How Consumers Navigate High-Stakes Purchases In AI Mode via @sejournal, @Kevin_Indig

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AI Mode is compressing the stage where buyers compare, reject, and discover brands on their own. Our new usability study of 185 documented purchase tasks shows that 74% of AI Mode final shortlists came directly from the AI’s output – no external check, no triangulation, no second opinion.

This analysis will cover:

• How the comparison search phase has collapsed.
• What this means for brands competing in categories with high competitor AI Mode saturation.
• The three levers that determine whether your brand shows up.

Why We Conducted The Study

AI transforms Search from a list of results to a list of recommendations (shortlist). Until now, we have no idea how users treat AI shortlists. Do they take it at face value or thoroughly validate it?

That’s why I partnered with Citation Labs and Clickstream Solutions to record real users and their interactions when facing high-stakes purchases. This usability study of 48 participants completing 185 major-purchase tasks reveals that AI Mode operates as a recommendation environment, not a comparison one.

In traditional search, people click through results, comparing across sources to assemble a candidate set. In AI Mode, they accept the AI’s candidates and move on. 74% of AI Mode shortlists came directly from the AI’s output with no external check. In traditional search, more than half of users built their own shortlist from scratch.

The study covers four categories (televisions, laptops, washer/dryer sets, and car insurance). Participants completed tasks using both AI Mode and traditional search in a within-subjects A/B design, producing 149 AI Mode task observations and 36 search observations. The behavioral patterns are consistent enough across categories and participants to carry weight. (Full study design is at the end.)

From Garret French, founder of Citation Labs:

“In AI Mode, buyers often use a shortlist synthesis to shortcut the cognitive effort of Standard Searching and comparing. This raises the value of onsite decision assets and third-party sources that provide AI with clear trade-offs, specific evidence, and sufficient contextual structure to describe a brand’s offering with confidence.”

From Eric Van Buskirk:

The absence of narrowness frustration is the most intellectually significant finding. 15% in AI Mode vs 11% in Search, with no meaningful statistical difference. That’s the finding that rules out the obvious alternative explanation: that users accepted the AI’s shortlist because they felt trapped. They didn’t push back. They weren’t frustrated. They were satisfied. That makes the acceptance harder to dismiss.

Here’s what happened.

1. 88% Of Users Took The AI’s Shortlist Outright

Across the laptop and insurance tasks, where participants used both search surfaces (classic search and AI Mode), the gap in constructing a product shortlist was stark.

Definitions:

• AI Adopted: The participant took the AI’s recommended candidates as their shortlist with no changes or external verification.
• User Built: The participant ignored the AI’s (or Search’s) suggestions and assembled their own candidate list from independent sources.
• AI Verified: The participant started with the AI’s candidates but checked them against an outside source (a retailer site, a review, a manufacturer page) before finalizing.
• Hybrid: The participant combined AI-suggested candidates with at least one candidate they found independently.

In classic search, 56% of participants built their own shortlist from multiple sources. In AI Mode, only 8 out of 147 codeable tasks produced a genuinely self-built shortlist. The user’s comparison process didn’t just shrink when using AI Mode. For most participants, it didn’t happen at all.

64% of AI Mode participants clicked nothing at all during their task. They read the AI’s text, sometimes scrolled through inline product snippets, and declared their finalists. The no-click rate varied by category:

Insurance participants delegated most heavily. Washer/dryer participants clicked the most, likely because appliance decisions involve specific physical constraints (capacity, stacking compatibility, dimensions) that the AI summary didn’t always resolve.

The 36% who did interact with individual results within AI Mode broke into 2 groups:

• About 15% of the AI Adopted group (17 of 117 participants) verified inside AI Mode: They opened inline product cards or merchant pop-ups to check a price or spec, then returned to the AI’s list.
• Others used follow-up prompts as verification tools, asking the AI for prices or narrowing by constraints.

A separate 23% of all AI Mode tasks involved at least one visit to an external website, mostly retailers (Best Buy appeared in 10 of 34 tasks with external visits) and manufacturer sites. The destination pattern matters: Users left AI Mode to confirm a candidate they’d already accepted from the AI’s list, not to find new ones.

Of the 117 participants who adopted the AI’s shortlist directly, roughly 85% showed no internal verification behavior at all. Participants who built their own lists took an average of 89 seconds longer and consulted more than twice as many sources.

• “Given that the first paragraph says Lenovo or Apple… going with that,” said one user about laptops when searching via AI Mode. Position one in the AI response was the entire decision.
• Another AI Mode user remarked: “I liked it more than anything else I’ve ever used for product searching. It made it a lot quicker to find the options.” They experienced speed as a valuable feature, not a shortcut.

In classic search, the pattern reversed. Nearly 89% of participants clicked on something.

• One insurance participant clicked out to Progressive and GEICO independently, read both landing pages, consulted an Experian article, and then arrived at a shortlist.
• A laptop participant applied hardware filters and flagged a review score discrepancy: “It shows 4.6 out of 5 stars for the reviews, but when you actually click the link: not reviewed yet.” Active skepticism of aggregated data was a behavior absent from AI Mode transcripts.

2. The AI’s Top Pick Becomes The User’s Top Pick 74% Of The Time

Just like in classic search, the top answer carries outsized weight. 74% of participants chose the item ranked first in the AI’s response as their top pick. The mean rank of the final choice was 1.35. Only 10% chose something ranked third or lower.

Position one in the AI’s output carries an outsized advantage because of where it sits: inside a curated section that typically contains two to five items, after the AI has already done the filtering. The first item is the AI’s top pick. When people engage with AI mode, we know they read almost all of the output: The first AI Mode study found users spend 50 to 80 seconds reading AI Mode output, more than double the dwell time on AI Overviews. Users are reading carefully. They just read within a set the AI already narrowed.

However, 26% of participants in this study overrode rank order. The driver: brand recognition. They spotted a brand lower on the list and preferred it regardless of where the AI placed it. TV and laptop categories saw this most, where participants arrived with existing preferences for Samsung, LG, Apple, or Lenovo. But overriding rank did not mean rejecting the AI’s output: 81% of rank-override participants still chose from the AI’s candidate set.

3. The AI’s Words Become The Trust Signal

“Travelers and USAA actually tell me how much, whereas State Farm and GEICO give percentages. Just knowing the exact amount makes me want to pick Travelers or USAA right off the bat.”

That quote captures a core pattern in AI Mode trust. The AI’s formatting shaped the decision: Dollar amounts versus percentage discounts determined which brands made the shortlist.

AI framing (37%), meaning how AI talks about the product, and brand recognition (34%) were the top 2 trust drivers in AI Mode. They run nearly even:

• Brand recognition led when participants arrived with brand preferences.
• AI’s wording filled the gaps where participants didn’t already have preferences.

In classic search, the dominant trust mechanism was multi-source convergence: Participants built confidence by checking whether multiple independent sources agreed about a product.

Essentially, users triangulated. One checked Progressive, then GEICO, then an Experian article. Another compared aggregated star ratings against reviews on the actual site. They were building a case from separate inputs.

That behavior was almost absent in AI Mode (5%). Instead, AI framing (how the AI worded its description of a product) and brand recognition were the top 2 trust drivers.

The split between these two signals tracked closely with product category:

For televisions and laptops, where most participants arrived with existing brand preferences, brand recognition dominated. For insurance and washer/dryer, where participants had less prior knowledge, AI framing dominated.

When you lack a prior view, the AI’s description becomes the trust signal. In AI Mode, the synthesis is the corroboration. Participants treated the AI’s summary as if the cross-checking had already been done for them.

The first study showed a related pattern from the supply side: AI Mode matches site type to intent, surfacing brands for transactional queries and review sites for comparisons. This study shows the demand side of the same behavior: When the AI surfaces a brand the user already knows, brand recognition drives the decision; when it doesn’t, the AI’s own framing fills that role. The site-type matching and the trust mechanism reinforce each other.

4. If You’re Not In The List, You Don’t Exist

Purchase outcomes in AI Mode concentrated heavily. For laptops, three brands captured 93% of all AI Mode final choices. In classic search, the distribution was broader: HP EliteBook variants appeared three times, ASUS once, and other brands got consideration they never received in AI Mode.

Two distinct problems emerged:

1. Brands that never appeared in the AI’s output were never considered. Participants didn’t see them, so they couldn’t evaluate them. The AI decided who made the list, not the buyer.
2. Brands that did appear but lacked recognition faced a different problem: They weren’t seriously considered. Erie Insurance showed up in AI Mode results, but multiple participants eliminated it on name recognition alone. The brand was present but hadn’t built enough awareness to survive the moment of selection. One participant dropped a brand because it lacked a hyperlink in the AI output, reading that formatting gap as a credibility signal: “There’s not even a link there.”

Another participant said when using AI Mode: “I’m already eager to believe these are good recommendations because it mentions LG and Samsung, two brands I consider very reliable.” The AI didn’t say those brands were better. The participant inferred it from familiarity.

Participants didn’t feel constrained by the narrower set. Narrowness frustration appeared in 15% of AI Mode tasks and 11% of classic search tasks, statistically indistinguishable. The option set shrank, but the feeling of having enough options didn’t change. The most skeptical AI Mode participant in the comparison set, who complained the AI kept pointing to “teen drivers, teen drivers, teen drivers,” still chose GEICO and Travelers: the consensus AI result.

5. Users Leave To Buy, Not To Research

23% of AI Mode tasks involved an external site visit, but keep in mind these prompts reflect high-stakes situations. In standard search, that figure was 67%.

The volume difference matters less than the intent difference:

• AI Mode participants who left went to retailer sites and manufacturer pages to verify a price or spec for a candidate they’d already selected.
• Standard Search participants left to discover candidates: Reddit for peer opinions, editorial review sites for expert takes, insurance aggregators for comparison.

In the first AI Overviews study, we found that high risk leads users to verify AI claims more and reference against answers from other users on UGC platforms (like Reddit).

In this study, Reddit appeared in 19% of standard search tasks and only twice across all 149 AI Mode sessions. The peer-opinion layer that shapes a large share of traditional Search barely exists in AI Mode behavior.

There’s irony in that pattern. Google leans heavily on Reddit content to train its models. However, the source that users rely on most in standard search is the one they almost never visit when the AI synthesizes those same sources for them.

The first study found the same pattern at a different scale. Across 250 sessions, clicks were “reserved for transactions:” Shopping prompts drove the highest exit share, while comparison prompts drove the lowest. The exit destinations were retailers and brand sites, not editorial or peer-opinion sources. Six months and a different task set later, the pattern holds: When users leave AI Mode, they leave to buy.

6. 3 Levers: Visibility, Framing, And Pricing Data

Three things that excite me most about the study:

First, we can apply the mental model of rankings (higher = better) to AI Mode as well. Most users choose the first product. Now, we can apply this to prompt tracking by focusing more on prompts that lead to shortlists and use our position as a goalpost.

Second, trust trumps rank. We know this since the first user behavior studies I published, but this study reinforces the importance of building trust with users before they search. It’s the ultimate cheat code.

Third, we now know buyers trust AI’s recommendations. Obviously, there’s a high risk here if the AI is wrong, but seeing how quickly buyers take the AI’s recommendation also shows us how fast consumers adopt AI. It truly is the future of Search.

Keep in mind:

1. Visibility at the model layer is the new threshold. If AI Mode doesn’t surface your brand, you have a visibility problem at the model layer. Query your own category the way a buyer would (i.e., “best car insurance for a family with a teen driver,” “best washer dryer set under $2,000”) and document which brands appear, in what order, and with what framing. Do this across multiple prompt variations. Do it regularly, because AI responses shift over time.

2. How the AI describes you matters as much as whether it appears. Brands cited with concrete attributes (specific model, specific price, named use case) held stronger positions than brands described generically. The content on your site that the AI draws from not only affects whether you show up, but also how confidently and specifically you show up. A brand with structured pricing data, clear product specs, and explicit use cases gives the AI better material to work with.

3. For categories with context-dependent pricing, AI Mode creates a false-confidence problem. 63% of insurance participants were rated overconfident about pricing. They accepted AI-quoted rate estimates without checking whether the figures applied to their actual state, driving record, or current insurer. They made elimination decisions based on numbers that may not have applied to them. Where shopping panels showed explicit retailer-confirmed prices (washer/dryer), 85% of participants understood pricing clearly. Where they didn’t (insurance, laptops), confusion and overconfidence filled the gap. Structured pricing data through Merchant Center feeds and schema markup is the most direct lever for brands selling physical products. For services, the lever is editorial: Make sure your landing pages and FAQ content frame pricing as conditional (“your rate depends on X, Y, Z”) so the AI has that framing to draw from.

Study Design

Citation Labs and Clickstream Solutions ran this as a remote, unmoderated usability study with 48 U.S.-based participants recruited through Prolific. Each participant completed up to four major-purchase shortlisting tasks across televisions, laptops, washer/dryer sets, and car insurance.

The comparison between AI Mode and traditional standard search used a within-subjects A/B design: Participants used both surfaces, not one or the other. Significance calculations were normalized for the exact number of participants in each group (149 AI Mode task observations, 36 standard search task observations). This matters because the groups are unequal in size, and raw percentage comparisons between them would overstate confidence without that correction.

Sessions were screen-recorded with think-aloud audio. Trained analysts annotated each recording for behavioral markers (click-through, shortlist origin, trust signals, external site visits) and qualitative markers (stated reasoning, brand mentions, frustration signals). The 185 task-level observations provide a larger analytical base than the 48-participant headcount suggests, but confidence intervals remain wider than a large-scale survey. Findings are directional, not population-level estimates.

Notes on terminology used throughout this report:

• Shortlist: The final set of brands a user would consider buying from.
• AI Adopted: The participant took the AI’s recommended candidates as their shortlist with no changes or external verification.
• User Built: The participant ignored the AI’s (or Search’s) suggestions and assembled their own candidate list from independent sources. In Search, when there was no AIO present, they had no option for relying on AI suggestions.
• AI Verified: The participant started with the AI’s candidates but checked them against an outside source (a retailer site, a review, a manufacturer page, further prompting, or interaction with a panel outside the main AI text block ) before finalizing.
• Hybrid: The participant combined AI-suggested candidates with at least one candidate they found independently.
• AI framing: The specific words and structure the AI used to describe a product, such as labels like “best for affordability” or explicit price comparisons.
• Brand recognition: The user chose or eliminated a brand based on prior familiarity, not the AI’s description or any external research.
• AI trust (general): The user accepted the AI’s output as credible without citing a specific reason, such as a particular label or description.
• Source trust: The user trusted a recommendation because of where it came from, such as a retailer, manufacturer, or named publication surfaced in results.
• Multi-source convergence: The user built confidence by checking whether multiple independent sources agreed on the same recommendation.
• Rank override rate: The share of users who chose a brand other than the AI’s top-ranked option, regardless of whether they stayed within the AI’s candidate list.

Featured Image: Tapati Rinchumrus/Shutterstock; Paulo Bobita/Search Engine Journal

The Top 6 Search Engines Market Share & The AI Search Engines To Watch via @sejournal, @MattGSouthern

For more than a decade, Google’s share of global search traffic barely moved. It hovered between 91% and 93%, and the SEO industry built its workflow around that reality.

As of March 2026, StatCounter data shows Google at 90.01% of worldwide search traffic, with Bing at 4.98%, Yahoo at 1.39%, Yandex at 1.34%, DuckDuckGo at 0.76%, and Baidu at 0.55%.

Google’s share has fluctuated between roughly 89% and 93% since 2015. It dipped below 90% in each of the final three months of 2024 and again in February 2026 before ticking back above that line in March. At Google’s scale, every tenth of a percent represents millions of searches.

Meanwhile, AI search tools like ChatGPT and Perplexity are growing fast outside of traditional measurement, and platforms like Amazon and TikTok continue to capture search behavior that never touches a traditional engine.

Here we will walk through the top six search engines that account for the measurable search market. I’ll also explain where AI search engines fits into the picture and what all of this means for where you invest resources.

1. Google

Google still leads by a wide margin, with nine out of every 10 searches worldwide running through its engine, according to StatCounter. Alphabet, Google’s parent company, generates most of its revenue from search advertising and continues to invest in AI-powered features.

The numbers look different by market and device. In the United States, Google holds 84.13%, with Bing at 10.52% and Yahoo at 2.86%. On desktop globally, Google’s share drops to roughly 82%, while Bing picks up over 10%. Mobile is still Google’s strongest position at over 94%.

The biggest change to Google’s search results over the past year has been the expansion of AI Overviews. These AI-generated summaries appear directly on the results page and answer the query before a user clicks anything. Several studies suggest rising rates of zero-click searches on Google. For SEO professionals, this means competition for clicks is intensifying even as Google’s overall share holds steady.

The challenge is familiar to anyone who has optimized for Google over the past few years. The traffic potential is still unmatched, but earning clicks means competing against Google’s own SERP features. Featured Snippets, People Also Ask boxes, local packs, shopping carousels, and AI-generated summaries all sit above or alongside organic results. Paid search costs continue to rise in competitive verticals. And even ranking in the top three organic positions doesn’t guarantee a click the way it once did.

None of that changes the math. Google is still where the traffic is. If you are going to invest in one search engine, it should be this one. But the cost of competing keeps rising, and the share of clicks reaching external sites is being squeezed by more on-SERP answers. Diversification is worth serious consideration.

→ Further reading:

2. Microsoft Bing

Bing has posted the most notable gains in market share among traditional search engines over the past two years. Its 5.01% global share may look small, but in the U.S., it climbs past 10%, and on desktop it’s even higher.

Microsoft has paired Bing’s existing distribution advantages with Copilot integration, adding a conversational AI layer alongside traditional search results.

The volume gap between Google and Bing is obvious. But that gap also means fewer brands competing for the same positions, which is worth considering when you are deciding where to allocate budget.

There’s a strategic reason beyond market share to pay attention to Bing. ChatGPT’s search functionality relies on Bing’s index for retrieving web results. If that connection holds, content that performs well in Bing has an additional path to being surfaced through AI-generated answers in ChatGPT.

Optimizing for Bing overlaps with Google in many areas. The Bing Webmaster Guidelines cover familiar ground, such as content quality, backlinks, and page speed. Where SEO professionals who work across both engines tend to notice differences is in how each engine weighs keyword specificity, how each handles mobile versus desktop indexing, and how social signals factor into results. If you are optimizing for Google already, much of that work carries over to Bing, but reviewing Bing’s own guidelines is worth the time to catch the gaps.

For teams already running Google Ads, testing Microsoft Ads is a low-friction starting point. Microsoft Ads supports direct campaign imports from Google Ads, which reduces the setup time. From there, you can evaluate performance independently and adjust bids and targeting for Bing’s audience.

→ Further reading:

3. Yahoo

Yahoo holds 1.39% of the global search market share and 2.86% in the U.S.

Yahoo’s search results are powered by Bing through a long-standing partnership. The index and ranking algorithms are the same, where Yahoo differs is in its ecosystem. Yahoo Mail, Yahoo Finance, and Yahoo News still draw audiences, and those users often search within the Yahoo environment rather than switching to Google.

If you are optimizing for Bing, you are also reaching Yahoo’s audience. The combined Bing-Yahoo share in the U.S. is over 13%, which represents a pool of traffic that most SEO strategies ignore. There’s no separate Yahoo optimization playbook. The work you do for Bing carries over.

Where Yahoo becomes strategically relevant is in paid search. Microsoft Ads campaigns serve across both Bing and Yahoo properties. This means a single paid search setup gives you access to the combined audience without additional management overhead.

→ Further reading: New Yahoo Scout AI Search Delivers The Classic Search Flavor People Miss

4. Yandex

Yandex holds 1.34% of the global search market share, but that number undersells its importance in one region. In Russia, Yandex powers roughly 72% of all searches, according to StatCounter. If your business operates in or sells to the Russian market, Yandex is the primary search engine, not an alternative.

A leak of approximately 44 GB of Yandex source code in 2023 exposed over 17,800 ranking factors. The leak gave the SEO community an unprecedented look at how a search engine’s algorithms work. Yandex’s algorithms differ from Google’s, but the leak provided useful insights into ranking signal categories that many SEO professionals have incorporated into their broader thinking.

The leaked code confirmed that Yandex’s algorithms place greater weight on geolocation than Google’s, making local optimization especially important. Domain age, content freshness, and user behavior signals like click-through rate and dwell time all carried weight in the leaked ranking factors. Because of lower competition, paid search on Yandex can be less expensive per click than on Google or Bing. But success requires native-language content and cultural fluency that goes well beyond translation.

Yandex also offers its own webmaster tools, advertising platform (Yandex Direct), and analytics suite (Yandex Metrica), all designed for Russian-language users. Most English-language SEO teams can safely skip Yandex, but international teams working in Russian-speaking markets can’t.

→ Further reading: Yandex Search Ranking Factors Leak: Insights

5. DuckDuckGo

DuckDuckGo holds 0.76% of the global search market share and 1.84% in the U.S. Its growth has been steady rather than explosive, driven by users who prioritize privacy.

Unlike Google, DuckDuckGo doesn’t track users or build advertising profiles based on search history. This privacy-first positioning has attracted a consistent user base. In Europe, where data privacy regulations like GDPR have raised awareness of tracking practices, DuckDuckGo and similar engines like Ecosia have picked up small but steady gains.

DuckDuckGo’s search results draw from multiple sources, including Bing’s index and its own web crawler. There’s no need to optimize for DuckDuckGo separately. Strong performance on Bing generally translates to visibility here as well.

DuckDuckGo does offer its own advertising platform, which serves keyword-based ads through a partnership with Microsoft Advertising. Brands in privacy-sensitive verticals like healthcare, financial services, or cybersecurity may find that visibility on a privacy-focused engine resonates with their audience.

→ Further reading:

6. Baidu

Baidu holds 0.55% of the global search market share, a number that reflects its almost exclusively Chinese user base rather than a lack of scale. Within China, Baidu commands over 53% of the search market according to StatCounter and processes billions of searches daily.

Baidu has invested in AI through its ERNIE large language model series. The company’s AI assistant reached 200 million monthly active users by January 2026, according to the South China Morning Post, and Baidu unveiled its latest model, ERNIE 5.0, in November 2025. The company also made ERNIE Bot free for individual users starting April 2025. Baidu is integrating AI-generated answers into search results in ways similar to Google’s AI Overviews.

For businesses targeting Chinese consumers, Baidu isn’t optional. But the optimization work looks different than what you do for Google. Content must be in Mandarin, hosted on servers accessible within China, and compliant with Chinese internet regulations. The algorithms also differ. SEO guides for the Chinese market typically emphasize domain age, meta tags, and page load speed as ranking factors that carry more weight on Baidu.

Baidu operates its own advertising platform (Baidu Tuiguang), webmaster tools, and analytics suite. Setting up these tools from outside China adds complexity. Most teams will need a partner or team member with direct experience in the Chinese market. Unless you have a specific China market strategy with native-language resources, Baidu is unlikely to be part of your SEO roadmap.

→ Further reading: Baidu Ranking Factors: A Comprehensive Data Study

AI Search Engines: ChatGPT Search and Perplexity

The market share numbers above don’t capture what may be the most important change in search behavior over the past two years. AI search engines like ChatGPT Search and Perplexity don’t appear in StatCounter’s data. They don’t function as traditional search referral sources that tracking scripts measure in the same way.

The usage numbers are growing fast. ChatGPT has reached 900 million weekly active users, OpenAI reported in late February 2026. That is up from 800 million in October 2025. Perplexity CEO Aravind Srinivas said at Bloomberg’s Tech Summit in June 2025 that the platform processed 780 million search queries in May 2025, up from 230 million less than a year earlier.

In terms of traffic sent to websites, AI platforms are still small. Conductor’s 2026 AEO/GEO Benchmarks Report found that AI referral traffic accounts for 1.08% of total web traffic across 10 industries it studied. ChatGPT drives 87.4% of that AI referral traffic. An SE Ranking study found that AI traffic to websites grew roughly sevenfold between early 2024 and mid-2025.

These tools work differently from traditional search engines. Instead of returning a list of links, they provide synthesized answers with source citations. Users can ask follow-up questions, refine their research, and get summaries without clicking through to individual pages.

For SEO professionals, this creates a new optimization challenge. Appearing as a cited source in ChatGPT or Perplexity answers is becoming a separate discipline from ranking in Google’s organic results. The SEO industry has started calling this “Generative Engine Optimization,” or GEO, though the field is still young and best practices are evolving.

Early data from Conductor’s benchmarks offers some initial signals about Google’s AI Overviews, specifically. In Conductor’s analysis of over 21 million Google searches in September 2025, 25.11% triggered an AI Overview, with rates varying widely by industry. The types of pages cited in those overviews also varied by sector. Whether those patterns hold across ChatGPT, Perplexity, and other AI platforms is less clear, which means tracking your visibility across multiple AI surfaces is becoming necessary.

The first step is to start monitoring whether your content appears in AI responses. Several SEO platforms have added AI visibility tracking tools. From there, the optimization work resembles what has always worked in SEO. Clear, authoritative content with well-organized data and transparent sourcing is the foundation.

For more on the growing AI search landscape, see our guide to the best AI search engines.

→ Further reading:

Search Beyond Traditional Engines

Amazon

Amazon doesn’t appear in StatCounter’s search engine rankings, but it’s one of the most important search platforms for anyone selling products online. Survey-based research has consistently found that a large share of online product searches begin on Amazon (56%, according to Jungle Scout) rather than on a traditional search engine.

Amazon’s search algorithm, originally known as A9 and widely referred to as A10 in the seller community, is built around purchase intent. Amazon has not officially confirmed an algorithm version change, but third-party sellers and agencies have documented differences from earlier versions, including greater weight on external traffic and seller authority.

For ecommerce brands, the key is balancing Amazon marketplace investment with direct-to-consumer search traffic. Amazon gives you access to buyers at the point of purchase, but the platform controls the customer relationship and takes a cut of every sale. Brands that invest only in Amazon search lose the ability to build direct relationships with their customers, while brands that ignore Amazon miss the audience that starts their product research there.

Amazon also offers its own paid advertising through Sponsored Products, Sponsored Brands, and Sponsored Display campaigns. These ads appear within Amazon’s search results and on product detail pages, and they run on a cost-per-click model. For product-focused businesses, Amazon advertising is a separate budget line from Google or Microsoft Ads, and it requires its own keyword research and bid management.

→ Further reading:

TikTok

TikTok has more than 170 million users in the United States and over 1.6 billion worldwide. The platform has become a search and discovery tool for younger audiences looking for recommendations, product reviews, and how-to content.

TikTok’s regulatory path in the United States went through a turbulent period. Congress passed legislation in 2024 requiring ByteDance to divest its U.S. operations or face a ban. TikTok briefly went dark in January 2025. Following multiple executive order extensions, a deal was finalized on January 22, 2026. The resulting TikTok USDS Joint Venture LLC put US operations under majority control of American investors, including Oracle and Silver Lake, with ByteDance retaining a 19.9% minority stake.

With the ownership question resolved for now, TikTok is still a growing discovery platform. Its recommendation algorithm surfaces content based on engagement signals rather than keywords, which means optimization for TikTok search looks nothing like traditional SEO. Content format, hook quality, and engagement metrics matter more than backlinks or domain authority.

For brands, TikTok’s search function is most relevant as a discovery channel. Users search TikTok for product recommendations, restaurant reviews, travel ideas, and tutorials. The content that performs well in TikTok search tends to be short, visually engaging, and structured around a specific question or problem. If your audience skews younger and your content lends itself to video, TikTok search is worth testing as a supplementary channel.

→ Further reading:

What Market Share Changes Mean for Your SEO Strategy

Google is still the leader of search, but its share is dropping while AI search tools are growing fast, and alternative platforms serve specific audiences that Google doesn’t reach as effectively.

Here’s how to think about resource allocation.

Google should get the majority of your SEO investment, because it’s where most of your audience searches. But the expansion of AI Overviews means you need to optimize for visibility within Google’s AI-generated feature as well.

Bing deserves more attention than most teams give it. Between Bing, Yahoo, and the reported connection to ChatGPT’s web search, Bing’s reach is wider than its global share suggests. On the organic side, reviewing your Bing Webmaster Tools data and ensuring your site is properly indexed is a quick win most teams skip.

The traffic volume for AI search engines is small, but the growth rate is steep. Start tracking whether your content appears in ChatGPT and Perplexity responses. If you aren’t being cited, start with the same content fundamentals that drive good SEO.

Vertical platforms matter for specific audiences. If you sell products, Amazon search optimization isn’t optional. If you target younger demographics, TikTok search and discovery behavior is a factor you can’t ignore. Neither of these platforms runs on traditional SEO signals, so they require dedicated strategy and budget.

Regional engines serve their markets. Yandex and Baidu are essential for Russia and China, respectively, but require native-language teams, localized hosting, and familiarity with platform-specific tools and regulations.

The data makes a case for diversifying beyond Google. The harder decision is how much to invest in each channel based on your specific audience, market, and goals. The answer will be different for every business, but the days of building an entire search strategy around a single engine are over.

More Resources:

Featured Image: Accogliente Design/Shutterstock

MCP, A2A, NLWeb, And AGENTS.md: The Standards Powering The Agentic Web via @sejournal, @slobodanmanic

This is Part 3 in a five-part series on optimizing websites for the agentic web. Part 1 covered the evolution from SEO to AAIO. Part 2 explored how to get your content cited in AI responses. This article goes deeper: the protocols forming the infrastructure layer that make everything else possible.

The early web needed HTTP to transport data, HTML to structure content, and the W3C to keep everyone building on the same foundation. Without those shared standards, we’d have ended up with a fragmented collection of incompatible networks instead of a single web.

The agentic web is at that same inflection point. AI agents need standardized ways to connect to tools, talk to each other, query websites, and understand codebases. Without shared protocols, every AI vendor builds proprietary integrations, and the result is the same fragmentation the early web narrowly avoided.

Four protocols are emerging as the foundational layer. This article covers what each one does, who’s behind it, and what it means for your business. Throughout this series, we draw exclusively from official documentation, research papers, and announcements from the companies building this infrastructure.

Why Standards Matter

Consider how the original web came together. In the early 1990s, competing browser vendors and incompatible standards were fragmenting what should have been a unified network. The W3C brought order by establishing shared protocols. HTTP handled transport. HTML handled structure. Everyone agreed on the rules, and the web took off.

AI is at a similar crossroads. Right now, every major AI company is building agents that need to interact with external tools, data sources, other agents, and websites. Without standards, connecting your business systems to AI means building separate integrations for Claude, ChatGPT, Gemini, Copilot, and whatever comes next. That’s the M x N problem: M different AI models times N different tools equals an unsustainable number of custom connections.

What makes this moment remarkable is who’s building the solution together. On Dec. 9, 2025, the Linux Foundation announced the Agentic AI Foundation (AAIF), a vendor-neutral governance body for agentic AI standards. Eight platinum members anchor it: AWS, Anthropic, Block, Bloomberg, Cloudflare, Google, Microsoft, and OpenAI.

OpenAI, Anthropic, Google, and Microsoft. Competing on AI products, collaborating on AI infrastructure. As Linux Foundation Executive Director Jim Zemlin put it: “We are seeing AI enter a new phase, as conversational systems shift to autonomous agents that can work together.”

This is a bigger deal than most people realize. Competitors building shared infrastructure because they all recognize that proprietary standards would hold back the entire ecosystem, including themselves.

MCP: The Universal Adapter

What it is: The Model Context Protocol (MCP) is an open standard for connecting AI applications to external tools, data sources, and workflows.

The official analogy is apt:

“Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect electronic devices, MCP provides a standardized way to connect AI applications to external systems.”

Before MCP, if you wanted your database, CRM, or internal tools accessible to an AI assistant, you had to build a custom integration for each AI platform. MCP replaces that with a single standard interface. Build one MCP server for your data, and every MCP-compatible AI system can connect to it.

The numbers are striking. MCP launched as an open-source project from Anthropic on Nov. 25, 2024. In just over a year, it reached 97 million monthly SDK downloads across Python and TypeScript, with over 10,000 public MCP servers built by the community.

The adoption timeline tells the story. Anthropic’s Claude had native MCP support from day one. In March 2025, OpenAI CEO Sam Altman announced support across OpenAI’s products, stating: “People love MCP and we are excited to add support across our products.” Google followed in April, confirming MCP support in Gemini. Microsoft joined the MCP steering committee at Build 2025 in May, with MCP support in VS Code reaching general availability in July 2025.

From internal experiment to industry standard in 12 months. That pace of adoption signals something real.

What this means for your business: If your data, tools, or services are MCP-accessible, every major AI platform can use them. That’s not a theoretical benefit. It means an AI assistant helping your customer can pull real-time product availability from your inventory system, check order status from your CRM, or retrieve pricing from your database, all through one standardized connection rather than platform-specific integrations.

A2A: How Agents Talk To Each Other

What it is: The Agent2Agent protocol (A2A) enables AI agents from different vendors to discover each other’s capabilities and collaborate on tasks.

If MCP is how agents connect to tools, A2A is how agents connect to each other. The distinction matters. In a world where businesses use AI agents from Salesforce for CRM, ServiceNow for IT, and an internal agent for billing, these agents need a way to discover what each other can do, delegate tasks, and coordinate responses. A2A provides that.

Google launched A2A on April 9, 2025 with over 50 technology partners. By June, Google donated the protocol to the Linux Foundation. By July, version 0.3 shipped with over 150 supporting organizations, including Salesforce, SAP, ServiceNow, PayPal, Atlassian, Microsoft, and AWS.

The core concept is the Agent Card: a JSON metadata document that serves as a digital business card for agents. Each A2A-compatible agent publishes an Agent Card at a standard web address (/.well-known/agent-card.json) describing its identity, capabilities, skills, and authentication requirements. When one agent needs help with a task, it reads another agent’s card to understand what that agent can do, then communicates through A2A to request collaboration.

Google’s own framing of how these pieces fit together is useful: “Build with ADK, equip with MCP, communicate with A2A.” ADK (Agent Development Kit) is Google’s framework for building agents, MCP gives them access to tools, and A2A lets them talk to other agents.

Here’s a practical example. A customer contacts your company with a billing question that requires a refund. Your customer service agent (built on one platform) identifies the issue, passes the context to your billing agent (built on another platform) via A2A, which calculates the refund amount and hands off to your payments agent (yet another platform) to process it. The customer sees one seamless interaction. Behind the scenes, three agents from different vendors collaborated through a shared protocol.

The enterprise adoption signal is strong. When Salesforce, SAP, ServiceNow, and every major consultancy sign on to a protocol within months, it’s because their enterprise clients are already running into the multi-vendor agent coordination problem that A2A solves.

NLWeb: Making Websites Conversational

What it is: NLWeb (Natural Language Web) is an open project from Microsoft that turns any website into a natural language interface, queryable by both humans and AI agents.

Of the four protocols covered here, NLWeb is the most directly relevant to this series’ audience. MCP, A2A, and AGENTS.md are primarily developer concerns. NLWeb is about your website.

NLWeb was introduced at Microsoft Build 2025 on May 19, 2025. It was conceived and developed by R.V. Guha, who joined Microsoft as CVP and Technical Fellow. If that name sounds familiar, it should: Guha is the creator of RSS, RDF, and Schema.org, three standards that fundamentally shaped how the web organizes and syndicates information. When the person behind Schema.org builds a new web protocol, it’s worth paying attention.

The key insight behind NLWeb is that websites already publish structured data. Schema.org markup, RSS feeds, product catalogs, recipe databases. NLWeb leverages these existing formats, combining them with AI to let users and agents query a website’s content using natural language instead of clicking through pages.

Microsoft’s framing is deliberate: “NLWeb can play a similar role to HTML in the emerging agentic web.” The NLWeb README puts it even more directly: “NLWeb is to MCP/A2A what HTML is to HTTP.”

Every NLWeb instance is automatically an MCP server. That means any website running NLWeb immediately becomes accessible to the entire ecosystem of MCP-compatible AI assistants and agents. Your website’s content doesn’t just sit there waiting for visitors. It becomes actively queryable by any AI system that speaks MCP.

Early adopters include Eventbrite, Shopify, Tripadvisor, O’Reilly Media, Common Sense Media, and Hearst. These are content-rich websites that already invest heavily in structured data. NLWeb builds directly on that investment.

Here’s what this looks like in practice. Instead of a user navigating Tripadvisor’s search filters to find family-friendly restaurants in Barcelona with outdoor seating, an AI agent could query Tripadvisor’s NLWeb endpoint: “Find family-friendly restaurants in Barcelona with outdoor seating and good reviews.” The response comes back as structured Schema.org JSON, ready for the agent to present to the user or act on.

If your business has already invested in Schema.org markup (and Part 2 of this series explained why you should), you’re closer to NLWeb readiness than you might think.

AGENTS.md: Instructions For AI Coders

What it is: AGENTS.md is a standardized Markdown file that provides AI coding agents with project-specific guidance, essentially a README written for machines instead of humans.

This protocol is less directly relevant to the marketers and strategists reading this series, but it’s an important piece of the complete picture, especially if your organization has development teams using AI coding tools.

AGENTS.md emerged from a collaboration between OpenAI Codex, Google Jules, Cursor, Amp, and Factory. The problem they were solving: AI coding agents need to understand project conventions, build steps, testing requirements, and architectural decisions before they can contribute useful code. Without explicit guidance, agents make assumptions that lead to inconsistent, buggy output.

Since its release in August 2025, AGENTS.md has been adopted by over 60,000 open-source projects and is supported by tools including GitHub Copilot, Claude Code, Cursor, Gemini CLI, VS Code, and many others. It’s now governed by the Agentic AI Foundation, alongside MCP.

The file itself is simple. Plain Markdown, typically under 150 lines, covering build commands, architectural overview, coding conventions, and testing requirements. Agents read it before making any changes, getting the same tribal knowledge that senior engineers carry in their heads.

GitHub reports that Copilot now generates 46% of code for its users. When nearly half of code is AI-generated, having a standard way to ensure agents follow your conventions, security practices, and architectural patterns isn’t optional. It’s quality control.

Why this matters for your business: If your development teams use AI coding tools (and most do), AGENTS.md ensures those tools produce code that matches your standards. It reduces agent-generated bugs, cuts onboarding time for AI tools on new projects, and provides consistency across teams.

How They Fit Together

These four protocols aren’t competing. They’re complementary layers in the same stack.

The stack works like this: MCP provides the plumbing for agents to access tools and data. A2A enables agents to coordinate with each other. NLWeb makes website content accessible to the entire ecosystem. AGENTS.md ensures AI coding agents build correctly. And the Agentic AI Foundation provides the governance layer, ensuring these protocols remain open, vendor-neutral, and interoperable.

The parallel to the original web is impossible to ignore:

• HTTP (transport) maps to MCP (tool access) and A2A (agent communication).
• HTML (content structure) maps to NLWeb (website content for agents).
• W3C (governance) maps to AAIF (governance).

What’s different this time is the speed. HTTP took years to gain broad adoption. MCP went from launch to universal platform support in 12 months. A2A grew from 50 to 150+ partner organizations in three months. NLWeb shipped with major publisher adoption at launch. AGENTS.md reached 60,000 projects within its first few months.

The infrastructure is being built at internet speed, not standards-committee speed. That’s partly because the companies involved are the same ones building the agents that need these protocols. They’re motivated.

And these four aren’t the only protocols emerging. Commerce-specific standards are building the transaction layer: Shopify and Google co-developed the Universal Commerce Protocol (UCP), launched in January 2026 with support from Etsy, Target, Walmart, and Wayfair. OpenAI and Stripe co-developed the Agentic Commerce Protocol (ACP), which powers Instant Checkout in ChatGPT. CopilotKit’s AG-UI protocol addresses agent-to-frontend communication, with integrations from LangGraph, CrewAI, and Google ADK. We’ll cover the commerce protocols in depth in Part 5.

What This Means For Your Business

You don’t need to implement all four protocols tomorrow. But you need to understand what’s being built, because it shapes what your website, tools, and teams should be ready for.

If you’ve already invested in Schema.org markup, NLWeb is your closest on-ramp. It builds directly on the structured data you already maintain. As NLWeb adoption grows, your Schema.org investment becomes the foundation for making your website conversationally accessible to AI agents. Keep your structured data current and comprehensive.

If you have APIs or internal tools, consider MCP accessibility. Making your services available through MCP means any AI platform can interact with them. For ecommerce, that could mean product catalogs, inventory systems, and order tracking becoming accessible to AI shopping assistants across ChatGPT, Claude, Gemini, and whatever comes next.

If you’re evaluating multi-vendor agent workflows, A2A is the protocol to watch. Enterprise organizations running agents from multiple vendors (Salesforce, ServiceNow, internal tools) will increasingly need these agents to coordinate. A2A is the emerging standard for that coordination.

If your development teams use AI coding tools, adopt AGENTS.md now. It’s the simplest protocol to implement (it’s a single Markdown file) and the one with the most immediate, tangible benefit: fewer bugs, more consistent output, faster onboarding for AI tools on your codebase.

The underlying message across all four protocols is the same: the agentic web is being built on open standards, not proprietary ones. The companies that understand these standards early will be better positioned as AI agents become a primary way users interact with businesses.

These aren’t things you need to implement today. But they are things you need to understand, because Part 4 of this series gets into the technical specifics of making your website agent-ready.

Key Takeaways

• Four protocols form the agentic web’s infrastructure. MCP (tools), A2A (agent communication), NLWeb (website content), and AGENTS.md (code guidance) are complementary layers, not competitors.
• The speed of adoption signals real urgency. MCP reached 97 million monthly SDK downloads and universal platform support in 12 months. A2A grew from 50 to 150+ partner organizations in three months. These are not experiments.
• Competitors are collaborating on infrastructure. OpenAI, Anthropic, Google, and Microsoft are all building shared protocols under the Agentic AI Foundation. This mirrors the W3C moment that unified the early web.
• NLWeb is potentially the most relevant protocol for website owners. Built by the creator of Schema.org, it turns your existing structured data into a conversational interface for AI agents. Every NLWeb instance is automatically an MCP server.
• MCP is the universal adapter. Build one MCP connection to your data, and every major AI platform (Claude, ChatGPT, Gemini, Copilot) can access it. No more building separate integrations for each platform.
• Start with what you have. Schema.org markup readies you for NLWeb. Existing APIs can become MCP servers. AGENTS.md is a single file your dev team can create today. You don’t need to start from scratch.

The original web succeeded because competitors agreed on shared standards. The agentic web is following the same playbook, just faster. The protocols are being established now. The governance is in place. The agents are already using them.

Up next in Part 4: the hands-on technical guide for making your website ready for autonomous AI agents, from semantic HTML to accessibility standards to testing with real agent tools.

More Resources:

This post was originally published on No Hacks.

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