

People in the English-speaking countries generally don’t have government issued ID beyond a driver’s license. That’s also true for the UK. Historically, ID cards are connected to military conscription. The UK could rely on the Navy for defense and did not maintain vast land armies like the continental nations. Political initiatives to introduce ID cards are usually rejected by voters as totalitarian overreach.
The former slave states in the US have a history of using procedural rules to exclude blacks from voting. After the end of slavery, there was formally equality before the law. So, laws were created to maintain the status quo that were non-discriminatory on their face. EG literacy tests. This not only targeted blacks who were denied an education. Administering such tests was fully in the hands of local elites. They could be made arbitrarily hard to black people, while politically reliable white illiterates could be excused.



As you can tell from the previous answers: It depends.
The bigger an LLM is, the more power it uses. AI models can be quantized or distilled to yield smaller but less capable models. Providers may try to route you to the cheapest model that can handle your prompt.
Another question is the length of the output. The length of the input matters less but might be relevant for processing long texts.
The energy used for training is relatively insignificant once you average it over its lifetime. The energy efficiency of a particular data center will certainly matter more.
Providers like OpenAI claim that the typical query uses about 0.3Wh. That’s about the same as an idling phone charger uses in an hour; ie charger plugged into the outlet but not into the phone.