Don’t Roll Your Eyes: Looking the Part Could Land You That Job
Applying to be a programmer? Better grab a pair of glasses. Different jobs favour certain looks, new research shows.
4 min
Think appearances don’t matter if you’re applying for a job online? New research shows that looking the part is very much part of the equation.
Your credentials and referrals may get you on the shortlist. Even if the whole process takes place online, though, it’s rare that a hiring manager won’t check out your LinkedIn profile. Making the final cut can come down to nailing a specific professional look, according to a new study published by the Harvard Business School.
Analysing 63,000 job openings and the more than 160,000 freelancers who applied for them over a six-month period, researchers found that certain accessories or physical features gave candidates an edge in landing the job—even after controlling for race, age and gender. Researchers used computer vision technology and machine learning to help classify which attributes made someone be perceived as a better fit for a job, then examined what role that played in hiring.
Different jobs favoured certain looks. The analysis showed that men wearing glasses and having a computer visible in the photo were perceived to be a better fit for a software programming assignment than men without glasses, boosting their chances of getting it. A beard gave them a slight edge, too.
With design and media-related jobs—one of two broad job categories examined in the study—flashing a smile and using a photo with high image quality was also important. Women sporting reading glasses and an “artistic” look were seen as a better fit for graphic design jobs than other women.
The researchers, from Harvard and the University of Southern California, found that certain photo features could tilt the selection process when profiles included equally high ratings from previous clients. The advantage could be roughly the equivalent of a 5% pay differential.
On the other hand, the study suggests that looking the part for a job doesn’t rely just on a candidate’s gender, ethnicity and age. Rather, paying attention to the details of a profile photo can go a long way, recruiters say.
“We would be fooling ourselves to say it’s not part of the package,” says Jessica Vann, founder of Maven Recruiting Group, a San Francisco job-placement firm. While not as important as job or communication skills, “it’s a piece, for sure.”
It’s generally a good idea to have a neutral background and no children, pets or celebrities in the photo. Vann, whose firm specialises in placing executive assistants and chiefs of staff at Silicon Valley companies, says she has counselled job seekers to eschew an obviously AI-generated photo or tone down the makeup.
In a CivicScience poll of more than 2,000 people conducted online last week, about half of respondents said they had used a professional-looking photo of themselves in some capacity; 82% agree that appearance makes a difference in a job offer.
Title VII of the Civil Rights Act of 1964 prohibits employers from discriminating because of race, gender and religion, among other factors. But other aspects of personal appearance—whether height, weight or hairstyle—aren’t necessarily covered by the federal statute, says Steven Pearlman, a labor attorney at Proskauer Rose in Chicago. Plus, it’s often difficult to legally prove whether such biases were the reason for a candidate’s rejection.
Brent McCreary, a theatre ticketing director in New York, has found certain photo details can swing a hiring manager’s decision either way. His professional profile picture usually shows him with a favourite celebrity. At one point it was Britney Spears. Now it’s Kelly Clarkson.
The choice worked against him when he lost out on a revenue management job at a theme park three years ago. In the rejection note, the interviewer suggested a more professional LinkedIn photo.
A month later, though, the executive director of a San Francisco-based streaming platform contacted him. The job he’d applied for was already filled but she noticed his photo. “Your personality and background seem so fun and special,” she wrote in a LinkedIn message. When another project-management job opened soon after, McCreary got it. The job turned out to be a better fit for him, too, he says.
“The company I ended up working for was one where I kind of jelled with the organisation,” he says.
Looking the part is often informed by stories and stereotypes, career coaches say. “You see it in books and movies,” says Catherine Fisher, a LinkedIn career expert who studies data and trends on the professional social media network.
Every industry has its own sartorial vibe, from the fleeced vests and sweatshirts of Silicon Valley to the traditionally suited-up finance crowd in New York.
“You always think hoodies are related to tech companies, but that doesn’t mean I have to wear one,” Fisher says. By the same token, angular bobs and big sunglasses have come to be associated with the fashion industry, though “not everyone in fashion looks like Anna Wintour,” she says.
That’s rapidly changing as home and work life become more mixed, Fisher says. More than half of working Americans say that how they present themselves at work has changed since the pandemic, according to a poll of 2,000 people conducted last year by LinkedIn. Two-thirds said they thought that managers and co-workers were more accepting of different ways of dressing and styling than several years ago.
Alice Stephenson, a 42-year-old lawyer, says that for much of her early career, she dressed the part and concealed her piercings and tattoos. “I wore a stereotype of what a professional looked like,” she says. “I never felt comfortable or able to express my own individuality or creativity through my appearance.”
That changed after she started her own law firm. In her photo on the firm’s website, in her email signature and on LinkedIn, she is wearing a friendly smile, a blue sleeveless dress and a visible sleeve of tattoos.
“I want to look friendly and approachable,” says Stephenson, who lives in Amsterdam. “That’s key to my brand.”
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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.
Technology and semiconductor shares fell sharply as investors questioned the debt financing the AI infrastructure boom and confronted higher rates and Chinese competition.
4 minA 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.
The debt behind the AI build-out
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.
Inflation changes the calculation
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.
China adds another source of pressure
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.
A reset rather than the end of the boom
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.
Fact box
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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