Economist Shane Oliver's advice for investors: follow these simple rules for optimum results

AMP chief economist and head of investment strategy, Dr Shane Oliver has amassed vast knowledge over four decades in the financial field, yet his key tips for average investors centre around keeping things very simple. Create a long-term plan and stick to it. Invest in a mix of high-quality assets including bonds, shares and property. Don’t follow the crowd. Use the magic of compounding by reinvesting your returns. Don’t invest in products or companies you don’t understand. And maintain an optimistic view.

“We have a knack for overcomplicating investing,” Dr Oliver says.

Buying quality assets at good prices (determined by looking for low price-to-earnings ratios and high dividend yields with shares) and holding for the long term is a simple approach that works, Dr Oliver says.

“The cheaper you buy an asset the higher its return potential. Flowing from this it follows that yesterday’s winners are often tomorrow’s losers – as they get overvalued and over-loved.”

Diversifying investments is crucial. Lower-risk assets like cash and bonds give investors comfort but lower returns. So, growth assets have to be in the mix. He also notes that compounding interest “is key to growing wealth”. Dr Oliver says a dollar invested in Australian cash in 1900, with interest earnings reinvested, would be worth about $259 today. If it was invested in shares, with dividends reinvested, it would be worth $879,921.

“Yes, there were lots of rough periods along the way for shares, but the impact of compounding returns on wealth at a higher long-term return is huge over long periods. The same applies to other growth-related assets such as property.”

Fear stops many investors from even getting started, and fear can drive panic in financial markets when economic crises inevitably occur. “Much has happened over the last 40 years with each new crisis invariably labelled ‘unparalleled’ and a ‘defining event’ …” Dr Oliver says.

He’s witnessed the 1987 crash, the recession Australia “had to have” in the early 1990s; the tech wreck in 2000; the GFC in 2007/2008; and the pandemic. Despite such events, ASX shares have delivered positive returns in roughly eight out of 10 years since 1900.

“So, getting too hung up worrying about the two or three years in 10 that the market will fall risks missing out on the seven or eight years when it rises.”

When it comes to investor psychology, Dr Oliver puts it plainly: people can get irrational. This causes many stocks to rise well beyond what they are fundamentally worth. Dr Oliver says investors tend to look for evidence that confirms their views (‘confirmation bias’), become overconfident when influenced by ‘the crowd’, and have a lower tolerance for losses than gains.

“While fundamentals may be at the core of cyclical swings in markets, they are often magnified by investor psychology if enough people suffer from the same irrational biases at the same time,” he says. “This ‘safety in numbers’ approach is often doomed to failure. The problem is that when everyone is bullish and has bought into an asset there is no one left to buy but lots of people who can sell on bad news.”

Dr Oliver says investors need to know themselves and manage any psychological weaknesses.

“It’s also about knowing how you would react if your investment just dropped 20% in value,” he says. “If your reaction were to be to want to get out, then you will either have to find a way to avoid that as you would just be selling low and locking in a loss or if you can’t then you may have to consider an investment strategy offering greater stability over time and accept lower potential returns.”

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.

Economist Shane Oliver’s advice for investors: follow these simple rules for optimum results

After more than 40 years observing and commenting on the Australian economy, Dr Oliver says there’s a key pathway to growing wealth when investing in shares

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

Playing a different game

Overlooked