How to Build a Tech Career That Compounds (Even as AI Rewrites the Work)
- scottshultz87
- 9 hours ago
- 9 min read
Recently, I’ve had several conversations with peers and co-workers about the advice we’d give college students—and people early in their technology careers—when they ask a few core questions:
What should I be focusing on to build a long-term career in tech?
How will AI impact my career?
What advice would you give someone pursuing a degree in Computer Science, MIS, or a related field?
They’re the right questions. And honestly, I wish I’d had someone to ask early in my own career.
Over 30+ years in technology, I’ve learned a few durable realities about building a successful career as a technologist—some learned easily, and many more through the “school of hard knocks.”
Reality 1: Graduation is not the finish line.
In tech, education doesn’t end when you leave school—it becomes a lifelong practice. Embrace continuous learning, or choose a career where the knowledge base changes more slowly.
Reality 2: Technical skill is only as valuable as the business outcome it enables.
Your tech skills matter—but they compound in value when you understand how the business works, what it’s trying to achieve, and what “success” actually means in that environment.
Reality 3: Most business leaders don’t have time to learn the complexity—your job is to translate it.
Leaders aren’t paid to master technical depth; they’re paid to make decisions. Your job is to make the tradeoffs legible and make complexity usable: communicate tradeoffs clearly, connect decisions to outcomes, and deliver results without requiring others to become technologists.
Reality 4: Your reputation is built on reliability, not brilliance.
The fastest way to grow your career isn’t being the smartest person in the room—it’s being the person others can depend on to deliver.
In practice:
Meet commitments (or renegotiate early).
Write things down. Close loops. Follow through.
Make your work legible: status, risks, next steps.
Reality 5: You will be paid for judgment, not effort.
Effort doesn’t scale. Outcomes do. As your career progresses, the real differentiator becomes how you make decisions under uncertainty.
In practice:
Learn to frame tradeoffs (speed vs quality, cost vs risk, build vs buy).
Separate “interesting” from “important.”
Make the decision, document the rationale, and learn from results.
Reality 6: Scope is the currency of career growth—and you earn it through trust.
Promotions are less about time served and more about expanding the size of the problems you can own.
In practice:
Start by owning small, clearly scoped outcomes.
Become the person who reduces chaos: clarity, structure, delivery.
Ask for problems with real impact, not just more tasks.
Reality 7: Communication is a technical skill.
If you can’t explain what you’re doing and why it matters, you will eventually cap your influence—no matter how strong your engineering is.
In practice:
Write a crisp one-pager before you build.
Speak in outcomes: “reduces incident volume by 20%,” not “migrated to Kubernetes.”
Tailor the message: executives want risk, cost, and timeline; engineers want architecture and failure modes.
Reality 8: AI will change how you work—your advantage will be how well you direct it.
AI compresses undirected work. Advantage shifts to people who can specify, integrate, and verify. It will eliminate a lot of undirected work: boilerplate code, first drafts, basic analysis, and repetitive tasks.
Your edge becomes:
asking better questions,
designing better workflows,
verifying outputs,
and applying judgment.
In practice:
Use AI to accelerate: research, code scaffolding, test generation, documentation, refactoring, analysis.
Treat AI like a junior teammate: helpful, fast, sometimes wrong.
Build a verification habit (tests, constraints, validation, peer review).
Reality 9: Fundamentals compound; tools churn.
Frameworks change. Cloud services change. “Hot stacks” cool off. The fundamentals keep paying dividends.
Fundamentals that compound:
Data structures & algorithms (enough to reason clearly)
Systems thinking (latency, throughput, failure modes, observability)
Databases & data modeling
Networking basics
Security basics (threat models, least privilege)
Product thinking (who is the user, what is success)
Reality 10: Relationships and integrity are force multipliers.
People remember who made their lives easier, who told the truth early, and who was safe to work with.
In practice:
Give credit. Share context. Don’t hoard information.
Disagree without making it personal.
Escalate risks early and propose options, not complaints.
Considering choosing a career in technology?
If you’re a college student or early-career professional aiming for a long, impactful career in technology, the biggest mistake you can make is treating “tech” as a single lane.
It isn’t. Technology is now an operating layer for almost every industry. That means there are incredible careers available—but also plenty of roles where tech is theater and delivery is improvisation (e.g., “We put AI in the deck, now please build it by Tuesday.”).
So the better question isn’t “Should I go into software?” It’s:
“How do I build a career that compounds across waves of technology, business cycles, and organizational dysfunction?”
Below is the advice I’d give—professional, practical, and slightly allergic to hype.
The Big Shift: Tech Careers Are Becoming “Portfolio Careers”
The old model was: pick a role (developer, sysadmin, analyst), climb a ladder, retire with a pension and a printer you stole from the office.
The new model is closer to a portfolio:
You’ll hold multiple “identities”: builder, operator, communicator, strategist, risk manager.
You’ll switch domains: finance, healthcare, logistics, cybersecurity, AI—often without changing your core skills.
Your “job security” is really “skill liquidity.” Your assets are capabilities that transfer.
This shift isn’t theoretical. Major employers are signaling that the skills gaining value fastest include AI and big data, networks and cybersecurity, and technological literacy, with curiosity and lifelong learning rising in importance too.
Translation: it’s not just “be technical.” It’s “keep learning and keep becoming more useful.”
Step 1: Stop Thinking “Good Job / Bad Job.” Think “Good Economics / Bad Economics.”
Some industries are structurally tough—volatile demand, weak pricing power, brutal competition. Those conditions can turn even smart people into exhausted people.
Tech now contains both “good economics” and “bad economics” inside the same company, sometimes on the same floor. Here’s the key pattern:
Good economics (and usually better careers)
Work that scales via:
products and platforms (reusability)
network effects or data flywheels
regulation/security requirements
switching costs and operational integration
durable business outcomes (revenue, cost, risk)
Bad economics (and often “bad jobs”)
Work that scales via:
labor hours
commodity delivery
weak differentiation (“we can do that too”)
constant cost pressure and churn
This is why “software services” can be a fantastic apprenticeship or a treadmill. It depends on whether you’re building scarce expertise or just burning hours.
Step 2: Broaden Your Target Beyond “Software” to the Real Tech Career Map
A long career in tech is not “coding for 40 years.” It’s moving up and across the map:
Builders
Software engineering
Data engineering
ML engineering
Security engineering
Hardware/embedded (in some industries, increasingly relevant)
Operators
SRE / reliability
Observability and incident response
Cloud/platform engineering
IT operations and service management (still a massive lever in enterprises)
Translators
Product management
Solutions architecture
Technical program management
Customer engineering / field engineering
Governors
Privacy, risk, and compliance
AI governance and model risk management
Security leadership and policy
Here’s the punchline:
Impact comes from being able to build, run, and steer systems—not just write them.
And tech labor demand is still real. For example, BLS projects 15% growth in software developer/QA/test roles from 2024–2034, with roughly 129,200 openings per year on average. That’s not “everyone gets a job instantly,” but it does mean the obituary for tech careers is premature.
Step 3: Learn the Two Skills AI Won’t Make Less Valuable—Judgment and Integration
AI changes the work. It doesn’t remove the work.
Nikesh Arora, CEO of Palo Alto Networks, recently argued that AI job-loss fears are overstated and that we may need multiples more skilled people to implement and manage these systems.
And that tracks with what companies actually struggle with:
(A) Judgment under constraints
AI can generate code, text, even architectures. But it doesn’t own the consequences.
Judgment looks like:
choosing tradeoffs (speed vs reliability, cost vs resilience, convenience vs security)
recognizing second-order effects
deciding what not to build
designing for change, not perfection
(B) Integration into real business workflows
In most organizations, the hard part is not invention. It’s integration:
AI into line-of-business applications
internal systems with external SaaS ecosystems
security/compliance requirements into shipping velocity
The winners will be people who can turn “cool” into “reliable”—and then into “adopted.”
Step 4: Become a “Learn-It-All” Without Becoming a “Hype-It-All”
Satya Nadella has popularized a mantra worth tattooing on your professional habits:
“Don’t be a know-it-all; be a learn-it-all.”
The witty add-on is: don’t be a hype-it-all either.
Here’s what “learn-it-all” means in practice:
You can learn new tools fast without losing fundamentals.
You can ask better questions than your peers.
You treat every project like a chance to build reusable patterns.
You invest in feedback loops (users, metrics, postmortems).
And yes, AI should accelerate learning—not replace it. The “AI era advantage” isn’t that you can prompt. It’s that you can learn faster than the problem changes.
The World Economic Forum explicitly elevates curiosity and lifelong learning as rising in importance. That’s not motivational fluff. It’s a labor market signal.
Step 5: Treat Security, Trust, and Risk as First-Class Career Accelerators
Early-career professionals often avoid security and risk because it feels like “slow work” or “someone else’s job.”
That’s a mistake.
Bruce Schneier’s classic line remains a brutally accurate framing: “Security is a process, not a product.”
Meaning: the environment is adversarial; therefore, the work is never finished—and expertise stays valuable.
Now widen that beyond cybersecurity to “trust” broadly, especially with AI.
NIST’s AI Risk Management Framework emphasizes that managing AI risks helps enhance trustworthiness and public trust, and NIST leadership has described the framework as a way to operationalize trustworthy and responsible AI.
Career insight:
If you can build systems that are secure, compliant, resilient, and explainable, you become the person executives want near the steering wheel, not just near the keyboard.
Step 6: Move “Up the Stack” Into Decisions That Are Hard to Change
Even if you don’t want the title “architect,” you want the capability.
Martin Fowler has discussed the popular definition of architecture as “stuff that’s hard to change,” and argues that good architects reduce friction to change.
That idea applies beyond software:
In data: schemas, governance, and contracts are hard to change.
In security: identity models and trust boundaries are hard to change.
In product: positioning and workflow adoption are hard to change.
In operations: incident response maturity and observability are hard to change.
So here’s the move:
Execute tasks early; earn the right to own decisions.
Step 7: Learn Business Like It’s a Second Major (Because It Is)
If you want impact, you must be bilingual: technology + business outcomes.
That means being able to frame your work in terms of:
revenue growth (acquisition, conversion, retention)
cost containment (automation, cloud efficiency, support load reduction)
risk (security, compliance, downtime, reputational exposure)
speed (time-to-value, cycle time, deployment frequency)
quality (defects, reliability, customer trust)
This isn’t “soft stuff.” This is what gets funded.
If you can walk into a room and say:
“This integration reduces churn because onboarding becomes faster.”
“This observability change reduces MTTR and prevents SLA penalties.”
“This security pattern reduces breach likelihood and audit cost.”
“This AI feature needs governance or we’ll create unbounded risk.”
…you become dangerous (in the best way).
The Practical Playbook: 10 Moves That Build a Long Tech Career
Pick a compounding specialty (AI/data production, security, platforms, reliability, integration).
Build “T-shaped” capability: deep in one area, competent across adjacent areas.
Ship proof-of-work: projects with users, not just assignments with grades.
Write clearly: design docs, postmortems, decision records—clarity scales.
Get good at integration: APIs, events, data contracts, and workflow adoption.
Treat security as a default constraint, not a compliance afterthought.
Use AI to accelerate learning, not to avoid learning.
Measure outcomes: reliability, cost, adoption, customer impact—not just output.
Build your network deliberately: mentors, peers, operators, product people.
Optimize for trajectory, not title: choose roles that increase your rate of learning and your surface area of impact.
Bottom Line
A long, impactful career in technology today is less about being “a software person” and more about being a systems-and-outcomes person.
The durable path looks like this:
Learn fast (be a learn-it-all).
Build judgment (tradeoffs, constraints, consequences).
Master integration (where most value gets stuck).
Own trust (security, risk, governance, resilience).
Speak business (make technology legible as value).
Technology will keep changing. That’s the deal. Your advantage is building a career architecture that keeps paying you back.
#TechCareers #CareerAdvice #EarlyCareer #ComputerScience #MIS #SoftwareEngineering #AI #ArtificialIntelligence #CareerGrowth #Leadership #ContinuousLearning #ProfessionalDevelopment
References
World Economic Forum — Future of Jobs Report 2025 (fastest-growing skills; curiosity and lifelong learning).
U.S. Bureau of Labor Statistics — Software Developers, QA Analysts, and Testers (2024–2034 outlook; openings per year).
NIST — AI Risk Management Framework (AI RMF 1.0) (trustworthiness, risk management).
NIST News Release — AI RMF helps operationalize trustworthy/responsible AI (Locascio quote).
Bruce Schneier — “Security is a process, not a product.”
Martin Fowler — architecture as “things perceived as hard to change” and making change easier.
Satya Nadella — “learn-it-all” framing and learning culture commentary.
Nikesh Arora (Palo Alto Networks) — AI job-loss fears overstated; need for more skilled people.
BLS projection: Occupational Outlook Handbook
NIST Risk Management Framework Aims to Improve Trustworthiness of Artificial Intelligence
Most jobs are not going anywhere soon; AI is good for deterministic problems right now: Nikesh Arora
Comments