So what do we do with this moment? Here are a few takeaways I’ve seen play out firsthand:
This is not a passing trend
Think of it like the early days of electricity. At first, people just replaced steam engines with electric motors, same workflows, slightly better efficiency. But over time, the factory was redesigned around electricity application. We’re now at the AI equivalent of that phase.
The true power of electricity wasn’t just in generating energy, but in enabling new tools, light bulbs, refrigerators, and everything else that changed daily life. AI is similar. Its value shows up when people build useful things with it. Applications, tools, and services that solve problems in new ways. This will be the next turning point.
Coding became the perfect entry point for AI
Interestingly, coding emerged as an early AI success. Tools like Bolt, Cursor, and Lovable saw quick adoption, while big AI players released their own coding agents, such as OpenAI’s Codex, Anthropic’s Claude Code, and GitHub Copilot Agent. They are pushing AI-powered development into the mainstream. But why coding?
There are few main reasons:
- Code has rules and structure, which makes it easier for models to generate syntactically correct output.
- Code can be tested, if it runs, it’s probably doing something right.
- Coding is one of the fastest ways to create real impact, because it leads to working software that people can actually use. It’s different from math or science, where even a great idea might take years of testing, validation, and approval before the world sees its value. With code, you can build something today and have someone using it tomorrow.
Additionally, abundant training data of public code repositories accelerated learning.
But AI-generated code isn’t always good code. It’s trained on tons of open-source code, and not all of it is high quality. A lot of that code might be outdated, messy, or full of workarounds. Even if the AI gives you something that runs, it might not be secure, clean, or built to last. That’s why human engineers still matter, we’re the ones who check the work, fix the rough edges, and make sure it actually holds up in the real world.
Accessibility is the real revolution
LLMs didn’t just get smarter, they were used by everyone. That’s what changed everything. You didn’t need to understand machine learning or write custom models anymore. AI is now in the hands of every engineer, designer, marketer, and student. That’s what makes this the turning point. You no longer need to build the model. You just need to use it well and build with it.
You can’t ignore it anymore.
If you’re still treating AI like a nice-to-have, you’re already behind. It’s not a future skill. It’s a now skill. And it’s becoming a baseline expectation for how teams work, think, and build.
You don’t need to master AI to keep up. But you must try it, see what it’s good at, and where it still messes up. Think of it like a fast but forgetful teammate, you’ll get more out of it when you know when to help steer it.
AI isn’t here to steal your job. It’s just another tool. The people who’ll do well are the ones who aren’t afraid to try, learn, and adjust. Keep an open mind. Make it work for you.