The Convergence of AI and Blockchain: A 15-Year Horizon

Artificial Intelligence (AI) and blockchain were expected to revolutionize the world. However, things have taken a bit longer to manifest.

Artificial Intelligence (AI) and blockchain were touted to revolutionize the world, but things have taken a bit longer to manifest. Here's how they can converge and what challenges lie ahead.

Artificial Intelligence (AI) and blockchain were expected to revolutionize the world by now, but things have taken a bit longer to manifest. Both technologies have advanced significantly, but their convergence and mainstream adoption still face several challenges and opportunities.

In this article, we'll explore why AI and blockchain need to converge, the specializations forming amongst Large Language Models (LLMs), and why we expect 15 more years to see commercially viable applications go mainstream.

The evolution of AI: Specialization and cost challenges

As AI continues to leapfrog expectations, we're witnessing a trend towards specialization in LLMs. Models like Claude, developed by Anthropic, are already becoming popular among developers for technical tasks and coding assistance. Others focus on specific industries or use cases (e.g., ChatGPT for more general audiences, Gemini for copywriting, and Perplexity for general research).

This natural specialization reflects the growing demand for precision in AI applications, particularly in enterprise settings. However, this progress also comes at a cost.

Despite ongoing efforts to optimize AI models, the financial burden of using LLMs at scale remains significant. OpenAI’s GPT-4, for instance, charges $0.03 per 1K tokens for input and $0.06 per 1K tokens for output. Their o1 (‘Strawberry’) model, which focuses on reasoning, is being priced at $15 per 1 million input tokens.

These costs can quickly become prohibitive for businesses looking to integrate AI across multiple departments. For instance, a large e-commerce company might use an LLM to personalize product recommendations, generate marketing copy, and even assist customer service representatives.

While using an LLM for each task might be ideal from a performance perspective, the costs could quickly become unsustainable, especially considering that LLMs require continuous fine-tuning and maintenance.

To address this challenge and make AI more accessible to a broader range of applications, we need to explore alternative approaches that can reduce the overall costs of deploying and using AI models at scale.

One promising solution lies in converging AI with blockchain technology, specifically Scalable Blockchain Technology (what we call “SBT”), which offers several unique advantages for AI applications.

Blockchain: A potential solution for AI’s pain points

The convergence of AI and blockchain can pave the way for a new era of decentralized intelligence, where data privacy, security, and ownership take center stage. Here's how blockchain can address some of AI's most pressing pain points:

Data privacy and ownership: By integrating AI models with decentralized blockchain networks, we can create a system where data is no longer centrally controlled or owned. Instead, individuals and organizations could securely contribute their data to a collective pool, ensuring that AI models have access to a diverse and privacy-preserving dataset.

In this scenario, data contributors would retain ownership and control over their data, and they could choose to opt out or revoke access at any time. This approach aligns closely with the principles of Web3 and decentralized data governance, empowering individuals to participate in the data economy without sacrificing their privacy.

Secure and immutable data input: Another key benefit of converging AI and blockchain is the ability to ensure the integrity and immutability of data used to train and operate AI models.

In a decentralized AI system, data would be securely recorded on the blockchain, making it virtually impossible to tamper with or manipulate. This immutable data record would serve as a single source of truth, ensuring that AI models are always operating on the most accurate and up-to-date information.

By combining the strengths of AI and blockchain in this way, we can create a new generation of AI models that are not only powerful and efficient but also privacy-preserving, secure, and transparent.

Several initiatives are already exploring the potential of blockchain for secure and privacy-preserving AI applications. For instance, the European Blockchain Services Infrastructure (EBSI) is examining how blockchain can be used to create a trusted and secure environment for deploying AI models.

Similarly, projects like Ocean Protocol are developing decentralized data marketplaces that could revolutionize how AI models access and use training data. And projects like Teranode are showcasing what's truly possible at scale—something AI systems need since they deal with infinitely larger datasets than traditional ones.

Roadblocks on the path to convergence

Despite the potential for synergy between AI and blockchain, several significant roadblocks stand in the way of seamless integration:

Nascent regulatory frameworks: Both AI and blockchain are still emerging technologies that are rapidly evolving. As a result, regulatory frameworks governing their use and application are still nascent and vary widely across jurisdictions.

This lack of clear and consistent regulation poses a challenge for businesses and technologists seeking to converge AI and blockchain in a legally compliant manner.

For instance, some jurisdictions might have strict data privacy laws that limit the use of certain AI techniques, while