Our Research Agenda: The Path to AGI

In our founding announcement, we stated our mission: to build safe and broadly beneficial Artificial General Intelligence (AGI). This is not a goal we take lightly, and it requires a research agenda that is both ambitious in its scope and rigorous in its execution. This post outlines the core pillars of our technical approach.

Our work is guided by the thesis that progress toward AGI will come from the iterative development of increasingly capable and general models, coupled with a foundational commitment to safety and alignment at every stage.

1. Scaling and Architectures

The scaling hypothesis—that increasing the computational resources and data used to train models leads to greater capabilities—has been one of the most successful paradigms in AI research. We will continue to explore the frontiers of this approach. Our work here includes:

• Novel Architectures: Designing next-generation model architectures that are more efficient, scalable, and capable than current systems.
• Data Curation: Developing new techniques for creating and filtering vast, high-quality datasets to train more knowledgeable and robust models.
• Efficiency at Scale: Researching methods to reduce the computational cost of training and inference, making powerful AI more accessible and sustainable.

2. Foundational Capabilities

Beyond simply scaling, we are focused on pushing the boundaries of what AI models can do. Our research into foundational capabilities is centered on:

• Reasoning: Moving beyond pattern recognition to build models that can perform complex, multi-step reasoning and logical deduction.
• Multimodality: Developing systems that can understand and process information from multiple modalities—text, images, audio, and more—to build a richer, more comprehensive understanding of the world.
• Reinforcement Learning: Using reinforcement learning to train agents that can plan, strategize, and learn from interaction to solve complex problems.

3. Safety and Alignment

We believe that safety is not a separate field but an integral part of building powerful AI. Our safety research is woven into every aspect of our work and includes:

• Interpretability: Creating new techniques to understand the internal workings of our models. We cannot ensure the safety of systems we do not understand.
• Robustness: Stress-testing our models against adversarial attacks and unforeseen scenarios to ensure they are reliable and predictable.
• Value Alignment: Researching methods to ensure that the goals and behaviors of our AI systems are robustly aligned with human values.

This agenda is a starting point. As we learn more, we will adapt and evolve. We are committed to sharing our progress openly and collaborating with the broader research community to navigate the path to safe and beneficial AGI.