Who Are We WritingThought Leadership For Now?

I think we may be writing thought leadership for AI now

Not because AI has money, authority, or a procurement department. It does not. Not yet, anyway.

But increasingly, it is the first reader.

It crawls the article, decides which parts are worth extracting, rewrites them into an answer, and may give the human enough information that they never visit the source.

Very efficient. Especially for everyone except the person who did the work.

That is a strange audience model. We still say we are writing for founders, buyers, operators, and the hidden people inside a company who can quietly kill a purchase. But between us and them now sits a machine reading at industrial scale and returning very little traffic.

Apparently, the middleman no longer has to introduce the two parties.

AI is already my researcher, editor, sparring partner, occasional second brain, and, more often than I care to admit, my first brain. On a busy day, half my thoughts appear to be sitting in a server farm.

I am not arguing that we should keep AI out of the writing process. I am asking whether it has also become the main audience.

If the buyer asks ChatGPT, Google AI Mode, or another answer engine instead of visiting our site, who did I actually write this for? The person making the decision, or the machine deciding which pieces of my argument survive the trip?

The answer cannot be "write for AI." That produces the same lifeless material the machine already makes for free, and the internet has a generous supply.

But pretending AI is not standing between the idea and the reader is becoming equally ridiculous.

Thought leadership was already in trouble

Too much of it was ordinary marketing content wearing a blazer: five trends shaping the future, seven lessons from successful leaders, and a conclusion nobody could possibly disagree with.

AI can summarize the accepted view, smooth out the language, add a tasteful amount of confidence, and produce a conclusion that sounds important without creating any risk for the person whose name appears above it.

If that was the whole product, then yes, its importance is diminishing. It probably deserved to.

Thought leadership should contain thought. That means judgment, a position, evidence, and enough specificity that another informed person can disagree. AI did not remove the need for those things. It exposed how often they were missing.

The human reader did not disappear

The 2025 Edelman-LinkedIn B2B Thought Leadership Impact Report surveyed nearly 2,000 management-level professionals. Sixty-four percent of target decision-makers and 63 percent of hidden decision-makers said they spend more than an hour a week consuming thought leadership. Fifty-six percent of target buyers and 55 percent of hidden buyers said they use it to evaluate vendors.

Edelman and LinkedIn both have an obvious interest in the category, so I would not carve those percentages into a monument. The more useful point is that the people who affect a purchase are still looking for ideas before they volunteer for a sales call. Many of them are the finance, legal, security, procurement, and operating people a marketer may never know by name.

They are still human. They still decide whether an argument is credible, whether a company understands the problem, and whether somebody should take the vendor seriously.

The reader did not vanish. The route to the reader changed.

The machine may read it first

Pew Research Center analyzed 68,879 Google searches made in March 2025. When an AI summary appeared, users clicked a traditional result in 8 percent of visits. Without the summary, they clicked a result in 15 percent. A link cited inside the AI summary received a click in just 1 percent of visits.

That is more than a change to the search page. The answer engine has become an intermediary between the person with the question and the person who did the work.

Cloudflare's 2026 data found that 52 percent of AI crawler requests were for training, up from 22 percent in spring 2025. Across leading AI companies, it observed crawl-to-referral ratios ranging from 118 pages crawled for every referred visitor to nearly 50,000 pages per visitor.

Google says overall organic click volume has remained relatively stable and that the clicks it sends are becoming more valuable. Both things can be true. Google can send billions of clicks in aggregate while AI answers remove a large share of the visits an individual publisher once expected from informational searches.

Whatever the final ratio becomes, the old bargain is changing. We publish. A platform reads. The platform may deliver our conclusion without delivering our audience.

A citation is useful. It is not a relationship.

If an AI answer carries our name, our evidence, and our point of view into a buying conversation, that influence has value. It may shape a shortlist before we know the shortlist exists.

But we should not confuse being used as a source with owning an audience. A person who never reaches our site cannot join our list, explore the rest of our work, see the full argument, or notice where the machine flattened an important qualification.

This makes thought leadership harder to measure. Traffic still matters, but branded search, direct visits, citations, sales conversations, invitations, and buyers repeating our language may tell us more than a pageview report. Influence has always been messy. AI has made it messier and given the mess an API.

For 25 years, publishers accepted crawling because search engines returned traffic. AI systems weaken that exchange. We should be clear-eyed about the economics even while we make our work easy to find.

Do not write for AI

Write for the person making a decision. Format the work so a machine can carry it there without mangling it.

Those are different jobs.

An answer engine benefits from direct definitions, descriptive headings, named sources, clear comparisons, and sentences that survive being quoted out of context. A 2026 preprint studying citations across ChatGPT, Google, and Perplexity found that pages contributing more to generated answers tended to be structured, semantically aligned with the question, and rich in extractable evidence such as definitions, numbers, comparisons, and procedures. The research is young, but none of that requires us to sound like a machine.

Writing only for extraction creates the same generic sludge AI already produces for free. There is no competitive advantage in becoming easier to summarize because you removed every surprising thought.

The better rule is simple: write for the decision and format for retrieval.

AI can help with the work. It cannot own the judgment.

I use AI in the writing process. I have already argued that we should judge the finished work instead of treating the tool as a verdict. I am not changing that position here.

AI can find a weak claim, challenge a conclusion, organize research, suggest a clearer structure, and rescue a sentence I have made unnecessarily difficult. That is useful leverage.

But if the machine supplies the premise, the examples, the position, and the conclusion, I am not leading anyone's thinking. I am laundering consensus through my byline.

The part that has to remain ours is the part with consequences: what we believe, why we believe it, which experience changed our mind, where the conventional answer fails, and what we are prepared to recommend under our own name.

AI can be half the brain. It cannot be the opinion.

Publish something the machine could not have known without you

That might be original research. It might be a decision you made, a pattern you observed across twenty years, a technical tradeoff that failed in production, or a position that becomes obvious only after living with the consequences.

Name the evidence. Link to the source. Separate what the data proves from what you infer. Make the main point clear enough to survive a summary. Keep enough personality in the piece that a human wants the full version.

Then distribute it where people already spend time. Put the sharpest version on LinkedIn. Put the compact argument on X. Let the canonical article hold the evidence and context. Waiting politely for every interested person to discover the company blog has become an eccentric marketing plan.

Google's own guidance for AI search features says there is no special AI schema or machine-readable file required. It recommends the familiar work: make the page crawlable, keep important material in text, use structured data that matches the visible page, and create helpful, reliable, people-first content.

That advice is self-serving, but it is still directionally right. Machines need access and structure. People need a reason to care.

So who are we writing for?

We are writing for people who increasingly ask machines what to read, what to consider, and sometimes what to think.

Thought leadership is not becoming less important. Average thought leadership is becoming worthless. The path is less direct, attribution is worse, and the standard for having something worth repeating is higher.

The AI may be the first reader. It is not the final buyer, the skeptical CFO, the security lead, the board member, or the person whose reputation rides on the decision.

That is still who we are writing for.