The Beginner's Guide to AI Governance

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Introduction<br>I recently finished the London School of Economics' programme on AI Law, Policy, and Governance. What follows is a series built from the notes I took along the way. This has been written for people who want to understand how AI is being regulated without reading the regulations themselves. The seven lessons roughly follow the six modules of the course, but I have split and rearranged the sequence where it made more sense to do so. I also added material to cover recent updates in the world of AI, since the info provided in the LSE course itself was current as of early to mid 2025.<br>This is not a substitute for the programme, so if this subject interests you, I do recommend you follow the course. Nothing here is endorsed by LSE, and the notes reflect my own reading on the subject. AI governance moves very quickly, so some material will go out of date in a few months - if you see something that is dated, please reach out to me here.<br>The guide is divided into seven lessons:<br>Lesson 1: Why AI is a policy problem<br>Lesson 2: How AI rules get made<br>Lesson 3: Six ways to govern AI<br>Lesson 4: Why the EU wrote the AI Act<br>Lesson 5: Complying with the EU AI Act<br>Lesson 6: China, the UK, and the US<br>Lesson 7: The International arena and the future of AI governance<br>Lesson 1: Why AI is a policy problem<br>A lot of people carry misconceptions about AI from things they see on films, or see on headlines. In the programme we learnt that AI is not simply another software that can answer your questions, and neither a "sentient presence", a robot that can make decisions on our behalf.<br>AI is the use of computational algorithms to interrogate data at speeds no human can match, learning through mathematics, and analysing and recommending in ways people cannot. So, searching for a word on this document is not AI, while Spotify serving you a playlist assembled from your listening history, and what other millions of users have preferred, is AI.<br>The point here is that "unless you've agreed to define AI in a common way", setting rules or frameworks for governance becomes very difficult, in Professor Evans' own words.<br>A GPT, moving unusually fast<br>Economists call innovations that shape entire economies rather than single industries as 'general purpose technologies', GPTs. Steam power, the internal combustion engine, electricity, IT, the internet, these are all GPTs. Jovanovic and Rousseau back in 2005, identified three markers of GPTs:<br>Pervasiveness - it shows up across industries and sectors<br>Improvement - performance rises over time while the cost of use stays low<br>Innovation spawning - it makes new products and processes possible that weren't feasible before<br>Artificial Intelligence has all three, but what makes it different from its predecessors is the speed of adoption. While electricity needs grids, and computing needed hardware, networks, and services, AI runs on general purpose technologies that already exist. There is no infrastructure lag to slow diffusion down.<br>N.B - The 'GPT' in general purpose technology is an economics term that predates AI entirely. It has nothing to do with the generative pre-trained transformer architecture behind OpenAI's ChatGPT.<br>The macro picture<br>Work - General purpose AI models can automate a lot of parts of jobs that depend on thinking, learning, memory etc. Roles with high exposure to this, like telemarketing or clerical work, are the most obvious. Reports of AI ending human labour are likely to be greatly exaggerated, and new roles will emerge that don't currently exist. At the same time, displaced workers actually reaching these new roles depends on reskilling rates and individual worker characteristics, which is a very different claim from "it will all work out".<br>Employers face their own version of the problem too - the productivity gains with AI are real, but only if the workforce is trained and workflows are redesigned. There are also ethical costs to manage like job losses, AI-enabled surveillance that can erode workers rights, and management by algorithm, which strips employees of their agency in how they do their own work.<br>Regulatory competition - A global study on public trust found that 71% of people expect AI to be regulated, which aligns with an accompanying finding (Gillespie et al., 2023), that 61% believe that AI's long-term impact on society is uncertain and unpredictable. Governments hear this, but because each country or jurisdiction sets its own rules, the field has become ripe for regulatory competition. States are building more favourable environments to attract investment, and companies engage in regulatory arbitrage by simply relocating to the friendliest one.<br>There is a positive thing in being the first in this space, however. The EU's experience with GDPR produced the "Brussels Effect", where large tech firms complied with the higher European standard and then applied it globally, because it would be more costly for...