Learning programming is more than becoming familiar with a programming language. Picking a good programming language and learning its syntax, however, is (only) the first step.
And after these five stages, you gradually get a better hang of what a complete program looks like and moving from a basic programmer to a software developer and eventually a software architect, but there's still more: multiple programming languages, cloud platforms, DevOps tools, performance tuning, security best practices, domain specific topics, and on and on. Programming is a lifelong learning journey!
Today, with AI-driven code generators readily handling syntax, libraries and even framework wiring, simply knowing the pieces isn’t enough. The real differentiator - the true "holy grail" of programming - is deep domain expertise: the ability to understand your field’s unique challenges and translate them into robust, maintainable code.
(Nowadays with AI code generators abundant, those few topics are becoming more and more foundamental and less and less dominant on its own, more importantly, it's the sixth stage:
Whether you’re building real-time control systems in robotics, designing quantitative models in financial analysis, architecting generative workflows in automated design, or tackling complex data pipelines in bioinformatics, it’s your specialized knowledge that unlocks the highest value. By combining programming fundamentals with in-depth subject-matter insight you’ll be the one guiding AI assistants and custom code alike toward solutions that truly move the needle.
The same applies to designing a programming language - it's not just about syntax. In fact, a language can have extremely limited syntax (think of those node-based shader graphs in 3D software), yet still be powerful enough to express complex behaviors. What really makes a language useful is the combination of its syntax, built-in libraries, tooling, and the patterns you can build on top of it.