Is entry-level hiring in tech really down, or is it just a temporary cycle?

Multiple datasets point to a sustained decline in new grad hiring relative to 2019 at major tech firms, alongside a notable drop in global entry-level postings since early 2024—signals that look structural, not merely seasonal.

What’s the single biggest reason entry-level roles feel harder to get?

AI makes many junior starter tasks—drafting, summarizing, basic analysis—cheap to generate, so employers push entry-level expectations upward toward judgment and accountability.

If AI can do routine work, what can a new graduate do that still matters?

Build proof of outcomes: ship small projects, document decisions, and show you can define problems, choose tradeoffs, and deliver results using AI as leverage.

Why are recent college grads struggling even when overall unemployment is lower?

Recent graduates’ unemployment and underemployment can remain elevated even when overall unemployment is lower because the bottleneck is specifically in early-career pathways and junior role availability.

Does this mean everyone should become a solo founder?

No. But the rise in solo founders suggests the cost of execution is falling, enabling individuals to produce credible work artifacts faster—useful whether pursuing a job, promotion, or business.

What’s the most practical high-agency metric?

Your say-do ratio: how reliably you turn stated intent into a finished artifact or measurable result.

The Entry-Level Hiring Collapse in Tech: How AI Rewrites Careers in 2026

Maurice Bretzfield
Jan 22
6 min read

From Silicon Valley to Boston, the first rung is vanishing, and careers are shifting from ladder-climbing to proof-building.

Executive Overview

Entry-level hiring at major technology firms has fallen sharply since 2019, with new graduates now representing a much smaller share of new hires, signaling a structural shift rather than a temporary slowdown.
Global data shows a sustained decline in entry-level job postings since early 2024, tightening the funnel for early-career talent across regions and industries.
Recent college graduates face elevated unemployment and underemployment even as overall unemployment remains lower, revealing a specific breakdown in early-career pathways.
Generative AI now performs many of the routine, low-risk tasks that once required newcomers, removing the bottom rungs of the traditional career ladder.
The emerging advantage belongs to individuals who can produce visible proof of outcomes, leverage AI as a multiplier, and maintain a high say-do ratio in their work.

A career once began with work nobody glamorized. You summarized meetings. You cleaned the data. You drafted memos that would be rewritten. You produced the first versions so others could deliver the final word. You learned by doing tasks whose failure cost was low but whose exposure to real organizational dynamics was invaluable. Those tasks were not the work. They were training.

They existed because organizations needed a safe way to turn inexperienced people into capable ones. In exchange for patience, companies received a pipeline of talent that understood how decisions were made, how incentives distorted information, and how judgment formed under pressure.

Then the bargain changed.

The entry-level hiring collapse in tech is not a hiring freeze or a cyclical slowdown; it's a structural re-pricing of how organizations train, trust, and deploy early-career talent in an AI-saturated economy.

What the data is really saying

According to an analysis by SignalFire, entry-level hiring at major technology companies has declined by more than fifty percent since 2019. New graduates now make up a historically small share of Big Tech hiring, even as overall headcount has stabilized in many firms.

Globally, data from Randstad reported by the World Economic Forum shows that job postings requiring zero to two years of experience declined sharply through 2024 and into 2025. This contraction is not isolated to software engineering; it appears across marketing, analytics, operations, and professional services.

In the United States, the unemployment rate for recent college graduates reached 5.3 percent in Q3 2025, higher than the overall unemployment rate of 4.4 percent reported by the Bureau of Labor Statistics later that year. Data from the Federal Reserve Bank of New York shows that underemployment among recent graduates remains unusually high.

This divergence matters. When overall unemployment is lower but early-career unemployment is higher, the problem is not “the economy.” The problem is the on-ramp.

Why entry-level work vanished first

The tasks that once justified hiring newcomers—drafting summaries, preparing reports, cleaning datasets, synthesizing research—are precisely the tasks generative AI now performs cheaply and quickly. This is not about perfection. It is about adequacy.

A first draft does not need judgment. It needs existence.

When AI can generate that existence in seconds, organizations stop paying salaries for it. This is AI replacing entry-level tasks, not because the models are better than humans, but because the economics have flipped. The cost of producing preliminary work has collapsed.

Historically, entry-level roles absorbed inefficiency in exchange for learning. That inefficiency is no longer tolerated when a machine can do the same work faster, cheaper, and without onboarding.

The result is predictable: entry-level jobs now require experience that entry-level roles no longer provide.

The entry-level hiring collapse in tech signals a new career bargain

In the old bargain, organizations paid people to learn. In the new one, they pay people to perform.

This explains why job descriptions labeled “entry-level” now demand autonomy, stakeholder fluency, and judgment. They are junior only in compensation, not in responsibility.

The ladder disappears while people are still standing on it—a literal career ladder collapse unfolding in real time.

This entry-level hiring collapse in tech is most visible in U.S. innovation hubs like San Francisco, Seattle, Austin, and New York, where junior hiring pipelines historically fed the broader technology economy. The shock feels sharper there because the ladder used to be sturdy.

What disappears when the ladder breaks

The most important loss is not jobs. It is apprenticeship.

Routine work once provided three essential functions:

A low-risk environment for mistakes
Repeated exposure to real organizational decisions
Gradual accumulation of tacit knowledge

When those tasks vanish, so does the primary mechanism for developing judgment inside institutions.

Organizations now face a choice. They can intentionally rebuild apprenticeship, or they can accept a brittle talent pipeline fed only by those who arrive already trained—often through privilege, networks, or unpaid labor.

Many will choose the latter by default.

Why “high agency” becomes non-optional

Agency is not optimism. It's the capacity to turn intent into evidence.

In a world where output is abundant, credibility becomes scarce. Employers no longer buy potential. They buy reduced uncertainty. That uncertainty is reduced by proof.

This is why careers increasingly reward individuals who produce artifacts rather than credentials. A shipped project, a documented workflow, a working system—these reduce hiring risk far more effectively than a resume bullet.

The most reliable metric is simple: your say-do ratio.

If you say you can analyze, build, automate, or decide, the market asks one question: Where is the evidence?

The solo-builder signal

At the same time, entry-level hiring contracts signal another trend. The share of startups founded by solo founders has increased markedly since 2019. This is not a coincidence.

AI lowers the cost of execution without lowering the cost of judgment. Individuals can now prototype, test, and ship work that once required teams.

This does not mean everyone should start a company. It means the barrier to producing credible work has fallen, while the barrier to being hired has risen.

The runway is extending even as the ladder collapses.

How to build proof without permission

Waiting for a job to grant you meaningful problems is no longer a reliable strategy. The problems are everywhere; what’s scarce is the willingness to own them.

Pick a domain. Pick a workflow. Rebuild it with AI leverage. Publish the result.

Operations candidates can design intake-to-decision systems. Marketing analysts can automate competitive research and experimentation. Product thinkers can ship small tools with feedback loops. The deliverable is not the output. It is the record of judgment. That record is what machines cannot replace.

What organizations must confront

This is not only a worker problem. It is an organizational one.

Companies that eliminate entry-level work without replacing apprenticeships will struggle to find “experienced” talent later. They will have optimized away their own future.

The solution is not nostalgia. It is a redesign.

Entry-level roles must become structured judgment labs, not task sinks. Novices should own bounded outcomes, work with AI tools, and explain decisions. Learning velocity—not keystrokes—must be the metric.

If organizations fail to do this, apprenticeship will move outside their walls, and talent will arrive already shaped elsewhere.

The future of careers is visible now

AI did not end work. It ended practice as a paid activity.

The people who thrive will not do so because of mindset alone, but because they internalize a different operating model: build proof first, permission second. The alternative no longer produces outcomes.

FAQs

Q: Is entry-level hiring in tech really down, or is this temporary? A: Yes. Multiple datasets show a sustained decline in new-grad hiring relative to 2019, confirming that the tech industry's entry-level hiring collapse is structural rather than cyclical.

Why do entry-level jobs now require experience? A: Because the tasks that once provided training are automated. Employers now expect judgment earlier since routine work no longer justifies hiring.

Q: What can new graduates do that AI cannot? A: Define problems, make tradeoffs, communicate reasoning, and take responsibility for outcomes—especially when using AI as leverage.

Q: Why are recent graduates struggling more than the general workforce? A: Early-career pathways have narrowed, creating higher unemployment and underemployment for new grads even when overall unemployment is lower.

Q: Does this mean everyone should become a founder? A: No. But building visible, outcome-driven work is increasingly essential whether you seek employment, advancement, or independence.