IvanaSPEAR
Founder & CIO of Spear Invest, Portfolio Manager of SPRX ETF
Better chips explain falling AI token prices, while rising GPU rents reflect demand for Nvidia's hardware. B200 rentals rose 28% in the first eight months of 2026 even as token prices more than halved since May. A Vera Rubin rack is estimated at ~$7.8M, almost twice a Blackwell GB300 rack's price, but Nvidia says Rubin cuts cost per token by up to 10x versus Blackwell; its September agentic-coding benchmark puts the gap at up to 45x. x.comÂ
Nvidia's long-term case is a bet that open, general-purpose AI wins a very large part of the market, and open-weight models are gaining share fast: on Vercel's AI Gateway they went from 28% of tokens in June to 62% in August. The largest closed-model labs, such as OpenAI and Anthropic, and specialized workloads at Google and Meta will move toward custom silicon. But thousands of companies deploying open models need general-purpose compute, Nvidia's sweet spot. x.comÂ
More AI tokens mean more compute demand for Nvidia even as pricing power starts to slip. OpenRouter data via the FT show weekly token use up ~250-fold since January 2025; the FT links the surge to agents' long call chains. Bloomberg's breakdown of a 1 GW AI campus that could cost roughly $30B puts well over half the bill in chips and servers, most landing at Nvidia as GPU racks. Servers wear out in 5-6 years, making AI capex a recurring chip refresh. x.com x.com x.com x.com x.com x.com x.comÂ
Lower AI token prices drive more usage, making GPUs scarcer and raising rental rates for neocloud capacity. Gavin Baker reports rising GPU rental prices even as token prices fall; the GPU constraint never left, and higher rental prices make the ARR math work again. x.com x.comÂ
Falling AI token and compute prices are a perfect storm for Nvidia. Token costs are down about 30% since the start of June, falling on 15 of the past 18 days, while compute rental prices are 16% off their recent highs. The bull argument that this doesn't matter for Nvidia because the Silicon Token LLM index only tracks spot prices does not hold. x.comÂ
Nvidia GPUs may retain high residual values while memory, power, data centers and chips remain scarce. Ornn Data's chart shows B200 rentals in October still more than twice their January level, with older H200 rentals higher still. This may last longer than expected, but normal depreciation returns once newer generations ramp and supply catches up. x.com x.comÂ
AI data-center spending should reach chips first, then racks, power and cooling, benefiting Nvidia, AMD, Vertiv, Bloom Energy, Credo, Marvell, Lumentum and Micron. BofA forecasts the global data-center market at about $1T in 2026, $2.1T in 2028 and nearly $3T by 2030, a 37% annual rise; roughly 80% of a new AI site's $57M per MW cost goes to servers, networking and storage. 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
Rising rental rates even for older GPUs strengthen the case that IREN and CoreWeave's compute assets retain value. The quoted a16z data puts year-to-date rental gains at 160% for H200s, 120% for B200s and 35% for H100s; even six-year-old A100s rent for more than at the start of the year. x.comÂ
AI agents' productivity over human labor should lift GPU demand and rates for neoclouds. The bottom looks in for CoreWeave, Nebius, Boost Run, Sharon AI and IREN. Gavin Baker says token prices can fall as revenue per GPU rises; paraphrasing Elon, even $38 per GPU-hour can pay off. CoreWeave's five-year fixed contracts may lag current rates; Boost Run has less capacity locked in. If SpaceX takes more growth, slower cash burn could rerate neoclouds, though smaller customers may pay less. x.com … Show more
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
