OP says one query uses 0.3 Wh. Driving an electric car for 10 miles = 3,000 Wh which is roughly 10,000 Wh per hour.
I'm not sure how many queries is equivalent to an hour of Claude code use, but maybe 5 seconds, which means an hour of continuous use = 216 Wh, or ~50x less than an electric car.
A coding agent runs near-constantly, so of course it'd require a lot more compute than running even, say, a multi-minute query with a thinking model every hour. How much exactly is pretty hard to calculate because it requires some guesswork, but...
For a long input of n tokens from a model with N active parameters, the cost should scale as O(N n^2) (this is due to computing attention - for non-massive n, the O(N n) term is bigger, which is why API costs per token are fixed until a certain point and then start to rise). From the estimates from [1], it's around 40Wh for n=100k, N=100B. I multiply by 2.5 to account for Opus probably being ~2.5x larger than gpt-4o, and also multiply by 2 to pessimistically assume we're always close to Opus's soft context limit of 200k (it's possible to get a bigger context for extra cost, but I suspect people compact aggresively to not have to use it). That gets me 7.2J/t, which at a rough throughput estimate of 20t/s gives me power of 144W. Like a powerful CPU or a mediocre GPU, and still orders of magnitude lower than a car.
It is not only about raw power consumption. Comparing driving an electric car with using AI only in kW hides a major point: Hyperscale datacenters are massively centralised, which brings it's own problems; a lot of energy is used for cooling, and water consumptions is enormous. Charging electric cars at home is distributed and does not suffer from the same problems as the centralised hyperscalers do. Also, running AI models at home is not much different than a gaming session :)
This is an incredible sequence of assertions, every single one of which is very incorrect.
"A lot of energy used for cooling": hyperscale data centers use the least cooling per unit of compute capacity, 2-3x less than small data centers and 10-100x less than a home computer.
"Water consumption is enormous": America withdraws roughly 300 billion gallons of fresh water daily, of which IT loads are expected to grow to 35-50 billion gallons annually by 2028. Data center water demands are less than a rounding error.
"distributed and does not suffer from the same problems": technically correct I guess but distributed consumption has its own problems that are arguably more severe than centralized power consumption.
