Bitcoin Over Ethereum: Arthur Hayes Foresees the Cryptocurrency of Choice for AI

The rapid advancement of artificial intelligence (AI) brings with it the need for a robust and efficient payment system. BitMEX co-founder Arthur Hayes believes that Bitcoin (BTC) is the ideal currency to meet the requirements of AI. In this article, we will explore Hayes’ arguments and delve into why Bitcoin is well-suited to fulfill the payment needs of AI.

The need for a payment system for AI

To effectively integrate AI into various applications and services, an indispensable component is a payment system that is available at all times, digital, and completely automated. AI operates round the clock and requires a payment rail that can seamlessly handle transactions at any hour of the day. Traditional banking systems fall short in meeting these fundamental requirements.

Blockchain-based systems as a solution

Hayes argues that a blockchain-based payment system can overcome the shortcomings of traditional banking. Blockchain technology offers the potential to provide uninterrupted services regardless of geographical boundaries. This decentralized approach allows for a 24/7 payment solution that aligns perfectly with the non-stop operations of AI. Additionally, an AI payment rail must be censorship-resistant, with transparent and clear rules. Blockchain technology, specifically Bitcoin, fulfills these criteria. While not all blockchains are inherently permissionless and censorship-resistant, Bitcoin’s decentralized nature and consensus mechanism make it a suitable choice.

Bitcoin’s suitability as a currency for AI

Bitcoin, as a permissionless and censorship-resistant cryptocurrency, is well-suited to serve as a payment rail for AI. Its decentralized network ensures that no central authority can control or manipulate transactions. Changes to Bitcoin’s rules require a public proposal, followed by majority consensus from the network. This feature guarantees censorship resistance, making Bitcoin an ideal choice for AI transactions.

The role of stablecoins in digital networks

While stablecoins allow the circulation of fiat currency and even gold on digital, decentralized networks, there is a caveat. The reserves backing these stablecoins need to be held by centralized entities. This centralized control contradicts the principles of decentralization and introduces potential risks. In contrast, Bitcoin’s decentralized design ensures that no single entity can manipulate its value or control its reserves.

Bitcoin’s advantage in maintaining value against AI’s electricity needs

Hayes argues that Bitcoin is uniquely positioned to maintain its value over time when compared to AI’s electricity requirements. With a programmatically capped supply of 21 million coins, Bitcoin’s scarcity and fixed issuance prevent inflationary pressures. Furthermore, Bitcoin’s mining process directly ties its value to electricity consumption. This connection serves as a robust foundation for Bitcoin’s long-term worth, making it resistant to arbitrary manipulation.

Conclusion: Bitcoin as Good Money with Inherent Value

In conclusion, Arthur Hayes presents a compelling case for Bitcoin as the ideal currency for AI. Its 24/7 availability, censorship resistance, transparency, and ability to maintain value over time make it the perfect fit for the payment needs of AI. While other digital assets and stablecoins may serve a purpose in decentralized networks, Bitcoin’s fundamental properties position it as good money with inherent value separate from the utility of other assets. As AI continues its relentless progress, Bitcoin stands ready to power the seamless and efficient transactions that this transformative technology demands.

Explore more

Is the Mistic Backdoor Hiding in Your Security Tools?

Introduction The emergence of the Mistic backdoor represents a sophisticated advancement in the arsenal of modern cybercriminals, specifically those operating within the niche of Initial Access Brokering (IAB). This malicious software, also identified by some security researchers as MLTBackdoor, has been actively infiltrating corporate environments throughout the first half of 2026. Its primary strength lies in its ability to camouflage

Is the Redmi 17C the New King of Budget Smartphones?

Dominic Jainy is a seasoned IT professional with a deep understanding of how hardware evolution impacts the budget mobile market. Today, he breaks down Xiaomi’s latest strategic move with the Redmi 17C, a device that surprisingly leaps over a generation to deliver high-refresh-rate displays and massive battery life to the entry-level segment. We explore the balance between essential utility features,

How Can PowerTool Speed Up Business Central Data Migrations?

Modern enterprises frequently encounter significant friction during ERP transitions because traditional data migration methods often fail to accommodate the sheer volume and complexity of contemporary datasets. In 2026, the demand for agility within Microsoft Dynamics 365 Business Central has reached a point where standard configuration packages, while functional for small tasks, often act as a bottleneck for larger implementations. The

How to Move Beyond the Portal to a True Developer Platform?

Dominic Jainy stands at the forefront of the modern cloud-native movement, possessing a deep technical mastery of artificial intelligence, machine learning, and blockchain architectures. With years of experience navigating the complexities of large-scale IT infrastructures, he has become a leading voice in the evolution of platform engineering. His perspective is shaped by the practical realities of moving beyond simple automation

Will AI Token Costs Soon Surpass Developer Salaries?

Recent financial projections indicate that the cost of maintaining high-frequency artificial intelligence interactions is rapidly approaching the median annual compensation of experienced software engineers in the global market. As the software development industry undergoes a radical transformation, the traditional overhead associated with human labor is being challenged by the sheer volume of data processed through large language models. This shift