AI Replacing IT Jobs: Hype vs Reality

Your LinkedIn feed is full of doom: “AI will replace developers.” “Learn to code? Don’t bother.” “IT is dead.”

Meanwhile, your cousin who just finished a coding bootcamp can’t find work, and that senior developer you know just got laid off after 15 years.

So which is it? Is AI coming for every IT job, or is this just another overhyped technology cycle that’ll blow over?

Neither. The truth is messier, more nuanced, and actually more actionable than the clickbait suggests. Let’s look at what’s really happening.

The Numbers That Actually Matter

Here’s the headline stat that’s causing panic: according to IEEE Spectrum, U.S. programmer employment fell 27.5% between 2023 and 2025. That’s not a typo. More than a quarter of programming jobs disappeared in two years.

But here’s what the same data shows: software developer employment—a distinct, more design-oriented position—only fell 0.3% in the same period. Information security analyst jobs are actually growing.

This isn’t “AI is replacing IT.” This is “AI is replacing some IT work while creating demand for different IT work.”

The World Economic Forum’s Future of Jobs Report 2025 projects that by 2030, 170 million new roles will be created while 92 million are displaced—a net gain of 78 million jobs globally. The catch? Those new jobs require different skills than the ones disappearing.

Who’s Actually Losing Jobs

Let’s be specific about where the pain is concentrated:

Role Type	Change (2023-2025)	Primary Cause
Entry-level programmers	-25% to -27%	AI handles routine coding tasks
QA testers (manual)	-27%	Automated testing tools
Basic IT support (L1)	-38% ticket resolution	AI chatbots handling simple issues
Traditional sysadmins	-2.6% projected through 2033	Cloud automation
Software developers	-0.3%	Slight decline only
Security analysts	+33% projected through 2033	More threats, more work

The pattern is clear: repetitive, well-defined tasks are being automated. Complex problem-solving, security work, and strategic thinking are not.

The Entry-Level Crisis Is Real

Let’s address the elephant in the room. If you’re trying to break into IT right now, the job market is genuinely harder than it was three years ago.

According to Stanford University research cited by Fortune, entry-level employment in software engineering and customer service declined by roughly 20% between late 2022 and mid-2025. The percentage of workers aged 21-25 at major tech firms dropped from 15% to 6.8% in that same period.

This isn’t doom-mongering. It’s data.

The reason? AI tools are genuinely good at the tasks typically assigned to junior workers: writing boilerplate code, handling basic support tickets, generating documentation, running routine tests. Companies can now assign more work to mid-level employees augmented by AI, reducing their need for entry-level hires.

But here’s what this doesn’t mean: it doesn’t mean entry-level IT work is impossible. It means the bar has moved. The “entry level” of 2020—knowing basic syntax and passing a certification exam—isn’t entry level anymore.

If you’re building your first IT career, the skills that get you in the door have changed. We’ll cover what those are below.

Five Myths About AI and IT Jobs

Myth 1: “AI Will Replace All Programming Jobs Soon”

Dr. Eric Schmidt, former Google CEO, made headlines predicting that “within one year, most programming work will be done by AI.” That was in 2024. It’s 2026. Most programming work is still done by humans.

What actually happened: AI tools like GitHub Copilot made developers more productive. A ServiceNow and Pearson study found that sysadmins save roughly 12 hours per week using AI tools—but they’re using that time for higher-value work, not getting laid off.

The prediction that AI would automate programming missed something fundamental: most programming isn’t writing code. It’s understanding what to build, why it matters, how it fits with existing systems, and what happens when things break. AI is great at the “write code” part. It’s still terrible at the rest.

Reality: AI is automating routine coding tasks, not replacing developers who can architect solutions, debug complex systems, and understand business requirements.

Myth 2: “Certifications Are Worthless Now”

If AI can write code, why bother getting certified?

Because certifications never proved you could write code. They proved you understood how systems work. That knowledge is more valuable now, not less.

Here’s why: when AI generates code or configurations, someone needs to verify it’s correct, secure, and appropriate for the environment. That someone needs to understand networking fundamentals, security principles, system architecture—exactly what certifications teach.

A CompTIA Security+ certification doesn’t mean you can write firewall rules from scratch. It means you understand why certain configurations are secure and others aren’t. AI can generate a firewall config in seconds. Can it explain why that config is appropriate for your specific compliance requirements? Can it identify when its output conflicts with your existing architecture?

Reality: Certifications validate the knowledge needed to supervise, verify, and correct AI-generated work. If anything, understanding fundamentals matters more when you’re checking AI’s homework.

Myth 3: “Only AI Specialists Will Have Jobs”

According to the World Economic Forum, 77% of AI jobs require master’s degrees, and 18% require doctoral degrees. If “just become an AI specialist” is the answer, most IT professionals are out of luck.

Fortunately, that’s not the answer.

The fastest-growing IT roles include big data specialists, fintech engineers, AI/ML specialists, and software developers. But they also include cybersecurity professionals (33% growth through 2033), cloud engineers, and DevOps specialists—roles that existed before the AI hype and will exist long after.

You don’t need a PhD to build a career in cybersecurity or cloud computing. You need practical skills and the ability to keep learning.

Reality: The IT job market is diversifying, not consolidating around AI expertise. Traditional infrastructure and security roles are growing faster than average.

Myth 4: “Remote IT Jobs Are Safe Because AI Can’t Fix Hardware”

This one’s half-true, which makes it dangerous.

Yes, AI can’t physically replace a server or run cable. But the jobs being most affected by AI are largely remote-friendly positions: programming, support, testing, documentation. The MIT study on AI job displacement found that occupations requiring bachelor’s degrees have higher AI applicability than those requiring physical presence.

What’s actually safe? Jobs requiring unpredictable physical work, jobs requiring real-time judgment in ambiguous situations, and jobs where the cost of AI error is unacceptable (security, compliance, architecture).

The irony: the “safe from AI” jobs often require being on-site. The jobs most susceptible to AI often offered the best work-from-home options.

Reality: Remote-friendly desk work is more exposed to AI than hands-on roles. Career resilience might mean accepting some trade-offs on flexibility.

Myth 5: “Just Wait It Out—This Is Like Every Other Tech Hype Cycle”

Remember when cloud computing was going to eliminate all on-premises IT jobs? When DevOps was going to make ops teams obsolete? When low-code platforms were going to replace developers?

Those technologies did transform the market. They just didn’t eliminate jobs—they changed which skills were valuable. Sysadmins who learned cloud survived. Developers who understood CI/CD thrived. Those who didn’t adapt struggled.

AI is following the same pattern, but faster. The 27.5% drop in programmer employment happened in two years. Previous technology shifts played out over a decade.

Reality: This is similar to past disruptions in kind, but not in speed. Waiting it out means falling behind while the market reshapes around you.

What’s Actually Growing

Not everything is doom and gloom. Several IT specializations are seeing strong growth despite—or because of—AI advancement:

Cybersecurity (33% growth through 2033)

Information security analysts are projected to grow nearly eight times faster than average occupations. AI creates new attack vectors, generates more code that needs security review, and enables more sophisticated threats.

AI can help with threat detection, but it can’t do the job without human intelligence and intuition. New threats require creative thinking to identify. Complex breaches need human judgment to assess and contain.

If you’re considering getting into cybersecurity, the timing is actually good. Entry-level security positions are less affected by AI than entry-level programming because security requires judgment calls that AI can’t reliably make.

DevOps and Cloud Engineering

The BLS projects slight decline for traditional sysadmins, but DevOps and cloud roles tell a different story. Companies need people who can:

• Design and maintain the infrastructure AI tools run on
• Implement CI/CD pipelines that include AI-assisted code review
• Ensure AI deployments are secure, monitored, and compliant
• Handle the inevitable outages that automated systems can’t fix themselves

As one DevOps manager put it: “AI won’t replace DevOps. It’ll supercharge it—standups become data-driven, CI/CD pipelines self-optimize, and QA leans on AI for test creation. But someone still needs to make trade-offs and handle outages.”

Building hands-on skills with automation tools makes you more valuable, not less.

AI Integration Specialists

You don’t need a PhD to help companies implement AI tools. Someone needs to:

• Evaluate which AI tools fit specific business needs
• Configure and customize AI solutions
• Train teams on effective AI use
• Monitor AI outputs for accuracy and bias
• Build workflows that combine AI capabilities with human judgment

This is an emerging role that combines IT generalist knowledge with specific AI tool expertise. If you understand how enterprise IT actually works—not just how AI demos work—you have something most AI specialists lack.

Architecture and Strategy Roles

Senior positions requiring system design, business alignment, and long-term planning are largely insulated from AI disruption. AI can generate code for a microservice. It can’t determine whether your organization should use microservices at all.

The path to these roles runs through the mid-level positions that are most at risk. Which brings us to the hardest part of this whole situation.

The Mid-Career Squeeze

If you’re five to ten years into an IT career, you’re in an uncomfortable spot. Entry-level workers are competing with AI for junior tasks. Senior architects are safe because their work requires institutional knowledge and judgment. You’re in the middle.

The traditional path—do your current job well, wait for promotion—isn’t reliable anymore. Mid-level roles that primarily execute well-defined tasks are exactly what AI automates best.

This isn’t a reason to panic. It’s a reason to be intentional about your next move. The questions to ask yourself:

Am I building unique knowledge, or just accumulating years of experience?

Ten years of doing the same thing isn’t the same as ten years of expanding capabilities. If your daily work doesn’t teach you new skills, your experience is depreciating while AI catches up.

Could AI do 80% of my current tasks if given the right prompts?

Be honest. If the answer is yes, your value lies in the 20% that requires human judgment—or in your ability to supervise and correct the AI doing the other 80%.

What do I know that’s specific to my organization or industry?

Domain expertise is harder to automate than generic technical skills. The developer who understands healthcare compliance or financial regulations has something AI doesn’t.

If you’re feeling the career plateau, AI pressure might be accelerating it. The solution is the same either way: actively develop new capabilities rather than coasting on existing ones.

What Actually Protects Your Career

After all that, here’s the practical part: what should you actually do?

1. Learn to Work With AI, Not Against It

This sounds like corporate platitude, but it’s concrete advice. AI tools are force multipliers. The developer who can effectively prompt Copilot writes more code than one who refuses to touch it. The support tech who knows when to escalate from the chatbot handles higher-value tickets.

The sysadmin study I mentioned earlier found that AI saves about 12 hours per week of routine work. That’s 12 hours you can spend on higher-value tasks—if you know how to use the tools.

Practical steps:

• Actually use GitHub Copilot, ChatGPT, or Claude in your daily work
• Learn what they’re good at (boilerplate, syntax, documentation) and bad at (architecture, debugging complex issues, understanding context)
• Build workflows that combine AI capabilities with your judgment

2. Double Down on What AI Can’t Do

AI struggles with:

• Ambiguous requirements: “Make it faster” means nothing to AI without specific metrics
• Novel situations: AI pattern-matches on training data; truly new problems break it
• Accountability: No AI signs off on a change that could take down production
• Politics: Understanding who needs to approve what and why isn’t in any training set
• Physical presence: Hardware still needs human hands

If your work involves translating vague business needs into technical requirements, handling unprecedented problems, taking responsibility for critical systems, navigating organizational dynamics, or physically interacting with infrastructure—lean into those aspects.

3. Build T-Shaped Skills

“T-shaped” means deep expertise in one area plus broad familiarity across related domains. This combination is particularly valuable now because:

• Deep expertise lets you catch AI errors in your specialty
• Broad knowledge helps you integrate AI tools across different contexts
• The combination is hard to automate

For example: deep expertise in Kubernetes plus working knowledge of security, networking, and CI/CD tools. Or: deep expertise in penetration testing plus familiarity with cloud architecture, compliance frameworks, and developer workflows.

If you’re not sure where to develop depth, cybersecurity and cloud platforms are reasonable bets based on current growth projections.

4. Get Comfortable With Continuous Learning

Workers can expect 39% of their current skill sets to become outdated or transformed between 2025 and 2030. That’s not a one-time adaptation. That’s ongoing.

The professionals who thrive won’t be the ones who learned the “right” skills once. They’ll be the ones who can learn new skills efficiently and repeatedly.

This is actually encouraging if you have a track record of learning. Your ability to pick up new technologies quickly is a durable skill that AI doesn’t touch. If you’ve successfully transitioned from help desk to sysadmin or from traditional ops to DevOps, you’ve already demonstrated the core capability that matters.

5. Build Things That Demonstrate Judgment

A portfolio of projects matters more than ever, but the bar has changed. Anyone can prompt AI to generate a CRUD app. What demonstrates value is:

• Projects that solve actual problems in specific contexts
• Technical decisions you can explain and defend
• Trade-offs you evaluated and conclusions you reached
• Problems you encountered and how you diagnosed them

Your home lab isn’t impressive because you set up Kubernetes. It’s impressive because you can explain why you chose Kubernetes over Docker Swarm for your specific use case, what problems you hit, and how you troubleshot them.

For hands-on practice, tools like Shell Samurai can help build practical Linux and command-line skills that remain fundamental even as higher-level tools change.

The Honest Bottom Line

AI is genuinely changing the IT job market. Some roles are disappearing. Entry-level hiring is down. The skills that got you hired five years ago may not be enough five years from now.

But IT careers aren’t going away. They’re changing shape. The 27.5% decline in programmer jobs coincides with growth in security, DevOps, cloud, and AI integration roles. The World Economic Forum projects net job creation, not destruction.

The professionals who struggle will be those who assume their current skills will remain valuable indefinitely. The ones who thrive will be those who treat learning as an ongoing investment rather than a one-time expense.

AI isn’t coming for “IT jobs.” It’s coming for routine, well-defined tasks that can be automated. What remains—and what’s growing—is work that requires judgment, context, creativity, and accountability.

That’s still plenty of room for a career.

Frequently Asked Questions

Will AI completely replace software developers?

No. AI is automating routine coding tasks, which has reduced demand for entry-level programmers doing basic implementation work. But software development is mostly requirements analysis, system design, debugging, and maintenance—work that requires understanding context and making judgment calls. Software developer employment fell only 0.3% even as programmer roles declined sharply, because the job is more than writing code.

Which IT jobs are safest from AI automation?

Roles with the strongest growth projections include cybersecurity (33% through 2033), DevOps and cloud engineering, and positions requiring physical presence or real-time judgment in ambiguous situations. AI integration specialists—people who can implement and manage AI tools rather than just use them—are also in growing demand. The common thread: work that requires contextual judgment rather than executing well-defined tasks.

How do I future-proof my IT career against AI?

Build skills in areas AI struggles with: handling ambiguous requirements, managing novel situations, taking accountability for critical systems, and understanding organizational context. Learn to use AI tools effectively so you can work faster while supervising their output. Develop deep expertise in one area (security, cloud, etc.) while maintaining broad knowledge across adjacent domains. Treat learning as ongoing rather than something you finished years ago.

Is it still worth learning to code in 2026?

Yes, but with adjusted expectations. Pure coding—translating clear specifications into working code—is increasingly automated. The value is in understanding what to build, why, and how it fits with existing systems. Learning to code is still the best way to develop that understanding. Focus on programming fundamentals and problem-solving rather than just syntax, and expect that AI will be part of your workflow from day one.

Are IT certifications still valuable with AI?

More valuable, arguably. Certifications validate knowledge of how systems work—understanding that’s necessary to evaluate, supervise, and correct AI-generated configurations. A Security+ certification doesn’t prove you can configure a firewall from scratch; it proves you understand why certain configurations are secure. That judgment is exactly what AI lacks.