Once upon a time, good behavior and a high school diploma qualified us for a career-long job in an automobile manufacturing plant.  Good behavior still matters, but that high school diploma isn’t worth much, and it will likely be trumped by artificial intelligence or a tireless robot.

Of its 33,000 employees in this quarter, Tesla fired 400-700 employees as a result of a performance review.  Most of those fired were performing administrative functions, not production line tasks.  While the production of new autos fell sharply below plan during the review period, at least some of that failure was attributable to a parts shortage that left production line workers crafting parts by hand.  The production shortfall seemed to come from a failure to order an adequate supply of parts.  That is an administrative failure and should not be blamed on the production line.

Some believe the Tesla layoff is an attempt to align employee behavior closer to the example set by Elon Musk, Tesla’s smart, hard-working leader.  Others believe that the performance review behind the reduction is a ruse meant to avoid the advance notice obligation for employee layoffs that some states require.  Many of those who survived the performance review received a substantial bonus, and Tesla continues hiring for its worldwide sites.  Tesla is a fast-growing employer that is fixated on worker productivity as the gauge for workforce merit.

GM has cut employment several times during the last year.  Between November 2016 and May 2017, it announced a total of 5,000 layoffs, and in October 2017, it announced it will lay off another 1,500 people for a 5-week period.  GM has 219,000 employees, so its labor force adjustments of 5,000 to 6,500 amount to just 1% to 1.3%.  Each of these labor force adjustments was related to adjusting the inventory for major elements, such as transmissions.

GM, unlike Tesla, is a mature firm showing almost no growth.  Its additions and reductions in employee headcount are done frequently and for mundane reasons such as inventory control, changes in total demand for automobiles, and the steady drift toward SUVs, pickups and electric cars.  GM has decades of experience in adjusting its workforce to automation, and especially in the use of industrial robots.  The major losses in employment at GM occurred decades ago as the giant right-sized itself to capture the productivity available from automation in its US and overseas operations.

In other sectors such as finance, there are pockets of skilled workers who will be challenged by automation.  For decades, brokerages have shed floor traders in favor of trading computer networks.  There are still analysts whose personal advice is valued by retail and institutional investors (e.g., Dick Bove – a very successful banking analyst, and Craig Moffett – a successful Telecom analyst).   But many brokers and hedge funds have relied on artificial intelligence to pick winners and losers in stocks and bonds.  Displaced analysts usually are sharp enough and skillful enough to find new employment, but perhaps at a lower salary.

Deutsche Bank’s CEO believes that automation will sharply reduce the headcount of employees over the next 5 to 10 years.  One of the automation tools behind that conviction is blockchain, a very secure strategy for maintaining financial records and legal contracts.  Unlike AI, blockchain does not deliver deeper insights, rather it delivers an ultra-secure, highly organized record of complex topics.  Blockchain was originally associated with Bitcoin, but it has shown attractive capabilities for negotiating, organizing and securely storing complex contracts such as IPOs, international trade, bond issues and insurance.  IBM, UBS and Commerzbank are collaborating on the development of blockchain approaches for trade finance, a multiparty environment that involves lawyers, manufacturers, bankers, transport firms and government agencies.  Currently that environment is subject to errors and misunderstanding.  The blockchain approach will displace many clerks and attorneys, resulting in enormous cost savings.

Workers in auto manufacturing and the financial sector are not uniquely exposed to AI and robots poaching their jobs.  Even in fields such as architecture and medicine, there are AI and robotic advances that will displace some specialists.  Overall, about 38% of U.S. jobs are at risk of being affected by automation by the early 2030s, with Germany following closely behind at 35%, and with the UK and Japan at 30% and 21%, respectively.

Among notables close to the AI and robotic developments, Bill Gates and Elon Musk have a different view on the risks that AI and robots pose for us.  They have different levels of fear that super-smart computers may at some time shed their obligation to protect us.  However, they agree that we need to be flexible and continually retrain ourselves to remain relevant in the job market. 

We should not sit idle, hoping that government will rescue us when automation inevitably occurs in our field.  What will you do to burnish your skills in directions that AI or robots will find hard to match?