Search News You Can Use - SEO Podcast with Marie Haynes

Google's latest earnings call reveals a massive shift in search and AI infrastructure. While Gemini 3.5 Pro is delayed, new Flash models are already changing how AI Overviews function and how agents interact with your data.

In this video we discuss how Google's ad revenue from websites publishing ads is down slightly but Cloud revenue is significantly up.

Did you know Google is not actually a search engine? More in this video!

Blog post that goes with this episode:
https://www.mariehaynes.com/the-future-of-search-ai-what-i-learned-from-googles-latest-earnings-call/

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0:00. Google's earnings call and financial overview: Marie introduces Google's earnings call, noting that despite strong revenue growth, Google is currently cash flow negative due to heavy investments in AI infrastructure.

1:07. Gemini model releases and future roadmap: Google announced new Flash models like Gemini 3.6 Flash while actively testing Gemini 3.5 Pro and working on their Gemini 4 run.

2:16. Gemini 3.5 Flash-Lite in Search: Google Search has begun routing conversational queries to Gemini 3.5 Flash Lite to enhance intent understanding in AI Overviews and AI Mode.

3:26. Security models and CodeMender: Google introduced specialized cyber models designed to continuously scan codebases and patch software vulnerabilities automatically.

6:04. Hands-on experience with Anti-Gravity and AI development tools: Marie shares her positive experience using Gemini 3.6 Flash within the Anti-Gravity IDE to quickly build and deploy web pages.

8:47. Google's strategic positioning with AI models: Marie highlights that Google's strategy focuses on fast, reliable, and cost-effective models built for high-volume daily use rather than purely chasing peak coding benchmarks.

10:41. Expansion of AI Overviews in commercial search queries: Google is expanding AI Overviews to more commercial product queries, emphasizing the importance of being an authoritative resource for users.

11:53. Increased ad integration in AI Mode: Google is leveraging AI model capabilities to better comprehend complex queries, allowing them to serve more targeted ads inside AI Mode.

12:42. Shift toward agentic search and connected apps: Search is evolving into an agent manager where users can perform real-world tasks, such as ordering groceries through third-party app integrations.

15:32. E-commerce and agent payment protocols: Google is pushing seamless purchasing options like Buy with Google Pay and laying the groundwork for autonomous AI agent transactions via security protocols.

17:04. Revenue shifts between AdSense and Google Cloud: Recent financial metrics show a minor dip in display ad network revenue alongside massive revenue growth in Google Cloud driven by enterprise AI adoption.

20:40. Intelligent eyewear and multimodal AI opportunities: Google's upcoming smart glasses will gather multimodal real-world data, creating new avenues for interactive software and content creators.

23:25. Capital expenditure and compute resource allocation: Google spent billions on AI servers and data centers to meet enterprise demand while dedicating vital compute resources toward ongoing AGI research.

26:42. Progression from AGI to Artificial Superintelligence (ASI): The discussion transitions to how current AI systems are moving beyond narrow task intelligence toward full AGI and eventually recursive self-improvement through ASI.

In my last video, I introduced the concept of Google's Open Knowledge Format (OKF). Today, I'm showing you exactly what I've built with it. I have created a personal brain that I use every day to automate complex SEO tasks, generate proposals, and analyze Google search updates. We walk through the structure of an OKF file, including the YAML frontmatter and how it differs from traditional RAG. If you want to make your data and processes agent-ready, my hope is that what I'm building will help you build your own OKF brain.Here's the first video on OKF: https://www.youtube.com/watch?v=MY9F9K7wWX4 To build something similar, give these links to your agent (Claude Code / Cowork, ChatGPT Codex or my favourite, Google's Antigravity) and try this prompt:"I want to build an OKF system similar to Marie's. Read these links and then give me some ideas of what this would look like. Then, ask me questions one at a time so that together, we can decide what we want to build:https://cloud.google.com/blog/products/data-analytics/how-the-open-knowledge-format-can-improve-data-sharing/https://github.com/GoogleCloudPlatform/knowledge-catalog/blob/main/okf/SPEC.md https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94fhttps://www.mariehaynes.com/okf/https://www.youtube.com/watch?v=MY9F9K7wWX4https://youtu.be/esYAIA6lU-s"

Google just announced the Open Knowledge Format (OKF), a new way to structure your knowledge for AI agents. This standard uses simple Markdown to make business and personal insights accessible to the agentic web. In this video, I break down why this changes SEO, how to build your first OKF bundle, and the potential for selling expert knowledge directly to AI systems.Google's blog post on okf:https://cloud.google.com/blog/products/data-analytics/how-the-open-knowledge-format-can-improve-data-sharing/The Spec.md file for OKF on Github:https://github.com/GoogleCloudPlatform/knowledge-catalog/blob/main/okf/SPEC.mdAndrej Karpathy's LLM-Wiki:https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94fJoin me in my community at the Search Bar:https://community.mariehaynes.comhttps://mariehaynes.com/join - for more on the paid areas.0:00 - Introduction to Google's Open Knowledge Format (OKF)1:03 - How OKF changes SEO and makes businesses accessible to AI agents1:43 - Selling proprietary knowledge and OKF bundles2:21 - How to use NotebookLM and Gemini to learn the new standard3:02 - What is OKF? (Markdown files and the LLM Wiki pattern)4:01 - Breaking down the OKF spec (Human and agent-friendly format)5:03 - Terminology: Creating "Knowledge Bundles" and directories5:51 - Structuring concepts as single Markdown files6:30 - Understanding YAML front matter (Type, title, descriptions, tags)7:18 - Formatting the body, citations, and cross-linking7:40 - Organizing files: Index files, log files, and using Git/Obsidian9:11 - Practical examples: BigQuery data vs. triggered Playbooks11:12 - Andre Karpathy’s LLM Wiki pattern explained13:48 - Letting the LLM write and maintain your knowledge wiki14:34 - Why OKF is the "new schema" and future revenue streams17:15 - How agents discover your OKF (llms.txt) and "Semantic Unbaking"

You can build an AI dashboard so easily - even if you don't know how to code! In this episode, Laura Tobin from the Search Bar community shares the incredible system of AI agents she built using Claude and Co-work. As a food blogger facing the shifts in AI changing Search, Laura decided to pivot from fighting the technology to using it to become a "one-person content machine."She walks us through her "Command Center," a custom dashboard that manages her entire workflow, from post pipelines to client shoots. We also discuss the "uncanny valley" of AI content and why her audience rejected AI avatars, teaching us a valuable lesson on where AI belongs in your business.The discussion following the webinar is so good. We share the different avenues we are exploring in building an AI brain. Where to connect with Laura Tobin:A free 30 minutes exploratory session:https://calendly.com/laura-tobinfooddigital/30minEmail:Laura@tobinfooddigital.comLaura's Bloghttps://yourguardianchef.com/Laura's youtube channel:https://www.youtube.com/@yourguardianchefLaura's Instagram account:https://www.instagram.com/yourguardianchefJoin Marie's community, the Search Bar for real time Search and AI News:https://community.mariehaynes.com/Join the Search Bar Pro area so you can join our weekly meetings:https://www.mariehaynes.com/join/Timestamps: 0:01 - Introduction: Meet Laura and her Claude-powered content system.1:02 - Laura's Pivot: Becoming a one-person content machine after Google's update.3:03 - AI Video Testing: Why human authenticity still beats AI avatars.6:07 - System Setup: Centralizing goals and workflows into a "Second Brain."8:34 - The AI Team: Meet Laura's specific agents (Kiara, Francesca, Julia, Rosella).10:25 - Command Center: Walkthrough of her custom dashboard and time-tracking tool.13:48 - Social Batching: Automating schedules and building a reusable content bank.17:04 - Photography Days: Managing recipes, shoot schedules, and client quotes.19:30 - Results: How the system saves time and improves audience engagement.21:46 - Q&A: Moving assets to Google Drive and republishing YouTube Shorts.24:04 - Q&A: Using Claude Co-work and why Notion acts as the system's memory.28:20 - Global Instructions: Using .md files to run agents and saving token costs.32:46 - Getting Started: How Laura built it all without a coding background.36:13 - Choosing an App: Debating Notion, Craft, and Obsidian for data storage.41:06 - The Future: Discussing open-source agents and auto-building systems.46:37 - AI Goals: Using agents for task execution vs. life optimization.48:45 - Conclusion: Wrap-up and how to contact Laura for consulting.

Google has just released a major update to Search Console, providing website owners with dedicated performance reports for Generative AI features including AI Overviews, AI Mode, and Discover AI. In this video, I walk through where to find this new report, why impressions matter even without click data, and the new regulatory changes that forced Google's hand.

0:00 - Introduction: Google introduces new AI tracking data within Search Console.
0:37 - Official Announcement: Google launches Search Generative AI performance reports.
1:22 - Report Walkthrough: Looking at the new report, which tracks impressions only.
1:58 - Google's Blog Post: Reviewing Google's updates on AI insights and best practices.
2:23 - Links & Sources: Discussing inline citations and how to become a "preferred source."
3:11 - AI Guidance: Discussing updated rules regarding LLM.txt and optimizing for AI agents.
3:45 - The Opt-Out Toggle: Introducing the setting to block Google from using your site for AI.
4:46 - The Risk of Opting Out: Why blocking AI might completely kill your search visibility.
5:37 - Personal Story: How the speaker updated her site to correct inaccurate AI information.
7:08 - UK Rollout: Explaining why the feature is currently limited to a subset of UK users.
7:32 - Regulatory Pressure: How UK regulations forced Google to add these transparency tools.
9:11 - Impression Counting: Explaining how unique URLs count as impressions in AI Overviews.
11:12 - Missing Query Data: Why complex, multi-modal search inputs make keyword tracking impossible.
12:53 - Missing Click Data: Analyzing what content earns clicks vs. ignored "commodity content."
15:08 - Brand Value: Why being cited in AI answers is still valuable for brand awareness.
16:33 - Analysis Strategy: How to pair impression data with standard metrics to optimize content.