What Leads Where: The Decision Tree

The four scenarios are not independent futures — they are outputs of a branching decision tree whose critical nodes are governance choices, energy policy, and competitive dynamics. The flowchart below maps the pathways from current conditions to each destination state.

The Four Scenarios in Detail

Resource Wars in the AI Age: The Fifth Scenario No One Is Pricing

The four scenarios in Part IV’s framework address governance, energy, corporate concentration, and geopolitical decoupling. There is a fifth dynamic that sits underneath all of them and has not received adequate analytical attention in most AI-focused research: the extent to which AI’s physical resource requirements are already reshaping state behaviour, military posture, and the calculus of territorial control. This is not a future risk. It is a present one — and the pattern of recent US foreign policy activity provides the clearest evidence.

Why AI Creates Resource Imperatives for States

The connection between AI capability and physical resource control runs through three pathways. First, compute requires minerals that are geographically concentrated and partially controlled by adversary states — gallium, germanium, rare earths, cobalt. A state that controls or secures alternative supply chains for these materials has structural advantage in the AI race that persists regardless of software advances. Second, AI infrastructure requires power at unprecedented scale — and the states with the largest clean energy endowments (hydroelectric, geothermal, wind, solar) will host AI infrastructure at lower cost and with lower climate liability than those without. Third, the economic surplus generated by AI dominance is large enough to fund military and geopolitical projection at a scale that makes the resource competition self-reinforcing.

The US-China dynamic makes this explicit. China’s October 2023 export restrictions on gallium and germanium were not merely a trade retaliation to US chip controls — they were a demonstration of the resource leverage that China holds over the semiconductor supply chain. China controls approximately 80% of global gallium production and 60% of global germanium refining. A sustained supply restriction would not halt US AI development overnight, but it would impose significant cost increases and sourcing delays on the chip manufacturing base that US AI depends on. China’s rare earth processing dominance (roughly 85% of global capacity) amplifies this leverage. When Beijing discusses “resource weapons,” it is not being metaphorical.

The Ukraine Minerals Dimension

The clearest example of AI resource logic entering active geopolitical negotiation is Ukraine. The minerals agreement between the United States and Ukraine, signed in principle in early 2025 after intense negotiation, explicitly linked US security guarantees and reconstruction support to US access to Ukrainian critical minerals — specifically the titanium, lithium, and neon gas deposits that are strategically significant for semiconductor manufacturing. Ukraine’s geological survey estimates it holds approximately 20% of global titanium reserves, significant lithium brine deposits in the Donetsk region (currently occupied), and is a major source of neon gas used in the excimer lasers that ASML lithography equipment depends on.

This is not coincidental. It is a signal that the US has concluded — at the strategic planning level — that the critical mineral supply chain for AI and semiconductor manufacturing is a national security priority equivalent to oil supply security in the 20th century. The minerals deal with Ukraine is the first overt example of AI resource logic shaping US foreign and security policy in a live conflict context.

Greenland: The Starkest Resource Case

The Trump administration’s publicly stated interest in acquiring Greenland — expressed in 2019 and returned to with greater intensity in 2025, including suggestions that military options were not off the table — is best understood through this resource lens rather than as idiosyncratic behaviour. Greenland holds one of the world’s largest undeveloped rare earth deposits (Kvanefjeld/Kuannersuit), significant oil and gas resources, and occupies a strategic position in Arctic routes that are becoming commercially and militarily significant as ice cover declines. It also represents an opportunity to deny Chinese mining access — multiple Chinese mining consortium bids for Greenlandic mineral projects have been blocked on national security grounds.

From a conventional diplomacy perspective, the suggestion of acquiring an autonomous territory of a NATO ally is extraordinary. From an AI resource strategy perspective, it is consistent: Greenland’s rare earth deposits, if developed under US-aligned governance, would represent a material reduction in Chinese leverage over the semiconductor supply chain. The willingness to absorb the diplomatic cost of making this interest explicit — even if the acquisition itself never occurs — signals how seriously the resource competition is being taken at the policy level.

From Chip War to Resource War: The Escalation Pathway

The current phase of competition — chip export controls, mineral export restrictions, intelligence infrastructure exclusion — is best understood as the opening moves of a resource competition whose ultimate stakes are AI dominance. The escalation pathway is not hard to trace: if AI capability compounds with compute access, and compute access depends on mineral supply chains, and mineral supply chains are concentrated in politically contested geographies, then the logic of securing those supply chains through political, economic, or — in the limit — military means is structurally analogous to 20th century oil resource competition.

The 2003 Iraq War is instructive not as a direct precedent but as a demonstration of how states behave when they conclude that a critical resource is controlled by a hostile actor and that control is existentially important. The calculation failed in execution, but it was rational in its premise. A state that concludes that rare earth and semiconductor mineral supply chains are the oil of the AI economy will eventually develop a foreign policy that reflects that conclusion — whether through commercial competition, political pressure, sanctions, or ultimately something harder. The pattern visible in 2024–25 US foreign policy suggests that conclusion has already been reached at the strategic planning level. The overt component of the response is early stage. The covert component — intelligence operations, grey-zone activity around mining infrastructure, competitive investment in alternative supply chains — is likely further advanced than is publicly visible.

Does AI Reverse Multipolarity? The Case for a New Unipolar Moment

The post-Cold War international order has been characterised — with increasing accuracy since roughly 2008 — as multipolar: the United States as the leading but no longer unchallenged power, with China, the EU, Russia, India, and regional powers each asserting spheres of influence that constrain American primacy. The question that AI raises, and that most geopolitical analysis has not yet seriously engaged with, is whether AI capability sufficient to produce an overwhelming and durable advantage in economic output, military capability, and intelligence processing would structurally reverse multipolarity — effectively reproducing a unipolar world, but with AI rather than nuclear weapons and dollar dominance as the foundation of power.

The Argument for AI-Driven Unipolarity

The argument rests on three claims. First, AI capability advantages compound: a state with a significant lead in frontier AI at T₀ will have faster economic growth, better military capability, and more effective intelligence at T₁ — and those advantages fund further AI investment, widening the lead at T₂. Unlike conventional military hardware, which depreciates and requires maintenance, AI models improve in deployment and generate the data that trains successor models. The advantage is self-reinforcing in a way that most prior sources of national power are not.

Second, the inputs to AI are heavily concentrated geographically and institutionally. Frontier AI development currently occurs at perhaps five to eight organisations globally, all of them in the United States or closely allied with US institutions. The compute substrate — Nvidia GPUs, TSMC-fabricated chips, hyperscaler cloud infrastructure — is predominantly US-designed or US-allied. The training data advantages accrue to organisations with access to the largest English-language internet corpora, which are US-domiciled. China is the credible second, but the gap between US and Chinese frontier capability — while smaller than three years ago, partly due to DeepSeek’s efficiency innovations — remains material. No other actor is currently in serious contention.

Third, AI-enabled economic productivity, if it compounds at rates that some researchers project, could produce US economic output growth that leaves the rest of the world so far behind that the structural basis of multipolarity — rough economic parity among major powers — is simply eroded away. A United States growing at 10%+ annually because of AI-driven productivity while China grows at 4–5% and the EU at 2–3% would, within a decade or two, have an economic base so large relative to competitors that conventional balance-of-power dynamics cease to operate.

The Counterarguments — Why Unipolarity May Not Materialise

The unipolarity thesis, while analytically coherent, faces several serious counterarguments that Fenrir believes are strong enough to prevent a clean outcome.

Diffusion is not containable. The history of transformative technology is that advantages erode faster than incumbents expect, because knowledge diffuses and because the technology itself lowers the cost of subsequent adoption. DeepSeek-R1’s demonstration that frontier-capable models can be trained at dramatically lower compute cost than previously assumed is a concrete example: Chinese researchers, denied access to the best hardware, innovated around the constraint and produced architectures that reduce the compute advantage of US AI labs. This pattern — disadvantaged actors innovating more efficiently out of necessity — is well-documented in technology history. It is not clear that the US lead in frontier AI is as durable as the unipolarity thesis requires.

Multipolarity has institutional momentum. The international institutions, alliances, trade frameworks, and legal norms built around a multipolar world — the UN Security Council structure, WTO, the IMF voting framework, regional security architectures — do not simply dissolve because one power’s GDP grows faster than others. They create friction, delay, and political costs for unilateral action that constrain even a dramatically more powerful United States. The US already has the world’s largest economy and most powerful military, and it has not achieved unipolar dominance. The question is whether AI advantage is qualitatively different — and that depends on how quickly it translates into deployable geopolitical power versus GDP growth that takes years to convert into military and institutional leverage.

Coalition against dominance is the most reliable dynamic in international relations. If it becomes apparent that AI is producing a durable and accelerating US power advantage, the political incentive for other major powers — including current US allies who are not themselves on the frontier — to form countervailing coalitions is strong. This is the oldest mechanism in the balance-of-power tradition, and it has never been more dormant than for a generation at a time. An AI-powered US that overreaches on resource control, technology exclusion, and economic coercion would likely accelerate rather than prevent coalition formation against it.

The honest assessment: AI is more likely to produce a new phase of contested unipolarity — in which the United States is structurally the most powerful actor but does not achieve the uncontested dominance that the term “unipolar” implies — than to resolve the current multipolar tension definitively in either direction. The resource competition, the chip war, and the geopolitical manoeuvrings are not the prelude to a stable new order; they are the symptoms of a transition period whose outcome is genuinely uncertain.

The Governance Variable: Why the Next Five Years Are Critical

The flowchart’s most important node is not the start state (AI rapid advancement) or the end states (the four scenarios) but the decision nodes in between. Technology paths exhibit strong path dependency: once infrastructure is built at scale, once regulatory frameworks are established, once competitive positions are locked in, reversing direction is enormously costly. The window for governance intervention that shifts the trajectory from Scenario C or D toward Scenario A is finite and closing.

The EU AI Act represents the most developed governance framework currently in force, but its limitations are instructive. It addresses risk tiers for deployed applications but is weaker on foundation model obligations. Its enforcement depends on national market surveillance authorities whose capacity varies enormously across EU member states. It applies within the EU but not to AI systems deployed globally that affect EU citizens. And it was negotiated before the current generation of autonomous agent systems existed — meaning its risk classification framework is already partially obsolete.

Labour Market Disruption: The Jobs Question

The labour market impact of AI is the most politically sensitive and analytically contested dimension of the technology. The historical debate between technological optimists (who argue that automation always creates more jobs than it destroys, as each prior wave has done) and structural pessimists (who argue that cognitive AI is fundamentally different from physical or narrow automation) has not been resolved. What can be said with confidence is that the magnitude and speed of current disruption exceeds prior technology transitions by a significant margin.

Energy, Water & Resource Pressure: The Infrastructure Cost of Intelligence

The energy implications of AI are large enough to materially affect utility sector investment theses, grid planning horizons, carbon trajectory models, and — increasingly — the geopolitics of resource access. They are also among the most underreported dimensions of the AI buildout in financial media, which tends to cover the technology through the lens of the companies building it rather than the infrastructure it consumes. This section examines the electricity demand profile, water stress, semiconductor supply chain resource requirements, and the emerging dynamic by which AI’s physical infrastructure needs are beginning to reshape both corporate strategy and state-level resource policy.

The Electricity Demand Shock

U.S. data centers consumed approximately 4% of total national electricity in 2024. The IEA projects this will more than double by 2030. A single ChatGPT query consumes approximately 10 times the electricity of a standard Google search. Training a large frontier model at GPT-4 scale requires electricity equivalent to the lifetime consumption of roughly 100 U.S. households. These are not abstract statistics — they aggregate into grid planning problems that utility commissions, transmission operators, and capacity market designers are now grappling with in real time.

The geographic concentration compounds the challenge. Data center demand is not distributed evenly across the grid. Northern Virginia (Loudoun County alone hosts more than 25% of the world’s data center capacity) accounts for a disproportionate share of PJM load growth. The PJM capacity auction for 2025–26 saw data centers contribute an estimated $9.3 billion price increase, translating to residential electricity bill increases of $16–18 per month in Ohio and western Maryland. Pew Research cites a Carnegie Mellon University study estimating that data centers and cryptocurrency mining could increase average U.S. electricity bills by 8% by 2030, with local increases exceeding 25% in the highest-demand markets.[1] This is a regressive transfer: household ratepayers in AI-proximate states bear infrastructure costs for technology whose economic benefits accrue globally.

The grid investment response is already underway but faces a multi-year lag. PJM has approved the largest transmission expansion in its history — approximately $50 billion in new transmission investment — with AI demand as the primary driver. Similar programmes are active in ERCOT (Texas), MISO, and SPP. For utility analysts, the investment thesis is straightforward: rate base expansion at regulated utilities serving AI-dense territories is a multi-decade growth story. The risk is demand-side: if AI efficiency improvements or a hyperscaler capex rationalisation reduces data center build rates, utilities face stranded capital in transmission and generation assets that ratepayers ultimately bear.

The Carbon Lock-In Problem

The energy source question is where AI’s environmental paradox sharpens into a genuine policy crisis. AI is being deployed, with genuine effect, to accelerate clean energy materials science, improve grid management efficiency, and sharpen renewable generation forecasts. Simultaneously, the electricity demand it creates is being met, in significant part, by gas-fired generation — because clean alternatives cannot be deployed fast enough to match the pace of data center growth.

Greenpeace East Asia reported a 4.5× year-on-year increase in AI chip manufacturing emissions between 2024 and 2025 — driven by the energy-intensive nature of advanced semiconductor fab processes at TSMC, Samsung, and Intel foundries.[4] This is upstream of the operational electricity story and largely invisible in corporate carbon accounting frameworks that focus on Scope 2 emissions (purchased electricity) rather than Scope 3 (supply chain). The full lifecycle carbon footprint of AI infrastructure — chip fabrication, data center construction, cooling systems, operational power — is substantially higher than the operational electricity figures suggest.

The carbon lock-in dynamic operates through committed infrastructure. When a hyperscaler signs a 15–20 year data center operating agreement, the power demand profile that agreement creates is effectively locked in for that period. If the generating capacity contracted to serve it is gas-fired (because new nuclear and utility-scale solar with storage cannot be commissioned fast enough), that gas demand is similarly locked in. At the aggregate scale of the 2024–2026 hyperscaler capex programme — $400+ billion per year — the locked-in fossil demand being created is a material input to climate transition models that is not adequately reflected in most net-zero scenario analyses.

Water Stress: The Less-Visible Constraint

Data centers cool their servers primarily through evaporative water cooling systems. The water demand is large and geographically concentrated. A University of Houston study estimates that data centers in Texas alone will use 49 billion gallons of water in 2025, rising to 399 billion gallons by 2030 — equivalent to drawing down Lake Mead by over 16 feet annually.[2] ChatGPT reportedly uses approximately 500 millilitres of water per conversation for cooling — a figure that, at 100 million daily active users conducting multiple sessions, aggregates to volumes that are material at the watershed level.

The conflict with agricultural and human consumption is already live in water-stressed geographies. In central Arizona, data centers approved under pre-AI-boom planning assumptions are now competing for water allocations with agricultural users in a state that already overdraws the Colorado River. In Goodyear, Arizona, a proposed data center campus was rejected by the city council specifically because its projected water consumption exceeded available supply — the first prominent example of water, rather than power or planning permission, becoming the binding constraint on AI infrastructure expansion.

The international dimension is starker. In Chile — a leading data center hub for South America — AI infrastructure projects are being built in the Atacama Desert region, where water scarcity is an extreme constraint and where the same lithium extraction operations that supply battery supply chains for AI hardware also consume enormous volumes of water. In India, data centers proposed in water-stressed states such as Maharashtra and Rajasthan face local opposition from agricultural communities that mirrors the solar farm conflicts of the previous decade.

Critical Minerals: The Upstream Resource Dependency

The semiconductor supply chain that powers AI is itself deeply resource-intensive and geographically concentrated. The minerals required for advanced chips, cooling systems, server hardware, and the clean energy infrastructure needed to power data centers sustainably represent a set of supply chain dependencies that are underappreciated relative to the attention given to the compute layer itself.

China’s October 2023 export restrictions on gallium and germanium were not an isolated trade measure — they were a deliberate demonstration of leverage over the semiconductor supply chain. In a geopolitical context where the United States has used chip export controls to constrain Chinese AI capability, China has demonstrated an ability to apply reciprocal pressure through mineral supply chain chokepoints. The strategic calculus is explicit: the same resource dependencies that the AI buildout is creating are becoming instruments of geopolitical coercion. This dynamic connects directly to the Fracture scenario in Part IV and to the broader pattern of resource-competition-driven foreign policy examined there.

The Energy Transition Paradox in Full

The paradox at the heart of the AI energy story is not merely that a technology promising to accelerate clean energy is consuming dirty energy. It is that the investment capital and engineering talent currently being deployed into AI infrastructure — data centers, chips, cooling systems, power distribution — is capital and talent that might otherwise be deployed into grid decarbonisation, battery storage, and clean generation. The opportunity cost is real even if it is invisible in the financial accounts of the companies making these investments.

The IEA’s April 2025 Energy and AI report projects that data center electricity demand will equal the entire current electricity consumption of Japan by 2030 — a remarkable figure that has received far less analytical attention than it deserves. The path to meeting that demand cleanly requires a nuclear renaissance, an acceleration of utility-scale storage, and transmission grid investment at a pace that no major electricity market has achieved in the modern era. The probability that all three materialise simultaneously, at sufficient scale, to prevent the AI energy buildout from adding net carbon load is, in Fenrir’s assessment, low. The Depletion scenario in Part IV is partly a consequence of this dynamic playing out over the next decade.

Capital Concentration: Who Captures the AI Dividend

The distributional question in AI is not merely about jobs. It is about who owns the systems, who captures the productivity surplus they generate, and whether that surplus flows broadly through wages and lower prices or narrows into the accounts of capital owners and equity holders at a handful of dominant firms.

The concentration data is stark. Crunchbase data for 2025 shows that OpenAI and Anthropic alone captured 14% of all global venture investment — two companies out of hundreds of thousands globally.[3] The five hyperscalers (Microsoft, Amazon, Alphabet, Meta, Oracle) account for the overwhelming majority of AI infrastructure capex. The pattern is self-reinforcing: scale in data and compute creates capability advantages that attract more customers, generating revenue that funds further scale investment. The barriers to entry at the frontier model level have grown, not shrunk, as the field has matured.

The Productivity Surplus Distribution Problem

The critical macroeconomic question is not whether AI generates a productivity surplus — it does and will at increasing scale — but whether that surplus flows broadly through the economy via lower prices, higher wages, and improved public services, or whether it accumulates as supernormal returns for the companies owning the AI systems. Prior technology waves have generally distributed gains relatively broadly over time, via competition eroding rents. The concern with AI is that network effects, data advantages, and compute scale create moats that competition cannot erode — resulting in persistent monopoly or oligopoly rents that concentrate capital rather than diffusing it.

Misuse, Misinformation & the Regulatory Vacuum

The dual-use problem — that the same capabilities that make AI valuable for legitimate purposes also enable harm at scale — is not a theoretical concern. It is already manifesting across a range of domains where the regulatory framework is inadequate.

The Regulatory Patchwork

The Creative Economy: What AI Destroys Before It Builds

The creative economy is experiencing AI disruption in real time, and it is worth distinguishing between the short-run effects (which are destructive for specific categories of workers) and the longer-run picture (which may see AI expand the creative economy while redistributing value within it).

The near-term harms are concentrated and measurable. Stock photography platforms have reported revenue declines of 30–50% in certain categories since the introduction of high-quality text-to-image models. Survey data from the Graphic Artists Guild (2024) found that 87% of illustrators reported AI competition affecting their income. Entry-level copywriting rates have compressed significantly in markets where AI-generated content is accepted by clients. The workers most affected are those doing standardised, repeatable creative tasks at volume — a category that turns out to be quite large within the creative economy.

The legal framework is unresolved at a structural level. The New York Times v. OpenAI litigation, Getty Images lawsuits, and numerous class actions by artists contesting the use of their work in training datasets have not yet produced settled law. The EU AI Act requires disclosure of copyrighted training data, but retroactive remedy for works already incorporated into existing models is legally and practically complex. Until training data compensation frameworks are established, the economic incentive structure systematically favours AI developers at the expense of the human creators whose work trained the systems.

The Cybersecurity Crisis: AI as Weapon, AI as Target

Cybersecurity is the downside risk that every major institution acknowledges and almost none has adequately addressed. AI has not invented new categories of cyberattack — it has fundamentally changed the cost, scale, and sophistication of existing ones, while simultaneously creating new attack surfaces through the AI systems themselves. The threat environment in 2025–26 is not an extension of the 2020 threat environment; it is qualitatively different.

Three changes in the threat environment are structural. First, AI has eliminated the skill floor for sophisticated attacks. Hyper-personalised phishing — emails that reference a target’s recent activity, mirror their writing style, and correctly name their colleagues — was previously the work of skilled social engineers operating at low volume. LLMs now generate thousands of such messages per hour, each contextually accurate, each indistinguishable from legitimate communication. The State of AI Cybersecurity 2026 report identifies hyper-personalised phishing as the top concern (50%), followed by automated vulnerability scanning and exploit chaining (45%) and adaptive malware and deepfake voice fraud (40% each).

Second, AI has compressed the full attack lifecycle. IBM’s X-Force Threat Intelligence Index 2026 found that over five years, major supply chain and third-party breaches quadrupled — reflecting a shift toward targeting interconnected systems and trusted integrations rather than a single organisation’s perimeter. CrowdStrike documented that over 90 organisations had legitimate AI tools exploited to generate malicious commands and steal data, with ChatGPT mentioned in criminal forums 550% more than any other model. Agentic AI — autonomous agents with access to internal data and APIs — is what IBM calls the most helpful insider threat: broad access is required for the agent to function, and that access becomes the attack vector.

Third, nation-state escalation is unambiguous. In January 2026, Salt Typhoon, the China-linked threat actor, was confirmed to have achieved persistent access to US congressional communications. This is not classic espionage; it is systematic infrastructure penetration enabling both intelligence collection and, in an escalation scenario, operational sabotage. The first half of 2025 saw a 65% year-over-year increase in ransomware incidents affecting government bodies — 208 confirmed attacks. Healthcare remains the highest-cost target at $9.77 million per breach, with documented cases of patient harm from ransomware-induced operational disruption.

The systemic risk dimension is the one markets are least pricing. AI-managed power grids, water systems, and financial market infrastructure present concentrated attack surfaces whose disruption would cascade across sectors. The 2021 Colonial Pipeline incident — a single ransomware attack on scheduling software that caused fuel shortages across the US Southeast — is a preview of what concentrated AI infrastructure dependency enables. AI security spending is projected to grow from $25.35 billion in 2024 to $93.75 billion by 2030 at a 24.4% CAGR, but most of that spending will chase yesterday’s threat rather than tomorrow’s attack vector.

The Mineral Race: Resource Competition, Coercion, and the Risk of Conflict

The AI buildout is, at its physical base, a competition for specific rocks. Advanced AI chips require gallium, germanium, and rare earth elements. The clean energy infrastructure to power them requires lithium, cobalt, and copper. The geographical distribution of these materials — and critically, the processing capacity to turn raw ore into usable inputs — is deeply concentrated and, in critical cases, controlled by states that are strategic competitors of the leading AI powers. This is not background context. It is becoming a primary driver of state foreign policy behaviour and a structural risk to the AI investment thesis.

The US State Department stated explicitly at the February 2026 Critical Minerals Ministerial: “Today, this market is highly concentrated, leaving it a tool of political coercion and supply chain disruption, putting our core interests at risk.” The same day, the US signed bilateral critical minerals frameworks with multiple partner nations and launched the Forum on Resource Geostrategic Engagement. The US National Security Strategy, released November 2025, highlighted securing critical mineral supply chains as a matter of national security. In January 2026, President Trump signed an executive order on adjusting imports of processed critical minerals to reduce foreign source reliance.

Critical minerals have become the 21st-century “oil” — lithium, cobalt, rare earths, and semiconductor inputs are now explicit levers of state power, as demonstrated by China’s 2024–2025 export controls and the scramble by the US and allies to secure alternative supplies. Geology drives geopolitics: these minerals are geologically fixed and highly concentrated. By mid-2025, with the two superpowers locked in a trade war, Beijing demonstrated its willingness to weaponise its control of heavy rare earths through a series of export restrictions, though China eventually agreed to suspend implementation until late 2026.

When major producers have announced export controls on gallium, germanium, and graphite, it caused immediate price spikes and panic among manufacturers worldwide — not random market fluctuations, but calculated geopolitical moves creating a dangerous feedback loop of price volatility, disrupted investment, and supply chains used as diplomatic weapons.

The pattern of US foreign policy engagement with resource geographies in 2024–25 reveals a strategic posture that has moved beyond commercial competition into coercion: Venezuela’s coltan and oil, Greenland’s rare earth deposits and Arctic position, DRC cobalt, Ukrainian titanium and lithium deposits in Russian-occupied territories. In each case, the US has employed sanctions, political pressure, military posturing, or direct deal-making. Trump’s stated interest in Greenland — including suggesting military options against a NATO ally’s territory — represents a public articulation of coercive resource logic that would have been inconceivable in the post-Cold War era.

The convergence of these dynamics — AI capability competition, mineral supply concentration, weakening international institutions, and the accelerating pace of technological change — is assembling the structural conditions for resource conflict. This is not a prediction of imminent great power war. It is an observation that the post-WW2 UN-led order, which constrained great power resource competition within diplomatic channels, is visibly under strain in ways that direct economic and security incentives are driving rather than moderating. The AI mineral race is one of several forces simultaneously degrading the institutional friction that historically prevented resource competition from escalating into open conflict.

The Productivity Case: Evidence and Honest Caveats

The headline productivity numbers for AI are compelling but must be read carefully. The evidence for transformative productivity gains is strong at the task level and weak at the macroeconomic level — which is exactly the pattern we should expect at this stage of diffusion. When electrification arrived in factories in the 1890s, individual electric motors were clearly more efficient than the steam systems they replaced. The economy-wide productivity boost did not appear in the statistics until the 1920s, when factory layouts had been fully redesigned around the new technology. The question is not whether AI can make individual tasks faster — it demonstrably can. The question is whether that task-level efficiency has yet propagated into organisational restructuring sufficient to show up in national output data.

The two strongest pieces of evidence for near-term impact are: first, Goldman Sachs’ Q4 2025 earnings analysis which found median productivity gains of approximately 30% in organisations that have deeply integrated AI into software development and customer service workflows[1]; and second, multiple controlled studies showing measurable output improvements for knowledge workers using AI assistants — GitHub Copilot studies showing 55% faster task completion for coding exercises, and Stanford/MIT research showing 14–15% output increases for customer service workers with AI assistance.

Goldman’s 2023 foundational estimate — a 7% global GDP uplift equivalent to nearly $7 trillion — rests on three mechanisms: labour cost savings from automation, new job creation in AI-enabled roles, and a productivity boost for non-displaced workers who are augmented by AI tools.[2] McKinsey’s higher-end estimate of $2.6–$4.4 trillion in annual value focuses on the use cases where impact is most concentrated: customer operations, software engineering, marketing, and R&D.[3]

The Healthcare Revolution: From Diagnosis to Drug Discovery

Healthcare is the sector where AI’s positive case is most empirically grounded and the stakes are highest. Three distinct impact channels are worth separating: diagnostics and clinical decision support; drug discovery and development; and pandemic preparedness and population health.

Scientific Acceleration: Compressing the Innovation Cycle

Beyond healthcare, AI is beginning to function as an accelerant of the broader scientific enterprise — not by replacing human scientists but by dramatically expanding the hypothesis space they can explore. The implications for climate technology, materials science, and fundamental physics are material.

The common thread across these domains is that AI excels at pattern recognition in high-dimensional data — precisely the challenge that creates bottlenecks in scientific research. The bottleneck in drug discovery is not lack of scientific ideas but the inability to evaluate millions of molecular candidates experimentally. The bottleneck in materials science is synthesis time. The bottleneck in climate modelling is compute time. AI attacks all three.

Democratisation of Expertise: The Access Dividend

Perhaps the most underappreciated positive externality of AI is what it does to access to expertise. For the 8 billion people who are not in the top decile of income in their country, access to high-quality legal advice, medical second opinions, financial planning, and specialised educational support has always been rationed by cost and geography. AI changes that equation materially.

Legal & Financial Access

Access to justice is among the most persistent inequalities in developed democracies — not merely in the Global South. In the United States, roughly 80% of low-income people with civil legal needs receive inadequate or no legal help (Legal Services Corporation, 2022). AI legal tools capable of drafting documents, explaining rights, and identifying relevant case law do not replace litigation representation, but they substantially close the information gap between a layperson and a trained attorney for the large proportion of legal situations that do not require courtroom advocacy.

Financial planning has a similar structure. The advice provided by a fee-based wealth manager — goal-setting, tax optimisation, asset allocation, estate planning — is genuinely valuable but only accessible above a net worth threshold that excludes the majority. AI financial planning assistants can deliver a credible version of that service at near-zero marginal cost. The implications for wealth accumulation across income distribution are potentially significant, though regulatory barriers to AI financial advice (particularly around personalised recommendations) remain real.

Education & the Personalised Learning Dividend

The global teacher shortage — estimated at 44 million by UNESCO — creates a structural access constraint in education that will not be solved by training more teachers in time for current student cohorts. AI tutoring systems offer a partial solution: adaptive, patient, available at 2am, capable of explaining the same concept twelve different ways until it lands. Khan Academy’s Khanmigo has demonstrated measurable improvements in student outcomes in controlled settings. The democratisation of Socratic tutoring — historically available only to the wealthy through private instruction — is a genuine positive externality of AI deployment.

Error Reduction, Decision Quality & Scenario Analysis

A category of AI benefit that receives less attention than productivity is error reduction in domains where mistakes are costly — or fatal. Cognitive fatigue, attention bias, and pattern-matching limitations are well-documented sources of human error in medicine, aviation, financial risk management, and infrastructure maintenance. AI systems that do not tire, do not anchor on prior diagnoses, and can process far more variables simultaneously are structurally better suited to these detection tasks.

Finance: Risk, Fraud & Scenario Analysis

In financial services, AI fraud detection systems now operate at a scale and speed that human analysts cannot approach — processing millions of transactions in real time, identifying anomalous patterns that would be invisible in manual review. Major card networks report fraud detection improvements of 30–50% following AI system deployment, with lower false positive rates that reduce friction for legitimate customers.

Scenario analysis and stress testing is a directly relevant application for Fenrir’s readership. AI can evaluate a portfolio against thousands of macroeconomic scenarios simultaneously — far beyond the three-to-five scenarios that traditional risk management frameworks use. This is not a theoretical capability: institutional asset managers and risk teams at major banks have been deploying AI-assisted scenario analysis tools since 2023, with material improvements in tail-risk identification. The ability to construct non-linear, correlated stress scenarios (rather than simple parallel shifts) is particularly valuable in environments like 2025–26, where macro variables are moving in novel combinations.

Infrastructure & Predictive Maintenance

Predictive maintenance — using sensor data and ML to anticipate equipment failure before it occurs — is one of the cleanest AI ROI stories in the industrial economy. McKinsey estimates that AI-enabled predictive maintenance can reduce machine downtime by 30–50%, extend equipment life by 20–40%, and reduce maintenance costs by 10–25%. The power generation, oil and gas, and aviation sectors have been early adopters; the railroad and utilities sectors are scaling deployments now. For utility analysts in Fenrir’s coverage universe, this is a directly material investment thesis: asset owners using AI-predictive maintenance have structurally lower capex replacement cycles.

AI as Creative Collaborator, Not Just Replacement

The narrative that AI inevitably destroys creative work misses an important part of the picture. In architecture, AI generative design tools can produce thousands of structural variants optimised simultaneously for cost, energy performance, and aesthetic criteria — expanding the design space that architects can explore in a given project timeline. In film production, AI pre-visualisation tools allow directors to iterate on visual storytelling at a fraction of the cost of physical shooting tests. In music production, AI tools handle technical tasks (mixing, mastering, sample clearance matching) that previously consumed studio time at the expense of creative work.

The distinction that matters analytically is between AI as a creative collaborator — expanding what individual practitioners can produce and explore — and AI as a replacement that commoditises creative output entirely. The former is already happening and is broadly positive. The latter is a real risk at specific nodes of the creative economy (stock photography, entry-level copywriting, generic illustration) but is not the complete picture. The artists and practitioners who integrate AI effectively into their workflow are, at present, more productive and more competitive than those who do not — which is the same dynamic that characterised earlier waves of digital tools in creative industries.

What Is This Technology, and Why Now?

Artificial intelligence has been a research field since the 1950s, but the version reshaping markets and capital allocation in 2026 is fundamentally different from anything that came before it. The key distinction is generality: modern large language models and multimodal systems can perform an enormous range of cognitive tasks — writing, coding, analysis, translation, image generation, scientific reasoning — using a single underlying architecture. That is new. It is also why every prior forecast about AI’s economic impact has been wrong, usually by underestimating the pace and scope of adoption.

Three technical developments created the current moment. First, the transformer architecture (Vaswani et al., 2017) proved that attention mechanisms could scale to arbitrary sequence lengths, providing the mathematical foundation for modern language models. Second, the empirical observation that increasing model scale — parameters, data, and compute — reliably improved capability (the “scaling laws” literature, Kaplan et al., 2020) gave practitioners a roadmap: spend more compute, get a better model. Third, the deployment of ChatGPT in November 2022 was the first time these capabilities were packaged into a consumer interface that a non-technical user could immediately exploit, triggering mass adoption at a speed that no enterprise software product had achieved before.

The Technology Stack

AI is not a single product but a layered stack. At the base is compute infrastructure — GPUs, TPUs, custom silicon — concentrated in a handful of chip designers (Nvidia, AMD, Google, and increasingly Amazon and Microsoft with custom ASICs). Above that is cloud infrastructure: the hyperscalers who rent compute capacity. Above that are foundation model developers (OpenAI, Anthropic, Google DeepMind, Meta, Mistral, and Chinese labs including DeepSeek and Baidu). Above that are the application layer companies building products on top of foundation models. The investment thesis, the competitive dynamics, and the risk profile differ substantially across each layer.

A Concise History: From Turing to Transformers

The doubling time of AI training compute has averaged roughly six months since the deep learning era began — compared to Moore’s Law’s eighteen months.[1] That pace means capability improvements that once took a decade now arrive in two years. It is also why most five-year-old AI forecasts look deeply conservative today.

The Capital Race: Numbers That Demand Attention

The economic mobilisation around AI is now large enough to materially affect GDP accounting, energy grids, and credit markets. The numbers are striking not because they are large in isolation — global GDP is $110 trillion — but because of the concentration, the acceleration, and the leverage involved.

The concentration of this spend warrants attention. RBC/Bloomberg data shows that Microsoft, Amazon, Alphabet, Meta, and Oracle account for the bulk of the increase, with the four largest spenders generating approximately $400 billion in trailing twelve-month free cash flow — meaning most current AI infrastructure is being funded internally rather than externally.[2] That changes if growth rates remain elevated: Bank of America estimates that AI capex will consume 94% of operating cash flows by 2025–26, up from 76% in 2024 — leaving little margin for error.[3]

The National Competition

AI is not just a private sector race. National governments have concluded that frontier AI capability is a strategic asset, and the policy response — investment mandates, export controls, infrastructure subsidies — is now a material input to corporate strategy. Below is a condensed view of the major national programmes.

Where AI Lands: A Sector-by-Sector Analysis

AI is not a single application landing in one place. It is a general-purpose cognitive substrate being absorbed — at different speeds, depths, and with different risk profiles — by every sector of the economy. What follows is a detailed sector-by-sector analysis of the impact vectors: where AI is already producing measurable change, where the potential is large but diffusion is early, and where the disruption is structural rather than incremental. Each sector maps to the deeper analysis in Parts II through V of this series.

Current Penetration: How Deep Is Adoption Actually?

Market commentary frequently conflates AI hype with AI deployment. The two are not the same. As of early 2026, enterprise AI adoption in the United States is real but concentrated, with meaningful productivity effects visible in only a limited set of use cases — primarily software development and customer service.[4] The broader productivity statistics remain unmoved, consistent with the historical pattern that general-purpose technology productivity benefits lag deployment by a decade or more.

The penetration picture matters for investors because the productivity dividend thesis depends critically on when diffusion reaches sufficient scale. The historical analog — electrification, the PC — suggests the economy-wide inflection point arrives roughly when ~50% of businesses have adopted the technology. We are nowhere near that threshold today outside of the technology sector itself. That either means the bears are right that AI hype has outrun reality, or it means the productivity windfall is genuinely ahead of us rather than behind us. We lean toward the latter, but the timeline is longer than equity multiples currently imply.