Site icon Canadian Technology Magazine

AI just took all our jobs — why entry-level roles are under threat and what we can do about it

Sofia Alvarez
AI just took all our jobs

AI just took all our jobs

AI is no longer a distant sci‑fi threat. It’s in products we use every day and in the tools companies are adopting to build faster, cheaper, and at a scale that would have been unimaginable a few years ago. That shift is showing up in the labor market already — not evenly, but concentrated where young, early‑career workers traditionally get their start.

This piece breaks down the most telling evidence: warnings from industry leaders, eye‑opening corporate anecdotes, major studies and reports, and what they collectively suggest about the future of entry‑level jobs by 2030. I’ll also walk through concrete examples, the economic mechanics behind the changes, and practical steps workers, businesses and policymakers should consider.

Table of Contents

🧭 What’s actually happening right now?

There’s a growing chorus of experts and executives saying the same uncomfortable thing: AI is not just augmenting work — in many cases it’s replacing tasks that once required an entry‑level human. What used to be a steady stream of low‑barrier jobs — bank tellers, data entry clerks, junior paralegals, certain types of editorial roles — is being automated or materially reduced by generative AI and other large language models (LLMs).

Some people frame this as “AI will create more new jobs than it destroys,” but others argue that the kinds of jobs created will be fewer, highly concentrated, and demand specialized technical skills. That disagreement matters because if most new roles require advanced training, those displaced from entry‑level positions may not easily transition into them.

📊 The evidence: reports, studies and blunt executive quotes

There are three types of evidence worth paying attention to: public statements by leading technologists and executives, corporate behavior, and academic/econometric studies. Taken together, they form a consistent pattern.

“Rich people are going to use AI to replace workers. It’s going to create massive unemployment and a huge rise in profits.” — perspective echoed by several AI researchers

The Stanford research, in particular, is alarming because it isolates an age cohort and occupations most exposed to AI and finds a roughly 13% relative reduction in employment for young workers in those fields. That reduction is not seen among older, established cohorts in the same roles, suggesting AI is substituting entry‑level tasks rather than displacing veteran workers — at least, for now.

👶 Who is getting hit hardest — and why it matters

The disruption is most visible among entry‑level, early‑career workers. Think bank tellers and clerks, junior paralegals and document reviewers, data entry workers, ticket agents, and similar roles that typically require fewer years of experience and are task‑oriented. Why? Because many of those roles are composed of repeatable, predictable tasks that current AI models handle well.

Entry-level jobs serve a critical social and economic role: they’re the bridge into stable, higher‑paying careers. They allow recent grads and young workers to gain experience, build professional networks, and develop soft skills. If AI eliminates a large portion of those bridges, the downstream consequences are more than individual unemployment — they affect income trajectories, homebuying, career formation, and broader consumer demand.

Here are some concrete categories flagged as declining (based on cross‑report comparisons):

These are often the first paid experiences that young workers accumulate. The loss of these roles risks creating a bottleneck where a whole cohort struggles to get the practical track record employers look for when promoting to mid‑level positions.

🌱 Why the WEF’s “7% net growth” headline is misleading

The World Economic Forum’s headline projection that jobs will grow by 7% can look reassuring — until you unpack the composition of that growth. The gross numbers show around 170 million jobs created and 92 million displaced. But the newly created roles are heavily concentrated in technical and AI‑adjacent fields: big data specialists, FinTech engineers, AI and machine learning specialists, software developers, security specialists, and roles tied to autonomous systems.

That creates two key problems:

  1. Skill mismatch: New jobs often require advanced technical skills. How many entrants can retrain quickly enough to become a “big data specialist” or an “AI engineer” in five years? Not many without substantial training and support.
  2. Scale and demand limits: Even if AI‑related jobs grow 80–120% in percentage terms, the absolute number of humans needed to fill millions of roles is limited. You can’t realistically create tens of millions of truly new, high‑paying AI jobs overnight to absorb displaced workers from vastly different fields.

Put differently, the WEF’s numbers assume waves of retraining and mobility that may not materialize in practice. They also concentrate new employment gains in sectors where the economic winners are often firms that capitalize on scale and automation — which can exacerbate wage inequality.

🕹️ Real‑world examples: coding, game building, and the new “vibe” of work

Anecdotes from startups and builders illustrate the scale of change. One founder reported that an app his small team built — using AI tools for design, copy, data work and large parts of the codebase — required far fewer engineers than a similar project a few years ago. Where once a product might have taken dozens or hundreds of person‑months, the same product can now be prototyped by one or two people in a fraction of the time and with only thousands of dollars of AI API costs.

Another striking example: a solo developer used LLMs and generative tools to build a language‑learning game with speech and translation features in three to four months. Previously, teams of 10 people working for a year or more might have been needed. The biggest manual sticking point remains custom animated sprites and highly bespoke creative assets — though that gap is narrowing rapidly as generative image and video models improve.

These “vibe coding” workflows don’t mean software engineers are obsolete. They do mean that many mid‑task and support roles around development, content generation, translations, and junior QA can be dramatically reduced or redefined.

💰 Productivity gains, inequality and the “capitalist” angle

One economist’s boiled‑down perspective: AI increases productivity — we can get more output for the same or fewer hours worked. That’s good for GDP, theoretically. But productivity gains don’t automatically lead to broad‑based gains for workers. When productivity increases if the capital owners — those who own the AI systems — capture most of the value, profits rise but wage growth may lag.

Some leading researchers have framed the risk bluntly: AI may make a few people and firms much richer while leaving most people worse off. This outcome is not caused by AI alone, but by how the economic system allocates the gains from automation. If displaced workers can’t access the new high‑skilled roles, or if wages for the remaining roles stagnate, you end up with rising inequality and smaller middle‑class employment.

Two macro implications commonly discussed:

🎓 What employers, platforms, and governments are doing (and should do)

Some industry players are beginning to react. One major AI developer announced initiatives that sound like a blend of job placement and education: an AI‑driven training “university” and job‑matching platform intended to help workers learn AI tools and find roles that use them. The classes themselves are often taught or supported by AI, meaning scale can be extremely high and marginal cost low.

These are promising directions, but several caveats matter:

Other policy ideas under consideration or debate include:

🛠️ How workers can prepare — practical steps

If you’re starting your career or thinking about how to remain resilient, here are pragmatic strategies:

None of these are guarantees, but they improve resilience and adaptability in a labor market where tasks — not always entire jobs — are the unit of change.

🔍 Limitations and uncertainty: what we don’t know

Forecasting is hard, especially when the technology is accelerating. A few open questions matter:

Researchers caution it’s still early to draw firm causal links between AI adoption and macro employment trends. Yet the pattern is consistent with AI substituting entry‑level tasks and enabling higher productivity. That’s an important signal, not definitive proof, but a signal policymakers and organizations should take seriously.

🔔 Conclusion: the future won’t be the same — and we need to act

There’s no single “AI will take all our jobs” verdict that fits every sector. What is clear is this: AI is already reshaping the entry points to the labor market. The data and corporate signals suggest a meaningful reduction in the availability of low‑barrier, early‑career roles in AI‑exposed fields. That creates a policy and moral problem. If young workers can’t get those first steps, entire career pathways narrow and inequality can deepen.

We should prepare for a world where productivity soars and job composition changes. That means rethinking how entry‑level experience is earned and validated, investing in accessible retraining and apprenticeship systems, and designing safety nets that allow people to retrain without losing their lives. It also means asking the economic question plainly: who will capture the gains from this productivity — and how can society ensure those gains support broadly shared prosperity?

Ignoring the problem because “new jobs will appear” is a passive strategy. The data suggests a proactive approach — by companies, educators, and governments — is required to ensure that automation’s benefits don’t concentrate among a small few while the rest of the population bears the cost.

❓ FAQ

Will AI take all jobs?

No — not all jobs. But AI is highly effective at automating repeatable, predictable tasks. Jobs made primarily of such tasks are at the highest risk. Roles requiring deep human judgment, complex social interactions, or highly bespoke creativity are currently less exposed, though the boundary is shifting.

Is the pessimistic view — that entry‑level jobs will disappear — supported by evidence?

There’s growing evidence supporting that concern. Multiple studies and corporate data show notable declines in employment for early‑career workers in AI‑exposed occupations. The most rigorous work so far suggests a relative employment decline of roughly 13% among 22–25‑year‑olds in highly exposed fields compared to their peers.

Won’t new AI jobs absorb displaced workers?

Some new jobs will be created, often in AI, data, security, and autonomous tech. But those roles typically require higher technical skills. The scale of hires and the speed of retraining will determine whether displaced workers can realistically move into these positions. The simple math and skill mismatch make this an uncertain path for many.

What should a young person entering the job market do?

Focus on hybrid skills: domain knowledge combined with AI literacy. Seek apprenticeships, real project work, and roles that develop human capabilities that AI struggles with (empathy, persuasion, complex negotiation). Learn to use AI tools to increase productivity — that skillset will be valuable across many jobs.

What can policymakers do?

Policy options include funding large‑scale retraining and apprenticeship programs, supporting paid internships for young workers, experimenting with income supports during retraining, incentivizing firms to employ and train early career hires, and requiring impact assessments for large labor‑displacing AI deployments.

Are tech companies doing enough?

Some are investing in training and job‑placement programs, but progress varies. Private initiatives can scale quickly, yet public oversight and public investment are necessary to ensure equitable access and to deal with the broader societal impacts.

How will inequality be affected?

AI could increase productivity and overall wealth. But unless gains are shared, inequality may rise: owners of capital and highly skilled workers capture a disproportionate share, while displaced early‑career workers lose out. Fiscal policy and corporate behavior will play decisive roles.

Is it too late to act?

It’s not too late, but the window to shape outcomes is closing. Early investments in retraining infrastructure, apprenticeships, and inclusive policies can smooth transitions. The sooner societies act, the better the chances of minimizing harm and maximizing shared benefits.

What are the most immediate signs to watch?

Watch labor market flows for early‑career cohorts, corporate adoption rates of generative AI in production environments (especially where tasks are repeatable), the scale and uptake of AI training programs, and whether new job growth is concentrated in a small number of technical roles or broadly diffused across sectors.

How can employers help?

Offer paid internships and apprenticeships, design jobs that combine human oversight with AI tools, invest in reskilling current staff, and collaborate with public institutions to build credible, transferable certification for AI‑augmented skillsets.

Where can I learn more?

Look for recent labor economics research on AI exposure by occupation, reports from major global institutions, and case studies of firms adopting AI in production. Also watch job demand trends for “generative AI” and “AI tool” skills — these are rising fast and indicate where employers are investing.

 

Exit mobile version