From Atari to Go to StarCraft, games have driven some of the biggest breakthroughs in AI. Now, we’re partnering with game developers to prototype new gameplay experiences that push the frontiers of both gaming and AI. Since DeepMind’s foundation in 2010, the constrained yet rich worlds of games have played a critical role in understanding intelligence. They have driven some of our biggest AI breakthroughs, from mastering Atari to helping solve protein structure prediction - and they are still at the heart of what we do.
Gaming is in GDM’s DNA. Demis Hassabis, one of Google DeepMind's founders, is himself a former game developer, as are many of us in the GDM team. Together, we have decades of hands-on experience in game development and a deep respect for the craft of making games.
We’ve always been clear that doing AI research with games requires deep partnership with game developers - like our major new research partnership with Fenris Creations and the EVE Universe that we unveiled earlier this year, and the work we’ve done together with acclaimed studios like Hello Games, Coffee Stain Studios, Foulball Hangover and others.
Games as the engine of AI research #
Our journey began when a small team trained a deep neural network to play Atari 2600 games directly from raw pixels. The Deep Q-Network (DQN) learned to play 49 different games — from Pong to Breakout to Space Invaders — without any game-specific engineering. The 2015 Nature paper on DQN helped catalyze the modern era of deep reinforcement learning.
From there, we attempted to master more complex games, with each milestone producing more capable and general systems. [AlphaGo](https://deepmind.google/research/breakthroughs/alphago/) defeated world champion Go player Lee Sae Dol in 2016 — a feat many experts thought was still a decade away. [AlphaGo Zero](https://www.nature.com/articles/nature24270) surpassed every previous version by learning entirely from self-play, with no human data at all. [AlphaZero](https://www.science.org/doi/10.1126/science.aar6404) generalized this approach to master chess, shogi, and Go with one algorithm, while [MuZero](https://www.nature.com/articles/s41586-020-03051-4) learned to play without even knowing the rules. In 2019, [AlphaStar](https://www.nature.com/articles/s41586-019-1724-z) reached Grandmaster level in *StarCraft II*, navigating real-time complexity and imperfect information.
For each game, AI enriched the playing experience. AlphaGo's famous [Move 37](https://deepmind.google/research/breakthroughs/alphago/) was a play so unexpected that professional commentators initially thought it was a mistake, overturning centuries of received wisdom in Go and inspiring experts to explore new strategies. AlphaZero similarly inspired entirely new lines of play in chess. Crucially, the spirit of exploration that succeeded in games had profound impacts for other AI systems: [AlphaFold](https://deepmind.google/science/alphafold/) applied these foundations to help solve the 50-year grand challenge of protein structure prediction, a breakthrough which was recognized with the 2024 Nobel Prize in Chemistry.
From mastering games to understanding them #
Our earlier work demonstrated that AI could master any game given a clear objective and enough training. But the real world doesn't come with scores and rule books — which led us to ask a fundamentally different question: can AI understand and interact with any game world the way a person would?
This is the challenge behind SIMA, our Scalable Instructable Multiworld Agent. Rather than optimizing for a high score, SIMA is a generalist agent that ‘sees’ what a player would see on screen, understands natural language instructions, and acts through ordinary keyboard and mouse controls — requiring no APIs or source code access.
Powered by Gemini, our frontier AI models, SIMA 2 acts as an interactive companion capable of real-time reasoning and conversation. It achieves human-like play across complex 3D research environments and video games including No Man's Sky, Valheim, Hydroneer, and more.
For game developers, a truly general gaming agent would unlock AI capabilities that work with existing games — no modifications to the game code required. This could power entirely new gameplay, from AI companions that genuinely understand the game world to Non-Player Characters (NPCs) that adapt and respond in ways that scripted systems never could. A general gaming agent could also transform how games are made. During development, when the game changes with every commit, such agents could enable truly robust QA testing. Post-launch, when new content is introduced or players behave unpredictably, they could adapt in real time — generalising to new situations without needing to be re-scripted.
To develop SIMA agents safely and responsibly, we've partnered with acclaimed game studios and we are building a growing portfolio of games for AI research. This allows us to challenge our agents with ever more complex tasks that may one day transfer to solving problems in the real world.
Exploring new frontiers of AI and games research, in partnership with game developers #
Game studios bring expert craft, extraordinary game worlds, and deep knowledge of their players. We bring frontier AI — from Gemini to research in generative interactive environments and embodied agents — research expertise, and our team’s unique game development background and years of experience building AI for interactive environments. Together, we focus on discovering breakthrough experiences — never-before-seen gameplay that wouldn't be possible without AI.
It's not about the tech, it's about fun experiences. So we take a ‘show, don't tell’ approach with our partners. Our team works hand in hand with game developers, exploring new ideas and building playable prototypes to find the fun.
Our latest research partnership with Fenris Creations, the independent studio behind the EVE Universe, represents a new chapter in our history of AI research in games.
Fenris Creations has spent more than two decades building one of the most extraordinary persistent worlds in gaming. EVE Online, launched in 2003, is a massively multiplayer space simulation where thousands of players share a single universe that has evolved continuously for more than 20 years. Its player-driven economy features real supply-and-demand dynamics and trade networks spanning thousands of star systems. Its landscape — shaped by alliances, conflicts and diplomacy — is driven by human interaction.
For AI research, this is a golden opportunity. It's a living, evolving world that demands precisely the capabilities we believe are essential for frontier AI: Continual learning: Acquiring new skills without forgetting what came before, in a constantly changing world.** Memory:Accumulating and retrieving knowledge across timescales that extend far beyond today's model context windows. Long-horizon planning**: Reasoning over weeks, months, or even years.** Complex multi-agent dynamics:**Navigating cooperation, competition, negotiation, economics, and emergent social behavior at scale.
These challenges sit at the core of our broader research initiative to create systems that learn continuously from experience, with the rate of learning accelerating over time. We believe these frontier capabilities could unlock new gameplay experiences in the future.
Our research partnership extends across Fenris Creations' expanding universe, offering distinct environments for AI development. While EVE Online offers a large-scale single-shard persistent universe, EVE Vanguard is played from a first-person perspective, bringing ground-level, fast-paced tactical decision-making to the broader persistent world. This creates opportunities to study agents operating across multiple levels of abstraction, from twitch-level tactics to galaxy-spanning strategy.
Furthermore, EVE Frontier — with its programmable "Smart Assemblies” and its open, extensible architecture — offers an open-ended environment where the very rules of the world can change, demanding agents that adapt to entirely new game mechanics.
Our collaboration has already delivered real player value: the Aura Guidance system uses Gemini to deliver player-generated knowledge, based on real Rookie Help questions and answers, to help new pilots.
Our longer-term research program begins with an offline instance of EVE Online, which is a safe sandbox, separate from live players. It then progresses through EVE Frontier as a space to study how humans and agents can coexist in a persistent and open-ended world. Only when capabilities are mature would we consider bringing them to EVE Online and EVE Vanguard with the aim of enriching human play.
The road ahead #
Games have always been a mirror for intelligence. As they become more complex, more persistent, and more open-ended, so do the AI systems we build to navigate them.
We are aiming for the same ultimate goal we always have: AI as a catalyst, not a replacement. Our long-term ambition is to unlock breakthrough gameplay experiences, make games more accessible and more personalized, and — as we've seen from AlphaGo to AlphaFold — apply what we learn in games to problems in the real-world and to advance scientific discovery.
We're grateful to all of our game partners on this journey, and to Fenris Creations for joining us on this next chapter. We can't wait to share what we discover.
Acknowledgements #
We’d like to recognize the many teams across Google DeepMind for their contributions over the years to advancing AI research safely and responsibly in games.
Special thanks to all of the game developers who partnered with us: Coffee Stain (Valheim, Satisfactory, Goat Simulator 3), Fenris Creations (EVE Online, EVE Vanguard, EVE Frontier), Foulball Hangover (Hydroneer), Hello Games (No Man's Sky), Keen Software House (Space Engineers), RubberbandGames (Wobbly Life), Strange Loop Games (Eco), Thunderful Games (ASKA, The Gunk, Steamworld Build), Digixart (Road 96), and Tuxedo Labs & Saber Interactive (Teardown).