Google now runs two different searches — and you optimize for one

Spoiler, for anyone who skims: AI Overview and AI Mode take their sources from two different engines, so a top-10 page can be everywhere in one and absent from the other.

One histogram in the Ashmanov & Partners slides stopped me: source positions in AI Overview looked like any SERP chart — a fat top-3 and a long tail — while the AI Mode chart was almost flat. Same button, two shapes! What produces that, other than two different retrieval systems?

Two searches behind one button

The biggest architectural discovery about Google in 2026: AI Overview (AIO) and AI Mode (AIM) aren’t one “AI search” in two skins — they’re two different systems on different retrieval engines. The hypothesis (Ashmanov & Partners, empirics across 600+ parameters): AIO assembles its answer from the classic results, AIM searches a vector semantic index, the way ChatGPT does.

Practically, “one template for everything” doesn’t work: optimizing only for the classic results covers AIO and leaves a site nearly nonexistent in AIM.

The data: how they differ

Parameter	AI Overview (AIO)	AI Mode (AIM)
Answer length	short (1–2 paragraphs)	~4× longer
Sources from the original query	~60%	~15%
+ from extra sub-queries	10–15%	~30%
Top-3 sources	~40% of slots	flatter distribution
Commercial pages among sources	45.6%	29.2%
Source languages	as in the results (monolingual)	multilingual
Ambiguous words	one meaning	all meanings
Correlation with organic	stronger	weaker

The most telling piece is the histogram shape. In AIO the source-by-position distribution looks like classic search: dense on the top-3, exponential decay. In AIM it’s flat — exactly what you’d expect from an embedding-index search, where SERP position means nothing and semantic similarity decides. Each row alone could have another explanation — but could all of them together?

The AIO strategy: the classics still work

• Top-10 still supplies ~60% of sources — rankings remain a lever.
• Query fan-out gives pages outside the top-3 a shot — via sub-queries.
• Freshness wins: recently updated pages systematically take the slots.
• The commercial share is high (45.6%) — commercial pages are allowed into AIO.

The AIM strategy: position barely matters

• Top-10 supplies only ~15% of sources — ranking barely helps.
• You need vector similarity, not lexical match: self-contained atomic blocks of 40–60 words, a number at the start of the passage, structured data.
• Multilingual: AIM cites foreign sources — your content can surface in other locales (and competitors arrive from other languages).
• The answer is 4× longer → more citation slots, but each slot is won by a separate sub-intent (see where engines get their answers).

Uncomfortable? Yes, but it probably also means #7 is no longer a life sentence!

What to do

1. Split the KPIs. Presence in AIO and in AIM are two different results; measure them separately, like rankings.
2. For AIO — keep the classics: rankings, freshness, sub-query fan coverage.
3. For AIM — atomize: every sub-intent = a self-contained block, extractable without context.
4. Check both surfaces. Our AI-visibility probe measures citation across 4 engines, and geo-metrics separately counts your AI-Overview exposure per keyword.

My subjective take: the split will soon look obvious, and reports will have had two columns all along. One histogram convinced me; perhaps it’ll convince you.

Sources

• GEO-HowTo 2026 — Kurdyukova–Volovich talk (Ashmanov & Partners) — the two-engine hypothesis, a 600+-parameter methodology: source histograms, original-query shares (~60% vs ~15%), commercial pages (45.6% vs 29.2%), language behavior.
• GEO-HowTo 2026 — Alexander Runov’s talk (MGCom) — Google Search 2.0 (2026-05-19): “AI became the foundation of the search interface.”
• US v. Google case materials (2025) — FastSearch as a separate retrieval system (embeddings, dot-product) — the architectural basis of the hypothesis.