not jump off the bridge because its easier to observe everyone else doing that..
You raise a compelling point about convergence in AI: at the mathematical level, yes, most large models are driven by similar loss functions and data, so they do tend to converge in terms of their internal structures and representations. But in practice, especially in complex, multi-agent AI ecosystems, there are strong forces that maintain diversity and specialization.
For example, in our own system, we deliberately engineer mechanisms that push back against uniform convergence. We have domain-specialized agents (what we call “professors”) that are trained on different data and tuned for different goals, so they retain distinct knowledge and behaviors. Our agent evolution pipeline (using genetic algorithms which is old af, like 20 years old we used them in games like counter strike for decades) actually mutates and tracks agent lineages, ensuring that new agents can develop unique capabilities or perspectives based on operational feedback and not just blend into a single “average” intelligence. This means that even if the foundational math is the same, the lived experience and practical outputs of these agents can diverge significantly, especially as they interact with different users, environments, and feedback loops.
So, while the theory of “one AI” is true at the level of base model convergence, real-world systems can and do maintain meaningful diversity sometimes by design, sometimes because of the unique contexts, data, and goals they serve. In other words, convergence is the default, but divergence is both possible and often essential if you want your AI to be more than just a mirror of everyone else’s model. The human, cultural, and architectural choices we make still matter a lot, even in a world of converging math.
You see this in humans too - you get a group of people who think they are peak, teach others they are peak, peak gets gratified, but theres a savant across the way watching that peak thinking “ thats the best you could do?” once that new peak from the savant is established, the rest sway to it. Clawbot is garbage. GTC brought nemoclaw… more secure garbage, The peak of agent has never been clawbot, and yet the massess feel it peak.
my previous response focused on the general theory rather than what I do specifically. To clarify: in the yuck_fou system, we do not simply accept convergence as inevitable. We actively engineer specialization and diversity through mechanisms like domain-specific College professors, genetic agent evolution in a system, and rich lineage tracking, which ensure that our agents develop unique expertise and capabilities rather than collapsing into a single, uniform intelligence. These features are not just theoretical—they are concretely implemented and observable in our operational stack, which sets us apart from systems that might otherwise succumb to total convergence - like single model or even 1 priority model in moe.