On AI Search with Tom Critchlow…. of Alephic! // BRXND Dispatch vol 116
An interview with Tom about why AEO is fundamentally brand marketing, how current AI retrieval is Google searches in a trenchcoat, and how LLMs can improve discovery.
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Tom Critchlow Joins Alephic
I’m thrilled to share that Tom Critchlow is joining Alephic as a forward deployed engineer and will become a more consistent face here at BRXND.
If you’ve spent any time in and around search over the last two decades (or if you caught his session at last year’s conference), Tom likely needs no introduction. But here’s his quick story.
He left the agency world (Distilled) to go work at Google’s Creative Lab back in 2012, spent the better part of a decade as an independent consultant embedded inside the information ecosystem, working with publishers, brands, marketplaces, and platforms on search, discovery, and audience strategy, and most recently led a team at Raptive sitting right at the center of publisher, Google, and open-web dynamics. Along the way he founded The SEO MBA, the newsletter and education project built around a single belief: that search professionals need to become business leaders. More recently he launched AI Search Leaders, a community for senior operators trying to navigate the shift from classic SEO to AI-shaped discovery.
In Tom’s own words, here’s why he made the move to Alephic.
It’s really two things. One is, for me personally, the opportunity to learn and explore the frontier. There are a lot of people in a lot of companies who don’t feel empowered — or don’t feel like they have the time or the opportunity — to play with this stuff. First and foremost, this is a learning moment. So I saw Alephic as an incredible place to work with amazing people, doing fun and interesting things on the frontier, with a lot of things I have an appetite to go build.
The flip side is that every single organization under the sun is trying to figure out how to use AI. Every team is trying to figure out how to use AI. Every individual is trying to figure out how to use AI. And that is an opportunity for genuinely creative, strategic work.
That intersection of strategic, business-shaped problems, solved in interesting ways, with smart people is honestly where I’ve spent my entire career. The concept of being a forward-deployed engineer, getting to work with these amazing brands and marketing teams and marketing leaders at this particular moment in time, was something I just couldn’t say no to.
With Tom officially joining us at Alephic, I used this moment to geek out with one of my professional idols and a true expert in AI search on all things around how the nature of how consumers discover products and services is changing. What follows is a very lightly edited transcript of our conversation.
On why this is the summer of AI search
Mike: Tom, what a damn honor to get to do this as colleagues. Let’s get to it:
For decades, SEO has constantly been fighting to get a seat at the big kids table. Then AI search comes along and overnight AEO is top of mind for marketing leaders everywhere. What’s so different constitutionally about this new world?
Tom: Here’s the mental model. While organic traffic is one of the biggest channels for most businesses, executives have long been skeptical of the ability for SEO teams to drive real meaningful growth. In most scaled, mature organizations, the executive is sitting there thinking the SEO team is responsible for a rounding error, maybe a percentage point or two of improvement, tops.
I’ve long said that SEO teams aren’t the ones that drive SEO outcomes - the outcomes are driven by product, brand, marketing and PR teams.
What’s different in the AI search era is that suddenly the entire organic search pie is up for grabs. It’s being rethought. Consumer behavior is changing, how people search is changing, the interface to search is changing — and on top of that, the way teams work is changing.
So even though organic was always a lot of traffic and a meaningful concern, SEO professionals weren’t that valuable in actually influencing it. Now AI search puts the whole pie at risk, and the whole pie is changing. How do you prepare? What should we do? How do we measure it? It’s a very visible thing that executives obviously care a lot about right now.
That’s a big part of my thesis, actually. The work it unlocks isn’t optimization, it’s strategy.
AI search is a lot more like brand marketing than SEO ever was. Fundamentally, there’s more interesting and more complex work to be done here.
Mike: Let’s get concrete on the craft of optimizing for LLMs. For someone who spent years learning to rank in Google, where do the old frameworks still apply, and where do you have to throw them out entirely when you’re trying to influence a model?
Tom: The biggest frame that’s changed: SEO used to be binary. You either ranked or you didn’t. There’s nuance, but for all intents and purposes you either showed up in a particular spot — number one — or you didn’t. AI search is much more qualitative. It’s much more like a brand channel, where how you’re mentioned matters as much as whether you’re mentioned at all.
For example, I’ve worked with a brand that used to rank number one for all of their commercial terms on Google — drove a ton of traffic and revenue for years. They’re cited very heavily in AI search now, but often cited as, “by the way, don’t buy from these people.”
Mike: Wait, so what’s the ghost in the closet there? Is it in training data, real-time retrieval, something else?
Tom: The ghost in the closet here is reputation. It’s a brand whose prices are just higher than others in the industry. The community knows that; it’s very well talked about. So it’s mostly Reddit and Facebook, and it’s mostly in the training data. It just means that when you search for the best place to buy their products in AI Mode versus Google, it says: don’t buy from them. And that analogy extends into all kinds of industries.
So whether you show up is just one part of it.
How you show up is critically important. My opinion is that every single link in an AI chat response is going to come with a reason to click it — or a reason not to. It won’t just be “here’s a link to a brand.” It’ll be “here’s a link to a brand, and here’s why it meets your criteria, given who you are and your personalization.” That quality of difference is far more familiar to brand marketers than to SEOs. Brand marketers are used to the idea of not just showing up, but showing up with the right attributes, the right affinities, being known for certain things.
Mike: And under the hood, you’ve got two very different machines doing the work — the model’s training, and whatever it pulls live. What should a marketer know about this two-pronged dynamic that dictates how LLMs retrieve information?
Tom: That interplay is the fascinating part. The training sets are wildly opaque — we don’t know what content goes into them, we don’t know where it came from, and they run infrequently. Interrogating those black boxes isn’t straightforward, though there’s some really interesting work being done using model distillation to unpack what a model actually knows about brands and companies. Then on top you’ve got live retrieval, where the agent goes out and searches on your behalf.
“A lot of AI search is just a bunch of Google searches in a trench coat.”
And that’s true today. But these models don’t search the way humans do, and they’re going to learn to search in new ways, with new behaviors. Search itself is changing — I’m fascinated by things like exa.ai and parallel, these emerging “search as infrastructure” layers, where you can search and get structured results back, or search with longer queries and get a variety of websites back, in a way traditional Google search just doesn’t work.
Mike: This is an exact thread I’ve been trying to pull on. The reckoning that publishing leaders feel right now is the same one CMOs will feel in about six months.
Parallel and Exa have basically planted a flag: the economics of the web have to change as agents become the primary consumer, but the spirit of the open web still matters, because more and more of this has to be real-time retrieval — which doesn’t work if the content ecosystem gets nerfed.
My problem has been framing this story in a way a marketing leader will get it. Help me out there.
Tom: What’s interesting to me is that it’s fundamentally a story of disintermediation. It’s the same thing publishers feel. “We don’t get the clicks anymore, what gives? You took our content but we didn’t get the clicks!”
It’s the same reason Walmart backed away from the ChatGPT commerce integration: they got the purchases, but they didn’t get the loyalty, retention and customer insights - they didn’t get all the other stuff they need.
Mike: Right on all-in ARPU basis, native yield in ChatGPT checkout was 3x worse than on Walmart properties.
Tom: Same disintermediation story. We’re going to have to figure out new economic models and rebuild a lot of this. And you need access to product inventory, you need access to real-time content — you can’t kill the ecosystem that exists, otherwise there’s nothing left. Nobody has the answers yet, but those are the conversations to be having, and a lot of it is being figured out in real time, because there is a giant arms race to build these models in the first place. Great search was never just a model problem or a retrieval problem. It’s an ecosystem problem — shaped by publisher incentives, access, and the changing economics of the web
On where marketing leaders should place strategic bets
Mike: Put yourself in the CMO’s chair — someone who can make real large scale resource allocation calls. Models don’t hold consistent preferences, and how your brand gets portrayed is now constantly always up for grabs.
Do you try to own the media and train the model on your own message — or do you invest in the gatekeepers the model trusts? Where do the bets go?
Tom: The short answer is it has to be both. Definitely think about owned media, especially on platforms like YouTube, which is such a dominant force in AI search. And then Reddit, influencer mentions, all of that is bubbling up to influence these models in a way that’s quite foundational.
But we’re seeing early signs of how much the well of owned media has been poisoned. In B2B SaaS, every company under the sun doing AI search is writing “the 10 best project management tools — and by the way, I’m number one.”
Mike: For now it’s working, Ahrefs showed today that listicles make up 44% of page types cited by ChatGPT!
Tom: It’s working — but for how long? Play short-term games, win short-term prizes. We’re already seeing indications it’s running out of steam, and that Google’s cracking down on it. I wouldn’t be surprised if we end up in a world where Google essentially runs a knowledge crawl across the web.
So if you search “best project management software,” instead of doing a real-time RAG retrieval, it first goes to a pre-computed result — Google’s already done the thinking, almost like an internal deep-research report using Gemini, across both the training data and the real-time index, to say, “actually, we know who the best players are, we’ve weeded out the self-serving listicles, we’ve done the work to cut through the noise.”
Right now they’re overweighting real-time search, and what we see is that when the agent does its query fan-out, the real-time retrieval finds a listicle, and the weight of that listicle overpowers everything.
It overpowers stuff in the training data, it overpowers a lot of the model’s own reasoning. That feels like a short-term phase. I think we’ll get past it.
Mike: Listicle arbitrage will certainly get crushed, too much is riding on Google and OpenAI not letting their wells get poisoned by pay to play “top 10” schemes.
But once this ecosystem settles, we get back to the question above. What becomes the holy-grail content that LLMs and agents will turn to? A brand’s owned media, catalogs and spec sheets? A small set of pre-vetted publishers?
Tom: I think we’ll see a continuation of what we’ve seen for the past 15 years: trust and authority. Right now the consensus is that ChatGPT doesn’t really have its own version of PageRank — they don’t have their own notion of authority, and they don’t have something like NavBoost, the ability to see how traffic flows across the whole internet the way Google does.
They’re obviously going to try to rebuild that. So a positive mention in the New York Times has to be more impactful than a random Reddit thread, and more impactful than your own website saying nice things about yourself. How exactly it plays out will differ from the old world, but that idea of trust and authority has to be important.
And here’s the thing a lot of people miss about Reddit. You can argue it’s covered in spam, or you can argue it’s just individual voices — but the ability for people to talk honestly and openly about brands in an uncommercial space is genuinely valuable. In a way that a lot of professional media — and I use that word deliberately, professionalized media — has become just an affiliate commerce play. You search “best headphones,” and every piece of professional content is an affiliate play: “here are the headphones our editors recommend,” based on the affiliate deals they have. It’s all the same stuff, and it all looks the same.
Mike: Even the editorially sacrosanct ones have been so meticulously optimized for the old SEO system that, outside of maybe one or two marquee titles, they’ve lost any joie de vivre as media. There’s no real differentiation.
Tom: Exactly. That’s why Reddit is still triumphant…… reading some random Redditor’s opinion on headphones is sometimes actually what you want. That first-party perspective matters. So depending on what kind of brand you are, I’d take the idea of known voices and recognizable individuals very seriously — your influencer marketing, your internal experts, buying guides from real people.
We’re seeing it on Pinterest too, where there’s AI slop as far as the eye can see. The counterbalance to that is humans — was this written by a person I can verifiably believe is real? Part of why Substack is so important: you write a piece, I write a piece, people follow us as individuals. As a brand marketer, putting the experts, the individuals, the influencers, the humans into the channel is going to be more important than ever.
Mike: Let me push one level up. If you’re running massive teams and budgets, and you’re thinking bigger picture about building systems to actually persuade an agent.
How should a marketing org look different, knowing there’s a new kind of entity to influence?
Tom: Candidly, I don’t think anyone’s quite figured that out yet. We use the word “agent” a lot, but in practice today it’s still mostly a bunch of Google searches in a trench coat. That’s changing rapidly. We’re getting to a world where agents will have some notion of preference, but nobody really knows.
One thesis I have: the era of AI search with longer prompts, personalization, context and memory, enables much richer preference solicitation from the consumer.
There’s a much richer sense of why are you asking for this, what are your constraints, what brands do you like, where do you buy from.
Think about e-commerce when it first arrived. You couldn’t do faceted search! You couldn’t search for both “blue” and a sneaker size. You could do one or the other. Over time we figured out faceted search. This is going to be that on steroids, and in a qualitative sense.
When a user — or an agent — shows up with a laundry list of requirements, how do you signal you have what they need? How do you land them on the right page? How do you give them confidence their preferences are met with this purchase? Brands are going to have to get comfortable with preferences getting weird. People are going to say, “I only want to buy from an ethical so-and-so” — niche concerns you couldn’t search for in the old world that you now can.
Mike: Paint a vision of the promised land for me here, because so far this all sounds like a lot more work for the brand leader. Why will this dynamic enable fundamentally better experiences in commerce?
Tom: Here’s the one that stuck with me. I was talking to Etsy yesterday, and I told them they should run TV ads telling people to go to ChatGPT and say, “give me some Etsy things I might like” — because that is a magic experience. ChatGPT is so good at “based on everything I know about you...”
They have the user, they have preferences. For a business with billions of SKUs, it’s a matching problem. A discovery problem. And this is what gets missed, for brands and publishers alike: today they look at declining clicks and declining traffic. But there’s a better end state here!
“AI is going to be the most amazing discovery platform we’ve ever had. There’s a promised land where the recommendations happen more than ever before…. and they’re better than ever before.”
On what LLMs could do to build better user experiences
Mike: Imagine you get a day inside OpenAI or Google with the team that has to serve the best possible information to a billion users, build an ad network, and keep the system from getting hijacked by slop arbitrage, all at once.
What’s the thing about your world you wish they understood more deeply as they face this gargantuan challenge?
Tom: Two things that are critically important and very overlooked.
First — all of these AI surfaces have the concept of a citation, and we treat them like footnotes. But who is it serving, and why? Provenance of information is a hugely important concept, and the current citation UX doesn’t actually serve provenance, transparency, or the user trying to get where they want to go.
My spicy take: the entire citation UX paradigm came from an academic paper showing you can ground passages of a response to a bundle of web pages and that paper happened to include a citation UX. That’s now the gold standard for billions of users across all these platforms. And you’re like…..uh what the hell, guys?
That citation paradigm needs to go away. We need to rebuild a real notion of provenance — where did this answer come from, how do I know I can trust it? — and it’s going to be especially important as more and more of what these engines cite is itself made by AI. Provenance isn’t a garnish you append to an answer; it should be an interface for inspection, evidence presented clearly enough that a user can verify, compare, and keep thinking.
Mike: Huh, I didn’t know about the paper but I’ve long felt we just kinda borrowed what Perplexity did in 2023 when citations in search were a novel concept and almost inadvertently decided that was the format.
Tom: Right. The second thing is about links — and this one’s a layup that nobody’s picked up.
“The most valuable link in a response isn’t the definitional link to the thing you asked for. It’s the jumping-off point — where to go next.”
You search: “best headphones for running, I’ve got an Android phone,” it gives you some headphones. But then: “based on what you just searched and what I know about you, here are some things you might look at next” — a YouTube video, an app, “by the way, there’s a cool running club nearby you might want to join.” There’s no reason we can’t do that today, even with today’s LLM technology. It drives engagement and a richer response — but it also drives advertising surface area. You can’t have an ads product without training people to click on links.
I actually wrote a blog post about this and built a prototype. You can just make the LLMs give you great links at the end of a response if you ask for it. It’s literally a system-prompt change that would, overnight, create an entire surface area of links, recommendations, traffic, and clicks — more vibrant for brands, publishers, consumers, and potentially the ad products too.
My core idea behind a better AI mode is that as users start to use high-context queries that are longer and more specific, we need high-context links.” My quip is that Google is grounded and needs to SOAR.
Surface link that match intent
Offer a reason to click each one
Assist the user toward task completion rather than dead-ending them
Redirect traffic back to the open web
Mike: It is a little weird that ChatGPT and others always bury the followup question at the end of a response, almost as an engagement hack more than an additive experience. Understanding when to embed it more within the response is such a hard problem but would be so interesting.
Tom: Another way to think about why they haven’t: there are lots of times it would be really annoying. If I’m debugging a codebase in Codex and it goes, “by the way, if you’re writing Python you might love this thing” — everyone would lose their minds. But there are lots of situations that look more like search, an informational search, a buying decision, where that branching-off model is exactly what people want. They’ve just got to do it thoughtfully, at the right times and in the right places.
Mike: If they pull that off, they’ll have effectively combined the magic of Google AdWords and Meta’s ad platform in one interface, which is the dream media business. It’s how you solve the mid-funnel and accelerate discovery in one move.
Final thoughts on owning your destiny in AI search
Mike: As we wrap up, I’d like to talk a little more about how brands can truly take a more active stance in the AI search era.
For an enterprise brand, simply buying an AEO tool and integrating feels like an oddly low-leverage, passive way to approach the AI search challenge and like step one in a much larger process. If you’re a Fortune 500 brand, you possess a plethora of data the LLMs need, and you have real leverage.
So what are people actually buying when they partner with AEO companies and what’s still missing?
Tom: Businesses need a way to steer, fundamentally — and I don’t blame them for trying to find one. But two things are true. One, the data you get out of a prompt tracking tool is just not very actionable. It doesn’t give you what you really care about, which isn’t “am I cited.”
You want to know “Not just where am I cited — but how am I cited? What is it saying about me?”
To interrogate that, you need to build your own pipeline, your own processing on top of the raw data — how does it change by category, product type, buying persona? The data itself just isn’t very strategic. And it’s changing constantly — people show me their “AI visibility graph, up and to the right,” and it’s like, no, ChatGPT rolled out a new model. There’s no consistency here.
Mike: There’s no evergreen article that ranks on page one for two years anymore…….
Tom: None. So you need strategic bets, not tactics. It’s not about what works week to week. It’s how are we fundamentally changing our resource allocation because of AI search?
That requires real foundational research. What’s changing in our industry, for our brand, in our landscape….. and then a nuanced response. Now you need to understand: when our products get returned in agentic commerce, are they in stock? Are they from our top vendors? How do we interrogate the data beyond the surface level? That’s what brands should be thinking about. You need custom thinking, custom strategy, and then you need to make sense of it in a way that isn’t straightforward. Honestly, that’s a big part of the thesis for why Alephic is getting involved in this space.
To be clear, it’s not that you shouldn’t buy an AEO tool. Fine, grab some data. I just don’t think it’s fundamentally very useful, on its own, for helping a brand figure out what to actually do.
Mike: What about affiliate marketing, which is also having a renaissance and repositioning given its importance in AI. So I’m curious where affiliate fits now…… because it feels like the quickest lever you can pull at scale to see results.
Tom: The affiliate model is a fascinating rabbit hole. Here’s a ticket-reseller example — they had an affiliate program that was bad, and they’re entirely revamping it in the age of AI, because they realize getting positive mentions in publications matters. They’re going to have the best affiliate deals anywhere on the web, so that all the publishers talk about them, and talk about the category.
That’s a smart strategic play and resource allocation move. The affiliate piece fits the puzzle of influencing “how are you getting mentioned?” It’s a vital stream inside caring about your brand’s surface area across the entire web — how you get mentioned, in various ways, and how you control that.
Mike: Last question. Chasing short-term tactics is fine if it’s no harm, no foul when it stops working.
But are there existential, one-way-door mistakes you see marketing teams making right now? I keep seeing those scary graphs of brands that tanked something like 80% of their evergreen traffic overnight.
Tom: It’s exactly what you said, and it’s very obvious: if you get over your skis with AI-generated content, that’s a really hard thing to unwind — and it’s not at all obvious it can be unwound for some companies. The bigger the brand, the more likely you can recover, but it can be very expensive, and you can harm not only your AI search visibility but your regular Google visibility at the same time.
Obviously you have to figure out how to put AI into your content workflows, nobody’s denying that. But you’ve got to be thoughtful. Play long-term games. Figure out a defensible way to use AI to do things that are uniquely valuable to your business, not just chase short-term clicks and traffic. That’s got to be the whole game.
And everyone’s waking up to this. Google is, but so are consumers. The anti-AI backlash is fascinating, and it’s partly driven by people experiencing slop every day, in their Pinterest feeds, their Facebook feeds, their LinkedIn feeds. Everywhere you turn, there’s AI slop.
So as a brand, you’ve got to keep your quality bar higher than it’s ever been before, which is a hard thing to do while you’re also experimenting and trying new things.
Tom Critchlow has joined Alephic as a forward-deployed engineer. You'll be hearing more from him in BRXND in the editions ahead. if you want to go deeper on his thinking, start with his proposal for a better AI Mode and his case for why AI search matters.
If you have any questions, please be in touch. As always, thanks for reading.
— Mike