The upcoming quarterly earnings report from NVIDIA (NVDA.US), often referred to as the world's most critical stock and the dominant force in AI chips, is set to serve as a major stress test for the AI computing power investment theme. Scheduled for release after the U.S. market close on Wednesday, the results are highly anticipated by global investors focused on AI infrastructure. These investors are seeking confirmation that the chip giant's profits are growing robustly in line with the massive AI capital expenditure trends, estimated between $650 billion and $700 billion, from the U.S. hyperscale technology companies.
Concurrently, recent announcements from these hyperscalers about developing more cost-effective, self-designed AI ASIC chips signal potential risks to NVIDIA's long-standing dominance in the core AI chip market. After fueling a significant bull market over the past three years, NVIDIA's stock, a heavyweight in the Nasdaq 100 and S&P 500 indices, has seen a modest gain of only about 2% year-to-date in 2026. This muted performance is attributed to concerns over an "AI apocalypse narrative" triggered by products from Anthropic, which have impacted software stocks and highly-valued tech giants, coupled with increased competition from rivals like AMD and the hyperscalers' push for multi-vendor strategies with alternative chips like TPUs.
The chart illustrates NVIDIA's 2026 stock performance compared to the MAGS ETF and the S&P 500 index. Alongside AMD (AMD.US), which plans to release a new version of its flagship AI server cluster later this year, Google, under Alphabet, has emerged as a formidable competitor. A recent agreement to supply its self-developed TPU AI computing clusters to Anthropic, the developer of the Claude chatbot, positions Google as a direct challenger in the AI infrastructure space. Reports also indicate Google is in talks with Meta Platforms (META.US), one of NVIDIA's largest customers, to supply TPU-based infrastructure.
NVIDIA's earnings are highly event-driven. Options markets imply an expected stock price movement of approximately ±5% following the report. Given its market capitalization of around $4.7 trillion, this equates to a single-day valuation swing of about $226 billion. Furthermore, its roughly 7.8% weighting in the S&P 500 means its performance alone could mechanically induce significant market volatility.
Following substantial gains since 2023, the stocks of the U.S.'s seven largest tech companies by market cap, including NVIDIA, have experienced turbulence in 2026. Investors are questioning whether the continuous massive investments in AI computing infrastructure—projected to surge 60% to over $700 billion this year for the top four U.S. tech firms—can generate returns strong enough to justify their elevated valuations. This group, known as the "Magnificent Seven" and comprising Apple, Microsoft, Google, Tesla, NVIDIA, Amazon, and Meta Platforms, represents about 35%-40% of the S&P 500 and Nasdaq 100. They are seen as the primary drivers of the market's record highs and are considered by top Wall Street firms as the portfolio best positioned to deliver outsized returns during what is viewed as the most significant technological shift since the internet era.
To defend its near-monopoly in AI infrastructure and capitalize on the burgeoning AI inference wave, NVIDIA secured a landmark deal valued at an estimated $20 billion late last year to license chip technology from AI chip startup Groq. Analysts widely view this move as bolstering its leadership in the rapidly growing AI inference market, which involves trained AI models answering questions and executing complex workflows in real-time. NVIDIA also recently agreed to sell millions of AI chips to Meta, though the financial terms were not disclosed.
However, as the biggest beneficiary of the AI boom, NVIDIA itself has raised questions about the sustainability of AI infrastructure spending. Reports suggest the chip giant may scale back a potential $100 billion investment in OpenAI, a major customer, to a smaller $30 billion commitment. While NVIDIA's AI GPUs dominate the AI training segment, which requires powerful, versatile computing clusters and rapid iteration, the inference segment prioritizes cost per token, latency, and energy efficiency after AI models are deployed. Google's positioning of its Ironwood TPU as "built for the AI inference era," emphasizing performance, efficiency, and scalability, underscores this shift.
NVIDIA's deal with Groq, essentially a non-exclusive license for inference-focused AI chip technology coupled with the acquisition of Groq's founder, CEO, and key engineering talent, is seen as a direct response to the cost and latency challenges in AI inference. This strategic move, combined with intensifying competition from Google's TPUs, highlights NVIDIA's efforts to maintain its approximately 80% market share in AI chips through a multi-architecture approach, strengthening its CUDA ecosystem, and acquiring top AI chip design talent to secure its position across the full AI stack.
The market urgently needs to see if NVIDIA's profit and revenue growth can continue to meet or exceed lofty expectations against the backdrop of massive tech capital expenditures. Strong forward guidance is also critical. "This particular quarterly report is especially important because there are significant concerns about the future of AI infrastructure spending—worries that we might be in an AI bubble," stated Ivana Delevska, Chief Investment Officer at Spear Invest, a firm that holds NVIDIA stock via an ETF. "Demonstrating that profit growth hasn't actually slowed down will be very important."
According to Wall Street analyst estimates compiled by LSEG, NVIDIA is expected to report a profit surge of over 62% year-over-year for the quarter ending in January. While impressive, this represents a slowdown from the previous quarter's 65.3% growth due to a more challenging year-over-year comparison. Total revenue for NVIDIA's fiscal fourth quarter is projected to jump over 68% to $66.16 billion. Analysts anticipate management will guide for first-quarter fiscal 2027 revenue to grow another 64.4% to $72.46 billion. Notably, while NVIDIA has beaten revenue expectations for 13 consecutive quarters, the magnitude of these beats has narrowed as analyst expectations have become more stringent following its unprecedented market cap ascent past $5 trillion.
RBC's equity analysis team expects the chip giant to issue revenue guidance for the quarter ending in April that is at least 3% above consensus. Spear Invest's Delevska, a long-term NVIDIA bull, believes the guidance could exceed consensus by as much as $10 billion, representing a beat of over 13%. A recent Bank of America report suggests the global AI arms race is still in its "early to middle stages." Vanguard, a major global asset manager, noted in a research report that the AI investment cycle might only be 30%-40% complete towards its eventual peak, but also cautioned that the risk of a correction in large-cap tech stocks is indeed increasing.
Wall Street firms like Morgan Stanley, Citi, Loop Capital, and Wedbush argue that the global AI infrastructure investment wave, centered on computing hardware, is far from over and is merely at its beginning. Driven by an unprecedented surge in demand for inference computing power, this investment cycle could reach a total scale of $3 trillion to $4 trillion by 2030. Demand for DRAM and NAND memory chips remains robust, with prices for products like DDR4/DDR5 and data center SSDs showing strong expansion. This is largely because the deluge of AI computing demand has elevated the importance of memory chips in AI training and inference systems to unprecedented levels. The ongoing exponential growth in global AI computing demand, which currently far outpaces supply, is clearly reflected in the exceptionally strong recent earnings from chip leaders like Taiwan Semiconductor Manufacturing Company (TSM.US) and ASML.
For U.S. stocks, which have traded sideways with high internal volatility recently, NVIDIA's earnings report is pivotal. It addresses not just the strength of NVIDIA's own growth trajectory but also the validity of the core investment thesis: whether robust AI capital expenditure will translate into realized profits that can justify current valuations, thereby dispelling "AI bubble" concerns. Year-to-date in 2026, the S&P 500 has seen only a slight increase (around 0.2%), but underlying sector performance has diverged significantly, with software and services sectors under pressure due to AI disruption fears. Broader Wall Street surveys also indicate market uncertainty for 2026, influenced by trade tensions and AI infrastructure spending volatility, with valuations (forward P/E around 21.6x) remaining sensitive. Consequently, commentary from NVIDIA's management during the earnings call regarding customer order visibility, the return on investment cycle for AI capex, and the competitive landscape will be closely watched as a barometer for risk appetite across the high-beta AI infrastructure ecosystem, including cloud providers, supply chain companies, data center power providers, and AI software firms.
Statistics indicate that earnings from NVIDIA, the highest-valued company in the U.S. and global markets, are likely to cause significant market turbulence. The implied ±5% stock move corresponds to a $226 billion valuation swing. A scenario where earnings merely meet expectations or guidance is perceived as weak could trigger a synchronized de-risking event across the semiconductor supply chain, cloud providers, and software stocks, potentially pushing short-term volatility (VIX) higher. Conversely, significantly better-than-expected results and strong guidance that reinforce the "AI computing bull narrative" could lead to a rapid recovery in risk appetite and a decline in volatility.
Analysts still expect strong demand for NVIDIA's high-priced AI chips, which act as the "brains" in servers processing massive AI workloads and are expected to capture the lion's share of tech giants' massive spending on expanding or building new AI data center capacity this year. NVIDIA executives hinted in January about discussions with major customers regarding data center orders for next year, leading several Wall Street analysts to predict an update to the company's disclosed order backlog for AI computing infrastructure, which was initially reported at a cumulative $500 billion for 2025-2026 last October.
However, the most significant constraint on NVIDIA's growth may be supply chain bottlenecks, particularly in chip manufacturing capacity, which limits the pace of AI chip shipments. NVIDIA and its competitors are fiercely competing for production capacity on advanced nodes at TSMC. "We believe NVIDIA will easily meet expectations, but given TSMC's capacity constraints, it's difficult to see significant upside beyond that," wrote Jay Goldberg of Seaport Research Partners in a report.
A potential boost to NVIDIA's revenue and profit outlook could come from a resurgence in AI chip sales to China, which were previously restricted by U.S. export controls. NVIDIA's CEO, Jensen Huang, stated last month that he hopes the company will receive permission to sell its high-performance H200 AI chips in China, noting that relevant sales licenses are being finalized. Rival AMD (AMD.US) has already reintegrated sales of high-performance AI chips to China into its current-quarter forecasts after receiving licenses to ship modified data center CPUs and GPUs.
NVIDIA is expected to report an adjusted gross margin of 75% for the fiscal fourth quarter, an improvement of over 1 percentage point from the prior year. Analysts generally do not expect the company to be adversely affected by the global memory chip shortage, citing NVIDIA's pricing power and its likely pre-allocation of high-bandwidth memory (HBM) supply for the full year and into 2027, which should insulate it from the impact of soaring memory prices.