How to Retire Better, From Retirees Who Learned the Hard Way

Thousands of Americans retire every day short on cash, friendships and plans.

Many retirees say they realised too late how they could have prepared for a more financially secure and rewarding postwork life. They would have focused on saving more money to cover the higher cost of living. Or they would have put more time into building relationships, taking better care of their health or cultivating new pursuits.

One reason retirement is so hard to prepare for is we often lack models of postwork life to emulate, retirees and financial advisers say. Though our culture is awash with images of professional success, we are a little hazier on what retirement success looks like and what it takes to achieve.

To sharpen that picture, we asked retirees about what they would do differently if given a second chance. Their regrets offer insights that can help people think and plan better at every life stage.

“Regret makes us feel bad, but it can help us do better,” said Daniel Pink, who researched people’s relationships to regret across a range of areas for his book “The Power of Regret.”

Here are three lessons retirees say they wish they had known sooner.

Investing for retirement means more than money

Jim Pilzner, a retired entrepreneur, regrets not setting goals for himself when he retired about four years ago. Now 78, he found there is only so much golf to play and only so many lunches to go to.

“I would counsel my younger self, and any other active, achieving person to recognise what drives them and what success really means,” said Pilzner.

He eventually figured out that the two things that motivated him most during his career—taking action and learning new things—were the same recipe he needed for retirement.

So this spring he enrolled at University of Nevada, Reno with two classes (earning a 4.0) and will be full-time in the fall. He is studying for a degree in political science and history.

Retirees frequently don’t realise how much their career provided a sense of identity and self-worth. Many fail to grasp the need to plan for a different source of purpose in retirement, said Betty Wang, a financial adviser in Denver.

People carefully plan how they will spend money in retirement but often give far less thought to how to spend their time.

Jay Holt, 74, regrets not retiring sooner. He planned to spend his postwork years playing polo. But in 2015, he fell while playing and had to give up the sport.

The resident of Cincinnatus, N.Y., who retired in 2013 at age 64, now wishes he had had a few more years in which to enjoy this activity.

Relationships are the key to retirement

The best predictor of longevity, health and happiness in later life is the quality of your relationships. That is the finding of the Harvard Study of Adult Development, which has followed families for decades.

Dan Roberts, 72, in Idyllwild, Calif., wishes he had kept up with former colleagues for personal and professional reasons.

Roberts retired about 18 months ago. Soon after, his son and his family, who were living just two hours away, moved to New Zealand.

Roberts and his wife, Robin Roberts, said only two visits a year are doable on their budget. He said he would have been able to afford more-frequent trips had he kept the door open to contract work by maintaining both his relationships with former colleagues and a project-management certification.

“We miss our grandchildren terribly,” his wife said.

David Edmisten, an adviser in Prescott, Ariz., said clients sometimes regret delaying retirement for this reason. The extra years working come at the cost of missing time with family and friends and postponing trips, he said.

“Some even had people close to them pass away and regret not being able to spend more time with their loved ones while they still could,” Edmisten said.

Retirement is longer than you think

Arthur Parmentier, 69, regrets retiring at 65, rather than working a few more years, partly because he missed out on a few more years of contributions to his retirement account.

The Providence, R.I., resident claimed Social Security at 65, accepting a lower monthly benefit than he would have received by waiting.

“Had I waited two more years or maybe three, I would have been quite comfortable, but right now, I’m living on Social Security and trying not to touch my IRA,” said Parmentier. “I think now that I may live well into my 80s, so I have to be prepared for that and make sure my IRA will last me throughout those years.”

The life expectancy for a 65-year-old is 84 for men and nearly 87 for women, according to projections by the Society of Actuaries based on 2019 data. Surveys suggest many Americans vastly underestimate those numbers. Of 1,500 adults ages 45 to 80 polled by the Society of Actuaries in 2015, 41% of pre retirees and 37% of retirees underestimated their life expectancy by five or more years, while 14% of pre retirees and 18% of retirees underestimated it by two to four years.

Social Security allows people to start their retirement benefits any time between ages 62 and 70, and increases the payment for every month of delay.

For many, the math favours starting at 70, when monthly benefits before cost-of-living adjustments are 76% higher than at 62, according to Laurence Kotlikoff, a Boston University economist.

A person who postpones benefits until age 70 instead of 62 would have to live to at least 80 to come out ahead, said Kotlikoff, founder of MaximizeMySocialSecurity.com, which advises people on claiming decisions.

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