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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

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BY ASHLEY COUTO

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 analyzed 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 analyzed 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 favored 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: Illustration: Getty Images

BY ASHLEY COUTO

Sourced from Inc.

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Polaroid’s new anti-AI billboard is an important reminder to touch grass

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…Before it’s too late.

Polaroid has been on a mission to bring back the joy of analogue, embracing back-to-basics branding that reminds us to touch grass and unplug from the digital world. Continuing this trend, the brand’s latest campaign, ‘The best of Summer is Analog’ is perhaps the most provocative yet, promoting the launch of the Go Generation 3.

In an increasingly online world, Polaroid’s simple yet poignant anti-design campaign is a wonderful example of how stripped-back visuals and powerful copy can be just as effective as a gaudy ad campaign. Equal parts provocative and poignant, Polaroid’s new campaign is a refreshing voice among the digital noise.

Polaroid campaign

(Image credit: Polaroid)

Taking a stand against over-digitisation, Polaroid’s campaign centres around a large billboard situated on Coney Island beach, reading “Go jump in some water before the data centers drink it all up”. With its handwritten-style font and candid Polaroid snap, the design has a signature human warmth that counteracts its subtly foreboding message.

The campaign will continue across New York, South Korea and London, with a station overtake at King’s Cross, alongside placements across Bethnal Green and Hackney. Celebrating the joys of an unplugged existence, statements including “You can’t bask in blue light”, “Dance like nobody is recording” encourage audiences to live in the moment, while playful sarcasm comes through with billboards bearing “What a glorious day to stare into various screens for hours on end”.

Polaroid campaign

(Image credit: Polaroid)

“When we stopped asking ‘How do you make instant cameras appealing to Gen Z?’ and started asking ‘Why should Polaroid exist at all in an AI era?’ we knew we were on to something,” says Patricia Varella, creative director at Polaroid. “For Polaroid, the simple act of existing is already an act of rebellion. While our campaigns are provocative and challenge our relationship with technology, we’re not anti-digital. We know we have to live alongside it, but we’re deeply pro-human, and know what humanity gives us. And we know what we stand to lose if we don’t protect it. That’s a fight worth fighting.”

Feature image credit: Polaroid

By 

Natalie Fear is Creative Bloq’s staff writer. With an eye for trending topics and a passion for internet culture, she brings you the latest in art and design news. Natalie also runs Creative Bloq’s 5 Questions series, spotlighting diverse talent across the creative industries. Outside of work, she loves all things literature and music (although she’s partial to a spot of TikTok brain rot).

Sourced from CREATIVE BLOQ

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The digital world disruption

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By Jim VandeHei

The way your work shows up in a digital-dominated world is changing lightning fast, starting with the total collapse of Google Search traffic.

Why it matters: Google has basically stopped sending people to websites (including our site) for answers and information. Instead, it’s using AI to answer them on its platform, in its words.

  • Chartbeat data shared with Axios shows Google Search traffic to publishers fell 34% over the past year.
  • That pain is regressive. Over the past two years, small publishers lost 60% of referrals from search overall, medium publishers 47%, large publishers 22%.
  • This is a gut punch to all of us who operate online because people who search arrive with specific needs and intent, so they tend to be more valuable than a random passer-by from X or Facebook.

At the same time, LinkedIn, Reddit and other massive platforms are getting jammed with AI-created content, making it harder to stand out — or decipher real from fake.

  • To that end, Substack announced a partnership last week with AI-detection software Pangram, specifically calling out LinkedIn while arguing that “platforms that reward fakeness will create a race to the bottom.”

Between the lines: Any smart person who primarily does business online is already anticipating the shift to an LLM, ChatGPT-like world.

  • It starts with GEO — the term for shaping how content shows up inside LLMs like Claude and ChatGPT. This is the modern version of SEO. It’s early, and it’s a black box. But there’s no time to waste.

The future: My theory is all of us will need to rethink and reformat our content to meet human AND AI needs.

  • Turns out, our Smart Brevity format — most important point first, context second, information stacked in order of importance, often in bullet form — works wonders with AI.
  • We’re among the top publishers surfaced by the leading LLMs, so we’re working with companies to see if they can achieve similar results.

It’s crucial to get ahead of this problem. At Axios, we’re already figuring out how we can:

  • Show up inside LLMs now.
  • Improve that standing.
  • Offset reliance on search and open web.
  • Connect with audiences in a post-open-web world. (Direct relationships via things like newsletters and events are a good start.)

What’s next: The agent wave is coming. Smart publishers have largely stopped treating AI engines as a referral source and now treat them as a distribution layer.

  • Soon, the “customer” reading a website, comparing prices and learning about a product will often be someone’s AI agent as the middleman.

Feature image credit: Illustration: Brendan Lynch/Axios

By Jim VandeHei

Sourced from AXIOS

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‘If You Can’t Measure It, You Can’t Scale It’: Meta’s Helen Black On Creator Marketing’s Performance Shift

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By Mia Rogers

For years, creator partnerships have sat somewhere between PR, social and brand marketing, valuable for attention, but rarely tied to sales or ROI.

According to Meta ANZ’s head of connection planning Helen Black, that distinction between attention and outcome no longer exists.

As brands demand clearer evidence that marketing dollars are working harder, creator partnerships are increasingly being judged on the same metrics as paid advertising. It’s a shift changing where budgets are flowing.

Creator content is quickly becoming one of the more powerful performance levers available to marketers, provided brands start treating it like media.

The World Advertising Research Centre (WARC) found that brands are projected to spend US$37 billion (A$55.4 billion) on creators in 2026, up 26 per cent year on year. It also reported that 61 per cent of marketers plan to increase investment in creator marketing in 2026.

“I think what’s changed is that creator marketing has crossed the line from being an awareness play to being a genuine performance channel,” Black told B&T.

“Brands now have the proof that it drives business outcomes and I think that’s what’s unlocking budgets.”

For example, Australian skincare brand Bondi Sands has built its creator strategy around a wide network of influencers. It reportedly works with up to 250 creators a year and allocates up 85 per cent of its media budget to social, according to a 2020 Afterpay case study.

Its approach spans different roles across the funnel, with nano creators used for product education and conversion, while larger creators support product launches and brand awareness.

 

The model reflects the broader challenge that Black outlined: brands are no longer just looking for creators to generate attention, but for a pipeline of content that can be tested, measured and deployed across campaigns.

The challenge, Black argued, is that many brands are failing to get the full value from the creator campaigns they are already running.

She believes the problem isn’t the quality of the content, but what happens after it’s published.

 

“They do all this brilliant organic creator work. They’ve got this great content, but don’t necessarily amplify it through paid,” said Black.

It’s an issue that reflects a broader challenge in the industry.

Creator marketing is often managed by social, PR or specialised influencer teams, while paid media has sat somewhere else entirely.

The result is content that generates strong engagement organically, but never receives the advertising budget needed to reach a wider audience.

One of the reasons creator marketing has traditionally struggled to attract larger budgets is measurement.

Although engagement metrics, including views, comments and shares are easy to track, proving whether creator campaigns actually changed purchasing behaviour is far more difficult thus making marketers hesitant to shift budgets into creator partnerships.

“If you can’t measure it, you can’t scale it,” Black said.

Take H&M for example. Its US summer campaign diversified its traditional fashion creative by instead relying on content from local creators, amplified through Meta’s partnerships ads, delivering a 94 per cent lower cost per brand consideration lift than the broader campaign. It also doubled incremental return on ad spend both online and in store.

 

The example reflects a broader shift in how creator content is being used. Rather than sitting alongside advertising, it’s increasingly becoming the advertising itself. Black noted that around 70 per cent of what someone sees comes from accounts they don’t already follow, served on relevance rather than existing follower ties.

 

For Black, the biggest opportunity isn’t finding more creators. It’s making better use of the creator content brands are already producing.

As creator marketing becomes a bigger part of media plans, she believes the brands that succeed will be those that stop treating creator campaigns as standalone moments and instead build them into broader advertising strategies.

“Why would you have it sitting there and not be using it to the full extent that it can do?” Black said.

“Amplify it. It’s going to make your paid work harder… it’s unlocking measurable growth.”

Feature image credit: Helen Black

By Mia Rogers

Sourced from B&T

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Amazon’s Ad Sales Surges 26% On Back Of Sponsored Products, Live Sport

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By Arvind Hickman

Amazon’s advertising business is growing faster than the majority of its competitive set due to demand for sponsored product listings and broadening its inventory through Prime Video, live sports, Twitch and its wider display network.

Advertising revenue climbed 26 per cent year on year to $19.8 billion in the July quarter, outpacing Amazon’s overall revenue growth of 20 per cent. It is on track to generate annualised revenues of almost $80 billion, cementing its position behind only Alphabet and Meta among the world’s largest advertising platforms.

Amazon CEO Andy Jassy said that although sponsored products continue to be the backbone of its advertising revenue, there have been solid gains elsewhere.

“Increasingly more shoppers are discovering products in our agentic and conversational experiences, including in Alexa+ and Alexa for Shopping. Shoppers who click a Sponsored Prompt convert to a sale 48 per cent more often and spent 21 per cent more on average than those who don’t,” he said.

He said there is also growth and engagement in Prime Video Ads and Live Sports.

Amazon introduced more than 30 new advertisers to the NBA in its first year of an 11-year streaming deal with the US basketball competition.

Inventory on Thursday Night Football, NBA, WNBA and NASCAR all sold out.

Per Jassy, “Advertisers are increasingly investing in multi-sport strategies, with brands activating across multiple sports seeing 2.3 times higher unduplicated reach compared to single-sport advertisers. And multi-sport viewers are driving 12 per cent higher spend and 17 per cent more orders on Amazon.”

A third contributor is the use of AI-powered tools such as an ads agent to help advertisers optimise campaigns across the Amazon ecosystem.

The advertising performance formed part of a standout quarter for the Seattle-based company. Total revenue rose to a record $200.6 billion, while Amazon Web Services accelerated to 37 per cent growth — its fastest expansion in more than four years — as demand for AI infrastructure continued to surge.

Last week, Meta revealed it had also posted strong Q2 growth of 27 per cent dues to increases in ad cost and volume.

Feature image credit: Amazon CEO Andy Jassy.

By Arvind Hickman

Arvind writes about anything to do with media, advertising and stuff. He is the former media editor of Campaign in London and has worked across several trade titles closer to home. Earlier in his career, Arvind covered business, crime, politics and sport. When he isn’t grilling media types, Arvind is a keen photographer, cook, traveller, podcast tragic and sports fanatic (in particular Liverpool FC). During his heyday as an athlete, Arvind captained the Epping Heights PS Tunnel Ball team and was widely feared on the star jumping circuit.

Sourced from B&T

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The Future Of Paid Social Media: Will Creators Need To Pay To Play?

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By Lyn Wildwood

Is social media heading toward pay-to-play?

More creators are boosting posts, organic reach is shrinking, and the algorithm feels harder to work with than ever.

But does that actually mean you have to pay just to be seen? Or is the situation being misunderstood?

That’s what this post breaks down.

You’ll also find the one thing you need to focus on towards the end.

Let’s get into it.

What is paid social media?

Paid social media is a method to increase organic reach by boosting individual social media posts.

It’s a technique social media creators and brands alike can use to try and beat the algorithm by paying to have their content reach more users on social media.

“Reach” is a metric that measures the number of social media users who had your post in their feed when they browsed social media. It essentially tells you the number of users who had the potential to engage with your post.

There are two types of reach: organic reach and paid reach.

Organic reach refers to reach that comes from the algorithm naturally. Paid reach refers to reach you paid to acquire.

What are the differences between organic and paid reach?

Organic reach and paid reach are similar in that they both refer to methods that are designed to help you reach more users on social media. At the same time, each one plays a different role in how you approach marketing.

Targeted marketing

With organic reach, you make posts about topics related to your niche in an attempt to find your target audience on a particular social media platform.

By making videos about topics your target audience cares about and by using keywords related to those topics in captions and text overlays, you send signals to that platform’s algorithm that lets them know that your videos are relevant to users who have expressed interest in those topics.

With paid reach, you pay to reach a specific audience.

When you boost a post, most platforms allow you to choose different parameters that control who the post is shown to. Possible parameters usually include age demographics, genders and specific topics.

The idea is that by choosing your parameters, you not only increase your reach overall, you also increase it among users who are most likely to buy your products.

Expanding reach

With organic reach, you trust the algorithm to show your post to users who have shown interest in the topic your post is about.

Even so, social media users still need to interact with your post and every other post you publish in order for the algorithm to pick up on your video and begin showing it in feeds.

If your growth and engagement rates are low, your reach will be as well.

With paid reach, you pay to guarantee that your post will be shown to part of your target audience.

Campaigns

Most social media platforms allow you to view analytics for individual posts, but for the most part, you’ll need to monitor and record data for campaigns on your own.

This is where paid reach has the upper hand.

When you boost a post, you create a campaign within the platform’s advertising section. That campaign usually includes specific analytics for engagement metrics related to your post.

You can then use these metrics to optimize your campaign while it’s live.

Building loyalty

Unfortunately, social media users are growing tired of ads.

This is especially true given the fact that TikTok Shop has turned the platform into a hub for infomercials.

For this reason, it may be better to stick with organic reach.

Trust between you and your audience is very important. Being relatable and personable can do a lot to build trust, but a giant “sponsored” label can really turn users off and only cause them to scroll as quickly as possible.

Plus, if you boost too many posts that don’t do well, you’ll see a drop in your overall engagement rate, which will only make it harder for you to build relationships with brands and potential sponsors.

Which platforms allow you to boost posts?

  • Instagram
  • Facebook
  • TikTok
  • YouTube
  • Twitter (X)
  • Pinterest

You need to convert your Instagram account into a business account if you want to boost posts.

Then, you can go to your profile, tap the post you want to boost, and tap Boost to set up a campaign.

This is how it’s done on Facebook as well. You go to your page, find the post you want to boost, then tap Boost.

Personal accounts and business accounts alike can boost posts on TikTok. Government and political accounts aren’t allowed to.

TikTok’s help docs also state that your post must use an original sound or a sound that can be used for commercial purposes.

You can boost a post or a live. To boost a post, find it, tap the More options, and use the Promote button to set up your campaign.

In YouTube, you can set up simple promotion campaigns in YouTube Studio or more advanced campaigns in the YouTube Ads section of Google Ads.

On Twitter, visit your profile, find the post you want to promote, tap the post activity icon, then tap Promote.

Finally, Pinterest requires you to have a business account as well.

If you do, go to your profile, find the pin you want to boost, and tap the promote button.

How much does it cost to boost posts?

Unfortunately, this is difficult to determine because advertising costs differ for everyone. They’re determined by the target audience you choose as well as your competition.

Costs usually come from clicks, meaning the users who click on your ads. You are not charged for the amount of users your post reached.

Fortunately, while everyone’s cost-per-click (CPC) is different, you can set a maximum budget per campaign so you never over spend.

Is organic reach declining?

Unfortunately, studies indicate that organic reach is declining.

According to a study of Instagram and Facebook, which was conducted by SocialInsider, Instagram had an average organic reach of 4% in 2024 while Facebook’s was 2.6%.

The company runs these tests every year, and they discovered that Instagram had an 18% decrease in organic reach year over year from 2023 to 2024.

Social Status tracks Facebook’s average organic reach on a monthly basis.

They found that average organic reach was 1.72% in February of 2025, down from 2.16% in August of 2024.

Is paying to boost posts worth it?

With organic reach declining, you may be wondering if now is the perfect time to start promoting your posts on whatever social media platform you use.

Unfortunately, some creators do not find success with this and only wind up wasting their money. Social media users tend to ignore ads, which means promoted posts, which are labelled as ads, often have fewer engagements.

And some creators simply don’t understand that money does not make up for social media content that’s just not that good.

Here are a few numbers from creators who have tried promoting their posts:

TikTok creator @thekoolkaycee used TikTok’s promotion feature to gain more followers.

She spent $10 for one day and only gained seven new followers as well as 1,100 video views. This works out to $1.43 per follower.

She spent $20 on a second campaign and gained 26 followers and 3,100 video views. This works out to $0.77 per follower.

This means if her goal is to gain 1,000 followers, which isn’t very much on TikTok, she’d have to spend between $770 and $1,430.

Hootsuite ran a small experiment involving $75 spent boosting a single post.

The post reached 7,447 users but only received 189 interactions. Overall, it had a click-through rate of 2.7%.

Should you boost posts?

You should try to boost posts on your chosen platform at least once just to see how it works for you.

Unfortunately, the figures I reported above are anecdotal.

Something you need to understand is that just because you promote a post that doesn’t mean that post will earn more engagements.

Just like organic reach, you need good content to succeed with paid reach.

The best thing you can do for your brand is work on creating better content.

Not only will this increase your odds of earning more organic reach, it’ll also increase your numbers from paid reach campaigns.

But proceed with caution. Some Instagram users have reported receiving much fewer organic engagements after they boosted posts for a while then stop.

While this is only a conspiracy theory right now, creators suspect Instagram is intentionally decreasing organic engagements in an effort to convince them that they need to boost posts in order to succeed.

Note: For a unique perspective on boosting posts on Facebook and ad copy, check out Adam’s article; Why I Write Bad Facebook Ad Copy On Purpose.

The rise of ad-free platforms

Decentralized social media platforms are here, and they’ve come to shake up the world of social media as we know it.

Decentralized social media platforms are social networks that are designed to work on any server, even a server you operate yourself.

This puts data and content moderation back into the hands of the user. In fact, privacy and data security are among the top reasons why more users want decentralized social media platforms.

Another reason is the way these apps are funded. Most are ad free, finding other ways to promote themselves, including user donations and sponsorships. Some are operated on a blockchain network that uses a specific cryptocurrency, which helps fund operation costs.

Whatever the case may be, these platforms don’t use traditional advertising models, so you won’t be able to boost posts on them.

The most popular decentralized social media platforms are Twitter competitors Bluesky and Mastodon.

Bluesky has gained huge momentum since it was created by Twitter’s former CEO Jack Dorsey.

In fact, the platform has over 23 million users and continues to gain more as X makes more and more changes to its platform and the reputation of its CEO, Elon Musk, continues to fall.

How to win on social without paying for reach

Now, I mentioned the importance of creating better content earlier. That’s a subjective topic and there are a lot of variables to consider.

But we need to look at what “better” means in the eyes of social media algorithms. And specifically what you can do to win without paying vast amounts of cash to popular social media platforms.

It all comes down to retention.

Each social media site will have an algorithm that works slightly differently but generally, it’s all about retention.

The longer you keep people glued to your content, the more of a push it will get from the algorithm.

Now, algorithms work by pushing your video to a small test audience. If retention rates are high, it will push your video out to a wider group of people, and if retention remains high, they’ll continue pushing it.

This does mean that you can sometimes reupload videos and get more views. It’s often that first test audience that will either boost or bury your video.

So, keep working on crafting your social media strategy around retention rates and you’ll start seeing better results.

I’d also recommend using social media analytics tools to get a better understanding of which videos are performing and which aren’t.

Sometimes it’ll literally come down to the test audience. Other times, there will be something you did differently in one video that you can apply to future videos.

For example, Adam Connell (founder of Blogging Wizard) noticed that shorter videos on TikTok tend to get more views. So, he experimented by speeding up videos and re-uploading them. Each video he sped up and reuploaded would get around 3x the views than the original slightly longer videos.

The original videos were 30-40 seconds and the sped up videos came in at below 20 seconds. Around 1.8x the original speed.

The sped up videos did sound a bit weird in terms of the audio, however. But it highlighted the importance of focusing on making videos that come in at below 20 seconds.

It’s these kind of insights that we can pull from our analytics. This is just one example. I’m sure you’ll be able to find other ways to increase your organic reach buried in your analytics.

If in doubt? Export your analytics from whatever tool you’re using and run them through an AI tool. Even something like Gemini would be able to spot some pretty useful patterns.

Final thoughts

So, should you embrace paid social media and start boosting your posts?

That’s a difficult question to answer. And I don’t think anyone but you can answer that question.

If algorithms aren’t picking up your content then it may be time to revisit your content and start by improving that first before you spend any money.

If you do go ahead and spend money on social media ads of any kind, you’ll need to ensure that you cap your spending and start small.

The best mindset to adopt going in is to be prepared to lose your money. It’s a gamble, after all.

You’ll also need to run a few different experiments to figure out the best way to make boosting posts work for you. And make it work on that platform you’re using.

They all work slightly differently so understanding the nuances of each one will be critical.

Finally, if you haven’t already, I’d recommend reading Adam’s post on how the blogging landscape has changed. It provides some guidance on how to adapt your blog to the shifting sands we find ourselves having to work with.

By Lyn Wildwood

Lyn Wildwood is a member of the Blogging Wizard content team and a freelance writer for hire with over a decade of experience in the marketing space. She’s an expert when it comes to blogging and WordPress. She also has a strong background in testing social media software.

Sourced from bloggingwizard

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Google Cloud faces 48% revenue reliance on OpenAI and Anthropic in 2027

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By Luis Rijo

OpenAI lost $20.9 billion in 2025 and cannot pay its bills from cash flow, Ed Zitron says. What happens to the ad platforms financing the record AI buildout?

EZ Primary Research chief executive Ed Zitron used a Bloomberg interview published on July 31, 2026 to argue that the capital expenditure wave lifting technology stocks rests on two loss-making customers, citing UBS estimates that OpenAI and Anthropic will account for 27% of Google Cloud revenue this year and more than 48% next year.

The conversation, released on the Bloomberg Podcasts YouTube channel after a week in which Microsoft, Amazon, Meta, and Alphabet reported quarterly results, drew more than 420,000 views by August 2. “Everyone is buying into these stocks because they believe all of that CapEx is going towards diverse and spread out AI demand, when in fact, what it’s actually doing is helping create infrastructure for two unprofitable, unsustainable companies,” Zitron said, according to the interview.

The claim lands on numbers that investors rarely see broken out. According to UBS estimates cited by Zitron, OpenAIand Anthropic together will generate more than $124 billion of Google Cloud revenue next year, with Anthropic alone contributing $76 billion in 2027. Zitron noted that OpenAI’s position as a large Google Cloud customer remains little known, a detail he attributed to UBS analyst Stephen Ju.

Two customers behind the cloud growth

Microsoft shows a similar pattern, according to the interview. Zitron cited Barclays estimates placing the two AI companies at 13% of revenue this year and 18% next year for a cloud operation he described as a much bigger business than Google Cloud. His own reporting on OpenAI’s finances produced a sharper figure. According to Zitron, 69% of the year-over-year growth in the Microsoft Intelligent Cloud segment during 2025 came from OpenAI. Without that single customer, the segment would have grown 8%, which he characterized as barely beating inflation.

“Everyone is being sold what I consider kind of a lie. It’s honestly kind of a scandal,” Zitron said during the interview.

The concentration extends beyond the hyperscalers. According to reporting by The Information referenced in the conversation, 89% of revenue at the largest AI companies comes from OpenAI and Anthropic alone. When a host asked whether the industry must be concentrated to some degree, given the cost of building capable models, Zitron clarified that his concern targets the concentration of revenue in two companies rather than the concentration of compute itself.

The scale of the projected payments raises its own question. “How is Anthropic going to afford that? They burned tens of billions of dollars,” Zitron said, referring to the $76 billion UBS estimate for 2027.

OpenAI’s balance sheet and a delayed listing

Zitron brought direct knowledge of OpenAI’s accounts to the discussion, having reported the company’s audited financials for the Financial Times. “It’s a company just burning cash. They lost $20.9 billion in 2025,” he said. More than $800 million of OpenAI’s revenue that year came from SoftBank for a program called Crystal Intelligence, according to Zitron, who said he could find no evidence of activity connected to it. SoftBank holds a large shareholding in OpenAI without board seats, he added.

The listing timeline compounds the pressure. OpenAI had been expected to go public this year, but the New York Times reported the company is considering a delay until 2027, according to the interview. “That’s lethal for a number of people,” Zitron said.

The structural problem, in his telling, sits in the cash cycle. “OpenAI and Anthropic need continual flows of capital. They do not pay their bills out of existing cash flow,” Zitron said. Any interruption to that capital, he argued, becomes the first domino.

Circular structures and owned infrastructure

Asked whether the companies running the hyperscalers failed at due diligence, Zitron offered a different reading. The platforms “did the due diligence in the sense that they said, we are going to create our largest customers and we’re going to own large parts of them,” he said. The arrangement extends to hardware. According to Zitron, Broadcom sells tensor processing units to Google, the chips are then sold to Anthropic and rented back to Anthropic through Google. “Google gets to double up on revenue,” he said.

The funding picture follows the same loop. OpenAI and Anthropic have raised between $200 billion and $300 billion, according to Zitron, yet the effective total runs higher because Microsoft, Google, and Amazon built their infrastructure for them. He referenced testimony from the trial between Sam Altman and Elon Musk, in which a Microsoft executive put that infrastructure cost at $100 billion, before settling on a figure of roughly $70 billion to $80 billion of capacity the AI companies never had to pay for. The exchange turned briefly to Enron and WorldCom as reference points for fiduciary responsibility, with the explicit caveat that no comparable conduct was being alleged.

PPC Land coverage documents how deep the ownership ties run. Microsoft and Nvidia committed a combined $15 billion to Anthropic in November 2025, valuing the company at approximately $350 billion, while Anthropic committed to purchasing $30 billion of Azure compute capacity – a $3.2 billion gain on that stake lifted Microsoft’s net income 31% in the quarter reported on July 29, 2026. The customer-concentration risk Zitron describes has already reached credit ratings elsewhere: S&P Global Ratings cut Oracle to BBB- in July 2026, noting that OpenAI accounts for roughly half of the $638 billion in remaining performance obligations Oracle carries.

The data center mathematics

Zitron then worked through the demand side of the buildout. Sightline Climate identified about 190 gigawatts of data center capacity in planning or construction as of February, according to figures he cited. Applying a power usage effectiveness rating of 1.3 – the ratio between total facility power and the power reaching computing equipment – and $12 million per megawatt, Zitron calculated that the facilities would require more than $1.6 trillion in annual revenue to justify themselves. “Having two customers is not going to do that,” he said. Even the heaviest spender could not close the gap; he estimated OpenAI would need to spend $400 billion a year, funding he does not believe the company will secure.

Construction timelines add friction. Data centres take 12 to 36 months to build depending on size, according to the interview, slower than the market narrative assumes.

The transformation of the platforms themselves troubles Zitron as much as the arithmetic. Amazon, Google, and Microsoft have shifted from cash-generating businesses with low asset intensity into what he called “bulbous, GPU filled asset mongers,” filled with semi-built data centres serving two or three customers. Meta invests in AI as well but does not yet sell compute capacity, he noted. For his thesis to hold, Zitron argued, nothing dramatic needs to happen: OpenAI and Anthropic would simply have to grow to an implausible size to make the capacity pay off, because otherwise demand for compute at scale does not exist.

Why this matters for the marketing community

The companies named throughout the interview are the companies that sell most of the world’s advertising, and the spending Zitron questions runs through the earnings reports marketers watch each quarter. Alphabet raised its 2026 capital expenditure guidance to $195 billion to $205 billion on July 22, 2026, reported negative free cash flow of $5.9 billion for the quarter, and now carries $98.2 billion in long-term debt, up from roughly $16 billion a year earlier. The company had already raised approximately $85 billion in equity in June 2026 to fund infrastructure it describes as supply-constrained against demand, a framing that sits at the opposite pole from Zitron’s reading. Meta reported quarterly capital expenditures of $31.08 billion on July 30, 2026, against $17.01 billion a year earlier, with free cash flow falling to $784 million. Microsoft’s capital expenditures rose 70% to $41.0 billion in its June quarter, with calendar 2026 spending expected around $175 billion. Advertising revenue funds a substantial share of those budgets, which means ad businesses now bankroll infrastructure whose demand case rests, in Zitron’s account, on two unprofitable tenants.

The exposure runs in the other direction too. The same AWS infrastructure serving Claude and GPT-5 powers programmatic bidding, creative generation, and Amazon’s Rufus shopping assistant, so the operational continuity of AI ad tooling depends on the economics Zitron disputes. OpenAI itself has entered the advertising market that would need to fund it, with projections of $102 billion in advertising revenue aimed at Google’s $224 billion search business. Concentration compounds the stakes: OpenAI, Google, and Anthropic held more than 84% of the AI agent market as of May 2026, leaving marketers who build on these systems dependent on a narrow set of suppliers whose financing Zitron considers unsustainable.

The productivity question raised near the end of the interview cuts closest to marketing employment. One host framed the best case for the spending as productivity gains in which fewer people do more work, noting serious implications for the labour force if the bet succeeds and for the stocks if it fails. The marketing industry has already placed that bet with its own money. Agency leaders named AI their top investment priority for the second consecutive year, with 77.7% of vice presidents and above planning to increase AI spending, while 39.9% of agencies conducted layoffs within the preceding 12 months. AI sales and marketing investment reached $3.7 billion globally in the first part of 2026. Whether tokens sold by two cash-burning model companies can substitute for salaried expertise remains contested territory: one analysis argues cheaper marketing work expands total demand for marketers rather than shrinking it, while task-level measurement shows models covering a fifth of job tasks without revealing whether those are the tasks that matter. If Zitron is right about the financing, the industry restructuring its workforce around these systems has tied its labour model to companies that, in his words, do not pay their bills out of existing cash flow.

Timeline

Summary

Who: Ed Zitron, chief executive of EZ Primary Research, speaking on Bloomberg, with claims involving OpenAI, Anthropic, Google, Microsoft, Amazon, Meta, SoftBank, Broadcom, and estimates from UBS, Barclays, Sightline Climate, and The Information.

What: Zitron argued that AI capital expenditure builds infrastructure for two unprofitable companies, citing UBS estimates that OpenAI and Anthropic will supply 27% of Google Cloud revenue this year and more than 48% next year, worth over $124 billion, alongside OpenAI’s $20.9 billion loss in 2025 and a calculation that planned data centres require more than $1.6 trillion in annual revenue.

When: The interview was published on July 31, 2026, following the week in which Microsoft, Meta, Amazon, and Alphabet reported quarterly results, and had drawn more than 420,000 views by August 2, 2026.

Where: The interview took place in the Bloomberg Interactive Brokers studio and was distributed globally through the Bloomberg Podcasts YouTube channel.

Why: The revenue concentration matters to the marketing community because advertising income funds the hyperscaler infrastructure in question, AI advertising tools run on that same infrastructure, and agencies restructuring their workforces around AI have tied their labour models to two companies that, according to Zitron, do not pay their bills out of existing cash flow.

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

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Why every web page now needs to work like a homepage

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Visitors enter anywhere, so every page must orient them.

I can usually tell when a website has been designed around an outdated assumption. The Homepage is polished and persuasive. Move one level deeper, however, and the experience collapses. Service pages lack context, articles offer no route forward, and case studies assume prior knowledge.

This is important because people don’t arrive politely through the front door. They enter through whichever URL answers their question, whether that is a search result, an article, a portfolio link, or a product recommendation. In practice, the first page someone sees is their Homepage.

I don’t mean every page should imitate the Homepage. I mean every page must be capable of welcoming a stranger.

For help building your own site, see the best blogging platforms or the best website builders for small businesses.

The Homepage-first journey is fictional

Many website projects are still planned as neat diagrams: Homepage, navigation, enquiry. It looks sensible in a sitemap. Real behaviour is far less obedient.

Search engines surface relevant pages, not necessarily the root domain. Colleagues and friends share page URLs. Campaigns use dedicated landing pages and AI-assisted search recommends specific resources and not company home pages that weren’t cited.

Contentsquare’s 2026 benchmark report draws on 99 billion web sessions across more than 6,500 websites, showing the scale and fragmentation of digital journeys.

Search engines surface relevant pages, not necessarily the root domain. Colleagues and friends share page URLs. Campaigns use dedicated landing pages and AI-assisted search recommends specific resources and not company home pages that weren’t cited.

Contentsquare’s 2026 benchmark report draws on 99 billion web sessions across more than 6,500 websites, showing the scale and fragmentation of digital journeys.

A service page should identify the service, the intended customer, and the outcome. An opening that explains that a company replaces damaged underground pipes without excavating an entire driveway establishes scope, relevance, and distinction.

Clarity here is not simplistic copywriting. It reduces the interpretation visitors must perform before deciding whether to stay.

Self-sufficient does not mean repetitive

The obvious danger is duplication. Once teams accept that every page must introduce the business, they often paste the same company paragraph, accreditation strip and oversized call to actions across every template.

That creates consistency, but not necessarily usefulness.

A Homepage caters to multiple audiences. An internal page has a narrower job, so it should be more specific, not simply smaller

A page should carry enough information to stand on its own while showing evidence relevant to the decision being made. Someone reading a technical guide may need an author biography, review date and links to deeper resources. A prospective buyer needs specifications, availability and guarantees about returns. On a location page, service coverage and local proof matter more than the company’s full history.

A Homepage caters to multiple audiences. An internal page has a narrower job, so it should be more specific, not simply smaller.

Put trust where the claim is made

Instilled website on three mobile screens

(Image credit: Instilled)

One of my strongest opinions about website design is that centralised proof is wasted proof.

Businesses commonly place their best testimonials, certifications and case studies on the Homepage, then expect visitors to seek them out. Most will not. If a service page makes a claim about response times, specialist knowledge or measurable results, the evidence should appear beside it.

That could mean placing a relevant quotation below the process section, linking a performance figure to a case study or displaying the certification required for that work.

Trust is contextual. A five-star review about friendly communication does little to support a claim about technical capability. Strong pages match the proof to the concern occupying the visitor at that moment.

Designers treat navigation as a hierarchy of movement: Homepage, then category, then subcategory, and then a single page. Visitors will move back and forth instead.

Someone reading a blog may want to read one more guide before considering a service. Shoppers often seek another item for comparison. A case-study reader might want to examine the methodology rather than immediately completing a contact form.

Internal links should reflect likely decisions rather than merely mirror the sitemap. Google recommends logical site structures, links between relevant pages and concise anchor text that describes the destination. That is useful SEO guidance, but it is also basic courtesy.

‘Learn more’ places the burden on the visitor. ‘Compare installation options’ tells them what lies ahead.

The next step is not always conversion

There is a persistent belief that every commercially useful page needs a prominent enquiry button. Calls to action are measurable, familiar and easy to defend in a design review.

Sending everyone towards the same form can be intellectually lazy.

Still, sending everyone towards the same form can be intellectually lazy.

A visitor may be researching, comparing, or checking a single detail. The right next step depends on their readiness. Sometimes it is requesting a quote. Elsewhere, it is examining specifications, reading a case study, checking service areas or understanding the process.

I prefer to design for progression, without pressuring visitors. Each page must help visitors make the next useful decision, even if it doesn’t immediately generate a lead. Premature conversion pressure can make a website feel less helpful when visitors are looking for reassurance.

Audit the pages people actually enter

Homepage redesigns attract attention. Entry-page audits are often more valuable.

Use analytics to identify the service pages, articles, products and case studies through which new users arrive. Review each one without relying on knowledge gained elsewhere.

Check if the page is understandable, establishes ownership, presents relevant proof and offers clear progression. Test it on mobile, where breadcrumbs disappear, headings wrap, and supporting context is pushed below the opening section.

The goal is to remove hidden dependencies that make internal pages work only for visitors following the intended path.

The homepage still matters as the broadest expression of the brand. But it no longer owns the first impression.

That responsibility is now distributed across the website. Every significant page is a potential front door, and I believe it should be designed with the confidence, context and care that position deserves.

Feature image credit: Getty Images

By 

Sue Vervaet is a WordPress web developer and Head of Digital at Instilled. She is a highly experienced WordPress web developer with specialist knowledge in custom-built websites.

Her role is to turn written content and design concepts into websites that are easy to use, functional, secure and SEO friendly. With expertise across front-end development, web design, SEO, analytics and content management systems, Sue helps ensure each website is built with both users and search engines in mind.

Sourced from CREATIVE BLOQ

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How to Track Your Brand’s AI Visiblity in 2026

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By Simon Moser, edited by Maria Bailey

With 94% of enterprise executives ramping up AI visibility spending but a third of marketers unsure how to measure it, here’s the five-layer system that separates real impact from dashboard theatre.

Key Takeaways

  • Track citations, mentions, and recommendations as three separate metrics — lumping them together lets you celebrate movement that never turns into revenue, because being visible in an AI answer and being recommended by it are not the same thing.
  • Platforms like Peec, Semrush, and Ahrefs are useful monitoring infrastructure but not ground truth; the strongest setup is hybrid — automated tracking for broad patterns paired with monthly manual checks across ChatGPT, Claude and Gemini on the prompts that actually drive pipeline.

According to a recent report, 94% of 250 surveyed enterprise C-level executives plan to ramp up spending on AI visibility efforts in 2026. However, while almost all executives agree that generative engine optimization had a positive impact on their business in the previous year, a HubSpot study showed that 32.5% of marketers have no clue how to monitor AI citations — let alone measure their impact.

Unlike traditional search optimization, tracking a brand’s AI visibility isn’t as easy as opening Search Console. For many businesses, it’s not even as easy as signing up for an Ahrefs subscription — although there are already similarly designed products available. The truth is that the most effective AI visibility tracking requires a layered approach. Here’s the system I’ve been running since the start of 2026.

1. Get clear on what you’re actually tracking

Before you touch a single tool, decide what success looks like. In my experience, most founders lump together several very different signals and then wonder why their reporting tells them nothing useful.

The first is citations. A citation is when an AI engine links to your website or clearly uses your page as a source inside its answer. It is the closest thing AI visibility has to a traditional SEO signal, which is why so many teams start there.

The second is mentions. A mention is when your brand name appears inside the response, whether or not the AI links back to you. Mentions matter because they show your brand is part of the model’s vocabulary on a topic. But mentions can also flatter you. A brand can be mentioned as a passing example and still lose the commercial intent of the query.

That is why I treat recommendations as a third and separate metric. This is the question that matters most: When someone asks for the best option, does the AI actually suggest your product, company or service, or does it just acknowledge that you exist? As I wrote in my previous Entrepreneur piece on how AI recommends local businesses, being visible and being recommended are not the same thing.

If you only track citations, you can end up celebrating movement that never turns into revenue. Track citations, mentions and recommendations separately, or your reporting will blur the thing you actually care about.

2. Build a prompt library that sounds like a real customer

Nothing in AI visibility works without a serious prompt library filled with the questions a real buyer would ask to discover a brand like yours.

I always start manually. Before I ask any AI tool for help, I write the first 10 to 20 prompts myself. That matters because you already know the language your customers use, the objections they have and the competitors they compare you against. Start with the obvious commercial prompts, then expand into comparison queries, pain-point queries, and local variations.

Good prompt libraries also need specifics. Add city names where geography matters. Add competitor names where comparison matters. Add budget, company size, use case or industry where those filters would realistically shape the answer. OpenAI’s own data shows how conversational ChatGPT usage has become, which means generic one-line prompts often miss how people actually search.

Once you have that manual base, use Claude or ChatGPT to generate variants and cluster them by intent.

It’s better to have 50 good prompts than 300 bloated ones. Too few prompts and you miss the long tail. Too many, and you start tracking noise instead of buying intent.

3. Use platforms for scale, but understand their limits

A growing number of tools now cover AI visibility directly, including Peec, Semrush, Ahrefs and DataForSEO. What makes them useful is not just that they collect data. It is that they make the data operational.

A good platform can track multiple engines at once, automate daily checks, visualize trend changes, generate reports for your team and often let you set a location. Some also suggest new prompts to monitor, identify competitors you had not considered and surface content gaps that may be hurting your visibility. Once you spend the time setting them up properly, the maintenance burden is relatively low.

But there is a big catch. A lot of this tracking still depends on search-enabled environments, model snapshots or vendor-specific ways of querying the models.

That matters because the answer a user gets from a live AI session can look very different depending on whether web search is active, what context is available and how the system decides to compose the response. In other words, platform data can be directionally useful without being a perfect reflection of what every real user sees.

This is where teams get overconfident. They subscribe to a dashboard, see a neat visibility chart and assume they now understand the market. They do not. They understand one layer of it.

That does not make the tools useless. It just means you should treat them as monitoring infrastructure, not ground truth. For a useful overview of how these products fit together, this guide on measuring AI visibility in 2026 is a solid reference point.

4. Keep a manual tracking layer for the prompts that matter most

The most labour-intensive part of AI visibility tracking is also the most revealing. Once a month, I like to take the most commercially important prompts from my library and run them manually across ChatGPT, Claude and Gemini in fresh chats.

The point of doing this is control. You can test the exact prompt phrasing, add the location directly into the query when geography matters and compare outputs side by side. You also get the full richness of the response instead of a summarized score inside a platform dashboard.

From there, I save the responses and use a high-reasoning model to analyse them. I want a clean breakdown of how often my brand was cited, how often it was mentioned, whether it was actively recommended, how prominently competitors appeared and what patterns keep repeating across answers. You can also use this layer to ask for hypotheses about why certain competitors keep outperforming you on specific prompts.

This approach takes more effort, but it gives you something automated tools often flatten: context. You see not just whether your brand showed up, but how it showed up and what narrative surrounded it.

In practice, the best setup is usually hybrid. Use a platform subscription to monitor broader patterns, and use manual checks on the prompts that actually matter to your pipeline.

5. Measure business impact, not just AI visibility

Visibility is interesting. Impact is what pays for the work.

The most obvious place to start is Google Analytics. Track identifiable AI referral traffic where possible and monitor how those visitors behave compared with other channels. That still will not show you the full picture, because some people will discover your brand through an AI answer and come back later through a branded search, direct visit or referral.

That is why I also like simple operational fixes. Add “AI assistant” as an answer option to your “How did you hear about us?” field. If your business uses sales calls, train the team to ask whether the lead first heard about you through ChatGPT, Claude, Gemini or another AI tool. It sounds basic, but this kind of qualitative data becomes surprisingly valuable once patterns start repeating.

Watch for indirect signals too. When your recommendation rate improves on important prompts, do branded search, demo requests and direct traffic rise soon after? If your visibility numbers look better but none of those downstream indicators move, something in the chain is broken.

By Simon Moser,

Founder of Polygrowth. Simon Moser is a European communications specialist and entrepreneur. He is particularly focused on new markets, such as high-tech, fintech and cannabis. In 2018 Simon co-founded Strain Insider, which has since become the leading online media outlet in the European cannabis market.

Edited by Maria Bailey

Sourced from Entrepreneur

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I’ve Watched Every Marketing Trend Die Since 2011. This 1 Strategy Never Stopped Working

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BY DAVE KERPEN

Providing value for free is the growth strategy that’s outlasted every algorithm change.

When I wrote Likeable Social Media in 2011, one chapter drew more scepticism than any other. The advice: provide value to your audience — genuinely useful, expert help — completely free. Executives would push back in every meeting: “Why would we give away what we sell?” Fifteen years, several companies, and an eight-figure exit later, I can report the results: every platform from that era changed beyond recognition. Facebook reach collapsed. Twitter became X. Algorithms rose, fell, and rose again. And through every single shift, as I’ve told founders here before, exactly one strategy never stopped working: being genuinely useful, for free, in public.

It’s the closest thing to a law of gravity that marketing has.

Here’s why it works, and why it keeps working when everything else breaks. Every algorithm every platform has ever built is, underneath the math, trying to answer one question: what do people actually find valuable? The tactics that game that question expire every eighteen months. The content that honestly answers it gets rewarded by every algorithm, forever, because it’s what the algorithm is looking for. When you provide real value, you’re not optimizing for the platform. You’re optimizing for the thing the platform is optimizing for. That’s why it’s permanent.

But the deeper reason has nothing to do with algorithms. Free value is how strangers learn to trust you before they’ve risked a dollar on you. Every useful thing you publish is a free sample of your competence. A restaurant gives away tastes; you give away answers. By the time a reader becomes a buyer, the sale is already made — you made it weeks or years ago, one helpful post at a time. Nearly everything I’ve built, from my books to my companies to a LinkedIn following of 650,000-plus, grew from that single flywheel.

Now, the objection I’ve heard for fifteen years: “If I give away my expertise, nobody will pay for it.” Here’s what actually happens, in my experience across hundreds of brands: the people who consume your free value and never pay were never going to pay. They were never your customers — but they are your marketers, sharing your work with people who will pay. Meanwhile, real buyers don’t want your information; they want your implementation, your product, your time, your accountability. Knowing how isn’t the same as having it done. I can read a hundred free articles about how to sell a company; Carrie still built an entire M&A advisory practice, The Whisper Group, because knowing and doing are different products.

And in the AI era, this strategy now has a caveat and an upgrade. The caveat: generic value is now worthless. AI can generate infinite “5 tips for better marketing” posts, and everyone knows it. The upgrade: what AI cannot generate is your value — your real numbers, your scars, your specific stories, your contrarian takes earned from actual experience. The bar for “valuable” moved up, and it moved directly toward the people with real stories to tell. That’s good news for every founder reading this, because you have something the content farms never will: you were actually there.

How to run this playbook in 2026:

Answer real questions.

The questions your customers ask you every week are your content calendar. Answer them fully and publicly — don’t tease, don’t gate the good part.

Give away the how, sell the done.

Publish your methodology. Your process is not your product; your execution is.

Be specific enough to be un-generatable.

Numbers, names, mistakes, screenshots. If ChatGPT could plausibly have written it, don’t post it.

Stay patient.

This flywheel is slow for months and unstoppable for decades. Most people quit at month three. That’s precisely why it still works.

Every gimmick you’re being pitched right now will be dead by next year. Generosity won’t be. Be useful, be free, be everywhere — and let compounding do what it does.

Feature image credit: Adobe Stock

BY DAVE KERPEN

Sourced from Inc.

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