🍪 Cookies, Games, and AI: What Game Design Can Teach Us About Building Better AI Products

In Designing Games, Tynan Sylvester shares a deceptively simple story:

A child desperately wants cookies on the kitchen counter. He tries everything—climbing a stool, tricking his brother, even training the dog. But when a cookie finally falls to the floor by chance, he picks it up, eats it… and walks away, bored.

It’s a moment that captures something powerful—not just about game design, but about human motivation.

And, surprisingly, it also tells us something important about designing AI systems.

🎮 What Game Design Knows About Engagement

In game design, balance isn’t just about fairness or difficulty levels. It’s about meaningful challenge—crafting systems where the player feels that their choices matter.

Sylvester warns against “degenerate strategies”: overly efficient choices that trivialize a game. If players find a single solution that always works, they stop exploring. The experience flattens. The game loses its spark.

This is where the cookie story hits hard. When the challenge disappears, so does the engagement.

🤖 And What This Means for AI

We often treat AI design as a matter of inputs and outputs: faster answers, better predictions, streamlined results. But the real question is:

Are we designing experiences that people want to return to?

Because in AI, just like in games, the absence of challenge—or worse, shallow decision-making—leads to disengagement.

If your AI tool always gives the same suggestion… If it flattens choice into efficiency… If it rewards users too easily…

Then you’ve built a system that hands out cookies, but offers no reason to care about them.

⚖ Applying Game Balance to AI Systems

So what can we borrow from game design? Three key principles:

1. Fairness Isn’t Optional

In games, fairness means starting on equal footing. In AI, it means:

• Preventing bias in algorithms

• Ensuring equal access and experience

• Being transparent about how decisions are made

Fairness is especially crucial when AI is used in high-stakes contexts: hiring, finance, education, healthcare.

2. Depth Over Options

Adding more features or choices doesn’t automatically create better systems. What matters is:

• Are the choices meaningful?

• Can users experiment and find their own path?

• Does the system respond thoughtfully to different inputs?

A good AI tool should reward curiosity and allow for non-obvious solutions.

3. Avoid the Cookie Drop

Ask yourself: When does your system remove friction so much that it loses meaning?

Challenge doesn’t mean frustration. But it does mean users should feel a sense of discovery, learning, and mastery over time. That’s what keeps them coming back—not just results, but growth.

🧠 For AI Builders and Designers

If you work in AI product development, consider this your design prompt:

• Don’t just optimize—humanize.

• Don’t only streamline—challenge and reward.

• And don’t assume more features = better experience. Sometimes less is more—if it’s more thoughtful.

Whether you’re building a game, a chatbot, or a decision engine, the goal is the same:

Create systems that people care about.

Because the most valuable technologies aren’t the ones that just give us what we want.

They’re the ones that make the journey worth it.