"a burst of technology democratization as chatbot-style tools are used by people in fields from medicine to marketing to create their own programs, tailored for their industry, fed by industry-specific data sets."

Most striking was the counterintuitive finding that reasoning models actually reduced their computational effort as problems became more difficult, despite operating well below their token generation limits.

The use of the terms megawatts and kilowatts as descriptive of battery energy storage is to effectively convey the instantaneous power contribution of battery storage as comparable to the power produced by grid-level generators. We recognize that energy capacity in the context of energy storage typically refers to the total energy a battery can hold in watt-hours, kilowatt-hours, megawatt-hours, etc. However, for statewide planning and reliability purposes, understanding the peak power capability of battery energy storage systems allows for the integration of data with the nameplate capacity of traditional power generation units serving the grid. It is in this context that battery systems are able to be effectively compared for their ability to serve the grid over short periods of time, typically two to four hours per day depending upon system conditions.

Though the AI systems proved adept at quickly finding relevant code sections, they stumbled when it came to understanding how different parts of software interact. The models often suggested surface-level fixes without grasping the deeper implications of their changes.