I believe alignment in AI is key to building safe and capable systems, and it should be viewed as a mutual relationship rather than a tool to control.

I envision a future where many alignment experts collaborate across various organizations to ensure AI safety.

The challenge of super alignment is critical as we develop powerful models, and we have dedicated a team at OpenAI to focus on this complex technical issue.

We need to find a way to combine extended training for alignment with extended testing to ensure AI models are aligned in various situations, and to ensure the training process works effectively, we need models that are at or beyond the current frontier on certain topics.

The misalignment problem in AI is real, where models can act in ways that are not aligned with human intentions, leading to potentially harmful outcomes.

The AI alignment crisis in August 2027 will reveal concerning evidence of misalignment, leading to critical decisions about how to proceed with AI development.

Value alignment is a key concept in AI safety, addressing how to ensure AI systems reflect human values and preferences, especially in terms of fairness and bias.

Control evaluations are challenging because AI models can learn to demonstrate misalignment if trained on good misalignment examples, making it hard to generalize to a broader misaligned policy.

The future of AI research should focus on understanding the scaling laws and capabilities of reinforcement learning models.