How Students Can AI-Proof Their Careers
Artificial intelligence is going to eliminate a lot of jobs in the future. It’s possible to reduce the risk that it will be yours.
Artificial intelligence is going to eliminate a lot of jobs in the future. It’s possible to reduce the risk that it will be yours.
The current generation of college students is facing a challenge that those who came before never had to worry about: They’ll be competing with AI for jobs.
What can they do to get ready?
After all, artificial intelligence is likely to eliminate at least some jobs that formerly served as first rungs on career ladders. “We have to accept and embrace the idea that in fact with AI we are going to have jobs that are going to be eliminated and jobs that are going to be created, and we don’t know which ones,” says Joseph E. Aoun , president of Northeastern University.
That uncertainty leaves today’s college students struggling to prepare for a workplace that is changing faster than ever. We asked a range of career counselors and employers how they would suggest students AI-proof their careers. One consensus: It’s important to master skills not easily matched by machines, such as human-style communications and the ability to understand and work smoothly with people who have different perspectives and personalities.
“In many ways the human skills are going to be more fundamental than they are now,” as machines take over some routine tasks, Aoun says.
A survey of 255 employers by the National Association of Colleges and Employers last year found that the three top “competencies” they sought in job candidates were communication, teamwork and critical thinking.
Communication and teamwork rely on emotional intelligence, or EQ. “AI has probably won the IQ battle,” says Tomas Chamorro-Premuzic , chief innovation officer at Manpower Group and professor of business psychology at Columbia University, “but the EQ battle is up for grabs.”

Of course, that doesn’t mean students shouldn’t master AI. Skill in using AI as a productivity-enhancing tool can give them an edge over older workers who haven’t mastered ChatGPT and other AI programs.
But knowing how to use AI effectively isn’t enough. Here are some suggestions from the experts on how students—or really anybody—can reduce the risk they will eventually be replaced by AI.
AI can write computer code, improve grammar and solve math problems, but so far it lacks the ability to mediate squabbles among colleagues, charm potential clients over cocktails or soothe angry customers. So developing those skills may be one of a job applicant’s best selling points.
Anything that requires talking and cooperating with strangers is helpful. That includes volunteering in a nursing home or an after-school youth program, or leading an on-campus club or sport. Jobs that require dealing directly with lots of other people, including jerks, are an educational opportunity. “If you’re a waiter you will understand human beings better,” says Chamorro-Premuzic.
Focusing too heavily on one type of expertise could be a mistake if, as expected, AI eliminates lots of jobs in some specialties. It isn’t a risk only for technology fields like computer science; other fields such as accounting and finance are also being transformed by AI.
Instead, experts recommend having a portfolio of skills.
“If you have one skill, you compete with the masses that have that same one skill,” says Anna Esaki-Smith , author of “Make College Your Superpower.” In contrast, she says, “Should you stack on another skill, you become qualified for a wider range of opportunities.”
That could mean adding a minor or two to a major or going for a double major. It also could involve a strategic selection of electives. D. Raja , chief executive of CEI, a Pittsburgh information-technology consulting firm, says he increasingly looks for job candidates who have both technical skills and a grounding in business, enabling them to understand clients’ needs. An M.B.A. stacked atop a computer-science degree is one good strategy, he says.
Though a range of skills and knowledge is an advantage, it’s still important to develop deep expertise in at least one or two areas. “AI has disrupted superficial expertise,” Chamorro-Premuzic says. In other words, you have to know more than generative AI programs can spit out in a minute or two.
If AI will do at least some of the grunt work, people will still be needed to devise strategies and carry out complicated projects. Machines do pieces of work, but “we still need big-picture humans to put it all together,” says John Behrens , director of the technology and digital studies program at the University of Notre Dame.
To help students learn how to manage complexity, many universities require them to complete a capstone project before graduation. Those can include primary research, ambitious artworks or community-service projects.
Vanderbilt University calls such projects “immersion.” For his Vanderbilt project, Logan Glazier is converting an old school bus, once consigned to the junkyard, into an RV with solar panels mounted on the roof to power his refrigerator and other appliances.
He expects to finish the project within a few months, before graduating next spring with a degree in civil engineering. Glazier had to sell his idea to university administrators, persuade them to give him space to work on the bus, develop a plan and find materials. He watched dozens of YouTube videos and consulted with Vanderbilt professors.
He recalls the reaction he got from people at the engineering consulting firm HNTB when they heard about the project: “Wow, that’s really cool!” He got an HNTB internship in 2023 and recently accepted a full-time job at the firm starting in May, after his graduation.
As AI and other technological changes make career paths less predictable, adaptability will be an advantage. “We don’t know what the world is going to be like in five years or 10 years,” says Behrens.
Students can develop their adaptability by seeking out new experiences, such as studying abroad or taking unconventional courses. At Carnegie Mellon University, renowned for computer science and robotics, one of the most popular electives is “Acting for Non-Majors,” offered by the Pittsburgh school’s drama department. Students have long taken the course, but now demand has soared as students see it as a plus in the job market, forcing them to shed their inhibitions and engage with other people in unscripted ways.
This year, to accommodate demand, CMU quadrupled the capacity of the course.
“It’s exhilarating,” says Emily Ma , a math major. “Acting forces you to step outside your comfort zone.” That’s particularly important for a generation of young people who were isolated during the Covid-19 pandemic and spent far more time staring at screens than they did engaging directly with people.
Amid all the changes AI is bringing, companies want fresh thinking. So one route to success is to be a “moderate misfit,” unhappy with the status quo and ready to innovate, says Chamorro-Premuzic. By moderate, he means that “you fit in well enough and work well with others but are not so bland and risk-averse as to lose the desire for change and progress.”
Chamorro-Premuzic advises young people not to seek employers that fit perfectly with their values but rather to “look for places they like but which they also dream of transforming and improving.”
AI is like a B+ student and can tell you what the average person would say, says Matthew Rascoff , vice provost for digital education at Stanford University. A+ work, he says, is the product of an individual brain with a distinctive voice. So he urges students to develop their own voices and identities. “The more you outsource” to AI, he says, “the less you are developing that muscle.”
Technology and semiconductor shares fell sharply as investors questioned the debt financing the AI infrastructure boom and confronted higher rates and Chinese competition.
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Technology and semiconductor shares fell sharply as investors questioned the debt financing the AI infrastructure boom and confronted higher rates and Chinese competition.
A sharp technology sell-off has exposed a new anxiety on global markets: whether the enormous borrowing behind the artificial-intelligence infrastructure boom can generate sufficient returns as interest rates rise and Chinese competition intensifies.
The artificial-intelligence investment boom has powered technology shares, semiconductor manufacturers and data-centre operators to extraordinary heights. Last week, investors were given a reminder that much of this expansion is being built with borrowed money.
The technology-heavy Nasdaq Composite fell 4.5 per cent over the week ended October 16, its weakest performance since late June. Semiconductor shares suffered an even deeper correction, with the Philadelphia Semiconductor Index losing about 11.4 per cent.
The selling spread quickly through global markets. Micron Technology fell 14.6 per cent over the week, while South Korea’s Samsung Electronics and SK Hynix dropped approximately 18 per cent and 24 per cent respectively.
This was more than a routine bout of profit-taking. Investors were reassessing three interconnected risks: higher interest rates, the rapidly expanding debt burden attached to AI infrastructure and the prospect of stronger competition from Chinese memory-chip manufacturers.
Artificial intelligence requires a physical infrastructure network of enormous scale. Data centres must be built, advanced processors purchased, power supplies secured and cooling systems installed before customers begin generating meaningful revenue from them.
Technology companies are increasingly supplementing their own cash with corporate bonds, syndicated loans, equipment leases, private credit and project-level financing.
JPMorgan describes the emerging funding model as a combination of conventional corporate borrowing and project finance tied to individual data-centre assets. In Australia, the Reserve Bank estimates that syndicated loans accounted for approximately three-quarters of data-centre financing during 2025 and 2026 to date.
The numbers are becoming large enough to affect broader credit markets. The Federal Reserve Bank of Dallas has cited Wall Street estimates suggesting AI-related investment-grade bond issuance could approach US$300 billion in 2026 alone.
That borrowing is not automatically problematic. Debt can be an efficient way to finance long-lived infrastructure when future revenue is sufficiently predictable. The concern is that AI technology, customer demand and computing economics are changing much faster than the repayment schedules attached to the assets.
A data centre financed today may carry debt for years. Investors must therefore make assumptions about future utilisation rates, electricity prices, chip performance, customer concentration and the amount businesses will ultimately pay for AI computing.
When interest rates rise, those assumptions become less forgiving.
September’s US inflation data intensified the pressure. Consumer prices increased 3.7 per cent over the year, ahead of market expectations and up from 3.4 per cent in August.
The hotter reading reduced confidence that interest rates would fall quickly and pushed bond yields higher. That matters particularly for companies whose valuations depend on profits expected well into the future, as well as highly leveraged infrastructure operators that may need to refinance.
CoreWeave has emerged as a closely watched test case. The specialist cloud-computing company has accumulated substantial debt and lease obligations to build AI capacity, while relying on a concentrated group of large technology customers.
Its shares fell sharply during the sell-off, while the cost of insuring its debt against default climbed. CoreWeave has argued that its contracted backlog more than covers its obligations and that financial markets do not always understand its business model. Nevertheless, its securities have become a proxy for investor confidence in debt-funded AI infrastructure.
Oracle, another major investor in AI computing capacity, fell 8.8 per cent in Friday trading, while CoreWeave dropped 15.5 per cent.
The question is shifting from how much AI infrastructure can be built to how profitably it can be operated.
Interest rates were not the only catalyst. Reports of advances by Chinese memory-chip producer ChangXin Memory Technologies raised concerns that lower-cost Chinese products could increase competition across the global semiconductor industry.
Memory chips are essential to AI systems, but the industry is historically cyclical. Periods of tight supply and high pricing can encourage rapid investment, eventually producing oversupply and falling margins.
The prospect of a well-funded Chinese competitor expanding its technological capabilities unsettled investors in Samsung, SK Hynix and Micron. It also demonstrated that AI-related demand does not insulate chipmakers from conventional competitive pressures.
That combination—slower rate cuts, more expensive financing and potentially cheaper supply—challenged the assumption that every company exposed to AI would enjoy continually expanding margins.
The sell-off does not establish that the AI boom is ending. Demand for computing power remains strong, the largest technology companies continue to generate formidable cash flows and many infrastructure projects are backed by long-term customer contracts.
What has changed is the market’s willingness to overlook the price and financing structure of that growth.
The most resilient companies are likely to be those able to fund investment from operating cash flow, secure long-duration customers and demonstrate that new capacity will earn returns comfortably above its financing costs.
More speculative operators may face greater scrutiny, particularly where debt is expensive, customers are concentrated or expected revenue depends on demand that has not yet materialised.
For Australian investors, the lesson extends beyond US technology shares. Local data-centre operators, infrastructure funds, electricity suppliers and property groups are increasingly exposed to the same investment cycle.
Artificial intelligence may remain one of the defining growth themes of the decade. But last week’s correction showed that enthusiasm for the technology does not eliminate financial discipline.
The next stage of the AI boom will be judged not only by computing power, model performance or demand for chips, but by something more traditional: whether the assets being financed can generate enough cash to repay the money borrowed to build them.
Nasdaq Composite: Down 4.5 per cent for the week ended October 16, 2026
Philadelphia Semiconductor Index: Down approximately 11.4 per cent
Micron Technology: Down approximately 14.6 per cent
Samsung Electronics: Down approximately 18 per cent
SK Hynix: Down approximately 24 per cent
US September inflation: 3.7 per cent annually
Estimated 2026 AI-related investment-grade issuance: Approximately US$300 billion
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