Companies replaced entry-level workers with AI. Now they are paying the price

Isaac, 33, has been a mid-level software development engineer at a Big Tech firm for four years, and noticed entry-level job postings dropping at his workplace at the start of 2025. The work, however, didn’t vanish with them. Tasks once handled by junior engineers—like writing and testing code, fixing bugs, and contributing to development projects—were absorbed by senior staff, often with the assumption that AI would make up the difference.

And while AI has sped up the velocity of shipping code and features, there are fewer people to do tasks like designing, testing, and working with stakeholders, which AI has zero grasp on. The cracks have been hard to ignore. “Seniors are burning out, and when they leave, there’s no rush to replace them, because ‘the AI will do it’!” Isaac says. Worried that he’ll become the next strung-out senior, he’s looking for his exit, ideally at a smaller tech firm. (Isaac spoke to Fast Company under a pseudonym to avoid possible retaliation.)

The shift is striking, given how recently corporate America was courting Gen Z with fanatic fervor. Organizations raced to prove they understood younger employees. They flooded LinkedIn with thought leadership on the multigenerational workplace of the future, and retooled benefits programs to include wellness stipends and mental health days. Reverse mentorship programs, through which younger employees share knowledge and perspectives with more senior colleagues—touted by companies like Target, Accenture, and PwC—promised to give junior employees a voice in shaping culture and strategy. Some firms even brought Gen Z voices into the boardroom.

Yet now, in the case of firms like Isaac’s, entry-level workers, once heralded as essential to innovation and growth, are struggling to get a toe—let alone a foot—in the door. Internships, starter jobs, and junior roles, the indispensable on-ramps to white-collar careers, have been evaporating for several years due to cost pressures and post-pandemic belt-tightening. Since 2023, entry-level job postings in the U.S. have sunk 35%, according to labor research firm Revelio Labs.

The advent of AI is accelerating the entry-level apocalypse. Two-fifths of global leaders revealed that entry-level roles have already been reduced or cut due to efficiencies made by AI conducting research, admin, and briefing tasks, and 43% expect this to happen in the next year.

“While there’s steady hiring or even growth in the skilled trades, we’re seeing entry-level vacancies fall significantly in tech and customer service and sales roles,” says Mona Mourshed, founder of the workplace development nonprofit Generation. “Being in the business of training and placing people into entry-level roles, we find it deeply concerning.” Graduates are clearly not okay—but neither are the companies that decided they could do without them.

AI at work: the supercar with no driver

The logic was seductive in its simplicity. Cut costs, move faster, shrink training budgets, let AI and a leaner workforce handle the rest. In reality, it’s producing something else entirely: flattened teams with little agency, endless cycles of rework, and exhausted senior employees juggling all task levels at once. 

One redditor who posted about how their company has stopped hiring entry-level engineers, received hundreds of other responses as others chiming in with similar stories. One commenter noted:  “Not sure what the plan will be after the knowledge transfer is over.”

Isaac has watched this dynamic unfold firsthand. Leaders at his company see AI as a force multiplier, and are fixated on shipping features quickly. Isaac can see their point: “[AI] can straight up write better, faster, more legible code than most developers,” he admits. However, he points out, “any seasoned engineer knows the hard part isn’t writing the code, it’s the design and testing.” Yet, there’s far fewer people to delegate this work to, so senior developers are left to do this on their own.

Compounding the problem is the fact that AI doesn’t understand the problem it’s meant to solve. Left unchecked, it can go rogue. Isaac recalls multiple instances of chatbots deleting production stacks—unprompted—because they couldn’t figure out how to solve an issue. “Without an expert who knows how to prompt and guide it, AI is just a supercar with no driver,” he says. The team has seen their workload steadily increase in line with automation, so the time savings it creates have had little impact. Many seniors have checked out, with several burned out engineers signed off for medical leave.  

Research from the project management platform Asana underscores this growing “efficiency illusion.” While 77% of workers are already using AI agents and expect to hand more off to them in the next year, nearly two-thirds say the tools are unreliable, and more than half say agents confidently produce incorrect or misleading information. The result is time down the drain: a U.S. study found that employees are spending an extra 4.5 hours a week fixing AI workslop. 

“AI can make work look faster on the surface, but it can also create a lot of cleanup work—double-checking outputs, correcting errors, and redoing steps that were based on faulty information,” Mark Hoffman, Asana’s Work Innovation Lead, tells Fast Company. When something goes wrong, accountability is murky, he adds, and the responsibility often falls back on the employee to catch errors, explain outcomes, and manage the risk. It’s driving up already record-high levels of burnout; 77% of knowledge workers say their workloads are unmanageable, and 84% are digitally exhausted.

When errors slip through, the consequences are costly and embarrassing. Three-quarters of Americans report at least one negative consequence from poor AI outputs, including work rejected by stakeholders (28%), security incidents (27%), and customer complaints (25%). In October, Deloitte was forced to refund the Australian Department of Employment and Workplace Relations after a report was found to contain AI hallucinations and workslop. In the past, newbie consultants would have handled tasks such as this. However, notably, Deloitte cut its graduate cohort by 18% and slashed hundreds of early-career roles earlier that summer. 

The demographic time bomb

Not only are workloads increasing, by hollowing out their junior ranks, businesses are putting themselves squarely in the path of a slow-burning demographic time bomb as seniors begin to retire in record numbers.

From 2024 to 2032, 18.4 million experienced workers age 55 to 64 with postsecondary education are expected to retire, but only 13.8 million younger workers (currently age 16 to 24) are entering with equivalent qualifications. Even in an AI-powered economy, where certain jobs will be automated, companies still need humans with judgment-, context-, institutional-, and sector-specific insight. 

Yet plenty are making moves—at least for today—to wipe out the training ground that turns beginners into experts.

“There won’t be an endless supply of experienced hires to fall back on, so everyone will be fighting for the limited, increasingly expensive talent with domain expertise,” says Cali Williams Yost, futurist and founder of flexible-work consulting firm Flex+Strategy Group. “Companies have maybe five years to train younger workers to take over and gain the niche knowledge, so AI has something to augment.”

Moe Hutt, an entry-level recruitment marketing expert and director of consulting at recruitment marketing agency HireClix, has watched clients scale back or abandon entry-level hiring, citing AI-aided workflows and economic uncertainty. Hutt points to the less visible fallout within organizations beyond damaging the talent pipeline. “It’s human nature to want to help,” she says. “When there’s no release valve of training juniors, it creates friction everywhere.” 

For middle and senior management, delegating, teaching, and watching someone grow is a reward for the experience. Research consistently shows that sharing knowledge and mentoring improves motivation, boosts psychological well-being, and reduces burnout among experienced employees. With no one to train or teach, disengagement spreads, eroding a workforce where most people have already checked out.

Being AI-savvy and being prepared for the demographic cliff aren’t mutually exclusive. Organizations can build pro-worker environments where employees are augmented with AI, without hollowing out their future talent pipelines. PwC—admittedly, another firm which has been open about its cuts to entry-level recruiting, at least in the U.K.—is experimenting with what that balance could look like by training junior accountants to become managers of AI. Entry-level employees gain early exposure to leadership and accountability, while the firm builds a cache of managers that are fluent in both human judgment and machine output. It’s proof that efficiency and succession planning can coexist.  

This matters because disappearing entry-level jobs aren’t just a problem for the corporate workforce—it will be a societal crisis, too. A functioning society depends on younger generations steadily taking over from older ones. 
AI might be able to write the code, but without people trained to guide it, question it, and eventually replace their elders, there will be no one left to keep the lights on.