Petter Törnberg

It’s official! 🎉 Very happy to announce that I’ll be joining the University of Lucerne as Professor of Political Science! And even better, my wife Juliana is also taking up a position at Lucerne! 😊 Excited to return to Switzerland together, and meet all the wonderful new colleagues! ⛰️

The Platform Society described how digital platforms came to reorganize social life. We are now entering a new phase: the AI Society Generative AI is changing not only how information circulates, but how knowledge is produced, economies are organized, and political life is structured.🧵 New paper!

The Platform Society concentrated power over communication. The AI Society extends that power deeper: into the infrastructures through which we know, decide, and think. The central battle of the AI society may thus be over the control the conditions of thought itself.

The Platform Society described how digital platforms came to reorganize social life. We are now entering a new phase: the AI Society Generative AI is changing not only how information circulates, but how knowledge is produced, economies are organized, and political life is structured.🧵 New paper!

The shift is economic. Platforms built monopolies through network effects: users became dependent, services “enshittified,” and monopoly rents followed. AI society extends this logic—but through new lock-ins: control over the infrastructure through which cognition itself is organized.

The Platform Society described how digital platforms came to reorganize social life. We are now entering a new phase: the AI Society Generative AI is changing not only how information circulates, but how knowledge is produced, economies are organized, and political life is structured.🧵 New paper!

In The Platform Society, van Dijck, @tpoell.bsky.social & de Waal described how social and economic life was increasingly organized through corporate platforms—through datafication, commodification, and algorithmic selection. That transformation reshaped markets, institutions, and public life. 🧵

Social media has defined our attention landscape for two decades. It is now coming to its end. What comes next, and how will AI reshape the attention economy? 🧵 New essay for Informational Democracy informationaldemocracy.substack.com/p/after-soci...

What is replacing it marks a recentralization of allocation of attention: from networks to models. In effect, a return to broadcast, but without the regulatory structures we built around the broadcast paradigm.

Social media has defined our attention landscape for two decades. It is now coming to its end. What comes next, and how will AI reshape the attention economy? 🧵 New essay for Informational Democracy informationaldemocracy.substack.com/p/after-soci...

The social media paradigm rested on a simple bargain: users supplied the content, and users distributed it. Attention flowed through networks of friends and followers, competing for attention under the rules defined by the platforms.

Will AI give us a shared basis for political knowledge, or will Democrats and Republicans end up asking increasingly different chatbots? New preprint: We find a self-reinforcing loop that can pull AI models apart politically. We call it the Centrifugal Alignment Spiral 🧵 arxiv.org/pdf/2609.28486

More broadly, we must recognize that model alignment is emerging as an important political arena. We cannot leave it to market share, smoke-filled rooms, or opaque feedback pipelines. It must be made subject to democratic deliberation.

Will AI give us a shared basis for political knowledge, or will Democrats and Republicans end up asking increasingly different chatbots? New preprint: We find a self-reinforcing loop that can pull AI models apart politically. We call it the Centrifugal Alignment Spiral 🧵 arxiv.org/pdf/2609.28486

Step 3: We put these measured effects into an agent-based model of repeated choice and learning. Tiny initial differences attract different audiences, and those audiences push the models further apart. ꩜ A model that starts slightly left or right spirals in that direction.

Will AI give us a shared basis for political knowledge, or will Democrats and Republicans end up asking increasingly different chatbots? New preprint: We find a self-reinforcing loop that can pull AI models apart politically. We call it the Centrifugal Alignment Spiral 🧵 arxiv.org/pdf/2609.28486

Step 2: We trained identical copies of three open models on feedback from 90% Dem vs 90% Rep audiences (a deliberate stress test). On unseen political questions, paired models chose different answers 12–41% of the time. Every run moved in the expected partisan direction.

Will AI give us a shared basis for political knowledge, or will Democrats and Republicans end up asking increasingly different chatbots? New preprint: We find a self-reinforcing loop that can pull AI models apart politically. We call it the Centrifugal Alignment Spiral 🧵 arxiv.org/pdf/2609.28486

The spiral needs three things: 1) politically different users pick different models 2) models learning from their users' feedback drift apart 3) those differences then shape who picks which model next

Will AI give us a shared basis for political knowledge, or will Democrats and Republicans end up asking increasingly different chatbots? New preprint: We find a self-reinforcing loop that can pull AI models apart politically. We call it the Centrifugal Alignment Spiral 🧵 arxiv.org/pdf/2609.28486

Honored to be part of this amazing group of scholars working on Informational Democracy at the Max Planck Institute! We'll be exploring how AI and changing information systems are reshaping democracy, public debate, and collective decision-making. Follow along: informationaldemocracy.substack.com