Jo Bhakdi
Investor, Tesla & AI Stocks
Model safety testing could become the next training, a massive new market for Nvidia and compute providers. New models will be placed in testing and monitoring harnesses that require huge compute capacity, likely some form of inference, though that call is low-confidence. Nvidia may be pushing for regulatory guarantees that could make this a 10x of the current compute market. x.com
Nvidia faces the risk of a ban on artificial superintelligence, which the market will probably start pricing in by the end of next year. More people will see ASI's risk as existential, and by 2029 frontier training runs and ASI attempts will be made illegal. Nvidia has the most to lose, because a ban would cost it, at least for a while, its central advantage in training chips, while hyperscalers and AI labs focus on inference. Nvidia is smart enough to get ahead in inference too. x.com x.com
Enterprise and neocloud demand is creating a second wave of AI infrastructure spending, with tailwinds for Nvidia and Super Micro. Dell forecasts 200 GW of AI data center growth through 2030, half driven by these customers, and estimates a $1 trillion AI infrastructure opportunity over the next four years. AI servers could enter a new growth phase in 2027, with higher shipment volumes and more revenue per deployment. 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
Guaranteeing AI data-center debt could eventually leave Nvidia unable to obtain financing. Per The Information, Nvidia is in talks to provide billions in guarantees on Nscale debt to develop a 460-megawatt data center; one day, its card may read "Denied!" 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
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.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 gets paid for AI infrastructure while the companies funding AI buildouts bear the spending. Alongside Apple's edge hardware business, Nvidia's position as a supplier distinguishes it from the AI spenders that are losing to companies collecting the cash. x.com
Nvidia's capacity commitments argue against AI orders drying up soon. Its filings show mostly memory and manufacturing commitments rising from $119 billion to $279 billion in one quarter, with about $175 billion covering 2027-28. Fewer new data-center projects could reflect power, land and building bottlenecks rather than weaker demand; a mid-teens multiple still reflects a fair worry that profits may not last. x.com
Nvidia could become the weakest link in AI's circular financing economy as it takes on more customer risk. It is turning to insurers to cover customers' GPU debt, and if insurers require Nvidia to keep backstopping deals, its liabilities will keep growing on top of $245B of supply chain commitments and prepayments as of last quarter. Such large counterparty conflicts are unwise. Amazon is also looking to move $8B of Nvidia Blackwell GPUs into an SPV to take on more debt for its AI buildout. … Show more
Nvidia's push into open-weight AI should make compute demand explode by making AI far cheaper. In one week it announced a $13B acquisition of Hugging Face and a $6B licensing deal with Poolside to build frontier open-weight models. Open-weight models cost less than OpenAI's and Anthropic's closed models and can be tuned into specialized models that beat frontier ones on specific tasks at a fraction of the token cost. Cheaper AI means more usage, more tokens and more compute. x.com
