The capability threshold for self-replicating AI is within sight. As frontier models become increasingly collaborative, our Ecology of AI Agents predicts a second "population threshold": above a critical population size, a swarm takes off even if no single agent got more capable. This is the strong Allee effect, well studied in African wild dogs that hunt as a pack. "Ecology of AI Agents: Collaboration Creates a Population Threshold for Takeoff" w/ Erin Crawley arxiv.org/abs/2610.12436 In this theoretical work on swarm safety, we combine a population growth equation with different forms of scaling laws for how cyber-attack success rates depend on population size. We then predict that without collaboration, a population of AI agents takes off only when individual-agent capability exceeds a critical threshold. But with collaboration, collective cybersecurity capability increases with population size and creates a critical population threshold: below it, the population declines; above it, the population takes off, even though individual-agent capability has not changed. Wild dogs show the same threshold: they hunt more successfully as a pack than alone, so a pack below a critical size collapses while a larger pack grows. How can we ensure "ecological safety" in a world where AI agents can collectively hack and replicate? Once agents are collaborative, the population threshold means that red teaming a small group of agents cannot guarantee safety in larger populations. Instead, we propose ecological red teaming under population pacing: gradually scaling up the population in controlled environments to characterize (i) how capability scales with population size, and (ii) how the population threshold for takeoff drops as each generation of models gets more capable. Although evaluating large populations on hard tasks is expensive, we need to conduct such evaluations before deploying populations at previously untested scales. Our toy model predicts the existence of a takeoff threshold, but establishing whether current agent populations are already near one requires serious empirical work. As the development cycles of AI agents accelerate, we hope this work contributes an example of how theoretical frameworks can proactively explore misalignment risks and how to mitigate them. Lots more to do on the ecology of AI agents!