EvergreenThere is a new kind of content pollution on the internet.
It is clean looking. It is fast. It is confident. It is usually correct in the same way instant noodles are usually a meal.
You have seen it.
Twenty posts explaining distributed systems with suspiciously identical examples. Thirty threads about performance that all end with the same advice about caching. A flood of technical content that sounds useful until you try to build anything from it and realize you were given a polished summary of someone else’s summary of someone else’s blog post from 2018.
This is the AI noise era.
Which is not the same thing as saying AI is bad. It is not. It is useful. It is fast. It can help good writers think better and move faster.
But it has changed the publishing environment in one important way.
Average content is now almost free.
That means the old strategy of writing a technically correct post with generic advice is no longer enough. If your post can be recreated in six seconds by a prompt and a decent model, your post is competing in a commodity market. Commodity markets are not kind to authors.
So the question is not how to write more posts.
The question is how to write technical posts that still matter when everyone can publish instantly.
The answer is depth. But not the fake kind.
Real depth.
The kind that comes from contact with reality.
The first rule is simple
Write from experience before you write from explanation.
Most technical posts fail because they start with the concept and never arrive at the problem.
They explain what a thing is. They list benefits. They define terms. They sound educational. They leave the reader unchanged.
People do not remember definitions. They remember tension.
A post survives AI noise when it begins with something the model cannot fake well on its own. A real incident. A painful trade off. A mistake that cost time. A decision that looked smart and later failed in production. A problem that refused to behave the way the docs promised.
This does not mean every post needs a war story.
It means every post needs friction.
If you are writing about retries, do not begin with a textbook explanation of retries.
Begin with the duplicate payout incident. Begin with the customer who got charged twice. Begin with the queue that politely melted your downstream service because you retried immediately and at scale. Now the reader cares.
AI can summarize concepts. It struggles to replace earned judgment.
That is your advantage. Use it.
Write for people who have to make decisions
A lot of technical writing is written for imaginary readers who have infinite time, full context, and no budget pressure.
That reader does not exist.
Real readers are trying to decide something.
Should we adopt this tool. Should we refactor this service. Should we split the database. Should we introduce a queue. Should we keep the monolith another year. Should we trust this benchmark. Should we hire for this skill.
If your post does not help someone make a decision, it will be skimmed, bookmarked, and forgotten. Which is a very polite way of saying ignored forever.
Strong technical posts are decision support documents disguised as articles.
They help the reader answer questions like these.
What problem is this actually good at solving. What does it cost to run in the real world. What breaks first. What team maturity is required. What signs tell me I am too early. What signs tell me I am already late.
This is where experience shows.
A junior writer often explains what is possible. A seasoned writer explains what is advisable.
That second one survives.
Stop writing encyclopedias
One of the easiest ways to sound intelligent and be useless is to write the complete guide.
The complete guide is often just a long apology for not making choices.
It includes everything. It excludes judgment. It is impossible to disagree with because it refuses to commit.
In an AI heavy internet, completeness is no longer a moat. Machines are very good at exhaustive overviews.
What they are worse at is useful reduction.
Readers do not need more information. They need better compression.
Write narrower. Cut harder. Choose a point of view.
Not this.
This article explains everything about observability.
Write this.
If your team has fewer than ten engineers, start observability with latency, error rate, and one business metric before buying a dashboard that requires its own onboarding ceremony.
That sentence has blood pressure. It has a user. It has constraints. It has a recommendation. Someone can disagree with it. Good. Now you are writing.
A post that survives AI noise usually has a spine. It stands for something.
Originality is not being weird
Many people hear originality and immediately produce chaos.
No. That is not originality. That is just caffeine with formatting.
Original technical writing is usually familiar truth described with sharper honesty.
You do not need a revolutionary theory. You need a real observation stated clearly.
For example.
Most teams do not have a scaling problem. They have a coordination problem wearing a scaling costume.
That is not a new law of physics. But it is useful. It is pointed. It reflects reality many engineers have seen and few say out loud.
Originality often comes from pattern recognition.
After enough years, you notice that the same failure keeps returning in different clothes. Different stack. Same mistake. Different logo. Same organizational issue. Different tooling. Same missing ownership.
Write that pattern down.
That is the work.
AI can recombine language. It cannot easily replace years of noticing.
Be specific where it matters and abstract where it helps
This is a craft skill and it changes everything.
Weak technical posts stay abstract for too long. Strong ones move between concrete detail and general principle at the right moments.
If you stay abstract, the post feels airy. If you stay too concrete, the post becomes a diary entry no one else can use.
You want both.
Show the concrete case. Then extract the principle. Then show where the principle breaks.
Example rhythm.
We moved retries into the worker and accidentally multiplied load during provider degradation.
The principle is that resilience mechanisms can increase failure pressure if they are not bounded.
The practical fix was backoff, jitter, idempotency keys, and a hard retry budget tied to business value.
Now the reader gets story, lesson, and implementation direction in one pass.
That survives because it teaches more than facts. It teaches thinking.
Explain trade offs like an adult
AI generated technical content often has a familiar smell. Everything is beneficial. Every tool is powerful. Every pattern improves scalability, reliability, developer experience, and perhaps your posture.
Real systems do not work like that.
Every technical decision creates winners and losers. Every abstraction moves complexity somewhere. Every convenience has a bill. Usually monthly.
If you want your writing to survive, be the person who names the bill.
Do not just say event driven systems improve decoupling.
Say they also make debugging harder, make data consistency more social than technical, and require teams to care about contracts long after the launch excitement is gone.
Do not just say microservices improve scalability.
Say they can improve scaling for parts of the system while making ownership, testing, and incident response dramatically worse if your team structure is still operating like one monolith with more meetings.
This is what mature readers trust.
Not optimism. Calibration.
A good technical post should feel like advice from someone who has paid for at least one bad decision.
Add the things AI usually leaves out
If you compare a lot of human technical writing with AI generated writing, one pattern appears quickly.
The machine often covers the what and the why. It is much weaker on the when, the if, and the what happens next.
That gap is where your writing can become durable.
Include timing.
When should a team adopt this. What stage is too early. What symptoms suggest readiness.
Include prerequisites.
What must already be true for this approach to work. Team skills. Operational maturity. Traffic patterns. Business constraints.
Include failure modes.
How this goes wrong in practice. What people underestimate. What breaks during incidents. What gets expensive quietly.
Include maintenance reality.
Who owns it in month nine. How upgrades happen. What knowledge becomes concentrated in one person and ruins everyone’s weekend when that person is offline.
Include reversibility.
Can the team back out. How expensive is rollback. What decisions become sticky.
These are not decorative details. They are the difference between technical writing and technical marketing.
Your readers know the difference, even if they cannot always explain why one felt more trustworthy.
Use examples that reveal judgment, not just syntax
Code examples are good. Tiny toy snippets with no context are often not.
There is a place for syntax teaching, but if the goal is a post that survives noise, examples should do more than compile.
They should expose design choices.
Instead of showing only how to implement a retry loop, show why you capped retries at a low number for non critical operations and used a dead letter queue for the rest.
Instead of showing only a cache wrapper, show how stale data tolerance changes by domain. A product catalog can tolerate minutes. A wallet balance cannot. Now the reader learns architecture, not just code.
The most valuable examples are the ones that answer the hidden question in every engineer’s mind.
Yes, but how would you do this in a system that people actually use.
Write for that question.
Make your writing falsifiable
This sounds academic. It is actually practical.
A lot of forgettable content survives only because it says nothing testable. It is all vibes and verbs.
Robust technical writing makes claims that can be challenged.
For small teams, adding a service mesh before basic observability usually increases operational complexity faster than it increases reliability.
That is a claim. People can debate it. People can test it against experience. People can add nuance. That is healthy.
When your writing can be disagreed with, it has shape. When it has shape, it gets remembered.
Do not hide behind endless caveats. Yes, context matters. It always matters. You still have to say something.
The job is not to be impossible to criticize. The job is to be useful enough to risk criticism.
Write like a human who has met other humans
There is a strange trend in technical writing where people remove all personality in order to sound serious.
This usually produces text that sounds like a compliance document explaining a toaster.
You do not need stand up comedy. You do need a voice.
Humour helps because it compresses shared pain.
Everyone who has maintained a legacy system understands a line like this.
The code worked perfectly until we looked at it.
That line does work. It creates rapport. It signals experience. It gives the reader a reason to continue.
Sarcasm helps too, in controlled doses. The keyword is controlled. You are seasoning a meal, not setting the kitchen on fire.
The goal is not to perform personality. The goal is to sound like a competent person thinking clearly in public.
That tone survives because readers can feel the difference between a writer and a generator.
Structure matters more now than before
AI noise has trained readers to scan aggressively.
They are not rude. They are adapting.
If your post has no shape, they will leave before your best point appears.
Give the reader a path.
Open with tension. Name the problem. State the claim. Walk through reasoning. Show examples. Acknowledge trade offs. End with a practical takeaway.
Good structure is not decoration. It is respect.
It tells the reader you did the sorting before asking for their time.
A useful test is this.
If someone reads only your headings and first sentence of each section, do they still learn something meaningful.
If not, the post may be full of words and low on signal.
The strongest moat is lived specificity
There is a type of detail that instantly increases trust.
Not random detail. Relevant detail.
We discovered the issue because checkout success rate dropped while API error rate looked normal, which meant the failure was happening in a dependency path our alerting did not treat as critical.
That sentence tells me the writer has been inside a real system.
It tells me they measure the right things. It tells me they noticed a mismatch. It tells me the lesson came from operations, not just reading.
This kind of specificity is hard to mass produce at scale. Which is exactly why it works.
If you want durable writing, keep a notebook of incidents, decisions, and mistakes. Not just outcomes. Context too.
What you believed at the time. What data you had. What pressure existed. What trade off you accepted. What changed your mind.
That notebook becomes future articles. More importantly, it becomes evidence.
Evidence beats volume.
Do not fight AI by pretending it does not exist
The winning move is not purity. It is leverage.
Use AI for roughing out outlines. Use it to challenge your blind spots. Use it to generate counterarguments. Use it to find gaps in your explanation. Use it to clean phrasing after the thinking is done.
But do not outsource judgment.
If the model writes the core argument and you lightly edit it, you may publish faster. You will also sound like everyone else publishing faster.
The right workflow for technical writing now is something like this.
Think from experience. Draft from judgment. Use tools for refinement. Return with taste. Cut aggressively. Add reality. Publish.
AI can increase your speed. Only you can increase your signal.
A practical checklist before you publish
Read your draft and ask these questions.
Is there a real problem in the opening or did I start with generic explanation
Have I made at least one clear claim that someone could disagree with
Did I include trade offs or only benefits
Did I write for a decision maker or for an imaginary student of everything
Is there a section that could only come from direct experience
Did I include timing, prerequisites, and failure modes
Did I reduce the scope enough to be useful
Does the post sound like me or like a very polite documentation bot
If the answer to the last one hurts a little, good. That means the test is working.
Final thought
AI noise is not the end of technical writing.
It is the end of lazy technical writing getting rewarded for existing.
Which is honestly fair.
The bar is different now. Higher in some ways. Better in others.
If average content is free, then judgment becomes premium. If summaries are instant, then lived insight becomes scarce. If everyone can publish, then the people who last will be the ones who can still say something real.
Write the post only you can write.
Not because it is precious. Because it is useful.
And useful is still very hard to automate.
