--- The Blunt Reality of Prompt-to-Earn: Great Hype, Immature Tech 🛠️ I recently dove into the world of Prompt-to-Earn (P2E) development during the Covalent Speedrun, attracted by the promise of generating functional dApps in mere seconds using AI. My experience, after attempting several projects and reviewing the ecosystem, leads to a blunt conclusion: Prompt-to-Earn is too raw. While the concept promises to democratize development, the current reality is that AI project generation is woefully ill-equipped for anything serious. It's a fantastic starting line, but it's miles away from the finish line for production-grade applications. --- The AI's Critical Failure Point: Complex Logic and Security The primary weakness of the current P2E model is the logic chasm. The moment a project requires anything beyond a simple, one-step function or a basic user interface, the generated code is guaranteed to fail. What the AI excels at are applications with minimal, non-stateful logic: Simple Frontend Games: The ecosystem is dominated by classics like Tic-Tac-Toe, Snake, Coin Toss, and basic Pinball. These projects rely on straightforward, isolated game loops or minimal data interaction. Basic Utilities: Simple calculators, timers, or aesthetic tools like the Graphic Generator and Time Zone Converter are achievable. Minimalist Dashboards: Generating a basic UI that pulls and displays data (like the X Analytics or DeFi Pool Dashboard) is possible, but any complex filtering or interaction logic quickly breaks the build. The Code Quality Problem During development, the AI-produced code consistently exhibited numerous errors and security weaknesses. This forced a deep dive into manual debugging and code review. This completely defeats the "seconds-to-dApp" promise, as creating a truly functional and secure application requires the exact expertise the P2E model is supposed to eliminate. You cannot create serious, secure, or reliable smart contracts without going deep into researching Solidity, JSX, and best practices. The AI is an incomplete tool that trades speed for stability and security. --- Data Speaks: The 26 Survivors Out of all the deployed applications, only a small fraction were actually working, and their nature clearly illustrates the AI's current limitations. The functional projects confirm that the AI is best used for a proof-of-concept shell, not a final product. CategoryWorking App ExamplesUnderlying RequirementSimple GamesCOVALENT SNAKE, Tic-Tac-Toe, NEON PLINKO, Minimal ChessStateless, simple loops, minimal external data.Basic UtilitiesSPOOKY TYPING TEST, Time Zone Converter, Graphic GeneratorSimple utility functions, no blockchain interaction needed.Web3 DashboardsX Analytics, Layer 2 Speed Analyzer, Blockchain Speed MonitorPrimarily basic data retrieval and display (read functions).Niche/AdvancedBase Token Launcher, Predict MarketThese likely work only if their logic is extremely simple or relies on basic template code. --- Conclusion: An Accelerator, Not a Replacement Prompt-to-Earn, in its current iteration, sucks as a replacement for skilled development. It cannot handle the complexity of modern decentralized applications (dApps) like complex AMM logic, secure financial protocols, or robust, state-heavy games. However, it is a phenomenal tool for rapid prototyping. If you need a boilerplate React component, a simple web game to build upon, or a basic display for data, P2E can save you time. But for any serious builder looking to deploy funds or manage user state, the generated output must be treated as un-audited, vulnerable, draft code that requires a substantial amount of manual work from an expert. The current P2E model is an accelerator for simple templates, but not a solution for sophisticated development. The promise of generating complex, secure dApps in seconds remains firmly in the realm of hype.