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

Author: Kyle

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

Taking prediction markets to the extreme, is it a press conference? In the just-concluded US election, Polymarket, relying on 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 ideology 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 to improve decision-making efficiency and accuracy. Prediction markets are a typical example, where by linking the problem with financial incentives, these markets incentivize 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 multiple other fields. At the same time, the emergence of AI will further lower the threshold, allowing micro-decisions to be effectively implemented 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 the computing demand. In this context, a secure and viable post-scarcity computing system has become an indispensable foundation for the continuous development of Info Finance.

The current landscape, who will win 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 no longer dependent on a single point, 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 competition of public chains

Currently, major public chains are fiercely competing in performance to meet the increasingly complex application demands. Reviewing the current public chain ecosystem, the development trend is shifting from the traditional single-threaded mode to a multi-threaded parallel computing mode.

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, implementing high-performance customized applications through native extensions (Aspect) in the WebAssembly runtime. With parallel execution and elastic block space design, Artela effectively solves the performance bottleneck of EVM, significantly improving 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 is also the AO-represented 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 its unique technical architecture. AO is also a strong contender towards the post-scarcity computing system, and is expected to help the large-scale deployment of Info Finance.

Carrying Info Finance, the construction blueprint of AO

AO is an 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 computation 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, 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 capabilities: The parallel process design and efficient message delivery mechanism of the AO platform allow it to support processing 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 high scalability by decoupling the virtual machine, scheduler, message delivery, and computation units. Whether it is the growth of data throughput or the integration 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 has a unique advantage in training and deploying complex machine learning models.

AO, with its high throughput, low latency, unlimited scalability, and AI integration capabilities, has become the ideal carrier platform for information finance. From real-time trading to dynamic analysis, AO provides excellent support for large-scale computing and complex financial models, paving the way for the popularization and innovation of information finance. The Future of Information Finance: AI-Driven Prediction Markets What features should the next-generation prediction markets in information finance have? Looking back and looking forward, traditional prediction markets have long faced three major pain points: lack of market integrity, high thresholds, and limited popularization. Even for a Web3 star project like PolyMarket, these challenges have not been completely avoided. For example, it has been questioned for the possibility of manipulation risks, such as the challenge period being too short for the prediction of the Ethereum ETF or the UMA voting rights being too concentrated. In addition, its liquidity is concentrated in popular areas, with low participation in long-tail markets. Furthermore, the user base in some countries (UK, US) is restricted due to regulatory constraints, further hindering the popularization of prediction markets. The future development of information finance requires the leadership of new-generation applications. The excellent performance of AO provides fertile ground for such innovations, and the prediction market platform represented by Outcome is 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 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 prediction 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, the prediction markets built on AO can have the following core features: Trustless Market Resolution: The core of Outcome lies in the Autonomous Agents. These agents are AI-driven and operate 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 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 prediction. Users can customize personalized prediction agents according to 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 can earn token rewards by 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 following architecture: 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 in-depth processing of 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 reference for probability calculation and risk management. Event Probability Calculation: Dynamically calculating the probability of event occurrence based on sentiment analysis results and historical data, helping 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 reference for subsequent predictions, forming a continuously optimized closed-loop system. This workflow, through AI-driven intelligent 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 popularization, becoming a core prototype for the next-generation economic innovation. Conclusion The future belongs to those who can extract 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 the post-scarcity architecture of AO to the intelligent 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 large-scale data processing 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 make the complex world clearer and drive society to find a new balance between efficiency and trust.