5 Things I Learned Reading 25 Books on Technology

I have been an ardent reader ever since I was a kid, but last December I decided to go for an experiment. I set myself the goal to read 50 books on business and technology in 2021. What would I learn from them that regular publications would not teach me?

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It is halftime and I am through most of the tech literature, so it is time to draw a first conclusion. I have decided to summarize it along the line of five lessons I want to share with you. Some are surprising, some are counterintuitive, but all of them confirm that the new economic order this newsletter chronicles is inexorably gaining ground.

Lesson 1: Legacy systems are the biggest drag on companies

Technological progress has been one of the most reliable constants in human history. It is no wonder that the question of when to switch to a new system and trash the old has been keeping managers up at night. Whether they managed factory floors in the dawn of the steam engine or today ponder the switch from mainframes to the cloud.

Yet there are two particularities of our age that make the legacy-switch exigent. First, the accelerating nature of technology (see also point 3 below) makes technological progress exponential. This makes it extremely tricky to find the right timing. Start too early, and you will waste millions and leave scorched earth, so that investors’ wallets will stay sealed for quite some time. Switch too late, and competitors will leave you behind, an impact sometimes felt for decades on the balance sheet. Kodak and Nokia still serve as reminders of what happens to those companies failing to jump in time onto the bandwagon of progress.

Second, today the complexity and the interdependency of systems is unprecedented. Unlike changing from one method of production to another, in the digital age it is not enough to get the direction right.

A micro-problem on the level of software code can have devastating consequences. Examples abound in which old software or missing security updates left doors wide open to hackers, resulting in large-scale customer data theft or the complete corporate paralysis. The Equifax hack is just the most prominent case.

Yet instead of replacing outdated software, new systems are simply added onto it, making cost, complexity, and vulnerability spiral out of control. Banks, for example, spend 7 out of 10 IT-dollars on keeping the lights on legacy applications and every third fintech says legacy is the major challenge when partnering with a big company.

But even more critical than security and performance is the inability to take advantage of technological progress. Almost all blitzscaling companies have recently started as a tabula rasa. Old and inflexible systems put incumbents at a disadvantage.

Luckily, there are tested strategies even for those companies that refuse to hit the reset-button. Setting up subsidiaries or connecting inbred systems with application programming interfaces (APIs) can go a long way and is often the more prudent decision. Eventually, however, migration will be inevitable.

Yet legacy shouldn’t only be bothering corporations, but governments too. An economy’s most critical infrastructure is the most prone to legacy and its problems. We speak primarily about industries such as finance, cybersecurity, and governmental bodies. To their credit, those have been the first to adopt new tech, but that’s also why their systems sometimes date back to the middle of the 20th century. To protect a country’s competitiveness, it will take bold executives and broad national strategies that incentivize tech adoption.

Lesson 2:  A slowing Moore’s Law points to a new techno-economic paradigm

At the beginning of all legacy stands innovation. Science unearths natural phenomena; technology harnesses them. And eventually an economy arises from the sum of its technologies and their interplay.

While individual technologies have been studied extensively, its principles have at times been neglected. But to understand and fight legacy systems and to use new tech to gain a competitive advantage, we need to understand the general mechanisms driving progress.

I am sure you are all familiar with the exponential acceleration of innovation, as brilliantly illustrated by Ray Kurzweil in The Singularity is Near. Over the course of history, the intervals between technological advances get continuously shorter. The gap between the development of spoken language and writing was longer than the gap between writing and printing, which again was longer than the gap between printing and computers. You get the idea. It is the reason we are so bad at predicting the future. Note here that technology is very broadly defined. It does not necessarily require the exploitation of a physical phenomenon, but it might also be a behavioral one. In this regard the invention of speech or a monetary system is tech too.

The Nature of technology, a classic by Brian Arthur, reveals the mechanism behind this quickening pace: Recursiveness. This simply means that new technologies build on modules of already existing ones. Ergo, the more knowledge we accumulate, the more there is to build on and hence new tech can emerge more frequently. It is a spiral comparable to a viral post on twitter. Every new reader acts as a node that brings a host of other potential readers, and so on.

Eventually, the growth of specific strands of technology – e.g., integrated circuits – will mature and slow down. They will not grow exponentially forever. At this point, however, a new paradigm starts taking hold and the overall acceleration of technological progress continues.

That we are currently in a decelerating phase is best shown by a very prominent principle. Moore’s Law is a precept that describes the halving of size and costs of computing chips roughly every 18-24 months. It has been working for more than half a century, but lately it is pushing the boundaries of physics. Microchips cannot infinitely grow smaller. Exponential growth in future cannot be accomplished by simply doing more of the same. That is why completely new materials such as graphene are surfacing that will radically speed up the progress again. It is symptomatic for a starting paradigm transition felt all over the digital world.

Lesson 3: The leaders of the next techno-economic revolution are generalists

In the past, the idea of technological determinism was highly popular. This belief held that the invention of a new technology shaped the culture and social structure in an inevitable way. Free will does not exist in this worldview. After being repeatedly refuted in its hard version, technological determinism has fallen from grace and today we know that even the business potential of a technology is never self-evident. It takes clever outsiders to the technology that apply it in a novel way and (re)structure organizations around it.

But how does an organization make sure it hires and promotes this type of people? Research shows there is one quality in leaders that is by far the best predictor of whether they will be capable of harnessing the potential of the new technology: Range.

Range means that a person is or was exposed to many different domains of business, science, and technology. The more diverse those fields, the better. David Epstein has written an amazing book on this subject called “Range”. He shows that this principle holds true not just for business but for all walks of life, from sports (Roger Federer) to art (Van Gogh) and even military operations. The more varied an individual’s background, the likelier he or she will be a game-changer in the field.

A similar argument is taken up by Scott Hartley in “The Fuzzy and the Techie”. The humanities produce so-called fuzzies with no specific engineering knowledge, but they turn out to be at least equally important to technological advancement as the hardcore techies.

So, if you are looking for a manager to take your company to the next level, check more than just the university major and the latest job title, but make sure to check the breadth of the knowledge just as well as the depth.

Lesson 4: Most disruptors are not startups, but giants from other industries

Startups are hyped as the largest threat to incumbents. In reality, most Goliaths get butchered by other Goliaths. A study of the last 150 years showed that three out of four game-changing innovations come from incumbents. The likes of Amazon and Dell are the exception rather than the rule.

Most startups die, some partner with bigger competitors, and others manage to carve out a profitable niche. Those startups that seem to be too good to be true, often are. In some extreme cases, there is even flat-out fraud. My favorite account, Bad Blood by John Carreyrou, tells the story of the presumed blood-testing disruptor Theranos. The fall of this Silicon Valley darling magnifies the dangers when the precept of hyperscaling meets a lack of expertise and youthful hubris.

Management theory makes the distinction between de novo market entrants and diversifying market entrants. The former are complete newcomers, the classic Davids. Diversifying market entrants, on the other hand, are firms that have been successful in other arenas and strive to expand their reach. Think about Nokia and the iPhone. The Scandinavian mobile phone maker was not displaced by a newly founded firm, but by the deep-pocketed colossus from Cupertino. What happened was that technological advancement suddenly turned the cellphone into a minicomputer. This meant that momentum shifted to companies with core competencies in building apps and processors rather than antennas and ergonomic buttons.

The same is happening today with blockchain, AI or cloud computing. Just look at who is running the world’s largest datacenters. It is companies like Amazon or Google. And instead of bitcoin, currencies in the digital world will be run by banks like JP Morgan Chase and the 86% of central banks that are working on central bank digital currencies right now. That is not to say that incumbents can lean back. On the contrary: It is more difficult to fend off attacks by challengers that you cannot buy easily, that don’t need to first capture a customer base, and whose very existence does not depend on another funding round.

Lesson 5: Hyperscaling has never been easier

Having just listed some major handicaps of greenfield challengers, it is important to note that never in history have they held better cards than today. Yes, they are unlikely to bring an entire industry to its knees, but there is no better point in history to be a founder than today. The media has created an unprecedented startup hype, which in turn has flushed billions into new ventures led by brilliant entrepreneurs and has nurtured an almost limitless supply of young talent. The rise of clusters around the world makes it easier to tap into like-minded people. And the rise of technology lets you build and manage a company on a shoestring budget. When Facebook bought Instagram, it was worth 77 million USD per employee.

There have been disruptors before the path from garage to millionaire was so broad. One book that stuck with me was Shoe Dog, an autobiography by the Nike-founder Phil Knight. He tells in stunning detail how difficult it was to obtain funding from banks, even after rising to an industry behemoth. This was a time before venture capitalists were scouring Silicon Valley and readily opening their wallets for founders with little more than a grand vision. And Nike had to build an entire logistics network, stock up its inventory, and invest in expensive offline advertising. Going international was a mega-project, as was opening a new office within the US. The average founder today can do all this in just a fraction of the time and at negligeable costs. 

But one thing remains as true for today’s startups, as it was for Nike: Despite stellar growth, there is no such thing as “too big to fail.” It was eye-opening for me to read how despite the skyrocketing revenues, publicity and profits, Nike was still teetering at the verge of collapse.

The essence

Most of these lessons show that a new economic order is dawning. The burden of legacy weighs heavier than in any other point in history. At the same time building and scaling challengers is easier and faster than ever. The combinatorial effect of maturing technologies will make it easier for diversifying market entrants to endanger incumbents in other industries. The effect is also about to hit the breaking point very soon and unleash completely new rules that govern the economy. Blockchain, machine learning, hardware – they are all marching on with giant steps.  And to navigate those transformative times it takes a new breed of managers, which are not the greatest specialists, but those with the widest experiences and the capabilities to integrate them.

I will draw on these themes in future articles of The New Frontier, but if you want to find out more about the specific books on my reading list, then read the short reviews on twitter and goodreads.