The New York Times and other outlets have raised a clear caution: the recent surge in AI funding may be more circular than it first appears. Large platform providers and AI developers are forming interlocking deals that mix purchases, equity stakes and long term licensing commitments. The result can be a self reinforcing spending loop that boosts headline totals while obscuring true external demand.

What circular investment means

In practice, circular investment refers to arrangements where companies:

• buy cloud capacity or AI products from one another
• take equity stakes or make strategic investments in partners
• sign reciprocal consumption commitments and licensing deals that guarantee future internal revenue

Put simply: platform A commits capital to developer B, and B in turn commits to buy services from A or signs licensing deals that channel value back into the network. This can create apparent market traction even where broad end user adoption is limited.

Key themes to watch for

• Scale: Big numbers in planned AI spending across infrastructure, cloud and licensing can mask how much of that demand is internal.
• Interlocking deals: Reciprocal cloud commitments, equity for service arrangements and long term licensing create layered dependencies.
• AI valuation risk: Converting internal commitments into headline revenue can inflate company valuations and hide weak unit economics.
• Shifting monetization: Moves from litigation over data use toward negotiated licensing deals monetize publisher content and add contractual complexity.

Common forms of circular arrangements

• Large cloud providers pre committing capacity for major model developers
• Platform investments into startups that then rely on the same platforms for production
• Long term licensing deals that channel content monetization back into platform economics

Practical implications for businesses and investors

For people evaluating AI vendors or potential investments, the central questions are about signal versus noise. Does reported growth reflect broad external adoption or a closed loop of pre committed spending? Consider these steps:

• Due diligence: Ask for transparent unit economics and proof of diverse external customers rather than concentrating on headline spending figures.
• Contract scrutiny: Review licensing and cloud agreements for minimum consumption guarantees, exclusivity clauses and equity for service swaps that tie fortunes together.
• Risk assessment: Model scenarios where external demand slows or licensing terms change to understand potential valuation downside.

Context from broader AI investment trends

Generative AI and agentic AI attract heavy investment, with small language models or SLMs emerging as strategic components of enterprise deployments. That investment is often necessary to build production scale where specialized chips, data center capacity and long term R D are required. Still, scale alone does not remove the structural risks introduced by interlocking deals.

Minimal industry takeaway

This pattern echoes past tech cycles where inter company commitments propped up headline growth while masking underlying weakness. The best positioned companies will show independent revenue traction, clear unit economics and contractual clarity that supports scalable adoption by independent end users.

Conclusion

The AI era will continue to reward large investments that build capability. But when much of that spending circulates within a small set of interconnected players, the boom becomes more brittle. Evaluate AI vendors with careful due diligence, focus on external demand, and watch for signs of circular investment and interlocking deals that could amplify AI valuation risk.