Tools & Platforms

AI is gutting the next generation of talent: In tech, job openings for new grads have already been halved

Published

1 month ago

August 15, 2025

Kenneth Kang, a computer science graduate, spent his first year out of college applying for more than 2,500 jobs. He got 10 interviews.

“It was very devastating,” he told Fortune. “Honestly, I thought that having a 3.98 GPA, getting recognition letters, and having an interesting experience in the past, perhaps I could get a full-time job offer easily. But that was not true.”

Kang, who lives in Portland, Oregon, eventually landed a job at Adidas, where he had interned the previous summer, after more than 10 months of endless job applications. His experience is actually better than that of many of his fellow grads; one of his classmates, he said, has been on the job hunt for two years.

For many new graduates, the first rung of the corporate ladder is getting harder to reach. Entry-level roles, typically defined as positions requiring no more than one year of prior full-time experience and providing on-the-job training, are becoming increasingly rare in many white-collar industries.

Job postings are down, internships are converting to fewer permanent roles, and some employers now expect “entry-level” hires to arrive with skills once taught in-house.

Artificial intelligence is accelerating this trend by automating junior-level tasks and giving companies an incentive to delay or reduce early-career hiring. Experts warn that while this may cut costs in the short term, it could weaken the leadership pipeline in the years ahead. In the tech sector, hiring for new graduates in the 15 largest companies fell by over 50% since 2019, according to a report from VC firm SignalFire. Before the pandemic, new graduates made up 15% of Big Tech hires, now, that number has dropped to just 7%.

“I feel like it’s getting worse over time,” Kang said. “AI is taking over, which is creating limited jobs or just pushing companies to look for very high-level candidates. I feel like it’s very unfair.”

A weak training pipeline

For the last few decades, climbing the corporate ladder has been a relatively straightforward process. Graduates started with an entry-level role where companies invested in training and development before steadily promoting from within.

Truly entry-level roles have been disappearing for some time as companies increasingly expect new hires to arrive with skills and experience that once would have been taught on the job. But as the AI efficiency drive eats away at even more entry-level roles, the hiring market for new grads is getting difficult.

Companies are being more selective about who and where they hire while they attempt to integrate AI, and entry-level roles are feeling the worst of this impact because the technology is particularly effective at automating tasks handled by junior workers, such as data cleaning, summarization, and basic QA.

According to data from Handshake, a Gen Z-focused career platform, entry-level job postings for traditional corporate roles decreased by approximately 15% last year.

Internship conversion rates are also slipping. In 2023–24, only 62% of interns received full-time offers, pushing the overall conversion rate below 51%, the lowest in more than five years, according to the National Association of Colleges and Employers. Hybrid interns converted at even lower rates than those who were in person.

One of the ways to succeed in the age of AI is to leverage expertise to work productively with more generalized AI tools. But if traditional career trajectories run dry, what happens when companies run out of experts?

“I’m sure there’s going to be big skills gaps,” Stella Pachidi, a Senior Lecturer in Technology and Work at King’s Business School, said. “I think that the traditional ways in which we have seen people developing expertise could easily vanish.”

Studies are increasingly pointing to AI as one of the drivers behind the shrinking job market, particularly for entry-level roles. In the U.S., in the first seven months of 2025 alone, AI was cited as a reason for just over 10,000 job cuts, according to new data from outplacement firm Challenger, Gray & Christmas. The firm ranks AI among the top five stated causes of workforce reductions this year.

These disruptions could cause problems down the line.

“If a lot of firms are cutting, cutting, cutting at the entry level, there’s a fear that they might actually miss out on the talent that’s going to create their pipeline going forward, that’s going to become the managers, executives, etc.,” Tristan L. Botelho, associate professor of organizational behavior at Yale School of Management, told Fortune.

“Everyone is just focused on the current efficiencies and not necessarily thinking further about the future,” Pachidi added. “How will their organizations be doing? What kind of value will they be creating in the future, and will they have experts?”

Concern about tomorrow’s talent

A looming skills gap is something a lot of executives are worrying about, according to Nick South, the managing director and senior partner of Boston Consulting Group.

Although he sees the disruption of entry-level jobs as a “short-term” issue and believes that long-term AI will actually create new jobs, this brief disruption could be a painful one.

“At the point in time for an individual, this is incredibly disruptive, and as a society, we need to help people with reskilling,” he said.

There’s also the question of how to prep young people for an AI world. Some argue that the rise of AI might rewrite the traditional path from education to entry-level jobs entirely.

“The middle ground of knowledge workers is likely to become less important,” Rob Levin, McKinsey senior partner and a leader of QuantumBlack, McKinsey’s AI arm, said. “And I worry about how we are going to incent folks to deeply specialize and get companies to train folks in those deep specialties that they need. Will there be new vocational schools or things?”

At universities, professors and students have both realized that the majority of work done on some university courses can be assisted, if not near-totally automated, by AI.

Students were some of the first to realize ChatGPT’s ability to write essays and to summarize long texts. But while students may find their academic load is significantly lightened by AI tools, professors told Fortune they were worried about the prospect of a generation lacking critical skills and traditional education.

A study from MIT suggested that LLM use can reduce neural engagement and harm learning in students, especially for younger users (researchers caution that the findings are early and not yet peer-reviewed). The study also found that ChatGPT users specifically had the lowest brain engagement and “consistently underperformed at neural, linguistic, and behavioral levels.”

Graduates taking action

Eva Selenko, a professor of work psychology at Loughborough Business School, thinks that educational systems and the job market are more likely to adapt to a generation of AI-boosted talent than to cope.

“I think we need to educate people to use AI tools to the best of their expertise,” she said. “I do absolutely feel for those graduates. On the other hand, you know, they are super, super, highly educated people. They have drive, they have creativity.”

Young job-seekers are already taking some of this upon themselves.

While job hunting, for example, Kang founded a startup as a way to gain experience, since employers were requiring years of experience even for entry-level positions. He formed a group with other computer science graduates in similar situations to do tech consulting for clients at low cost, to build their resumes.

“I’m not just sitting here applying for jobs and just biting my nails,” he said. “I’m here to continually do other activities along the way.”

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Tools & Platforms

The rise of AI tools forces schools to reconsider what counts as cheating

Published

59 minutes ago

September 14, 2025

The Editors

By JOCELYN GECKER
Associated Press

The book report is now a thing of the past. Take-home tests and essays are becoming obsolete.

Student use of artificial intelligence has become so prevalent, high school and college educators say, that to assign writing outside of the classroom is like asking students to cheat.

“The cheating is off the charts. It’s the worst I’ve seen in my entire career,” says Casey Cuny, who has taught English for 23 years. Educators are no longer wondering if students will outsource schoolwork to AI chatbots. “Anything you send home, you have to assume is being AI’ed.”

The question now is how schools can adapt, because many of the teaching and assessment tools that have been used for generations are no longer effective. As AI technology rapidly improves and becomes more entwined with daily life, it is transforming how students learn and study and how teachers teach, and it’s creating new confusion over what constitutes academic dishonesty.

“We have to ask ourselves, what is cheating?” says Cuny, a 2024 recipient of California’s Teacher of the Year award. “Because I think the lines are getting blurred.”

Cuny’s students at Valencia High School in southern California now do most writing in class. He monitors student laptop screens from his desktop, using software that lets him “lock down” their screens or block access to certain sites. He’s also integrating AI into his lessons and teaching students how to use AI as a study aid “to get kids learning with AI instead of cheating with AI.”

In rural Oregon, high school teacher Kelly Gibson has made a similar shift to in-class writing. She is also incorporating more verbal assessments to have students talk through their understanding of assigned reading.

“I used to give a writing prompt and say, ‘In two weeks, I want a five-paragraph essay,’” says Gibson. “These days, I can’t do that. That’s almost begging teenagers to cheat.”

Take, for example, a once typical high school English assignment: Write an essay that explains the relevance of social class in “The Great Gatsby.” Many students say their first instinct is now to ask ChatGPT for help “brainstorming.” Within seconds, ChatGPT yields a list of essay ideas, plus examples and quotes to back them up. The chatbot ends by asking if it can do more: “Would you like help writing any part of the essay? I can help you draft an introduction or outline a paragraph!”

Students are uncertain when AI usage is out of bounds

Students say they often turn to AI with good intentions for things like research, editing or help reading difficult texts. But AI offers unprecedented temptation, and it’s sometimes hard to know where to draw the line.

College sophomore Lily Brown, a psychology major at an East Coast liberal arts school, relies on ChatGPT to help outline essays because she struggles putting the pieces together herself. ChatGPT also helped her through a freshman philosophy class, where assigned reading “felt like a different language” until she read AI summaries of the texts.

“Sometimes I feel bad using ChatGPT to summarize reading, because I wonder, is this cheating? Is helping me form outlines cheating? If I write an essay in my own words and ask how to improve it, or when it starts to edit my essay, is that cheating?”

Her class syllabi say things like: “Don’t use AI to write essays and to form thoughts,” she says, but that leaves a lot of grey area. Students say they often shy away from asking teachers for clarity because admitting to any AI use could flag them as a cheater.

Schools tend to leave AI policies to teachers, which often means that rules vary widely within the same school. Some educators, for example, welcome the use of Grammarly.com, an AI-powered writing assistant, to check grammar. Others forbid it, noting the tool also offers to rewrite sentences.

“Whether you can use AI or not depends on each classroom. That can get confusing,” says Valencia 11th grader Jolie Lahey. She credits Cuny with teaching her sophomore English class a variety of AI skills like how to upload study guides to ChatGPT and have the chatbot quiz them, and then explain problems they got wrong.

But this year, her teachers have strict “No AI” policies. “It’s such a helpful tool. And if we’re not allowed to use it that just doesn’t make sense,” Lahey says. “It feels outdated.”

Schools are introducing guidelines, gradually

Many schools initially banned use of AI after ChatGPT launched in late 2022. But views on the role of artificial intelligence in education have shifted dramatically. The term “AI literacy” has become a buzzword of the back-to-school season, with a focus on how to balance the strengths of AI with its risks and challenges.

Over the summer, several colleges and universities convened their AI task forces to draft more detailed guidelines or provide faculty with new instructions.

The University of California, Berkeley emailed all faculty new AI guidance that instructs them to “include a clear statement on their syllabus about course expectations” around AI use. The guidance offered language for three sample syllabus statements — for courses that require AI, ban AI in and out of class, or allow some AI use.

“In the absence of such a statement, students may be more likely to use these technologies inappropriately,” the email said, stressing that AI is “creating new confusion about what might constitute legitimate methods for completing student work.”

Carnegie Mellon University has seen a huge uptick in academic responsibility violations due to AI, but often students aren’t aware they’ve done anything wrong, says Rebekah Fitzsimmons, chair of the AI faculty advising committee at the university’s Heinz College of Information Systems and Public Policy.

For example, one student who is learning English wrote an assignment in his native language and used DeepL, an AI-powered translation tool, to translate his work to English. But he didn’t realize the platform also altered his language, which was flagged by an AI detector.

Enforcing academic integrity policies has become more complicated, since use of AI is hard to spot and even harder to prove, Fitzsimmons said. Faculty are allowed flexibility when they believe a student has unintentionally crossed a line, but are now more hesitant to point out violations because they don’t want to accuse students unfairly. Students worry that if they are falsely accused, there is no way to prove their innocence.

Over the summer, Fitzsimmons helped draft detailed new guidelines for students and faculty that strive to create more clarity. Faculty have been told a blanket ban on AI “is not a viable policy” unless instructors make changes to the way they teach and assess students. A lot of faculty are doing away with take-home exams. Some have returned to pen and paper tests in class, she said, and others have moved to “flipped classrooms,” where homework is done in class.

Emily DeJeu, who teaches communication courses at Carnegie Mellon’s business school, has eliminated writing assignments as homework and replaced them with in-class quizzes done on laptops in “a lockdown browser” that blocks students from leaving the quiz screen.

“To expect an 18-year-old to exercise great discipline is unreasonable,” DeJeu said. “That’s why it’s up to instructors to put up guardrails.”

___

The Associated Press’ education coverage receives financial support from multiple private foundations. AP is solely responsible for all content. Find AP’s standards for working with philanthropies, a list of supporters and funded coverage areas at AP.org.

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Tools & Platforms

Vibe coding has turned senior devs into ‘AI babysitters,’ but they say it’s worth it

Published

2 hours ago

September 14, 2025

Dominic-Madori Davis

Carla Rover once spent 30 minutes sobbing after having to restart a project she vibe coded.

Rover has been in the industry for 15 years, mainly working as a web developer. She’s now building a startup, alongside her son, that creates custom machine learning models for marketplaces.

She called vibe coding a beautiful, endless cocktail napkin on which one can perpetually sketch ideas. But dealing with AI-generated code that one hopes to use in production can be “worse than babysitting,” she said, as these AI models can mess up work in ways that are hard to predict.

She had turned to AI coding in a need for speed with her startup, as is the promise of AI tools.

“Because I needed to be quick and impressive, I took a shortcut and did not scan those files after the automated review,” she said. “When I did do it manually, I found so much wrong. When I used a third-party tool, I found more. And I learned my lesson.”

She and her son wound up restarting their whole project — hence the tears. “I handed it off like the copilot was an employee,” she said. “It isn’t.”

Rover is like many experienced programmers turning to AI for coding help. But such programmers are also finding themselves acting like AI babysitters — rewriting and fact-checking the code the AI spits out.

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A recent report by content delivery platform company Fastly found that at least 95% of the nearly 800 developers it surveyed said they spend extra time fixing AI-generated code, with the load of such verification falling most heavily on the shoulders of senior developers.

These experienced coders have discovered issues with AI-generated code ranging from hallucinating package names to deleting important information and security risks. Left unchecked, AI code can leave a product far more buggy than what humans would produce.

Working with AI-generated code has become such a problem that it’s given rise to a new corporate coding job known as “vibe code cleanup specialist.”

TechCrunch spoke to experienced coders about their time using AI-generated code about what they see as the future of vibe coding. Thoughts varied, but one thing remained certain: The technology still has a long way to go.

“Using a coding co-pilot is kind of like giving a coffee pot to a smart six-year-old and saying, ‘Please take this into the dining room and pour coffee for the family,’” Rover said.

Can they do it? Possibly. Could they fail? Definitely. And most likely, if they do fail, they aren’t going to tell you. “It doesn’t make the kid less clever,” she continued. “It just means you can’t delegate [a task] like that completely.”

“You’re absolutely right!”

Feridoon Malekzadeh also compared vibe coding to a child.

He’s worked in the industry for more than 20 years, holding various roles in product development, software, and design. He’s building his own startup and heavily using vibe-coding platform Lovable, he said. For fun, he also vibe codes apps like one that generates Gen Alpha slang for Boomers.

He likes that he’s able to work alone on projects, saving time and money, but agrees that vibe coding is not like hiring an intern or a junior coder. Instead, vibe coding is akin to “hiring your stubborn, insolent teenager to help you do something,” he told TechCrunch.

“You have to ask them 15 times to do something,” he said. “In the end, they do some of what you asked, some stuff you didn’t ask for, and they break a bunch of things along the way.”

Malekzadeh estimates he spends around 50% of his time writing requirements, 10% to 20% of his time on vibe coding, and 30% to 40% of his time on vibe fixing — remedying the bugs and “unnecessary script” created by AI-written code.

He also doesn’t think vibe coding is the best at systems thinking — the process of seeing how a complex problem could impact an overall result. AI-generated code, he said, tries to solve more surface-level problems.

“If you’re creating a feature that should be broadly available in your product, a good engineer would create that once and make it available everywhere that it’s needed,” Malekzadeh said. “Vibe coding will create something five different times, five different ways, if it’s needed in five different places. It leads to a lot of confusion, not only for the user, but for the model.”

Meanwhile, Rover finds that AI “runs into a wall” when data conflicts with what it was hard-coded to do. “It can offer misleading advice, leave out key elements that are vital, or insert itself into a thought pathway you’re developing,” she said.

She also found that rather than admit to making errors, it will manufacture results.

She shared another example with TechCrunch, where she questioned the results an AI model initially gave her. The model started to give a detailed explanation pretending it used the data she uploaded. Only when she called it out did the AI model confess.

“It freaked me out because it sounded like a toxic co-worker,” she said.

On top of this, there are the security concerns.

Austin Spires is the senior director of developer enablement at Fastly and has been coding since the early 2000s.

He’s found through his own experience — along with chatting with customers — that vibe code likes to build what is quick rather than what is “right.” This may introduce vulnerabilities to the code of the kind that very new programmers tend to make, he said.

“What often happens is the engineer needs to review the code, correct the agent, and tell the agent that they made a mistake,” Spires told TechCrunch. “This pattern is why we’ve seen the trope of ‘you’re absolutely right’ appear over social media.”

He’s referring to how AI models, like Anthropic Claude, tend to respond “you’re absolutely right” when called out on their mistakes.

Mike Arrowsmith, the chief technology officer at the IT management software company NinjaOne, has been in software engineering and security for around 20 years. He said that vibe coding is creating a new generation of IT and security blind spots to which young startups in particular are susceptible.

“Vibe coding often bypasses the rigorous review processes that are foundational to traditional coding and crucial to catching vulnerabilities,” he told TechCrunch.

NinjaOne, he said, counters this by encouraging “safe vibe coding,” where approved AI tools have access controls, along with mandatory peer review and, of course, security scanning.

The new normal

While nearly everyone we spoke to agrees that AI-generated code and vibe-coding platforms are useful in many situations — like mocking up ideas — they all agree that human review is essential before building a business on it.

“That cocktail napkin is not a business model,” Rover said. “You have to balance the ease with insight.”

But for all the lamenting on its errors, vibe coding has changed the present and the future of the job.

Rover said vibe coding helped her tremendously in crafting a better user interface. Malekzadeh simply said that, despite the time he spends fixing code, he still gets more done with AI coders than without them.

“‘Every technology carries its own negativity, which is invented at the same time as technical progress,” Malekzadeh said, quoting the French theorist Paul Virilio, who spoke about inventing the shipwreck along with the ship.

The pros far outweigh the cons.

The Fastly survey found that senior developers were twice as likely to put AI-generated code into production compared to junior developers, saying that the technology helped them work faster.

Vibe coding is also part of Spires’ coding routine. He uses AI coding agents on several platforms for both front-end and back-end personal projects. He called the technology a mixed experience but said it’s good in helping with prototyping, building out boilerplate, or scaffolding out a test; it removes menial tasks so that engineers can focus on building, shipping, and scaling products.

It seems the extra hours spent combing through the vibe weeds will simply become a tolerated tax on using the innovation.

Elvis Kimara, a young engineer, is learning that now. He just graduated with a master’s in AI and is building an AI-powered marketplace.

Like many coders, he said vibe coding has made his job harder and has often found vibe coding a joyless experience.

“There’s no more dopamine from solving a problem by myself. The AI just figures it out,” he said. At one of his last jobs, he said senior developers didn’t look to help young coders as much — some not understanding new vibe-coding models, while others delegated mentorship tasks to said AI models.

But, he said, “the pros far outweigh the cons,” and he’s prepared to pay the innovation tax.

“We won’t just be writing code; we’ll be guiding AI systems, taking accountability when things break, and acting more like consultants to machines,” Kimara said of the new normal for which he’s preparing.

“Even as I grow into a senior role, I’ll keep using it,” he continued. “It’s been a real accelerator for me. I make sure I review every line of AI-generated code so I learn even faster from it.”

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