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Microsoft launches Publisher Content Marketplace for AI licensing

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The future of remarketing? Microsoft bets on impressions, not clicks

Microsoft Advertising today launched the Publisher Content Marketplace (PCM), a system that lets publishers license premium content to AI products and get paid based on how that content is used.

How it works. PCM creates a direct value exchange. Publishers set licensing and usage terms, while AI builders discover and license content for specific grounding scenarios. The marketplace also includes usage-based reporting, giving publishers visibility into how their content performs and where it creates the most value.

Designed to scale. PCM is designed to avoid one-off licensing deals between individual publishers and AI providers. Participation is voluntary, ownership remains with publishers, and editorial independence stays intact. The marketplace supports everyone from global publishers to smaller, specialized outlets.

Why we care. As AI systems shift from answering questions to making decisions, content quality matters more than ever. As agents increasingly guide purchases, finance, and healthcare choices, ads and sponsored messages will sit alongside — or draw from — premium content rather than generic web signals. That raises the bar for credibility and points to a future where brand alignment with trusted publishers and AI ecosystems directly impacts performance.

Early traction. Microsoft Advertising co-designed PCM with major U.S. publishers, including Business Insider, Condé Nast, Hearst, The Associated Press, USA TODAY, and Vox Media. Early pilots grounded Microsoft Copilot responses in licensed content, with Yahoo among the first demand partners now onboarding.

What’s next. Microsoft plans to expand the pilot to more publishers and AI builders that share a core belief: as the AI web evolves, high-quality content should be respected, governed, and paid for.

The big picture. In an agentic web, AI tools increasingly summarize, reason, and recommend through conversation. Whether the topic is medical safety, financial eligibility, or a major purchase, outcomes depend on access to trusted, authoritative sources — many of which sit behind paywalls or in proprietary archives.

The tension. The traditional web bargain was simple: publishers shared content, and platforms sent traffic back. That model breaks down when AI delivers answers directly, cutting clicks while still depending on premium content to perform well.

Bottom line. If AI is going to make better decisions, it needs better inputs — and PCM is Microsoft’s bet that a sustainable content economy can power the next phase of the agentic web.

Microsoft’s announcement. Building Toward a Sustainable Content Economy for the Agentic Web

Read more at Read More

Web Design and Development San Diego

Inspiring examples of responsible and realistic vibe coding for SEO

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Vibe coding is a new way to create software using AI tools such as ChatGPT, Cursor, Replit, and Gemini. It works by describing to the tool what you want in plain language and receiving written code in return. You can then simply paste the code into an environment (such as Google Colab), run it, and test the results, all without ever actually programming a single line of code.

Collins Dictionary named “vibe coding” word of the year in 2025, defining it as “the use of artificial intelligence prompted by natural language to write computer code.”

In this guide, you’ll understand how to start vibe coding, learn its limitations and risks, and see examples of great tools created by SEOs to inspire you to vibe code your own projects.

Vibe coding variations

While “vibe coding” is used as an umbrella term, there are subsets of coding with support or AI, including the following:

Type Description Tools
AI-assisted coding  AI helps write, refactor, explain, or debug code. Used by actual developers or engineers to support their complex work. GitHub Copilot, Cursor, Claude, Google AI Studio
Vibe coding Platforms that handle everything except the prompt/idea. AI does most of the work. ChatGPT, Replit, Gemini, Google AI Studio
No-code platforms Platforms that handle everything you ask (“drag and drop” visual updates while the code happens in the background). They tend to use AI but existed long before AI became mainstream. Notion, Zapier, Wix

We’ll focus exclusively on vibe coding in this guide. 

With vibe coding, while there’s a bit of manual work to be done, the barrier is still low — you basically need a ChatGPT account (free or paid) and access to a Google account (free). Depending on your use case, you might also need access to APIs or SEO tools subscriptions such as Semrush or Screaming Frog.

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To set expectations, by the end of this guide, you’ll know how to run a small program on the cloud. If you expect to build a SaaS or software to sell, AI-assisted coding is a more reasonable option to take, which will involve costs and deeper coding knowledge.

Vibe coding use cases

Vibe coding is great when you’re trying to find outcomes for specific buckets of data, such as finding related links, adding pre-selected tags to articles, or doing something fun where the outcome doesn’t need to be exact.

For example, I’ve built an app to create a daily drawing for my daughter. I type a phrase about something that she told me about her day (e.g., “I had carrot cake at daycare”). The app has some examples of drawing styles I like and some pictures of her. The outputs (drawings) are the final work as they come from AI.

When I ask for specific changes, however, the program tends to worsen and redraw things I didn’t ask for. I once asked to remove a mustache and it recolored the image instead. 

If my daughter were a client who’d scrutinize the output and require very specific changes, I’d need someone who knows Photoshop or similar tools to make specific improvements. In this case, though, the results are good enough. 

Building commercial applications solely on vibe coding may require a company to hire vibe coding cleaners. However, for a demo, MVP (minimum viable product), or internal applications, vibe coding can be a useful, effective shortcut. 

How to create your SEO tools with vibe coding

Using vibe coding to create your own SEO tools require three steps:

  1. Write a prompt describing your code
  2. Paste the code into a tool such as Google Colab
  3. Run the code and analyze the results

Here’s a prompt example for a tool I built to map related links at scale. After crawling a website using Screaming Frog and extracting vector embeddings (using the crawler’s integration with OpenAI), I vibe coded a tool that would compare the topical distance between the vectors in each URL.

This is exactly what I wrote on ChatGPT:

I need a Google Colab code that will use OpenAI to:

Check the vector embeddings existing in column C. Use cosine similarity to match with two suggestions from each locale (locale identified in Column A). 

The goal is to find which pages from each locale are the most similar to each other, so we can add hreflang between these pages.

I’ll upload a CSV with these columns and expect a CSV in return with the answers.

Then I pasted the code that ChatGPT created on Google Colab, a free Jupyter Notebook environment that allows users to write and execute Python code in a web browser. It’s important to run your program by clicking on “Run all” in Google Colab to test if the output does what you expected.

This is how the process works on paper. Like everything in AI, it may look perfect, but it’s not always functioning exactly how you want it. 

You’ll likely encounter issues along the way — luckily, they’re simple to troubleshoot.

First, be explicit about the platform you’re using in your prompt. If it’s Google Colab, say the code is for Google Colab. 

You might still end up with code that requires packages that aren’t installed. In this case, just paste the error into ChatGPT and it’ll likely regenerate the code or find an alternative. You don’t even need to know what the package is, just show the error and use the new code. Alternatively, you can ask Gemini directly in your Google Colab to fix the issue and update your code directly.

AI tends to be very confident about anything and could return completely made-up outputs. One time I forgot to say the source data would come from a CSV file, so it simply created fake URLs, traffic, and graphs. Always check and recheck the output because “it looks good” can sometimes be wrong.

If you’re connecting to an API, especially a paid API (e.g., from Semrush, OpenAI, Google Cloud, or other tools), you’ll need to request your own API key and keep in mind usage costs. 

Should you want an even lower execution barrier than Google Colab, you can try using Replit. 

Simply prompt your request and the software will create the code, design, and allow testing all on the same screen. This means a lower chance of coding errors, no copy and paste, and a URL you can share right away with anyone to see your project built with a nice design. (You should still check for poor outputs and iterate with prompts until your final app is built.)

Keep in mind that while Google Colab is free (you’ll only spend if you use API keys), Replit charges a monthly subscription and per-usage fee on APIs. So the more you use an app, the more expensive it gets.

Inspiring examples of SEO vibe-coded tools

While Google Colab is the most basic (and easy) way to vibe code a small program, some SEOs are taking vibe coding even further by creating programs that are turned into Chrome extensions, Google Sheets automation, and even browser games.

The goal behind highlighting these tools is not only to showcase great work by the community, but also to inspire, build, and adapt to your specific needs. Do you wish any of these tools had different features? Perhaps you can build them for yourself — or for the world.

GBP Reviews Sentiment Analyzer (Celeste Gonzalez)

After vibe coding some SEO tools on Google Colab, Celeste Gonzalez, Director of SEO Testing at RicketyRoo Inc, took her vibing skills a step further and created a Chrome extension. “I realized that I don’t need to build something big, just something useful,” she explained.

Her browser extension, the GBP Reviews Sentiment Analyzer, summarizes sentiment analysis for reviews over the last 30 days and review velocity. It also allows the information to be exported into a CSV. The extension works on Google Maps and Google Business Profile pages.

Instead of ChatGPT, Celeste used a combination of Claude (to create high-quality prompts) and Cursor (to paste the created prompts and generate the code).

AI tools used: Claude (Sunner 4.5 model) and Cursor 

APIs used: Google Business Profile API (free)

Platform hosting: Chrome Extension

Knowledge Panel Tracker (Gus Pelogia)

I became obsessed with the Knowledge Graph in 2022, when I learned how to create and manage my own knowledge panel. Since then, I found out that Google has a Knowledge Graph Search API that allows you to check the confidence score for any entity.

This vibe-coded tool checks the score for your entities daily (or at any frequency you want) and returns it in a sheet. You can track multiple entities at once and just add new ones to the list at any time.

The Knowledge Panel Tracker runs completely on Google Sheets, and the Knowledge Graph Search API is free to use. This guide shows how to create and run it in your own Google account, or you can see the spreadsheet here and just update the API key under Extensions > App Scripts. 

AI models used: ChatGPT 5.1

APIs used: Google Knowledge Graph API (free)

Platform hosting: Google Sheets

Inbox Hero Game (Vince Nero)

How about vibe coding a link building asset? That’s what Vince Nero from BuzzStream did when creating the Inbox Hero Game. It requires you to use your keyboard to accept or reject a pitch within seconds. The game is over if you accept too many bad pitches.

Inbox Hero Game is certainly more complex than running a piece of code on Google Colab, and it took Vince about 20 hours to build it all from scratch. “I learned you have to build things in pieces. Design the guy first, then the backgrounds, then one aspect of the game mechanics, etc.,” he said.

The game was coded in HTML, CSS, and JavaScript. “I uploaded the files to GitHub to make it work. ChatGPT walked me through everything,” Vince explained.

According to him, the longer the prompt continued, the less effective ChatGPT became, “to the point where [he’d] have to restart in a new chat.” 

This issue was one of the hardest and most frustrating parts of creating the game. Vince would add a new feature (e.g., score), and ChatGPT would “guarantee” it found the error, update the file, but still return with the same error. 

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In the end, Inbox Hero Game is a fun game that demonstrates it’s possible to create a simple game without coding knowledge, yet taking steps to perfect it would be more feasible with a developer.

AI models used: ChatGPT

APIs used: None

Platform hosting: Webpage

Vibe coding with intent

Vibe coding won’t replace developers, and it shouldn’t. But as these examples show, it can responsibly unlock new ways for SEOs to prototype ideas, automate repetitive tasks, and explore creative experiments without heavy technical lift. 

The key is realism: Use vibe coding where precision isn’t mission-critical, validate outputs carefully, and understand when a project has outgrown “good enough” and needs additional resources and human intervention.

When approached thoughtfully, vibe coding becomes less about shipping perfect software and more about expanding what’s possible — faster testing, sharper insights, and more room for experimentation. Whether you’re building an internal tool, a proof of concept, or a fun SEO side project, the best results come from pairing curiosity with restraint.

Read more at Read More

Web Design and Development San Diego

LinkedIn: AI-powered search cut traffic by up to 60%

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AEO playbook

AI-powered search gutted LinkedIn’s B2B awareness traffic. Across a subset of topics, non-brand organic visits fell by as much as 60% even while rankings stayed stable, the company said.

  • LinkedIn is moving past the old “search, click, website” model and adopting a new framework: “Be seen, be mentioned, be considered, be chosen.”

By the numbers. In a new article, LinkedIn said its B2B organic growth team started researching Google’s Search Generative Experience (SGE) in early 2024. By early 2025, when SGE evolved into AI Overviews, the impact became significant.

  • Non-brand, awareness-driven traffic declined by up to 60% across a subset of B2B topics.
  • Rankings stayed stable, but click-through rates fell (by an undisclosed amount).

Yes, but. LinkedIn’s “new learnings” are more like a rehash of established SEO/AEO best practices. Here’s what LinkedIn’s content-level guidance consists of:

  • Use strong headings and a clear information hierarchy.
  • Improve semantic structure and content accessibility.
  • Publish authoritative, fresh content written by experts.
  • Move fast, because early movers get an edge.

Why we care. These tactics should all sound familiar. These are technical SEO and content-quality fundamentals. LinkedIn’s article offers little new in terms of tactics. It’s just updated packaging for modern SEO/AEO and AI visibility.

Dig deeper. How to optimize for AI search: 12 proven LLM visibility tactics

Measurement is broken. LinkedIn said its big challenge is the “dark” funnel. It can’t quantify how visibility in LLM answers impacts the bottom line, especially when discovery happens without a click.

  • LinkedIn’s B2B marketing websites saw triple-digit growth in LLM-driven traffic and that it can track conversion from those visits.
    • Yes, but: Many websites are also seeing triple-digit (or more) growth in LLM-driven traffic. Because it’s an emerging channel. That said, this is still a tiny amount of overall traffic right now (1% or less for most sites).

What LinkedIn is doing. LinkedIn created an AI Search Taskforce spanning SEO, PR, editorial, product marketing, product, paid media, social, and brand. Key actions included:

  • Correcting misinformation that showed up in AI responses.
  • Publishing new owned content optimized for generative visibility.
  • Testing LinkedIn (social) content to validate its strength in AI discovery.

Is it working? LinkedIn said early tests produced a meaningful lift in visibility and citations, especially from owned content. At least one external datapoint (Semrush, Nov. 10, 2025) suggested that LinkedIn has a structural advantage in AI search:

  • Google AI Mode cited LinkedIn in roughly 15% of responses.
  • LinkedIn was the #2 most-cited domain in that dataset, behind YouTube.

Incomplete story. LinkedIn’s article is an interesting read, but it’s light on specifics. Missing details include:

  • The exact topic set behind the “up to 60%” decline.
  • Exactly how much click-through rates “softened.”
  • Sample size and timeframe.
  • How “industry-wide” comparisons were calculated.
  • What tests were run, what moved citation share, and by how much.

Bottom line. LinkedIn is right that visibility is the new currency. However, it hasn’t shown enough detail to prove its new playbook is meaningfully different from doing some SEO (yes, SEO) fundamentals.

LinkedIn’s article. How LinkedIn Marketing Is Adapting to AI-Led Discovery

Read more at Read More

Web Design and Development San Diego

Are we ready for the agentic web?

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Are we ready for the agentic web?

Innovations are coming at marketers and consumers faster than before, raising the question: Are we actually ready for the agentic web?

To answer that question, it’s important to unpack a few supporting ones:

  • What’s the agentic web?
  • How can the agentic web be used?
  • What are the pros and cons of the agentic web?

It’s important to note that this article isn’t a mandate for AI skeptics to abandon the rational questions they have about the agentic web. 

Nor is it intended to place any judgment on how you, as a consumer or professional, engage with the agentic web.

LinkedIn poll on Copilot Checkout

With thoughts and feelings so divided on the agentic web, this article aims to provide clear insight into how to think about it in earnest, without the branding or marketing fluff.

Disclosure: I am a Microsoft employee and believe in the path Microsoft’s taking with the agentic web. However, this article will attempt to be as platform-agnostic as possible.

What’s the agentic web? 

The agentic web refers to sophisticated tools, or agents, trained on our preferences that act with our consent to accomplish time-consuming tasks.

In simple terms, when I use one-click checkout, I allow my saved payment information to be passed to the merchant’s accounts receivable systems. 

Neither the merchant nor I must write down all the details or be involved beyond consenting to send and receive payment.

For fun, I put this question to four different AI models, and the responses are telling: 

  • Copilot: “The agentic web is an emerging layer of the internet where AI agents can reason and take actions on behalf of people or organizations, turning expressed intent into outcomes while preserving user choice, transparency, and control.” 
  • Gemini: “The agentic web is an evolution of the internet where autonomous AI agents – rather than just human users – interact with websites, APIs, and each other to perform complex tasks and make decisions on our behalf. “
  • Perplexity: “The agentic web is an emerging phase of the internet where autonomous AI agents act as the primary interface, navigating, interpreting, and taking actions across websites and services on behalf of users with minimal human involvement.” 
  • Claude: “The agentic web is an evolution of the internet where AI agents autonomously navigate, interact with, and complete tasks across websites and services on behalf of users.” 

I begin with how different models answer the question because it’s important to understand that each one is trained on different information, and outcomes will inevitably vary.

It’s worth noting that with the same prompt, defining the agentic web in one sentence, three out of four models focus on diminishing the human role in navigating the web, while one makes a point to emphasize the significance of human involvement, preserving user choice, transparency, and control.

Two out of four refer to the agentic web as a layer or phase rather than an outright evolution of the web. 

This is likely where the sentiment divide on the agentic web stems from.

Some see it as a consent-driven layer designed to make life easier, while others see it as a behemoth that consumes content, critical thinking, and choice.

It’s noteworthy that one model, Gemini, calls out APIs as a means of communication in the agentic web. APIs are essentially libraries of information that can be referenced, or called, based on the task you are attempting to accomplish. 

This matters because APIs will become increasingly relevant in the agentic web, as saved preferences must be organized in ways that are easily understood and acted upon.

Defining the agentic web requires spending some time digging into two important protocols – ACP and UCP.

Dig deeper: AI agents in SEO: What you need to know

Agentic Commerce Protocol: Optimized for action inside conversational AI 

The Agentic Commerce Protocol, or ACP, is designed around a specific moment: when a user has already expressed intent and wants the AI to act.

The core idea behind ACP is simple. If a user tells an AI assistant to buy something, the assistant should be able to do so safely, transparently, and without forcing the user to leave the conversation to complete the transaction.

ACP enables this by standardizing how an AI agent can:

  • Access merchant product data.
  • Confirm availability and price.
  • Initiate checkout using delegated, revocable payment authorization.

The experience is intentionally streamlined. The user stays in the conversation. The AI handles the mechanics. The merchant still fulfills the order.

This approach is tightly aligned with conversational AI platforms, particularly environments where users are already asking questions, refining preferences, and making decisions in real time. It prioritizes speed, clarity, and minimal friction.

Universal Commerce Protocol: Built for discovery, comparison, and lifecycle commerce 

The Universal Commerce Protocol, or UCP, takes a broader view of agentic commerce.

Rather than focusing solely on checkout, UCP is designed to support the entire shopping journey on the agentic web, from discovery through post-purchase interactions. It provides a common language that allows AI agents to interact with commerce systems across different platforms, surfaces, and payment providers. 

That includes: 

  • Product discovery and comparison.
  • Cart creation and updates.
  • Checkout and payment handling.
  • Order tracking and support workflows.

UCP is designed with scale and interoperability in mind. It assumes users will encounter agentic shopping experiences in many places, not just within a single assistant, and that merchants will want to participate without locking themselves into a single AI platform.

It’s tempting to frame ACP and UCP as competing solutions. In practice, they address different moments of the same user journey.

ACP is typically strongest when intent is explicit and the user wants something done now. UCP is generally strongest when intent is still forming and discovery, comparison, and context matter.

So what’s the agentic web? Is it an army of autonomous bots acting on past preferences to shape future needs? Is it the web as we know it, with fewer steps driven by consent-based signals? Or is it something else entirely?

The frustrating answer is that the agentic web is still being defined by human behavior, so there’s no clear answer yet. However, we have the power to determine what form the agentic web takes. To better understand how to participate, we now move to how the agentic web can be used, along with the pros and cons.

Dig deeper: The Great Decoupling of search and the birth of the agentic web

How can the agentic web be used? 

Working from the common theme across all definitions, autonomous action, we can move to applications.

Elmer Boutin has written a thoughtful technical view on how schema will impact agentic web compatibility. Benjamin Wenner has explored how PPC management might evolve in a fully agentic web. Both are worth reading.

Here, I want to focus on consumer-facing applications of the agentic web and how to think about them in relation to the tasks you already perform today.

Here are five applications of the agentic web that are live today or in active development.

1. Intent-driven commerce  

A user states a goal, such as “Find me the best running shoes under $150,” and an agent handles discovery, comparison, and checkout without requiring the user to manually browse multiple sites. 

How it works 

Rather than returning a list of links, the agent interprets user intent, including budget, category, and preferences. 

It pulls structured product information from participating merchants, applies reasoning logic to compare options, and moves toward checkout only after explicit user confirmation. 

The agent operates on approved product data and defined rules, with clear handoffs that keep the user in control. 

Implications for consumers and professionals 

Reducing decision fatigue without removing choice is a clear benefit for consumers. For brands, this turns discovery into high-intent engagement rather than anonymous clicks with unclear attribution. 

Strategically, it shifts competition away from who shouts the loudest toward who provides the clearest and most trusted product signals to agents. These agents can act as trusted guides, offering consumers third-party verification that a merchant is as reliable as it claims to be.

2. Brand-owned AI assistants 

A brand deploys its own AI agent to answer questions, recommend products, and support customers using the brand’s data, tone, and business rules.

How it works 

The agent uses first-party information, such as product catalogs, policies, and FAQs. 

Guardrails define what it can say or do, preventing inferences that could lead to hallucinations. 

Responses are generated by retrieving and reasoning over approved context within the prompt.

Implications for consumers and professionals 

Customers get faster and more consistent responses. Brands retain voice, accountability, and ownership of the experience. 

Strategically, this allows companies to participate in the agentic web without ceding their identity to a platform or intermediary. It also enables participation in global commerce without relying on native speakers to verify language.

3. Autonomous task completion 

Users delegate outcomes rather than steps, such as “Prepare a weekly performance summary” or “Reorder inventory when stock is low.” 

How it works 

The agent breaks the goal into subtasks, determines which systems or tools are needed, and executes actions sequentially. It pauses when permissions or human approvals are required. 

These can be provided in bulk upfront or step by step. How this works ultimately depends on how the agent is built. 

Implications for consumers and marketers 

We’re used to treating AI like interns, relying on micromanaged task lists and detailed prompts. As agents become more sophisticated, it becomes possible to treat them more like senior employees, oriented around outcomes and process improvement. 

That makes it reasonable to ask an agent to identify action items in email or send templates in your voice when active engagement isn’t required. Human choice comes down to how much you delegate to agents versus how much you ask them to assist.

Dig deeper: The future of search visibility: What 6 SEO leaders predict for 2026

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4. Agent-to-agent coordination and negotiation 

Agents communicate with other agents on behalf of people or organizations, such as a buyer agent comparing offers with multiple seller agents. 

How it works 

Agents exchange structured information, including pricing, availability, and constraints. 

They apply predefined rules, such as budgets or policies, and surface recommended outcomes for human approval. 

Implications for consumers and marketers 

Consumers may see faster and more transparent comparisons without needing to manually negotiate or cross-check options. 

For professionals, this introduces new efficiencies in areas like procurement, media buying, or logistics, where structured negotiation can occur at scale while humans retain oversight.

5. Continuous optimization over time 

Agents don’t just act once. They improve as they observe outcomes.

How it works 

After each action, the agent evaluates what happened, such as engagement, conversion, or satisfaction. It updates its internal weighting and applies those learnings to future decisions.

Why people should care 

Consumers experience increasingly relevant interactions over time without repeatedly restating preferences. 

Professionals gain systems that improve continuously, shifting optimization from one-off efforts to long-term, adaptive performance. 

What are the pros and cons of the agentic web? 

Life is a series of choices, and leaning into or away from the agentic web comes with clear pros and cons.

Pros of leaning into the agentic web 

The strongest argument for leaning into the agentic web is behavioral. People have already been trained to prioritize convenience over process. 

Saved payment methods, password managers, autofill, and one-click checkout normalized the idea that software can complete tasks on your behalf once trust is established.

Agentic experiences follow the same trajectory. Rather than requiring users to manually navigate systems, they interpret intent and reduce the number of steps needed to reach an outcome. 

Cons of leaning into the agentic web 

Many brands will need to rethink how their content, data, and experiences are structured so they can be interpreted by automated systems and humans. What works for visual scanning or brand storytelling doesn’t always map cleanly to machine-readable signals.

There’s also a legitimate risk of overoptimization. Designing primarily for AI ingestion can unintentionally degrade human usability or accessibility if not handled carefully. 

Dig deeper: The enterprise blueprint for winning visibility in AI search

Pros of leaning away from the agentic web 

Choosing to lean away from the agentic web can offer clarity of stance. There’s a visible segment of users skeptical of AI-mediated experiences, whether due to privacy concerns, automation fatigue, or a loss of human control. 

Aligning with that perspective can strengthen trust with audiences who value deliberate, hands-on interaction.

Cons of leaning away from the agentic web 

If agentic interfaces become a primary way people discover information, compare options, or complete tasks, opting out entirely may limit visibility or participation. 

The longer an organization waits to adapt, the more expensive and disruptive that transition can become.

What’s notable across the ecosystem is that agentic systems are increasingly designed to sit on top of existing infrastructure rather than replace it outright. 

Avoiding engagement with these patterns may not be sustainable over time. If interaction norms shift and systems aren’t prepared, the combination of technical debt and lost opportunity may be harder to overcome later.

Where the agentic web stands today

The agentic web is still taking form, shaped largely by how people choose to use it. Some organizations are already applying agentic systems to reduce friction and improve outcomes. Others are waiting for stronger trust signals and clearer consent models.

Either approach is valid. What matters is understanding how agentic systems work, where they add value, and how emerging protocols are shaping participation. That understanding is the foundation for deciding when, where, and how to engage with the agentic web.

Read more at Read More

Web Design and Development San Diego

Google Ads API update cracks open Performance Max by channel

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Is your account ready for Google AI Max? A pre-test checklist

As part of the v23 Ads API launch, Performance Max campaigns can now be reported by channel, including Search, YouTube, Display, Discover, Gmail, Maps, and Search Partners. Previously, performance data was largely grouped into a single mixed category.

The change under the hood. Earlier API versions typically returned a MIXED value for the ad_network_type segment in Performance Max campaigns. With v23, those responses now break out into specific channel enums — a meaningful shift for reporting and optimization.

Why we care. Google Ads API v23 doesn’t just add features — it changes how advertisers understand Performance Max. The update introduces channel-level reporting, giving marketers long-requested visibility into where PMax ads actually run.

How advertisers can use it. Channel-level data is available at the campaign, asset group, and asset level, allowing teams to see how individual creatives perform across Google properties. When combined with v22 segments like ad_using_video and ad_using_product_data, advertisers can isolate results such as video performance on YouTube or Shopping ads on Search.

For developers. Upgrading to v23 will surface more detailed reporting than before. Reporting systems that relied on the legacy MIXED value will need to be updated to handle the new channel enums.

What to watch:

  • Channel data is only available for dates starting June 1, 2025.
  • Asset group–level channel reporting remains API-only and won’t appear in the Google Ads UI.

Bottom line. The latest Google Ads API release quietly delivers one of the biggest Performance Max updates yet — turning a black-box campaign type into something advertisers can finally analyze by channel.

Read more at Read More

Web Design and Development San Diego

How to build a modern Google Ads targeting strategy like a pro

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Search marketing is still as powerful as ever. Google recently surpassed $100 billion in ad revenue in a single quarter, with more than half coming from search. But search alone can no longer deliver the same results most businesses expect.

As Google Ads Coach Jyll Saskin Gales showed at SMX Next, real performance now comes from going beyond traditional search and using it to strengthen a broader PPC strategy.

The challenge with traditional Search Marketing

As search marketers, we’re great at reaching people who are actively searching for what we sell. But we often miss people who fit our ideal audience and aren’t searching yet.

The real opportunity sits at the intersection of intent and audience fit.

Take the search [vacation packages]. That query could come from a family with young kids, a honeymooning couple, or a group of retirees. The keyword is the same, but each audience needs a different message and a different offer.

Understanding targeting capabilities in Google Ads

There are two main types of targeting:

  • Content targeting shows ads in specific places.
  • Audience targeting shows ads to specific types of people.

For example, targeting [flights to Paris] is content targeting. Targeting people who are “in-market for trips to Paris” is audience targeting. Google builds in-market audiences by analyzing behavior across multiple signals, including searches, browsing activity, and location.

The three types of content targeting

  • Keyword targeting: Reach people when they search on Google, including through dynamic ad groups and Performance Max.
  • Topic targeting: Show ads alongside content related to specific topics in display and video campaigns.
  • Placement targeting: Put ads on specific websites, apps, YouTube channels, or videos where your ideal customers already spend time.

The four types of audience targeting

  • Google’s data: Prebuilt segments include detailed demographics (such as parents of toddlers vs. teens), affinity segments (interests like vegetarianism), in-market segments (people actively researching purchases), and life events (graduating or retiring). Any advertiser can use these across most campaign types.
  • Your data: Target website visitors, app users, people who engaged with your Google content (YouTube viewers or search clickers), and customer lists through Customer Match. Note that remarketing is restricted for sensitive interest categories.
  • Custom segments: Turn content targeting into audience targeting by building segments based on what people search for, their interests, and the websites or apps they use. These go by different names depending on campaign type—“custom segments” in most campaigns and “custom search terms” in video campaigns.
  • Automated targeting: This includes optimized targeting (finding people similar to your converters), audience expansion in video campaigns, audience signals and search themes in Performance Max, and lookalike segments that model new users from your seed lists.

Building your targeting strategy

To build a modern targeting strategy, you need to answer two questions:

  • How can I sell my offer with Google Ads?
  • How can I reach a specific kind of person with Google Ads?

For example, to reach Google Ads practitioners for lead gen software, you could build custom segments that target people who use the Google Ads app, visit industry sites like searchengineland.com, or search for Google Ads–specific terms such as “Performance Max” or “Smart Bidding.”

You can also layer in content targeting, like YouTube placements on industry educator channels and topic targeting around search marketing.

Strategies for sensitive interest categories

If you work in a restricted category such as legal or healthcare and can’t use custom segments or remarketing, use non-linear targeting. Ignore the offer and focus on the audience. Choose any Google data audience with potential overlap, even if it’s imperfect, and let your creative do the filtering.

Use industry-specific jargon, abbreviations, and imagery that only your target audience will recognize and value. Everyone else will scroll past.

Remember: High CPCs aren’t the enemy

Low-quality traffic is the real problem. You’re better off paying $10 per click with a 10% conversion rate than $1 per click with a 0.02% conversion rate.

When evaluating targeting strategies, focus on conversion rate and cost per acquisition, not just cost per click.

Search alone can’t deliver the results you’re used to

By expanding beyond traditional search keywords and using content and audience targeting, you can reach the right people and keep driving strong results.

Watch: How to build a modern targeting strategy like a pro + Live Q&A

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AI-Powered Functionality in Google’s SEO Tools

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Google’s been quietly upgrading Search Console and Analytics with AI. No fanfare. Just better data filtering. They sit quietly inside platforms you already use, like Search Console and Google Analytics, and they change how data is surfaced, filtered, and interpreted.

These updates don’t power AI Overviews or conversational search. They work behind the scenes in platforms you already use. Google is using AI to reduce manual analysis, surface issues faster, and help marketers understand complex datasets without exporting everything to spreadsheets.

Indexing patterns and performance trends are easier to spot, even if the underlying work still requires human judgment. Google’s automating the diagnostics. You still handle the strategy.

Key Takeaways

  • Google’s embedding AI into Search Console and Analytics 4 to cut down on manual data analysis. The AI handles filtering and pattern detection—you still make the decisions.
  • AI-powered features focus on filtering, pattern detection, and prioritization rather than execution.
  • Google Search Console AI helps surface performance insights faster.
  • Google Analytics 4 uses AI for anomaly detection, predictive metrics, and guided analysis.
  • Predictive metrics in GA4 (like churn probability) give you directional guidance, not guarantees. Use them to build hypotheses, not to replace analysis.

Why Google Is Embedding AI in SEO Tools

Google’s SEO tools have always produced more data than most teams can realistically analyze. As sites grow, so do performance reports and behavioral metrics. AI helps Google address that scale problem.

AI-Powered configuration in Google Analytics.

The main shift is from reactive analysis to proactive surfacing of insights. Instead of expecting marketers to manually filter reports, compare date ranges, and segment data, Google is using AI to highlight patterns and outliers automatically.

Search Console now groups issues more intelligently, with clearer prioritization, and more context around what matters. Analytics delivers automated insights, anomaly detection, and predictive metrics.

An example of Search Console grouping with AI.

The most practical benefit is time savings. AI-powered filtering lets you type what you want to see instead of clicking through multiple dropdowns. You can ask for specific trends, segments, or anomalies and let the system do the slicing for you. That alone removes a lot of friction from daily SEO work.

Your SEO expertise still matters. AI just handles the mechanical steps that used to slow you down. Google’s goal is to help marketers spend less time finding the signal and more time deciding what to do with it. For teams managing complex sites, this automation is table stakes.

If you want to understand how AI fits into broader SEO workflows, check out our guide on AI SEO.

AI Features in Google Search Console

Google Search Console has gradually introduced AI-assisted functionality that focuses on diagnostics and data interpretation rather than automation.

As a start, Search Console’s performance reporting benefits from smarter analysis. The platform highlights notable changes in clicks, impressions, and rankings without requiring manual comparison. This helps teams catch traffic drops or unexpected gains earlier, before they become larger problems.

Conversational-style filtering saves even more time. Instead of manually applying multiple filters, marketers can describe what they want to see, and Search Console narrows the data automatically. This reduces the time spent digging through reports just to answer basic questions.

Here’s how it works in practice: Instead of clicking Performance > Filters > Query > Contains > ‘product name’ > Apply, you type ‘show me queries for product pages with declining CTR.’ The AI interprets your request, applies the right filters, and shows you the data. That’s the time savings—going from five clicks to one typed question.

An AI query workflow.

Note: Conversational filtering is rolling out gradually and may not be available in all Search Console accounts yet.”

AI won’t fix your indexing issues or update your site. It finds problems faster so you can fix them yourself. The value comes from speed and clarity, not automation. For SEO teams, this shortens the path between detection and action without removing human oversight.

AI Features in Google Analytics 4

This is partly because GA4 handles more complex event-based data and cross-device behavior.

Analytics Advisor is the most visible AI feature. Currently in Beta and not available for everyone yet, It automatically flags unusual patterns, such as sudden traffic spikes, drops, or changes in engagement. These insights appear without manual configuration and are designed to draw attention to potential issues or opportunities.

Analytics Advisor in GA4.

Source

To access Analytics Advisor, click the lightbulb icon in the top right corner of any GA4 property. The insights refresh daily and highlight metrics that deviate from your baseline. You might see ‘Pageviews from organic search increased 47% compared to last week’ with a link to explore the affected pages. That’s faster than manually comparing week-over-week reports.

Predictive metrics add another layer. Examples include purchase probability, churn probability, and revenue prediction for eligible properties. These metrics help teams forecast outcomes based on historical behavior rather than relying purely on past performance.

Predictive metrics in GA4.

Predictive metrics require at least 1,000 positive and 1,000 negative examples of the target event over 28 days. If your site doesn’t meet that threshold, you won’t see predictions for purchase probability or churn. This makes the feature more useful for high-traffic e-commerce sites than small content publishers.

Another important use of AI in GA4 is automated anomaly detection. The platform monitors metrics continuously and alerts users when behavior deviates from expected patterns. This can surface tracking issues, campaign impacts, or site problems more quickly than manual review.

GA4’s AI points you toward what matters. You still handle the investigation. Teams still need to validate data quality, understand context, and decide how insights should influence strategy.

Other Google Tools Getting Smarter With AI

Beyond Search Console and GA4, other Google tools now have AI-supported features. Several other Google tools marketers use regularly now rely on machine learning to guide decisions and reduce manual work.

Google Analytics 4’s predictive metrics extend beyond reporting. They influence how audiences are built and activated, especially when connected to Google Ads. This allows marketers to target users based on likely future behavior rather than past actions alone.

Google Ads leans on machine learning to suggest budget shifts, adjust bids automatically, and test creative variations. You can accept or reject these suggestions, the control stays with you. These systems focus on optimization suggestions rather than forced changes, leaving final control with advertisers.

Here’s what matters: diagnostic AI explains what’s happening now. Predictive AI estimates what comes next. Diagnostic AI explains what is happening now and why. Predictive AI estimates what might happen next. Both influence how marketers act, but they serve different purposes. Understanding which type of insight a tool provides helps teams decide how much weight to give its recommendations.

This changes your daily workflow. Instead of checking reports manually and looking for problems, you respond to flagged issues. Instead of building audience segments from scratch, you refine AI-generated segments. The shift is from ‘find the problem’ to ‘validate the finding.’ That’s faster, but it requires trust in the system’s baseline accuracy.

Should You Trust AI to Support Your Reporting?

Google’s using AI to decide what you see first in your reports. That raises control questions. These tools influence what you see first, what gets flagged, and what feels urgent.

Trust the insights. Verify the recommendations. AI supports reporting by prioritizing information, not by defining truth. Understanding its role helps teams use it effectively without losing oversight.

Is AI Taking Too Much Control?

One concern is that AI-driven data points could push marketers into autopilot mode. When tools highlight issues automatically, it’s tempting to assume they reflect the full picture.

AI helps you see more. It surfaces technical problems and data anomalies that teams often miss because they’re buried in reports or obscured by volume. AI helps surface data anomalies that teams might miss due to scale or limited time. It reduces the chance that important issues stay hidden in reports.

Don’t follow every data point blindly. AI recommendations are based on models and thresholds that may not reflect business context. Treat insights as starting points, not final answers. Validation still matters.

Who Really Gets the Advantage?

People assume big brands with more data get better AI insights. Not true. Everyone has access to the same tools.

The advantage goes to teams that actually use the insights. A local contractor who spots a data anomaly flagged by Search Console and acts on it outranks a national franchise that ignores the same alert.

AI lowers the barrier to analysis, but it doesn’t guarantee better outcomes. Interpretation and execution still determine results.

FAQs

Does AI in GA4 replace manual analysis?

No. AI highlights anomalies and predictions, but analysts still need to validate findings and decide how to act.

Are predictive metrics in GA4 always accurate?

Predictive metrics are estimates based on historical data. They provide directional guidance, not certainty.

Conclusion

AI makes Google’s SEO tools more efficient. It doesn’t replace the need for strategy. You still need to validate insights, understand your business context, and decide how to act on recommendations. The teams winning with these tools treat AI as an assistant, not an autopilot. 

They use automated insights to find problems faster, then apply their own expertise to fix them. That combination (AI-powered detection plus human strategy) is what drives results. Start by exploring the AI features already available in your Search Console and GA4 accounts. Check what Analytics Advisor has flagged. Look at how Search Console groups your indexing issues. 

See if the insights align with what you’re already tracking manually. Then decide where automation saves you real time. 

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What is the open web?

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The open web is the part of the internet built on open standards that anyone can use. This concept creates a democratic digital space where people can build on each other’s work without restrictions, just like how WordPress.org is built. For website owners, understanding and leveraging the open web is increasingly crucial. Especially with the rise of AI-powered systems and the general direction that online search is taking. So, let’s explore what the open web is and what it means for your website.

What is the open web?

The open web refers to the part of the internet built on open, shared standards that are available to everyone. It’s powered by technologies like HTTP, HTML, RSS, and Schema.org, which make it easy for websites and online systems to interact with each other. But it is more than just technical protocols. It also includes open‑source code, public APIs, and the free flow of data and content across sites, services, and devices. Creating a democratic digital space where people can build on each other’s work without heavy restrictions.

Because these standards are not owned or patented, the open web remains largely decentralized. This allows content to be accessed, understood, and reused across devices and platforms. This not only encourages innovation but also ensures that information is discoverable without being locked behind proprietary ecosystems.

The benefits of an open web

The open web is built on publicly available protocols that enable access, collaboration, and innovation at a global scale. 

The most important benefits include:

  • Collaboration and innovation: Open protocols enable developers to build on each other’s work without proprietary restrictions.
  • Accessibility: Users and AI agents alike can access and interact with web content regardless of device, platform, or underlying technology.
  • Democratization: No single company controls access to information, giving publishers greater autonomy.
  • Inclusion: The open web creates a more level playing field, where everyone gets a chance to participate in the digital economy.

The open web vs the deep web

To give you a better idea of what the open web is, it helps to know about the “deep web” and closed or “walled garden” platforms. The deep web covers content not indexed by search engines, while closed systems or walled gardens restrict access and keep data siloed.

On the open web, anyone can access information freely. A good example of that is Wikipedia. Accessible to anyone looking for information on a topic and anyone who wants to contribute to its content. Closed-off platforms, like proprietary apps or social media ecosystems, create places where content is only available if you pay or use a specific service. Well-known examples of this are social media platforms such as Facebook and Instagram. Another example is a news website that requires a paid subscription to get access.

In essence, the open web keeps information discoverable, accessible, and interoperable – instead of locked inside a handful of platforms.

AI and the open web

The popularity of AI-powered search makes open web principles more important than ever. Decentralized and accessible information allows AI tools to interact with content directly and use it freely to generate an answer for a user. 

“We believe the future of AI is grounded in the open web.” 

Ramanathan Guha, CVP and Technical Fellow at Microsoft. 

Microsoft’s open project NLWeb is a prime example. It provides a standardized layer that enables AI agents to discover, understand, and interact with websites efficiently, without needing separate integrations for every platform. 

What this means for website owners

For website owners, including small business owners, embracing the open web means making your content freely available in ways that AI can interpret. By using structured data standards like Schema.org, your website becomes discoverable to AI tools. Increasing your reach and ensuring that your content remains part of the future of search. 

Yoast and Microsoft: collaborating towards a more open web

Yoast is proud to collaborate with NLWeb, a Microsoft project that makes your content easier to understand for AI agents without extra effort from website owners. Allowing your content to remain discoverable, reach a wider audience with and show up in AI-powered search results.  

The open web strives towards an accessible web where content is available for everyone. A web where it doesn’t matter how big your website or marketing budget is. Giving everyone the chance to be found and represented in AI-powered search. NLWeb helps turn this vision into reality by connecting today’s open web with tomorrow’s AI-driven search ecosystem 

Read on: Yoast collaborates with Microsoft to help AI understand Open Web »

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Discord as an Engagement and Digital PR Platform

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Discord has moved far beyond its gaming roots. Today, it’s becoming a direct access channel for brands that care about real engagement and meaningful digital PR outcomes.

This isn’t a Discord 101 guide. Most marketers already understand what the platform is and how servers work. Most marketers don’t know how to use Discord for engagement and PR, even as email pitches fail and social algorithms tank reach.

Discord matters now because it removes friction. Brands get real-time access to fans, creators, journalists, and niche communities without algorithmic interference. Over 200 million people use Discord monthly, and brands from Shopify to The New York Times now run active servers. Conversations happen in the open, persist over time, and create context that traditional channels struggle to replicate.

Brands can show up consistently in spaces people actually want to join. That changes how relationships form and stories emerge.

In this article, we’ll break down how marketers and PR teams can use Discord to drive engagement, support press outreach, host event-style campaigns, and turn community activity into earned media.

Key Takeaways

  • Discord works best as a relationship channel, not a broadcast platform. Engagement comes from participation, not posting frequency.
  • PR teams can use Discord to build trust and shared context before any formal outreach happens.
  • Features like roles, private channels, and stages support controlled access for media and creators.
  • Event-driven engagement inside Discord often creates moments journalists and creators want to reference.
  • Earned media from Discord grows out of visible conversation, not promotional messaging.
  • Most brands fail on Discord by broadcasting instead of conversing. The platform rewards brands that facilitate discussion, respond quickly, and give members real access to decision-makers.

Why Discord Is More Than Just a Community Platform

Discord gets grouped with other community tools, but that undersells what it actually does.

Discord is an owned communication layer. Members opt in. Conversations persist. There’s no feed to fight and no algorithm deciding who sees what. Engagement teams tired of declining social reach find that valuable.

The Discord interface.

Source

The platform has also expanded into professional and brand-led use cases. B2B companies, SaaS platforms, media brands, and creator-led businesses now use Discord to host product discussions, feedback loops, and industry conversations. These servers often function as always-on focus groups where insight flows both directions.

Shopify hosts channels for developers and partners. Notion uses Discord for product feedback and feature requests. These aren’t gaming communities—they’re professional spaces where brands get direct access to customers, partners, and media without paying for ads or fighting algorithms.

For PR teams, Discord introduces something email can’t replicate: visible context. Journalists and creators don’t just receive a message. They see how a brand responds to questions, explains decisions, and engages with its community over time.

A tech journalist following a SaaS brand’s Discord sees how they handle bug reports, communicate delays, and support users. That context makes it easier to cover the company fairly when news breaks. Email alone can’t build that kind of ongoing visibility.

The Adobe Photoshop discord interface.

That ongoing presence builds familiarity before coverage is ever discussed. Discord blends access, continuity, and transparency into a single environment, which sets the foundation for both engagement and digital PR.

Core Features That Make Discord Ideal for Engagement and PR

Discord’s strength is ongoing conversation, not one-way distribution. That distinction changes how engagement and digital PR teams plan campaigns.

Chat channels stick around. Conversations don’t disappear after a day or get buried by new posts. Conversations don’t disappear after a day or get buried by new posts. A strong AMA thread, product debate, or media Q&A can remain active and searchable for weeks, giving journalists and creators extended context without repeated outreach.

Roles and access control make Discord viable for PR use cases. Teams can create press-only channels, creator lounges, or embargoed spaces tied to launches. Access feels intentional rather than promotional, which increases participation and trust.

Here’s how that works in practice: You can create a #press-only channel where journalists see embargoed announcements, background context, and Q&A access before public launches. A #creators channel might include early product access, collaboration opportunities, and direct messaging with your team. Fans see neither of these spaces—they get their own channels focused on community discussion and support. That segmentation makes Discord feel exclusive and valuable to each group.

Editing roles on Discord.

Events, stages, and AMAs introduce timed engagement bursts. Moderated formats work well for leadership conversations, briefings, and launches. These events concentrate attention while still allowing real interaction.

Stages support up to 1,000 listeners with interactive Q&A. That’s enough for most brand events without requiring webinar software or event platforms. The recording stays in the channel afterward, so people who missed the live session can still participate in the discussion.

Integrations extend Discord’s usefulness. Feedback tools, shared resource hubs, and workflow automations connect Discord activity to broader marketing and PR efforts. Instead of living in a silo, Discord becomes part of day-to-day operations.

The key advantage is flexibility. Discord lets teams design micro-environments around how people actually communicate.

Using Discord to Build Journalist and Creator Relationships

Most PR teams still rely on cold email, despite falling response rates. Journalists and creators increasingly prefer communication that feels conversational and contextual rather than transactional.

Discord makes non-pitch engagement possible. Skip the ask. Invite journalists and creators into private or semi-private channels first. These spaces offer early context, background discussion, or access to subject-matter experts without pressure.

Buffer runs a Discord server where journalists can ask the CEO or product team questions directly. No PR gatekeepers. No scheduling calls. Just post a question in the #media channel and get a response within hours. That accessibility makes Buffer easier to cover than competitors who require formal interview requests and two-week lead times.

Buffer's Discord Server.

Direct access to decision-makers changes expectations. Journalists can ask follow-up questions, clarify details, or observe how a brand thinks before deciding whether a story fits. Creators can explore ideas collaboratively rather than responding to a single brief.

Here’s a simple journalist outreach flow:

  1. Create a private #press channel with embargoed access
  2. Invite 10-15 journalists who cover your industry (not thousands)
  3. Share early context on product launches, company updates, or industry insights
  4. Let them ask follow-up questions async
  5. When a story fits, the relationship already exists

Over time, transparency and responsiveness in chat build trust faster than long email threads. When a pitch does make sense, the relationship already exists.

This approach works particularly well for tech, SaaS, and creator-driven industries where speed, access, and nuance influence coverage decisions.

This approach doesn’t work for every brand. Mass consumer brands or highly regulated industries might struggle with open-channel discussions. But for companies selling to creators, developers, or digital professionals, Discord shortens the relationship-building cycle from months to weeks.

Event-Based Engagement: How to Use Discord for Launches, AMAs, and More

Smart brands treat Discord like a live venue, not a static community.

Product launches often include countdown channels, staged reveals, and post-drop discussion. Leadership teams host AMAs. Engineers, designers, and product managers run Q&A sessions that surface both feedback and insight.

Source

Good events take prep work.. Clear goals, advance question collection, and active moderation improve outcomes and keep discussions focused.

Effective Discord events typically include:

  • Before the event:
    • Announce 3-5 days early with clear agenda
    • Create dedicated event channel
    • Collect questions in advance via Google Form or channel thread
    • Assign at least 2 moderators
    • Test Stage or voice channel setup
  • During the event:
    • Pin the event agenda
    • Start with 3-5 pre-submitted questions to build momentum
    • Let mods filter and prioritize live questions
    • Keep responses under 3 minutes each
    • Screenshot strong quotes for later use
  • After the event:
    • Post a recap with key quotes, decisions, or takeaways
    • Thank participants by name
    • Share recap as blog post or social content
    • Leave the channel open for continued discussion

Most effective Discord events run 45-60 minutes. Longer sessions lose energy. Shorter sessions feel rushed. Plan for 10-12 questions max, with flexibility for strong follow-ups.

Events focused on audience value beat pure announcements every time. These moments also create reusable assets. Quotes, insights, and screenshots often become blog content, social posts, or supporting material for PR outreach.

Driving Earned Media Through Discord Engagement

Growing your Discord server matters less than what happens inside it.

Active communities generate stories organically. Journalists reference AMA insights. Industry newsletters cite ongoing discussions. Blogs quote real community sentiment.

Community-driven narratives often outperform traditional press releases because they show participation rather than positioning. Readers trust stories that reflect real dialogue.

A transparent Q&A or high-energy discussion thread can become the foundation for coverage. Discord surfaces narratives that feel timely, authentic, and grounded in lived interaction.

To maximize earned media potential from Discord:

Make conversations screenshot-friendly. Clear usernames, well-formatted responses, and threaded discussions make it easier for journalists to reference your server.

Highlight notable members. When industry experts or recognizable creators participate in your Discord, that increases media appeal.

Track quotable moments. Assign someone to screenshot strong quotes, insights, or exchanges during active discussions. These become PR assets.

Pitch the conversation, not just the product. Send journalists a link to an active discussion thread, not a press release. Let them see the community energy firsthand.

Common Mistakes When Using Discord for PR and Engagement

The biggest mistake? Treating Discord like a broadcast channel.

Post links without conversation and your server dies.. Members expect response and interaction, not scheduled promotion.

Another issue is weak moderation. Servers without clear purpose or active moderators lose focus fast, which discourages journalists and creators from participating.

PR teams also create friction when they treat creators or journalists like captive audiences. Discord works because participation is voluntary and collaborative.

Guide discussion. Share insider context. Show up consistently. Respect the community’s time.

“Mistake #1: Broadcasting Without Responding Posting ‘Check out our new blog post!’ and disappearing doesn’t work. People expect you to discuss the post, answer questions, or explain why it matters. If you’re not ready to engage, don’t post.

Mistake #2: No Clear Server Purpose Servers that try to be everything—community hub, support forum, news feed, social network—confuse members. Pick 2-3 core functions and build around those. Zapier’s Discord focuses on automation discussion and customer success. That’s it.

Mistake #3: Treating Journalists Like Fans Journalists don’t want hype. They want context, access, and honesty. A press channel filled with marketing language gets ignored. Background information, data, and direct responses get used.

Mistake #4: Inconsistent Presence Posting daily for two weeks, then ghosting for a month, breaks trust. If you can’t maintain active engagement, don’t launch a server. Better to have no Discord than an abandoned one.

Mistake #5: Over-Moderation or Under-Moderation Too many rules kill discussion. No rules create chaos. Find the balance: clear guidelines, active mods who participate (not just police), and flexibility for organic conversation.”

Tools, Bots, and Setups to Maximize PR ROI

The right setup makes Discord manageable for small teams and scalable for larger ones.

Roles segment audiences cleanly. Press, creators, and fans shouldn’t share the same access paths. Clear onboarding channels explain where to engage and what matters.

Bots support efficiency:

  • Event scheduling and reminders
  • Moderation and automation
  • Engagement and activity tracking

Larger teams often use ticket-style workflows to route media requests or creator inquiries without cluttering channels.

The goal is structure without rigidity. Discord should feel organized, not over-engineered.

A Discord Bot.

Source

Here are the bots worth using:

For Events: Sesh – Schedules events with automatic reminders. Members RSVP directly in Discord, and the bot pings them 15 minutes before start time.

For Moderation: MEE6 – Auto-moderates spam, assigns roles based on activity, and sends custom welcome messages to new members. Free tier handles most small-to-mid sized servers.

For Analytics: Statbot – Tracks message volume, active members, peak engagement times, and channel-level activity. Shows which conversations generate the most participation—useful for PR teams measuring impact.

For Workflow: Zapier’s Discord integration – Connects Discord to Google Sheets, Notion, or your CRM. Auto-post media inquiries to a tracking sheet or notify your team in Slack when someone joins your press channel.

For Ticketing: Ticket Tool – Creates private support threads for media requests, creator pitches, or partnership inquiries. Keeps channels clean while routing requests to the right team member.

FAQs

How do you engage a Discord community?

Run regular events like AMAs, Q&As, or feedback sessions. Assign roles that give members status and access (not just colors). Recognize active contributors publicly. Create channels for member-led discussions, not just brand announcements. Give people reasons to return daily, like ongoing conversations or exclusive content drops.

How can you increase Discord engagement?

You can run a small server (under 500 members) with one dedicated person spending 30-60 minutes daily. Larger servers need at least 2-3 moderators to handle different time zones and maintain a consistent presence. Consider community volunteers once your server reaches 1,000+ active members.

What’s the minimum team size needed to run a Discord server effectively?

What’s the minimum team size needed to run a Discord server effectively?

Expect 3-6 months before Discord activity generates measurable earned media. Relationships take time. The first month focuses on setup and onboarding. Months 2-3 build conversation patterns. Months 4-6 typically produce quotable moments and media references. Results accelerate after you establish a consistent presence and trust.

How long does it take to see PR results from Discord?

Expect 3-6 months before Discord activity generates measurable earned media. Relationships take time. The first month focuses on setup and onboarding. Months 2-3 build conversation patterns. Months 4-6 typically produce quotable moments and media references. Results accelerate after you establish consistent presence and trust.

Conclusion

Discord rewards dialogue over distribution. That makes it a natural fit for engagement and digital PR teams focused on relationships rather than reach.

Brands that use Discord well create space for trust, transparency, and real participation. Those signals translate into earned media, stronger creator relationships, and long-term community value that social platforms can’t replicate.

Start small. Launch a focused server with clear purpose, maybe a press channel and a creator lounge. Host one monthly AMA or live event. See what surfaces organically before scaling up. The platform rewards consistent, genuine engagement more than polished campaigns.

Discord won’t replace your email list or social media presence. But for building the kind of relationships that lead to coverage, partnerships, and authentic advocacy, it’s one of the most effective channels available right now.

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Yahoo debuts Scout, an AI search and companion experience

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Yahoo today launched the first version of its AI-powered answer engine, Yahoo Scout. Scout is available at scout.yahoo.com and is embedded across Yahoo’s network, including Yahoo News, Finance, Mail, and Search. Think of it as a Yahoo-branded AI companion designed to guide users directly within Yahoo’s properties.

What is Yahoo Scout. Yahoo Scout is Yahoo’s take on an AI search engine and companion, similar to Google’s AI Mode or OpenAI’s ChatGPT, but with a distinct Yahoo flair. The goal is to give Scout a real personality — fun, engaging, and easy for people of all ages to use and understand, Yahoo told me.

  • When you first visit Yahoo Scout, you’re greeted by a playful homepage with a search box, a catchy slogan, and an animated icon that makes the experience feel friendly and inviting.
  • Below the search box, Yahoo offers suggested searches, with filters for topics like news, finance, sports, shopping, and travel.
  • On the left, Scout shows your past queries, making it easy to jump back in where you left off.

Here’s a screenshot of the homepage. This one features a cowboy hat, but other versions include a crystal ball, a gold medal, a walking cartoon brain, and more.

Yahoo Scout’s advantage. The Yahoo Search team gave me early access to Yahoo Scout. While the interface feels familiar if you’ve used competing tools, the Yahoo-specific elements clearly set it apart.

Yahoo’s advantage over many AI search competitors is its massive, built-in audience across Mail, News, Finance, and Search. It has more than 500 million user profiles and deep data on queries, usage, intent, and behavior. Yahoo also maintains over one billion knowledge-graph entities and tracks 18 trillion consumer events and signals across its properties. Together, this gives Yahoo the ability to deliver more personal AI-driven search experiences and more accurately categorize queries.

Yahoo is the second largest email company and third largest search engine, the company told me.

Yahoo Scout can pull rich content from across Yahoo directly into its responses. This includes features like Yahoo Finance widgets, detailed financial data, tables and citations, weather, news, and more.

  • “Search is fundamentally changing, and our team has been inspired to use our decades of experience and extremely rare assets to create something uniquely useful for Yahoo’s hundreds of millions of monthly users. This beta launch is just the starting point. From search to our industry-leading verticals, Yahoo Scout will help our users accomplish their goals online faster and better than ever before,” said Jim Lanzone, CEO of Yahoo.

Sending traffic to you, the publisher. Scout is closely tied to Yahoo’s original mission: being a trusted guide to the internet, Lanzone said. From the ground up, Yahoo built Scout to honor the open web by driving traffic downstream to content creators.

Yahoo Scout responses use large, wide blue highlights across the text. When you hover over them, you can click through to the original source.

Each response also includes a “featured source” that’s easy to spot and select. Scout further emphasizes content with tables and imagery while surfacing relevant news articles and sources throughout its answers.

Early AI search engines did little to send traffic back to the sources behind their answers, Lanzone said. Yahoo wanted to set an example for how to do this the right way. There isn’t enough revenue for every publisher to rely on licensing deals with AI companies, and historically, the model that worked best was simple: send traffic back to the original sources.

Here’s an example of how Yahoo Scout links to its sources:

When you hover over the blue highlights, the source appears, and you can click through to visit it. The purple “Read more” featured-source section also aims to drive traffic downstream.

CTR expectations. I asked Yahoo about the expected click-through rate from Scout to publishers. They said they don’t know yet. Yahoo plans to learn from real-world usage once Scout goes public and iterate to improve downstream clicks. This is Scout’s first release, and real user data should be telling.

They expect queries in Yahoo Scout to be longer than in Yahoo Search, with lighter ad loads and a much higher click-through rate than the industry average.

Yahoo also told me it plans to give publishers access to impression and click data in the future, possibly through a Yahoo Webmaster Tools–style product. Crawling and indexing would remain separate, since that layer is still powered by Microsoft Bing.

Yahoo Scout in every Yahoo property. You’ll be able to access Yahoo Scout across all Yahoo properties.

  • Yahoo Mail will summarize emails with AI and extract actionable items, such as adding events to your calendar.
  • Yahoo Search will add AI summaries powered by Scout.
  • Yahoo News will surface key article highlights and include the daily digest audio summary.
  • Yahoo Finance will introduce a new Analyze button powered by Scout.

Examples of Yahoo Scout in action. Here are a few examples of Yahoo Scout. It’s not perfect, but for a six-month project, I’m impressed.

I asked Scout for help explaining how SEO works, and it delivered a solid response. SEO is complex, and not everyone will agree with every detail, but the answer was thoughtful and useful. There are citations throughout the summary:

I then asked it to share sources for finding content on the topic as a follow-up. There were clear missed opportunities to link out more, which I pointed out to Yahoo, and they agreed.

I asked Yahoo Scout how to navigate to the sources it mentioned, and at that point, it did provide links:

Here’s a screenshot of another citation that appears when you hover your mouse cursor over it.

Here are some other searches I tried:

  • Entertainment: Scout incorporates news articles, with larger graphics in clickable card formats.
  • Finance: Yahoo brings in Yahoo Finance. I was unable to generate stock charts, although in a demo I was given, I was shown that live. So maybe it was being worked on during my tests:
  • Weather: I was testing this Sunday morning, as the big snow storm was touching down in New York:

I was able to get a Yahoo Weather chart:

With tips on how to stay warm:

  • Sports: The Super Bowl is coming up, and I was hoping to get some predictions:

As a lifelong Jets fan, I asked whether the team has any chance of winning the Super Bowl in the next 10 years. The answer wasn’t encouraging, but I was happy to see a chart embedded directly in the response.

  • Shopping: And then Yahoo gave me some advice on how to dress during this weather:

Ads and commissions. Yahoo Scout will show ads at the bottom of some responses. It will also monetize commerce-related queries through affiliate commissions, a common web revenue model.

  • Yahoo told me the ads are still powered by Microsoft Advertising, but Yahoo controls how those ads appear within these interfaces.
  • These ads will be charged on a CPC basis, not an impression basis, as some other AI engines announced.

Here is a screenshot of a Progressive Insurance ad for questions about car insurance.

Here is a screenshot of product results that are labeled, “Yahoo may earn commission from these links.”

How Yahoo Scout came about. For about three years now, Yahoo has been hinting about making a return to the search game. In 2009, Yahoo made a deal with Microsoft to have Microsoft power Yahoo Search and that was the end of Yahoo building its own search technology. Literally, Yahoo has outsources Search since then and has not done its own search technology until now, with Yahoo Scout.

That is until now. About six month ago, Yahoo acquired Eric Feng’s company to lead up consumer search at Yahoo. Eric Feng is known for co-founding an online video platform startup called Mojiti, which was acquired by Hulu in 2007, in which Eric became the founding CTO and head of product at Hulu. But before that, he worked at Microsoft in the Research labs, working on solving problems with Search.

“Yahoo’s deep knowledge base, 30 years in the making, allows us to deliver guidance that our users can trust and easily understand, and will become even more personalized over the coming months,” said Eric Feng, Senior Vice President and General Manager of Yahoo Research Group, the creators of Yahoo Scout. “Yahoo Scout now powers a new generation of intelligence experiences across Yahoo, seamlessly integrated into the products people use every day.”

Jim Lanzone, the CEO of Yahoo, who in his own right has a long history in search, as the CEO of Ask.com for many years, told me that Eric Feng has been instrumental in building out Yahoo Scout in the past 6 months. And there is so much more to come, this is just the first public release and you can expect many more interations and improvements to Yahoo Scout in the near future.

Anthropic. Yahoo Scout is not built on its own LLM, Yahoo partnered with Anthropic to use Claude as Yahoo Scout’s primary foundational AI model. Anthropic is one of the top artificial intelligence companies in the market. It has arguably the best AI for coders and coding frameworks named Claude. Anthropic was founded in 2021 by former members of OpenAI, including siblings Daniela Amodei and Dario Amodei, who serve as president and CEO, respectively. In September 2023, Amazon announced an investment of up to $4 billion. Google committed $2 billion the next month. As of November 2025, Anthropic has an estimated value of $350 billion.

While the foundational AI models use Anthropic, Yahoo has customized it and incorporates Yahoo’s proprietary data to make it unique and useful. Doing these searches on Anthropic will not give you anywhere close to the same experience as you would get on Yahoo Scout.

“When you’re serving hundreds of millions of users, you need AI that can do more than retrieve information – it has to reason, synthesize, and explain. Yahoo is building toward a more personalized, trustworthy kind of search, and Claude’s ability to deliver that quality of guidance at scale is at the heart of Yahoo Scout,” said Ami Vora, Head of Product at Anthropic.

Microsoft Bing. Plus, Microsoft Bing data is also incorporated into Yahoo Scout. The underlining search index is from Bing, but the responses, ranking, and experience is all Yahoo. “Yahoo Scout also builds on Yahoo’s long-standing relationship with Microsoft by leveraging Microsoft Bing’s grounding API. By combining this API with Yahoo’s trusted data and content ecosystem, Yahoo Scout ensures that answers are informed by authoritative sources from across the open web, Yahoo wrote.

Plus, Yahoo is also joining Microsoft’s Publisher Content Marketplace pilot. Microsoft’s Publisher Content Marketplace can help support revenue for publishers, the company said. Yahoo wrote this is, “reflecting a shared commitment to expanding publisher reach, connecting original work with new audiences, and supporting sustainable revenue opportunities for publishers.”

Hallucinations. I asked about hallucinations and Yahoo told me they put in a lot of guardrails to prevent hallucinations as much as possible. The Yahoo entity graph, the news content, and other Yahoo-specific data are used to ground the responses so that communications should be minimal and less than some other AI engines. In fact, they believe the hallucination rate would be “very low” compared to other AI engines.

Agents. Many AI engines are releasing agentic experiences, AI agents, to complete tasks for you. Google, OpenAI and Microsoft are investing big time into this.

Yahoo Scout has added some elements of this including inside of Yahoo Mail to add calendar events, smart compose features and more. Yahoo promises a lot more to come on this front.

Why we care. It’s an exciting time for search. For someone like me who has spent more than 20 years in search, it’s nice to see Yahoo step back into the space. Watching industry veterans like Jim Lanzone, Eric Feng, and Brian Provost take on search with AI is making it fun again, and I’m excited to see what Yahoo does next.

Availability. The Yahoo Scout answer engine is available today in beta for U.S. users at Scout.Yahoo.com and in the Yahoo Search app on iOS and Android. For more about Yahoo Scout, see this help document.

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