Despite its name, the infrastructure used by the “cloud” accounts for more global greenhouse emissions than commercial flights. In 2018, for instance, the 5bn YouTube hits for the viral song Despacito used the same amount of energy it would take to heat 40,000 US homes annually.
Large language models such as ChatGPT are some of the most energy-guzzling technologies of all. Research suggests, for instance, that about 700,000 litres of water could have been used to cool the machines that trained ChatGPT-3 at Microsoft’s data facilities.
Additionally, as these companies aim to reduce their reliance on fossil fuels, they may opt to base their datacentres in regions with cheaper electricity, such as the southern US, potentially exacerbating water consumption issues in drier parts of the world.
Furthermore, while minerals such as lithium and cobalt are most commonly associated with batteries in the motor sector, they are also crucial for the batteries used in datacentres. The extraction process often involves significant water usage and can lead to pollution, undermining water security. The extraction of these minerals are also often linked to human rights violations and poor labour standards. Trying to achieve one climate goal of limiting our dependence on fossil fuels can compromise another goal, of ensuring everyone has a safe and accessible water supply.
Moreover, when significant energy resources are allocated to tech-related endeavours, it can lead to energy shortages for essential needs such as residential power supply. Recent data from the UK shows that the country’s outdated electricity network is holding back affordable housing projects.
In other words, policy needs to be designed not to pick sectors or technologies as “winners”, but to pick the willing by providing support that is conditional on companies moving in the right direction. Making disclosure of environmental practices and impacts a condition for government support could ensure greater transparency and accountability.
I don’t want to doxx myself or blow my own horn. The programming I do, and many developers do, is not something ChatGPT or Bing AI or whatever it is called can do.
At best, it is a glorified search engine that can find code snippets and read -but not understand- documentation. Saves you some time but it can’t think and it can’t solve a problem it hasn’t seen before, something programmers often have to do a lot.
But after you’ve written the code, don’t you find that the LLM is great at documentation?
Dude, if you’ve never used copilot then shut up and don’t say anything.
Don’t pretend like you write code that doesn’t benefit from AI assisted autocomplete. Literally all code does. Just capitalization and autocompleting variable names with correct grammar is handy, let alone literally any time there’s boiler plate or repetition.
Lmao, the idea that you having an NDA makes you work on super elite code that doesn’t benefit from copilot if hilarious. Ive worked on an apps used by hundreds of millions of people and backend systems powering fortune 10 manufacturers, my roommate is doing his PhD on advanced biological modelling and data analysis, copilot is useful when working on all of them.
We have had IDEs for decades
Oh do tell us again how you haven’t used copilot without saying the words ‘i haven’t used copilot’. Stackoverflow’s professional developer survey found that 70% of devs are using AI assistants, you think none of them have heard of an IDE or Intellisense before?