Is the Stock Market Near Its Top?

The third season of the terrific show “The Bear” blends family dysfunction with the ups and downs of high-end restaurants. With markets chasing new highs—get out those Dow 40000 hats—this column is about a different kind of dysfunctional beast. Is the market bear dead, or is it about to sneak up on us?

A U.S. equity strategist told me the story of a Japanese portfolio manager who sat in his office in July 1987 asking for stock ideas. The strategist’s model was based on a proprietary survey of investor sentiment, though it never really worked. Nonetheless, he read off a list of dozens of stocks. The portfolio manager then asked if he would kindly put in an order for 20,000 shares of each. The Dow Jones Industrial Average peaked at 2722 in late August and crashed 22.6% on Oct. 19.

A friend was a portfolio manager of a massive growth-stock fund in 1999. He told me he bought shares of Yahoo, Cisco, F5 Networks, Infosys and others every day because money flowed into his fund every day. The tech-heavy Nasdaq index peaked on March 10, 2000. As money began to flow out, he had to sell every day. By year’s end, Nasdaq had fallen by more than half.

I met Cathie Wood as she was filing papers for her “disruptive innovation” funds—to “change the way the world works.” Her ARK Innovation exchange-traded fund, ARKK, launched in October 2014 and charges 0.75% management fees. In 2020 it was up 153% as stimulus money flew in, driving more buying. ARKK peaked in February 2021 with $28 billion in assets. Since then, its net asset value is down 70%, even amid a roaring bull market, especially in tech. Morningstar recently calculated that Ms. Wood’s Ark Invest funds have destroyed more than $14 billion in wealth. One of my favorite Wall Street sayings is, “Don’t mistake a bull market for brains.”

In almost every bull run, stock momentum lures in investors at the worst moment, I call them momos, ensuring they get burned when the buying stops. Since 2009, excepting a few brief sell-offs, cash has been trash. That made some sense during the era of zero interest rates. But now with higher inflation and short rates above 5%? Confusing. Maybe investors are already anticipating another Donald Trump antiregulation pro-growth presidency, forgetting that he is married to a growth-killing pro-tariff agenda. Is the bear dead, or does it have a long fuse?

Predicting stock markets is a fool’s errand. My Series 7 test for General Securities Representative Qualification lapsed long ago, so you won’t get investment advice from me. But there are warning signs.

Have we run out of buyers? Sometimes there are triggers that scare them away: oil shocks, viruses, bank failures. But sometimes they simply collapse from exhaustion. More than 40% of households reportedly own stocks—a higher percentage than in 2000. It was 20% in 2010. Some market indicators also point to asset managers being fully invested. Who’s left to buy?

Market breadth is concerning. The 1973 market peak was driven by stretched valuations of the Nifty Fifty, which included IBM , Coca-Cola and GE but also Polaroid and Xerox . Fifty? Now it’s the Magnificent Seven: Alphabet , Amazon , Apple , Meta , Microsoft , Nvidia and Tesla . Seven? Artificial-intelligence hype, way ahead of even the rosiest of realities, drove Nvidia to make up almost a third of the S&P 500’s first half gains. Another quarter came from Amazon, Meta, Microsoft and Eli Lilly . Maybe fat bulls need Mounjaro.

Stock values feel divorced from reality. The so-called Warren Buffett indicator—the ratio between total stock-market value and gross domestic product—was 138% in March 2000. It’s now 196%. Certainly not a buy signal. And Bitcoin, my go-to bubblicious bat signal, is down about 20% since March. A dead canary?

“Don’t worry, be happy,” the bulls sing. Inflation is slain, and the Fed will cut rates. But investors won’t like the reason for those cuts. We’re already seeing earnings disasters—Nike, Walgreens , Lululemon , Delta and Wells Fargo . If the economy slows, earnings glitches and stock implosions become contagious. Plus, banks’ exposure to commercial real estate is scary, with buildings being dumped at huge haircuts almost weekly. This is now infecting rental buildings, and there are signs of a private housing glut. Inventory in Denver is up nearly 37%. Sure, markets climb a “wall of worry,” and bull markets tend to last longer than people expect, but sometimes the nightmares are real. Recessions are like honey to bears.

Even writing about the bear is bullish. Bull runs end when everyone is a believer. Still, another favorite saying of mine is, “No one’s ever lost money taking a profit.” Someday, cash will be king again. I prefer to buy stocks when everyone hates them.

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 the Stock Market Near Its Top?

Don’t let the hum of the bull tune out signs warning that a bear may be lurking.

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

Playing a different game

Overlooked