Artificial Intelligence (AI) and crypto are arguably the two most disruptive technologies of our time, each with the potential to reshape industries, economies, and even society as a whole.

Many investors are now realizing that these two seemingly distinct innovations are beginning to intersect, potentially in massively transformative ways. As AI advances, concerns over centralization, privacy, and transparency are surfacing—issues that can be addressed by decentralized technologies. Understanding this intersection is something our team has been closely tracking given both the near- and long-term consequences—and potential opportunities.

 

Centralization concerns with a potentially $15T market 

 

AI’s potential is immense, with speculation its market value may reach $15 trillion by 2030. ¹ Companies like Google, Microsoft, and OpenAI are leading the charge, creating powerful AI models and tools that are driving this exponential growth. However, this dominance by a few major players has sparked concerns about centralization, privacy, and control. As AI becomes more integral to everyday life—shaping everything from healthcare and finance to media and entertainment—the question arises: Who controls this powerful technology?

The fact that a handful of corporations are responsible for developing and deploying the most advanced AI systems raises important questions:

1. Who influences the output of these AI models?

2. What are the incentives of these companies?

3. Will these businesses act in the best interest of society in the long run?

These concerns strike at the heart of a major issue: trust. Trust in AI models, trust in the companies behind them, and trust in the data they are built upon. Increasingly, this centralization has drawn attention to crypto assets and their underlying ethos of decentralization, transparency, and privacy as potential solutions to these challenges.

 

Decentralizing AI through blockchain-based technologies

 

The inherent value proposition of decentralized networks lies in their ability to distribute control, ensuring that no single entity can dominate. Blockchain networks, dependent on crypto assets, offer a decentralized infrastructure that can democratize access to AI development and deployment. Instead of relying on a few large tech corporations, blockchain-based AI platforms aim to create a more open, transparent, and equitable AI landscape.

Unlike social media and the largest current internet platforms, which became dominant platforms via a centralization of services, it will be far more difficult for AI to scale and live up to its potential without decentralization given the inherent trust issues that come with this technology (e.g., deep fakes, data control, etc.). 

This shift from centralization to decentralization is critical for maintaining the integrity and fairness of AI systems as they continue to evolve. This is why there has been the development of blockchain platforms specifically designed to decentralize AI (e.g., Bittensor, Near), which allow developers to contribute to and access AI models without the gatekeeping of a centralized authority. The result is a more diverse ecosystem where AI innovation can thrive unencumbered by the control of a few large companies. 

In addition to these decentralized AI platforms, crypto assets are being used in a variety of innovative ways to address other critical issues in the AI space. Decentralized blockchain networks, such as Chainlink (LINK), can be used to improve data sources for training models while decentralized high-performance computing networks can help make AI models more resilient (e.g., Render). Smart contract platforms, including Solana (SOL), are also used for code execution and information registry, which raises demand for these networks native tokens. 

 

Crypto’s role in AI’s long-term development

 

While the relationship between AI and crypto is still in its early stages, the potential for long-term synergy is immense. Investors have already started paying attention, as AI-focused crypto assets have outperformed other segments of the crypto market in recent months. However, this initial investor interest is just the tip of the iceberg. As AI matures over the next 5, 10, or 15 years, crypto will increasingly play a pivotal role in solving some of the biggest challenges with AI. Here are how crypto technologies might provide solutions:

 

1. Concentration of Power in AI

• Challenge: Access to cutting-edge AI technology is currently concentrated in the hands of a few large corporations, creating barriers for smaller companies and independent developers.

• Crypto Solution: Blockchain can reduce barriers to entry by decentralizing AI infrastructure. For instance, mining or staking mechanisms can lower the cost of AI computing power, making it accessible to a broader pool of developers and researchers. Additionally, decentralized platforms can crowdsource computing resources, reducing the dependency on large tech corporations.

 

2. Ownership and Control of Data

• Challenge: In the current AI landscape, data is controlled by a few firms that monetize it without necessarily compensating the original data owners. This lack of transparency and fairness erodes trust in AI systems.

• Crypto Solution: Smart contracts can enforce ownership rights and facilitate automated, transparent payments for data usage. This would allow individuals to retain control over their data, while companies can still access the data they need to train AI models—creating a more equitable ecosystem. Blockchain ensures that all transactions and data exchanges are transparent and immutable, further enhancing trust.

 

3. Accuracy and Reliability of AI Models

• Challenge: AI models are only as good as the data they are trained on. Ensuring that the data used to train AI is accurate, trustworthy, and reflects reality is a significant hurdle, especially when misinformation and fake data proliferate.

• Crypto Solution: Blockchain’s ability to verify data provenance and ensure data quality through decentralized consensus mechanisms can be applied to AI training datasets. By utilizing a distributed network of validators, blockchain systems can certify that the data used in AI models is accurate, up-to-date, and tamper-proof.

 

4. Responsiveness to New Information

• Challenge: Centralized AI models often struggle to incorporate new, relevant information in real-time, leading to outdated or incorrect outputs.

• Crypto Solution: Decentralized validators on a blockchain can facilitate faster incorporation of new data into AI models. By leveraging a global network of validators, AI systems can be updated in real-time, ensuring they remain responsive to current events and information. This decentralized approach also mitigates the risk of a single point of failure or bias, which is more common in centralized systems.

 

AI and crypto as complementary forces

 

AI tokens are currently worth over $40 billion,² and we expect this number to grow alongside the technology’s development, driven by:

• crypto improving AI models; 

• AI models adoption of blockchains as physical infrastructure, ID platforms, and payment systems; and 

• smart contracts platforms are increasingly used for AI code execution and information registry.

While the full intersection of AI and crypto remains to be seen, it is clear that these two technologies will play significant roles in shaping each other’s futures. Crypto’s decentralized, transparent, and privacy-preserving frameworks provide a natural counterbalance to the growing centralization of AI. As AI continues to develop and take on greater importance in the global economy, digital assets will likely serve as both a critical infrastructure component and a tool to address some of AI’s most pressing challenges.

As more projects emerge at the intersection of AI and blockchain, investors will have the opportunity to capitalize on a transformative technological shift that could reshape industries from finance and healthcare to media and beyond. The convergence of AI and crypto represents not just an evolution of two powerful technologies, but a fundamental reshaping of the technological landscape.

 

 ^{1 https://www.pwc.com/gx/en/issues/data-and-analytics/publications/artificial-intelligence-study.html}

 ^{2 CoinGecko}

 

 

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