertzel, delivered a keynote at the Ai4 conference in Las Vegas, examining what it will take to reach AGI beyond scaling large language models. He identified reasoning, persistent memory, continual learning, creativity, and grounding in the world as central capabilities for systems that can generalize beyond their training. “The goal is to make real thinking machines,” he said. Dr. Goertzel described an AGI that can enter a novel environment, work out what is happening, ask questions when necessary, and learn from what it encounters. Such a system needs to form abstractions, generalize from experience, retain what it has learned, and connect knowledge across time. Memory was a major focus. “Lifelong learning depends on lifelong memory,” Dr. Goertzel said. He discussed OmegaClaw, which combines a continuously running agentic loop with symbolic working memory and long-term declarative and episodic memory, allowing an agent to retain experience over extended periods and build knowledge through continued interaction. The keynote also addressed what it means for such a system to be beneficial. “Alignment is ill-posed unless you ask: aligned with what?” Dr. Goertzel noted that human values are dynamic and heterogeneous, making alignment a question that extends beyond instruction following and behavioral constraints. The system's design must account for values, self-understanding, and moral agency. Dr. Goertzel also discussed the architecture being developed through Hyperon and OmegaClaw. Hyperon combines neural, symbolic, and evolutionary paradigms, while OmegaClaw brings persistent symbolic memory into a continuously running agentic system. He described these approaches as part of a broader effort to develop AGI beyond a single-model paradigm. He also addressed the infrastructure underlying AGI development, emphasizing open-source and decentralized approaches that allow a broader range of people and organizations to participate in developing and governing advanced AI systems.

How does the scale of current AI infrastructure investment affect the economics of the AI industry, and what happens if AGI arrives on a different timeline than expected? In a new analysis, our CEO, Dr. @bengoertzel, examines three possible trajectories for AI progress and the financial obligations created by today’s infrastructure buildout. His central argument is that a financial crisis could emerge across fast, moderate, and pessimistic AI scenarios, meaning that a future market correction would not necessarily tell us much about whether the underlying technology is progressing toward AGI. The analysis then considers what changes if decentralized AGI succeeds. A global network using heterogeneous hardware and token-based funding would have a different capital structure from the debt-financed infrastructure being built by major technology companies, with no comparable refinancing gates and a capacity to absorb hardware released during a downturn.