Copyright © Stephanie Pistel.

The launch of ChatGPT in late 2022 was the iPhone moment for generative artificial intelligence: a technology started to move into the hands of everyone, not just experts. And, since AI is a general-purpose technology, it will induce more change than the iPhone did. It will change everything everywhere all at once. The movie of the same name inspired Miriam Meckel and Léa Steinacker in their new book: Alles überall auf einmal (engl. everything everywhere all at once).

There aren’t that many general-purpose technologies in the history of tech. The computer is one good example, the internet is another. For earlier generations, think electricity, or the steam engine. What all these have in common is that they impact almost all parts of our collective and individual lives: our societies, economies, and the way we work, shop, live, or entertain.

What we put under the AI moniker these days is, in many ways, the culmination of decades-long development, starting with the very early roots of computing, networks, and artificial intelligence. Steve Jobs famously dubbed the computer “a bicycle for the mind”. Similarly, the authors describe AI systems as “steam engines of the mind, our cognitive GPS systems or simply climbing frames for thinking”.

For them, it’s clear that these are tools and need to be treated as such. They don’t subscribe to any of the doom theories that have been richly in supply of late. Rather, they talk about the immense opportunities, and discuss how to set the right course. Here, it certainly helps that the authors are both social scientists, which gives them a broad perspective on the topic. They are definitely well-versed in the subject matter.

Humans aren’t machines

Their overall theme is the delicate relationship between humans and machines. Following the European tradition, they maintain a fundamental difference between the two: humans aren’t machines, and machines, however intelligent they may become, don’t become humans. Anything else would lead to a mechanistic view of the world and of human nature.

We haven’t yet solved the problem of human intelligence and consciousness: we don’t really know what it is. There is a tendency to understand these deeply human phenomena in terms of machine logic. This could lead, as the authors point out, to a self-fulfilling prophecy:

Instead of being proud that there could be something in our human nature that computers and artificial intelligence do not have, we are doing everything we can to make ourselves more and more machine-like.

Our human physicality with everything that goes with it – emotions, hormones, digestion – is what makes us human and distinguishes us from machines. In this view, “artificial intelligence” is a misnomer. Or, as researchers Drew McDermott and Melanie Mitchell put it, a “wishful mnemonic”:

That’s when the creator of a computer program names a function after what they wished it was doing, rather than what it’s actually doing.

Miriam Meckel and Léa Steinacker propose that we understand AI as “data-driven human amplifiers”. This, they argue, would avoid problems we face today of defining intelligence, making clear who is holding the reins, and maintaining the connection between mind and body as a characteristic of the human being.

On that rationale, they don’t shy away from the thorny issue of regulation. One chapter of the book is dedicated to the state of AI regulation (at the end of 2023) in the EU, the USA, China and at a global level. It ends with a plea to focus on the immediate risks such as data protection, discrimination, content moderation, liability and sustainability. In general, they argue in favour of regulation in order to create a reliable framework for innovation:

With the right rules, we can shape the future and write technology and even world history.

AI and the future of work

It seems to be almost a consensus that AI will impact many, if not most jobs. Think about other general-purpose technologies, and you’ll see that computers and the internet affect nearly any job today, be it directly or indirectly.

The effect of AI goes beyond that. It touches on ontological questions, ones that concern the human condition. In 1917, Sigmund Freud coined the term “slights to humanity” (Kränkungen der Menschheit) for disruptive scientific discoveries which, according to his theory, have called into question people’s self-image in the form of a narcissistic injury.

Back in 1994, the German physicist and philosopher Gerhard Vollmer pointed out ten mortifications of humanity. Among them was the prospect of machines (artificial intelligence) that match and even surpass our intellectual achievements. Human beings define themselves and their value partly through work and through self-perception as a unique species. AI threatens both definitions.

But, as we have argued before, the future of work is what can’t be done by machines. The technology we call AI today will automate at least some parts of almost every job. If things go well, this should make our jobs easier, not harder. And we’ll figure out entirely new things to do, to develop, to make, to produce. Things that only become possible through the use of new technology.

Inevitably, this will create new jobs, change existing ones and, yes, render some obsolete. If a machine can do your job, better watch out. Or, as a 2015 study quoted by the authors said:

Machines will take on more repetitive and laborious tasks, but seem no closer to eliminating the need for human labour than at any time in the last 150 years.

While almost a decade has passed since 2015, I still agree with that quote. We shouldn’t forget that everything machines can do leads to commodification, but this also means that value moves elsewhere. Commodities are cheap, abundant and replaceable. Value is expensive, scarce, and irreplaceable.

The book gives a well-informed, detail-rich and broad perspective on the subject, listing a ton of useful sources in the appendix. If you need an overview (and can read German), this book is for you.

Copyright © Stephanie Pistel.