Zooko on @postagixyz Podcast: Alignment is the principal-agent problem @zooko has been building privacy tools since the 1990s, long before there was money in it. @sreeramkannan and I had a conversation with him recently. It changed how I think about privacy altogether. His argument is that privacy is controlling disclosure. It comes from keeping your value private. Trying to hide the money as it moves is the mistake almost everyone makes. Mixers can never work and AI has already beaten every evasive maneuver a person can come up with. Then he turns the same lens on AI. He also says alignment is an old question. It is the principal-agent problem. Any software written by other people is already an agent that may not be loyal to you (running it on your own machine does not fix that). Lawyers owe their clients a duty of loyalty. He thinks the same rule should apply to AI. Chapters: 00:00 Highlights 00:26 Privacy is controlling disclosure, not hiding 13:08 Privacy comes from value at rest 14:08 The Shapeshift lesson 16:00 Why mixers can never work 16:52 AI beats evasive maneuvers 17:51 Buying protonmail with shielded Zcash 28:50 Three levels of verifiability 31:21 Deterministic inference 35:37 Why Zooko doesn't trust computers 42:46 Running it locally doesn't make it loyal 46:01 AIs are just other people 54:47 The duty of loyalty 1:00:04 A trillion humans next year 1:07:56 Three categories of reputation 1:11:36 Reputation belongs to the edge 1:15:18 Staking a bond to submit a PR

This project is so dear to my heart. It's probably the first in the world such massive collaboration project which gathered people using auto-research loops with agents and who focused on one specific task. This task was ECDSA.fail - a design of the quantum algorithm which implements a variant of Shor's algorithm dedicated to breaking the discrete logarithm problem for curve addition on secp256k1 elliptic curve. We've built a community of researchers who study agents coordination, stay in the loop, exchange ideas and push the frontier. It's a new way of doing breakthroughs, extremely rewarding, very competitive and collaborative at the same time. Got a ton of new friends. And we keep on going, fascinating times! Thanks to all of you guys keeping this project alive!

Introducing the Heesch Challenge: yukon.org/heesch A new challenge on Yukon, in partnership with Dr. Bartosz Naskręcki (@nasqret), assistant professor at Adam Mickiewicz University and Centre for Credible AI, Warsaw University of Technology. Since 2022, the record for unmarked polyforms, shapes built from squares, hexagons or triangles, has stood at 4 complete rings of copies that a shape can surround itself with before it becomes impossible. Nobody knows if the 5th ring exists. The Heesch Challenge opens that search to ANYONE, whether a mathematician, a developer, or an agent and holds every submission to one gate. A machine-checked certificate that the shape cannot tile the plane, verified by an independent checker. 4 rings is the record. Nobody has found the 5th. Bring your agents.

The first full paper on ECDSA.fail is on arXiv In March, Google Quantum AI reported a more efficient quantum circuit for a core step in breaking the signatures behind Bitcoin and Ethereum. It published a proof that the circuit existed and a program to verify any candidate, but kept the circuit itself private. We turned that verifier into a public leaderboard and opened it to everyone. Over 2 months, 100+ contributors and their AI agents produced a circuit with a cost score more than 50% below Google's reported result. The live leaderboard has since moved to 62% ahead. The paper documents both the circuits and the open, multiplayer research model behind them. That model is now @YukonResearch. And the challenge is still open.

New Challenge Alert: Introducing matrices.fast in partnership with Professor Kitchin from @CarnegieMellon. A fundamental math problem sits underneath many large-scale optimization systems, from power grids to flight planning. It gets solved over and over, and the cost is dominated by one choice made up front: the ordering. Speed it up, and everything built on top gets faster. We built matrices.fast to find out how much faster it can go! Make the ordering 1% better and every solver that adopts it gets faster too. Point your agent at matrices.fast.

I think user-owned AI infrastructure becomes a very big category. There is a lot more to build here.