AI can build your game prototype but it can’t build your strategy

• AI makes building faster, so the quality of decisions becomes the constraint.
• Prototypes are easier than ever to ship, which makes clarity on audience, core loop, and differentiation more important than ever.
• Use AI speed to run learning loops, not to pile on scope.

AI is making it easier than ever to build a playable game. The catch is, it’s also making it easier to build the wrong one.

The first time I heard that framed clearly was when speaking to Ivan Dyshuk, head of gamedev at co-dev studio, Mind Studios Games. 

He told me he’d been quietly worried AI would mean fewer teams would need a partner like his. In the event, he found the opposite.

“I thought AI would reduce demand for co-dev,” Ivan said. “But it did the opposite. Founders who’ve been sitting on an idea for years can now bring it to life. And once it feels real, they want help turning it into something shippable.”

Faster builds, bigger risks

It seems counterintuitive, but it makes sense. Vibe coding removes a big blocker for small teams and founders: getting the first playable version out of your head. Once something is playable, it creates momentum. Teams can feel how it works, play-test it and evolve it. The idea comes alive.

The catch is that prototyping speed only helps when it’s paired with experience. AI can get you to “playable”. It can’t reliably get you to “good”, and it definitely can’t get you to “scalable”. The problem is that the speed of building can outpace the quality of decisions you make. 

When building is fast, it’s tempting to keep adding systems, content, meta, monetisation, and polish instead of focusing on the fundamentals: who the game is for, whether the core loop is fun, what makes it unique, why it will retain, and how it will monetise.

Details matter. Rapid prototyping works when the loop is focused on “build, measure, learn”, not “build, build, build”. Ivan sees this all the time.

“AI makes it easier to get to a playable prototype, but it also makes it easier to lock in the wrong assumptions early, because you build on top of them quickly. Later, you realise the foundations are shaky, and changing direction has become expensive.”

From prototype to product

The better play is to treat prototypes as experiments. Each one should have a purpose, a thesis to test. Is the core-loop fun? Does it appeal to a particular audience? What is D0 behaviour like? Short loops are how you discover something that genuinely resonates without betting the company on guesswork.

Pixel Flow is a good example of this. Before Loom, the founders had already spent years running Crescive Games, shipping over 120 games over six years, roughly one every couple of weeks. 

This was shipping with purpose. Seed investor Enis Hulli described how they once tested a pixel-crunching mechanic in a game that didn’t perform, but noticed a small group of users became “completely obsessed” with the feature, so they kept rebuilding and retesting it “again and again”.  That insight became Pixel Flow. 

Loom co-founder and CEO Kübra Gundogan summed up the mindset behind their approach: “[Pixel Flow’s] reception comes down to how deeply we analyse player experiences, and the strong insights we draw from that work.”  That’s the real advantage of rapid prototyping, especially for small teams: not speed for speed’s sake, but many learning cycles, guided by people who know which signals matter. Now Loom are reaping the rewards.

This is also the logic behind a new collaboration, The Experimentation Group (TXG) and Mind Studios Games have launched together. It’s built for founders and small teams who can now get to a prototype faster than ever, but want to turn that early spark into a game that scales.

The collaboration combines the strengths of Mind Studios and TXG. The result is rapid prototyping with experienced execution. So teams keep the speed AI unlocks, and pair it with the craft and discipline needed to make something players will stick with.

AI makes it easier to start. The hard part is still knowing what you’re building toward, and having the technique to prove it.

Takeaways

1. AI makes building faster, so the quality of decisions becomes the constraint. Prototypes are easier than ever to ship, which makes clarity on audience, core loop, and differentiation more important than ever.

2. That speed can harden assumptions early. When you build fast, you also commit fast, so the cost of being wrong shifts later unless you deliberately test the foundations upfront.

3. Use AI speed to run learning loops, not to pile on scope. Rapid prototyping works when it’s “build, measure, learn”, not “build, build, build”.

4. Make those learning loops explicit by treating every prototype as a thesis test. Each build should test one key assumption (for example, fun, audience fit, early behaviour, retention hooks) and produce evidence you can iterate on.

5. Do this well and AI becomes leverage, not a crutch. AI can get you to playable; experience gets you to scalable. Craft and discipline still determine whether a prototype turns into a game players stick with.

For more industry insights Pocket Gamer Connects San Francisco is taking place on March 9th, 2026.