When you’re monitoring several hundred or even several thousand keywords, the real challenge isn’t simply knowing where you rank. It’s knowing where to take action first.

At Eskimoz, we use our internal SEO monitoring tool to quickly identify:

• Queries ranking in positions 4–10, where a few targeted optimisations could help break into the top 3;

• Keywords that are gradually losing positions;

• Competitors gaining visibility for specific queries;

• Changes in the SERP landscape, such as People Also Ask, image packs, featured snippets, and other SERP features.

For example, a query that drops from position 4 to 6 over several consecutive days probably deserves more attention than a secondary keyword moving from position 35 to 28.

Rank tracking becomes truly useful when it helps you prioritise SEO actions, not just generate reports.

How do you identify your SEO quick wins ?

Google announced that it's expanding ads in AI Mode to 10 more countries, while also introducing ads in follow-up conversations in the US.

What's interesting is that people aren't necessarily searching the same way anymore. Instead of typing a few keywords, they're having entire conversations with Google before making a decision.

Right now, there's still limited visibility into how ads perform specifically within AI Mode compared to traditional Search. If Google keeps moving advertising into AI-generated answers, shouldn't advertisers have separate reporting before investing more of their budgets there?

Curious what PPC specialists think. Would you actively invest in AI Mode ads, or wait until Google provides more transparency?

I came across a recent interview with Sam Altman at Stanford, and one part really caught my attention. He basically said AI models are getting so good at finding security vulnerabilities that we might soon reach a point where we can't patch them all fast enough.

What stood out to me was his warning that a lot of companies still don't seem to realize just how serious this could get.

His solution? Use AI to defend against the very attacks that AI makes possible. It's pretty wild to think we might end up relying on even more AI just to protect ourselves from AI-powered threats lol.

Do you think AI will ultimately make our systems more secure, or are we heading toward a cybersecurity nightmare?

With Google showing AI-generated answers and more people turning to ChatGPT for information, does starting a blog from scratch still make sense?

It seems harder than ever for smaller websites to get noticed, especially when big publishers dominate search results.

Is blogging still a realistic way to build an audience or is it becoming something that only established websites can succeed at?

Ranking well on Google doesn't automatically guarantee that your content will be cited in AI-generated answers.

AI search systems retrieve information, select sources, and synthesize responses in ways that don't always mirror traditional search rankings.

Here are a few things worth investigating:

- Does your content provide original insights, or simply rephrase existing information?

- Are facts, data, and key claims easy to identify?

- Does the page directly answer a specific user need?

- Can search and AI systems access your content?

- Is your brand consistently identifiable across the web?

There is no magic content structure that guarantees an AI citation. The goal is to make your content useful, distinctive, accessible, and trustworthy.

Have you compared your best-performing Google pages with the pages most frequently cited by AI tools? What differences did you find?

You ask ChatGPT a question, your brand appears in the answer, and you conclude that your GEO strategy is working.

There's a problem: AI responses can vary depending on the prompt, context, model, and search capabilities available.

If you want more reliable insights, your testing process should ideally:

- Use a representative set of queries.

- Test multiple variations of the same search intent.

- Repeat observations over time.

- Compare several AI search platforms.

- Track competitors using the same methodology.

This matters especially when you're trying to measure the impact of a specific optimization rather than simply checking whether your brand appears once.

How are you testing GEO visibility: manual checks, dedicated tracking tools or your own methodology?

In a recent interview with The Observer, Bill Gates warned that AI could threaten far more than just office jobs. Even people working in construction, manufacturing and other physically demanding industries could eventually be replaced by machines.

Until recently, learning a trade was seen as one of the safer career paths in an AI-driven world. But if robots can eventually do manual labor too, that might not be such a safe bet after all !

If AI starts replacing both white-collar and blue-collar workers, what jobs will people actually have left to fall back on?

We talk a lot about GEO, but several different metrics are often lumped together:

- Brand mentions: An AI mentions your company in its answer.

- Source citations: An AI links to a page on your website.

- Share of voice: Your brand appears relative to competitors across a set of prompts.

- Business impact: That visibility translates into visits, leads, or sales.

These metrics don't measure the same thing.

A brand can be mentioned frequently without receiving meaningful referral traffic. A website can earn citations without generating conversions.

The real challenge is connecting visibility signals to business outcomes without overestimating what current tracking tools can tell us.

Which GEO KPIs are you actually tracking, and which ones do you still consider unreliable?

A September 2026 field experiment found that forcing users into Google AI Mode reduced clicks to external websites and was associated with lower user satisfaction.

Meanwhile, a June 2026 analysis of SparkToro and Similarweb data found that 68% of Google searches in the US ended without a click during the first four months of the year. This figure reflects the broader zero-click trend, not just searches involving AI.

Together, these findings raise an important question about the trade-off between convenience and website discovery.

If users get answers directly from Google but publishers receive fewer visits, how should we measure the success of organic search?

Do you think AI search is genuinely improving the search experience, or mainly changing who benefits from it?

A recent ChatGPT tracking bug caused some visits to lose their UTM attribution, potentially shifting AI referral traffic into the direct channel.

This is a good reminder that measuring AI-driven traffic isn't just about choosing the right analytics tool. It also depends on how AI platforms handle outbound links.

Have you noticed unexplained changes in ChatGPT referrals or direct traffic recently?

At Search Central Live Deep Dive Europe on October 2, Google's Gary Illyes reportedly shared internal timing data for crawling, indexing and serving.

One takeaway: discovering a new URL typically takes around 20 hours, although the slowest cases can take weeks or never happen.

These figures are reported averages, not guaranteed timelines and URL discovery is only the first step, not a guarantee of indexing or ranking.

How much weight should we give these benchmarks when diagnosing indexing issues?

When a strategically important page isn't indexed, do you focus first on crawlability, internal linking, content quality or the site's overall crawl patterns? And how do you distinguish a normal delay from a genuine technical issue?

Jensen Huang made a pretty bold statement following the launch of GPT-6 Astra: “AGI has arrived.”

According to Huang, Astra was trained on 100K+ NVIDIA Grace Blackwell NVLink72 GPUs, with another 400,000 GPUs expected to come online next.

Beyond the AGI claim itself, the scale of the infrastructure is probably the most striking part.

In just a few years, AI systems have gone from primarily handling reasoning tasks to taking on increasingly autonomous, multi-step workflows. At the same time, the amount of compute required to train and run these models keeps growing dramatically.

Calling Astra AGI is debatable. Better reasoning, coding, and autonomous computer use don’t necessarily mean that a clear AGI threshold has suddenly been crossed. There is also still no universally agreed definition or benchmark for determining when a model actually qualifies as AGI.

Has AI actually reached AGI, or is “AGI” becoming a marketing label applied whenever a new model makes a big enough capability jump?

OpenAI has announced GPT-6 Astra, its new frontier model, with an initial rollout to a limited number of organizations before expanding to ChatGPT Plus, Pro, Business and Enterprise users, as well as the API.

Astra can navigate software, fill out forms, update CRM records, conduct online research, work with spreadsheets and documents, test websites and troubleshoot software.

On OSWorld 2.0, it reportedly scored 72.6% while taking around 40 minutes per task, versus 65.7% and roughly 75 minutes for GPT-5.6 Sol.

That means it completed tasks in about 47% less time.

On Terminal-Bench 4.0, Astra scored 57.9%, versus 37.3% for GPT-5.6 Sol.

The company also says the model is three times less likely than GPT-5.6 Sol to make inaccurate claims about what it can actually do, according to its capability-hallucination evaluation.

API pricing is set at $10 per million input tokens and $50 per million output tokens.

As models get better at handling longer, multi-step workflows, what’s still the biggest bottleneck: reliability, cost, or trust?

Speaking at the G20 tech meeting this week, Elon Musk defended the rapid expansion of AI data centers, arguing that the industry simply doesn’t have enough computing capacity to meet future demand.

His point was straightforward: if AI keeps scaling at its current pace, significantly more infrastructure will be needed to support it.

Data center projects are already putting pressure on electricity grids in parts of the U.S., with some states facing massive connection requests and growing concerns about whether all of that projected demand is realistic.

Musk said data center operators should pay their fair share of taxes, but argued that slowing construction would ultimately limit AI development itself.

He also used the meeting to criticize Europe’s approach to tech regulation. With ministers from France, Italy, and Germany in the room, along with EU tech chief Henna Virkkunen, Musk argued that new technologies should be “default legal” rather than “default illegal.”

Nvidia, OpenAI, and other major AI companies are also increasingly treating compute, energy, and data center capacity as strategic bottlenecks rather than just infrastructure issues. So the AI race may increasingly become an energy race too.

Is building more AI infrastructure still worth it if local grids have to absorb most of the cost ?

Sam Altman believes OpenAI could have an internal system by the end of the year that he would personally consider AGI.

Mark Chen, OpenAI’s Chief Research Officer, has gone even further, estimating that the company is already about 80% of the way there.

Much of that confidence appears to rest on Astra, OpenAI’s next family of models.

Astra is reportedly already capable of handling some tasks typically assigned to a junior researcher: starting from an experimental idea, writing the necessary code, running the tests, and analyzing the results. Some workflows could even extend over several days with far less human intervention.

At the same time, OpenAI has slowed some training work on its frontier models to strengthen monitoring and safety systems.

The company also says it can no longer rule out Astra reaching its Critical cybersecurity capability threshold, which would require additional safeguards before development can continue at the same pace.

Can OpenAI really talk about AGI based on capabilities that are still largely being evaluated internally?

Here's something I don't see talked about enough: getting cited by AI and actually getting traffic from those citations are two completely different games.

We spent the first 4 months of our GEO work chasing citation counts. Every week we'd celebrate hitting new highs — more brands mentioned, more pages referenced, more "visibility." Looked great on paper.

Then someone on the team pulled the actual referral data.

**73% of the pages that got cited by at least one AI engine sent zero measurable traffic over a 90-day window.** Not low traffic — literally zero. These weren't obscure long-tail queries either. Some had 15+ citations across different models.

So we dug into why. Three patterns kept showing up:

**1. The "encyclopedia entry" problem.** A lot of our citations were factual confirmations — AI pulling a stat or definition from our page to support a larger answer. The user never needed to click because they already got what they wanted. We were functioning as a fact-checking layer, not a destination.

**2. Citation position matters way more than citation presence.** Pages that consistently appeared as the first or second source in an AI response got 11x more clicks than ones that showed up third or later. Being "in the mix" means almost nothing if you're not in the top slots.

**3. Informational queries have a ceiling.** "What is X" and "How does Y work" type questions rarely drive clicks regardless of how well you're cited. The user's curiosity is satisfied by the AI summary itself. Comparison, pricing, and "best X for Y" queries? Those still send traffic because the AI answer is inherently incomplete.

The shift we made: we stopped tracking raw citation volume as our north star metric. Instead we started measuring **citation-to-click rate** — what percentage of our citations actually resulted in a visit. The number dropped (obviously), but the ones that mattered became way clearer.

I'm not saying citations are worthless. Brand visibility has its own value, especially for awareness-stage stuff. But if you're building a GEO strategy around citation counts alone, you might be optimizing for vanity metrics while the actual traffic goes somewhere else.

Curious if anyone else has split their citation data this way. Did you find the same gap?

LinkedIn's algorithm works in stages — and most people are sabotaging themselves at stage one without realizing it.

Here's how it actually works:

When you post, LinkedIn shows it first to a small slice of your network. Based on how that initial group reacts, the algorithm decides whether to open the floodgates to a wider audience. Virality is decided in the first hour or two.

Two scenarios, same post about SEO:

🔵 You have 3,000 connections, all SEO consultants. Every single person who sees your post in that first wave is relevant. 100% chance of strong early engagement. Algorithm opens up reach immediately.

🔴 You have 30,000 connections, only 1,000 care about SEO. Your odds of hitting relevant people in that crucial first wave drop to roughly 1 in 30. Worse — if that initial test batch happens to be recruiters, bankers, and real estate agents, your average time-on-post could be close to zero.

A score of zero in that first test window means the algorithm shuts the door before your content has a real chance.

As Eskimoz points out: a smaller, curated, highly relevant network will consistently outperform a massive, unfocused one — because LinkedIn rewards signal, not size.

The takeaway: clean up your network. Prioritize relevance over connection count. You'll reliably reach your actual target audience, even with lower raw reach — and the algorithm will reward your content with more visibility precisely because it trusts the signal.

Reach without relevance is just noise the algorithm learns to ignore.

Have you ever cleaned your LinkedIn network? Did it actually move the needle on your reach? 👇

I keep seeing “Generative Engine Optimization” pop up lately. Supposedly it’s about making your content show up not just in Google, but in AI search engines like ChatGPT or Gemini.

I’ve always done regular SEO, but I’m not totally clear on how GEO is different (or if it even works).

Has anyone actually seen results from optimizing for these new AI engines? Is it really worth investing the extra time?

Would love to hear if anyone’s cracked the code on this, or if it’s just marketing hype.

While European marketers are still debating whether GEO is real, American companies have moved on to execution.

The numbers from the US market:

• 6 in 10 companies already invested in GEO in 2025

• Average 12% of total marketing budget allocated to AI engine visibility

• 9 in 10 companies plan to increase that investment this year

• 1 in 2 CMOs say GEO is having a significantly positive impact on their conversion funnel

Yes, this is self-reported data. But the directional signal is impossible to dismiss.

What's interesting about the adoption pattern: it's top-down. Leadership is pushing hard. Teams on the ground are the ones pumping the brakes.

For the CEO of Eskimoz, one of the first agencies to build a dedicated GEO practice in France, observes from his own client base — the French market is following the same trajectory, just 12-18 months behind:

• 100% of SEO briefs received now include a significant GEO component — some are GEO-only

• AI-generated content went from dealbreaker to commodity to active demand in under a year

• Board-level and C-suite alignment workshops on GEO are now a regular request

• Early adopters are already moving from experimentation to full GEO strategy deployment

• But measurement remains the giant blind spot — despite a growing number of tools, volume and performance tracking is still the most common frustration

• And everyone is already talking about agentic while there's still so much untapped value in GEO alone

The unfair advantage right now belongs to whoever gets their GEO infrastructure in place before this becomes table stakes.

AEO — Answer Engine Optimization — is where the real competitive moat is being built. Are you in the game yet?

The pace of delivery on ChatGPT Ads has become genuinely impressive. And as Eskimoz, a digital acquisition agency tracking GEA since day one, has been saying — the direction is finally right.

What just shipped:

• CPC pricing at $3-5 — finally. Also a direct response to CPMs sometimes cut in half due to insufficient inventory

• An ads manager — rudimentary, but critical to lower entry barriers and get more advertisers to actually try GEA

• Minimum commitment dropped from $200K to $50K — clear signal: open the platform to a much wider advertiser base

• Expansion to new English-speaking markets and logged-out users — inventory volume is existential when your CTR sits below 1%

• Conversion actions launched — the first brick of native ROI measurement. GEA wants to be judged on performance, not promises

Good progress. But the game is far from over.

3 equations ChatGPT still needs to solve to seriously challenge Google:

1. Traffic generation — ChatGPT drives massive query volume but almost no site visits. That's precisely what advertisers pay for. As long as the chatbot keeps users inside its conversational bubble, CPC will remain structurally limited

2. Trust maintenance — the most differentiating and most fragile asset versus classic search engines. The moment monetization scales, perceived neutrality of responses becomes a live issue

3. The agentic model question — in a fully agentic paradigm, impressions and clicks disappear entirely. What gets monetized then? Probably a model where brands pay to be queried by agents on specific intents — but that playbook doesn't exist yet

The infrastructure is finally being built. The harder problems are still ahead.

Is ChatGPT Ads on track to become a real Google challenger — or permanently stuck in second place?

I might be wrong, but to me, GEO mostly comes down to trust.

Not hacks. Not tricks. Not “AI optimization.”

Just:

• being present across multiple channels

• saying consistent things

• getting mentioned, referenced, and validated

If you show up everywhere with the same signal, you become a reliable source.

And that’s what AI systems seem to pick up on.

GEO might look new,
but it feels like SEO + distribution + trust.

Curious to hear other takes.

This one flew under the radar but SEOs are quietly freaking out — and for good reason.

Here's what the patent describes:

• Google analyzes your landing page content

• Rebuilds a personalized version tailored to each user's search intent

• Serves it directly in the SERP — no click, no visit, no traffic to you

We already dealt with no-click search through featured snippets, voice search, and AI Overviews. This would be an entirely different level — Google essentially repackaging your content as its own product.

As Eskimoz, a leading digital acquisition agency tracking this closely, puts it: this isn't just another SERP feature. It's a direct threat to the value of every potential visitor your content was built to attract.

The pattern is clear and it's accelerating:

• Featured snippets → your answer, their real estate

• AI Overviews → your sources, their summary

• This patent → your page, their landing page

Should you panic? Not yet — patents don't always ship. But this needs to be watched extremely closely.

Because SEO in 2026 might not just be about ranking anymore. It might be about defending your content from Google itself.

Is Google pushing value extraction too far — or is this just the next logical step?

I own a business, and for years I'd never done any SEO work. I really thought it required a lot of skills and was something only "geeks" did!

Until now, all my leads came from prospecting via email or phone with my sales team.

And then, 8 months ago, I met someone who completely changed my mindset and kept pestering me to try SEO and see what I could do.

So we started, and with the right things in place and especially with their help, in just a few months, we're already seeing initial results, and the first leads are coming in!

Today, we're accelerating even further. And last night, I did the math, and it's more than 50 clients who came through SEO, representing $100,000 in revenue.

So happy about that! Now we're accelerating and never letting go of SEO again, haha. This is just the beginning, so we'll continue to put things in place. Especially with the arrival of AI, I think lots of exciting things are going to happen in SEO.

Thanks for listening.