Nvidia has committed five billion dollars to Safe Superintelligence, the Ilya Sutskever venture that had raised roughly three billion in total and carried a thirty-two billion dollar valuation last year. Nvidia’s own framing emphasizes that the investment comes with GPU access sufficient to raise SSI’s available compute by an order of magnitude. That second clause is the whole story, and the dollar figure obscures it. A single investor writing a check larger than a company’s entire prior funding history is unusual.

Every dollar of AI capital expenditure eventually lands somewhere concrete: a wafer, a switch, a cooling loop, a power purchase agreement, a SaaS seat. Tracking that flow from the hyperscaler earnings call down to the individual equity ticker is the only way to separate durable exposure from sentiment-driven noise. The chain below runs twelve tiers deep rather than the usual eleven, because the packaging layer that sits between memory and networking has become too structurally important to leave folded into a generic equipment bucket.

The case for owning Marvell almost always opens with custom AI chips, and that is the part of the story where Marvell is structurally second. The company and Broadcom are the two names that turn hyperscaler in-house silicon designs into manufacturable chips, the application-specific processors that Amazon, Microsoft, Google and Meta increasingly favor over off-the-shelf GPUs for inference. But Marvell holds roughly 15 percent of that market against Broadcom’s 55 to 60 percent, and Counterpoint expects Broadcom to command about 60 percent of custom AI silicon by 2027.

NVIDIA and Corning Incorporated have announced a multiyear partnership to scale U.S.-based optical connectivity manufacturing for AI infrastructure. The deal commits Corning to a tenfold expansion of its domestic optical connectivity manufacturing capacity, a greater than 50% increase in U.S. fiber production, and the construction of three new facilities across North Carolina and Texas — creating more than 3,000 jobs in the process. To understand why this matters, it helps to understand what optical connectivity actually is and where it sits in the AI infrastructure stack.

A working reference for the vocabulary of modern AI — organized by conceptual layer, not alphabetically. Each definition is written for practitioners and informed generalists: precise enough to cite, plain enough to share. Foundation Layer Large Language Model (LLM) A neural network trained on vast quantities of text to predict and generate language. LLMs learn statistical patterns across billions of documents, enabling them to answer questions, write code, summarize text, and engage in dialogue.