Power, Windows, and Dependencies

Exercise: Map it out

Contested Questions in AI Governance

How should we handle open weights as models get more capable?

When, if ever, should frontier development be slowed - and by whom?

What even are the prospects for international AI governance?

How much direct control should different governments have over AGI?

How should we handle the geopolitical implications of catastrophic dual-use capabilities? 

Tracking Hub

Finding Your Leverage Point

More Career Resources

Next Steps: Fellowships

Congratulations! 

State of Play at the Frontier

Evaluating Claims

The Evals Theory of Change

METR

Epoch

The System Card

Beyond Corporate Promises

Exercise: Briefings

Reframing the debate

Visions of Victory

Face off

Argue it out (with yourself)

Optional but highly recommended

Unit 4 will force you to go deep on one of AI Governance's key contested questions. For this test run we will focus on the question: **[How should we handle open weights as models get more capable?](https://bluedot.org/courses/ai-governance/4/2) **

In the future, groups will be able to vote on which question they are most interested in and focus on that question in their session - see the other chunk titles for things we may cover. 


The relevant question used to be "should we be open?" - kind of like asking whether we should connect our very best AI models to the internet. We all know how that went.
But that question is no longer live. Open models are now genuinely good, and there are a lot of them. Beyond the models themselves, we're seeing open training code, data, RL environments, and checkpoints. Training 70B+ models on decentralized consumer hardware is feasible. With a single H100, you can run plenty of models downloaded from HuggingFace - where you'll also find lovely abliterated and unsafe variants. That said, open models do provide real economic benefits, enable privacy-conscious adoption, and draw power away from the frontier triplets (OpenAI, Anthropic, Google DeepMind).


1. State your position. Choose an actual position on the spectrum and be specific about where you sit and why!
2. State your level of uncertainty. What are you least confident about? What would change your mind? What's the strongest argument against your position?
3. Check coherence with your strategic vision. Maybe go back to the strategic visions table. Does your position on this question match what your chosen vision implies?



Write ~1 page on where you stand!

Under construction - for now "How should we handle open weights as models get more capable?" is the way to go! 


Frontier AI Governance

The Proposal Landscape

Race Conditions, Power Concentration and Governance in the Limit