The following article was mostly written by Brian Arthur of McKinsey in 2017. I have added content and edited it heavily to update its story to today’s realities and to add context to our investing thinking. It smartly describes the disruption that is rippling through the world today at an ever-increasing pace. The fading disorderly events of the pandemic, disruptive political policy at every level, and the relentless evolution of creative destruction are being supercharged by technological disruption and innovation at massive scale. The world is spinning seemingly faster into greater complexity. We believe a big picture framework starting with a historical roadmap is a good place to frame how best to position for the future from an investment posture.
Through Artificial Intelligence (AI), we are creating an intelligence that is external to humanity and housed in the virtual economy. This is bringing us into a new economic era—a distributive one—where different rules apply.
That shift of course has been going on for a long time. It’s been driven by a succession of technologies—the Internet, the Cloud, big data, robotics, machine learning (ML), and now artificial intelligence (AI)—together powerful enough that economists agree we are amid a digital economic revolution.
So, in what way exactly are the new technologies changing the economy?
The main feature of this autonomous economy is not merely that it deepens the physical one. It’s that it is steadily providing an external intelligence in business—one not housed internally in human workers but externally in the virtual economy’s algorithms and machines. Business and engineering and financial processes can now draw on huge “libraries” of intelligent functions and these greatly boost their activities—and bit by bit render human activities obsolete.
The economy has arrived at a point where it produces enough in principle for everyone, but where the means of access to these services and products, jobs, is steadily tightening. So, this new period we are entering is not so much about production anymore—how much is produced; it is about distribution—how people get a share in what is produced. Everything from trade policies to government projects to commercial regulations will in the future be evaluated by distribution. Politics will change, free-market beliefs will change, social structures will change.
We are still at the start of this shift, but it will be deep and will continue to unfold indefinitely in the future.
Thinking generationally.
How did we get to where we are now? About every 20 years or so the digital revolution morphs and brings us something qualitatively different. Each turning changes the economy.
The first turning, in the 1970s and ’80s, brought us integrated circuits—tiny processors and memory on microchips that miniaturized and greatly accelerated calculation. Engineers could use computer-aided design programs, managers could track inventories in real time, and geologists could discern strata and calculate the probability of finding oil in the ground. The economy for the first time had serious computational assistance. Modern fast personal computation had arrived.
The second turning, in the 1990s and 2000s, brought us the connection of digital processes. Computers got linked together into local and global networks via telephonic or fiber-optic or satellite transmission. The Internet became a commercial entity, web services emerged, and the cloud provided shared computing resources. Everything suddenly was in conversation with everything else.
It’s here that the virtual economy of interconnected machines, software, and processes emerges, where physical actions could now be executed digitally. And it’s also here that the age-old importance of geographical locality fades. Davis Rea could contract relatively cheap but highly skilled labour in India to help with our software design. Modern globalization had arrived, and it was very much the result of connecting computers.
The third turning began roughly in the 2010s, and it has brought us something that at first looks insignificant: cheap and ubiquitous sensors. We have radar and lidar sensors in our cars, gyroscopic sensors, magnetic sensors, blood-chemistry sensors, pressure, temperature, flow, and moisture sensors, by the dozens and hundreds all meshed into wireless networks to inform us of the presence of objects or chemicals, or of a system’s current status or position, or changes in its external conditions.
These sensors brought us data—oceans of data—and all that data invited us to make sense of it. If we could collect images of humans, we could use these to recognize their faces. If we could “see” objects such as roads and pedestrians, we could use this to automatically drive cars.
As a result, in the last ten years or so, what became prominent was the development of methods, intelligent algorithms, for recognizing things and doing something with the result. And so, we got computer vision, the ability for machines to recognize objects; and we got natural-language processing, the ability to talk to a computer as we would to another human being. We got digital language translation, facial recognition, voice recognition, inductive inference, and digital assistants.
What came as a surprise was that these intelligent algorithms were not designed from symbolic logic, with rules and grammar and getting all the exceptions correct. Instead, they were put together by using masses of data to form associations: this complicated pixel pattern means “cat,” that one means “face”—Jennifer Aniston’s face. This set of Jeopardy! quiz words point to “Julius Caesar,” that one points to “Andrew Jackson.” This silent sequence of moving lips means these spoken words. Intelligent algorithms are not genius deductions, they are associations made possible by clever statistical methods using masses of data.
Of course, the clever statistical techniques took huge amounts of engineering and several years to get right. They were domain specific, an algorithm that could lip read could not recognize faces. And they worked in business too: this customer profile means “issue a $1.2 million mortgage”; that one means “don’t act.”
Computers, and this was the second surprise, could suddenly do what we thought only humans could do—association.
Fourth Turning: The Data Era. The coming of external intelligence.
*AI - Artificial Intelligence, ML – Machine learning, AR – Augmented reality, VR – Virtual reality
As we progress through the Data Era — which is centered around the convergence of new data technologies such as AI/ML, AR/VR, and automation — all industries will need to maintain/ramp up investment in these new areas to defend their positions. While navigating these challenging times, they need to manage these costs/ investments efficiently to generate a strong relative profit. Smarter and more efficient investment through this downturn will bolster our confidence that Amazon, Apple, Goggle, Meta, and Microsoft - to name a few - can maintain or potentially enhance their competitive positioning beyond 2023. Failure to keep up to the rush of innovation could be a terminal event. In order to survive, it will be crucial for companies to invest heavily in technology in order to remain competitive and relevant. Many will fail.
It would be easy to see artificial intelligence as just another improvement in digital technology, and some economists do. But I believe it’s more than that. “Intelligence” in this context doesn’t mean conscious thought or deductive reasoning or “understanding.” It means the ability to make appropriate associations, or in an action domain to sense a situation and act appropriately. This fits with biological basics, where intelligence is about recognizing and sensing and using this to act appropriately.
Thus, when intelligent algorithms help a fighter jet avoid a mid-air collision, they are sensing the situation, computing possible responses, selecting one, and taking appropriate action to avoid disaster.
Once built, there doesn’t need to be a human controller at the center of such intelligence; appropriate action can emerge as the property of the system learning from its own experiences. Driverless traffic when it arrives, will have autonomous cars traveling in special lanes, in conversation with each other, with special road markers, and with signaling lights. These in turn will be in conversation with approaching traffic and with the needs of other parts of the traffic system. Intelligence here—appropriate collective action—emerges from the ongoing conversation of all these items. This sort of intelligence is self-organizing, conversational, ever adjusting, and dynamic. It is also largely autonomous. These conversations and their outcomes will take place with little or no human awareness or intervention.
The interesting thing here isn’t the form intelligence takes. It’s that intelligence is no longer housed internally in the brains of human workers but has moved outward into the virtual economy, into the conversation among intelligent algorithms. It has become external.
This shift from internal to external intelligence is important. When the printing revolution arrived in the 15th and 16th centuries it took information housed internally in manuscripts in monasteries and made it available publicly. Information suddenly became external: it ceased to be the property of the church and now could be accessed, pondered, shared, and built upon by lay readers, alone or in unison. The result was an explosion of knowledge, of past texts, theological ideas, and astronomical theories. Scholars agree these greatly accelerated the Renaissance, the Reformation, and the coming of science. Printing, argues commentator Douglas Robertson, created our modern world.
Now we have a second shift from internal to external intelligence, and because intelligence is not just information but something more powerful—the use of information—there’s no reason to think this shift will be less powerful than the first one. We don’t yet know its consequences, but there is no upper limit to intelligence and thus to the new structures it will bring in the future.
How this changes business.
To come back to our current time, how is this externalization of human thinking and judgment changing business? And what new opportunities is it bringing?
Some companies can apply the new intelligence capabilities like face recognition or voice verification to automate current products, services, and value chains. And there is plenty of this now, but increasingly these once thought of wonders will be commonplace. There will be a relentless push for more.
In doing this, businesses can reach into and use a “library” or toolbox of already-created virtual structures as Lego pieces to build new organizational models. One such structure is the blockchain, a digital system for executing and recording financial transactions.
The result, whether in retail banking, transport, healthcare, or the military, is that industries aren’t just becoming automated with machines replacing humans. They are using the new intelligent building blocks to re-architect the way they do things. In doing so, business will cease to exist in their current form.
So, we will see both large dominant tech leading companies and shared, free, autonomous resources in the future. And if past technology revolutions are indicative, we will see entirely new industries spring up that we hadn’t even thought of.
Reaching the ‘Keynes point’.
Of course, there’s a much-discussed downside to all this. The autonomous economy is steadily digesting the physical economy and the jobs it provides. It’s now a commonplace that we no longer have travel agents or typists or paralegals in anything like the numbers before; even high-end skilled jobs such as radiologists are being replaced by algorithms that can often do the job better.
Economists don’t disagree about jobs vanishing; they argue over whether these will be replaced by new jobs. Economic history tells us they will. The automobile may have wiped out the carriage-manufacturing industry, but it created new jobs in car manufacturing and highway construction. Freed labor resources, history tells us, always find a replacement outlet and the digital economy will not be different.
Some will argue that slowing population growth and an aging demographic will offset some of the pain felt in labour markets.
I am not convinced. Revolutions always leave some behind. As disparities grow, social upheaval intensifies. We all see its complex face manifest on our screens today.
Offshoring in the last few decades has eaten up physical jobs and whole industries, jobs that were not replaced. The current transfer of jobs from the physical to the virtual economy is a different sort of offshoring, not to a foreign country but to a virtual one. If we follow recent history, we can’t assume these jobs will be replaced either.
In fact, many displaced people become unemployed; others are forced into low-paying or part-time jobs, or into work in the gig economy. Technological unemployment has many forms.
Universal Social Income will be the next debate.
We believe we have reached the “Keynes point,” where enough is produced by the economy, both physical and virtual, for all of us. (If total US household income of $8.495 trillion were shared by America’s 116 million households, each would earn $73,000, enough for a decent middle-class life.) And we have reached a point where technological unemployment is becoming a reality.
Will the machines do our work and pay for the displacement of our jobs? Tax the robots? Or are we becoming the product of the machines? Dystopian visions can easily be conjured. Already some are calling for a pause on artificial intelligence availability.
We have entered the Distributive Era.
A new era brings new rules and realities, so what will be the economic and social realities of this new era where distribution is paramount?
All these challenges will require adjustments. But we can take solace in that that we have been in a similar place before. In 1850’s Britain, the industrial revolution brought massive increases in production, but these were accompanied by unspeakable social conditions, rightly called Dickensian. Children were working 12-hour shifts, people were huddled into tenements, tuberculosis was rife, and labor laws were scarce. In due time safety laws were passed, children and workers were protected, proper housing was put up, sanitation became available, and a middle class emerged. We did adjust, though it took 30 to 50 years—or arguably a century or more. The changes didn’t issue directly from the governments of the time, they came from people, from the ideas of social reformers, doctors and nurses, lawyers and suffragists, and indignant politicians. Our new era won’t be different in this. The needed adjustments will be large and will take decades. But we will make them, we always do.
