@inbook{vealegorwa2026badcomputersch1,
  author = {Veale, Michael and Gorwa, Robert},
  title = {Introduction},
  booktitle = {Bad Computers},
  booksubtitle = {How the Wider AI Stack Can Stop the Worst AI Misuse},
  chapter = {1},
  pages = {1--18},
  abstract = {This chapter sets up the book’s scope and approach. We argue that in order to understand policies and enforcement in the AI stack, we need to pay much more attention to a much wider array of intermediaries. Rather than focusing on the big AI labs training models, or on evaluations of frontier systems, we examine the past, present and emerging practices of many of the intermediaries which malicious users of AI interact with and through today: cloud providers, operating systems and hardware, network services, model hosting platforms, communications services and more. Code-based “technoregulation,” private governance and law in this area are fast growing and entwining. Trying to animate any single layer of the AI stack to fully achieve a complex policy aim against motivated, malicious users of these computational systems usually brings far too many trade-offs to rights and freedoms, if such an approach even works at all. More promising is what we call full-stack AI governance, where interventions are spread across many actors, coordinating to leave technological “loops” — governance residue like watermarks, logs, hashes and blocking capabilities — that other stack actors can “hook” into. We outline the book’s structure and approach — looking back before the generative AI rush to historical analogies that bring significant lessons for today, while deeply probing practices and research we see in the current moment for lessons for the future.},
  publisher = {MIT Press},
  location = {Cambridge, MA},
  date = {2026-10-02},
  pubstate = {forthcoming},
  url = {https://homepages.ucl.ac.uk/~ucqnmve/badcomputers/chapter/1/},
  note = {Preprint, 2 October 2026},
  langid = {english}
}