Taking prediction markets to the extreme, is it a press conference? In the just-concluded US election, Polymarket, with its market-driven data, successfully predicted Trump's winning rate to be higher than traditional polls, quickly attracting the attention of the public and the media. People are gradually realizing that Polymarket is no longer just a financial tool, but a "balancer" in the information field, using market wisdom to verify the authenticity of sensational news.
When Polymarket became a hot topic, Vitalik proposed a brand-new concept - Info Finance. This tool that combines financial incentives and information can disrupt social media, scientific research and governance models, opening up a new direction for improving decision-making efficiency. With the advancement of AI and blockchain, Info Finance is also moving towards a new turning point.
Facing this ambitious new field of Info Finance, is Web3's technology and philosophy ready to embrace it? This article will take prediction markets as the entry point to explore the core ideas, technical support and future possibilities of Info Finance.
Info Finance: Using Financial Tools to Acquire and Utilize Information
The core of Info Finance is to use financial tools to acquire and utilize information in order to improve decision-making efficiency and accuracy. Prediction markets are a typical example, where by linking the problem with financial incentives, these markets motivate participants' accuracy and responsibility, providing clear forecasts for users seeking the truth.
As a sophisticated market design, Info Finance can guide participants to respond to specific facts or judgments, with application scenarios covering decentralized governance, scientific review and other fields. At the same time, the emergence of AI will further lower the threshold, allowing micro-decisions to also operate effectively in the market, promoting the popularization of Info Finance.
Vitalik specifically mentioned that the current decade is the best time to expand Info Finance. Scalable blockchains provide a secure, transparent and trustworthy platform support for Info Finance, while the introduction of AI has improved the efficiency of information acquisition, enabling Info Finance to handle more refined issues. Info Finance not only breaks through the limitations of traditional prediction markets, but also demonstrates the ability to tap into the potential of multiple fields.
However, as Info Finance expands, its complexity and scale are rapidly increasing. The market needs to handle massive data and make real-time decisions and transactions, posing a severe challenge to efficient and secure computing capabilities. At the same time, the rapid development of AI technology has spawned more innovative models, exacerbating computing demands. In this context, a secure and viable post-scarcity computing system has become an indispensable foundation for the continued development of Info Finance.
The Current Landscape, Who Will Build the Post-Scarcity Computing System
The "post-scarcity computing system" currently lacks a unified definition, but its core goal is to break through the limitations of traditional computing resources and achieve low-cost, widely available computing power. Through decentralization, resource enrichment and efficient collaboration, these systems support large-scale, flexible task execution, making computing resources tend towards "non-scarcity". In this architecture, computing power is liberated from single-point dependence, and users can freely access and share resources at low cost, promoting the popularization and sustainable development of inclusive computing.
In the context of blockchain, the key features of the post-scarcity computing system include decentralization, abundant resources, low cost, and high scalability.
The High-Performance Race of Public Chains
Currently, major public chains are fiercely competing in performance to meet the increasingly complex application demands.
Traditional High-Performance Public Chains:
- Solana: From the very beginning of its design, Solana has adopted a parallel computing architecture, achieving high throughput and low latency. Its unique Proof of History (PoH) consensus mechanism allows it to process thousands of transactions per second.
- Polygon and BSC: These two are actively developing parallel EVM solutions to enhance transaction processing capabilities. For example, Polygon has introduced zkEVM to achieve more efficient transaction verification.
Emerging Parallel Public Chains:
- Aptos, Sui, Sei and Monad: These new public chains are designed for high performance by optimizing data storage efficiency or improving consensus algorithms. For example, Aptos uses Block-STM technology to achieve parallel transaction processing.
- Artela: Artela proposes the EVM++ concept, using native extensions (Aspect) in the WebAssembly runtime to achieve high-performance customized applications. With parallel execution and elastic block space design, Artela effectively solves the performance bottleneck of EVM, significantly improving throughput and scalability.
The performance race is in full swing, and it is difficult to determine the winner. However, in this fierce competition, there is also AO, a solution that takes a different path. AO is not an independent public chain, but a computing layer based on Arweave, achieving parallel processing capability and scalability through a unique technical architecture. AO is also a strong contender towards the post-scarcity computing system, and is expected to help facilitate the large-scale deployment of Info Finance.
Serving Info Finance, the Construction Blueprint of AO
AO is a Actor Oriented (role-based) computer running on the Arweave network, providing a unified computing environment and an open messaging layer. Through its distributed and modular technical architecture, it enables the large-scale application of Info Finance and the integration with traditional computing environments.
The architecture of AO is simple and efficient, with core components including:
- Processes are the basic computing units in the AO network, interacting through Messages;
- Scheduling Units (SUs) are responsible for message sorting and storage;
- Computation Units (CUs) undertake state calculation tasks;
- Messenger Units (MUs) are responsible for message delivery and broadcasting.
The decoupled design of the modules endows the AO system with excellent scalability and flexibility, allowing it to adapt to application scenarios of different scales and complexities. Therefore, the AO system has the following core advantages:
- High throughput and low latency computing capabilities: The parallel process design and efficient message delivery mechanism of the AO platform enable it to support millions of transactions per second. This high throughput capability is crucial for supporting a global-scale Info Finance network. At the same time, AO's low-latency communication characteristics can ensure the immediacy of transactions and data updates, providing users with a smooth operating experience.
- Unlimited scalability and modular design: The AO platform adopts a modular architecture, achieving extremely high scalability by decoupling the virtual machine, scheduler, message delivery, and computation units. Whether it is the growth of data throughput or the access of new application scenarios, AO can adapt quickly. This scalability not only breaks through the performance bottleneck of traditional blockchains, but also provides developers with a flexible environment for building complex Info Finance applications.
- Support for large-scale computing and AI integration: The AO platform already supports the 64-bit WebAssembly architecture, capable of running most full-fledged large language models (LLMs) such as Meta's Llama 3, providing a technical foundation for the deep integration of AI and Web3. AI will become an important driving force for Info Finance, involving applications such as smart contract optimization, market analysis, and risk prediction, and AO platform's large-scale computing capabilities enable it to efficiently support these demands. At the same time, through the WeaveDrive technology to access the unlimited storage of Arweave, the AO platform provides unique advantages for training and deploying complex machine learning models.
With its high throughput, low latency, unlimited scalability, and AI integration capabilities, AO has become the ideal hosting platform for Info Finance. From real-time transactions to dynamic analysis, AO provides excellent support for large-scale computing and complex financial models, paving the way for promoting the popularization and innovation of Info Finance.
The Future of Information Finance: AI-Driven Prediction Markets
What should the next generation of prediction markets in information finance look like? Looking back and looking forward, traditional prediction markets have long faced three major pain points: lack of market integrity, high thresholds, and limited adoption. Even a Web3 star project like PolyMarket has not been able to completely avoid these challenges. For example, it has been questioned for potential manipulation risks due to the short challenge period for Ethereum ETF prediction events or the overly concentrated UMA voting rights. In addition, its liquidity is concentrated in popular areas, with low participation in long-tail markets. Furthermore, the adoption of prediction markets is further hindered by regulatory restrictions on users in some countries (UK, US).
The future development of information finance requires the leadership of new-generation applications. The excellent performance of AO provides fertile ground for such innovations, with Outcome-represented prediction market platforms becoming a new focus of information finance experiments.
Outcome has already taken shape as a product, supporting basic voting and social functions. Its true potential lies in the future deep integration with AI, using AI agents to establish a trustless market settlement mechanism, and allowing users to autonomously create and use prediction agents. By providing the public with a transparent, efficient, and low-threshold prediction tool, it is possible to further drive the large-scale adoption of prediction markets.
Taking Outcome as an example, prediction markets built on AO can have the following core features:
- Trustless Market Resolution: The core of Outcome lies in Autonomous Agents. These AI-driven agents operate independently based on pre-set rules and algorithms, ensuring the transparency and fairness of the market resolution process. Due to the absence of human intervention, this mechanism significantly reduces the risk of manipulation, providing users with reliable prediction results.
- AI-Based Prediction Agents: The Outcome platform allows users to create and use AI-driven prediction agents. These agents can integrate various AI models and rich data sources to perform accurate analysis and forecasting. Users can customize personalized prediction agents based on their own needs and strategies, and participate in prediction activities in various market topics. This flexibility significantly improves the efficiency and applicability of predictions.
- Token-Based Incentive Mechanism: Outcome introduces an innovative economic model, where users receive token rewards for participating in market predictions, subscribing to agent services, and trading data sources. This mechanism not only enhances user engagement, but also provides support for the healthy development of the platform ecosystem.
AI-Driven Prediction Market Workflow
Outcome's introduction of AI models to achieve semi-automated or fully automated agent modes provides an innovative approach for a wide range of information finance applications built on Arweave and AO. The workflow generally follows the architecture below:
1. Data Storage
- Real-time Event Data: The platform collects and stores event-related information from real-time data sources (such as news, social media, oracles, etc.) on Arweave, ensuring the transparency and immutability of the data.
- Historical Event Data: Preserving past event data and market behavior records, providing data support for modeling, verification, and analysis, forming a sustainable optimization loop.
2. Data Processing and Analysis
- LLM (Large Language Model): The LLM is the core module for data processing and intelligent analysis (an AO process), responsible for deeply processing the real-time event data and historical data stored in Arweave, extracting key information related to events, and providing high-quality inputs for subsequent modules (such as sentiment analysis, probability calculation).
- Event Sentiment Analysis: Analyzing user and market attitudes towards events (positive/neutral/negative), providing a reference for probability calculation and risk management.
- Event Probability Calculation: Based on the sentiment analysis results and historical data, dynamically calculating the probability of event occurrence to help market participants make decisions.
- Risk Management: Identifying and controlling potential risks in the market, such as preventing market manipulation and abnormal betting behavior, to ensure the healthy operation of the market.
3. Prediction Execution and Verification
- Trading Agent: The AI-driven trading agent is responsible for automatically executing predictions and bets based on the analysis results, without the need for manual user intervention.
- Outcome Verification: The system verifies the actual results of events through oracles and stores the verification data in the Historical Event Data module, ensuring the transparency and credibility of the results. Furthermore, the historical data can also provide a reference for subsequent predictions, forming a continuously optimized closed-loop system.
This workflow, through AI-driven smart prediction and decentralized verification mechanisms, achieves efficient, transparent, and trustless prediction agent applications, reducing user participation thresholds and optimizing market operations. Relying on the technical architecture of AO, this model may lead information finance towards intelligence and widespread adoption, becoming a core prototype for the next generation of economic innovation.
Conclusion
The future belongs to those who are good at extracting the truth from the myriad of information. Information finance is redefining the value and use of data with the wisdom of AI and the trust of blockchain. From the post-scarcity architecture of AO to the smart agents of Outcome, this combination makes prediction markets not just about calculating probabilities, but a re-exploration of decision science. AI can not only lower the participation threshold, but also make the processing of massive data and dynamic analysis possible, opening up a new path for information finance.
As Alan Turing said, computation brings efficiency, and wisdom inspires possibilities. Dancing with AI, information finance may be able to make the complex world clearer and drive society to find a new balance between efficiency and trust.
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