Why I Attend Fewer AI Events Than I Used To

I used to enjoy attending AI conferences and meetups. Whenever I saw an event about large language models, AI agents, startups, or AI applications, I would sign up. I also followed countless online interviews, podcasts, and technical forums. Partly because I wanted to stay up to date with the latest developments in the industry. Partly because I hoped to learn from people doing remarkable work.

But at some point, I realized I was attending fewer and fewer of them. Not because AI has become less exciting. Quite the opposite. AI is evolving so quickly that I've come to realize something: Attending a thousand talks is far less valuable than building one real project yourself.**

Knowledge Is Getting Cheaper. Practice Is Becoming More Expensive.

In the past, the greatest value of industry events was access to information. The latest trends, technical insights, and practical experience often couldn't be found anywhere else.

Today, that's no longer true. A great keynote is usually summarized in articles within days. Official blogs, YouTube, LinkedIn, and X are full of detailed analyses. You can even ask AI to synthesize perspectives from multiple sources and produce a high-quality summary in minutes.

Knowledge has become incredibly inexpensive. What has become truly expensive is something else: **Time.** Attending an in-person event often means:  Two or three hours at the venue. Travel time. Rearranging your schedule for the day.

If the content turns out to be average, you've lost more than two hours. You've lost what those two hours could have created. That is the real opportunity cost.

Growth Comes From Solving Problems, Not Collecting Knowledge

The biggest change in how I learn didn't come from AI. It came from building my own products. When I was only listening to other people's experiences, I always felt I had learned a great deal. But once I started developing real products, I discovered that reality is far messier than any conference case study.

Questions like:

• Why is address recognition still inaccurate?
• Why do users visit but fail to return?
• Is an AI recommendation engine really worth the additional token cost?
• Will merchants continue updating their content over time?
• What kind of data actually matters to community managers?

No conference can give you these answers. These questions only appear when real users begin using what you've built. And every time you solve one of them, you gain more than an answer. You develop judgment. That kind of growth cannot be replaced by any course or presentation.

AI Is Changing the Way We Learn

For years, the common advice was simple: **Learn first. Practice later.** Because building things was expensive, we tried to master the theory before taking action.

But AI has dramatically lowered the cost of development, design, research, and experimentation. As a result, the learning process is changing. More and more, it looks like this: **Build → Encounter Problems → Learn → Keep Building**

Start first. When you don't know something, ask AI. When you hit a bug, research it. When users don't engage, improve the product. Learning is no longer a one-time transfer of knowledge. It's a continuous cycle of feedback and iteration. And that's where real growth happens.

How I Decide Whether an Event Is Worth Attending

This doesn't mean conferences have lost their value. I've simply added one question before I register: **Will this event provide something that's difficult to get online?**

For example:

• Meeting people I might collaborate with in the future.
• Having meaningful conversations with speakers about challenges I'm actively facing.
• Trying products that haven't been released publicly yet.
• Joining a community where genuinely valuable relationships can be built.

If the answer is yes, I'm still happy to go. Because the most valuable part of a great conference is often not the slides on stage. It's the conversations afterward.

But if the event mainly offers public knowledge, trend analysis, or familiar case studies, I increasingly prefer learning online. It's more efficient and better aligned with how learning works today.

In the AI Era, Knowledge Is No Longer the Scarce Resource

Information used to be a competitive advantage. Today, almost anyone can access the same knowledge within minutes. The real differentiator is no longer who knows more. It's who can turn knowledge into action.

Some people attend dozens of conferences every year and save hundreds of articles, yet never begin their first project.

Others build a small product, a simple website, or a modest automation tool—and by solving real problems, steadily develop stronger judgment, sharper execution, and better instincts.

Those capabilities cannot be acquired by listening. They are earned by building.

AI isn't just changing the way we work. It's changing the way we learn.

Perhaps the best learning path today is no longer: **Consume more.**

It's: **Build something. Hit real problems. Then keep learning.**

Because in the end, your rate of growth has never been determined by how many talks you've attended. It's determined by how many real problems you've solved with your own hands.