Problem

We live in the AI era.

Tech megacaps including Meta, Amazon, Alphabet and Microsoft intend to spend as much as $320 billion combined on AI technologies and datacenter buildouts in 2025, with applications like ChatGPT reaching 100 million users faster than any product in history. It is projected that AI could contribute up to $20 trillion to the global economy by 2030—reshaping healthcare, finance, and manufacturing at breathtaking speed. Over 80% of organizations report integrating AI into core processes, underscoring a seismic shift that stands to redefine our future more profoundly than any previous technological revolution.

Crypto keeps redefining global finance.

Fueled by a wave of crypto ETF filings from major asset managers and regulatory progress in North America, Europe, and Asia, crypto has entered a pivotal phase of mainstream acceptance—reflected by over 420 million global users in 2023. It is estimated that the market could surpass $5 trillion by 2030, establishing cryptocurrencies as a core pillar of modern finance. This shift from speculative hype to institutional-grade investment signals a new era poised to reshape global finance more profoundly than any innovation in recent memory.

However, in the AI x Crypto age, there are a few major problems to address.

Absence of a Liquid Compute Asset Market

In the AI era, as Sam Altman said, "compute will be the currency of the future". Meanwhile, enterprise-grade GPUs can be viewed as a new class of commodities.

Take another physical commodity, gold, for example. Before the advent of modern futures trading (1970s) and the first major gold ETFs (early 2000s), gold’s total market value was only in the low trillions. Today, driven by transparent markets and easy investor access, gold’s global market capitalization has soared above $12 trillion.

Compute as commodities is like gold before 1970s, lacking a liquid, transparent, and efficient market. Compute assets such as enterprise-grade GPUs are in extremely high demand but cannot be traded or leveraged effectively, limiting their economic value proposition.

Capital Barriers for Cloud and Data Centers

Cloud and data centers—which collectively house trillions of dollars’ worth of compute assets—are essential to delivering the raw processing power that underpins modern AI. Yet they are also among the most capital-intensive businesses in tech, with the leading providers (AWS, Azure, Google Cloud) investing tens of billions of dollars annually in data center construction, networking infrastructure, and high-performance GPUs.

A single NVIDIA H200 card can cost upwards of $30,000, and outfitting a large-scale facility can quickly escalate into the hundreds of millions. As demand for AI workloads grows exponentially, the need to finance and refinance these expensive expansions becomes a critical bottleneck—one that, without innovative financial solutions, could limit how quickly the cloud and data centers can scale to meet the world’s computing needs.

Limited Investment Opportunities

Compute captures the majority of value in the AI supply chain. For large AI projects, spend on compute can be a substantial fraction of total cost—often cited anywhere from 30% to 60% of the overall operational spend for AI (covering both training and inference).

Major chipmaker such as NVIDIA has gross margins as high as 60%, while cloud providers and data centers have a margin of 30% - 60%.

The global cloud compute sector has the size of more than $600 billion in annual revenue as of 2024, with a compound annual growth rate (CAGR) of 12–18% over the next five years.

However, for individual and institutional investors, the options to participate in such a substantial, high growth, and high margin sector are relatively limited. Beyond purchasing stocks of semiconductor manufacturers like NVIDIA, there are few avenues to directly invest in the compute assets that drive the AI revolution.

Crypto’s Need for Real-Yield Assets

The crypto sector faces a significant challenge: a shortage of real-yield assets. Many of the high returns advertised in crypto rely on token inflation, which isn’t sustainable over the long term.

Although many innovative projects aim to bring real-world returns into the space, they often involve hurdles like strict KYC requirements, lock-up periods that reduce liquidity, or ongoing trust and security concerns. As a result, real-world asset (RWA) solutions have gained only modest traction, with on-chain T-bills serving as the primary example of genuine yield.

This lack of secure, liquid, and high-yielding real-world assets on-chain makes it harder for the industry to attract long-term capital and integrate more deeply with traditional finance. Without better, more accessible real-yield options, crypto risks remaining dependent on inflation-based rewards, which can undermine both its credibility and its future growth.