Summary: High school students are asking the blunt question: "What can my career look like, and how do I get there?" The answer is not a single path. Rapid advances in AI, shifts in funding for science, and changing employer expectations create uncertainty — and opportunity. This post lays out what five seniors told WIRED, extracts practical lessons, and offers clear choices students can act on now. Read the stories, then decide: which path fits you — and what will you say "no" to when people push a one-size-fits-all plan?
Interrupt — You don’t get to wait for the future; you have to build a credible position inside it. Engage — Ask better questions than the headlines do. "What can my career look like, and how do I get there?" What would you answer if that were asked of you? What does your "no" sound like when someone tells you to lock into a single major because it’s safe?
Why this matters now
AI is changing what skills employers value and how work gets done. Public funding shifts are slowing some research programs. Jobs that look stable today may require very different skills in five to ten years. That alone could paralyze students — or force them to be strategic. The five seniors we spoke with illustrate the real, practical responses students are already building. Their choices show that tech careers remain attractive, but not in the old, linear way.
AI Security and Development — Laksh Patel (Willowbrook, IL)
Laksh saw the rise of large language models and worried about privacy exposure. He worked on an algorithm to filter private data during training so models won't leak API keys or other sensitive bits. He’s treating AI as both an engineering and a security problem. He believes expertise formed now will carry influence as the field matures.
Lessons and tactics:
- Skill stack: build programming fundamentals, statistics, and security principles. Learn how models are trained and where data leaks occur.
- Portfolio over pedigree: Laksh is preparing project-based proof — code, datasets, writeups. Employers and labs value demonstrated ability.
- Path options: traditional CS degree, apprenticeship at a startup, or research internships. A degree remains useful as a baseline credential; real work speaks louder.
Open question: If you wanted to protect people’s data during model training, what would be your first experiment?
Healthcare and Neurodegenerative Disease Research — Amelia Andrea Ramirez (New York City)
Family illness pushed Amelia toward pediatric neurology and patient care. She wants to translate scientific findings into real help for communities with limited resources. She’s wary of AI’s effect on cognition and fears overreliance will weaken clinicians’ judgment.
Lessons and tactics:
- Dual fluency pays: combine clinical knowledge with data literacy. Learn basic coding, data interpretation, and how imaging or biomarkers are used in diagnosis.
- Human work retains value: empathy, bedside reasoning, and communication are hard for machines to replace. Train those skills deliberately.
- Ethics and advocacy: students who care about access can steer careers toward public health, policy, or community medicine rather than pure lab work.
Mirroring her worry: you’re concerned about AI making people less able to think for themselves. How would you balance using AI as a tool while keeping clinical reasoning sharp?
The Importance of Curiosity in Science — Jiondae Dewald (Lambertville, NJ)
Jiondae is a "why" person. Biology appeals because it builds on itself, and discoveries reveal layers we didn’t know existed. He worries that AI erodes curiosity by sprinting to answers and discouraging depth.
Lessons and tactics:
- Guard curiosity like a muscle: practice sustained investigation — not just fast answers. Work on lab projects, not only quick online solutions.
- Teach and test: explaining a concept to peers forces deeper understanding. Use AI as a sparring partner, not the final arbiter.
- Career framing: aim for roles where curiosity is rewarded — research, translational science, problem-focused medicine.
Empathize: frustration at a culture of shortcuts is valid. Which small habit could you adopt to force yourself to explore a question three layers deeper?
Engineering and Systems Thinking — Simon Tchira (Miami, FL)
Simon prefers concrete answers and is drawn to industrial engineering because it studies whole systems: people, logistics, materials, spreadsheets. He views his role as improving systems and believes human oversight will remain necessary.
Lessons and tactics:
- Systems skills: learn process mapping, basic operations research, and how to collect and interpret key performance indicators.
- People-first engineering: communication, change management, and stakeholder alignment are as important as math.
- Apply broadly: internships in manufacturing, supply chains, or service operations reveal how theory maps to messier reality.
Open question: Which system around you could be measurably better in one month with a small experiment? How would you measure success?
Machine Learning and Medical Imaging — Jayden Jeong (Lexington, KY)
Jayden’s first science fair used computer vision to count bacteria colonies. He now wants to work in machine learning for medical imaging. He’s skeptical of the hype: he thinks many startups are repackaging existing tech and that timelines for full automation are longer than headlines claim.
Lessons and tactics:
- Start small and public: build reproducible models with clear evaluation metrics. Share code and results so others can verify your claims.
- Read the literature: understand where models fail — rarity, dataset shift, adversarial inputs — and build projects that address those failure modes.
- Network in two lanes: clinical partners (doctors, radiologists) and algorithmic peers. Medical impact depends on both.
Confirming a suspicion: hype often masks hard limits. What weak spot in current models would you try to fix first?
What employers and grad programs will actually look for
Across these stories the signal is consistent: demonstrable work, reasoning ability, and communication matter more than a perfect transcript. Degrees open doors but projects, internships, and collaborations keep them open. Employers want people who can describe what they built, why they chose an approach, and what failed.
Tangible checklist:
- A portfolio: code repos, lab notes, poster PDFs, or short project explanations.
- Interpersonal proof: letters, references, or recorded presentations showing you can explain work to different audiences.
- Domain depth: a few focused projects in a field (security, imaging, systems) rather than thin coverage across many.
How to build signal now (practical steps)
Start with small bets you can prove. Pick one problem; ship something measurable. Combine this with reading and mentorship.
- Project-first learning: pick a simple dataset, ask a real question, and document results. Public writeups help more than grades.
- Internships and research: apply to local labs, clinics, or startups. Even short stints teach context and give references.
- Cross-disciplinary skills: pair programming with domain expertise — e.g., code + biology or code + security.
- Ethics and communication: practice explaining trade-offs, risks, and limitations. That makes you trustworthy.
On risk, failure, and claims
Dreams are valid. Failures don’t disqualify you; they teach which assumptions were wrong. If someone tells you a specific role is dead, say "no" to panic. Say "no" to lazy predictions. Instead ask: what skills will remain valuable if the tech shifts, and how can I prove I have them?
Use commitment and consistency: pick a small, public promise (a project demo, a poster at a fair) and keep it. Social proof follows — peers, teachers, and early employers notice visible progress.
Policy and social context — why funding shifts matter
When public funding shrinks, discovery slows and certain research roles dry up. That pushes talent toward private labs and startups, where different skills are prized: speed, product focus, and fundraising awareness. Students should be aware: policy shapes opportunity. If you care about public science, consider combining clinical or research paths with advocacy or nonprofit work.
Ethics and the human factor
All five students share a theme: human judgment and ethics still matter. AI will change tools, but trust, care, and real-world testing remain human tasks. If you can argue clearly for safety, fairness, and patient-centered design, you will be invited into decision-making circles.
Final, practical decision framework
When you choose a path, ask three questions and answer them honestly:
- What problem would I be excited to work on daily?
- Can I show work on that problem within six months?
- What skills will still be valuable if the tools change?
If you can answer those, you’ve moved from uncertainty to a testable plan. If you cannot, that's an honest signal to experiment more before locking in.
Call to dialogue
I’ve repeated the students’ biggest concern back to you: "What can my career look like, and how do I get there?" Which of these five stories speaks to you — Laksh’s security focus, Amelia’s clinical drive, Jiondae’s curiosity, Simon’s systems view, or Jayden’s measured skepticism? Reflect, then answer: which of these would you explore first, and what single experiment will you run in the next 30 days to prove it?
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