As artificial intelligence becomes ever more capable, is any job secure? “I’ve sort of convinced myself that the safest job in the world is probably gardener,” the FT’s chief economics commentator Martin Wolf recently confessed. That seemed right. There are some things the computers just can’t do.
The next morning the FT published “The gardens that AI grew”, describing intelligently automated drip irrigation, pest detectors, laser scarecrow systems and a solar-powered weeding robot. Oof.
It’s not entirely clear how much the laser scarecrow and the robot weeder really will threaten the jobs of human gardeners, but the prospect reminds us that there is a distinction between a job and a task. Most jobs are bundles of interconnected tasks.
A gardener needs to do everything from mowing and weeding to diagnosing a pest infestation, designing an outdoor space, or — hardest of all — communicating with a difficult client. Different AI systems could well help with most of these tasks, although the likely outcome is not that the job of gardener disappears, but that it changes shape.
The question is, how will each new AI application change the shape of what we do? And will we like the reworked jobs available to us on the other side of this transformation?
Generative AI may be new but these questions are not. They run all the way back to the Luddite protests of the early 1800s, when highly skilled textile workers saw machines doing the hardest parts of their job, allowing them to be replaced by low-paid labourers with far less expertise.
And the answers to those long-standing questions? They depend both on the technology and on the job. There’s a lesson to be drawn from two contrasting precedents: the digital spreadsheet, and warehouse guidance earpieces such as the “Jennifer unit”. The digital spreadsheet, which hit the market in 1979, instantly and flawlessly performed work previously done by accounting clerks, but the accounting profession simply moved on to more strategic and creative problems, modelling different scenarios and risks. Who doesn’t want a creative accountant?
The Jennifer unit is a headset to guide warehouse pickers as they scurry around grabbing merchandise off shelves, whispering in their ears as it tracks their last move and guides their next one. The unit removes the last vestige of cognitive load from a physically demanding job that was already mind-numbing. It is a stark contrast to the digital spreadsheet, which excised the most tedious part of a varied and highly skilled job. The lesson: AI can make a boring job even more boring and an interesting job even more interesting.
New data and a new perspective on these questions come from MIT researchers David Autor and Neil Thompson. Autor and Thompson begin a new research paper titled “Expertise” by posing a question: would we expect accounting clerks and inventory clerks to be similarly affected by automation?
There are several well-established approaches to analysing this question, and all of them suggest that the answer is “yes”. Back in the day, both types of clerk spent a lot of time performing routine intellectual tasks such as spotting discrepancies, compiling inventories or tables of data, and doing simple arithmetic on a large scale. All of these tasks were the kind of things that computers could do, and as computers became cheap enough they took over. Given the same tasks faced the same sort of automation, it seems logical that both jobs would change in similar ways.
But that is not what happened. In particular, say Autor and Thompson, wages for accounting clerks rose, while wages for inventory clerks fell.
This is because most jobs are not random collections of unrelated tasks. They are bundles of tasks that are most efficiently done by the same person for a variety of unmysterious reasons. Remove some tasks from the bundle and the rest of the job changes.
Inventory clerks lost the bit of the job requiring most education and training (the arithmetic) and became more like shelf-stackers. Accounting clerks also lost the arithmetic, but what remained required judgment, analysis and sophisticated problem solving.
Although the same kind of tasks had been automated away, the effect was to make inventory clerking a job requiring less training and less expertise, while accounting clerks needed to be more expert than before.
The natural worry for anyone hoping to have a job in five years’ time is what AI might do to that job. And while there are few certainties, Autor and Thompson’s framework does suggest a clarifying question: does AI look like it is going to do the most highly skilled part of your job or the low-skill rump that you’ve not been able to get rid of? The answer to that question may help to predict whether your job is about to get more fun or more annoying — and whether your salary is likely to rise, or fall as your expert work is devalued like the expert work of the Luddites.
For example, generative AI systems are great brainstormers. They make unexpected connections and produce lots of varied ideas. When I’m running a role-playing game, that’s great. They accelerate the preparation and let me get straight to the good stuff, which is sitting around the table with my friends pretending to be wizards.
For someone whose job offers occasional oases of creative brainstorming in a desert of menial administration, the emergence of industrial brainstorming engines might be rather less liberating.
Or consider that gardener. Perhaps the worst part of their job is trying to compose emails to desk-based clients who seem far more fluent in the medium than someone who spends most of their time outside. Laser scarecrows and robot weeders be damned. What the gardener needs is an AI secretary, scribe and editor. And the technology for that is already here.
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