The Embarrassment of Having to Explain Your ‘Monster’ Diamond Ring
Couples find that lab-grown diamonds make it cheaper to get engaged or upgrade to a bigger ring. But there are rocky moments.
4 min
Wedding planner Sterling Boulet has some advice for brides-to-be regarding lab-grown diamonds, which cost a fraction of the natural ones.
“If you’re trying to get your man to propose, they’ll propose faster if you offer this as an option,” says Boulet, of Raleigh, N.C. Recently, she adds, a friend’s fiancé “thanked me the next three times I saw him” for telling him about the cheaper lab-made option.
Man-made diamonds are catching on, despite some lingering stigma. This year was the first time that sales of lab-made and natural mined loose diamonds, primarily used as center stones in engagement rings, were split evenly, according to data from Tenoris, a jewellery and diamond trend-analytics company.
The rise of lab-made stones, however, is bringing up quirks alongside the perks. Now that blingier engagement rings—above two or three carats—are more affordable, more people are dealing with the peculiarities of wearing rather large rocks.
Esther Hare, a 5-foot-11-inch former triathlete, sought out a 4.5-carat lab-made oval-shaped diamond to fit her larger hands as a part of her vow renewal in Hawaii last year. It was a far cry from the half-carat ring her husband proposed with more than 25 years ago and the 1.5-carat upgrade they purchased 10 years ago. Hare, 50, who lives in San Jose, Calif., and works in high tech, chose a $40,000 lab-made diamond because “it’s nuts” to have to spend $100,000 on a natural stone. “It had to be big—that was my vision,” she says.
But the size of the ring has made it less practical at times. She doesn’t wear it for athletic training and swaps in her wedding band instead. And she is careful to leave it at home when traveling. “A lot of times I won’t take it on vacation because it’s just a monster,” she says.
The average retail price for a one-carat lab-made loose diamond decreased to $1,426 this year from $3,039 in 2020, according to the Tenoris data. Similar-sized loose natural diamonds cost $5,426 this year, compared with $4,943 in 2020.
Lab-made diamonds have essentially the same chemical makeup as natural ones, and look the same, unless viewed through sophisticated equipment that gauges the characteristics of emitted light.
At Ritani, an online jewellery retailer, lab-made diamond sales make up about 70% of the diamonds sold, up from roughly 30% two years ago, says Juliet Gomes, head of customer service at the company, based in White Plains, N.Y.
Ritani sometimes records videos of the lab-diamonds pinging when exposed to a “diamond tester,” a tool that judges authenticity, to show customers that the man-made rocks behave the same as natural ones. “We definitely have some deep conversations with them,” Gomes says.
Not all gem dealers are rolling with these stones.
Philadelphia jeweller Steven Singer only stocks the natural stuff in his store and is planning a February campaign to give about 1,000 one-carat lab-made diamonds away free to prove they are “worthless.” Anyone can sign up online and get one in the mail; even shipping is free. “I’m not selling Frankensteins that were built in a lab,” Singer says.
Some brides are turned off by the larger bling now allowed by the lower prices.When her now-husband proposed with a two-carat lab-grown engagement ring, Tiffany Buchert, 40, was excited about the prospect of marriage—but not about the size of the diamond, which she says struck her as “costume jewellery-ish.”
“I said yes in the moment, of course, I didn’t want it to be weird,” says the physician assistant from West Chester, Pa.
But within weeks, she says, she fessed up, telling her fiancé: “I think I hate this ring.”
The couple returned it and then bought a one-carat natural diamond for more than double the price.
When Boulet, the wedding planner in Raleigh, got engaged herself, she was over the moon when her fiancé proposed with a 2.3 carat lab-made diamond ring. “It’s very shiny, we were almost worried it was too shiny and was going to look fake,” she says.
It doesn’t, which presents another issue—looking like someone who really shelled out for jewellery. Boulet will occasionally volunteer that her diamond ring came from a lab.
“I don’t want people to think I’m putting on airs, or trying to be flashier than I am,” she says.
For Daniel Teoh, a 36-year-old software engineer outside of Detroit, buying a cheaper lab-made diamond for his fiancée meant extra room in his $30,000 ring budget.
Instead of a bigger ring, he got her something they could both enjoy. During a walk while on an annual ski trip to South Lake Tahoe, Calif., Teoh popped the question and handed his now-wife a handmade wooden box that included a 2.5-carat lab-made diamond ring—and a car key.
She put on the ring, celebrated with both of their sisters and a friend, who was the unofficial photographer of the happy event, and then they drove back to the house. There, she saw a 1965 Mustang GT coupe in Wimbledon white with red stripes and a bow on top.
Looking back, Teoh says, it was still the diamond that made the big first impression.
“It wasn’t until like 15 minutes later she was like ‘so, what’s with this key?’” he adds.
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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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