This week, 1,100 leaders from 60 countries are gathered in London for The Economist Enterprise’s 5th Annual Commercialising Quantum Global 2026. The UK Minister for Science is on stage. NVIDIA’s VP of Quantum is on stage. The Deputy Chief Science Officer of the US Space Force is on stage. The heads of quantum at Barclays, Lloyds, Sanofi, and E.ON are in the room.

Last week I spoke at #Deep Tech WeekSeattle in #QForce — a panel on building the deep tech and quantum workforce. This week I am part of a #WomenInCloud
panel in the #WICxMarketMaker Summit 2026 talking about the physics of
commercializing deep tech.

Here are five observations around quantum from these Seattle discussions that I suspect are common to the dialogue in London this week:

Quantum is not one market.

Quantum is three markets on three different timelines. The harvest-now-decrypt-later risk is a time-critical security problem that is in the process of being addressed today by Post Quantum Cryptography (PQC). Quantum Sensing is delivering commercial advantage now. Q-CTRL’s navigation system outperformed classical GPS backups by 100X in real-world flight tests in 2025. Quantum Computing is 3-7 years from broad commercial deployment.

Talking about quantum as a single market, just like talking about AI as a catch-all, is misleading. It overlooks very different product, problem, and market attributes across three distinct technology domains.

Problem-Solution fit is unusually difficult in quantum.

The wrong question is “how can quantum improve our current processes?” That imports classical computing assumptions into a fundamentally non-classical problem space. Not every problem benefits from quantum.

The right question is: where does our most important problem hit an exponential scaling wall that quantum can solve? Does quantum offer orders-of-magnitude advantage there, or just incremental improvement?

That question requires technical depth most commercial teams don’t yet have. It’s also why the translation layer matters so much. The translators aren’t just explaining the technology. They’re working within their teams to identify the use cases, many of which are some of the most natively complex and mathematical use cases that exist. Discerning which quantum computing use cases will bring the greatest commercial value will require deep industry expertise married with category creation muscle.

The quantum tech stack requires architectural re-engineering.

Quantum is foundationally different from Cloud and AI because of this point, and the GTM implications naturally follow.

Cloud infrastructure can be plugged into existing architecture. Workloads can be moved to the cloud one use case at a time without touching the underlying architecture - same applications, same data, same workflows, just running on someone else’s servers. The integration surface is relatively shallow: APIs, connectors, lift-and-shift migrations. Architecture could be revisited during a cloud migration but it is an option, not a necessity.

AI integration goes deeper but still largely augments existing systems. You add a model to your existing data pipeline. You embed a copilot into your existing application. You call an API. The underlying architecture — the databases, the compute, the software stack — remained largely intact. AI sits on top.

Quantum requires something more fundamental: a re-engineering of the computational workflow itself. Quantum processors operate on qubits — superposition, entanglement, interference — that have no direct equivalent in classical computing’s binary logic. To extract value from a quantum processor you need what researchers call a hybrid classical-quantum architecture — where classical computers handle data preparation, pre-processing, orchestration, and post-processing, and quantum processors handle only the specific computational core where quantum advantage exists.

This is not an integration. It is a re-engineering of the computational workflow from the ground up — identifying which part of the problem benefits from quantum, designing the handoff between classical and quantum systems, managing the error correction and noise mitigation that quantum hardware requires, and then connecting the output back into classical infrastructure in a way that makes it usable.

You can’t interface a quantum processor with a classical system the way you integrate an API or migrate a workload. You have to design a hybrid classical-quantum architecture from the ground up — identifying which specific part of the problem benefits from quantum, redesigning the handoff between two fundamentally different computational paradigms, managing error correction at the hardware level, and connecting the output back into classical infrastructure in a usable form. The middleware to do this reliably at scale is still being built. The skills to design it are scarce, and the investment required is architectural, not incremental.

As a result, the Quantum ROI model is more surgical.

Cloud ROI was horizontal and broad. AI adoption has been characterized by early, rapid growth with the full ROI still to be determined. Both Cloud and AI are not comparable to quantum as their market adoption is virtually ubiquitous and the tech stacks and not transformational.

Quantum ROI follows directly from the first principles question. If you’ve identified the right problem, the investment becomes deliberate and the outcome measurable. If you haven’t, you’re spreading budget across vague exploration and getting nothing back from any of it. You measure in option value — what is the cost of not having this capability when the threshold is crossed? — not in quarterly returns. The ROI is built use case by use case and not peanut-buttered across an organization or an infrastructure layer.

Category Creation then becomes a key asset.

Even when organizations identify the right use case — where quantum provides genuine, outsized advantage — that insight has to be converted into market category creation and buyer education. In most technology markets that work happens quickly. In quantum it adds material time to an already extended GTM timeline.

You are not just selling a product. You are building the buyer, defining the category, writing the playbook, and educating a market that doesn’t yet know what questions to ask. That is not a sales problem. It is a market formation problem. And it is one of the most underestimated dimensions of quantum commercialization.

The translation layer is a key bottleneck.

The people who can sit between a quantum research team and an industry problem owner, fluent in both “languages,” are the scarcest resources in the ecosystem today. But this is more than a talent gap. The translators aren’t just bridging science and industry. They’re building a market that doesn’t yet exist. Category creation is a fundamentally different job than driving GTM into an established category.

The Economist holding this event for the fifth consecutive year is itself a signal. When the world’s most credible business publication dedicates its platform to quantum commercialization — and fills the room with 1,100 leaders from 60 countries — the question stops being “is quantum real?” and starts being “are you positioned for it?”

More in my Ecosystem Entanglement™ series — link in comments.

#QuantumComputing #EcosystemEntanglement #DeepTech #CommercializingQuantum #QuantumAdvantage #QForce

I had just graduated from college and taken a job with Xerox. (I truly had no idea what I was doing in terms of my career, which is a longer, different story). I was beginning to learn that I loved technology. I can still vividly remember sitting in a conference room playing with the Xerox Star workstation. The mouse, the icons, the graphical interface. I felt a visceral excitement. I thought the technology was magical. Xerox PARC had created what I saw as a masterpiece. I sold some of those early Star workstations to a Washington DC publishing firm.

Most people know what happened next. The ability to fully commercialize that extraordinary technology at scale was lost on Xerox. Apple and Microsoft built billion-dollar empires on what Xerox invented.

That commercialization challenge continues today, charged by hyperbolic innovation cycles. For deep tech companies it is not just difficult; it is a categorically different challenge.

The 2023 European Deep Tech Report found that deep tech ventures typically need 35% more time and 50% more capital than traditional startups to reach $5M in recurring revenue. The average time to Series A is approximately 18 months longer than for a software startup. And while a software product might reach market readiness in 18 months, deep tech products typically require five to ten years. Researchers call this gap the Valley of Death.

CB Insights analyzed hundreds of startup post-mortem reports and found one cause above all others: 42% built something with no existing market need. They failed not because the technology didn’t provide value, but because the market was not built to recognize it.

True innovation rarely originates from a focus group. Technology that is new, different, and ahead of its time does not arrive with a ready, educated market. The paradigm shift that creates extraordinary value is the same thing that makes it difficult to build the market. Creating use cases that never existed, reshaping how problems can be solved, in many cases involve significantly changing human behavior.

That is when go-to-market strategy, from the outset, is existential.

In 1994 mathematician Peter Shor developed an algorithm that changed everything we thought we knew about computational barriers.

Classical computers take what computer scientists call a brute force approach, factoring large numbers by testing possibilities sequentially, one by one. For large enough numbers the problem becomes effectively unsolvable. No shortcut and a lot of grinding. Time is the key variable that supports success.

Shor’s Algorithm doesn’t brute-force the problem. It finds the hidden periodicity in the number, an underlying pattern invisible to classical approaches, and collapses what was laborious and time-driven into something more immediately solvable. Shor’s Algorithm is the reason quantum computing threatens RSA encryption. It doesn’t work harder. It reframes the problem entirely and in doing so, collapses the time to resolution.

Most deep tech founders take a similar brute force approach, exhaustively working through every relationship, every demo, every warm introduction, one by one, with no map of the underlying pattern. It works eventually, but only if you have the time.

Deloitte’s Tech Trends 2026 is plain about the consequence: “The S-curves are compressing. The distance between emerging and mainstream is collapsing. The traditional playbook assumed you had time to get it right. That assumption no longer holds.”

Deep tech companies already operating under 35% longer timelines and 50% higher capital requirements cannot afford to brute-force their go-to-market. There is little runway for trial and error. Finding the pattern early drives outsized results.

Ecosystem intelligence is your Shor’s Algorithm.

It finds the underlying pattern — the precise buyer, the genuine white space in the ecosystem, the right channel motion, the acquirer already visible but perhaps not yet apparent — and collapses complexity into something addressable and executable.

The four gaps I see most consistently in deep tech companies aren’t four separate problems. They are the hidden periodicity waiting to be found:

· Who is the precise buyer — and what specific, urgent problem does this solve for them that nothing else does?

· Where does this sit in the ecosystem stack — genuine white space or entrenched player?

· What is the right channel motion — embedded technology, joint sales, licensing, or a combination?

· And who, in three to five years, will want to acquire this — and are you building toward them today?

Find the periodicity across those four dimensions and the market complexity begins to resolve. Target customers become easier to prioritize. The channel becomes clearer. The exit becomes visible.

Miss it and you’re running a classical algorithm in a quantum market — brute-forcing a problem that Shor’s Algorithm could solve, if you let it.

If you are enjoying the physics and want to learn more about Shor’s Algorithm from Peter Shor himself - check out this YouTube video:

Quantum physics turns out to be a surprisingly illuminating lens for a foundational pillar of company success: ecosystem strategy. A little physics. A lot of business reality. And a bit of fun.

This series is called Ecosystem Entanglement™ — A practitioner’s view on the physics of ecosystems and the commercialization of deep tech in hyperbolic markets.

It is written for founders, investors, private equity and enterprise leaders, as well as for anyone who has ever said “we’re not ready for ecosystem yet.” Feel free to bring your questions. Look forward to the dialogue.

A note on the quantum physics: we borrow from it lightheartedly throughout this series. Not to oversimplify a beautiful science, but because entanglement, superposition, decoherence, and Einstein’s famous skepticism turn out to be surprisingly illuminating lenses for the strategic challenges of building and scaling technology companies today.

The physics is real. The business challenges are real. The connection between them is — we’d argue — a little bit spooky.

Electrons don’t “know” they’re being watched. To measure them you use light (photons) which physically disturbs their wave-like nature. The disturbance isn’t intentional. It’s caused by the nature of the instrument being used to measure. The closer and more entangled the observer, the more contaminated the result.

The result is never purely the state of the particle. It is the state of the particle as changed by the act of looking at it. It’s not about consciousness or intent. It’s just physics.

Founders and innovators have exactly this problem.

You are running the newest version of your product or your latest idea past your network. Your Rolodex is your “instrument of measure.” You want feedback from your network about the value of what you have created.

Think about who your Rolodex represents. The colleague who backed you in your last role. The investor who believes in you as a person. The customer who bought from you before and wants to see you win again. The friend who has watched you build something from nothing and is genuinely rooting for you.

These are not neutral observers. They come to every conversation already entangled with you — with your history, your credibility, your shared relationship. Their bias isn’t dishonesty or flattery. It’s something more fundamental: the relationship itself changes what they measure and how they report it.

That’s the Observer Effect at work. The result is contaminated by proximity, loyalty, and genuine goodwill. Your friendlies respond not just to the technology — but to you. They cannot separate those two things and neither can you.

So you walk away thinking you have validated your product. But what you actually need is to validate your market.

The question shouldn’t be “is the technology interesting?” You need to answer a harder question — one that demos, Rolodexes, and networking events can’t answer:

How does this technology create value within the ecosystem around it?

Who are the natural buyers — and what specific, urgent problem does this solve for them that nothing else does? Where does this sit in the stack — and is there genuine white space, or does an entrenched player already own that position? What are the right channel motions — embedded technology, joint sales, licensing, or a combination? Which investors will find the thesis compelling? Who will help embed, propel and drive your vision forward? And who, in three to five years, will want to acquire this — and are you building toward them today?

Those questions span product, investors, channel, and exit. They are ecosystem questions — not technology questions. And they require an undisturbed measurement that is not proximate to you. They require you to reach into the space where you don’t know what you don’t know; built not from who you know, but from what the market actually tells you when proximity isn’t distorting the result.

That’s what a well-researched go-to-market strategy does. It removes the Observer Effect. It maps the white spaces, the product integration points, the channel dynamics, the buyer personas, the acquisition signals — and builds a clear picture of where the technology creates genuine, differentiated value in the world around it.

And once you have that strategy then take it to your network based on their alignment to your go-to-market plan. Identify exactly who you need to reach — the right level, the right function, the right company — and then use your relationships to open those specific doors. Don’t waste your time on meetings that simply seek product validation. Be deliberate and understand what each outreach could bring you in the context of your longer-term business goals, and drive the desired outcome.

Your Rolodex is not a market strategy, and it can’t truly validate your product. No matter how brilliant an offering, if there is no market for it, no ecosystem to propel it, it is not valuable. But in the hands of someone who has done the ecosystem work first — who knows precisely where the technology creates value and who benefits from that value — the Rolodex becomes a remarkably powerful tool.

Build the ecosystem GTM strategy at formation and then activate your network against that plan.

If you are enjoying the physics and want to learn more about the Observer Effect and Wave-Particle Duality - check out this YouTube video:

Quantum physics turns out to be a surprisingly illuminating lens for a foundational pillar of company success: ecosystem strategy. A little physics. A lot of business reality. And a bit of fun.

This series is called Ecosystem Entanglement™ — A practitioner’s view on commercializing deep tech in hyperbolic markets.

It is written for founders, investors, private equity and enterprise leaders, as well as for anyone who has ever said “we’re not ready for ecosystem yet.” Feel free to bring your questions. Look forward to the dialogue.

A note on the quantum physics: we borrow from it lightheartedly throughout this series. Not to oversimplify a beautiful science, but because entanglement, superposition, decoherence, and Einstein’s famous skepticism turn out to be surprisingly illuminating lenses for the strategic challenges of building and scaling technology companies today.

The physics is real. The business challenges are real. The connection between them is — we’d argue — a little bit spooky.

#ecosystems #quantum #commercialization #startup #valuecreation

Entanglement creates capabilities neither particle could achieve alone. It is the basis of quantum computing power, quantum cryptography, and quantum networking.

Entanglement can also drive errors to propagate instantly. Decoherence (interference from the environment) can corrupt the entangled state. And once entangled, you cannot cleanly separate without consequences.

Albert Einstein hated this idea. He called it “spooky action at a distance,” and he meant it as a criticism. Surely, he argued, the universe couldn’t actually work this way. There had to be a rational, local explanation. Objects affect what’s immediately around them. Nothing reaches instantly across distance.

He was wrong. In 2022, physicists Aspect, Clauser, and Zeilinger won the Nobel Prize in Physics for proving that quantum entanglement is real. Particles genuinely share a unified state regardless of distance. Some theorists believe entanglement is the fundamental glue that holds space-time itself together. That without it, the fabric of reality doesn’t cohere.

Ecosystem is the same kind of glue for a company — the non-separable connective tissue between product, partnerships, competition, revenue, retention and exit. When it’s built in from the beginning everything compounds. When it’s ignored, decoherence can set in. The connection between what you’re building and where the market is going breaks down.

Ecosystem is not simply a channel engagement model that is bolted onto a sales strategy for a Series B raise. It is not just joint pipeline calls or market placements. Ecosystem informs the strategic architecture of your company and its ultimate value. It’s a foundational component everything else should be built on from the very beginning.

When you embed ecosystem into your company from formation, your product decisions, your partnerships, your positioning, your exit thesis become entangled in that same quantum sense. Non-separable. The state of one changes all the others.

Quantum physics turns out to be a surprisingly illuminating lens for a foundational pillar of company success: ecosystem strategy. A little physics. A lot of business reality. And a bit of fun.

Starting next Tuesday I’m launching a series called Ecosystem Entanglement™ — a practitioner’s view on commercializing deep tech in hyperbolic markets.

It is written for founders, investors, private equity and enterprise leaders, as well as for anyone who has ever said “we’re not ready for ecosystem yet.” Feel free to bring your questions. Look forward to the dialogue.

A note on the quantum physics: we borrow from it lightheartedly throughout this series. Not to oversimplify a beautiful science, but because entanglement, superposition, decoherence, and Einstein’s famous skepticism turn out to be surprisingly illuminating lenses for the strategic challenges of building and scaling technology companies today.

The physics is real. The business challenges are real. The connection between them is — we’d argue — a little bit spooky.

#ecosystems #quantum #commercialization #startup #valuecreation