**TL;DR:** In May 2026, the world exists in a parallel reality of energy blockades and AI booms. This article argues that rising physical costs from geopolitics are clashing with AI's high infrastructure CapEx. As technology pricing escapes the reach of consumers and the energy base remains locked by conflict, we may be heading toward a macro storm of tech-bubble burst and vicious stagflation.

Introduction: Observations on the Fault Line

In early May 2026, the world is in a bizarre parallel reality: the Strait of Hormuz remains under a de facto blockade, and Ukrainian drones continue to strike Russian refineries; yet, thousands of miles away on Wall Street, tech stocks are dancing under the banner of AI.

As an observer working on the front line of the semiconductor industry, I feel a strong, physical sense of unease. This unease stems from the massive disconnect between efficiency feedback at the execution level and the grand narratives of the capital markets.

I. The “Liquidity Deadlock” of Hormuz

The current situation is far more than simple energy price volatility. The Strait of Hormuz is in a “reciprocal blockade” between US and Iranian forces. Despite the deployment of carrier strike groups, this “power rebalancing” has not restored navigation; instead, it has made the risks more structural and long-term.

The core contradictions are:

• Physical Disruption: 20% of the world’s crude oil supply is trapped in the Persian Gulf. Global JIT (Just-In-Time) supply chains are facing a mechanical failure as their “lubricant” runs dry.
• Invisible Cost Transmission: While crude reserves within the Gulf are sufficient, the “War Premium” caused by the inability to enter or exit has locked oil prices at high levels.

II. The “Impossible Triangle” Behind AI Euphoria

Capital markets are betting heavily that AI can hedge against the recessionary risks brought by geopolitics. I believe this logic is extremely fragile. From an execution perspective, the AI boom is facing an intersection of “physical costs and shrinking demand”:

1. Pressure on Raw Materials: Due to the energy crisis and geopolitical conflicts, PCB (printed circuit board) costs have surged. Upstream supply chain pressures are being passed down, leading to shortages across GPUs, CPUs, and memory (DRAM/NAND).
2. The Diminishing Returns of Efficiency: While AI shows incredible performance in basic development and standardized content generation (allowing many beginners to rapidly productize), it often falls into a “mediocrity trap” when solving complex systemic problems. For large enterprises, the invisible costs of maintaining and auditing AI-generated code are eroding the efficiency dividends promised by the technology.
3. The Power and Resource Sink: Big Tech’s investment in data centers has reached astronomical levels. Not only must they endure hardware price hikes, but they also face additional “resource taxes” from state governments for their massive power consumption.

III. Cost Transmission: LLMs Enter a Price Hike Cycle

Large AI vendors are beginning to raise prices as they struggle with skyrocketing costs. Pricing strategies in the LLM space are undergoing structural adjustments through tiered pricing, high-end subscription levels, and restructured API billing logic.

Here is a summary of the latest LLM pricing trends in the first half of 2026:

1. Microsoft: Full-Scale Commercial Price Hikes

Microsoft announced its largest price increase in years in early 2026.

• Effective Date: July 1, 2026.
• Changes:
  • Microsoft 365 Commercial: Office 365 E3 (without Teams) rose by 14%, Microsoft 365 E3 by 11%.
  • Copilot Strategy: While independent SKU prices didn’t rise directly, Microsoft began bundling Copilot with higher-priced packages and introduced a more expensive Copilot Pro Max tier for users needing higher concurrency and priority access to the latest models (like GPT-5.4).

2. Anthropic (Claude): Introducing “Ultra-Premium” Modes

Anthropic achieved indirect price hikes through functional unbundling:

• Max Tier: Introduced Claude Max plans at $100/mo and $200/mo, targeting developers using Claude Code and large-scale context processing.
• API “Fast Mode”: In the latest Claude 4.6, low-latency responses require “Fast Mode,” priced at 6x the standard rate (e.g., input jumping from $5/MTok to $30/MTok).

3. Google (Gemini): Tiered Granularity and API “Corrections”

Google’s strategy is the most complex, showing a trend of “cheap low-end, expensive high-end.”

• Consumer Edition: Launched Google AI Ultra at $249.99/mo, including 30TB of storage, Veo 3.1 video permissions, and the autonomous assistant Project Mariner.
• API Billing Controversy: In March 2026, developers found Gemini API bills surging. Officials explained this was a “fix” for previously under-billed “Thinking Tokens” and “Grounding,” viewed by developers as a retroactive price hike.

4. OpenAI: Shifting Toward Enterprise

Preparing for an IPO in late 2026, OpenAI is under immense revenue pressure:

• Enterprise Focus: Tightening resource quotas for the individual Plus version to push users toward ChatGPT Team or Enterprise.
• GPT-5 Series Pricing: With the release of GPT-5.4, OpenAI has adopted a “discount old models, premium new models” strategy. Inference costs for top-tier models (especially Agent-capable versions) are 30%-50% more expensive than the previous generation.

Summary and Observations

The current industry pricing logic revolves around three points:

1. From “Flat Rate” to “Consumption-Based”: $20 no longer covers everything; “fast” or “strong” now requires $100+ tiers.
2. Explicitizing Hidden Costs: Separate charges for web search, AI drawing, and long-term memory storage.
3. B-Side Shouldering the Burden: Indirectly forcing enterprises to pay for AI costs through bundled “full stack” upgrades.

IV. The Convergence of Pain: The Road to “Double Stagflation”

This pressure eventually reaches the average consumer. A dangerous feedback loop is forming:

1. Structural Rise in Unemployment: Major companies are pushing large-scale layoffs in the name of AI to pursue “efficiency.”
2. Supply-Side Inflation: Rising energy costs push up living expenses, while AI premiums inflate the cost of electronics and software.
3. Defensive Household Contraction: As unemployment expectations meet high prices, households are forced to cut spending, further killing the ROI for businesses.

This is “Double Stagflation”: technology-driven productivity dividends fail to cover their high underlying costs, while simultaneously destroying the consumption capacity of the market.

V. Echoes of History: 1978 Meets 2000

Looking at 2026 through the lens of history, this may be a fatal encounter between “Stagflation” and a “Tech Bubble”:

• The 1978 Backdrop: Hyperinflation caused by an energy crisis, combined with shrinking purchasing power. When gas prices and gadget prices double simultaneously, the only rational choice for consumers is to cut all non-essential spending.
• The 2000 Script: Frantic infrastructure building (routers then, GPUs now), only to find that terminal ROI couldn’t close the loop. When the first giant admits that “trillions in investment cannot be recovered,” the stampede begins.

VI. Future Paths

• Path A: The Depression Theory (70% Probability): A freeze in demand leads to an overnight shift from “supply shortage” to “overcapacity” in AI infrastructure. The world enters a long correction period similar to post-2000, but with severe inflation due to geopolitical conflicts.
• Path B: The Miracle Theory (30% Probability): AI achieves an exponential breakthrough in power grid efficiency or material production by late 2026. The incremental value generated successfully offsets the cost losses caused by the blockade.

Conclusion

I personally lean toward the former. When the pricing of technology escapes the reach of the consumer, and the energy base is locked by war, the euphoria is often the final light before the curtain falls.

This article was recorded in May 2026. I hope that when looking back at a future moment, these “subjective observations” will provide a authentic footnote for the reality of that time.

Disclaimer: The content of this article is for personal study and research records only. All analysis was completed with the assistance of AI tools and does not constitute any investment advice. Investing is risky; please be cautious.