The question is no longer whether AI search is taking traffic, it is how much. A r/degoogle thread asked if DuckDuckGo will overtake Google; the sharper answer in the comments was that the real disruption is people searching through LLMs instead of search boxes at all. The numbers back that up, and they change what "winning search" means.
The 2026 numbers
A few verifiable data points, sourced this month:
- AI Overviews now appear in roughly a quarter of Google searches, about 25.8% by late 2025, up from ~18% in early 2025 (Exposure Ninja, AI Search Statistics 2026).
- When an AI Overview is present, clicks to websites drop sharply. A 2026 field experiment measured around a 38% fall in clicks; click-through on the blue links can roughly halve when an Overview shows.
- ChatGPT hit about 900 million weekly active users in February 2026, more than double a year earlier (reported via OpenAI figures). Whole categories of "look it up" queries now happen inside a chatbot, never touching a SERP.
The takeaway is not "Google is dying." It is that a growing share of answers are delivered without a click, either inside an AI Overview or inside a chatbot. Ranking #1 still matters, but it no longer guarantees the visit.
Ranking is not the same as being cited
In classic SEO you optimized to rank. In AI search you optimize to be cited, quoted as a source inside the generated answer. That is the discipline now called GEO (generative engine optimization) or AEO (answer engine optimization). The mechanics differ from blue-link SEO:
- Answer-first structure. Models pull the passage that most directly answers the query. Lead with the answer, then support it. Buried answers do not get quoted.
- Atomic, self-contained sections. AI chunks pages. A section that only makes sense after the one above it will not survive being pulled out of context.
- Entities, numbers, and freshness. Cited content skews fresh, much of what AI quotes was updated recently, and concrete (named brands, prices, versions) over vague prose.
Reddit is a leading indicator
One practical signal: Reddit threads frequently precede AI citations on the same topic. LLMs lean heavily on forum discussion for "what do people actually recommend" queries, so presence in the relevant Reddit conversation is directionally upstream of showing up in AI answers. You can monitor both surfaces programmatically:
import os, requests
H = {"Authorization": f"Bearer {os.environ['SCAVIO_API_KEY']}", "Content-Type": "application/json"}
# What the live SERP says about your topic (with PAA + related)
serp = requests.post("https://api.scavio.dev/api/v1/google",
headers=H, json={"query": "your topic", "light_request": False}).json()
# What Reddit is saying, the leading indicator
reddit = requests.post("https://api.scavio.dev/api/v1/reddit/search",
headers=H, json={"query": "your brand"}).json()The SERP call returns People Also Ask and related searches, the question space AI fans your query into. The Reddit call surfaces the discussions that often get cited before your own page does.
What to do now
You cannot directly optimize for a black-box model, but the inputs are knowable. Structure pages answer-first and atomically. Put real numbers, named entities, and recent dates in. Track the People Also Ask space so you cover the sub-questions AI breaks a query into. Watch Reddit for your topic, because that conversation is often upstream of the AI answer. Being cited, not just ranked, is the game now, and the pages that win it are concrete, fresh, and structured to be quoted.