alt Hacker NewsThere are thousands of ways to calculate carbon that are all valid, that’s why a similar usage amount in AWS and Azure will give you wildly different numbers. We prioritise consistency, coverage, and transparency. If the users understand where the numbers come from, and we are applying the similar data science across all clouds, then you have comparable numbers. We get our numbers audited by 3rd parties regularly to ensure robustness and credibility, but an accurate number for your entire AWS environment isn’t useful if you are just trying to calculate the difference between an AMD instance family, and a Graviton instance family. This is where we focus our methodology and why it works inside of Infracost.
A big focus now is applying this same level of rigorousness to different AI models and their impact. Batching, caching, model size and manufacturer are the choices engineers are making now. We want to ensure that choices being made are cost and carbon efficient.
Curious to know what decision you're making at the moment that's triggered you looking into your own methodology?
While there are thousands of valid ways to do the calculation. Their results, if they are different, denote different consequences.
I take it from what you say here that you specialise in accuracy and consistency of measurement as a service and let the client judge for themselves what meaning to derive from them. It feels like it might be an invitation to Goodhart's law.
I'm in no decision making position myself (that said, had a few face to face conversations with people writing position papers). My interest is primarily in understanding what has the best outcomes and the ability of strategies to affect those outcomes.
To put an absurd case. Imagine adding a gadget to generators to use all of the CO2 as part of a cyanide manufacturing process which is then emitted. It gives you great CO2 emission numbers, but public health outcomes less so.