At Dartmouth, ambition has a rhythm: countless recruiting emails, coffee chats between classes, LinkedIn notifications as return offers circulate through group chats. Traditional career paths with clear recruiting cycles, such as consulting, finance, tech, medicine and law feel familiar because generations before us have walked them. There’s comfort in that familiarity, in knowing that the path is set.
I’ve watched friends map their lives around these cycles, sometimes years in advance. Applications open earlier every year, often before students have had the space to figure out what they are actually good at, let alone what they care about. The D-Plan, designed to encourage exploration and off-term discovery, increasingly feels optimized for internship sequencing: secure an off-cycle internship, land a junior summer role, return to campus with an offer in hand. Decisions that feel enormous are made under the pressure of deadlines that leave little room for reflection.
For a long time, I assumed this structure existed because it was rational. If you followed the path, took the right classes, joined the right clubs, and networked with the right alumni, you would be rewarded with stability. But over the past few years, that assumption has started to crack — not because people stopped working hard or caring deeply about their futures, but because the environment itself has shifted, driven by the rapid advance of artificial intelligence.
Following a well-defined route no longer guarantees insulation from disruption. In some cases, it may even increase risk exposure. The more predictable and standardized an entry-level role is, the easier it becomes to automate or reorganize around new tools. Entire categories of work that once served as training grounds — such as structured analyst tracks and junior legal support roles — are being reshaped, if not disappearing entirely, faster than institutions can respond.
What makes this moment unsettling is not just the speed of change, but the lack of precedent. Artificial intelligence is a general-purpose technology that touches nearly every professional domain. We do not yet know which roles will disappear, which will change and which have yet to exist.
As someone about to step off the undergraduate conveyor belt into post-graduate life, my goal is no longer optimizing for a single job title or grinding toward a role that may not exist in five years. Instead, it’s developing skills that travel across contexts: comfort with ambiguity, the ability to learn quickly, and the confidence to iterate in public. It means seeing a career as a portfolio of projects and experiments with independent work launched between classes and ideas tested during off-terms. There is no guaranteed offer letter at the end, no predefined ladder to climb.
This suggests that Dartmouth students, with flexible off-terms, close faculty access and an unusually tight alumni network, may have more autonomy than we realize. We are in a brief and unusual window where the cost of experimentation is low, while the risk of clinging to the status quo is unusually high.
In this window, we as students have a unique opportunity to experiment. Institutions move slowly, and norms often lag behind technology. This gap creates space, especially for students, to shape their own trajectories if they are willing to step off the beaten path and build alongside AI rather than trying to outrun it.
This moment will not last forever. As AI becomes more deeply integrated into institutions, today’s flexibility may harden, and the paths that now feel unconventional may eventually become formalized, competitive and just as crowded as the ones many of us are currently trying to escape.
Preparing for the future may require letting go of some of the scripts that worked so well in the past because the world they were built for no longer exists. The challenge now is not to find the “right” path, but to stay adaptable as the ground shifts beneath us and to build a life that can move with it.
Eliana Stanford is a member of the Class of 2026. Guest columns represent the views of their author(s), which are not necessarily those of The Dartmouth.
