JUNE 10, 2025

• What Is Inverse Prompting—And Why Should You Care?
• What Apple's 'Anti-Reasoning' Research Misses
• THE HEADLINES
• Read, Annotate, Understand Research Papers with AI

What Is Inverse Prompting—And Why Should You Care?

Chapter 1 Excerpt from Becoming Irreplaceable: How We Evolve in the Age of AI

Most people treat AI like a vending machine. You punch in a request—social media post, email draft, a quick summary—and out it comes, neat and fast. That works. But if that’s all you ever do with AI, you’re missing the real power.

The idea behind Inverse Prompting is simple: Instead of asking AI for answers, you ask it to ask you questions.

I discovered this while using AI to help with my daily work—press releases, scheduling, writing, brainstorming. Over time, I noticed what it got right... and what it didn’t. It wasn’t intuitive. It couldn’t anticipate nuance. It didn’t know me.

So I flipped the script. I started giving it context—what I was working on, what I was trying to say—and told it to ask me smart questions. What I got back wasn’t perfect, but it triggered better ideas. Stronger insights. A clearer voice. The more questions I answered, the more I saw my thinking sharpen.

For example, prepping a presentation? Don’t ask AI to write it. Ask it to challenge you:

• What’s the one thing your audience must walk away with?
• Why should they care?
• What would someone who disagrees with you say?

Now you’re thinking. You’re refining. You’re preparing. That’s the real value.

Inverse Prompting is built on that process. It puts you back in the driver's seat. And in a time when we’re all tempted to let the machine “just handle it,” that choice matters. Because if you hand over your thinking entirely, you risk sounding stilted, derivative—or worse, wrong.

We’ve seen lawyers cite fake cases, students turn in AI-written essays, professionals speak in jargon-filled nonsense because they didn’t fact-check or rewrite. They assumed AI wouldn’t steer them wrong. But AI doesn’t know the stakes. You do.

What surprised me most about this process? How powerful it made me feel—not the tool. The better I got at prompting, reflecting, and revising, the more the tool became what it should have been all along: an amplifier, not a replacement.

If that sounds like the kind of relationship you want with AI, then this chapter—and this book—is your first step.
It’s not just about asking better questions. It’s about letting the questions sharpen you.

​CLICK HERE FOR THE PRINT VERSION​

​CLICK HERE FOR THE ELECTRONIC VERSION​

What Apple's 'Anti-Reasoning' Research Misses

‘The Illusion of Thinking’ Goes for More Than AI

When Apple quietly released its paper “The Illusion of Thinking,” it fired a subtle but significant shot across the bow of AI optimism.

The researchers dissected modern Large Reasoning Models (LRMs) like Claude 3.7, DeepSeek-R1, and OpenAI’s o-series, concluding that their much-hyped reasoning skills may be more illusion than intelligence. It’s a deeply thoughtful, rigorously designed piece of research.

But here’s the twist: while the paper convincingly shows the limits of current AI reasoning, it also exposes the cracks in our definition of reasoning. The researchers might be right that these models aren’t doing “true reasoning,” but if we’re honest, neither are we.

Let’s unpack the findings first, then circle back to what the paper misses — and what Reddit got right.

​CLICK HERE TO SEE THE 4 THINGS APPLE MISSED​

THE HEADLINES

Read, Annotate, Understand Research Papers with AI

​

​

​Read All mAIn Street Back Issues Here​

Your input fuels the content. Let me know how I did today!