Game Design Mechanics

Agentic AI is 𝗻𝗼𝘁 about wrapping prompts around a large language model. It’s about designing systems that can: → 𝗣𝗲𝗿𝗰𝗲𝗶𝘃𝗲 their environment → 𝗣𝗹𝗮𝗻 actionable steps → 𝗔𝗰𝘁 on those plans → 𝗟𝗲𝗮𝗿𝗻 and improve over time And yet, many teams hit a wall—not because the models fail, but because the 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 behind them isn’t built for agent behavior. If you’re building agents, you need to think in 𝗳𝗼𝘂𝗿 𝗱𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻𝘀: 1. 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝘆 & 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 → Agents must decompose goals into steps and execute them independently. 2. 𝗠𝗲𝗺𝗼𝗿𝘆 & 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 → Without memory, agents forget past context. Vector DBs like FAISS, Redis, or pgvector aren’t optional—they’re foundational. 3. 𝗧𝗼𝗼𝗹 𝗨𝘀𝗮𝗴𝗲 & 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 → Agents must go beyond text generation—calling APIs, browsing, writing code, and executing it. 4. 𝗖𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗶𝗼𝗻 & 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻 → The future isn’t just one agent. It's many, working together—planner-executor setups, sub-agents, role-based dynamics.     Frameworks like 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵, 𝗔𝘂𝘁𝗼𝗚𝗲𝗻, 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻,𝗚𝗼𝗼𝗴𝗹𝗲'𝘀 𝗔𝗗𝗞, and 𝗖𝗿𝗲𝘄𝗔𝗜 make these architectures more accessible. But frameworks alone aren’t enough. If you’re not thinking about: • 𝗧𝗮𝘀𝗸 𝗱𝗲𝗰𝗼𝗺𝗽𝗼𝘀𝗶𝘁𝗶𝗼𝗻 • 𝗦𝘁𝗮𝘁𝗲𝗳𝘂𝗹𝗻𝗲𝘀𝘀 • 𝗥𝗲𝗳𝗹𝗲𝗰𝘁𝗶𝗼𝗻 • 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗹𝗼𝗼𝗽𝘀 …your agents will likely remain shallow, brittle, and fail to scale. The future of GenAI lies in 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝗶𝗻𝗴 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗯𝗲𝗵𝗮𝘃𝗶𝗼𝗿, not just fine-tuning prompts. 2025 is the year we go from 𝗽𝗿𝗼𝗺𝗽𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 to 𝗔𝗜 𝘀𝘆𝘀𝘁𝗲𝗺 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘀. Let’s build agents that don’t just respond—but 𝗿𝗲𝗮𝘀𝗼𝗻, 𝗮𝗱𝗮𝗽𝘁, 𝗮𝗻𝗱 𝗲𝘃𝗼𝗹𝘃𝗲.

Scientists just published something in Nature that will scare every marketer, leader, and anyone else who thinks they understand human choice. Researchers created an AI called "Centaur" that can predict human behavior across ANY psychological experiment with disturbing accuracy. Not just one narrow task. Any decision-making scenario you throw at it. Here's the deal: They trained this AI on 10 million human choices from 160 different psychology experiments. Then they tested it against the best psychological theories we have. The AI won. In 31 out of 32 tests. But here's the part that really got me... Centaur wasn't an algorithm built to study human behavior. It was a language model that learned to read us. The researchers fed it tons of behavioral data, and suddenly it could predict choices better than decades of psychological research. This means our decision patterns aren't as unique as we think. The AI found the rules governing choices we believe are spontaneous. Even more unsettling? When they tested it on brain imaging data, the AI's internal representations became more aligned with human neural activity after learning our behavioral patterns. It's not just predicting what you'll choose, it's learning to think more like you do. The researchers even demonstrated something called "scientific regret minimization"—using the AI to identify gaps in our understanding of human behavior, then developing better psychological models. Can a model based on Centaur be tuned for how customers behave? Companies will know your next purchasing decision before you make it. They'll design products you'll want, craft messages you'll respond to, and predict your reactions with amazing accuracy. Understanding human predictability is a competitive advantage today. Until now, that knowledge came from experts in behavioral science and consumer behavior. Now, there's Centaur. Here's my question: If AI can decode the patterns behind human choice with this level of accuracy, what does that mean for authentic decision-making in business? Will companies serve us better with perfectly tailored offerings, or with this level of understanding lead to dystopian manipulation? What's your take on predictable humans versus authentic choice? #AI #Psychology #BusinessStrategy #HumanBehavior

How to choose the right monetization model for your game? Consider both player and studio views. I’ve prepared 6 main types with: 🟢 The pros 🔴 The cons But first, consider these 4 factors: 1. Game Genre • Premium models fit narrative ones. • Free-to-play suits casual or MMO games. 2. Target Audience • Casual players prefer free models. • Hardcore players will pay for more. 3. Game Lifespan • Subscriptions fit long-term games. • Casual games may fit in-game payments. 4. Game Design & Value • Value should justify the purchase. • Don’t frustrate players with pay-to-win. It’s not just about revenue. It’s about what fits your game best. P.S. The Play-to-Earn model is highly speculative, but the success of ‘Off The Grid’ made me include it on the list.

In the clutter of D2C brands, customization can make you win. Last weekend, I was trying to buy a gift for my friend's anniversary, but every option felt generic. Basic. Non-memorable. Then, I found a leather wallet and cardholder set online where I could add their initials, choose the leather texture, and even include a hidden photo inside. Suddenly, it became a gift they’d remember. This experience made me realize that as the landscape matures, we’re moving from an era of 'product-market fit' to 'product-person fit.' Here’s why I think mass customization is becoming the new competitive advantage in retail: 1/ The New Consumer Psychology Five years ago, customization was a luxury add-on. Today, it's becoming the baseline expectation. When I asked my teenage nephew why he refused a popular sneaker brand, his answer was telling: "If I'm wearing the exact same thing as everyone else, what's the point?" The data confirms it: > 60% of Millennials and Gen Z prefer customized products. > More surprisingly, they’re 4x more likely to recommend brands that offer customization. 2/ The Business Transformation The most fascinating insight I’ve discovered as an investor: Customization is creating an entirely new business model. Take Traya – they analyze your background, health, diet, and lifestyle through a 30-question diagnostic, then create regimens with 4x higher efficacy. The result? ₹7Cr → ₹300Cr in 2.5 years. Or Bombay Shirt Company – by letting customers design everything from the collar to the thread, they’ve achieved what seemed impossible: mass-produced customization at scale. 3/ The Economic Advantage When we analyze the unit economics, customized products are creating an unfair advantage: > Customer acquisition costs drop by 35% (word of mouth increases). > Return rates fall by 55% (customers keep what they helped design). My favorite examples: > Perfora’s name engraving on toothbrushes. > Mokobara’s luggage monograms (they started it). > Lenskart.com’s custom-fit frames. Yes, it adds cost and effort. But it makes you stop while you’re scrolling. And it makes the customer feel like the ONLY customer. That’s everything today. 😉 Which customized product experience has impressed you the most? #ConsumerTrends #Customization #Retail #D2C

*What Brands Can Learn from Dharna Durga's Unique Brand Integrations* 🎯 If you've seen Dharna Durga's content, you know how effortlessly she blends brand collaborations into her storytelling. Here's why her approach works — and why brands should take notes: 1/ Fresh, Viral, and Value-Driven Content Dharna brings fresh perspectives that audiences love. Her content delivers value first, making it naturally shareable and enjoyable — the perfect formula for virality. 2/ Content Comes First, Brand Follows She integrates the brand into her content — not the other way around. This subtle but crucial difference makes her brand mentions feel organic, adding to the storyline instead of disrupting it. Audiences appreciate this authentic flow. 3/ Creators Know Their Audiences Best Great creators understand their followers — what they love, what they skip, and what truly resonates. Brands that trust creators with creative freedom gain better visibility, reach, and engagement. It's a win-win. 4/ Celebrity-Level Impact through Genuine Integrations On Dharna's branded reels, even celebrities have commented on how seamlessly the brands fit into her content. Some even say, "You should do more of these!" — proof that authentic, story-driven collabs enhance brand presence and attract high-level engagement and credibility. What This Means for Brands: Stop forcing salesy ads that audiences skip or scroll past. Audiences today are smart — they can spot inauthentic content a mile away. Instead, let creators do what they do best: build engaging, relatable stories that naturally integrate your brand into their narrative. Time to rethink your influencer marketing strategy?✌️

Imagine launching a game without a publisher's backing. That happened in 2012, leading to an industry-wide shift towards the live-service model. Today, free-to-play isn't just a novelty; it's the norm, driven by in-game purchases that keep studios thriving." In 2012, facing a do-or-die scenario without a publisher, a studio named Digital Extremes took a bold step by self-publishing Warframe using a live-service model. This approach, which has become the gaming industry's mainstay, involves offering free games and monetizing through in-game purchases. This model saved their studio and reshaped how games are developed, released, and sustained financially. Here’s how this model has evolved and why it’s celebrated and criticized within the gaming community. 🚀 Innovative Launch: Initially a survival tactic, the live-service model allowed Warframe to launch without upfront costs, relying instead on in-game transactions. 💡Warframe's commitment to player satisfaction is evident in its continuous updates. Thanks to the invaluable feedback loop with its players, the game stays fresh and engaging. This approach not only keeps the game relevant but also encourages players to continue investing in it. 🎨 Cosmetic Profits: Cosmetic items like scarves and helmets have become a significant revenue stream, proving that aesthetics can be as valuable as functionality. 🐳 Whale Economics: A small percentage of players, known as 'whales,' contribute the majority of revenue, sustaining extensive development teams. ⚖️ As the live-service model gains traction, it's more than just revenue growing. The model also attracts regulatory attention, with entities like the FTC scrutinizing its practices. This underscores the importance of ethical monetization, a key consideration in the industry's evolution. #LiveServiceGames #FreeToPlay #InGamePurchases #GamingIndustry #DigitalExtremes #Warframe #GameDevelopment #VideoGames #GamerEconomics #FTC #Monetization #GamingCommunity #PlayerFeedback #CosmeticItems #GameUpdates

Zomato faced a big problem: How can we turn app browsers into loyal customers? The goal was clear, improve the user experience with personalized restaurant suggestions. But there were a few challenges too: 🔴 Understanding user preferences from massive data. 🔴 Combining multiple data sources for meaningful insights. 🔴 Developing accurate recommendation algorithms. 🔴 Processing data in real time to keep users engaged. 🔴 Building trust in the recommendations to ensure they felt helpful, not intrusive. To tackle this, Zomato used a structured approach: 🟢 Data Collection and Cleaning - They collected user behavior data (searches, clicks, abandoned carts). - They analyzed restaurant details (cuisine types, delivery times, ratings). - Past orders were also analyzed for trends. 🟢 User Segmentation - Users were grouped based on age, location, past orders, and browsing habits. - This helped them identify patterns and preferences. 🟢 Developing the Recommendation System - Combined collaborative filtering (what others like you prefer) and content-based filtering (what matches your past orders). - Fine-tuned algorithms with ongoing testing for better accuracy. 🟢 Implementation and Testing - They rolled out the recommendations and tested them through A/B experiments. - Adjusted based on user feedback and data performance. 🟢 Continuous Improvement - Introduced feedback loops for real-time adjustments. - Regular updates ensured the system stayed relevant to evolving user needs. And, the impact was impressive: ⬆️ 35% more time spent on the app by users receiving personalized suggestions. ⬆️ 28% higher click-through rates, showing better engagement. ⬆️ 22% increase in orders per user per month due to tailored suggestions. ⬆️ 18% boost in retention rates, turning occasional users into loyal customers. ⬆️ 12% higher average order value, leading to revenue growth. ⬆️ 15% jump in monthly revenue, proving personalization works! I see this as the perfect example of using data to deepen customer relationships. It's not just about the tech—it’s about understanding people and making their experience smoother and more personal. 📊 Data is the secret to building trust and loyalty. What do you think? Can other industries learn from Zomato’s success? How can personalization improve your industry? #zomato #deepindergoyal

Balance Sheet Optimisation: A Prudent Approach to Sustainable Growth Banks operate in a highly regulated and competitive environment, where balance sheet optimisation is essential for long-term sustainability. Striking the right balance between liquidity, profitability, and risk requires a structured and strategic approach. Balance sheet optimisation involves managing assets, liabilities, and capital efficiently to enhance returns while maintaining regulatory compliance and financial stability. It requires an in-depth understanding of key metrics such as the Liquidity Coverage Ratio (LCR) and Net Stable Funding Ratio (NSFR) to ensure liquidity resilience, Risk-Weighted Assets (RWA) to manage capital efficiency, and Net Interest Margin (NIM) to maximise profitability. Effective duration and basis risk management also play a critical role in mitigating interest rate risk. A well-optimised balance sheet delivers benefits beyond regulatory compliance. It strengthens financial stability, enhances shareholder value, and enables institutions to navigate economic cycles with greater resilience. However, achieving this requires careful consideration of several key factors. Liquidity management remains a priority, as maintaining an adequate liquidity buffer is essential for financial resilience. Banks need to align funding sources with asset maturities, optimise their high-quality liquid asset (HQLA) portfolios, and conduct stress tests to assess potential liquidity risks. At the same time, holding excessive liquidity can reduce profitability, making it crucial to find an optimal balance. Capital efficiency is another important consideration. By effectively managing RWAs, banks can allocate capital to areas that generate the highest risk-adjusted returns. Strategies such as optimising credit exposures, diversifying assets, and implementing capital-light business models can enhance return on equity (ROE) without breaching regulatory constraints. Interest rate risk and market risk also require close attention. Effective asset-liability management (ALM) strategies help banks navigate interest rate volatility, ensuring that duration mismatches do not erode profitability. Hedging strategies, dynamic repricing approaches, and robust risk modelling contribute to stronger interest rate risk management. Diversification of funding sources is essential to reduce refinancing risk and enhance stability. Over-reliance on a single funding channel can expose banks to disruptions, while a well-diversified funding structure—including retail deposits, wholesale funding, and capital market instruments—improves resilience. Credit risk optimisation plays a crucial role in enhancing risk-adjusted returns. Banks that refine risk-based pricing, improve borrower selection, and implement effective portfolio diversification strategies can strengthen credit risk management while maintaining growth potential.

64% of gamers say ads ruin their gameplay. 46% admit they’ve actually made a purchase because of them. Paradox? Or maybe the future of monetization. 🎮 According to Bain & Company’s Gaming Report 2025: Breaking Boundaries to Win, the share of gamers irritated by ads in games rose by 5 p.p. year over year. At the same time, 6 p.p. more people declared that ads had prompted them to buy. This tension is one of the industry’s biggest challenges today: how to monetize without losing player attention. For years, the gaming business model rested on three pillars: – boxed sales at a fixed price (for two decades around $60–70), – add-ons and microtransactions, often causing frustration, – free-to-play, where only “whale” players drive revenue. But the reality has shifted. $70 for a new title today is worth less than a cartridge in the ’90s. AAA budgets reach hundreds of millions, while players expect more and more free content. The gap was inevitable. That’s why the market is testing new solutions: – subscriptions (Game Pass, PlayStation Plus), though it’s still unclear if big titles can sustain this model, – programmatic in-game ads (Roblox in 2024 launched self-serve video ads for 97M daily users), – rewarded ads (e.g. Google + Roblox: a 30-second video in exchange for an in-game reward), – branded experiences (7-Eleven and iHeartMedia building their own worlds in Roblox, where ads become play). The trend is clear: it’s not just exposure that matters, but context and integration. We see the same in streaming. Ads that appear naturally within the stream are received very differently than interruptions that block gameplay. The question is balance. On one hand, the industry needs revenue. On the other, every failed integration pushes players to the competition. The future belongs to those who design formats that respect time and immersion. Advertising in games won’t disappear. But to work, it must become part of the experience. 🔑 👉Will gamers accept ads if they get real value in return? #gaming #monetization #advertising #creatorseconomy #inStreamly

Memory & personalization might be the real moat for AI we’ve been looking for. But where that moat forms is still up for grabs: •App level •Model level •OS level •Enterprise level Each has very different dynamics. 🧵 ⸻ 1. App-level personalization Apps build their own memory & context for users. Examples: •Harvey remembering firm-specific legal knowledge for law firms •Abridge capturing patient conversations & generating notes for doctors •Perplexity building long-term search profiles for individual users ➡️ Most likely in vertical applications with focused use cases and domain-specific data. This is where Eniac Ventures is currently doing most of our investing ⸻ 2. Model-level personalization The model itself becomes personalized and portable across apps. Examples: •ChatGPT memory & custom instructions •Meta’s LLaMa fine-tuned on personal embeddings ➡️ Most likely in general-purpose assistants and broad horizontal use cases where user context needs to travel across apps. ⸻ 3. OS-level personalization Personalization happens at the OS level, shared across apps & devices. Examples: •Google Gemini native to Android •Apple (maybe) embedding Claude via Anthropic ➡️ Most likely in consumer devices and mobile ecosystems where platforms control distribution. ⸻ 4. Enterprise-level personalization Each enterprise owns and controls its own personalization layer for employees & customers. Examples: •Microsoft Copilot trained on company data •OSS models (LLaMa, Mistral) deployed on private infra with platforms like TrueFoundry •OpenAI GPTs fine-tuned & hosted in secure enterprise environments ➡️ Most likely in highly regulated industries (healthcare, financial services) where data privacy and compliance are critical. ⸻ Why it matters: Where memory & personalization “land” may define who captures AI value. Different layers may win in different sectors. Where AI memory lives may reshape who captures the next decade of value.