Paradigm Shift in Computing World
🧠Key Argument: What we are witnessing today is more than just a technological upgrade — it is a Kuhnian paradigm shift in the nature of computation itself. As quantum computing matures, it could redefine the foundations of AI, software development, and how we model reality — marking a transition as profound as the shift from Newtonian mechanics to relativity.
In the famous debate between Karl Popper and Thomas Kuhn, Popper argued that science is a search for truth through deductive falsification — meaning scientific theories must be testable and open to being proven false. Kuhn, however, presented a contrasting view in The Structure of Scientific Revolutions, arguing that science does not progress toward an absolute truth, but instead evolves through paradigm shifts — fundamental changes in the basic concepts and experimental practices of a scientific discipline.
To illustrate Kuhn’s idea, consider how early physics assumed that light travels in straight lines, but later discoveries showed this isn’t always true — especially in quantum mechanics and general relativity. Similarly, time was once considered constant, until Einstein showed it’s relative depending on the observer’s frame of reference. These examples illustrate that scientific “truths” are often temporary and context-dependent.
Today, we are witnessing a paradigm shift in computing.
Since the end of World War II, computation has been dominated by classical binary systems, operating on 0s and 1s. This digital paradigm underpins modern computers and artificial intelligence. However, with the advent of quantum computing, which applies principles of quantum mechanics — such as superposition and entanglement — we are no longer restricted to bits that are just 0 or 1, but now work with qubits that can represent both simultaneously and values in between (in a probabilistic sense).
This unlocks exponentially greater computational power, allowing us to solve problems that are currently intractable, particularly in AI, cryptography, material science, and complex simulations.
However, computational power isn’t the only bottleneck — energy availability is also a critical constraint. Modern data centers and AI models require massive amounts of energy. Expanding power infrastructure — such as building nuclear or hydroelectric plants — takes years, if not decades.
Therefore, quantum computing, with its potential to perform computations more efficiently, may alleviate the energy burden of AI and usher in a new computational paradigm — just as Kuhn described.