Here’s how not to lose your job to AI

Making you think differently – is what a good book does. Epstein’s bestseller Range makes an engaging counter-intuitive case for cultivating a broad wide ranging generalist perspective.

“Range makes a compelling case for actively cultivating inefficiency. Failing a test is the best way to learn. Frequent quitters end up with the most fulfilling careers. The most impactful inventors cross domains rather than deepening their knowledge in a single area. As experts silo themselves further, while AI threatens the jobs once reserved for highly focused humans, people who think broadly and embrace diverse experiences and perspectives will increasingly thrive,” believes Epstein.

Analytics wizard who restores old cars, or the auditor who bakes cakes isn’t wasting their time.

Impossible to miss AI’s strengths

Unearthing research, turning mountains of data into forecasts, AI works at lightening speed.

Yes, that power comes with limits, of course: models can miss context and mirror biases. And AI responses can speak with confidence, when in reality the facts are thin. Consensus view is that AI works best in collaboration with human intelligence, including judgment, creativity, and empathy.

Deliberate practice or dabble?

The 10,000-hour rule suggests that if you want to become a ‘master’ at anything you need to put in the years of deliberate practice. In this school of thought, anyone who wants to develop a skill or lead their field should start early, focus intensely, and rack up as many hours of deliberate practice as possible.

Argument is that if you dabble or delay, you’ll never catch up to the people who got a head start. But thanks to Epstein, on a closer look at research on the world’s top performers, from professional athletes to Nobel laureates, shows that early specialisation is the exception, not the rule.

Epstein’s research examined the world’s most successful athletes, artists, musicians, inventors, forecasters and scientists. In most fields, especially those that are complex and unpredictable, generalists, not specialists, are primed to excel.

Generalists often find their path late, and they juggle many interests rather than focusing on one. They’re also more creative, more agile, and able to make connections, their more specialised peers can’t see.

Wicked or kind?

Do specialists get better with experience? Yes, would be the answer one would think. But when one can’t spot patterns, have trouble chunking information and data, or when the rules keep changing even the 10,000-hour specialists begin to flounder.

Psychologists Gary Klein and Nobel laurate Daniel Kahneman teamed up to try and determine whether experience inevitably led to expertise. What they found was that it depended entirely on the domain in question – whether they were working in a ‘kind’ or ‘wicked’ area.

“Narrow experience made for better chess and poker players and firefighters, but not for better predictors of financial or political trends, or of how employees or patients would perform.

The domains Klein studied, in which instinctive pattern recognition worked powerfully, are what psychologist Robin Hogarth termed ‘kind’ learning environments. Patterns repeat over and over, and feedback is extremely accurate and usually very rapid. In golf or chess, a ball or piece is moved according to rules and within defined boundaries, a consequence is quickly apparent, and similar challenges occur repeatedly.”

“Drive a golf ball, and it either goes too far or not far enough; it slices, hooks, or flies straight. The player observes what happened, attempts to correct the error, tries again, and repeats for years.

That is the very definition of deliberate practice, the type identified with both the ten-thousand-hours rule and the rush to early specialisation in technical training. The learning environment is kind because a learner improves simply by engaging in the activity and trying to do better,” explains Epstein.

Confusing, constantly changing

Kahneman was focused on the flip side of a ‘kind’ learning environments – known as ‘wicked’.

In wicked domains, the rules of the game are often unclear, or incomplete, and may change without notice. There may or may not be repetitive patterns and they may not be obvious, and feedback is often delayed, inaccurate, or both.

Strange thing is that in devilish wicked environments, those with bundles of learning may find that all that experience may reinforce the wrong lessons. Their interpretation of a problem, their diagnosis may be totally off.

In a ‘kind’ domain putting in the hours, noticing the patterns is where the specialists can thrive – but it’s also where AI’s computing power intelligence can give humans a run for their money.

Classic example is radiologists, specialist physicians who are trained over the years to spot cancerous growth. While it was initially thought that AI would put radiologists out of work, the opposite has happened.

Radiologists are using AI as a co-diagnostician and taking their specialist expertise to the next level focusing on the tricky ‘wicked’ cases.

Case of radiologists using AI is the best case scenario. Impact on AI on the demand for entry level intern-like positions is not so upbeat.

Business professor Abbie Griffin has made it her work to study modern Thomas Edison, what she calls ‘serial innovators’ Her findings should sound familiar by now.

Innovators have a ‘high tolerance for ambiguity’ – they are ‘systems thinkers‘ who apply ‘additional technical knowledge from peripheral domains’ often ‘repurposing what is already available‘ and are ‘adept at using analogous domains for finding inputs to the invention process’ with an ‘ability to connect disparate pieces of information in new ways’.

In other words: they are able to connect the dots. They have range.

David J. Abbott is a director at aCatalyst Consulting. [email protected]