Google Search Central Live Toronto: What It Means for Your SEO

On April 21, 2026, Toronto hosted the first Google Search Central Live in Canada. Five speakers directly from Google – Martin Splitt, Danny Sullivan, Daniel Waisberg, Annanya Raghavan, and Ryan Levering. I wasn’t there. Saarbrücken isn’t exactly around the corner from Ontario.

What I did have: JC Chouinard’s excellent slide documentation of the entire event. Jean-Christophe Chouinard – SEO strategist at Tripadvisor, technical SEO specialist, and one of the most thorough documentarians in the English-speaking SEO community – photographed nearly all the slides and added his own notes. Merci, JC. Seriously. This article wouldn’t exist without his work. If you’re not already following him: JC Chouinard on LinkedIn – highly recommended.

I’m not reproducing his report here. What I’m providing is an analysis for the international SEO community: what do these signals mean concretely for you as a webmaster, SEO consultant, or content manager? What confirms what I see in my daily work – and where are there honest contradictions to what I’ve previously recommended?

Transparency note upfront: There is one point in this article that directly contradicts my previous recommendation on llms.txt. I won’t downplay it – I’ll address it clearly.

Google Search Central Live events are closed events. No livestreams, no official slide downloads. What the community gets out of them depends entirely on whether someone attended – and whether that person has the energy to document what they experienced.

JC Chouinard did that. He photographed nearly all the slides, added his own notes, and compiled a community summary. That’s not a given – that’s work. That’s exactly why I’m explicitly crediting his source, linking directly to it, and recommending you check it out yourself – the slides contain visual details I’m not reproducing here.

My article is an interpretation and analysis – not a duplicate. The original source is at jcchouinard.com.

One of the clearest points from Toronto, confirmed by Martin Splitt and implicitly by several slides: AI has massively raised the bar for indexing. Because content production has become so cheap, Google has raised the quality threshold for inclusion in the index. That’s not a community claim – it was communicated directly in Toronto.

What this means in practice is something I see in my technical audits daily: clients show me Search Console reports with hundreds of URLs in “Crawled – currently not indexed” status – believing it’s a JavaScript rendering problem or a crawl budget issue. That’s almost never the case. Google read those pages. Google decided they weren’t worth including.

What helps? Not mass content. Not generic overview pages that repeat the same thing as the top five Google results. The focus must be on content that genuinely contributes something: proprietary data, personal experience, an original perspective. Anyone who hasn’t internalized this will notice in 2026 that more and more pages disappear into index limbo.

More on how Google handles crawling and indexing internally in my article Crawling & Indexing: How Google Finds and Stores Your Content.

An important point was clarified in Toronto that the community often conflates: Google is not fundamentally against AI-generated content. What Google’s “Scaled Content Abuse” algorithm targets is the mass production of content without real informational gain – regardless of whether a human or machine produced it.

This is an important distinction in practice. I hear from agencies and in-house SEOs who say: “We only use AI for first drafts, then a human revises it.” That sounds reasonable. It is – if the revision is genuinely substantial. But if it only shifts commas and rearranges sentences, you’ve still produced Scaled Content Abuse, because the informational content is identical to what’s already on the web.

Furkan Özkaya – one of the most active community voices in the post-Toronto discussion – put it plainly: AI content only works if it’s been thoroughly revised by a human to the point where it takes 2-3 hours per piece. That aligns exactly with my own production practice for this blog.

This point from Toronto made waves – rightfully so. The community recommendation used to be: if you don’t want your content used for AI Overviews, block Google-Extended. In Toronto, that was corrected.

The reality: because your page is already in the Google index, Google can still use the content through fanout mechanisms for AI responses. Blocking changes nothing about your visibility in AI Overviews: your content is still used and still cited. The only lever that actually controls citations is data-nosnippet – but that also removes snippets from classic search results.

What actually works: the data-nosnippet attribute on specific content you want to protect. That’s surgically precise – and the only mechanism that, according to Google, actually prevents specific text passages from appearing in AI responses. JC Chouinard rightly notes in his commentary that this is a double-edged sword: it also reduces classic SEO benefits like Featured Snippets.

The robots.txt debate around Google-Extended is over. Anyone who has been blocking it expecting protection was relying on a false measure. More on how fanout mechanisms work in AI Overviews in my article on How Google AI Overviews Work. The difference between AI Mode and AI Overviews – and why it matters for visibility – is here: Google AI Mode vs. AI Overviews.

One more detail from Danny Sullivan’s session that sharpens the discussion: the Gemini model running in AI Overviews and AI Mode is the same model as in the standalone app – but Google “shapes” it differently in Search. That explains why responses to identical queries can diverge depending on the entry point. For optimization, this means there’s no separate “AI Mode strategy” – the same quality fundamentals apply everywhere.

Now to the uncomfortable part. In Toronto, both the Google speaker and Glenn Gabe in the community discussion confirmed: there is no SEO benefit in converting your website to Markdown. And there is no SEO benefit from an llms.txt file.

I published a detailed guide to llms.txt on this blog – recommending implementation. That now directly contradicts Google’s official position as repeated in Toronto.

My honest assessment: the contradiction is real, but it’s more precise than it looks at first glance. When I recommend llms.txt, I don’t mean classic Google SEO benefit – I mean the benefit for LLM crawlers like GPTBot and other systems outside the Google ecosystem. My own server log analyses show that GPTBot actively crawls the file. That’s not a Google topic.

The same argument applies to Markdown: for Google, there’s no added value. Period. For LLMs that directly consume content, however, clean structured markup is still easier to parse than nested HTML with ad widgets and cookie banners. That’s not a contradiction – those are two different audiences.

My takeaway and recommendation: cleanly separate all GEO/LLM recommendations between “works for Google” and “works for other AI systems”. That’s a distinction still missing too often in practice.

Ryan Levering’s session on Structured Data, Quality & AI was, according to JC Chouinard’s notes, one of the most substantial parts of the event. An important detail I hadn’t consciously communicated before: the Rich Results Testing Tool is directly integrated into Google’s internal indexing stack – the generic Schema Testing Tool (schema.org Validator) is not.

What this means: if you want to know whether Google is correctly processing your markup and whether it qualifies for Rich Results, the Rich Results Testing Tool is the only reliable reference. The Schema Validator only tells you whether your JSON-LD is syntactically correct – not whether Google will do anything with it.

Also: Structured Data in the e-commerce space is evolving. New use cases were hinted at – concrete specifications haven’t been published yet, but it’s worth keeping an eye on the Google Search Central documentation. From my e-commerce SEO work, I regularly see that schema implementations exist but are outdated or incomplete – leaving valuable Rich Results potential unused.

A complete overview of why structured data is strategically important right now is in my article Are Structured Data Your Advantage for AI Overviews?.

Annanya Raghavan’s session on Google Trends gave me one of the most interesting strategic outlooks of the event. The upcoming API updates for Google Trends will make the tool significantly more useful for data analysis: you’ll be able to query at the individual term level, with configurable time windows (daily, weekly, monthly) – and the data will be consistently scaled, meaning comparable across terms.

Important distinction: Trends and Keyword Planner measure fundamentally different things. Trends shows relative interest over time – cross-platform across YouTube and Search. Keyword Planner shows absolute search volumes for advertising purposes. For identifying emerging topics and “breakout” queries, Trends is the superior tool – also because the data is deliberately delayed by 48 hours to prevent spam manipulation.

For practice: I use Google Trends far too rarely in a systematic way for keyword research. That will change once the API updates are available – especially for seasonal trend analysis in e-commerce projects.

One topic that was raised in Toronto but gets lost in many recaps: Agentic Search. Google made clear that autonomous, task-executing searches are currently relevant primarily in e-commerce contexts – scenarios where AI independently compares products, fills shopping carts, or triggers bookings.

For e-commerce operators, this is a relevant early signal: those who set up clean product data, Schema Markup, and structured purchase flows today are better positioned for when Agentic Search rolls out more broadly. For content sites, blogs, or service providers, there’s no urgent action required – but it’s a topic worth monitoring.

JC Chouinard also summarized a number of community reactions in his post that I don’t want to skip in my analysis. Some of them hit exactly the points I regularly discuss in my consulting work.

GEO vs. SEO: Orit Mutznik argues GEO is simply an evolution of SEO – the term difference is semantic. Kristine Schachinger calls “GEO” a marketing term invented by VCs to drive tool sales. My take: both are right. The concept isn’t new, but the implementation requirements are – and dismissing it as pure rebranding underestimates the change.

Position is no longer a stable metric: Dmitrij Zatuchin describes rankings not as a fixed number but as a distribution that can shift hourly. That aligns with what I observe in client reports: volatility is the 2026 default.

Proprietary Data: Cyrus Shepard highlights that proprietary data was the strongest correlating variable for success after recent updates. It’s a signal I see implemented too rarely in practice – yet it’s the one thing AI cannot replicate.

Mark Williams-Cook puts it most bluntly: any content that looks like the top-5 results, just lightly rewritten, is “commodity” – and will be penalized by future updates. That hits hard for many blogs running exactly this pattern.

Information Gain Score: Chris Long predicts that the Information Gain Score – Google’s measure of how much new knowledge a piece of content adds relative to what’s already indexed – will be weighted more heavily going forward. That’s the algorithmic counterpart to what Toronto kept emphasizing at a content level: only content with genuine informational value has a long-term place in the index.

What am I taking from Toronto that I’ll apply in my consulting work tomorrow? Three things:

First: every page with “Crawled – currently not indexed” status is now a quality task, not a tech ticket. The question is no longer “has Google seen the page?” – but “why did Google decide not to include it?”

Second: the Google-Extended debate is over. No meaningful protection from robots.txt blocking. Anyone who wants to specifically protect content uses data-nosnippet on the specific sections they don’t want cited.

Third: Schema Markup is not a nice-to-have. The Rich Results Testing Tool is the only valid reference for Google compliance. And the new e-commerce use cases are coming – those with clean implementations now will benefit from the updates.

One more time explicitly: this article would not have been possible without JC Chouinard’s work. Originalbeitrag: Google Search Central Live Toronto Slides (April 2026). LinkedIn: Jean-Christophe Chouinard.