Reddit Is Deciding What AI Says About Your Brand. You Cannot Control It, So Here Is What You Can Do

Reddit Is Deciding What AI Says About Your Brand. You Cannot Control It, So Here Is What You Can Do

By Stephen Honight, Founder of Lmo7

Most brands assume that if they fix their own website, they fix how AI describes them. That is the comfortable version. The uncomfortable version is that a model can look straight past your clean PDP and your carefully written brand page, and describe you instead using whatever the last Reddit thread said.

We see this pattern often in our Share-of-Model work. A brand with a strong site, good structured data and accurate product content still gets summarised by a model in language that came from somewhere else entirely. When you trace where that language came from, it keeps landing on the same handful of sources. Reddit is usually near the top. YouTube is often right behind it.

This is one of the harder things to explain to a brand team, because it feels unfair. You spent the money on the site. You cannot spend the same money on a forum you do not own. My sense is that most teams have not fully internalised the mechanism yet, so let me walk through why it happens before getting to what you can actually do about it.

Why models trust other people more than they trust you

Start with what an AI model is doing when someone asks it a shopping question. It is not reading your website the way a shopper does. It is pulling together an answer from a wide set of sources it has learned to weight for reliability, and then summarising. Your own site is one input. It is not the most trusted one.

There are three reasons a model leans toward third-party sources over your owned content.

The first is an independence signal. A brand describing itself is expected to be positive. That is priced in. A group of strangers on a forum describing a product carries more weight precisely because they have no reason to flatter you. Models are trained to treat independent commentary as closer to the truth than marketing copy, so a Reddit thread about your category can outrank your own product page in the model’s internal sense of what to trust.

The second is freshness and volume. Your PDP is one page that changes slowly. A category subreddit produces a steady stream of new posts, questions, complaints and recommendations. That volume and recency give the model a lot of material to draw on, and a lot of signal about what real buyers actually say and ask.

The third is that this is where the conversation already lives. When a shopper asks a model “what is the best electrolyte drink for long runs” or “which probiotic actually helped people with IBS”, the model has seen those exact questions asked and answered on Reddit thousands of times. Your PDP has never had that conversation. The forum has had it every week for years.

Put those together and you get the situation most brands are now in. The model has a rich, independent, constantly refreshed source describing your category, and a static self-authored page describing your product. It knows which one to lean on.

Why one bad thread can undo a good page

Here is the part that worries brand teams the most, and rightly so. Because the model treats independent commentary as more trustworthy, a negative thread can override a polished brand page. You can have every approved claim on your site, and still get described using the objection that keeps coming up in a forum.

The mechanism is the same one that makes third-party sources valuable. Independence cuts both ways. If the independent source is critical, the model does not discount it for being critical. It weights it as honest. So a recurring complaint about taste, or price, or a side effect, or delivery, can become part of how the model summarises your brand, even when your own content never mentions it.

This is not the model being hostile. It is the model doing exactly what it was built to do, which is to reflect what independent voices say. The problem is that independent voices are not evenly distributed, and they are not always right, and they are almost never under your control.

This is not one problem. It is a different problem per engine

One more thing to get straight before the playbook, because it changes what you actually do. The source a model leans on is not the same across engines.

In our client work the pattern is consistent enough to plan around. Reddit shows up heavily across ChatGPT and Gemini. YouTube scores especially well in Gemini, to the point where a strong set of category videos can move how Gemini describes a brand. Retailer and specialist-press pages carry weight in some categories and barely register in others. The mix shifts by category too. A spirits brand and a supplements brand do not share the same citation landscape.

So a monitoring approach that only checks one engine, or that treats “AI search” as a single surface, will miss where the real influence sits for your category. You have to look at each engine separately and see which sources it actually pulls from for your products.

What you cannot do, said plainly

Let me be honest about the limits, because the worst thing an agency can do here is sell you a fix that does not exist.

You cannot control Reddit. You cannot delete threads you do not like. You should not astroturf, and if you try, it tends to backfire in exactly the communities that feed the models. You cannot make a forum say what you want, and you cannot buy your way to the top of an organic subreddit the way you would with an ad.

Anyone telling you they can “fix your Reddit presence” the way they would fix a title tag is selling you something that is not real. This is the long game. It sits on the authority track, not the quick-wins track, and it moves in months, not days. I would rather say that up front than let a brand think a content sprint solves it.

What you can actually do

Now the useful part. You cannot control the off-site conversation, but you are not powerless in it either. There is a real playbook here, and it works if you treat it as ongoing rather than a one-off project.

Monitor which sources the models actually cite for your category. This is the foundation and it is the part most brands skip. Before you touch anything, find out which threads, subreddits, channels and pages the models are leaning on when they describe your products, and do it per engine. This is core Share-of-Model work. It turns a vague anxiety about Reddit into a specific list of sources you can watch.

Show up authentically where the rules allow. Most communities have clear norms about brand participation. Some welcome a verified brand account answering questions honestly. Others do not. Where genuine participation is allowed, being a useful, transparent presence over time is worth far more than any one-off push. The goal is to be a source the community trusts, which is slow by design.

Seed genuinely useful answers, not marketing. The content that earns its place in these communities answers a real question a buyer has. Fit, dosage, comparison, what a product works for and what it does not. That is the same honest, specific information that helps a model describe you accurately. Useful content and citable content are the same content here.

Correct factual errors, carefully. When a thread contains a plain factual mistake about your product, a correct product spec, an out-of-date price, a claim that was never true, there is often a legitimate way to correct it. Do it as a brand, openly, once, without arguing. Models pick up corrections over time, and an accurate record is the thing you are actually building.

Build the third-party evidence base the model can lean on instead. This is the real long-game move. If the only independent material about your product is a handful of old forum complaints, that is what the model has to work with. If there is a strong body of reviews, honest video content, specialist coverage and genuine community discussion, the model has better material to draw on. You are not deleting the bad thread. You are giving the model something better to weight above it.

That last point is where the YouTube signal matters, and it is worth pulling out on its own. Reddit is the off-site source you cannot control. YouTube is the off-site source you largely can. It is one of the strongest citation sources we see, especially in Gemini, and unlike a forum you can actually produce and optimise the content yourself. A focused set of honest, useful category videos, titled and described clearly, is one of the few off-site levers where the effort you put in maps directly to the citation signal you get back. If Reddit is the risk, YouTube is the response you have the most control over.

Where we see this in real client work

This is not theory. It sits underneath a lot of the work we do.

Our Share-of-Model programmes for enterprise clients like Haleon and Brown-Forman include cited-source and domain analysis, which is exactly the exercise of finding out which independent sources the models lean on for each brand and category. That analysis is what turns “the models are saying something odd about us” into a specific, trackable source list.

With Trip Drinks, part of a 60-day engagement was identifying and addressing the sources the models referenced most heavily, alongside the site and content work. Over that period Trip’s average position across the major models moved from 7th to 3rd and AI referral traffic rose by around a third. The off-site citation piece was part of that, not a footnote to it.

The pattern across all of it is the same. The brands that improve their AI description are the ones that stop treating their own website as the whole battlefield and start managing the independent sources the models actually trust.

So what should you do next

If you take one thing from this, it is that your owned content is necessary but not sufficient. The independent sources are doing at least as much work in how AI describes you, and you have to manage them deliberately.

My recommendation would be to sequence it like this.

Start by finding out which sources the models actually cite for your category, per engine. Without that you are guessing. This is the Share-of-Model diagnostic and it is the honest entry point. If you want the raw citation-source data to read yourself, that sits in our DaaS tier at the data-and-upskilling level, from £250 plus VAT a month.

Then act on the two levers you can move. Get your own content and PDPs accurate and useful so the model has good owned material to draw on, and build the off-site evidence base, with YouTube as the channel you control most directly. For a single brand that wants us running the monitoring and the content and authority track hands-on, that is the Challenger Agentic Stack, with Tracking and Content Optimisation for AI Search Visibility at £1,000 a month.

If you are a multi-brand business and need this mapped across a portfolio, with off-site citation tracking sitting alongside a full Share-of-Model programme and the stakeholder alignment to act on it, that is Enterprise work. It starts with a workshop and a baseline rather than execution.

The brands that win here are not the ones with the cleanest website. They are the ones who accept they cannot control the conversation and get to work on the parts they can.

Stephen Honight is the Founder of Lmo7, an AI-native agency helping consumer brands win in AI-powered discovery and agentic commerce. Lmo7 works with brands including Trip Drinks, Veloforte, Brown-Forman, Haleon, Pelotan and Symprove across Amazon, D2C and AI search. If you want to know which sources the models actually trust when they describe your brand, that is where we start.