Cooking the Digital economics :)

AI as the enabler for succeeding in Digital and Industrial worlds

Preface.

Being involved in different aspect of digital economy, working in the teams of Strategists, Innovators, M&A experts, AI experts, ui/ux, big data etc., implementing real products for digital economy, one can realize, that the changes companies are dealing with, are much bigger and far more solid and structured, than one might think about it. In this article we try to explain what Digital Economics is, how it relates to Industrial ones, what is Cyber-physical economics and what it all means for people, business and governments. We also discuss why exponential technologies like AI are disrupting industries. At the end of the article authors will present their view on AI inbuilt in an ecosystem of digital products significantly fostering the ecosystems value supported by real cases implemented in different countries within different clients.

…Everything which can be digitized will be digitized. Everything which is digitized may be copied. Everything which is copied will lose its price and becomes free…

Kjell A. Nordstrom

Digitization Symptoms

Due to the active development of the technologies last decades and especially the last years there are many things changing around us. We will try to introduce them based on 6D of digitization, introduced to us by SingularityU. So, the Digitization is driven by two types of technologies Deceptive and Disruptive. Both are very important for the Digitization. First group are the so called “deceptive” (deceptive technologies operate primarily in the realm of our minds and can potentially be misused to manipulate and control people). Arising out of the diversity, these innovations are not changing our life drastically, but making it more comfortable and joyful. The second group are Disruptive. Serious life breakers, in a sense, that you can always say that’s how it was before and now that is how it is today — completely different. All of them have similar characteristics, which are causing similar symptoms for the commodity products — Dematerialization, Demonetization and Democratization. Dematerialization is easy — meaning in the past one had a physical calculator with buttons and solar batteries and now it is in your cell phone being completely digital, the end for productions and work places. Next is Democratization — being digital it has no physical limits and can be distributed among all people having cell phones, nowadays all people. Which also means the speed of distribution of these products. Think about Radio took 38 years to get 50Mln clients, Angry Birds did it in about 35 days. Democratization means digital products can be equally accessed by all people, especially if… Now comes the third one — Demonetization, which means free of charge at some sense. Open the Apple play and enjoy the number of calculators which are for free available from across the Globe. These are the features of real digital products — non material, being released from physical limitations of material world, become distributed or Democratic and by being so (see the citation above) become free of charge, demonetized. Monetization principles for the companies which are developing these products are not always straight forward, complex and heavily depend on the business model. These are the symptoms.

Digitization of the Industrial Economics

If one thinks seriously about the symptoms and investments of companies into Digital, he will realize, that it’s already a serious thing. Evolving from toys, the industry is jeopardizing in high power polynomial growth, being close to exponential at some points. Why that happens? The answer is simple, Digital economy is created to serve humans and humans love that. Every “digital something” conference authors attended over the last 5 years starts with a smiling man standing in the middle of the slides. That is how we all buy it. Professionals (digital product owners) created an industry to analyze user personas, value, ui/ux etc. etc. to understand how to make something that has intrinsic value for the end customer. But is this the real reason for the companies being successful in digital? If one thinks one layer deeper the answer is quite clear. What is the biggest inefficiency of modern economics? The answer is — Principal agent conflict.

What is the Principal-Agent Problem? (https://www.investopedia.com/terms/p/principal-agent-problem.asp)

The principal-agent problem occurs when a principal creates an environment in which an agent’s incentives don’t align with those of the principle. Generally, the onus is on the principal to create incentives for the agent to ensure they act as the principal wants. This includes everything from financial incentives to avoidance of information asymmetry.

What Is Asymmetric Information? (https://www.investopedia.com/terms/a/asymmetricinformation.asp)

Asymmetric information, also known as “information failure,” occurs when one party to an economic transaction possesses greater material knowledge than the other party. This typically manifests when the seller of a good or service possesses greater knowledge than the buyer; however, the reverse dynamic is also possible. Almost all economic transactions involve information asymmetries.

So, what it all means, that people like digital services first, due to the fact, that these are solving their asymmetry of information leading to solvency of the principal agent conflict in many deals that these people do daily. The asymmetry arises of us being material and limited with the barriers of time and space. Think about how many shops are you going to visit to find the best price-value-time2get coffee? Now think about the car, the house? In general, its fine for people to have the asymmetry of information in a small deal, since you will not fly to Australia from Germany for the coffee best matching your criteria triangle. Long distances stop people from wasting time for the best and people are ok with locally (in terms of space) best. But the idea of being at the not optimal consumption curve in terms of daily purchases is always in the brain. Just think about people reading social media or journals, realizing that they missed best offer for vegetables or missed the place for vocation? That is what marketplaces solved in the most efficient way. Marketplaces offered people completely full access to all producers and their ratings and vice versa about clients eliminating information asymmetry and guaranteeing the rights and information about buyers for sellers, sellers about buyers and gifts and money exchange. By solving this inefficiency the world has changed forever, as well as the underlying economics. Markets become more and more transparent. This fact caused many problems for producers of products. Why? Because the transparency supported with globally efficient logistics, infrastructure as a service, access to financing, global payment systems (we call these all Infrastructure) caused extreme competition at the layer of products engineering, their production and realization. Therefore, one might all the time hear words agile, time to market, faster, more, client centric, portfolio of services etc. One can visualize the new business model of digital economy in the following way, we call it N1M1. N clients get their products from 1 marketplace which provide access to M products hosted, produced and delivered through one global infrastructure. That is a limit case of course, but the principles are correct. These changes all the MBA principles regarding how to manage companies producing N products with average life span of a year or even 6 months, know knowing how clients preferences change going forward, thus running huge portfolios in a complete uncertainty with a huge risk at the balance sheet, generating shareholder value and not revenue. https://rushkoff.com/digital-trends-isnt-digital-economy-making-life-better/

That is exactly the impact of the digital economics, by fostering changes using the speed of light, Democratization and Dematerialization pared with Demonetization, Traditional competitive barriers of Time and Space, those which protected businesses for the last few centuries nowadays are transforming, forming new competitive barriers of new — Digital economy, which are the derivative of traditional ones, namely Risk and Speed. That is why at any conference today, where people discuss Digital transformation nobody knows what to do. There is no way found to manage this new business long term. They are all “bubbles” from the point of view of traditional MBAs. Again, I refer to Prof. Rushkoff,

To Rushkoff, the constant corporate requirement to grow is not just hurting the digital economy but destroying the planet itself. Sure, digital technology allows for transparency, but it also tends to accelerate and amplify the shortcomings of corporations. Today, companies aren’t necessarily created in order to generate revenue but rather, to get snapped up by bigger companies.

Basically, the sustainability all corporations are aiming for is now in the past, the future is unknown, but we can try to think about the evolution of N1M1 business model. I call it a spiral of Digital economics. I used UBER as an example for mobility platforms development and my private understanding of how UBER will evolve in future.

or, if one wants, simplified representation in 3D. The thing is that depending on the point in time different business model will become competitive and technologies are responsible for shifts.

These are the basic principles of Digital economy and the answer why monopolies will emerge and why they are going to be unsustainable in future. Basically, that means, depending on how fast technologies develop in a certain industry the less sustainable any business model is. That is a serious challenge for the mankind going forward — knowing how to deal with this complexity and being able to fail, learn, succeed, learn and fail again. Each cycle of the is the disruptive change in Digital economy.

Digitization. Postindustrial economics crack

Nowadays we are living in a very interesting time, which we call a crack of traditional postindustrial economics into three different ones. Namely: Digital, Industrial and Cyber-physical. The crack happened since the digital products follow 6D principles, being not limited by material transfer, they spread with the speed of lite, follow their spirals, appear, grow and disappear, making clients happy. The industrial economics still struggles from inefficiency, principal agent conflicts due to the information asymmetry. Now, imagine the trade, as information exchange of industrial economics happens in Digital economics solving all the inefficiencies. Opening new markets, making payments and goods exchange safe Industrial Economics will be involved in Digital economics being the market place for Industrial ones, jeopardizing competitions of Industrial economics.

Assuming the winner takes it all principle of digital economy, we will get global winners and loosers on a world scale. Digital economics was newer planned to be a country level economics — it is going to be a global marketplace for industrial economics and physical people squeezing each cent of inefficiency to all possible limits. In case industrial economies will not learn how to apply exponential technologies, to increase efficiency, the game will be over. There is no time for attempts anymore.

The real globalization under the “winner takes it all” principle is in the nearest future. And that is where exponential technologies become of the strategic importance for local industrial economies of country scale.

What it means for people, business and governments.

Change of the economic systems. Borders of what we understand as economy or the ways how we as the society, create monetary value, considerably change. Historically we all acted within the barriers of time and space (with all its consequences for export and import of physical products, with protection consumers and the quality standards, with a monetary and tax policies), more and more we move into the world with barriers of Speed and risk (taking into account that how many risks we can and we want to afford — as societies, states, corporations and individuals and as quickly we can act and react).

Change of the growth model. This change of the main barriers leads to a new paradigm of “growth”. We used to have long S curves to create value by attraction of investments for an accelerated growth to a maturity. Nowadays, only the combination of a set of small and dynamic S-curves will lead to the borderline growth. Theories of formation of optimum portfolios in the context of holdings and S-curves will become a basis for the economy of the enterprise.

Change of the business purpose. The change of the economics model followed by the change of the micro economics model will have its fundamental impact towards how we organize, and which competencies do we need to succeed going forward, equally true for the states, corporations and individuals. To succeed we need to not only limit our dependency on bureaucratic models of organizations, but also a transition from just understanding of what is right to an execution of that.

Change of a business model of the organization. This new form of success of the organization (the business purpose) will lead to emergence of new structures in our economies. It will change the roles all of us are playing in the economy. We will see decrease in horizontal and vertical thinking. Instead we will see emergence of N1M1 model playing more and more dominating role.

Change of the society model. All this required from us to rethink our understanding of society, institutes of our society and those roles which we all play. We need to rethink approaches who gives capital to whom, under which conditions and for which purpose.

Now let’s summarize changes for the society, business and states.

Changes for the business.

  • Immediate realization of traditional microeconomics on a global scale with a focus on portfolio management approaches in the absence of traditional competitive barriers of time and space.
  • Risk and speed are the new competitive barriers for business.
  • Big corporations will be dismissed by a holding of small companies.
  • Jeopardizing competition for talents replacing fixed incomes with shares in business.

Changes for the society

  • Release of a high volume of an unqualified workforce to the market.
  • Serious increase of the social tensions where states need to react by rebalancing social systems in a right way while having high economic volatility and sharpening global fight for markets.
  • Necessity to get education and change profession many times in life.

Changes for the states / governments

  • Necessity to invest in the infrastructure to be able provide competitive advantages for business and receive dividends from it in the future to cover social obligations.
  • Local market liberalization / de-monopolization to allow globally competitive companies to appear.
  • Human potential and its management systems needs to become the core of economics growth since business will not be able to stay globally competitive not having access to the required human potential.
  • Ability to manage social stability in case of high economic volatility becomes a key success factor for the state government. Ability to act if required becomes of strategic importance.
  • Cyber risks become essential in digital economics. Governments need to be able to protect its infrastructure from any risks.

But among all these risks, there is a new opportunity arising: ability to create and optimal model for macroeconomics management, will release colossal financial resources, reduce the risks, increase the financial stability and reduce risks of economical agents.

AI as a competitive advantage in Digital Economics.

The real revolution within economical systems might happen due to the fast development of ecosystems, data availability and successes in machine learning and especially the AI. Why it happens is that before last years business was always functioning in some local optimum of space and time competitive barriers, realizing the existing supply and demand curves. According to some assessments nowadays business only realizes about 10–20% of all possible demand and supply curves. The reason for this is that business is not entering in supply and demand curves, where the number of hurdles is above 3 to 5. These hurdles might be different, absence of logistics, complex regulation, sanctions etc. One can consider wheat global trade example. We have analyzed data for global trade with agricultural product (42000 pairs of country-product) and revealed an inefficiency of about 20% for one of the leaders in global agriculture trade. Another interesting thing we have found is that the amount of trades could have been significantly higher. Nevertheless, due to the information asymmetry, in this case driven by too big volumes of data for analysis, countries / business try to find locally optimal solution or are driven by political decisions. Based on optimization algorithms, high volumes of data and many different optimization routines and models we have been able to build a recommendation system which would find the best matching curves of supply and demand maximizing profits of exporters. After we realized that that was possible, we have decided to try to go to the producers of agricultural goods and try to optimize their plans (crop rotation plans) in order to maximise their profits based on the export politics and global markets demand. The interesting thing was that by building an optimal strategy for crop rotation based on predicted demand for special crops at the global markets it is possible to significantly increase the income of producers (up to 26% Revenue increase for farmers) and up to 16% of Revenue increase on a country scale due to optimal trade (all numbers are based on real projects the team has done in the past). These fantastic numbers are achieved only using existing supply and demand curves. Another interesting judgement one can make is that if the farmer behaves in a way that is not only optimal for him, but also for the state both will win at the end.

Now, if somebody goes further and tries to solve the hurdles, e.g. a bank, this organization would be able to create incredible amount of new business. What do we mean by that, is that using big data technologies, paired with the possibility to forecast supply and demand, having the optimization routines based on hybrid models of markets one would be able to create new curves for supply and demand, thus constructing new value generating new supply and demand chains with a much higher efficiency. A good example would be a producer of wheat not being able to export his wheat to a country where he sees the biggest price for his goods due to the absence of logistics, e.g. sea port. Many banks are looking for good cases to invest in building infrastructure, but due to the absence of information about our farmer might not consider the deal. Working in ecosystems of value creation rather that industries would allow bank to see better risks thus being much safer in terms of capital adequacy, while solving the hurdles for business and allow it to create new supply and demand curves. In case government would find the ways to synchronize businesses around global opportunities assuming the winner takes it all principles, it all would serve for good both, the business and the state releasing huge liquidity volumes to finance risky project, e.g. development of the disruptive technologies within R&D institutions. Below we can share our experience in building a prototype of such a system.

One can foresee the same picture for e.g. Banking industry.

All these ideas are supported by real world project our team did over the last 15 years and my personal opinion, that we can solve really serious problems of the global trade and local country production problems using the AI on a much bigger scale and with a much higher effectiveness and efficiency, than it is used nowadays.

Special thanks to Stefan Dierks for many of these enlightenment.

Currently AP in MHP (Porsche company) Digital services accelerator. Ex. head of Deloitte Analytics, ex. Siemens AI engineer. PhD in AI @ TUM, Honored professor