On September 12, Anthropic CEO Dario Amodei called publicly for independent monitoring of frontier AI models, industry-wide regulation, and a global framework for managing increasingly capable systems. Within hours, OpenAI CEO Sam Altman and Elon Musk expressed support for the direction.^[2][6]

This follows Amodei’s June proposal that governments adopt mandatory pre-release testing for frontier AI, drawing an analogy to regulatory approaches used in areas such as pharmaceuticals and aviation. His position was clear: if a model does not meet sufficiently high safety standards, its release should be blocked or reversed.^[3]

At Seattle’s Mobile Future Forward conference on September 10, Chetan Sharma interviewed T-Mobile President of Technology and Chief Technology Officer John Saw in a session titled “Engineering the Nervous System of Physical AI.” The program described a future in which intelligence is “dissolving into the physical world — embedded in surfaces, infrastructure, and environments — creating a layer of continuous perception and response that operates below the threshold of human awareness and above the threshold of human control.”^[1]

These two parallel conversations materially impact how AI is governed. One speaks to governing AI before it is released. The other is about how to perceive and respond to AI once it is embedded in the physical world.

An important question emerges: How do we regulate AI once it is no longer simply a model, but an evolving system operating in the real world?

That is where the real risk lies. The ability to govern in production relies on the distinction between Human-Time and AI-Time.

Pre-release testing is necessary. It is not enough.

Testing an AI model before release is essential. It gives regulators and developers a point-in-time assessment of what a system can do and whether it meets defined standards.

But the system that is tested before release may not be the system operating months later.

AI-enabled systems can be updated. They can gain access to new tools and data. They can interact with other systems and agents. They can operate in environments that were not present during testing. In some applications, they may learn from real-world use or change through approved update processes.

The FDA already recognizes the reality and the challenges of evolving AI models in medical devices. Its framework for predetermined change control plans allows certain planned modifications to AI-enabled devices to be managed over the product lifecycle, while emphasizing ongoing monitoring, evidence, risk management and the ability to detect and respond to changes in performance.^[4]

The European Union’s AI Act similarly extends obligations beyond deployment. High-risk AI providers are required to monitor systems throughout their lifetime, and serious incidents must be reported within specified timeframes.^[5][13]

These are important steps, and they acknowledge that production AI needs to be regulated and monitored. However, they still largely operate within a Human-Time regulatory model: something happens, the operator records it, and a regulator reviews the evidence afterward.

The harder question is what happens when the system itself is operating faster than humans can observe, interpret or reconstruct events. This is the AI-Time problem.

One Potential Answer: The Network

John Saw speaks about networks that provide “deterministic connectivity required for real-time control,” where the network stops being “a passive data pipe” and becomes “the active nervous system of physical intelligence itself.”^[8]

He describes the transition from informational tokens to what he calls “kinetic tokens” — data that drives action in the physical world, whether through a drone, robotic arm, vehicle or other machine. These applications require ultra-low latency, synchronization across devices and continuous learning at the edge.^[7][8]

T-Mobile’s Chief Network Officer, Ankur Kapoor, who also spoke at MFF, said in August that machine-time decisions are already production reality inside the carrier: the network responds to real-time demand and adjusts itself, and “that all happens completely autonomous. No humans involved.”

He named what the network holds that no one else does: “Nothing in the physical AI space is going to work without context … We understand what the context is.”^[14]

AT&T sees the same thing from the other side of the industry. Shawn Hakl, SVP of Product for AT&T Business and another MFF speaker, describes the carrier’s custody of an AI transaction end to end: “I have the edge device. I have a RAN (Radio Access Network). I can take that packet, put the deterministic routing around it. I can make it a private connection with policy controllers built in. I can transport that to Nvidia compute that sits at the edge of the network.”^[20] His design rule for the agentic era is blunt: “You’ve got to make it agent consumable.”^[20] And AT&T’s leadership has put the dependency in one line: “AI doesn’t exist without our connectivity … plain and simple.”^[21]

This “layer of continuous perception and response” – the network - could enable AI governance, not only when a model ships, but as it grows and evolves in its real-world applications. That makes the network a candidate for the governance layer. A network is no longer simply carrying information from one place to another. It becomes part of the environment in which AI perceives, decides and acts.

Regulation requires evidence in AI-Time

Humans work in hours, minutes and seconds. The systems increasingly making decisions around us may operate in microseconds or faster. So if those systems are going to be regulated, how do we establish what happened, when it happened, and in what sequence, reliably, at the speed at which the system actually operated?

That means the nervous system Saw describes must do more than simply carry the action. It has to provide the context Kapoor describes and more: timestamp the action contemporaneously, sequence it correctly against every other event it touched, and record it in a form that cannot be altered and that a third party can verify.

Regulators have never operated at the speed of the systems they regulate. The SEC does not stop a trade mid-transaction. It reconstructs the sequence afterward, and it can only do that because the rules force every venue to stamp each event against a common clock. Regulation of fast systems has always been evidentiary rather than real-time, and that evidence has always been based on time.

AI-Time in Financial Markets

Financial markets offer a useful analogy, not because physical AI is identical to electronic trading, but because financial regulators have already confronted the same fundamental problem: you cannot put a human regulator inside every transaction. Instead, the regulatory system creates an evidentiary record, and the regulator reconstructs what happened afterward with a confidence that depends entirely on the quality of that record.

The SEC’s Consolidated Audit Trail requires every reportable event to be captured so it can be linked through the lifecycle of an order, from generation through routing, modification, cancellation and execution. It also sets clock-synchronization standards by participant: exchanges must hold their clocks within 100 microseconds of NIST time, broker-dealers within 50 milliseconds, and manual-order clocks within one second, while firms whose systems produce timestamps finer than a nanosecond must truncate at the nanosecond.^[9][10] An order on a US equities exchange today is matched in microseconds.

Spoofing, placing orders you intend to cancel to move the market price, is prosecuted by reconstructing sequence and intent from those records. In 2020 the CFTC (Commodity Futures Trading Commission) ordered JPMorgan to pay $920 million after its traders placed “hundreds of thousands” of futures orders “with the intent to cancel those orders prior to execution.”^[11] That case covered eight years of conduct, took years to reconstruct, and the actors were people.

Visa reported in December 2025 that agent-initiated transactions had been completed on its network with more than 100 ecosystem partners and projected that “millions of consumers” would be using AI agents to buy things by the 2026 holiday season.^[12]

With all this in mind, now put an AI agent on each side of a trade, negotiating and executing in microseconds across venues whose clocks are allowed to differ by tens of milliseconds. Which order came first? Was the cancel sent before or after the fill? Did the agent see the price it claims it saw? The evidence a regulator would need is an event record that is contemporaneous to the microsecond, sequenced across systems that do not share a clock, unalterable after the fact, and referenced to something the agent’s operator does not control.

And today’s infrastructure does not deliver time across disparate systems at AI-Time precision. The same rulebook that lets firms timestamp in nanoseconds permits two broker-dealers’ clocks to disagree by up to 50 milliseconds, fifty thousand times less precise than the one-microsecond events they are recording.^[9][10]

Who establishes the authoritative sequence? And, more importantly, who can independently prove that sequence?

What happens when the regulated system is also the recordkeeper? A log tells us what a system says happened. A regulatory evidentiary layer needs to establish that the record itself can be trusted.

That distinction matters enormously when the entity generating the evidence is also the entity being regulated.

The Industry’s Current Answer: Guardrails, Monitoring and a Human Who Verifies

The technology industry has not ignored what happens after launch. At GTC in March, Nvidia CEO Jensen Huang put the risk plainly: “Agentic systems in the corporate network can access sensitive information, execute code, and communicate externally. Obviously, this can’t possibly be allowed.”^[15]

Nvidia’s remedy is a build-deploy-run stack in which run-time guardrails “actively block unsafe model behavior” and “with continuous monitoring … model safety becomes enforceable.” However, by May, Nvidia’s own engineers were writing that scaling agents “with structural transparency and operational integrity requires more than runtime guardrails.”^[16]

Qualcomm CEO Cristiano Amon described the operating model for an agent on a device in one sentence: “It plans, it executes, you verify.”^[17] Kedar Kondap, Qualcomm’s SVP of Compute and Gaming, expects “hybrid orchestration” of AI across device and cloud to be “where the industry is going”;^[18] Ronnie Vasishta, Nvidia’s SVP of Telecom, describes AI-native networks that are “self-adapting.”^[19]

Taken together, these perspectives describe a world in which AI action is distributed across the device, edge, network and cloud, and in which the infrastructure itself is continuously changing.

Put simply: guardrails decide whether an action is allowed. Monitoring tells the operator what the system is doing. Verification adds a human check. But none of those, by themselves, answers a regulator, an insurer, a counterparty or any other independent party asking:

What exactly happened? When did it happen? What else was happening at the same time? Was the action appropriate? And how do we know the record is genuine?

Regulating Production AI in AI-Time

None of this is an argument against pre-release regulation. It is an argument that pre-release regulation is just the first half of the problem.

Pre-release testing tells you what the model was capable of on the day it shipped. It tells you nothing about what the model actually did on a Tuesday six months later, at 2:14 p.m. and 37 microseconds, when an agent was negotiating with a system it was never tested against.

This second half of AI governance is not simply post-deployment monitoring. It is a post-deployment regulatory engagement model for AI that evolves and acts in the real world, one that treats every action the system takes as an event that happened at a specific instant, in a specific order, relative to every other event it touched.

That is where governance has to live. Today no one outside the system that acted can prove any of it, and no one can certify, for a regulator or any other third party, an auditable sequence of events across the parties involved.

The next generation of AI regulation therefore requires something we do not yet have at scale: an evidentiary layer capable of operating in AI-Time.

It needs a trusted, compliant and auditable record whose temporal resolution, sequencing and integrity are appropriate to the system being governed, and whose credibility does not depend entirely on the organization that operated the AI.

If AI is going to live in the physical world, regulation needs to meet it there.

Let’s have this conversation now, while the world’s AI leadership is acknowledging the need to monitor, regulate and control AI models.

___________________________________________________________________________

Part of the Ecosystem Entanglement™ series — a practitioner’s view on the physics of ecosystems and the commercialization of deep tech in hyperbolic markets.

Author Lori Salow Marshall is the Co-founder and Managing Member of The Quantum Links™ Group.

Disclosure: Marshall advises companies in the physical AI space who are seeking to solve the regulatory and management challenges of AI-Time vs. Human-Time.

Sources

[1] Mobile Future Forward 2026 agenda and topic descriptions, Seattle, September 10, 2026 — https://www.mobilefutureforward.com/agenda/ and https://www.mobilefutureforward.com/our-topic/

[2] The Washington Post, “Anthropic’s Amodei calls for AI oversight, joined by Altman and Musk,” September 12, 2026 — https://www.washingtonpost.com/technology/2026/09/12/anthropic-ceo-dario-amodei-calls-ai-industry-slow-down/

[3] Axios, “Anthropic CEO says government should block dangerous AI,” June 10, 2026 — https://www.axios.com/2026/06/10/anthropic-ceo-government-block-dangerous-ai

[4] FDA, “Marketing Submission Recommendations for a Predetermined Change Control Plan for Artificial Intelligence-Enabled Device Software Functions,” final guidance, December 4, 2024 — https://www.federalregister.gov/documents/2024/12/04/2024-28361/

[5] Regulation (EU) 2024/1689 (AI Act), Article 72, Post-market monitoring (applies to high-risk systems from August 2, 2026) — https://artificialintelligenceact.eu/article/72/

[6] Axios, “Anthropic, OpenAI CEOs call for slowdown in AI development,” September 12, 2026 (Amodei essay: “We must slow the pace at which we improve the capabilities of AI models”; Altman on X; Musk: “Dario is right”) — https://www.axios.com/2026/09/12/anthropic-ai-amodei-pacing; The Wall Street Journal, “Anthropic Boss Warns AI Industry Must Slow the Pace,” September 12, 2026 — https://www.wsj.com/tech/ai/anthropic-boss-warns-ai-industry-must-slow-the-pace-a4267b56; Bloomberg, “Anthropic CEO Says It’s Time to Slow Pace of Improving AI Models,” September 12, 2026 — https://www.bloomberg.com/news/articles/2026-09-12/anthropic-ceo-says-it-s-time-to-slow-pace-of-improving-ai-models

[7] Fierce Network, “Exclusive: T-Mobile’s John Saw explains kinetic tokens and why they matter in 6G,” February 23, 2026 — https://www.fierce-network.com/wireless/exclusive-t-mobiles-john-saw-explains-kinetic-tokens-and-why-they-matter-6g;

[8] John Saw, “Why Physical AI Begins with Intelligent Networks,” T-Mobile Newsroom, February 17, 2026 — https://www.t-mobile.com/news/network/why-physical-ai-begins-with-intelligent-networks

[9] CAT NMS Plan, “What are the CAT clock synchronization standards for CAT Reporters?” — https://www.catnmsplan.com/faq/r1; FINRA Rule 6820 (Clock Synchronization) — https://www.finra.org/rules-guidance/rulebooks/finra-rules/6820

[10] Federal Register, July 22, 2025, SR-FINRA-2025-012 (FINRA Rule 6860 timestamp granularity; relief to April 8, 2030) — https://www.federalregister.gov/documents/2025/07/22/2025-13732/; National Law Review summary — https://natlawreview.com/article/finra-aligns-cat-timestamp-requirements-sec-relief-and-extension-2030

[11] CFTC Release 8260-20, “CFTC Orders JPMorgan to Pay Record $920 Million for Spoofing and Manipulation,” September 29, 2020 — https://www.cftc.gov/PressRoom/PressReleases/8260-20

[12] Visa, “Visa and Partners Complete Secure AI Transactions, Setting the Stage for Mainstream Adoption in 2026,” December 18, 2025 — https://usa.visa.com/about-visa/newsroom/press-releases.releaseId.21961.html

[13] Regulation (EU) 2024/1689 (AI Act), Article 73, Reporting of serious incidents (15 days; 2 days for widespread infringement; 10 days for death) — https://artificialintelligenceact.eu/article/73/; Article 12, Record-keeping — https://artificialintelligenceact.eu/article/12/

[14] SDxCentral, “T-Mobile US network chief touts AI transformation and where telecom is best positioned to lead” (Ankur Kapoor, EVP and Chief Network Officer, T-Mobile), August 20, 2026 — https://www.sdxcentral.com/analysis/t-mobile-us-network-chief-touts-ai-transformation-and-where-telecom-is-best-positioned-to-lead/; Kapoor spoke at MFF 2026 on “Silicon, Signal, Service: Earning the Right to Win in the AI Economy” [1]

[15] VentureBeat, “Nvidia’s agentic AI stack is the first major platform to ship with security at launch, but governance gaps remain” (Jensen Huang, GTC keynote, March 17, 2026) — https://venturebeat.com/security/nvidia-gtc-2026-agentic-ai-security-five-vendor-governance-framework

[16] NVIDIA, “Safety for Agentic AI” blueprint (build, deploy and run stages; NeMo Guardrails; continuous monitoring) — https://github.com/NVIDIA-AI-Blueprints/safety-for-agentic-ai; NVIDIA Technical Blog, “NVIDIA-Verified Agent Skills Provide Capability Governance for AI Agents,” May 19, 2026 — https://developer.nvidia.com/blog/nvidia-verified-agent-skills-provide-capability-governance-for-ai-agents/

[17] The Register, “‘Resistance is futile,’ says Qualcomm CEO” (Cristiano Amon, Computex, June 2, 2026) — https://www.theregister.com/personal-tech/2026/06/02/qualcomm-ai-agents-will-be-as-transparent-as-they-will-be-inescapable/5249894

[18] Tom’s Hardware, “Qualcomm roundtable interview transcript” (Kedar Kondap, SVP and GM, Compute and Gaming, Qualcomm), June 4, 2026 — https://www.tomshardware.com/pc-components/cpus/qualcomm-roundtable-interview-transcript-svp-of-compute-and-gaming-talks-snapdragon-c-rtx-spark-and-the-agentic-ai-future; Kondap spoke at MFF 2026 on “Silicon at the Edge of Intelligence” [1]

[19] AI Magazine, interview with Ronnie Vasishta (SVP, Telecom, NVIDIA), May 6, 2025 — https://aimagazine.com/interviews/ronnie-vasishta; Vasishta spoke at MFF 2026 on “Silicon, Signal, Service: Earning the Right to Win in the AI Economy” [1]

[20] SDxCentral, “AT&T sees agentic AI making IoT sexy again” (Shawn Hakl, AT&T Business), March 26, 2026 — https://www.sdxcentral.com/analysis/att-sees-agentic-ai-making-iot-sexy-again/; TelecomTV, “MWC26: AT&T Business on the impact of AI, APIs and telco collaboration,” March 2026 — https://www.telecomtv.com/content/spotlight-on-5g/mwc26-at-t-business-on-the-impact-of-ai-apis-and-telco-collaboration-55092/; title per AT&T newsroom, March 17, 2026 — https://about.att.com/story/2026/cisco-ai-grid-with-nvidia.html; Hakl spoke at MFF 2026 on “Silicon, Signal, Service: Earning the Right to Win in the AI Economy” [1]

[21] SDxCentral, “AT&T CEO John Stankey stays strong on the carrier’s AI-support stance,” July 22, 2026 (Q2 2026 earnings call; line attributed to CFO Pascal Desroches and cited by Stankey) — https://www.sdxcentral.com/news/att-ceo-john-stankey-stays-strong-on-the-carriers-ai-support-stance/; AT&T Q2 2026 earnings transcript — https://investors.att.com/~/media/Files/A/ATT-IR-V2/financial-reports/quarterly-earnings/2026/2Q-2026/t-usq-transcript-2026-07-22.pdf

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