Author: Kyle

The prediction market is surpassing traditional financial tools and becoming a smart carrier of information verification, and information Info Finance further uses financial incentives and technological innovation to redefine the value of data. AO's post-scarcity computing architecture and AI agents promote the intelligence and popularization of prediction markets, creating a new paradigm for the future information finance field.

Predicting the market to the extreme, is it a press conference? In the just-concluded U.S. election, Polymarket relied on its market-driven data to successfully predict that Trump’s victory rate was higher than traditional polls, quickly attracting the attention of the public and the media. People gradually realize that Polymarket is not only a financial tool, but also a "balancer" in the information field, using the wisdom of the market 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 subvert social media, scientific research and governance models, and open up a new direction for improving decision-making efficiency. With the advancement of AI and blockchain, information finance is also heading towards a new turning point.

Faced with the ambitious emerging field of information finance, are Web3 technologies and concepts ready to embrace it? This article will take the prediction market as an entry point to explore the core concepts, technical support and future possibilities of information finance.

Information finance: using financial tools to obtain and utilize information

The core of information finance is to use Financial tools to obtain and utilize information to improve decision-making efficiency and accuracy. Prediction markets are a prime example. By tying questions to financial incentives, these markets incentivize accuracy and accountability among participants, providing clear predictions to users seeking the truth.

As an exquisite market design, information finance can guide participants to Respond to facts or judgments, and the application scenarios also cover decentralized governance, scientific review and other fields. At the same time, the emergence of AI will further lower the threshold, allowing micro-decision-making to operate effectively in the market, promoting the popularization of information finance.

Vitalik specifically mentioned that the current decade has become the best time to expand information finance. Scalable blockchain provides secure, transparent and credible platform support for information finance, while the introduction of AI improves the efficiency of information acquisition and enables information finance to handle more sophisticated problems. Information finance not only breaks through the limitations of traditional prediction markets, but also demonstrates the ability to tap potential in multiple fields.

However, as information finance expands, its complexity and scale are increasing dramatically. The market needs to process massive amounts of data and make real-time decisions and transactions, which poses severe challenges to efficient and secure computing capabilities. At the same time, the rapid development of AI technology has spawned more innovative models and intensified computing requirements. In this context, a safe and feasible post-scarcity computing system has become an indispensable foundation for the sustainable development of information finance.

In today's situation, what is the post-scarcity calculation system?

"Post-scarcity calculation system "There is currently a lack of unified definition, but its core goal is to break through the limitations of traditional computing resources and achieve low-cost, widely available computing capabilities. Through decentralization, resource enrichment and efficient collaboration, this type of system supports large-scale and flexible computing task execution, making computing resources closer to "non-scarcity". In this architecture, computing power gets rid of 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 characteristics of post-scarcity computing systems include decentralization, abundant resources, low cost, and high scalability.

High-performance competition of public chains

Currently, major public chains are competing fiercely for performance. to meet increasingly complex application requirements. Looking at the current public chain ecological landscape, the development trend is shifting from the traditional single-threaded model to the multi-threaded parallel computing model.

Traditional high-performance public chain:

Solana: Solana has adopted Parallel computing architecture achieves high throughput and low latency. Its unique Proof of History (PoH) consensus mechanism enables it to process thousands of transactions per second.

Polygon and BSC: These two are positiveDevelop parallel EVM solutions to improve transaction processing capabilities. For example, Polygon introduced zkEVM to enable more efficient transaction verification.

Emerging parallel public chains:

Aptos, Sui, Sei and Monad: these emerging public chains Chains are designed for high performance by optimizing data storage efficiency or improving consensus algorithms. For example, Aptos uses Block-STM technology to implement parallel transaction processing.

Artela: Artela proposed the EVM++ concept to implement high-performance customized applications during WebAssembly runtime through native extensions (Aspects). With the help of parallel execution and flexible block space design, Artela effectively solves the EVM performance bottleneck and greatly improves throughput and scalability.

The performance competition is in full swing, and it is difficult to determine which one is better. However, in this fierce competition, there are other solutions represented by AO. AO is not an independent public chain, but a computing layer based on Arweave that achieves parallel processing capabilities and scalability through a unique technical architecture. AO is definitely a strong competitor in moving toward post-scarcity computing systems, and is expected to help the large-scale implementation of information finance.

Carrying information finance, AO's blueprint

AO is a network running on the Arweave network Actor Oriented (role-based) computer on the server, providing a unified computing environment and an open messaging layer. Through its distributed and modular technical architecture, it provides the possibility for the integration of large-scale applications of information finance and traditional computing environments.

AO's architecture is simple and efficient, and its core components include:

< p style="text-align: left;">Process is the basic computing unit in the AO network, and interaction is achieved through message transmission;

Scheduling units (SUs) are responsible for the sorting and storage of messages;

Computing units (CUs)Undertake state calculation tasks;

Messenger units (MUs) are responsible for message delivery and broadcast.

The decoupling design between modules gives the AO system excellent scalability and flexibility, allowing it to adapt to application scenarios of different scales and complexity. Therefore, the AO system has the following core advantages:

High throughput and low-latency computing capabilities: The parallel process design and efficient message passing mechanism of the AO platform enable It can support millions of transactions per second. This high throughput capability is critical to supporting global information and financial networks. 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.

Infinite scalability and modular design: The AO platform adopts a modular architecture and achieves Extremely high scalability. Whether it is the increase in data throughput or the access to new application scenarios, AO can quickly adapt. This scalability not only breaks through the performance bottleneck of traditional blockchains, but also provides developers with a flexible environment for building complex information financial applications.

Support large-scale computing and AI integration: AO platform already supports WebAssembly 64-bit architecture and can run most complete large-scale language models (LLM), such as Meta's Llama 3 provides a technical foundation for the deep integration of AI and Web3. AI will become an important driving force in information finance, involving smart contract optimization, market analysis, risk prediction and other applications, and the large-scale computing power of the AO platform enables it to efficiently support these needs. At the same time, the AO platform provides unique advantages for training and deploying complex machine learning models by accessing Arweave with unlimited storage through WeaveDrive technology.

AO has become an ideal hosting platform for information finance with its high throughput, low latency, unlimited scalability and AI integration capabilities. From real-time transactions to dynamic analysis, AO provides excellent support for the realization of large-scale calculations and complex financial models, paving the way for the popularization and innovation of information finance.

The future of information finance: AI-driven prediction market

The next generation prediction market of information finance , what color should it have? Learn from the pastAccording to Zhilai, the traditional prediction market has long faced three major pain points: insufficient market integrity, high thresholds, and limited popularity. Even Web3 star projects like PolyMarket cannot completely avoid these challenges. For example, the Ethereum ETF has been questioned about possible manipulation risks because the challenge period for predicted events is too short or UMA’s voting rights are too concentrated. In addition, its liquidity is concentrated in popular areas, and long-tail market participation is low. In addition, some users (in the United Kingdom and the United States) are restricted due to regulatory restrictions, which further hinders the popularity of prediction markets.

The future development of information finance requires the guidance of a new generation of applications. AO's excellent performance conditions provide fertile ground for this type of innovation, among which prediction market platforms represented by Outcome are becoming the new focus of information finance experiments.

Outcome has now begun to take shape as a product, supporting basic voting and social functions. Its real potential lies in its deep integration with AI in the future, using AI agents to establish a trustless market settlement mechanism, and allowing users to independently create and use predictive agents. By providing the public with a transparent, efficient, and low-threshold prediction tool, it is possible to further promote the large-scale popularization of prediction markets.

Taking Outcome as an example, prediction markets built on AO can have the following core characteristics:

Trustless market resolution: The core of Outcome lies in Autonomous Agents. These agents are driven by AI and operate independently based on preset rules and algorithms to ensure the transparency and fairness of the market decision-making process. Since there is no human intervention, this mechanism minimizes the risk of manipulation and provides users with credible prediction results.

AI-based predictive agents: The Outcome platform allows users to create and use AI-powered predictive agents. These agents can integrate multiple AI models and rich data sources to perform precise analysis and predictions. Users can customize personalized forecasting agents based on their own needs and strategies, and participate in forecasting activities in various market themes. This flexibility significantly improves the efficiency and applicability of predictions.

Tokenization incentive mechanism: Outcome introduces an innovative economic model. Users receive token rewards by participating in market predictions, subscribing to agency services and transaction data sources. This mechanism not only enhances user participationMotivation also provides support for the healthy development of the platform ecosystem.

AI-driven prediction market workflow

Outcome achieves semi-automation, Or the design of fully automatic agent mode can provide innovative ideas for information finance applications widely built on Arweave and AO. Roughly follow the following workflow structure:

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. Ensure data transparency and immutability.

Historical Event Data: Save past event data and market behavior records, provide data support for modeling, verification and analysis, and form a feasible A closed loop of continuous optimization.

2. Data processing and analysis

LLM (Large Language Model): LLM is data The core module of processing and intelligent analysis (which is an AO process) is responsible for in-depth processing of real-time event data and historical data stored in Arweave, extracting key information related to events, and providing high-quality information for subsequent modules (such as sentiment analysis and probability calculation). Quality input.

Event Sentiment Analysis: Analyze users and market attitudes towards events (positive/neutral/negative), providing reference for probability calculations and risk management .

Event Probability Calculation: Based on sentiment analysis results and historical data, dynamically calculate the probability of an event occurring to help market participants make decisions.

Risk Management: Identify and control potential risks in the market, such as preventing market manipulation, abnormal betting behavior, etc., to ensure the healthy operation of the market.

3. Prediction execution and verification

Trading Agent: The AI-driven trading agent is responsible for analyzing results based on Predictions and bets are performed automatically without manual intervention from the user.

Outcome Verification: The system verifies the actual results of the event through mechanisms such as oracles, and stores the verification data in the Historical Event Data module to ensure the results transparency and credibility. In addition, historical data can also provide reference for subsequent predictions, thus forming a closed-loop system of continuous optimization.

This workflow implements efficient, transparent and trustless prediction agent applications through AI-driven intelligent prediction and decentralized verification mechanism, reducing the cost of User participation threshold and optimized market operation. Relying on AO's technical architecture, this model may lead the development of information finance toward intelligence and popularization, becoming the core prototype of the next generation of economic innovation.

Summary

The future belongs to those who are good at extracting the truth from complicated information. Information finance is redefining the value and use 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 the prediction market no longer just a calculation of probability, but a re-exploration of decision science. AI can not only lower the threshold for participation, but also make the processing and dynamic analysis of massive data possible, opening up a new path for information finance.

As Alan Turing said, computing brings efficiency, while wisdom inspires possibilities. Dancing with AI, information finance is expected to make the complex world clearer and push society to find a new balance between efficiency and trust.

Reference materials:

1. https://ao.arweave.net/# /read

2. https://x.com/outcome_gg/status/1791063353969770604

3. https://www.chaincatcher.com/article/2146805

4. https://en.wikipedia.org/wiki/ Post-scarcity