When you polish your LinkedIn headline or your portfolio summary, you are usually writing for a person: a recruiter scanning for a few seconds, a hiring manager deciding whether to message you. That person still matters. But before they ever see you, something else often decides whether you show up at all. Some recruiters now describe who they want to an AI, and the AI picks which profiles to put in front of them. Someone searching your name may read an answer a machine assembled, without ever opening your page. In other words, the first thing reading your profile is increasingly software, and if the software cannot understand you, the person never gets the chance to.

Here is the reassuring part, and it is worth holding onto before anything else: writing for the software and writing for a human pull in the same direction. Both reward the same thing, clarity. You are not learning to write for robots at the expense of real readers. You are learning to be clear about what you do, which has always helped a human reader and now helps the machine that reaches them first. Everything below comes back to that.

So this is not a reason to panic, and it does not mean cramming your profile with keywords. It means understanding two shifts in how people and companies find information now, and writing so you come through clearly on both. Here is what is happening, and what to do about it.

The web is answering questions without sending anyone anywhere

Start with how people find information at all now. In the first four months of 2026, about 68 percent of Google searches in the United States ended without a click to any website. That figure comes from SparkToro, working with clickstream data from Similarweb, and it has been widely reported, including by Search Engine Land. It is up from around 60 percent two years earlier. Put plainly, for every thousand searches, fewer than three hundred send someone out to an actual page. The rest are answered on the results page itself, increasingly by an AI summary.

This matters to you because it changes what it means to be found online. If someone searches your name, or a skill you have, the answer they see may be assembled by software that reads pages and summarises them, without the person ever landing on your profile. If your LinkedIn summary or portfolio is written only for a human who has already arrived, it is written for a moment that happens less often than it used to. You also need to be understood by the software doing the summarising.

Some recruiters are now searching through an AI

The same shift is happening inside hiring, and this is the part that affects you most directly. LinkedIn has rebuilt how recruiters find people. Its recruiter tools now run on a system its engineers call MUSE, which reads profiles by meaning rather than by exact keyword. Instead of typing a rigid Boolean string, a recruiter can now describe who they want in plain language, something like a backend engineer with payments experience, open to remote work, and the system interprets that and returns people it judges to fit.

Read that carefully, because it changes the old advice. For years the guidance was to stuff your profile with exact keywords so a search would catch you. That still helps, but it is no longer enough. The system is now trying to understand what you do, so a profile that clearly explains your work in real language can be matched to a recruiter's description even when the exact words differ. A profile that is vague, or padded with buzzwords that do not describe anything concrete, is harder for that system to place, so you surface less often.

What this means for how you write about yourself

Both the software and the human want the same thing, clarity and specifics. Here is how to give them that.

• Say what you do, in plain words. A headline like “passionate technologist driving innovation” tells a person nothing and gives the software nothing to match. “Backend developer building payment systems with Node and Postgres” tells both exactly what you are. Concrete beats impressive every time, because concrete is what can be understood and matched.
• Write your summary as a clear description. Assume a machine may read it and use it to decide whether you fit a search, so state plainly what you build, the tools you use, and the kinds of problems you solve. A clear first two lines do more for you now than a clever one.
• Use the real words for your work. Name the languages, the tools, the domains, the kind of product. If you do quality assurance, say quality assurance and say what you test and how. The semantic systems understand real descriptions, and a human reading it gets a truer picture too.
• Make every claim specific enough to be checked. In place of “experienced in data,” name the databases, the scale, the sorts of problems you solved. Specifics are legible to software and convincing to people, and they are the thing generic profiles cannot fake.

Where this leaves you

The recruiter skimming for seconds and the AI ranking a thousand profiles are trying to answer the same question: what does this person do, and does it fit what I need. Answer that plainly, early, and in real words, and you make yourself easy to find in a market where being found is half the battle.

It is worth putting an afternoon into this. Rewrite your headline and the first lines of your summary so anyone, or anything, reading them knows within seconds what you do and what you are good at. At AmaliTech we push people to describe their work in concrete terms for exactly this reason: the clearer you are about what you can do, the easier it is for the right opportunity to find you.

Images sources: AmaliTech, Igor Omilaev on Unsplash