This spring, I asked AI to build me a tool to explore how public sources around the world talk about geopolitics. I wanted to see which themes come up in Asia, which in Europe, and which in the United States. Within a few minutes, I had a dashboard in front of me. Just a couple of years ago, it would have taken a team and a week of work. I didn’t write a single line of code. I gave instructions, and the machine did the rest. My first reaction was excitement: I can ask for more. And more. A new viewpoint, a new country, a new time frame, a new set of sources. Everything is just one command away.
But information alone doesn’t create anything
A large amount of information means very little on its own. AI can learn to find what is relevant, if it is guided well. But someone still has to give that guidance, to decide what to ask and what matters in each situation. That someone is me. This is where my journalism education and twenty-five years of experience come in. I can instruct AI to build a tool not because I know how to read the news, but because I know how to see the big picture. I can tell how things connect, what causes what, and what matters most. And I can explain it in a clear way. AI now produces information faster than ever, but it hasn’t made this skill unnecessary. It has made it the place where real insight is created.
Of course, this doesn’t apply only to my small geopolitical experiment. It applies to many organizations right now. There is more information, more signals, more options, and more viewpoints than anyone has time to take in. Still, someone needs to say which of them deserve attention. Sometimes that means spotting new market signals early. Sometimes it means seeing changes in the operating environment before they happen. Sometimes it means comparing strategic options or finding out which message really connects with different audiences. AI can bring more observations to the table than ever, but it still doesn’t know which of them matters most in a given situation.
This is where Miltton’s perspective gets interesting. We don’t see AI only as technology. We see it as a new way to explore the things we have always cared about: politics, business, society, and people. It’s not just about having more data. It’s about finding better ways to notice what deserves attention.
I’m not a developer, and I won’t become one. But I can look at ten different options and tell you which one is truly interesting, relevant, or useful. With this skill, I build tools that don’t just produce more options. They help people make better choices. That is an important skill in the age of AI.
When everyone has access to the same almost endless pool of information and the same tools, the difference is not who can get the machine running. The real difference is being able to say: this matters, and this is where we should go next. That is why the few-minute experiment from this spring didn’t stay just an experiment.
It turned out that others were also interested in how geopolitical themes are emphasized in different parts of the world, which patterns keep coming back in the discussions, and which important signals are easy to miss. As we developed the idea further with Miltton’s experts, the small experiment started to grow into something more lasting. Not because AI did everything. But because experts knew how to ask it better questions.
