Why I may ‘hire’ AI instead of a graduate student

From ScienceMag:

The other day, a new research idea struck me. The conceptual path was clear, but the execution would require real effort—synthesizing the literature, writing code, training models, performing statistical analysis. Just a few years ago, the next step would have been a no-brainer. I would recruit a graduate student into my lab and allow them to run with the project, providing guidance along the way. This time, an uncomfortable thought crept into my head: Should I just give these tasks to artificial intelligence (AI) rather than take a chance on a student?

I thought about the skills I had when I started graduate school more than a decade ago, and how much mentoring it took to get me where I am today. I had zero research experience when I emailed faculty to say I was interested in computer science Ph.D. programs. I did my basic due diligence, reading up on what they worked on. But sitting in their offices, listening to them talk about robotics, algorithms, and natural language processing, I had little to no clue what these concepts really meant.

One professor saw past my ignorance and agreed to take me on. I was incredibly grateful for the opportunity, but the first few months were a harsh reality check. I worked tirelessly—reading, summarizing, drafting ideas, and trying to make sense of it all. Yet, whenever I would present my work to my adviser, she would look at the nonsense I had presented, give me feedback, and send me back to start from scratch.

I thought about quitting. I felt I was constantly disappointing her. But she didn’t give up on me. Perhaps she believed in my potential, perhaps she saw I was doing the best I could, or perhaps she simply believed in the process of cultivating a scholar. It took a year or so of immense patience before I finally produced something we could build on. From there, I slowly transformed from a clueless novice into a junior colleague.

Years later, when I became a professor, I watched my own students struggle to make progress, just as I once had. My calendar filled up with meetings where my main job was to untangle their confusion. Eventually, though, the investment paid off, and I experienced the deep satisfaction of watching them transform into capable junior collaborators.

Now, AI has introduced a new option. It is certainly no extraordinary intellectual partner. But it can competently perform a lot of the work I need immediately; AI requires no ramp-ups, no meetings, and absolutely no emotional support. It is forcing a quiet, uncomfortable shift in my mindset.

The issue is not whether my students are valuable. In the long run, they are invaluable. The issue is that their value emerges slowly, whereas AI delivers immediate returns. I feel somewhat embarrassed to admit how tempting this is. In our culture, preferring an algorithm to a trainee feels like a betrayal of the academic mission.

Yet I see these calculations shaping the labs around me. Close colleagues are quietly refraining from taking on as many students as they used to. When they do take students, they are noticeably pickier.

My immediate instinct is to expect any student I recruit in this new environment to contribute at a much higher level from the outset. But to meet those elevated expectations, a student would likely rely heavily on the same AI tools I could turn to on my own. In the process, they may bypass the valuable experience of struggling through early tasks and learning from their mistakes. Students, I worry, could simply become an intermediary between the raw idea and the AI’s output.

For faculty, meanwhile, the pressure to produce remains relentless and the scientific pace is unforgiving, making a productive and frictionless AI even more tempting. The real danger I see is not that AI will entirely replace graduate students in the foreseeable future. It is that the default assumption that taking on students is simply part of any professor’s academic journey will quietly erode. In some cases, the most pragmatic solution could be to use an AI.

I’m not sure where that will leave students who start with no research experience. Personally, I am seriously tempted not to take a chance on a novice for my new project—which means today, I probably wouldn’t recruit my younger self.

Read More