There is a familiar signal that someone may be looking for a new job: the sudden professional headshot on LinkedIn. A new profile picture is rarely the only change. Workers also revise their summaries, tweak their job titles and rewrite descriptions of previous roles. Sometimes they return to jobs they left years ago and change how those experiences appear on their profiles.
In new research with Nick Bloom and Gideon Moore at Stanford, we use monthly vintages of Revelio Labs data to observe these changes over time. We focus on what we call “time travelers”: workers who change the title or description of a job more than 60 days after leaving it.
While someone is still in a position, an updated description might reflect a genuine change in responsibilities. However, once the worker leaves a position, the nature of work is fixed. A later edit changes only how the worker chooses to present that experience in the future when they are making the edits.
This behavior is common. Around one in five U.S. users in our sample retroactively edited at least one previous position between 2020 and 2026. These are not primarily small corrections made shortly after departure: the median edit occurred 4.5 years after the position ended.
A profile refresh often comes before a job switch
The timing of these edits suggests that workers are not revisiting their histories at random. Among workers who change employers, the probability that a worker edits their profile begins to rise about six months before the transition. It climbs sharply in the final few months, peaks close to the employer change, and then falls back toward its earlier level. By contrast, among workers who never change employers, editing remains much lower and comparatively flat over the same time window.
Workers are 66% more likely to change employers during the period in which they update their profiles. The timing suggests that many workers revise their profiles as part of preparing for a job search. A stronger profile may also help them attract recruiters, which can help them land a new job. Either way, retroactive edits are a strong signal that a job transition may be approaching.
A professional profile is therefore not simply an archive of what someone did. It is also a marketing document, adjusted for the opportunity the worker hopes comes next.
Workers rewrite the past for today’s labor market
Because these edits apply to completed jobs, sometimes even from many years ago, analyzing these edits offer an unusual way to observe how workers’ perceptions of employer demand change.
Normally, an increase in a keyword on professional profiles could mean that more workers are doing that kind of work, or simply that they are becoming more likely to describe existing experience using that term. Time-travel edits help distinguish between the two. The job itself is already over, so only its presentation can change.
The clearest example is artificial intelligence. Following ChatGPT’s launch, workers started adding AI-related language to descriptions of jobs they had already left. The increase is visible in both descriptions and job titles, although it is stronger and more volatile in descriptions. At the peak, the rate of adding AI terms was more than five times its earlier baseline.
DEI language follows the opposite trajectory. During the earlier part of the sample, workers generally added DEI references to previous jobs. Around early 2025, however, net additions turned sharply negative, particularly in job titles. The pattern partially rebounds afterward, but remains weaker than before.
Remote-work language shows a similar, though more gradual, reversal. References to working from home were frequently added in the period after the pandemic, but that pattern has steadily weakened, and workers have recently been removing language on remote work.
These changes do not necessarily tell us what employers objectively value. They tell us what workers believe will make them more attractive to employers. That belief matters in its own right: workers respond not only to labor demand, but also to their perception of it.
AI is also changing how profiles are written
Workers are not only changing what they emphasize in their profiles, but increasingly, how they write them as well. After ChatGPT’s launch, words strongly associated with LLM-generated writing—such as “delve,” “underscore,” and “showcase”—rose rapidly in retroactive job-description edits. Before ChatGPT, these markers appeared in roughly 0.3% of edits. By late 2023 and early 2024, their share had climbed above 3%.
The rate has since fallen substantially, settling below 1% by early 2026. Even so, it remains well above its pre-ChatGPT baseline. The pattern suggests a sharp initial wave of AI-assisted rewriting, followed by a partial normalization rather than a full return to earlier writing styles.
AI therefore appears twice in the profile-editing process. Workers increasingly present themselves as having AI skills, and many are also using AI tools to help edit their profiles.
Who rewrites their professional history?
Time travel is not evenly distributed across the workforce. Education matters, although the pattern is not simply that more education leads to more editing. MBA holders have the highest rate of retroactive edits: 29% of users with an MBA have retroactively edited a previous position. They are followed by workers with other master’s degrees, at 26%, and bachelor’s degrees, at 24%. Doctorate holders are much closer to the overall average, at 20%, while workers with associate’s degrees have the lowest rate, at 16%.
Editing is also particularly common among younger workers. More than 38% of workers under 30 retroactively edited a previous position, compared with fewer than 10% of workers aged 60 or older. This is both understandable and rational: younger workers are still shaping their career profiles and are more likely to switch up how they represent themselves.
The behavior is also concentrated in knowledge work. Nearly one in three workers in technology and information time travel, compared with roughly one in seven in real estate. Product managers, UX researchers, and workers in AI leadership roles are among the occupations with the highest rates. With these roles facing some of the highest expected changes with AI, it makes sense that workers are repositioning themselves for the current labor market more and are adding more explicit language on some of the topics they have been working on.
Higher-ranked MBA alumni show fewer signs of AI-assisted editing
The workers most likely to edit their profiles are not necessarily the most likely to show signs of AI-assisted writing, but rather those for whom writing may require more effort. LLM-associated language is most common among workers with high school and associate’s degrees and least common among PhD holders. MBAs use these markers somewhat more frequently than other master’s graduates. Even within the MBA population, that is most likely to retroactively edit their profiles, AI-assisted writing is uneven.
Graduates of higher-ranked MBA programs are less likely to introduce LLM-associated language into their profile edits. The likelihood rises fairly steadily as school rank declines, with graduates from several top-ranked programs showing particularly low rates of LLM-associated writing.
This pattern matters beyond profile writing itself. Professional profiles are partly a signaling device: workers decide how much effort to put into presenting their experience, while employers may infer something about both their abilities and that effort from the result. LLMs lower the cost of producing polished professional writing, which can be especially valuable for workers who would otherwise find it more difficult or time-consuming to present their experience well.
But lowering that cost also weakens the signal contained in the writing itself. A polished profile may once have reflected some combination of writing ability, attention to detail, and the effort someone invested in presenting themselves. When an LLM can produce similar prose almost instantly, those things become harder to distinguish.
The differences we see across education levels and business-school rank are consistent with this idea: workers who may have more to gain from writing assistance are also more likely to show signs of using it. AI is therefore not only changing how professional histories are written; it may also be changing what employers can infer from how well they are written.
