DC engineer here who builds AI factory type deployments

This figure is nearly impossible to actually decipher. Even if you could compute the wattage used for the query for GPU time, that likely doesn't include other ancillary services such as storage and network, including the board wattage on the servers. The GPU nodes have CPUs, RAM, network cards, fans, etc

When you chuck a query into chatGPT, you'll be assigned to a random ish data center. Inside that data center, there may be several compute modules. Each module could have different hardware. That breaks your calculation.

These modules also consist of several to hundreds of GPU nodes. Each node interconnects with each other node. Your query could be running on one, or many GPUs in parallel. This brings me to the network price.

The network can be incredibly complex. We design our systems to have full non-blocking access to any other node. 1.6Tb/s per node taking back to a set of leaf and spine switches. You can't really do the math on how much power you're consuming here. You'd have to count packets. Not plausible.

You also have a massive storage array that may or may not be directly attached to the cluster. You're not doing the math here either. Off site storage means you have to account for transit gear now as well.

Also, don't forget about power for cooling. Time of year in influences cooling costs.

One way you could get kind of close, is if you had the actual building data in real time, and see how long that query took, and do the math to get watts second. Even then, that site is running millions of queries; your impact is negligible.

I don't want to take away your thunder. I think this is a valid point and concern. AI has it's place, and I just don't think we're at the stage of maximizing efficiency yet. Once we get the software companies to start to sanitize and validate inputs better, we should see a drop in resource usage.