For most of the past two years, the scarce input for AI was chips. Increasingly it is electricity. Companies can order GPUs, but they cannot easily get a few hundred megawatts of grid power delivered to a site, and the wait for a new grid connection in many US regions now runs for years. That is why data center deals are announced in gigawatts, why tech companies are signing contracts to restart nuclear plants, and why some sites are building their own gas turbines.

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.

WiGig had a brief moment of consumer visibility around 2017 to 2019. A handful of laptops from Dell and Lenovo shipped with 60 GHz modules. A small number of docking stations used WiGig to replace the DisplayPort and USB cables between a laptop and a desk setup. Then it went quiet, consumer products quietly discontinued, and the technology receded from mainstream WiFi discussions. The conclusion most drew was that WiGig had failed.

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.