Canvas in AI Mode is designed to help users organize and plan projects or delve into deeper research. The feature now supports the ability to draft documents or create custom tools within Google Search. Users can describe an idea to Canvas and watch as it generates the code to transform that idea into a shareable app or game.
For the last two decades, the internet has operated on a fairly predictable exchange: businesses create content to capture attention, and search engines reward that volume with traffic. The goal was almost always discovery. If you could just get enough people to land on your blog post or landing page, a certain percentage would inevitably buy what you were selling. This incentivized a specific kind of marketing that was loud, promotional and focused on getting the click at all costs.
AI is no longer just influencing search results. It is replacing the website as the first - and often only - customer touchpoint. Platforms like ChatGPT, Perplexity and Google's AI experiences now synthesize answers instead of returning ranked links. As discovery shifts from clicks to conversations, visibility becomes probabilistic, zero-click and increasingly detached from traditional SEO metrics - forcing a new set of risks and responsibilities onto the C-suite.
AI search builds on the same signals that support traditional SEO, but adds additional layers, especially in satisfying intent. Many LLMs rely on data grounded in the Bing index or other search indexes, and they evaluate not only how content is indexed but how clearly each page satisfies the intent behind a query. When several pages repeat the same information, those intent signals become harder for AI systems to interpret, reducing the likelihood that the correct version will be selected or summarized.
I tend to use different search engines for different purposes. For example, when I want better privacy, I use DuckDuckGo. When shopping, I might use Amazon, and if I need AI, I might opt for Perplexity. In other words, multiple configured search engines are necessary. For most browsers, you configure individual search engines. Some browsers even allow you to configure a search engine such that it'll be used by first typing a keyword in the address bar (such as duck for DuckDuckGo).
It's increasingly important for publishers to understand when, how and why their content is being used as a source inside AI tools and platforms (often without attribution or traffic). The search landscape has changed exponentially this year, as more people turn to AI tools and platforms like ChatGPT to get information. And though referral traffic from AI platforms is still minuscule, many publishers are starting to track where they are cited in AI-generated responses to users' queries.
Unlike earlier search engines that primarily matched strings of text, AI search engines interpret the meaning behind the query, allowing for more accurate and relevant results. Examples of AI search engines include Google's recent AI integrations , Microsoft Bing's AI enhancements, and specialized platforms employing AI to tailor search results based on user behavior and preferences. These engines dynamically learn and improve their algorithms to respond more intelligently over time, a capability traditional search lacks.
There is a persistent myth of objectivity around AI, perhaps because people assume that once the systems are deployed, they can function without any human intervention. In reality, developers constantly tweak and refine algorithms with subjective decisions about which results are more relevant or appropriate. Moreover, the immense corpus of data that machine learning models train on can also be polluted.
It's never been easier to ditch Google search. Just ask Mohamed Mura, a 37-year-old professional based in London, who began pulling back from the search engine during the pandemic. Instead of typing into Google, he turned to TikTok for questions like "how to change a watch band." With the launch of ChatGPT in 2022, Mura's Google usage dropped further. He said the AI chatbot felt like a "second brain or agent" he could bounce ideas off of.
Adobe Inc. is nearing a deal to buy the marketing software company Semrush Holdings Inc. for $1.9 billion, the Wall Street Journal reported, a takeover that would mark its first since the failed $20 billion acquisition of Figma Inc. in 2022. Adobe is set to pay $12 a share for Semrush, which had a market value of a little over $1 billion as of Tuesday, the Journal reported, citing people familiar with the matter. Semrush shares surged 71% in premarket trading to $11.55.
Apparently, the new it product for agencies is to offer tools and services that help brands understand how they appear in "zero-click" AI-driven search. These tools are hard to resist. What brand doesn't want to know what LLMs are saying about it? For example, many brands who signed up for one such tool, called Lorelight, were looking to improve how they rank and are described by AI search engines. "They basically wanted a secret hack that would suddenly allow them to rank in ChatGPT,"
