Shor's Algorithm: Don't "Brute-Force" Your Commercialization Strategy
The Ecosystem Entanglement™ Series - Post 3: A practitioner’s view on commercializing deep tech in hyperbolic markets
It’s always fascinating to look back and realize you have touched a bit of history — usually without knowing it at the time.
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.