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When I was 12 years old, my parents enrolled me in a kids’ coding class at the YMCA. This was 1983—before the internet—typing code from magazines like Compute! into a computer with a green-on-black screen and seeing what it did. And the experience would go on to shape the course of my life.
I’ve been in software for more than 30 years, most of them at Intuit. I started there as a software engineer in 1999 and today am its chief technology officer. In that time, so much has changed about this profession—from the way we mentor to the way we code. Today, agentic AI technology can take high-level directions, look at an existing code base, pull in the right set of data, do a web search to look at the current ecosystem, and then plan out and perform a sequence of actions normally expected from a junior engineer. This provides a true end-to-end “done-for-you” experience. Put simply, I can see why people might feel like everything is changing for software engineers.
But even in this fast-changing field, there are throughlines. I may not be working in BASIC on the same Apple 2E from coding camp, but the foundational skills that help me break down complex problems, ask the right questions, and code durable solutions are as important as they’ve always been. No one needs me to describe how this industry has changed. Instead, here are three things that haven’t.
1. The “why” is as important as the “how”
Strategic thinking has long been part of a software engineer’s job, to go beyond coding to building. Working in service of a larger purpose helps engineers develop more impactful solutions than simply coding to a set of specifications. With the rise in AI-assisted coding—and, thus, the ability to code and build much faster—the “why” remains at the forefront. We drive business impact by delivering measurable customer benefits. And you have to understand a problem before you can solve it with code.
As machines tackle the parts of the job that deliver relatively standard pieces of a solution, the other part of an engineer’s role—that of a cognitive architect—takes on new weight. The key differentiator lies in the ability to effectively use AI to augment human capabilities. Time previously spent on routine coding tasks can now be devoted to strategic decisions, allowing engineers who are just starting out to practice critical thinking skills earlier in their careers and offering seasoned engineers more opportunities to leverage their expertise for competitive advantage.
2. Curiosity is key
The best engineers are inherently curious, with an eye for detail and a desire to learn. Through the decades, that hasn’t really changed; a learning mindset continues to be important for technologists at every level. I’ve always been curious about what makes things tick. As a child, I remember taking things apart to see how they worked. I knew I wanted to be an engineer when I was able to put them back together again.
The continuous advancement of technology makes it impossible for the day-to-day work of a junior engineer to look the same year over year. In the 1980s, an entry-level coder might have been tasked with writing simple programs in assembly language, but by the ‘90s this was made nearly obsolete by higher-level languages like C++. Similarly, in the early 2000s, we needed humans to manually parse and clean large files, and by the 2010s we could automate data cleaning with scripting and ETL tools.
AI may be exponentially accelerating the pace of change in our day-to-day work, but those who enter the field with curiosity and a hunger to make things more efficient, effective, and intuitive will continue to find success, even as the way they apply that curiosity continues to shift.
3. Leadership skills aren’t just for managers
Not every great coder aspires to be a people leader; I certainly didn’t. I was introverted growing up. But as I worked my way up at Intuit, I saw firsthand how the right leadership skills could deepen my impact, even when I wasn’t charged with leading anybody. I’ve seen how quick decision making, holistic problem solving, and efficient delegation can drive impact at every level of an organization. And these assets only become more important as we fold AI into the process.
Communication skills, for example, have taken on new significance. When we convey all the relevant information needed for a counterpart to provide an adequate response—whether it’s a colleague, a customer, or AI—we reach better outcomes faster. It’s always been critical to understand the context around a problem in order to choose and code the right solution. But engineers now need to be able to adequately explain that context to AI in a clear, direct way to efficiently delegate portions of their work, in order to leverage AI to operate more efficiently and increase productivity internally. At Intuit, we see up to 40 percent faster coding using generative AI code assistants. Communication skills are key in getting these outcomes—and, as a result—driving faster innovation for customers.
AI is changing the trajectory of software engineering. And as long as we continue to practice the foundational skills our industry was built on, it will be as rewarding and exciting a career in the future as it was for me 30 years ago.
Alex Balazs is CTO at Intuit.
