For a Good Job by 30, Do This in Your 20s

Skepticism about the value of college is growing, but earning a four-year degree by your mid-20s is the surest route to a good job by age 30.

That is a key takeaway from a new analysis by Georgetown University’s Center on Education and the Workforce that aims to identify the paths that bring people to good jobs. The findings are important as companies, individuals and families are trying to better understand how college degrees affect career outcomes.

Georgetown researchers examined government data for more than 8,000 Americans born in the early 1980s from adolescence through age 30. They identified 38 decision points that could influence workers’ ability to land what they deemed a good job by age 30—one that pays the minimum for economic self-sufficiency, a median annual salary of $57,000.

Pursuing a bachelor’s degree made more of a difference than any other decision that researchers analysed.

“The main road to a good job is still to go get the BA,” said Anthony Carnevale, who directs the Georgetown centre.

The researchers focused on people who didn’t go directly from high school to college, because the cohort that graduated college in their early 20s had a high rate of good job outcomes.

Millions of people start bachelor’s degrees, but don’t finish them by their mid-20s. Those non-finishers have a 40% chance of getting a good job by 30, Georgetown data show. If they eventually earned a bachelor’s degree by age 26, they would have a higher chance—56%—of getting a good job, Georgetown estimates.

Even starting a bachelor’s degree by age 22 made a difference for some high-school graduates. People who pursued an associate degree, skills training or certificate had a 29% shot at a good job, compared with 23% for those who didn’t pursue higher education by that age.

College Debt—and Payoff

Escalating college costs have complicated people’s decision to attend, said Zack Mabel, an author of the Georgetown report and a research professor of education and economics at the university.

The expected payoff to getting a bachelor’s degree is higher than it has ever been, Prof. Mabel said, but added, “with the rising cost of college, and the increasing debt that students and families have to take on, the risk of pursuing higher education is higher than it’s ever been.”

Some 56% of respondents to a recent Wall Street Journal-NORC poll said a four-year degree isn’t worth it, because students often leave with large student debt loads and no specific job skills. Ten years ago, 40% of people polled thought a college degree wasn’t worth it.

Dany Nguyen, 30 years old, started a job in Austin last year as a software developer for General Motors after a decade of working while going to school.

Mr. Nguyen, who graduated from high school in 2010, said he spent four years stocking shelves at a store, running food orders at a restaurant and working at a banquet hall while taking community-college classes at night. Though exhausting, the arrangement ensured he could pay his bills and tuition. He got skills and connections that led to better paying roles, he said, including an inventory job with a dental-product company that he learned about from a co-worker at a different job.

Mr. Nguyen ultimately transferred from community college to California State University, Long Beach, and finished his bachelor’s degree in management information systems last year. Today, he is making more than ever and sees the benefit to working his way through school.

“Being able to combine both school teamwork and work teamwork, you’re able to do your job efficiently,” he said.

Salaries for college graduates are higher than those without degrees, but data analysed by the Federal Reserve Bank of St. Louis shows the gap in net worth between college grads and non grads has narrowed significantly. One reason is the high cost of college, with many grads’ higher earnings offset by student debt.

Renee Wooten worked while attending a for-profit university, delivering pizzas and fielding queries at a call centre, then turning a contract position in the video game industry into a full-time job with benefits. Mr. Wooten, 33, makes six figures as a video game producer but says having $40,000 in outstanding student debt is stressful.

“I don’t know if I would do it again,” Mr. Wooten said, adding that an associate degree to start may have been a better choice. “I’ve been dumping my bonuses and my tax returns into my student loans, just for them to be eaten up by interest.”

Industries Matter

Some companies have eliminated bachelor’s-degree requirements for hires, though almost 70% of the new jobs created in the U.S. between 2012 and 2019 were in occupations that typically require a four-year degree or higher for entry, according to Opportunity@Work, a nonprofit.

Georgetown’s analysis showed several other early-career decisions can help put 20-somethings on the path to a better-paying job if they don’t go to college after high school. Steady work between the ages of 20 and 22 and avoiding resume gaps in these years can help, researchers said, because hiring managers are more likely to hire experienced people who are actively working.

Industries count, too. Working at age 22 in a blue-collar job or in tech or finance, rather than fields such as education, food services and the arts, also helped raise the chance of getting a higher paying role. Still, workers who took one of those paths had no more than a 25% chance of landing a good job by 30. Those pathways proved more effective when combined with attending college.

Diego Padilla faced a choice in 2020 while in his late teens: Continue his internship with JP Morgan Chase, assisting clients with transactions such as opening accounts and withdrawals, or accept a full-time job managing a grocery store.

Mr. Padilla, then a fresh high-school graduate enrolled in community college, was drawn to the stability of a full-time job. But he wondered where he could go if he stayed at the bank. Now 22, Mr. Padilla has a full-time role with Chase, finished his associate degree and transferred to Chicago where he works with Chase clients.

Mr. Padilla is taking online classes in pursuit of his bachelor’s degree while working full time. After that he said he wants to get an M.B.A.

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