Scientific research is not a linear pipeline but a dynamic system built upon the ever-shifting interactions among three elements — research objects, tools, and researchers. And sustained progress depends on how quickly insights circulate within this network, not on optimizing a single node in isolation. With the impending arrival of more general artificial intelligence, we stand at a critical point in how AI might change scientific research in a systemic manner. Recent “AI for Science” achievements—from protein-structure prediction to accelerated climate simulations—have proven the value of task-level AI-driven solutions. Yet, potential still remains unrealized when these advances are siloed in disciplinary “archipelagos”. This paper argues that the real prize is systemic: AI that simultaneously expands the research objects’ data landscape (AI for Data), rewires computational research tools (AI for Computation), and co-creates hypotheses with researchers (AI for Innovation). When these three pushes converge, AI stops being merely a revolution of tools but becomes the tool of revolution—a catalyst that raises the frequency, breadth, and depth of discovery across disciplines. By enhancing the full research triad rather than isolated nodes, AI can raise the overall tempo and scope of discovery in a measured, discipline-agnostic way.
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