As artificial intelligence begins to erode traditional entry-level roles, a growing number of recent graduates are reconsidering their immediate career paths and turning instead to graduate school, not as a default next step, but as a calculated response to a shifting labor market.

New data from education firms Jenzabar and Spark451, published by CNBC, show that nearly 78% of prospective students considering postgraduate education plan to enroll within the next 12 months, up from 69% a year earlier. The increase suggests a renewed pull toward advanced degrees, even as the broader economic backdrop does not fully resemble a downturn.

Historically, graduate school enrollment rises during recessions, when job opportunities shrink and workers seek to reskill.

Kristin Blagg of the Urban Institute said the pattern remains relevant.

“We know that there is a trend to go back to school to re-skill during a recession,” she said. In uncertain periods, “people shelter in higher education,” adding that “it makes sense that it’s counter-cyclical.”

What distinguishes the current cycle is the disconnect between headline economic strength and underlying anxiety. According to the Bureau of Labor Statistics, the U.S. economy added more jobs than expected in March, while the unemployment rate edged down to 4.3%. Yet for younger workers aged 16 to 24, unemployment remains elevated at 8.5%, pointing to a labor market where access, rather than availability, is becoming the central challenge.

That challenge is increasingly tied to structural change. Artificial intelligence is not simply reducing hiring volumes; it is altering the composition of jobs. Entry-level roles, particularly in administrative, analytical, and support functions, are among the most exposed to automation. Companies are beginning to reorganize hiring around this reality, with some executives openly citing AI as a reason to slow recruitment or cut junior positions.

At the same time, geopolitical uncertainty is compounding economic unease. Consumer confidence fell sharply in April amid concerns over the Iran war and its potential spillover effects. Blagg noted that such uncertainty can influence decision-making: “That is something that could push people to think about other opportunities.”

Yet the response from students is not straightforward. Christopher Rim, chief executive of Command Education, said the current environment is producing hesitation rather than a clear shift.

“What we’re seeing right now amongst our clients is actually the inverse of that dynamic,” he said, referring to past downturns.

While interest in graduate school is rising, so is skepticism.

“Students are approaching graduate school with extreme caution,” he said. “Recent college graduates are generally uncertain about whether a graduate degree is worth the investment, especially given how fast the labor market is shifting.”

This caution reflects a more forward-looking concern. For many, the question is not just whether graduate school improves immediate prospects, but whether it will remain relevant by the time they graduate. The pace of technological change has introduced a new layer of risk, where skills acquired today may face rapid obsolescence.

Even so, advisers argue that advanced degrees still offer a form of protection. Eric Greenberg of Greenberg Educational Group said, “Concern about getting a job right out of college is leading to more interest in graduate school.”

He added that the trend is “even more magnified because it’s not only about what’s going on today, but what is going to happen in the not-so-distant future.”

“Graduate school is much more of a hedge now,” Greenberg said. “If somebody has more education, more knowledge, more of a skill set, they will typically get a better job. It’s kind of like an insurance policy.”

The framing of graduate education as an “insurance policy” underscores how its role is evolving. It is no longer simply a pathway to advancement, but a buffer against uncertainty. That shift is also evident in how prospective students are evaluating programmes.

According to the Jenzabar/Spark451 survey, career outcomes and practical experience now rank among the most important decision factors. Internships, job placement support, and industry alignment are taking precedence over traditional academic markers. Mike McGetrick of Spark451 said institutions must “demonstrate real, tangible return on investment,” signaling a more transactional approach from applicants.

Despite the rising interest, enrollment trends have yet to fully reflect this shift. Graduate enrolments remained broadly flat in fall 2025, with private nonprofit institutions recording a slight decline, according to the National Student Clearinghouse Research Center. The expectation is that 2026 could mark a turning point if current intentions translate into actual enrolment.

The financial calculus, however, remains a significant constraint. While data from the Bureau of Labor Statistics show that advanced degree holders typically earn more and face lower unemployment, the cost of obtaining those credentials is substantial.

Analysis from the Urban Institute indicates that the median debt for master’s degree graduates is about $54,800, rising sharply to $173,180 for professional degrees such as law or medicine. By comparison, those with only a bachelor’s degree carry a median debt of roughly $27,300.

Christopher Rim emphasized the stakes involved. “Graduate school is an investment,” he said, adding that the current environment is forcing a more deliberate approach. “This market is pushing students to a more general understanding that graduate school is not a casual next step, but should be an intentional and strategic stepping stone toward clear professional goals.”

Policy changes are set to further reshape the equation. New borrowing limits introduced under legislation signed by Donald Trump will cap federal loans for graduate students at $100,000 over a lifetime, with a $200,000 limit for professional programmes. Grad PLUS loans, which previously allowed borrowing up to the full cost of attendance, will be eliminated entirely.

Blagg said the implications of these changes remain unclear. “Up until recently, you could borrow up to your cost of attendance [for advanced degrees], so we had people borrowing quite a lot,” she said, adding that “we don’t really know yet what that will do for overall debt.”

The reforms, which take effect for new borrowers from July 1, are likely to constrain access for some students while forcing others to weigh more carefully the return on investment.

Together, the data point to a transition in how graduate education is perceived and used. It is no longer simply a refuge during economic downturns, nor a guaranteed pathway to upward mobility. Instead, it is becoming a strategic response to structural disruption, particularly the rise of artificial intelligence and the narrowing of traditional entry points into the workforce.

Many Americans are now turning to ChatGPT, Claude, or Gemini for financial guidance, but the usefulness of what they get back depends far less on the sophistication of the AI and far more on how skillfully they phrase their questions.

This, according to Andrew Lo, director of MIT’s Laboratory for Financial Engineering and principal investigator at its Computer Science and Artificial Intelligence Lab.

“I think that there’s a real art and science to prompt engineering,” he said in a recent web presentation for Harvard University’s Griffin Graduate School of Arts and Sciences, first published by CNBC.

AI can deliver clear, high-level explanations on many topics. It is often very good at outlining why diversification matters, when exchange-traded funds might outperform mutual funds, or the basic mechanics of retirement accounts. Yet experts are quick to highlight its serious limitations when the conversation turns personal or precise.

The Clear Limits of AI in Personal Finance

Andrew Lo stressed that AI still struggles with individualized planning. Tax strategies are a prime example. While it can discuss general rules or potential deductions, asking it to run detailed calculations based on someone’s actual situation is risky.

“When it comes to very, very specific calculations of your own personal situation, that’s where you have to be very, very careful,” Lo said.

Another persistent weakness is hallucination—the tendency of large language models to invent plausible-sounding answers that are simply wrong. Lo finds this especially troubling in finance.

“One of the things about [large language models] that I find particularly concerning is that no matter what you ask it, it’ll always come back with an answer that sounds authoritative, even if it’s not,” he said.

But despite these shortcomings, adoption is surging. An Intuit Credit Karma poll of 1,019 adults released in September found that 66% of Americans who have tried generative AI have used it for financial advice. Among millennials and Gen Z, the share exceeds 80%, and 85% of those who received recommendations went ahead and acted on them.

Lo’s bottom-line advice is pragmatic: “[People] should be using AI for financial planning — but it’s how they use it that’s important.”

Crafting Prompts That Actually Work

The difference between generic advice and genuinely useful guidance often comes down to the prompt itself. A vague question like “How should I retire?” typically produces boilerplate answers that are of little practical value—“garbage in, garbage out,” as Lo put it during the Harvard webinar.

A far stronger prompt, he explained, gives the AI clear context and structure: “Assume you are a fee-only fiduciary [financial] advisor. Here are my goals, constraints, tax bracket, state, assets, risk tolerance and timeline. Provide me with, number one: base case strategy. Number two: key assumptions. Three: risks. Four: what could invalidate this plan. Five: what information you are missing, and in particular, what are you uncertain about.”

By explicitly instructing the model to act as a fiduciary, legally bound to put the client’s interests first, and by demanding transparency on assumptions, risks, and gaps in knowledge, users extract far more thoughtful and cautious responses.

Certified financial planner Brenton Harrison, founder of New Money New Problems, echoed the point.

“Even if it’s the best model in the world, if it’s fed a bad prompt,” it will only be able to do so much, he said.

He noted that a strong prompt must contain enough specific detail for the AI to tailor its output rather than fall back on generic platitudes.

Lo described the process as iterative, almost conversational. It often takes more than 20 back-and-forth exchanges to refine the answer until it feels reliable.

“It’s a process of trial and error,” he told CNBC.

Practical Techniques to Sharpen Results

One of Lo’s most useful shortcuts is what he calls “reverse engineering” the prompt. After receiving a solid answer, simply ask the AI: “What prompt should I have asked you in order to generate the answer that I was looking for?”

The response can then be saved and reused for similar future questions, making prompt engineering much more efficient over time.

He also recommends pressing the model to reveal its own limitations. After getting what seems like a good answer, follow up with targeted questions such as: “What kind of information did you not have in order to be able to make that recommendation, and that could lead to some unreliable outcomes?”

Or: “How convinced are you that this is the correct answer? What kind of uncertainties do you have about the answer, and what kinds of things don’t you know that you need to in order to come up with a conclusive answer to the question?”

These probes help cut through the false sense of authority that large language models routinely project.

Harrison takes verification one step further. He instructs the AI to list its sources and, when possible, to limit those sources to reputable, verifiable ones.

“If you don’t require it to verify the sources, it’ll give an opinion, which isn’t what I’m looking for,” he said.

Why Human Judgment Still Matters

Even the best-crafted prompts cannot fully replicate the nuance a human advisor brings. Every person’s financial life contains layers of context—family obligations, emotional tolerance for risk, shifting life circumstances, and subtle tax interactions—that are difficult to capture completely in text.

“Looking to [AI] for advice implies you are giving it enough information to form an opinion and make a recommendation, and that’s a step further than I’d go with AI,” Harrison said.

He pointed out that a skilled planner teases out subtleties through conversation that a user might not even realize they need to include in a prompt. That human element remains hard to replicate.

The takeaway from both experts is consistent and reassuring: AI can be a powerful, accessible tool for financial education and initial planning, but it works best as a well-informed assistant rather than a replacement for professional judgment.

The real skill—and the real protection—lies in learning how to ask better questions, verifying every output, and knowing when to bring in a qualified human advisor for complex or high-stakes decisions.

In an era when financial lives have never been more complicated, experts have noted that mastering the art of prompting may be one of the most practical financial skills anyone can develop. This means that those who treat AI as a conversation partner rather than an oracle will get far more value, and far less risk, from the technology.