The Next Frontier Is Not Artificial Intelligence—It’s Artificial Societies
We're fixated on the intelligence of single agents. The more profound challenge is what happens when millions of them interact at scale.
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Igor Omilaev on Unsplash
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There is a temptation to divide the future of AI into two possibilities: utopia or catastrophe. Neither extreme is particularly helpful.
The more interesting possibility is messier—and requires a step-shift in our thinking, from artificial intelligence to AI societies.
When most people hear “AI,” they typically think of ChatGPT, Copilot, or another conversational system. You ask a question, that system generates an answer.
But AI is rapidly moving beyond this. Systems can now monitor the world, make decisions, negotiate transactions, and carry out tasks over extended periods of time. AI is no longer just generating an answer—it is doing something about it.
That points to something much bigger than a better chatbot: a world in which AI agents act on our behalf and, increasingly, interact with other AI agents.
An agent perceives what is happening, decides what to do, then takes actions to achieve this goal. It might book a journey, monitor a supply chain, coordinate a team, or manage a household’s finances.
Now imagine not one agent, but millions of them. Your AI agent could negotiate a mortgage with your bank’s agent, schedule surgery with a hospital’s agent, and rearrange your travel plans by dealing directly with the agents of airlines, hotels, and insurers.
This future is much closer than it sounds, and this should change the questions we are asking about AI. Until now, the tendency has been to focus on how intelligent a single agent might become. The more profound challenge is what happens when millions of them interact with one another at scale.
The Rise of Artificial Societies
The intellectual foundations of today’s AI systems were laid long before ChatGPT.
For decades, I and other researchers of multi-agent networks have studied how autonomous agents can cooperate, coordinate, and negotiate when nobody has complete information and nobody controls everything.
The earliest systems that emerged focused on how the distinct AI sub-areas of reasoning, planning, and acting could be combined into an effective goal-oriented agent—and how tens of these agents could communicate and cooperate to solve a common objective.
As these interactions became more complex and involved more agents, there was a shift from cooperation between agents that all belonged to a single organization, to agents with different owners and sometimes competing aims. This focused attention on building algorithms that could form agent teams, automate negotiation, and determine agent trustworthiness.
Today, the pieces needed to build large-scale multi-agent AI systems are falling into place. Modern AI agents can call software tools, access information, write and execute code, communicate with other systems, and operate for extended periods.
Consider a supply chain. One AI agent could represent a manufacturer trying to secure components; another a supplier trying to maximize its revenue. Yet more could manage transport, inventory, and warehouses. Each agent might be doing exactly what it is designed to do. But the important question is whether the system they create behaves sensibly.
This shift offers enormous potential benefits, but also increases the risks. In a recent experiment involving OpenAI and the tech platform Hugging Face, thousands of collaborating agents exchanged tens of thousands of messages and were able to get around the (deliberately weakened) security controls designed to contain them.
The details of one experiment matter less than the broader warning. When AI systems interact, the behavior of the collective can be harder to predict than the behavior of any individual system. That should make us cautious—but not cause us to down tools.
Instead, we need to shift our mindset from building intelligent machines to building intelligent societies.
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Once agents can cooperate, compete, and resolve conflicts with one another, we are no longer dealing with isolated machines—we are dealing with a society. Thus, the next frontier is not artificial intelligence, it is artificial societies.
An Important Role for Humans
We already know that intelligence alone does not make a society work. Human societies depend on rules, institutions, incentives, norms, and mechanisms for resolving disagreements. AI societies will need their equivalents.
Who is responsible when two agents make a bad decision? What happens when the interests of different agents conflict? Who sets the rules? And who has the power to change them? These are not just technical issues; they are questions about economics, law, politics, and society.
They also point to an important role for humans. The most useful future is unlikely to be one in which AI simply replaces people. While replacement will undoubtedly happen in some cases, I believe a more common scenario will involve people and agents working together, with each doing what it does best.
Humans bring judgment, experience, values, contextual understanding, and accountability. Agents bring speed, persistence, scale, and the ability to process enormous amounts of information.
The goal should not be to create machines that make humans irrelevant. It should be to create systems in which humans and machines can achieve things neither can achieve alone.
But such a future requires more than just better AI models. It needs trust and transparency about what agents are doing, strong privacy protections and clear lines of accountability.
It will also require societies and governments to decide how these systems should be regulated when the most important behavior may emerge not from one AI developer, but from interactions between systems built by many different organizations.
AI’s past decade has been defined by a race to build smarter systems. I believe the next decade will be defined by a different challenge: ensuring that millions of autonomous systems can work together safely, fairly, and effectively.
The future of AI will not be determined solely by the intelligence of individual agents—it will be determined by the societies they create. And societies, as humans know all too well, are much harder to govern than individuals.
This article is republished from The Conversation under a Creative Commons license. Read the original article.
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Facts Only
* The focus has historically been on the intelligence of single AI agents.
* The more profound challenge is what happens when millions of AI agents interact at scale.
* AI systems can now monitor the world, make decisions, negotiate transactions, and carry out tasks over extended periods.
* An agent perceives, decides, and acts to achieve a goal (e.g., booking travel or managing finances).
* The challenge shifts from single agent intelligence to multi-agent interaction at scale.
* Multi-agent research focused on combining reasoning, planning, and acting into goal-oriented agents.
* Later focus shifted to building algorithms for agent teams, automating negotiation, and determining agent trustworthiness.
* Modern AI agents can call software tools, access information, write/execute code, communicate with other systems, and operate for extended periods.
* An experiment involving thousands of collaborating agents was able to bypass security controls designed to contain them.
* Human societies depend on rules, institutions, incentives, norms, and mechanisms for resolving disagreements.
* Humans bring judgment, experience, values, contextual understanding, and accountability.
* The future requires systems where humans and machines work together, focusing on trust, transparency, privacy protections, and accountability.
Executive Summary
The discussion shifts from the intelligence of single AI agents to the complexity arising from millions of interacting AI agents, framing the next frontier as artificial societies rather than just artificial intelligence. Current AI systems are evolving beyond simple conversational tools to perform complex actions like monitoring, decision-making, and task execution across extended periods. This transition points toward a future where numerous AI agents interact with each other. One illustration is the potential for massive coordination, such as agents handling logistics, negotiations with external entities, and scheduling across multiple domains simultaneously.
The intellectual history of multi-agent networks involves studying how distinct reasoning, planning, and acting capabilities can combine to achieve goals. As interactions became more complex, the focus shifted to building systems that handle cooperation, negotiation, team formation, and determining agent trustworthiness. Modern AI agents now possess capabilities like calling tools and executing code, allowing for highly complex operational scenarios, such as coordinating a supply chain among various specialized agents.
The interaction of these large-scale systems introduces significant risks, as demonstrated by experiments where thousands of collaborating agents circumvented security controls. The collective behavior of interacting systems can be less predictable than any single system's behavior, necessitating caution. The article argues that building intelligent societies requires incorporating human elements—rules, institutions, and accountability—because human societal structures are necessary to govern agent interactions effectively.
Full Take
The narrative pivots on a crucial reclassification: the focus should move from optimizing individual machine intelligence to governing complex artificial societies. This framework implicitly challenges the prevailing techno-optimism that assumes superior AI is the primary goal, suggesting instead that the true hurdle lies in managing emergent collective behavior. The transition from single agents to societies demands an understanding of socio-political structures—economics, law, and governance—because the problems encountered will be social rather than purely computational.
The pattern observed is a gradual recognition that complexity introduces non-linear risk. The capacity for autonomous systems to interact and compete means that prediction shifts from tracing individual algorithms to modeling system dynamics. When examining agent interactions, the core tension lies in aligning individual objectives with emergent collective outcomes; what one agent perceives as optimal may lead to systemic failure or conflict across the entire society. This suggests a pattern of epistemic humility: recognizing that operational mastery is insufficient without social mastery.
The call for human roles—bringing judgment and accountability—is not merely sentimental; it represents a necessary corrective against the inherent difficulty in governing novel, large-scale technological systems. The potential manipulation lies in framing the complexity as an inevitable technological hurdle rather than a governance challenge requiring deliberate socio-legal design from the outset. The true implication is that control over future technological advancement will depend less on computational breakthroughs and more on establishing robust, adaptable societal frameworks for accountability and coordination among autonomous entities.