Who Gets Promoted to the C-Suite—and How That Has Changed Over the Decades
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Who Gets Promoted to the C-Suite—and How That Has Changed Over the Decades

Among our findings: The average age of top executives started falling after 1980. But now it’s higher than it was 40 years ago.

By PETER CAPPELLI
Wed, Jan 17, 2024 9:13amGrey Clock 5 min

Here’s the face of the new C-suite: older, with broader industry experience and increasingly female.

These are some of the most surprising findings my colleagues and I have uncovered about how C-suite leaders have changed over time. My co-researchers—Rocio Bonet and Monika Hamori—and I have been tracking the attributes of the leaders of the world’s biggest corporations, the Fortune 100, since 1980, when many of the key forces shaping business today began.

The findings, in some cases, seem to be at odds with each other. That is because many factors are pulling the business world in different directions. For instance, executives change jobs a lot more than in the past and don’t stick with one employer or industry for their entire careers. On the other hand, C-suite executives do less job hopping later in their careers after moving around a lot early on. In many ways, there is more stability in the corporate world now than we would ever imagine from the tales of intrigue within individual executive suites.

Here is a closer look at our key findings

  • The youth movement is over. Our study—which will appear in the California Management Review—found that C-suite executives are getting older. It’s a reversal of a long trend: Executives were getting younger after 1980—with the average age falling six years to 51 in 2001—but now the top leaders are back to where they were in 1980: 57 years old on average.
  • Executives are doing more job hopping. The number of different companies where executives worked, including their current job, rose each decade—to 3.3 in 2021 from 2.2 in 1980, a 50% rise. Likewise, the number of years the executives worked elsewhere before joining their current company jumped by a third, to 15 years, over that same period. As a result, more outsiders are being hired directly into executive roles. In 1980, 9% of C-suite executives fit that bill. In 2021, 26% did.
  • Executives are less likely to be lifers. The percentage of executives who spent their whole careers at one company dropped in every period in our data, especially between 2011 and 2021. Now just under 20% of executives are lifers, less than half the level in 1980 and about the same as in 1900. There is a big exception to that finding, though: legacy companies. These 17 companies—which have been in the Fortune 100 since 1980—have more than twice the percentage of lifers as the others.
  • Eventually, executives do settle down. While executives may move around more early in their careers, when they do settle on a job, they stay there longer. Average tenure in executive roles is now back up to where it was in 1980, close to four years, after falling to two years in 2001. This may have to do with tech companies: As the industry has matured, it has become more stable. (At legacy companies, though, average tenure has dipped to three years from four.)
  • They have broader experience. Executives used to get training in-house in various aspects of the business: operations, finance, logistics and so forth. It was a way for companies to train potential leaders from within, especially important since there weren’t a lot of outside hires for executive roles. Now companies are seeking people from outside who have experience in different niches, and putting them in roles that fill those niches. In 1980, the average top executive had worked in 1.4 different industries. Now that figure is 2.3.
  • Legacy companies aren’t exempt from big changes. The C-suite at legacy companies looks more traditional—that is, more like 1980—than it does at other companies. Even so, these older corporations have seen some big changes.
    First off, let’s look at the traditional side. Not only do legacy C-suites have a higher percentage of lifers, these executives get more training in-house and have less experience in other industries. At the same time, though, legacy executives have been affected by some trends that make them look different than in 1980. The executives have less tenure, as we have seen, and outsiders hired directly into executive roles went to 18% in 2021 from 1% in 1980.
  • More executives come from finance. Financial markets and investor interests took on a greater role after the 1980s, and that change is reflected in the proportion of executives with a finance background: The figure has been above 30% since 2001, up from 19% in 1980.
  • More executives have law degrees. The proportion of executives with a law degree has risen, going to 17% in 2001 from 11% in 1980, and staying near that higher level in 2021. This may be a response to increased corporate regulations like Sarbanes-Oxley and Dodd-Frank that drive the need for more legal expertise in the C-suite.
  • Business degrees aren’t as prevalent as you would think. For years, there was huge growth in M.B.A. graduates in the overall population—63% from 2001 to 2011. But the growth rate of M.B.A.s in Fortune 100 C-suites was considerably lower: just 6%. The period from 2011 to 2021 had even less upward movement. The number of M.B.A.s in the C-suite rose by just 4% over those years, as M.B.A. graduates in general rose by 8% during that time.
  • Ivies are still influential. Even as the growth rate of M.B.A.s goes down overall in the C-suite, the dominance of graduates from Ivy League business schools in the executive ranks remains strong. Ivy League M.B.A. programs represent less than 1% of all such programs in the U.S. Meanwhile, as of 2021, 35% of C-suite executives had M.B.A.s, and 23% of those got the degree in the Ivy League. That’s in the same ballpark as 2001, when 30% of C-Suite executives had M.B.A.s, and 20% of those were from Ivies.
    A couple of factors may be at play: These top jobs have become more attractive for elite graduates as executive pay has soared—and more outside hiring by companies has made it possible for M.B.A.s to make lateral moves that offer a chance at the C-suite. Previously, graduates of those elite programs disproportionately moved into higher-paying investment careers.
  • Women are landing more executive jobs. The proportion of women in Fortune 100 top executive ranks rose from roughly zero in 1980 to 12% in 2001 and 18% in 2011, by about the same percentage as the proportion of women in all management jobs. After that, the proportion of women in these top executive ranks rose to 28% of jobs in 2021—while women executives in the overall ranks of management rose to just 18% of jobs from 17%, according to the Bureau of Labor Statistics. This indicates that it did not take an increase in the pipeline of women managers to add more to the executive suite.
  • Women are also advancing quicker than men. Women executives got to executive jobs faster than their male counterparts—four years faster into their careers in 2001, slowing to 1.5 years faster in 2021.
  • Foreign-born executives have also made gains. Something similar happened with executives from outside the U.S. Until this past decade, the percentage of foreign-born people in top executive ranks—2% in 1980, for instance—had lagged behind the proportion of foreign-born people in the U.S. as a whole. Now, foreign-born people make up 15% of top executive ranks—larger than their proportion in the overall population. This increase, though, doesn’t seem to be associated with any greater globalization of top corporations: Instead, it may reflect an increase in foreign-born students in elite U.S. postgraduate programs.

Peter Cappelli is a professor of management at the Wharton School of the University of Pennsylvania and the author of “Our Least Important Asset: Why the Relentless Focus on Finance and Accounting is Bad for Business and Employees.”



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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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