Fiat currencies function as the most efficient instruments of resource extraction in human history, sustained only by the state's ability to guarantee exchange for essential survival materials. Since basic necessities like food and reproduction cannot justify continuous monetary expansion, the system artificially inflates survival costs around specific 'materials' to rationalize plunder. This mechanism, termed 'development,' creates perpetual supply-demand mismatches to validate previous extractions. The US dollar system relied on oil as this anchor material for 50 years following the 1974 Saudi agreement.

However, structural failures emerged after 2024 when Saudi Arabia began accepting RMB for settlements, OPEC ceased automatic reinvestment of oil revenues into US Treasury bonds, and the Federal Reserve accelerated money printing. With the oil-backed era collapsing, the system has identified AI as the new anchor material required to sustain the dollar for the next 50 years, a dynamic defined as the 'USD-AI Ponzi scheme.'

The immediate challenge for the US dollar system involves filling liquidity gaps through debt issuance or direct money printing. With China's holdings of US debt dropping from 1.3 trillion to 0.7 trillion and overseas investors reducing exposure, the Federal Reserve faces a binary choice that inevitably dilutes purchasing power. This dilution directly conflicts with investor expectations for real returns exceeding inflation. Consequently, the system requires a 'material' with demand growth sufficient to absorb the newly printed money. Without such an asset, the dollar would drive up prices, erode purchasing power, and trigger a sell-off where volume exceeds buyer capacity, forcing further intervention and exacerbating inflation. The continuous creation of bubbles and the search for new liquidity sinks are thus inherent requirements for the survival of the US dollar system.

Applying the Three-Circles Theory reveals why traditional investors dismiss the AI Ponzi argument while crypto-native participants recognize the pattern. Skeptics cite OpenAI's 25 billion annual revenue and productivity gains from ChatGPT as proof of utility.

However, this mirrors the 2023-2024 debate regarding Layer 2 (L2) and Zero-Knowledge (ZK) technologies, where real costs and existence did not guarantee sustainability without external liquidity injection. The AI industry's annual expansion rate exceeds 1 trillion, far outpacing actual revenues generated by AI companies. Most of this revenue stems from labor replacement, leading to irreversible job losses. These entities survive solely on continuous external funding. Woofun AI notes that once liquidity inflows into the US dollar system halt, AI asset valuations will collapse, tech capital expenditures will slow, and semiconductor stocks like NVDA will revert to normal cycle levels, replicating the trajectory of L2 and ZK assets.

The current state of the semiconductor industry parallels the L2 and ZK landscape of 2023 and 2024, as both rely on external liquidity to sustain growth metrics. Current 'fundamentals' resemble Total Value Locked (TVL), address counts, and trading volumes seen in crypto markets, all driven by liquidity rather than intrinsic utility. The core objective in both scenarios is to legitimize liquidity-driven models, a strategy reminiscent of political rhetoric suggesting cryptocurrency usage for debt repayment. The upstream components of the AI ecosystem, including terminal applications, large tech companies, and chip designers, form a mutually supportive closed loop. This structure generates internal revenue, stabilizing the total market value without money leaving the circle. OpenAI's 25 billion revenue, Azure's AI business growth exceeding 50% year-on-year, and the Mag-7 stock price highs exemplify this self-reinforcing mechanism, mirroring the Federal Reserve-Treasury-bank triad on a smaller scale.

Microsoft's funding of OpenAI to develop services on Azure illustrates the upstream mutual-support system that dictates the scale of AI capital expenditures and downstream supply chain orders. This reciprocal investment maintains high market valuations, allowing shareholders to exit near entry values. This expectation of liquidity encourages retail investors, ETFs, and 401k plans to continuously purchase AI assets, driving the scheme's expansion at an annual rate exceeding 1 trillion. Downstream companies operate similarly to dividend-paying systems, investing in fixed-cost manufacturing facilities like SK Hynix's HBM production, Lumentum's optical module lines, and TSMC's wafer services, waiting for upstream orders to distribute profits or conduct buybacks. Woofun AI analysis suggests that the Ponzi nature of these downstream firms stems not from their operations but from the premium value added by the upstream ecosystem's influence. Excluding the AI boom revaluation, these companies would revert to normal cycle-level valuations.

Speculative funds in the secondary market operate independently of supply chain constraints, moving based on prevailing narratives rather than primary market alpha. When primary market gains reflect beta factors, such as the consensus that NVDA will continue rising, speculative capital seeks undervalued assets within the same narrative framework regardless of supply chain position. Under the narrative of 'AI as an industrial bottleneck,' companies across the spectrum compete for the same liquidity pool. When a technology-based Ponzi scheme collapses, it does not necessarily spawn new industry leaders but rather validates the narrative, drawing attention to lower-market-cap companies and accelerating capital turnover in the secondary market. This mirrors the 'diversion of funds' strategy employed by traders, where the focus shifts from fundamentals to narrative adoption rates.

Trading strategies in this environment prioritize buying assets at low prices and early stages, a principle aligned with General P's 'perilla leaves' theory and the meme-as-cult concept. While value investing and long-term holding are debated, the lifespan of market narratives has significantly shortened since the 2001 dotcom bubble burst. The Three-Circles Theory describes this as 'the audience determining the structure of the market.' Success depends on identifying one's position in the queue of liquidity hunters before arriving too late to become transactional cost. Woofun AI observes that the fundamental quality of an asset matters less than the number of believers in its narrative. Early actors reap benefits regardless of the tools used, provided they act before liquidity expectations shift. Future analysis will focus on using fund flow data to determine relative market positioning.