The AiFi Summit 2024 Devcon, co-hosted by GAIB, Codatta, and Kite AI (formerly ZettaBlock) at the Park Hyatt Bangkok, has successfully concluded.
On November 12, the AiFi Summit 2024 Devcon co-hosted by GAIB, Codatta, and Kite AI (formerly ZettaBlock) at the Park Hyatt Bangkok concluded successfully. The AiFi Summit registered 1,300 participants, with over 500 attendees. 27 projects and investment institutions, including Paypal, BNB Chain, Base, NEAR Protocol, Story Protocol, 0G, Aethir, io.net, Exabits, Plume, Space and Time, Hyperbolic, Faction, Hashed, and Coinbase Ventures, delivered impressive speeches.
Sarah, the head of the Asia-Pacific region at BNB Chain, delivered the first keynote speech. She mainly introduced the construction of the entire BNB Chain ecosystem, various support policies for developers, and updated the audience on the current progress of BNB Chain in AI applications.
Following that, in the second keynote speech, Kony, the CEO of GAIB, shared his views on the potential opportunities in the current computing power market. He mentioned that AI is the most important era after the mobile internet, and computing power has captured a significant portion of value in the AI boom. Compared to other financial assets, investing in GPU computing power assets can yield returns that other assets cannot match. However, the current issue in the GPU market is the inefficiency in connecting participants on both sides: on one side are operators who have to bear huge financing costs when scaling GPU operations for external financing; on the other side are investors who find it difficult to invest directly in computing power assets and usually can only choose to invest in semiconductor stocks like Nvidia. GAIB aims to provide investors with more decentralized, transparent, and AI cash flow-based on-chain assets by tokenizing computing power assets and their returns and providing liquidity.
The first roundtable discussion at the AiFi Summit focused on the theme: “AiFi: Financialization of AI & Compute Assets.” Core members from GAIB, Exabits, io.net, Aethir, WitnessChain, and Plume discussed the current opportunities, challenges, and industry regulations surrounding AiFi.
Jonathan, the CIO of Exabits, mentioned that currently, if users want to use GPUs, they can only turn to major cloud service providers like AWS or Azure. However, these platforms tend to serve large enterprises, which limits the development of startups. We need more democratic and open GPU resources to support small and medium-sized enterprises. In the Web3 world, everyone can become an investor in GPUs to break AWS's computing power monopoly, which presents a huge industry opportunity.
Asa, the head of the Asia-Pacific region at io.net, pointed out that there are still 50% of GPUs in independent data centers outside the three major cloud providers that are not fully utilized, as these data centers lack opportunities to reach users. However, GPUs need to ensure continuous operation while facing maintenance issues. Building an incentive mechanism to protect the interests of investors and other participants is a significant challenge in the AiFi track.
Kartik, the ecosystem head at Aethir, mentioned that the system simultaneously has demand-side computing power, operating parties, and investors. Convincing them to participate in a market that operates based on on-chain mechanisms and ensuring the needs of all parties is full of challenges. Regulatory risks exist in that, in certain countries and regions, incentivizing data center services through tokens may cause some trouble, so compliance boundaries need to be defined in customer agreements.
Ranvir, co-founder and CEO of WitnessChain, proposed that computing power, as a new asset, requires a new pricing mechanism. There is no unified formula to calculate the commodity price of computing power; different platforms and GPUs have cost and performance differences, and GPUs with different performances contribute differently to the same task, creating new opportunities for designing financial mechanisms.
Teddy, the CBO of Plume, also mentioned that when new assets emerge, we need to approach regulation cautiously. There is already a certain compliance framework for AI-related assets that makes asset trading legitimate and feasible, which is also what Plume is helping ecological projects to achieve.
In the following keynote speech, Yi, the CEO of Codatta, explained how decentralized data trading drives AI towards AGI and Codatta's position and mission in this process. He mentioned that only vertical domain data can enhance the reasoning and planning capabilities of foundational models in specific fields, and only by collecting a large amount of data from different vertical domains can AGI be achieved. Each piece of data we provide as data contributors can actually be applied to multiple different scenarios, with different companies commercializing each scenario. This means that the vertical domain data we provide will generate revenue over time, which is why we view data as an asset. Therefore, we need to make data asset trading easier and achieve relatively fair pricing in the market.
The second roundtable discussion focused on the Open Data Economy, with core members from Spheron, Theoriq, Space and Time, Hyperbolic, Base, and Nevermined discussing the current state of the AI data ecosystem, infrastructure support, and future ecosystem needs.
Ron, co-founder and CEO of Theoriq, mentioned that we currently see many applications beyond simple conversational bots and governance bots on DAOs. These applications combine the cooperation of multiple agents, and beyond the crypto field, they are increasingly appearing in marketing, analytics, and other scenarios. Many people believe that the greatest use of data is in training models, but we see that data plays an increasingly significant role in decision-making processes. Different agents obtaining different data and collaborating can create the greatest value.
Scott, co-founder and CTO of Space and Time, stated that Space and Time is currently building a rules engine for agent systems using smart contracts, allowing agents to use your funds in a trustless environment, achieving the ideal on-chain form of agents. Space and Time's products enable users to query the historical behavior of agents and establish strict execution policies for agents.
Don, CEO of Nevermined, believes that to succeed in the data market, two conditions must be met: one is to monopolize data trading, and the second is to impose restrictions on data assets to prevent data contributors from uploading meaningless assets. A feasible approach is to build analytical tools around data assets in relevant scenarios, maximizing data value extraction and profit.
As one of the hosts, Chi, CEO of Kite AI, announced a brand upgrade during her keynote speech, launching a new AI platform, Kite AI, during the summit. She discussed the current challenges of centralized AI development and how Kite AI expands the boundaries of AI through its solutions. She mentioned that due to the lack of data distribution channels and data ownership confirmation mechanisms, a large amount of personal and even enterprise data is difficult to be utilized for training large models. Over the past year, the proportion of datasets on the internet with open-source licenses has dropped from 95% to 75%, making it challenging for companies doing model training to obtain the best quality data for their models and achieve breakthroughs in model performance. The industry needs decentralized AI solutions to acquire more valuable data.
In the third roundtable discussion, team members from GM Network, Mind Network, 0G Labs, NEAR Protocol, and Chainbase discussed how Web3 companies can participate in AI competition, data privacy, application implementation, and other topics.
Max, a founding team member of GM Network, mentioned that users have been generating a large amount of data, but this data has not been well utilized, causing it to lose value. We need to combine the collected data with AI to make smart devices smarter.
Leon, the head of the Asia-Pacific region at Mind Network, pointed out that while there are no perfect data privacy protection measures in reality, different methods combined may explore feasible solutions. To protect user privacy, Mind Network is currently encrypting data at three different levels: one is encrypting data in distributed storage, another is encrypting through fully homomorphic encryption during GPU computing, and the third is encryption at the application level.
Chris, an AI researcher at 0G Labs, mentioned that in traditional AI models, even with open-source models, it is difficult to know what data was used in training and how they will perform in new scenarios, making model results hard to trust. 0G has a solid data storage infrastructure, allowing data to be directly loaded from the cloud into the training process, and in the future, it can achieve building safer and more trustworthy models through personal verification of data.
Chris, COO of Chainbase, mentioned that there are currently two narratives in the market: one is crypto for AI, and the other is AI for Crypto. The use of crypto to solve the problems of large companies controlling data, computing power, and models has been discussed extensively. However, many AI for Crypto use cases have recently emerged, such as truth terminal and AI payments, with more and more projects beginning to collaborate to support the AI ecosystem. Users are very concerned about whether data can earn money, and the key task of the platform is to solve how to distribute profits between data contributors and consumers. Developers are not a vision-driven group; the most important thing is to help them save time and make money.
In the subsequent keynote speeches, Bu Fan, Head of IPFi at Story Protocol, and Prakarsh, ecosystem head at Spheron, shared their views on the decentralization of AI assetization and how their organizations are adapting to this trend.
Bu Fan mentioned that there are already many practical scenarios combining AI and Crypto in the market. The first is user-facing chatbots, where creators create AI characters and issue commercial licenses on-chain; the second is AI meme coins, where creators can legally connect with source IP assets on-chain and issue tokens; the third is providing model training data (such as images), which can continuously generate revenue through on-chain royalties. However, these are just very early applications, and the models have not yet matured. Creators can continue to explore scenarios that combine AI and Crypto. The Story Protocol focuses on standardizing IP activities through tokens and disseminating IP in various forms. He believes that most AI is also a form of IP, and if IP can be assetized, then AI can also be assetized. For example, images used to train AI models can be IP, and the AI model itself can also be IP. When the AI model generates new content, IP distribution transactions can occur on-chain to achieve assetization.
Prakarsh mentioned that in the AI era, computing power will become the underlying anchoring asset for most agents and AI applications. Distributed computing power will have many application scenarios, and they currently see promising scenarios including, first, knowledge sharing between hospitals while protecting data privacy, and second, AI dialogue systems supported by local computing power and models, ultimately forming a personal AI system.
The fourth roundtable focused on how to connect the Crypto and AI worlds, where investors discussed the current issues faced by centralized AI systems and how Crypto + AI can break through in certain areas.
Hiroki, the research head at Lemniscap, pointed out that there are two difficulties in building a decentralized AI network: one is that the scalability of a distributed computing network is hard to compete with centralized competitors, and the other is that the quality of data contributed by individuals is difficult to control.
Will, an investment partner at Faction, stated that currently, you can have AI plan your entire vacation, but the plan cannot be executed because AI cannot help you make payments. Will believes that AI agents need to have crypto wallets, which would act as bank accounts, and there will be huge opportunities in the payment technology stack, as all financial transactions will flow through these agents.
Ryan, an investment partner at Coinbase Ventures, believes that most models currently can only access public data and cannot obtain sensitive private data such as financial and medical data. Crypto can promote model access to private data pools, enhancing AI performance in specific fields. Agent systems currently cannot complete very complex tasks; they do not actually know how to understand the content of smart contracts and take action. We need large models that can acquire, understand, and provide human-readable interpretations of smart contracts.
Dan, an investor at Hashed, pointed out that the incentive system for distributed AI is not very well developed. In the entire AI value chain, only a few people have made significant positive contributions, but their contributions are not reflected in the incentives. The lack of a good distribution mechanism has led to unfair distribution. Additionally, community-owned models must be secure and controllable, returning ownership of parameters to the community for research, rather than providing a black box like centralized companies. If the model involves scenarios like emotional companionship, it should be governed in an open environment.
Sylvia, a director at Bullish Capital, mentioned that the design of incentive models must fully consider what the actual needs are. For example, if edge devices are needed, it is essential to consider how to find them among numerous decentralized computing devices. Therefore, without clarifying the model architecture optimization issues, it is impossible to design a truly effective incentive model.
The above is a complete review of the AiFi Summit 2024 Devcon. Even in the face of challenges such as regulation and incentive mechanisms, the AiFi track is also full of opportunities. With the market reaching new highs and the AI track heating up in all aspects, the industry is showing a positive trend, with talent continuously pouring in and more innovations emerging.
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