Is AI Removing the First Rung of the Career Ladder?

For generations, the route into professional life has followed a relatively predictable pattern. You graduate, secure a junior position, take on the less glamorous tasks, learn from more experienced colleagues and gradually build the judgement required to progress.

Artificial intelligence is beginning to disrupt that model.

The concern is not simply that AI could replace jobs. It is that many of the tasks AI performs particularly well are the exact tasks traditionally given to people at the beginning of their careers: research, data analysis, first drafts, basic coding, document review, administration and customer support.

If those tasks disappear, an uncomfortable question follows: how do people gain the experience required to become tomorrow’s experts if they can no longer access the jobs that once taught them?

Entry-level workers appear to be feeling the impact first

There is growing evidence that early-career employment is under particular pressure.

The World Economic Forum reports that more than one in three young workers globally are employed in occupations with medium to high exposure to AI-driven task change. Separate research cited by the Forum found that US entry-level job postings had fallen by 35% over an 18-month period, with AI identified as a significant contributor.

Research highlights evidence suggesting that younger workers in highly AI-exposed occupations have experienced a noticeably weaker employment market than more experienced workers in the same fields.

AI is not necessarily responsible for every missing vacancy. Economic uncertainty, changing employment costs and cautious hiring also play an important role. CIO notes that blaming every decline in junior recruitment on generative AI would oversimplify what is happening.

Nevertheless, AI is changing the economics of entry-level hiring.

An experienced employee equipped with AI can suddenly complete work that might previously have required several junior employees. From a short-term financial perspective, that can look extremely attractive.

But there is a problem.

We might be automating the way people learn

Entry-level work has never just been about completing basic tasks.

Those tasks are also how people develop professional judgement.

A junior analyst checking datasets learns to recognise when the numbers do not make sense. A graduate consultant conducting research begins to distinguish useful information from noise. A new recruiter speaking to hundreds of candidates develops an understanding of people and markets that cannot simply be downloaded from a textbook.

Technology careers provide an especially clear example.

A junior software developer might traditionally begin by fixing bugs, writing relatively simple pieces of code, documenting systems and supporting testing. Individually, these tasks may seem straightforward. Collectively, however, they teach someone how a production environment works, why particular architectural decisions were made and what happens when something goes wrong.

CIO makes exactly this point: a developer working on smaller defects gradually begins to understand the wider architecture around them. If AI performs much of that work instead, the organisation gains efficiency but potentially removes part of the learning process that turns a junior developer into a senior engineer.

The World Economic Forum has since raised the same question specifically around software engineering: if companies significantly reduce junior developer opportunities today, where will future senior developers come from?

What does this mean for junior tech talent?

For graduates hoping to enter technology, this shift could be particularly significant.

Generative AI tools can already write and explain code, suggest fixes, generate unit tests, produce technical documentation, query data and assist with troubleshooting. These capabilities overlap heavily with work that has historically been allocated to junior developers, engineers, analysts and IT professionals.

That does not mean junior technology careers are disappearing.

It does mean the definition of “junior” is changing.

Employers may increasingly expect entry-level candidates to arrive capable of using AI tools productively rather than spending their first few years learning tasks that AI can already perform. The World Economic Forum recently reported that employers are becoming considerably more likely to expect new entrants to demonstrate skills that previously might have developed later in their careers.

For someone trying to break into software engineering, cloud, data, cybersecurity or infrastructure, simply knowing how to execute a technical task may therefore become less differentiating.

Understanding why something works becomes more important.

A developer who can generate code with AI but cannot explain it, test it, identify security issues or understand how it interacts with the wider system will have limited value.

Likewise, an aspiring data professional needs more than the ability to ask an AI tool to produce a query. They need to understand whether the data is reliable, whether the analysis answers the right question and whether the resulting conclusion makes sense.

The same principle applies in cybersecurity. AI can accelerate investigation, summarise alerts and assist with detection, but junior professionals still need to understand risk, identify unusual behaviour and know when an automated recommendation should be challenged.

This creates a different kind of early-career technology professional: one who is expected to combine technical fundamentals with AI literacy, problem solving and judgement much earlier.

So how should graduates respond?

The answer is unlikely to be competing against AI.

Instead, graduates need to become particularly good at working with it.

AI literacy is rapidly becoming part of basic professional literacy. Young professionals should understand how to use AI tools to research problems, generate ideas, accelerate development and automate routine work.

But equally important is knowing where AI falls short.

Graduates who can question an output, validate its accuracy and recognise when human intervention is required will be more valuable than those who simply know how to produce something quickly.

For aspiring technology professionals, building evidence of real-world ability will also become increasingly important.

Rather than relying solely on a computer science degree or technical certification, candidates can demonstrate skills through personal projects, GitHub repositories, internships, open-source contributions, hackathons, labs or freelance work.

A junior developer who can talk through a system they have built, the problems they encountered and why they made particular technical choices demonstrates something AI cannot easily manufacture: experience of solving an actual problem.

Human capabilities will also remain important. Communication, curiosity, adaptability and the ability to work with non-technical stakeholders are increasingly valuable when technology itself makes technical execution easier.

Employers also have a responsibility

The solution cannot simply be telling graduates to become more employable.

Businesses need to reconsider what entry-level employment looks like in an AI-enabled organisation.

That does not mean preserving repetitive work simply because previous generations had to do it. Automating monotonous processes can be hugely beneficial.

Instead, businesses need to distinguish between low-value work and valuable learning experiences.

The World Economic Forum suggests redesigning junior positions so that newcomers move earlier towards judgement-based work. Entry-level employees could review AI outputs, identify errors, investigate unusual cases, test hypotheses or monitor AI-supported workflows rather than simply executing repetitive tasks themselves.

For technology teams, that might mean changing how junior engineers are developed.

Instead of asking a graduate developer to spend months producing boilerplate code, organisations could use AI to accelerate that work while deliberately exposing them to code reviews, architecture discussions, debugging, customer requirements, testing and production incidents.

The objective should be to use AI to compress the learning curve, rather than remove it.

Mentoring becomes especially important in this environment. Junior employees still need experienced colleagues who can explain why something has been designed a particular way, how technical decisions affect the business and where the risks lie.

Without that knowledge transfer, organisations risk becoming dependent on a shrinking group of experienced professionals while failing to develop the next generation behind them.

Universities and educators need to adapt too

Education cannot remain unchanged while the jobs students are preparing for evolve.

For technology students in particular, teaching someone to produce code that an AI assistant can generate in seconds will not be enough.

Technical fundamentals remain essential, but students increasingly need to understand systems thinking, software architecture, cybersecurity, responsible AI use, critical evaluation and how technology solves real business problems.

Universities and businesses may therefore need to work much more closely together.

Placements, apprenticeships, internships, project-based learning and partnerships with employers can give students exposure to the messy realities of production technology before graduation.

This matters because AI can provide an answer immediately.

What it cannot automatically provide is the years of context that allow an experienced professional to know whether that answer is the right one.

Rebuilding the first rung

AI will undoubtedly remove certain tasks from entry-level jobs. In many cases, that is progress.

The greater danger would be assuming that because AI can perform junior-level tasks, businesses no longer need junior people.

Today’s graduate developers are tomorrow’s software architects. Today’s junior security analysts are tomorrow’s CISOs. Today’s cloud engineers, data analysts and support professionals will become the technology leaders organisations depend on in the future.

Businesses cannot expect an endless supply of experienced technology professionals if nobody is given the opportunity to become experienced.

The challenge is therefore not to protect entry-level work exactly as it exists today.

It is to redesign the first rung of the career ladder.

For graduates, that means developing strong technical foundations while learning how to use AI intelligently and critically. For employers, it means using AI to accelerate junior development rather than simply removing junior headcount. And for educators, it means preparing students for a technology industry where knowing how to perform a task matters less than understanding, challenging and improving the result.

AI may be changing the beginning of our careers.

The question now is whether we allow the first rung of the ladder to disappear, or use technology to build a better one.