When Will AI Search Overtake Google?
Large Language Model (LLM)-powered assistants (like ChatGPT, Bard, and Bing Chat) are rapidly changing how people find information. This report projects when such AI-driven search will overtake traditional search engines (e.g. Google) in global consumer usage. We examine current adoption trends, growth rates, user behavior shifts, and industry forecasts to identify a “tipping point” where LLM-based search surpasses classic search in daily usage share and query volume. The focus is on 2025 through 2030, with data-driven milestones and a forecasted intersection of adoption curves around the end of the decade.
Google Still Crushes AI Tools in Search Volume
Traditional search engines still dominate overall query volume as of mid-2025. Google alone processes on the order of 15+ billion searches per day (well over 5 trillion annually) and maintains roughly 90% of the global search market share. By contrast, ChatGPT – the leading LLM-based assistant – handles an estimated tens of millions of “search-like” queries per day in 2025. In other words, Google Search’s daily query volume remains vastly higher – SparkToro estimated that in 2024 Google handled roughly 373× more queries than ChatGPT, and all AI-powered search tools combined made up less than 2% of the market. Even Bing (the #2 traditional engine) sees hundreds of millions of searches each day, an order of magnitude above ChatGPT’s query count. LLM-based search accounts for about 5.6% of desktop search traffic in the U.S. as of June 2025 (up from roughly half that a year earlier), according to the Wall Street Journal — still a small fraction of traditional search volume, but growing rapidly.
However, the landscape is starting to shift. Google’s search traffic has continued to increase into 2025 (over 20% year-over-year growth in 2024) in part due to new AI-powered features in Search. At the same time, ChatGPT’s adoption has been explosive – it reached 100 million users within 2 months of launch (the fastest-growing consumer app ever) – and by late 2024 it was reportedly logging around 1 billion interactions per day. By early 2024, ChatGPT’s web traffic even surpassed Bing’s in volume, making it arguably the second-most used search tool on the web in some analyses. In short, Google’s lead remains enormous in absolute terms, but AI assistants are rapidly narrowing the gap from a zero baseline. Users are increasingly turning to LLM-based tools for information queries, signaling a gradual shift in the search landscape as we head further into 2025.
Rapid Adoption of LLM Search
Consumer uptake of LLM-based tools has been remarkably fast. A March 2025 survey found 52% of U.S. adults have now used an AI LLM (e.g. ChatGPT), signaling mainstream awareness. Among LLM users, two-thirds report using them “like search engines” for information retrieval. In other words, a significant share of the population is already turning to chatbots for search-like queries. This adoption cuts across demographics – while younger, educated users lead slightly, even 53% of U.S. adults earning under $50k have used LLMs. LLMs appear to be one of the fastest-adopted technologies in history.
Several factors drive this growth: conversational convenience, always-on assistance, and rapid improvements in capability. Unlike traditional search, an LLM agent can engage in multi-turn dialogue, provide direct answers with context, and even perform tasks (coding, writing) beyond static information lookup. This versatility has led to surging usage rates. OpenAI’s ChatGPT went from launch in late 2022 to 800 million weekly active users by April 2025 – an 8× increase in just 18 months. By mid-2025 it was handling 1 billion searches per week (roughly 143 million per day) as users increasingly treat it as an information source. Other LLM-powered assistants (Anthropic’s Claude, Google’s Bard/Gemini, etc.) are also growing, though they remain much smaller than ChatGPT so far.
Voice assistants are another vector accelerating AI search adoption. Globally, voice-enabled AI assistants (Siri, Alexa, Google Assistant, etc.) have proliferated – 8.4 billion voice assistants are in use by 2025, almost doubling from 4.2B in 2020. About 20-30% of consumers use voice search regularly, often for quick queries. As these voice interfaces integrate advanced LLMs, they effectively become conversational search engines, further shifting queries away from traditional typed search. The convenience of asking a question out loud and getting a spoken answer (e.g. via smartphones or smart speakers) has normalized AI-assisted search in daily life.
From Search Bar to AI Chat
Crucially, consumers are learning when to use LLM assistants versus a traditional engine. Studies show that 98% of ChatGPT users still also use Google – they are not abandoning one for the other outright, but rather allocating different query types to each. Simple factual or navigational queries (“weather tomorrow”, “Facebook login”) still default to Google’s quick answers. Google’s familiarity and speed make it the go-to for one-off facts or transactional searches. However, for complex, open-ended tasks – e.g. planning travel itineraries, researching a topic in depth, troubleshooting code, brainstorming – users increasingly prefer AI assistants. ChatGPT can synthesize information from multiple sources and provide a personalized, conversational response that would otherwise require many Google queries and clicks. This emerging division of labor in search is evident: users report turning to Google for quick answers but using ChatGPT for detailed explanations, creative ideas, and multi-step research.
Younger demographics especially are embracing “AI-first” search habits. Nearly 80% of Gen Z have used generative AI tools, with almost half using them weekly. A majority of these young users say AI makes finding information easier (72%) and helps them learn faster. They are comfortable asking chatbots for homework help, product recommendations, or advice – queries that older users might still direct to Google or specific websites. Additionally, specialized search alternatives like TikTok (for how-tos, trends) and Reddit (for human reviews) are diverting searches from Google. In fact, “reddit” is now one of the most searched terms on Google itself, reflecting how people seek community-sourced input to validate AI or search results. All these trends indicate a broad fragmentation of search behavior: consumers are no longer relying on a single platform, but rather using a mix of AI assistants, social platforms, and traditional engines based on the context of their query.
How Google Is Fighting Back with AI
Facing this shift, incumbent search providers are aggressively integrating LLM technology into their products. Google launched its Search Generative Experience (SGE) in 2023-2024, augmenting search results with AI summary “Overviews”. Early results showed increased user engagement – Google’s CEO noted higher search usage and satisfaction among those using AI Overviews. Internally, Google acknowledges the landscape change: in late 2024, CEO Sundar Pichai called 2025 “critical” to address the ChatGPT threat. Google is reportedly investing $75 billion in AI to bolster its search AI capabilities, including developing its own advanced models (e.g. Gemini). The head of Google Search, Elizabeth Reid, even suggested the classic Google search bar will become “less prominent over time” as AI interfaces take center stage.
Microsoft has taken a different tack – rather than defending an existing monopoly, it partnered with OpenAI to leapfrog Google. Microsoft’s $13 billion+ investment in OpenAI brought GPT-4 into Bing in early 2023, spurring a surge of interest. Within a month of adding the AI chat feature, Bing exceeded 100 million daily active users for the first time (still a single-digit share of the market, but a notable bump). Microsoft reports that roughly 1/3 of Bing’s daily users engage with AI chat and that AI features increased overall time spent on Bing. Additionally, new AI-centric search startups (Perplexity, Neeva before its pivot, etc.) have drawn significant venture funding, and OpenAI itself is exploring a dedicated AI search engine as of 2024. In China, Baidu introduced its Ernie AI chatbot into search, and other regional engines are following suit. Across the board, massive investment is flowing into AI-driven search, signaling industry consensus that LLMs are the future interface for information retrieval.
AI Could Overtake Google Search by 2028
When will LLM-based search overtake traditional search? Based on current trajectories, multiple analyses converge on the late 2020s as the critical inflection period. Key data points and projections include:
2025: LLM usage still <5% of global search queries. Google remains ~90% of the market, but AI chat queries are growing exponentially. ChatGPT’s query volume is on track to reach hundreds of millions of searches per day (it hit ~143M/day by mid-2025). By 2025, over half of consumers have tried LLM search and 34% use an LLM daily or near-daily. Milestone: OpenAI’s ChatGPT crosses 1 billion weekly searches and 800M users.
2026: Inflection point begins. Gartner predicts that by 2026, traditional search engine volume will drop 25% as users turn to generative AI assistants — a shift that could mean Google’s query count peaks and starts to decline to around 10–11 billion per day (down from roughly 14 billion), while AI-powered queries continue their exponential rise. In practical terms, this could mean Google’s own query count peaking and starting to decline (~10-11B/day, down from 14B) while LLM queries continue to rise. Milestone: AI chat integrated into most search platforms (e.g. Apple potentially launches an AI search tool), and a quarter of all search queries could be handled by LLMs (per Gartner’s scenario).
2027: Early signs of parity in specific domains. Research suggests that by late 2027, AI-driven search traffic could deliver equal — or even greater — economic value to traditional search traffic, even if raw volume is lower, thanks to significantly higher conversion rates. An Ahrefs study found AI search visitors convert up to 23× better than regular search visitors, while Semrush data indicates that AI-driven traffic achieves, on average, a 4.4× higher conversion rate than traditional organic search. If these patterns hold, AI-powered channels could match Google’s business impact as early as Q4 2027. Some niche sectors may already see AI tools surpass Google in share of queries (e.g. coding help, certain research domains). In fact, early market data suggests that in areas like programming assistance, academic research, and complex product recommendations, AI-first search platforms are already capturing a majority share of queries — in some cases exceeding 60% — well before the projected 2028 tipping point. Milestone: Internal data shows AI searches overtaking traditional search for digital marketing queries by early 2028 if trends continue.
2028: Tipping point approaches. Gartner projects that by 2028, organic search traffic to websites will be down 50% or more as consumers fully embrace generative AI search. In other words, roughly half of search activity may be happening through AI assistants instead of classic search engines by 2028. Research from Semrush even predicts that AI-powered search could overtake traditional search traffic entirely by the first half of 2028 – potentially marking the crossover earlier than many industry forecasts suggest. Similarly, other market analyses suggest LLM-based platforms will capture between 30% and 50% of the search market by 2028, depending on the metric and region — with some high-engagement categories, like in-depth research or technical problem-solving, already leaning toward AI-first search dominance. Milestones: Google’s AI-driven “SGE” likely becomes the default search mode, and AI-first search engines handle an estimated 30-40% of informational queries across industries. This year is a plausible “crossover” in certain metrics (e.g. time spent or number of informational queries on AI platforms vs Google).
2030: LLM search overtakes traditional search in general consumer usage. By 2030, extrapolating current growth, AI-powered assistants are expected to handle a majority of search queries worldwide. Industry analyst Kevin Indig’s modeling (using Similarweb traffic trends) predicts ChatGPT’s traffic will surpass Google’s by around October 2030. Based on mid-2025 Similarweb data, Google Search is generating roughly 136 billion monthly visits compared to about 4 billion for ChatGPT — meaning that, to meet this forecast, AI-powered platforms would need to sustain their current double-digit monthly growth rates while Google’s traffic trends downward. In this scenario, LLM-based systems collectively would command over 50% of global search query volume by 2030, marking the definitive tipping point where AI search dominates. Google will still generate enormous query volume, but much of it may come from users asking Google’s own AI (Bard/SGE) for answers, blurring the line between “traditional” and “AI” search. Milestone: By 2030, LLM assistants become the first preference for finding information for most users – effectively “Google” becomes just one of many AI-powered or hybrid search options, rather than the default starting point.
All forecasts carry uncertainty, but the consensus is that late this decade (2028-2030) will witness the crossover. By that time, LLM-based search will likely have 30-50%+ usage share, exceeding the old query-and-click model. Some optimistic scenarios even envision Google’s share dropping to ~20% by 2027 in certain verticals, with ChatGPT and others absorbing the rest. More conservative outlooks (e.g. Gartner) still see at least half of search queries shifting to AI by 2028. Our forecast aligns with these, pegging 2029-2030 as the period when AI-driven search usage definitively surpasses traditional search worldwide.
What Will Speed Up (or Slow Down) AI Search Takeover?
Several drivers will determine how quickly LLM search overtakes traditional search:
Quality and Trust: LLMs need to continually improve accuracy and cite reliable sources. Increased trust (already ~70% of consumers trust AI results to some extent) will encourage more users to switch fully to AI for answers. Google’s integration of citations and real-time data into its AI results, as well as OpenAI’s move to connect ChatGPT to the live web, are addressing this. If by ~2025-2026 LLMs can reliably answer most factual queries with sources, users will have less need to “double-check” on Google.
User Experience & Convenience: LLM assistants offer a conversational, one-stop experience (no multiple clicks), which users find appealing for complex queries. As interfaces improve (e.g. voice integration, multimodal capabilities, memory of past queries), they will attract more search share. Voice search growth also plays a role – speaking a query to an AI assistant that talks back is a natural evolution. By 2030, we expect voice and chat-based search to converge, providing instant answers on-the-go, which traditional web search can’t match for convenience.
Integration into Daily Tools: AI search will become embedded in productivity apps, browsers, and operating systems. For example, Microsoft is embedding ChatGPT (via Copilot) across Office and Windows, so users can ask questions without opening a browser at all. If asking your desktop or AR glasses an question yields an immediate AI answer, the need to “Google it” diminishes. This ambient integration could dramatically boost LLM query volume by the late 2020s, accelerating the crossover.
Economic and Content Ecosystem: One challenge is the sustainability of the web content ecosystem. Traditional search drives traffic to websites; AI answers often quote information without a click-through, which has already led to 60% of Google searches ending with no click. If publishers restrict content access or if regulations intervene (to ensure AI tools aren’t anti-competitive), it could impact AI search growth. Conversely, if new monetization models (like AI-native ads or affiliate links in answers) are implemented, AI search could scale faster. By 2030, the advertising and revenue model for search will likely be reinvented to accommodate AI – e.g. sponsored chatbot responses – which could further tilt business incentives toward LLM-based search.
Competition and Default Habits: Google’s response will affect the timeline. Google may push its own AI mode (Bard/SGE) to all users by default. If Google successfully retains users within its ecosystem by offering the best of both worlds (trusted AI answers with the option of traditional results), the “overtaking” might be less visible as a Google vs. ChatGPT battle – instead, Google’s search itself becomes LLM-powered. In that case, the tipping point could arrive as Google’s search product transforms into an LLM-first experience by 2030, effectively meaning LLM search has overtaken the old link-based search within the dominant platform. On the other hand, if an independent AI provider (OpenAI or others) captures a large user base directly, that would mark a more distinct overtaking of Google. Current signals (e.g. OpenAI’s plan for a search engine, and ChatGPT becoming a household name) suggest a real possibility of an external AI platform rivalling Google’s scale by 2030.
2030 Is When AI Search Takes the Crown
All indicators point to a transformative shift in how people search for information over the next 5-7 years. By 2030, LLM-powered search is projected to eclipse traditional search engines in global usage – a historic changing of the guard in consumer technology. We expect the crossover around 2028-2030, when more daily queries worldwide go through AI assistants than through keyword searches. This will be driven by LLMs’ continued exponential adoption, improvements in AI capabilities, and user preference for convenient, conversational answers. Notably, “overtaking” does not mean search engines vanish overnight – rather, they will evolve or integrate these AI capabilities. In fact, by 2030 the distinction between an “LLM-based assistant” and a “search engine” may blur, as most search platforms will have become AI-centric.
In practical terms, the milestone to watch is when LLM-based systems account for >50% of search queries and traffic. Current data and forecasts suggest this is likely by the end of this decade (around 2030), with some metrics reaching parity even sooner (e.g. half of informational searches via AI by 2028). The transition is already underway: users are dividing their searches, businesses are adapting SEO for AI, and search giants are reinventing themselves as AI companies. The adoption curves are on a collision course, and if present trends hold, 2030 is set to be the year LLM-powered search becomes the new dominant paradigm.
Sources: The projections and data above are drawn from a range of authoritative sources, including analyst reports, consumer surveys, and public disclosures by the companies involved. Key references include SparkToro’s 2024 search volume research, Gartner’s AI adoption forecasts, Kevin Indig’s industry analysis, and usage statistics from OpenAI and others. These provide a robust, evidence-based foundation for predicting when and how LLM-based search will overtake traditional search in the coming years.
Prepare Your Business for the AI Search Era
The shift from traditional search to AI-first platforms is accelerating — and the tipping point may arrive sooner than most forecasts suggest. Organizations that act now can adapt their SEO strategies, optimize content for AI-driven discovery, and integrate LLM-powered tools into daily operations. TTMS supports companies worldwide in leveraging AI technologies, automating critical workflows, and ensuring their digital presence remains competitive in the new search landscape. Let’s explore how your business can lead — not follow — in the AI search era. Talk to our experts!
Will ChatGPT completely replace Google Search by 2030?
While forecasts suggest ChatGPT and other AI-powered assistants could surpass Google in global search share by 2030, complete replacement is unlikely. Instead, search is expected to evolve into a hybrid model where AI tools handle most complex and conversational queries, while traditional engines remain relevant for quick facts, local information, and transactional searches.
How will AI search change SEO strategies?
AI search shifts the focus from ranking for keywords to being cited as a trusted source within AI-generated answers. This means optimizing content for clarity, authority, and relevance to AI models, while also monitoring “share of voice” in AI responses. Businesses will need to adapt by creating content formats that AI tools can easily summarize and reference.
Is AI-powered search more accurate than traditional search engines?
Accuracy depends on the query type. For in-depth, multi-step, or creative tasks, AI assistants like ChatGPT often provide richer, more contextual responses. However, for real-time, fact-based queries, traditional engines with live indexing still hold an advantage — though this gap is narrowing as AI integrates real-time data sources.
What industries will benefit most from the rise of AI search?
Sectors requiring personalized advice, problem-solving, or detailed explanations — such as education, healthcare, travel, software development, and legal services — stand to gain the most. These industries can leverage AI search to deliver tailored recommendations and solutions directly to users without multiple clicks.
How can businesses prepare for the AI search tipping point?
Companies should start by auditing their content for AI-readiness, ensuring it’s authoritative, well-structured, and easy for AI to parse. They should also monitor how often their brand appears in AI responses, experiment with conversational content formats, and integrate AI tools into customer-facing workflows to stay competitive in the evolving search landscape.
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