Article URL: https://thinkingmachines.ai/blog/the-future-worth-building-is-human/ Comments URL: https://news.ycombinator.com/item?id=48904905 Points: 54 # Comments: 34

The mission of Thinking Machines is to build AI that extends human will and judgment. Artificial intelligence can do more every day, but deciding what it should do is up to us: individuals, organizations, humanity as a whole. These decisions require knowledge and judgment that people acquire through continuous contact with the work, increasingly done alongside AI. Shaping the goals of advanced intelligence is also a continuous process of feedback, learning, and realignment. Most AI in use today is trained in a handful of places and then frozen. It isn’t shaped by the people it serves, and doesn’t learn much from the work they do together. Extending human will and judgment calls for AIs as diverse and distributed as people themselves are. This is the path we have chosen. We believe the future worth building is human — shaped by human knowledge, guided by human will, and decided by human judgment. What follows is the case for that future, and the work we’re doing to bring it about. AI exists to serve the work that we do. This work runs on knowledge of how things are done and what is worth doing, knowledge that is generated continuously by people engaged in the work. Think of a chef crafting a new recipe or a shopkeeper rearranging the items and prices on display. They are pursuing a complex set of goals and applying know-how that isn’t immediately legible to outsiders. This knowledge is constantly updated through feedback; it’s not a static repository that can be written into a database. It’s local — a different restaurant or shop pursues different outcomes by different means. The collective knowledge of shops and kitchens is scattered across every shopkeeper and chef.Michael Polanyi, The Tacit Dimension (1966) The dispersion of knowledge is a collective strength; it’s the source of variety, adaptability, and resilience of the overall system. It’s the reason that free markets outperform planned economies. Central planning fails not because of insufficient intelligence, but because of the nature of productive knowledge: tacit, local, fleeting, and held privately by those who acquired it through their work.Friedrich Hayek, The Use of Knowledge in Society (1945) Attempting to aggregate knowledge for the use of a centralized intelligence faces the same challenge. There are domains where intelligence alone is sufficient, and where autonomous AI doesn’t require human participation to race ahead. Two examples are chess, where the strongest engines are trained purely on self-play, and math, where frontier models are solving long-standing problems on their own. These examples share two traits. First, the goal given to AI is static and expressible: to win a chess match, to prove a theorem. Second, these domains don’t contain hidden knowledge. The rules of chess and math are universal; the board is visible to all. Outside the board, intelligence alone is not enough.