How AI Decides What Products to Recommend (And Why You Should Care)

I asked ChatGPT “What’s the best project management tool for small teams?” and it recommended Asana, Monday.com, and ClickUp as top choices. Then I asked Claude the exact same question and got a completely different list including Trello, Notion, and Linear.

Same question, different AI model, and completely different product recommendations. How exactly do AI models decide what to recommend in the first place? And what’s going to happen when millions of people start using AI instead of Google for their buying decisions?

Table of contents

AI Doesn’t “Search” For Products

Most AI models aren’t browsing the web in real-time to find products unless they’re specifically using search APIs as described earlier. Instead they pull recommendations from their training data which includes product reviews, articles, Reddit discussions, and blog posts, supplemented by search API results if they have web access enabled, and occasionally query structured databases including product APIs and review aggregators.

The answer you get depends entirely on which sources the model used and how recent that data is.

The Training Data Problem

GPT-4’s training data cuts off in late 2023, and Claude’s cutoff is similar, which creates serious problems for product recommendations. If your product launched in 2024, the model simply doesn’t know it exists and can’t recommend something it’s never encountered. If you changed pricing dramatically, got acquired by another company, or went out of business entirely, the model might not know unless it’s actively searching the web in real-time. These models are fundamentally working from stale data that gets more outdated every single day.

What Makes A Product “Recommendable”

The formula is brutally simple where high mention volume combined with positive sentiment equals AI recommendations. If Notion appears in 10,000 training documents with mostly positive context, it will get recommended constantly, while an obscure but excellent tool with only 50 mentions gets buried and essentially invisible.

This creates devastating popularity bias where the most talked-about products get recommended more frequently, which leads to them being talked about even more, which leads to more recommendations in a self-reinforcing flywheel. If you’re not already in this flywheel, you’re fighting a brutal uphill battle.

Source Authority Matters

AI models weigh sources differently where a TechCrunch review carries vastly more weight than a random personal blog post. A comprehensive review in a major publication outweighs a casual passing reference, and authentic user reviews on Reddit or Hacker News carry significant weight because they signal genuine user experience. This is essentially PageRank applied to language models where certain sources are inherently more trusted. If your product is only mentioned on your own website, the AI won’t recommend it because models need third-party proof of legitimacy.

The SEO → AI Shift

Traditional SEO optimization focused on ranking #1 on Google through tactics like keyword optimization, building backlinks, and improving site speed. AI recommendations require a fundamentally different strategy focused on maximizing mention volume across the entire internet, cultivating positive sentiment through authentic good reviews, getting coverage from authority sources like major tech publications, maintaining current information through constantly publishing fresh content, and implementing structured data with proper schema markup.

This isn’t about ranking #1 in search results anymore. It’s about being mentioned favorably across the internet so frequently that AI models naturally absorb your product into their knowledge base.

The Review Site Problem

AI models aggressively scrape major review platforms including G2, Capterra, TrustPilot, Reddit, and Hacker News, where positive reviews directly translate to AI recommendations. But review sites have serious built-in biases including pay-to-play dynamics where vendors can buy better placement, rampant fake reviews that are hard to detect, and competitor manipulation through coordinated negative reviews. AI systems ingest all this data indiscriminately without verifying authenticity or filtering out manipulation, which means gaming review sites directly translates to gaming AI recommendations.

How Companies Are Gaming The System

Savvy companies are already gaming this new system through several coordinated tactics. They flood the internet with mentions through press coverage, guest blog posts, and podcast appearances to maximize volume. They specifically optimize for “X vs Y” comparison content that directly shapes how AI models compare products. They aggressively pursue listings on aggregator sites like Product Hunt and AlternativeTo that AI models heavily reference. They systematically generate positive reviews across multiple platforms, and they diligently publish structured data with proper schema markup that makes their product information machine-readable.

This represents the new SEO game with completely different rules, and most small companies don’t even realize they’re supposed to be playing.

What Happens To Unknown Products

If you’re building something genuinely new and innovative, the harsh reality is that if AI doesn’t know about you, you’re functionally invisible to millions of potential customers. When users ask “What’s the best tool for X?” and your product isn’t mentioned anywhere in the model’s training data, they’ll try the alternatives the AI suggests and buy something else instead, never knowing you exist. With traditional search you could at least rank for specific keywords and get discovered organically, but with AI recommendations you’re either in the model’s knowledge base or you’re not, with no middle ground.

This creates a severe discovery problem where new products struggle to gain traction, while established products keep getting recommended in a self-reinforcing cycle. Innovation becomes harder when discovery mechanisms favor what’s already popular.

The Paid Placement Question

Currently there’s no direct paid placement in AI recommendations since they’re based purely on training data and search results. But this won’t last long because Google makes all its money from advertising, and AI search products need sustainable revenue models eventually.

Within two years I’d bet we’ll see sponsored recommendations appearing in AI responses, affiliate links embedded in product suggestions, paid priority placement for companies willing to pay, and exclusive brand partnerships with AI platforms. AI recommendations will inevitably become pay-to-play just like Google search already is.

How To Get Your Product Recommended

The winning strategy requires coordinated effort across multiple channels simultaneously. Create content everywhere including your own blog, guest posts on related sites, podcast appearances, and active social media presence. Aggressively pursue coverage by major tech publications since one TechCrunch article carries more weight than 100 random blog posts combined. Systematically encourage authentic user reviews on platforms like Reddit, Hacker News, and G2, but absolutely don’t fake reviews because that damages credibility. Implement structured data including proper schema markup and API integrations that make your product machine-readable. Specifically optimize for comparison search content by creating comprehensive “X vs Y” comparison pages. Stay current by regularly updating your content so AI models see you as actively maintained rather than abandoned.

The User Perspective

You need to be genuinely skeptical of AI product recommendations because the AI isn’t objective, isn’t personally testing these products, and is simply regurgitating whatever got mentioned most frequently in its training data. Popular doesn’t automatically mean best for your specific situation.

Always follow this process: ask multiple AI models for recommendations to see what overlaps and what differs, do independent research beyond what the AI tells you, actually try free trials yourself before committing money, check recent reviews from the last few months rather than relying on stale sentiment, and actively consider newer products that AI models might not know about yet.

Treat AI recommendations as a useful starting point for research, definitely not as your final answer.

What This Means Long-Term

If AI becomes the primary discovery mechanism for products, we’re likely heading toward a dangerously consolidated market structure. The flywheel dynamics are brutal where AI recommends a product, which drives more users to that product, which generates more reviews, which makes AI recommend it even more, accelerating the cycle. New entrants struggle to break in while established players increasingly dominate, and innovation potentially slows as the market ossifies.

Unless AI systems dramatically improve at surfacing genuinely innovative new products, rigorously verifying review authenticity to prevent gaming, personalizing recommendations based on individual needs rather than popularity, and consciously avoiding popularity bias that reinforces existing winners.

We’re essentially replacing Google’s SEO game with AI’s mention game, where the rules are different but the fundamental consolidation problem remains exactly the same.

My Take

I personally use AI for product recommendations all the time because it’s dramatically faster than reading 10 comparison articles manually. But I absolutely don’t trust it blindly as gospel truth. I always cross-reference across multiple AI models, check authentic discussions on Reddit where people have skin in the game, and try products myself rather than taking anyone’s word for quality. AI is pattern-matching what got written about most frequently, which is genuinely useful for discovering options, but it’s definitely not the whole picture.

If you’re building products in this environment, you need to aggressively get your mentions up across the internet, systematically generate authentic reviews on major platforms, and make absolutely sure AI models know you exist.

If you’re buying products guided by AI recommendations, remember that AI reliably shows you what’s most popular based on historical data, not necessarily what’s actually best for your specific needs. There’s a crucial difference.