Trust on the internet used to be a fairly simple calculation. You looked for familiar names, recognizable brands, maybe a blue checkmark, and you made a judgment call. Today, that math often fails.

AI has changed the game. Deepfakes are convincing. Entire personas can be spun up in minutes. Fraud doesn’t look sloppy anymore—it looks professional. And in many cases, it looks exactly like the people and platforms we already rely on.

That’s the backdrop for my latest episode of the TechSpective Podcast, where I sat down with Oscar Rodriguez, who leads product efforts around trust at LinkedIn. The conversation quickly moved past features and announcements and into a much bigger question: how do we decide who to trust online when it’s getting harder to tell what’s real?

LinkedIn has become my primary social platform over the past few years—partly by default, partly by design. As other platforms drifted further into chaos, LinkedIn positioned itself as the place where professional identity still mattered. But even there, the ground is shifting. The platform is more social than it used to be. The conversations are broader. And the risks are higher.

In this episode, we dig into that evolution—not just how LinkedIn has changed, but why it’s changing and what that means for the people using it every day. We talk about professionalism as a concept, how it’s expanded beyond résumés and job postings, and why trying to rigidly police what “belongs” on a professional platform misses the point.

At the same time, we don’t ignore the downside of that openness.

One of the recurring themes in our conversation is signal versus noise. When you’re interacting with people you don’t know—often several degrees removed from your own network—what clues do you rely on to decide whether someone is legitimate? Mutual connections? Profile history? Gut instinct? Verification badges?

Those signals matter more than ever, and not just on LinkedIn. As Oscar explains, trust has become a portable problem. We’re constantly being asked to prove who we are, where we work, or whether we belong—often across dozens of platforms that don’t talk to each other. That friction creates opportunity for abuse, but it also forces a conversation about how trust should work at internet scale.

We also get into how AI is accelerating the arms race. The same tools that make it easier to create content and connect at scale also make it easier to deceive. Fraudsters don’t need to sound unprofessional anymore. Bots don’t look like bots. And “doing your own research” is a lot harder when expertise itself can be convincingly faked.

Rather than offering simple answers, this episode focuses on the trade-offs. How much friction is acceptable in the name of safety? What does verification actually prove—and what doesn’t it prove? Should trust be assessed once, or continuously? And who ultimately bears responsibility when things go wrong: the platform, the user, or both?

Listen to or watch the full episode of the TechSpective Podcast with Oscar Rodriguez to hear the full conversation.