Finn Stockinger
Equity Analyst & Investor, Technology Research
NVIDIA's advantage extends beyond GPUs into engineering work that improves customers' AI economics. Its work with Meta on Andromeda redesigned ad-retrieval models and pipelines, processing over 1 billion ads per second on one Hopper GPU server with 100 times less network traffic and 300 times fewer FLOPs per inference. Meta says further optimization is underway for Grace Blackwell. x.comÂ
NVIDIA GPUs' fungibility is their most important advantage because AI workloads three to five years out remain uncertain. NVIDIA VP Ian Buck says the same GPUs handle training, inference, reinforcement learning and simulations and can remain economically productive for years, even as newer generations arrive; he also cites ongoing hardware and software improvements. ASICs can be more efficient for specific workloads, but GPU flexibility has great value in this market. x.comÂ
Meta's AI-driven ad revenue can justify sustained AI spending, arguably a bigger NVIDIA positive than its slight capex-guidance lift. Meta raised the low end of 2026 capex guidance from $130B to $135B, left the $145B ceiling unchanged and lifted the midpoint just 1.8%. Its goal of over 10x better Muse compute-cost efficiency over the next year complicates GPU-order growth even as total serving costs are expected to rise. Spending looks durable, not necessarily set for faster NVIDIA orders. x.comÂ
Meta's data edge and poaching of top talent support its in-house AI development against rivals buying projects to integrate. Only Google matches its data; Anthropic and OpenAI cannot really compete without funding. Meta is taking some of Google's ad share, which should reduce Google's free cash flow. x.comÂ
Low Anthropic spending relative to customer expenses points to an enterprise AI adoption gap that leaves Nvidia reliant on intermediaries able to build their own chips. Anthropic's S-1 says over half the Fortune 500 were $100k+ annual customers as of September, yet spending among its publicly traded customers averaged under 0.1% of expenses in Q3 2026. That measures one provider and cannot show whether AI deployments succeed; Nvidia needs more enterprises buying its compute directly. x.comÂ
AI software and hardware advances make GPUs and power more valuable, creating huge long-term tailwinds for compute providers and AI model and app businesses. Each new software generation increases a GPU's value; each hardware generation increases the value of a gigawatt of power. That favors Nvidia, Broadcom, Intel and AppLovin. x.comÂ
Nvidia's system components can reduce the idle time that erodes returns on expensive AI chips. CPUs, accelerator interconnects and server networking help operators such as Nebius and IREN keep machines doing useful work; solving the next bottleneck could win Nvidia more of the data-center equipment budget even when a rival chip looks appealing alone. Buyers must compare the cost of completed work across the whole system. x.comÂ
Nvidia's AI profits are real, but its compute demand depends heavily on fragile venture funding. Per Chamath, 1% of firms account for 90% of AI spending, and 72 cents of each dollar comes from unprofitable VC-backed firms. Venture money passes through startups to Nvidia and cloud suppliers; if the next raises fail, concentrated orders could contract together. x.com x.com x.comÂ
AI infrastructure bottlenecks could give eight stocks as much as 18x upside. Nvidia supplies frontier-model GPUs; Broadcom and Marvell supply custom silicon and networking; TSMC makes advanced chips; Micron supplies sold-out HBM; Vertiv provides power and cooling; Palantir monetizes enterprise AI; Alphabet spans TPUs, research, cloud and apps. The list tracks where AI infrastructure spending is flowing. x.com x.comÂ
Nvidia's CUDA retains developers, putting it among 12 stocks that could dominate the decade ahead. Nebius's GPU rental price rise signals demand; Fabrinet and Credo supply AI connectivity. DraftKings has betting scale; Microsoft has Azure and an OpenAI partnership; Figma anchors design work; Shopify powers stores. TeraWulf pivots power sites to AI leases; Constellation, the largest US nuclear operator, signs big-tech power deals; Meta spends on AI; Uber has autonomous-vehicle partnerships. x.com … Show more
Nvidia's case is a lot weaker than two years ago, because large providers such as OpenAI and Meta are likely to design custom chips tuned to their own models. Nvidia wins only if workloads and models stay diverse. The claim that frontier labs are decoupling from Nvidia is doubtful, since OpenAI's recent efficiency gains likely came from Blackwell, but it holds for Google and Amazon. Nvidia also needs to own the open-source ecosystem, which is contradictory by definition. x.com x.comÂ
Nvidia can capture more AI infrastructure spending by supplying Vera CPUs and networking alongside GPUs. Oracle outlined an initial supply of 150,000 Vera CPUs, and OpenAI says it is putting them into production for agent tool execution; reducing CPU waits could improve cost per task and GPU-memory use. Benefits from independently scaling CPUs and GPUs still need validation in utilization and completion times. x.comÂ
