◼️Market Overview and Business Model
MARKET
Based on the analysis of the public financial reports of fintech and blockchain companies, it was determined that given the budgets spent by large companies on maintaining IT infrastructure, the current state of development of the Web3 industry, and that no less than 46% of the net income of such companies goes towards IT infrastructure support, a conservative approach among such innovative companies can expect expenditures on NTP services to be about 1/100 of the IT budget of Web3 companies, or 0.46% of the net income of Web3 companies.
For example, Amazon has been allocated 2 $bln investments into Network Time Protocol.
Google has the Quick protocol which uses same technologies in NTP sector.
COSTS
Thus, for a company the size of Coinbase, NTP services would cost $1.18 million per month ($772 million*0.0046/3), taking into account the large scale of their IT infrastructure. Considering the methodology for calculating the potential budget that could be allocated for NTP solutions as 0.46% of the net revenues of Web3 companies, and also a minimum service cost of $5,000 per month, the list of potential NTP clients from decentralized Web3 companies includes organizations whose net income over the last year exceeded $13 million.
High Demand of Immense computational power
Immense computational power is essential in the realm of data computing and real-time data solutions. The need for unparalleled speed is growing exponentially, driven by the demand for instantaneous data processing and analysis. This directly impacts the time spent on development, the efficiency of product enhancements, and the resources allocated for these tasks. The ability to perform complex computational processes more quickly and cost-effectively is not just a desire but a necessity in our rapidly evolving technological landscape. Efficient real-time data solutions are crucial for businesses to remain competitive, enabling them to make informed decisions promptly, enhance user experiences, and optimize operations dynamically.
Direct Relationship Between Computing Power and Advanced Technologies
Market Dynamics
Competitive Advantage: Companies that leverage advanced computing power gain a significant edge over competitors. They can innovate faster, reduce costs, and bring new products to market more quickly.
Market Expansion: The growth of the computing power market is closely tied to the adoption of advanced technologies. As more industries recognize the benefits, the demand for high-performance computing continues to rise, further driving down costs through economies of scale.
The graph shows the rapid and significant decrease in costs and time for technological leading corporations due to advances in computing power. Here's a breakdown of the key points:
The graph illustrates the exponential growth in computing power, which is directly reducing costs and time for leading tech companies. This trend highlights how the rapid advancements in computing are fueling innovation, enhancing efficiency, and providing a competitive edge in the market. The continuous improvement in computing capabilities is a key driver behind the accelerated pace of technological progress and market expansion.
Growth of artificial intelligence
To ensure the performance and growth of artificial intelligence, immense computational power is essential. This need for unparalleled speed is growing exponentially. It directly impacts the time spent on development, the efficiency of product enhancements, and the resources allocated for these tasks. The ability to perform complex computational processes more quickly and cost-effectively is not just a desire but a necessity in our rapidly evolving technological landscape.
Before AI
General-Purpose Computing: Prior to AI, most computing tasks were handled by general-purpose processors like CPUs. These processors are versatile but not optimized for the specific needs of AI computations.
Limited Specialization: The market for specialized processing hardware was small. High-performance computing was mainly used in fields like scientific research and financial modeling, with limited application in everyday technology.
Cost and Performance: General-purpose processors offered moderate performance improvements and cost reductions over time, following Moore's Law. However, the growth was relatively linear compared to the exponential needs of AI.
After AI
AI-Specific Processors: The rise of AI has driven the development of specialized processors, such as GPUs, TPUs (Tensor Processing Units), and other AI accelerators. These processors are designed to handle the large-scale parallel processing required for AI algorithms.
Exponential Performance Gains: AI-specific processors provide exponential improvements in performance and efficiency for AI tasks compared to general-purpose CPUs. This leads to significant reductions in the cost and time required to process large datasets and train AI models.
Market Expansion: The demand for AI-capable hardware has surged, leading to a rapid expansion of the computing data market. Companies invest heavily in AI infrastructure, driving innovation and competition among hardware manufacturers.
The graph illustrates the distribution of raw performance in terms of decisions made per hour by AI, comparing performance before and after the implementation of AI. The x-axis represents the number of decisions per hour, while the y-axis represents the density of those decisions. Before AI, the performance distribution peaks around 2 decisions per hour, indicating a lower overall decision-making capacity. After AI implementation, the distribution shifts to the right, with a peak around 3 decisions per hour, and extends further along the x-axis, demonstrating that AI significantly enhances decision-making performance. This shift highlights the increased efficiency and productivity resulting from AI adoption.
Significant shift in the computing data market thanks to AI-specific processors provides exponential performance improvements, reduce costs, and accelerate innovation. This transformation drives the expansion of the computing data market, reshaping the landscape of technology and enabling new applications and industries to leverage the power of AI.
BUSINESS MODEL
Our main focus
Grace leverages the Web3 model as a method of payment for services, utilizing decentralized practices. This not only provides secure and transparent transactions but also aligns with the growing trend towards decentralization, enhancing trust and innovation in her business operations. GRACE NET has a few ways to be acquired by Web3 market to improve current technological solutions. The success of this business model is influenced by carefully balancing open accessibility for basic use to encourage widespread adoption, with premium and enterprise-level services that offer significant added value. Engagement with the community, transparent pricing, and continuous innovation are key to sustaining long-term growth and relevance in the rapidly evolving blockchain sector.
Grace employs both B2B (Business-to-Business) and B2C (Business-to-Consumer) models strategically, allowing her to cater to a broad market spectrum. This approach is beneficial as it diversifies her revenue streams and mitigates risk by engaging with both corporate clients and individual consumers. From a perspective point of view, the B2B model enables Grace to establish long-term contracts and stable partnerships with other businesses, ensuring consistent revenue. Meanwhile, the B2C model allows her to tap into the mass market, driving growth through high-volume sales and direct customer engagement.
Technology Partnership Programs
Collaborate with blockchain platforms, decentralized application (dApp) developers, and infrastructure providers to integrate GRACE NET as a standard feature. This could involve revenue-sharing models or co-marketing agreements.
Developer Ecosystem Incentives
Create incentives for developers to build on or with GRACE NET, such as hackathons, grants for innovative projects, or revenue-sharing for widely adopted applications. This accelerates GRACE ecosystem growth and GRACE NET adoption.
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