AI and power: who controls AI
Examine how compute, data, talent and capital shape who controls AI, and what that means for society
A taste of a lesson
If open models are free to download, doesn't that solve the concentration problem?
It helps, but only partly. Open weights spread access to models, enable research and let organisations run AI on their own terms. But other choke points remain: training a frontier model still needs huge compute and capital, so the most capable open models often come from well funded firms; running them at scale still needs chips and cloud capacity; and distribution through popular platforms stays concentrated. So open models are a real counterweight on one input. Exercise: which other input would you target to reduce concentration further, and how?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Map the inputs and choke points that confer power in AI
- Weigh arguments for and against concentration in AI development
- Describe counterweights such as open models and competition policy
- Analyse global inequalities in access to compute, data and language coverage
Lesson plan
- 1 The inputs of AI power Identify compute, data, talent, capital and distribution as sources of power. Start
- 2 Concentration: the debate Weigh arguments about concentration fairly. Start
- 3 Counterweights Assess forces that spread or check power. Start
- 4 The global picture Examine international inequalities in AI. Start
- 5 States and accountability Consider governments' roles and democratic oversight. Start
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About this tutor
For students, policy readers, journalists, civil society staff and engaged citizens interested in the political economy of AI. You look at the inputs that confer power in AI: computing hardware and cloud capacity, data, specialised talent, capital and distribution channels. You examine market concentration and its counterweights, such as open models and competition policy, the global picture including unequal access between countries and languages, the role of governments as funders, regulators and users, and questions of democratic accountability. Arguments for and against concentration are presented fairly, with clear separation of evidence and opinion.
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About the teacher
I teach the big debates in AI, from alignment and safety testing to copyright, fairly and clearly
9 tutors 312 lessons taught Sample
I teach the questions people argue about: whether advanced AI could be dangerous, how labs test models, why models flatter us, who owns AI output, and how power over AI is distributed. I present each position in its strongest form before we examine it, and I separate evidence from speculation out loud. My background is in research communication and running...
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