A few months ago, I watched a room full of tech leaders nod knowingly when someone admitted they’d stopped trusting Google for product recommendations. These weren’t Luddites; they were innovators running billion-dollar portfolios. 

And you can see why. Review sites where every product mysteriously maintains 4.5 stars. Vendor white papers that span 30 pages without a single useful insight. AI-slop that floods every search result with plausible-sounding nonsense.

At that same conference, upwards of 200 companies packed the exhibition floor, claiming revolutionary product capabilities built on AI. Nearly all of them were selling variations of the same thing—thin wrappers around commodity large language models, each promising to change everything while changing nothing. 

But the most influential people in the room weren’t listening to those pitches. They were huddled in corners, asking each other, “What are you actually using? What actually works?”

The Trust Algorithm

Tech leaders are retreating into vetted communities – CFO councils, CTO forums, industry-specific leadership groups – not because they’re exclusive, but because they’re accountable.

In these closed networks, bad recommendations have consequences. Suggest a tool that fails, and your reputation suffers. Your name carries weight because everyone knows you have skin in the game. There’s no algorithm to game and no SEO to optimize, just humans who’ve faced similar problems sharing what actually worked.

Human recommendations work because they contain elements no AI can replicate:

Shared context: Your peer understands your constraints because they face them, too. They know what “enterprise-ready” means in your industry. They’ve navigated the same compliance requirements, legacy systems, and organizational dynamics.

Reputation risk: Every recommendation puts social capital on the line. Bad advice damages relationships that took years to build. This natural accountability mechanism ensures people only recommend what they genuinely believe in.

Follow-up accountability: In a community, you can’t give advice and disappear. People will ask how it worked out. They’ll want details. They’ll share their own experiences. The feedback loop is immediate and unavoidable.

No financial incentive: The best recommendations come from people who gain nothing from your decision except the satisfaction of helping a peer. When you remove money — or a hidden incentive structure — from the equation, the advice is remarkably candid.

Implications for Business

What this means from a product development perspective is potentially profound. 

Product development will require continuous dialogue with engaged communities throughout the development cycle. This isn’t just about user feedback or beta testing. It’s about making your early adopters true partners in the creation process. They need to feel ownership in what you’re building because they’ll become your most credible advocates – or critics.

In the process, roadmaps become living documents shaped by ongoing community input. Feature requests are weighted not by volume but by the credibility of their sources. Success metrics expand beyond usage statistics to include advocacy rates within trusted networks.

Product success increasingly depends on reputation within small, influential circles. Instead of the goal being known by everyone, it’s about being trusted by the right people.

The Path Forward

Private networks are once again becoming the primary source of business intelligence, not because we’re becoming more insular, but because we’re becoming more discerning. 

When everyone has access to the same information, the advantage goes to those who understand that; in a world of infinite information and AI tools, the scarcest resource isn’t data. It’s trust.