America’s Green Skills Gap Raises Concerns About Energy Transition

Green skills in the U.S. aren’t growing as fast as green jobs, pressuring companies to get creative to find the workers they need to carry out the energy transition and take advantage of the historic amount of money pouring into climate technology.

Generous incentives in last year’s Inflation Reduction Act have prompted billions of dollars in clean-energy investment announcements that are forecast to create millions of U.S. jobs. But recent data shows strong growth in demand for green skills exacerbating an already tight market where demand outstrips supply.

In 2022, the number of U.S. LinkedIn profiles with at least one green skill grew around 8.4%, compared with a 20% rise in green job postings on the platform, according to data from LinkedIn provided to The Wall Street Journal. The online professional network defines green skills as those that make economic activities more environmentally sustainable, such as carbon accounting, hydrogen engineering and battery manufacturing. It considers green jobs to be ones which include climate action objectives such as removing pollution and preserving natural resources.

Likewise, more than 114,000 U.S. clean-energy jobs were created in 2022, according to last week’s annual employment report from the U.S. Department of Energy. Every state recorded an increase in these jobs and the rise outpaced employment growth in both the wider economy and the overall energy sector. More than 40% of all U.S. energy jobs last year were in clean energy, defined as ones that include technologies aligned with a net-zero future such as electric vehicles, renewables or hydrogen.

“We need this concentration of workers with green skills to be higher,” said Sue Duke, head of global public policy at LinkedIn.

The IRA earmarked around $369 billion of government incentives for energy and climate-related programs over 10 years. The legislation was predicted to create more than nine million clean-energy and climate-related jobs over the next decade, according to the Political Economy Research Institute at the University of Massachusetts Amherst.

The utility-scale clean energy industry has announced more than $150 billion of investment as well as 18,000 new manufacturing jobs associated with new or expanded facilities since the IRA was passed in August 2022, the American Clean Power Association, an industry group for wind, solar and battery storage, said in April.

“The transition to a cleaner economy, as envisioned by many policy makers, will involve building a vast amount of infrastructure,” said Kenneth Gillingham, economics professor at the Yale School of the Environment.

“There has certainly been an uptick in hiring,” Gillingham said. “But much of the building of infrastructure and clean energy is yet to come if the goals of the IRA come to pass.”

The current green skills supply shortage has raised concerns about how U.S. companies will find the workers they need as the IRA-related projects are built and eventually start operating. However, there are some mitigating factors.

“Today’s workforce is not ready for such a scaling up, but the skills needed are not always specific to green technology,” Gillingham said. There are also plenty of traditional economy roles that can quite easily transition to green jobs, such as from construction, electrical work and engineering, he said.

To help close the green skills gap, companies are getting creative. For newer, fast-growing roles such as sustainability manager and energy auditor, some businesses have recruited workers without prior green job experience, according to LinkedIn. It also said around half of the solar consultants and waste managers hired in the U.S. had no prior experience.

Businesses are also upskilling current workers and hiring people from areas of the economy that are shrinking. For example, Gillingham said coal-power plant workers are being trained to run renewable-energy farms, operate electric-vehicle charging networks or expand transmission lines.

Universities are also stepping up to help close the green skills gap. For example, the Yale School of the Environment has begun a “major push” into certification programs to train professionals with new greenskills that they can quickly bring into their companies, said Sara Smiley Smith, associate dean at the school.

Yale offers two online certification programs, each taking roughly 11 months to complete. One is on financing and deploying clean energy, while the other concerns restoring, conserving and sustainably using tropical forests. It is developing three more certifications and experimenting with the Coursera learning platform.

“The applications to the master’s program I run here have doubled in the last three years,” said Steven Cohen, director of Columbia University’s Master of Science in Sustainability Management. Cohen added: “The growth of programs to educate people to play sustainability roles in private corporations is exploding.”

The need for green skills varies across industries: The green shift in the energy and transport workforces are quite pronounced as they develop lower-carbon energy sources and electric vehicles, respectively.

More than 84% of net new electric power generation jobs last year were in clean energy such as renewables, geothermal and nuclear, although oil and gas jobs grew too, with only coal jobs falling, according to the U.S. Department of Energy. However, LinkedIn data shows that even within the U.S. oil-and-gas industry there has been a steady increase in the share of its workforce acquiring green skills, which has risen to 22%, well above the average across U.S.industries, at around 12%.

Likewise, there were more than 200,000 jobs added last year in the clean energy vehicle sector, reflecting year-over-year growth of more than 20%, according to the U.S. Department of Energy.As of 2023, nearly 11% of U.S. transport workers, such as employees of carmakers, have green skills, according to LinkedIn. The U.S.’s share of auto workers with electric vehicle-related skills rose by 68% between 2018 and 2023.

In contrast, the U.S. finance industry is lagging behind the national average despite many businesses talking up how they are increasingly basing their investments on environmental, social and governance criteria. The proportion of U.S. financial workers with green skills reached 8% in 2023, but is growing faster than most industries with a 14.8% year-over-year increase.

—Rochelle Toplensky contributed to this article.

Corrections & Amplifications
Yale School of the Environment offers two online certification programs to train professionals on new green skills, both taking roughly 11 months. A previous version of this article incorrectly said Yale’s course on financing and deploying clean energy was 10 months and a second course on restoring, conserving and sustainably using tropical forests was eight weeks. (Corrected on July 7)

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