Technology trends rise and fall in waves. As one new innovation grows more powerful and popular, another begins to die out. We don’t see many horse-drawn carriages around for a reason. People drive cars now.
Until autonomous vehicles come to rule the roads, that is.
Many people become overwhelmed as the regular cycle of tech waves crashes over them again and again. But one way to keep this from happening is by creating a structure or framework you can continually add new pieces of data.
Here’s how Elon Musk put it:
“One bit of advice: It is important to view knowledge as sort of a semantic tree — make sure you understand the fundamental principles, i.e. the trunk and big branches, before you get into the leaves/details or there is nothing for them to hang on to.”
That’s one way to think about it. If you have a framework in your mind on which you can hang individual facts, then you’re able to see all of the data in context—the big picture.
The more your knowledge is organized into frameworks and models, the better you get at making sense of news, facts, data, and other information that is continually bombarding you. From there, it’s easier to incorporate this information into your thinking.
When it comes to technology and science, try using the Carlota Perez framework.
Carlota Perez, currently Centennial Professor at London School of Economics, researched the major technological disruptions since the Industrial Revolution. And she found there are two phases to every tech revolution.
1) First comes the installation phase, beginning with “irruption” and leading to “frenzy.” In the installation phase, the technology makes it to market, people begin investing, and the infrastructure is built.
Then comes the crash. Expectations were too high. People invested too much too soon. The frenzy simply can’t be sustained, and the market corrects. In the long run, the crash actually helps, because it leads to the second phase.
2) The second phase is known as deployment. It’s characterized by two periods known as “synergy” and “maturity.” In this phase, people are more realistic about the opportunities presented by the technology, and they’ve figured out how to profit from it through dependable business models.
The internet is a good example from recent history. Irruption and frenzy occurred in the 90s, leading to the Dotcom Bubble and eventually the Dotcom Crash in 2000. But afterwards, people got a grip on how to work with the technology and invest prudently. As the internet grew into its synergy and maturity phases, huge companies were born.
The model is not perfect but is applicable to a wide range of industries and situations.
Electricity, motor vehicles, rail transport, and many other technological revolutions have gone through the cycles in Carlota Perez’s model.
It fits many technology cycles in medicine and science as well. Take gene therapy, for example. It started out with so much promise. My sister’s middle school science fair project in the mid-1980s was entitled “The advent of gene therapy”, but there were all kinds of disappointments (the deal of Jesse Gelsinger at University of Pennsylvania in 1999) that caused people to abandon it. But here we are, years later, and it looks as though gene therapy is getting increasingly closer to .
Even our drug development work at Morphic Therapeutic fits into the framework. The integrin class of receptors we’re targeting was discovered by our founder, Tim Springer, back in the 80s. And initially, there was plenty of investment in it.
But using that discovery to create a small molecule drug turned out to be much harder than anyone anticipated. The bubble burst, and many of the largest pharmaceutical companies left the field. It took another decade of research, but now we’ve figured out many of the technical issues that led to the failures. And we’re poised to create a new generation of drugs that would have been impossible before. I’d say we’re somewhere in the deployment stage at this point, hopefully trending towards maturity. This is the portion of the Carlota curve where you hit the “accelerator.”
You can use this framework to test your own assumptions.
You don’t have to be an expert in a certain subject to understand where a given technology lies within the framework.
When you come across a new technology or discovery, ask yourself where it fits into this model. Is it in the installation or deployment phase? How far into that phase? Why do you think so? Are there arguments for placing it somewhere else?
The purpose of this exercise is to put a stake in the ground somewhere. Your assessment is never going to be perfect. But asking these questions gives you insight into whether you should act on a particular piece of information.
Should you bet your career on an investment if it looks like the technology is in the frenzy stage of installation? Maybe, but at least you will realize the forces at work. There are a different set of risks at the various different stages.
The model is a thinking tool. It won’t provide you with an 100% accurate answer, but it will help you develop some important insight by placing things in a framework you can understand.
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