The AI industry today is heavily dependent on a web of circular funding and commitments. Major tech giants and startups are locked in a cycle where investments and revenue backlogs feed into each other, creating a self-sustaining but risky ecosystem. This financial loop raises questions about the true potential and stability of AI advancements.

The Circular Flow of AI Investments

Several of the world’s biggest companies are funneling billions into AI startups, which in turn commit huge sums back to cloud providers like Google Cloud, Amazon Web Services, and Microsoft Azure. For example, Anthropic has pledged to spend $200 billion on Google Cloud over five years, while also committing billions to Amazon and Microsoft. These commitments are not just spending; they are reflected as revenue backlog for the cloud providers, boosting their stock prices and reinforcing the cycle.

In total, Anthropic and OpenAI alone have committed hundreds of billions of dollars to cloud infrastructure. The four major cloud providers plan to spend nearly three-quarters of a trillion dollars on AI infrastructure in 2026, almost doubling the previous year’s investments. This enormous spending is driven largely by the expectations of future profits, but many of these investments are based on promises rather than proven results.

The Risks of Relying on Promises

Many experts point out that this circular system is built on unproven promises. The industry is pouring a huge amount of money into AI models that are still largely experimental. For instance, a recent report from Anthropic showed a significant gap between what AI could theoretically do and what it is actually doing in the real world. This gap suggests that AI’s real-world impact may be much less than industry hype implies.

Some analysts warn that these circular deals might be masking deeper issues. If AI models don’t improve in reliability and practical usefulness, the entire funding cycle could collapse. A study on AI model reliability found that, despite rapid growth in capability, models are not becoming more dependable. This mismatch could mean that the current investments are building on shaky foundations, risking a major setback if expectations aren’t met.

Despite these concerns, many industry leaders remain confident. They believe that with deeper deployment and continued innovation, AI will eventually deliver on its promises. The industry’s optimism is partly driven by the sheer amount of money being invested, and partly by the belief that current limitations are temporary. Whether this confidence is justified or not remains an open question.

Inspired by

• https://www.thealgorithmicbridge.com/p/how-the-ai-industry-runs-on-its-own

Sources

• substack.com
• theinformation.com
• reuters.com
• cnbc.com
• anthropic.com