The technology industry has a hiring problem that nobody talks about honestly enough: it is not a shortage of people who want tech jobs. It is a shortage of people who arrive at the starting line of a tech career with the foundations that make them genuinely ready to grow into the roles that matter.
And that shortage, more often than most people in the industry acknowledge, traces back to choices made — or not made — in high school. The student who arrives at a computer science degree having already built things, having a genuine feel for mathematical reasoning, and having engaged seriously with the hardest academic material available to them is in a categorically different position from the student who arrives with a vague interest in technology and a plan to figure it out from there.
This is not an argument for pressure or for treating childhood as career preparation. It is an honest account of why the academic foundation built during secondary school matters more than the tech industry typically acknowledges — and how students who are genuinely interested in tech careers can make choices that compound into real advantages.
Why the Tech Industry Is Actually About Foundations
There is a persistent myth in popular discourse about technology careers that goes something like this: because the field changes so fast, specific knowledge becomes obsolete quickly, and what matters is the ability to learn rather than any particular body of knowledge. This myth contains a kernel of truth and a significant misleading implication.
The kernel of truth is real. The specific frameworks, languages, and tools that are dominant in industry do shift, and the practitioner who cannot learn new things will struggle. The misleading implication is that foundational knowledge — mathematics, logic, computer science principles, systems thinking — is somehow in the same category as specific tool knowledge, and therefore equally disposable.
It is not. The practitioners who consistently outperform across multiple technology cycles and multiple tool generations are the ones with deep foundations. They can pick up a new language quickly because they understand what languages are and what problems they solve. They can debug novel systems because they understand how systems fail. They can design solutions because they understand the mathematical and logical structures that solutions are built from. These foundations are slow to build and long-lasting in their value — and they are built primarily through the kind of sustained, rigorous academic engagement that secondary school and early university provide.
The Advanced Coursework Question
For students serious about technology careers, the question of how much academic challenge to take on in secondary school is one that deserves a direct answer: more is almost always better, up to the point where you are genuinely overwhelmed rather than productively stretched.
The most relevant advanced coursework for tech career preparation includes mathematics at the highest level available — calculus is important, statistics is important, and any exposure to discrete mathematics or linear algebra is genuinely valuable for understanding the mathematical underpinnings of computer science. Computer science courses at any level are worth taking if available. Physics builds the kind of systems-thinking and quantitative intuition that transfers broadly into engineering and development contexts.
The ranking of these exams and coursework by difficulty is less important than whether you engage with them genuinely. A student who takes AP Computer Science A and actually builds the understanding of object-oriented programming, algorithms, and data structures it offers has done something valuable. A student who memorises enough to get a good grade without internalising the underlying principles has a line on their transcript and a gap in their preparation.
The value of advanced coursework for tech career preparation is not primarily about the signal it sends to universities — though that signal matters. It is about the substantive preparation it provides for the technical depth that university-level computer science and engineering programmes require, and that the tech industry subsequently expects.
How the Academic Landscape Is Shifting
The academic pathway into technology careers is not static, and students planning their high school trajectory in 2025 and 2026 are navigating a landscape that has shifted meaningfully from what it looked like even a few years ago.
Advanced placement courses in mathematics, computer science, and related subjects have been evolving their curriculum to reflect changes in how the industry and universities think about the foundational skills students need. Understanding these changes — not just which courses are available, but what the current versions of those courses actually emphasise and how they align with university expectations and industry needs — is part of making good academic decisions.
The integration of AI and machine learning concepts into even introductory computer science curricula reflects a real shift in what the industry expects practitioners to understand. Students who encounter these concepts at the high school level, with enough mathematical foundation to understand them properly rather than just interact with them as black boxes, are better positioned for the direction the field is moving than those who encounter them for the first time in a university course.
Similarly, the emphasis on data — data structures, data analysis, data literacy — that has grown substantially across technology roles means that the student who has engaged seriously with statistics and mathematical reasoning is in a better position across a broader range of technology careers than the one whose quantitative preparation is narrower.
Choosing the University Programme That Sets You Up
The university programme choice is where the high school preparation either compounds into real advantage or fails to connect to an environment where it can grow.
The technical programmes that produce the strongest technology practitioners share some characteristics worth understanding. They have rigorous mathematical and theoretical foundations that ensure graduates understand computer science, not just how to use current tools. They have substantial project and applied work that develops the ability to actually build things, not just understand them abstractly. And they have connections to industry — through research opportunities, internships, and faculty with real industry experience — that give students exposure to how the field actually operates outside of academic contexts.
The programme name matters less than the curriculum. A strong computer engineering programme, a mathematics and computing degree, or a software engineering programme at a research-active institution can all provide the foundation that a technology career needs. The question to ask is: what does a graduate actually know how to do, and does that align with what the industry actually requires?
This matters particularly because the gap between the best and average technology programmes at the university level is large. Graduates of the strongest programmes arrive at their first jobs ready to contribute; graduates of weaker programmes often need eighteen months to two years of on-the-job learning to reach the same point. That gap compounds — earlier contribution leads to earlier advancement leads to a career trajectory that diverges significantly over time.
The Self-Directed Learning That Separates Good From Great
Whatever academic foundation is built through school and university, the technology practitioners who advance furthest consistently have one additional quality: they learn continuously outside of formal structures.
This quality is partly temperamental — some people are genuinely driven to explore things they do not understand, while others are more comfortable operating within the known. But it is also a habit that can be built deliberately, and building it during high school and early university establishes patterns that compound over a career.
The specific content of self-directed learning matters less than the habit itself. The student who picks up a new programming language over a summer because they were curious what it could do, or who works through a mathematics textbook chapter because a concept was unclear, or who builds a project that required them to figure out things they had not been taught — these students arrive at every stage of their career with more than the formal curriculum provided. And in a field that changes as fast as technology does, that excess is precisely what keeps practitioners relevant as the landscape shifts.
The Honest Path
The honest account of the path from high school academic choices to a meaningful technology career is not a guaranteed formula or a set of boxes to check. It is a process of building genuine foundations — in mathematics, in computational thinking, in problem-solving under ambiguity — that make every subsequent stage of the career more navigable.
The students who take this seriously — who choose the harder coursework and engage with it genuinely, who build things independently, who develop the habit of learning beyond what is required — consistently outperform those who do not, at every stage of the journey from student to professional.
This is the aspect of tech career development that the industry’s recruitment conversations least often acknowledge: the most durable advantages come from foundations built long before the first job search begins. The professionals who get the most from technical training, certification courses, and continuing education later in their careers are the ones who arrived with the foundations to make that learning connect to something real. Building those foundations is a high school and early university project that pays dividends for the entirety of what follows.