Why AI and Automation Technologies should provide impact beyond productivity.
DISCLAIMER: This article is not a scientific study, it simplifies and scratches along the fields in finance, technology, sociology and philosophy. If you are a researcher in any fields of those particular fields, I am more than happy to hear your feedback.
The first question to clarify when talking about AI is its definition. There are dozens of definitions out there that categorize the field in various dimensions. All of them are logic in its own way, but for this kind of analysis we need a practical view on AI.
While the first industrial revolution lead to the automation of knowledge work, the digital revolution is already leading towards the automation of knowledge work.
Hard physical labour was gradually handed over to cranes, machines and robots. I guess today no one wants to work as a plow, crane of conveyer belt anymore.
I. The Finance and Economics View:
But in order to understand the role and impact of automating digital labour, we need to understand how the economic machine on a fundamental level works. One of the clearest views on productivity and its role for the global economy has hedge fund legend Ray Dalio and his team at Bridgewater Associates. In his famous youtube video “How the economic machine works”, Ray Dalio lays out the principles that drive economic growth. Within the next paragraph, I want to get across the essence and hence link it to technological advancement and the advent of AI and automation technologies.
In the early days of trade and economic activities in general, everyone´s spending was someone else´s income.
So in a world without credit, the only way to increase your buying power (spending) is to increase your income. This requires you to do more work in a given period of time (productivity).
The same applies on a bigger scale in the M&A world for instance. When a company is being bought with borrowed capital and the debt burden is loaded onto the acquired company. With its future profits the acquired company works towards amortization and ultimately offsetting the debt burden. The same logic basically applies to all asset classes that are built on leveraged finance (borrowed capital, also known as credit). The faster an asset is amorized (e.g. through productivity increase), the faster it becomes a bankable asset in your balance sheet, extends your creditworthiness and ultimately your credit line at the bank.
According to Dalio, credit is also what makes the economic machine a bit more complicated and leads to long-term and short-term debt cycles.
If you lay them on top of each other, you see that both the long term debt cycle as well as the short term debt cycle gradually follow the productivity line. Looking further into the debt cycles, Dalio concludes with three critical rules of thumb.
- Don´t have debt rise faster than income,
- Don´t have income rise faster than productivity, and
- Do all you can to raise productivity
II. The Technology and Innovation View
So, if productivity is such a big economic driver, let´s take a look to what extend technological advancements have influenced the global economy in the past centuries.
Major technological advancements have fueled innovation through new products, services or business models. Be it the invention of the steam engine, the commercialisation of electricity with its famous “Hurley washing machine moment” or the internet. More importantly, all of those innovation built up on one another:
- Steam engine → trains → long distance travel → remote places → need for long distance communication (telegrams)
- Edison inventing the light bulb → GE digging up streets to wire cities and connect houses to the electricity network → the upcoming of new appliances → the cabling infrastructure being used for telephone lines
- Telephone lines built the foundation for the Arpanet → the arpanet advanced to the internet and was commercially used → the use of internet forced to participants to transform formerly analog data into digital data
- Increased digital data led to bigger needs for computational power and data storage → Data (information) is being stored in data warehouses → the accumulation of data (information) led to ideas in structuring and ultimately using data (information) across domains for different purposes → computational power and availability of digital data enable us to make use of algorithms that have been around for a long time → we call it AI and start to think what cognitive tasks it can do better than a human
Some of these events overlap. For instance there was the telegram before the lightbulb and the Dartmouth conference with the invention of the term AI before the Arpanet. Furthermore, there were also other factors that fueled productivity such as, for instance, women entering the working world after WWII. But still, there is a guiding thread leading towards productivity increased and technological innovation.
III. The Sociology and Philosophy View
That graph shown above is what the past can tell us, but what could be the dynamics for the future. From a European view, there are merely two scenarios.
Scenario A: We harvest the potential of radical automation and invest in new technologies that promise to deliver new products, services and business models. If we follow this guiding thread we may as well come to the conclusion that there is laying great potential for productivity increase in AI and automation technologies. Many experts claim that every job that doesn´t involve creativity will be subject to automation. If we think this forward, it means there will be only a hand full of job categories in the long term.
Scenario B: The other option we need to talk about is scenario B in the graph — Stagnation. A flattened curve is also a more realistic scenario than you people think. According to Harari, a major driver for the 20th century productivity boom was a combination of cheap and abundant energy and cheap and abundant raw materials. In a world where there is constantly more pressure on restricting resources, there is a natural threshold to productivity provided by the energy sector. I call this “living-with-less” scenario. We can observe the “living-with-less” scenario already in some parts of the energy market where a downsizing of nuclear energy markets (by governments) lead to forced (and paid through auctions) production stops in energy demand peaks.
Yes, we are in a productivity hamster wheel. And yes, technological advancements look promising to being able to create new spaces beyond our current horizon, but we already see resource constraints on today´s horizon. That is why there is currently happening a shift from shareholder capitalism to stakeholder capitalism.
A recent client discussion may sum this up properly. Our industrial client told us, they are leveraging economies of scales already to a maximum extend. The data supported his claim entirely. So he asked me why should they need to digitize this use case that is running on 99,9% efficiency?
He is right. There is no digital dictate around digitization for the sake of digitization. But looking at new fields with a fresh pair of eyes might change the game, for your organization, for your employees, for your customers or your community. The royal road leads towards digitization for the purpose of productivity, profitability while finding technologically solutions to long term challenges.