Working with Artificial Intelligence: What Can I Do as an Individual?

We are living in the age of artificial intelligence, and there is very little left in our daily lives that does not involve AI in some way. Ethical questions around AI have started to be discussed. Laws related to AI are even beginning to appear. While we were dreaming about what we could do with artificial intelligence, discussions suddenly shifted to "Will AI take our jobs?"

So how much are we, as individuals, actually part of the AI age? Or let me ask it this way: what have we personally done for AI today?

In this post, I want to explain where I stand-not on the side that "watches from afar," but on the side that "does what it can." Not big moves, but small and honest steps.

Thousands of starfish have washed up on the shore.
A child is picking them up one by one and throwing them back into the sea.
An adult says:
"You can't save them all. What difference will it make?"
The child throws another starfish into the sea and says:
"It made a difference for that one."

You've probably heard the starfish story at least once. If not, it's written above. This story always reminds me that even small things matter. When it comes to AI, whenever I open LinkedIn, I see some people in my personal network doing really good work. To be honest, I "envy" them. Because they took a step that changed the life of a starfish washed onto the shore. This "envy" is not a bad feeling-it's more like a direction sign.

When I look at myself, I see that I want to do something, but I don't actually do it. The kind of situation people describe as "all talk, no action." Luckily, I also realize that I'm not alone when I see demotivating or fake-smiling comments under those works (hey friends, I'm talking about you-and people like me).

Let's be honest with ourselves: artificial intelligence is a big opportunity for us. An opportunity to finally catch a development train that we failed to catch in the past. A chance where every step-big or small-will matter.

With this post, I'm questioning where I personally grab onto this opportunity, and I'm leaving a note for myself in history. Maybe it also gives you a chance to question yourself.

Does Working with AI Mean Training Models?

First of all, "working with artificial intelligence" does not only mean training models, fine-tuning them, or optimizing an existing algorithm.

In my opinion, these are the hardest, most valuable, and most scientific parts of AI. Still, there are many areas where you can honestly say, "I work with AI."

Infrastructure Instead of Big Breakthroughs

During the gold rush in the United States in the 1890s, the people who made the most money were not the gold miners. They were the ones selling shovels, pickaxes, and mining equipment. Today, the most valuable material in the AI field is datasets that are cleaned, filtered, and labeled by humans. For me, this is what finding a realistic lane in AI looks like: understanding where value is created, rather than trying to build everything from scratch.

Why Is Data the Real Value?

The work done by people who develop AI algorithms is very valuable, but without data, it doesn't mean much. For example, the word2vec application developed by Tomáš Mikolov and his colleagues, mentioned by Cem Say in his TEDx talk, could have remained just a theory-or failed to create a real impact-if it didn't have Google's data behind it.

word2vec is an application that converts words into numerical vectors, allowing words to be manipulated mathematically. It is based on the idea that words with similar meanings tend to appear close to each other.

Similarly, without platforms like Stack Overflow and GitHub which I'm sure my developer friends follow the development approach known as Vibe Coding would not have progressed this fast.

Does Everyone Have to Be a Scientist?

On a global scale, the number of people with a scientist-level mathematical background is not very high. It wouldn't be my place to evaluate this specifically for Turkey. But I also don't think we are in a very strong position among OECD countries in mathematics or verbal skills. At this point, I remember something Ali Nesin once said.

Ali Nesin: If you start learning something by asking "What will this be useful for?", you can only become an engineer. You repeat what others have already found. You can build an electric car or a computer because they already exist. But you cannot invent the computer, the internet, or go to space you can only go after others have gone. To discover something new, you must deal with topics whose usefulness you don't yet know. Because you never know what will be useful, or when.

I can't speak for anyone else, but you don't need to be a scholar to clearly see that something is wrong. And this problem doesn't seem easy to fix anytime soon. When you think about this, how do you feel?

Since I won't be the one making a big breakthrough in AI, I think it makes more sense to focus on what I can do. And that realization alone helped me define my own lane more clearly.

For example:

I don't have the mathematical background to deeply understand LLM (large language model) architectures,
I don't have the knowledge, experience, or financial resources to train a model from scratch,
I also don't have the experience or budget to fine-tune an existing base model for a specific need,

But not having these things does not stop me from:

trying to learn the mathematics behind LLMs, even if it's late,
trying to understand ML-related structures,
running conceptual experiments,
creating customized datasets,
doing comparison (benchmark) studies to see which model is "better" from my point of view.

I'm trying to take a step by opening a new lane for myself and focusing on work I can realistically do.

As I think about my own place in AI, I can't ignore how that place is shaped by the country I live in.

Toward the end of 2025, a local LLM model called "Kumru" was released. I was incredibly happy. I imagine the people who developed "Kumru" felt the same justified pride as the engineers who worked on the "Devrim" automobile. But the outcome was the same for both products. They were criticized much more harshly than they deserved, and people shared AI-generated memes just to make themselves look funny.

I think these reactions come from "jealousy," "perfectionism," or more simply, "ignorance." When it's something that comes from us, we are often the first to attack it. There must be a solid sociological explanation for this. If a sociologist is reading this, I'd really like to hear their thoughts. At least if the problem is diagnosed, maybe a treatment can be applied for future generations.

Local Production, Good Intentions, and Real Life

Before wrapping up, I don't want to skip a topic that naturally connects to this. Because individual production is shaped not only by intention, but also by access, resources, and real-life conditions. In 2026, a new Customs Tax update entered our lives. This update was designed to protect local tradespeople and producers. However, even before the first month of the year ended, there were already news stories about it being misused in bad faith.

I can say clearly that while preparing this legal update, no one really thought about people doing scientific work, makers who they called "inventors", or students who want to buy materials to experiment and apply what they've learned. None of the required products are mass-produced in Turkey. The number of businesses that can buy separate parts and assemble them is also very limited. In short, the saying "go farther and fare worse" fits the situation perfectly.

Still, I'm one of the optimistic ones. I see this as an opportunity for those who understand it. And for those who take action. When constraints become unavoidable, creativity is no longer optional, it becomes necessary.

For those who still don't get it, let me give an example from cinema. When shooting a film, you can't include everything exactly as you want. A film has a structure and a story. On top of that, there are censorship rules applied by countries for various reasons. Directors still manage to say what they want without breaking these rules. They imply, speak indirectly, or use symbols. They use their creativity.

Working with AI is a bit like that. I believe that people in Turkey who work with AI or want to can find creative, even radical solutions despite all the difficulties.

Closing Words

To sum it up, if someone truly wants to do something, they will find a way and work with AI. I've chosen a path for myself and I'm continuing on it. Maybe I'll have to turn back from a dead end, or maybe I'll end up somewhere completely unrelated but I don't plan to stop walking.

Maybe in this age, the real issue is not being the fastest runner, but being the one who keeps walking. I'll keep walking from here.

Sources and Further Reading

Below are some people and initiatives in the AI field that I follow and learn from.

People and Companies I Follow

Reading Resources

The Intelligence of the Future: Bias-Free Robots | Cem Say | TEDxIstanbul A talk by Cem Say on artificial intelligence.
Mathematics and Philosophy - 1+1=1 | 19.10.2021 A TV program with Ali Nesin and Dücane Cündioğlu.
Word2vec An application developed by Tomáš Mikolov and his colleagues that represents words as vectors.