Context Widows Or, of GPUs, LPUs, and Goal Displacement (artificialbureaucracy.substack.com)

A recent article critically examines the role of large language models (LLMs) in scientific discovery, arguing that the prevailing debate often misframes the essential questions regarding their integration into the science-making process. Instead of focusing on what LLMs can "do" scientifically, the discussion should shift towards how these models can be effectively integrated into existing scientific frameworks, which are historically shaped by institutions and metrics developed since the 1960s. This change in perspective is significant because it highlights that LLMs are not essentially disruptors of the scientific process but rather intensifiers of existing practices, potentially reinforcing problematic institutional logic and metrics that shape research priorities. The article references concepts like "goal displacement," where measures of success evolve into ends in themselves rather than tools for genuine assessment. LLMs have the potential to amplify the output of scientific literature but also risk further entrenching a system already optimized for quantifiable metrics rather than qualitative insights. The implications for the AI/ML community are profound; rather than merely enhancing the production of academic papers, there is a pressing need to rethink how LLMs can facilitate deeper explorative and connective roles in science, thus fundamentally reshaping our understanding of knowledge generation.