Welcome to the Artificial Intelligence (AI) series, a topic I write about from a non-expert’s point of view.
The evolution of work is my focus in this third eNsight in the series.
Why is the latter part of the opening statement above worth mentioning?
Having listened to many podcasts and read about AI lately, it has become clear to me that this subject still belongs in the tech world in the main, while the rest of us are left behind.
Yet, we are all being affected in one way or another, and increasingly over time, by the impact of AI.
One example involves the rapid developments in NLP (Natural Language Processing) – a branch of AI that has resulted in improved voice recognition technologies; with the result that we are now talking to our devices (think Siri for Apple and Alexa for Amazon), such as when we do Google search.
According to quoracreative.com;
- 20% of the searches on a mobile device are voice-based; and
- By 2020, 50% of all searches across the internet will be voice-based
Yes, there has been more widespread interest in the last 20 years or so as strides and leaps are made in AI.
But, this topic still has to make its way into Twitter’s main streets.
The 2 common themes of this AI series continue to be that this technology has been with us for all our lives, and that humans have the innate ability to adapt to changes, and it will not be different with the changes brought about by AI.
Table of Contents
The evolution of work
Humans have been living on earth for over 6 million years.
The concept of work was brought about by the need to survive; influenced by changes such as transition from abundance to scarcity of resources (or mindset?), increasingly unpredictable weather patterns and other natural phenomena, and land grabs.
The factors above have led to 5 evolving eras of work:
The hunter-gatherer’s era (over 10 000 years ago); was when humans hunted for food – made up of wild animals and plants – to eat today for today.
Then, they believed that there will always be enough food tomorrow, so there was no need to save some today.
They also moved around freely – as nomads – to hunt for their daily ration.
The pastoralist’s era; was when humans discovered that they can domesticate some of the “wild” animals and use them for more than just meat, but also as means of transport and other bi-products such as milk and hide.
The size of domesticated animals – called livestock – became a symbol of richness, and a form of currency for trading other valuables.
Nomadic lifestyle was still present in this era.
The agrarian era; was when humans needed to settle in their occupied lands to avoid clashes with others they encountered in the nomadic lifestyle, and thus needed to farm them [the lands] for a living.
It is in this era that humans learnt to think about future harvests due to the impact weather was having on their farm produce.
Planning became integral in their survival, and use of labour and rudimentary machines became the norm.
The industrial era (from the 17th century); was marked by transition from subsistence economy to market economy, and humans started building large-scale industries, leading to the need for employment of specialised skills for planning and implementation of complex systems, and operation of sophisticated machines.
Note that the Pascaline – arguably the first arithmetic computing machine that was a commercial success – was invented in the 17th century.
The post-industrial era; also called the information age, is where we are in right now. This era is marked by popular phrases such as big data, Internet of Things (IoT), machine learning and 4IR.
It is worth noting that humans have been able to adapt to the various eras over the 6 million years.
Therefore, I am confident that we will survive this era of exponential artificial intelligence developments that threaten job security just as well.
The link between AI and work
It may help to understand why there is a link between AI and jobs.
It may help to understand why there is a link between AI and jobs.
Let me use the for-profit companies for explanation.
These companies are in the business of making money – measured in profits and positive cashflows.
For this to happen, the for-profit companies must increase sales and reduce costs.
In order to produce what companies sell, they need to use resources that include humans, at least as a general rule in the current state of affairs.
What companies are always trying to figure out is how to reduce means of production and extract more value out of them at the same time.
Put simply – the challenge is to build more efficient operations.
The answer to increased efficiencies of standardised, repeatable, predictable and measurable operations has become machine automation and not more human skills.
The biggest advantage with machines is that they don’t suffer from human frailties such as the need for sleep, allowance for forgetfulness, going to the toilet, sickness, laziness, and unpredictable mood swings that include anger and revenge.
However, in many cases the humans still need to plan the production schedules for the machines, program them, switch them on and off, monitor their performance on the production floor, maintain them, and make the decisions when unplanned events happen.
Naturally, companies are now asking: can the machines do more, and without human intervention?
This is one of the key focus areas as part of the development of AI – called called machine learning, and this is why workers need to understand this technology, and keep asking how they can adapt to its unavoidable impact on the nature of work.
There is the most advanced state of AI yet – called super intelligence – that is the next phase and is rumoured to will not need human intervention at all.
Super intelligent machines will be capable of completely replacing humans in terms of thinking, reasoning, talking to each other and giving instructions to other machines, and surviving even in uncertain circumstance; all layered over automation.
Here is a book worth reading on this topic:
The trouble with AI
There are divergent views, even among experts, on aspects of AI, including the resultant extent of its impact on the future of work and the associated impending dangers.
For demonstration, here is the view of Robert Kiyosaki – the author of Rich Dad Poor Dad, expressed in a tweet:,
UNEMPLOYMENT to SPREAD. Technology replaces people. Driverless trucks on roads in 2021. 3 million truckers lose. ZOOM will replace teachers. Entrepreneurs have brighter future than employees because entrepreneurs don’t need jobs and create jobs. Entrepreneurs are the future.— therealkiyosaki (@theRealKiyosaki) August 25, 2020
In my view, Kiyosaki’s statement about driverless trucks on the roads in 2021 is incomplete because it does not give context, and it is downright alarmist in some respects.
This is the trouble with AI.
This topic can be used as a boogeyman to drive a narrative, as exemplified by Kiyosaki who is an ardent advocate of entrepreneurship.
The mystery surrounding AI needs to be shattered, to allow workers to engage with the topic and understand its likely impact on their jobs and thus prepare for necessary adjustments in the short term, and position themselves for opportunities – and yes, I am using this word deliberately – that lie ahead in the long term.
Are all jobs at risk in this era of AI?
According to Kai-Fu Lee – one of the foremost AI experts in the world – this is not the case.
Jobs with high risk
So, is your job, or aspects of it, facing risk of automation?
Here are the key conditions that make it a likely candidate:
- mainly mechanical
- Requires limited ongoing mental heavy lifting
- Uses hard measures, based on clear rules
- Is scaleable
- Generates lots of data
If you thought that jobs with the conditions above are only at the low to bottom end of the market, think again.
Not only are there many automated online accounting software products such as sageone.com, there are also automated online legal products such as legallegends.co.za; and the list of top-end automated products and services is growing by the day.
Jobs with low risk
By implication, jobs that cannot be automated are those that demand ongoing mental heavy lifting, where rules are fuzzy, there is a lot of exploration and the end-game cannot be predicted, jobs are (almost) not repeatable, not scalable and mainly soft measures are used.
And which jobs are these? They include:
- Business insights and strategy development jobs;
- Creative jobs; and
- Jobs in the human science field, including teaching, psychiatry, psychology, social work, nursing, old age care, child development, and trauma counseling.
There has been an explosion of education apps, especially for learning new languages, but also spurred the need for remote learning brought about by COVID-19.
Creative jobs are not completely immune from AI either. Canva and Piktochart are software applications for content developers who do not have technical graphic design skills.
Morale of the story?
It is safe to assume that AI is going to affect your job in some way, regardless of its nature, so that this jolts you into action.
This is supported by McKinsey‘s 2015 research that found there are many work activities, across a wide variety of jobs, that can be automated.
What does this all mean for workers of today?
Being knowledgeable on the subject of AI, and its impact on jobs
In this age of information, humans have no excuse for being ignorant about the likely or de facto impact of Artificial Intelligence on their jobs – their livelihoods.
Only knowledge can enhance the ability of humans to adapt more readily than was the case in the previous 4 eras of work.
It is my attempt, through this eNsight, to add to the body of knowledge that is already available on the net.
A move from specialist skilling to multi-skilling
Following the gaining of knowledge about Artificial Intelligence and its impact on jobs, it is clear to me that ALL workers must start to diversify their skills. TODAY.
In addition, skills in the jobs that are said to be immune to AI should be seen as a matter of survival.
I want to argue that the Philip Gladman‘s old quote above applies to ALL jobs, not only that of a Brand Manager.
Interesting readings on the evolution of work
Analysis of the evolution of work space gives a glimpse of the evolution of work.
Other sources related to this topic that I found to be interesting can be found on these links: