Which projects combining crypto and AI can help break down AI monopolies?
TechFlow Selected TechFlow Selected
Which projects combining crypto and AI can help break down AI monopolies?
The integration of artificial intelligence and blockchain will clearly play a crucial role in shaping various industries.
Navigating Web3 tides with focused insightsAuthor: Reflexivity Research
Compiled by: TechFlow
Recently, the artificial intelligence (AI) industry has been making headlines for both positive and negative reasons. While you may be familiar with recent events at OpenAI and have explored the current state of AI technology, you might not yet understand how AI interacts with blockchain. In this week's report, we will explore some applications attempting to combine AI and blockchain technologies, as well as insights into these applications and the future trajectory of the AI industry over the coming years.
What is Artificial Intelligence? How Is It Related to Cryptocurrency?
Before diving into the technical details and applications that integrate AI with blockchain, it’s important to first understand the basics of artificial intelligence and how leading teams and individual developers have advanced the field to its current state.
ChatGPT has been one of the most talked-about applications in the tech industry over the past year and is currently the most widely recognized and consumer-friendly AI application. Today, we’ll briefly introduce the fundamental concepts behind ChatGPT technology and explain why it performs so exceptionally.
The core technology behind ChatGPT and other chat-based AI models is large language models (LLMs). These complex AI systems are essentially a combination of deep learning techniques and massive datasets, which together enable AI models to predict and summarize knowledge.
Interaction between humans and LLMs occurs through natural language processing (NLP), with most LLMs specifically designed for NLP tasks. When users ask a chatbot to answer certain types of questions, the AI leverages its underlying technology, training data, and capabilities to provide the best possible response.
LLMs are built upon transformer models—commonly referred to simply as "transformers"—a type of neural network highly effective at predicting text and understanding contextual relationships between words. Because transformer-based LLMs excel at natural language processing, they perform remarkably well on everyday human tasks such as solving math problems, generating code, or even writing short reports and offering revision suggestions.
This capability is precisely why chatbots like ChatGPT, Microsoft Bing, and Claude have achieved tremendous success, almost single-handedly sparking an AI revolution. While many believe AI could eventually surpass human intelligence, there is currently no evidence suggesting this will happen anytime soon. Regardless, the potential of integrating AI models into human workflows—and their increasingly impressive capabilities—demonstrates that AI is here to stay, whether we embrace it or not. However, you may wonder how these AI models can align with the permissionless nature of cryptocurrency and blockchain. Let’s explore the synergies between them and examine how these two advanced technologies can work together.
How Can Cryptocurrency Empower AI Applications?
The cryptocurrency industry is one that consistently generates news coverage across media and social platforms. Starting from Satoshi Nakamoto’s whitepaper in 2008, crypto has evolved into a $1.5 trillion market, with globally recognized financial institutions continually applying for various cryptocurrency ETFs.
Generally speaking, it’s difficult to explain the unique benefits of blockchain technology to those outside the industry, especially in developed countries where traditional finance is already robust. However, in underdeveloped regions, the advantages of distributed ledger technology in financial transactions become much clearer—mainly due to corrupt financial institutions and governments still holding power worldwide. National currencies frequently devalue, and the majority of the global population still lacks access to banking infrastructure. In such contexts, blockchain’s distributed ledger technology offers distinct advantages.
Cryptocurrency serves as a means of providing banking services to the unbanked, giving individuals the opportunity to become their own financial regulators. Whether storing crypto in cold wallets or using numerous DApps within the crypto ecosystem, users can enjoy bank-like services.
Blockchain’s inherent characteristics—transparency, security, and decentralization—can significantly improve how AI stores, shares, and utilizes data. Blockchain can provide immutable, distributed ledgers for AI-driven transactions and decisions. This integration has the potential to enhance trust in AI systems, reducing concerns about data manipulation or misuse.
One key area where cryptography can assist AI (and vice versa) is data management and security. AI systems require vast amounts of data to learn and improve. By leveraging blockchain technology, this data can be securely and transparently shared across platforms and stakeholders. This ensures data integrity while opening new pathways for collaborative AI development, breaking down data silos that hinder innovation.
The combination of AI and blockchain can also create legitimate decentralized autonomous organizations (DAOs). These DAOs, governed by smart contracts and driven by AI algorithms, could operate, make decisions, and execute transactions independently without human intervention. Historically, DAO governance in crypto has been suboptimal, as human emotions and economic incentives often override the original purpose of the DAO. Integrating AI can automate DAO management processes, reduce reliance on human intermediaries, increase efficiency, lower costs, and ultimately transform industries.
Another promising avenue is using blockchain to incentivize AI data generation and sharing. Through tokenization, individuals and organizations can receive economic rewards for contributing valuable data to AI models, fostering a more collaborative and inclusive AI ecosystem.
Decentralized finance (DeFi) is another sector poised to benefit from AI. Their convergence could give rise to what some call decentralized artificial intelligence (DeAI). This approach could enable individuals and small entities to access AI tools previously available only to large corporations, democratizing and broadening access to AI technology.
The fusion of cryptocurrency and AI has the potential not only to transform finance but also many aspects of our digital lives. By combining the strengths of both technologies, we can expect future AI systems to be more accessible, secure, transparent, and efficient. With that said, let’s now analyze how the AI industry currently operates and what it’s capable of today.
Breaking Down the Opacity of AI
Drawing parallels between how cryptocurrency is transforming financial systems and how AI is revolutionizing intelligent production reveals similarities that support their integration.
Currently, companies like OpenAI, Google DeepMind, and Anthropic are heavily investing in AI research and development.
Current Opportunities in Crypto and AI
Now that we’ve covered the foundational synergies between AI and cryptocurrency, we can delve deeper into some of the leading projects in this space. While most of these projects still face challenges—such as developing mainnets, building loyal user bases, and gaining broader attention within the crypto community—they are all at the forefront of this rapidly evolving industry.
Bittensor: A Decentralized Network for AI Models
Bittensor is one of the most popular and mature projects in the intersection of cryptocurrency and AI ecosystems. It is a decentralized network aiming to democratize AI by creating a platform for multiple decentralized marketplaces (or "subnets") unified under a single token system. Its mission is to build a network capable of rivaling large-scale AI supercompanies like OpenAI, using unique incentive mechanisms and advanced subnet architecture. Bittensor’s system can be viewed as a machine that efficiently transfers AI capabilities onto the blockchain.
The network is managed by two key participants: miners and validators. Miners submit pre-trained AI models to the network and are rewarded for their contributions, while validators ensure the validity and accuracy of model outputs. This setup creates a competitive environment that incentivizes miners to continuously improve their models for better performance and higher rewards (in native token $TAO). Users interact with the network by sending queries to validators, who then distribute these queries to miners. Validators rank the miners’ responses and return the top-ranked result to the user.
Bittensor’s approach to model development is unique. Unlike many AI labs or research institutions, Bittensor does not train models itself, as training is complex and expensive. Instead, the network relies on a decentralized training mechanism. Validators evaluate models submitted by miners using specific datasets and score each model based on criteria such as accuracy and loss functions. This decentralized evaluation ensures continuous improvement in model performance.
Bittensor’s architecture includes the Yuma consensus mechanism—a hybrid of proof-of-work (PoW) and proof-of-stake (PoS)—which allocates resources across the network’s subnets. Subnets are self-contained economic markets, each focusing on different AI tasks such as text prediction or image generation, and can choose to join or exit the Yuma consensus based on their functionality.
Bittensor represents a significant step toward decentralizing AI, offering a platform for developing, evaluating, and improving various AI models in a decentralized manner. Its unique structure not only incentivizes the creation of high-quality AI models but also democratizes access to AI technology, potentially transforming how AI is developed and used across industries.
Akash: Open-Source Supercloud
Akash Network is an innovative, open-source supercloud platform designed to securely and efficiently buy and sell computing resources. It enables users to deploy their own cloud infrastructure while also allowing them to trade unused cloud capacity. This flexibility not only democratizes cloud resource utilization but also provides cost-effective scaling solutions for users expanding their operations.
At the heart of Akash’s system is a reverse auction mechanism: users submit bids for their computing needs, and providers compete to fulfill them, resulting in prices significantly lower than traditional cloud systems. The platform is powered by reliable, mature technologies like Kubernetes and Cosmos, ensuring a secure and stable environment for hosting applications. Akash’s community-driven model gives users a voice in the network’s development and governance, making it a truly user-centric public utility.
Akash defines infrastructure using a simple, YAML-based stack definition language (SDL), enabling users to create complex deployments across multiple regions and providers. Combined with Kubernetes—the leading container orchestration system—this feature ensures both deployment flexibility and application security. Additionally, Akash offers persistent storage solutions, preserving data across restarts, which is particularly beneficial for applications managing large datasets.
Overall, Akash stands out as a decentralized cloud platform offering a unique alternative to the monopolistic nature of current cloud providers. By tapping into millions of underutilized data centers worldwide, Akash reduces costs while increasing speed and efficiency for cloud-native applications. Free from proprietary languages and vendor lock-in, Akash provides a versatile, accessible platform for a wide range of cloud-based applications.
Render: A Platform Expanding Access to Computing Power
Render is a blockchain-based platform designed to meet the growing computational demands of media production, especially in augmented reality, virtual reality, and AI-enhanced media. It leverages idle GPU cycles to connect content creators needing computing power with suppliers who have available GPU resources. By using blockchain technology, Render ensures secure and efficient processing of GPU-intensive tasks, including AI-driven content creation and optimization.
At the core of Render is its integration with AI, which plays a crucial role in content creation and workflow optimization. The network supports AI-powered tasks, enabling artists to use AI tools to generate assets and enhance digital artworks. This integration allows for the creation of ultra-high-resolution 3D worlds and optimizes rendering workflows—for example, through AI denoising. Furthermore, Render’s use of AI extends to managing large art collections and streamlining rendering pipelines, greatly expanding creative possibilities.
Render’s ecosystem functions as a marketplace for GPU resources, serving artists, engineers, and GPU node operators alike. It democratizes access to computing power, enabling both individual creators and large studios to undertake complex rendering projects at low cost. Transactions within the ecosystem are conducted using the RNDR token, creating a vibrant economy centered around rendering services. As AI continues to reshape digital content creation, Render is positioned to play a pivotal role in enabling new forms of creative expression and technological innovation in digital media.
Gensyn: A Decentralized Compute Platform
Gensyn is a project at the intersection of AI and cryptocurrency focused on overcoming the resource constraints inherent in modern AI systems. It aims to break down the barriers posed by the enormous resource requirements needed to build foundational AI models. Gensyn’s solution is to create a blockchain-based decentralized protocol that efficiently harnesses global computing resources.
Gensyn addresses the rapidly growing computational demands of AI, which are beginning to outpace available resources. For instance, training large models like OpenAI’s GPT-4 requires immense computing power, creating significant barriers for all parties involved. This necessitates a system that can effectively utilize all available computing resources, overcoming the limitations of current solutions—which are either too costly or insufficient for large-scale AI workloads.
Gensyn tackles this challenge by creating a decentralized protocol that connects and verifies off-chain deep learning work in an economically efficient way. The protocol faces several challenges, including work verification, market dynamics, upfront task estimation, privacy concerns, and the need for effective parallelization of deep learning models. It aims to build a trustless computing network that provides economic incentives for participation and a method to verify that computations are performed as promised.
The Gensyn protocol is a trustless Layer 1 protocol for deep learning computation that rewards participants who contribute computing time and execute ML tasks (ML being a type of compute task within Gensyn). It uses multiple technologies to verify completed work, including probabilistic proofs of learning, graph-based pinpointing protocols, and Truebit-style incentive games. The system involves various actors—submitters, solvers, validators, and challengers—each playing a specific role in the computational process.
In practice, the Gensyn protocol involves multiple stages—from task submission to contract arbitration and settlement. It aims to create a transparent, low-cost market for ML computation, enabling scalability and efficiency. The protocol also offers GPU-equipped miners the opportunity to repurpose their hardware for ML computation at potentially lower costs than mainstream providers. This approach not only solves critical AI compute challenges but also aims to democratize access to AI resources.
Fetch: An Open Platform for the AI Economy
Fetch.ai has a longer development history compared to the previously mentioned projects and offers a diverse range of services. At its core, Fetch is an innovative project at the intersection of AI and cryptocurrency, aiming to revolutionize the way economic activity is conducted. The foundation of Fetch’s products lies in AI agents—modular components designed to be programmed for specific tasks. These agents can autonomously connect, search, and transact, forming dynamic markets that transform traditional economic interactions.
One of Fetch’s key offerings is enabling legacy systems to integrate with AI. This is achieved by connecting existing application programming interfaces (APIs) with Fetch.ai agents—a fast and seamless process that doesn’t require modifying the underlying business applications. These agents can combine with others on the network, unlocking new use cases and business models. Moreover, they possess the ability to negotiate and transact on behalf of users, allowing users to monetize their deployments.
Additionally, these agents can deliver inferences from machine learning models, enabling users to monetize insights and enhance their ML models.
Fetch has also launched Agentverse, a no-code management service that simplifies the deployment of AI agents. Just as no-code platforms like Replit and GitHub Copilot have made coding accessible to the masses, Fetch aims to democratize Web3 development in its own distinctive way.
With Agentverse, users can easily launch their first agent, significantly lowering the barrier to entry for advanced AI technology. In terms of AI engines and agent services, Fetch leverages large language models to discover and route tasks to appropriate AI agents. The system not only enables monetization of AI applications and services but also serves as a comprehensive platform for agents—including building, listing, analyzing, and hosting.
The platform enhances usability through features like “Search & Discovery” and “Analytics.” Agents can register within Agentverse, which employs LLM-based directed search to actively discover agents on the Fetch.ai platform. Analytics tools help improve the effectiveness of agent semantic descriptors, enhancing their discoverability. Additionally, Fetch.ai integrates an IoT gateway for offline agent clusters, allowing them to collect data and batch-process it upon reconnection.
Finally, Fetch.ai offers hosting services for agents, providing all Agentverse functionalities except hosting. The platform also leverages Fetch.ai’s Web3 network to introduce an open network for agent addressing and naming—an innovative DNS-like addressing method that incorporates blockchain technology into the system.
Overall, Fetch.ai delivers a multifaceted platform that merges AI and blockchain technologies, offering tools for AI agent development, machine learning model monetization, and a pioneering approach to search and discovery in the digital economy. The integration of AI agents with blockchain paves the way for automating and optimizing various processes in a decentralized and efficient manner.
The Next Steps and Predictions for Both Industries:
The seamless integration of artificial intelligence and blockchain technology represents a major advancement for both fields. This convergence is not merely the fusion of two cutting-edge technologies, but a transformative synergy that redefines the boundaries of digital innovation and decentralization.
As demonstrated by projects like Fetch.ai, Bittensor, Akash Network, Render Network, and Gensyn, the potential applications of this integration reveal the vast possibilities and significant advantages of combining AI with blockchain.
Looking ahead, the convergence of AI and blockchain will clearly play a crucial role in shaping multiple industries. From enhancing data security and integrity to creating new models of decentralized autonomous organizations, this integration promises more efficient, transparent, and accessible technologies. Particularly in decentralized finance (DeFi), the emergence of decentralized artificial intelligence (DeAI) could dismantle traditional barriers that favor large corporations, democratizing access to AI tools. This would lead to a more inclusive digital economy, where individuals and small organizations can leverage AI capabilities once out of reach.
Moreover, the integration of these technologies is expected to address some of the most pressing challenges in both domains. In AI, issues like data silos and the massive computational power required to train large models can be mitigated through blockchain’s decentralized data management and shared compute capacity. In blockchain, AI can enhance efficiency, automate decision-making, and improve security mechanisms. As the industry evolves, developers, researchers, and stakeholders must continue exploring and leveraging the synergies between AI and blockchain. By doing so, they will not only advance both fields but also drive innovation across the entire digital landscape, ultimately benefiting society as a whole.
Join TechFlow official community to stay tuned
Telegram:https://t.me/TechFlowDaily
X (Twitter):https://x.com/TechFlowPost
X (Twitter) EN:https://x.com/BlockFlow_News
@reflexivityres
