We are building the autopoietic intelligence engine—systems that generate their own supervision, architect their own successors, and embody physics as natively as they process text. The systematic path to superintelligence.
The "Scaling Laws" era of merely adding parameters to static transformers is hitting diminishing returns. The next trillion-dollar unlock is not in training larger models on the same human internet—but in building systems that move from imitation to innovation.
Learning from humans → Learning from physical reality and self-play.
Superintelligence is no longer a question of if, only who builds it and how.
To engineer Recursive General Intelligence (RGI): agents that iteratively rewrite their own cognitive and control architectures to solve challenges beyond human data distributions. Self-improving systems that generate their own training data, optimize their own learning algorithms, and evolve their own architectures—the foundation for aligned superintelligence.
A systematic path from cognitive foundation to recursive superintelligence.
Solve reasoning grounded in physical reality. Current LLMs are fast, intuitive thinkers, but prone to hallucination. We are building a Generalist Cognitive Core grounded in Latent World Models and Search-Guided Learning.
Agent-as-a-Service API outperforming Gemini 3 Pro class models on complex multi-step long-horizon planning: coding, math, legal discovery, logistics.
The Universal VLA Controller for Any Morphology. We download the Phase I Cognitive Core into physical reality. Robot control as a generative modeling problem.
SUPERSEED OS—a universal brain for the $500B industrial robotics market. The "Android of Embodied AI."
Automated Architecture Search & The Singularity Flywheel. The agents stop being the product and become the researchers.
A Superintelligence Service. An oracle for grand challenges—fusion reactor stability, protein folding, planetary logistics.
Architecting autonomous recursive evolution—infrastructure where agents design their own successor architectures. Stabilizing post-training feedback loops where models fork, optimize, and merge weights, creating a flywheel of capability scaling that remains interpretable and steerable.
Developing a generalist cognitive core integrating large multimodal reasoning VLAs with predictive world models and long-term memory. MCTS guided by dense value functions for deep lookahead. Open-ended self-play RL creates an autocurriculum with unlimited experiential data for superhuman capabilities.
Extending the cognitive core into physical reality. Cross-embodiment deployment across diverse morphologies such as humanoids, drones, autonomous vehicles. Flow matching and efficient world models enable generalist robot policies treating control as joint generative video-action processes.
Algorithms prioritizing novelty and diversity to discover behaviors outside training distributions. Beyond objective-based gradient descent—evolutionary dynamics for automated architecture search and curriculum generation. Drawing from natural evolution's power to create complex organisms from simple building blocks.
The winner will be the lab that builds the Data Generator. Our Phase I engine creates higher-quality training data than any competitor can scrape from the web. Infinite synthetic supervision.
While others build chatbots, we are building agents that can act. The real economic value of AGI is in moving atoms, not just bits. Modularity and composability of skills is the only scalable path.
Our cost of intelligence drops exponentially as our models optimize their own training code. We are not building a model, we are building a self-improving organism.
We have the architecture defined. Now we need the fuel to ignite the first recursive loop.