We had been arguing for forty-five minutes about the "right" way to structure our API response format.
One developer insisted on nested objects for clarity. Another demanded flat structures for simplicity. A third wanted hybrid approaches for different endpoints. Everyone had compelling reasons. Everyone cited best practices. Everyone was convinced their logic was objectively superior.
Then our product manager asked a simple question: "What problem are we actually solving?"
Silence.
We weren't solving a problem. We were defending logical frameworks we'd each internalized from different past experiences, different mentors, different architectural philosophies. We dressed our preferences in the language of objectivity—"clearly," "obviously," "logically"—but we were really just advocating for the patterns that felt right to us.
That's when I realized: developers don't argue about logic. We argue about which axioms to accept as true.
The Logic Trap
Developers worship logic. We're trained to think in boolean values, deterministic outcomes, and provable correctness. We believe—genuinely, deeply—that our technical decisions flow from pure reason, objective analysis, and rational evaluation of tradeoffs.
This belief is comforting. It's also mostly wrong.
Logic isn't a lens that reveals truth. Logic is a tool that processes inputs into outputs. The quality of logical reasoning depends entirely on the quality—and choice—of its starting assumptions. Change the axioms, and the "logical conclusion" changes with them.
When two developers reach different "logical" conclusions about the same problem, they're not disagreeing about logic. They're disagreeing about which assumptions to privilege.
The developer who insists REST APIs are "obviously better" than GraphQL isn't reasoning from pure logic—they're reasoning from a set of assumptions about what matters (simplicity, cacheability, statelessness) that someone else might not share. The developer who advocates for microservices isn't being more logical than the monolith advocate—they're just weighting different tradeoffs (scalability vs. complexity) differently.
Every technical opinion that feels "logical" to you is built on a foundation of assumptions you don't even realize you're making.
The Axioms We Don't See
The reason logic feels objective is that we're blind to our own axioms. We don't experience them as choices—we experience them as obvious truths about the world.
"Clean code is always better." This feels self-evident until you're at a startup racing to validate market fit, where clean code might mean death by premature optimization. The axiom that "code quality matters most" is situational, not universal—but developers who internalized it in stable enterprise environments might never question it.
"Performance should be measured in milliseconds." This feels logical until you're building for regions with unreliable connectivity, where availability matters more than latency. The axiom that "fast is good" assumes a context where fast is actually the binding constraint.
"Explicit is better than implicit." This Python principle feels like pure wisdom until you're maintaining a codebase where explicitness created so much boilerplate that the actual business logic disappeared. The axiom that "clarity through explicitness" is always superior ignores the clarity that comes from removing noise.
"Code should be self-documenting." This feels obvious until you're reading undocumented legacy code that made perfect sense to its author but is incomprehensible to everyone else. The axiom that "good code needs no documentation" only works if everyone shares the same mental models.
These aren't absolute truths. They're contextual heuristics that became invisible axioms through repetition and social reinforcement.
The Cultural Logic We Inherit
Most of your "logical" conclusions aren't derived through careful reasoning—they're absorbed from the engineering cultures you've been embedded in.
If you started your career at Google, you internalized axioms about scale, automation, and testing that feel objectively correct but are actually optimized for Google's specific context. If you came up through startups, you absorbed different axioms about speed, pragmatism, and acceptable technical debt.
The senior engineer who says "we should never ship without 80% test coverage" isn't being more logical than the one who says "ship fast and fix bugs in production." They're applying different axioms about what constitutes acceptable risk.
The developer who insists on extensive code review isn't objectively correct—they're privileging quality and knowledge sharing over velocity. The developer who advocates for moving fast and breaking things isn't reckless—they're privileging learning and iteration over stability.
Both positions are logical given their axioms. Neither position is objectively true without context.
The Invisible Trade-Offs
Every technical decision is a tradeoff between competing values. But we rarely make these tradeoffs explicit. Instead, we argue as if our preferred tradeoff is the only logical choice.
Consistency vs. Flexibility: The developer who demands strict coding standards is prioritizing consistency. The one who argues for flexibility is prioritizing adaptation. Both are logical. Neither is universally correct.
Abstraction vs. Concreteness: The engineer who builds elaborate abstraction layers values reusability and maintainability. The one who prefers concrete implementations values clarity and simplicity. Both have valid logical foundations.
Innovation vs. Stability: The team that advocates for cutting-edge technologies values learning and competitive advantage. The team that sticks with boring, proven tech values reliability and reduced cognitive load. Both are making rational choices based on different risk tolerances.
The conflict arises not from flawed logic, but from unexamined assumptions about which values matter most. When we make these axioms explicit, technical arguments often dissolve—not because someone was wrong, but because everyone was optimizing for different things.
When "Best Practices" Become Dogma
The most dangerous axioms are the ones we call "best practices"—because calling something a best practice implies it's objectively superior, when it's usually just widely applicable within certain contexts.
"Never use global state." Best practice in large applications with many developers. Potentially premature complexity in a 500-line script. The axiom that "global state is bad" is context-dependent, not universal.
"Always use semantic versioning." Valuable for libraries with many consumers. Potentially meaningless overhead for internal tools. The axiom that "versioning discipline matters" depends on your ecosystem.
"Prefer composition over inheritance." Generally good advice in object-oriented programming. Sometimes creates unnecessary complexity when inheritance is genuinely the right model. The axiom that "composition is always better" isn't logic—it's a heuristic that works most of the time.
When best practices become unquestioned axioms, they stop being useful guidelines and become obstacles to clear thinking. The developer who can't articulate why a best practice applies in their specific context isn't thinking logically—they're following rules.
The AI Mirror
Working with AI has made this blindness to axioms painfully obvious. When you ask Claude Sonnet 4.5 to solve a problem one way and GPT-5 suggests a completely different approach, you're not seeing one correct answer and one wrong answer. You're seeing different axioms in action.
Claude might prioritize safety and correctness. GPT might prioritize creativity and flexibility. Both are being "logical" according to their training objectives. Neither is objectively right.
The same thing happens when you use tools like the AI Fact-Checker or AI Literature Review Assistant—different models will emphasize different aspects of truth depending on their underlying assumptions about what makes information reliable.
This is exactly what happens in technical debates between developers. We're not arguing about facts or logic. We're arguing about which framework of assumptions should guide our interpretation of those facts.
Using platforms like Crompt AI to compare multiple AI perspectives makes this explicit: the "right answer" often depends on which assumptions you bring to the question. The value isn't in finding the one correct response—it's in understanding how different starting points lead to different but equally valid conclusions.
The Developer Who Gets This
The most effective developers I've worked with aren't the ones with the strongest logical reasoning. They're the ones who can articulate their own axioms and understand when those axioms don't apply.
They don't say "this is obviously the right approach." They say "given these constraints and these priorities, this approach makes sense—but if we were optimizing for something different, we might choose differently."
They don't argue that their solution is objectively better. They explain which tradeoffs they're making and why those tradeoffs seem appropriate for the current context.
They treat their logical conclusions as conditional—true given certain assumptions—rather than absolute.
This doesn't make them wishy-washy or uncertain. It makes them precise. They're clear about what they believe and why, but they're also clear about the boundaries of that belief.
The Practice of Questioning Axioms
Developing awareness of your own axioms is uncomfortable because it requires doubting things that feel obviously true. But it's learnable.
Before making a technical argument, list the assumptions it depends on. If you're advocating for microservices, what are you assuming about your team size, deployment capabilities, and organizational structure? If those assumptions don't hold, does your conclusion still follow?
When someone disagrees with your "logical" conclusion, try to identify the axiom mismatch. Don't ask "why don't they see the obvious logic?" Ask "what are they assuming that I'm not?" Often the disagreement evaporates once the underlying values are made explicit.
Deliberately argue the opposite position. If you think approach A is clearly better than approach B, spend thirty minutes building the strongest possible case for approach B. This forces you to see which axioms make B preferable, revealing that your preference for A wasn't logic—it was a choice of values.
Use tools like the AI Debate Bot to challenge your technical positions. Having AI argue against your conclusions reveals assumptions you didn't know you were making. The Sentiment Analyzer can help you identify when your language reveals hidden certainty about debatable axioms.
The Meta-Axiom
Here's the uncomfortable truth: even the principle that "we should question our axioms" is itself an axiom. It's not objectively true that explicit reasoning about assumptions leads to better outcomes—it's a value choice to prioritize examined beliefs over intuitive expertise.
Some contexts genuinely benefit from operating on trusted heuristics without constant philosophical interrogation. Sometimes the developer who just builds the damn thing based on pattern recognition outperforms the developer who's paralyzed by epistemic uncertainty.
The point isn't to eliminate axioms—it's to see them for what they are: choices, not truths.
When you argue that your technical approach is "more logical," you're really arguing that your axioms are more appropriate for the context. That's a legitimate argument to make. But it's a different argument than claiming logical superiority.
The Shift in Technical Discourse
Once you understand that logic is always downstream of axioms, technical debates change fundamentally.
Instead of: "Your approach doesn't make sense."
Try: "I'm optimizing for X, and your approach seems to deprioritize X. Can you help me understand what you're optimizing for instead?"
Instead of: "That's obviously the wrong choice."
Try: "That choice seems to assume Y, but in our context I think Z matters more. Am I missing something about why Y is important here?"
Instead of: "Any competent developer would choose A over B."
Try: "In my experience, A tends to work better when you care about [specific outcome]. What outcomes are you prioritizing that make B more attractive?"
This isn't weakness or relativism. It's precision. You're still advocating strongly for your position—but you're being honest about what that position depends on.
The Real Question
The next time you're in a technical argument that feels like it's about logic, ask yourself:
"What am I assuming is true that someone could reasonably believe isn't true?"
If you can't identify any assumptions—if your position feels like pure logic with no underlying beliefs—you're not seeing your own axioms. Which means you're not actually thinking as clearly as you think you are.
The developers who advance fastest aren't the ones with the most rigorous logic. They're the ones who can see their own axioms clearly enough to adapt them when the context changes.
Logic is a powerful tool. But like any tool, its utility depends entirely on understanding when and how to apply it—and having the humility to recognize when your application is based on choices, not certainties.
-ROHIT V.
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