The one thing that had me reading to the end was the mention near the top of the environmental impact of LLMs, but he never got back to it.
I'm currently writing an onboarding doc for my team, encouraging LLM use for some tasks. (OK, well, I'm actually procrastinating by reading HN).
At the same time, I'm in a darkened office with tinfoil on the windows and a fan pointed at me because it's hell outside and it has been for weeks, and every year it seems to get hotter and hotter and we have longer and longer heatwaves.
This seems ... discordant, at a minimum.
Really, _should_ we be using these things to speed up, say, dependency updates if the cost is the planet? I wanted to know what the author thought about that.
I won't belabour the point, but I think someone needs to correct their misconceptions. You're comparing a single, average person's usage of AI in a day -- not the billions who actually use it. Then you are comparing it to an American's two-hour commute in an oversized SUV. (A commute on the Tube costs about 0.02991 kg per passenger, per kilometre.)
What about training those models? Or the usage of all of the data centres? The projections are that by 2028 a fifth of all energy consumption in the U.S. alone will be for AI.[1]
This is actually a good link. Thank you. It's usage that is the big driver, and LLMs are a big driver in emissions and other damage to the environment.
I'm currently writing an onboarding doc for my team, encouraging LLM use for some tasks. (OK, well, I'm actually procrastinating by reading HN).
At the same time, I'm in a darkened office with tinfoil on the windows and a fan pointed at me because it's hell outside and it has been for weeks, and every year it seems to get hotter and hotter and we have longer and longer heatwaves.
This seems ... discordant, at a minimum.
Really, _should_ we be using these things to speed up, say, dependency updates if the cost is the planet? I wanted to know what the author thought about that.