u/tjrobertson-seo

TL;DR: getting AI content to rank is easy, getting it to sound like you is the hard part. What worked for us was having the model extract style guidelines from our own writing, curating that list by hand, and feeding it only the guidelines instead of writing samples.

We've been making content with AI since ChatGPT came out, and honestly the ranking side of it is the easy part now. You match your headings to what AI is searching for, answer the question right after the heading, then back it up with a fact or a quote or a table. Machines eat that up.

The annoying part is content written that way comes out really dry and academic. Which is fine if you only care about ranking, machines like dry academic writing. But most of us also have humans reading the stuff and caring about the brand, so it matters.

First thing everybody tries is telling the model to add more warmth and personality, and it's just cringe. It'll give you a personality, it's just an unlikable one, and it still reads as AI. Telling it not to sound like AI doesn't do anything either, you can't really ask it to stop being itself.

What helped was calling out the specific tells. No "it's not x, it's y" framing, cut the empty adverbs (absolutely, actually), don't force every sentence to be a mic drop, no em dashes. That gets it to stop sounding like AI, but it still won't sound like you specifically.

Giving it your old articles as samples kind of backfires too, at least for us. It doesn't know what you liked about them so it grabs the wrong stuff and starts repeating the same phrases over and over.

The thing that worked better: in a separate chat, dump a bunch of your writing in and have it write out a long list of style guidelines it thinks it can pull from your writing. Then go through and keep only the ones you agree with. When you go to write real content, you just give it the curated guidelines, no samples, no example phrases.

Then it's just feedback over time. Every draft you mark up, and if you end up rewriting a chunk yourself, paste your version back and ask it what rule it should add. We've got clients past 100 guidelines at this point and the newer models handle a list that long no problem.

One thing I'd flag if you try this: make sure none of the voice guidelines end up breaking the SEO structure you started with. Easy to fix the tone and quietly wreck the part that was getting you ranked.

I've been on Claude every day for work for two years and it's still amazing, but I'm actually looking at moving our team to ChatGPT now, because of how ridiculous the pricing is.

What makes the pricing feel crazy to me is how subsidized the subscriptions are. A $20 plan gets you roughly $200 of usage, and a $200 plan gets you around $5,000 worth. So on the max plan you're paying about 4% of what you'd pay per token. If you use these things heavily you basically have to be on a subscription.

I'm guessing those subsidized plans came from back when the model makers just wanted to grab users and weren't thinking about revenue, Anthropic especially. Now they're compute constrained and they care about revenue, so it seems they're doing what they can to push us onto usage and to lock you in. Like how if you're on a third-party harness you can't use your subscription anymore, you have to pay usage.

Fable 5 is what really brought it to a head for me. I think we could agree it's the best model out right now, but you can only use it on a subscription through July 7th, then it's usage only. A few days ago I had it refactor a codebase in a single prompt, ran about an hour, ~5 million tokens. On usage that one prompt would've been over $250. On my subscription it was about $10. Given the task it honestly would've been worth $250, but paying $10 and knowing that's going away is a tough pill. Across our team of 25 that's the difference between about $5k a month and $125k a month.

Meanwhile OpenAI just put out ChatGPT 5.6 Sol. From what we know, it probably won't be quite as good as Fable 5, but it's about half the cost per token ($30 per million vs $50) and it tends to use way fewer tokens on the same task. So really we should be comparing it to Opus 4.8, which is $25 per million output, only a little cheaper than Sol, except Sol burns fewer tokens. If OpenAI lets us run Sol on the subscription, it's a better model for less than what we'd pay for Opus.

The main takeaway for me is just don't get stuck with one provider. We keep our knowledge base and skills in GitHub so we can point at whatever model makes sense and switch without redoing everything.

So last week for a few hours Google apparently pushed out an experimental feature by accident. A bunch of local businesses logged into their Google Business Profile and there was a new messaging tab. GBP had messaging years ago, but this one was AI-powered, which is the part that's actually interesting.

Nothing official from Google. But Darren Shaw posted about it, and a Google PM actually replied with a link to an article about Google's new RCS platform for businesses, so those two things are pretty clearly connected. And it kind of looks like this might not be just a local business / GBP thing either.

The way I read it, Google's trying to help businesses stand up AI chatbots, and for GBP they're basically going to do it for you. The assumption is it'll pull from your profile and your website to answer questions. So the obvious stuff: keep your listing and your site up to date, and make sure the site actually has all the info a customer might want. That's worth doing regardless of any chatbot.

The other thing that's probably related is Google announced the Open Knowledge Format a little while back, basically a standard for structuring your knowledge base for AI. I'd be surprised if these chatbots don't end up being able to connect to an OKF bundle.

Even if you don't want a chatbot on your site, you're going to want a knowledge base. So if there's one takeaway it's just start building that now. Collect all the info about your business and your industry you're willing to make public and get it into a format AI can parse easily. We're doing it for all our clients at this point, and I think in a year or two it's just going to be mandatory.

The bigger picture is AI is turning into the thing people go through to interact with your business, and if it can't get what it needs from you it'll just point them at a competitor instead.

Hi r/SEO_for_AI, and thanks to David and Ann for setting this up.

https://preview.redd.it/anyn9g5svbah1.png?width=450&format=png&auto=webp&s=6b6db31ac8039a190324d0dfcd7993fe7ef48e15

I'm TJ Robertson. A bit of background so you know what I can speak to. I've been doing SEO for 17 years. In May 2025 I started my own agency, TJ Digital, and it's grown to $150k in monthly recurring revenue and 26 people since then. That growth came entirely from short-form video, mostly TikTok, not from the channels agencies usually rely on.

What keeps clients around is that we've gotten reliable at increasing visibility in AI search, mainly Google's AI results and ChatGPT. Getting a brand cited in the answers and recommended as the solution, consistently. We track more than 2,500 prompts across ~40 industries. Most of what I know comes from real results across a lot of verticals.

After 17 years in SEO, this is the biggest shift I've seen, and I'm glad to talk about it. I'm an open book.

The AMA goes live July 2. Leave your questions on AI search visibility, getting cited and recommended in ChatGPT and Google AI Mode, content strategy for AI search, or how we built the agency, and I'll work through them on the 2nd.

Google rolled out another spam update and like always they pointed everyone back at their spam policies. The new part is a section that basically says manipulating generative AI results violates the policy. The thing they're really going after is people getting their brand recommended on the pages AI cites before it answers. Usually that's either comments on places like Reddit or paying a publisher to recommend you in an article. They're saying doing either one now risks a penalty, as in Google could just stop recommending you.

Where I get skeptical is that this is really hard for them to detect. Unless someone is just blasting recommendations for their brand all over the internet, how does the algorithm actually know if I placed a recommendation somewhere or paid a publisher to? They can pattern match and probably be right more than half the time, but they're going to get a bunch of false positives too, which means penalizing businesses that didn't do anything and making their own results worse in the process.

The other thing is you don't even need to spam for this to work. Google cites the same handful of pages over and over, so a couple of well placed recommendations can do a lot. The most extreme one I've seen, we got a client added to one listicle that gets cited on basically every search for their service. The article looks the same as it did before, still just a top companies list, it just lists them at number one now instead of some other company. That one change took them from showing up in about 20% of the prompts we track to 40%.

So yeah, if you're running some automated tool spamming recommendations everywhere, I'd be careful with this update. But getting a few recommendations placed on the specific pages AI is already telling you it trusts, I think that's fine. It's basically link building for AI search at this point.

OpenAI dropped the 5.6 announcement and I've been going through what we actually know so far.

Three models: Sol, Terra, Luna. None are public yet so we're working off what OpenAI put out. From what they've shared, Terra is supposed to match 5.5 capability at about half the cost, Luna cheaper and faster but less capable. OpenAI is genuinely winning on the cost side right now. Anthropic keeps shipping smarter models but without the pricing coming down, and that gap is getting harder to ignore if you're doing high-volume API work.

The Sol vs. Mythos question is where I'm more skeptical. OpenAI is positioning Sol as a Mythos competitor, but the benchmark situation is not convincing. They're only showing a handful, they picked the ones where their model does well (which every model maker does), and when they do include Mythos in the comparison, it's Mythos Preview, which is over two months old. If Sol were actually in that territory it seems like it would be easier to demonstrate.

5.5 is already a capable model and 5.6 Sol looks like a real upgrade. But "comparable to Fable or Mythos" is a high bar and I don't think they've cleared it yet.

One thing worth remembering if you're on a subscription: you're already paying somewhere between 4% and 12% of actual API usage cost. So the raw cost comparison between the platforms matters a lot less if your work fits in a subscription tier. At that point you're mostly comparing model quality, and for anything ambitious, I still think Claude is the better call right now.

Fable 5 is supposed to be coming back soon too.

I run an SEO agency and lately I get this a lot, people come in saying they want to rebuild their site for AI. And I'm not against it, there are decent reasons to rethink how your site is structured so AI can actually use it. But it's not going to help you rank in Google or get recommended by ChatGPT, and I think a lot of people have those two things mixed up.

The thing most people don't realize is that when someone asks ChatGPT to recommend a company that does what you do, the AI isn't actually looking at your website at all. It takes the prompt and runs a bunch of searches in a regular search engine (the fan-out stuff), pulls back somewhere between 1 and 200 pages, figures out which ones it trusts and which are relevant, and grabs passages from those. If those passages end up recommending you more than your competitors, you're the one it recommends. That's pretty much all it's doing right now.

So all the technical stuff people obsess over, llms.txt, markdown versions of your pages, structured data, WebMCP, agentic discovery, none of that is what's getting you recommended today. It's whether your content is relevant to what they searched and whether the AI trusts the source. For us it works out to something like 70% content and 30% authority building, and honestly that ratio's been pretty stable.

None of this means you should ignore making your site easy for AI to use, it'll matter more soon (Google's going to start sending agents to fill out forms and use tools on your site). But if your main goal is showing up in AI search, the rebuild isn't really what gets you there, it's the content. The rebuild is more of a "this makes it easier for us to maintain the site with AI later" thing, which is fine, just a different reason than people usually walk in with.

I run a marketing agency so this is mostly about marketing, but it probably applies more broadly.

The way I see it going: a year from now the AI a company uses has its own accounts in everything. Its own Slack, its own Gmail, Notion, whatever you're running. It can update the website, at least on staging, and honestly I think plenty of companies will let it touch production. And it has access to all the info the business has, the stuff on your site, in your docs, and the stuff that only lives in people's heads, all organized so the AI can actually read it.

And it's maintaining that knowledge base itself. It's reading every Slack message, email, and meeting transcript and deciding if there's anything new it should add. For companies that set it up right, people end up doing very little hands-on work. The job is mostly just talking, to each other and to the AI, and then the AI goes and does the computer work.

So the marketing team isn't really making the blog posts and the social posts and the email anymore. They're building and maintaining the systems that do that. A system that takes some announcement and turns it into posts, one that updates the site when there's new public info, one that drafts the newsletter and runs the ads.

There's still a human review step on the important stuff, at least for a while, but the person isn't usually making the edit themselves. They tell the AI what to change, and the AI also takes that and applies it to future projects, not just the one in front of it.

You also have to think about budget. This isn't free and it gets more expensive as the models get smarter. I'd assume at least 20% of payroll going to tokens. Some companies are already over 50% and I don't think that'll be unusual in a year. If you've set it up right it's still a drop in the bucket compared to what it produces, but it's a real number.

Basically no one has all of this built yet (we don't have it fully built either), so it's not like you're behind. But it's the direction I'd be moving.

Not the model, not the harness. Just a folder. That's where I've landed and it made the whole thing a lot simpler to think about.

Quick context on how I got here. A year ago if someone said they built an agent they usually meant an n8n workflow or some custom-coded scaffolding, the stuff we'd now call a harness. Those were kind of a pain to set up, they worked all right, and honestly at this point they're more or less obsolete. What changed for me was Claude Code and OpenClaw showing how good and how general-purpose a harness could be, and then Codex catching up once OpenAI realized they were behind.

The harnesses are improving so fast that building your own doesn't really make sense anymore. And since they keep leapfrogging each other, I want whatever I build to be portable enough to move from one to the next. Which kind of forces the question: if it's not the model and not the harness, what's actually the part that's mine?

For me the answer is the folder. The harness already handles the capabilities, tools, file access, the loop, all of it. What it doesn't have is knowledge about a specific business and clear instructions on what to do with it. Give it enough of both and it's honestly surprising how much it'll handle, pretty much anything that gets done on a computer for the business.

The thing that made it click for me is that a website is also just a folder, mostly html. The difference is we don't have an agreed-on way to arrange the agent files yet the way we do for a site. Google's Open Knowledge Format might end up being that, not sure yet, so for now I just structure it myself.

One practical note: at the agency I run we build this folder before we do any real work for a client now, because everything else we've got sits on top of it. If the folder isn't there we can't really start.

Still figuring out the best way to organize the inside of it, which is the part I don't think anyone's nailed down yet.

Saw this today and figured people here would care. Google put out what's basically the first standard for how you structure your company's knowledge so AI can actually use it. They're calling it the Open Knowledge Format.

Honestly the format itself isn't the exciting part to me. It's that there's a standard at all. Right now everyone building anything with AI is figuring out how to organize their knowledge from scratch, in isolation. If we all structure it the same way, then any agent or prompt or skill can just assume that structure, and we can actually share and build on each other's stuff instead of reinventing it every time.

The format's about as simple as it gets. No SDK, nothing to install. It's just markdown files in nested folders. Each folder can have an index file that describes what's in it, and every file has a little chunk of YAML frontmatter at the top (title, description, tags, type, resource, timestamp, that kind of thing). Only a few fields are required and you can add whatever custom ones you want.

It's v0.1 so it'll definitely change, probably mostly from people using it and figuring out what actually works.

For what it's worth we already build something like this for clients (we call it a brand ambassador) and we're figuring out how to line ours up with this now. I don't think there's a huge rush, but it's close enough to what people already do as best practice that adopting it now seems pretty safe. And if you haven't started writing your company's knowledge down somewhere structured at all, that part I'd just start on regardless of the standard.

Been spending a lot of time on this lately because clients keep asking about it. Wherever someone first hears about a business now, they tend to go straight to ChatGPT or Gemini and ask what it thinks. And honestly that opinion matters more than where you rank, sometimes more than a recommendation from an actual person, because people just trust what the AI says.

That's the customer side. Once an owner realizes that's happening, the natural move is to go check it themselves, ask the AI what it thinks of their own business, and then ask it why it said that. The thing is, the AI doesn't actually know why. It's answering that question the same way it answers everything else, off its training data, and the training data doesn't have the reason it recommended you or didn't. So you get a confident answer that's basically made up.

What actually works is looking at the pages it cited right before it answered. Most of the time the response is just summarizing whatever was on those pages. Once you see that, it gets pretty obvious what to do. If you want it to say good things about you, the pages it's pulling from need to say good things about you.

Two ways to go at it. You can try to change the pages it's already citing. There's some easy stuff there, like a directory listing that's out of date, a Reddit thread you can leave a comment on, maybe getting a few customers to drop reviews on a review site it's citing. But you run out of that quick, and emailing publishers asking them to say nicer things about you in their article is a 5% response rate situation, if that.

The other way, where most of the opportunity is, is just making those pages yourself on your own site. When someone asks about your brand specifically that's a branded search, and your own site is the biggest authority in the world on your own brand. So figure out the questions people actually ask about your company and put up content that answers them directly, with the question right in the title or a heading. If it's in a heading and the AI still isn't picking it up, I'd just make a standalone page with that question as the title.

Reviews page, awards page, FAQ page are all good places to start if you don't know where to begin.

Running a mid-sized agency and this has been on my mind. Back in 2023 everyone was talking about that idea of making an AI version of yourself and charging people for access to it, and I remember thinking it was nuts. Why would anyone pay for an AI trained on your stuff when they could just go use ChatGPT.

But they were kind of right, just not the way it got pitched. People are already paying for access to AI agents, it just looks like hiring an agency that happens to run most of its work through AI.

For us our people are still our number one asset, but honestly the number two asset at this point is our Claude skills. I never thought a pile of markdown files would be worth much, but we've put thousands of hours into them now. Everything we deliver still has a human on it, but most of the actual work gets done by Claude or ChatGPT or Gemini, and I assume more of it gets handed off to those workflows over time while the people spend more of their time on the relationship and judgment side.

What's interesting is lately I'm getting clients straight up asking if they can just have the AI. Like "can you train my team on your workflows." I don't really have a good way to do that right now, but even if I did I have no clue how I'd price it.

On one hand it's a bunch of markdown files. Maybe a couple database connections or an MCP server behind it. So I get why someone wouldn't want to pay much for that. On the other hand the work these things put out is really good, and we only use AI where it actually does a better job than a person would, so it's not like the output is worth less.

I don't have an answer, it's just one of those open questions I figure we're all going to have to work out at some point. If anyone here has had clients ask for the workflows directly and actually landed on a way to price it, I'd take it.

I run an SEO/AI search agency and I get emails pretty much every day from business owners who are worried they're falling behind. They've figured out that the main thing using their website now is an AI agent, not a person, and they want to rebuild everything for that and get ahead of their competitors. Which is great, most businesses aren't thinking about this at all yet.

What I tell them is, look, we can sit here and speculate about what the web's going to look like a year from now and start building toward it. And I actually do think we're getting close enough to make some decent guesses. But if you want to chase that stuff you're taking on risk. So before you do any of that, just do the things that are going to be useful no matter what happens, and that also help you right now.

There's basically three:

1. Make an internal knowledge base for your company. Just document everything about the business in a way AI can understand. You're going to need it before you do anything sophisticated with AI anyway, and it helps you now.
2. Whatever you're fine sharing publicly should be on your website, and set up so it's easy for AI to find it.
3. Start tracking how AI actually does its research and recommends a brand when someone searches for what you sell. Figure out what kinds of pages and articles it likes to cite, and have versions of those on your site. And pay attention to which review sites, directories, and forums it cites, and get your brand on those.

That's it. It's the most effective marketing you can do right now and I don't really see any of it getting less important.

Been using Claude Artifacts the same way for months, so when Sites in Codex dropped it clicked for me right away. It's basically Artifacts and Dashboards combined with auth on top. One of those things you don't realize you needed until you've done it once.

The use case that actually matters is team decisions. Normally you write up a doc and then schedule a meeting to walk everyone through the doc. With this you just dump all the info into Codex, it spins up a little web app, you share it, and people log in with their ChatGPT account. Way better than a document, honestly. It's basically custom software for one specific purpose, built in a few minutes.

So yeah, I think every business should be using this right now.

The thing I keep coming back to though is that these apps are completely isolated from everything else you're doing. They're great for pulling information together and coordinating a decision, but that information and the decision you landed on still need to end up back in your actual knowledge base somewhere. Otherwise it just lives in this one-off app and nobody can do anything with it later.

And that's the part that makes me think it's a transition phase. If you want AI to actually run your business at some point, you kind of need one knowledge base where every piece of info has a single canonical version written for the AI. If your stuff is scattered or duplicated across a bunch of separate tools and one-off apps, you're never really going to be able to hand it all over to AI cleanly.

I think within a couple years we end up with AI operating systems that own and maintain all the company info, and tools like this get absorbed into that. It's useful right now, I just don't think it's what this is going to look like in two years.

Either way, building out that knowledge base now seems like the move. Feels like it's going to be required no matter who ends up building the platform.

Posting this because the AI content threads here usually go one of two ways, either "it's all slop" or "just use ChatGPT." For us it's landed somewhere in between. We run content for clients in some pretty dense niches (immigration law, cybersecurity, finance, education) and after a lot of trial and error we can get AI output to the quality the client would've produced in-house. It took a real system though, and the part most agencies skip is the part that actually matters.

Most AI content fails for one reason. The model has no clue who the client is. It doesn't know their services, their positioning, or the stuff their team only picked up from years of doing the work. So before we write anything, we build the client a knowledge base. Company info, products and services, ICP, every important page on their site and when to link to it, brand voice, and a pile of past writing we want to match. We keep it in Google Drive so it plugs straight into Claude, and we just keep adding to it over time instead of trying to get it perfect upfront.

After that it's a chain of steps rather than one prompt. One step researches the term and works out the real search intent. Then deep research (still ChatGPT for that part, it's ahead of Claude here imo). Then a drafting step loaded with our SEO and fact-density rules. Then a revision step that runs the draft against every piece of feedback we or the client have ever given on that brand. Then a fact check on every claim and every link, so we're not shipping invented stats or accidentally linking to a competitor.

Couple of things I'd flag if you go down this road. Topic selection matters more than the writing does. If you're producing stuff nobody, human or AI, is actually searching for, none of the pipeline saves you. We use Peec to see what AI is citing for a client's space and work backwards from there. And if you batch-generate raw content at scale and ship it, you can get the client penalized, so that's the real risk to watch, way more than the AI writing itself.

So I posted a few days ago that 4.8 had mixed reviews and honestly I was kind of in that camp. First day it felt verbose, a little sterile, sort of academic. I think a couple things were going on, including what looked like it forcing disagreement to avoid being sycophantic and then over-explaining why it was doing stuff. That seemed to calm down after a day or two, though that's just my anecdotal read, could be a system prompt tweak, could be me adjusting.

But the bigger thing I figured out is that I was using it like an older model. Giving it explicit step by step instructions for everything. And with 4.8 that kind of backfires, it overthinks the task and burns through tokens.

Where it actually shines for me is when I just give it a clear goal and let it figure out the steps itself. Not shorter prompts, I still load in as much context as I can, just point the instruction at what I'm actually trying to accomplish instead of spelling out the how. Treat it like a smart senior person on your team rather than something you have to hand-hold.

The place this clicked hardest was building skills for Claude. It's better than me at articulating what each skill should do. And that lines up with the system card stuff Anthropic put out, I think they said it's around 4x more likely to catch bugs in code it wrote.

Kind of a weird realization that the model is getting good enough to improve the things you build with the model. Anyway, if 4.8 felt off to you at first, might be worth trying the goal-first approach before writing it off.