Ikeanomics

This is the valuing of your own labor rate that takes place after your third or fourth trip back from Ikea. You know you have saved some money buying a set of shelves that you need to assemble but part of the decision was made by assessing how much time it would take to put the item together vs how much you saved.

Ikea is in the business in devaluing our self-perceived labor rate so that they can charge the most for a flatpack item such that the discount achieved justifies the hours needed to assemble the item. (This is the same model used by meal-kit businesses, or at least it should be.)

For items that do not require assembly then the trade-off people have to make is their willingness to buy products that do not comply with the standards for that type of product bought everywhere else. For example, a desk lamp from Ikea requires no assembly so there is no time-cost to save. To justify the lower price there must be an investment from the customer. In this case it is the willingness to accept a non-standard shade fitting. The same is true of other non-assembly products sold at Ikea.

For those folks interested in alternative data, there could be a macro signal regarding wage earnings and wage growth buried in this data. Comparing the price of an Ikea product with a similar (assembled) non-Ikea product over time could be a useful economic indicator.

When Do We Start Working For Computers?

I have done some quick back-of-envelope calculations on the progress of AI, trying to estimate how much progress has been made vs. how many job-related functions and activities there are left to automate.

On Angel List and Crunchbase there are a total of 4830 AI start-ups listed (assuming both lists contain zero duplicates). To figure out how many unique AI tools and capabilities there are, let’s assume the following:

  1. All these companies have a working product,
  2. Their products are unique and have no competitors,
  3. They are all aimed at automating a specific job function, and
  4. These start-ups only represent 30% of all AI-focused company universe.

This gives us a pool of 16,100 unique, operational AI capabilities. These capabilities will be in deep domains (where current AI technology is most successful) such as booking a meeting between two people via email.

If we compare this to the number of domain specific activities in the world of work, we can see how far AI has come and how far it has to go before we are all working for the computers. Using US government data, there are 820 different occupations, and stock markets list 212 different industrial categories. If we make the following set of assumptions:

  1. 50% of all occupations exists in each industrial category,
  2. Each occupation has 50 discrete activities.

This gives us a total of 4.34 million different occupational activities that could be automated using AI. In other words, at its most optimistic, current AI tools and processes could automate 0.37% of our current job functions. We have come a long way, but there is still a long way to go before we are out of work.  As William Gibson said, “the future’s here, it’s just not widely distributed yet”

Automation: A Bright Future

From reading many articles and posts about the threat of AI to the job market, I am coming to the view that any automation, whether or not it is as result of AI, is good for long term economic prospects. Like most economists I have painted a simplistic view of the economic cycle, none-the-less I have faith that automation is a force for good.

Automation will help decouple the relationship between reducing employment and increasing inflation, a relationship that can quickly turn an economic booms into a recession.

The accepted view is that rising demand not only increases companies’ profits, it also raises inflation as prices rise in response to demand. Rising demand for a company’s products and services will lead to more hiring to increase output. As economies approach full employment, the cost of labor for companies faces two inflationary pressures; the first is response to increased demand for labor, and the second is in response to increased prices lead to hire wage demands. This leads to a familiar cycle: boom -> increasing inflation -> correction in the economy -> increased unemployment and reduced inflation/prices -> boom -> etc.  

Inserting automation into this cycle will allow companies to increase productivity without increasing labor cost – which erode profits and break the growth cycle. Increasing company profits will lead to increased share prices for public companies. Since many people’s retirement savings are invested in the stock market in one form or another, as companies profits grow, so will the value of people’s retirement savings. This will help make it easier for people to make the decision to retire. In short, the right amount of automation could a) reduce an economy’s overall demand for labor, and b) provide sufficient long term stock market gains to support a growing retired section of the population. This latter point is interesting since automation could reduce the overall demand for labor. If the pool of workers chasing fewer jobs is too large then wages would fall leading to deflation and a stagnated economy. The ideal outcome is that people remove themselves from the labor market, because they can afford to retire sooner, leaving the right balance between jobs and workers. The right balance of labor supply and demand will allow for moderate inflation, GDP growth, and a stock market that can support an growing number of liberated workers.

From an employment point of view, automation may also create the need for new jobs that do not currently exist. For example prior to 2007, a marketing department in a company did not need a Social Media Manager, similarly there were no Gas Station Attendants prior to the invention of the car. In other words, automation will reduce the need for labor in current roles, as companies look to increase productivity without baking in more labor costs, it will also create new roles as the labor force becomes liberated from repetitive tasks.

One area this is happening is in data analysis and data engineering.  My web app Knowledge Leaps is designed to automate the grunt and grind of data engineering. I am building it because I want people working in similar industries to be liberated from the chore of data management, so that they  can focus on interpretation and application of the findings.