AI Tools: The Era of Education is Over, It's Time to Experiment
The last two decades were defined by continuous education, but with the rise of AI, traditional learning methods are no longer sufficient. The author argues that success today lies not in theoretical courses, but in hands-on practice, experimentation, and building a personal connection with technology.
The last two decades can be framed as the "era of education." Millennials grew up on their parents' academic expectations and the familiar path: army, trip, bachelor's degree. Around the years 2017–2020, the concept of "Life Long Learning" also took root, and with it grew an entire industry of courses and training that offered professional studies in small pieces. Since then, the equation has been simple: identify the changes in the market, map the gaps in knowledge and capabilities they are expected to create, and try to bridge them through learning.
Now, as the AI storm sends arms into almost every field, the instinct is to go and learn AI. But perhaps for the first time, we are facing a challenge that cannot be solved through learning alone. Learning is no longer necessarily the gap, nor is it necessarily the solution.
A year and a half ago, I decided to start doing Vibe Coding. It sounded so complex to me that the task became a monster I was afraid to touch. I assumed that since I had never written a line of code, it would take me a long time to learn. But if there is a muscle I have developed well in a decade of entrepreneurship, it is the ability to simply get started. I followed the relevant feeds, learned concepts, read about people who build, paid for Claude and Gemini, and took Anthropic courses. And then something surprising happened: I discovered that it is much easier to build than I thought. Every time I encountered a difficulty, I simply asked the tool, and it answered in a clear, accessible, and simple way. It was amazing to see how quickly one can turn an idea into a link that can be sent to friends.
As the models improve, so does their ability to make themselves accessible to everyone. And thus, almost anyone can learn AI on the go. A study published by Anthropic on June 16, 2026, after seven months of tracking 400,000 sessions of Claude Code users, found that user success depends mainly on their level of expertise in the field of the task they performed, and not on their level of knowledge in programming. The gap between users from computer professions and users from other fields was small throughout the study period. It has never been so accessible to learn something new.
So why does the demand for AI courses continue to grow? There are several reasons for this. The first is that AI is scary. And it is also developing rapidly, so the feeling is that one must learn. The second is that the hype has already reached the labor market. Company announcements, layoffs, and organizational changes reinforce the feeling that the ability to work with AI is becoming a threshold requirement in many professions. And the third reason is that most of us just need a framework. Me too. But the main factor that separates those who live AI from those who remain outside the event is actually persistence and joy. Frequent changes make it difficult to persist over time, and to keep up, curiosity for experimentation and creation is required. In other words, it is harder to stay on the AI train than to get on it.
For persistence, one can find an answer through frameworks and communities. But how do you create joy? Until today, we could ask ourselves what we like to do, what we are good at, and what interests us. AI also requires a lot of experimentation and practice – in formulating an idea, planning, characterization, integration, control, and correction. It is not natural for everyone, and not everyone enjoys it. Is it possible to adopt AI without "loving it"? Probably yes, but not everyone will be at the same point on the adoption scale, and that is okay. But our place on the scale is not a decree of fate. Joy comes with action. And this is the heart of the matter: to make time, to despair, to try again, and to develop every week a little more the personal connection to AI through daily tasks.
I have read enough predictions about AI in the last year that were refuted earlier than expected, so I will not end with another prediction. But as long as AI is here, developing and creating resonance, make time for it and increase joy. Maybe AI will like you back too.
Adi Sarga-Ken is an entrepreneur and an expert in curriculum development and career management.
