The impact of automation and robotics on the labor market

Seems like a lot of bright people are seriously investigating the impact of automation and robotics on the labor market nowadays. According to most, we're heading some serious storms in this regard. Not only is automation destroying plenty of blue-collar jobs, but with the avenge of smarter devises and self-learning software, even white collar jobs could suffer from this trend in the long-run.

However, as Andrew McAfee brilliantly explains in this TED talk, as automation first and foremost benefits the corporate profits, it will no longer do so in the future if this keeps on destroying jobs - companies will simply have no consumers to sell their products to if all jobs would disappear. This somehow pleas for a guaranteed minimum wage (or a different way to re-distribute corporate profits, if you'd ask me, this could be done in other ways than a minimum wage).

The vision of labor-free citizens that spend their time thinking, discussing, making art and designing new things while robots do the hard work, is reappearing. Sounds pretty sixties and tech-utopian, perhaps. But why not? Whether we want it or not, we are already facing a completely new logic in the way our society gets organized. Trends like 3D printing, climate change, crowdsourcing, sharing economy etc etc already are impacting the very fabric of our society. If you project all of these trends on a larger scale (okay, a very dangerous thing to do for any forecaster) you see a completely new society emerge.

How exactly this will unfold is obviously very hard to predict. No doubt the path to this transition will be painful for many, as is the case with all transitions of this magnitude. But if we're all looking in the same direction, it might lead to something better at the end... it just might...

The art and science of prediction (Nate Silver's 'The Signal and the Noise')

“This book is less about what we know than about the difference between what we know and what we think we know”, writes Nate Silver somewhere at the end of his book. He could not have phrased it more beautifully. Our brain is a remarkable instrument that, when it comes down to predicting almost anything, contains a substantial amount of techniques to mislead us. Of course, these techniques often helped us to survive in tougher times, but they fail us miserably in the area of future-watching. Just as an example: apparently we have a bad habit of neglecting certain risks just because they’re too hard to measure…. Not sure how far this will get us in terms of survival, however…

The solution? Statistics. According to Nate Silver, that is. He’s not saying statistics can predict everything (or, for that matter, anything) –‘all statistic models are faulty, but some are useful’- but at least it can provide us with a more accurate picture of the (possible) future(s).

Chapter by chapter Nate applies this thought on very specific subjects, ranging from the American elections (his specialty), to weather forecasting, earthquakes, economic growth and climate change, to more tangible topics such as poker, chess, or beating the stock market (which, he concludes, is impossible).

Fascinating material, and some conclusions linger on for quite some time. For instance: the level of uncertainty of prediction increases when the system we study is non-linear and dynamic (which sounds intuitive at first, but is very often forgotten, for instance when governments predict next year’s GDP), or when the data we start with is not accurate enough (like with weather forecasting). We also need to watch out not to ‘overfit’ our models (adapting it with the data we have at hand), as we often do with predicting earthquakes. Sometimes we can use models developed for one system onto another system, like applying the power-law distribution we use for earthquakes onto the event of future terrorist attacks (which results in a somewhat bewildering conclusion).

The central thought Nate Silver uses across his book is the formula of Bayes which, for the curious people among you, looks like this:

[xy] / [xy + z(1-x)]

Where

X = the initial likeliness you would attribute to an event.

Y = the ‘positive possibility’ (the likeliness of an event without taking x into account)

Z = the ‘negative possibility’ (the likeliness of the event not taking place).

Believe it or not, but with this formula you can even calculate the likeliness that your partner is cheating on you! The key figure in the formula is the ‘x’, the likeliness you grant to an event accuring –which implies that the overall likeliness gets more accurate as you gather more experience or knowledge about the event (if your partner cheated on you in the past, the ‘x’ would increase, and hence the overall likeliness in a particular event as well).

Strangely enough this thought process can be applied to virtually any system (or prediction), though dependent on a number of factors it can produce totally different outcomes, with different levels of accuracy. The world will never be predictable, but with a sensible use of statistics it can become a little more so…

Fascinating reading…

Meet the new Meat

...have to love this quote, just because it makes you think "A vegetarian in a Hummer is more ecological than a meat eater on a bike"... meat has some future though:

Why CSR is moving to the core of corporate strategy

It still strikes me that people look surprised when I talk to them about CSR (Corporate Social Responsibility). To many, CSR is just a fashionable word for ‘greenwashing’, a handy way to disguise activities that are far from ‘responsible’ at all. Others see CSR as an argument to attract a new breed of clients, or a way to attract Generation Y employees who –according to plenty of surveys- would prefer to work for a socially responsible employer.

To counter this skepticism I often explain that CSR is increasingly getting to the core of corporate strategy. Even better: in many industries CSR has become an imperative. To show why, let us take a look at this graph:

The vertical axis shows the source of motivation for CSR: is this imposed by society, or is it a company’s own initiative? The horizontal axis shows the impact of CSR on a company: does it involve its core business, or does it only impact the ‘business periphery’ (the context) of a company?

We now obtain 4 segments, each reflecting a different behavior to CSR:

The lower left segment (‘must do’s’), where CSR is imposed by society but touches the business’ context, is where we find activities that companies must comply with. A fair treatment of employees, honest communication, compliance with the law, etc. Nothing too exciting here.

In the upper left segment (‘good to do’) we find the numerous corporate foundations that each company from a certain size feels obliged to possess. This space is pretty crowded, and hence offers little room for competitive differentiation. In a certain way this segment is responsible for the skepticism I talked about earlier: what good can we expect from the foundation of an oil company when, due to bad practices, the mother company causes a natural disaster?

But it starts to get really interesting once the CSR activities touch the core business of corporations. The lower-right segment (‘major threat’) is clearly a danger zone companies would want to avoid. Think of how BP had to review its activities after the disaster in the Gulf of Mexico. Or think of Coca Cola, whose products were boycotted for years throughout India because of its bad management of water in a plant in the Southern State of Kerala. This factory was located in a region that is increasingly suffering from water shortage, hence the anger of the local farmers for Coca Cola’s water spoilage, and ultimately the boycott throughout India. To be fair with Coca Cola: they since reviewed the water usage  of their factories, and even win sustainability awards with it. In each threat lies an opportunity…

Now to my point: in order to still gain competitive advantage from the overpopulated ‘corporate foundation’ activities, and to avoid the threat of society imposing changes on companies, more and more companies move to the upper right segment (‘major opportunity’), where the core activities of a company are used to do social good. Google.org is a good example of this, since it uses plenty of Google’s tools at the disposal of aid organizations after a natural disaster. Some would call this a clever way to position its products favorably (‘cause marketing’), but I just wonder how many day-to-day users of Google know about this initiative…

Other examples include the many traditional energy providers who increasingly put sustainable energy at the core of their long-term strategy. Or take technology company Siemens, that recently completed a reorganization where each business unit now focuses on resolving one specific societal challenge (ageing population, mobility, sustainability, etc). But the most impressive example is probably Umicore, who transitioned from a (dirty) mining and natural resources company into a world-class recycling company, and now consistently appears in the lists of most sustainable companies worldwide.  

This movement becomes inevitable in many industries. The two ‘forces’ we discussed are driving companies to put their core products at the service of their CSR activities. But there is a third force at play: in this segment companies face an increasing competition from social entrepreneurs, who very often threaten the overall business model of the industry, and at any rate are eating market share from traditional players. Pure providers of green energy are a good example of this, but also the multiple initiatives that have to do with the ‘sharing economy’, which are forcing traditional players to at least ask themselves the (vital) question whether they should join or fight this movement.

Admittedly, these three forces will play with different intensity dependent on the industry you’re in, but from the multiple examples I have seen in recent times, I can only conclude that virtually no industry will remain unaffected by them.