🔥 The hottest programming language in 2025: English
AI is reshaping software engineering by automating routine tasks, shifting focus to critical thinking, and unlocking career and entrepreneurial opportunities.
AI is changing the way we work in software engineering. Routine tasks that once consumed hours of our time are now being automated, freeing us time for “something else”.
Coding itself is evolving into something more accessible and faster, emphasizing critical thinking over memorization. Developers who don’t understand this will lag behind the rest of the world.
⭐ In this post you'll learn
How coding is shifting from syntax memorization to natural language programming.
How experienced developers can still leverage their knowledge
How AI lowers technical barriers, opening up entrepreneurial and career-advancements opportunities
🕐 Our working hours are highly occupied by undifferentiated work
In software engineering, much of our time is consumed by tasks that don’t directly contribute to innovation—tasks like taking meeting notes, fixing syntax errors, and managing JIRA tickets. Jeff Bezos called this “undifferentiated heavy lifting.” While essential, these tasks don't move the needle on creativity or innovation.
AI tools are changing this dynamic. By automating repetitive work, engineers can refocus on understanding customer needs and delivering innovative solutions. Personally, I’ve seen this in action. I use Amazon Q in IntelliJ to instantly resolve syntax errors. I rely on ChatGPT and Cedric daily, generating test cases, scenarios, and even Tampermonkey scripts. These tools don’t just save time—they enable me to channel energy into solving meaningful problems. This shift not only accelerates productivity but also positions engineers for career advancement.
Software engineers are uniquely poised to benefit from this AI-driven revolution. With coding itself evolving, the advantage is even greater.
🔄 The rules have changed
Coding is no longer a barrier to entry. AI tools like ChatGPT make it easier than ever to move from idea to implementation. I’ve experienced a significant productivity boost—tasks that once took hours are now completed in minutes.
This accessibility is a double-edged sword. Basic coding skills aren’t enough anymore. Developers need advanced skills and substantial projects to stand out in a competitive job market.
Everyone’s baseline has gone up.
The good news is that zero to one is faster than ever. AI allows us to create working software rapidly, iterate on it, and gather feedback sooner. This agility transforms the way we build and ship products.
🤖 Shift to natural language coding
Coding is transitioning into a declarative paradigm. Instead of writing low-level code, developers now describe requirements in plain language, and AI generates the code. I’ve experienced this firsthand, using tools like Claude 3 to write code snippets, review outputs, and refine them into production-ready solutions.
This shift changes the game. Success depends less on memorizing syntax and more on critical thinking, project planning, and clear communication with AI tools. It’s about explaining the “why” and “what,” rather than just the “how.”
🧠 The benefit of experience in the new era
This isn't to say a new grad will take the job of a senior engineer.
Experience is no longer just about knowing how to do something, as proper guidance can enable almost anyone to accomplish tasks. Instead, experience is about anticipating the types of challenges you'll encounter and understanding the solutions that have been implemented in the past.
LLMs make you move faster, but moving faster in the wrong direction serves no purpose.
Experienced engineer will identify the wrong options before they even start to climb those ladders.
🌟 Opportunities for developers
AI tools empower even beginners to build complex applications. This levels the playing field but also raises the stakes. Developers who leverage these tools can stand out by demonstrating high-level application design and problem-solving skills.
For instance, I’ve used LLMs to quickly generate and refine integration tests, speeding up code reviews and ensuring better quality. These tools help me focus on architecture and logic—areas that truly showcase expertise.
Career enhancement
AI accelerates the creation of impressive projects. Using LLMs, I’ve built scripts, iterated on complex workflows, and enhanced code reviews, all of which add value to my brag docs and portfolio.
These projects bridge the gap between beginner skills and professional expectations, making resumes more impactful.
Entrepreneurial opportunities
With technical barriers reduced, developers can identify real-world problems and build software solutions more easily. Whether it’s creating a business or demonstrating technical capabilities, the opportunities are immense. For example, rapid prototyping with AI allows us to test ideas, gather feedback, and iterate faster than ever before.
🎯 Conclusion
AI tools are reshaping software engineering. They automate the mundane, amplify productivity, and open doors to new opportunities.
I encourage developers to embrace these tools, become proficient on them, and focus on meaningful projects. This change is positive, you’ll do more meaningful work.
Before, a new grad who knows how to code was getting a job. Now it's not the case anymore. Companies will value more than ever problem solving and your thinking process. They will value the problems you have already solved and your pattern matching with past experiences.
Being able to translate thoughts to code? That's a commodity now.
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. Sometimes we are so caught up in something that is working like your successful newsletter that we don't have time for something else. This shows Jordan had a big opportunity here!
Great article. From engineers to leaders, leveraging Generative AI is a must in today's fast moving world. By the way, I'm glad I recently doubled own on my English :)
Rapid prototyping is where I most benefited as a senior engineer from AI. I was tasked with creating a POC using Graph Databases (Amazon Neptune) for one od our features. But as someone who never worked with GraphDBs, I had to realize it is an entirely different world, with separate programming languages and
Db implementations. ChatGPT helped me ship a starter POC to validate some basic assumptions without learning an entirely new language.