An InSpace perspective

There’s a lot of noise right now around the idea of “ads in ChatGPT.” I got triggered by the article on Search Engine land https://searchengineland.com/openai-insists-chatgpt-ads-werent-ads-465781

Some of it is speculative, some of it reactionary, and some of it is driven by a familiar fear: that generative AI will simply become another pay-to-play surface, where whoever bids the highest gets inserted into answers, regardless of whether they actually belong there.

I don’t think that’s how this will play out. 

To be clear upfront: what follows is not a leaked roadmap, nor a confirmed OpenAI policy. It’s a hunch — but a very educated one — grounded in how LLMs already work today, how trust functions in AI systems, and how OpenAI, Google, and Microsoft are all converging on the same fundamental constraint.

That constraint is relevance.

Why relevance is not optional in an LLM environment

People don’t use LLMs the way they use search engines. They don’t scan ten blue links, cross-compare sources, or tolerate obvious commercial bias. They ask a question and expect the assistant to reason its way toward an answer that makes sense.

That expectation is the entire product.

The moment a large language model injects something that feels irrelevant, self-serving, or unjustified, it doesn’t just weaken an ad experience — it undermines the perceived intelligence of the system itself. In other words, the assistant doesn’t just lose trust in that answer; it loses trust altogether.

This is why I find it extremely unlikely that future ads in ChatGPT (or any comparable LLM interface) will function like traditional PPC. You don’t get to “buy” your way into a reasoning chain unless your presence actively supports the answer being constructed.

And we already know this is how these systems behave today.

OpenAI has been clear that ChatGPT Search selects sources based on a mix of intent understanding, relevance, recency, and credibility. Google’s SGE explicitly states that generative answers are grounded in the same high-quality signals that power traditional search, only compressed into a conversational interface. Microsoft has publicly confirmed that ads in Bing Chat are relevance-gated by machine learning before they ever appear.

None of these platforms can afford to do otherwise.

Credibility as the real gatekeeper

What’s interesting is that credibility, in an LLM context, is not a single score or label. There is no public “AI Quality Score” you can look up. Instead, credibility emerges from a combination of signals that, taken together, answer a simple internal question:

Can I justify referencing this source as part of a trustworthy answer?

That justification depends on whether your content is topically aligned with the query, whether it is readable and structurally clear, whether it reflects domain expertise rather than surface-level repetition, and whether it fits naturally into the intent behind the question being asked.

If you sell car polish and someone asks, “What’s the best way to polish my car?”, it makes sense — logically and contextually — that your product might appear as part of that answer. But that only holds if the surrounding content demonstrates that you actually understand the problem, explain the process clearly, and offer a solution that aligns with the user’s goal.

If, on the other hand, your site exists only to sell, without showing understanding, explanation, or trustworthiness, then even a paid placement would feel wrong. And in an LLM interface, “feels wrong” is not a UX issue — it’s a system failure.

Why intent matters more than traffic

Another reason ads in LLMs will behave differently is the type of queries people ask. Most LLM interactions sit squarely in the top- and mid-funnel. Users are exploring, learning, comparing, and trying to understand — not immediately transacting.

That doesn’t mean monetization isn’t possible. It means monetization must respect the journey.

LLMs infer intent not just from keywords, but from linguistic cues, task framing, and learned behavioral patterns. They distinguish between someone trying to understand how to do something and someone ready to buy the tools to do it. Ads, if they appear, will need to align with that inferred intent, otherwise they simply don’t belong.

This is why I believe the path to successful LLM advertising runs straight through organic relevance. If your site is not already positioned as a credible answer to a given intent, paying to appear there won’t fix the mismatch. It will expose it.

The self-reinforcing loop that nobody is talking about

Here’s where things get genuinely interesting.

If credibility and relevance determine whether you are cited organically and whether you are eligible for future ads, then optimization stops being a one-way street. It becomes a loop.

Imagine this scenario: your content isn’t being surfaced by an LLM, either organically or commercially. Instead of guessing why, you ask the system directly what’s missing. It tells you where your explanations are thin, where intent alignment is unclear, or where credibility signals are weak. You improve those areas — possibly even with the help of the same model — and suddenly your visibility changes.

In that sense, the system doesn’t just enforce the rules; it teaches them.

This doesn’t mean the loop is perfectly fair, or that it can’t be gamed. But it does mean we’re moving toward an ecosystem where paying for visibility without earning relevance becomes increasingly difficult, if not impossible.

And that’s a fundamental shift from everything we’ve known about digital advertising so far.

Why this matters for brands right now

Even if ads in ChatGPT are still experimental, the direction is already visible. Relevance, readability, intent alignment, and credibility are no longer just “SEO best practices.” They are prerequisites for participation in AI-driven discovery — paid or unpaid.

The brands that win in this next phase won’t be the ones with the biggest budgets. They’ll be the ones whose content actually deserves to be part of the answer.

That’s not a guarantee. It’s a hunch. But it’s a hunch supported by how these systems already behave, by what the platforms themselves are saying, and by the simple reality that an assistant that stops being helpful stops being used.

How InSpace helps you prepare

At InSpace, we don’t approach this as an ad problem or a traffic problem. We treat it as a visibility-through-credibility problem.

We help brands understand how LLMs interpret their content today, where intent alignment breaks down, where credibility signals are missing, and how to structurally improve content so that it becomes eligible — first for organic AI citations, and eventually for whatever commercial layers follow.

Whether that means cleaning up content bloat, rebuilding topical authority, improving readability, or mapping intent more precisely across the funnel, the goal is the same: make your brand make sense to the machine before you ask the machine to promote it.

If you want to know where you stand in this emerging ecosystem, we can help you find out — and more importantly, help you fix it.

Because if this hunch is right, the future of visibility won’t be bought.

It will be earned, reinforced, and amplified.

Footnotes

[^1]: OpenAI, Introducing ChatGPT Search — emphasis on “original, high-quality content from the web.”

[^2]: Zapier, How ChatGPT sources information — breakdown of relevance, credibility, recency, and intent signals used.

[^3]: Google, SGE Quality Principles — generative results rooted in Google’s highest information quality bar.

[^4]: Search Engine Land, SGE and YMYL limitations — showing Google avoids generating answers where credibility cannot be guaranteed.

[^5]: Microsoft, Bing Chat Ads Announcement — confirming relevance gating for chatbot ads.

[^6]: Google SGE, Intent-Aware Generative Results — “tailored to the user’s intent and stage in the buyer journey.”