Selling Dreams in the Age of Speculative Technology

In today’s financial markets, a troubling asymmetry has emerged between those who sell technological dreams and those who buy them. Founders, CEOs, and early investors often understand how distant true commercialization remains, while many young retail investors—driven by optimism and headlines—see only the promise, not the timeline. This imbalance creates a structural divide: the informed side monetizes expectations, while the uninformed side absorbs the losses.

Quantum computing offers a striking example. The 2025 Nobel Prize in Physics was awarded to John Clarke, Michel H. Devoret, and John M. Martinis “for the discovery of macroscopic quantum mechanical tunnelling and energy quantisation in an electric circuit.” Their work revealed that quantum effects—once confined to the microscopic world—can emerge in circuits large enough to see, laying the foundation for today’s superconducting qubits. Quantum systems promise radically new ways of computation through superposition, entanglement, and tunnelling—phenomena that classical chips cannot truly replicate. Yet the road from elegant physics to practical machines remains brutally hard. Qubits are fragile, error rates stubbornly high, and environmental noise constantly threatens to erase their delicate states. The Nobel Prize rightly honors a fundamental scientific breakthrough, but it doesn’t mean quantum computers are anywhere near replacing GPUs and HBM-powered systems. The discovery is profound—but the commercialization story is still very much a dream under construction.

Interestingly, quantum computing stocks have surged sharply over the past eight months (Feb 2, 2025-October 2, 2025), recovering from a major crash earlier this year. The rebound came after a steep decline triggered by Jensen Huang’s remarks suggesting that quantum computing is unlikely to become a reality in the near future. Although Huang later softened his position, I still agree with his initial assessment. The excitement that followed feels less like renewed scientific optimism and more like another speculative cycle fueled by narrative rather than progress. Most of the field’s current funding still comes from government research programs, not private demand, yet market narratives continue to portray quantum computing as an imminent revolution. When expectations race ahead of the underlying science, disappointment becomes inevitable. And when executives quietly sell shares while retail investors rush in, the market stops being a platform for innovation and instead becomes a mechanism for wealth transfer—from hope to disillusionment.

At the technical level, many current efforts in quantum computing for scientific computation neglect fundamental concepts from numerical analysis, including conditioning, stability, and error propagation. A common misconception—shaped by the success of AI—is the assumption that quantum computing will naturally follow a similar trajectory; this is a flawed analogy rather than a reasoned inference. The issue is further compounded by repeated appeals to “exponential speedups” without adequate consideration of constants, input/output costs, noise, and overall practical feasibility. In this context, claims that numerical analysis problems can be solved using quantum computing effectively amount to asserting that stable continuum computations can be carried out on a noisy, probabilistic device—a proposition that is internally inconsistent from a numerical analysis perspective.

Quantum computing may indeed prove transformative in theory, but in practice—when measured by economic efficiency, energy use, and scalability—it is likely to be outperformed, at least for now and for the foreseeable future, by the relentless progress of GPUs coupled with HBM memory. 

A similar illusion persists in robotics. Human-like robots command attention because they resemble us, yet their enormous energy demands and control complexity make them inefficient for most practical purposes. Just as airplanes do not flap their wings like birds—because biological flight relies on elastic deformation and continuous compliance adjustment that rigid structures cannot replicate—robots lacking intrinsic elastic adaptability face fundamental limits in performing human-like tasks. Bipedal locomotion requires constant dynamic stabilization, precise foot-contact transitions, and intricate sensorimotor coordination—tasks that drain vast computational and power resources. If humanoid robots continue to falter in diverse, unpredictable environments, they will remain spectacles rather than tools—initially fascinating, then repetitive—sustained by grand narratives and relentless storytelling to justify continued funding. Achieving autonomous operation without external power, while maintaining elastic adaptability and seamless, human-like motion, remains an unsolved challenge bordering on the impossible with current technology.

This critique, however, should not obscure a crucial reality: robotics is becoming indispensable in aging societies. As the working-age population shrinks and demand for healthcare and elderly support grows, automation is no longer optional. In hospitals, nursing facilities, and private homes, assistive robots can reduce physical strain on caregivers, handle routine logistics, and help older adults maintain independence. Robotics that prioritizes function over form offers genuine social and economic value in a world where human labor is increasingly scarce.

Recently, some robotics companies have demonstrated fine hand manipulation—one of the field’s most difficult technical challenges. While impressive as demonstrations, it remains unclear whether such extreme dexterity is truly necessary in real industrial, medical, or service settings. In most cases, “good-enough” precision suffices, and the pursuit of human-level dexterity risks over-engineering. These efforts often resemble demo-driven academic R&D, where increased sophistication undermines robustness and long-term consistency.

Viewed this way, simpler robotic platforms—wheeled systems, tracked robots, or articulated arms—may lack theatrical appeal, but they are already delivering measurable gains in manufacturing, logistics, and healthcare. Yet, as recent stock-market trends show, investment continues to flow disproportionately toward humanoid robots, while practical collaborative and industrial systems receive far less attention. It is a familiar pattern: in technology markets, spectacle often outperforms substance—at least until the stories stop selling.

Another example can be found in nuclear fusion research, a field that has promised a breakthrough for decades. Fusion is often described as the ultimate clean energy source—“the power of the sun on Earth.” The vision is inspiring, but the reality remains punishingly complex. Each new experimental reactor demands tens of billions of dollars and decades of construction, yet delivers only marginal progress toward the elusive goal of sustained net energy gain. Despite these persistent difficulties, the narrative of “near-future success” keeps the funding cycle alive, attracting new investment every few years. In practice, fusion has become less a race toward commercialization and more a long-running story that sells hope—a scientific ideal repackaged as a financial instrument.

While it is important to pursue ambitious dreams, it is equally crucial to acknowledge reality and advance them with the right balance of expertise and accountability—optimizing personnel and funding to safeguard public resources rather than draining a nation’s precious tax money. 

The contrast is instructive: technological progress often advances through practicality, not spectacle. Across fields, the message is the same. Imagination drives innovation, but expectation must remain grounded in physics, economics, and time. When markets price in dreams faster than science can deliver, hope turns volatile. Investors — especially younger ones — deserve not cynicism but realism: a recognition that even the most promising technologies evolve incrementally, not explosively. Innovation indeed thrives on belief, but belief is strongest when guided by understanding