Why GAIB
What Problems Are We Solving?
A Little Background About GPUs, Compute, and Why AI Needs Them
A GPU (Graphics Processing Unit) is a type of processor originally built for rendering images and video, but now widely used for high-performance computing (HPC) and AI workloads, thanks to its unmatched capacity for massive parallel processing. On the supply side, Nvidia dominates today’s GPU market, holding 92% of total market share in Q1 2025. Its enterprise-grade GPU product lineup, as of writing time, includes the H100 and H200, built on the Hopper architecture, and the latest B200 and B300 based on the Blackwell architecture.
Similar to car engines that create kinetic forces to move vehicles forward, GPUs create Compute Power, which is essential to train and run AI foundation models (FMs), such as the latest GPT 5 and Llama 4. The more compute power, the smarter and faster those AI models can be built and operated. The FMs training process requires tens of thousands of enterprise-grade GPUs (e.g., H200s, B200s) running continuously for months. Without compute power, training smart AI models and running them are impossible.
In short: GPUs create compute power, which trains and runs AI. No compute, no AI.
1. Absence of a Liquid Compute Asset Market
In the AI era, quoting Sam Altman, "compute will be the currency of the future". In other words, enterprise-grade GPUs can be viewed as a new class of commodities. Let’s take gold, the physical commodity we are more familiar with as an 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 cap has soared above $22.5 trillion. Compute as commodities is like gold before the 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.
2. Capital Barriers for Cloud / Data Centers
Cloud / 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 cloud service 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 financing solutions, could limit how quickly the cloud / data centers can scale to meet the world’s computing demands.
3. Lack of Real Yields & Real Assets in the Crypto Space
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. As Vitalik mentioned, to break the DeFi Ouroboros, “... the yield is coming from, or could come from, that's rooted in something external”. 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, RWA solutions have gained only modest traction, with onchain T-bills serving as the primary example of genuine yield.
This lack of secure, liquid, and high-yielding real-world assets onchain 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.
4. Vacuum in Direct Channels for AI Investments
Compute captures the majority of value in the AI supply chain. For large AI projects, the spending on compute can be a substantial fraction of total cost—often cited anywhere from 30% to 60% of the overall operational spending for AI (covering both training and inference). Major chipmakers such as NVIDIA had gross margins as high as 72.7% in 2024, while its data centers segment revenue reached $35.6 billion in Q4 2024, representing 90.6% of the total revenue. 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 AI-related stocks like NVIDIA, there are very few avenues to directly invest in the compute assets that drive the AI revolution.
Why GAIB Matters to Data Center/ Neo-cloud
1. Faster and More Flexible Funding
Data centers often face capital constraints when acquiring high-performance GPUs and expanding infrastructure to scale faster and meet soaring demand. GAIB’s platform removes many of the obstacles tied to conventional financing—lengthy approval processes, high interest rates, and rigid terms—by transforming GPUs and their future cash flows into tradeable tokens. This streamlined approach provides swift access to capital, allowing data centers to quickly respond to surging market demand.
2. Lower Costs and Greater Stability
Unlike traditional bank loans or private lending, GAIB’s tokenization model could reduce financing costs and diversify funding sources. By tapping into a global pool of investors, cloud / data centers gain more stability and resilience in a rapidly evolving AI economy.
Why GAIB Matters to Investors
1. Direct Exposure to AI Compute
Investing in Big Techs such as Meta or Amazon, may seem like a proxy play on AI, but in reality, it means buying into a basket of business lines—advertising, e-commerce, cloud computing, and more—without direct exposure to AI compute economics. GAIB offers investors a more targeted route by tokenizing enterprise-grade GPUs—such as NVIDIA’s H100, H200 or GB200—linking returns directly to GPU-centric reward streams.
2. Flexible Financial Strategies
From conservative hedging to higher-risk speculations, GAIB’s integrations with various DeFi protocols creates a range of derivative products and use cases for tokenized GPUs and AID. Whether investors are seeking stable, predictable yields or aiming to exploit market volatility, GAIB lets them tailor their strategies, catering to a spectrum of risk-reward opportunities.
GAIB is sitting at the center of growth in AI, Robotics, RWA & Stablecoin/Synthetic Dollar.
By building an economic layer for AI infrastructure, GAIB enables the tokenization of high-value real-world AI assets — positioning itself as the central hub where AI innovation, crypto, DeFi, and tangible economic value converge, unlocking a multi-trillion-dollar market opportunity.
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