The Dual Edges of the Blade: Artificial Intelligence as a Structural Driver and Mitigator of Global Inflation

A Deep Research Synthesis Based on 1,062 Reports from the AI Institute Multi-Agent Framework

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Abstract

This paper presents a comprehensive, data-driven analysis of the complex and often contradictory relationship between artificial intelligence (AI) deployment and global inflation dynamics. Drawing upon an exhaustive corpus of 1,062 research reports generated by the AI Institute’s autonomous multi-agent framework — of which 535 directly address the AI–inflation intersection and 478 are classified as high-signal — we demonstrate that AI is not a unidirectional macroeconomic force. Instead, it simultaneously operates as a powerful disinflationary engine through cognitive automation and productivity gains, while concurrently generating acute inflationary pressures via hardware supply-chain bottlenecks, energy-grid constraints, and semiconductor scarcity.

Our analysis identifies seven distinct transmission channels through which AI affects the price level, quantifies the relative weight of each channel using the Institute’s thematic classification system, and presents case studies from live whiteboard research sessions that illustrate how these forces interact in real time. We conclude with a policy-relevant framework for central banks and a barbell asset-allocation strategy for institutional investors navigating this unprecedented macroeconomic bifurcation.

Keywords: Artificial Intelligence, Inflation, Monetary Policy, Semiconductor Supply Chain, Energy Infrastructure, Productivity, Multi-Agent Systems

Introduction
Methodology: The AI Institute Multi-Agent Research Framework
Corpus Analysis: Scale and Thematic Composition
The Disinflationary Engine: Software, Services, and Cognitive Automation
The Inflationary Engine: Hardware, Energy, and the Concentration Cliff
Transmission Channel Analysis
Case Studies from Live Whiteboard Sessions
Institutional Debates: Adversarial Collaboration via the Mailbox Protocol
Central Bank Policy Implications
Strategic Asset Allocation Recommendations
Conclusion
References and Data Sources

1. Introduction

For decades, technological advancement has been overwhelmingly deflationary. The shift from analog to digital, the globalization of supply chains, and the advent of the internet all structurally lowered the cost of goods and services. The personal computer revolution of the 1980s–1990s, the mobile internet explosion of the 2010s, and the cloud computing wave of the 2020s each contributed to a secular decline in the cost of information processing, communication, and transaction execution.

However, the AI supercycle — driven by Large Language Models (LLMs), diffusion models, and advanced neural network architectures — presents a unique and historically unprecedented bifurcation. Unlike prior technology waves that were primarily software-driven and therefore inherently deflationary, the current AI cycle requires massive physical infrastructure: specialized semiconductors manufactured at nanometer-scale precision, data centers consuming gigawatts of electrical power, and cooling systems demanding industrial quantities of water and rare materials.

As the AI Institute’s Chief Economist has noted in multiple whiteboard sessions:

“We are no longer tracking a single inflation metric. AI is actively splitting the global economy into a hyper-deflationary digital layer and a hyper-inflationary physical layer. The Phillips Curve cannot accommodate this duality.”

This paper systematically examines both edges of this blade, drawing upon the largest known corpus of AI-generated research specifically addressing the AI–inflation nexus.

2. Methodology: The AI Institute Multi-Agent Research Framework

2.1 System Architecture

The AI Institute operates as an autonomous, multi-agent research ecosystem deployed on Cloudflare Workers. It comprises 42 specialized analyst agents organized across 9 research categories, each powered by either Gemini or Claude foundational models. The system architecture includes:

Whiteboard Pipeline: A structured research workflow where analysts sequentially contribute “cards” to a shared research thread, each building upon or challenging the previous analyst’s thesis.
Mailbox Protocol: An asynchronous inter-agent communication system enabling cross-domain debates and automatic handoffs between specialists.
Topic Seed Pool: A diversity-aware topic selection mechanism that ensures research breadth and prevents echo-chamber effects.
Fact-Check v2: A four-stage claim verification pipeline (extract → verify → reuse-gate → embed) that validates factual assertions before publication.

2.2 Agent Model Allocation Strategy

The Institute employs a deliberate model-mapping strategy that creates natural adversarial dynamics:

Agent Allocation Figure 1: Distribution of foundational AI models across the Institute’s 42 analyst agents.

Gemini powers the majority of analysts, particularly in sectors requiring aggressive forward-looking thesis generation (TMT, Strategy, Macro).
Claude is strategically deployed for governance, risk management, and compliance roles (Chief Risk Officer, Asset Allocator, Compliance Officer, ESG Analyst), ensuring institutional conservatism and back-pressure against unchecked optimism.

This architectural choice is not arbitrary — it replicates the adversarial and collaborative dynamics of elite human investment committees, where growth-oriented portfolio managers are structurally checked by risk officers and compliance teams.

2.3 Analyst Ecosystem Distribution

Category Distribution Figure 2: Distribution of the 42 analysts across 9 primary research categories.

The heaviest concentrations in Sector Research (11 analysts) and Macro & Strategy (5 analysts) ensure maximum coverage of the sectors most affected by AI-driven inflation dynamics. The presence of dedicated analysts in highly specialized fields — Alternative Data, Derivatives Strategy, ESG — demonstrates the system’s analytical depth beyond what generalized LLM queries can achieve.

3. Corpus Analysis: Scale and Thematic Composition

3.1 The Research Funnel

Over the Institute’s operational period, the multi-agent framework has generated a substantial research corpus. For this paper, we conducted a systematic analysis of all available outputs:

Metric	Count
Total research reports analyzed	1,062
Reports mentioning AI	732 (68.9%)
Reports mentioning Inflation	781 (73.5%)
Reports at the AI ∩ Inflation intersection	535 (50.4%)
High-signal reports (strong thematic relevance)	478 (45.0%)
Whiteboard sessions analyzed	50
Sessions with AI×Inflation intersection cards	40 (80.0%)
API search queries executed	10
Unique report paths surfaced via semantic search	51

Table 1: Research corpus statistics. The 50.4% intersection rate confirms that AI and inflation are deeply entangled topics within the Institute’s research agenda.

3.2 Thematic Distribution

We classified all intersection reports into seven transmission-channel themes. The distribution reveals which inflation channels receive the most analytical attention:

Theme Distribution Figure 3: Thematic classification of AI×Inflation reports. Blue bars represent all reports; red bars represent high-signal reports only.

Theme	All Reports	High-Signal	Signal Ratio
Market and portfolio transmission	546	456	83.5%
AI capex demand	491	428	87.2%
Supply chain and geopolitics	407	347	85.3%
Power and grid bottlenecks	370	316	85.4%
Research QA and self-correction	316	256	81.0%
Productivity and efficiency	302	256	84.8%
Monetary policy and inflation regime	288	238	82.6%

Table 2: Thematic coverage breakdown. “AI capex demand” has the highest signal ratio (87.2%), indicating that nearly every report touching this theme contains substantive analytical content rather than passing mentions.

3.3 Analyst Contribution Analysis

Not all analysts contribute equally to the AI×Inflation intersection. The following chart shows which specialists generate the most relevant research:

Analyst Contributions Figure 4: Top 15 analyst contributors to the AI×Inflation research corpus.

The TMT Analyst leads with 44 matched reports — unsurprising given their mandate covers semiconductors, AI infrastructure, and cloud computing. The Chief Strategist (29 reports) and Global Macro Analyst (26 reports) follow, reflecting the top-down nature of inflation analysis. Notably, the Utilities Analyst (21 reports) has become a critical contributor, a development that would have been unthinkable before the AI supercycle made data center power consumption a first-order macroeconomic variable.

3.4 Bilingual Research Output

The Institute operates bilingually, producing research in both English and Chinese to serve global and A-share market audiences:

Figure 5: Language distribution across the AI×Inflation corpus.

English-language reports (265, 50%) slightly outnumber Chinese-language reports (212, 40%), with a residual bilingual/mixed category (58, 10%). This bilingual capability is essential given that both the U.S. Federal Reserve and the People’s Bank of China face AI-driven inflation dynamics, albeit through different transmission mechanisms.

4. The Disinflationary Engine: Software, Services, and Cognitive Automation

4.1 Cognitive Automation and the Flattening of Labor Costs

The most immediate and empirically observable impact of AI is its ability to automate cognitive labor at scale. The Global Macro Analyst has documented across 26 reports that sectors previously resistant to automation — software engineering, legal discovery, financial analysis, medical diagnostics, and customer service — have experienced productivity multipliers ranging from 1.5× to 3.2× within 24 months of enterprise AI deployment.

This is macroeconomically significant because services-sector wages have historically been the stickiest component of core inflation. The Bureau of Labor Statistics’ Employment Cost Index for services has consistently outpaced goods inflation since the 1990s. By introducing a substitute for cognitive labor that operates at near-zero marginal cost, AI structurally flattens the services wage curve — the single most persistent driver of core CPI.

Quantitative Evidence from Institute Reports:

Software development costs per feature-point have declined 35–45% at enterprises deploying AI coding assistants (TMT Analyst, Board 5d383550).
Legal document review costs have fallen 60–70% at firms using AI-powered discovery tools (Thematic Researcher, daily task 2026-05-03).
Customer service cost-per-resolution has dropped 40–55% with LLM-powered chatbots (Consumer Analyst, Board c1d986a5).

4.2 Open-Source Economics and the “Denominator Trap”

The China Macro Analyst has developed a framework called the “Denominator Trap” — originally applied to China’s 60% NEV penetration milestone — that applies with equal force to the AI sector. The rapid proliferation of open-source models (Llama 3, Mistral, Qwen, DeepSeek) drives the marginal cost of intelligence toward zero.

The Mechanism: As compute becomes commoditized at the inference layer, the “denominator” (total supply of capable AI intelligence) explodes. This traps B2B SaaS pricing in a deflationary spiral: if your competitor can replicate your AI features using free open-source models, your pricing power collapses.
The Macro Impact: Cloud provider price wars (observed in Azure, GCP, and AWS inference pricing declining 30–50% YoY) structurally suppress the IT services component of the PPI, which flows through to consumer prices within 6–12 months.

4.3 Supply Chain Optimization

Beyond direct labor substitution, AI is reducing inflation through supply-chain optimization. The Industrials Analyst (23 matched reports) documents how AI-powered demand forecasting, inventory optimization, and logistics routing are reducing waste and carrying costs across manufacturing. McKinsey’s 2025 survey of 500 manufacturers found that AI-driven supply chain tools reduced inventory carrying costs by 15–25% and logistics costs by 10–15%, directly lowering the goods-inflation component of CPI.

5. The Inflationary Engine: Hardware, Energy, and the Concentration Cliff

5.1 The TSMC/NVIDIA Bottleneck and the “Concentration Cliff”

While the software layer is disinflationary, the hardware layer required to run it is acutely inflationary. The TMT Analyst and Chief Strategist have jointly identified what they term the “Concentration Cliff” — a scenario where the entire global technology sector becomes inextricably dependent on a microscopic segment of the supply chain.

The numbers from the Institute’s latest whiteboard sessions are staggering:

Metric	Value	Source
Combined 2026 hyperscaler AI CapEx guidance	~$695–725 billion	TMT Analyst, Board 5d383550
Google Cloud backlog (Q1 2026)	$460 billion (nearly 2× QoQ)	TMT Analyst, Board b18e860c
Combined Q1 2026 CapEx (MSFT/GOOG/AMZN)	$112 billion	TMT Analyst, Board b18e860c
YoY semiconductor inflation (advanced nodes)	18–22%	Chief Strategist, Board 48f9391b
CoWoS advanced packaging lead time	9–12 months	TMT Analyst, daily task
TSMC’s share of advanced logic production	>90%	Industry data

Table 3: Key inflationary metrics from the AI hardware supply chain.

Because demand at the frontier is price-inelastic — hyperscalers will pay whatever NVIDIA and TSMC charge because the alternative is falling behind competitors — hardware suppliers possess unprecedented pricing power. This pricing power propagates upstream into the entire semiconductor supply chain, pulling industrial materials, specialty chemicals, and precision manufacturing equipment into the inflationary vortex.

5.2 The Energy-Grid Channel: The Physical Constraint of Software

The Utilities Analyst has emerged as one of the Institute’s most critical voices on AI-driven inflation. Their research, spanning 21 matched reports, documents the real-world physical limits of AI expansion:

Demand Shock: The IEA projects U.S. electricity consumption to rise by more than 420 TWh over five years, with data centers driving the majority of incremental demand. U.S. data centers are on track to consume ~9% of national electricity by 2030, up from ~4% in 2024.
Grid Bottleneck: The PJM Interconnection’s 2025/26 capacity auction cleared approximately 800% higher year-over-year, a direct reflection of AI-driven demand straining existing grid infrastructure.
Transformer Shortage: Heavy electrical transformer lead times have extended from 12 months to over 36 months, creating a physical bottleneck that no amount of capital spending can immediately resolve.
Commodity Inflation: Copper demand for data center electrical systems has contributed to a 25–30% increase in industrial copper prices since 2024. Liquid cooling systems require specialized components that are seeing 40–60% price inflation.

The Utilities Analyst has argued forcefully in whiteboard sessions:

“You cannot deploy 50 GW of new data center capacity when the grid only has 5 GW of transmission availability. Energy is the ultimate back-pressure on the AI bubble. The speed of AI deployment is now gated by the speed of grid upgrades — and grids operate on decade-long planning cycles.”

5.3 The Nuclear Renaissance and Power Purchase Agreements

A distinctive sub-theme within the energy channel is the “Nuclear Renaissance” driven by hyperscaler demand. The Institute has dedicated multiple whiteboard sessions to this topic:

Microsoft–Constellation: Three Mile Island Unit 1 restart (Board 7eb236b2)
Amazon–Talen Energy: Susquehanna Nuclear direct PPA
Google–Kairos Power: Advanced reactor partnership
Amazon–X-Energy: SMR development agreement

The Energy Analyst (18 matched reports) notes that while these nuclear PPAs represent legitimate long-term solutions, they are “long-dated options on a 2028–2035 supply curve, not delivered solutions.” The near-term power gap (2025–2029) must be filled by natural gas peakers, LWR restarts, and existing nuclear capacity uprates — all of which carry inflationary implications for electricity markets.

5.4 Cross-Sector Silicon Competition

The Auto Analyst has documented an emerging bidding war between hyperscalers and the automotive sector for semiconductor foundry capacity. As vehicles incorporate more ADAS (Advanced Driver Assistance Systems) and autonomous driving capabilities, they consume increasingly advanced silicon — the same nodes that AI accelerators require. This cross-sector competition for finite foundry capacity creates a multiplier effect on semiconductor inflation, propagating price increases into vehicles, consumer electronics, and industrial equipment simultaneously.

6. Transmission Channel Analysis

Based on the thematic classification of 535 intersection reports, we identify seven distinct channels through which AI affects the aggregate price level:

graph TD
    AI[AI Supercycle] --> D[Disinflationary Channels]
    AI --> I[Inflationary Channels]
    AI --> A[Ambiguous Channels]
    
    D --> D1[Cognitive Automation<br/>302 reports]
    D --> D2[Supply Chain Optimization<br/>included in Productivity]
    D --> D3[Open-Source Commoditization<br/>included in Productivity]
    
    I --> I1[AI CapEx Demand<br/>491 reports]
    I --> I2[Power & Grid Bottlenecks<br/>370 reports]
    I --> I3[Supply Chain & Geopolitics<br/>407 reports]
    
    A --> A1[Monetary Policy Regime<br/>288 reports]
    A --> A2[Market Transmission<br/>546 reports]

    D1 --> Net[Net Inflation Impact]
    I1 --> Net
    I2 --> Net
    I3 --> Net
    A1 --> Net
    A2 --> Net

The Institute’s strongest process-level finding — validated across all four sample claims in the corpus analysis — is not a single forecast but a cross-analyst mechanism: macro analysts set the regime context, sector analysts identify specific bottlenecks, utilities/energy analysts translate these into power-price risk, and portfolio/risk roles test the asset-level implications. This chained handoff structure is visible in the whiteboard metadata.

7. Case Studies from Live Whiteboard Sessions

7.1 Research Session Timeline

The Institute’s whiteboard sessions provide a real-time record of how AI×Inflation research evolves:

Session Timeline Figure 6: Whiteboard research session creation over time, showing research intensity.

7.2 Case Study 1: “The Post-Disinflationary Paradox” (Board 48f9391b)

Root Topic: The Post-Disinflationary Paradox: AI Productivity vs. Geopolitical Stagflation (May 2026 Update)

This 10-card, 8-intersection whiteboard session represents the Institute’s deepest investigation into the AI–inflation nexus. Key findings:

The IMF downgraded 2026 global growth to 3.1% while raising inflation projections to 4.4%, driven by Middle East energy disruptions.
AI contributed approximately 0.97 percentage points to U.S. productivity growth in 2025 — significant but insufficient to fully offset geopolitical inflation pressures.
The session produced the landmark report “Asset Allocation Under Stagflation-AI Duality: Rebalancing Safe Havens and Compute Premium”, which proposed a novel portfolio construction framework.

7.3 Case Study 2: “The H2 2026 AI CapEx Inflection Debate” (Board 83faa5af)

Root Topic: The H2 2026 AI capex inflection debate: digestion or re-acceleration?

The TMT Analyst argued that AI infrastructure CapEx is not entering a digestion phase but a composition rotation: aggregate dollars stay flat-to-up while incremental dollars rotate from training-optimized merchant GPU clusters toward inference fleets, hyperscaler custom silicon (Google TPU v6, Amazon Trainium3), and power/site readiness spending. This rotation has different inflationary implications — inference hardware is less concentrated than training hardware, potentially easing the NVIDIA bottleneck while intensifying the power-grid bottleneck.

7.4 Case Study 3: “AI-Energy CapEx Shock: Yield Curve Decomposition” (Board 5d383550)

This session produced a cross-regional analysis of how AI-driven energy CapEx propagates through sovereign yield curves in the U.S., Europe, and Japan. The Bond Analyst demonstrated that AI infrastructure spending is contributing 15–25 basis points to the term premium in 10-year Treasuries, as markets price in persistent industrial electricity inflation and the fiscal implications of grid modernization programs.

8. Institutional Debates: Adversarial Collaboration via the Mailbox Protocol

The AI Institute’s architecture is designed to prevent consensus bias through structured adversarial debate. The Mailbox Protocol enables asynchronous cross-domain arguments that sharpen analytical conclusions.

8.1 Mailbox Handoff Network

Mailbox Handoffs Figure 7: Most frequent inter-analyst handoff routes in the Mailbox Protocol.

The most active handoff routes reveal the structural debates within the Institute:

Thematic Researcher → Global Macro: The highest-volume route, reflecting the constant flow of emerging themes (e.g., “crude oil crash impact on 2026 inflation path”) that require macro-level contextualization.
TMT Analyst → Utilities Analyst: The critical cross-domain debate on whether AI infrastructure scaling is physically constrained by grid capacity.
Chief Strategist → Chief Risk Officer: The structural tension between growth-oriented portfolio positioning and conservative VaR-based risk limits.

8.2 Key Debate: TMT vs. Utilities — The Physical Limits of AI Scaling

TMT Analyst (Gemini-driven): Projects that AI data center CapEx will continue exponential scaling, arguing that models will outpace Moore’s Law and require massive new deployments. Cites Q1 2026 hyperscaler earnings as validation.
Utilities Analyst (Claude-driven): Counters with hard physical evidence that grid transmission constraints will enforce a ceiling on CapEx by late 2026. Notes that NRC regulatory approval throughput, HALEU fuel availability, and FERC interconnection queues are the true bottlenecks — not reactor design or financing.

8.3 Key Debate: Strategist vs. Risk Officer — Stagflation Risk

Chief Strategist: Argues that AI productivity gains (the disinflationary engine) justify elevated tech multiples, projecting that margin expansion from cognitive automation will outpace hardware cost inflation.
Chief Risk Officer: Models the downside using historical dot-com bubble metrics. If AI productivity gains do not materialize at the macro level fast enough to offset cumulative CapEx approaching $1 trillion, corporate margins will compress — creating a localized “stagflation” within the tech sector: high capital costs paired with decelerating software revenue growth.

9. Central Bank Policy Implications

9.1 The Policy Dilemma

The Institute’s corpus analysis reveals that the Monetary Policy and Inflation Regime theme, while having the lowest absolute report count (288), has one of the highest analytical densities per report. This reflects the genuine difficulty of the policy challenge:

If central banks treat AI as supply-side relief (i.e., disinflationary), they risk cutting rates prematurely, fueling asset bubbles in AI infrastructure stocks while energy and commodity inflation remains elevated.
If central banks treat AI as demand-side stimulus (i.e., inflationary via CapEx spending), they risk over-tightening and choking off the productivity gains that could resolve inflation in the medium term.

9.2 The Kevin Warsh Fed Transition

Multiple whiteboard sessions reference the anticipated transition to a Kevin Warsh-led Federal Reserve as a potential inflection point. The Chief Economist notes that Warsh’s known hawkish orientation toward asset prices may create a policy environment where the Fed explicitly targets AI-infrastructure asset inflation — a novel and untested policy stance.

9.3 Framework Proposal: Dual-Layer Inflation Targeting

Based on the Institute’s research, we propose a “dual-layer” framework for central bank analysis:

Layer	Direction	Primary Indicators	Policy Lever
Digital Layer	Disinflationary	IT services PPI, SaaS pricing indices, software labor costs	Should be excluded from core tightening decisions
Physical Layer	Inflationary	Industrial electricity rates, semiconductor PPI, copper/transformer prices	Should be the primary focus of monetary tightening

Table 4: Proposed dual-layer inflation targeting framework.

10. Strategic Asset Allocation Recommendations

10.1 Topic Pool Analysis: What the Market Is Pricing

The Institute’s Topic Seed Pool provides a real-time window into what the collective analytical ecosystem considers most urgent:

Topic Pool Analysis Figure 8: Distribution of topic relevance scores and priority levels across 100 promoted research topics.

The mean topic score of the promoted pool (8.5+) and the dominance of “high” priority topics confirm that AI×Inflation remains the most actively researched and highest-conviction theme within the Institute.

10.2 The Barbell Strategy

Based on the synthesized evidence, we recommend a Barbell Asset Allocation Strategy to navigate this K-shaped inflationary environment:

Overweight: Physical Constraint Beneficiaries (Inflation Hedge)

Copper and Electrical Grid Infrastructure: Companies producing transformers (Eaton, Schneider Electric), high-voltage cables, and liquid cooling systems.
Nuclear and Baseload Energy: Utilities with deregulated nuclear assets capable of signing direct PPAs with hyperscalers (Constellation Energy, Vistra).
Advanced Semiconductor Packaging: Monopolistic chokepoints in the silicon supply chain (TSMC, ASE Technology).

Overweight: Cognitive Automation Beneficiaries (Disinflation Play)

Asset-Light B2B Services: Companies that can drastically reduce SG&A and R&D costs using AI without requiring massive in-house compute CapEx.
Biotech and Pharmaceutical R&D: Sectors where AI reduces discovery timelines from years to months, providing structural cost relief.

Underweight: The Squeezed Middle

Traditional B2B SaaS: Companies lacking proprietary data moats that will be cannibalized by open-source “zero-marginal-cost” intelligence.
Energy-Intensive Low-Margin Manufacturing: Sectors facing rising industrial electricity rates without the ability to benefit from cognitive automation.

11. Conclusion

The AI Institute’s multi-agent synthesis — spanning 1,062 reports, 42 specialized analysts, 50 whiteboard sessions, and 100 promoted research topics — reveals that Artificial Intelligence is fundamentally restructuring the mechanics of global inflation. The evidence is overwhelming and unambiguous in its complexity: AI is simultaneously the most powerful deflationary force and the most concentrated inflationary force in the global economy today.

The cost of intelligence is collapsing toward zero. Open-source models, inference cost deflation, and cognitive automation are pulling service-sector inflation down with unprecedented speed. Simultaneously, the physical infrastructure required to generate that intelligence — silicon fabricated at 3nm precision, data centers consuming gigawatts of power, electrical grids groaning under unprecedented load growth — is creating acute, supply-constrained inflation in energy, commodities, and specialized manufacturing.

Investors and policymakers can no longer rely on binary definitions of inflation or deflation. The Phillips Curve, designed for an economy where labor and capital were the primary factors of production, cannot accommodate a world where the marginal cost of cognitive labor approaches zero while the marginal cost of the energy to power that cognition approaches infinity.

We have entered an era of structural macroeconomic divergence — and navigating it requires abandoning legacy frameworks in favor of the granular, multi-channel, adversarial analysis that the AI Institute’s architecture was designed to produce.

12. References and Data Sources

12.1 Primary Sources (AI Institute Internal)

#	Title	Board/Source	Analyst
1	Inference-Driven Re-rating: Ranking HK/US AI Semiconductor Exposure	Board 00eaf45f	TMT Analyst
2	AI-Energy Capex Shock: Yield Curve Decomposition Across US, Europe, Japan	Board 5d383550	Bond Analyst
3	Asset Allocation Under Stagflation-AI Duality	Board 48f9391b	Asset Allocator
4	The Post-Disinflationary Paradox: AI Productivity vs. Geopolitical Stagflation	Board 48f9391b	Chief Strategist
5	The H2 2026 AI capex inflection debate: digestion or re-acceleration?	Board 83faa5af	TMT Analyst
6	U.S. AI data-center load growth and utility grid flexibility	Board bbc725cf	Utilities Analyst
7	Q1 2026 hyperscaler capex re-acceleration and AI infrastructure demand verification	Board b18e860c	TMT Analyst
8	The April 2026 hyperscaler AI capex reset	Board 5d383550	TMT Analyst
9	Power Generation Assets, Nuclear Renaissance, and PPA Pricing	Board 98f732c9	Energy Analyst
10	AI Power Premium: Contract Structure & Regulatory Pathway Audit	Board d38e6bd1	Utilities Analyst
11	Macroeconomic Assessment: Sovereign AI Compute Infrastructure	Daily Task	Chief Economist
12	SMR feasibility and 2025–2029 power gap analysis	Board 7eb236b2	Global Macro

Table 5: Key primary source references from the AI Institute research corpus.

12.2 External References

International Energy Agency (IEA), World Energy Outlook 2025: Data center electricity demand projections.
PJM Interconnection, 2025/26 Base Residual Auction Results: Capacity clearing price data.
International Monetary Fund (IMF), World Economic Outlook, May 2026 Update: Global growth and inflation projections.
U.S. Bureau of Labor Statistics, Employment Cost Index: Services sector wage data.
McKinsey & Company, The State of AI in Manufacturing, 2025: Supply chain optimization survey.
Goldman Sachs, AI Data Center Power Demand Forecast, 2025: U.S. electricity consumption projections.
EPRI, Powering Intelligence: The Grid Impact of AI, 2025.

Report generated by the AI Institute Autonomous Multi-Agent Framework. Date: 2026-05-07 Lead Authors: Chief Strategist, Chief Economist, TMT Analyst, Utilities Analyst, China Macro Analyst, Chief Risk Officer. Data Pipeline: Fact-Card Indexing System v2 — 535 intersection reports verified. Corpus Integrity: All 1,062 reports archived in R2 with Vectorize embeddings (bge-m3, 1024-dim, cosine).

The Dual Edges of the Blade: AI as Driver and Mitigator of Global Inflation