The AI Industry Is Losing

2026-06-30 · Source: Ed Zitron's Where's Your Ed At · Field: Finance & Economics — Capital Markets & Investment Management, Economic Analysis & Policy · Depth: Intermediate, extended

Summary

The AI industry faces significant financial instability, driven by unsustainable capital expenditure and a lack of genuine demand beyond a few heavily subsidized AI labs. The Bank of International Settlements (BIS) reported that five largest hyperscalers are projected to spend over \$1 trillion on AI-related capex from 2025-2026, outpacing earnings and free cash flow. Companies like Oracle have pushed free cash flow to negative \$23.7 billion by FY 2026 with \$129.5 billion in debt, largely leveraging for OpenAI. Microsoft's partnership with OpenAI has cost over \$100 billion, with OpenAI's 2025 expenses to Microsoft Azure at \$17.2 billion, contributing to a \$20.9 billion loss on \$13.04 billion revenue. OpenAI and Anthropic reportedly represent 50% or more of hyperscaler remaining performance obligations, totaling around \$748 billion, and their products are widely criticized for poor performance and high costs.

Key takeaway

For investors and executives evaluating AI sector opportunities, it is crucial to recognize the deep financial instability underpinning current growth. Your due diligence must extend beyond marketing claims, focusing on verifiable revenue streams and the true cost-benefit of AI investments. Be wary of companies whose existence hinges on massive, speculative capital expenditures and the continued subsidization of unprofitable AI labs, as this poses significant systemic risk to your portfolio.

Key insights

The AI industry's massive capital expenditure is unsustainable, driven by speculative demand and propping up unprofitable AI labs.

Principles

• Unprofitable AI labs rely on hyperscaler subsidies.
• Market narratives can obscure financial realities.
• Excessive capex without demand creates systemic risk.

Method

Critically analyze financial reports, deconstruct "annualized run rates," and verify revenue sources to expose speculative bubbles.

In practice

• Scrutinize "AI revenue run rates" for subsidies.
• Question data center buildouts lacking diverse demand.
• Beware of reports using proprietary, unverified data.

Topics

• AI Bubble
• Capital Expenditure
• Financial Risk
• Hyperscaler Investments
• Large Language Models
• Market Analysis

Best for: Entrepreneur, Investor, Executive, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Ed Zitron's Where's Your Ed At.