Artificial intelligence is fundamentally changing the landscape for technology graduates entering the workforce, with recent research indicating significant displacement in traditional entry-level positions. A Stanford University study reveals that employment for workers aged 22-25 has declined approximately 13% since generative AI achieved widespread adoption, particularly affecting roles like software development and computer programming.
The findings raise critical questions about career pathways for technology professionals just beginning their journey in cybersecurity, software engineering, and related fields. As AI tools demonstrate increasing capability to handle tasks traditionally assigned to junior staff, the industry faces a fundamental shift in how new professionals build foundational skills and advance their careers.
Cybersecurity Leaders See AI as Tool Rather Than Threat
Despite growing concerns about AI displacement in entry-level technology roles, cybersecurity executives maintain that security positions may prove more resilient than other technical careers. The volume-driven nature of security work creates persistent demand for human expertise that automation cannot fully replace.
Marshall Erwin, chief information security officer at Fastly, argues that security roles face different dynamics than standard development positions. “I think the concern about AI tooling possibly replacing entry-level roles should be a little bit less of a concern when it comes to security roles than other basic developer roles,” Erwin stated. “The basic reason for that is, even putting aside your sort of new AI trends, any security team is really struggling with volume.”
The cybersecurity field confronts an ongoing challenge managing the sheer quantity of alerts, threats, and incidents that security teams encounter daily. Organizations cannot simply expand headcount indefinitely to address these volume issues. Instead, Erwin views AI as enabling human professionals to focus on high-impact problems requiring judgment and expertise.
“I think AI is really going to be helpful in that respect,” Erwin explained. “We need better ways to filter through that noise and manage the volume of what we’re seeing on a day-to-day basis and that’s a problem we’ve had for a long time, and these tools are going to help us with that.”
Entry-Level Roles Will Transform Rather Than Disappear
Jason Manar, CISO at Kaseya, acknowledges that AI will reshape junior positions while maintaining that opportunities will persist for professionals willing to adapt to changing requirements. The transformation represents evolution rather than elimination of entry-level careers.
“There will be new opportunities, and you see this now with various engineering positions where there are more positions for generative AI and new positions within engineering,” Manar noted. “So, is there consolidation that may take place with entry or junior level jobs? Yes, absolutely. Will there still be some type of junior job or entry level job? There will. I just think that’s going to be redefined as to what that is.”
This perspective suggests that while traditional entry-level roles may contract, new categories of junior positions will emerge focused on AI implementation, oversight, and integration. The skills required for these positions will differ from historical entry-level expectations, demanding greater technical sophistication and adaptability.
However, Jessica Sica, CISO at Weave Communications, observes that breaking into cybersecurity was already becoming increasingly difficult even before AI’s current impact. The field paradoxically faces growing demand while simultaneously raising barriers to entry.
“Everybody says the security industry is growing rapidly, but it’s getting harder and harder to get in,” Sica stated. “And I think part of that is maybe more and more people want to get into security, but the entry level jobs I think are getting more difficult. Companies are getting more demanding.”
Sica anticipates AI will accelerate this trend, with automation handling tasks that previously provided learning opportunities for junior professionals. “I think some of those tasks are going to be replaced with AI,” she explained. “Not sure that we’re there yet, but when that happens, how do you get in and how do you move up if those entry-level jobs are getting harder to find?”
Recent Graduate Perspective: Higher Performance Expectations
Mudit Sinha, AI lead at Lineaje, graduated from the University of Massachusetts at Amherst just two years ago and successfully navigated the challenging entry-level market through strategic networking. His experience illustrates both the obstacles and opportunities facing recent graduates.
Sinha secured his first position by directly contacting the company’s chief technology officer, demonstrating initiative that helped him stand out among candidates. “I was looking more into data analytics and a mix of AI back in the day, and the company had just started doing AI,” Sinha recalled. “I did about three to four rounds of interviews, most of them with the CTO, the one with the CEO.”
His recommendation for current graduates emphasizes proactive outreach, personal introductions, and showcasing relevant projects that demonstrate value to potential employers. This approach requires candidates to operate more entrepreneurially than previous generations who could rely primarily on traditional application processes.
Regarding AI’s impact on the cybersecurity industry, Sinha views the technology as elevating performance standards across all experience levels rather than eliminating positions. Junior professionals now face expectations that would have previously applied to more senior roles.
“Everyone is expected to perform at a level higher than they previously were expected to perform,” Sinha explained. “Juniors are now expected to do the same output as, let’s say, senior SDEs (software development engineer) or senior researchers because they have access to this really powerful tool. So, entry level people should not have the expectation that they are going to be treated as entry level applicants.”
Adapting to Elevated Entry-Level Standards
This reality requires fundamental mindset shifts for technology graduates entering the workforce. The traditional apprenticeship model where junior staff learned through relatively simple tasks no longer applies when AI can handle those same assignments more efficiently.
Sinha advises candidates to embrace this challenge by deeply understanding AI capabilities, maintaining genuine interest in cybersecurity beyond job security considerations, and developing strong interpersonal skills that remain distinctly human.
“Ultimately people like interacting with other people, and that interaction piece is not really going away with AI, as far as I’ve seen,” Sinha noted. “Be sociable, be open to opportunities, and if you have the opportunity, keep talking to upper management or whoever you can.”
The emphasis on soft skills reflects recognition that while technical capabilities matter, communication, collaboration, and relationship-building provide competitive advantages that AI cannot replicate. These human-centered skills may prove more valuable than ever as routine technical tasks become automated.
Implications for Technology Career Development
The Stanford study’s findings combined with industry leader perspectives paint a complex picture for technology graduates. Traditional pathways into the field face disruption, but opportunities remain for candidates who approach their careers strategically.
Key adaptations include accepting that entry-level no longer means basic expectations, investing in continuous learning about AI tools and applications, developing strong interpersonal and communication skills, networking proactively rather than relying solely on formal applications, and demonstrating capability to perform at levels previously expected of more experienced professionals.
The transformation also raises questions about how the industry develops talent when traditional learning opportunities disappear. If AI handles tasks that previously helped junior professionals build skills, organizations must create new mechanisms for developing expertise and institutional knowledge.
For educational institutions, the shift demands curriculum adjustments that prepare graduates for elevated entry-level expectations. Technical proficiency alone no longer suffices when AI can match or exceed human performance on many coding and analysis tasks. Programs must emphasize problem-solving, system thinking, and human judgment alongside technical skills.
The cybersecurity field’s persistent talent shortage suggests the industry cannot afford to eliminate entry-level positions entirely. However, the nature of those positions will continue evolving as AI capabilities expand. Graduates who recognize this reality and position themselves accordingly will find opportunities, while those expecting traditional career progressions may struggle to gain footing in an increasingly competitive landscape.