The raw numbers
Entry-level hiring in the top 15 tech companies (Google, Meta, Microsoft, Apple, Amazon, etc.) fell 25% from 2023 to 2024. The fall continued through 2025 and accelerated in 2026.
Yale Insights published an analysis under the title: "The Real Job Destruction from AI Is Hitting Before Careers Can Start" — AI's real damage hits before careers begin.
The mechanism
Traditionally, tech companies hired juniors for tasks requiring supervision: simple code reviews, bug triage, documentation, manual testing, data entry. Those were the "five tasks" a senior gave a junior so they could learn.
Now, all those tasks are done by Cursor, Claude Code, Codex. Faster, cheaper, no mentorship needed. The junior becomes obsolete before having experience.
The expert paradox
If no one hires juniors, where do the seniors come from in 5 years? That's the question current management isn't answering. Every generation of seniors was once junior; cutting the pipeline now creates a structural medium-term problem.
Some argue AI will make everyone productive without needing the traditional "career path". Yale is skeptical: tacit knowledge transfers in mentor-junior interaction, not by reading manuals.
Who hires and who doesn't
Reduce hiring: tech giants (Google, Meta, Microsoft), banking, consulting, big law.
Maintain or increase: healthcare (especially nursing), skilled trades (electricians, welders), very specific startups (AI/ML, agentic systems), industries with complicated regulation (compliance officers, AI auditors).
Individual responses
Strategies working for Gen Z looking for entry-level:
(1) Vertical specialization: instead of "software engineer", "ML engineer specialized in biotech compliance". More niche, less AI competition.
(2) Builder portfolio: show finished products, not GPA. Companies value "what you did with AI" more than "what grades you got".
(3) AI fluency: master AI tools. A junior who uses Cursor + Claude well does the work of 3 traditional juniors.
(4) Soft skills: negotiation, presentation, sales — areas where AI is assistant, not replacement.
Responses companies need
For companies aware of the problem:
(1) Rebrand entry-level: instead of "mechanical tasks", convert the first 6 months into structured learning programs with intensive mentorship.
(2) AI-augmented training: give juniors AI tools + supervise how they use them to accelerate the learning curve.
(3) Internal pipeline: paid apprenticeships, "junior fellow"-style programs with growing responsibility.
Universities in crisis
Universities face a secondary problem: if entry-level work disappears, what do they sell with their degrees? Coding bootcamps are in free fall (many closing). MBAs are under pressure too.
Top universities are pivoting: focus on research, AI/ML, scientific areas (not pure application). Second-rate ones are in serious trouble.
The global problem
The phenomenon is not only US. UK, Germany and Japan report similar falls in junior tech hiring. In LATAM, the dynamic is different: local companies have less pressure (yet) and the lower cost of local juniors remains defensible.
But remote jobs from US companies paid in USD also reduced, affecting Latin American developers who depended on that income source.
What we do at VuraOS
As a company building AI products, we try the opposite: we hire juniors and pair them with seniors + AI. The hypothesis: a junior with good mentor and AI tools grows faster than the traditional junior without AI.
So far it works: our juniors deliver senior output by their third month (with supervision). But it requires serious investment in mentorship — something many cost-optimized companies don't want to do.
Conclusion
The entry-level crisis is AI's most underestimated impact on work. It generates a medium-term pipeline problem and an immediate individual problem for millions of young people. The answer isn't pessimism — it's adaptation: specialization, builder mentality, AI fluency. And on the company side: invest in training, don't cut it. Those who do it well will have the best talent in 5 years.