Variation 123: Will AI Displace Jobs, and Should the So-Called Corporate “Bullshit Jobs” Be the First to Go?

Title: Will AI Eliminate “Bullshit Jobs” Before More Traditional Roles?

As artificial intelligence continues to advance, many are questioning which jobs will be affected first. A common assumption is that AI will render many roles obsolete, but the order of which jobs disappear first isn’t entirely clear. Specifically, there’s an interesting debate about the so-called “bullshit jobs”—positions often criticized for their lack of tangible productivity, such as project managers, consultants, and other corporate administrative roles.

These roles—frequently involving preparing PowerPoint presentations, responding to endless emails, and participating in unproductive meetings—are often viewed as unnecessary or redundant. Given their nature, one might expect AI to automate or eliminate these positions early in the technological transition.

But why does it seem that such corporate or administrative jobs are at risk first? Logically, if AI can perform routine tasks and streamline processes, these roles, which primarily involve administrative overhead, should be the first to disappear. Conversely, roles like housekeepers or factory workers—whose work involves tangible, manual labor—are often perceived as more resilient.

Adding to this complexity is the question of educational backgrounds. Why are degrees in fields like humanities, languages, design, or computer science seemingly more vulnerable to displacement by AI than fields such as economics, finance, or administrative studies?

The reality is nuanced. While some professions are more susceptible to automation due to their repetitive nature, others involve skills that are harder to replicate, such as critical thinking, creative problem-solving, and emotional intelligence. The discrepancy may also stem from the way different jobs are structured within organizations rather than the jobs’ intrinsic value.

Ultimately, the impact of AI on employment hinges on a multitude of factors—what tasks can be automated, how industries adapt, and how society values different types of work. Understanding these dynamics is essential to preparing for the future of work in an AI-driven landscape.