Yesterday morning, while getting on the Metrobus at Zincirlikuyu, everyone had the same thing in their hands: a phone. Some were looking at stock screens, some were adjusting the times of their appointments. The young man sitting next to me on the bus was doing a final check of the presentation he had prepared for a job interview.
At one point he stopped and leaned back toward the screen. His astonishment was obvious. The presentation’s design, its text, even the answers to the tough questions he might be asked, had all been produced within a few minutes by the GenAI he was using.
Until a few months ago, we were working by struggling with AI. Now we simply describe the result we want, and a task that would have taken us days to grind through is finished in a matter of minutes. Incredible. Yet for many of us, especially those using free versions, the AI in our minds is still the 2023 version: confident but wrong. In reality, over the past year, even if they still get lazy from time to time, models have generally started to write better and reason better.
What is more, this improvement has accelerated further. Because AI is now also helping with the processes that improve itself, which increases the momentum. So in the race between models, the speed of progress is no longer just a matter of more data, more money, more chips. AI is scaling, and the intelligence we build is now participating in improving its own intelligence.
As individuals, it has become mandatory for us to spend at least one hour every day using these tools to experiment with doing our real work better, and to keep discovering the expanding possibilities day by day while continuing to develop our skills. The issue is not simply writing a report faster. We need to track which parts of our work are now being automated, and which parts will remain under human accountability and responsibility.
And there is the opportunity side. Even a small neighborhood business can run analyses that only large companies used to be able to do. A student can work as if they had a personal tutor. The cost of turning an idea into a prototype is dropping. In other words, this wave is not only reducing work and employment; it is also opening doors to entry, intensifying competition, and accelerating the creation of new small businesses.
Perhaps the antidote to the frightening employment scenarios of the new era is that, with these capabilities, everyone can create new micro-enterprises and produce value at a larger scale than before. But as long as the critical source of that broadly produced value is GenAI, the lion’s share of it will continue to be transferred to the center. And that is a major problem.
States, especially countries above a certain scale like Türkiye, must develop their own GenAI infrastructure no matter what. It is as if electricity has been discovered and the electric motor has entered industry, but electricity can be produced only in two or three countries worldwide. We are facing something far worse than that scenario.
In terms of knowledge and skill, AI has already surpassed humans by a large margin. If there are any remaining areas, it will surpass us in those soon as well. The core issues of the new era are whether people can make their companies, education, and sense of justice keep pace with this speed, and whether countries can manage the global relationships of dependency.
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