Introduction
Both Quantum computing (“quantum”) and Artificial intelligence (“AI”) are widely hailed as transformative technologies for the 21st century. The question “Will quantum be bigger than AI?” invites us to compare two very different technological trajectories: one (AI) already in broad deployment and commercial use, the other (quantum) still largely in its infancy. The aim here is to unpack what “bigger” might mean, examine where each stands today, how they might evolve, how they might interact, and what scenarios are plausible for the future.
Definitions and Scope
First, we need to clarify what we mean by each:
- Artificial Intelligence (AI): Broadly speaking, AI refers to systems that perform tasks that normally require human intelligence — perception, language understanding, decision-making, pattern recognition, etc. It includes sub‐fields like machine learning (ML), deep learning, natural language processing.
- Quantum Computing (QC): Quantum computing leverages quantum mechanical phenomena (superposition, entanglement) to process information in fundamentally different ways from classical computing. In short, quantum computers use qubits rather than classical bits, enabling, in principle, massive parallelism or new kinds of algorithms.
When asking “bigger”, we might mean: bigger economic impact, broader societal penetration, more fundamental change, or even existential shift in computing capacity.
Current Status: AI vs Quantum
AI today
- AI is already widely deployed — in consumer services (recommendations, voice assistants, image recognition), enterprise analytics, automation, etc.
- The market for AI is projected to grow very large: e.g., some estimates suggest the global AI market might grow from a few hundred billion USD today into the trillions within a decade.
- Because AI builds on current classical computing infrastructure (GPUs, data centres, classical algorithms) its commercialisation path is relatively clear and progressing rapidly.
Quantum today
- Quantum computing remains in an early stage: hardware is noisy, error-prone, coherence times are limited, large-scale fault-tolerant quantum computers are not yet broadly available.
- Market size is much smaller in comparison: one article notes quantum computing today may be only ~0.2 % of the AI market size.
- Many of the most promising quantum use-cases (e.g., large-scale quantum simulation, cryptography, materials discovery) are still research/proof-of-concept rather than mainstream commercial.
Thus, as of now, AI is far ahead in terms of deployment, revenue, ecosystem, and visible impact. Quantum is “waiting in the wings” with huge promise but limited real-world penetration.
Comparative Trajectory: Will Quantum Surpass AI?
We can examine several axes of comparison:
1. Economic scale and market growth
- AI is already large and growing fast.
- Quantum is much smaller now, though projections anticipate growth. For example, quantum computing revenue is expected to grow, but from a low base.
- Because AI is already so far ahead, for quantum to “be bigger”, it will either need to grow much faster or replace significant portions of existing computing paradigms.
2. Technological maturity and deployment timeline
- AI uses classical hardware, well-understood algorithms, and large ecosystems.
- Quantum still faces major hardware / error-correction / scaling challenges.
- Many experts believe quantum will have meaningful applications in the next 5-10 years, but not yet general replacement of classical computing.
- Because AI is already mature in many domains, quantum would need to leap ahead.
3. Breadth of application
- AI touches many domains: consumer apps, business intelligence, automation, healthcare, finance, etc.
- Quantum’s strength lies in specific classes of problems: optimization, simulation of quantum systems/materials, cryptography.
- So far, quantum’s potential is deep but narrow compared to AI’s breadth.
4. Fundamental transformation vs incremental improvement
- AI has brought transformational change in many sectors (automation, data insights). But many of those improvements are incremental (better models, more data).
- Quantum holds the promise of more radical change: solving previously intractable problems, enabling new kinds of simulation, opening new scientific frontiers. In that sense, quantum’s upside may be larger. For instance, quantum + AI could enable molecular simulations that are today impossible.
- But “potential” doesn’t guarantee “realised”.
5. Synergy and dependencies
- One important factor: quantum and AI are not necessarily competitors — they can be complementary. Many research papers emphasise how quantum computing could accelerate or enable advanced AI (quantum machine learning), and how AI could help quantum (error correction, optimization).
- Therefore, the question of “which is bigger” might be less meaningful than “how will the interplay evolve”.
Likely Scenarios and Strategic Outlook
Given the above, we can outline a few plausible scenarios for how quantum and AI might evolve over the next 10-20 years:
Scenario A: AI continues to dominate, quantum remains niche
In this scenario, AI expands in scope and capability, continues to drive massive economic value, while quantum finds limited niche applications (e.g., cryptography, scientific simulation) and never displaces AI or classical computing on a broad basis. The quantum market remains modest relative to AI.
Scenario B: Quantum emerges as a major accelerator, but not a replacement
Here, quantum begins to be integrated into AI and other workflows: quantum-accelerated AI, quantum co-processors, hybrid quantum-classical systems. The impact grows significantly, maybe reaching “comparable size” to AI in certain domains (e.g., drug discovery, materials, logistics), but AI remains broadly used; quantum augments more than replaces. In this case, quantum becomes a major force but in partnership with AI.
Scenario C: Quantum leaps ahead, becomes the foundational computing paradigm and outstrips AI
In the most ambitious scenario, quantum computing achieves fault-tolerant scale, large-qubit hardware becomes affordable, quantum algorithms become mainstream, and many problems currently tackled by AI or classical computing shift to quantum platforms. In this world, quantum becomes the “bigger” technology — not by replacing AI consensus usage, but by enabling capabilities far beyond what AI today can do, thereby creating new markets and value streams that dwarf those created by AI.
Which of these is most likely? Many specialists believe the progression will be closer to Scenario B than C, at least in the next decade, because of the maturity gap. Many caution against over-hyping quantum as replacing AI immediately.
Challenges and Risks
There are major hurdles if quantum is to become “bigger” than AI:
- Technical/engineering risk: Qubit coherence, error correction, scaling to large qubit counts, hardware costs. Until those are solved, many quantum claims remain aspirational.
- Economic/market risk: Investment may slow; quantum may not find commercial uses at scale quickly enough. For example, some firms reduced quantum research spending as they focus on AI.
- Ecosystem and software risk: Quantum algorithms require new paradigms; existing software/tools are classical. Building an ecosystem is non-trivial.
- Competing technologies and alternatives: Classical computing and AI hardware continue improving (e.g., specialized chips, optical computing). Quantum must overcome improvements in the “old” technologies.
- Societal/regulatory risk: Quantum’s implications for encryption and security raise regulatory and geopolitical risks. AI too has its ethical and societal risks. But quantum’s potential to disrupt cryptography and simulate materials at scale could lead to broader systemic changes.
- Timing risk: Even if quantum’s promise is immense, the timing matters: if quantum becomes impactful only after AI has saturated, then quantum’s “bigger” moment might come late or be overshadowed.
My Assessment & Conclusion
Putting all this together, here is my considered view:
- In the short to medium term (next 5-10 years), AI will continue to dominate in terms of direct economic impact, broad adoption, and visible societal change. Quantum will make incremental inroads, show high-value niche applications, but will not surpass AI in market size or reach.
- Over the medium to longer term (10-20 years), quantum has the potential to become bigger than AI in the sense of enabling previously impossible capabilities (e.g., full molecular simulation, materials discovery, cryptography/shattering, optimization at truly massive scale). But this outcome is contingent on major technical and ecosystem breakthroughs.
- Likely, the real story is one of hybridisation: AI and quantum will be deeply intertwined rather than one superseding the other. Many tasks that AI now handles may migrate to quantum-accelerated hardware or hybrid quantum-classical systems. AI models themselves may be trained or supported by quantum processors.
- The question of “bigger” may shift away from “which technology wins” toward “which combination creates new value”. In this sense, quantum’s biggest role might be as a multiplier for AI and classical computing, rather than as a standalone competitor.
- From a strategic standpoint (especially if you’re in business, research, policy or investment), it is wise to keep pace with AI developments today, and monitor quantum progress carefully — but not to assume quantum will overtake AI imminently. Being prepared for the quantum-augmented future is prudent.
In short: Yes, quantum could become bigger than AI — but not yet, and not without important caveats. At present, AI is the mainstream powerhouse; quantum is the latent giant. The next decade will likely be about how they converge, rather than one completely eclipsing the other.