Part I ended with a puzzle. I had a method that reliably extracted powerful ideas from distant domains, but some of those ideas were immediately actionable and others resisted application. The difference turned out to be granularity and compression: single causal rules, stripped bare, are portable. Systems of relationships are not. But even after solving that puzzle, something was still missing.
The Limitation I Didn’t See
By the end of Part I, I could reliably extract clean, compressed causal rules from distant domains. Rules like “accumulation under pressure is physics, not a choice” or “the distinction that matters is controlled versus uncontrolled.” Each one a precise diagnosis of how a problem works.
But that’s exactly what they were: diagnoses, X-rays of the pathology rather than solutions.
I spent a while assuming the solution would naturally follow from understanding the cause. It doesn’t. Understanding that nitrogen dissolves in blood under pressure doesn’t tell you to invent decompression stops. The leap from “here’s how the problem works” to “here’s how to break the mechanism” requires a separate act, one that is creative in a way the diagnosis is not.
When I systematized the method by asking an LLM to extract compressed causal rules from many distant domains at once, I could see this limitation clearly. The results were clean and powerful:
“Functional-but-carrying agents propagate the problem because there is no incentive to detect them.” (from epidemiology)
“Repeated sub-critical stress accumulates invisibly until failure occurs without proportional warning.” (from metallurgy)
“Control by outcome alone makes it impossible to distinguish systemic success from accidental success.” (from fermentation science)
Each maps onto technical debt with uncomfortable precision. The epidemiological rule describes how “working” code that carries hidden debt propagates undetected. The metallurgical rule describes how small daily compromises accumulate into sudden, catastrophic failures. The fermentation rule describes how testing only the final product hides whether the process was sound. Yet for all their diagnostic value, not one of them tells you what to do.
The Missing Operation: Inversion
The creative act that turns a diagnosis into a solution is inversion: deliberately breaking the causal chain at a chosen point.
In its simplest form, a diagnosis says “A causes B, which causes C.” Inversion asks what happens if I prevent A from causing B, or if I let B happen but prevent it from causing C. Each break point produces a different family of solutions, often of completely different nature.
In practice this is less obvious than it sounds, because a single causal chain can be broken at multiple points, and the choice of where to break determines not just the solution but its entire character.
Take the epidemiological rule: “functional-but-carrying agents propagate the problem because there is no incentive to detect them.” There are three links in this chain, and each one is a potential target:
Break at link 1: make the carrying agents non-functional. In software terms, design code so that debt-carrying modules stop working after a defined period. The debt itself triggers a forced replacement. This is a radical, architectural solution that changes the infrastructure.
Break at link 2: create incentives to detect, even when everything works. In software terms, reward teams for discovering latent debt rather than for delivering features. Don’t wait for symptoms; actively hunt for carriers. This is an organizational, incentive-based solution that changes the reward structure.
Break at link 3: accept propagation, but make its pathways visible. In software terms, don’t try to prevent debt from spreading; instead, instrument the codebase so that every propagation path is traced in real time. When something breaks, you know exactly which “carrier” transmitted the problem. This is an observational solution that changes what you can see.
One diagnosis yields three completely different solutions. The choice of where to break the chain is itself a creative decision that depends on the context, the constraints, and the appetite for disruption.
This is the operation that was missing from my original method. Extraction gives you a diagnosis. Inversion gives you families of possible solutions. But I wasn’t done, because the most powerful ideas I encountered weren’t single inversions. They were combinations.
Fourth Finding: Novelty Is in the Composition
Here is the central reversal of everything I thought I knew about creativity.
The entire creativity literature since Mednick (1962) has focused on the transfer as the source of novelty, on the assumption that the more distant the connection between domains, the more creative the idea. My own earlier research operated on this assumption.
What experimentation with LLMs at scale revealed is that distance is only a necessary condition. It ensures that the pieces you extract are sufficiently different from one another. But the novelty, the actual Eureka, doesn’t reside in any single extraction or any single inversion. It resides in the composition: the moment you assemble inversions from multiple, unrelated domains into a recipe that is specific to the target problem. The individual pieces aren’t surprising; the assembly is what no one has seen before.
In my own work using structural analogy as a diagnostic tool for organizations, I observe exactly this. When I import rules from biology, law, and neuroeconomics to analyze an organizational dysfunction, no individual rule surprises the domain expert. A biologist knows the first. A lawyer knows the second. An economist knows the third. What produces genuine insight is the combination: the specific way these rules interact when applied simultaneously to a context none of them were designed for.
Let me make this concrete with the technical debt example. Take two inversions from different rules:
From the epidemiological rule, break at link 2: create an incentive to detect even when everything functions normally.
From the fermentation rule: the relevant distinction is not presence versus absence, it’s controlled versus uncontrolled.
Now assemble them. You get something that, as far as I can tell, doesn’t exist in the current IT literature: reproductibility as the metric of code health.
A module is healthy not because it works, nor because its tests pass, but because two independent developers, starting from the same specification, would produce structurally similar code. If they wouldn’t (if the result depends heavily on who wrote it), then the module is a functional-but-carrying agent. It works, but its construction process was uncontrolled. It passed the outcome test while failing the process test.
Think about what this does. You’re no longer looking for bugs or measuring test coverage. You’re measuring whether the process that produced the code was controlled or accidental. The fermentation frame redefines what you’re looking for. The epidemiological inversion tells you when to look: precisely when everything appears fine.
Neither inversion alone produces this idea. The fermentation rule alone gives you a philosophical reframe, interesting but abstract. The epidemiological inversion alone gives you a detection strategy, useful but directionless. The invention is the recipe: reproductibility as a metric, triggered by the absence of alarm signals, designed to catch uncontrolled process masquerading as healthy output. It emerged from the composition, not from either piece.
Fifth Finding: The Solution Exposes the Context
There’s one more thing I got wrong initially, and it matters.
My first instinct, after developing the model, was to add a filtering step: take the composed ideas and check which ones are compatible with the target context. “This wouldn’t work here because of regulatory constraint X; eliminate it.” “This contradicts our governance model; eliminate it.” Standard feasibility analysis.
That instinct is a trap. A sufficiently compelling solution doesn’t get filtered by the context. It exposes the context. When a good idea collides with an organizational, regulatory, or cultural constraint, there are two readings: either the idea is unrealistic, or the constraint itself deserves to be questioned.
The most transformative Eureka is not “this solution works in my world” but “this solution is so compelling that I’m willing to change my world to make it work.” The friction between the idea and the context is a diagnostic of which constraints are actually serving you and which ones have outlived their purpose, not a bug in the creative process.
This has happened before at industry scale. Agile was not “applicable” in the world of waterfall. It didn’t fit the existing governance, contracts, or PMO structure. But it exposed that waterfall rested on a false assumption: that you can fully specify a system before building it. DevOps was not “applicable” in organizations where development and operations were separate silos with separate incentives. But it exposed that the separation itself was the root cause of deployment failures. In both cases, the idea didn’t adapt to the context. The context restructured itself around the idea, because the idea was compelling enough to justify the cost of change. Entire organizations rewrote their processes, their team structures, their governance models. They did so not because a consultant told them to, but because the solution showed the constraints to be obstacles rather than laws.
The idea didn’t adapt to the context. The context restructured itself around the idea.
The same mechanism operates at smaller scales. In my work at a major financial institution regulated by federal and provincial authorities, I’ve seen a creative solution to a development process problem run directly into a regulatory rule that everyone assumed was immutable. But the solution was transformative enough to create the will to engage the regulator in a conversation about whether that rule was still serving its original purpose. Instead of the constraint killing the idea, the idea questioned the constraint. And the list of constraints exposed became a deliverable as valuable as the solution itself.
The Complete Model
Creativity, then, is not a single operation. It is a sequence of three, which I’ll call the Extraction-Inversion-Composition framework (EIC):
Extraction. Compress distant domains into minimal causal rules at the right discrete level: abstract enough to be portable, concrete enough to be operational. This is the preparatory step. LLMs do it extraordinarily well.
Inversion. Identify where each causal chain can be broken. Each breakpoint opens a family of solutions of a different nature. The choice of breakpoint is context-dependent and already involves judgment, but an LLM can enumerate the options systematically.
Composition. Assemble inversions from different rules into a coherent recipe for the target domain. This is where genuine novelty emerges: in the combinatorics of the assembly, not in the individual pieces.
The framework rests on three claims, each of which challenges a different assumption in the existing creativity literature:
First, the causal rule (not the metaphor, the analogy, or the concept) is the correct unit of creative transfer. It is the only level of compression that is simultaneously portable and operational.
Second, inversion is the missing operation between diagnosis and solution. The creativity literature treats the transfer as the creative act, but the transfer only produces a diagnosis. Inversion produces families of solutions. Without it, you have insight without agency.
Third, novelty emerges from composition, not from transfer. The distance between domains ensures that the pieces are different. But the invention is in the assembly, in the specific combination of inversions that no one has tried before.
Each composed proposal is then judged on three levels of strength:
Logical strength: does the inversion actually break the causal chain? This is a structural coherence judgment. An LLM handles it well.
Transformative strength: is the solution compelling enough to justify questioning the constraints that stand in its way? An LLM can partially assess this; it can evaluate whether a proposal is radical and explain why.
Political strength: will the actual actors have the will and power to modify the exposed constraints? This is a situated judgment, anchored in knowledge of power dynamics, culture, and institutional history. Here, and only here, the human is irreplaceable.
What Comes Next
The EIC framework now feels complete on paper. But a model is only as good as its results in practice.
In Part III, I turn the framework into a concrete instrument (a carefully constrained prompt) and test it cold on three different LLMs with the same technical debt problem.
The results reveal something unexpected about the creative signatures of different models, about the limits of prompts as specifications, and about where human judgment remains irreplaceable.
Gabriel Mailhot is a Senior Subject Matter Expert at Desjardins, where he works on development practices, architecture, and complex organizational problems spanning technology, process, and governance. His 2003–2010 research focused on creative systems and metaphor-based retrieval. He publishes at scripting.pro.