New Paradigm of AI Data Economy: Looking at DIN's Ambitions and Node Selling from the Perspective of Modular Data Preprocessing

Go2Mars Research
2024-08-15 11:48:27
Collection
AI is undoubtedly one of the hottest tracks, whether it's Silicon Valley's OpenAI or domestic companies like Moonshot and Zhipu Qingyan, a host of new entrepreneurs and traditional giants have joined this AI revolution. It not only leads the trend in the technology sector but is also one of the most outstanding areas in this year's cryptocurrency market.

Preface

In today's global landscape, AI is undoubtedly one of the hottest tracks, with emerging entrepreneurs and traditional giants like OpenAI in Silicon Valley and domestic players like Moonshot and Zhipu Qingyan joining this AI revolution. It not only leads trends in the tech field but is also one of the most prominent areas in this year's cryptocurrency market. Looking at the projects on major CEXs this year, despite recent market turbulence, the AI leader Bittensor (TAO) still leads all new coins with a return rate of over 5 times. As AI technology continues to develop and apply, data, as the cornerstone of AI development, is becoming increasingly important.

Under the Tide of the AI Era, the Importance and Potential Value of Data Have Reached Unprecedented Heights

According to statistics, mainstream AI large model companies currently need to process and consume hundreds of millions of datasets each year, and the effectiveness and accuracy of this data directly affect the training results of AI models. However, the cost of data acquisition is also continuously rising, becoming a major challenge faced by AI companies.

Performance Optimization Supported by Increasing Data Consumption

In the current market, large model companies handle and consume a vast amount of data each year. For example, OpenAI used about 45TB of text data to train the GPT-3 model, while the training cost for GPT-4 reached as high as $78 million; Google's training cost for its Gemini Ultra model is approximately $191 million. This enormous demand for data is not limited to OpenAI; other AI companies like Google and Meta also need to process massive amounts of data when training large AI models.

The Need to Focus on Data Effectiveness

Effective data needs to possess high quality, be unbiased, and have rich feature information to ensure that AI models can learn from it and make accurate predictions. For instance, OpenAI used text data from various sources, including books, articles, and websites, to ensure the diversity and representativeness of the data when training GPT-3. However, the effectiveness of data depends not only on its source but also involves multiple stages such as data cleaning, labeling, and preprocessing, which require significant manpower and resource investment.

The Economic Aspect Cannot Be Ignored: The Cost of Data Collection and Processing

In actual AI model training, the costs of data collection, labeling, and processing are often underestimated, but these costs can be quite significant. Specifically, data labeling itself is a time-consuming and expensive process that often requires manual labor. Once data is collected, it also needs to be cleaned, organized, and processed so that AI algorithms can effectively utilize it. According to a McKinsey report, the cost of training a large AI model can reach millions of dollars. Additionally, the construction and maintenance of data centers and computing infrastructure for AI companies also represent a substantial expense.

In summary, training large AI models relies on a large amount of high-quality data, and the quantity, effectiveness, and acquisition costs of this data directly determine the performance and success of AI models. In the future, as AI technology continues to advance, how to efficiently acquire and utilize data will become a key competitive factor for AI companies.

Modular Data Preprocessing Layer: A Decentralized AI Data Solution Based on Blockchain

Against this backdrop, DIN (formerly known as Web3Go) has emerged as the first modular AI native data preprocessing layer. DIN aims to enable everyone to provide data for AI and earn rewards through decentralized data verification and vectorization processing, leading a data economy trend where individuals can monetize their personal data, and businesses can acquire data more efficiently and economically. Currently, DIN has secured $4 million in seed funding from Binance Labs and an additional $4 million in pre-listing financing from other institutions, communities, and KOL networks, with a current valuation of $80 million, demonstrating the market's high recognition of its immense potential and future development. Its partners include Polkadot, BNB Chain, Moonbeam Network, and Manta Network.

DIN's Data Preprocessing Node -- Chipper Node

DIN has a very clear market positioning, dedicated to establishing a decentralized data intelligence network in the AI and data fields. Chipper Node plays a crucial role in the DIN ecosystem, responsible for data verification, vectorization processing, and reward calculation, making it a core component of DIN's data preprocessing layer. To promote the data economy more widely, DIN has opened public sales of Chipper Nodes to incentivize more users to participate in the development and maintenance of the network and earn rewards, creating a positive cycle that promotes the collaborative development of the DIN ecosystem and the data economy.

The node selling model, as an emerging token issuance method, has rapidly gained popularity in the crypto market due to its unique advantages. Compared to traditional public sales, it offers investors more flexibility and potential returns. The core of this model is that by selling nodes, the project can better incentivize early participants while ensuring the decentralization of the network and maximizing economic benefits.

DIN's node selling plan will be conducted in phases, including pre-sale rounds, whitelist sales, and public sales, with different participation conditions and reward mechanisms for each round. The distribution and unlocking rules for node token rewards have also been carefully designed to ensure market price stability and long-term returns for investors. By purchasing and operating DIN's Chipper Node, users can not only participate in data verification and vectorization processes but also receive generous $DIN token rewards.

As the AI and data markets continue to evolve, DIN is poised to become a leader in this field. The following sections will delve into DIN's Chipper Node sales model and its unique advantages in the market, revealing its future investment potential and development prospects through an analysis of return rates and payback periods.

Expected Return Rate and Payback Period Analysis

DIN's node selling plan will be conducted in phases, including pre-sale rounds, whitelist sales, and public sales, with different participation conditions and reward mechanisms for each round. The distribution and unlocking rules for node token rewards have also been carefully designed to ensure market price stability and long-term returns for investors. By purchasing and operating DIN's Chipper Node, users can not only participate in data verification and vectorization processes but also receive $DIN token rewards from node mining. Below is a detailed analysis of the expected return rates and payback periods for DIN's node sales.

DIN's Sales Plan

  1. Node token reward distribution scheme: DIN's node token accounts for 25%, with 50% unlocked in the first year. In addition to the node mining rewards themselves, there will also be additional $DIN token airdrops for $xDIN holders, unlocking 100% at TGE; simultaneously, 13% of tokens will be airdropped to Chipper node holders, with linear unlocking over six months after TGE. This distribution scheme helps maintain the stability of the token market price and reduces price fluctuations caused by a large influx of tokens into the market in a short time.

  2. DIN's node sales are divided into three stages: pre-sale rounds, whitelist sales, and public sales. Each stage has different sales prices and conditions to attract different types of investors. The pre-sale round primarily targets early product users and core community contributors; the whitelist sales round is aimed at specific institutions, communities, and KOL partners; while the public sales round is open to the general public.

  3. Invitation mechanism: DIN has introduced an invitation mechanism, allowing existing users to invite new users to purchase nodes, with both parties receiving additional token rewards. This mechanism not only effectively expands the user base but also enhances community activity and loyalty.

Prices and Return Cycles for Different Node Tiers

The total supply of $DIN is 100 million. Compared to other DePIN projects that have also opened node sales and raised $10 million before TGE, io.net currently has an FDV of $1.5 billion. Using this as a benchmark, assuming the price of $DIN after TGE is $15 and the number of active nodes is 50%, we can estimate the expected returns and payback periods for investors in each stage within a year (excluding airdrop rewards).

  • The pre-sale Tier 1 nodes are free for eligible $xData Chip NFT holders and some community contributors, so there is no payback concern; they can also start mining early, converting their wafers into airdrop points $xDIN and locking in their share of the $DIN token airdrop.

  • The whitelist sales Tier 2 nodes are priced at $99, with first-year rewards of 106 $DIN, corresponding to $1,590, and buyers will break even in 27 days.

  • The public sales are divided into two phases, the first phase (Tier 3 - 5) and the second phase (Tier 6 - 10). Tier 3 nodes are priced at $149, with first-year rewards of 133 $DIN, corresponding to a value of $1,995, and buyers will break even in 36 days. Tier 6 is priced at $300, with first-year rewards of 265 $DIN, corresponding to a value of $3,975, and buyers will still break even within three months.

Compared to other mainstream projects like Aethir and CARV that have recently opened node sales, DIN's node sales offer advantages in price, unlocking speed, and reward mechanisms. Aethir's node tokens unlock over four years, resulting in a longer payback period, while CARV, despite adopting a multi-round sales strategy, does not achieve the overall return rate of DIN. Meanwhile, DIN's node sales, through faster unlocking speeds and flexible reward mechanisms, allow investors to receive returns in a shorter time while maintaining market price stability, reducing investment risks.

DIN's Technical Strength and Market Potential

Technical Strength

As the first modular AI data preprocessing layer, DIN stands out in technological innovation and unique advantages. DIN's core technology provides efficient and reliable data preprocessing services through decentralized data verification and vectorization processing. This technology not only improves the efficiency of data processing but also ensures the security and privacy of the data. Additionally, DIN's Chipper Node has significant advantages in data verification and reward calculation, allowing node holders to directly participate in the operation and maintenance of the network, further enhancing the decentralization and robustness of the network.

Market Potential

The enormous potential of the AI and data market is a significant driving force behind DIN's development. With the rapid advancement of artificial intelligence and big data technologies, the demand for high-quality data is growing. DIN, through its innovative technology and business model, can provide efficient data preprocessing services for AI models, significantly reducing the costs of data acquisition and processing. This positions DIN favorably in a competitive market, with immense market potential and development prospects.

Capital Background

DIN's strong capital background and supporters further enhance its market competitiveness. DIN has completed $4 million in seed funding and $4 million in pre-listing financing, with a current valuation of $80 million. Notably, DIN has received support from top investment institutions like Binance Labs, which not only provides ample funding security for the project but also offers strong resources and network support for its future development.

Conclusion

Despite the recent shocks to the global capital market and the subsequent crash in the cryptocurrency market, the current panic sentiment in the secondary market has not completely dissipated. However, participating in node sales may be a higher odds choice during market turbulence, offering more reliable node reward returns than the secondary market. Through detailed node token reward distribution and flexible sales methods, DIN provides investors with high return rates and short payback periods. As macro conditions stabilize and interest rate cuts materialize, an expected bull market may return in the second half of the year. As a comprehensive integration of modularization, DePIN, and AI narratives, DIN is expected to lead a trend in the private data economy against the backdrop of rapid AI development, and its performance in future markets is worth looking forward to.

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