For a long time, we were all hand-wringing over the shortage of software developers. School districts rolled out coding curriculums. Colleges debuted software “labs”. “Bootcamps” became a $700m industry.
Today, we have the opposite problem. Thousands of trained, entry-level engineers that no one wants to hire.
Just as software finished eating the world, zero interest rates ended. Companies optimized for cash and slowed hiring. The market didn’t shrink, but stopped growing at the breakneck pace we all expected.
The result: a glut of entry level talent groomed for jobs that never materialized.
This would explain a more competitive entry level market. But it doesn’t explain the entry-level market shrinking, despite overall industry growth.
In short: demand for senior talent is rising, but has fallen off a cliff for juniors.
AI didn’t create this trend - there was already a bias for senior talent pre-2022 - but it gave leaders a convenient justification to exacerbate it.
In terms of speed, price, and quantity, juniors can’t compete with LLMs. Code is now a commodity.
It sounds logical because it’s true, but it misses the big picture.
Here’s the core misunderstanding:
The job of software engineering is not “writing code”.
Coding: translating a process into something a machine can understand and execute
Engineering: sustaining and evolving interconnected, ever-changing systems
When we talk about “the code” we often actually mean the systems that contain code.
These systems are complex. They include layers of interwoven dependencies that evolve unpredictably. Between unique technology choices, historical decisions, and company-specific processes, every system’s “code” is its own special snowflake.
Fail to understand your special snowflake, and things break. Ex: If Apple makes an update, you can’t respond if you don’t know how your mobile app connects to your billing system.
It’s why the best engineers try to write as little code as possible. They understand that each additional line is another thing that must be understood, remembered, and maintained.
That understanding, or institutional knowledge, lives in human minds.
And humans quit. They move. They retire. They die.
This has been and remains the primary function of juniors: to ensure an evergreen supply of future seniors.
Until AI solves human mortality, this will not change.
In fact, for “AI-first” companies, this is doubly true. Absent expertise and institutional knowledge, AI is just expensive (and potentially destructive!) autocomplete.
Companies who have stalled hiring to “wait out” the full impact of AI are prioritizing a hypothetical optimization problem over an impending inevitability.
Not to mention, it screws over an entire generation.
Gen-Z doesn’t know life before the internet. The pandemic stole their formative social and professional years.
To their colleagues - with a shared understanding of How Things Are™ and Back In My Day™ - they’re practically feral.
There’s some scientific truth to this: 20-somethings are inherently narcissistic. Wisdom requires having a full frontal lobe.
But this is what functional societies do. We lament how uniquely terrible the youth are… then teach them anyway.
AI has changed a lot, but it hasn’t eliminated our responsibility to our young people. Leaders who don’t prioritize the next generation are failing to pay forward what was given to them.
Most leaders aren’t trying to undermine over society or create existential risks to business continuity.
But when timelines are tight, budgets are tighter, and things need to get done… “hiring juniors” quickly becomes a nice to have.
Reframe the problem: you’re not “trying to hire juniors”, you’re laying the infrastructure for a fundamentally stronger organization.
An engineering org that “can’t afford” junior talent is incredibly fragile.
Teams that can easily absorb junior talent have systems of resilience to minimize the impact of their mistakes. An intern can’t take down production because no individual engineer could take down production!
This doesn’t mean you need unit tests for every edge case or rock-solid infrastructure. It can be as simple as a formal mentorship program… or implementing common-sense policies like “don’t deploy on Friday”.
These guardrails and programs aren’t “for juniors” - all engineers are benefitting from a system that promotes growth, experimentation, and learning. AKA: The foundation of innovation.
When you get this right, hiring entry-level engineers feels natural and obvious.
That feral hunger and drive (only healthy when you don’t have a full-frontal lobe) is a powerful force. While writing this, I spoke with 24+ technical leaders. Every single one could name an exceptional junior engineer they were mentoring.
"Some of our top engineers at Finley were all entry level hires,” says Kevin Suh, CTO at Finley, “It’s impossible to replicate that energy, internal motivation, and resourcefulness."
This isn’t “grind” or “grit” or “fire” - it’s the miracle of early learning.
The magic of those working toward true mastery is inspiring and necessary in its own right, but proximity to the high-growth only juniors can experience is infectious and energizing.
Perhaps the most compelling reason to hire juniors? AI itself.
The biggest barrier to AI transformation has been workforce buy-in. Gen Z not only leads in AI adoption, but acts as an accelerant: nearly two-thirds of Gen Z workers help their older colleagues learn AI tools and workflows.
At the same time, AI has also brought down the cost of onboarding.
“Our top juniors are now getting up to speed in 3 months,” says Arjun Kannan, CTO of Residesk, “AI has made coaching much easier because the kids are learning the basics on their own.”
If you already have the infrastructure, hiring juniors may be one of the highest-leverage AI bets available today.
“Your greatest alpha as a hiring manager is finding people who are young, smart, and unproven,” says Randy Brown, CTO of Scout, “and that’s outrageously easy to do right now.”
If you can see through the hype, you gain a huge advantage. Not only do you get top talent in a favorable market, you get an injection of eager, learning energy that can propel your entire team into the future.
That future will be shaped by AI, but AI can’t change everything. Systems - and society - still require care and renewal to endure.
You remembered that spring would come again… and faithfully planted seeds.
A huge thank you to the small village that helped read drafts, share quotes, and provide feedback on this article: Kathryn Minshew, Randy Brown, Kevin Suh, Arjun Kannan, Matthew Casey, Charity Majors, Sadam Iqbal, Maria Ashby, @bentloy, and Bea Arthur.
About the Author:
Christine Miao is the creator of technical accounting–the practice of tracking engineering maintenance, resourcing, and architecture. It visualizes the most complex technical problems - think: breaking up monoliths or cleaning up tech debt - in a way that anyone can understand.