There’s no modality that isn’t handled by AI. And AI systems reach even further, planning commercial and marketing campaigns, automating social media postings, … Most of this was unthinkable a mere ten years ago.
But then, the primary machine learning-driven algorithms did their initial steps: out of the research labs, into first products. They began to curate content on YouTube and social media sites. They began recommending movies on Netflix and songs on Spotify. The ranked search results. They played strategic games on par with humans. The final rise of AI-enabled has been spectacular.
AI within the workplace
And the workplace isn’t immune against this. As an undergrad, I used to be studying construct hyperplanes, centroids, and backpropagation rules, and for many of my studies, AI was mostly considered a tutorial research direction. Since I entered the job market, this has modified A LOT. Employers and employees alike realized the potential of AI for work. In most (digital) workplaces, AI is rapidly becoming an invisible co-worker.
Many dedicated AI tools already made the leap onto our desktops: programmers use AI-assisted coding tools, data analysts prepare pipelines from single sample files through AI, and designers draft faster with AI-generated visuals. These tools undeniably make work easier. But additionally they raise a deeper query:
What’s one’s work?
What is really my very own work? Do I still must interact with my code, with anything, really, intimately?
The more we AI-ify our workflows, the less we want to interact with our work material. It’d well end up that we now not to grow to be experts, possessing deep knowledge a few fairly narrow topic, but relatively shallow surfers, taking an AI-glimpse here and there.
In other words, we grow to be mere managers of how work is completed by AI. Notice there’s no “our” in front of .
Is that, can that be fulfilling? Can we not need some sense of depth in our work?
I well remember a time after I needed to handle multiple concurrent projects. At the moment, which was before AI took hold within the offices, I used to be often switching between three different and mostly unrelated projects per day. Along with semi-urgent interruptions, one can imagine that there was not much time to spend prolonged time on a single topic; before I could go deep enough into any topic to make actual progress, I already had to change.
Nowadays, AI systems often act as proxies, stopping us from needing to interact with a project in the primary place. Though we may be working on a single project only, we – which ends up in the query:
If we use AI to do our work, what’s our work, then?
Is our work simply doing more work? AI is usually hailed as allowing us to do more, which means that, given the identical working times, we want to interact with the fabric even less.
This suggests that, by definition, we cannot gain profound experience in a single topic.
This, further, implies that we could, in principle, do any job that is said enough to our skills.
Which, finally, implies that anyone else could do our job.
We’re, thus, replaceable as soon as AI automation scales.
How can we prevent this?
Use AI deliberately: Think first, prompt later
For my part, the one way* is: use AI deliberately, selectively. Don’t outsource your considering. Don’t let your ability to think deeply and critically decay through unconscious non-use.
It’s completely nice — often even smart — to make use of AI tools for the truly boring tasks that any decently expert person could do. For programmers, secure (within the sense of not making us dumber) uses of AI include: summarizing codebases, creating README documents, generating boilerplate, or loading and cleansing data.
But when the duty at hand requires human judgment, interpretation, or specific design selections and tradeoffs, that’s when it’s best to resist the temptation handy it off. These are precisely the moments where you construct the expertise that keeps you irreplaceable.
To make this more concrete, you should use this straightforward heuristic when deciding on using AI assistance:
- Task which are Low-stake, repetitive, well-defined → Let AI help.
Examples are: formatting code, generating test stubs, writing SQL queries. - Task which are high-stake, ambiguous, or require human judgment → Do it yourself. Examples are: designing system architecture, interpreting experiment results, making ethical decisions.
This rule of thumb keeps the “boring” stuff automated while protecting the work that really builds your expertise. To integrate the heuristics into each day practice, it’s best to Intentionally pause before a task. Ask yourself:
Then, if the goal is knowing → start manually. Code the primary draft, debug yourself, sketch the design. When you’ve thought it through, you’ll be able to augment your works with the output of an AI system.
Nonetheless, if the goal is mere output → let AI speed up you. Prompt it, adapt it, and repeat with the following task.
Consider it as a mantra: “Think first, prompt later.”
Then, at the tip of a piece week, you’ll be able to reflect back: which tasks did you outsource to AI this week? Did you something from those tasks, or simply complete them? Where could you will have benefited from engaging more deeply?
Closing thought
It seems that, as AI is increasingly more utilized in the workplace, our real job may not be to churn out more output with AI. As an alternative, our job is to interact directly with the fabric when it matters — to construct the sort of judgment, insight, and depth that no system can replace.
So, use AI deliberately. Yes, automate the boring parts, but protect the parts that make you grow. That balance is what is going to keep your work not only worthwhile, but in addition fulfilling.
* A non-alternative for many machine learning folks who spent considerable time constructing a profession in data science: switching careers to do something manual and offline. Examples are construction work, hair dressing, waiting, etc.
