What does it mean to write well when machines are part of your audience?
Machines now read your technical docs, product copy, and landing pages. They compare you to competitors, decide whether to recommend you — or skip you entirely. Some of them buy the product.
They evaluate all of this based on your writing.
Machines have made bad writing harder to fake
A human reader might give you a few sentences of grace. A machine won't. It either gets it or it doesn't.
This is a good thing. Whether you're writing API docs, an onboarding tooltip, a product changelog, or a blog post — it forces you to do what you should have been doing all along:
- Lead with the answer. If a reader (or a model) stops after your first paragraph, did they get the point? If not, you buried the lede.
- Make structure obvious. Answer engines extract meaning from how you organize information, not just what you say. Headings, clear paragraphs, logical flow — these aren't formatting choices, they're comprehension choices.
- Demonstrate authority. Buzzwords and jargon signal insecurity. Concrete examples and clear explanations signal that you actually know what you're talking about.
- Earn trust in the first sentence, not the fifth paragraph. Developers and AI systems alike are ruthless about this. Get to the point.
None of this is new advice. It's just that machines have raised the bar for following it.
AEO is the next version of SEO
AEO forces you to think about how meaning is extracted, not just how pages are ranked. You're not writing for someone who might click — you're writing for a system that might quote you, paraphrase you, or skip you entirely in favour of someone who said it more clearly.
The bar is different. With traditional search, you could get away with a decent title and some keyword density. With answer engines, the content itself has to hold up.
- Structure matters.
- Clarity matters.
- Authority matters.
Your content is your best marketing. If an answer engine can't extract a clear, trustworthy response from what you've written, it won't surface you. No amount of backlinks or keyword optimization saves you from unclear writing.
My checklist for writing that works for both
It's not complicated — most of it is just good writing discipline. But having it written down keeps me honest.
On the page:
- Speak your customer's language, not yours. Keyword stuffing is dead. What works now is using the exact words your prospects use. This means digging into support tickets, hopping on sales calls, reading every customer review. The goal is to pick up their language and write in it — not the language your marketing team invented in a brainstorm.
- Build topical authority. Don't write one post about a topic and move on. Go deep. Cover the topic from every angle your audience cares about. High-signal sites that demonstrate genuine expertise in their domain perform better — always.
- Original insights win. The internet is drowning in rewritten versions of the same advice. If your content says something novel — your own data, your own experiments, a perspective nobody else has — it gets cited, shared, and remembered. Machines and humans both gravitate toward the thing they haven't seen before.
- One idea per section. If a section covers two topics, split it. Machines parse meaning per heading block. So do readers who are scanning.
- Headings that describe. Your H2s should read like a table of contents. If someone only reads your headings, do they get the gist? If not, rewrite them.
- Heading hierarchy matters. H1 for the page title. H2 for major sections. H3 for subsections. Never skip levels, never use a heading just because you want bigger text.
- Write like you talk. If you wouldn't say "leverage synergistic methodologies" out loud, don't write it. Plain language isn't dumbing things down — it's being clear on purpose.
- Short paragraphs, short sentences. Your readers are on a phone, distracted, impatient. Walls of text lose humans and confuse machines trying to extract a specific answer.
- Be specific, not superlative. Don't say your product will change someone's life. Say exactly which feature improves which workflow, and by how much.
- Back every claim with evidence. Stats, benchmarks, customer reviews, case studies — anything that can be vouched for. Unsupported claims get ignored by humans who are skeptical and by machines that are trained to surface trustworthy sources.
- Interlink. This is honestly one of the most underrated things you can do. Every page on your site should connect to related pages on your site. You're building a web of context that helps both readers and crawlers understand what you're about and how deep your coverage goes.
- Freshness matters more than ever. The AI world moves fast. Content can turn stale in weeks. That docs page you wrote six months ago? The API might have changed a gazillion times since then. You need to keep up — audit, update, and ensure you always have the latest and most useful content on your site.
Off the page:
- Distribution is half the battle. You could do everything right on-page and still lose. Off-page matters. You need to be present, spoken about, mentioned, linked to. Write guest posts, get quoted, show up in communities. The best content in the world doesn't work if nobody knows it exists — and answer engines pay attention to whether the rest of the internet trusts you, too.
For AI specifically:
- Think in tokens, not pages. LLMs don't read your entire 3,000-word article the way a human might. You need to convey your message precisely in the first 200 words or so. If the core value isn't there, the model has already moved on.
- Write for your ICP's reasoning path. LLMs personalize answers based on what they know about the person asking — their company size, industry, integrations, budget. Your content needs to match that reasoning. Be specific about who your product is for, who it's not for, and what makes it the right fit for your exact audience. The more precisely you define this, the more likely a model recommends you to the right person.
- Cover the fan-out. One user prompt spawns dozens of sub-queries behind the scenes — comparisons, integrations, compliance, alternatives, pricing. You don't need a page for every sub-query, but you need to cover the themes. If a model is looking for "how does X compare to Y" and you haven't written that page, your competitor gets the citation.
- Don't sleep on non-blog pages. Pricing pages, support docs, case studies, testimonials, "who it's for" pages — these are getting cited by LLMs now. SEO teams historically ignore them, but if a model is evaluating your product and your competitor has a case study in the right vertical and you don't, you lose.
It's a lot to keep up with. Every day there's a new tactic, a new framework, a new thing someone swears by on X. But I think the fundamentals are these — and they haven't changed as much as the noise would have you believe.
This only covers half the picture
There are two sides to writing for machines: discovery and implementation.
This post is about discovery — how machines find your content, evaluate it, and decide whether to surface you. Getting cited. Getting recommended. Getting chosen.
But there's a second side: writing so that an agent can actually use your product. Discover your API docs is one thing. Read them, call the right endpoints, pass the right parameters, handle edge cases — that's a different discipline entirely. It touches your docs, your SDKs, your error messages, your onboarding flows. Less about visibility, more about usability for a non-human user.
I'll write about that separately.