Current AI models suffer from 'jaggedness'—narrow expertise without broad generalization—due to overreliance on reinforcement learning tuned to specific tasks, unlike humans who generalize efficiently through evolved priors and emotional value functions. Pre-training has driven scaling but faces diminishing returns as data becomes scarce, prompting a shift back to fundamental research. SSI’s approach prioritizes continual learning during deployment, treating superintelligence as a dynamic process rather than a static system. This enables iterative improvement and real-world robustness, akin to how humans learn over time. Safety is central: future AGIs should care for sentient life, not just obey commands, potentially through architectures inspired by brain-like elegance. Self-play and multi-agent frameworks offer scalable training with minimal external data, fostering diverse problem-solving. Ultimately, progress depends not on compute alone but on research taste—the ability to recognize simple, powerful ideas that generalize across domains.