Beyond Google – AI Search Alternatives (Bing, Perplexity & More)

The search landscape is no longer dominated solely by Google. A new wave of AI-powered search engines and assistants has emerged, offering users conversational answers, summaries with citations, and innovative features that challenge the traditional “ten blue links” model. This article explores the key alternatives beyond Google – from Microsoft’s Bing Chat to independent platforms like Perplexity AI and others – and examines what they mean for marketers in the era of Generative Engine Optimization (GEO).

We’ll look at how these AI-driven search tools work, real-world adoption trends in 2024–2025, and practical steps for optimizing content for them. While Google still commands the lion’s share of search traffic, early adopters of AI search are often tech-savvy and influential. Understanding these platforms now can give marketers an edge (and many tactics will carry over to Google’s own AI search features). 

Bing Chat’s GPT-4 Integration and Influence

Microsoft made headlines in early 2023 by integrating OpenAI’s GPT-4 into Bing and launching the new Bing Chat feature ( ^[1] ). This marked one of the first major attempts to embed generative AI directly into a mainstream search engine. Instead of the familiar page of ranked links, Bing’s AI mode delivers a conversational answer that synthesizes information from across the web and presents it in narrative form, with footnote citations linking to sources ( ^[2] ) ( ^[3] ). Users can type natural language questions and receive an AI-generated summary or explanation, often combining information from multiple webpages.

Crucially, Bing’s implementation addresses a key concern with generative AI: each answer includes references to the websites used, usually denoted by numbered footnotes that users can click for verification ( ^[2] ). This not only lends transparency and credibility, but also creates a pathway for web traffic – if a user wants more detail or to verify the AI’s answer, they can click the citation and visit the source site. 

How Bing Chat Works: On the backend, Bing Chat marries Microsoft’s search index (Bing’s web crawling and indexing capabilities) with the language understanding of GPT-4 ( ^[3] ). Essentially, when a user asks a question, Bing’s system retrieves relevant content from live web results, then the GPT-4 model “reads” those results and composes a coherent answer in real time, complete with supporting references. This is a form of Retrieval-Augmented Generation (RAG) in action.

Because it uses current web content, Bing Chat can provide up-to-date information, overcoming the “knowledge cutoff” limitation of static trained models. Microsoft initially rolled out Bing Chat via a limited preview (waitlist) in February 2023 and later expanded access, including integration into the Edge browser’s sidebar and Bing’s mobile apps ( ^[4] ). By mid-2023, Bing Chat was accessible to all users of Microsoft Edge, effectively reaching millions by being baked into the default Windows web experience. 

A New User Experience: The introduction of Bing’s chat mode fundamentally changes how some searches work. Users can now have a multi-turn conversation refining their query – asking follow-up questions or for clarifications – much like they would with a human expert. This conversational capability means search is becoming less of a one-and-done query and more of an interactive dialogue ( “from queries to conversations”). Bing’s AI remembers context within a session, so it can tailor answers based on previous questions, making the experience more intuitive and personalized ( ^[5] ).

From the user’s perspective, Bing Chat offers several distinctive benefits: 

Direct Answers, Fewer Clicks: Instead of scanning multiple sites, the user often gets the answer they need summarized in one go. Bing’s AI-generated response often fulfills the query without the user needing to click any result ( ^[6] ). For example, a query like “What are the health benefits of green tea?” might yield a concise paragraph citing a few authoritative health websites, rather than ten separate links to sift through. This improves convenience but also means reduced click-through to websites overall, as the AI has “pre-read” the content for the user. 

Citation Footnotes and Transparency: Unlike a raw ChatGPT response, Bing’s answers clearly highlight their sources. Small superscript numbers link to references, and users can expand a references pane or hover to see the URLs. For instance, Bing might answer a question and include “ ^{[1] [2] [3]} ” footnotes – clicking those reveals the websites (like “MayoClinic.org” or “Healthline.com”) it pulled information from ( ^[2] ). This system was lauded for bringing some credibility and accountability to AI answers ( ^[7] ). It also provides an opportunity for content publishers: if your site is one of those cited, you gain visibility and potential traffic. 

Dynamic Query Refinement: Users can ask follow-ups like “What about for weight loss specifically?” and Bing’s AI will remember you were asking about green tea and adapt the answer ( ^[8] ). This conversational refinement means long-tail questions that might not have a pre-written FAQ on your site could still be answered by the AI drawing from your content (if your content is comprehensive). 

Integrated Visuals and Features: Microsoft has enhanced Bing Chat with multimedia and interactive elements. The AI can deliver charts, graphs, or images alongside text when relevant ( ^[9] ). It can also perform certain actions like generating product comparison tables or integrating with shopping data. This blurs the line between search, content, and commerce within the chat interface. For example, an AI answer about “best smartphones under $500” might show a comparison chart of models with specs, plus links to buy – all without leaving the search page. 

Influence and Adoption: Bing’s move gave it a burst of attention. Within a month of launch, Microsoft announced Bing had reached 100 million daily active users, an all-time high, partly thanks to “the million+ new Bing preview users” trying the chat feature ( ^[1] ) ( ^[10] ). To put that in perspective, Google has over 1 billion daily users, but for Bing it was a significant milestone ( ^[11] ). Microsoft noted that about one-third of Bing Chat users were new to Bing, indicating the AI feature attracted people who normally defaulted to Google ( ^[12] ). On average, users were engaging in roughly 3 chats per session, with over 45 million chats conducted in the first month ( ^[12] ) – evidence that many found the conversational search useful enough to dig deeper. However, Bing’s overall search market share remains relatively small. By late 2023, estimates put Bing at around 3–4% of global search queries, barely up from pre-chat levels ( ^[13] ). For example, StatCounter showed Bing at ~3.95% worldwide vs. Google’s 89.5% in mid-2025 ( ^[14] ). In the U.S. desktop search market Bing fares a bit better (7–12% share) ( ^[15] ), but it’s still a minority player. In other words, the AI chat infusion did not instantly topple Google’s dominance in terms of market share ( ^[16] ). Many users continue Googling out of habit or because they still prefer traditional results for certain tasks. Nonetheless, for marketers, Bing Chat’s influence is bigger than its raw share numbers suggest.

Precedent and Competition: Bing’s success in incorporating GPT-4 pressured Google to accelerate its own AI projects (like Bard and the Search Generative Experience). It proved the concept that AI answers with citations could be done on a large scale. Microsoft essentially made Google “dance” (as CEO Satya Nadella quipped ( ^[17] )) – forcing the 800-pound gorilla of search to respond. This opened the door for more AI search alternatives as well, creating a more fragmented search ecosystem where early adopters explore multiple tools. 

Traffic and SEO Impact: Bing Chat’s habit of answering questions outright means some searches that used to result in a click to a website now result in zero-click answers. Publishers have expressed concern that being merely a citation at the bottom of an AI-generated paragraph yields less traffic than being a top blue link. Indeed, users can get what they need from the summary and not click through at all ( ^[6] ). However, when users do click a Bing Chat citation, they come with high intent – they are actively seeking to validate or get more detail than the summary provided. Some websites have seen referrals from Bing’s footnotes (e.g., in their analytics, traffic from bing.com with query parameters indicating the new Bing) and consider that a new source of visits. It’s not yet massive, but if Bing grows or if those users are valuable (say, B2B researchers), it can be meaningful. 

New SEO Criteria – “Optimizing for the AI”: Unlike classic SEO where you optimize for an algorithmic ranking, here you’re trying to get selected and quoted by an AI. Bing has outlined some factors it uses to decide which sites to cite. These include trustworthiness and authority of the domain, content relevance to the question, clear organization and formatting, up-to-date information, and consistency of information across sources ( ^[18] ). Only a few sources will be shown for any answer (often 2–4 footnotes), making it an “exclusive club” compared to a typical SERP with 10 results ( ^[19] ).

For marketers, this raises the stakes: being cited by the AI could be as valuable as ranking #1, whereas being ignored by the AI might mean your content is practically invisible for that query. We’ll discuss optimization tactics in section 7.4, but Bing specifically has hinted that well-structured, authoritative content stands a better chance ( ^[20] ).

For example, pages with clear subheadings, bullet points, FAQ schema, and concise factual statements might be easier for the AI to extract and quote ( ^[20] ). Sites that already followed best practices for Featured Snippets or Answer Boxes in Google (concise answers to common questions) have a head-start, since Bing’s AI often scoops up those same answer-friendly snippets.

In summary, Bing’s GPT-4 integration has transformed its search experience into something of a hybrid between a search engine and an AI assistant. Users get quick, conversational answers with sources, and can refine through dialogue. It hasn’t dethroned Google, but it has attracted a niche of users and signaled where search is heading.

Marketers should familiarize themselves with Bing Chat not only for the direct opportunity (reaching Bing’s smaller but possibly growing user base), but also because it’s a bellwether for AI-driven search norms – what works for Bing Chat (e.g. being cited for providing a clear factual answer) is likely to be similar for Google’s AI summaries and other platforms. If your content strategy adapts to win a spot in Bing’s AI answers now, you’ll also be positioning yourself well for AI features across the search spectrum. 

Real-World Example: Imagine a travel website that has the most authoritative content on “safest destinations for solo travelers in 2025.” In the old paradigm, you’d want to rank in the top results on Google or Bing for the query “safest places for solo travel.” In Bing Chat’s paradigm, you’d want your site to be one of the sources the AI pulls into a concise answer. If your article has a neat bullet list of “Top 10 Safest Destinations” with a brief explanation for each (and you’re a well-regarded site), Bing’s AI might respond to a user’s question with a paragraph summarizing a few top destinations and cite your list as the source ( ^[20] ) ( ^[21] ).

The user gets an immediate answer (“According to TravelSafe.com and two other sources, the safest solo travel spots include Japan, New Zealand, and Iceland due to low crime rates and traveler infrastructure…”) with your site in the footnote. The user may or may not click through – but they have seen your brand as a trusted source. This kind of brand visibility via AI citation is a new outcome to optimize for.

Perplexity AI: The Rise of a Citation-Focused Answer Engine

Among the new class of AI-powered search tools, Perplexity AI has quickly emerged as an influential player. Launched in late 2022 by a San Francisco startup, Perplexity is often described as an “answer engine” or “AI search engine” that provides direct answers with source citations for virtually any question ( ^[22] ).

In practice, using Perplexity feels a bit like using a supercharged Google combined with an AI assistant: you ask a question in natural language, and Perplexity returns a succinct, well-structured answer (often a few paragraphs or a list) compiled from information found on the web, with footnote numbers linking to the original sources. For example, if you ask “What are the latest trends in e-commerce for 2025?”, Perplexity might output a short summary of key trends (say, “AI-powered customer service, AR/VR shopping experiences, and sustainable packaging”) and each statement would have a tiny number like ^[1] or ^[2] that corresponds to references – perhaps a Forbes article or a market research report that it used ( ^[22] ). Clicking the footnote drops you directly into that source’s webpage (at the section relevant to the info). 

Focus on Trustworthy Sources: Perplexity’s philosophy is “accurate, trusted, and real-time answers” ( ^[23] ). It tries to ground every part of its answer in content from reputable websites. In essence, it is performing dozens of traditional search queries behind the scenes, analyzing the results with an LLM, and then synthesizing a coherent answer. This rigorous approach aims to reduce hallucinations and ensure that the AI isn’t just making things up – if Perplexity says something, you can inspect exactly where it got it from on the internet. Because of this design, authoritative and well-written content is more likely to be featured.

Perplexity tends to cite news sites, reference sources like Wikipedia/Britannica, scholarly articles, well-known blogs, and other high-quality web pages. Low-quality or spammy sites are generally filtered out or simply not chosen by the AI as part of the answer. For marketers, this means that E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) principles remain crucial – if your site is a respected authority in its field, Perplexity is more likely to pick up your content when relevant. Conversely, thin content or SEO-gimmicky pages are unlikely to be favored by this kind of AI meta-search. 

Growth and Adoption: Though not (yet) a household name, Perplexity AI has seen impressive growth since its inception. By the end of 2024, the startup was valued at around $9 billion after a major funding round ( ^[24] ) – a testament to investor confidence in AI-driven search. It reportedly reached about 10–15 million active monthly users in 2024, up sharply within a few months ( ^[24] ) ( ^[25] ).

Usage statistics show surging engagement : Perplexity answered 500 million queries in all of 2023, but then handled **250 million queries in just one month (July 2024) ( ^[26] ). That indicates a rapidly growing user base and frequency of use. In fact, by 2025 it was processing on the order of 30 million queries per day (around 780 million in May 2025) according to company statements ( ^[27] ). These numbers, while smaller than Google’s billions of daily queries, are significant for a relatively new service.

For comparison, Perplexity’s website received about 73 million visits in May 2024, which was roughly 12% of the traffic ChatGPT’s site got that month ( ^[28] ). This is notable because ChatGPT had a much bigger media profile, yet Perplexity quietly amassed a sizeable following (especially among researchers, students, and professionals seeking direct answers with citations). Demographically, Perplexity’s usage has had some interesting patterns. A large portion of its early user base came from outside the typical Western markets – for instance, a significant share of traffic has been reported from Indonesia and India, besides the US ( ^[29] ). This suggests Perplexity’s appeal as a free, reliable Q&A tool found global resonance, possibly in markets where access to high-quality information is valued and English is commonly used online. It also has mobile apps on iOS and Android, extending its reach in regions where smartphones are primary internet devices ( ^[30] ). 

Product Features and Differentiators: What sets Perplexity apart from both Google and even Bing Chat?

A few key aspects:

1. Always Cited, Inline: Every factual assertion in a Perplexity answer is accompanied by a citation link. The interface often shows the answer with superscripts, and at the bottom a list of sources used. This is more akin to an academic paper or Wikipedia article style of answering. It means even a lay user can verify information immediately. Many have praised this citation-first approach as a gold standard for AI transparency ( ^[31] ). In contrast, Google’s AI summaries (SGE) initially did not cite specific sentences unless you expanded them, and ChatGPT free version doesn’t cite at all. 
2. Concise, Stackable Information: Perplexity tries to keep answers concise and to-the-point. It often aggregates multiple sources – e.g., if answering a question about climate change effects, it might pull one line from NASA, another from a UN report, etc., blending them into a single coherent paragraph. This ability to synthesize across sources (thanks to the LLM’s language skills) allows a more comprehensive answer than any single source alone. It also means the tone is neutral and informational, as it’s essentially remixing what others have said (versus injecting a distinctive AI “voice”). Users can ask follow-up questions to delve deeper or clarify, making it conversational in that sense ( ^[32] ). 
3. Real-Time and Unrestricted Web Access: Unlike some AI bots that have knowledge cutoffs or limit browsing (e.g., default ChatGPT knows little beyond 2021 data unless using plugins), Perplexity is built from the ground up to search the web. It performs live web searches when you query it ( ^[33] ), ensuring it can retrieve the latest information. If you ask “Who won the UEFA Champions League this year?” or “What is the current price of gold?”, Perplexity will go find a current source and give you the answer with today’s info, complete with the source link (which might be a news article from hours ago). This real-time capability keeps answers from going stale and makes the tool useful for newsy or time-sensitive queries (a space where static LLMs fall short). 
4. “Copilot” and Deep Research Modes: Perplexity has innovated with different modes for users. Beyond the basic Q&A interface, they introduced features like Copilot, which allows for a more interactive session where the AI will proactively ask you clarifying questions or present multiple facets of an answer. They also have a “Deep Research” mode (for paid subscribers) that can autonomously dig much deeper into a topic – performing dozens of searches and reading hundreds of sources before compiling a longer, report-like answer ( ^[33] ). This is like having a virtual research assistant gather and summarize extensive information. For example, someone doing market research can pose a broad question and get a multi-section answer complete with references. 
5. Multi-Model and Customization: Perplexity’s Pro subscription (and by 2025, perhaps even free version in some cases) allows users to pick which underlying AI model to use for generation ( ^[34] ). They’ve integrated various large language models on the backend – OpenAI’s latest GPT-4.1, Anthropic’s Claude, and even other emerging models like Meta’s Llama-based models or xAI’s Grok (the Perplexity Wikipedia entry lists those engines, implying either current or planned support) ( ^[35] ) ( ^[34] ). This is quite unique: it means the service is somewhat model-agnostic and willing to plug in the best available AI brains while maintaining the same user-facing search experience. For marketers, this doesn’t change how you optimize content, but it’s interesting in that Perplexity isn’t tied to a single AI provider – it can leverage improvements from multiple fronts (e.g., if one model is better at coding answers, another at general knowledge). 
6. Publisher Partnerships: Notably, Perplexity in mid-2024 announced a Publisher Program to share ad revenue with content creators whose pages are frequently cited ( ^[36] ). This is a very important development for the marketing and publishing community. One fear about AI answers is that they “steal” content from websites and reduce the incentive for those sites to create content (since users might not click through). By offering to share some revenue, Perplexity is acknowledging that it owes a debt to the content creators it summarizes. The details of the program aside, it signals an ecosystem approach: they want quality publishers to be on board rather than hostile. Marketers should keep an eye on such programs – it could mean that having your content cited not only yields brand exposure but potentially direct compensation in the future (a new kind of SEO monetization!). 

Why Perplexity Matters for GEO: For Generative Engine Optimization, Perplexity is a prime example of a platform where “content quality meets AI”. Traditional SEO practices (like keyword optimization or backlink building) matter less here than content substance and clarity. If you produce the best answer to a question, Perplexity’s AI is likely to find it and use it (regardless of whether your site is #1 on Google for that query or not). Thus, a smaller site with an excellent, well-sourced piece of content can get featured in Perplexity answers if it’s truly authoritative.

From the marketer’s perspective, you’d want to ensure your content is AI-friendly:

1. Write clear, factual statements that are easily quote-worthy (the AI might literally quote a full sentence or two from your page ( ^[21] )).
2. Provide concise summaries or bulleted takeaways in your articles – these often get extracted.
3. Maintain accuracy and updated info (the AI may prioritize more recent sources for current questions ( ^[37] ), so an article updated in 2025 might outrank a stale 2020 page in the AI’s eyes for a “2025 trends” query).
4. Establish your site’s authority (through content depth and perhaps off-page signals) so it’s among the trusted pool the AI considers.

Perplexity’s user base might still be modest compared to Google Search, but it includes a lot of inquisitive, high-intent users. These could be researchers, students, professionals – people looking for detailed answers. If your content gets cited, you not only gain a potential click, you gain credibility by association (“as cited on Perplexity AI” is almost like being cited in a research paper!). Some companies have observed that even when direct traffic from these AI engines is low, having their brand appear in AI-generated answers can boost brand recognition and trust.

In one case study, a blog that optimized for AI answers saw its content share in AI responses rise from 2% to 12% (meaning its material was showing up a lot more in answer boxes) while traditional traffic stayed flat – indicating the benefit was mostly in audience awareness rather than clicks ( ^[38] ) ( ^[38] ). Another organization’s whitepaper became the primary cited source in many AI summaries on chatbots, which coincided with a 30% increase in demo requests and more LinkedIn mentions of their brand ( ^[39] ) ( ^[40] ).

This suggests that when your content is the reference for an AI’s answer, readers implicitly value it, and that can translate to downstream actions (like seeking a demo of your product, or simply remembering your brand favorably).

In conclusion, Perplexity AI represents where search is heading: ask a question, get a synthesized answer with trusted sources, and move on with confidence in the information. Its rise in 2024/2025 underscores that users are seeking efficient yet credible answers. Marketers should view Perplexity as both a testing ground for GEO techniques (since it has one of the more stringent citation mechanisms) and as an opportunity to reach a growing set of users who prefer AI-assisted search. By ensuring your content can easily be discovered and cited by Perplexity, you not only cater to that platform’s users but also prep your content to be consumable by any similar AI systems that come along.

Other AI-Driven Search Tools: DuckDuckGo, Neeva, You.com, Brave, and More

Beyond the high-profile players (Google’s AI efforts, Bing Chat, and independent upstarts like Perplexity), there’s a broader ecosystem of search engines experimenting with AI. Many of these alternatives have smaller market shares, but they often pioneer features that later become standard. They also serve specific user niches (privacy-conscious users, early tech adopters, etc.). Let’s survey a few notable ones.

DuckDuckGo and its AI “DuckAssist”/Duck.ai

DuckDuckGo is well-known as a privacy-focused search engine that doesn’t track users. In early 2023, DuckDuckGo jumped into the AI fray by launching DuckAssist, an AI-powered instant answer feature integrated into its search results. DuckAssist was essentially an LLM (from OpenAI/Anthropic) that would summarize information from certain sources (initially, Wikipedia and Britannica ) to directly answer user queries ( ^[41] ). It was presented as an extension of DuckDuckGo’s Instant Answers (the boxes that provide quick info, like definitions or weather) but now with AI-generated phrasing.

For example, if you searched DuckDuckGo for a question like “What is the capital of Australia?”, traditionally you’d just get a snippet from a source. With DuckAssist (when it triggers), you might see a highlighted box saying: “Australia’s capital is Canberra, located in the Australian Capital Territory…” which is an AI-summarized sentence drawn from Wikipedia, accompanied by a link to the source for verification.

DuckAssist did not have a full chat interface and did not take follow-up questions – it was a one-shot answer aimed at factual questions, especially those answerable by encyclopedic knowledge ( ^[42] ). DuckDuckGo explicitly mentioned this was an experiment to make search results more useful while maintaining privacy (no login required, and queries still anonymous). By March 2023, DuckAssist rolled out to all users for relevant queries, and DuckDuckGo noted it was the first in a series of AI-assisted features ( ^[43] ).

Importantly, DuckAssist would only trigger when it was pretty sure it could find a correct answer (to avoid hallucinations). It was conservative and focused on non-controversial factual questions – essentially letting the AI summarize Wikipedia, which is a high-quality source for many factual queries. The CEO, Gabriel Weinberg, even said they “fully expect it to not be perfect” but that it should help with roughly straightforward questions ( ^[44] ).

Moving into 2024 and 2025, DuckDuckGo expanded on this with a broader AI strategy: They took DuckAssist out of beta and started sourcing information from across the web, not just Wikipedia ( ^[45] ). This means DuckDuckGo’s AI summaries (now often just called “AI Instant Answers” or “AI Search Answers”) can draw from multiple websites, similar to how Bing or Perplexity do, while still citing them. An illustration in DuckDuckGo’s announcements showed that the AI summary box displays tiny favicons or domain names indicating which websites were used ( ^[46] ) – reinforcing that transparency.

DuckDuckGo introduced Duck.ai, an actual conversational chatbot mode accessible via their platform ( ^[47] ) ( ^[48] ). Users can click to enter a chat where they can ask follow-ups. True to its ethos, no account is needed and privacy measures are in place (DuckDuckGo routes queries in a way that masks the user’s IP from the AI providers) ( ^[49] ). Interestingly, Duck.ai allows the user to toggle between multiple models, including OpenAI and Anthropic models (GPT-4 variants and Claude) as well as open-source ones like Llama and Mistral ( ^[49] ).

This multi-model approach is in line with DDG’s desire for independence – not putting all eggs in one AI basket, and giving users control. 

User Control on Frequency: DuckDuckGo’s implementation is very user-centric. In settings, users can choose how often they want to see AI answers: e.g., “Occasionally”, “Often”, or “Always”. Even at the highest setting, DuckDuckGo was initially only showing AI summaries for about 20% of searches (since it won’t force it if not confident) ( ^[50] ). Users can also turn it off entirely. This is different from Google’s approach where SGE, if enabled, shows up for most queries automatically. DuckDuckGo recognizes that some of its audience might be skeptical of AI or prefer classic results, so it provides that flexibility.

From a market share perspective, DuckDuckGo is a minor player, but not insignificant: it handles hundreds of millions of searches per month and has steadily grown in the past decade due to its privacy proposition. As of 2025 it holds roughly 0.6–0.8% of global search market share (closer to ~2% in the US) ( ^[51] ). This is smaller than Bing or Yahoo, but its users are very loyal. Many DuckDuckGo users set it as their default for ideological reasons (avoid Google tracking) and thus may not use Google at all. That means if you ignore DuckDuckGo entirely, you might be missing a subset of users – often tech-savvy, privacy-conscious individuals (including some journalists, developers, etc.).

For marketers, optimizing for DuckDuckGo’s AI isn’t radically different from general GEO principles. The engine is likely pulling answers from sources like Wikipedia (so having your brand or product represented accurately on Wikipedia can indirectly help). It also likely favors universal factual info – so if you have content you want featured, it helps if that content is referenced by known authorities or structured in a way the AI can trust. Perhaps the simplest tactic is: if there are common questions in your domain, ensure that either your site or a site that cites you provides a clear answer on Wikipedia or similar sources, because DuckAssist was heavily reliant on that.

One interesting angle is community Q&A content :

DuckDuckGo might not use Reddit or StackExchange as sources for DuckAssist (since it started with encyclopedias), but the general trend is that these conversational search tools do incorporate forum content (as we’ll see with You.com). It’s not far-fetched that Duck.ai’s web-wide answers might sometimes draw from forums if relevant. So participating in those discussions (Reddit, Quora, StackExchange) where appropriate can seed content that later gets summarized by various AI engines.

In summary, DuckDuckGo’s venture into AI (DuckAssist and Duck.ai) shows even privacy-first companies see the value in AI summarization. They’ve implemented it in a restrained, user-friendly way – aligning with their brand (no tracking, give user choice, cite sources). While their audience is smaller, they often represent a high-value demographic and early adopters. Marketers with a focus on tech or privacy-aware consumers should ensure their SEO strategy includes DuckDuckGo (e.g., making sure their content renders well there, perhaps getting listed in DDG’s instant answers if possible). And since DuckDuckGo sources content from similar pools as others, the same optimizations that help you on Google/Bing (clear answers, structured data, authority) will help on DuckDuckGo’s AI answers too.

NeevaAI – An Early Innovator (Now Defunct but Influential)

Another name worth mentioning is Neeva, a startup-led search engine that was one of the first to integrate LLM-based answers with citations. Neeva was founded by ex-Google executives and launched in 2021 as an ad-free, subscription-based search alternative. In January 2023 – right on the heels of ChatGPT’s public debut – Neeva announced “NeevaAI”, a feature that would answer queries with a summarized response and cite the sources used, very much like what Bing and Google would later attempt ( ^[52] ).

In fact, NeevaAI beat both Microsoft and Google to the punch – it was deployed to users in early 2023 before Bing’s February GPT-4 chat launch and while Google’s SGE was still in the lab ( ^[52] ). NeevaAI’s answers looked similar to what we see now on Perplexity or Bing: a few paragraphs answering a question, with annotations linking out. It was praised for being the “ world’s first LLM-powered answer engine with reliable citations ” by Neeva’s founders ( ^[53] ). They saw it as a way to make search more user-friendly (no ads, no clutter, just answers) while still respecting content creators (via citations).

However, Neeva faced a steep uphill battle in the search market. Despite the innovative product, it struggled to attract enough users to sustain its model ( ^[54] ). People are so accustomed to free search (and to Google) that getting them to switch – let alone pay a subscription – was extremely challenging. By May 2023, Neeva announced that it would shut down its consumer search engine on June 2, 2023 ( ^[55] ). The founders cited the difficulty of breaking user habits and the tough economics (especially in an environment where generative AI itself was changing the landscape) ( ^[56] ).

Indeed, as a small company, competing with Microsoft (which can afford to give away GPT-4 in Bing for free) and Google (ubiquitous default engine) was nearly impossible. Neeva’s data showed maybe ~600,000 users at peak – not enough to justify the expenses. While Neeva’s life as a search engine was short, its impact was notable: It validated the concept of integrated AI answers. Early adopters who tried NeevaAI in Jan/Feb 2023 got a glimpse of the future. In many ways, Neeva’s approach presaged what others did – and it likely spurred the giants to move faster.

There’s a bit of irony that Neeva introduced a feature (AI answers) to out-compete Google, but Google and Bing’s swift responses meant Neeva lost its unique edge within months. It highlighted the importance of citations in AI answers. Neeva strongly emphasized “with citations” in its announcements, framing it as a positive differentiator. Now, citations have become a standard expectation for credible AI search (Bing does it, Google SGE does some version of it, DuckDuckGo does it, etc.). Neeva arguably helped set that standard.

After shutting down search, Neeva was acquired by Snowflake (a cloud data company) in mid-2023 ( ^[57] ). The team’s expertise is being applied to enterprise AI search (like searching within business data), which is outside our scope. But it means the tech lives on elsewhere. For marketers, the direct relevance of Neeva now is minimal (since it’s offline), but there’s a takeaway: innovation can come from anywhere, but distribution is king in search.

Neeva’s failure underscores that even if you have great GEO strategies, you must focus on platforms that have users. It’s a reminder to keep an eye on newcomers but also to calibrate effort vs reach. If you optimized heavily for NeevaAI early on, you may have gotten some benefit for a few months, but that effort would have been short-lived. On the other hand, if you applied those same content improvements for GEO across the board (clear answers, etc.), you would reap benefits on Bing, Google, and others too.

In essence, Neeva’s story is a cautionary yet insightful footnote in the AI search saga – showing both how quickly the landscape can shift and reinforcing that Google’s dominance is hard to crack (even Bing, with all its AI splash, gained <1% market share from it ( ^[16] ); Neeva gained far less). Nevertheless, the concepts pioneered by NeevaAI (generative answers with citations) are here to stay, carried forward by those who survived. So one could say Neeva was a trailblazer that set the stage for GEO, even if it didn’t get to enjoy the spoils.

You.com and YouChat – The AI-Native Search Experience

Another notable player in this space is You.com, a search engine startup founded by Richard Socher (former Salesforce chief scientist) in 2020. You.com launched with the idea of a highly customizable search (users could personalize sources and apps on the results page). But its biggest pivot was going all-in on AI assistance. In December 2022, You.com introduced YouChat, which was effectively the first search-integrated chatbot akin to ChatGPT that could handle live web queries ( ^[58] ) ( ^[59] ).

This made You.com arguably the first mover in combining a conversational LLM with web search results – even before Bing did.

 YouChat’s capabilities: It launched using a version of GPT-3.5, tailored for conversation and equipped with real-time internet access ( ^[58] ) ( ^[59] ). This meant YouChat could answer questions about current events or recent information by fetching data from the web on the fly, then formulating an answer (with citations for the sources it used). It was essentially a more “alive” version of ChatGPT, directly embedded in a search engine interface. Early users noted that YouChat did provide source citations in its responses, although its accuracy and quality varied. The fact it could do things like summarize yesterday’s news or provide code answers with references was impressive for the time ( ^[60] ). 

Rapid Iterations: You.com iterated quickly on YouChat: In February 2023, they released YouChat 2.0, which improved conversational abilities and integrated what they called “Apps” into the chat experience ( ^[61] ). Essentially, YouChat could use specialized modules or search verticals when needed (for example, if you asked for a coding answer, it might pull from StackOverflow; if you asked about stock prices, it could pull a finance chart). They blended a custom LLM (codenamed C-A-L: Chat, Apps, Links) that could decide when to show you a chart, image, or a specific snippet from an app ( ^[61] ).

This was a step toward multimodal answers – not just text, but visual elements and formatted data. It allowed users to get rich results in one interface (like a mini data table or an image result) alongside the AI’s narrative. In May 2023, YouChat 3.0 came out, boasting deeper integration of community content and real-time info ( ^[62] ). Notably, YouChat 3.0 could directly pull content from Reddit, Stack Overflow, TikTok, Wikipedia, and more into its answers ( ^[62] ).

For example, if you asked for opinions on “best programming laptop”, the AI might actually fetch a relevant Reddit thread or StackOverflow posts and incorporate insights from them (citing them). It was like having a meta-aggregator that knows where the discussions are happening. This is a clear demonstration of a trend: community-driven content often holds answers that AI can summarize. Many technical or niche questions are extensively discussed in forums; YouChat tapped into that. You.com’s approach, being a startup, was bold and experimental.

It even allowed some level of user customization – you could upvote or downvote sources, and the search had tiles for different services (like a StackOverflow tile or a Twitter tile). It wasn’t just about Q&A; they also had a suite of AI “apps” (for writing help, coding, etc.), positioning themselves as an “AI hub”. 

User Base and Impact: You.com is much smaller in usage compared to others, likely in the low millions of queries per day range. It’s not a top search engine by market share (not in StatCounter’s top 5 globally, for instance). However, it garnered attention among the AI community and early adopters. It also likely has a significant number of users via partnerships (for instance, they launched a YouChat integration on WhatsApp to let people use AI search in messaging ( ^[63] )).

For marketers, You.com might not drive noticeable traffic (unless you have a highly tech audience that you suspect uses it). But the features pioneered by You.com tell us where search experiences are heading: 

Real-time, on-the-fly citation of community content: If an AI can leverage Reddit or Q&A forums to answer questions, then even if your site isn’t the one being cited, the content about your product on those forums could surface. For example, if people on Reddit have discussed your product’s pros and cons and someone asks an AI “Which is better, Product X or Product Y for __?”, the AI might incorporate those opinions from Reddit (and cite the Reddit thread).

Thus, your brand’s presence and reputation in online communities can directly influence AI answers. It’s a new form of off-page SEO: ensuring that experts and users speak positively and accurately about you on public forums could pay dividends when AI summarizes “what people are saying.” 

Structured data and app integrations: You.com’s blending of structured results (like showing a chart or a code snippet) means that providing data in structured formats (APIs, code blocks, graphs) can be advantageous. If you have an open data source or an API, an AI engine might use it to present info. For example, an e-commerce site that provides a public API for product specs might see AI search tools use that to answer “compare these two products.” 

The multi-turn conversation is front and center. YouChat’s existence inside a search engine affirms that search is becoming conversational. Strategies like having content in FAQ format, or anticipating follow-up questions users might ask, can align well with how these AI handle multi-turn sessions. In summary, You.com and YouChat serve as an innovation lab for AI search. While their direct reach is limited, they demonstrate how AI can integrate diverse content sources (including UGC – user generated content) and how user interaction might work (e.g., switching contexts, apps).

Marketers should note the importance of community content and the fact that content from Reddit/StackExchange often ranks high in credibility for many questions. Indeed, Microsoft’s Bing AI also often pulls answers from StackOverflow for coding queries – and as a result, users can get those answers without visiting StackOverflow, a phenomenon observed and discussed in developer circles ( ^[64] ).

This means companies that rely on those forums for traffic (like StackOverflow) are being disrupted, but also that if your company has domain experts, encouraging them to contribute on StackExchange or similar can increase the chances that their answers (with maybe a subtle mention of your product if allowed) get propagated by AI. Just be sure any participation is genuinely helpful and not spammy – the AI will pick up quality, not marketing fluff.

Brave Search and Others

A quick mention goes to Brave Search, a privacy-centric engine launched by the makers of the Brave Browser. In March 2023, Brave Search rolled out an AI Summarizer feature ( ^[65] ). This was not a chatbot per se, but an AI-generated summary that appeared at the top of search results for certain queries. It used in-house LLM technology (not OpenAI’s) to compile a concise answer from multiple web sources ( ^[66] ).

Brave explicitly designed it to always cite sources via clickable links within the summary text ( ^[67] ). For example, if you searched “What happened in the East Palestine, Ohio incident?”, Brave’s Summarizer might produce a few sentences giving the gist and include references like “[source: NYTimes][source: EPA]” as hyperlinks embedded in the summary. They bragged that unlike some AI chat tools that might fabricate, their system only pulled from actual web results and gave attribution ( ^[68] ). They also highlighted that it was done in a privacy-preserving way on their own infrastructure.

Brave’s search share is small (comparable to DuckDuckGo’s range), but it’s noteworthy because they took a stance of not relying on Big Tech models – they trained their own and scaled it to handle all Brave queries (which at one point was 22 million queries per day by their claim) ( ^[68] ). For marketers, Brave’s Summarizer again reinforces the pattern: if your content has clear, authoritative info, it might get distilled into an AI summary. The good part is the user sees your link right there if they want more. The bad part is, again, they might not click if the summary suffices. But better to be cited than omitted. 

Other notable AI-centric search tools include: 

• WolframAlpha, an old player (launched 2009) which is a “computational answer engine.” It’s not an LLM, but worth noting as it directly answers factual and mathematical queries from its curated knowledge base. It became relevant to AI chat when it was integrated as a plugin to ChatGPT for factual math/science answers. If you have content in the scientific or quantitative realm, WolframAlpha integration or optimization (structured data that it can ingest) might be something to consider. 
• Kagi – a small subscription search engine that introduced its own AI summary feature called “Oracle”. It’s niche but shows even boutique engines are adding AI summaries. 
• Ask.com (Ask Jeeves) – historically known for Q&A style, but it hasn’t been a major AI player recently. 
• Baidu’s ERNIE Bot and Yandex’s attempt – outside English markets, players like Baidu (China) and Yandex (Russia) have launched their own LLM-based search assistants. For example, Baidu’s ERNIE Bot (launched 2023) can answer questions in Chinese in a ChatGPT-like fashion, integrated into Baidu search.

While our focus is English markets, marketers targeting those regions should consider similar GEO strategies (ensuring content is accessible to those engines, possibly optimizing for how they pick sources, etc.). The overarching theme with all these alternatives is that AI-driven search is not a one-company phenomenon – it’s industry-wide.

Even if some engines die off (like Neeva) or remain small, they contribute ideas that larger engines adopt. For instance, the idea of revenue-sharing with publishers started with smaller ones (Neeva talked about it, Perplexity is doing it) and now even Google is reportedly exploring ways to compensate publishers for AI usage. So, paying attention to the whole ecosystem can give you an early look at trends that might go mainstream.

Optimizing for Alternative AI Search Platforms (GEO Best Practices Across Engines)

Now that we’ve reviewed the key players beyond Google, the question for marketers is: How do we optimize for these AI-driven search experiences? The good news is that there is a lot of overlap – generally, what’s good for one tends to be good for others, because ultimately these systems all aim to provide users with accurate, authoritative answers. That said, there are some nuances. Here we’ll outline strategies to improve your visibility on Bing Chat, Perplexity, DuckDuckGo’s AI, and similar platforms.

Ensure Crawlability and Open Access

First and foremost, your content must be accessible to these engines. Unlike traditional search (where Google’s crawler was king), now multiple AI systems might try to read your site – Bing’s crawler, perhaps OpenAI’s browser (for ChatGPT plugins or Bing’s backend), Perplexity’s crawler, etc. Make sure you are not inadvertently blocking these.

Check your robots.txt – while most AI search tools obey standard robots rules, you might consider explicitly allowing known AI user agents if applicable. Conversely, if there’s content you don’t want used by AI, that’s a separate discussion (some publishers block OpenAI’s GPTBot now). But for GEO, assume you want maximum exposure. 

No Paywall (for Key Content): AI answers cannot retrieve content behind logins or strict paywalls. If you run a gated content site, consider offering a summary or portion that’s publicly accessible, so AI engines have something to work with (and then can cite you, perhaps leading users to sign up for the full piece). If everything is locked away, you’ll be invisible to AI search. Some sites are experimenting with “noai” metatags to opt out of LLM training – but note that opting out might also exclude you from being cited in answers. This is a strategic choice: do you prefer not to be consumed by AI, or do you want the exposure? Most marketers will lean towards exposure, given proper credit.

Emphasize Authoritative, Well-Structured Content

1. Authority & Trust: All these AI systems heavily prioritize trusted sources. Bing’s selection criteria explicitly include Domain Authority and Content Trustworthiness ( ^[18] ). Perplexity similarly tends to quote highly credible domains. DuckDuckGo started with sources like Wikipedia. To be seen as authoritative: Build your domain’s expertise in a niche – cover topics comprehensively and accurately (depth of content). Get quality backlinks and mentions from other reputable sites (traditional SEO off-page still matters for signaling authority). Ensure your content is factual, up-to-date, and error-free. AI models cross-check facts across sources ( ^[69] ); if your data is an outlier or outdated, it might be skipped in favor of a source that has more consensus or recent info. 
2. Clear Structure & Formatting: AI answers often extract specific sentences or bullet points from content. Thus, how you format content can influence whether the AI finds it easy to use: Use Headings and Subheadings (H2, H3, etc.) that clearly delineate sections and indicate the topic of each section. For example, an FAQ page with questions as headings and answers below is excellent fodder for AI. Bullet Points and Numbered Lists are AI-friendly. If you have “Top 5 reasons” or steps in a process, list them cleanly. Bing’s AI, for instance, has been seen quoting bullet lists directly (with a citation) because it’s a concise piece of info. 
3. Schema Markup: Implement structured data like FAQ schema, How-To schema, etc. ( ^[70] ). While we don’t have direct proof that Perplexity or Bing’s AI actively parse schema, it certainly can’t hurt – and Bing’s own advice to marketers includes using schema to help it understand content ( ^[70] ). If nothing else, the presence of schema might assist the underlying search index (Bing/Google) in knowing your page has Q&A or how-to content, which could then be fed to the AI layer. 
4. Tables and Charts: Some answers might present data from tables (Brave’s Summarizer can highlight parts of a table on a page, for instance). Use tables for data comparisons where appropriate, with clear labels. Provide alt text for charts with key takeaways (since the AI might read that). 
5. Consistent Terminology: AI models pay attention to wording. If you have an important fact, phrase it in a straightforward, unambiguous way. For example, instead of burying the answer to a question in a verbose paragraph, use a declarative sentence that could stand alone. E.g., “Yes, Product X is water-resistant up to 1 meter for 30 minutes (IP67 rating).” That sentence is pluckable by an AI and could appear in an answer about “Is Product X water resistant?” – with a citation to you. 
6. Include Sourceable Quotes: This is an interesting tactic suggested for Bing ( ^[71] ). It means writing short, impactful sentences that an AI might find convenient to quote directly. Think of them as pull quotes or sound bites. E.g., “According to [Our Company] research, nearly 60% of shoppers now use AI assistants for product discovery.” If that is in your content and it’s a compelling stat (and you are a known source), an AI summary about e-commerce trends might pull that sentence and cite you ( ^[71] ). Journalists do this too when writing articles – they look for quotable lines. Now AI is doing it algorithmically.

Leverage Community and Off-Page Content

As noted, community forums and Q&A sites play a big role in AI answers, especially for niche and long-tail queries. Marketers should not ignore this indirect avenue: Identify key forums for your industry. It could be Reddit (there’s a subreddit for almost every topic), Stack Exchange network, Quora, or specialized forums. For developer tools, StackOverflow is king; for consumer products, maybe Reddit or Quora. Contribute value in those communities. This isn’t traditional “link building” – you’re not there to drop your link (in fact, overly promotional posts will be downvoted or removed).

Instead, have genuine participation: answer questions, provide expert insights, correct misconceptions about your product or industry. Over time, some of those posts could become reference points that AI models pick up on.

For example, if multiple Reddit users (including possibly your experts incognito) mention that your software has a certain feature that competitor lacks, an AI summarizing “Software X vs Y” might reflect that point, citing the Reddit thread. Encourage user reviews and Q&A on third-party sites. Google’s own generative summaries have shown content from product review sites or Q&A on e-commerce pages.

Bing and others might incorporate things like Amazon’s “Most asked questions” or StackExchange answers. While you can’t directly control user-generated content, you can foster a positive presence. For instance, ensure your product’s FAQ on Amazon is answered (even if you as a brand representative answer it). The more high-quality content exists about your brand on the open web, the more likely AI finds something relevant to quote. Monitor AI outputs for your brand.

Periodically, try asking these AI engines questions about your company or product: “What does [My Company] do?” or “Is [My Product] good for [use case]?” See what comes up. Does the AI mention incorrect info?

If so, trace the citation – maybe a forum post has wrong data. That gives you a clue where to jump in and clarify in the source community. This is a new kind of brand monitoring. It’s akin to checking search results for your brand, but now you check AI answers for your brand.

One caveat: as of now, Bing Chat and others often will not mention a lesser-known brand in an answer unless specifically asked (to avoid sounding like endorsement). But if asked directly about you, the answer’s accuracy matters. Also, for general questions like “best project management software”, an AI might list a few tools – will yours be among them? That depends on whether the sources it draws from talk about your tool in that context. So you’d want to be present in “Top 10” list articles or discussions, etc.

Technical SEO and Speed – Still Important

The AI layer doesn’t eliminate the need for strong technical SEO: 

• Site Speed and Performance: If an AI search tool is fetching your page in real-time to get info (which is how Bing operates – it fetches live content to feed GPT-4), a slow site could be a hindrance. If your page takes too long to load, maybe the engine skips it or times out. Bing’s index likely caches content, but freshness matters, so it might be hitting your site. Ensure fast response times. Also, mobile-friendliness and good UX indirectly matter since Bing and others won’t consider a poor quality page authoritative. 
• Meta Tags and Snippets: While an AI doesn’t just parrot your meta description, having a concise meta description or snippet can influence what the search index thinks your page is about, and that context can feed into AI answer selection. Plus, Google’s SGE sometimes highlights key sentences from a page – which often come from a well-written introductory paragraph. So continue writing clear intros and use meta tags appropriately. 
• Content Freshness Signals: Use dates on your content where applicable (and update them when you refresh an article). If Bing sees two articles on “AI search trends” and one was updated in 2025 and the other in 2022, it will likely favor the newer for a question about current trends ( ^[37] ). Perplexity also tends to fetch the latest info. This doesn’t mean old content is useless (evergreen content still works for timeless questions), but for anything time-sensitive, keep it fresh. 
• Crawl Depth: Ensure important content isn’t buried. Traditional SEO audits (fix broken links, have good sitemaps, etc.) remain valuable because if your content isn’t indexed properly, the AI can’t use it.

Ethical SEO and “GEO” Tactics

It might be tempting to try to manipulate AI answers. For instance, one could think “maybe I can get my site cited by feeding misleading information somewhere.” Be very careful here. Black-hat SEO tactics in the AI era (like creating spammy Q&A pages just to get quoted, or gaming prompt answers) can backfire severely. The AI models are getting better at cross-verifying and ignoring outliers ( ^[69] ).

Additionally, search engines have teams looking at the quality of AI answers – if they find people trying to trick the system, they’ll patch it (just as Google patched many SEO loopholes over the years).

Instead, focus on ethical influence :

Provide genuinely useful content even if the immediate click doesn’t come. This builds trust such that when an AI or human encounters your brand, they see it as helpful. For example, a financial company might publish a free dataset or tool. Perplexity’s answer might use that data – even if users don’t click, they see the company name and perhaps associate it with expertise. 

Avoid AI-generated content for SEO (ironically) unless you thoroughly fact-check it. Low-quality content won’t get cited by these AI engines; they’re picking the cream of the crop. So churning out 100 AI-written articles likely won’t help (and could harm your human SEO too). Quality over quantity is more crucial than ever. 

Mark your content clearly where needed. If you have sections that summarize others’ data, cite them (the AI might give you credit for being transparent). If you have original research, highlight that – use wording like “In our 2025 survey of 500 CEOs…” so the AI knows this is proprietary info from you. 

Monitor and adapt: This is new territory for everyone. Keep an eye on how these AI answers evolve. They might start showing more source links or fewer. They might change which types of content they favor as the models improve. Staying updated (via SEO news, experiment yourself, etc.) is part of GEO.

In essence, optimizing for alternative AI search platforms is about making your content as AI-ready as possible : accessible, authoritative, structured, and present wherever the AI might look (your site and the broader web). The nice side-effect is that these optimizations typically improve user experience for human visitors too – clarity, structure, trustworthiness are universally good practices.

Should Marketers Care About These Alternatives? – Reach vs. Opportunity

After examining Bing, Perplexity, and others, you might wonder: given their relatively small market shares compared to Google, how much effort should you really allocate here? It’s a valid question. Google still accounts for ~90% of global search traffic ( ^[14] ); in many organizations, resources are limited, and Google SEO (plus maybe a bit of Bing SEO) has been the primary focus for years. 

The current reach of alternatives: 

• Bing : ~3-4% worldwide share (a bit higher on desktop, and in certain countries like U.S./UK). Despite Bing Chat’s hype, it only nudged this a little upward (e.g., from ~3% to ~3.4% globally in 2023) ( ^[13] ). If you’re strapped for time, you might think “Why bother? That’s tiny.” However, consider that 100 million people use Bing daily ( ^[1] ). It’s not negligible. And some demographics (like Windows 11 users with the integrated Bing Chat, or corporate users on default Edge) might be heavily represented. 
• Perplexity : not a traditional “search engine” in share stats, but 73 million visits in a month and growing ( ^[72] ). It likely has a few million regular users who ask many questions. These users might skew toward researchers, students, and professionals. 
• DuckDuckGo : ~1% share globally ( ^[51] ), but with over 100M queries per day reported in 2022 (and likely more now). A niche but loyal base, including influencers who publicly advocate it (even Twitter’s ex-CEO Jack Dorsey has endorsed using DDG) – these users can be evangelists. 
• You.com, Brave, etc. : collectively well below 1%, but they often attract early adopters and tech enthusiasts. These are the people who write blogs, Reddit posts, or news articles – i.e., they punch above their weight in shaping opinions. If an influential tech blogger consistently sees your site cited in Perplexity or Bing, they might mention that in an article. 
• ChatGPT itself : Let’s not forget, some users bypass search and just use ChatGPT or other assistants to find information (“answer engines” without a traditional search engine). ChatGPT had 600 million visits in May 2024 ( ^[73] ) (though not all for search-like queries). If you have content that is embedded as part of the training data or via plugins, it could influence answers. However, with ChatGPT’s default model being a black box (no live web, no citations), optimizing for that is more about providing good data to the training corpus (which is indirect and not something you do on the fly).

That’s a complex topic. Given the above, marketers should care strategically : 

Early Movers Advantage: The fact that these platforms are smaller actually means less competition in them right now for GEO. Many businesses have not yet systematically tried to optimize for being cited in AI answers. By starting now, you can carve out a presence. For example, maybe few of your competitors have bothered to ensure their FAQ content is in Q&A form and well-cited – if you do it first, Bing’s AI might start preferring your site for answers in your sector. Capturing visibility on Bing Chat or Perplexity in 2024 could be analogous to capturing top Google ranks in the early 2000s – easier before everyone else piles on. 

Influential Audience: As mentioned, early adopters of AI search skew tech-savvy, and often influencers or decision-makers. Think of developers (using Bing Chat integrated in VSCode or StackOverflow discussions), journalists (experimenting with Perplexity to gather facts for an article and seeing your brand cited), or executives (asking ChatGPT/Bing for quick insights). If your site is frequently cited in these AI-generated answers, it subconsciously builds credibility with these users. Even if they don’t click immediately, your brand may be seen as a go-to authority. Then, when they encounter your brand elsewhere, there’s recognition. It’s akin to brand impressions in display advertising – sometimes you’re doing GEO for the branding as much as the click. 

Cross-Pollination to Google: Many GEO tactics for Bing/Perplexity will help with Google’s Search Generative Experience too. Google’s SGE, while outside this article’s scope, also presents AI summaries with cited links (in labs as of 2023). The way to get cited by Google’s AI is very much to have the same qualities: authoritative, concise content that directly answers questions. So by optimizing for the alternatives, you are indirectly preparing for Google’s imminent AI-dominated results. It’s a form of future-proofing your SEO. As one SEO expert put it, “view GEO as an extension of SEO rather than a replacement” ( ^[74] ) – the core idea is still to produce great content; we’re just tuning it for AI consumption. 

Incremental Gains: Even if Bing only brings, say, 5% of your search traffic, that might still be significant in absolute terms. If your site gets 1,000,000 visits from Google a month, an extra 50,000 from Bing is not trivial – especially if competition for those users is lighter, conversion rates could be higher (maybe Bing users are less inundated with options). Similarly, a handful of mentions on Perplexity that lead researchers to cite your study, or a few thousand DuckDuckGo users who happen to be highly relevant (e.g., privacy enthusiasts who might love your privacy-friendly product) – those are valuable. Marketers often chase every bit of market share; ignoring even a 5-10% segment entirely would be a missed opportunity. 

Case Study – B2B Tech: Consider a B2B software company targeting developers. Traditional SEO might bring in organic traffic via Google, but many developers are now using Stack Overflow less and tools like GitHub Copilot or Bing Chat more ( ^[64] ). If your product documentation is top-notch and accessible, a dev might ask Bing Chat a question and get an answer citing your docs – saving them the trip to StackOverflow and directly building trust in your product’s resources. That developer may not click right then (they got the answer), but next time they have a deeper issue, they might recall “that answer was from Company X’s docs, maybe I should check their site.” Moreover, we saw earlier how Stack Overflow’s traffic has dipped partly because AI is serving answers directly ( ^[64] ).

If competitors relied solely on community answers for visibility, and you instead ensure your official content is good enough to be used by AI, you could leapfrog in visibility. All that said, prioritization is key. The advice isn’t to abandon Google optimization in favor of Bing or others, but rather to incorporate GEO strategies into your overall search marketing plan. It might mean reallocating some content resources to update FAQ pages for AI-friendliness, or spending a bit of time on Bing Webmaster Tools (yes, Bing has its own) to see how you’re performing there. It could involve monitoring where your content is being cited by these AI engines – which itself could become a new metric (perhaps “AI Citation Count” will be a KPI in future SEO reports, analogous to search impressions).

Looking ahead, it’s quite possible that alternative engines could grow. For example, if Microsoft continues to invest (it’s leveraging Bing Chat across Windows, Office, and more), Bing’s share could inch upward, or at least Bing’s influence might extend beyond bing.com (e.g., Bing answers powering things in other apps). Similarly, Apple has been rumored to be working on AI search capabilities – if they launch something on iPhones, that could be a new channel overnight. By getting experience with optimizing for Bing/Perplexity, you’ll be better prepared for any newcomer. In conclusion, while Google remains the primary focus, forward-thinking marketers should absolutely care about the AI search alternatives. The risks of ignoring them include missing out on early adopter audiences, ceding mindshare to competitors who do engage there, and being unprepared when Google fully rolls out similar paradigms.

On the flip side, the benefits of engagement include enhanced brand authority (through citations), additional traffic (even if modest, it can have high ROI if it’s cheap to get), and crucial learning that can inform your overall content strategy. As one LinkedIn analysis noted, neglecting GEO now could relegate your content to a mere footnote in others’ AI responses ( ^[75] ) – meaning you might only be referenced as supporting someone else’s more prominently featured info. Better to strive to be the primary source being quoted.

By capturing visibility on platforms like Bing Chat and Perplexity today, you not only gain an edge in those arenas, but you also refine the skills and content quality needed to thrive in the evolving world of AI-assisted search. In the next articles, we’ll look at measuring GEO success and preparing for future trends – but as far as where to optimize, remember that Google may be king, but it’s now ruling a more crowded kingdom. The savvy marketer will build presence in all the places the audience seeks answers, however small, because those small streams can collectively turn into a significant river of opportunity – and they often carry the most influential currents. 

Key Takeaway: Alternative AI search engines might individually have modest reach, but they attract influential early adopters and pioneer features that larger engines emulate. Marketers should proactively optimize for these platforms – it’s largely an extension of good SEO/GEO practices – to gain brand visibility and be ahead of the curve. The effort is justified not just by the direct traffic available now, but by the strategic advantage of positioning your content for an AI-driven search future where being the trusted cited source is gold. ( ^[39] ) ( ^[64] )