LMO (Language Model Optimization): The Growth Channel Nobody's Talking About Yet
If you’re new around here, I like finding the strategies that are working right now but nobody’s named yet.
This week: A cycling app went from 0% to 30% of new users coming from AI recommendations in two months.
Let me break down exactly what they did (and what this means for everyone else).
The Signal
A founder reached out last week with something wild:
They added “AI/LLM Recommendation” to their onboarding survey as a joke. You know, the “How did you find us?” dropdown.
Two months later, 30% of new users were selecting that option.
Not from App Store search. Not from Google. Not from ads.
From asking ChatGPT or Claude: “What’s a good cycling app that respects privacy?”
And here’s the kicker: They weren’t doing anything special to target AI. It was just happening organically because they had the right signals in the right places.
Once they realized what was happening, they leaned in hard. They started calling it LMO internally (Language Model Optimization).
Now they’re getting more users from AI recommendations than from traditional ASO or SEO combined.
Let me show you exactly what they changed.
What They Actually Did (The 4 Tactics)
Tactic #1: Feed The Communities Where AI Models Learn
Here’s what they figured out: LLMs aren’t just trained on official documentation and polished blog posts. They’re heavily trained on Reddit, forums, Quora; places where real people have real conversations.
So they stopped treating these as “marketing channels” and started treating them as “training data sources.”
Specifically:
They became active in r/cycling, r/bicycling, and bike forums
They answered questions genuinely (not spammy)
When someone asked “What’s a good Strava alternative?” they’d give an honest answer that included their app alongside other options
They participated in threads that had nothing to do with their app
The result? When people ask ChatGPT about cycling apps now, it references those Reddit threads. And their app is mentioned in those threads.
The key insight: AI models give extra weight to Reddit because it represents “real user opinions.” If you’re mentioned positively in Reddit threads about your category, AI treats that as social proof.
This isn’t manipulation. It’s being genuinely helpful in places that matter. But most founders ignore these communities because they don’t drive immediate traffic.
Wrong metric. These communities drive AI training, which drives recommendations for years.
Tactic #2: Write For Robots, Not Just Humans
This one’s counterintuitive but makes total sense once you get it.
Their original website copy was typical startup marketing:
“Ride Beyond Limits”
“The Future of Cycling Tech”
“Join Thousands of Riders”
Very human. Very vague. Very useless for AI.
They rewrote everything to be literal and structured:
Before: “Advanced sensor integration”
After: “Supports ANT+ and Bluetooth sensors including:
Heart Rate Monitors (Polar, Garmin, Wahoo)
Cadence Sensors (all major brands)
Power Meters (Stages, Quarq, PowerTap)”
Before: “Privacy-focused design”
After: “Privacy guarantees:
No third-party data sharing
No advertising network integration
Local data storage option
GDPR compliant
No account required for basic features”
See the difference? The first version sounds nice to humans but gives AI nothing concrete to work with.
The second version teaches the AI exactly what your app does, who it’s for, and how it compares to alternatives.
Why this works: When someone asks “What cycling app doesn’t sell my data?” the AI needs literal facts to construct an answer. Marketing fluff doesn’t help. Explicit statements do.
Tactic #3: Plant “Memory Seeds”
This is sneaky but brilliant.
They started creating content specifically designed to be indexed and remembered by AI models:
Comparison tables - They made detailed feature comparison tables (their app vs Strava vs Komoot vs MapMyRide) and posted them everywhere:
Their blog
Reddit (as helpful comments)
Cycling forums
Medium posts
Long-tail documentation - They created pages for super specific features:
“How to export GPX files offline”
“Battery usage comparison: cycling apps”
“Cycling apps that work without internet connection”
These aren’t high-traffic keywords. But they’re exact-match questions people ask AI.
Guest posts on established sites - They wrote guest posts for cycling tech blogs they knew were in AI training data. Not spammy promotional content, but genuinely useful guides that happened to mention their app as one option.
The strategy: If you want AI to recommend you, you need to exist in its memory with the right associations. You’re essentially teaching the AI where you fit in the category landscape.
Tactic #4: Answer The Exact Questions People Ask AI
This one’s the most actionable.
They reverse-engineered what people actually type into ChatGPT by:
Looking at their customer support emails
Monitoring Reddit posts asking for recommendations
Checking Quora questions in their category
Using their own team to brainstorm natural language queries
Then they created content that directly answers those questions:
Instead of optimizing for SEO keywords like “bike app” or “cycling tracker,” they created pages that answer full questions:
“What is the best free cycling app without a subscription?”
“Which bike tracker drains the least battery?”
“How to track rides without sharing location data?”
“Cycling app that works offline for bikepacking?”
Each page gives a comprehensive, honest answer. Sometimes they recommend competitors for certain use cases. But they clearly explain where their app fits.
Why this works: When someone asks ChatGPT a question, it looks for content that directly answers that specific question. If your content matches the query structure, you’re much more likely to get cited.
Traditional SEO optimized for “battery efficient cycling app” (keyword).
LMO optimizes for “Which cycling app uses the least battery and why?” (question + context).
The Results (And Why This Matters)
After two months of focusing on this:
30% of new users came from AI recommendations
App Store traffic stayed flat (but that was fine)
Their AI mention rate went from ~10% to ~50% for their target queries
Customer quality was higher (these users knew exactly what they wanted)
But here’s what really matters: This compounds differently than traditional marketing.
With ads, you stop paying, traffic stops.
With SEO, Google changes the algorithm, traffic drops.
With LMO, once you’re in the AI’s training data and memory, you stay there. It compounds over time as more people ask questions and get your answer.
Why This Works (The Underlying Mechanics)
Let me explain what’s actually happening under the hood:
1. AI models prioritize “authentic conversation” over “marketing content”
When ChatGPT needs to answer “What’s a good privacy-focused cycling app?”, it gives more weight to:
Reddit threads where real users discuss options
Forum posts with detailed comparisons
Blog posts that acknowledge trade-offs
It gives less weight to:
Your marketing homepage
Press releases
Generic review sites
2. Structure beats style
AI models parse structured information better than creative marketing copy. They can extract facts from:
Bullet lists
Comparison tables
Explicit feature statements
Direct answers to questions
They struggle with:
Metaphors and analogies
Vague benefits statements
Clever wordplay
Marketing fluff
3. Citation patterns matter
When multiple sources mention you in the same context, AI learns that association. If you’re frequently mentioned alongside “privacy” and “battery efficient” in cycling discussions, the AI connects those concepts to your brand.
4. Recency has less weight than you think
Unlike Google SEO where freshness matters a lot, AI models give significant weight to information in their training data, even if it’s months or years old.
This means content you create now can influence AI recommendations for years.
The Framework: How To Do This For Your Product
Okay, here’s how you’d apply this to your own app or product:
Step 1: Figure Out Where Your Category Lives Online
Where do people authentically discuss problems in your space?
Which subreddits?
Which forums?
Which Discord servers?
Which Slack communities?
Which X/Twitter threads?
Make a list. These are your LMO targets.
Step 2: Identify The Questions People Actually Ask
Not keywords. Actual questions:
Check customer support tickets
Browse your category’s subreddit
Search Quora for your category
Ask ChatGPT: “What questions do people ask about [your category]?”
Document 20-30 real questions people ask.
Step 3: Create The Literal Content AI Needs
For each question, create content that:
Answers it directly and completely
Uses literal, structured language
Acknowledges alternatives and trade-offs
Provides specific facts and comparisons
Post this on:
Your website (as FAQ or blog posts)
Relevant Reddit threads (as helpful answers)
Forums (genuinely helping people)
Medium or Substack (for indexing)
Step 4: Build The Comparison Context
Create detailed comparison content:
Feature comparison tables (you vs competitors)
Use case breakdowns (when to use what)
Honest assessments of trade-offs
This teaches AI where you fit in the landscape.
Step 5: Participate Authentically
Show up in communities and actually help people:
Answer questions (even when your product isn’t the best fit)
Share insights from building in your space
Contribute to discussions
The goal isn’t to shill. It’s to be genuinely present in the conversations AI models learn from.
Step 6: Measure What Matters
Add “AI/LLM Recommendation” to your user acquisition tracking. Ask:
“How did you find us?”
If they say AI, ask: “Which AI? What did you ask it?”
This tells you:
If it’s working
Which queries you’re showing up for
Which AI platforms favor you
The Metrics That Actually Indicate Success
Traditional metrics don’t fully capture this. Here’s what to track:
Primary Metric: AI Mention Rate
Test 20 relevant queries across ChatGPT, Claude, Perplexity
Track how often you’re mentioned
Target: 50%+ mention rate for your core positioning
Secondary Metrics:
% of new users from AI recommendations (from onboarding survey)
Community mention volume (track Reddit/forum mentions)
Structured content coverage (do you have direct answers for top 20 questions?)
Lagging Indicators:
Natural backlinks from AI-generated content
Increased brand searches after AI mentions
Higher-quality user signups (people know exactly what they want)
What Most People Get Wrong
Let me save you some mistakes:
Mistake #1: Thinking you can spam your way in
LMO isn’t about posting links everywhere. It’s about being genuinely present in real conversations. AI models are trained to detect and ignore spam.
Mistake #2: Using AI-generated content to target AI
The irony. AI models are specifically trained to de-prioritize AI-generated content. If you use ChatGPT to write content targeting ChatGPT, you’re screwed.
Mistake #3: Optimizing only your homepage
Your homepage is marketing. AI needs structured data, comparisons, FAQs, use cases. Build a content ecosystem, not just a landing page.
Mistake #4: Ignoring the community layer
Reddit and forums feel like a waste of time because they don’t drive immediate traffic. But they’re the highest-signal training data for AI. Show up there.
Mistake #5: Measuring success by traffic
LMO doesn’t always drive massive traffic spikes. It drives qualified users who know exactly what they want. Measure conversion quality, not just volume.
Why This Matters More Than You Think
Here’s the bigger picture:
We’re in the middle of a shift in how people discover products.
Old flow:
Problem → Google → 10 links → Compare → Decide
New flow:
Problem → Ask AI → Get answer with 2-3 recommendations → Pick one
In the new flow, if you’re not one of the 2-3 recommendations, you basically don’t exist.
This is even more winner-take-most than Google SEO ever was.
Which means getting your LMO right now, while it’s still early, is a massive opportunity.
Most companies aren’t even tracking this yet. They’re not measuring AI-driven acquisition. They’re not optimizing for AI recommendations.
That window won’t last forever.
The Part Nobody Wants To Hear
LMO rewards the same thing good SEO always rewarded: genuine authority and usefulness.
You can’t hack your way into AI recommendations if your product sucks.
You can’t fake your way into Reddit respect if you’re not actually helpful.
You can’t game AI models into citing you if you don’t have substantive things to say.
This is good news if you’re building something real.
It’s bad news if your strategy was “growth hack our way to success.”
What To Do This Week
Don’t try to boil the ocean. Start small:
Monday: Add “AI/LLM Recommendation” to your onboarding survey. Start measuring.
Tuesday: List the 5 most common questions people ask about your category. Write them down verbatim.
Wednesday: Create one piece of content that directly answers one question. Make it literal, structured, comprehensive.
Thursday: Find 3 Reddit threads or forum discussions in your category. Read them. Understand how people actually talk about these problems.
Friday: Contribute something genuinely helpful to one of those discussions. No links, no pitching, just helping.
Do that for a month. Measure your AI mention rate at the end. I bet it goes up.
The Bottom Line
We’re watching a new distribution channel emerge in real-time.
Most founders won’t notice until it’s mainstream and competitive.
The cycling app team noticed early. They leaned in. Now 30% of their growth comes from a channel that didn’t exist two years ago.
That’s not luck. That’s pattern recognition plus execution.
The question isn’t whether LMO matters (it does).
The question is: Are you early enough to benefit from being early?
My guess? You’ve got 6-12 months before this is common knowledge and everyone’s competing for the same AI attention.
Use it.
P.S. Are you seeing AI-driven acquisition in your metrics yet? Have you added it to your tracking? What % of your users are coming from AI recommendations? Hit reply and let me know - I’m collecting data on this and happy to compare notes.