Too many new AI things — so I stopped learning

It’s not giving up.

It’s that I finally realized: faced with the new tools, features and concepts that pop up every day, I don’t need to seriously learn every single one.

This is a field that never runs short of fresh concepts and new tools:

AgentKit, Codex, Google Antigravity, Gemini Spark, Managed Agents, MCP, A2A, Agent Harness…

Every one looks important. Every one seems to say “learn me now or fall behind.”

But I calmed down and thought about it:

Do I actually have a problem right now that this solves?

A lot of the time, I don’t.

So I made myself a simple set of levels:

Level one: know what it is. Spend 15 minutes, read an introduction, and get a rough idea of what problem it solves. For concepts like MCP, harness and guardrails, knowing what they’re talking about is enough for now.

Level two: try it once on a real task. Spend 1–2 hours using it to solve a small problem of my own. For example Codex, Open Design or new skills: not watching tutorials or playing with demos, but trying it on a real task. Then decide: does it have anything to do with me?

Level three: make it part of my own workflow. Spend 10+ hours really building it into daily work. For example my content publishing flow, my debugging flow, my product development flow. Only problems that keep coming back like these are worth learning deeply.

Then I found that most new tools only need level one. A few deserve level two. Very few need level three.

Because AI changes too fast. A lot of tool-level knowledge may be out of date before you’ve really used it.

So the steadier way isn’t to chase tools, but to come back to my own needs:

What tasks do I have right now? What bottleneck have I hit? Can this tool solve it?

Let needs lead the learning. Not tools.

“I’ll learn it when I have a real problem” isn’t necessarily lazy.

Sometimes it’s a strategy.