The money and infrastructure race is getting crowded

One thing that stands out right now: AI and quantum computing are no longer living in separate corners of tech strategy. Big companies are increasingly spreading massive R&D budgets across multiple advanced technologies at once, which McKinsey recently described as a new class of “omniscaler” competitors. [1]

That matters because both AI and quantum computing want the same things:

  • elite researchers
  • advanced chips
  • huge compute budgets
  • specialized infrastructure

And those resources are not unlimited.

The semiconductor side is especially important here. ASML pushed back this week on the idea that owning advanced chipmaking machines instantly unlocks cutting-edge manufacturing, noting that building leading-edge chips still requires enormous expertise and long-term effort. [3] That’s relevant because both modern AI systems and future quantum systems depend on extremely hard-to-build hardware ecosystems.

AI is winning the “usable today” argument

Quantum computing still gets framed as “future tech,” while AI is already showing up in products people touch daily. That gap is shaping where companies place bets.

India’s growing semiconductor push, including Tata Group’s chip ambitions, is being discussed alongside expectations that 2024 could become a breakthrough period for quantum commercialization. [5] But even in those conversations, AI tends to dominate near-term business urgency because companies can deploy it immediately.

You can see the same pressure in cybersecurity. Banks are already preparing for AI-led cyber threats and coordinating with dozens of OEMs to strengthen defenses. [6] AI is creating operational problems companies need to solve right now, which naturally pulls funding and attention toward AI deployments instead of longer-horizon quantum projects.

That’s the “frenemy” dynamic: AI may eventually benefit from quantum advances, but today it also competes with quantum for talent, capital, and executive focus.

The interesting part is where they start helping each other

Even with the competition, the overlap is getting harder to ignore.

A lot of researchers believe AI could help automate parts of quantum system design, error correction, and materials discovery. Meanwhile, quantum computing still holds long-term potential for solving certain optimization and simulation problems that are difficult for classical AI systems.

The reality, though, is more grounded than the marketing headlines. Advanced computing breakthroughs still depend on years of engineering work, supply chains, and specialized manufacturing capability — not just flashy demos. [3]

That’s probably the biggest takeaway right now: AI is moving fast because it already fits into existing computing infrastructure, while quantum computing is still trying to build its own. The next decade may be less about one replacing the other and more about which companies can afford to pursue both at the same time. [1]

Sources