The Bureau of Labor Statistics (BLS) projections for industry and occupational employment are developed in a series of six interrelated steps. Each step is based on a different model and a set of related assumptions namely labor force, aggregate economy, final demand (GDP) by consuming sector and product, industry output, industry employment, and job openings by occupation. Outputs from aforementioned steps are key inputs to steps following.Two key assumptions for this model are dependency on aggregate demand growth and long-run full employment equilibrium. However, these fail to take into account the recent explosion in technological disruption. In this model, joblessness is driven by a decrease in aggregate demand. Furthermore, BLS extrapolates employment over a 10-year period with a 2-year revision cycle, which is not in tandem with the current rate of technological innovation and automation. While aggregate demand has indeed decreased since 2009, employment is less dependent on consumption because of automation and technological increases in productivity. The decoupling of consumption spending from employment projections makes previous models that depend on this relationship shaky at best. The intervening effects of automation technologies is stark: it’s predicted that 47% jobs will be automated within 20 years and people are being impacted at pace much rapid than they can be retrained for new careers or industries. A disruption forecast index can be built utilizing more recent markers of such change including twitter hashtags, publications, patents, google trends etc. Such an index can help measure impact of emerging technologies on the labor market.