Advances in artificial intelligence have led to claims that formal standards in scientific communication are becoming obsolete. If AI systems can parse PDFs, extract figures, infer metadata, and answer questions from unstructured text, why invest in shared formats and schemas at all? This framing misunderstands the role standards play. Standards do not merely serve machines; they shape interfaces, incentives, and social norms, determining what kinds of scientific work are produced, shared, and rewarded.

There is a parallel in software development: AI-assisted coding succeeds not because of chat interfaces alone, but because it builds on decades of tooling—IDEs, language servers, testing frameworks, and structured project context. By contrast, today’s scientific AI is forced to operate on PDFs, a print-era artifact that erases structure and constrains what researchers share. Chat interfaces layered on top of PDFs may improve access, but they do not directly change the underlying incentive system nor support or encourage richer, more reliable scientific workflows.

Meaningful transformation requires standards that treat research outputs as modular, contextualized objects—linking data, code, analyses, provenance, and computation as first-class components. Such standards enable new interfaces, support AI agents, and create pathways for social change by reshaping credit, transparency, and reproducibility. In an AI-mediated future, standards are not less relevant; they become the primary substrate through which scientific values are encoded and sustained.