For Working Women in India, Staying Safe Can Feel Like a Full-Time Job
Daily planning around ensuring their safety, and reassuring family members, is invisible labour that women do to be able to work
Daily planning around ensuring their safety, and reassuring family members, is invisible labour that women do to be able to work
When Ajita Topo , a cook in an affluent neighbourhood in Delhi, leaves work in the evening, she holds her bag like a shield against her chest, keeps her fists clenched and carries a black umbrella with a very sharp end to ward off a possible attack.
She makes sure to wear lots of layers—no matter how hot it is—to deter someone from trying to grope her chest, and secures her bun with a sharp metal stick as an additional weapon.
Topo isn’t being paranoid. Last year, she was followed by two men when she left work after 10 p.m. She managed to scare them away by shouting as she passed homes with guards outside.
“Workplace, public transport, public places, we feel safe nowhere,” said Topo, the sole breadwinner for her two children. “The only solution is to stay alert at all times.”
For many women in India, taking steps to ward off a violent attack—and reassuring their families they are safe while at work and on their commutes—is an invisible form of labour that is a central element of their work life.
The killing and rape of a trainee doctor in the city of Kolkata in August was a fresh reminder for Indian women who work of the dangers lurking in public spaces where women are far less visible than men, from the deserted corners of a hospital or corporate park, in public transport or on city streets.
The 31-year-old was found dead in a seminar hall of the state-run hospital after she went on break during a night shift. A volunteer at the hospital has been arrested as a suspect. The killing prompted protests by women across the country.
Only 30% of women in India aged 15 to 64 were in the labour force in 2022, according to the International Labor Organization, as conservative values lead many families to discourage women working outside the home. In the U.S., the corresponding rate is 68%.
A decade ago, Indian laws dealing with crimes against women were overhauled in the wake of the gang-rape of a 23-year-old woman on a moving bus. But women’s rights advocates say that entrenched patriarchal attitudes haven’t budged significantly, and are at the root of frequent violence against women, both in the home and outside of it.
In other widely publicised incidents, female call centre workers have been assaulted and killed as they commuted to or from night shifts. Last year, a receptionist disappeared from the hotel where she worked and was found dead days later. Each time such a crime takes place, women experience another jolt of fear for their own safety.
Piyali Maiti , clinical director for counselling operations at 1to1help, which manages corporate assistance programs for employees, said worries sparked by the Kolkata attack have cropped up in conversations with employees and their family members in recent weeks.
Sometimes “young professionals are working out in a different city. So family members are also part of our concern group,” she said. “When we have sessions with them, they raise concerns for their child’s safety. ‘What can I do as a parent to make sure my child is safe?’”
For women working in healthcare, where they are a larger share of the workforce compared with many other occupations, but late night shifts and long hours are also common, the Kolkata attack has resonated especially deeply.
Amrita Bhattacharya , a 29-year-old medical resident at another hospital in the city, said she previously felt comfortable walking the distance—less than a mile—from her work to the hospital-provided women’s residence on her own at night. Now she asks a male colleague or friend to accompany her.
Earlier she used to chafe against rules at the residence, which include locking the facility’s doors after a certain hour.
“But now after the horrific incident, I feel being put under lock and key is a must in a society like ours,” she said. “It’s a strain to deal with such fear every day.”
Sexual-assault statistics indicate that women in India are more likely to be raped by a family member, romantic partner or friend, than an acquaintance, employer or stranger. Still, the highly publicised attacks on working women dovetail with existing beliefs held by many that a woman’s place is in the home, shaping decisions made by women and their families.
A Pew Research Center survey in 2019 found 40% of Indians think it is better for a marriage for men to provide while women take care of the home and children, significantly higher than the global median of 23%.
Some women choose to withdraw from work as soon as economic circumstances permit, while others prefer lower-paid work options close to home over travelling to more distant locations.
That perception that working in public is unsafe and homes are safe shows up in India’s newest employers—gig-work platforms. Companies that focus on deliveries have struggled to hire significant numbers of women; the lone exception is Urban Company, a platform that offers personal-care and repair services at home.
The pervasive sense that public spaces are unsafe also shapes company policy. Delivery platform Swiggy at one point limited delivery hours to before 6 p.m. for female workers. The company said in a 2021 blog post on its efforts to hire more women that it reversed the policy after realising it was barring women from earning during peak dinner hours.
Economists and researchers say the low share of Indian women in the workforce is costing India economic growth.
Economic privilege can offer an additional layer of security—such as a personal vehicle for commuting to work, or access to ride-share services that come with some inbuilt safety measures. But, nevertheless, the sense of insecurity persists.
Vidhi Pandey , a digital-marketing professional, has an almost 90-minute commute between her home in Gurgaon and her office in the Indian capital, and frequently attends evening events. If an international client is involved, her day can end at 2 a.m.
“Although my husband and mother have grown accustomed to my unconventional working hours, announcing a late night at the office still makes them lose their sleep,” said the 43-year-old.
Her mother frequently lobbies her to look for something closer to home even if it means earning less. But she has resisted the pressure. Instead, she reminds her mum of her extensive safety checklist.
When she books a cab, she makes sure to look at the driver’s performance rating and reads riders’ feedback regarding his behaviour. If she is wearing a skirt, she changes into something more covered up before getting into a taxi for her ride home.
On her phone, she keeps the Delhi Police panic button app open throughout the ride. The app, developed after the 2012 bus attack and called “Himmat” or “courage,” allows the user to quickly send an alert to the Delhi Police, with a link to the user’s location on Google Maps, as well as notifications to family and friends.
She makes sure to share her live location and trip status with her husband and at least one friend. Sometimes she keeps a phone call going with her mother or a friend throughout the car ride.
“I keep thinking about how to escape if something goes wrong,” said Pandey. “I dream of a day when our society is safe enough for me to travel alone anytime, anywhere without any fear or worry.”
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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.
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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