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Prompts to programming: Vibe coding vs. traditional coding a website
Good vibes can spark great ideas, and now they can even help you build a website. With vibe coding, you don’t need to start by writing lines of code. Instead, you tell AI what you want to create, refine the results through conversation, and watch your website take shape. While it won’t replace every aspect of traditional development, it’s opening the door for more people to bring their ideas online.
In this guide, we’ll explore what vibe coding is, how to build a website with it, and how it compares to traditional coding.
What’s the vibe around vibe coding?
The term vibe coding was coined in February 2025 by computer scientist Andrej Karpathy, co-founder of OpenAI and former AI leader at Tesla. He used it to describe a new way of building software: instead of writing code line by line, you simply explain what you want to create and let AI generate the technical implementation. In Karpathy’s words, you “say stuff, run stuff, and copy-paste stuff, and it mostly works.“
At its core, vibe coding is about shifting your role. Instead of acting as the developer who writes every line of code, you become the creative director. You describe your vision, review what the AI generates, ask for changes, and continue refining the result until it matches what you have in mind. Rather than focusing on syntax, frameworks, or debugging, your attention stays on the bigger picture: what you want to build and how it should work.
Overall, if I had to describe vibe coding in one sentence, “I’d say it feels less like programming and more like collaborating with an AI that speaks both your language and the language of code.“
What can you build with vibe coding?
Vibe coding isn’t limited to generating snippets of code. Today’s AI-powered tools can help you create everything from simple landing pages to complete websites, web applications, dashboards, and internal business tools!
With vibe coding, you can:
- Build websites and landing pages faster. AI can generate layouts, content, and page structures from a simple prompt, helping you move from idea to first draft in minutes.
- Start with ideas instead of code. Rather than thinking about HTML, CSS, or JavaScript, you focus on your goals, audience, and the experience you want to create.
- Generate a complete website foundation. AI can create pages, navigation menus, sections, and design elements that you can build on.
- Focus on creativity rather than the technical setup. Spend more time shaping your messaging, branding, and design while AI handles much of the repetitive work and offloads technical tasks.
- Customize and iterate quickly. AI-generated websites can be refined through multiple iterations, allowing you to adjust layouts, content, and other design elements until they match your needs.
- Explore different ways to present the same content. AI can quickly generate different design approaches for the same information, making it easier to compare options and find the one that works best for your audience.
For example, you could ask AI to create three versions of the same interface: one with low-contrast colors, one with vibrant colors, and one with a dark-mode option.
Understanding vibe coding for building a website
When the concepts of vibe coding are applied to website design, it means you can converse with an AI platform and bring your vision to life online without worrying about complex concepts like HTML, CSS, JavaScript, or other programming languages. Simply explain your brand in plain English and let AI handle much of the technical work.
For example, you could prompt an AI with something like:
“Create a modern website for a local coffee shop with warm colors, an online menu, customer testimonials, and a contact form.”
This conversational approach makes website creation much more accessible, especially for entrepreneurs, freelancers, creators, and small business owners who have ideas but little or no coding experience.
That said, vibe coding doesn’t eliminate the need for human input. AI can generate a strong starting point, but creating a successful website still depends on having a clear goal, understanding your audience, and reviewing the output before going online.
Key benefits of vibe coding a website
Vibe coding is changing the way people approach website creation. Instead of spending weeks learning programming languages or waiting for a first draft, you can start with an idea and let AI help you bring it to life.
For anyone with a website idea, that shift means more time to focus on what they want to achieve and less time spent worrying about the technical details. Even experienced developers are embracing vibe coding for rapid prototyping and repetitive development tasks because it helps them move from concept to execution much faster.
Here are some of the biggest reasons why vibe coding is gaining momentum:
Launch ideas in hours, not weeks
Every website starts as an idea, but turning that idea into something people can visit has traditionally taken time. Vibe coding shortens that journey considerably. By describing your website in natural language, AI can generate a functional first draft in minutes. Instead of staring at a blank screen, you’re reviewing something tangible and deciding how to improve it. That faster start means you can validate ideas, collect feedback, and get your business online sooner.
Also read: New: Yoast AI Content Planner turns a blank post into a structured draft
Experiment and prototypes faster
One of the biggest advantages of vibe coding is how quickly it lets you test and refine ideas. Instead of spending days or weeks building a prototype from scratch, you can describe your vision in plain language and let AI generate a working first draft in minutes.
Whether you’re exploring different layouts, validating a business idea, or creating a website for a client, you can quickly experiment, gather feedback, and improve the result through conversation, making it easier to iterate before investing significant time or resources.
Iterate through conversations
Perhaps the biggest shift isn’t that AI writes code, it’s that building a website becomes a conversation.
Instead of manually editing every element, you can ask AI to redesign a section, rewrite your homepage copy, add a testimonials section, or make the layout feel more modern. Each prompt builds on the previous one, allowing your website to evolve naturally over time.
Can you really build a website using prompts?
The answer is yes, but it’s not because AI has replaced websites with something entirely new. Instead, it has changed how we build them.
Think of prompts as the new interface for website creation. Instead of dragging elements onto a page or writing HTML and CSS, you describe your business, goals, and the kind of website you want. Modern AI website builders then translate those instructions into a functional first draft, which you can continue refining through conversation.
Also read: Perfect prompts: 10 tips for AI-driven SEO content creation
That doesn’t mean prompting replaces creativity or strategy. AI can generate the website, but it still relies on your direction. The clearer your vision and the more context you provide, the closer the final website will be to what you imagined.
How to vibe code a website in 5 steps
Building a website with vibe coding isn’t about finding the perfect prompt; it’s about following the right process. The more clearly you communicate your goals and refine the results, the closer your website will be to what you originally envisioned.
Here’s a simple five-step framework to help you get started.
1. Define the purpose and style of your website
Before you open an AI website builder, spend a few minutes thinking about what you want to build. The better you define your vision, the better AI can translate it into a website.
Start by answering a few simple questions:
- Who is the website for?
- What do you want visitors to do?
- How should your brand feel?
- What pages or features do you need?
For example, instead of writing: “Build me a business website.”
Try something more descriptive:
“Create a modern website for a boutique coffee roastery. Use warm earth tones, clean typography, and large product images. Include a homepage, About page, online shop, customer reviews, and a contact form. The overall style should feel welcoming, premium, and easy to navigate.”
It explains the website’s purpose, functionality, and personality, which is important for launching as a brand or business that people are searching for.
Also read: What is search intent and why is it important for SEO?
2. Choose an AI website builder
Once you have a clear idea of the website you want to create, the next step is choosing the right AI website builder.
AI tools can support website development in different ways. For example, coding agents such as Claude Code offer specialized capabilities for building websites, but they typically require you to work in a development environment or via a command-line interface.
On the other hand, AI website builders offer a more accessible alternative: instead of working in a terminal or configuring a development environment, you can describe what you want through a conversational interface and start with a functional website that you can refine.
If you’re building a website with AI, the Bluehost AI Website Builder is a great place to start. It asks a few questions about your business, goals, and preferred style before generating a personalized website that you can continue to customize. Because it’s built into Bluehost’s hosting platform, you can go from idea to a live website without juggling multiple tools or worrying about technical setup.
From there, your role shifts from building pages manually to guiding AI toward the website you envision.
3. Write your first vibe prompt
Your first prompt lays the foundation for everything that follows, so it’s worth spending a little extra time getting it right.
This is where the idea of context engineering becomes useful. Rather than trying to cram every instruction into one clever prompt, think about the information AI needs to understand your project: what you’re building, who it’s for, how it should look and feel, what pages it needs, and what you want visitors to do.
You can build this context around:
- your business or project
- your target audience
- your preferred design style
- the pages you need
- important features or functionality
- your brand’s tone of voice
The key idea is simple: don’t focus on writing a clever prompt; focus on giving AI the context it needs to make good decisions. And as you continue vibe coding, you can add or refine that context through subsequent conversations rather than trying to get everything right in your first message.
4. Refine your website through conversation
Don’t expect your first prompt to produce the perfect website, and that’s completely normal. Vibe coding is an iterative process. Once AI generates the first version, continue the conversation by making small, focused improvements.
For example, you could ask AI to:
- make the hero section more visually engaging
- rewrite your homepage copy in a friendlier tone
- replace placeholder images with a cleaner layout
- add customer testimonials
- improve the mobile experience
- make the call-to-action button more prominent
Making one change at a time usually produces better results than asking AI to redesign the entire website in a single prompt. Think of each prompt as another design review. With every iteration, your website moves closer to the experience you originally imagined.
5. Review, publish, and keep improving
Before publishing your website, take some time to review everything AI has generated.
Check that the content accurately represents your business, all links and forms work correctly, and the design looks good on both desktop and mobile devices. This is also the perfect time to replace placeholder text, add your own images, and make sure the website reflects your brand.
Once you’re happy with the result, connect your custom domain and publish your website.
One of the biggest advantages of vibe coding is that the process doesn’t stop after launch. Need to add a new service, update your homepage, or create a landing page for a campaign? Simply return to the AI website builder, describe what you want, and continue refining your website through conversation instead of starting from scratch.
How to avoid the vibe coding doom loop
One of the biggest advantages of vibe coding is how quickly you can turn an idea into a working website. But as you continue refining your prompts, there’s a chance you’ll run into what’s commonly known as the vibe coding doom loop.
This happens when AI gets stuck trying to solve the same issue repeatedly without actually fixing it. It may suggest several “solutions,” but each one either introduces a new problem or brings you back to where you started. Instead of moving your website forward, you end up going in circles.
The doom loop usually happens when AI loses track of the bigger picture. This can occur if your original prompts are too vague, you keep changing your website’s direction midway through the process, or you request too many unrelated changes at once. As the conversation becomes more complex, the AI may struggle to connect all the pieces together, leading to inconsistent results.
How to fix this situation
The good news is that the vibe coding doom loop is usually avoidable. A few simple habits can help you keep AI on track and make the website-building process much smoother.
Get clarity on what you want
As mentioned earlier, context engineering is about giving AI the information it needs to make better decisions, rather than relying on increasingly elaborate prompts. Before asking AI to make changes, consider whether you’ve provided enough context about what you’re trying to achieve, who the website is for, and what you want the change to accomplish.
For instance, if you’re refining your homepage, explain the outcome you’re aiming for and provide relevant context about your audience, messaging, layout, or user experience. This gives AI a better basis for making the change and reduces the chances of it solving one problem while creating another.
Manage AI mistakes instead of chasing them
It’s important to remember that AI isn’t perfect. Treat each suggestion as a draft rather than a final answer. Review every change before moving on, make one improvement at a time, and don’t hesitate to undo or rephrase a prompt if the output isn’t what you expected. A little oversight goes a long way in preventing small issues from turning into bigger ones.
Vibe coding vs. traditional coding: What’s the difference?
By now, you’ve probably noticed that vibe coding doesn’t completely reinvent website development; it simply changes the way you interact with it. Instead of writing every line of code yourself, you’re guiding AI with prompts and refining the results through conversation.
That doesn’t mean traditional coding has become obsolete. Developers still rely on it to build highly customized websites, complex web applications, and features that require complete control over every aspect of the codebase. Vibe coding, on the other hand, focuses on speed, accessibility, and helping people bring ideas to life without getting bogged down in technical details.
Ultimately, both approaches can produce excellent websites. The biggest difference isn’t the final outcome; it’s the path you take to get there.
| Website aspect | Traditional coding | Vibe coding |
| Primary input | Programming languages like HTML, CSS, JavaScript, and PHP | Natural language prompts |
| Required skills | Coding and web development knowledge | Clear communication and prompt writing |
| Development process | Manual coding and debugging | AI-assisted generation and refinement |
| Time to first draft | Usually longer | Usually much faster |
| Customization | Unlimited control | High, depending on the AI tool |
| Best suited for | Complex, highly customized websites and applications | Business websites, portfolios, landing pages, MVPs, and rapid prototyping |
| Barrier to entry | Higher | Lower, making it accessible to non-coders |
Vibe coding a website: FAQs
Who should vibe code a website?
Vibe coding is a great fit for entrepreneurs, freelancers, creators, marketers, and small business owners who want to launch a professional website without having to learn to code. It’s also useful for designers and developers who want to prototype ideas quickly before investing time in a full-scale build. If you have a clear vision but limited technical experience, vibe coding can help you bring that vision online much faster.
Which tools are best for website vibe coding?
If you’re building a website, an AI website builder is the easiest place to start because it combines AI-generated design, hosting, and customization in one workflow. For example, the Bluehost AI Website Builder helps turn your prompts into a personalized website that you can continue to refine through conversation. If you’re creating an online store, Bluehost AI Store provides AI-powered ecommerce capabilities, while the Bluehost AI All Access Pack brings together multiple AI tools for building, managing, and growing your website.
Once your website is live, you can complement it with Yoast SEO AI+. It helps you prepare for the future of search by monitoring how your brand appears in AI-powered search experiences, tracking AI visibility over time, and providing insights into your brand’s presence alongside competitors.
How long does it take to create a website with an AI website builder?
The answer depends on your website’s complexity, but one of the biggest advantages of vibe coding is how quickly you can create a functional first draft. Many AI website builders can generate a website within minutes, while the remaining time is usually spent refining the design, content, and functionality to match your goals.
If you’d like a detailed breakdown of what influences the timeline, read our guide on how long it takes to build a website with AI.
Your prompts build the site. SEO helps people find it
Creating a website is an exciting milestone, but publishing it is only the beginning. Whether you’ve built your website through traditional development or vibe coding, it still needs to be discovered by the people you’re trying to reach. That’s where SEO comes in.
Today, your website isn’t just being read by search engines. AI assistants and other intelligent systems are also interpreting your content to answer questions, recommend businesses, and surface relevant information. That means your website needs to communicate clearly with both people and machines.
If you’re running a WordPress website, Yoast SEO Premium helps lay that foundation. It automatically generates structured data that helps search engines and AI systems better understand your pages, your content, and your expertise. Features such as schema aggregation create a unified view of your website’s structured data, while the Bot Blocker gives you more control over which AI systems can use your content for training.
A smarter analysis in Yoast SEO Premium
Yoast SEO Premium has a smart content analysis that helps you take your content to the next level!
SEO doesn’t stop once your website is published, either. Creating optimized titles and meta descriptions for every page can be time-consuming, especially as your website grows. Yoast SEO Premium helps speed up that process with AI-powered suggestions that can draft SEO titles and meta descriptions for individual pages or in bulk. Every suggestion remains fully editable, giving you the final say before anything goes live.
The post Prompts to programming: Vibe coding vs. traditional coding a website appeared first on Yoast.
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The August 2026 SEO Update by Yoast recap
Each month, we host the SEO Update by Yoast, covering the latest in search and AI. In this edition, Carolyn Shelby and Alex Moss discussed Google’s new tools for publishers, the impact of AI on search visibility, and how to adapt to the evolving digital landscape.
Watch the full recap on YouTube to dive deeper into these topics, hear examples, and get answers to audience questions.
Key stories from August 2026
Google Trends adds comparison over time data
Google Trends now allows users to compare search interest over time, providing deeper insights into keyword trends and seasonality.
Why it matters:
– Helps identify long-term trends and seasonal fluctuations in search behavior.
– Useful for content planning, especially for topics like news, e-commerce, and seasonal events.
Actionable takeaway:
– Use Google Trends to refine your content strategy by focusing on rising or consistently popular topics.
– Compare related keywords to understand user intent shifts over time.
Explore Google Trends.
Cloudflare introduces new controls for AI content access
Cloudflare announced new default settings for AI crawlers, blocking “Training” and “Agent” crawlers by default for new domains starting September 15, 2026. However, Google has stated it will not honor these blocks for its search crawlers.
Why it matters:
– Publishers now have more control over how AI systems access their content.
– Google’s refusal to honor these blocks means AI-driven search visibility may still be impacted.
Actionable takeaway:
– Review Cloudflare’s new AI traffic settings to understand how they affect your site.
– If you rely on AI-driven traffic, ensure your content remains accessible to Google’s crawlers.
Read Cloudflare’s announcement.
Google updates product structured data guidance
Google has updated its product structured data guidelines, emphasizing the importance of rich snippets for e-commerce sites. The update includes new properties like “category” to improve product visibility in search results.
Why it matters:
– Structured data helps search engines better understand and display product information.
– E-commerce sites can benefit from enhanced visibility in rich results and AI-driven search.
Actionable takeaway:
– Audit your product schema to ensure compliance with Google’s updated guidelines.
– Use Yoast SEO to implement structured data effectively.
View Google’s Product Structured Data Guidelines.
Google Search Console expands reporting for social and video platforms
Google Search Console now includes platform properties, allowing creators to track how their content on Instagram, TikTok, X, and YouTube performs in Google Search and Discover.
Why it matters:
– Provides a holistic view of how social and video content contributes to search visibility.
– Helps creators and businesses optimize their content for broader reach.
Actionable takeaway:
– Set up platform properties in Google Search Console to monitor social and video performance.
– Compare data across platforms to identify gaps and opportunities.
Learn more about platform properties.
Google’s Preferred Sources button
Google has expanded its Preferred Sources feature, allowing users to prioritize specific publishers in their search results. Publishers can now embed a button on their sites to encourage readers to add them as a preferred source.
Why it matters:
– Publishers can increase visibility in AI Overviews and Top Stories by becoming a preferred source.
– Users benefit from personalized search results tailored to their trusted sources.
Actionable takeaway:
– Add the Preferred Sources button to your website to encourage readers to prioritize your content.
– Monitor performance in Google Search Console to see how preferred source status impacts visibility.
Guide to Preferred Sources in Google Search.
AI Overviews can now generate images
Google’s AI Overviews now have the capability to generate images directly in search results, enhancing the visual appeal of AI-driven answers.
Why it matters:
– Publishers must ensure their content is descriptive and accurate to avoid misinterpretation by AI image generators.
– Visual content in AI Overviews can drive engagement but may also raise copyright concerns.
Actionable takeaway:
– Provide detailed descriptions in your content to guide AI image generation.
– Monitor AI Overviews for inaccuracies and report issues to Google.
Read About AI image generation in search.
Google claims AI search sends billions of clicks to websites
Google stated that its AI search features are sending billions of clicks to websites weekly, though the quality and intent of these clicks remain debated.
Why it matters:
– AI-driven search is becoming a significant traffic source for publishers.
– Not all clicks may be valuable, as some could be generated by AI systems rather than human users.
Actionable takeaway:
– Monitor AI-driven traffic in Google Search Console and Microsoft Clarity.
– Focus on optimizing for high-intent queries to maximize the value of AI-generated clicks.
Read about Google’s AI search clicks claim.
Google loses key claims in lawsuit against SerpApi
A U.S. federal court dismissed key claims in Google’s lawsuit against SerpApi, a move that could preserve access to third-party SEO tools that rely on SerpApi for data.
Why it matters:
– The outcome ensures that SEO professionals can continue using tools that depend on SerpAi.
– Highlights the ongoing tension between Google and third-party data providers.
Actionable takeaway:
– Stay informed about legal developments that could impact SEO tooling.
Read about the SerpApi lawsuit outcome.
Microsoft Clarity adds branded and non-branded AI query reporting
Microsoft Clarity now offers AI query reporting, allowing website owners to distinguish between branded and non-branded queries that lead to AI citations.
Why it matters:
– Provides insights into how AI systems cite your content.
– Helps identify opportunities to improve visibility for high-value queries.
Actionable takeaway:
– Set up Microsoft Clarity to track AI query performance.
– Use insights to refine content strategy and target high-intent queries.
Explore Microsoft Clarity’s AI query reporting
Anthropic adds watermarks to Claude content
Anthropic announced that Claude models will now include watermarks in AI-generated content to comply with EU regulations and improve transparency.
Why it matters:
– Watermarking helps distinguish AI-generated content from human-created content.
– Businesses must ensure compliance with emerging AI regulations.
Actionable takeaway:
– Familiarize yourself with AI watermarking and its implications for content creation.
– Use AI tools responsibly and transparently to maintain trust with audiences.
Learn about Claude’s watermarking
Sign up for the next SEO Update by Yoast
The next SEO Update by Yoast is on September 25, 2026, at 4:00 PM CET (10:00 AM EST). Sign up to join live!
The post The August 2026 SEO Update by Yoast recap appeared first on Yoast.
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Google AdSense to change how it counts impressions: Begin-to-Render
Google notified AdSense publishers that it is switching how it counts impressions for display ads. Google will switch to a Begin-to-Render methodology that will count an impression only once the ad has loaded and rendered on the user’s device.
What’s changing. Google wrote:
- “Currently, AdSense counts an impression when an ad starts to download to a user’s device.”
- “Beginning on February 17, 2027, we will move to the Begin-to-Render methodology. This means an impression will only be counted once the ad has successfully loaded and has started to render on the user’s device.”
Google will only count the impression when the ad fully loads.
The impact. Google said that when this change goes live, “you may see a change in total impressions.” Google said “this is because impressions that started to download but never actually rendered will no longer be counted (e.g., if a user left the page before the ad began to render).”
More details. Google posted a help document with more details that says “Currently, Ad Manager and AdSense already use or are compliant with begin-to-render for native, app, and video inventory impression counting. The shift to count display ads via BTR unifies our impression counting methodologies.”
Why we care. If you run AdSense ads on your site, expect impressions and maybe earnings to be lower after February 17, 2027. Advertisers may notice more engagement on their ads for less money, I assume.
This is a big change for AdSense display ads and we have several months to prepare for it.
Read more at Read More
10 technical SEO audit mistakes that lead to bad recommendations
Most technical SEO audits produce plenty of findings. Then the document sits in a shared drive for six months, and nothing gets deployed.
Sometimes that’s the client’s fault. More often, it’s the audit’s. The findings were never validated, ranked by a tool’s notion of severity, or written in a way no developer could act on.
Here are 10 mistakes that keep showing up — and what to do instead.
1. Crawling without JavaScript execution enabled
Screaming Frog will show you both versions of the page in a single crawl, as long as JavaScript rendering is on and you’ve enabled storing both the original and rendered HTML.
The comparison shows you any body copy, internal links, canonical elements, or meta robots directives that exist in the rendered DOM but not in the initial HTML response.
You can also see the diff side by side in the View Source tab.
Google renders most pages without issue, but content that only appears after JavaScript runs is still less reliable. A blocked resource, a script error, or a timeout can leave it out of the index entirely.
Most AI crawlers don’t execute JavaScript at all, so a page can rank in Google and still be invisible to the systems generating AI answers.
If you find a gap, confirm it with the URL Inspection tool in Search Console. That gives you Google’s own view of the rendered page, which is harder for a developer to argue with than a screenshot from a third-party crawler.
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2. Ignoring the Page indexing report in Search Console
You’ll find it under Indexing > Pages. It’s the only report where Google tells you directly whether a URL is indexed, crawled but not indexed, discovered but not indexed, a soft 404, or something else.
The Page indexing report looks like this:
Not every URL in the “Not indexed” bucket is a problem, which is where people go wrong with this report. Alternate page with proper canonical tag, excluded by noindex tag, and page with redirect are all normal outcomes of a site that’s set up correctly.
The exclusions worth investigating are the ones you didn’t expect. Pages you want ranking that sit in Crawled – currently not indexed, or a Discovered – currently not indexed count that keeps climbing.
3. Sampling URLs at random instead of by template
Pull URLs by page type rather than at random, so you’re covering product pages, category pages, blog posts, filtered views, paginated series, and whatever else the site generates. Most technical issues worth reporting are template issues.
Get the canonical rule wrong on a product template, and you’ve broken it on all 40,000 product pages at once. If your sample includes three blog posts and a contact page, you’ll miss that and report something trivial instead.
Sampling by template also makes the fix cheaper to scope. A developer can estimate “change the canonical logic on the PDP template” in about a minute. Nobody can estimate a list of 40,000 URLs.
Dig deeper: Technical SEO testing: How to build a stronger experiment
4. Auditing from a single data source
Every tool is blind to something. A crawling tool only finds what’s linked or what you feed it, so orphaned pages stay invisible unless you supply them.
Search Console tells you Google’s verdict but not the reason behind it. Analytics only records visits where the tracking code runs, so crawler activity mostly doesn’t show up.
Server logs are the only source that shows every request Googlebot or AI crawlers make to your server and what they get back. Rate limiting, intermittent 5xx errors, and crawl activity concentrated on URLs you don’t care about only turn up here.
If you don’t have server logs, use the Crawl Stats report in Search Console. It’s sampled data, but you can still use the crawl request breakdown to see examples of URLs Google requested.
You don’t need all of them for every finding. But anything you’re about to hand to a development team should be confirmed in at least two places, and when two sources disagree, that disagreement is usually the more interesting finding.
5. Treating tool classifications as facts
Crawlers report missing titles and H1s on pages where the content renders fine, and they log 429 and 503 status codes that the site only returned because the crawl was running too fast.
Before a finding goes in the report, open the page and check it yourself. To confirm a status code like the example above, run a curl command.
It takes a couple of minutes per finding, and it prevents a developer from spending half a day chasing a problem that was never there. Developers who’ve been sent after one phantom issue tend to read the rest of your document with suspicion.
6. Documenting symptoms instead of causes
“The site has 12,000 duplicate URLs” is an observation, not a finding. The finding is whatever produces them, which might be faceted navigation without parameter handling, session IDs appended to URLs, or a CMS that generates a second copy of every page under a different path.
A developer can delete the 12,000 URLs in an afternoon. They come back the next time someone adds a filter because nothing about the underlying behavior changed. Tracing a duplicate back to its source takes longer than exporting the list, and it’s the part of the job a tool can’t do for you.
7. Prioritizing by tool severity instead of business impact
A crawler assigns severity based on the type of issue. It has no idea which templates generate revenue, which categories the business is pushing next quarter, or which pages the sales team sends prospects to. So you get audits where tons of low-value warnings sit at the top of the list, and a rendering failure on the highest-margin product template sits on page four.
In the example below, it looks like there are some high-priority issues with Page Titles: Outside <head>. What Screaming Frog doesn’t know is that all those URLs come from a template the team is deleting in the upcoming redesign.
Fixing this means asking the client questions the tool can’t answer. What are the priority products or services? Which pages convert? What’s launching this year? Then rank your validated findings against those answers instead of against a severity column.
Dig deeper: The biggest technical SEO time-wasters to avoid
8. Recommending changes without understanding site architecture
Redirects, canonical changes, URL removals, and noindex directives all have second-order effects. A noindex on a filtered category eventually cuts off the internal links to the products underneath it. A batch of old URLs redirected to the homepage will often end up classified as soft 404s.
Before you recommend any of these, map what links to the pages in question and what they link to in turn. Check whether they appear in navigation, sitemaps, or breadcrumbs. What you want to know is whether the page is the only route to something else, and whether the pages it links to have another way in.
9. Writing recommendations developers can’t act on
“Improve site speed” isn’t a recommendation. Neither is “fix canonicalization” nor “strengthen internal linking.”
A usable recommendation includes the affected URLs or templates, the root cause, the expected outcome, and enough detail for someone to estimate the work. If a developer has to come back and ask what you actually want, the ticket goes to the bottom of the backlog and stays there.
Compare “improve site speed” to something a developer can pick up, like “The LCP element on the PDP template is a hero image loading through a lazy-load script, so it needs loading=”lazy” removed and fetchpriority=”high” added, with LCP under 2.5 seconds.”
10. Prescribing the implementation instead of the outcome
Write the outcome and the constraints. The canonical on paginated pages needs to be self-referencing. Primary product content must be included in the initial HTML response. Then let the developer decide how.
You can absolutely suggest an approach if you have one, and on smaller sites you might be right. But you don’t know the framework’s limitations, what else depends on that component, or what the team already has planned for that part of the codebase.
Acceptance criteria give a developer something to build against and something to check their work against when they’re done. A prescription just invites a debate about whether your approach is the right one.
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What a good audit looks like
A crawler produces a list of problems in 10 minutes. Clients are paying for everything that happens after that, when someone checks which of those problems are real, determines which ones matter to the business, and assigns a cost to each fix.
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Google vs. Microsoft AI Max: What’s the same and what’s different
With the global launch of Microsoft AI Max for Search campaigns, it’s worth spending some time unpacking where the features align and differ from Google’s version.
Much of the core functionality is the same:
- Search term matching expanding your reach beyond static keyword lists. AI Max uses your keywords, ads, and landing pages, along with intent and contextual signals, to uncover relevant searches you may not qualify for with keywords alone. This is especially helpful for complex conversational queries.
- Text customization allowing ads to adapt to high-value placements and prospects. AI Max uses your existing assets and website content to generate and test additional messaging variations. It then selects the most appropriate combinations at auction time, helping you to deliver more relevant ad creative, including in AI-native experiences.
- Final URL expansion that routes people to the page on your site that best matches their intent. Instead of always sending traffic to a static landing page, AI Max can route people to the page that best matches what they’re looking for. This ensures a more consistent user experience across query, creative, and website.
However, there are some key nuanced differences. We’ll dive into:
- Where Google and Microsoft AI Max are the same.
- Key points of differentiation between Google and Microsoft AI Max.
- How to leverage AI Max successfully in new and existing account structure.
(Note: I am a Microsoft Advertising employee and wrote this post as platform-agnostically as possible. Features discussed in this post are based on publicly available help documentation as of September 2026.)
What’s the same between Google and Microsoft AI Max?
Aside from the core functionality, the following AI Max mechanics remain the same across both Google and Microsoft.
AI Max is a setting, not a campaign type.
Unlike Performance Max (PMax), Demand Gen, Audience ads, and other unique campaign types, AI Max represents optional settings within Search campaigns. These settings are designed to work better together, and advertisers tend to see the most benefit when they opt into all three.
Here’s how advertisers benefit when they leverage all three core AI Max features across Google and Microsoft:
- Search term matching allows for net new queries and that can bring the advertiser into auctions their static ad might not accurately reflect. By allowing the text to adapt in the moment, you’ll ensure your ads are relevant for those new queries.
- To deliver the most relevant landing page for unique queries, Final URL expansion can be really helpful. Final URL expansion is linked with Text asset generation as platforms need to be able to adapt creative to make a promise the dynamic landing page can deliver on.
- If you’re going to allow for text generation, you’ll get the most bang for your optimization buck by also allowing for intelligent matching of URLs and relevant queries.
That said, if advertisers want to begin with a more conservative test, they absolutely can.
For example, an ecommerce brand selling products with similar margins might feel more comfortable testing URL expansion because it allows them to get fuller coverage of their products without building out unique ad groups, but don’t want to leverage search term matching. While Brand Controls exist and can help ensure certain brands aren’t included (more on that later), it’s fair for the advertiser to start with just Final URL Expansion and Text customization.
Whether an advertiser tests just one, two, or all three parts of AI Max, these tests can be done safely with experiments. While both platforms support controlled testing, Google’s AI Max experiments are designed to run within the existing campaign by diverting traffic, whereas Microsoft Advertising’s Search Experiments compare a standard campaign against a cloned test version with AI Max enabled.
Here is guidance on how to experiment with AI Max:
- Start with your strongest campaign. Choose a campaign with stable performance and enough volume to generate results. Experiments work best when there’s enough traffic to detect meaningful differences.
- Create a 50/50 split. Split traffic evenly between the control and experiment. Keeping traffic balanced makes it easier to determine whether performance differences are due to the change you’re testing.
- Let the experiment learn. One common mistake is ending a test too quickly. New bidding strategies and AI-powered features need time to learn. If you’re testing AI Max specifically, go directly into an A/B test and allow sufficient learning time before drawing conclusions.
- Measure business outcomes, not just clicks. Focus on:
- Conversion rate
- Cost per acquisition (CPA)
- Return on ad spend (ROAS)
- Changes in revenue or conversion value
Conversion-based bidding is a core part of search term matching
AI Max’s search term matching relies heavily on conversion data in order to be successful. This is why both Google and Microsoft AI Max require conversion-based bidding when enabling this part of AI Max.
Conversion-based bidding works when there’s accurate conversion data flowing into the platform. It is ideal to have at least 15-30 conversions in a 30-day period before working with conversion-based bidding.
If your brand likely won’t hit the threshold, it makes sense to wait on trying AI Max until you can or to use micro-conversions with strategic conversion values. This means setting conversion values aligned to each stage of the journey and TROAS that reflects the focus on important steps.
For example, if a brand wanted to secure applications for their financial product, they might include conversion goals reflecting different completion milestones (beginning, mid-way, completed, and accepted). These conversion goals would get a profit-related conversion value associated:
- Beginning application: $10
- Mid-way: $20
- Completed: $50
- Accepted: actual conversion value using offline conversion uploads.
Brand controls exist on both, though there are some differences in what’s available.
Google and Microsoft both understood advertisers need messaging and branding controls to ensure ad creative stays within style guides. That’s why both AI Max variants allow advertisers to set brand inclusions, exclusions, term exclusions, and message constraints.
To make it easier to understand the mechanics for each, here’s the breakdown:
Google:
- Brand inclusions: 10 brand lists per campaign, with up to 5,000 brands per list
- Brand exclusions: 10 brand lists per campaign, with up to 5,000 brands per list
- Term exclusions: 25 per campaign
- Message constraints: 40 per campaign
Microsoft:
- Brand inclusions: 20 brand lists per campaign, with up to 100 brands per list
- Brand exclusions: 20 brand lists per campaign, with up to 100 brands per list
- Term exclusions: 25 per campaign
- Message constraints: 40 per campaign
Microsoft offers disclaimers that do not take up ad real estate, and work with AI Max. Google is piloting disclaimers as of this writing that would take up description line #2.
What’s unique between Google and Microsoft AI Max?
While most of the core AI Max functionality remains the same, there are a few key differences to account for when moving between ad platforms.
Consolidation of settings vs being able to pick and choose all three.
AI Max features work better together, so it makes sense to opt into all three. However, if you want to test individual settings before committing to the full suite, Microsoft makes this easier by offering all three features as opt-in toggles.
Google opts all advertisers who turn on AI Max at the campaign level into search term matching, which can be turned off at the ad group level.
While Microsoft maintains all existing ad group targeting settings (including location targeting, scheduling, and time zone selection), there are no AI Max-specific ad group settings.
Google also supports Locations of interest, URL inclusions, and Brand inclusions at the ad group level.
This means Google’s AI Max management asks advertisers to make more decisions at the ad group level, while Microsoft focuses AI Max settings at the campaign level.
Matching mechanics and search term transparency are different.
Both Google and Microsoft recommend leaning away from syntax-oriented keywords as they can get in the way of being eligible to serve for complex and longer queries, especially in AI experiences.
However, Google and Microsoft take different approaches to matching and search term reporting. Microsoft offers full search term reporting for any query resulting in a click for AI Max, PMax, and traditional Search and Shopping campaigns. These can be found in the reporting templates, Search term and Search term landing page.
Google hides some search terms for privacy reasons, which also means there isn’t full transparency on whether the query is relevant. To mitigate this, Google allows for more close variant mechanics in negative keywords.
When it comes to matching, due to different ecosystems, there are inherently different signals about how Google and Microsoft will match user queries to advertiser campaigns. Here’s a breakdown of the main signals by each ad platform:
Google:
- YouTube data
- Previous search behavior
- Conversion data
- Landing page
- Other keywords in the ad group
- Audiences: In-market, demographics, and other 1P audiences like Customer Match).
Microsoft:
- LinkedIn data
- Previous search behavior
- Conversion data
- Landing page
- Other keywords in the ad group
- Audiences: Impression-based remarketing, In-market, demographics, and other 1P audiences like Customer Match
How to leverage AI Max in existing and new account structures?
AI Max brings the best of AI functionality to search campaigns, and it’s understandable that advertisers will want to test some or all of the AI Max feature suite. However, there are some key considerations for accounts bringing AI Max into their structures.
Here are the top five considerations:
- Do you trust your conversions measurement and do you meet conversion thresholds?
- Will your landing pages be a help or a hindrance in conveying what your brand offers to AI systems as they address human questions?
- Are your existing ad assets on brand or do they have serious deviation?
- Is PMax already part of your account structure?
- Have you budgeted for the targets you’re setting?
Let’s dig deeper into each one.
Do you trust your conversion measurement and do you meet conversion thresholds?
AI Max and conversion-based bidding are joined at the hip because conversions are a critical signal AI Max uses to help advertisers connect with the right customers at the best ROI. If your campaigns don’t have accurate conversions feeding into the system, or don’t have enough conversion data (15-30 conversions in a 30-day period is the minimum), it will be very hard for AI Max to make intelligent matching choices.
If you have an existing account with at least 90 days of accurate conversion data, AI Max is a no-brainer.
Newer accounts should work on building conversion data so they can leverage AI Max and conversion-based bidding once the account ramps up.
Will your landing pages be a help or a hindrance in conveying who you are to the ad platform?
Landing pages are a critical signal that ad platforms use to understand your utility to a potential user. If your landing page is accessible in phrasing and visuals, this translates to easier content consumption for AI and humans alike.
A really great example of this is including alt-text on images and videos on the landing page, which ensures there can be no doubt about the subject matter. On the flip side, if you don’t make it clear what you’re offering and why your customers enjoy working with you, it can be hard to translate those messages to AI creative and matching.
A common mistake brands will make is disallowing all bots from crawling their landing pages, which deprives AI from critical insights into who you are and how you can help your customers.
A good way to understand whether your landing pages are AI Max compatible is to plug them into the PMax campaign creation flow. If you strongly disagree with the assets Google or Microsoft generated for you, that can be a sign to adjust landing page copy/mechanics before turning on AI Max.
Are your existing ad assets on brand or do they have serious deviation?
Both Google and Microsoft AI Max rely on existing text assets to inform potential new ad creative. Beyond adding in Brand Controls (including term exclusions and message constraints), it’s really important that your ad assets reflect the guidance you share.
For example, if you give a style guide note that all headlines should be sentence-cased, but your existing headlines are title-cased, that can cause confusion in the system. It’s important to audit your ad creative and landing pages for phrasing choices that might not align or were included unintentionally.
Is PMax already part of your account structure?
AI Max takes the best parts of PMax AI and layers them as optional boosts to Search campaigns. That means there are inherently fewer net-new opportunities for AI Max to unlock in accounts already running mature PMax campaigns.
However, AI Max can still add value when PMax is focused primarily on Shopping, is budget-constrained, or isn’t fully capturing your search opportunity. Rather than evaluating AI Max and PMax in isolation, focus on whether the combination is driving incremental account-level growth in conversions, revenue, or efficiency
PMax, by its nature, is cross-channel, and has a strong affinity for ecommerce. It can be useful to have AI Max as a search specific tool to go after parts of your business you don’t want exposed to non-search inventory.
In short, running AI Max and PMax in the same account isn’t inherently good or bad. If you want the full AI performance lift, Performance Max will have an easier time delivering that because it’s not restricted to search only surfaces. AI Max represents the useful AI gains of PMax in choice oriented and search-specific experience.
Have you budgeted for the targets your setting?
AI Max requires leaning into conversion-based bidding (“Smart” on Google and “Auto” on Microsoft). One of the biggest reasons any campaign can fail is it’s asked to go after too many targets for the budget.
For example, if you’re targeting customers for your plumbing business, it won’t be useful to ask the same campaign to go after minor repairs and a burst pipe. This is because the services have different costs, levels of urgency, and service capacities.
As a general rule, all services/products within a campaign should be within 20-30% of each other. If there’s too much variance, make sure you’ve set up accurate conversion values/TROAS goals, URL exclusions, and budgeted enough to cover the spread.
Final takeaways
Ultimately, Google and Microsoft’s AI Max are fairly similar. The differences have more to do with platform-specific mechanics.
Both agree that you’ll see the best results when you opt into all three core AI Max features.
Google puts more AI Max functionality at the ad group level, while Microsoft makes it a campaign-level choice.
Both platforms actively take on advertiser feedback, so if there is a preference for one style of management vs another, it’s worth sharing through support.
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Anthropic AI watermarking: What it means for content and SEO
On Aug. 11, Anthropic announced it would begin adding machine-readable watermarks to Claude’s outputs. The reaction was immediate and predictable. LinkedIn and X filled with the usual takes:
- “All AI writing is now fully traceable!”
- “This is the death knell for AI content farms!”
- “SEO is dead. Again.”
Sensing the uproar, Anthropic quickly followed up with a blog post, FAQs, and a technical demo showing that the watermark had no practical effect on output quality.
A few days later, Dario Amodei posted on X about AI’s broader crisis of trust, arguing that the public’s skepticism runs deeper than any one company’s messaging.
The technical explanations were clear. The demo was impressive. Yet public reaction remained largely negative.
In this article, I want to separate the hype from the reality and explore why what appeared to be a straightforward regulatory compliance announcement may instead become a flash point dividing the Eloi who embrace AI from the Morlocks who oppose it.
A quick history of watermarking
Craftspeople have marked their work for centuries.
In 1266, the English Parliament required bakers to use distinctive marks on their bread. By 1282, papermakers in Fabriano, Italy, were creating translucent watermarks with wire molds embedded in the paper.
The principle was simple: this is someone’s work, and the maker should be identifiable.
In the digital era, stock image libraries adopted the same idea. You’ve seen Shutterstock’s repeating patterns and Getty Images’ overlays stamped across preview images. The goal was the same: identify the original creator and discourage unauthorized use.
The EU rule Anthropic is answering
Anthropic’s decision is a direct response to Article 50(2) of the EU AI Act (Regulation 2024/1689). The provision requires providers of systems that generate synthetic text, images, audio, or video to mark those outputs in a machine-readable format so they can be detected as artificially generated or manipulated. The technical measures must be effective, interoperable, robust, and reliable, “as far as this is technically feasible.”
That final phrase carries significant weight. It’s not a precise legal standard.
To give companies a practical compliance path, the EU published a Voluntary Code of Practice on Transparency of AI-Generated Content. Most major providers (Anthropic, OpenAI, Google, Meta, Microsoft, Mistral, Cohere) signed it. xAI did not.
What ‘text watermarking’ actually means here
The term itself is causing confusion, so it’s worth being precise.
Traditional text watermarking typically relied on orthographic steganography: inserting hidden characters, zero-width spaces, or other invisible markers into finished text. These methods alter the form of the text. Once you know what to look for, they’re relatively easy to detect and remove.
Anthropic is using a different approach: statistical, or generative, watermarking.
When a language model generates text, it doesn’t always choose the single most likely next word. Instead, it samples from a range of plausible candidates. That controlled randomness helps keep the writing from becoming flat and repetitive. Statistical watermarking replaces some of that randomness with choices guided by a secret key. To the user, the output still appears natural. To the provider, the sequence of choices creates a detectable statistical signature.
Anthropic has said the method doesn’t insert hidden characters, identify individual users, or have any practical effect on output quality. A developer also released a demonstration tool based on the SynthID-Text approach. The engineering is sound.
Yet public reaction remained largely negative, even after Anthropic’s explanations.
That’s because the company answered the technical objections while largely missing the concerns that matter most to the people who use these tools every day — or who still need convincing to use them.
The real problems
1. It treats AI use itself as the problem
Imagine buying a set of kitchen knives and having the government assign someone to monitor you around the clock to make sure you don’t stab anyone. Don’t worry, they say. As long as you only use the knives to cut vegetables, you’ll be fine.
That’s the logic behind this approach.
Historically, watermarking existed to protect creators. Here, it’s meant to protect the potential victims of people who use AI.
Yes, scammers will use AI for fraud. Yes, people will be misled by synthetic content.
Those risks are real. But this policy rests on the assumption that the default use of AI is suspect, so the tool itself must bear a permanent mark.
Anyone who’s worked in SEO has seen this pattern before: white text on white backgrounds in the 1990s, paid links in the 2000s, private blog networks in the 2010s. The tactics worked for a while, then the market and the platforms adapted.
We didn’t need a special regulatory regime treating every form of content creation as potentially fraudulent. Existing fraud and consumer protection laws, along with Google’s incentive to protect the quality of its search results, were enough.
AI is a tool. It can be used well or poorly. Building the system on the assumption that users can’t be trusted isn’t a good way to earn their trust.
2. A positive detection becomes a Scarlet Letter
This is the practical issue that matters most to people doing the work.
Statistical watermarking can’t distinguish between high-value and low-value uses. If Claude performs light editing, rewriting, translation, or tone adjustment, the output can still carry a watermark. The watermark indicates the text was processed by Claude, not that Claude was the original author.
That distinction will be lost on most people. In practice, a detected watermark is likely to become a negative signal — a sign that the work is somehow less legitimate. Ironically, the people producing the lowest-value content will have the strongest incentive to strip or evade the watermark. Its absence will prove almost nothing.
The technique also isn’t especially durable. Just when we thought we were past the endless “we cracked Google’s algorithm” cycle, we’re about to start the same cat-and-mouse game again. Once reliable detectors exist, people will test how much paraphrasing, human editing, or multi-model processing it takes to weaken the signal.
3. It treats writing like a math problem to be optimized
I studied both computer science and English. When I read Anthropic’s explanations, the computer scientist in me was intrigued. The description of the sampling process was clear, and the demonstration tool was genuinely instructive.
The English major in me cringed.
Read these three sentences and see if you can spot the difference:
- Four score and seven years ago our fathers brought forth on this continent, a new nation, conceived in Liberty, and dedicated to the proposition that all men are created equal.
- Eighty-seven years ago, our forefathers established upon this continent a new nation, born in liberty and devoted to the principle that all men are created equal.
- Fourscore and seven years past, those who came before us brought into being on this continent a new nation, conceived in freedom and committed to the truth that all men are created equal.
From a narrow technical perspective, all three are grammatical, coherent, and “high quality.” From the perspective of someone who values good writing, only one is doing the work of literature. The other two are competent paraphrases.
An engineer or computer scientist might not even notice the difference. Readers will.
AI writing already has recognizable patterns: a heavy reliance on em dashes, the familiar “It’s not X, it’s Y” construction, overuse of words like “delve,” “leverage,” and “underscore” where simpler language would do, neatly balanced but empty phrasing, and a lack of specific, independently verifiable details that could only come from real experience.
Adding a statistical bias on top of those tendencies introduces another artificial constraint on the output. The stronger the required signal, the more constrained — and less human — the writing is likely to feel.
4. It applies a regional rule globally
Anthropic didn’t write the EU regulation; it’s simply responding to it. Still, the decision to apply the watermark worldwide at launch, rather than limiting it to the jurisdictions where the law applies, was deliberate and speaks volumes.
The company’s stated reason was the “lack of a durable way to scope the feature by region.” That may be technically inconvenient, but it’s hardly impossible.
Companies routinely adapt product behavior to local legal requirements. Choosing not to do so here — especially for a user base that extends well beyond the EU — suggests a surprising disconnect from its users, many of whom are sophisticated enough to switch to open-weight or non-watermarked models when they want maximum flexibility.
The deeper problem
On the surface, the past week looks like a tech company solving a technical problem to meet a regulatory requirement. To Anthropic’s credit, it moved first and was transparent about the change.
Where it went wrong was the audience it seemed to be addressing. Its explanations were clear to people who already understand how language models work. They did little to address the broader crisis of trust.
A few days after the announcement, Dario Amodei posted on X that the public’s negative view of AI is fundamentally a crisis of trust.
- “I do agree that the public has a negative view of AI (and that this is a big problem), but I don’t think it is primarily caused by me or any other AI leader warning about AI’s risks. I think it is fundamentally a crisis of trust.”
He has the diagnosis right. What’s less convincing is the cure.
He went on to argue, correctly, that glitzy marketing won’t fix the problem, and neither will simply claiming AI will cure cancer. The real solution, he suggested, is actually curing cancer.
That framing misses the point. It’s a blind spot shared by many AI executives.
AI won’t cure cancer. Humans will.
AI can surface connections, identify patterns, and accelerate parts of the work. But it’s still a tool. Behind every meaningful result is human judgment and human responsibility.
The same gap appears at a more ordinary level.
Outside of work, AI has improved my life. I’ve already shared how it helped me improve my health. I’ve also used it to plan vacations, adapt recipes, repair my car, and research my family history.
None of those uses will change the world. But they changed mine. Not because I picked the right model, but because I knew how to use it.
I’ve found the same is true for many long-time SEOs. Good SEOs know how to ask questions. We know how to challenge what a computer gives us, refine our prompts, and decide when to accept an answer and when to push back.
Most people haven’t had that experience. Their exposure to AI is largely limited to viral videos and a steady stream of horror stories: mass layoffs, data centers straining local resources, and executives accumulating fortunes that would make the old robber barons blush. With all due respect to Amodei, actually curing cancer won’t change any of that.
Talking as though the technology itself will deliver the breakthrough turns people into spectators instead of participants. Worse, some hear that message and conclude the companies quietly share Agent Smith’s view in “The Matrix”: humans are the problem, and AI is the solution.
What will close the gap is the same force that drove mainstream internet adoption in the 1990s: people discovering tangible benefits in their own lives. That happened because the early internet was built in a spirit of openness rather than control.
The internet scaled because its architects favored open protocols and worked in a culture that was skeptical of concentrated power, whether in government or corporations. Vint Cerf, Bob Kahn, Tim Berners-Lee, Jon Postel, Linus Torvalds, Richard Stallman, Paul Mockapetris, and many others still aren’t household names. Most never became multimillionaires or sought public recognition, yet their contributions to daily life are immeasurable. The political class’s greatest contribution was restraint.
Today, the major AI labs are responding to pressure by adding constraints and tightening control. Too often, the visible motivation seems to be who can produce the biggest exit. That’s a very different spirit from the one that built the early internet.
What actually matters
There’s a useful parallel here for SEOs. You’ve always been able to distinguish between using a technique to create real value and using it to game the system.
This article is a good example. I wrote it the old-fashioned way, drafting it myself and using AI only for research.
Once I had a draft, I used AI to organize, prune, and refine it. I didn’t blindly accept every suggestion. I pushed back and, in some cases, overrode it.
A good example is the H.G. Wells “The Time Machine” analogy above. AI kept urging me to expand that paragraph and explain the reference. I said no. I think enough of this audience will get it immediately. The rest of you can spend five seconds Googling it (or, better yet, check the book out from your local library).
The difference between quality work and slop isn’t whether it passes a detection tool. It’s whether people engage with it, share it, and convert. Everything else is secondary.
It’s also telling which tool I chose. I’ve been using Claude all month for real work. For this piece, I switched to Grok precisely because it doesn’t fingerprint its output.
Part of that decision was rational. Part was emotional. Companies ignore that mix at their own risk.
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What’s new in Yoast SEO 28.4: find, fix, and generate at scale
The Yoast SEO Bulk Editor already helps you update titles, meta descriptions, and focus keyphrases across your whole site, filter by content type, and let Yoast AI draft metadata you approve before it saves. This update builds on that: it’s now easier to spot exactly which posts need attention, catch AI suggestions that need a second look, and get started without leaving your regular post list.
Here’s what’s new.
Find weak fields in seconds
The Filters dropdown now includes “needs improvement” options for SEO title, meta description, social title, and social description. Turn one on, and the bulk editor shows only the posts where that field is empty or scoring poorly, using the same scoring your post editor already relies on.
Once you’ve found them, you don’t have to select each one by hand. New quality-based options in the Select menu let you select every post on the page that needs improvement in a single click, using that same score, so what you filter and what you select always agree.
Prefer working row by row? Shift+click in the Bulk Editor now selects a range at once, the way you’d expect from any table.
Catch weak suggestions before you publish
When you generate SEO titles, meta descriptions, or social copy in bulk, the AI does its best work with posts that already have some content to draw from. Previously, posts with very little written content could still generate suggestions, even though the input was thin, so you had no way to know which ones needed a closer look.
That’s why the Bulk Editor now gives you a built-in guardrail: after a generation run, it flags exactly which generated posts had limited content, so you know which ones are worth a second look. Add a bit more content, regenerate, and you’re set.
Start bulk editing from your regular post list
You no longer need to open the bulk editor separately to get started. Select posts, pages, or products from their regular overview screens, choose “Bulk edit” from the bulk actions dropdown, and your selection carries straight into the bulk editor, ready to go.
Keep working, even when your site is hard to reach (rolling out)
If Yoast AI can’t reach your site, for example because it’s behind a firewall, offline, or has the REST API disabled, you’ll now see an option to connect your site to MyYoast right from the error message, instead of a dead end. Once connected, Yoast AI keeps working even on trickier hosting setups. This is rolling out gradually, so you may not see it yet. You don’t need to do anything to prepare, you’ll be invited to connect right when it’s useful to you.
The post What’s new in Yoast SEO 28.4: find, fix, and generate at scale appeared first on Yoast.
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Calls and clicks keep falling as Google Maps becomes the destination
In February, we wrote that local rankings were holding steady while calls and website clicks quietly disappeared. By April, our data pointed to something much more dramatic, enough that we declared “local SEO” was dying on stage at BrightonSEO.
We were wrong about the scale.
Q1 data was revised retrospectively. After testing the corrected numbers against a full second quarter, we found a clearer, more specific story than either version suggested.
Why Q1 looked like a crisis
Sterling Sky and Jepto’s analysis of 179 Google Business Profiles found that AI-powered local packs often show just two businesses instead of three, frequently without a click-to-call button, and surface only 32% as many unique businesses as the traditional Map Pack. Most rank trackers couldn’t see any of it.
Joy Hawkins, Claudia Tomina, and Matt McGee were independently reporting the same pattern: rankings held steady while performance declined.
Our initial reading of the Q1 data, presented at BrightonSEO in April, looked even more severe. Actions and impressions appeared to have fallen by roughly half across our U.S. portfolio. The drop was dramatic enough that we advised some agency customers to invest in paid search.
It didn’t hold up.
The corrected Q1 number, and what Q2 adds
Rechecking Q1 revealed a much less dramatic picture. Year over year:
- U.S. website clicks and calls each fell 15.8%, while direction requests rose 31.3%.
- Desktop Search impressions increased 12.3%.
- Mobile Search impressions fell 20.6%.
- Desktop Maps impressions declined 17.9%.
We don’t put much weight on the mobile Maps figure alone. A small number of large advertisers can significantly shift that metric.
Q2 data tests that corrected the baseline.
U.S. calls and website clicks are still falling, down 11.9% and 12.5%, and direction requests are still growing, up 21.1%, all three decelerating against Q1’s sharper moves.
The standout change is desktop Maps: down 17.9% in Q1, up 3.2% in Q2, a genuine reversal, specific to the US. Mobile Maps impressions are up 30.4%, desktop search is up 13.9%, and mobile search is down 20.1%, enough on its own to erase the other gains.
Our read is that U.S. customers are increasingly completing the entire journey within Google Maps rather than starting on a search results page. The reversal in desktop Maps impressions suggests that the shift is no longer limited to mobile.
Two ranking systems and reviews as the connective tissue
Traditional Maps rankings still rely on proximity, relevance, engagement, and prominence. The signals behind AI Mode and Gemini differ: web context, entity matching, brand authority, and review sentiment are layered on top of the Google Business Profile.
One system determines whether you appear on the map. The other determines whether Google’s AI trusts what it knows about your business enough to answer a customer’s question directly, without a click.
Reviews are becoming the raw material for that second system. Google now prompts reviewers with structured tags such as atmosphere, price, and cleanliness, rather than relying solely on free text, and also encourages customers to review businesses they’ve visited.
It’s building cleaner data so its AI can answer questions without sending users to a website first — a plausible explanation for direction requests rising while calls and website clicks decline.
The EU and UK aren’t a slower version of the US
The picture outside the U.S. is different. In the EU, desktop Maps impressions fell faster in Q2, dropping from -16.5% in Q1 to -34.7%, even as direction requests continued to grow, slowing from +21.7% to +13.1%.
The U.K.’s smaller dataset showed the opposite reversal. Mobile Maps impressions swung from +22.4% in Q1 to -70.8% in Q2, while direction requests and website clicks continued to rise.
Neither market appears to be simply trailing the U.S. They’re following different paths.
Rank tracking alone won’t tell you this
SOCi’s 2026 Local Visibility Index found AI platforms recommend far fewer locations than Google’s 3-pack: 1.2% on ChatGPT, 7.4% on Perplexity, and 35.9% on Google. A key reason is profile accuracy, which averages 68% on ChatGPT and Perplexity versus 100% on Gemini, which pulls directly from Google Maps data. Search Engine Land covered the report in detail.
Reviews matter here, too. Locations recommended by ChatGPT average 4.3 stars, while those recommended by Perplexity average 4.2, suggesting review quality is becoming a gate for AI recommendations, not just a ranking signal.
What this means for your reporting
If your reporting still leads with call volume, you’re measuring only part of the customer journey. Direction requests deserve equal weight. Businesses with the most complete, accurate Google Business Profile data capture more of the remaining clicks, calls, and directions.
For multi-location brands, agencies, and SMBs focused on the bottom of the funnel, local SEO has grown up. It’s starting to look a lot more like SEO.
More detail on GMBapi’s Q2 Local SEO trend data is available here.
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SEO for Doctors: a 10-Step Plan to Get Found Locally and Earn Patient Trust
Patients often search before they call, even when a physician already referred them. SEO for doctors helps clinics and medical practices appear when patients compare symptoms, treatment options, specialists,…
The post SEO for Doctors: a 10-Step Plan to Get Found Locally and Earn Patient Trust appeared first on Mangools.
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