Buzzwords are not understanding

Software engineering has a strange failure mode: the more abstract a discussion becomes, the easier it is to sound competent without saying very much. A proposal becomes “event-driven, cloud-native, AI-first, loosely coupled and scalable.” A team wants “platform engineering with shift-left observability.” An architecture is “DDD with clean boundaries and an asynchronous microservice approach.” Every word can describe something real. The problem begins when the words are doing the thinking.

The useful rule is not the familiar slogan that if you cannot explain something simply, you do not understand it. Taken literally, that is too strong. Some ideas are genuinely difficult, some audiences lack the prerequisites, and a simple explanation can be confidently wrong. The better rule is narrower and more useful for engineers: if you understand a technical idea, you should be able to unpack its label into a mechanism, an example, a trade-off and a boundary. You may still need technical language. You should not need it to hide the missing explanation.

Jargon is compression#

Good jargon is one of the most useful things experts invent. Saying “idempotent” is faster than saying “you can perform the same operation again without changing the final state after the first successful application.” Saying “eventual consistency” is faster than re-explaining a distributed replication model every time two engineers discuss it. Saying “circuit breaker” saves a paragraph when both people already know the failure-control pattern behind the name.

That is what technical language is for: compression. A short label stands in for a larger model that the people in the conversation already share. The label is not the knowledge. It is a pointer to the knowledge.

Labels can also hide a tidy but unsupported history. The software-term origin checks show how an early dated use can disprove a later coinage story without proving who first invented the term.

The distinction matters because the compressed form and the empty form can sound identical. An engineer who deeply understands event-driven architecture and an engineer who learned the phrase yesterday can both say, “We should make this event-driven.” The difference appears only when someone asks what that means for this system. Which state change becomes an event? Who publishes it? Who owns the schema? What happens when delivery is duplicated? What happens when a consumer is down for six hours? Which operations still need a synchronous answer? How will we debug one business transaction across the boundary?

If the phrase expands into concrete answers, it was compression. If it expands into more phrases, it was camouflage.

The decompression test#

A practical way to test understanding is to decompress the concept. Take away the fashionable label and see whether the model underneath still stands. For almost any software concept, six questions expose the difference quickly.

Consider “microservices.” A weak explanation says they are independently deployable services that improve scalability and team autonomy. Those are recognisable properties, but they are still close to the brochure. A decompressed explanation says something more operational: instead of one process owning several business capabilities together, we put selected capabilities behind separate runtime and deployment boundaries. Calls that used to be in-process can now cross a network. Data ownership has to be divided. Deployments can move independently, but cross-service changes need explicit contracts. A failure in another service is now a normal runtime condition. Observability has to reconstruct behaviour across processes. That can be worth it when independent change and ownership are valuable enough. It can be absurd for a small product whose main problem is simply shipping features.

For concrete follow-through, compare bounded contexts and who owns each model with events and what their delivery promises. These are two different questions hidden inside the same architecture label.

The second explanation is longer because the point of the test is not to produce the shortest sentence. It is to prove that the short sentence is backed by a working model.

What buzzword bingo looks like#

Buzzword bingo is rarely a single ridiculous sentence. More often it is a conversation that never reaches a mechanism. Each unanswered term is replaced by another term at the same level of abstraction.

“Why Kubernetes?” — “Because we need a cloud-native platform.”
“What does cloud-native buy us here?” — “Elasticity and resilience.”
“What has to become elastic?” — “Our microservices.”
“Which workload currently fails because it cannot scale independently?” — silence.

The individual claims may all be true in some system. The problem is that none of them has been connected to this system. The vocabulary keeps moving sideways instead of downward.

The same pattern appears around AI. “We should make the product agentic” can describe a legitimate architecture. But the useful discussion starts one level lower: what decision is the model allowed to make, what tools can it call, what state does it observe, what must be deterministic, what is the recovery path after a wrong action, and why is a model-driven loop better than a normal workflow for that part? Without those answers, “agentic” is not yet a design. It is a direction-shaped word.

Architecture is especially vulnerable because many architecture words name qualities rather than implementations. “Scalable,” “decoupled,” “modular,” “resilient” and “flexible” all sound positive in isolation. None tells you whether the proposal is good until the quality is attached to a workload, a change pattern, a failure mode and a cost. A system is not meaningfully “scalable” in the abstract. It scales something, under some load, by paying some price.

Simple does not mean shallow#

There is an easy mistake on the other side: treating all specialist language as pretentious and all simple explanations as superior. That fails too. Precision sometimes requires vocabulary. You cannot discuss transaction isolation seriously forever using only “the database keeps things separate.” At some point the difference between read committed, repeatable read and serializable matters. You cannot replace every discussion of covariance, consensus, backpressure or vector clocks with an analogy and expect the engineering decision to remain precise.

The goal is therefore not simple words at all costs. The goal is controlled abstraction: move up into concise technical language when it saves time, and move back down into concrete behaviour when the decision needs inspection.

Experts often sound simple not because the subject is simple but because they know which complexity matters. They can remove detail without removing the mechanism. That is harder than adding vocabulary. A weak explanation often includes every noun the speaker knows. A strong explanation chooses the few distinctions that change the conclusion.

This is also why “explain it to a five-year-old” is a poor literal standard for engineering knowledge. A five-year-old does not have the prerequisites for most engineering concepts. A much better target is: explain it to a competent engineer who has not worked in this specific area. You can assume programming, networks and databases. You cannot assume they share your local acronyms, vendor language or architecture fashion.

Use it in real engineering#

The decompression test is most useful where software teams are tempted to reward fluency instead of reasoning: architecture reviews, design documents, planning meetings, hiring and technical leadership.

In an architecture review, replace “Is this a good microservice architecture?” with “Which boundaries need to change independently, which calls become remote, who owns each piece of data, and which new failure modes are we accepting?” Replace “Is this event-driven?” with “Which fact is published, who can act on it later, and what happens when delivery is late or duplicated?” Replace “Is this scalable?” with “Which resource hits a limit first, at what load, and how does this design move that limit?”

In a design document, watch for adjectives with no observable consequence. “Fault-tolerant,” “flexible,” “enterprise-ready,” “future-proof” and “AI-powered” are not automatically bad words, but each should create a follow-up question. Tolerant of which failure? Flexible for which expected change? Future-proof against which plausible future? AI-powered where, making what decision, with which fallback?

Use the architecture decision record guide to turn those answers into a decision with context, rejected alternatives and accepted consequences. The reusable Markdown template gives you that structure without a signup; fill in the evidence you actually have rather than a verdict you have not yet reached.

In hiring, the test is even more revealing. Asking a candidate to define eventual consistency mostly measures whether they have heard the standard definition. Give them a small system instead: one user changes an address, two replicas temporarily disagree, and a read lands on the older replica. Ask what the user can observe, what guarantees the product actually needs, and how they would choose between stronger consistency and lower coordination. Vocabulary may appear naturally, but the score comes from the model underneath it.

And in leadership, clarity is not “dumbing things down.” It is part of the job. A senior engineer who can discuss a system only with other people who already know the same vocabulary is transferring terminology, not understanding. The more expensive the decision, the more important it becomes to expose the mechanism in language that the people responsible for the consequences can interrogate.

That is the practical version of “if you cannot explain it simply, you do not understand it.” Simplicity is not proof of understanding, and complexity is not proof of ignorance. But an engineer who genuinely understands a concept can usually move between levels: from the short expert label, down to the concrete mechanism, across to an example, and back up to the trade-off that makes the decision worthwhile.

The buzzword is allowed to stay. It just has to open.

Questions people also ask

5 QUESTIONS

▸ Does being able to explain something simply prove that you understand it?

No. A polished simple explanation can be memorised, copied, or wrong. Simplicity is a useful diagnostic, not proof. Real understanding survives follow-up questions about mechanism, edge cases, trade-offs, alternatives, and what evidence would change the decision.

▸ Are technical terms and buzzwords the same thing?

No. A technical term is valuable when it gives a precise shared name to a concept. It becomes empty jargon when the term is used instead of explaining the concept, especially when nobody can say what changes in the system because of it.

▸ Why is software engineering especially vulnerable to buzzword bingo?

Software has fast-moving trends, abstract systems, expensive decisions, and many concepts that are hard to inspect directly. That makes concise labels useful, but it also makes them easy to use as status signals or as substitutes for concrete reasoning.

▸ How can I test whether I really understand a technical concept?

Explain it without the label, describe the mechanism step by step, give a concrete example, name the main trade-off, describe one failure mode, and say when you would choose an alternative. If one of those collapses, that is the part to learn next.

▸ Should engineers avoid jargon in technical discussions?

No. Shared jargon can make expert communication much faster. The rule is not to ban specialised words; it is to be able to unpack them on demand. Compression is useful only when the underlying meaning is still available.