May 4, 2016, 1:17 AM ETPanel

Jobs and Technology: Is Any Job Truly Safe?

Martin Ford states that no job is truly safe in the long run, with machine learning poised to encroach on fundamental human capabilities previously thought unique.
Michael Chu notes that while less than 5% of full occupations can be automated, 45% of specific activities within jobs are susceptible to automation using current technology.
Alan Kruger highlights that approximately 25% of U.S. work could be offshored, a figure that overlaps significantly with the potential for automation.
Research indicates that over 100% of the 9.1 million net jobs created in the U.S. over the last decade have come from alternative employment sectors (freelancing, contracting, temp work), leaving traditional employment flat.
Uber X driver numbers have doubled every six months for the last four years, representing exponential growth in the platform economy compared to the linear growth of Uber Black.
The panel estimates that 80% to 90% of news articles could be written entirely by software within five to ten years, signaling the automation of high-skill knowledge work.
The average age of an advanced skilled manufacturing worker is 56, creating an immediate demographic opportunity for job growth in this sector.
By 2030, a workforce crisis is projected where there will be a shortage of workers with the necessary skills to fill open positions.

The post-Great Recession recovery is characterized as the weakest in modern history due to significant household wealth loss and housing value collapse rather than solely policy failure or tech shifts.
Productivity growth has stalled recently, a phenomenon experts attribute to measurement issues (consumer surplus not captured in GDP), a slowdown in capital investment, and the "Sola Paradox" delay between tech adoption and productivity gains.
A "reverse specialization" trend is observed where consumers using DIY tools (e.g., booking travel) may spend more time than previous experts (travel agents), temporarily lowering measured productivity.
Wage stagnation and inequality are linked to technology destroying demand for low-skill labor while decoupling productivity gains from wage growth for decades.
Kate Mitchell argues that technology is not inherently aimed at eliminating jobs but at increasing productivity, which historically creates new sectors and opportunities.
Martin Ford counters that the decline of manufacturing displaced low-educated workers into low-wage service jobs, and these positions are also now at risk of automation.

The share of workers in alternative work arrangements has grown significantly, with contracted-out labor increasing five-fold over the last decade.
The "platform economy" (e.g., Uber, TaskRabbit) currently represents only about 0.5% of the total workforce, though it is a leading indicator of future trends.
Experts express concern that the gig economy offers unstable income, lacks benefits, and fails to build long-term career relationships, altering the psychological and financial security of workers.
Martin Ford warns that the gig economy is often a precursor to full automation, citing Uber's heavy investment in self-driving cars as an example of a long-term elimination of the human driver role.
Kate Mitchell notes that the gig economy has reduced unemployment rates (8% of Uber drivers were unemployed prior to joining) and provided flexibility for parents and caregivers.
A significant skills gap exists, with 600,000 skilled manufacturing jobs currently unfilled and 80% of new jobs requiring digital literacy.

Martin Ford proposes a Guaranteed Basic Income (GBI) of $10,000 to $30,000 to provide a safety net, arguing it is a necessary adaptation as traditional employment erodes.
Michael Chu suggests expanding the Earned Income Tax Credit (EITC) rather than a blanket GBI, as it maintains work incentives.
Alan Kruger advocates for a "grand bargain" to create a new category of employment for gig workers, offering benefits like civil rights protections and healthcare contributions without mandating overtime rules.
Kate Mitchell emphasizes the separation of benefits from employment (building on Obamacare) and public-private partnerships, such as coding academies in prisons, to address skills gaps.
Panelists suggest that reducing work hours could be a solution to distribute available work, though this trend has stalled due to connectivity and cultural shifts in the last 40 years.
Government investment in fundamental research (e.g., the Internet, genomic mapping) is cited as essential for driving the next wave of productivity, rather than regulating specific automation technologies.
Experts reject the idea of government limiting technology adoption, arguing instead for facilitating transitions and investing in infrastructure and education.
A "scenario planning" approach is recommended, where businesses and policymakers prepare for multiple potential futures ranging from high job creation to massive displacement.

The panel agrees that future job growth will likely occur in sectors involving human emotion, creativity, and complex collaboration, which machines currently cannot replicate effectively.
Martin Ford argues that not everyone can transition to high-level creative or emotional roles, necessitating safety nets regardless of new job creation.
Alan Kruger points out that historically, the vast majority of job growth comes from occupations that did not exist in previous decades, suggesting unpredictability in future labor markets.
The panel notes a potential feedback loop where technology-driven inequality reduces consumer demand, thereby slowing investment and further technological adoption.
Demographic shifts, particularly aging populations in advanced economies, mean that future economic growth will rely entirely on productivity gains rather than workforce expansion.
The "experience economy" is identified as a sector where human interaction adds value that cannot be automated, such as healthcare decision-making and artistic performance.