NextFin News - In late November 2025, Michael Burry, renowned investor and head of Scion Asset Management, escalated his critique of the artificial intelligence (AI) sector by taking large put option positions totaling over $1 billion on major AI-related stocks, most notably Nvidia and Palantir. This move marked a sharp public confrontation between Burry and Nvidia, a tech giant valued in the trillions and a central player in AI hardware provision. The contention centers on fundamental disagreements about the true economics underlying the AI boom, particularly regarding asset depreciation, spending incentives, and financial reporting. Nvidia responded with an internal memo directly addressing Burry’s critique, denying allegations of inflated valuations and disputing the depreciation concerns. The debate has since permeated financial newsrooms and investor communities, symbolizing wider market uncertainty over AI’s sustainability.

Burry’s skepticism stems primarily from his analysis of accounting methodologies used by AI adopters, especially the depreciation schedules applied to costly GPUs powering AI workloads. He argues that the prevailing practice—commonly spreading AI hardware costs over several years—misrepresents the rapid obsolescence of these technologies. His valuation model suggests a more appropriate useful life closer to three years rather than the longer periods used, which inflates short-term earnings and masks economic realities. If asset life estimates do not align with rapid generational improvements in AI chips, companies could face steep write-downs and diminished returns by as soon as 2026. Burry stresses that this issue reflects a timing mismatch, not fraud, but one with profound consequences for profitability and market valuations.

Compounding these concerns, Burry warns that much of the current AI infrastructure spending is driven by competitive fear—firms investing heavily to avoid being left behind in AI adoption—even when the economic payoff is uncertain or marginal. This psychological dynamic distorts capital allocation, potentially pushing expenditures beyond sustainable economic return points. Nvidia’s soaring demand is largely a function of this dynamic, raising the key question of whether AI spending will endure if capital costs rise or returns underwhelm expectations.

Another contentious area surrounds vendor financing. Critics, including notable analysts like Jim Chanos, suggest that some AI infrastructure sales might be artificially supported through vendor financing schemes to maintain sales momentum. While Nvidia vehemently denies these claims, the possibility of such mechanisms adds another layer of risk to the sector’s robustness if purchase incentives decouple from end-user demand. The lack of transparent evidence keeps this debate ongoing but highlights investor caution over demand quality.

Additionally, Burry highlights Nvidia’s extensive use of stock-based compensation, which, while standard in the tech sector, indirectly impacts long-term owner earnings via shareholder dilution. Nvidia defends its compensation practices as compliant, yet this tension illustrates the divergence between reported accounting profits and underlying cash economics important to investors assessing real shareholder value.

Of particular analytical interest is Burry’s comparison of Nvidia’s current valuation trajectory to Cisco’s during the late 1990s dot-com boom. Cisco had solid revenues and innovative products but suffered a valuation bubble that collapsed when the tech spending frenzy cooled, imposing years of recovery. This analogy encapsulates Burry’s view that despite Nvidia’s critical role in powering AI, its market value could suffer sharp corrections if AI spending slows or earnings disappoint—especially given macroeconomic pressures in 2025, including tighter capital conditions under President Donald Trump’s administration.

Nvidia’s public rebuttal memorandum attempted to clarify its accounting policies, reject allegations of vendor financing, and argue that AI demand in enterprises, data centers, automotive, and edge computing remains broad-based and resilient. However, Burry countered that the memo misrepresented his focus, which is primarily on AI hardware purchasers’ economic realities rather than Nvidia’s internal accounting. The sharp tone of this exchange underscores a rare and intense dispute between a major corporate titan and a seasoned market skeptic, drawing elevated attention from regulators, market participants, and analysts.

This confrontation reflects systemic tensions in the AI tech boom where rapid innovation cycles, massive capital expenditures, and lofty valuations collide with enduring challenges in financial discipline and transparency. The sector’s health hinges on whether depreciation schedules and capital spending align with AI’s true productive lifespan and economic contribution. Data through 2026-2028 will be critical, especially regarding impairments, spending moderation, and the emergence of alternative chip suppliers like Broadcom.

From a market impact perspective, Burry’s sizable put positions signal a bet on significant downside risk, contrasting with Nvidia’s conviction in persistent, diversified AI-driven demand. This dichotomy places investors at a crossroads: adhere to euphoric projections of an AI-driven future or heed warnings of a classic technology investment bubble akin to previous eras. Given the interplay of AI’s transformative potential with financial market realities and the macro-policy environment under the Trump presidency, near-term volatility in AI hardware stocks seems probable.

Looking forward, the debate highlights broader trends shaping AI investment dynamics. First, accelerated innovation demands more conservative asset accounting practices reflecting shortened useful lives. Second, investor and corporate governance will increasingly focus on clear disclosures about capital spending incentives and financing structures to discern real demand. Third, diversification of AI hardware suppliers may temper Nvidia’s dominant position, impacting its valuation multiples. Lastly, the macroeconomic context—with possible interest rate shifts and regulatory scrutiny—will influence capital availability for hefty AI infrastructure build-outs.

Ultimately, the Burry–Nvidia divide marks a crucial inflection point for the AI ecosystem. It signals that while AI’s technological revolution progresses, financial frameworks must evolve to capture the sector’s underlying economics accurately. Investors, corporate management, and policymakers should pay close attention to earnings quality, depreciation policies, and market signals in the coming quarters. The resolution of this dispute will define investor confidence and valuation paradigms in one of the most consequential technology booms in recent history.

According to FinTech Weekly, this rare public clash between a multi-trillion-dollar company and an influential contrarian investor exemplifies the complexity and tension in today’s AI investment landscape, urging a cautious yet informed approach to AI sector exposure in 2026 and beyond.