Is This 1987 All Over Again? What’s Driving the Market Meltdown?

Financial markets are supposed to capture the wisdom of the crowd, but on Monday the crowd ran in all directions waving its hands in the air screaming. Japan’s stock market fell the most in 37 years with a 12% plunge that wiped out all its gains for the year, while in the U.S. the VIX index of implied stock volatility briefly had its biggest rise ever. Panic hit.

The selloff was triggered by Friday’s jobs data prompting a sudden switch in the economic narrative from soft landing to hard landing. Add to the mix a period of deflating hype about artificial intelligence and a Bank of Japan rate rise designed to strengthen the yen. News that Warren Buffett’s Berkshire Hathaway had sold half its Apple shares and boosted its cash pile added to the pain.

But the triggers couldn’t possibly justify the scale of the moves. When a new trigger arrived, in the form of better-than-expected data on the service sector, markets partially rebounded and the Vix fell sharply — again, far more than the data could justify.

The selloff—which at one point had chip maker Nvidia down 15%—was so big because investors had been all-in betting that things would work out well. Now things have calmed a bit, the question is whether the unwind of these bets, and the leverage behind them, is done. If it resumes, will the selloff feed back into higher savings and a weaker economy or, worse, hit the financial system?

The extreme examples of past effects from big market falls are 1987’s crash, 1998’s Long-Term Capital Management blowup and 2008’s global financial crisis. History is never perfect, but so far this looks more like a (much milder) version of 1987 than it does the other two.

In 1987, the stock market had its biggest one-day fall ever, with the S&P 500 down more than 20% on Black Monday in October. Investors had built up excessive leverage after a stunning 39% gain in the year to August’s high, and the crash led both to big margin calls and to badly designed automated trading that exacerbated the selling. But the Federal Reserve poured liquidity into the banks, brokers didn’t default and the market made back all its losses within two years. The economy was fine.

The good news was that 1987 was all about markets: They went up, they went back down, no one else was hurt. The S&P made 36% in the eight months to its August 1987 peak, similar to the 33% it rose in the eight months to the end of June this year. As in 1987, this year’s gains came in spite of tight monetary policy and higher bond yields. Just like today, in 1987 investors were on edge and ready to sell to lock in the unexpected profit. The losses are smaller so far, but lucrative trades have reversed , just as they did for the market as a whole in 1987.

In 1998, the situation was much worse, although stocks recovered more quickly. Highly levered hedge fund LTCM was crushed when Russia’s domestic debt default created a flight to safety. LTCM was big enough that it threatened to bring down Wall Street institutions. The Fed cut rates three times and pulled together a group of banks to rescue the firm and wind down its trades slowly. Stocks took just four months to recover, but the easy money helped stoke the dotcom bubble, which popped two years later and led to a mild recession—and gigantic losses for investors in tech stocks.

We don’t know yet if any hedge funds have been taken out by the big moves in markets, which have brought heavy losses for those engaged in the “ carry trade ” of borrowing cheaply in yen and buying higher-yielding currencies such as the Mexican peso or dollar. Large swings in Treasurys on Monday might also have hurt, given the large positions hedge funds hold. Traders are betting that the Fed will slash rates, with a super-sized cut of 0.5 percentage points priced into futures for the September meeting (and far more earlier in the day).

The really bad outcome would be a repeat of 2008, but it seems highly unlikely. True, some large U.S. banks failed last year, due to bad bets on government bonds. But banks are much less leveraged than they were, and the system is less exposed to a liquidity crisis, as private lenders have taken on much of the risk that used to sit in banks. Big losses are entirely possible, and private funds could hit trouble, but that would take time and wouldn’t create the same system-wide crisis.

The ideal would be that excess in the stock market unwinds as in 1987 without creating wider trouble, hopefully more gradually than in 1987. AI enthusiasm could deflate stock prices much more—even after falling 30% from its June high, Nvidia has still doubled in price this year. But the market is already much closer to normal, with Monday’s falls leaving the Nasdaq 100 index up only 6% this year, and the S&P 7%.

If panic continues to abate, the Fed cuts and nothing breaks in the financial system, we should count ourselves lucky. But it would be good if investors could remember the sinking feeling they had on Monday morning, and try to be a bit wiser and less speculative.

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.

Is This 1987 All Over Again? What’s Driving the Market Meltdown?

Past routs offer lessons after Black Monday Morning

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

Playing a different game

Overlooked