Markos
Founder of Asset Alliance Investment Group
NVIDIA's expanding AI stack will keep even customers building their own ASICs buying from it. Nemotron, NVLink Fusion's link to custom silicon, Spectrum-X and BlueField show that reach, while NVHBM extends it into memory base dies. The reach is expected to deepen into optics, with Spectrum-X Photonics already showing the direction. Reflection AI is another push as Jensen competes with frontier labs. 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
A rising value of compute per watt should benefit AI cloud, chip, connectivity and power suppliers. Elon Musk says that value will rise over time. Nebius has Microsoft and Meta deals; Marvell benefits from custom silicon demand; Credo connects GPUs; Vistra has long-term nuclear supply deals with Amazon and Meta; Nokia's Nvidia partnership could help it capture data-center networking growth. x.com x.comÂ
Nvidia's neocloud orders increasingly require its own credit support. Per The Information, it is in talks to guarantee billions of Nscale debt for a 460MW data center; Nscale needs an IPO to fund $24B in Nvidia systems committed for 2026-27. Separately, a quoted article says US officials identified Alice Huang, scrutinized for possibly routing Nvidia chips to China, as a Chinese intelligence asset with direct access to Jensen Huang. This is just the tip of a very big iceberg. x.com x.com x.comÂ
AI data-center spending flows beyond GPUs and operators into networking, optics, power and cooling. Nvidia, AMD, Broadcom and Marvell supply chips; Arista, Nokia and Credo connectivity; Applied Optoelectronics and Lumentum optics; Vistra, Constellation and Bloom power; Vertiv and Modine cooling. Meta's $140B spending this year illustrates the scale, per a commenter with whom the point is agreed. x.com x.comÂ
Nvidia's integrated AI software stack could give it an edge beyond chip performance and pricing. @semivision_tw says the completeness of that stack may become the next competitive battleground for AMD, Broadcom and Marvell; Nvidia's open models can also drive demand for its compute, as @benitoz describes. x.com 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
Nvidia retains pricing power if its full system keeps reducing customers' cost per effective AI task. Strong AI demand alone does not ensure profits across the chain: rising chip, memory and optical costs burden buyers, while tighter financing and slow collections threaten operators. Large customers developing their own chips could pressure Nvidia's margins even as the market grows. x.comÂ
NVIDIA's free-model plan could defend chip demand as the two largest AI labs cut reliance on its hardware. OpenAI is building a chip with Broadcom; Anthropic mostly uses Google and Amazon chips. NVIDIA is putting together Reflection AI (in talks), Hugging Face and Groq for free models and paid chips. At 11x expected 2029 earnings versus ~19x for Microsoft, Alphabet and Amazon and 32x for Apple, its price reflects fears of slower AI spending and lost customers. NVIDIA is a 'Maximum Opportunity'. … Show more
Nvidia's biggest customers scaling their own chips threatens its long-term weight in a portfolio. Microsoft, Meta, Amazon and Google are building proprietary silicon; as AI workloads shift from training to inference, the shift toward ASICs should accelerate, with ASIC volumes expected to surpass GPUs by 2030. That is why Nvidia should not be overweighted for the long term. x.comÂ
Inference's move toward edge devices and dense, memory-hungry racks should drive demand for specialized chips, links, power delivery and cooling. McKinsey projects inference at 60% of AI computing demand by 2030. Ambarella makes on-device AI chips; Astera Labs links GPUs, memory and networks; Credo supplies short copper connections; Rambus earns from memory interfaces; Navitas reduces power waste; Modine cools dense racks; and Super Micro builds inference-specific server racks at volume. x.com … Show more
