tbd/con 2026 | the future of ai is tbd.
AI isn't arriving cleanly. a virtual conference for people who have more questions than answers. september 23-24, 2026, on gather.town.
Europe 2031 — What getting AI wrong means for us
A five-year scenario about AI and Europe's impending slide into irrelevance, with a 2034 epilogue that describes how the collapse of the European model could have been prevented.
The Era of Experience & The Age of Design: Richard S. Sutton, Upper Bound 2025
Request for Proposals: The Launch Sequence | IFP
Apply to our rolling effort to find, scope, and build the most important projects to prepare the world for advanced AI
The EU AI Act Is Ready – Interdependent Thoughts
A final draft of the European AI Regulation is circulating (here’s an almost 900 page PDF). The coming days I will read it with curiosity.
What the hell happened with AGI timelines in 2026?
Import AI 461: "Alignment is not on track"; FrontierCode; and synthetic research interns
Where are your agents right now?
A Short Guide to Data Strikes and Conscious Data Contribution in the Context of 2026 Frontier AI
Back to the basics of data leverage.
Inside the AI Index: 12 Takeaways from the 2026 Report | Stanford HAI
The annual report reveals a field hitting breakthrough capabilities while raising urgent questions about environmental costs, transparency, and who benefits from the technology.
Selective Optimism: a critique of AI 2040
Some context for this post: I’ve been working part-time as a consultant for the AI Futures Project over the last year.
The future belongs to those who can refute AI, not just generate with AI
Why verification, not prompting, could shape the next decade of engineering
1/7 Proud to share our paper, accepted as an oral at ICML '26! We highlight how Big Tech’s influence on AI R&D drives damaging outcomes, and what we as researchers can do about it. We also discuss underlying economic causes. See link for paper, and below for a brief summary arxiv.org/abs/2512.03077
1/7 Proud to share our paper, accepted as an oral at ICML '26! We highlight how Big Tech’s influence on AI R&D drives damaging outcomes, and what we as researchers can do about it. We also discuss underlying economic causes. See link for paper, and below for a brief summary arxiv.org/abs/2512.03077
