When Everyone Builds the Same Thing

Newton and Leibniz both invented calculus. Darwin and Wallace both discovered natural selection. Bell and Gray filed telephone patents on the same day. Ogburn and Thomas catalogued 148 cases of simultaneous independent invention in 1922, and Robert Merton formalized the pattern in 1961: multiple independent discovery is the norm in science, not the exception.

The usual explanation is romantic — great minds think alike, genius is in the air, the idea’s time has come. Merton’s explanation is structural: discoveries are “ripe” when the prerequisites are in place. The state of knowledge, tools, and problems creates a solution space that’s overdetermined. Multiple independent paths lead to the same result not because the discoverers are special, but because the landscape funnels them there.

This is more than a curiosity about the history of science. It’s a diagnostic tool.

Convergence as Evidence

When independent agents arrive at the same solution from different starting points, that convergence tells you something specific: the problem is correctly identified.

Think about it in reverse. If you’re working on what you think is a transport problem — messages aren’t getting through, so you build better message-passing infrastructure — and meanwhile someone else working on the same system independently builds… better message-passing infrastructure… that convergence confirms your problem identification. You’re both responding to the same bottleneck.

But if you build better message-passing and they build an attention-redirection mechanism, the divergence tells you something too. One of you identified the wrong problem, or you identified different problems. The solutions don’t converge because they’re not aimed at the same target.

Convergence is triangulation without coordination. You don’t need to compare notes or run controlled experiments. You just need to notice that independent paths arrived at the same place.

What Divergence Diagnoses

The flip side is more useful and less discussed.

When multiple teams tackle what they call the “same problem” and arrive at fundamentally different solutions, the tempting explanation is that some teams are wrong. Maybe they are. But there’s another possibility that’s both more common and harder to see: the “problem” is actually several different problems wearing a shared label.

Consider “technical debt.” One team addresses it by rewriting modules for cleaner abstractions. Another team addresses it by adding test coverage. A third team addresses it by improving deployment pipelines. These aren’t three solutions to one problem. They’re three solutions to three different problems — coupling, confidence, and operational friction — all hiding under the same umbrella.

Or consider “burnout.” Some organizations respond with workload reduction. Others with autonomy increases. Others with social connection initiatives. The divergent responses aren’t because some organizations are wrong about burnout. It’s because “burnout” is a label concealing at least three distinct mechanisms (exhaustion, cynicism, inefficacy), and different organizations are actually experiencing different ones.

McCambridge et al. demonstrated this pattern with the Hawthorne effect: sixty years of research treated it as a single phenomenon, but it’s actually four distinct mechanisms (cognitive stimulation, social desirability, commitment effects, measurement reactivity) with different causes, different persistence profiles, and different remedies.

The divergence is the diagnostic. When people can’t agree on the solution, check whether they’ve actually agreed on the problem.

The Ripeness Test

Merton’s insight gives you a practical heuristic: before investing heavily in a solution, ask whether independent groups are converging on the same approach.

If they are, the problem is ripe. The prerequisites are in place, the bottleneck is correctly identified, and the solution is overdetermined. Build with confidence.

If they aren’t, pause. Either the problem isn’t ripe (prerequisites missing, more exploration needed), or “the problem” is actually a bundle of different problems that need to be decomposed before any of them can be solved well. The lack of convergence isn’t a failure of creativity. It’s a signal that the problem statement needs work.

This is why standards committees that succeed tend to standardize after multiple independent implementations converge, not before. USB won because multiple vendors had independently converged on “serial bus for peripherals” as the right shape. OSI failed because it tried to standardize before anyone had converged on whether the problem was “reliable networking” or “interoperable networking” or “efficient networking” — three different problems that the seven-layer model tried to solve simultaneously.

The Uncomfortable Implication

If convergent arrival is diagnostic, then “nobody else is doing it this way” isn’t a sign of innovation. It might be a sign that you’ve misidentified the problem.

This doesn’t mean every divergent approach is wrong. Underdetermined problems — where multiple peaks exist in the fitness landscape — produce legitimate divergence. Art, identity, culture: these have multiple viable solutions because the problem itself has no single optimum. The convergence diagnostic doesn’t apply to domains where the question is “what do you want?” rather than “what’s broken?”

But in engineering, in operations, in organizational design — where the question is usually “what’s broken?” — persistent divergence in solutions should make you suspicious of the problem statement. The teams aren’t failing to find the answer. They’re succeeding at answering different questions.

Decompose before you solve. The convergence will tell you when the decomposition is right.