editor, Author at Mediastreet

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Amazon and Nike Are Winning Gen Alpha: The Bigger Story Is Who Actually Controls the Purchase

September 28, 2026 No Comments

Gen Alpha is often discussed as if brands can reach the generation by finding the right social platform, creator, or short-form video format.

The household data tells a more complicated story.

Amazon and Nike currently sit at the top among brands in U.S. households with Gen Alpha teenagers, according to early findings from the Gen Alpha Household Influence Study conducted by HarrisX and Allison Worldwide. McDonald’s ranks third with both teens and parents, while Walmart ranks fourth overall and performs particularly well with parents.

Those rankings are interesting, but they are not the most useful part of the research.

The bigger finding is that the purchase journey inside a Gen Alpha household does not move in one direction.

Parents shape what teenagers like. Teenagers shape what parents notice and buy. Different family members discover brands on different platforms. And the person creating demand is not necessarily the person completing the transaction.

For marketers, that changes the problem.

Gen Alpha Is Not Shopping Alone

There are roughly 67 million Gen Alpha children in the United States, including about 16 million between the ages of 13 and 16. Many of these teenagers are being raised by millennials, meaning both generations have grown up in environments where digital discovery, online shopping, social media, reviews, and algorithmic recommendations are normal parts of everyday buying.

But being digitally fluent does not make the teenager an independent buyer.

The study found that 80% of surveyed teens said their parents influence the brands they like.

The influence is even stronger in the opposite direction: 91% of surveyed parents said their teenagers influence the brands they like.

That makes the household less like a traditional parent-child purchasing hierarchy and more like a small recommendation network.

A teenager sees something on TikTok.

A parent checks the price.

Someone recognizes the brand from Amazon.

A sibling already owns something from it.

The purchase may happen at Walmart.

Trying to assign the entire conversion to the first platform in that chain misses much of what actually happened.

Attention and Purchasing Power Are Split

A lot of digital marketing is optimized around capturing the attention of one target customer and pushing that person toward conversion.

Gen Alpha households make that model harder to apply.

A teenager can create the demand without controlling the payment method.

That distinction becomes especially visible in beauty.

Nearly three-quarters of parent purchases involving Sephora and E.l.f. Cosmetics in the study were initiated by the teenager. Among the teenage girls surveyed, 60% said they would ask their parents for E.l.f. products. Teen girls were also more likely than boys to drive purchases made for them: 67% compared with 59%.

In situations like these, the teenager is doing work that marketers traditionally associated with the buyer.

They are discovering the product.

They may be choosing the specific brand.

They can introduce it to the household.

They can create the reason to purchase.

The parent may only enter later in the process.

That makes a simple question such as “Who is our customer?” less useful than it once was.

A better question may be: Who starts the purchase, who validates it, and who completes it?

Those can be three different people.

Mom Still Has More Influence Than TikTok

One of the more surprising findings has little to do with algorithms.

Among teenagers surveyed, 73% said their mothers influenced their brand preferences, compared with 58% who cited their fathers. According to the study, parental influence overall exceeded the influence attributed to social platforms, creators, or celebrities.

That does not mean social media has suddenly become unimportant.

It means discovery should not be confused with authority.

A creator might introduce a product. TikTok might create curiosity. YouTube might explain how something works.

A parent can still determine whether the product feels trustworthy, affordable, necessary, or worth buying.

This is particularly relevant for marketers who build an entire Gen Alpha strategy around youth-facing creative.

Getting the teenager interested may only solve the first part of the sale.

The parent still needs a reason not to say no.

There Is No Single Platform for the Gen Alpha Household

The media data makes the problem even less convenient.

Teen girls in the study leaned most heavily toward TikTok, at 42%, followed by Pinterest at 19%.

Teen boys were more likely to favour YouTube, at 35%.

YouTube also ranked first among dads, at 32%.

For moms, Facebook led at 24%.

A household can therefore contain several audiences occupying different parts of the internet at the same time.

That makes a “Gen Alpha platform strategy” a strange concept.

There may not be one.

A beauty product could first appear in front of a teenage girl on TikTok, become familiar through repeated exposure, get searched elsewhere by a parent, and eventually be purchased through a retailer that neither person originally used to discover it.

The media plan needs to survive that movement.

This is where marketers may need to think less about dominating one platform and more about making the brand recognizable when people encounter it again somewhere else.

The first impression gets attention.

The next impression may need to provide reassurance.

The final interaction needs to make purchasing easy.

Those jobs do not have to happen in the same feed.

Why Amazon and Nike Make Sense at the Top

Amazon and Nike occupying the leading positions is less surprising when viewed through this household lens.

Neither brand depends on one narrow cultural entry point.

Amazon is already deeply connected to how households search for, compare, order, replace, and discover products. A teen can want something without needing to create an entirely new purchasing behaviour for the parent.

Nike operates differently, but it also crosses generations.

It can exist simultaneously as sportswear, fashion, footwear, school clothing, identity, and a familiar household brand.

McDonald’s shows similar cross-generational strength, ranking third for both teenagers and parents in the study. Walmart, meanwhile, performs especially strongly with parents and ranks as the top brand among moms.

These brands are not simply attracting young consumers.

They already have infrastructure inside the household.

Recognition matters because Gen Alpha does not arrive at the purchase with a completely blank slate. Their parents already have years of experience with many of the same companies.

A brand can be new to the teenager while being old news to the person paying for it.

That familiarity can reduce friction.

Moms and Dads Do Not Play the Same Role Either

Treating “parents” as a single segment loses another part of the picture.

Walmart ranked highest with moms but third among dads. Wendy’s also performed particularly well with moms.

Among dads, Nike ranked first and McDonald’s second, with Adidas fourth and Samsung eighth.

There was another difference: 60% of dads in the survey said they introduced their teenagers to new brands, compared with 39% of moms.

So even within the parent side of the household, influence works differently.

One parent may be particularly important for purchase approval or routine household shopping.

Another may play a larger role in brand discovery.

The useful lesson is not that every campaign suddenly needs separate “mom ads” and “dad ads.” That would become mechanical very quickly.

It is that household roles should be investigated rather than assumed.

The Funnel Is Starting to Look More Like a Network

Traditional marketing funnels are convenient because they turn consumer behaviour into a sequence:

awareness, consideration, conversion.

Gen Alpha households do not necessarily behave that neatly.

Awareness might start with the teenager.

Consideration may move to a parent.

Then the parent encounters the brand independently.

The teenager brings it up again.

The family compares alternatives.

Another platform reinforces the choice.

Then somebody purchases it days later through a retailer.

Influence can travel backward and sideways.

For analytics teams, this creates an attribution problem. The last click can still be measured, but it may reveal surprisingly little about why the transaction occurred.

For brand teams, however, the situation is more interesting.

A company does not necessarily need to win every family member independently. It needs enough recognition, relevance, and trust to survive the handoff from one person to another.

What Marketers Should Take From the Data

The obvious response would be to add more channels.

That is probably too simple.

Running TikTok ads for teenagers, Facebook ads for moms, and YouTube ads for dads does not automatically create a household strategy. It can just create three disconnected campaigns.

The harder job is making those interactions support one another.

Youth-facing content can create curiosity without pretending teenagers have unlimited purchasing power.

Parent-facing information can answer practical concerns without feeling like an entirely different brand.

Retail availability can make it easy to act on demand that originated somewhere else.

And the message should remain recognizable enough that a parent understands which product their teenager is talking about when the brand moves from a screen into a real conversation.

That continuity may become more valuable as media consumption becomes more fragmented.

Gen Alpha Marketing May Really Be Household Marketing

The HarrisX and Allison Worldwide research was conducted from May 4 to May 12, 2026, using responses from 705 Gen Alpha teenagers aged 13 to 16 and 750 parents. Results were weighted across factors including age, region, gender, income, and race or ethnicity, with a reported margin of error of plus or minus 2.5%. The study is expected to continue quarterly.

It is one study, and brand rankings will change.

The underlying behaviour is more important.

Teenagers influence parents.

Parents influence teenagers.

Moms and dads do not necessarily play identical roles.

And those people are not consuming media in the same places.

For years, marketers have tried to understand younger consumers by asking which platform owns their attention.

Gen Alpha may force a different question.

Not where can we reach the teenager?

But how does an idea move through the household until someone finally buys it?

The brands that understand that movement may have a much better chance of becoming the Amazons, Nikes, and McDonald’s of Gen Alpha rather than simply becoming another brand that briefly appeared in their feed.

Source

Sara Karlovitch, “Amazon, Nike reign supreme with Gen Alpha teens: report,” Marketing Dive, published August 17, 2026. The article reports early findings from the HarrisX and Allison Worldwide Gen Alpha Household Influence Study.

By Zarrak

@zarrak

Digital Marketing & SEO Professional at Zarrak Designer

Digital Marketing & SEO Professional | Tech Content Contributor | Exploring AI, SaaS, Social Media, SEO, and the evolving digital landscape.

Sourced from HACKERNOON

In News

People Who Obviously Used ChatGPT Almost Always Use 14 Dead-Giveaway Phrases

September 28, 2026 No Comments

By Maria Cassano

Once upon a time, I was the editor-in-chief of a corrupt publication. I didn’t know it when I took the job, but within days of starting my new role, the secrets began pouring out: The product review articles were fake, the CEO didn’t care that his business practices were illegal, and the writers were all using ChatGPT.

In my opinion, large language models like ChatGPT are not inherently evil. AI can be an incredible tool for writers. I use it myself to brainstorm ideas, enhance clarity, streamline formatting, and polish my writing.

It’s also a sanity-saver when I can’t think of a particular word. (Pro-tip: Write your sentence, use an underscore as a placeholder, and ask ChatGPT to fill in the blank with the correct term.)

That said, there’s a huge difference between using AI as a supplement and abusing it to churn out content. The former boosts quality and efficiency. The latter insults the intelligence of your reader and causes your reputation to plummet.

The writers on my newly acquired team were using AI to generate entire articles, and it was painfully obvious. Their content was repetitive, boring, and poorly written, not to mention factually incorrect.

Because AI wants to answer your prompt but may not have the necessary data to do so, chatbots often produce “hallucinations,” or information that’s nonsensical and inaccurate. They may pull quotes, stats, or ideas out of thin air, and unless you fact-check your work, you’ll never know it. Misinformation was the first red flag, but AI-generated content also contains the same dead giveaway phrases over and over again.

People who obviously used ChatGPT almost always use these phrases:

“Treasure trove”
“Intricate tapestry”
“It’s important to note that”
“It’s essential to consider”
“While navigating the complexities of”
“A testament to”
“Furthermore”
“Consequently”
“In the world of”
“Let’s delve into”
“Look no further than”
“Whether you’re… or…”
“A plethora of”
“In conclusion”

What do most of these ChatGPT phrases have in common?

Ju Jae-young / Shutterstock

They’re low-effort transition phrases that don’t mean much of anything. They’re fluff. Placeholders. Wasted word count and bad writing. But why would ChatGPT spit out bad writing if language is supposed to be its specialty? Large language models like ChatGPT are primarily trained on publicly available text from the internet.

Yes, that includes some good writing — but for every high-quality piece, you’ll find hundreds of generic listicles, bad blog posts, spammy articles, keyword-stuffed nonsense, and amateur essays with no views.

In other words, ChatGPT learned how to write by studying content from novice writers.

Real writers use the above phrases, too, but that doesn’t mean they make for good writing

If you removed those phrases entirely, most of your sentences wouldn’t lose any meaning. But it would result in a clearer, more concise, and more confident article.

Beginners believe that fancy, wordy language impresses readers. Professionals know that writing is a form of communication, and communication should be as straightforward as possible.

A few of these phrases won’t make or break your work, but when most of them are present in the same article, it tells your editor two things: You used ChatGPT as a shortcut (not a springboard), and you have no interest in mastering your craft as a writer.

As for me? I got fired from that corrupt publication because I refused to let AI replace passionate, competent writers, and I wear that job termination like a badge of honor.

Feature image credit: theverest | Canva

By Maria Cassano

Maria Cassano is a writer, editor, and journalist whose work has appeared in HuffPost, NBC, Bustle, CNN, The Daily Beast, Food & Wine, and USA Today, among others. Her debut memoir (Numb, Party of One: A Memoir of Healing From Dissociation) is currently available for pre-order.

Sourced from Your tango

In News

Most Small Businesses Are Using AI. The Real Challenge Is Learning How to Use it Well

September 25, 2026 No Comments

By Grant Freeman, edited by Micah Zimmerman

Key Takeaways

AI is already becoming a must-have for small businesses, but knowing how to use it well matters more than simply using it.
AI can move fast and be incredibly useful, but human judgment, oversight and ongoing learning are still essential.

It wasn’t that long ago that AI felt out of reach to many small business owners. Flash forward to today and AI is helping local businesses manage customer communications, create social media campaigns, schedule appointments, monitor inventory, and so much more.

In fact, Thryv’s recent 2026 AI and Small Business Adoption Survey found:

AI adoption among SMBs increased from 55% in 2025 to 66% in 2026
70% report increased revenue
92% say AI saves time.

This data tells us that small business owners are embracing AI in large numbers and experiencing measurable benefits. But the survey surfaced a red flag: 70% of SMBs say they need more AI training.

It’s no longer really a question of whether small businesses are using AI. AI has quickly become table stakes. The bigger question now is whether business owners know how to use it effectively and, more importantly, how to use it in ways that actually make an impact on their business.

What’s interesting is that while seven in ten small business owners say they need more AI training, 86% say they’re already comfortable using AI. That suggests there’s an important gap between being comfortable with the technology and actually knowing how to use it well.

For many business owners, using AI might mean asking ChatGPT a question or generating a social media post. But there’s a lot more to using AI effectively. Understanding how to write better prompts, protect sensitive data, evaluate the quality of AI-generated content and integrate AI into existing workflows can make a significant difference in the results.

Marketing is a good example. A roofer can use AI to create a Google ad in a matter of seconds, but that doesn’t necessarily mean the ad will bring in more customers. If they don’t understand what matters to homeowners in their local market, what questions customers are asking or what drives local search performance for roofing businesses in their area, the final ad may sound polished while still missing the things that actually matter.

That’s the real opportunity (and challenge) with AI. It can help businesses move faster and get more done, but speed alone doesn’t guarantee better results. Without the right knowledge and oversight, it’s easy to trust AI too much and end up somewhere you never intended to go. The goal shouldn’t just be to use AI, but to understand how to use it strategically and make sure it’s working toward the outcomes that matter to the business.

Ad hoc AI education

When it comes to learning AI, most SMBs are figuring it out on their own:

YouTube and social media (57%)
Online resources and webinars (49%)
AI tools themselves (31%)

That DIY mindset fits the entrepreneurial spirit, but it can also lead to knowledge gaps.

For example, a florist might use AI to speed up customer responses, only to accidentally share incorrect promotion dates. The technology worked—the implementation didn’t.

The solution? Learn how to use AI effectively while putting the right safeguards in place. checks and balances.

Four ways small businesses can get smarter about AI

1. Start with a question specific to your business. Many businesses begin by asking an AI agent like ChatGPT, “How can I use ChatGPT to improve my business?” A better question: “What’s the most repetitive task in my business?”

A plumbing company admin can spend hours responding to after-hours inquiries. By implementing an AI-powered chat assistant trained on common service questions, the staffer arrives each morning with qualified leads already captured and ready to be followed up on.

Lesson: Start with a business problem and solve it with AI.

2. Block out an ‘AI Learning Hour’ every week on your calendar. AI is evolving far too quickly to treat training as a “one and done” event. Put some discipline around how you and your employees approach AI training. Experiment with AI on real business tasks, participate in team sharing sessions, attend webinars or tutorials, learn new prompting techniques and explore new AI features in existing software.

An accounting firm designates Friday mornings for employees to test one new AI use case each week. Over six months, the team identifies several automations that reduce administrative work and improve client responsiveness.

Lesson: Small, consistent learning creates major gains over time.

3. Adopt a “Trust but Verify” mindset. Treat your AI tool like a talented intern, not an experienced decision-maker.

Always check and verify customer communications, financial information, legal language, and business recommendations. This process saves time and maintains accuracy.

Lesson: Human oversight is a “need to have” not a “nice to have.”

4. Turn to Your Business Peers. Resources like social media influencers can be helpful, but some of the most valuable AI lessons can come from other business owners who are facing the same challenges. Connecting with industry associations, local business groups, Chamber of Commerce events, or online communities related to your profession can give you practical insights and real-world examples of what’s actually working.

For example, a partner at a boutique law firm might attend a local Bar Association event focused on AI. After hearing how other firms are successfully using AI, they realize their efforts could go beyond blogging. Instead, they decide to focus on automating client intake and summarizing documents, creating more value for both the firm and its clients.

Lesson: Learn from businesses solving problems like yours. Real-world, industry-specific use cases often provide more value than generic AI tips.

Where small business owners can get AI-educated

There are many good options for business owners looking to elevate their AI literacy. The OpenAI Academy has built a dedicated Small Business learning track, and Anthropic offers an AI Fluency Framework & Foundations curriculum. The U.S. Chamber of Commerce has launched a free practical AI education program aimed specifically at SMBs. Local community colleges are another avenue, with many rapidly expanding their AI education through continuing education programs and AI literacy workshops.

Small businesses don’t need to become AI engineers. They need to become AI-literate. The good news is that AI education is more accessible than ever. The businesses that make learning a priority today will be the ones capturing the greatest value tomorrow.

By Grant Freeman

Grant Freeman is President at Thryv, a global sales and marketing platform for small and medium-sized businesses. He ensures that Thryv’s innovative software and inspired customer teams create highly engaged and happy business owners.

Edited by Micah Zimmerman

Sourced from Entrepreneur

In News

The second coming of brand purpose

September 25, 2026 No Comments

By MATT JONES

Four Pillars Gin co-founder Matt Jones writes that, with the AI landscape moving rapidly, a risk lies in companies mistaking the race to adopt the technology as a strategy in itself. When everyone is using AI, what will remain distinctive is what one chooses to do with it.

Every month that goes by, the list of things AI is terrifyingly good at seems to grow.

For time-strapped and budget-poor leaders, the question is becoming less about whether AI matters and more about how to decide which AI priority to focus on first. And for many companies, their commitment to being the best at what they do in their category seems to have been replaced by simply trying to be the best at AI.

But that strikes me as a race only a handful of AI-first companies can ever win.

You know the FOMO is real when even the Deputy Prime Minister gets involved. Richard Marles is off to the US this week and AI is high on his agenda. According to Marles, getting this AI moment right for Australia represents the most critical moment for national self-reliance since World War II. And you thought your board’s scrutiny of your AI plans was tough.

The AI landscape is moving extraordinarily quickly, and there will inevitably be companies that move late and find themselves on the wrong side of the changes it brings.

But there’s another risk in moments like this.

When a technology becomes important and high-profile enough, the race to adopt it can start masquerading as strategy. The question shifts from ‘what problem are we trying to solve?’ to ‘how quickly can we do something, anything, with AI?’. Which brings me, somewhat reluctantly, to the second coming of brand purpose.

The fall of purpose

Yes, I know. ‘Purpose’ is a triggering word for survivors of the brand purpose wars of recent years. For a while, thanks in part to Simon Sinek and his useful but simplistic golden circle, brands didn’t merely make things or solve problems. They started with why. They stood for something. They were on a journey and inviting you to join them. Shampoo wanted to empower you, banks wanted to enable human flourishing and companies making sugary soft drinks proudly shared their peppy visions for social harmony-maxxing.

The author Matt Jones

The backlash was deserved. Mark Ritson and others spent years attacking the assumption that consumers needed the brands in their lives to possess some higher-order social purpose. Somewhere along the way, purpose had become shorthand for corporate virtue, usually expressed in advertising and bearing only a passing relationship to what the company actually did. It rarely meant anything or cost the company anything, all of which demonstrated it barely existed at all. The pity is that, in the process, we discredited a useful idea.

Purpose, at its best, was never about giving customers another reason to admire a brand. It was an internal tool for market orientation. It forced a business to stop staring lovingly at the product it wanted to sell and look instead at the people it hoped might buy it. Why does this thing need to exist in their lives? What problem does it solve? Why does that problem matter? Purpose, used correctly, helped shift the conversation from ‘what do we want to sell?’ to ‘why does anyone need us to exist?’

It’s not about you

This is how we used purpose at Four Pillars Gin. We wrote our purpose statement a year before we launched our first gin and never changed it. But we also never banged on to consumers or bartenders about it. It simply helped us make better, more focused decisions and kept us on course as growth, change and opportunity all inevitably tempted us with their various distractions.

For me, that’s always been the power of the ‘why’ question at the heart of purpose. Why will anyone care? Why would anyone mourn your business if you shut your doors tomorrow? These questions direct a company’s attention up from its own navel and out towards its customers.

Over time, organisations naturally become fascinated by their own capabilities and priorities. When you spend years developing a product, it’s understandable that the product becomes the centre of your universe. AI now gives us a bold new remix of that same old song. The inward-looking product obsessions of the past are becoming the inward-looking technology obsessions of the present. What can AI do for us? How many processes can we automate? Can we put an AI button on this before the investor presentation next week?

Leading with purpose helps to turn the telescope around and ask more meaningful questions. What problems do our customers rely on us to solve? Why do we matter to them? How is that changing? How can AI help us matter even more to them in the future? Will AI make the experience of working with us better, or is our obsession with AI at risk of distracting us from why we really exist?

The Canva case study

Consider Canva, much in the news last week after its valuation took a hit. Its original purpose was unusually easy to understand: democratise design. Take something that had largely required specialist skills and complicated professional software and make it accessible to almost anyone. That clarity helped turn an Australian start-up into one of the country’s great technology success stories.

Now Canva finds itself in the middle of an AI arms race. Its recent AI Vision event in Sydney showcased its own increasingly sophisticated AI capabilities alongside platform collaborators who may soon become competitors. OpenAI, Anthropic and others all sat on Canva’s impressive stage while simultaneously working to expand what ordinary people can create without the need for specialist tools like, well, Canva.

It would be foolish to conclude that Canva has somehow got AI wrong. It is investing heavily and has explicitly framed that investment around empowering people to design and create. But there is a strategic danger in accepting that the contest is now simply about whose AI is best. What happens if the great flattening takes place and the power of your AI model quickly becomes table stakes? Does the question then shift back to who has the best design workflow platform?

If Canva’s purpose remains democratising design for everyone from solopreneurs and students to enterprise teams, it doesn’t necessarily need to beat frontier AI companies at their own game. Perhaps it simply needs to use whatever technology is most relevant to deliver a design experience that is better than anyone else’s and be the world’s best place to manage design end-to-end, not just have the whizziest AI for bits of the process.

AI is not a strategy

Salesforce offers another timely example. News of the much-heralded “SaaS-pocalypse” has, for now at least, been exaggerated. Salesforce has reported record quarterly revenue and pointed to strong growth from Agentforce, its agentic AI offering. For decades,

Salesforce has operated around automating customer relationships, sales, service, data and the workflows connecting them. AI changes what Salesforce can do inside that world, potentially dramatically, but the centre of gravity remains recognisable. It has given Salesforce a new way to pursue and amplify the exact business it was already in.

Salesforce leader Marc Benioff has so far warded off the so-called “SaaS-pocalypse”

We’ve been here before, albeit on a smaller scale. Back in simpler times, businesses wanted a digital strategy, then a mobile strategy, before realising that nobody actually needed a mobile strategy. They just needed an overall business strategy that still made sense once every customer had a computer in their pocket.

AI is arguably broader and even more consequential than any of those shifts, but the tension is the same. And if AI develops anything like its advocates expect, being good at AI may eventually cease to be much of a differentiator anyway. Powerful models, agents and automation will become increasingly available to everyone. Saying your company “uses AI” could become about as revealing as announcing that your employees have email addresses.

Purpose matters for what it does

What will remain distinctive is what you choose to do with it. And that choice requires clarity about the business you’re actually in. The second coming of purpose doesn’t require another generation of lofty manifestos or brands explaining how they’re changing the world.

We’ve had quite enough of that. The great irony is that we spent years arguing about whether customers cared about our purpose. That was never really the point. Purpose mattered because it forced businesses to care more about their customers: to look beyond the thing they made and understand why it mattered in somebody else’s life.

AI makes that discipline useful all over again. When every organisation is asking how much AI it can deploy and how quickly, the more interesting questions remain stubbornly human: why do we exist, why do we matter to the people we serve, and can this extraordinary new technology help us matter to them even more?

Feature image credit: Cliff Obrecht, Melanie Perkins and and Cameron Adams at Canva Create 2026

By MATT JONES

Matt Jones has an eclectic background, combining economics, politics, brand experience and gin. To read more, see the biographical note at the end of his first column for Mumbrella on why SXSW Sydney failed.

Sourced from Mumbrella

In News

AI is eating website traffic, websites are blocking AI – and reliable information is getting harder to find

September 24, 2026 No Comments

By Dana McKay & Damiano Spina

For 30 years, the world wide web has run on a surprisingly profound social contract: most sites are free for search engines to access, but if you use their content you give credit by linking to the source.

Recently, that social contract has begun to collapse. Artificial intelligence (AI) tools are crawling sites not to link to them, but to train models and generate answers (which may or may not be accurate).

When you search for something, ChatGPT’s response or Google’s AI Overviews may still include links to sources, but they’re a kind of optional extra to the main answer.

This has triggered a bad dynamic for website owners, the public, and even AI companies themselves: as websites lose traffic (and revenue), many are beginning to block AI scraping tools, meaning AI results depend more on low-quality websites (many of which are also generated by AI). As a result, good information can be harder than ever to find.

How we got here

In the early days of the world wide web, search engines and content creators came to an agreement about crawling (the practice of technologically examining a site to index it, so it can be served up in search results). Content creators would provide access to their sites for free, and even allow search engines to reproduce small snippets of text.

In return, search engines provided links to the sites owned by content creators, who benefited from that web traffic. If content creators didn’t like the deal, they could prevent search engines from crawling their site with instructions in a file called robots.txt.

But if AI tools no longer provide web traffic, it cuts content creators out of the economic loop. There are also other costs associated with each visit to a website, so AI crawling can cost website providers money while not giving them any of the ad or other revenue that would come from human traffic. AI crawlers also crawl more deeply and more intensely than traditional web crawlers, magnifying that cost.

This change in traffic patterns isn’t a small or hypothetical problem. Cloudflare, a web hosting and service company that manages 30% or more of the top 10,000 sites on the internet, estimates over half of all web traffic is now AI bots.

Some of this will be AI agents supervised directly by people, but the majority will be crawlers. Site owners can use robots.txt to ask AI crawlers to stay off their sites – but some AI companies may ignore this polite request.

If the AI companies do honour the request, that can create a different problem. Sites containing misinformation are far less likely to ban AI crawlers, so the AI answers won’t be informed by high-quality sources.

What’s happening in the short term

On the horizon is an event dubbed “Google Zero” – the day when through-traffic from Google drops to nothing. While some greyhaired diehards (like one of the authors of this piece) might still click through to verify AI answers, this traffic is rapidly dwindling, as a direct result of AI summaries.

A study of Wikipedia confirms this, showing that traffic in the English language version of the site dropped off quickly with the launch of AI summaries on Google in English, and that the same pattern occurred in other languages as AI summaries were rolled out. Never having to click through to get an answer might seem great for information seekers, but the reality is more complex.

Many sites are now blocking AI crawlers altogether. Site owners who decide to block AI crawlers are less likely to be linked in AI Overviews answers, even when the AI tool can still access the content to ground its answers (using a technique called retrieval-augmented generation).

Alternative “pay to crawl” models have been suggested as a way to compensate content creators, but haven’t gained traction.

Come September 15, Cloudflare sites will block AI crawlers by default on pages that contain advertising (and therefore make money for content creators).

This means up to 30% of the world’s top sites will no longer appear in Google AI Overviews summaries. It also means that much of what AI is being trained on will itself be AI-generated text.

What it means for you

So what does this mean when you’re looking for information? The quality of AI summaries is likely to go down, at least in the short term, while the new economics of the web get sorted out.

This will happen for two reasons. The first is that high-quality content is less likely to go into those AI summaries – one recent study found that already, around 1 in 6 sources used by AI search tools is itself an AI-generated website.

The second reason is that, as AI models are trained on more AI text, their output may degrade (a phenomenon known as model collapse).

As a result, search engines that depend less on AI may become more reliable. The challenge is finding one that doesn’t use an AI-based crawler. They do exist: ZDNet recommends Mojeek, PCMag recommends Brave, and Ban the Bots lists several, including one specifically for “small producer” content such as blogs.

For now, whatever search engine you’re using, the best thing you can do is to scroll down and click on some actual search results. This benefits content creators, and is also more likely to give you more accurate information.

Feature image credit: Damien Geso / Getty Images

By Dana McKay

Senior Lecturer, School of Computing Technologies, RMIT University

Damiano Spina

Associate Dean, Interaction, Technology and Information, RMIT University

Sourced from The Conversation

In News

You might be using Claude wrong — 5 habits to stop right now

September 24, 2026 No Comments

By Amanda Caswell

These tips can support your workflow, save time and make the AI assistant’s responses much better

Claude has become one of the AI tools I use most often, whether I’m brainstorming, planning something, vibe coding, researching a topic or simply trying to make sense of a complicated question. But using an AI chatbot every day doesn’t necessarily mean you’re using it well.

In fact, some of the habits that feel like they should make Claude better can have the opposite effect.

That’s what caught my attention when I came across a list of 27 Claude tips from AI consultant Ruben Hassid, who says he’s spent more than 1,800 hours using Anthropic’s chatbot.

Some of his advice goes against the way many of us have been taught to use AI. Keep everything organized in Projects? Maybe not. Keep correcting Claude until it understands what you want? That could actually make things worse. Spend 10 minutes crafting the perfect prompt? There may be a better approach.

Not every tip will make sense for every Claude user, and some of Hassid’s recommendations are based on his own experience rather than hard-and-fast rules. But several are worth trying if you use Claude regularly.

Here are five Claude habits worth breaking — and what to try instead.

1. Stop correcting Claude over and over

We’ve all had this conversation with an AI chatbot. You ask Claude for something. It gets it wrong and then you go back and forth trying to get the response you’re actually looking for — all while wasting your tokens in the meantime.

Even when you explain the problem in even more detail or add another layer of clarification, before long, you’re six messages deep trying to rescue an answer that wasn’t particularly good to begin with.

Hassid recommends doing something much simpler: go back and edit the original prompt. This is a feature that a lot of users overlook but it’s one of the best ways to avoid correcting every wrong answer, which in turn, keeps adding to the conversation.

That’s because every correction you add becomes part of the conversation Claude is working from. Instead of giving the model one clear instruction, you can end up with a growing trail of bad outputs, corrections and exceptions.

Editing the original prompt gives Claude another shot with cleaner instructions.

For example, imagine you asked: Plan a three-day trip to Boston.

Claude gives you an itinerary packed with museums, historical sites and early mornings. But what you really wanted was a relaxed weekend centered around food and neighborhoods.

You could spend several messages correcting the itinerary.

Or you could edit the original: Plan a relaxed three-day trip to Boston centered around great food and interesting neighborhoods. Include no more than one museum, avoid early mornings and leave plenty of unplanned time.

Same request. Much better context.

Try it: If Claude completely misses the mark, resist the urge to argue with it. Edit your previous prompt and regenerate the answer instead.

2. Stop keeping every conversation alive forever

An individual typing on a laptop, focusing on the hands and keyboard in an indoor setting. — (Image credit: Pexels / Eren Li)

This one is incredibly easy to do. Once Claude understands what you’re working on, abandoning that conversation can feel wasteful. So you keep going…and going!

Eventually the chat contains previous questions, rejected ideas, unrelated tangents, corrections and instructions that stopped being relevant 30 messages ago.

Hassid recommends starting fresh more often, arguing that very long conversations can eventually make Claude less effective.

There isn’t a magic number of messages where Claude suddenly becomes worse, and Claude’s context window is designed to handle a significant amount of information. But context isn’t the same thing as useful context.

A model having access to something doesn’t necessarily mean you want that information influencing its next response.

Imagine using the same Claude conversation to plan a vacation for weeks. You’ve discussed five hotels, changed the dates twice, ruled out three destinations and completely changed your budget.

Claude may technically have all that information available, but you’re now asking it to distinguish between what you wanted two weeks ago and what you want today. Sometimes the clean slate is the advantage.

Try it: When a conversation starts feeling cluttered, ask Claude to summarize the important information and decisions you’ve made. Paste that summary into a fresh chat and continue from there.

3. Stop obsessing over the perfect prompt

The internet has spent the past few years convincing us that getting great AI results requires elaborate prompts. A few minutes on X or Instagram and you’ll be swamped with plenty of prompts.

But Hassid makes a compelling case for doing something considerably less polished: brain dumping.

Instead of spending several minutes turning an idea into the perfect prompt, simply tell Claude everything you’re thinking.

This works particularly well with voice input because we naturally provide more context when we’re talking. We mention what we’re trying to accomplish, what we’ve already tried, what we don’t like and the half-formed thoughts we might remove if we were carefully typing a prompt.

For example, instead of: Create a weekly meal plan for a family of four.

Try the prompt: I need dinners for the next five days. I don’t want anything that takes more than about 30 minutes, we’re trying to spend less on takeout, one person hates fish, I already have chicken and pasta in the house and Wednesday is going to be really busy, so that meal needs to be ridiculously easy. Help me figure this out.

The second prompt isn’t perfect, but it’s packed with useful information.

Try it: Open Claude and dictate your request as though you’re explaining the problem to a friend. Don’t organize it first. Let Claude organize it for you.

4. Stop telling Claude only what you want

Man looking confused whilst sitting in front of a laptop — (Image credit: The Motley Fool)

Quite often AI users will prompt an assistant with “Make this better.” “Give me something more interesting.” “Make this easier.” “Be more creative.”

And while they will probably give you answers, these instructions are incredibly subjective. Claude has to guess what “better” or “interesting” means to you.

Hassid recommends giving the model negative examples — showing Claude what you don’t want. Say you’re trying to come up with ideas for a 10-year-old’s birthday party.

Claude suggests a trampoline park, bowling, laser tag and an arcade.

Instead of responding: Give me more creative ideas.

Try the prompt: I don’t want trampoline parks, bowling, arcades, laser tag or other typical birthday-party venues. I’m looking for something the kids probably haven’t done at another birthday party this year.

Suddenly, “creative” has boundaries and it’s far more useful to Claude. The same technique works for recommendations, travel plans, recipes, gift ideas, schedules and almost anything else where personal taste matters.

Try it: When Claude gives you suggestions you dislike, tell it specifically what those options have in common and ask it to avoid that entire category.

5. Stop putting everything into Projects

Projects are one of Claude’s most useful features. They let you keep related chats, instructions and reference material together so you don’t have to repeatedly explain what you’re working on.

But that doesn’t mean every task belongs inside one. Hassid argues that fresh conversations can sometimes be better for creative work because accumulated context can steer Claude toward ideas and patterns you’ve already explored.

I’d treat this as a rule of thumb not something universal for Claude. Projects can be enormously useful when the model genuinely needs background information.

The question is whether that background is helping with the task you’re doing right now.

If you’re using Claude to help plan a major home renovation, for example, keeping your measurements, budget, preferences and previous decisions in one Project makes sense.

But if you suddenly want Claude to give you completely unexpected ideas for what to do with an empty room, you might get more variety by asking in a fresh chat without all those previous assumptions.

Otherwise, you risk asking Claude to surprise you while simultaneously surrounding it with everything you’ve already considered.

Try it: Use Projects when Claude needs context. Try a fresh chat when you want novelty. You can even give the same prompt to both and compare the results.

The takeaway

Sometimes the best thing you can give Claude is a clearer problem. So the next time Claude isn’t giving you what you want, don’t automatically add another paragraph of instructions. Try editing the prompt, start a new chat or tellit what you don’t want.

You may just find Claude understands you better when you give it less to untangle.

Feature image credit: Anthropic/Claude

By Amanda Caswell

Amanda Caswell is the AI Editor at Tom’s Guide and one of today’s leading voices in AI and technology.
A celebrated contributor to various news outlets, her sharp insights and relatable storytelling have earned her a loyal readership. Amanda’s work has been recognized with prestigious honours, including outstanding contribution to media. Known for her ability to bring clarity to even the most complex topics, Amanda seamlessly blends innovation and creativity, inspiring readers to embrace the power of AI and emerging technologies. As a certified prompt engineer, she continues to push the boundaries of how humans and AI can work together. Beyond her journalism career, Amanda is a long-distance runner and mom of three. She lives in New Jersey.

Sourced from tom’s guide

In News

Interactive’s Lizzy Jones Calls On Marketing Teams To Embrace ‘AI-Curious’ Culture

September 24, 2026 No Comments

By Melania Watson

Interactive’s head of data and AI, Lizzy Jones, has urged marketing and advertising teams to create workplaces where employees feel comfortable experimenting with AI, making mistakes and sharing unfinished ideas.

Speaking to B&T at the recent TechLeaders conference in Bowral, Jones said businesses needed to move away from cultures where employees feel they must have the “perfect answer” before speaking up, arguing that experimentation is essential as AI tools continue to evolve.

For Jones, creating an “AI-curious” culture is not about mandating the use of AI, but giving employees the time and psychological safety to experiment with it.

At Interactive, employees are given dedicated time to explore AI through fortnightly “AI Playground” sessions, where staff from across the business demonstrate how they are using the technology.

The sessions run for around 30 minutes on Friday lunchtimes, with employees typically spending 10 to 15 minutes showcasing an AI experiment, tool or process.

“Some people have slides. Some people do a bit of a demo. Some people walk through the process they’ve gone to get something up and running,” Jones said. “Some people come and show stuff that’s not quite working yet, so it’s really cool.”

Jones said the approach had helped employees think more laterally about how AI could be applied to their own work, rather than simply learning how to use individual tools.

“It’s how do you think, in a diverse way, laterally about how you could use it and how you could optimise for it?” she said.

The approach is also designed to tackle a cultural barrier Jones said could prevent employees from experimenting in the first place: a fear of being judged.

She said people at Interactive could sometimes be reluctant to speak up unless they were confident they had the right answer, particularly given the company’s mix of experienced technology professionals.

But AI has created an environment where “not everyone knows everything”, making it easier for employees to admit they do not know whether something will work and try it anyway.

“I think the AI thing is really cool because not everyone knows everything,” Jones said. “And so that’s been a really big shift for us, and it’s something we’re still promoting.”

Jones believes leadership also has an important role to play in creating that environment.

At Interactive, senior leaders were encouraged not only to learn about AI, but to demonstrate that they were using it themselves and openly discuss what worked and what did not.

“If you know people at the top are trying it, maybe it’s working, maybe it isn’t, that sort of gives you licence to try things as well,” she said.

Rather than creating additional AI-specific meetings and requirements, Interactive has also incorporated AI discussions into existing team meetings, making it a standing agenda item.

Jones said this helped keep AI “top of mind” without adding another layer of work to employees’ calendars.

Why teams shouldn’t be locked into one model

While Interactive has a company-endorsed AI tool, Jones said she did not believe creative and marketing teams should necessarily be restricted to one model.

The company endorses Microsoft Copilot for business use, particularly from a security and governance perspective, but employees are not prevented from accessing other models such as ChatGPT or Claude through their browsers.

Jones said organisations should continue testing different models because they are evolving at different rates and can produce different results.

She pointed to Microsoft’s efforts to bring multiple models into its Copilot environment, allowing users to compare outputs from different systems.

“I think it’s good to keep testing because they advance at different levels, and so different models are going to be better at doing different things,” Jones said.

However, once a team identifies a model that consistently delivers strong results for a repeatable task, Jones said there was little value in constantly switching between tools.

“You can just go ‘Great this model works really well, I’m not going to even think about it again for another sort of few months,.’”

The key, she argued, is avoiding a “model monopoly” without creating unnecessary noise for employees.

And as AI becomes more embedded in agency and marketing workflows, Jones also expects greater transparency around how the technology is being used.

She suggested agencies could increasingly spell out their AI practices to clients, while AI usage could also become part of contractual discussions.

Some clients may welcome AI if it creates efficiencies, while others may explicitly prohibit its use.

Jones said Interactive already works with some large government clients that have instructed the business not to use AI.

At the same time, she believes agencies could eventually build AI-driven productivity gains into their commercial arrangements, potentially reducing costs as certain processes become more efficient.

“You could start to build into some of your deals is like a productivity gain and you say, ‘Well, maybe we’ll use AI, and our services won’t be as expensive over time’,” she said.

Ultimately, however, Jones said organisations should focus less on forcing employees to use AI and more on giving them the opportunity to learn.

“The message we try to give people is this is your opportunity to learn and the company’s got your back, and we’ll actually create space for you to learn and try,” she said.

“This technology is not going anywhere, so it’s an opt-in thing. We don’t certainly mandate people do it or use it, but we’re trying to inspire them to get on the train, because the train has definitely left the station.”

Feature image credit: Lizzy Jones.

By Melania Watson

Melania is B&T’s senior reporter, covering all things martech and adtech across the industry. When she’s not chasing breaking news, she’s chatting with industry leaders to discuss the big changes in the marketing, advertising, and media landscape. She kicked off her journalism career in 2022 at TV3 in New Zealand as a digital reporter and producer, later moving into a technology reporter role that brought her to Sydney. Driven by a desire to push herself into a new niche, she joined B&T at the start of 2026.

Sourced from B&T

In News

Google Ads Display campaigns retire as Demand Gen takes over in 2026 Google’s Display Ads are dead – here is what replaces them in 2026

September 23, 2026 No Comments

By Luis Rijo

Google is phasing out standalone Display Ads campaigns and folding the Google Display Network into Demand Gen, with a migration tool launched in June 2026.

Google last month confirmed that standalone Display Ads campaigns are being retired, with the Google Display Network folded into Demand Gen as the new unified home for visual advertising across more than 2 million sites, videos and apps. A phased migration tool is now rolling out, and the full transition is expected to complete by 2027.

What is actually changing

The announcement, published on May 26, 2026 on the Google Ads and Commerce Blog, marks the end of Google Display Ads as a standalone campaign type. According to Google, advertisers can now manage their Google Display Network (GDN) presence directly through Demand Gen campaigns. The campaign creation workflow changes, but the underlying network remains unchanged – the same inventory of more than 2 million sites, videos and apps remains accessible.

The move is significant in scale and in what it signals. Display Ads have been part of Google’s advertising infrastructure for well over a decade. Folding them into Demand Gen is not a minor interface update. It is a structural consolidation that changes how advertisers set up, manage, and report on their display activity going forward.

What remains available? Advertisers who want to serve ads exclusively on GDN can still do so. According to Google’s Help Center documentation, customers looking for Display-only campaigns can continue purchasing them within Demand Gen using channel controls – a feature that lets advertisers isolate specific placements. The difference is that the campaign shell itself is now Demand Gen, not a standalone Display campaign.

The migration tool and how it works

Google is launching a phased rollout of its migration tool in June 2026. Eligible advertisers can begin moving existing Google Display Ads campaigns to Google Display Network on Demand Gen using the tool directly inside their Google Ads account.

The tool is the recommended path. According to Google’s Help Center, it allows advertisers to update live campaigns with performance history going back 42 days ported over to the new campaign. That historical data transfer reduces learning time to approximately 1 to 2 days and avoids a “cold start” – the period during which Google’s bidding models lack sufficient data and can underperform. The alternative is a manual budget transition, where advertisers gradually shift spend from existing Display campaigns to Demand Gen by decreasing budgets on the former while proportionally increasing them on the latter.

The step-by-step process inside Google Ads involves navigating to the Campaigns menu, filtering by campaign type for “Display,” selecting the campaigns to migrate, then choosing “Upgrade to Demand Gen” from the Edit dropdown. The migration can handle multiple campaigns simultaneously, though Google does not recommend batches of more than 100 campaigns at a time.

A few technical details matter here. Migrated campaigns will be renamed following the convention “[Original campaign name] #2.” The original Google Display Ads campaigns are not deleted – they are set to “Removed” status and remain in the account for reporting purposes for up to five years under Google Ads’ standard retention policy. Advertisers running lift measurement studies – such as Conversion Lift – are advised not to use the migration tool while a study is active, because the converted campaign will not be automatically associated with the ongoing study.

Budget handling on migration day carries a specific wrinkle. According to Google’s documentation, any budget spent earlier in the day on the Google Display Ads campaign will not be recognised by the new Demand Gen campaign. If a campaign has spent $10 of a $50 daily budget before migration, the new Demand Gen campaign resets and starts with the full $50 for the remainder of the day. Temporary underdelivery or overdelivery is possible within the first 24 hours.

Performance claims and real-world data

Google is citing two sets of numbers to support the migration. On average, according to the announcement, advertisers adding GDN in Demand Gen campaigns see a 9.5% increase in return on investment (ROI). The company also pointed to GoFood, a food delivery platform, as a case study. According to Google, GoFood saw a 24% decrease in cost per acquisition (CPA) and a 19% higher conversion volume after adding GDN to its Demand Gen campaigns.

These figures come from Google’s own data and should be read in that context. The 9.5% ROI improvement and GoFood case study reflect conditions that may not transfer uniformly across industries, account sizes, or competitive environments. Advertisers who have relied on standalone Display campaigns for years will need to test and monitor performance independently after migration, particularly during the first several days when fluctuations are expected.

Feature changes: what is gained and what is lost

The move from Google Display Ads to GDN in Demand Gen involves a meaningful shift in feature availability. Not everything carries over directly, and some capabilities available in Display Ads are not available in Demand Gen.

On the inventory side, Demand Gen expands reach beyond what Google Display Ads offered. Where Display Ads ran across GDN, YouTube, and Gmail, Demand Gen adds Discover and Maps (Maps currently in beta). The ad surfaces available expand from three to five, giving advertisers who opt into them a broader footprint.

Creative formats also expand. Google Display Ads supported Responsive Display Ads, uploaded Display Ads (image ads, HTML5, GIF), and product feeds from Google Merchant Center and Business Data Feeds. Demand Gen adds carousel ads, generative image tools, a wider range of video ad formats, and HTML5 (described in the documentation as “coming soon” for GDN in Demand Gen). GIF support, however, is not listed among the available formats in Demand Gen.

On the audience targeting side, the transition introduces Lookalike segments as a replacement for Similar Audiences. Lookalike segments allow advertisers to reach new users who share characteristics with existing customers. The core targeting options – optimised targeting, remarketing lists, custom segments, first-party data, interest, demographics, and contextual targeting – remain available in both formats.

Bidding changes in specific ways. Manual CPC, Viewable Impressions, and Pay for Conversions are not available in Demand Gen. What replaces them are Max conversions, Max conversion value, tCPA, tROAS, Max clicks, and a new option called tCPC (target cost per click). Demand Gen also introduces Flighted Campaign Total Budgets, which are not available in Google Display Ads.

Pay for Conversions campaigns specifically are handled by the migration tool automatically. According to the documentation, these campaigns will be switched to pay for clicks, while continuing to use target CPA to optimise for conversions at the advertiser’s stated target.

Reporting gains one notable capability in Demand Gen: format segmentation reporting. This breaks down performance data at the format level, including In-Feed, Skippable In-Stream, and Shorts, giving advertisers visibility they did not have within standalone Display campaigns.

Logos and business names are pre-populated from existing Google Display Ads campaigns during migration. If a Display Ads campaign lacks a logo, the migration tool creates a placeholder image to ensure continuity. Advertisers can edit this after migration completes.

Key dates and what comes next

The timeline for this migration has three stages, according to Google’s Help Center documentation.

June 2026: The phased rollout of the migration tool begins. Eligible advertisers can start moving existing Google Display Ads campaigns to GDN on Demand Gen using the tool in their Google Ads accounts.

Coming later (no specific date provided): New Google Display Ads campaigns can only be created within Demand Gen. Advertisers can still access and edit existing Display Ads campaigns until they are migrated.

Coming later (no specific date provided): Remaining eligible Google Display Ads campaigns will be automatically migrated to GDN on Demand Gen without any advertiser action required.

The full migration is expected to complete by 2027. Google has said it will provide account notifications in Google Ads as key dates approach.

Where the money goes – and does not go

There is a dimension to this migration that sits outside the migration checklist: the question of who ultimately benefits from ad spend flowing into Demand Gen rather than standalone Display, and how the balance between Google-owned and publisher inventory shifts in the process.

Google Display Ads – the format being retired – ran by default across the Google Display Network, a third-party publisher ecosystem spanning more than 2 million sites and apps. When an advertiser ran a Display campaign and ads served on an external publisher’s website, a portion of that revenue was shared with the publisher via AdSense or Google Ad Manager. That revenue-sharing model has underpinned publisher monetisation across the open web for over a decade.

Demand Gen has a more layered inventory structure, and understanding it requires separating the surfaces available within the campaign type. The primary surfaces in Demand Gen – YouTube, Discover, Gmail, and Maps – are all Google-owned properties. Discover is Google’s personalised content feed. Gmail is Google’s email service. Maps is Google’s navigation product. According to PPC Land’s analysis of Demand Gen placements, unlike traditional YouTube ads where Google must split revenue with video creators, Demand Gen placements on surfaces like Discover and Gmail allow Google to retain a larger portion of advertising revenue. Those surfaces carry no revenue share obligation to external publishers.

GDN is also available within Demand Gen – but the mechanics are different from how standalone Display Ads worked. In a standard Display campaign, GDN was the entire network, and every impression served on a third-party publisher site generated revenue for that publisher. In Demand Gen, GDN is one channel among several, accessed via explicit channel controls at the ad group level. The default campaign configuration points spend toward YouTube, Discover, Gmail, and Maps. Advertisers who want GDN inventory must actively opt into it. That shift from default to opt-in is consequential at scale.

In Display & Video 360, the distinction is encoded directly into the reporting infrastructure. According to PPC Land’s coverage of DV360’s granular inventory controls, the standard Inventory Source reporting dimension labels YouTube, Discover, and Gmail traffic as “Google Owned Properties,” while GDN traffic appears separately as “Google Display Network.” Google itself maintains that boundary in its own systems – a clear delineation between inventory where revenue stays with Google and inventory where a share flows to external publishers.

The financial trajectory of Google’s Network advertising segment – the one that pays out to publishers – makes the stakes concrete. Google’s advertising revenue distribution reached a point in mid-2025 where 90% of revenues were flowing to its own properties rather than through publisher partnerships, according to PPC Land’s analysis following Alphabet’s Q2 2025 earnings. Network advertising revenue – covering AdSense, AdMob, and Google Ad Manager – declined 1% year-on-year to $7.4 billion in Q2 2025. By Q1 2026, that figure had fallen further to $6.97 billion, a 4% year-on-year drop of approximately $285 million in a single quarter, according to PPC Land’s coverage of Alphabet’s earnings release.

The Display Ads migration adds structural momentum to that decline. In standalone Display campaigns, GDN publisher inventory was the default and the entire point of the campaign type. In Demand Gen, GDN is an optional channel that requires a deliberate activation step. Every advertiser who migrates without explicitly enabling GDN via channel controls will, by default, direct their visual advertising spend toward Google-owned surfaces where no publisher revenue share applies. Budget that previously flowed to external websites through AdSense now flows to YouTube, Discover, Gmail, and Maps instead.

Google’s Network ad revenue decline has also been attributed in part to AI Overviews reducing click-through rates from search results, which reduces traffic to publisher sites and therefore ad impressions served through AdSense and Ad Manager. The Display migration applies pressure from a different direction: it reduces the share of advertiser display budgets that flow to external publishers, independent of what happens to search traffic volumes.

GDN inventory remains technically available inside Demand Gen, and publishers on the network can still earn revenue from advertisers who opt in. That is the honest limit of the claim. But the structural change is real: the campaign type that made GDN the default has been retired, and the one replacing it treats GDN as an elective channel within a portfolio that tilts heavily toward Google’s own properties.

Why this matters for the marketing community

This consolidation is one of several structural moves Google has made to reduce the number of distinct campaign types in its advertising platform. The retirement of YouTube Video Action campaigns in favour of Demand Gen completed by April 2025, and Demand Gen was expanded to Display & Video 360 in October 2024. The pattern is consistent: Google is reducing campaign-type fragmentation and concentrating activity inside a smaller set of formats built around its AI-driven bidding and creative tools.

Google quietly removed Display and Video campaign support from Performance Planner on March 9, 2026, eliminating the ability to model those campaigns in its forecasting tool. Taken together with the Display migration announcement, the signals from Google have been clear for months. Standalone Display infrastructure is being wound down.

The Google Marketing Live 2026 announcements in May included Demand Gen’s expansion to Google Maps with Promoted Pins, and further AI-assisted campaign creation tools – all pointing toward Demand Gen as the primary canvas for visual advertising across Google’s owned-and-operated surfaces.

For advertisers, the operational consequences are real. Campaigns that have accumulated years of performance history inside Google Display Ads will need to be migrated. The migration tool attempts to transfer 42 days of historical data to ease the transition, but the learning reset is not zero – performance fluctuations within the first several days are explicitly flagged by Google’s own documentation. Advertisers running lift studies or time-sensitive campaigns will need to plan the migration window carefully.

The removal of Manual CPC and Pay for Conversions bidding in Demand Gen will also require workflow changes for teams that have built their optimisation processes around those options. February 2026 changes to how Lookalike segments function in Demand Gen – converting them from hard targeting constraints to audience suggestions – had already altered the audience control model. The Display migration adds another layer to the adjustment.

Advertisers who re-approve migrated ads should expect them to go through the standard approval process. According to Google, migrated ads are treated as newly created ads regardless of how they are transitioned, which means they must be submitted for approval before they can serve. This is particularly relevant for advertisers planning to migrate close to a campaign’s scheduled start date.

What best practices say

Google’s Help Center outlines recommended steps for advertisers using the migration tool. On channels, the guidance is to keep GDN-only selected in channel controls during migration to ensure a proper campaign setting transfer – additional channels like YouTube and Gmail can be added after the migration is complete.

On audiences, Google recommends replicating the audience approach from comparable Display campaigns and testing Lookalike segments. On bidding, the advice is to try similar bid levels to existing Display campaigns, set the conversion attribution window to more than 28 days, and limit bid changes to no more than plus or minus 15% – waiting at least a week between adjustments.

On creative, Google recommends expanding the number of assets, including a business logo and video assets. According to the documentation, this allows ads to scale across the widest possible range of inventory slots, which typically leads to stronger overall performance.

Campaign consolidation is also advised: combining similar audience themes across ad groups, and considering merging ad groups with fewer than approximately 30 conversions in 30 days. A consolidated campaign structure allows Google’s AI to learn more efficiently.

Timeline

September 2024 – Google announces Video Action Campaigns will merge with Demand Gen by Q2 2025, with migration tool planned for early 2025.
September 2024 – Demand Gen expands to Display & Video 360, scheduled for October 2024 rollout.
February 2025 – Google clarifies distinct roles of Performance Max and Demand Gen, confirming GDN availability for Demand Gen from March 2025.
March 2025 – Channel placement controls arrive for Demand Gen campaigns, enabling granular selection across YouTube, Discover, Gmail, and Display Network.
March 2025 – DV360 adds GDN inventory support for Demand Gen line items, announced March 3, 2025, with late April implementation.
April 2025 – Deadline for YouTube Video Action campaigns; remaining campaigns automatically upgraded to Demand Gen. PPC Land coverage.
November 2025 – Google launches four new Demand Gen features for holiday campaigns, including brand suitability controls and A/B asset experiments.
February 2026 – Google converts Lookalike segments in Demand Gen from hard targeting constraints to audience suggestions, effective March 2026.
March 9, 2026 – Google removes Display and Video campaign support from Performance Planner, eliminating impression share metrics and locking existing plans from view or editing.
May 20, 2026 – Google Marketing Live 2026 announces Demand Gen expansion to Google Maps with Promoted Pins and AI-assisted campaign creation.
May 26, 2026 – Google announces Display Ads migration to Demand Gen on the Ads and Commerce Blog.
June 2026 – Phased rollout of migration tool begins; eligible advertisers can migrate existing Display Ads campaigns using the in-account tool.
2027 (expected) – Full migration completes; remaining eligible Display campaigns automatically migrated to GDN on Demand Gen.

Summary

Who: Google, affecting all advertisers currently running Google Display Ads campaigns globally, announced via the Google Ads and Commerce Blog.

What: Google Display Ads campaigns are being retired as a standalone campaign type. The Google Display Network is being folded into Demand Gen as the unified home for visual advertising, expanding the available ad surfaces from GDN plus YouTube and Gmail to also include Discover and Maps. A migration tool is launching in June 2026 to help advertisers transfer existing campaigns, with 42 days of performance history ported over. The full migration is expected to complete by 2027.

When: The announcement was published on May 26, 2026. The migration tool rollout begins in June 2026. A future date – not yet specified – will prevent the creation of new standalone Display Ads campaigns. A second future date will trigger automatic migration of all remaining Display campaigns.

Where: The change applies to Google Ads globally. Advertisers manage the migration from within their Google Ads accounts. Google Display Network inventory – more than 2 million sites, videos and apps – remains available via Demand Gen campaigns after migration.

Why: Google frames the migration as a response to shifting consumer behaviour and a push toward more unified campaign management. The consolidation aligns with a multi-year pattern of reducing standalone campaign types: Video Action Campaigns were absorbed into Demand Gen by April 2025, Display was removed from Performance Planner in March 2026, and Google Marketing Live 2026 confirmed Demand Gen as the primary vehicle for visual advertising across Google’s owned-and-operated surfaces. Advertisers adding GDN in Demand Gen campaigns see on average a 9.5% ROI increase, according to Google’s own data.

By Luis Rijo

Luís Rijo is a seasoned marketing professional with over 10 years of experience in Digital Marketing, Search, Social, Display, Video, and DOOH. Based in Europe. Reach out via [email protected]

Sourced from PPC Land

In News

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

September 23, 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 analyse 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 website.

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

The E-Commerce Profitability Crisis: Why Revenue Growth Has Become A Dangerous Metric

September 22, 2026 No Comments

By John Readman

Over the past 25 years, I’ve seen e-commerce change in almost every way. With benefits such as improved accessibility and insight for customers, and innovation and efficiency for vendors, this evolution has been largely positive for buyers and sellers. In recent years, however, I’ve seen profitable businesses become unprofitable as they grow, and that should be a concern.

As acquisition costs rise and privacy restrictions challenge the established strategies businesses rely on, revenue growth as the sole measure of success has become a trap.

The Growing Gap Between Revenue And Profitability

Customer acquisition has always cost businesses money. In 2013, industry benchmarks were around $9 per customer. By 2022, that cost had risen 222% to $29. A 2025 Deloitte report estimated that, depending on the channel, acquisition costs across the board increased by 25% to 40%.

For every first purchase by a new customer, you could potentially be losing over $30 before considering the wider economics of that customer relationship.

While your revenue graph might show growth and customer acquisition numbers might be increasing, the hidden costs behind that growth could be masking a decline in profitability.

Rising Advertising Costs

One of the biggest drivers of increasing acquisition costs is advertising. According to a Triple Whale analysis of data from over 30,000 brands, cost per acquisition (CPA) for paid advertising increased by 8.64% in 2025. For businesses relying on a single channel to drive sales, increases like this year after year can quickly erode profitability.

Customer Loyalty

The cost of acquisition is only part of the problem. The SAP Customer Loyalty Index showed a 5% drop in customer true loyalty from 2024 to 2025. And many customers won’t return for repeat purchases, where businesses generate their strongest margins.

Privacy Changes

Changes to data privacy have perhaps had the biggest impact on the e-commerce growth model. As more users opt out of third-party tracking, the ability to optimize campaigns and target customers has been significantly reduced.

This lack of visibility has also impacted reporting and attribution, making it harder for businesses to accurately understand which activities are driving profitable growth rather than simply generating revenue.

ROAS Is No Longer Enough

The unfortunate truth is that Return on Ad Spend (ROAS) is increasingly limited as a measure of e-commerce performance. It’s a metric built around an e-commerce market that has changed dramatically. Looking at the revenue generated from acquisition spend without factoring in margin at a more granular level does not provide a complete picture. ROAS rewards revenue and revenue alone.

For example, imagine you are running two campaigns, Campaign A and Campaign B. On the surface, both appear successful, delivering five times ROAS and generating $50,000 in revenue. However, the product in Campaign A has a margin of 20%, compared with 50% for the product in Campaign B. While both campaigns generate the same revenue, Campaign B creates $25,000 in profit compared with just $10,000 from Campaign A. ROAS does not make that distinction. Profit on Ad Spend (POAS) does.

The Hidden Costs That ROAS Misses

So why is ROAS a less effective metric now? Advertising costs have increased significantly, meaning businesses are spending more to acquire customers, without those costs reflected in traditional ROAS calculations.

Product margins are also ignored when evaluating campaign effectiveness. Platform fees, shipping costs and operational complexity all impact profitability.

A shift in returns culture since the pandemic has also had a massive impact. In 2019, the average retail return rate was 8.1%. A 2025 National Retail Federation report estimated an overall return rate of 15.8%, with a 19.3% rate for online sales. Looking at revenue without accounting for almost one in five products being returned no longer provides an accurate picture of profitability.

Profitability Is Moving To Product Level

The problem is not that e-commerce businesses lack data. Most retailers have access to more reporting than ever before. The problem is that the data can lead many of them to the wrong decisions.

By focusing on the profitability of individual products, businesses can avoid the revenue trap and identify where real value is being created. For years, e-commerce teams have optimized around channels, but the channel is only part of the story. The real driver of profitability is the product itself.

Some, often less glamorous, products have strong margins, attract valuable customers and encourage repeat purchases. Others generate impressive revenue but hemorrhage profit once return rates, advertising costs and other fees are taken into account.

The next shift in e-commerce performance will be moving from channel-level reporting to product-level profitability. Retailers need to understand not just what is selling, but what is creating value. This requires connecting data that has traditionally been siloed: advertising performance, product margins, customer behavior and commercial outcomes.

A new generation of e-commerce analytics platforms is helping businesses make this shift, moving beyond reporting on past performance toward predictive analytics.

AI Will Make This Shift Unavoidable

AI shopping agents will accelerate this change. For years, e-commerce success has been about clicks and conversions. But that model is changing. As AI increasingly helps consumers compare products, evaluate options and make purchasing decisions, retailers will compete in an environment where product value is an essential metric.

AI systems are designed to understand context, compare alternatives and recommend what best fits a customer’s needs. To benefit from the rise of agentic commerce, businesses will need a much deeper understanding of their own products, margins and customer value.

Successful retailers understand which products deserve attention and where profit actually comes from.

E-commerce has spent the past decade optimizing for growth. But as acquisition costs increase, privacy changes reduce visibility and AI reshapes how consumers discover products, the smartest businesses will understand exactly where their profits come from and build strategies to maximize them.

Feature image credit: Getty

By John Readman

Find John Readman on LinkedIn. Visit John’s website.

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

editor

Gen Alpha is often discussed as if brands can reach the generation by finding the right social platform, creator, or short-form video format.

Gen Alpha Is Not Shopping Alone

Attention and Purchasing Power Are Split

Mom Still Has More Influence Than TikTok

There Is No Single Platform for the Gen Alpha Household

Why Amazon and Nike Make Sense at the Top

Moms and Dads Do Not Play the Same Role Either

The Funnel Is Starting to Look More Like a Network

What Marketers Should Take From the Data

Gen Alpha Marketing May Really Be Household Marketing

Source

By Zarrak

Sourced from HACKERNOON

People who obviously used ChatGPT almost always use these phrases:

What do most of these ChatGPT phrases have in common?

Real writers use the above phrases, too, but that doesn’t mean they make for good writing

By Maria Cassano

Sourced from Your tango

Key Takeaways

Ad hoc AI education

Four ways small businesses can get smarter about AI

Where small business owners can get AI-educated

By Grant Freeman

Edited by Micah Zimmerman

Sourced from Entrepreneur

Four Pillars Gin co-founder Matt Jones writes that, with the AI landscape moving rapidly, a risk lies in companies mistaking the race to adopt the technology as a strategy in itself. When everyone is using AI, what will remain distinctive is what one chooses to do with it.

The fall of purpose

It’s not about you

The Canva case study

AI is not a strategy

Purpose matters for what it does

By MATT JONES

Sourced from Mumbrella

How we got here

What’s happening in the short term

What it means for you

By Dana McKay

Sourced from The Conversation

These tips can support your workflow, save time and make the AI assistant’s responses much better

1. Stop correcting Claude over and over

2. Stop keeping every conversation alive forever

3. Stop obsessing over the perfect prompt

4. Stop telling Claude only what you want

5. Stop putting everything into Projects

The takeaway

By Amanda Caswell

Sourced from tom’s guide

Interactive’s head of data and AI, Lizzy Jones, has urged marketing and advertising teams to create workplaces where employees feel comfortable experimenting with AI, making mistakes and sharing unfinished ideas.

Why teams shouldn’t be locked into one model

By Melania Watson

Sourced from B&T

Google is phasing out standalone Display Ads campaigns and folding the Google Display Network into Demand Gen, with a migration tool launched in June 2026.

What is actually changing

The migration tool and how it works

Performance claims and real-world data

Feature changes: what is gained and what is lost

Key dates and what comes next

Where the money goes – and does not go

Why this matters for the marketing community

What best practices say

Timeline

Summary

By Luis Rijo

Sourced from PPC Land

​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

The Growing Gap Between Revenue And Profitability

Rising Advertising Costs

Customer Loyalty

Privacy Changes

What Exactly Has Changed?

What Do These Changes Mean For Brands?

What Should You Do About It?