How to Lose Money on the World’s Most Popular Investment Theme

There are lots of embarrassing ways to lose money, but it is particularly galling to lose when you correctly identify the theme that will dominate the market and manage to buy into it at a good moment.

Pity the investors in the three artificial-intelligence-themed exchange-traded funds that managed to lose money this year. Every other AI-flavored ETF I can find has trailed both the S&P 500 and MSCI World. That is before the AI theme itself was seriously questioned last week, when investor doubts about the price of leading AI stocks Nvidia and Super Micro Computer became obvious.

The AI fund disaster should be a cautionary tale for buyers of thematic ETFs, which now cover virtually anything you can think of, including Californian carbon permits (down 15% this year), Chinese cloud computing (down 21%) and pet care (up 10%). Put simply: You probably won’t get what you want, you’ll likely buy at the wrong time and it will be hard to hold for the long term.

Ironically enough, Nvidia’s success has made it harder for some of the AI funds to beat the wider market. Part of the point of using a fund is to diversify, so many funds weight their holdings equally or cap the maximum size of any one stock. With Nvidia making up more than 6% of the S&P 500, that led some AI funds to have less exposure to the biggest AI stock than you would get in a broad index fund.

This problem hit the three losers of the year. First Trust’s $457 million AI-and-robotics fund has only 0.8% in Nvidia, a bit over half what it holds in cybersecurity firm BlackBerry .

WisdomTree ’s $213 million AI-and-innovation fund holds the same amount of each stock, giving it only 3% in Nvidia.

BlackRock ’s $610 million iShares Future AI & Tech fund was also equal weighted until three weeks ago, when it altered its purpose from being a robotics-and-AI fund, changed ticker and switched to a market-value-based index that gives it a larger exposure to Nvidia.

The result has been a 20-percentage-point gap between the best and worst AI ETFs this year. There is a more than 60-point gap since the launch of ChatGPT in November 2022 lit a rocket under AI stocks—although the ETFs are at least all up since then.

The market has penalized being equal weighted recently, instead rewarding big holdings in the largest stocks.

Jay Jacobs , U.S. head of thematic and active ETFs at BlackRock, says it is best to be market-value weighted when a theme has winner-takes-all characteristics, which he says generative AI has. When the firm’s AI fund included robotics it was spread across a lot more stocks that didn’t compete with each other, so equal weighted made more sense.

For investors, it isn’t so simple. Global X takes the opposite approach with its two $2 billion-plus AI funds, AIQ and BOTZ. BOTZ only buys stocks that focus on AI and robotics, but takes larger positions. AIQ spreads its bets on AI and tech more widely, and its 3% cap on its biggest holdings each time it rebalances means it has far less in Nvidia than BOTZ, with a cap of 8%. AIQ still managed to beat BOTZ this year, though.

So far, so confusing. The basic lesson: Picking among funds within a theme is hard, and depends on luck as well as close reading of the fund’s documents. A more advanced lesson is that it is hard to pick a theme in the first place, or to stick with it. The three problems:

1. Defining the theme is hard . Nvidia features in the anti-woke YALL ETF, which pitches itself as for “God-fearing, flag-waving conservatives.” The chip maker is also held by vegan, gender-diverse and climate-action ETFs. Its shares are clearly driven by the prospects for AI, but it is still big in computer-game and bitcoin ETFs, where its chips were originally used.

2. Timing the theme is even harder. Get in too early, and there aren’t any companies to buy. Get in when the funds are being launched, and the chances are the theme is already widely known and overpriced, as there are typically large numbers of launches during bubbles and late-stage bull markets.

“They are trendy by design,” says Kenneth Lamont, a senior researcher at Morningstar. “They play to our worst instincts, because we’re narrative-driven creatures.”

A recent example was the race to launch clean-energy and early-stage-tech ETFs during the bubble of late 2020 and early 2021. Performance since then has been miserable as prices corrected, with many of the ETFs halving or worse.

Dire timing is common across themes: According to a paper last year by Prof. Itzhak Ben-David of Ohio State University and three fellow academics, what they call “specialized” ETFs lose 6% a year on average over their first five years due to poor launch timing.

3. Long-term investing is pitched by fund managers as the goal for thematic investing, to hang on until the theme bears fruit. But even investors who really want to commit to a theme for the long run often find it hard, as so many funds are wound up, merged or change strategy when they go out of fashion.

The boom in internet funds of the late 1990s vanished after the dot-com bubble burst, with few surviving to see the internet theme blossom a decade later, while six of the 50 “metaverse” funds launched after Facebook switched to Meta Platforms in 2021 have already shut, according to Lamont.

The oldest thematic fund, the DWS Science and Technology mutual fund, started as the Television Fund in 1948 before adding electronics, and has gone through at least four other names. I only have data back to 1973, but it has lagged far behind the wider market since then, despite golden ages for television, electronics, science and now tech. (Yes, it has a lot of Nvidia.)

So what to do? At a very minimum, don’t buy based on the name of a fund. Look at the holdings, look at the index it follows and how it is structured, and consider whether it does what it says. Then think about just how expensive the idea has already become. Watch for the theme coming into fashion and getting overpriced, as that is a good time to sell (or to launch a fund).

But mostly, look at the fees: They will be many times higher than a broad market index fund, and the dismal history of poor timing suggests that for most people they aren’t worth paying.

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