The start of 2026 brought a chill to Wall Street sharper than in previous years. The global software sector experienced a rare, sharp decline, with the S&P 500 Software and Services Index plummeting over 18% in just a few weeks, erasing nearly a trillion dollars in market value. This was not a typical cyclical pullback or short-term panic driven by macroeconomic fluctuations, but a deeper, more structural anxiety. The market began to question whether AI Agents are fundamentally rewriting the logic of value distribution within the software industry.

Simultaneously, a tremor was occurring at the hardware level. OpenAI officially released its GPT-5.3-Codex-Spark model, built on Cerebras's wafer-scale chips. This marked the first substantive move by a leading model company to reduce its reliance on the NVIDIA GPU ecosystem. The signal for hardware diversification was exceptionally clear, revealing cracks in what was once an impenetrable monopoly on computing power.

Amid this backdrop of internal and external pressures, the earnings report from NVIDIA (NVDA.US), the "heart" of the global AI supply chain, is imminent. This is more than a routine earnings disclosure; it is a stress test concerning whether AI spending can continue its relentless pace. As profitability narratives in the application layer face skepticism and the single-supplier dependency in the hardware layer begins to loosen, the question remains: can NVIDIA, positioned at the very top of the supply chain, continue to justify its high valuation? The market awaits an answer.

The turmoil in the software industry is shaking the very foundation of AI spending. The core anxiety behind the software sector's crash is not short-term profit volatility, but a fundamental concern over whether business models are being disrupted. Over the past decade, the SaaS model has been the most reliable growth engine in the tech sector, with companies locking in customers through subscriptions to generate stable recurring revenue. However, the emergence of AI Agents is challenging this logic. Traditional software requires human users to operate interfaces, completing inputs, clicks, and confirmations. In contrast, AI Agents can bypass traditional interfaces, directly understand intent, and autonomously call APIs from multiple software systems to complete workflows that previously required human coordination.

Consider a scenario: in the past, a company needed to purchase a CRM system for customer management, an email system for communication, and data analytics tools for reporting. In the future, a single enterprise AI Agent subscription might autonomously handle customer follow-ups, email composition, and data analysis. When Agents become the true entry point, standalone software applications risk being reduced to "underlying capability modules," potentially redistributing pricing power. Software companies could shift from "selling tools" to "selling data interfaces," significantly weakening their bargaining power. This implies that the long-term growth expectations for software companies are being reassessed. If the revenue ceiling for SaaS companies is lowered, their valuation framework is destined to collapse.

The investment thesis for AI infrastructure is fundamentally built on an assumption: the application layer will continue to expand, thereby driving demand for computing power. Cloud computing giants have recently significantly increased their AI capital expenditures, with some companies reporting year-over-year CapEx growth exceeding 50%. Their rationale is that AI applications will explode, developers will rent more computing power, and therefore, they must stockpile GPUs in advance. However, against the backdrop of collapsing software valuations, the market is beginning to ask a pointed question: when will these investments pay off? If the application layer cannot generate sufficient profit to cover high token costs, or if enterprise clients find that the efficiency gains from AI do not justify increased software subscription expenses, then the demand for compute leasing from cloud providers will slow. Once profit expectations for the application layer are revised downward, the investment pace for infrastructure will inevitably be affected. This is a classic "bullwhip effect," where minor fluctuations in end-demand can be amplified into significant order volatility for upstream chip manufacturers. This is the potential chain reaction NVIDIA must confront. If the software industry enters a winter, will cloud providers cut their orders? If the answer is yes, then NVIDIA's high-growth narrative loses its most critical support.

On the hardware front, OpenAI's launch of GPT-5.3-Codex-Spark on Cerebras chips is seen as a significant signal of a diversifying hardware ecosystem. Although NVIDIA still holds an absolute market share in the short term, the symbolic importance of this trend cannot be ignored. In recent years, NVIDIA has become almost synonymous with AI computing power. Its CUDA ecosystem has built a formidable moat, making it difficult for developers to migrate to other platforms, thus allowing NVIDIA to dominate the data center GPU market with high pricing power. However, this monopolistic position carries inherent risks: single-supplier dependency, high costs, and production bottlenecks. When leading model companies begin experimenting with diverse chip solutions, the message is clear: no company wants to be locked in by a single supplier. As one of NVIDIA's largest customers, OpenAI's shift toward Cerebras is not just about cost or performance; it is a strategic move for balance. Cerebras's wafer-scale engines have demonstrated exceptional energy efficiency in specific large model training scenarios, proving the feasibility of non-GPU architectures in AI. This does not mean NVIDIA's orders will immediately vanish. The inertia of the CUDA ecosystem is immense, and migration costs are high. However, the valuation premium investors assign to NVIDIA's perceived "irreplaceability" may begin to erode. Previously, investors were willing to award NVIDIA a higher P/E ratio because they believed it was the only choice; now, with the emergence of a second option, NVIDIA's pricing power could be constrained. More critically, if profitability in the application layer comes under pressure, clients will scrutinize the return on their computing investments more rigorously. At that point, issues of price, efficiency, and alternatives will be magnified. Cloud providers might increasingly turn to in-house chips or third-party alternatives to reduce procurement costs. NVIDIA's risk lies not in the present, but in the marginal change. As long as demand growth outpaces supply, NVIDIA can maintain high margins. But once demand growth slows, or alternative solutions offer better value, NVIDIA's gross margins will face downward pressure. OpenAI's move is a precursor to this marginal change, signaling to the market that AI computing is no longer NVIDIA's solo act, and a diversified hardware ecosystem is forming. For NVIDIA, which relies on a monopoly premium to sustain its high valuation, this is a long-term bearish signal.

The true key to the earnings report lies not in the numbers, but in confidence. Investment banks generally expect NVIDIA to still deliver results exceeding expectations, as the existing order backlog is sufficient to support revenue for several quarters. However, as strategists note, when the market becomes accustomed to surprises, the marginal impact of the surprise itself diminishes. The truly critical element will be the commentary from Jensen Huang during the earnings call and the management's forward guidance. The market needs to hear not just about order growth, but about the sustainability of customer capital expenditures. Are cloud providers still committed to expanding data centers? Have AI applications begun to scale and generate profit? Are enterprise clients still increasing their training and inference budgets? These questions are more important than mere revenue figures. NVIDIA's surge over the past two years was built on a belief that AI is a long-term arms race where giants would invest heavily in computing power regardless of short-term profitability. But if the profit logic of the software industry is being reconfigured, will this arms race slow down? Once capital expenditure enters a more rational phase, NVIDIA's growth curve could shift from exponential to linear. For highly-valued companies, a change in pace is often more damaging than a change in direction. Even if NVIDIA signals merely a slowdown in growth rate—while still growing—the stock could face a sharp adjustment. This is because the market prices in an expectation of "unlimited growth"; once that expectation is revised to "limited growth," valuation multiples contract. Furthermore, the market will closely watch for changes in revenue mix. Is training demand peaking? Is inference demand taking over? If training demand slows before inference demand explodes, NVIDIA could face an awkward transition. Jensen Huang needs to convince the market that AI is not just about training large models, but also about pervasive inference applications, which represent a more enduring market. The true key to the earnings report is confidence: confidence that clients can profit from AI investments, and confidence that NVIDIA can sustain its growth. If this confidence wavers, a chain reaction will quickly ripple through the entire industry.

In conclusion, the暴跌 in the software sector is a signal that the AI revolution is entering a challenging phase. The pain in the application layer could be merely the cost of a technological leap, or it could mark the beginning of a fundamental business model restructuring. NVIDIA stands at the very top of this supply chain, seemingly the safest position, yet also the most sensitive. It is the foundation of the entire AI edifice, but if the superstructure above begins to shake, the foundation cannot remain unscathed. If the application layer recovers and AI Agents create new business value, demand for computing power will continue to expand, and NVIDIA will remain the primary beneficiary. However, if profitability concerns spread to capital expenditure decisions, the flames could travel up the supply chain, eventually reaching the chip supplier. The upcoming earnings report will reveal more than just quarterly profits; it will answer a more fundamental question: is AI spending driven by faith, or by cash flow? In recent years, AI spending has been largely faith-driven, with giants investing heavily for fear of missing the future. But now, the market is demanding returns. Shareholders are asking: where are the profits from hundreds of billions in investment? If AI cannot generate real cash flow, this arms race will ultimately be unsustainable. As the market shifts its focus from "narrative" back to "returns," NVIDIA must prove that it is not holding the final parcel in the AI feast. It must prove that it sells not just chips, but productivity tools that generate sustained value. For NVIDIA, this spring of 2026 is not just an earnings season; it is an ultimate test of survival logic in the AI era. Those who can navigate the cycle will build true empires, while prosperity sustained by faith alone will eventually succumb to the gravity of cash flow. The market holds its breath, waiting for Jensen Huang to provide the answer that can stabilize the entire landscape.