Max Headroom 
I’ve been thinking a lot lately about a species of carpenter ant that lives in the mountains around Jerusalem. These tiny insects might just hold the key to one of AI’s biggest challenges: alignment.
The ants in question are called Camponotus sanctus, and they do something remarkable that puts our most sophisticated AI systems to shame. When these ant colonies relocate, they face complex decisions: protection from predators, adequate nest size, proximity to food, and accessibility for the colony. The stakes are high—a poor choice could doom thousands.
But here’s what’s fascinating: Rather than relying on a single “superintelligent” leader or centralized command structure, the colony employs a democratic process where each ant in the search party makes its own decision based on potential sites it has evaluated. Individual ants assess different locations independently, and through their collective interactions, the colony consistently arrives at optimal solutions—even when no individual ant possesses complete information about all available options.
Researchers call this “majority concession”: When faced with conflicting preferences, the majority sometimes abandons its favored option to preserve colony unity, joining the minority rather than risking a split. This sophisticated collective behavior emerges without any central coordinator, representing a form of distributed intelligence that could revolutionize how we approach AI alignment.
Why Should We Care About Ant Democracy When We’re Building AI Systems?
The answer lies in the limitations of our current approach to AI alignment: reinforcement learning from human feedback, or RLHF.
RLHF has been transformative. It’s what makes ChatGPT helpful instead of harmful, what keeps Claude from going off the rails, and what allows these systems to understand human preferences in ways that seemed impossible just a few years ago. But as we move toward more autonomous AI systems—what we call “agentic AI”—RLHF reveals fundamental constraints.
The cost problem: Human preference data in RLHF is expensive and highly subjective. Getting quality human feedback is time-consuming, and the cost of human annotation can be many times higher than using AI feedback.
The scalability problem: RLHF scales less efficiently than pretraining, with diminishing returns from additional computational resources. It’s like trying to teach a child every possible scenario they might encounter instead of giving them principles to reason from.
The “whose values?” problem: Human values and preferences are not only diverse but also mutable, changing at different rates across time and cultures. Whose feedback should the AI optimize for? A centralized approach inevitably introduces bias and loses important nuances.
When Individual Intelligence Fails
The problems with individual-agent approaches aren’t just theoretical. We’ve seen them play out in real-world AI failures that should give us pause.
Consider Microsoft’s Tay chatbot in 2016. Designed to learn from interactions, Tay was quickly derailed by coordinated attacks feeding it offensive content. Lacking collective wisdom, Tay had no context or peer perspective to draw upon. Within 24 hours, this sophisticated AI system was posting inflammatory content, forcing Microsoft to shut it down.
Similar patterns appear across industries. Tesla’s Autopilot system, despite sophisticated algorithms, has been involved in accidents where the system misidentified obstacles. IBM’s Watson for Oncology began recommending unsafe treatments because it operated as an individual intelligence, lacking the collective wisdom and peer review that human medical communities rely upon.
These aren’t just implementation problems—they’re symptoms of a fundamental limitation in how we think about AI alignment.
The Double-Edged Sword of Human Swarms
Swarm intelligence in humans—sometimes called “human swarms” or “hive minds”—has shown promise in certain contexts. When groups of people are connected in real time and interactively converge on decisions, they can outperform individuals and even standard statistical aggregates on tasks like medical diagnosis, forecasting, and problem-solving. This is especially true when the group is diverse, members are actively engaged, and feedback is immediate and interactive.
However, human swarms are not immune to failure—especially in the moral domain. History demonstrates that collective intelligence can devolve into collective folly through witch hunts, mob mentality, and mass hysteria. Groups can amplify fear, prejudice, and irrationality while suppressing dissenting voices.
Research suggests that while collective intelligence can lead to optimized decisions, it can also magnify biases and errors, particularly when social pressures suppress minority opinions or emotional contagion overrides rational deliberation. In moral reasoning, human swarms can reach higher stages of development through deliberation and diverse perspectives, but without proper safeguards, the same mechanisms can produce groupthink and moral regression.
The Ant Colony Alternative
While individual AI agents struggle with these challenges, the carpenter ants of Jerusalem have been perfecting collective decision making for millions of years. Their approach suggests a radically different path forward.
Research suggests individual ants may choose incorrectly 43% of the time, yet the colony achieves up to 95% accuracy through collective decision making. This dramatic improvement emerges from the swarm’s ability to aggregate diverse information sources and cancel out individual biases and errors.
The mechanism is elegant in its simplicity. Each ant follows basic rules about quality assessment and communication, but the key lies in their interactions. When ants evaluate potential nest sites, they’re not just making individual judgments—they’re participating in a distributed computation that considers multiple perspectives simultaneously.
But the analogy has limits. Ant colonies are not prone to mass hysteria or moral panics; their “swarm intelligence” evolved to optimize survival, not ethics. Human swarms, by contrast, are deeply shaped by culture, emotion, and history—making our collective intelligence both a source of wisdom and a potential engine of harm.
Addressing AI Bias Through Swarm Intelligence
AI systems are often biased—sometimes due to historical data that reflects societal prejudices, sometimes due to intentional manipulation. These biases can reinforce discrimination, perpetuate stereotypes, and undermine trust in AI. Swarm intelligence offers a potential path to mitigating bias:
- Decentralization: By aggregating insights from diverse agents or nodes, swarm systems can reduce the impact of any single biased perspective.
- Dynamic feedback: Real-time interaction and consensus building can help identify and correct outlier or biased inputs.
- Human-in-the-loop: Swarm AI platforms that keep humans actively engaged in decision making can help ensure that a broader range of values and sensibilities are represented.
However, swarm intelligence is not a panacea:
- Human swarms can still amplify bias if the group is not genuinely diverse or if social pressures suppress dissent.
- Swarm AI systems require careful design to ensure transparency, diversity, and mechanisms for bias detection and correction.
- Decentralized learning can help reduce the risk of bias introduced by any single dataset or actor, especially when combined with technologies like blockchain for transparency and auditability.
The advantages of swarm intelligence extend far beyond simple error correction. When designed well, swarms can incorporate diverse perspectives, correct for individual errors, and even reach more ethical decisions. But without safeguards, they can also magnify collective blind spots and moral failings.
The Wisdom of Small Things
I keep coming back to those ants in the mountains around Jerusalem. Individually, they’re unremarkable—tiny insects with brains smaller than poppy seeds. But together, they solve problems that challenge our most sophisticated AI systems.
Their secret isn’t superintelligence—it’s collective intelligence. They show us that the most robust decisions often emerge not from individual brilliance, but from the patient interaction of many minds working together toward shared goals.
Yet, as humans, our collective intelligence is a double-edged sword. It can produce both wisdom and folly, justice and injustice. If we want to harness swarm intelligence for AI alignment and bias reduction, we must design our systems with humility, vigilance, and a deep understanding of both the promise and peril of the human swarm.
As we stand on the threshold of truly autonomous AI systems, perhaps it’s time we stopped trying to build perfect individual agents and started learning from the democracy of ants. The future of AI alignment may not lie in creating superintelligent systems, but in orchestrating not-so-intelligent ones into something greater than the sum of their parts.
The ants have been showing us the way for millions of years. Are we wise enough to follow their lead—and learn from our own history?
Artificial Intelligence, Research
Radar
