Self-driving trucking startup Ike has released an economic analysis showing that instead of eliminating truck driving jobs, Ike's approach to automation shifts the task from long-haul to short-haul jobs for drivers.
The study, conducted by Yale economist Dr. Charles Hodgson, suggests that 210,000 long-haul driving jobs could be replaced by automation by 2030.
But the model also finds Ike's approach could create nearly 140,000 local truck driving jobs during that same period.
Further mitigating the losses from the long-haul sector, Hodgson's analysis also projects 78,000 retirements from that sector of the industry in the next 10 years.
The automated trucking future has generated plenty of sturm und drang, with technologists, labor groups and even presidential candidate Andrew Yang forecasting a profession decimated by self-driving commercial vehicles.
"The first thing I get asked at job interviews, barbeques, is this question: ‘Aren't you putting truck drivers out of work?'" Ike co-founder and CEO Alden Woodrow told FreightWaves.
"There's this idea of the robotic apocalypse. But if you make a reasonable set of assumptions and apply the approach we are taking to trucks, we think there could be a pretty good outcome for drivers."
So what are the assumptions?
First, Hodgson's model uses the Ike approach as a baseline. Under that model, autonomous trucks focus solely on highway driving, leaving human drivers to handle all non-interstate transportation.
Central to the model is the idea of transfer hubs, where loads are exchanged between self-driving trucks and human-driven trucks.
In that scenario, an increase in automated miles on the highway actually creates local miles driven by truckers. Hodgson's analysis assumed an average of 25 miles of local driving on each end of a journey.
Crunching The Numbers
Among the study's other assumptions are that automation will roll out gradually in different parts of the country, and that the number of long-haul truck drivers in the United States has been vastly overstated, registering in the hundreds of thousands, not the often cited 3 million, a figure that actually refers to drivers of all sorts: short-haul, garbage truck drivers and the like.
Hodgson bundled these assumptions with government data about the demographics of truck drivers, trucking supply and demand, as well as the cost of trucking.
He then estimated how many individual jobs would shift from long haul to short haul by dividing the mileage projections by the number of miles truckers drive annually.
The results yielded an upfront job loss, offset by retirements and the addition of new short-haul jobs.
The Ike Model
Like many company-sponsored research studies, the Ike analysis might appear biased in favor of, well, Ike, inasmuch as the results validate the startup's approach to autonomous trucking.
But the study builds on other recent reports dialing back some of the fear around automation, suggesting it won't make truckers obsolete.
And if the Ike study is self-serving, it is consciously so. The results are "absolutely connected" to Ike's transfer hub model, Woodrow said.
While some analyses examining the (dire) impact of automation on drivers "are very rigorous and thoughtful, they assumed that one automated truck is equal to one lost driver," he explained. "We're trying to preserve a role for drivers."
That said, Ike is not claiming the numbers are exactly right. "If you change the assumptions, you change the answers," Woodrow said.
To broaden the discussion, the startup is open sourcing all of the code and the data from Hodgson's analysis on Github.
Even assuming the numbers are correct, plenty of concerns about the impact of automation on drivers remain: for example, whether those short-haul jobs are going to be good jobs and what wages they will pay.
"How do we make sure that the people who are shifting work from long haul to regional home operations, that those are still high-quality jobs?" Woodrow mused. "That is a good conversation to have."