Introduction: AI Is Changing the Future of Coding
Artificial intelligence is changing how people build software. Developers can now use AI to write code faster and complete tasks that once required much more manual work. But this does not mean coding skills are becoming useless. Instead, AI is changing which skills matter most. The ability to understand technology, solve problems, and work with AI is becoming increasingly important.
This shift is also changing how young people should prepare for the future. The goal should not be to create people who only know how to write code. It should be to create builders who can take an idea, use code and AI, and turn it into something real. Spencer Thomason, founder of Startup Hack, brings a workforce perspective to this issue through his experience as a lead developer, fractional cto, development team leader, and workforce development trainer.
How AI Is Changing Software Development
AI has made software development faster. Developers do not have to manually write every line of code anymore. AI can generate large amounts of code and help developers complete tasks more quickly. This allows developers to spend less time on repetitive coding work and more time thinking about the larger problem.
However, faster code generation does not remove the need for developers to understand programming. Developers still need to know what the generated code is doing. They need to understand algorithms, data structures, architecture, and the principles behind software development. AI can produce code, but developers still need to determine whether that code is good and acceptable.
Why Coding Fundamentals Still Matter
One of the biggest questions for parents is whether children still need to learn coding when AI can already write code. The discussion provides a clear answer. Coding fundamentals still matter because they help people understand and control what AI creates.
A developer who understands programming can look at AI-generated code and identify potential problems. They can understand how different parts of a system work together. They can also make better decisions about how a solution should be designed. This makes programming fundamentals more valuable, not less valuable, in an AI-assisted development environment.
Why AI Integration Skills Are Becoming More Important
AI is not only about generating code. One of the biggest challenges is integrating AI with existing systems. Businesses already depend on different applications, databases, platforms, and older technologies. Developers need to understand how AI can connect with these systems and work within existing environments.
The interview specifically points to mainframes as an example. A large percentage of the world still runs on mainframes, and connecting these systems with AI is not a simple task. This creates a need for people who understand both software development fundamentals and AI integration. That is where AI engineers and forward-deployed engineers can become especially valuable.
What Should Kids Learn in the AI Era?
Children should continue learning programming, but the focus should move beyond syntax. They need to understand how technology works and how to solve problems with it. Programming gives children a way to develop logical thinking and understand how software systems operate.
Problem-solving is especially important. Engineers need to reason, use logic, learn new concepts, and understand how different systems connect. Learning programming fundamentals, data structures, algorithms, and system integration can give young people the foundation they need to work effectively with AI later.
How AI Is Changing What Kids Learn
AI is already changing how coding education works. Instead of teaching traditional programming separately from AI, educators can combine the two. Children can learn the fundamentals of coding while also learning how to use AI-assisted development tools.
Students need enough technical knowledge to understand what they are asking AI to do. They also need to recognize when AI produces an incorrect result. AI has improved significantly, but it can still make mistakes. Teaching children how to verify AI output can help them become better users of the technology.
The Entry-Level Developer Job Is Changing
The entry-level technology job market is becoming more difficult. Young people can no longer assume that completing a course or earning a degree will automatically demonstrate that they are ready to work as developers. Companies increasingly want to know what candidates can actually build.
The interview also questions whether traditional college education is always necessary for someone entering coding. AI can help people learn faster, and practical experience can provide another path into the technology workforce. The key point is not that education has no value. The point is that people need practical skills and real evidence of what they can do.
What Employers Actually Want From Young Developers
For someone who wants to become a developer, one of the clearest pieces of advice is simple: start building. A portfolio can demonstrate practical ability in a way that a list of completed courses may not. Employers want to see what candidates have created.
Shipping a project makes that evidence even stronger. It is one thing to create an application during a course. It is another thing to deploy it on the web, try to get users, collect feedback, and figure out how to improve it. These experiences show that a person can take an idea beyond development and into the real world.
Coder vs. Builder: What Is the Difference?
The difference between a coder and a builder is important in the AI era. A coder may create small projects to practice programming. A builder starts with an idea, a problem, or an interest and tries to create something that people can actually use.
Builders also follow an iterative process. They launch a product, receive feedback, make changes, and release improvements. They continue this process until the product develops real traction. This approach combines technical ability with creativity, problem-solving, and an understanding of what users need.
Why Shipping Projects Matters
Shipping a project creates a completely different learning experience. A project that only exists on a developer’s computer remains mostly theoretical. Once people can actually use it, the developer has to deal with real problems.
Users may discover issues that the developer never expected. The developer then needs to understand those problems, fix them, and improve the product. This is why shipping provides valuable experience. It shows that someone can move from an idea to a working product that exists outside a classroom or local computer.
Degree vs. Real-World Projects
The interview presents a clear comparison between academic credentials and real-world experience. Imagine one candidate with a computer science degree but no experience shipping products. Another candidate may have built and launched five impressive projects.
The second candidate can demonstrate practical experience. Those projects provide evidence that the person knows how to build and ship. Academic knowledge can still matter, but employers also need people who can turn knowledge into working products. For development roles, real-world proof can become a major differentiator.
The Problem With “Don’t Use AI” in Education
There is also a growing gap between what students hear in education and what companies expect in the workplace. The interview highlights how students can be told not to use AI in school, while employers may expect candidates to understand how to use AI.
This creates a difficult situation for students. Instead of simply telling them to avoid AI, education can focus on teaching them how to use it intelligently. Students need to understand what AI can do, where it can fail, and how to verify its output. This approach can prepare them for the actual technology environment they will encounter in the workforce.
AI Creates an Opportunity for Young Entrepreneurs
AI also creates an opportunity for teenagers and young adults to build businesses. AI tools allow people to experiment and create products faster. The important distinction is how they use that speed.
Someone can use AI simply to complete tasks. Another person can use AI to build faster while also learning faster. The second approach can help create stronger engineers because they continue developing their own knowledge while using AI as a powerful tool.
A Practical Path for Kids
Different age groups can approach technology in different ways. For children around 10 to 12, the focus can remain on fundamentals, coding logic, games, and projects. The goal is to make building enjoyable and help children become comfortable creating things.
During middle school, students can move into real programming languages such as Python and learn software development. During high school, they can begin focusing more heavily on AI-assisted development and more sophisticated applications, games, and software. Independent projects can then help students develop confidence and practical experience.
Follow Their Interests
Children are more likely to stay engaged when they build things they actually enjoy. Some children are interested in games. Others like robotics, websites, or hardware. Those interests can become starting points for learning technology.
The interview emphasizes following children’s interests rather than forcing every child into the same path. A child who enjoys games may eventually move toward programming. Another child may become interested in hardware and AI servers. As children grow older, these interests can naturally lead them toward more advanced programming and technology.
Conclusion: The Future Belongs to Builders
AI is not making coding irrelevant. It is changing the role of the developer. Syntax may become less important, while problem-solving, system understanding, integration, and technical judgment become more important. The strongest developers will not simply ask AI to write code. They will understand the technology, verify the results, and use AI to build and learn faster. For children and young developers, the best preparation is to learn the fundamentals, build real projects, ship them, collect feedback, and improve them. The future will favor people who can turn ideas into working products. This builder-focused mindset also reflects the practical technology and workforce perspective behind startuphakk, where the emphasis is on using modern AI while understanding how to build real software.
