**Fostering Innovation Through Openness in AI**
The spirit of openness drives innovation, and the latest developments in artificial intelligence (AI) have highlighted its worldwide significance and reach. As the integration of resources enhances computing power, we may face centralization challenges, where entities with advanced computing capabilities could dominate the landscape. This centralization could stifle the innovation process. However, decentralization and Web3 technologies present promising alternatives to uphold the openness of AI.
**Decentralized Computing for Model Development**
**Crowdsourced Computing (CPUs + GPUs)**
*Supporting Perspective:* The crowdsourcing model, akin to those employed by companies like Airbnb and Uber, can be reimagined for computing resources. This approach would pool idle computing power into a marketplace, potentially providing cost-effective computing solutions tailored for specific applications while offering censorship-resistant resources for training models that might be subject to future regulations or prohibitions.
*Counter Perspective:* The effectiveness of crowdsourced computing may be limited in achieving the necessary economies of scale for high-performance tasks, as most high-end GPUs are not owned by consumers. This notion of decentralized computing seems at odds with the principles of high-performance computing.
**Decentralized Inference**
*Open-Source Model Inference Decentralization*
*Supporting Perspective:* Open-source models are rapidly approaching the performance levels of their closed-source counterparts and are gaining popularity. Centralized services like HuggingFace and Replicate for model inference raise privacy and censorship concerns. Decentralized or distributed service providers could effectively address these issues.
*Counter Perspective:* Local inference, supported by dedicated chips adept at managing large parameter models, may ultimately be more successful. Edge computing offers solutions for enhancing privacy and resisting censorship.
**On-Chain AI Agents**
*Leveraging Machine Learning in On-Chain Applications*
*Supporting Perspective:* AI agents, which necessitate a transaction coordination layer, can leverage cryptocurrency payments, as they are fundamentally digital and cannot utilize traditional banking methods. On-chain AI agents reduce platform risks, such as unexpected alterations in plugin architectures by organizations like OpenAI, which can disrupt services abruptly.
*Counter Perspective:* Presently, AI agents like BabyAGI and AutoGPT are not fully production-ready. Moreover, developers of AI agents can utilize payment solutions such as Stripe without depending on cryptocurrency. The argument concerning platform risk has been previously utilized to advocate for crypto, but it has yet to come to fruition.
**Data and Model Ownership**
*Autonomous Management and Value Collection for Data and Machine Learning Models*
*Supporting Perspective:* Users who generate data should retain ownership, rather than the companies that collect it. As data is a vital resource in the digital age, concerns about its monopolization by large tech firms and inadequate monetization are pressing. A more personalized internet necessitates portable data and models, enabling users to transfer data seamlessly across applications, similar to moving cryptocurrency wallets between decentralized applications. Blockchain technology could offer a viable solution to data sourcing challenges, especially given the rise in fraudulent activities.
*Counter Perspective:* Data ownership and privacy may not be high priorities for users, as indicated by the substantial registration figures for platforms like Facebook and Instagram. Trust in established organizations like OpenAI may overshadow concerns regarding data ownership.
**Token-Incentivized Applications (e.g., Companion Apps)**
*Imagining Crypto Token Rewards*
*Supporting Perspective:* Crypto token incentives have proven effective in fostering network growth and user engagement. Many AI-focused applications are predicted to adopt this approach. The AI companion market holds immense potential, potentially evolving into a multi-trillion dollar industry. Historical data, such as the $130 billion spent on pets in the U.S. in 2022, indicates a robust market for AI companions. AI companion applications have already demonstrated significant user engagement, with average session durations exceeding one hour. Platforms incentivized by cryptocurrency could capture a substantial share of this and other AI application markets.
