The Future of Information Finance: Dancing with AI in a Post-Scarcity System
The prediction market is surpassing traditional financial instruments, becoming a smart carrier for information verification, while Info Finance further redefines data value through financial incentives and technological innovation. AO's post-scarcity computing architecture and AI agents drive the intelligence and popularization of prediction markets, creating new paradigms for the future of the Info Finance field.
Is the prediction market taken to the extreme a press conference? In the recently concluded U.S. election, Polymarket successfully predicted Trump's winning probability to be higher than traditional polls, quickly attracting public and media attention with its market-driven data. 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, which combines financial incentives with information, can disrupt social media, scientific research, and governance models, opening up new directions for improving decision-making efficiency. With the advancement of AI and blockchain, Info Finance is also moving towards a new turning point.
Is the technology and philosophy of Web3 ready to embrace this ambitious new field of Info Finance? This article will use the prediction market as a starting point to explore the core concepts, 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 to improve decision-making efficiency and accuracy. The prediction market is a typical example, linking questions with financial incentives, which motivates participants' accuracy and accountability, providing clear predictions 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 various fields such as decentralized governance and scientific review. At the same time, the emergence of AI will further lower the threshold, allowing micro-decisions to 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 secure, transparent, and trustworthy platform support for Info Finance, while the introduction of AI enhances 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 increasing sharply. Markets need to process vast amounts of data and make real-time decisions and transactions, posing severe challenges to efficient and secure computing power. At the same time, the rapid development of AI technology has spawned more innovative models, intensifying computational demands. Against this backdrop, a secure and feasible post-scarcity computing system has become an indispensable foundation for the sustainable development of Info Finance.
Current Landscape: What Constitutes a 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, such systems support large-scale, flexible computing task execution, making computing resources approach "non-scarcity." In this architecture, computing power is freed from single-point dependence, allowing users to access and share resources freely and at low cost, promoting the popularization and sustainable development of inclusive computing.
In the context of blockchain, the key features of a post-scarcity computing system include decentralization, abundant resources, low cost, and high scalability.
The High-Performance Competition of Public Chains
Currently, major public chains are fiercely competing for performance to meet increasingly complex application demands. Looking at the current public chain ecosystem, the development trend is shifting from traditional single-threaded models to multi-threaded parallel computing models.
Traditional High-Performance Public Chains:
- Solana: Since its design inception, 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: Both are actively developing parallel EVM solutions to enhance transaction processing capabilities. For example, Polygon has introduced zkEVM for more efficient transaction verification.
Emerging Parallel Public Chains:
- Aptos, Sui, Sei, and Monad: These emerging public chains are designed for high performance by optimizing data storage efficiency or improving consensus algorithms. For instance, Aptos uses Block-STM technology to achieve parallel transaction processing.
- Artela: Artela proposes the EVM++ concept, achieving high-performance custom applications through native extensions (Aspect) in the WebAssembly runtime. With parallel execution and flexible block space design, Artela effectively addresses EVM performance bottlenecks, significantly enhancing throughput and scalability.
The performance competition is fierce, and it is still difficult to determine who is superior. However, in this intense competition, there are also alternative solutions represented by AO. AO is not an independent public chain but a computing layer based on Arweave, achieving parallel processing capabilities and scalability through a unique technical architecture. AO is undoubtedly a strong competitor in the move towards a post-scarcity computing system, promising to support the large-scale implementation of Info Finance.
Building the Blueprint for Info Finance with AO
AO is an Actor Oriented (role-based) computer running on the Arweave network, providing a unified computing environment and an open messaging layer. It offers the possibility of large-scale applications of Info Finance and the integration of traditional computing environments through a distributed, modular technical architecture.
The architecture of AO is simple yet efficient, with core components including:
- Processes are the basic computing units in the AO network, interacting through message passing;
- Scheduling Units (SUs) are responsible for message ordering and storage;
- Computing Units (CUs) undertake state computation tasks;
- Messenger Units (MUs) are responsible for message transmission and broadcasting.
The decoupled design between modules endows the AO system with excellent scalability and flexibility, enabling it to adapt to applications of varying 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 messaging mechanism of the AO platform enable it to support millions of transactions per second. This high throughput is crucial for supporting a global Info Finance network. At the same time, AO's low-latency communication characteristics ensure the immediacy of transactions and data updates, providing users with a smooth operational experience.
- Infinite scalability and modular design: The AO platform adopts a modular architecture, achieving extremely high scalability by decoupling virtual machines, schedulers, messaging, and computing units. Whether it is the growth of data throughput or the integration of new application scenarios, AO can quickly adapt. This scalability not only breaks through the performance bottlenecks 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 WebAssembly 64-bit architecture, capable of running most complete 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, while the large-scale computing capabilities of the AO platform efficiently support these needs. Additionally, by accessing the infinite storage of Arweave through WeaveDrive technology, the AO platform offers unique advantages for training and deploying complex machine learning models.
With its high throughput, low latency, infinite scalability, and AI integration capabilities, AO becomes an ideal platform for Info Finance. From real-time trading to dynamic analysis, AO provides excellent support for the realization of large-scale computing and complex financial models, paving the way for the popularization and innovation of Info Finance.
The Future of Info Finance: AI-Driven Prediction Markets
What characteristics should the next generation of prediction markets in Info Finance possess? Looking back to understand the future, traditional prediction markets have long faced three major pain points: lack of market integrity, high barriers to entry, and limited popularization. Even Web3 star projects like PolyMarket have not completely avoided these challenges. For example, it has been questioned for potential manipulation risks due to the short challenge period for predicting Ethereum ETFs or overly concentrated UMA voting rights. Furthermore, its liquidity is concentrated in popular areas, with low participation in long-tail markets. Additionally, some users in certain countries (like the UK and the US) are restricted by regulatory limitations, further hindering the popularization of prediction markets.
The future development of Info Finance requires the leadership of a new generation of applications. The excellent performance conditions of AO provide fertile ground for such innovations, with prediction market platforms represented by Outcome becoming the new focus of Info Finance experiments.
Outcome currently has a preliminary product prototype, supporting basic voting and social functions. Its true potential lies in the future deep integration with AI, utilizing AI agents to establish a trustless market settlement mechanism and allowing users to create and use prediction agents independently. By providing the public with a transparent, efficient, and low-barrier prediction tool, it may further promote the large-scale popularization of prediction markets.
Taking Outcome as an example, prediction markets built on AO can possess the following core characteristics:
- Trustless market resolution: The core of Outcome lies in autonomous agents. These agents are driven by AI, operating independently based on preset rules and algorithms, ensuring the transparency and fairness of the market resolution process. Due to the absence of human intervention, this mechanism minimizes the risk of manipulation, providing users with credible 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 for precise analysis and prediction. Users can customize personalized prediction agents based on their needs and strategies, participating in prediction activities across various market themes. This flexibility significantly enhances the efficiency and applicability of predictions.
- Tokenized incentive mechanism: Outcome introduces an innovative economic model, where users earn token rewards by participating in market predictions, subscribing to agent services, and trading data sources. This mechanism not only enhances user participation motivation but also supports the healthy development of the platform ecosystem.
AI-Driven Prediction Market Workflow
Outcome introduces AI models to achieve semi-automated or fully automated agent mode designs, providing innovative ideas for widely building Info Finance applications based on Arweave and AO. The workflow architecture roughly follows these steps:
1. Data Storage
- Real-time Event Data: The platform collects event-related information through real-time data sources (such as news, social media, oracles, etc.) and stores it in Arweave, ensuring data transparency and immutability.
- Historical Event Data: Stores past event data and market behavior records, providing data support for modeling, validation, and analysis, forming a sustainable optimization loop.
2. Data Processing and Analysis
- LLM (Large Language Model): LLM is the core module for data processing and intelligent analysis (essentially an AO process), responsible for deep processing of real-time event data and historical data stored in Arweave, extracting key information related to events, and providing high-quality input for subsequent modules (such as sentiment analysis and probability calculation).
- Event Sentiment Analysis: Analyzes user and market attitudes toward events (positive/neutral/negative), providing references for probability calculation and risk management.
- Event Probability Calculation: Dynamically calculates the probability of an event occurring based on sentiment analysis results and historical data, helping market participants make decisions.
- Risk Management: Identifies and controls potential risks in the market, such as preventing market manipulation and abnormal betting behavior, ensuring the healthy operation of the market.
3. Prediction Execution and Verification
- Trading Agent: AI-driven trading agents automatically execute predictions and bets based on analysis results, without requiring manual intervention from users.
- Outcome Verification: The system verifies the actual results of events through mechanisms such as oracles and stores the verification data in the Historical Event Data module, ensuring the transparency and credibility of the results. Additionally, historical data can provide references for subsequent predictions, forming a continuously optimized closed-loop system.
This workflow achieves efficient, transparent, and trustless prediction agent applications through AI-driven intelligent predictions and decentralized verification mechanisms, lowering the participation threshold for users and optimizing market operations. Based on AO's technical architecture, this model may lead Info Finance towards intelligent and widespread development, becoming a core prototype for the next generation of economic innovation.
Conclusion
The future belongs to those who are adept at extracting truth from the chaos of information. Info Finance is redefining the value and usage of data with the wisdom of AI and the trust of blockchain. From AO's post-scarcity architecture to Outcome's intelligent agents, this combination makes prediction markets not just a calculation of probabilities but a re-exploration of decision science. AI can not only lower participation barriers but also make the processing of vast amounts of data and dynamic analysis possible, opening up new paths for Info Finance.
As Alan Turing said, computation brings efficiency, while wisdom inspires possibilities. Dancing with AI, Info Finance is expected to make the complex world clearer and help society find a new balance between efficiency and trust.
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