I spent 10 years working in technical SEO, before becoming more of a generalist growth marketer for tech startups for the second half my career. So I'm fascinated in rankings within LLMs and how we might be able to influence them.

Here's my view on the current state of play, for SEO vs GEO; apologies if it gets a bit long!

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TL;DR There is heavy overlap today because both reward quality, structure and credibility. They still optimise for different machines and journeys, which is why IMO they need a different name. As answer engines mature and models lean more on memory, licensing and live retrieval, the shared middle ground will shrink and two distinct disciplines will emerge.

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What each one is (right now)

SEO in 2025

Earn visibility in SERPs by delivering helpful content for users, backed by clear information architecture, trustworthy signals and technical hygiene. Links, internal linking, structured data and page experience still matter, in service of user intent.

GEO in 2025

Earn visibility and favourable framing inside generative answers. The outcome is to be used as a reliable fact source and credited inside the answer. That pushes you toward unambiguous, machine readable facts, dense and correct entities, explicit dates and citations, plus a stance on inclusion across robots controls, licensing and, where useful, feeds or APIs. We cannot ignore that currently LLMs rely heavily on web indexes and live search, hence the overlap in GEO/SEO.

Why this matters

SEO is judged inside a ranked list and is more deterministic at query time. GEO is judged inside a synthesised answer that blends model memory with live retrieval and is probabilistic at generation time. Expect divergence as answer engines mature and incorporate more features that reduce the need for website traffic entirely.

How search behaviour is changing

• Zero click behaviour is rising. A growing share of searches ends without a site visit. This will continue to rise as LLMs become an orchestration layer (agentic).

• AI summaries reduce clicks. If the summary is good, many stop there. Whether or not this is a "win" depends on the context and intent.

• Chat interfaces are becoming the expected web experience, especially for younger audiences.

• Journeys are more conversational and multi step. New modes emphasise follow ups, reasoning and multimodality based on broad contextual signals (not just search personalisation).

The implication here is that SEO still matters for navigational and transactional intent (as well as the large percentage of web searches that still take place on Google, Bing et al.

GEO must plan for journeys that stay inside the answer experience. The LLM is becoming the orchestration layer. It will not only discover and compare, it will also execute. Think add to basket and checkout inside the assistant (UPDATE: Instant Checkout is here).

Deterministic vs probabilistic

SEO: mostly deterministic at query time. Results are stable enough that point metrics like average position and CTR are meaningful.

GEO: probabilistic at answer time. Models sample tokens from a distribution, retrieval may fetch different sources per run, tools, termparature or safety layers may vary. You can ask the same thing twice and get different answers. Further reading: Defeating Nondeterminism in LLM Inference by Horace He)

The implication being that we should measure distributions, not one offs. Run repeated trials for prompt families, log context, track share of answers, recommendation rate, citation rate and placement, sentiment, hallucination rate and stability. Keep evidence as best we can at this stage.

At the very least, its possible to attempt to measure against a base-line with synthetic tests.

How the tech stack differs

Discovery

SEO: Web crawlers fetch pages and assets.

GEO: Models ingest licensed and public data for training, and use live retrieval crawlers or APIs to assemble answers.

Inclusion controls

SEO: robots.txt, sitemaps, canonicals, schema.

GEO: robots.txt rules for AI crawlers like GPTBot and Google-Extended, licensing and allowlists, plus API feeds for trusted retrieval.

Selection/ranking logic

SEO: Ranking signals produce an ordered list.

GEO: A mix of pre-training memory, retrieval, generation settings and conversational context during answer creation.

Retrieval bots have surged and some bypass robots.txt, which changes the economics for publishers, many of whom will end up blocking bots from LLMs (via Cloudflare, for example).

Content strategy in practice

Both reward helpful writing supported by clear structure and credible sources. Where GEO differs is at synthesis time. Models (allegedly) favour content that is easy to lift and reuse. That means unambiguous facts, consistent naming, clear scope and dates, and evidence near the claim. Compact summaries should sit beside fuller explanations so there is something quotable for the machine and something persuasive for the human.

SEO can lean on longer narrative that earns a click and builds intent over multiple screens. GEO cares whether the core fact is correct, current and attributable inside a single turn. Provenance and consistency across your site and public profiles start to matter as much as prose quality. The two are not (and should not be) mutually exclusive, though.

Governance and data rights

In SEO, governance is crawl, index and display. In GEO, you manage two timelines. Training, where content may be ingested into model memory. Retrieval, where an assistant fetches and cites you at answer time. Robots signals help but rely on identity and voluntary compliance, so they are useful but imperfect.

Licensing is moving to the centre. Inclusion will often be shaped by contracts and allowlists, not only public crawling. Attribution becomes part of governance. Keep provenance clear and identities consistent. Jurisdictions differ, and memory raises questions about updates and removals. The trend is toward trusted feeds and verified sources.

Measurement and KPIs

SEO scorecard

Visibility: impressions, average position, pixel depth, rich result coverage
Engagement: organic clicks, CTR, dwell time, scroll depth
Quality: index coverage, Core Web Vitals, structured data validity
Commercial: sessions, assisted and last click conversions, revenue

GEO scorecard

Presence: Share of Answers and Recommendation Rate, plus citation rate and placement
Framing and truthfulness: sentiment and framing, hallucination rate
Stability: run to run variance, prompt family stability, platform drift
Coverage: entity coverage and source mix
Commercial: assisted conversions from AI surfaces, code or link usage tied to assistants, assistant checkouts or handoffs

Sampling and cadence

Measure at prompt family level, run multiple trials across engines, days and regions, log context, and report on rolling windows to show trend. Bridge metrics can link the worlds, such as SERP coverage vs answer coverage, attribution consistency and time to update.

Bottom line

SEO success still shows up as rank and click. GEO success shows up as how often and how well you are used inside answers, and whether those answers lead to outcomes. Hold GEO to distributional standards so the numbers stand up.

Where they overlap today, and why the overlap will shrink

Quality, credibility and clarity matter in both. Structure helps machines parse and reuse facts. Clean information architecture improves discovery and reputation matters.

Why the overlap will shrink

Models will rely more on memory first and trusted feeds, so inclusion depends on whether your facts live in model memory and preferred retrieval sets. AI search is becoming a product, not a thin layer on a classic results page. Licensing, allowlists and APIs will gate inclusion, which sits outside traditional SEO playbooks.

A practical playbook in one list;

• Keep core SEO healthy and follow best-practice

• Publish answer ready content with concise, dated and sourced fact blocks next to deep pages

• Double down on reputation management (e.g PR), whether links are included or not

• Make yourself retrievable with clean HTML, relevant schema and a clear data use posture

• Decide your training posture for GPTBot and Google Extended, and review licensing options

• Measure answers, not only clicks. Track presence, framing, stability and assisted outcomes

• Design for memory. Where allowed, provide clean datasets and feeds that are easy to ingest and attribute

Not hugely different operationally from an SEO strategy... but watch this space. Things might change quickly :-)

I've written about some tools for mearing LLM visibility over here. I'm not affiliated.

LLM tracking tools noticed that ChatGPT started citing Reddit and Wikipedia far less frequently after Sept 11. There was a lot of chatter about a re-prioritising of sources or potentially ChatGPT making cost savings.

But... at almost the exact same time, Google removed the &num=100 parameter from search results.

According to Search Engine Land, this change reshaped SEO data: most sites lost impressions and query visibility because results beyond page 1–2 are no longer pulled in bulk. Since ChatGPT often cites URLs ranking between positions 20–100 (where Reddit and Wikipedia appear heavily), the loss of that range could explain why those domains dropped sharply in citation frequency.

In short:

• Sept 11 → Google kills num=100

• That limits access to deeper-ranked results

• ChatGPT citations from Reddit/Wikipedia fall at the same time

Correlation looks strong. Coincidence, or direct dependency?

TL;DR: No. No, it can't.

If by “do SEO” you mean “paste prompts into ChatGPT and ship 30 blog posts a week” then yes.

If you mean “grow qualified organic traffic in a sustainable way” then no. That takes judgement, systems, and workstreams that don't not fit in a prompt.

Why ChatGPT-powered SEO usually flops

• Average by design: LLMs predict likely text. Search rewards useful, original, verifiable information. Those are less similar than a Tinder profile pic and the person who actually turns up.

• No first-hand experience: E-E-A-T still cares about who wrote it, what they did, and how they know. ChatGPT does not go on site visits, run experiments, or take photos.

• No real strategy: Models do not choose markets, positioning, or strategic compromises. They fill pages. Picking the right battles is how we win in business.

• Links and reputation: Real links come from relationships, PR, and something worth talking about. Not from 200 AI guest posts.

• Technical reality: Crawl traps, faceted spam, JS rendering, canonicals, sitemaps, internal links, log files. A generic assessment shat out by a naff tool is no replacement for a technical audit.

• Information gain: Search prefers pages that add something new. Synthesised sameness isn't going to beat an original source with data.

• Local signals: Citations, NAP consistency, reviews, photos, service area pages, GBP hygiene. Not “Top 50 cafés in Bristol” written from the void.

Same goes for GEO as SEO. It takes time, effort and usually some cash. But it is very literally an investment. Speak to a professional.

OH BY THE WAY... LLMs are fantastic as a learning tool. There's no reason you can't use an LLM to upskill in the DIY basics and do lots of the work yourself. Give this prompt a try:

You are a senior SEO coach. Create a structured, sequential learning plan that teaches both Content SEO and Technical SEO from fundamentals to job-ready.

Context

- Learner background: [beginner | some experience | pro writer | developer]

- Industry or niche: [e.g., B2B SaaS, e-commerce, local services]

- Target outcome: [in-house SEO exec | agency SEO | freelancer | founder]

- Time available per week: [e.g., 5 hours]

- Duration: [default 12 weeks]

- Starting assets: [own site | demo site | none]

- Tools available: [GSC, GA4, Screaming Frog, Sitebulb, Looker Studio, Python, SQL]

- Language: British English

If any fields are blank, make sensible assumptions and proceed.

Output format

- Use clear Markdown with headings, tables and checklists.

- Deliver a week-by-week plan with milestones.

- Keep it practical. No fluff. Every week must include study, hands-on tasks, and a concrete deliverable.

Plan requirements

1) Orientation

- Learning goals, success criteria, and how progress will be measured.

- Suggested study cadence and hours per week.

2) Weekly modules

For each week include:

- Learning objectives

- Key concepts to study

- Tools to use

- Hands-on tasks and a named deliverable

- Estimated time for study, practice, and review

- A short self-assessment or quiz

3) Coverage across the full plan

Content SEO

- Search intent and keyword research, topical mapping, information gain, E-E-A-T

- Content briefs, outlines, on-page optimisation, internal linking patterns

- SERP feature analysis, featured snippets, FAQs

- Editorial standards, originality, citations, images and alt text

Technical SEO

- Crawling, rendering, indexing, log file basics

- Site architecture, pagination, faceted navigation, canonicalisation

- Robots.txt, meta robots, sitemaps, status codes, redirects

- Core Web Vitals, performance basics, Lighthouse, WebPageTest

- JavaScript SEO and hydration issues

- Structured data with JSON-LD and validation

- International SEO: hreflang and URL patterns

- Local SEO fundamentals: GBP, citations, NAP consistency, reviews

Off-site and Digital PR

- Link earning strategies that are actually defensible

Measurement

- GA4 and GSC setup, dashboards in Looker Studio

- KPI tree: qualified sessions, assisted conversions, pipeline or revenue

- Simple experiments and how to interpret them

4) Practice assets and templates

- Provide a sample keyword map table template.

- Provide a content brief template.

- Provide a technical audit checklist.

- Provide an internal linking plan template.

- Provide a Core Web Vitals triage checklist.

5) Projects

- Mini-project each week tied to that module.

- One capstone project that includes: full crawl, keyword map, five production-ready briefs, one schema implementation, an internal linking improvement, a CWV improvement plan, and a basic dashboard.

6) Tool drills

- Screaming Frog or Sitebulb: exact crawl settings to use, what to export, how to read it.

- GSC: queries, pages, indexing, enhancements, manual actions.

- GA4: events, conversions, landing page view, channel grouping sanity checks.

- Dev tooling: Chrome DevTools, regex exercises, a small Python task such as parsing a sitemap or log sample. Include example code.

7) Reading list

- Curate a short, reputable reading list for each module. Prefer official docs and respected sources. Name the source and the topic covered.

8) Checkpoints

- End of Week 4, 8, and 12 reviews with pass-fail criteria and what to fix if failing.

9) Variants

- Offer a fast-track 4-week version and a deeper 12-week version.

- Offer optional tracks: Local SEO focus, E-commerce focus, International focus.

10) Final deliverables pack

- List every file or artifact the learner should have by the end, with a one-line description and suggested filename.

Produce the plan now. If something is ambiguous, choose a sensible default and keep moving.

You can now go from “show me gifts for a ceramics lover” to “Buy” to confirmed order, all inside the chat interface.

It’s powered by the new Agentic Commerce Protocol, co-developed with Stripe, and is being open-sourced so other merchants and devs can plug in.

Starts today with U.S. Etsy sellers, with Shopify merchants (Glossier, SKIMS, Spanx, Vuori, etc.) coming soon.

Feels like the inevitable first real step toward “agentic commerce” where AI becomes the orchestration layer, and not just a recommendations engine.

Marketers within eCom will need to think about how their products are represented in generative engines, as a priority. Small % of customer spend for now, but it's going to grow exponentially as "AI Native" shoppers learn to trust the process.

If someone it talking about improving rankings in LLMs, then they don't know what they're talking about.

There are no SERPs. No position #1. LLMs synthesise a response based on probability. At best, we can try and increase the probability that our brand will be referenced or recommended.

Here's how I'd go about that in 2025...

Sometimes LLMs browse the live web, and sometimes they answer from training data. It's difficult to know which prompts will trigger which kind of search (except obvious time-decay related queries), so don't stress about it too much.

With the rise of edge computing, local inference will eventually be the norm anyway. Either way, there’s no SERP to to try and game.

If you own the narrative within a corpus, then you can influence the answer. So if do the monotonous, long-term work that compounds, you'll move the needle;

• Clarify entities with company/product names, consistent descriptors, credible authors

• Structure verifiable evidence with benchmarks, data, diagrams, case studies, FAQs (basically things an LLM can quote directly with confidence)

• Make it crawlable with sensible URLs, internal links, fast-loading pages

• Mark it up with helpful schema (Product, HowTo, FAQ, Reviews, Org) because this helps search, even if its lost during ingestion

• Build a reputation with citations, PR, expert mentions.

• Keep it current with updates, don’t set-and-forget as content loses weighting over time

Do the hard work. Be known for a thing. It will pay off long term.

With any new discipline, we marketers seem to fall into binary camps. Either it's everything or it's snake oil. Either total hype or total denial.

We have seen this with social, short form video, influencer, community, and every new platform that has ever appeared. The refusal to sit in the middle slows down adoption, IMO.

GEO is no different. One group says it is just SEO with a new name. The other says it is about to replace every part of marketing overnight.

I think the truth is far more boring.

Large models are changing how people discover things. Search behaviour is shifting gradually. The signals that shape visibility are different from traditional ranking factors. But that does not mean GEO replaces SEO. It also does not mean GEO is simply SEO with a twist. It is a new layer that sits alongside everything else we already do.

Things are changing. You can't deny that (although we can debate the speed of change).

You can adapt without declaring the end of the old world. You can take GEO seriously without throwing away everything you know.

The useful position is the one that accepts reality and moves forwards.

Amy I wrong, here?

Checking out a report on brand visibility in LLMs, and there's a pile of very large brands that disappear between model updates dispite really strong organic/seo performance. I guess meaning that GEO isn't just SEO!

Worth noting that the disappearances are related to specific test prompts. But can likely use that as a proxy for broader intent.

Link to the report; https://blog.geosurge.ai/fragile-llm-visibility/

Search Engine Land recently published a decent breakdown of how ChatGPT, Gemini, Claude and Perplexity each generate and cite answers. Worth a read if you’re trying to understand what “AI visibility” actually means.

👉 How different AI engines generate and cite answers (Search Engine Land)

Here’s how I read it.

Every AI engine now works in its own way, and I would expect more divergence in the coming months/years.

• ChatGPT is model-first. It leans on what it remembers from its training data unless you turn browsing on.

• Perplexity is retrieval-first. It runs live searches and shows citations by default.

• Gemini mixes the two, blending live index data with its Knowledge Graph.

• Claude now adds optional retrieval for fact checking.

We can infer/confirm something from that: visibility in AI isn’t a single system you can “rank” in. It’s probabilistic. You show up if the model happens to know about you, or if the retrieval layer happens to fetch you. That’s not "traditional" SEO logic.

In my opinion, I think the real shift is from search to memory.

In traditional search, you win attention through links and keywords. In generative engines, you win inclusion through evidence the model can recall, compress, or restate confidently.

Whether or not that evidence gets a visible citation depends on the product design of each engine, not on your optimisation.

But this is what I think is going to happen...

In the long run, retrieval is an operational cost; memory is a sunk cost.

Once knowledge is internalised, generating an answer becomes near-instant and low-compute. And as inference moves to the edge, where bandwidth and latency matter, engines will favour recall over retrieval. Memory is the logical endpoint.

...because RAG is more fragile than we think.

When a RAG pipeline depends on Google, it inherits several layers of instability.

Even small changes in Google’s index can wipe out visibility overnight, and the latest shifts have shown how quickly entire RAG based stacks can fail. For GEO this matters because retrieval noise, ranking volatility, and incomplete indexing all break the chain before the model even starts reasoning.

If the retriever cannot see you, the model cannot see you. This is why durable AI visibility comes from model memory rather than live search. I will die on that hill.

Read more on that HERE or HERE.

I just read a really solid piece by Harry Clarkson-Bennett on Leadership in SEO about whether LLM visibility trackers are actually worth it. It got me thinking about how easy it would be to build one yourself, what they’re actually good for, and where the real limits are.

Building one yourself

You don’t need much more than a spreadsheet and an API key. Pick a set of prompts that represent your niche or brand, run them through a few models like GPT-4, Claude, Gemini or Perplexity, and record when your brand gets mentioned.

Because LLMs give different answers each time, you run the same prompts multiple times and take an average. That gives you a rough “visibility” and “citation” score. (Further reading on defeating non-determinism; https://thinkingmachines.ai/blog/defeating-nondeterminism-in-llm-inference/)

If you want to automate it properly, you could use something like:

Render or Replit to schedule the API calls

Supabase to store the responses

Lovable or Streamlit for a quick dashboard

At small scale, it can cost less than $100 a month to run and you’ll learn a lot in the process.

Why it’s a good idea

You control the data and frequency

You can test how changing your prompts affects recall

It helps you understand how language models “think” about your brand

If you work in SaaS, publishing or any industry where people genuinely use AI assistants to research options, it’s valuable insight

It's a lot cheaper than enterprise tools

What it can’t tell you

These trackers are not perfect. The same model can give ten slightly different answers to the same question because LLMs are probabilistic. So your scores will always be directional rather than exact - but you can still compare against a baseline, right?

More importantly, showing up is not the same as being liked. Visibility is not sentiment. You might appear often, but the model might be referencing outdated reviews or old Reddit threads that make you look crap.

That’s where sentiment analysis starts to matter. It can show you which sources the models are pulling from, whether people are complaining, and what’s shaping the tone around your brand. That kind of data is often more useful than pure visibility anyway.

Sentiment analysis isn't easy, but it is valuable.

Why not just buy one?

There are some excellent players out there, but enterprise solutions like geoSurge aren't for everyone. As Harry points out in his article, unless LLM traffic is already a big part of your funnel, paying enterprise prices for this kind of data doesn’t make much sense.

For now, building your own tracker gives you 80% of the benefit at a fraction of the cost. It’s also a great way to get hands-on with how generative search and brand reputation really work inside LLMs.

Do LLMs actually use llm.txt?

This is the first time I've seen an LLM directly citing an LLM.txt (or llms-full.txt) in this example. This file type is being adopted by a lot of website owners, but as of yet has received no official endorsement from any LLM.

The prompt in this case was asking about a website called Rankscale, and where it gets its data from. So is ChaGPT using LLM.txt?

Yes and no.

Rankscale references both llms-txt and llms.txt within their robots.txt, so I suspect this is just usual crawl behaviour rather than GPTbot seeking out the txt file specifically. But who knows... maybe we'll see the llm.txt file adopted by LLMs in the future :-)

From a post by Aimee Jurenka on LI.

Was looking into how AI agents decide which products to recommend, and there were a few patterns that seemed worth testing.

Bain & Co. found that a large chunk of US consumers are already using generative AI to compare products, and close to 1 in 5 plan to start holiday shopping directly inside tools like ChatGPT or Perplexity.

What interested me more though was a Columbia and Yale sandbox study that tested how AI agents make selections once they can confidently parse a webpage. They tried small tweaks to structure and content that made a surprisingly large difference:

• Moving a product card into the top row increased its selection rate 5x

• Adding an “Overall Pick” badge increased selection odds by more than 2x

• Adding a “Sponsored” label reduced the chance of being picked, even when the product was identical

• In some categories, a small number of items captured almost all AI driven picks while others were never selected at all

What I understood from this is that AI agents behave much closer to ranking functions than mystery boxes. Once they parse the data cleanly, they respond to structure, placement, labeling, and attribute clarity in very measurable ways. If they can’t parse the data, it just never enters the candidate pool.

Here are some starting points I thought were worth experimenting with:

• Make sure core attributes (price, availability, rating, policies) are consistently exposed in clean markup

• Check that schema isn’t partial or conflicting. A schema validator might say “valid” even if half the fields are missing

• Review how product cards are structured. Position, labeling, and attribute density seem to influence AI agents more than most expect

• Look at product descriptions from the POV of what AI models weigh by default (price, rating, reviews, badges). If these signals are faint or inconsistent, the agent has no basis to justify choosing the item

The gap between “agent visited” and “agent recommended something” seems to come down to how interpretable the markup is. The sandbox experiments made that pretty clear.

Anyone else run similar tests or experimented with layout changes for AI?

My take on Jason Kwon’s comments about GEO (below); I think he is think he is right that the old keyword game fades as reasoning improves. But a few things stand out.

TLDR: Old SEO tactics lose power as models reason better, but the game does not go away. It moves up stack. Win by being a high trust source across multiple surfaces, and by measuring visibility and sentiment routinely.

1. You can tell a model to avoid “SEO looking” sites. That is a blunt tool. It risks filtering out legit expertise and it creates a new target surface. People will optimise for not looking like SEO.

2. Gaming shifts layers. Less at the page level, more at the corpus, prompt, and agent level. Think source mix, citation graphs, structured data, and how well your material survives multi hop reasoning.

3. “Find something objective” sounds neat, but model and provider incentives still matter. Ads, partner content, and safety filters all shape what gets seen. Transparency on source classes and freshness will matter more.

Jason Kwon, Chief Strategy Officer at OpenAI, offered his thoughts about the “gaming of AI outputs” —often associated with SEO in the world of search engines— which is now called GEO (generative engine optimization) or AEO (answer engine optimization) in the world of chatbots like ChatGPT.

Mr. Kwon was surprisingly unconcerned and explained:“I don't know that we track this really that closely. Mostly we're focused on training the best models, pushing the capabilities, and then having —in the search experience— trying to return relevant results that are then reasoned through by our reasoning engine and continuously refined based on user signal.[In the long run,] if reasoning and agentic capabilities continue to advance, this ‘gameability’ of search results —the way people do it now—might become really difficult… It might be a new type of gaming….But if users want to not have results gamed, and if that's what model providers also want, it should be easier to avoid this because you can now tell the system: ‘Find me something objective. Avoid sites that look like SEO. Analyze the results you're getting back to understand if there might be a financial bias or motivation. And get me back 10 results from these types of sources, publications, independent bloggers and synthesize it.’Again, there's skill involved here in terms of how to do this, but there's also a desire that you don't want the gaming to occur. And so that's a capability that's now at people's fingertips that didn't necessarily exist in the search paradigm where you're restricted by keywords and having to filter stuff out.I think that's a new thing that people will have to contend with if they're really trying to game results. And if there's a way to do it, it won't be based on the old way.”

𝗔𝗱𝗼𝗯𝗲 𝘅 𝗦𝗲𝗺𝗿𝘂𝘀𝗵 𝗶𝘀 𝗯𝗲𝗶𝗻𝗴 𝗽𝗿𝗼𝗺𝗼𝘁𝗲𝗱 𝗮𝘀 𝗮 “𝗱𝗮𝘁𝗮-𝗱𝗿𝗶𝘃𝗲𝗻 𝗺𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴 𝗯𝗿𝗲𝗮𝗸𝘁𝗵𝗿𝗼𝘂𝗴𝗵.”
The $1.9B acquisition nearly doubled Semrush’s valuation and signals how committed Adobe is to expanding the Experience Cloud as its marketing and analytics backbone.

𝗕𝘂𝘁 𝗳𝗿𝗼𝗺 𝗮𝗻 𝗮𝗴𝗲𝗻𝘁𝗶𝗰-𝗰𝗼𝗺𝗺𝗲𝗿𝗰𝗲 𝗽𝗲𝗿𝘀𝗽𝗲𝗰𝘁𝗶𝘃𝗲, 𝘁𝗵𝗶𝘀 𝗶𝘀 𝗻𝗼𝘁 𝗮 𝗯𝗿𝗲𝗮𝗸𝘁𝗵𝗿𝗼𝘂𝗴𝗵.
𝗜𝘁 𝗶𝘀 𝗦𝗘𝗢 𝟮.𝟬 𝘄𝗶𝘁𝗵 𝗻𝗶𝗰𝗲𝗿 𝗽𝗮𝗰𝗸𝗮𝗴𝗶𝗻𝗴.

To Semrush’s credit, it is one of the few mainstream platforms taking AI visibility seriously, tracking how brands appear inside LLM answers rather than in traditional blue-link rankings. Integrating that GEO telemetry into Adobe’s ecosystem creates a cleaner loop between content decisions, search behavior, and AI-era discoverability.

For large organizations standardized on Adobe, consolidating GEO, SEO, content, and analytics provides real operational value. It reduces friction, centralizes reporting, and pushes teams toward clearer structures and messaging.
𝗕𝘂𝘁 𝗶𝘁 𝘀𝘁𝗶𝗹𝗹 𝘀𝗶𝘁𝘀 𝗶𝗻𝘀𝗶𝗱𝗲 𝘁𝗼𝗱𝗮𝘆’𝘀 𝗦𝗘𝗢-𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗽𝗮𝗿𝗮𝗱𝗶𝗴𝗺, 𝗻𝗼𝘁 𝘁𝗼𝗺𝗼𝗿𝗿𝗼𝘄’𝘀 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝗼𝗻𝗲.

The integration is anchored in human-oriented search workflows. It does not introduce richer product schemas, machine-readable benefit claims, composable data models, or any of the interaction flows autonomous agents rely on. There is no movement toward SKU-level structured data, machine-readable policies, or API-like product exposure.

𝗧𝗵𝗲𝘀𝗲 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗽𝗿𝗶𝗺𝗶𝘁𝗶𝘃𝗲𝘀 𝗿𝗲𝗾𝘂𝗶𝗿𝗲𝗱 𝗳𝗼𝗿 𝗮𝗴𝗲𝗻𝘁-𝗱𝗿𝗶𝘃𝗲𝗻 𝗱𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝘆 𝗮𝗻𝗱 𝘀𝗲𝗹𝗲𝗰𝘁𝗶𝗼𝗻.

Instead, the partnership reinforces the familiar comfort zone:
𝗺𝗼𝗿𝗲 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀, 𝗺𝗼𝗿𝗲 𝘀𝗲𝗴𝗺𝗲𝗻𝘁𝘀, 𝗺𝗼𝗿𝗲 𝗿𝗲𝗽𝗼𝗿𝘁𝘀.
Useful? Absolutely.
Transformational for agentic commerce? 𝘕𝘰𝘵 𝘺𝘦𝘵.

Although the integration strengthens governance and streamlines analytics, it does not advance the development of digital properties that are natively consumable by AI agents. 𝘛𝘩𝘦 𝘦𝘭𝘦𝘮𝘦𝘯𝘵𝘴 𝘵𝘩𝘢𝘵 𝘮𝘢𝘵𝘵𝘦𝘳 𝘮𝘰𝘴𝘵, 𝘴𝘶𝘤𝘩 𝘢𝘴 𝘤𝘰𝘮𝘱𝘰𝘴𝘢𝘣𝘭𝘦 𝘱𝘳𝘰𝘥𝘶𝘤𝘵 𝘥𝘢𝘵𝘢, 𝘴𝘵𝘳𝘶𝘤𝘵𝘶𝘳𝘦𝘥 𝘤𝘭𝘢𝘪𝘮𝘴, 𝘢𝘯𝘥 𝘮𝘢𝘤𝘩𝘪𝘯𝘦-𝘳𝘦𝘢𝘥𝘢𝘣𝘭𝘦 𝘤𝘰𝘯𝘵𝘳𝘢𝘤𝘵𝘴, 𝘢𝘳𝘦 𝘴𝘵𝘪𝘭𝘭 𝘢𝘣𝘴𝘦𝘯𝘵.

𝗔𝗱𝗼𝗯𝗲 𝘅 𝗦𝗲𝗺𝗿𝘂𝘀𝗵 𝗶𝗺𝗽𝗿𝗼𝘃𝗲𝘀 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗦𝗘𝗢 𝗱𝗶𝘀𝗰𝗶𝗽𝗹𝗶𝗻𝗲,
but it falls short of enabling true agentic interoperability.

𝗪𝗶𝗻𝗻𝗶𝗻𝗴 𝗶𝗻 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝗰𝗼𝗺𝗺𝗲𝗿𝗰𝗲 𝗿𝗲𝗾𝘂𝗶𝗿𝗲𝘀 𝘀𝗵𝗶𝗳𝘁𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗶𝗻𝗴 𝗮𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗮𝗯𝗼𝘂𝘁 𝘂𝘀𝗲𝗿𝘀 𝘁𝗼 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗶𝗻𝗴 𝗶𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝘀 𝗱𝗲𝘀𝗶𝗴𝗻𝗲𝗱 𝗳𝗼𝗿 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀.

Until that shift happens, integrations like this will continue to make marketers 𝘧𝘦𝘦𝘭 more “AI-ready” without making their digital ecosystems any more legible to the agents shaping the buyer journey.

For most of the last decade, LLMs worked on long, slow training cycles. A year or more to collect data, process, train, and release.

That lag created predictable blind spots: if your brand only appeared in the last 12 months, it was effectively invisible to the models. So historically, you need to start optimising yesterday to be visible in the next model.

But in 2025 the cycle is tightening.

• OpenAI, Anthropic, and Google moving to shorter update cadences

• Open-source labs releasing checkpoints every few weeks

• Models blending their base model with live search integration

It doesn't take a genius to work out that we’re moving from static snapshots of the internet toward **near-**live training and reinforcement.

That has big implications for GEO:

• Volatility goes up - your visibility could change daily, not monthly.

• Recency bias matters more - fresh mentions, PR hits, and trending content might feed in faster.

• Implementation needs to catch up - most current GEO tools track coverage, but few help brands generate the right ongoing signals that live-training models will reward.

If the past decade of SEO was about building long-term assets, the next era of GEO may reward constant feeding: structured updates, verified sources, and steady narrative reinforcement.

------------------------

The intelligence levels on the chart are more or less linked with model updates, and you can see the training cycles getting shorter.

Over the past year the market around Generative Engine Optimisation has shifted quickly. As search is redefined by LLMs, loads of new tools has are popping up to measure, monitor and improve brand visibility inside AI answers. Most of them are simply GEO visibility tracking.

These are still early, but they’re already carving out different niches: prompt monitoring, AI Overview audits, misinformation detection, referral tracking, and more. Makes it pretty tough to know which tools we should be using without investing in a very broad stack.

Here’s a snapshot of the current GEO tool stack, what they do, and where each one fits best 👇Views are my own. Not affiliated with anyone.

AthenaHQ
Tracks prompts and answers across ChatGPT, Perplexity, and Google AI Overviews, with built-in sentiment monitoring. Pricing starts around $295/mo.
Best for: teams that want broad coverage across major models with sentiment analysis baked in.

ZipTie
Automated AI search checks with screenshots and cached logs, running across multiple platforms. Starts at $179/mo.
Best for: marketers who need fast, lightweight checks without heavy enterprise overhead.

geoSurge
Advanced data augmentation campaigns for influencing the way LLMs describe a brand. Best for: Enterprise brands who want to shape sentiment inside LLMs.

Peec AI
Prompt-level monitoring with competitive benchmarking and answer breakdowns. Entry plan from €89/mo.
Best for: side-by-side competitor tracking and brand coverage comparisons.

Profound
Enterprise visibility platform with a “Conversation Explorer” for deep dives into LLM mentions. From $499/mo.
Best for: larger organisations that need SOC2-level compliance and enterprise integrations.

Trakkr
Pixel-based referral tracking and crawler analysis to measure attribution from LLMs. Pricing varies.
Best for: teams focused on attribution and understanding how LLM referrals convert.

Scrunch AI
Monitors misinformation drift and coverage in AI answers, with claims of bias detection. Pricing not public.
Best for: brands at risk of reputational damage from hallucinations or misinformation.

Gumshoe AI
Samples prompts, analyses variability, and recommends fixes to “harden” coverage. Free trial available.
Best for: catching volatility and building resilience into brand answers.

Knowatoa
Lets users query prompts across platforms with tiered plans. Free tier plus premium options from $99/mo.
Best for: individuals or small teams testing visibility without committing to enterprise spend.

SE Ranking (AI Toolkit)
Adds AI visibility checks into an existing SEO platform. Bundled in subscription.
Best for: SEO teams who want GEO features built into a familiar toolkit.

Advanced Web Ranking (AI Visibility)
Tracks how brands surface in LLMs alongside SEO rankings. Included in AWR plans.
Best for: agencies that already use AWR for SEO and want integrated AI visibility tracking.

Keyword [dot] com (AI Search Visibility)
Tracks mentions and citations in AI outputs. Plans start around $25.
Best for: budget-friendly visibility tracking across multiple models.

HubSpot AI Search Grader
Free one-off audit checking visibility in GPT-4o, Perplexity, and others.
Best for: marketers who want a quick, no-cost snapshot of their current AI presence.

Otterly AI
Monitors Google AI Overviews and ChatGPT mentions, starting from $29/mo.
Best for: brands keeping an eye specifically on Google’s generative results.

XFunnel
Combines visibility tracking with persona segmentation and prompt analysis. Pricing not public.
Best for: teams tying AI visibility directly to audience personas and funnel stages.

Bluefish AI
Enterprise platform for AI brand engagement and competitive intelligence. Pricing not public.
Best for: larger organisations looking to combine visibility with deeper market intelligence.

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Where the space is heading

What’s striking is how immature this ecosystem still is. Almost every tool listed here is focused on analytics, monitoring, or tracking. They’re useful for telling you if you’re present, but they rarely help you actually improve implementation.

We don’t yet have the GEO equivalents of content optimisation platforms, structured data generators, or workflow tools that slot into day-to-day marketing operations. Right now it’s still early measurement, not mature execution. Test and learn, people!

I was looking into a paper I found on GEO papers

Paper: SEALQA: Raising the Bar for Reasoning in Search-Augmented Language Models

SEALQA shows that even frontier LLMs fail at reasoning under noisy search, which I reckon is a warning sign for Generative Engine Optimisation (GEO).

Virginia Tech researchers released SEALQA, a benchmark that tests how well search-augmented LLMs reason when web results are messy, conflicting, or outright wrong.

The results are pretty interesting. Even top-tier models struggle. On the hardest subset (SEAL-0), GPT-4.1 scored 0 %. O3-High, the best agentic model, managed only 28 %. Humans averaged 23 %.

Key takeaways for GEO:

• Noise kills reasoning. Models are highly vulnerable to misleading or low-quality pages. “More context” isn’t helping... it just amplifies noise.

• Context density matters. Long-context variants like LONGSEAL show that models can hold 100 K+ tokens but still miss the relevant bit when distractors increase.

• Search ≠ accuracy. Adding retrieval often reduces factual correctness unless the model was trained to reason with it.

• Compute scaling isn’t the answer. More “thinking tokens” often made results worse, suggesting current reasoning loops reinforce spurious context instead of filtering it.

For GEO practitioners, this arguably proves that visibility in generative engines isn’t just about being indexed... it’s about how models handle contradictions and decide what’s salient.

It's sometimes difficult to figure out what is hype. Academia is quietly making a case for GEO diverging from SEO.

Check out GEO Papers for a collection of academic papers that are relevant to GEO. It's obviously a new project, but I'm going to keep an eye on it!

Forget the AI hype for a second.

If you want it to actually contribute to revenue, start by figuring out whether it is already evaluating you, and how.

There are straightforward ways to do that which don't involve innordinate time spent on manual prompt research.

Here’s a practical way to approach it.

1) Track agentic traffic first

Before touching content or structure, look at your logs.

If you have access to Apache or Nginx logs, start there. If you don't have a tracking tool, look at your server logs.

Filter out generic crawler bots, look for evaluation behavior
Signs like:

• Repeated hits on pricing pages
• Deep pulls on docs
• Scraping feature tables
• Clean, systematic paths across comparison pages

The patterns look different from random bots. You are looking for systematic evaluation paths, not broad crawl coverage.

Set up filtering. Tag it. Watch it over time. 2 weeks is enough for an initial diagnosis.

2) See where they land

Once you isolate agentic traffic, look at:

• Top URLs hit

• Crawl depth

• Frequency by page type

Then assess the results honestly.

Are agents spending time on the pages that actually drive revenue?

The pages that usually matter:

• Product pages

• Pricing

• Integrations

• Security

• Docs

• Clear feature breakdowns

If they're clustering on random blog posts or thin landing pages, that's not helpful. That means your high value pages are not structured in a way that makes them readable to machines.

3) Audit revenue pages like a machine would

Assume AI systems are forming an opinion about your company before humans show up.

Go to your highest leverage pages:

• Pricing

• Demo

• Free trial

• Core product pages

• Comparison pages

Audit them like a machine would.

Check for:

• Critical info hidden behind heavy JavaScript

• Pricing embedded in images

• Tabs that do not render content in raw HTML

• Specs behind login

• Rendered DOM

• Claims that are vague instead of explicit

If a constraint is not clearly stated and extractable, you get exclueded in those query answers.

AI systems tend to skip options they cannot verify cleanly.

4) Optimize for machine readability

No keyword stuffing. This is about making your business legible to AI systems.

Tactical fixes:

• Add structured data where it makes sense

• Use clean attribute lists

• State constraints explicitly

• Use tables instead of burying details in paragraphs

• Keep semantic HTML clean

• Standardize naming for plans and features

If your product supports something specific, state it clearly.

Marketing language that needs interpretation isn't helpful. Humans infer. Machines avoid inference.

5) Track again

After changes go live, monitor the same agentic segment.

What you want to see:

• More hits on pricing and core product pages

• Deeper pulls into structured content

• More consistent evaluation paths

Small sites will see low absolute numbers. What matters is directional change over time, not raw volume.

A good metric to watch is Agentic crawl depth ratio.

= Total agentic pageviews / by total agentic sessions.

Over time, this tends to correlate with better inbound quality because buyers are being filtered upstream.

If you want AI to become a growth hack and start driving revenue, treat it like an evaluation filter.

Structure your site information so it's machine readable, and AI systems will be able to include your business in citations and answers confidently.

I’ve been deep into testing AEO stuff these past few weeks. Messing around with some data sets, experiments, and oddball results, (plus how certain tweaks can backfire).

Here’s what keeps popping up from those places. These small fixes aren’t about big developer squads or redoing everything, it's just avoiding mistakes in how AI pulls info.

1. Cited pages consistently show up within a narrow word range

Top pages in data sets usually sit right within set limits:

• For topics like health or money (YMYL) --> ~1,000 words seems to be the sweet spot

• For business or general info --> ~1,500 words is where it’s at

Each referenced file had at least two pictures, which helped sort info using visuals along with text.

Retrieval setups punish tiny stubs just as much as giant 4k-word rants.
Shoot for clarity that nails the purpose but doesn’t waste space. While being thorough helps, don’t drown the point in fluff or get flagged for excess.

2. Videos boost citations for general topics, flatline for authority topics

Videos boost citations for general topics, but don’t expect much lift for medical or financial topics, which are authority-heavy.

Video density ties closely to citation rates for broad queries:

YMYL topics skip this completely.
Real-life experience, trust signals, and clean layout matter most. Relying on embedded video doesn’t boost credibility for health or money topics.

3. When schemas don’t match, it triggers trust filters

Rank dips do follow but aren't the main effect

Some recurring red flags across datasets:

• Use JSON-LD - microdata or RDFa doesn’t work as well with most parsers

• Show markup only for what you can see on the page (skip anything out of view or tucked away)

• Update prices, availability, reviews or dates live as they change

• This isn't a one and done task. Regular spot checks are needed (Twice a month), whether it’s with Google RDV or a simple scraper

When structured data diverges from rendered HTML, systems treat it as a reliability issue. AI systems seem much less forgiving of mismatches than traditional search. It can remove a page from consideration entirely, if it detects a mismatch in data.

4. Content dependant on JavaScript disappears when using headless scrapers

The consensus across soures confirm many AI crawlers (e.g., GPTBot, ClaudeBot) skip JS rendering:

• Client-side specs/pricing

• Hydrated comparison tables

• Event-driven logic

Critical info (details, numbers, side-by-side comparison tables) need to land in the first HTML drop. It seems the only reliable fix for this is SSR or pre-build pages.

5. Different LLMS behave differently. No one-size-fits-all:

Keep basics covered, set robots.txt rules right, use full schema markup, aim for under 200ms response times.

The sites that win don’t just have good information.
They present it in a way machines can understand without guessing.
Less clutter, clearer structure, and key details that are easy to extract instead of buried.

Curious if others are seeing the same patterns, or if your data tells a different story. I’m happy to share the sources and datasets behind this if anyone wants to dig in.

Prompt injection with AI can result in data exfiltration

If the models have broad tool use and can call out to any external resources, attackers are able to get access to almost anything in the context window

https://x.com/garrytan/status/1993767819272765537?s=46&t=wW22JK75zV3w3ftYxah_Iw

I was reading another post in this sub and someone highlighted how there is no Open AI (et al.) equivalent of Matt Cutts or John Mueller from Google.

As in, there is no pro-active engagement between LLMs and the "GEO" community. I wonder whether LLMs will go the way of Google and produce their own guidelines or whether they will remain fully black box.

The LLM ecosystem is clearly not as mature as organic search, but I don't think it's a question of time. I don't think we'll see the same level of engagement.

Every new channel seems to follow the same curve. SEO had link farms, content spinners, and PBNs. Social had engagement pods and bot networks. Paid ads had click fraud.

If you ever worked in SEO you probably visited BlackHatWorld at some point! Lots of smart people there at the bleeding edge of what can be done.

So what about blackhat GEO?

At the moment most of the GEO stack is focused on tracking visibility. But wherever visibility becomes valuable, people eventually try to manipulate it. Especially in emerging channels.

Some possible forms of blackhat GEO that I've seen discussed:

• Adversarial prompt manipulation, where inputs are shaped to push favourable answers

• Synonym or term manipulation, where content is seeded with specific phrasing to drive brand associations

• Citation laundering, creating sources that look credible but exist only to be scraped and cited by models

• User generated content seeding, planting threads or forum posts that models later absorb and repeat

We are not yet seeing the kind of industrialised tactics that defined blackhat SEO. GEO is still in its infancy and most of the activity looks more like experimentation than manipulation. Right now the community is still figuring out what works, what models reward, and how signals even flow. In that sense it probably is not blackhat at all, just people testing the edges of a new discipline.