On Content vs. Knowledge with Alex Springer // BRXND Dispatch vol 130
An interview with Alex about why models sometimes err on the side of a lie, what a CMO should demand from OpenAI before spending a dollar, and why the LLM keeps telling you to buy used golf clubs
As we build towards this year’s BRXND NYC conference, I’m continuing our series of interviews with founders, technologists and marketing leaders innovating in the problem space around how AI is fundamentally reshaping the way consumers build preference and purchase brands.
If there’s someone you think I should get to know, please drop me a line at mike@brxnd.ai. If you’d like learn more about our November 5 show at the Times Center, check out details here or click the button below to grab an early bird ticket.
On Content vs. Knowledge for LLMs
Much of the modern web is a corrupted morass of arbitrage hacks from a bygone era that have rendered media utterly impossible to consume. You feel this most viscerally when you look for a simple chili recipe and have to read 5,000 words about how Grandma Ethel survived dysentery on the Oregon trail before you even get to put the onions in the pot.
That mess is now the raw material feeding every LLM on earth, which is why the machines spend so much of their energy sifting five thousand words of Great Plains hardship to find "one large onion, diced." For Alex Springer, this is the largest opportunity for CMOs. AI systems don't want to consume content, they simply want the knowledge buried inside it. And the vast majority of knowledge about products is owned by brands directly.
Alex has spent a decade building marketing, measurement and reporting teams and technologies across the US, UK, and Europe. His primary focus is transparency, accuracy, and accountability in AI systems. He serves as tech lead for the SPUR Coalition of publishers (founding members include the BBC, Guardian, Telegraph, Sky, and the FT), is Director of OpenAttribution.org, the open standards body for content attribution in AI systems, and co-leads the AI Taskforce for the UK based APMA.
He is quite simply the most erudite man I know on how AI agents intersect with content and my go to WhatsApp sparring partner for dive bar conversations held across an ocean on all things about how LLMs view the world.
A look into our general Tuesday morning DMs and lightly edited version of our conversation follows below:
Mike: Alright Alex, Other than pedantic nerds like us, who’s actually digging into what makes an agent pick one product over another? And what are they finding?
Alex: So the team working on the UCP (Universal Commerce Protocol) at Google set up this lab where they’re loading in a bunch of different product catalogs and running experiments to figure out what makes agents buy what. We are still at this rudimentary level of saying, okay, here’s ten laptop chargers, all features being equal - which does the agent buy? The cheapest one, obviously (spoiler: this doesn’t bode well for brands charging a premium based solely on brand value).
But there were interesting edge cases where say nine of the chargers had amazing features; however, the “lesser” charger had better safety ratings from a registered safety organization - and the agent always bought that. I’d say this makes understanding the bias of a model - all of the big frontier models are tuned to prioritise safety - a critical thing to understand.
I was at a talk from one of their engineers and he was talking about this “quirk” - I pulled him aside afterward and said “I know you said that as a throwaway statement, but marketers are so desperate for this information that whole companies are raising millions on much shakier intel.”
Whenever Google’s engineering teams meet up with their biz dev folks (won’t be long) and get these insights into their ads and search products it’ll knock out half the GEO offerings out there. Until then I suppose the GEO LinkedIn-industrial complex can make a few hundred grand on consulting engagements based on these little hacks?
Mike: But do these safety ratings even have to be real? Or is this another case where we can throw “eco-friendly” or “organic” on everything and fool the models?
Alex: Well, that’s the right question. Because if I throw out official sounding acronyms, most of the LLMs will just be like, “oh yeah, sure, that must be a thing.” It’s a probabilistic token predictor going, yea “ABCA-certified” does totally sound like a real thing, why not? Don’t believe me? Pick an even slightly obscure acronym and ask AI Overview about it.
Here’s Google’s response to the UK CMA when it was suggested that its existing mechanisms regarding factuality were not good enough:
“Google receives … feedback via the thumbs up / thumbs down mechanism every day. These simple visual icons allow both publishers and users to quickly flag if something is incorrect, with a specific option to select ‘not factually accurate’.” Link
“Publishers” in this context is anyone whose content is being scraped and regurgitated. I’d bet 99% of brand marketers and 100% of consumers didn’t know that it was up to us to help double check every answer on Google’s behalf.
So now we have these answer engines mediating our access to information without being held accountable for incorrect statements by any of the consumer protections we in many ways took for granted. We need to think about how you signal authority in a verifiable way to anything that gets injected into the agent that’s doing the stuff. And that’s the tricky part.
Mike: Ok, let’s zoom out a bit. Influencing LLMs is obviously a Herculean abstract challenge…and a whole cottage industry that is less than two years old.
Who has the best framework for where a CMO or head of AI transformation can truly dive in and start putting tangible points on the board right away?
Alex: I recently had a great conversation with the chief AI officer at an international, multi-brand retailer - someone who is also deeply involved in broader industry standards work. He’s basically going piece by piece through their brands saying: marketers, get your house in order, and the first thing you do is align product feeds and owned and operated site content.
Forget the third-party content for now. We can’t control that, it’s messy, the LLMs are changing the algorithm every day, and we’re not going to get into the SEO battle again. They don’t care about that, they care about our product data being well organized.
It was the same conversation I had a bunch at Cannes this year. I’m a brand: the first thing I do is make sure I’m clear, consistent, available and accessible with my data. Then I can go try to mess with whatever third party wrote a good or bad thing about me.
Particularly with the sea of content laundering and shitty arbitrage URLs trying to pump authority by not blocking while premium publishers block and negotiate better deals with the models. It’s such a mess in there that the CMO looks at it, goes a little cross-eyed, and goes back to what they can control.
The first question I ask a CMO? What is it you’re known for? How do you describe your brand/product/service? Then you go ask ChatGPT the same question and do the hard work of reconciling the inevitable disparity by tracking the inconsistency across the web. Because the LLMs don’t have opinions - they learned to talk about your brand from somewhere.
Mike: “Fix your feed” is a pretty anticlimactic headline man…….
Alex: People look at me with a lot of disappointment when I say that. They’re like, “but that’s not clever! I thought listicles were the answer!”
LLMs have exposed that all of our data is really messy, globally. And LLMs are going to force us to finally fix that, instead of pretending we’re going to fix it. The only way to do that is to finally do what we said we would do for years and get our data in order.
You want a slightly better headline? If your brand info and image isn’t accessible, accurate, consistent, and observed - forget about how the answer engines talk about you. That is just a symptom - the root cause is closer to home.
LLMs have exposed that all of our data is really messy, globally. And LLMs are going to force us to finally fix that, instead of pretending we’re going to fix it.
Mike: So how can brands make sure they are marching in a direction that is long-term aligned with how the models want to consume information vs. playing short-term arbitrage games?
Alex: I think doing stuff like using LLMs to blast LLM-written content about your brands across Reddit will bite people. It already is — they’re downranking LLM-written content left and right. The temptation to do it at scale is probably the biggest gotcha. And Reddit’s deal is expiring soon. Looks likely enough it won’t renew so Reddit’s not going to be ranking for much longer either.
There’s also a poisoning-the-well issue. All the model builders I’ve talked to are pissed off when they realize — sweet summer children that they are — that half the internet is biased, because we’ve been biasing it forever via advertising. If models are going to tell the “truth” they need consistent and ideally true information, and the internet is full of paid-for, semi-false information. The more that’s true, the more they run away from various sources.
Too many brands are putting a lot of short-term pressure on third party publishers, basically paying a lot of money to write a whole lot of stuff that caters to the brand. Increasingly that’ll be cross-checked, and increasingly the models will back off of it which hurts both brand and publisher in the long run.
If models are going to tell the “truth” they need consistent and ideally true information, and the internet is full of paid-for, semi-false information.
Mike: Oh man, I played this game hard when I was a growth leader two years ago in Google’s glue on Pizza era. Pumped out both owned programmatic SEO and worked with basically glorified content mills to get pretty small DTC sites showing up in AI overviews for “best x for y” searches. But, it always seemed like an ephemeral thing.
Alex: Yup, the original version was “just suck up everything”. Now there’s more of an ethos of “what is the least amount of stuff we need to ingest that teaches our model?” And then, how do we get really, really specific about high-quality inputs?
Mike: There’s a bit of a contradiction here though. LLMs clearly go to third parties because they know they can’t trust what brands write about themselves without some kind of independent validation.
Alex: Yes… but think about what is the most authoritative thing about a product that actually has any kind of accountability. It’s the information provided directly by a brand. You shared something with me from Lily Ray the other day about that right?
Mike: Yup, GPT-5.6 is increasingly going directly to brands and manufacturers to retrieve product, feature, specification, and pricing information
Alex: I trust brands talking about themselves, because I know that there are consumer protections if the brand lies to me about itself. If a publisher review fudges some numbers or goes out of date, there isn’t necessarily the same recourse.
And there are areas where the models intentionally still get things wrong. The system prompt for most major LLMs at the moment includes something along the lines, multiple times, of don’t quote from more than one source more than fifteen words in a row. Because if you do that, we suddenly get hit with copyright infringement lawsuits. They haven’t gotten worse at being able to reproduce content at the model level!
There’s no accountability mechanism for a lie, but there is an accountability mechanism for theft. So they sometimes err on the side of lie.
No brand is ever going to sue Google for using their Merchant Center product feed data. So the model can go, oh cool, yeah, you can quote that directly, that’s great.
Mike: So how does a brand-side marketing leader start to actually own shaping the narrative AI weaves about their products beyond just exposing accurate information?
Alex: A lot of the work I do is trying to empower publishers and brands to own their own content and serve it out in smarter ways, as opposed to just handing their content over the platforms and hoping that their general models serve as decent sales reps.
At core what AI systems need isn’t content - it is knowledge. The current system is super inefficient because for decades we’ve been really good at hiding the knowledge inside of ever longer content (see the damn recipe blogs that no one wants to read - recipes are too short to serve a bunch of ads on though!). And right now the only recourse really is for the AI system to take it all and then do a lot of processing, sifting for relevant knowledge.
At core what AI systems need isn’t content - it is knowledge.
Mike: There’s some irony there in that I’d argue that “hiding knowledge inside of ever longer content” is the fatal flaw in LLM writing as well…..
Alex: But if we start thinking instead, I as a CMO own my own content, own my own IP, and I can enrich it how I want and experiment with ways to make it more attractive — I can turn my content back into knowledge!
For a long time agents have had more immediate concerns: “how do I make sure I’m not getting prompt injected, and how do I not blow up my token count?” That’s finally evolving into, “how do I put stuff in my context window that’s going to create better outputs and outcomes.”
That is the real opportunity - and you can see it in nascent efforts from CDNs to make “markdown versions” of the web, in content grounding services serving “chunks” instead of articles… but who better than the original content owners and creators to offer their distilled “knowledge as a service”!?
It’s scary out there and things are moving fast - whatever form this new knowledge economy takes I think it’s a safe bet that the marketing leaders that own their own knowledge will do better than those who give it all away and hope for the best.
Mike: Does this imply that brand-side CMO’s have more leverage with LLMs than they’ve been exerting? AI providers need up to date brand-side content to best serve their users! What should brands ask for in return?
Alex: If you’re a FORTUNE 500 advertiser right now, you say: look, OpenAI, we have billions in advertising I would happily put through ChatGPT. I cannot do that as long as I’m in the dark about how you’re targeting. And I can’t do it if I don’t know whether you’re saying hand lotion causes hairy palms and then……boom here’s my Neutrogena ad!
I think that the leverage has to be tied to ad spend rather than any notion of withholding access to content as publishers are doing. Can’t imagine many CMOs who are going to say, “oh, I’m going to take my Merchant Center feed away from Gemini.”
That said - your product data is valuable to these models. Which does give a brand leverage.
Mike: Can you get a bit more concrete– if you’re a CMO with the power to spend nine figures in ChatGPT Ads, what do you ask for concretely from OpenAI before you move forward with a large ad buy?
Alex: Citation and grounding manifests for conversations in which my brand is listed - you have to be able to know what content (yours and others) was shown to a user, what informed the answer.
Really understanding grounding—which is to say deeply knowing which content from where is influencing the agent at time of inference — is the main rallying cry on my side.
This isn’t just a brand safety issue from the consumer point of view - media companies are getting aggressive and suing when their content is used without license. Not just the grounding services but the agent builders using those services - why wouldn’t they happily sue the advertisers that are paying for the content theft as well?
If we don’t know the inputs for an AI system, we can’t trust the outputs.
Mike: What about an ad placement next to an organic prompt response that says you shouldn’t buy a given product at all?
Alex: The thing that should scare performance marketing types most is that if I say I’m going to go buy some golf clubs, the first thing the LLM often says is go buy used. I have to practically beg ChatGPT to suggest I buy a new car. It wants to give me tips on making my old one last.
That should scare the shit out of the entire industry, because its priorities are not to go sell me new stuff. Its priorities are to save me, as the end user, money. Figuring out how to efficiently deliver ads that drive any kind of engagement and real action — in the face of a machine that is trying to more demonstrably serve the user— that is causing an ad tech crisis, to say the least!
Mike: It will be really interesting to see if this dynamic holds as OpenAI really hits the throttle on their ads business or if the training is tweaked to make the underlying environment more ad-friendly. OpenAI swears that will never happen….but a year ago Sam also swore they’d never run ads!
Alex: This gets back to the biases trained into the models and harnesses. They don’t “think” buying used is inherently better, or that climate change is real, or that democracy is good - they have been tuned that way. Again we need visibility - otherwise it is just a couple of key presses from nameless engineers and we won’t know for ages that our “thinking partners” now think something very different.
Mike: Take the last word here, how is the work you are doing both at OpenAttribution and with the SPUR Coalition helping to provide more transparency in how LLMs form their opinions and notions of brand?
Alex: I firmly believe that we as consumers, marketers, writers… humans deserve to know why a generative AI system believes what it believes and responds how it responds. Everything else proceeds pretty naturally from that premise - and from that transparency we could build a functioning knowledge economy that benefits all sides (including the model builders).
SPUR and OpenAttribution give us the opportunity to convene and find common cause around that concept. The media companies that form the SPUR Coalition have a very immediate need to solve this problem - AI platforms are substituting their content products, denying them user visits, and in most instances refusing to engage in constructive solutions with the broader industry. Industry coalitions can and have been a useful trigger for that engagement. OpenAttribution offers a similar convening across the brand and advertiser space.
We are working on pragmatic solutions - we’ve set up a suggested common language for how AI systems ingest and use content and data as part of their answer generation process and we engage with AI builders, brands, publishers, and regulators along with other standards organisations in order to promote transparency and accountability in content usage. This transparency directly serves brands just as much as anyone.
OpenAttribution also works directly with brands to assess and improve their posture is taken to AI - data availability, bot management, etc.
I’d throw out an invitation to your audience Mike. Brands can either wait it out and hope that the content producers win their battle and just take whatever data Google, OpenAI, etc deign to hand out - or they can recognise that their clout, money, and connections give them a say.
Brand safety, accuracy, accountability and ownership should still matter - even if for this moment, many seem willing to pretend like they don’t.
Alex Springer has spent a decade building marketing, measurement and reporting teams and technologies across the US, UK, and Europe. His primary focus is transparency, accuracy, and accountability in AI systems. To get in touch with him, please email alex@openattribution.org.
To suggest additional builders that I should feature in this series, please get in touch at mike@brxnd.ai
If you have any questions, please be in touch. As always, thanks for reading.
— Mike