Evolution of Technology with Simon Jagers

It’s my pleasure to welcome back Simon Jaegers to the podcast.

He is one of the founders of Samotics in 2015. His professional career has been spent in technology, usually evolving around either making data, storing it, or processing it. Samotics provides condition monitoring technology based on electrical signature analysis.

In this episode we covered:

When we talk about technology evolution, is it similar to how people, animals, reptiles evolve as well? Or is it something completely different?
Why is it useful to understand that algorithm?
What influences the growth of technologies?

When we talk about technology evolution, is it similar to how people, animals, reptiles evolve as well? Or is it something completely different?

I think it is very similar and certainly to the extent that they evolve according to a similar process. There’s an algorithm involved, often referred to as a Universal Darwinism, which basically says you have mutations, something changes; then you have selection. The environment sort of selects whether this mutation is a better for the environment at that present moment.

That same algorithm applies to technology as well. The algorithm working on the available technology is the initial conditions, combining and recombining several technologies. The market then decides if the new product is a new innovation. Is this mutation a better fit for my current environment? What they have in common is that process, which is mutation and selection.

Why is it useful to understand that algorithm?

If we can use this model of how technology is involving to better predict which technologies will catch them and which ones will likely fail, we will probably be at least somewhat more effective in introducing new technologies and reaping the benefits of all these innovation projects.

What influences the growth of technologies?

There is a set of available technologies that can be combined. Some of them include:

Universal Darvin algorithm
Universal Darwinism algorithm
Laws of nature
Rules of thumb
Tendencies
Complexities

Technologies on many occasions create more complexity and not complexity you get from a user perspective, but simply a more complex product. And that complexity typically helps it to have more survival skills. Having a deep understanding of ideally the technology, the market is beneficial when it comes to predicting what’s coming next.

Where do you see these rule sets and tendencies in action with examples?

Alexander Graham bell invented the phone. Because it’s typically a combination recombination of technologies, that same principle was invented at exactly the same moment in Europe as well. You could talk and if you want to do that more efficiently, if you’re going to make it more complex, then at some point you’re going to have to be able to connect one phone, two more phones via telephone system.

And in the early days, the switchboard was a human powered switchboard. If you look at that system and want to make that more efficient, and cheaper, you probably want to automate the switch board. At some point we could send photos via our phones. But as a Testament of how the algorithm worked at the time, people thought there are better systems to transfer images. So that kind of died down, but the phone learned how to an image in and out of itself. At some point we got mobile phones; we’ve got various networks. Today, phone is not just a telephone. It’s a mobile computer that has internet that has text messages you can navigate using your phone.

We say one in four pilots fails. Is that because we have certain mutations that haven’t been fully vetted in selection yet?

For some things to succeed, you’re always going to have a lot of things that fail. I think it’s simply part of the process. I think the cell mutations are going to happen. The selection process takes time, and that can prolong with lots of capital. It’s basically part of the process.

Is this evolution the same for physical products and tools?

There is a big difference. That difference is also the reason why we will see much foster innovation moving forward because essentially digital products are not shackled by physical constraints. So that same process of digitization, we hop into Maslow’s law. Doubling in speed cost every 18 months or so, that deceptively small growth moving into a disruptive phase where it will disrupt a market or a niche then becoming deemed materialized, then monetized and democratized; that’s how digital technology tend to evolve. That is unshackled from physical constraints and therefore goes much, much faster.

It’s not one technology developing all these different pieces; cloud computing, the AI, etc. Is that true?

Absolutely. The combination of all of those technologies is almost by driven by an invisible hand exploring all the available possibilities of technology, whether it is AI and vibration, AI and ultrasound, AI, and ASAP cloud, etc. Everything is going to be explored and when there is a market for it, which essentially means that if there is enough demand and the combination of those technologies, the new invention or that product can be delivered at a better price. It can also add more value than it costs to produce, that’s likely going to stick around.

We’re relying on these predictions to make decisions about our equipment. What if we got some of those assumptions wrong or we’re still early in the evolution and they’re not quite right?

If those assumptions are right, that electrical signature analysis is almost by definition the way to go. This is because it is the most elegant, the most energy efficient way to achieve all those things, which is reliability. Sustainability and flexibility allow you to detect failures. It allows you to measure performance and energy efficiency on a highly granular level, and it allows you to control the speed and torque of that motor, which determines the quality and qualities of the stee, how they are being processed.

If any of those assumptions is wrong, it could mean that we will not move beyond a very niche product. Ultimately there could be something that’s not yet out there that will do all of those things massively better. There’s no ecology to sustain it.

What is the role of other assumptions in this development of technology?

I think the point is to always keep an open mind as to how evolving you’re developing your product and simply accepting that to stay in the game, to be selected, to be retained. You need to be flexible enough to adapt to random events that cannot be foreseen. It is a model of thinking about the future.

What do you want our listeners to take away from this conversation today?

I simply love technology. It makes sense to have a framework when thinking about the future. This conversation provides a framework for thinking about how technology will change and how it will apply to you, your industry, and so on. People get to learn how they can leverage technology, how to think about technology in the future and how to get engaged with it.

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Evolution of Technology with Simon Jagers

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