On Labor Day, a question of what will work look like in 5 years? It is an interesting question because most of us would think that much of work today would look like work from the past few years. If you look at the infrastructure of urban environment, we consistently build office buildings for people, although the office looks and feels much different than the world of cubicle, the work remains similar. We continue to build houses and condos and hope people will commute to work and shop in shopping malls, even though it is possible to shop online anytime you want to. We hope that in many ways the future will look like the past and individually we all benefit.

In a book called Reinventing Capitalism in the Age of Big Data by Vicktor Mayer-Schonberger and Thomas Ramge published by Basic Books, NY, 2018. The book is a few years old but the message is valid.

The key difference between conventional markets and data-rich is the role of information flowing through them and how it gets translated into decisions. In data-rich markets, we no longer have to condense our preferences into price and can abandon the oversimplification that was necessary because of communication and cognitive limits. That makes it possible to pair decentralized decision-making, with its valuable qualities of robustness and resilience, with much-improved transactional efficiency.

There is a critical link between market efficiency and information flows, Information can make or break a market – not only does information have to travel throughout the market, but only does information have to travel throughout the market, but it must travel at low cost.

With money, market participants not only know what something is worth on the market. Once we put a value on something, using money, we can trace that value; we can record and compare value over time, thus creating an informational link between the past and future, and maintaining an external, more objective basis for mutual trust among market participants.

Money may have not have been invented to facilitate transactions on the market, but it surely has made markets work more efficiently.

After money, was no longer tied to gold (1971, President Nixon took the US off the gold standard) money became purely informational. Today money is moving from physical to virtual – further emphasizing money’s informational role.

The fundamental shift in the economy, the driver is data. Data is the new grease for the wheels of the market. It helps market participants to find better matches.

Thanks to digital networks, massive amounts of data now can flow quickly, easily and cheaply between transactional partners, whether they are near each other or thousands of miles apart.

Consider Amazon’s product-recommendation engine at first glance at first glance, it’s a matching system. It quite successfully matches our preferences with available products and makes recommendations about what we should order. But that is only half the story. Amazon captures our preferences not from us directly but from the comprehensive data stream it gathers about our every interaction from its website – what products we look at, when and for how long we look at them, which reviews we read. Amazon looks for unique patterns in the data that reveal our preferences. Identifying such patterns enables Amazon to statistically deduce our wants and needs without having to ask us directly and it does not know why we prefer one thing over another; it just takes into account the fact that we do.

Amazon’s strategy is not unique; its is representative of Big Data, an approach to data analysis that aims to capture data comprehensively by looking for complex patterns embedded in the data. By concentrating on pattern analysis, Big Data approaches is that the pattern one is looking for is not defined from the outset; it emerges as huge amounts of training data are analyzed.

For the system to work: two conditions must be met. First, huge volumes of data are needed initially for machine learning systems to train themselves and make explicit what is embedded in the data. The second thing needed is frequent feedback so that over time it can self-adjust based on the specific and changing circumstances.

Firms exist wherever they can organize human activity more efficiently than the market can. Firms persist when they operate more efficiently than the firms, they compete against.

Whether a focus on efficiency is a suitable strategy depends on 2 factors: are there existing inefficiencies within an industry that can be eliminated? The traditional network airline business model, for example was wasteful enough that more efficient, low-cost airlines could take over a substantial share of the air-travel market. By contrast modern supermarket chains are comparatively efficient in their operations that new entrants have much less opportunity to disrupt them.

The second aspect is time. Relative efficiency improvements almost always result in an advantage that is only temporary as other firms catch up. That is why firms competing on commodity products often start a new round of economizing the moment they conclude the previous one. Over time, as inefficiencies are eliminated, there is a diminishing marginal utility to pushing for automation, process efficiencies and other cost-cutting measures.

Two options for firms

Focus on cost; – for every doubling of total units sold, the cost of producing any good or service drops by between 20 and 30% according to The Boston Consulting Group and Bain & Company.

If a company successfully automates and optimizes decision-making, cutting down on overhead and permanently reducing head count. The strategy will disrupt its workforce, but keeps the structure in place of centralized management in place.

Option 2 is break the centralized management with a more decentralized style which allows for more simpler, faster, flatter model.

Both options are data driven and one wonders what happens to the people in the firms. Much of the economy depends on people spending, if firms use data and machine learning more and more, there is a need for less humans, Government policies on some sort of UBI ( universal basic income) will have to come into play; greater taxes for the firms are highly successful; tax credits for actually hiring people. No one knows what the ultimate solution should be.

Linking to dividend paying stocks, how does your company use AI and big data? What do they do different? how are they more focused on making revenues? what is the business strategy? these types of answers allow you to determine if you like the answers and you think the firm is on the correct track. There are many unknowns in investing and only time will tell if you made the correct solution, however you still need to make decisions.

There are more questions than answers, till the next time – to raising questions.