The OP argues that the core pillar of the current AI rally is not machine learning broadly, but frontier large models that depend on massive training and inference compute. If OpenAI and Anthropic cannot convert model capabilities into sufficiently high revenue and profits, the expansion of data centers, GPUs, memory, storage, and cloud computing will face a severe return-on-investment shortfall. The evidence chain begins with user behavior: although high-end models such as Fable have briefly demonstrated the ability to generate applications rapidly, they are primarily reconstructing existing products, and their capability improvements are insufficient to justify high token prices. Enterprises are shifting toward subscriptions, older models, or open-weight alternatives such as Qwen because of cost, while developers can switch providers frequently, leaving model vendors without strong customer loyalty or a pricing moat. The OP further argues that the failure of frontier labs would cause existing compute demand to fall sharply and leave GPUs, storage, and memory purchased at high prices in oversupply, hurting compute supply-chain companies such as NVDA, MU, SNDK, CRWV, and NBIS. It would also weigh on AMZN, ORCL, MSFT, SoftBank, and CSCO, which provide cloud resources to labs or have undertaken data center construction commitments. The macro narrative is that approximately $2.5 trillion in AI spending will not be recovered, and capital providers are gradually transferring risk through low-volume premarket rallies, intraday rotation, and capital moving into non-semiconductor sectors, with the problem potentially becoming visible when Anthropic goes public and discloses its financials. The OP gradually reduced and ultimately eliminated risky assets in a 401(k), while taking positions in late-2027 put options on SNDK, NVDA, PLTR, TQQQ, and ORCL, and identifying NBIS, MU, and CRWV as similarly exposed names. In the comments, the OP disclosed that the ORCL contracts expire on June 17, 2027, with a $150 strike, but did not fully provide the other strike prices, position sizes, or costs. The principal valuation anchors are Anthropic's potential valuation of approximately $2 trillion and NVDA's market capitalization of approximately $5 trillion, rather than discounted cash flow or sum-of-the-parts valuation. Potential catalysts include weaker NVDA guidance, Anthropic's public offering documents revealing its cost structure, consumer pressure, and enterprises cutting AI budgets. The thesis implicitly assumes that model performance is approaching a plateau, that lower-priced models will not increase total compute consumption through demand elasticity, that training accounts for approximately 70% of compute demand, and that committed projects cannot readily be repurposed. The largest flaw is that it equates broad AI spending forecasts with LLM compute capital that has already been lost, and directly extrapolates from the potential commoditization of model providers to the destruction of value across the entire compute supply chain. Open models still require local or cloud inference, and lower prices could also expand use cases. In addition, the OP acknowledges that the PLTR position reflects personal animosity; the OP's experience of being laid off and stated motive of making a 'revenge trade' weaken the analysis's objectivity. Claims that capital providers are manipulating premarket trading and can avoid lock-up restrictions by shorting after an IPO also lack trading data and legal support. The post contains no image attachments and does not provide complete profit-and-loss figures, price targets, or scenario probabilities.