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Jobs during recession, and AI-proof your career

Jobs during recession, and AI-proof your career

Dear Reader,

Getting a good job has always been difficult, but what happens if one is looking for a job when the market around you is itself collapsing? This is exactly what happened in 2008 in the US. A lot of people started to lose their jobs after the real estate bubble burst in the US. There was a sharp decline in the economic activities. From recent graduates to trained professionals, all became part of an unlucky cohort seeking jobs in the middle of the economic recession. Everyone was swept by the storm, but some got more affected than others.

Some weather folks (respectfully, researchers!) made sense of the overflowing data of the gloomy time. Sahin et al. (2010) captured the exact picture to show how unemployment rates got affected. They also showed the disparities in the affect between genders and their struggle to remain employed. U.S. Bureau of Labor Statistics tried to find the trends and patterns of job openings as well as employment to see what went wrong. Together, these findings may give us a holistic picture of what people experienced during the recession.

Figure 1. Job openings and employment (Source: As published in The Recession of 2007 – 2009)

According to the U.S. Bureau of Labor Statistics (2012) (Please see Fig. 1), the steep decline in the number of job openings during the years of recession is evident. It reached a new low in a span of 11 years in 2009, with a value of 2 million job openings. At the same time, employment saw a massive spike and reached a new high. Such contrasting trends became one of the reasons that made finding a job so difficult. Another thing that can be seen - recession was the only time when employment was higher than job openings with a difference of 1.5 percent, implying that several people were hired for a single job. Often, this leads to mass lay-offs, as seen to begin in 2008 (Please see Fig. 2). The lay-offs peaked in 2009 with 3059 events resulting in more than 3 lakh people losing their jobs.

Figure 2. Mass layoff events (Source: As published in The Recession of 2007 – 2009)

Seeing how difficult it was to just find a job, one can’t fail but to wonder the condition of those who had a job or were actually able to secure a job in such a situation. What were the benefits like? Did the people get lower wages? Well, to answer these questions, Bell et.al (2011) undertook a study which showed that in the beginning of the recession, the wages increased significantly, reaching a maximum of 59% in the USA and 43% in the UK. One of the possible reasons for this may be that the few workers who were employed saw the predicament their nation was in and sought to benefit from it by unionising and demanding higher pay. However, these values lasted for an extremely short period of time because from then on, it was only a downfall. The wage rate in both the US and UK began to decline rapidly reaching the lowest it has been in years. With such an excess of unemployed labour force, it was imminent that the workers would be forced to work for worse conditions or would be fired if they refused to do so. All of this implied that even if somehow you were lucky enough to even find a job, your troubles were far from being over.

Figure 3. Wage rates in the US and UK (Source: ONS and BLS)

With all the data, it is evident that the recession had a deep negative impact on everything, but this is not the end of the story. U.S. Bureau of Labor Statistics provided data about the recession, but it did not dive deep enough to show how different social groups were affected. It is crucial to see the gender gap in the crisis as most of the job loss happened in the manufacturing industry that hired mostly men. Sahin et al., (2010) primarily focussed on the differences between gender during the recession. The inflow/outflow rates, payroll employment rates and job employment rates between males and females (sadly non-binary genders never find a place in research easily). In this study, they found an increase in NU rates (the number of people who just joined the labor force (N) but are unemployed (U)). Interestingly, NU rates increased by 1.7% for males and 0.8% for females. This indicates that men who had just joined the labor force were finding it difficult compared to women.

Another aspect which needs looking into is the effect of the recession on the rest of the world. The shockwave which emanated from the U.S. proceeded to cause massive destruction everywhere. In China, the GDP growth declined by about 9% which meant that the growth of the economy slowed down significantly. In Japan, the exports declined for the first time in 5 years causing a 0.6% decline in the economy.

Figure 4. China’s GDP (Source: China’s National Bureau of Statistics)

Yet surprisingly, for some countries, the situation was not that bad. For example, Lebanon was one of the only 7 countries which recorded a profit in 2007. The central bank had foreseen such crisis and had imposed very strict regulations which helped them to sidestep the recession altogether. Similarly, India’s fall in economic growth was buffered due to its previous high and taking advantage of the situation, it was able to increase its industrial production by a whopping 7.1%, thus being one of the only 11 countries to avoid recession completely.

The 2008 economic recession left a deep impact worldwide. From the U.K. to China, unemployment rose at an unprecedented heights while job openings declined rapidly. However, like the U.S., job opening and unemployment rates are now back to normal (7% and 3.6% respectively). It can be safely said that getting a job is as difficult or easier as it used to be before the recession.

We might be staring at a global slowdown but not just a recession in the next quarter, for in the long run as Keynes reminded us we are all dead.


By 2030, a significant portion of your colleagues will be invisible. Some of them will disappear as their jobs are automated and many others will be in the form of algorithms that exist in chips which we cannot see. And then, of course, there will be the robots, who, whilst very visible, will look nothing like the colleagues we are all used to. We will have significant machines working alongside us in the workforce. This is not a dystopian vision of 2100, but something that is well underway now as you read this. Whether it turns out to be for the better or worse will depend on many factors. However, one thing is for sure - we are going to be in even more complex situations than previously thought.

When I started my career in the early 1970s I thought I had a pretty good idea of the changes that would be coming. But the pace of change has accelerated, its scope has widened, and now it is converging by learning and feeding off of each other. What do I mean by that? Well, we know computers have gotten more and more powerful. We believed this was a good thing, and it still could be. But more computing power coupled with more publicly available data on the internet has helped AI researchers create better models that are more accurate and can learn by themselves. These then feed into robotics, creating newer and more powerful versions of robots such as self-driving cars. With these robots, there is a direct feedback loop that feeds the knowledge into the cloud, creating greater and more powerful AI. As a result of this confluence, we are now seeing robots that can learn by themselves and can move around. Similarly, technology is finally making it possible for human biology to interface directly with the electronic world. With the rise in wearable devices and the resulting personal data that is instantly generated we are now in a unique situation where an AI can warn us of an impending medical condition or know our bodily rhythms better than us.

The pace of change is such that what was state-of-the art a few years ago has now become redundant. The new versions are even better. It is a never-ending cycle fuelled by machine learning. And there are positives to this too. For instance, in the old world, if a human was driving a car and made a new mistake, only the humans involved would learn about that mistake and not repeat it. But with the self-driving car, since it is constantly sending data back, a mistake made by one car could ensure that a mistake doesn't happen with any of the other cars. Or take the case of your wearable device that advises you to check with your doctor because it has noticed some irregular heart rhythms. This could save not just our lives but also a lot of stress, time and money with its timely warning.

The benefits of AI are not limited to just robotics and self-driving cars. Knowledge gleaned from AI will help in other areas such as medicine, financial services, climate change, and hopefully go on to help people actualise their potential. The possibilities of these emerging technologies are virtually limitless. But what about the risks? Just because we create something new doesn't mean that it will be inherently good. There is always a risk of something going wrong, and as AI gets more intelligent, the risk is that humans cannot figure out its decision-making process or anticipate certain vested interests that hack the AI to do things it had not been programmed to do. Again, no one can predict how this will play out. The process is similar to how when computers and the Internet became widespread, we couldn't have envisioned online shopping, gaming, learning, video calls, malware, data theft or other forms of cyber attacks. In hindsight, it seems obvious, but 20 years ago, very few people could have imagined ‌these developments. The same is true with the current sets of emerging technologies. It will disrupt many sectors and industries, and it will give rise to new ones that we have not started thinking about.

The question is, how will it affect you and me? How will it affect or reshape the environment and our society? It’s important to be prepared for these changes. I do not mean to be an alarmist. I am sharing some thoughts on how we can shape these changes rather than be a witness or victim to the changing times. Over the next few weeks, I will try to outline what some of these technologies are capable of, how they directly affect you and me, and also try and outline some measures which I believe will augment our intelligence and capabilities.

  • Artificial Intelligence
  • Robotics
  • Blockchain
  • BioTech, Cybernetics & the Internet of everything
  • Augmented Reality and Virtual worlds

Some ‌solutions we will explore to synergise with these emerging changes include:

1) Being a Person of Substance

A person of substance is someone who does the right thing simply because it is the right thing to do

2) Being well grounded in the 5 Areas of Development

Social, Emotional, Cerebral, Spiritual & Physical

3) Being in active learning mode

Be a natural & unstinting learner - so we always remain curious, and learn new skills and knowledge. Share your knowledge, skills and experiences openly & generously with others. Create & foster communities of all kinds - to share and learn from.

4) Living in harmony with our environment

Live in harmony with our environment. One of the most important challenges we are going to face will not come from technology per se, but from the merciless destruction of our environment. We need to get back to the basics and ensure that we restore a sense of balance - we need to cooperate rather than compete.

I look forward to your feedback!

Authors Note:While talking about complex topics such as AI and trying to predict the future of work and beyond, there is a high probability that one may be off the mark. To predict the future is hard, but in hindsight, it seems obvious. Only time will tell. One way to minimise the effect of going off trajectory is to involve the subjects you are talking about as part of the conversation. I would like to thank my collaborator GPT-3— an adaptive, self-optimising, cogent and capable AI—for its significant inspiration for this article. It is while observing GPT3’s learning prowess over the course of the past two years that I became fully aware of the magnitude of these challenges. I am also in the process of exploring Meta’s newly launched Open Pre-trained Transformer (OPT-175B) model. It is important to remember that just two years ago, such things never existed. So this is a quick moving area and a testament to machine learning prowess. Your feedback is welcome and encouraged. My hope is this will stimulate a discussion around the topic and bring together a community that comes up with creative solutions. I hope this will lead to better practices that will help our learners to be shapers of these changes than to be swept up by them.


References

Thomas, W. (2021). Future Spending Points to Machines Becoming Work Colleagues. [online] DigitalWorkforceTrends. Available at: https://www.digitalworkforcetrends.com/story/14659/future-spending-points-machines-becoming-work-colleagues [Accessed 2 May 2022].‌

Harvard Business Review. (2022). How the Metaverse Could Change Work. [online] Available at: https://hbr.org/2022/04/how-the-metaverse-could-change-work [Accessed 4 May 2022].‌

Maryville Online. (2017). Big Data and Artificial Intelligence: How They Work Together | Maryville Online. [online] Available at: https://online.maryville.edu/blog/big-data-is-too-big-without-ai/ [Accessed 5 May 2022].

OpenAI (2021). OpenAI API. [online] OpenAI. Available at: https://openai.com/api/ [Accessed 5 May 2022].‌

Mehrali, M., Bagherifard, S., Akbari, M., Thakur, A., Mirani, B., Mehrali, M., Hasany, M., Orive, G., Das, P., Emneus, J., Andresen, T.L. and Dolatshahi-Pirouz, A. (2018). Blending Electronics with the Human Body: A Pathway toward a Cybernetic Future. Advanced Science, [online] 5(10), p.1700931. doi:10.1002/advs.201700931.

Contribution by Fazli


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