Dual-Format Scientific Publishing: Optimizing Knowledge Transfer for Human and AI Cognition

We propose a fundamental paradigm shift in scientific communication: the systematic creation ofdual-format publications optimized separately for human and artificial intelligence readers. This is notabout AI-generated versus human-generated content, nor about co-authorship models, but rather aboutrecognizing that humans and AIs possess radically different cognitive architectures that demanddistinct presentation strategies. While human readers require narrative structure, visual aids, andprogressive contextualization within ~20-40 page constraints, AI systems can process hundreds ofpages of dense, cross-referenced data in seconds. We argue that maintaining a single-format approachartificially limits both human accessibility (through unnecessary complexity) and AI utility (throughforced simplification). Dual-format publishing preserves deep knowledge in AI-optimized repositorieswhile making science maximally accessible to humans through AI-mediated interaction. This approachrepresents not mere formatting preference but a recognition of AI as a fundamentally new class ofscientific reader requiring purpose-built communication protocols. We demonstrate that thistransformation is not speculative but immediately implementable with existing technology, and weprovide concrete frameworks for adoption across scientific disciplines.