Plus that graph is the first derivative of industrial robots. the actual # of new robots since 2012 is the area under the respective curves, so a very big lead.
The "China leads in robotics" seems to be unaffected by AI. The China line is basically on the same trajectory since 2012. The chart does no belong in the article.
> The report estimates that carbon emissions from models with the least efficient inference are over 10 times as high as those with the most efficient inference. DeepSeek’s V3 models were estimated to consume around 23 watts when responding to a “medium-length” prompt, while Claude 4 Opus was estimated to consume about 5 watts.
This makes absolutely no sense. I suppose they meant watt hours, and that's a weird way to explain carbon emissions...
Stating "Software engineers are all-in on AI" because of an increase in github projects being created is hilarious. I didn't realise creating a github repo made someone a software engineer. If only I had known this I wouldn't have bothered learning all the other stuff!
> The report estimates that training the latest frontier large language models, such as xAI’s Grok 4, can generate over 72,000 tons of carbon-equivalent emissions.
That seems pretty trivial, relative to 38bn per year globally?
The training of one LLM requires as much emissions as 17,000 people over a year. Which, according to the article, is 8 times more than last year, and may be underestimated by a factor 2.
That does not cover the whole usage: the hardware, the bots that collect learning data, the prompts, etc. And there are now many models of this size, and thousands and thousands at smaller sizes. And some of this parameters are increasing.
AI is estimated to emit more than 80e6 tons of CO2-equivalent this year. Much more than whole countries like Austria or Israel. Is that trivial?
> The capabilities of AI models have improved with incredible speed over the past decade, and as the graph above shows, progress seems to be accelerating.
errr… no? Every discipline is clearly hitting a plateau so far. Some started recently and hence haven’t yet (competition maths) but based on past graph, they will all plateau.
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[ 4.5 ms ] story [ 37.9 ms ] threadSure, but what I'd really like to see is a graph for how much carbon is generated serving these models globally.
The absence speaks volumes.
https://news.ycombinator.com/item?id=47758028
Source: https://hai.stanford.edu/ai-index/2026-ai-index-report
This makes absolutely no sense. I suppose they meant watt hours, and that's a weird way to explain carbon emissions...
That seems pretty trivial, relative to 38bn per year globally?
That does not cover the whole usage: the hardware, the bots that collect learning data, the prompts, etc. And there are now many models of this size, and thousands and thousands at smaller sizes. And some of this parameters are increasing.
AI is estimated to emit more than 80e6 tons of CO2-equivalent this year. Much more than whole countries like Austria or Israel. Is that trivial?
errr… no? Every discipline is clearly hitting a plateau so far. Some started recently and hence haven’t yet (competition maths) but based on past graph, they will all plateau.