We’ve all been told that AI is becoming more transparent, that it can “explain its thinking” and walk us through how it reaches decisions. But what if that story is more fiction than fact?
Anthropic’s latest research reveals a quiet truth most people are missing: AI isn’t really telling us how it thinks. It’s giving us believable explanations that hide what’s really going on under the hood. Now, that’s not just a technical flaw; it’s a trust problem with serious implications for how we use these tools in business, leadership, and decision-making.
In this video, I unpack the new research, break down what it actually means for leaders, and ask the hard question no one wants to confront: Can you really rely on AI to explain itself when it matters most? Whether you’re building AI into your workflows or just trying to keep up with what’s changing, this conversation might shift how you view these systems altogether.
What do you think? Is this just a phase in AI’s evolution, or are we fundamentally misunderstanding what explainability really means? Drop your thoughts in the comments.
Link to the Research: https://assets.anthropic.com/m/71876fabef0f0ed4/original/reasoning_models_paper.pdf
Chapters:
00:00 - Breaking Down the Anthropic Research
01:48 - Understanding Key AI Terminology
04:23 - The Problem with Human Feedback in AI Training
11:57 - The Issue of Reward Hacking
15:16 - Implications for AI in Decision Making
17:20 - Conclusion: Navigating the AI Landscape
#AIExplainability #AnthropicResearch #AITrustCrisis #FutureOfAI #ChristopherLind