Perplexity's monthly active users in India amounted to around *** million as of the second quarter of 2025. The AI startup saw a whopping *** percent year-on-year increase in MAUs in the country. In fact, India is the platform's leading market in terms of MAUs.

When Aravind Srinivas and team launched Perplexity AI in 2022, few predicted the tool would one day challenge Google’s throne in search. Fast forward to 2025, and Perplexity AI is not just a niche experiment; it’s redefining how people find answers online. With a sleek interface, lightning-fast generative search, and...

The number of downloads of the search-focused AI platform, Perplexity, in India amounted to about *** million, as of the second quarter of 2025. The AI startup saw a whopping *** percent year-on-year growth in downloads in the country. Perplexity seeks to leverage the Indian market and its sizable user base in a bid to surpass its rival OpenAI, which has already secured the lead in the United States.

Comparison of Output Tokens per Second; Higher is better by Model

Comparison of Seconds to First Token Received; Lower is better by Model

In 2024, OpenAI's ChatGPT was by far the most widely used AI-powered tool among developers over the past year, with 82 percent of developers reporting regular usage. GitHub Copilot ranked second at 44 percent, while Google Gemini came in third at 22 percent. Other notable tools included Bing AI and Visual Studio Intellicode, both of which are owned by Microsoft. Tools such as Claude and Perplexity AI saw lower but still notable usage rates. Traditional tools like WolframAlpha maintained a steady user base at four percent, overtaking newer tools such as Meta AI and Amazon Q.

Comparison of Seconds to Output 500 Tokens, including reasoning model 'thinking' time; Lower is better by Model

The artificial intelligence landscape in the United States continues to evolve rapidly, with numerous startups achieving unicorn status in 2024. Among these, *** stands out with a market value of ** billion U.S. dollars, followed by ************* at **** billion U.S. dollars and ********* at *** billion U.S. dollars. This surge in AI unicorns reflects the growing investor confidence and market potential in the AI sector. Growth of AI unicorns The proliferation of AI unicorns has been remarkable in recent years. Throughout 2021 and early 2022, more than *** AI startups per quarter reached the billion-dollar valuation milestone. This trend continued into 2024, with ** new AI unicorns emerging worldwide. The consistent growth in AI unicorns underscores the sector's dynamism and the increasing integration of AI technologies across various industries. Notable valuations and funding rounds While xAI's ** billion U.S. dollar valuation is impressive, it is surpassed by other AI giants in the U.S. ****** claimed the top spot with a valuation of *** billion U.S. dollars by the end of 2024, while ********** secured a ** billion U.S. dollar valuation. ********** also made headlines in the fourth quarter of 2024 by raising ** billion U.S. dollars in the largest equity deal for any AI unicorn globally. ****** followed with a significant *** billion U.S. dollar equity investment, demonstrating the substantial capital flowing into leading AI companies.

According to a November 2024 survey, four out of 10 consumers worldwide reported using an AI-based tool for travel planning. While ** percent of respondents used artificial intelligence both when planning a trip and during a vacation, ** percent of the sample relied on AI tools only for travel planning.

AI in Consumer Decision-Making: Global Zero-Party Dataset

This dataset captures how consumers around the world are using AI tools like ChatGPT, Perplexity, Gemini, Claude, and Copilot to guide their purchase decisions. It spans multiple product categories, demographics, and geographies, mapping the emerging role of AI as a decision-making companion across the consumer journey.

What Makes This Dataset Unique

Unlike datasets inferred from digital traces or modeled from third-party assumptions, this collection is built entirely on zero-party data: direct responses from consumers who voluntarily share their habits and preferences. That means the insights come straight from the people making the purchases, ensuring unmatched accuracy and relevance.

For FMCG leaders, retailers, and financial services strategists, this dataset provides the missing piece: visibility into how often consumers are letting AI shape their decisions, and where that influence is strongest.

Dataset Structure

Each record is enriched with: Product Category – from high-consideration items like electronics to daily staples such as groceries and snacks. AI Tool Used – identifying whether consumers turn to ChatGPT, Gemini, Perplexity, Claude, or Copilot. Influence Level – the percentage of consumers in a given context who rely on AI to guide their choices. Demographics – generational breakdowns from Gen Z through Boomers. Geographic Detail – city- and country-level coverage across Africa, LATAM, Asia, Europe, and North America.

This structure allows filtering and comparison across categories, age groups, and markets, giving users a multidimensional view of AI’s impact on purchasing.

Why It Matters

AI has become a trusted voice in consumers’ daily lives. From meal planning to product comparisons, many people now consult AI before making a purchase—often without realizing how much it shapes the options they consider. For brands, this means that the path to purchase increasingly runs through an AI filter.

This dataset provides a comprehensive view of that hidden step in the consumer journey, enabling decision-makers to quantify: How much AI shapes consumer thinking before they even reach the shelf or checkout. Which product categories are most influenced by AI consultation. How adoption varies by geography and generation. Which AI platforms are most commonly trusted by consumers.

Opportunities for Business Leaders

FMCG & Retail Brands: Understand where AI-driven decision-making is already reshaping category competition. Marketers: Identify demographic segments most likely to consult AI, enabling targeted strategies. Retailers: Align assortments and promotions with the purchase patterns influenced by AI queries. Investors & Innovators: Gauge market readiness for AI-integrated commerce solutions.

The dataset doesn’t just describe what’s happening—it opens doors to the “so what” questions that define strategy. Which categories are becoming algorithm-driven? Which markets are shifting fastest? Where is the opportunity to get ahead of competitors in an AI-shaped funnel?

Why Now

Consumer AI adoption is no longer a forecast; it is a daily behavior. Just as search engines once rewrote the rules of marketing, conversational AI is quietly rewriting how consumers decide what to buy. This dataset offers an early, detailed view into that change, giving brands the ability to act while competitors are still guessing.

What You Get

Users gain: A global, city-level view of AI adoption in consumer decision-making. Cross-category comparability to see where AI influence is strongest and weakest. Generational breakdowns that show how adoption differs between younger and older cohorts. AI platform analysis, highlighting how tool preferences vary by region and category. Every row is powered by zero-party input, ensuring the insights reflect actual consumer behavior—not modeled assumptions.

How It’s Used

Leverage this data to:

Validate strategies before entering new markets or categories. Benchmark competitors on AI readiness and influence. Identify growth opportunities in categories where AI-driven recommendations are rapidly shaping decisions. Anticipate risks where brand visibility could be disrupted by algorithmic mediation.

Core Insights

The full dataset reveals: Surprising adoption curves across categories where AI wasn’t expected to play a role. Geographic pockets where AI has already become a standard step in purchase decisions. Demographic contrasts showing who trusts AI most—and where skepticism still holds. Clear differences between AI platforms and the consumer profiles most drawn to each.

These patterns are not visible in traditional retail data, sales reports, or survey summaries. They are only captured here, directly from the consumers themselves.

Summary

Winning in FMCG and retail today means more than getting on shelves, capturing price points, or running promotions. It means understanding the invisible algorithms consumers are ...

Comparison of sequences designed by SPIN-CGNN, RosettaFixBB, OSCAR-design, ProteinMPNN, and PiFold on CATH4.2-StructNR193 and PDB-StructNR156 test sets according to perplexity, median sequence recovery, median relative deviation of the frequency of amino-acid residue types, the median relative BLOSUM score, the fraction of low complexity regions, conservation of hydrophobic and hydrophilic sequence positions, the mean steric clash count of refolded structures, and the difference between refolded and target structures in term of RMSD, GDT-TS and TM-score.

Count of simulated mutation tree data sets, out of 15 per column, where a model had the best log perplexity ratio (P) or relationship reconstruction loss (R).

Introduction

In 2024, the Global AI Search Engine Market was valued at USD 17.3 billion and is projected to reach nearly USD 73.7 billion by 2034, expanding at a CAGR of 15.6% during 2025–2034. The growth is driven by the increasing integration of generative AI, natural language processing, and voice-enabled search technologies across industries. Organizations are investing heavily in AI-based platforms to enhance search relevance, improve personalization, and support multilingual accessibility, which is accelerating adoption worldwide.

One of the top driving factors behind this market’s growth is the explosion of digital data and the need for intelligent processing to sort through vast volumes efficiently. Users now want search results that are tailored to their unique preferences and context, which AI technologies like deep learning enable. Another important driver is the increased adoption of cloud computing, providing the infrastructure needed to support scalable AI search solutions. The rise of voice assistants and visual search methods also expands how users interact with these search engines, contributing further to demand.

Demand for AI search engines shows significant growth not only among consumers but also enterprises. Consumers rely on AI search for everyday needs such as finding local services or translating languages, while enterprises use AI-powered search tools to quickly access complex data across departments, improving operational efficiency and decision-making. Large enterprises lead adoption due to their need for handling immense data, but smaller companies are catching up through affordable, cloud-based options that streamline customer support and research.

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In 2024, about 45% of U.S. adults used AI-powered search engines at least once a month, showing that mainstream adoption has already begun. Perplexity.ai emerged as a strong example, growing from 10 million monthly active users in mid-2023 to over 30 million by Q1 2025, reflecting the pace of adoption. Globally, around 27% of enterprises integrated AI search tools into their internal systems, while 51% of Gen Z turned to AI platforms for academic queries. In India, more than 60% of AI search traffic came from mobile, and 38% of users trusted AI search results over traditional engines.

The marketing landscape has also been reshaped by AI-driven search adoption. According to Gitnux, 61% of marketers saw increases in organic traffic due to AI, while 72% considered it central to future strategies. Nearly 95% of customer interactions are expected to be managed without humans by 2025, and by 2024 about 80% of search queries were projected to be handled by AI-powered agents. This signals a structural shift in how companies approach engagement, customer support, and visibility online.

Among smaller businesses, adoption momentum is accelerating, with 55% planning to expand AI investment in search marketing. AI-powered personalization is playing a key role in this trend, helping improve customer targeting and increasing conversion rates by up to 20%. Together, these figures illustrate how AI-driven search is not only transforming consumer behavior but also redefining enterprise strategies, marking a rapid global shift toward automated and intelligent engagement.

Questo dataset contiene i prezzi previsti dell'asset Perplexity tokenized stock (PreStocks) per i prossimi 16 anni. Questi dati sono calcolati inizialmente con un tasso di crescita annuale predefinito del 5% e, dopo il caricamento della pagina, presentano una componente di scala mobile in cui l'utente può regolare ulteriormente il tasso di crescita in base alle proprie proiezioni positive o negative. Il tasso di crescita regolabile massimo positivo è del 100 percento, mentre il tasso di crescita regolabile minimo è del -100 percento.

With an estimated market share of ***** percent during the period between April 2024 and March 2025, ChatGPT was the most popular artificial intelligence (AI) chatbot globally. The Chinese company DeepSeek had a **** percent share, which was just above Google's Gemini, which had a **** percent share, and Perplexity, which had a **** percent share.