The ‘October Theory’ of Changing Your Life
People are using the beginning of fall as the best time to reset their goals and values, inspired by a social-media trend
3 min
October is traditionally the time to break out the cozy sweaters and consume as many pumpkin-spice drinks as possible. Instead, people are now using it to reset their goals.
Dubbed “October Theory,” these people are rethinking their approach to the last three months of the year. They’re using it as a time to set goals, pick up new habits and reflect—essentially taking on the role New Year’s plays.
October Theory is the latest “theory” social media has latched onto. Between the uneven job market, inflation, and the usual daily grind, people are looking for something they can control. Setting goals and improving their lives —whether it’s their health, finances or mindset—is something they are gravitating toward.
Sarah Stone, a 35-year-old Realtor in Kansas City, Mo., says October is a better time to reflect on the previous nine months and also home in on what she wants to achieve in the last few months of the year. This month, she’s decluttering her home and purging habits such as too much impulse shopping at TJ Maxx.
“It feels almost like the beginning of the year is in the wrong place on the calendar,” says Stone.
October can feel like an introspective time for people since the seasons are changing, a new academic school year has started and the current year is on its way out, says Laurie Kramer, a licensed clinical psychologist and a professor of applied psychology at Northeastern University. The Jewish new year—Rosh Hashana—also takes place in September or October, giving millions a time to reflect.
“This is a great time, 90 days from the new year, from the holidays, to reassess, see where you are with things,” Kramer says.
Start now, win later
October Theory is catching on partly because it sets someone up for success by the time January rolls around, say fans of the trend. Instead of picking up a new habit in the dead of winter—at the same time everyone else is trying to make it to the gym, for instance—it has already been in place for three months.
Every new year, Allison Bucheleres, a 30-year-old lifestyle and fashion content creator in Miami, tries to set new goals. Often, she fails because she doesn’t have a routine in place to make it happen.
Most of her goals this month revolve around setting new daily routines, such as waking up at 7 a.m., journaling her thoughts and writing self-affirmations to reframe her thinking. Around the middle of the day, she’ll repeat her positive phrases—at times over 100 of them—and will sometimes write one on a sticky note to post on her bathroom mirror.
Bucheleres’s newest self-mantra: “I can control my work and my self belief, but not the timing.”
Simple behaviours that are easy to repeat could take as few as 30 times to become a habit. More complex ones, such as going to the gym, could take up to three months of daily practice, says Wendy Wood, professor emerita of psychology and business at the University of Southern California.
The best time to change behaviour is during a big life change, such as moving to a new house or starting a new job or relationship—regardless of whether it’s in January or October, she says.
“You have a sort of window of opportunity to make decisions about what you want to do without your old habits getting in the way,” Wood says.
Making the most of 2024
Others view October as a last chance to fulfil the goals and aspirations they set months ago.
That includes Mateo Pérez, who is in the final stretch for his weightlifting and running regimen. The 19-year-old sophomore, who is majoring in creative advertising at the University of Miami, is also working on an application to transfer to New York University for the fall 2025 semester. Pérez wants to finish the application by the end of this semester in December.
“Right now, it’s like a reflection of this whole year and how can we make the most of the last three months,” Pérez says.
Psychologists say being introspective—at any time of the year—helps people develop habits and routines. It is often the key to following through on your goals.
Two Octobers ago, Kelly Sites, a 38-year-old customer-support manager and content creator, decided to stop living overseas. By February, she had moved to Kansas City, Mo.
This year, she’s trying to set up a daily meditation and breathing practice, and eat more whole foods. In a TikTok post on Oct. 2, Sites encouraged people to go to their photo albums and type in October to see how much their lives have changed in the 10th month of the year.
“It’s this idea of hibernation, seasons changing,” Sites says. “There’s always seeds of my life that were planted in October that changed the rest of the year.”
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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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