AI is clearly the real disruptor right now, but why is everyone suddenly hyper-focused on quantum? The hype often makes it sound like quantum computing is about to reshape the entire industry, yet we are still many years away from anything commercially transformative. Today’s quantum systems operate with a few dozen to a few hundred physical qubits, most of which are noisy, short-lived, and require extreme environmental control. Meaningful results typically rely on heavy error mitigation inside highly specialized laboratory conditions.
Another point that rarely gets discussed is cost per qubit. While estimates vary by architecture, the effective cost of producing and maintaining a single usable qubit is extremely high once fabrication yield, cryogenics, control electronics, calibration, isolation, and ongoing operational overhead are included. Conservative estimates often place the real-world cost per qubit in the tens of thousands of dollars or more, and these qubits are still error-prone. Scaling by orders of magnitude would require billions in infrastructure investment alone.
Equally important is where we are in our fundamental understanding of quantum computing itself. Despite decades of research, we are still at a very early stage when it comes to modeling large-scale quantum behavior. Error correction remains theoretically sound but practically expensive, coherence times are fragile, and there is no consensus on a dominant hardware architecture. In many respects, quantum computing is still in a pre-industrial phase, closer to the vacuum-tube era than to anything resembling a mature computing platform.
I was at a quantum computing talk at a University recently, and during the Q&A session, a surprising number of questions were actually about AI. How will quantum affect AI? Will it accelerate AI? When will AI run on quantum systems? That contrast felt telling. AI is already having very real, measurable impacts on society today. It is actively reshaping industries, redefining what jobs will exist, and changing how people interact with technology. Yet the average person still struggles to clearly explain what AI actually is, never mind quantum computing.
Quantum physics truly is the next frontier. It is beautiful in its complexity and already forcing revisions across physics, mathematics, materials science, and information theory. But we are still at the stage of discovering what the right questions even are, let alone finding answers. The science itself is unsettled, and the engineering challenges dwarf anything we have faced in classical computing. Over the next decade, principles of physics that we have considered fundamental truths will be rewritten at least a dozen times.
Quantum research is important and worth pursuing, but it is not an imminent replacement for classical systems. For the next decade, real-world progress will come from AI, specialized accelerators, NPUs, and distributed computing architectures working at scale. Quantum will have its moment, but today it remains a long-term scientific frontier, not a near-term commercial revolution.
For now, our focus needs to remain on the real-world challenges created by the explosive growth of AI. That means improving efficiency in ASICs, GPUs, and NPUs, addressing power consumption and heat generation, and rethinking how compute resources are deployed and shared at scale. These are solvable problems with immediate impact, and progress here will shape the practical future of computing far sooner than any speculative quantum breakthrough.
In the meantime, we need our best and brightest minds, especially the next generation of scientists and engineers, pushing the boundaries of quantum physics and imagining possibilities that are still decades away. The true “next space race” will be led by those who are just beginning their academic journeys today, and its breakthroughs will come not as short-term products, but as foundational scientific advances that reshape how we understand reality itself.