Several converging observations motivate this analysis. First, consumer technologies smartphones, laptops, and increasingly automobiles exhibit strong convergence in form, capability, and usage patterns. Performance continues to improve, but largely within stable design envelopes, suggesting maturation rather than rapid conceptual diversification. Second, in several foundational scientific domains, progress increasingly appears cumulative and technically demanding, with fewer frequent, field-defining conceptual ruptures relative to earlier historical periods, despite substantial ongoing advances. Third, many prominent recent breakthroughs most visibly in artificial intelligence have been driven primarily by scale, data availability, and engineering optimization built on established theoretical foundations, rather than by the introduction of fundamentally new elementary theories.

 Taken together, these observations raise a policy-relevant question: are we experiencing a genuine plateau in discovery, or is the character of progress changing in ways that require different interpretation and institutional response? This article synthesizes economic, technical, and scientific evidence to address that question and proposes concrete institutional actions. The sections that follow summarize the strongest empirical findings on discovery dynamics and examine their implications for the future of computation and scientific innovation.

Empirical Evidence That Discovery Is Becoming More Resource-Intensive

A growing body of economic research suggests that ideas are, empirically, becoming harder to find. Bloom and colleagues show that across multiple sectors, increasingly large research teams and resource inputs are required to achieve levels of scientific and technological advance comparable to those of previous decades. Their analysis documents a rising input-per-output ratio in innovation, indicating declining productivity of traditional research investments across many domains.

 In computing, a similar pattern is visible in hardware progress. The long-standing pace described by Moore’s Law has slowed, and its physical and economic limits are increasingly apparent. While engineering advances in architecture, specialization, and energy efficiency continue to deliver meaningful gains, straightforward transistor-density scaling is no longer the dominant driver of visible performance improvements. Technical commentary and industry analyses document this transition and its implications for device- and system-level innovation.

 Machine learning provides a complementary illustration. Empirical scaling laws show that recent improvements in large language models and related architectures have followed predictable relationships with model size, data volume, and compute expenditure. At present, capability gains are largely achieved through scale and engineering refinement rather than through simple conceptual breakthroughs, even as these systems demonstrate striking practical utility. The transformer architecture and subsequent scaling work exemplify this dynamic.

 Finally, frontier scientific experimentation increasingly depends on high-cost, capital-intensive infrastructure. Large accelerators, advanced fusion facilities, and similar installations have enabled significant progress but also impose substantial financial and organizational barriers to entry. The National Ignition Facility’s recent results illustrate both the power of such investments and the degree to which modern discovery is shaped by large-scale engineering and long-term institutional commitment.

Mechanisms Shaping the Tempo and Visibility of Breakthroughs

Several interacting mechanisms help explain why contemporary progress may appear slower or less revolutionary.

 Exhaustion of low-hanging fruit. Early phases of scientific and technological development often resolve the most accessible problems first. Remaining questions tend to be conceptually subtler and technically more demanding, increasing the effort required per unit of discovery.

 Rising costs and capital intensity. Many modern research frontiers require substantial capital investment and long planning horizons, reorganizing how research agendas are set and which questions are pursued.

 Incentive and institutional effects. Funding allocation mechanisms, evaluation metrics, and corporate investment horizons often favor lower-risk, incremental projects that reliably yield measurable outputs. Empirical economic studies indicate that such incentives can shift research portfolios away from speculative, high-risk work with potentially transformative payoff.

 Scale-driven engineering gains. In several domains particularly machine learning rapid application-level progress has been achieved through scale and engineering optimization rather than through new core theories, accounting for both impressive practical results and persistent conceptual uncertainty.

 These mechanisms are not mutually exclusive. In practice, they interact to shape both the pace and the form of contemporary discovery, reinforcing the need to distinguish between incremental improvement within mature paradigms and the rarer emergence of genuinely new conceptual frameworks.

Contemporary Technical Landscape: What It Is and Why It Appears Incremental

 Moore’s Law and the changing driver of progress. What Moore’s Law is. Gordon E. Moore observed in 1965 that the number of components (transistors) that could be economically placed on an integrated circuit was increasing at an approximately exponential rate. This empirical observation later termed Moore’s Law became both a descriptive rule and an industry-wide coordination mechanism for performance expectations. The companion concept of Dennard scaling explained why power density and clock frequency could increase alongside transistor density for several decades. When Dennard scaling ended, transistor density alone no longer produced proportional gains in performance or energy efficiency.

 Historical context: switching elements through time. The historical sequence of switching technologies illustrates how paradigm shifts reconfigure the computational design space. Early electromechanical relays, used in telegraphy and telephone exchanges, were replaced by vacuum tubes to enable high-speed electronic switching. Vacuum tubes, in turn, gave way to semiconductor transistors in the mid-twentieth century, enabling integrated circuits and dramatic reductions in size, cost, and power consumption. Subsequent integration produced the microprocessor and mass consumer computing. Each transition opened architectural possibilities that were not merely “faster relays,” but qualitatively different substrates for computation. As transistor scaling slows, industry emphasis has shifted toward architectural specialization (GPUs, tensor accelerators), heterogeneous systems, and software–hardware co-design as the principal levers of progress highly productive, but less visually dramatic per generation than earlier substrate changes.

 AI as scale, architecture, and a tool for humans (responsible usage). Recent progress in machine learning, particularly since the transformer era, illustrates how architectural innovation combined with scale (data, compute, and optimization) can yield rapid growth in system capabilities. Foundational algorithmic ideas backpropagation, gradient-based optimization, and probabilistic modeling are decades old. Transformer architectures (introduced in 2017), together with empirically observed scaling laws, enabled the construction of substantially larger and more capable models. In practice, modern AI is best understood as a powerful, general-purpose set of tools that augment human judgment and productivity when used responsibly. Framing AI primarily as an autonomous cultural actor risks obscuring the engineering-tool nature of most real-world deployments. Consequently, AI should be treated as a technology that requires explicit governance structures, ethical frameworks, and responsible operational practices, in the same way as other transformative technologies. International policy efforts and ethics frameworks increasingly aim to make that stewardship systematic.

 Emerging substrates: photonics, quantum, and neuromorphic approaches. Photonics, quantum hardware, and neuromorphic substrates each offer domain-specific advantages: photonics for extremely high bandwidth and low-latency analog or optical signal processing; quantum platforms for particular classes of simulation and optimization problems; and neuromorphic devices for energy-efficient, sparse, event-driven computation. Recent experimental progress in all three areas is substantial, including integrated photonic accelerators and continued improvements in qubit coherence and fidelity. At present, however, these substrates function primarily as specialized computational tools rather than as fully general, unifying paradigms. The critical question is whether, and under what conditions, these substrates catalyze genuinely new theoretical abstractions about computation, or whether they remain primarily hardware-level performance multipliers.

Historical Precedents

 Historical episodes demonstrate a recurring pattern: conceptual reframing that resolves persistent anomalies often precedes broad and durable scientific revolutions. Three canonical examples are particularly instructive.

 Thomas Young and the wave theory of light. In the early nineteenth century, Thomas Young’s interference experiments produced clear fringe patterns that a purely corpuscular model of light could not naturally explain. Young’s work, together with later analytic elaborations by Fresnel and others, shifted the dominant conceptual account toward the wave theory of light. At the time, debates about the nature of light were active; many contemporaries favored the corpuscular tradition established by Newton. Young’s experimental results helped displace entrenched expectations about the completeness of existing optical theory. This episode illustrates how carefully designed experiments that expose anomalies can force reconsideration of foundational assumptions.

 Max Planck and quantization. Around 1900, unresolved discrepancies in black-body radiation spectra resisted explanation within classical electrodynamics and thermodynamics. Planck’s audacious hypothesis that energy exchange occurs in discrete quanta resolved the empirical regularities and initiated the development of quantum theory. Planck’s proposal was initially puzzling to many of his contemporaries, and its implications were not immediately understood. This episode illustrates how confronting persistent empirical anomalies can motivate entirely new conceptual machinery. While some late-nineteenth-century physicists believed their field was nearing completion, historians and philosophers of science have since nuanced this narrative, noting that significant anomalies were already recognized. Nevertheless, the broader lesson holds: anomalies, combined with a novel conceptual postulate, can reconfigure an entire domain.

 Non-Euclidean geometry and Riemannian reconceptualization. The nineteenth century saw the development of geometries not constrained by Euclid’s parallel postulate, producing self-consistent alternative systems developed by Lobachevsky and Bolyai. These advances culminated in Riemann’s formulation of differential geometry, which later supplied the mathematical language used by Einstein to formulate general relativity. This case demonstrates how mathematical reconceptualization can enable new physical theories and, eventually, new technologies. Collectively, these examples show that conceptual revolutions often arise from rethinking axioms, principles, or explanatory primitives, rather than merely building better instruments.

From Tools to Ideas: Why Instruments Alone Rarely Suffice

 Substrate innovations tend to produce three broad categories of outcomes:

1. Tool amplification. The new substrate accelerates tasks already meaningful under existing theory (for example, faster dense linear algebra).
2. Niche expansion. The substrate enables new but domain-limited capabilities (for example, optical interconnects or specific quantum simulations).
3. Conceptual rupture. More rarely, a substrate motivates or enables new abstractions that reframe computation itself.

 At present, most technological progress aligns with the first two outcomes. To trigger conceptual rupture, research efforts must deliberately pursue theoretical constructs that cannot be recovered by simply scaling existing ideas.

Pathways to Create New Computational Revolutions

Drawing on empirical work on discovery difficulty and the historical precedents discussed above, the following practical strategies are intended to increase the probability of conceptual breakthroughs in computing.

 Prioritize concept-driven, long-horizon funding. Funding agencies should support multi-year programs evaluated primarily on conceptual ambition and designed to tolerate null or negative results, thereby reducing short-term optimization pressures.

 Invest in hybrid theory–experiment centers. The co-location of theoretical computer scientists, physicists, mathematicians, and systems engineers can accelerate cross-disciplinary abstraction and idea transfer.

 Build shared open infrastructure for emergent substrates. Shared photonic foundries, public quantum testbeds, and neuromorphic prototyping platforms can lower capital barriers and broaden the community exploring new abstractions.

 Support conceptual prizes and carefully framed challenge problems. Prize mechanisms should be designed to reward novel abstractions and explanatory frameworks rather than incremental performance improvements alone.

 Reform evaluation metrics. Academic and industrial evaluation systems should explicitly recognize theoretical novelty and enabling artifacts such as frameworks, formalisms, and proof concepts alongside benchmark-driven performance gains.

 Nurture interdisciplinary training that privileges abstraction. Graduate curricula that integrate mathematics, theoretical physics, and systems design can cultivate researchers predisposed to invent new computational formalisms.

 Cultivate ethical, integrative research cultures. Historical precedents from medieval Islamic philosophers like Nasir al-Din Tusi and Avicenna (Ibn Sīnā), whose ethical writings were embedded within scholarly norms, to modern institutions like Bell Labs demonstrate that enduring contributions arise from cultures that value integrity, stewardship, and intellectual risk-taking over short-term metrics. Institutional policies must actively protect exploratory scholarship and mentor researchers in responsible, open scientific practice. A revolution's impact is measured not only by its power but by the wisdom of its application.

Conclusion: Cautious, Constructive Optimism

 Computing today is technically rich: engineering improvements and substrate innovation continue to yield high-value outcomes. Yet many advances appear incremental because prevailing paradigms have matured. Historical precedents suggest that paradigm shifts follow conceptual reframing that resolves anomalies and opens new classes of inquiry. While it is impossible to guarantee when the next conceptual rupture will occur, policy choices and institutional design can increase its likelihood. Longer-horizon funding, hybrid theory–experiment centers, shared infrastructure, prizes for conceptual novelty, reformed evaluation metrics, interdisciplinary training, and a stronger ethical research culture all make conceptual breakthroughs more probable.

 This is pragmatic optimism. The next revolution in computing need not be miraculous; it can emerge from deliberate structural changes that reward abstraction, protect intellectual risk-taking, and connect new substrates to novel theoretical questions. Under these conditions, the steady and rigorous work being conducted today can plausibly seed the conceptual revolutions of the coming decades.

References

 Kuhn, Thomas S. The Structure of Scientific Revolutions. University of Chicago Press, 1962.

 Moore, G. E. “Cramming More Components onto Integrated Circuits.” Electronics (reprinted), 1965.

 Bloom, Nicholas; Jones, Charles I.; Van Reenen, John; Webb, Michael. Are Ideas Getting Harder to Find? NBER Working Paper No. w23782, 2017.

 Vaswani, A.; Shazeer, N.; Parmar, N.; et al. “Attention Is All You Need.” Advances in Neural Information Processing Systems 30, 2017.

 “Young’s experiment (double-slit).” Encyclopædia Britannica.

 “Planck’s radiation law.” Encyclopædia Britannica.

 “Non-Euclidean geometry.” Encyclopædia Britannica.

 Integrated photonics and computational accelerators. Nature reviews and technical articles.

 Quantum Index Report 2025. MIT.

 National Academies of Sciences, Engineering, and Medicine. On Being a Scientist: A Guide to Responsible Conduct in Research. 4th ed., National Academies Press, 2024.

 Tusi, Nasir al-Din. Akhlaq-i Nasiri (The Nasirean Ethics). Routledge / classical editions.