The reason why we gather at NEXT Conference in Hamburg every year has a lot to do with the meteoric rise of information networks over the past decades. This year’s gathering brought heaps of insights from today’s frontiers of the digital revolution:

• the physical world
• the Global South
• energy
• brain-computer interfaces
• biological computing
• democracy
• VR/AR
• automotive
• and creativity.

The common denominator is the networked character of information processing. In his recent book Nexus, Yuval Noah Harari levels out common distinctions by ultimately reducing all living things to information-processing entities and thus making distinctions that are based solely on the channels through which networks absorb and process information and react to irritations.

We already live in a hyper-networked reality, where the digital revolution continues to embed networking principles across all facets of life, organizations, and societies. With physical AI, we try to make sense of the vast amount of data from sensors in the physical world. Our energy grids are transforming into more decentralised networks, modelled after the internet. Similarly, democracy is up for a networked reboot: the shift from the consumer to the citizen.

Brain-computer interfaces help us better understand how our brains work and give us new modes of interaction with digital systems. Vice versa, biological computers – mini-brains in a dish – follow similar tasks the other way around, by giving us working lab models of the brain. The brain is a network. Meanwhile, the Global South is quickly turning into a more networked, digital society than the West. Automotive will emerge from its current midlife crisis with borderline disorder as a truly digital, networked industry.

The network society

The rise of networks is everywhere. Through this lens, we can better understand what’s happening in every part of our world. As long ago as the 1990s, sociologist Manuel Castells coined the term network society to describe a shift that started to occur way before we denerded the internet through the web. In his view, networks have become the basic units of modern society.

This framework helps us anticipate innovation and change, suggesting that wherever networks are not yet dominant, they soon will be. Physical AI is the Trojan Horse for networked information technology in all parts of the physical world. Think of it as a software update for the internet of things.

Electricity grids are transitioning from heavily centralised to decentralised networks, in which the number of energy producers is rising sharply. With solar panels on the roof and batteries in the cellar and the car, households are becoming prosumers. At first glance, it looks like the back channel that TV cable networks had to be retrofitted with after the arrival of the internet. However, the new network paradigm will soon have an impact on the entire business model – just as the internet has had on television.

For democracy, it’s a similar story. The internet had long promised more democratic participation, self-governance, and transparency. The reality, however, is characterised by powerful tech companies, manipulated opinion and the rise of autocratic regimes. This can lead to what Jon Alexander dubbed the return of the subject story, where people are subject to authorities and their democratic rights are severely restricted. Instead, he advocates for a renewed focus on empowering citizens within the network society, restoring the democratic promise of interconnectedness.

The dichotomy of tech

Interestingly enough, the same dichotomy is at work in information technology itself. It vacillates between centralisation and decentralisation. It isn’t fully networked yet. Artificial neural networks have driven much of the recent rise of AI. They are modelled after the biological neural networks we find in our brains. Now, these biological networks itself give us new insights, tools, and interfaces.

The brain is a better computer, because it is a network. As such, it is intrinsically learning:

there’s no real difference between thinking and learning.

The brain operates through the network of its synapses, creating new ones all the time. We can now take insights from research at Cortical Labs or into brain-computer interfaces that teach us the inner workings of the brain and apply them to computers. This is a virtuous cycle: computers allow us to better understand the brain, and then build better computers and software based on that knowledge.

Biological systems, like the brain, are way more powerful and efficient than the tech we have today. This also applies to energy systems.

In nature, nothing is lost, only transformed.

Networks are systems

Thus, we finally arrive at systems theory. Now this is something we have discussed for years on this blog. In 2022, it even was the theme of our yearly conference. Networks, like the brain, are autopoietic systems:

Think systems, e.g. ecosystems, that are autopoietic, i.e. reproducing themselves. Artificial intelligence, or machine learning, is just that: a self-reproducing system. These systems are structurally coupled with their environment, but not controlled by it.

Autopoiesis isn’t necessarily a bad thing. Think of the living cell – biologists describe their chemistry as autopoietic systems. The identical concept has also been applied to cognition, systems theory, and sociology. If we reuse it to describe hybrid analog/digital systems, we can probably draw lots of valuable conclusions from that.

For example: Systems change all the time. They are not static. And they aren’t more important than their environment. Nature itself is comprised of autopoietic systems, like the biological cell. And maybe it’s just the modern project of humanity that’s ending with the rise of hybrid analog/digital systems: the project to conquer the whole world and put it under human control.

Loss of control, self-regulation, or self-reproduction are all characteristics of networks, or autopoietic systems. The network model of society, economy, and democracy is a significant shift away from the old industrial, mass communication and mass consumption model.

For the automotive industry, this shift will be particularly hard. The car is an icon of the industrial society. It’s perhaps the ultimate consumer product. Cars signify freedom, mobility, and wealth. They have shaped our cities and landscapes. Apart from the electrification and digitisation of the car, the question remains: how will we transfer all this to the network society?

How will we integrate these values into a new automotive paradigm? The rise of networks fundamentally reshapes our understanding of society, economy, and democracy. By embracing the principles of connectivity and decentralization, we can navigate the complexities of our increasingly networked world.