If you’re worried about being displaced by AI in the near future or struggling to find a job after high school, your concerns are valid. It is believed that AI has already replaced 15% of jobs since 2018 and this trend is accelerating.
To understand why I’m writing this article, you need to know what I do. I teach high school students about Artificial Intelligence; four classes worth. I have also taught Computer Science courses for many years. I’ve spent over 22 years in technology, working with everything from web development to server administration, but teaching AI today is like the old saying about building the plane while flying it.
Here’s the truth: AI wasn’t rolled out with lots of thought on how students and teachers (and the general public) would use it. There was no proactive framework of any kind created for institutions to implement it or use it. It just happened. We started using it, and now we’re scrambling to create frameworks and documentation for teaching it. This is also affecting the teachers who are not Computer Science based, and how they are implementing AI in their classroom, but that is another story. The bottom line is that it just showed up one day and we started using it, those of us that could use it. So I’m going to do my best to address the question that’s on everyone’s mind, especially my high school students: What does AI mean for my future career?
Note: Throughout this article, I’ll use “AI” as a general term to describe computer systems that mimic human decision-making capabilities. While there are technical distinctions between AI, Machine Learning, Neural Networks, and Generative AI, for our purposes here, these nuances aren’t as important to understand as the job market impact.
The Question Everyone’s Asking
“Why should I go into Computer Science if AI will be able to do these jobs?”
“Why do I want to become a designer if AI can design on its own?”
“What will I do if I don’t have the skills to work in an AI driven industry?”
I hear these questions every week, especially from senior students. These are fair concerns. It’s hard to imagine investing years and money into a career that might not exist when you graduate from a post secondary school. The truth is complicated: AI will replace some jobs, but it won’t replace all jobs. At least not yet. And understanding the difference matters more than you think.
AI Today: The Current Landscape
AI models are everywhere; in our phones, web apps, and standalone services. Companies are racing to stake their claim in the market. Claude AI positions itself as better at coding in multiple programming languages. ChatGPT excels at documentation and image generation. Perplexity focuses on research papers. Magicschool.ai was designed with the educator in mind. No single system does everything well, but each is trying to carve out its territory.
For this article, I’m focusing on web development, programming, and design because those are the areas my students ask about most and where the impact is most direct. In my classroom, tasks that once took hours of development now take minutes with AI. The results aren’t always perfect, but they’re often accurate enough to be useful especially if you know how to write effective AI prompts (a topic for another time).
How AI Actually Works (The Version You Need to Know)
Having a basic understanding of how these systems work will help you predict what kinds of jobs they’ll affect. AI models create content by recognizing patterns in massive training datasets (training data for AI) billions of lines of code across multiple programming languages. They use your prompt (or existing code) to predict what needs to be built next. The more specific your input (prompt), the more accurate the output.
That’s really all you need to know. We’re not trying to build our own AI here we’re trying to understand how to work alongside it.
What AI Does Well
Having a simple understanding of how AI works helps to understand significantly what AI or LLM’s do well. AI excels at repetitive tasks and pattern recognition. If a job follows general rules and involves finding patterns in data, AI can probably handle it. Here are some examples:
In Web Development:
- Generating form validation code
- Building basic CRUD (Create, Read, Update, Delete) database operations
- Creating HTML and CSS for standard page layouts
- Configuring JSON or XML setup files
- Writing boilerplate code for common functions
Beyond Development:
- Data entry
- Basic bookkeeping and invoice processing
- Generating product descriptions at scale
- Creating routine reports (earnings summaries, weather updates, sports scores)
Before AI, these tasks fell to junior developers, data entry clerks, or administrative staff. They’re essential but repetitive. And that’s exactly where AI shines.
What AI Struggles With
Here’s what many people don’t realize: AI struggles with business logic and organization-specific rules. These rules are unique to specific companies or institutions, and they typically live in internal documents, proprietary databases, or institutional knowledge not in the public datasets that train AI models.
Let me give you a real-world example from education. AI can calculate a student’s GPA easily that’s just math. But determining scholarship eligibility? That’s complicated. It requires interpreting rules about GPA thresholds, extracurricular activities, residency requirements, and financial need. These rules change yearly, sometimes mid-year. They involve exceptions and special cases. They require understanding intent, not just following formulas.
Another example: legal compliance frameworks. A company might need to determine if a product meets FDA regulations, which vary by product type, ingredients, intended use, and recent rule changes. AI can provide general guidance, but the final determination requires human expertise and accountability.
AI struggles with:
- Dynamic, context-dependent rules
- Interpretation of intent
- Decisions requiring accountability
- Nuanced judgment calls
- Situations where the “right” answer depends on factors not easily quantified
The Middle Tier: Where Most of You Will Work
Let’s talk about something I haven’t mentioned yet; the jobs between “entry-level data entry” and “AI specialist.” This is where most of my students will start their careers, and this is where the picture gets more interesting.
Mid-level developers and designers aren’t being replaced, they’re being augmented. In my classroom, I’ve watched students use AI to generate a basic webpage structure in minutes. But then they spend the next hour customizing it, fixing the AI’s assumptions about what they wanted, integrating it with existing systems, and making sure it actually solves the client’s problem.
That’s the pattern I see emerging: AI handles the routine parts while humans focus on the creative, strategic, and interpersonal aspects. The developer who used to spend 60% of their time writing boilerplate code and 40% on architecture and problem-solving? Now they spend 90% on the interesting stuff.
The catch? You need to understand both domains. You need to know enough about coding to recognize when AI-generated code is wrong, inefficient, or insecure. You need design sensibility to know when an AI-generated layout misses the mark. You need to understand the business problem deeply enough to craft the right prompts.
This is the real answer to “why bother learning this if AI can do it?” Because understanding what AI does makes you exponentially more effective at using it. It’s the difference between someone who can use a calculator and someone who understands mathematics well enough to know which calculation to run.
Jobs Most At Risk
Based on what we know about AI’s strengths, here are roles facing the most disruption:
Administrative and Clerical:
- Data entry clerks
- Transcriptionists (meeting notes, audio-to-text)
- Basic bookkeepers (expense categorization)
- Appointment schedulers
Customer Service:
- First-line support representatives
- Chat support agents
- Call center operators (for routine inquiries)
Content Production at Scale:
- Generic product description writers
- Basic SEO content writers
- Routine news reporting (earnings, weather, sports)
Entry-Level Technical Roles:
- Manual QA testers
- Junior programmers (writing only boilerplate code)
- Code documenters
- Basic bug reproducers
Notice a pattern? These are roles where the work is highly standardized, follows clear rules, and involves minimal custom decision making.
Jobs AI Is Creating
Here’s the other side of the story: AI is creating entirely new roles across multiple industries.
AI Decision Designer bridges data science, policy, and UX design. This role designs how and when AI makes decisions, ensuring human oversight for accountability. Someone needs to determine: Should this AI approve loan applications automatically, or flag edge cases for human review?
Prompt Engineer crafts and tests the inputs that guide AI systems. They maintain prompt libraries, establish quality metrics, and essentially “program” AI through language rather than traditional code.
AI Ethics Compliance Officer ensures AI systems align with legal requirements and ethical standards. As AI becomes more prevalent in hiring, lending, and healthcare, someone needs to audit these systems for bias and compliance.
AI Filmmaking Specialist uses text-to-video and audio sync tools to produce cinematic content. This combines traditional filmmaking skills with AI tool expertise.
AI Training Data Specialist curates and labels the datasets that train AI models, ensuring quality and reducing bias. I will note on this point that as of the writing of this article, a new company had emerged that would label your data without user intervention. Therefore, labeling data is something that will probably be replaced by new data labeling engines emerging as of this writing.
The list keeps growing. These roles didn’t exist five years ago, and many don’t require traditional computer science degrees. They require a combination of domain expertise and AI literacy.
What This Means For You: Practical Action Steps
Whether you’re entering the workforce or considering a career change, there are concrete steps you can take to remain competitive.
Understand the “Skills-First” Shift
Employers are increasingly prioritizing demonstrated skills over formal degrees. Mentions of degree requirements in AI-related job postings have dropped roughly 15% since 2018, while demand for specific skills Python programming, cloud platform experience, prompt engineering is surging. Don’t take my word for it, search online.
Consider Industry Certifications
Certifications from Google (ML Engineer), Microsoft (Azure AI Engineer), and IBM (AI Engineer) are increasingly recognized as degree alternatives. These programs offer several advantages:
- Faster completion (months vs. years)
- More practical, hands-on focus
- Lower cost
- Regularly updated to match current industry needs
For students not interested in a four-year degree, or displaced workers looking to transition quickly, certifications provide a legitimate pathway into AI-adjacent roles. Review the AI-900 beginners exam certification here.
NOTE: Students or displaced workers can start today for no cost at all the following free courses in AI. Taking these courses will give you a chance to understand if this is something that you might want to pursue further or not. See below:
Build a Skills Hierarchy
Think about your learning in layers:
- Foundation Skills (Always Valuable)
- Programming logic and problem-solving
- System design and architecture
- Understanding of data structures
- Critical thinking and debugging
- AI-Adjacent Skills (Increasingly Necessary)
- Prompt engineering
- Understanding of model capabilities and limitations
- Basic machine learning concepts
- Data analysis and interpretation
- Human-Centered Skills (AI Can’t Touch These)
- Client communication and requirements gathering
- Creative problem-solving for novel situations
- Ethical decision-making
- Leadership and team collaboration
- Teaching and mentoring
The most valuable professionals will be those who combine all three layers, people who understand the technology deeply, can leverage AI effectively, and excel at the human elements of work.
Timeline Expectations
Students always want to know: When will this happen? Here’s my honest assessment:
- Now to 3 years: Entry-level displacement accelerates, mid-level roles increasingly incorporate AI tools
- 3-7 years: New AI-hybrid roles become standard, educational programs adapt to emphasize AI literacy
- 7-10 years: Major workforce restructuring, but likely not wholesale replacement—think “evolution” more than “revolution”
Those estimates are rough estimates and no one can give you a definitive answer. I am trying to be optimistic here but in the context of education, I would venture to say even longer only because I feel larger, public education systems take longer to implement solutions being created by private industry.
Additionally, something to keep in mind is this. The AI industry is moving at such a fast pace, that these numbers or statements could be off or completely different by the time of this publishing or before it even gets indexed on Google. A little side note, we teach students that AI’s are trained with labeled data. Just yesterday I received notification that now there is a company that created a labeling data engine (Roboflow). So, you no longer need to label your data to teach an LLM. Every day there is new information about advances in AI and the industry progress very quickly.
The students I’m teaching today will graduate into a job market significantly different from today’s, but also one still recognizably dependent on human expertise, judgment, and creativity. The most important key point to keep in mind is, what should my training be and how do I get there.
A Real Example From My Classroom
Let me share something I see every week. I assign my web development students a project: build a responsive website for a fictional small business. Here’s what happens:
Students who use AI without understanding web development produce technically functional but generic sites. The AI generates clean HTML and CSS, but the layouts feel template-like. The color schemes are safe but uninspired. The user experience works but doesn’t delight.
Students who understand web development deeply and then use AI? They produce exceptional work. They use AI to generate the boilerplate structure, freeing their time for custom animations, thoughtful user flows, and creative problem-solving. They recognize when AI suggests outdated practices or inefficient solutions. They iterate quickly because AI handles the tedious parts.
The difference isn’t the tool. It’s the user’s depth of understanding.
This is the model for the future: AI as a powerful amplifier of human expertise, not a replacement for it. AI will provide humans the ability to at a higher cognitive level of thinking.
My Advice
I’ve watched technology eliminate jobs (if you are old like me, you can remember going to a store to purchase a CD. Those store clerks were displaced once music went online and users were purchasing and downloading music from services like Napster), transform jobs (accountants today versus 1990’s), and create entirely new industries (social media management didn’t exist when I started my career). Here’s what I’ve learned:
The people who thrive through technological change are those who stay curious and adaptable. They learn new tools. They understand foundational principles deeply enough to apply them in new contexts. They focus on solving problems, not just executing techniques.
If you’re worried about AI taking your job, the solution isn’t to avoid AI but rather it’s to become the person who understands both the technology and the human context well enough to use it effectively. Learn to code not just so you can write code, but so you understand what good code looks like and can evaluate AI-generated solutions. Study design not just to design, but to develop aesthetic judgment that AI lacks.
Build skills in layers. Master the foundations. Add AI literacy. Cultivate the human skills that AI can’t replicate. The combination makes you invaluable.
Conclusion
Will AI take your job? The honest answer is: it depends on what your job is and how adaptable you’re willing to be.
If your career path relies entirely on repetitive tasks that follow predictable patterns, yes AI will significantly impact your opportunities. But if you’re building expertise that combines technical knowledge, creative problem solving skills, and human judgment, you’re positioning yourself for a career that AI will enhance rather than eliminate.
The future belongs to those who can work with AI, not just alongside it or in fear of it. Understanding what AI does well and what it struggles with isn’t just academic, it’s practical career planning. The students sitting in my classroom today won’t just compete with AI; they’ll leverage it to accomplish things neither humans nor AI could do alone.
That’s not a guarantee that the transition will be smooth or that everyone will find their footing easily. Change is hard, and technological disruption creates real challenges for real people. But it’s also true that every major technological shift has created more opportunities than it eliminated. Albeit, just different ones than existed before.
Your job isn’t to predict the future perfectly. It’s to build a foundation strong enough to adapt to whatever comes next. Learn deeply. Stay curious. Focus on developing judgment and creativity alongside technical skills. And remember: the goal isn’t to compete with AI but rather it’s to become the kind of professional who knows exactly how to make AI do the heavy lifting while you focus on the work that actually matters.
Last final words, if you are a student who is in his beginning high school career, take as many AI classes as are being offered at your school. This will only help you advance your future career not hinder it. If you are a displaced worker, start to build your future career with free courses and certifications as time permits. Build your skill set expeditiously but consistenly. In either case, start building those important skillsets today, the future is being built in real time and there is still room for your contribution.