Many businesses today run “in the cloud”. What this often means is that they have abstracted out the hardware entirely. Large corporations like Amazon, Google, Microsoft or IBM operate the servers. The business only needs to access the software, remotely.
In theory, this means that you can adjust your capacity for just your needs. If you need only twelve servers most of the year, then you pay for only twelve servers. And on the specific days when you need 100 servers, you pay for the 100 servers on these days only. You may even use “serverless” computing and pay just for what you use, saving even more money.
Is this the whole story?
I am not so sure.
A tremendous benefit of cloud computing for the developers and operation people is that it cuts through the red tape. If you are using the cloud, then a developer can get ten more servers at the click of a button. I have met credible people from well-known businesses who told me that their developers have practically an unlimited ability to allocate new cloud resources.
If we make it easy for developers to quickly use lots of computing resources, these developers might effectively think of computing and storage as infinite. It also wipes away all incentives to produce efficient systems.
You may end up warming up the planet. It is not a joke: training a single machine-learning model can have over four times the footprint of a car over its entire lifetime.
Your ten servers end up needing a much more complicated architecture than your single laptop. But that is not obviously a negative for some developers who get to try out fancier-than-needed software, always useful on a resume.
Developers are not alone. When I take pictures these days, I never delete them. They get uploaded to the cloud and I forget about them. When I managed the scarce storage on my PC where digital pictures could be stored, two decades ago, I would spend a lot of time deleting bad pictures. I no longer care.
Further reading: The computing power needed to train AI is now rising seven times faster than ever before and Facebook AI research’s latest breakthrough in natural language understanding is running up against the limits of existing computing power. (credit: Peter Turney)