ChatGPT stopped asking the crowd. After the switch from GPT-5.5 to GPT-5.6, ChatGPT seems to have lost some of its interest in Reddit. Yes, we already know that. But something else caught my attention: .gov and .edu domains have gained significantly more visibility in citations. Since the model switch, we’re seeing: 🔹 .edu: +160% Citation Share 🔹 .gov: +71.5% Citation Share That looks like a pretty clear shift toward more authoritative domains in retrieval. 👉 Could this be an attempt to reduce the impact of spam and low-quality content? What’s even more interesting is that we can see the same pattern inside the Query Fan-Outs. So the site: operator currently appears to play a major role in determining which domains are explicitly targeted during retrieval. And, unsurprisingly, the largest share of those fan-outs goes to brands offering specific products or services. ChatGPT frequently uses site: to search those domains directly. But .edu and .gov are also showing a surprisingly meaningful presence among all site: operator mentions in Fan-Outs: 🎓 .edu → 5.6% 🏛️ .gov → 4.0% And Reddit? Only around 0.4%. In other words, Reddit is barely being targeted through site: searches in Query Fan-Outs. --------- I guess it won’t be long before we see the first GEO tactics and partnerships designed specifically to get mentioned on .edu and .gov domains. Until the next model switch comes along and decides that a completely different pattern is suddenly more important. 😄 Welcome to GEO. cc @lilyraynyc @glenngabe @CyrusShepard @chris_nectiv

Reverse-engineered ChatGPT Deep Research and Agent Mode's click curve. For years, we've had click curve studies for Search Engines. But what about AI search? 🤖 Based on my recent research, I was able to reverse-engineer ChatGPT Deep Research and Agent Mode's click curve. Note: While analyzing the WebSocket traffic, I also discovered that Agent Mode sessions can be reconstructed in exactly the same way. I'll share more about that in a separate post, but one finding immediately stood out: 👉 Deep Research and Agent Mode follow almost identical source selection behavior. That allowed me to combine data from both systems and build a much larger dataset for this analysis. ⚠️ One important note: This is not the definitive click curve for ChatGPT. Like every click curve study, it's based on a sample, and there are certainly factors that still need further investigation. However, I believe it provides one of the first data-driven insights into what an AI click curve could look like. Here are the three findings👇 🥇 1. AI agents start at the top Position 1: 28.1% Position 2: 18.2% Position 3: 11.6% Position 4: 9.1% Position 5: 9.1% So somehow ranking still matters. 🔄 2. Top-ranked sources get revisited Position 1 sources are revisited in 73.5% of sessions, compared to 36.4% for position 5. Being ranked first doesn't just increase the chance of getting opened. It increases the chance of becoming part of the model's reasoning process. 🎯 3. Attention compounds Across an entire research session, Position 1 accounts for 40.7% of all source opens, while Positions 1 and 2 together receive 62% of the agent's total attention. 📌 What "position" means here: Each query the agent runs returns a ranked list of results. Position 1 is the top result in that list. The search engine used in these sessions was always Bing. More research is coming soon 🚀 I've linked my earlier article on ChatGPT Deep Research in the comments. 🔻