The Jargon Trap: Why Engineers Can Never Seem to Speak Plain Language

An Industry-Wide Problem Behind a Simple Complaint

"Please speak plain language — I have no idea what you're saying." This line, which sparked a discussion on Hacker News, captures a long-standing yet rarely addressed problem in the tech industry: a communication chasm between technical practitioners and ordinary people, built from terminology, acronyms, and jargon.

The discussion wasn't exactly viral (18 upvotes, 9 comments), but it struck a nerve with many tech professionals. Because nearly every engineer, product manager, or architect has faced this exasperated request — in meeting rooms, in front of clients, or while trying to explain their work to family. The problem isn't that the other person isn't smart enough; it's that we habitually wrap perfectly explainable ideas in a language only insiders understand.

How Tech Jargon Forms

A Byproduct of Efficiency

Specialized terminology exists for good reason. When two database experts discuss "sharding" or "eventual consistency," these terms carry precise technical meanings that dramatically improve communication efficiency. Among peers, jargon functions as a highly efficient compression codec — a single word conveys a wealth of shared understanding.

Take "sharding" as an example: it's a strategy for splitting a large database into smaller pieces distributed across different servers. It was first widely adopted at massive-scale internet services like Google and Facebook to solve the bottleneck of a single server being unable to handle enormous volumes of data. "Eventual consistency" comes from the CAP theorem in distributed systems theory (which states that consistency, availability, and partition tolerance cannot all be achieved simultaneously), referring to a system that allows brief data inconsistencies but guarantees eventual convergence. These two concepts are instantly understood among database experts, but for non-technical people, they require substantial prerequisite knowledge — which perfectly illustrates the double-edged nature of specialized terminology.

The problem is that this encoding only works when both parties share the same "decoder." The moment the other person in the conversation lacks the relevant background, these efficient terms become pure noise. Engineers often don't realize they've switched to the "internal channel," because in their daily work, this language is their native tongue.

Identity and Technical Gatekeeping

More subtly, jargon sometimes mutates into an identity marker and power symbol. Using complex terminology can project a professional image and even unconsciously erect exclusionary barriers. When someone says "we need to refactor the observability pipeline for this microservice" instead of "we need to make it easier to see what's going wrong with this system," the former sounds more authoritative — but also more alienating.

The two concepts involved here are themselves quite illustrative. Microservices architecture is a design pattern that breaks an application into multiple independently running small services, rising to prominence in the mid-2010s with Netflix and Amazon as early practitioners. Observability is a concept borrowed from control theory into software engineering in recent years, encompassing three pillars — logs, metrics, and traces — with the goal of enabling operations teams to infer a system's internal state from its external outputs. These terms are everyday language in DevOps and SRE (Site Reliability Engineering) circles, but stacked in a single sentence, they form a nearly impenetrable semantic barrier for outsiders.

This tendency is particularly prevalent in the tech industry. Some commenters pointed out that jargon abuse sometimes serves precisely to mask unclear thinking — people who truly understand something can often explain it in the simplest language.

Why Speaking "Plain Language" Is So Hard for Engineers

The Curse of Knowledge Erases the Beginner's Perspective

There's a concept in cognitive science called the "Curse of Knowledge": once you've mastered a piece of knowledge, it becomes nearly impossible to imagine how someone without that knowledge thinks. This concept was formally introduced by economists Colin Camerer and others in a 1989 study, later popularized by Stanford's Elizabeth Newton in her famous experiment — participants tapped out a song's rhythm on a table and predicted 50% of listeners would guess the song, but the actual success rate was just 2.5%. The tappers, with the melody already playing in their heads, simply couldn't understand why listeners couldn't hear it.

Engineers immersed in technical details for years have long forgotten the state of not understanding these concepts. This cognitive bias is particularly deadly in technical communication because technical knowledge is often layered: to understand container orchestration, you first need to understand containers; to understand containers, you need to understand virtualization; to understand virtualization, you need to understand basic operating system principles. Experts have internalized these layers as intuition and find it difficult to reconstruct the step-by-step path a beginner needs.

This explains why "speaking plain language" is so difficult — it's not simply swapping out words, but actively performing cognitive perspective-taking, rebuilding an explanatory path that starts from the other person's existing knowledge. This requires extra mental effort, and in fast-paced work environments, people tend to default to the path of least resistance.

Translating from Abstract to Concrete Is an Advanced Skill

Converting abstract technical concepts into concrete everyday language is itself an advanced skill. It requires the speaker to first truly grasp the essence of a concept, then find appropriate analogies and scenarios. For example, explaining "cache" as "keeping frequently used things within arm's reach so you don't have to go to the warehouse every time," or explaining "API" as "a waiter at a restaurant who relays messages between the kitchen and your table."

This methodology aligns with physicist Richard Feynman's teaching philosophy. The core steps of the Feynman Technique are: choose a concept, try to explain it as if teaching a child, identify where you get stuck, go back and relearn, then simplify and re-explain. Feynman himself was famous for making deep physics concepts like quantum electrodynamics vivid and accessible. He once said, "If you can't explain it in simple terms, you don't understand it well enough." In the field of technical writing, this principle is equally revered — excellent technical documentation often requires the author to make multiple transitions from concrete to abstract and back to concrete.

People who can do this tend to have a more thorough understanding of the technology itself. This is also why great developer advocates and educators are so rare — clear expression is the externalization of deep understanding.

From Communication Struggles to Career Competitive Advantage

Technical Communication Is an Undervalued Core Competency

In the tech world's value hierarchy, coding ability and architecture design are often placed at the top, while communication is dismissed as a "soft skill." But the reality is that as careers progress, whether someone can explain complex problems clearly increasingly determines the upper bound of their influence.

In recent years, Silicon Valley's talent evaluation systems have undergone subtle but definitive shifts. Amazon's Leadership Principles explicitly include communication-related dimensions like "Earn Trust" and "Have Backbone; Disagree and Commit." Google's internal research initiative Project Aristotle, through analysis of over 180 teams, found that the most critical characteristic of high-performing teams wasn't individual members' technical ability, but psychological safety and clear communication. At Staff Engineer level and above, "ability to influence cross-team decisions" and "ability to clearly convey technical trade-offs to non-technical stakeholders" have become hard evaluation criteria in promotion reviews. Technical depth is the entry ticket, but communication ability determines the ceiling.

Whether advocating for resources from leadership, explaining solutions to clients, or building team consensus, it all fundamentally comes down to a competition in the ability to "speak plain language." Those who can fluidly switch between technical depth and clear expression tend to take on more critical roles.

Practical Tips for Improving Technical Communication

For technical practitioners who want to improve in this area, here are several actionable principles:

• Assess your audience first: Before speaking, gauge the other person's knowledge level and switch to the appropriate level of abstraction. The same technical decision should be framed completely differently for a CEO versus a fellow engineer on your team.
• Use analogies and metaphors: Map unfamiliar concepts onto familiar everyday scenarios. A good analogy doesn't need to be 100% precise — it just needs to provide scaffolding for understanding.
• Proactively expand acronyms and terminology: The first time a term or acronym appears, always spell it out and explain. Don't assume the other person knows K8s is short for Kubernetes, and certainly don't assume they know what Kubernetes is.
• Check for understanding in real time: Observe the other person's reactions and confirm whether understanding has landed, rather than barreling ahead. A simple "Does that make sense?" is far more effective than an uninterrupted monologue.
• Self-check by simplifying: If you can't explain something in simple terms, it might mean you haven't fully thought it through yourself. Treat "speaking plain language" as a tool for testing the depth of your own understanding.

Conclusion: True Expertise Makes the Complex Simple

"Please speak plain language" — on the surface, it's a communication failure. At a deeper level, it reflects a cultural inertia in the tech industry. Jargon itself isn't inherently sinful; it's a lubricant for professional collaboration. But when it becomes the default mode of expression, or worse, devolves into a tool for obfuscation and showing off, it becomes an obstacle to delivering value.

In an era where AI technology increasingly permeates every industry and collaboration between technical and non-technical people grows ever more frequent, the ability to communicate clearly matters more than ever. As Large Language Models (LLMs) and generative AI tools become widespread, a new communication scenario is emerging: non-technical people need to interact with AI systems through natural language to accomplish tasks that previously required programming skills. The rise of prompt engineering is fundamentally about "how to make machines understand your intent through clear natural language" — which in turn trains people to express themselves more precisely and structurally. The boundary between technical and non-technical is blurring, and clear communication is the bridge connecting the two.

True expertise isn't about being incomprehensible — it's about making the complex simple.