AI Is Ready. We Aren’t: The Race to Build the Guardrails with Tiffany Wang

AI is getting more capable by the day. Our ability to know when to trust it isn’t keeping up.

In this episode, Tiffany Xingyu Wang, author of the forthcoming The Trust Code and a longtime leader in digital trust and online safety, joins Philip Meissner to explore one of the defining challenges of the AI era: not whether we should trust AI, but what we should trust it with.

Tiffany shares why:

• Our instincts can mislead us. Generative AI sounds confident, helpful, and remarkably human, making the signals we normally use to judge trust less reliable.
• Both too much and too little trust are dangerous. Overtrust creates risk, while undertrust can stall innovation. The answer is learning to calibrate trust.
• AI capability is outpacing its guardrails. Organizations are adopting AI faster than they are building the accountability and safeguards needed to use it responsibly.
• Human judgment must remain in the loop. As AI takes on more tasks and decisions, critical thinking and human agency become more important, not less.

Tune in for a timely conversation about AI, trust, guardrails, and how to stay in control as intelligent systems become part of everyday life.

Timestamps:

00:00 Intro

00:40 Why Trust Is AI’s Biggest Challenge

02:27 What Does It Mean to Trust AI?

08:38 Overtrusting vs. Undertrusting AI

13:38 Why Tiffany Wang Wrote The Trust Code

17:35 How to Calibrate Trust in AI

22:07 Three Questions Leaders Should Ask

24:59 How to Use AI Responsibly

31:18 Lessons From a Career in Tech

35:40 Reasons for Optimism About AI

Connect with Tiffany Xingyu Wang:

linkedin.com/in/tiffanyxingyuwang

What’s Next is a podcast exploring how technology, innovation, and global systems are shaping the future.

Hosted by Philip Meissner, each episode features conversations with global leaders, builders, and thinkers working at the forefront of change.

🔗 Connect with Philip Meissner:

https://www.philipmeissner.com/

https://www.linkedin.com/in/pmeissner/

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