u/lightsiteai

The debate around SEO vs GEO is mostly missing the point.

It reminds me of the early cloud days. Some people were saying cloud was exactly the same thing as an on-premises data center, and in a way, they were right.

Technically, it was mostly the same thing. The real difference was how budget was allocated, CAPEX vs OPEX, and the skill set needed to manage the new environment.

I think GEO is similar.

GEO is not just another SEO tactic, and it is not another dashboard category. In practice, it is becoming a resource allocation problem and a skill set problem.

The companies that win in AI search will not be the ones publishing the most content or tracking the most prompts. They will be the ones that are easiest to understand, verify, and recommend.

Try a simple test inside your company.

Ask 10 people: Who are we? What do we do? Who are we really for?

Sounds simple, right?

In my experience, you will often get 10 different answers. Sometimes very different answers.

That is the essence of GEO.

If your own team cannot describe the company consistently, why should ChatGPT, Perplexity, Gemini, or Claude understand you clearly?

This is why I think a new role is coming: Brand Authority Architect.

Someone who sits between SEO, brand marketing, PR, content, product marketing, and leadership.

The best way to describe this role is as a kind of compliance officer for marketing.

Their job is to align the brand message across every source, cut budget from work that creates noise, like useless AI slop blog production, and invest in the channels that actually build authority.

If this sounds like nothing new, that is because maybe it is not. It is almost the same ingredients, just a completely different cocktail.

Technically, an SEO executive can become a Brand Authority Architect.

But in my experience with people in this space, most will not adapt. They will keep shipping small technical website fixes while the real budget and influence move somewhere else.

Wdyt?

The debate around SEO vs GEO is mostly missing the point.

It reminds me of the early cloud days. Some people were saying cloud was exactly the same thing as an on-premises data center, and in a way, they were right.

Technically, it was mostly the same thing. The real difference was how budget was allocated, CAPEX vs OPEX, and the skill set needed to manage the new environment.

I think GEO is similar.

GEO is not just another SEO tactic, and it is not another dashboard category. In practice, it is becoming a resource allocation problem and a skill set problem.

The companies that win in AI search will not be the ones publishing the most content or tracking the most prompts. They will be the ones that are easiest to understand, verify, and recommend.

Try a simple test inside your company.

Ask 10 people: Who are we? What do we do? Who are we really for?

Sounds simple, right?

In my experience, you will often get 10 different answers. Sometimes very different answers.

That is the essence of GEO.

If your own team cannot describe the company consistently, why should ChatGPT, Perplexity, Gemini, or Claude understand you clearly?

This is why I think a new role is coming: Brand Authority Architect.

Someone who sits between SEO, brand marketing, PR, content, product marketing, and leadership.

The best way to describe this role is as a kind of compliance officer for marketing.

Their job is to align the brand message across every source, cut budget from work that creates noise, like useless AI slop blog production, and invest in the channels that actually build authority.

If this sounds like nothing new, that is because maybe it is not. It is almost the same ingredients, just a completely different cocktail.

Technically, an SEO executive can become a Brand Authority Architect.

But in my experience with people in this space, most will not adapt. They will keep shipping small technical website fixes while the real budget and influence move somewhere else.

Wdyt?

The debate around SEO vs GEO is mostly missing the point.

It reminds me of the early cloud days. Some people were saying cloud was exactly the same thing as an on-premises data center, and in a way, they were right.

Technically, it was mostly the same thing. The real difference was how budget was allocated, CAPEX vs OPEX, and the skill set needed to manage the new environment.

I think GEO is similar.

GEO is not just another SEO tactic, and it is not another dashboard category. In practice, it is becoming a resource allocation problem and a skill set problem.

The companies that win in AI search will not be the ones publishing the most content or tracking the most prompts. They will be the ones that are easiest to understand, verify, and recommend.

Try a simple test inside your company.

Ask 10 people: Who are we? What do we do? Who are we really for?

Sounds simple, right?

In my experience, you will often get 10 different answers. Sometimes very different answers.

That is the essence of GEO.

If your own team cannot describe the company consistently, why should ChatGPT, Perplexity, Gemini, or Claude understand you clearly?

This is why I think a new role is coming: Brand Authority Architect.

Someone who sits between SEO, brand marketing, PR, content, product marketing, and leadership.

The best way to describe this role is as a kind of compliance officer for marketing.

Their job is to align the brand message across every source, cut budget from work that creates noise, like useless AI slop blog production, and invest in the channels that actually build authority.

If this sounds like nothing new, that is because maybe it is not. It is almost the same ingredients, just a completely different cocktail.

Technically, an SEO executive can become a Brand Authority Architect.

But in my experience with people in this space, most will not adapt. They will keep shipping small technical website fixes while the real budget and influence move somewhere else.

Wdyt?

We are a small but growing B2B SaaS company looking at AI SDR tools specifically for outbound calling.

Would be great if you could answer:

• What tool are you using?
• How are you using it?
• Is it calling cold leads, inbound leads, old pipeline, demo no shows, something else?
• Does it actually book qualified meetings?
• What breaks or feels awkward?
• Would you trust it with your main prospect list?

Please recommend only from personal experience

Recently, I have noticed an explosion of posts claiming to have discovered some unique insight about AI search / GEO.

Things like:

• what affects AI search traffic
• what builds authority
• what makes ChatGPT or Perplexity cite a brand
• what role Reddit, backlinks, llms.txt, structured data, or community mentions actually play
• which tactics supposedly move the needle

Some of these posts seem useful and kind of make sense, but many others feel like they are based on tiny datasets or are simply hard to verify.

I’m curious how people here think about this.

When you see research or data about AI search published on Reddit, do you actually trust it? Do you act on it, or do you mostly treat it as vibes?

How do you decide what is worth believing?

The reason I’m asking is that I personally love data, but I also know there are many ways to interpret the same dataset. An insight does not always mean an actionable insight. In fact, more often than not, it probably is not actionable.

I’m wondering whether posts based on real and relatively large datasets are genuinely useful to people here, or whether they mostly add more noise to an already confusing space.

AI bot traffic to your websites. Do you:

- Care about it?

- Analyze it for patterns?

- Understand what these patterns mean for business?

- Try to correlate it with off site activities?

Would love to learn your view on it,

AI bot traffic to your websites. Do you:

- Care about it?

- Analyze it for patterns?

- Understand what these patterns mean for business?

- Try to correlate it with off site activities?

Would love to learn your view on it,

AI bot traffic to your websites. Do you:

- Care about it?

- Analyze it for patterns?

- Understand what these patterns mean for business?

- Try to correlate it with off site activities?

Would love to learn your view on it,

I’m part of the team at an AEO platform. We posted some analytics here before, but most of it was about technical bot behavior patterns across our client base.

This time, we asked our AI agent to analyze anonymized data across our clients and look specifically at what kinds of pages actually get human traffic and conversions from AI search.

There is a pattern.

When tested at scale, human visitors from AI search usually don’t land on homepages, pricing pages, or generic product pages.

They land on pages that directly answer something - this part is probably sounds trivial so here are some concrete examples.

Top 4 patterns that worked in temrs of landing human visitors from AI:

A. Listicle with audience + geography qualifier

Example: /blog/best-[category]-for-[audience]-in-[region]

This was one of the strongest informational patterns. The winning pages looked like:

“Best spend management software for small businesses in the US”

Pattern: Best [category] for [audience] in [region]

Why it works: LLMs love comparison answers, and the title matches how people actually ask prompts. Usually the prompt includes the category, the buyer type, and the geography.

B. Tool-named technical how-to

Example: /blog/automating-[workflow]-with-[named-tool]

These did surprisingly well

Pattern: [verb] [outcome] with [named tool]

The best pages named a specific product, library, or workflow. Not a broad thinkpiece. More like:

“Automating GitHub issue creation with Claude Code”

Lesson: blog titles that name a specific tool often perform better than generic concept posts because LLMs treat them almost like documentation.

C. Template / utility pages

Example: /templates/[artifact]

This was the most underrated category.

Template pages worked both as informational answers and as useful tools. They also converted much better than regular editorial pages because the intent was already clear.

Examples:

• /templates/invoice
• /templates/estimate
• /templates/crm

If the audience would download a checklist, calculator, template, or worksheet, it should probably have its own indexable page.

D. Narrow-vertical how-to

Example: /how-[specific-audience]-can-[specific-action]

These are cheap to write and surprisingly durable.

Examples:

• how attorneys can use YouTube Shorts
• resources for deaf interpreters

The pattern is simple: pick a narrow audience that big publishers ignore and write the specific how-to they need.

What this means for content structure:

Slug patterns that worked:

• best-[category]-for-[audience]-in-[region]
• how-[audience]-can-[action]
• [verb]-[outcome]-with-[named-tool]
• /templates/[artifact]

Slug patterns that did not show up much:

• “The Future of X”
• “Why X Matters”
• generic thought-leadership noun phrases

The first sentence also matters. The best pages usually answer the title immediately instead of opening with context.

Another pattern: one named entity per post. A tool, a vertical, or a region. Posts without a named entity were much weaker.

Our main takeaway: AI visitors land on answers, not positioning.

I’m part of the team at an AEO platform. We posted some analytics here before, but most of it was about technical bot behavior patterns across our client base.

This time, we asked our AI agent to analyze anonymized data across our clients and look specifically at what kinds of pages actually get human traffic and conversions from AI search.

There is a pattern.

When tested at scale, human visitors from AI search usually don’t land on homepages, pricing pages, or generic product pages.

They land on pages that directly answer something - this part is probably sounds trivial so here are some concrete examples.

Top 4 patterns that worked in temrs of landing human visitors from AI:

A. Listicle with audience + geography qualifier

Example: /blog/best-[category]-for-[audience]-in-[region]

This was one of the strongest informational patterns. The winning pages looked like:

“Best spend management software for small businesses in the US”

Pattern: Best [category] for [audience] in [region]

Why it works: LLMs love comparison answers, and the title matches how people actually ask prompts. Usually the prompt includes the category, the buyer type, and the geography.

B. Tool-named technical how-to

Example: /blog/automating-[workflow]-with-[named-tool]

These did surprisingly well with technical audiences.

Pattern: [verb] [outcome] with [named tool]

The best pages named a specific product, library, or workflow. Not a broad thinkpiece. More like:

“Automating GitHub issue creation with Claude Code”

Lesson: blog titles that name a specific tool often perform better than generic concept posts because LLMs treat them almost like documentation.

C. Template / utility pages

Example: /templates/[artifact]

This was the most underrated category.

Template pages worked both as informational answers and as useful tools. They also converted much better than regular editorial pages because the intent was already clear.

Examples:

• /templates/invoice
• /templates/estimate
• /templates/crm

If the audience would download a checklist, calculator, template, or worksheet, it should probably have its own indexable page.

D. Narrow-vertical how-to

Example: /how-[specific-audience]-can-[specific-action]

These are cheap to write and surprisingly durable.

Examples:

• how attorneys can use YouTube Shorts
• resources for deaf interpreters

The pattern is simple: pick a narrow audience that big publishers ignore and write the specific how-to they need.

What this means for content structure:

Slug patterns that worked:

• best-[category]-for-[audience]-in-[region]
• how-[audience]-can-[action]
• [verb]-[outcome]-with-[named-tool]
• /templates/[artifact]

Slug patterns that did not show up much:

• “The Future of X”
• “Why X Matters”
• generic thought-leadership noun phrases

The first sentence also matters. The best pages usually answer the title immediately instead of opening with context.

Another pattern: one named entity per post. A tool, a vertical, or a region. Posts without a named entity were much weaker.

Our main takeaway: AI visitors land on answers, not positioning.

I’m part of the team at an AEO platform. We posted some analytics here before, but most of it was about technical bot behavior patterns across our client base.

This time, we asked our AI agent to analyze anonymized data across our clients and look specifically at what kinds of pages actually get human traffic and conversions from AI search.

There is a pattern.

When tested at scale, human visitors from AI search usually don’t land on homepages, pricing pages, or generic product pages.

They land on pages that directly answer something - this part is probably sounds trivial so here are some concrete examples.

Top 4 patterns that worked in temrs of landing human visitors from AI:

A. Listicle with audience + geography qualifier

Example: /blog/best-[category]-for-[audience]-in-[region]

This was one of the strongest informational patterns. The winning pages looked like:

“Best spend management software for small businesses in the US”

Pattern: Best [category] for [audience] in [region]

Why it works: LLMs love comparison answers, and the title matches how people actually ask prompts. Usually the prompt includes the category, the buyer type, and the geography.

B. Tool-named technical how-to

Example: /blog/automating-[workflow]-with-[named-tool]

These did surprisingly well with technical audiences.

Pattern: [verb] [outcome] with [named tool]

The best pages named a specific product, library, or workflow. Not a broad thinkpiece. More like:

“Automating GitHub issue creation with Claude Code”

Lesson: blog titles that name a specific tool often perform better than generic concept posts because LLMs treat them almost like documentation.

C. Template / utility pages

Example: /templates/[artifact]

This was the most underrated category.

Template pages worked both as informational answers and as useful tools. They also converted much better than regular editorial pages because the intent was already clear.

Examples:

• /templates/invoice
• /templates/estimate
• /templates/crm

If the audience would download a checklist, calculator, template, or worksheet, it should probably have its own indexable page.

D. Narrow-vertical how-to

Example: /how-[specific-audience]-can-[specific-action]

These are cheap to write and surprisingly durable.

Examples:

• how attorneys can use YouTube Shorts
• resources for deaf interpreters

The pattern is simple: pick a narrow audience that big publishers ignore and write the specific how-to they need.

What this means for content structure:

Slug patterns that worked:

• best-[category]-for-[audience]-in-[region]
• how-[audience]-can-[action]
• [verb]-[outcome]-with-[named-tool]
• /templates/[artifact]

Slug patterns that did not show up much:

• “The Future of X”
• “Why X Matters”
• generic thought-leadership noun phrases

The first sentence also matters. The best pages usually answer the title immediately instead of opening with context.

Another pattern: one named entity per post. A tool, a vertical, or a region. Posts without a named entity were much weaker.

Our main takeaway: AI visitors land on answers, not positioning.

I’m part of the team at an AEO platform. We posted some analytics here before, but most of it was about technical bot behavior patterns across our client base.

This time, we asked our AI agent to analyze anonymized data across our clients and look specifically at what kinds of pages actually get human traffic and conversions from AI search.

There is a pattern.

When tested at scale, human visitors from AI search usually don’t land on homepages, pricing pages, or generic product pages.

They land on pages that directly answer something - this part is probably sounds trivial so here are some concrete examples.

Top 4 patterns that worked in temrs of landing human visitors from AI:

A. Listicle with audience + geography qualifier

Example: /blog/best-[category]-for-[audience]-in-[region]

This was one of the strongest informational patterns. The winning pages looked like:

“Best spend management software for small businesses in the US”

Pattern: Best [category] for [audience] in [region]

Why it works: LLMs love comparison answers, and the title matches how people actually ask prompts. Usually the prompt includes the category, the buyer type, and the geography.

B. Tool-named technical how-to

Example: /blog/automating-[workflow]-with-[named-tool]

These did surprisingly well with technical audiences.

Pattern: [verb] [outcome] with [named tool]

The best pages named a specific product, library, or workflow. Not a broad thinkpiece. More like:

“Automating GitHub issue creation with Claude Code”

Lesson: blog titles that name a specific tool often perform better than generic concept posts because LLMs treat them almost like documentation.

C. Template / utility pages

Example: /templates/[artifact]

This was the most underrated category.

Template pages worked both as informational answers and as useful tools. They also converted much better than regular editorial pages because the intent was already clear.

Examples:

• /templates/invoice
• /templates/estimate
• /templates/crm

If the audience would download a checklist, calculator, template, or worksheet, it should probably have its own indexable page.

D. Narrow-vertical how-to

Example: /how-[specific-audience]-can-[specific-action]

These are cheap to write and surprisingly durable.

Examples:

• how attorneys can use YouTube Shorts
• resources for deaf interpreters

The pattern is simple: pick a narrow audience that big publishers ignore and write the specific how-to they need.

What this means for content structure:

Slug patterns that worked:

• best-[category]-for-[audience]-in-[region]
• how-[audience]-can-[action]
• [verb]-[outcome]-with-[named-tool]
• /templates/[artifact]

Slug patterns that did not show up much:

• “The Future of X”
• “Why X Matters”
• generic thought-leadership noun phrases

The first sentence also matters. The best pages usually answer the title immediately instead of opening with context.

Another pattern: one named entity per post. A tool, a vertical, or a region. Posts without a named entity were much weaker.

Our main takeaway: AI visitors land on answers, not positioning.

I’m part of the team at an AEO platform called LightSite AI. We posted some analytics here before, but most of it was about technical bot behavior patterns across our client base.

This time, we asked our AI agent to analyze anonymized data across our clients and look specifically at what kinds of pages actually get human traffic and conversions from AI search.

There is a pattern.

When tested at scale, human visitors from AI search usually don’t land on homepages, pricing pages, or generic product pages.

They land on pages that directly answer something - this part is probably sounds trivial so here are some concrete examples.

Top 4 patterns that worked in temrs of landing human visitors from AI:

A. Listicle with audience + geography qualifier

Example: /blog/best-[category]-for-[audience]-in-[region]

This was one of the strongest informational patterns. The winning pages looked like:

“Best spend management software for small businesses in the US”

Pattern: Best [category] for [audience] in [region]

Why it works: LLMs love comparison answers, and the title matches how people actually ask prompts. Usually the prompt includes the category, the buyer type, and the geography.

B. Tool-named technical how-to

Example: /blog/automating-[workflow]-with-[named-tool]

These did surprisingly well with technical audiences.

Pattern: [verb] [outcome] with [named tool]

The best pages named a specific product, library, or workflow. Not a broad thinkpiece. More like:

“Automating GitHub issue creation with Claude Code”

Lesson: blog titles that name a specific tool often perform better than generic concept posts because LLMs treat them almost like documentation.

C. Template / utility pages

Example: /templates/[artifact]

This was the most underrated category.

Template pages worked both as informational answers and as useful tools. They also converted much better than regular editorial pages because the intent was already clear.

Examples:

• /templates/invoice
• /templates/estimate
• /templates/crm

If the audience would download a checklist, calculator, template, or worksheet, it should probably have its own indexable page.

D. Narrow-vertical how-to

Example: /how-[specific-audience]-can-[specific-action]

These are cheap to write and surprisingly durable.

Examples:

• how attorneys can use YouTube Shorts
• resources for deaf interpreters

The pattern is simple: pick a narrow audience that big publishers ignore and write the specific how-to they need.

What this means for content structure:

Slug patterns that worked:

• best-[category]-for-[audience]-in-[region]
• how-[audience]-can-[action]
• [verb]-[outcome]-with-[named-tool]
• /templates/[artifact]

Slug patterns that did not show up much:

• “The Future of X”
• “Why X Matters”
• generic thought-leadership noun phrases

The first sentence also matters. The best pages usually answer the title immediately instead of opening with context.

Another pattern: one named entity per post. A tool, a vertical, or a region. Posts without a named entity were much weaker.

Our main takeaway: AI visitors land on answers, not positioning.