Coding Against the AI Tide
Why Choosing the “Hard Way” is My Quiet Act of Resistance in the AI Age
Series: AI Ethics & Inclusion
Essays and reflections on building AI that works for everyone, at every age.
I still write code by hand—not to be fast, but to keep my mind sharp. Learning to code in my 50s was tough but rewarding—a way back into deep thinking and creativity. In an AI-driven world, staying mentally engaged isn’t just personal—it’s about making sure older voices stay part of the conversation. Coding is my quiet act of inclusion and resilience.
Introduction
I still write Python code.
Not every day. Not always for serious projects. Sometimes just for the joy of solving a problem. Sometimes to see if I still remember how to clean a messy dataset or create a map or chart. But I code. By hand. Often from scratch.
In a world where generative AI can churn out entire programs, apps and websites in seconds, this might seem a little old-fashioned — or even inefficient. But I don’t code to be fast.
I code to think.
There’s a kind of deep, focused satisfaction that comes from sitting with a problem and figuring it out line by line. Python, for me, is logic made legible. It’s crisp, structured, but forgiving. It invites experimentation. And more than anything, it makes me feel sharp — like my mind is being stretched in the best possible way.
At 55+, that matters.
From Spreadsheets to Syntax Errors
When I began my Master’s in Data Science in my early 50s, I naively thought I’d be working mostly in Excel — maybe making dashboards or drawing some elegant charts. I had no idea what I was in for.
The reality hit fast and hard: coding in Python, R, SQL… debugging, writing functions, wrangling data. It felt like being thrown into a foreign country where I didn’t speak the language — and everyone else seemed less than half my age.
At first, I was terrified. Truly. I didn’t grow up coding. I didn’t even consider myself technical. But slowly, something shifted. The logic started to make sense. The tools became less intimidating. And somewhere along the way, I fell in love with it — especially Python and particularly Pandas. It was clean, intuitive, and just challenging enough to keep me mentally on my toes.
In tech spaces — especially those driven by AI — that late start can be a quiet act of resistance. We’re so used to seeing “innovation” tied to youth that we forget how valuable it is to have different life stages represented in problem-solving.
What I thought would be a pretty Excel-based journey turned into something much deeper: a way back into structure, creativity, and deep thinking.
Keeping My Brain in the Loop
Now, in my late 50s, I still write Python because I want to stay sharp. I don’t use ChatGPT to write my code for me. Not because I’m a purist — but because I like to work it out myself. I want to feel the satisfaction of solving something. Of remembering how to simply slice a list or build a function that just works. The joy of seeing my own code run never gets old.
Sure, my code isn’t always the most Pythonic or elegant. But it works. And more importantly, it’s mine. There’s something quietly powerful about continuing to challenge your brain in mid and later life. It’s not about proving you can keep up — it’s about reminding yourself that you still want to.
That you still have ideas worth building, and a mind fully capable of building them.
The Ethical Thread
This brings me to AI ethics — because this isn’t just personal. It’s political, too.
Ethical AI isn’t just about biased training data or black-box algorithms. It’s about who gets to stay in the conversation. If AI development teams skew young and fast-moving, the tools they create may unconsciously favour cognitive styles that prize speed over depth — unintentionally sidelining those who learn or work differently.
Age is often invisible in tech inclusion efforts. We talk about gender and race (as we should), but age rarely makes the cut. And yet, older adults are often assumed to be slower, less capable, or uninterested in learning tech — even when that couldn’t be further from the truth.
I started learning to code in my 50s. It was hard. It was humbling. But it was also one of the most empowering decisions I’ve ever made. It opened my mind to lifelong learning, something essential as we hurtle into the AI world.
Choosing When to Automate
I don’t reject AI tools. I use them all the time. I do turn to ChatGPT when I am a bit stuck, or when Stack Overflow doesn’t give me the answer (although I still prefer the community on there). But I also believe in choosing when to automate and when to stay mentally engaged. That choice — to stay in the loop — matters.
Automation is great when it frees us up for higher-level thinking. But when we automate everything, we risk outsourcing the very mental friction that keeps us sharp, alive, and connected to the problem. For older professionals, that “mental friction” isn’t just personal enrichment — it’s a safeguard against a future where certain groups are quietly deemed obsolete because their way of thinking doesn’t align with automation’s pace.
Coding is one way I stay sharp. One way I stay relevant. And in a world where older minds are often overlooked, I think that’s a quietly radical act.
Coding to Calm the Over-Stressed Mind
Coding also calms me. There’s something settling in the familiarity of writing the same lines of code and getting the results you know you want. During a recent AI skills class on using complex context prompts to generate highly polished and top quality photographic images and develop a marketing strategy, I found myself zoning out. I just wasn’t engaged — it wasn’t my thing. I would get to it when I felt ready to do so.
So I quietly went back to a topic modeling project I’d been working on in the background. Just me, my data, and my brain. And that moment felt like home.
Age-Inclusive AI Starts with Real Participation
To me, age-inclusive AI means more than designing tools for older users. It means recognizing older builders, thinkers, and contributors. When older coders and thinkers are absent from AI development, we risk designing tools that bake in ageist assumptions — and those assumptions can spread invisibly, shaping hiring systems, educational tools, and even cultural norms.
So yes, I still write Python. Not because I have to. But because I want to. Because it keeps me thinking. Because it reminds me that my brain still works — beautifully, stubbornly, creatively.
And because the ethics of inclusion aren’t just about fairness. They’re about possibility — the possibility of building AI that reflects the full range of human minds, across every age and stage of life.
💬 Questions to Ponder
Have you ever learned a hard skill later in life — especially one you never expected to enjoy? What was that experience like for you?
Do you still enjoy doing things the “hard way” — like writing your own code — even when AI can do it faster? Why or why not?
What keeps your brain sharp these days? Is it tech-related, creative, physical — or something else entirely?
How do you feel about automation and cognitive effort — do you ever worry that we’re outsourcing too much thinking to machines?
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AI Ethics & Inclusion
This post is part of my AI Ethics & Inclusion series, where I reflect on fairness, trust, and how AI can better serve people at every age.