Landing a Job Is All About Who You Know (Again)
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    HOUSE MEDIAN ASKING PRICES AND WEEKLY CHANGE     Sydney $1,655,421 (-1.37%)       Melbourne $932,308 (-8.79%)       Brisbane $1,123,003 (-1.81%)       Adelaide $1,037,564 (+0.65%)       Perth $1,067,431 (-3.14%)       Hobart $841,531 (-0.24%)       Darwin $833,085 (+2.22%)       Canberra $997,488 (-1.28%)       National Capitals $1,117,351 (-1.89%)                UNIT MEDIAN ASKING PRICES AND WEEKLY CHANGE     Sydney $809,969 (-3.22%)       Melbourne $565,142 (-9.84%)       Brisbane $739,115 (-5.33%)       Adelaide $586,922 (+1.33%)       Perth $628,011 (-0.24%)       Hobart $600,183 (+4.66%)       Darwin $485,553 (-1.34%)       Canberra $494,366 (+1.35%)       National Capitals $631,659 (-2.57%)                HOUSES FOR SALE AND WEEKLY CHANGE     Sydney 15,318 (+1,415)       Melbourne 16,910 (+2,370)       Brisbane 12,398 (-503)       Adelaide 4,278 (+579)       Perth 9,664 (+855)       Hobart 813 (+96)       Darwin 197 (+12)       Canberra 1,334 (+129)       National Capitals 60,912 (+4,953)                UNITS FOR SALE AND WEEKLY CHANGE     Sydney 9,649 (-1,019)       Melbourne 6,724 (-1,041)       Brisbane 2,551 (+198)       Adelaide 669 (+230)       Perth 1,787 (+79)       Hobart 166 (+9)       Darwin 207 (-9)       Canberra 1,280 (+68)       National Capitals 23,033 (-1,485)                HOUSE MEDIAN ASKING RENTS AND WEEKLY CHANGE     Sydney $850 (-$10)       Melbourne $600 ($0)       Brisbane $720 (+$20)       Adelaide $650 ($0)       Perth $750 ($0)       Hobart $620 (-$10)       Darwin $850 (+$50)       Canberra $710 (-$10)       National Capitals $730 (+$7)                UNIT MEDIAN ASKING RENTS AND WEEKLY CHANGE     Sydney $800 (-$20)       Melbourne $610 (-$10)       Brisbane $680 (+$60)       Adelaide $540 ($0)       Perth $700 ($0)       Hobart $530 (+$23)       Darwin $680 (-$40)       Canberra $580 (-$10)       National Capitals $652 (-$2)                HOUSES FOR RENT AND WEEKLY CHANGE     Sydney 6,408 (-212)       Melbourne 7,769 (+707)       Brisbane 3,720 (+239)       Adelaide 1,232 (-47)       Perth 2,223 (+72)       Hobart 199 (-38)       Darwin 85 (+31)       Canberra 376 (-70)       National Capitals 22,012 (+682)                UNITS FOR RENT AND WEEKLY CHANGE     Sydney 9,911 (-226)       Melbourne 6,187 (+102)       Brisbane 2,194 (-1,277)       Adelaide 443 (+20)       Perth 714 (+16)       Hobart 80 (+10)       Darwin 154 (+42)       Canberra 770 (+1)       National Capitals 20,453 (-1,312)                HOUSE ANNUAL GROSS YIELDS AND TREND       Sydney 2.67% (↑)      Melbourne 3.35% (↑)      Brisbane 3.33% (↑)        Adelaide 3.26% (↓)     Perth 3.65% (↑)        Hobart 3.83% (↓)     Darwin 5.31% (↑)        Canberra 3.70% (↓)     National Capitals 3.40% (↑)             UNIT ANNUAL GROSS YIELDS AND TREND       Sydney 5.14% (↑)      Melbourne 5.61% (↑)      Brisbane 4.78% (↑)        Adelaide 4.78% (↓)     Perth 5.80% (↑)        Hobart 4.59% (↓)       Darwin 7.28% (↓)       Canberra 6.10% (↓)     National Capitals 5.36% (↑)             HOUSE RENTAL VACANCY RATES AND TREND       Sydney 1.4% (↑)      Melbourne 1.5% (↑)      Brisbane 1.2% (↑)      Adelaide 1.2% (↑)      Perth 1.0% (↑)        Hobart 0.5% (↓)       Darwin 0.7% (↓)     Canberra 1.6% (↑)      National Capitals $1.1% (↑)             UNIT RENTAL VACANCY RATES AND TREND       Sydney 1.4% (↑)      Melbourne 2.4% (↑)      Brisbane 1.5% (↑)      Adelaide 0.8% (↑)      Perth 0.9% (↑)      Hobart 1.2% (↑)        Darwin 1.4% (↓)     Canberra 2.7% (↑)      National Capitals $1.5% (↑)             AVERAGE DAYS TO SELL HOUSES AND TREND       Sydney 33.2 (↑)      Melbourne 31.7 (↑)        Brisbane 38.7 (↓)     Adelaide 31.4 (↑)        Perth 43.6 (↓)     Hobart 27.6 (↑)      Darwin 36.0 (↑)      Canberra 31.1 (↑)      National Capitals 34.2 (↑)             AVERAGE DAYS TO SELL UNITS AND TREND       Sydney 30.1 (↑)      Melbourne 29.3 (↑)      Brisbane 37.9 (↑)        Adelaide 31.6 (↓)     Perth 45.3 (↑)      Hobart 28.5 (↑)        Darwin 24.2 (↓)       Canberra 31.3 (↓)       National Capitals 32.3 (↓)           
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Landing a Job Is All About Who You Know (Again)

Networking is making a comeback as employers drown in computer-generated job applications

By CALLUM BORCHERS, LINDSAY ELLIS
Fri, May 31, 2024 9:02amGrey Clock 6 min

Nine-hundred eighty-three people applied online for a job posted recently by tech recruiter Rob Tansey. The candidate who got the offer wasn’t one of them.

Tansey, who scouts potential hires for aviation-software maker Veryon, received a half dozen referrals from a woman he knew from past job searches. One of those six quickly became the front-runner. That’s often how Tansey operates: He estimates that just 40% of successful applicants come in cold through his company’s job portal.

“There’s an idealist in me that wants to look at all the résumés,” he says. “The reality is you just can’t.”

Who-you-know networking is back. As the number of job applicants has swelled in recent years, the key to landing a new position often turns on a personal connection that can pluck your résumé out of online obscurity and ensure it’s seen by a real person.

Behind the resurgence: frustration with the digital slog that bogs down U.S. hiring. Many hiring managers and applicants agree that the ease with which job hunters can respond to help-wanted postings has broken the online-application process by creating high volumes of candidates that hiring managers can’t hope to parse through. Meanwhile, applicants say automatic screening tools are shutting them out of opportunities.

Reverting to referrals threatens to undermine corporate diversity efforts, which were supposed to be aided by online applications. Software promised to democratise hiring by reducing human biases, but wider talent pipelines have overwhelmed some employers to the point where they’re reaching for what’s worked in the past.

To promote the use of connections, some employers, like software giant Dassault Systemes , have increased cash rewards for employees whose recommendations lead to hires. Others, including the University of Miami and its health system with 20,000 total employees, have launched new referral-bonus programs. Corporate hiring software can allow companies to identify referred candidates first, boosting those applicants over the competition.

Whether recommendations come from in-house or outside the company, the advantages are significant, according to data compiled by the hiring-software company Greenhouse. For roles that were posted on Greenhouse job boards and filled in the first quarter of this year, applicants with referrals had a 50% chance of advancing past an initial résumé review, compared with 12% odds for other external candidates.

Thirty percent of eventual hires had referrals, even though people with referrals represented just 5% of the applicant pool.

“Candidates are so desperate to get noticed, and they’re asking, ‘What’s the cheat code? What’s the way to get through the filters?’” says Jon Stross, Greenhouse’s co-founder. “Get referred. It really increases your chances of getting through the first filter.”

AI arms race leads to frustration

The renewed reliance on networking comes as applicants and hiring managers are struggling to navigate the software that now dominates recruiting.

Companies are swamped by applicants in part because many job seekers are newly relying on ChatGPT and automated bots to fire off large volumes of résumés, or using other shortcuts like LinkedIn’s “easy apply” button.

Some human-resources professionals lament that cover letters sound eerily similar, as if written by the same nonhuman.

Candidates fume when they click “submit” and don’t hear back—and sometimes even when they do. Amanda Palasciano, in Red Bank, N.J., scored an interview for a senior copy-manager position as she sought a job in advertising. The catch was that her interviewer was an avatar, not a person.

The video call was awkward. Palasciano, 42, had a short window to record her response to each question. She didn’t know where to look. Soon after, she was rejected.

“It put a bad taste in my mouth about the company,” she says. “If that’s how much reliance you put on brand-new tools over people, this isn’t the right place for me.”

She decided to stop applying online and started asking former colleagues about short-term openings , aiming to convert one to a full-time job.

“This front door is locked,” Palasciano says. “We’re going through the back door, or we’re not working.”

Fewer applicants, higher quality

Lindsay Broveleit, a Minneapolis-area vice president at the marketing agency Matato, didn’t bother posting a job listing online when she needed to fill a midlevel role this spring.

She feared that listing the position on LinkedIn or the company’s website would bring throngs of low-quality applications—and she was anxious about how truthful AI-enhanced applications might be.

“A couple of years ago, I would have absolutely pursued more digital channels that are more public-facing,” she says. Not now. “We don’t want that many applicants. We want good ones.”

She thought to tap her and her colleagues’ networks to fill the role, but she soon reconsidered—even those personal channels have become oversaturated with first- and second-degree connections looking for work.

Employees are “inundated with people asking for their help to navigate into open positions,” she says.

She contracted a staffing agency instead.

Whether using agencies or their own employees, more companies are turning to people to vet prospects.

Laserfiche, a maker of content-management software, has a longstanding employee-referral program and a renewed commitment to contact everyone who is recommended by an employee.

The guaranteed outreach does not ensure an offer, says Vice President of People Jenny Bode, but it is a chance for candidates with unconventional backgrounds to make a case for themselves. With about 400 applicants for a typical opening, that’s an opportunity not afforded to everyone.

Bode values referrals partly because one helped bring her to Laserfiche in 2022. She wasn’t even looking for a job when she heard from a former co-worker she’d kept in touch with.

“She reached out to me and said, ‘I have this position. Do you want to interview for it?’” Bode recalls.

Alison Mincey introduced referral bonuses of up to $2,500 when she became chief human resources officer at the University of Miami in 2022. She estimates one in 10 hires now starts with an employee referral. The program, she says, also helps with retention: Employees are more engaged when their input is valued, and newcomers are more likely to stay and thrive when they already know a co-worker.

A flawed but indispensable system

On Greenhouse’s platform, when employees submit referrals, the software prompts them to “consider referring people from underrepresented backgrounds.”

That can be a challenge. People tend to know—and therefore recommend—people like themselves, says Ruth Thomas, chief of research and insights at PayScale, a compensation-management firm. (PayScale itself has lately doubled its usual share of new hires found through referrals, to 30%.)

Referral networks tend to reinforce demographic imbalances . A PayScale survey of 53,000 workers found that white men land more jobs and bigger raises through referrals than others because they are more likely to be connected to corporate decision makers.

Hiring managers might say, “They were in my club at Princeton, and therefore, they’re a good person,” says John Horton, an associate professor at the Massachusetts Institute of Technology’s Sloan School of Management and co-author of a recent paper on artificial intelligence in hiring. “If people start to retreat away from an open market, and it happens much through who you know and references, that would be bad.”

Cisco Systems is leaning more heavily on referrals for hiring than it has in the past. But the network-equipment company knows the approach can run counter to diversity efforts, so it also launched an effort to hire more people of colour and workers without four-year degrees, says Macy Andrews, vice president of employer branding for people and purpose. That hiring initiative looks to identify candidates with transferable skills rather than mandating college. Cisco so far has made over 100 hires through the initiative.

The company is looking to cut through the online-application morass in other ways as well. Cisco recruiters now visit college campuses with preprinted offer letters, so they can snap up students who make strong impressions.

“There is a bigger point here, which is that the process of sending in résumés and having thousands—sometimes millions—to go through isn’t the wave of the future,” she says.

No connection, no luck

Samuel Joynson, of Venice, Fla., launched a thoroughly modern job hunt after he lost his job when his company went through a reorganisation. He enlisted ChatGPT to edit his résumé and draft emails and cover letters.

The language didn’t sound like him—so he changed his approach.

Joynson, 28 years old, switched to writing his own letters and emails, investing about two hours into each application. He also pushed himself to contact everyone he knew at major tech companies including Apple, Google and Microsoft , where he talked to a friend from college. On calls, he asked them about their experiences at the companies, the office environment and remote-work policies.

“I took the approach of making it as human-based as possible,” he says.

Joynson’s old-fashioned strategy paid off. Many conversations led to introductions to other employees within his target companies. He landed a role and started as a senior technical program manager at Microsoft in April.

Francisco Denis recently discovered that connections—or lack thereof—can make all the difference. He says he was a finalist for a project-manager role at Disney but was told by a recruiter that the company decided to hire someone who had worked there in the past and could plug in immediately.

Denis, 40, has applied online for more than 100 jobs this year after moving on from an unsuccessful startup. His efforts have yielded only a handful of interviews, a jarring turn from a couple of years ago, when he was routinely headhunted. He’s pouring energy into networking for the first time in his career, reconnecting with former co-workers at companies he’d like to join. He often shares his résumé and describes his arduous job search to show that he’s not looking for a handout.

Still, even a system that relies on human connections can be gamed. On Fishbowl, an app for workplace venting and career advice, users have started a forum dubbed “Job Referrals!” It has drawn hundreds of thousands of members seeking endorsements from people they don’t know.

“Anyone willing to provide a referral to Amazon ?” one user posted.

In lieu of a handshake or business card, another offered an emoji: “Looking for a referral for Spotify, thanks in advance :).”

Bogus referrals could be win-wins for the people who give and receive them. The applicants gain an edge and, if they’re hired, their sponsors might be entitled to referral bonuses.

The companies that extended the offer? They’re left with one more hiring strategy that can’t be trusted.



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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.

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The AI Boom Faces Its First Serious Debt Test

Technology and semiconductor shares fell sharply as investors questioned the debt financing the AI infrastructure boom and confronted higher rates and Chinese competition.

By Ruba Jaajaa
Mon, Oct 19, 2026 4 min

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.

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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