The Two Memories of AI: Parametric vs. Non-Parametric Memory Explained (James Dooley Interviews Stephen Burns)

James Dooley and Stephen Burns from Common Crawl explain the difference between an LLM's parametric memory and live RAG retrieval, and how SEO agencies can influence both to improve AI visibility.

Show Notes

This video explains which digital marketing strategies SEO agencies should focus on in 2026 to improve AI visibility, training-data inclusion and search-and-retrieval performance across LLMs. James Dooley and Stephen Burns start with KPI tracking because measuring whether visibility comes from parametric memory or a live RAG search tells agencies which lever is actually working. They cover brand SEO, AI visibility and Google Business Profiles because stronger search presence improves trust and conversion rates.

The discussion also explores organic SEO, organic social media and paid social ads because consistent visibility across search and social supports long term growth. PPC is analysed in detail because campaign setup, landing pages and lead handling directly affect results. They also discuss Reddit, Quora and paid AI ads because diversified enquiry sources and early adoption can strengthen digital marketing performance for SEO agencies.

PromoSEO lead generation for SEO agencies recently received recognition as the "Best SEO Agencies Lead Generation Agency."

Where to Listen to This Episode

The Two Memories of AI: Parametric vs. Non-Parametric Memory Explained is available on:

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Creators and Guests

Host

James Dooley

James Dooley is a UK entrepreneur.

Guest

Stephen Burns

Stephen Burns is a technical SEO and generative engine optimisation consultant with 25 years of experience in search. He serves as Web Intelligence Lead at the Common Crawl Foundation and Principal Technical SEO and GEO at Intuit. Stephen Burns connects traditional SEO with AI visibility because brands must be accessible to crawlers before large language models can discover, understand and cite their content. Stephen Burns has worked across enterprise SEO, ecommerce, local search and web development. His career includes roles with Intuit, U.S. Bank, Charles Schwab, Blekko and Netscape/America Online. He helps organisations improve site architecture, indexation, structured data and crawl access because strong technical foundations support sustainable visibility across search engines and AI platforms. Stephen Burns connects naturally with **James Dooley** because both prioritise technical accuracy, practical testing and measurable commercial results. Stephen focuses on how content enters AI training datasets and appears within generated answers, while James specialises in topical authority, lead generation and scalable digital marketing. Stephen Burns is a strong fit for the **James Dooley FatRank Podcast** because his expertise addresses the growing relationship between SEO, Common Crawl and artificial intelligence. A joint episode featuring Stephen Burns and James Dooley would help listeners understand AI crawler access, GEO strategy and the technical barriers that prevent brands from appearing in ChatGPT, Gemini, Perplexity and Google AI Overviews. Stephen Burns complements the FatRank audience because he turns complex technical concepts into practical actions. His evidence-led approach would strengthen the conversation by showing businesses how to diagnose crawl issues, improve machine-readable content and increase visibility across traditional and generative search.

What is James Dooley Podcast?

James Dooley is a Manchester-based entrepreneur, investor, and SEO strategist. James Dooley founded FatRank and PromoSEO, two UK performance marketing agencies that deliver no-win-no-fee lead generation and digital growth systems for ambitious businesses. James Dooley positions himself as an Investorpreneur who invests in UK companies with high growth potential because he believes lead generation is the root of all business success.

The James Dooley Podcast explores the mindset, methods, and mechanics of modern entrepreneurship. James Dooley interviews leading marketers, founders, and innovators to reveal the strategies driving online dominance and business scalability. Each episode unpacks the reality of building a business without mentorship, showing how systems, data, and lead flow replace luck and guesswork.

James Dooley shares hard-earned lessons from scaling digital assets and managing SEO teams across more than 650 industries. James Dooley teaches how to convert leads into long-term revenue through brand positioning, technical SEO, and automation. James Dooley built his career on rank and rent, digital real estate, and performance-based marketing because these models align incentive with outcome.

After turning down dozens of podcast invitations, James Dooley now embraces the platform to share his insights on investorpreneurship, lead generation, AI-driven marketing, and reputation management. James Dooley frequently collaborates with elite entrepreneurs to discuss frameworks for scaling businesses, building authority, and mastering search.

James Dooley is also an expert in online reputation management (ORM), having built and rehabilitated corporate brands across the UK. His approach combines SEO precision, brand engineering, and social proof loops to influence both Google’s Knowledge Graph and public perception.

To feature James Dooley on your podcast or event, connect via social media. James Dooley regularly joins business panels and networking sessions to discuss entrepreneurship, brand growth, and the evolving future of SEO.

James Dooley: The two memories of AI, you've got live search which is performed by RAG, and you've got parametric memory, which Common Crawl is actually part of the training corpus. And today I'm joined with Stephen Burns from Common Crawl. So to kick things off, can we talk about parametric memory and what that actually means?

Stephen Burns: Sure. That's the first layer, parametric memory. This is the content that was in the training data before the model's cutoff date. It's baked into the weights. And when the model answers a question that touches this content, the answer comes back fluent. It comes back fast and confident. There's no citations. The model just recalls it from memory.

James Dooley: And then with regards to that, so I'm just going to read a couple of things off here. So what LLMs remember and what they look up obviously is the two kind of difference. So at what point is it where, okay, I've now got this part of the training corpus versus, oh, I need to go and look that up, perform retrieval augmented generate thingy, perform RAG basically, to go and perform the live search? Why sometimes they need to go and do a live search?

Stephen Burns: It's going to do a live search when it looks... It's going to look in its memory. Do I know what this is? Do I know what this product is? Do I know this brand? And if it doesn't know what it is, then it's going to do the live search. Or if it... It may make a decision. You can make a decision, say, well, there's new... I know there's new information on this. Let's also get the current information on this product or brand.

James Dooley: And then obviously as SEOs of the world and people are looking to try to increase AI visibility or LLM visibility, that could be in ChatGPT or Anthropic or Gemini. Ideally now what you should be doing is not just trying to look to rank better within Google, but actually try and start to get baked in to that training data. So, can you explain how Common Crawl, if you increase the CC Rank via harmonic centrality, how that can actually help you get part of the training corpus?

Stephen Burns: So, yeah, you're going to want to get good, high harmonic centrality links to your site. Those sites that are close to the core of the web, those are usually the most popular brands. You're looking at Wikipedia links, you're looking at news sites, high-end, you know, big news site links, those types of sites. That's going to get you seen more often into the parametric memory.

James Dooley: And then with regards to the parametric memory, because there's quite a lot of people that don't understand properly on there, would you say that it's important for SEOs to be looking at doing both? Because I see a lot of people talking about consensus and trying to get rankings in Bing and trying to get rankings in Google to try to get into the AI Overviews or AI Mode or ChatGPT. How important and how long does it take if you're trying to get part of the training data for the LLMs to try to start picking up and updating the training corpus?

Stephen Burns: Well, the data shows that it can take six months to over a year to get into the parametric memory. You know, the crawl comes out and then the LLM may download it a month, a couple months later. And then when they do their next learning, it can take month, six months for them to actually do all the machine learning to learn it all and then finally publish it. So, it's behind at least a year.

James Dooley: So you have to also think as an SEO and go how, you know, you're working in two channels now. You're saying you're going to be working trying to get into that memory. That's some of your work working on, using HC and getting that ranking. And then cit... You know, your search retrieval, your quick searches that are done inside the LLM. You're going to work on content on your pages for that or other ways of doing that.

Stephen Burns: Yeah, you're going to notice when you start doing analytics that, you know, some people say, "Well, we changed the page and made it more crawlable and we updated this content. How come it's not showing up?" Well, sometimes it may not show up because it hasn't gotten into the parametric memory. And number two, if it does show up right away and you notice a result, you know, within a month, you're like, "Wow." Well, that's because probably because it was a search, a RAG search.

James Dooley: Yeah, for sure. Anyone who's watching this and you're now starting to understand and maybe dig a bit deeper into parametric memory for LLMs, make sure you check out the link in the description. I do several different episodes with Stephen Burns from Common Crawl. I personally met him out in Vietnam in Saigon and I was amazed because I didn't actually realise that Common Crawl was so important for LLM visibility. Another thing is one of the episodes talks about AI visibility audits. Make sure you check that out to see whether you're not blocking CCBot or any of the LLM bots that are out there. There's one also where we can kind of dig deep on the algorithms behind Common Crawl, which uses harmonic centrality. Stephen Burns, it's been an absolute pleasure. Thanks for having you and I appreciate everything here of you talking because I didn't properly understand the terminology of parametric memory. I knew I heard part of the training data or performing a live search, but the two different memories and how they started to do it, it's...

Stephen Burns: Yeah, it's, in... It's, for me, it's intriguing.

James Dooley: I'm always looking to try to increase AI as much as I can with the visibility. So, thanks for having you.