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In 2026, brands need content that demonstrates deep topical understanding to rank in traditional search and earn citations in AI-generated answers. That means marketers should move beyond generic keyword lists and optimize content around relationships, entities, and the questions buyers actually ask.

This guide walks through what semantic keywords actually are, how they differ from outdated LSI tactics, and outlines a repeatable, step-by-step process for finding and using them in 2026 — whether a brand is optimizing for Google, AI Overviews, or answer engines like ChatGPT.

Table of Contents

• What are semantic keywords in SEO?
• Semantic Keywords for AEO vs. Traditional SEO
• How to Find Semantic Keywords
• How to Use Semantic Keywords on a Page
• Semantic Keyword Research Tools
• Frequently Asked Questions About Semantic Keywords

Semantic keywords are terms that are semantically related to a page’s topic and keyword intent. They help search engines interpret context beyond exact-match phrases. Think of them as the words, phrases, and concepts that naturally surround a topic and signal the real subject of the content. For example, if the primary keyword is “email marketing software,” semantic keywords might include:

• “Drip campaigns”
• “Automation workflows”
• “Open rate”
• “List segmentation”
• “A/B testing”

Semantic keywords often include synonyms, modifiers, and related questions that a comprehensive piece on the topic would naturally cover.

Why are semantic keywords important in SEO and AI search?

I asked Kelvin Çobanaj, CEO of ZeroRank, why semantic keywords matter for SEO and AI search optimization. Çobanaj points to two reasons these high-intent keywords matter.

First, he says, “With traditional SEO, semantic keywords are mostly variations of the same search so that a page can rank for more queries.”

When Google encounters a piece of content that uses the right cluster of related terms, it gains greater confidence that the page genuinely covers the subject rather than merely mentioning a keyword in isolation. That confidence translates into better rankings and, increasingly, a better chance of being cited in AI-generated answers.

The second reason? Semantic keywords support topical authority when used across a topic cluster to answer the questions buyers are asking. That helps brands build a connected set of content that both Google and AI engines can understand.

Çobanaj says, “With AI search, I focus more on covering the full topic and common questions, not just keyword variants. That gives AI enough context to include the brand in its answer.”

Semantic Keywords vs. LSI Keywords

To clarify, LSI keywords are not the same as semantic keywords, and the term itself is outdated.

LSI (Latent Semantic Indexing) refers to a mathematical technique introduced in a 1988 research paper that analyzes word co-occurrence patterns in documents. In plain terms, LSI looks at which words are most likely to appear together.

Google’s own John Mueller confirmed on X in 2019 that Google does not use LSI. Modern search engines rely on far more sophisticated natural language processing (NLP), including transformer models like BERT and MUM, which understand language contextually in ways LSI never could.

LSI tools often spit out loosely related terms based on statistical co-occurrence. Semantic keyword research, on the other hand, focuses on meaning: what concepts, entities, and questions does a searcher expect your content to address?

Pro tip: If a tool markets itself as an “LSI keyword generator,” the underlying functionality might still be useful. But take the time to evaluate whether it’s surfacing truly semantic relationships or just word co-occurrence data.

Semantic Keywords vs. Entities

Entities are uniquely identifiable things, such as people, brands, tools, places, or concepts, that search engines recognize as distinct objects in the world.

Entities anchor the meaning of ambiguous terms. For instance, the entity “Apple Inc.” is distinct from the entity “apple (fruit),” and Google’s Knowledge Graph understands the difference.

Semantic keywords and entities are related but not interchangeable. Semantic keywords are the broader set of related terms and phrases that deepen a topic. Entities are the specific, named things within that semantic field.

A strong page uses semantic keywords to build context and references entities to anchor specificity. For example, in an article about “project management software,” semantic keywords might include “task tracking,” “team collaboration,” and “workflow automation.” Entities within that same piece would be “Asana,” “Monday.com,” “Jira,” and “Gantt chart.”

Semantic Keywords vs. Topics

A topic is the broad subject your content addresses. Semantic keywords are the specific terms and phrases that fill that topic with substance. Think of the topic as the container and semantic keywords as the ingredients that give it substance.

A content strategy should start with topic selection (often organized into pillars or clusters). Then, semantic keyword research fills in the details for each page.

Without semantic keywords, a topic-based approach remains shallow. With them, content signals the depth and expertise that both human readers and AI systems seek.

Semantic Keywords for AEO vs. Traditional SEO

Traditional SEO has always rewarded pages that demonstrate topical relevance through related terms. That hasn’t changed.

Answer engine optimization (AEO) has changed how marketers structure content. With AEO, marketers organize content so that answer engines like ChatGPT, Perplexity, and Google’s AI Overviews can extract, synthesize, and cite it in their responses.

In traditional SEO, semantic keywords improve a page’s matching of search intent.

In AEO, semantic keywords play a slightly different role. When a page clearly defines the relationships between concepts — using specific, unambiguous language — AI engines are more likely to trust, reference, and reuse it.

Pro tip: AEO Grader is a free tool that shows how answer engines like ChatGPT, Perplexity, and Gemini currently represent your brand based on their training data. Before investing time in AEO optimization, run an audit to understand your baseline. The tool scores your brand out of 100 across five dimensions: sentiment, presence quality, brand recognition, share of voice, and market position.

Here’s how the role of semantic keywords differs across the two approaches:

Bernard Huang, founder of Clearscope, put it simply when I asked him about the overlap between the two strategies.

He says, “I see a lot of teams treating AEO and SEO like two totally separate things, and honestly, it’s the biggest resource waste out there right now. Both come down to the same goal: creating content that genuinely covers a topic well. When you do good semantic keyword research and map out the concepts and relationships around a topic, you’re building content that works for traditional search and AI engines at the same time.”

The takeaway? Semantic keywords aren’t a separate project for AEO. The same research process strengthens both your traditional rankings and your AI visibility. The difference shows up in execution: AEO demands clearer definitions, more explicit entity references, and content structured for passage-level extraction.

Dive deeper into AI search optimization with HubSpot’s AEO Guide.

How to Find Semantic Keywords

Semantic keyword research starts with a primary query and a clear page goal. Before marketers open any tool, they need to know two things: what they’re writing about and what action they want the reader to take. Here’s a step-by-step workflow brands can repeat for every piece of content they create.

Step 1: Map your personas to their prompts.

Before touching a keyword tool, identify the actual questions your buyers type into ChatGPT, Perplexity, or Google when they’re actively evaluating a solution.

Çobanaj says, “Teams often focus only on keyword tools, but analyzing real questions, comparisons, and prompts gives you a much better picture of what content needs to cover.”

That lines up with what Lindsay Boyajian-Hagan, VP of Marketing at Conductor, said on a recent episode of the Found in AI podcast: the most valuable content starts by mapping your personas to the prompts, and it’s especially valuable when revenue is on the line.

These aren’t top-of-funnel curiosity questions. These are the specific, high-intent prompts a buyer uses when they’re comparing solutions, evaluating tradeoffs, or building a business case for their team.

For each persona, document:

• Role and decision-making context. What’s their title? What are they responsible for? Who do they report to?
• Pain points at the decision stage. What specific problem are they trying to solve right now? Not in theory, but in this quarter?
• Money prompts. The actual questions they’d type into an AI engine when they’re ready to evaluate, compare, or buy.

For example, if you sell project management software and one of your personas is a VP of Engineering at a mid-market SaaS company, their money prompts might look like:

• “Best project management tool for engineering teams using Jira and GitHub”
• “How to migrate from Asana to [your product] without losing sprint history”
• “[Your product] vs. Monday.com for technical teams with legacy integrations”

These prompts are the foundation of your semantic research. They tell you exactly which concepts, entities, and tradeoffs your content needs to address from a buyer intent perspective, not from a keyword volume perspective.

Pro tip: Don’t guess at your money prompts. Pull them from sales call recordings, demo request forms, G2 reviews, and Reddit threads where people actively discuss your category. The language your buyers are actually using is almost always more specific and more valuable than what a keyword tool suggests.

Step 2: Map your primary keywords to prompts and queries.

Once a marketing team understands who they’re writing for and what their customers are asking, they can connect primary keywords to those prompts. That’s where traditional keyword research meets AI-era strategy.

Take a primary keyword — let’s say “email marketing software” — and ask: Which of my personas would search for this, and what would their full prompt look like?

A CMO at an early-stage startup might prompt differently than an email marketing manager at an enterprise company. For example, the CMO asks, “What’s the most cost-effective email marketing platform for a team of two?” The enterprise manager asks, “Best email marketing software with advanced segmentation and Salesforce integration.”

It’s the same primary keyword, but completely different semantic profiles.

When marketers map keywords to specific persona prompts, they can see which semantic terms belong on each page and avoid trying to make one page serve every audience. Document this mapping in a table like this:

This table serves as the bridge between persona research and the rest of the semantic keyword workflow. Every step that follows — SERP analysis, tool-based research, AI engine prompting — is now filtered through the lens of specific buyer intent, not just keyword volume.

Step 3: Analyze the SERP for your primary keyword.

Search for primary keywords in Google and study the first page of results. Google’s People Also Ask (PAA) box is one of the most accessible sources of semantically related questions.

Click on several PAA results to expand the list. Google dynamically generates related questions, helping you uncover dozens of queries from a single starting point. Pay attention to:

• The People Also Ask box
• Related searches at the bottom
• The types of content ranking (lists, how-tos, comparisons, etc.)

Note recurring subtopics and terms across the top-ranking pages. Then, cross-reference these against your persona-to-prompt mapping from Steps 1 and 2. Do the SERP feature results align with what your buyers are actually asking, or is there a gap?

Daniel Horowitz, Enterprise SEO at Salesforce, told me that many teams stop their semantic keyword research before reaching this step. He added, “I always want to see how the topic is actually being framed across rankings, AI answers, People Also Ask, forums, documentation, and competitor pages. That’s where you start to see which entities recur, which subquestions matter, where you can add value with an FAQ section, and which phrasing keeps showing up.”

Step 4: Use a dedicated semantic keyword tool.

Tools like Semrush’s Keyword Magic Tool, Ahrefs’ Keywords Explorer, or a specialized semantic tool like Keywords People Use can surface related terms you won’t find by manually scanning SERPs. Enter your primary keyword and look for:

• Related keywords grouped by topic cluster
• Questions containing your primary keyword
• Long-tail variations that reflect specific use cases
• Entities (brand names, tools, standards) that frequently co-occur

Step 5: Prompt AI engines directly.

AI engines often collapse queries into broader intent clusters, so the terms and questions they surface can point to concepts your content may need to address. To mine AI answers for semantic keywords, open an AI tool, enter the money prompts from the persona mapping in Steps 1 and 2, then note:

• Which subtopics does the AI cover in its answer?
• Which entities (tools, brands, concepts) does it reference?
• What follow-up questions does the engine suggest?

Perplexity and Google’s AI Mode are useful places to look for semantic signals in follow-up questions. By using the persona prompts rather than generic keywords, brands get a much more accurate picture of the semantic landscape their content needs to cover.

However, Horowitz encourages caution with this approach due to the personalized nature of these engines. He says, “Personalization and output variability mean you have to be careful. What you see in ChatGPT or Perplexity is useful as a signal, but not reliable enough to treat as a source of truth. I still trust the SERP, first-party data, and actual performance much more.”

Step 6: Pull insights from voice-of-customer data.

Semantic keyword research benefits from voice-of-customer inputs, such as sales calls and reviews. Take the time to review:

• Customer recordings
• Support tickets
• Product reviews on G2 or Capterra
• Community discussions on Reddit

Look for the specific language buyers use to describe their problems or evaluate solutions. Those phrases often translate into long-tail keywords and natural-language prompts people use in AI engines.

Step 7: Map your semantic keywords to an entity map.

After gathering a raw list, it’s time to organize it. Group the semantic keywords into clusters, such as:

• Core concepts
• Related entities
• Common questions
• Use-case modifiers
• Comparison terms

These clusters create an entity map, a visual or structured representation of how all these terms relate to each other and to the primary keyword. The map tells content strategists and writers which sections to include, which entities to reference by name, and where to go deeper.

Pro tip: If you’re using Content Hub, you can turn this process into templates, briefs, and reusable content patterns that support extractable answers at scale. It’s especially useful for teams producing content across multiple pillar topics.

Step 8: Run a quick audit with AEO Grader.

Before diving into content creation, run a quick AI visibility check with AEO Grader. That tells brands where they’re starting from and what gaps their content needs to close.

AEO Grader also surfaces competitors and their share of voice in AI answers. It shows where rivals are being cited instead of you, and which topics need deeper coverage to close the gap.

Using these insights, brands can turn content planning into a strategic exercise: not just creating content for its own sake, but building the citations and brand presence needed to claim share of voice in AI-generated answers.

How to Use Semantic Keywords on a Page

Finding semantic keywords is half the job. Next, marketers need to place them strategically without stuffing or forcing them into the content. Here’s a quick guide to semantic keyword placement, plus a before-and-after example to show the difference.

Where to Place Semantic Keywords

• Title and H1: Your primary keyword belongs here. Semantic keywords can support the subtitle or the first paragraph.
• H2s and H3s: Use semantic keywords as subheadings where they naturally define a section’s scope.
• Opening paragraph: Introduce your primary keyword and 2 to 3 core semantic terms within the first 100 words. Research shows that 44.2% of all ChatGPT citations come from the first 30% of a text, so the intro carries extra weight.
• Body content: Weave semantic keywords into explanations, examples, and comparisons. Distribute them naturally across sections.
• Alt text and image captions: Use semantic terms to describe visuals. That’s an often-overlooked placement with real impact.
• FAQ sections: Structure questions around the semantic queries from your research. Answer each one in 2 to 3 sentences so that answer engines can extract the answer more easily.
• Internal links: Use semantic keyword variations as anchor text when linking to related content. That strengthens your site’s semantic map.

Before and After Example

Before (primary keyword only, no semantic depth): “Email marketing software helps you send emails. The best email marketing software has features for sending emails and managing your email list. If you need email marketing software, look for one that fits your email marketing needs.”

After (with semantic keywords integrated): “Email marketing software gives B2B teams the tools to build automated drip campaigns, segment subscriber lists by behavior or lifecycle stage, and track engagement metrics like open rate and click-through rate. The strongest platforms in 2026 also integrate with your CRM for lead scoring and support A/B testing across subject lines, send times, and content blocks. If you’re evaluating options, prioritize workflow automation, deliverability tracking, and native analytics.”

Notice that the second version doesn’t force unrelated terms. It naturally covers the concepts buyers expect, including drip campaigns, segmentation, open rates, CRM integration, A/B testing, workflow automation, and deliverability. These are semantic keywords doing their job.

Pro tip: Resist the urge to add every semantic keyword from your research to a single page. A focused page with 10 to 15 well-placed semantic terms will outperform a page that tries to cram in 50 of them.

For teams looking to build semantic optimization into their writing process, Marketing Hub includes built-in SEO recommendations that flag missing opportunities and help teams plan promotion around answer-ready content. The SEO tools surface optimization suggestions as you write, which helps teams manage content production across multiple pages.

Semantic Keyword Research Tools

Not every keyword tool works for semantic research. Some still operate on exact-match logic. Here are the five tools I’ve found most useful for surfacing genuinely semantic relationships, along with notes on where each works best and where it may fall short.

1. HubSpot SEO Marketing Software

SEO Marketing Software is an integrated suite of tools within Marketing Hub. It’s especially relevant for a semantic keyword strategy. It lets users map pillar pages to subtopic content and visualize how their semantic clusters connect. Essentially, it builds the entity map I talked about in Step 8 of this guide, but within the platform where content actually lives. For teams managing dozens or hundreds of pages, that visibility into how topics relate to each other is what keeps a content architecture from becoming fragmented.

The Google Search Console integration also pulls keyword impressions and CTR data directly into HubSpot, allowing marketers to see which semantic terms are driving traffic and which they’re ranking for but underperforming.

And for teams considering AEO alongside traditional SEO, HubSpot also includes AEO Grader and HubSpot AEO, which complement its SEO tools. Brands can benchmark AI visibility and see how answer engines represent the brand, all within a single platform.

Key Features

• Topic cluster planning and content strategy tool
• On-page SEO recommendations prioritized by impact
• Keyword tracking and analytics dashboard
• Google Search Console integration, content performance reporting
• Native integration with HubSpot’s CMS and content management tools

Best for: Marketing teams already using HubSpot, or considering it, who want semantic keyword optimization built into content creation and analytics.

Pricing: Marketing Hub starts at $20/month per seat, billed monthly. Pricing and feature availability vary by plan.

What we like: HubSpot stands apart from the other tools on this list because it connects SEO recommendations to the same place where users build pages, write blog posts, send emails, and track leads. You get on-page SEO recommendations surfaced directly in the editor as you write, which means semantic optimization happens in real time rather than as an afterthought.

Where it falls short: HubSpot’s SEO tools are not a replacement for dedicated keyword research platforms. Think of it as the execution-and-monitoring layer, not the primary research tool. The strongest workflow pairs HubSpot with a dedicated research tool that enables deep semantic research, then brings those insights into HubSpot, where the content is actually built, published, and measured.

2. Semrush

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Semrush is an SEO and competitive research platform that helps marketers research keywords, analyze competitors, audit websites, and plan content. For semantic keyword research, it’s useful because it provides teams with a large keyword database, topic research tools, and intent-based groupings that reveal related terms, subtopics, and questions around a primary keyword.

Key Features

• Keyword Magic Tool: Semrush’s Keyword Magic Tool includes more than 25 billion keywords for keyword and topic discovery.
• Topic Research: The Topic Research tool helps teams identify content gaps and related subtopics.
• Intent grouping and SERP insights: Semrush groups related terms by intent and helps marketers identify SERP features that may shape content strategy.
• Content optimization: ContentShake AI helps teams turn keyword research into optimized drafts and content recommendations.

Best for: Teams doing both SEO and AEO who want enough data to build a complete semantic map from a single platform.

Pricing: Starts at $139/month for the Pro plan, or $117.33/month when billed annually.

What we like: The Keyword Tool is the most comprehensive semantic research starting point I’ve used. It automatically groups related terms by subtopic, saving hours of manual clustering. The Topic Research tool is particularly strong for identifying content gaps, showing marketers what questions and subtopics the top-ranking pages cover that their content doesn’t.

Where it falls short: Pricing can be steep for smaller teams. The sheer volume of data can also be overwhelming without a clear research framework, which is why starting with a page goal (Step 1) matters so much.

3. Ahrefs Keywords Explorer

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Ahrefs Keywords Explorer helps marketers research keywords, evaluate ranking difficulty, estimate traffic potential, and understand how related terms connect to larger parent topics. For semantic keyword research, its parent topic and traffic potential features are especially useful because they help teams decide whether related keywords should appear on the same page or require separate content.

Key Features

• Keyword data: Ahrefs provides keyword data from major search engines to support broader semantic research.
• Parent topic: Ahrefs identifies parent topics so teams can decide whether related keywords should be on one page or on separate pages.
• Traffic potential: Ahrefs estimates traffic beyond raw search volume, helping teams prioritize keywords more realistically.
• Keyword difficulty: Ahrefs scores ranking difficulty to help teams weigh opportunity against competition.
• Content gap analysis: Ahrefs helps teams compare competitor rankings and identify missing topics.

Best for: Teams that are already doing SEO who need to layer semantic research into competitive analysis.

Pricing: Paid plans start at $29/month for Starter. Lite starts at $129/month.

What we like: Ahrefs’ parent topic feature is underrated for semantic research. It automatically identifies when multiple keywords should target the same page rather than separate pages, preventing content cannibalization.

The traffic potential metric is also more useful than raw search volume. It estimates how much traffic a brand would actually receive from ranking, accounting for clicks absorbed by SERP features and AI Overviews. For competitive semantic analysis, the Content Gap tool is excellent.

Where it falls short: Less intuitive than Semrush for pure semantic discovery, the tool is built primarily around keyword-level data, so users will need to do more manual work to group terms into semantic clusters.

4. Surfer SEO

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Surfer SEO is a content optimization platform that analyzes top-ranking pages and turns those patterns into writing recommendations. For semantic keywords, it’s most useful during the drafting and editing stage because it shows writers which related terms, entities, headings, and content elements appear across competing pages.

Key Features

• Content editor: Surfer’s NLP-driven editor scores content in real time as writers add semantic terms.
• SERP Analyzer: Surfer analyzes top-ranking pages to show patterns in structure, content depth, and related terms.
• Content audit: Surfer audits existing pages and recommends updates based on current SERP patterns.
• Semantic term suggestions: Surfer identifies semantic terms from top-ranking pages and suggests where to add them naturally.

Best for: Writers who want to focus on drafting rather than research.

Pricing: Paid plans start at $49/month for Discovery, billed yearly. Standard starts at $99/month, billed yearly.

What we like: Surfer is the best tool I’ve used for semantic keyword implementation, not research. Its content editor analyzes the top-ranking pages for your keyword and generates a list of semantic terms to include, with a real-time score that tracks optimization as I write. It’s like having a semantic checklist built into your writing process.

Where it falls short: Not a standalone semantic research tool, users still need something like Semrush or Ahrefs for the initial research phase. Surfer works best as a companion tool for the writing and optimization step.

5. KeywordsPeopleUse

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KeywordsPeopleUse is a focused keyword research tool that surfaces questions, entities, semantic maps, and related queries from sources like Google Autocomplete, People Also Ask, Reddit, and Quora. For semantic keyword research, it helps marketers see how people phrase questions around a topic and which concepts Google appears to associate with that topic.

Key Features

• Semantic keyword generator: Keywords People Use extracts entities, questions, and related queries from Google data.
• Semantic maps and clusters: The tool groups related keywords and concepts, helping marketers see how topics connect.

Best for: Solo marketers and small teams who are conscious of their budget.

Pricing: Paid plans start at $15/month for Lite, which includes 150 credits/month.

What we like: This is the most focused semantic keyword tool on the list. While the others are full SEO suites, Keywords People Use shows users the semantic relationships Google associates with any topic.

The entity extraction feature is especially useful for AEO, as it highlights the specific entities and concepts that AI systems would expect to find in authoritative content.

Where it falls short: Doesn’t include search volume, keyword difficulty, or competitive analysis. You’ll need to pair it with a traditional keyword tool to get the full picture.

Frequently Asked Questions About Semantic Keywords

Are LSI keywords real?

The technique called Latent Semantic Indexing is real. It emerged in 1989 as a method for analyzing word co-occurrence patterns in documents. However, Google does not use LSI in its search algorithm. Modern search engines use more advanced NLP techniques, including transformer-based models like BERT and MUM, that understand contextual meaning in ways LSI cannot.

When people refer to “LSI keywords” in an SEO context, they usually mean semantically related keywords, which are valuable. The terminology is just outdated. Focus on semantic keyword research using modern tools and frameworks, and ignore any tool that claims to use Google’s “LSI algorithm.”

How many semantic keywords should I add to a page?

There’s no universal number, but as a practical guideline, most well-optimized pages benefit from 10 to 20 strategically placed semantic keywords. The emphasis should be on relevance and natural integration, not volume. A page that uses 12 semantic terms with clear, contextual placement will usually perform better than one that forces 40 loosely related terms into the copy.

Use an entity map from the research phase to prioritize core concepts and high-intent terms. Then layer in supporting entities and question-based keywords. If a term doesn’t fit naturally, leave it out. Over-stuffing semantic keywords creates the same readability problems as old-school keyword stuffing.

What is the difference between semantic keywords and entities?

Semantic keywords are the broader set of related terms, phrases, and concepts that help search engines understand a page’s topic and intent. Entities are a specific subset of uniquely identifiable things, such as people, brands, tools, places, or concepts, that search engines recognize as distinct objects in the world.

A page about “project management software” might use the semantic keywords “task tracking,” “team collaboration,” and “workflow automation.” The entities on that page are specific named things: “Asana,” “Monday.com,” “Jira,” and “Gantt chart.” Semantic keywords build topical depth, while entities anchor specificity.

How do I find semantic keywords for free?

Semantic keyword research uses SERP features like People Also Ask and related searches. Each of these features reveals semantically related terms and questions that Google associates with the topic.

Beyond Google, prompt AI engines like ChatGPT (free tier) or Perplexity to generate related concepts, entities, and follow-up questions. Google’s Natural Language API also offers free entity analysis for small volumes.

Where should semantic keywords go on the page?

Brands should naturally distribute semantic keywords throughout their content. The highest-impact placements are the introduction (first 100 to 150 words), H2 and H3 headings, the opening sentence of each major section, FAQ answers, image alt text, and internal link anchor text.

Avoid concentrating all of the semantic terms in one paragraph. The goal is for the entire page to demonstrate depth on the topic, so related terms should appear throughout the piece wherever they belong in context. If a semantic keyword only fits in one place, that’s fine. Force-fitting it elsewhere will hurt readability.

Build for meaning, not just keywords.

Semantic keywords have changed how marketers approach content optimization. In 2026, search engines and AI systems alike reward content that demonstrates genuine understanding of a topic, not just surface-level keyword placement.

I’ve found that teams that treat semantic keyword research as an input to content strategy — not a checklist item — create stronger content that ranks, earns citations, and attracts more qualified prospects. And the brands that invest in semantic keyword research now are building the foundation for visibility across both traditional SERPs and AI-generated answers, which are quickly becoming the default search experience.

Use the tools and steps in this guide to build a repeatable process, and benchmark your progress with AEO Grader to see how your brand appears in the AI engines where your buyers start their research.

The geographically observant will note that Japan is actually about 2,500 miles north of the equator, so doesn't sit in the southern hemisphere. But the sport is rolling with it, somewhat in the spirit of Australia's participation in Eurovision.

All teams will play each of the six in the opposing hemisphere once, with three rounds of fixtures staged in July and another three in November.

Their results will rank the teams within their own hemisphere, from one to six.

On the final weekend in November, there is a three-day play-off event staged at Allianz Stadium in Twickenham. The sixth-placed team in the northern hemisphere plays off against the equivalent in the southern hemisphere standings and so on, culminating in the two top-ranked sides taking each other on. The winner of that final match is crowned the inaugural Nations Championship winner.

However, there is also a parallel, Ryder-Cup style hemisphere title. The winner of each of the matches on the play-offs weekend will earn one point for their hemisphere, except for the contest between the two top-ranked teams which delivers two.

The first hemisphere to earn four points over the weekend will be crowned winners.

For the vast majority of the past 120 years, the southern hemisphere sides have been rugby's jet set.

They have won nearly 57% of the 1,062 Tests between the two groups over more than 125 years of action.

That win rate has fluctuated over time though.

Initial dominance in Test rugby's early days weakened with fixtures drying up for South Africa, one of the south's powerhouse sides, as rugby, fitfully, joined the rest of the world in a boycott of its apartheid political regime.

Fiji and Japan, who played their first Tests against top northern hemisphere teams in 1964 and 1971, struggled for victories in those initial encounters.

However, South Africa's return to international action and the south's more ardent adoption of professionalism widened their advantage over the north, with a generational New Zealand side winning more than 91% of their meetings with Six Nations opposition in the first decade of the new millennium.

Things have been tighter in recent years though.

In fact, the decline of two-time world champions Australia and, by their own stratospheric standards, New Zealand, combined with the emergence of Ireland as a genuine superpower, mean the North have shaded an almost 50:50 split of results so far this decade.

If the two hemispheres have been more closely matched on the scoreboard, there remain some significant tactical differences between them.

Overall there has been a stylistic shift to keeping the ball on the pitch and in the hand in the past couple of years.

Compared to 2021-2023, the average ball-in-play time in a Test has risen by nearly two and a half minutes, with the volume of ball-carrying correspondingly up by 17%.

However, northern hemisphere side kick the ball an average of 80 metres further from open play and make 25 more tackles per game.

The southern hemisphere sides also conform to some age-old stereotypes, recycling the ball 10% faster from rucks and powering their way to 18% more post-contact metres.

Which individual players are likeliest to tilt the title the way of their team - and hemisphere?

With the finals of the United Rugby Championship, England's Prem, France's Top 14 and Super Rugby all having taken place over the past two weeks, the world's best players are coming into the tournament on the back of some high-level domestic rugby.

Data experts Oval Insights' match ratings, which reflect a player's net impact on their side's scoring opportunities, say that the Ireland and Leinster second row Joe McCarthy, with an average rating of 8.5 out of 10 over his five most recent matches in the past 60 days, is the world's most in-form player.

New Zealand's Jordie Barrett and England's Tommy Freeman, who like McCarthy are fresh from lifting club silverware, are second in the standings, with France scrum-half Maxime Lucu and Australia centre Len Ikitau not far behind. %!s()

In this article, I’ll walk you through how to define, evaluate, and implement AI search optimization. I’ll even clearly outline how it differs from, but does not replace, SEO.

Table of Contents

• What is AI search optimization? And why does it matter?
• How AI Search Finds and Cites Your Content
• How is AI search optimization different from traditional SEO?
• How to Optimize Content for AI Search Citations
• How to Optimize Technical Structure for AI Search
• How Off-Page Signals Strengthen AI Visibility
• What Not to Do for AI Search Optimization
• How to Measure AI Visibility and Operationalize Your Plan
• Preparing for AI Agents and What Comes Next
• Frequently Asked Questions About AI Search Optimization

What is AI search optimization? And why does it matter?

AI search optimization is the practice of making a brand and its content more likely to be mentioned and cited by answer engines like ChatGPT, Perplexity, AI Overviews, and Gemini. AI search optimization is known by many names, including generative engine optimization (GEO), AI SEO, and LLM optimization (LLMO), but at HubSpot, we call it answer engine optimization (AEO).

AEO builds upon SEO and does not replace it; they remain distinct but complementary practices, which I’ll detail in a section below.

By optimizing for AI search, brands can expect to see:

• Increased brand visibility. AEO can help brands get recommended in answers generated in Google AI Mode, ChatGPT, Claude, Gemini, and more. Even if a user never clicks through to your website, they can find out details about your product that are highly tailored to their specific situation.
• More qualified leads. Traffic that comes from AI search tends to be higher intent than traffic that comes from traditional search. Why? In an attempt to provide a personalized result, answer engines essentially pre-qualify users by asking follow-up questions targeting sub-queries.

To be clear, AI search traffic is still small compared to traditional search. However, it has an outsized impact on conversions. AI traffic grew 66.02% in 2025 (faster than every channel except paid search), while accounting for only 0.14% of visits, according to Semrush. The latest data I could find shows that AI search is still less than 1% of the total share, according to Ahrefs May 2026 data. But again, that doesn’t tell the whole story when AI answers are influencing purchases without buyers clicking links.

People are increasingly using AI answer engines to get recommendations. AI search optimization puts you in control of the narrative that answer engines put out.

How AI Search Finds and Cites Your Content

1. Training data (parametric knowledge) - This is the knowledge baked into a model during training. An engine can mention your brand from what it absorbed before its knowledge cutoff, but you can’t optimize for it directly, because training runs on a fixed snapshot of the web you don’t control. However, brands can indirectly increase the likelihood of future inclusion by building a strong, consistent presence across authoritative websites, news coverage, research publications, and other trusted sources that are likely to be included in future training datasets.
2. Live web search (RAG) - For the most part, when marketers talk about AEO, they are talking about live web search. In other words, they’re trying to create content that gets cited in answers that are generated after searching the internet.
3. Indexed content - This is a newer, emerging field of AEO, about which little is known. As I wrote about in how to get indexed by ChatGPT, OpenAI stores the pages its crawler discovers in its own index and can surface that cached content in a future answer, separate from any live web fetch.

Content Types That AI Search May Cite

An answer engine can pull from properties you own or from third-party platforms where your brand shows up. Content types it may cite include:

Where Brands Can Appear in AI Search

Getting cited isn’t the only way to show up. A brand can surface in an AI answer in a few different forms.

Inline Citations

A linked reference attached to a specific claim inside the answer, usually a small chip or number right after the sentence it supports. It tells the reader exactly which statement came from your page, and clicking it sends them straight to that source.

Unlinked Named Mentions

Your brand is named directly in the answer text with no hyperlink attached. An engine can recommend you this way without sending a click, which is why these mentions are worth tracking even though they don’t show up as referral traffic.

Comparison Tables

An AI-generated table that lines up several tools or brands across shared criteria like best use case, strengths, and drawbacks. Being included as a row puts you in the engine’s consideration set for that query, and the cells become the engine’s summary of how you stack up against competitors, accurate or not.

Source List

A rail or panel listing every page the engine pulled from to build its answer, shown alongside or below the response. A page can land here even when it isn’t tied to any single sentence, so a brand can appear in the source list without earning an inline citation.

Rich Product Results

Product results with details like images and pricing, surfaced for shopping queries. ChatGPT, for example, shows products through its merchant program.

How is AI search optimization different from traditional SEO?

There’s been much debate about whether AEO is actually a thing, or whether it’s just traditional SEO masquerading as something new and exciting. AEO is definitely distinct from SEO. And here’s where they differ:

• JavaScript can block answer engines that wouldn’t block Google. Some answer engine crawlers, including OpenAI’s, can’t render JavaScript at all, so any content a script loads is invisible to them. Googlebot renders JavaScript in a second indexing wave, so a page that ranks fine in Google can still be unreadable to ChatGPT.
• Answer engines handle long, conversational prompts. Unlike a Google Search keyword that might be a few words, prompts submitted to answer engines like ChatGPT can be paragraphs long.
• Answer engines retrieve passages, not whole pages. An answer engine pulls specific chunks from a page to assemble its response instead of ranking the URL as one unit, as iPullRank founder Mike King has documented.
• AEO is a multi-engine game. SEO has been overwhelmingly Google-focused; AEO spreads visibility across ChatGPT, Perplexity, Gemini, Copilot, and more, each with its own crawler and citation patterns.
• Unlinked mentions carry real weight. Answer engines treat brand mentions as entity and authority signals even without a hyperlink, while traditional search has leaned more heavily on backlinks.
• Visibility matters more than the click. In AEO, the goal is to be named as the recommended answer; in SEO, the goal is to get a click to your page.

For deeper reading, check out our article on how SEO has evolved over the years.

How to Optimize Content for AI Search Citations

Content optimization for AI search comes down to two questions: how you format your answers so an engine can lift them cleanly, and what signals you attach to those answers so the engine trusts them enough to cite. Here’s how to optimize both.

How can I format answers for AI extraction?

Answer first, add details after.

Begin by answering the implied question directly, ideally in a subject-predicate-object format (aka, “semantic triple”). Then, you can share the details. Too often, we invert this when we write, leading with a whole bunch of preamble before we finally get to the punch.

Here’s a real-life example from an article I wrote pre-AEO and how I would reword it for AI search optimization:

Before AEO:

“According to Omnisend, a series of three shopping cart abandonment emails results in 69% more orders. So you can see why reminding buyers of what they left behind in their carts is powerful, right?”

How I would rewrite that for AI search optimization:

“Buyers who receive cart abandonment emails are more likely to complete their purchase. A series of three shopping cart abandonment emails leads to 69% more orders, according to Omnisend.”

Conduct prompt research.

Similar to how keyword research informs SEO strategy, prompt research guides your AEO strategy by helping you discover the queries and follow-up questions a customer might ask an answer engine. This gives you the opportunity to structure your content around those questions and, hopefully, win the citation.

There are two main ways to approach prompt research:

1. Manually. Pose the questions your customers would ask to ChatGPT, Gemini, and Perplexity on a regular schedule. To reduce the impact of previous conversations or personalization, use a fresh chat, Temporary Chat (where available), or a private browsing session. Then, record which sources each engine cites and what follow-up questions it raises. That running log shows which prompts your content already wins and which ones competitors own.
2. Using AEO tools. HubSpot AEO automates that tracking and recommends which prompts to monitor, building those suggestions from your company profile, competitor set, and industry. AEO in Marketing Hub Professional and Enterprise takes it further: It reads your connected CRM data to suggest prompts tied to the questions your actual buyers ask, and sharpens those suggestions as your business changes.

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Structured data may help.

Schema markup, the specialized code that labels your content type for crawlers, may help boost AI citations, according to HubSpot’s State of AEO 2026. Which schema types matter for which engines is covered in the technical structure section below.

Focus on off-site signals.

Answer engines verify credibility through third-party sites, such as review sites and social media. Google AI Overviews gets 51% of its citations from off-site sources like review platforms, according to research by the AEO agency Fan Out. The research also found that Reddit and YouTube make up more AI citations than all other off-site platforms combined, making them particularly high-value for brands looking to boost off-site signals.

What claims and author signals should I add?

Show credibility with an on-page author bio.

An on-page author bio carries slightly more citation weight than a byline alone, per State of AEO 2026. The same report found that those trust signals matter most in AI Overviews, Gemini, and Perplexity, the three engines most responsive to experience, expertise, authoritativeness, and trustworthiness (E-E-A-T). Give each author a bio that names their years of experience, areas of expertise, and any credentials or publications that explain why they can speak on the topic.

Then keep that identity consistent wherever it appears. An answer engine forms a clearer read on an author when the same name shows up the same way across your site, LinkedIn, Crunchbase, G2, and other trusted profiles.

Back up claims with original data or external research.

Answer engines favor pages that back up what they assert. Including statistics and data on a page correlates with more citations, most strongly in AI Overviews and ChatGPT, and outbound links show the same pattern, with the biggest lift in AI Overviews and Gemini, according to State of AEO 2026.

First, publish original data when you have it. First-party research, survey results, or proprietary benchmarks give an answer engine a fact it can’t find anywhere else, which positions your page as the source to cite. Second, when a claim isn’t yours, attribute it to a credible source and link out to the original. A statistic with a named source and a working link reads as more verifiable than a bare assertion.

How to Optimize Technical Structure for AI Search

Now, let’s move on to technical optimization that shapes whether answer engines can read and trust your page: the markup that describes it and how that markup gets rendered.

What schema and HTML help AI understand context?

Schema markup and semantic HTML give answer engines structural cues that help them interpret a page and the relationships between the entities on it. FAQ sections paired with schema markup correlate with higher citations in Gemini, Google AI Mode, and Perplexity, according to HubSpot’s State of AEO 2026.

Schema’s role is debated, though. Google advises site owners not to overfocus on structured data and says no special schema is required to appear in its AI features, per Google’s generative AI optimization guide. A small controlled experiment cuts the other way: Among three near-identical pages, only the one with well-implemented schema triggered an AI Overview and earned the highest organic rank, though the authors call the result inconclusive, according to Search Engine Land. The takeaway is that schema works best as a supporting signal when it accurately maps entity relationships, not as a guaranteed boost.

For HTML, Google says it’s generally a good idea to use semantic markup when possible because it helps screen readers parse and navigate your structure.

Pro tip: Run any markup through the Schema.org validator and Google’s Rich Results Test before publishing.

When should you use server-side rendering?

Use server-side rendering (SSR) or static site generation whenever you need answer engines beyond Google to read your content. As covered earlier, many AI crawlers can’t execute JavaScript, so anything a script loads after the initial response stays invisible to them. SSR and static generation fix this by delivering fully populated HTML in the first response, before any client-side script runs.

How Off-Page Signals Strengthen AI Visibility

Off-page signals are references to your brand on sites you don’t own. Earlier, I covered why Reddit and YouTube carry so much citation weight. Two more off-page levers deserve attention: earned media and the local or ecommerce details that feed Google’s specialized results.

How can PR and bylines boost authority?

ChatGPT leans heavily on publishers, drawing 78% of its citations from vendor- or publisher-controlled sources, which makes earned media one of the most direct routes to a ChatGPT citation, according to Fan Out’s analysis of 33,000+ AI citations. News and media sites make up 9.5% of all ChatGPT citations, according to Semrush.

The practical play is digital PR, getting your experts quoted and published on high-authority outlets. A byline on a trusted publication ties an author’s name to an authoritative domain, reinforcing the entity recognition I described in the author-signals section. Mentions in respected publications build that authority whether or not they link back.

How should local and ecommerce details be optimized?

For shopping and local queries, Google AI Overviews are the wrong place to concentrate. AI Overviews show up for just 3.2% of shopping searches and 7.9% of local searches, according to an Ahrefs study. The shopping opportunity sits in conversational engines instead, where product listings and landing pages were cited in 86% of ChatGPT queries and 84% of Perplexity queries tested, per HubSpot’s State of AEO 2026.

For ecommerce, here are three ways to optimize for AEO:

• List products on marketplaces. AI citations in shopping categories cluster around a few retailers. Amazon earned 17.99% of AI citations in consumer staples and Walmart 6.25%, per Conductor’s 2026 AEO & GEO Benchmarks Report.
• Optimize category pages, not just product pages. Category pages drew 15.96% of AI citations in ecommerce, according to Wix Studio’s AI Search Lab (share of total citations).
• Surface detailed reviews. User reviews were cited in 90% of ChatGPT queries tested, per State of AEO 2026.

The payoff is conversion. ChatGPT-referred ecommerce visits convert at 11.4% against 5.3% for organic search, according to Similarweb’s 3rd Annual Global Ecommerce Report. If you generate product data with AI, label it per Google Merchant Center policy.

Local AI visibility is harder to earn than a map-pack spot. Multi-location brands surfaced in ChatGPT recommendations only 1.2% of the time versus 35.9% in Google’s local 3-pack, and just 45% of retail brands leading traditional local search carried into AI recommendations, according to SOCi’s 2026 Local Visibility Index (vendor data). To close that gap, complete your Google Business Profile and keep your name, address, and phone number identical across every directory an engine reads. Add LocalBusiness schema to each location page so engines can parse hours, service area, and category without guessing.

What Not to Do for AI Search Optimization

The flip side of optimizing for AI search is knowing which tactics waste your time. Most AI search “hacks” fall apart under scrutiny, and a few can actively hurt you. Here’s what to skip.

Don’t create special files just for AI.

You don’t need llms.txt files, separate Markdown versions of your pages, or any other machine-readable format to show up in generative AI results. Google states plainly that its search features, including AI Overviews and AI Mode, don’t use these files. Maintaining llms.txt won’t help or hurt your visibility, according to Google’s AI search optimization guide. Serving a bot-only version of a page carries a real downside, too: Publishing separate content for crawlers and users can read as cloaking, which violates Google’s spam policies.

Don’t over-chunk your content as a gimmick.

Logical structure helps, as the earlier sections on passage retrieval covered, but artificially fragmenting a page into one-sentence paragraphs and FAQ-style snippets because you think models prefer bite-sized text is a different move. Google’s Danny Sullivan has told creators not to do it, according to Search Engine Land. A well-structured page already creates natural retrieval boundaries through clear headings, logical sections, and focused paragraphs. It’s good practice to develop one idea per paragraph, but manufacturing extra fragmentation prioritizes perceived ranking signals over readability.

Don’t publish commodity or mass-produced content.

Recycling what’s already been said gives an answer engine no reason to cite you over the original source. Using AI to spin up high volumes of unoriginal pages designed to game rankings is classified as scaled content abuse and violates Google’s spam policies. The work that earns citations is the opposite: people-first content with a first-hand perspective, original data, or expert insight that can’t be found anywhere else.

Pro tip: If a tactic asks you to create something only a bot will ever see, treat that as a red flag. The lasting plays for AI search are the same ones that serve readers.

How to Measure AI Visibility and Operationalize Your Plan

Answer engines changed what you measure. Clicks still matter, but they no longer capture the full picture because a buyer can read an AI answer about your brand and form an opinion without ever landing on your site. Measuring AI search means tracking how often answer engines mention you, whether those mentions are accurate, and how that visibility shows up in the pipeline.

How can I assess AI visibility with a grader?

Start with a baseline. Before you can improve how answer engines represent your brand, you need to know how they represent it today.

HubSpot’s AEO Grader runs a free, one-time diagnostic that scores how ChatGPT, Perplexity, and Gemini currently describe your brand, returning a composite score out of 100 across sentiment, presence quality, brand recognition, share of voice, and market competition.

Because AEO Grader accepts any brand name, you can run the same check on a competitor and compare where they show up and you don’t. A grader is a single moment in time, though, not a monitoring system, so it tells you where you stand today but not how that’s trending.

Best for: Teams that want a quick read on AI brand perception before committing to ongoing measurement

How do I connect visibility to pipeline?

Visibility only matters if it leads somewhere, and the early data suggests AI-referred visitors convert at a higher rate than other channels.

Looking across all channels, AI-referred visitors in that same Microsoft Clarity dataset converted at about three times the rate of other traffic sources overall. The pattern holds because people use answer engines to research and compare before they click, so the ones who reach your site arrive further along in their decision.

HubSpot’s own results point in the same direction. After focusing on AEO, HubSpot grew qualified leads from AI by 1,850%, with those leads converting at three times the rate of leads from other sources.

To connect that thread, your AI visibility data has to sit next to your demand data. AEO in Marketing Hub tracks brand visibility alongside campaign metrics, so you can see whether a lift in citations corresponds to a lift in form fills.

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Preparing for AI Agents and What Comes Next

AI agents are moving from answering questions to completing tasks. Browser agents like OpenAI’s ChatGPT agent and Perplexity’s Comet can now navigate sites, fill forms, and act on a user’s behalf inside a logged-in session. Commerce agents go a step further: ChatGPT can surface products and, through Agentic Commerce Protocol, hand a purchase to the merchant’s own systems.

The readiness work is mostly an extension of what already earns citations. Agents read the rendered page and rely on structured, machine-readable signals, so the pages an agent can parse and act on are the same clean, well-structured pages I described in the technical and off-page sections. Where agents add a wrinkle is action: Agents can only reliably buy, book, or submit when the relevant controls are exposed in an accessible, machine-interpretable way.

You don’t need a stack overhaul to get ready. Try these steps first:

• Serve content in the initial HTML response so agents that can’t run JavaScript still see it.
• ChatGPT’s commerce experience relies on structured product feeds, so keeping pricing and inventory synchronized is important.
• Label key actions like buy, book, and contact with semantic markup rather than script-only buttons.
• Keep your entity details consistent across the profiles that agents check.

Most organizations won’t need a new CMS. In many cases, improving rendering, structured data, accessibility, and product feeds is enough. Agents act on the pages they can already read, which is the same foundation AEO has asked for throughout this guide.

Frequently Asked Questions About AI Search Optimization

How long does it take to see results from AI search optimization?

There’s no fixed timeline, and it depends on which lever you pull. Technical fixes like server-side rendering can make a page citable as soon as engines recrawl it, often within days or weeks. Authority signals move more slowly: Earned media, consistent entity details, and training-data inclusion compound over months. Set expectations accordingly, and track movement with ongoing monitoring rather than waiting for a single before-and-after read.

Who should own AI search optimization across marketing and SEO?

AEO works best as a shared responsibility rather than a single owner. Your SEO or content team is the natural lead, since the on-page and structural work overlaps heavily with what they already do. But because citations also depend on earned media, consistent brand profiles, and product data, AEO pulls in PR, brand, and web teams too. Assign one person to coordinate, then make the supporting functions accountable for their piece.

Do I need to rebuild my site or change CMS to optimize for AI search?

No. You don’t need to overhaul your tech stack, switch CMS platforms, or add AI-only files to compete. Google states its AI features require no special structured data, chunking, or llms.txt files, and that maintaining them won’t help your visibility, per Google Search Central. The fixes that matter most are crawlability and rendering, which I covered in the technical structure section above.

How does AI search optimization impact paid search and social?

Differently for each. On paid: Bidding on a keyword doesn’t earn your page a spot in an AI Overview, and only 5% of AIO SERPs also showed PPC ads, according to Semrush. On social: Answer engines lean heavily on community and video platforms, with Reddit and YouTube driving more AI citations than all other off-site sources combined, per Fan Out.

It's hard to articulate exactly what's happening here, so I'll put it like this: say a car like the Mercedes-AMG GT63 is 50% sporty and 50% practical, the C2S is 100% sporty and 100% practical. No, I don't know how that's possible, either, but I'll do my best to try to explain it. Fair warning, though, if you're sick of stories where journalists glaze Porsche (and the 911 specifically), you can stop reading here. It's not our fault. Tell those Germans to make worse cars if you've got a problem.

Full Disclosure: Porsche lent me a fully fueled-up 911 Carrera S to do with as I pleased for a week.

Lead from behind

Andy Kalmowitz / Jalopnik

The heart of the Carrera S, like all 911s, is in its ass. The twin-turbocharged 3.0-liter flat-6 now pumps out an incredibly healthy 473 horsepower and 390 pound-feet of torque, nearly the same amount of power as a 997 Turbo. Don't worry, this isn't just a retune of the base 911 Carrera's engine, either. To help it gain 85 hp and 59 lb-ft of torque over the standard car, it makes use of the turbos and intercoolers from the 992.1 Carrera GTS.

When adding in the brilliant 8-speed dual-clutch PDK transmission, launch control, the Sport Chrono package and a foot of Pirelli rubber out back, the rear-wheel-drive 992.2 Carrera S can lay down a 0-to-60-mph time of 3.3 seconds according to Porsche — and in Car and Driver's testing, it hit 60 mph in a hardly believable 2.7 seconds. It crossed the quarter-mile marker in 10.8 seconds at 128 mph in C/D's test, on the way to its Porsche-claimed 191-mph top speed. It goes without saying that this machine can really move, far quicker than anyone would expect from a car that's just a notch above base. The thing is, though, it can do this all day. The Carrera S shook off 5,000-rpm clutch dumps as if I was gently pulling away from a stop sign.

Andy Kalmowitz / Jalopnik

While the Carrera S was undoubtedly wonderful in a straight line, it was even better on the twisty roads of Bear Mountain in the Hudson Valley on an early morning drive. Like all Porsches, its steering is as direct, weighted just right (if you don't mind a bit of heft) and flickable as anything on the road, aided by the PASM sport suspension option that lowers the coupe by 0.4 inches. Even in Sport mode, it didn't ride terribly roughly, though. Porsches never really do. Instead, it was wonderfully compliant over the rough pavement I found on my 500-something-mile test through New York, New Jersey and Pennsylvania.

It's a car that allows its driver to push for hours, simply because it's so easy to do so. Every input is proportional to every output, which means the Carrera S is incredibly predictable to drive. There's just so much mechanical grip from the Pirelli P-Zero tires. Of course, getting into trouble is always possible — especially at the speeds the C2S is capable of — but the way the rear end lets go isn't reminiscent of the "widowmaker" 911s of old. This is really hard driving on easy mode.

Daily driver

Andy Kalmowitz / Jalopnik

Since the 911 Carrera S makes sporty driving so easy, daily driving and road trips are even more of a cinch. It's truly the super sports car that could be driven every day, just because of how easy it is to use. All the creature comforts anyone could ever want in a daily are here and accounted for, thanks mostly to the Premium package my tester was equipped with, which included a Bose sound system, ventilated front seats, adaptive cruise control, and a 360-degree camera. It's also available with other daily-friendly features like rear-axle steering for maneuvering in tight spaces, a front-axle lift and the wonderfully comfortable 18-way adjustable Adaptive Sports Seats Plus that makes even the longest drives feel relaxing. Aiding in those long drives was the extended range fuel tank option, which let me throw a massive 22.1 gallons of gas in the 911. That's pretty sick, but it costs about the same amount as the car's MSRP to fill it.

Andy Kalmowitz / Jalopnik

Outside of the comfort features, the 992.2's cabin was just a nice place to be... for the front two passengers, at least. In the rear, well, good luck. Those are for my cats and luggage, which served as a great overflow zone once I filled up the car's 4.8-cubic-foot frunk. Still, as I was one of the two people in the front, I was able to enjoy my tester's Bordeaux Red leather interior. It was very '80s in the best way possible.

The car also has all of the normal tech features anyone could want from a daily driver without overwhelming the senses. The 10.9-inch center screen was clear and easy to use, and it doesn't take up much real estate on the dashboard, plus there are still plenty of buttons for the less tech-inclined. Those folks will probably be pretty bummed about the 12.65-inch curved gauge cluster, but times are changing, and the cluster can change with it.

Andy Kalmowitz / Jalopnik

All of this adds up to a car that can be driven every single day with almost no compromises. Sure, the engine is maybe a touch louder than the most comfort-oriented people would want from their daily, but that's a small price to pay. It's smooth as hell on the highway, the PDK transmission shifts up and down its eight gears with ease to return some pretty decent fuel economy, the fast-acting front-lift system means the expensive front bumper won't get the crap scratched out of it, and there's even plenty of storage space. It's hard to argue with this amount of brilliance.

Porsche pay

Andy Kalmowitz / Jalopnik

There is but one downfall for this car: its price. The 911 Carrera S is not a cheap car. You knew that, but I don't think you really realize how expensive it has gotten — and it's only getting more expensive. Technically, the car I drove was a 2025 model with a starting price of $153,695, including destination. With options, it came out to $178,825. 

Now though, we've got 2027 pricing, and it's gone up considerably. The car now starts at $158,500, but since this is Porsche, there are going to be a hell of a lot of options added on that you're going to need. For example, you're going to want to shell out $3,680 (2027 pricing) for the 18-way seats. They're that good. Then, add in that $5,590 Premium package. We also can't forget about the chassis needs like rear-axle steering ($2,220), PASM ($1,280) and the front axle lift system ($3,160). Are you going to buy a Porsche without the Sport Chrono pack or the bigger fuel tank? I didn't think so. Hand over $2,540 and then another $240, please.

Andy Kalmowitz / Jalopnik

There were other odds and ends my car was fitted with, but honestly, I'd say it was pretty lightly optioned in Porsche-land. Still, the Ice Grey Metallic paint was $880, the 20-inch front and 21-inch rear wheels were $2,790, and that gorgeous red interior was $3,300. All in all, my tester came out to a fairly high $184,230, including destination. I suppose if you want one of the best cars on sale today, you're going to have to pay for it.

It's paradoxical, and yet it works

Andy Kalmowitz / Jalopnik

There's an argument to be made that the Porsche 911 Carrera S is both the best daily driver and the best weekend car on sale today. I understand that doesn't make much sense on the face of it, and you're probably asking yourself how it could be both. I'm here to tell you that it technically can't, but it is. It's paradoxical, and yet it works. If you don't trust me, trust J. Robert Oppenheimer.

There's just nothing else on the road today that combines the fun-to-drive attitude of a sports car with the true daily driveability of a luxury car — then turning it up even further. Yes, there are other 911s that do roughly the same sorts of things, but they all start to either lean too far in one direction or the other. The rear-wheel-drive Carrera S is the sweet spot, and that's why you've got to pay up for the privilege of driving it.

Getting in this car makes every single drive special, turning the mundane into the magical. It's not a car that meets the occasion. It makes the occasion, because that's what a truly special car can do. %!s()

“We knew that the [merger] needed to happen first and then once we grew together as a community then we could take on that [renaming] task,” he said. “This is a change we’re making very thoughtfully.”

Richard Culatta, CEO of the International Society for Transforming Education, told attendees that the name change is intended to reflect a global focus on aligning instructional strategy, technology use and educator practice to improve student outcomes and engagement.

“We believe that this name most effectively captures what both legacy organizations were always about,” he said. “Our new name shifts the focus from how we do it, to why we do it. And it shows how serious we are about transforming learning together.”

Some attendees expressed enthusiasm over the new name. “Oh, I'm excited,” said Elizabeth Diamond, an associate professor at Temple University in Philadelphia. “Words are so important, and those words are where we’re headed as teachers.”

Julie Keller, also a Temple University associate professor, added, “There’s power in the words, and it really brings together what we’re trying to do.”

Other attendees, including legacy-ASCD member Ruth Letang-Horton, vice president of the North American Division of SDA, were less enthusiastic. “I feel like the ASCD part is really lost,” she said. “Your feeling is like, ‘Wait a minute, what about ASCD?’ It’s because I’ve been an ASCD member for decades.”

The new name is the latest phase of the merger between ISTE and ASCD, which happened in 2023. According to Culatta, membership, educator certifications, the ISTE Standards and professional learning programs will continue without interruption under the new brand.

Read the full press release here.

(Editor’s note: EdSurge is an editorially independent newsroom of the International Society for Transforming Education.) %!s()