Exploring the Innovative Path of AI and Web3: A Comprehensive Guide from Concept to Market Application
With the rapid development of Artificial Intelligence (AI) and Web3 technologies, choosing and establishing the path for AI projects has become increasingly complex and full of opportunities. In this wave of transformation, how to successfully launch and develop an AI project from concept to implementation is a pressing question for every entrepreneur and tech enthusiast. Do you know how to leverage Web3 technology to start a new chapter for AI projects in the context of the AI era?
On June 11 at 21:00, the 44th TinTinWeekly event was successfully held. The live broadcast invited DIN community head Stan, Zero1 Labs BD head E, KIP Protocol CEO & co-founder Julian Peh, KIP Protocol AI head Jennifer Dodgson, TARS BD head Susie, and Nesa CTO Dr. Harry Yang. They deeply analyzed the basic steps and core technical architecture for starting an AI project. This online event attracted over 20,000 viewers, many of whom interacted closely with the guests during the live broadcast.
🕹️ Replay it on X Space: https://twitter.com/i/spaces/1OwGWYVjqDnxQ
How to Successfully Launch an AI Project?
When starting an AI project, the entrepreneurial team faces a series of complex and critical decisions. How to transform a concept into an actual market application and stand out in the fierce market competition is a challenge every entrepreneur must face. Stan first pointed out that the primary step in launching an AI project is to clarify the specific problem to be solved. Secondly, ensuring data quality is crucial. Then, it is necessary to choose appropriate tools and technologies to build the core technical architecture, including establishing data infrastructure, developing or utilizing existing AI models, creating APIs, and ensuring security. Additionally, scalability must be considered to ensure the project can meet specific needs and goals. These are the aspects that need to be focused on when launching an AI project.
E, from Zero1 Labs, also emphasized this point, noting that many people mistakenly believe that AI can quickly solve all problems, but in reality, the core of AI is to solve specific issues. Moreover, a strong team is indispensable. The project development requires extensive experimentation to ensure the team can fully test and deploy machine learning frameworks in the Integrated Development Environment (IDE).
Zero1 Labs is the first inclusive Proof-of-Stake-based Decentralized Artificial Intelligence (“DeAI”) ecosystem committed to fostering AI innovation through Zero Construct Program (ZCP).
Zero1's core is Zero Construct Programme (“ZCP”) an incubator for open-source AI developers with grants, fast-track launch, AI expertise, and developer resources. ZCP is also the heart for launching thoroughly vetted AI projects ensuring a successful go-to-market.
Jennifer shared her views based on her experience. Entering the AI field does not require a lot of computing resources and funds, especially with the development of small models. She emphasized the feasibility of using quantized models on mobile devices. Jennifer also mentioned that the initial intention of the KIP Protocol was to build a decentralized AI application, which later developed into an infrastructure platform that anyone can use to monetize existing models or develop applications using any model. Jennifer suggested that the best way is to start directly and use platforms like KIP Protocol to achieve goals.
Susie agreed with the previous guests’ views, mentioning that many small AI projects focus on very niche areas with fierce competition. Regarding the choice of algorithms and models, she shared TARS protocol’s experience in choosing the ChatGPT-4 model. Despite its speed and cost not being optimal, it was the best choice in terms of quality and widely adopted. She also mentioned that synergy with partners is crucial when starting an AI project. Choosing the right model and algorithm and collaborating with partners can increase the project’s practicality.
Dr. Harry Yang emphasized the importance of vision. He believes that clarifying the problem to be solved is the primary task in launching an AI project. Secondly, he mentioned that a solid team is key to success, including researchers and engineers from both academia and industry. He shared technical considerations when building a project, especially the importance of scalability and infrastructure. He pointed out that the system needs good scalability and high availability to support a large user base. He also mentioned that usability is a crucial factor for the project’s success, designing a simple user interface so users can easily get started and operate.
How AI Projects Move from Technical Concept to Market Application
Identifying Market Needs and MVP Development
So, how can cutting-edge technical concepts be transformed into actual market applications? Understanding market needs and identifying potential users is the first step in turning AI projects into market applications. Stan emphasized developing a Minimum Viable Product (MVP) to demonstrate the core functions of the AI solution and collect feedback from early users to make necessary adjustments to the project. He pointed out that formulating a comprehensive market and sales strategy, especially in the Web3 field, has become a trend. Additionally, collaborating with other projects or companies can enhance product functionality and expand market coverage, which is an excellent strategy for startups.
Collaboration Between Technical and Marketing Teams
Turning an AI project from a technical concept to a market application requires close cooperation between technical and marketing teams. The technical team is responsible for building the actual product, technology, and software, while the marketing team is responsible for bringing the product to market. E mentioned that during product development, attention must be paid to UI/UX design to ensure the product is simple, easy to use, and provides a seamless user experience. Additionally, product scalability is key. Zero1 Labs’ Key Maker platform is a decentralized AI community that helps users develop on the foundation, thus expanding the entire ecosystem.
User Positioning and Experience
Julian Peh believes that the core of decentralized AI lies in clear user positioning and optimizing user experience. When developing applications, it is essential first to clarify who the application is designed for and what specific problems it aims to solve. While user experience is important, it must be based on clear user positioning. KIP Protocol’s infrastructure can help reduce testing time and cost, enabling rapid iteration and testing.
Susie emphasized the importance of user-friendly interfaces and good user experience when designing AI projects. She believes that without a very user-friendly interface and experience, large-scale user adoption and improved user satisfaction cannot be achieved. She pointed out that market research is crucial, including feasibility testing, beta testing, and prototype testing. She particularly emphasized the importance of listening to the community’s voice, mentioning that TARS protocol is undergoing a rebranding to respond to community members’ needs. Additionally, decentralized identity verification plays a key role in ensuring project compliance and security.
Dr. Harry Yang described the adjustments Nesa has undergone in turning technical concepts into market applications. He mentioned that from the initial technical concept to the current market application, they have experienced many changes. During this process, they faced challenges in obtaining computing resources, running models, and ensuring privacy and security, which were crucial reasons for their shift in direction. He suggested that entrepreneurs should not be afraid to adjust the project’s direction and should continuously optimize products based on user feedback. Additionally, price rationality and how to compensate community contributors are factors that need to be considered.
How to Design AI Projects to Better Meet User Needs
User-Centered Approach
Designing AI projects requires a user-centered approach, focusing on solving real user problems and enhancing user experience. Stan explained that user-centered design and extensive usability testing are crucial to ensure the product is easy to use. Allowing users to customize AI functions according to their needs while ensuring transparency and explainability so that users can understand the AI’s decision-making process is essential. Furthermore, establishing a continuous user feedback and improvement mechanism is vital for maintaining the relevance and effectiveness of AI projects. This way, AI projects can be continuously optimized to meet user needs and achieve success.
E emphasized that one of the purposes of decentralization is to allow users to control their own data and decide how it is accessed, used, and monetized. Developers should also focus on the following key points when creating AI projects:
- Sustainability: Focus on how to ensure the project’s sustainable development before starting it or the company. Conduct extensive research and choose the most suitable tools for the project while considering cost issues.
- User Needs: Understand the community, know who the users are, and diversify budgets according to user needs.
- Tool Selection: Choose appropriate tools, consider the cost of supporting users, and developers need to understand the tools and resources they use to expand from there.
Obtaining User Feedback
Jennifer shared her experience in obtaining user feedback. She pointed out that many active users in the AI industry are also the most active creators, and their innovative behaviors can inspire product development and help developers better understand market needs. She shared an instance where users on TikTok discovered a relatively old but very interesting GPT hack, which helped them understand market needs and new usage methods.
Susie agreed with Jennifer’s view and further emphasized the importance of market research and community feedback. She pointed out that without a very user-friendly interface and experience, large-scale adoption and improved user satisfaction cannot be achieved. Market research, including feasibility testing, beta testing, and prototype testing, is crucial.
Privacy Protection and Technical Assurance
Dr. Harry Yang, CTO of Nesa, shared Nesa’s experience in designing AI projects to meet user needs. He pointed out that understanding user needs and designing AI products based on user experience is key. To address users’ reluctance to share private information, Nesa emphasizes Zero-Knowledge Proof (ZK Proof) and Trusted Execution Environment (TEE) in project design to ensure that user information is encrypted end-to-end and cannot be seen or used by others. This way, users can trust that their models are private and can only be used by themselves.
Financing Strategies for AI Startup Projects
Stan proposed several effective financing strategies for AI startup projects:
- Angel Investment and Venture Capital: Angel investors and venture capitalists who focus on AI and technology startups can provide significant financial support to startups.
- Grant Applications and Competitions: Applying for various grants and participating in competitions can ensure funding sources and increase project visibility.
- Crowdfunding Platforms: Raising funds through crowdfunding platforms can also build a community around the project, enhancing user engagement.
- Strategic Partnerships: Establishing strategic partnerships with large companies can provide additional funds and resources.
- Revenue Model Development: Developing revenue models early on can provide financial support for the continuous growth of the project.
When selecting appropriate financing strategies, entrepreneurs need to adjust based on the specific stage and development needs of the project. For the startup phase, as E mentioned, support from angel investors, friends, or family is crucial, and self-funding can also be considered. Emphasizing proof of technical concept, as Julian Peh pointed out, through programming competitions and hackathons can help demonstrate the project’s technical potential and attract more investor attention.
In terms of team building, having a strong founding team and viable technical proof of concept are important factors in attracting venture capital interest and investment. Simultaneously, establishing a supportive ecosystem for project development, such as TARS receiving support from the Solana Foundation, can provide important momentum and resource support for the project’s growth. Finally, controlling expenses and optimizing costs are key to successful financing. Nesa’s practice of managing funds cautiously and leveraging cloud services to optimize costs demonstrates how to financially support project development effectively, ensuring long-term financial health and sustainable growth.
Advantages of Decentralization for AI Projects
Data Privacy and Increased Transparency
The primary advantage of decentralization in AI projects is data privacy and security. Stan pointed out that decentralized systems reduce the risk of centralized data breaches and enhance transparency and trust through blockchain technology. Additionally, decentralization minimizes reliance on a single entity, reducing the risk of single points of failure. Dr. Harry Yang added that decentralized AI projects are more private and secure than monopolistic AI players because computation is distributed, and models are sharded, ensuring that no single node can see the inference process, training process, or data, making the entire process 100% private and secure.
Transparency and trust are other significant advantages of decentralization. Stan mentioned that decentralization provides transparent and tamper-proof records through blockchain technology, enhancing system trust. Dr. Harry Yang further noted that this transparency allows anyone to view and challenge the consensus, ensuring the entire process is open and transparent.
Incentive Mechanisms and Community-Driven Development
Decentralized platforms encourage data sharing and collaboration through token-based incentive mechanisms. E pointed out that these incentive mechanisms allow users and developers to customize and create their products, accelerating innovation. This competitive environment benefits users the most, as they can quickly discern which projects are meaningful and learn from successful ones. Jennifer Dodgson added that decentralization enables everyone to protect their intellectual property while selectively disclosing or keeping it private and profiting from it. Community-driven development on decentralized platforms promotes innovation and diverse attempts.
Ownership and Control
Decentralization gives users more ownership and control. Jennifer Dodgson noted that in the decentralized field, users can regain the right to choose, whether it’s about protecting intellectual property or deciding on the openness and confidentiality of models. She believes that this control is crucial for the future development of AI, as more people try different things, promoting the prosperity of AI.
Cost Efficiency and Scalability
Decentralized systems also have significant advantages in cost efficiency and scalability. Susie mentioned that decentralized systems can allocate resources more effectively. With modular design, people can access systems more easily and obtain distributed resources from around the world. Compared to some expensive public cloud services, decentralized solutions are more cost-effective. She believes that these advantages help drive the development of the AI industry.
Conclusion
In the wave of AI and Web3 technologies, entrepreneurs and tech enthusiasts can fully leverage the advantages of decentralization, from data privacy protection and transparency to incentive mechanisms and cost efficiency, continuously innovating and optimizing projects.
These technologies not only provide a solid foundation for the initiation and development of AI projects but also open up new possibilities for future technological development. Through continuous learning and community collaboration, explorers in the AI and Web3 fields are sure to encounter more opportunities and breakthroughs, injecting continuous momentum into global technological innovation.
