Is Germany’s Economic Model Truly Kaputt?

More than two decades after Germany was famously called “the sick man of the euro” by The Economist magazine, investors must wonder if the country’s industrial heart is once again critically weak. This week brought more dismal German economic data: Industrial production fell 1.5% in June from the previous month, worse than analysts expected. Though figures released on Friday showed a rise in exports, the volume of goods Germany sends abroad is still close to lows plumbed in the 2009 global financial crisis.

German gross domestic product has clocked three quarters of negative or zero growth , making the country the worst performer among major eurozone nations since 2019. Previously it was the top performer. How long this “slowcession” lasts is a crucial question for picking stocks in Europe. Despite the recent economic reversal, the DAX has been by far the best equity index in the eurozone over 20 years, returning almost 360%.

By comparison, investing in long-stagnant Italy yielded a paltry 140% return. German industrial output has been in decline since 2018, when global vehicle sales fell for the first time in almost a decade. A postpandemic rebalancing of spending toward services has made the situation worse. Growth in China, the fourth-largest market for German exporters, has slowed.

On Thursday, shares in Siemens —the largest industrial firm in Europe—fell 5% after it cited these two factors as the cause of a fall in orders during the second quarter. Some of the grit that has gotten into the German economic machine might be hard to dislodge . Chinese carmakers have turned from partners into fierce competitors as Volkswagen , BMW and Mercedes-Benz play catch-up in electric-vehicle technology.

It isn’t just China that is seeking to substitute imports for domestic products; the Biden administration is copying Beijing’s playbook. There is also energy. At the same time as German industry has lost Russia as its main source of cheap gas, Berlin has closed the country’s last three nuclear power plants. Angst has gripped German officials and executives in an echo of worries voiced at the start of the millennium, when unemployment surged and globalisation ravaged factories.

Back then, the response was a policy package that prioritised international competitiveness, incentivising the creation of low-pay “minijobs.” The government embraced fiscal austerity and nudged unions to push for wage restraint. The result was a 20-year decline in unemployment and a current-account surplus that reached an eye-watering 8% of GDP even as the U.S. ran huge deficits. Many economists praised German labor flexibility and fiscal austerity.

Conversely, critics pointed out that surpluses made most households worse off, and that Germany’s factory-job losses were just as large as America’s. Politics aside, it was largely a fortuitous jump in foreign demand that drove growth, allowing the nation to solidify gains in industries where it already had an advantage.

“Over the last 20 years, Germany always had an external sugar daddy: China, the eurozone and then the U.S.,” said Carsten Brzeski , chief economist at ING.

The flaw in this model was that it outsourced economic policy, leading to problematic dependencies on geopolitical rivals. It also fostered an excessive focus on old winners at the expense of new digital technologies and renewable energy. Bearish investors are right that it will take years to rectify these problems, particularly given the complexity of consensus-based German politics. Yet the German export-led model also got a lot right. As in China or South Korea, it channeled demand toward higher-productivity, higher-wage firms.

Unlike in the U.S., German manufacturing became more complex. That allowed the country’s industrial base to survive better than in other Western countries. In a world where nations are scrambling to reshore industries, Germany already has them. The readiest answer to its growth challenge isn’t to turn away from manufacturing but to double down by taking a page from Chinese and now U.S. industrial policy.

The German government is already doing this with semiconductors as part of the European Chips Act. Back in June, it signed off on 10 billion euros (around $11 billion) in subsidies for American chip maker Intel to build two plants, and earlier this week it committed €5 billion to help Taiwan’s TSMC set up a factory with local partners like Infineon.

A similar approach is needed to upgrade the country’s power generation and transmission and accelerate the transformation of carmakers and other industrial incumbents. Long-term energy guarantees could stem cost swings in the meantime. Given its political influence over the European Union, it seems hard to imagine that the bloc’s green-economy push could somehow leave Germany in a less dominant position . Historically, this is one patient that always leaves the hospital.

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