Stocks Are Crashing—That’s a Great Reason to Sit Tight

The red numbers in your 401(k) today might appear to vindicate warnings about an artificial-intelligence bubble and infirm economy. But don’t tilt your portfolio toward full pessimism just yet.

The S&P 500 was down 3% Monday, with the Nasdaq falling even further. Investors have been selling the year’s best performers, concerned that disappointing second-quarter results from big technology companies such as Alphabet , Tesla and Intel are a sign that the AI frenzy is a fad. Also, consumer discretionary stocks have become the worst-performing sector in the S&P 500, as lacklustre labour-market reports have raised worries that the Federal Reserve made a mistake by waiting until September to cut interest rates.

Overseas, the Stoxx Europe 600 closed almost 5% below where it was a week ago, whereas the Swiss franc, a common haven asset, is up roughly 4%. The most eye-popping moves happened in Asia, though, where the Nikkei 225 plunged 12.4% Monday in the worst trading session since Oct. 20, 1987—the day that followed Wall Street’s infamous Black Monday.

Yet it is precisely the breakneck speed with which Japanese equities tumbled that should give most investors a reason to remain calm.

As a guideline, sudden market selloffs are less dangerous than those that unfold progressively over time. This is because investors who rationally price in bad economic data often do so slowly, as it trickles in. Flash crashes, conversely, are often a sign that some tidbit of bad news made speculative bets go awry, triggering a cascade of trades, many of them automated.

Japan is particularly prone to such reversals because interest rates there are so low that many investors use them to fund higher-yielding investments in other currencies. Whenever markets get jittery, these “carry trades” tend to unravel, pushing up the yen and hitting Japanese stocks, many of which are diversified exporters that do better when global growth accelerates. Amplifying this tendency, Japanese stocks had this year become extremely popular among global investors.

The timing of the rout also points a finger at the Bank of Japan , which last week decided to tighten monetary policy for the first time in 17 years with the explicit goal of boosting the yen. Investors who rushed to cover their bets then triggered the reversal of stretched trades elsewhere, including in the U.S.

One of the most striking features of the S&P 500 for most of this year has been its extremely low volatility. Until July, the Cboe Volatility Index, or VIX, was at 2019 levels, and kept sliding lower even as investors made big changes to their monetary-policy forecasts.

While the VIX is often dubbed Wall Street’s “fear gauge,” the options contracts it is based on often themselves influence volatility. Whenever investors make bets against market swings, as they have recently in the U.S. by buying lots of structured products , the banks that sell those options are forced to take the other side. These hedges then suppress volatility in the stock market.

The flip side is that whenever a panic breaks through this feedback loop, volatility skyrockets. As the stock market opened Monday, the VIX hovered above 50, making it the highest weekly jump since the onset of the pandemic, though it later fell below 40.

This suggests the selloff is disproportionate, especially looking at the historical record: 87% of the time, investors who bought the S&P 500 on days when the VIX closed at 30 or higher ended up making money a year later.

The second-quarter reporting season has brought mostly good news, with 78% of the S&P 500 firms that have reported so far beating analysts’ earnings estimates—compared with a 74% 10-year average. Both AI-related companies and the rest are reporting net income above what was forecast a month ago. Overall, the U.S. economy still looks robust: The unemployment rate has gone up because the labor force has expanded.

Also, looking at S&P 500 returns since 1994 shows that selling based on the previous day’s falls is a bad strategy. Electing to move into cash after large monthly declines fared better, but still less well than sitting tight.

This isn’t to say that concerns about an economic slowdown or high tech valuations aren’t warranted. Investors have reasons to diversify away from the AI trend or swap more cyclically exposed stocks for more “defensive” names. Indeed, selling out of stocks after particularly exuberant days and months has historically tended to be a winning move. But hindsight is a terrible guide to investing your savings.

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.

Stocks Are Crashing—That’s a Great Reason to Sit Tight

The sudden selloff in Japanese equities and a surge in the VIX suggest the current rout is being exaggerated by trend chasers

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

Playing a different game

Overlooked