How to Lose Money on the World’s Most Popular Investment Theme
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    HOUSE MEDIAN ASKING PRICES AND WEEKLY CHANGE     Sydney $1,766,859 (+1.43%)       Melbourne $1,057,987 (+0.39%)       Brisbane $1,163,157 (+0.28%)       Adelaide $1,009,467 (-0.01%)       Perth $1,020,350 (+0.19%)       Hobart $791,751 (-1.26%)       Darwin $858,973 (-1.15%)       Canberra $977,332 (+0.59%)       National $1,153,623 (+0.63%)                UNIT MEDIAN ASKING PRICES AND WEEKLY CHANGE     Sydney $799,653 (-0.43%)       Melbourne $527,118 (-0.21%)       Brisbane $750,759 (+2.47%)       Adelaide $572,120 (-0.11%)       Perth $584,687 (+3.51%)       Hobart $537,541 (+0.78%)       Darwin $464,817 (+5.17%)       Canberra $479,787 (-2.83%)       National $611,752 (+0.90%)                HOUSES FOR SALE AND WEEKLY CHANGE     Sydney 11,234 (-975)       Melbourne 13,637 (+555)       Brisbane 7,179 (-18)       Adelaide 2,226 (-10)       Perth 5,278 (+91)       Hobart 845 (+12)       Darwin 149 (+4)       Canberra 953 (+13)       National 41,501 (-328)                UNITS FOR SALE AND WEEKLY CHANGE     Sydney 9,137 (+7)       Melbourne 6,987 (+129)       Brisbane 1,327 (-19)       Adelaide 346 (-4)       Perth 1,153 (-22)       Hobart 162 (-11)       Darwin 241 (+1)       Canberra 1,132 (+2)       National 20,485 (+83)                HOUSE MEDIAN ASKING RENTS AND WEEKLY CHANGE     Sydney $800 ($0)       Melbourne $580 ($0)       Brisbane $670 ($0)       Adelaide $630 (+$5)       Perth $700 ($0)       Hobart $600 ($0)       Darwin $775 (+$25)       Canberra $700 (+$5)       National $690 (+$5)                UNIT MEDIAN ASKING RENTS AND WEEKLY CHANGE     Sydney $750 ($0)       Melbourne $595 ($0)       Brisbane $660 ($0)       Adelaide $545 (+$15)       Perth $650 (-$10)       Hobart $473 (-$8)       Darwin $600 ($0)       Canberra $570 (+$5)       National $615 (+$)                HOUSES FOR RENT AND WEEKLY CHANGE     Sydney 5,382 (+1)       Melbourne 7,710 (-16)       Brisbane 3,999 (+82)       Adelaide 1,520 (-4)       Perth 2,404 (+80)       Hobart 171 (+18)       Darwin 81 (-2)       Canberra 420 (-23)       National 21,687 (+136)                UNITS FOR RENT AND WEEKLY CHANGE     Sydney 7,614 (-242)       Melbourne 5,976 (+59)       Brisbane 2,021 (+30)       Adelaide 407 (+7)       Perth 754 (+55)       Hobart 66 (+3)       Darwin 153 (+4)       Canberra 669 (-18)       National 17,660 (-102)                HOUSE ANNUAL GROSS YIELDS AND TREND         Sydney 2.35% (↓)       Melbourne 2.85% (↓)       Brisbane 3.00% (↓)     Adelaide 3.25% (↑)        Perth 3.57% (↓)     Hobart 3.94% (↑)      Darwin 4.69% (↑)      Canberra 3.72% (↑)      National 3.11% (↑)             UNIT ANNUAL GROSS YIELDS AND TREND       Sydney 4.88% (↑)      Melbourne 5.87% (↑)        Brisbane 4.57% (↓)     Adelaide 4.95% (↑)        Perth 5.78% (↓)       Hobart 4.57% (↓)       Darwin 6.71% (↓)     Canberra 6.18% (↑)        National 5.23% (↓)            HOUSE RENTAL VACANCY RATES AND TREND       Sydney 2.0% (↑)      Melbourne 1.9% (↑)      Brisbane 1.4% (↑)      Adelaide 1.3% (↑)      Perth 1.2% (↑)      Hobart 1.0% (↑)      Darwin 1.6% (↑)      Canberra 2.7% (↑)      National 1.7% (↑)             UNIT RENTAL VACANCY RATES AND TREND       Sydney 2.4% (↑)      Melbourne 3.8% (↑)      Brisbane 2.0% (↑)      Adelaide 1.1% (↑)      Perth 0.9% (↑)      Hobart 1.4% (↑)      Darwin 2.8% (↑)      Canberra 2.9% (↑)      National 2.2% (↑)             AVERAGE DAYS TO SELL HOUSES AND TREND         Sydney 26.8 (↓)     Melbourne 27.3 (↑)        Brisbane 28.3 (↓)     Adelaide 24.6 (↑)      Perth 34.2 (↑)      Hobart 27.2 (↑)      Darwin 25.9 (↑)        Canberra 25.8 (↓)     National 27.5 (↑)             AVERAGE DAYS TO SELL UNITS AND TREND       Sydney 26.7 (↑)      Melbourne 27.8 (↑)        Brisbane 27.9 (↓)       Adelaide 24.9 (↓)     Perth 33.9 (↑)        Hobart 25.8 (↓)     Darwin 26.7 (↑)      Canberra 37.3 (↑)      National 28.9 (↑)            
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How to Lose Money on the World’s Most Popular Investment Theme

Pity the investors in the three artificial-intelligence-themed ETFs that managed to lose money this year

By JAMES MACKINTOSH
Mon, Sep 2, 2024 11:03amGrey Clock 4 min

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.



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A divide has opened in the tech job market between those with artificial-intelligence skills and everyone else.

By CALLUM BORCHERS
Thu, Oct 2, 2025 4 min

There has rarely, if ever, been so much tech talent available in the job market. Yet many tech companies say good help is hard to find.

What gives?

U.S. colleges more than doubled the number of computer-science degrees awarded from 2013 to 2022, according to federal data. Then came round after round of layoffs at Google, Meta, Amazon, and others.

The Bureau of Labor Statistics predicts businesses will employ 6% fewer computer programmers in 2034 than they did last year.

All of this should, in theory, mean there is an ample supply of eager, capable engineers ready for hire.

But in their feverish pursuit of artificial-intelligence supremacy, employers say there aren’t enough people with the most in-demand skills. The few perceived as AI savants can command multimillion-dollar pay packages. On a second tier of AI savvy, workers can rake in close to $1 million a year .

Landing a job is tough for most everyone else.

Frustrated job seekers contend businesses could expand the AI talent pipeline with a little imagination. The argument is companies should accept that relatively few people have AI-specific experience because the technology is so new. They ought to focus on identifying candidates with transferable skills and let those people learn on the job.

Often, though, companies seem to hold out for dream candidates with deep backgrounds in machine learning. Many AI-related roles go unfilled for weeks or months—or get taken off job boards only to be reposted soon after.

Playing a different game

It is difficult to define what makes an AI all-star, but I’m sorry to report that it’s probably not whatever you’re doing.

Maybe you’re learning how to work more efficiently with the aid of ChatGPT and its robotic brethren. Perhaps you’re taking one of those innumerable AI certificate courses.

You might as well be playing pickup basketball at your local YMCA in hopes of being signed by the Los Angeles Lakers. The AI minds that companies truly covet are almost as rare as professional athletes.

“We’re talking about hundreds of people in the world, at the most,” says Cristóbal Valenzuela, chief executive of Runway, which makes AI image and video tools.

He describes it like this: Picture an AI model as a machine with 1,000 dials. The goal is to train the machine to detect patterns and predict outcomes. To do this, you have to feed it reams of data and know which dials to adjust—and by how much.

The universe of people with the right touch is confined to those with uncanny intuition, genius-level smarts or the foresight (possibly luck) to go into AI many years ago, before it was all the rage.

As a venture-backed startup with about 120 employees, Runway doesn’t necessarily vie with Silicon Valley giants for the AI job market’s version of LeBron James. But when I spoke with Valenzuela recently, his company was advertising base salaries of up to $440,000 for an engineering manager and $490,000 for a director of machine learning.

A job listing like one of these might attract 2,000 applicants in a week, Valenzuela says, and there is a decent chance he won’t pick any of them. A lot of people who claim to be AI literate merely produce “workslop”—generic, low-quality material. He spends a lot of time reading academic journals and browsing GitHub portfolios, and recruiting people whose work impresses him.

In addition to an uncommon skill set, companies trying to win in the hypercompetitive AI arena are scouting for commitment bordering on fanaticism .

Daniel Park is seeking three new members for his nine-person startup. He says he will wait a year or longer if that’s what it takes to fill roles with advertised base salaries of up to $500,000.

He’s looking for “prodigies” willing to work seven days a week. Much of the team lives together in a six-bedroom house in San Francisco.

If this sounds like a lonely existence, Park’s team members may be able to solve their own problem. His company, Pickle, aims to develop personalised AI companions akin to Tony Stark’s Jarvis in “Iron Man.”

Overlooked

James Strawn wasn’t an AI early adopter, and the father of two teenagers doesn’t want to sacrifice his personal life for a job. He is beginning to wonder whether there is still a place for people like him in the tech sector.

He was laid off over the summer after 25 years at Adobe , where he was a senior software quality-assurance engineer. Strawn, 55, started as a contractor and recalls his hiring as a leap of faith by the company.

He had been an artist and graphic designer. The managers who interviewed him figured he could use that background to help make Illustrator and other Adobe software more user-friendly.

Looking for work now, he doesn’t see the same willingness by companies to take a chance on someone whose résumé isn’t a perfect match to the job description. He’s had one interview since his layoff.

“I always thought my years of experience at a high-profile company would at least be enough to get me interviews where I could explain how I could contribute,” says Strawn, who is taking foundational AI courses. “It’s just not like that.”

The trouble for people starting out in AI—whether recent grads or job switchers like Strawn—is that companies see them as a dime a dozen.

“There’s this AI arms race, and the fact of the matter is entry-level people aren’t going to help you win it,” says Matt Massucci, CEO of the tech recruiting firm Hirewell. “There’s this concept of the 10x engineer—the one engineer who can do the work of 10. That’s what companies are really leaning into and paying for.”

He adds that companies can automate some low-level engineering tasks, which frees up more money to throw at high-end talent.

It’s a dynamic that creates a few handsomely paid haves and a lot more have-nots.

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