The contemporary global economy is experiencing an unprecedented transformation driven by the accelerating convergence of scientific fields and technological paradigms. Artificial intelligence, quantum technologies, advanced sensor systems, nanotechnology, and biomedical innovations are no longer evolving in isolation but are increasingly intertwined through complex trajectories of co evolution, institutional interaction, and market driven adaptation. This article develops a comprehensive theoretical and empirical synthesis of how scientific knowledge and technological innovation evolve in this converging environment. Drawing strictly on the provided body of literature, this study integrates evolutionary theories of science, innovation management, and technological change with emerging evidence from artificial intelligence and quantum technology research. The objective is to articulate a unified framework that explains how disruptive and general purpose technologies emerge, spread, and reshape industries, research systems, and societies.

The article first situates technological change within long run evolutionary perspectives of science, building on Kuhnian paradigm shifts, Lakatosian research programmes, and modern scientometric models of scientific growth and collaboration. These theoretical foundations are then linked to Coccia’s systemic purposeful conception of technology, which conceptualizes technological change as the outcome of cumulative, interactive, and goal oriented processes within complex socio economic systems. The dynamics of technological parasitism, technological convergence, and the rise of general purpose technologies are shown to provide an explanatory structure for understanding why certain technologies such as artificial intelligence and quantum computing achieve dominant roles in economic and scientific development.

Methodologically, this study adopts a qualitative comparative and evolutionary approach grounded in scientometric, bibliometric, and theoretical models developed in the science of science tradition. Instead of numerical modeling, the research reconstructs the pathways of technological evolution by synthesizing patterns observed across multiple research domains, including sensor technologies, cancer diagnostics, nanomedicine, artificial intelligence, and quantum computing. This approach allows for a detailed understanding of how funding structures, international collaboration networks, disruptive firms, and institutional frameworks jointly influence the direction and speed of technological progress.