Usage of Large Language Models (LLMs) and Generative AI is widespread, but building this first generation relied on large-scale scraping from scholarly publishers. As the AI market matures, LLM companies are beginning to understand that the most valuable use cases, which have real business and societal impact, depend on outputs that are accurate, reliable, and trustworthy. Delivering that level of quality requires training content that is curated, verified, and contextualized, placing scholarly publishers in a stronger, more strategic position as use of LLMs moves from novelty to infrastructure.
In this webinar, we’ll put forward a deliberately utopian vision of collaboration between publishers and LLM companies, asking what it would take to move from friction to partnership. Together, we will set out the conditions, incentives, and shared responsibilities that will make high-quality, publisher-anchored AI not just possible, but sustainable.
We’ll examine:
Where publisher content delivers unique, defensible value in AI workflows
How trust, provenance, and curation become competitive advantages in LLM outputs
What meaningful collaboration between publishers and AI companies could look like in practice
The strategic choices publishers face now to shape the next phase of AI development
This session will think practically about the steps needed to get from today's tensions to tomorrow’s opportunities.
