Code Like a Girl
What if we stopped automating the things we enjoy?
When I was a kid, I had an old black-and-white CRT TV, with three channels and knobs you had to turn to change channels or adjust the volume. It was already an antique by 1980s standards, but it was the “kids’ TV,” and I was perfectly happy with it. We had a newer one in the living room, with both a remote control and little buttons hidden behind a flap.
But according to my father, “remote controls are for lazy people,” so for a while he kept getting up to turn the volume up or down, which, from the perspective of my five-year-old self, seemed like a rather absurd way to defend the value of effort.
We used to call the remote “la zapette” in France back then (a slightly outdated word that I still quite like). It had already eliminated one particularly painful task: getting off the sofa. My kids grew up with voice assistants, and I’m not sure whether I should be proud or embarrassed about this, but both of them counted “Hey Google, play Netflix” among their first official sentences.
♦Eventually, maybe my father had a point: they literally named the first remote control “Lazy Bones.” (Source: Remote control advertisement, 1951 — © Zenith Radio Corporation.)Today, I think back to my button-operated TV with a bit of nostalgia. Not because I reject technology, but because there was something to do. That feeling of fiddling with it, going a little too far, then turning back until the picture was stable and you finally found the right channel. That whole “tinkering” phase was part of the TV time my parents allowed me, and it was a ritual I loved.
Sure, it was less efficient than a voice command. But between wanting to watch TV and finally getting a clear picture, there was a whole little journey. And maybe it’s that little bit of the journey that I miss today.
With AI, I feel like we’re doing the exact opposite: trying to make that journey as short as possible.
Keeping humans in the loop. To do what, exactly?I’ve been working in data and AI for about fifteen years. Automating things is literally part of my job. Yesterday with data processing pipelines, today with AI agents. So let’s just say I’m not exactly an enemy of technology.
But for a while now, there’s been a question nagging at me. I feel like we spend a lot of time asking what we can automate, and much less time asking what we should automate.
And the method is more or less always the same. We start with an existing process, look at what could be automated, then estimate what we could gain in time, efficiency or cost. If an agent can write meeting minutes, produce code or carry out several steps for us, why keep doing it manually?
In corporate talk, we systematically hear the same reassuring disclaimer: “Of course, our AI is human-centric. We keep humans in the loop.”
OK, but what exactly are they doing in that loop?
If I exaggerate a little, we often end up with a pretty simple model:
The AI does the work.
The human reads it, checks it, approves it.
Of course, that supervision is essential in many situations. But I still struggle to see how putting a human somewhere in the loop is enough to make a system human-centric, especially if their role is essentially to check what the machine has produced.
AI is supposed to be our tool. We should probably avoid optimizing everything so much that we end up becoming a tool in the chain.
Because if we really want to keep a place for humans in work, I hope we have something more to offer them than quality control.
And more importantly, what happens if checking, correcting and approving eventually becomes the main part of our jobs?
The day I started getting bored with AII used to love coding.
Not just having code that worked. Coding.
I loved the hands-on side of it: starting with an idea, writing a few slightly shaky lines of code, running them, seeing that of course they didn’t work on the first try, debugging, and trying again until finally the expected result appeared in the terminal.
There’s a little bit of magic in that. I’ve always loved it.
I also loved learning. Every time I changed jobs, I discovered new languages, new libraries, new frameworks. I dug around forums, tried things out, struggled with completely incomprehensible errors.
Basically, I spent an absurd amount of time searching, trying things and getting things wrong. A beautiful waste of time, by the usual standards of a process optimisation consultant.
When coding assistants arrived, I loved them. I would code, something would break, and I’d copy the error message into the assistant to understand what was wrong.
It was a bit like having a senior colleague looking over my shoulder, always available, but without the sighing, the “did you read the docs?” and the fear of asking a stupid question.
AI removed some of the frustration without removing the pleasure of doing the work.
But the tools kept improving. Today, I can ask them to produce a large part of the code for me. I explain exactly what I want, go and get myself a coffee, and by the time I come back, the code is already there.
All that’s left is to review it. Hard to complain.
At first, you happily delegate the parts you enjoy least (let’s be honest, nobody likes writing unit tests). And then, little by little, automation starts eating into the fun part too.
Congratulations, I’m more productive now. I’ve also become the reviewer of the activity I loved most.
And I have to admit, that leaves me with a strange question: if my work becomes more efficient but I enjoy doing it less, can we really say it has become “better”?
Don’t worry, I’m not planning to start an association for the defence of hand-written code. Not everyone shares my passion for debugging.
But the whole thing did make me realise something: when we decide to automate a task, we spend a lot of time looking at what we’re going to gain. Much less at what we might lose.
♦I wasn’t planning to start the association. But now that we have the logo… (AI Generated image)Coding literally means “producing code,” sure. But it’s also experimenting, searching, testing, understanding, trying something improbable just to see what happens, learning and sometimes even having fun (Please tell me I’m not the only one ! ).
So yes, if an AI assistant can do in a few minutes what would have taken me a whole day, it seems silly not to use it. But are we sure we haven’t left something interesting behind along the way?
And what if my story about being a nostalgic developer were just one example among many?
WALL-E or Severance?OK, by this point, you can probably see where I’m going with this. My brain, raised on a steady diet of science fiction, was never going to stop at a nice little story about code and productivity. If I’m going to think about a problem, I might as well immediately imagine how badly it could go. People who know me know that I can go on about these topics for quite a while.
Dystopias are unbeatable when it comes to existential questions: they take an idea, push it to its most extreme consequences and force us to ask where the line is.
Imagine we just keep happily optimizing, automating and delegating to machines everything they can do for us. Not three months from now. Not next year. Let’s push the slider much further, just to see. What might the worst-case scenario look like?
Fortunately, screenwriters have already imagined quite a few ways for humanity to mess things up. And two of them immediately came to mind.
The first is WALL-E.
You know the humans in their floating chairs? They no longer need to walk, wait, search or even really look around. Everything comes to them, immediately. Their entire daily life is designed to make things easier for them.
From an OKR perspective, “remove all friction from the user experience” can officially be marked green. Mission accomplished. The problem is that apparently nobody thought to add “still want to do anything” to the KPI list.
Let’s just say that if this was supposed to be the ultimate goal of human progress, perhaps we should have defined the success criteria a little better. Because by gradually removing every constraint, they also let a whole bunch of things disappear that are much harder to fit into a dashboard: curiosity, movement, encounters, discovery, wonder.
The most unsettling part is that nobody wakes up one morning and decides to give all of that up. Every small step backwards seems perfectly logical. Why walk when you can be carried? Why search when a machine can search for you? Why do something yourself when you can delegate it?
♦I still wonder how they automated toe scratching. (Source : Screenshot from Wall-E(2008) — © Disney/Pixar)OK, don’t worry, I don’t think subscribing to ChatGPT Plus is the first step towards the floating chairs in WALL-E. But I like what the film forces us to look at: if we remove all friction, what else do we remove without really noticing?
And then there’s Severance.
And strangely enough, that one speaks to me even more when I think about work. In the show, without spoiling anything, the employees don’t spend their days doing nothing. Quite the opposite. They work. A lot, actually. They process data, follow procedures, check that everything is compliant. They know exactly what they have to do.
The small problem is that their work is so compartmentalised that they have no idea what they are really contributing to, or what happens before or after their part. It’s hard to get more literally “disconnected from your work” than that.
Of course, this is a dystopia, so nobody is suggesting that your next copilot is going to turn the open-plan office into Lumon Industries.
♦I know, I know, I’ve been complaining a little… but their computers do look pretty cool. (source : Screenshot from Severance — © Apple TV)In the show, the distance between the characters and their own work is pushed to the extreme. And maybe that’s what bothers me most. If tomorrow AI produces, builds, searches and experiments while we spend more and more of our time reviewing, controlling and approving what it has done, we are still working.
We are even still “in the loop.” That famous loop.
It’s just that our place in it has changed. By constantly optimizing, we risk eventually reducing the human role to a very specialised one: supervising, checking, approving. Ok, it’s efficient. But how do you keep feeling truly involved in an outcome when you took part in none of the experiments, none of the mistakes, none of the choices that got you there?
Because all those little, not-very-optimized detours have a purpose too: understanding what we’re doing, knowing what we brought to it, sometimes feeling proud of it and, ultimately, putting a little bit of ourselves into it.
And unlike the characters in Severance, we don’t have one part of our brains dedicated to work and another one for the rest of our lives. How we feel at work, how interesting we find it, how much we want to invest ourselves in it, inevitably spills over a little.
In the end, my two dystopias offer two fairly unappealing versions of the future: having almost nothing left to do, or carrying on working without really understanding what we’re contributing to. So naturally, whenever I hear people talking about the “future of work,” I’d quite like us to avoid having to choose between the two.
Now mix them together, add a few AI agents and there you go: my dystopian brain is delighted. Personally, I find it pretty terrifying.
“The more senior people will manage agents”Recently, I was talking with my manager about the future of the Data Scientist profession. His view, in short: “The more senior people will manage agents.”
That sentence bothered me a little.
Not really because I think that future is impossible. Quite the opposite. What surprised me was that it was presented as the natural, even desirable evolution of the profession: you start by doing the work, you become an expert, then one day you gradually stop doing it and start supervising agents that do it for you.
And apparently, that’s supposed to be good news. Maybe I’m just a bit old-fashioned. But I chose this profession because I also enjoy doing it.
So the idea that the ultimate achievement of my career would be to sit back back and watch agents have all the fun instead of me leaves me, let’s say, moderately enthusiastic. I have to admit, I’d quite like to still be doing a little Data Science when I grow up. And finally become that senior colleague who sighs over a junior’s shoulder and asks: “Did you read the docs?” It would be a shame if AI took that away from us too.
♦How I picture myself leading my future army of AI agents. (Source : Screenshot from Toy Story(1995) — © Disney/Pixar)A little resistance to automationI think I want to defend a small form of resistance.
Not against AI. That would be fairly ironic given my job. Not against agents or automation either. But against the idea that just because we can automate something, we necessarily should.
Some tasks are tedious, repetitive and honestly not very interesting. If a machine can take them off our hands, great. But other tasks give us something too: autonomy, mastery, interaction, creativity, sometimes simply the pleasure of doing them. Just because these things are difficult to measure doesn’t mean they don’t matter.
♦It started as a joke. Now there are two logos. I’m starting to worry. (AI generated image)So maybe, alongside the usual questions like “how much time will we save?” or “what’s the ROI?”, we should ask another one: Do we actually want to stop doing this?
And, more importantly, ask the people who actually do the work.
For those of us who work in AI, I think that is also a responsibility. Automation doesn’t just remove a task. It also reshapes the work that remains. We don’t get to decide how work is organised on our own, obviously, but we do have a role to play in bringing something other than productivity into the equation.
Maybe that’s what truly human-centric AI is, in the end. Not simply keeping a human somewhere in the loop to click “Approve,” but asking what place we actually want to leave for them.
I don’t want to go back to three channels and the knobs on my old TV.
I’m not against the remote control.
I just want to make sure that, in our obsession with building the perfect remote, we don’t end up forgetting that sometimes there was something interesting about turning the knob.
♦Kind of wish changing channels still felt like wielding Buzz Lightyear’s laser. (Source : Zenith Flash-Matic advertisement, 1955 — © Zenith.)♦What if we stopped automating the things we enjoy? was originally published in Code Like A Girl on Medium, where people are continuing the conversation by highlighting and responding to this story.