Recently, cryptocurrencies themed around artificial intelligence (AI) were all the rage. Experts called for an AI revolution in the cryptocurrency markets. But what lies behind the hype surrounding the fusion of AI and blockchain technology?
Currently, according to CoinGecko, the combined market capitalisation of digital assets in this narrative stands at over USD 44 billion. At one point, any project with the term “AI” in its pitch automatically became a crypto-Twitter favourite and was being shilled by self-proclaimed crypto experts on Twitter. However, this narrative soon changed for the better. Some AI crypto projects were unable to deliver on their promises. How these AI crypto projects branded themselves vs what they became was a topic of open discussion. This is when users began paying closer attention to detail and this pushed projects to be practical when setting expectations for its users. So how did this AI hype and market narrative come about? What are some top projects building in this space? How does the future for this sector look?
Key takeaways
- Funds raised by companies in the artificial intelligence (AI) sector since 2019 stands at over USD 220 billion. Demand for AI tools and high-end hardware rises.
- To meet surging demand, decentralised solutions emerged, and crypto markets saw a new narrative: the confluence of AI and blockchain technology.
- AI-powered onchain agents and decentralised physical infrastructure networks (DePINs) are the two main AI-themed sectors in crypto.
- Bittensor, Fetch.ai, Render Network and Worldcoin are the top AI-themed crypto projects in terms of market capitalisation and popularity.
- Zero knowledge (ZK) machine learning (ML) shows promise as a possible solution to balance the data-intensive nature of AI and the cypherpunk privacy-first ethos of crypto.
- The sector is promising, however, multiple challenges currently exist which include service provider reliability and lagging demand.
- While the possibilities for AI and crypto are exciting, it would be wise to not have unrealistic expectations of what could be possible.
Development of open markets
Free, open markets have always incentivized leaps in human productivity. The industrial revolution was the biggest jump we witnessed in this regard – something that forever changed the way markets function. From transportation to manufacturing, it offered a better lifestyle and brought about a structural change in the job market. Fast forward, the dot com boom also radically changed our daily life. It brought more investments into technology companies and led to the displacement of less efficient ones. Companies that leveraged tech took the lead. This lead is now widening with the advent of artificial intelligence.
AI companies have brought about a revolution in increasing human efficiency and the sector has attracted investments. According to Statista, since 2011, total funding into this sector has been over USD 284 billion while in 2023 alone, total funding of companies offering AI services exceeded USD 40 billion. The breakthrough in AI was brought about by Open AI’s large language model (LLM) ChatGPT which put the technology into the hands of the common man through a simple chatbot interface. Most AI solutions in the market were based on generative AI (Gen AI).
The AI Phenomenon
For context, generative AI models are those that can generate text, images, videos or other data in response to prompts (requests). This includes ChatGPT which reached a million in user base in merely 5 days. It currently has over 180 million monthly active users. This growth inspired further research and developments in the AI chatbots sector. For example, Google launched Bard, then ended the year with another one, Gemini. Baidu launched Ernie. Meta put out various AI tools. Amazon launched Amazon Q, although only in preview. Image creating tools also proliferated and improved. Midjourney led the way in terms of providing the richest and most complex output but OpenAI’s DALL-E too advanced rapidly.
Meanwhile, even open-source efforts emerged. HuggingFace has now become a leader in open-source generative AI. This even led to structural changes in the job market. For example, companies increased job postings for machine learning (ML) engineers to build AI systems. For the uninitiated, ML is a branch of AI that focuses on enabling a machine to imitate intelligent human behaviour. It involves training AI algorithms on various data sets to ensure it provides an optimal output for a specific input.
Cost of AI infrastructure
AI models are computationally intensive to run. The hardware resources required to run an AI program can vary depending on several factors like the complexity of the program, size of the datasets required to train the model and the desired performance level. While computer central processing units (CPUs) were key in running these complex programs, graphic processing units (GPUs) were the game changer. CPUs are versatile and can handle a variety of input types. However, GPUs enable parallel processing in computer systems. This helps process data several orders of magnitude faster than a CPU. With most AI models built around graphics rendering (DALL-E and Midjourney, for example) and natural language processing (ChatGPT is an example), the demand for GPUs went up.
Every tech enthusiast soon wanted to customise open-source AI models according to their requirements and on their own machines. Developers and tech companies therefore began buying GPUs in bulk. This led to a demand explosion and a spike in prices across the globe for GPUs. Once it became difficult to get their hands on such hardware to run models, developers looked to non-traditional rails to do the same – the decentralised web. Thus a new wave came about: the crossover of AI, decentralised computing and cryptocurrencies.
AI and Crypto
AI-themed cryptocurrency projects and tokens are ones that either provide AI solutions or remotely provide resources to run AI tech. They mark a key narrative in the blockchain industry. By combining AI and blockchain technology, AI-based crypto projects aim to solve issues such as GPU scarcity and help users who want to leverage AI but lack the technical know-how. Implementing AI on blockchains will also allow smart contracts to securely query machine learning models. This will create a new area of blockchain technology known as zero-knowledge machine learning (ZKML).
Decentralised Physical Infrastructure (DePIN)
The surge in popularity of AI chatbots like ChatGPT and Bard sparked an increased need for graphics processing units (GPUs), which are crucial for running AI. This caused a global GPU shortage, prompting cloud giants like Amazon Web Services (AWS), Microsoft and others to limit GPU access. As wait times stretched to months developers began considering DePINs which can connect people who need computing power with unused CPU or GPU resources. These are some examples of decentralized providers:
- Render Network: Bridges the gap between node operators and users requiring 3D rendering services. These can be simple images, animations or motion graphics. Users submit rendering requests which are fulfilled by node operators using their GPU resources. Node operators receive RNDR tokens as compensation.
- Io.net: An open-source GPU network dedicated to machine learning applications that secured USD 30 million funding, currently valued at USD 1 billion. It partnered with Render and Filecoin, and allows Apple users to contribute to the network through support for Apple silicon chips, promoting retail participation.
- Akash Network: A decentralised open-source cloud computing platform that connects server owners in need of computing power to host applications through its marketplace. The network runs on a Tendermint-based blockchain using the Cosmos SDK. Currently, it has a computation resource pool of 74 GPUs and 5.6K CPUs. A USD 3.5 million incentive program is underway to attract more GPU providers and allocate Nvidia chips to the network.
- Grass: Previously known for providing residential proxies and unused internet bandwidth, Grass pivoted to machine learning data. Offering web scraping services to meet the needs of AI applications. Grass engages users in data labeling and is looking to create automated models for these tasks. Their goal is to establish a data infrastructure which can be leveraged by any onchain AI model.
Onchain AI Agents
AI has opened new ways to design smart contracts for decentralised finance (DeFi) dapps. Tools and software can make on-chain transactions easier for end users, reducing the need for manual execution. AI smart contracts agents can also handle complex tasks. For instance, in blockchain based marketplaces or games, AI characters could interact independently, carrying out actions based on user inputs. Decentralized infrastructure suits AI agents because there are fewer barriers than in traditional financial systems. For example, crypto marketplaces do not require personal identity verification (KYC), a standard in traditional financial services. AI agents make it easier for new blockchain users to interact with the technology. They connect directly with the underlying smart contracts, allowing execution of tasks too complex for regular users.
Some use cases:
- Automation in DeFi e.g. lending protocols: Lending platforms could employ machine learning (ML) models to dynamically adjust parameters in real time. Currently, lending protocols rely heavily on off-chain models managed by their respective decentralised autonomous organisations (DAOs) or sometimes even private organizations for determining parameters like collateral factor, loan-to-value (LTV), liquidation thresholds and other risk factors. A possible approach would be to use open-source models trained by the community, which can be publicly monitored and validated.
- On-chain credit assessment: A ML model can use data from a user’s wallet activity, past transactions and behaviour onchain to create a clear and trustworthy evaluation of their creditworthiness. Open-sourcing these models would be a stepping-stone for permissionless collateral-free lending to gain adoption.
- Gaming: Blockchain-based gaming environments can leverage AI to enhance their offerings. It's development can assist in the creation of visual game content including characters, objects and environments. Additionally, non-player characters (NPCs) can leverage AI to adapt and customize themselves based on a player’s profile and in-game surroundings, elevating the immersive experience.
Onchain AI projects
These concepts may sound futuristic. However, groundwork is being carried out by many protocols. Protocols currently are very basic in terms of decentralising AI services. They are either done manually by blockchain network securing entities called nodes or provide tools to developers to launch AI-powered agents onchain.
Two of the most popular projects in this space are Bittensor and Fetch.
- Bittensor: Allow users to rent out their AI models to be used in various applications. Bittensor has multiple subnets, each specializing in a particular machine learning task or providing specific resources. These subnets offer a variety of services such as AI chatbots, financial prediction tools, pre-trained models, data storage and more. To access these services, users must go through subnet validators, who serve as the gatekeepers for each subnet. According to Tao Stats, 32 subnets and 1001 active miners exist at the time of writing.
- Fetch.ai: A platform that provides tools and software for developers to create AI agents. Recently, Fetch launched Fetch Compute – a program investing USD 100 million to purchase Nvidia H100s and A100s. These GPUs will be used by developers and users for computing tasks on the Fetch platform. By contributing to the network, users who stake the project’s native FET token can earn Fetch Compute Credits for access to GPU use.
ZKML: The future?
The integration of AI and cryptocurrency requires balance. While AI systems require extensive data, the crypto’s cypherpunk culture emphasizes privacy and anonymity. This poses a challenge in improving the accuracy of AI models without compromising data integrity. This is where zero knowledge proofs (ZKPs) come in. ZKPs offer a potential solution by allowing verification without exposing sensitive information. They allow a person (prover) to show that something is true without sharing additional details. When combined with machine learning (ML), this technology enables the generation of results without revealing the sensitive data used for training.
This technology guarantees both computational accuracy and data privacy. This allows AI models to demonstrate their effectiveness publicly while maintaining secrecy. The convergence of ZKPs and ML is known as ZKML – zero knowledge machine learning. Interested readers can dive deeper into ZKP technology through our blog on the topic here. Worldcoin is a notable project that utilizes ZKML technology. Its ZK-based iris recognition system verifies the uniqueness of individuals without disclosing specific iris features or model parameters. ZKML has numerous potential applications. For instance, decentralized social media platforms (like Lens Protocol or Farcaster) could incorporate ZKML to develop tailored social feed recommendations based on each user’s blockchain activities and interactions.
The challenges of combining AI with blockchain technology
AI relies heavily on computational power and data availability. While the past year has seen significant traction for GPU networks leading to a crowded market, data-focused projects have received less attention. However, this trend is changing, with growing emphasis on data-centric initiatives. The potential for incentivized, crowdsourced data networks remains largely unexplored and holds promising prospects. There is market optimism about the potential of combining cryptocurrency and artificial intelligence. AI-related cryptocurrencies continue to be highly popular. This year, the global AI market is projected to reach USD 305 billion and have a CAGR of over 15% until 2030. Meanwhile, the GPU-as-a-service market is expected to expand significantly, to USD 28.7 billion by 2030. Despite these promising figures, there are still challenges to overcome in this field.
Growth in GPU networks depends on balancing supply and demand. To expand, networks must increase demand from paying customers and attract and fulfil the supply of computational resources. However, a challenge exists in that suppliers require income to participate while customers need enough suppliers to meet their needs. This is because even though centralised computing services face a supply crunch, the supply of decentralised computing resources far exceeds demand. For instance, Akash Network’s utilization rate has dropped from 40% in January to around 30% currently.
Prioritizing cost or computing power?
Cheaper decentralized compute networks face challenges in attracting users who can get their needs met by traditional GPU providers, especially since the cost savings are not as substantial as advertised. Moreover, the demand for these networks is further complicated by the different utilization rates. Low-end GPUs are often underutilised while high-end ones like the Nvidia A100s and H100s are consistently operating at maximum capacity, leading to limited availability and undermining potential supply.
When AI companies and users compare cloud computing services from web2 and web3 platforms, they prioritize both cost and reliability. While web3 platforms like DePIN offer lower costs, they often face issues with reliability and reputation. Therefore, improving on reliability will strengthen demand. At the same time, offering token rewards and sharing in the network’s growth incentives will attract suppliers to join the network. This approach provides economic incentives for new suppliers to join and contribute to the network’s growth.
Conclusion
Many cryptocurrencies labeled as AI are currently riding a wave of hype, leading to inflated prices for these projects. This excitement around AI resembles the initial hype around crypto and blockchain. While crypto and blockchain have had real impacts and will continue to evolve, they have not lived up to all the exaggerated claims made at the beginning. The same could happen with AI: the hype might overshadow its potential, causing disappointment.
Many use cases are still in their infancy and most projects may not endure beyond this market cycle, although a select few might. Investors need to be prudent to meticulously select their investments based on fundamentals rather than solely riding the current hype wave. Artificial intelligence, blockchain and cryptocurrencies have enormous potential for collaboration, offering many advantages. The possibilities are vast and exciting, but we must avoid having unrealistic expectations in terms of what could be possible.
