  Large generative AI models (LGAIMs), such as ChatGPT, GPT-4 or Stable
Diffusion, are rapidly transforming the way we communicate, illustrate, and
create. However, AI regulation, in the EU and beyond, has primarily focused on
conventional AI models, not LGAIMs. This paper will situate these new
generative models in the current debate on trustworthy AI regulation, and ask
how the law can be tailored to their capabilities. After laying technical
foundations, the legal part of the paper proceeds in four steps, covering (1)
direct regulation, (2) data protection, (3) content moderation, and (4) policy
proposals. It suggests a novel terminology to capture the AI value chain in
LGAIM settings by differentiating between LGAIM developers, deployers,
professional and non-professional users, as well as recipients of LGAIM output.
We tailor regulatory duties to these different actors along the value chain and
suggest strategies to ensure that LGAIMs are trustworthy and deployed for the
benefit of society at large. Rules in the AI Act and other direct regulation
must match the specificities of pre-trained models. The paper argues for three
layers of obligations concerning LGAIMs (minimum standards for all LGAIMs;
high-risk obligations for high-risk use cases; collaborations along the AI
value chain). In general, regulation should focus on concrete high-risk
applications, and not the pre-trained model itself, and should include (i)
obligations regarding transparency and (ii) risk management. Non-discrimination
provisions (iii) may, however, apply to LGAIM developers. Lastly, (iv) the core
of the DSA content moderation rules should be expanded to cover LGAIMs. This
includes notice and action mechanisms, and trusted flaggers. In all areas,
regulators and lawmakers need to act fast to keep track with the dynamics of
ChatGPT et al.
