As a third-year student in college, many would argue the ideal scenario is having some sort of plan lined up by mid-March or by the end of April for summer internships. However, that’s hard to achieve in this day and age. Some students are stuck in what I like to call the “application trenches.”
In the age of AI, new large language models (LLMs) are reducing the need for soft skills and basic, entry-level jobs that can provide income to college students looking to grow their skillset and financial portfolio.
You’d expect that in the 21st century, it would be easier to find a job. But, reports have found that “41% of Class of 2025 students had applied to at least one internship through Handshake, compared to 34% of Class of 2023 students at the end of their undergraduate career.” Personally, I have been stuck in the application trenches since late November and early December.
Although the University of Denver (DU) offers the Burwell Career Center for Achievement and Career Peer Advisors, the most they are able to do is direct students to job portals, resume building and personal branding to ace interviews.
While those are important goals of any career center, the solution involves focusing more on the demand for job openings rather than just the supply — especially given that opportunities are declining in the first place. According to Science Direct, innovation plays a direct role in increasing gross domestic product (GDP), as new technologies and more efficient processes allow businesses to produce more value and expand economic output. This connection suggests that fostering innovation could help counteract the decline in available internships by stimulating job creation.
As Adam Smith notes, the “division of labor enhances productivity by increasing workers’ specialization,” allowing workers to become more efficient and identify improvements in the production process.
Similarly, Smith introduces the concept of the division of labor, arguing that specialization improves productivity. As workers focus on specific tasks, they not only become more skilled but also more likely to identify inefficiencies, which can lead to technological improvements and greater overall production.
The simplification of tasks, in this case, can be linked to how AI substitutes for “easy” and manual jobs such as data entry and website management. Basic organizational work — from creating workflows to writing product reports — is now increasingly handled by LLMs that companies use to boost efficiency and streamline operations.
While businesses are still investing in these AI systems, they often see a stronger return on investment by reducing labor costs, consolidating teams, and automating repetitive tasks. In this sense, AI is viewed as more “profitable” not because it eliminates costs entirely, but because it allows companies to produce similar or greater output with fewer human resources.
Smith’s idea of the “simplification of tasks” helps explain why internships are becoming harder to find, as interns typically occupy the lowest rung of the workforce hierarchy. Tasks that were once delegated to interns — such as data entry, basic research or content drafting — are now increasingly handled by AI systems. As a result, many of these entry-level responsibilities have disappeared, reducing the need for interns altogether.
In response, Big Tech companies are prioritizing more “experienced” candidates who already possess technical skills such as programming in languages like Python or Java, data analysis, machine learning fundamentals and familiarity with tools like SQL or cloud platforms. This shift has increased demand for candidates with STEM backgrounds while making it more difficult for students without those skills to gain initial work experience.
But how do we define “experience” in a rapidly evolving technological-centered society? Everyone has access, and access is expanding to reach people from all backgrounds. AI skills are now being taught in a range of classes, including seminars and classes centered on global issues involving writing annotated bibliographies.
Well, my friends, experience is subjective, and thus employers seek out ideal candidates based on buzzwords which an AI resume-scanning platform can generate for them. It’s a wonderful world we live in, isn’t it?
