By Stephen DeAngelis
The term “polycrisis” (introduced by the French complexity theorist Edgar Morin and brought into contemporary strategic discourse by the historian Adam Tooze of Columbia University) has earned its place in the global vocabulary. It describes a world in which multiple, interconnected crises (geopolitical conflict, energy disruption, trade fragmentation, food insecurity, monetary policy paralysis) reinforce one another in ways that defy linear analysis. We are living through a textbook case. The Iran war has effectively closed the Strait of Hormuz, removing approximately 20% of the world’s seaborne oil supply from reliable transit.
Brent crude has surged past $106 a barrel. Urea prices have climbed approximately 50%, with ammonia, phosphate, and potash rising sharply. The head of the International Energy Agency has called it “the greatest global energy security threat in history,” warning that more oil has been lost than in both 1970s crises combined and that fertilizer, chemicals, and helium supply chains face simultaneous disruption. Central banks from Washington to Frankfurt are trapped between rising inflation and weakening growth. And all of this is unfolding against the backdrop of an expanding tariff regime, with Section 301 investigations targeting 16 economies for industrial overcapacity and a further round covering approximately 60 trading partners for forced labor practices.
But the polycrisis, as commonly described, captures only the base condition. There is an exponent.
The Exponent, AI, and Quantum Computing
I want to introduce a concept I am calling Polycrisis², the polycrisis squared. The notation is deliberately heuristic rather than algebraic, a shorthand for the non-linear dynamics that defy simple quantification. The base is what we see in the headlines, specifically the cascading, mutually reinforcing geopolitical and economic shocks that have defined the past several years. The exponent is the consequential change being driven simultaneously by two technologies, artificial intelligence and quantum computing, which are compounding it at an unprecedented rate.
The scale of the AI transformation alone is staggering. Morgan Stanley Research estimates that approximately $3 trillion in AI-related infrastructure investment will flow through the global economy by 2028, with more than 80% of that spending still ahead. Corporations expect to double their AI expenditure in 2026 alone. Block, the payments company, cut nearly half its workforce (over 4,000 positions) citing AI’s ability to automate fraud detection, risk assessment, and customer support. Reuters reported that Meta was planning to eliminate approximately 16,000 positions (20% of its workforce) in cuts widely attributed to its accelerating AI infrastructure investment, which Meta has guided at $115 to $135 billion in capital expenditure for 2026 alone. An estimated 45,000 tech jobs have been eliminated globally in the first quarter of 2026, with roughly 20% attributed directly to AI replacement.
These shifts are structural. The World Economic Forum’s Future of Jobs Report 2025 projects 170 million new roles created and ninety-two million displaced between 2025 and 2030 across all drivers of structural change, including, but not limited to AI. But within that net-positive headline lies an asymmetry that matters. Capital captures productivity gains immediately, while labor absorbs displacement on a two-to-four quarter delay. Layer that delay onto an energy-driven inflation surge and constrained monetary policy, and the compounding effect on household economics becomes severe.
Quantum computing compounds the picture in a different way. Google shortened its post-quantum cryptography migration timeline to 2029, warning that “a cryptographically relevant quantum computer is not forever a decade away.” The company confirmed that “store now, decrypt later” attacks (in which adversaries harvest encrypted data today with the expectation of decrypting it once quantum systems mature) are already underway. Only 9% of organizations have any plan for transitioning to quantum-resistant encryption. The World Economic Forum has warned explicitly about a “quantum divide” in which wealthy nations and large corporations become quantum-safe while the rest of the world gets cut off from global trade and finance overnight.
This is already playing out in real time across multiple domains simultaneously. AI-generated deepfakes depicting fabricated missile strikes on Tel Aviv, US bases in Riyadh, and port infrastructure in Bahrain have flooded social media since the Iran war began. The New York Times identified more than 110 unique deepfakes in a two-week period, many produced by state-linked influence networks and amplified across the Russian, Chinese, and Iranian information ecosystems. Corporate decision-makers monitoring the Strait of Hormuz crisis cannot distinguish real damage assessments from synthetic fabrications without investing in verification infrastructure that most organizations do not possess. Meanwhile, cybersecurity researchers have confirmed that harvest-now-decrypt-later campaigns are accelerating. Adversaries are quietly exfiltrating encrypted corporate data, trade secrets, and supplier agreements with the expectation of decrypting them once quantum capability matures. The data cannot be unharvested. The exposure is permanent and cumulative.
This is the exponent. AI is restructuring labor markets, competitive dynamics, and the speed at which decisions must be made, all in the middle of an economic environment already destabilized by war, energy disruption, and trade fragmentation. Quantum computing is quietly undermining the cryptographic foundations on which global commerce and national security depend. Neither operates in isolation from the base polycrisis. Each amplifies it.
Why the Squared Function Matters
A simple polycrisis is, at least theoretically, manageable through coordination. Central banks can consult. Alliances can negotiate. Supply chains can reroute. The difficulty increases dramatically, but the tools are familiar.
Polycrisis² is different because the exponent changes the very nature of the problem, beyond its magnitude.
A tariff shock is manageable. A tariff shock during an energy crisis is harder. A tariff shock during an energy crisis while AI is displacing your workforce, rewriting your competitive landscape, and degrading the reliability of the information on which you make decisions, while the cryptographic infrastructure protecting your intellectual property faces an advancing existential threat, which is a qualitatively different problem.
AI degrades the information environment in which leaders make decisions through deepfakes, AI-generated misinformation, algorithmic amplification of negative sentiment, and the steady erosion of the trusted analytical baseline on which organizational decision-making depends. Quantum computing, meanwhile, has already changed the behavior of adversaries who are harvesting data now against the eventuality of being able to break encryption. The compounding effect is non-linear, and each crisis amplifies the others in ways that cannot be decomposed into their individual parts.
Here is how the squared function works in practice. A supply chain disruption caused by the Strait of Hormuz closure is a manageable crisis. Companies have playbooks for rerouting, repricing, and reallocating. But when the same company must simultaneously evaluate whether its rerouted supply chain communications are being harvested by adversaries exploiting quantum-vulnerable encryption, and whether the AI-generated market intelligence guiding its repricing decisions has been contaminated by synthetic misinformation, the problem changes in kind. It becomes one of epistemic integrity. The organization cannot trust the data on which it would normally base its response. It cannot trust the communications through which it coordinates that response. These are crises that degrade the very tools available to address them.
This distinction, between crises that test an organization’s capacity and crises that compromise its ability to perceive and respond, is the analytical core of the Polycrisis² concept. It is what separates a difficult operating environment from one that is qualitatively different. And it is why the organizational response must be architectural rather than incremental. You cannot improvise your way through a disruption when the instruments of improvisation themselves are compromised.
This framework has limits that deserve acknowledgment. The Sense, Think, Act & Learn architecture assumes that an organization possesses a minimum threshold of data infrastructure, institutional willingness to share date and insights across functions, and leadership continuity manage the buildout. Where any of these preconditions is absent, the architecture will have difficulty taking hold regardless of the quality of the technology. It also assumes that the sensing layer can be deployed faster than the environment degrades it, an assumption that a sufficiently rapid cascade of compound disruptions could defeat – we hope this is not the case. These are the boundary conditions. They do not invalidate the framework. They define the conditions under which it must be deployed with discipline, and the conditions under which an organization should focus.
Leadership Matters — But Not the Way You Think
The instinct, when facing compounding complexity, is to look for strong leaders. And leadership does matter, profoundly. The organizations that navigate Polycrisis² will be led by people who possess the vision to see what is changing, the judgment to distinguish signal from noise, and the courage to act before the data is complete.
But we need to be precise about what we mean. Leadership in this context means something beyond individual heroism, beyond the mythology of the lone executive who sees what no one else can see and wills the organization through a crisis by sheer force of character. That narrative is compelling, and also dangerous, because it substitutes personal grit for systemic capability, and in a Polycrisis² environment, individual grit without a system for resiliency is simply a more admirable form of fragility.
The organizations that relied on heroics during COVID-19 (where a handful of gifted operators improvised solutions on the fly) often discovered that those solutions could not scale, could not be replicated, and evaporated the moment those individuals moved on. The crisis was survived despite the absence of organizational capability. That works once. It does not work when the disruptions are continuous, overlapping, and compounding.
Enlightened leadership in the age of Polycrisis² means building the systems that make the organization resilient independent of any single leader’s brilliance. It means recognizing that your job as a leader is to ensure that the organization can sense, interpret, respond, and learn from disruption structurally, whether you are in the room or not.
The Enterprise Intelligence Layer, Sense, Think, Act & Learn
The Polycrisis² condition described above is an environment that must be navigated, and navigating it requires a fundamentally different organizational architecture.
If individual heroism is insufficient, what takes its place? An enterprise intelligence layer, a systemic capability embedded in the organization’s operating architecture, which allows it to dynamically Sense, Think, Act & Learn. The idea of iterative organizational response is not new. Boyd’s OODA loop, Deming’s Plan-Do-Check-Act, and the organizational learning tradition of Argyris and Schön all describe cycles of environmental sensing and adaptive response. But those frameworks were designed for human-speed decision cycles, such as a fighter pilot’s cockpit, a factory floor, or a management offsite. They assume that the sensing, interpretation, and response can be performed by individuals or small teams operating within a single domain. What the Polycrisis² environment demands is fundamentally different, specifically the same business and analytic logic applied across dozens of interconnected domains simultaneously, at machine speed, with autonomous propagation of insight across functional boundaries, embedded in the organization’s data architecture, AI systems, and autonomous decision infrastructure. The distinction is architectural. When an energy shock simultaneously disrupts shipping routes, triggers tariff recalculations, shifts demand patterns, and creates workforce reallocation pressure, all while the information environment is being contaminated by AI-generated misinformation, no human team running an OODA loop in a conference room can process the interactions fast enough. The system must do it, or it does not get done.
Sense means building the instrumentation to detect disruption early and across the full dimensionality of the operating environment. Not just monitoring the variables you already know matter, but maintaining awareness across adjacent domains (geopolitical, technological, regulatory, competitive, environmental) where the next compound shock is likely to originate. Most organizations have point-monitoring systems. Very few have the integrated sensing architecture that a Polycrisis² environment demands.
Think means applying analytical capability, increasingly AI-driven, to interpret what the sensing layer detects. This is where raw signals become actionable intelligence. It requires modeling cascading effects across interconnected systems, specifically how an energy disruption propagates through shipping costs into supplier economics, into competitive pricing, into demand signals, and into workforce planning. Organizations that can think across these dimensions in near-real-time will see the shape of compound crises before they fully materialize.
Act means translating insight into response at the speed the environment requires. In a Polycrisis² world, the traditional cycle of quarterly strategy review, annual planning, and committee-based decision-making is structurally too slow. The answer is pre-mapped response options, delegated decision authorities, and the organizational muscle memory to execute scenario-based responses without waiting for consensus that will never arrive in time.
Learn is the most critical, and most neglected, capability. It is the mechanism by which the organization improves with each disruption rather than merely surviving it. Learning means the models become more accurate, the sensing architecture expands to include the signals that were missed, the response playbooks are updated with what actually worked, and the institutional knowledge base deepens structurally. An organization that senses, thinks, and acts but does not learn will make the same mistakes in different configurations. An organization that learns will find that each cycle of disruption leaves it measurably stronger than the last.
These four capabilities apply with equal force to the quantum dimension of the exponent. The sensing challenge is to track the quantum threat timeline as an active risk already shaping adversary behavior. The thinking challenge is to model the cascading consequences of a cryptographic breach across the entire data estate, including which intellectual property, which supplier agreements, and which customer records have already been harvested by adversaries waiting for decryption capability. The imperative action is to begin the migration to post-quantum cryptography now, years before a cryptographically relevant quantum computer is announced. The National Institute of Standards and Technology has already published the standards. Enterprise migration timelines of twelve to fifteen years mean that organizations beginning today may not finish before the threat materializes. And the learning opportunity is to treat each phase of cryptographic migration as a chance to discover where encryption is actually embedded, where dependencies hide across the vendor ecosystem, and where the organization’s cryptographic architecture is fragile in ways that have nothing to do with quantum computing, and everything to do with decades of accumulated technical debt.
What makes this more than a framework is the underlying architecture. A genuine enterprise system of intelligence shares knowledge across the organization through enriched ontologies, structured representations of domain knowledge that allow insights generated in one part of the business to be understood, contextualized, and applied in another. When a supply chain disruption is detected, the implications for pricing, promotion, production planning, and financial forecasting should propagate simultaneously and autonomously, through an autonomous decision science platform that connects sensing, reasoning, and action into a single coherent system. The goal is to make decisions at the speed of the market, not at the speed of the organization chart. Enterprises that achieve this operate at a structural clock speed that turns volatility into advantage.
This is an engineering challenge, and a practical one. Building the Sense, Think, Act & Learn capability requires the integration of AI systems, data architecture, decision science, and organizational design into a coherent operating layer. The technology to do this exists today. The question is whether leadership will commit to the investment before the next compound disruption makes the decision for them.
The Gravity of Transformation
Describing the required architecture is the easy part. Building it inside a living enterprise, while that enterprise is simultaneously managing the disruptions the architecture is meant to address, is something else entirely. The difficulty of this transformation deserves candor, because underestimating it is how organizations end up with pilots that never scale and strategies that exist only in slide decks.
The first obstacle is structural. Most large enterprises are organized to prevent exactly the kind of cross-functional intelligence sharing that a Sense, Think, Act & Learn architecture requires. Supply chain, finance, marketing, and commercial teams operate with different data, different key performance indicators, and frequently competing incentives. The enriched ontologies described above (the connective tissue that allows insight in one domain to be contextualized and applied in another) cannot be overlaid on an organization whose fundamental design fragments knowledge into silos. The transformation is as political as it is technological. It requires dismantling institutional boundaries that have existed for decades, and that have constituencies invested in their preservation.
Navigating this political reality requires deliberate sequencing. The most successful transformations begin with the most willing function, typically a business unit leader who is already experiencing the pain of fragmented intelligence and is looking for a better answer. That initial partnership establishes a working proof point. From there, the architecture extends to adjacent functions where the value is visible, and the political cost of exclusion begins to exceed the cost of participation. The executive sponsor for this kind of transformation must sit at the enterprise level, such as a CEO, COO, or chief strategy officer with the authority to arbitrate across functional boundaries. Without that sponsorship, architecture will be optimized within silos rather than across them, which is precisely the failure mode it is designed to prevent.
The second obstacle is capability. The overwhelming majority of Fortune 500 companies do not currently possess the data architecture, the decision science talent, or the organizational design expertise to build what is being described here. The technology exists. The institutional capacity to deploy it is not yet scalable. This is a recognition that the gap between what the environment demands and what most organizations can deliver is substantial, and that closing it requires sustained investment in people, infrastructure, and institutional redesign that extends well beyond a technology procurement decision.
The third obstacle is temporal. Building an enterprise intelligence layer is a multi-year, board-level commitment. The paradox is that this commitment must be sustained through the very disruptions it is meant to address. An organization cannot pause the polycrisis while it upgrades its decision architecture. It must transform while under fire, running quarterly earnings, managing supply chain volatility, responding to regulatory shifts, and navigating workforce transitions simultaneously. The organizations that wait for stability before beginning will discover that stability is not returning.
A fourth obstacle deserves separate mention, specifically cryptographic debt. The quantum threat compounds every difficulty described above. An organization already struggling to integrate its data architecture across functional silos must now also inventory every cryptographic dependency in that architecture (every certificate, every key exchange, every hardware security module) and develop a migration plan that coordinates with vendors, suppliers, and partners who face the same challenge on their own timelines. NIST has published the post-quantum cryptography standards. CISA has issued federal procurement guidance requiring quantum-resistant products. Google has already migrated its own services to post-quantum key exchange. The standards exist. The government mandates are arriving. And peer-reviewed research estimates that large enterprises will require twelve to fifteen years for complete migration, a timeline that, if quantum capability arrives by 2030, leaves a multi-year window of structural vulnerability that no amount of heroic leadership can close after the fact.
There is, however, a practical answer to these obstacles, and it begins with a single business process. The organizations that have successfully built enterprise intelligence did not attempt to transform everything at once. They identified one high-value process (demand sensing, promotion optimization, supply allocation) where the intelligence layer could demonstrate a measurable financial return within quarters, not years. That initial return creates a profit engine. The margin improvement from the first process funds the buildout of the next, which funds the next, which progressively extends the architecture across the enterprise. The transformation finances itself. In practice, the right initial process (chosen for its data readiness, its margin sensitivity, and its cross-functional visibility) should be self-liquidating within the first year of operation, ideally within the same corporate fiscal year. That is the threshold that converts a transformation initiative from a strategic bet into a funded program with its own P&L justification. This is how the investments are de-risked in practice, by converting each phase from a cost center requiring board-level faith into a revenue contribution that justifies the next phase on its own terms. The organizations that stall are invariably the ones that try to build the entire architecture before demonstrating value. The ones that succeed start with a single proof point that makes the financial case undeniable and then compound.
One caveat deserves honesty. Not every first proof point succeeds. The process chosen may underperform, the data may prove less ready than anticipated, or the organizational learning curve may be steeper than projected. The discipline required is to treat an underperforming proof point as a learning cycle, not a failure verdict, and instead to diagnose why the return fell short, adjust the process selection criteria, and redeploy. The organizations that abandon architecture after a single disappointing quarter are making the same mistake as those who never started. They are confusing a difficult implementation with a flawed strategy. The strategy is sound. Execution requires persistence and the willingness to learn from what the first cycle reveals.
All of this is reason for urgency. The difficulty of the transformation is precisely what makes it a source of competitive advantage, because most organizations will not do it.
Systemic Resilience as Competitive Advantage
Resilience is too often framed as a defensive posture, the ability to absorb shocks and keep operating. That framing is incomplete. It undersells the strategic opportunity.
Organizations that build genuine systemic resilience (the Sense, Think, Act & Learn architecture described above) outperform. They outperform because they see dislocations earlier, interpret them more accurately, respond more quickly, and learn more deeply from each cycle. Over time, that structural advantage compounds. The resilient organization is harder to break, faster to adapt, more precise in its resource allocation, and better positioned to exploit the opportunities that disruption always creates for those who can recognize them.
The evidence is emerging. BCG’s 2026 CEO survey found that nearly all chief executives believe AI agents will produce measurable returns this year, and that half believe their job is on the line if AI does not pay off. The World Economic Forum’s Industry Strategy Meeting in Munich heard executive after executive describe the shift from “efficiency to resilience” as the defining strategic reorientation of the moment. BCG’s multi-year research found that the top 5% of AI-mature companies (those that have moved beyond pilot-phase adoption) are achieving 1.7 times the revenue growth and 3.6 times the total shareholder return of lagging firms, though, as with any AI-maturity segmentation study, these figures reflect some selection bias toward organizations that were already structurally advantaged before their AI investments began.
Resilience is the foundation of competitive advantage in a volatile world.
The best-run organizations of the next decade will not be the ones with the largest balance sheets or the most diversified portfolios. They will be the ones that have built the systemic capability to sense disruption, think through its implications, act with precision, and learn from every cycle, continuously, structurally, and enterprise scale.
That is the strategic prize. Polycrisis² will sort organizations into two categories, those that built the architecture and those that did not. The sorting is already underway.
The Imperative
The disruption we are living through will persist. The base polycrisis (geopolitical, economic, energetic, alimentary) is real and intensifying. The exponent (AI and quantum computing simultaneously restructuring the competitive, informational, and security landscape) is accelerating. The product of the two is the new operating environment.
The entry point is specific. Identify one high-value business process where an enterprise intelligence layer can demonstrate a measurable financial return within a single fiscal year. Let that return fund the next process, and the next. The transformation that seems impossibly large when conceived as a whole, becomes manageable and self-financing when begun at the right starting point.
Polycrisis² is a description of where we are. The question, for organizations and individuals alike, is whether we will match the complexity of the challenge with the sophistication and elegance of our response, or whether we will continue applying linear solutions to an exponential problem.
The architecture for resilience exists. The leadership models are clear. The technology is available. What remains is the decision to build.
Polycrisis²™ is a trademark of Stephen F. DeAngelis.
Sense, Think, Act & Learn™ is a trademark of Enterra Solutions.
Stephen F. DeAngelis is the founder, president, and CEO of Enterra Solutions and Massive Dynamics, two companies that apply artificial intelligence and advanced mathematics to complex enterprise challenges. His career spans international relations, national security, and commercial technology. He has served in visiting research affiliations with Princeton University, the Oak Ridge National Laboratory, the Software Engineering Institute at Carnegie Mellon University, and the MIT Computer Science and Artificial Intelligence Laboratory. He is a founding member of the Forbes Technology Council. DeAngelis holds patents in autonomous decision science and has been recognized by Forbes as a Top Influencer in Big Data and by Esquire magazine as the “Innovator” in its Best and Brightest issue.
About the Stephen DeAngelis Explainer Brief Series
The Stephen DeAngelis Explainer Brief series applies critical reasoning to the complex issues facing society today. In an era of compounding uncertainty and deepening division, the series aims to build understanding and community by making consequential topics (from artificial intelligence and geopolitics to organizational resilience and national competitiveness) accessible through rigorous analysis, current evidence, and honest assessment. Each installment is written in the belief that better explanations lead to better decisions, and that informed citizens and leaders are the foundation of a stable functioning society. Published under the DeAngelisReview imprint – www.deangelisreview.com.
