Existential Hope

What is intelligence for? In a rare collaboration between top universities and 3 frontier labs, we all agree that alignment should move beyond pathologizing to a positive focus on flourishing. We need north stars not just barbed wire. A close historical analogue comes from psychology. For much of the twentieth century, mainstream psychological science organized its aims around diagnosing, predicting, and treating dysfunction: depression, anxiety, psychosis, addiction, and other forms of impairment. That focus was justified and socially urgent, and it produced progress. Yet the field also discovered a systematic limitation. The constructs and instruments that reliably detect pathology do not, by default, specify what counts as a life well-lived. The turn toward positive psychology expanded the scientific target space by developing distinct theories, taxonomies, and measures for wellbeing, strengths, virtue, purpose, wisdom, meaning, and prosocial functioning, alongside interventions to boost these capacities beyond the status quo. As AI becomes embedded all over society and everyday sensemaking, a solely negative posture risks optimizing our information ecology for risk avoidance rather than human development. It may reduce catastrophic errors but leave agents in a local optimum of superficial and `soulless' assistance, where subtle misalignments abound. It also reveals that alignment is not a purely technical problem. We have to cut across vast disciplines because questions about the good life demand insights from philosophy, pychology, neuroscience, economics, and beyond. We need to work together to build AI systems that explicitly understand, model, and enhance human, animal, and ecological flourishing. The core challenge is therefore to build systems that can represent and reason about wellbeing as a structured manifold of human goods, trade-offs, and temporal dynamics, while enabling individuals and communities to retain agency over what counts as better in their context. While some may explicitly desire a system that is strictly and indiscriminately instruction-following, others must have the genuine option to choose systems configured to support their long-term growth or specific ethical commitments. This distinguishes *consented guidance*, where a user authorizes a system to help align their immediate actions with their higher-order goals, from *technocratic imposition*, ensuring that the pursuit of flourishing remains an exercise of, rather than an infringement upon, human agency. It gives me optimism that we found common ground on such a profoundly complex issue as the end game(s) of AI. Because when learning become cheap, we need to take a serious look at what intelligence is actually for.

Alignment research often has to focus on averting concerning behaviors, but I think the positive vision for this kind of training is one where we can give models and honest and positive vision for what AI models can be and why. I'm excited about the future of this work.