2025. Robots here are defined as fully autonomous machines that can be programmed to do several jobs. Between 1993 and 2007, the stock of industrial robots increased by 1 per 1,000 workers in the US and 1.6 per thousand in Western Europe. There was a negligible adoption of robots in education research and development and exactly zero in other non manufacturing industries.
Just because there's a robot...
Today's technopanic* about "AI" and robotics is unusual in that it began life as a media narrative some four years ago, rather than arising from a genuine technical or scientific breakthrough. Beginning with Erik Brynjolfsson and Andrew McAfee's 2013 book The Second Machine Age – a reprint of a self-published book the authors had put out in 2011 – and subsequently Martin Ford's The Rise of the Robots in 2015, "thinkfluencers" competed to be doomsayers of an economic apocalypse. The idea spread like wildfire through the op-ed pages, think tanks and quangos.
For once, the technology industry was caught on the wrong foot. It scrambled to fix that – but all it had were 20-year-old techniques. Nonetheless, by focussing the computing power of huge server farms, and in particular GPUs, to harness neural networks (now rechristened as the much sexier "deep learning") on narrow demo-friendly challenges such as image recognition and text processing, Silicon Valley could boast of impressive new "breakthroughs" giving the apocalyptic thinkfluencers the material they needed to sustain their narrative. However, serious problems arose when Silicon Valley tried to apply its know-how to more challenging tasks.
In the past year Google, Apple and most recently Uber have reset their self-driving vehicle initiatives, having realised it's much more complicated than they thought. The AI autopilots weren't up to the job. Google had clocked up several million miles of automative driving over a decade, but now Alphabet has spun out the project as Waymo.
... doesn't mean you'll use one
Here's where it gets interesting. Acemoglu and Restrepo note that: "There is renewed concern that with the striking advances in automation, robotics, and artificial intelligence (sic), we are on the verge of seeing them realized. The mounting evidence that the automation of a range of low-skill and medium-skill occupations has contributed to wage inequality and employment polarization adds to these worries."
Then there's a big caveat.
"These concerns notwithstanding, we have little systematic evidence of the equilibrium impact of these new technologies, and especially of robots, on employment and wages."
Even if the presumed technological advances materialize, there is no guarantee that firms would choose to automate; that would depend on the costs of substituting machines for labor and how much wages change in response to this threat.
In other words, it may be advantageous not to buy the robot to replace the workers.
"Second, the labor market impacts of new technologies depend not only on where they hit but also on the adjustment in other parts of the economy. For example, other sectors and occupations might expand to soak up the labor freed from the tasks that are now performed by machines, and productivity improvements due to new machines may even expand employment in affected."
So how is that labour substitution coming along?
In a little-noticed interview this week, Lior Ron of Uber's Otto division struck a new, conciliatory tone. Ron agreed that there is a national shortage of trucking jobs and positions his technology as a trucker's companion – a "co-pilot" – not her (or his) replacement.
And when Alphabet spun out its self-driving cars unit, allowing it to focus on licensing more modest gains, Waymo CEO John Krafcik said: "We are not in the business of making better cars. We are in the business of making better drivers." Ah.
Otto. Well done, chaps.
Just as neural nets helped speech recognition make big gains, it isn't hard to envisage the classification benefits of new deep learning tools as being genuinely useful in other narrow fields of archiving and data an