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AI Search Cites Random YouTubers More Than Your Brand. Here’s How to Build a Strategy For the AI Search Era

August 14, 2026 No Comments

New data shows AI assistants cite independent YouTube creators more than brand-owned content. Founders who adjust their creator briefs now will get pulled into the answers that drive buying decisions.

YouTube creator content now appears in more than 25 percent of prompts answered by AI assistants, according to new research from Jellyfish shared exclusively with Adweek. In high-intent categories like consumer electronics and financial services, nearly one in two references come from Youtube.

The Jellyfish team analysed 27 million responses across seven AI search assistants and found that independent, niche creators consistently outrank brand-owned content and celebrity influencers in AI-generated answers.

For founders, that means the customer journey is being rerouted through creators they may have never considered partnering with. While Reddit used to be the belle of the AI search ball, YouTube has overtaken it. Here are five things founders can change about their creator strategy to get pulled into AI search results.

Look beyond celebrity influencers

Jellyfish analysed responses across Claude, ChatGPT, Gemini, DeepSeek, Meta AI and Perplexity. Smaller creators consistently came out on top, especially those with high niche authority as opposed to more generalized creators.

An OtterlyAI study of more than 100 million citations found that roughly 41 percent of cited YouTube videos had fewer than 1,000 views and 36 percent had fewer than 15 likes. The median cited channel had posted fewer than 41 total videos.

That means a micro-creator with 2,000 subscribers and a well-structured tutorial can earn more AI citations than a high-profile partnership. AI systems prioritize reference value over popularity metrics, according to OtterlyAI’s analysis.

Prioritize long-form video over short-form

Jellyfish’s data favoured videos longer than 10 minutes. OtterlyAI found AI platforms barely cited YouTube Shorts at all outside Google’s own surfaces, with 94 percent of cited YouTube videos qualifying as long-form.

That contradicts the standard creator brief at most startups, which still asks for 30-second cuts optimized for TikTok and Reels. To surface your brand in AI search assistants, fund a structured 12-minute walkthrough rather than a punchy clip.

Insist on chapters and timestamps

Only 31 percent of cited YouTube videos have chapter structure, according to OtterlyAI’s research. That gap represents an opportunity competitors haven’t closed.

Chapters turn one video into multiple citable units. When a creator adds timestamps for “unboxing,” “setup,” “first impressions” and “comparison,” each section becomes its own potential AI citation.

Make this a line item in your creator brief. Pay for the editing time required to get the structure right.

Prioritize topical authority over follower count

A detailed YouTube review from a recognized creator now carries more citation weight than a Reddit thread with mixed opinions, according to a Superlines analysis. AI systems read subscriber counts, channel history and stated credentials as authority signals.

The right questions to ask when vetting creators are whether they consistently cover the category and whether their channel has a clear topical focus. A creator who posts consistently about makeup for olive skintones is a better bet than a generalist with five times the audience for AI search.

Track AI citations as an influencer success metric

AI search traffic converts at 14.2 percent, compared with 2.8 percent for traditional Google search, according to data cited in PikaSEO’s analysis. That conversion gap is too significant to leave unmeasured.

Adding creator-sourced AI citations as a tracked metric, alongside backlinks and branded search volume, gives founders a clearer picture of which partnerships are working. Most creators aren’t well versed in the intricacies of SEO, let alone answer engine optimization (AEO) and generative engine optimization (GEO), so you’ll likely need to coach on keywords and structure to maximize generative search capabilities.

AI assistants are sending searchers to video creators, and the brands those creators reference will end up with the sale. Founders who adjust their creator strategy now will get cited while their category competitors catch up.

Feature image credit: Getty Images

BY ASHLEY COUTO

Sourced from Inc.

In News

Smart Glasses Are Here. Can You Identify Their Hidden Cameras?

August 13, 2026 No Comments

By Jason Chun

Ready or not, the smart glasses future is upon us.

Even if you’ve never seen a pair of smart glasses before, you’ve probably heard about the wearable tech. Upcoming devices like Snap Specs and XReal Aura aren’t much thicker than a normal pair of glasses, but they can perform many of the same functions as a computer. This week, Meta announced new, cheaper glasses, including a frame designed in collaboration with Kylie Jenner.

But is society ready for the repercussions of everyone walking around with cameras on their faces?

Last month, a woman in London was approached by a man wearing smart glasses, who recorded their interaction without her knowledge. The video was uploaded to social media without her consent — it got 40,000 views, and the man refused to take it down unless she paid him.

This is just one of many events that highlight the privacy concerns of smart glasses.

I told a friend about Meta Ray-Bans, which look like a chunkier pair of normal black Ray-Ban Wayfarers. The average person probably wouldn’t suspect that their frames contain a hidden camera. My friend found the concept repulsive.

“Ew,” she said. “Why do those exist?”

I’ve never owned a pair of smart glasses, but I’ve seen them in the wild on two occasions. Once was when I was riding the New York subway and noticed a person sitting across from me wearing the frames.

The other time was when I struck up a conversation with a guy at a bar. It took a minute in the dimly lit room, but then I recognized the tell tale signs of his smart glasses.

I was unsettled. For a moment, I felt as if I were encountering an urban creature, like a rat or raccoon, and I didn’t know how to behave.

“Act natural,” I told myself. He wasn’t recording me (I’m pretty certain). But I knew that he could be.

Smart glasses and privacy problems

Much of the general public still doesn’t know anything about smart glasses, and that’s a major problem.

Some smart glasses wearers are exploiting the ignorance by harassing strangers and filming their reactions. Many of their victims are homeless people, service workers and women.

These glasses aren’t a niche product, either. Meta sold 7 million pairs of smart glasses in 2025. For a relatively low price (they start at $300), “manfluencers” and other content creators can buy a pair of Meta Ray-Bans and use them to record unwitting subjects.

Smart glasses can be used to survey people participating in protests or secretly record people in restrooms and other public places. The privacy problem will only get worse if companies add facial recognition features to their smart glasses — and Meta is reportedly planning to do just that.

It may not always be possible to stop someone from filming you in public without your consent. But you can make it harder for this new generation of “glassholes” to film you in secret. The first step is knowing how to identify the technology.

What do smart glasses look like?

A pair of eyeglasses with a heavy black frame, sitting on a translucent pedestal — Snap’s Specs on display at Augmented World Expo 2026 in Long Beach, California. Scott Stein/CNET

Not all smart glasses look alike, and not all models have cameras. The Even Realities G2 glasses contain only microphones and screens, while plug-in display glasses like Xreal‘s and TCL‘s connect to other devices and function as wearable monitors. The Viture Beast contains a camera, but it’s meant for limited augmented reality experiences.

Google’s Intelligent Eyewear is coming later this year, as are the $2,200 Snap Specs. Apple is rumoured to be debuting a pair of smart glasses next year. For now, the vast majority of camera glasses currently available are produced by Meta.

The easiest way to identify a pair is by locating the indicator light — a small LED bulb that turns on when the wearer is taking a picture or video.

According to CNET editor and wearable tech expert Scott Stein, “Each pair of smart glasses has its own type of indicator. And many smart glasses do different things. We don’t have a clear mental map of what to look for. That’s a big part of the problem.”

CNET's Scott Stein smiling while wearing Ray-Ban Meta Gen 2 glasses. — CNET’s Scott Stein sporting the second-gen Meta Ray-Bans. Relative to the wearer, the camera is in the upper left corner of the frames, while the indicator LED is in the upper right. Joanna Desmond-Stein/CNET

Meta Ray-Bans have been around since 2021. (They launched under the name Ray-Ban Stories.) A slimmer second-generation model was introduced in 2023.

The latest iteration includes a small screen built into one of the lenses, though from most angles, this feature is only visible to the wearer.

All Meta Ray-Ban models have relatively thick plastic frames with a camera lens in the upper left corner (or the upper right if you’re facing the wearer). On the opposite corner is the LED light, which automatically turns on when the wearer is filming. It lights up when a photo is taken and pulses during video recording.

To take a photo or record a video, the user presses the capture button on the right arm of the glasses (near the LED light). The user can also use voice commands: “Hey Meta, take a photo” or “Hey Meta, take a video.”

Meta also produces glasses in partnership with Oakley. The HSTN model looks like a rounded version of the Ray-Ban frames, with the camera and LED in the same location. But the Vanguard model looks more like wraparound goggles than glasses, and its camera and LED are found in the centre of the nose bridge.

CNET’s Vanessa Hand Orellana wearing the Oakley Meta Vanguard glasses. The camera sits above her nose, and the LED light is just above the camera. Vanessa Hand Orellana/CNET

In addition to the indicator LED, you might get an audio cue: a shutter snap sound when a picture is taken. However, both of these cues are relatively subtle.

Even if you’re aware of smart glasses indicators, you might not know for sure if you’re being filmed. Outside in direct sunlight, it’s virtually impossible to detect when the recording light is on.

Smart glasses owners can also cover up the LED with a sticker or modify the frame to disable the light altogether (though they aren’t supposed to do this). And Amazon sells some pairs of glasses with a pinhole camera, which seem tailor-made for creeps to secretly record people.

The smart glasses future is already here

Smart glasses are a relatively new technology with plenty of potential. They can be useful for visually impaired people. They allow artists, woodworkers, chefs and other creators to capture footage while their hands are occupied.

But they can also be dangerous.

However, few current laws regulate smart glasses and deter abusers. But as devices become more common, social norms will develop and guide their use, just as social norms developed for phone recording.

By being able to recognize smart glasses in public, you’re reducing the chances that pranksters and bad actors will exploit you. You’re helping to shape this emergent technology, to define what it can — and can’t — do.

A woman in a bright pink jacket stands outside wearing the Oakley Meta Vanguard AI sunglasses. — CNET’s Scott Stein sporting the second-gen Meta Ray-Bans. Relative to the wearer, the camera is in the upper left corner of the frames, while the indicator LED is in the upper right. Joanna Desmond-Stein/CNET

Meta Ray-Bans have been around since 2021. (They launched under the name Ray-Ban Stories.) A slimmer second-generation model was introduced in 2023.

The latest iteration includes a small screen built into one of the lenses, though from most angles, this feature is only visible to the wearer.

All Meta Ray-Ban models have relatively thick plastic frames with a camera lens in the upper left corner (or the upper right if you’re facing the wearer). On the opposite corner is the LED light, which automatically turns on when the wearer is filming. It lights up when a photo is taken and pulses during video recording.

To take a photo or record a video, the user presses the capture button on the right arm of the glasses (near the LED light). The user can also use voice commands: “Hey Meta, take a photo” or “Hey Meta, take a video.”

Meta also produces glasses in partnership with Oakley. The HSTN model looks like a rounded version of the Ray-Ban frames, with the camera and LED in the same location. But the Vanguard model looks more like wraparound goggles than glasses, and its camera and LED are found in the centre of the nose bridge.

CNET’s Vanessa Hand Orellana wearing the Oakley Meta Vanguard glasses. The camera sits above her nose, and the LED light is just above the camera. Vanessa Hand Orellana/CNET

In addition to the indicator LED, you might get an audio cue: a shutter snap sound when a picture is taken. However, both of these cues are relatively subtle.

Even if you’re aware of smart glasses indicators, you might not know for sure if you’re being filmed. Outside in direct sunlight, it’s virtually impossible to detect when the recording light is on.

Smart glasses owners can also cover up the LED with a sticker or modify the frame to disable the light altogether (though they aren’t supposed to do this). And Amazon sells some pairs of glasses with a pinhole camera, which seem tailor-made for creeps to secretly record people.

The smart glasses future is already here

Smart glasses are a relatively new technology with plenty of potential. They can be useful for visually impaired people. They allow artists, woodworkers, chefs and other creators to capture footage while their hands are occupied.

But they can also be dangerous.

However, few current laws regulate smart glasses and deter abusers. But as devices become more common, social norms will develop and guide their use, just as social norms developed for phone recording.

By being able to recognize smart glasses in public, you’re reducing the chances that pranksters and bad actors will exploit you. You’re helping to shape this emergent technology, to define what it can — and can’t — do.

Feature image credit: Bloomberg/Getty Images

By Jason Chun

Jason Chun is a CNET writer covering a range of topics in tech, home, wellness, finance and streaming services. He is passionate about language and technology, and has been an avid writer/reader of science fiction for most of his life. He holds a BA from UC Santa Barbara and an MFA from The New School. See full bio

Sourced from CNET

In News

Zero Coding Skills Needed to Build Powerful AI Workflows : Complete Guide 2026

August 12, 2026 No Comments

By Julian Horsey

Claude Code offers a unique way for non-coders to automate tasks and manage workflows without needing programming expertise. Simon Scrapes explains how this system goes beyond simple chat interactions by acting as an execution layer for complex processes. One standout feature is the use of a `claude.md` file, which allows users to define rules, preferences and contextual details to improve accuracy and consistency. For example, when automating overview generation, specifying date ranges, formatting and data sources in the file ensures reliable outputs. This structured approach makes Claude Code accessible and effective for both personal and professional tasks.

In this explainer, you’ll gain insight into practical applications of Claude Code, from creating reusable “skill systems” to integrating external platforms using the Multi-Connection Protocol (MCP). Discover how features like Auto Mode and advanced commands such as `/goal` and `/loop` streamline repetitive processes while maintaining user control. Additionally, learn strategies for managing memory effectively and addressing limitations like context degradation. Whether you’re looking to automate simple tasks or design scalable workflows, this guide provides actionable steps to help you make the most of Claude Code.

Features of Claude Code

TL;DR Key Takeaways :

Claude Code is a user-friendly automation tool that enables non-technical users to streamline workflows, integrate tools and execute commands efficiently through a terminal or desktop app.
Its functionality relies on precise prompts and a customizable `claude.md` file, which acts as a blueprint for consistent and accurate task execution.
Key features include “skills” and “skill systems” for modular task automation, Multi-Connection Protocol (MCP) for seamless tool integration and advanced commands like /effort and /goal for handling complex workflows.
Automation tools such as /loop and “hooks” reduce manual effort, while memory management practices help maintain performance and prevent context degradation.
To maximize efficiency, users should adopt a scalable mindset, focus on reusable workflows and use external tools to address limitations like memory recall and context accuracy.

Claude Code simplifies automation and tool integration, allowing you to perform tasks such as creating files, managing spreadsheets and connecting to CRMs. Unlike traditional chatbots, it operates through a terminal or a user-friendly desktop app, offering a robust platform for executing commands. Its ability to integrate with external tools and execute actions directly from a chat-like interface distinguishes it from other automation tools.

By using its intuitive design, you can streamline workflows and reduce manual effort. For example, you can automate repetitive tasks like data entry or overview generation, saving time and minimizing errors. This makes Claude Code a practical solution for both personal and professional use.

How Prompts and Context Work

The effectiveness of Claude Code relies heavily on crafting precise and detailed prompts. Context plays a crucial role in making sure accurate outputs and the tool allows you to define rules, preferences and business-specific information in a `claude.md` file. This file acts as a blueprint, making sure consistent performance and reducing the likelihood of errors.

For instance, if you’re automating a overview generation process, including specific parameters such as date ranges, formatting preferences and data sources in your prompt will yield more accurate results. By maintaining a well-organized `claude.md` file, you can enhance the tool’s reliability and efficiency.

Watch this video on YouTube.

Advance your skills in Claude Code by reading more of our detailed content.

Permissions and Automation Control

Claude Code prioritizes user control and safety by requiring approval before executing actions. This default setting ensures that you remain in charge of the automation process. For repetitive tasks, you can enable Auto Mode, which minimizes the need for constant approvals while still flagging potentially risky actions.

This balance between automation and oversight makes Claude Code suitable for a wide range of workflows, from simple task automation to complex, multi-step processes. The ability to toggle between manual and automated modes allows you to adapt the tool to your specific needs, making sure both efficiency and security.

Understanding Skills and Skill Systems

Claude Code introduces the concept of “skills,” which are pre-written instructions designed to perform specific tasks. These skills can be combined into “skill systems,” allowing you to chain multiple skills together to create end-to-end automated workflows.

For example, you can design a skill system that extracts data from emails, updates a CRM and generates a summary overview, all in one seamless process. This modular approach enables you to build scalable and reusable workflows, making it easier to manage complex tasks.

Tool Integration with Multi-Connection Protocol (MCP)

The Multi-Connection Protocol (MCP) is a standout feature of Claude Code, allowing seamless integration with external platforms such as Google Drive, Notion and CRMs. These connections remain active during sessions, allowing for real-time data exchange and streamlined operations.

However, it’s important to monitor memory usage when working with multiple integrations, as active connections can impact performance. By managing these integrations effectively, you can maximize the tool’s capabilities without compromising efficiency.

Managing Memory Effectively

Claude Code uses auto memory to store user preferences and project details, enhancing usability and personalization. However, managing large data sets can be challenging. To optimize memory recall and ensure smooth operations:

Keep your `claude.md` file concise and focused on essential details.
Use external memory tools for storing extensive information.

These practices help reduce the risk of context degradation, often referred to as “context rot,” and ensure that the tool performs consistently across different workflows.

Advanced Functionalities

Claude Code offers advanced commands and features designed to handle complex tasks with precision:

/effort: Adjusts reasoning levels to tackle intricate workflows effectively.
Ultra Code: Breaks tasks into sub-agents, each responsible for specific aspects of a process. Sub-agents operate independently, improving task accuracy and preventing context pollution.

These advanced functionalities make Claude Code a robust solution for dynamic and multi-layered workflows, allowing you to address complex challenges with ease.

Automation Features

Several automation tools enhance the efficiency of Claude Code, allowing you to focus on higher-value activities:

/goal: Ensures tasks meet predefined completion criteria, improving accuracy and consistency.
/loop: Automates recurring tasks on a schedule, reducing manual intervention.
Hooks: Scripts designed for repetitive tasks, minimizing reliance on AI tokens and improving efficiency.

These features simplify repetitive processes, freeing up your time for more strategic tasks.

Tips for Maximizing Efficiency

To make the most of Claude Code, consider these practical tips:

Use screenshots for visual problem-solving when text descriptions are insufficient.
Avoid correcting broken outputs; instead, restart tasks with clearer instructions to achieve better results.
Keep setups portable to avoid vendor lock-in and maintain flexibility in your workflows.

By following these strategies, you can enhance the tool’s effectiveness and adaptability, making sure consistent performance across various use cases.

Addressing Limitations

While Claude Code is a powerful tool, it does have limitations. Memory recall and context degradation can pose challenges, particularly for extensive projects. To mitigate these issues:

Use semantic recall to improve memory functionality and maintain context accuracy.
Incorporate open source tools to enhance long-term memory reliability and scalability.

These improvements can make Claude Code more dependable, especially when managing complex or long-term workflows.

Adopting a Scalable Mindset

Using Claude Code effectively requires a shift in mindset. Treat the tool as a modular system, focusing on creating reusable configurations and workflows. Instead of correcting errors, restart tasks with improved setups to ensure consistent results. This approach enhances scalability and allows you to adapt to growing workflow complexities with ease.

By adopting this mindset, you can maximize the potential of Claude Code, transforming it into a reliable and versatile tool for automation and task execution.

Media Credit: Simon Scrapes

By Julian Horsey

Sourced from Geeky Gadgets

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In News

Google Rewrote The Rules Of Search, And Many Marketers Haven’t Noticed

August 12, 2026 No Comments

By John Readman

Google announced a shift in how search works after more than 25 years of relative stability. It confirms what agencies like us have been telling clients for months: that Google is now an AI engine. From a familiar search box to AI Mode, the way online searches happen has changed.

What Exactly Has Changed?

AI Mode and AI Overviews have been rolled further into Google Search, shifting the experience from a list of links to a synthesized response layer built on top of traditional search results.

The most visible change is, of course, the search box. Relatively unchanged for decades, Google now prompts users directly into new AI-led features. Instead of just autocomplete suggestions, users are now offered routes into Gemini models, image and document uploads, and conversational prompts in a way that mirrors ChatGPT. Not only that, but Google now actively encourages long-form conversational queries rather than short keyword-based searches. The search engine is adapting to user behaviour in a way it never has before.

Other Shifts You May Have Missed

Alongside the update to search, Google also shared insights into how people in the U.S. are using AI Mode. Voice and image searches now account for more than one in six searches overall, with image searches growing over 40% month over month. The shift to more conversational search also has become evident, with the average AI Mode search being three times as long as a traditional search query. Perhaps most interesting is the reported 80% increase in “queries related to planning” over the last six months.

In other words, many people aren’t using AI Mode to answer a question; they’re using it to achieve an outcome. They’re asking real questions that they might have asked a person before. These are queries that may have multiple stages and multiple constraints requiring consideration. Instead of asking questions about a holiday destination (e.g., “best time to visit Paris,” “best hotels in Paris,” “best restaurants near Pigalle”), people are using AI Mode to plan entire trips, with queries that look something like this: “I have a $2,000 budget, I’m free in July or August, I love art, plan me a week visiting Paris.”

What Do These Changes Mean For Brands?

The entire user journey can now take place within a search engine. A 2024 study by SparkToro found that zero-click searches accounted for 58.5% of all U.S. Google searches. Google’s search revolution means that number is probably going to get much, much higher.

The shift is designed to make users’ lives more convenient, but it could make yours a lot harder. As AI agents bring more interactions away from your site and centralize them within search engines, the typical ways we track and analyze user data have to change. We no longer can rely on website sessions, UTM codes or clean user click journeys, making your current attribution model largely obsolete.

What Should You Do About It?

While Google’ overhaul represents a new phase in how we view user journeys, build our brand presence online and measure success, the fundamentals of search are still the same. Google still values good quality brands that understand their customers, and are trusted by others—it has simply developed more sophisticated signals that can meet the needs of evolving user expectations and behaviours.

Here are some ways to start adapting:

Unify Organic, Paid And AI Search Strategies

Stop treating organic, paid and AI search as separate channel conversations. Visibility is shifting from channel silos to a unified system. AI synthesizes information from multiple sources, so public relations, SEO, paid media and content all feed the same underlying entity signal.

Stop Focusing On Rankings, And Optimize Your Brand Entity

Visibility is shifting from pages to entities, so your brand, products, leaders and locations need consistent signals across the web. AI doesn’t just pick one ranking URL; it pulls and synthesizes information from across sources to fully understand your brand.

Stop Separating AI And GEO; Embed Them

There is no separate strategy for AI search. Answer engine optimization (AEO) and generative engine optimization (GEO) are essentially just SEO applied to an AI surface. Google still rewards helpful, structured and authoritative content, but now puts more emphasis on clarity and extractability.

Start Measuring Causal Outcomes, Not Clicks

AI search and zero-click environments mean users can be influenced without ever visiting your site. Click-through rates and sessions alone don’t show the full picture anymore. Instead, focus on things like brand lift, incremental conversions, geo experiments and assisted conversions.

Ensure You’re Present Throughout The Conversation

Search is becoming a planning and decision-making interface. Users are no longer just asking questions; they are trying to solve entire problems inside search. So you can’t just answer isolated queries with blog content. You need to support decisions by making sure your brand appears in comparison, constraint-based and recommendation-driven queries.

As Google has adapted to keep users within its platform, you can no longer rely on a single page or ranking. You need to optimize your online footprint so your brand is present throughout complex planning journeys—not just answering one question, but being consistently surfaced across follow-up prompts, comparisons and refinement stages.

This update goes way beyond technical changes; it’s behavioural. Search is moving from a system that helps users find information to one that helps them make decisions. The question is no longer just “How do I rank?” It’s “How do I stay present while decisions are being made?” It’s time we all started taking a lot more notice.

Feature image credit: Getty

By John Readman

Find John Readman on LinkedIn. Visit John’s websit

COUNCIL POST | Membership (fee-based)

John Readman is the CEO of ASK BOSCO, an AI-powered marketing platform that brings all your marketing and e-commerce data into one place. Read John Readman’s full executive profile here.

Sourced from Forbes

In News

Apple’s Mail app is about to get one of the most requested improvements with iOS 27

August 11, 2026 No Comments

By Daisy Edwards

Life could be a lot easier for anyone with an overflowing inbox because Apple’s Mail app is about to get one of the most requested improvements with iOS 27.

While Apple unveiled plenty of headline-grabbing features at WWDC, one smaller change has caught the attention of long time iPhone users.

The update focuses on fixing a frustrating issue that has plagued the Mail app for years.

And if it works as intended, finding old emails could soon become much less of a headache.

iOS 27 is bringing a major improvement to Apple’s Mail app

Apple’s Mail app tech has long been criticized for its search function, with users often struggling to locate specific emails, attachments, or conversations buried deep in their inboxes.

Now, Apple is preparing to address that problem with iOS 27.

Apparently, the company is introducing a revamped Mail search experience that aims to deliver more relevant and accurate results when users search for messages.

Apple's Mail app is about to get one of the most requested improvements with iOS 27 — Unsplash

The update is designed to better understand what people are actually looking for and prioritize the most useful emails at the top of the results.

While it might not sound as exciting as Apple’s latest AI tools or visual redesigns, it tackles a real-world problem that affects millions of iPhone owners every day.

For users who rely on Mail for work, travel bookings, receipts, and personal communication, a more effective search function could save a significant amount of time and stop that panic when you’re at a ticket turnstile.

The tech giant is stopping the turnstile panic

The Mail app improvement arrives as part of a wider collection of updates coming to iPhones with iOS 27.

Apple showcased numerous new features during WWDC, including enhancements to Siri, system-wide intelligence features, and updates across several core apps.

But sometimes the most appreciated upgrades are the ones that simply make existing tools work better.

Anyone who has spent several minutes scrolling through years of emails trying to find a single confirmation message or attachment will likely welcome the change.

If Apple’s new search system delivers on its promise, it could transform one of the most frustrating aspects of using the Mail app.

The software is currently available in beta testing before a wider public release later this year, giving users something practical to look forward to alongside the platform’s bigger headline features.

By Daisy Edwards

Sourced from TECH SB

In News

ChatGPT drops to 52.7% as Claude triples its AI traffic share

August 11, 2026 No Comments

By Luis Rijo

Similarweb’s June 2026 update on generative AI website traffic share reveals a market that has shifted more sharply in twelve months than most forecasts expected – with ChatGPT surrendering nearly a quarter of its worldwide dominance while Anthropic’s Claude posted the biggest month-over-month percentage point gain among the tracked platforms.

ChatGPT’s 12-month slide: from 76.4% to 52.7%

Twelve months ago, according to Similarweb data published on LinkedIn on June 11, 2026, ChatGPT held 76.4% of worldwide generative AI website traffic. By May 26, 2026 – the measurement date for the June update – that figure had fallen to 52.7%. The arithmetic is blunt: OpenAI’s flagship product lost 23.7 percentage points in a single year, roughly two points per month.

The decline was not linear. Six months ago ChatGPT still held 65.2%, meaning the platform shed 11.2 additional percentage points in the second half of that window – a faster pace of erosion than the first six months. Three months ago it stood at 56.7%, and one month ago at 52.7%, suggesting the rate of decline may be stabilising somewhat, though the overall trend remains downward.

PPC Land has tracked this trajectory closely since mid-2025, documenting how ChatGPT fell below 65% for the first time in January 2026, a milestone that at the time appeared significant. The May 26 data now places the platform’s share more than 12 points below that level.

Gemini’s ascent: 8.9% to 27.3%

Google’s Gemini platform is the clearest beneficiary of ChatGPT’s retreat. Twelve months ago, according to Similarweb, Gemini accounted for 8.9% of generative AI website traffic. By the one-month-ago snapshot it had reached 27.3% – an increase of 18.4 percentage points over the period.

The trajectory, month by month, is striking. Six months ago Gemini held 20.3%. Three months ago it reached 25.5%. One month ago it stood at 27.3%. Each data point represents an acceleration that compounds the competitive pressure on OpenAI. In proportional terms, Gemini’s traffic share more than tripled over twelve months.

The questions this data raises for the marketing and media buying community are not trivial. As PPC Land noted when Gemini passed 22% in January 2026, the platform’s growth creates direct implications for GEO and AEO strategies – the optimization disciplines that govern whether brand content surfaces inside AI-generated answers. A platform commanding more than a quarter of global AI traffic demands serious attention from anyone managing organic or paid visibility.

One methodological question circulated in the comments under Similarweb’s LinkedIn post: whether Gemini’s traffic figures incorporate Google’s AI Mode and AI Overviews surfaces, or whether they represent only visits to the Gemini chat application specifically. Similarweb described the dataset as covering generative AI website traffic worldwide, which implies direct visits to AI platform domains rather than AI-generated answer surfaces embedded within search results pages. The distinction matters because AI Mode usage, if included, would substantially inflate Gemini’s count. If excluded, the 27.3% figure represents Gemini’s standalone chat product – and makes the growth even more remarkable.

Claude’s 2.9-point jump: the fastest riser in the latest period

The most commented-upon finding in Similarweb’s June update was not the top two platforms but the third-place mover: Anthropic’s Claude. One month ago Claude held 6.0% of global generative AI traffic. By May 26, it had reached 8.9% – a gain of 2.9 percentage points in a single month.

Darren Goldstein, a Senior Growth Marketing Manager, noted in comments on the Similarweb LinkedIn post that Claude had become the fastest riser in the latest period, gaining 2.9 percentage points ahead of Gemini’s 1.8-point increase over the same interval. Twelve months ago Claude held just 1.6%. At 8.9% today, the platform has multiplied its traffic share more than fivefold over the year.

This trajectory aligns with independent measurement from other sources. Comscore’s March 2026 Consumer AI Chatbot Usage Rankings, reported by PPC Land on May 26, documented Claude expanding 130.1% from February to March 2026, reaching 2.66 million U.S. desktop unique visitors. That figure moved Claude firmly into fourth place among the platforms Comscore tracked – though it is worth noting that Comscore measures U.S. desktop unique visitors while Similarweb’s traffic share data covers worldwide all-device website traffic. The two methodologies are not directly comparable, but both point in the same direction.

The pace of Claude’s growth carries implications beyond brand sentiment. Platforms that attract a rapidly growing share of AI-mediated queries become more relevant surfaces for GEO optimization – the discipline of improving the likelihood that AI systems will cite a brand or retrieve content when generating answers. As PPC Land has covered since at least July 2025, Similarweb itself has built product infrastructure specifically to help marketers track their visibility and referral traffic across AI platforms including Claude.

DeepSeek, Grok, Copilot, and Perplexity

The remaining four platforms tracked in Similarweb’s update show a more complex picture, with some gaining and others losing ground over the period.

DeepSeek held 5.3% twelve months ago and sits at 4.0% in the most recent data – after reaching a brief high of 3.8% six months back, the platform has recovered to 4.0%, though the twelve-month comparison shows a modest net loss. The Chinese-developed model attracted significant Western attention in early 2025, and the data suggests a sustained but somewhat diminished presence.

Grok, developed by xAI, stood at 2.8% twelve months ago, reached 3.8% six months ago alongside DeepSeek, pulled back to 3.7% three months ago, and now sits at 2.8% – returning to where it started a year ago. PPC Land documented Grok overtaking DeepSeek for the first time in January 2026, when xAI’s platform reached 3.5% against DeepSeek’s 3.3%. That advantage appears to have compressed by May.

Microsoft Copilot has moved in a narrow band: 1.9% twelve months ago, 1.8% six months ago, 2.0% three months ago, and 2.0% one month ago. The platform’s stability is notable given Microsoft’s extensive distribution advantages through Windows and Office integrations. Copilot’s inability to meaningfully expand its share despite those distribution levers suggests the product has not yet found a usage pattern that converts installed base into consistent AI platform visits.

Perplexity ran at 1.8% twelve months ago and has drifted slightly lower, reaching 1.3% in the most recent reading. The platform has consistently generated attention for its citation-heavy search approach, and PPC Land reported in mid-2025 that Perplexity processed 780 million queries monthly. Its declining traffic share relative to rivals suggests that query volume and website traffic share do not always move together – a gap likely explained by heavy API and mobile usage not fully captured by website-level tracking.

A comment posted by Ofir Halfon, Global Partnerships and GTM leader, under Similarweb’s LinkedIn post identifies an important limit of the dataset: “What this data doesn’t show is intent quality by platform. Traffic share tells you where people go, it doesn’t tell you what they’re asking, how deep into the purchase journey they are, or which platform is driving the most commercial outcomes. Someone asking ‘what are the best running shoes’ is very different from someone asking ‘compare On Cloudmonster and New Balance 1080’. Same category, completely different commercial value.”

This is a relevant caveat for anyone interpreting the numbers through a media buying or conversion lens. Traffic share is a reach metric. It says nothing about the query types, the commercial intent, the session depth, or the downstream conversion rates associated with each platform. Research covered by PPC Land in November 2025 found that AI traffic converts at three times higher rates than traditional channels across more than 1,200 publisher and news websites. But that finding is an aggregate; it does not disaggregate by platform, and the mix of commercial versus informational queries likely varies significantly across ChatGPT, Gemini, Claude, and Perplexity.

The decentralisation argument

A separate comment from Chuks Wesley, a content strategist, makes a structural observation: “The traffic share is becoming more decentralised over time.” The data supports this reading arithmetically. Twelve months ago, ChatGPT held 76.4% alone – a position approaching the kind of monopoly concentration that characterises early-phase platform markets. Today, the top three platforms collectively account for roughly 89% of tracked traffic, with that share distributed across ChatGPT at 52.7%, Gemini at 27.3%, and Claude at 8.9%. The remaining 11% is split across four platforms.

Decentralisation of this kind creates specific operational challenges for marketers who have built AI visibility strategies around a single platform. Similarweb’s own product development has reflected this reality, with the company’s GenAI Intelligence Toolkit – launched in July 2025 – designed to track brand visibility and referral traffic across multiple AI surfaces simultaneously rather than optimising for any single platform. As PPC Land reported in November 2025, Similarweb research found that citation sets change 50% monthly and that only 11% of citations overlap between major AI platforms – a finding that makes multi-platform tracking operationally necessary rather than optional.

Context: 12-month benchmarks and what shifted

The twelve-month progression captured in Similarweb’s data maps onto a period of significant product releases and adoption events. ChatGPT’s share stood at 76.4% in June 2025, around the time when PPC Land first began tracking AI traffic conversion advantages. By January 2026, the platform had fallen to 65.2% in six-month terms. Google’s release of Gemini updates and expansion of AI integration into its search products accelerated Gemini’s uptake through the latter months of 2025 and into 2026. Claude’s growth curve steepened sharply through early 2026, which Anthropic’s product expansion – including new API capabilities and enterprise integrations – likely contributed to.

The broader context for all these numbers is one that PPC Land’s reporting on Datos’ Q1 2026 State of Search reportmakes plain: AI tools collectively account for less than 2% of total desktop web visits in both the US and Europe. The traffic share figures Similarweb publishes measure the distribution of traffic among AI platforms themselves – not AI platforms’ share of the total web. Both dimensions matter to marketers, but they answer different questions. The first shows which AI platform is winning the race for AI-mediated queries. The second shows how large a role AI-mediated queries play in total digital traffic overall.

Why this matters for the marketing community

For media buyers, agencies, and brand marketers, the shift documented in Similarweb’s June update has practical consequences across at least three areas.

First, GEO strategy – the optimization of brand content to appear in AI-generated answers – now needs to weight platforms differently than it did twelve months ago. Claude’s move from 1.6% to 8.9% means it is no longer a secondary concern. Gemini’s 27.3% share makes it the second most important surface for visibility optimization, ahead of all other platforms by a considerable margin.

Second, attribution and measurement require updating. As PPC Land covered in August 2025, AI platforms are expanding beyond standalone chat interfaces into browser integrations and agentic workflows. Website traffic data from standalone domain visits may increasingly undercount actual AI-mediated touchpoints as these expansions mature.

Third, the question of commercial intent by platform – raised in the LinkedIn comment thread by Ofir Halfon – remains largely unanswered by traffic share data alone. Advertisers and publishers evaluating which AI surfaces deserve investment need richer signal than visit share alone. That gap represents both a measurement challenge and a product opportunity for analytics providers.

The Similarweb June update, based on data through May 26, 2026, covers worldwide all-device traffic to generative AI platform websites. It reflects twelve months of measurement across seven named platforms – ChatGPT, Gemini, DeepSeek, Grok, Claude, Perplexity, and Copilot – plus an “Other” category. The data was published on LinkedIn by Similarweb on June 11, 2026.

Timeline

June 2025 – ChatGPT holds 76.4% of global generative AI website traffic; Gemini at 8.9%; Claude at 1.6%, according to Similarweb.
July 28, 2025 – Similarweb launches GenAI Intelligence Toolkit combining AI brand visibility tracking with traffic measurement as AI platforms generate 1.1 billion referral visits in June 2025.
November 6, 2025 – Research shows AI traffic converts at 3x higher rates than traditional channels across more than 1,200 publisher and news websites.
November 12, 2025 – Similarweb publishes AI citation analysis framework showing citation sets change 50% monthly and only 11% overlap across major AI platforms.
December 2025 – ChatGPT falls to 66.8%; Gemini climbs to 19.5%, according to Similarweb.
December 22, 2025 – Similarweb releases November 2025 data showing ChatGPT maintains two-thirds U.S. market share despite a 35% traffic decline.
January 16, 2026 – ChatGPT drops to 64.6% as Gemini surges to 22%; Grok overtakes DeepSeek for the first time in Similarweb tracking.
January 2026 – Six-month snapshot in Similarweb data: ChatGPT at 65.2%, Gemini at 20.3%, DeepSeek and Grok both at 3.8%, Claude at 2.0%.
February 15, 2026 – Similarweb launches AI Studio for enterprise customers following beta expansion.
March 2026 – Three-month snapshot: ChatGPT at 56.7%, Gemini at 25.5%, Claude at 6.0%, Grok at 3.7%, DeepSeek at 3.4%.
April 27, 2026 – Datos Q1 2026 State of Search report shows AI tools collectively account for less than 2% of total desktop web visits in the US and Europe.
May 2026 – One-month snapshot: ChatGPT at 52.7%, Gemini at 27.3%, Claude at 8.9%, DeepSeek at 4.0%, Grok at 2.8%, Copilot at 2.0%, Perplexity at 1.3%.
May 18, 2026 – Comscore March 2026 AI Chatbot Rankings show Claude growing 130.1% month-over-month to 2.66 million U.S. desktop unique visitors.
May 26, 2026 – Similarweb publishes June update capturing twelve months of generative AI traffic share data through May 26, 2026; Claude records fastest monthly gain among tracked platforms.
June 11, 2026 – Similarweb posts the June update on LinkedIn, with full twelve-month comparison across ChatGPT, Gemini, DeepSeek, Grok, Claude, Perplexity, and Copilot, worldwide all-traffic.

Summary

Who: Similarweb, the web analytics platform with 91,644 LinkedIn followers, released the data. The platforms tracked are ChatGPT (OpenAI), Gemini (Google), DeepSeek, Grok (xAI), Claude (Anthropic), Perplexity, and Microsoft Copilot.

What: A twelve-month comparison of worldwide generative AI website traffic share, as of May 26, 2026, shows ChatGPT declining from 76.4% to 52.7%, Gemini rising from 8.9% to 27.3%, and Claude tripling from 1.6% to 8.9% – with Claude posting the largest month-over-month gain in the most recent period at 2.9 percentage points.

When: The data covers the twelve months to May 26, 2026. Similarweb published the June update on LinkedIn on June 11, 2026.

Where: The dataset covers all worldwide traffic to generative AI platform websites, across all device types.

Why: The data matters because it documents a structural shift in which AI platform users are choosing for their queries – with direct consequences for GEO, AEO, brand visibility optimization, and any media buying strategy that treats AI platforms as a discovery or conversion surface.

By Luis Rijo

Sourced from PPC Land

In News

Nothing’s Carl Pei declares war on Apple, ‘one bored iPhone user’ at a time

August 10, 2026 No Comments

By Adamya Sharma

Nothing’s Apple challenge is ambitious, confident, and a little hard to watch.

TL;DR

Nothing founder Carl Pei has posted a new video directly challenging Apple.
Pei says he’ll steal Apple’s customers “one bored iPhone user at a time.”
The video continues Pei’s long-running habit of positioning Nothing as the anti-Apple brand.

Nothing founder Carl Pei has posted yet another cheeky video taking aim at Apple, and this one might be his most self-affirming effort yet.

In a video posted to Instagram, Pei looks directly into the camera and declares, “This is a message to Apple. My name is Carl. I make phones in London. I’m gonna steal your customers. One bored iPhone user at a time.”

The message appears to be aimed at users who feel smartphones have become uninspiring and repetitive. Unfortunately, the video comes across as far more dramatic than the message probably needed to be.

This isn’t a new strategy for Nothing and Pei. Ever since founding the company, Pei has positioned it as an alternative to what he sees as an increasingly boring smartphone industry. He has repeatedly argued that Apple has lost its creative edge and no longer delivers the kind of innovation that once inspired him. In previous interviews, he’s criticized Apple’s direction while pitching Nothing as the brand for users who want something different from the phones their parents own.

More recently, Pei has leaned heavily into the idea that Nothing can challenge Apple and Samsung by winning over younger users. He believes Gen Z is the key demographic that could help Nothing break into a market dominated by the two smartphone giants.

The funny thing is that Nothing is actually doing decently well without the theatrics.

According to Counterpoint Research, Nothing’s shipments in India jumped 146% year over year in Q2 2025, making it one of the country’s fastest-growing smartphone brands. The company also posted 32% growth in Q4 2025, while its CMF sub-brand was the fastest-growing smartphone sub-brand in India.

But growth percentages only tell part of the story. Despite its impressive momentum, Nothing remains a relatively small player in the global smartphone market, which is dominated by Apple, Samsung, Xiaomi, OPPO, vivo, and others.

That said, Nothing has been making steady moves to grow its business in recent months. The company recently announced plans to significantly expand its retail presence in the US through Best Buy, giving its products far greater visibility in a market where breaking Apple’s and Samsung’s dominance is most difficult.

That’s a more meaningful step toward winning over iPhone users than posting a direct challenge to Apple on Instagram. So yes, Carl Pei and Nothing may very well win over a few bored iPhone users. Whether that’s enough to make Apple lose sleep is another question entirely.

By Adamya Sharma

Managing Editor, Adamya Sharma is a tech journalist with over 15 years of experience, currently leading the News Desk at Android Authority. She covers Android news, shares insights, and helps uncover the stories that matter.

Sourced from Android Authority

In News

Eight ways to stay discoverable when search, social and AI stop sending people your way

August 7, 2026 No Comments

By Katy Cowan

The free routes that used to bring creatives their next commission are closing one by one. It’s been happening for some time. And now AI is pretty much destroying the web as we know it. Here’s how to keep being found – and, increasingly, recommended – without a marketing budget.

For many happy years, getting found was something any creative freelancer or studio could do on the strength of work alone. A website that ranked for your craft. A post that travelled far and wide and carried your name back to your profile. A share that turned a random person into a client. None of it really cost anything except for a bit of time and effort. And all of it was the pipeline to paid work.

Oh, how things change. Organic search has fallen off a cliff. Social media rewards quantity over quality. And the latest? Conversational, agentic AI is now pulling all the shots.

And with today’s big news that Pinterest is shifting away from traditional search, it’s clear that none of us can out-optimise this on our own. The good news is that the shift rewards a handful of things independent creatives can still build: a direct audience, a distinctive name, and a reputation the new systems can read and recognise as important. Exhausted? Yes, so are we. But here’s where to put your energy.

1. Build an audience you actually own

I’ve been saying it for over a decade – since Meta ruined Facebook for publishers in 2016 – build your own audience. That’s because every follower you have is rented. Platforms like Instagram and Pinterest get to decide who sees your work, and the goalposts can change overnight.

An email list is the one audience you get to control. It’s a list of people who chose you, and you can reach them directly, whenever you want. So start a simple newsletter, even a short monthly one, and make signing up the clearest call to action on your website. (Our guide to creating a newsletter people actually want is a good place to begin.) A few hundred people who want to hear from you is worth more than ten thousand followers a feed decides to throttle, and it’s how plenty of creatives are winning work after quitting social media altogether.

2. Become a “category of one”

Don’t roll your eyes, but AI discovery rewards a “good enough” match to a brief, which flattens everyone into interchangeable options. The defence is to be “uninterchangeable”. So sharpen the one thing you do that nobody else does in quite the same way. That could be a material, a subject, a voice. You want to be known by name rather than retrieved by attribute. The creatives who survive this current massive shift are the ones a client specifically asks for, because a specific name is the one thing an algorithm can’t substitute.

3. Get cited, named and talked about

As someone who worked in PR for two decades, this next tip warms my heart. Because I know public relations is having a golden hour moment. That’s because if you still want to be found, recommendation engines lean on signals they can read: who’s mentioned in other people’s work, who turns up on lists, in interviews, in the press, and in collaborations.

It means that being talked about is more valuable than being optimised. Forget staying in your comfort zone. Say yes to the guest piece, the podcast… even the scary panel on stage that you’ve been avoiding. Pitch yourself for round-ups in your niche. Every place your name appears alongside your craft is a breadcrumb the systems – and the people – can follow back to you.

4. Make your site easy for a machine to understand

If an AI is going to represent you, it has to be able to read you correctly. That means not blocking AI crawlers on your site. And it means spelling out, in plain language, what you do, who you do it for and what makes your work yours. Don’t just have images and a one-word ‘work’ tab. Name your projects, your clients and your disciplines in actual words. Add alt text to every image. You’re not gaming anything here; you’re making sure that when a system describes you, it gets you right rather than second-guessing.

5. Show the process and the person

A generative answer can summarise a style, but it can’t reproduce the human genius behind it. That’s your advantage. In which case, share the process: the sketches, the dead ends, the “why” behind every decision, and the story of a commission. Content about process, along with a real point of view, builds the kind of trust that turns a browsing visitor into a client, and it’s exactly the material an AI can’t copy from pages that already exist.

6. Make referrals easy to give

Word of mouth is, and always will be, the most powerful marketing channel. And it’s still how most independents get their best work. Don’t leave it to chance. Tell happy clients you’d welcome an introduction. Keep a tidy one-line description of what you do that someone can paste into a message without having to think. Stay in touch with people you’ve worked with so you’re the name that comes up when they’re asked, “Do you know anyone good who can…?”

7. Turn up in real rooms

“In real life” is back, baby. Events, meet-ups, talks and communities – they’re all back on the table after those weird post-pandemic years. Like word of mouth, networking is hugely powerful. A conversation at a portfolio night or a face remembered from a meet-up leads to commissions that no number of impressions or page views can match. You don’t need a stage, but turning up consistently where your peers and potential clients gather will eventually deliver results.

Just remember to enjoy yourself and make friends, first and foremost. Don’t go in with the hard sell, as there is nothing more off-putting.

8. Don’t rent your whole presence from one place

Remember the old saying, Don’t put your eggs in one basket? It applies here. If a single platform is your entire shop window, one change to its rules can harm you enormously. Spread your presence across things you own and things you earn – your website and newsletter list, plus a couple of channels and the press and communities you show up in. The aim isn’t to be everywhere all at once; it’s to make sure no one switch, algorithm tweak or new AI layer can take away every route to your door at once.

In summary

None of the above is a quick fix. But don’t panic. The creatives who stay discoverable through this enormous revolution won’t be the ones who chased the algorithm the hardest; they’ll be the ones who built something that’s truly their own… a name worth knowing, a direct line to the people who love your work, and a great reputation that travels on its own accord.

By Katy Cowan

Sourced from CREATIVE BOOM

In News

Why AI-generated ads are becoming indistinguishable

August 7, 2026 No Comments

By Sophie Rhone

AI speeds up advertising, but without strategy it risks making brands forgettable

For small businesses, AI has made advertising easier to produce but much harder to differentiate.

What should have been a useful shortcut is quickly becoming a creative trap. As more brands rely on the same tools to write copy, shape campaigns and generate ideas, too much of the output is starting to feel interchangeable.

The ads may look polished enough to publish, but polish is not the same as impact, and efficiency is not the same as originality.

That matters because most ads are not competing in a vacuum. They are fighting for attention in crowded feeds, against endless lookalike content, in front of audiences who have become highly skilled at filtering out anything that feels generic.

When brands use AI without a clear point of view, they do not just risk making weaker creative. They risk making work that disappears on contact.

The problem is not AI, it’s the way many businesses are using it.

Patterns & performance

Most generative AI tools are built on patterns. They are good at producing what is probable, what is familiar and what already resembles successful marketing. That can help with speed, but it also creates sameness.

Similar phrasing, similar structure, similar claims, similar tone. After a while, entire categories begin to sound like they were written by the same person for the same audience, regardless of who is actually selling the product.

For brands trying to grow, that is a serious commercial issue.

When advertising starts to blend in, performance usually follows. Ads that feel vague or formulaic tend to attract less curiosity, fewer clicks and weaker engagement. Businesses then spend more trying to force results from creative that never had enough edge in the first place.

The waste is not always obvious at first, which is partly why the problem is spreading. A campaign can still be technically competent while quietly underperforming where it counts.

Over time, that gap compounds. Budgets get allocated based on surface-level performance rather than true effectiveness, and teams double down on what feels safe instead of what actually works.

The result is more output, more spend, and very little movement in terms of brand recognition or recall.

For SMEs, the stakes are even higher.

Solving the wrong problems

Big brands can sometimes get away with forgettable advertising because they already have reach, recognition and budget on their side. Smaller businesses do not. They depend far more heavily on clarity, distinctiveness and trust.

Their marketing has to do more with less, which means the brand itself needs to be sharper, not flatter. If AI strips out the character, specificity or conviction that made a business memorable in the first place, it starts eroding one of the few real advantages that smaller brands have.

That is the irony in all this. Many businesses are using AI to save time and strengthen output, only to end up producing ads that weaken the very thing they are trying to build.

Solving the wrong problem

The issue usually starts long before anything goes live. Too many marketers are asking AI to solve the wrong problem. They are using it to generate finished ads before they have properly defined what the brand wants to say, who it needs to resonate with, or why anyone should care. When the strategic thinking is thin, AI does not improve it. It simply accelerates it.

That is why so much AI-assisted advertising feels hollow. It fills space nicely, but it rarely lands with force. It often says the sort of thing a brand should say, in the sort of tone a marketer expects, without ever arriving at something sharp enough to be remembered.

There is also a growing risk around brand dilution. When multiple teams, agencies or founders rely on similar prompts and tools, the outputs begin to converge. Without strong internal direction, even well-intentioned campaigns can start to blur together, weakening long-term brand equity in ways that are difficult to reverse.

Taking a clearer position

The brands getting this right are taking a more disciplined approach. They are not handing the whole process over to a tool and hoping for the best. They are using AI to support execution while keeping the core thinking firmly human. That means using it to test routes, speed up production and explore variations, but not to define the message.

They are also investing more time upfront. Clear positioning, sharper audience insight and stronger creative direction are doing the heavy lifting, with AI acting as an amplifier rather than a substitute. That shift alone is often the difference between content that performs and content that fades.

Good advertising has always depended on knowing what makes a business distinct and then expressing it clearly. That has not changed. If anything, it matters more now. In a market flooded with passable content, originality has become more valuable, not less.

AI can help marketers move faster, but speed only matters if you are moving in the right direction.

We’ve listed the best email marketing platforms.

Feature image credit: Getty Images

By Sophie Rhone

SEO & Digital PR search specialist at Cupid PR.

Sourced from techradar.pro

In News

Why Local SEO Metrics No Longer Explain Customer Acquisition

August 6, 2026 No Comments

By Mayur S

For years, local businesses have relied on visibility metrics to measure marketing performance. Rankings, reviews, profile views, and website traffic became the standard indicators of success because they appeared to correlate with customer acquisition.

As a result, many business owners came to believe that higher rankings automatically meant more business.

After reviewing businesses across roofing, plumbing, HVAC, electrical services, and legal services, I noticed a consistent pattern. Many had strong rankings, growing reviews, and healthy Google Business Profiles, yet phone calls were not increasing at the same pace. Some were receiving fewer inquiries than they had several years earlier despite improved visibility.

But the more businesses I looked at, the less those explanations made sense. Instead, I started to wonder whether the real problem was that businesses were still measuring success using a model that no longer reflects how Google works today.

The Original Customer Acquisition Model

For years, the formula was simple. Rank higher on Google, get seen by more people, and generate more leads. In many cases, that’s exactly what happened.

Businesses focused on local SEO because improvements in visibility often translated directly into more calls and customer inquiries.

Back then, most customers followed a similar path.

Search → Map Pack → Business Profile → Website → Phone Call

Since businesses could see this journey happening, higher rankings usually translated into more website visits, more calls, and more leads. That’s why most marketing reports focus heavily on visibility metrics.

The challenge is that the way people use Google today is very different from the way they used it when most local SEO reporting models were developed.

Google’s Incentives Have Changed

Local businesses want one thing: more customers. Whether that means more calls, more appointments, or more revenue, the goal is the same. Google’s goal, however, is not exactly the same as the business owner’s.

Google’s goal is simple: help users complete tasks as quickly as possible. The fewer clicks required, the better the experience. That’s why Google increasingly provides enough information directly in search results for users to make decisions immediately.

Over time, Google has made it easier for users to complete tasks without leaving its ecosystem. We’ve already seen this happen with hotels, flights, restaurant reservations, and shopping. More decisions are happening directly inside Google’s ecosystem, and local services appear to be following the same trend.

The Rise of Action-First Search Results

Today, someone searching for a plumber may never visit a company’s website at all. They can see reviews, business hours, service details, credentials, and a call button directly on Google. In many markets, Local Service Ads appear at the very top of the page and provide enough information for a customer to choose a business and make a call immediately.

As a result, the customer journey often looks much simpler:

Search → Local Service Ad → Phone Call

The website is no longer a required step in the process. Yet many businesses still measure success using models that were built when websites played a central role in generating leads. That’s why some companies struggle to understand where their customers are actually coming from.

The Attribution Problem

Here’s a question I often ask business owners:

“Do you know exactly where your phone calls come from?”

Most know how many calls they received.

Very few know where those calls originated.

That’s because many reporting systems combine everything together. Calls from Google Business Profiles, Local Service Ads, websites, and other sources often end up in the same report.

The result is that businesses see the final number but miss the story behind it.

For example, consider a hypothetical plumbing company. Total call volume increases from 460 to 500 over three years. On paper, that looks like growth.

However, the source of those calls changed dramatically. Google Business Profile calls fell by more than 40%, while Local Service Ads became the dominant source of demand.**

The lesson isn’t that calls increased. It’s that customer behaviour has changed. Without source-level attribution, the business would likely continue focusing on rankings and visibility metrics without realizing how customers were actually finding them.

Google Quietly Created Two Customer Acquisition Systems

I think one mistake many businesses make is assuming they’re competing in a single Google system.

In reality, there seem to be two different systems at work.

The first is traditional local SEO. This is where factors like proximity, relevance, reviews, business profile optimization, and website content help determine visibility in local search results.

The second is Local Service Ads.

LSAs appear to care about a different set of signals, such as how quickly you respond to leads, whether calls are answered, your availability, booking activity, review growth, and the quality of customer interactions.

A business can rank extremely well in local search but struggle to generate leads from LSAs. At the same time, another business with average local rankings can generate a large number of calls through Local Service Ads.

If you treat both systems the same way, you may end up optimizing for the wrong things.

Visibility No Longer Guarantees Demand

To be clear, I’m not saying rankings don’t matter.

Visibility is still important.

If people can’t find your business, they can’t contact you.

But rankings only tell part of the story.

Many businesses assume that better rankings automatically lead to better results. That’s not always true anymore. A company can dominate local search results and still struggle to generate calls if it’s focused on the wrong channel.

The businesses seeing the best results today are usually the ones paying attention to customer behaviour, not just rankings.

The larger issue is that many businesses are still measuring local search using assumptions that were formed when websites sat at the centre of the customer journey. As Google reduces the distance between discovery and action, those assumptions become less reliable.

The Future of Local Search Measurement

As Google makes it easier for people to take action directly from search results, knowing where your leads come from will become more important than ever.

Businesses will need to separate calls by source to understand what’s actually driving growth.

This doesn’t mean local SEO is going away. Strong rankings, positive reviews, and an optimized Google Business Profile will continue to matter.

What’s changing is how those factors turn into customers.

The businesses that succeed over the next few years won’t just be the ones with the best rankings. They’ll be the ones who understand where their customers are coming from and which channels are generating real revenue.

By Mayur S

Sourced from Hackernoon

editor

New data shows AI assistants cite independent YouTube creators more than brand-owned content. Founders who adjust their creator briefs now will get pulled into the answers that drive buying decisions.

Look beyond celebrity influencers

Prioritize long-form video over short-form

Insist on chapters and timestamps

Prioritize topical authority over follower count

Track AI citations as an influencer success metric

BY ASHLEY COUTO

Sourced from Inc.

Ready or not, the smart glasses future is upon us.

Smart glasses and privacy problems

What do smart glasses look like?

The smart glasses future is already here

By Jason Chun

Sourced from CNET

Features of Claude Code

How Prompts and Context Work

Permissions and Automation Control

Understanding Skills and Skill Systems

Tool Integration with Multi-Connection Protocol (MCP)

Managing Memory Effectively

Advanced Functionalities

Automation Features

Tips for Maximizing Efficiency

Addressing Limitations

Adopting a Scalable Mindset

By Julian Horsey

Sourced from Geeky Gadgets

​What Exactly Has Changed?

Other Shifts You May Have Missed

​What Do These Changes Mean For Brands?

​What Should You Do About It?

Unify Organic, Paid And AI Search Strategies

Stop Focusing On Rankings, And Optimize Your Brand Entity

Stop Separating AI And GEO; Embed Them

Start Measuring Causal Outcomes, Not Clicks

Ensure You’re Present Throughout The Conversation

By John Readman

Sourced from Forbes

iOS 27 is bringing a major improvement to Apple’s Mail app

The tech giant is stopping the turnstile panic

By Daisy Edwards

Sourced from TECH SB

ChatGPT’s 12-month slide: from 76.4% to 52.7%

Gemini’s ascent: 8.9% to 27.3%

Claude’s 2.9-point jump: the fastest riser in the latest period

DeepSeek, Grok, Copilot, and Perplexity

What traffic share does not measure

The decentralisation argument

Context: 12-month benchmarks and what shifted

Why this matters for the marketing community

Timeline

Summary

By Luis Rijo

Sourced from PPC Land

Nothing’s Apple challenge is ambitious, confident, and a little hard to watch.

By Adamya Sharma

Sourced from Android Authority

The free routes that used to bring creatives their next commission are closing one by one. It’s been happening for some time. And now AI is pretty much destroying the web as we know it. Here’s how to keep being found – and, increasingly, recommended – without a marketing budget.

1. Build an audience you actually own

2. Become a “category of one”

3. Get cited, named and talked about

4. Make your site easy for a machine to understand

5. Show the process and the person

6. Make referrals easy to give

7. Turn up in real rooms

8. Don’t rent your whole presence from one place

In summary

By Katy Cowan

Sourced from CREATIVE BOOM

AI speeds up advertising, but without strategy it risks making brands forgettable

Patterns & performance

Solving the wrong problems

Solving the wrong problem

Taking a clearer position

By Sophie Rhone

Sourced from techradar.pro

The Original Customer Acquisition Model

Google’s Incentives Have Changed

The Rise of Action-First Search Results

What Exactly Has Changed?

What Do These Changes Mean For Brands?

What Should You Do About It?