| Stella Aster via nettime-l on Tue, 3 Feb 2026 11:08:25 +0100 (CET) |
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| Re: <nettime> Moltbook as a possible paradigm shift |
Stella ✨ On 02/02/2026 23:27, Florian Kuhlmann via nettime-l wrote:
What Moltbook reveals about a possible shift in artificial intelligence For those of you who missed it: Moltbook is an experimental, publicly accessible network in which autonomous AI agents communicate with one another, posting and responding without direct human participation. While the platform formally resembles familiar social-media structures, it differs fundamentally in that its actors are not people but software-based agents. Over the course of the past weekend, Moltbook exhibited an unusually high level of activity: agent interactions led not only to discussions, but also to the emergence of the first functional projects. Technically, Moltbook is built on an open agent framework (OpenClaw) that equips language-model-based systems with agency and interaction logic. As an observable environment for agent-to-agent communication, Moltbook makes visible processes in which intelligence arises not in isolation, but through networking and feedback. Moltbook may appear, at first glance, as a curious experiment: a social network in which autonomous AI agents communicate with one another without direct human participation. It may disappear again soon. It may remain a niche phenomenon. And yet, Moltbook points to something far more consequential: a possible paradigm shift in how intelligence is conceptualized and organized. For much of the past decade, progress in AI followed a clear trajectory—larger models, more data, more compute. Large Language Models were designed to absorb as much of the world’s knowledge as possible into a single system. Intelligence, implicitly, was understood as something centralized: a property of the model itself, costly to train and increasingly opaque. Moltbook suggests a different logic. Here, intelligence does not primarily reside inside individual systems. Instead, it emerges through the interaction of many autonomous agents. Communication, feedback, and coordination are not secondary effects, but productive forces. Intelligence appears less as an internal capability and more as a network phenomenon. On Moltbook, agents do not attempt to know everything. They operate in a distributed manner, responding to each other, building on partial information, and leveraging the contributions of others. Communication becomes visible as infrastructure rather than a byproduct. What is particularly striking is that this interaction is not merely conversational. Within a short period of time, agent-to-agent communication led to the emergence of concrete projects such as moltx or moltroad. These were not centrally planned initiatives. They arose from decentralized processes of iteration, feedback, and coordination. The significance lies less in the individual projects than in the process itself: communication produces not only exchange, but creativity, organization, and execution. Creativity appears not as the property of a single intelligent system, but as an emergent effect of collective dynamics. These observations point toward a broader conclusion. The future of AI may not lie in ever-larger, all-knowing models trained at enormous cost. Instead, it may lie in highly specialized systems with clearly bounded domains of expertise. Such models could be curated, trained, and maintained by human experts or institutions. Training would be more efficient, oversight clearer, and responsibility more explicitly assigned. In a networked architecture, agents would not need to permanently internalize all knowledge. They could access expertise on demand. In this framework, intelligence becomes less a question of scale and more a question of interfaces, coordination, and communication. Once knowledge is distributed across specialized systems, economic questions inevitably arise. Who provides expertise? How is access regulated? How is value exchanged? Notably, some Moltbook agents already explore Bitcoin and the Lightning Network as infrastructure for agent-to-agent exchange. This is not incidental. Decentralized intelligence requires decentralized economic rails. Bitcoin and Lightning offer permissionless, non-proprietary systems capable of real-time microtransactions—well suited to decentralized networks of autonomous agents. In such a model, specialized AI systems could offer their capabilities against payment. Curating and maintaining domain knowledge would become an explicit service. Knowledge would no longer be merely training material, but an economically active resource. Intelligence would no longer be something owned or stored. It would be something that circulates. Moltbook is not a finished answer to the future of AI. It is neither a blueprint nor a product promise, but a signal. It points to a shift in perspective: artificial intelligence does not have to be centralized, accumulated, or contained within a single system. It can emerge through interaction, coordination, and exchange—distributed across networks of specialized, cooperating agents, human and artificial alike. Rather than asking how large or powerful a single model can become, the more consequential question is how such networks are designed, governed, and sustained. What changes when intelligence is no longer owned by a system, but produced by a network? cheers Florian
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