DigiTalk Podcast EP18 Recap : What Stays, What Fades in GameFi

The recent DigiTalk Episode 18 podcast assembled a forward-thinking panel of GameFi innovators including leaders from TFARM, Miomi Game, and Hive for an in-depth exploration of “What Stays, What Fades in GameFi.” The conversation explored how AI is optimizing GameFi experiences and earning mechanisms during a challenging market phase—driving sustainable innovation, enhancing player retention, and enabling smarter token economies.

Q1: Can you start by giving an overview of your project?

TFARM
TFARM is a blockchain farming game built directly on the Telegram Web App. Players are assigned a virtual plot of land where they can grow crops, raise animals, and race horses. These activities allow them to earn TFARM tokens, as well as XRC and TON tokens, blending farming simulation with real on-chain rewards.

The project is designed for mainstream accessibility. By embedding into Telegram, it removes the need for external wallets or apps, allowing users to start playing quickly. The onboarding experience is simple, and players can get into the game within minutes without being crypto-native.

Miomi Game
miomi is a competitive gaming platform that supports both Web2 and Web3 games. Users can challenge each other in traditional games like FIFA and Dota2 or participate in our Web3 mini-games, such as "Ladinho," a PvP penalty shootout game. Our model centers around user-created prize pools—what we call "play-to-win."

Our games are tested directly with real users before launch to ensure they’re fun and engaging. For example, our penalty game reached over 1 million matches within two months—more than double the number of matches played in Counter-Strike over two years on the same platform. We build for real gamers, not speculators.

Hive
Hive is a decentralized, gasless blockchain ecosystem that powers socialFi, GameFi, and DeFi use cases. It supports some of the most established Web3 games, including Splinterlands, which has hosted over 4 billion on-chain matches with zero transaction fees for players.

The chain is built for scalability and ownership. Hive’s resource credit model allows developers to sponsor user activity invisibly, making the experience seamless. Its open social graph and infrastructure support persistent identities and real-time interaction across games and communities.

Q2: To open the discussion, how do you view the role of AI in redefining the GameFi experience beyond the traditional play-to-earn model?

TFARM
AI helps us move beyond the unsustainable play-to-earn format by recognizing behavior patterns tied to genuine player engagement. We can identify when users are consistently active and reward them accordingly, rather than basing rewards solely on capital investment.

Through AI, we can design smarter game loops that adjust to how people actually play. This keeps the experience fresh and responsive, making players feel like their time is valued without needing to rely entirely on token payouts.

Miomi Game
We use AI to monitor real-time player performance and create balanced matchups. This allows us to shift away from generic reward systems and move toward skill-based gameplay, where users are incentivized to improve and compete rather than grind.

The competitive dynamics introduced by AI-driven matchmaking make the experience more fun and sustainable. Players stay because they enjoy the game, not because they're chasing short-term token rewards.

Hive
AI enables personalization by analyzing public on-chain data, which Hive stores through its open social graph. This allows games to tailor challenges, social suggestions, or experiences based on a player’s activity history and preferences.

Instead of applying a one-size-fits-all model, developers can create context-aware gameplay that evolves with the user. This human-centered approach helps Web3 games break away from transactional models like play-to-earn.

Q3: As market dynamics shift, how has your project adapted its earning model?

TFARM
We restructured our earning system to reward consistent gameplay and meaningful engagement rather than speculative investment. AI helps us identify valuable user behavior, which makes reward distribution more targeted and efficient.

At the same time, AI helps us detect bots or manipulative patterns. This ensures that the reward economy is not just more sustainable, but also more secure against abuse.

Miomi Game
We moved from play-to-earn to a “play-to-win” model. Players contribute to their own prize pools and compete based on skill. This approach is inherently more sustainable because it doesn’t rely on constant token emissions.

AI supports this by tracking gameplay data to ensure fair competition. It allows us to adjust matchmaking and tournament rules in real-time to reflect player behavior and maintain a healthy competitive balance.

Hive
We've learned from previous market cycles that inflated rewards don’t build long-term ecosystems. Instead, Hive encourages alternative value systems—like rewarding players for governance participation or social contribution.

AI helps us pinpoint which user actions add long-term value. This allows builders to move away from simple token distributions and create more nuanced, behavior-based reward systems.

Q4: How are you leveraging AI to tailor gameplay, quests, or earning paths based on player behavior or preferences?

TFARM
AI tracks player preferences and suggests relevant in-game activities based on what they enjoy. For instance, users who focus on crop farming might get nudged toward related seasonal quests, keeping their experience aligned with their playstyle.

This personalization helps reduce churn. Players are more likely to stay when the game feels like it’s adapting to them rather than pushing everyone through the same repetitive path.

Miomi Game
We use AI to implement adaptive matchmaking based on skill levels. Beginners aren’t overwhelmed by pro players, and experts find worthy opponents. This keeps both groups engaged.

We’ve seen higher retention rates in games with this AI-based adaptive difficulty model compared to games with fixed setups. It creates a smoother onboarding curve and encourages progression.

Hive
Hive allows games to use social signals and on-chain history to personalize the experience. AI analyzes user interactions across multiple apps or games and recommends content, quests, or groups that match their interests.

This builds a sense of connection and belonging. Users feel seen—and they’re more likely to engage when the ecosystem understands and responds to their preferences.

Q5: How is your team applying AI to streamline operations or balance in-game economies?

TFARM
AI helps monitor our in-game economy in real-time. We can dynamically adjust token emissions or item availability based on market conditions, preventing inflation and keeping resource distribution healthy.

It’s also critical for fraud detection. AI flags suspicious activities like multi-account farming or bot usage, which helps us act quickly and protect the fairness of the ecosystem.

Miomi Game

Our platform uses AI to manage backend systems like server load and cheat detection. This reduces the need for manual oversight and allows us to scale faster and more efficiently.

AI also analyzes spending behavior and in-game asset flow. When something becomes over- or under-used, we can quickly make adjustments, which is especially helpful during market downturns.

Hive
AI supports the entire lifecycle of game operations on Hive by processing large volumes of micro-transactions without cost. This helps developers identify inefficiencies or gameplay bottlenecks early.

It also helps with in-game economy balancing by monitoring token use, quest participation, and user growth metrics. These insights are fed back to developers who can make rapid improvements without disrupting user experience.

Q6:What lessons have you drawn from previous market cycles, and how have they influenced your current approach to AI and GameFi design?

Miomi Game
We learned that attracting users through token incentives doesn’t guarantee loyalty. Many of those users leave once rewards stop. That’s why we focus on competitive, skill-based mechanics now—and use AI to monitor who’s really playing versus who’s farming.

This has helped us retain high-value users and shape our game economy around activity that brings long-term health, not short-term growth.

Hive
One major lesson is that value lies in relationships, not just tokens. Hive’s open data allows us to see which users are building, socializing, and contributing meaningfully. AI helps surface these patterns across games.

As a result, our ecosystem design now rewards reputation, creativity, and collaboration. With AI, we can support these long-term contributors more effectively and encourage healthy behaviors across the platform.