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WHAT I’M WATCHING AS $NVDA PREPARES ITS BIGGEST EARNINGS REPORT YET
Nvidia is set to report Q4 earnings on Wednesday, and anticipation couldn’t be higher. Wall Street has its sights set on revenue, which is expected to surge 73% YoY, with EPS climbing from $0.52 to $0.82. But at this stage, beating expectations has become routine. Investors aren’t just watching for another "triple beat" -- they’re scrutinizing the underlying currents. How long can Nvidia sustain its gravitational pull on AI infrastructure spending? Will supply constraints finally ease, unlocking another leg of exponential growth? And, perhaps most critically, is the AI arms race evolving in a way that shifts hyperscalers’ investment calculus, potentially reshaping the trajectory of Nvidia’s dominance?
For now, the AI demand machine remains unstoppable. Nvidia still commands over 90% of the AI accelerator market, a position that would be almost unthinkable in any other high-growth tech sector. The largest cloud hyperscalers -- $AMZN, $MSFT, $META & $GOOGL -- are collectively pouring over $320B into AI infrastructure in 2025 alone, an astonishing level of capital deployment that ensures Nvidia's GPUs remain the backbone of AI compute. The next-generation Blackwell architecture is already sold out for the next 12 months before mass production has even started. Demand isn’t just exceeding expectations -- it’s obliterating them. And now, with OpenAI, SoftBank & $ORCL spearheading Project Stargate, a private-sector AI infrastructure buildout with a staggering $500B budget, the AI investment cycle is accelerating at a pace that continues to defy conventional growth curves.
Yet even in an environment of seemingly unlimited demand, supply constraints have been Nvidia’s Achilles’ heel. Over the past year, the bottleneck hasn’t been wafer production -- it’s been advanced packaging. $TSM, Nvidia’s primary foundry partner, has struggled to keep up with demand for CoWoS (Chip-on-Wafer-on-Substrate), the sophisticated packaging process essential for Nvidia’s high-performance AI chips. But this is about to change. TSMC has now committed to over 150% ramp in CoWoS capacity through 2026, a scaling effort that could unleash a torrent of previously untapped revenue. If Nvidia can finally match supply with demand, its already staggering growth trajectory could steepen even further. Historically, the company has exceeded EPS estimates by an average of 10-12% per quarter. If these supply-side constraints ease faster than expected, Nvidia’s actual earnings trajectory could blow past even the most bullish projections for FY26.
But supply is only one side of the equation. The other, far more nuanced factor is how AI compute demand itself is evolving. DeepSeek, a Chinese AI startup, has thrown a wrench into the hyperscaler investment narrative. By demonstrating that large language models can be trained using lower-cost Nvidia chips -- rather than its cutting-edge accelerators -- DeepSeek has raised an uncomfortable question: Is AI compute efficiency improving in ways that could ultimately reduce demand for Nvidia’s highest-margin products? Right now, hyperscalers are still in full-throttle expansion mode, but there’s a real possibility that over the next few quarters, companies like Amazon, Microsoft, and Google begin optimizing their AI capex strategies in response to DeepSeek’s innovation. The timing is critical. Hyperscalers set their 2025 investment budgets before DeepSeek’s breakthrough gained widespread recognition. If Nvidia’s guidance reflects even a hint of hyperscalers rethinking their spending trajectory -- it could mark the first signal that the AI-driven capex cycle is transitioning from a phase of unchecked expansion to one of strategic optimization.
Despite these emerging complexities, Nvidia’s long-term dominance remains largely unchallenged. The company is no longer just a semiconductor juggernaut -- it is methodically transforming into the de facto AI infrastructure layer for the global economy. CUDA, Nvidia’s proprietary AI software ecosystem, has become the standard for machine learning and neural network development, creating an ironclad moat that forces enterprises to remain within its ecosystem. The company is also aggressively expanding into AI-driven software and enterprise solutions, rolling out subscription-based products that could fundamentally shift its revenue model from cyclical hardware sales to high-margin, recurring revenue streams. If Nvidia successfully transitions to a hybrid AI hardware-software model, its valuation ceiling could rise even higher than today’s already lofty levels.
Valuation remains a hotly contested topic. Nvidia is trading at 34x NTM earnings -- a number that, at face value, might seem inflated given the historical boom-and-bust cycles of the semiconductor industry. But viewed through the lens of its actual growth trajectory, the stock appears significantly undervalued. Nvidia’s PEG ratio, a measure that adjusts valuation based on earnings growth, now sits at 0.7, signaling that Nvidia is trading at a major discount. With consensus EPS growth projections of 48% in FY25 and 26% in FY26, Nvidia’s valuation remains tethered to its fundamentals. But if the company continues its pattern of obliterating expectations -- the market may still be underestimating just how far Nvidia’s pricing power can extend.
This earnings report isn’t just another checkpoint -- it’s a potential inflection point. The market isn’t just pricing in past performance anymore -- it’s searching for signals about the future of AI investment itself. If Nvidia reaffirms strong hyperscaler demand and downplays the impact of DeepSeek, the stock could soar to new highs. But if guidance hints at a slowing AI capex cycle or a shift toward efficiency over raw compute spending -- the market could reassess the long-term trajectory of AI infrastructure investment.
Regardless of what happens in the near term, Nvidia remains the single most critical player in AI. The AI revolution is still in its infancy, and Nvidia is the indispensable enabler of that transformation. Whether it’s AI model training, cloud infrastructure, autonomous robotics, or next-gen semiconductor development, the world’s most influential companies are designing their futures around Nvidia’s technology. The question isn’t whether AI compute demand will grow -- it’s whether Nvidia will continue capturing the lion’s share of that demand. And as the company prepares to unveil its latest results -- that is the single most important narrative investors will be watching.
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翻译自 英语
我正在关注$NVDA准备其迄今为止最大的收益报告
Nvidia 将于周三公布第四季度业绩，人们对其的期待值非常高。华尔街将目光投向了营收，预计该季度营收将同比增长 73%，每股收益将从 0.52 美元攀升至 0.82 美元。但在这个阶段，超出预期已经成为常态。投资者不仅在关注另一个“三重胜率” --还在密切关注潜在的趋势。Nvidia 对 AI 基础设施支出的吸引力能维持多久？供应限制最终会缓解，开启另一轮指数增长吗？也许最关键的是，AI 军备竞赛是否会以改变超大规模企业投资策略的方式发展，从而有可能重塑 Nvidia 的主导地位？
目前，AI 需求机器仍然势不可挡。Nvidia 仍然占据着 AI 加速器市场 90% 以上的份额，这一地位在任何其他高增长科技领域几乎是不可想象的。最大的云超大规模提供商-- $AMZN 、 $MSFT 、 $META和$GOOGL --仅在 2025 年就向 AI 基础设施投入了超过 3200 亿美元，这一惊人的资本部署水平确保了 Nvidia 的 GPU 仍然是 AI 计算的支柱。下一代 Blackwell 架构在开始大规模生产之前，未来 12 个月的库存就已经售罄。需求不仅超出预期--而且正在超越预期。现在，随着 OpenAI、软银和$ORCL牵头实施星际之门项目，这是一个私营部门的 AI 基础设施建设项目，预算高达 5000 亿美元，AI 投资周期正在以继续违背传统增长曲线的速度加速。
然而，即使在需求看似无限的环境下，供应限制也一直是 Nvidia 的致命弱点。在过去的一年里，瓶颈不是晶圆生产-- ，而是先进的封装。 $TSM Nvidia 的主要代工合作伙伴台积电一直在努力满足对 CoWoS（晶圆上芯片上基板）的需求，这是 Nvidia 高性能 AI 芯片必不可少的复杂封装工艺。但这种情况即将改变。台积电目前承诺到 2026 年将 CoWoS 产能提高 150% 以上，这一扩大规模的努力可能会释放出大量以前未开发的收入。如果 Nvidia 最终能够实现供需平衡，其本已惊人的增长轨迹可能会进一步陡峭。从历史上看，该公司每季度的每股收益平均超过预期 10-12%。如果这些供应方面的限制比预期更快地缓解，那么 Nvidia 的实际盈利轨迹可能会超过 2026 财年最乐观的预测。
但供应只是等式的一边。另一个更为微妙的因素是人工智能计算需求本身如何演变。中国人工智能初创公司 DeepSeek 给超大规模企业的投资叙事带来了阻碍。通过证明可以使用低成本的 Nvidia 芯片--而不是其尖端加速器--来训练大型语言模型，DeepSeek 提出了一个令人不安的问题：人工智能计算效率的提高是否最终会减少对 Nvidia 利润率最高的产品的需求？目前，超大规模企业仍处于全速扩张模式，但在未来几个季度，亚马逊、微软和谷歌等公司很有可能开始优化其人工智能资本支出策略，以响应 DeepSeek 的创新。时机至关重要。在 DeepSeek 的突破获得广泛认可之前，超大规模企业就设定了 2025 年的投资预算。如果 Nvidia 的指引反映出超大规模企业正在重新考虑其支出轨迹--的迹象，那么这可能标志着人工智能驱动的资本支出周期正在从不受控制的扩张阶段转变为战略优化阶段的第一个信号。
尽管出现了这些新出现的复杂情况，但 Nvidia 的长期主导地位仍然基本未受挑战。该公司不再仅仅是半导体巨头--而是正在有条不紊地转变为全球经济事实上的 AI 基础设施层。Nvidia 专有的 AI 软件生态系统 CUDA 已成为机器学习和神经网络开发的标准，创造了一条坚固的护城河，迫使企业留在其生态系统内。该公司还积极扩展到 AI 驱动的软件和企业解决方案，推出基于订阅的产品，这可能会从根本上将其收入模式从周期性的硬件销售转变为高利润的经常性收入来源。如果 Nvidia 成功过渡到混合 AI 硬件软件模型，其估值上限可能会比今天已经很高的水平更高。
估值仍然是一个备受争议的话题。Nvidia 的市盈率为 34 倍-- ，从表面上看，考虑到半导体行业历史上的兴衰周期，这个数字似乎被夸大了。但从其实际增长轨迹来看，该股似乎被严重低估。Nvidia 的 PEG 比率（一种根据收益增长调整估值的指标）目前为 0.7，这表明 Nvidia 的交易价格大幅折价。鉴于市场普遍预期 2025 财年每股收益增长 48%，2026 财年每股收益增长 26%，Nvidia 的估值仍与其基本面挂钩。但如果该公司继续其超越预期的模式--市场可能仍低估了 Nvidia 的定价能力可以延伸到多远。
这份收益报告不仅仅是另一个检查点--而是一个潜在的拐点。市场不再只是根据过去的表现定价-- ，而是在寻找有关 AI 投资本身未来的信号。如果 Nvidia 重申对超大规模计算的强劲需求并淡化 DeepSeek 的影响，该股可能会飙升至新高。但如果指引暗示 AI 资本支出周期放缓或转向效率而非原始计算支出--市场可能会重新评估 AI 基础设施投资的长期轨迹。
无论短期内发生什么，Nvidia 仍然是 AI 领域最重要的参与者。AI 革命仍处于起步阶段，而 Nvidia 是这一转变不可或缺的推动者。无论是 AI 模型训练、云基础设施、自主机器人还是下一代半导体开发，世界上最具影响力的公司都在围绕 Nvidia 的技术设计自己的未来。问题不在于 AI 计算需求是否会增长-- ，而在于 Nvidia 是否会继续占据这一需求的最大份额。随着该公司准备公布其最新业绩--这是投资者关注的最重要的叙述。 |
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发表于 2025-2-23 18:11:32
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