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What One Should Know About Automation & Future Skill Demand

Star investor Marc Cuban argues that the future in-demand skills might be creativity and critical thinking, because the next innovation wave will lead to the automation of automation.1 In his opinion the coding-frenzy and tech-centricity of today’s education advocates is wrong, since the skill-demand will change drastically during the next decade.2

The Automation of Automation

Code will be written by artificial intelligence and jobs - high or low qualification - could be automated in most professions. At least latest research by Google, Open AI and other entities, indicate that AI will be able to automate its own creation.3,4 Hence, we might see the automation of software development, data science and related fields that are currently highly sought-after and responsible for the upcoming shifts in the job market.

This would be a development, surpassing the job market implications of computerisation predicted by Carl Frey and Michael Osborne in their ground-braking study from 2013, where the two researchers analysed the automation potential of 702 professions.5

So it seems that most jobs could be automated and we might need a universal basic income and additional taxes for technologies making these jobs redundant.6,7

Robots - Friend or Foe?

Why Automation is More Complex than Most People Think

Even though it might be possible to automate most jobs, there are other factors in this equation that are often neglected. Here is our list:

  1. Cost of Job Automation: As long as the marginal cost of robotic labour is higher than human labour, it is not cost-efficient to automate certain jobs.
  2. Economies of Scale: As soon as economies of scale reduce the marginal costs of robotic labour regarding a certain job/task, these jobs will eventually become redundant.
  3. Remuneration: Automation might lead to excess supply of workers willing to work for less than they are currently paid.
  4. Entry Costs: Furthermore, startups and SMEs that might want to automate production cannot afford the initial cost of robots, development of sophisticated AI or simply lack the training data, meaning that they are forced to work with humans.
  5. Regulations: Even though it might be possible to automate certain tasks from a technological and scientific perspective, it might be the case that regulations inhibit these developments. Responsible for this, might be lags in revisions of certain laws or regulations, unsolved ethical questions or security threats, which are crucial when discussion AI.
  6. Slow Adaptation: Across industries we see varying adaptation rates of technologies, which might be traced back to a lack of awareness, knowledge gaps, lack of talents, lack of clear strategy and unwillingness to invest. (Currently, we see a lack of engineers and talents at the intersection of business and new technologies, which leads to communication issues, wrong expectations and poor strategic decisions)
  7. Data: Even though we have an abundance of data, we still do not use most of it. Hence, it is still hard to train AI in certain fields. (Data availability and market transparency in the German real estate sector is worse than for example in the UK or US, which makes training supervised ML models harder)
Besides these points, there are many other variables influencing the automation process. Some of them are industry specific, people-driven or of social, economic or environmental nature.

Skill-Gap – What to learn?

As described, automation will not happen overnight and certain jobs might take longer to be automated than others. Hence, at least in our opinion, it is advised to develop an understanding for technologies, allowing to assess innovations in the context of your industry.

“What should my children and I learn to be fit for the job market of the future?”

It might not be necessary to become a developer, yet it is reasonable to know if a problem could be solved by programmatic means. You should also be able to assess if a technology project is really necessary and if the generated output is meeting your requirements. Thus, knowledge at the intersection of your current task (May it be teaching biology in school, accounting, construction or investment management) and technology is essential. Adding to this, a broad knowledge base will help you to develop new ideas exploiting the potential of technologies in your business sphere.

Creativity and critical thinking are skills that are, were and will be useful. Even though if some employers do not appreciate it, they should.

In conclusion the answer to the skill-question might be: “Flexibility, learning and adaptability.”

Talents of tomorrow must be flexible thinkers, curious about new developments and eager to learn as well as possess a high level of adaptability.​

Experience Based Economy - An Idea

It is our guess that we will develop an experience-based economy, where humans value human to human interactions more than mere productivity. As long as humans feel emotionally attached to each other, we are likely to accept flaws, imperfection and non-standardised services/products, mirroring our own perceived individuality. Humans will probably still enjoy a meal that was prepared by a chef, listen to a tour guide explaining the history of the old-town in Prague and prefer a unique wooden table produced by their local carpenter. We might not buy these services or products because of their sole quality, but the experience and emotions we connect to their creation. Hence, we might develop an experience based economy.

Please share your thoughts.​

We wish you creative ideas,

Viktor Weber

Sources

1 Mikel, B. (2017) “Mark Cuban Says This Will Soon Be the Most Sought-After Job Skill”, Inc. [Online] 21 February 2017, Available at: http://www.inc.com/betsy-mikel/mark-cuban-says-this-will-soon-be-the-most-sought-after-job-skill.html (Accessed: 22 February 2017).

2 ibid

3 Simonite, T. (2017) “ AI Software Learns to Make AI Software”, MIT Technology Review [Online] 18 January 2017, Available at: https://www.technologyreview.com/s/603381/ai-software-learns-to-make-ai-software/ (Accessed: 22 February 2017).

4 Duan, Y.; Schulman, J.; Chen, X.; Bartlett, P.; Sutskever, I. & Abbeel, P. (2016) „RL2 : Fast Reinforcement Learning via Slow Reinforcement Learning“, Under review as conference paper at ICLR 2017, Available at: https://arxiv.org/pdf/1611.02779.pdf (Accessed: 22 February 2017).

5 Frey, C. & Osborne, M. (2017) “The Future of Employment: How Susceptible are jobs to computerisation?”, Technological Forecasting and Social Change, Vol. 114, pp. 254 – 280.

6 Sodha, S. (2017) “Is Finland’s basic universal income a solution to automation, fewer jobs and lower wages?”, The Guardian [Online] 19 February 2017, Available at: https://www.theguardian.com/society/2017/feb/19/basic-income-finland-low-wages-fewer-jobs (Accessed: 22 February 2017).

7 Waters, R. (2017) “Bill Gates calls for income tax on robots”, Financial Times [Online] 19 February 2017, Available at: https://www.ft.com/content/d04a89c2-f6c8-11e6-9516-2d969e0d3b65 (Accessed: 22 February 2017).

8 Graetz, G. & Michaels, G. (2016) “Robots at Work”, CEPR Discussion Paper, No. DP10477, pp. 1 – 69.

9 ibid

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