Project summary
Feature-length documentary that captures the intellectual and cultural zeitgeist surrounding frontier AI at the moment before AGI.
We conduct in-depth interviews and present a rigorous, multi-perspective exploration of AI existential risks and potential benefits. We also created pedagogical explanations of Recursive Self-Improvement and related concepts.
Our goal is to deepen public understanding and encourage broader engagement with questions of AI safety and governance. We specifically want to help elites and people in power to understand existential risk - not just economic impacts like job loss. We will convey the main ideas behind runaway AI improvement (RSI), Gradual Disempowerment, Alignment, etc.
The tone of the documentary will be sophisticated, but also accessible to no-experts.
Some of the people we've interviewed so far:
Sizzle reel: https://www.youtube.com/watch?v=5RSv3wmDIpY
Project website: https://aimachinegod.com/
What are this project's goals? How will you achieve them?
To release and widely distribute a documentary film that captures this unique moment in history and educates the public about X-risk.
How will this funding be used?
The majority of funds will be used to pay an editor. Some of it will be used for equipment and travel expenses to promote the film and obtain a distribution agreement.
Who is on your team? What's your track record on similar projects?
Steve Hsu: Professor of theoretical physics and serial startup founder. Hsu is the host of the Manifold podcast and has founded several venture-backed startups. He is an experienced team leader and also an exceptional communicator of technical ideas.
John Greer: Writer and former startup founder who has shot over thirty long-form interviews including with prominent people in AI safety, philosophy, technology, and the broader Bay Area intellectual scene.
Lei Huang: AI research scientist and ethnographer with experience as a storyteller. Huang has produced and hosted talks and forums related to both technology and history.
What are the most likely causes and outcomes if this project fails?
The most likely failure is the project isn't watched by as many people as we hope and it doesn't sway public opinion or cause more people to work in AI safety.
How much money have you raised in the last 12 months, and from where?
We've been self-funded and are just starting to raise money for the project.