Economic Education

Is It Worth Starting a Career in IT Today?

IT is no longer the easy ladder it looked like during the boom years. But for people who choose the right entry point, learn with AI instead of against it, and build durable judgment, it remains one of the stronger career bets.

Market Lens

Market Lens

— 10 min read
Is It Worth Starting a Career in IT Today?

Why This Topic Matters

Starting a career in IT today is still worth considering, but the answer is no longer the simple “learn to code and get rich” story that sold so many bootcamps and online courses.

The market has changed. Big technology companies hired aggressively during the pandemic, then cut jobs as growth slowed, interest rates rose, and budgets shifted toward artificial intelligence infrastructure. At the same time, software has not stopped spreading. Banks, hospitals, logistics companies, retailers, factories, governments, schools, energy companies, and small businesses still need systems, data, security, automation, support, and product improvement.

So the honest answer is this: IT is a good field if you treat it as a long-term profession, not as a quick escape hatch. The entry level is harder. The middle of the career can still be excellent. The long-term outlook is strong for people who keep learning, understand business context, and can use AI tools without becoming dependent on them.

This article is global-first. Pay, hiring rules, degrees, taxes, visas, labor protections, and discrimination laws vary by country. Use the examples as signals, then check local data before making a career or education decision.

The Core Idea

IT is not one job market. It is several markets stacked together.

There is software engineering: building applications, platforms, internal systems, mobile apps, and tools. There is infrastructure: cloud, networks, reliability, DevOps, platform engineering, and data centers. There is cybersecurity. There is data work: analytics, data engineering, business intelligence, machine learning, and AI operations. There is enterprise technology: ERP, CRM, identity, integrations, low-code tools, and business process automation. There is support and operations. There is product management, design, technical writing, sales engineering, implementation, and customer success.

That distinction matters because the weakest part of the market is often the generic junior path. A beginner who says “I know JavaScript and want any remote developer job” faces heavy competition. A beginner who can help a small company secure accounts, automate reporting, clean data, integrate tools, or support a specific business workflow has a clearer opening.

The best question is not “Is IT worth it?” It is “Which IT problem can I become useful at solving?”

The Background: A Tougher Market, Not a Dead One

The current technology labor market is mixed. Layoffs and restructuring have made headlines, especially in large U.S. technology companies. Many firms are spending heavily on AI infrastructure while trimming teams elsewhere. That has made the market feel unstable, particularly for junior workers.

But long-term employment projections still point to growth in core computer occupations. The U.S. Bureau of Labor Statistics projects software developers, quality assurance analysts, and testers to grow much faster than the average occupation from 2024 to 2034. It lists a May 2024 median annual wage of $133,080 for software developers and projects roughly 129,200 annual openings across software developers, QA analysts, and testers. BLS also projects information security analysts to grow 29% from 2024 to 2034, with a May 2024 median annual wage of $124,910.

The World Economic Forum’s Future of Jobs Report 2025 also expects technology-related roles to grow quickly. It names big data specialists, fintech engineers, AI and machine learning specialists, software and applications developers, and security management specialists among fast-growing roles. It also warns that skills will keep changing, which means the field rewards people who keep updating their toolkit.

That is the balance: the short term is competitive; the medium and long term remain attractive.

Where Your Chances of Getting Hired Are Bigger

Your best entry point depends on your background, location, and tolerance for uncertainty.

If you are early in your career, your chances may be better in practical business IT than in prestigious pure software jobs. Look at roles such as support engineer, QA analyst, implementation specialist, junior data analyst, business systems analyst, CRM administrator, cloud support associate, security operations analyst, automation specialist, or technical customer success. These jobs may not sound as glamorous as “AI engineer,” but they put you near real systems and real users.

If you already have domain experience, use it. A nurse learning health IT, an accountant learning ERP systems, a logistics worker learning data analytics, or a marketer learning automation may have a stronger story than a generic beginner. Domain knowledge is underrated because AI can generate code, but it cannot easily understand every business constraint, messy process, and customer nuance without human guidance.

If you want a more stable path, consider cybersecurity, cloud infrastructure, identity and access management, data engineering, enterprise systems, compliance technology, and reliability work. These areas are tied to operational risk. Companies may cut experimental projects, but they still need systems to run, data to move, users to log in, and attackers to be kept out.

If you want a more promising but volatile path, consider AI tooling, machine learning operations, developer productivity, data platforms, robotics software, fintech infrastructure, security automation, and industry-specific AI products. These can pay well and grow fast, but the tools and employers may change quickly.

Short-, Mid-, and Long-Term Prospects

In the short term, expect a crowded entry level. AI coding tools, bootcamp graduates, remote applicants, laid-off workers, and outsourcing all increase competition. Employers can be pickier. A portfolio of toy apps is less persuasive than evidence that you can solve a real problem, work with other people’s code, write clearly, test your work, and learn a business domain.

In the mid term, IT remains a strong career if you build depth. The valuable worker is not the person who can produce the most code snippets. It is the person who can understand a problem, choose a sensible design, avoid security mistakes, communicate tradeoffs, and maintain systems after launch.

In the long term, retiring in the field is realistic, but not by standing still. Many people can spend decades in technology by moving from junior execution to senior engineering, architecture, security, product, management, consulting, teaching, compliance, or specialist domains. The field changes too quickly for a single toolset to last a lifetime. The career can last if the learning habit lasts.

The retirement question is therefore less about whether “IT” survives and more about whether your role stays attached to valuable judgment. Routine task execution is exposed. Systems thinking, security judgment, business translation, architecture, incident response, and leadership are harder to replace.

Which Areas Look Stable, Promising, and High-Paying

The more stable areas tend to be infrastructure, cybersecurity, enterprise systems, regulated-industry technology, data engineering, and internal business systems. These are not immune to layoffs, but they are tied to ongoing operations.

The more promising growth areas include AI and machine learning, data platforms, security automation, cloud cost optimization, privacy engineering, fintech infrastructure, healthcare technology, energy systems, robotics, and tools that help companies use AI safely. The World Economic Forum’s job-growth rankings support the broad direction: AI, big data, fintech, software development, and security are all growth themes.

The higher-paying areas usually combine technical depth, business value, and scarcity. In many markets, strong compensation appears in software engineering, cloud infrastructure, security, data engineering, machine learning, quantitative finance, enterprise architecture, engineering management, and product leadership. Stack Overflow’s 2025 Developer Survey shows senior executives and engineering managers among the highest median salary roles globally, and it also shows wide country gaps, with U.S. salaries much higher than many other markets.

Do not chase pay alone. The highest-paying path may also demand harder interviews, longer hours, more volatility, or expensive cities. A slightly lower-paid security, infrastructure, or enterprise role in a stable employer can be a better life decision than a high-status job that burns people out.

Gender and Region Gaps Are Real

IT is not a perfectly open meritocracy. Gender and region gaps matter.

OECD research shows women remain underrepresented in ICT specialist jobs. Across OECD countries, men are several times more likely than women to work as ICT specialists, and women represented only a minority of the ICT specialist workforce in the countries studied. That affects networks, mentorship, promotion, pay, and the feeling of belonging.

Region gaps are also large. Stack Overflow’s 2025 survey shows wide salary differences by country. The same job title can pay very differently in the United States, Germany, the United Kingdom, France, India, Romania, Brazil, Nigeria, the Philippines, or Indonesia. Remote work can narrow some gaps, but it does not erase them. Employers often localize pay, visas restrict mobility, and global competition can pressure rates.

For career planning, this means two things. First, do not compare your early salary only with social-media screenshots from high-cost U.S. markets. Second, do look for ways to access better markets: English fluency, open-source work, internationally legible portfolios, cloud certifications where useful, public writing, remote-friendly communication, and specialization in globally demanded tools.

Product Jobs vs Consulting and Services Jobs

Product companies build and sell software or technology-enabled products. Services and consulting companies sell expertise, implementation, customization, support, outsourcing, or transformation projects.

Product jobs can offer deeper ownership. You may work on one platform for years, understand users, improve architecture, and see the compounding effect of good decisions. Pay can be high, especially at successful software companies. The downside is that product companies can be cyclical. If growth slows, funding tightens, or strategy changes, teams can be cut quickly.

Consulting and services jobs can offer faster exposure. You may see many clients, industries, systems, and messy real-world constraints. For beginners, this can be a powerful learning environment. Implementation roles in cloud, ERP, CRM, cybersecurity, data, and automation can create strong career capital. The downside is that services work can be deadline-heavy, client-driven, and less focused on long-term technical elegance.

Which is better? For deep engineering craft, product companies often win. For employability, domain exposure, and practical business understanding, services can be excellent. A common path is to start in services or implementation, learn real customer problems, then move into product, architecture, security, or independent consulting later.

Does Open Source Matter?

Open source matters, but not in the simplistic “make a GitHub and get hired” way.

It matters because public work can prove taste, persistence, collaboration, and communication. A small accepted documentation fix, useful bug report, plugin, test improvement, or issue discussion can say more than a polished portfolio project no one uses. Stack Overflow’s survey also shows that connection to an open-source project remains one factor developers consider when judging technology tools, though practical qualities such as API usability, reliability, and quality rank higher.

For hiring, open source helps most when it is relevant to the role. A DevOps candidate contributing to infrastructure tooling, a data engineer improving documentation for a data library, or a frontend developer fixing accessibility issues in a real project has a clearer signal than someone with dozens of unfinished repositories.

If open source feels intimidating, start with documentation, examples, tests, translations, issue reproduction, or small bug fixes. The goal is not fame. The goal is public evidence that you can work in a shared technical environment.

Does AI Matter?

Yes. AI matters a lot, but not because it removes the need to learn.

AI tools are becoming part of ordinary software work. Stack Overflow’s 2025 Developer Survey says most respondents use or plan to use AI tools, and many professional developers use them daily. But the same survey highlights frustration with AI answers that are almost right and with debugging generated code. That is the key lesson: AI raises the value of judgment.

For beginners, AI is both a gift and a trap. It can explain unfamiliar code, generate examples, help you practice, write tests, summarize documentation, and speed up small tasks. But if it does the thinking for you, you may never build the mental model employers pay for. In interviews and real work, you still need to debug, reason, communicate, and own the consequences.

The safest strategy is to become AI-capable, not AI-dependent. Learn fundamentals: programming, databases, networks, security, operating systems, HTTP, cloud basics, data modeling, testing, version control, and clear writing. Then use AI to move faster.

Practical Takeaways

If you are deciding whether to start, use a business-case lens.

The customer is your future employer or client. Their problem is not that they need another person who has completed a course. Their problem is that systems break, data is messy, customers need tools, attackers are active, costs are rising, and AI creates both opportunity and risk.

Your job is to become useful against one of those problems.

Pick a starting lane:

  • Software development if you enjoy building and debugging products.
  • Cybersecurity if you like risk, investigation, systems, and discipline.
  • Data analytics or data engineering if you like measurement, databases, and business questions.
  • Cloud, DevOps, or platform work if you like infrastructure and reliability.
  • Enterprise systems if you like business processes and practical automation.
  • Technical support or implementation if you need a realistic entry point and want exposure to real customers.

Then build a small proof of work. Solve a real problem for a local business, nonprofit, school, friend, or personal workflow. Document what you did, what tradeoffs you made, and what you learned. Employers do not only need code; they need evidence of judgment.

How to Think About the Opportunity

Before spending serious money on a degree, bootcamp, certification, or long unpaid learning path, run a small validation test.

Spend 30 days studying one lane. Build one small project. Talk to five people who work in that lane. Read 20 job postings in your region and 20 remote postings. List the repeated skills. Apply to a few internships, apprenticeships, support roles, junior roles, or freelance tasks if appropriate. Watch what the market says.

If every posting asks for cloud, SQL, Python, Linux, and customer communication, do not spend six months only building a pretty frontend clone. If local employers hire ERP analysts and support engineers, do not ignore that because social media told you every successful person is a Silicon Valley developer.

The smallest sensible test is not “quit everything and become a programmer.” It is “can I become measurably useful in one technical lane within three months?”

Risks, Limits, and Common Mistakes

The biggest mistake is entering IT with a passive learning plan. Watching tutorials is not the same as becoming employable. You need projects, explanations, mistakes, feedback, and real constraints.

The second mistake is chasing only the fashionable layer. AI is important, but many companies still need boring work done well: permissions, integrations, reports, migrations, testing, backups, monitoring, documentation, and support.

The third mistake is ignoring communication. Technical skill gets you considered. Clear writing, calm debugging, honest estimates, and the ability to explain tradeoffs often get you trusted.

The fourth mistake is assuming one region’s job market is universal. Local demand matters. Remote work exists, but global competition is intense.

The fifth mistake is believing that AI either destroys the field or guarantees success. Neither is true. AI changes the work. It makes weak task-only skills less defensible and makes strong problem-solvers more productive.

Final Takeaway

It is still worth starting a career in IT today if you are prepared for a harder, more selective market. The field still has strong pay, global demand, and long-term relevance. But the easy-entry story is gone.

The best path is practical and specific: choose a lane with real demand, learn fundamentals, use AI as a tool, build proof of work, understand business problems, and keep updating your skills. If you want stability, look toward cybersecurity, infrastructure, data engineering, enterprise systems, and regulated-industry technology. If you want upside, look toward AI tooling, security automation, data platforms, fintech, and product engineering.

You can retire in IT, but only if you keep becoming useful as the work changes. That is the real bargain of the field: it can pay well and stay interesting for decades, but it never lets you stop learning.

Sources

Read more