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Biotech Investor, AI-Driven Drug Discovery
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.com
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.com
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.com
NVIDIA's Apple-like ecosystem moat lets it charge for software as well as hardware while still offering customers the best value per dollar. Jensen Huang said, via Tom's Hardware, that AI-factory agent software could cost about $2,000 per GPU annually; on a potential 5 million GPUs, that would be $10B a year. If that fee does not make NVIDIA uncompetitive, it likely has more than a $2,000 annual per-GPU total-cost advantage over AMD. The licensing model itself is unappealing. x.com
Google's allocation of massive TPU capacity to Anthropic strains its own AI effort while its researchers face compute constraints. CNBC reported internal tension; Anthropic competes with Gemini. Google should keep more TPUs for its own AI and buy more NVIDIA GPUs to rent to cloud customers, serving CUDA developers alongside Gemini. Selling TPU systems changes the equation, but Alphabet does not disclose TPU margins, so NVIDIA-like economics cannot be assumed. x.com
NVIDIA's GPU value pool faces architectural competition and shorter useful lives than ten-year assumptions imply. Etched and other accelerator designs may challenge performance within five years, while power limits favor relentless gains in performance per watt; past GPU generations improved more than 2-3x, and industry experts reportedly used four-year lives before GPT. The range of outcomes remains wide. x.com x.com x.com
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.com
Broadcom's integrated optics, electronics and packaging could lower custom-chip system costs because separate suppliers defend their margins. At similar valuations, it is preferred to Nvidia and has more room to beat expectations. Google TPU v8 shipments are estimated at 4.0M in 2026 and 9.1M in 2027, assuming Broadcom supplies the remainder after MediaTek's allocation. Anthropic financing adds value only if chip profit exceeds likely support payments and capacity costs. x.com x.com
Nvidia's funding of future customers in Germany further blurs how much chip demand it paid for itself. Jensen Huang said Nvidia invested almost $1B there in the last year and is investing in four more companies this week; German taxpayers are helping too. Finding buyers beyond Big Tech is smart, but the more sales come back from Nvidia's own checks, the harder genuine demand is to gauge. x.com
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.com
AMD could see much more accelerator demand as OpenAI and Anthropic allegedly increase their orders by multiple gigawatts. Nvidia's only real edge over AMD may soon be its size and resources; Lisa Su is a master operator. x.com
