Evo-Devo Series, Part 6: Convergence — Different Paths, Same Solutions

26 Sep, 2025

Nature’s Echoes

Evolution doesn’t always invent something new.
Often, different lineages arrive at strikingly similar solutions to life’s challenges. This is convergent evolution:

• Birds, bats, and insects all evolved wings.
• Dolphins and bats both evolved echolocation.
• Eyes, in some form, have evolved independently at least 40 times.

When the constraints are the same, and the goals are survival, solutions converge.

The Red Queen Effect

Convergence often emerges through coevolution and arms races.
Predators get faster → prey get faster.
Viruses mutate → immune systems adapt.
Competitors race ahead, but in the end, they often settle into similar strategies, shaped by the same pressures.

This “running to stay in place” is known as the Red Queen effect. Rivalry doesn’t just drive difference—it funnels species toward shared survival tactics.

Convergence in AI

We see similar dynamics in artificial intelligence:

• Neural network architectures: CNNs, transformers, and even biologically inspired models tend to discover similar feature hierarchies.
• Adversarial robustness: Competing defenses often converge on comparable strategies, like smoothing gradients or augmenting data.
• GANs and adversarial training: Generators and discriminators push each other forward, often forcing convergence toward realism and detection stability.
• Scaling laws: No matter the training recipe, performance curves tend to align around predictable scaling patterns.

Different paths, same outcomes—because the constraints (math, data, compute) are universal.

When Convergence Helps (and Hurts)

Convergence can be a double-edged sword:

• ✅ Strengths:

  • Robustness through tested strategies.
  • Predictable, interpretable pathways.
  • Efficiency in solving recurring problems.

• ❌ Weaknesses:

  • Loss of diversity (monocultures are fragile).
  • Blind spots (shared strategies may share vulnerabilities).
  • Stagnation if every path collapses to the same solution.

In ecosystems and in AI, convergence provides stability—but at the cost of variety.

Designing for Convergence (Without Collapse)

The challenge for AI architects is not whether convergence happens—it will—but how to guide it:

• Encourage pluralism of approaches, even if outcomes align.
• Use adversarial pressure productively, to refine robustness.
• Watch for systemic vulnerabilities when everyone converges on the same playbook.

Convergence is inevitable, but collapse is not.

Closing Reflection

Evolution teaches us that constraints shape outcomes. Wings, eyes, echolocation—nature found them again and again.
AI is no different: whatever paths we take, many solutions will rhyme.

The question is: will convergence bring resilience, or fragility?

Coming Next: Divergence

In Part 7, we’ll flip the lens—exploring how lineages, and AI systems, split apart into radically different paths, fueling diversity.