Photon Capital
Photonics & Semiconductor Research
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
Nvidia accelerators' residual value and resale liquidity could lower hyperscaler financing costs and sustain hardware demand. Jensen Huang calls AI chips the lowest-risk asset, arguing their versatility across workloads makes them viable loan collateral. H100 and B200 have reportedly held or gained value; B200 trades at 158% of its original purchase price in secondary markets. Collateral value still depends on workload compatibility and system configuration. 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
Physical AI could open a new demand curve for Nvidia before humanoids ship in volume, as the biggest chip opportunity may first sit in data centers training robots. Figure reportedly plans about 100,000 Vera Rubin GPUs from 2027, roughly 230MW mostly for training. Per Goldman, a humanoid holds only a few thousand dollars of chips, but each robotics company chasing frontier capability may become a major compute customer. Some demand is circular; training bills pay off only if fleets grow large. … Show more
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
Enterprise agents widen NVIDIA's opportunity beyond inference because reasoning, tool use and company-data workflows need infrastructure and controls outside the model. Satya Nadella describes those needs; Vera CPUs, OpenShell and Sentry on BlueField are the NVIDIA read-through. x.comÂ
Cerebras should dominate the enormous fast-inference market because agentic workflows often run steps sequentially, making each step's speed critical. Its newly announced Oracle partnership brings its ultra-fast inference to Oracle Cloud Infrastructure. Cerebras delivers inference speeds up to 13x faster than Nvidia GPUs, and its chips should eventually reach every major AI cloud. 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
Nebius' software efficiency and a possible end to heavy AI financing support a re-rating. Per @DrTomsLens, its engineers got about 43% more video output from the same GPUs, cutting compute cost per second by 30%; newer hardware alone is not enough without capable software teams. Oracle expects to fund growth from free cash flow in FY2029, suggesting capital-heavy AI providers may eventually outgrow debt and dilution. Google Cloud reported 103% revenue growth amid strong AI infrastructure demand. … Show more
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 faces rival chips from every Mag 7 customer, which has an incentive to cut GPU spending and improve margins on its AI buildout. If they can make a GPU 85% as good for 50% less, it is worth using instead of NVIDIA's; the shift will take time. NVIDIA makes a GPU for $5,000 and sells it to those customers for $50,000, leaving them an incentive to save 90% by building their own. x.com x.com x.com x.comÂ
Nvidia can raise prices 15% and still leave customers plenty of margin because Vera Rubin delivers 2-30x more throughput per megawatt than Blackwell Ultra, depending on interactivity. A megawatt of Vera Rubin racks should generate at least twice the revenue of Blackwell racks, and much more in some configurations. SpaceXAI also appears to be a new standalone Vera CPU customer; Nvidia says Vera completes tasks up to 1.8x faster than x86 CPUs. x.com x.comÂ
