Intersection of AI and Crypto

Written by Reflexivity Research

Intersection of AI and Crypto

The artificial intelligence industry has been making headlines lately, for reasons good and bad. While you’re probably well aware of the recent drama surrounding OpenAI and have maybe explored the capabilities of existing artificial intelligence technology, you probably haven’t thought much about how artificial intelligence can interact with blockchain-based systems. In this week’s report we’ll be covering a handful of existing applications attempting to leverage both artificial intelligence and blockchain technology, along with some information concerning the future of these apps and the artificial intelligence industry in the years to come.


What is AI and how does it relate to crypto?

Before we get into project specifics and some of the more technical details, it’s important to cover some of the basics around artificial intelligence technology and how the talented teams and individual developers within the industry have got us to today.

There’s a strong chance you are already familiar with ChatGPT, the most popular and widely recognized consumer-facing AI application that has consumed the tech industry’s attention over the last year – today we’ll briefly explain the concepts underpinning this technology and how it’s able to perform so competently at, well, basically everything it’s asked to compute.

The core piece of technology powering ChatGPT and other consumer chat-based models is what’s known as the large language model, otherwise known as an LLM. These complicated pieces of AI tech are essentially a combination of deep learning techniques / algorithms and very large data sets that work together to create an artificial intelligence model capable of predicting and summarizing knowledge.

Interactions between humans and LLMs are handled via natural language, with most LLMs being built specifically with natural language processing (NLP) in mind. A user asks a chatbot to answer some type of question in natural language, with the chatbot then using its underlying technology, training data and capabilities to provide an answer to the user as best as it can.

LLMs are built upon transformer models, commonly referred to as transformers. These are a type of neural network that excel at predicting text and learning the context behind words. Because LLMs that use transformer models excel at NLP, they’re able to work really well for common tasks that humans need everyday, things like solving math problems, generating code templates or even writing shorter reports or suggesting edits.

Because of this, chatbots like ChatGPT, Microsoft Bing AI and Claude have seen immense success and have almost single handedly sparked an AI revolution. While many believe that AI systems might eventually gain capabilities and intelligence greater than humans, there is little evidence to suggest that this will happen anytime soon. Regardless, the possibilities that come from these models integrating with human workflows and the extremely promising existing capabilities prove that AI is here to stay, whether we all like it or not. But you’re probably wondering how these models can fit in with crypto and the permissionless nature of blockchains, so let’s explain the potential synchronicities and examine these two radical forms of technology.


How can crypto help enable AI applications?

The crypto industry is one that’s consistently discussed on the news, in large media outlets and across other social media platforms every single day. What started with a single whitepaper written by Satoshi in 2008 has transformed into a $1.5 trillion market with a flurry of looming ETF approvals or denials from the largest financial institutions in the world.

It’s often difficult to describe the innate benefits of blockchain technology to industry outsiders, mainly because the financial industry is very well developed and smooth in the majority of first world countries. Outside of places like the United States, it’s much easier to explain and show the power of permissionless ledgers for financial transactions, largely due to the corrupt financial institutions and governments that unfortunately still hold power in every area of the world. Currencies are regularly getting debased in nations across the globe, with a large majority of the world’s population still without access to banking infrastructure that’s often seen as an afterthought in the United States.

Crypto is a way of banking the unbanked, a technology that offers an opportunity for individuals to become their own overseer of financial operations, whether they’re holding crypto in a cold storage wallet or utilizing the numerous decentralized finance applications available across the crypto ecosystem. The promise of permissionless finance can’t easily be described, but the revolution occurring each day cannot be understated.

A blockchain’s inherent characteristics of transparency, security, and decentralization can significantly contribute to the way AI data is stored, shared, and utilized. This amalgamation of technologies promises to enhance trust in AI systems by providing an immutable ledger for AI transactions and decisions, thereby reducing concerns over data manipulation or misuse.

One of the critical aspects where crypto can assist AI (and vice versa) is in the realm of 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 different platforms and stakeholders. This not only ensures the integrity of the data but also opens up new avenues for collaborative AI research and development, breaking down data silos that often hinder innovation.

The integration of AI and blockchain could lead to the creation of legitimately decentralized autonomous organizations (DAOs). These DAOs, governed by smart contracts and powered by AI algorithms, could operate independently, make decisions, and execute transactions without human intervention. Historically, DAO management in crypto has been less than ideal as human emotions and financial incentives often obscure the initial purposes of a DAO. Implementing AI systems could revolutionize industries by automating processes and reducing the need for intermediaries, thereby increasing efficiency and reducing costs.

Another promising area is the use of blockchains as a means to incentivize the generation and sharing of AI data. Through tokenization, individuals and organizations could be rewarded for contributing valuable data to AI models, fostering a more collaborative and inclusive AI ecosystem.

Decentralized finance (DeFi) is also a potentially huge benefactor of AI, potentially creating what could be referenced as decentralized AI (DeAI). This approach could democratize access to AI technologies, allowing individuals and small entities to access AI tools and services that were previously the domain of large corporations.

The convergence of cryptocurrency and AI holds the potential to transform not only the financial sector but many aspects of our digital lives. By combining the strengths of both technologies, we can look forward to a future where AI is not only more accessible, but more secure, transparent and potentially even more efficient. Speaking of, let’s breakdown the current workings of the AI industry and how it currently functions.


Breaking down the opaque walls of artificial intelligence

Comparing the overhaul of the financial system via crypto to the intelligence revolution occurring through the production of artificial intelligence systems, we can draw some very relevant similarities and make a case for the combination of the two.

In the present day, artificial intelligence companies like OpenAI, Google’s Deepmind, Anthropic and many, many others conduct their research and operations under closed doors.


Current opportunities in the crypto & artificial intelligence landscape

Now that we have covered some of the basics around AI and Crypto synergies, we can take a closer examination of some of the leading projects within the sector. While most of these are still actively working to bootstrap their networks, acquire a loyal user base and gain attention from the broader crypto community, they are all working at the forefront of the industry and represent a good representation of this rapidly growing sector.


Bittensor, a network of decentralized artificial intelligence models:

Bittensor is by far the most popular and well-established project building within the crypto & AI ecosystem. Bittensor is a decentralized network designed to democratize the field of artificial intelligence (AI) by creating a platform for numerous decentralized commodity markets, or ‘subnetworks’, united under a single token system. Its mission is to build a network that rivals the capabilities of large super corporations in AI, such as OpenAI, by employing unique incentive mechanisms and an advanced subnetwork architecture. Bittensor’s system can be thought of as a machine, facilitated by blockchains, to transfer AI capabilities on-chain efficiently.

The network is managed by two key players: miners and validators. Miners submit pre-trained AI models to the network and receive rewards for their contributions, while validators ensure the validity and accuracy of the models’ outputs. This setup creates a competitive environment where miners are incentivized to continually improve their models for better performance and greater rewards in $TAO, the network’s native token. Users interact with the network by sending queries to validators, who then distribute these to miners. The validators rank the outputs from these miners and return the highest-ranked responses to the user.

Bittensor’s approach to model development is unique. Unlike many AI labs or research organizations, Bittensor does not train models due to the high costs and complexity involved. Instead, the network relies on decentralized training mechanisms. Validators are tasked with evaluating the models produced by miners using a specific dataset and scoring each model based on certain criteria, such as accuracy and loss functions. This decentralized evaluation ensures a continuous improvement in model performance.

The architecture of Bittensor includes the Yuma Consensus mechanism, a unique hybrid of both Proof of Work (PoW) and Proof of Stake (PoS), which distributes resources across the network’s subnetworks. Subnetworks are self-contained economic markets each focusing on different AI tasks, like text prediction or image generation, and can choose to opt in or out of the Yuma Consensus depending on their functionality.

Bittensor represents a significant step in the decentralization of AI, offering a platform where diverse AI models can be developed, evaluated, and improved 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 in various sectors.


Akash, an open-source supercloud:

The Akash Network is an innovative, open-source Supercloud platform designed for buying and selling computing resources in a secure and efficient manner. It is built with the vision of providing users the power to deploy their own cloud infrastructure as well as to buy and sell unused cloud resources. This flexibility not only democratizes cloud resource utilization but also offers cost-effective solutions for users needing to scale their operations.

At the core of Akash’s system is a reverse auction mechanism, where users can submit bids for their computing needs and providers compete to offer services, often resulting in significantly lower prices compared to traditional cloud systems. This system is underpinned by reliable and well-established technologies like Kubernetes and Cosmos, ensuring a secure and dependable platform for hosting applications. Akash’s community-driven approach ensures that its users have a say in the network’s development and governance, making it a truly public and user-centric service.

Akash’s infrastructure is defined using a simple-to-use, YAML-based Stack Definition Language (SDL), which allows users to create complex deployments across multiple areas and providers. This feature, combined with Kubernetes, the leading container orchestration system, guarantees not only flexibility in deployment but also security and reliability in application hosting. Furthermore, Akash offers persistent storage solutions, ensuring data retention even after restarts, which is particularly beneficial for applications managing large datasets.

Overall, Akash Network stands out as a decentralized cloud platform, offering a unique solution to the monopolistic nature of current cloud service providers. Its model of utilizing underutilized resources across millions of data centers globally not only reduces costs but also enhances the speed and efficiency of cloud-native applications. With no need for proprietary language rewrites and no vendor lock-in, Akash presents a versatile and accessible platform for a wide range of cloud-based applications.


Render, a platform for expanding access to compute:

The Render Network is a blockchain-based platform designed to address the growing computational demands in media production, particularly in fields like augmented reality, virtual reality, and AI-enhanced media. It leverages unused GPU cycles to connect content creators needing computational power with providers who have available GPU resources. This decentralized approach, facilitated through blockchain technology, ensures secure and efficient processing of GPU-based tasks, including AI-driven content creation and optimization.

Render Network’s core offering is its integration with AI, which plays a crucial role in both content creation and process optimization. The network supports AI-related tasks, enabling artists to use AI tools for generating assets and enhancing digital artwork. This integration allows for the creation of ultra-high resolution 3D worlds and optimized rendering processes, like AI denoising. Additionally, Render Network’s use of AI extends to managing large-scale art collections and optimizing the rendering workflow, thus broadening the possibilities in creative processes.

The ecosystem of Render Network functions as a marketplace for GPU resources, serving various stakeholders such as artists, engineers, and node operators. It democratizes access to computational power, enabling both individual creators and larger studios to undertake complex rendering projects affordably. Transactions within this ecosystem are facilitated using the RNDR token, creating a vibrant economy centered around rendering services. As AI continues to reshape digital content creation, the Render Network is poised to become a key player in facilitating new forms of creative expression and technological innovation in the digital media landscape.


Gensyn, a decentralized compute platform:

Gensyn is an AI and cryptocurrency project focused on addressing the computational challenges and resource limitations inherent in state-of-the-art Artificial Intelligence (AI)systems. The project aims to overcome the barriers to AI advancement caused by the enormous resource requirements needed to build foundational models. Gensyn’s approach is to create a decentralized, blockchain-based protocol for efficiently leveraging global compute resources.

The background of Gensyn highlights the increasing computational complexity of AI systems, which is outpacing the available compute supply. For instance, training large models like OpenAI’s GPT-4 requires substantial resources, creating significant barriers for all parties involved. This dynamic has led to demands for a system that can efficiently use all available compute resources, addressing the limitations of current solutions, which are either too expensive or insufficient for large-scale AI work.

Gensyn aims to solve this problem by creating a decentralized protocol that connects and verifies off-chain deep learning work in a cost-efficient manner. This protocol faces several challenges, including work verification, marketplace dynamics, ex-ante work estimation, privacy concerns, and the need for effective parallelization of deep learning models. The protocol intends to build a trustless compute network with economic incentives for participation and a method to verify that computational work has been performed as promised.

The Gensyn Protocol is a layer-1 trustless protocol for deep learning computation that rewards participants for contributing their compute time and performing ML tasks. It uses a combination of techniques to verify the work completed, including probabilistic proof-of-learning, a graph-based pinpoint protocol, and a Truebit-style incentive game. The system involves various participants such as Submitters, Solvers, Verifiers, and Whistleblowers, each playing a specific role in the computational process.

In practice, the Gensyn Protocol involves several stages, from task submission to contract arbitration and settlement. It aims to create a transparent, low-cost marketplace for ML compute, enabling scalability and efficiency. The protocol also offers an opportunity for miners with powerful GPUs to repurpose their hardware for ML computation, potentially at a lower cost compared to mainstream providers. This approach not only addresses the computational challenges of AI but also aims to democratize access to AI resources.


Fetch, an open platform for the artificial intelligence economy: has been around longer than some of the previously mentioned projects, with a large variety of services offered on its website. At its core, Fetch is an innovative project at the intersection of artificial intelligence (AI) and cryptocurrency, aimed at revolutionizing the way economic activities and processes are carried out. The foundation of Fetch’s offerings is its AI agents, designed as modular building blocks that can be programmed to execute specific tasks. These agents are capable of autonomously connecting, searching, and transacting, thereby creating dynamic marketplaces and altering the traditional landscape of economic activity.

One of the key services offered by Fetch is the ability to make legacy products AI-ready. This is achieved by integrating their APIs with Agents, a process that is quick and does not necessitate altering the underlying business application. The AI agents can be combined with other agents in the network, opening up possibilities for new use cases and business models. Furthermore, these agents possess the capability to negotiate and transact on behalf of users, enabling them to earn from their deployment.

Additionally, these agents can provide inferences from machine learning models, allowing users to monetize their insights and enhance their machine learning models.

Fetch also introduces Agentverse, a no-code managed service that simplifies the deployment of AI agents. Just like legacy no-code platforms are gaining traction (Replit)and services like Github’s Copilot making writing code accessible to the masses, Fetch is working to further democratize web3 development in its own unique way.

Through Agentverse, users can launch their first agent effortlessly, which significantly lowers the barrier to entry for using advanced AI technologies. In terms of AI Engine and Agent Services, Fetch utilizes large language models (LLMs) to discover and directtask execution to the appropriate AI agents. This system not only monetizes AI apps and services but also serves as a comprehensive platform for agent services including building, listing, analytics, and hosting.

The platform enhances its utility with features such as Search & Discovery and Analytics. Agents can be registered in the Agentverse for active discoverability on’s platform, which employs a targeted LLM-based search. Analytical tools are available for improving the effectiveness of an agent’s semantic descriptors, thereby enhancing their discoverability. Moreover, incorporates an IoT Gateway for offline agents, enabling them to collect messages and process them in batches upon reconnection.

Finally, offers hosting services for managed agents, providing all the features of Agentverse besides hosting. The platform also introduces an open network for agent addressing and naming, leveraging’s Web3 network. This aspect signifies a novel approach to Web DNS addressing, integrating blockchain technology into the system. Overall, presents a versatile platform that merges AI and blockchain technology, offering tools for AI agent development, machine learning model monetization, and a groundbreaking approach to search and discoverability in the digital economy. This combination of AI agents and blockchain technology paves the way for automating and optimizing various processes in a decentralized and efficient manner.


Next steps and projections for both industries

The seamless integration of artificial intelligence and blockchain technology represents a pivotal advancement in both sectors. This combination is not just a mere fusion of two cutting-edge technologies, but a transformational synergy that redefines the boundaries of digital innovation and decentralization. The potential applications of this integration, as explored in various projects like, Bittensor, Akash Network, Render Network, and Gensyn, demonstrate the vast possibilities and significant benefits of combining AI’s computational power with blockchain’s secure and transparent framework.

As we look toward the future, it is evident that the convergence of AI and blockchain will play a crucial role in shaping various industries. From enhancing data security and integrity to creating new models of decentralized autonomous organizations, this amalgamation holds the promise of more efficient, transparent, and accessible technologies. Particularly in the realm of decentralized finance, the emergence of decentralized AI (DeAI) could democratize access to AI technologies, breaking down the barriers that have traditionally favored large corporations. This could lead to a more inclusive digital economy where individuals and smaller entities can leverage AI tools and services that were previously out of reach.

Furthermore, the integration of these technologies is poised to address some of the most pressing challenges in both domains. In AI, issues like data silos and the immense computational resources required for training large models can be mitigated through blockchain’s decentralized data management and shared computational power. In the blockchain space, AI can enhance efficiency, automate decision-making processes, and improve security mechanisms. As we advance, it is crucial for developers, researchers, and stakeholders to continue exploring and harnessing the synergies between AI and blockchain. By doing so, they will not only contribute to the growth of these individual fields but also drive innovation across the digital landscape, ultimately benefiting society as a whole.



‍This article is written by Reflexivity Research.

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Disclaimer: This research report is exactly that — a research report. It is not intended to serve as financial advice, nor should you blindly assume that any of the information is accurate without confirming through your own research. Bitcoin, cryptocurrencies, and other digital assets are incredibly risky and nothing in this report should be considered an endorsement to buy or sell any asset. Never invest more than you are willing to lose and understand the risk that you are taking. Do your own research. All information in this report is for educational purposes only and should not be the basis for any investment decisions that you make.

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