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The next chapter: 

Why quantum is the catalyst AI has been waiting for
Articles Back
By
Rav Hayer
Published: November 03, 2026  | Last updated: November 03, 2026

Generative AI has reshaped business over the last two years. It creates, reasons and automates. But the current path of classical AI is hitting a wall: real limits on energy, compute power and reliability.

 

Throwing more power at existing systems will not open up the next level of value. We need a new way to solve problems that classical computers cannot handle: extreme optimization, uncertainty and physical complexity. Quantum computing is that answer. It is moving from a science experiment to real engineering.

 

Leaders face two immediate realities. The first is defense. You cannot ignore the security risk: bad actors are harvesting encrypted data now to decrypt it later, so you need to secure your digital foundations today. The second is offense. You need a strategy to build quantum capabilities now.

 

Leaders who treat this as both a security priority and a growth engine will capture the market. Analysts estimate this market could be worth $1.3 trillion by 2035.

Infographic with three cards showing how quantum computing, sensing and communication create value. 01 Breaking barriers (computing): quantum AI handles problems with too many variables for classical AI, such as logistics networks and financial risk models. Takeaway: problem complexity, solved directly. 02 Physical interaction (sensing and simulation): quantum sensing raises precision, and simulation designs materials on a computer instead of in a lab, with uses in medical imaging, navigation and molecule design. Takeaway: precision beyond classical limits. 03 The talent gap (people and infrastructure): quantum capability can’t be bought off the shelf, so advantage goes to organizations that build teams and skills early. Takeaway: built ahead of the curve, not bought later.
Infographic with three cards showing how quantum computing, sensing and communication create value. 01 Breaking barriers (computing): quantum AI handles problems with too many variables for classical AI, such as logistics networks and financial risk models. Takeaway: problem complexity, solved directly. 02 Physical interaction (sensing and simulation): quantum sensing raises precision, and simulation designs materials on a computer instead of in a lab, with uses in medical imaging, navigation and molecule design. Takeaway: precision beyond classical limits. 03 The talent gap (people and infrastructure): quantum capability can’t be bought off the shelf, so advantage goes to organizations that build teams and skills early. Takeaway: built ahead of the curve, not bought later.

The strategic context

 

Classical AI is linear and binary. It is strong at finding patterns, but it is limited by the laws of classical physics.

 

By 2030, demand for compute will exceed what silicon can physically provide. The problems we want to solve are getting too big: global trade logistics, dynamic risk models, scientific discovery. These involve too many variables and too much uncertainty. Even with powerful GPUs, we are questioning whether we can build enough power plants to run the data centers required.

 

Quantum is different. It does not just process faster. It processes differently.

 

A classical computer tests one path, hits a wall, and tries another. It solves problems through brute force. A quantum computer evaluates every path at once and identifies the exit directly.

 

This ability to work with probability instead of only binary data is the change that matters. It allows AI to move from making guesses based on patterns to finding answers grounded in physics.

The defensive mandate

 

There is a catch. Before quantum helps us, it creates risk. It breaks our current security models.

 

Quantum computers will eventually break the encryption we use to protect everything today. This creates exposure for data encrypted now that needs to stay secret for decades.

 

Hackers are already using a "harvest now, decrypt later" strategy: stealing banking records and state secrets now, and planning to decrypt them once the hardware catches up.

 

This is why we are seeing a global shift. The World Economic Forum and national cyber agencies are telling organizations to move to post-quantum cryptography. This is not a quick fix. Critical systems like energy grids and satellites last for decades, so they need to be secure against threats that do not exist yet.

 

The mandate is clear: do not wait for the computer to be built. Secure your data now.

The pivot to engineering

 

For decades, quantum was a science topic. Now it is engineering, built around reliability and commercial use.

 

We are not replacing classical computers. We are moving to hybrid systems. CPUs handle the logic. GPUs handle AI training. Quantum processors, or QPUs, handle the complex optimization tasks the others cannot touch.

 

The conversation has changed. The theory phase is done. We are looking at commercial viability, and we care less about qubit counts and more about error rates and cost. Can it solve a specific business problem faster and cheaper?

 

This is a signal for leaders. Quantum is no longer a research topic. It is a capability you need to build, test and integrate into your strategy.

The economic opportunity

 

We need to stop thinking about just the computer. We need to think about the quantum economy, which spans computing, sensing and communication.

Side-by-side comparison of classical and quantum computing. On the left, classical sequential search is shown as a single stepped line from start to exit, with dotted dead ends, reaching the exit after several attempts. Result: correct answers, but cost rises quickly as variables grow. On the right, quantum parallel evaluation is shown as several paths fanning out from start and converging on the exit at once. Result: the best path is identified directly, even as the number of variables grows.
Side-by-side comparison of classical and quantum computing. On the left, classical sequential search is shown as a single stepped line from start to exit, with dotted dead ends, reaching the exit after several attempts. Result: correct answers, but cost rises quickly as variables grow. On the right, quantum parallel evaluation is shown as several paths fanning out from start and converging on the exit at once. Result: the best path is identified directly, even as the number of variables grows.

This ecosystem opens value in three specific ways:

 

  • Breaking barriers. Classical AI struggles once a problem has too many variables. Quantum AI is built for exactly that scale, processing massive datasets for logistics or financial risk at a speed classical systems cannot match.

  • Physical interaction. Quantum sensing raises the ceiling on precision, improving medical imaging and navigation. Quantum simulation lets us design new materials and molecules on a computer instead of in a lab.

  • The talent gap. Competitive advantage will belong to a select few. You cannot buy this capability off the shelf in 2030. You have to build the talent and infrastructure today.

Strategic outlook

 

The quantum era is already emerging.

 

The defensive play is to upgrade your encryption immediately. The proactive play is to embrace quantum AI as the driver of future value. We are leaving the era of artificial intelligence and entering the era of quantum intelligence.

 

The question for CEOs is simple: are you preparing now with intent, or will you be scrambling later with urgency?