The Sim to Real (Gen AI) Gap

I was listening to a Google DeepMind podcast recently and a robotics researcher was talking about how they teach robots to complete tasks in simulation environments and robots perform beautifully time and time again, predictably. Everything works. Every movement lands.

Then they move the robot into the real world, ask it to do the exact same task—and everything falls apart. Maybe it’s a tiny glare off a metal ball, just enough to throw off depth perception. A single, unaccounted-for variable, and the whole system gets confused.

Maybe a plate warps ever so slightly because of humidity. Maybe the background noise is louder than expected… or there are unexpected bursts.

The world that we live in is messy, unpredictable, dynamic, and definitely never perfect.

I learned that in robotics that failure is called the sim-to-real gap.

And I couldn’t stop thinking about how it reminded me of what a lot of us are feeling with generative AI right now.

The “AI Demo-to-Reality” Gap

The AI Demo-to-Reality Gap

We’ve all seen the demos. The One-Click-Magic-AI-Wand tools. The mind-blowing videos generated from just a sentence. Custom agents that build your entire app while you sip your morning coffee. The ones that build your website with just a prompt. The AI assistants that do the work of your entire marketing team.

The tools look incredible, flawless in the demos. The content is breathtaking. The results are almost too good to be true. That’s because… it is.

What we’re seeing is the Gen AI version of a simulation:

• A tightly controlled environment

• Cherry-picked inputs

• Hidden scaffolding (hello, internal-only tools and pro-level users)

• Often, features that aren’t even available yet

• Highly curated outputs that get showcased… edited to enhance speed, accuracy, and capabilities

The expectations we form from seeing these demos are sky-high.

And then we get access—and the gap hits us. Hard. Because our real life isn’t curated.

And when we try to make that same magic happen in our environments… it sputters.

So I’m calling that that friction the AI Demo-to-Reality gap. And it’s not a tech problem, it’s a marketing one.

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I’ve been in marketing long enough to know the game. There’s always going to be a little sparkle and shine. That’s fine. Expected, even.

But right now, the disconnect between what’s demoed and what’s delivered feels unusually wide. We’re not just promised capabilities—we’re promised transformation.

Meanwhile, real people are handed these tools with zero context, no training, and wild expectations. They’re told “this will change everything”… and left to figure it out themselves.

It’s not just unfair—it’s setting people up to fail.

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So What Do We Do With This Gap?

We name it. We normalize it.

We don’t let it convince anyone that they’re “bad” at AI—or that they’re behind. We need less sparkle and more support. Less hype and more help.

We need:

• Reality

• Transparency

• Training

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We are not robots in simulation (well… some would argue otherwise 😬). We are human beings with unorganized stickies, confusing slack threads, forty-seven open tabs, and maybe a kid yelling in the background.

If you’ve tried the tools and felt underwhelmed—you’re not alone. It’s not you.

You’re just living on the real side of the gap.

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