This might be controversial, but someone has to say it. The entire AI in game development industry is built on a house of cards. After 8 years deep in this world, seeing it from every angle—as a founder, an investor, and a guy who just loves to see cool stuff get built—I’ve learned that most of what people are selling is snake oil. I’ve burned through cash, I’ve backed winners like Anthropic and OpenAI, and I’ve built and sold my own companies. I’m here to tell you what actually works.
My journey wasn’t a straight line. With MovieLaLa, we were obsessed with recommendation algorithms. We thought a smarter algorithm was the key to everything. We spent hundreds of thousands on it. Later, with RemoteTeam, we focused on simple, practical automation that solved a real, painful problem for businesses. Guess which one got acquired by a giant like Gusto? It was the “boring” one. That’s the kind of experience that shapes your thinking.
So, let’s cut the crap. Here are the eight biggest lessons I’ve learned about using AI in game development, the stuff no one at a big tech conference will tell you.
1. Your “AI” is Probably Just a Fancy Script
I see it all the time. A startup pitches me their “revolutionary AI-powered animation system.” I look under the hood, and what do I find? A complex state machine with a thousand if-else statements. It’s clever, for sure. But it’s not AI. It’s a script. A very, very fancy script.
Let’s be honest with ourselves. True AI learns and adapts. It makes decisions based on patterns it has discovered in data, not just rules you hard-coded. Most of the “AI” in games today is just a deterministic system that produces a predictable output. Your NPC that “intelligently” finds cover? It’s probably running an A* pathfinding algorithm that’s been around since the 90s. That’s not a knock on it—A* is fantastic. But calling it AI is just marketing fluff.
Why does this matter? Because when you think you’re doing AI but you’re just scripting, you limit your thinking. You’re not building systems that can surprise you or create truly emergent behavior. You’re just building a more complicated version of what we’ve had for decades. The first step to actually innovating is to call things what they are.
2. Stop Chasing AGI for Your NPCs
Every game developer dreams of creating NPCs that feel truly alive. NPCs that can hold a conversation, that have their own goals, that react to the world in unscripted ways. We’re chasing the ghost of AGI (Artificial General Intelligence) to stick it inside a video game. It’s a noble goal. It’s also a massive waste of time and money for 99% of studios.
I’ve seen teams burn their entire seed round trying to build a dynamic dialogue system that can handle any player input. The result is always the same: a buggy mess that’s worse than just writing good, old-fashioned branching dialogue. You don’t need an NPC that can pass the Turing test. You need an NPC that serves its purpose in the game—to be a believable quest-giver, a challenging enemy, or a funny sidekick.
Well-designed behavior trees, clever smoke-and-mirrors, and a bit of randomness will get you 90% of the way there for a fraction of the cost. Look at the companies I’ve invested in, like Scale AI or Hugging Face. They are focused on the practical application of AI, on solving real-world data and model problems, not on building a conscious robot. Your game doesn’t need a sentient shopkeeper. It needs a shopkeeper that sells you a health potion when you need one.
3. The Money is in “Boring” AI
This is the big one. Everyone is obsessed with the sexy side of AI: generative art, AI-composed music, dynamic storytelling. These are exciting frontiers, but they are not where you’ll find a reliable return on investment. The real money, the real efficiency gains, are in the “boring” applications of AI.
What do I mean by boring? I mean things like:
- Automated QA testing: An AI that can play your game 24/7, trying to break it in ways human testers would never think of. It can find bugs, performance issues, and exploits while your team sleeps.
- Procedural Content Generation (PCG): Not just for creating infinite random worlds, but for the small stuff. Using AI to generate variations of rocks, trees, and buildings. Automating the placement of foliage. Creating endless loot variations with balanced stats.
- Smart Asset Management: An AI that can automatically tag your entire library of 3D models, textures, and sound files. Imagine typing “rusty metal barrel, dented, with a lid” and instantly getting every matching asset in your project. That’s a real time-saver.
These things don’t make for a flashy headline. But they will save you thousands of hours of manual labor. That’s what we did at RemoteTeam. We didn’t build a robot CEO. We built tools that automated payroll and compliance. It was a real problem, and solving it created real value. That’s the lesson. Find the boring, repetitive, and expensive parts of game development and aim your AI efforts there first.
4. Generative AI is a Tool, Not a Creator
I love Midjourney. I’ve lost hours playing with it. The potential for AI-generated art, music, and animation is incredible. But right now, it’s a tool for inspiration, not a replacement for artists. It’s a co-pilot, a brainstorming partner.
Using generative AI effectively is a skill in itself. It’s about prompt engineering, about knowing how to guide the model to get what you want, and then taking that output and refining it. The best concept artists I know are using AI to generate a dozen ideas in ten minutes. Then they pick the best one and spend the next ten hours painting over it, fixing the weird six-fingered hands, and adding the human touch that makes it art.
If you just take the raw output from a generator and stick it in your game, it will feel soulless. It will have that generic, uncanny AI sheen to it. The same goes for AI music and AI animation. Use these tools to break your creative blocks. Use them to generate textures and placeholder assets. But don’t fire your art team. The soul of your game comes from your human creators, and no algorithm can replicate that. Not yet, anyway.
5. Data is Your Most Valuable Asset, Not Your Algorithm
Everyone is obsessed with having a proprietary algorithm. But the truth is, most of the cutting-edge models are becoming open source. The real competitive advantage isn’t your code; it’s your data.
A unique, high-quality, well-labeled dataset is worth its weight in gold. If you want to build an AI that generates unique monster sound effects, you need a massive library of monster roars, hisses, and growls, all meticulously labeled. That’s your moat. Someone else can copy the algorithm, but they can’t get your data.
Start thinking like a data company. Every time a player plays your game, they are generating data. How do they move? What do they buy? Where do they get stuck? This data can be used to train an AI to balance your game’s difficulty, personalize the experience, or even design better levels in the future. Stop throwing that data away. Collect it, clean it, and guard it. It’s the fuel for any real AI you’ll ever build.
6. The User Experience is Still King
You can have the most advanced AI system in the world, but if it makes the game less fun to play, it’s a failure. I’ve seen games with “intelligent” enemies that were so good at predicting the player’s moves that they were impossible to beat. It was a technical marvel, but it was a miserable experience.
AI should always serve the player experience, not the other way around. Sometimes, that means making your AI dumber. An enemy that occasionally makes a mistake is more fun to fight than a perfect aimbot. A procedural level generator that creates a few memorable, handcrafted-feeling moments is better than one that creates an infinite but bland landscape.
Before you implement any AI feature, ask yourself: “Does this make the game more fun?” If the answer is no, then it doesn’t matter how clever the tech is. Don’t get so lost in the technical weeds that you forget you’re supposed to be making a game.
7. Small, Specialized Models Beat Giant, General Ones
The industry trend is towards bigger and bigger AI models, trained on the entire internet. These models are powerful, but they are also slow, expensive, and unwieldy. For game development, a better approach is often to use a collection of small, specialized models.
Instead of one giant “NPC brain” model, you could have:
- A small model that’s an expert at navigating your game’s terrain.
- Another model that’s trained to generate witty one-liners in your game’s specific tone.
- A third model that’s an expert at choosing the right weapon for a given combat situation.
These smaller models are faster, cheaper to run, and easier to control. You can fine-tune them on a specific dataset to get exactly the behavior you want. It’s a microservices approach to AI. It’s less glamorous than saying you have a single, god-like AI running your game, but it’s a hell of a lot more practical.
8. Your Biggest Gains Will Come From AI in Your Tools, Not Your Game
This is the lesson that ties it all together. For the next few years, the most significant impact of AI in game development won’t be seen by the player. It will be felt by the developers.
The revolution is in the tools. It’s AI-powered code completion that writes boilerplate for you. It’s AI-assisted level design that suggests layouts and populates them with assets. It’s AI-driven project management that can predict bottlenecks in your production schedule.
By embedding AI into your development pipeline, you free up your human team to focus on what they do best: being creative. You automate the grunt work so they can spend more time on the art, the story, and the gameplay. This is how you build a better game, faster and cheaper. It’s how I built my businesses—by focusing on tools that make people more efficient.
The Real Future
So, what’s the takeaway? Forget the hype. Forget the dream of sentient AI. Focus on the practical. Focus on solving real problems for your players and your developers. Use AI as a tool to enhance human creativity, not replace it. The studios that understand this are the ones that will still be around in ten years. The rest will be a footnote in the history of a hype bubble that was bound to pop. Now go build something real.
Frequently Asked Questions
Can I implement all of these at once?
I'd strongly recommend against it. Pick the 2-3 items that resonate most with your current situation and focus there. Trying to do everything simultaneously is a recipe for doing nothing well.
Are these recommendations still relevant in 2026?
Absolutely. While specific tools and tactics change, the underlying principles remain consistent. I update my thinking regularly based on what I'm seeing in the market and across my portfolio companies.
How were these items selected?
Each item on this list comes from direct experience, either from building my own companies or from patterns I've observed across the 200+ startups I've invested in. I prioritize practical, actionable items over theoretical concepts.
How do I know which items apply to my situation?
Start by honestly assessing where your biggest bottleneck is right now. The items that address that specific constraint will give you the highest return on your time and energy.