Further Thoughts on Infinite Workloads.
I previously wrote about infinite workloads and also why there is always too much to do.
I thought it would be worth exploring this topic again in light of the advances of artificial intelligence, especially LLMs (Large Language Models).
I have for sure been a power user of AI since it became commercially available, even going as far as coding my own Python scripts to help me make more efficiency. But, in the excitement of being able to do things that I could not do before, or even just being able to do more in general, I forgot about the primary rule when dealing with an infinite workload: you cannot outwork infinity.
The reason you cannot work against infinity is because every item of work that you get done creates more work. Every email you send elicits a reply. Every time you drive a project forward, new tasks move into your todo list.
For me, it has now reached the point that I can hardly manage just keeping track of my todo list, which takes an increasingly large amount of time, because I have so much on my plate. This is because AI has accelerated my work so much, that even more work ends up on my desk.
I started to go down the rabbit hole and consider if there were other AI solutions or approaches to further increase productivity, but then I finally remembered that when dealing with an infinite workload there is only one solution: prioritisation.
We have to get comfortable with the fact that things will slip.
We cannot and should not aim to complete everything, because in an environment of infinite work, this is an unattainable goal. In fact, the idea of completion itself is a fallacy when you’re dealing with infinity.
There’s an old saying, “He who chases two rabbits catches neither.” This underlines the importance of focus and selection. Now, what happens to someone if they try and chase an infinite number of rabbits?
This is essentially the paradox of infinite workload.
The more we try to handle, the less effective we become, even when we’re armed with the most powerful AI tools. AI makes the problem worse, not better if its used just to improve productivity.
In this regard, the role of AI should not only be about making us work faster or managing more tasks, but also about helping us prioritise better. Imagine an AI tool that understands not only the task content but also its contextual importance in the grand scheme of things. What if your AI tool could suggest which task to tackle first based on a mixture of urgency, importance, and your own work rhythm?
By integrating AI and machine learning into our productivity tools, we can potentially transition from being overwhelmed by infinite workloads to navigating them strategically. The algorithm can learn your patterns, deadlines, preferences, and more. It might even help you spot when you’re overcommitted and need to delegate or defer tasks.
However, the AI’s assistance would be just half the battle won. The other half is on us, the users. We must be ready to take those AI-driven suggestions, to say “no” when needed, and to focus on the tasks that truly matter. We must develop the ability to accept that some tasks will indeed slip and that it’s alright.
Remember, perfection is not attainable in a world of infinite work. What is attainable, though, is purposeful progress. Instead of being guided by the sheer volume of work, we should let our priorities drive us. The advancement of AI and its integration into our lives should be a tool to reinforce this, not just a means to create more work.
To conclude, as we further embrace AI in managing our workload, let’s not forget the basic principle of dealing with infinity: prioritising. It’s not about outworking infinity; it’s about working intelligently within it. AI can potentially transform our approach to work, but only if we allow it to guide us towards what’s important, rather than merely increasing our output. In the world of infinite workloads, smart work is indeed the key to success, not hard work alone.
