Latest news with #Liner
Mint
04-05-2025
- Business
- Mint
Google is planning to bring ads to AI chatbots: Here's how it will work
Google has long been the dominant player in the search market, but the tech giant is finally seeing some competition to its dominant revenue stream, with generative AI coming into the picture. As it turns out, Google is now planning to display ads within conversations with AI chatbots in a bid to maintain its position in the digital advertising space, according to a report from Bloomberg. Reportedly, Google's AdSense for Search network, which is responsible for showing ads within search results on websites, has been expanded to include conversations with chatbots run by AI startups. The move comes after the company conducted tests last year and this year with a handful of AI search app startups, such as iAsk and Liner. Google is reportedly running these experiments with AI startups in order to test the waters with advertising in the relatively new world of AI. Commenting on the report, a Google spokesperson told Bloomberg, 'AdSense for Search is available for websites that want to show relevant ads in their conversational AI experiences.' Notably, Google's search business has been under threat since ChatGPT's first public debut in late 2022, amid fears that users would turn to the chatbot to find answers to their questions. Much has changed since then, with Google developing its own AI chatbot, generally considered to be on par with OpenAI's offering, while also adding AI overviews to search, allowing users to get quick answers to questions without opening websites. However, the tech giant is now facing newer challenges, including from the likes of Aravind Srinivas-led Perplexity AI, which uses various AI models, including OpenAI's and China's DeepSeek, to provide quick results to users' queries. Meanwhile, Google is also facing scrutiny at home, with a US federal judge finding that the company violated antitrust laws by monopolising the open web digital advertising market. The remedy hearing for this case is currently underway and could have a major impact on the development of Google Search for years to come. First Published: 4 May 2025, 07:21 AM IST
The Star
01-05-2025
- Business
- The Star
Google places ads inside chatbot conversations with AI startups
The Google AdSense network, which traditionally displays ads in search results and in the margins of websites, has expanded to include conversations with chatbots operated by AI startups. — Bloomberg Google's ad network has begun showing advertising within the flow of conversations with chatbots – part of Alphabet Inc's efforts to keep its edge in digital advertising as generative artificial intelligence takes off. Earlier this year, the Google AdSense network, which traditionally displays ads in search results and in the margins of websites, has expanded to include conversations with chatbots operated by AI startups. Google made the move after conducting tests last year and earlier this year with a handful of startups, including AI search apps iAsk and Liner, according to people familiar with the matter who asked not to be identified discussing private information. Showing ads alongside its own search results is the heart of Google's business, bolstered by a business that serves up advertising across much of the web. That empire has come under threat as new entrants like OpenAI and Perplexity AI seek to siphon off the search giant's audience with products aiming to help users find what they are looking for more quickly. As Google invests heavily to protect its lead, finding the best way to monetize generative AI will be crucial, said Tomasz Tunguz, a general partner at Theory Ventures. "Feedback loops are incredibly important,' Tunguz said. "Having greater visibility into what's working' is essential to success. A Google spokesperson confirmed that "AdSense for Search is available for websites that want to show relevant ads in their conversational AI experiences.' Startups iAsk and Liner declined to comment on their relationship with Google. Regulators are increasingly scrutinising Google's influence over the advertising economy that underpins the open web. Google Search, an industry juggernaut, yielded more than US$198bil (RM854.27bil) in revenue in 2024, almost 60% of Alphabet's annual sales. In April, a federal judge found that the Alphabet unit violated antitrust law in the markets for advertising exchanges and tools used by websites to sell ad space, known as ad servers. The company has argued that it has a dominant position because its tools are effective and easy to use. Now, with its burgeoning business relationships with startups, Google aims to profit even if its share of the search market dwindles. Running experiments with AI startups will allow the company to test the waters for advertising in the relatively new world of AI chats. Generative AI startups are increasingly exploring advertising-based business models to offset the high costs of answering users' questions with artificial intelligence. For example, before inviting users to ask follow-up questions, iAsk shows ads below its AI-generated responses. In addition to Google, startups such as Koah Labs have begun allowing brands to serve ads to the chatbot audience. AI search startup Perplexity, one of the most prominent players using AI to reshape internet services, establishes relationships directly with brands that want to buy ads on the site, according to a person familiar with the matter. Perplexity allows brands to sponsor follow-up questions to users' queries. To keep its product accessible for students, a key audience, Liner has focused on delivering a select number of ads tailored to users' searches. People using generative AI tend to click on fewer links – which may pose challenges for startups seeking to make money through online advertising, where brands often pay per click. But Liner users, who come to the site for research, often click on links, and the longer queries allow for more targeted advertising, Chief Executive Officer Luke Jinu Kim said in an interview. With its advertising, Liner is trying to achieve something that is "more like a very early version of Google search ads,' Kim said, adding that he hopes the site will show a small number of ads that are highly related to the query. – Bloomberg
Bloomberg
30-04-2025
- Business
- Bloomberg
Google Places Ads Inside Chatbot Conversations With AI Startups
Google's ad network has begun showing advertising within the flow of conversations with chatbots — part of Alphabet Inc.'s efforts to keep its edge in digital advertising as generative artificial intelligence takes off. Earlier this year, the Google AdSense network, which traditionally displays ads in search results and in the margins of websites, has expanded to include conversations with chatbots operated by AI startups. Google made the move after conducting tests last year and earlier this year with a handful of startups, including AI search apps iAsk and Liner, according to people familiar with the matter who asked not to be identified discussing private information.
Arabian Post
24-04-2025
- Science
- Arabian Post
Liner Edges Ahead in AI-Powered Research Battle
Deep research, once the domain of academics, analysts, and professionals poring over databases and archives, is rapidly being transformed by artificial intelligence. Tools like Liner, ChatGPT, and Perplexity have redefined what it means to explore a subject in depth. These platforms promise not only to automate research but to enhance it—consolidating data, extracting patterns, and offering structured, referenced summaries that would normally take hours or days to compile. Yet despite their shared aim, each platform brings distinct strengths and limitations to the table. The core idea behind these platforms is to go beyond mere data retrieval. Deep research tools are expected to contextualize information, synthesize insights, and present arguments in a way that aligns with academic and professional standards. This isn't simply about answering a question—it's about understanding why the answer matters, how it was derived, and whether the sources used are reliable. The user, whether a student, a journalist, or a corporate strategist, depends on clarity, speed, accuracy, and trustworthiness. That's where the divergence begins. Testing three complex questions across all platforms illuminated major differences. The first and most noticeable contrast appeared in response times. Liner consistently delivered results in under two minutes, even when faced with multi-layered prompts involving statistics, case studies, and longitudinal data. ChatGPT, operating under its GPT-4.5 framework, was considerably slower—taking more than 15 minutes in some instances. This delay is likely linked to the tool's attempt to provide more nuanced, human-like responses, but in environments where time is critical, the tradeoff becomes an obstacle. Perplexity struck a middle ground, balancing speed and detail more effectively, although it occasionally lagged when prompted with nested or ambiguous queries. Beyond speed, the second point of divergence lies in reliability and citation integrity. When examining the accuracy of each tool using a recognized metric—OpenAI's SimpleQA benchmark—Liner scored 95.3, a clear lead over ChatGPT's 62.5. Perplexity landed just behind Liner at 93.9, demonstrating strong parity in understanding direct and fact-based inquiries. This gap in performance indicates that while ChatGPT excels in conversational coherence, it sometimes falters in delivering pinpoint accuracy when stakes are academic or legal in nature. Its preference for blog content or Wikipedia citations occasionally undermines its utility in rigorous settings. Liner's edge here stems from its source prioritization and integration with curated databases. Instead of pulling from a broad and often inconsistent web, Liner tends to lean on academic journals, verified industry reports, and governmental datasets. This makes it particularly useful in fields where citations must hold up to scrutiny, such as policy research or financial forecasting. While Perplexity also provides references, they vary in quality and are not always traceable to original documents. Liner, by contrast, typically includes clickable source chains and detailed metadata, providing transparency and accountability—features that are often dealbreakers for serious researchers. Usability and readability form the third pillar of differentiation. Each tool attempts to simplify the research output for end users by segmenting answers, linking references, and offering suggested follow-ups. Liner distinguishes itself again by providing visual aids—charts, graphs, and interactive tables—particularly in economics and business contexts. This collaboration with Tako, an analytics visualization partner, allows users to digest dense datasets at a glance, something neither ChatGPT nor Perplexity currently matches at scale. Even when dealing with qualitative questions—those that rely less on data and more on discourse—Liner's structure-oriented response style creates a noticeable user experience advantage. ChatGPT, while fluid and often more conversational, sometimes meanders in tone or includes speculative commentary unless tightly constrained. Perplexity, though more focused, can produce rigid or formulaic responses that lack the natural flow needed to synthesize subjective or interdisciplinary topics. Where the comparison becomes nuanced is in the balance between human-like interaction and structured output. ChatGPT remains unparalleled in mimicking human dialogue and crafting responses that feel personalized. For journalists or creative professionals exploring themes or ideating around a topic, this natural tone can be a creative asset. But when precision and academic rigor are non-negotiable, this stylistic flexibility becomes a potential pitfall. The platform may inadvertently introduce interpretative bias or dilute its own claims by relying on lower-grade citations. Conversely, Liner's format is ideal for those looking to plug results directly into a report, brief, or paper. Its ability to extract and format source content into bullet-pointed frameworks, annotated visuals, and context-aware overviews ensures that users spend less time editing and formatting the results. This doesn't mean it is flawless—there are occasional formatting glitches, especially when integrating tables with textual outputs—but its design remains more conducive to professional and academic use. Perplexity often appeals to users looking for a blend between the two extremes. Its UI is cleaner than ChatGPT's, its results more modular than Liner's, and its focus on conciseness ensures that the information presented doesn't overwhelm. However, its major drawback lies in source depth and specificity. While it is commendable in general web research, its limitations become visible when tasked with field-specific exploration such as advanced medical literature, case law, or geo-political analysis. It provides a well-packaged generalist overview but rarely dives deep enough to stand on its own in a footnoted academic context. Another area where Liner stands apart is its responsiveness to iterative refinement. Users can tweak their prompts, narrow the scope of queries, or expand on specific angles without restarting the entire session. It remembers context more effectively and allows for branching exploration—something ChatGPT only handles within limited session memory and Perplexity struggles with unless queries are restated clearly each time. From a user experience standpoint, aesthetics and interface design also play a subtle but important role. Liner's dashboard is intentionally minimalist, with collapsible citation panels and customizable output formatting. ChatGPT leans into its chat-style layout, which, while user-friendly, lacks scalability for research-heavy tasks. Perplexity's search-focused interface mimics traditional search engines, which can be comforting for first-time users but feels limiting over extended research workflows. Price is another factor that could sway users, especially students or freelancers. ChatGPT operates on a freemium model, where advanced capabilities require a subscription. Liner also uses a tiered approach, with most of its deep research functionality behind a paywall. Perplexity currently offers more free access but with noticeable tradeoffs in output complexity and customization.
