The convergence of AI and blockchain: A 15-year prediction

This post is a guest contribution by George Siosi Samuels, managing director at Faiā. See how Faiā is committed to staying at the forefront of technological advancements here.

Artificial Intelligence (AI) and blockchain were expected to revolutionize the world. However, things have taken a bit longer to manifest. So, it’s crucial to understand the challenges and opportunities ahead. This article explores 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. While 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, as well as the inherent bias that comes into systems.

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. While 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.

Blockchain: A potential solution for AI’s pain points

Scalable Blockchain Technology (what we call “SBT”) offers promising solutions to some of AI’s most pressing challenges, particularly regarding data privacy, security, and ownership.

The European Blockchain Services Infrastructure (EBSI) is already exploring blockchain’s potential for secure and privacy-preserving AI applications. Similarly, projects like Ocean Protocol are developing decentralized data marketplaces that could revolutionize how AI models access and use training data.

However, projects like Teranode are probably the most exciting because they show 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, several significant roadblocks stand in the way of seamless AI-blockchain integration:

• Scalability: Current blockchain networks struggle to match the processing speeds required for AI applications. Ethereum, one of the most popular blockchain platforms, can only process about 15-30 transactions per second (tps). Bitcoin (BTC) can only run seven tps on average.
• Energy efficiency: AI and blockchain are notorious for their high energy consumption. Training a single AI model can emit as much carbon as five cars in their lifetimes. Microsoft (NASDAQ: MSFT) is now even tapping into nuclear energy to fuel its AI energy needs.
• Regulatory hurdles: Regulations like the EU’s General Data Protection Regulation (GDPR) pose challenges for blockchain-based AI systems—particularly concerning data privacy and the right to be forgotten—since many of these systems are designed to retain data.

The 15-year horizon

Given these challenges, we predict 15 more years for a more commercially viable convergence of AI and blockchain. Gartner’s Hype Cycle for Emerging Technologies places AI and blockchain at different stages of maturity, suggesting that complete integration is still some years away.

Several factors could accelerate this timeline:

• Quantum computing: The development of practical quantum computers could revolutionize both AI and blockchain. IBM’s (NASDAQ: IBM) plans for a 1000+ qubit quantum computer by 2023 mark a significant milestone, though practical applications may take longer to materialize.
• Regulatory developments: As governments and international bodies develop more comprehensive AI and blockchain regulations, we may see increased pressure for transparent and secure data usage methods.
• Technological breakthroughs: Advancements in areas like Zero-Knowledge Proofs (ZKPs) and Layer 2 scaling solutions could address current scalability and privacy concerns for popular blockchains. However, once again, some solutions (like Teranode) offer true Layer-1 scaling.

Conclusion

The convergence of AI and blockchain represents a massive opportunity for the tech world. While significant challenges remain, the potential benefits in terms of data privacy, security, and “decentralized intelligence” are immense. As we navigate this 15-year journey, it’s crucial for businesses, policymakers, and technologists to collaborate in addressing the current limitations and paving the way for a more energy-efficient, intelligent future.

At Faiā, we’re committed to staying at the forefront of these technological developments. We help our clients navigate the complex landscape of emerging technologies—from AI to blockchain—while keeping their cultural integrity intact. As these technologies mature, we anticipate exciting new possibilities for creating value and solving complex problems in ways we’re only beginning to imagine.

In order for artificial intelligence (AI) to work right within the law and thrive in the face of growing challenges, it needs to integrate an enterprise blockchain system that ensures data input quality and ownership—allowing it to keep data safe while also guaranteeing the immutability of data. Check out CoinGeek’s coverageon this emerging tech to learn more why Enterprise blockchain will be the backbone of AI.

Watch: Transformative AI applications are coming

title=”YouTube video player” frameborder=”0″ allow=”accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share” referrerpolicy=”strict-origin-when-cross-origin” allowfullscreen=””>