The future of information finance: Post-scarcity systems and AI

 Author: Kyle

Prediction markets are surpassing traditional financial tools and becoming a smart carrier for information verification, while Info Finance further redefines data value with financial incentives and technological innovation. AO's post-scarcity computing architecture and AI agents promote the intelligence and popularization of prediction markets, creating a new paradigm for the future of information finance.

Prediction markets are played to the extreme, is it a press conference? In the just-concluded US election, Polymarket successfully predicted that Trump's chances of winning were higher than traditional polls with its market-driven data, which quickly attracted the attention of the public and the media. People gradually realized 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 moving towards a new turning point.

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

Information Finance: Using Financial Tools to Obtain and Use Information

The core of information finance is to use financial tools to obtain and use information to improve decision-making efficiency and accuracy. Prediction markets are a typical example. By linking problems to financial incentives, these markets incentivize the accuracy and responsibility of participants and provide clear predictions for users who seek the truth.

As a sophisticated market design, information finance can guide participants to respond to specific facts or judgments, and its application scenarios also cover multiple fields such as decentralized governance and scientific review. At the same time, the emergence of AI will further lower the threshold, so that micro-decision-making can also 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 a 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 the potential of 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 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 needs. In this context, a safe and feasible post-scarcity computing system has become an indispensable foundation for the sustainable development of information finance.

Today's landscape, who is the post-scarcity computing system?

"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 capabilities. Through decentralization, resource enrichment and efficient collaboration, such systems support large-scale, flexible computing task execution, making computing resources close to "non-scarcity". In this architecture, computing power is free from single-point dependence, and users can freely and low-cost access and share resources, promoting the popularization and sustainable development of inclusive computing.

In the context of blockchain, the key features 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 needs. Looking at the current public chain ecosystem, the development trend is shifting from the traditional single-threaded mode to the multi-threaded parallel computing mode.

Traditional high-performance public chains:

• Solana: Since its inception, Solana has adopted a parallel computing architecture to achieve 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: Both are actively developing parallel EVM solutions to improve transaction processing capabilities. For example, Polygon introduced zkEVM to achieve 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 example, Aptos uses Block-STM technology to achieve parallel transaction processing.

• Artela:Artela proposed the concept of EVM++ and implemented high-performance customized applications in the WebAssembly runtime through native extensions (Aspect). With the help of parallel execution and elastic block space design, Artela effectively solves the performance bottleneck of EVM 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 also alternative solutions represented by AO. AO is not an independent public chain, but a computing layer based on Arweave, which achieves parallel processing capabilities and scalability through a unique technical architecture. AO is definitely also a strong competitor in the move towards a post-scarcity computing system, and is expected to help the large-scale implementation of information finance.

Carrying information finance, AO's blueprint

AO is an Actor Oriented computer running on the Arweave network, providing a unified computing environment and an open messaging layer. It provides the possibility of integrating large-scale applications of information finance with traditional computing environments through a distributed and modular technical architecture.

AO's architecture is simple and efficient. Its core components include:

• Process is the basic computing unit in the AO network, and interacts through message transmission;

• Scheduling Units (SUs) are responsible for message sorting and storage;

• Computing Units (CUs) are responsible for state calculation tasks;

• Messenger Units (MUs) are responsible for message transmission and broadcasting.

The decoupling design between modules gives the AO system excellent scalability and flexibility, enabling 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 power:The parallel process design and efficient message passing mechanism of the AO platform enable it to support millions of transactions per second. This high throughput is essential to support a global information and financial network. At the same time, AO's low-latency communication characteristics can ensure the immediacy of transactions and data updates, bringing users a smooth operating experience.

• Unlimited scalability and modular design:The AO platform adopts a modular architecture, which achieves extremely high scalability by decoupling virtual machines, schedulers, message passing, and computing units. 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 finance applications.

• Support for large-scale computing and AI integration:The AO platform already supports the WebAssembly 64-bit architecture and can run 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 information finance, involving applications such as smart contract optimization, market analysis, and risk prediction, and the large-scale computing capabilities of the AO platform enable it to efficiently support these needs. At the same time, through the WeaveDrive technology to access Arweave with unlimited storage, the AO platform provides unique advantages for training and deploying complex machine learning models.

AO has become an ideal carrier platform for information finance with its high throughput, low latency, unlimited scalability, and AI integration capabilities. 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 promoting the popularization and innovation of information finance.

The future of information finance: AI-driven prediction market

What should the next generation of prediction market for information finance have? Looking back, traditional prediction markets have long faced three major pain points: insufficient market integrity, high thresholds, and limited popularization. Even Web3 star projects such as PolyMarket have not been able to completely avoid these challenges. For example, the Ethereum ETF was once suspected of manipulation risks because the challenge period of the prediction event was too short or the UMA voting rights were too concentrated. In addition, its liquidity is concentrated in hot areas, and the participation in the long-tail market is low. In addition, the users in some countries (the United Kingdom and the United States) are restricted due to regulatory restrictions, which further hinders the popularization of prediction markets.

The future development of information finance needs to be led by a new generation of applications. AO's excellent performance conditions provide fertile ground for such innovations, among which prediction market platforms represented by Outcome are becoming a new focus of information finance experiments.

Outcome has already taken shape as a product, supporting basic voting and social functions. Its real potential lies in the future deep integration with AI, using AI agents to establish a trustless market settlement mechanism, and allowing users to independently create and use prediction agents. Only by providing the public with a transparent, efficient, and low-threshold prediction tool can it 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 is autonomous agents. These agents are AI-driven and operate independently based on preset rules and algorithms to ensure transparency and fairness in the market resolution process. Since there is no human intervention, this mechanism minimizes the risk of manipulation and provides users with reliable forecast results.

• AI-based forecast agents:The Outcome platform allows users to create and use AI-driven forecast agents. These agents can integrate multiple AI models and rich data sources for accurate analysis and forecasting. Users can customize personalized forecast agents according to their own needs and strategies, and participate in forecasting activities in various market themes. This flexibility significantly improves the efficiency and applicability of forecasts.

• Tokenized incentive mechanism:Outcome introduces an innovative economic model where users are rewarded with tokens by participating in market forecasts, subscribing to agent services, and trading data sources. This mechanism not only enhances the motivation of users to participate, but also provides support for the healthy development of the platform ecosystem.

AI-driven prediction market workflow

Outcome provides innovative ideas for information finance applications widely built on Arweave and AO by introducing AI models to achieve semi-automatic or fully automatic agent mode design. It roughly follows the following workflow architecture:

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 to ensure the transparency and immutability of the data.

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

2. Data Processing and Analysis

• LLM (Large Language Model):LLM is the core module of data processing and intelligent analysis (that is, 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:Analyze the attitudes of users and the market towards events (positive/neutral/negative), and provide reference for probability calculation and risk management.

• Event Probability Calculation:Based on sentiment analysis results and historical data, dynamically calculate the probability of an event 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 automatically executing predictions and betting based on the analysis results without manual intervention by the user.

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

This workflow realizes efficient, transparent and trustless prediction agent applications through AI-driven intelligent prediction and decentralized verification mechanisms, lowering the threshold for user participation and optimizing market operations. Relying on AO's technical architecture, this model may lead information finance to develop towards intelligence and popularization, and become the core prototype of the next generation of economic innovation.

Conclusion

The future belongs to those who are good at extracting the truth from the complex 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 it possible to process and dynamically analyze massive amounts of data, 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 promote 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