At a critical turning point in this new technological era, huge trends in the fields of artificial intelligence (AI) and Crypto have begun to merge, bringing profound changes to the entire industry. The emergence of breakthrough technologies such as ChatGPT in the AI ââfield will attract a staggering $25 billion in investment in 2023 alone, a fivefold increase from the previous year. This surge not only demonstrates continued confidence in the potential of AI, but also reignites heated discussions about the convergence of AI and Crypto. Notably, Ethereum co-founder Vitalik Buterin made an important contribution on this topic, providing insights into the promises and challenges of integrating AI with Crypto.
Recently, OpenAI launched the Sora model, an innovative text-to-video model, which has attracted the attention of the technology community and demonstrated the rapid pace of artificial intelligence development. OpenAI CEO Sam Altman has proposed a bold plan to raise $7 trillion for chip design and manufacturing, highlighting a strong commitment to the evolution of artificial intelligence while also triggering in-depth thinking about the potential of the encrypted artificial intelligence market.
Although many of these collaborative applications are still in their early stages, the market remains optimistic.
Walking in different paths
Traditionally, the two have been viewed as opposing forces: Crypto focuses on decentralization, while AI favors centralization. This stark contrast is vividly illustrated by Peter Thiel and further elaborated in an in-depth discussion by a16z Cryptoâs Ali Yahya. However, recent developments reveal an unexpected convergence that promises to reshape digital innovation. In exploring this dynamic intersection, we discovered the huge potential for collaborative synergy between AI and Crypto.
This integration takes full advantage of the strong advantages of encrypted networks in data ownership, transparency and ethical governance, complements the advanced capabilities of AI, and provides novel solutions to the centralization challenges in the AI ââindustry:
Ensure data ownership: With blockchain technology, users can encrypt and regulate access to their data, providing them with the means to oversee the use of data by AI systems.
Increased transparency: The immutable nature of blockchain acts as a transparent ledger, facilitating verification and authentication of data used in AI models.
Achieving direct data monetization: Blockchain facilitates the direct monetization of user data, encouraging data sharing by providing economic incentives while ensuring personal control.
Reduce the energy consumption of AI: By adopting energy-efficient mechanisms such as proof of stake, blockchain is expected to minimize the energy requirements of AI training, thereby promoting sustainable progress in AI development.
Advancing Ethical AI: Blockchainâs inherent transparency and inclusivity can promote more ethical AI practices, eliminating the secrecy often associated with AI innovation.
AI and Crypto innovation bridge the gap and shape future advantages
zkML: An innovative move to advance AI privacy protection
While the capabilities of modern AI are impressive, they also raise pressing concerns about user privacy, security, and autonomy. In the process of AI model training, centralized data aggregation directly challenges personal privacy rights, especially in a single technology ecosystem, where it is difficult for users to control their own data.
In response to this challenge, innovation guided by the concept of decentralized encryption has emerged, where cryptographic technologies such as zero-knowledge proofs (ZKP) enable privacy-preserving machine learning without sacrificing sensitive user data. Despite the many advantages of these methods, there are still some challenges compared with traditional large-scale data aggregation practices, including issues with computational efficiency, model accuracy, and debugging.
It is worth noting that zero-knowledge machine learning (zkML), led by teams such as Modulus Labs and EZKL, has made significant progress, marking the rapid development of this field. As hardware acceleration technology continues to improve, there is optimism about the prospects of zkML.
Authenticity challenges in the era of deepfakes
In an age where deepfake technology is spreading, protecting the authenticity and trustworthiness of digital content is crucial. Blockchain technology is expected to significantly facilitate the creation of decentralized and tamper-proof identity registration systems. This registration system maps public keys to real identities, providing an easy way to establish trust and hold people accountable for malicious behavior.
Worldcoin, co-founded by Sam Altman, is one of the most compelling crypto protocols to address current challenges. The goal is to achieve a global registration of every individual through Orb biometric scanning to reliably differentiate between humans and machines. The protocolâs incentive mechanism uses a dedicated blockchain token called WLD. As of now, the Worldcoin team has made significant progress in 120 countries around the world, attracting more than 3.8 million people to register.
Another initiative to address this issue is the Digital Content Provenance Recording (DCPR) standard launched by the Arweave and Irys (formerly Bundlr) teams. This standard makes full use of Arweave blockchain technology to timestamp and verify digital content, providing users with reliable metadata and helping to assess the credibility of digital information.
Addressing bias in AI models
As AI models become increasingly integrated into our daily lives, there are widespread concerns about their potential for bias. For example, AI-powered chatbots may quietly exert influence among consumers, subtly guiding them to choose specific products or ideologies, leading to the breakdown of trust with far-reaching consequences.
Bittensor, a decentralized computing protocol, combats AI bias by incentivizing diverse pre-trained models to compete for the best response. Validators reward high-performing models while weeding out poor-performing and biased models. By fostering an open and collaborative environment across various models and datasets, Bittensor is expected to advance AI while proactively combating the negative impacts of bias.
Although Bittensor is still in its early stages of development, it has already made significant progress, with 32 specially customized sub-networks suitable for specific use cases such as text prompts, image generation, price prediction, data scraping, storage, etc.
Driving the rise of AI development through increased accessibility
The surge in AI and machine learning (ML) workloads has created huge demand for high-performance graphics cards, such as the Nvidia A100. However, the huge capital costs associated with computing and storage can exclude many people, leaving AI development largely monopolized by tech giants. In response to this challenge, an emerging marketplace similar to the âAirBnB of graphics cardsâ has emerged, allowing individuals and organizations to rent unused GPU resources to meet the needs of AI researchers and developers.
Decentralized computing markets, such as Akash Network and Render Network, are designed to solve the efficiency problem of underutilized GPU resources by connecting GPU owners with AI developers seeking computing power. By leveraging these decentralized computing platforms, a new batch of computing resources becomes accessible, enabling individuals around the world to monetize their idle computing power. At the same time, it provides AI developers with flexible access to computing resources, free from the constraints of centralized giants.
By leveraging blockchain technology to eliminate profit-seeking and additional cost intermediaries, these decentralized networks can provide services at a fraction of the cost of their centralized counterparts. Akash Network even boasts of rates that are just one-fifth of traditional costs. Additionally, Render Network, which focuses on the GPU market for 3D image rendering, experienced significant usage surge in 2023.
Looking forward
When looking at the current status of the AI ââand Crypto fields, it is obvious that both have strong technical capabilities, but each also faces significant shortcomings. Despite its capabilities, cryptography still lacks mainstream maturity for widespread adoption. At the same time, the centralized control of AI by Big Tech companies has raised concerns about a monopoly on the technology.
While this synergy is still in its early stages, projects combining AI with Crypto are building an infrastructure for scalable on-chain AI interactions. This promising momentum is expected to continue growing in 2024 and beyond. All of this depends on market participants viewing these assets as a counterweight to the potential dominance of major centralized players such as OpenAI.
Careful integration of these revolutionary technologies will subtly expand the ways to address their respective weaknesses. This points to a future where blockchain-based AI builds a paradigm that preserves privacy while opening the door to potential use cases. The prospects for decentralized computing, zkML, and AI agents are promising, laying the foundation for a deeply connected AI and crypto future. Their potential is huge and stems from a grassroots developer community that has formed spontaneously and is committed to advancing the application of technology in a way that is fair and accessible to everyone.
