Let me repeat the title, because it's the whole point: Is quantum a bubble?

This is purely my take. I don't expect you to agree with all of it. I only hope it helps you build your own intuition.

It's probably one of the most pressing questions you can ask right now, whether you are an investor deciding where to put money, a student betting your career on the field, or a professional already working in it.

But there's a distinction that matters before we answer it.

When people ask, “Is quantum a bubble?”, they're often asking one of two very different questions:

Is the technology fake? Or has the money gotten ahead of the reality?

Those aren't the same question. A technology can be real while the market around it is overheated. Keeping those two questions separate is where clear thinking starts.

So let me give you both sides.

Quantum computing is genuinely a different computational paradigm from ordinary computing. That word — different — is the one that matters.

This isn't about making today's computers faster. Quantum computers work in a fundamentally different way, which could let them solve certain problems that classical (today’s) computers can't solve efficiently.

Think of it as inventing a new tool you simply didn't have. Or adding a brand-new piece to the game of chess. Suddenly, whole strategies open up that didn't exist a moment ago.

As I put it in one of my LinkedIn posts: quantum computers are like planes, and classical computers are like cars. One isn't an upgrade of the other. They're built differently, for different jobs.

We don't have to take this on faith.

The clearest examples are Shor's algorithm and Grover's algorithm.

Shor's algorithm showed, theoretically, that a sufficiently powerful quantum computer could factor large integers dramatically faster than the best known classical methods. That's important because the security of widely used public-key cryptography relies on the difficulty of problems such as factoring and discrete logarithms (I wrote an episode on how today’s algorithm works):

Grover's algorithm showed a different kind of advantage: a quadratic speedup for unstructured search (I covered this in more detail in last week's episode):

But here's where a lot of the hype quietly leaks in.

Shor's and Grover's are the famous examples for a reason. Algorithms that provide a large, broadly useful quantum advantage are rare. We don't yet have a deep library of them.

Algorithm design is its own research frontier, running in parallel with the hardware. People are inventing and refining new quantum algorithms all the time. Some offer modest advantages. Some only help with narrow classes of problems. Others look promising until a clever classical method catches up.

The flip side is just as true: new algorithms continue to appear, and a major breakthrough could arrive tomorrow, in twenty years, or never.

We genuinely don't know.

That's the promise: the underlying science is real, the theoretical advantages are real, but the map of practical applications is still incomplete.

Now the hard part.

Here is why.

We’re used to working with matter in bulk. Take something as simple as mixing salt into water. At the microscopic level, an enormous number of atoms and molecules are interacting, but we don’t need to control any of them individually. We simply mix the salt and water, and the laws of physics take care of the rest.

A quantum computer is almost the exact opposite.

Instead of dealing with matter in bulk, you have to prepare and control individual quantum systems—systems that are incredibly small, incredibly sensitive, and easily disturbed. A tiny vibration, temperature change, electromagnetic fluctuation, or other environmental disturbance can corrupt the information you are trying to preserve.

That means controlling physical systems at microscopic, sometimes nanoscopic, scales with extraordinary precision. And these systems can be agonizingly fragile (check episodes #29 to #32).

In some experiments, even vibrations caused by metro trains hundreds of metres away can matter. That’s the level of fragility we are dealing with.

And the challenge becomes harder as the computer gets bigger.

A useful quantum computer doesn’t simply need lots of qubits. Those qubits need to preserve quantum information long enough to perform useful computations. The operations, or gates, need to be extraordinarily accurate. And because errors are unavoidable, the system needs powerful error correction to detect and suppress them (more on error correction here).

So adding more qubits isn’t automatically progress. A machine with a thousand noisy, unreliable qubits could be far less useful than one with a much smaller number of high-quality qubits (more on this here).

The real challenge, then, isn’t simply building a bigger quantum computer.

So, is that impossible?

Here's where it gets genuinely uncertain, and where I would urge some humility on both sides.

History keeps teaching us the same lesson: humans have a habit of blowing past “impossible.”

The semiconductor revolution. Airplanes. More recently, AI.

Time and again, roadblocks that looked permanent turned out to be temporary.

Never say never.

The story that resonates most with quantum, for me, is ASML and EUV lithography.

If you're not familiar: ASML is the dominant supplier of EUV lithography machines — the machines used to manufacture the most advanced semiconductor chips in the world that run our computers and smartphones. A very interesting video explaining the science and history behind ASML, do watch it:

The road to EUV was extraordinarily long. Companies, governments, universities, and research organizations spent decades working on the technology. There were enormous technical obstacles, huge costs, and repeated setbacks.

Eventually, it worked.

And the result is one of the most important pieces of infrastructure in modern computing. But let me be honest about this example, because it's usually told dishonestly.

ASML is a survivor.

For every ASML, there are technologies where patient money was simply lost, where investors held on for decades and the breakthrough never came.

“Never say never” cuts both ways.

The lesson isn't “quantum will definitely work because ASML did.”

The lesson is narrower and more useful:

The science can be real and the engineering can still take decades.

We understood gravity in the 17th century thanks to Newton. We didn't build rockets until the 20th.

Quantum may be similar.

Or it may not.

You can line quantum up next to computers, rockets, and AI, things people once thought were impossible, that eventually happened and changed history.

But there's one thing about quantum that is genuinely, structurally different from all of those.

And this is the part I wish more people talked about, because it's a major engine behind the funding.

National security.

Remember Shor's algorithm?

It's not just an academic curiosity. A sufficiently powerful fault-tolerant quantum computer could threaten widely used public-key cryptographic systems that protect communications, financial systems, government infrastructure, and much of the internet.

That single fact changes the economics of the entire field.

In many technologies, funding follows demand: someone wants the product, so money flows toward building it.

Quantum has that too. But layered on top is something far more powerful and far less rational:

Fear.

No government can afford to assume that a rival will never get there first.

That's because a cryptographically relevant quantum computer could eventually compromise information protected by today's vulnerable encryption systems — including data that may have been collected and stored years earlier.

So the funding isn't necessarily a bet that quantum will be profitable next quarter. In part, it's an insurance policy against being the one who's caught unprepared.

That's why the money can be unusually deep and unusually patient — and why it is less tethered to near-term commercial results than money in many other technology sectors.

It also helps explain the wave of startups.

Where governments open the taps, companies appear to catch the water, each demonstrating another proof-of-principle step to justify the next round of funding.

Some of those companies are doing real work. Some are, frankly, catching water.

But here's the truth no amount of money can buy around:

Quantum takes the time it takes.

The real open question was never whether the underlying physics works.

It's whether the engineering problems can be solved at a useful scale — and whether the patience, capital, and incentives remain strong enough to fund the long, unglamorous middle.

That part is genuinely unclear.

Now we can come back to the question we started with.

The technology and the money around it are two different things, and they can move independently.

The physics is real. The theoretical algorithms are real. The national-security motive means funding isn't likely to disappear overnight simply because commercial applications take longer than expected.

But whether any particular company's valuation, or any particular five-year timeline, is reasonable is a separate question entirely.

They often travel together.

It happened with the early internet. Plenty of companies were wildly overvalued. The internet was still real.

So a lot depends on which question you're asking — and which company you're asking it about.

The science is real.

The timeline is nobody's to promise.

And the money is a bet on patience, competition, and the possibility that the technology eventually becomes strategically or commercially important.

And that's what makes the next few years so interesting: we get to watch the science unfold.

New algorithms, better error correction, new hardware, and experiments that push the boundaries of what these machines can actually do. I will try my best to break down the science as it happens.

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