How Data Hoarding Is the New Threat to Privacy and Climate Change
Big Tech needs to get better at energy efficiency
As machine learning and other data-intensive algorithms proliferate, more organizations are hoarding data in hopes of alchemizing it into something valuable. From spy agencies to network infrastructure providers, data collection is part and parcel of the digital economy. The best data can be combined with clever algorithms to do incredible things — but digital hoarding and computationally-intensive workloads have externalities too.
The electrical costs — and therefore the environmental impacts — of computation are both extraordinary and growing. Modern machine learning (ML) models are a prime example. They require an enormous amount of energy in order to process mountains of data. The computational costs of training ML models have been growing exponentially since 2012, with a doubling period of 18 months, according to OpenAI. In recent months, similar studies have shown that the electrical costs of cryptocurrency and video streaming are also significant and growing.
Producing this electricity creates literal exhaust in most cases — there are precious few server farms running on 100% renewable energy — and with climate change looming large, it’s time we acknowledge the environmental impact of computation. Just like wrapping every little thing in a plastic bag is, some of our CPU usage is frivolous and wasteful.
Computer science and engineering experts have been complaining about this for years. Some point out that we went to the moon with only 4kb of RAM. Others detail how slow and bloated modern software is. Jonathan Blow went so far as to warn about the impending collapse of the entire software engineering discipline due to intergenerational knowledge loss.
Most of the time this argument is positioned in terms of engineering elitism. Its supporters nostalgically harken back to a time when it really meant something to be a software engineer. They scold beginners for not knowing better while flaunting their beautiful hair, tinged with the silvery gray of experience. Despite the condescension, they’re not completely wrong.
As computers got faster and faster, computer programs actually got slower. End-users…