Ep. 34 Found in AI: Bing’s AI Performance and ChatGPT Ads

In this episode of Found in AI, Cassie breaks down Bing’s new AI Performance dashboard inside Bing Webmaster Tools and OpenAI’s rollout of ads in ChatGPT — and explains why this week marks a maturity moment for AI search.

Cassie walks through what Bing’s citation metrics actually measure, how grounding queries differ from traditional Google Search Console data, and why these insights should be used to validate entity authority, not just track keywords. She also unpacks what ChatGPT’s new ad layer signals about monetization, visibility, and the emerging “AI SERP” environment.

Using the Freshness, Structure, Authority (FSA) framework, Cassie explains how brands should approach measurable AI visibility and what changes are now required as AI interfaces include both organic citations and sponsored placements.

In this episode, you’ll learn:

• What Bing’s AI Performance dashboard actually measures (and what it doesn’t)
• How grounding queries differ from Google Search Console query data
• Why you should use Bing to validate entity authority and GSC to guide keyword targeting
• How Freshness, Structure, and Authority show up in Bing’s recommendations
• What ChatGPT’s ad rollout means for organic AI visibility
• Why AI engines are starting to resemble conversational SERPs
• How brands should think about organic and paid visibility in AI interfaces

Transcript: What Do Bing’s AI Performance and ChatGPT Ads Mean for Search?

Hey, welcome back to Found in AI.

I’m Cassie Clark — AI search optimization expert, fractional content strategist, and the host of the show where we talk about AI search optimization, GEO, and AEO strategies, and what all this means so we don’t get lost in this new wave of user search behavior.

Today is Thursday, February 12th, and we have some genuinely significant updates this week.

If you’ve been spending time on LinkedIn, YouTube, or Reddit — anywhere people are talking about AI search or ChatGPT — you’ve probably seen the headlines already. ChatGPT officially launched ads inside the chat interface this week. And Bing released a new feature inside Bing Webmaster Tools called AI Performance.

In the same week, we got both measurement and monetization.

Generative search has sometimes felt theoretical, experimental, or maybe just SEO under a different name. This week makes it clear that something more structured is forming. So let’s walk through what these updates from OpenAI and Microsoft actually mean for AI search strategy.

Bing AI Performance in Webmaster Tools

Let’s start with Bing’s AI Performance feature because this is the update that has the most direct strategic implications.

ChatGPT ads are important, and we’ll get there. But this is the first major search engine building native reporting around AI-generated answers. We’ve seen hints of this direction before. Back in January, Bing released its AEO-GEO e-commerce guide, which I covered in a previous episode. That guide outlined best practices. This new AI Performance dashboard moves from guidance into measurement.

This feature allows publishers to see:

• Total citations in AI-generated answers
• Which URLs are being cited
• Average cited pages per day
• Grounding queries
• Citation trends over time

There are third-party AI visibility tools on the market, and many of them provide helpful insights. But this is Bing’s own reporting inside Webmaster Tools. It functions similarly to Google Search Console, but instead of showing traditional search data, it surfaces visibility within AI answers.

Some people will naturally ask whether Bing’s data matters given Google’s dominance in traditional search. That’s a reasonable question. However, the larger signal here isn’t about market share — it’s about the fact that a major search engine is building infrastructure around AI-driven discovery and publishing that data publicly.

That shift is significant.

Total Citations and What They Signal

The first metric shown in the AI Performance dashboard is total citations. This reflects how often your content appears as a cited source in AI-generated answers.

It does not show prominence, ranking, or placement within the answer. It simply measures frequency. But frequency still provides insight. If your content is repeatedly cited, that indicates the engine is retrieving and trusting your content consistently enough to reference it.

That repeated retrieval is a signal of credibility within the model’s ecosystem.

The dashboard also shows average cited pages per day. This metric reveals how many unique pages from your site are being cited within a given timeframe.

If citations are concentrated on one or two pages, that suggests your authority is clustered. If citations are distributed across many pages, that points to broader entity authority across your site. Over time, a site with strong structural clarity and topical depth should see citation distribution widen.

This is where the FSA framework — Freshness, Structure, and Authority — becomes particularly useful. When structure and authority are consistently applied across content, retrieval patterns tend to expand rather than concentrate.

Grounding Queries and How They Differ from Google Search Console

One of the most interesting features in Bing’s AI Performance dashboard is grounding queries. These are the phrases the AI system used when retrieving your content.

At first glance, grounding queries look similar to query data inside Google Search Console. And yes, Google Search Console also provides keyword-level insights.

However, I would use these two data sources differently.

Bing’s grounding queries are useful for validating entity authority — understanding how the model categorizes your brand and which conceptual themes you are consistently associated with. They show how AI systems interpret your content at a semantic level.

Google Search Console, on the other hand, remains incredibly valuable for guiding keyword targeting and optimizing content around traditional search demand.

One helps you understand how AI understands you. The other helps you determine what to build next.

They complement each other, but they are not interchangeable.

Page-Level Citation Activity and Strategic Expansion

The AI Performance dashboard also includes page-level citation activity, showing which URLs are cited most frequently.

Microsoft’s accompanying blog explains how to use this data. They recommend validating which pages are already referenced, identifying frequently cited content, and improving clarity and structure on indexed but less-cited pages.

Viewed through the lens of the FSA framework, this guidance aligns clearly with Authority and Structure principles.

If a page is being cited for specific grounding queries, it likely reflects clear subject focus and domain expertise. That is an authority signal.

The logical next step is expansion. Deepen adjacent subtopics. Reinforce related entity relationships. Build content clusters around what is already working.

While much of this work happens on your own website, it shouldn’t remain isolated there. Off-site signals continue to matter in AI retrieval environments. Mentions on YouTube, LinkedIn, Reddit, third-party publications, review sites, and through digital PR partnerships all contribute to how authority travels across the web.

AI visibility is influenced not just by what exists on one page, but by how consistently your expertise appears across platforms.

Structure, Evidence, and Freshness in AI Retrieval

Microsoft also emphasizes improving structure and clarity, including clear headings, tables, and FAQ sections.

This is not merely formatting advice. Structured content improves machine legibility. When information is clearly organized, it is easier for AI systems to retrieve and reference accurately. That directly aligns with the “S” in the FSA framework.

They also recommend adding examples, data, and cited sources. Authority is strengthened when claims are supported. AI systems are more comfortable referencing content that provides evidence rather than unsupported assertions.

Finally, freshness remains a critical factor. Bing references regular updates, current information, and the use of IndexNow.

Freshness has always mattered in search. In AI-driven retrieval systems, update velocity becomes even more relevant. If systems are pulling from indexed content pools, the most current and consistently maintained content gains an advantage.

Taken together, Bing’s recommendations map closely to the Freshness, Structure, and Authority framework. The difference now is that these signals are measurable within an AI-specific dashboard.

ChatGPT Ads and AI Monetization

Now let’s turn to the second major update: ChatGPT ads.

OpenAI rolled out ads to logged-in adult users on the Free and Go tiers. Plus, Pro, Business, Enterprise, and Education tiers remain ad-free.

In their announcement, OpenAI emphasized that ads do not influence answers, ads are clearly labeled, advertisers do not have access to chats, and sensitive topics are excluded.

The underlying theme here is trust. The separation between organic answers and sponsored placements is central to maintaining user confidence.

What is new is how ads are matched. They are contextual to the conversation topic and informed by prior interactions. This is not traditional search advertising in the sense of keyword-based blue links. It is contextual advertising embedded within a conversational reasoning interface.

That distinction matters because it represents a shift in how monetization appears within AI-driven environments.

The Convergence of Measurement and Monetization

When viewed together, these two updates represent a structural change.

AI visibility is now measurable through Bing’s AI Performance dashboard. At the same time, AI interfaces are being monetized through clearly labeled sponsored placements.

This creates an environment where organic citation surfaces and paid placements coexist within the same conversational interface. Whether we label these platforms search engines or answer engines, users are relying on them for information discovery and decision-making.

For brands, this introduces two parallel but connected strategies.

Organic citation visibility is influenced by Freshness, Structure, and Authority. Paid visibility is influenced by relevance and budget allocation. They operate on separate systems, but they share user attention within the same interface.

Visibility in AI environments is becoming layered.

A Maturity Moment for AI Search

This week feels like a maturity moment for AI search optimization.

Bing is building GEO tooling directly into Webmaster Tools. OpenAI is formalizing monetization inside AI interfaces. Infrastructure is forming around AI-driven discovery.

When infrastructure solidifies, behavior follows.

If you have already been applying Freshness, Structure, and Authority principles to your content strategy, these new tools provide additional clarity into how AI systems interpret and retrieve your content.

If you are beginning to explore AI visibility more seriously, this is a practical moment to experiment and build familiarity while the ecosystem continues to evolve.

What this week demonstrates is that AI search is no longer purely experimental. It is measurable. It is monetized. And it is developing quickly.

I’m Cassie Clark. If you’re interested in running an AI search visibility audit before making changes to your current strategy, you can find more information in the show notes or at CassieClarkMarketing.com.

Until then, stay visible.

If we haven’t met yet….

Hi, I’m Cassie, a fractional content strategist for early-stage startups who focuses on AI search optimization.

If AI-generated answers are already influencing your buyers, your content strategy needs to account for that. I build content programs that connect strategy with execution: clear positioning,  systems, and content that actually drive revenue. If you’re ready to stop guessing and start growing, here’s how we can work together.